From 59ea9f69a955774e058391bb57500ced792b2637 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 6 Sep 2023 17:55:47 -0400 Subject: [PATCH 1/8] docsrc/generated --- docsrc/generated/giddy.directional.Rose.rst | 26 ----------- docsrc/generated/giddy.ergodic.mfpt.rst | 6 --- .../generated/giddy.ergodic.steady_state.rst | 6 --- .../giddy.ergodic.var_mfpt_ergodic.rst | 6 --- .../giddy.markov.FullRank_Markov.rst | 30 ------------- .../generated/giddy.markov.GeoRank_Markov.rst | 30 ------------- docsrc/generated/giddy.markov.LISA_Markov.rst | 31 ------------- docsrc/generated/giddy.markov.Markov.rst | 30 ------------- .../generated/giddy.markov.Spatial_Markov.rst | 43 ------------------- docsrc/generated/giddy.markov.homogeneity.rst | 6 --- docsrc/generated/giddy.markov.kullback.rst | 6 --- docsrc/generated/giddy.markov.prais.rst | 6 --- .../generated/giddy.markov.sojourn_time.rst | 6 --- .../giddy.mobility.markov_mobility.rst | 6 --- docsrc/generated/giddy.rank.SpatialTau.rst | 22 ---------- docsrc/generated/giddy.rank.Tau.rst | 22 ---------- docsrc/generated/giddy.rank.Tau_Local.rst | 22 ---------- .../giddy.rank.Tau_Local_Neighbor.rst | 22 ---------- .../giddy.rank.Tau_Local_Neighborhood.rst | 22 ---------- docsrc/generated/giddy.rank.Tau_Regional.rst | 22 ---------- docsrc/generated/giddy.rank.Theta.rst | 22 ---------- docsrc/generated/giddy.sequence.Sequence.rst | 22 ---------- .../giddy.util.fill_empty_diagonals.rst | 6 --- docsrc/generated/giddy.util.get_lower.rst | 6 --- .../generated/giddy.util.shuffle_matrix.rst | 6 --- 25 files changed, 432 deletions(-) delete mode 100644 docsrc/generated/giddy.directional.Rose.rst delete mode 100644 docsrc/generated/giddy.ergodic.mfpt.rst delete mode 100644 docsrc/generated/giddy.ergodic.steady_state.rst delete mode 100644 docsrc/generated/giddy.ergodic.var_mfpt_ergodic.rst delete mode 100644 docsrc/generated/giddy.markov.FullRank_Markov.rst delete mode 100644 docsrc/generated/giddy.markov.GeoRank_Markov.rst delete mode 100644 docsrc/generated/giddy.markov.LISA_Markov.rst delete mode 100644 docsrc/generated/giddy.markov.Markov.rst delete mode 100644 docsrc/generated/giddy.markov.Spatial_Markov.rst delete mode 100644 docsrc/generated/giddy.markov.homogeneity.rst delete mode 100644 docsrc/generated/giddy.markov.kullback.rst delete mode 100644 docsrc/generated/giddy.markov.prais.rst delete mode 100644 docsrc/generated/giddy.markov.sojourn_time.rst delete mode 100644 docsrc/generated/giddy.mobility.markov_mobility.rst delete mode 100644 docsrc/generated/giddy.rank.SpatialTau.rst delete mode 100644 docsrc/generated/giddy.rank.Tau.rst delete mode 100644 docsrc/generated/giddy.rank.Tau_Local.rst delete mode 100644 docsrc/generated/giddy.rank.Tau_Local_Neighbor.rst delete mode 100644 docsrc/generated/giddy.rank.Tau_Local_Neighborhood.rst delete mode 100644 docsrc/generated/giddy.rank.Tau_Regional.rst delete mode 100644 docsrc/generated/giddy.rank.Theta.rst delete mode 100644 docsrc/generated/giddy.sequence.Sequence.rst delete mode 100644 docsrc/generated/giddy.util.fill_empty_diagonals.rst delete mode 100644 docsrc/generated/giddy.util.get_lower.rst delete mode 100644 docsrc/generated/giddy.util.shuffle_matrix.rst diff --git a/docsrc/generated/giddy.directional.Rose.rst b/docsrc/generated/giddy.directional.Rose.rst deleted file mode 100644 index ce4a363..0000000 --- a/docsrc/generated/giddy.directional.Rose.rst +++ /dev/null @@ -1,26 +0,0 @@ -giddy.directional.Rose -====================== - -.. currentmodule:: giddy.directional - -.. autoclass:: Rose - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Rose.__init__ - ~Rose.permute - ~Rose.plot - ~Rose.plot_origin - ~Rose.plot_vectors - - - - - - \ No newline at end of file diff --git a/docsrc/generated/giddy.ergodic.mfpt.rst b/docsrc/generated/giddy.ergodic.mfpt.rst deleted file mode 100644 index 2395811..0000000 --- a/docsrc/generated/giddy.ergodic.mfpt.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.ergodic.mfpt -================== - -.. currentmodule:: giddy.ergodic - -.. autofunction:: mfpt \ No newline at end of file diff --git a/docsrc/generated/giddy.ergodic.steady_state.rst b/docsrc/generated/giddy.ergodic.steady_state.rst deleted file mode 100644 index 01a5d26..0000000 --- a/docsrc/generated/giddy.ergodic.steady_state.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.ergodic.steady\_state -=========================== - -.. currentmodule:: giddy.ergodic - -.. autofunction:: steady_state \ No newline at end of file diff --git a/docsrc/generated/giddy.ergodic.var_mfpt_ergodic.rst b/docsrc/generated/giddy.ergodic.var_mfpt_ergodic.rst deleted file mode 100644 index 67c5966..0000000 --- a/docsrc/generated/giddy.ergodic.var_mfpt_ergodic.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.ergodic.var\_mfpt\_ergodic -================================ - -.. currentmodule:: giddy.ergodic - -.. autofunction:: var_mfpt_ergodic \ No newline at end of file diff --git a/docsrc/generated/giddy.markov.FullRank_Markov.rst b/docsrc/generated/giddy.markov.FullRank_Markov.rst deleted file mode 100644 index 2ba4ed0..0000000 --- a/docsrc/generated/giddy.markov.FullRank_Markov.rst +++ /dev/null @@ -1,30 +0,0 @@ -giddy.markov.FullRank\_Markov -============================= - -.. currentmodule:: giddy.markov - -.. autoclass:: FullRank_Markov - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~FullRank_Markov.__init__ - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~FullRank_Markov.mfpt - ~FullRank_Markov.sojourn_time - ~FullRank_Markov.steady_state - - \ No newline at end of file diff --git a/docsrc/generated/giddy.markov.GeoRank_Markov.rst b/docsrc/generated/giddy.markov.GeoRank_Markov.rst deleted file mode 100644 index dadebca..0000000 --- a/docsrc/generated/giddy.markov.GeoRank_Markov.rst +++ /dev/null @@ -1,30 +0,0 @@ -giddy.markov.GeoRank\_Markov -============================ - -.. currentmodule:: giddy.markov - -.. autoclass:: GeoRank_Markov - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~GeoRank_Markov.__init__ - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~GeoRank_Markov.mfpt - ~GeoRank_Markov.sojourn_time - ~GeoRank_Markov.steady_state - - \ No newline at end of file diff --git a/docsrc/generated/giddy.markov.LISA_Markov.rst b/docsrc/generated/giddy.markov.LISA_Markov.rst deleted file mode 100644 index b2d588b..0000000 --- a/docsrc/generated/giddy.markov.LISA_Markov.rst +++ /dev/null @@ -1,31 +0,0 @@ -giddy.markov.LISA\_Markov -========================= - -.. currentmodule:: giddy.markov - -.. autoclass:: LISA_Markov - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~LISA_Markov.__init__ - ~LISA_Markov.spillover - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~LISA_Markov.mfpt - ~LISA_Markov.sojourn_time - ~LISA_Markov.steady_state - - \ No newline at end of file diff --git a/docsrc/generated/giddy.markov.Markov.rst b/docsrc/generated/giddy.markov.Markov.rst deleted file mode 100644 index d13288a..0000000 --- a/docsrc/generated/giddy.markov.Markov.rst +++ /dev/null @@ -1,30 +0,0 @@ -giddy.markov.Markov -=================== - -.. currentmodule:: giddy.markov - -.. autoclass:: Markov - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Markov.__init__ - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~Markov.mfpt - ~Markov.sojourn_time - ~Markov.steady_state - - \ No newline at end of file diff --git a/docsrc/generated/giddy.markov.Spatial_Markov.rst b/docsrc/generated/giddy.markov.Spatial_Markov.rst deleted file mode 100644 index 930540d..0000000 --- a/docsrc/generated/giddy.markov.Spatial_Markov.rst +++ /dev/null @@ -1,43 +0,0 @@ -giddy.markov.Spatial\_Markov -============================ - -.. currentmodule:: giddy.markov - -.. autoclass:: Spatial_Markov - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Spatial_Markov.__init__ - ~Spatial_Markov.summary - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~Spatial_Markov.F - ~Spatial_Markov.LR - ~Spatial_Markov.LR_p_value - ~Spatial_Markov.Q - ~Spatial_Markov.Q_p_value - ~Spatial_Markov.S - ~Spatial_Markov.chi2 - ~Spatial_Markov.dof_hom - ~Spatial_Markov.f - ~Spatial_Markov.ht - ~Spatial_Markov.s - ~Spatial_Markov.shtest - ~Spatial_Markov.x2 - ~Spatial_Markov.x2_dof - ~Spatial_Markov.x2_pvalue - - \ No newline at end of file diff --git a/docsrc/generated/giddy.markov.homogeneity.rst b/docsrc/generated/giddy.markov.homogeneity.rst deleted file mode 100644 index 6458e39..0000000 --- a/docsrc/generated/giddy.markov.homogeneity.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.markov.homogeneity -======================== - -.. currentmodule:: giddy.markov - -.. autofunction:: homogeneity \ No newline at end of file diff --git a/docsrc/generated/giddy.markov.kullback.rst b/docsrc/generated/giddy.markov.kullback.rst deleted file mode 100644 index 25fd4ae..0000000 --- a/docsrc/generated/giddy.markov.kullback.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.markov.kullback -===================== - -.. currentmodule:: giddy.markov - -.. autofunction:: kullback \ No newline at end of file diff --git a/docsrc/generated/giddy.markov.prais.rst b/docsrc/generated/giddy.markov.prais.rst deleted file mode 100644 index 5639f37..0000000 --- a/docsrc/generated/giddy.markov.prais.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.markov.prais -================== - -.. currentmodule:: giddy.markov - -.. autofunction:: prais \ No newline at end of file diff --git a/docsrc/generated/giddy.markov.sojourn_time.rst b/docsrc/generated/giddy.markov.sojourn_time.rst deleted file mode 100644 index c733e78..0000000 --- a/docsrc/generated/giddy.markov.sojourn_time.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.markov.sojourn\_time -========================== - -.. currentmodule:: giddy.markov - -.. autofunction:: sojourn_time \ No newline at end of file diff --git a/docsrc/generated/giddy.mobility.markov_mobility.rst b/docsrc/generated/giddy.mobility.markov_mobility.rst deleted file mode 100644 index da38b5c..0000000 --- a/docsrc/generated/giddy.mobility.markov_mobility.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.mobility.markov\_mobility -=============================== - -.. currentmodule:: giddy.mobility - -.. autofunction:: markov_mobility \ No newline at end of file diff --git a/docsrc/generated/giddy.rank.SpatialTau.rst b/docsrc/generated/giddy.rank.SpatialTau.rst deleted file mode 100644 index f0313f2..0000000 --- a/docsrc/generated/giddy.rank.SpatialTau.rst +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.SpatialTau -===================== - -.. currentmodule:: giddy.rank - -.. autoclass:: SpatialTau - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~SpatialTau.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/generated/giddy.rank.Tau.rst b/docsrc/generated/giddy.rank.Tau.rst deleted file mode 100644 index 181a8f4..0000000 --- a/docsrc/generated/giddy.rank.Tau.rst +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Tau -============== - -.. currentmodule:: giddy.rank - -.. autoclass:: Tau - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Tau.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/generated/giddy.rank.Tau_Local.rst b/docsrc/generated/giddy.rank.Tau_Local.rst deleted file mode 100644 index 01347ea..0000000 --- a/docsrc/generated/giddy.rank.Tau_Local.rst +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Tau\_Local -===================== - -.. currentmodule:: giddy.rank - -.. autoclass:: Tau_Local - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Tau_Local.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/generated/giddy.rank.Tau_Local_Neighbor.rst b/docsrc/generated/giddy.rank.Tau_Local_Neighbor.rst deleted file mode 100644 index 6a9d2b2..0000000 --- a/docsrc/generated/giddy.rank.Tau_Local_Neighbor.rst +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Tau\_Local\_Neighbor -=============================== - -.. currentmodule:: giddy.rank - -.. autoclass:: Tau_Local_Neighbor - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Tau_Local_Neighbor.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/generated/giddy.rank.Tau_Local_Neighborhood.rst b/docsrc/generated/giddy.rank.Tau_Local_Neighborhood.rst deleted file mode 100644 index ff3b6a2..0000000 --- a/docsrc/generated/giddy.rank.Tau_Local_Neighborhood.rst +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Tau\_Local\_Neighborhood -=================================== - -.. currentmodule:: giddy.rank - -.. autoclass:: Tau_Local_Neighborhood - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Tau_Local_Neighborhood.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/generated/giddy.rank.Tau_Regional.rst b/docsrc/generated/giddy.rank.Tau_Regional.rst deleted file mode 100644 index 3896417..0000000 --- a/docsrc/generated/giddy.rank.Tau_Regional.rst +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Tau\_Regional -======================== - -.. currentmodule:: giddy.rank - -.. autoclass:: Tau_Regional - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Tau_Regional.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/generated/giddy.rank.Theta.rst b/docsrc/generated/giddy.rank.Theta.rst deleted file mode 100644 index e84caba..0000000 --- a/docsrc/generated/giddy.rank.Theta.rst +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Theta -================ - -.. currentmodule:: giddy.rank - -.. autoclass:: Theta - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Theta.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/generated/giddy.sequence.Sequence.rst b/docsrc/generated/giddy.sequence.Sequence.rst deleted file mode 100644 index e368ce5..0000000 --- a/docsrc/generated/giddy.sequence.Sequence.rst +++ /dev/null @@ -1,22 +0,0 @@ -giddy.sequence.Sequence -======================= - -.. currentmodule:: giddy.sequence - -.. autoclass:: Sequence - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Sequence.__init__ - - - - - - \ No newline at end of file diff --git a/docsrc/generated/giddy.util.fill_empty_diagonals.rst b/docsrc/generated/giddy.util.fill_empty_diagonals.rst deleted file mode 100644 index 5daece7..0000000 --- a/docsrc/generated/giddy.util.fill_empty_diagonals.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.util.fill\_empty\_diagonals -================================= - -.. currentmodule:: giddy.util - -.. autofunction:: fill_empty_diagonals \ No newline at end of file diff --git a/docsrc/generated/giddy.util.get_lower.rst b/docsrc/generated/giddy.util.get_lower.rst deleted file mode 100644 index 8ed893e..0000000 --- a/docsrc/generated/giddy.util.get_lower.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.util.get\_lower -===================== - -.. currentmodule:: giddy.util - -.. autofunction:: get_lower \ No newline at end of file diff --git a/docsrc/generated/giddy.util.shuffle_matrix.rst b/docsrc/generated/giddy.util.shuffle_matrix.rst deleted file mode 100644 index d691283..0000000 --- a/docsrc/generated/giddy.util.shuffle_matrix.rst +++ /dev/null @@ -1,6 +0,0 @@ -giddy.util.shuffle\_matrix -========================== - -.. currentmodule:: giddy.util - -.. autofunction:: shuffle_matrix \ No newline at end of file From 16dd0dba9550edbd405380bb77acd62deda85810 Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 6 Sep 2023 17:56:11 -0400 Subject: [PATCH 2/8] add doc build workflow --- .github/workflows/build_docs.yml | 65 ++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 .github/workflows/build_docs.yml diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml new file mode 100644 index 0000000..b012b34 --- /dev/null +++ b/.github/workflows/build_docs.yml @@ -0,0 +1,65 @@ + name: Build Docs + + on: + push: + # Sequence of patterns matched against refs/tags + tags: + - 'v*' # Push events to matching v*, i.e. v1.0, v20.15.10 + workflow_dispatch: + inputs: + version: + description: Manual Doc Build + default: test + required: false + jobs: + docs: + name: build & push docs + runs-on: ${{ matrix.os }} + timeout-minutes: 90 + strategy: + matrix: + os: ['ubuntu-latest'] + environment-file: [ci/311.yaml] + experimental: [false] + defaults: + run: + shell: bash -l {0} + + steps: + - name: checkout repo + uses: actions/checkout@v4 + + - name: setup micromamba + uses: mamba-org/setup-micromamba@v1 + with: + environment-file: ${{ matrix.environment-file }} + micromamba-version: 'latest' + + - name: reinstall for version + run: | + pip install -e . --no-deps --force-reinstall + + - name: make docs + run: | + cd docs + make html + + - name: commit docs + run: | + git clone https://github.com/ammaraskar/sphinx-action-test.git --branch gh-pages --single-branch gh-pages + cp -r docs/_build/html/* gh-pages/ + cd gh-pages + git config --local user.email "action@github.com" + git config --local user.name "GitHub Action" + git add . + git commit -m "Update documentation" -a || true + # The above command will fail if no changes were present, + # so we ignore the return code. + + - name: push to gh-pages + uses: ad-m/github-push-action@master + with: + branch: gh-pages + directory: gh-pages + github_token: ${{ secrets.GITHUB_TOKEN }} + force: true From ef8e04755618ef39b4d66350935c7d29f3087bfe Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 6 Sep 2023 17:56:29 -0400 Subject: [PATCH 3/8] add doc deps --- ci/311.yaml | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/ci/311.yaml b/ci/311.yaml index 850a5d3..1da37a3 100644 --- a/ci/311.yaml +++ b/ci/311.yaml @@ -17,3 +17,10 @@ dependencies: - pytest - pytest-cov - pytest-xdist + # docs (this env only) + - nbsphinx + - nbsphinx-link + - numpydoc + - sphinx>=1.4.3 + - sphinxcontrib-bibtex + - sphinx_bootstrap_theme From bda2abe9a1ad49dcbecf856bf20e7383419b9c3d Mon Sep 17 00:00:00 2001 From: James Gaboardi Date: Wed, 6 Sep 2023 18:01:48 -0400 Subject: [PATCH 4/8] rename docsrc/ -> docs/ --- docs/.buildinfo | 4 - docs/.nojekyll | 1 - docs/DirectionalLISA.html | 895 -- docs/DirectionalLISA.ipynb | 607 - {docsrc => docs}/DirectionalLISA.nblink | 0 {docsrc => docs}/Makefile | 3 +- docs/MarkovBasedMethods.html | 1644 --- docs/MarkovBasedMethods.ipynb | 1598 --- {docsrc => docs}/MarkovBasedMethods.nblink | 0 docs/MobilityMeasures.html | 693 - docs/MobilityMeasures.ipynb | 342 - {docsrc => docs}/MobilityMeasures.nblink | 0 docs/RankBasedMethods.html | 1951 --- docs/RankBasedMethods.ipynb | 1873 --- {docsrc => docs}/RankBasedMethods.nblink | 0 docs/RankMarkov.html | 1515 --- docs/RankMarkov.ipynb | 1348 -- {docsrc => docs}/RankMarkov.nblink | 0 docs/Sequence.html | 786 -- docs/Sequence.ipynb | 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When it is not found, a full rebuild will be done. -config: 1f8f5620c92b2c73ace58b81fd15c5ff -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/.nojekyll b/docs/.nojekyll deleted file mode 100644 index 8b13789..0000000 --- a/docs/.nojekyll +++ /dev/null @@ -1 +0,0 @@ - diff --git a/docs/DirectionalLISA.html b/docs/DirectionalLISA.html deleted file mode 100644 index e14067b..0000000 --- a/docs/DirectionalLISA.html +++ /dev/null @@ -1,895 +0,0 @@ - - - - - - - - Directional Analysis of Dynamic LISAs — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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This page was generated from DirectionalLISA.ipynb. -Interactive online version: -Binder badge

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Directional Analysis of Dynamic LISAs

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This notebook demonstrates how to use Rose diagram based inference for directional LISAs.

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[1]:
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import libpysal
-import numpy as np
-from giddy.directional import Rose
-%matplotlib inline
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[2]:
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f = open(libpysal.examples.get_path('spi_download.csv'), 'r')
-lines = f.readlines()
-f.close()
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[3]:
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lines = [line.strip().split(",") for line in lines]
-names = [line[2] for line in lines[1:-5]]
-data = np.array([list(map(int, line[3:])) for line in lines[1:-5]])
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[4]:
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sids  = list(range(60))
-out = ['"United States 3/"',
-      '"Alaska 3/"',
-      '"District of Columbia"',
-      '"Hawaii 3/"',
-      '"New England"','"Mideast"',
-       '"Great Lakes"',
-       '"Plains"',
-       '"Southeast"',
-       '"Southwest"',
-       '"Rocky Mountain"',
-       '"Far West 3/"']
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-
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[5]:
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snames = [name for name in names if name not in out]
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[6]:
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sids = [names.index(name) for name in snames]
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[7]:
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states = data[sids,:]
-us = data[0]
-years = np.arange(1969, 2009)
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[8]:
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rel = states/(us*1.)
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[9]:
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gal = libpysal.io.open(libpysal.examples.get_path('states48.gal'))
-w = gal.read()
-w.transform = 'r'
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[10]:
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Y = rel[:, [0, -1]]
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[11]:
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Y.shape
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[11]:
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-(48, 2)
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[12]:
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Y
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[12]:
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-array([[0.71272158, 0.83983287],
-       [0.91110532, 0.85393454],
-       [0.68196038, 0.80573518],
-       [1.181439  , 1.08538102],
-       [0.96115746, 1.06906586],
-       [1.25677789, 1.39952248],
-       [1.14859228, 1.00773478],
-       [0.9535975 , 0.9765967 ],
-       [0.82090719, 0.86781238],
-       [0.85088634, 0.82257262],
-       [1.12956204, 1.05319837],
-       [0.9624609 , 0.86064962],
-       [0.95542231, 0.93021289],
-       [0.92674661, 0.96547951],
-       [0.77267987, 0.79775169],
-       [0.75234619, 0.90588938],
-       [0.81803962, 0.90671011],
-       [1.09462982, 1.20319339],
-       [1.09098019, 1.27472145],
-       [1.08107404, 0.86920513],
-       [0.98409802, 1.07035913],
-       [0.62643379, 0.75604357],
-       [0.93039625, 0.9110376 ],
-       [0.85870699, 0.86161958],
-       [0.93091762, 0.97368683],
-       [1.18091762, 1.02422404],
-       [0.97627737, 1.08493335],
-       [1.17309698, 1.277308  ],
-       [0.76120959, 0.83142658],
-       [1.19212722, 1.2125199 ],
-       [0.79405631, 0.87902905],
-       [0.80787278, 0.99159371],
-       [1.01955162, 0.89586649],
-       [0.83524505, 0.89497115],
-       [0.9580292 , 0.9027308 ],
-       [0.99165798, 0.99830879],
-       [1.00286757, 1.02884998],
-       [0.73540146, 0.81242539],
-       [0.7898853 , 0.96152507],
-       [0.77085506, 0.86987664],
-       [0.87695516, 0.93946478],
-       [0.80943691, 0.79446876],
-       [0.88112617, 0.96214684],
-       [0.92805005, 1.09988062],
-       [1.06491137, 1.06588241],
-       [0.7278415 , 0.78693295],
-       [0.97679875, 0.93929069],
-       [0.93508863, 1.20891365]])
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[13]:
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np.random.seed(100)
-r4 = Rose(Y, w, k=4)
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Visualization

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[14]:
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r4.plot()
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[14]:
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-(<Figure size 432x288 with 1 Axes>,
- <matplotlib.axes._subplots.PolarAxesSubplot at 0x1a2a7efb00>)
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-_images/DirectionalLISA_15_1.png -
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[15]:
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r4.plot(Y[:,0]) # condition on starting relative income
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[15]:
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-(<Figure size 432x288 with 2 Axes>,
- <matplotlib.axes._subplots.PolarAxesSubplot at 0x1a2aa8ccc0>)
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-_images/DirectionalLISA_16_1.png -
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[16]:
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r4.plot(attribute=r4.lag[:,0]) # condition on the spatial lag of starting relative income
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[16]:
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-(<Figure size 432x288 with 2 Axes>,
- <matplotlib.axes._subplots.PolarAxesSubplot at 0x1a2ac076d8>)
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-_images/DirectionalLISA_17_1.png -
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[17]:
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r4.plot_vectors() # lisa vectors
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[17]:
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-(<Figure size 432x288 with 1 Axes>,
- <matplotlib.axes._subplots.AxesSubplot at 0x1a29fafb38>)
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-_images/DirectionalLISA_18_1.png -
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[18]:
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r4.plot_vectors(arrows=False)
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[18]:
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-(<Figure size 432x288 with 1 Axes>,
- <matplotlib.axes._subplots.AxesSubplot at 0x1a2ae61f28>)
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-_images/DirectionalLISA_19_1.png -
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[19]:
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r4.plot_origin() # origin standardized
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-_images/DirectionalLISA_20_0.png -
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Inference

-

The Rose class contains methods to carry out inference on the circular distribution of the LISA vectors. The first approach is based on a two-sided alternative where the null is that the distribution of the vectors across the segments reflects independence in the movements of the focal unit and its spatial lag. Inference is based on random spatial permutations under the null.

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[20]:
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r4.cuts
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[20]:
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-array([0.        , 1.57079633, 3.14159265, 4.71238898, 6.28318531])
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[21]:
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r4.counts
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[21]:
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-array([32,  5,  9,  2])
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[22]:
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np.random.seed(1234)
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[23]:
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r4.permute(permutations=999)
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[24]:
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r4.p
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[24]:
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-array([0.028, 0.   , 0.002, 0.004])
-
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-

Here all the four sector counts are signficantly different from their expectation under the null.

-

A directional test can also be implemented. Here the direction of the departure from the null due to positive co-movement of a focal unit and its spatial lag over the time period results in two two general cases. For sectors in the positive quadrants (I and III), the observed counts are considered extreme if they are larger than expectation, while for the negative quadrants (II, IV) the observed counts are considered extreme if they are small than the expected counts under the null.

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[25]:
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r4.permute(alternative='positive', permutations=999)
-r4.p
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[25]:
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-array([0.013, 0.001, 0.001, 0.013])
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r4.expected_perm
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[26]:
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-array([27.24824825, 11.56556557,  2.43443443,  6.75175175])
-
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-

Finally, a directional alternative reflecting negative association between the movement of the focal unit and its lag has the complimentary interpretation to the positive alternative: lower counts in I and III, and higher counts in II and IV relative to the null.

-
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[27]:
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r4.permute(alternative='negative', permutations=999)
-r4.p
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[27]:
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-array([0.996, 1.   , 1.   , 0.996])
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- - -
- -
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-

- Back to top - -
- -

- -

-

- © Copyright 2018-, pysal developers.
- Created using Sphinx 5.0.2.
-

-
-
- - \ No newline at end of file diff --git a/docs/DirectionalLISA.ipynb b/docs/DirectionalLISA.ipynb deleted file mode 100644 index fd9d8ce..0000000 --- a/docs/DirectionalLISA.ipynb +++ /dev/null @@ -1,607 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Directional Analysis of Dynamic LISAs\n", - "\n", - "This notebook demonstrates how to use Rose diagram based inference for directional LISAs." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import libpysal \n", - "import numpy as np\n", - "from giddy.directional import Rose\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "f = open(libpysal.examples.get_path('spi_download.csv'), 'r')\n", - "lines = f.readlines()\n", - "f.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "lines = [line.strip().split(\",\") for line in lines]\n", - "names = [line[2] for line in lines[1:-5]]\n", - "data = np.array([list(map(int, line[3:])) for line in lines[1:-5]])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "sids = list(range(60))\n", - "out = ['\"United States 3/\"',\n", - " '\"Alaska 3/\"',\n", - " '\"District of Columbia\"',\n", - " '\"Hawaii 3/\"',\n", - " '\"New England\"','\"Mideast\"',\n", - " '\"Great Lakes\"',\n", - " '\"Plains\"',\n", - " '\"Southeast\"',\n", - " '\"Southwest\"',\n", - " '\"Rocky Mountain\"',\n", - " '\"Far West 3/\"']" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "snames = [name for name in names if name not in out]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "sids = [names.index(name) for name in snames]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "states = data[sids,:]\n", - "us = data[0]\n", - "years = np.arange(1969, 2009)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "rel = states/(us*1.)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "gal = libpysal.io.open(libpysal.examples.get_path('states48.gal'))\n", - "w = gal.read()\n", - "w.transform = 'r'" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "Y = rel[:, [0, -1]]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(48, 2)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.71272158, 0.83983287],\n", - " [0.91110532, 0.85393454],\n", - " [0.68196038, 0.80573518],\n", - " [1.181439 , 1.08538102],\n", - " [0.96115746, 1.06906586],\n", - " [1.25677789, 1.39952248],\n", - " [1.14859228, 1.00773478],\n", - " [0.9535975 , 0.9765967 ],\n", - " [0.82090719, 0.86781238],\n", - " [0.85088634, 0.82257262],\n", - " [1.12956204, 1.05319837],\n", - " [0.9624609 , 0.86064962],\n", - " [0.95542231, 0.93021289],\n", - " [0.92674661, 0.96547951],\n", - " [0.77267987, 0.79775169],\n", - " [0.75234619, 0.90588938],\n", - " [0.81803962, 0.90671011],\n", - " [1.09462982, 1.20319339],\n", - " [1.09098019, 1.27472145],\n", - " [1.08107404, 0.86920513],\n", - " [0.98409802, 1.07035913],\n", - " [0.62643379, 0.75604357],\n", - " [0.93039625, 0.9110376 ],\n", - " [0.85870699, 0.86161958],\n", - " [0.93091762, 0.97368683],\n", - " [1.18091762, 1.02422404],\n", - " [0.97627737, 1.08493335],\n", - " [1.17309698, 1.277308 ],\n", - " [0.76120959, 0.83142658],\n", - " [1.19212722, 1.2125199 ],\n", - " [0.79405631, 0.87902905],\n", - " [0.80787278, 0.99159371],\n", - " [1.01955162, 0.89586649],\n", - " [0.83524505, 0.89497115],\n", - " [0.9580292 , 0.9027308 ],\n", - " [0.99165798, 0.99830879],\n", - " [1.00286757, 1.02884998],\n", - " [0.73540146, 0.81242539],\n", - " [0.7898853 , 0.96152507],\n", - " [0.77085506, 0.86987664],\n", - " [0.87695516, 0.93946478],\n", - " [0.80943691, 0.79446876],\n", - " [0.88112617, 0.96214684],\n", - " [0.92805005, 1.09988062],\n", - " [1.06491137, 1.06588241],\n", - " [0.7278415 , 0.78693295],\n", - " [0.97679875, 0.93929069],\n", - " [0.93508863, 1.20891365]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Y" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(100)\n", - "r4 = Rose(Y, w, k=4)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "r4.plot(Y[:,0]) # condition on starting relative income" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "r4.plot(attribute=r4.lag[:,0]) # condition on the spatial lag of starting relative income" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "r4.plot_vectors() # lisa vectors" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "r4.plot_origin() # origin standardized" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Inference\n", - "\n", - "The Rose class contains methods to carry out inference on the circular distribution of the LISA vectors. The first approach is based on a two-sided alternative where the null is that the distribution of the vectors across the segments reflects independence in the movements of the focal unit and its spatial lag. Inference is based on random spatial permutations under the null." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0. , 1.57079633, 3.14159265, 4.71238898, 6.28318531])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r4.cuts" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([32, 5, 9, 2])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r4.counts" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "np.random.seed(1234)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "r4.permute(permutations=999)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.028, 0. , 0.002, 0.004])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r4.p" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here all the four sector counts are signficantly different from their expectation under the null." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A directional test can also be implemented. Here the direction of the departure from the null due to positive co-movement of a focal unit and its spatial lag over the time period results in two two general cases. For sectors in the positive quadrants (I and III), the observed counts are considered extreme if they are larger than expectation, while for the negative quadrants (II, IV) the observed counts are considered extreme if they are small than the expected counts under the null." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.013, 0.001, 0.001, 0.013])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r4.permute(alternative='positive', permutations=999)\n", - "r4.p" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([27.24824825, 11.56556557, 2.43443443, 6.75175175])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r4.expected_perm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, a directional alternative reflecting negative association between the movement of the focal unit and its lag has the complimentary interpretation to the positive alternative: lower counts in I and III, and higher counts in II and IV relative to the null." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.996, 1. , 1. , 0.996])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "r4.permute(alternative='negative', permutations=999)\n", - "r4.p" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docsrc/DirectionalLISA.nblink b/docs/DirectionalLISA.nblink similarity index 100% rename from docsrc/DirectionalLISA.nblink rename to docs/DirectionalLISA.nblink diff --git a/docsrc/Makefile b/docs/Makefile similarity index 91% rename from docsrc/Makefile rename to docs/Makefile index 5353fe4..6361ef0 100644 --- a/docsrc/Makefile +++ b/docs/Makefile @@ -23,8 +23,9 @@ github: @make html sync: - @rsync -avh --exclude '.nojekyll' _build/html/ ../docs/ --delete + @rsync -avh _build/html/ ../docs/ --delete @make clean + touch .nojekyll clean: rm -rf $(BUILDDIR)/* diff --git a/docs/MarkovBasedMethods.html b/docs/MarkovBasedMethods.html deleted file mode 100644 index 1f604bf..0000000 --- a/docs/MarkovBasedMethods.html +++ /dev/null @@ -1,1644 +0,0 @@ - - - - - - - - Spatially Explicit Markov Methods — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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This page was generated from MarkovBasedMethods.ipynb. -Interactive online version: -Binder badge

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Spatially Explicit Markov Methods

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Author: Serge Rey sjsrey@gmail.com, Wei Kang weikang9009@gmail.com

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Introduction

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This notebook introduces Discrete Markov Chains (DMC) model and its two variants which explicitly incorporate spatial effects. We will demonstrate the usage of these methods by an empirical study for understanding regional income dynamics in the US. The dataset is the per capita incomes observed annually from 1929 to 2009 for the lower 48 US states.

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Note that a full execution of this notebook requires pandas, matplotlib and light-weight geovisualization package pysal-splot.

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Classic Markov

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giddy.markov.Markov(self, class_ids, classes=None)
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We start with a look at a simple example of classic DMC methods implemented in PySAL-giddy. A Markov chain may be in one of \(k\) different states/classes at any point in time. These states are exhaustive and mutually exclusive. If one had a time series of remote sensing images used to develop land use classifications, then the states could be defined as the specific land use classes and interest would center on the transitions in and out of different classes for each pixel.

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For example, suppose there are 5 pixels, each of which takes on one of 3 states (a,b,c) at 3 consecutive periods:

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import numpy as np
-c = np.array([['b','a','c'],['c','c','a'],['c','b','c'],['a','a','b'],['a','b','c']])
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So the first pixel was in state ‘b’ in period 1, state ‘a’ in period 2, and state ‘c’ in period 3. Each pixel’s trajectory (row) owns Markov property, meaning that which state a pixel takes on today is only dependent on its immediate past.

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Let’s suppose that all the 5 pixels are governed by the same transition dynamics rule. That is, each trajectory is a realization of a Discrete Markov Chain process. We could pool all the 5 trajectories from which to estimate a transition probability matrix. To do that, we utlize the Markov class in giddy:

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import giddy
-m = giddy.markov.Markov(c)
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-The Markov Chain is irreducible and is composed by:
-1 Recurrent class (indices):
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-0 Transient class.
-The Markov Chain has 0 absorbing state.
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You may turn off the summary for the Markov chain by assigning summary=False when initializing the Markov Chain.

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m = giddy.markov.Markov(c, summary=False)
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In this way, we create a Markov instance - \(m\). Its attribute \(classes\) gives 3 unique classes these pixels can take on, which are ‘a’,‘b’ and ‘c’.

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print(m.classes)
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print(len(m.classes))
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In addition to extracting the unique states as an attribute, our Markov instance will also have the attribute transitions which is a transition matrix counting the number of transitions from one state to another. Since there are 3 unique states, we will have a \((3,3)\) transtion matrix:

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print(m.transitions)
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The above transition matrix indicates that of the four pixels that began a transition interval in state ‘a’, 1 remained in that state, 2 transitioned to state ‘b’ and 1 transitioned to state ‘c’. Another attribute \(p\) gives the transtion probability matrix which is the transition dynamics rule ubiquitous to all the 5 pixels across the 3 periods. The maximum likehood estimator for each element \(p_{i,j}\) is shown below where \(n_{i,j}\) is the number of transitions from state -\(i\) to state \(j\) and \(k\) is the number of states (here \(k=3\)):

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\begin{equation} -\hat{p}_{i,j} = \frac{n_{i,j}}{\sum_{q=1}^k n_{i,q} } -\end{equation}

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print(m.p)
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-[[0.25       0.5        0.25      ]
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This means that if any of the 5 pixels was in state ‘c’, the probability of staying at ‘c’ or transitioning to any other states (‘a’, ‘b’) in the next period is the same (0.333). If a pixel was in state ‘b’, there is a high possibility that it would take on state ‘c’ in the next period because \(\hat{p}_{2,3}=0.667\).

-
-
[8]:
-
-
-
m.steady_state  # steady state distribution
-
-
-
-
-
[8]:
-
-
-
-
-array([0.30769231, 0.28846154, 0.40384615])
-
-
-

This simple example illustrates the basic creation of a Markov instance, but the small sample size makes it unrealistic for the more advanced features of this approach. For a larger example, we will look at an application of Markov methods to understanding regional income dynamics in the US. Here we will load in data on per capita incomes observed annually from 1929 to 2010 for the lower 48 US states:

-
-

Regional income dynamics in the US

-

Firstly, we load in data on per capita incomes observed annually from 1929 to 2009 for the lower 48 US states. We use the example dataset in libpysal which was downloaded from US Bureau of Economic Analysis.

-
-
[9]:
-
-
-
import libpysal
-f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
-pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
-print(pci.shape)
-
-
-
-
-
-
-
-
-(81, 48)
-
-
-

The first row of the array is the per capita incomes for the 48 US states for the year 1929:

-
-
[10]:
-
-
-
print(pci[0, :])
-
-
-
-
-
-
-
-
-[ 323  600  310  991  634 1024 1032  518  347  507  948  607  581  532
-  393  414  601  768  906  790  599  286  621  592  596  868  686  918
-  410 1152  332  382  771  455  668  772  874  271  426  378  479  551
-  634  434  741  460  673  675]
-
-
-

In order to apply the classic Markov approach to this series, we first have to discretize the distribution by defining our classes. There are many ways to do this including quantiles classification scheme, equal interval classification scheme, Fisher Jenks classification scheme, etc. For a list of classification methods, please refer to the pysal package mapclassify.

-

Here we will use the quintiles for each annual income distribution to define the classes. It should be noted that using quintiles for the pooled income distribution to define the classes will result in a different interpretation of the income dynamics. Quintiles for each annual income distribution (the former) will reveal more of relative income dynamics while those for the pooled income distribution (the latter) will provide insights in absolute dynamics.

-
-
[11]:
-
-
-
import matplotlib.pyplot as plt
-%matplotlib inline
-years = range(1929,2010)
-names = np.array(f.by_col("Name"))
-order1929 = np.argsort(pci[0,:])
-order2009 = np.argsort(pci[-1,:])
-names1929 = names[order1929[::-1]]
-names2009 = names[order2009[::-1]]
-first_last = np.vstack((names1929,names2009))
-from pylab import rcParams
-rcParams['figure.figsize'] = 15,10
-plt.plot(years,pci)
-for i in range(48):
-    plt.text(1915,54530-(i*1159), first_last[0][i],fontsize=12)
-    plt.text(2010.5,54530-(i*1159), first_last[1][i],fontsize=12)
-plt.xlim((years[0], years[-1]))
-plt.ylim((0, 54530))
-plt.ylabel(r"$y_{i,t}$",fontsize=14)
-plt.xlabel('Years',fontsize=12)
-plt.title('Absolute Dynamics',fontsize=18)
-
-
-
-
-
[11]:
-
-
-
-
-Text(0.5, 1.0, 'Absolute Dynamics')
-
-
-
-
-
-
-_images/MarkovBasedMethods_23_1.png -
-
-
-
[12]:
-
-
-
years = range(1929,2010)
-rpci= (pci.T / pci.mean(axis=1)).T
-names = np.array(f.by_col("Name"))
-order1929 = np.argsort(rpci[0,:])
-order2009 = np.argsort(rpci[-1,:])
-names1929 = names[order1929[::-1]]
-names2009 = names[order2009[::-1]]
-first_last = np.vstack((names1929,names2009))
-from pylab import rcParams
-rcParams['figure.figsize'] = 15,10
-plt.plot(years,rpci)
-for i in range(48):
-    plt.text(1915,1.91-(i*0.041), first_last[0][i],fontsize=12)
-    plt.text(2010.5,1.91-(i*0.041), first_last[1][i],fontsize=12)
-plt.xlim((years[0], years[-1]))
-plt.ylim((0, 1.94))
-plt.ylabel(r"$y_{i,t}/\bar{y}_t$",fontsize=14)
-plt.xlabel('Years',fontsize=12)
-plt.title('Relative Dynamics',fontsize=18)
-
-
-
-
-
[12]:
-
-
-
-
-Text(0.5, 1.0, 'Relative Dynamics')
-
-
-
-
-
-
-_images/MarkovBasedMethods_24_1.png -
-
-
-
[13]:
-
-
-
import mapclassify as mc
-q5 = np.array([mc.Quantiles(y,k=5).yb for y in pci]).transpose()
-print(q5[:, 0])
-
-
-
-
-
-
-
-
-[0 2 0 4 2 4 4 1 0 1 4 2 2 1 0 1 2 3 4 4 2 0 2 2 2 4 3 4 0 4 0 0 3 1 3 3 4
- 0 1 0 1 2 2 1 3 1 3 3]
-
-
-
-
[14]:
-
-
-
print(f.by_col("Name"))
-
-
-
-
-
-
-
-
-['Alabama', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']
-
-
-

A number of things need to be noted here. First, we are relying on the classification methods in mapclassify for defining our quintiles. The class Quantiles uses quintiles (\(k=5\)) as the default and will create an instance of this class that has multiple attributes, the one we are extracting in the first line is \(yb\) - the class id for each observation. The second thing to note is the transpose operator which gets our resulting array -\(q5\) in the proper structure required for use of Markov. Thus we see that the first spatial unit (Alabama with an income of 323) fell in the first quintile in 1929, while the last unit (Wyoming with an income of 675) fell in the fourth quintile.

-

So now we have a time series for each state of its quintile membership. For example, Colorado’s quintile time series is:

-
-
[15]:
-
-
-
print(q5[4, :])
-
-
-
-
-
-
-
-
-[2 3 2 2 3 2 2 3 2 2 2 2 2 2 2 2 3 2 3 2 3 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3
- 2 2 2 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4
- 3 3 3 4 3 3 3]
-
-
-

indicating that it has occupied the 3rd, 4th and 5th quintiles in the distribution at the first 3 periods. To summarize the transition dynamics for all units, we instantiate a Markov object:

-
-
[16]:
-
-
-
m5 = giddy.markov.Markov(q5)
-
-
-
-
-
-
-
-
-The Markov Chain is irreducible and is composed by:
-1 Recurrent class (indices):
-[0 1 2 3 4]
-0 Transient class.
-The Markov Chain has 0 absorbing state.
-
-
-

The number of transitions between any two quintile classes could be counted:

-
-
[17]:
-
-
-
print(m5.transitions)
-
-
-
-
-
-
-
-
-[[729.  71.   1.   0.   0.]
- [ 72. 567.  80.   3.   0.]
- [  0.  81. 631.  86.   2.]
- [  0.   3.  86. 573.  56.]
- [  0.   0.   1.  57. 741.]]
-
-
-

By assuming the first-order Markov property, time homogeneity, spatial homogeneity and spatial independence, a transition probability matrix could be estimated which holds for all the 48 US states across 1929-2010:

-
-
[18]:
-
-
-
print(m5.p)
-
-
-
-
-
-
-
-
-[[0.91011236 0.0886392  0.00124844 0.         0.        ]
- [0.09972299 0.78531856 0.11080332 0.00415512 0.        ]
- [0.         0.10125    0.78875    0.1075     0.0025    ]
- [0.         0.00417827 0.11977716 0.79805014 0.07799443]
- [0.         0.         0.00125156 0.07133917 0.92740926]]
-
-
-

The fact that each of the 5 diagonal elements is larger than \(0.78\) indicates a high stability of US regional income dynamics system.

-

Another very important feature of DMC model is the steady state distribution \(\pi\) (also called limiting distribution) defined as \(\pi p = \pi\). The attribute \(steady\_state\) gives \(\pi\) as follows:

-
-
[19]:
-
-
-
print(m5.steady_state)
-
-
-
-
-
-
-
-
-[0.20774716 0.18725774 0.20740537 0.18821787 0.20937187]
-
-
-

If the distribution at \(t\) is a steady state distribution as shown above, then any distribution afterwards is the same distribution.

-

With the transition probability matrix in hand, we can estimate the mean first passage time which is the average number of steps to go from a state/class to another state for the first time:

-
-
[20]:
-
-
-
print(giddy.ergodic.mfpt(m5.p))
-
-
-
-
-
-
-
-
-[[  4.81354357  11.50292712  29.60921231  53.38594954 103.59816743]
- [ 42.04774505   5.34023324  18.74455332  42.50023268  92.71316899]
- [ 69.25849753  27.21075248   4.82147603  25.27184624  75.43305672]
- [ 84.90689329  42.85914824  17.18082642   5.31299186  51.60953369]
- [ 98.41295543  56.36521038  30.66046735  14.21158356   4.77619083]]
-
-
-

Thus, for a state with income in the first quintile, it takes on average 11.5 years for it to first enter the second quintile, 29.6 to get to the third quintile, 53.4 years to enter the fourth, and 103.6 years to reach the richest quintile.

-
-
-

Regional context and Moran’s Is

-

Thus far we have treated all the spatial units as independent to estimate the transition probabilities. This hides an implicit assumption: the movement of a spatial unit in the income distribution is independent of the movement of its neighbors or the position of the neighbors in the distribution. But what if spatial context matters??

-

We could plot the choropleth maps of per capita incomes of US states to get a first impression of the spatial distribution.

-
-
[21]:
-
-
-
import geopandas as gpd
-import pandas as pd
-
-
-
-
-
[22]:
-
-
-
geo_table = gpd.read_file(libpysal.examples.get_path('us48.shp'))
-income_table = pd.read_csv(libpysal.examples.get_path("usjoin.csv"))
-complete_table = geo_table.merge(income_table,left_on='STATE_NAME',right_on='Name')
-complete_table.head()
-
-
-
-
-
[22]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
AREAPERIMETERSTATE_STATE_IDSTATE_NAMESTATE_FIPS_xSUB_REGIONSTATE_ABBRgeometryName...2000200120022003200420052006200720082009
020.75034.95611Washington53PacificWA(POLYGON ((-122.400749206543 48.22539520263672...Washington...31528320533220632934349843573838477407824158840619
145.13234.52722Montana30MtnMTPOLYGON ((-111.4746322631836 44.70223999023438...Montana...22569243422469925963275172898730942326253329332699
29.57118.89933Maine23N EngME(POLYGON ((-69.77778625488281 44.0740737915039...Maine...25623270682773128727302013072132340336203490635268
321.87421.35344North Dakota38W N CenNDPOLYGON ((-98.73005676269531 45.93829727172852...North Dakota...25068261182677029109296763164432856358823900938672
422.59822.74655South Dakota46W N CenSDPOLYGON ((-102.7879333496094 42.99532318115234...South Dakota...26115275312772730072317653272633320359983818836499
-

5 rows × 92 columns

-
-
-
-
[23]:
-
-
-
index_year = range(1929,2010,15)
-fig, axes = plt.subplots(nrows=2, ncols=3,figsize = (15,7))
-for i in range(2):
-    for j in range(3):
-        ax = axes[i,j]
-        complete_table.plot(ax=ax, column=str(index_year[i*3+j]), cmap='OrRd', scheme='quantiles', legend=True)
-        ax.set_title('Per Capita Income %s Quintiles'%str(index_year[i*3+j]))
-        ax.axis('off')
-        leg = ax.get_legend()
-        leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))
-plt.tight_layout()
-
-
-
-
-
-
-
-_images/MarkovBasedMethods_46_0.png -
-
-

It is quite obvious that the per capita incomes are not randomly distributed: we could spot clusters in the mid-south, south-east and north-east. Let’s proceed to calculate Moran’s I, a widely used measure of global spatial autocorrelation, to aid the visual interpretation.

-
-
[24]:
-
-
-
from esda.moran import Moran
-import matplotlib.pyplot as plt
-%matplotlib inline
-w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
-w.transform = 'R'
-mits = [Moran(cs, w) for cs in pci]
-res = np.array([(mi.I, mi.EI, mi.seI_norm, mi.sim[974]) for mi in mits])
-years = np.arange(1929,2010)
-fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (10,5) )
-ax.plot(years, res[:,0], label='Moran\'s I')
-#plot(years, res[:,1], label='E[I]')
-ax.plot(years, res[:,1]+1.96*res[:,2], label='Upper bound',linestyle='dashed')
-ax.plot(years, res[:,1]-1.96*res[:,2], label='Lower bound',linestyle='dashed')
-ax.set_title("Global spatial autocorrelation for annual US per capita incomes",fontdict={'fontsize':15})
-ax.set_xlim([1929,2009])
-ax.legend()
-
-
-
-
-
[24]:
-
-
-
-
-<matplotlib.legend.Legend at 0x7fd3508ea7b8>
-
-
-
-
-
-
-_images/MarkovBasedMethods_48_1.png -
-
-

From the above figure we could observe that Moran’s I value was always positive and significant for each year across 1929-2009. In other words, US regional income series are not independent of each other and regional context could be important in shaping the regional income dynamics. However, the classic Markov approach is silent on this issue. We turn to the spatially explict Markov methods - Spatial Markov and LISA Markov - for an explicit incorporation of space in understanding US -regional income distribution dynamics.

-
-
-
-

Spatial Markov

-
giddy.markov.Spatial_Markov(y, w, k=4, m=4, permutations=0, fixed=True, discrete=False, cutoffs=None, lag_cutoffs=None, variable_name=None)
-
-
-

Spatial Markov is an extension to class Markov allowing for a more comprehensive analysis of the spatial dimensions of the transitional dynamics (Rey, 2001). Here, whether the transition probabilities are dependent on regional context is investigated and quantified. Rather than estimating one transition probability matrix, spatial Markov requires estimation of \(k\) transition probability matrices, each of which is conditional on the regional context at the preceding period. The regional -context is usually formalized by spatial lag - the weighted average income level of neighbors:

-
-\[z_{r,t} = \sum_{s=1}^{n} w_{r,s} y_{s,t}\]
-

where \(W\) is the spatial weight matrix and \(w_{r,s}\) represents the weight that spatial unit \(s\) contributes to the local context of spatial unit \(r\) at time period \(t\).

-

Similar to the construction of a Markov instance, we could create a Spatial Markov instance by utilizing the \(Spatial\_Markov\) class in giddy. The only difference between the adoption of \(Markov\) and \(Spatial\_Markov\) class is that the latter accepts the original continuous income data while the former requires a pre-classification/discretization. In other words, here we do not need to apply the classification methods in -mapclassify as we did earlier. In fact, the Spatial Markov class nested the quantile classification methods and all we need to do is set the desired number of classes \(k\) when creating the \(Spatial\_Markov\) instance. Here, we set \(k=5\) (quintile classes) as before.

-

Different from before, quintiles are defined for the pooled relative incomes (by standardizing by each period by the mean). This is achieved by setting the parameter \(fixed\) as True.

-
-
[25]:
-
-
-
giddy.markov.Spatial_Markov?
-
-
-
-
-
[26]:
-
-
-
sm = giddy.markov.Spatial_Markov(rpci.T, w, fixed = True, k = 5,m=5) # spatial_markov instance o
-
-
-
-

We can next examine the global transition probability matrix for relative incomes.

-
-
[27]:
-
-
-
print(sm.p)
-
-
-
-
-
-
-
-
-[[0.91461837 0.07503234 0.00905563 0.00129366 0.        ]
- [0.06570302 0.82654402 0.10512484 0.00131406 0.00131406]
- [0.00520833 0.10286458 0.79427083 0.09505208 0.00260417]
- [0.         0.00913838 0.09399478 0.84856397 0.04830287]
- [0.         0.         0.         0.06217617 0.93782383]]
-
-
-

The Spatial Markov allows us to compare the global transition dynamics to those conditioned on regional context. More specifically, the transition dynamics are split across economies who have spatial lags in different quintiles at the preceding year. In our example we have 5 classes, so 5 different conditioned transition probability matrices are estimated - P(LAG0), P(LAG1), P(LAG2), P(LAG3), and P(LAG4).

-
-
[28]:
-
-
-
sm.summary()
-
-
-
-
-
-
-
-
---------------------------------------------------------------
-                     Spatial Markov Test
---------------------------------------------------------------
-Number of classes: 5
-Number of transitions: 3840
-Number of regimes: 5
-Regime names: LAG0, LAG1, LAG2, LAG3, LAG4
---------------------------------------------------------------
-   Test                   LR                Chi-2
-  Stat.              170.659              200.624
-    DOF                   60                   60
-p-value                0.000                0.000
---------------------------------------------------------------
-P(H0)           C0         C1         C2         C3         C4
-     C0      0.915      0.075      0.009      0.001      0.000
-     C1      0.066      0.827      0.105      0.001      0.001
-     C2      0.005      0.103      0.794      0.095      0.003
-     C3      0.000      0.009      0.094      0.849      0.048
-     C4      0.000      0.000      0.000      0.062      0.938
---------------------------------------------------------------
-P(LAG0)         C0         C1         C2         C3         C4
-     C0      0.963      0.030      0.006      0.000      0.000
-     C1      0.060      0.832      0.107      0.000      0.000
-     C2      0.000      0.140      0.740      0.120      0.000
-     C3      0.000      0.036      0.321      0.571      0.071
-     C4      0.000      0.000      0.000      0.167      0.833
---------------------------------------------------------------
-P(LAG1)         C0         C1         C2         C3         C4
-     C0      0.798      0.168      0.034      0.000      0.000
-     C1      0.075      0.882      0.042      0.000      0.000
-     C2      0.005      0.070      0.866      0.059      0.000
-     C3      0.000      0.000      0.064      0.902      0.034
-     C4      0.000      0.000      0.000      0.194      0.806
---------------------------------------------------------------
-P(LAG2)         C0         C1         C2         C3         C4
-     C0      0.847      0.153      0.000      0.000      0.000
-     C1      0.081      0.789      0.129      0.000      0.000
-     C2      0.005      0.098      0.793      0.098      0.005
-     C3      0.000      0.000      0.094      0.871      0.035
-     C4      0.000      0.000      0.000      0.102      0.898
---------------------------------------------------------------
-P(LAG3)         C0         C1         C2         C3         C4
-     C0      0.885      0.098      0.000      0.016      0.000
-     C1      0.039      0.814      0.140      0.000      0.008
-     C2      0.005      0.094      0.777      0.119      0.005
-     C3      0.000      0.023      0.129      0.754      0.094
-     C4      0.000      0.000      0.000      0.097      0.903
---------------------------------------------------------------
-P(LAG4)         C0         C1         C2         C3         C4
-     C0      0.333      0.667      0.000      0.000      0.000
-     C1      0.048      0.774      0.161      0.016      0.000
-     C2      0.011      0.161      0.747      0.080      0.000
-     C3      0.000      0.010      0.062      0.896      0.031
-     C4      0.000      0.000      0.000      0.024      0.976
---------------------------------------------------------------
-
-
-
-

Visualize the (spatial) Markov transition probability matrix

-
-
[29]:
-
-
-
#we use seaborn to create a heatmap (`pip install seaborn` to install seaborn if you do not have it)
-import seaborn as sns
-sns.set()
-
-fig, ax = plt.subplots(figsize = (5,4))
-im = sns.heatmap(sm.p, annot=True, linewidths=.5, ax=ax, cbar=True, vmin=0, vmax=1,
-                          square=True,  cmap="YlGn",fmt='.3f')
-
-
-
-
-
-
-
-_images/MarkovBasedMethods_60_0.png -
-
-
-
[30]:
-
-
-
fig, axes = plt.subplots(2,3,figsize = (15,8))
-
-for i in range(2):
-    for j in range(3):
-        ax = axes[i,j]
-        if i==1 and j==2:
-            ax.axis('off')
-            continue
-        p_temp = sm.P[i*3+j]
-        im = sns.heatmap(p_temp, annot=True, linewidths=.5, ax=ax, cbar=True, vmin=0, vmax=1,
-                          square=True, cmap="YlGn",fmt='.3f')
-        ax.set_title("Spatial Lag %d"%(i*3+j),fontsize=13)
-
-
-
-
-
-
-
-_images/MarkovBasedMethods_61_0.png -
-
-
-
[31]:
-
-
-
fig, axes = plt.subplots(2,3,figsize = (15,8))
-
-for i in range(2):
-    for j in range(3):
-        ax = axes[i,j]
-        if i==0 and j==0:
-            im = sns.heatmap(sm.p, annot=True, linewidths=.5, ax=ax, cbar=True, vmin=0, vmax=1,
-                          square=True, cmap="YlGn",fmt='.3f')
-            ax.set_title("Global",fontsize=13)
-        else:
-            p_temp = sm.P[i*3+j-1]
-            im = sns.heatmap(p_temp, annot=True, linewidths=.5, ax=ax, cbar=True, vmin=0, vmax=1,
-                          square=True, cmap="YlGn",fmt='.3f')
-            ax.set_title("Spatial Lag %d"%(i*3+j),fontsize=13)
-
-
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-_images/MarkovBasedMethods_62_0.png -
-
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The probability of a poor state remaining poor is 0.963 if their neighbors are in the 1st quintile and 0.798 if their neighbors are in the 2nd quintile. The probability of a rich economy remaining rich is 0.977 if their neighbors are in the 5th quintile, but if their neighbors are in the 4th quintile this drops to 0.903.

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We can also explore the different steady state distributions implied by these different transition probabilities:

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[32]:
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print(sm.S)
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-[[0.43509425 0.2635327  0.20363044 0.06841983 0.02932278]
- [0.13391287 0.33993305 0.25153036 0.23343016 0.04119356]
- [0.12124869 0.21137444 0.2635101  0.29013417 0.1137326 ]
- [0.0776413  0.19748806 0.25352636 0.22480415 0.24654013]
- [0.01776781 0.19964349 0.19009833 0.25524697 0.3372434 ]]
-
-
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The long run distribution for states with poor (rich) neighbors has 0.435 (0.018) of the values in the first quintile, 0.263 (0.200) in the second quintile, 0.204 (0.190) in the third, 0.0684 (0.255) in the fourth and 0.029 (0.337) in the fifth quintile. And, finally the spatially conditional mean first passage times:

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[33]:
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print(sm.F)
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-[[[  2.29835259  28.95614035  46.14285714  80.80952381 279.42857143]
-  [ 33.86549708   3.79459555  22.57142857  57.23809524 255.85714286]
-  [ 43.60233918   9.73684211   4.91085714  34.66666667 233.28571429]
-  [ 46.62865497  12.76315789   6.25714286  14.61564626 198.61904762]
-  [ 52.62865497  18.76315789  12.25714286   6.          34.1031746 ]]
-
- [[  7.46754205   9.70574606  25.76785714  74.53116883 194.23446197]
-  [ 27.76691978   2.94175577  24.97142857  73.73474026 193.4380334 ]
-  [ 53.57477715  28.48447637   3.97566318  48.76331169 168.46660482]
-  [ 72.03631562  46.94601483  18.46153846   4.28393653 119.70329314]
-  [ 77.17917276  52.08887197  23.6043956    5.14285714  24.27564033]]
-
- [[  8.24751154   6.53333333  18.38765432  40.70864198 112.76732026]
-  [ 47.35040872   4.73094099  11.85432099  34.17530864 106.23398693]
-  [ 69.42288828  24.76666667   3.794921    22.32098765  94.37966594]
-  [ 83.72288828  39.06666667  14.3          3.44668119  76.36702977]
-  [ 93.52288828  48.86666667  24.1          9.8          8.79255406]]
-
- [[ 12.87974382  13.34847151  19.83446328  28.47257282  55.82395142]
-  [ 99.46114206   5.06359731  10.54545198  23.05133495  49.68944423]
-  [117.76777159  23.03735526   3.94436301  15.0843986   43.57927247]
-  [127.89752089  32.4393006   14.56853107   4.44831643  31.63099455]
-  [138.24752089  42.7893006   24.91853107  10.35         4.05613474]]
-
- [[ 56.2815534    1.5         10.57236842  27.02173913 110.54347826]
-  [ 82.9223301    5.00892857   9.07236842  25.52173913 109.04347826]
-  [ 97.17718447  19.53125      5.26043557  21.42391304 104.94565217]
-  [127.1407767   48.74107143  33.29605263   3.91777427  83.52173913]
-  [169.6407767   91.24107143  75.79605263  42.5          2.96521739]]]
-
-
-

States in the first income quintile with neighbors in the first quintile return to the first quintile after 2.298 years, after leaving the first quintile. They enter the fourth quintile 80.810 years after leaving the first quintile, on average. Poor states within neighbors in the fourth quintile return to the first quintile, on average, after 12.88 years, and would enter the fourth quintile after 28.473 years.

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Tests for this conditional type of spatial dependence include Likelihood Ratio (LR) test and \(\chi^2\) test (Bickenbach and Bode, 2003) as well as a test based on information theory (Kullback et al., 1962). For the first two tests, we could proceed as follows to acquire their statistics, DOF and p-value.

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[34]:
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giddy.markov.Homogeneity_Results(sm.T).summary()
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---------------------------------------------------
-             Markov Homogeneity Test
---------------------------------------------------
-Number of classes: 5
-Number of transitions: 3840
-Number of regimes: 5
-Regime names: 0, 1, 2, 3, 4
---------------------------------------------------
-   Test                   LR                Chi-2
-  Stat.              170.659              200.624
-    DOF                   60                   60
-p-value                0.000                0.000
---------------------------------------------------
-P(H0)        0        1        2        3        4
-    0    0.915    0.075    0.009    0.001    0.000
-    1    0.066    0.827    0.105    0.001    0.001
-    2    0.005    0.103    0.794    0.095    0.003
-    3    0.000    0.009    0.094    0.849    0.048
-    4    0.000    0.000    0.000    0.062    0.938
---------------------------------------------------
-P(0)         0        1        2        3        4
-    0    0.963    0.030    0.006    0.000    0.000
-    1    0.060    0.832    0.107    0.000    0.000
-    2    0.000    0.140    0.740    0.120    0.000
-    3    0.000    0.036    0.321    0.571    0.071
-    4    0.000    0.000    0.000    0.167    0.833
---------------------------------------------------
-P(1)         0        1        2        3        4
-    0    0.798    0.168    0.034    0.000    0.000
-    1    0.075    0.882    0.042    0.000    0.000
-    2    0.005    0.070    0.866    0.059    0.000
-    3    0.000    0.000    0.064    0.902    0.034
-    4    0.000    0.000    0.000    0.194    0.806
---------------------------------------------------
-P(2)         0        1        2        3        4
-    0    0.847    0.153    0.000    0.000    0.000
-    1    0.081    0.789    0.129    0.000    0.000
-    2    0.005    0.098    0.793    0.098    0.005
-    3    0.000    0.000    0.094    0.871    0.035
-    4    0.000    0.000    0.000    0.102    0.898
---------------------------------------------------
-P(3)         0        1        2        3        4
-    0    0.885    0.098    0.000    0.016    0.000
-    1    0.039    0.814    0.140    0.000    0.008
-    2    0.005    0.094    0.777    0.119    0.005
-    3    0.000    0.023    0.129    0.754    0.094
-    4    0.000    0.000    0.000    0.097    0.903
---------------------------------------------------
-P(4)         0        1        2        3        4
-    0    0.333    0.667    0.000    0.000    0.000
-    1    0.048    0.774    0.161    0.016    0.000
-    2    0.011    0.161    0.747    0.080    0.000
-    3    0.000    0.010    0.062    0.896    0.031
-    4    0.000    0.000    0.000    0.024    0.976
---------------------------------------------------
-
-
-

From the above summary table, we can observe that the observed LR test statistic is 170.659 and the observed \(\chi^2\) test statistic is 200.624. Their p-values are 0.000, which leads to the rejection of the null hypothesis of conditional spatial independence.

-

For the last (information theory-based) test, we call the function \(kullback\). The result is consistent with LR and \(\chi^2\) tests. As shown below, the observed test statistic is 230.03 and its p-value is 2.22e-16, leading to the rejection of the null.

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[35]:
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print(giddy.markov.kullback(sm.T))
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-{'Conditional homogeneity': 230.0266246375395, 'Conditional homogeneity dof': 80, 'Conditional homogeneity pvalue': 2.220446049250313e-16}
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LISA Markov

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giddy.markov.LISA_Markov(self, y, w, permutations=0, significance_level=0.05, geoda_quads=False)
-
-
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The Spatial Markov conditions the transitions on the value of the spatial lag for an observation at the beginning of the transition period. An alternative approach to spatial dynamics is to consider the joint transitions of an observation and its spatial lag in the distribution. By exploiting the form of the static LISA and embedding it in a dynamic context we develop the LISA Markov in which the states of the chain are defined as the four quadrants in the Moran scatter plot, namely, HH(=1), -LH(=2), LL(=3), HL(=4). Continuing on with our US example, the LISA transitions are:

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[36]:
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lm = giddy.markov.LISA_Markov(pci.T, w)
-print(lm.classes)
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-[1 2 3 4]
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The LISA transitions are:

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[37]:
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print(lm.transitions)
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-[[1.087e+03 4.400e+01 4.000e+00 3.400e+01]
- [4.100e+01 4.700e+02 3.600e+01 1.000e+00]
- [5.000e+00 3.400e+01 1.422e+03 3.900e+01]
- [3.000e+01 1.000e+00 4.000e+01 5.520e+02]]
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and the estimated transition probability matrix is:

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[38]:
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print(lm.p)
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-[[0.92985458 0.03763901 0.00342173 0.02908469]
- [0.07481752 0.85766423 0.06569343 0.00182482]
- [0.00333333 0.02266667 0.948      0.026     ]
- [0.04815409 0.00160514 0.06420546 0.88603531]]
-
-
-

The diagonal elements indicate the staying probabilities and we see that there is greater mobility for observations in quadrants 2 (LH) and 4 (HL) than 1 (HH) and 3 (LL).

-

The implied long run steady state distribution of the chain is:

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[39]:
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print(lm.steady_state)
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-[0.28561505 0.14190226 0.40493672 0.16754598]
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again reflecting the dominance of quadrants 1 and 3 (positive autocorrelation). The mean first passage time for the LISAs is:

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[40]:
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print(giddy.ergodic.mfpt(lm.p))
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-[[ 3.50121609 37.93025465 40.55772829 43.17412009]
- [31.72800152  7.04710419 28.68182751 49.91485137]
- [52.44489385 47.42097495  2.46952168 43.75609676]
- [38.76794022 51.51755827 26.31568558  5.96851095]]
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-
-

To test for dependence between the dynamics of the region and its neighbors, we turn to \(\chi^2\) test of independence. Here, the \(\chi^2\) statistic, its p-value and degrees of freedom can be obtained from the attribute \(chi\_2\). As the p-value is 0.0, the null of independence is clearly rejected.

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[41]:
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print(lm.chi_2)
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-(1058.2079036003051, 0.0, 9)
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Next steps

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  • Simulation/prediction of Markov chain and spatial Markov chain

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References

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- Back to top - -
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- -

-

- © Copyright 2018-, pysal developers.
- Created using Sphinx 5.0.2.
-

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- - \ No newline at end of file diff --git a/docs/MarkovBasedMethods.ipynb b/docs/MarkovBasedMethods.ipynb deleted file mode 100644 index 748effe..0000000 --- a/docs/MarkovBasedMethods.ipynb +++ /dev/null @@ -1,1598 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Spatially Explicit Markov Methods \n", - "\n", - "**Author: Serge Rey , Wei Kang **" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "This notebook introduces Discrete Markov Chains (DMC) model and its two variants which explicitly incorporate spatial effects. We will demonstrate the usage of these methods by an empirical study for understanding [regional income dynamics in the US](#Regional-income-dynamics-in-the-US). The dataset is the per capita incomes observed annually from 1929 to 2009 for the lower 48 US states.\n", - "\n", - "* [Classic Markov](#Classic-Markov)\n", - "* [Spatial Markov](#Spatial-Markov)\n", - "* [LISA Markov](#LISA-Markov)\n", - "\n", - "Note that a full execution of this notebook requires **pandas**, **matplotlib** and light-weight geovisualization package pysal-**splot**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Classic Markov\n", - "\n", - "```\n", - "giddy.markov.Markov(self, class_ids, classes=None)\n", - "```\n", - "\n", - "We start with a look at a simple example of classic DMC methods implemented in PySAL-giddy. A Markov chain may be in one of $k$ different states/classes at any point in time. These states are exhaustive and mutually exclusive. If one had a time series of remote sensing images used to develop land use classifications, then the states could be defined as the specific land use classes and interest would center on the transitions in and out of different classes for each pixel.\n", - "\n", - "For example, suppose there are 5 pixels, each of which takes on one of 3 states (a,b,c) at 3 consecutive periods:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "c = np.array([['b','a','c'],['c','c','a'],['c','b','c'],['a','a','b'],['a','b','c']])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So the first pixel was in state ‘b’ in period 1, state ‘a’ in period 2, and state ‘c’ in period 3. Each pixel's trajectory (row) owns [Markov property](https://en.wikipedia.org/wiki/Markov_property), meaning that which state a pixel takes on today is only dependent on its immediate past. \n", - "\n", - "Let's suppose that all the 5 pixels are governed by the same transition dynamics rule. That is, each trajectory is a realization of a Discrete Markov Chain process. We could pool all the 5 trajectories from which to estimate a transition probability matrix. To do that, we utlize the **Markov** class in **giddy**:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The Markov Chain is irreducible and is composed by:\n", - "1 Recurrent class (indices):\n", - "[0 1 2]\n", - "0 Transient class.\n", - "The Markov Chain has 0 absorbing state.\n" - ] - } - ], - "source": [ - "import giddy\n", - "m = giddy.markov.Markov(c)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may turn off the summary for the Markov chain by assigning `summary=False` when initializing the Markov Chain." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "m = giddy.markov.Markov(c, summary=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this way, we create a **Markov** instance - $m$. Its attribute $classes$ gives 3 unique classes these pixels can take on, which are 'a','b' and 'c'." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['a' 'b' 'c']\n" - ] - } - ], - "source": [ - "print(m.classes)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3\n" - ] - } - ], - "source": [ - "print(len(m.classes))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to extracting the unique states as an attribute, our **Markov** instance will also have the attribute *transitions* which is a transition matrix counting the number of transitions from one state to another. Since there are 3 unique states, we will have a $(3,3)$ transtion matrix:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 2. 1.]\n", - " [1. 0. 2.]\n", - " [1. 1. 1.]]\n" - ] - } - ], - "source": [ - "print(m.transitions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above transition matrix indicates that of the four pixels that began a transition interval in state ‘a’, 1 remained in that state, 2 transitioned to state ‘b’ and 1 transitioned to state ‘c’. Another attribute $p$ gives the transtion probability matrix which is the transition dynamics rule ubiquitous to all the 5 pixels across the 3 periods. The maximum likehood estimator for each element $p_{i,j}$ is shown below where $n_{i,j}$ is the number of transitions from state $i$ to state $j$ and $k$ is the number of states (here $k=3$):\n", - "\n", - "\\begin{equation}\n", - "\\hat{p}_{i,j} = \\frac{n_{i,j}}{\\sum_{q=1}^k n_{i,q} }\n", - "\\end{equation}" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.25 0.5 0.25 ]\n", - " [0.33333333 0. 0.66666667]\n", - " [0.33333333 0.33333333 0.33333333]]\n" - ] - } - ], - "source": [ - "print(m.p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This means that if any of the 5 pixels was in state 'c', the probability of staying at 'c' or transitioning to any other states ('a', 'b') in the next period is the same (0.333). If a pixel was in state 'b', there is a high possibility that it would take on state 'c' in the next period because $\\hat{p}_{2,3}=0.667$. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.30769231, 0.28846154, 0.40384615])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.steady_state # steady state distribution" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This simple example illustrates the basic creation of a Markov instance, but the small sample size makes it unrealistic for the more advanced features of this approach. For a larger example, we will look at an application of Markov methods to understanding regional income dynamics in the US. Here we will load in data on per capita incomes observed annually from 1929 to 2010 for the lower 48 US states:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Regional income dynamics in the US\n", - "Firstly, we load in data on per capita incomes observed annually from 1929 to 2009 for the lower 48 US states. We use the example dataset in [**libpysal**](https://github.com/pysal/libpysal) which was downloaded from [US Bureau of Economic Analysis](https://www.bea.gov)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(81, 48)\n" - ] - } - ], - "source": [ - "import libpysal\n", - "f = libpysal.io.open(libpysal.examples.get_path(\"usjoin.csv\"))\n", - "pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])\n", - "print(pci.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first row of the array is the per capita incomes for the 48 US states for the year 1929:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 323 600 310 991 634 1024 1032 518 347 507 948 607 581 532\n", - " 393 414 601 768 906 790 599 286 621 592 596 868 686 918\n", - " 410 1152 332 382 771 455 668 772 874 271 426 378 479 551\n", - " 634 434 741 460 673 675]\n" - ] - } - ], - "source": [ - "print(pci[0, :])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to apply the classic Markov approach to this series, we first have to discretize the distribution by defining our classes. There are many ways to do this including quantiles classification scheme, equal interval classification scheme, Fisher Jenks classification scheme, etc. For a list of classification methods, please refer to the pysal package [**mapclassify**](https://github.com/pysal/mapclassify). \n", - "\n", - "Here we will use the quintiles for each annual income distribution to define the classes. It should be noted that using quintiles for the pooled income distribution to define the classes will result in a different interpretation of the income dynamics. Quintiles for each annual income distribution (the former) will reveal more of relative income dynamics while those for the pooled income distribution (the latter) will provide insights in absolute dynamics." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Absolute Dynamics')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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NzFYT0nI8IOksSXXi/PtkSXcT5sk3SWomqWmsW+w8OQOFzvnjzpN/A3+R1EpSNUlHSaoFLCUklC0scDIGaCnpKkm1JNWX1DuWfQacIqlxTCVyVdqzi4tod5cq6UoVCPlL6qYuzGwtIRnOuYQo2CLgLkLm4ZRxhKVGuYlrERLglMZthFwsU4DJhORxGSWOM7NXCattxkg6pLj6zjnnnHPOOedcVWBm9wBXAzcRgha5hNxfLxBOtJtImCN/QZhv31EOfRY35/9V7O9jwik+dxFOAN4A3Am8J2lV+o6R2O6JhO22iwin7B0bi4cR5vw5wOuExR1JfyAEkFZJ+lVZP2NZFHv6j3PloWnTpta+ffvKHoZzzjnnnHPOlUne1q2sXbGMzevXA7B3y9bU3GuvYp+bNGnSMjNLP/jF7eYyTlTrXFm0b9+eiRMnVvYwnHPOOeecc65UNm/YwIcvjOKTl19A1TpzxOk/IHvcm9Taqw4D/3gfWdWqFfm8pDlFVnC7pQoNqqSfJe2cc84555xzzu1O8vPzmPL2m7w3chgbVq+ie9/j+e65g6jfuCnN2rbnxb/8nslvvsoh/U+t7KFWGZKGABebWZ/KHktFKzaoIikHaA5sA/KAqcAThKMFCz0O2TnnnHPOOeec253NnTKZsU88wtI5s2nVpRtnX3szLTrvv728c6+jaHvgwbw3chhdvnM0e9VvUGFjiXPzVkArM1uWuP8pcAjQwcxyKmwAVVxBgRxJQ4F5ZnZTRfWbaaLa082sPuF4pj8C1wGPVtSgyosk397knHPOOeecc65EVi1ayH/+fCfP/O5GNm9Yz2lXXce5t921Q0AFQBLHDrmUzRs38N7I8jhop1izgfMS/fdgx6OH3S5W0iOVV5vZi8D/ARdIOjAeffRnSXMlLZb0oKQCs/RIul7SLElrJU2VdHaibI6kw+P7AZJMUvd4fZGkF+L7XpImxCy/CyX9XVLNRDsm6aeSviJkD0bSAZLekLRC0gxJ55Tw6+Scc84555xz7lvg/WdG8NjVlzNn8qf0OXcwQ/7yT7ocdTSSCqzftE07Dul/KpPffJUlOV9X9PCGAYMT1xcQdpIAIOlUSZ9KWiMpV9KtibLakoZLWh7n0x9Lah7Lhkj6Os7VZ0saEO93kvS/+MwySU9KapRos42k5yQtjXX+nhxsjBWsjG2enLifI+mExPWtkoZnMM6Gkh6NsYD5ku6Ixzh3BR4EjpK0Lj53KTAAuDbeeym2cV18dm2MDxxflm9IaY5Uxsw+AuYBRxNWruxPWG7UGWhNOA+7ILPiMw0JxyIPl9Qylo0D+sX3fYGvgWMS1+Pi+zzgl0BT4CjgeOCKtH7OAnoD3STVBd4ARgD7EI6B+oekbiX82M4555xzzjnn9mBL5+YwYfQIOvXsxY/ve5jeZ59DjZq1in3uOz8cQO169fjfYw9RwSfsfgA0kNRVUjXC/Da5RGY9IejSCDgVuFzSWbHsAsJcvA3QBLgM2BjnzPcDJ8cdKt8BPovPiHB8cSuga3z2VoDY/xhgDtCeEAt4OjGW3sAMwtz9buBRFRaZ2lGB44xlQwmpSToDhxKOer7YzKbFehPMrJ6ZNTKzh4EngbvjvdMldSEcQX1E/Kz9Ccc2l1qpgirRAqAxcCnwSzNbEc+Z/j3hG7sTM3vGzBaYWb6ZjSSsJOkVi8cRgicQAi9/SFxvD6qY2SQz+8DMtsX9Yg8l6qX8IY5nI3AakGNmj8VnPgWeBX5Uhs/unHPOOeecc24Pkz3uLbKqVeeEi39Kvb0bZ/xc7Xr16HPeYOZPz2bG++9U4AiBb1arnAhMA+anCsxsrJl9Eefck4Gn+Ga+vJUQpOhsZnlxbr0mluUDB0ray8wWmll2bG+mmb1hZpvNbCnwl0R7vQjBll+b2Xoz22Rm4xPjnGNmj5hZHvA40JKQr7U4BY4zrlY5Bbgq9rcE+CuFxB8KkQfUIizAqGFmOWY2qwTP76QsQZXWhES3dYBJcXnNKuBVoMCztyUNlvRZou6BhKgVhKDJ0XHlSjVgFPBdSe0JUarPYhv7SxojaZGkNYQgTtO0rnIT79sBvVN9xn4HAC3K8Nmdc84555xzzu1B8rZtY9q7b9PxsCOo06BhiZ8/8NgT2adDJ8YN/zdbN22qgBFuNww4HxhCYusPgKTekt6O23FWE1ZvNE089xrwtKQFku6OgYX1hBQflwELJb0s6YDYXnNJT8ftMmsIq2JS7bUhBE62FTLORak3ZrYhvq2X4efbaZyEuX2NOMbU3P4hwo6UjJjZTOAqwmqbJfGztcr0+YKUKqgi6QhCUOUFwjKc7nF5TSMza2hmO32hJLUDHiEstWliZo2AKYTlRKkPtwG4EngnRswWEVbCjE+cNPRPYDqwn5k1AG5MtZGQXG+VC4xLjK9RXPpzeWk+u3POOeecc865PU/O55PCkcn9Tii+cgGysqpx3JCfsG7Fcj584ZlyHt03zGwOIWHtKcBzacUjgBeBNmbWkJBnJDXn3mpmt5lZN8IWn9OI+VnM7DUzO5GwmmQ6Ye4OYRGDAT3i/Hsg38y/c4G2Kt0BMevZMcHu9kUPRYwzF9gMNE3M7RuYWffUowX0s9M9MxsRTwhqF8vvKsX4tytRUEVSA0mnEfZJDTezzwlf7L9K2ifWaS2pfwGP140DXhrrXUhYqZI0jhB0SeVPGZt2DVAfWAOsi9Gz4oIjY4D9JQ2SVCO+joiJbJxzzjnnnHPOObLHvkWdho3ocMjhpW6j9QHd6Hr0sUx86VlWLVpYjqPbyUXAcXGVSVJ9YIWZbZLUi7CiBQBJx0rqEXOhrCFss8mPq1HOjLlVNgPrCNuBUu2tA1ZLag38OtHXR8BC4I+S6sYEs9/NcPyfAefG+XlP4IfFjdPMFgKvA/fE2ERWTKSb2o60GNhXiYNs4r2Oiba7SDpOUi1gE2GRSD5lkGlQ5SVJawmRod8Q9lFdGMuuA2YCH8TlQG8CXdIbMLOpwD3ABMIH6wG8l1ZtHOGb9k4h1wC/IvxgrCUEdEYWNfCY5+Ukwj6rBYTVL3cR9lE555xzzjnnnPuW27BmNbMmfUTXPv2oVr00Cy++ccz5Q8iqVp2xwx4tp9HtzMxmmdnEAoquAG6P8/ebCWk1UloAowmBimmE+fYwQlzgasJ8eQUhZ0pq8cJtwGHAauBlEitjYq6U0wlJY+cSDrP5vww/wm+BTsDK2MeIDMYJYcVKTWBqfHY0YXUNwP+AbGCRpGXx3qOE/CmrFE4UrkU4bGcZITawD3BDhmMukCo4M7FzAPTs2dMmTizov3nnnHPOOeecq1yfvPISbw99iMF3/41m7TqUub2P/jOad0cM5Qc33k77gw8DQNIkM+tZ5sZdlVKWRLW7DUkDJL1e2eNwzjnnnHPOOVf1ZI97k306dCqXgArAYaecSaMWLXl76MPkbSssj6vbE2QcVJF0vqSJktZJWijpFUl9KnJwpSGpvSRLJssxsyfN7KRyaDtHUumyFjnnnHPOOeecq3KWzpnNktmz6N63/KZ61WvUoN/gS1ixYB6fvTamXNqM89ElMfdJ6t7FksaWSweF9ztW0sUV2cfuLKOgiqSrgXsJmX+bA22BfwBnVtzQnHPOOeecc865ipU97i2yqlXngO8eU67tdjzsCDoccjjvPzOC9atWllez1YBflFdjFamUpwLtdooNqkhqCNwO/NTMnjOz9fGIo5fM7NeSakm6N54fvSC+rxWf7SdpnqRrYkRtYTz1J9X2UEkPxHOw10r6UFKnRPkBkt6QtELSDEnnJMr2knSPpDmSVksaL2kvvklquyquqjlK0hBJ4xPPdk+0u1jSjYnx3JGo10/SvPh+GCGY9FJs99rSfcmdc84555xzzlUFedu2MW38WDod3os6DRqWa9uS6HfBJWzbsoXxTz9RXs3+CfiVpEaF9FngHFpSh5isNStePyJpSeK5YZKuymQAkk6T9Fls731JByXKciRdJ2kysF5S9Xg9P875Z0g6PtbNknS9pFmSlksaJalxLHtZ0pVp/U6WdHbJvlwVL5OVKkcBtYHnCyn/DXAkcAhwMNALuClR3gJoCLQmHPv0gKS9E+XnErL97k04RehOgLik6Q1CFuB9Yr1/SOoWn/szcDjh3OrGwLWEo5BS4cVGZlbPzCYkByupPuGEoleBVoRMxW8V90Uws0GEjManx3bvLu4Z55xzzjnnnHNVV87nk9iwehXd+x1fIe03brUvh51yBlPefqO8mpwIjCWciruDoubQZjabcJrOobH6McA6SV3jdV/CKTtFknQo8G/gJ0AT4CHgxdTCiug84FSgEeGEn58BR5hZfaA/kBPrXQmcFftuRTjN54FY9jgwMNHvwYSYwsvFjXFXyySo0gRYZmaFZdcZANxuZkvMbCkhQDIoUb41lm81s/8SzrhOHrn8vJl9FNt/khCcATgNyDGzx8xsm5l9CjwL/ChG134M/MLM5ptZnpm9b2abM/g8pwGLzOweM9tkZmvN7MMMnnPOOeecc845twfJHvsWdRo2ov3Bh1dYH0d+/1watWhZfMXM3QxcKalZ2v1C59CxfBzQV1KLeD06XncAGgCfZ9D3pcBDZvZhnIc/DmwmLLRIud/Mcs1sI5BHOMa4m6QaZpZjZrNivcuA35jZvDiXvxX4Ydw29CKwv6T9Yt1BwEgz25LBGHepTIIqy4GmReyHagXMSVzPife2P58WkNkA1EtcLyqkrB3QOy4pWiVpFSGA0wJoSlg9M4uSa1PK55xzzjnnnHPO7SE2rFnNrEkf0bVPP6pVr7j0H7Xq1OHCvzxYbu2Z2RRgDHB9WlFRc2gIQZV+hFUq7xBWvPSNr3fNLD+D7tsB16T10YYdYwC5ibHOBK4iBEyWSHpaUqtEW88n2plGCMI0N7NNwEhgYFxUcR4wLIPx7XKZBFUmECJPZxVSvoDwxUhpG++VVS4wzswaJV71zOxyYBmwibCUKJ1l0G7HQsrWA3US1y3Syotr2znnnHPOOefcbmD6e++Qn7eN7n0rZutPUla1auXd5C3AJYQtMSlFzaEhBFWOJgRWxgHjge+S4dafRB93pvVRx8yeStTZYd5sZiPMrA8hbmDAXYm2Tk5rq7aZzY/ljxOCQscDG9JTe1QVxQZVzGw1YXnRA5LOklRHUg1JJ0u6G3gKuElSM0lNY93h5TC2MYTlPoNifzUkHSGpa4yg/Rv4i6RWkqrFhLS1gKWE3CqFBU7GAC0lXaWQZLe+pN6x7DPgFEmN45Ko9EQ9i4to1znnnHPOOefcbiJ73Jvs06ETzdp1qOyhlFhcATIS+HnidqFz6PjMV8BGQq6ScWa2hjDH/QGZB1UeAS6T1FtBXUmnxtylO5HURdJxca6+KfafWhHzIHCnpHaxbjNJ208YjkGUfOAequgqFcjwSGUzuwe4mpCAdikhovQz4AXgDkKynMnAF8An8V6ZmNla4CRCcp0FhG1CdxH2Y0FIzPMF8DGwIpZlmdkGQrLb9+IyoiMLaPdE4PTY5lfAsbF4GGEfWQ7wOuGHNOkPhADSKkk7JQZyzjnnnHPOOVf1LZ0zmyWzZ9G97wmVPZSyuB2om7rIYA4NIXiy3MxyE9cizOOLYrGPiYQVMn8nJJadCQwp4rlawB8Ju00WERLo3hDL7iPkTnld0lrgA6B32vNPAD0on4UbFUJmvqPFVbyePXvaxIkTK3sYzjnnnHPOOcfYJ/7Fp6+O4ScPPl7uRykXRtIkM+u5SzorR5I+IRw+80Il9D0YuDRuH6qSMlqp4pxzzjnnnHPO7Qnytm1j2vixdDq81y4LqOyuJHUHugKfVkLfdYArgId3dd8lUeFBFUkmqXN8/6Ck3ybKLpe0WNI6SU3Kud+2sd1yzwjknHPOOeecc273lPP5JDasXkX3fhWfoHZ3JukuYArwJzObU1z9ErbdT9K8Isr7E1KPLAZGlGff5S3joIqk8yVNjIGKhZJekVSiJThmdpmZ/S62VwP4C3BSzEi8vGRDL7avubHdvPJs1znnnHPOOefc7it77FvUadiI9gcfXtlDKRFJOZK2xANikvc/jYsZ2pdnf2Z2XXz7RHm2m2Hfr5lZXTM708y27er+SyKjoIqkq4F7gd8DzQnHJv8TMBJEAAAgAElEQVQDOLOo54rRHKgNZJf0wZhl2LcuOeecc84555zL2IY1q5k16SO69ulHterVK3s4pTEbOC91IakHUKc0DUnaLb8AVU2xgQlJDQlZhX9qZs+Z2Xoz22pmL5nZryX1kjQhnoizUNLfJdUspK2hku6QtD8wI95eJel/sfw7kj6WtDr++Z3Es2Ml3SnpPWAD0DHe+52k9yStlfR6KmonqX2M1lWP1xdKmhbrfS3pJ2X5wjnnnHPOOeec271Mf+8d8vO20b3vbrv1ZxgwOHF9AYmVJPF4408lrZGUK+nWRFlqjnyRpLnA/yS9LOnKZAeSJks6O73jDNu+QNJcScsk/SZRvleMB6yUNBU4ohy+FlVCJqs9jiKsKHm+kPI84JdA01j3eEIymUKZ2ZdA93jZyMyOk9QYeBm4H2hC2Br0clqulUHApUB9ILWn63zgQsLRTDUJRy0XZAlwGtAg1v+rpMOKGqdzzjnnnHPOuT1H9rg32adDJ5q161DZQymtD4AGkrrG/KHnsuNxw+sJQZdGwKnA5ZLOSmujLyH5bH/gcWBgqkDSwUBrwtw8XSZt9wG6EOICN0vqGu/fAnSKr/6EYNAeIZOgShNgWWH7mMxskpl9YGbbzCwHeIjwTSqpU4GvzGxYbOspYDpweqLOUDPLjuVb473HzOxLM9sIjAIOKWScL5vZLAvGAa8DR5dinM4555xzzjnndjNL58xmyexZHNjvhMoeSlmlVqucCEwD5qcKzGysmX1hZvlmNhl4ip3n57fGHSgbgReB/SXtF8sGASPNbEt6pxm2fZuZbTSzz4HPgYPj/XOAO81shZnlEhZT7BEyCaosB5oWtt9K0v6SxkhaJGkNIe9K04LqFqMV36w+SZlDiJKl5Bbw3KLE+w1AvULGebKkDyStkLQKOKWU43TOOeecc845t5vJHvcmWdWqc8B3S7MGoEoZRtixMYS0JLKSekt6W9JSSauBy9h53rt9Xm1mm4CRwMCYt/S82P5OMmy7sPl5K3acz5fraUKVKZOgygRgM5C+rCfln4QVJfuZWQPgRkClGMsCoF3avbYkom6AlaJdJNUCngX+DDQ3s0bAf0s5Tuecc84555xzu5G8bduY+u5YOvXsxV71G1T2cMokHm88m7BQ4Lm04hGE1SdtzKwh8CA7z3vT59WPAwMIW3Y2mNmEQrrOpO3CLATaJK7bZvhclVdsUMXMVgM3Aw9IOktSHUk14sqPuwn5TdYA6yQdAFxeyrH8l7Ds6HxJ1SX9H9ANGFPK9pJqArUI51xvk3QycFI5tOucc84555xzroqb/dkkNq5ZTfe+u/3Wn5SLgOPMbH3a/frACjPbJKkXYUVLkWIQJR+4h0JWqZS27YRRwA2S9pa0L3BlcQ/sLjI6ltjM7gGuBm4iBCZygZ8BLxASw54PrAUeISwdKjEzW05IJHsNYcvRtcBpZrasNO2ltb0W+DnhG7kyjvfFsrbrnHPOOeecc67qm/3Jx9SqU5f2B+8ZZ5XEfKETCyi6Arhd0lrC4ohRGTb5BNCDHZPellfbALcRtvzMJuQ3LSp4s1uRWal21DhXIj179rSJEwv6b94555xzzjnnKtajv7iExq3bcPa1N1faGCRNMrOelTaAIkgaDFxqZn0qeyy7m4xWqgBIGiJpfOLaJHWO7x+U9NuyDkbSAEmvl7Ud55xzzjnnnHMOYO3yZaxatJC23Q8uvvK3kKQ6hFUoD++Cvk6QlFPR/exKxQZVJOVIKnLjmZldZma/K+tgzOxJM/NcJ84555xzzjnnykVu9mQA2nTvUckjKZs4N18iqW7i3sWSxpahzf6EFB+LCYlo08uHS3os7V5fScsltSxtv3uSjFeqOOecc84555xzu5u5UyZTu34DmrVtX67tbtq0kGXLx5ZrmxmoBvyivBozs9fMrK6ZnWlm2wqo8gvgZEknAkiqTcileo2ZLSxJX5Kql33EVU+5BFUkDZV0R3zfT9I8SdfEKNpCSRcm6jaU9EQ823qOpJviedg7bDFS8NfYxhpJX0g6sDzG65xzzjnnnHNuz2dmzM3+nDbdDkRZ5bemYOvWlXzy6fl8/vlFzJ79N3ZhrtI/Ab+S1KigQkkHSHpD0gpJMySdE+93kLQqMfd+RNKSxHPDJF2V3l48UOZK4OG4QuYWYJaZDY3P1ZZ0f5z3z5f0F0k1Y9kJcXXNjZIWEYIx6eP9paQpklqV8etSaSpqpUoLoCHQmnDU0wOS9o5lf4tlHYG+wGDgwgLaOAk4Btg/1j+HcCqQc84555xzzjlXrNVLFrN22VLadD+o3NrMz9/C5C+uYPPmRTRpcixfz76Xr7++Z1cFViYCYwmn8O4gBj3eIGzj2Qc4F/iHpG5mNhtYAxwaqx8DrJPUNV73BcYV1KGZPQN8AjwFXBpfKTcDPYGDYtvfBW5IlO8L1APaEvK2JMd7OzAA6GtmC4r/6FVTRQVVtgK3m9lWM/svsA7oIqka4Rt7g5mtNbMcwlnYgwppoz5wAOGUomklXV70bRWjgV9I+kzSxHivcYxYfhX/3DveV4wszpQ0WdJhiXYuiPW/knRB4v7hsf2Z8Vnt+k/pnHPOOeecc0VL5VNpW05BFTNj+ozfsmrVR3Q94C4OPuhhWrX6P3Lm/JOZM/+wqwIrNwNXSmqWdv80IMfMHjOzbWb2KfAs8KNYPg7oK6lFvB4drzsADYDPi+jzCuA4wjw/N3F/AHCrmS01syXA7ew4v98Wy7eY2cZ4T5LuIwRyjourYXZbFRVUWZ62H2sDITrVFKhBOJ86ZQ5hRcsOzOx/wN+BB4Alkh6W1KCCxrsnOtbMDkkc2XU98JaZ7Qe8Fa8BTgb2i69LgX9CCMIQlnb1BnoBtyRWG/0TuCTx3Pcq/uM455xzzjnnXMnkZk+mTsNGNG7dplzamzv3ERYuHE2H9lfSosUZSFkc0OVO9t13MHNzH2XGl7dill8ufRXGzKYAY/hmTpfSDugdt/mskrSKEPRIBVHGAf0Iq1TeIax46Rtf71oRAzezxcAyIDutqBVFz+8Xm9mWtGeaABcDd5rZmsI/6e5hVyeqXUZYgdIuca8tML+gymZ2v5kdDnQjbAP6dYWPcM91JvB4fP84cFbi/hMWfAA0ilmc+wNvmNkKM1tJWEb2vVjWwMw+sBCGfSLRlnPOOeecc85VCSGfymTadD+I8lhcv3Tp68ycdTf77HMqHTp8kytWEvvvdzNt217C/PnDmT79N5jllbm/YtxC+IfuZAAjFxhnZo0Sr3pmdnksHwccTQisjAPGE7brFLr1JwMLKHp+X9DSnWXAGcBwSUeWst8qY5cGVSz8ZI0C7pRUX1I74GpgeHpdSUdI6i2pBrAe2ARUbMhvz2HA65ImSUrtd2ue2D61CGge37cm/MeXMi/eK+r+vALuO+ecc84551yVsXLhfNavXEHbA8u+9Wft2mymZF9NgwYH0a3r3TsFaSTRudN1tG//MxYsHMXUqdeSn1/QYTrlw8xmAiOBnydujwH2lzRIUo34OiKVN8XMvgI2AgMJwZc1hKOUf0DpgypPATdLahq3I/2WAub3BYz/LUJ+1f9I6llc/aqsMo5UvpIQJPmaEBkbAfy7gHoNCNmBVxKWEC0nZDp2xetjZocRtvb8VNIxycK4wqTCN/tJulTSREkTly5dWtHdOeecc84559x2qXwqZU1Su3nzYj6ffCk1ajTioB4PUa1a7QLrSaJTx1/SsePVLFr8AtlTf0l+/tYy9V2M24G6qQszW0s48OVcwgqSRcBdQK3EM+MI6TpyE9ciJKItjdsIuVimAJOBD4E/ZPKgmb1KWG0zRtIhpey/0mkXHv3kKoGkWwmJgi8B+pnZwriFZ6yZdZH0UHz/VKw/g7AcrF+s/5N4/yHCnruxwNtmdkC8f16yXmF69uxpEydOLPfP55xzzjnnnHMFeemvf2TBV9O59IHHSr39Jy9vI5M+OY8NG2Zx+GGjqF+/a/EPAXPm/ouZM/9A06Yn0OPA+8nKqoWkSYmcl24PURkrVVwFklRXUv3Ue0KkcgrwIpA6wecC4D/x/YvA4HgK0JHA6rhN6DXgJEl7xwS1JwGvxbI1ko6Mp/4MTrTlnHPOOeecc5XOzMid+gVty5BPxSyfqVN/zdq1U+je/d6MAyoA7dpezP7738qyZW8yefJl5OVtKtUYXNVXvbIH4Mpdc+D5+D+O6sAIM3tV0sfAKEkXEbZTnRPr/xc4BZhJOKXpQgAzWyHpd8DHsd7tZrYivr8CGArsBbwSX84555xzzjlXJSzPncPGNavLtPXn69n3smTpK3TufAPNmh5f4ufb7DuILNVg+oyb+HzyxaUeRyYkPQjMN7PfVfTzZe1rT1NsUEVSDlAH6GBm6+O9i4GBZtavogYmaSww3Mz+VVF97InM7Gvg4ALuLwd2+j9BzK/y00La+jcF5Lsxs4nAgWUerHPOOeecc85VgLkxn0rbUgZVFi56gZycB2jV8hzatrmo1ONo3fpcsrJqMnXadaVuA0DSq8BHZnZz2v0zgYeAfc2s1Jlxzeyyiqj7bZDp9p9qwC+KrVUFSPLVN84555xzzjn3LZabPZmGzVvQoNk+JX521aqJTJt2A3s3OpIuXW4r83HMLVt+n8MOfbJMbQCPAwO182AGAU8WFVDxOXLFyjSo8ifgV5IaFVQo6QBJb0haIWmGpHPi/Q6SVknKitePSFqSeG6YpKsyGYCk0yR9Ftt7X9JBibIcSddJmgysl1Q9Xs+XtDaO6fhYN0vS9ZJmSVouaZSkxrHsZUlXpvU7WdLZGX6dnHPOOeecc85Vovz8POZNnUKbbiVfpZKXt4EvpvyU2rVb0aPHA2Rl1SyXMe29d6+yNvEC0AQ4OnUj5r48DXhC0lBJd8T7/STNi3PiRcBj8f61khZKWiDpYkkmqXMsK+j5ayQtic9cmOg3WXdvSWMkLZW0Mr7ft6wfdneSaVBlIuHUl1+lF8RkqG8Qjkbeh3B80z8kdTOz2cAa4NBY/RhgXeqcbKAvGZyHLelQwjaUnxB+kB4CXpSUPBrqPOBUoBHQCfgZcISZ1Qf6Azmx3pXAWbHvVoQjmx+IZY8TzuxO9Xsw0Bp4ubgxOuecc84555yrfEtzZrNp/Tradu9R4mcXLfoPW7Yso2vXP1KjRoFrCrbLzs5mzJgxbN1aoccmA2BmG4FRhINCUs4BppvZ5wU80gJoDLQDLpX0PeBq4ASgM+G016K0ABoS5sMXAQ/EIE66LELQph3QFtgI/D2zT7VnKMnpPzcDV0pqlnb/NCDHzB4zs21m9inwLPCjWD4O6CupRbweHa87AA0IZ1oX51LgITP70MzyzOxxYDNwZKLO/WaWG3/Y8ghncXeTVMPMcsxsVqx3GfAbM5tnZpuBW4EfxiVRLwL7S9ov1h0EjDSzLRmM0TnnnHPOOedcJcuN+VRKmqTWzMid9wT16nWjUcOiTz6ePHkyo0ePZuLEiTzzzDPk5eWVerwl8Dhh7lo7Xg+O9wqSD9xiZpvjHPkc4DEzyzazDYR5cFG2Eg4r2Wpm/wXWAV3SK5nZcjN71sw2mNla4E7CAoZvjYyDKmY2BRgDXJ9W1A7oHbflrJK0ChhAiGxBCKr0I6xSeYew4qVvfL1rZvkZdN8OuCatjzaElSYpuYmxzgSuIvygLJH0tKRWibaeT7QzjRCEaW5mm4CRhL1qWYTVL8MyGJ9zzjnnnHPOuSpgbvZk9m61L/UaNynRc6tWfcj69V/SZt/BReZRmTJlCs8//zzt2rWjf//+fPnllzz//PPk52cytS09MxsPLAPOktQJ6EXYMVKQpXF+m9KKxJw57X1BlqfladkA1EuvJKmOpIckzZG0hjDnbySpWjHt7zFKmrDmFuAT4J7EvVxgnJmdWMgz4wg5WebF9+OBB4FNZLD1J9HHnWZ2ZxF1bIcLsxHACEkNCNuF7iKsPMkFfmxm7xXSzuOEQMp4YIOZTchwjM4555xzzjnnKlF+Xh7zp2fTtc+xJX42d94wqldvRPPmpxdaJzs7m2effZa2bdty/vnnU7NmTfLy8njzzTepWbMmp59+epkT2xbjCcIKlS7Aa2a2uJB6lna9EEjmOmlTTuO5Jo6lt5ktknQI8ClQoV+EqqQk239SK0BGAj9P3B5D2DIzSFKN+DoilTfFzL4i7KsaSAi+rAEWAz8g86DKI8BlknorqCvpVEn1C6osqYuk42LOlU2x/1TY8EHgTkntYt1m8Riq1GecEOveg69Scc4555xzzrndxuKvZ7Jl48YSb/3ZtGkBy5a9QetW51CtWu0C60ydOpXRo0fTpk2b7QEVgD59+nD00UfzySef8Prrr2OWHs8oV08Q8qJcQuFbfwoyCrhQUldJdYDfltN46hPm26viATC3lFO7u40SBVWi24G6qYu4b+okQoLaBcAiwqqQZBLZcYTlQ7mJaxFWvRTFYh8TCT80fycklp0JDCniuVrAHwlLoxYREujeEMvuI+ROeV3SWuADoHfa808APYDhxYzPOeecc84551wVMXdKSNnZpoRJaufNH4GZ0br1wALLp02bxujRo9l3330ZMGAAtWrVYtXWbUxdtxGA4447jl69ejFhwgTGjct07UDJmVkO8D5hTv5iCZ57BbgfeJswn/4gFm0u45DuBfYizL0/AF4tY3u7HVVwFK3UJH1CSIzzQiX0PRi41Mz67Oq+91Q9e/a0iRMnVvYwnHPOOeecc3uw0Xf+lvWrVnLBnzI/gCYvbzPvvd+HRg0P56CDHtypfPr06YwaNYpWrVoxcOBAateuzeqt2zjj05nM2rCJsb0OoHOd2uTn5/Piiy/y2Wef0b9/f4466qgd2pE0ycyKzoC7i8SdJVOAWmm5U1wJlWalSoWT1B3oStiLtav7rgNcATy8q/t2zjnnnHPOOVc6edu2Mn/61BKvUlm85CW2bl3BvvsO3qlsxowZjBo1ipYtW24PqGzKy2fIlNl8vWEzNbOyuG3mAgCysrI4/fTT6datG6+99hqTJk0ql89VXiSdLalWPBr5LuAlD6iUXZULqki6C3gduM7M5pTw2SGSxpeh7/7AUkLOl8KyKDvnnHPOOeecq2IWfjWDbVs2lyifipkxb94T1K27H3vvvePKki+//JJRo0bRokWL7QGVfDOunDaXCavWc1/XtlzdrjlvLF/DuBVrAahWrRrf//736dy5My+99BJTpkwp189YRj8BlgCzCCfgXl4ejUrKltSvPNraHRUbVJGUI2mLpKZp9z+VZJLal+eAzOw6M2ttZveXZ7sZ9v2amdU1szMzjdgVFMiRNFTSHRUzSuecc84555xz6XKzvwCJNl0zX6myes0nrF2bzb5pxyh/9dVXjBw5kn322YdBgwax1157YWbcMnM+Ly1dxc2dWvH95ntzSZtmtKtdk1tmzmdbfkitUb16dc455xzatWvHc889x4wZM8rl80m6QdIrafe+KuTeuenPm9n3zKyhmTU2s7PNbGF5jMvMupvZ2PJoa3eU6UqV2cB5qQtJPYA6FTIi55xzzjnnnHOuhHKzJ7NP+47Urlcv42fmzRtG9er1adF8+4GwzJw5k6effppmzZptD6gAPJi7lEfmLeOSfZtyeZtmANTKyuK3nVoxff0mRixcvr2NmjVrct5559GiRQtGjRrF7Nmzy+MjvgN8R1I1AEktgRrAoWn3Ose6bhfINKgyjHAWdsoFhBNyAIjHG38qaY2kXEm3JspqSxouabmkVZI+ltQ8lg2R9LWktZJmSxoQ73eS9L/4zDJJT0pqlGizjaTnJC2NdXbIQiTpz5JWxjZPTtzPkXRC4vpWScMzGGdDSY9KWihpvqQ7JFWLyX0eBI6StC4+dykwALg23nsptnFdfHatpBmSjs/wa++cc84555xzrghbt2xmwZfTSrT1Z/PmJSxZ8gotW/6Q6tXDAbcbNmxg5MiRNG3alMGDB1OnTlhL8Pzildw2awGnN2vEbZ1b77Cq5dRmDTmyYV3umr2INdvytt+vXbs2AwcOpHHjxowYUS7ZJT4mBFEOiddHE07zmZF2bxbwG0n3JB+W9KKkX8b3XSWNjXPYbElnJOoNlfQPSa/EOe17klpIujfOs6dLOjRRf/s8O86xR0l6Is59syX1TNQ9LMYO1kp6RtLI3X2XR6ZBlQ+ABvELX41wfHLyuOH1hKBLI+BU4HJJZ8WyC4CGQBugCXAZsFFSXcKRTiebWX3gO8Bn8RkBfwBaERLWtgFuBYj9jwHmAO2B1sDTibH0JvxQNQXuBh5V8ie+cAWOM5YNBbYRIn6HEo6QvtjMpsV6E8ysnpk1MrOHgSeBu+O90yV1AX4GHBE/a38gJ4MxOeecc84555wrxsIvp5O3bRttD8w8qDJ//lOY5bFv4hjlzz77jK1bt3L22WdvD6iMX7mWn0+by1GN6vK3rm3JSpteSuL2/VqzYus27s1ZvENZnTp1GDx4MA0bNizDpwvMbAvwIXBMvHUM8C4wPu3eO8DjwHmSsuIYmwInACMk1QBeIuQy3Qe4EngyzltTzgFuIsyrNwMTgE/i9WjgL0UM9QzCHL0R4djnv8cx1ASeJ8yvGwNPAWeX+AtRxZQkUW1qtcqJwDRgfqrAzMaa2Rdmlm9mkwlfnL6xeCshSNHZzPLMbJKZrYll+cCBkvYys4Vmlh3bm2lmb5jZZjNbSviGpdrrRQi2/NrM1pvZJjNL5jSZY2aPmFke4QepJdA8g89X4DjjapVTgKtif0uAvxICS5nKA2oB3STVMLMcM5tVguedc84555xzzhUiN3syyspi3wO6Z1Q/P38L8xc8RZMmfalTp328l8/HH39M27ZtadGiBQDZ6zZy4Rez6VinFkMP7EDtagVPoQ+qX4dzWjTmX/OWkrNx8w5l9evX5/LLyyUnLMA4vgmgHE0Iqrybdm+cmX0ErAZSOyTOBcaa2WLgSKAe8Ecz22Jm/yMsXNie8gN4Ps6JNxECIZvM7Ik4zx5JWGxQmPFm9t9YdxhwcLx/JFAduN/MtprZc8BHpfsyVB0lDaqcDwwhsfUHQFJvSW/H7TirCas3miaeew14WtICSXfHwMJ64P9i3YWSXpZ0QGyvuaSn43aZNYRVMan22hACJ4Ulkl2UemNmG+LbTDbVFThOoB1hidXCuDRqFfAQIaKXETObCVxFWG2zJH62Vpk+75xzzjnnnHOucHOnTKZFp/2ouVdmqT+XLHmVLVuW0iZxjPLMmTNZuXIlvXr1AiB30xbO/3wW9atX46mDOtKwRvUi27yhY0uqZ4nfzVqwU1m1atVK8GmK9A7QR1JjoJmZfQW8T8i10hg4kG/yqTwOpJbhDCTMeSEsUsg1s/xEu3MIu0BSkktuNhZwXdQce1Hi/QagtqTqsd/5ZmaJ8twi2tktZBxUiccbzyas2ngurXgEYVlPGzNrSMgzovjcVjO7zcy6Ebb4nEbMzxJP2zmRsJpkOvBIbO/3gAE9zKwB4QcgtcYqF2gbvykltZ4dE+y2SHy+wsaZS1ju1DRu72lkZg3MLBUCTf5AUNg9MxthZn0IQRojnAvunHPOOeecc64MtmzayKJZX5Yon0ruvCfYa6/2NG589PZ7H3/8MfXq1eOAAw5g5dZtnP/5LDbm5/PkQR1pVbtmsW22qFWDK9vuw8tLV/P+ynWl+iwZmEBIW3EJ8B5A3AmyIN5bYGaprLjDgTMlHUxIq/FCvL8AaJPaGhS1JbEbpYIsBFqnpedoU8F9VriSrFQBuAg4Lq4ySaoPrDCzTZJ6EVa0ACDpWEk9Yi6UNYRtNvlxNcqZMbfK5v9n777ju6zO/4+/3pmEQMIIW/ZWlswKCqhoVbRYWy1Fa/XbaX+dVrtrtdV+Wzu/fmvtlorbuiqjZSjgYIsgM4AQwsyC7J3r98d9Bz6EhHwSgmC/1/Px4NF87nPOfZ/7k/SPc3nOdQFFBMeBau9XBORL6gHcHfGs1QS/jJ9JSg4TzE6Kcv7vADMlxYfJcj7e2DzDMlMLgV9JSpEUoyCRbu1xpMPAeeH5MCKu9Yu492BJl0lKBMoIInuRUUHnnHPOOeecc82wf9sWaqqrow6qFBRspKBgPeeddwu1cYW8vDx27NjBmDFjqFQMn353NxmlFcwe1o+hbZKinssXe3amR2I8P9q5n2qr77+/nx4zKwXWAncSHPup9UZ4bXlE330EyW3nAM+HYyHIy1JCUFwlXtJU4DpOzFV6JqwgSI3xZUlxkmYQpPf4QGtSUMXMdpnZ2nqavgT8WFIhcA/wbERbV4JENgUEuViWEfxSYwh+6QeAPIKcKbUHze4DRhOcAZtHxM6Y8FzWdQRJY/cC+wiOEUXjh0B/4Ej4jMgUzA3NE4IdKwnAlnDsPwh21wC8CmwGDknKCa/9lSB/ylFJLxHkU/kZkEOwFaoz8N0o5+ycc84555xzrgGZmzcSExtHj8FDo+q/b98cYmNb073bsf/Gzpo1a4iJiWHMmDF8dete1uQX87vzezOxffTlmQGSYmP4Qf/uvFtUyrOH8po0tgmWEawpI3OLvh5eq1tK+e/AcI6vbWsT3l4HXE2wRv09cKuZbTtTE4547g0EmzWOEpxImUuwyeIDS3YGomfO1TV27Fhbu7a+eJxzzjnnnHPONd8T3/sGsfEJzLyv8QwLFRW5vPnWxXTrdhNDBt8XXqvg17/+Nf369WP8dTP40Mqt3NmnC9/q262Ru9XPzLj27R1kllWwYsJQkuOCfCqS1pnZ2EaGtyhJkwmOAfW2c3DxL2kV8Acze/Rsz6W5mnr85yRhDesWqSsd1rR+vPGep/UMkzTgDNx3qqR9LX1f55xzzjnnnHP1Ky8p5vB7u6I++nPgwDPU1FTQ87xPHbu2adMmysrKGD9+PItzg0K1n+jaodlzksSPB/Qgq6KK/92b1ez7nK6w8MrXgL+cKwEVSVMkdQ2P/3waGAH8q4XufS6bApQAACAASURBVMrYhKQiSf0aam+uRoMqkvZIKg0ncCicaNP2QL0PwnlOO9vzcM4555xzzjn3/ti3dRNmNfQa1nhQpaamin37n6BD+0kkJwf/nd3MWL16NZ07d6Z3794syS1gQOtEeiclnta8xqQmc0OX9vwhM4t9ZRWUlZWd1v3g2Jo3K8xLWnvts5KW1tN3KMERm27Ab1vgudPqXLtN0hsNjTmFwcCGcG7fBD4e5jE948ysjZm919L3jbaCznVmtlhSV4Kyw98Fvt/Sk3HOOeecc84556KVuXkjcfEJdBs4pNG+2TmLKC8/xOBB9x27tm/fPg4dOsT06dMprTHeOlrEbd3TmjSHiooKCgoKyM/PP+F/hxaV8M8uA5n1ymIu27y6ye/WgFiC3Sc/PVUnM9sKJJ+qz9lgZn8C/nS251GXpNgwf2uTNTVR7SGCoMqoOk3tJc2TVChplaT+EZObKGmNpPzwfydGtPWVtCwctwg44a9X0ockvRUmfN0QZiVulKQB4X3zJeVIeqaBftMlrZdUIClT0r0RbX3Co0KflrQ3vM/3I9qTwl07RyRtAcZFMzfnnHPOOeeccy1j77sb6D54CHHx8Y323bdvDq1anUda2qXHrq1evZrExERGjBjBm0cKKa8xLu+YEtWzV61axc9//nN++tOf8rvf/Y45c+bw8ssvs3TpUrZv305CYT6XlRwhPa07vS7/cLPfsY5fAHdJaldfo6QhkhZJypO0XdJN4fW+4bo6Jvz8Z0lZEePmSPp6cycl6TuSdoVr+y2SPhrRdpukNyX9JpzDe2Gc4LZwHZ4VHgWq7T9b0h/C9ygM1/a9wzaF98kK1/HvShoWMZVTxSaOpQIJn/GIpPmSioFLJSVK+mW4/j8czqHR0k/R7lSpncR5BBmCX63TNDO8/jZBduEHCEoXdyCo3vNV4CngRmCepAFmlktQfWcFcCUwIez7cvisHuHnTxGcsboceF7SEDPLbmSqPyEog3wpQdWehpIBFRNU9tkMDAMWSXrHzF6K6HMxwRalQcBqSS+EUb8fEVQS6k8QAVzQyJycc84555xzzrWQjHffIXvvHi67/QuN9i0s2sbRo6sY0P/bSEHi2KKiIjZv3sy4ceNITExk8Z4sWsfGMKFd4xs8Nm3axIIFC+jTpw/9+vUjNTWVlJSUY/8bFxcstW+pqmbiqq38o1XH03vZ49YCS4G7gB9ENoTHghYRVOS9mqDqzyJJm8xsi6QC4EJgHTAZKJI0NFzfTgF+fRrz2gVcQlDt9kbg8XDdX3u0ZwLwF6AjQSXep4FXCKr6TiFY6z9vZkVh/5uB6QTlnx8EniBYm18Zzn0QQbXgIQRHiWrVG5toYM6zgGuAawniBj8jWN+PAioJ4hX30Ejl3mh3qrykoFxyJpBFEFCI9KKZrTazKoKXrd3JMh3YYWZzzKzKzJ4CtgHXSepFsLvjh2ZWbmbLCb7UWrcA881svpnVmNkigj+ga6KYbyXQG+huZmVmVu9ZLzNbambvhvffSBD4mVKn231mVmpmGwjOfo0Mr98EPGBmeWaWCTwUxbycc84555xzzp0mM+PNp+fQtmMnhl/W+C6QgwefR0qge/ebjl17++23qampYdy4cZgZS/IKmNK+LYkxp14m7927lxdffJFevXpx8803M3nyZEaOHEnfvn3p0KHDsYAKQHJcLN/r1523C0qa/7Inuwf4iqROda5fC+wxs0fD9fd64HmCIAcEpZinhGk9AP4Rfu4LpBCsdxvyUrjL5KikowRlmI8xs+fM7EC4tn4G2AGMj+iyO5xXNfAM0BP4cRgLWAhUEARYas0zs+VmVk6QeuQiST0J1vptCYIpMrOtdXKyNBSbqM/LZvammdUQlHX+PPCNcI1fSHDEqqGAzDHRBlWuN7O2wNRw8nUPmR2K+LkEqE1k2x3IqNM3A+gRth0xs+I6bbV6AzfW+cVdTJBopzHfAkSws2SzpP+qr5OkCZJek5QtKR/4YhPfLbOBuTvnnHPOOeecO0Pee3s1B3du50Mfm0lcQsIp+5oZOdmL6dBhIvHxwamZ6upq1q5dS79+/UhLSyO9pJx9ZZWNHv3Jzc3lqaeeIjU1lZkzZxIfxbGjG7u25+Zuza8mVJeZbQLmAt+p09QbmFBnDX0zUBtEWUawpp8MLCfY8TIl/Pd6GFxoyPVm1q72H/ClyEZJt0p6J+K5wzhxbX044ufS8D3qXossiHNsrR3uXskj2DTxKvA74GEgS9KfJEX+0hpav9cncj3fCWgNrIt4h3+F10+pqTlVlgGzgV9GOeQAwS82Ui9gP3CQ4LxTcp22WpnAnMhfnJklm9nPopjnITP7nJl1B74A/F71l1F+Evgn0NPMUoE/EARjonGQILpW39ydc84555xzzp0BVlPDm0/PoX237lww5fJG+xcXp1NatpdOaccL2KSnp1NQUMC4cUFqzCVhKeXLOrQ9xX2KeeKJJwC4+eabad26dVTzjZH41ZAWXy7+CPgcwYaFWpnAsjpr6DZmdkfYvozgiM7U8Oc3gEkEQZVlzZ1ImO/kz8CXgY5h0GUT0a+t63Nsra2g+nAHgvgCZvaQmY0Bzic4BnR3M58RWWY6hyCwc0HEd5dqZo1WPm5SUCX0W+AKSSMb7QnzgUGSZimoQ/0Jghefa2YZBMd57pOUIOli4LqIsY8THBP6sKRYSa0kTQ3zupySpBsj+h0h+LLqi7q1BfLMrEzSeIIzVdF6FviupPbhs77ShLHOOeecc84555ph+4rXyd67h4tuvJnYuMbThGZnLwREWkRQZfXq1aSmpjJo0CAgCKqcn9yK7q3q3/VSWVnJ008/TX5+Pp/85Cfp2LHFcqQ0i5ntJDhG89WIy3MJ1t+fkhQf/hunoLwyZraDIHBwC0HwpYBgB8nHOI2gCkGOUQOyASTdTrBT5XRcI+liSQkEOVNXmllm+D4TJMUT5Egto/61fpOEu3T+DPxGUmcI8rxKavRsWZODKmGS2McIznE11jeX4FzXN4FcgmM515pZTthlFkHCmjyCSNtjEWMzgRnA9wh+OZkEEaho5jwOWCWpiGAnytcaqEf9JeDHYb6YewgCJdG6j+DIz26CpLhzmjDWOeecc84551wT1VRX89ZzT5DWqw9DLrokqjHZOYtJTRlFYmJwkiM7O5vdu3czduxYYmNjKayqZlV+UYNHf2pqanj55ZfJzMzkox/9KL16nTOHFH5MRNnkMA/IlQR5QA4QHIX5OZAYMWYZkBuut2s/iyCxa7OY2RbgVwRFaA4TJMh9s7n3Cz1JECPIA8YQBIIgyP3yZ4LNExkEcYZfnOazan0b2AmsDJP6LiYoWnNKMrPG+jh32saOHWtr164929NwzjnnnHPOfYC9++pCFv7xIWbc/UMGjJ3QaP+ysgO8+dYl9O//Lfr0DqoEzZ8/n3Xr1nHnnXeSnJzM3KyjfHbzHl6+cAAT2p182mPx4sW88cYbTJs2jYsvvrjZc5e0zswaqkzrQpJmA/vM7AeN9T0XNOf4j3POOeecc845976qqqxkxT+eotuAwfQfM77xAQS7VAA6pV0BQHl5Oe+88w4XXHABycnBJo8leQWkxsUyJuXkUsrr1q3jjTfeYMyYMUyaNKmF3sT9J/nABFUkLZX02bM9D+ecc84555xz77+NixdQmJvNpJmfQoouB2pO9mJat+5PcnI/ADZs2EBFRQXjxwdBGTNjSW4BUzu0JS7mxHvu3LmTuXPn0r9/f6655pqon3m2SJot6f4Wute9kh5viXud4hnWQEGZ073vVEn7Wvq+DWk0qCJpj6SsyCo9kj4raekZnZlzzjnnnHPOOQdUlpWx6sVn6TVsBL2Hj4puTGU+R46uOlb1x8xYs2YN3bp1o0ePoGjOpqJSsiqqTsqncvjwYZ599lk6d+7MjTfeSGxsbMu+UDOEa/NSSUWSDoVBlEar07zfwnlOa7xn/czstg/K0R+IfqdKLPC1MzkR55xzzjnnnHOuPm8v+Ccl+UeZ9Ilbox6Tm7sUsyo6dQqO/uzZs4fs7GzGjx9/bNdJbSnlSyNKKRcUFPDEE0+QmJjIrFmzaNWqVQu+yWm7LizzOwq4EPjuWZ7P/3nRBlV+AdwlqV3dBklDJC2SlCdpu6SbwusTwuhZbETfj0raGP48XtIKSUclHZT0u7BcUm3fKyRtk5Qv6XdE1LiW1F/Sq5JyJeVIeqK+uTnnnHPOOeec+2ArKy5izSvP02/MeLoPGhL1uOycxSQkdCIlZSQQlFFOSkpi2LDj1X4X5xYwqm1rOiXEA1BdXc1TTz1FWVkZs2bNIjU1tWVfpoWY2SHg3wTBlUjtJc2TVChplaT+tQ2SJkpaE66x10iaGNHWV9KycNwiIC3yppI+JOmtcP2+QdLUaOYpaUB43/xw7f5MA/2mS1ovqUBSpqR7I9r6hEeFPi1pb3if70e0J4W7do5I2kJQDfh9E21QZS2wFLgr8mJ4JGgRQbmjzgSlm34v6XwzW0VQN/qyiCGzwr4A1cA3CH5ZFwGXE5Q4RlIa8ALwg7B9FxCZFUjAfwPdgaFAT+DeKN/FOeecc84559wHxNpXXqS8uJhJN93SeOdQdXU5ubnLSEu7HCmG/Px8tm3bxoUXXkh8fBBAya2oYl1BCdMijv6sXbuWgwcPMmPGDLp169bi79JSJJ0HXE1QAjjSTOA+oH3Y9kDYvwMwD3gI6Aj8GpgnqWM47klgHcH6+yfApyOe1SMcez/QgSAu8LykTlFM9SfAwnA+5wH/20C/YuBWoB0wHbhD0vV1+lxMUOL4cuAeSUPD6z8C+of/Phw59/dDUxLV3gN8pc4Xdy2wx8weNbMqM1sPPA/cGLY/BXwSQFJb4JrwGma2zsxWhuP2AH8EpoTjrgE2m9k/zKwS+C1BjW3CsTvNbJGZlZtZNsEfRO1Y55xzzjnnnHP/AUryj/L2/JcZPHEynfv0i3rckSNvUV1dfOzoz4YNGzAzxo07volh2ZFCDI7lUykpKeG1116jX79+nH/++S36Hi3oJUmFQCaQRRBQiPSima02syrgCY7vZJkO7DCzOeEa/ClgG3CdpF4Euzt+GK6xlwOvRNzzFmC+mc03sxozW0Sw8eKaKOZbCfQGuptZmZm9UV8nM1tqZu+G999IEDeou8a/z8xKzWwDsAEYGV6/CXjAzPLMLJMgcPS+iTqoYmabgLnAdyIu9wYmhFuAjko6CtwMdA3bnwRukJQI3AC8bWYZAJIGSZobHhEqAH7K8S1G3Qn+SGqfbZGfJXWR9LSk/eHYx6mzPck555xzzjnn3Afbqpeeo6qygok33tykcdk5i4mNTaZD+4sA2L59Oz169KB9+/bH+izJLaBjfBwj2yYBsHTpUsrLy/nwhz98Llf6ud7M2gJTgSGcvA4+FPFzCVCbyLY7kFGnbwbQI2w7YmbFddpq9QZurLPuvxiIZivPtwhOmqyWtFnSf9XXKUwf8pqkbEn5wBeb+G6ZEW113/OMampJ5R8BnyP44iGY+DIzaxfxr42Z3QFgZlsIXuhqTjz6A/AIQWRsoJmlAN/jeN6UgwRHegBQ8BfdM2LsTwEDhodjb4kY65xzzjnnnHPuA64gJ5sNi+ZzwZRpdOjeo/EBIbMacnIW07HjFGJiEikqKmL//v0MGjToWJ9qM17LK+Cyjm2JkcjOzmbNmjWMGTOGLl26nInXaVFmtgyYDfwyyiEHCIIjkXoB+wnW3+0jK/6GbbUygTl11v3JZvazKOZ5yMw+Z2bdgS8QpAupr4zyk8A/gZ5mlgr8gejX+CfED+rM/YxrUlDFzHYCzwBfDS/NBQZJ+pSk+PDfuIizTRB8OV8DJgPPRVxvCxQARZKGAHdEtM0DLpB0g6S48Hld64wtAvLD8113N+U9nHPOOeecc86d21a+8DSYcdHHZzZpXEHBO1RU5NApLTj6s2PHDoATgirrC0rIq6zm8g7B0Z9///vfJCQkcOmll7bQ7N8XvwWukDSy0Z4wn2DtPktSnKRPAOcDc8PTJGuB+yQlSLoYuC5i7OMEx4Q+LClWUitJU8O8Lqck6caIfkcINkfU1NO1LZBnZmWSxhNsyojWs8B3JbUPn/WVJow9bU3dqQLwYyAZwMwKgSsJkuEcINiO83MgMaJ/7VmoV80sJ+L6XQRfVCHwZ4JgDeF9cwjysvwMyAUGAm9GjL0PGA3kEwRgXmjGezjnnHPOOeecOwcdOXSATa8tYsQVV5OS1rlJY7OzFyPF0bHjVADS09Np27YtXbse/+/0S3ILiBVM7dCWHTt2sHPnTqZMmUJycnIDdz1ZWVkZWVlZTZpbSwrziz5GkP+0sb65BDlRv0mwxv4WcG3EGn0WMAHIIzih8ljE2ExgBsHpkmyCnSt3E108YRywSlIRwU6Ur5nZe/X0+xLw4zBfzD0EgZJo3UdwQmY3QVLcOU0Ye9oUpCtx7swaO3asrV279mxPwznnnHPOOfcBMO+hX7Bz7Uo++9BfSG7XvvEBEVasvIJWid248MLHqKqq4sEHH2T48OFcd93xzRdXrtlO69gYnh/Zj0ceeYSamhq+9KUvERcXF9UzzIwnn3yS3bt3c+edd9K6detGx0haZ2Zjm/Qy7pzXnJ0qJ5H0B0k/jKLfUkmfbaCtl6QiSbEtMSfnnHPOOeeccx88OXv3sO2t5Yy++iNNDqgUF++ipOQ90sKqPxkZGVRUVJxw9OdweSUbi0q5vGMKa9euJScnhyuvvDLqgArAtm3b2LFjB1VVVWzcuLFJczxdkm6T9EbEZ6vNUxLt2jyKZ9wsaeHp3uf/gkb/aiTtIcim2z3y+I6k9QTlmfqa2RdPdyJmtpfj2Xudc84555xzzv0f9PaCfxKXkMDY625o8tjsnMUAdEq7HAiO/sTFxdG3b99jfV7NKwBgYnICi8ISyoMHD476GeXl5SxYsIDOnTsTGxvLunXrmDBhwhmvGBSuzevdpFCrJdbm4X2eICjJ7BoR7U6V3cAnaz9IGg40vr/JOeecc84555yLUnlJCdveXM6QiVNIatO2yeOzsxfRtu0wWrXqjpmxfft2+vXrR0JCwrE+S3IL6JYYz+E1K5tVQnn58uUUFBQwffp0xo4dS3Z2Nvv27WvyXN1/hmiDKnOAWyM+f5qIxDWSZku6P+LzDEnvSCqQtEvSVRFje0t6U1KhpIWS0sIxfcJtS3Hh576Slof9Fkt6WNLjEc94TtIhSflhvwvqzOdhSfPC8ask9W/SN+Occ84555xz7n219Y2lVJaXMWLah5s8trw8i4KCd45V/cnOzubo0aMnHP2prDGW5RUysXU8a1avbnIJ5aysLFasWMGoUaPo3bs3w4YNIz4+nrfffrvJ8z0TItfmYYWefZK+KSlL0kFJt0f0TZX0mKRsSRmSfiApJmw7dsRIgd+E9yiQ9K6kYWfnDc890QZVVgIpkoaGOU9mEpRVOklY/ugxgmzA7QhKKe+J6DILuB3oDCQQVAGqz5PAaqAjcC/wqTrtCwiqAnUG3ubkrUkzCbIAtwd2Ag+c+hWdc84555xzzp0tZsbGxQvo1KcfXfsPanxAHTk5SwCjU5hPJT09HYCBAwce67M6v4jC6hra7tza5BLKZsa8efNISEjgiiuCZyQmJjJs2DA2bdpEWVlZk+f8PugKpAI9gM8AD0uqTVTzv2FbP4KKvbcSrNXrupJgXT8o7H8TQQUhR9MS1dbuVrkC2Arsb6DfZ4C/mdkiM6sxs/1mti2i/VEzSzezUoIySaPq3kBSL4LSS/eYWYWZvUFQfukYM/ubmRWaWTlB0GWkpNSILi+a2WozqyIIuJz0HOecc84555xz54ZDu9LJztjNyGlXNSs/SXbOIpJa9SI5OQjIpKen07VrV1JTjy8Tl+QWEgfEbtnY5BLKGzduJCMjg2nTpp0wbvTo0VRWVrJp06Ymz/l9UAn82MwqzWw+UAQMjtgs8d1wXb0H+BUnb2aovUdbYAhBBeGtZnbw/Zn+ua+pQZVZwG1EHP2pR09g1ynaD0X8XEL9yWm7A3lmVhJxLbP2B0mxkn4WHi0q4PhOmLQmPsc555xzzjnn3Dlg4+J/EZ/YiiGTpjZ5bFVVEXl5K0jrNA1JlJSUkJmZecLRH4Alufn0LDpCl3apjB8/Pur7l5aWsnDhQnr06MHo0aNPaDvvvPPo3LnzOXMEqI7ccKNBrdq1cRoQD2REtGUQ7Gg5gZm9CvwOeBjIkvQnSSlnbsofLFEHVcwsgyBh7TXAC6fomgmcbv6Sg0AHSZHJcHtG/DwLmAFMI9h+1Ce8fmbTLTvnnHPOOeeca3HlJcVse2s5Qy6eQmLrptdEyc1bjlnFsXwqO3fuxMxOCKpkllWwvaScrof2NbmE8muvvUZJSQnTp08nJubEZbQkRo8ezYEDBzh06FADdzjn5BDsQOkdca0XDZxIMbOHzGwMcD7BMaC7z/gMPyCaslMFgqM9l5lZ8Sn6/BW4XdLlkmIk9ZA0pCkPCQM4a4F7JSVIugi4LqJLW6Cc4BxXa+CnTXoL55xzzjnnnHPnjC2vv0ZVeTkjp13drPHZ2YuIj+9AamqwiyQ9PZ3k5GS6d+9+rM+CgzkATGod36QSygcOHGDNmjWMGzfuhPtFGjFiBLGxsefqbpWTmFk1QTqOByS1ldQbuJN6cqdKGidpgqR4oBgoA2re1wmfw5oUVDGzXWa2tpE+qwmS2/wGyAeWcWL0K1o3AxcRBE7uB54hCKRAcPwogyCKtoUgka5zzjnnnHPOuQ+YIEHtv+jSbwBd+g1o8viamkpyc18jreOlxMTEUV1dzc6dOxk4cOAJu0qe37WXlNJibrl8atQ5W2pqapg7dy6tW7fmsssua7Bf69atGTp0KBs3bqSysrLJ73CWfIUgSPIe8AZBsZi/1dMvBfgzcIRgHZ4L/OJ9muM5r9H9TmbWp4HrVRw/bnNbnbYXgRfrGTO1zufZwOzw5z0R98PMdgGX1H6W9AywLWwrIjj+E+mxiLF157MUOK++93DOOeecc845d/Yc3LGNnL17uOJzX27W+KNHV1NVVXis6k9mZiZlZWUnHP3ZdziLzcQxOa6Srl27Rn3vt99+mwMHDnDDDTfQqlWrU/YdM2YMmzZtYsuWLYwcObJZ73IqddbmsyOuR66jb4v4eSl11sGR9zCzI8AtDTxrNsfX6kuAEc2d93+6ph7/ed+EW4z6h0eIriIIorx0tuflnHPOOeecc67lbFz8L+JbJTFk0uRmjc/OXkRMTCs6dLgYCI7+xMbG0r//8VSfs1etoyo2jlsuiP7YT3FxMYsXL6ZPnz4MHz680f69e/emffv2H5gjQK5lnLNBFYJ62ksJSj49BNxhZusb6ixptqT7z/SkJBVJ6nemn+Occ84555xz/+nKiorY/tbrDL14CglJTU9Qa2Zk5yyiQ4eLiY1NAoKgSp8+fUhMTASgsLCQpYVlJFgNl3ZNO9XtTrBo0SIqKiqYPn16VMeFYmJiGD16NBkZGeTk5DT5XVqCpD9I+uFZefj/UY0GVSTtkVQaBhMOh8GLM16e2MxeMbOeZtbazAaZ2aNn+pnRMLM2Zvbe2Z6Hc84555xzzn3QbXn9VaoqKxjRzAS1hYWbKC8/dOzoT25uLjk5OScc/Vmzdh3vdezK5NRkkmKj21eQkZHBO++8w8SJE+nUqVPU8xk1ahSSzshulXBtXiEprc719ZJMUh8z+6KZ/aTFH95Cwnk2PXHOOSzanSrXmVkbYDQwFvjBmZuSc84555xzzrn/dLUJarv2H0iXvv0bH1CP7JxFQAxpHYMksunp6QAMHDgQgKqqKuZuS6ckMYmP9oguOFJdXc28efNITU1l8uSmHUlq27YtgwcPZsOGDVRVVTVpbJR2A5+s/SBpOEFFXHeWNLX6z35gATBMUqqkv0o6KGm/pPslxQJIuk3SG5J+KemIpN2SjoUeJS2V9BNJb0oqlLSwNtomqZWkxyXlSjoqaY2kLpJulLQucj6S7pT0ct15Stoq6dqIz3GSsiWNDj8/J+mQpHxJyyVdENF3tqSHJc0L57ZKUv+I9mORNUnTw6hggaRMSfc25ft0zjnnnHPOuf+r9m/fQu6+vc3epWJWQ1bWv2nXbiwJCR2AIKjSqVMnOnQIPm/ZsoUtrdsRC0zrmBLVfVetWkVWVhZXX301CQkJTZ7X6NGjKS4uPhbgaWFzgFsjPn+aiKItkWkxJE2VtE/SNyVlhWv32+v0PdXad4ikRZLyJG2XdFNE2zWStoTj9ku6K6Ltc5J2huP+Kal7eH152GVDeBLmE5LaS5obrtePhD9/oIrMNCmoIqkncA2wniATcBUwALgQuBL4bET3CcB2IA14EPirTjyINoug9HJnIAGo/SV8GkgFegIdgS8CpcA/gb6Shkbc41NE/AFFeIqI6B3wYSDHzGr3YC0ABobPfht4os74mcB9QHtgJ/BAPc+AoPzUrUA7YDpwh6TrG+jrnHPOOeeccy60cfG/SEhKYsjE5iWoPXjwH5SU7KR7t2CtX1ZWRkZGxglHf1auWkVGl55c0r4NqfGNFr+lqKiIpUuXMnDgQAYPPnVS28JXX+PA975P7l//StGbb1KVlwfAgAEDSElJOVMJa1cCKZKGhpsaZgKPn6J/V4L1dQ/gM8DDktpHtNe79pWUDCwiKLPcOez3e0nnh+P+CnzBzNoCw4BXw3GXAf8N3AR0IyjB/DSAmdX+okeGaTWeIYhJPAr0BnoRrP1/1/Sv5exp/K8q8JKkKiAfmAf8BUgH2plZKVAs6TfA54E/hmMyzOzPAJL+Dvwe6AIcCtsfNbP0sP1Z4CPh9UqCYMoAM9sIHNudoqCs8i3A98PdJX2AufXM90lgvaTWZlZCEMB5qrbRzI7V3g53lxyRlGpm+eHlF81sddj+BPDr+r6UsERVrY2SngKm4FWKnHPOOeecc65BpUWFpK98g2FTryC+WBOgmAAAIABJREFUkVLF9amsPMLOXQ+SmjqWrl2D/669a9cuampqjgVVMjMz2XSkgCMDkrimU7uo7rt06VKqqqq46qqrGkxOa2bk/e1vZP3yV8S0bk1+cfGxtrguXWg1ZAiD+vRm7c6dZG/ZQtqQISimRWvE1O5WWQZsBfafom8l8GMzqwLmSyoCBhMEZ6Dhte+1wJ6I3KbrJT0P3EgQhKkEzpe0ISzNfCTsdzPwt9oNDZK+S7De7mNme+pOzsxygedrP0t6AHgt6m/iHBBtUOV6M1tc+0HSeCAeOBjxhxYDZEaMqQ2eYGYlYb829bUDJRFtcwh2qTwtqR1B1O37ZlYJ/B14StIPCHapPGtm5XUna2Y7JW0FrpP0CkHA5sJw7rEE0bcbgU5ATTgsjSBodKq5nUDSBOBnBJG5BCAReK6+vs4555xzzjnnAluWvUp1ZSUjpl3VrPE7d/2CqqoChgz+8bHgR3p6OklJSZx3XnB6ZNWqVezt2hMBV6WlNnrP7Oxs1q1bx7hx4+jYsWO9fayykkM/uZ+jzz5L26uvovt//zdWVkbZtm2Ubd1G2dYtlG/dRte334ZrrmbpPfcwYvceEocMadZ7NmAOsBzoS/0nNyLlhgGVWnXXtw2tfXsDEyQdjWiPC58N8DGCXKs/k7QR+I6ZrQC6E5wGAcDMiiTlEuyU2VN3cpJaA78BriLYLQPQVlKsmVU38m7nhGiDKnVlAuVAWp1f0GkLgyf3AfdJ6gPMJzhG9FczWympAriEYPfJrFPcqvYIUAywxcx2htdnATOAaQS/1FSCqFrjNbJO9iTB1qSrzaxM0m8JgjPOOeecc8455+oRJKhdQLcBg+ncp1+Tx+fnr+fAgWfp1fO/aNMmOKJTU1PDjh07GDBgALGxsRQUFLBlyxYOTLqacanJdE6Mb/S+ixcvJiEhgSlTptTbXl1UxP6vfZ3iN9+k4+c/T6evfy3YgdKqFckf+hDJH/rQsb59ysvZ/Le/kZGQwKThI6jYtq3J79kQM8uQtJsgNcdnWuzGJ8oElpnZFQ3MYQ0wQ1I88GXgWYLNEQcIAjLAsWNEHWl4N803CXbOTDCzQ5JGEaQbac76/Kxo1h4kMzsILAR+JSlFUoyk/pLq/+trAkmXShoe7igpINhWVBPR5TGCQEalmb1xils9TZDn5Q6C4EettgQBoVyCLMk/PY3ptgXywoDKeE4d5HHOOeecc865//P2b91M3oF9zdqlYlbN9u0/IjGhM337fvX4Pffvp6Sk5NjRn7Vr13I0oRV7Y+K5OopdKnv27GH79u1cfPHFJCcnn9ReeeAAGbNupnjVKrrd/xM63/mNUx7piUlMZOzkyRRVVlL0yZn0eerJBvs202eAy8ysuNGezTMXGCTpU5Liw3/jwlwuCZJuDlNoVBKs22vX7E8Bt0saJSmRYL29KuLoz2EgMpLWliCPylFJHYAfnaH3OWNO52DXrQRHXrYQ7PT4B0EimtPVNbxXAcH5sGUc32JE+PMwTp2MpzbwswKYCDwT0fQYQbKc/eHcV548OmpfAn4sqRC4hyA655xzzjnnnHOuARsWLyCxdTKDJ17S5LH79j9BYdFmBg78PnFxx0+xpKenI4kBAwZQWVnJ2rVrKRo6AoBrOp06qFJTU8PChQtJSUnhQxG7TWqVbtrM7k98gsoDB+j1pz/S7uMfj2qugwcPpnXr1mckYa2Z7TKztS1+4+P3LyTYpDCTYPfJIeDnBCkvIEjHsUdSAUFxmZvDcYuBHxLkSTkI9A/vUete4O8KKv3eBPwWSAJyCNbm/zpT73SmyMzO9hyaRFISkAWMNrMdZ3s+Ljpjx461tWvP2P/nnXPOOeeccx8AJQX5/OmOTzP88qu4/L++2KSx5eXZrFg5jdSUUYwaNfuERLKPPPIISUlJ3Hbbbbzzzju89NJLvHXlx6iOj2fJuFPnM3n33Xd5/vnnuf766xk1atQJbYWvvsr+b95FXPv29PzjH0gcOLBJc164cCErV67kG9/4BikpKevMbGyTbuDOeS2SgljSvZIa3DkiabOkqS3xLILjPGs8oOKcc84555xzHyxbli2huqqKkc04+rNz58+oqalg8OB7TwioHD16lMOHDzNo0CDMjJUrV5LUtTvvlldzTdqpq/5UVVWxZMkSunbtyogRI05oy3vsMfb9vy+TOGAAfZ55uskBFYDRo0dTU1PDhg0bmjy2pUkySQPCn/8g6YcRbXdIOiypSFL9WXqb/9xe4X1jW/K+54qogyqSbpP0rqQSSYckPRJW52mUmV1Qp/xws0jaA3yNIJmNc84555xzzrkPCDNj45J/033QUNJ69WnS2CNHVnLo8Ev07v05Wrfue0Jbeno6AIMGDWLv3r0cOnSIyhFjMBo/+rN69WqOHj3KFVdcQUyYI8Wqqjj0k/s5/NP/pu20y+n92N+J69SpSfOtlZaWRq9evVr0CJCkWZLWhoGKg5IWSLq4Kfcwsy+a2U/C+8UTlFK+0szahGWOW4yZ7Q3v+4Go5tNUUQVVJH2T4PzU3QTVcj5EkNF3kaSEMze9E5lZHzPrbWbr369nOuecc84555w7fZmb3+XIwf1NTlBbU1PB9vR7adXqPPr0vuOk9vT0dDp06EBaWhqrVq2iVatWbEpKoU9SAkOSWzV435KSEpYvX86AAQPo378/AFZTw76vf50jTzxBh9tvp8dvf0tMUlL986qopiq3tNH5jxkzhry8vCjf9tQk3UmQh+SnQBegF/B7ggq3zdUFaAVsbsZ8JKlFTsB8UDX68pJSCEocf8XM/mVmlWHm3puAPsAtYdcESY9JKgyP+4yNuMceSdPCnxMl/VbSgfDfb8OswM4555xzzjnn/gPl7s9k5QtPk5iczKCLmrSpgszMRyku3sHgQT8iNvbEAEdFRQW7d+9m0KBB5Ofns3XrVoaMHsOb+SVck9buhGNCdb3++uuUl5dzxRXHqwYXzJ1L0eIldL77Lrp8+1sotv4TK+V7Czj0i1Uc+sVa8p7ZTtXRsgafM3ToUNq3b9+kd66PpFTgx8D/M7MXzKw4XJ+/YmZ3SxovaUWYBPagpN81tAlC0mxJ90saBGwPLx+V9GrYPlHSGkn54f9OjBi7VNIDkt4ESoB+4bWfSHozjAkslJQW9u8THj2KCz/fLmlr2O89SV847S/nLIomojSRIGr1QuRFMysC5gO1f4EfIShj3A74J0HZ4/p8n2CnyyhgJDAe+EFTJ+6cc84555xz7sw4/N5OdqxZQWlhQbPvUXz0COvmvczj3/06s++8g31bNjHho58gPiH6/6ZeVnaA93b/L2lp00hLu+yk9vfee4/q6moGDRrEmjVrACgcMJRKs1Me/Tly5AirV69m1KhRdOnSBYCa0lKyfv0bWl1wAR1uv73ecTWl5WT9fhFZD6+nNDuPrbnbKdmQxaFfriV/wW5qyqpOGpOQkMBXvvKVqN/5FC4iWJu/2EB7NfANIC3sezlBxdoGmVk6cEH4sZ2ZXRaWNp4HPAR0JDgaNK9OrpVPAZ8nKImcEV6bBdwOdCaoFHxXA4/NAq4FUsL+v5E0+lTzPJfFRdEnDcgxs5P/OoISSWMIIltvmNl8AElzgK83cL+bCXa9ZIV97wP+SFB2yTnnnHPOOefcWVR0JI/n7v8+5cXFAKT17M155w/jvKHBv+R2De+6qCgrZeealWx9/TUyNr6DWQ1d+g1g6q2fY8ikyaccW5/0HfcDxqCB9S8X09PTSUxMpHv37jz33HMMHjyYRSWVdEmIY3RK6wbvu2TJEiRx6aWXHruWN3s2VYcO0eOXv0AxJ+4/qDxwgLwnX6LsvRRiU3pTmL+N29p3JatVN76Z8W9mXf0JCpfto3jNIdpe3os2E7qhuOP3iIlpkRMyHWl4bY6ZrYv4uEfSH4EpBMeFmmI6sMPM5oSfn5L0VeA6YHZ4bbaZHTsuFO4IejQM0iDpWYKNF/XNc17Ex2WSFgKXAC1fe/p9EE1QJQdIkxRXzy+vW9gOQd3qWiVAqwbGdOd4JIvw5+5NmLNzzjnnnHPOuTPktUf/SFVFBR+583vkHdhH5pZ32bx0Ce/8O1gLt+9+Hj2HDgsCLecPIzm1PRnvvsPW119jx5oVVJWXk9KpM+Ovv5GhF0+l43k9mzWPnNylZGf/m/79vklS0nkntZeWlrJt2zb69+/Pli1bKC0tZeS48Xx3XyE3dW1PTANHf/bv38+mTZuYPHkyKSkpAFQeziLnz3+h7ZVX0npskMnCamoofmsFR556irKdZbQa/gli2sZgw2u4Y/8AqiuqmJAsflsznoHr/sGl37yLggW7yX/lPYrfOkDKVX1JGtbxlEeQmiiXhtfmhEd5fg2MBVoTrPfX1e0XhbprdsLPPSI+Z9Yzrm5MoE19N5d0NfAjYBDB6ZnWwLvNmOc5IZqgygqgHLgBeLb2oqQ2wNXA94CT/8IbdoAgyW1tVKtXeM0555xzzjnn3Fm0Y80K0le9ycUzb2XghCCNxoSP3kR1VRVZu3eRueVd9m/bzLa3lrNxyb8AiEtMpKq8nFbJbTj/kksZesml9Bg09KTdHk1RXV1Oevp9tG7dj169PlNvnwULFlBaWsqkSZN4+eWX6dKlCxkpHSitKWB6p/oL1ZoZCxcuJDk5mUmTJh27nv3Q/2CVlXS+65tUHz3K0Rdf4sjTT1F5IIek8f9F0oUjiO/eirazLuC2f2zgYP5R+ndKZsuREjrEww/LBvLcW0vo/dkZlG0/Qv6C3eQ9sZWE3imkXtOXxN4pzf4uItSuza8H/lFP+yPAeuCTZlYo6evAx5vxnNo1e6RewL8iPlsz7kuYT/V54FbgZTOrlPQS0GKRp/dbo0EVM8sPj+j8r6QCYAlBhOr3wD5gDvDdJjzzKeAHktYQ/CLuAR5v6sSdc84555xzzrWc8pJiXv3rI3Tq1Yex191wQltsXBzdBg6m28DBMOPj1NRUk71nN/u2biLvwD76jBpD31FjiYuPb5G5ZOz9I6Wle7lw1GPExJycg2XLli1s3LiRKVOmUFFRQVZWFh/5yEeYnZNPu7hYLmpX7yYJtm/fTkZGBtOnTycxMbhv2dat5L/wIh1uu42Y5GR2Tb+W6txcWk/8CEkTrsGqY0m9sjfJF/fgruc3snpPHm0TY9mTW0JKUhylEiVJKdz9713MmbCXpCG9aTWwPSXrDpO/aA/Zj2wgaXjaaX8n4dr8HuBhSVXAQqASmAZcSpDfpAAokjQEuAPIbsaj5hOs/2cRbKz4GHA+MPe0XyLItZIYzqsq3LVyJbCpBe59VkSzUwUze1BSLvBLoD/BL+ol4GYzK2/idqb7CRLSbAw/Pxdec84555xzzjl3lrz+5GyKjx5lxl0/IDbu1EvFmJhYuvQbQJd+A1p8HiUlGWRkPEKXztfSocOkk9qLioqYO3cu3bp1Y/LkyTz33HMkJSUx5IJhLFq9nWkdU4iPOXmNWl1dzaJFi+jYsSOjRwd5Uc2Mwz/7ObGpqaTd8UUOP/gg1YXFdPrWo5SlVxKbmkSHmUNI6NGGh1/bwQtv70dA55RW/OGWMcTFxnDjH1ZgreJZ02kQv3hgDj94+NsoPp7k8V1JGtmJotf3Ubh8X4t8N2b2K0mHCIq9PAEUEhzxeYAg6PEn4FsEO1aeAU7O7tv4M3IlXQv8D8Hul53AtWaWc+qRUd27MMzP8ixBcOUVgkI3H1gya9auHeeaZOzYsbZ27dqzPQ3nnHPOOedcPfZt3cQz936HMdOvZ+qtnz2rc3n33S+Tm7eMiz60mMTELie0mRnPPPMMO3bs4Atf+ALx8fE89NBDTJo0icTRE7hxwy4eHdaHq+s5/rNmzRrmzZvHzJkzGTJkCACFr77Kvi/9P7r88AckDR/Onpk3k/LRB7HqNiRf1I3Uq/sSkxDLP9Zlctdzwb6Aa4Z35b4rh7LttX0UHymn5/SezPzzSqqrqimtquH3aQe5+u7Pn/Ds6sIK4lIS15nZ2DP0tbmzpEVSEDvnnHPOOeec+2Cqqqhg4Z9+R0qnLky66ZazOpf8/PVkZS+gV6/PnRRQAdi4cSPbtm3jsssuo3PnzsfKKI8bN475OfkkxYgpHU7OX1JeXs7SpUvp1asXgwcPBsAqKsj6+YMk9OtHu49/nEM/uZ/EUTdg1W3oMGsI7WcMICYhlpfW7+fuMKDyrWmDuLVNO164fw0bX93HrvXZlGzI4++fmYDFxBAn+M6BFDLeWH3C82PbJrT0V+XOEedkUEWSSWrxfWSSpkpqmX1XzjnnnHPOOfcfYNWLz3DkwD6u+Nz/I75Vq7M2DzNjx86fkZCQRq+eJyenzc/PZ/78+fTq1YuLLrqIkpIS1q1bx9ChQ2mbksKC7Hwu65hC69iTl7nr16+nuLiYK6+88lg1niNPP01FRgadv3U3Ba+8QvmuQyT0vpykkZ1oPaITAH9+/T2+/sw7SPDd4b1IWHCQdQsyaNcpidosGBkLMui6IovHLxtKkmIoSEjmS0+9Q3l+wZn7ss4QSX3C9Xi9578kfU/SX97veZ3LGg2qSNojqUJSWp3r68Mvu8+ZmpxzzjnnnHPOuTMne+8eVr/8D86ffBl9Ro4+q3PJyVlCfv5a+vb9GnFxySe0mRkvv/wyNTU1XH/99cTExLB8+XIqKiqYOnUq7xSUcKiikqvTUuu9d0ZGBu3bt+e884LCtdVHj5L98O9JnjiRpFGjyPr1b2h9yRdQQhztpvejrLKau57bwAPzthIjuNWSqXo9m04929JzaAdy9hXROVYM6d6aw1XGijcOkPavvcwjhT+rLVPb9mP2A/+kpvSkysfNJum7khbUubajgWszW+zBEczsp2bWIufDwljDtJa419kUVaJaYDfwSeB/ASQNJ6gl3WQN1dR2zjnnnHPOOff+qampZuEfHyIxuc1Zz6NSU1PFzl0P0rp1P7p3u/Gk9rVr1/Lee+8xffp0OnToQF5eHqtXr+bCCy+kc+fO/GnXAeIE0zrWX7p437599O59vEpwziOPUFNYSOdvf5uc3z2MkvoQk9SLlCt6c6CqijseWcmmA4UAXF8Yz/BebTh/RnfW/3svRw+VMLRVDKOv7EW7a/pR87fNpK8+zBsxsVx5SXe6b82hb3YpiTG92X/fWyR0a0Niv/qDPU20HPiOpFgzq5bUDYgHLqxzbUDY170Poj3+M4egjnStTwOP1X6QND3cuVIgKVPSvRFttduHPiNpL/CqpHmSvhL5AEkbJX207oOjvPenJe2VlCPp+xHtSZJmSzoiaQswLsr3dc4555xzzrn/aO/8ay6HdqZz6W2fJ6lt/cGI98vBg89RUrKLAf3vJibmxLLMeXl5LFy4kH79+jF2bJDndfHixcTGxnLppZdiZszPzufidm1pF3/yvoGCggIKCwuP7VIp372bvCeepN3HPgbAkWdfIGn8bcR3S6b6wk7MeOgNtoYBlRkJyXzl86MZPvU8lj+9g9Ij5VyUHMuFU3qQenVfAKbdfj4deiRz9Eg5S9Zl0ffzI1n+4W58mWIerSmlzKooWnWoJb6mNQRBlFHh50uA14Dtda7tMrMDkv4nXEMXSFon6ZLaG0kaL2lt2HZY0q/rPOvmBtbY90p6PPw5mvX438P1+FZJ36pNxyFpDtALeEVSkaRvhdc/ImmzpKOSlkoaGnG/PZLuCmMH+ZKekXT2zquFog2qrARSJA2VFAvMBB6PaC8mCLq0A6YDd0i6vs49pgBDgQ8DfweOZUCSNBLoAcyr59nR3PtiYDBwOXBPxBf/I4IS0P3D5346yvd1zjnnnHPOuf9YBdlZvPH0HPqOGsOQiZPP6lyqq0t4b/f/kJo6hrS0K05oq6mp4cUXXyQmJoYZM2YgiczMTLZs2cLEiRNp27Yt20vKeK+0nKs71b8bZN++IK1mbVAl65e/IiYhgbSvfoXD999Pq2HXg7Wi3fUD+O3cbeSVVVINzOjXiV9+/xL2bs5l0d+20KFdApMTRa+xXWg3Y8Cx3CySmH7HCGLiYsg7UMwLv3ybj4/syehRHfhbTBUz9mfS8dunf7TKzCqAVUDtL2wy8DrwRp1rtbtU1hAEWzoATwLPRQQh/gf4HzNLIVgvP1vncQ2tsetzqvV4H6AfcAURMQAz+xSwF7jOzNqY2YOSBgFPAV8HOgHzCYIukVl+bwKuAvoCI4DbTjGv90VTEtXW7la5AtgK7K9tMLOlZvaumdWY2UaCL2JKnfH3mlmxmZUS1KEeJGlg2PYp4Jnwj+QEUd77PjMrNbMNwAZgZHj9JuABM8szs0zgoSa8r3POOeecc879xzEzFv/lYQCmffb/HQsOnC179/6ViopsBgz49klzWbFiBZmZmVxzzTWkpqZiZixcuJA2bdowceJEAOZn5yPgqgbyqezfv5/Y2Fi6du1K8cpVFC1ZQscvfIGS1asp27af+F5TSB7XlSNt4nhifSYCLhmQxr3XD+Ol36xn07L9XDCyIxPKK2l/fkc63DQIxZw4z5S0JCbe0B8zKDpSxgu/WMeXp1zApR2Mo7Gt+OiDC+qdWzMs43gA5RKCoMrrda4tAzCzx80s18yqzOxXQCJB8AOgEhggKc3MisxsZZ3nNLTGrs+p1uM/NbMjZraPxtfjnwDmmdkiM6sEfgkkARMj+jxkZgfMLA94heM7dM6apgZVZhFEgh6LbJA0QdJrkrIl5QNfBNLqjM+s/cHMyoBngFskxRDka5lT30OjvHfkXqoSoE34c/fI5wIZjb6lc84555xzzv0H2/bmMna/s46LP3krKZ06n9W5VFTkkLH3z3Tq9GHapY45oS0rK4tXX32VIUOGMGLECAC2bdtGZmYmU6dOJTExEf4/e+cdnlV9PfDPebM3ISFAQkICIewlGwRRVFBAXDgQFHddrbZWa4dtrVpna1tXRQsCLhwgiiigsmdQNiRsssjeO+97fn/cG3gTkhAgiPr7fp4nT+4933XufZM/znnPABZnFzIwOIC2Pl4n7A9WpEq7du3wECHz2WfxjGxPq2uvIeu55/EbdgcOfy+Cx8Xyp7c2U60Q4O3Bo/068vGziRQcLeWiCbHEpxTjGxdC2E3dkAa6CwH0Ht2BtnHBOBwOnNUu5r/wHX+fPJLOWsKeap+WemUrgfNFpDXQRlX3AmuB4baslz0HO1Vmt50qUwCEcNyWvh1IAPaIyCYRmVDvnMZs7IZorj3uft0QkbjZ7KrqstdEnaZePwjNdqqo6mGsgrWXA5/UG34XK/okWlVDgNeB+u5OrXf/NnATVohQmaqua+To5uzdGBlAtNt9TDPXGQwGg8FgMBgMBsPPjrKiQr6d9Qbt47vSb+z4c60OBw++jMtVQXzn39aRO51O5s+fj4+PDxMmTEBEcDqdLF26lPDwcPr37w/AkfJKtpeUN5r643Q6SU9PJyoqisIFn1K5ezcRv/kNebPeBu84HP4dCLksjsStWSzLLQKB33Rox4q3dhHYypdJU7sStD4Dr/YBhN/SE/HyaPRZHA7hwmndqKl2EhEXjJevBwtf2sLr1w4jpiK/pV7ZOiznyJ3AGgBVLQLSbVm6qh6066c8ghUtEqqqrYBCbFtaVfeq6o1ABPAs8JGIBNQ/7AzJADq43UfXG6/vI0gHjlUTFitsKRq3LJkfI6cSqQKWN+siVS2tJw8C8lS1QkQGY0W0NIntRHEBL9JIlMrp7u3GPOAxEQkVkQ7AAydbYDAYDAaDwWAwGAw/V1bMfpPKslIuvfsBHI7GHQQ/BGVlB0lLf4/IyBvw94+rM7Zy5UoyMjKYMGECgYFWMMLmzZvJy8vjkksuwcPD0n1xTiEAlzfiVMnOzqa6upqoNm3Ifukl/Pr2xad7d3LnfoDfgJvwjglCu7TiqQU7UIERrYMo+y6PLgMiuGJqAtWfHcCztQ/ht/XC4Xu8CK6rooaSNWkULj5ITWHlMXlYZCADL4/l8PZcBk+MIyjMl2Vv7+d/01qmc7BdTiMR+DVW2k8tq21ZbT2VIKAGyAY8ReRx4Fg1YhGZKiJt7GiQgtrHahElj+Nuj0cB99cbz8Sqt+I+f7yIjBERL+A3QCVWJM6PllNyqqjqflVNbGDoXuAJESkGHufEIjeNMRvoTd2ity21N8BfscKHDgJLaNp5YzAYDAaDwWAwGAw/W/ZuXMuuVd8y+MrJhMfEnmt12Lf/BRwOb+LifllHnp6ezsqVK+nTpw89evQAoKKiguXLlxMbG0tCQsKxuYuzC+kR4EusX8PpNbVFagM3bKQmO5uIRx8l65ln8O1xJYgPIZM689GsnWyTGgS4OsCKMBlxSTQFc3bj8Pekze298QiwUouqs8rI/3QfGU9vpOCzAxSvTOXo84kULD6Iq6wagPPGdqR1ZAAbPj3A+Hv7EBETxJI3d7Xkq1uBFWGy2k22ypbVOlW+Ar4EkrFs4grqpt+MA3aKSAlW0dobbIdNS/IEkIpljy8DPsJyktTyd+CPdqefh1U1CauY7X+AHGAiViHbE2qv/pgQ1foRNz/g4SI3A3ep6vnnTAnDD8LAgQM1MbEhf5zBYDAYDAaDwWA42+Slp/LO7x+idVQ01//lWTy9Gq4/8kNRWPg9iZuvJS7uQTrFHU8oUFX+97//kZeXx/3334+fnx8AX3/9NatWreKuu+4iMjISgN0l5Vy4KYnH4trzq9i2DZ6zYMECkpOSmPTZ5/h1707ojTeS/vtnCRj9ewJHRJEa4st9n27jsJeL6f06ELEyj97D29H5SBGoEnF3Xzxa+1KRlEfJ2nQq9xaAh+Dfpw2BwyNxBHpRtPQwZd9nIT6eBI3uQNCISDJTS/j4uc30GhnF8Gvi+WbObsbd2Xuzqg48+2/3x4mI3IPlvKnfeOYnzYlNvH8gRMQfKwrlVRGJxfJeealqTQNzfw90UtU7flAlDQaDwWAwGAwGg+EnTlVFOQtffBoPL28mPvTYOXeoqCp79z2Dt3cbYqInc7izAAAgAElEQVRvqzO2Y8cOUlJSmDhx4jGHSmFhIevWraN3797HHCoAr6Vk4edwMC0qrNGz0tLSiPD0xJWTQ+hNU8h86u/4Db4dR5A32jeCT17cyGE/F0G+nozGl91AVEoxWu0i/JYelO/OpWRdBs68ChzB3gRf0pGAIe1wBHih6kLEQevruhI0qgOFXx6i6MtDlK5NJ/jijvQd3YGt36bSZVBbxt7Ry6p4co4QkdHAXFXtcLK5LXhme6z0nnVAF6x0npd/qPN/KE7qVBGRx4BRqnqZm2wvsK8B2Z9U9f1m7DkWq9jtMqxCtE1+sKr69Mn2bC4icgi4Q1WXtdSeP0ZExAMr1y5NVSeISBzwPhAGbAamqWqViPhgpWENAHKB61X1kL3HY1h1dJzAL1X1K1s+DitEzAN4U1Wf+UEfzmAwGAwGg8FgMDQLVeWr1/9NXloq1/7xbwSHtznXKpGTs4zCwkS6dv0bnp7Ha6NWV1ezdOlS2rVrd6wQLcA333yDqjJmzJhjsvSKKj7JzGd6VDitvRo2aysqKsjOziYqLR2frl2p2LULPONw+Lcn+PI4Fr+bxEIfK7Pkyct7kPTOAWIi/fAurGR55odk/+4IqgoCatdU1W0KbxzP9ghtH8nNz7+CV7sAwqf3pPJgIYWLD5L/yV46h/tR3NqHb+fs5vo/DT7j92bbsm2xaqU4gV1Yttwbdm2UHxvewH+BOKy6Le8Dr55Tjc4CzYlUWQn8TkQ8VNVpe5u8gP71ZPEcz99qEts4P/bfc677ov9M+RWwm+PFiJ4F/qmq74vI61jOktfs3/mqGi8iN9jzrheRHsANQE+s1lbLRKQ2efEV4BKs/LhNIrJQVVs0SdBgMBgMBoPBYDCcOZsXLSB53SpGTplOTK++51odXK4a9u1/Hn//TkS2v67O2Nq1aykqKuLqq6/G4bDKfx49epStW7cyfPhwWrVqdWzuG6nZKHBXh8adROnp6QCE7E0m5OZbyHl9JgEX/hmf+FbsSilhTWo+OYEu4iMC6ZDnJLXSSUxhOUfK9xHk3Zrw6I74RAXjEeyNiAMRoPY3QkVJMd9/+Rl71qyg12irEK1PXAht7ulLxa5cCr86RG+Xi7yyGra9l9RSr3Ciqi4TkRDgAqwvu4cAt7bUAS2F3UG4F4CIeDaUlfJzoDmFajdhOVH62fcjgW+BpHqy/aqaLiL/EpEUESkSkc12KycARGSwiCTaY5ki8o96Z90kIkdEJEdE/uC27i8iMte+jhURFZFbGpnrJyJvi0i+3ZP7ERFJtcfmYLVV/kxESkTkEVt+hYjstAvkLBeR7m77HbL7e2+z+3t/ICK+zXm55wq709F44E37XoCLsAoDgdXO+kr7epJ9jz0+xp4/CXhfVStV9SCwDxhs/+xT1QN2waD37bkGg8FgMBgMBoPhR0TKru2sfGcmXQYPZ9AV15xrdQDIyPiQsrL9xHd+BIfj+Hf8RUVFrF69mu7duxMbG3tMvmTJEvz8/Bg58phZSWF1DXPSc7miTStiGilQC8eL1EaIg6qUFLwTJoKHNzUDItj05WGWhVg2/qs39GPbt6mE+0KQKCV+RYx/8feMfeI3jL77TkbeeAvn3zCNEddPY8R1NzF88k0MnzyFC6ffRZuYWBI/+wT3WqUigl/PcNr+agCh13QhyNeTiF25LfUKAVDVQlVdCFwP3CIivUTER0ResO3kTBF5XUT8GlovIr8Tkf0iUiwiu0TkKrexwyIywL6+yba/e9r3t4vIAvt6sIiss+3oDBF5WUS83fZREbnPzmrZa8u6ichSEckTkSQRqetZ+wlyUqeKbThvAEbZolFYlYVX15PVRqlswnK2tMZK7fnQzQnxL+BfqhoMdObETj7nA12BMcDj7s6NBmhs7p+BWKzcrUuwqgfXPss04AiWdy9QVZ+zoy/eAx4E2gBfYDldjv0xYPX2HocVttQHmN6EXj8GXsLqSV4bAhYGFLh5BlOBKPs6CrsKtD1eaM8/Jq+3pjG5wWAwGAwGg8Fg+JFQnJfD5y89S6t2kYy958EfRXaA01nGgYP/IiRkAOHhdVsMf/3117hcLi655JJjsn379nHgwAFGjRp1rL4KwOz0XEqdLu6NiWjyvCNJSQQVFREx+VpK1uzGq8NQAs7vwNfzD7Ap2EWJ08UlPdpSs6+EssIqunh4sL9sK6MevxuPQO8m9wbLeTJw4tXkph7h0NbvThz3EAIGtaPdwwPZd5bev6puxLLJRgLPAAlY9ng8lp32eCNL99trQrC65s61M1DA6i402r6+ADjAcdv/AnscrBSkh4BwYBiWbX5vvXOuxIqk6SEiAcBSLD9BBFZmxKt2lsRPlua2VF7B8Zc4EsupsqqebAWAqs5V1VxVrVHVFwEfLOcHQDUQLyLhqlqiquvrnfNXVS1X1a3AVqCp+LTG5l4HPK2q+aqaCvz7JM92PbBIVZeqajXwAuAHDHeb829VTVfVPOAzjkfo/OgQkQlAlqpu/hHocpcdmZSYnZ19rtUxGAwGg8FgMBj+X+Csqeazfz5DdUUFk37ze3z8/c+1SgAcOvxfqqqy6RL/uzpOntTUVLZu3cqwYcNo3bo1AC6XiyVLlhAaGsqgQYOOza10uZiRms0FoUH0Dmr8uVSVtNRUwgoK8Qxvg1fkSMQHtuVWcDS7lNWOKjwdwvPjupP48V4CHS78KSFmymB8gwKb/Uxdh48ksHUYiZ990ugcv1Y+jHxieKPjLUA6VlDDXcBDqpqnqsXA01iOixNQ1Q9tG9elqh9gRZLUFn5ZgeU8AcvW/7vb/TGniqpuVtX1tu1/CKt+Sv3OPn+39SkHJgCHVHWmveZ74GNg8pm+gHNJc50qK4HzRaQ10EZV9wJrgeG2rJc9BztVZredKlOA5fkKt/e5HctztkdENtkOAHeOul2XAU39NTc2N5K6kRTu1w0RidW3GwC7wE8KdaMvTkWvc80I4Aq7iNH7WGk//wJaiUhtfF0HIM2+TgOiwcpzw/q8ct3l9dY0Jj8BVX1DVQeq6sA2bc59QSyDwWAwGAwGg+H/A8tnv0lG8h7G3vMgYR1izrU6lJensm3bLzh06GUiIsYTEnLesTFV5csvvyQgIKBOis/WrVvJyspizJgxeHoeTxP6+Gg+WVU13HeSKJWc/fspFyG6YwxFS1bj2a43NV3C2LEmgzUxntS4lOn9ozj48laKK10k+HqRGZpKl+EjTunZPDy96D9uIkd2bCXr0IHG53k11/Q+LaKw6qX6A5vtdJwC4EusbIwTEJGbRWSL29xeHLfbVwAj7cgVD6wMkxFide0NAbbYeySIyOciclREirCcOOH1jnK3xzsCQ2rPtM+9CWh3hs9/TmnuJ7sO6+XdCawBUNUiLI/YnUC6qh6066c8ghUtEqqqrbDSScRes1dVb8QK9XkW+MgOAWpJMqjbTSi63rjWu0/H+nCBY/VHomnEUfBjR1UfU9UOqhqL5ZX8RlVvwqqDc6097RbgU/t6oX2PPf6NWgmBC4Eb7Ly8OKwWWBux0ru6iEicnSJ1gz3XYDAYDAaDwWAwnGN2rfyGLV8tYsCEq+g67PxzqovLVcnBQ6+wfsNYcvNW07nzI/Ts8UKdOTt27CA1NZUxY8bg42PVR6mqquKbb74hKiqKnj17Ht9PlVdTsugd6MfI0Ka/507+2IociRs6FGdhKIqyamsuJW29+b6wFH9PBzdtKyG5sApfUXw0g353X9Xkno3R5+JxePn6kfj5/NNafyaIyCAsp8oCoBzoqaqt7J8QVT3hRYlIR2AGcD8QZtvtOzhut+/DCiZ4AFhp2/5HsSJhVrt1GnoN2AN0sUt8/L52Dzfc7e8UYIWbfq3sshz3tMCrOGc0y6lih+okAr/GSvupZbUtq62nEoTV3ikb8BSRxznefQYRmSoibewPocAWt3Trp3nAYyISKiJRWH8o7mRi1Vtxnz9eRMaIiBdW7+xKrEicnxOPAr8WkX1YNVPesuVvAWG2/NfA7wBUdSfWu9mF5eG8T1Wddt2V+4GvsLoLzbPnGgwGg8FgMBgMhnNI1qEDLJ3xCtE9ejNqyvRzqktu7grWb7iMAwf+QXjYhQwbuoTYjnfjcByvVVJVVXWshXK/fscrLCQmJlJcXMyll15aJ01oSU4R+8oquS8moskaMa6yMo7s2oWHKr47k/CKGU5xoBeFZdV86l0JwN01XlQEepFT6aKTjweew4IIadN09Etj+AYE0mfMpSStXUlxbs5p7XGqiEiwnfnxPjDXLosxA/iniETYc6JEZGwDywOwnB3Z9rxbsbv0uLECy+6rrZ+yvN49WPZ/EVAiIt2AkzlHPgcSRGSaiHjZP4NOUkv1R8+pxCCtwIowWe0mW2XLap0qX2EZ4MlYKTUV1A33GQfsFJESrJSUG2yHTUvyBFahnoPAMqyONpVu438H/miHGz2sqklYxWz/A+QAE7EK2Va1sF4/OKq6XFUn2NcHVHWwqsar6mRVrbTlFfZ9vD1+wG39U6raWVW7qupiN/kXqppgjz31wz+ZwWAwGAwGg8FgcKeipISF/3ga34AAxv/qERweHudEj/LyNLZtv4ctW29DxEG/vrPo3ftlfH0jT5i7bt06ioqKGDdu3LEWyk6nk/Xr1xMbG0vHjh3rzH/lSBbRvt5MaNPqhL3cKViwgJygQNq1bk3F+lTEy49t2ZUc7uxLenEFYQjXd2vHAW8HnoCvTwq9rh13Rs993mWTUFW+W3zWg/g/E5FiLDv7D8A/ON5O+VGsrq3r7XScZRyvb3oMVd0FvIiVkZIJ9MbOSHFjBZbTZGUj9wAPA1OAYiyHzgdNKW7XebkUK9shHSv65VmsOqw/WcS99dPPERG5B8t5U79gjuEHZODAgZqYmHiu1TAYDAaDwWAwGH52qMvF/Oee4PC2LVz/l78TmfDDf/HvclVy5MhbHDz0CgBxsfcTE3MbDkfD9nJRURH/+c9/6NKlC9ddd7yr7vbt2/n444+58cYb6dr1uD9gY0EJV3y/jye7RHFHh8brNarTSfL48XwweDD9Y2Lo830wNaFt+SS3mjdCKqkG/p7QnssndmPun9YR5w3n/SKadj0SzvgdfP6v5zj4fSJ3vTqrweLAIrJZVQee8UGGHxWeJ5/y08IuptMJy+vWBSud5+VzqpTBYDAYDAaDwWAwnCU2fvoRB79PZMxt97SYQ6WmppTy8kOAAxEHIPZvByKCiMexsZKSJJL3Pkl5+SHatBlLQpc/NhiZ4s6yZctOaKGsqqxdu5awsDC6dOlSZ/6rKVmEenpwY/vWTe5b8u235BQW4XI4CNmTjkdQV3ZWKNtaKzVOiAv04frp/fnqxbUIQliHAtr1GHNa76g+AydcRdLalWz/5isGTji9+iyGnx4/O6cK4I3VyikOq27L+8CrZ+swEZkFpKrqHxsZLwH6uKfVGAwGg8FgMBgMBkNLUJSdxfqP3ydhyAj6Xnp5i+xZVZXDpsRrqKhIbfYaP79Y+vWdSVjYqJPOTU1NZdu2bZx//vmEhoYekx8+fJiMjAwmTJhwLB0IYG9pBV/mFPHr2LYEnCStKXfmLAo6WyU0WxeE4gyvYVu5k1WeVnWHP1zdm7L8Ug7vr6CtZw397m2o5Mjp0a5zFzr06MV3ixfSf9xEPDx/POa2iIzGqr3S4WRzW+i8Q8AdqrrshzjvXHLST9l+Gf5AnKqW2rI7gKmqOvpsKdbQhyAi021Zo2WsVfUwJxbZOWc0VG3ZYDAYDAaDwWAwGFqCle/OAhEuuPmOJou3Nhens5Jt2++hqiqHHt2fw9MzCFUXigvUVffa/u3w8COizaWNpvq4U9tCOTAwsE4LZbBqrPj5+dGnT5868tdSsvB1CLdFNZ72A1C+bRvlmzdTdPtt+JdXEBzWm8PqYGNwFQ6F+PAAxnSPYPFfl+DEi/jhgfgEtmwz2oETrmbBc0+QvGEN3UecnQoUIjIFq8lIN6x6JluAp1R1dZMLDWeF5rrOPIBfYfWdNrQQIuKhqs5zrYfBYDAYDAaDwWD46ZGWtJuktSsZes0NBIc37XBoDqrKnqTfU1j4Hb16vUzbiMtaQMu6bN++ndTUVCZNmnSshTJATk4OSUlJjBo1Cm/v4x2CjlZW89HRfKZEhhHu3bT5mjdrFo6gILK9vQnLrkREWFdeyXf+NQDcdWE8OcmHScv0JNS7hr5TLjqjZ9k293l2r1lBaPsownsMJaxrf9rGdSY0sgOJn31Ct+GjWsTR5Y6I1HZs/QVWo5gqrIYwk6jbVKYlzzR2axM0t/vP88DDItJgmWUR6SYiS0UkT0SSROQ6Wx5nd9lx2PczRCTLbd0cEXnwdJUXkd+JyH4RKRaRXSJyldvYdBFZIyL/tHU4ICLDbXmKiGSJyC1u82eJyOv2cxSLyAq7fzdi8U97TZGIbBcR92iYUBFZZK/bICKd3fZVEYl3O+M1EflCREqBC0XER0ReEJEjIpJp6+B3uu/EYDAYDAaDwWAw/PxRl4vls2cQGNqaQVdc0yJ7Hj78X44eXUCnuIfOikOlqqqKZcuW0b59e/r27VtnbP369Xh4eDB48OA68jdTs6lR5RfRTTuNqtPSKPpqCb6TryUvP5+2jvZkq4tl/k68gdZ+Xkzo047EGd9TpcJ5k+LO6Fk2fvoRSz9bQWlJOXv3HOLbDz/moyf/yH/vuYXS3CyyDu5j4T+eZtuyL0lL2k1lWekZnQcgIiFY3W7vU9VPVLVUVatV9TNV/a1tW74kIun2z0si0mD4kIh0F5Hltq28U0SucBtryG4dLyLf2/Zwioj8pd5+00TksIjkisgf6o01W6+fIs11qiRi9aV+uP6AiAQAS4F3sdor3wC8KiI9VPUgVt/q/vb0UVg9rGurJ11A3T7Xp8p+YCQQAvwVmGsXqq1lCLANCLP1ex8YBMRjtVF+WUTc03NuAv4GhGOFUL1jyy+1dU+wz7oOyHVbd4N9fihWC6um2gxPsceDsDyJz9j79rP1igIeb+bzGwwGg8FgMBgMhv+H7FmzgqP7kjn/xlvw9j3z72Szsr9i/4Hnadt2IrGx97WAhnVRVVasWHFCC2WA0tJStmzZQp8+fQgMPG6eFdc4mZ2ew4SIVsT6NW2D582eAyKUX2Cl3EQ4wllZ5SQZJ1XAzSNiOfTlSrLKQgjyVbpeFH/az7Fm3lxWvTuLbsFZ3HL7OO79z2v84o7RXDugmgvb7ifB9whtfMo4mLiWpTNe5v3Hf8vLt15/WufVYxjgC8xvZPwPwFAs27IvMBg4ofaniHgBnwFLsGz4B4B3RMS9/XJ9u7UUuBloBYwH7hGRK+39egCvAdOASCz72712S7P0+qnSXKcKWIb+AyJS30U4ATikqjNVtUZVvwc+Bibb4yuAC0SknX3/kX0fBwQDW5s4c4HtOSsQkQLqFZxV1Q9VNV1VXar6AbAX6wOq5aCtlxOrZ3Y08ISqVqrqEqxQKff/pkWqulJVK7E++GEiEg1UY/0xdcNqQ71bVTPc1s1X1Y2qWoPliOnXxDN9qqprVNUFVAJ3AQ+pap7dt/tpLCeNwWAwGAwGg8FgMJxAdWUFK997m7ad4ukx8sIz3q+4eCc7d/6G4OB+dO/2TIumrKgqe/bs4b///S9r1qyhd+/edOzYsc6cxMREampqGDZsWB353PRcimpc3Bsd0eQZzuJiCj76iOBx40grKAAFv2o/PvKpJhDwcsC1PUPJWFZIiQsGXZNwWs+oqqyY8xbrP36f3j07cFlkEh69r0bCOhFwycN0fOQrznvyG8be+Quu6F3GA11Wc0fnjVzZNZPzz2t7yuc1QBiQY9udDXETlr2bparZWF/8T2tg3lAgEHhGVatU9Rvgc+BGtznH7FZVrVDV5aq63b7fBryHFSQBcC3wuZst/SfAdRp6/SRpdjliVd0hIp9j5W/tdhvqCAyxnR7u+86xr1cAVwCpwEqsiJdpQAWwynYuNMaVDRWqdbu/GatAT6wtCsSKMqkl0+263H6O+jL3SJUUt+ctEZE8IFJVvxGRl4FXgI4i8gnwsKoW2dOPuu1RVm/P+qS4XbfBKgK82e2fWrBq2BgMBoPBYDAYDAbDCSR+Np+S3BzGP/Aw4jiV78lPpLIym63b7sLLqxV9er+Oh4dvi+ioqiQnJ7N8+XIyMjIIDQ3lyiuvpHfv3nXmVVdXs3HjRuLj44mIOO48qXK5eCM1mxGtAukX7N/kWQXzPsRVWkrrW6dz+LMvCdUANjg9SPWoxkvgyj5tSX79PbJqeuPnJyQMjzr153G5+Pp/r7F16WL6XzaRC6vmIdoXwjrXnRjcHgbdgXfXybx5/1RG9A+nV9tyOu//7JTPbIBcIFxEPBtxrEQCh93uD9uyhual1LPFD2NlTdTibrciIkOwsix6YXXc9QE+dN+vdq6qloqIe2ZHc/X6SXKq/4F/Bu7kxJe9QlVbuf0Equo99vgKrBSd0fb1amAEZ5j6Y9c7mQHcD4SpaitgB5ZT4nSJdts/EGgNpAOo6r9VdQDQAytd57eneYa6XedgOXZ6ur27ENMxyGAwGAwGg8FgMDREcV4OGxd+RMKQEXTofmZNT53OCrZt/wXV1YX07fMGPj4tU+w2KSmJN954g/fee4+KigomTZrE/fffT79+/fCo1xJ5+/btlJaWnhClMj+zgIzKau6LaTpKRauryZs7F//Bg/Ht0YPMslLCnUG85VVJBILT5aT37gX4FcSS61T6Xx6Hh8epmcEup5MvX3uJrUsXM/jKyVx4xVgkPRF6XtXoGv/gEDqPuoxlifmUXv4aPLL/lM5shHVY2Q5XNjKejhX0UEuMLWtoXnRt7VO3uWlu91p3Ce8CC4FoVQ0BXue47Z1BXVvaHyuq5lT1+klySn9NqroPK43ml27iz4EEuzCNl/0zqLZuiqruxXIcTMVyvhRhRZBcw5nVUwnA+qCzAUTkVs68lfLlInK+iHhj1VZZr6op9vMMsXPPSrGibJqKsGkWtmdwBvBPEYkAEJEoEWm5ZukGg8FgMBgMBoPhZ8Pq92ajTiejpt56RvuoKrv3/I6ioi306vkPgoJ6nPF+ycnJzJgxg/fee4/y8vJjzpT+/fuf4EypXbNu3Tratm1Lp06djsldqryakkX3AF8ubB3U5LlFXy2hJiOD1rdOJ21VIpUOF8XOIDJEqfFUbixdic+RcrI1CC8vBz1HnlqUirOmmkX/fp5dK79hxPXTGHnjLcjuhdZgj8Z8GxYDxk/C6XSy5avPwafp52gOqlqIVZbjFRG5UkT8bfv7MhF5Disl548i0kZEwu25cxvYagNWhsUj9vrRwESsGqSNEQTkqWqFiAzGqrlSy0fABDdb+gnq+hqaq9dPktOJFXsCy6EBgF0H5FKsOiDpWKkwz2KFA9WyAshV1RS3ewG+O43za8/dBbyI5a3LBHoDa053P5t3saJx8oABWI4gsGq/zADysUKVcrE6IrUEj2IVt10vIkXAMqBr00sMBoPBYDAYDAbD/zeO7t/LrpXfcN74KwmJaHfyBU1w6NDLZGZ+RudOv6VNm0tPex+Xy0VycjJvvvkm7777LmVlZVxxxRU88MADjTpTatm/fz/Z2dkMGzasTo2Tr3OLSCqt4L6YiCZrn6gqef/7H95xcQRecAH7lm0G4HN86IwwIONrWmfv4bzY8aRXK10Gt8Xbt9kVMKipqmLhi0+TvH41o2++g6FX28Vmd86H9v2gddMdhELbRxE/cAhblnxBdWVFs89tClV9EasExh+xAgxSsLI3FgBPYjWZ2QZsx7K3n2xgjyosJ8plWNkTrwI3q+qeJo6+F3hCRIqxnCLz3PbbCdyHZU9nYNnNqW5rm6XXTxVRrR/V8/8TEZkFpKrqz6YK8Y+JgQMHamJi4rlWw2AwGAwGg8Fg+Emiqrz/50cpOJrObS+9gY9/03VGmiIz6wt27HiAdu2uokf350+5aGteXh4HDhxg//79HDx4kIqKCkJCQhg1ahR9+/bF07N5jovZs2eTlZXFgw8+WGfNpO/2klpRxfqhPfByNK5b8ddfk3rf/bR/6kmCx43nw6dmkuSVw8yKflxRtJaYvO1cdPEdlO0KZUu5k2seGUC7TiHN0q26ooIFLzzJkR1bufj2e+l7id1iOv8Q/KsvXPxXivuOJT9/HdHRtzb6DtP27OL9Pz/CmNvuof+4CZtVdWCzFDD8ZGi+m85gMBgMBoPBYDAYDOeE5PVrSE/axSV33X9GDpWiom3s2vVbQkIG0L3bU81yqJSVlXHw4MFjjpSCAqtHSXBwMN26daNz585079692c4UgKNHj3LgwAHGjBlTZ92GghI2FJbyZJeoJh0q6nKR/a9/492xIyGTJpE7ZznZnmUUu/y5On8DkYXb8T3vEiIrOvKNq5DQdv60jQtulm6VZaV88sxfyUjew2X3PkSPURcdH9y5AABX9yvYtPZu1OsANeWt6NT16gb3iuzanfbxXdm8aEGzzjb89PjJOlVEZCdwn6ouP9e6GAwGg8FgMBgMBsPZoqaqipXvzKRNTCy9LrzktPepqspj27Zf4O0dRp/er+Jw+DQ6NzMzk+3bt7N//34yMjIA8Pb2Ji4ujmHDhtG5c2fCwsJOu/3y+vXr8fLyYsCAAXXkLx/JorWXB1PahzWy0qLoi8VUJicT+cIL4OFB0c4ScgNKqChzEVu4hV2hfXn+8hvJnL2HvEoXw4dHNktXZ001Hz/1OJkH9zHhwUdIGHp+3Qk750PkeWzeshn1OoCz2pd9+58kJGgEYZEntk0WEQZOvIrP/vnMyV/Kz4QfKgtEREqAPqp64GyeczJO6lQRkceAUap6mZtsL7CvAdmfVLWp4jYthqr2bOH9prfkfgaDwWAwGAwGg8HQEmz+4lOKsjOZ/KencDgar1FyMlLT3qGyKpPBgxbi7R3e6Lzk5GTmzZuHy+WiQ4cOjB49mk6dOhEVFdVkjcZasiEAACAASURBVJTmUlxczLZt2xgwYAD+blE3u0vKWZpbxCNx7fBvokOP1tSQ85//4JOQQPDll1GWeIB8L09UlKj8/ewO6k7c5TdQsyqdFBHEAV2HNq8GzYb5H5KxL4mJD/3uRIdK3kHI2EJu70fJzJ2Bt08QQW8HUHpvJquW/IkLx/+LkDZ+J+wZP3gY8YOGwbxFzXtBjSAih4C2gBOrgcpi4H5VLTmjjX+i/Fi65jYnUmUl8DsR8VBVp4i0B7yA/vVk8fZcg8FgMBgMBoPBYDC0AKUF+WxcMI/OA4cQ06vvae/jclWRlvYOYWGjCQpq/Pvp7du3M3/+fCIiIpg6dSqBgS1vt27cuBGXy8XQoUPryF8+kkWAh4Nboxp3+AAULlhA1eHDdHjlZcThoOCLXaRJPgA1AVF8638+y6PbULEmmZQqJbZ3OP7B3ifVKyflMBvmzyMsoQd7svLYOX8+NTU1OJ1OnE4n3XIWMwBYul1pM2o/gR/6UhGSgPfaMgJHfMOiGfOYcNd1BIfXdaw4HB5MevgP8NsWCdyYqKrLRCQK+AqrYO3vWmJjw+nRnO4/m7CcKP3s+5HAt0BSPdl+4A8i8qL7YhFZKCIP2dfdRWS5iBSIyE4RucJt3iwReVVEFotIiYisEZF2IvKSiOSLyB4R6e82/5CIXGxf/0VE5onIbBEptvce6Db3PBH53h77UEQ+EJGfTbVhg8FgMBgMBoPB8PNkzQdzqKmq5oKpt53RPllZX1JVlU10h1sanbNp0yY+/vhjoqOjmT59+llxqFRVVbFp0ya6detGWNjxFJ/D5ZUsyMpnWmQYoV6Nf/fvqqoi+9VX8e3Th8CLLqI6rxxnWTBHnEegxsVHrUdzaa9IfDZmke3lQUWFk+7D259UL5fLyeJXX0IdDg6rF8nJyRw+fJijR4+Sl5dHaWkpHYu/52hNF/wT1iBFQrH/GJbHxlLqvBRHkQetuv6PBS9uoDivZTr9NIWqpmFFqvQSkRAReUtEMkQkTUSeFBEPABGZLiKrReQF264+KCLuGSfLReRvtv1dLCJL7LbHiIiviMwVkVzbht8kIm1FZLKIbHbXR0R+LSKf1tdTRHaLyAS3e08RyRaR8+z7D0XkqIgUishKEenpNneWiLwiIots3TaISGe3cRWRePt6vG3zF4lIioj8pYVe9Uk5qVPFbre0ARhli0YBq4DV9WQrgbeBG0XEAWB/GBcD74qIF/AZsASIAB4A3hER9/bB12F52sKBSqx2yd/Z9x8B/2hC1Suw+mq3AhYCL9s6eAPzgVlAa6we2Ved7LkNBoPBYDAYDAaD4VySdegA279dSv9x4wltH3VGe6Wkvo2/fydatz7/hDFVZdWqVSxatIiEhASmTp2Kr6/vGZ3XGFu2bKGiooJhw4bVkb+eko0D4e7oNk2uL/hgHjXpGUQ8+CtEhANvfglAtlcV3kGRFFS4uLtzBFUHC0nz9cI/2JuOvZquzwKw4oN3yTqwl8p2MVxzw408/PDDPPjggzzwwAPce++93DbpQsJrUtnn05OAdkmEHerCmBf+xWWXXcYG8UOKxuETmop32OcseG4jJfmVp/+SmoGIRAOXA99j2bo1WNkj/YFLgTvcpg/BCooIB54D3pK6BWamALdi2enewMO2/BYgBIgGwoBfAOVY9naciHR322MaMLsBVd8DbnS7HwvkqOp39v1ioIt99nfAO/XW3wD8FQgF9gFPNXAGWOlQN2P5A8YD94jIlY3MbVGaE6kCsILjDpSRWE6VVfVkK1R1I1AIjLHlNwDLVTUTGAoEAs+oapWqfgN8Tt0XPF9VN6tqBZYjpEJVZ6uqE/gA6w+kMVar6hf23DlAbWzcUKw0p3+rarWqfgJsbOZzGwwGg8FgMBgMBsMPjqqyfPab+AYGMfTqG0++oAkKC7dQVLSFDh1uxv7+u845S5cu5euvv6Z3795cf/31eHl5ndF5jeFyuVi/fj1RUVHExMQck2dXVfNeRi6T24XS3qfxNB1XWRk5//0v/oMH4z9sGIUbU/HOCWZP5SHE08EeZwC9o0KI2llAlZ8nqekldB3aDkcT9VkA1i3/ls2ffgitwpj+m0fp1atXnXFV5eC7bwLgjNyNo1zoOnUGIsLgwYMZPHgwKw62wquqG216fUpleSrzn1lHaeFZcawsEJECrCCHFcCbWM6VB1W1VFWzgH9i2eK1HFbVGbat/DbQHqs2Sy0zVTVZVcuBeRzPSKnGcqbEq6rTttWLVLUSyz6fCmBHl8Ri2ff1eRe4QkRqi+dMwXK0AKCq/1PVYnvPvwB9RcS97/V8Vd2oqjVYDpd+NICqLlfV7arqUtVt9hkXNPwKW5bmOlVWAueLSGugjaruBdYCw21ZL47XU3kb++Xav+fY15FAiqq63PY9DLi7XDPdrssbuG8q/uyo23UZ4Csinva5aaqqbuMpTexjMBgMBoPBYDAYDOeU/YkbSNm5jeGTp+B7hmk4qamz8fAIpH27ugH7LpeLhQsXsnbtWgYNGsRVV13VIoVoGyMpKYm8vDyGDRtWpxPPm6k5VLqUe2Mimlyf9847OHNyCL//AQo+2UfxJwcprMpinmcpAHuKvXigezsq9xWQ1TYAddFk6o/T6eSLL75g5ewZOBwObvrDE7Rvf+L8zYsP0yrnK476RFMTl0v74KvxDe9wbHzs2LF07hzPuq09EW9PuvR6idK8cuY/tYayoqpTfU0n40pVbaWqHVX1XizniBeQYafoFAD/xYr8qOWYrayqZfZlYEPjWLZ07dgcrLot74tIuog8Z2eggGX3T7EjXqYB82zHSB1UdR+wG5hoO1auwHK0ICIeIvKMiOwXkSLgkL3MvahOY7rVQUSGiMi3dmpRIVZUTdPFeVqI5jpV1mGF/dwJrAFQ1SIg3Zalq+pBe+5cYJKI9AW6A7UNudOBaKnrGo0B0s7oCU5OBhBVL7wp+iyfaTAYDAaDwWAwGAynhbOmmhVz36J1VDR9L77s5AuaoLIyi8ysL4iMnIyn53F7tKamhg8//JDvv/+eUaNGcfnll+NwNNc8PB09KlmxYgUhISF07348a6S4xsnMtGzGtwkh3r/xlCNnURG5b76F/9ARFK8RSjcdZXPxVj7JXkwvPx8UwREQSr+UCsTPg/0ZpbTrFEJou4AG9ystLWXOnDl8t+QLPEuLuGDqbbSL6XjCvH2bs9izaBVtvA6SGlKCw+lF56GP1Znj4eHB5MmTCQzsyJEj/ajpnEvf1jMpzq9i/hMrKC9pcceKOylYpTPCbWdLK1UNboluuXamx19VtQcwHJiAlWKDqq4HqrCyVqZwPJiiIWpTgCYBu2xHC/a6SVglQ0Kwol0ATqdP97tYaUnRqhoCvH6a+5wyzfqvscOAEoFfY6X91LLalq10m5uKVdx2DvCxvRasuixlwCMi4iUio4GJWHVQzibrsFpO3W8XxZkEDD7LZxoMBoPBYDAYDAbDabHlqy8oOJrB6Gm34zjDyJG0tHdRraFD1NRjssrKSt599112797N2LFjueiii6j7HXTLUlFRwZw5c8jMzGTcuHF1omFmp+dSVOPi/pi2TewAuTNn4SosRALHUFVYyd81m305X9IpqAdeYTXkufy4r1c0VUn5VHQPoyCznO4jGo5SOXr0KDNmzCDl4AGC8jOJTOjOeeMmnDAv63ARX8/aRb+IjZT5OihM8KBDx2l4eYWeMNfX15cpU6aQld2HioowaiakMsjzW4oKnXzyp2VnzbGiqhlYdUtfFJFgEXGISGcROePUFxG5UER620Vvi7DSgdwzT2Zj1TKtVtXVTWz1Pladl3uwo1RsgrAcQrmAP/D0GagbBOSpaoWIDMZy2PwgnIorcgVWCJH7y1ply+q3Un4b6I2bt8oueDsRuAzIAV4FblbVPaeudvOxz70auB0owEpJ+hzrwzMYDAaDwWAwGAyGHw3lxUWs+/hdYvueR1z/gSdf0AQuVyWpae8SHnYh/v6xAJSVlTF79mwOHjzIpEmTTigY29KUlZXx9ttvk56ezuTJk+tEqVQ4Xfw3JYtRoYH0C/ZvdI+qjCzy3pqJZ+R5lHbuwnXVRVTkfQcIg3sMJ6somzwCGZPvQnw9OFzmxNPbQfyAE9OJduzYwZtvvonT6aSrrwOtqebSu3+J1IvSKcmvYNGr2/AL8iK+fAEHIoIQD29iOt7ZqJ6hoaHccP1N7E0eQlV1JoG/DGBI0DYKSz345LEvT/3lNZ+bsQrM7gLysZq8nLzl0clpZ+9VhJXCs4K6ESlzsEqBzG1qE9vxsw4r2uUDt6HZWCVB0mzd15+BrvcCT4hIMfA4Vm2YH4TGe1XVQ1UfAx6rJ5tHw8oewQpDWl5v/k4aKRajqtPr3b+JVXSn9n6fu76qGut2/Zd6aw/hFuqjqom4FbQRkQ1YnYgMBoPBYDAYDAaD4UfDuo/eo6qs/IxbKANkZn5BdXUuHaKtNsplZWXMmjWL3NxcrrvuujoOjrNBaWkps2fPJicnhxtuuIGEhIQ64x9m5pFVVcMr3RuPUqlKLyH1V8+gVZWkXHojd2Rm0suziv6l+wnyi8VzQiwy+xt6to3ClZSP36go9n2VQvyACLx9j5u7LpeLr7/+mjVr1hAdHc2QhM589fILDL/uJsI61K0OUV3pZNGr26iudDK69TLwLCGrYxiRkZPx8TnuqNmbWcCG1P3c0L8vng7rrJiYGC666E62bd+L6hwG/fkTHP9cy9r0uDN+n+42cD15IVYUyD0NjM3C6g7kLnO3lUc3Nl9V38OtqGwDZGN13anjVKlv29uyMQ3ISrDSf9yZ7TY+vd785UAHt3v35/gIywH0g9Nsp0pzEZESrGiWN+sVhz1niMgbQABWS6ibgD7AWXUVGgwGg8FgMBgMBsOpkJuWwpYli+hz8VjCY2LPaC9VJSV1Fv7+8bQOHYGqsmDBAnJzc7npppvo1KlTyyjdCMXFxbz99tsUFBQwZcoUOnfuXGfcqcorR7LoF+TP+aEn1h5VVUrXZ5D34XdU7lpGas8R3FkgXBEfynXf72aDs5hBE6bx2YZdAFzuGY54C5kB3lRXOuk+PLLOfmvXrmXNmjUMGDCAiy4YxdxHf0l4dEcGT7r2hHO/nrWL3NQSLhrlIPCjmRyZ5AfioGPMXcfmLUvaz0PLHwDfFF7Y6UVCaDxdQruQEJpAQngCgRG3UF39JJu/+zUX/vlLgt/+HGa01Ns9u4iIAl3cap80xj3AJruRzZmctxyYawdW/OQ4qVNFRB4DRqnqZW6yvcC+BmSvYUWIBAMvtby6p00i8Des9J8DwLV2CJLBYDAYDAaDwWAw/ChYOfd/ePn4MnzyTWe8V1HR9xQX76BrwhOICOvXryc5OZlx48addYdKYWEhb7/9NsXFxUydOpXY2NgT5nyeXcCh8ire6hV5Qj0XV2UN+fOSKd+ZS3XaEpzq4k+RI3noongmLUtmU3kSHh4+RF0wgh2vvkuMeBJ2yEngqGg2JmYREuFH+/jjXXmdTicbNmygU6dOTJw4kWVvvkJpfj5X/Ob3eHjWbR+dmpTP/u+zGXxJWxwv3oXfRS52RPrTrt0k/PysIIm5mxN55rvfID7FtKm+ioziXDxalbM2fS0L9y88tleg+BHlncYXi6YwauSZf6Yicgir9kicqpbasjuAqfUjTk5hz+WchkPD1kWAxSJSDVTYQ7U1Xp462za3iPwFq93z1JPNPZs0p6bKSqzWyR4AItIeq2VT/3qyeOB9VQ1Q1eF2d6AfBar6hqq2VdVAVe2jqovOtU4Gg8FgMBgMBoPBUMuhbd9z4LtNDLnqOvxDWp3xfikpb+PpGUT79leRkZHB0qVLSUhIYMiQIS2gbePk5+czc+ZMSktLmTZtWoMOFVXl5cNZxPv7cFl4yAnjhV8eonxXLlU9HZRv+5olHYfw2O1jmHIoD8WPlNJkOg0dyd3vbSPEVUykT2scnh7UdGtN+t4Cug9vX8dRs2fPHoqLixkyZAipu3awdelizrt8Iu3ju55wduKiQwS08iZ80T/x9CggM15xOZSOHX8BwHPffskzW+7Dw6uKsTHjqfRdT0DZ5RzYMYUPxi1m+XXLmXHpDB4Z9AgXxY6lpMaHr3N38ofVf2ipV+wB/OpMNxGL0273pKqxqtoRq+XxB6oaBLQGrsKqxbLZ9hP87GnOS9yE5USprUkyEvgWSKon26+q6SKiIhIPICKXi8guESkWkTQRebh2UxGZJCJbRKTI7ks9zpZHishCEckTkX0icqfbmr+IyDwRmW3vuVNEBrqNP2qfUywiSSIyxm3dXPs61tbxFhE5IiI5ItJif+EGg8FgMBgMBoPBcCq4XE5WzHmLkIi2nHd5/RITp05F5VGysr8ksv111NR48OGHH+Lv78+kSZPOapef3NxcZs6cSUVFBTfffDMxMTENzlueV8z2knLujYnAUU+f6qwySjdkUNI9lCXvvoHT4cEFf3uEMeVC5YEaEgt2o9SwxNWRIxnZtJIyokqCCBjSnr1bcxCBbkPr2vIbN26kVatWxHXsyJI3/kNIRFtGXDftBL3SkvJJ31tAQlAaFWvX0GZKb1IifYkIHY2/fyce/GwOsw89hq9HIM+d/zRe2/K4M+1SaP0quaVl3DlnE4FerRjafijTekzjqVFPMWfsHP4eWcV9/m1a6jU/DzwsIg163kRkuIhsEpFC+/dwt7HlIvKUiKzB6sw7B8uWf1lESkTkZbetLhaRvSJSICKvSDP+cOwWzDuB67HqrfzGPjdURD4XkWwRybevOzS0h4i0F5FtIvJb+75B/4DtP/g9cL2t+1ZbfquI7LZ9AgdE5O6T6X2mnNSpYnfP2QCMskWjsLr+rK4nq98BCOAt4G7ba9UL+AbAbnE0G/gt0Mpef8he8z6QCkQC1wJPi8hFbnteYc9phdWH+mV7z67A/cAg+7yxbns2xPlAV2AM8LiInN0qTQaDwWAwGAwGg8HQADu+WUrOkUOMuulWPL28Tr7gJFhtlJ106DCNL774gvz8fK655hoCAgJaQNuGycrKYubMmdTU1DB9+nSioqIanfufI1m09/Hi2rYntiYu/OIgLg8Hj25MZOShTfhfdz29wttRsHAfOeVlpJdvxxnchs8zvLizl/WuOtGWgPOj2LMug5heYQS08jm239GjRzl8+DCDBg1iw/wPyM9I45I7H8DL1/eEszd9cRA/fwfBHzxL0Lhx5ITuxOnpILrTQ0yZ9zzLcp+nlUcsn10+B/kklfuO3sDF+UOYVfAr2rdZyNaUIu57fxXupUXbtOlFZLtb6RJ2+ExerzuJWA1hHq4/ICKtgUXAv4Ew4B/AIhEJc5s2DbgLqwXxdCzb/n47q+N+t3kTgEFY9Uivw7Kvm4WqOoFPsRw2YPkdZgIdgRigHNuOr6d/HFaHoZdV9Xlb3KB/QFW/xGrB/IGte197fpatezBwK/BPETmvubqfDs0N91nBcQfKSKwXv6qebEUD66qBHiISrKr5qvqdLb8d+J+qLlVVl6qmqeoeEYkGRgCPqmqFqm7B6gB0s9ueq1X1C/uDmgPUvjwn4GOf56Wqh1R1fxPP9FdVLVfVrcBWt30MBoPBYDAYDAaD4QehsqyMNfPmEtWtB12GjDjj/ZzOStLS3iM8fAzJyXls3bqVUaNGNZiG01IcPXqUWbNmATB9+nTatWvX6NzNhaWsLSjhF9Ft8K7Xxrhibz4Ve/L4wKuayfuX4uHnR8z028mds4uqihLWlZdQU5XOBo9O3HZ+JxxHDxLhCiFySGfSU0soLayi+/ATo1Q8PT2JDgtl08KP6XnBxXTs04/6pO8tIC2pgNislXgH+dPm3itJaVVCgMZz/aez2FExh2ifgSwa/BrlryfTJT+KlKGlhN/Wi9CKQOaWTOe88F0s21HKnR/NpcZVc2zvXj1/TVDQ4DN4wyfwOPCAiNQPfxkP/B979x0fVZU+fvzzzGTSe0ISSKWFECBIkaYgCCpdESxYEGxr37Wuvbd1V8W1rG0V6ypFinSQKgIhIARCC6T3kF4nk5nz++MOMECACNn9rr8979crL51zzzn3zE384z6e8zzpSqmvlFLNzuo9+4EJLn1mKaXSnNdtZ7jH60qpSqVUDsZJlVMf2pkVYBwHQilVppSap5SqV0rVAK9walXgROd9nlNKfQzQyvjACZRSS5RSh5VhPUZ+l6Gn698WWhtU2QBc7Ix8tXNm9/0FI9dKMMYulJZ2qkwGxgLZIrJeRI4WQY8GWgp4dADKnQ/6qGzANcxZ5PLv9YCniLg5MxP/CXgeKBGR70TkxJTPJzp5nlNTTmuapmmapmmapv0bJS+YTX1VJcNvvr1NjuaUlCzGZivH12cKixcvJiYmhmHDhp194DnKyclh1qxZuLm5MX36dMLCws7Y/72cEgLdzNzUPuSEduVQVC3JpM7LzOqCg/TL3EHwzdOo/LEQR10jm2vtVLrtxYHQrs9FTAsxc6SmnISgOALGdGTfpgI8fS3E9Qo9Nmd9fT2pqakkJSWx48cf8PD24ZJpt7W4rm1LMvFwayZsx1winnmG4ryPOYI3D2aGUKBW0M9vAt/6P0XNrHRK7WV82PdHBl15BZ7xQbS7qzfuJgt/rxnCFYF1rN4ewKTv/kxuTS4AJpMHAy48U2Xi30YptQdYDDx+0qUOGO/Prk5+n85t5W3O9305EigHEBFvEflIRLJFpBojdhB4NEer041APieWRW5NfOAEIjJGRLY4jwtVYsQjQk/Xvy20NqiyGQgA7gA2ATgT0RY42wqUUpknD1JKbVNKXQmEAQuA2c5LuUDnk/s75wsWET+XthiMh3tWSqlvlVIXY2wrUsBfWjNO0zRN0zRN0zTtP62qpJjtSxeSOHQEEV3iz3s+pRS5uV/g5RnPqlWHcXNzY/LkyZjN5rMPPgc7duxg1qxZeHt7M2PGDEJDz/zueqCukWVHqrg1KhQftxPXVL+9GFtRHW9Za3k4cznmwEBMQRdjy6vlcPYuDrp50lSVRkVgHH8ZcQG/Lt+MIFx4y6U0NjSTmXqEbgMjMLsdf8XduXMnzc3NdIuJ4nDKFvqMHo+Xr9/Jy6LwcBV5+yuIPrSUwJHD8b3sEjZX7+Ol7EhqLbuZGngff8m/hrqNBeyLzeOPHf/C7aPuORYEc2/vQ7t7L8AS5Mkz1R243suTtLRBTJp3O4sOLzrhOFAbeg7jXdw1wFCA8S7s6uT36ZMX0+aLcybAnYBxugWM3CrdgIFKKX+On3hxjSI+DxwBvnUJtpwtPnDC2kXEA5gH/A0IV0oFAktPuk+ba1VQRSnVgHF26yGOPxgw8qo8RAu7VETEXURuFJEA57aiasDhvPxPYIaIjBQRk4hEikiCUioXYwfMayLiKSJJGEeFvj7bGkWkm4hc6nyQjRjntBxnGaZpmqZpmqZpmvZ/YsO3sxAxcfHUW9pkvqqq7dTUplFQMJqioiKuvPJKAgJOra5zvux2O8uXL2fRokXExcVxxx13EBR0an6UE8YoxV8zC/Eymbgt8sRTKw5rM1Urs8jzNtFQ8CvR2fvwG3szjfvrsdalscW/O8memfg21zL5iquo+XYfh83FdO7YCb/gAA4mF+OwqxOO/jgcDpKTk4mJiSHzl/W4eXhwwRXjW1xbypJM3B0NRFemEP7M0yz+5S1eLvXFZinlRY/XuGV7T+wVjVRP8OQh71e5tuf1dA3qesIcbgEetLurNx6dArivwYs7JJiGnGk8ueF5Ht3wKFXWqnN82i1zntT4HnjApXkpEC8iN4iIm4hch3GsZvEZpioG2qTGtvOe3YF/YVQAest5yQ/j/bzSedLluRaG24BrAB/gSxExtSI+UAzEyfEqRu4YKUFKgWYRGQNc3hbf7Ux+Swml9Rg7Tn52advobGvp6A8YSXCynFt87sLY0oNSKhln0higyjn30YjaVCAOIyo1H+NM1epWrM8DeB0julXkXNcTrftqmqZpmqZpmqZp/zn5+/dycPNG+k+4Gr+QtjmdkJv3BZWV8ezZU8GAAQNISEhok3ldNTQ08O2337JlyxYGDhzIjTfeiJeX1xnH5DRYmfTrIRaXVnFPTDtC3N1OuF6zPg9HjY1Xayr446HlWGI70VyXiFuYg82F9fzgbye25gABPhG03+tDiVs1taqBpD69UUqx75cCwuL8CYk8fkIlPT2dyspKkhLi2ffzOpIuvQJv/1MDTMWZ1eTsLSc6cwUdHnuYLLHz1MEf8UHxdf3fuHBnAO5RfoTcn8QzZa8T4RPBXb3vavF7mjzdCJ3RA+9+4dzc7M4f6yOIq3+S1Vk/MXnR5HN42mf1IkYQAjByl2AkaX0YKAMeA8YrpY6cYY53gCnOqjx/P8d1XCcitRjv9ouc9+6nlCpwXp8JeGG8q28Blrc0ibNIztVAOPCZM1hypvjAHOc/y0Rkh/OY0AMYJ2QqgBuc6/m3cjt7F4NS6glOClIopWZz/EjP0TbXrTWjzzDffIyHcnJ7HsYfQktjnj/pcxbHt/KkAi1m/3Edd9KYo23DT7dOTdM0TdM0TdO0tqQcDtZ99Sm+QcEMmNg2L9uNjQXk5W3g4IFJREREcNlll7XJvK6OHDnCv/71LyoqKpg4cSJ9+569qMq8onIeP5gHwPvdY5gcEXzC9eZKKzUb8tns4WDA4U14HynCc9DjmAO9yNrwObM6X0mFNJLUVMylMdPBochLaMSSbqFbt26U5tRQll/HJTd0O2He5ORk/Pz8qDqQBkC/8Ve1uL7kH/Zhaa4jPqYJ/6uuZOKsGSRaI3il4DY8HR74j47Fb1gUX+37ioMVB5k5fCbeFm9sVjvbl2VRll/LkMldCIowYhtiNhE0pStuQR6MXZ1DaG4Iewe/y1bH+WemUErFnfQ5F/A8qe1noN9pxg9voW0zEH9S28nvy9PPsKbnMY7unJYzuHLyvT9qaV1KqUZglEu/M8UHVeqG5AAAIABJREFUyjCq+rq2vQ+8f6b1tLVWB1U0TdM0TdM0TdO087d/03qKDh1k9D0Ptlja91zk5H7D/n1DcDjcmDJlCpY2KM3sKj09nblz52I2m7nllluIjT05dceJqmzNPJGezw/FFQwI8OG97jHEeHmc0q96RRZ2h4OPa4uYmbYSrz5DwK0TuGcyM3IkhywOnoptYJDtKtzxIPDmBPbN3kC3bt3w8PBg36YszBYTXS8MPzbnkSNHOHz4MEMHD2LPN5+QcNEl+IeemkC3OKuanPQ6OhduIOqjZ3h9/ULCrRX8JfdBPLwdhEzvjXu0H0V1Rby/832GRg5lRPQI0rcVs2neIeoqrWBxkPtyOReO70ify2IwmU2ICP6jYjEFetBvbjrBm5sZcvX7LD7jKRzt90oHVTRN0zRN0zRN0/5DbNZGNvzrC8I6diZx6Ig2mdNub2TzLzuoqurGVVeNP2vC2N9CKcXmzZtZtWoVYWFhTJ06lcDAwDOO2VJZy337sim02vhzxwjujwnHzXRqrtCm3Brqfy1hjqmJ2wvXYrI1YY6diFuEJ+8lZ7IlJokpHdsxJMuGn3sgITf3IKephIaGBnr17MXO1Tns3VRA1/7heHgdf7VNTk7GbDZjPlJEs9XKhafZDbTls8242Wz0uaYPWR7uLD/wCe/m3Yu4lRF+9zBMoUZ+1L9u+yt2ZefemIdY+PZOCtIr8Q03saT7uxzxLmZk5lTsCxSHd5Ry6bTuhEYZx5B8+0dg97YQ+dVe6n44pa7L75aIrAO+Vkp9+n+9lv8Grc6pIiLTRWS3iNSLSJGI/ENEzvxfk6ZpmqZpmqZpmnbM9iULqS07wohpdyCm35Li8vR27vyOzMyuJCR0oHfv3m0yJ4DNZmPBggWsXLmShIQEbrvttjMGVGwOxesZhVz96yHcRPixT1cejItoMaCilKJySQZ1bsLGqkySUjfgO2wiQjA/F6bwTXQvepgsPIQZf0cwlV1r8OoWTGpqKl6eXhxYUcemuYeI6RHCxdceTxprtVrZuXMn3bvFk7ZmBZ37DyQ0+tRdNUW7csgrsdDJvo/Qm67j7oWv8EzR9fgrD4I6Lcbk3NmyKX8T6w5v5NaqJ1k/M5eyglqi+tTzSfjDlHpnMt7akw2Rn7Ey/jMKCkuY/WoyyT9mYG82aqYEJIbgNSMR2qBctohkiUiJiPi4tN3uDHJo/0da9V+xiDyMUZ74UYzSyoMwEsuuEhH3FvrrHTCapmmapmmapmkurPV1pCz+gU79BhCV2LNN5qysrGDFioN4ezdx1VXTjpX5PV81NTV88cUX7Nq1i+HDh3PNNdfg7n7Kq98xGfVWJuxIZ2Z2Mde1D2Z1/270DfA5bf+GPWU0ZVXzvq2OR7OXYw4IRPyGU9nBxHP29oQ4THzQuT2OzHp+Lf+JjlcPxmq1sn//Acw1wRQcquaSG7ox9u5eePocP+q0c+dOmpqaCG5upLG2hgFXTmnx/r98uAG35gYGPjaJV9cs54bS9nRv7Eht53/g39dIDdpoa+TLeT9y085nYU8g3QaGEhC+ib/XP0+Nt41OYfGsCt3HmIQJtLNU822vlzgUuJ1tS7KY/eo2SrKrAYiKD6HT4y2m/zwXZuCPbTWZdv7OGlQREX/gBeB+pdRypZTNmez1WowsvDeJyPMiMldEvnZW+pnuLJX8uIgcFpEyEZntLJ90dN5pIpLtvPaMM+o2ynnNQ0RmikiB82ems1QyIjJcRPJE5GFnlK5QRGa0/aPRNE3TNE3TNE1rO78uX4y1ro4hU25ok/msVitffvkJdjuMn9AXzzbKz2Kz2fjzgiXM8wyifuzV7O7Ync8Kyvi6oIx5ReUsLa1kTVk1mytr+bW6ni/zjzAq5QBZDVY+7RHH2wkx+LqZTzu/anZQtSyTPDcFVWmEHErDe9h1NJs9eTQrjyazhfdCgzGnV5Ju/RVbR4WXXxALvliH3d5MsEc01z5xIT2HRZ4QRFJKkZycTIf27Tn08zoiE3rQIb77KffPnr2SQtWBblEN5AUFUZe6kSuqhlAct4iQ5i3QbSxFmVX888WfSNp7OUERvlx8jT/pm9/gS8dCSoKtuLt5kFmbRc/QnszOW8CuoEIGhPdiV8w8lnX7mIKSQua8nsLm+YdottnxD2ib3w3wV+CRlk6NiEiCiKwSkXIROSAi1zrbBzpPm5hd+k4SkVTnvw8Qkc0iUul8v37PdfOEiFwmIvtFpEpE3sOl8IuIdBaRNc73+iMi8s3/2omW1uwoGYKRUfgH10alVK2ILAUuAw4AV2LUlZ6GUd74fuAq4BKMOtF/x8jCO1VEEoEPMKoDJQOvApEu0z+FsRvmAkABC4GngWec1yMwdsxEOu8/V0QWKKUqfsN31zRN0zRN0zRN+49oaqhn+5IFdOp7IeGdupz3fA6Hg7lz51JRUUfffgfpnvBSG6wSGu0ObtmQwvqoBNyAX+sckFF41nEXB/ry9+4xdPA8/W6Wo2p/KcBe3sg79iqe2LsY946dUdKbf3rVs8/hy3MOd6IqbDjiLexYsZJLRj3C3NdTOFy/H09fH25+dBQW91NfZTMyMigrK2Nglzj2lpUy6o57Tuljr6xk2/z9uPnF0/eBMTz81bs8dWQ0xe1yqek0n15lI1g7r4C9GwuotzRRP/AAiSZ/Vn20gJ0XNpHVrg6AnqE9efGiF4n2iyajKoN/7v4nSzKWID5Cx4Bavvd5jUE5E2EFpG8tYEjSqYlyz1EKsA54BOMdGQDnkaBVwLPAGKAXxsmSPUqprSJSB1zq7ANGueFvjz4W4EHn3FHAMuAeYKaIhGLEAmZgvJffB9wFfHX01sBrwAbAH5iHUQ3oT231hf/btSaoEgocUUo1t3CtEKNc0wFgs1JqgbO9QUTuAu5zlkhGRJ4HckTkZmAK8KOz3BMi8ixGPemjbsTYGVPivP4CRsmlo0EVG/Cic01LnTWxu2HUvNY0TdM0TdM0Tfuv8uuKJTTW1jBo8vVtMt/KlStJT0+nc5dk+ve7HZdNCOcsr7GJm3ccYJ/JizHWKj69YhgCWB2KBoeDRruDBoeDBruDRoeiwfnZ3SQMC/LD1IqjR/Y6G1U/5bDNZGdoRQqWkkI8r3mGzSJ8VdfMsEYTl/l74xHlR3LtKjx8LyRlOZg9a2j2rWTgwItaDKiAkaDWx9uboh1bCY2Jo+MF/U/pk/7KexT7D6L3QH/+sf4X/liSRIVXHbW9/kZ4SSM/Fd5MxuECSrseINlzNlPSEtiem8P+0T7sNGVjFjOP9H+E8V2v4/GD+eys2cvnPTvyysWvcM8F9/D5ns+Znz6fZo9mdnZeQnq7Xxl56EZWbLCd76/H1bPAJhF5x6VtPJCllPrc+flXEZmHsfHhBeBfwFSMQIsfMBYjMINSarvLPFki8hHG5oiZzn5pSqm5ACIyE3j4aGel1CHgkPNjqYi8BTzXll/2v11rgipHgFARcWshsNLeeR0g96RrscB8EXG4tNmBcKCDa3+lVL2IlLn06wBku3zOdrYdVXbSWuoB31Z8F03TNE3TNE3TtP+opsYGUhbPJ+6CfrTv0u2859u2bRtbtmwhruMROnWqJzx84nnPuaG8hj+kZVFvtXJ19j7evulazM4giZdZ8DKboA2qNFevzsZhtfNlUwmvJi/Ga8DFFNsieYFaQu3CM6E+0Ajm4RFkvBGIydKHyPhAAnvVkb9OkZSU1OK8FRUVHDhwgN4xkWRs38DY+x85Jb9M7caf2XPIgjlCYRkUzYXfZeGugnCbUEh5ZRXZh68jo9BC6KV21u37ipE72lERWM7WKRb21+/Dz+LHV2O/osoUweUp6eRbmwhwM3Plr+l83rMjA02h/NE2naurL2K2dRFLgjZQEXiA2Re8Tt+c0ef/8JyUUntEZDHwOLDP2RwLDBSRSpeubhzfUfIt8IuI3A1cDexQSmUDiEg88BbQH/B2jjsaaDn53V2JyLHPIhIOvAMMBfwwUoz8T50gaU2i2s2AFePBHyMivhjbin5yNqmTxuUCY5RSgS4/nkqpfIwdLlEuc3kBIS5jCzD+KI6KcbZpmqZpmqZpmqb9ruxauZTGmmoGt8EulcOHD7N06VJi40KIilpOXOxdmEznHu1QSvFudjHX7zqMV1Mjk7av5ZERF+Hh4XHeaz2ZraSe2i2FLKSJP5RvBFsTpriJPGduxKrgmcZ6vGoFNSCceR/sRszR9Bruz/h7e7M/fS/h4eGEhbV8jGbbtm0IUJu+F/924XQbPPSE6/baOg69/A7FYf1JGB7F4bkbiG9qT94IB1W2OaiqYA4XjqX/ldFs/+UtBu0NprZ/KHMvymF//WFCPENYPGkJK6r9mLgjHQeKhX26srx7J8LswvU7DvPVZzuoWpJJiCOQh/s/zLJxS7i79924eZvZ1OmHFtd9Hp4D7uB4Go1cYP1J79++Sqm7AZRSezE2K4zhxKM/AP8A9gNdlVL+wJMcz5tSCEQf7ShGpCraZeyrGLGAXs6xN7mM/Z9w1qCKUqoKY7vQuyIyWkQsIhIHzAbyOB75OtmHwCsiEgsgIu1E5ErntbnABBEZ4kyA8zwnPvh/AU87x4RibG/6+rd+OU3TNE3TNE3TtP9LtsZGtv34A7FJfVpMmvpblJaWMnv2bNq1a0di9814eobTvv3VZx94GjXNdm7bk8UrGYWM8LEwZtNyLuuZSKdOnc5rnS2xldRTPucgjcCvtlxit/6E36hJfNjkyW57M1dXN9E/pD2WSF92ZtVis9rw9FzD0Ov6UV5RTn5+Pr169Wpx7qamJnbs2EGnsBBKMtLpP2ESJvOJx6HKP/+cQ979cLMI+UcKGVbfgRXRu+h7YXtq6/ZSfGg0g4cqNmd8QEAZpIw3Mzc4GavdSqBHIO9f/hX3HSznxcMFXBEawOr+3ei2swLTmzv46KdKetYrnrjAix+ndyL8/j74DYsiNCyCey64h1VTVvFgvwfb9Hk6j918z/E0GouBeBG52fnObhGRC0XE9Y/uW4zKQcOAOS7tfkA1UCsiCcDdLteWAD1E5Gpnld8HMHKcuo6tBapEJBKjYvD/lFaVVFZKvYERrfobxsPeihEJG6mUsp5m2DvAImCliNRg5DsZ6JwvDSOR7XcYka9aoARjRwzAyxhJclKB3cAOZ5umaZqmaZqmadrvxq7Vy2iormLw5KnnNU9dXR3ffvstbm5ujBvXg9q6rcTG3InJdG47Sg7WNTJm+0FWlFXxTFw4F25eTViAP6NGjTqvdZ6sudJK+dyDFL+9nYbCWt5w1HFfxjLMgYFs9RrCtzTR22rijgCFanLQNCCCnL3lNNVtpdeIfogIe/bsAThtUGX37t00NjZiKs7Dyz+AnsNP/A7KZiN/wWqKw/rTLiGAi3PN/OKXythpk9m+4WMczRbiq0rxuMCLspS9LBhexAFyCfMOwyxm7h74FjfurWZTZS2vxUfxaY84PA5WUrUsE8+EEOLv7csPY5IY1y6AFwpLef5QPg51/CCHj8WHW3ve2qbP1elFwAdAKVUDXA5cj3HKowj4C0YRmaP+hZErZY1S6ohL+yMYu1dqgE8wgjU45z2CkZfldaAM6Apschn7AtAXqMIIwLT5lpz/dq3JqQKAUuqfwD9Pc+35FtocGOey3jrNmFnALDh2lOg5jJ0vKKUaMSJgD7Qwbh0uR4ecbXGt+xaapmmapmmapmn/GTZrI9sWzSOmZxKRCYnnPE9zczPff/891dXVzJgxg9LSJ3F3D6VDh+vOab4fSyr50/4cvEwm5vTuQsUv69lWUcH06dPb7NiPvc5GzbpcajcXgILGpFBu3pvNWEc6XvtSsU+6mxeUgwi78KeiPXjEDsJvWBRrfynCzWLH2rSLxGH3opQiNTWV2NhYAgICTrnP0TLK7Xy8KEpJ4aLrbsbicWL54qrVa9nTbiwe7kJCfhV57qU4xoWyb2ER9tB1eBS3o98AD1786A1WDyjB4uVJz+AEdpTsYFjiczycZaGTl4mvk7rS088bW1Ed5d8fxBLlS8jUBMRiwgJ83COOp9Pz+TC3lGKrjZndY/AwtWofQ6uc/N6rlMrFqNR79PMBYNwZxufQwsYKpdQGIOGk5mddri8H4k8zZxpG8RpXb55uDf8/arvfcAtEZLiI5J3m2gQR8XaWfvobxo6UrPO8X5aItG1oVdM0TdM0TdM07Rzs/mkF9VWVDJ58wznPoZRi0aJF5OTkMGnSJPz8Simv2ERMzO2YzZ5nn8CFzaF48VABd6RlkeDjyaoL44moKGHbtm0MGjSIuLi4c17nUQ6rneqfcih6Yxu1P+fj3TsMdUdPrj+cj4encO32BVg6deHP9s7YlOKmokK6du6LW6gXNTH+5B+ooNm6jU79+uIXHEphYSFlZWWn3aWSk5NDcXEx/nWVWDy9uODyU2MKm384SG1gF3oHmhGx8VXiesLSksjPXYLZvYFepXtZldfE+s55NHgrhnS4iJTiFIIjZjCnphPXRASxsn88Pf28sdfZOPLlXsTDTOjNiYjl+Cu1WYRXu0byVKf2zC+p5MZdGVQ328/7mbYFEZklIv/20x8iEiciynlU6H9Cq4MqInKDiKSISK2IFIrIMhG5+DzufSXGtqQCjC1E1yulTk52q2mapmmapmma9rvT3NRE8qJ5RCX2JCqx5znPs3HjRlJTUxkxYgQ9e/YkM+t9LJYgIjv8tkDNtqo6Lks5wAe5JUyPDGV+ny4Eo1i4cCHBwcFceuml57xGANXsoHZTPkV/3Ub1qmw8ugQS/qe+yNg4bpm3E5vdwUdeB3EUFLAsYSKpJphQL4y2VEOzO4GTupC8NAsPL2isSqb3ZWMASE1NxWQykZjY8k6frVu34ilQeiCNpFGj8fQ9sSjs/uV7yDAn0iXQRrjNxF8jv2CU41r2bSwkql8y3s2eeDUH8XZdCkcCmhjfZTwrslfgCLiCbM9Lebd7DH/vHouPmxlld1D+7T7sVVZCbu6OOeDUXT0iwv2x4bzbPYYtVbVctSOdImvblFN2biIocW5MONp2u4isa5MbaOekVUEVEXkIo0b1qxglkWOADzACI+dEKXW7MyNxgFJqpHOrkus9z7/QuqZpmqZpmqZp2v+B3WtWUFdRfl67VNLS0lizZg29evVi2LBh1NSkUVa2lujoGbi5+Zx9AqDS1sxjB3KZsCOdmmY7X/bqyOvxUbibTKxcuZLq6momTZqEu7v7Oa1R2R3UbS+m6M0UKn/MwBLmTbt7ehN6cyL2YE/u+DKFvIoGPuvnhvr8E+ouGMyb7h0Y0ezG5PTleIb1w2dgBCVWO4WHqnCz7CMgLIS4pD44HA727NlD165d8fb2PuXepaWl7Nu3jwiaEDHRb9yJr6cVRXWsX1hIu/p8uuDJyoBfaB/Qm+INDpIuB4dbGu1zK3jIw5v8sEYGhl3IwkOLsHr1I7zDDFb2j+eaiOBj81UtycR6uIqgq7viEeNPwc9bOfTyX7BXVp68NK6JCOabpM5kNzYxbvtBDtY1ntPzbYEZI9nsv83/0i6TtnDWoIqIBGAkwLlXKfWDUqpOKWVTSv2olHpURDxEZKaIFDh/ZopIiwfxRKS7iKwTkUoRSRORiS7XZonIP0RkqYjUASNEZJyI/Coi1SKSKyLPnzTfzSKSLSJlIvLUSddavS5N0zRN0zRN07S20myzkbxwLpEJiUT3aPnYytmUlZUxf/58oqOjmThxIiJCZtb7uLn5ER017azjlVIsKK5gaPJ+vi4o4w/R7dgwIIHLQ428JIcOHWL79u0MHjyY6OjoU8Y3NTZjrT/9DgtbUR2VizMofDWZijkHMXlbCL21J6F39MIjxh+HQ/Hg9zvZnlPBe0ND8HvpSWjfgfsix9JVmZiQt4cOiZdi8vfAf3QcWxdl4u3vRln2SpJGjUFMJrKysqitrSUpKanFNaxduxYLisr0fSQOG4FfcOjx9VntLPswFbE10jHcj2ZpZn7oFiJ+7kvSpVGE99qCYGJJoxs/ByrC7YFsL0vF5t6Rfl3/zJJ+3eniffx4Vd22Imp/KcA0uD0rxMYdH6xl12NPsHfdOnaPGk3F4iWcfPDikmA/FvTpQpNSTNyRftbfWSv9FXhERAJPviAiCSKySkTKReSAiFx7UpdQ5/UaEVl/tFKvc6wSkXtFJB1Id7a943wPrxaR7SIy1KX/AOdJlmoRKRaRFnOpishk5w6bns7Pc0SkSESqRGSDiPRog2fyf6o1EajBGMlv5p/m+lPAIOACjPrUC4GngWdcO4mIBfgR+AwjK/HFwEIR6e+yS+UGYCwwHnB3zjsNSAN6AqtEZKdSaoGIJGLU0x6LUY3oNU5MYNuqdWmapmmapmmaprWlPWtXUVtexui7H0REzmmONWvWICJcc801WCwWamsPUlq6gri4+3Bz8zvj2OwGK48fzGNteQ29/bz4JqkTSX7Hd3o0NDSwaNEiQkNDGTFixCnjG2ttzP1LClVHGgiN8iWyWxCR8UFERPpg319O3Y4SbPm1YBa8ugfj3T8Cz/ggxHT8u768ZB/L9hTx/LAOdHrzcZpFeKHfDGqUFw9YzfRqKEbcBxB0VRdy06soyaqmfacSKvPkWPWe1NRU3N3diY8/NUdqQUEBe/fupauXmaLmZvpPOF5aWinF2q/3U1FUT/+cpbQPvYFvQ5YSmd+LpEtiGDI5lk2/zKe+1sKHvp4EV1ooD7FjxZ/B3V/go6RuuLskmK09XMHS+fv4yRfWp6TTsNnBOEcaf3ziBap9/bhk8y88/sST+M5fQNxLL2Dp0OHY2F5+3izu25WbUjM44WjGuUsB1mFU7Hn6aKPzSNAqjASzY4BeGO/Pe5RSe53dbsRIZLsVeAP4BuO9/KirMCr2Njg/b8PYYFGFsTtmjojEOQvLvAO8o5T6yll45pQzbiIyA+O9fJSzBDTAMuBWoAmjOtE3GO/sv1utOf4TAhxRSjWf5vqNwItKqRKlVClGSaWbW+g3CPAFXldKNSml1mDU0natLbZQKbVJKeVQSjUqpdYppXY7P6dyvAQUwBRgsVJqg7Os8zOA4xzWpWmapmmapmma1ibszTaSF8yhfXwCMb16n9Mc+fn5pKWlMWTIEPz9/QHIyv4As9mHmOjppx1ncyjezS5mePJ+kqvqeLlrJEv7xZ8QUAFYsWIFNTU1TJo0CYvFcuL67Q6Wf7KHmopG+oyKwcPTTMnGfEpnpVH6xjYqf8ygtqIRe/9wQh/uR8hNiXglBJ8QUPl43WE+25TJ9V2DGfD+K1gLCnm29zR2Kl8et3vRPfUrvBLG4pUUimdCMFt/zMAvxIOC/QuJH3QR3gGB2Gw29u3bR/fu3U9ZI8BPP/2El72JI3t20vXCwYREHt9ts2d9PunbiolvSMEz8XIqTA3MD1rPJX6XMey6eI6UrcFmK+fLekXPKkVliB9W5WBYj5f5tHdv3E0mlFJszy7nqdm7uPiTzTzmqGdLs5WLe0Dni4UfxlxOpDWfy1nN+sFDuO7Fd9leUMrBseMp/+prlP14gtpYLw/WXHhycZ3z8ixwv4i0c2kbD2QppT5XSjUrpX4F5mGUQz5qicv781PAYBFx3ab0mlKqXCnVAKCU+lopVeac702M8szdnH1tQBcRCVVK1Sqltpy0xj8BjwLDXQIqKKU+U0rVONfwPNDbeTrmd6s1O1XKMLYJuZ0msNIByHb5nO1sa6lfrrPUsmvfSJfPua4DRGQgRj3snhg7VzyAOa7zHe2rlKoTkbJzWJemaZqmaZqmaVqbSFv3EzVlpVx+533ntEtFKcWqVavw9vZmyJAhANTXZ1JcvITYmNuxWIJaHJdSVcejB3LZV9fI2NAAXu4aSQfPU/OkHDx4kJ07dzJ06FAiIyNPub5p7iHyD1Qw8upORDQ7iGuw4fAyg6eZ2mBPshocZObW4CjIQ9bkExbrR7sYPxprbdSUN7KlrIa5Uk+3Jhg96z2kNI03LryZ7KBY3jT7Em5NJbjzSMTTQuCEzmTsLOVIbi3dBjgoPVRD71FGgtr09HSsVmuLR3+ysrLI3L0Lv4IM/ELbcemtdx27VpRZxc9z0omK8yBqxS782g/hvXaziTnSi+FX9UJMwv7Mj6loFjyrmin3CcZur2Z4z9d574LBAHy4/jDfbs0ip7wRd+BiMdE7cSmHOnixQKZgsdr505IvuOmKaiRnE4OitvKPkCd48NHnuGztOh577TUqFy+mw0sv4uncZWMxnduOpZYopfaIyGLgcWCfszkWGCgirgle3ICvXD67vj/Xikg5J75Xn/w+/ghwm7OPAvyBo2esbsPYxbJfRDKBF5RSi12GP4qxySHPZT4z8ApGoKcdxzdFhGLshvldak1QZTNgxdgKNLeF6wUYv8A05+cYZ1tL/aJFxOQSWIkBDrr0Obn6z7fAe8AYpVSjiMzk+C+xEOh+tKOIeGPsqvmt69I0TdM0TdM0TTtv9uZmti6YTUSXeGJ79z2nOQ4dOkRWVhZjxozBw8NICZmV9Q9MJndiYm5rcczfs4t5LaOQ9h4WZvXsyOh2Lf+P//r6ehYtWkRYWBiXXHLJKdfTNuazb10ew+MD8F2XRy3gmRCMT/9wPLsFIWYTCYCtyU5RRhUFByvJP1DBga1FePu7U+IrLDA3kODnxd9KNmAu3cGnPcZRGtWX70cnEhZYQcHjaZh730DgxC6Ij4XkxZkEhntTdGg2IVExRHY3Umykpqbi4+NzSplnpRQr5s3BJzcdv3ZhXPPsK/gGGclkG2qaWPHxHnwCPbigdg11vaeSY6phedAG7q58ichuQewr2khTbSr7qsw8UF7PjA7VXJLwJO/1vRwR4d3Ve3hzdTYJQek8FODN5VXx7Byylr8HTiajOYhLS7K4++036HdrH7w3Ga/H00sqSOhxmK8jPmHppSPY2vMC3vjkb1ivnkzonXcQ8oc/YPJo8/SezwE7gDedn3OB9Uqpy85yB5+KAAAgAElEQVQw5tiuFOeRnWBOfEdWLteHAo8BI4E0pZRDRCoAAVBKpQNTRcQEXA3MFRHX9/HLgeUiUqSUmudsuwGj2M0oIAsIAI7N+Xt11uM/SqkqjO1F74vIVSLiLSIWERkjIm9gHMl5WkTaiUios+/XLUy1FagHHnOOHw5MAL47w+39gHJnQGUAxi/hqLnAeBG5WETcMaJkrt+ntevSNE3TNE3TNE07b3s3rKG6tITBU6ae0y4Vh8PBqlWrCAoKol+/fgA0NORRVLyAyA7X4+4eesqYXTX1vJ5RyLh2AWwYkHDagIpSRvnk+vp6Jk2ahJvbif9/Pf9ABdnz0rk8yJ2A0np8+ofT/okBhE5LxCsxBDEff9WyuJuJTghm4MROXP1oP+6ceQmD7uvFFw3VxIR68753Fubl37MkbhD1/ccz59FLiOrlS9FLb+LRYzLunfzw7hvGoe3FlBfU0W2AF8WHDxoJakVoaGggPT2dnj17YjafWBQ2ee1qalJ+xtPfn+uee+1YclqHQ7HqszTqa5q4/ObONO4uxd8zkC/DFxJe3ZlxY4ZSVFfE9ylG4ZxpmWX87G1iYMdbeX+g8ftasSeHt1ZnMaj9Dv7ROYwrarvzz8tCedB3Eg3mMD50t/HMc08QH6/wLvga+s2AG+fhpXy4ZHsmD6dP4c3YJprDg7nr8Zd4c9w0Sj/4B5lXTaI+JeU3/z2cifNIzffAA86mxUC8s5iLxflzoYh0dxk21uX9+SVgi1Iql5b5Ac1AKeAmIs9i7FQBQERuEpF2zg0TR3fHuJ5KSQNGY8QRJrrMacU4DeONUV34d69VJZWd56cewkiEU4oRBbsPWAC8jJEsJxXYjREte7mFOZowgihjgCMYJZmnKaX2n+HW9wAvikgNRlBktst8acC9GLtZCjEiXHkuY1u1Lk3TNE3TNE3TtPNlb25m6/zvCe/UlY4X9D+nOVJTUykpKWHkyJHHgh7Z2R8CZmJi7zilf7ND8cj+XNq5u/Fmt2h83cyn9Dlqy5YtHDhwgMsvv5z27dufcK18ZwlVn+0hydOMd5QvYff3Iejqrpj9WldmuaSmkVs+S8bNJLxam0fDR2+yLawbbtMe4IPHL8FcUUDWtddhCrgI8XAneEoCyqHYtjiL4A4+lOVtwM3dg8RhRtLc3bt3Y7fbTzn6U5B+gJ8/fR+xuHPjS3/FL+R4kGnb4kxy91Uw7Lp43FPWYuoyjlRzNZsCfmGYjMY3Vrh71R/o5V5NZZ0nsfXN7IoaxidD/wTA3oIK/vjdDuL8c3i930A258Rx3Qg/vjY1MyMylHU9Y+j59CNY/KFdx8Mw8T2YMBO6jkLu2oS070uPfeVcsvoqFnetZECAL4tHX8bkp97lcLOQfdO/Jb3ni4APgFKqBmN3yPUYu0+KMBLBum6R+RZjh0s50A+46QxzrwCWY5wsyQYaOfF40GggTURqMZLWXn80F8tRSqldGLlePhGRMcCXzrnygb3AyXlYfpdaXX9aKfUNRmbeljzA8QiZ65h1uFTkcQZCTt1nZlyb3kLbXFo+cnT0+hfAFy5Nr7hcazzdujRN0zRN0zRN09rSvp/XUVVSzIjpd57TLhWbzcaaNWvo0KEDiYmJADQ2FlJQOI8OHa7B0yPilDEf55Wyu7aBT3vEEWA5/atdXl4eq1atIiEhgYEDBx5rb65opGJxBta0MiwoPMZ2InRo5G9af1W9jVs/30Z5jZUXasux/PQmOf7hhLz2BtMuiqduy1byHngA986jcWuXSOCEzrgFe7J/cyGVxfWMnN6VlR+sJ+GiYXj6+GK329m0aRORkZF0cKmiU5xxiDkvP4XDZOKSO+4nMOz488jafYSUpVkkDI6g+0XtyZyVi1e7SL4KX4BvUxA3jbqKFza/gEfTYQL9FSH5Zezy8uXjqz5HRCirtTLjszV4mK28cWk7ns8KZnkfNxK8PfhnQgz9AnwofugWbIUlxEwwYbpzOUS6HO/yb49pxnLsq54gassnVH83mc+v/pi54RfzgoJbHn6RKWvXwRuPtPq5tkQpFXfS51yMSr1HPx/AqO7T0tjpZ5lbTvpsx6jSc6tL8xsu11sMyCilsnA5zqOUSgHCXbpcedKQL8+0rt+DVu1U0TRN0zRN0zRN01pmb7axdf73hMV1plPfAec0R3JyMtXV1Vx22WWYnOV8s3M+ARzExvzhlP7ZDVb+mlnIFaH+jDvNkR8wyifPnTsXf39/rrzySkQER5OdqlXZFL25nYa9ZexvtOM+tTvthkX9poDKhoOlXPH2evYVVHNLXR0RG97DavEg6sMPGXdRPJU/zCfn9ttx7zwYS+wovPuG4TOwPXa7g21LMgmN9qW+chc2ayO9LxsLGLtUqqqqGDZs2LG1lGRlMOflp2lW4N33IvpfdLwKcPWRBlZ/vpeQKF+GTe1G/eYdmEL6s8ZUw56AjQxqHkWqezKrc1YzwNef+mZIKmkgqO90LGYLNruDGf9cSFm9mVcureXL7E4sD3fjofo9rCz/kn6bX6b+2cGUL91KUA8TPuOmQV4KpHwOv34D+duNhZgtmEf/DdvkD/FucOD3za1cX/o1awYmEOfryZzRI1v9XLXfl1bvVNE0TdM0TdM0TdNOteWH76ksKmTSn587p10qDQ0NbNy4kS5dutCxY0cArNZSCgq+IyJiEl5eJ1bpUUrx5wN5mER4tevpAyFH86hUV1dz66234uXlRf2uUqqWZmCvaqKhnRcbD1XT5+ouxF7QrsU5WlJnbea1Zfv4eksOcWLmeocicctH+NsaCP1sFpG9u1AycyZlH36Ez9DRmNtPwS3Mm6BJXRER9v9SQPWRRsbe04sNX35BeKcuRHTuisPhYOPGjYSHhxPvrJpTmp3JnJefBpOZ2qiOjB83/ljQqbHOxrKPdqMUjL6zJxZ3M/mLDoBEsyBiLWaHG1MGTODhrffi6xFJT490qksdoISYQcaBhke+nUdqoQ+PDT3E4aKhzAkzcXf2ch4r/RSaG3E01FG4LAQ3b6FdQhGsOykNiJhg2kLoOAwAS6+pNIUlYPtmHP4/voBb4T7Wj/6Av2QU82yrn7D2e3LGnSoislxEXmyh/UoRKRKR/9qgjIjMEhGdQ0XTNE3TNE3TtH+bwvQDbJ0/mx6XjKRT3wvPaY6NGzfS2NjIqFGjjrVl53yEUs3Exd51Sv95xRWsq6jhyU7tiWyhbPJRycnJ7N+/n1GjRhEVFUXD3jLK/7Ufk4+FpkujWZleTczACC4YFX3aOU6WklXO2Hc28s2WHIbihg92OifPonNVPtHvvE2H3t0peORRyj78CP/J1+Le7QbEYibkpkTEYsJuc5CyNIvwjv5YLCUcyc0myVlGee/evZSVlTF06FBEhCM5Wcx56SnMbhasHROI7NT5WLClobaJhTN/pbywjstv60FgmDcNBwsxEc18qSfDbw1J1sG8V/IBIaY6bg/MwyzQJ7+O5tjB4BfOeysWsTDNm8mJh4mxDeONUGHkkXSevqAjXDML3Dw5sjeIphoL7d/5DPMLxfBEHjyWiePeXZRY/kju5kjq378NqguPPSP38D6437WdkugIvFNmI7Mu5ZkOrctPcyYikiUio1w+Xy8iFSLSYooN7T/jbMd/vgBuklNDnzcD3yilmlt7o//mAIymaZqmaZqmadpvZbM2suz9t/ANDmHE9DvPaY6qqiq2bt1K7969iYgw8oQ0WovIz/+GiIir8faOO6F/WVMzzx7Kp6+/N9MjT60GdFRBQQErV64kPj6ewYMHo5Siem0u5mBPTFd1YdViI7Ax/MZurdpd02iz89qyfVzz4WZqqhrp1NxEyKE1PLniZQYXpRHx9NME9OlNzvQZVC9dSuhDD+HR7VqayxsJviEBt0AjX+reTQXUVlgZOKETqauX4e7lTfeLLkEpxcaNGwkJCSExMZGyvBxmv/QUJjc3uk64hhqbnVGjRiEi1Fc3sfDtX6korGfs3UnE9jQq+ZZ+t4t6FOsikrGbmmgf0Z4+9vU8FFZLkLmBlMPudK6x4t57Kst2rOWtddCvfR43hV/Ig97Qqb6Kj/qEYW4ogy8m0lDtS9k+LwKmTMZ36FBw8wAPP+p27ifjhjsp+2oODeVeZC92I+/m8TRlZx57Xhbv9gRN20JGUjdMBanYPzi3Y2GnIyK3AO8D45RS69t0cu03OVtQZQEQAgw92iAiQRgZfL8UEQ8R+ZuI5IhIsYh8KCJezn7DRSRPRP4sIkXA5y5tj4lIiYgUOss0jxWRgyJSLiJPutzLQ0RmikiB82emiHicNP/DLnPNcF67E7gRo3xzrYj82JYPTdM0TdM0TdM0bcM3s6gozGf03Q/i4e1zTnOsXbsWgBEjRhxry876EKUcdIy795T+zx/Op7rZzpvdojGfJhjS2NjInDlz8PHx4aqrrkJEsB6uwpZbg+fACJZ9uAcPLzfG3NULN8vpKwYdtSe/ionv/sxH6zOItNYybvdS/rLyZf6wZxEdenQl+pNP8B08mKzrp9KYlkbkzLfx6HwFjXvLCRjbCc/OgQA0N9lJWZZF+y4BBEeaOLjlZxKHXYrF05ODBw9SXFzM0KFDqSouZPaLT2Iymbjyz8+RsieNzp07ExcXR12VlQVv7aCqpIFx9yYR28MIqDSml2Oq9+ErsXLEZy1RBDDcdxZJ3g7WVfvwwYEAEqtAmSzs9Y3hkfmldPCr4i89o/mDzR031cw3Pf3xTf0GVjyB6nw5hTujcQsJJfyxx4z1V1RQ8PgT5Nx6G5iEmFmz6LJuPaFThlN7qJaMceMpefNN7LW1AFgsQURPWMnei/phVdW/4a/izETkD8CbwBVKqV+cbXOcp0mqRGSDiPRw6T9LRN4XkSUiUiMiW0Wks/OaiMjbznfqahHZLSI9ndfGicivzvZcEXneZU5PEflaRMpEpFJEtolIOP+DzhhUcZZEmg1Mc2m+FtjvLI/0OhAPXAB0ASLhhKNiEUAwEAvc6dLm6dL3E4xSTv0wgjfPiEhHZ9+ngEHO+XsDAzDKOrvOH+Cc6zaMGthBSqmPMSoVvaGU8lVKTWjNw9A0TdM0TdM0TWuNrNRf2bliMX3HXklMz6SzD2hBcXExO3fuZMCAAQQGGoGHxsYC8gu+p0P7a/DyOvFYzobyGuYUVXBvTDjdfb1anFMpxaJFi6isrGTKlCl4e3sDULMuF5OvhfXbSqivaWLM3Un4BHi0OMdRzXYH76xOZ+J7P1Oblc09u37g/RWvMDX9J8KGDSHu++/o+NWXiLs7WVOn4qitJfbLL7DEXkj16my8+4The9HxCj57NuRTX9XEwImd2LthDfbmZnpfNgalFBs2bCAwMJCErl1Z9OarOBwOrnnmVQ5k59LQ0MDIkSOprbCy4K1fqamwMv7+3kR3Dza+s0NROieNEmVnT+gWatyPMCK4hN2N7mxxn8CCSkWv/YFcoZpoih3E7bOzEEx81KeRh8uDKfAy8VmUiZiVD0Lq9zDiaY6UX4Q1/RARLzyPyc+Pqh8XkzFuPFWLFxNy5510WrgQn0EDMXl70+7lf9D5yZH4R9VS9smnHL5iNBXfz0bZ7VgsgSQMm8/+kZed099IC+7GKKU80llZ56hlQFcgDNjBqZV7rwdeAIKAQxyvnHs5MAzjvT4A432/zHmtDiMWEIhRVehuEbnKee0WZ/9ojI0YdwEnlFT+X9GaIzlfAItF5D5nmeJpwBfOI0F3AklKqXIAEXkVo/b1E86xDuA5pZTVeR3ABryilLKLyHfAx8A7zrraaSKyFyOAkomx2+R+pVSJc/wLwEfAM875bcCLzmNIS501srvx/0m9a03TNE3TNE3T/vs01tay4h8zCe4QxcVTp519wGmsXr0aT09Phg49djCAzKz3AYiLu+eEvvV2B48eyKWTlwcPxp5+Q0BKSgp79+5l1KhRxMTEANCUV4P1UCUVUX7k7ynnslsTCY/zP+PaDhZVc/uX27FkpPNo+lqG5e8Ckwnfiy7Gp39fHA31lM2ahS07h8aDB3GPiSH6448Qj2BK3tuJpb0PQVd3MaoNORQ7V+WwdVEGUQlBdOgSwPL3lhGZ0IPQ6FgyMjLIz89n3LhxrPn8Q47k5TD5iRfwDArml1++onv37vh5BjP/rR001DQx8f7etO8SeGytDamlSDV8LI3Qfj6+dhPrq90YnXgvX6d+RJc8HwaH+eNed4Q3i8IprgvmkwvS+LS4H1tjPfmrRw2Dl0yHZitM/Y5GOnHk3in4T5iAR9d4cu/8A3UbN+LZqxcxn3+GZ7duADgcigabHR8PNyzXvf3/2Lvv6Kiq9eHj3z0zKZPeOymkUELoAiJFKSIdFGwIAip2L6j3WrBg7wUbFlCxIYqK9NC7QAiEkEJI771N2qTM7PePM4QAAYJwf69ez2etLCb7nLPnnGFkrf24n+fBry4J15RMivMiKXr+eSq//x7vJ5/AfvBg+vT7EWW/wmUbDewAjrcdlFJ+eeq1ZUdJpRDCWUpZbRn+TUp5yHL8e+Bdy3gz4Ah0BQ5JKZPbzLmzzVvECyFWAMNRMlqaUYIpYVLKeCD2Sjzc39FFgypSyr1CiDJgihAiBmW3yI2AJ2AHxLbJwRNA2/1jpZZATFvllp7XcDqSVdzmeAPgYHntB2S3OZZtGWs7V9u6LvVtrlWpVCqVSqVSqVSqK277159RX13F5Mefwcr6wrs9zicrK4vU1FRGjRrVupukoSGXwsJV+Pvdhq2t3xnnv5tVRLaxiVW9Q7HVtp9wUFhYyKZNmwgLC2Pw4MGt4zU7cpFWGvYnVBA13J+IAT7nvS+TycyTv8ZzbOsB5iVvpF/JSUw6W7RuwciKTOr37KJ+zy7QarHy98c6KAi3O2fhMW8ewtaeko+PIbQC95ndEVZapeXx10kUplXTuY8n187oQnbCMaqKCxl88wwAdu/ejaOjI9qKYpL37GDwzTMI7tWX6OhompubGdBnCL+9fYTGhhYm/as3PiGnW0jXGTIo+T2FLGwp8tlFRksLTkZPAkO7sDt3JxoJQ1McmBSVQq3RlqWV43gy+DApZeGs6O7FXY1lzNxzC7gGw60rMOn9KZg5E62zE9adQ8iYOBGEwPvpp3CdMQOhVZa7ZbWNPPTDEY7mVDHr6iDuvzYMt+nL0X82jKCwQmpmvUnJex+QM/cuHK69Fi9LCtEVcD9K9sZSIcRdUkophNCi7DyZjrJON1vO9QBOBVWK2szRum6WUm4XQnyEUp8lSAjxK/C4lNIghBiIkp3SA7AGbICfLXN8i7JL5UchhAvwHbBQStl8pR7076KjxWO/Qdmh0gWIllIWCyE0KAGQSCll/nmuk5d5fwUoqUOJlt8DLWMdcbnvrVKpVCqVSqVSqVRnOHlgr7Lwnz4Dn9DwPzWHlJItW7bg5OTEwIEDW8czsz5GCA3BwfefcX5ibQNLcku4zdeNIa6O7c7Z2NjIzz//jJ2dHVOnTm1tO9xcUk9DYjnpJolroCODp4Wd955+jcnmudUJWNfX8Nn+z7HW6LDqMhY7Tw02wa5YB96GdXAQ1oGBWPn7I6yszri+4rtkWkrr8bgrCq2LDcn7C9izMhUEjJzdjS4DfRBCEL9lI3pHJ8IHXkNOTg5ZWVlc3a8vu79ZSkjvfgyaegvV1dUcOnSIyLDe7P4qi+ZGE5Pn98Yr6PQOm/KKveSuWYVHw018TC3mlmqE1GB2aKC/d3/eP/I+4YV23Ox7El1DDTONzzDB/QRBdRrm9O7B8NoKXoydDuGj4MYvMJl05N5zD40pKVgFBFC2+APshw/D97nnsPI/3dY6LreK+7+LpaKuiWERnizbm8kPB3O4a0gI901cgt3Pt+HkF43D+nVUfvstZUs+JWPSpEv/orSvGBgJ7AI+QQmy3A5MBkYBWShpOZUomx4uSkr5AfCBEMILZTvNv1GyQ34APgLGSimNQoj3UQI1WIInLwAvCCGCgQ1ACrDsCjzj38qlBFWeAXoCCwCklGYhxBfAe5bUoBIhhD/QQ0oZfYXubwXwjGWHjESpwfJdB68tBjpfoftQqVQqlUqlUqlU/3C1lRVsWfoJPqHhDJgy/U/Pk5SURH5+PpMnT8bKEpior8+iqOhXAgJmYWNzOr3HJCWPncjFVafjuVC/dueTUrJ27VoqKyuZPXs29vani+YaduZiArKaJVPujmy3MO3++Cye/imOrBYrMEtej/0OR1MLLre/gtcjo9DaW51zzdlqdubSkFiO8/gQzF52bPz0OJnHyvALd2Hk7G44uSs1YGoqykg7fID+E6ais7Jiz5496PV6Mjetxt7VjbEPPYbQaNi9ezeiyRZDnBtSmpnyaB88Ak4HlIqL15G162t8Tj7AAZpJdzShcY3BttmeBVfP562Yt0BKXjEYCLCr5rGWeRj1emY1H2NavwUEGRv4/OgdaIc+Ctc9jam2jpy778KYmAg6Hea6OvzffQfHsWPP6I60MiaHZ1cn4uVkw/czarBq/o0HB17HsthAPtiexnK9FcsC76L/0WVoAgfhfvfdOE+dSukHH0JiwkU/x46QUhYIIUYCu4QQ7wGpQCNKLRQ74NWOziWEuAql1uoRlBoqRk7vdHEEKiwBlQEowZvNluuuA8qAJMCAkg5k5h/oYt1/AJBSZgH7AXtgTZtDT6AUuTkghDAAW1F2s1wpLwOHgXiUnLEjlrGOWAZ0t1QiXn0F70mlUqlUKpVKpVL9w0gp2fL5h7QYjdzw4KNodR39/9NnMplMbNu2DU9PT3r16tU6npn1IUJYERR47xnnf5lXRlxNPS+H++Nq1f57HjlyhISEBK677jqCgoJax1uqjNQfLSGr0cQ1d3TFxcvuzOsOpzD9qR+4/YdEslqs8DbVs9ohhbDSDGz73YT3gtEdCqg0pFRg2JyNvrcnZU42/PjSQbITyxl8UxhTFvRpDagAJGzfgjSb6TnyBgoKCkhNTcWpqQ5jVQUTFzyF3tGJ8vJyjh08gXt1XxDinIBKXt535Eb/it/Rh0nXankVI33kYZp0Rnp06sr+/P0YTUZerLOim00BX5ivZ6P5al4R0czr+S8QJr5PXoDzTZ/AyGcxGWrImT0HY3ISNhERaKysCFn1M07jxrUGVBpbTDz923Ge+OU4Azu7seJOPYai56io2Et5/jPc5H8/30/bwtjwfG45OZxDIoqWtY/SlH8Mnbs7vi8suujneCmklDnACGAaSoHabCAfJchxKTVGnVCax1Ra5igH3rIcewB4UQhRg7LBoW1RGB9gFUpAJRll58y3f/Jx/tY6/C+BlPLadsaMwNOWn7OP7QQCLjRmqYcizjpnyFnzP2L56cj8wW1ep6J0DVKpVCqVSqVSqVSqy3J8+2YyjsRw3ex5uPt3uvgF5xEbG0tFRQW33XZba4pOXV06RUVrCAyci42NZ+u5ucYmXsssZISbI5O9XNqdr6qqiujoaEJCQhgyZMgZxwp+TQOzRNvLi/D+p3e/HN8Vw9trj7NL542QjgghuSvKlQUj+pM7dgLCrTN+L/8Lje3Fl4vNxXVUrEgBTzuONZpJXnIc9wAHJs/vg7v/meUuzSYT8dujCe7VFxcfX6JXrkSn0VCXdJTRc+9rTafavmEfzhU9sXGwZuqjfXD1UXbeSCnJzPiAmq15+GTOYb8rPF9ZTe+aYtKC9mFrtuP2rrezYNcCbqptYGppKdvruvOK9k7e0H3NC92eIlevYUXaB4TM/BK8I2mprCRnzlyaMjJwv3M25UuX4v3001j5+rbed1G1kfu/j+VoThX3XxvKg0PtOXLkRqxt/Ynq/RNmYzpFRb9RXLKB0T5ruT7AlwNFvfDOyEOz9BYOjfqFSYO6XfSzvJi2613L75kodU3a802b82afdd1OLGtpKeU2lIyU9t5vFUrgpL1jK1AyS/7x/lx49SxCiE+BfCnlS1divsv1V7sflUqlUqlUKpVK9fdVVVzEzm+WEtijF33GTPjT81RXV7Nt2zZCQkKIiIhoHc/M/ACt1pagwHmtY1JKnjqZh5TwRpdOZ6SgtD1n3bp1SCmZPHlya5AGoDqvBplSQZmVlkEzlGSCyqJSnnplJdF2QWg17ggkfs62LL69H/2CXMmeMQ/ZZMTr6YXYBLUfxGmrKb+Wsi+PU2E2E1dmxJBaTd8xQQyYEILW6tykiIwjMdSWlzFizr2UlJSQnJyMdVkhkUOG03PUDQDkpBZR9Ic1VtY6bny8X+vuGilNHE14AZvtdrgXTmJLoBUbqgppMQm8XGpIsCvilohbeOngSwxqMPJcWTnpZnfu0/yb8fbJJPvdzj53W14r2snQme+AnRst5eXkzJ5DU04Ofm+/RdGLL2EbGYnrjNtb73lnWimPrE2gXicYOzWCIocW1sXMxt7cyLPNj9FwuIB1fXvSrdsAIiKeo7R0C4VFvxLZspk0Dy1O1fU4xM9h4oFnz/k8VP8bLhpUEUJkoXTc8ZNSlrUZP4qyEyRESnnff+0O/4S/2v2oVCqVSqVSqVSqvyez2cSmT95Do9Ew5v75CE2HKiicQ0rJmjVrMJvNTJw4sTVIUlubQnHJeoKC7sPa2r31/PWl1WwtN7Ao1I9Ottbtznns2DHS0tIYO3YsLi6ngyAmk5kTSxPwA0JmdENnpaWhycTcT3ZxTB+Iqw4qTDpu7h/AcxMjcbDRUfHdrzQc2Yv9dXfgOv3qc97L1GLGUNZAdUkDVSX1lKdXU55YTq1J0mCSOHnYMvWxvviFtR+MkVJyZOMaHNzcCe07gJ9/WglmMz56a0bd/QBCCGoqjGz4WKk7Mu7BHq0BlcYWI5sPPkbEvijsKyKJ6+9GZEMKL2XpGKKpItM9BmtsEELgZihicWkFdXprZhc/hb21mRutM5gVPIopFQXMufkh0OpoLikhZ85cmgsK6PTZpxg2bcJUUYH/p5/yZnYJB6pqSaiupwYJvV0BWF1Xw5MNb+FuziHW/U3udk69BWYAACAASURBVOnHJ7klzE3IZGO/CBx0enx8JuHjM4nGxmKKin6nMGMpOudyHjM/yuUUHhVCbEJpefzcWeOTgc+AgLM64/5lCCG+BvKklM/8/76X/4aO7lTJBG4DPgQQQkShFMD5yxFCaNu0bFapVCqVSqVSqVSqPy123WryTyQy9sFHcfLwvPgF53H06FHS09MZO3Ysbm5ureOZmR+i1doTFHhX61idycTzafl0t7fl7oD237O2tpZNmzbRqVMnrrrqqjOOHfo1DZ+GZswBjrh1c6Opxcx938RwtNEWnZBIGxs+vbEnN/RQWis35ZdQ8vbraD1C8H/jUZCQHldCwckqqkrqqSppoKbciDSfbrBqJcDRWktAT3c8gp3oMdwf6wukC8Ws+YXcxHhGzLmXsvJykk+kYFtTydT/PImVjS111Y389m4szY1mfK9pITBcSVfaXVpEVuwTXHN4DFZ1/pimdObaxjQe/iIRnX8v/E1w1O04kzpPZFvi93xfXIZWp+FJ8x3k6nx5VfMTz3R/GPemFl6/to8SUCkuJufO2TSXlBD4+Weg01G18ifcZs3kVWtnlmUX494MdSV1dLG35cEBQUQ626Mp/ICC3ENERDzP6ICpAPR2suPmuHTmn8jhi8jg1mCZjY03QUHzCOx0NzW/TqWw4fClfF3asxx4RQjxvJSybafbmcD3lxJQEULo/qoBmL+jjoZZv0VpqXzKnbTJ0RJCfC2EeNny2kMIsc5SILZCCLHH0n4ZIcQTQoh8IUSNECLFUrEYIYSNEOJ9IUSB5ed9IYSN5dhsIcTetjcjhJBCiLA2771ECLFBCFEHXNf2flQqlUqlUqlUKpXqzyjNyWLfym8JHzCYbkOv+9PzVFdXEx0dTVBQ0BkBkJqaJEpKN9Kp02ysrFxbxz/ILiG/sZlXIwLQadrvirthwwaam5vPSfvJOl6GYW8BVkLgOzUMk1myYGUcu9LK0bc0EuBoRfT8Ya0BFSkleQ8/hWxqwOeVV8hMreHHlw+x6bMEkvcX0lDTjFeQI/1uCGLU7G5MmB7GWFcrJndx5uaXr+b6e6PoOyboggGV7Pg49q74hi5XD6X3mAn8svwrkJJxU2/E1dcfY10zaz+Io7bSiMEtgRHjBpNvbOKh+Djy9/+boX/ciK7RH6+5PfGyr2T/wlfZ6d+HqCYtmZ5/IAQcLzjIR8UluJslK317sqZqBP0McRwPnUCmgxXv2FXh4u5Lc0EB2TNn0VJaSuDSL9D37k3R84vQeXuz/sbbWZZfhndZE/U78nnK14sdk/tys78HbrXRFOR+jr/fbQT4z2x9tiGujiwM9WNdaTVLckvPeXah0eA04Ru61IV0/AvTvtWAOzC0dW4hXIEJwDeWNfXbQogcIUSxEOJTIYTect61Qog8y3q8CPiqzdh/hBAlQohCIcQUIcQ4IcRJy1r+6TbvdaE1+6m5Hmsz1xzLsXnADOA/QohaIcTay/0g/mo6ulPlADBTCNENOAncClxD+514HgPygFMh1UGAFEJ0AR4CrrK0gAoGTvXzWmg5rzdK6+TfUVo4dzTx7HZgHMoXyhq4o4PXqVQqlUqlUqlUKtU5mhuNrF/8Jjb2Doy658F2a5p0xKl2x2az+ZwASEbmYnQ6RwI7nd6lklHfyJKcEqZ5uzLIxaG9KUlKSiIpKYmRI0fi4eHROl5baWTH18kMt9Ni3dkZnZ8DT/4az/rjhXg01VKvteaLe67By8m29ZrSj1ZiTPqD2nELWL+riYqCBFx97Lj+rkhC+3mhaRPUqY8roWJNGrYBjnjOiURjd/HOQIbSEtZ98CZu/gFcf98jHNkaTXFtHf6uTvQcdh1NxhbWfhBHVXE9NW5JuPUN4/3yBtbHH+HF0u/pcWwmWltbvO7qBy0VZMy+l7cH3oUOibXPNk76HaKzYwj/OnmQLk3NHIjw4Z2T8/GV1Qxzref14HBuKS9i9LQbaMrLJ+fOOzEZDAQuW4q+d2/Kly6l8eRJSl55nYUFlfgZJZVHSvnsjn5cH6kEnqqrj3LixFO4uAwkIuL5c74LD3Ty5KihjpfTC+jpqGeIq+MZx81WDiSEPg2Mv+jndT5SygYhxE8omx12W4ZvBk5IKY9ZWiuHoqypm4EfUDr2PGU51wdwA4JQNlcMtIzZAv7AbJQuQFuAfkAgcFgIscJSEPdia3YfwNky12hglRBitZTycyHEYNT0H+D0bpVdKC2T8s9zXjPgCwRJKdOAPQBCCBNgg9LmuNTSpvmUGcDDUsoSy7kvoOSFdTSo8ruUcp/ltfHP/oOnUqlUKpVKpVKpVAA7vv6c8vxcpj39EnZOzn96nri4uNa6J23TfgyG45SVbaVzyHysrJwAJQCzMDUPa43g2VC/dudraGhgw4YN+Pj4MHjw4NZxs8nM5qWJ+Eoz1lKD04hOvLw+mZ8O5xHhqOFkjQOLu5oI9z694K9PzCX51y1kDlpEXb0Hrk6y3WAKQN2hIip/S8U62AmP2ZFobC6+lGxpamLNu69ibmlh8uMLMZQUs3nDOnB256ZZc2hpMrH+43hKc2tpGqxhlT6CXBdv/PKO82HBGgIT7kTjocP77qvAXM/J2+exrP9U0hwlbn4fk2Cbz9Xeg7gqMZrr6hs4FuLAu2XP0tiiY1zJDr69aR6+xhZeHtmP5vx8smfOxFxfT+CXX6KP6kFTXj6lH32MadhwZnsE4y81lO7J5YkxXVoDKkZjAfHH78PG2oeeUR+j0SiBpJqmGt6MeZOYohiu8rmKSX7DSa71ZV5iFlv6d8Hf1hopJRlHDrH7+6+pyM/t2BfmwpYD64QQD1k65c4ClgtlATwP6CmlrAAQQryKElg5FVQxA89LKRstx0FZu78ipTQJIX4EPgcWSylrgEQhRBLQC6UcyMXW7M3Ai5a0og1CiFqgC5fW3vlv6VKDKruBENqk/rTjLWARsNnyF/W5lPJ1KWWaEGK+5VikECIaeFRKWYBSCDe7zRzZlrGOuiLfUJVKpVKpVCqVSqVK3ruT49s3M3DqzQT17P2n56murmbTpk3npP0AZGS+j07nTKdOs1vHNpVVs6OihhfC/PC2aX8XSHR0NHV1dcyYMQOtVts6fnBNJkXp1Uzw02PtoWdJeglf7stkYIgbBzMrmJWzl0kvvgCANEvSDhWx//MYarvMwNlVy/U3dm03mAJQsyef6vUZ2ES44n5HNzTW2nPOOZuUkq3LPqE4I40p/3kWU3Mzq956mUY3P3p0j8TZ2ZU1nxyjILWK3UOd2OWnw8lkz+NB3txwYCMux29DG2yF9+z+IJuJnfcg67uOY59PBnbe32Fr48iiAa9RuP057qmuZr+XPdts7yU22ZX7gqtJCpxEnr0VP2qrcXT1pODNpzFVVRH8/XfYdu+OlJKil15ECsFDE2/FSaOhYls+o7t4cd+wUABMpnri4+/DZDLSp893rSlaMUUxLNy7kJL6Egb5DmJbzjZWp60G6wAM3i8wJSaGDx2byF4dTV5yAq6+/kx6fCGP/7T+op/bRT7TvUKIMmCKECIGGADciJIlYgfEttlgIDidGQJQagnEtFXeph5pg+XP4jbHG4BT26UutmYvP6tOS32ba/+ndTioIqXMFkJkoqTZ3HWB82pQUoAeE0L0ALYLIWKklNuklD8APwghnFCiWm+gFNYpQNmGlGiZJtAyBlBHm6K4Qgif9t62o8+hUqlUKpVKpVKpVOdTWZjPli8+xq9LdwZPn/Gn57lQ2k919VHKy3cS2vlxdDpl50i9ycyzafl0sbdlrn/7xWnT09OJi4tjyJAh+Pr6to5nJ5ZzJDqbgT3c0OTV8IeXFYu3pzE8woO9qWVcXZjAQ8NCwMqKtNgSYtZlUlFYh11zAwN8q+j30px2gylSSmq252LYko2+hztut3ZF6DpWljN+6yYSd25l0E23IiX88Oy/afT0Q2i0BAwYzDuLD2OfWsu6/nY0+AtGJcbw8uSxWB07howfAmH1+MweDUIS/cTjbAnowx+Bv1NnV0yYfhj39Z/I0j3PsCI/nX32evJDh/Dtnv6M7OqJPucgm4feyMySYq69ZQwmgwHDxo04T5qEbffuANRER1O3azc/3TaHfGc3HA+X0cnehndu7oVGI5DSTGLSv6mpTaZXz89xsA+nydTEh0c/ZHnicjo5dmL52OX08uxFi7mFuJI4dufvJjptJQHxNuxLi6fJRqIZEUr42GkE+vft8HfnIr5B2aHSBYiWUhZbapg2AJFSyvNllFzumvlCa/aL+Z9er1/KThVQgimuUso6IUS71wohJgAngHSgGjABZktNFX9gH2BE+Us/FTlbATxjibZJlNyv7yzHjqHsbOltmXfRJd6zSqVSqVQqlUqlUl1US1MTa99/A61Ox/hH/o1Ge/EdGedzvrQfUGqpWFm5ERBwuhfIh9nF5Bmb+bV3EFbtBDgaGxtZu3Yt7u7uDB8+vHW8qaGFHd8k4+ZrR0CTiWonK+YfzuK6Lp4cy60mQNbzeOJq3Bdv5NDaTA5vyMLZSUfkyRX4a4oI+/JXxHkCKtUbs6jdnYddXy9cb4pAaDtWZqHg5Am2f/UZQb36orO25ve3X8YtJIwqe3cqvP1Z9mMWvbOaKBvmwWtjg9my9DN8fHxwSW2gdo89DQEnCZ09G7SCZa/8hy2eJpK8v0UnnaFoLl69svj3nn/zekkZZiFo7uHLsqR5ONlKnu5axI2eE+lU18zz1w8AoHrtWqTRiMvNNwNgqqmh6JVXKQjuzJdDRtEt20hmuZHvHrgGZ72yQygz8wNKSzcRHvY0Hh7XkVKRwlN7nyK1MpXp4dOZ5jGNfav3EV0ZzahRo4gM70Jt0jE0m3bTAvzRdziNPbUUlv/Adzt2oNfpO/TZdcA3KLVMegILLH9XZiHEF8B7ltSgEiGEP9BDSnk5nZzbutCa/WKKgc5X6D7+ci4pqCKlTO/AaeHARyhbkCqBT6SUO4QQPYHXgW4o+Vb7UfK+QCl46wTEW37/2TKGlPKkEOJFYCtKIOYp4N5LuW+VSqVSqVQqlUqluphd331JaVYGU/7z7GW1TzYYDOdN+6mqOkxFxR7Cwp5Ep7MHIKuhkU9yS5jq5cJg1/YzJrZv305VVRVz587Fyup0atCBNRnUGZoYc0MQLdFZvEsD14R5UGxopKnFxDM7l+A38QYSYms4vCGLLn08CfzlfUxFBwn4+SeEVftpRtUbM6ndnY/9IF9cJoW2G3hpT11VJWvfew0HNzds9HatXX9OOHnTmJeLuTaY3llNRI0NYtjkUA4ePEhdXR3DHHtRG12EwecQAbPGITXw2OIHOeiZSI1tBQNdxrD14NXoPfbyR/F2olpgfF09WZ30bK17i5TiRr66sx9vHd5Lqb+OJcXZOLhehZSSqpU/Ydu9O/oekQCUvPc+zeXlvHDXvxjeYsPuxELent6L7n5KbZvi4nVkZn2Ir+80/Pzv5KuEr/jw6Ic4WTvxatSrlMeX89Pmn3B2dsbVxZmNX33OzspiZEsLkcNGMujmGcTkGYivruXnQXMw1h1nd95uYojp0Gd4IVLKLCHEfpRaJ2vaHHoCJdBxQAjhgVIDdQlwpYIq512zd8Ay4GchRBWwU0o55Qrd01/CRYMqUsrg84y3oORpgVIp+NT4e8B77Zwfj5Lz1d5cRuARy097x18BXmkz9F2bY7PbOf+cMZVKpVKpVCqVSqU6n9RD+4mLXke/8VMI7TfwT89zKu3HZDKdk/YjpZnUtNewtvYgwP90w9JnUvPRCcHzYf7tzpmTk8PBgwcZMGAAgYGBrePFWQaO78wjapg/tUeKqMJMVaA9XnZW7Esv4x3XIgKqCinvO5W9P6cS3N2NsIQDNOTsx+2ee9FHdm/3/RqzqpWAykAfXCaHdrjzkdlkYt3iN2gwGHD19ePkgb1cc/MdxPuFUhu9HunUja4pkl6jOnHNpM60tLSwb98+hjn0QnugGoPPQazGNdOgceP2T28i2zUdJ6Mr/+o+n/e3NiNEC1rX3US4RvBZSTHNOsHhwPl8t62RmYOCaD65htUBA5iSks/YO68HwBgfT+PJk/gsWgRAQ3w8lStWsHr49XTv1pNNv53k9oGBTOsXAEB9fSZJyU/g7NwfR//7uHvL3cQWxzLabTS9qnsRuyYWO72eiUN6UX/oKEdj9mJraqTZ3pmmgE5kVvhi/DaH26x1+NXV8U3sCaZ28mGM3Qye7XAflguTUl7bzpgReNryc/axnUDAhcbOWt+fGhty1vztrtnPM39wm9epKF2D/iddavqPSqVSqVQqlUqlUv1PqS4pJvrTxfiEhjP09jsva65jx46RmprKDTfccE7aT0HBSgyGOLp3ewutVkkH2VxWzdZyA8+F+uHTTnHa5uZm1qxZg7OzMyNHjmwdN5vM7Pz+BHZO1uh9bNAfM7LaWcOwbt68FZ3Co9d1pscLr1J97e3EbCjD29mayKwijHu+xCo4FK+HH2j3/qXJTOVvaWidbXAe1/mSWknv/v4r8pISsHVwoKqkiEmPPs1B386kfL8cWzsn/FLd6TLIh2tuCkMIQXx8PMEVrkS0eFAfkEhlr7UEei5j9tf3k++QQe/Cnnj18eCdw7/RUH0/Nl7rmdF9Gv9yicTuyExOhoby1qH+hHlZ8cAAe64/1puQmmZGFB/CWq+0L6786SeEnR1OE8YjW1pIfmohtU4uZM+8i/0bMugZ4MxzE5TgktncQmLSv9FobCiwH89D625D36TnHjkHm9hU/LS/M9bTiEddGo07y1me2Rc7jZnI7pFU+s4itTSWYmMyhpoCvOlBpNGOyrpGktLz0Jgu9Mmp/s7+a0EVIcQiIExKeYcQIhBIApzbVBdWqVQqlUqlUqlUqv+vTC0trF/8JtIsGf+vJ9Dq2k+H6QiDwcDGjRsJDAxkwIAzN+k3NZWRlv4WLi4D8fGZCkCDycwzqfmE29lwT0D76Ua7d++mrKyMO+64Axsbm9bx4zvzKcutJeymEKqjM7ETGqLGhfHAj3GMifTm9qoE0l0HcURcg7NG0N8WzOk/Iltq8X97KcLaut33q9mTT0txPe4zu6Ox6XhNmRP7dxO7fjVCo8HKRs/0Z18l2tqJnzdupm9jA8HWvTE72DD05nCEEJhMJko2pjKgJQxzWBW5we/g6PsEz3z6EXl+qfTLiSDL9yQJJUm0lN2FRlfL2xMnMSH0euo/CsdoreFN46tU1jfz5ZyreGHvdircg7lz20Y6D+8FKLVTDBs24jxhPFoHB2I//hS79DR+mf8kWYcr0WkFn8zoi62V8pw5OUsxGI6SZO7LiW2v80KdF+H1ZQTwKtY0gwlM9QFkVHdld74DDeZaZox0wStvOfS5CtlnHklJSWzYsIHs+v1cM+Ia9F2ieCytkIf8PZVWLVeYECIYpeWx1Vnddzp6/dNAZynl3ec5ngXcLaXcehm3+T/tokGVK/EhSilz+Ie0U1KpVCqVSqVSqVR/H3t//IbCtBQmzH8SF+/2Go12zIXSfgDS0t7EZKqjS5cXWnd/fJxTQo6xiVW9Q9stTltYWMi+ffvo1asXYWFhreM1FUYOrsnAoaszKbvSuBFb/ihdx7YlENXpal4I8qLo1yLiOo3HXqdhzI2dkSmbKFt5EO+FC1tri5ytpcJIzbYcbLu7o4907/Czl2ZlsPGjdwDwCQ1n8uPP8FOdiTeOJHNLXhqBvuHUHbVixKwwbOyUoFXGT0foUeNHY5CZnM5PY7aLYvm3BaT5xeFaZ88J9xQ83IMpLtTSXBfCQ6N8mBjej4o/FuJWUcbmoBlsTTHx/MTuZKbsZLVnCHPT8hG5+wnpMxMAw7p1yIYGXG6+mfS0TDSffcqx3v2pdu1JalwBX88ZQICr0mi2tjaFjMz3saYbs/btxFk2YaaAGrsgiJgBYcNIL+vMllXVaDQnqGlYx9DbZ+M1fhKsqIW1jyD0rkRGTiAkJITNmzezd+9e3JOTmd17IB/ll3b48zwfy9rcG6UZzCnXX86cUspXL+d6lZr+o1KpVCqVSqVSqf6hMo7GcHjtr/QaPY4uVw+5+AUX0Dbtx939zIBEZVUMhUW/EBR4Lw724QBkNzTyUU4xk7xcGOLqeM58JpOJNWvWoNfrGTNmzBnH9qw8SZ3ZzLGaKp5stiGj7jgHbSSdy1MIr0xke3ofaq2GYG0jmfr8QDTpx8n5YDGOY2/A9Y7220RLKalakw4CXCaFXvBZzSYTtZXlGEpKKC/IZde3yzCbTEQMGsLYhx5jeXEVC0/mcWdWArY21pjSfPDp7ETXQUrQqnp7NvpjRnLtKrEd+Dst1S1s3hZBtW0ZBttyPFvsMNk5k1WdjWPtQhwdbXhoeG8a6rKw2fMZBr0T/0oby6huXkzoZsPwQ750aWxmYFEMJZ5euPkHIKWkcuVP2HTvhm2PHhy56z7CkVTf/iDr9hewYFQEwyOU3UFmcxOJSY+jkc502ncCWwl7gh4h8oa7cfMNwmyW7PstnbgtOXgFSgpTthHQrQf9J04FjRZu+RaWT4JVc2Hmr9gFD2HKlClERUWxdu1abLdtYEZIBO927Kt0MRPbbniw7FT5U4QQuj+zu0V1pg4HVYQQs4G7gQMorZWrgAeklBstx0OAr4G+lnNS2lwbTJstSUKIOcB/UIrZlAJvSCk/s5x7LUoh2vdQKhibgKellF9Zjo9HqTIcitKyeZmUctGlP7pKpVKpVCqVSqX6p6opL2Pjx+/hGRjMtbPazXzosFPdftpL+zGbm0lJeRZbW39CQh5qHX8uLR+NECwK9Wt3zj/++IPCwkKmT5+OnZ1d63jmsVJOHitlrz+8XGOmpqWCjU5afjEP5zX9UBx1mWSWB4Jsxsk1lvIkK5qeWoh1UBC+L7183hopxsRyjCcqcB4fgtbZmtqKciqLCjCUllBdUoyhtARDaTHVpSXUlJcizeYzru91/ThGzr2fL/JKeS6tgGkN5ejLign2uYqKbMGwh7sgNALD9hxqNueQpinCZnQJFVU7SUgbgF1FV+Kj3qWnY1fia05g1WLFzOBXWHJCy0uTw7DRQdb6GYTUN/Oczb9wstfz+rhOPLrrEAanTiwvOMAfqQl0HzYCIQQN8fE0njiBz6LnOXDwMD327yZh6q28c7CSa7t48vCI0zt/MrM+oqrqJK5HOxFsOsHOzrO4dtZLADTWN7N5WSI5iRVEDvOlMPlLhBCMffBRNBpLepS1Pcz4Gb68AVbcBrPXg29PQkNDeeCBB9ixYwcHDhzo+BfqMggh/IBPgSFABcpa+wvLsUVAD8AITAIeFUIEYCnbYTlnJsp62wHOjAMJIQYAi1E6+jYAvwCPSimb/vtP9td1qTtVBgLLAQ+UdsjLhBD+UkoJ/AD8gbL9aCCwHvj9PPOUABOADGAYsFEIESOlPGI57gM4A/7AaGCVEGK1lLISqANmAYkoX4gtQog4KeXqS3wWlUqlUqlUKpVK9Q9kNpnY8OHbmJqamLDgSXTnqS/SEVJKfv/99/Om/eTmfkVdXSo9oz5Dq1WCI1vLDUSXGVjY2Rc/23Pfu7y8nJ07d9K1a1e6dz/doafJ2MKOFSlsdjOzoKoWO60L2+wKWdESyINeroy/qQdrPndGJ2sIdDlCTsEJ8ub/jKuxCR59BI2d/pz3am5qpCw9i+afi2m2aWTj9iWULs/EWGM4fZIQOLi64eTpjX+Xbjh6DKeyMJ+0mAPoHR0Z++BjBPfqw5KcEl5IL2CCs56gw0dx8PSl4pgtkUP98ejkgGFrNoatOeQ7VBPrnEJE/fc01bqhjbuTP7q9jJuNC8m16QC8d+17vLtGh79LIzdf1Ymc9E/wTU4iy6Yz3xr68FX/ChZuiWWzbyCPn8zCq28PmqPXE9KnH2ApUKvX4zh+PMVz5qG1d2CJXX88rWx47+beaCzpVtWGY6SmfkXD8YGMafid3Q4hDJu5WJmjqI4NS45jKG3g2hldqC3bR8HJZMY99BhOnl5nfpB2bjDzV1g2Br67Ce6KBrfOWFtbM2bMGKKiolhk6UD0X/YjkAD4AV1R1svpUsrtluOTgekoa2oblI0MAAghuqO0YR4HHARe48yuPiZgAXDYMr4ReAB4/7/4PH95lxpUyW4T5VoOfAJ4CyGsgauAUVLKRmC3EGLt+SaRUq5v8+suIcRmYChwKqjSDLxo2Yq0QQhRC3QBDljaNZ0SL4RYAQwH1KCKSqVSqVQqlUqluqg/fllBXnICYx96DDe/gItfcAExMTGkp6czYcKEc9J+jMYCMjI/wMNjFJ6eo5Qxk5lnUvMIs7Ph3k7nFqc9VZtFq9Uybty4M3aWHFybwerGWkZSQTebTiRpTvJScydGO9nxyL39WftRPIbyRnqf/Iqrfv+CsqVLqdxzhJM9wklbuRy3Pdvodf14mhrqKc3JojQ7k6rCAnq7Xke4Uz/2la1G62ND+FWD8AgMwc0/AGcvbxzdPdFZKbVQaisr2Pjxu+QcjyN8wGBG3/swegdHPsou5uWMQiZ6ujApK4Ej9fX4yf4Y7XQMuCGQiu+SaUgsxxSuZ1XB73gEb0NPEyUH7yPX+WsKnaoJsA6gvKmSOZFzaKntyrHcw7x+YxRNDSdo+uMNbBvNPNV0G7OtDXxtDGOTnzUPpx/l0W7N7E4uQ6vTERjZC1NtLYb1G3AaP46dew/QJSGO1WNvJ8+oYdWcvrjaK4Esk8lIbOxC0uOGcWfDr2Rp9XDTG2iEhuyEcjYvS0SjFUxe0BuNpozNS76ny9VD6Trk2va/DM4BMPM3+HIMfDsV5kaDo5Ly5OfX/o6kP2G1EOJUys5OYP6pA0KITsA1wHhLC+Q4IcRSlADKqaDKH202JDSctXNpGrBOSrnbMt+zQOv2KillbJtzs4QQn6GsxdWgyiUoOvVCSllv+QtwQNm5UimlrGtzbjbQqb1JhBBjgeeBCEAD2AHH25xSflZuV73lfRBCDAReR9mlvdLl7AAAIABJREFUYo0SXfv5Ep9DpVKpVCqVSqVS/cNIKTm0+mcO/PIjkcNH0X3odZc1X1lZGZs3byY8PJx+/fqdc/zkyRcBSUT4c61j72cXk9XQxMpeoViftasF4MiRI2RlZTFx4kScnJxax0tza1iyPxNHWcI0m0BKZCGPmTyJsLbinQeuJnpZEiVZBqKSvyZ4RA8ajh2jctmXuEyfzsRFz3PywF4O/b6KHV8rLWicvX3wDAwmqtcIfE74YhXlxB23fXA6paUd6bGHiF7yPs2NjYye9xBRI5RaLx9kF/NqRiFTvFx42knHl4cPExHcg8oDkhGTAjB8mUhLRQPO40NYnPY1qSHbGGNXQ1X6tejyk9g+6iT+dv7k1ebhbefN/L7zmfjRPgLd7JjS24Pjh+bSK8vIPnMUBhlFahdXtvpa82TRBubXrYTBMWT+9ggB3aOwsrWl8scfkQ0NONw0jeannqHUzYNluij+MyaCngEurc9z9Og7HNjflVtbNmMljHzSdQhvBF9P3NYc9v+Shpu/A+Puj0LvIPj2yXewc3Fl1N0PXrjNtGcEzFgFyyfCd9Ng9jrQu5z//Es35QI1VfyACillTZuxbKB/m99zLzC3X9vjUso6IUR5m/eKQEkJ6o+yhtcBsWdP8k9zpQrVFgKuQgj7NoGVQECefaIQwgYl92oW8LuUslkIsRroaAP0H4CPgLFSSqMQ4n2UoI5KpVKpVCqVSqVStctsMrH9q085tmUj3YZex+h5D17WfCaTid9++w0rKysmTZp0zkK7rGw7pWVbCO38b/R6fwB+La7k/exibvFxY7jbucVpDQYDmzdvJjg4mL59+56+d7PktS+PkinK+FLrSpM0skjYoNPoWDpvELHrMslNquAq/wIcdxzBcczD5M+fj023bng/sxCNVkvXa4bTZfAwKgrycHRzx1pvhzRLSj6Jw+TQiNeNkecNqDQ3NbL7u6+Ii16HS2AwvrPuZ729G68cy+BYTT1VLSZu9Hbl/YgAvlq2FAcHBxqSPejma4vTgQLM1lo8745iRdlqfuFnnnXQYzIK7LZ7sObWbKgR5NfmA/DkgCdZG19IYoGBt6f3Ijd2CSE7bLA2NfNey23YDbBlq4stz9kW8UDKGzDtK6orKqgoyKPnqLGnC9R27cquxBTCstJ5f8Qs+oR6cdeQzq3PlJwczaZNNVwjk+gss3nB3Y2p1zxNzPosYtZlEtrHk5Gzu2Nlo2XrsiVUFuQx7ZmXsXXoQFPbgH5w63fw/c1KjZWZv4LVualX/wUFgJsQwrFNYCUQyG9zzjlr9DYKUeqlACCEsAPabr9aAhwFbpNS1ggh5qPsbvlHOzc0+idIKbNR8qpeEEJYCyGGABPPc/qp3SWlQItl18qltIFyRIm+GS2Fcm6/jFv/nyOEsBVCHBJCHBNCJAohXrCMhwghDgoh0oQQKy0pWwghbCy/p1mOB7eZ6ynLeIoQYkyb8RssY2lCiCf/r59RpVKpVCqVSqW6FM2NRn5/5xWObdnIgMnTGPvgo2h1Vpc15549e8jPz2fChAk4Op4ZIDGZGkg5+QL29uEEBs4F4EBVLfOTcxjkbM+bXc5NOZJSsn79ekwmExMnTjwjSLPs5yQ21pTweksjDlonvtU0kih0fDytF9pGSdK+QvqM9Mdl4xLshw6l9J13wGwmYPH7aGxsWucRQuDu3wlrvVLbpe5AIc15tbhM6IxGf+7/by9pbGZNfAKLH3+EuOh1JPQewnNjZnN/WTMf5xRT0dzCBE8X3u/aiQ+7BRJ3JJbCwkJCXHoR1ggRDc1Y+Trg9mAUbxV9xLtJ73J3Qx+cnUphdySVDwdxtDqRJnMTTtZOhDiF0MXpap5dnUCUtyMDY5Kw/z0cx5YYfjVfQ+3IAexxceXFEE8e2L8A/PpC5FQyjyqbJUL69MOYkEhjcjL6qVOx//xTsn0C2OvWi7en90JrqaOSmprIqlV7CNQUcq35ADscXcgKG4421puYdZl0vdqH6+/pgZWNloyjMRzbvJ5+46cQFNW741+Q0BFw4+eQ84fSFcj032+yI6XMBfYDr1nWhT1Rmsx818EpVgEThBBDLOvFFzkzZuAIGIBaIURX4P4rd/d/X1eypfLtKEVsK1AK1n4DnLPPyRLRegT4CSW4shZYcwnv8wDwjhDiI2CXZZ4rup/qb64RGCGlrBVCWAF7hRAbgUeB96SUPwohPkX5j2uJ5c9KKWWYEOJW4A3gFkuRoluBSJRtYFst270APkYpIJwHxAgh1kgpk/4vH1KlUqlUKpVKpeqIekM1q994kaL0VEbOvZ/eY8Zf9pz5+fns2rWLqKgoIiMjzzmelfUxRmMeffv8gEZjTXq9kTnHMwnUW/NVVAg27aT9JCUlkZKSwujRo8+ozbInoZi3D6exsDaVUJchxGoNfGPSseiazgzq7ctPrx3GwdWGCJlAaUUFwtoKY0ICAR99iHVg4HmfwWRopDo6C5twF/S9zqztsqeihpfS8tEe3Mm1f2yi0dqG4zfejV/PvrzkqKe3ox3dHfTYak8/R01NDdu2baOTTyChJ6zxsNXgMNiPpuscmb39LlLKU7klfQbhw77BnO1D15kPcFPcwwD09OhJfFk8/+n/JI+tjKelxczCYokGM01Wb9OkEbw14FkyNVa8Gu7P3MxvoaYAbloKQpAZdxhnbx9cff0pWvI5wtaWA2VVdC0p4rmR83hqQiRB7vatn/OqVT/hbFPG7dr91OqcedrNjicN84jZkUXXq324bmY3NBpBvaGa6CWL8QgMZsitsy7tSwLQ40aoL4cNj8Paf1369X/ObSjdfwqASuD5tulCFyKlTBRCPIiSHWKPkuqT1+aUx4HPUTr5HgVWAiOu3K3/PV00qCKlDG7z69dnHRNtXmegFJttb44s2qT3SCk/RlmYt3fuTs6sMHzGPUgpV6FE0FTtsHRiqrX8amX5kShf9lO7epYDi1CCKpMtr0H5XD8SSlh8MvCjpfBwphAiDTjVHy7N8veNEOJHy7lqUEWlUqlUKpVK9ZdSVVTIL689R215ORMfe4rwq66+7Dmbm5v57bffcHR0ZNy4ceccr6tLIztnKT4+U3F1HUhZUwsz4jPQCMH3PTvjanXuEqy+vp4NGzbg6+vLoEGDWsfTSmq49/tYbq08xFC3EVRoGlhggls6ezJ7Yjfid+RRnlfLmHsiMSx6HZ2vL7XbtuM2dy6Oo0Zd+LNZl4E0mXGdHNa6KyalzshL6QUcP5HM9TFb8MlJw7VHbyY+sADPs4rwnm3z5s20NLfQL8cfF63AYXIoGZ2LePj3WVhVOzDrxEKC+61EI5roe9XLPJT9IY2mRqI8ojBJE/4O/mTnRHAoK41nsCJUc5xK90/wqatgco8lZNrqeatLADOdBXz7HnQZB8HX0NLURE5CPD2uG425rp7q9euxGTUKr5UrOB4Yjr7vYGYMVIJLsbGxrFu3FgeHEmY7HUFbWMFjnYIYXTObvINGug46HVCRUrLl8w9prKtl2jMv//kOUQPuUQIrO1/7c9e3cdba/NRYFmeutfNQOu22d/2ii41JKZejrBdPeaXNsd0oHYXaeo5/uCuS/nMhQohAIUStEOL8FY9UV5QQQiuEiENpXb0FSAeq2hT/zUNpV43lz1wAy/FqlLy51vGzrjnfuEqlUqlUKpVK9ZdRlHaSH559HGNtLdOefeWKBFQAtm7dSllZGVOmTEGvP7NOhpSSlJTn0Wr1hIc9SYPJzOzjGRQ1NvNNVAhBept259y8eTP19fVMmjQJrVZZNlXWNTHjswMMKvuDaY59QKvhPnMLvV3seGluf+oNTRxam0FAV1e861NpTE3DVF6Ovl8/vBbMb/d9TjGmVNAQX4bTdYHoPPSUNjXzn5RcpkfvwnnFZ8z6ZQlBFcWMmHMvc5556aIBlYyMDI4fP05UUydsm/XUDvbnN7to5myaQ1BeL6YnPI6rrgKnwMN0cpnOz6aTHC87jpfei3t63kNieSIjve5m8dZ0rte0MJYmysI+JaelM3N6vEqya2fe7dqJmX4esPstaKqFUYsAyEtOoKWpkZA+/TCsX4+sr+d4sxk3QxUroibw5rReCCE4duwYa9euxc2tjMn+B3DOT+FAt9E05Y/AO6mHElCZ1a211XLCji2kxRxgyG134hkYfPEvxoUMfwKuf/ny5vg/JoTYKYS4+//w/RYJITqapvSXctGdKkKILJT0Dz8pZVmb8aNAbyDEEh1rl5QyB0vnHtX/DSmlCegthHABfuPcaOL/CSHEPGAeQOAFth6qVCqVSqVSqVRXUsaRGNa+/zr2zi7c+NQLl902+ZT09HQOHjzIwIED6dy58znHi4vXUFl1gC5dXkJn5c6DidnEGur5IjKYfs72550zLi6OIUOG4Ovr2zr+wppEPAvjuEPnhquNN6+a6zFba/nswaux1mnYuzqdZqOJoTeHUbbgHtBq0djb4//uOwir89eLkc0mKn9PR+epRzfEj8VZxSw/nkTfQ1uZlRqPtY3t/2PvrKOjur42/JyZycQ9gRBPsODu7oWipQIVKAXqLXWqtL+6l7bUaGlxKC7B3UJwCASIkRAhbhMfO98fd4AACaUt1L77rDWrM+ceu3eGrnXe7P1u2t85lna3j8TeqeY9V8dYWsmaxStxtToSaA7liJtgv90X7Duyn8EJDxFU2ALPsmRCQ7+hEjtK/AfwzbYp6DV6Ft6+kNf2voa3PoDV+zyoI0p5zupFefBnbLEbxs6GjYj2aMWXTYK5288LCs7BoZ+g7TjwbQxA8rHD6Oz0BDVtQfq7HyHCw/HfuZ19jVoz7v4h+Lk7UFJSwoYNG/DxMdM+eCP1TxRjCuvF7IQwOqQPuEZQKcy6wI7ZMwlu3pJ2Q0b85jP4TYSArk8BT//JaUR34GMUiwYLcAZ4Rkp56E/O+xbQQEp5/x8c3xulZHO5rakIxdvlkz+7txtce76U8ub8I/8D3KinSjJKbtbXAEKIFigllFT+wUgpi4QQO4AugIcQQmeLRgnksgN0Bkrp63QhhA5wB/KrtV+k+pja2q9efyZKzh3t27e/nsu0ioqKioqKioqKyk0hZtsmtv70DXVCwxk19U2cPTxvyrwVFRWsXr0aHx8f+teQWmMyGYhPeA83t1YE+I/hvXOZrM0tYlp9f4bWqdkC0mg0snbtWry9venVq9el9p1xOew/cIwp5Rk08hvNDmlkm8bC8sld8HV1IOtcMWeiMmkzIBixex2VJ06AEAR8/hl2dete9z4M29OwFFQSOzqUt3ceIDx6K/eePYJWq6Pt0FF0GD4aJzf3G3om5/fHsWZzJEWyhO6eXdh8PpsdTX+mKkVyX9xrOFZ5UD9nJ8HFW8h+qAo3n4E8tvMlJJLpfaaTVZ7FgawDhFdM42RRJTNwQ++1jaOt+7I8O5AE91C+bliH0X5eyoLb3gatHfR+BQBptZJ09CCBzVpgTkiiMjaWlKYtCDJWcbrfWD5rrQTUb9y4EZOpivCgNbRNkghHT2ZXDKFlaiPqtXG6QlCxmM1smPEZGp2W2x5/DlGD/83fgRDCDYhEMYddglIApgeKp+Y/gQtSykCblUQAyh/X9wghbpdSbvub93ZLudFfyDyUEsgXGY9iRAuAEOJ2IcQxIYRBCJFmU7ouXgsVQkjbgf1iGNE7Qoh9QogSIcRmIYRPtf6dhRBRQogiWwWb3n/mBv+/IYTwtUWoIIRwRDGUPQPs4HK5q/HAatv7NbbP2K5vt/myrAHG2KoDhQENgYPAIaChrZqQHsXM9vcYDauoqKioqKioqKjcdKSU7FuygC0zvyakZRvufvODmyaoAKxfv57S0lJGjRqFXQ2RIOfOfY7JVEhE43eYn1nAjNQcxvl781iQbw2zKWzfvp2ioiKGDRt2ac7SKjOvLT3G8IL9dPIdQjYW3haVfHJHC5oHe2K1SnYvjsfZXU+r9o5kv694dXg//hjOXWpOcbJWmak4lUfBsngMu9KJCtAye/M8Rsz5hDZxR2ndfzCTv/qRXvc/dEOCislQycYZy5mzcTHlVHF7p8FsyYtneYtP8c4M4+5TL+Kp9aJdyhzCs7Zh99FtWGUV75w9SqWlktENR9MjsAc/xfyEXVkXTqQ4MQELzfS5GIcG8GVWfRLdQ/hSnGF0kC0AIf0wxK5UIj5c/QA4u38PxdlZNOvZl6IlS5B6Pf5xp9nWshuvTRyEEIK4uDhiY2MJCY2jQ24VdsX5nPB7DePxRpSGZTBycqdLggrA3sVzyUyIY8DkJ3H19qnp9v8uGgFIKRdJKS1Sygop5WYpZQyAEEIjhHhdCHFeCJEjhJgrhHC3XesthKhuOIsQIkUI0V8IcRvwKkqxklIhxIlq3UJqO7fXhlRIl1JOA35CKYRycc0vbXqBQQhxRAhRoyerEMJOCLFICLFcKNWF7YUQ04UQF2yv6bY2Z2AD4G/be6kQwl8I0VEIsd+mKWQKIWbYzq63hBsVVaIBNyFEE5s3yhiuLMtUhiK6eAC3A48JIUZeZ757gQlAHRSF7QUAIUQAsA54F/CytS8XQtT+fyKVq6kH7BBCxKAIIFuklJHAVOA5m+GsNzDL1n8W4G1rfw54GRTnZxQF9DSwEXjC9o/XDDwJbEIRa5bY+qqoqKioqKioqKj8LVitFrbM/Jro5Yto1rs/I198A72D428PvEFiY2M5efIkPXv2JCDgWjvBouIjpGfMJzDwfg4Zg3g5Pp2+Xq683zDwitLI1UlPT+fAgQO0b9+e0NDQS+2fboojMC2KTk6N0WsceIEKJnYOYXgHJVj89N4L5KaW0HV0A/KmvYY0GnFo0Rzfx66sbmvKq6Bkbwa5P53kwtvR5M8/gyEmlyO6XNIPfk37k/tp0b0XE6fPpP/Ex3Dxur5vCijC1fmdZ/jh82+JzjtJQ99QHnn6MX4+t5LN9WfTN+leeiTfTUCgMx2PfYRXSRJBs2eSXr6G6DJ7kssN1HOuxyudXiGuII7t505Qmn4brSjjfpxx63SSn44XkOAZyBvZa7mr++iLC8OWaeDsa0ujUSJKopbMxzc4lAYt22KIjCTHzROp0RD+6BN4u9hTVVXFunXrcHO30NouCt/0LDL9J7J3bxAJvoe4++HuVwgqSUcOcHjtCloNGELjLjWe9y9jroINU+GrNrDiETi+EIprDOC/WcQDFiHEHCHEYCHE1Yrhg7ZXHyAcxYJjxm9NKqXcCLwP/CqldJFStqp2ucZz++9gBdDWJn6Acj5tjXLWXwgsFUI4VB9gCwxYhRKBc7eU0gi8BnS2jW2FUkDldSllGTAYJUrGxfa6gJIa9Szgg5K10Q+livAt4feUVL4YrbIL5TB96Rdjq9hzkRghxCKgF8rDqIlfpJTxAEKIJcBwW/v9wHop5Xrb5y1CiMPAEK50IFapBZtS2aaG9nNcrt5Tvb0SuKuWud6jmttztfb1wPprR6ioqKioqKioqKj8tZiNRtZ//SkJB6PoNOoeut1zf61Cxh+hpKSEyMhIAgIC6NHj2oO2xVLJmTNTcXDwp6rOkzwck0KEswMzm4Wi09S8D7PZzJo1a3BxcbkilejI+UIidxxmvCGOhsGPskWYCA3x4KXhStnmylIT0auT8G/oQZ2sQ2QeOIBwcCDwyy8BDZUJhVSeLaAyrhBzXgUAujpOuHQLoNjfjm/mfIzfhXPU79SNHvfcj3dAUE3bqxFjfjk75q3nQGEseo0dI/sO42hFPMOX3YkQOsYefw1nkwfturnjOfNZhJSEzJlNVOViEssMLC10xNPBk2/7fYu91p6Zx37AeGEMeqvgbZFPoNdHzMloyfyGU+henMWjY95SvEgA4jfC+X1w+2dg7wpA7K6tFGVlMvKlNyjZsAFreTm+5eXs7DWUJ/q0BpRIIIPBQPvmG2iaaKTMuSmrjt5Ggu9h3AaWEeYZdun+DLk5bPzmC+qE1af3uN/wZy1OhyXjIeMwhPaAxC0Qs1i55lUfwntBWC/lmvNvi1U3gpTSYPNUmQr8CPgJIdYDk6WU2cB9wOfVqrS+ApwSQkz4E8vWdm6/US6gVCbyAMqklNUDMz4TQrwONAYuRse4ofxB/wQwxZZBAcq9PSWlzLHt5X/AD8AbNS0qpTxS7WOKEOIHFH1i+u/c/w3xe0WV3UAY1VJ/AIQQnYAPgeYoCpY9sPQ6c2VVe1/OZSPbEOAuIcSwatftUFJXVFRUVFRUVFRUVFRULmGsKGf1p++SeiqGPuMn0/ZmmIpWQ0rJ6tWrMZlMjBo16lJlnuqcO/c55eXJ+Debz72ns3DVaZnfMhwXXe3FT/ft20dOTg5jx47FwUH5Q32V2cLLy44zMH8nTT27AhoWiwp+HdfjUjRF9OokjBUWug3xI2vsRAD8P/iAijgzJd9HI40W0Answz1w6eqPQ4QXOi8HivNymfvWq/jm59D+4Wfo1e/65ZaveAZWSdrW00Tu20yOKCbcK5gyD0eePzkNgz6fjmlDaZLbGScXe/qM9MQ0TYmYCZkzmzXmI8QmzWN1kT31XAKYNWgW/i7+pOTGsvaoxFQRwgfaNbSyW8wWz9t5I/xpnAqq+HFI38uCisUMW94E7wbQVnEtMBuN7F++mHoNGxPetiPn3v+EcnsHLEJwx5svApcjgQKDUulQbEBbVUlk7iQqw/PZ7beYyDaRl+7RYjaxdvqHWK1Whj3z8vXLJ5/bCcseUiJV7p4LTUeA1Qo5sZC8G87tgpglcPhnpb9fC0VgCetV+5w3+l1IeQYlGgUhRARK9sh0FP9Tf+B8te7nUc771zfZuT61ndtvlABAohjXIoR4AZiIsleJIqJUTynqjHL+H1tNUIGa782/tkWFEI2Az4H2KF6wOuBIbf3/LDcsqkgpzwshklGiRiZedXkhSmjRYCllpRBiOlc+nBslDZgnpZz8B8aqqKioqKioqKioqPw/odxQzIoP3iInJYnBTzxH0559b/oahw4dIjExkSFDhuDjc+3xpqjoMKlpP+PjP46nUr0wmKtY07Yh9exrP5Tn5uaye/dumjVrRuPGjS+1f7MjCbeEvQSYy2ng1oqNmBjWKRhPZ2WunPMGYvdeoFWfICo+eQNZWYnroIHoAttRsOAsDhFeOHf0w76BBxr9ZUEnLzWFue++gba8HO9Hn6dXr543fP9VmSXsWrCJAyWn0Qot7rpAFhJJvimTDtm30SS3CxqrjoYd6tKhnZbcJyaBRhA8ezY/Gjaw+vR35JjtCHf1Z9aQBXg5eEGlga8WvoQp/1FGa3Zxh906Tvb6jIdkeygx8kPzUDwdqz2/4/MhLw7uma+Y1AIntmygND+PwY8/R+Xp0xhjY3ECYsY+REf/OlgsFtasWYOjo6SF+07qnsrneMVINPWbMd/rcUY3Go2/y+Uz+e75v5CVGM+w517Bw68eNSIl7JuumOV6N1T249tIuabRKOKJXwvo8gRYTHDhmCKwJO+CgzNh/29m4vwupJRnhRCzgUdsTRdQghQuEgyYgWwUAeJSoRmbpUd1i41bVVRkFHBUSllm8095CSUVJ1ZKaRVCFKJEslxkMxADbBNC9LZF4MDle7toOxFsa6tt798Bx1DEmRIhxDNc9he96fyeSBVQxBRP20OpPtYVKLAJKh1Rcq82/4H9zAcOCSEGAVtRVKrOQKKUMv26I1VUVFRUVFRUVFRU/l9gyMth2XvTKMnNYcQLr1O/3TVZ7n+azMxMNm3aRIMGDejQocM11y2WCk6feQkHh0BWaMZzqrSIuS3CaOZSu5eLyWRixYoV6PV6Bg8efKn9bJaBhZsOcU/RYZrVGQUIIjVmlgyJAJRokd2L43F01dPYPpG8qCi0np74vjCNvJ/i0Ae74n1/E4TuSsvMtNMnWfHxO5QILenjn+GVnt1v6N6llKRtPM26/ZvJ1hTjYnEnyv0Y6R7raJc+kCFnH0WLjobt69B+SBhOJRmkPjgBodXi/8ss3suazarEVYCGCEcNP9/+K672nlCQTPoPA4kufo0gkcsLwkDa0F3cW16OqczIPdKBAY2rBVYYy2DH+xDUCSKGKk0V5RxYtYTgFq0Jbt6SY8++jF4IilzdGfXikwBERUWRk5ND8ybbaZ0mKKMOJ8z3kdhsE5ocDQ+3fPjSEgkHoji6YQ1tBw+nUaduNT+QymJY9TicjYRmo2D4DLC/TtCG1g6COiqvXi+CqQJSo+F/f1z4s0Wm3I7ifZIuhAhCiVCJtnVZBEwVQmwAcrnsk2IWQsQDDkKI21HO6a+iZJdcJBsYIITQSCmtf3iTyj4Fiogzyfa6mDLkiiLy5AI6IcTLKJEqVyCl/FgIYc9lYSXPdm+vCyEOoYgo07js8ZqN4hHqLqUsrraWASi1PbfHbOveEn6XqCKlTKrl0uMoOVEzUDxXlqDkTf0upJRpQogRKLW3F6EYzBxEeQgqKioqKioqKioqKv/PyU9PY9n7b2CqqGD0a28T2KT5TV+jsrKSJUuW4OzszKhRo2r0aElK+pSKivNYGy5kZmIR4/29Gehz/co5mzZtIjMzk7Fjx+LiohzKLVbJ1GUn6Je3AyetJ2FO4azHxN3dQnC0RZyc2Z9JdrKBPmPCyZ8yUimf/O13FC5LQWgFXvdeK6jE7d/D+hmfUezmxfphE1jXq/MNec1IiyR3+VmWnoqkQhjJdyhlg+cW2mT2p/e5+9BKHcG+FTTzycA55yAVnxeQt2cPQq/H5+fveC75U6IuRAHQxtHMG+0fx9Xek9KcAzh+N5gPqh4nGy9+kFasbZ5lsjSTZzQTmFDKe49cJWrs/wZKs+HueZfSgY6uX0OFoZjuYx4gPzEZzeZ1aKTE+PAj6J0cyc/PZ+fOndSpk0NHcz72xXlsKXyVhnfV4/NzKxjXdBy+TkqQRlFWJhu/m45fg0b0vL8W65Hs0/Dr/VCYAoPeh86PX05NulHsHKF+n9835lpKgE4oxUc8UFJqIoFviHk9AAAgAElEQVQXbdd/RhEzdgMOKIVFngKQUhYLIR5HqcajRTlvVw9aWIrib5ovhEiWUrb9A/vzF0KUokSeFANRQG8p5UXRZxOKX0o8SqGbL1AyVa5BSvmOTVjZKoToi1LIxg0liuXift+19T1r83Q9Z4vAaYpiqDsTJTLmGPArcPND2WyIK1OVVFRuDe3bt5eHDx/+u7ehoqKioqKioqLyLyYrMZ7lH76FRqNh9KtvUyc0/KavIaVk6dKlnDlzhgkTJhAcHHxNn8LCgxw9NhYv/wd5tFCpULO9Q2Ocr+OjEhMTw4oVK+jWrRsDBgy41P7TnnOsXfgrPQv20TloMgE6T57XVrL4f/2w02mpLDOx4M1oPOs60TbmSyoOHcJz/Dj04XdQfjwHnwnNcWh0ZSGYI+tWs3PeT1iCw/m2zz3M7NDsNwUfAKvRQv6CMxxOPMEBuwQOexwlqLA1zbK7IqQOv+yDhJ7fhFNFDgDCyQmdlxd2gYHYvfo0U86+T1xhHBLJoDohDHZIpkH9Z8lPX03E7r2sLe3JVPPDPKjT8LC7D28M9GZdvgG7Y/n8OqIVXRtUS7EqzVEq69TvC/fMA6CitIRZT00isGkLhj//KpuH3EnQ+bPk+dahx7YtCJ2OuXPnkp6eTLemi+l5Kp9zZe1JjviU+XU/Jr4wnvV3rMfTwROz0ciiaS9SnJPFAx9+hXudGqxHTi6DNU+B3gXumg2htUSy3CBCiCNSyvZ/ahKVfxy/N/1HRUVFRUVFRUVFRUXlL+f8yeOs/uRdnNzdufO1d2v3vviTHDx4kNOnTzNgwIAaBRWLpZwzZ6bi6BDMIu4ntbKYlW0aXFdQyc3NZe3atQQHB9O37+U/mKcVlDMz8hB3Fh3E2b4ZgTov1mJkfK9w7GzzHVybTFWZidZ1M6g4dAhdQADOfcZTvCoJt/7BVwgq0mpl14JfOBK5Eq82HZnWbjBjgv1uSFCxlBrJ+eUkpRlFHHCIp0rCoLNPoJFavO2z6NzeCZ/Q7mg9h6Pz8kTr5YXGUUl1Si5O5sEtj5Bdno1E8mCDAbSqXAsSzp9+j5CTet4wTGa5tRdh2jIetPjxU39f1hUUYRdXxIQGda8UVAB2fgjmSuj35qWmw2uWU1VRTrd77mfNJz/QOOUMAH5vvoXGzo5jx46RnJxMg/r7aZ+lx2zWcVTzGOUtj3D05FHe6/4eng7K89o5bxY5yUmMePGNawUViwk2vwEHvoOgzoqg4nZrfm8q/35uuqgihPgeyJBSvnOz51ZRUVFRUVFRUVFR+f9H/IF9rP/qEzz9Axn96tu4eHrdknUyMjLYtGkTjRo1okuXLjX2SUz6mIrKVCrqL2buuWIeDfKls0ft/hpGo5ElS5ag1+u58847L1UQklLyyvIYeuTsQCsFbUMGYzVJou2sPN5ficDJTSvh1K50mnb2xfjJM6DR4P/xdxSuOId9I09c+14WfcwmExu//YK4qN00HTCENyJ6EKjT8lb9WoukXKIyp4T0Hw8jSiVL9YdBCvzyWqP3rWLQ/e0IbVxztSApJZvPb+bt/W9TbipDSiv3+jrRumo1CKjjNpAjWySPF/ejEj197aN5pWoA2wfV49uiItxzqqhTInl5cMSVE+clwJHZ0P4h8GkAQFlRIUc3rCWia0/SC0yEzP9eeb73jKVVv96UlpayefMmPDyK6eCYiUvSeXaVTCbkvjAeix1P36C+DAtXisyejdrNic3raD/sDhq073Tl2uYqmD8aUvZAp8dg4DuXDHJVVGrihkUVIcROoBXgJ6Wsqq2flPLRm7AvFRUVFRUVFRUVFRUVTm7fzJaZM6jXsDGjpr6Jg8vvrep6Y1RUVLB06VJcXV0ZOXIkGo3mmj6FhdGkp8/D3X8iL2Y40dBJw8thtUcwSCmJjIwkNzeXcePG4eZ22Zdz6ZF0Co/toU15Om4uQ/AzaVmFkcl9GqDRaJBSsmdxPPbOdtRb8z5WkwnvJ5/BsLUQrYser3saI2yllqvKy1j96XukxcbQ494HmdugPZm5RaxtEX7dCBopJfsObcdntQWNRcu+8nKMnpW4GvS0G1WXTr2aYbGUUloah8lswGIuwWQ2YDYbSCxKYWbCLk4ZcrATAimtjPc20sevFYaSCnKq+vLp6macMfkToTvDJ9o5eJg+5XgTN6aJMgJNgrwTeXw2uQtOel31TUHks6B3hl5TLzVHr/gVi9lE8+F3s/epF2hjMlIVFEzL114GYOPGjVRVVdKq8Tbqny4ky9gITeeH+OzCW7jqXZnWZRpCCAouZLD5h6/xb9SE7mPGXftQdrynCCojvoU299X67P4OhBApwCQp5da/ey8ql7khUUUIEQr0QDGcGY5iDFNTP62U0nKzNqeioqKioqKioqKi8v+XvNQUtvw4g5CWrRn+/KvY2TvcknWklKxatQqDwcCECRNwcnK6po/ZXMbpM1NxdAxhjnUM2UYDkW0b4aC9Vny5yJEjR4iJiaFPnz6Eh1/2f8kpqeTzlQcZVRSNxi6IdmFtMJeaOG0neap3KADxB7PJTCqmQ2ge1sjj6BvUR+PSHWNmIXUebYXWWYmeMBmrWP7+NLLPJTL4iedIiGjDstjzvBDqR1t351r3djjrMNvXbOCucz2ostixr8xElksyGquF2ye2IyX/E37dmEldp1w04rIPZ5kFNhjs2FuqQ5FrBHZC8ErzodzW9GmS0nfx875g9mR0QWhLCKg7j3VFGymzjOZMWF1eCNNQT2jI2ZrKxC5hdAy7Kuro2HxF1Bg6HVwUQ1lDbg4xWzfSvM8Aps9fy8MJp7BqtDT57hs0ej0JCQmcOnWK4JAYOhY5o6lK47jDsySG7+BM7Bm+7PMl3o7emIxVRH7xAVo7O26f8hJa3VXH4ZS9sO8raPfgP05QUfnncqORKuNQSjUdAMZjE1VsdbErUGpG9wJGCCHuB9KllK8LIdYC1W2OnYCHpJSzhRBdgS+BRigOwFOklFG2eXcCe1AcelsC+4F7beWUEEIsRRF5HIETwGNSylhUVFRUVFRUVFRUVP4TSCnZ/ssP2Ds5M+SpF26ZoAIQHR1NXFwcgwYNIigoqMY+iUkfU1mZQVH4ryxNNvBsSF3auF0rvlzkwoULbNiwgfr169OjR48rrr21+hQdL2xHJ614uN+GT6mZFRh5uH9DhEZgrDSzf0UiPn72uMx9G7RavB7+iLKoAjxG1Ecf5Aooz2jTd1+SmRDH8OdexaV1e6YejKOtmxPPhNRgvArEF8bz3a5ZtDjYhLGWXhRZJftLS6jQlFPlVEBEQxMzoyNZe24cJqsdLnpJUz9BM389FboktuUvpJJCAFztPRjfbDz3NL4HZ50Liw+l8l6kjgpTZ3Ree+nWIJXp+U5oijScCHuAKRF26KQVcTCPcA8nXhzU+MrNlebA5tchuCu0HX+pOWrZQoRGsMo7gkkzlGI3fq++jH2DBlRVVREZGYmzczltvZPxOnqOYxUjcX8ggp+OPcSI+iPoG6z42Oz45QdyU1O44+W3cPPxvXLtiiJY+Sh4hStVfv7BCCE0KGWRJ6OciTcCT9mq/MwBYqSUnwkhAlCq/DwppfxGCFEfOAT4AO7APJSKQjpgH/ColDL92hVVrkftsuqVjAMW2F6DhBDV/4XeC7yHUgt6b/VBUsphUkoXKaULcBeQhVJv2gtYB3wFeAOfA+uEEN5XzTsBqAPoUcoiXWQD0NB27ahtXyoqKioqKioqKioq/xHi9u8h7fRJuo95AEdXt98e8AdJS0tjy5YtRERE0Llz5xr7FBREkZExH1f/ybyZbk9zF0eeDa1ZtIDLqUROTk7ccccdV6QSbTyVRWL0HkLKz6Nz6EbbYD9MSNL1gjY9FI+UY5tTKSs2En5oJsJqwevhlyjbX4BjK1+cO19ON9q/bBFxUbvpPnY89Tt2YcqZVKqskhlNQtBpri37uyl+K99+upqhO29joLU+uToTu8pSMUoHLI7H0GhNzEgKZ0XiMPo18eej0S0Y3iaEnEotc0/GsDotUhFULK6Ea8Yw1u8HWrrcQXymmaEzdvDqylMEuFygd+PP+L5/OF97vI5b+jqWBT3NAw08QUD3Aklubhmf3tXyUsnoS2yYCqZyGPYl2J5ZfkYap3dtp7JdTzou+AkHYxUOHTrgdZ8SSbJjxw6Ki4tpEL6LiLhKDBZfLL1e4P3EN6njVIepHZUUovjovZzcvpmOI+8irE0NBXjWvwiGC3DHj0rq0T+bB22vPkA44ALMsF3bBfS2ve8FnAN6Vvu8R0ppRdECfkEJkAhGCZa4OIfK7+A3I1WEEN1RHvQSKWWeECIJRfD4wtZltZRyn+19ZU21z4UQjYA5wB1SyjQhxANAgpRynq3LIiHE08AwYLat7RcpZbxt/BKUtCMApJQ/V5v7LaBQCOEupSy+sdtWUVFRUVFRUVFRUfmnYqysYNf8n6kTWp8W/QbdsnXKy8tZtmwZbm5ujBgxgprOMmZzKWfOvoyjYyg/mu+k2FzK0tb10dfguQJK9Mjq1aspLi7mwQcfxNn58gE9raCcN3+NZlThPuxdg/B0a49noZHlGHm4fyOERmDIq+DY5lSCXPJxSTqIfUQLLIZG6Hx1eN7R8NIez+7bxf5lC2nWqx8dR9zJj+m57C4s5dPGQYQ72V+zr1O5p9j5y1nurupAsIOWRLscjpflYGdtjF3VDvL89OyrDEW61OPnB1vTN6IumaWZrM/7lHzvozh5g7POnfbukzEXd+RUejkfxyYDyQAIbQnDG69iiN8hejedgzExGNP2z/mw0QN8FTySTq5OTHR05ek1R3i4ZzjtQq5K+4nfBLEroM9r4NvoUnPUkgWg13O6HEacjgFHR4K+nI4QgjNnzhAdHU29egl0NjnhUHaWI87vs9srkpT4FGYNnIWr3hWL2czuhbPxDQmj2933X/ulnVwGJ5dA71chsN1v/Wz+CdwHfC6lPAcghHgFOCWEmIAiqnxmi2bpCXwMvGEb18t2HSllPrD84oRCiPeAHX/ZHfyHuJH0n/HA5oupN8BCW9tFUSXteoOFEO7AauB1KeXFSBZ/4PxVXc8DAdU+Z1V7X46iviGE0KJExtwF+AJWWx8fFM+XfzVCCEcpZcXfvQ8VFRUVFRUVFRWVv4sDK5dQmp/H0ClT0WhqN1r9M1itVlatWkVJSQkTJ07E0VYe+GoSEz+ksvIC2WG/sjalhFfD69HEpea+oKQSnT17loEDB15RkrnSZOHxOdH0SN+IXpqRoi8t/J0x5lVQpNfQsFsgAFErEhFIgrZ8ATodzn1ewJxvwvv+lmjslWeRmRDHxu+mExDRjP6Tn+RsWSXvnctkkI8b99W7tjJSdlk2c35axQOmHnjpNRzWJZHnaEGX04Bz5nOIYAdMVh39u3fg6X4R2Okkb0a9ycqElUgkrnpXnmrzFHc2vBO7apVw4nMzmbLhc5ILirjP9TBdQww08B1HZWwAWVFJvNu2BVu92jHSyw3P5FJePJxIfV9nnhvQ6MoNVpVA5HPg2wS6PXN538lJxEfv5VizLjy/eDYAgZ9/js7Li+zsbFauXIm7exmtg89S58B5kozdMd/ThoUHH+H+JvfTsV5HAE7t2EJxdhajXn4Tjfaq31NxOqx7DgI7QI/na/1e/2FcfZ4+j3K2ryulTBJClAGtUSwz3gEmCiEao4gqXwEIIZxQzvS3ARfrcruqPqm/n+uKKkIIR+BuQCuEuChy2AMeQohWts+yxsFcyvVaCOyQUs6sdukCSvRLdYJRcsF+i3uBEUB/IAUlF6wQuFZW/neyF7hCHhVCREgpz/5N+1FRUVFRUVFRUVH5yyjMzOBI5Eqa9uxLQOMmt2yd/fv3Ex8fz+DBgwkICKixT37+bjIuLMLJ/3HeSdfT1s2ex4Pq1DpnamrqpVSi6iWZpZS8vvwY4ceXUK8yA4+gEbjp/fHIq2Q5Rib2b4TQasiIKyTpaC71c3fgUFWIx7h3MWVW4TW2MXZ1FP8WQ14Oqz55Bxcvb4Y//yqVQsMjsedx1Wr5tHHQNdE2KRkp/Lh4Fg8XDsBOK9lhH4tfGz0xm/zY4lSGq5OgozDRe+hwendoRnJxMs/ueJak4iQcdY483upx7mt6H3aaK8sKpxSnMGX3Y+SJbL7VVlDHrYo8jRPOSSOJjcngpR72nNe1YnBOEps2O2Cn0TCqTQBP9WuAg91Vwsb2d8GQARM3g05/qXnd/NlU2jsyOPoIDiYjbkNvx7VPb8rKyli0aBFarYnGjdcTcdYZs9WO8r7TeOfUa4S5hzGl7RRAMfKNXr4I/8ZNCWt9VdqP1ar4qFjMMOoH0N5wcdy/m6vP08GAGci2fd4F3AnopZQZQohdKIERnsBxW5/ngcZAJylllhCiNXCM/865+i/jt341IwEL0AIwVmtfguKz8lu8BzgDU65qXw98LYS41zbXaKApEHkDc7oCVUA+ivHtP9tF6AYRQgxDeQYuQoggKWX1CKBfUcpZq6ioqKioqKioqPxnkVKyY/ZMtHZ29Lxvwi1bJzU1la1bt9K0aVM6duxYY5+yskROxU7Byakh31QNp8JazldNgmv0KlH6l7Fs2TLc3d2vSSVaEJWEceMsQiozaDXwQeIOedKukZ7KnAosei1BXQOxWiV7liTgpK0i8MxqHFr1wmKog3OXeji1UoQcY0U5qz56G7PRyN3T3sfB1Y1Jp1JIqqhkccv6+OoV4aOgoIBTp04RczKG8qJ8HjYMoEJYKeknaeCSxle/tuOAm8RTZ2WgUzahwfXp2b4Z807PY/qR6ZisJnwcfVg5fCUeDh7X3Oux9H08tWMKWnMVs7KyaORejyhvCz6lt7EtsZSXu7lgbynh8xPv865hMo/2CmNC11DquNVgNpx+GA78AB0mQdDl72LfsWMUnjpGlXsobc9tQ+PlRb333sNisbB06VJKSgy0aLmJplWhuBfvJdbjeRbbryGvIo/5febjoFPWOrFpHaWFBdz+9EvXpndFf6tUGhr+NXjXr/0H889jETBVCLEByEU5E/8qpTTbru8CPuVy1d6dtjF7qkWhuKL4qBTZPE/f/Iv2/p/jt0SV8SjeJqnVG4UQM1DChn6rPvZYwA/F8+Ri2yNSygVCiKEo1X++AxKBodVSjK7HXGAQkAEUoOSHPXYD4/7pnAKCUNKY5gghQlDuMRMw/Z0bU1FRUVFRUVFRUfkrOHf0IMnHj9DrgYk4e3j+9oA/QFpaGkuWLMHDw4Phw4fX6KNSZczj+ImJCGFHQr0ZbD1XyjsNAmjgVHMFIovFwooVKygrK2PSpElXpBIdTsrm0E+fE1yRRp8JT3JiuwvBATo8cypZgZEH+jVC6DTE7s4gP6OU5rHz0Ont0Td+AF1dZzxuV0oxW60W1n39KXnpqdwx9U28A4P5IiWL9XnFvN3An9Z2SvTNqVOnyMjIQCMFLewD6FDSlxyrBae70zh5YRFfbHmYIkc7BjpVMbStI0cPG2nZrSUPbXqII9lHcNI5Ya+1Z/Zts68VVIoz2LRrGq8WHKCe2cx3wp+gEe+SoI3Fmv4LX2QNYkNbJ1xKDWyJnURK2MNEPTEAF/tajp0WE6x5GlzrQb9pl5ozKqpYPfdnPOwcuGPfLkAQ/ONMNPb2rFu3jpSUFFq2SqaOixG/PTHkWCJIu60zaw49z6OtHqW5T3Pleywv58DqZYS2aktg0+ZXrp11Crb9DyKGQpsHfuNX84/jZ5QUoN2AA7AJeKra9V0ooslu2+e9KAEJu6v1mY6SVZKHEvnyGUpQhcrv5LqiipTytlral6BEmNR07cFq70OvM/c1aS7VrvW+6vNsbAa2UspSlPSf6sytbZ1/C1LKZOBbIcQpKeVuAFsJrBAUwUVFRUVFRUVFRUXlP4vZaGTHnB/xCgiizW3Dbvr8Ukr279/P1q1bcXNzY8yYMTg4XCuSWCwVxJyYjNGYh1+zhTx0ppwuHs5MDPSpcd6qqiqWLFlCUlISw4YNo169y9V5cgpLWfzhuwSXp9J9wuOUFYdTXpJOSIADlWVGHPU6fLsEUFlm4sDqJDzKUvDNPYbLXZ+AVoP3vREInWKIu3vBbM4dOUjfhx4ltHU7NucV83FyFqN83fE5vI/PTpwAwM/Pj0Hd++F2ogr3XHsSKi1UdltASnweX514DJ3Q8paugOFPDuGbb77BI9yDyfsno0FDfff6nDecZ+bAmYS4VcsuuXAcGTWDuWmb+cTTg5Z480r7aeR7tiAhP5ayvGV8YZrG6fq+eBSZ2JA1nUCdlaDRT4LddY6c+76EnFgYswgcLld4+nzDJvwvJBORWoq9xYzng+NxbNaMQ4cOcejQIZo1c8DdfS91D4Sjl0kYBrzN+zHv0sSrCQ+3fPjSPEfWraKyxED3MVclWZgqYcVkcPBQKg3VIKz9E7nqfP227VVTvziqpfHYCrrorupzgctVgi7yw83Y5/83bkrSmBDieyBDSvnOzZjv/zMXBRXb+wyUaBUVFRUVFRUVFRWV/zSH166gODuLO19/F63u5npblJeXs2rVKuLj44mIiGDEiBE1GtNKaSH29HMYSk7SpPl3PJrmhKSC6RHBaGo4eJeVlbFgwQIyMzMZPnw4bdu2vXStsrKKr19/g3qGZJreNZHwpj1Y+sEh2rSvg0dCAaswcn+fRmj0Wg6vOkdlmYkWZxbh2GE0mNzxuq8xOk9F9InZtokjkStpPWgobQYNJbG8kidOn6eJo56m+zYTk5lJly5daNu2Le6VDmTOOYGlVMvhMjOVjZeTW1SXGYkjcZSS9+0zGfzOQ8z/dT4maWKBaQGt6rbC19GXyORI3u76No3cWvPT7iROx8VRlJVMYbmRbIc2uDuH0bUin2DXDI6efxO/vGyyNfX4Tvcqybr63OPqyhdNjWi+26ZU0rGrObIHgLxE2PUxNB0JEUMuNe9PSsZz1TzcyiXhhdnoAgKoO3UqKSkpbNiwgbCwenh6fYFdchPCK/dS1ng8X5nXUWYq44MeH1zyfik3FHNk3UoadupK3fAGV669/R3IOQ33LgXnmsWy/wJCiFKg5cUqQf8EhBCvAuFSykl/915uFjdSUjkFJbTIv3p6jhDiGIqjcJiU8tFbtsObgBBCAg2llIl/915UVFRUVFRUVFRUVK7EkJfDgVVLadSpGyEtWt/UuVNTU1m2bBllZWUMHjyYjh071pjyA5CQ+CG5uZtp1PANfihuysHiXL5vGkKI47UligsLC5k3bx4Gg4F77rmHiIiIS9csZhNfvz4Nj7wEPAfcy213jGT5J0dwcLHDtaiCcsBbp8Ojqz8FmWXE7EzD/8I+3LVm7AIG4do7EMcIpYpP6qkYts36ltBWbekzfjIGs4UHTyajk5Ku0VsprSjj3nvvpVGjRpQdziJ75XEKKSamxAFz3dMUmQbwXZoON7OZ7+rl0v65h1gUvYiks0kkeSXxQrcXqLJU8cnhTxgVdj9HTjXg1Tmb8XFMp3fdKCIaX8DHNQd3+9JL92eW3uSXRfBL6RPsdovASSOY2zyMgT7usOYp0DlAh4m1fylSQuQziugy+ONLzcaqSjZP/xBHk4nOSedBoyFkzmyKiopYsmQJnp6ehIZEUlXuSJPzF7A6+7KzdSd2HPqAF9u/SH2Py74oh9Ysx1RZdW0J5XM7Yf8MxcOl0cDr/HL+edhKJ/eUUg6u1pYAJNbQ9oaU0uVv2OZ1kVL+JzxRq3OjEnAyij/K1wBCiBYoOVkqtxghRD2gQEpZ9XfvRUVFRUVFRUVFReVWsGvuLAB6jbvOQfx3YrVaiYqKYtu2bXh4eDBx4kT8/f1r7Z+WNoe0tJ8JCnyQ4/Yj+SExhYkBPoyse623S1ZWFvPnz8dsNjNu3LgrSidbzCZm/e9/aNJiKW03nOcn3Uvsngyykw106FwHz7OFLMLI5F4NEHote5fEozVVEJ6yDseBb6IPc8NtQCigVEJa+/n7eNYLYOgzU0Gj4cmTySSXVzLsxD78dBrGTJ6Mt6cXhaviKYvOJs4xidO5Xjg4V3DB1JL5WWX4VJXzc9MKgh68hyk7piAPSjzsPHjn3nc4X57KlB1P4y7bMnd9U/QimTHOG+ndcQtSKymwOpBQYaKx7230CB2LKc6DWWfK+TnEjkqdYLy/N8+H1cNHr4PSXDjxK7S+9/oRIMfmKQaxw74C17qAkp614NuvcMnJICK9DJ3Vgu9zzyJ9fVn8889YLBbatS6nwnQazxNd8dWsIanLK7x9bDrt67bn/qaXxZOSgjyOb4ykac8+eAde/m6oKISVj4F3Qxjwr0yy2A28fLHsse2saAe0uaqtAVf6p6jcQjQ32G8eV1b7GU81HxMhxGwhxLu2972FEOlCiOeFEDlCiEwhxISr+n4jhFgnhCgRQhwQQtSvdj1CCLFFCFEghIgTQtxd7doQIcRp27gMIcQL1a5NFkIk2satEUL429ov/phOCCFKhRD3CCE8hRCRQohcIUSh7X3g73hufyXzgLNCiE//7o2oqKioqKioqKio3GzOnzxO/IF9dBp5F24+tZcr/j2UlZWxcOFCtm7dSpMmTXjkkUeuK6jk5m4lPuFdfHz6IwKe55mzqbRzc+LNBteOSU5O5pdffkGj0fDQQw9dJaiY+fXj9ymJP05i/QG8/NxEKkqM7F+ZRN0wFzzOFhCDmf529rh1C+D8qXzSzhQSmrwOtzZ3oPPwwntsBEIrqCwtZeVHbyM0Gka+NA17J2c+PneBzfkGuiTE0LOOF5MmTcLbw4usWVGURWez02cbew0SLU7EGR2YZywjoCyfBR0h+ME7eHTLoyTFJ+FT5cPg/sNZeeo8T29/HnOFP5XnbuM53XL2ur/K0O4nEHonfigO4eMsRzq2+p4hAR8RudGFwcVGvq6vp6unCzs7RvBB4yBFUAE49BNYqqDLE7V/OSXZsPl1COkObS8fMY9v3UBB9G4MrkGE5WdhFxKC134/qegAACAASURBVKRJrFy5kpycHLp3aEF51SzMGW1pxx4M/q0Ym7qces71+KTXJ2jE5aPtgRW/YrVa6XLn2MvrSgmRz0JZDoz+EfT/yhiBQygiysVwrh7ADiDuqrYkKeUFIYQUQjSA3zxLjxBCHBdCGIQQSUKI22zt/razdYHtrD252pi3hBBLhBBzbXPGCiHaV7s+1bZOie1c36/auPm296G2PY4XQqQKIfKEEK/dqod3q7hRUSUacBNCNBFCaIExwPzr9PcD3IEAYCLwjRCiusQ7BvgfSp3sRJTSywghnIEtKC7EdWz9vhVCNLWNm4VSPcgVaA5st43rC3wA3A3UA84DiwGklD1tY1tJKV2klL/a7vsXFBPYYJRSUjNu8Fn8pUgp+0spw4Cf/u69qKioqKioqKioqNxMLGYz23/5Afe6frQfdsdNmfP8+fN8//33JCcnM2TIEO66664aDWkvYjDEcCr2GdxcmxMe8RmTYlOx1whmNgtFr7nyuBQbG8v8+fNxc3Nj4sSJ1KlzWQSymM2smf4RmScOcbheL9546WHsdVqiViRSVWnGs6QSAAcHHU3Gt0DqteyZdwqn8ixCjBew8++K15jGaN3ssVosrJ3+IcU52Qx//lU86vqxIjWL6am5NM48z6P1AxkzZgz2enuyFx7Eck6yOXwp0QYdvmUhxAnJSvsqwg0XWDDAG7+7hvDY1sc4m3uWbmU9EY7uPLophZ8T30BrtWdqjpYo3XM83SuY3GHDKLFm8UuOlWyjmV/6/QxnghgSHcfUAPB00bO0VTjz2jekoXO152qqgEM/QqPbwKdh7V/QhpcUo9hqBrGZCXFs/+UH0uqFMzI6CiEEQd9/x65duzh79iztmnfGYPgUq9mF3no3MBmYrC3C3yWAWYNm4eN4OSqmKCuTk9s307L/INzr+F1e9+gciF0JvV8B/zY38lP6xyGlNAIHgItn3J7AHpTqPtXbaopSqe0s3RElYOJFwMM2PsU2ZjGQjmIHcifwvu3sfZHhtj4ewBpsZ2ohRGPgSaCDbb1B1easie5AY6AfME0I0eS6D+Ifxo2KKnA5WmUAcIbrG6iagLellCYp5XqgFOUhXWSllPKgrY72Ai6rakOBFCnlL1JKs5TyGLAcuKvavE2FEG5SykIp5VFb+33Az1LKo7Y0mVeALkKI0Jo2J6XMl1Iul1KWSylLUESdXr/jWdx0hBDP2v7bzCZcXYGU8uxfvysVFRUVFRUVFRWVW8exjWspyEijz/jJ6PT6PzWX1Wplz549zJ49Gzs7OyZNmnRd/xSAiooMTsRMRq/3pkXLmbySmE98WSXfNQ0lwOHK/Rw8eJClS5fi7+/PhAkTcHd3r7a2hfVff8q5Q/vZ492VJ5+cgL+HIxcSikiKzsLRS0Njs+C8m5ZeL3TCoYEHMdtSMRisNExajVO7h3HrH4JDA+Xv0Dvn/kTqyeP0n/w4gU2aszsphSnx6dQtKWR6y/r079cPIQT5K2MwnzZxOGwdG4sqaZLTlVg7M2udqmhSeJ65oxvgNaQPj299nISsBIbnj8VUWsmWEm9cA2fhoC1mYWY845sHoHkyipW+GrKyl7C7REdQveF8GvEzH0WZGKsvJdtVx+dh/mzp0YweXm5cw4nFUJ4PXZ6s/UuK2wCnV0GvF8FHMY8tLy5izecfYHByo9W5bBzMRjzuHE1ieTm7du0iPLAxpRmbcPBIo7XPOOzPLGOBmxtV3uH8NOinKwQVgKhlC9FodXQadc+V60Y+B/X7Qvdna9/fv4NdXBZQeqCIKnuuattVw7jaztITUc7SW6SUVillhpTyrBAiCOgGTJVSVkopj6P8ob96BsteKeV6KaUFRS9oZWu3APa29eyklClSyqTr3NP/pJQVUsoTwIlq8/wr+D222vNQFK8wfruEcb5NMLlIOVDdJCerlmshQCchRNFVe5xnez8aeB34UAgRA7wspdyPopxd/FEgpSwVQuSjRMqkXL05IYQT8AVwG0q0DIDrxTy037i3W8Vx23/fByKEEBVALHASOCWljPyb9qWioqKioqKioqJy0yktLGD/soWEtWlPeNuOf3geq9VKbGwse/bsIScnh+bNmzN06NDrRqcAmEwGTsRMxGo10rbNAhbnCZZlF/JSmB+9vFwv9ZNSsmPHDnbv3k2jRo2488470V8lAO1ftoj46L3s9ezMoLvvoVsDHywWK8fmn0HjLulu1pDuIOg3tQsarYZyg5GDK+Pwzo8jILwTjs2DcO0TBMCJLRs4tnEt7W4fSYs+A9l79DiTs0rR6/TMbdOIVkEBABRtSaDykIET/tv42niKkclTiHEws9neSKuCZH4c3wGXzq14ctuTpKWmMTh/GMaqctIdw2jp/SV7NVVMpw5NJ8wlSlbw3d4nucvxLPnCmTs6z2XVCT13VBnQuguecfPgqdZBOGuv+dvvxS8B9n8D9VpBaPdrr1tMcOAH2PkB1GkKXacowywWIr/8mBJDMUnNujNm709oXFywTprEqvnz8XavizGxCv8Bkfh6DcF5z1yytTrWBTWvUVDJS03hzN6ddBg+GhdPxeiX1GhY+iDUawl3zwVNLffw72E38IQQwgvwlVImCCGygTm2tubUHKlS21k6CFhfQ39/FG/Pkmpt54H21T5ffa53EELopJSJQohngLeAZkKITcBztjLONVGbPvCv4IZFFSnleSFEMjAERc26FaQBu6SUA2rZwyFghBDCDiWcaAnKj+ACiiADXEoj8qb2aJrnUSJnOkkps4QQrYFjVKvl/VchhIiQUp6VUu4AkFKOsLW7AM2AFkB/QBVVVFRUVFRUVFRU/jPsWTgbi8lEn/GTrxtNUhv/x959R0dVrQ0c/u2ZJJPeE1JIQhJCQoAQIKFXQUBARJSiFBt67WLHa8WuiMq1IiLSe5feewslIYV0SO+9TDKT2d8fZ9CoIISrn1c9z1pZZM45e88+c2bB2i97v6/RaCQuLo7Dhw9TVlaGu7s7d9xxBx07drxmfyZTI+fjH6Wu7iKRkQtIafLhtdRUbnJ1YHpAqx+va2pqYsuWLZw5c4YuXbowatQotL8ILKTFnOD42hUkOYTh2XsYDw8IQjZJ0ubFg74eD6FBIwRdHu+KRqtsFDg87xhNJg1hlfHoujyA64RQhEaQFR/H3gVfExjZjb5338PW7dt5vQZqXD1ZFu5PZy8liFB9NIvavYUkuh9jlvYotyVMJ1Yn2W1tILo0jW8eHohN5zCe2PMEpcml9C/vT7XUkiT86W89kyVWNjwbcCsBnR/gkdOzOZp7iGe8JbYWOjp3Wc/08w0cs2zk1kYL3uwTgrf9bweoSN0Jpakw9tsft/T86NJR2PKsUsI4ZBiMnA0WSlDq8MrFZCfEcaDnCF4xJyu2eeVllq9ejYXGCtL8CBjxKVZWLjgUWmJXms6XbTryxYiFuNm4/WoYR1YtwcrahujRdygHipJg2Xhw9IVJa0Dn8Ks2f0HHUFJtPAgcAZBSVgkh8szH8qSUmb9s9Btz6Wwg+JfXo8yxXYUQDs0CK/789o6V5u+3DFgmhHAE5gIfAFOu+y7/QlpaAP4BwEVKWSuE+H2Lxyt+QImcTcGcEwVla1ANkI6yDegHKWWlEKIKMJmvWQ4sF0IsQ9ma9C5wQkp50Xy+EAhCyd8C4ICSR6XCHM17/Q+4l+t1VgjxDfCGlLL88kEpZQ3KfrkTf9rIVCqVSqVSqVSq/5JBr6eqpIiq4qIf/6wsKiT52CG6jxmHi7dvi/prbGzkzJkzHD16lKqqKry9vRk/fjxhYWFoNNfObiCl5MKFlykvP0Z4+4/APpppp5Lx1FnweXgAGnNQoL6+nrVr15KWlkb//v0ZNGjQr4I15fm5bP18NmU2nmS2G8768ZE0VTZSvCSR6pwqjogmHsUa3cggdO5KYtTC9DJSUwz45R/GtdMY3Ca1R2tvRUVBPps/eQ9nLx9GPvUCe/bs5bOCSrIDQnm/rQ8DzAGVurhiKjZdJN3pPO/Z7lECKlZa9lsb6F2awtdP34IuNJCndj+F8byRyJpIijWuxDR6crfrG3zrYsPIwBFkW9pyx+Y7sbWw5Y2wbjjWHsbU5gvGxNdT1mDgrSLBtAkdEdrrCHgd+1wJXHQY89OxmmLY9RrELgMnP5i4DEJH/Bh0ST1xlFMb11AQ3oMhZ8/gUF+HiIxkU04ODfWN2Bd0InTgcTQ2megcH8R93wecc3Dh/vEbrxhQyU9LJu3UcfqMn4yNvQNUZMPisWBhA1PW/3Y1or8QKWW9ECIGeAZzblKzw+Zju3/ZRghhxdXn0vOBnUKIH1CS3noDDuYtQEeB98xJbduhxAMmXWuM5pwqvihBHz3K3Psvv0ToaloUGLnGPqj/mpSyWggxFPjY/KNB2VP1jPmSKcDn5pwjyZgfqJRytxDiVZT8Ky7AUZQkt5e9gbIcygZ4CPgUJRluCUoEbjbQ7G+A/1fdUbYipQohZgJf/olbkFQqlUqlUqlUqhuWfvoEWedjzcGTYqpKi9FXV/3sGo1Wi4ObO+169qXH7eOv0tOv6fV6Tp06xfHjx6mtrcXf359bb72Vtm3btmilS0bmJ+QXrCMwcDqtvMYwKS6D4kYjm7qG4GqpTI+Ki4tZvnw5FRUVjBo1iqioqF/106ivZ+NH71BnlOxoPYwlU7tjmV5J4ZpUqusNvGuq50ONLU0BDrj3UaoINTWZ2PXhHiwNVoQ5u+FyWwS6AEca6upY/+GbAIx54VXOnY9nUVoWZ8Ojmeztyr1+SkJcfWo5pSsSybLL4C3HzdyWMJ1zFhr2WxsZUJrMFy+OQRfoxzPbn0EXq8Or0Ys8nR8HK124y+ttvnWxpJ1zOw5kH6S+qZ7xoeOZFNCF1MQniHF6ic8veePZYOS783oGPNDl2gEVKX8qj3zzW6C1BFMTnF4Ae96Exjolh0n/58HK7sdmZXk5bP/qE+z9g0l19Ob+k8swWlhwtF9fykpKcSjpSFgPsHBfjcahNyW7P8daSgImrsbF9srBkcMrFmPj6ETXEaOhthQW3w6NtXDfVnAJuGKbv7ADQC+UQMplh1BWoFytlPLV5tInhVKt9xOUVB+FwGPABeAu4GuUOXM58LqU8ldBmyvQAe8D7VFyuRxFmYf/LQkp5Z89BhUghBgDzEL50j0rpdz2Jw/pdxUVFSVjYmL+7GGoVCqVSqVSqf4AUkqOrVnOsTXLsNRZ4+jhiaO7B44enji4m39398TRwxM7Fxc0LchrUVdXx4kTJzhx4gR6vZ7g4GD69+9PQEDLJ8o5uctITn4VH+/xhIW9y8cXC5l1sYAP2rXmHl9lsp6cnMzatWuxtLRk/PjxV3wfKSVb5nzIheOH2dhqFE9NGcHg3AZqj+VjctHxSFEZD1vqiLC0wvfZKCycdQDse2U5iSWtiMjfT/s7JuA2JRwpTWz48C0uxZ3ljn+/Rb2ljk+37mBz575EOTuwKjIYK42GxuxqCr85Q4E2j1c8lnBL4uNKQMXGyIDiRL56ZRyWAb7MWD8DqwQrbLQ2FNgEs73IgTG+H7HLsRw/Bz+yq7Pp59uP56Kew9fGgYMnbuc77mNfUzf6N1nw+oFyAseFYRvh8dsfZnEKbHlGCaj49YTJa6AkRdnqk3cWAvvDiNng0e5nzRr19Sx7+VnqKitY2/Mu3vr6LRxrazg9cQKZUuJY0Z52IW3xiH6d2vo81qfW81lOHvW9HsVm2HtXHEpWfCyr33qZgVMfpNvNQ2DhaCg4r6xQadOnxd+TP4oQ4rSU8tcROtVf2h+xhUd1A6SUG4QQW4GngRVCiCMoyXzUqj8qlUqlUqlUqv9ZpqYmds//kvN7dtBhwBBufuhxtBa/zzSjurqauXPnUlNTQ1hYGP369cPXt2XbhS4rLt5JcvLruLkNIjT0LQ6UV/PRxQLubOXCVB83pJQcPHiQffv24e3tzcSJE39W4ae501s2kHzsEEddetJ3QG+GFBqoOZaPbS9vnj9+iTBLDV2wwOW24B8DKhlLtpJU7I5XeTzB0Tfjemc7hBAcWPI9mWdjGDLtMbQubsxfvpKdnfvR2saa7zoFYqXRYCiqo2j+WcpEGW+6r2BE0hPmgIqB/gXxfPH8rVj4+/Dqklexz7BH56ijxsKLrXlO3BTwFbtsywl2Dia9Ip1/RfyLx7s8jpRNbD39JG82PU8WATzl6MSkNTk4RHn9dkDFoIdDs+HwJ2BlC6M+hfa3Klt9YhaAvSfcMR863vGr/CpSSnZ8/R/KcnMwjnmY0VvW4VRTzbmePciUEvvqYIL8Qwgdup+s7CSWlNoys6SSJkdfbAa9esXhSCk5vGIR9m7udL5pCKy6B/LOwPjF/1MBFdXf1x8aVBFCfA/kSClf+YPfpw2QCVj+ourQX40tcBpYiLLkKk4I8TXwqpSy8k8dmUqlUqlUKpVK9QuGBj1b/jOL9JgT9Lh9An0mTL6hpLNXYjKZ2LBhA3q9nmnTptG6desb7qui8jTxCdNxdOxEp47/Ia/RxKOJlwi1s+bDUD8aGxvZsGEDSUlJREREcOutt2JpaXnFvrIT4ji4dAEXHYIxhA/geR83ajakY9fLmyXlNVyQDSwRDujauWDbTUl6W33uPAd2VmCltaazdyBukyPQ2FoSv28Xp39YT+SwUQRE9+Lz7xawJbwHFjodSzsH4WppgbGygcJ5Z6gyVvOW12JuTn6Es1oN+2wM9Ms/z+eP3oQmuA1vzn0T6yJr7HxtsKmHb/I86Ba4kFPWWXRw60BCaQKT20/mscjHAFiWuIJXq+5Cq7VhYbAfHRemoXG3wenWoKt/kOn7lNUpZRlK0tk2fSH7pLLVR18BPR6GQS+B9ZWDUac2rSXl2CE6jbmbb1MvMvPUEZLCwkht0wbbutYEuIfRZ3IjcfFfEVNnRf8KHf76WhgzTwngXEHGmZPkpyZz84OPY7HtaUjbBbfOgfajWvANUalu3DWDKkKIiyiT/UApZa352DRgspRy4B86un8Ac6mpaPNPMNCIUl55jvnPyUCiEGKslFJNWqtSqVQqlUql+p9QX13F+g/fJD81mcH3P0LksJG/a//Hjh0jPT2dUaNG/VcBldradGJjH0Kn86ZzxDzKmix5ID6DRpNkfsc2NFRVsnz5coqLixk6dCi9evW6amCourSEzZ9+QI3OmWM+Q1jRP4S6VWno2rmQGuTAZ0uS+Upjj5VOi+vYEIQQGIuL2ff6Guq8B9BDluAxoiu6Nk7kXkhk17wv8O8USc/xk/lu4UI2BXak0saOFZ0CCba1pqnWQME3p6mvreM9r8UMTP0XZ4Rgn42BPnnnmTO1O7punXn787fRVmhxDrPBLyeLZ0uG0y54BSlWF4hqFUVMYQx3hNzBC9EvYALeSTzNl0UdaGtZxuKu3XBYnYG+zoD7fR3QWF1ha1ZpOmyeDhcPgqUNWNpC6g7lx9ZdWRHS/wWlbPEVNNTVsmf+VyQd3k9I997Mq/Zk+sZPyQxsQ1xkZ6wbPGht15FbHgnmTMJoyps0JNd68UJxshK8CR1xxX5ryss4sPg7XLx96GA4CHErYdAr0O3eG/uy/A8TQtQ0e2kLNACXc3H+S0q59P9/VCq4/pUqWuAplKo6fwhzPeu/8iqTG/UsSlmsr4DjwGkpZWOz84uEEC8C36GUWFapVCqVSqVSqf5UlUWFrH3vdaqKCxn99EuE9Oj9u/afm5vLnj17aN++Pd26dbvhfhoaCjkXex9CaOkSuYBjNVY8npRMtbGJuR3aIAry+Gb1aqSUTJo0ibZt2161L6PBwOaP36O2rp71rcbywchIbDdlonG3xnJsMI/MOsB4rAiVGpxvDUbrpKNJrydm8jNkB95HgLEYv4gQHAf7U1lUyMbZ7+Dk6cktTzzHqjVr2ODsQ5azBx+H+dHXxQFpaCJ//mkMZQ185LWUnhn3cVoK9toa6JMbx5yx4dgM7MXLi1/GvsIe13BB1/TD3Ft5H62DVpJvFUc/334cyj3ELW1u4dWer1LcaOSppHT2lWsZpDnFN90nIGLKqLhQhvOtQVj52P90w+l7IWGjUi65Ou+n4y6B4N8TWncHv+7gGvTrMsrNn2VyEls/+4jq0mJ6j5vEpdY9GfTxG+jtbImJisJWr8PLMpLbpnchM2cGDfoClpbasbDJFdFkhFs+uGL/RRczWP/hmzTU1DBmRAe0Jz6F6Aeh/3M39F35Xyel/PHhmBc+TLvOpLGqP9i1a44pZgHPCSGcf3lCCBEmhNglhCgTQiQLIX6ZwtvdfL5aCHFACBHQrK0UQjwmhEgFUs3H5gghsoUQVUKI00KIfs2u7y6EiDGfKxRCfHylwQoh7hBCXBRCdDS/Xi2EKBBCVAohDgoh/meCE1JKPynleCnlJ1LKY78IqFy2AAj7/x6bSqVSqVQqlUr1S0UXM1j+6nPUVZZz5ytv/+4BlYaGBtasWYO9vT233nrrDW8nMhqrORf7AAZDOR0ivmV2ngUTYtNxsbBgW9cQHFMTWbx4Mfb29jz00EO/GVAB2L/wG/LTktnuOpC7b+pKxKFCANzv6cArS2OxNUoe1FhjHeaKbVdPpMlE2qR7SPAejW1TDR1b+eI6IRSDsYENs97CZDRy2/OvsmvvPjYbNCT4BPKYvyd3eyvlgvM2xkOegS88VxB56W7OmJSASu+883w8xBerkYN4cv2T2F6yxcnDxJDETTxcNRmXwDVU6mIZFjCMQ7mHGNh6IG/1fZsVBRX0O3mBw+W13M985nbpja5UQ8XWTKzDXLHrrVQooioPVk5RquecXawEVJz8YfRnMCMbHj0Goz6ByLvALfiqARVTUxNHVy9j5RsvIgRMnPkBQUNvZ/uKdURmpXCsbx9s9E24i16Meaor1fU7KCzcxI4qC15wH45j6m7o9wy4Bv6q7/TTJ1nx2gsATJw0CP8Ln0KH268agPknEEJohRCvCiEyhBAlQoill+fvQoh7hBApQgg78+vbhRA5QggX8+uvzK+rhBAnhRA9m/XbRwhx1nyuQAhx5WzB/2DXG1SJAfYDPwv7mR/KLpTyxJ4oZYy/FEKEN7tsEvAW4I6yneWXy5LGAD2Ay21OAZGAq7nf1UIIa/O5OcAcKaUjylaZVb8cqLkc1AfAECllvPnwNiDEPMYzVxjD/7pi4KY/exAqlUqlUqlUqn+2rPhYVr7xIhqtBXe9OYvWYb///1Vu2bKFiooK7rjjDmxtr5xH41pMpkbizj9KbW0qru2+YGqaNZ9nFTHZ240tkUEk79nJjh07CA0NZdq0abi6uv5mf/H7dhG7axvnXLriFdGdKflGjOV63KaEczC1lO1ZpbyvtcPC2gKXsUqJ59xnnyXB2Am9zoWujo64jQjEwsOGrZ/NpjQ7i1HTX+R8ajqbsgs41rYTIz2ceDnIG4DK8/nImCq2Oh2ibe4YYoyCPbYGeuXH81FXG7TjRzBtyzScUpyw1sGYopXc1/QMFgFr0Vuf5/a2t7M7azc9vHrwcPd3mBB7iWeTs2mjKeRdOZ1HQrpjbx1G2fILaGwtcRnXDiFNcGIufN4dUraDVqfkRhnzFUyPg65Twdrxuj7/yqJCVs58iWNrlhHWZwBTPvgMlzbteGXBQe45tZEDAwdgaTDg3tiN25+KxtKulIQL/yazQUOPxlB6HvkGWkdDn+k/61dKyektG9k4621cfVsz6enH8Dz5hlJp6Pa50IKqUn9DzwFDgb5Aa5Sqsp8ASCkXAueB2UKIVihlku+TUpab2x4DOgFuwEaUOfjlpEKfA++a5+AhwIb/n9v562hJotrXgCNCiDnNjo0CLkopF5hfnxVCrAXGATPNx7ZIKQ8CCCFeBiqFEH5Symzz+feklGWXO5RSLmnW/2whxCtAKBCL8sVoK4Rwl1KWoGyXaW46cD8wUEqZ06zP7y7/LoR4AygXQjj9VZK/SqXu9YE/exwqlUqlUqlUqn+uC0cOsO2LT3D18WXsSzNxcHP/3d8jNjaWuLg4Bg4ceEMlkwGkNJGUNIPy8qPk+n7Bw2nOSKlnbocAhjvZsnLlCjIyMhgwYAADBgxAo/nt/2cuzEhj97dfUuzgT5pfP5Y4OWE8U4LLne1ocNHx+txjfCBsCTAJXMYEo3XUUfSfz8g8mUN+p1sJsZb4tHfDvpcPh1YsJD3mOIPufYgKqWHtqTPs7TaQTva2fNY+AI0QGCrrKVwVT4FVKU0l4Zw2aNlja6BHQRIftNFjemAS92y7B6+LXtgb7LA35jCGmTT5rkJjk8aksEmsTFlJe/dOhLR9nVtOZ2Kj1fCCSyydyt6ijd/9tPadTMW6NIwl9bg/0AltVSIsfUophezbDYqTwc4d7t0CTi3LZ5N05AC7530BSEY8/izt+w2iuLqBB787yshd33GmexQmjYaQDC0DPhqKcytrTsQ8SYNRjy7HjnG5+yFoIExYCpbWP/bbZDSy7/u5xO7aRkj33tzyyHQsl49R8rvcMR8sdC3+rvzNPIyS9zQPQAgxE0gQQtxvnk8+hBJY6QuskFLuutxQSrno8u9CiHeBl4EgIBllDt5OCOEmpSwF1Dyfv3C9K1Uwr/r4AZjR7HAA0EMIUXH5B2Vlileza7Kb9VEDlAE+VzoPIIR4TgiRZN6qUwE4oaxyAXgAaAdcEEKcEkL8MqXz88AXzQMq5mVQ7wsh0oUQVcBF86nf/18BlUqlUqlUKpXqbyjmh/Vs+c8sfNqFMWHmB39IQKW0tJQtW7YQEBBA//79b7iftPQPySrcxhrHL3ghz4tgG2t2R4cy2NaS77//nszMTG677TYGDRp0zYBKXVUlm2a/i97Chh/cBvNhRAC6MyU4DGiNTVdP3vz8FE9gRQ8scB7TFtvOnlT+8AP58xaRFDoZR0tJmJMNLuPakXhoL6c2rqHzzbfg1K4Dy7ftYGdkX1x1ViyKCMJWq0GaJOfn70Vj1JBSp+G0wZbdtga6Fybzvkseqve7YwAAIABJREFUjU/exT3b7sGqyBq/Kj/SjK583DQK27ar0dimM63TNNamrsXDdSDZrjP4JKuUW9ydWOi5i85lbxLQ+h7atn2J+vMl1J4qwKFvK6zTPoRvBkJlLgx6GUpSbyig0lBXx7bPZ7P1P7Nw8/Nn6oef0b7fIBLyqxi84hQdU7ZQG9yKWns7ok/F03vmU3j4O5B58Stqq87RlNLIlNyL0H403L0KdD/ld9HX1rD+g5nE7tpG9G13cuvTM7A8vwSyT8Dw95Uyzv9gQtkj5wdsbTYvP4sy33cDMAdE1qPsEPn4F+1fMqfyqATKAWt+mi/fA0QAKUKIE0KIYf8f9/RX0tKSyq+jbJ+ZbX6dDRyQUt78G238Lv8ihLBH2dbTLNMRstn5fsALwGAgQUppEkKUAwJASpkK3CWE0ABjgTVCCLdmfQ0FtgshCqSUa83H7gZuA4agBFScUL4o/8zNdiqVSqVSqVQqVQskHtrHgcXzadejD7c8/iwWVla/+3sYjUbWrl2LRqNh7Nix1wx2XE1W9gKOZm3na8uvyax24jF/T2YEelNVXsb8JUuoqanh7rvvJiQk5Npjamxk0+x3qSovY12r23iiRxiBRwuxDnfDcVgbtq1Opne1ke7CEufb22Lfw5u6s2fJfeFFktvfj1FnT1drS9zHtqUgP42dcz/Dv2MEHUfezjeLFrOzc28MVjoWRwThpVN2WpzasAufIkdOmAq5oHdmt72BqOIM3tMkUfX8wzy68wE0pZGEF3tQLG2ot3WhR4dNJFWk8ljkY8xPXI7e/UHidN3xNcHiToEEVc7j4qWv8PWdTEjIKzSVN1C+LhUrdyOOF8ZCdTZE3a8EM1ZNBVvXFgdU8lIusPXzj6gqKqLXnXfRc+xEshqNvBiTxp68AkYXHMLC1Zr6Bg19jhwh6N6n8WnvSWVVLBkZn+ByoY7BxfXQZTKMmgPan6apFYUFrP9gJhUFeQx9+Ek6DRoKFVmw+w0IHgydJ7b4e/J3I6WUQohcYKyU8vSVrhFCdAfuAlYD/0GZIyOEuBl4AmW+nIQyT67mpzl4EjBBCKFFSfexTgjhcpVcoP9ILQqqSCnThBArgSdRlg79ALwvhJgCrDBfFgnUmD98gBFCiL7ASZTcKsebbf35JQfAiJJDxEIIMQP4ceOeEGIysENKWWyOvgGYmrVPAIYDO4QQBinlJnOfDUApSumpP6yCkUqlUqlUKpVK9XfSUFfHgcXz8W4bysjpL6D5g3JW7N27l7y8PCZMmICTk1OL20spyclZxjepx1ksZuEgbFge4c8gN0dyc3NZulRJqXjPPfdcV3lmk6mJrZ99RO6FBHZ43kxUpw6MPFeBpZcdrhNCyYorQXM6nyiNFjk8APse3jRmZZF13/0UenSj2KML4dZavLp6YvA2senfSqWfvvc+zJJly9kVEkmBjQMLO7Sho4OSNyY+7jSeJ63IpIpLVc7scGzEp66Sd2qPUfDugzyyYQ4NRdMYZCpBI2oY3t6G7Z4nOJgTw2ORj/F1xnmKPd/EpHHkodYevBjoRWHW52Re+gofn4mEtnsdTJKypXHQWIdr1aMIL3cYvwuERklM28KASm1FOcfXrSB21zYc3Ny58/X3SHD35e7zFzlcUk5UZgp356SibTLSNi2TjvGxWEdE02bKKIzGWs6cfZiQpGoCShqg1+Mw9O2fJZrNTU5i46y3kCYTd778Fn4dIkBKpbwzwK2f/mMT017B1yhz8/ullNlCCE+gh5RysxDCFliCUnl2BUrajvvNaTIcULb4FANWwKsoK1UAEEJMRUnpUWpeySJptjBC1fKVKgBvAlMApJTVQoihKMuHPkZZXhQLPNPs+mUoK1x6oaxymfwbfe8AtgMpQC1KYp3mAZjhwMfmL8UlYKKUsr55RnApZax5W9AWIYQBWAQMA3JRth69CjxyA/etUqlUKpVKpVL9oxxft4K6qkpuf+G1PyygkpaWxtGjR4mKiqJ9+/Ytbl9QV8HchFVsrXbnkniEfs62fBEeiKfOktTUVFatWoWdnR2TJ0/G3f3a25aklOz7/htSTx7lVKt+mPw780yxRKPT4nZvB6rL6slblki4Rsu5Ds6MHuiPsbiYi3fdTb20JqX9JNystYR66LAd6sPKd2dgMjUx5NFnWbl2Hfu8g0h1acXMtj4MdVcCSLmlOTSsyUaLA0mV1uyzMVArTLx3cStbnhjPF4tSaNJPoL9VGl6aakZFerLMOY2Dlw4yOvh2ZuVArfM0Qmy0/Cc8mC6OtmRe/ILMi//B23scYe3ehIyjlK7NprGsNa66L7G4+VHo9Rjkx7Y4oNJYX0fMD+uJ2bweo6GR4IE3k9J/BKPL9BTkZxKddYl7LyZiQSOts3PoHHsO+7p6XO+ZiscTjyOE4Hz8i3SMTcWjzEDjwBlYDZjxswBJ0uH97Ph6Dg5u7tz+4hu4+vgqJ2JXQPoeuGUWOPu3+PvyN/YhyoKDvUIIL6AQWAxsRtlpkng5F6p5UcQOIcRe8/nbgXSUFSqzUAIsl41CmYPrUHZ+jJdSGv5f7ugvQig5a1SqP1ZUVJSMiYn5s4ehUqlUKpVKpbpOpbnZLHr+ccL7D2bYw0/+Ie9RU1PDV199ha2tLQ899BCWlpbXbgQ0mEzsKqliWW4WByoMNKGlvVUN97UJZbKPOxohOHfuHJs2bcLT05NJkybh4OBwXX2fWL+KwysWkerZjVOeffnWyRWPIj2e/4pAuFiT/N4JHIwmvnWQvPZSP0RFOZl33okhv4DzvWdQbuPPQFstAQ92YNu6z7gYe4YRT7/E7pOn2WvvztGAMO7zdefdEF+EENQaatk+ZwnRJe05Ut1EjDCw1t7A7ZmHiYvqRnqpDTpdDc/q9lPQ6E6Yvweng3PZenErHdwiOCoG02jblX/52PFKSFssNYJLl+aSlv4hXi5DCC/3h9i1lBZPQm/qjnOb09hPGAMubSAnpkUBlSajgbg9Ozi+dgV1lRVYd47mZI+h7NLYIIHbKippFRuDoBorvTV9ju3CszAPq7bB+H74IdbhSsHXguzV6NY8jFOlkdJBL+Ex4Ke0ndJk4ujqpRxft5LW7Tsy+tl/Y+Ng3rxQUwSfR4NHGNy3DW5wm9ifRQhxWkoZ9WePQ/X7upGVKiqVSqVSqVQqlepvTErJ/oXzsLDS0e+uqX/Ie5hMJtavX09DQwNTp069ZkBFSsm56npWFZSxobCccmMTzpQzUnOKaSGD6O7T98frDh8+zO7duwkMDGTChAlYW1v/Zt+XJRzYw+EViyj06MABp57MdXfF41IdrneHYeFpQ/qHMTgYTbwp6nl0SndEVSWZd47DmF9AQafbKLHyI8JK4NXflxPH15F5Nob+Ux9k/5lYDls7cjQgjLGtXHjHHFBpMjWxcOUXjC7pxQV9E/nSwE67RvyrCjnXKYLMMkvaB51mYeVaVtTdhIOdHfFBJWy9uBUnnTtHtCMwWHfgnbbePODXCoCsjC9JuzibVjUOhB9cgZTWlFrMosEUiPNof+x791NutgUBFWkykXz8MIdWLKKqsIAa/7b8MHgi2Z6tCbS25KlKA9rTJ6gx5COMVrQphegDixEmE0533on3zDcQWmWlk74sAZuVj+BQa+Rsn4fp1iyg0lhfx9bPPyY95jgdB93MkGmPorVo9r3Y+jwY6mD0Z3+5gIrq70sNqqhUKpVKpVKpVKqfyThzkouxZxg49UFsnZz/kPc4fvw46enpjBw5klatWl31usIGA6sKylhVUEZqXQM6IehtlUG0YQn9XRyJ6DALKytlW4/JZGL79u2cPHmSjh07MmbMGCwsrm/Kk3nuNDu+nkONWxDr7Pvwnq0jQdn1ON/eFut2LmTPOYuuppHXRT3ePXzo7Ig5oJJPY0hXUloNw9NCQ9vWdmTpUjm9ZSMRN48gNr+YI9KSA8ER3OzmyJwwfzTmbS7fHPiSofFRlBpNpDXVsN9WSy2SIHcTsTWOdAw5wvripWwx9qccJ0R4I+svrUcIHbmuT9BkGcicMH8mtHKCtD1kX5hFqm0qnsUNhBc5Iwe8QWlSXxqy9bjc2Q67KPPn3IKASmrcWXYu/g59ViYlrq3Yf8sUGkI6MNrdiU4p+WQdOkllUz4gaKx04PYLB7C/mAqA20MP4vnMT5khZMUlmuYPxr7ewNrONzPu5g9+PFdRWMCGD9+kLC+HQff+iy7DR9E8zQNJP0DiBrjpVfBod13PVPVzQgh/IBFwklI2/dnj+bu45t8wQoiLKCWQfaSUJc2On0VJShsopbx4owMQQuwHlkgpv73RPlQqlUqlUqlUKtXvw9jYyL6F83D19SNy2Mjfvf+amhrOnz/P7t27CQsLIyrq6rshUmv1jDqTSqWxiWhHO94KaCKwcAaiPpmgoOm0CXgEpTCoUkFo3bp1JCYm0rNnT4YOHXrdVYQK0lLY/PF7GJ1ascR+EE9aONDHqMFtWnssve0p+CoWUVrPp9pGzttqmN3Lh8xx4zDm5qINCiGx22NYlDYSaaPB2F3Lnq++wr9TF3K1NhytaWBvx570dLbjmw5tsNQogYL1yevovMsfrbQgtqmEZOyIszDSwZBDbKM/Pt7nWFW4gBTLLpzTByOCBKtLViOxpNH3XQxaT74Ob8NoJy18P4ocwxlSQuzxMHrTofdH4N6Nku8TacypwnVCKLaR5rLD1xlQOZSUzP7F32KbnkSVvRPnhowjvN8g3tVpKTl4huSdiZwX9QhpQaXBHf/kBIYlHUDr4IBJSFxH98djaBvY+w4UJSKLEqEsA51W8F1oCFNGLfnxvbLiY9n8yfsgJXf8+00COkX+fDD1FbDlWWjVCfo8dV3P9O/ov52bSymzAPurnVfdmOtdqZKJUn7pMwAhRCeUSjoqlUqlUqlUKpXqb+T0lg1UFhZwx8tvob3OVR7XYjAYSElJITY2ltTUVKSU+Pn5MXr06J+vRmim3GBk6vkMLIRgX1Q7HKs3k5IyEwsLJzp2WYKLS48fry0uLmbt2rUUFBQwdOhQevfufd1jKy/IY90HMzFa2fK9/VDu1Ngzwd0Rt6kd0FhrKZoXh7GwjjXSyDpTA58ODqVsyl0Yc3KxCgrk4p1vU3ayhJ52Whx7u7Lm+7dx9vKmwS+Yo2XV7I7sR0cHGxZ1CsJGqwR5ThWcompZMdGmcM7KEor0Tuy1q8SjsZYEq9Y4OF9ko2Y9jRZebDb2xeSoZ4NpA2isEQFz0Es7FnRsw80OGlg6jrzGcyS3s8fddRAdI74EvaD4uwQMuTW43hWGbScP5WZL0q4roLLmUh7G6Y8ysKwCk5MrjrKJQSeOcCEhlgNODiAEbhVVtC7Ox6Y8n0CKcbGuwm6MB9q6dHSORoRmJaxZCUKDySWASmsD5QG2HHKwY8iQ1VhbWCOl5Oz2H9i/aB6uPq0Z8/yrOHt5/3pAu16F2mK4eyVory/vzt+YOjf/H3O9f0suBqZifnDAPShVdd4GEEI4mc/dAtQB84B3pZQmIcS9wDTgOPAAUAE8KqXcJoR4B+gH9BRCfAp8L6V8XAgxBxgLOAGpwHQp5SHze70BhAN6lCzFWcA9UsoY8/kZwIOAJ0rloJellOtb/tGoVCqVSqVSqVT/LNWlJRxfv5K20b1oE9Hlv+pLKXOcQ2xsLPHx8ej1ehwcHOjduzedO3fG09Pzqm0NJsm0+Ivk6g2s7OSLKesVLhRuxNWlLx06zP5xu4+UkpiYGHbs2IGVlRUTJ04kLCzsusdYV1nBundfp9FgZJHbCHpYOPJ0uA/uE8IQGij+9jyNebUcrDPwrVMj/fxc6PDGYxiys7EKCsIw4wsuLMsgRKchoKcnm/fOAUDXKZojxeXs6jaQAFsdyyKCcbBQcopkVWXxw8JtTNMPJEdTR1aVE8dti6nW2CAt7bDSlbEuOA7XhHQWtXqNuuJydnrsRGfljNF/DpUmLUsjAulrK2DpOMqqz3ChkxOurn3oFPEFsl5QMv88hsI63Ca3xybc7fIDga3PKb/f88NVAypfZxWR9eF7TM3NwRThQ6W3LaU2NdhYFNGvqQr3hkocRQ1Wjnq0LqafPzd9PY0mG2TURERAN6Rne/IN8aRkzqLR2MjWSmdu7TqHQOdAmowG9sz/ivN7dxIc1YMRjz+Llc0VYgMZ++HMIugzHXwif33+n+dac/OR5t+DgUpgvpTyDfO5NihBGUsppdG8a+QQcBMQARwD7r68CkYI0ROlym84SvXdp6SU+//g+/vLud6gynFgihCiPUq544lAH8wPDuWBOgFBgBuwE8gH5pvP9wAWAu7AQ8B8IYSvlPJlIUQffr395xRK6eZK4ClgtRCijZRSbz4/GiXocp95DJ8DPc3n0lECNQXAOGCJEKKtlDL/Ou9VpVKpVCqVSqX6Rzq4dAHSZGLg1AduuI/y8nLi4uKIjY2lrKwMCwsL2rdvT2RkJIGBgdfckiOl5OXUHI5U1PBxiCsibTIFtekEBT3zs+0+tbW1bNy4kZSUFIKDgxkzZsx1V/gBaNTXs+79mVSVlrLWYxR+lm58MLCdsmUFKF2aRMOlamJqjfwQYElTRQNPbpiF8VIWVkFBOH+xkDWzz+OqFXTp7cX+5KVUFBbgPXA4x4rK2dV9MG7WVqzsHIyblTLtqmms4aPFX/J46QjqtCbOlWsosCsmxtIerbYOgG/76wk+uII1Hk9xqbCS0+6nsXFyo8z7XfRSw+rOQXSzlrBsPLUlpzgf7YWtrR+dOn6GrBOUfBuHoUSP+9RwrENdf7rhpE2QsQ+Gvw/Ofr/6PExS8mZ6HltOxDAn7SS+t1fjoD3z4/kmLGly8uFSkxfHa+wx2nnRp0snvFoHYrJ2J+vlT9AnX6TN8mVowsKor88i6cLLlJcfpVx48EWBgUej32Bwm6HUVpSz6eP3yEtOpOfYCfQeNwlxpe9FYy1sfgpcg2HgjF+f/2e61ty8FiXokgB0BHYJIc5JKTdcpb+7URZHZAPbgOeAGUIIX2ALMAXYDgwG1gohwqSUxVfp6x+pJev5LkfEDgBJQK75uBblQUZKKauBaiHEbJQP/3JQ5ZKUch6AEGIh8CXQCiXw8StSyiXNXs4WQrwChAKx5mOHpZRbzf0tBqY3a7u6WduVQoiXgO7Axhbcq0qlUqlUKpVK9Y+SkxTPhSMH6HnHRJw8vVrcvnlOE4A2bdrQr18/wsPD0el0193P/NwSFuWV8mhrZ9rmP05tfRZdIr/H1bXPj9ekpqayYcMG9Ho9w4cPp3v37tedPwWgyWhk88fvUZSZzm7P4Qhrb+beEYFntA9SSio3Z6BPKCVB30RKgA3HS0qZmbwB+4wLWAUH03rJctZ+EIdokvTp2YrjeRu5dP4s3n0GcbK0il09hqCzsmR157b4WFsBYJIm3l43m6kZw7G2hMN1dRisa9koQSP0NDXpeHukln4Hn2aD/QQSizUkOCeg9XekxGUGJrSsjQyio07C0gkY8k4Q2zcYoZF0jpiHqNNR/G0cTeUNuN8bjnVbl59uuLEWtv8bPDtA9IO/+jwaTSaevpDNvvSLvHxqNRG9shBSsrnhAVqF9aX9zT3ZXyR5fXMiNXojTw0J4aH+QVhqNUiTifwXZ1B/Lgnfz/6DLjSErOwFpKfPRggNl3R9+TTtNI93eYpx7cZRmJHGxo/eob66ilHTXyS0V7+rP6h970L5Rbh3K1jaXPfz/Qe42tycX6wkiRNCLAcGAFcLqiyQUqYACCFWoSxgAJgMbL0870YJzsQAI1AWTKjMWhpUOQgEoiwvuswdsERZDnTZJcC32esfgydSyjrzvsmrJsgRQjyHslXIB5CAo/l9ftUfynYjayGEhXkJ01TgGaCN+bz9L9qqVCqVSqVSqVSqZkymJvYumIuDmwfdb7vzBtqb2LBhA4mJifTt25eoqCicnVteNWhvaRWvpeYyzM2eW6peorommYhOX/8YUDEYDOzevZsTJ07g6enJ1KlTf7Ny0JVIKdn1zWdcjD3DafdB5Nu1YfWUKHzClbwjNYdyqTmaxyUgx9aSjQ01jM88RHTSYayCgwlctZK9c5Mpr2igX7gLiTW7SDl+CJ8e/Thb3cienjfTZGnJms7BBNr+FEz6av93DD3ZBy8rLQmyhipTE0frc6hybwtSwwMDLZic9B5bZDSxNT6kOKVQ5ltHrvPbWGssWBcZTIhlEyybgCn7KHGDotAbc+gauQTLRg+K58XRVNWI+30d0QU5/fymD82Gqhy4Yx5ofz4FrDE28fCJ8+hOneLp8lOMddpJk1FLaudFDB11E5VGI89tiGdHQiGd/ZyZdWcEIZ726GNjKdu2jartOzAWFuIxfTqa3m2IOT2BqqqzuLkN5KzswCexC5jcfgrTOjxAwoE97P72S2wcHJn45oe0Cgy++oPKiYHjX0LUA9Cmz9Wv+2e62twcIUQP4H2UVSpWgA5Y/csOmvnl3PryPD0AGCeEuLXZeUtg33818r+h6w6qSCkvCSEyUSJTzdcDlgAGlA890XzMn2bRsmt13fyFEKIf8ALK8qIEc16WcuDKGax+3jYAJZ/LYOCYlLJJCHHuetqqVCqVSqVSqVT/VOf37KD4Uiajpr+Ipc66xe137dpFfHw8gwcPpl+/31h58BtSavX8K+EiYXbWPGL6mKrKU3QI/xh390EAFBYWsnbtWoqKiujRowdDhgzB0rLlSUsPL1tEwoE9ZDh357RjGEvviaJtmBJQqYstonJrJuXWFsSW1JPQxY7wPUe4N3YjuuBgAlevInFTNskpFYT62FJsd5yEnbvx7d6H2Doje3veTJWFFasjggi3/2llxY7EPXht9qKdlY6L0kBalRZ97SGO+SjBgoEdTbyqOcSufEtOyc5kOGRwwS2Fatd3cbHUsToymACtElCRl46QPGQoFQ0xdAj/GAc6UvRNHKYaA+4PdEQX4PjzGy5Nh6OfQcQECPh5At/M0lI+WfkDwUUXCTJlcTfrMeo1aB/bT2f/9mw4l8vMzYnUNTYxY3gok9301C2eS/rWbRjy8hCWltj164fDS89RFppJ3MnRWFjY0SH8Yw5XNvLx8TcZGTiC0cZeLHrhCUpzsvANC2f0M//+7VLdxkbY+Dg4eMOQN1r8jP/ufmNuDrAMJT3GLVJKvTl36Y0sMsgGFkspf720SfUzLU3n/QDgIqWsFUJcbtsErALeMa8ScUVZKfLRdfZZiJKL5TIHwAgUAxbmxLOOV2p4BXYoQZpiACHEfSgROpVKpVKpVCqVSnUF9TXVHF6xGL/wTrTr2bfF7Y8ePcqxY8fo3r07ffu2vD1AmbnSj06j4RXrpdSW7CA09C28vEYjpeTEiRPs2rULa2trJk2aREhIyA29T8zqtZzctJoSh45sde7KV3d1ISpMSZirT6+gbFUKDY5WHMqqIaWTDZbb1vFY/CZ0bYMJXL2awqPFHNmfi6u9JZYBScRs3YJvt54ckNac6d6LIgsdizsFEuVk9+N7JhUmk/1dGUMtW5FvaCJJm4uf8RDPeA8FJEFeBr6NtmP/ikMckYPIss/irNtZtJ6PYm/jy4YubfHRGGH5BLh0hKxhk8mr20qbgEfxsL2F4uYBFf9fTJukhG0vgFYHN7/54+Ha2lrWb95N6oVYXKWko7Gcsdp1NFZrKB7wCa18Q3l8+Vm2xOZxi10t022y0L73KdmXssDCArvevXB74lHo6UO1IZHk/C+pyUymleco2rV7lYP553j7+GsM13cldGMVW3M+xNXXj5FPPk+7Xn3RaLRXf0hSwpZnoDgJ7l4F1tc7FfzHudLcHJT5dJk5oNIdJWfKzhvofwlwSggxDNiNskqlJ5Ampcz5L8f+t9KioIqUMv0qp55ASVabgVKVZx7w3XV2OwdYKIR4BGUZ09MoiXBSUJLsfIISJbue8SWa87kcA0woS6GOXOc4VCqVSqVSqVSqf5yjq5bQUFvLoPv+ddXyxldz/vx5du7cSXh4OMOHD29xe1DyeTwQn0m+3sDHbvuRxcsIDnqO1r53K5P/9etJS0ujXbt2jB49Gnv7q2YRuCppksR9v4kDOxZQbRfECrc+vDa6A8MjfAAwFNZSujgRaW/JnqxaLnnp6brqc6ILL6ALa0+bFcuoOV/OnnXpCK3AJyKfmC1r0UZE835IDwqcPXDUavi6vT8DXH9Klhsfd5EdS48xVhtEFUZywxfQo6CSRwwDMQprHG0NbJjUnZPz7mO/HESebR6n3U9j6zyAIps+bOoUiI+2CZZNhMxDFI+cTlr1Ejw9biHA8zHzlp8G3B/o9OuACsCFLZC2G4a9Cw5eGAwGdvywlzOxpzBJI5aNrRgSVEeP3E/Rl2o5W3MTXUbczbSFMWj27WJNzkHsCrIxaTToekdj9fBNGMI0lDYkkFE5k6akWgBsbYOI6DQXD48hnMg9zhfLX+POjABsKksRre0Y+dQLtOvZ57eDKZftfgPOLob+z0O7YS1+1v8UvzE3fxQlL+nnKDlXVgEt3osnpcwWQtwGfAgsR1lMcRJ45MZG/PclpJTXvkql+i9FRUXJmJiYP3sYKpVKpVKpVKpmii9lsvjFp+g8dASD73+4RW0zMjJYsmQJfn5+TJ48+Ya24kgpeT45hyX5pbzmlkhoyav4+0+jbfAMLl26xNq1a6mrq2PYsGFER0ffUNDGUFxH0rzt7IldSK1dKxa4j+T5kR15eICSz6OpsoGiL89hMkr2VxjIqzhL31OLsDfocbltNL7vvUN9XAkHvksgo8FEcJdaEvbOJTM0kjUDx+LSZOTJEH+mtPbA3lw2OT+9kqMbU4grimGSqQMaCyM5vV+iVUUHZh8wsdttHFpNEzufGkDZ2sf4oSiIYpti4v0SqNdYk+Mxk/kR7RnhrIPlEyHjANWjXuN07QJsbYOJDF1E+XepGEv1V86hAtBYB1/0ACs7ePgQJeWVLF6wjMraMrSN7mT7tmdmp3S89r5AXbGhcaEWAAAgAElEQVQV8ad98F2xiae355CfkMLXh2dDv1YwxB99q0qq6hKRshEAe7tQnJyjcXGOxtk5Gp2ulZKXZ+cKDq1ejGONBc6+vvQdN5l2PfpcubLPlRyZA7teg+hpMOIjuIHn/b9MCHFaShn1Z49D9ftq6fYflUqlUqlUKpVK9TcgpWTv93PR2dvTe/ykFrXNz89nxYoVuLu7M3HixBsKqADMyylmSX4p9zoXEFryKj7e4wkKfIFDhw6xb98+XFxcePDBB/Hyank1ImmS1BzJ49Kmk+zPWYre1oXFbsN4bsRPARWT3kjJggRM+ibiNCYs4hcz6tIhCm1dCJ7+OJ7330t9fAkJixPJaDBh9KknYe9ckoM6cKLrICYWXeTd20dia65uVJxVzYlNGVyKLyXf9QLjjR2wsjCR0/1tfK2HsOOH/exu8wIg+XZqNNWHP+OHoiDKdGVUh9dQWV5Naaun+XfbIEa42CpbfjIO0DD6A2LrF2Nh4UinkM8pX5CmlE2+N/zKARWAw59AZRbcu4XY+EQ2bdyMyQhVFp2JHxXKmqbdOO55ldoadzIPWlL/0H08sjmLpPwqlpfso/zJBhoDMhEiCwdtR/z8puLsFI2zcxSWlj8tfJAmE0lHDnBo1SKqCwrROGrp98jDRPcfcf3BFIAzi5SASsc74JZZf7uAiurv65pBFSHERWCalHL3Hz8clUqlUqlUKpVK9f8h5fhhchLjGTLtMWzsHa7dwKy8vJylS5f+mN/ExubGSt3uLq3ijbQ8BtnXMLj8cTw9bsHP7yWW/R979x1dRbU9cPx7bknvvfcQQui9g/SugCgdpYpd7MqzPMvDZ0MRe0HpJYCAgjSB0AKEDiEhEFJI7/X28/vjXjQIKLH81Od81mJ5Z+bM3HNn0LVmu8/ey5Zx4cIFmjdvzvDhwxvVjvkKU2k9ZavTKc/IZU/Raurt7FnqPYiHhrZidm9rQEWaLJQuScVYVEehSzluiW8RUVvA3qAWjHx8Kj6DB1GfVkb24lRS6sxUOhqwO/MJFZFxqP1CmF2YwV1TpuBgb4/ZbGHnl6mkHyrE3kkDzUsZdjEBLzsVuS3fIzRiFCmvLeetJlPBoGFWr3B8yw6w6pSOKrtq/Lr5sytzNzUe4xgZ1pb7w/zg28fgwk7Mw9/mpOUbjMYK2jZfStWSYoxFdfhMSbi6bXJDZRdh3zsYmo3h2+MlHD++Ha3BnUqnlhzpE8BW3Xoc9ryGwaUt2avyuRATyQeGBC6WV7OoqR511gEM4WZiYp4mOGgcGo3zdb+mtqKcLe+/zaUTR6l2k6R2rGfe1E+J9Iy67vgbOrsBNj4EMf3gtg+hMcGYfyAhxF3Ao0A0UAWsA56WUlb8mfP6p1IyVRQKhUKhUCgUin8Ys8nEnqWL8I2IokXfATd9Xm1tLUuWLMFkMjF16lTc3W+QJfEL9pZXc8+ZSzRxMDKx+h58vLrj4vIgH330CXV1dQwbNox27do1ermPtEhqk/Op/DYTvaxnb+06ai0GVvmNYPbQ9tzbO8Y6TkrKE8+jO19GvfEw9usWobNz4qPmI3jm3iH49OmFoaCWy4vPsLfehA4L9oVLcIuKQu/kTnM7FZMmTsTBwdop6fTuy6QfKqRN/zCMAZUYVnsT4qCiIGYt4V3GcObfi3i2ZVcslQHEBzozMkLH0lUp1GrraTegBW+nfoDJsSXNQ0bzRlwo4uQqOPwpsst9pNqfoKroOM2bvod+lcSYX4v3pGY4NLlBQAVg81MUCH/WFDSnpOw4TjVhVHs1YVNnJw6ULcbhyAeYY28l47/HKXFyYnnCbWRXmlg0viW+j9xF3gw1zk5RhIXejRDXr4OSdfI43773Brq6GjI6qDgaUMjngz9vfEDl4i5InAYhHeCOr0Bj17jz/2GEEI9i7ZY7BdgBBAPvA9uEEN3klTVaP47XSClN//8z/ee46RCgEEIlhJgrhMgSQhQJIb4SQrjbjn1pe7gIIYKFEFIIcZ9tO1oIUWY731MIsUkIUSyEKLd9DvljfppCoVAoFAqFQqG4nrN7dlJVXEj3sZNurngoYDAYWLZsGZWVlYwbNw4/P79Gf2+B3sjsM5e4/fgFPNUm7tM9jJ9bU6oqJ/DVV8vRarXMmDGD9u3bNzqgYirXUfLZKSq+voAqzIkD5s2UV5SwznsgU4d35b5bYn4YW7U1i5r96ejOfoj528844h/HvA4TefKB4fj16YWlzsi5T09ypNqEziChZgs+od6Uunjj4ubO5MmTcXa2Zm/oao0c3pRJSFNPAto4Urq6lHgHLcUByYSPGEHOwi082VxDfWUv7DWCeYMDWLp6HTq1nv63RLMiZyNG4Yhz4H180SIa++JUa9ZGeDcyY/woLNpEVPijiA1BGHJr8B4fj2NTrxveB3nuW46cz+NTy2hqavS4l7VA5Z/AsaaXOJj6IJ5HPkB2mEnWRgMmk5nvm7bijF04i6d1osmejVSGZGHyMhAd/dh1Aypmk4ltiz9izStzKZEVrO2UTUpgAe/2fZcE74RGPTMup8CKCeAdA+NXWuu/KG5ICOEGvAg8IKXcIqU0SikvAXcAEcBEIcQLQog1QoglQogq4C7bu/hTQogLQohSIcQqIYRXg+tOtr3nlwoh/iWEuCSE6Gc7Zi+EmC+EyLP9mS+EsLcd6y2EyBVCPGqLEeTbOvD+ozQmr+ou259bsLZAdsHa/xqsVYV72z73wtoFqGeD7SQppcX2fV8A4UAYUN/gGgqFQqFQKBQKheIPZjaZOLh2Jf5RsUS2vrmamWazmTVr1pCXl8fo0aMJDw9v1HcaLZL3s4volpzKt8WVzPQu52XjDIIdXEk7N4Dvv99HQkICs2bNanT9FCkltSmFFM4/iiGnBrfbIjlYvpH8i+ls9unDuBG9rwqo1B4ronzZRuqSXkJ/8QzvtxzFJ81vZeHkjgT26o40S5IWHOVIXj0legtm/V7cfOsodPfHztGRKVOm4Or643Kpw99kYqg30WpAMHsXHKedvQNlbheJnNyH0iV7eSwgg9KSuwDBv2+LYfmazzAIEwNawgFKyK66iN53NovbtMHHUgsrJ4KDO6X9Z5GZtYAAv5E47+iGIbsKr3FxOCZ43/Be6KorWJ24lk30w9s9COfcVviGuRLq/CabTt6Hh64ERn1KWUE8+uMnOB3ky67wgayY1YWW2nqKP/uQ2lFa3N3a4OPT9yfP0MiW4+t5bc6dnNy0kbTQas4NdeH+/k+y7fZtdArs1KjnRnEaLLkdnLxh0jpw/JnMG8UVXQEHYG3DnVLKGuBboL9t163AGqxdf5Zi7dZ7G9Z38yCgHFgIIIRohjXTZQIQCLhjzX654lmsrZRbA62AjsDcBscDGpwzDVgohPhHPczGLP+ZALwlpbwIIIR4Gjhti0Ttxtq2SYU1mPJf4F+283rZjiOlLAUSr1xQCPEK8P1v/REKhUKhUCgUCoXi5pxNsmap9LnJFspSSjZt2kR6ejpDhw4lPj6+Ud+3t7yap9NzOV+np6dzHWON83EvScHOLpbDh7pSVVX4q5f7mGuNVKw9T/2ZUuwi3PAc04SdiZ9yMeUQu717cOttg3mgb+wP4/U5VRS+ugD9qTXU+oTxVKexVDg4sXJ0FGHdO2CxSNa/foj8S7UIrQVj1SrcfVWU+4Yj1BomT56Mh8ePRVrLC2o5vesysR392fbVfvpoPal1KCdiZmeqdhzmYcMGcmseAKmlR6wXSckv42OIomVgOrLFFNbteYx616F82H448c4OsHI6VGRjnLics5eex9kpFp/kcRgyq/C6Mw6nFr43vBeXL19m9eJPqDQG0TYogNyj4XSL/J7Y+q9wqNVj6fowql6Poc8ppPDtURS6ObE7tgefPDSMGD8Xch9+gZquBkyOeluWivVZZJRnsD5jPUeSNtMqxR4VAjksgSeGzSTGM+aG8/lZFTmweCSoNDB5Pbg2vhDxP5QPUHKD5Tz5QDsgDTggpVxv218vhLgHuF9KmQsghHgByBZCTAJuBzZKKffajj0HPNjguhOwZsYU2Y6/CHzEj+/7RuDftjl9K4SoAeKAg7/Tb/7La0xQJQjIarCdZTvfX0p5QQhRizV61QN4CZgmhIjDGlR5F0AI4QS8DQwCrkSvXIUQaiml+Tf9EoVCoVAoFAqFQvGzzCYTyetW4R8VQ1TbDjd1zsGDBzl27Bg9e/akQ4ebOwcgT2fghQt5bCiqIFhr5Bm7xSTUfIOjYxMq9dPYm2TA09ODGTPu+FXdferPlVG+Jh1LvQn3wZG49AjmQOJyTu/4jsPubek3ahQPNgiomCp15D44F0PqNuqbdWJ65EgsWFg5MpTo7u2pLtOR+FYKtSV6nBx0lOV/SmjzeC47emIym7lr8mR8fHyumsP+xAx0DrWknU2nL15IrYHwma3Rp2fzSOrrnJdTkSZvHLQqpPgvHiUJeLtU0PnO/zDsm7EY7aKY0+5B+nm7wd75cG4TcsCrnKvbiNFYTvilpzFm1OF5exOcWt94udXhw4fZvHkzrpZq7vAsI/20JxODHsetPou9Pl1oNvptvALjsdTVkf7I45iQpESE8cSzDxHt50LtgQNU7tpM7X/VeHn1wNOzMzuzd/LJyU9ILTpDp1QvOmS74BQawJhHn8cnMLTRz+sHtSWw+DbQ18Dd34JXI2uw/LOVAD43qJMSaDsOkPOTY+HAOiGEpcE+M+CP9T3/h/FSyjohRGmDcdeLAwQ12C79yVzqsK5q+cdoTFAlD+vDuCIMMAGFtu3dWKNcdlLKy0KI3ViL53gCx21jHsUateokpSwQQrQGjgFKvyyFQqFQKBQKheIPlrp3F5WFBfR+/F83lRWSn5/Ptm3biIuL45Zbbrmp7zBYLHyUU8zblwoxSxNjNdsYaFiEh1ME1eZZ7Nyhx2Ix0a5de/r379/o7j4Wg5nKby5Sm1yANsAJn6nN0QQ4ciBxOQfWLOOsSxydxkzgoX4/BlTMtfVkTbwXw/mDGBK6My1yBHohWT4inPjubblwtIhtX54FvYUIx1rS8j6m1cChpNWbqa+sZMqUKfj7+181j5yzZezJTiLEEkQ/jTf2ahPed8UidAYe3/wAx51vxVxhzeQI99mCU4kPdqgYd9czTN79MHqziT4JzzI7LBAy98COFyFhJIVRoRSdfYvAuimoTnviMSoG53ZXf3dDe/bsYefOncQ61zCybhVFBTHc6rWWQodgHor6D/f1m4ynsz1Zn39F2Qcf4FBdwclwf7rcPZPoYB+k0UjBK69QP8oFs6ac6KhHSc5PZs6uOTSVodx1rAXmokraDx9F97GTUGt+XftsAHRVsGQUVF62LvkJaP7rr/XPdADQA6OAVVd2CiFcgMHAM0AIIH9yXg4wVUq576cXFELkY31Hv7LtCDRcY3YlDnDGth1m26ewaUxQZTnwpBBiM1AMvAqsbBCV2g28Aay2be+ynZPUIAvFFWsdlQpbYZznf9v0FQqFQqFQKBQKxc2wmM0kr12JX0Q00e06/uJ4g8HAmjVrcHZ2ZsSIETcVhNlfXsPjaTlcqNfTUXWacZaFRDh4oDNNZ9f3evT6Olq2bEnv3r3x8rpxsdUb0WdXUb4yDVOZDpeeIbgPCKe2qpxlz71E6fkznHNuQsId03mk/w/viJirqsi8cxrGzNPowzrzWPgoKtVmFg0MJaFTC75feo6zSXm4aqCZs46DhUvpM/1ejuUWUFqaw8SJEwkODr5qHpX1lbyw+VU61Pekr4M7Gq0e9wleOPq48dRbg9nr3RljYScctSrsNLlYNGcJKu/GLX378uq5T8mpOEVg6CO807IDojof1kwF7xh0A+eSdnwMzjTDdV9P3PqF4dIx8Lr3QkrJjh072Lt3L61DXRmR8zZSqgm1P8O+Vg8x3m0oz8dG4Lp5K8kLFuBeVsAlr1CK432wbxpD14EDAShbupT6ggyqu6vw8x1MJR48uns2HcrCaJ6iQesAg59+kcjW7Rr9vK5SU2StF1N4BsYuh/Auv+16/0BSykrb8psFtiK0Dbv/5AKLgaevc+qHwCtCiClSyiwhhC/QVUr5NdbaKweFEF2BI8ALXJ30sByYK4Q4jDVY8xyw5A/5gX9TjQmqfI41zWcP1uI432EteHPFbqxBkz227b2AU4NtgPnAMqxpSXnAm1gL5igUCoVCoVAoFIo/UOreXVQU5nPrY3NvKkCyefNmSktLmTJlyg+dbn7Oyeo6xp44jxelPC4/opt9DUbjWPbsrqe+vpqmTZvSp0+fX9U1SJotVO3Ipvr7HNTu9vjOaIF9lAdnkg+yeeHbmAx6DgX1Y8zYkYzr9GNyvSE/n0tjJmAuLcIQM4R3owaSYWfkmYFNaRbrz+pXD1NeUIePM7RTSfZVbmLE089w8Mw5srKyGD16NFFRVy9POZR/iHnrFzAsaxy9HJ1R2dWgGV2FR0xvXn15CN8GhKLPHUaIhz25FfVogtfSu7wz7j4+rLTsZ/el5Wg9BrGqx2S0FiOsmgLGeuSUTaReeAmLyYDf3sk4tw7EtW/Yde+HxWLhu+++Izk5mS7uxQzIeRsBXLbrTuad8xiTY+CuS+eJeOlFLAWZ1Lj6kNq2PWWmMuycHLh1xn0IlQpTcTEl7y1EPy0AC5cJCJ3FjJ0P4FmmJv4Q+MfGMuzhJ3HxbHwA7Co5h2HVJKivgNGfQZObb+OtuJqU8r+25TlvANFAFbAemCCl1N/g3+13sAZKtgohgoAiYCXwtZTyjBDiAWAF4Iz1nb0Ia0YMwMuAG3DStr3atk9hI6T8aWaQQvH7a9++vTxy5MifPQ2FQqFQKBSKfySL2cwXc+5B6+DIpHnv/GJQ5cyZM6xevZru3bvTr1+/X7x+cX0V/Q+dxGiu5y3Hj3CVvTlwQEd1dQ3R0dH06dPnmmyPm2UsqqNsZRrGyzU4tfXDY0Q0ZpWFL959n+rD2ymx80bTbzJzbu+Bp7MdYM3iKF+5kqJX5iGlQLaexHL3lixz1XNLnC8PhAVyYN0F1I5qXO2q6WZy4ahpDz0fn8aBo8c4dOgQAwcOpEuXH7MpdCYd7x57lx0HDjLp/Cy6OTkiHcrRDTpE004v8dF/x7HAsxRD1v2EeriQWaZH65XEA15QlutAbit7kquW4erZj/UDX8PP3g42PwnJH8KYReS415Ce/gJ+aZPxF7fhO70FQnNts1aLxcLGxBUcO5NOLw7Qm2SkVHHM5Rk8Z9zLrA1bmZK4jHZppyh3dCU7KoTLaj2O7h60HTScVgOH4uhi7V6U99TTlO7fSPELJvwDbuWD/FqSL+1jSkoCdkLL5NcW4ODyG8pjSAlHPoPNT4FbEIxdCgEtfv31/uaEEClSyptrufUnsS0lqgBipZSZf/Z8/g4ak6miUCgUCoVCoVAo/obO7dtNRUE+Ix595hcDKhUVFWzYsIHg4OCbqqNSUXWKKcdOUWxuwn8cDnHuUDfKy0sIDQ1l1KjRREZG/qo5SympTS6gYtNFVHYqvCfG49jch4Mn0tmy4HVcq/PJD2rLpAcfoFXkj11x6g4fJv+llzGkn0fYu6Lp9iCbZQCbvU1Eax0ZVKpmb/J5fOM9qCk+Q3dTBJn25xn0/BwOpaRw6NAhunTpclVA5WzpWZ5JegZzpiPTL95HRyctRodyym5ZQtsOi1j06QMs9MnCculhPB0dKK3ORWi0PBwnKDvpRLGvJLlqGYE+/dgw6HUc1Bo4tcYaUOl8H3WRrclIHoZzeQt8aofgPbvZdQMq5sp81n21kNOlGgaQRAdxFqPZnlMh88nwD6d09v28e+wAdVp7zkWHc8lJhWugF32HjSKhd1+0dj/Wr6k7dozK9esxvhQN4gK7al3YnfMt9+T2QF+ey6gXX/ttARVjPXzzKBxfCjH9YdTH4PQbM14UfwghxHCsS4kE1gyYU8ClP3NOfyd/SlBFCCGxRr4y/ozvVygUCoVCoVAo/iksFjMH167ENyyCmPadf3as2WwmMTERKSWjR49GrVbfcKyUkpycL/jvhQyOcgcztTlc3FqFv78/48cPJTY2ttEtkn+YR62R8sTz6M6WYh/rgdeYOCrVMG/+EuwPrsVeCHxvm8Ujdw5DWMzo0tLRpZ6latM31O7dC4DKzR+7Lg+zx+hKUoSG0EITA0wqylQ1tLgtlFPbExmh7kqpWyVdn5rMqVOn2bFjBy1atKB///4AmCwmPj/9OR8c/4BW5T25LfM22jqpqdXoKO4+j3YdlvL5upd4V3sQVeY9gBs+6j2kGrsxqvVp7Mrj0HGJZIdNNPHvz8r+/0Wj1kBRKmx4AMK6YOn3L06njEcY1QSen4nP9OaonX9SDLa+HGPSO6w5cIk0GcEw7xyal2ZgNqs40+R9DmzcRa9TL2MRKs4H+5Pp6YhXdBMG3zqaJp26ofrJc5RmM4UvvYxs5kW5dxom1x58eGYV4/S90J29RI/xdxHUpHGts69SnmVd7pN/Ano9Cb2eAtW1QSLFn0sIMQFrc5lsrPVYBNa6KmNlgyUtQogPgctSypf+lIn+xf1iUOV6ARBbX+sYKeVEIURvYImUMuQPm6VCoVAoFAqFQqH4VdL27aE8/zLD5zyN+IUX26SkJHJychg5cuTPFpI1GEo4m/okO0srSBRz6aWqR+xIIS4ujjFjxqDR/Pr/d6s7X07ZqnQsdUbch0bh2CWQpfsz2L/0M2IrzmDyCGJEx854pKWQNXYJ+rQ0pN5a/kE4OgKgCYnDoeU9HDLYk9vKjYhTZUSYtAQ1dSeig4Xvl7zHCNfh1LtaaPH4YC5cvMiGDRuIiori1ltvRaVSUVJfwuO7H+dI4RHuMMyi7YXmtHRUU2o2U9T1eVq2/BdfJH3Fh9U70FycSbU5iC6eq9lbPoJo/1JuiepHyrcbOeN9nGZhPfii7+uoVWprB5yVk8DOBcYs4lLWp1TXniQw9V4C7uyG1sfxx5uhr4aDH2LY9wErDL24SATDmrjTPO0zLFLNibB5VL73Hn0r8sjy8SDDzx3/tu25dcRowlu0vmFQq2JNIrqzZ9EtaIZQVfBK2jG6a1vjtO0yoa3a0mH4qF/9/MjYAYnTwGKBcSshbtCvv5biKkKIS8B0KeX23+N6UsqlwFLb5vSfGXfP7/F9/6uU5T8KhUKhUCgUCsX/KIvFzIG1K/EJiyC2w893W8nKymL37t20bNmSVq1a3XBcWdk+zpx9lHyjlg/VbxNmsRCzezvN4uMZPXr0rw6oSJOFyq1Z1OzJRePniM/dCVi87Zn84hIiMrcRYyzHp9JI+xNJsDuJKldXHJo1w3P8eOybNKF2/z6qNm7CvlVXtGHjOWdQoW/uiePxUlyFmi4jQynM2MSJj9MZ4DcM4WhP1P0dyS8uYNWqVfj5+XHHHXeg0Wg4UXyCOd/PoUpfxRPiv/ifdqSZk5p8o4VLEetpFT+MpalH+PzyZtQZs6gVgQz0/Jzv9F3RqlSMHdiLpHVfoLcrxy8+gvf72AIqZhOsnQllF2HKRiplMZmXFuBa0ImInpOwj3T/8YZU5sInfdDVlLPM8W5yhBNDYgJoce5FjMKFZMMofN56DSchORwZQHrHbtw7eQpB0bE3vsmAuaKC4rffRjUonnL1cZJq3HFV+9HuoAMmF8Hg++b8YvDtuiwW2PsW7HwZ/OLhziXgHd346ygUfzO/KagihHAGNgP2Qoga2+4mWHtjvwPEY22hnAjMkVIaGpzez9ae2RdrdOx+qVTNVSgUCoVCoVAofjdp+5Moz8tl+CNP/eyLcn19PWvXrsXDw4MhQ4Zcd4zFYuRi5jtkZX2IxrEJH2v/g6nOQtfk7bRpFs/IkSN/drnQzzEW11G2wlqM1rlTAO5DozAV5TP34cU0rziMvdlED0dPIjq2wSGhGQ4JCWhDQhBCYMzPJ/fhh9GdOInTkDsQ2j5kG6HY14Hy42VU2EP/XnqOJf6XpvYdaBE4nhoXNcFTWlBprmHp0qU4OzszYcIE7O3tWXluJfMOzyPAKYAX7RbieKCOWEc1hWrJMUMxLTsUsqbQg6UZW1Gdn4Ve7csslwV86uiFuawpnboEs3XXYpoZJfpWahb2eQ2NSmMt2vrNHEjfDEPfxBzajtO7hqDRuxLj/zRObX7SFen7/1BXV88Sn6cpKKthQKAbbdKeox4PThz0JyhrA8UuTqzo2w/74bfzWtd22N1EMKT43XcxV1dTc3sQOr2GnVVq7ivsTG5BCmPmvoyTu0fjH6CuEtbNhrRvoPntMOJdsPvljlGK34cQYgbwJOCFtQvvPVLKPCFEBJAJaKWUJtvYXVhXmnwqhLgLa+ZLd2FNa3oLmIC1228WME5KeVoIsQjIlVLOFUJ4Yl0q1AlrPGGf7ftyG1w/CegDtAQOAOOllCW246uBHoAjcAKYLaU888fdnT/ebwqqSClrhRCD+cnyHyFEIPAI1vVYIVgDL/dibc90xTCgA9b2TCnARmDLb5mPQqFQKBQKhUKhsLJYzBxMXIFPaDixHbvecJyUko0bN1JdXc3UqVNxcHC4Zkx9fQ6nzzxMVdVxggLv4GNmcLqgikGnk+kZF/PDkpnGklJSd7iQio0XEFoV3pPicYj3onzFCtZ/uRGVnwNaAdMWfIyHf8A159fs3UfeY48hjUbcHnwBfVYgl/QWzltAn1fLYbsKejvsp2qLO329xmMRajY0dWb62JYYjDqWfLYEKSUTJ05E66hl7r65bLiwgR7BPZhp9yh163MJc1CjD3fm4IkKQrp+xyZ9MOvO70Ckz8Kk9uQdu//wgq8ac/Y0XDztSa3byvAiA7oAA2+MeA2tylYfZdc8OPol9HgMOkwnbd+z6EQW0bqX8Bzc/OofVnSO2uNr+dJpJqXltfRx1dDx8ktUGZzI2a7Gq6aE3W3b8cbY6cxqEcdjEQG/WL/GXF1N2ReLKF+xErvZt1Bl2Mx3lXY84DCF7INb6Dx6HGHNb5yhdF1V+ZC+BUfVn6YAACAASURBVA68B2WZMGgedLoHfmUtHUXjCSH6AP8BBgBnsBaaXQH0bOSlBtjOaQJUAk2xdgH6KRXwBXAHoAY+B94DbmswZjwwGMjBGgt4DHjKdmwzMBUwAK9hTbBo3ci5/qX8Ict/pJQpDTYvCSE+AnpxdVBlnpSyAqgQQnyP9UYqQRWFQqFQKBQKheJ3kH5gL2V5uQx7+MmfzVI5duwYZ8+epW/fvoSEXFsmsbw8mRMnZwLQPOEdtps7szwtlzZZaYwKD2LYsGG/KqBiqTNSvvY89adLsY/xwOuOJpirism+eyrZJ1LZ1743zarPMOSRp68JqEiLhZL3P6Bk4UI0AWE49JpNTaYnJ+rNFJskwteOlKIkBtbn096uHy5enpwMsuPVJvYs7hmPxMzSpUuprq7mrrvuQmev497N95JalsrsVrO5VT2G0q9SCbNT4dgzmM3bU3HwzmdXoIWt6UcgfRaonVmqfZF3Q/0pzJoE0hlzxHGGXMwGjRfPTHwardoWUDnyBeyeB20mQp+5FKZuJ1+/Aq+KQYSPGHtNQMSw/WWWiZGU6jV0M+bRtWI5lVUOFG53okrrynuPzmFvbFPejAvjzsCf76hjqa+nfNkySj/+BHNlJS6DB3KqxRHqDYIE97vJ/3IXIfHN6TJ67C8/NCmh4BSkbbZm3OQds+73ioIpGyGi200/f8XvZgLwuZTyKIAQ4mmg3Jal0hhGwBVrMOWQlDL1eoOklKVYV6Jg+75XgO9/MuwLKWW67fgqYESD8z9vcO4Ltrm6SykrGznfv4ybCaqYgZ+Un0aL9aZflxCiCdbUofaAk+17Un4yrKDB5zrgN/TrUigUCoVCoVAoFFdYLGYOJK7AOySMJp1u/KJbXFzM5s2biYyMpFu3a8dVVZ3kxMmZ2NsH0LrVp6QZvXjySDrBFcXc6+vC0MGDf1VARZdRTvmqdMy1RtwHR+LcLZCKlSsoevMtLCo1Xw2YSLOsrUT3HkR856vnpc/I4/LjT6BPTUET2hlDy4kcq1NRbDSh1gj8WqvIPPAV97h1JNSzOypPO5K6+PCQuZr5TUPxkyZWrkykoKCAsWPHkiWyeGLTE0gkC/supEV1AiWLzuKuFriOiiU5bT+GOg/Otz3M1sxLcH4WdkLDErvn+b5pd3ad64+02OPUOp2Ygt346NsxdMRQ3F1s9VHOfWNd9hM7AIbNR1dczLnMZ7AjkIR+r17TOtmclcyqdDV5+NKlLIlb3JKpKnWkaJc730d14ZP776PawZ7lzSPp4eV6w3ssDQYqEhMpef8DTMXFOPfsge9DD3FQtQ37/I2cF23w2JKNyc6OIQ8+dk2HoB+Y9JCZZA2ipG2BqlxAQEgH6PscxA0B36ZKdsqfJwg4emVDSlkjhCgFgoHLN3sRKeVOIcR7wEIgXAixFnhMSlnVcJwQwgl4GxgEeNp2uwoh1FJKs237uu/6Qgg18AowBmsZEIttjA/W7Ji/pZsJqmQDEUDDSFUkkG77fL06KB8Ax7CuwaoWQjwM3P4b5qlQKBQKhUKhUChuUvrBfZRdzmHoQ0/cMEvFZDKRmJiIRqNh5MiR1wRHamrPc/zEVLRaD9q0+ZJavJhw+CQOBgNPOkmGDRzS6JbJFr2Zys2Z1B7MR+PriN/kZiAryLn7buoOH8a5e3fW9hlHxMb3sPMPY9j0WQCYqwzUnSym6rsDVG96G6mvwtDnQdIC2nA5rw6QRLX2oK5yD07H6rjDfyxqrRa3fuFcaOXFoyczGO7rTkJJHgsXb0an0zF8xHB26Xax8OBCYj1jmd97Ph4X7SldcRaBxHlcU6rdLpKxyInigJNsrbmE5cIsnC1mljs9z+ZOj/PWIX+Q0LxLGoWFS2ldMZiQ0BDatW5n/cHZB2HNVAhqg7z9C4qz93D+9H8wOZXROmYJdm7uV98fi4WvVy0mg0g6V+9ngHsyVfmOnD8YwJKhU/l6SC+8tBo2tIwi3uXHLkGr0laxO3c3ZosZi9lEkyNFdN2cjWepnuxIZ3aMieZSZAXiwlxG2J/BQW1PXGZ3zmRvZ+RTz+Pq5XPtw8rcA4c+gQs7wVADWieI7gO3PA2xA8HFt1HPXvGHyQPCr2zY6p56Yw2o1Np2OwFXgiPXrqOzkVK+C7wrhPADVgGPA//6ybBHgTigk5SyQAjRGuu7/838x2A8cCvQD7gEuAPlN3nuX9bNBFVWAnOFEKewPrA+wHDgSvnwQsD7Jyk7rlgfWo0QoikwGyj+XWeuUCgUCoVCoVAoriEtFg4mrsArOJQmnW+cpbJz504KCgoYN24cbm5uVx2rr8/l+PG7EEJNm9ZfotH4cseOZMrVDjynNTCm74BGB1R0FyooTzyPuVyHS/dg3PqHUrF6FUVvvolQqwl8+SXS2/Tk8otP46cSTHx6LkIvKVl2hvrUUowXd6M7vZLKkA7kdb2bggITIr8OoYZmXarJS9pAG5c+uHp6oot0IeTOePQuWu47ko6vVkOXM4dJTD1LUFAQfYf0ZX7afHbl7mJo1FCe6/wcxn2lVGxJo9os8bgzDumfw5Z3z2IimE1eyZguzsTTWMd8n495tv0HHDpQBxYTt3RNJqXoa4YZhqExaxg+bLg1QFV0DpbdCe4hlA97mgvHplJZl4JG7U3ToHl4h3e+5h5tX/0ZJ2t96Knfxy0uh6jJs2fr2dasfeRRdoX70tzJgcUto/C3ty4kkFLyztF3+Oz0Z0S4htM+3ULvLfn4FegoDHEm8Z5o8pp5EKQqY6jII0AUocGMunYqKTu20374KKLadLj2YWXthyWjwdELWoyxZqNE9gTttfV2FP/vtEKIhg9iObBcCLEMayLEq0CylPISgBDiMjDRVpJjCnDdlkxCiA5Y66UcxRqM0fFjJklDrlib0VQIIbyA5xsxd1dAD5RiDfS82ohz/7JuJqjyb9ufvVjTey4AE6SUpwGklOeEEMuBi7Z0nmZYC9F8DDyBNWq1EmswRqFQKBQKhUKhUPyB0pP3U5qbzZAHH0eluv6SjpKSEg4cOEDbtm2Ji4u76pheX8yx45Mwm+to23Y59vahzNiymzNOXkylllm9ezUqoGIxmKnacoma/Xmo3TS4dAZZc4icac9Td+QIzj16EPjvF6nz8OGzJ16hib6QPvc+hpPRhaIFxzBV1mLKWU1OXjk5PV6gUnjhWKvC0VWLrrYYJ6e9lH5vRzef2yjEwrlO3owc2QyAJ1KzyazXM/psMnnlxfTv35/IFpHM2jGL3Opcnur4FONixlKxLoO6o0XkGSw4D4/CPiSPte+cwlARwvqILVTm3oGvrpLhzVMZH/Qmlv2lCEsNbdqtJz03k9sNtyOLJZ27dMbf3x+q8mDJaCrd7LjYIZqy1HvQGDzwz51C7NAHsA/0vOY+7d+3j/2pl+lr2UNX7VHqS7Usz+zFluf/xT6NhX7ebnzULBxnjfWZWqSFVw6+wqr0VcymF4OXF6E7dQq7yEi85t+DV2s13iVbKCn5HoulHq3WGz+/O3EU3dnw6mcExDSh+9hJ1z6w0guwYgJ4hMP0beB47VwVf6pvf7L9CtZskkSs7+v7gYYFcmYA72MNYHxmO349bliX9URhDah8B7x+nXHzgWVACdakize5ukjtz/kKGIg1i6bMNu/ZN3nuX5ZQuhgr/j+0b99eHjly5M+ehkKhUCgUCsX/NGmx8NUTD2Axm5ny5sIbBlVWrFjBxYsXefDBB3Fx+bG0odFYydGj46irz6Ztm69wd2/LG9t28YbanZ4qIyt7dfjZgIq5uhpdairG3MsYc3MxXK7FYmqO0LpjuLAD/dl1YDYAoHJ3x/+Jx3EfNQqAx99eSWDyEoI792FYt3FUfH0BRAWXzu7gvHN76pwDcfN1oEWvEE4mZXExdxcutUdI8OxNC/f2nNRYyO0ZyIz+sQghWHnpMg9lFtMmO40x5lpGjBiBxcnC1O+mUlJfwnt93qONS0tKF6diyKriXL0Z+y6BxPWqZ/X8k5irAtgYtZPMki54a+uw6+hCBgE4p5RgliWERy8mrCSAqOoo7O3t6dmzJ507d0ZtqKZ6WT8uepRQ4qlGo/bE6/xgvEoG4DetHVpfp2vu24kTJ1i3bh3dOMQt5v2YqtWsOdOd5c+8RAoWpgR580psCBqV9d4bLUbm7p3LjrRvePlkHOHbzqIK9cfugd7URJZQUrqrQSBlIH5+Q/D06IjZZGHl809Qnp/HpNfewd3vJytB6srgs/7Wf87YYS1Aq/jdCCFSpJTt/+x5KH5ff0j3H4VCoVAoFAqFQvH/79yBJEpyshhy/6M3DKhkZWVx7tw5brnllqsCKmZzHSdOTKO2LpNWrT7B3b0tu06nskA6EWIxsqhX+58NqOgzM8maNBlzSQmotNg3uw1tdF8QNQgO4trLF6/xL2IXEow2JASNnx/CVhx16Y4TeBxeg9o7hP4hQ6hYm4HKIY9TR4+SETQcT09B15HxFDjBgi++I6hgGz7mWloGTSDWLpC6BE8GjYtHpVFjsVj47nAKT1Ra8NPVMTcugi4dO1CqK2Xad9Morivmo/4f0cwcS9F7xzFVGzhSZ0YT60mzbrUsfessqho/tocncaG8Gw4xdmRHh+Krs+B1uBSzuEwbz+00udQBLVo6duxIr169cHZ2pqbiFBeTJlIcWYNG5Uq4x1Tsv26FxsEF35kt0Xhdu3wmIyODr7/+mubyHH0s+zHXq/j2dAdWzH2JFGnhX9FB3Bvq+8O915v1PLbrMUr27uST7S7Y1ZxF91wMFUGZWCyL0VZ4Exg4Ej/fwXh4dESlsr7y6evq2PDmyxRcOM/wOU9fG1AxGWDVZKjIhskblIDK35QQ4kPgspTypT97LvDXm88f4ReDKkKIS8B0KeV22/ZYrIVob5NS7v5jp6dQKBQKhUKhUChuhtGgJ2npInwjoojr1vO6Y6SUbNu2DVdXV7p06fLDfotFz8mTs6msOkGL5u/h7dWdgpJSHswsBEcXlneKx0l94y4/xvx8sqdNA4uFgFffR5fmgLnShHPnQNwHR6KyH3rDc9PzKzi2+D0iNI6MjLmb+pRihP0Zjh26wKWwwQREO5AW78EHm48Qm72LhJpzaJxCGBw1E6c6gcfwaIK7BCKEoKysjPUbNvCeWzDS3YsvOzSjTYAfJfUlTNs6jcK6Qj7s9yFxJaEUrzgOWjUHdBbMPo60HVDHkvkXsav3Yb/7Lk5Xd0e0c6fC05nRdg4k7ckhQn2OBC7jVNEU3whf7hx2Jz4+PlgsJs6efZz8/LWo7S1EOg3Az3sulYuzUbvZ4zO9BRoP+2t+e25uLitXriTQUshI8xakSbD9RGtWzf0vR6SFl2KCmRH6Y0HYWmMtj347m2YrUrj3mAVTPzvKRrtgkOcI9B9NgP+IqwIpP5xXUU7if56nNCeLwffNubYjlJSw8SG4lASjPoXwLij+emzv5kFAkJSypMH+Y0BrIFJKec+fNL3r+qvN54/QqEwVIcQUrK2Sh0opb7QWS6FQKBQKhUKhUPw/O/rN11SXFjP4vkdumKVy9uxZcnNzGTFiBHZ2dgBYLCZOn5lDWfle4pu+hp/fQIxGI9P3HKbIM4B3I3yJdXW+4feaysrInjoNS009Pg+8S+1hHWp3NT7TmuIQ+/P1OHRGM++8vpDOUkvPiEnIcgMYt3H0pIHssMH4xrvybGExkd8dYkjpfrTmepq3GUULUzzSKPG+Ox6HJtbvOHXqFBs2bOBocDR5nr68FRdCmwAfSutLmf7ddApqC3i/7/s0zQ+hdOVZNAHO7CnWUaMSRPcrY90HFTgYvDhl/p5kVQ/s2rpQ7+XMv339WLZmP31U6bibTZTb6xkyYgi9W/X+4Xfk5a0gv2AtoZfriYx+BLPXdEq+PIvGywHf6S1Qu9ld89tLSkpYtmwZzsYa7jIuAwlJxxJYM/dtdlksPBsVeFVApUJXwesLJzBh5UU8BNS8HEqV1wVcnJvSqulnuLm1vO49Li/II/GVf1FXWcltTzxH5JXORA0lvQEnlkHvp6HlmJ99Zoo/XSYwDlgAIIRogbXo61/OT9os/8+66abyQohZWIvQDLwSUBFCrBZCFAghKoUQe4QQCQ3GLxJCLBRCfCOEqBZCJAshom3HhBDibSFEkRCiSghxSgjR3HZsqBDimG1/jhDihQbXdBBCLBFClAohKoQQh4UQ/r/TvVAoFAqFQqFQKP6WaivKSV6/mpgOnQlNuP7LtclkYvv27fj6+tK6dWvAmrlyLm0uxcVbiI15lqCg2wF4YfsejngGMNZZzR2RwTf8XnN1NTnTZ2Cu1eB22+vUn9Hh1M4f/4fb/mJABeD1zzYyqNpM78Cx2DmDKXsRKWkWssMGENrOh9fLixlesoV+Rduxx43BQ54loaYJwk6D372tfgioHD58mMTERGRENMnhTRnq6864QG/KdeXM2DaDyzWXWdh3Ic0KwyhbmYZduBuHTJLSCj3qyEPsX1mFg8GdTGMS27174tzBlRovD+aoNRxak0hPzqJRVXLY7zAzZ8y8KqBiMlRyMe0VPCqMxPrfhclzKiVfnkHr64jvzOsHVKqqqli8eDGivoapus9RqyycOBbJ2rkL+dYieSTcnwfCf3zNKSi8yIbpg5jwyQXsu7tQNs+Oau8coqLm0KH9+hsGVAounGf5vx7HUF/PmOdeuX5A5XQi7HwZWt4JvZ78xWem+NMtBiY32J6CtQAs8MN7+Mu2zz5CiE22d+cyIUSSEEJlO/akEOKy7V09TQjR17bfXggxXwiRZ/szXwhhbzt2lxBib8PJCCGkECKmwXd/IIT4VghRC9zScD7/q242U2U20B3oK6U80WD/ZmAqYABeA5ZiTTu6YiwwGGtbpi+xViYeCwwAegJNgEqgKVBhO6cW61+SM0BzYJsQ4riUcj3WvzDuQCjWVkytsbZzUigUCoVCoVAo/rH2rliM2Wik54S7bzgmJSWF8vJyxo8fj0qlQkpJRsZ/yM9fTUTE/YSFTQXg65TjfKl1J04aeKP9ddrt2lh0OnJn349FxOHUbRCgxefuWBzivG5qzt/tP0fvM2VEet+Cxl9Hzda3OW3Xmcsh3YnrEcS8nGx6Z28goPYyGqfeDO85CLvUcuwi3PCe1Ay1s7Wt8N69e9m+fTvhcXF8EtkSX4vkjbhQKvWVzNg6g+yqbN7r+x4JRRGUrTyHXbgbpx205BwvpcL5GyrSOuFkcKbQbjfr3Xvi2cmTGjuYef4U5Zcv4KuCUx6nyXA7z2u9XyPBJ+HHH2E2cmnbbRgdDcS6DKM+4BHKlqSiDXbB9+4EVE7aa353fX09S5Ysob6qgik1X+LqqCPrqA+rnvmC1SbJPaG+PBH5Y72TzC2JFDz3PK3sTJS84oHRsxh3t/bEN30VZ+frdscF4NLJY2x44xUc3dwY/cxLeAVdJziWcwjWzYawLjBiATSyTbbiT3EQmCSEiAfSsb5fdwOuF7h4FMgFrqQ8dQakECIOuB/oIKXME0JEAFfS2561jWsNSOBrYC7WTj03YzwwBBgG2AETG/Hb/pZuNqjSH/geONVwp5Ty8yufbRkl5UIIdyllpW33OinlIdvxpViXDgEYsfaobgocklKmNrjmrgZfcdLWrrkXsN52njcQI6U8CaTc5PwVCoVCoVAoFIr/SUWXLnJ61zbaDRmBZ+D1s0p0Oh27du0iMjKS2NhYALKyPyY75zNCQiYTFfkwABfzC3iysAYHO3uWd275Q7eZn5JGI5fnvAiug7APCcGprT8ew6JQOd7c60XGqQIC1p7H0ykas08ONSsWkBo2ijzvdrTsF8qC/FxankvEvz4PB+fBDGndBW1aOU7t/PEcGYPQWINCO3fuJCkpifjmzdnSpC2ZpVWsbh2NylLLjG0zyazMZEGfBbQsiaFsRSp2oW6cESZSD5Zy2WkPbsYuOJnsKXXfxVJ6E9rJA7+iC7TIy8RskeS66jnptRWD2sDo2NEMihz0448w1KFLHE+OTxYBIg61z/OULbcGbXzuSkDlcO29qKurY+nSpZQUFTK+ejnBrhUUHndly71vsMgEU4K8eT46CCEEFr2e9GcexfLtdmqHS+oGCdTqeuKi/01w8DhsCQfXlbpvN1sWvo13cAijnn4RFy/vaweVZcLyceAWBHcuBc21NV8Uf1lXslV2A6lYWxRfjxEIBMKllBlAEoAQwgzYA82EEMVSyksNzpkAPCClLLKNfRH4iJsPqnwtpdxn+6xrTPv1v6vGZKrMBT4VQkyTUkohhBpr5skYrJEvi22sD9bsE4CCBteoA1wApJQ7hRDvAQuBcCHEWuAxKWWVEKITMA9rlood1oe92naNxVizVFYIITyAJcCzUkpjI3+3QqFQKBQKhULxtyelZPfiT3FwcaXzqHE3HLd3717q6+vp378/Qgjq6jK5ePFt/HwH0yT2Xwgh0Ol0TEs+TaWbF0uahhDkdG2nGgCLwUjes4vAZSAaO/Ca0AzH+Ou8tF9HQUE1J744QnyFCoSWYv12HBZtIK3NLPKdm9J+aARLygoJPrKcIF0+7m4jGBjbGlV+De5DInHpEWwNOFgsbNmyhUOHDtG6bVu2x7Rkc2EFL8cG09JZMmPrTDIqMni3z7u0KW9C6fJzaENcOVFTSfoFExc89xFQ3RIXi5ocryOsMvWgV3Q1IaeOojWbOW9xJz8smRLtGZw0Tvg7+PNEhyd+/CG6Slg2lguOp0DtRKDzPMpXp2Mf5Y73lARUdtfWtKmurmbx4sWUFBUzqmotMW6FFJ92obBvd15wjOTOAC/+0yQEIQTSbObcw7Mxpe8j698SJ28zPj59iWvyIg4OgT97j1O++ZpdX31CSHxzbn18Lg7OLtcOqq+AZXeCxQQTVoPzzT0/xV/GYmAPEEmDpT/X8TrwArDVFtz4WEo5T0qZIYR42HYsQQjxHTBHSpmHtRBuVoNrZNn23aycRoz9n3CzNVUKgb5AD+B9277xwK1AP6xLciJs+28qFCWlfFdK2Q5ohnUZ0OO2Q8uADUColNId+PDKNaWURinli1LKZkBXrClFk6+5uEKhUCgUCoVC8Q9wIeUQ2adP0vX2cTi4XOflGaisrOTgwYO0aNGCoCDru9H5jHmoVPY0afI8QlizPuZsTyLV3Yd7vZzoG+R33WsZ8mvIe34rqJuicasl8JluNxVQqag1sP7Dg1S9dYj4ChXnq49RU5qIw3frONdtDvnOTel8WxS7qEa783OCdfkEeI9icFgL1DUGvCc1w7WnNeBgNpvZsGEDhw4donOXLhyMa8OawgqeigzgTj8HZm2dRXp5OvNvmU+7inhKl51DG+xMSt5l0i+YSPNJIqi6Oe4WFRnBORyXYYx1OU3U5XPU2PuwQXiQFp1ItUMGIS4h6Mw6Xu3xKk5aWy3QmmJYNIyq8hQK/O0J+D/27js8iqp74Pj37mZTNr33TkLvVUBAQMCCinQVULHriwVFRREbdlEUQUAREQFBEUSCgPQOoQUIkN57L5vdbLm/P2aRFrC8+vMV5/M8PmZn7s7c2VmUOTn3HKc7MH6fS5DrY/i5vIbm9Cow1V1w/ZWVlSxcuJCK8nKGVKyhjUcOJWc8cIs2c0+ze7g1wIuZLcLRCIGUkuQXJ9NQv4vi5y24B7rTpvUs2rWdd8WAipSSHUsXsW3xAuK69WT41FebDqhYzUrr5IoMGL0E/OJ+9f6p/rdIKbNRCtbeCKy6wrhaKeVkKWUMcAvw1NnaKVLKpVLK3kAkyjKft+1vK7BvOyvCvg2UUh2/FMUVQlzUl1s57R+6qH+w39z9x77WagCwXQjxAZCKUtekHOWDfeO3HksI0RUloHMY5cYYOZfp4g5USCmNQohuKMGbjfb3XQeUAclADUo6kw2VSqVSqVQqlepfxmoxs2PJ5/iEhNFu4A2XHbd161aklPTv3x+AiordlJX9TGzsFJyclFILi/cfYrWLL10x82L79pccQ1oltTtyqdmQic0IjgGpBE2991fnaDRb+fGHU0Tuz6eLRk+RsYBieYyItCRMeYWcHvIqRQ1e9B4Zx3HHBooXzCbEVER04Bi6e0ah1Qp8722PY6gSHLBYLKxatYrk5GT69evHrvB4FuWU8Eh4ABODXXno54c4XXmamX1n0rWmNeVLT+EQpGf/6TPkmgNIDthBbEVHXG1mzkRVoqmvobuujDxXP/JFFOnm7ThHbcDNyZPaRgv15nre6fMOHQLsZSOrcuGr25DV+aRe1xmHxhL0P12Dl99qtLXZiBIzrLoPdHpofiO0HUGJRzu+Wroco6GB3nnr6BacTlmmO+4hBr6MH0bbsDhmt4xEa18mcfzdlzDVJlBztxU3j050aT8fne7KRX+tFgub5n/Mye2baX/9DfS/96GmO0BZGiFhMmRuh1vnQPS1v3oPVf+zJgLeUsp6IUSTz/VCiJuB00A6ymoSK2Cz11QJBXajPIs3cK6myjLgRSHEQZQAyUsoK0QAjqFktnSwH/flv+C6/nF+V0tlKWWOEKI/SqrRtyipQPlABcoaq4d/46E8gA+AGJSbuAElNQngEeB9+/Kg7cAKwMu+LwglcyUMqAO+QUl9UqlUKpVKpVKp/lWObUygsrCAYc9OR+vQ9F/ri4qKOHr0KD179sTb2xubzUJK6mu4OEcQEX43AMezc3m1yoyPVsNX13bi4hoI1ppGyhafxJxXhznvEE6xDQQ+P/WKc7PaJOt2ZUHCKXrgTq3FRErVBsJKUnBNS4f4lqSMmkVREfS9oznFXlaOvP8uwaYi2oSOp41TEDp/F/wmtEbrqdT6aGxsZMWKFaSlpTF48GAOhsQwO6OQ8SG+DPMsZ8y6+ymoK+C9vu/Rw9Ce8q+T0fo6sudYEoW6CE4Ebad5aVccHErJDizGw2Ckwd2bteGdsOVZMDotxsn7FK46N6pMVdwQfQPPdXsOH2d74d2yVFh8G5hqKbt9GlVFMwk4NQ7XmTQyZQAAIABJREFUMBdcylYj2o6AYfMhdx8cXwknV5N/YidLuB2Elj65G+gdkkpViRuGth1xakjkULuHmNc6Cp29ds2xBe/SUL2c+tttePj0p3O72Wg0V651UlNWwk9zPiT3ZBI9R95Jj+FjLryHtUWQulH5J30bNNbCtZOh451XPK7qf5uUMv03DIsDZqOU66gE5kgptwoh2qGU3GiJkqiwB3jA/p7XUZ7Xk+yvV9q3IaVMEUK8CvyMEoh5HnjwT7mgfzAh5b8uO0f1N+jSpYtMTEz8u6ehUqlUKpVKdVVoqKtl4aT7CYyNY/jUVy8JhJz11VdfkZ+fz+OPP46Liwt5eUs4kzKdtm3mEBAwmFqDgQE/76fA1YM17WLo7H9hRoSUkvIvkzGeKadh/3z0HQIJefcdhLaJLAj7+K0nikj79jjXG3UIKcku2khA9Sk4k4IuLAzXByaxMy2I0txa+o9rQWOQhiWvTCPQWEyPyPuJ0njh3NIHnzEt0Dgp5zEajSxdupScnByGDh3KscAIpqbmc3uAJ51tW5h9ZDZ+ej/e7P0mratjKF+cjMYddp84TYlbDElB22hV2hWjWxoNLtXUSUdMbbryrYsX3skn0PgsQqOrAiR+zn681PMl+oX3O3dhBUdhye0gNNjuXMHelMnYahtpXjwb/9BvEQfmwKMHLlhKk5mexrJlS3G01DOmZAUh3pU0GrRkxw2ieUkCS1s8xG0j30SvVSoyHF4xm4bMWRi72vDxH06HNm9dsRitlJLkHVvY8sU8pJQMuPchWvcdADYr5B9SgigpG6DI/mzsHgLxgyD+BogfrHb6+RsIIQ5JKbv83fNQ/bl+V6aKSqVSqVQqlUql+vvt+3YZJoOBvuMmXjagkp6eTnp6OoMGDcLFxQWzuZqMzA/x8uqOv/8gbDYbD23eQ467H9MD3S4JqAA0JJViPF2B8cS3OMW5EvL2W78EVGyNVmwGCzaDGZvBjKmmkRWbU+hR0kC81oX8kl3oy/cTkHIGrZ8fftNexNx9COsXnKLRaODGh9qiDdKy6NlnCTFV0Df6EQJwxe3aUDxviEbYszcMBgNLliyhqKiIESNGkOwXwtRTOfT3MGPKm8GHRfu5PvJ6pl8zHadsG2VfnQSdiZ1J6ZR6xHA8aBvxVS2o8jmKTWslxRZE1JDr+Lq4Cs/TCWj8V6PVaLBhY3jccJ7q8hQejh7nPoSs3bBsDDh7wrjVZKZuxGjLIbJ2Kn63ByHmfA5tR14QUDlz5gwrVqxAWqDV0T0EtqjCbNNxKLgtXUs3Uunkyy23TP0loJK4fgGGsg8xd5UEBT9AqxZTLntfAQzVVWxaMJu0g/sIa9mGIRPvw7PyCHx3P6T9DA0VIDQQ3h0GTIe4QRDYWg2kqFR/gT8UVLH3sc4EdFJKyx94/1QgRkp532X2ZwH3SSl//iPzU6lUKpVKpVKprlYVBXkc3biOtv0H4R8R1eQYm83Gxo0b8fLyolu3bgBkZn6E2Vz9S7efWTv3sdnNj0FaKw+3anbJMaz1ZipXnsJaXYBjbCecW3ag5JPj2AxmrAYLWC4tbXgDUFWTTUleAh4ZZ9C4u+P75JP4jLuL9ORatsw6jou7I8OndEC6WJj51DNENNbTL+phPDVOeN3aDLfuSjFWs9nMsWPH2LVrF3V1dYwZM4ZU7wCeOJFFB80pclLn0Gg18fI1L3N73O0YT5ZTtvw00lLD7sxCyjxiOBm0g0ijP7VeKVRJF05o4+k3ojNzkjPxLP4Cnf8hpE1HoKsvr/V6je7B3S+8oNPr4Nt7wSsCxq2mOt1EbvV8XK1tiB4+Ac226WA1QZ9znYGSkpL4/vvvsZmtdDmwmUGtTyMdNdzUaTYBMV34T4AjPdydEHpPAPbu+AxT6dtY4yVhIc/QvMVDV7z/qQf3smn+bBoblKBa50gbYvkQqC8Fva8SQIkfBLH9weXKtVhUVx8hxMtAMynlXZfZfxJ4VEq57f9zXlezXw2q2AMcgShFbc4a9N+cVEr5m4vaqlQqlUqlUqlUqnO2L1mIg6MjPUddviZGUlISxcXFDB8+HAcHB+rr08nLX0JIyCjc3VtyJCOLD40awjAy79quTR6j9NMd2Bq1aNwD0eidQQq03k7oQt3QuDqg0evQ6B0Qzg58uus07FtO2+wThJeVo9fp8LnvPnzvm4jGw4OD67I4+GMmwbGeDHmwLRZbA+89OYUYq6BvxP3onZzwvaslznHe1NXVcfDgQQ4ePIjBYCA4OJhhw4aR4ebNQ8fOEFW/gvyKn2jh04K3+7xNtD6SqjXp1O8rxGoqYXdpLeXu4WQE7SLIpsPkVMFRSygm3zh6DIph3p59eNkWoHEvQloduavNSCZ1nHSuuw+AlLDzfdjyOoR0hDtXYkixkXHkHazRtbTs8CraxnI4+Dm0Gw1+SlDqwIEDJCQkYDWZ6blnC31apeHgYePTvvP4uNtQWrq5XPAZ7z7wGabiN5EBEBXyMrEtxl32npoM9WxdNJ+T2zcTEB3LDRPG4Xf0ffguAYI7wKjFEN4DNL+1wavqn0oIcTcwGYhFaeDyPfC8lLLq194rpWz9187u3+e3ZqoMPT9rxJ6p8ocIIRz+SHaLSqVSqVQqlUr1b5d9/CgZhw7Qe+wEXL2azkIwm81s2bKFkJAQWrdWnp9S095Ao3EmNuZJ6uvrefxYCmYPPxZ1jMXF4dL6KGVfbsBSqgchcQx2w/eetjh4XlowVUrJtJWJuG7+mAHpZ3A3NuI1ejR+jz6CLiAAS6OVjZ+fJC2xhBY9gugyshkLl6+jcvNyWjiF0TP0Fpy8XfC7uzVVGgMbf/iBY8eOYbVaiY+Pp2fPnkRGRnKgup77Dm7Dt3wOtaZcxrUaxxOdnkCUWyhecAhLmYmGzO3scYqk0suHEt89eAIGqWO9OY7OzSNwbOXNss2L8XRfBVJDoHMYMwe8RXv/i7odNRpgzaNwcpWyrOeWjzGcqKX4hz1U9tpIoP8tePq0h5+eB2sj9HlGaWe8cydbt2zB2mCkz47NdGuei2tgI5VD5/Jo51GXfHbbD8/BUvQ+wgmaBb1BZKvRV7zvG+bOoq6ynB63j6FHZAPa1cOU9sjXvwY9HgGtWtnh30AIMRmYAkwANqN08ZkDbBJC9Po75/Zv9af8yRNChKB05emN0gnobSnlAvu+l4E2KF1+zvbGDuO8lCQhxDiUisJuwMyLjt0NmIVSmbgB+A54SkrZ+GfM/WojhAgHFqNkF0lgvpRylhDCB6VbUhSQBYySUlYKZbHmLJQe5wbgbinlYfuxJgAv2g/9upTyS/v2zsAiwAVIAB6XasVjlUqlUqlUqr+UzWZl++LP8PAPpPONt1523P79+6mpqWHYsGFoNBrKy7dTXr6NZs2eR6fz5YUf15HiHcYkP1fa+Hhe8v7KVetoOO6EcACXlt743NEKjWPThWnfXXuM6M9fpntOJloPD8Jnz8Gtt/JcV19lImFuEiU5tbTpE0JBfSUJzy+ikw1cg+7Ax9Ebx3APDNe5seLn1aSmpuLg4ECHDh245ppr8PPzA+BYTT0Tds1DX/E1Ho7uvDFwLr1CelG3M5vqhGxsjfVUJq8lMeZGyvSlGFwP4yi1ZDb6s12GMaFXNIc0taQdeBm95zG0Nh+8XDWsHrYCd0f3Cy+oOg+W3wGFSTDwZej1BIZjpVSsOENF9x8QDoJmcc8oHXUSF0L7Mdi8o1nw3WoKTxyD+lqu37qNVtFFeEY1QP9peHe+45LPbevRmVhLPkFrgrjwtwnrMKLJz9dsMrJz2ZccWb8W75Awxk5+nODj78NPuyG6DwydBT4xl/0uqK4uQggP4BXgXinlT/bNWUKIUSjlOc4u+XEUQiwGhgE5wAQpZaL9GFnYS20IIZyAt4GzUb8VwLNSStP/ywVdJf6scOZy4AQQArRAiZKlSym32PffCowExgNOwLNn3yiEaAXMRXmo3w+8idIy+Swr8CSQaN++HqXt8od/0tyvNhZgspTysBDCHTgkhNgE3A1sllK+JYR4DngO5T7cgNJqKw7ojnIvutuDMNOBLijBmUNCiB+klJX2Mfej3K8EYAjKfVGpVCqVSqVS/UVObP2Z0pwsbn7iWRwcHZscU19fz86dO4mPjyc6OhqbzUxK6gxcXCIJDxvP+j17WenmT5zGxpTWl9ZRqVydQO12GxoXJ1w6+OIzquUvBWMv9tmaA7R980miqiqgXRvi5n6KlHrqDxVTnFLJtj1FNFpsdNVrCUkqJRaQLi2okybco/wweJpZX72fghWF6PV6+vXrR9euXXF1df3lHPsqypm4+Rl0hoN0CerJu31m4GVypmhGAtY6DywlZyh1a2Bf8xupdD+FzcGA1ujLVpsvOVpPnru5JQvO7MFoW4DOrYqWXl05VXWQGb0/vTSgkrMfvrkLzA0wdjkyfjD1+wqp+iEda4syqjy2Exn+MM7OIbD1WbCaqe02iZfnLsS9NA+HijIG7NxFREgVfq3roNN4pXXxeaSUbDjyAg7l36ArETQLeY3QHk0HVMpys/lh5ptUFuTRacjN9I6sQbd+LOic4ZbZ0PEutfDsv09PwBlYdf5GKWWdECIBuB44g5LMcDtwD0rywmygRxPHe8G+vQPKM98alF+qT/uL5n9V+q1BldVCiLNLdrYBT5zdYc+M6AXcJKU0AkeFEJ+hBFDOBlX2SilX239uuKiS9QjgRynlDvvxpgGPnd0ppTx03tgsIcQ8oC9qUKVJUspCoND+c60Q4hRKStitQD/7sC9R7uOz9u2L7Zkm+4QQXkKIYPvYTVLKCgB7YGaIEGIb4CGl3Gffvhi4DTWoolKpVCqVSvWXaWwwsPubrwiJb0l8j95NjpFS8uOPP2I2mxk4cCAA+flfYzCk067tPPLzi3mtoBqLbyCfdW6Jw0XBkooV66nbY0Xj4oVTMw98x7S67Hx+WLCK9h+9goulEevI4cT/53mqN+bSkFRKvsnGEYMVrQZc9RayTGfIqsygygb6Fp1wiNKQkXGQxpJG/Pz8GDp0KO3atUOn011wjg0lJTyx9XEcjCe5t93jTGo9jsolCRQkaRGOHkjjEbK7deHgmQzqPA4DAmt1C1Y5OmFzcuTZG1vywcEFaD0ScHLw5pluL/DWwRkMjxtOr9CLVkkc/grWPQWeYTBhLRbHaCoXnsCUWoVjvBe5reega/AhKvJBqCmAxC9IiRrOh1//hH99FR65GfQ9eAxfPwMhXauh2UC4aeYFQY96UykJO0fho8nBMUUQE/gCoQPHNvn5piXuJ+Hj99A5OTHioQlEnp4FO45Dq1vhhnfBPfCy90Z1VfMDyi5TTqMQ6IwSVNklpUwAEEJ8xXnP7xe5E/iPlLLEPvYVYB5qUOV3+a1BlduuUFMlBKiQUtaety0bJcPhrNwrHDvk/P1SynohRPl554pHWRLUBdDb53zo4oOoLmW/Tx1RMkoC7QEXgCKU5UGgBFzOvz959m1X2p7XxHaVSqVSqVQq1V9k73fLMVRXcduUaZdttZuUlMSpU6cYOHAgAQEBmM2VZGR+hI93L1xdezL1x+/JjGrNsxF+NL+oYGr5kg0YjmgRTno07g74jm/T5DlkYyN7XphBs7UrqHfSYXn0SSIC+lLywWGEg4Zjnk5kZdWRr7WSo0umQ9F2sFqx+jTDFOCBzXgG11xX2rZtS6tWrYiOjkbTRGHV5fn5vLzzSXSm00zpNp2h6c7kzHwTh+B+CId6XK515ufUjmRkJdLoWYHO5E2lIZgfnJ3xd9fSt5MPHxx5BgfPFKLce7JwyOtM3DiRAH0AT3d5+tyJrBbYNA32zYGYfsjhCzEkm6n68RBIiddtsRhiT1J1/ADN41/BwcEd07bp1Fpd+DwvEv/GCiKTT9D9xGkcApyI6FeLCGgNIxeB9lyQ6Ez+OlKPP4WP1oLbWgdieryE/4hLCw1LKdn//Qp2r1hCYFQMt3Z3xn3HA+AaAKO/hpY3//qXRXU1KwP8LlOnNNi+H5TnvbMMgPNl3hOC8ux+VrZ9m+p3+DOW/xQAPkII9/MCKxFA/nljrlRvoxClXgoAQgg94Hve/rnAEWCsPfPiCZTsFtUVCCHcUOrPPCGlrDn/f75SSimE+MtroAghHgAeAIiIiPirT6dSqVQqlUp1VUrdv4fEtato238Qwc2aNzmmqqqKhIQEwsPD6dmzJwAZGbOwWGpp1uwFvv5xHZtCm9HG2YFJMed+HyalpGzBzxjTnUFjRAgNPqNbNllDpTE7m1OPPo5P2hkK/HwJuXkK7gU+GIrKcOsVSkK1gcqdJeQ5m/A2rcW3MAeL3h1jcBTuvgF0aNOKli1bEhYW1mQg5axPsrL4eN9kdKY0pnWaRrfXE6hy6oYudAC6IBtySA+WLNxCtVMK0smKW0MARlnIdy4xtA10wuaVx5rC19HqTYxoPpmXuk/gg0MfkFmdybyB83BzdFNO1FAJK++BjK3Q/WGsPV6icmUmxtMVOEZ74DMiHo23A8cPvINeH0tw8ChW7Emk++EElnAHeqORTomJNMvMpiG2OS37nECj84U7VoKTsrTIZrPw85En0FStx60aPNb602rqQpxbXZoFZDYZ2TB3Fmf27qRFr74MCk5Dd2wRdJoA178KLl6/9SujunrtBUwoS3tWnN1of/a7AZjKhaU0fk0BEAmctL+OsG9T/Q7/dVBFSpkrhNgDvCmEeBqIByaipBL9Ft8C+4UQvYEDwKvA+f+VdUdpE1UnhGgBPAyU/rfzvpoJIXQoAZWvpZRn19sVCyGCpZSF9uU9Jfbt+UD4eW8Ps2/L59xyobPbt9m3hzUx/hJSyvnAfIAuXbqohWxVKpVKpVKpfqeSrAwSPnmf4GbN6X/PQ02OsdlsrF69GpvN9ktx2rq6FPILlhIaegcnT1axEFesOkfmtGuG1v7LNmmxUTJ7K+YiZzDlI5xDce0ahHOzCx/epZRUr15DwauvYjNbqWxzPXFxI9DUanDtHoTHdeF8sz+P4p3FNIo6fIsWggBTQDyBMd0ZNLw3YeEhl82wOf88r6em8fWhKegaM3ijx2u0fT0Bjf9taPSe6AdFsutkMScXrsSkL0JYnWjvfJzjFj8WWm+gW6SeFOtqrJotCE0w7w9YwOCwNhwtOcqik4sYET+CnqFKwInSM7BsDFTlIofOpkE7mMqPjiHNNtxvCke2raSgZinluTsxGDIIiprFoO+SGJs9m6/l7dBoY8DO7XhVVFPeezg9W61BY7bAXWvBIxiA6voMtu0bi5sow2W/Br/SfsR++gFaN7dLrr2mrIQ1786gJDuDa++4m666A4gDy6HfVOj37CXjVf9OUspq+xKdj4UQNVzY/ScP+Ap4/nccchnwohDiIEoixEvAkj931le/P6tQ7ViU7j8FQCUw/fzlQlcipTwphHgUWAq4oiz1OX95ydMoD+ZTUDJWvgH6/0nzvurYu/l8DpySUp7fSekHlLZbb9n/vea87Y8JIZajFKqttgdeNgBvCCHO9uobhNL7vEIIUSOE6IGyrGg88PFffmEqlUqlUqlU/zKG6ipWv/sazq5u3PL0C5ctTrt//36ysrIYOnQoPj4+SClJTZ2BVuuK3mU0M7bsJbNFZ16IDiLe1RkAaZMUz9yOpcIRW80RdLHXII02PG+MvuT45Z99Run7M6n3icC39wP4ufrj2iUIjwEROHg5882ebFK/z8QTCw6Vy8AlACe3Qdx8Z29a9Aj+TddqlZLJyaf4Kek5HM25vNv7bVq+vQ48ByBcPcmK9CLxu8PUep7G6mJE42FmgnUBb1VOZL3sSFhkIid1m9Doq9E6X8d3N71GrJsnRouRabunEeQaxOTO9qKxuQdgyQhwcMQ6ai1liRoqi76hsVU2pvBsUo0nsR1Wmp84OYdRIEdz9wFvbstYR4EpHHdDLX03bcei9aV0zHP083ofUV8FE36AACUB/2TWInJTZ+DWaMNjpY7owS/h+/zYJgNLeadPsnbmm1gaGxn27EvEVG+BbfOg+8PQd8pv+vxU/x5Synfs5TLeA2JREhBWA3dKKU2/Fry8yOuAB5Bkf73Svk31Owi1E+7VxZ7xsxM4Dtjsm6eiBEBWoKR0ZaO0VK6wB2Fmo3TwMQD3nNdu6177ewFmSCm/sG/vwrmWyutRihtd8YvUpUsXmZiY+GddpkqlUqlUKtVVzWoxs/K1FylOT2X0K28TFBvX5LiSkhLmzZtHbGwsY8cqD+1lZVs4lnQ/0VHPsmy9hQXxXWnh6c66rs1/yVIpnb8VU4YD1spdeA0bSe22QnzHtcKlte8Fx6/56Sfyn3gSTWhnnDtPpMK1kjaPDkHnpwdg6f4sdi9JoaVZi7n2W9A6Ehg7hiEPtMMn2PWS+TbFZLNxf9JxDiS/gJOlgA/7vEfchwlYDe3Q+Eaxq95Cri4Lg1sOEg3RzU8xJH0nD5oe5ZRXMU4+e0FrwGyNJSRyLN/2GY6PTvnd8XsH3+PL5C+Zf/18rgm5BrJ2w9JRlAR7UxxzLVW1J2jUK6sdhHDA3b01np6d8fLsjIGW3JVQQLlLNbcd2oST3p2gskJ6bttDUWBvnMeM5drGKYiqbBj3PUT0wGKpY+uhB9DU70eXIfBYH0iLGZ/j3KJFk9eetHkDmz+fi2dAALc+Mw3fvARYPwXa3wG3fgJXWCal+ucRQhySUnb59ZGqf5I/nKliL4KaCeguU334/LH9gCVSyt+zvkv1B0gpdwGXC08OaGK8BB69zLEWAgub2J4INF29TKVSqVQqlUr1X5FSsvnzueSfPslNj0+5bEDFarXy/fff4+joyNChQxFCYLM1kpI6A70+hsRDHvwY4IlNp+Oj1lG/BFQqV+3AmK5B1p8h4Ln7KP/sDC7t/C4JqDQcPUr+lOfAJwZLm1s46nmYoc8/gUaj1Fv5clcaq79LY6DZEXPDLoRWR0DsaG5/ugvOrrpL5tuUWouVcYePcCZtGs7WEmb3/4jouRswV8eiDYxih6GGTPfTWHR12Jw03BT+PSKtjpvdB2AMW4mTxgzG1lS5D+XGVr2Y1SICZ60SiDhacpTFyYsZGT9SCaikb4VlY0mPDSYrsAaNcTeu1uaEBIzAJ7QHHh7t0GqVAr5bUkq4f38q8TVJDD56EquXH5HZWXQ9epgTLe+n2R196VwyCVGRAXeugIgelFUmsv/wRJxkHW4/afCxXk/Ml++idbs0uGS1WNi2+DOObviRqPaduGnSFJzT1ykBleY3wS0fqwEV1R8ihPgUyJdSvvYr47ahPKN/1sS+CCAZ8JRSWv+SiV5FfjWoIoTIAu77rct5VCqVSqVSqVQq1R935KcfOb5lI92HjaZFzz6XHbd9+3YKCwsZNWoU7u5KYdSsrLk0NGShd3mONSW1ZLZqwYvRwb8s+6ndtpfanbUIzAS9dDtV3+agcdLiNTT2gmM35uWR8/AjWHTuuHa9nwPORxj57Iu/BFTmbjzO4o253NHgiNWcgaAYr5DR3Pp4598cUCltNDMm8RCFmdNxkRXMGfAJUV/txJDpjkNEK9bbMijwygapwT1C0NM8ny8rfdgRoQeOYq3pgKfXLWQ1i+GJ6CCmRAehsQeOGiwNvLj7RYJdg5ncZTKkbIRv7iIlpjW5gTl4FPQmPvhlPG6ORGjPBS9sNskb21P5oqSAIcfXEWy1YPHyo2VyMt2LD7G381Ta3dmH1hmPQfEJGLMUS0RnTiRPpbTwG1yqwOsrRyLumI7vyNFNLvdpqK1h7QdvkXsyic43D6PPHXejSd0Aqx+GqGthxELQ/llVGlRXE/uzeQgQIqUsO2/7EaADEC2lbLr40u8gpcwBLi3+o2qS+qdVpVKpVCqVSqX6H5GddJRtixcQ26UHvUZdvu9DXl4eO3fupH379rSyd5KpqTlOVvYcPD0H8e2mSvZ26U8ndz0PRwQAUH8wkYqlp9B4ReJ/fydMqQ005tTiPSoerfu5ei3WmhpyHniQxnoTntc+R2LjIW566XG0Dkqw5M2VO1l8sIb767UIWy3ScgxX39u55YnOuHo5/eo11lis7Kys5bXTJ6jNeQUXWc28gXOJWn2IqoO11MV1Z6P2AAaHeixWR9w6JJFRnsgiJ2+kowPmyq4Ea4ZgbBVDnk7yQfNwxgZfmGXz8ZGPya7J5rNBn+Gavg3rNw9wOqofRcGH8KroR5t+M3EK87xwXkYzd6xNorg+k3H7EtD6BCKdnOmamEh316MU9ehFj1GDiT7xCOQdQA5fSJG7gdO7+mK1VuO2S4M+MYTm7y3AuXl8k9denJHGDzPfpL6ynCGPPEnrvgMgcyesvBuC28PYZaBz/tXPUPWvlolS0/RjACFEW0D/t87oX+4355QJIbRCiPeEEGVCiAzgpov23yOEOCWEqBVCZAghHmziGJOFECVCiEIhxD3nbfcUQiwWQpQKIbKFEC8KIdR8N5VKpVKpVCrVv0ZlYT5rP3wT39BwbnzsKcRlln80NjayatUq3N3dueGGGwCw2Uwkn3oGnc6Hffti2BXfAYuDjlktI9AKQUNSEsXvrkHrE4fXTZFoXN2pXp+Fc3Nv9B0Dfjm2NJvJm/Q4pqxs3Ls+TIYsotODt+Hu64fZbOaZeT8wP7GGOw1WHG0abOa9OHoMZeh/uuId1HQNFZuUHK818FF2MbcdTqXlziQeSvyJupzp6Knh80HzidmSQvH6FJJaRLPG8SANopFCpxqcu2/nm9oT7HHwwFTWn/r0ZxkWP4myzs2odNawvH3sJQGVw8WHWZK8hNHNR9O9PB/jstc5ETaUovBD+FqG0PGWeZcEVE4U1tDj+4M4p21m5N71EBiO3mhicEoGzaLAq4UR/zEvEH1mMmRup2HwixwyLiX51NOIjBp83nXAWzOStssTLhtQObF1E8teegZpszH6lbeVgEr+YVg2Fnyi4c5vf2nFrFJdwVcozULOmgAsPvtCCLFICPH6ea9vFUIctTc2euO3AAAgAElEQVQbSRdCDDnvvZFCiN32Z/iNQgg/+3uihBBSCOFgfx0thNhhH/ezEOITIcSS886xUghRJISoto9rfdF8PhFCrLO/f78Q4sLUuH+435Opcj9wM9ARqEdp2Xu+Evv+DKAPsF4IcVBKedi+PwjwRGn5dD3wrRBitZSyEiXK5gnEAL7ARqAQpYuNSqVSqVQqlUp1VTMZ6ln9zmtoNFpumzINR5fL/+J506ZNVFRUMH78eJydlayGjIxZ1NenUlkxjr3CmzTvAKbFBBPn6ozx1Cnyp7yHc4f7cW7tgWv3KEo+PoJG74D3yPhflqhIKSl85RUM+/ah7TieWg8vdN2cierQmYqKCp77bB0bK3wYazHhbfHCZtmLg1M/bny4M4HRHhfMscJsYUdFLZsrathWUUtpowVhqyfavJ+oms3UNOTg5eTFnAGfEbEvi8OrEznaPohqTTYOJm/2Rm+nb2QjywvzkcYgqnIexNvZk0fuas0bxWX4a3UsaRfzy7KmsxosDUzbPY0QtxCe1EZStXwbmVFdqYheR6DbMFp3fYeLf3f7+eEc3jt1mpv3fU+gxoG64HAic3Np5xlOTmAUXbxfprH1OAJT34eUnyjq2p+ThrkIg8DrOy2FFV60+OBTglp2avJ+Wcxmtn4xj6TNPxHRph03Pf4seg9Ppa3zkuHg4q0UunX1bfL9KtVF9gHjhBAtgRRgDNCLJrr2CCG6oQRcRqC0Xw4Gzo/c3QHcAOSiNCB5GniuiXMuBXYDA4FuQAJKF9mz1gP3Ao3A28DXKMuRzhpjP89h4Etghn3bVeH3BFVGAR9KKXMBhBBvAv3O7pRSrjtv7HYhxEbgWpQPDsAMvGovapsghKgDmtt7Yo8BOkgpa4FaIcT7wDjUoIpKpVKpVCqV6ipns1lZN+sdqooLGfHi63gGBF12bFpaGgcPHqRHjx7ExMQAUF19hOycBdhsPdmX4syBazrTyUPPQ+H+mNLSyH3oKZy7PI7W1xGf0a2pXJGCpdKI/wPt0LqdW/ZT/tlnVH/7HbXxg/EP78oZl70MHT2VgoICpiz4kV0NoQxzNBJW5Y20piK07Rh4byciWinBAKuUfJFfxqriSo7UGJCAt1ZDZ6dCMPzM6eJt1FqNtPFtw6iOrzI4ajANW3ezav0Bslt5oLfZaLS6s73dGm4N8OCr3CyspgCqcx6ka0QofQbH8FJWEZ089CxqG42/46W1Wz46/BE5tTks9r2X2lU2CmOhImYdwYGjaNlqhhJQsZhg03RsJafIKC3lOmMFnUpd2eg6kAYHHb1TdtMnNBFHVxudAal1xolKSF5DeqwvWc5J6LdpMO7Qsuu2Ljzw6AJcdC5N3q+aslLWznyDovRUut46gt6jx6HRaqEqB74aBhoHGL8aPEL++BdI9W90NltlO3AKyL/MuInAQinlJvvri8d9IaVMARBCrABuufgA9qK1XYEBUspGYJcQ4vyAytkGJ2fHvwxUCiE8pZTV9s3fSykP2Pd/Dcz8rRf6T/B7giohKBGss7LP3ymEuAGYDsSjLCvSo7T1Pav8oi5BBpTiN36A7qLjZaNktKhUKpVKpVKpVFe1nUu/JPPoIQbe9yjhrdpedpzBYGDNmjX4+fkxYIDS1NFqbSD51DNohA/bDkSzvXt/bFots1pEYM3OJvveiTi1uQ+N3g2/u9tSf6CYhhPleN4QjVPUuSUwNT/9ROn7MykI7UR8y9s5VLeJgc8/idls4fXF69nVEMrN3hZiM/RIWQ4iiGtHdyC+mxIAyjM28lhyNvuq6+ngrmdSmAe6+t3szVnDscozuDi4MDT2ZkbGj6SVr1ID5vjaH1m77zCN/lrizGFs9TxJTswJRvt5syDrDBaTPzU5D+LvaSOrWTo7s/SE2NIIqdjK9B0SjdBc8A/AlqzNvG4Yj9+pdhQ1X0lF5CZCQ8bSvPmrSkBFSljzGBxfwUmX5lBt4ExxS06GtsXdUM8gSyGNwbEcdexATKdo/KIDsJxYhi55DemRegpMDni/q+WHcA2+b93Lf7o/+cu5L5Z9/CjrZr2D1WLmlqemEte9p7KjrhQW3wamOrhnHfheVSshVP8/vgJ2ANGct/SnCeEoWSWXU3Tez2efzy8WAlRIKQ3nbcu1HxshhBYl82Qk4A/Y7GP8gLNBld9ynn+s3xNUKcT+wdlFnP1BCOGEshxoPLBGSmkWQqzm8q19z1eGksUSidK26eyxLxdtU6lUKpVKpVKprgont28mce0qOgy+ifbX33DFsQkJCdTX1zN27Fh0OiVLIz39PQyGTI4dH8L2DtdToHFkSesoIivLyb7nXnThA9F6ROE9ojm2BgvVCZk4t/TBrc+53182HD1K3pRnyfeJpkWniaTWHqbTIyNw8/bh06Xfs7HKnx7uRlpmWJE4IqWOLje2of0A5dFgdXElU1JysUl4KdxGWelyVh9Yh8FioLl3c6b1mMaN0Tfi5qg8R1mtVtbMX0hScT4eWj19TS15P3wx5pAGRvt6MjfjNOZGX2pzHsDZEYraGDFr2xNt2U+0eQf1UmKVVqSU2KTtl59D6vyYm/scYfWhVLWZT3nIXkJD76R5/MvnlvxseR2Or+AT75FY91Sh8Q2gLDyAGLMVnfMN7CnT0KJnML1HNMPRCaq/GYJnSiLZgXqqtrlRaPBgwU0WHhkynWFxw5q8T1JKDv7wHbuWLcY7JJRbJk/FN9T+GFWcDMvvgNoiJUMl6PJBNJXqcqSU2UKITOBGlGyUy8kF/tuoXSHgI4TQnxdYOT8ucAdwK8rSoCyUsh6V/LZYwFXh9wRVVgCThBA/otRUOX+tlSPgBJQCFnvWyiDgxK8dVEpptacazRBCjAd8gKeA937H3FQqlUqlUqlUqn+UvNMn2TT/YyLatKPf+PuvODYpKYkTJ05w3XXXERKiLBWprNxPbt4iiotbsTF8KCku7sxsHk5vs4Hsu+9GuESiC++Ha49gnJp5UfLREbSejvicV0elMS+PzIceoczRg6juD1NhLsXlukAi2rTnwOGjfHrciq9o5NrcKhBxSGstra9tTo/bYqi1WJmamsfKokq6eOiZ4JXD23uVR4QhUUMY1XwUbf3aXtBWuOxUBl8t/oZqJxNx5jC6WmN5Ouo9nEP1DPPS81HGKRobfajLeRCdxomGdhmY9QN5Kz6Mu0M7ABf2wpA2iTG5nNod2TTmGIBaarq8S4lPMmGh44iPn/7L+Uv3fo7/zvdYq72GwI15pLVpj9nRkXb6SAqzInF1d+SmR1sQ1dYPq6ma6gU98SrKo0DoqU1owbyeRk5ECT7sN4duwd2avE8mg4ENcz8k9cAe4nv0ZvDDj+PobF8adHI1rH4EnNxg/BqI6P57vzIq1fkmAt5SyvqzBWWb8Dmw0f4MvxV7TRUp5enfehJ7ACcReFkI8SLQGRgKrLUPcQdMQDnKapU3/tDV/IP9nqDKApSlPceAGpSgR38AKWWtEGISSuDFCeUD/uEyx2nKf1CK1WYARvu5Fl7xHSqVSqVSqVQq1T9UeV4Oa955DY+AIG5+4jm0Dpf/a3lqaipr1qwhPDyc3r17A2Cx1HEyeQqNjZ58Z72To95BPBEZyEgHKznj78FmFLj2nIiDvyueN0RTsfQU1rpGAh5uj0avZLlYa2rIuO8BDAYTumsno3VwJscjiZuGP0d5eTlTvz9Ojc2Dp43ZSDoibfXEdIym353NOVRj4JHkbPKMjUyOCqSZ5QCv7J5OC58WzB4wGz8XvwuuwVRnZMsHqzlkysbq2EjPxubEyVCeiXwP70hfbvQ08V5qMsZGLxpy7gebG9bWOzF63ck9oX7cHXrh8WxGC/UHi6nbk4+10oRWU4qHbg2F15ZR6HCasLBxxMcpAZVKs4Ufdq/kji3PkJjdDEulEyc6d8BDoyFIdqcww5EW1wTRa0Qczq46jGUpmBb1w6uunlyjJ9n+Y5k84md83AJYMvATYjxjLn9P33+DqqIC+o6bSOebblMCOjarkiGzayaEdYVRX4FH8H/z9VGpkFKm/4YxB+xddz9AWSpUDDwK/Oagit2dwCKUwMkB4BtAa9+3GBiMstKkApgGPPw7j/+PJqSUf/ccVP8CXbp0kYmJiX/3NFQqlUqlUqn+dnUV5Syd9jQ2i4Wxr72HZ0DgZcempaWxbNky/P39GT9+PHq90hUo+dQLFBQsZ3nuY/wYeR3DA72Z6a4ld+JErOWVeIz+AFu9IHBSRwzHSqnZmI3XrbG4XaNkuUizmYyJ92NITCS516P08m3DwboNDJ7xNE5u7jz24XLWl3rxmC4d55LmIBsJbhbMjY934JOCMj7ILiLEyZE5rSI5nb+Kdw6+Q/eg7szqPwtX3bnWyhazlSPLE0k8kEm5dxpaAf3N7WgIkUxzeZs2EW0Z6FrNW6nJGM0emLMmYrT4o4tdj6nZvbT38OC7jrE42ttLW8oaqNtTQH1iMbLRiqNzFu7Wr7HFWUlt4UN5XSJhYROIj5uG2V44N+H4Lr7Y+B8KD3pyOKoT6XHN8HfyRua1ws3NhX53KdkpAOU/zcPp6FT0JguFju1Y330os3K/ooN/B2b1n4WPs88l98hmtXI4YQ27V36No7MLNz/x7LnaOA2V8N39kLYJOk2AG98FB6c/5Xuk+ucRQhySUnb5u+fx3xJCfAOcllJO/7vn8r/g92SqqFQqlUqlUqlUqv+CyWBg1ZvTMdbVMfrlt64YUElPT2f58uX4+fldEFApL99JYeFydpTeyE+R/ejp5cabTlZyxt2NzWDA99GPMaY04ntXSyxVJmo2ZePS3h/XHkp2hJSSvJdepvHAfrZ0upPhvm04XX2ArpPGoPf0YuH3m9hQ6sH1ukKcioJBY8MzwI+2E1sy8kQGiTUGRgR680ZcKIuOz2HB8QUMjBjI233exlGrdBOyWW0kb83i8KozaNwqKPFJw1Pq6eXeheXN1rCxejPDmt1KZ006M1JO0WB2Q+SMw2gJQBe0Ccf4u3B2cGJBmyh0QmBMr6JuVz7G0xUgQO+ThVvNBwivejK79yDPuA9NQwnNmj1PeNi9rC+r5rX0ArQFKSxe9By5mT7s6t2b0sBAvGUoMjuGFj2C6T1SyU5pzMsnb/7DBDvvRGeRFLWdyMtutezL/YpbYm/hpWtewkl7aTCkMO0MmxZ8QmlWBjGdujLw/kdx97Fn1ZScUuqnVOXCzR9Al3v/5G+TSvX/QwjRFSULJROlzMetwFt/66T+h6hBFZVKpVKpVCqV6v+B1WLmh/dnUJ6fy7BnpxMYffn6kRkZGSxbtgwfH58LAipmcw3Hkp4i3dCCJQH3EK13Zq6zhaLx94GUBL02n9qtNbj1CsExwoPijw7j4OeC9+3NztUWmTefuu9Xsa759YyMvJbShjw8BkUS1rINSafTmHmghiDMtC+ygMYHByeBbVw0N55MRyNgbqtIbvH3YMb+GaxMWcnwuOFM6zENrUaLtEnSDpdwYNlxfC0adN55nNHlEyj9aD+oE88VTqO6rpqXr5mGvnIN008nYzC74pIzmtLGMBw8DxLc+WbSrS582zoKn3ITJatOYM6rQ6N3wD2+DLfC6QhDPgXXDiTDKQ1zw25CQkYRE/MUp4x6Hj+azr6qOsYnbueepfMo03mw9YZ+NLi44l4dhadjM/rZa6fYGhsp+/RTyo6+T0xYKdg0nBkynUlZa6kuqebla17m9rjbL6gLA2Ay1LNr+WKObkzAzcuboU89T1y3nufGJf8A3z+k1E+5+0eI6PHXfKlUqovYWxo3k1Le9Xv2NTF2EZAnpXwRCAJWAb5AHvCwlPLInzjtf7QrBlWEEHXnvdSjFKCx2l8/KKX8+q+amEqlUqlUKpVKdbWQUrLh04/IOXGMIY88SVT7Tpcdm5mZydKlS/Hx8WHChAm4up5bTnPo8BQqbJIPnV/E1dGRhY6NVN39IBpnZ0JnLaBydSm6cHc8BkVRtugk0mTF9762aJyUv/ZXr19P+Ycfsi2sIzd2Go6lzkheQBY33/os9fX1PL40EaPNhcdMWVi1HZGygey7WvJFQRE9PF35uFUkgTqYsmMKG7M3cl/b+5jUcRJCCEwGM+tmHsCpxEwHJ8lujxMUaCtoHtgK87UmHjn0CAGu/tzbrC8nUt9hXZkFg8UF79xbyWuMRqtPp8O1bdll9uOVmGBaHSijZFseGr0DXn01uGY8j8g+QnnLzqSG+VJvSsTLrTvxcS/i5taS97KKeD8rhXblxSz5eiGhp5KojPVmc6d+2NDiWR5Puy4d6WXPTqnbvZui117D2uYUcSFVmHUurLv2EV47vYhQt1DmDJxDc5/ml9zHlH272PrlAuqrKuk4+GZ6jR6Hkz3ohc0GW2fAzvcgtAuMXqLWT1H96YQQdwOTUTr71ADfA89LKav+ivNJKddyrjCt6iJXDKpIKX/pHy2EyALuk1L+/FdPSqVSqVQqlUqluprsWvYlp3ZupdfocbTuO+Cy47Kysvj666/x9vZm/PjxFwRU0tK/o7x+B29bZ9LgpGeptgHbxIfQ+voS/tnnVP1QBgh8x7agdmsujZnVeI+MRxekHKPh6FFyn3mW0z5RRA59CI9CwSHTHgb9ZzIIwdOf/URmozuTnNKxVLUBWU/umHi+rK/hzmAf3mkejsnSwGObn2Bv4V6e7vI0E1pPAMBYb2bD2/to0WDD5mokQXeUeo0RXStHPhfzKT5UjEBQWF/E3DNFANgavQnLG0C2pRk4VtCvnyvrzbHc4ubKLd/lUVtiQN/GFS/d52j2L6HeP4TU6/tQbkrGRRNBu7Zz8fO7HoDX0gv5LC2XDzetpu1Pa0HYKLsukC0BfdA1GGmTbaNNXB4ehTlUvFGHuaCA+v17cbzbTJyhCqOHDzOa9+WH1JUMihzEKz1f+aUF9FnVJUX8/Plcso4eIiA6ltuemUZQbNy5AQ1VsOp+SN0IncbDje+p9VNUfzohxGRgCjAB2AyEAnOATUKIXn/n3P6t/qvlP0IILTAVuAfwADYAj0opq4QQE4AXgI72Nk/DUDr8tJVSVgoh5qK0YvJAqT48SUq5z37cXsBslMibAfhCSvn8fzNXlUqlUqlUKpXq73B0wzoOrPmWdgOH0H3YqMuOOxtQ8fLyYsKECbi5nXuor6rKJyXtFT7SPEu+LohPqcf7oUdwCAsjYuFC6vfXYs6vw3dcK8wlBmq35eLaNQjXzkrNlsa8PFLvf4hSJw8yx01hdLaNXEMa3SfdiYu7B4vX7+anQicGOxag+z/2zju6qqLrw8++yU3vhSRAAoQOoXekI12kShUBEVEUKygWxIZiFwS79CaggAgoICC99xBIQiAkpPd+c8t8f5xLDAiCgr6+73eetbLInT0zZ58zh6zML3v2Tg4BsRHfLYzFUsyQYF/eqx1KnimXib9M5EzmGd6860361egHQEmBme0z91LfYiPBKY3tTqcpFTP7g/aTWZwJQLCTI3WdinF3COdAXDcuZFaiu4rkkDGcEmWjR/tcdkoXqtsUU9algJsj/g0P43r+LcwOQmz79iQaYjBYLlGj+vOEho7GYHBGKcX0c5dIWfEtG75fimNpKUZvC7Hd6rDfsRnBycm02bsPJ7MZywnIcXfH4OGBCnDB42kh7HIGORUqM8Y/iPiME7zQ8gWG1xl+1XEfq8XC4R/XsP+7FYjBQKcHxtOk5z0YHBx+W7z0aFg+DHLioc+HWv6Ua44M6ejcLiLiBbwGPKiU+snefFFEhqDlO7n/mv5GtOo8TsDw68y3CmgPuKJV+X1UKRVZrouviGwAOgBngBFXqg6JSFtgFlqF4GjgSaXUXrttB7AbrVpwQ7RyzmOA2WgawDngPqXURXv/WcBAwBuIAZ5SSu36K8/oP8Ht5lSZjJaoph1a4prP0co1jVVKLRSRe4EPRGS63Xa/UirbPnYfmiBTgKa0rRKRcKWUGU1QeUsptUpEPIF6t+mnjo6Ojo6Ojo6Ozj9OzMG9/DL/c8KbtaTrg4/+LjfHFeLj41m6dCne3t6/E1SKigr5dec4VrqP4IShEdMt+dR85jGcatYg7OuvKb1spWBvEh7tKmEMdiN1znGMIe743KuV/rXm5nJ29DhKSkrZMPoFHkk1YbYa8OkbTnCNWpyJS2TmznQqSRERyVbE4EdifVcWBygGBvnyUZ0w0opSmbBlAon5iXzU6SM6h3UGoDC3hE0fr6OFJZhzzgnskhic3J1wbe5GfkIenmLgAb8iGrhVZte50Xx9IRwfCvjEbRkfGnuTletNlzYxxLn1wlxs4p29hQRUTMM750UMMWkUNOnLMd+zlJrPUTFkCOHhT+PspCWCtdpsfLFgOe3nf0Xl9FQALtavSHaDSkQTjkd6Ou26DKb2a69h8PDA4OaGGAzk556i4PvBhMSnkFCxOoNcbPg6GFnUcxENAhuUPXelFPEnj/Hr4m/ISIinRos2dBk7AU//q8s7E70ZvhunRaWM/hGqtLlj74+OzjW0BVzQ8puUoZQqEJGNQDc0wQIRcQVWA+lo+3DrdX7+bAIeBEqBd4ClQONy9mFAL+AosBCYAQwTET9gA/AEsBy4D9ggIjWUUpnlxvYAMtD2/vuAiWgRNvOA6WjBGQCHgNeBXOBJNG2gqlKq5M8/on+e2xVVHkFboCQAEXkNiBSRB5VWq/lh4BSa6LJCKbXlykCl1KIr34vIW2hRLeFoL4EZqCUi/vZFOXCbfuro6Ojo6Ojo6Oj8o1w+F8XG2e8TUr0W9zz53NWRDeW4dOkSS5cuxcvLi9GjR+Pp6VlmKywsZOOm8ez1q8cv0pMHTbl0emYiro0aEfrF5yizkezVx3AK9cSjXUXSvzqFCPiPrIsYHVClpURNeAySLrOs/9M87OeOW4YzqaHJNOs2hBJTKY8s3E+pzcjI4iRsDk3J8LOwoIEb9wR6M7tOGEn5iYzbPI780nw+7/Y5LYJboJRix7mdJK8+TYfihhx0ieSYSsPF0YXSRlmsjN9MuLOVpzyc8Uhuy7TzbThrC+Ve9yiCq3kzNW4EBbnutKofi4tLD06VlvLxuQKaGj/AJW071OpJftv7OZbwGiJGWjT/Hi+v3wSP/D17OT5jBp3j4rA6G3APLSalaW0uuNQkXfnhkZXGA8+9RIUqlcrG2GwW4mM+xmPTTEKyTBysFM5DxlI6VurMm+3exNvZGwBlsxF7eD8H1qwiNS4Gz4BA+k2ZRo3mra5eOKVgz8ew9TUIaQjDloF35Tv4Buno/I4AIEMpZbmOLRlohraf9gJ+Qos+edK+N/8dSql5V763J7HNFhFvpVSuvXmNUuqg3b4U+NDe3geIUUottn9eLiJPoEWhLLC3zS8X1bIJqHcllYg9QuaNcn4sKefWByLyMlDb7v+/nr8sqogmc4UCG0Wk/CIZ0LICZyilMkVkDfAo2oMvP/4FtBCgYEChKW4BaC/BaOBVIFpEYoFXlFI//1VfdXR0dHR0dHR0dP5JspISWfvu63j4+9P/+VcwOrtct19CQgJLlizB09OTMWPGXCWo5OXl8cMPzxNZ0Yfl8gA9i3MZ+eyjuLVuReicOYiTK2kLT4BB8BlUk8yFZ7DllRIwvgGOAa4opTg3dRoOx4+wuMMDPHJPU5zXZZFjzKDxxIEAPPX1Fi6ZXHnSKRZrTgNKjMV83rUiPQK8+KxeVdKKkhm3eRzFlmLm9ZhHXf+6HEo5xNd7v2TgqbZ0KG7IAXWOuDwPHLxSiAuM5GDSGe51sDA53ZmvTzXnc2tf/IxmuoQXsjkxlJLTHji6pDKqvoUKpW2Z4WTh0YQYhqQ9hqFyA+i2kVxfV46feBBHB0+ahL6C29n9kL2C4sgzpG6KpTjBQrCbhcBW+bhXKWWPoTm7VAsM2AjIusz90966qlx1UdEFYg4/RviB/XgU2fiqUjXmOiuebvosY+qPQUSwWiyc3fMrB9etJutyAj5BIXR7eBL1OnTB0Wi8euHMxfDDJDi1CuoPhH5zwcntzr9IOjpXkwEEiIjjdYSVELsdoDVgBIbfSFCxp/KYgRZlEgjY7KYAtIgRgJRyQ4qAKyF0FYH4a6aMR8vvcoXUct8XX+dz+fytk4Fx9nkVmih0TUjYv5e/LKoopZSIXAYGKqWOXK+PiLREO7u1Cu38VD97ezdgEnA3EAUIkG//F6VUFDDUvtDDgO9FxFcpVfpX/dXR0dHR0dHR0dH5JyjMyea7t6ZjcHBg0Auv4+blfd1+ly9fZvHixXh4ePwuQiUnJ4fVq9/kUrVS5sskOuRl8syLT+LVqROVPv4Ig7Mz2etiMV8uwG9kXXJ/OI85tQj/0fVwDvMCIP6TT1Ebf2BNg56MnTKSos924WIMoeqjHXBwdGTR1iP8lAC9nBJxTK6IzcHG571D6BzgxZf1q5JVnMa4n8dRYC5gXo95lFhKeGjzQ6RfuMxrCRPxtnhyJj2Oy86VKAo+SIYxnXNOp/kwM4+AnEDusz1BrDWQKv5mUnJtbIt1J8SxiLF+DvSx1OVUoo3xLR3pmBnJyzkfYhj6FdS9l+ycA5w4Phonp0CaVpyKy4LhmHJspJ/2If+SM1YXA2ltggnPvUxkUW12WjtjMoBjfhZ+5mKGvzyjTFBRykbi5SWkHXmdBqezQDkwMSiQWN9A5rV/h6ZBTTGXmojcvpVD678jLz2NgLCq9HliCrVat7t+dFFeEqwYAUnHocs0aP+snj9F559iH1pF3oHAyiuNIuKBdkznRaAysBk4CfwiIp2UUqnXmWsE2v78buAiWj6TbOx78puQBFS5pi0MLTrmTyEi7dHSgXQFIpVSNhG5VT/+Fdzu8Z/PgZn24z4JIlIBaKWUWi8ibsAStFJPK4Bj9n7zAE+0Iz7paElzpqFFqgAgIg8AG+yRLrloatV1FTYdHR0dHR0dHR2dfwtKKTbO+YCivByGvvI2PsHXL6ebmprK4sWLcXNzY/To0Xh5eZXZMjMzWbHiY13ilj8AACAASURBVDJrZfGV4XGaX47jpZmvEnDPPYS8+QZiNFJ0Mp3Cfcl4tKtE8fE0THG5+A2tjWttPwBS1v1I8adz2BXWlN7vvEDCV0uo69QCx/a+OAX78vqaoyw4kEwVcqh32YY4+rGyvRtNK3rxTUQ18k1ZPLT5IbJN2bzf4X1mH53Nrsu76FnUnhfjn8diE2LijxLt25DU4N0YlAUP772sjDfxtfMrLCitiIso3FHEZxppgAujcaaVzRMnSzFZjqeY2qAOweZ0Pgsz4zBgPzgYycz8lZOnHsXVNYwmdedi/HIgyccDyT5roMRg5LsOPSj2r8nQrfPZ3KgzSZVDcLRZcY2PpX6jxtw9/jFc3LU/gJeUJBEVNRXjuV9oEl1EptGFcQHe1KjZm9VtpuNiNXJw3WqObFhLUW4OIbXq0GXsI4Q3bXHD3DckHIJvR0JpoXbcp07vO/sC6ej8AUqpXHvKjU9EJI+rq/8kAouBF+x93xURZ34TVjKumc4TTaDJBNyAt/6EKxvtPoxAE3cGoeVB/fEv3JYnYEHTBhxFZCpapMp/DbcrqryLFia0TUSC0UJ6FqPVsP4AOKOUmg8gIqOAn0Vkm90+ADiPFqHyHtpDvMI9wIf2l+AiMMSewFZHR0dHR0dHR0fnX0vMwb1cOnWcLmMnEFyj1nX7ZGRksGjRIoxGI6NHj8bb+7dIlrS0NJYu/YKiuol84fAYEfExvPn1XKq+8zbefbTT9JaMYrK/i8EY6onNZKE4MhPvPuG4NakAQO7hI6S/+ALn/KtR+4OZpK9fTrglAmsFyGpYiftn/UpsehF1nHLol5mMzdiMQ+E2AuoGsKBBOCXmXMZvHk9qUSpzuszhk2OfEJ0VzcfqVWrHVyDLbOXS+Z+JD76b+ICDuNsU1Z2P0SGpK/dbuhJXYsMJKFJCHaOF0WEu9PVOxTl9Aw5pW/jWtzuv1nyCUgdn1jWsh59/d+3e03/m9Okn8XCvReNG81Dzn+DiqkJKco1srNaG8+0H0+rUObzSNrKlR2dsjkbcclJxzs7g7gcnUK9DF0QEpRQpKWuJjn6VKnFZVI0v4LirO1NCQni0zUvcE9abwz98z9FN6zAVFlKlYRNaDRhC5boRNxZTAI4vg/VPgldFeGAdVKh7Z14aHZ0/gV0syQTeR6uWmwesBUYqpUzl32Gl1Bv2PfVWEelyzVSL0BLJXkYrOjMNLW3HrfiQKSL3oFX/+QyIBe65jnBzK/yMFuESDRSiFb5J+Avz/MeQGxyx0tG5ozRv3lwdPnz4P+2Gjo6Ojo6Ojs7fhtlUwvxnHsXZzZ1RM2dd9+hIdnY28+fPx2KxMHbsWAIDA8tsycnJLF48H2u988x1e4TqyRf5bPcOar46HWOQJpgos420z45jyTbh1qQChXuT8OxYGe9e1QAoibtA5OBhZIsThR98TuXCGEyb0gj1rMP6Zn68fyQeN4OFds4JdDY6kH4xnHRPK0eGVufbJjWwWAsYv3k8cblxzO06ly3xW1gbtYb5he/im+hEgsnMqfRvKfEcSLrHRQxeF6hgUXQ3teNhHLmAFRAq+mUypUY6A7I2QeIhAOJDuzCl+iR2Kj9ae7vzQZ1QqrtpweopKT9wJmoynp4NadxoHsVfzyR57neUGJz4senDeHhE4Ju9h8zADHJ9fPB1MFB67jiVqlSj96TJZRFBpaWZnD03jcyUn2gY54h/cirfe7izqmZb3ur0Hk7Jxfz8xWyykxKp0aINrQYMIbh6zT9eWKsFtk6HfXOgWge4byG4+d3m26Lz/xEROaKUav6f9kPnznK7kSr/GkRkJDBaKdX97x5/u9fS0dHR0dHR0dH53+Pguu/Iz0in1/Rnriuo5Ofns2jRIkpLSxkzZsxVgoqWsHYxqtYlPnd7mLDsJBY4QtVP51wVPZGzIQ5zUiHurYIp3JuEW7MgvHpWBaA4IZHTw0dhtVi5/PIMOvqVsn/RTtpVGMBqVysfH46nsVcxEZZoGntUJDnKC6tRceyeqixrUgNlK2bi1onE5sQyu8tskgqS+D7qO77IfB2fDCcii0rY4DKPMJ/hKKd8gv1OkVkSQMvSFow1KBJtVoyORcwJWkePzB+1jA7BDbF2mc78oF7MSDFjEJhZvSIPVPTHYL+vpKSVRJ19ER+fljSsO5esN94ka+V6LH5Gvm8+jQCzF8XG7cTVNOBmdicwJ42SlETaDhxC64HDcHDUtjQZmTs4c+Y5DIU5tIw24pqVwvt+PkibSXxd72EOrlrB0Z/W4xUQyKCX3qBqwyY3X9SiLK1c8vlt0HIC9JgBDsabj9PR+S/GXoimplIq9j/ty7WIyBjgIaVUO/vnAqChUiruP+XTH4oq9go9HZRSvcq1xQCx12mbppRa8VecEJFOwBKl1HVrkNnPVfVWSnW4pj0ALUlOU6XUUrS62n+JPzP+dq+lo6Ojo6Ojo6Pzv0VuWgqHflhN7TbtCa3X4Hf2wsJCFi1aRGFhIaNGjSI4OLjMduHCBZYtWwZhyXzlO5qgkmxWN2lExfCqV81RdCKdwv3JuNT1o/BgCi51/fAdWBMRoSgphZNDRmIoKiLq2bfp374GK1+eSueAkZxXFlY72niwejG2y6ep4RxOyllfbA5GdvcIYtFdtXBUJh795THOZJ7hg04f4O3kzdO/PMW7GVMIyfThSFExX1b9lHaXhuNiMFLPPYadpgDqqxAedCwh3+yCr1MKW2QaAY7VtASu9QcQ7VKJZ85e4nBSEV38PHm3diiVXZzK7ikhYSHRMa/j79eBOhWmc3nsIxQfP4lLzVKWN3wX37xSsv33ogxQKz2d1MwEjP7+9Jv+FpXrRgCglJW4uI+5GP8pFSyVqX48CUwFTAsNp0+vuVTOcmf51KfJTUulcY97aD9iNE4urn+8oErBiRWwZRoU50Df2dBs9F9/QXR07gAichEt/0k1pVShve0h4H6lVKe/OOcOtL3417fhVy20SkKd0aoOxaOVVp6llLL+1XlvBaWUx817/b3cLFJlJzBVRByUUlYRCUF7SE2uaath7/t3sQR4U0SqKaUulGsfBpxSSp3+o8E3KDmlo6Ojo6Ojo6Ojc0fYsegbxGCg46hxv7OVlJSwZMkSsrOzGTlyJKGhoWW2mJgYvl2+HOWfyfxKw/FVRazr2JGK7ldv+s0ZxWR/H4NjBVdKzmXhFOaF3/A6iINQmJrO0ftG4pafS8yUtxg5pD3fPP80dVxb4yyuHI3w5GnvJE4dPU0lazg5FwOxGqzsuCeQb3o0wEUsPP7LExxPP847Hd6hUWAjhq0fxgvJD1EnO5TjxSbm1viUrgn34Wnyp5W7YpvBhJuDIy+YfbBYXajtGMlGz3k49PoE6g/ErGDupVQ+PHUOdwcDc+qGMSjI96qom4sXP+d83HsEBnanWt4wLg0eiSU/h8pts1hT5UU80qPICrDgk51L7aREzkgJte/qyN0PTSxLRmsypRMZ+RTZ2fuoXlSXkGP7yBJY0Lg3kzq8x6nVa1i19Sd8gkMYOn0mletF3HwxU8/Ahmfh0l6o1BxGroaKjf/im6Gjc8dxAJ7kzyWW/R2i/We87Qo7IlIdOADMBxoopZJFpDYwHS0Jbc6fnM/h7xZi7jSGm9gPoYkoV36KtAe2A+euaTuvlEoCEJE6IrJFRLJE5JyIDLkymYj0FpEzIpIvIpdFZLKIuAObgIoiUmD/qljeCaVUIrANGHWNfw+gJdhBRMaIyO5y11Ii8pg9iibG3tbd7lOuiHwqIr/alb0bjX9ERGJEJEdE5tpfvOv1nSUiCSKSJyJH7GWhdHR0dHR0dHR0/h9w8eQxYg/to/WAoXj6B1xlKy0tZenSpaSmpjJkyBCqVatWZrsQHc3yJUtQLtksrDsINzGzrk2r3wkqtiIzmYvOAGDJNuEY6EbA6HoYnBzIz8jk0OCReOSkkzDlTfoP78aHL7+Ge64jNT0aUNAogBbVCjl19BD+RVUxpQVhpYRtfQL5qnsjPAw2nt7xNAdTDvLmXW/SNawrU3ZMZlRcT1pm1+d0sZnPqn5N35QheOSH0MzVgYtuuyiUUlYVV8Vi9aCd4yF+visTh0kHIGIQJwqK6XH4HDMvpNAz0JudreowONivTFCxWAqIinqB83HvEVShL8G76pH40KPkYaN6txSOVepBet5lsgIshCcl4Z90gVh3A70ef5Y+T0wpE1Sysw9y8FBfTKmHqXvGSNUjuzntZGRfnxkMDXuGdS+9xKlfNtO870AeePeTmwsqpnz4+SX4vB2kR0HfWTBuiy6o6PzbeA+YLCI+1zOKSFsROWTf8x4SkbblbDtEZIaI7AGK0IrMtAfm2Pfhc8pNdff19sLX4TVgr1LqGaVUMoBS6pxSaoRSKsd+3VUikmL3aaeI1C/n0wIR+UxENopIIdBZRLxFZJGIpItIvIi8LCLX1S7s+/Ya5eaaKyIb7JrDAbvoc6Xv37Jv/0NRRSlViqY6XTl20wHYBey+pm2n3Ul3YAuwDKiAFknyqYjUs/f9BpiglPIEIoBt9rClXkCSUsrD/pV0HXcWUk5Usatfje3XuhH9gVZAPftRodVoJab80YShtn8wFrQqRC2AhsAQtOzI1+OQ3Rc/uz+rRMTlBn11dHR0dHR0dHT+R7BazGyf/wU+QSE0u2fAVTaz2czy5ctJTExk8ODB1Kr1WzWgjMREVixejDKWsLR5P4yi+L5pXcLcro5kt5VayVgQiSWzGAQc3I0EjovA4GYkJyObfYNG4ZeZROrk1+kyoCNvPTsVl8vnaFFpIAY/Fyy1S9m6dSte+VWR3MpYVQ6/9Pbj6x5N8XZUTPl1Crsv7+aVNq/Qt3pfPjz0IS1OVqdLTkuiiq3Mq7SCPhmDMGQF09A1GdfAjzhlMHDGUoF0my893M+y+NF+0OsdbM5evBOXTO8j0WSaLcyPqMqX9asS6PRbDpLs7IMcONiHpOTVhAaNxXN2IZmzPyWqRgNqdo5jv3NLfqUKpUYHapktZOanYqlckftnzipX3cdGfPwXHD06ksCLGTQ5mIp3ViprarXD54EduO03sWbmqzi7ujH8jffoeP+DGJ3/4FdzpeD09zCnhZaMtslIePwINBsDhpv9DVpH5x/nMLADmHytQUT8gA3AbLQ974fABhHxL9dtFPAwWhTJGLT9/eP2ffjj5frd6l74brR99h+xCaiJphEc5fepNEagHR/yRNMaPgG8gXCgI1owxdibXOMKw9CEHl+0qkQzytn+ln37rfyU+JXfBJT2aA991zVtv9q/vwe4qJSar5SyKKWOAd8B99ntZjSBw0spla2UOvonfF0DBJVT2h4ANiml0v9gzNtKqSylVDHQG4hUSn1vPwo0G0i5yTVnKqVylFKX0CJ0ritTK6WWKKUy7ff8AeAM1P4T96ajo6Ojo6Ojo/NfyLFN68lKSqTT6PE4Gn8TD6xWK6tWreLChQv069ePevXqldkKU1JYMmcOea4OrGzbA6s4sLxBCLW8g66aW1ltZC07S+mlfHAQxEEIGBeBg5cz2Zm57BkympC0eDInv0rjbs2Z9dQTeKZF06zhODxtTpS0dWftjz/gkVsVp4JQrNYktnfz5quebfF1FF7c9SLbErYxteVUBtcazPrY9Tj9Wkjf7I7ElFhZ4f8TndPvRWX608TtKM51vmGRqRn5NieOWirTrWIJn77wJFKpCSabjcfOxPNRfCqDgnzZ2bIOvQJ9yj0PEzGxb3P02AgEA3WszyCPb6No3wG+bT2IiLqn+dGhB9udW+FSZCOoYhDJscepXL8Bw998H99gLZDdbM7lxMkJJJ98i4ijOdSJTeecdzAJo76jbtXn2fLaW5zZtY3WA4dy/8xZhNS8ya/kGTGwuD+sHgvugTBuK9z7Cbj7//E4HZ3/LK8Ak0Qk8Jr2PkCMUmqxfW+6HDgL9C3XZ4FSKtJuN//BNW5pL4wm3iT/kbNKqXlKqXyllAl4FWgkIt7luqxTSu1RStnQNINhwAv2MReBD/j9qZUbsUYpddC+519a3u+/a99+K9V/dgKP2VWvQKVUjIikAgvtbRH8lk+lCtBKRMqfm3JECysCGAS8DMwUkZPAVKXUvltxVClVJCKrgAdEZB8wEnj2JsPK17euWP6zUkqJSOJNxpcXXYqA6ybBEZHJwDj7NRTgBQRcr6+Ojo6Ojo6Ojs7/BgXZWez7bjnVmjSnerOWZe02m43vv/+e6Oho+vTpQ+PGv+1FTElJLHv3XS6GhPBzizaYBRbWcqRxQK2r5lZKkbXyHCVnswBwCnLHd2htjAGupGXksXfYWGokxZL77DQqNglnwfNPY7DZqN/jSarFOEOEF0t/+R63nGq4FlfCYjnPro6+fHZvFwKMBl7c/SI/XfyJZ5o9w8i6IzmTdpqotXsZkdWb8yYLa90P0SyzE5ic6OC5m8PVD7AhqQ8Rks9mS1VaVfNlzri2ODg6kGex8uCpC+zOKeDl8BAeC6twVe6U/PxIIs88S2FhDIHmu3Cbk0nemdnYKoeyLKI3fZ2384NbL4psblhNQXj5pZKzZysNu/aky4OPlFX3ycs7yZFjDxEWm0CDhCIKHI2c7TyFoEpD2LHway6fPUOFqtUZMPVVgqpV5w8pLYJd78Oe2WB0g17vQYtxYPh91SYdnX8bSqnTIvIjMBWIKmeqiJYktjzxQKVynxO4NW5pLwxkAiE3mkREHNCiRe4DAgGb3RQA5F7HpwB+S3Z7hWvv4S/5/Xft229FVNmHFnozHtgDoJTKE5Eke1tSueSxCcCvSqlu15tIKXUI6CciRuBxYCUQinZDt8JCYC3wPVpo0Pqb9C8/bzJQVl3IfibsutWG/gz2c1jPAV3RImFsIpLNHUj6o6Ojo6Ojo6Oj8+9l17IFWErNdHpg/FXtmzdvJjIykm7dutGiRYuy9tL4eH547XWO14vg56atcMTEgvAS7qrU69qpyVp2luJTGSDgdXcVPDuFIg5CUmYeu0Y8RMPEKAqefAFDiDvrZ04n3+hD50FPUOe4CQlwZmnsZlxzquFaEoLVFMn+Vu7MGnIvFYwOTNszjY0XNvJk0ycZGzGWtItRbPx2PiOy+xNnNrHFGEet7CY4KQc6e21lZdAJVicMo4/jeWKtAQRWqsKXY1rh7OhAisnMiBPniS4q4ZO6YdwX7Fd2DzabhUuXviTuwmwcrS5U+C4Ex+2HkOrVSbr/UVI3/0JTt1h+CumGl7WIHENDwi07KTx1gY6jxtGsT3/7cR/F0ZiPsETOoll0Pl7FVhKqtcX77tnEr9/Ehs+exdXTi24PTyKi890YbiaM5FyCRf0gKw4aDoPub4BHhdt7GXR0/nmmox2l+aBcWxJaoEN5woCfyn2+du99q3vxG7EVLXhi/g3sI4B+aMeELqJpC9ful8v7kIEWrVIFOGNvCwMu346Tf+e+/aaiilKqWEQOA89w9Xmk3fa2reXafkSLQhkFXCmv3BgoAM6jqVM/KqVyRSSP31SqVMBfRLyVUrncmF1o2YO/BFbYc77cKhvQEvD0t/v5CBD8x0NuCU/AAqQDjqKVf/a6A/Pq6Ojo6Ojo6Oj8S7l8LoozO7fRot9g/Cr+9gfU2NhY9u/fT8uWLbnrrrvK2kvOnePXl15ma9v2bG7cCndy+bxKKu2qXB3Rbiu1kvHNKUrj8xFXBwIejMA5VPvV8lJ6PjsemEiLS6cofvQpUsnh3OKVXHKrwuAhjxO+Jwu8nViRuxuH3FBcTBWwlBzheIQD7465nxBnR17Z8wrr49bzeOPHeajBQ2T9tIkV+35kmGkY52357LHlElJYHS+D0MXnZ7Z47uerjEnc6xSNCSMZPnVYPqYF7s6OxBSWMPzkebLMVpY0DKeT32+/AhcVXSAycjJ5+cdxi3TDa0EJblWq4/DOi3x8rpj2v8wnvWl1sn18qWe9wDFrT6qkr8daVEC/yS9To3kr7bmZMvhx9320On+GykklFLr6UHLfHFLizKx96UUspWaa9elPm0HDcHZzv/nC5SXBwr5QnA2j10O1Djcfo6PzL0QpFSsi3wJPAKfszRuBT0RkBFoAwyCgHtr+90akouUu+atMBw6JyHvAB0qpFHvi2FfRAik8ARNaRIsbN6laZK8wvBKYISIPoOU/eQZ4/zZ8hL9x336rmZd+RUsqs7tc2y57W1kpZaVUPtAd7QxUElrozTtoZ5VAOwd10S6oPIJ2hAel1FlgORBnzy58VfWfcvMrtGo/Vez/3jJKqQw0UeddtAWth5bkx/Rn5rkOP6Mpf9FoYUkl3HpIlY6Ojo6Ojo6Ozn8ZNpuV7Qu+wMPXj9YDh5a1FxUVsXbtWgIDA+nW7bfA7eLjxzky6QlWt+3IxsZt8CGDz0NO06761YJKaUI+Ke8eojQ+Hwd/F0KmtiwTVGJT8/h5zCRaXDiCacxDRKbHcm7HZo75NmX4qGcJ35uFyQjLsvdgyNEEFXPxbs5VKWD6xIlUcnbktX2vse78OiY2msj4mqNIfmU6P25czUDTMGIkg8PFZryLg6jgaKCHz88cd97OawVP0Mx4GR8p5ZxTTRY81A5fdycO5RZy79EYSqyKNU1qlAkqSikS4uZxYG8v8tNO4PuNAyF7G1D5g8/59eHneHdfJP7ZRzjRpjklPu4MtP1EQkl9KiR+hwuKYa++Q43mrVBKkZL6I6fWt6LPweNUTirB1OQBUjsuZNlX69m5ZB6V60Yw+v25dBo17tYElfxUTVApzIT71+iCis7/Aq8DZS+/UioTLc/ps2h73ueAe+x74RsxCxgsItkiMvvPOqCUOg+0AaoCkSKSi5ZX9TCQj7Zvj0eLNDkD7L+FaScBhUAcmgaxDJj3Z327hr9t3y6aTvH/D3tJpkRgpFJq+3/an/91mjdvrg4fPvyfdkNHR0dHR0dH57Y5+ctPbPlyDr0nTaZuu06AJiasXr2aqKgoxo8fT0iIlmKgcO9ezk6ewmf3DWdTozZU4hIfBB6gXcRbXKkQqqw28rYlkL/tEigwhrhTYWJjxKjZNxxPJO7FV7g7bh+mQUPYn32JvKxsfg3qzKuD+hP8cwKFVis/chqn/Co4WF0pLdpGQnAxj097g6oerryx/w1WR6/m4YYPM87YmeQpz3E2MICIiuOINaZwJtMbg82Jak4GWnj9RLTDz4wyv0RFhxzudkjkkgTx8sRRhAd68HNGLhMiLxLibGRFo+pUcdX+fmqzWTm2sR85blE4nxGCz7TE2vcBTplKOXEyEgebCQeLheoliTTwukhd20nW5d/LxcuZVKgSTv/np+HpF4DJlEr06an4HdpApRQT2e4+2DrNYfvmg8SfPIZfxcp0Gj2eao2b3fqiFWbAgj6QkwCjvoew1nfuhdDRuUVE5IhSqvl/2g+dO8ut5FT5n0FEeqCViC4GpqCdn7oVpUxHR0dHR0dHR0eHkoICdi9fRKU69alzV8ey9lOnThEZGUmXLl3KBJW8LVu4NOU5vhgzng0NWxNOLG95/8xd9T8tE1TMaUVkfXsO8+UCMIBjkBuBExoiRgMlZisz1hwn7LN3uDv5FCU9evJr/CkKcGJz2ADe7d6eCpsukWexsJl4XPJrI85CUf4asr3MTJg6k2oebsw4MIPV0asZFzGO4UdciJ81koLW/Wjk342zzglEpftisBlp4GqgvsdmEtnAaMt0XKSYdoYUCsSNFx8eTnigB4suZzA1OpFGnm4sbhhOgJO2nVBWK2e+GUZOjShcTtUkx38kB2ukk3dgP1YE99I8upfsIcIrBkd3K4nFDfmuuCeJyRlUb96aPpMm4+jsTFLSSjL3v0zNs+k4ldrYEdoEHIZx9MOvcHJxpfPo8TTq3qcsee0tUZQFi/pD9kUYuUoXVHR0dO4od0RUEZFXgRpKqfv/jO06fRcAiUqpl++EX9ehDVrokBNa6FF/e7llHR0dHR0dHR0dnZuyZ+USSgoK6DJ2QlmFm9zcXDZu3EhoaGhZHpWcNWtJeukl5kyYxA8NWlFPzjDN7TvuarQQg0GL7DBdzCXjm9NauWRnBwzuRgLHNcDg4khsWgGT5+9h+A+zaZgZR373LuxKiSHTPYQtIb14tWYNgrckkmezst2chVNJGNk+JpwuLaTQx53hL75JLV8f3j74Nt+e+5YHqg6h/yfHST98AsfezxFiCGOHx1HSUqrgohxo6e5IVbetpPMDQ9RrWJWZDo5pGMXG/fcPp3qwN+/EJfNRfCpd/bz4MqIK7g5aQlibyUTcGw+T2uEoBZlh7MpuhSH3AgajI1UM5xls24KnUzEFZlcO5A8iurg9+Y7HsKZG07zvQNqPGI3JdJkzBydT4cgOGqSZuOhkZEWdroTH1ibh1I806NqDdsMewM3L+/oLcyNKcmHJQMg4B8NX6Ed+dHR07ji3LKqIyBi0s1nVgTxgDVrt6Jw/GvdvQin1KlrCHB0dHR0dHR0dHZ0/RXr8BU5s3kjDbr2oUFXL62iz2Vi7di1Wq5UBAwbg4OBA9qpVpEx7hbmPPcPaiBY0Vsd5zmkJbRsvw9HREwBLZjGZi89g8HACqw1lUwSOi8DgYWTV4QQ+WrGXV/Z8RVheCrGN6xGdGs8F/wh2+3fgCYMX9aJySEext9iEwerLmaAMqp1djFO16kx88XXcPT1599C7LD+7nGHuHen73I+YHH1wH/Q+yuTAV4FrMCa0IABn2nu4EOS8jRy+Y7C8iclqpbEhj2BDPp179KZOtco8ey6BZclZDA/x471aoTgaNEHJmpdH/KQJJHY/hNXmxuno1tT0L6Z9zjrCTCkU4IIpxoGj+U05VOEZnALAWrwWMjPp/sgTRHTqSkLCQnIPvkmd6ByczDbm+fqyO7AZ7Q4GcDnlND0eeZKIztctLvrHmPJhyWBIOQ1Dl0CNrnfsXdDR+acRkYJyH93QcoNa7Z8nKKWW/vNe6cAtiioi8ixakpvRwC9oNaI/BbaIyF1/NFZHR0dHtY+xqwAAIABJREFUR0dHR0fnvx2bzcq2+V/g7O7OXUN/C8A+ePAgFy5coG/fvvj5+VEcGUny628w94kpfFe3Ka1sh3jS+DWtGi/H2Vkr22srtpCxIBJlUxgMYC2yEvhwQ0o8jExbeYIDu47z/sGv8S7O41DVIHI93Njq050E5zAmZDvR2d3GeZsiqsBCgasLl/zOUevUeqo0aU6/p6fi6OTMB4c/YEnUEvpnhzPg7V9waXkPDlXuJd9SwNuh8wi72J3KNg86ubnj47STAsNyBstMCkptBIuZRo5J1KkXQcsWzXgo8gI/ZeTxdJUgnqsWXBahY05NI2H8eFKbn8Uaojhzsi13WY/QLvMw231bEZXXnirLd5MX3JAj1R8kuHYBCadWYHR2Ycj0t/GqaOD4vv6EHDtAg3QTJv9wxnkaKDIH0X67M0XWbAa9+DphEY3+/IKVFsKyoXD5CNy3AGr3vBOvgY7OfwyllMeV70XkIvCQUmrrjUfo/FPctPqPiHgBrwGTlFI/KaXMSqmLwBC0DL/3X9PfKCLLReQ7EXG6znyrRCRFRHJFZKeI1L+mi6+IbBCRfBE5ICLVy41tKyKH7GMPiUjbcrYdIvKmiOwVkQIRWS8i/iKyVETy7P2rlus/S0QS7LYj9rrVOjo6Ojo6Ojo6OldRWlLMuvfeJDHqNO1HjMHVQ4s2SUtLY+vWrdSqVYumTZtiLSgk6Zln+WroaL6r25R2lr08YZhFs0Zf4O6uRbYoq43MpVFYMosxBrhiyTbhP6ouMWLl3k92E7l9P5/unYtHSR77qlYgvlZb5vgNIEtCeSzPmR6eTuwvsXIu30psiBFj8BFqnVpP/Y5dGTD5ZRydnPn46McsPLOQ3tHuDP8iBt8Rr+BQ8V7iHBKZVG0mAUmtqGYOoKuLNz4uhygxLGCI4W1yShXOykBXl4v4+fnRrU9vRp7SBJW3albi+fCQMkHFFHeB+OHDyfO6QFE7C4kJ9fAottGS43zUbAkRqidhS/eQFtiMkw0mENYokbhDC/CrGMrItz7E7LKbCxu7U3/7HoIyLRS0ncTAoACKsnxo/aszzi6uDH/j/b8mqJiLYflwuLQPBn4J9e69Y++Cjs6/FRFxEJFpIhInIhn2fbCP3VZHRCwiMlZEEkUkXUSmlBs7095/uX0fflJEGpezh4rIOvu8cSLySDnbXSJyzL6vThGRt+3t7iKyQkSy7BV+D4iIr93mJyKL7P0TRGS6XEk0pdkniMg5+9gNIvJb3fp/IbdSUrkt4AJ8X75RKVWAVge7LBZPRFyBtWihSEOUUqXXmW8TUBOtHPNR4NowpWFoIo4vEAvMsM/tB2wAZgP+wIfABhHxv2bsKLRImurAPmA+Wm3rKLQa2lc4BDS225YBq0TE5WYPQ0dHR0dHR0dH5/8P+ZkZrJj+PBeOHaHrg4/SsGsPACwWC2vWrMHJyYl7770XESH1jTc47OTKirZdaFl6mAkOs2jUYDbe3treRClFzg/nMcXm4FzDl9KEAnzuDWdlei4DPt1L+IUTfLh7LlZLCfvq1GRDlfuY59yaxgZPZjQIo7m7kV9zzaSbFdG1zbRx/hWHI9tp3ncgPR59inxLAU9vf5p5p+fR7TiM2+eF/6NfYS2qzA6vgzxb5T2qJbUjojiMrq5euLtdwsznDDPMJN1kAOVEX+9EXMRK9wEDGR6VwMHcAj6tV4UHKweWPZPiEyeIHzECs7GIrLFCQYEv59Ma86BpJUfrPcOIo0fJ+fxzkoLbkN5rIn4h+zi7ay1123dm4MvPcyn2aVx/eJEGZ3Jw9K9L0YObGFMchf8ZMy0OuFKhWnVGzPgQ/0qhf37BLCb4dhRc2An9PoUGg+/Ie6Cj81/AZKA70A6oDJiBj8rZHYDmQA2gNzBDRMLL2QeglS32QTud8jFoYg3avn8vUBHoCbwoIlcydc8B3lJKeaHt89fa2x9COxlTCQgAHgeu6ANLgVwgHGgJ9EfbxyMiQ4GngL5AEHAMWPKXn8o/wK0c/wkAMpRSluvYkoFmwDnAC63u8wngSXWDWs1KqbL60vYkttki4q2UyrU3r1FKHbTbl6KJJwB9gBil1GL75+Ui8gTaw15gb5tvr5ONiGwC6l0JiRKRVcAb5fwovzAfiMjLQG27/zo6Ojo6Ojo6Ov/PSb1wnrXvvEZpSTEDpk6/qoTvzp07SU5OZujQoXh4eJD7ww+kbdzIjHc+xduay3jjLOrXeZOAgC5lYwr2JFF4IAWXun6URGVhbB7E5JhkfopMYWLaXvrsW0OBs5EddTuzpHInzEZnZnSpRUNfV5KWRnOwyEaRsw1jrXTaZp4iMfIkHe5/kBZ9B7I3aS8v73iBbFMW9++wMty3P8be/TEllfB1he9Z4/cL9VLvonVufTq7uuPiWITN9jYjDG+SZHJCKQd6B2XgkZtJ6+49GJ+UT2JJKfMjqtEt4LfksAU7d5L45FM4BPiT/aIHltJIDl/szeSieaRVaEPIjjTytm4iIbQLrg+OpuDIIjITL9Fx1Diqt61E0vpO1IlJwYAjqssrWFo/whO/TCJgVxY1Ezyo3aY9PSc+jaPT7wLeb47VDKvGQuwW6DsLGg+/rfXX0fkv4xHgfqVUEoCIvAZEisiD5fpMV0qVAIdE5CzQEIiz27YppbbYxy4Gxtrb2wEuSql37J+jRWQ+WkDDr2jiTS0R8VdKZaJV28XeHghUV0qdRgtqQESqAB2Ae5VSZqBIRGbb51tov483lVLR5e6jSESClFKpd+ZR3VluRVTJAAJExPE6wkqI3Q7QGjACw28kqNhVrhnAfWgP2GY3BaApVQAp5YYUAVfOjlUE4q+ZMh5N+bpC+YdcfJ3P5c+hTQbG2edVaKJQwPX81tHR0dHR0dHR+f9F7OEDbJj9Lq4eXgx7/T0Cw6qW2RISEti1axeNGzembt26lF68SMqrrzHrsWdJc/XgBfUatSrfR8WK95WNKY7KJHdDHM7h3pTEZGOt5M6o2ESSsvJ4/dRyWlw4SaaHB+vvfoRvCaZ+RS/mjmhK9N5Ezqw9R4ZFUeieS8V6eajoI1y+dJGeE5+mRrt2vLN/JkvOLaVSJry9zY0WQ16jOM6T/JxcZlT6mmOeUYTl1OPu9Lvo5OmIs9kJB9sLjHCcTnyJK0oZ6BRWSFD6RUJr1Waq8iTPZGZFo+q09in79ZmctWtJfnkazrVqYprWhuLsz4mKb0dP0z7cHIW07W5YTm0isV5/KjxyD3tWfoyyWRkw9RWc1I/IgvHUyDNjCWuOod+X2Pyq8dKWZ/FZH0/FTHdaDRjKXUNGIoZbCaa/hpwE+P5huLQXer0Hzcbcxurr6Px3Idq5vFBgo4iU34sb0E55AFiVUhnlbOX32nDjfXgVoKqIlC9Q4wBcyecyGq0YTLSIxAKvKKV+Br4BgoHVIuIBLAKm2edzAdKvHCe0+xlb7nqfi8jcctezoEXf/NeKKvvQjvMMBFZeabQ/mF7Ai2g3uBk4CfwiIp1uoCKNAPoBdwMXAW8gG5Dr9L2WJLQHXJ4wtOiYP4U9f8pzQFcgUillE5Fb9UNHR0dHR0dHR+d/FKUURzf+wI7FXxNUrQb9n5uGh69fmd1kMrFmzRq8vb3p2bMnqrSUy89OZn9EYzbVakgXy680c0ujevgzZWNKkwrIWn4WxyA3zOnFmJwcGJGcimNhOu8dn0fN1FRyqlRlTven2J9u4YE2VZjcvRY/zTtF3ulcTECuzwVqRjiRd3A3BdlZ9J8yDUs1b4atHkBsSQI9jth41LkH/hMnkL8rjQSXJN4JnU+CMQX/wkr0SepFxwAzLjmhuBqnc7/rc5zPc0MhNKuWT6OiZMyeXswKqYVVwZomNYjwdCu7h8xv5pH23nu4tWmN6+vjOX9uNBnZYRRYA7mrcC0Jp6tTGhlNaudH8O5ZhW3z38E3pBJ9nhiPbd+jVIg+i83ohLXvRzg2HYsC3tv6Os7fRuJb5EaPR58kotPdf23RItfC+ifAZoUBX0KjoX9x9XV0/jtRSikRuQwMVEodudYuIrcTPJAAnFVKNbjBtaOAofYAimHA9yLiq5QyAa8Ar9iPGf0MRKIdIyoAfG8QjJEATFFKfXcbPv+j3FRUUUrl2kNuPhGRPK6u/pMILAZesPd9V0Sc+U1YybhmOk80gSYTrQzUW3/C1412H0agiTuDgHrAj39ijvJ+WIB0wFFEpqJFqujo6Ojo6Ojo6Pw/xWa1sm3Bl5zYvIGaLdvS6/FnMDpfnXJv8+bNZGVlMWbMGFxcXEid+Q6Z588zc+an+FlyGOn4JbVqfYijo/ZHXmteKZkLIxEXBzAIpsJSJtgKqJR3kQknFhGWmUNOmw48GjYASx7MHdGUZlV9WffWQfLSSzEYFYWBZ+jSri7Hv12I1Wxm0Euvs61kPx+vm41bkZWX9nrTe8wMSpMDKdiZxi6vo6wK3EgKuThbPOh3aTAdg7NwS2vBJbe5PG64n6w8N0BoUD2VwR6uRKfms7FpB4wuLqxpVINwN2dAE5nSP/yIzK++wrNXT/xff5mtu3rhYHBmQ+lgFqVMI/eyJ9lnIX/Uq1gDU9m17GuqN29Fx67+OK/sgVtRKYU1WuLWfxniEUiRuYgPV78MG6PwETfue+l1wiIa/vkFKy2En6bC0UVQqRkM+hr8wm8+Tkfnf5PPgZki8qBSKkFEKgCtlFLrb3Pe3QAi8pT9Gha0fbijUuqoiDwAbFBKZYpILtopECUid6MFRpwF8uzjbEqpCyKyH3hXRF4HCtHyoQYppXbbr/GyiJxWSp2zJ7ft8m8WWW6ppLJdLMkE3ke74Ty0BDQjlVKmcmE7KKXesAsrW0WkyzVTLQJ6AJeBLLTwn0dv0YdMEbkHmAV8hhYedM91hJtb4We0CJdotEX8CE0R09HR0dHR0dHR+X+IqaiIH2e9w8XjR2hx7yDaDx/9u2Mo0dHRHDlyhLZt21K1alUKdu4ka8EC3p/2FtlOLkzjXSoH3EVgQHcAbKVWMhZFYi0yo8K8sJ3P5VWKqJd9mn4nVlIxp4CzHe/haZ+O1Pd3Z+6IpqRmFbHplf0UldgQdxNejXJo4h3CgXmf4uLpSbcXnmf6/7F339FRFe0Dx7+zJcmm90oKofdeFERAEEUFFUUBFVBUsKKICiqgAgqKDRVRQIpSRLpSBASp0gkEQgmk9952N5vdnd8fd3nfvIqCYPvpfM7hnOzeuffO3lnKPMw8z5FX2W9Jom2ykxc9+lN38rMULj+Hs7iQz0JXUqZPpMQOdjcHdyUPoVNUKn7ZN7I8cC0fWjvjqIxEJyTjb4uhhT6YDRs2sL9ec9xDwlnWOp4Idy2fiXQ6yXvjTUoWLcL/nnsIfvllFq8ZTJ3AIpYWjeDT1I9wmCElIRrdSzMQxlSOLd9Ap759aGVYjc/ag1hNbpgHvIdXCy09w6HkPXz1yVRCM4BAH4a+9DbBdWJ++4DlJMDXD0FRMnR9Bnq8BHrjVX0HFOX/uelo6TW+F0KEo22VWQRcVVBFSlkjhOgLzEBbTOEGnHT9DHAr8I4rBpCKVrCmxlWxZxZauo0KtOS0y1znDAKmoQVcvNDyukxx3W+JEMITbcVLNNrOlg3A3zaoIn4h/Ymi/K7at28vDx48+Fd3Q1EURVEU5WfKC/JZNe1VirIy6DXiMVrecNPP2pSUlDBnzhy8vLx45JFHkMXFpNx+B9uuvZ6Jtwykj2M3wwyz6NxpEyZTFNIpKV5yCktiIdmRnkRmmVkgrbhZd9NxzzpCK8xs7nIH74R04b7OMbx8S1PWbDlH2bpMHE5wBhXTtk8I2bu2kppwmLpt2uPRszFTjk/HKm08dCSAoUNnoPOMp2DZSSpkFW9FzqVHURlL/bzI9kmlb/JQgoJTuDfvbjb7/MiHIpCi8nB8PQx8OaIzQboqPpszh9SAEHI79+TL1vUINGr/5yodDnInTaJ0+dcEDh2K75jneOfL1+kc+wWHy65lSEIhkezh1MFmeLy0CIs9nfUfzqDHtUG0MG9AX11NUaPmBPRfhcEzlJoaG5/Nn0j5tgR0UhB/Uw9uH/w0BuNvDIQ4nbBvFmyZBJ5BcMdsiL/+kqcpyt+BEOKQlLL9X90P5fd1WStVFEVRFEVRFOWfKO98MivfnKRtqxn3GrEtW/+sTXFxMfPnz8fpdHLXXXeh1+lIf+EFivQG3u51GyG2Uu41fkh8/LOYTFoNhfLNaZiPF7DVA3pkVrGXGiIdm2mwazOBVVYWdr6HlRGdeOfOFvRtGcGnHx1En1SFSQ8ejYtp1TqA3QtnU2O10vn++1lT8S0bklYQXyiZ4H4Xrd9+kZLvM7DuTOKMRyrLwmYzKK0Zn4R4kul3hG7nBpIfdIJH8geT63aaZe4tKCp0EOnvwbonulKSm8Gcr1dQYXTH0qkby9vUx9ugB0Da7WSPG0/5unUEjRqJ24hRjP70C+6ov5xiayh9t9qICNpDTm4cwR+uw2rJYdPk9+ne2kLbktVUeBux9H2JkJZjEUJw7MhO1s2egUeJHUesD/c9NZk6dRr89sGqzIfVoyB5CzTqC/0+BK+gS5+nKIryB7qqoIoQ4hMgS0r5+iUbX+X5V3svRVEURVEURakt5+xpVkydgLuXFwMnTCXoIttQioqKmD9/Pna7naFDhxIaGkrh7E+p2vsjb06eQaWbBy/o3yPQFE90nWEAVB3KpXBbOh85qxlhdSdP56TGvJLGO3fjZ7Exo+N9nGp8DSvub4evm4FZL+3CrcxBkAdE9dZjzsxmy6z5hMTF42wTyHM5U7HoHdydHMgzgz/Es24T0ucfxJhmZ73/TizGxfRLe4L5/sdJDt5L+/SbCQg8y8N5g6jUFfOsKYb0whoahfuwZlRndvywnT179lDk5Uv1tT1Y0L4J7q6tTtJmI+u5sVR89x0ho0djHPogo2at5o6YuegMdup8EUiD4M1Ya3zxm/w91Y5q1kyeynVNM2lXfZbiYB+MQ74hNKA15vIylnwymdJDSThMDsKG9GHwrU+hu5LqPme3wOqRUF0Bfd+GDiNAqBoTinI5hBCTgPpSyvuEEDFo23f8pJSOv7Zn/wy/GlQRQmwE9kspJ/zk/f7AbKDORcosXzYp5cg/oq2iKIqiKIqi/JrsM0msmDoRk68vAydMxTc49GdtCgsLWbBgAQ6Hg6FDhxIeHo75yBEKPviADSNGsi8okv4cpq7jEI0bLUenM1KdVs7J5aeZ5DTzos6Emw7O5X1Jg8MHMNkcvNpxGPqu3Vg7qA0nj+WybfE5THaICJA07OfF3mXzKMnNIbZTW5YaN3O6upLmRUaer/84rV9/iOrsSs6/swODWTAvdAnXW5NIz53BKt9tJEbsoH5BW24OPcx1qY9wUsCzBm/KympoVcePeYOb8cWihWRmZnIyIo7Qa67j4+bxGHRacMJZXU3mU09R9cMOwsa9iLxzIC++/zk311lFYGA2bt+E0Nh5GKOXE/2wr7CZ3Fj18ji61D9Ma2sOJeGheA/9AaN7OAc2rmb74s/BZievuTuPjZxGfMgVrE5x2GHLRNj7IYQ2hQfWQljTqxx9Rfn/SQiRCoyQUm65VNtfIqVM539LKStX6VIrVRYAU4QQE39S7uh+4MtfC6gIIQxXE3BRFEVRFEVRlD9C1ukkVr4xAU8/fwZOeAOfoJ9XGy0oKGDBggU4nU6GDh1KWFgYjvJyssc8R0GTpnzUohMRNeXcYXiLqKhB+Pm1wV5m5dxnx3hOmnlC50EcOvZlfEmj4wfQ2yXjO4+gwx29GdO9PpsXnyTzcDH+OqhTT49HXDYbZy7Fw9uH8qbwWuBK/MyCl+x9uPvp19GbTGTtPo3tm2wq9VVsDnmffvpr+DFrEMeDd3Mg5ltCqyJ5PjCRemcfYY0MYJqowV7tpE20P5O6+fHp7E+wOp1sadKBLq1b8mbDOuhcqz2cZjMZjz+O+cd9hL/6KhXXd+fDD2Zyfb2NhISm4bHNRMTxYgK6mJFdn4Xo9qx9YzydojfTrKKYsuh4/B7YSWFWPms+fozytEzyA61E338Db9zwHEbdFSSRra6Erx+Es5u0lSk3Tgaj6WqHX1EU5Xd1qbV3q4Eg4LoLb7hKGt0KLBRCzBdCTHa9310IkSmEeEEIkQt87nr/eSFEjhAiWwgxQgghhRD1Xccudv4YIUS+65zhte5bu22AEOIbIUSBEKLE9XOd3++xKIqiKIqiKP9EmadOsGLqBLz8Axk48eIBlfz8fObPn4+UkmHDhhEWFoZ0OMh5ZQLVBQW8PvghrAY3xnovwsvNl3rxY5E1TtI/PMp7NVX0EkauwUBixirqH9+H06nj5W6jePCJuxjeIIKlr+8j83AxDdx1xLcxklv+LXu+/hK3QHe+anacVdGp9K9qxNq71nPvQ28DBo7O24Rcl88pUzJZQW/QyeN5dif15IzfUXbWXY6fw4N5ZBNw9j7elHWZgg2HlLSI8GJonWJWrfiaKi8fFrfpTq92rZlWK6DiqKwkfcTDmPftJ/LNNzjfuAnz5nxE+8brCAlNw7TOF98N7sRcXw1hLaD7i3w35w3a+C+hWUUxlfXa4jtsP6mnzrDopdHk5aRxrKOdeya+yVO9x11ZQKU8Bz6/GZI3wy0ztF8qoKIoAAghhgkhdgkh3nbNh1OEEDfXOl5XCPGDEKJCCLEZCK51LM41Jze4Xg8XQiS52p4XQjxaq+2l5ui3CCGOCCHKhRAZrm1G/zq/ulJFSmkRQnwFPADscL09EDglpUwQP9/HGA4EArGATghxE/AscAOQAnx6if6EA35AFNAb+FoIsVpKWfKTdjq0oM1AQA/MAz4Ebr/E9RVFURRFUZR/qcyTiax8cxLeQcEMnDAV74DAn7XJy8tj4cKFCCEYOnQoISEh2FJTyX5xHJajR1kxdjzHA8IYbDxLmPV7GjR9F4PBh7RZCXxXUUUOTl7GRHrWViKPf0+VcOP9m55i8mO3Ur6vgDXbTmM0wHXeegobVXN49xwcNdWciylmZ7M0Gpv9eL/TZFo27Q5AysETmNekEVDjxXa/9XSKt3Hk7LuknTaT5XuWLQ0W4CkN9Mtqw+NVvUjEC6jBqBc09pP0MZzk6JF8Khs2Y3FYPZ6Jj+C5uHAu/DveUVpK+oiHsZ46Rdjbb7HdIUn85kuat9iOr3cRxhVR+G4rIf6ROhgq0uHO2exZN5NGzlnEF5mxNLkB77uXs3vfRnbPnEW5yYb97hbM7DkRXzffKxuo3OOw+B6wlsHgr6BB7ysccUX5R+uEtrMkGHgEmCuEiHLtMFkM7AVudLX7FljzC9fJR1s0cR7oBmwQQhyQUh52Hf+1OXoVWqzgBNAc2CyEOCqlXP27f9q/sctJVLsA+EYI8YSU0or20Bb8QlsnMFFKWQ0ghBgIfC6lPOF6PQkY8iv3qgFec20bWi+EqAQaAT/WbiSlLKJWnWohxBRg22V8FkVRFEVRFOVfKOPEMVZOexXf4FAGTpiKl3/Az9rk5uaycOFCdDodw4YNIygwkOIvvyT/7RkIo5GiSa8yLyCWGIeZvuINAgO6EhZ2G1krz5KWXspsWcUX0p2q/KN4H1lJidGL5YNf4PVbruPA56cpzTMT5AUdjAaS6pZy5vs52A021l6Ti9MdxseO4J7uT6ITOirLyklYuInYrHDK3cpJi5zD9W3HsGGFHaulikTfFPY2mQ32AHJTHudjpydN0HFPHX82FJXT0FBEa1sKVRjJubYna4y+vFIvksdj/ps7xl5URPqDD2FLScFn+jS+ysigpPgsjVr9gJepArmiGSHfnyb4sZvxLJ4LvV/j8KkNROe9TUy+BVurARhum8WH66ZStXQvZi9Jl2ee4OZm/a58oM5ugeVDwd0XHtwI4S2u/FqK8s+WJqX8DEAIsQD4GAgTQrgBHYBernn5DiHEul+6iJTy21ovfxBCfIe2U+VCUOUX5+hSyu21zj0mhFgCXI+24+Vf45JBFSnlLiFEIXC7EOIA0BG48xeaF7gCLxdEAgdrvc64xO2KfpKHxcxFkugIITyBd4GbgAt/I/oIIfQqg7GiKIqiKIpSW3piAqumvYZfaBh3vzLlogGVnJwcFi5ciMFgYNiwYfjabGSMGEHVnr14XXcdpufH8nBiCjUGIy/5r0VfYaFRo1fJ35ND1f4cXpYVvGErxcvpS9nh+RS4+3Hs6ckM9oliy8wE3EyCxv6CWJ2B/aFpZH+/lOIQHyyhLehU5k54VCPM5jqs+PE89pTztEiURDlD2OG5g/DGfpRbJzF9WS7JRifngnMx1p0LDk/aZFxDT48C2pibcqa+O5+kpXGNsYiImgIiYmPZ2rgd31udvNGwDsOj/rvVyXLiBFmjn8FeUIDl1UmsSkgAQyGNW3+P0c2OZeX1NPl+B6Z77ibEshBZpyPHDPmEHZ9DVK6VmnZDOX/tKKbPu5dG26rBz8TI194hIjT2ygfq4Dz49jktEe3gr8A38sqvpSj/fLkXfpBSml2rz7zRVq6USCmrarVNA6IvdhHXtqGJQEO0HSGewPFaTX5xji6E6AS8ibZKxQ1wB5Zf1af6f+hySyovRFuh0gjYJKXM+4V28ievc4DauU4uOpBXYIyrL52klLlCiNbAEUDVVVMURVEURVH+I+3YUVZPfw3/iEjufnkynn7+P2uTnZ3NokWLMBqNDB06FP3OnZyfMhXpcBA+aRLlPXowYM9RzviHMsq3CO+ypcTVfQZbtj9Va48xTZYxuCyBZn5dKf1xOg67g+SRE4lMN3EkM53gGB0NK52464xsMx6mbN9W8urWxcsjiAq7CaddUJGSRFVqEgD+Ti9Kdb7sFCYSLY0oP+wBZIAXGPXFmOI+JaTGjWfzBU4fX7LLbaw17UZm1nC9HoQUdO7WjY/8ojhYbuH9xjHcE6FtdZJSUrp0KXlT34DgYJLHPMvBhASM/mZaNNmE0+BB0Zp+dNi6HGOXHsQ2S0eeriJjB3r6AAAgAElEQVQpPpCQw58RmVeNtf0jzI2KZd2CYfQ8GIxXSAjDX3//os/2sjidsGUC7JkJDW6Eu+aBu8+VDrmi/NvlAAFCCK9agZUYfj5XRwjhjrYD5AFgjZSyRgixmsufVy9GS8Nxs5TSKoR4j1r5W/4tfktQ5WWgJfDMb7j+V8A8IcQitOjYK7+te7/IB7AApUKIQLTImqIoiqIoiqL8R2rCYda8NZmAiEjuemUKnr5+P2uTlZXFokWLcHd3577+/TFPnEjllq2Y2rUj8o2pHJGCBw+dodgvhGeC3elS/hp6z3hCfO4na8YxNjpKiSvYyI2ht2M5uwJ9fiqre4wjOFGP2bOa6BZ2GmW5YTEY2VK1GWvGaYobtMQkDCQRyYh7bqV1rC/fr1lJ+EkTJVRx3pBBvsFKQxw0BJxOA3bpSZF7Nt7CiVdmL3ToOADoK2uwGtxIqfEjKjKCh/u0wSc0lGGnszhRYeGTZnH0C9WCHY7KKnInTKB8/XoM3bqxo307Ms6dQx9uo229tVh0weStG0TXrXMw1GtI/FM3IFY9SFq9KAKTthFeUE1ms0E85UimYts2+hyKICA8kkETp115QKXGAisfgaS10OFhuOlN0F/uFEVRlJ+SUqYJIQ4CrwohxqPtNLkNWHuR5hdWlxQAdteqlRuBxMu8nQ9Q7AqodAQGA99d7Wf4/+ay/sSSUqYKIfYArbj4YPzSeRuEEB+g5TtxAq+jRcGqr6Cvtb2HFhUrBLKBGagktYqiKIqiKIpLytFDrHl7MoFR0dz10usXDagkJyezbNkyvLy8GBAfT8mQ+3BWVRH6/PMEDn2Ar86m8HxGCcLozqfxITRxLCGtIJOWzReS+sEJMqqLKMxfTr+gm9Dln8B6ahvftRtNoKxDdJMAdIZ0GqX5UeiuY0fecqqFBVtcY+y4k+rXjBnDe5ORe4hD75+hS0VDcvXZFNv9cC+KwWQQ2GQldq9ycvwSkbIEf3SUGipppU+nRf0hWA6bmGbUkWCp4ZleDXnqhvrk2ezcm3COFEs1nzevS+9g7XNbT58m6+nR2NLT8XrqKdbrBPm5uehj7XSKXk6FqEvG+uF0++EjjH5exH30JnJZbyo99fjkFRBYaWNXRBeesPxIg/wQ+h6uQ0B4JAMnTL3ygEplPiwZBFmHoM9U6PwY/LwQhqIov91gtDyoxWgJaxcCP/uNKqWsEEI8hbYYwh1Yx2+Y7wOPATOEEB8CP7iuc4V/IPz/JbTkwH/SzYRoghb1cv/JvizlH659+/by4MGDl26oKIqiKIpylfJTz7P45TEERcVw1yuTMXn/fCtJQkICa9asISQwkBsyMnCsWYtH06ZETnsTt/r1efVIErNLqwmxVrG4XSPC9YkcOz6SsNB+2NYNxJGfwca8ZTTzbk47Q2PK9r7DviYPUe0TT4uekVTkHKd5TiipnrA36wss/gEIkyfnHYHEte7C+FtbsXXFEhocD8cg9ZyttpJs8cDkY6TG6sDhcJJf9xTrAucT5F6Jn8PAaelkusOPHp0WsW9JJi9gplhIZgxsxa0tI0mqtHDfsfOU2h3Mb16X6wJ9kFJStmIFua9PRu/ri8/k11l+7Bjl5cV41C2kdfh6ymQrktcPo8ueOQRWphG18CNqjj5BQFoKNTodOgfMDWrIR35mBrj3xP/bDPxDw68uoJJ1GJYP0wIrAz6DJrdd3aAryv8DQohDUsr2f3U/lN/XH762TghxB7AeLeHNNGDdLwVUhBDbgS+klHP+6H657jcJqC+lvO/PuJ+iKIqiKIryx7JZLXzz/nQ8vH0YMP7VnwVUpJTs2bOHzZs3U8fdnU5LluIoLSX48ccJHvkoZp2OoXuPsb1a0qyiiCXdO6KrOcSx44/h7d0Yy+47MeSeY2v+1/gag2nrdw0lBz7hUPMnsHiG0+HOOIqPH6RlQThJnnYO5i7FGhaBXRg47Izlibt60yPIxIkZ39CqPI48ew0JZvAMD8Tb5KCyuBpHZAWrQj/G7JVLH72Zo1Y3knVOXvVuSs9On7B29nEmOKvw8jKybGgHWkf7s7O4ggcTU/DS61nTpj7NfTxxms3kvvoqZWvW4nlNZ0wvvcSX69bi7nGKxm0S8PcopJzrSf72XlofW0ZgyVl8ZjxKevpjNE9LQwKWGg9Gh4eQHuLFG6FPcm7uKvyuJqBiKYXvX4cDc8EnHIZ9C3Xa/T6DryjKn0oIMQQYKqW88a/uy1/pkkEVIURXYDrQDHAAScBoKeWBy7zHo8B817k/oC0RuuqAhhCiO/A9WvZhgFJgD/DWb+jbFXHd+wspZZ1LtVUURVEURVH+PN/Pm01JThZ3vzzlZ5N+p9PJpk2b2LdvHzH5+XTc/gO+XboQ+twYPBo1Is1Szb37E0lxQJ/iLGb17YnZst8VUGmIe+ZErImJ/JC/Covei1vjB1N4aC2H695HldGLyFvqkJd8gDb54Rw3VXKodBOO0EgKnV6c92rEx/d1x+tQFoVLzuKNDwer7FT7Q1CdADJPlWDwc7K3+QoSvHfR1cdA55IyZum98BA65rZ8mubRg/lo5j5m2s00CvVm3kMdifAzsSynmDGn06nv6cGXLeoSJauoTs4mc/RobOfOE/zEEzgH3Mnyle8RV3cXvr655FRFYHSbRvLXwcSnbqVO9m7sE1tzXv8B1x4qBuCcI4ThMR40iu3IB8HD2Pruu/iFhl1ZQEVKOPYVfPcSmIug06PQYzx4/HxblqIov04IcS9artPmQBWQgrbdZ5b8E7eiSCm/BL78s+73d/WrQRUhhC/wDTAKbX+UG1rN6svOiSKlvOlqOngJ2VLKOkKrHxUFPALsFELcIqXc+gfeV1EURVEURfmbOblzGyd+2ELnAfcS07zl/xyrqanh688+43R+Pg1On+Eam42weXPx6twZgJ3FFQxPSMZWU8NDpVlMvP0WKir2cvz4KEwe9ak8MBbLiUMcKlxHkZs/rZo8SNnJ4ySE9cKGnTMtfYgpOkaztCDOGArZbzuE9AkgwR5JTLOWLGvVgPKFJ6gptXPOUUVypRuhcUZKM+04S0vJbHyEDb5fUi8glEnVZnKzHbzt60Mzgz/v3bIQf2c4z7+3h9U2K73rB/P+A+0wGfW8lZLDjNQ8uvmZmGPfg/dnwyk9lELu4SB0Pv7EzJ1DfoQH+3cOp3GTs1TavFl9/j5ujr2fM8syCC5JpGHBCkpe9UZ4/kinAxXoHZK9hDMq3o1H2oyil601G96dceUBlYLT8O0YSN0JUe1gyNcQ2fr3GnZF+VcRQowBngceBzYBlUBr4DlgLlefv/TCfQwqZcfludRKlYYAUsolrtcWamXzFULogPHAw4AJ2Ag8KaUsu9hqDiFEKjDCdd/x2lviduCclLKVq1msEGI3WqWhvcBgKWXhr3XSFY3LBCa4qgFNA9q77vk+cCfgB5xFW2Wz86fXEEIY0RL4uAGD0MpITQMGupp8Bbzg6vsGwF0IUVnrOdUB3geauJ7TCuBZKaXt1/quKIqiKIqiXL2SnCy2zPmYqMbNuGbAoP85Vnr4CEuXLSXXZKJNaird7xuC7803I3Q6pJR8klHAa8lZ+JsreMZaxKN33kZZ2R4Sjo0EewzpK/piK19CvjWNIs9IimPuJCStmCPeTXCrzmNupB9PBeRSP8FEoiGLI4bzmKUXO+3xjOnTkRuya6hYeJJCfTknq4xUOp14B3uQlWLDHl3C8pAPEb52RoY1pdfJ7/hI+LDF14fbwq9lwg3vU5xpZdCcnRyx1/BI22hevKsFNpuNJ/YlscLioH/aYcYtnU5BkYOsSgPIADxDLIQN8OeEbhVFyWsIDIStKT05WTGAIX51SFmRjs2YT0vDpxS9ZCfIXEqTA+UYpIPDOl9eaBTGR12mInensG7VVMLi63Pni5N+W0DFZoYdb2mlkt084dZ3oe0w0Ol+38FXlH8JIYQf8BrwgJRyRa1DR4AhrjbuwBS0eaw7sAp4RkppcR1/GG1eGwjsAkZKKbNdxyTwBDAabd5bVwhxIzATCEdbldIMWCSlnCOEGAaMkFJ2dZ1/WXPvf5pLBVXOAA4hxAJgKfCjlLKk1vFhrl89gHy0oMSHwP2/dlEp5UYhxFQuvv1nMHAzkIEWvHgOePFyPozLSuCxWnW5D6B98cqAp4HlQog4KaX1wglCCBPwNVopqfuklA4hxGtAZ7SonwTWAC9LKV9xlZr6acAoAm0J1kG0AMsGtK1O7/2GviuKoiiKoii/kb2mhm/em47eYKDvk8+h0+sBsKWnk/ree6x3OCjz86N3aCjXjh+PcHMDwCElzyWlsSSvlLqFOYx2t3P3nf0pKdnNkcOPUphYh+JDYVhqvsXDzYeChjewxVaPCaUGkoQ/wWUnmBoezo1xUOe4lX1u+WTpi0l3+JNojGZxt/Z4bc+h0lrDfpFFQVEY7u5lOBx+lFVa2d30awrtu7nH1I3uuYfRH9jKs/WDSDMZeMr7Vgbau5Lw/mKWHkwj2mnn6XAj9Xdu4tjiDMbdNIAjjZozfO1X3L9xJY4gT9xbNcO3RQfcGjekMnAHe4uW4zSvpLQknvdP3kuDoCbcXlIDP6wnRu4nqOMxSm+30yDNnZj0POxOQaFOzyftrmNR+9c4MHcBaceO0KLnjfQcPhKD67ldltMbYcNYKE2HVoOh92vgHfIHjL6i/KtcgxYoWfMrbd4E6qHNY2vQquZOAMYJIXoCb6CVTT4BvI02z+9W6/zbgU6ARQgRjDZPHoZWFehxtAUVi37h3pece/8T/WpQRUpZ7sqp8gLwGRAuhFgPPCylzEOLhr0jpTwPIIQYByQKIYZfRZ8+l1KecV3vK6Dfbzw/G22ViT9QJaX8otaxGUKIl4FGQILrPV+0FTYJwNO19qANQVt1k+/qy6vAbOCVi91USnmo1stUIcRs4HpUUEVRFEVRFOUPtePLeeSnnqP/2FfwDQ5BOhzkv/U2qatX80O367D5+jJowAAatmjxn3NyCgqYtnUnpJ3joWordaMi6d9vCCmnNrJv42sUHKuL3aLDx6hD37Q/84mmvMrBy3ZvMqodxGZ8x4IGDQgM9aZVahoH3IqxYGOfLYoaYxCrQ+KRWzIo8rOyo7gcaQ3Gw82KpdqPnIhTHPNYwCPb9dQ7aQe+51ic4N3bgxDA+KUOWqauJpvV+KLtbwfguI60+tGMve9x0gPDmbbvY+7pEIzbK1vRBUYBUF5+nKRT46isSKLcGkzxyUYMLPuOmOpArCu3E1hwDFsnC+V3OKkxOGmXYMC/PJs8mxdhblXs6DSIifWfYP3rUzCXldL7kSdpeUOfyx+M0gzY8AKc/hZCmsCw9RDX5XcaaUX51wsGCmtvyxFC7AGaogVbbkL7I6OllLLYdXwqWmBlHNocd56U8rDr2DigxBX4SHVd8o1a594NnJBSrnS9/gBt0cNFXcbc+x/pkolqpZRJaJEphBCNgS/QAgWDgEggrVbzNNc1w66iT7m1fjYD3r/x/Ci0lSWlAEKI54CH0Poq0YIowbXadwaMwKCfJPW52GeL/KWbCiEaAu+gbTvyRHsOh36pvaIoiqIoinL1kg/u48iGdbS9uR/123dCSknu5Mmc2/Qdu/rciM7Tk+H33UdUVBQVFRUkJiaScPw4udnZ+AGV7h5EBAaQmZHB9DffwFiUibEwlCCPcPIjOzLLpw4lFgd9In3pXOmgoqqaJqe/JKVTCw7rQnnEfJJkQwVuDgPf1tTDw82TxcYIyDOzPzibzOQAhM6EDh2Veid7Yj/h5hOpDD5cic5dT0CrclY1NTIr1oe67lG8VfcZfNoEM23taQ6WWunl4eDZbmY88ndzojSdUS3GYjV6sjSolK5jPwa99s95p9NOWtonpKTOBHxISrqOwoJGBB48iT1MT+fojZS09ORA5zDcgmpoWBxM6NlMdDUWtlii6O6VRU6DnjRw68fXk8bh6R/Ava9OJ7xeg8sbiBor7J0JO2aAENrKlM6Pgd74h429ovwLFQHBtfOdSCmvBRBCZKLNwz2BQ1raUUBbcKB3/RwJHL5wQEpZKYQoQptDp7rezqh1v8jar6WU0nWfi7qMufc/0m8qqSylPCWEmI9W0Qe0VSGxtZrEAHYgD+1Bel44IITQA7XX/P1RWYnvAA5LKauEENehJfG5AS3C5hRClKB9sS74DjgGbBVCdHetwIH/frYTrtcxrvd+qe+z0PayDZJSVgghRgN3/Z4fTFEURVEURfmv8sICNs16j9C4elw3RFsoXTT7U1I2bGT7TX3wCQhg4MCB5ObmsnXrVlJSUpBSUunjx/H4ZkRUlTIiOpTsIycwpyZRHRxBdVA01YGx/OiI5Kg9lOtiA+hv9CZ7bx4Op53WCR/h1Taep0Qs9xgTseiqCbH587E9HA+jnkWekQirZK1MxZkcjk5UI5wenA7ZRVTeeiYsNaN3SHxbGfGvm8bE+DA2Gd3oHdubyV0mcyLDzIQFq+jAfl4MP0ao4zTiiGRTndsY1W4mAW5ufNWmIY29TP95DmZzGidPjqGs/AhOW2v2HWyIf1Y5A3Z8icDAEdt1+DQ/RzvO0ONcGvrz7uhqzpBSGcBiXRSDg/NwEMKx0mtJXDuT2JZt6Pvkc3j6XmZlnjObtNUpJSnQtD/cOAX8o/+IIVeUf7u9aIlo+6Pl8PypQrT8ns2klFkXOf4/83chhBcQBNRuW3uum4OW2uJCe1H7dW2XOff+R7pU9Z/GwC3AMillphAiGm2Fyo+uJkuAF4QQG9DykUx1tbULIc4AHkKIW9ACF+PRliRdkAf0FkLopJTOq/kQrsGNREuCO4L/bhnyQQvyFAAGIcSLaNGy/yGlnO5K6HMhsFLo+mwvCyEOoH2xJqCt0rnQ9yAhhJ+UsqzWvcqBStdzG+W6r6IoiqIoivI7czocrJ/5Fg67nVtHP4/BaKR05SoyZs1iT/9+GE0mQkJCmDt3Lna7HT9fX+rFx/OpKZhk/1Bu3b6SRmcT2Acg9IQ1KcK7YR6zTw4lhlKaGzJpYyrEL7OKzNxgokU18funYYr0Z3xYW/o7T+CJoKG1HhOd7ugMgiV+kRjKnWyy5WEvD0UHVLiXU+FczPBN6XiUW/Hp2JDgiB+x+NgYERdHosPBEy1H8rBXQ07OH01E5laWiEIwggxtg7XhS7zu35t5pdDS28SilvGEuWurP6SUZGcv42zyFKRTkJPUleSCusQnn8M9s5rTzYZiaWsmsNUKYnMLkVVGjPYanNLCrsIY3gvz5rF6QQSdTGd51Q3k5fxApzvu4dqBg9Hp9L/2+DXF52HjeDizAYIbwv2roF7PP3LYFeVfTUpZ6kpL8bFrDrwJraRyS8ALcKKl7XhXCPGElDJfCBEFNJdSbkKb4y4RQiwGktDm7/tqbf35qW+BD13FZb4BRqIlrL2Yy5p7/xNdaqVKBVqSmmeFEP5oW2q+Aca6js9DC2bsADzQBvVJAFcFoMeAOWjLjaajVei5YDlwH1AkhEiRUra9gv5HuirwCLRkOHuA7lLKC0GfTWj5Us6gfdne5X+XM/2HlPJ1V2BliyuBz2S0L8GxWv2d7Gp7SgixBDjvWoHTFG1v2ado0bkjwDJA/a2iKIqiKIryB9i7YglZp07S94kxBEREUbljB5kTJvDjrbdgNhhwWiyknEvGVzqQBZkUZVQz+7ZhVHr6cvf2lXTz9qIwpBcOUyV1eyymsCaQiQdH0jE0midva0rWzlMcPbmfQvfTuAWfxZmYgAjxYun1fWhqPoe/9KRNdTOelFU49DqWBAbhWSzZ5pGFtTAEgZMc02ZuPLKVkNwKTK2aEdqqBE/bdk4HujMmtAFZNhvTjHHcvH4KoqaKetKNJGdLvOqPwv+OwSTpAhh1Mo3TpVYeqRPC+PgIPPRa5ZxqWyGnTo2nsHAr+tJwDie0w+zwxZRaSKJfPxpEWolsv5C2lceJPmTDaHdQFXYt3x01097/PF1D0omJ64oz4RSLcjrh1JvpP/YV6rfvdOmHbzPDrndh9/va9p7er0OnkWD4DYlsFUW5Iq4FAVlo886FaPPc82h5UPegLYCYAPzoSjSbhbarYpOUcosQ4hW0VS4Brvb3/sq9Cl15VT4AFqBV/znIxcs2X/bc+59G/G8aEUX5Y7Rv314ePHjwr+6GoiiKoij/AOmJCSyf/DLNut3ATY+NxnI8kdShQznc5VrOBgUBoK8swzP7PCFR0VjqNeHt2DbY9HrG6y10MzVk45LTFMQepFurOeRbQthz7FlG3dGFMJMHWxckUZpnpnGnMDx++ILj7sXkRkT85/6htmC6OJvxoLOQUp2OecHu1Cvy43BUERmJvjixE547m+anTuIWF0dovxZ4FS9CCjvbYtx5Q4RSabfzbm4eHfHkG1t7vrG2opVsy/A72uDZIZw5mQVMPpeDv1HP+41j6BH03//wLSjYQtLJF7HXlFP5YySHa7phrHaw11yPXt4hxEWtpKXHRqKzLRgdEtn4Vo7bW7P5mx8o9q0hqYuT2T5xJG/dy57CWIKj6tBv7AQCIqJ+/cFLCUnrYNN4KMuAFndruVN8fzHtoKIotQghDkkp2//V/bhSQggd2kKJIVLKbX91f/4uflNOFUVRFEVRFEX5K5nLSlk/820CIqK44cGR2NLTyRg5kvNNm/43oGKuoFV0BDdPfZMt5zN4LL0YISUfhfngPOzFpITjuDfYw31NF1NcHkPDile5Z3RHDmxMY9fWE3gHuNN3WD0cE1+gJi2B2269i3M9e7Noy37iLT501zfkIWchxToDMwKt1CsMJb2pjfQ93iDttDgxk3B7AaFjRhEgNiIyZ1MREsb3flbeqvFFL63MK5dUxz9Nm6NN8JIevK7zpOsDLSiv68PDx86zrbiCG4N8eadxDMFu2j/Z7fZKTh9/hdySteiz9KQkdiLDVI+cGh+cXn6MjVlJfQ4SVmhB54S8+N4Edx/Pt8s3cG7/D5yLrMLeux4ftB7Hts/mkFEYR+M2Lbhx9ESMHh6//uALz8L6sXB+G4Q2U1V9FOVfQgjRB9iHlqtlLNoukR9/9aR/mT89qCKEGAIMlVLeeIl2nwBZUsrX/5yeKYqiKIqiKH9n0ulkw8fvYq2q5M5xryKqqkgb8TC5fn4cqF8PAJ3VTLv4GPo8/Dif/3iIV806PKWTd0OD+WpFOrurLfRouI17Gq9CFDXlptAZVDUPYfm0Q5QVWGjeLYo2rXXkjhiOoySfwEfGMj+6PvPXF9DLGkB/9zgedxaTo9Mzzj+PjsVNKG/jxsHtFvRS0PLY+wT4Wqk3+i6Mh99ACh0FsdEcKStiSk0gwVLwcaOHWVTQnbk/ZtNRZ+A1Tx/ih7dgu4eT0QdOYXY4ebNhHYZGBnGhgkdh+mZOnnieGmM5ut3e7HfeSpXJDatbBU+FrCC+PBNTvpNqvYHNYd2J6jWBGK9IFk6fREl2FgcbF9PhtgHc6ujEmldexlFTw02PPUPTbj3/c4+LP3QJ+z6B714BowlumgYdRvyn6pCiKP9416CVZHYDTgK3Syktf22X/l4uuf1HCJEKjJBSbvlTeqT8I6ntP4qiKIqiXK2D61bywxfzuOHBUbTs2p20YcMpzMpic9+bsdntCJuVa+rHccPQh5m6YQufeAQT6LTTPQc2niykBskzrbfQPGwdPgUdiImdzonTlZzam4tvsAc97m+CX14iWU+OBqmjZsRLPFZmoKjMzHDrefqa2vKcqCZJBw/7n2VoaQccLX1YvSsHo11P64T3MIZV02ZABIaUdTiDG2CrSmej3sCk4EAaeUbyRs+5TFidza7kQu7BjSeD/fEb2pTJxcUsyC6imbcHs5rG0dBLWzlSmreP0/vGUemdhr5QYEnpwVFLMI3Faa712E+YpQIJpHvWYWade/BqPYgxDeMpPnmMNe9OxVxjZk+7Mh675UWMO9JI2LyBsPj69H1yLIGRl9juU2OBb56BhCXQ8Gbo9wF4h/7xA60o/1BXu/3HNTePBCJdxVUuvH8EaA3U/ZWks5dz/e3AF1LKOVd6jX8jFWJWFEVRFEVR/vbyUs6xc8lC6nfoTMsevcl84gkqTp9m16B7sVVXI2psdGnaiJ6Dh/Lexi3MMoUQZLNh/bGEb6xOWhuNPNP1G2rcvsUj41bKqh9i5cJz6PSC1r2i6XBLHBVLvyBz+tvofCI50vdJXsl0cE3JHp7DhDPwWl6QZpJ0grv8Exha2g3R2I9VezJwsxtod+QdymPM9HywO/oD7yPdvRGFZ5kb4s8n3r5cE9GZJ5q9zvB5SWSXWBiHBwMahZHdL5ZByemcNVczMjqEcfERuOt0VJQmcnrPi5R5JKEDAo43Jp84oiwH6cVZjNJBpdODQ4buvBn1IOUtGjO9UTRNjTqOrl/NjsXzKfG2caqHgUltp3B89jKKMtNpf9uddL33fvQG468/8LIsWDYEso/A9S/C9S+ATvdnDLWiKL8uBa0i70wAIUQLwPMv7dG/3BWvVBFCPIyWYTgQ2AWMlFJmCyHi0AbaKKW0u9puxxXxEkIMc12vq6sM1DvAELTqQWnAICllohBiPpAppXxZCBEALEKrRGQAdrvul1nr+jvRqu20RKvfPfhC9E4IsRy4DjABCcAoKeWJK3heyhVSK1UURVEURblSNdVWvnhxNDaLmfunz6R02nRKV6xkz/BhZFos4LDTrXkTet4zhOXbdzC22h1hdyL3ltDUrOOB5iE0iPiIfHsiVQmPU5Adg9AJmnWLpG2fWEwekDtxEmWrV2OIbMu3bQayveoE0dV5VAR24YjRmzIk6Bzc5HeIl0puwBDvz/ITGRjNetoefZfU+FJue/ZRPDc8A0CZjwevRwazyaajb92+9Ax6kjHLEnFzSKY4POh8XQyrWvoy6Vw2AUY9M5vE0i3Qh6qqFM7seYFi3SGEFQLSfPF2+hJWmIwvlVTrDGQFRJGY1499jhvY2RaGmKqoX5BJznp0zEkAACAASURBVJlT5KeeRzqdpIZVwc2NGezsyb4li/Hw8uamx58lrmWbSz/wtL3w1f3aSpU7ZkOTW//gEVaUf4ffaaXKHKC/lLKD6723gRK0SrV1XT/PBG4GzGgllqdKKZ0X5uJoOVEeQqvu+5iUcoMQYgrwIlCDVhp5vpTyCSHE+8CdgB9wFhgtpdzpuvcktEq4VuAOIB0t1cdB1/EXgYeBULRKQC9JKVdd6ef/u7qilSquksNvADcCJ4C3gaVAt994qRtd5zREK4ncGG1gf0oHfA4MRCvPPA/4ELi9VpvBaF+cDGADWonjF13HNgAPAjZgGlopqNa/sa+KoiiKoijKX2D7wjkU52Rx10uvUzV/AWUrVnLygfu1gIrTSffWLek+YCC7Dh1mYoXE4a7H+2Ap91WaGHBHNPm5U0g6Hkt5ymB0egPNro+kXZ9YvPzdqcnPJ+2Rp7AmJKBrdCsfxTbmhDWVdN8mHNW1whNo6O/G8cCddLWdZVzWo+ijfFiVlIXRLGh35B0ONiritnHj8fx6BABZYb68FBLMoSo7DzR9AK/K2xm5KIGGegNTpSdRAxrwvIeVdclZNCvOYVjqUcxJVrb7b8fhn4nOIYnIsRGS5yCkqhAngvP6KLYHdiCv4CbMhyopNOXiWzOX/l+XUgkcdzNiDXXndHwZ2QFmbrtmEJG7KthzeAHxbTvQZ+TTePr5X/phH5gLG54H/xgY+g2ENv5Dx1ZRlN/sR+B+IUQTtPLF9wJd0IIqoAVU/IB4IAj4DsgB5rqOd0IrjxwMPALMFUJESSlfEkJ04efbfw4Ar6HN158Glgsh4qSUVtfxfmhBl+GuPnwIdHYdO4e2uCEXuBv4QghRX0qZ83s9jL+DK93+MwSYJ6U8DCCEGAeUuFap/BY1gA9aMGW/lDLpYo2klEVotbRx3W8K8NMSTp9LKc+4jn+FNrgXzp9X69xJrr76SSnLfmN/FUVRFEVRlD/R2QN7ObZlI+1vuxPfE6fJ/XgWGQPuJNFmAym5vm0rut8+gFOnT/Pc+TxKgsLxPlzIkFIj994eyYFdaynKHIRO6GnerQ5tb4rDO8AdgKq9e0l9+hnsVWbWtBvMoqgW1OiMmJw13KA3cb3Rg61NssmqXM74os70Kn8UQ7An36YUIioctDvyHttbVHH9mGeJXvko4CSjbhxjPOwkVdl5us2zHD/ZmjVHz9BLZ2S8hw/Fd8bSNy+T3HI93Q9t5O6gTfg2zkPnbsOr0k5kspXQfBtGp6QIf753NudkgS/VVUaotgLfABBqCiauRQvKQwQ7nQnsqTmGh5uJfvX68ahsx9EFS0ivKKfHsEdoc9Ntv56MFsBerQVTDs2H+r1hwBwwXUYQRlGUv8Ii4AHgByAJyHK9r0cLsrSWUlYAFUKIGcD9/Deokial/AxACLEA+BgIQwt8/IyU8otaL2cIIV4GGqHtAAHYJaVc77reImB0rXOX1zp3mStu0BFYcyUf+u/qSoMqkcDhCy+klJVCiCIgiv8O6CVJKb8XQnwIfATECiFWAs9JKctrtxNCeALvAjcBAa63fYQQeimlw/W69pfADHi7ztUDU9AiYyGA09UmGC3apiiKoiiKovwNVRYX8d3smYTWrUersGhynh5Ncc/u7DEYQEq6tmlFj9vvJDMzkzH7jpEa3QDTqRIGnIdW0UY2LUsH0Yy6LRx0G9QN7wAt+at0OMh87wMqPvuMTJ9Qplz/MAVefjSsSubWsFhuNodhCTAwx3s7/c6G0tr8DNIAxqbBbDySg6PcQrsjH7C9lYnYh2+k05pnwFlDbvNrGetIJclq4NnWL7PqhyiOZ2XzKO48EOzH5/ElzMrJxmQ183zBQlq03YlbTQ3h+VYiM6vxqnFgF24cl005KJuxv6YBEXZ3/Kor0Msqqvzc8ezZhv7XNWVr0XbeO72UfHM+dbzrMLbt83RyNubkho3sPvgRgVHR3PniJELj4i/9oCty4asHIGMfdH0Wer4MOv0fO7iKolyNRcAOtO0+C2u9HwwY0dJqXJCGNk+/4D/zZiml2RVw9f6lGwkhnkPbKhQJSMDXdZ+fXQ9tHu4hhDBIKe1CiAeAZ4E413Hvn5z7j3ClQZVsIPbCCyGEF9rSoiygyvW2J3AhOBL+SxeSUn4AfCCECAW+Qqt9/cpPmo1Bi4Z1klLmCiFaA0fQamRfymCgP9ALSEVbClVymecqiqIoiqIofwHpdLLho3ew26q5ofet5I55nqpWLdkcolWf6dSyBb1uv5PCwkJe2fg9h+q1xC2jgttO2GnhrSc9rQb/unvpeltXYpv2/M91bXl57B8+iqDzSWyO6cDXDa+lQ/kBOmTn0bHBPbibwynSFSOKJc8UX4fFvRrvXlGU+nuxcfFJHGXltD36IXta1EcMsDNw6wywV1PScSDPVe7ihNXA8EZj+HhdEFXmcqbYJI3siTzu7cmPfh1pYUlglOd7BEaVEpdiISbLgg4o923EOmddjjnqkeJeF7/qeFoVC6SAwkYmwq8No009C1tSlnL3969hc9r4P/buOzyqKn3g+PedSe+dJJAGhN57EVBQFAsq2FDBvi4W1J+sbW1rwa7o2kFAUBEUpSNFKYK00FvoISG915nJlPP7497sRpYSQLGdz/P4MPfcdu4djJ4357xv77jePNXjKeLzfdj05Wzm7Z2BX3AIva+5ke5Dh+Ht63fqF300DWbcDPZyuGYytBv2K32jmqb9UpRSR0TkMHApRsCjThHGapAkjPLHAIk0fOLDzxKuikg/4BFgELDLzMvSoLG0iCRh5HMZBKxVSrlFZGtDzv2jaWhQxVtE6v9Ung5MF5EvMKYbjQPW15VvEpFs4GYR+Qi4BWh2vIuKSHeMfCmbMYIxdv47k6S+YMAGlIlIBPBMA/tdd64DKMYI9Iw7jXM1TdM0TdO030Dagtlk7tzGBcNGUPXkM9hjG7EwtTmI0KFlC4YMv4bKykpenTWHJc074V1i4+Itdrp4WSmrUMR1/YKBw0YTFvnfnJBrv1yAvPQMga5a3u94Fc6Aau7e/yUtnCHEdrgDsYSh3E4iicBVtA/boa+wF6Tzw9Zh5Mb1xr8mhw67P2VrmwEUD1rFc5u3Iu5aanrezoPVy9hm8+JvbcYwaXYU/rZ83sqfSHkb+FuP/yPH2phr1HSultnE5leResiBj9NJYeKlfF2UTH6FLxXeUYRVpNA5zx9ngJWg/lEMHJTAAUcaU3c/x/tLN+Hv5c/VqVdzQ/PrsO84wsa3Z7ApO4uQ6EYMvO1u2p1/Ed5+DQimKAVbpsGChyE4Du5YCrHtfsVvVNO0X9gdQLhSqlpE6sb1boyJCi+as0QiMGaKvN7Aa+Zj5GKpE4yRtLYQ8DITz4Y08FqBGEGaQgARuQ34U/6QaWhQZeEx2y9izCaZhbEc5yeMtVt17sJYmzUOY+3WTye4bgjGsp6mGAGVxcBrxzluPPAFRuQtB3iDnyepPZmpwMUY0bkSs9+jG3iupmmapmmado7lHzrA6ulTadaxK8ETP6XaamF+1854LFZSmjRh2IgbsdvtfDBjJl8ntQWHm/6ba+hZa6Xa4ya+1yT6Dbn7PwGVQ7llLBr7HOdv+o6s4BiWtG1Hs9qdtN+TR6N+dxLm1QYUuHCzMmwLnpahXNe8G1m7U9i43o3NIYhtBY1y9rKr9TUU95jCc4f2IsqNq9ONPGhfzeZq+Fv8cOZ94UvX5JXc0GwOPwb0Ywp34YeLN8J/onXOdzRNLya00kltbCc+UoPIzfTglEDCylKItkfglxRE72sTSWgfxIIj8xm95hEyKzOJD4xnbLexXNbkYg6v+onvP32RqpJiopNSuHTMP2jZ6zws1gYu2XFUwYL/g+0zoOn5xgyVgIhf7fvUNO2Xp5Q6eIJd92Mkqz2EMcaegFHopSHeBj4VkdEYS4weAr7DSIhbjTF2z2pg/3ab+VzWYkycmIpRxfdP55QllTXtl6BLKmuapmma1hBOu51pjz+I01bDgKIaqo9ksPT8/tgDQ4gMC+O+Bx7A7XYzZfp03g5tQpF/CL3TKrk404NbXDTu8z49LriFuMZXU1TlYPz0NXSc8jrtig+TFpdKbrTQuKKStjWBRA0cC3YLLtzMjljOwqQC3hz8T1r4xbJm1gF2r86h1tuGU9US6ArFY3EQlPgct9mNWfXu5hfyUFgZy4sLuKmqBft/6siR9uE81O0DpnkeYIV3T3qF+PGszyJi1rxMfJ4dd0AES5rdzao94ON0EFiViK89kebdGtFrcDKecBvT06czc99Myh3ltI9qz6i2o+gd0o3tixewbclCHDXVJLbrQPeh15DUofOpk9DWl7cTvroVSg7CgMeg/1idP0XTzpGzLams/T6daU6VPzwRScRYZxZaL9mtpmmapmma9htaMXUipbnZDAiIpOLgAVb27409IBg/X19G33cfSilmz5nDFJ9wCoPC6LyjisFH3HgsLpr0H0/HnsOJjb+K95cfYOVnc3lo4xf4umvZkJRAeSh0zykjse31+IS3A7uw2+8QTyVNoWubu1jY/RkOphXw6fQ1OGwuBMHL6UNB6CHSI2dwqWUN11RVgMULFdOKJ6O9WJ5XwLAdPrA7gnVdWvFi+zd43fIv9lhTeSDOj5t33Elc+k4sHmFPu1v4KLsToduz8Hf74l3ZgYRuqQy7phUZtoO8tftlFv6wEJfHxaDEQYxqO4pU7yTS5n3DlKUTcDudpPbsQ/ehw4ltlnp6L1Ypo7LPokeNqj6j5kJKv1/lO9Q07dwTkSnAUaXUkw04dgX/WzpZO0OnDKqISAZGLpIUpVS12XYncLNS6vxfq2PmfeOBeKVUUb32LUAnsz8ZZ3p9pVQmJ8lyrGmapmmapp1b+zf8xPbvv6N1eAzudZtY07Mz1WExWK1WRt9zD15eXixZsoRPyx0cSGlGq71VXLHHiVidNDn/NVp2uIiklDuZsPIg+W+N59l931PiH8j6lFhC8GewLRm/vldiFS9cuHk3fjY5HZJ4rdlUqrdk89HYZVBj/O9xiX8ue2N+pH3gWm4VSC3JwqI84BOM8gnkhaRWzM/bwBXrPDQ7mMKznYfyRNspTPK/jT2Syudea+g991kCamopimvFeMtDeG3LJcyaiTiiyfNvxdjHe5Dn3sH9P97D2ty1+Hv5c02LaxjZeiSRKpgNc2fxyZIXcDudtOl/AT2vvo7wuManeIvHYa+A+Q/CzlnQbCBc/TEERf/C356maeeKGRTpCMQqpRy/cXf+8ho6U8UKPMC5T/J6GBiBsSYMEWmPEeDRNE3TNE3T/kQqS4pY8tG/iQwKIWzDZn7q3IqK2CQQ4eaRIwkNDWXdunV8vvcwG9v1pOmhGq7dWou3Vy2NB71IUvOeNG/xKD/tL6Tg1Ve54eAqMiNC2N8klu618cQmXIQ10AgkZPrm80PfoyT7tiFsjZWtX6ZjxYrCSl7wQYobT+LvjsO0d1uwlFQbiVw73QRH0/CUHuHVyE7MLNjAJWke2mY14+VOo7g6cQ2rGnVhq+rCksNP0CFrDTX+vnze+EEO72uLt/8+lEWx39aMln278PTAGF7Z+DTLMpcR4x/DA10e4NoW1+LtgI1zZ/HNkgW4a520Pm8AvYbfcGbBFIDcbcZyn9IMGPiUUTLZYvnFvjdN084tEUkG+gHlwFDgq9+yP5pReachXgPGikjY8XaKSCsRWSoiJSKyV0SuM9tTRKRMRCzm9gQRKah33jQRefAk950GjKq3fQs/r8ONiPiKyOsikiki+SLyoYj4m/seFZH1ddmQRWS0iOwSET8RSRYRVW9fhIhMFpEcESkVkdn17nGXiBwwn2+uiMQ38L1pmqZpmqZpp6A8Hr57701cNhtNdu1jfeumVCS0BBGGDBlCSkoKu3fv5rMf17KsdTdSMmzclGbHz6uWJhf9i7ikVFq3fYmDBVUsfOQFhh1cxf7oCOxNe3Fp2FU0bnUjrkCjYMWKRpt5uvEESpcrmN6M8MxkrFix+ZRiTxrHmJBH+KBsNx1rXVhSB8Mlr0B8F9gyjdqso7xqa87nlt0M2ANJWYlMaHs78aE5eFq5SHP15Ycdf6dD1hp2xrXgo/yPyNofhytwN5X4sZgO3DLqInp0yOK6+cNZeXQlD3Z5kO+Gf8eNSdey7etvmHDf7WyaP5vUHn249c33GXLfw2cWUFEKNkyAiReC0w63LjDzp+iAiqb9wY0C1gFTMMbH/0NEwkVkvogUmmPb+SLS5JjDmonIBhGpEJE5ZpXduvO/EpE8ESkXkVUi0rbeviki8r6ILBKRKhFZIyKxIjLevFe6iHSud/xjInJQRCpFZLeIXP2Lvo3fgYbOVEkDVgBjgZ+t0RKRQGAp8DQwBGgPLBWRnWbG3wqgM7AJ6A9UiUhrpdQeYADw5knuuw4YKSKtMTIO3wD0BV6od8zLGCWbO2HU5P7C7MvjGMGgy4AnReRzjJk2A5VS9uMk9JoGVAFtzT/7mM83EHgJGAzswihH9aX5LJqmaZqmadpZ2jjvGzJ3bielsIwdyY2patoGZbHQqVMnevbsSWZmJpPmL2R+h34kZzsZscFGgLWW+MH/IiI2jg4d3+VwsYMPx4zjjj1L2B+bRHzrK4kNbYfTXY1dHCgFM3w3oTJTuMr2CILx/4IqsIRI7xVc6TsDf0cthCVBn/shNAE2fIRn/mNUFoRTXtqdz4Pz+GJAGZ2PWEk6FM/Gdn8nTzm5ocdeltgH8v3OO4mxF/JD6EVs3zGSyuh9uFU1O1xxeGLbMOGapkxOf40laUtoG9mWF897kThLFOtmfM6W7+bjrHXQqk9/eg2/gcjGCWf+Qm1lMG8M7J4DqYPhqg8hMPIX+rY0TfuNjcIYQ68H1olII6VU/jHHWIDJwHUYq04mAe/y8wq6ozCq5B7GmLjwDnCzuW8RcDtQC7wCfI4x3q5znXnuLoxKwWuBZ4CHgX+Z/bvAPPYgxsyaPOBa4DMRaa6Uyj3jN/A7czqJap8G1ojI28e0Xw5kKKUmm9tbRGQWxgv7F7ASGCAi2eb+r81tO0ZJ5W2nuG/dbJWVwB6M0sgAiBEZ+RvQQSlVYraNwwisPK6U8pj1uTcD1wOvKqW2HHsDEYnDCAhFKqVKzeaV5p83AZOUUpvNYx8HSkUk+WxyumiapmmapmmQuXMba76cSniNgyNR4diat8Vt9SIuLo6hQ4dSVFTEh9NnMrddL1LyFNesryHYy0ncRc8SFBVCp86fsK/Axfv/eJN7ts3mQEInWnS4jkBrOIXOIqK9o6h2eUirduPn6QBAcJSV4pgVNLF/zrU1+dhE8AlNhCveAeVCrXid6o2bqTgaRuXRRIq8nXwytISNSULzHD86H4ijtucYVhY6uKPvRrZUtmPR7rvxExufB91NfmZHaqK34cCbH2pbMvS8TnRpncW9K2+koraCMZ3HcH3iMLYtnMeCxQtwOuy07N2P3sNHENnkLIIpAAe+h/kPQUU2XPQc9L5fz07RtD8JETkPSAJmKqWKROQgcCNGqeP/UEoVA7PqnfcisPyYy01TSu009z8FbBWRW5RSbqXUpHrnPosx/g1VSpWbzd8qpTaZ+78F7lFKTTW3ZwD31etL/eVJM8zxdA9gzpm+h9+bBgdVlFI7RWQ+8BhGcKNOEtBTRMqOue408/NKjLVeR4FVGDNeRmLUzP5RKeU5xa2nmeelcMzSHyAaI8fKpnozTwQjGlfX7wwRWQ5cCrx3gnskACX1Air1xWMEZequVyUixUBjIOMUfdc0TdM0TdNOoCQnm7mvv4hvrZMyfx9cqW2p9fIhICCAUaNGUVZZxZsTP2Vu6660yPZiyOYawrxdxF70L3zDrXTuMoVdecI7T7/H/6XN5GjqhbRtfSXiESo8QrR3FJke2FHjweWBxNgCjkZO4RDpjCktJ8zjwRbZDP9+D6N8grF99gTlm7KoyArCbYuEoACWXR7P1GZHceKi255wetWG0/jip3lgRx5D2xyipMqHGfv+QbW/D69axxGQFU51WDp5nlA2WVrw9IhUfiydyD9WLaJ1RGve7fUmJT9uY8rrd+OsddCy13n0Gn4DUQlJZ/cyS4/A4icgfT5ENIXbFkFCj1/mi9I07ffiFmBJvUIuX5htPwuqiEiA2XYJEG42B4uItV7l26x6pxwBvIEoESkCXsSYJBEN1I3XozDyuADUnxljO872fwrCmJMc/g9INpuCzGv9aZxuSeVnMAIMb9RrywJWKqUuOsE5KzGW4Rw1P68GPsQIqqw8wTn/oZQ6IiKHMYIidxyzuwjjS2urlMr+n5MBEbkM6A18b/bj7uMclgVEiEiYUqrsmH05GIGjuusFApHUmzGjaZqmaZqmnR5bVSXfvvQMrpoaPFYLlmZtqPH2w2KxcOutt1JU7WT8h5+wqmVb2h3xpd/uGqJ9XEQPfBFLqI1OnWeQlu3D2+M+4el1n1PZaQStkgZQ4XKhxEqIl7DNUUuGTUj2S6MqfhazIku4p6ycHnYH9qhULJe8hnXvJgrffI2KPdXUVnqBNYTaFqlsjbHweYtMcqMyiC/z4+IKP9p3SSI+/Alu/P4wraLLaeHawX2HZnAoNJrxvuNJTrdTEbGLPE8w+TFdeXRQDW9s/xvljnJGN7+DVgcC+OHJcbidTlr26UevYTec/cwUpw1Wj4c140EsMOgZ6H0vePn+Ml+Upmm/C2be0OsAq4jkmc2+QJiIdDzm8IeBlkBPpVSeiHQCtgD1c2DU/+GTiJFKowhj5suVwIUYkwhCgdJjzm1on5OACcAgYK1Syi0iW8/kWr9npxVUUUodMKfzjAF2mM3zgZdFZCRGrhEw1ltVKaX2KKX2i4gNY33WS0qpChHJB4YD7zfw1ncA4Uqp6rrEsmZ/PCIyAXhLRO5TShWISGOgnVJqsYhEARPN89cDO0RkjlJq4THPlSsii4D3ReRejJwqvZVSq4DpwHQR+QJjhs44YL1e+qNpmqZpmnZm3C4X894YR3lBHgqFf0pLCnwDARg+fDgZVRY+mTaFvc2b0uFwMF0O2YnzVUT3fx1naCEdO01j3qFAvnj3C17YMB3pfT8JMW046nAThItQXy82VLuxyg6ap/7ApOh8BpbkMDG/ErwDUK2H4Nx6iIK7bqc61xcQ7I2iyYgNIcMXdjQvYUvLcrwsVq71D6Z3kyJaNB9LYNHljPhqG15+Nu7x/4zLMtewulEb5ka+Q9Mfj1IeuZtijz/ljdvRvNVC/rVhAe18WzCmZghZH6xiq8tF6/MG0HPY9UTEH5sz8jQpBXvmweJ/QnkmtBsOFz0PoWdYJUjTtN+7qwA3Rg7T2nrtM/l5cReAYIzJB2VmAtpnjnO9m0VkKkbg5DngazPoEQw4gGKMVSFnUwE4EFBAIYCI3Aa0O4vr/S6d7kwVMF74yLoNpVSliAzGSEbzJkZSnG0YU3zqrAR6KaWy6m23ot6ympNRSh08ye5HMfK9rDODKNnAB8Bi4GPgP0EUEbkD+ESM0szHGokxRSod8MFYc7ZKKbXMXGM2C2Pq1E8YCXM1TdM0TdO006SU4ofJH5K12/j9XGhic476G5V5+vbtS1p5AN99OZPKlEZ0OBRBy2wHCf4Woru/S034Idq3fZ/Xtoew4fO5vL7lWwIHPIZPYAw7axykuMsJCIpic7Udh28Wy8/fjDVrN+OzKohzOnB7h1O6zUHpV2tx2ax4AkLJTAjjUJAfKjwUv85NWRW9gwOOUnpGpXKZ7x6i/fxo2/Jz1MZI/rFqB4WWciaHvEHngn3MSL6AquZv0WTmTkojd1Hu8aY8oT010R/wU3oWd5T3h+05ZLjX0KbfQHpefe2Zl0aur3AvLHoEDq2AmLZGZZ/k887+upqm/Z7dAkxWSmXWbxSRdzGSzC6r1zweY2lQEcbKizf4eZJaMNJsTMEYl68ERpvtUzGS0GYDJcBT9fadFrNwzRsYiWw95rXXnMm1fs9EKfVb90H7C+jWrZtKS0v7rbuhaZqmadpvbNOC2ayYOhGA6JgEjsTE43a7SWnajO2+7UnfnkZwYx+ScpJJKHKRHOZNXMvPKEtaRqOm/+LRHe2oXJTG+3uWEdzlNmxevmy0F9CnphqfiKbstRdwwO3LovYv8UBFJgOrq3C7oGRPEEW7Q0CBq1kie3y8OOoNiR270OnyK1nsXsfEXZ8Q5B3EqMYJpNSuJzKyH6n+z3FkTi5PlZRSYjnKtOBXiK4tZVrrS+nSdTwb3t9AYdBmqpSiOLEbZVEfE5JWTPtDoQjQpv8gel51LWGxcWf/8uwVsPIVWP8h+ATCBf+EbneA9Ux+T6pp2rkmIpuUUt1+635ovyz9E/hPRkQmYVRkKlBKtTPbIoAZGMmBMoDrlFKlZvWktzHy1dQAt9arcnQL/y2f/YJS6lOzvStGRNMfo3zWA0pH5jRN0zRNa4BDmzf8J6ASHxxJRnwiboeD6EZxzCprQmXedppFC40zEomqcpHaJIi4iLkUJC3jQOST3LO6GVFrtzExew9BPUaT6XHyo+xleA34RLQiv3Ybex3t+LHlv5mWl06w20PxwRAKtwQiQSE4+nVgS3UpJS4HyR27cMM1I8gLtfGPdc9zoOwAFyf0Z7DPXqy1G2na+P8I3X0RSzem85qlmGHey/i7z3wcHl8+6DyckX1eY86baRQGbMWGm+rkHtQGfULzxRXElATRql9/+l53E6ExsWf/4qqLYMfXsPpNqCqALiON3CmBf6pcj5qmaX9If/igioicD3ymlDrLhal/GlMwapDXr5T0GPC9UuplEXnM3H4Uo4x0qvlPT4xlUz3rrbvrhrEGbpOIzDWrI30A3IWRo2YhRkbpRefguTRN0zRN+wMrOHyQ2a+9AECS1ZeMlu1wVFcTFhnDp4VJBLoLaR9qJzYrlQCnok2HKGJrlrKz5TI+936TzWvD6LYjg9cqS/FvfRXrayv4MaCIu0ut+Ee2pLJm798sqAAAIABJREFUAetrB7Ot8VJurthAiPJweFkUJPag+ppmbMjaT015HimdunLx8BHYY3x4afNbrFi7gkYBjXi+802ElnyGF4G0DJxC9Zwq3lZzCbTsZaHPAsJUNUtDe/ND87Y81uc+Fr67gxzLJmottdiSuqPck2k1r5IAgrjkvjG07nfB2b0wRxWkL4AdX8HBH0C5oUkPGDEdGnf9Bb4RTdO03wcRSQR2A6H1qhP9YZwyqCIiGcCdSqll5vYNGAPrq5RSp6zec4JrJgOHAW+llOtMrqEdn1Jqlfl+67sSON/8/ClGWetHzfap5kyTdSISJiJx5rFLlVIlACKyFLhERFYAIUqpdWb7VIy1eTqoommapmnaCVUWFzH9qbEoj4emNjdZA/tRVVZGUFgEU4qSifF10tW7gsjsVLAK3QY2JnTPKqb0yOIrNR421nD1gUIeUgrvuM58XZPLikBvXrB7ERzclMri6azwvpz84EMEyLdcYquhujqEwhvvZ+Pmddj3bqVpl+70Gn4D1vhw3t/6Pt+u+5YArwDu63QPPa2HKS6YQJjf+UQduZrd1Z9QbMlirG07wd52lob35tOkIXQOSeOB1hexfFoxB2vW4/SuoTapK755k2myrRq/2BhGPPICkY3PsKKPq9YIoOyYCekLwWWD0AToOwbaXwuN2v6yX4ymaX8o5tg8AEhRSlWbbXcCNyulzv+V7xsPxNcr54yIbMEoUpNyNoVczDwxQac88HfqtGaqmEtC3gQuU0r99Ot0SfsVNFJK5Zqf84BG5ufG/Lw++VGz7WTtR4/Trmmapmmadlw1lRVMefgeXE4nKWXVFFw+jJLiYvyCQplUlEKzUKFnbTFB2U2pDrBw/tVNKV6zicd6hnLANZKEzaWMOFrFMF8/rJZQJpXvYl5YIh9YFGFeiVQc+YL1Mb2ocbspCp/IM5udeLXzsKS4BXt+XEbTLt3pPXwEgYlxTN45mWnrpuFSLka0GsE1jVuSf+Rtim1ZRLkup6oijYqae+mVV4mfy82yiF68l3QtreL9eKtZKhGhd7Ny+kF25SzD6VOBJ7Y1wdsnEZBbS2i31twy5gW8ff1O7wV5PJC1zpiRsms22ErAPxw6jYD210FCT7BYfp0vR9O0PyIr8ABnV5XnTBwGRgD/BjCLvwSc4z78LjX4J7SI3I2RNfjiuoCKiPQSkZ9EpExEtplLceqOXyEiz4vIGhGpFJElZnUegFXmn2UiUiUivUXkWRH5rN75ySKi6kooi0iEiEwWkRwRKRWR2Sfo5xgR2S0iTURkp4hcUW+ft4gUiUjnhj73n405K+Wc5EARkb+JSJqIpBUWFp6LW2qapmma9jtiq6xkykN/p9ZWQ0JxBZVXDCe3uBgv/yAmFzelY5QXPey5hBxNpDjUSt9bWzIzPYPRXRPIt0XRZU0JDx2t4pqAYCxuJ1MKV/JpWBL/9PElzh1G1d6Z7I0LpcaRQGb0VB5Y6Ca8aTlFjjAyVAw3vvgGl499gmW1G7j0m0uZsGMCFyRewIzB73GxTzpH9o7Faa/A6hYC82bSbedu2maVsTqkG5d2eo9Xer3Gu4NH8lL3m4kI7c6mRdms37ECp28pXsExhKz/HJ98O9HD+nPnP14/vYBKTQmsHg9vd4TJQ2Dbl9BsIIyYAQ/vg8vfgqTeOqCiadqxXgPGikjY8XaKSCsRWSoiJSKyV0SuM9tTzHG7xdyeICIF9c6bJiIPnuS+0/h56eZb+HnKCUTEV0ReF5FMEckXkQ9FxN/c96iIrK83vh8tIrtExO90xv4icpeIHDCfb66IxJ/Gu/tVNHSmymjgPGCQUmobgIg0BhZglCL+DhgEzBKRVkqpuhH0jRh5O7IwloiMxcjn0R8j0hVWt/xHRC4+RR+mAVVAW/PPPsceICJPYyxHGaCUKjSXp9wMzDMPuRTIVUptaeBz/1nki0icUirXXN5T9y9PNlB/fmoTsy2b/y4XqmtfYbY3Oc7xx6WU+hijrDXdunXTyWw1TdM07S+kpryMTx+5H1tlBXGllajLruZwUREWHz+mlqYyIMGHRqV5hOYkUxDhRfxNzfi/w9nkpATTuXg9seuSucHhoG1wJO7ig8ws2cjk1Et5zMef7rXe2Hd9Q17CIXJqHiYn4nvuWZFHcEQlPgFO1uYmMvQfT7LFcpB3Zj9AdlU2PeN6Mqbj3/GrWErWrlsBsDo9NMnLJSHLjq/Lw/dhPXg9+VbSw9rxaosmnO/y4ejqfLbsLeXo/lLKfPfi8C/Ar9aN18aFlAfV0nL0cG7uf3fDX0zhPqN6z7bp4KyB5H4w6CloeSn4/mFnv2uadu6kYYzNxvLfwiIAiEggsBR4GmMc3h5YKiI7zfLGFUBnYBPGmLxKRForpfYAAzBWpZzIOmCkiLQG9gE3AH2BF+od8zLQDGNJkBOjrPPTwOMYwaDLgCdF5HOMmTYDlVJ2o37Kzxx37C8iA4GXgMHALuB14EvzWX4zDQ2qXAQsB3bUa7sZWKiUWmhuLxWRNIzAxadm22Sl1D4AEZkJDD2TTpqBgCFApJksFYxa2vUOkTeBHsAFSqlys/0z4CkRCVFKVWAEgKadSR/+4OZiRBJfNv+cU6/9PhH5EiNRbbkZeFkMjBORcPO4wcDjSqkSEakQkV4YiWpHYU7/0jRN0zRNq+N02PniqbHUlJUSUVmDf/+L2FZeDlYfPqtoxWVNvfEuzCc6J4nicC8KropjYn4hsT4VXJexlKBNF3C5l53EwHCcWetYXLSVDzqN4EnvAAbXeuHYM4cS/+VskRdw+B9h5KYNUF5NUD8HxY4AGo18jEcPjmN74XZahrfkg0EfkGLJY3/6XbjdFVhdHhJy7CQctePj8vBDaHdeb3ob271acrXNm3v2CPnzdjGz2kj9p8Kd5IbswkcVE5ifj6U0jwONq+g1chQ3d7791C9EKSNXyroP4MBSsPpCh2uh52iIbfcrfxuapv0JPQ2sEZG3j2m/HMhQSk02t7eIyCzgWuBfGGPoASJS94vxr81tOxACbDvFfetmq6wE9lDvF+xmZdm/AR3q5eYchxFYeVwp5RGRUcBm4Hrg1eNNdjjF2P8mYFK9irWPA6Uiknw2OV3O1unMVHkSmCgid5hLSJKAa+svrwG8MYIvdfLqfa7hzJPPJAAl9V7qscIwvsDr6wVUUErliMgaYLiIfIvx5Txwhn34QxCR6RizTKJE5ChGFZ+XgZkicgdwBLjOPHwhRhDsAMb3cxuAGTx5HthoHvdc3b8YwD38t6TyInSSWk3TNE3T6lEeDzOefYzy/DyCbA4i23Rhs8uFsliZUd2a61t4U1ZQRFJOIhWhXqwfEsXWqmquLt1GWMZh4g4MYoCfg0ifMOy7v2VD+QFe73UXT3r5MtjpRfXB76gun893nccS4hCGZCyBvEJcwzsS4plNWsubeL7wQ/Jr8nmh7wucFxFDevqjpNuz8XJ5SMk2ginebg9Lw3rxZsqtbJfmXLDLxmNHKhCgNMKP5HbhVFqLWbNzNeFlGQQ7bPg6HbjdDla3L+LSoXdwS4dTBFRqa2D7DCOYUrQXghrBBf+ErrdBUPQ5+T40TfvzUUrtFJH5GKtA9tTblYRRzbWsXpsX/51YsBJjosNRjJQcKzAmHtiBH5VSnlPcepp5XgrHLP0BojFyrGyqN/NEMHLA1PU7Q0SWY4xB3zvBPU429o/HCMrUXa9KRIox8nxmnKLvv5qGBlXyMZb3rATexwiyZAHTlFJ3ncF9j7cUpJqfJ7qJrfc5C4gQkTClVBn/qxRj5sxMEblaKbWm3r5PgTsxnnWtUuqEy1X+DJRSI06wa9BxjlXAvSe4ziRg0nHa0wD9KxVN0zRN045r1kvPkH/oAAF2JwnxzdkQFIQSC7Nq2nBray8OFpbT5mgTaoK9mH9hGPm1tfzz6E/kHvClZXE/egW68RM/qjZ8yD5bAS+cdy+PW7wY7PajOmc1rn2zmDH4EhIrm9KhchE+B3bhuH0U4TnvcDg8jKeDD1NiK+X13mPxKfyALZn78XZ6SDlqIyHHjrdbsSi8L2+l3MJudwrddlYzNj+X+DgHAa0qcNcWUJibydbFRxGliAMQCxFNmpAfXMOCiEOM6Hcnd3U4yf8CVxUYgZRNk8FWCrEd4OqPoO3V4OV7rr4KTdP+3J7BCDC8Ua8tC1iplLroBOesxFiGc9T8vBr4ECOocsrKvkqpIyJyGCMocscxu4sAG9D2RGNuEbkM6A18b/bjeGsnTzb2z8EIHNVdLxCI5CQpKc6FBlf/MWd9DAJWishbGOutNpq5UJZhzFLpBRxQSh09yaUACgEP0BRjPRbAVuBRMWpUl2Osu6q7d66ILALeF5F7MdZV9VZKrap3zAoRuQn4RkSuUEptMHfNxggENQJebejzapqmaZqmaadn3lsvc2T7FvxrnTSPSWJNbCMUwlxbS25vY2FzmZ0uGbE4Aqx8NiAIq8vNuIMrOLCvKV1cgXQNElxuJxVr36Sgtopn+t/Po1Z/Bip/nIVbcG+dxseXNqNF2VCa1O4iausirPeNZtf6r+jXvJq7k1pQVVvOI+1HYMt4Eo/TRdOjNhKybXh7FPMj+vNWyigO2BPptdXG87YjKPtqSvMPcjTfeAYVFE6x8iMwIhYCgug1aBC5CSVM3j2FQlshd7S7g9EdR5/4JWSuhxk3Q00RtLoMet0Dib3hf3MGaJqmnTGl1AERmQGM4b9pOuYDL4vISIxcI2DkN6lSSu1RSu0XERvGhISXlFIVIpIPDMcYMzfEHUC4Uqq6LrGs2R+PiEwA3hKR+5RSBWYe1nZKqcViFK2ZaJ6/HtghInPqpROpu87Jxv7Tgeki8gXGDJ1xwPrfcukPnGZJZaVUppkcZhVGNOtKjEDFdMANbMCYxXKq69SIyIsY68C8gUuUUkvNvxTbMaJcr/DzHCwjgbeAdMAHY5nRqmOuu1REbgfmicgQpdRmpZTNXEc2AvjmdJ5X0zRN0zRNOzWlFIs/GM++davxcbpJiUlmTVwcCljibMFtbSysrLHQZ38UTn8LnwwIJtEJ92YsJn1fR7parHQI9KHMkYtz7bvYHDae7jeGR3wiOE98UKV7qdo4gTeGhtOx7O8EqRKarZtAwENjmL1qMd1aFHNrfCy1FisXNb8dS/47tDhSQZNsO1YPzI06n/HJo8isjOe8DTZGB5bjcq8m+9BmgsIj6H/T7aR7Ipi8rZgW7gxiLZU0SkrCu73w4qE3KNpURPfY7rzS/xW6x3Y/8YvYNAUWjIWwBBg1Gxq1PVdfgaZpf03PYYyTAVBKVYrIYIwJEG9iVPvdBvxfvXNWAr2UUln1tltRb1nNySilDp5k96MY+V7WmUGUbOADYDFGAZP/BFHM1BSfiFGa+VjHHfsrpZaJyFPALCAc+AkjYe5vSowVIH9uZlWgFkqpm3/rvvxVdevWTaWlpf3W3dA0TdM07RemlGLJx/9m5w9L8HK7SYpJYWtMDApY427G0GZefOcJ5YKt/ni8hU8uCKZvleKy3EUc2t+Tzt4eWvsHklW1B0/ax/hV1fLEeffyt7BUeorgqsyh8sc3+K5Hf/y9rsBbuei2fhxx99zEnI2r8IRXMLPlAZR3ANYmjzLa/hYX7d5HeKWTb6IHMT5hJHnljbhgj4MrE/xwO9azf/0PePv60n3oNdSk9uHN5Ydx5h+kh89RfL2sRHQM46vqryiyG8GU0R1HnzyY4qqFxY/DxolGWeRrJoF/+ImP1zTtL0lENimluv3W/dB+Wac1U+XXJCIKSFVKHfiFrxuBMcVo5DHtK4DPlFITf8n7aZqmaZqm/VUoj4elE95l5w9LEI+H6PhUtkZEALBbkrm8qTDbGsUlW7zweAmfnh/MLbkumpR9T+ah3nTzs9PCN5R9FZup2TmZpHInz/e+nXvCUumKC6etCtvqd9mVegtB/u1Jrt1H7NpPib/nNhbu3ky+XyXL2mTi44LCiLE8VzKeIenpuN3e3NH6SdbaujN4vZNh7UNwt9rE9mVz8bhctL9wCEWpA3hmUxH5G9fTzy+LSO9y/GP8WR6ynOzibHrE9uDVjq+ePJgCUFUIM0dB5k/QZwxc+CxYrCc/R9M07RwQkQ+BbKXU8791X/7MThlUEZEMjASyKUqparPtTuBmpdT5Z3LTsw1oiMizwD8xliAB5AJLgBeVUrn1jrsLGI+RUHfVsdc5i3s317NeNE3TNE37K/N43Cz58B12rfweFAQ3acm+0FAA8r3j6BLjYaZfE65cr0Dg6wtCePJADWX2dZQf6Um3gCqa+kSws3wNFQdn0r7AwdtdrueWmC60wYmr1o191Xh2trqKhMv70mn6OFTGPqIfeYTvM3az35HBsl7FhDodXOLuyHlFL9D9cBaH/Ztwa7sXCEuPYGqHOIjexcY5E6kpLyOlex+ym1/AkzsrCDy4kU7+JfT2LUesQnr0Xnb67qRHVA+e7/j8qYMpADlb4Eszf8qwiUaZZE3TtF+ZOZ7uCMQqpRwnOk4p9fdz1qm/sIbOVLFilCIedzY3M2tX/1JZumYopW42c7K0wKi7vUlEutYFVpRSE4AJv9D9NE3TNE3TNMDjdrPw3TfY+9MqlFjwSWhBdlAQSoHDLwKfcCszI1px/WoHomDZoDBe215GmmsPAbmd6RpQTpJPNFvKvsfv0FLaZ9qY1mYIVyYOINUDVo8F25rXKL1yGBf17UjBY6MRb2/iP5nIyg0/sik3jSW9Swn0CJ/kFuAdvIq4nGoWRfZlTPKj9NuquLerm22LX6E0N5uo5q0p7Hkjr2TYSUrbymCfYqw+LpSPIj1wL/sD9tOpcScmdZzUsGAKwPaZMPd+CIyG2xdDfKdf96VrmqYBIpIM9MMo7jIU+OoEx1mVUu5z17O/LksDj3sNGCsiYcfbKSJ9RGSjiJSbf/apt2+FiLwoImuAGoza1v2Ad0WkSkTerXepC0Vkv4iUich7IqdOk66UciqldgHXY1QVeti8b7iIzBeRQhEpNT83OUH/40Rku4j8w9yOF5G5IlIiIgfMGS+IyCXAE8D1Zt+3me23icgeEakUkUMicrzSUJqmaZqmaX94bpeT+eNfYe9Pq/BYvfAktaY4KAiXEvAN4FBUOEviO3L9mlqsbtg5IJzXNhaxpvoIgTkt6RpYRpJPNJuL5hG3dznxmaVM6HAV57cYSnO3BR/AvvbfRNw3ihYxvuTdfy/eiQkkfzWTjenb+SH9O77rVYzLGs6X2ZnEKg+NSmt4IeVu/tb4SW7Y6uBy7x9Z8embONyKnG7XM1W1Je/IboZYt9PGO5/S4DxWxa5icZPFtOzSkk8u/4RPLv6kYQEVtwsW/xO+uQsad4W7luuAiqZp59IoYB0wBbilrlFEpojIByKyUESqgQvMthfM/fPMMWzdPx4RudXcd6rx/PMissYc7y4xk9DW7f9KRPLMc1eJyF8uQ3dDZ6qkASuAscCT9XeYOUsWYJRymg5cCywQkeZKqWLzsJHAEGAvxkyVxhx/+c/lQHcgBNgEzAO+a0gHlVJuEZkDXGw2WYDJwHUYM20mAe8CVx3T/xSMbMSvK6U+Npu/BHYC8RiZkJeKyEGl1HciMo7/Xf5TYPb9ENAfWCQiG5VSDcqgrGmapmma9kdgr6piwTuvkrFtMx5vH2oTW2L38cOprPh7CasSmpEdlsytyyuxuhRFvcJ4YlMuM2sraVTZmG5BpUR7RbMldyYp+7dAaRnju93EiCZ9iVNu/Cxe2Dd8TMSoy7Bv2Ejl0qWEXHEFsc8+w9IvprBwxyxW9CjG6teYqeXZNHE6qPQK4M72z7Hdmcwj2/ZiObqEI3Y72Sl9OWoNoXl1Bv183NT61rIjYC+ZIZl0bdKVh5s9zMDEgfh7+Tf8BdSUwNe3w6Hl0P0uuOQlsHr/ei9c0zTtf43CqOyzHqPKTiOllFkUnhuBSzHGpj4YpZMBUEpdUfdZRIYAnwDfN3A8fyPGeD4LWIQRF3jM3LcIuB2oxajg+zlGGee/jNNJVPs0Rgnkt49pvwzYr5SaZm5PF5ExwBUY0TOAKeZsEgBOMgHlZaVUGVAmIssxvowGBVVMOUAEgPkXYFa9e76IUYqpvjYYQaLHlVLTzeMSgL7AZUopO7BVRCZi/OX94Xg3VUotqLe5UkSWYMzG0UEVTdM0TdP+FAqPHGbuG+MoL8jD4+uPLbEFdi8/7MqbEIuDxS26Uuofy60rKvFyKXzaBDJqVw4zq13E2oLpGVRJmFckuzOmknxgO05HLa/3vpO/x3QiyFJJsCcM2+ZPCRnckYr583AcPETMo48SNupmvv3wHebkfMPWruVEBjbn/coDtMnNJD0wiZFtXyYsJ5cHdy6lOnc3gQnN2OCTSqpPEa2kgNzAHA4GHSSkUQhDU4dyWcplNAps1PAH97jhaBrs+85Y8lNdAEP/DV1G/XovW9M07ThE5DwgCZiplCoSkYMYAY+3zEPmKKXWmJ/txxt3i0gL4FNgmFIqS0RGcurx/GSl1D7z/JkYy44AUEpNqnftZ4FSEQlVSpX/Es/8R9DgoIpSaqeIzMeISO2ptyseOHLM4UcwZqPUyaJh8up9rgGCGto/U2OgBEBEAjD+cl2CUcMaIPiYtWU3AQeAr+tdIx4oUUpV1ms7Apyw9JUZ6XsGI7eLBSOx747T7LumaZqmadrv0p7VK1jy0TtYXG7cvoFUJ6ZSY/HFLoGEU87Slt2oDoxl1PIqvJ2Kxo18SM7N49sKL+Kc0Ce4lkBLGAfTP6bxoXSqxcqrfe/h4fCWBFjLiXZHYN/5Ff5tQyj95hssFguJEyfg37Mnn7w9jtnOeWS2sNE1pg+v7F1Co7ISZsdcwCOpD3Hpptmk7M7G7nFh6XkFK0uctPQqItc/l/2N93NR6kU82PxB2kS0Odkv9n7OXgEHf4B9i2H/YqgpBrFCUh+4dgokNDDviqZp2i/rFmCJUqrI3P7CbKsLqpx03C0iocAc4Eml1GqzuSHj+eOO00XECryIMbslGvCYx0Rh5Hz5SzjdksrPYMy+eKNeWw5GtKy+RH4+w0Qds//Y7bMmIhaMaNoys+lhoCXQUymVJyKdgC38PFHusxhBly9E5AYz2JIDRIhIcL3ASiKQfby+i4gvxoyYURiRQaeIzD7mPpqmaZqmaX84bpeLVZ9PZvPCOfh5oDwmjtqoeMqVHx7fUCJqC/ipWTsIjef6pRX4OhXtQq3YJYOVRdE0cpXRLzgAH4svmTs+JPpIOsV+QbzW+288HZxCqKWSQFcIts2T8A/ZT+2GfHzi29DkvfewNGrEuNcfY3Hg95RFuLgtvAP3bpqFl9vFE83GsDSkE3fOfxtrXi2RLVrzU3Q/PEX7aOlVRXpoOomdE/mu33f4WH0a9rClGbD3O2NGSsZq8DjBLwxSB0OLi6H5heB/3PSCmqZpvzoR8cdMbSEidUEOXyBMRDqa2yccZ5vj5S+A5fXSXkDDxvMnciNwJXAhkAGEAqX8xcbCpxVUUUodEJEZGOut6mZiLAT+LSI3AjOB4RjLauaf5FL5QNPT7+7/EhEvIBUjQBKLsb4MIBiwYSwlisAICB3LiRFVmw1MFZGR5hSon4CXRGQsxuyTOzBmtdT1/SIRsSilPBhr1XwxkuS6zFkrgzFysmiapmmapv0hVZeVMm/8y2Tv2YWPslCSkoo7IJhDrnCiI4IJrchkV1wyxDRnyJIyAmo99Az1YlfIbsoPNyfCk8n5IXGIWDi680siMnaTGRzDv/vczJsBOwjxRKGcVmrWjicoeBuxbUuxtAcV4Ma19lneOpjD7MijKG9v3nLChZvnkxEUw+0txhGftZcRiydhFV9Srx7Fe4f9aF28kwCrg/VRG+jTpQ9P9HwCq8V68ocsy4St02HXt1BoTsSOagG9RkPLIdCkB1hP93eQmqZpv4qrADfQHiN/SZ2ZGL/gP5UXgUCMqr71ncl4vk4w4ACKMVZrnFW14D+qM/mvxHMYiWcBI3eJiFwOvA18gLGc5vJ6U5KO523gUxEZDUxTSo05g35cLyJXYUTBcoClQFelVI65fzxGJK7I3P8GxySpNftfKyLDMP7STBKR24ERwIfmeaXAM0qpuhkwX2Ek/CkWkcNKqS7mmrOZGMGVecDcM3geTdM0TdO034WcfXuY+/o4bOWlKP8QihOa4rZ4sdGVQtckL6w5B8gKj6Y6uRP9l5QRYvfQv4kvy2QzXgdbESs76RvaHgcOKg5MI/rQJnZFJjOp92W877cIb9fdKGcZ1SvfA2ceRb6B1KYOI6R1NPaMDcwq+J7PYgNp6nLydvZRGrs8jE++kQ8iruH65VMIzSmlUWozvAfexlvLd9HTmg6+LpZHLeeabtcwpvOYEy/1cdpgz3zY+hkcWmm0JZ8HXV4yZqRENjt3L1rTNK3hbsHIbZJZv9GspvsO/12xcSIjMCYhlNb7+Xi3UurzMxjP15mKUSgmGyMNx1PA6IY9zp+HKPWLr8TRtP/RrVs3lZaW9lt3Q9M0TdO0k1BKsW3pIpZP/hCry0NlXCLOiBjKXD7khzTFJ6yWxvlHqPH1Z2eb8zl/eRVhNg8DunkzO2sn4dkJJHntp3NIN0o8uUQf+hdlOz2sjWvDjO5DeNPiIZTWqKr9VK/6AI+zEuf9qRS1Sgc81HpgZqkPaTVeDKi18WpOEVkh8dzW4mXCs48y8MeFWDweuvRvwvcx97Jj0zo6euXiCKplafgS7u15L7e1u+14DwY5m2HLZ7BjFjjKISwJOt0EnUZAWOI5f9eapv31iMgmpdQJc3Vqf0x6PqOmaZqmaZqGs9bBso/eZffq5XiJN2XNW+P28eNAbTjBSTGU+grdjxzGERhMWrOeDFleRbjNQ6+LXXy9PouYgmA6+OeQEtiNPDlCo23PU3bEwuKkHszpdDXjJYxQrLhyN2DbOAWi/bG/0JFSZxoxMZezytmDGRvHU+pr557Scm6EKK7NAAAgAElEQVSvrOK15rfxccR1XPnDlyRnHiQiwkO/ri6esF1P0ObldPQqoyK6gh8Cf+CpPk8xvMXwnz9UVSFsn2EEUwr3gJc/tLkSOt8ESeeBxfKbvGtN0zTtz0MHVTRN0zRN0/7iirOzWPDGOAqysyA4ktL4JNxKOGBpTEmbKIrdLnod3o0nshGLm3Thhh9thDsUrYfkMnu5D/FlZfQKaUSUX2vybdsJXPcxleUWprW+mI0tBvGuBBGKFcfehdTumYPq05jSO2px1G4jvumTjMsMJX3fc1i8XPw7r4hQ/8b06jYB3/z/Z+++o6wqr8aPf/ctc6fd6Y1pTKcMvVcBG6goRIOxYIklamJiYkn09xqNiRpNYowlaqq9915A6b0OMMwMTINpTO9z79z2/P44Z15GXgQEFdTnsxbLOf0558xacjb72buTH7/wME5vNxPOuxD/8FO44rX1jPVtJNLmpjallg1BG7j/pPuZnTF7/w11N8EHt0DROxDwQep4mPs3GHYuBEcevwetaZr2PSAiFwOXKaVO/7qPP9ZrfRUOOf1HRLr6LYZiFKHpa0d8jVLq+a9xbNp3iJ7+o2mapmknHq+nl3VvvMyGt15DKXClZOGNiKLDY6EhOZ11mTkMr61gRE0ZvqgUVsQO4+xNbpx+IXHqJratyiC1awdTY8cRZA2mvewDrCWfgfLwh3GX0500hAdUOGEKPDuewbNnHZ5zk2g5uQ6bPYrtMXfxj50roesNUr1e7m9q49msq3g2Zh4zln7C2NI1BEXFc8b1N/GvEj+rNhcy01FOaJCFHSnbKbOW8eDMB5maMnX/TdVsgpcvhe5GmPgTGLUQEgYfv4esaZpmOpbpPyJyG3CSUuqMfut2A6UHWfdbpdRLR3mdmcBzSqnUL9h+K3CmUuqkA9bHYdQkHaOU+l41bTlkpopSKrzvZxGpBK7qV7BV0zRN0zRN+5aq2LqJT//1KO1NjdhtobRm5KCsNmr8oewaPpjdCSmcVbyZlMYa2iMy8HdkcX6xi6ikIPxpH1O0dBB53q1Mip+Jix66Vv8de1MJXWEh3DTxZrLCE7hXheHwe7FUPYynfg8d14bSlV9Jb8h4fu+5Gjb8nQC7mdnTwzm+JM4f8ziOti4WvvA4iT0NDJwyi8iZP+TSd0pId5dxWlA9kTFRfBqzmBZp4Z+n/pNRCaP239TmZ+D9myA8Ca78GJJHH78HrGma9tVaDtwqIlallF9EBgB2YPQB63LMfb8uzwF3i0imUqqi3/oLgO2HC6iIiE0p5fsax/eNO6aJpCJiFZHfiki5iDSJyPMiEmVuGywiPhH5sYhUi0ijiNzS79j7zP1fFJFOEdkmIqP6bU8TkbfN85aLyLX9tk0VkS0i0iEi+0Tkj+b6MBF5SURaRKRNRNaJSLS5LUZEnjH3rxKRO81e3X3nvEZESsxj3xeRlGN5NpqmaZqmaSeizpYm3v3rH3njj3fiamzGm5RBS+5QvEoojktj8bSTqIxN4qqCtaQ01tAWnEtaeRqDGvzkn+KiNfllGlfHMzKwl2nxp9PurcW/+M/YGosoi0/nipNuZYozhrvFicPTSkTvXbQ17aXpZh9d+S0st/+Iq9sWElz6/3CpXVzX0kFQ9MVcMuxRBm0s4Edv/od4XxcnXXczq5NO4ZaXNjDNv40h1npShqbwduxbdNm6eHLOk/sDKr5eeOcX8M7PYeBU+MlSHVDRNO27ZgNGEKXvm3k6sAQoOWBdWV9HXPObfJH5jVsiIuf3nUxEzhSRnea3eI2I3CwiYcCHQLKIdJl/kvsPQilVDXxGv47ApksxugEhIpeLyMp+11Ii8jMzi2a3ue50c0ztIvKYiCwTkasOcfy1IrLb/M7/u5gtjA6y70Pm936HiGwSkelH8ay/lGOtqXIzcDowDaOF0hPAg0Bf2XUrMA4jWjYcWCUiryulys3tPwDmYbQo/gtGG+SZImLF6Jf9HLAAyAAWi0iRUmoZ8Chwr1LqVRFxYvTRBrjKvKcUwAuMZn8P7+cx2kNlAZHm+SsxWjv/CPglcDZQAdxpXnvWMT4fTdM0TdO0E0LA72frx++x8qVn8fW6CXY4aRyYDWJhH0FsHT2Byrhk8nt6mLNpFd2BLvAMIXdfPKG5DjKHPseqNXmEV0YzMSKIjPDptDWsx7buOSx+Nx/mzODR/LP4qQjnKyfULyEm/hV2N1lpudGFOziYR7gNV6eblPrbCODntlYrfxr8Dzw+4fyX/8PAtkpCM4eSNv8Kfrq4inj3FuaHVGMLEnanVPC663XSnek8dupjDIwYaNxYezW8fInR3WfajXDy7WCxHt+HrWma9hVTSnlEZB1wErDJ/O8KjCk3/dctByPhAFgE3AGcgfE9vkhEdiildgL/Ac5XSq0wExEylVLdInIGh5j+Y3oa+B3we/NagzACO2cd4pj5wETAZU4Veg24HHgH+BlwNfDsIY6fC4wHIsx7fRf46CD7bTDH1Q7cALwqIhlKKfchzn1MjjWoci2wsF8k7C6gUESu6LfPneYNbBCRYmAE0BdU+Uwptcg89ln2B2OmAcFKqfvN5V0i8iRGStEyjIBJnojEKqWagXXmfl4gHsg20442mOceiPELdo5Sygv0iMjD5vmeNu/jbqXUrn730SMiiUqp+mN8RpqmaZqmacdVXWkJi/71dxory3FipzEtj47wCFrFxuacYZQOSMcO/Hx3K/bqtfRYfES05OOzxTHgzD14Gxez6c2TiHNtYVLcDGIdyfQUvYXs+gAldh4ZewmfpI3gt4RyqteNv+g+4kdVs8NqoevH3VT70viP/TZGtrzO0vY1DPN4GB40iV+NvIExO9Yzae2nOAIecuctZBG5PPRWMbOde4m2tdAW2sbKmJUkO5O5Z/g9nJF5BnaL3bix8mXw2hVGpsqPnoMhZx/X56xpmvY1W4bxXfsgRlbKQxhBlWv6rfurue9coFIp9aS5vEVEXsdIWrgL49t5qIgUKKVagdYvMY43gcdFZIpSajVGlsqHSqnGQxzzR6VUC4CILAAKlVJvmMsPYyRsHMp9Sqk2oE1ElmAEcf5PUEUp9Vy/xQdE5HZgEFBwhPf2pR11UMVMt0kDPhCR/tVuLUCs+bNfKdXUb1sPEN5ved8XbBsIZIhIW7/tVqCvnstlGJGxXSJSCtyhlPoYI9qWBLwmIuEY6Ue/Nc8XDDSaWUJ94yztd70nROTv/a7nA1IBHVTRNE3TNO1byd3VxcqXnqFg8Yc4rHYcobHUpg2kLTiUjWmDKEvJwKYCLGj0Mm9HI2utBbiAiOYR1OXGMDn9n9SvnYC7eiAZ9jLGDDgP8ONa9xi+fVtxOyL53cQrKY9O5gGCmdC9jt4tTxM2MoItcb30jvKzyT0FT9r1ZG69kaWBDs7q9LI57X/4b1A6Z731MkPqC1GRA0j74dXcta6dMM8OFoRXIj4/W2O2EZwZzP0j7mdW+iwsfTO3lYLVj8DiOyE2Fy54HuJyj+ej1jRN+yYsB34mIjFAvFJqt4jUY8y+iAGGsb+eykBg4gHf1Db2Z4OcB9wO3Cci24BblVJrjmQQSqkeEXkVuFRE1gAXAzcd5rCqfj8n919WSikRqT7M8V8UO/gcEbkZuNK8hsLIbIk7zLmPyVEHVcwbrwHOVUptOnC7mdJztKqAYqXU8C+4dhHwI3Oa0AXAGyISrZTqxUhvukNEsoCPgUJgNdAFRKuDtzuqAm5RSr1+DGPWNE3TNE07ITRUlrNt8UcUrVyC1+0mzhbKnpQMWmLi2ZicRVlKBtaAYm5bN1dv9VBlqWWltRyLLwiPfxQVp7Yxac+bVL03C1zLmRA9hfTwIfQ2l+Da/CTW7hYaYwfy6/FX4A8O5wnaGe29n4YN1TAwgZLBTfTmK7ZbL2Ni+lB+v/5K2kXxg94Enhl8H+kVJVy07J9EeDtInnYmKyLH8viycmZE7SDdb6Nd2nENd/GbSb9h0oBJ9PtHMejthLevh51vwdB5MO/v4HAev4etaZr2zVmDUcriamAVgFKqQ0RqzXW1/YrHVgHLlFKnHexESqkNwDwRsQPXA69gJE18cXvgz3saeAt4A3BiTMc5lP7nrcNIYAD+N2HjUNONjohZP+XXwCkYmTABEWkF5NBHHptjnf7zBEZk6wqlVJWIJAATlVKHe6CHsxJARH5pXsOHUTfFppTaLCKXAu8rpZpFpB3jBSkRORUj/akY6DCPCyilKkRkLfAnEfk90A1kA4lKqZXmNW4355eVmHPKTtZBFk3TNE3Tvi28nl52rVlJwaIPqNtdgtVuJyUyjmqHhc1ZuWxOy6E0KR1rIMCs1mZ+WqRo6K3lY2s1fnzYPLFsThvOiIiV5C/NoKEpmBjLBiYkLyDUEoar8A26Kj8m2Ksozp7Mb4bOJ9lq5xHLLvJtv6d6rZMeZxwtJzfhyVE0xt5CXPVmflnxCgmBADnOc3kqZS7TPvqI0VUbsYRGkfKjW7h/WytBvc9zTpiNyO4I3MluLj37UkYPOEih2ZrN8Oa10LwbTvsDTPk5yNf6d2VN07QThlLKJSIbgRuBe/ptWmmu69+p9z2Mb/VLgL72yqMwkg3KMKYBvaeUaheRDiBg7lMPxIpIpFKq/RDDWQG0Af8EXlJKeQ6x74HeBx4VkfnmOK/FmHFyrJwYMYBGwCZG++eIr+C8h3SsQZU/YTz8z0QkCeMFPMvho1SHpJTyisiZwAPAbUAQsNP8GYz5YX8VEQdGsdnzzWNSgMcxUn06MYrTvmwecyFwP0bAJQyjrss95vVeFJFQjIyXNIz5ZB8COqiiaZqmadoJrbmmim2LP2Lnsk9xd3cRnZTM8Jx89tY1siI1l82ZQyhNSMUW8DOhvoqf1FhxtzWwxFaD3+InyB1HS3gWa4dHcFFRIWpjMt097zMscjRDIufgdTXQsP0Rwur24Ihy8P7wq3g0NpORWLk/uIlB6g7qtzhp63XSfHkHvjQhPP5XfLbp37xr6WWEx0ZB2j00N7m58PknSHA3kTR6OisSJ/KPnR8wPLySoW15WGwWZp4zk5ljZv7fm6zdAkvvh10fQmgcXPIWZM34xp+1pmnaCWAZMBkzEcG0AiPb5H9bKSulOkXkdIwaK3/FKH9RgBF8AaN7z6Pm7I8SjCk8KKWKReRFoNzcNrSvhmp/5syVZzCavDzzZW5AKdVk1lV5GCPj5XlgI9D7Zc5zEB9j1FnZhZFI8SCfn3b0tZCDz4bRtK/WuHHj1MaNG4/3MDRN0zTtO8Hv87J7/Rq2LfqQqp3bsVht5IydQIQXduytZnNePttSs6mKTcLm8zK8tpIL2304GrrYZavDTwCHKwEHA3ltZDzZ3fVM3BzA272aUH8t45PmkmBPpqNmBZ7tr2D39rJ30Kk8kH06pVYLJ2PjjsQgEsrPp60shJbGUGputiCxPlLUmdxT8SHbHUEMJpeVyb9hwrIVTCldgd0eTMJZC/lzdSHB4UsY35pLXG8cqVmpXHDuBYSHHzBFvnYrLLsfSj6A4CiYcj1MuAaCv/Z/eNQ0TfvKicgmpdS44z2OE42IWIBq4GKl1JLjPZ4v61gzVb5xInIxcJlS6vTjPRZN0zRN07Rvks/rpeCTD1j/9qv0tLcREZ/I5B9ehK+2kTVV1WzOGcq2/Km0hkcS6u5hfHkhM9vbiWsTKqUBZYMQTxLBXanszrPxSUYcCzfuIXJvIy7Xp2SHZjNqwOXg91G743GcpVvwx6fz5IhLeNsZTRLCPTiYnWfH8/ql1LdG0KXs7Lk9iPBgD86qBH4R+Jh2u50g50J2+kbwo5eeJK2rmvickSzKyGJT62PkORwMr5+Iwx7E2T84mxEjRny+bkrdNlh6H5S8D8GRMOt2mKiDKZqmaV+GiDwFVCulbj/eYzmQiMzG6OLrAm7BqHuy9rgO6igdcVBFRC4AfoVRUbgbqMBI1Xn8C4q/fi2UUs9jpAdpmqZpmqZ9L6hAgOI1K1j54jN0NNaTPmwkwy6/hurN2/hgx242Zw5m5+CJuIOCiWtrYtbODUzp6SWkW1GjWui2CJGWNKQ+iaBED09NCCGs08FP3yvD37kMS6CRiUmnkuUYQltHCbLmP4T4Xawecwl/ShuBEuFKHCxMjiJ+cAd1t11FwOugN9ZCxW+CyezopKLCx02RbTgsYexLup38LTXM2P4vHCjCTj6Tv/iXESKLOaVjErGuaLJzspl3zjwiIvoFSuq2GZkpxe+ZwZT/MYMpkcfv4Wuapp1gRGQpMBJIMpu1fBtNBl5gf6mP+Uop1/Ed0tE5ouk/InITRhXdn2HMU+rCKHJzM3DFV/UiRcSmlPJ9FefSTix6+o+maZqmHZ29OwpY/vyT1JeXEj8wk9HzFrB7cwFLe3rZmp5HaUIKAbGQUbeH4fV7GN9txeaBKkszFhEGhGXi2hOHwy5UjSjimQHTOWdTM9m71hNwbyI7ajT50VOwKzttpW8SVLiIurSJ3Dv8TEqDQpmBlZ/ZQ8gdGUTPe3fTsa4MlMAgD1XXhDCsspUXfDaejowgyDoQj+0yTlu8iJTOWpxJmbw3yE95yCqyurIZ0zaSIKud2bNnM2bMmP3ZKfWFsOReI5jiiITJP4WJ10JI1PF9+JqmaV+hr2L6j4hkYBSabQeuUUq9+gX7WYH/cIJmqnyXHDaoIiKRGB11Lv2ibjhmwdh7gPMBB/Am8Ku+SJOIXA38BojBKKhzbV+xGxFRGEV1fonR3SfTLKjzCEYF4OeBfOBZpdS/ReRy4Cql1DTz+IeAczFaS+0GfqmUWnF0j0P7uuigiqZpmqZ9OY17Klj+wlNUbt2EMy6eMfMWULmnho+6etk8cBC10fHYvR6G7Ckhv6mWET3hCLDX2oRYLGQlD8ZVGk1vm4Xw7LW8PjyJ2o5hLFi8BVvnUgY44slPmEWsJY6W9u0ErX8ZF/DCqAW8Gp/JQIQbCWFCvAf38odxl5SjMPKzHZN76JgjpJR3cnt0NKtDQvA5ZjFiVyJjC9dhtTvomTyUV0LexxGwMqv9dMI6rWRkZDBv3jyio6ONm/T7YOVfjak+QWEw6acw6TodTNE07TvpKwqq3AH0TZ3JU0rNNdc/hTGVZiAwA5gHLMQMqoiIE3gH2A7cAJwJ3I3RFbcd+I9S6nfmuTIwZqZcDvwBCAUeVErdY26fADwG5JnXfF4pdaO57VVgOhCCURj3OqVUobntTOAvGK2bO8xz/uVYnseJ4Eim/0zGCJS8fYh97sN4GaMAL0Yazx3AbSJyMvBH4HSgEOMhvgSc1O/4+cBEwCUiccBrGC/wHYzsmKsxugodzAbg9xi/CDcAr4pIhlLKfQT3pmmapmmadkLpaGpk9SvPU7j8UxyhoYxfcDF7m9r4e3ElmwYOpiEnhvDuDqZuX8Og1maGemPBEU25bR8iwtBBI+gtC6Nto43g6L04Zn3GXyIvZ9zaDmbufoZw6WB40mzSHNl09zbRvfkRrE0lbMw7jT9kT8dms3EDDs6RXtTGv9NWux1ltyCAP9ZG1Gmt2BI8UOrn0qQEamxBxPbM5aQllUS6yokfPoUXEmqoCXqD7I6JjOvKhIDitDNOY/z48VgsFuNGWyvhjWugai0MXwBn/AlCY47no9c0Tfs2uBSjm886YK2IJCql6s1tF2EES+ZiTKtZCCAisRjdbT/py1oRkW7zXIUYJT4WichWpdRb/a41DRiEETxZLyJvKKWKgIeAh5RSz4pIuHl8nw+BKwAPRvfd5zHiBGBkzpyvlFohItFA5lf1UI6nIwmqxAFN/afliMhqYChGsGUO8BNghFKqxdx+L0Zg5TaM1kz/VUptNrfdBrSagY9K85R/7HfsAqBQKfWGufwwxjSjg1JKPddv8QERuR3jxRccwb1pmqZpmqadENzdXax/+zW2fPAOgYCf7Cknsc/t49917WwaOIRmZxThXR3M2LaGIS3NDJEBqOg0drXvAQXD8wfBXhe1S5wE2TqZGP8SoyyLaC5IxlcXj8e1l6ExU8iNGE0g4KWn6C0Cuz5mX+pY7j7tNsqDwzkTG9epDuJqHsfq2UqX1YE3YMem/MSO68A3NZSo1m7WdQZxU3Iy4o1gSvEIcvcWoMJjCTn3Sv7c+iShlnrOaD6X0E5hQFoS8+fPJzY21rhRpaDgJfjgFhCBc/8FI84/vg9f0zTtW0BEpmFkorxitiUuwwikPGju8rZSapX5s9ucYpmM0Yb5aaXUn/vOpZRa2u/U28w2yjOA/kGVu8zZJwUiUoBRx6UII5EiR0TilFJN9Cswq5T6b7/x/g7j2z9SKdVuHjdURAqUUq1A67E9kRPDkQRVmoG4/vVOlFJTAESkGkjESAfa1K9quwBW8+dkYHPfBqVUl4g0AylApbm6f+/o5P7LZv/r6i8anIjcDFxpHqeACIxAkKZpmqZp2gnP43ax5cN32fvxf0iQesanJ9BKCCXeKran5tIYFkmKu5aZlRtJaWkh156FJT2LnTWlqLYAw6PaiGtxUfzpKLwqhpGh75EX+QFbrdn8s+Yq3G3VZIY6yE+/ihAJxle7gt6t7+BxRvP4jBv4MCqZYTTxkvUjxvAZNms7rYEwGouc+H0WZGo2yeeNxlL0FO7GVh6Ji+KZ4HDyqgcyttiO1V9L0sz5vGJxUNbxAIPdKQzvOBO7zc6s2bOYOHHi/uwUVyu8dyMUvgHpU+AHT0D0wOP7AjRN0749LsPINmkyl18w1/UFVaoOcsxZGDVRn+i/UkQmYsw4GYaR1eIADqzPsq/fzz1AX9/7KzFmixSLSAVG8OU9s47LPcACIB4ImPvHYcwsOQ+4HbhPRLYBtyql1hzZrZ+4jiSosgboxZiTdbCaKk0Y86jylVI1B9leixFNA0BEwoBYoP++/Qu71AGp/faX/sv9ich0jAK6p2BktwREpBUjqKNpmqZpmnbC8nk8FCz6kPVvv4q/s4krczYTYvVgTGOHszqBzs8fs5dkXveeQUdVOKMpJLe3nS27F7LNl47NWUNRSgXvto9lxN4kEnoKSAjqZuqA84gNSsDlqqJn7dMEPG0sHnkeDyYPJQIfd1mWcz6LUKqegrYUVEE84c092CaMJ/OGSwnsuI/Crf/gxSgni2PSiG63c2ZBOvFtXnoTUnCedgl3F39MYsQy5jROJtwTxuAhg5kzZw6Rkf269lSsgDevha59cPJvYdqvwGJF0zRNOzwRCcGoYWoVkb5ghwOIEpGR5vLBCqb+C4gGPhCROUqpbnP9C8CjwBlKKbeI/I0jTE5QSu0GLhQRC0Z909fMKUbnYsQNTsVIoIjEyEYR87gNwDwRsWPUVX0Fo77Kt9phgypKqTYRuQt4zAxwfIzRUnkEEIYRffoX8KCIXK+UahCRFGCYUupj4EXgRRF5ASNV6F5gXb+pPwd6H3hUROYD7wHXYhSsPRgn4AMaAZuI3IqRqaJpmqZpmnZC8vt87FiyiLVvvERXSzPBUdGMG9iKw+plwdC/UhyZQXx7MxNqishrryI9xMmokXkUtvaytKQVp93NdOdqyhrOo6p1EK3B8FkiOJrsTCqoJ8e1mozwPLKSLyDaHkNvoIOeTf/GX7uZirwzuCN7Mi1WO/MDwlUSRYU7h0vTc7ls2etkF1ViT0km8cHb8fs+45Vll/NKRDgVyYk4e0KZWJhF3t5OeoLsxM+9mNfa46go/S8TLG4y903HGeFk7nlzGTRo0P4b9nlgyT2w6iGIzYYrF0HKmOP3AjRN076d5gN+YDhGvZI+r2DURjmU6zG+2d8VkbPMKT1OoMUMqEzAmEb0yZEMREQWAh8rpRpFpM1cHTDP2Ysx2yUU49u/75ggjAyW95RS7SLSwf5Mlm+1I8lUQSn1JxGpwcgKeQYjqFKO0dFnNcYcqjswCuXEYWShPI7xoBeLyG8xslyizf0vOMS1msy6Kg8DT2MUttmI8XIO9DHwEbDLHNODHDzlSdM0TdM07bgKBPwUr1zG6tdeoL1+H47oGPZl5dOSncw11e/x7ICz2RQ0mAnFxQxqrSLZmcgpF15CVFIsL7/8X2pr2yHORaV/Ipbi2SgFG0N92DylzNldRFSggxznaDISTiFIgujx1tOz7Tn8lWvoThnLn079H9aHOBnu6eXPtnAcDjd/TKxnwWdv8vsPKpDgYGJv+Bm1OTXcv/sWPg5x4I6NxulPZGT5IEbsqkLhgmGTCRl1Fn/YUMyg6KeY2zUAh3IwecpkZs2cRVBQ0P6bbtwFb1wFdQUw9nKYfa/R5UfTNE37si4DnlRK7e2/UkQexfh2XvxFB5olNX4CPAW8LSLnAD/FqEn6KEbNlVeAI229Ngf4q4iEAnuAC5RSLhF5BqMzUQ3QAvwWuK7fcZdgJFBYgRKM+qvfeodtqXy8mSlF1cDFSqklx3s82tHRLZU1TdO07ysVCLB7/WpWvfI8LTVVBEXHUpA+hK2DRlMdncCr225kRMcufh1yI0ndncSGRTP7zNnkDM5g1doXWLK0Ep/fQnlkPsPKYonwQGlQN1b/dpLbykkOiiA3YiyJIen4lZ/u5iKshe8SaNtDIHkMr2afxNPRA4jye7jOGs5JYufN0L2MWPcCWWV7sTjDCV94ASvyanm95iOK7RaClSLalkVo3VjGb9tOkKcXW+ZgEk+/hH9vaaG1czvTgkuJdUcTHh/OJT+8hMTExP033dsJax6DlQ+CPQTmPQqDzzp+L0HTNO0E8FW0VNZOPCdkUEVE+vpuu4BbMNoqZ5lpStq3kA6qaJqmad83vT3dlKxewdZFH9BYWY4/IZl1g8ZSkDWMrpAwwnu6Ob/0E+5t/RvvM4udwdM5fc6pDBqcyoel77F8dS2Rtb202SMIbckjpTuURqsbpbYQ315Gdng2Wc7RhNmcuL09eCtXIrs/AgKojJm8kz6Wp8JC6RULZ4Q1cKXdR133PoILl5BUVo8tLIqoK69kRV4zT+x+gXqrItvrxxk6lNr205i2YTkR3YdaScsAACAASURBVO1YEpIJn3EJT+720NHRwvjonaT1+AlYA0yZMYU50+bsL0TrdcGGfxvBlJ5mGHI2nPkXcH7RTG5N07TvDx1U+W46YYIqIqKAXKVUqdl66ecYVYh3Ar9QSq07jmO7HLhKKTXNXO7CaCFdfrzG9G2jgyqapmna94EKBNhbuI3CpYvZvX4NXk8vLVlDWTt4HCUp2fitVgbW1zCwuoMBnRX8Th7HQzA1ZzyPIzuGpyo2saY+iNGF5cS6OunuTSa9NRO3BOi2bCW5Yw+DI/IZGJ6PVax0dNRiKXkfVbMRS2QqZJ/CWzEpPBseQhfBjEnYyg9y3ic5vP7z41RQ0mnjg1Yrey1W8ns9ZAQPY7lawElrFxHfUo84nVjGns/TDaGE99Yz2llJdK8fAFeCi19e8EuSY5KNE/o8sOVZWP5n6KyD7JPh5NshZew3/Qo0TdNOWN/1oIqIzASeU0odtNHMd9Vha6qISCVGkZnMvkrBInIVsFApNfNoLioiSzEe9r8Ptl0p9Tvgd4c5Rx5Gu6ZZgB1jLtdTwENKKf/RjOtIKaXCD7+XpmmapmnfF231+yhc9ik7l39KR2MDbXEDKJoyh8KUXFqdUdj8PkaX7WREVRddli5iLN1Mt2wkWnVQPfcW/uUp5J0tQxlSqphVvw0VsBHaNowYTxRt1h2kdO9lmnMoqcmT8eOjvX4zQTvehe4GrMljYeqveCsYno2MoiMQyvC4Qs5P2kR+UTjqfYU9YMOaFUfYuWdQPCDAkzveZKfykeH38kNfKp+E/JiErWs4r/o5cAQRyP8BL3niSKmpY46jHkeQwuVz0ZLcw9knnc1pg05DRCDgh22vwNI/QtseSJsE5/0bMqYd71eiaZr2nWN+m1+llFpsLl+AUct0vlJq2VGeMwOj7ZxdKeX7akb6/XJEhWoBK3AD/ar3Hg2ze9AxtzsWkWyM6UFPAsOVUnUiMgi4E6PicNuhjj/I+axfdyBG0zRN07TvFq/bza51q9i+5BNqigppc0azK388hacMpSkqDpQipa2R6Ws+ZWK9h6JIO0G2LsJwMHl4NrN2Psr22Fzmd4yGNsUZ21aTSjvSG0VM+2C6qSbau52pjhwSk8bi9Xvo2LMW687XsKOwZ8yA5NG8GdbLiyFOmn2hDIrczc8TtpJf1EXEC6VYe3oJzssg7hd/oGbkAO5dcgNrtteT4PNxXnci60Iuonnnds6tfR5sFvzJJ/FJcDJpvkbm2qoQoN5ej8pULJyxkKmpU41gilKw821Yci80FkPSCLj4Ncg5FeSY/6qnaZqmHYaIXAb8FThLKbX6eI/n++xIgyp/Bn4tIo8ppf5PwEJEpgAPAXkYnXhu6HuxZlbKKmAmMAZ4A5gOTDJ7YT+llLrePNWpIvIhEI/R9ed6dfD5SXcBq5VSN/atUEqVYLSB6hvTq+Z1QoAC4DqlVKG57SmMei0DgRkYvbI3AI8AZwA9GC2n7lVK/Z82TwdMVXoKo/NQBnASxnSli5RSZea+D2H0644EdgO/VEqtOMg9aZqmaZp2AuvpaKe+vJT6st3sK99N5bYttNmDKckbyc4F11Mfa9QNSWxvZvbmlcxev56Q9JFsCYWq6F4sSpGcm8rUURVYlt6HR8GPM/7AmPIacqt2YydAWGcW4g4hzr+TGaFpxEQMotfnprN0ERS/g9XqIChvDippJJ9E+njaAvu8UWSGVvKz5K3MqHThe6SIQE8PYVOnEnv1VbQMTeGupb/ik/eLcQYCXNjpYKflIjpK93Jq/TsouxV/wlQ2hcWQ6mhmmpTisXrYFV6BM9PJlROvZELShP3BlN2L4LO7oW4rxOXBgqdhyDnQV1dF0zRN+1qJyDUYszZmK6U2musmYQRZhmLM4rhBKbXU3LYUWAGcDIwA1mB8szYBy83Tthk5EJyG0cEnRym10Dw+g37ZLCISAzxg7hcCLFNKzT/IOH8BXAucjtG19zal1LvmNjtQB5ymlNryFT2a4+JIgyobgaXAzcDt/TeYD/R94BfAixi9p98XkRylVLO52yUYwYoSjEyVFA4+/WcuMB6IADYB72I8/AOdCtx2mDF/CFyB0cP7fowgzah+2y8CzjSvGQT8EyPwkQXEYvTorgP+c5jrgNEi+gxgM0Yb6HvY3zZ6A/B7oB0j2+dVEclQSrmP4Lyapmmaph0H/QMo9RWl1JeX0dnciAKaoxOoSMtj11k/pjYxDYD4zlZmFG3m1DXLyZRItmbmUTQ4F6t04lI2ktIVc4ZtZ1tXC//YNZWHG6v5Z/qPmbm1nHBPIzavk/DOgeSF7SPH4SRUjac30ERP6Qv4d65EhdnwnjQQ97B41nttvNLaQ5U7htSQWn47uoSTi5rpfWAdHr+fiDlzCLviEjY7m/l74SN88kYBNqU4v9NPg3subZVtjG5eRcBuRyXOZHOYk4FBDQyROlpD2lgfvouU7BRuGnUT45LMqf+BABS9AyseMNojR6bD/Mdh+PlgPdK/TmqapmlfgeuAacApSqkCABFJwfgmvwTj+/kU4HURGayUajSPuwjjm7UK41v5ZuBWjMSACiCqb/qP2TjmUJ4FuoB8879TDtxBRO4A5gMzlFKNZrvlhRjf+GB8i9d92wMqcORBFYA7gFVm5kV/ZwG7lVLPmssvmhGpszFqnICRjVLYd4B8cVrofWYmTJuILMEIghwsqBKLEfD4Qkqp//a73u+AVhGJVEq1m6vfVkqtMrd7MYIgo5RSnUCniDyA8Ut5JEGVN5VS681zPY8RIewbx3P99ntARG4HBmFkz2iapmmadpz5fV7qy0upKSmitqSI+vJSOpsb/3d7ICqG8sSB7B4xncrUXLrCnADEdLUzuWwHJ21ay4jKaspHzWBP1hBqxIVPNeEN9zM8r5m0mK3Uef38xXUVaxjFp6U/YbV1KtWVsYRLE6GdAxkSgGHRQdg9I+m1VtBZ/ArsKMAXG6DrAj+d44T1zWG8XzGFup5EksOauHVcIaevK6P3f1bQGxSEOn8uW2fFsrRtBZs2XUovivBAgLmdvVjapuOqDjCwvRhfkANJnMkWZzTptlrypIuG0Aa2R21nWNYw7h1xL6MSzH+H8nth+6tGN5+mXRCTBec8AiMuAFvQ8XhdmqZp33enAUuA7f3WLQQ+UEp9YC4vEpGNGIGLp811TyqldgGIyCvAOUdzcREZgBGciVVKtZqrl31+F/krMAGY1e/7+zngtyISoZTqwPjWfpbvgCMOqiildojIexjRrKJ+m5Ix0ov624ORjdKn6ggvs6/fzz3AFxWEbQYGfNFJRMSKkS2yAGMqUd8UnjiMjJEDxxTH/mK3fQ68h6Mat4jcDFyJ8ZwURhZO3BGeV9M0TdO0r5irq5O6XcXUFBdSU1JEfdlufF4PAGHRMdjDwmnLHUZhZAIV6YPYFz8AJRaCfF5SWxuYtKeEsTsLGFxaSs/AUexIymZZQhwWaccf0kt6ehPZybvAX02HxPKa9Se87R+LBQt/2/chy9tnUmuLIMgXSk5XHGOC4wi1ROD2luHd8jr+PeuR8HisYy4kKHUy73k8vLpG0eKFQUlObsrzMOajpfQ+v5Y9aWFsuS6PNUktlLjeRZVBos/HD3pc5LrDKemegr+qB2dXI73BoVgTp7MlMpFkSxV50kVjSCNronYwdtBYnhj+BPmx+cZD8rqNbj6rHob2vZA4DH74Xxg6HyzW4/j2NE3Tvveuw5g98m8RudIslzEQWCAiZ/fbz44RfOlzpN/ah5MGtPQLqBwoCvgJ8KN+ARWUUrUisgo4T0TexAjM3HCUYzihfNl8zTsxprg80G9dLcZL7C+dz2eYHFgX5Vj7OC8GzsMoVHswFwHzMKYJVWJM62nl80Vy+4+hCfBi3MdOc106UHMsgxSR6cCvMdKvCpVSARE5cByapmmapn2NetrbqNi6iZqSndSWFNFcvRcAi9VKQmY2+TNPoS4grOp0szMqkT0p2fQ6gkEpEjpbGVtZwvDyEsbs3EaKy0vz4EkUxuWxKi4Rm/hQwXXEJzWSm1qO3VINWGgLm8snchcfdkbh6YWzgkIYuWgTRQEPAWskA7oSmU46EaFOPFTQte1JVHkh4hxA8NgrcaeO4i8RXpb1+nB7FMMGOLndUc/Qdx6luLeS/45wsOFmB7X2XqCM7HY/13R3MckVYIdzGivdg/EU7SKiuw53aCS2uPFsj8sinkpypJKm4CY2RRcxKX8STw57kqyoLONh9XbCxv/C6kehuwFSx8OZf4a82boAraZp2omhHuP7chnwGEaQpQp4Vil19VGc72Df5t0YHYD7JPX7uQqIEZGog9VbxfjuXgi8IiI/6JsdYnoauAojDrFGKXVM39snii8VVDELs76MUT+lL93oA+AREbkIeAUj2DEUeO8Qp6rHqF1ytO4ENojIn4EHlFL7RCQHow3z9RgdgHoxMlpCOUzXIqWU30yBukdELgVigBuBvxzDGDHH4QMaAZuI3IqRqaJpmqZp2tco4PdTsXUTO5YsonzzegJ+P47QMJLzBjN46gwicgax0QPvVe5lqz2c1ogYAEJ7XWS01pNbs4cJJYUMESEyJZ+N4TlsGxFHgWrBJh7sIRXExjaRk7KHUEc1ABER4ylz/pLXugezsr2XYIswR4LJX1NLR8dntDg6CPWHMcubxwBbDH53C93bHyGwZzsSm0bIxJ/SMCiXu8NC2NbcA50BRg0I42LXdtyrn2JpipsH5lnoCLZhUX6GuO1c3NHMKT3dVIcO4cWEi/i0PoSxG9cw2LURZbUTnDKJkoh0nGoPGVJGs6OZbTG7mD5iOrcNv42UcDMp19UGax+HdU+Auw2yZsL0/0DGdB1M0TRNO8GYWR+nAMtE5EGM8hMbzFooizGyVCYBpUqp6sOcrhFjZkcWRtMZgK3Ab0QkHWOmx//WMzU7734IPCYiP8OoqTJZKbW83z5LReRi4A0RObuvVAbwFkYgKBH40zE8ghPK0VQW+z3G/CcAlFLNIjIXo/vP40ApMNesJPxFHgKeFpHrMCJqv/gyA1BKlYnIZOBuoFBEbBgZKU8CncAzGJWIa4AW4LcYEbxD+TlG959ywI3R/ee/hzzi8D7GyNjZhRHte5AjnwqlaZqmadqX1FpXw44liyhc/hndrS2ERkYx5sx55E6dQXVUAksaW3m4ooqSxgDKYsEWlUxyWxNDdxUwem8Z40WRm5VPVfwI1nrjeMdVR0igiTApJyGpkciYVhKja7BZOwGIiBhFZNzlrJCTeGqfh921vcSLl4uqFEmby/HZ62gLaYUgP6M9mYwOZEBvB64d/8BXs4nefCH0tMtoHTaZm6x2duxtRzq6yI93M9r1NtWNm/ljBnjPFIIDVvJckZzVWM9ZPQ14rFG8kngmV6dPIbdyM+mL1pLmBosjFHfuFCqsocRIO8mU0+JoYWN8OSePPpk78+8kLsSciezphnX/gFV/A3c7DDoLpt8EqWOP2zvUNE3TDk8ptVdETsbo3uPGmKnxJ4zmMX5gPYf/BkYp1SMi92DUT7UDc5RSi8xkim0Yszru5/M1WC7B+LYtxmj6soT9XYT6zrtIRK4A3hWRM5RSm5VSLhF5HbgQoyvwd4IcvGOxpn21xo0bpzZu3Hi8h6FpmqZ9B3ndbnatW8X2zz6hprgQsVjIHD2OATNOozwlh8WNbSxtaafHYgOliO9sJbW1kcyGGia1dZAXn0UgMpeC1k4q2/fip5H4sAaiovYRFtlMbPQ+guzdADgcSURHTSI6eiLesEm82Gzn6ZomWn1+hrcGmLS5lfCuGvzBHXgcxr8vpfrjmOjPJrLHg3vHq/hrt+Iea6XzNB+BoN9xryuVkrJWLNYGBsRuI5LV1ER2oUSIdkOOJ4HJbjeXdhdiB5ZGj+e9mJn4o5oZsqsA1xYHAa+FQFgE3XHJEGpMk++x9tAY0khbZBuzx8/mwiEXEumINB6azwObn4blf4auesidDSffDgNGHI9XqGma9r0gIpuUUuOO9ziOJ7MrUF5fu+bvAh1U0b4ROqiiaZqmfZX8Pi91pbvYufwzSlYvx+NyETkghZBT5lKZnc+Stm529PoBCHP3kNbaQFpLA9kNtWQHIvE7cinrcVBKF15LI3lhlaTH7CEyqo7IqAYcQS7g80GUqKiJtFsG8FlLJ4ubO1jc3E54h58Zxd1kVrdgtXXiDq3DZ+vBoewM8iczxJ9CSGcH3uJ38e3bjC1rKvXnbaYqOoRn9v2aoqoO7M4CIqI24XEYNQQzGxVZnigyLIn8yLuZAf4W6oLieDVhDqWRSQwLWoF/o52eMiv4A/jCIvDEDaDDaaUxpIGm4CaC48IYnzWGycmTGZ80nhBbiPHgAn6jm8+Se6FtD6RPgVPugIGTj8t71DRN+z75vgdVRCQG2AJc0n+60LfdMQVVROQpoFopdfsR7LsUeE4p9e+jvqD2raWDKpqmadqx8Lh6qN1dYnTsKd5J3e4SfJ5e/OFOemfOpTInnzVuRQsCSpHY0UJ6Sz0ZDdUMaGxEVCwVtly2WULoxM3g4GrGxxaSGrOHqKg6QkK6ALDbE4iNnfK/gRS7I5WCTheLmztYUt/GvtpuEtr9pLZ4ya7pINzjxxNWizukAb/4iQs4GepLI707CLV3Hf69q7EE6onMsdEe/iPWzt7KG/uGs8MF9sgCbKGlIJBVpxhbYyU+fDD54fuY5i1AIXwWM5EVMVMIjd5Nas0eGrfF42/yIErRGx5OVZqVvXFtdAT7GJoygrPzZjApedL+6T19lILi9+Gzu6GxCJJGwCl3Qs4pumaKpmnaN+TrCqqISAZQAdiVUr7D7DsT47s89asex2GuezXwN4zyH9d+k9f+uh1xTRUzKDISSFJK9X5tI9I0TdM07Xuvu62VmpKd1BTvpKa4kIbKclQggIiF8JxBNM27mPUxKewgCL8Ijk4Pqa0NjGquI6O6Amd7C+5ALKuDc3nXkY/T0s1pSdu4OqaIWGcF4eEtZiwhhKioiSQkzCQmeiqhoZm09Hj4dFcTW9e0sq92Hc4WHwntHub2gCAExIcKbsIfWk9rZDtWZSErkECuK5bIvSVQ8QTe7kYcySkkji0nNKGbN3sv4dHUTuobg7BFvk1ItI+4dmHGaj+TvFYcKYmMii8mOvARNZZ4/pm6kH2RUQwOXkRKwVo6F4VQ745AWby0xgaxMauVZmcUU1Im88expzM8MQf5ouBI+TL49PdQsxFic2HBUzBkHlgs3+Ab1TRN046ViFQCVymlFh/vsXxZSql/YdQt/c45oqCKGfmajlH59xzg1a9vSJqmaZqmfd/4fV6qdu6gbOM69mzbTGtdLQA2exADcgcxev75VGQO4ZOAg5U9HnxiIaKni+FNe8jaV8WA2jKCutrp9kewMTyPkrAJZES2cHbmNkKc1XSEgMfioNIbS5l7PI7uHOz+ZHAFE9jtgy4flq46bF1VhHb6ESAFSEERYvETYwMV0UZzUAN11hb8EiBcBTO6N520mlZCS95Ete+F6Ey6s3PITGiiKbqMe6zj+DQmAnfoasTqJtTvYPr2AHOrehiSAJEDunGKiw5rM4tjplARNYzwiEJiWjfSuzqF6n2JSCCAP0ixd6CwLjWI/KTT+d24c5iUmXjwQIqnB6rWQsUKKPsM6rZCRCqc8yiMvBCsR9OnQNM0TdO0gznS/6teCqwF1gGXcZCgiohEA88CE83zrgKuPaCFU7aIrAcGY1QI/rFSqsU8/lWMwE0IUABcp5QqNLc9BfQAmeY+BRitm281x1MPXKiU2mLufytwNZCA0W3nf5RSbx7hvWqapmma9g1wd3VRsXUjpRvXUbl1Ex5XD7YgB+nDRjD85NkkDRpKWUwiL+zZx6J2F65uKyGeLoY0VDO8spSE2nKsnU30BuzsCB9EScwkRma0MWlgAzlBTTR05rKy6WwSKuwktPtxugJEeftPe+4GuvFbAojNT5gKEImdyGAb0RYhxOanztrAHmsT262t+CVAaCCIDHcsyU0ukktWQVMxBEfSkJlLxcw0Inua+Ch5H5ucGbjCqhFLKQTsDJQwLi5q4/T6RiJSvATl+2i3hPFh/El8EjudsBAv060vErWjk9bPnHR1xaHEgzssmG2pHjoGjuDSkefxwIihBNutn3+Qvl6o3ggVy40/1Rsg4AWLDVLGwpz7YOyPwR78Tb5eTdM07WsiIlaMjjyXAx3AAwds/zHwayAVo2Xy/Uqpfxywz03AbzA6Bf0/pdST5vpIjK64Z2B8g/8LuFcpFfgab+lb7csEVf6KEVRZKyKJSqn6A/axYLQ0Ph+wYrQjfhSYf8B5ZmPM93oGeBjoq/r7IXAF4MH4BXkeGNXv2PPNYwuBD4A1wJ3ATcBd5vhmmfuWYQRf9gELgOdEJEcpVXeE96tpmqZp2tegrX4f5ZvWUbpxHdVFO1CBAKGRUQyaPI3scRNJGzaSol4/T5dU8MGeTtpqe7H7vGQ21TJsbxVJtXtxNJZi9XuoDBnIntQpxIwOJygKMlvD6WnKonWjYmCzj0FeBShwBrAnKrwh3bgsrVjc3YR3BUjviiJbJREsQYgILl8nLtVJQ5CbbbYWqi3N+CVAcMBOekcw6XsqSShegSXgQ9mCaR2QzqK5WVQPzuX/s/fe4ZakV33uu75KO598+nT36Tgz3ZODNIqjNGgkJKQHsDBcIYQk2xIYXZLB98IFjOVr4BIMCMP1BRsMAoFhEEFI1kgCJQRGcXLomencp0/cZ+dU6Vv3j9rdfaanZ6YnaRTqfZ56qnaFb3/f3nXOrvrVWr9V32hxbOIUSWkFEYWkxo7Rdr6jcIzXDdZYXItwCkpnT4m/mn41f7Pjm7ivdoDX2A/yimO30nxgluMbOxCbom5Ke6rI7fsmePHzvpPffOEr2DZRPPch2hSW74Bjn8lElJOfh2QICGy/Dl78A7DvlbD7xRBUnquvOicnJyfn2eNdwBuBG8ieEPzFedvXx9uPAq8AbhORL6rq7ePtC8AEWVDma4APiMhfq2qTTFCZAPYDM8DHgRXg957VEX0N84RGtSLyMrKoku2qWheRQ8DvqOqvP55RrYhcD3xKVafGrz8NfE5Vf3L8+krgTqCoqul5x04CTWBSVdvj94lV9V3j7T8EvFtVrxi/vgb4rKpOPsYY7gT+vap+8GI+lJxnntyoNicnJ+cbkzSJWX7oEMfvup2jX/4C9VMnAJhZ3M0lN76I/c97IeHOPXz82BKfXFrlLuPT9gsYa9ndWOWS5XUmluqUNx5kYbhCJB6bO/ex+fyDbNr9VOtldm0k7GgmOGeeoU0Ztl9aoT+7zH3JFxisrHB1Zx/X9i5jZ7qAiMFqyigdAKCOx2m3wXGzwZJpYMUSJA47NwfsefAO5pZPIEC/Ns+hXUU+e4PhxOJuenFEW49CsAlAcTDFZY0S38lxXlaqM92JMQpD6/ORwkv5y73fzN9vewHbk2W+9dStTN7fZLTiItaixiEpFKmXlN6NL+X7Xv0Wbtg1ey69JxrA0U/Bgx+BBz8Kg6xcM/NXwb5XwL6Xw56XQnHqK/jt5uTk5OQ8GZ6uUe0ZTxXgp4BbVfW3x+tfC3yMxzCqFZG/Jrs3/42xUe1tQPXMviKyTmbz8UVgCFyvqvePt30/WVbIq55qv7/euZhIlbcDH1fV8a83fzJe9+tbdxKR0njd64Azv+hVEXG2iCanthxyAvCAWRGpAz9PFlUyB5y5LJol83GBLMXnDMMLvD77KEZE3gb8GLB3vKoybisnJycnJyfnWaa1usLxu27n+N23c/Leu4lHQ8QYFi+/ile97Z04+y/nM0tr/EZnyD1Hm7SXM//7ovHZ0axzYLNLtDpksn6KA+37qaR90kqV5g2vYLV4LTMbJW74bIKjYGVAWg3ZfmWN2asLHHHvZuXIYXS1wPOOHeTm5JsBSG1CZEfEEuM7AUOJOea3OGLWqTstVKAQWvaurLHn8APM1TdIvCLL8/N8/JtfxB2Xz3DY3aDDSfA3gVOo6zDZnuWVR2t858ZpLt2xTMXJrmW7vYA/c27m1rnX8+W916Mi3HTik/zQx/8fnJMxWGXkeCTlGsMg5fB8yEtv/hf8m5feQuCO03t6G/DQRzMh5cinsmiUoAaXvQYOfgvsfxWU88ubnJycnG9AdvDoe+uziMjrybI6DpBllJSAe7bssnme+DLg3D2zd157J8giWnIeg8cVVUSkyDidR0RWx6sDYFJErjtv9x8HDgIvUtXVcaTKHcBWB7VdW5Z3AzFQB94CfBtwC3CcLNyoed6xF4WI7CHL+3o18E+qmo4jVfJ6gTk5OTk5Oc8C0XDAyfvu4fhdt3PirttprWXZtrW5bVx+0yuIphe4e6T8ifg8wBzt1RG4ExQqBba1GiycWEY3I9JWSHXU5WC4xEL3QQwVkvnraZWuZLJXZftxWEAZVNo4O0P2Xj6Lc2nM6skTNI92mPnUTl4bXorhAKkmhOmASEb4TgHHuHRQHpLTLJs1un4WpVLp9rn85AkWl5aotbqsT85w/65JPv/Kee5Y7JAWVxA5DYAJK0w3a1y6tsDLO5u8Olpj28JJ/FKK7oJWzeVvCs/n/y1+L/csXE0xHXHZyfv4nk/+V2ZOriNWsY5LVJsh8T2Wqmsc373JO1/xI7znitdmUSn1hzMR5dBH4NTnAc1MZp/3vZmQsucmcP3n6JvOycnJyfkqYYVH31sDICIBWTrQ24APqmo8jlS5mPvhOtk9+h7g/i1tn34mOv31yhNFqnw7mXHNNWReJ2e4lexL2kqVLGKkJSLTZMrY+bxVRP6QTDj5v4EPjEWPKhACm2Qq2i88yXFspQwomSHPGZOeq59Gezk5OTk5OTlj4iiksXSK+qkT1E+dYPXIQyw/eAibJrhBwI6Dz2d01c084BW5t1zmxOQEQz8AIIgjplstZk+tUl5vsWNjk8W0zUSyiek3EK1inFnwdmCnX0GQwSSyMAAAIABJREFUlghCwKT0ZppU5xwuvXqSKEroHWvi39/nsi/t5mquIdWUftKmpy3KXg1HXMQUOKRNlqMHaLh1BqWshPBUo8E1S0vMrjVoBw4P7iryV7dMcGhXTBpsApuQ+FTbk2w/tchVqxGv2zjFgeJJyvMhhekYqWVhtfWpgHtn9/I7xe/hfxVeylx7g0tOPsD3fem9VOtZsK11PeKJOZJShY4ZcWj+AZo7XX7kxh/kTZd9O86wBZ//bbjj/bB2b/ZBL1wLr/pJOPj6bPmxyiXn5OTk5HwjcivwwyLyYTJPlZ/css0nC4TYAJJx1MprgXufqNHxvfmtwM+Psz+myTJA/tMz3P+vK55IVHk78PuqenLrShH5LTKT2a31sd9LlhpUB5bJHIi3mtRCVh3oD8iq/3wG+IHx+j8kM6E9DTSAf7dl25NCVe8XkV8lM7K147b/8am0lZOTk5OT842KtSmt1ZVMPDl5fDw/QWt1BVVLwakwX9qDu/MK+i99CffUCjw4UWKtWkYlEy9qgx6z9Tq1RovtrQ121U9R7W1QGBjEmcE4s6i3H/VehF89Z6gaurA+G0N1k+t3VLl2wiFaV4KliMXbwGUWq9N0kgatdI2CU6biTVLyJjipTb7AYTZ1hV4hQY1BrGWmvsni8U2ipM2RbSkfe6lycrqFOFmGsjuYZG5jnv3rLjcfafDCzjK1bauU5iOKizGyW1GFYWA4Pl3l7rndfLR2M58dvYy5lXX233+Id5/6FfwwQgFbLBPO7SQpVYmssFpocffuf0DKPu+69p18z8E3Uzz5OfjAv8wiU9IIdjwPXv/LWUTK5K4LfS05OTk5OTmQZWYcIKuK2yETPb4JQFW7IvLDZMJLAHwI+Jsn0fYPkZnVHgVG4/f6789Yz78OeUKj2pycZ4LcqDYnJyfnqw9VZdBu0Vw5TWt1hebqMq2VZZqryzSXT5PEWZCqNQ7O4hWE8wdYqc1wslJmqRywXi4QuR4Abpow024y3dhkYX2ZfcsPM1VfwkiAcfdi/H2ovx2XGkImulhRRtURo0qfuNDH+jFzEzNcU5mi3A9xVxJm21UcHFJN6cQN4nSIa3xq3gzGOKxJh6NssGzW6TiDsyLKVLPJ9GaLKB3y4HyDf7o8pllpjcdt8Hrz7FgvcnAZXnW4yTXhMuWFkPL2mOJMhHEsqtD3fdqThjvm9/OpyRdzt72W7kaZHcun2Ld0mO3rS4gq1nFIyxMk1UniQplO6rBRjNncs8yqcx8i8ObL38y/3vU6avd/CO78E+gsQXEarnsz3PBW2HbVc3Mi5OTk5OR8RXi6RrU5X50846LK41UEyvnGJRdVcnJycr6yWJsS9vuMel1GvR7DXodBq0VrbfWseNJaWyYaDs8eo45LtLCbxvQ26hMLrE7MsTIxQb1SITXO2f1K4ZDJfpeJTovZ5jq7lx5m2+oJnDQhNQ7diUuxxQNUdDvVURlBUH9AYfYYxeopUrfLpoSMihWmvX1MxzWmeiXmWjVmhjUAEk3oxJvEaYhvfAJvgo4Tsmm6rNGhbtp0zQAdp8VMNpvMr63jDkOOT/T53IEODy92QQRNfZz+ThZXC9xwbMhNx5rsD+tUtw0pL0SUt8e4fgzAmjtFs+pwdNscn5p+EXdxHb21EttXTrNr+Rg71k7iWIsCaaFAWp4iqU4QuiXW1GFzfkhr7hD15AgAi5VFbtn5ct5hZph54H9mZZARuOSbzvmkuMFX5qTIycnJyXlO+VoSVcbVe9+vqr/7XPflfERkL3CMcbUjEbkN+FNVfd9z0Z+Lqf4DnP1QrwMWVDV81nqUk5OTk5OT85hEoyHNlWVOH7qX5QcPUV86QdjvY9OENEmxSUKaJNj0URUVz+J6Pr3KBOs7L2NtZjvrU/OsT83SqExinUw8EVWqoz5T/Q7XHl9iurnBTGOVmfUV3DgiKZXplyrUi2WObb+E0ztfzq5Rme11w8LQgyEEU8dJdv4jS5VjHC1sUEjn2T5aYLG/k+sGe9kf7sQdX4oM0yHDpMOabRE5EAc+reKATdOjLl36ZnS2/14UMlVvsdBsUWu1WC9EfGl/jz99VZ9+UdA0wAx2s+PIpdx4eMDNDy2zIzpMZWaYRaO81FKoZmLSwPgcr9Z4YG6Rv5+9kTvlagZrJRZOLLH7n45xy/pfnxVRkkKJdHKOsFQlLVXoacApzzBcqLNZ/gc66QaCcO3Utbxtx7t5nQ1YOPEF5NO/DaM2TO6Gm38arn8LTCw+eydJTk5OTs7XJSLyMuCXgavIvE8fAH5UVb/4NNt9D3Cpqr71abRRI/NNfROZF8saWerRz22pJPysoKqvfzbbfyIuSlQZK0EvJytv/K3Anz/Gfs6F1ufk5OTk5OQ8MapKOOjT3azT3dygs75Oe2ONxtJJNpdP02tukkbRo44zrosRg4hggbhYpl+q0StV6BcrDIpl+oUy3UqN+sQsm7UpQu9cBZnyaMB0r8NVJw9Ta7cotdo47SH91Kc6M0tp9zZGBxe5vxxwHx5BLOxuRFzT2OTg+pDysRpiXcQdEk09zMkdRzgxcYIg9blssJvnb9zM2waXUKIIQKopcToilhEDIzScARtuh3qhw4bpEMlYEFJw45CJZou96xtMNxrU2m1WaoY7LrH87bUhR7aD1SJmsIuFjSleeajHqx86xSXO3RSnYgpTMcFNluLkCGOUFGG1MsEdM1fzsZmb+ET5ZUQDn0tPHOLSOx/gNafPiChCUixnIkq5St+rsWGqrHsgM13iyVNscB+hHVJ0i7xk+0t49faXcHOYUD38SfjIz0HUg8JEFo1y3Zth7yvAmK/EqZSTk5OT83XGWLT4MJn36K1khrQvJyv48pwiIj7wCaAFvA44RFae+fuBFwIfeZLtueeVfP6q5qLSf0TkZ8mMZD8PHFDVN47X/wFZxZ89wCvJyiK/lXH6z7iqz9+Q1cT+EeBbgJ8DLiETaH5PVd8zbmsvWQjPO4D/SFYF6NdV9efH218I/BcyQ54h8Meq+mPjbX9OdkIVycx6fkBV7xtv+xYy455dZCY+v66quXvxV5g8/ScnJ+cbHbWWYa9Lv9mg26jT29ykU1+nvb5Op75Ot1Fn0GqRxo8WTSArazcslIhmtxNOzdOrTNIulGkWK7QLJXpBgUFQYOAFpM6Fn3EEcUSt36fUG+D0IuglRB0lsgUWpmrsna+wMOtBbUjdj3ggNhwZFJlrWXZvhhxotNjWUIJe+WybUXGdjcmjrEycIJAR1/UOcm3vUhbtNpyxYW2qCY64DImomw7r0mZN2myaHqGJzw4wSA1Bb8DMRpPFpWPM15dx05TVSZ879lvu3m+5b7cwMDXMYJGd7TLPOxHxxlMPss9bpzgVE0zFBJMJxmTXN7EYVkvT3DVxKR+dfjkfmfgmBk6R+foK1x39EvuPP0yt2cz66QeklUlGpQlWvVmW3Cl6xT7Btg20fJzN5DCxzb6fvbW93LhwI7dsexEv6GziP3gbPPx3kAyhNAOXvxGu/NZMSMlLIOfk5OTk8PTSf0TkRuDvVHXyMbYb4KeAd5HdF38U+CFVbYvIq8hSeRa37H8ceCdZoMXfkJVcDoEjqnrdOFPls2QGuNeSFYJ5y4WiTkTkncDPA5eoau8x+veT477NA6eAn1bVvxpve8d42xfIqgz/f8DPPs549vLI9J9Pj8f3u+O23gl8DvhXZELPu1X1tvF7/Qvg/wQWySok/ZKq/s6F+nyxXGz6z9uAXyMTVT4nIttUdW287S1kYskbydSyt447OwPcBnz8jL+KiPTHbd1HVub4b0XkTlX96y3v9TLgIJl48gUR+UtVfQD4DeA3VPWPRKTCI8sk3wb8S7Kyz78E/DFw/Xjb7wHfpaqfFZEpYN9FjjknJycnJ+dxUWsZdNr0W00G7RaDdov+eN7brNOur9NvNRl1u0SjIZz3IMOKYRgUGRVKDAslhjunGRbKDIsVBqUyg8oUvfIE3VKFTrH8KLFEVClEIX4Y40YxpX6PStRGohSNLBopaagQCWXxmCgV2Ttf5MA87Lkkplbqsy4xD43aHOr1ONSZ5/5mjYnTsNAUXt3o8x3tEaKZOBL7Ke3KKqcW1xAzYGda4+BgO6/pX8/k8OXZmDSlT8iq06ItfTbp0qBL1wkZbRFQCklAua0sNLvMrh9n8fRhCmNBqVcw3L1X+eDzhbv3uKwX5nD6O7hqM+Vff2Gdlw1OsVA4QmEiwd+RIONLxJF4LJemeWhiF5+rXcunqi/joeI+VAy1pMkNJ7/EP//S+5k+vYYXhZkvSrFCY2Yvx8p7OBFUiafaTM6tEHufYzM6jqI44nBF9Qpeu+3NPH/mGp6Hz+TGYTjyCfi738oq91QWMrPZK78Vdr8UnIvOsM7JycnJybkYHgJSEXkf8KfA51S1uWX7O8bTzcA6WRXc3wK+9/EaVdWPisgvcOH0n7cArycTQW4D/i2PLN98hluAjz6WoDLmCFkgxCrwncD7ReRSVV0Zb3/ReFzbAO+pjmdLW+8ji5b5PuD3RGSnZhEl62TaxVHgFcBtIvJFVb39Itq9IE/4iz/O29oD3KqqdRE5Qvbh/vp4lw+q6pmSxSPJDON2kJVMfp+q/sqZtlT101uavltE/gdZhMtWUeU/qOoQuEtE7iLzcXkAiIFLRWR2rI59bku7Z0s8jfPBmiIyoart8XFXishd45Nu64mXk5OTk/M1jiaW6HSPtBPhVD2k7DJMu3Sbdbr1DTr1DTob63Q2s3mvsYkXBBQqVYq1GsVKjUK1SrFao1ipUqjWsuVqFRD6rQb9ZpN+u0m/2aDfatJrNhi0mvRaTYwaHHEx4qBi6AcB3UJAp1hgUKrR23mQQanKoFxjWKwxLBQZ+gFDP3hECs75GGsph0PK0ZBav8d0owWjlHSkJCPBT3y2OcL+qT7bJkZMVmIm5iKqhZCiOyQwA1wGEIckYUh3oJzul1jubGPl6C6OD+ZwBvupDZSJgeWV8RnBJ7seSpyQfmWDzZkuEynsSkss6gSzySUEvSuJSenIgBY9jnobtM0J2jKgJX0SsWfH4ahDEPs4vRGzvTXmN06z+9RpJnpZtHLkwPFt8MnrhMPbDQ9tK9MoTrN9WOIlrS6/eGyVK8y9VMtfxHjAYqZNDbXIqfICD07t4PbJK/hk5SYeKBwAEVyNubT3EAdO3sVLT32CynqXoN9GrEXF0C7NsjS5k8PTVcyOHoXqEl39MN2kMe50ketnrue75l/HS4o7uGLYJ1i5B+76GKz8CqTjSOuJ3fCCd8GV3waLL8hTe3JycnJynjVUtTO+N/8JsjLHCyLyEeBd44CH7wF+TVWPAojI/wXcO47MeKr8vqo+NG7vVjIrkAsxA3z5Cfq/1ULkz8b9eyHwwfG6ZVX9zfFyIiJPZzwnVPW/jY97H1nGyzZgVVX/55b9PiMiHycTe549UQV4O1m0yZkwnz8Zrzsjqpy6wDFvILsq++2tK0XkRcAvkkWZ+GR1s8/3Z1ndsjwAKuPlf0VmfHNIRI6RiS8fHvu4/DyZ2jUHnLmSmyVLMfoO4GeAXxSRu4GfVNV/uohx5+Tk5OR8FZL2Y6ITHUZHmwwermM3IsTKo/YL0yE27eOkIRUpUC7sYsfUPpw9AWkaEw2HxKMR8cqQ4bE63dFp1FqyhwOCIDji4ogHKKkmjCRlrWBYLk+xvGsHm1dN0pqYpl2dZFAoMwwKqDz6xtpYSyEOKUUhQRwx2eswFyc4cYITp5g4RSILsRKkULKGsnGZqgbsmEvZMdNjutqm6teRqMmoMaK7mTJsl4ibcySrVdKkSDOusJHMY+MCkniY9NFpQFPjaeTGjII6id9jWIuw1jKReMymAXMU2GaqFGQP3XSUiSVej2NS5y45RdsM6MsjU7grtkA5CdgxLBJ0OhSbJ5nYPMZ0o05hLNhY4PQs3LHXsDRXYmO2hNZc9tqIa8IO35k0WeyexvTGAo8LUcll6AYsVac5PDvPl6au5NPlm7jHuYZYsqo5O4enuGr1Pl5w+h+ornUJWl2cOOufAgO/yvHaIsvbKgz2D4iCk2zGt2M1BWBPcQ+vmr2J509cxo2psNhexVm+A+78VRiML3/cIuy4Hl74Lli8EXbemJnNyqPPvZycnJycnGeDcQbHOwBE5HLg/cB7ge8mC2w4sWX3E2T3+9uexls+1r35+WwC2x+vIRF5G/BjwN7xqgrZPfsZztcVns54zvZbVQfjwI/KuB+vB/49WWaMIbMdueci2nxMHldUEZEi8F2AIyJnOhYAkyJy3Zl+XuDQ/0Z2zfYREXmdqvbH6/+ELGTn9ao6EpH38sgP8jFR1YeB7x7nir0J+MA4xehNZF4utwDHgQmyaBQZH/dF4NtExAN+kMzUZ9fFvGdOTk5OznNHEsf0mw3SVkhyesDwcAO7HOINs58uqymNcJV6eJq21vHnykxMLFANJilSQCJwhoo/dIhG0B0OaY9C1ruWkRFSAwmQFlziYo3EQCIQGyERiIzQCTzqtSqt2jSdSpaGs1U0cdKU2qhPbTRgurGOGyU4UYpEKU4CXmrwcSm4AUGxTLFUYaJcYWF6wEyxz2QhouJ1KbltPGkhtslo2GLQDhm0E0adEtHaPPHROZZ6c0S952OjR17PDAJLP4Chp0RBQlQKiUwb6wwwDCjZhFKqFKxQSoQJ61GzHkXx8cQhFYjFIXaU2A1p0adOyhdlRFdGqJz7mQ/UpWZLLMRVioMihW6XQnOT6toxip1TePG58sy9AE7Nwf1XCcmkYa4acVmlww0m4jWp5YwUkXQNcWKIMETGsBKUGNUM7XmHu2cu4U73eu7nah7iCkINqPVaXH78Af75+q3MbDYJWn2kP0DGlyOJ69MqTLA+U6S1w9LZ3qRpjjFK7wWg4lW4ZuYa3jR7Cy/yZrhy0KO8di/c/UnY+C+cvayZPQgHvhl2Pj8TUeavBMd7Zk/ynJycnJycp4iqHhp7nH7/eNUyWYbJGXaTXeqskQkUpTMbxoEJc1ube5rd+Tvg50SkvOXe/ywisodMI3g18E+qmorIncDWJxPn9+HxxvOUSuiJSAD8BZklyQdVNRaRvz6vH0+aJ4pU+XayUk3XkPmVnOHWcUcejx8k++A+JCJvGKf0VIHGWFB5IVka0ccvpqMi8lbgY6q6ISKt8Wo7bjMkU8dKwC9sOcYni2D58NjQpsO5SJacnJycnCeJWkschcSjEdFwQDQaEY+yiI9oNEKM4Ho+jufheB6u5+N6Ho7v47gerp+9ttbSa2zSa2xmpq2NTTobG7SWlunV6wx6baItN+hbEbIqN44RjBjU9QndgE6rSjce0KtU6Zcr2XyuzKAwQT9YYOAXGPgF7JNM0ShEIbVRn7lem10b6xSjlJoatnkBi7UqO+cK7NpXY6Lo45kuLkPEtkmSJlHUZNjv0himNPqGzsglGvpIs0hzVKQ1KsGoDMNpGO1GRxVIHpkSZLH0iwM6pS7d2RUir4urQ0ppSjFVPDU4ZI9fLKBWs4XzPPMV6LvQJ7tK2YpRwcPFVYOHg68u07bMjriA32tTbawx0Vij2F7D9NuYOD57bOoo69Nw50HDiTnD8hzUqhFXuSOuGsK1owKhBERBAJVJ1qYGnC7CKDCEgSFyDE2ZZp0F1nUb6+k2Vobb2ejOUj7cY7pV5/LGXdzU+gTlXhfRcz/jahyGhTKN2QXq08LGzib18ilC+zAAnvE4MHWAl8+8getq+3h+DNubpzCnvwS3/6eszDFAYTJL37nqn8GuF2RCSmHiSZ0nOTk5OTk5zybjyJQ3AH+mqksisossQuWMLcb/AH5CRG4jM2D9hfG+iYg8BBRE5A1k998/RRYscYY14DUiYlT1qdwv/xGZuPMXIvKjZP4vU+N1d5IFP+i4X2fMYq++YEvneLzxPIUuAueyZTbIUoxeD7wWuPepNghPLKq8nSyP6uTWlSLyW8B/JlOkLoiqqoh8H/AHwAdF5FuBdwO/Oj7+M2TizAXdiy/A64BfE5ESWejPm1V1KCJ/SFaZ6DTQAP4dWZmpM3wv8FtjNe5BslyznJycnJwLkMQxjdOnqJ86wcaJY9RPnaC9tkI4GBCNhiThs1e1zzdFSm6VolNlKpjHFAyqKVHcpi0DVopF1me20ZicpT0xQa9UpVcq0Q9K9ILiBSveBHFEMYooJyk7w5jJBGYKBQrGQazFpCmOVcSmmNRibIJJY0wa49qIqhOzZzplYTFmqthBh136rR6tVkS969FdK3L6+CTHRzVs7EPsIfEkTjKLlzh4iYOc9/BDSLEmQU1M6A4YeX1ip4sN6thCBJLiYHEAR0BFUcmuAmYsEIKoUKZAWQNKGlBQDweTTWpwcHAwuGdfn5uMBceCLy6ORog20MFRaN6D9OqYThftjtD+I6+p4pKlMaUs7RYenHU4NissTwv1GszHRbb1J9neKXNd7FE1Paq7NhlONTghFgYha62dLOluTvi7OcEi3W4FXVcKwwGT3RaTnQaT7QZ7Og9wWXzX2fdVEawXYIMCvelttAOfzWpKY7bFau04oWQPxM4IKDfNvIFrJi7h+lTY1d3EWb0bvvwhqD9Edj0nMH8FXPntsOuFsPhCmLk090PJycnJyflqp0tmwPpjIjJJVtXmw8D/Md7+38kiUv4eKAAfA34IYBxg8G7gdwEH+GVgaUvbf05WcGZTRI6p6vOeTMdUNRSRW4D/APwtmaCyRuaX8nlV3RSRXyWrIGTJTGf/8bHae6LxPFVUtSsiP0ymQwTAh8gqHz0tLqqkck7O0yUvqZyT89WFqtKtb7Bx8jj1k8fZOHmc9eNHaK2uoDa7mRYxBH4RjwCjDp74+KZI4JQoOCUKTpmiU8V1fDAukZNFVahaVFNUU+x42arFagKakqRxZibqlTBegdA4nC6knPISVguGRjGgXSrRLVfGoknx0YauqhTiiEKaUhQouS6FQoAX+BgD1sYkSUg6GCG9Ac4wxB8mBCNLIQTfCp4aPCBQ8FTxNcWzKa6muNbiqkViDx1VcEYBbuSikmJNhHUirAmxJiJxB+CEIClgQVIUBbGZIIKenVvRxw0wdVQI8AjUI8CjrAFlLYzn2XLRevhqSG2K1R6+eZiCs4QRRdTB4pLgkWJIFUQGOPQQOpiwgeluIN0+tp0Qtl3CjoemWadUlLCq1Gccjs86PDQnPDRnWZmBYSBI6uEPpyi2J5nruyyK5ZLJHrv3r1Cb7AIQpy5HhpdxZ3gNx/r7CAcBlWjAZK/JZGczE086Ddw0PTtuK4a4UMJ6PsZ1US8g9gtsFl3Wy0M6lTU2iscJ3REAZa/MgakDXDZ5GVdP7OcG67KrW8dZvQeW78gElDMP2qrbYfv1sPN5WTTKzuflUSg5OTk5Oc8JT6ekcs5XL7mokvMVIRdVcnKeOzS2dJfXWT50iNWjD7F8+BD11RPEybmok4JTpurNMOnPMxNsZ9Kfp+pNYcQhIqEvI+raZ8V2aDCgKyMGTsKgYIh8h9h3GXkBsXFIjUNqzJa5Ofs6EYM1DpHr0guK9IISAz94lNmnl8QUopBSFBPEipcKYgEb4SYRhWhEcNb4NSaIUwqxoRj5FKMCxbhEIS7j6LmAzEzciEmdCJUYKwkqCVZS1CQoKSopamw2F4uaGCsRmAQ1NrMzOw9XHUrq442jQx47UsTBxeCpc1Y48dUlUBdfHTx1cRCsWlJSElJCiYjMCLFt/HQTVzYInCWqzhKz7lE8E5LGQjI0pCOHZGRIzsyH2XI8nuxIED33OXdLhqUZl+PzwtFtKSfnlaVZiD0BBX9YZaJdYkfdYf9qwpXDLvv2NomvtISLgAf9uMjRxn6ON/fQbE8ifaU66DLZ2aTWa2O2XGOkxiEplLGulwknfgHrB8S+TzuArjekH7To+HW6fpuO3wGBXdVdHJw+yIGpA1xV2MblccJsewVZuxdW7ob6g+cElMo22HFDJqLsuCEzlq0uPCN/Rzk5OTk5OU+XXFT5+uQZEVVE5LeB06r6H5/i8T8F7FfVdz7F478HeLuqvvapHH9eW7cBf6qq73u6beWcIxdVcnKePdQq8dqA8EiLaKnLqNVlY+ME9c2TbHaX2BwuM0yzMrmCMOHPUQ4WEH+OoVel7Rdo+JaukzDwU4YeRL4h8R1S15B4LkPPZ+QFDL2AkeuhFHBTl2IElZGlPEwpj0a4qSC4me+JglEQBVHFqGIUjFUcq3ip4qaMJ4ubWvz0zHrBsY9O57ng+LEoMVZGpKaLlR7WDMCE4Fqsa7GOErugF5OCq+AgOJqZzGYpNgXKZKk2JQ0oEVBSn5IGOGqwW9KPDYKIQQCLkmoMDLE6QOlj6WIZkDJE6QNDjAwxDHDlzDQiMF0q6QYmjOgOHbpDj+HQZTj0SAYOMjAEfQiiRw8hFWiXhVYZWmVoV7J5oyIszcLJeaHv+ZQGRSY7LttasLMRs78+4LLBgJ0aE8wlDA/C6p4CxwoLbA6nabdrDFpFaCulTo/qoPuI9x35BYaFKolfQo1DybEYP8D6BUIX2kGXrt+i63XPTkN3xGxpnp2V7eys7mB7eTs7y9u52pTZN+wRbDwIq/fA6r3Q3eIGU9sJ267OhJMzIkrtcQsP5OTk5OTkPKd8NYoqIqLAZap6+Lnuy9cqTyiqiMhxslymHVvKKiMidwDXA/tU9fiz2MecrwNyUSUn55lDVUnqQ8IjbVr3nWLtwYdpdFZoRWu0knU64ebZfSWYYHNqJ0vTO1iZmqdZq+G4UE2HiEDo+sSuSyIGYx38RAhihyASCpFQDIXKMKEyiiiHKcUICrGTVVoZp7RwNsUlIUVBDVlIh2Tbxp4g2SRYEaxYUhOSypDYDEkkUwYcdShYn6INqGiBkvVwNUWIUYnAxKiTYJ2EWFIikxKamKHExJKe90FBiaydsp6ZZ8s+Lq5mkSMuDq6eiyRxMlmEM7+PNmsKQ4wjPZQmfRoMtEMkbayj5VHcAAAgAElEQVR0cOjiSZ/C1kn7FLWPm6bYREgSoZM6dFOHYewwig1RZEhiQxo52MggkcENwQ+hOIJC/MghWaBVgUYVNqvCZg02yy7tsken5NEvuwzLLnFZCBwoGUvZSSk7MVU3ZtILWQhCZlylNI66SRKh2Zuk2Zum260y6heJ+w7aBb8dUur1HpGxNCiU6JSn6BeqhF4ZdQJ8sUybEH/sa5OKpeU3aQQNmkGTYWnE3oU97J3cy47yDhbKC+yo7GB7MM38sIvbOAL1w1nazubDsPEQxOPiAeLA3OWwcDUsXJNN266B8swz8eeUk5OTk5PzFePpiioXEkBE5D3Apar6VhF5FfB+Vb3o6ji5qPL0eSKj2jMcI3MW/k0AEbmGLSWZcnJycnKePGGSErgXF40RbfbZ+PJhVu55gI3jR2l0V2hF6wzskG6lRrs6RW9+mnr1eaxPL9CamKFfqWJdh/lOk+2tTfa1Grzg5FE8mzzxG56Pm03DMly4Js+jMVZwNIv4cFQwCGacP2Mw+OpmAoYIKooVSyIJMX0iSWiQsiEXNqD31KGkAUX1mbFFiuqNBZQilbH3iI9PiBLaDiENYmkSyyapbDBihCMxRhKEGIcYQ4KRGEey156EBDKizICq9vHiJEutGRqq4xSbcOjQD10GI48kMfRioZ8Ibqx4cQmjF/6p9MYTwMiDfgH6gTAIDMOaMAocIl/oTxhG04Z4SmDK4hVSZtsJsxuWvSuG69cKOB0fAg/reYSuz8AEdJ2AjVKReqlAKwgYOA5dI7SsciK2+KOIwnBIud+jNDxXjtghpEzIoFCiV6nRmpphMDdB5JVIHIeCEzKnESWrlMf9t4R0/DZLYwElLIfs3r6bK2ev5A0zb+CK6YPswsU0jmeCyfLDUP9YJqC0TvKICoq1RZi9DG546zkBZe5y8AoXedbl5OTk5OTk5HxluVhR5Y/ISij/5vj128kce38OYFwfe0lVf0ZEZskq/ryM7KHafcArVdWKyE8APwzUyCo6vltVP3GeulYgcyV+PZkz8cPAG1V1TUTeAfwsWU3tOvAzqvrH4/XvVNWXjfujwI8APzp+r98HfmLch3cA7wLuIKsMtAL876r6ifGxnyZT9373Ij+bnJycr1PCwYDW2gqt1RU69XXSOEatxVqLWktqUyKrxGqJFCKFWCEyhtC4RMYhVOglSjNOaSVCSw1dHPrGJTIufppQSiNKSUQpjSgmMcUkphSHFOIYLwoxcUjbJDQrVTrVSdpX7aBdu4ZWdYJ+uQKSCRVenLDQ6nPJ+hovXjtMOerg6uCsMaoTl/DiWby4xlRSo4yLMQmOSXEkQUyCSIpIes5kVRSVrHTx2eoxCIJg1IyFkkwsMQgxKaHEhMSEkmTLEhOac+tGxKgozpbyvR4uHg4ldfEp4Vk3KxWsgq8OBfXxKGKkwNAautqjLZv0pU5PVhjRJtI2Vtu42qakbSq2w07bBwW1glqIrDCwhmHqEScOSeKQJIY0MdjEkKRCnAicmSIHd1ghGJZwLqDvhC40K9Auw6AkjHwY+RA5gjqCekARtAKmDG4Avm8JKgluGfwyFAIoGvCMUCP70UIAFbxejZlWg0p/xJHNa/hQ/2bupchILHbB4mxLcTXCTyOK8ZBSPKAU9imN+hTXN3BQZoCtMR2x69Et1egXajRm5xl6JUIvIPEUnJSSGzGZhlRTw2ziMS7GjMXSNV2afpcTXoeoGOHXfKamp7h85iDfXnoRVxIwN+wijaNw5Hb4wgegcRSSLVKcV4KZS2DxRrjuuzMRZfayrAKPXyYnJycnJyfnySMiZeA2IBCR3nj1AWAR+A3gCrJnY38B/Jiqbk0gvmVsgzEH/DHwg5qbr140FyuqfA74XhG5gqzm9JuBmxiLKufx42TlmebGr18MqIgcBH4QeIGqLovIXjLR5HzeDkwAu4CQLMVoOD5J/vP4+AdFZDsw/Th9/mfAjUCFrPTzg2RiDWSlqD4AzAJvAv5SRPapauMJPoecnJyvcawqm3HCepSwMopY63bZaLZodDs0Oj2a/T6dMKJnLUPjEPkBkRcQe5Mkvos1DonjkjoO1rnYf6FbUKU2SNjWCFlsJ6QOhL4wKkPfsSSuZgapWFQsQoqrFi9NKI9CJvshO9sjShvHCZIIRxOEBB0LIWcQ6+DGVbxohmpSZpeWmXc9Jh2Pou8hwcWYi2R+LYqSaJpNVolREhUSNWT1bmJSjUk0wsVl0pTwxMMVFxEz7pfNEoIEBEURIpQhMBAzdhnpM5A2Xa1jotME8RoTYRM/7lGJ+pTjIaUoxIsSNIIkMkSRQxIZCAVjHzmmBmUaPP5NusO5H6LIgdCD0FdCTxl5MKgIzQVolYVOBeISaFHxvJSSkzAtKRPqUrYeexkwGwxhUuhP+zRqHqkaNDK4dY+kXqTTqDGMSxgXxIXIMXQkoE/AQAMGNqCvAaF1wCpBFOKEEcXhgNKoz2LyWS4Uzxt6AcOgzCgo0itMsFFZYDRO7UodAWMxJsWThJoklMRQSVwm1QFSYAApaKqM0hGJnxBXgFpKdarK7Nwsi7UJdqsyHw2ZGnbxu2vQOgFH74Mvfehcug6A8WBqbyae7H8VzOyH6f0wc1nmhZKXL87JycnJyXlGUdW+iLye89J/xvfN/wb4EpnAchvwbuC9Ww5/I/ACsmc7XyYrNfzRr1DXv+Z5MncEZ6JVPgM8AJx+jP1iYDuwZ5yX9VkAEUnJakFfKSIbj+PDEpM9VLtUVe8m+1LPKG8WuFpETqrqClmUyWPxS2ORpCEi7yVLXzojqqwD7x2rb38mIj8OvGE8xpycnK8Q8WjE/Z/9JPFohFco4hUK2RQUUGvpt5p0N+u0V5dpHDnCoN0E1axkr1VQi6pirWKNQ+yWaFXn6dRmaZcLtMsFeqUy/WKZXqFMr1CiXyhhL3hDV0LKBXx/Ai+Js4oySUIQp9SGFq8fopqgCFbB2qx8cGpTrE1BU1ybleQtRjE7+ykzYUopVlxrESyYBGvirCSviVATP2553QtiDUZdxHoY6yJaQqyLYw2+CkUr7KTEHmeSminheO7ZPBNVJdWEQVon0ZNkNll9xOmSmi7q9BFnhJEQV0I8QgJCPBtTsIAqYhWsjnWSM8s6Nh6xJNYhti6RdYnHy4l1SKxDak0mxFgHk1oq4ZDJKGI+jJFISUeGNDLY+LFvuLuuxyDw6BWhVYLetNArQLeYlfy1ohgLTio46uIYF+M4GMdFcJDYRWI3c891lGxnRR3FCiQIKYIipCrgGBzPx3geFcdFxZAmDunQIU6E1RjWEsXEFidNMTY9N7cXTl067wsle2g0xAd8oCaG1HVQ1yH0C7S8Gp3KPNGUR+oa1IBjLJ6BgloKjkeAi4uhDOfJSOd8ZlJriZyE2I2zOtJFl3LJZ7ZUYKFcYGe5xM5CwKQITtSD7mqWnrN8Au4/CaP2I7vulWByD0zuhn0vz0ST6f2ZkDKxC8zFpbbl5OTk5OTkPHuo6pe3vDwuIr8DvJJHiiq/qKotoCUinyILbMhFlYvkyYoqfw/sI0v9eSx+BXgP8HHJSmT+V1X9RVU9LCI/Ot52lYh8jCzsaPm84/+ILErlT0VkEng/8NNj5e1/A/4t8Hsi8o/Aj6vqocfox6ktyyfIzHbPcPq8cKbzt+fk5DyL2DTl3k//Hf/rz/+YfvOJA8TEmcd4e8C/GkxA6pSwThHER9TFtQYvFTxgLoK5OlCH1EAvEIaBMvItsdslMZsgI4QRjh3i2BFGE1yb4hDjSgrohcUOFYz1MUmAkxRx1MMAKhZrYlJnROKMsM7YXdQF60JE1qRRL0tncRKKXkzRTbEqDEKfXuQRWUGtg7EujvUw6iMECAVStVhtUdURM8ZlQUrMUqXm1CiYIuYC3iyxjemka/TlJOI9wKR7BzvkNFUGpJGQDDNfkDQ0hEOXUegTjjzi0CEdGZIQRiODSfyn/F07JDgkBBfYljhKtwj9okevBJ2ZTChplIVGReiUoFeETlHoFaEfKNWRMNeGuZayre0wM3ApRw7lUYkoDQiNS5wYOM82RoG04BEViowmSiS42BRMajFpghfFOGmcCWpJjNFHCiJWLJEHsesQez6J65G4LuobpKw4bkoiHiNbYGjLRPhYcVAxWDHoeGJs1IsoIjGOifEJ8WWER4Ij4Aq4Cr518dOASQyT5312oQkZOSNiL6bn9RgGDl7BoxAUKBUC5hzDNmLmdcRc0mVmVKc6WMPrriFhF9oDOE8jeRRbRZNdL87mZ6c9UJp+VCnsnJycnJz/v707j7csKwu7/3vW2sMZ7lRjd1V3Vw9A20xNIAjmjYKaQEB4UfOSREEBhRiDAwoxoPJR0YQPkry8xigvgtAgs6JvwARoBQUShk6DNjTQDT3QU3V1zXc490x7r/W8f6x9q07drlvD7b419fOtz/6cffZ09z7rnntqP2et5zFmQwWOpmVbkZM6JhyXiFwNvIU0iqNDigF8ZdVmD0zM90mjPcwpOuWgiqreLSLfAX4IePkJtlsiDQF6jYg8AfgbEblRVT+tqh8APiAiM8AfAb9LymsyuX8FvAF4QzNE6OOkoTvvVNXrgetFpE0aevQO4PvWOJXLSPlcAHaRcrisuEREZCKwsgv42Cm8DMaYh0BV+fYNn+dz730niwf2k5VHb7XbfpqZYjOzfgavmxhk2zmUzzKMUzhNAYPaQRRSURkRoqTnwSljn4aYSIw4DU3lFqEYOcqxw6nDa47XEpg+8XkSiH50tEeJHxP9mMoPiX5AyJepy8Vj9slCQRHbzNRb6IxbTEnOVD6i016i2+rT8oFitIV8sI18sAUZrvEtvoC6VItYUaJGIhGPx8mlyKqb2KjKgBEDnWfk5gmyH+/uYJN8lV18GzdQxgsZy4sl+xdnuHVxB1mvpjV88GdvFOi3hX5X6Hegv1lZLpTlQhjmMCrS8JhBBsMs5Q2pPdQ+BbBCM187Ofr8eI8OxjlQQNulKjRt0ZT8NCidClq1Y/u4ZMewjdSzcGgKxi0YQNYb0Vrq40NkBIwcDPMW87ObWZyaY6k7w9LU7NHHqVl67SkiHmpF6ph62YSml00AYsQFkKBIjGRVTaseUWhNJoFSB3Slx1RYohuWaLGMjyN8SPtloUWmGTPRs1kjmdY8KLLz4F80CKlSTuWE6D2SCVnhaeU5ncIxVWZMtzyzZcamVsbmVsbm3DHtlDIqrh5CNYDeA3D4Nth/NyzsBp2ohOSy1HNk0+Vw8bXQmoNyBsrpNLVW5ieWldNQTFnQxBhjjDm33ANcQRo5suJKUooOOCb7+xH/Lymf6I+r6lLT0eGFG3mSjzSnmxDg5cCmptfIcfcVkecDtwJ3kL4HC0BscqpcAnweGJL6Oz/orkJEfoCUhPabwCIp6hZF5CJSfpZPNfv2SP2m1/IrInIDKcr2KlJ0bsV24BdF5K3Aj5CS9nz8VF4AY8zpqauKPd++hVs/8zluu/ELDAaL5K7kyqlruWLmGqZliiiz7FbhNqk5OHJkdRr+EdLIDADGLnJoqgcSKARaKCVKCbRUacVAqZGMgEOBJihBTKV8U6iEgFKRbncrlFoitQ9UrqZyFSM3IqwuzQuICmUs6YaC7miWTvR0g2NKHVPeUWae3OXkrk0pM/jYTn+9KmARatdjLIsMwwIjduNZwNEjACGrqfNA8B6NLTR0kboDsY1QIJqjMkLlIIXsoy17mJb7mJO78XGeMHBUy56q7xn3MpaXSvpLJXcsbcdNXMqoFXlg84B7rhbu3+LYPwOLXVjoSEq0WkAePJ1K6NaOdnC0g9CqHaUKeYQ8Cj5kdEJGJ2YQCqhyVHNizInOEZuhK5p71PvmeYaSoZIRyanqgtG4JFSKjAPZcEwxHNIaDmkN+nQHPdrD5YkhNMsEN2BhZhOHZy9icNkUw+k29VRJ3S5wLiJjGPS66Dij0JqdS33KxQVaIVDEmkwUx8qUgm9CBI0puKIp/YvTk+f7iFJClpF5pcyUTkvxXnEu4twI54eIU8Qpzivi0zxO6Tpluxe2ucCWMGImDCiGS0j/EPQPwNJBCOOTnsPEbyd0t6UcJpc9Ha69IvUk2XRFCqRM74T15P8xxhhjzLnmw8DrReRmUqeBHwT+T+AfNev3AltEZFZVV/qkTpPuq3sicg3wb4H9Z/a0L2yn9b8sVb3jFDZ7DPAHpES1h4G3qurfisi1wJtIAYwK+ALwM8fZ/2LgbaQkOj3SL857m+O9mjT0SIGbSL8Qa/koqVvTLKka0Tsn1t3QnOcB0i/eC1X14Clcm1mn+SrwpfkeO8uci8ucwpIUXlBUldivCYeGjPYvsXDHHpbvO0B1oM+w1+POxZu4t/8tStfmyVuexczma7nZj/nsWMl6Oa5K34Y7ydjiYLYQZjPI8wqyETUDhixRyTIVJUMkVZYhVZc52FSVGcvplwp2KnS0oK05s9qiXc/Q1oKOFnRo0dWSaTqU5MjKmCDhSIZTJRL9EsH1qKVHLQcYyAIDt4++38uS38OhfB8L2Zgl5+hHhy478iVHqycUAiWRAqWF0pJIS5VSlDZKm0hHI2GUMern9PsZC33hwLJQ9Nq0+u1jrieKcnBOuG+Lcu93Cbs3C/tnMka5Z8tixrb5jO6gZGqxQ2cxBwSn4GPA66nkAEmC8wTfJO11nug9URw+BvJqQF6PycKpt0dwnlHZZly0qIuS5bmtLOUXQ+nQPEckZQ0pQ0iVbGrgcJ0mUoLUSCTKQaJEokQC4Uip5iCBcdoCFSVIILqIOMFljtxntJ1jygldgSlRpohMU7OJEXM6YDYsMj0+THfwAPn4ELISNHsoylnoboHOFpi9FHY+Kc13tqbhNUU3DcPJWukxbzdTJ5UZzjvgC+tRYowxxjwy/HYz/S9gE6kjw4tV9esAqnqriHwQuFNEPPA4UvqMtwP/ntRj5cOkYIx5mMiFWCmpKan8mCZR7up1L2Oi/LI5M/LvepxuedsHgHQ/uq3I2FHmXFIW7ChzdrYKdpY5l7YKrmqXbM79g4Y5mLMrjgJhfkh9aEg4NKQ+PKI+NGC8r0eYHyH1se21OD7EF8d/z03FPIe27qTbvZyLR5vZejinWIiEmH4XtmbCRZmwNXe0XOCwW2KfHGbe7WNJ5pl3MJCjQ0dFU++UUoUST6meQgtKSkotKbVFSYsMT9aUAPY6MY/D6wgvA5A+wQ0JUlM107iZhi4wdDVDCQxcTd/VLPplDmVLHPI9DmQ9Dvgey34AmpKjtoeO7ihj05Jn66Iw1/NMDxztoaOsPD46FE+VOcbeE7wjqwNFCJR1oKgjRR0eNJV1QFSJThhljkNTwkJXWOg6llpCry30C2Gcp8DG9KCkPWxRVp5yHPGr/s4PixaL03P0212GRZtR2WJUNFPZYli0qPOC2qfcIUE8vg5IrCFUSKjIqgGu7uOqHi70gSFRKrxArkKhQhGhHZRWDUWAMkAelCKm16vKhVGhDMuacVZR+dRbKLqKlo6Yc5HtXuh6R+EdpfeUPqPMPG2f0cpyWt7TznPaPqdwHgd4VRzgtOmVouA04lBEFaeRctSjGC7glg9Ab3/qIRKPEwQSlwIc3ZVpW/N827HL2ptTYlaNJ5k0BUi6W9M+2frz1RhjjDHm/CIiX1HVp57t8zAPr3OqP7CIfIZUAuqPT7btmdbkd/kOkKtq3dTx/pCqvuesnth54uJe4DU3HqafjdjfDuzrOA5MldzcbfGZokVfju25Mpt5rmqXPKpTcuXKY6fkqnbJ9HEScl6o4jgQ+zVxuSL2K+JyhVYRv7lFtrWNny4Q99CDT6qKDmrq+RHhcAqYhJX55jH2j95wBg30wxL3cpA7sz67y8AD056D0x0Od6ZoZzNs121cvfjP+D/21hQP1BwYKzXgicx5KPKKg/nd3JLdzlddj7bzkM1QuxRAEYXN2uHSsJmtOsvWOM2sdslxBBlRMaKSIbUbEGRAlD5R5olumbEbsOj7HJIhB2XIfBwwrwN6oc8g9sjrSGsMrYr0OIb2WGmvzI+gPXa0xsJcDdtrwUehcp7KZ4wyzzjLGOUdhtkMozz1BNHjBQI1DUKqvVJ7R+1z+kWH+c4s891ZslBRVkPKakBZjyjqEUU9Ro47JPbB2sM0HflxCL3OFEvTczwwNcdyZ5phu0tVtqnLFsG3CCih7jPWRQJ9IkvgDhDcPMEfBA6h0k8HrFeuAcqmB03plLJQilxptZRCla54piQjE38kMWsUISLElQStOMYiDHG0xfMoV7ILx0V1zZZqxOxwiU7/MPng8Lp+j09IHIhPgQ+XQXtTCobMXAI7ngTd7TC1vQmWbDs6395s5X+NMcYYc94QkbeRCrP8zjr3/zXgKlV9xTr3fzHwUlV99nr2X3Ws8+Ke+6RBFRH5XuDNwONJ+VFuAX5JVW98KD9YRH6LVDb5Jx7CMWZI3Z/+ObCZNJTnLx/KeZ0qVX3umfg5F4p2rHj0wj0MZcQuGTJiSCUjxjIkUFE7x6AoWSq6LLRmONiZ5WB3E387PcNHWp1jjrU18zyq2+LxU22eON3m2ukOV3da5A9DcOFMCssV1b5l5u/Yzd477mTYG6CxRGOBhjTVOCoHIw+VE8YOgkA7QLtW2grdrmdmc5vZ7VN0t06RbWuTbWnhunlK5joKhKUx1eE+/X2HWNx/mMMHDnF4YYHF5R5LoyH9UDEQGDphkAlDn6ZBJgwdDKcdoznPsChY6k6zODXLUvciZuIlPHop8uhe5MlLgUcdrNn8nUi/Ug7UI/bWkQOuQn0f2vOM3H76/iB7c49mK0MWOpTaYU47bIlzbA3TbI0zFET2un3cI7v5mvsyh+O99Kt9FKOK9hjaI6WzEgAZNUGREZSVkNWe2eiYxXGp91TeUXlH7R1VNnfM89o5ghOCcwTn6DlhqQN0T/b7JKjLiC4j+rzp1VEwzlssdmfZvX0Hu3deyu5tOwj56iTtgCpFMzwmiqBNAEKdA420RkPaw2U6g2U6wz4SIyoOQfGaemNkqqmMs0ayGCiaqV1XlOMRnXqJKr+PUbaX3O2jHQ8zE0ZsCoG5GJlVYbPkzIlnRh3TCFNR6cRAK9SUoaKoR2T1CDmNoUGnrZyFmZ2w6Sq4fCfMXJqez16S8oHkrRQUQZrgiEw8XzXv/NHgyZFHZ8NjjDHGGHPeE5G7SFVrd6rqgYnlf08qg3ylqv7sQ/kZqvrGh7j/+4H3P5RjTBzrvLjnPmFQpQla/HdS7pI/BQpStZ3Rxp/aiYlIAXwamAeeQ0qOuxX4N8Dzjjf0B0BV303KsbL6eJmqnn5CBnNKXMzYNbqclnMUAtmqG5wxFcNBxVAqRlIxomIohxjL/Yz8kMW2stDNONAu2dPpcFd3mvd3tzLy6Wa1QLmmXfCkuRmunWnzxKkO13RbtPyZ/YZZVRlFZRAjgxBZrMbcv/swe/YusOfAAnt6S+yvxhzyynzuWCxz+u0OoysvppCMLEKm6aY5zdPcODfzEQRlORN6mbCcCctZyqMBS/j5eYoDNWVVUdQVCoy9o/L+SAnYOFvA7MWIXpSCM0FpBSijkkcoIhRB06MqRYDZGGnXabriYM2j7o7sXE5leeeDMh8iB3TIN92AOlsmFH3qzhLB91F/tMeFV5jTTWyKs8zVXea0y0yd01qeR5d2E+dvwh/ajS7shmqZHcBFwDDPGBQZozxnnJWMvWeUZwyKnGGecbjl2d9xRyoDrdk+QPA5lS+osoJxVjDOC8ZZSZ3nKT9InlP5nDrPqLOcKs+psoKqKBi0OgxaUwzLNtH7NMwkxjS0RLWZIkWoaddjLhr12HXXN2lVY8p6TLvu0Q3zbK0PsrM+yFZdZkDGkuQsS0bfZQzJGJEzdAU1BZGSoAU+1jgZojJGZIxzYzIqCsaUjGkxpq1jpnXIRRzkEnps9crMuKI1OMGfNpelKi8rFV/aK/NTUKxUgemmwEa2ajqyrISs3QxlkVR5JsbmMax6bJb7PPUSmdmZfoYxxhhjjDkV3wF+HPivACLyRFKpZHOWnDCniog8FfiUqs6tsd4Bvwb8a6ANfBL4BVVdEJHvJw3luXRi+7uAV5CCOR8j3f6MgDtU9UnN8J//SUqccy3wReBFk1G4iWO9AviPwKNUtbfG+b2uObftwL3Ar6vq/9ese1mz7n8DLyGVmvqNE1zPFRw7/OczzfX98UqeFuBLpApJ88ArVfUTzc/6KVJioEtJmZZ/V1X/6HjnfKG6fMfl+hsv/U0k5kjI0eCRKGgtSIBMSBVdnKPjHVM+Y8aXTPsCt8Y3zEMq5v2YA2XFni7cNV1w68w0d0212NMSglOuygJXdjpM5wXTec5UkTOVebre080cXe/SvHe0nGMQIr0QWA6RhdGY+cGAheUBC/0xS6OK5SrSi5GhKqMYU/mp6PDqcCp4dZSRI8GKdlCmapiulOlama6U2XFkpgrMVGldNwit+NC+RR9JZOAiAw99r/Q9LGcpl0QrCO0IrQCtKJTNVERZqei6Ul2WGgiqE/NQK2m4iEKlyqE44oAsMcyG1Fk/BVGyZXSizEwWhSmKZtjOLHPaZS52yUfLyPxudP4+4sK9DPr3cntngYOdNv2sQ8wy8Kl3gYojABLCg+IkCk3+jzZ1VhCzDHEeL0KO4hHwGeo96jPUpSo0qdfC8V/rVCvo6Fx6FlFi8xNjKg1DBAmIBJwEHAFPqjrkieREOjpiuy5xiS6yTReYoccMS+QEQjmD23QFMrcLpi6C4Tz09kFvH9rbiwzn1/U7oOKI5TS0ZnGdrUinSX7a2dxMW9JQlsnlrdkUFLFeHMYYY4wxG+6h5lRp7qf/GPhhVf3uZtl/JhWI+Q+k8sq/Bdynqq8Xka2kDgXfS6qc+w3gmaoaReS1wC8CM6RKQq9U1U9PjigRkVbz855LKtNwG/B8Vd3b3AP/BqmgzAHg9ar6/tU5TJt8p68Cfqn5WdcBr23O4WWke1kfl1oAABd3SURBVO+/B34S2AP8nKp+utn3M5yj6UEmnWz4z7eBICLvAT4EfElVJwe7v6yZfgDYR6rM8wekF2RNqvpJEXkjxx/+8yJSo90LfIKUrfh1xznMPwU+uVZApXEHqWfNA8C/AN4nIo9W1T3N+qc313URkK/3eiaO9R5Sb5mfAd4pIpdoilrtA54P3Ak8A/iEiNyoqn93Cse9IASp2V3e2/SoeLBSM0otKLQgjyVZKJBYQF1Q15FQVTCuKagopKZ0kam8YDbvcNl4lscuz+D3e1KIIFUCOZwF9rSUg2Wf2jkqJ9TOUTthIEJP0lCalSmKUASlFaHV9N7YGZSrIqmXRtC0fCJAkZx8WESlNQs6ZEEH9BjR04oDsWJEpNZIbCqUqEuP0cUjFUxiU6UkSEjzRBwOpx6nDtE0L+pwlUfGDlGPqG8KC6fjrxQYVtE0uVQ/ViVtlerJHn1UVs+DNue0IlfPFp1iU7yYTaHLJu2yKU6Rh0A1OIAcvo9R71ssDx7gO/U+7u/AwfYUg3aJdIQibiKPx/ZSCM6x1J1tEqlOUZctNCvwztFSpR0CZT3GU5PLmGkGzNBjsy6xjQU2s0hXBk2QI+CIE4/Hzq+sS8tOga56XCXmbWLRRbtbyTY/Cpl7MsztOmbyrdk1Dy8A9QiW90NvbxNsaR6RFARpzUJrZmI+TVJM4S04YowxxhhzofsS8JMi8ljS/fqPAf+YFFRZ7TXAfaTAB8D3ACoi3wX8PPDdqnp/04HgeIkrX0qqpnsZqTPEPwAGItIFfr/Z/1sisoOUjmMtPwo8FZgCPgV8ixSsgXQf/RHSffQ/B/5CRK5U1UMneR3OGScMqqjqYpNT5bXAO4CLReTjwL9W1b3Ai4G3qOqdACLyq8DXm54Z63Wdqn67Od6fAi9YY7stpJLJJzr/P5t4+uHm/J5GKrcMcL+q/tdmvm6S6qz3eu5W1Xc0+70HeCspWPOAqv6Pie0+KyJ/RQr2PGKCKmU1z6P3fpRYTBHKGUI+TZ1NE1ybSJsoJVGUMTV912eYVccNwBSa04oFRSw5FBUXKzQeIoz3E8MYxmO6QZjKC2aykk39Ljt9CxCcOASHSDMhONJjmhdqIjWBmppaK6pYMYo1Iw30QuRQjIxjZCyBkQTGUlM3wRD1TVDERYILBBeoXU0t4ZhAxIk4FQoycs3I8c18QU5GET05GR5HIFIRqCVQSXqsJVBRpfOXQEU4cl0Oh9fmsblq3wRm/MQr4SZeC9FmXmXiVRK6sWC6gunBmGx5kfF4kUF9H8v1ArvDErdoj2VfM/Ke6By0SBNdFBi0uyxNzaapO8NyZ5q6KCHLyRCmwogdg3181+Aeroq3sTMeYrpaZoplpujTYoQIDLKScXuW0NmKn76YYnYXrdl/hKwkGHXHyWNyPBpT1ZdYQ6zSMJVQTSxbmUIaBlPOpKBGOZ3my+n0vJjG+ezUgjMnkpWptO7spSff1hhjjDHGPBK9lzTa4rOknKe719iuAnYAlzfpMf4ngIgEoAQeJyL7VfWuE+y/hdQZ4ms0999NUCUCTxCRe5pOC3vWOAakkRqHgEMi8nuk4UsrQZV9wO81nRE+LCKvAZ7XXON54aSJalX1FlLvDUTkGuB9wMoLsRO4e2Lzu5tjXvQQzumBifk+KZp1PAdJvyBrEpGXAK8GrmgWTZEiYCvuXbXLQ7meI+etqv2mHPBUcx7PBX4TuBpwpDFvN5/CMS8Y45Cz5/DOiSVLzfRgRYy0NVJmHYpyFt/kdIhFm5A5xpky9kMWsx4DqqanxVEHgFwzyliQ6wJO/ZHqJUf7XzQ9L1SZ/JeSXnKkd4aKHukdEiUQiCfM2VFqRksLOuS0tEURsxTMUHCxyb8RwpGytFJXaBijoULrCtWaQKDW0AR3AkEDA61Z1pqgASXiJcNLnoItZHTJUk4WMjKXk0mGdzkO4cgQP+XIlR95HeToIJfQHL8+cg41tdaMCQStqQgEAuPQJ67qnaPAuCzplV167a30W12WO9Mszc0xnOoS8hJ8RoYwPe5z8Wgf14zu4cmjr/DYpTvZxCIFNf28zag1Q9WdI27fgnQvxk8/mWL2Utqzl5NP7zhSlaWdt2if2q+fMcYYY4wxF5L3Ap8jDff5kxNs959Iw4H+qrk/fbuqvklVbxeRX2rWPV5Ergderar3H+fnXAZ8SETmSLGAX1fVZRH5V6RRJe8Ukc8Dr1HVW9c4j8n77rtJ990rduuxOUlWrz/nnVZJZVW9VUTeTUoGC2ns1eUTm+wipWLYS3ohjiTMERHP0W5HsGYH+lP2KeA/iEhXVZdXrxSRy0m9a/4J8EVVDSJyE8feEq8+hxNdz7q+NhaREvhzUiTxo6paich/44S35uc+EXkO8F9I3cT+WFXfdKLt+90Zvv6UZ1IMK/LBiNZwQHswoBwNKccj8mpEXte4WCEqgCOMRwwHD4DsSUEAJwSRI5VaPLDJtSiLGfJyBpd3cVmJ+LxJNiqMfE1w1ZHzWGlwaYIKq/OaehwZjkwzPJ4Mh49NUERBVJGYkpG6OkBdoWGEViNCPWAcBoxjn1EYMB8HRA0cQxUfVxKaAiqoOqI0g1GcJ0h6jM4TnCN6T8xKgu8QMw9O8HVNFip8CPjYx8UaFyIuBkRjU6llZciOoOLQprRtdI4g/shj8J7gPLXLqJuEtpUvqLMOVZaStNY+bRe9Z9hu0+9OoZ0MCkFyIUNpj2tay2M6yyNmRj12jQ5z9dLNPH7pTi6WA3RkmeHUZnT2EvwlV9HZdg3llmfB7GUwvQO6W+n43LJsGWOMMcYYcwKqereIfAf4IVJOz7W2WyINAXqNiDwB+JsmDcWnVfUDwAea4jR/BPwuq9JeqGoFvAF4QzNE6OOkoTvvVNXrgetFpE0aevQO0miM47mMlM8F0j32ZPDmEhGRicDKLlL+1fPGyar/XEPqevNhVb1PRC4j9VD5UrPJB4HXNvWj9wNvbLatReTbQEtEngf8FSkBbDlx+L3As0TEqa6rVud7ScGdP2+ibN8GNjXLbgLuIt1V7m+u5aeAJ5zkmCe6nnWcIpAqJpXN8eqm18qzga+v94BnWxMg+0PgWaQxejeKyMdU9Ztr7dMKFY8aHEZV0NJRtTqMt86xnGVU/ug09p6Yg5YRLSKxgJBDyJsKNt4zJqc/Lql7nuzwmO2HDrDj8F42Lx1iemGR1nhAVlegMSVhbZpOELx4nHi8ZLiVeVbmHRorYhwT45gQx9TUCApOEJfK3mqWAhDDrEXft1jOOyx1uyzmcyznO6iKVDFmXBTUec6w3SJ0MijAF5FWVtHKBrT8kLYbUMqInDEFY3Idp6BLODpJDa4GVwu+AmqhdlMMJKeSjMplVJKloIhkVC6ndp5aMkBYybPqmoCQoEjQI0Ei11SwyWLEhzqV6A01ZQxkMZDFmiwOKWJFHgNbBoe5cnE3OznANg7TliExc4zb08TOZmTzNtz0RRSzu5ja9mO4TZfD3OXQ2ULXcn4YY4wxxhjzcHg5sKnpNXLc+3oReT6pSu4dwAJNjYomp8olwOeBITDgODlVROQHSAMBvgkskoYDRRG5iJSf5VPNvj1OnGjyV0TkBtJIjlcBb5lYtx34RRF5K/AjwGNJwZvzxsl6qiyREse8uunuM08qsfwrzfp3kXqkfI6UNeF64BcAmoo5rySNlfLAm0k34Cv+DPgJ4KCIfEdVn3I6J66qIxH5p6TI2V+TAip7SflSblDVgyLyf5MqCEVSt6jPn+Swa17Peqnqkoj8IqkkdQn8JedZ5O04ngbcPpF75kPAD5PebMe1Y+clvP63mpLnoYbBYeLyfuqlffTm72V5/n6GS/sYDxYIdSDUgaoXqKtAqCq0qtE6EIMSNFIXkdoJdSZUHc/SXJulokMvn+FAfjHLeZtl36Kfdah8hpeAlxrnUsUW72pEFNcMhmn6cVAwpmBEeeRxRB4qimZqhRFlGJNXgWwExUhwQ8jG4IcOFxVcyqrhKpAaGHu0V0BeIHmJL1pkRUmRd2gVJUWroCiEotxE3r4I35rBO9/0otEmn0nqUeMgBYvq1DMm1CNiPSRUA6p6mboaUo9HVNWQajzGiZJ5h3ce5xxeHM6lYJJzad43z73PUk4QlyGSI65EXIbIStUcl6bWbMpZspK7pL35yDUbY4wxxhhjNp6q3nEKmz2GVHhlG6lC0FtV9W9F5FrgTaQARgV8gVRsZbWLgbeRRm30gA+TOjdsI6XZ+BNSR4abgH97gvP4KCkfyyypGtE7J9bd0JznAdL9/AtV9eApXNs544QllY05HhF5IfAcVX1F8/wngaer6s+vtc9Tn/pU/fKXv7xxJ6WaqqZUfRj3YLwM42Y+jJuSsXLkUYGoNbX2CVWPOvQIcYgrZsk62/Gt7WStbfjWZiTvWtDAGGOMMcYY85A81JLK56OmpPJjmkS5q9e9jInyy+er08qpYszpEJGf4WjEsyci3zqF3baSopTm3GVtdG6z9jn3WRud+6yNzm3WPuc+a6Nzn7XR2XH5yTcx5xsLqpj12E1KNrTiUo5TxktV3w68/XQOLCJffqRFb8831kbnNmufc5+10bnP2ujcZu1z7rM2OvdZGxnz8LExDWY9bgQeIyJXikgB/Bjnf54YY4wxxhhjjDEPI1WV4w39ada9+3wf+gPWU8WsQ1MN6edJiXw98C5V/cZJdjPGGGOMMcYYYy4oFlQx66KqH2djSl2d1nAhc1ZYG53brH3OfdZG5z5ro3Obtc+5z9ro3GdtZMzDxKr/GGOMMcYYY4wxxqyD5VQxxhhjjDHGGGOMWQcLqpgNJyLvEpF9IvL1iWVPEpEvisjNIvKXIjLTLH+aiNzUTF8VkR+d2Oc5IvItEbldRF53Nq7lQnQ67TOxfpeI9ETk300ss/bZIKf5HrpCRAYT76O3TezzD5vtbxeR3xcRORvXc6E53feQiFzbrPtGs77VLLf22SCn+R568cT75yYRiSLyD5p11kYb5DTbKBeR9zTLbxGRX53Yxz6LNsBptk8hItc1y78qIt8/sY+9hzaIiFwmIn8rIt9sPl9e1SzfLCJ/LSK3NY+bmuXStMHtIvI1EXnKxLFe2mx/m4i89GxdkzHnDVW1yaYNnYBnAE8Bvj6x7Ebgmc38TwO/08x3gKyZ3wHsI+X+8cAdwFVAAXwVeNzZvrYLYTqd9plY/xHgz4B/1zy39jlH2gi4YnK7Vcf538D3AAJ8Anju2b62C2E6zfbJgK8BT2qebwG8tc+500ar9nsicMfEc2ujc6CNgBcBH2rmO8Bdzd8++yw6N9rn54DrmvntwFcA1zy399DGtdEO4CnN/DTwbeBxwJuB1zXLXwf8bjP/Q00bSNMmNzTLNwN3No+bmvlNZ/v6bLLpXJ6sp4rZcKr6OeDQqsVXA59r5v8a+L+abfuqWjfLW8BK0p+nAber6p2qOgY+BPzwhp74I8TptA+AiPwI8B1gsuKTtc8GOt02Oh4R2QHMqOqXVFWBPwF+5OE+10ei02yfZwNfU9WvNvseVNVg7bOxHsJ76MdJf8/sPbTBTrONFOiKSAa0gTGwiH0WbZjTbJ/HAX/T7LcPmAeeau+hjaWqe1T175r5JeAW4BLSe+A9zWbv4ehr/sPAn2jyJWCuaaN/Bvy1qh5S1cOktn3OGbwUY847FlQxZ8s3OPofnX8BXLayQkSeLiLfAG4GfrYJslwC3Dux/33NMrMxjts+IjIFvBZ4w6rtrX3OvDXfQ8CVIvL3IvJZEfm+ZtklpHZZYW20sdZqn6sBFZHrReTvROTfN8utfc68E72HVvwr4IPNvLXRmbdWG30EWAb2APcA/1lVD2GfRWfaWu3zVeAFIpKJyJXAP2zW2XvoDBGRK4AnAzcAF6nqnmbVA8BFzfxa7xd7HxlzmiyoYs6WnwZeKSJfIXVRHK+sUNUbVPXxwHcDv7qSb8CcUWu1z28B/4+q9s7WiZkj1mqjPcAuVX0y8GrgA7IqJ445I9Zqnwz4XuDFzeOPisg/OTun+Ii35ucQpAA/0FfVrx9vZ3NGrNVGTwMCsBO4EniNiFx1dk7xEW2t9nkX6Ub8y8DvAV8gtZc5A5ovwP4c+CVVXZxc1/QQstKvxjzMsrN9AuaRSVVvJXWDR0SuBp53nG1uEZEe8ARgN8d+i3hps8xsgBO0z9OBF4rIm4E5IIrIkDRe2trnDFqrjVR1BIya+a+IyB2k3hG7Se2ywtpoA53gPXQf8DlVPdCs+zgpT8H7sPY5o07hc+jHONpLBew9dMadoI1eBHxSVStgn4h8Hngq6dt1+yw6Q07wOVQDv7yynYh8gZTf4zD2HtpQIpKTAirvV9W/aBbvFZEdqrqnGd6zr1m+1v+tdwPfv2r5ZzbyvI0531lPFXNWiMj25tEBrwfe1jy/shkjjYhcDlxDSkB3I/CYZn1B+s/ux87CqT8irNU+qvp9qnqFql5B+vbpjar6B1j7nHEneA9tExHfzF8FPAa4s+n6uygi39NUW3gJ8NGzcvKPAGu1D3A98EQR6TR/654JfNPa58w7QRutLPuXNPlUIOUrwNrojDpBG90D/GCzrktKsnkr9ll0Rp3gc6jTtAsi8iygVlX7O7fBmtf0ncAtqvqWiVUfA1Yq+LyUo6/5x4CXNFWAvgdYaNroeuDZIrKpqRT07GaZMWYN1lPFbDgR+SAp4r1VRO4DfhOYEpGfazb5C+C6Zv57gdeJSAVE4JUT3+j+POmPugfepaqTiVLNOp1m+xyXqtbWPhvnNNvoGcBvT7yHfrbJNQDwSuDdpMSOn2gm8xCdTvuo6mEReQvp5k+Bj6vq/2i2s/bZIOv4O/cM4F5VvXPVoayNNshpttEfAtc1+deEVGnma81x7LNoA5xm+2wHrheRSOr18JMTh7L30Mb5x6TX+mYRualZ9mvAm4A/FZGXA3eTAsYAHydVALod6AM/BaCqh0Tkd0ifUwC/PfH/CGPMcUgaWmeMMcYYY4wxxhhjTocN/zHGGGOMMcYYY4xZBwuqGGOMMcYYY4wxxqyDBVWMMcYYY4wxxhhj1sGCKsYYY4wxxhhjjDHrYEEVY4wxxhhjjDHGmHWwoIoxxhhjjDHGGGPMOlhQxRhjjDHnNBF5n4hct2rZM0XkoIjsOFvnZYwxxhhjQRVjjDHGnOteBTxXRJ4FICIt4B3Aa1R1z8P1Q0TEP1zHMsYYY8wjgwVVjDHGGHNOU9WDwC8AbxeRLvCbwB2q+m4RcSLyayJyh4gcEJEPicgmgGbdR0TkARGZF5HPiMhjV47b9ID5QxH5pIgsA98nIs8XkVtEZElE7hORXz4rF22MMcaY84IFVYwxxhhzzlPVPwP+Dvgg8DPNBPDLwPOAZwCXAj3g9yd2/e/AY4CLga8D71116BcBbwCmgS8C1wEvV9Vp4FrgsxtwOcYYY4y5QIiqnu1zMMYYY4w5KRG5CLgD+HVV/S/NstuAV6jqZ5vnlwG3AR1Vjav23wrsB6ZUdVlE3geMVfWnJ7a5H/gN4MOqunQmrssYY4wx5y/rqWKMMcaY84Kq7gUOAN+YWLwL+MtmeM88cHOzfLuIeBF5s4jcKSKLwO3Nuq0T+9+76sf8KPAC4J5muNDTH/4rMcYYY8yFwoIqxhhjjDmf3Qc8S1XnJqaWqj4AvAT4IeAHgVng0c0+MrH/MV12VfUGVX0BsJ00dOhDG34FxhhjjDlvWVDFGGOMMeeztwFvFJFdACKyXURe0KybBkbAQaAD/McTHUhE2iLyIhGZUdUKWALiifYxxhhjzCObBVWMMcYYcz57C/BJ4NMisgR8AfjuZt11wP3N9I1m3cm8FLi7GS70cuAnHvYzNsYYY8wFwxLVGmOMMcYYY4wxxqyD9VQxxhhjjDHGGGOMWQcLqhhjjDHGGGOMMcasgwVVjDHGGGOMMcYYY9bBgirGGGOMMcYYY4wx62BBFWOMMcYYY4wxxph1sKCKMcYYY4wxxhhjzDpYUMUYY4wxxhhjjDFmHSyoYowxxhhjjDHGGLMOFlQxxhhjjDHGGGOMWYf/H3/yfdqUyjvIAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "years = range(1929,2010)\n", - "names = np.array(f.by_col(\"Name\"))\n", - "order1929 = np.argsort(pci[0,:])\n", - "order2009 = np.argsort(pci[-1,:])\n", - "names1929 = names[order1929[::-1]]\n", - "names2009 = names[order2009[::-1]]\n", - "first_last = np.vstack((names1929,names2009))\n", - "from pylab import rcParams\n", - "rcParams['figure.figsize'] = 15,10\n", - "plt.plot(years,pci)\n", - "for i in range(48):\n", - " plt.text(1915,54530-(i*1159), first_last[0][i],fontsize=12)\n", - " plt.text(2010.5,54530-(i*1159), first_last[1][i],fontsize=12)\n", - "plt.xlim((years[0], years[-1]))\n", - "plt.ylim((0, 54530))\n", - "plt.ylabel(r\"$y_{i,t}$\",fontsize=14)\n", - "plt.xlabel('Years',fontsize=12)\n", - "plt.title('Absolute Dynamics',fontsize=18)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Relative Dynamics')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "years = range(1929,2010)\n", - "rpci= (pci.T / pci.mean(axis=1)).T\n", - "names = np.array(f.by_col(\"Name\"))\n", - "order1929 = np.argsort(rpci[0,:])\n", - "order2009 = np.argsort(rpci[-1,:])\n", - "names1929 = names[order1929[::-1]]\n", - "names2009 = names[order2009[::-1]]\n", - "first_last = np.vstack((names1929,names2009))\n", - "from pylab import rcParams\n", - "rcParams['figure.figsize'] = 15,10\n", - "plt.plot(years,rpci)\n", - "for i in range(48):\n", - " plt.text(1915,1.91-(i*0.041), first_last[0][i],fontsize=12)\n", - " plt.text(2010.5,1.91-(i*0.041), first_last[1][i],fontsize=12)\n", - "plt.xlim((years[0], years[-1]))\n", - "plt.ylim((0, 1.94))\n", - "plt.ylabel(r\"$y_{i,t}/\\bar{y}_t$\",fontsize=14)\n", - "plt.xlabel('Years',fontsize=12)\n", - "plt.title('Relative Dynamics',fontsize=18)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0 2 0 4 2 4 4 1 0 1 4 2 2 1 0 1 2 3 4 4 2 0 2 2 2 4 3 4 0 4 0 0 3 1 3 3 4\n", - " 0 1 0 1 2 2 1 3 1 3 3]\n" - ] - } - ], - "source": [ - "import mapclassify as mc\n", - "q5 = np.array([mc.Quantiles(y,k=5).yb for y in pci]).transpose()\n", - "print(q5[:, 0])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Alabama', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'Florida', 'Georgia', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New Hampshire', 'New Jersey', 'New Mexico', 'New York', 'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']\n" - ] - } - ], - "source": [ - "print(f.by_col(\"Name\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A number of things need to be noted here. First, we are relying on the classification methods in [**mapclassify**](https://github.com/pysal/mapclassify) for defining our quintiles. The class *Quantiles* uses quintiles ($k=5$) as the default and will create an instance of this class that has multiple attributes, the one we are extracting in the first line is $yb$ - the class id for each observation. The second thing to note is the transpose operator which gets our resulting array $q5$ in the proper structure required for use of Markov. Thus we see that the first spatial unit (Alabama with an income of 323) fell in the first quintile in 1929, while the last unit (Wyoming with an income of 675) fell in the fourth quintile.\n", - "\n", - "So now we have a time series for each state of its quintile membership. For example, Colorado’s quintile time series is:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2 3 2 2 3 2 2 3 2 2 2 2 2 2 2 2 3 2 3 2 3 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3\n", - " 2 2 2 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4\n", - " 3 3 3 4 3 3 3]\n" - ] - } - ], - "source": [ - "print(q5[4, :])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "indicating that it has occupied the 3rd, 4th and 5th quintiles in the distribution at the first 3 periods. To summarize the transition dynamics for all units, we instantiate a Markov object:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The Markov Chain is irreducible and is composed by:\n", - "1 Recurrent class (indices):\n", - "[0 1 2 3 4]\n", - "0 Transient class.\n", - "The Markov Chain has 0 absorbing state.\n" - ] - } - ], - "source": [ - "m5 = giddy.markov.Markov(q5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The number of transitions between any two quintile classes could be counted:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[729. 71. 1. 0. 0.]\n", - " [ 72. 567. 80. 3. 0.]\n", - " [ 0. 81. 631. 86. 2.]\n", - " [ 0. 3. 86. 573. 56.]\n", - " [ 0. 0. 1. 57. 741.]]\n" - ] - } - ], - "source": [ - "print(m5.transitions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By assuming the first-order Markov property, time homogeneity, spatial homogeneity and spatial independence, a transition probability matrix could be estimated which holds for all the 48 US states across 1929-2010:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.91011236 0.0886392 0.00124844 0. 0. ]\n", - " [0.09972299 0.78531856 0.11080332 0.00415512 0. ]\n", - " [0. 0.10125 0.78875 0.1075 0.0025 ]\n", - " [0. 0.00417827 0.11977716 0.79805014 0.07799443]\n", - " [0. 0. 0.00125156 0.07133917 0.92740926]]\n" - ] - } - ], - "source": [ - "print(m5.p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The fact that each of the 5 diagonal elements is larger than $0.78$ indicates a high stability of US regional income dynamics system." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another very important feature of DMC model is the steady state distribution $\\pi$ (also called limiting distribution) defined as $\\pi p = \\pi$. The attribute $steady\\_state$ gives $\\pi$ as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.20774716 0.18725774 0.20740537 0.18821787 0.20937187]\n" - ] - } - ], - "source": [ - "print(m5.steady_state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the distribution at $t$ is a steady state distribution as shown above, then any distribution afterwards is the same distribution. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the transition probability matrix in hand, we can estimate the mean first passage time which is the average number of steps to go from a state/class to another state for the first time:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 4.81354357 11.50292712 29.60921231 53.38594954 103.59816743]\n", - " [ 42.04774505 5.34023324 18.74455332 42.50023268 92.71316899]\n", - " [ 69.25849753 27.21075248 4.82147603 25.27184624 75.43305672]\n", - " [ 84.90689329 42.85914824 17.18082642 5.31299186 51.60953369]\n", - " [ 98.41295543 56.36521038 30.66046735 14.21158356 4.77619083]]\n" - ] - } - ], - "source": [ - "print(giddy.ergodic.mfpt(m5.p))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thus, for a state with income in the first quintile, it takes on average 11.5 years for it to first enter the second quintile, 29.6 to get to the third quintile, 53.4 years to enter the fourth, and 103.6 years to reach the richest quintile." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Regional context and Moran's Is" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thus far we have treated all the spatial units as independent to estimate the transition probabilities. This hides an implicit assumption: the movement of a spatial unit in the income distribution is independent of the movement of its neighbors or the position of the neighbors in the distribution. But what if spatial context matters??\n", - "\n", - "We could plot the choropleth maps of per capita incomes of US states to get a first impression of the spatial distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas as gpd\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AREAPERIMETERSTATE_STATE_IDSTATE_NAMESTATE_FIPS_xSUB_REGIONSTATE_ABBRgeometryName...2000200120022003200420052006200720082009
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145.13234.52722Montana30MtnMTPOLYGON ((-111.4746322631836 44.70223999023438...Montana...22569243422469925963275172898730942326253329332699
29.57118.89933Maine23N EngME(POLYGON ((-69.77778625488281 44.0740737915039...Maine...25623270682773128727302013072132340336203490635268
321.87421.35344North Dakota38W N CenNDPOLYGON ((-98.73005676269531 45.93829727172852...North Dakota...25068261182677029109296763164432856358823900938672
422.59822.74655South Dakota46W N CenSDPOLYGON ((-102.7879333496094 42.99532318115234...South Dakota...26115275312772730072317653272633320359983818836499
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5 rows × 92 columns

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" - ], - "text/plain": [ - " AREA PERIMETER STATE_ STATE_ID STATE_NAME STATE_FIPS_x SUB_REGION \\\n", - "0 20.750 34.956 1 1 Washington 53 Pacific \n", - "1 45.132 34.527 2 2 Montana 30 Mtn \n", - "2 9.571 18.899 3 3 Maine 23 N Eng \n", - "3 21.874 21.353 4 4 North Dakota 38 W N Cen \n", - "4 22.598 22.746 5 5 South Dakota 46 W N Cen \n", - "\n", - " STATE_ABBR geometry Name \\\n", - "0 WA (POLYGON ((-122.400749206543 48.22539520263672... Washington \n", - "1 MT POLYGON ((-111.4746322631836 44.70223999023438... Montana \n", - "2 ME (POLYGON ((-69.77778625488281 44.0740737915039... Maine \n", - "3 ND POLYGON ((-98.73005676269531 45.93829727172852... North Dakota \n", - "4 SD POLYGON ((-102.7879333496094 42.99532318115234... South Dakota \n", - "\n", - " ... 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 \n", - "0 ... 31528 32053 32206 32934 34984 35738 38477 40782 41588 40619 \n", - "1 ... 22569 24342 24699 25963 27517 28987 30942 32625 33293 32699 \n", - "2 ... 25623 27068 27731 28727 30201 30721 32340 33620 34906 35268 \n", - "3 ... 25068 26118 26770 29109 29676 31644 32856 35882 39009 38672 \n", - "4 ... 26115 27531 27727 30072 31765 32726 33320 35998 38188 36499 \n", - "\n", - "[5 rows x 92 columns]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_table = gpd.read_file(libpysal.examples.get_path('us48.shp'))\n", - "income_table = pd.read_csv(libpysal.examples.get_path(\"usjoin.csv\"))\n", - "complete_table = geo_table.merge(income_table,left_on='STATE_NAME',right_on='Name')\n", - "complete_table.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "index_year = range(1929,2010,15)\n", - "fig, axes = plt.subplots(nrows=2, ncols=3,figsize = (15,7))\n", - "for i in range(2):\n", - " for j in range(3):\n", - " ax = axes[i,j]\n", - " complete_table.plot(ax=ax, column=str(index_year[i*3+j]), cmap='OrRd', scheme='quantiles', legend=True)\n", - " ax.set_title('Per Capita Income %s Quintiles'%str(index_year[i*3+j]))\n", - " ax.axis('off')\n", - " leg = ax.get_legend()\n", - " leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is quite obvious that the per capita incomes are not randomly distributed: we could spot clusters in the mid-south, south-east and north-east. Let's proceed to calculate Moran's I, a widely used measure of global spatial autocorrelation, to aid the visual interpretation." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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+w7TWSYXME43RO20GUBejB06Zts0sX0+Y2sTEY7THsyswT4hp3R+a0nPD6PVztcB8xzECpCEYvw5Pa63jTNv8H6VUNkaj+nsxqoBGmy37Mkavw9+VUp9jNG7tidF4+leMgPdxYJ1S6i3+7u14CKOXZXm9BGxUSuVi9LhKwuitNBz4t9b6RCHLWOxYlvGct4hKSrM055HFmLZhFcYQGmcwSjGf5/oxBC2SL611gul4/89Uev4Xf/fAa8O1x/9LU1C2AqOdTn3gEaATRQzVUUAHpdRnpuX7YQzU+rRZKfssjGtyjVJqAcb9tRnGj89FWuvgMm7eNxj7Zb3pvAjD6E3dVmv9Mn8HBguVUl9hBH4vcH31GpR8HyzMdddJGdO0hA0YHZa+U0rl9TZsgtGBZqrWOr6U97Fy0VqHKaU+xegBOce0fnuM7W6rtZ5sKrGdhNF7NArjmE/l2rZktVtVtey/kV8Yv942YNzwsjACpBUYVWXm813Xiw94EqNBYiZGwPNsgc9nYdxw+gL7MX6RHsCsp5lpvqEYv1jSgIMYF0ow8KPZPIsovrdjQ4w2a6cwqjouAku5tkdPMMYX92MYF0UaRvur5gXW9RJG4/0UjEDDp+D2YzTy3mmaRwP9C9tPGO0fQkxphWP0xrpmWyhFb0eMYvOfMAKpSxg9c6ZQoGeXKb3dGL/+9mGUskVybS86b9N2XTEtP9E03RojODxrOqZHgbGF5OVWYLMpjUSMLuidzT4PwLjR5H3+HdDI7HMvCukBZ/rsmrwW+Kw7sNa0D1JM+XsX41coFNLTzZLHsiznfCF5v25dpd328qZZ3vOIInoVUoprsrBja0pzlSnNMxhPWLhm2Yrkq5BtVBi95I5i3HOuYnxhdy/kvvM9RiP4vHvGRuD2Etaft41jMe4xSRjBzH8p0AsYY+DcHzHur2mm4/gZ4FmWbTJbnztGr8oYU56PA0+ZfT4OI5BMwzinuxc8ryjFfbCI41jUdVJimkVsS2G9HX8sMM91+wejtG0uxg/bDNPxe93s8xLvYwXPv2LuH4Xth7zz64gp/csYQf540+e+pv171vR5NEYvWrfSHOPa8FKmDRWiVJQxcGWs1rq4caGEEKJIynjm5mngTm2U9tYqch8UFSVtvoQQQgghqpAEX0IIIYQQVUiqHYUQQgghqpCUfAkhhBBCVCEJvoQQQgghqlCNHefLw8NDe3l5VXc2hBBCCCFKFBoaGqu1blDynDU4+PLy8mLPnj3VnQ0hhBBCiBKZBkEuFal2FEIIIYSoQhJ8CSGEEEJUIQm+hBBCCCGqUI1t8yWEEEIIQ1ZWFtHR0aSnW/z586KM7O3t8fT0pE6dOuVehwRfQgghRA0XHR1N3bp18fLyQilV3dm5aWmtiYuLIzo6mlatWpV7PVLtKIQQQtRw6enpuLu7S+BVzZRSuLu7V7gEUoIvIYQQohaQwKtmsMRxkOBLCCGEECVSSvHwww/nv8/OzqZBgwaMGDGi0tPu378/kZGRpZp31qxZzJ07t3IzVEESfAkhhBCiRE5OThw+fJi0tDQANmzYQLNmzcq0juzs7MrIWq0jwVcJrqRlsTU8trqzIYQQQlS7O+64gzVr1gCwdOlSRo8enf9ZfHw8d999N/7+/vTo0YODBw8CRknUuHHj6N27N+PGjSMyMpK+ffsSGBhIYGAg27dvByA4OJj+/ftz//33065dO8aOHYvWGgA3Nzesra3Jyclh4sSJdOzYET8/P957770q3gOWIb0dixGTlM64L0MIu5TEnPv8GdW1eXVnSQghhKg2Dz30ELNnz2bEiBEcPHiQSZMmsWXLFgBmzpxJQEAAP//8M5s2bWL8+PHs378fgKNHj7J161YcHBxITU1lw4YN2NvbEx4ezujRo/MfJ7hv3z6OHDlC06ZN6d27N9u2baNPnz6sXLkSgNDQUM6dO8fhw4cBSExMrIa9UHESfBXhfGIaY7/cxaWr6fg1c2XGqsN0aOZCh6au1Z01IYQQN7H//nKEo+evWnSdtzR1YeadHUqcz9/fn8jISJYuXcodd9xxzWdbt25lxYoVAAwcOJC4uDiuXjXyedddd+Hg4AAYY5ZNnz6d/fv3Y21tzYkTJ/LX0a1bNzw9PQHo3LkzkZGR9OnTJ/9zb29vTp06xZNPPsnw4cO5/fbbK7bh1aTWBV8Z2TlsPBZDelYOSoFCGX+VQgF2Nla0a+xCczeHcvdIOBOXwpgvdnE1PYvF/+hGS3cnRny4lWlL9rJ6eh9cHco/sJoQQghRm91111288MILBAcHExcXV6plnJyc8v9/7733aNSoEQcOHCA3Nxd7e/v8z+zs7PL/t7a2vq6NWP369Tlw4ADr1q3j008/5YcffmDBggUV3KKqV6uCr9xczXPLDrDm0IUS561rb0OHpi50bOpKh2bGX+8GzlhbFR+QRcQkMfbLXWRm57J0Sg86NjNKuj4aE8BDn+/kxeUH+GxcF+nyK4QQolqUpoSqMk2aNIl69erh5+dHcHBw/vS+ffuyZMkSZsyYQXBwMB4eHri4uFy3/JUrV/D09MTKyoqvv/6anJycUqcdGxuLra0t9913H76+vtf0vqxNalXwNXd9GGsOXeD5wW25s1NTNMZos/l/NaRk5nDswlUOn7vC4fNXWbzzDBnZuQA429nQ37cBwzo2ob9vA5zsrt38I+evMP6rEKysFN8/2hPfxnXzPwvycuPlYe3435pjfLHlFI/2a12FWy6EEELUDJ6enjz11FPXTZ81axaTJk3C398fR0dHvv7660KXnzZtGvfddx/ffPMNQ4cOvaZUrCTnzp3jkUceITfX+F5/4403yrcR1Uzl9SSoaYKCgnReAzyAH3af5aUVBxndrQX/d0/HUpc8ZefkcvJyCofPXWF3ZDwbjl4iLiUTOxsr+rVtwLCOjRnUrhGnYpOZsCAEZzsblkzpQSuP608GrTXTluxl/dFLLJ3Sg26t3Cy2vUIIIURRjh07Rvv27as7G8KksOOhlArVWgeVZvlaEXxti4hlwoIQerZ2Z8HErtSxLv8IGTm5mt2R8aw9fJF1Ry5y4Uo6NlYKaytFY1d7lkzujmd9xyKXT0rP4q6PtpGSkc2ap/rSoK5dkfMKIYQQliDBV81S0eCrxlc7RsQk8di3oXg3cOLjsYEVCrwArK0UPbzd6eHtzqsjbuHguSv8fvgCMVczeHlYOxq52Be7fF37OnzycCB3f7yNp5buY/E/umFTwTwJIYQQ4uZRo4Ov2OQMJi7cjZ2NNQsmdsXF3rK9DK2sFJ2b16Nz83plWq5dYxf+d7cfLyw/wLsbTvDS0HYWzZcQQgghblw1tshGa5jyzR5ikzP4ckJQsVWB1eH+Lp6M7tac+cEn2XzicnVnRwghhBC1RI0Nvs4mpLL/bCLvP9i5zCVTVWXmnR1o28iZ5344QGxyRnVn54aVm6uZs/Y43+48U91ZEUIIISqsxgZfV9KyeGVYO4Z2bFLdWSmSfR1rPhwdwNX0LF5YfoDc3JrZeaE201rzn1WHmR98kg83hlNTO4gIIYQQpVVjg68Wbo5M6etd3dkoUbvGLvxneHuCwy6zcHtkdWfnhqK15rVfj/Hdrig6NnMhJimDiJjk6s6WEELcdCIjI+nYseM102bNmsXcuXOrNB9eXl7ExsZWWXrBwcGMGDHC4uutscGXq0OdWjOK/LgeLbmtfSPe+v04h89dqe7s3DDeWX+CBdtO80hvLz4Z2wWArRFVd9EJIYSoPlrr/MFUbzQ1NviqTZRSzLnfn/pOdXjq+32kZmaXvJAo1sd/RvDRnxGM7taCV0fcQnM3R7zcHdkaLsGXEELUNP379+fpp5+mc+fOdOzYkZCQEMAoHRs3bhw9e/bEx8eHL774In+Zt99+m65du+Lv78/MmTMBo4TN19eX8ePH07FjR86ePXtdWnPmzMHPz49u3boRERGRv9zAgQPx9/dn0KBBREVFATBx4kR+/PHH/GWdnZ0Bo0Srf//+3H///bRr146xY8fmN2tZu3Yt7dq1IzAwkJUrV1bC3rJQ8KWUGqqUClNKRSilXi5inlFKqaNKqSNKqe8skW5N4uZky3sPduZ0bAqzfzla3dmp1b7aepq314Vxb0AzXr/776cZ9G7jwc5TcWTl3Ji/hIQQojZLTU1l//79zJ8/n0mTJuVPP3jwIJs2bWLHjh3Mnj2b8+fPs379esLDwwkJCWH//v2EhoayefNmAMLDw5k2bRpHjhyhZcuW16Xj6urKoUOHmD59Os888wwATz75JBMmTODgwYOMHTu20McfFbRv3z7ef/99jh49yqlTp9i2bRvp6elMmTKFX375hdDQUC5evGihvXOtCo/zpZSyBj4GBgPRwG6l1Gqt9VGzeXyAV4DeWusEpVTDiqZbE/Vq7cHjt7ZmfvBJ+vo0YLh/zegssCcynh/2nCUlI4eUzGxSM3JIzsgmNTOblMwc2jWuy/yxgdS18Dhq5bFk1xle+/Uod/g1Zs79/liZPQi9r48HS3ZFceBsIkFe8mgnIcRNbOHw66d1uBu6TYHMVFjywPWfdx4DAWMhJQ5+GH/tZ4+sKTa5opoBmU8fPXo0AP369ePq1askJiYCMHLkSBwcHHBwcGDAgAGEhISwdetW1q9fT0BAAADJycmEh4fTokULWrZsSY8ePYrMS146o0eP5tlnnwVgx44d+aVU48aN46WXXip2ewC6deuGp6cnAJ07dyYyMhJnZ2datWqFj48PAA8//DCff/55iesqK0sMstoNiNBanwJQSn0PjATMi3+mAB9rrRMAtNYxFki3Rnp2cFu2n4zj5ZUH6dTctdrHJztxKYmJC3ejFDSoa4eznQ2OttY0rWePo60Ndayt+Hn/OaYuDmXhI12xs7GutryuCI3mPz8fZmC7hrz/YMB1Tw7o6e2BUrAlPFaCLyGEqELu7u4kJCRcMy0+Pp5WrVrlvy8YoOW9L2y61ppXXnmFqVOnXvNZZGRkiQ/aNl9fSW3DbWxs8tuN5ebmkpmZmf+Znd3fjwe0trYmO7vqmgxZIvhqBphXykYD3QvM0xZAKbUNsAZmaa3XWiDtGqeOtRUfPhTAHR9u4Ynv9vHxmIBqC8ASUjKZ/PUe7OtYs3p6b5rWcyh0vl6t3Xl++QGeXbafeaMDsbaq+o4Oy/cYD07v3dqD+WMDsbW5vkbc1bEO/s1c2RYRy7OD21Z5HoUQosYorqTK1rH4z53cSyzpKsjZ2ZkmTZqwadMmBg4cSHx8PGvXruXpp5/On2fZsmUMGDCArVu34urqiqurKwCrVq3ilVdeISUlheDgYN58800cHByYMWMGY8eOxdnZmXPnzlGnTulqX5YtW8bLL7/MsmXL6NmzJwC9evXi+++/Z9y4cSxZsoS+ffsCRu/I0NBQRo0axerVq8nKyip23e3atSMyMpKTJ0/SunVrli5dWqb9VFpV9XghG8AH6A94ApuVUn5a60TzmZRSjwKPArRo0aKKsmZ5LdwdmfuAP88s28/Ad/5icp9WPN6/dZVW62Xl5DJtyV4uXknn+6k9igy8AO7r4klCaib/W3OM+o6H+Z9ZO6uqsDQkin/9dIg+bTz4YnwQ9nWKLn3r4+PBp3+dIik9q0ZUkwohxM3im2++4YknnuC5554DYObMmbRu3Tr/c3t7ewICAsjKymLBggX50/39/RkwYACxsbHMmDGDpk2b0rRpU44dO5YfPDk7O/Ptt99ibV1y7UtCQgL+/v7Y2dnlB0fz5s3jkUce4e2336ZBgwYsXLgQgClTpjBy5Eg6derE0KFDSyxVs7e35/PPP2f48OE4OjrSt29fkpKSyrajSkFVdNBKpVRPjJKsIab3rwBord8wm+dTYJfWeqHp/UbgZa317qLWGxQUpPfs2VOhvFW384lpzF0Xxsp95/BwtuW5wb482LV5lZQszfj5MIt3nuGdBzpxXxfPUi3zxu/H+OyvUzw9yKfKSpYW74hkxqojDPBtwCcPdyk28ALYfjKWMV/s4svxQdx2S6MqyaMQQlS3Y8eO0b59++rORpH69+/P3LlzCQoKumb6rFmzcHZ25oUXXqimnFWOwo6HUipUax1UxCLXsERvx92Aj1KqlVLKFngIWF1gnp8xSr0Jl+DTAAAgAElEQVRQSnlgVEOeskDaNVrTeg68+2BnVk/vTSsPJ/710yGGf7iFLeGV+yzIb3eeYfHOMzzaz7vUgRfAy0Pb8UAXTz7YGM7iHZGVlr88C7edZsaqI9zWvhGfjis58ALo0rI+9nWsZLwvIYQQtVaFqx211tlKqenAOoz2XAu01keUUrOBPVrr1abPbldKHQVygBe11nEVTbu28Pesxw9Te7L28EXe+P04474Koa+PB2O6tWBg+4YWbeS+42Qcs1YbJUn/HNquTMsqpXjjXj8SUrN4dfUR6jvZMsK/qcXyZu6Lzad4/bdjDO3QmA9HBxTaxqswdjbWdGvlzjYJvoQQosYIDg4udPqsWbOqNB+1hUXafGmtfwN+KzDtVbP/NfCc6XVTUkoxzK8JA9s35OvtkXy55TSPL9lLPcc63OnflPu6eNLJ07VCba2i4lJ5fEkoXh5OfDA6oFzVmzbWVnw0JoBxX+3i2WX7qedgSx8fj3LnqTAf/xnB2+vCGO7XhPcf6kwd67IVwPZp487//Xaci1fSaexqb9G8CSGEEJVNRrivYnY21jzarzXbXx7Ioke60tenAT/sOcvdH2/jtnf/Yn5wBGfiUsgp40O641MymfzNbrSGL8cH4VKBxuj2daz5ckJXWjdw5vFvQy32PMXsnFze/P04b68LY2TnpnxQjsALjMFWASn9EkIIUStVVW9HUYCNtRX9fRvS37chV9OzWHPwAitCo5mzNow5a8OwtbbCs74DLdwdaeFmvFq6O+FkZ010fBpR8alExadyJj6Vs/GpxKdkYm2lWDypG14exffmKA1Xhzp8NbErd83byqOL9/DzE70rFNBFxqbwzLL97D+byOhuzfnf3X7l7njQvrEL7k62bIuILVObNiGEEKImkOCrBnCxr8Pobi0Y3a0FkbEpbD8Zx5n4FM7Gp3ImLpXQyASSMq4d/M3aStGsngMt3BwZ2rExLdwc6dbKjcAW9S2Wr2b1HJg/NpCxX+7i2e/388X4oGtGnC8NrTXLdp9l9q9HsbFSfDQmoMLtyKysFL3aeLA1Ihatda15ALsQQggBEnzVOF4eTteVXGmtSUzN4kx8KikZ2TSv70jTevbXjQBfGbp7uzPzzluYseoI7/1xgudv9y31svEpmby84iDrj16iV2t33hnViSauRY83VhZ92rjzy4HzhMck07ZRXYusUwghRNGcnZ1JTrZMM5TyiIyMZMSIERw+fLjK0qysoTIk+KoFlFLUd7KlvpNttaT/cI+WHD53lXmbIriliQvD/Ep+ZmVwWAwv/niQK6lZ/Gd4eyb1blXmUrPi5LX72hoeK8GXEELcgLKzs7GxuTHDFGlwL0qklGL23R0IaFGP55cfIOxi0aP9nriUxHM/7Gfiwt24OdqyanpvJvf1tmjgBeBZ35FWHk4y3pcQQlSjyMhIBg4ciL+/P4MGDSIqKoqcnBxatWpl1NokJmJtbc3mzZsB46Hb4eHhpKSkMGnSJLp160ZAQACrVq0CYNGiRdx1110MHDiQQYMGXZdednY2Y8eOpX379tx///2kpqYCsHHjRgICAvDz82PSpElkZGQAxuOFYmON74k9e/bQv39/wCjRmjRpEv3798fb25sPP/wwP43XX3+dtm3b0qdPH8LCwiplv0nwJUrFzsaaTx/ugrOdDVO+2UNi6t8PJ9Vasz0ilokLQ7j9vc38dugCU/t5s2p6b9o3cam0PPVu487OU3Fk5eRWWhpCCCGK9uSTTzJhwgQOHjzI2LFjeeqpp7C2tsbX15ejR4+ydetWAgMD2bJlCxkZGZw9exYfHx9ef/11Bg4cSEhICH/++ScvvvgiKSkpAOzdu5cff/yRv/7667r0wsLCmDZtGseOHcPFxYX58+eTnp7OxIkTWbZsGYcOHSI7O5tPPvmkxLwfP36cdevWERISwn//+1+ysrIIDQ3l+++/Z//+/fz222/s3l3kg3gq5MYszxOVopGLPZ+O68JDn+3kyaX7+GJ8EGsPX+SLLac4cv4qHs52PD+4LQ/3aFklVaR92njw7c4o9p9NpKuXW6WnJ4QQNcUjax+5btoQryE81O4h0rLTmPbHtOs+H9lmJHe3uZuE9ASeC7522M2FQxeWKx87duxg5cqVAIwbN46XXnoJgL59+7J582ZOnz7NK6+8whdffMGtt95K165dAVi/fj2rV69m7ty5AKSnpxMVFQXA4MGDcXMr/J7evHlzevfuDcDDDz/Mhx9+yODBg2nVqhVt2xqPxZswYQIff/wxzzzzTLF5Hz58OHZ2dtjZ2dGwYUMuXbrEli1buOeee3B0dATgrrvuKtd+KYmUfIkyCWxRn9fu7sCW8FiC/vcHzyzbT3pWDm/d58fWfw7gyUE+VdY2rae3B1bKaPdVHtsjYjl87oqFcyWEEKJfv35s2bKFkJAQ7rjjDhITEwkODqZv376AUWOyYsUK9u/fz/79+4mKisp/VmJxD78u2Lu9pN7uNjY25OYatSPp6enXfGZnZ5f/v7W1NdnZ144qUJmk5EuU2YNdW3A2Po0D0Yk80tuL/m0bWrxNV2m4OtbBz7MeWyNiy/wg8N2R8YxbEIIC/nVHex7p7SVDVgghao3iSqocbByK/by+ff1yl3QV1KtXL77//nvGjRvHkiVL8oOrbt26MW7cOLy9vbG3t6dz58589tln/PrrrwAMGTKEefPmMW/ePJRS7Nu3j4CAgBLTi4qKYseOHfTs2ZPvvvuOPn364OvrS2RkJBEREbRp04bFixdz6623Akabr9DQUIYNG8aKFStKXH+/fv2YOHEir7zyCtnZ2fzyyy9MnTq1AnuocFLyJcrlhSG+LP5Hdwa2a1QtgVeePm3c2X82kaT0rFIvczkpgyeW7KV5fQf6+zZk9q9Hmb50H8kZVferRwghapvU1FQ8PT3zX++++y7z5s1j4cKF+Pv7s3jxYj744APAKFVq3rw5PXr0AIxqyKSkJPz8/ACYMWMGWVlZ+Pv706FDB2bMmFGqPPj6+vLxxx/Tvn17EhISePzxx7G3t2fhwoU88MAD+Pn5YWVlxWOPPQbAzJkzefrppwkKCsLauuTnKAcGBvLggw/SqVMnhg0bll9NamnKeOxizRMUFKT37NlT3dkQNdyOk3GM/mInX44P4rZbGpU4f06uZtxXuwg9k8BP03rTvkldPtt8ijlrj+Pl4cSnD3eRoSuEEDXOsWPH8qvlRPUr7HgopUK11kGlWV5KvkStFtiyHvZ1rFhz6AKl+SHx3oYTbD8Zx2t3d+SWpi4opXjs1tZ8N6UHV9OyGfnRNn7aF10FORdCCHGzkuBL1Gp2NtY80KU5P+07x2PfhnIlrejqx03HL/HRnxE8GNScUUHNr/msh7c7vz3VBz9PV55ddoD//HyIjOycys6+EEKIm5AEX6LWmz2yA/8Z3p6Nx2K4c97WQnswRiek8uyyA7Rv4sJ/R3YodD0NXez5bnJ3pvbz5tudUUz+eo+MISaEEMLiJPgStZ5Sisl9vVk2tQdZObnc+8l2luw6k18NmZGdwxNL9pKbq/lkbCD2dYpudGljbcUrd7TnzXv92BIeyysrD5WqOlMIISqb3ItqBkscBwm+xA2jS0s31jzVl57e7vz7p8M8s2w/KRnZvL7mGAeirzB3VKfrHlpelIe6teCpQT78GBrNBxvDKznnN7fMbCldFKIk9vb2xMXFSQBWzbTWxMXFYW9vX6H1yDhf4obi5mTLwoldmR8cwbsbThByOp4LV9J5tJ83Qzo0LtO6nr3Nh/OJabz/RzhN6zlc105MVEx6Vg7vbTjBV1tPc4dfE14e1o6m9RyqO1tC1Eienp5ER0dz+fLl6s7KTc/e3h5PT88KrUOGmhA3rO0nY3lq6X5aN3Di28ndqWNd9oLerJxcJi3azY6TcSyY2JV+bRtUQk5vPrtOxfHPFQeJjEvl1rYN2HkqDqVgar/WPHZraxxsSx6PRwghapKyDDUhwZe4oWVk52ClVLkCrzxJ6Vk88OkOzsan8sNjPenQ1NWCOSxZzNV0XBzqFNtWrayyc3KxtlKlGtVfa03omQRW7D1HcFgMQV5uTOzVksAW9cv8VICk9CzeWnucb3dG0dzNgbfu9adXGw+iE1J54/fjrDl4gSau9rw8rB13dWoqTx0QQtQaEnwJYWEXr6Rzz/xt5ORqfnqiN80quXosMTWTXw9eYOXeaPZGJeJka82Adg0Z2rExA3wb4mRX/hYDvx26wIvLD+Bga0P3Vm50M718G9W95mkFUXGprNwXzU/7znEmLhWHOtb0au1OSGQ8SenZ+DVzZUIvL0b4NylVYPhnWAz/XnmIC1fTeaRXK14Y0hZH22u3I+R0PLN/PcLhc1cJbFGPmXd2oFPzeuXeViGEqCoSfAlRCY5fvMoDn+ygST17lj/WC1eHOhZdf1ZOLsFhl1m5N5qNx2LIzMnFt1Fd7urclOiENDYcvUhsciZ2Nlb0a9uAoR0ac1v7Rrg6li4fWms+2hTBOxtO0Ll5PbzcHQk5Hc/5K8bDZl3sbejq5UaHZq7sOBnL7sgElIKe3u7cF+jJ0I6NcbKzISUjm5X7zvH19kgiYpJxd7JldLcWPNyjJc72NsQnZxKfmklCSiZxKcbf/dGJrDl4gTYNnZlzvz+BLeoXmc/cXM2PodHMWRdGbHIG/X0bMLmPN73buEtJmBCixpLgS4hKsj0ilgkLQ3CoY82dnZpyfxdPOjevV6GgICdX8+6GMJaGnCU+JRMPZ1vu6tSMewOb0cE0Cn/efHsi4/n98EXWHbnIhSvp2Fgp7g5oxrOD2xZbGpeelcPLKw7y8/7z3BPQjDfu9csvrYpOSCXkdHz+61RsCq0bOHFvoCd3BzQrcr1aa7ZFxLFoeyQbj1+iuFuJfR0rpvT1ZvrANtjZlK76NDkjm4VbT/P1jjPEJmfQrnFdJvVpxcjOTUu9DiGEqCoSfAlRifZGJfDN9kjWHrlIelYubRo6c38XT+4JaEYjl7J1P9Za88rKQ3y/+yxDOzRmVFdP+vo0KLGNmtaaA9FX+HnfOb4LiQJgfI+WTBvQBjcn22vmjU3OYOriUELPJPDC7W15YkCbYoPF5IxsnGytyxRQRsWl8svB89hYKdycbK951Xeypa6dTbkD1IzsHFbvP89XW09z/GISHs52jO/ZkrHdW+DubFeudQohhKVJ8CVEFbiansVvBy/wY2g0e84kYKWgX9sGPDmwDV1aupW4vNaa//vtGF9sOc30AW14YYhvufJxLjGN9zecYMXeaJxsbXi0nzf/6NsKR1sbwi4m8Y+vd3M5KYN3R3VmuH+TcqVRE+SVtH259RTBYZexr2PFOw/U7m0SQtw4JPgSooqdupzMir3RLN8TTVxKJv8c6suUvt7FlvZ8uDGcdzecYGIvL2beeUuF2zOduJTE2+vC2HD0Eh7OdowK8uSbHWdwsLXmy/FBN1TD9fBLSbyy8hChUQnMvqsD43p6VXeWhBA3OQm+hKgmV9OzeGn5QdYeucjttzTi7Qc6Fdowf8HW08z+9Sj3BjZj7v2drullWFGhZ+J56/cwQiLjuaWJC19OCLohBy9Nz8ph+nd7+eNYDE8N8uHZ23ykQb4QotpI8CVENdJa89XW07z5+3Ga1nNg/thAOjb7e2ywH/ac5aUfDzKkQyM+HhOITQXGICsuD4fOXcGnYd0besDS7Jxc/vXTIX7YE82Y7i14bWRHrC0YyAohRGmVJfiSZzsKYWHmD/rOzDYe9P3drii01vx26AIvrzhIXx8PPhwdUCmBV14e/D3r3dCBFxgPQn/rPn8e79+a73ZF8cSSvaRn5VR3toQQolhS8iVEJYpLzuCZZfvZEh7LAN8GbI2Ixd+zHov/0e26AUZFxXy19TSv/XqUHt5ufD4+CBd7y47DJoQQxZFqRyFqkJxczbxN4XywMZz2jV1Y+mgPiw/QKgw/7zvHC8sP4N3AiUHtG9Gwrh2NXOxpWNeOhnXtaehiZ9HHNIkbl9aak5dTqO9YR4Y0EaUiwZcQNVBETBKNXR1wrsCjgUTJgsNimLn6COcS0sjOvf7+Vs+xDi3dnWjt4YR3AydaeTib/jpJYHaTS8vMYfvJWP4Mi+HP45c5l5iGZ30HVk7rRcO6ZRvDT9x8JPgSQtz0cnM1CamZxCRlcOlqOjFJGVxOyuB8YhqRcSmcupzCBdOjlQCUAi93J6b09WZUkGeltccTNUtscgZrDl5g0/EYdpyKIzM7F0dba/q08SCwZX0+3BhOKw8nlk3tKT+cRLEk+BJCiFJIycjmdGwKp2JTOH05heATMeyLSqR1AydeGtqO229pJMNX3MDSMnMY8v5mouJT8fZwor9vQwa2a0jXVvXzH2H1Z1gMk7/eQ6/W7iyY2LXEp0+Im5cEX0IIUQ5aa9YfvcRba49z6nIKQS3r88od7Ur1xAJR+8xZe5z5wSdZ9EhX+vs2LHK+vOFh7g1sxjsPdJKAXBRKhpoQQohyUEoxpENj1j/Tj/+7x48z8anc98kOpi7eQ0RMcnVnT1jQiUtJfL75FPcFehYbeAGMCmrOs7e1ZeXec7yz/kQV5VDcyKQCWwghCrCxtmJM9xbcHdCUr7ac5tO/TrLuyF+0beRMr9Ye9PB2p4e3G/UcbUtemahxcnM1//npMM72NvzrjnalWuapQW24eDWNj/6MoLGrPQ/3aFnJuRQ3Mgm+hBCiCI62Njw5yIfR3VvwY2g020/GsWz3WRZtj0Qp6NDUhV6tPRjg25Cerd2rO7uilH7cG01IZDxv3edX6mEklFK8NrIjl65m8OqqwzSsa8ftHRpXck7FjUrafAkhRBlkZudyIDqR7RFxbD8Zy76oRDJzcnnzXj8e6taiurMnShCfksmgd4Jp3cCZH6b2LPNzVVMzsxn9+U7CLiWxZHIPurSsX0k5FbWNtPkSQohKYmtjRVcvN56+zYdlU3tyYObt9GvbgP/8fJjtJ2OrO3uiBG/8doyk9Gxev8evXA+0d7S14auJXWnkYs/kr3dz6rK0BRRlJ8GXEEJUgIOtNR+NCaCVhxOPf7tXvoxrsF2n4lgeGs3kvt74Nq5b7vV4ONvx9SPdsFKKCQtDiElKL3khIcxI8CWEEBXkYl+HBRO7Ym2lmPz1HhJTM6s7S6KAzOxc/v3zYTzrO/D0IJ8Kr8/Lw4kFE7sSm5TJpEW7Sc7ItkAuxc1Cgi8hhLCA5m6OfDauC9EJaUxbspesnNzqzpIw88WWU0TEJDN7ZAccbC3zGKlOzesxf2wgxy4k8fi3oXLMb2Jl/cElwZcQQlhIVy833rzPj+0n43h11RFqaoemm01UXCofbgxnWMfGDGzXyKLrHtCuIW/c48eW8Fj+ueKgHPOb1CfBJ8s0vww1IYQQFnRvoCcnLyfz8Z8nad3Aicl9vas7Sze92b8excZK8eqdt1TK+kd1bc7Fq+m8u+EETVzteXFI6cYOEzeGC1fSWLQ9skzLSPAlhBAW9vxgX05dTuH1347RysOJQe0tW9oiSm9fVAJ/HLvEi0N8aeLqUGnpPDmwDReupPPxnydp7GLPuJ5elZaWqFne3xBOWQs8pdpRCCEszMpK8e6oznRs6sr07/bxSfBJ0rNyqjtbN6V3N5zAzcmWib28KjUdYxDWDtzWviGvrj7Cp3+dJOaq9IK80UXEJLE89Cxje5RtjD8JvoQQohI42Frz1cQg+vh48Nba4wx+7y9+P3RB2gRVod2R8WwJj+WxW71xsqv8ih4bayvmjQ6kV2t33vz9ON3f2MioT3ewaNtpLkkgdkN6e10YjrY2TB/QpkzLWWSEe6XUUOADwBr4Umv9ZhHz3Qf8CHTVWhc7fL2McC+EuFFsi4hl9i9HCbuURLdWbrw64hY6NnO9Zp7cXM3RC1fZHH6ZLSdiSc7I5o17/a6bT5Te6M93EnE5mc0vDrBYD8fSCr+UxJpDF/jt0AVOXEpGKQhqWZ9hHZswsnPTUj/WSNRce6MSuHf+dp4b3JanBvmUaYT7CgdfSilr4AQwGIgGdgOjtdZHC8xXF1gD2ALTJfgSQtxMsnNyWbbnLO+sP0FCaiYPdPFkYq9WHD5/ha3hsWyNiCU+xeiu3q5xXRJTs7iSlsWHowMYfIu0GSur7SdjGfPFLmbeeQuP9G5VrXmJiElizcGL/HboAmGXknCytWZyX28m921FXfs61Zo3UT5aax78fCenLqfw14v9cbKzqfLgqycwS2s9xPT+FVPG3igw3/vABuBF4AUJvoQQN6MraVl8tCmcRdsjycox7r8eznb08/Ggb1sPerfxoGFde2KupjPlmz0cPHeFfw1rz+S+rVCq7I/DuRlprRn12Q7OxqcR/GJ/7OtUbalXccIuJvHBxhP8dugi9R3r8MSANjzco2WNyqMo2Z/HY3hk0W5mj+zAeFPnirIEX5aoBG8GnDV7Hw10N59BKRUINNdar1FKvVjUipRSjwKPArRoIQ+oFULceFwd6vDv4bcwpntLdpyMI7BlPXwb1b0usGroYs/3j/bk+eX7ef23Y5yKTWH2yA7UsZamuiXZEh7L7sgEXru7Y40Lanwb12X+2C4cOJvI2+vC+N+aYyzYeppnbmvLvYHNsLlJj29SehZZORpHW2vsbKxq9A+N3FzNW2uP08LNkYe6li9WqfQWiEopK+BdYGJJ82qtPwc+B6Pkq3JzJoQQ1aeVhxOtPJyKncfB1pqPRgcy1z2M+cEnORufysdjA3F1kKqqomiteWfDCZrVc2BUkGd1Z6dInZrX49vJ3dkWEcuctcd5acVBPtt8ktfv8aOHt3t1Z69K/RkWw9TFoWRmG08IsFLgZGuDo501jrY2ONlZ08TVgZZujrR0d6SFuxNe7o40redQLT9GVh04x/GLSXzwUGdsbcqXviWCr3NAc7P3nqZpeeoCHYFgUyTbGFitlLqrpKpHIYS42VlZKV4a2o5WHk7866dD3Dt/GwsndqOFu2N1Z61G+jMshgNnE3nzXj/sbGpWqVdherfx4OcnerPuyEXe/P04478K4cPRAQzt2Li6s1Yl9kUlMO3bvbRp4MyoIE9SMnNIy8whJTPb9DeH5PQsImNT2HziMhnZfz/CydpK0crDiQ8e6kyHplXTMSUjO4d31p+gQ1MX7vRvWu71WKLNlw1Gg/tBGEHXbmCM1vpIEfMHI22+hBCizHaeiuOxb0OxVopv/tGtyr5wagutNXd+tJWradlsfP7WWldFm5iaycSFuzkYncic+ztxf5eaW3JnCRExyTzw6XZcHOrw42O9aFC3+B6gubmamKQMzsSlcCY+lai4VJaGRNHC3ZGVj/eqkqrKhdtO899fjvL1pG7c2rbBNZ+Vpc1Xhc9MrXU2MB1YBxwDftBaH1FKzVZK3VXR9QshhDD08HZn5eO9sLOxYvTnO9l/NrG6s1SjrD96icPnrvLUIJ9aF3gB1HO0Zcnk7vRq7cELyw+wcNvp6s5Spbl4JZ0JC0KwtlJ8M6lbiYEXGKXAjV3t6e7tzqig5rwwxJd/Dm3HvqhEVh84X+l5vpyUwUebIujp7U4/H48Krcsi43xVBin5EkKIwp2NT2Xsl7uIT8lk4SNd6erlVt1Zqna5uZo7PtxCZnYu65/tV6sbrmdk5/D00v2sPXKRZ27z4WljDKnqzpbFXEnNYtRnO4hOSGXZ1J4VGssuN9co7UxIyWTj8/0rZTy3nFzNkl1nmLsujLSsHFY83gt/z3rXzVelJV9CCCGqVnM3R36Y2pOGLnaM/yqEbRGx1Z2lavfb4Qscv5jE07f51OrAC8DOxpqPxgRwfxdP3v8jnP/+cpTc3JpZUFJW6Vk5TP5mN6dik/l8fFCFBxG2slK8OuIWzl9J5/PNpyyUy7+Fnkngro+28uqqI3Rs5srvT/ctNPAqq9p9hgohxE2qsas9yx7tSUt3Rx5ZtJs/j8dUd5aqjdaaeRsj8GnozIgKNIKuSWysrZhznz+Terdi0fZIXvzxIFk5uSUvWINl5+Ty5NJ97DmTwHsPdqZ3m4pV3eXp7u3OHX6N+fSvk1y4kmaRdcYlZ/DSjwe475PtxCZn8NGYAJZM7k6bhnUtsv7Kf9iVEEKIStGgrh1Lp/Rg3IJdPLp4D/NGB940veTMBZ+4TNilJN55oBPWVjdO9ZyVlWLGiPbUc6zDuxtOsPrAORrWtaexqz2NXexp5GJPE1d7GrnaY2tthdaaHK3JydXkak1uLmigeys3mrtVb+9YrTUzVh1mw9FLzLrzFosHya8Ma88fx2J4e20Y7z7Yudzryc3VfBcSxdvrwkjJyObRft48NcgHZws/G1TafAkhRC13JS2LiQtDOBh9hXdHdWJk52bVnaUqNfrznUTGpfDXiwPKPe5STffH0UvsjUrg4pV0Ll5Nz/+bmplT4rI2VopRXZszfUAbmtZzqILcXis31wi8luyK4okBrXlxSLtKSWfO2uPMDz7JT9N6EdCifpmXPxufygvLD7DrdDw9vN2YPbIjbRuVvqSrSh8vVFkk+BJCiNJLzsjmH4t2szsynk8e7sKQDjdHCdiBs4mM/Hgb/76jPVP6eVd3dqqU1pqkjGwuXUknMycXayuFtVIopfL/z8jO4ZsdZ/h+dxQKxZjuLZjWvzUNXeyrJI85uZqXVxxkeWg0j93amn8O9a20zgPJGdn0fzuY5m4OZRp6QmvNst1nee3XoyhltCF7IMizzPmU4EsIIW5CKRnZjPlyF8cuXGXxpG50vwlGSn9iyV42h19m+8sD5SHVxYhOSOWjTREsD43GxkoxvmdLHru1Ne7OJQ/xUF7ZObk8v/wAq/af5+lBPjxzW+X32vxh91leWnGQDx7qXKoS4Jir6by88hCbjsfQw9uNuQ90wrN++apoJfgSQoibVHxKJvd/up3LSRn8MLUn7Zu4VHeWKs2ZuBQGzA1m6q2t+efQyqnKutGciUvhw40R/LQvGvs61tzp39iSEEIAABePSURBVJQ7/JvQq7W7RcdGy8zO5env9/H74Yu8OMSXJwa0sdi6i5M39ER8SiabShh64pcD55mx6jBpmTm8PKwdE3p6YVWBNoMSfAkhxE3sXGIa983fTq7WrHi8V7U3tq4sM34+zLLdZ9n6zwFVVo12ozh5OZn5f55k7eELpGTmUM+xDrff0og7/JrQu43HdYFYQkomp2JTOHU5mbMJaXh7ONG7jUehg6NmZOfwxJK9/HEshv8Mb8/kvlVbHbzrVBwPfr6TZ29ry9O3+eTn6UJiOtEJaUQnpPLXicv8fvginZrX491RnWjdwLnC6UrwJYQQN7kTl5J44NMduDnZsvyxnnhUYvVSdYhLzqD3W5sY2akZb93vX93ZqbXSs3LYfOIyvx26wB/HYkjOyMbVoQ63tW8EwOnYZE7FppCYmlXo8rc0caGvjwd9fRoQ5GU0cp+6OJS/TlzmtZEdGNfTq6o25RrTloSy6XgMHZu6Ep2QxqWkdMzDHVsbK54a2IbHbm1tsXHhJPgSQghB6Jl4xn65C5+GdVn6aA+Ld5evTu9tOMEHG8P547l+Fht76WaXnpXDlvBYfjt0gY3HLuFga00rDye8Gzjj7eGU/3/TevaEXUxiS3gsW8IvE3omgawcjZ2NFQ3q2nEuMY037/Xjwa4tqm1bzsanMm3JXpzsrPGs74hnfQezvw40drG3+GC8EnwJIYQAYOOxSzy6OJSe3u4smNj1hhiKIS0zh15vbqRLSze+nFCq7zpRiVIysgk5Hc/m8MscPneFh3u0vOmGO4GyBV83zs8gIYQQ1xnUvhFv3uvHiz8e5PnlB/jgwc4ValRcEywPPUtCahaP3XpzDS1RUznZ2TCgXUMGtGtY3VmpNST4EkKIG9wDQc2JS8nkzd+P06yeAy8Pq709A7Nzcvlyy2kCW9QjSB4oLmopCb6EEOImMLWfN9EJqXz610mauzkwtnvL6s5Suaw9cpGo+FT+Pbx9dWdFiHKT4EsIIW4CSilm3dmBcwlpvLrqCE3rOTDAt3ZVE2mt+eyvU3h7ODHY1BtPiNqo9re8FEIIUSo21lbMGxOIb6O6TF+yl6Pnr1Z3lspkx6k4Dp27wpR+3rW+3Zq4uUnwJYQQNxFnOxsWTOyKi0MdJi3azYUradWdpf9v786jpKzOPI5/H2kWQUFQVKIgGLeoqGCLiTOKSUxEnYiJMW6JuByJMYkmjkZM5ngcMxmXyUTjUUdxxN2YRLOQxGXUuCVRBBQQFGRxFwUFV2STO3/US+ylmiVd3u7q+n7OqdO33nvrXR5uF79+662qdfLAzAWc9etpbLZRV748pPbeSaeOxfAlSTVmy17dGHf8Xry3bCUnXDeRd5eW/wDN9mD+2x/wrZsnc8L1E9mwSyfGHrcn3Tq3/JUxUjUwfElSDfpUv55ceexQZi94j2/f+iQrPlzV1rvUyMoPVzHuL89xwH8/xJ9nLuCsA3fkztP2ZeiA3m29a1KrGb4kqUbtt0Nf/uOwXXn42YWc+/vptJcP3Z7y0luMvOKvnP/Hp9lrUB/u/f5wvv3Z7TrEB8RK4LsdJammHT1sAC8tWsKVD85l0x5dOfPAHdtkPz5Y/iEPzlrAH6fN587p89l8465ceexQDtp1SyK8uF4di+FLkmrcWQfuyKL3l3P5A3PotWFnTt4vzyfHL1m+kgdmlr7U+c8zF/DBig/p06MLJ++7Ld/93HZs3K1zlv2QcjN8SVKNiwh+8uXBvLt0JT+58xl6blj3sX4p8rSX3+J/HpzLA7MWsHTFKjbbqAtfGboVhwzux7BBfSr+hcdSe2P4kiTRaYPgkiP34J2lKzjnN0/Rs1tnDhrcr+LbefHNJXzj2sfptEFwxJ79ObgIXJ383C7VEMOXJAmALnUbcPU39uQb1z7O6bdNYaNudey7fd+KrX/J8pWMvmkSAL89dR+22bRHxdYtVRPP7UqS/q57lzrGjdqLbfv2YPSNk5n8wuKKrDelxA9un8as19/lsqOHGLxU0wxfkqRGenXvzI0nDWOLnl058fqJzHyt9V9DdM0j8/jjtPmcdeCODN+hcmfTpGpk+JIkNbP5xt246aS96dZ5A0aNe5w33lv2D6/rkdkLufCumRwyuB/fGv7JCu6lVJ0MX5Kksvr36c644/di8ZIVnPGrqaxatf4fwvrSoiV89xdPsv3mG3PxV3fzM7skDF+SpDXY5RO9OPdfdubhZxdy9cPz1uuxS5av5OQbJ7FqVWLscXvSo6vv8ZLA8CVJWotj9x7AIbv146f/N4tJzy9ap8eklDj7jqe8wF4qw/AlSVqjiOCCrwxmq0025LRfPMni95evcfyHqxIX3T2LP0x9lTO/uCP777h5pj2VqoPhS5K0Vj27deaKY4ay8L1lnHX71Ba/hPvVtz7g6Gse46qH5nJkfX9O3d8L7KWmDF+SpHUyeOte/PDgT3HfMwu49i/PNeu/e/p8Dvr5I8x45W1+9rXducgL7KWyvPpRkrTOjt9nII/Ne5ML75pJ/cA+7NF/E5au+JAf//FpbpnwIoO36sVlRw9h0GZe4yW1xDNfkqR1FhFcfPjubNGzG9+59QkmPr+IQy//C7dMeJFv7rctd3xrH4OXtBaGL0nSeunVvTOXHzOE195eyhFXPcqi91dw44nDOOfgT9Glzv9WpLXxZUdJ0nobMqA3F3xlMI/OfZMfHvIpNtuoa1vvklQ1DF+SpH/IEfX9OaK+f1vvhlR1PD8sSZKUkeFLkiQpI8OXJElSRoYvSZKkjAxfkiRJGRm+JEmSMjJ8SZIkZWT4kiRJyqgi4SsiRkTErIiYExFjyvSfERFPR8S0iLg/IrapxHYlSZKqTavDV0R0Aq4ADgJ2Bo6OiJ2bDHsSqE8p7QbcDlzc2u1KkiRVo0qc+RoGzEkpzUspLQduA0Y2HJBSeiCltKS4+xiwdQW2K0mSVHUqEb62Al5qcP/lYllLTgLuqsB2JUmSqk7WL9aOiK8D9cDwFvpHA6MBBgwYkHHPJEmS8qjEma9XgIZfa791sayRiDgA+BFwaEppWbkVpZTGppTqU0r1ffv2rcCuSZIktS+VCF8Tge0jYlBEdAGOAsY3HBARQ4CrKQWvBRXYpiRJUlVqdfhKKa0EvgPcAzwD/CqlNCMizo+IQ4th/wVsBPw6IqZExPgWVidJktShVeSar5TSncCdTZad26B9QCW2I0mSVO38hHtJkqSMDF+SJEkZZf2oifXyxmy47pDGy3Y5DIadDMuXwC1HNH/MHsfAkGPh/TfhV8c179/rRNj1cHj7ZfjNN5v37/Md2PGg0rb/8L3m/fudCZ/8LMyfBnef07z/8+fCgL3hxQlw//nN+0dcAP12g7kPwMM/bd7/pUths+1h1l3wt8ub93/laui1NUy/AyaOa97/tRuhx6bw5C0w5dbm/cf+Grp0h8evgRm/a95/wp9KP/96GTx7T+O+zt3g63eU2g9dDPMeatzfvTcceXOpfd958NLExv09PwGHX1Nq3zUGXnuqcf+mn4RDLyu1x58Gb85t3L/lYDjowlL7jpPhnVcb9/ffCw44r9T+5ddhyeLG/dsOh+E/KLVvPhxWLG3cv8OB8E+nldpN5x0495x7pbZzr3m/c6/Udu4176/1ubcGnvmSJEnKKFJKbb0PZdXX16dJkya19W5IkiStVURMTinVr8tYz3xJkiRlZPiSJEnKyPAlSZKUkeFLkiQpI8OXJElSRoYvSZKkjAxfkiRJGRm+JEmSMjJ8SZIkZWT4kiRJysjwJUmSlJHhS5IkKSPDlyRJUkaGL0mSpIwMX5IkSRkZviRJkjIyfEmSJGVk+JIkScrI8CVJkpSR4UuSJCkjw5ckSVJGhi9JkqSMDF+SJEkZGb4kSZIyMnxJkiRlZPiSJEnKyPAlSZKUkeFLkiQpI8OXJElSRoYvSZKkjAxfkiRJGRm+JEmSMjJ8SZIkZWT4kiRJysjwJUmSlJHhS5IkKSPDlyRJUkaGL0mSpIwMX5IkSRkZviRJkjIyfEmSJGVk+JIkScqoIuErIkZExKyImBMRY8r0d42IXxb9EyJiYCW2K0mSVG1aHb4iohNwBXAQsDNwdETs3GTYScDilNJ2wCXARa3driRJUjWqxJmvYcCclNK8lNJy4DZgZJMxI4EbivbtwOcjIiqwbUmSpKpSifC1FfBSg/svF8vKjkkprQTeBjatwLYlSZKqSru64D4iRkfEpIiYtHDhwrbeHUmSpIqrRPh6Bejf4P7WxbKyYyKiDugFvNl0RSmlsSml+pRSfd++fSuwa5IkSe1LJcLXRGD7iBgUEV2Ao4DxTcaMB0YV7a8Cf04ppQpsW5IkqarUtXYFKaWVEfEd4B6gEzAupTQjIs4HJqWUxgPXAjdFxBxgEaWAJkmSVHNaHb4AUkp3Anc2WXZug/ZS4IhKbEuSJKmatasL7iVJkjo6w5ckSVJGhi9JkqSMDF+SJEkZGb4kSZIyMnxJkiRlZPiSJEnKyPAlSZKUkeFLkiQpI8OXJElSRoYvSZKkjAxfkiRJGRm+JEmSMjJ8SZIkZWT4kiRJysjwJUmSlJHhS5IkKSPDlyRJUkaGL0mSpIwMX5IkSRkZviRJkjIyfEmSJGVk+JIkScrI8CVJkpSR4UuSJCkjw5ckSVJGhi9JkqSMDF+SJEkZGb4kSZIyMnxJkiRlZPiSJEnKyPAlSZKUkeFLkiQpI8OXJElSRoYvSZKkjAxfkiRJGRm+JEmSMjJ8SZIkZWT4kiRJysjwJUmSlJHhS5IkKSPDlyRJUkaGL0mSpIwMX5IkSRkZviRJkjIyfEmSJGVk+JIkScqoVeErIvpExL0RMbv42bvMmD0i4tGImBER0yLiyNZsU5IkqZq19szXGOD+lNL2wP3F/aaWAMellHYBRgCXRsQmrdyuJElSVWpt+BoJ3FC0bwAOazogpfRsSml20X4VWAD0beV2JUmSqlJrw9cWKaX5Rfs1YIs1DY6IYUAXYG4rtytJklSV6tY2ICLuA7Ys0/WjhndSSiki0hrW0w+4CRiVUlrVwpjRwGiAAQMGrG3XJEmSqs5aw1dK6YCW+iLi9Yjol1KaX4SrBS2M6wn8CfhRSumxNWxrLDAWoL6+vsUgJ0mSVK3WGr7WYjwwCriw+Pn7pgMiogvwW+DGlNLt67ri5995nhPuPqHRsgMHHshROx3FBys/4NT7Tm32mJHbjeSw7Q5j8dLFnPHgGc36j9zxSEYMGsFr77/GOY+c06x/1C6j2L///jz39nOc/+j5zfpH7zaaz3ziM8xcNJOLHr+oWf/pQ09nj833YMqCKfz8iZ836z972Nns1GcnHn31UcZOG9us/9zPnMugXoN48KUHuWHGDc36L9j3ArbssSV3P3c3v5z1y2b9P9v/Z/Tu1pvfzfkdv5/T7J+CKw+4kg3rNuS2mbdxz/P3NOu/bsR1AFw//XoeevmhRn1d67py1QFXAXDV1KuYMH9Co/5Num7CJZ+9BIBLJ1/K1IVTG/Vv0WMLLtz3QgAuevwiZi6a2ah/m57bcN4+5wFw3t/O44V3XmjUv1OfnTh72NkAjHlkDK+//3qj/t377s739vweAN9/4Pu8teytRv1799ubU3Y/BYBT7juFZSuXNeofvvVwjt/1eIBm8w6ce849555zz7nXlHNvzXNvTVp7zdeFwBciYjZwQHGfiKiPiP8txnwN2A84PiKmFLc9WrldSZKkqhQptc9X9+rr69OkSZPaejckSZLWKiImp5Tq12Wsn3AvSZKUkeFLkiQpI8OXJElSRoYvSZKkjAxfkiRJGRm+JEmSMjJ8SZIkZWT4kiRJysjwJUmSlJHhS5IkKSPDlyRJUkbt9rsdI2Ih8EIL3ZsBb2TcnWphXcqzLuVZl+asSXnWpTzrUl6t1mWblFLfdRnYbsPXmkTEpHX98spaYl3Ksy7lWZfmrEl51qU861KedVk7X3aUJEnKyPAlSZKUUbWGr7FtvQPtlHUpz7qUZ12asyblWZfyrEt51mUtqvKaL0mSpGpVrWe+JEmSqlK7CV8RMS4iFkTE9AbLdo+IRyPiqYj4Q0T0LJYPi4gpxW1qRHy5wWNGRMSsiJgTEWPa4lgqaX3q0qB/QES8FxFnNljWYeqynnNlYER80GC+XNXgMXsW4+dExGUREW1xPJWyvnMlInYr+mYU/d2K5TVbl4g4tsFcmRIRqyJij6KvluvSOSJuKJY/ExHnNHhMrT63dImI64rlUyNi/waP6WhzpX9EPBARTxfPF6cXy/tExL0RMbv42btYHsVxz4mIaRExtMG6RhXjZ0fEqLY6pjaXUmoXN2A/YCgwvcGyicDwon0i8OOi3R2oK9r9gAVAHdAJmAtsC3QBpgI7t/Wx5apLg/7bgV8DZxb3O1Rd1nOuDGw4rsl6Hgc+DQRwF3BQWx9bxrrUAdOA3Yv7mwKdar0uTR43GJjrfEkAxwC3Fe3uwPPF71YtP7d8G7iuaG8OTAY26KBzpR8wtGhvDDwL7AxcDIwplo8BLiraBxfHHUUdJhTL+wDzip+9i3bvtj6+tri1mzNfKaWHgUVNFu8APFy07wUOL8YuSSmtLJZ3A1ZfuDYMmJNSmpdSWg7cBoz8WHf8Y7Y+dQGIiMOA54AZDcZ3qLqsb03KiYh+QM+U0mOp9KxwI3BYpfc1p/WsyxeBaSmlqcVj30wpfWhdGjma0u+K86X0HNsjIuqADYHlwDvU9nPLzsCfi8ctAN4C6jvoXJmfUnqiaL8LPANsRenf+oZi2A18dJwjgRtTyWPAJkVdDgTuTSktSiktplTPERkPpd1oN+GrBTP46Bf5CKD/6o6I2DsiZgBPAacUYWwr4KUGj3+5WNbRlK1LRGwEnA38e5PxtVCXFucKMCginoyIhyJi32LZVpTqsFpHrAm0XJcdgBQR90TEExHxg2J5rdeloSOBXxTtWq/L7cD7wHzgReCnKaVF1PZzy1Tg0Iioi4hBwJ5FX4eeKxExEBgCTAC2SCnNL7peA7Yo2i3Ni1qYL+ukvYevE4FTI2IypVOdy1d3pJQmpJR2AfYCzll9vUqNaKku5wGXpJTea6sda0Mt1WQ+MCClNAQ4A7g1mlwj18G1VJc64J+BY4ufX46Iz7fNLraJFp9boPTHHbAkpTS93IM7sJbqMgz4EPgEMAj414jYtm12MbuWajKOUniYBFwK/I1SjTqs4g/8O4DvpZTeadhXnOXz4xPWUV1b78CapJRmUnp5hIjYATikzJhnIuI9YFfgFRr/Bbt1saxDWUNd9ga+GhEXA5sAqyJiKaVrETp0XVqqSUppGbCsaE+OiLmUzvq8QqkOq3W4msAa58rLwMMppTeKvjspXetyM7Vdl9WO4qOzXuB8OQa4O6W0AlgQEX8F6imdxajV55aVwPdXj4uIv1G6FmoxHXCuRERnSsHrlpTSb4rFr0dEv5TS/OJlxQXF8pb+L34F2L/J8gc/zv1ur9r1ma+I2Lz4uQHwb8BVxf1BxbUHRMQ2wE6ULgCdCGxf9Heh9AQ6vg12/WPVUl1SSvumlAamlAZS+kvsP1NKl1MDdVnDXOkbEZ2K9rbA9sC84lT5OxHx6eKdSMcBv2+Tnf8YtVQX4B5gcER0L36XhgNPW5e/L/saxfVeULrmhdquy4vA54q+HpQuop5JbT+3dC9qQUR8AViZUuqQv0PFcVwLPJNS+lmDrvHA6ncsjuKj4xwPHFe86/HTwNtFXe4BvhgRvYt3Rn6xWFZ72vqK/9U3Sn9lzgdWUPqr/CTgdEp/STwLXMhHHwr7DUqvw08BngAOa7Ceg4vxc4EftfVx5axLk8edR/Fux45Wl/WcK4c3mStfarCeemB6UZPLy9Wxmm7rO1eArxe1mQ5cbF3+Pn5/4LEy66nZugAbUXoH9QzgaeCsBuup1eeWgcAsShef3wds04Hnyj9TeklxWvFcOqX4d98UuB+YXdSgTzE+gCuK438KqG+wrhOBOcXthLY+tra6+Qn3kiRJGbXrlx0lSZI6GsOXJElSRoYvSZKkjAxfkiRJGRm+JEmSMjJ8SZIkZWT4kiRJysjwJUmSlNH/A3BxNE1iYx/eAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from esda.moran import Moran\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "w = libpysal.io.open(libpysal.examples.get_path(\"states48.gal\")).read()\n", - "w.transform = 'R'\n", - "mits = [Moran(cs, w) for cs in pci]\n", - "res = np.array([(mi.I, mi.EI, mi.seI_norm, mi.sim[974]) for mi in mits])\n", - "years = np.arange(1929,2010)\n", - "fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (10,5) )\n", - "ax.plot(years, res[:,0], label='Moran\\'s I')\n", - "#plot(years, res[:,1], label='E[I]')\n", - "ax.plot(years, res[:,1]+1.96*res[:,2], label='Upper bound',linestyle='dashed')\n", - "ax.plot(years, res[:,1]-1.96*res[:,2], label='Lower bound',linestyle='dashed')\n", - "ax.set_title(\"Global spatial autocorrelation for annual US per capita incomes\",fontdict={'fontsize':15})\n", - "ax.set_xlim([1929,2009])\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the above figure we could observe that Moran's I value was always positive and significant for each year across 1929-2009. In other words, US regional income series are not independent of each other and regional context could be important in shaping the regional income dynamics. However, the classic Markov approach is silent on this issue. \n", - "We turn to the spatially explict Markov methods - **Spatial Markov** and **LISA Markov** - for an explicit incorporation of space in understanding US regional income distribution dynamics." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### [Spatial Markov](http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.2001.tb00444.x/full)\n", - "\n", - "```\n", - "giddy.markov.Spatial_Markov(y, w, k=4, m=4, permutations=0, fixed=True, discrete=False, cutoffs=None, lag_cutoffs=None, variable_name=None)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Spatial Markov is an extension to class Markov allowing for a more comprehensive analysis of the spatial dimensions of the transitional dynamics (Rey, 2001). Here, whether the transition probabilities are dependent on regional context is investigated and quantified. Rather than estimating one transition probability matrix, spatial Markov requires estimation of $k$ transition probability matrices, each of which is conditional on the regional context at the preceding period. The regional context is usually formalized by spatial lag - the weighted average income level of neighbors:\n", - "\n", - "$$z_{r,t} = \\sum_{s=1}^{n} w_{r,s} y_{s,t}$$\n", - "\n", - "where $W$ is the spatial weight matrix and $w_{r,s}$ represents the weight that spatial unit $s$ contributes to the local context of spatial unit $r$ at time period $t$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similar to the construction of a **Markov** instance, we could create a **Spatial Markov** instance by utilizing the $Spatial\\_Markov$ class in **giddy**. The only difference between the adoption of $Markov$ and $Spatial\\_Markov$ class is that the latter accepts the original continuous income data while the former requires a pre-classification/discretization. In other words, here we do not need to apply the classification methods in [**mapclassify**](https://github.com/pysal/mapclassify) as we did earlier. In fact, the **Spatial Markov** class nested the quantile classification methods and all we need to do is set the desired number of classes $k$ when creating the $Spatial\\_Markov$ instance. Here, we set $k=5$ (quintile classes) as before.\n", - "\n", - "Different from before, quintiles are defined for the pooled relative incomes (by standardizing by each period by the mean). This is achieved by setting the parameter $fixed$ as *True*. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "giddy.markov.Spatial_Markov?" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "sm = giddy.markov.Spatial_Markov(rpci.T, w, fixed = True, k = 5,m=5) # spatial_markov instance o " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can next examine the global transition probability matrix for relative incomes." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.91461837 0.07503234 0.00905563 0.00129366 0. ]\n", - " [0.06570302 0.82654402 0.10512484 0.00131406 0.00131406]\n", - " [0.00520833 0.10286458 0.79427083 0.09505208 0.00260417]\n", - " [0. 0.00913838 0.09399478 0.84856397 0.04830287]\n", - " [0. 0. 0. 0.06217617 0.93782383]]\n" - ] - } - ], - "source": [ - "print(sm.p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Spatial Markov allows us to compare the global transition dynamics to those conditioned on regional context. More specifically, the transition dynamics are split across economies who have spatial lags in different quintiles at the preceding year. In our example we have 5 classes, so 5 different conditioned transition probability matrices are estimated - P(LAG0), P(LAG1), P(LAG2), P(LAG3), and P(LAG4)." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--------------------------------------------------------------\n", - " Spatial Markov Test \n", - "--------------------------------------------------------------\n", - "Number of classes: 5\n", - "Number of transitions: 3840\n", - "Number of regimes: 5\n", - "Regime names: LAG0, LAG1, LAG2, LAG3, LAG4\n", - "--------------------------------------------------------------\n", - " Test LR Chi-2\n", - " Stat. 170.659 200.624\n", - " DOF 60 60\n", - "p-value 0.000 0.000\n", - "--------------------------------------------------------------\n", - "P(H0) C0 C1 C2 C3 C4\n", - " C0 0.915 0.075 0.009 0.001 0.000\n", - " C1 0.066 0.827 0.105 0.001 0.001\n", - " C2 0.005 0.103 0.794 0.095 0.003\n", - " C3 0.000 0.009 0.094 0.849 0.048\n", - " C4 0.000 0.000 0.000 0.062 0.938\n", - "--------------------------------------------------------------\n", - "P(LAG0) C0 C1 C2 C3 C4\n", - " C0 0.963 0.030 0.006 0.000 0.000\n", - " C1 0.060 0.832 0.107 0.000 0.000\n", - " C2 0.000 0.140 0.740 0.120 0.000\n", - " C3 0.000 0.036 0.321 0.571 0.071\n", - " C4 0.000 0.000 0.000 0.167 0.833\n", - "--------------------------------------------------------------\n", - "P(LAG1) C0 C1 C2 C3 C4\n", - " C0 0.798 0.168 0.034 0.000 0.000\n", - " C1 0.075 0.882 0.042 0.000 0.000\n", - " C2 0.005 0.070 0.866 0.059 0.000\n", - " C3 0.000 0.000 0.064 0.902 0.034\n", - " C4 0.000 0.000 0.000 0.194 0.806\n", - "--------------------------------------------------------------\n", - "P(LAG2) C0 C1 C2 C3 C4\n", - " C0 0.847 0.153 0.000 0.000 0.000\n", - " C1 0.081 0.789 0.129 0.000 0.000\n", - " C2 0.005 0.098 0.793 0.098 0.005\n", - " C3 0.000 0.000 0.094 0.871 0.035\n", - " C4 0.000 0.000 0.000 0.102 0.898\n", - "--------------------------------------------------------------\n", - "P(LAG3) C0 C1 C2 C3 C4\n", - " C0 0.885 0.098 0.000 0.016 0.000\n", - " C1 0.039 0.814 0.140 0.000 0.008\n", - " C2 0.005 0.094 0.777 0.119 0.005\n", - " C3 0.000 0.023 0.129 0.754 0.094\n", - " C4 0.000 0.000 0.000 0.097 0.903\n", - "--------------------------------------------------------------\n", - "P(LAG4) C0 C1 C2 C3 C4\n", - " C0 0.333 0.667 0.000 0.000 0.000\n", - " C1 0.048 0.774 0.161 0.016 0.000\n", - " C2 0.011 0.161 0.747 0.080 0.000\n", - " C3 0.000 0.010 0.062 0.896 0.031\n", - " C4 0.000 0.000 0.000 0.024 0.976\n", - "--------------------------------------------------------------\n" - ] - } - ], - "source": [ - "sm.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Visualize the (spatial) Markov transition probability matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#we use seaborn to create a heatmap (`pip install seaborn` to install seaborn if you do not have it)\n", - "import seaborn as sns\n", - "sns.set()\n", - "\n", - "fig, ax = plt.subplots(figsize = (5,4))\n", - "im = sns.heatmap(sm.p, annot=True, linewidths=.5, ax=ax, cbar=True, vmin=0, vmax=1,\n", - " square=True, cmap=\"YlGn\",fmt='.3f')" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(2,3,figsize = (15,8)) \n", - "\n", - "for i in range(2):\n", - " for j in range(3):\n", - " ax = axes[i,j]\n", - " if i==1 and j==2:\n", - " ax.axis('off')\n", - " continue\n", - " p_temp = sm.P[i*3+j]\n", - " im = sns.heatmap(p_temp, annot=True, linewidths=.5, ax=ax, cbar=True, vmin=0, vmax=1,\n", - " square=True, cmap=\"YlGn\",fmt='.3f')\n", - " ax.set_title(\"Spatial Lag %d\"%(i*3+j),fontsize=13) " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(2,3,figsize = (15,8)) \n", - "\n", - "for i in range(2):\n", - " for j in range(3):\n", - " ax = axes[i,j]\n", - " if i==0 and j==0:\n", - " im = sns.heatmap(sm.p, annot=True, linewidths=.5, ax=ax, cbar=True, vmin=0, vmax=1,\n", - " square=True, cmap=\"YlGn\",fmt='.3f')\n", - " ax.set_title(\"Global\",fontsize=13) \n", - " else:\n", - " p_temp = sm.P[i*3+j-1]\n", - " im = sns.heatmap(p_temp, annot=True, linewidths=.5, ax=ax, cbar=True, vmin=0, vmax=1,\n", - " square=True, cmap=\"YlGn\",fmt='.3f')\n", - " ax.set_title(\"Spatial Lag %d\"%(i*3+j),fontsize=13) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The probability of a poor state remaining poor is 0.963 if their neighbors are in the 1st quintile and 0.798 if their neighbors are in the 2nd quintile. The probability of a rich economy remaining rich is 0.977 if their neighbors are in the 5th quintile, but if their neighbors are in the 4th quintile this drops to 0.903.\n", - "\n", - "We can also explore the different steady state distributions implied by these different transition probabilities:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.43509425 0.2635327 0.20363044 0.06841983 0.02932278]\n", - " [0.13391287 0.33993305 0.25153036 0.23343016 0.04119356]\n", - " [0.12124869 0.21137444 0.2635101 0.29013417 0.1137326 ]\n", - " [0.0776413 0.19748806 0.25352636 0.22480415 0.24654013]\n", - " [0.01776781 0.19964349 0.19009833 0.25524697 0.3372434 ]]\n" - ] - } - ], - "source": [ - "print(sm.S)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The long run distribution for states with poor (rich) neighbors has 0.435 (0.018) of the values in the first quintile, 0.263 (0.200) in the second quintile, 0.204 (0.190) in the third, 0.0684 (0.255) in the fourth and 0.029 (0.337) in the fifth quintile. And, finally the spatially conditional mean first passage times:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[[ 2.29835259 28.95614035 46.14285714 80.80952381 279.42857143]\n", - " [ 33.86549708 3.79459555 22.57142857 57.23809524 255.85714286]\n", - " [ 43.60233918 9.73684211 4.91085714 34.66666667 233.28571429]\n", - " [ 46.62865497 12.76315789 6.25714286 14.61564626 198.61904762]\n", - " [ 52.62865497 18.76315789 12.25714286 6. 34.1031746 ]]\n", - "\n", - " [[ 7.46754205 9.70574606 25.76785714 74.53116883 194.23446197]\n", - " [ 27.76691978 2.94175577 24.97142857 73.73474026 193.4380334 ]\n", - " [ 53.57477715 28.48447637 3.97566318 48.76331169 168.46660482]\n", - " [ 72.03631562 46.94601483 18.46153846 4.28393653 119.70329314]\n", - " [ 77.17917276 52.08887197 23.6043956 5.14285714 24.27564033]]\n", - "\n", - " [[ 8.24751154 6.53333333 18.38765432 40.70864198 112.76732026]\n", - " [ 47.35040872 4.73094099 11.85432099 34.17530864 106.23398693]\n", - " [ 69.42288828 24.76666667 3.794921 22.32098765 94.37966594]\n", - " [ 83.72288828 39.06666667 14.3 3.44668119 76.36702977]\n", - " [ 93.52288828 48.86666667 24.1 9.8 8.79255406]]\n", - "\n", - " [[ 12.87974382 13.34847151 19.83446328 28.47257282 55.82395142]\n", - " [ 99.46114206 5.06359731 10.54545198 23.05133495 49.68944423]\n", - " [117.76777159 23.03735526 3.94436301 15.0843986 43.57927247]\n", - " [127.89752089 32.4393006 14.56853107 4.44831643 31.63099455]\n", - " [138.24752089 42.7893006 24.91853107 10.35 4.05613474]]\n", - "\n", - " [[ 56.2815534 1.5 10.57236842 27.02173913 110.54347826]\n", - " [ 82.9223301 5.00892857 9.07236842 25.52173913 109.04347826]\n", - " [ 97.17718447 19.53125 5.26043557 21.42391304 104.94565217]\n", - " [127.1407767 48.74107143 33.29605263 3.91777427 83.52173913]\n", - " [169.6407767 91.24107143 75.79605263 42.5 2.96521739]]]\n" - ] - } - ], - "source": [ - "print(sm.F)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "States in the first income quintile with neighbors in the first quintile return to the first quintile after 2.298 years, after leaving the first quintile. They enter the fourth quintile 80.810 years after leaving the first quintile, on average. Poor states within neighbors in the fourth quintile return to the first quintile, on average, after 12.88 years, and would enter the fourth quintile after 28.473 years." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Tests for this conditional type of spatial dependence include Likelihood Ratio (LR) test and $\\chi^2$ test (Bickenbach and Bode, 2003) as well as a test based on information theory (Kullback et al., 1962). For the first two tests, we could proceed as follows to acquire their statistics, DOF and p-value." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--------------------------------------------------\n", - " Markov Homogeneity Test \n", - "--------------------------------------------------\n", - "Number of classes: 5\n", - "Number of transitions: 3840\n", - "Number of regimes: 5\n", - "Regime names: 0, 1, 2, 3, 4\n", - "--------------------------------------------------\n", - " Test LR Chi-2\n", - " Stat. 170.659 200.624\n", - " DOF 60 60\n", - "p-value 0.000 0.000\n", - "--------------------------------------------------\n", - "P(H0) 0 1 2 3 4\n", - " 0 0.915 0.075 0.009 0.001 0.000\n", - " 1 0.066 0.827 0.105 0.001 0.001\n", - " 2 0.005 0.103 0.794 0.095 0.003\n", - " 3 0.000 0.009 0.094 0.849 0.048\n", - " 4 0.000 0.000 0.000 0.062 0.938\n", - "--------------------------------------------------\n", - "P(0) 0 1 2 3 4\n", - " 0 0.963 0.030 0.006 0.000 0.000\n", - " 1 0.060 0.832 0.107 0.000 0.000\n", - " 2 0.000 0.140 0.740 0.120 0.000\n", - " 3 0.000 0.036 0.321 0.571 0.071\n", - " 4 0.000 0.000 0.000 0.167 0.833\n", - "--------------------------------------------------\n", - "P(1) 0 1 2 3 4\n", - " 0 0.798 0.168 0.034 0.000 0.000\n", - " 1 0.075 0.882 0.042 0.000 0.000\n", - " 2 0.005 0.070 0.866 0.059 0.000\n", - " 3 0.000 0.000 0.064 0.902 0.034\n", - " 4 0.000 0.000 0.000 0.194 0.806\n", - "--------------------------------------------------\n", - "P(2) 0 1 2 3 4\n", - " 0 0.847 0.153 0.000 0.000 0.000\n", - " 1 0.081 0.789 0.129 0.000 0.000\n", - " 2 0.005 0.098 0.793 0.098 0.005\n", - " 3 0.000 0.000 0.094 0.871 0.035\n", - " 4 0.000 0.000 0.000 0.102 0.898\n", - "--------------------------------------------------\n", - "P(3) 0 1 2 3 4\n", - " 0 0.885 0.098 0.000 0.016 0.000\n", - " 1 0.039 0.814 0.140 0.000 0.008\n", - " 2 0.005 0.094 0.777 0.119 0.005\n", - " 3 0.000 0.023 0.129 0.754 0.094\n", - " 4 0.000 0.000 0.000 0.097 0.903\n", - "--------------------------------------------------\n", - "P(4) 0 1 2 3 4\n", - " 0 0.333 0.667 0.000 0.000 0.000\n", - " 1 0.048 0.774 0.161 0.016 0.000\n", - " 2 0.011 0.161 0.747 0.080 0.000\n", - " 3 0.000 0.010 0.062 0.896 0.031\n", - " 4 0.000 0.000 0.000 0.024 0.976\n", - "--------------------------------------------------\n" - ] - } - ], - "source": [ - "giddy.markov.Homogeneity_Results(sm.T).summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the above summary table, we can observe that the observed LR test statistic is 170.659 and the observed $\\chi^2$ test statistic is 200.624. Their p-values are 0.000, which leads to the rejection of the null hypothesis of conditional spatial independence. \n", - "\n", - "For the last (information theory-based) test, we call the function $kullback$. The result is consistent with LR and $\\chi^2$ tests. As shown below, the observed test statistic is 230.03 and its p-value is 2.22e-16, leading to the rejection of the null." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Conditional homogeneity': 230.0266246375395, 'Conditional homogeneity dof': 80, 'Conditional homogeneity pvalue': 2.220446049250313e-16}\n" - ] - } - ], - "source": [ - "print(giddy.markov.kullback(sm.T))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### LISA Markov\n", - "\n", - "```\n", - "giddy.markov.LISA_Markov(self, y, w, permutations=0, significance_level=0.05, geoda_quads=False)\n", - "```\n", - "\n", - "The Spatial Markov conditions the transitions on the value of the spatial lag for an observation at the beginning of the transition period. An alternative approach to spatial dynamics is to consider the joint transitions of an observation and its spatial lag in the distribution. By exploiting the form of the static LISA and embedding it in a dynamic context we develop the LISA Markov in which the states of the chain are defined as the four quadrants in the Moran scatter plot, namely, HH(=1), LH(=2), LL(=3), HL(=4). Continuing on with our US example, the LISA transitions are:" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1 2 3 4]\n" - ] - } - ], - "source": [ - "lm = giddy.markov.LISA_Markov(pci.T, w)\n", - "print(lm.classes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The LISA transitions are:" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1.087e+03 4.400e+01 4.000e+00 3.400e+01]\n", - " [4.100e+01 4.700e+02 3.600e+01 1.000e+00]\n", - " [5.000e+00 3.400e+01 1.422e+03 3.900e+01]\n", - " [3.000e+01 1.000e+00 4.000e+01 5.520e+02]]\n" - ] - } - ], - "source": [ - "print(lm.transitions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and the estimated transition probability matrix is:" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.92985458 0.03763901 0.00342173 0.02908469]\n", - " [0.07481752 0.85766423 0.06569343 0.00182482]\n", - " [0.00333333 0.02266667 0.948 0.026 ]\n", - " [0.04815409 0.00160514 0.06420546 0.88603531]]\n" - ] - } - ], - "source": [ - "print(lm.p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The diagonal elements indicate the staying probabilities and we see that there is greater mobility for observations in quadrants 2 (LH) and 4 (HL) than 1 (HH) and 3 (LL).\n", - "\n", - "The implied long run steady state distribution of the chain is:" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.28561505 0.14190226 0.40493672 0.16754598]\n" - ] - } - ], - "source": [ - "print(lm.steady_state)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "again reflecting the dominance of quadrants 1 and 3 (positive autocorrelation). The mean first passage time for the LISAs is:" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 3.50121609 37.93025465 40.55772829 43.17412009]\n", - " [31.72800152 7.04710419 28.68182751 49.91485137]\n", - " [52.44489385 47.42097495 2.46952168 43.75609676]\n", - " [38.76794022 51.51755827 26.31568558 5.96851095]]\n" - ] - } - ], - "source": [ - "print(giddy.ergodic.mfpt(lm.p))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To test for dependence between the dynamics of the region and its neighbors, we turn to $\\chi^2$ test of independence. Here, the $\\chi^2$ statistic, its p-value and degrees of freedom can be obtained from the attribute $chi\\_2$. As the p-value is 0.0, the null of independence is clearly rejected." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1058.2079036003051, 0.0, 9)\n" - ] - } - ], - "source": [ - "print(lm.chi_2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Next steps\n", - "\n", - "* Simulation/prediction of Markov chain and spatial Markov chain" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### References\n", - "\n", - "* Rey, S. J. 2001. “[Spatial Empirics for Economic Growth and Convergence](http://onlinelibrary.wiley.com/doi/10.1111/j.1538-4632.2001.tb00444.x/full).” Geographical Analysis 33 (3). Wiley Online Library: 195–214.\n", - "* Bickenbach, F., and E. Bode. 2003. “[Evaluating the Markov Property in Studies of Economic Convergence](http://journals.sagepub.com/doi/abs/10.1177/0160017603253789?journalCode=irxa).” International Regional Science Review 26 (3): 363–92.\n", - "* Kullback, S., M. Kupperman, and H. H. Ku. 1962. “[Tests for Contingency Tables and Markov Chains](https://www.jstor.org/stable/1266291?seq=1#page_scan_tab_contents).” Technometrics: A Journal of Statistics for the Physical, Chemical, and Engineering Sciences 4 (4). JSTOR: 573–608." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docsrc/MarkovBasedMethods.nblink b/docs/MarkovBasedMethods.nblink similarity index 100% rename from docsrc/MarkovBasedMethods.nblink rename to docs/MarkovBasedMethods.nblink diff --git a/docs/MobilityMeasures.html b/docs/MobilityMeasures.html deleted file mode 100644 index 6333e38..0000000 --- a/docs/MobilityMeasures.html +++ /dev/null @@ -1,693 +0,0 @@ - - - - - - - - Measures of Income Mobility — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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This page was generated from MobilityMeasures.ipynb. -Interactive online version: -Binder badge

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Measures of Income Mobility

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Author: Wei Kang weikang9009@gmail.com, Serge Rey sjsrey@gmail.com

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Income mobility could be viewed as a reranking pheonomenon where regions switch income positions while it could also be considered to be happening as long as regions move away from the previous income levels. The former is named absolute mobility and the latter relative mobility.

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This notebook introduces how to estimate income mobility measures from longitudinal income data using methods in giddy. Currently, five summary mobility estimators are implemented in giddy.mobility. All of them are Markov-based, meaning that they are closely related to the discrete Markov Chains methods introduced in Markov Based Methods notebook. More specifically, each of them is derived from a transition probability matrix \(P\). Whether the final -estimate is absolute or reletive mobility depends on how the original continuous income data are discretized.

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The five Markov-based summary measures of mobility (Formby et al., 2004) are listed below:

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Num

Measures

Symbol

1

\(M_P(P) = \frac{m-\sum_{i=1}^m p_{ii}}{m-1}\)

P

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\(M_D(P) = 1- \left|det(P)\right|\)

D

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\(M_{L2}(P) = 1-\left|\lambda_2 \right|\)

L2

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\(M_{B1}(P) = \frac{m-m \sum_{i=1}^m \pi_i P_{ii}}{m-1}\)

B1

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\(M_{B2}(P) = \frac{1}{m-1} \sum_{i=1}^m \sum_{j=1}^m \pi_i P_{ij} \left|i-j \right|\)

B2

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\(\pi\) is the inital income distribution. For any transition probability matrix with a quasi-maximal diagonal, all of these mobility measures take values on \([0,1]\). \(0\) means immobility and \(1\) perfect mobility. If the transition probability matrix takes the form of the identity matrix, every region is stuck in its current state implying complete immobility. On the contrary, when each row of \(P\) is identical, current state is irrelevant to the probability of moving -away to any class. Thus, the transition matrix with identical rows is considered perfect mobile. The larger the mobility estimate, the more mobile the regional income system is. However, it should be noted that these measures try to reveal mobility pattern from different aspects and are thus not comparable to each other. Actually the mean and variance of these measures are different.

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We implemented all the above five summary mobility measures in a single method \(markov\_mobility\). A parameter \(measure\) could be specified to select which measure to calculate. By default, the mobility measure ‘P’ will be estimated.

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def markov_mobility(p, measure = "P",ini=None)
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from giddy import markov,mobility
-mobility.markov_mobility?
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US income mobility example

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Similar to Markov Based Methods notebook, we will demonstrate the usage of the mobility methods by an application to data on per capita incomes observed annually from 1929 to 2009 for the lower 48 US states.

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import libpysal
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income_path = libpysal.examples.get_path("usjoin.csv")
-f = libpysal.io.open(income_path)
-pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)]) #each column represents an state's income time series 1929-2010
-q5 = np.array([mc.Quantiles(y).yb for y in pci]).transpose() #each row represents an state's income time series 1929-2010
-m = markov.Markov(q5)
-m.p
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-/Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
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-array([[0.91011236, 0.0886392 , 0.00124844, 0.        , 0.        ],
-       [0.09972299, 0.78531856, 0.11080332, 0.00415512, 0.        ],
-       [0.        , 0.10125   , 0.78875   , 0.1075    , 0.0025    ],
-       [0.        , 0.00417827, 0.11977716, 0.79805014, 0.07799443],
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After acquiring the estimate of transition probability matrix, we could call the method \(markov\_mobility\) to estimate any of the five Markov-based summary mobility indice.

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1. Shorrock1’s mobility measure

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\begin{equation} -M_{P} = \frac{m-\sum_{i=1}^m P_{ii}}{m-1} -\end{equation}

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measure = "P"
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mobility.markov_mobility(m.p, measure="P")
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-0.19758992000997844
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2. Shorroks2’s mobility measure

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\begin{equation} -M_{D} = 1 - |\det(P)| -\end{equation}

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measure = "D"
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mobility.markov_mobility(m.p, measure="D")
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3. Sommers and Conlisk’s mobility measure

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\begin{equation} -M_{L2} = 1 - |\lambda_2| -\end{equation}

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measure = "L2"
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mobility.markov_mobility(m.p, measure = "L2")
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-0.03978200230815965
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4. Bartholomew1’s mobility measure

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\begin{equation} -M_{B1} = \frac{m-m \sum_{i=1}^m \pi_i P_{ii}}{m-1} -\end{equation}

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\(\pi\): the inital income distribution

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measure = "B1"
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pi = np.array([0.1,0.2,0.2,0.4,0.1])
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5. Bartholomew2’s mobility measure

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\begin{equation} -M_{B2} = \frac{1}{m-1} \sum_{i=1}^m \sum_{j=1}^m \pi_i P_{ij} |i-j| -\end{equation}

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\(\pi\): the inital income distribution

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measure = "B1"
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pi = np.array([0.1,0.2,0.2,0.4,0.1])
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Next steps

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  • Markov-based partial mobility measures

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  • Other mobility measures:

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    • Inequality reduction mobility measures (Trede, 1999)

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  • Statistical inference for mobility measures

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References

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- © Copyright 2018-, pysal developers.
- Created using Sphinx 5.0.2.
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- - \ No newline at end of file diff --git a/docs/MobilityMeasures.ipynb b/docs/MobilityMeasures.ipynb deleted file mode 100644 index 683234d..0000000 --- a/docs/MobilityMeasures.ipynb +++ /dev/null @@ -1,342 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Measures of Income Mobility \n", - "\n", - "**Author: Wei Kang , Serge Rey **\n", - "\n", - "Income mobility could be viewed as a reranking pheonomenon where regions switch income positions while it could also be considered to be happening as long as regions move away from the previous income levels. The former is named absolute mobility and the latter relative mobility.\n", - "\n", - "This notebook introduces how to estimate income mobility measures from longitudinal income data using methods in **giddy**. Currently, five summary mobility estimators are implemented in **giddy.mobility**. All of them are Markov-based, meaning that they are closely related to the discrete Markov Chains methods introduced in [Markov Based Methods notebook](MarkovBasedMethods.ipynb). More specifically, each of them is derived from a transition probability matrix $P$. Whether the final estimate is absolute or reletive mobility depends on how the original continuous income data are discretized." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The five Markov-based summary measures of mobility (Formby et al., 2004) are listed below:\n", - "\n", - "| Num| Measures | Symbol | \n", - "|-------------| :-------------: |:-------------:|\n", - "|1| $M_P(P) = \\frac{m-\\sum_{i=1}^m p_{ii}}{m-1}$ | P |\n", - "|2| $M_D(P) = 1- \\left|det(P)\\right|$ |D | \n", - "|3| $M_{L2}(P) = 1-\\left|\\lambda_2 \\right|$| L2| \n", - "|4| $M_{B1}(P) = \\frac{m-m \\sum_{i=1}^m \\pi_i P_{ii}}{m-1}$ | B1 | \n", - "|5| $M_{B2}(P) = \\frac{1}{m-1} \\sum_{i=1}^m \\sum_{j=1}^m \\pi_i P_{ij} \\left|i-j \\right|$| B2| \n", - "\n", - "$\\pi$ is the inital income distribution. For any transition probability matrix with a quasi-maximal diagonal, all of these mobility measures take values on $[0,1]$. $0$ means immobility and $1$ perfect mobility. If the transition probability matrix takes the form of the identity matrix, every region is stuck in its current state implying complete immobility. On the contrary, when each row of $P$ is identical, current state is irrelevant to the probability of moving away to any class. Thus, the transition matrix with identical rows is considered perfect mobile. The larger the mobility estimate, the more mobile the regional income system is. However, it should be noted that these measures try to reveal mobility pattern from different aspects and are thus not comparable to each other. Actually the mean and variance of these measures are different. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We implemented all the above five summary mobility measures in a single method $markov\\_mobility$. A parameter $measure$ could be specified to select which measure to calculate. By default, the mobility measure 'P' will be estimated.\n", - "\n", - "```python\n", - "def markov_mobility(p, measure = \"P\",ini=None)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from giddy import markov,mobility\n", - "mobility.markov_mobility?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### US income mobility example\n", - "Similar to [Markov Based Methods notebook](MarkovBasedMethods.ipynb), we will demonstrate the usage of the mobility methods by an application to data on per capita incomes observed annually from 1929 to 2009 for the lower 48 US states." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import libpysal\n", - "import numpy as np\n", - "import mapclassify as mc" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", - " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[0.91011236, 0.0886392 , 0.00124844, 0. , 0. ],\n", - " [0.09972299, 0.78531856, 0.11080332, 0.00415512, 0. ],\n", - " [0. , 0.10125 , 0.78875 , 0.1075 , 0.0025 ],\n", - " [0. , 0.00417827, 0.11977716, 0.79805014, 0.07799443],\n", - " [0. , 0. , 0.00125156, 0.07133917, 0.92740926]])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "income_path = libpysal.examples.get_path(\"usjoin.csv\")\n", - "f = libpysal.io.open(income_path)\n", - "pci = np.array([f.by_col[str(y)] for y in range(1929, 2010)]) #each column represents an state's income time series 1929-2010\n", - "q5 = np.array([mc.Quantiles(y).yb for y in pci]).transpose() #each row represents an state's income time series 1929-2010\n", - "m = markov.Markov(q5)\n", - "m.p" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After acquiring the estimate of transition probability matrix, we could call the method $markov\\_mobility$ to estimate any of the five Markov-based summary mobility indice. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Shorrock1's mobility measure\n", - "\n", - "\\begin{equation}\n", - "M_{P} = \\frac{m-\\sum_{i=1}^m P_{ii}}{m-1}\n", - "\\end{equation}\n", - "\n", - "```python\n", - "measure = \"P\"\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.19758992000997844" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mobility.markov_mobility(m.p, measure=\"P\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2. Shorroks2's mobility measure\n", - "\n", - "\\begin{equation}\n", - "M_{D} = 1 - |\\det(P)|\n", - "\\end{equation}\n", - "\n", - "```python\n", - "measure = \"D\"\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6068485462369559" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mobility.markov_mobility(m.p, measure=\"D\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3. Sommers and Conlisk's mobility measure\n", - "\n", - "\\begin{equation}\n", - "M_{L2} = 1 - |\\lambda_2|\n", - "\\end{equation}\n", - "\n", - "```python\n", - "measure = \"L2\"\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.03978200230815965" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mobility.markov_mobility(m.p, measure = \"L2\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 4. Bartholomew1's mobility measure\n", - "\n", - "\\begin{equation}\n", - "M_{B1} = \\frac{m-m \\sum_{i=1}^m \\pi_i P_{ii}}{m-1}\n", - "\\end{equation}\n", - "\n", - "$\\pi$: the inital income distribution\n", - "\n", - "```python\n", - "measure = \"B1\"\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.2277675878319787" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pi = np.array([0.1,0.2,0.2,0.4,0.1])\n", - "mobility.markov_mobility(m.p, measure = \"B1\", ini=pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5. Bartholomew2's mobility measure\n", - "\n", - "\\begin{equation}\n", - "M_{B2} = \\frac{1}{m-1} \\sum_{i=1}^m \\sum_{j=1}^m \\pi_i P_{ij} |i-j|\n", - "\\end{equation}\n", - "\n", - "$\\pi$: the inital income distribution\n", - "\n", - "```python\n", - "measure = \"B1\"\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.04636660119478926" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pi = np.array([0.1,0.2,0.2,0.4,0.1])\n", - "mobility.markov_mobility(m.p, measure = \"B2\", ini=pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "* Markov-based partial mobility measures\n", - "* Other mobility measures:\n", - " * Inequality reduction mobility measures (Trede, 1999)\n", - "* Statistical inference for mobility measures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "* Formby, J. P., W. J. Smith, and B. Zheng. 2004. “[Mobility Measurement, Transition Matrices and Statistical Inference](http://www.sciencedirect.com/science/article/pii/S0304407603002112).” Journal of Econometrics 120 (1). Elsevier: 181–205.\n", - "* Trede, Mark. 1999. “[Statistical Inference for Measures of Income Mobility / Statistische Inferenz Zur Messung Der Einkommensmobilität](https://www.jstor.org/stable/23812388).” Jahrbücher Für Nationalökonomie Und Statistik / Journal of Economics and Statistics 218 (3/4). Lucius & Lucius Verlagsgesellschaft mbH: 473–90." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docsrc/MobilityMeasures.nblink b/docs/MobilityMeasures.nblink similarity index 100% rename from docsrc/MobilityMeasures.nblink rename to docs/MobilityMeasures.nblink diff --git a/docs/RankBasedMethods.html b/docs/RankBasedMethods.html deleted file mode 100644 index e844174..0000000 --- a/docs/RankBasedMethods.html +++ /dev/null @@ -1,1951 +0,0 @@ - - - - - - - - Rank based Methods — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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This page was generated from RankBasedMethods.ipynb. -Interactive online version: -Binder badge

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Rank based Methods

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Author: Wei Kang weikang9009@gmail.com, Serge Rey sjsrey@gmail.com

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Introduction

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This notebook introduces two classic nonparametric statistics of exchange mobility and their spatial extensions. We will demonstrate the usage of these methods by an empirical study for understanding regional exchange mobility pattern in US. The dataset is the per capita incomes observed annually from 1929 to 2010 for the lower 48 US states.

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Regional exchange mobility pattern in US 1929-2009

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Firstly we load in the US dataset:

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import pandas as pd
-import libpysal
-import geopandas as gpd
-import numpy as np
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-geo_table = gpd.read_file(libpysal.examples.get_path('us48.shp'))
-income_table = pd.read_csv(libpysal.examples.get_path("usjoin.csv"))
-complete_table = geo_table.merge(income_table,left_on='STATE_NAME',right_on='Name')
-complete_table.head()
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AREAPERIMETERSTATE_STATE_IDSTATE_NAMESTATE_FIPS_xSUB_REGIONSTATE_ABBRgeometryName...2000200120022003200420052006200720082009
020.75034.95611Washington53PacificWA(POLYGON ((-122.400749206543 48.22539520263672...Washington...31528320533220632934349843573838477407824158840619
145.13234.52722Montana30MtnMTPOLYGON ((-111.4746322631836 44.70223999023438...Montana...22569243422469925963275172898730942326253329332699
29.57118.89933Maine23N EngME(POLYGON ((-69.77778625488281 44.0740737915039...Maine...25623270682773128727302013072132340336203490635268
321.87421.35344North Dakota38W N CenNDPOLYGON ((-98.73005676269531 45.93829727172852...North Dakota...25068261182677029109296763164432856358823900938672
422.59822.74655South Dakota46W N CenSDPOLYGON ((-102.7879333496094 42.99532318115234...South Dakota...26115275312772730072317653272633320359983818836499
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5 rows × 92 columns

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We will visualize the spatial distributions of per capita incomes of US states across 1929 to 2009 to obtain a first impression of the dynamics.

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import matplotlib.pyplot as plt
-%matplotlib inline
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-index_year = range(1929,2010,15)
-fig, axes = plt.subplots(nrows=2, ncols=3,figsize = (15,7))
-for i in range(2):
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-        ax = axes[i,j]
-        complete_table.plot(ax=ax, column=str(index_year[i*3+j]), cmap='OrRd', scheme='quantiles', legend=True)
-        ax.set_title('Per Capita Income %s Quintiles'%str(index_year[i*3+j]))
-        ax.axis('off')
-plt.tight_layout()
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years = range(1929,2010)
-names = income_table['Name']
-pci = income_table.drop(['Name','STATE_FIPS'], 1).values.T
-rpci= (pci.T / pci.mean(axis=1)).T
-order1929 = np.argsort(rpci[0,:])
-order2009 = np.argsort(rpci[-1,:])
-names1929 = names[order1929[::-1]]
-names2009 = names[order2009[::-1]]
-first_last = np.vstack((names1929,names2009))
-from pylab import rcParams
-rcParams['figure.figsize'] = 15,10
-p = plt.plot(years,rpci)
-for i in range(48):
-    plt.text(1915,1.91-(i*0.041), first_last[0][i],fontsize=12)
-    plt.text(2010.5,1.91-(i*0.041), first_last[1][i],fontsize=12)
-plt.xlim((years[0], years[-1]))
-plt.ylim((0, 1.94))
-plt.ylabel(r"$y_{i,t}/\bar{y}_t$",fontsize=14)
-plt.xlabel('Years',fontsize=12)
-plt.title('Relative per capita incomes of 48 US states',fontsize=18)
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-Text(0.5, 1.0, 'Relative per capita incomes of 48 US states')
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-_images/RankBasedMethods_6_1.png -
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The above figure displays the trajectories of relative per capita incomes of 48 US states. It is quite obvious that states were swapping positions across 1929-2009. We will demonstrate how to quantify the exchange mobility as well as how to assess the regional and local contribution to the overall exchange mobility. We will ultilize BEA regions and base on it for constructing the block weight matrix.

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BEA regional scheme divide US states into 8 regions: * New England Region * Mideast Region * Great Lakes Region * Plains Region * Southeast Region * Southwest Region * Rocky Mountain Region * Far West Region

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As the dataset does not contain information regarding BEA regions, we manually input the regional information:

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BEA_regions = ["New England Region","Mideast Region","Great Lakes Region","Plains Region","Southeast Region","Southwest Region","Rocky Mountain Region","Far West Region"]
-BEA_regions_abbr = ["NENG","MEST","GLAK","PLNS","SEST","SWST","RKMT","FWST"]
-BEA = pd.DataFrame({ 'Region code' : np.arange(1,9,1), 'BEA region' : BEA_regions,'BEA abbr':BEA_regions_abbr})
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Region codeBEA regionBEA abbr
01New England RegionNENG
12Mideast RegionMEST
23Great Lakes RegionGLAK
34Plains RegionPLNS
45Southeast RegionSEST
56Southwest RegionSWST
67Rocky Mountain RegionRKMT
78Far West RegionFWST
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region_code = list(np.repeat(1,6))+list(np.repeat(2,6))+list(np.repeat(3,5))+list(np.repeat(4,7))+list(np.repeat(5,12))+list(np.repeat(6,4))+list(np.repeat(7,5))+list(np.repeat(8,6))
-state_code = ['09','23','25','33','44','50','10','11','24','34','36','42','17','18','26','39','55','19','20','27','29','31','38','46','01','05','12','13','21','22','28','37','45','47','51','54','04','35','40','48','08','16','30','49','56','02','06','15','32','41','53']
-state_region = pd.DataFrame({'Region code':region_code,"State code":state_code})
-state_region_all = state_region.merge(BEA,left_on='Region code',right_on='Region code')
-complete_table = complete_table.merge(state_region_all,left_on='STATE_FIPS_x',right_on='State code')
-complete_table.head()
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AREAPERIMETERSTATE_STATE_IDSTATE_NAMESTATE_FIPS_xSUB_REGIONSTATE_ABBRgeometryName...200420052006200720082009Region codeState codeBEA regionBEA abbr
020.75034.95611Washington53PacificWA(POLYGON ((-122.400749206543 48.22539520263672...Washington...349843573838477407824158840619853Far West RegionFWST
145.13234.52722Montana30MtnMTPOLYGON ((-111.4746322631836 44.70223999023438...Montana...275172898730942326253329332699730Rocky Mountain RegionRKMT
29.57118.89933Maine23N EngME(POLYGON ((-69.77778625488281 44.0740737915039...Maine...302013072132340336203490635268123New England RegionNENG
321.87421.35344North Dakota38W N CenNDPOLYGON ((-98.73005676269531 45.93829727172852...North Dakota...296763164432856358823900938672438Plains RegionPLNS
422.59822.74655South Dakota46W N CenSDPOLYGON ((-102.7879333496094 42.99532318115234...South Dakota...317653272633320359983818836499446Plains RegionPLNS
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The BEA regions are visualized below:

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fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (15,8))
-beaplot = complete_table.plot(ax=ax,column="BEA region", legend=True)
-leg = ax.get_legend()
-leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))
-beaplot.set_title("BEA Regions",fontdict={"fontsize":20})
-ax.set_axis_off()
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Kendall’s Tau

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Kendall’s \(\tau\) statistic is based on a comparison of the number of pairs of \(n\) observations that have concordant ranks between two variables. For measuring exchange mobility in giddy, the two variables in question are the values of an attribute measured at two points in time over \(n\) spatial units. This classic measure of rank correlation indicates how much relative stability there has been in the map pattern over the two periods. Spatial decomposition of Kendall’s -\(\tau\) could be classified into three spatial scales: global spatial decomposition , inter- and intra-regional decomposition and local spatial decomposition. More details will be given latter.

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Classic Kendall Tau

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Kendall’s \(\tau\) statistic is a global measure of exchange mobility. For \(n\) spatial units over two periods, it is formally defined as follows:

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\begin{equation} -\tau = \frac{c-d}{(n(n-1))/2} -\end{equation}

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where \(c\) is the number of concordant pairs (two spatial units which do not exchange ranks over two periods), and \(d\) is the number of discordant pairs (two spatial units which exchange ranks over two periods). \(-1 \leq \tau \leq 1\). Smaller \(\tau\) indicates higher exchange mobility.

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In giddy, class \(Tau\) requires two inputs: a cross-section of income values at one period (\(x\)) and a cross-section of income values at another period (\(y\)):

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giddy.rank.Tau(self, x, y)
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We will construct a \(Tau\) instance by specifying the incomes in two periods. Here, we look at the global exchange mobility of US states between 1929 and 2009.

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tau = giddy.rank.Tau(complete_table["1929"],complete_table["2009"])
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There are 856 concordant pairs of US states between 1929 and 2009, and 271 discordant pairs.

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The observed Kendall’s \(\tau\) statistic is 0.519 and its p-value is \(1.974 \times 10^{-7}\). Therefore, we will reject the null hypothesis of no assocation between 1929 and 2009 at the \(5\%\) significance level.

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Spatial Kendall Tau

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The spatial Kendall’s \(\tau\) decomposes all pairs into those that are spatial neighbors and those that are not, and examines whether the rank correlation is different between the two sets (Rey, 2014).

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\begin{equation} -\tau_w = \frac{\iota'(W\circ S)\iota}{\iota'W \iota} -\end{equation}

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\(W\) is the spatial weight matrix, \(S\) is the concordance matrix and \(\iota\) is the \((n,1)\) unity vector. The null hypothesis is the spatial randomness of rank exchanges. The inference of \(\tau_w\) could be conducted based on random spatial permutation of incomes at two periods.

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For illustration, we turn back to the case of incomes in US states over 1929-2009:

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from libpysal.weights import block_weights
-w = block_weights(complete_table["BEA region"])
-np.random.seed(12345)
-tau_w = giddy.rank.SpatialTau(complete_table["1929"],complete_table["2009"],w,999)
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-/Users/weikang/Google Drive/python_repos/pysal-refactor/libpysal/libpysal/weights/weights.py:165: UserWarning: The weights matrix is not fully connected:
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tau_w.discordant
-
-
-
-
-
[16]:
-
-
-
-
-271.0
-
-
-
-
[17]:
-
-
-
tau_w.discordant_spatial
-
-
-
-
-
[17]:
-
-
-
-
-41
-
-
-

Out of 856 concordant pairs of spatial units, 103 belong to the same region (and are considered neighbors); out of 271 discordant pairs of spatial units, 41 belong to the same region.

-
-
[18]:
-
-
-
tau_w.tau_spatial
-
-
-
-
-
[18]:
-
-
-
-
-0.4305555555555556
-
-
-
-
[19]:
-
-
-
tau_w.tau_spatial_psim
-
-
-
-
-
[19]:
-
-
-
-
-0.001
-
-
-

The estimate of spatial Kendall’s \(\tau\) is 0.431 and its p-value is 0.001 which is much smaller than the significance level \(0.05\). Therefore, we reject the null of spatial randomness of exchange mobility. The fact that \(\tau_w=0.431\) is smaller than the global average \(\tau=0.519\) implies that globally a significant number of rank exchanges happened between states within the same region though we do not know the specific region or regions hosting these rank exchanges. A -more thorough decomposition of \(\tau\) such as inter- and intra-regional indicators and local indicators will provide insights on this issue.

-
-
-

Inter- and Intra-regional decomposition of Kendall’s Tau

-

A meso-level view on the exchange mobility pattern is provided by inter- and intra-regional decomposition of Kendall’s \(\tau\). This decomposition can shed light on specific regions hosting most rank exchanges. More precisely, insteading of examining the concordance relationship between any two neighboring spatial units in the whole study area, for a specific region A, we examine the concordance relationship between any two spatial units within region A (neighbors), resulting in the -intraregional concordance statistic for A; or we could examine the concordance relationship between any spatial unit in region A and any spatial unit in region B (nonneighbors), resulting in the interregional concordance statistic for A and B. If there are k regions, there will be k intraregional concordance statistics and \((k-1)^2\) interregional concordance statistics, we could organize them into a \((k,k)\) matrix where the diagonal elements are intraregional concordance statistics -and nondiagnoal elements are interregional concordance statistics.

-

Formally, this inter- and intra-regional concordance statistic matrix is defined as follows (Rey, 2016):

-

\begin{equation} -T=\frac{P(H \circ S)P'}{P H P'} -\end{equation}

-

\(P\) is a \((k,n)\) binary matrix where \(p_{j,i}=1\) if spatial unit \(i\) is in region \(j\) and \(p_{j,i}=0\) otherwise. \(H\) is a \((n,n)\) matrix with 0 on diagnoal and 1 on other places. \(\circ\) is the Hadamard product. Inference could be based on random spatial permutation of incomes at two periods, similar to spatial \(\tau\).

-

To obtain an estimate for the inter- and intra-regional indicator matrix, we use the \(Tau\_Regional\) class:

-
giddy.rank.Tau_Regional(self, x, y, regime, permutations=0)
-
-
-

Here, \(regime\) is an 1-dimensional array of size n. Each element is the id of which region an spatial unit belongs to.

-
-
[20]:
-
-
-
giddy.rank.Tau_Regional?
-
-
-
-

Similar to before, we go back to the case of incomes in US states over 1929-2009:

-
-
[21]:
-
-
-
np.random.seed(12345)
-tau_w = giddy.rank.Tau_Regional(complete_table["1929"],complete_table["2009"],complete_table["BEA region"],999)
-tau_w
-
-
-
-
-
-
-
-
-/Users/weikang/Google Drive (weikang@ucr.edu)/python_repos/pysal-refactor/libpysal/libpysal/weights/weights.py:170: UserWarning: The weights matrix is not fully connected. There are 8 components
-  warnings.warn("The weights matrix is not fully connected. There are %d components" % self.n_components)
-
-
-
-
[21]:
-
-
-
-
-<giddy.rank.Tau_Regional at 0x1a212d85c0>
-
-
-
-
[22]:
-
-
-
tau_w.tau_reg
-
-
-
-
-
[22]:
-
-
-
-
-array([[ 0.66666667,  0.5       ,  0.3       ,  0.41666667,  0.28571429,
-         0.5       ,  0.79166667,  0.875     ],
-       [ 0.5       ,  0.4       ,  0.52      ,  0.26666667, -0.48571429,
-         0.52      ,  0.53333333,  0.6       ],
-       [ 0.3       ,  0.52      ,  0.        ,  0.4       ,  0.88571429,
-         0.76      ,  0.93333333,  1.        ],
-       [ 0.41666667,  0.26666667,  0.4       ,  0.86666667,  0.47619048,
-         0.83333333,  0.86111111,  0.91666667],
-       [ 0.28571429, -0.48571429,  0.88571429,  0.47619048, -0.14285714,
-         0.42857143,  0.69047619,  0.14285714],
-       [ 0.5       ,  0.52      ,  0.76      ,  0.83333333,  0.42857143,
-         0.8       ,  0.06666667,  0.1       ],
-       [ 0.79166667,  0.53333333,  0.93333333,  0.86111111,  0.69047619,
-         0.06666667,  0.54545455,  0.33333333],
-       [ 0.875     ,  0.6       ,  1.        ,  0.91666667,  0.14285714,
-         0.1       ,  0.33333333,  0.        ]])
-
-
-

The attribute \(tau\_reg\) gives the inter- and intra-regional concordance statistic matrix. Higher values represents lower exchange mobility. Obviously there are some negative values indicating high exchange mobility. Attribute \(tau\_reg\_pvalues\) gives pvalues for all inter- and intra-regional concordance statistics:

-
-
[23]:
-
-
-
tau_w.tau_reg_pvalues
-
-
-
-
-
[23]:
-
-
-
-
-array([[0.586, 0.516, 0.196, 0.37 , 0.151, 0.526, 0.051, 0.104],
-       [0.516, 0.41 , 0.583, 0.114, 0.001, 0.532, 0.526, 0.472],
-       [0.196, 0.583, 0.102, 0.316, 0.011, 0.156, 0.001, 0.014],
-       [0.37 , 0.114, 0.316, 0.122, 0.41 , 0.034, 0.003, 0.026],
-       [0.151, 0.001, 0.011, 0.41 , 0.013, 0.344, 0.08 , 0.051],
-       [0.526, 0.532, 0.156, 0.034, 0.344, 0.324, 0.005, 0.056],
-       [0.051, 0.526, 0.001, 0.003, 0.08 , 0.005, 0.502, 0.136],
-       [0.104, 0.472, 0.014, 0.026, 0.051, 0.056, 0.136, 0.166]])
-
-
-

We can manipulate these two attribute to obtain significant inter- and intra-regional statistics only (at the \(5\%\) significance level):

-
-
[24]:
-
-
-
tau_w.tau_reg * (tau_w.tau_reg_pvalues<0.05)
-
-
-
-
-
[24]:
-
-
-
-
-array([[ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-         0.        ,  0.        ,  0.        ],
-       [ 0.        ,  0.        ,  0.        ,  0.        , -0.48571429,
-         0.        ,  0.        ,  0.        ],
-       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.88571429,
-         0.        ,  0.93333333,  1.        ],
-       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-         0.83333333,  0.86111111,  0.91666667],
-       [ 0.        , -0.48571429,  0.88571429,  0.        , -0.14285714,
-         0.        ,  0.        ,  0.        ],
-       [ 0.        ,  0.        ,  0.        ,  0.83333333,  0.        ,
-         0.        ,  0.06666667,  0.        ],
-       [ 0.        ,  0.        ,  0.93333333,  0.86111111,  0.        ,
-         0.06666667,  0.        ,  0.        ],
-       [ 0.        ,  0.        ,  1.        ,  0.91666667,  0.        ,
-         0.        ,  0.        ,  0.        ]])
-
-
-

The table below displays the inter- and intra-regional decomposition matrix of Kendall’s \(\tau\) for US states over 1929-2009 based on the 8 BEA regions. Bold numbers indicate significance at the \(5\%\) significance level. The negative and significant intra-Southeast concordance statistic (\(-0.486\)) indicates that the rank exchanges within Southeast region is significantly more frequent than those between states within and out of Southeast region.

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Region

New England

Mideast

Great Lakes

Plains

Southeast

Southwest

Rocky Mountain

Far West

New England

0.667

0.5

0.3

0.417

0.2856

0.5

0.792

0.875

Mideast

0.5

0.4

0.52

0.267

-0.486

0.52

0.533

0.6

Great Lakes

0.3

0.52

0

0.4

0.886

0.76

0.933

    -
  1. -
-

Plains

0.417

0.267

0.4

0.867

0.476

0.833

0.861

0.917

Southeast

0.286

-0.486

0.886

0.476

-0.143

0.429

0.690

0.143

Southwest

0.5

0.52

0.76

0.833

0.429

0.8

0.067

0.1

Rocky Mountain

0.792

0.533

0.933

0.861

0.69

0.067

0.545

0.333

Far West

0.875

0.6

    -
  1. -
-

0.917

0.143

0.1

0.333

0

-
-
-

Local Kendall Tau

-

Local Kendall’s \(\tau\) is a local decomposition of classic Kendall’s \(\tau\) which provides an indication of the contribution of spatial unit \(r\)’s rank changes to the overall level of exchange mobility (Rey, 2016). Focusing on spatial unit \(r\), we formally define it as follows:

-

\begin{equation} -\tau_{r} = \frac{c_r - d_r}{n-1} -\end{equation}

-

where \(c_r\) is the number of spatial units (except \(r\)) which are concordant with \(r\) and \(d_r\) is the number of spatial units which are discordant with \(r\). Similar to classic Kendall’s \(\tau\), local \(\tau\) takes values on \([-1,1]\). The larger the value, the lower the exchange mobility for \(r\).

-
giddy.rank.Tau_Local(self, x, y)
-
-
-

We create a \(Tau\_Local\) instance for US dynamics 1929-2009:

-
-
[25]:
-
-
-
tau_r = giddy.rank.Tau_Local(complete_table["1929"],complete_table["2009"])
-tau_r
-
-
-
-
-
[25]:
-
-
-
-
-<giddy.rank.Tau_Local at 0x1141187b8>
-
-
-
-
[26]:
-
-
-
pd.DataFrame({"STATE_NAME":complete_table['STATE_NAME'].tolist(),"$\\tau_r$":tau_r.tau_local}).head()
-
-
-
-
-
[26]:
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
STATE_NAME$\tau_r$
0Washington0.617021
1Montana0.446809
2Maine0.404255
3North Dakota-0.021277
4South Dakota0.319149
-
-
-

Therefore, local concordance measure produces a negative value for North Dakota (-0.0213) indicating that North Dakota exchanged ranks with a lot of states across 1929-2000. On the contrary, the local \(\tau\) statistic is quite high for Washington (0.617) highlighting a high stability of Washington.

-
-
-

Local indicator of mobility association-LIMA

-

To reveal the role of space in shaping the exchange mobility pattern for each spatial unit, two spatial variants of local Kendall’s \(\tau\) could be utilized: neighbor set LIMA and neighborhood set LIMA (Rey, 2016). The latter is also the result of a decomposition of local Kendall’s \(\tau\) (into neighboring and nonneighboring parts) as well as a decompostion of spatial Kendall’s \(\tau\) (into its local components).

-
-

Neighbor set LIMA

-

Instead of examining the concordance relationship between a focal spatial unit \(r\) and all the other units as what local \(\tau\) does, neighbor set LIMA focuses on the concordance relationship between a focal spatial unit \(r\) and its neighbors only. It is formally defined as follows:

-

\begin{equation} -\tilde{\tau}_{r} = \frac{\sum_b w_{r,b} s_{r,b}}{\sum_b w_{r,b}} -\end{equation}

-
giddy.rank.Tau_Local_Neighbor(self, x, y, w, permutations=0)
-
-
-
-
[27]:
-
-
-
tau_wr = giddy.rank.Tau_Local_Neighbor(complete_table["1929"],complete_table["2009"],w,999)
-tau_wr
-
-
-
-
-
[27]:
-
-
-
-
-<giddy.rank.Tau_Local_Neighbor at 0x114118f98>
-
-
-
-
[28]:
-
-
-
tau_wr.tau_ln
-
-
-
-
-
[28]:
-
-
-
-
-array([ 0.33333333,  1.        ,  1.        , -0.66666667,  0.        ,
-        1.        ,  0.        ,  0.5       ,  1.        ,  0.66666667,
-        1.        ,  0.6       , -0.33333333,  1.        ,  0.33333333,
-        0.        ,  0.5       ,  1.        ,  0.6       ,  0.        ,
-        1.        ,  0.33333333,  0.5       ,  1.        ,  0.        ,
-        1.        ,  0.        , -0.09090909, -0.5       ,  1.        ,
-        0.09090909,  0.        ,  0.63636364, -1.        , -1.        ,
-        0.33333333,  0.27272727,  0.45454545,  0.33333333,  0.33333333,
-        0.63636364,  0.81818182,  0.45454545,  0.81818182,  0.81818182,
-        0.81818182,  0.81818182,  0.        ])
-
-
-

To visualize the spatial distribution of neighbor set LIMA:

-
-
[29]:
-
-
-
complete_table["tau_ln"] =tau_wr.tau_ln
-fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (15,8))
-ln_map = complete_table.plot(ax=ax, column="tau_ln", cmap='coolwarm', scheme='equal_interval',legend=True)
-leg = ax.get_legend()
-leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))
-ln_map.set_title("Neighbor set LIMA for U.S. states 1929-2009",fontdict={"fontsize":20})
-ax.set_axis_off()
-
-
-
-
-
-
-
-_images/RankBasedMethods_58_0.png -
-
-

Therefore, Arizona, North Dakota, and Missouri exchanged ranks with most of their neighbors over 1929-2009 while California, Virginia etc. barely exchanged ranks with their neighbors.

-

Let see whether neighbor set LIMA statistics are siginificant for these “extreme” states:

-
-
[30]:
-
-
-
tau_wr.tau_ln_pvalues
-
-
-
-
-
[30]:
-
-
-
-
-array([0.463, 0.256, 0.165, 0.101, 0.316, 0.336, 0.237, 0.614, 0.292,
-       0.325, 0.33 , 0.675, 0.06 , 0.541, 0.412, 0.032, 0.594, 0.791,
-       0.575, 0.049, 0.209, 0.48 , 0.488, 0.457, 0.605, 0.409, 0.259,
-       0.018, 0.022, 0.405, 0.016, 0.25 , 0.001, 0.001, 0.045, 0.521,
-       0.167, 0.363, 0.635, 0.478, 0.417, 0.247, 0.282, 0.423, 0.578,
-       0.17 , 0.1  , 0.625])
-
-
-
-
[31]:
-
-
-
sig_wr = tau_wr.tau_ln * (tau_wr.tau_ln_pvalues<0.05)
-sig_wr
-
-
-
-
-
[31]:
-
-
-
-
-array([ 0.        ,  0.        ,  0.        , -0.        ,  0.        ,
-        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-        0.        ,  0.        , -0.        ,  0.        ,  0.        ,
-        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-        0.        ,  0.        , -0.09090909, -0.5       ,  0.        ,
-        0.09090909,  0.        ,  0.63636364, -1.        , -1.        ,
-        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-        0.        ,  0.        ,  0.        ])
-
-
-
-
[32]:
-
-
-
complete_table["sig_wr"] =sig_wr
-fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (15,8))
-complete_table[complete_table["sig_wr"] == 0].plot(ax=ax, color='white',edgecolor='black')
-sig_ln_map = complete_table[complete_table["sig_wr"] != 0].plot(ax=ax,column="sig_wr",cmap='coolwarm',scheme='equal_interval',legend=True)
-leg = ax.get_legend()
-leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))
-sig_ln_map.set_title("Significant Neighbor set LIMA for U.S. states 1929-2009",fontdict={"fontsize":20})
-ax.set_axis_off()
-
-
-
-
-
-
-
-_images/RankBasedMethods_63_0.png -
-
-

Thus, Arizona and Missouri have significant and negative neighbor set LIMA values, and can be considered as hotspots of rank exchanges. This means that Arizona (or Missouri) tended to exchange ranks with its neighbors than with others over 1929-2009. On the contrary, Virgina has significant and large positive neighbor set LIMA value indicating that it tended to exchange ranks with its nonneighbors than with neighbors.

-
-
-

Neighborhood set LIMA

-

Neighborhood set LIMA extends neighbor set LIMA \(\tilde{\tau}_{r}\) to consider the concordance relationships between any two spatial units in the subset which is composed of the focal unit \(r\) and its neighbors.

-
giddy.rank.Tau_Local_Neighborhood(self, x, y, w, permutations=0)
-
-
-
-
[33]:
-
-
-
tau_wwr = giddy.rank.Tau_Local_Neighborhood(complete_table["1929"],complete_table["2009"],w,999)
-tau_wwr
-
-
-
-
-
[33]:
-
-
-
-
-<giddy.rank.Tau_Local_Neighborhood at 0x114118f28>
-
-
-
-
[34]:
-
-
-
tau_wwr.tau_lnhood
-
-
-
-
-
[34]:
-
-
-
-
-array([ 0.66666667,  0.8       ,  0.86666667, -0.14285714, -0.14285714,
-        0.8       ,  0.4       ,  0.8       ,  0.86666667, -0.14285714,
-        0.66666667,  0.86666667, -0.14285714,  0.86666667, -0.14285714,
-        0.        ,  0.        ,  0.86666667,  0.86666667,  0.        ,
-        0.4       ,  0.66666667,  0.8       ,  0.66666667,  0.4       ,
-        0.4       ,  0.        ,  0.54545455,  0.        ,  0.8       ,
-        0.54545455, -0.14285714,  0.54545455, -0.14285714,  0.        ,
-        0.        ,  0.54545455,  0.54545455,  0.        ,  0.        ,
-        0.54545455,  0.54545455,  0.54545455,  0.54545455,  0.54545455,
-        0.54545455,  0.54545455,  0.4       ])
-
-
-
-
[35]:
-
-
-
complete_table["tau_lnhood"] =tau_wwr.tau_lnhood
-fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (15,8))
-ln_map = complete_table.plot(ax=ax, column="tau_lnhood", cmap='coolwarm', scheme='equal_interval',legend=True)
-leg = ax.get_legend()
-leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))
-ln_map.set_title("Neighborhood set LIMA for U.S. states 1929-2009",fontdict={"fontsize":20})
-ax.set_axis_off()
-
-
-
-
-
-
-
-_images/RankBasedMethods_69_0.png -
-
-
-
[36]:
-
-
-
tau_wwr.tau_lnhood_pvalues
-
-
-
-
-
[36]:
-
-
-
-
-array([0.585, 0.278, 0.104, 0.032, 0.024, 0.295, 0.43 , 0.225, 0.167,
-       0.02 , 0.548, 0.116, 0.023, 0.158, 0.017, 0.016, 0.075, 0.225,
-       0.168, 0.027, 0.505, 0.66 , 0.146, 0.605, 0.614, 0.37 , 0.08 ,
-       0.505, 0.059, 0.358, 0.541, 0.025, 0.185, 0.017, 0.225, 0.151,
-       0.541, 0.527, 0.191, 0.12 , 0.519, 0.427, 0.526, 0.442, 0.453,
-       0.528, 0.478, 0.617])
-
-
-
-
[37]:
-
-
-
sig_lnhood = tau_wwr.tau_lnhood * (tau_wwr.tau_lnhood_pvalues<0.05)
-sig_lnhood
-
-
-
-
-
[37]:
-
-
-
-
-array([ 0.        ,  0.        ,  0.        , -0.14285714, -0.14285714,
-        0.        ,  0.        ,  0.        ,  0.        , -0.14285714,
-        0.        ,  0.        , -0.14285714,  0.        , -0.14285714,
-        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-        0.        , -0.14285714,  0.        , -0.14285714,  0.        ,
-        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
-        0.        ,  0.        ,  0.        ])
-
-
-
-
[38]:
-
-
-
complete_table["sig_lnhood"] =sig_lnhood
-fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (15,8))
-complete_table[complete_table["sig_lnhood"] == 0].plot(ax=ax, color='white',edgecolor='black')
-sig_ln_map = complete_table[complete_table["sig_lnhood"] != 0].plot(ax=ax,column="sig_lnhood",categorical=True,legend=True)
-leg = ax.get_legend()
-leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))
-sig_ln_map.set_title("Significant Neighborhood set LIMA for U.S. states 1929-2009",fontdict={"fontsize":20})
-ax.set_axis_off()
-
-
-
-
-
-
-
-_images/RankBasedMethods_72_0.png -
-
-
-
-
-
-

Theta statistic of exchange mobility

-
-
-

Next steps

-
    -

  • theta statistic

    • -
-
-
-

References

- -
-
[ ]:
-
-
-

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- - \ No newline at end of file diff --git a/docs/RankBasedMethods.ipynb b/docs/RankBasedMethods.ipynb deleted file mode 100644 index 60b429a..0000000 --- a/docs/RankBasedMethods.ipynb +++ /dev/null @@ -1,1873 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Rank based Methods \n", - "\n", - "**Author: Wei Kang , Serge Rey **" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "This notebook introduces two classic nonparametric statistics of exchange mobility and their spatial extensions. We will demonstrate the usage of these methods by an empirical study for understanding [regional exchange mobility pattern in US](#Regional-exchange-mobility-pattern-in-US-1929-2009). The dataset is the per capita incomes observed annually from 1929 to 2010 for the lower 48 US states.\n", - "\n", - "* [Kendall Tau](#Kendall's-Tau)\n", - " * Classic measures:\n", - " * [Classic Kendall Tau](#Classic-Kendall-Tau)\n", - " * [Local Kendall Tau](#Local-Kendall-Tau)\n", - " * Spatial extensions:\n", - " * [Spatial Kendall Tau](#Spatial-Kendall-Tau)\n", - " * [Inter- and Intra-regional decomposition of Kendall Tau](#Inter--and-Intra-regional-decomposition-of-Kendall-Tau)\n", - " * [Local indicator of mobility association-LIMA](#Local-indicator-of-mobility-association-LIMA)\n", - "* [Theta statistic of exchange mobility](#Theta-statistic-of-exchange-mobility)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Regional exchange mobility pattern in US 1929-2009\n", - "\n", - "Firstly we load in the US dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AREAPERIMETERSTATE_STATE_IDSTATE_NAMESTATE_FIPS_xSUB_REGIONSTATE_ABBRgeometryName...2000200120022003200420052006200720082009
020.75034.95611Washington53PacificWA(POLYGON ((-122.400749206543 48.22539520263672...Washington...31528320533220632934349843573838477407824158840619
145.13234.52722Montana30MtnMTPOLYGON ((-111.4746322631836 44.70223999023438...Montana...22569243422469925963275172898730942326253329332699
29.57118.89933Maine23N EngME(POLYGON ((-69.77778625488281 44.0740737915039...Maine...25623270682773128727302013072132340336203490635268
321.87421.35344North Dakota38W N CenNDPOLYGON ((-98.73005676269531 45.93829727172852...North Dakota...25068261182677029109296763164432856358823900938672
422.59822.74655South Dakota46W N CenSDPOLYGON ((-102.7879333496094 42.99532318115234...South Dakota...26115275312772730072317653272633320359983818836499
\n", - "

5 rows × 92 columns

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" - ], - "text/plain": [ - " AREA PERIMETER STATE_ STATE_ID STATE_NAME STATE_FIPS_x SUB_REGION \\\n", - "0 20.750 34.956 1 1 Washington 53 Pacific \n", - "1 45.132 34.527 2 2 Montana 30 Mtn \n", - "2 9.571 18.899 3 3 Maine 23 N Eng \n", - "3 21.874 21.353 4 4 North Dakota 38 W N Cen \n", - "4 22.598 22.746 5 5 South Dakota 46 W N Cen \n", - "\n", - " STATE_ABBR geometry Name \\\n", - "0 WA (POLYGON ((-122.400749206543 48.22539520263672... Washington \n", - "1 MT POLYGON ((-111.4746322631836 44.70223999023438... Montana \n", - "2 ME (POLYGON ((-69.77778625488281 44.0740737915039... Maine \n", - "3 ND POLYGON ((-98.73005676269531 45.93829727172852... North Dakota \n", - "4 SD POLYGON ((-102.7879333496094 42.99532318115234... South Dakota \n", - "\n", - " ... 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 \n", - "0 ... 31528 32053 32206 32934 34984 35738 38477 40782 41588 40619 \n", - "1 ... 22569 24342 24699 25963 27517 28987 30942 32625 33293 32699 \n", - "2 ... 25623 27068 27731 28727 30201 30721 32340 33620 34906 35268 \n", - "3 ... 25068 26118 26770 29109 29676 31644 32856 35882 39009 38672 \n", - "4 ... 26115 27531 27727 30072 31765 32726 33320 35998 38188 36499 \n", - "\n", - "[5 rows x 92 columns]" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "import libpysal\n", - "import geopandas as gpd\n", - "import numpy as np\n", - "\n", - "geo_table = gpd.read_file(libpysal.examples.get_path('us48.shp'))\n", - "income_table = pd.read_csv(libpysal.examples.get_path(\"usjoin.csv\"))\n", - "complete_table = geo_table.merge(income_table,left_on='STATE_NAME',right_on='Name')\n", - "complete_table.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will visualize the spatial distributions of per capita incomes of US states across 1929 to 2009 to obtain a first impression of the dynamics. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "index_year = range(1929,2010,15)\n", - "fig, axes = plt.subplots(nrows=2, ncols=3,figsize = (15,7))\n", - "for i in range(2):\n", - " for j in range(3):\n", - " ax = axes[i,j]\n", - " complete_table.plot(ax=ax, column=str(index_year[i*3+j]), cmap='OrRd', scheme='quantiles', legend=True)\n", - " ax.set_title('Per Capita Income %s Quintiles'%str(index_year[i*3+j]))\n", - " ax.axis('off')\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Relative per capita incomes of 48 US states')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "years = range(1929,2010)\n", - "names = income_table['Name']\n", - "pci = income_table.drop(['Name','STATE_FIPS'], 1).values.T\n", - "rpci= (pci.T / pci.mean(axis=1)).T\n", - "order1929 = np.argsort(rpci[0,:])\n", - "order2009 = np.argsort(rpci[-1,:])\n", - "names1929 = names[order1929[::-1]]\n", - "names2009 = names[order2009[::-1]]\n", - "first_last = np.vstack((names1929,names2009))\n", - "from pylab import rcParams\n", - "rcParams['figure.figsize'] = 15,10\n", - "p = plt.plot(years,rpci)\n", - "for i in range(48):\n", - " plt.text(1915,1.91-(i*0.041), first_last[0][i],fontsize=12)\n", - " plt.text(2010.5,1.91-(i*0.041), first_last[1][i],fontsize=12)\n", - "plt.xlim((years[0], years[-1]))\n", - "plt.ylim((0, 1.94))\n", - "plt.ylabel(r\"$y_{i,t}/\\bar{y}_t$\",fontsize=14)\n", - "plt.xlabel('Years',fontsize=12)\n", - "plt.title('Relative per capita incomes of 48 US states',fontsize=18)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above figure displays the trajectories of relative per capita incomes of 48 US states. It is quite obvious that states were swapping positions across 1929-2009. We will demonstrate how to quantify the exchange mobility as well as how to assess the regional and local contribution to the overall exchange mobility. We will ultilize [BEA regions](https://www.bea.gov/regional/docs/regions.cfm) and base on it for constructing the block weight matrix. \n", - "\n", - "BEA regional scheme divide US states into 8 regions:\n", - "* New England Region\n", - "* Mideast Region\n", - "* Great Lakes Region\n", - "* Plains Region\n", - "* Southeast Region\n", - "* Southwest Region\n", - "* Rocky Mountain Region\n", - "* Far West Region\n", - "\n", - "As the dataset does not contain information regarding BEA regions, we manually input the regional information:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Region codeBEA regionBEA abbr
01New England RegionNENG
12Mideast RegionMEST
23Great Lakes RegionGLAK
34Plains RegionPLNS
45Southeast RegionSEST
56Southwest RegionSWST
67Rocky Mountain RegionRKMT
78Far West RegionFWST
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" - ], - "text/plain": [ - " Region code BEA region BEA abbr\n", - "0 1 New England Region NENG\n", - "1 2 Mideast Region MEST\n", - "2 3 Great Lakes Region GLAK\n", - "3 4 Plains Region PLNS\n", - "4 5 Southeast Region SEST\n", - "5 6 Southwest Region SWST\n", - "6 7 Rocky Mountain Region RKMT\n", - "7 8 Far West Region FWST" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "BEA_regions = [\"New England Region\",\"Mideast Region\",\"Great Lakes Region\",\"Plains Region\",\"Southeast Region\",\"Southwest Region\",\"Rocky Mountain Region\",\"Far West Region\"]\n", - "BEA_regions_abbr = [\"NENG\",\"MEST\",\"GLAK\",\"PLNS\",\"SEST\",\"SWST\",\"RKMT\",\"FWST\"]\n", - "BEA = pd.DataFrame({ 'Region code' : np.arange(1,9,1), 'BEA region' : BEA_regions,'BEA abbr':BEA_regions_abbr})\n", - "BEA" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AREAPERIMETERSTATE_STATE_IDSTATE_NAMESTATE_FIPS_xSUB_REGIONSTATE_ABBRgeometryName...200420052006200720082009Region codeState codeBEA regionBEA abbr
020.75034.95611Washington53PacificWA(POLYGON ((-122.400749206543 48.22539520263672...Washington...349843573838477407824158840619853Far West RegionFWST
145.13234.52722Montana30MtnMTPOLYGON ((-111.4746322631836 44.70223999023438...Montana...275172898730942326253329332699730Rocky Mountain RegionRKMT
29.57118.89933Maine23N EngME(POLYGON ((-69.77778625488281 44.0740737915039...Maine...302013072132340336203490635268123New England RegionNENG
321.87421.35344North Dakota38W N CenNDPOLYGON ((-98.73005676269531 45.93829727172852...North Dakota...296763164432856358823900938672438Plains RegionPLNS
422.59822.74655South Dakota46W N CenSDPOLYGON ((-102.7879333496094 42.99532318115234...South Dakota...317653272633320359983818836499446Plains RegionPLNS
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5 rows × 96 columns

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" - ], - "text/plain": [ - " AREA PERIMETER STATE_ STATE_ID STATE_NAME STATE_FIPS_x SUB_REGION \\\n", - "0 20.750 34.956 1 1 Washington 53 Pacific \n", - "1 45.132 34.527 2 2 Montana 30 Mtn \n", - "2 9.571 18.899 3 3 Maine 23 N Eng \n", - "3 21.874 21.353 4 4 North Dakota 38 W N Cen \n", - "4 22.598 22.746 5 5 South Dakota 46 W N Cen \n", - "\n", - " STATE_ABBR geometry Name \\\n", - "0 WA (POLYGON ((-122.400749206543 48.22539520263672... Washington \n", - "1 MT POLYGON ((-111.4746322631836 44.70223999023438... Montana \n", - "2 ME (POLYGON ((-69.77778625488281 44.0740737915039... Maine \n", - "3 ND POLYGON ((-98.73005676269531 45.93829727172852... North Dakota \n", - "4 SD POLYGON ((-102.7879333496094 42.99532318115234... South Dakota \n", - "\n", - " ... 2004 2005 2006 2007 2008 2009 Region code State code \\\n", - "0 ... 34984 35738 38477 40782 41588 40619 8 53 \n", - "1 ... 27517 28987 30942 32625 33293 32699 7 30 \n", - "2 ... 30201 30721 32340 33620 34906 35268 1 23 \n", - "3 ... 29676 31644 32856 35882 39009 38672 4 38 \n", - "4 ... 31765 32726 33320 35998 38188 36499 4 46 \n", - "\n", - " BEA region BEA abbr \n", - "0 Far West Region FWST \n", - "1 Rocky Mountain Region RKMT \n", - "2 New England Region NENG \n", - "3 Plains Region PLNS \n", - "4 Plains Region PLNS \n", - "\n", - "[5 rows x 96 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "region_code = list(np.repeat(1,6))+list(np.repeat(2,6))+list(np.repeat(3,5))+list(np.repeat(4,7))+list(np.repeat(5,12))+list(np.repeat(6,4))+list(np.repeat(7,5))+list(np.repeat(8,6))\n", - "state_code = ['09','23','25','33','44','50','10','11','24','34','36','42','17','18','26','39','55','19','20','27','29','31','38','46','01','05','12','13','21','22','28','37','45','47','51','54','04','35','40','48','08','16','30','49','56','02','06','15','32','41','53']\n", - "state_region = pd.DataFrame({'Region code':region_code,\"State code\":state_code})\n", - "state_region_all = state_region.merge(BEA,left_on='Region code',right_on='Region code')\n", - "complete_table = complete_table.merge(state_region_all,left_on='STATE_FIPS_x',right_on='State code')\n", - "complete_table.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The BEA regions are visualized below:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (15,8))\n", - "beaplot = complete_table.plot(ax=ax,column=\"BEA region\", legend=True)\n", - "leg = ax.get_legend()\n", - "leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))\n", - "beaplot.set_title(\"BEA Regions\",fontdict={\"fontsize\":20})\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Kendall's Tau\n", - "\n", - "Kendall’s $\\tau$ statistic is based on a comparison of the number of pairs of $n$ observations that have concordant ranks between two variables. For measuring exchange mobility in **giddy**, the two variables in question are the values of an attribute measured at two points in time over $n$ spatial units. This classic measure of rank correlation indicates how much relative stability there has been in the map pattern over the two periods. Spatial decomposition of Kendall’s $\\tau$ could be classified into three spatial scales: global spatial decomposition , inter- and intra-regional decomposition and local spatial decomposition. More details will be given latter." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Classic Kendall Tau\n", - "\n", - "Kendall's $\\tau$ statistic is a global measure of exchange mobility. For $n$ spatial units over two periods, it is formally defined as follows:\n", - "\n", - "\\begin{equation}\n", - "\\tau = \\frac{c-d}{(n(n-1))/2}\n", - "\\end{equation}\n", - "\n", - "where $c$ is the number of concordant pairs (two spatial units which do not exchange ranks over two periods), and $d$ is the number of discordant pairs (two spatial units which exchange ranks over two periods). $-1 \\leq \\tau \\leq 1$. Smaller $\\tau$ indicates higher exchange mobility." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In giddy, class $Tau$ requires two inputs: a cross-section of income values at one period ($x$) and a cross-section of income values at another period ($y$):\n", - "\n", - "```python\n", - "giddy.rank.Tau(self, x, y)\n", - "```\n", - "\n", - "We will construct a $Tau$ instance by specifying the incomes in two periods. Here, we look at the global exchange mobility of US states between 1929 and 2009." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "import giddy" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau = giddy.rank.Tau(complete_table[\"1929\"],complete_table[\"2009\"])\n", - "tau" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "856.0" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau.concordant" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "271.0" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau.discordant" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are 856 concordant pairs of US states between 1929 and 2009, and 271 discordant pairs." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.5188470576690462" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau.tau" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.9735720263920198e-07" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau.tau_p" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The observed Kendall's $\\tau$ statistic is 0.519 and its p-value is $1.974 \\times 10^{-7}$. Therefore, we will reject the null hypothesis of no assocation between 1929 and 2009 at the $5\\%$ significance level." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spatial Kendall Tau\n", - "\n", - "The spatial Kendall's $\\tau$ decomposes all pairs into those that are spatial neighbors and those that are not, and examines whether the rank correlation is different between the two sets (Rey, 2014). \n", - "\n", - "\\begin{equation}\n", - "\\tau_w = \\frac{\\iota'(W\\circ S)\\iota}{\\iota'W \\iota}\n", - "\\end{equation}\n", - "\n", - "$W$ is the spatial weight matrix, $S$ is the concordance matrix and $\\iota$ is the $(n,1)$ unity vector. The null hypothesis is the spatial randomness of rank exchanges. The inference of $\\tau_w$ could be conducted based on random spatial permutation of incomes at two periods. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "giddy.rank.SpatialTau(self, x, y, w, permutations=0)\n", - "```\n", - "For illustration, we turn back to the case of incomes in US states over 1929-2009:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/weikang/Google Drive/python_repos/pysal-refactor/libpysal/libpysal/weights/weights.py:165: UserWarning: The weights matrix is not fully connected: \n", - " There are 8 disconnected components.\n", - " warnings.warn(message)\n" - ] - } - ], - "source": [ - "from libpysal.weights import block_weights\n", - "w = block_weights(complete_table[\"BEA region\"])\n", - "np.random.seed(12345)\n", - "tau_w = giddy.rank.SpatialTau(complete_table[\"1929\"],complete_table[\"2009\"],w,999) " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "856.0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_w.concordant" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "103" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_w.concordant_spatial" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "271.0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_w.discordant" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "41" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_w.discordant_spatial" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Out of 856 concordant pairs of spatial units, 103 belong to the same region (and are considered neighbors); out of 271 discordant pairs of spatial units, 41 belong to the same region." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.4305555555555556" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_w.tau_spatial" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.001" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_w.tau_spatial_psim" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The estimate of spatial Kendall's $\\tau$ is 0.431 and its p-value is 0.001 which is much smaller than the significance level $0.05$. Therefore, we reject the null of spatial randomness of exchange mobility. The fact that $\\tau_w=0.431$ is smaller than the global average $\\tau=0.519$ implies that globally a significant number of rank exchanges happened between states within the same region though we do not know the specific region or regions hosting these rank exchanges. A more thorough decomposition of $\\tau$ such as inter- and intra-regional indicators and local indicators will provide insights on this issue." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Inter- and Intra-regional decomposition of Kendall's Tau\n", - "\n", - "A meso-level view on the exchange mobility pattern is provided by inter- and intra-regional decomposition of Kendall's $\\tau$. This decomposition can shed light on specific regions hosting most rank exchanges. More precisely, insteading of examining the concordance relationship between any two neighboring spatial units in the whole study area, for a specific region A, we examine the concordance relationship between any two spatial units within region A (neighbors), resulting in the intraregional concordance statistic for A; or we could examine the concordance relationship between any spatial unit in region A and any spatial unit in region B (nonneighbors), resulting in the interregional concordance statistic for A and B. If there are k regions, there will be k intraregional concordance statistics and $(k-1)^2$ interregional concordance statistics, we could organize them into a $(k,k)$ matrix where the diagonal elements are intraregional concordance statistics and nondiagnoal elements are interregional concordance statistics.\n", - "\n", - "Formally, this inter- and intra-regional concordance statistic matrix is defined as follows (Rey, 2016):\n", - "\n", - "\\begin{equation}\n", - "T=\\frac{P(H \\circ S)P'}{P H P'}\n", - "\\end{equation}\n", - "\n", - "$P$ is a $(k,n)$ binary matrix where $p_{j,i}=1$ if spatial unit $i$ is in region $j$ and $p_{j,i}=0$ otherwise. $H$ is a $(n,n)$ matrix with 0 on diagnoal and 1 on other places. $\\circ$ is the Hadamard product. Inference could be based on random spatial permutation of incomes at two periods, similar to spatial $\\tau$. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To obtain an estimate for the inter- and intra-regional indicator matrix, we use the $Tau\\_Regional$ class:\n", - "```python\n", - "giddy.rank.Tau_Regional(self, x, y, regime, permutations=0)\n", - "```\n", - "Here, $regime$ is an 1-dimensional array of size n. Each element is the id of which region an spatial unit belongs to." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "giddy.rank.Tau_Regional?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similar to before, we go back to the case of incomes in US states over 1929-2009:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/weikang/Google Drive (weikang@ucr.edu)/python_repos/pysal-refactor/libpysal/libpysal/weights/weights.py:170: UserWarning: The weights matrix is not fully connected. There are 8 components\n", - " warnings.warn(\"The weights matrix is not fully connected. There are %d components\" % self.n_components)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.seed(12345)\n", - "tau_w = giddy.rank.Tau_Regional(complete_table[\"1929\"],complete_table[\"2009\"],complete_table[\"BEA region\"],999) \n", - "tau_w" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.66666667, 0.5 , 0.3 , 0.41666667, 0.28571429,\n", - " 0.5 , 0.79166667, 0.875 ],\n", - " [ 0.5 , 0.4 , 0.52 , 0.26666667, -0.48571429,\n", - " 0.52 , 0.53333333, 0.6 ],\n", - " [ 0.3 , 0.52 , 0. , 0.4 , 0.88571429,\n", - " 0.76 , 0.93333333, 1. ],\n", - " [ 0.41666667, 0.26666667, 0.4 , 0.86666667, 0.47619048,\n", - " 0.83333333, 0.86111111, 0.91666667],\n", - " [ 0.28571429, -0.48571429, 0.88571429, 0.47619048, -0.14285714,\n", - " 0.42857143, 0.69047619, 0.14285714],\n", - " [ 0.5 , 0.52 , 0.76 , 0.83333333, 0.42857143,\n", - " 0.8 , 0.06666667, 0.1 ],\n", - " [ 0.79166667, 0.53333333, 0.93333333, 0.86111111, 0.69047619,\n", - " 0.06666667, 0.54545455, 0.33333333],\n", - " [ 0.875 , 0.6 , 1. , 0.91666667, 0.14285714,\n", - " 0.1 , 0.33333333, 0. ]])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_w.tau_reg" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The attribute $tau\\_reg$ gives the inter- and intra-regional concordance statistic matrix. Higher values represents lower exchange mobility. Obviously there are some negative values indicating high exchange mobility. Attribute $tau\\_reg\\_pvalues$ gives pvalues for all inter- and intra-regional concordance statistics: " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.586, 0.516, 0.196, 0.37 , 0.151, 0.526, 0.051, 0.104],\n", - " [0.516, 0.41 , 0.583, 0.114, 0.001, 0.532, 0.526, 0.472],\n", - " [0.196, 0.583, 0.102, 0.316, 0.011, 0.156, 0.001, 0.014],\n", - " [0.37 , 0.114, 0.316, 0.122, 0.41 , 0.034, 0.003, 0.026],\n", - " [0.151, 0.001, 0.011, 0.41 , 0.013, 0.344, 0.08 , 0.051],\n", - " [0.526, 0.532, 0.156, 0.034, 0.344, 0.324, 0.005, 0.056],\n", - " [0.051, 0.526, 0.001, 0.003, 0.08 , 0.005, 0.502, 0.136],\n", - " [0.104, 0.472, 0.014, 0.026, 0.051, 0.056, 0.136, 0.166]])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_w.tau_reg_pvalues" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can manipulate these two attribute to obtain significant inter- and intra-regional statistics only (at the $5\\%$ significance level):" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. , -0.48571429,\n", - " 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0. , 0.88571429,\n", - " 0. , 0.93333333, 1. ],\n", - " [ 0. , 0. , 0. , 0. , 0. ,\n", - " 0.83333333, 0.86111111, 0.91666667],\n", - " [ 0. , -0.48571429, 0.88571429, 0. , -0.14285714,\n", - " 0. , 0. , 0. ],\n", - " [ 0. , 0. , 0. , 0.83333333, 0. ,\n", - " 0. , 0.06666667, 0. ],\n", - " [ 0. , 0. , 0.93333333, 0.86111111, 0. ,\n", - " 0.06666667, 0. , 0. ],\n", - " [ 0. , 0. , 1. , 0.91666667, 0. ,\n", - " 0. , 0. , 0. ]])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_w.tau_reg * (tau_w.tau_reg_pvalues<0.05)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The table below displays the inter- and intra-regional decomposition matrix of Kendall's $\\tau$ for US states over 1929-2009 based on the 8 BEA regions. Bold numbers indicate significance at the $5\\%$ significance level. The negative and significant intra-Southeast concordance statistic ($-0.486$) indicates that the rank exchanges within Southeast region is significantly more frequent than those between states within and out of Southeast region." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "| Region | New England| Mideast|Great Lakes|Plains|Southeast|Southwest|Rocky Mountain|Far West|\n", - "|:-------------:|:-------------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|\n", - "| New England | 0.667| 0.5 | 0.3|0.417| 0.2856|0.5 | 0.792| 0.875|\n", - "| Mideast | 0.5 | 0.4|0.52|0.267| **-0.486**|0.52| 0.533| 0.6 |\n", - "| Great Lakes | 0.3 | 0.52 | 0 | 0.4 | **0.886**| 0.76 | **0.933**|1.|\n", - "|Plains| 0.417| 0.267| 0.4 | 0.867| 0.476|**0.833**| **0.861**| **0.917**|\n", - "|Southeast|0.286|**-0.486**|**0.886**| 0.476| **-0.143**|0.429| 0.690| 0.143|\n", - "|Southwest| 0.5 |0.52 |0.76|**0.833**| 0.429|0.8|**0.067**|0.1|\n", - "|Rocky Mountain|0.792| 0.533| **0.933**|**0.861**| 0.69|**0.067**| 0.545|0.333|\n", - "|Far West|0.875|0.6| 1.| **0.917**|0.143|0.1 |0.333| 0|" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Local Kendall Tau\n", - "\n", - "Local Kendall's $\\tau$ is a local decomposition of classic Kendall's $\\tau$ which provides an indication of the contribution of spatial unit $r$’s rank changes to the overall level of exchange mobility (Rey, 2016). Focusing on spatial unit $r$, we formally define it as follows:\n", - "\n", - "\\begin{equation}\n", - "\\tau_{r} = \\frac{c_r - d_r}{n-1}\n", - "\\end{equation}\n", - "\n", - "where $c_r$ is the number of spatial units (except $r$) which are concordant with $r$ and $d_r$ is the number of spatial units which are discordant with $r$. Similar to classic Kendall's $\\tau$, local $\\tau$ takes values on $[-1,1]$. The larger the value, the lower the exchange mobility for $r$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "giddy.rank.Tau_Local(self, x, y)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We create a $Tau\\_Local$ instance for US dynamics 1929-2009:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_r = giddy.rank.Tau_Local(complete_table[\"1929\"],complete_table[\"2009\"])\n", - "tau_r" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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STATE_NAME$\\tau_r$
0Washington0.617021
1Montana0.446809
2Maine0.404255
3North Dakota-0.021277
4South Dakota0.319149
\n", - "
" - ], - "text/plain": [ - " STATE_NAME $\\tau_r$\n", - "0 Washington 0.617021\n", - "1 Montana 0.446809\n", - "2 Maine 0.404255\n", - "3 North Dakota -0.021277\n", - "4 South Dakota 0.319149" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.DataFrame({\"STATE_NAME\":complete_table['STATE_NAME'].tolist(),\"$\\\\tau_r$\":tau_r.tau_local}).head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Therefore, local concordance measure produces a negative value for North Dakota (-0.0213) indicating that North Dakota exchanged ranks with a lot of states across 1929-2000. On the contrary, the local $\\tau$ statistic is quite high for Washington (0.617) highlighting a high stability of Washington." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Local indicator of mobility association-LIMA\n", - "\n", - "To reveal the role of space in shaping the exchange mobility pattern for each spatial unit, two spatial variants of local Kendall's $\\tau$ could be utilized: neighbor set LIMA and neighborhood set LIMA (Rey, 2016). The latter is also the result of a decomposition of local Kendall's $\\tau$ (into neighboring and nonneighboring parts) as well as a decompostion of spatial Kendall's $\\tau$ (into its local components)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Neighbor set LIMA\n", - "\n", - "Instead of examining the concordance relationship between a focal spatial unit $r$ and all the other units as what local $\\tau$ does, neighbor set LIMA focuses on the concordance relationship between a focal spatial unit $r$ and its neighbors only. It is formally defined as follows:\n", - "\n", - "\\begin{equation}\n", - "\\tilde{\\tau}_{r} = \\frac{\\sum_b w_{r,b} s_{r,b}}{\\sum_b w_{r,b}}\n", - "\\end{equation}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "giddy.rank.Tau_Local_Neighbor(self, x, y, w, permutations=0)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_wr = giddy.rank.Tau_Local_Neighbor(complete_table[\"1929\"],complete_table[\"2009\"],w,999) \n", - "tau_wr" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.33333333, 1. , 1. , -0.66666667, 0. ,\n", - " 1. , 0. , 0.5 , 1. , 0.66666667,\n", - " 1. , 0.6 , -0.33333333, 1. , 0.33333333,\n", - " 0. , 0.5 , 1. , 0.6 , 0. ,\n", - " 1. , 0.33333333, 0.5 , 1. , 0. ,\n", - " 1. , 0. , -0.09090909, -0.5 , 1. ,\n", - " 0.09090909, 0. , 0.63636364, -1. , -1. ,\n", - " 0.33333333, 0.27272727, 0.45454545, 0.33333333, 0.33333333,\n", - " 0.63636364, 0.81818182, 0.45454545, 0.81818182, 0.81818182,\n", - " 0.81818182, 0.81818182, 0. ])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_wr.tau_ln" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To visualize the spatial distribution of neighbor set LIMA:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AY0y5MeYIY8zdwEx3kRcyrSe2y/k/YW86zTbGRNvYR70x5kZjzGR3HwONMRdjS5eGA28mq4IbY+7JcKzGZYnhLuAabOn/u40x+7KEkIxtUoZtjMC2SdtgjNmfjBfbmVKNOz/dRPf5kM6uOns8lVJt00RNqSJjjHGwdyDHk7375iXu86ld3N3L2O+BkzLMa6+dXGfMzjDtVGzp/stpcUGGsXfcnuWmAo1AepuMLnEvrB43duDYb2KTkHe7s5/H3h3uyDFPVvfJVsLohV9gk4UyDrQpbEuy2uP/YROC9McDact1irFdnTvAKBGZmGGR5AX+S13ZT4pkldkT21gmOS+9em2HGWMS2BIVOLh6YVe2ucsY83djzCex1VAHcvD5maxWl+n8m+A+P5hhXqbP6TvbI/P53JnPxzuMMVuM7YL+A9iSvjBdTGg7Sg4MQbEX26Nm+vz/wX4ONmGTtP92clfJz0qbQ2Jk2L+IyN3YUtR/YUvS9rexSrK68DkZ5r07bZmurJNRe8dTKdU+TdSUKk63Yu/izyNzVbofY0vd7hKRTHdLy0QklwumZLW321JL78SOU3Zjh6Nu341uVbrkfiqAb7kvU9s//Q77/j7ntqdKlRxz7nf5aJckdqy2ygyzkqV/ybvNW7AXVseLyI2ZSiFFJCwi41MmJUurxnY1zjz6A/bi6XwOHjLgEGIHvz0C2+HI/xhjPpH+wA7JsBbbnfsh52IH/QqbxHw3NbkQkcEcOB/TO23orKewieE0EbkifaaInIkdumA3aVU+RWSwiEx240qdfrPYQYYPISIXYdtU7sRWNWxzW9mIyDlZSsCTJWmpF+5tnX9r3Oc5adsPYatpZpJ1ex39fIgdL+8Mt0OM1GWC2IQz/b3kTaZOlkSkBvu90we41RizJ23+5dhhKNYBszJVd8ywzb4Zpp2LrWa9kSzj7WXZlgA/x1Z5fRR4rzHmkA6Q0vwaW735s6nnpfsdnBzm4mdp6yRfz0v7rh6HHdS7ibS2qp05nkqp3GgbNaWKkDFmh4h8E/hOlvkrReRj2IvW5SLyD2x1lCD2IupUbNuHye3s6jfAB7F3T5eJyMPuNi7EdhYSoYPtc9qxwo03dRy1MHYMp3fG4jLGrBGRL2LH0XpJRO53389sbCnHSmx7iHy4ExgndiDgNdhhD47DDnOwFpvYJH0WW/3nVmyvk09jq0CNxHaSMB34EHZcKLBVjzYCHxSRZuxFngF+a4xZS27uEJFsVZtuMu2MlZfOvQOf3tYsmyvd519mW8AYkxCRX2N7Ofwk7bebbMsd2Lv25wOvum2VqrBtfIYC3zHGtNfJTU7ctpGXYbuN/7WbrD2PLTU6GvuZiAOXm0M7t/gstj3R17HvO+lLwC1iO7t5EXvO9sN2KHIitg3np9JuMGTbVjZ/ABrdc28NNrE9FXvuLQX+nbLsf7DH7iH3WDYAa40xv8WWcLwJfFnsYMQvY7873oP9PGZK7p7Afh98S0SOxCadGGNuS3kvuX4+Kt1Y14jIc9jPWgVwlrvsw+bQXgwzEpE7OFBNNVkT4Fr3/wvwF2NM6jl/uYhcjb1R8Ra2hPk87M2ZXwDfS9v+aRwYE+4J4KNp+SVk7vhjpYi8hv2+asJ2KnM69rw4z6T0gJuDm7BtHBuwvUxenyGGV1LfpzFmtYhcC/wQeFFs5zPN2Dano4E7jTHPpm7AGLNYRL6H7ZzkNfe7ugx7Q2Yg8DmTMni2q0PHUynVAaYIxgjQhz5K9UHKOGoZ5pVjL2iS4+0EMixzFLbK01rshcAO7N36BcDpacs+SYYxlbAXR7e6+2rCXvzNx/b4ZrAXOanL30KWsZTIMkYWB8bzKgduS9lXDLfTgizH4F3AY9gLwibsheV3gP4Zls34/nL4H3wAW53pv9hxhfa4x3A+MCTD8mXYC9LF2NKWJmwC9h9slaRBactPd+ftxl7kZh2HKm29NSn/+2yPqWnHd06mYwJMyGF/B42jhk0w6t331+aYVsAYbIKzhZTx+LIsm/EcSTsfv+r+Dxqw1aaeJmX8t1y3leP/fxzwI2xSvR9bpTaGvTA/Mss6yc/ALWnTT3HPm6fdc6LJPYZR7AXrUbluq414P4VtxxVz492BTbK+AvRJW9aPrcIbwyadhpRxEd3/233YmwkNwHJ3O4H0ZVPWuQybKDQkz8POfD6wN4S+gi0dWuce963Yat2fau886uBnJf3/NBM7Vt9b2MRlOzZhPz/L9q9oZ/sGWJNhve9iB3ne4x6vKPZmxCHfKzm8x3tyiCHbZ+o8bI+ae93z8QXsDYi29ne5u1y9u95C4D1Zlu3Q8dSHPvSR+0OM6UwbbKVUbyciZ2GTpG8bY25ob3mllFJKKZU/2kZNqRInIiMzTBsEfNt9+efCRqSUUkoppbSNmlLqeyJyDLaq0lZs24V3Y9sjLDDGdHQAaaWUUkop1UWaqCmlHsI2+j4PO1ZWI7atyq9ooxMJpZRSSinVfbSNmlJKKaWUUkoVGW2jppRSSimllFJFRhM1pZRSSimllCoymqgppZRSSimlVJHRRE0ppZRSSimliowmakoppZRSSilVZDRRU0oppZRSSqkio4maUkoppZRSShUZTdSUUkoppZRSqshooqaUUkoppZRSRUYTNaWUUkoppZQqMpqoKaWUUkoppVSR0URNKaWUUkoppYqMJmpKKaWUUkopVWQ0UVNKKaWUUkqpIqOJmlJKKaWUUkoVGU3UlFJKKaWUUqrIaKKmlFJKKaWUUkUm4HUASimlVDGpC0YuAG4B1gCrgWhtPPozL2NSSilVejRRU0oppQ7WBzjGfQBQF4wsqo1H3/AuJKWUUqVGqz4qpZRSB8t0E/PigkehlFKqpGmippRSSh1MEzWllFKe06qPBVK/YJ6gx7tbPX/bY+x/e4/XYagS89DUuUSHH+d1GL3e+17+GZM3v1So3ZVnmHZEXTAypTYeXVGoIJRSSpU2TRwK5yTgX0Cl14H0EAuB2R1ZIVhdthKY3D3hKJXZxv7hpYBmat2sX8P2p4FTPA7jYuBWj2NQSilVIrTqY+E8BxTsdnApEr+0eh2DKj3+REuL1zGUgkeOumKM1zEA7/E6AKWUUqVDE7UCqZ47vwX4IvAk0ORtNL2T+H3G6xhU6fEn4nqDoAC21Yw4rFV8GzwOY7/H+1dKKVVCtOpjAVXPnf8icFr9gnkVQAwY4XFIxUw6vIJPEt0RiFJtCSRa9LwrBPHx3Piz15wUe3S0h1HMrgtGHgB+DuwD4kAzsLw2HtWSVaWUUnmliVo3cDsOoXru/IwlPNVz5zfWL5h3L3B9QQPrWTpcOuYL+PSCWRVcIBHXktwCWTp2TuSk2KNeh3Gh+0j197pg5M/AjpTHttp4dFOmDdQFI6OAMdhkby+QcB+bauNRPZ+UUkoBWvWxu5wDvFq/YN5pbSzzL2BXgeLpiTpRoqZVH1XhiTF63hXIZc/f4XgdQxbnAr8AHgSeAF4F1tcFI0NSF6oLRgbWBSOvABuAZ4HXgTXAOnfa0rpgZG5dMNKngLErpZQqUlqi1j2WY5Owa+oXzHvKbZ9G/YJ5/YGbgaewd1P7exdi0et4oubXqo9K9WY7qoY2Ddi/1eswcuUD7q8LRjYDQ93HOKC6jXWmAT8D7qoLRm6qjUfv6PYolVJKFS1N1LpB9dz564BZ9QvmjeDghGMPMB6YDhzpRWy9mUiHczulVA/SFOhxo5vM6eR6FcDcumDkvcBK4Au18WhD3qJSSinVI2iiBtQvmPczoB+2+/z7q+fOX5uP7VbPnf9W2qTDgHcDfwV+ANwNDElfTwGdKFFDOt6uTamuEozeISiQFn+Z1yEUigAT3MepwC1AQ10wUgvUAgOxHZh8w7MIlVJKdTtN1KyRwHnAB4Hv1C+Ytxj4AzZp25zH/dwEfBXbFuE14HLg73ncfm/SiURNr5dV4YlJ6A2CAlk3YGLV0RsXex2GF+6tC0bOAS4FPpSc6LZ3e6SjHZDUBSOVgNTGozrcgFJKFTFN1Kyhaa9Pch/fr18w73Fs0vZQ9dz5Ozu7g/oF84KAA0wFfg8sA67o7PbUocQnesGsCk6MNo0slIH1m0t1DMozgTextTJSPQxsrgtGnsZ2THJrW0lbXTByJHY8z4uBurpg5NJC9zIZcxwBAqFwOF7I/SqlVE9U8oma25X+gCyzfdgfyDOBBfUL5r0NbHUf21L+3ortPGQkEHYfPuA/wD+AUcBEYL477aPAXUDUnX9ON7y1nq7jnYlo1UflAZ92+lgwawdNHnDS6n94HYZXxmWZPowDQwY4dcGIUxuPPpu6QF0wMhb4OrYWR/K79UPA43XByGLsDcQl7rw+QI37XI5NAGNdTehijjMBWyI4APhKV7allFKlouQTteq58039gnnfw/a01RY/NuEa1YHNvwu4FSgDXsB23/xF4BlsIjcQeB/wDeCajkXe63Wm6qNeMauC06qPhbNm8OFH7Kwc/NyAhm0zvI6lSP0WW8K2ANuzcPIRAoIZlv9Fjtu9tS4YucX9O1mqt7E2Hm2zVCzmOEOBS7AJ2jTgn8D/hMLh5hz3q5RSJU3HUbN+gy0V6w7l2KTjBOBX2LuT78c2Dv8ScAzwHFCqVXqy6fDF75Cpo/QYqoLTgtzCemnsHK1r2rZh2PbQH8XWBomQOUnriJuw471tAlZjf8c+WReM9E1fMOY41THHuTTmOH8H3sAmijcDA0Ph8HtD4fC+LsailFIlQxM1oHru/AbgzgLs6oPAh4E7sEnbOdgf0T9hEzrVBQOnDJ814aKpi7yOQ5UaTdQK6flxZ56YwPe213GUoDHYttUfBobXxqM/qY1H92RY7h/A74ApwKxQOPyVUDj8z1A4XF/AWJVSqlfQRM1VPXf+7cBZ2A4/8u0J4MvADdjx047GbR+AvdOo8mT0nAmzTrjp7CXi08GvVWFoX6MFJj4Wh8990+swSsRWbIcl1wKH1cajZ9XGo7+rjUfbSrq+i63SPzUUDr9RiCCVUqq30kQtRfXc+f/GJlD5vsj/Q/Xc+XcBfbE/eDFgL3Aatu2AOlSnr3+rhvaZefRnTn06n8EolZV2JlJwT00475TGQOUyr+PoZQx22JifYTsdmQgMq41Hz6+NR++ojUfX57KRUDj8cCgc/ksoHN7djbEqpVRJ0EQtTfXc+S8A38vjJjcC/6hfMO9E7I/fDmBS9dz59wLj87if3qZLBRUDIkNnHfHxmQvzFYxSqoiI8I8jLtUOKfIjDvwSmFgbjx5TG49+ujYe/U1tPPpmobvuV0opdTBN1DL7eZ62E3W39QqwGNt9/0DgPe78R/O0H5XB4GNGHSE+afU6DtW7adVHb6wcfvy0FvGv8TqOHqwJuBuYUBuPfrI2Hu2Oav9KKaW6oOS758/iTey4aP27sI3NwGPYsWvS/bx+wbyXu7DtUtDl699EvHWHL+ivbG1qqc5HQEplpoUOXjDikxZ/2b5AS4PXofQ0+7HVG++sjUc3eR2MUkqp7DRRy8AdW205cHIXNvMNDlShbAQqUua1AFuq585vrF8w73fAZV3YT2/V5UStNd66f8i0UW+8vWTt9HwEpJQqLsHW5j5ex9DDPAhcVRuPbvE6EICY45RhOx45HduGO/URAK4LhcOPeBehUiof6oKRMHYc4RXAf2rj0ajHIfUYmqhl9wKdT9T2ApOBtzkwUPYL7usKwHGTNMFWf9RE7VBdTtTKasqnTvrgsWvK+lYuXvfYypPyEZRS6XxGOxj1yvbq4RuG7tt4WPtLlrw4cDXw42JodxZznErgRuDjwNC02SuwQ9a8BCwpcGhKqTxzk7QngdHupLuBz7azzjDsOJCv1Majy7s1wCKniVp21wEjgEs6sW4fDpyE/8b+GB2LbbB9J7C7fsG8+diqlVd1PVSVjS/gHxd675Hjasb0X7h16fq+O6JbJrQ2xPUuvMob0V4fvaMNBHOxGrikNh59wetAAGKOEwZ+ih0OJ5UD3AL8Xygc1rbFSvUCdcHIeOwQVaNTJg/MsFw5MBs4A1s9+xxgJtBSF4x8C7itNh4tyQ6kxOhFRlb1C+aNw/7IdcbvgBur585f427rF8Bfq+fOf6R+wbw7seOqqexeAPJeZdEYE3/ulke3NW4O0c4pAAAgAElEQVTfPyLf21al6cGpn1q4avixs72OoxRd8MqChVPeXqrHPrs/Ax+rjUd3eR1IzHEmYccR/R1QmTJrN3ANcG8oHI57EZtSKv/qgpFx2JK09FoPBvgtcBd2XOHzsIlZTRubWwZ8tDYefTHvgRY57fWxDW6StaqTq28H1qa8/hnwWP2Ced9Ek7RcdMsdBBEJzrj5HBlzZmRxd2xflSDRYh2vNPsr2l+oNDVg24Nc6HWSFnOcaTHHuR9Yia3u+Di2wy6wPU+eFwqHf6lJmlK9R10wchi2JC1T1XQBPgK8DNwLXETbSRrYmzxL6oKRb7qlbyVDqz62bzV2AOwJdOx4fRq4pH7BvJ8AM4BaYCswJO8Rqg4Rn294+IKjhvefMHjh6wuemYXRClRK9US7qgbrb9ihHgK+XBuPrm13yW4Wc5zJ2HZmZe6kqe7jOOyd9itC4fBT3kSnlOoOdcHIGGySNi7Pm/YDNwAX1AUj02vj0fo8b78o6Y9c++qwDR8vAv4HW8/2uBzWKwOGA7emTNMkrYgMOnLE7MmXHr9w1R9fnpGIt+qtedUpWnncO7sqB+vn9oAo8PnaePSxXBaOOY4vFA7nvSecmOPMAD4DBLE3KMsyLPZH4JRQOPxavvevlPJOXTAyGnsTZnw37mYKUA1ooqYAW58+2QiyFTjcw1hUng2fOW72kGmjVz1z3cPjEi2JTBcUSrVDC2S98sbIGced7NQ9O7j+7RO9jsVD64A7gAVtNbaPOU41cBq2Lcg5QN+Y45wbCofz0uYj5jijgE8B1wLtVU2qAF6POU4Q29nWkcAaoB/2d3YdUBcKhzfnIzalVPerC0ZGYZO0UAF25y/APoqCJmrt2w3cDxyDloj1Sr4y/2FVw/ts2LdhdyG+XJRS+WIMfRt3DPI6DI+8AXwb+ENtPHpI+66Y4whwBAcSs1M5uHRrG7bdWM5ijlMFXIj9LRyA7bm4P7b2yGnkfvG0FjjfjT+SZRkTc5zngPk6lppSxa0uGBmJre4YLtAuS6aPDU3U2lE9d36ifsG8NdgfueexpWsjPQ2qNBSsmEJEyo+//qxQ4879L74w/7HJ/vJAY/PuxsGF2r/q2Yx2JuIdEcSYkrmzmuSrrvz5uD99/+opZ83Zl5wWcxwfMBE4AZiFTc5GZ9kEwB2hcHhfG/MPEnOcs4GfA2M7F/VBTqb9cUoF2z33gzHHeVcoHF6Yh/3mzD2e04E52KS0D/BGKBy+u5BxKFXs6oKREdgkbWIBd1sy3/uaqOXmSmznINOxY6Fpotb9Ct70p2JA1fEn3la7wl8WGLboS39uMa2JgC/ga64a0Xf9vvW7CnWXSCnVAf5ES+mMixjwLysbO2JRqG7BOSLyesxx7sVWIzwe+/vUN8ctbQfujjmOhMLhrN+1McfxYxO/j7sPL5QBf4k5zimhcLhLA9+61TOnYMdpOhxb4jgQ2IOtPbMb2Idth/4eYFjaJtpKfJUqVQ8Dkwq8Ty1RUwc5BvvF/hm0jVqvFqgITgE49c4L1hlj4r6AbyAwZNGX/hw3LYmgx+EppdLsrhy0ekDDtqFex1EIgz958a4hX/jIVSmTbu7kphYDtwPvjTnOLuyF1j+xVSH9wNnAu7GDUg/ofMR50x/4R8xxTgyFwxs6smLMcfpjq2teii0d60oReN47X1GqJ6sLRoZjbxQVmpaoqYMMwd5Jux37Za96OV/A9071nqZdDUtNSyKXnj5VCfIZvXbz0vPjzjJnr/g/r8MoDF/ebiKfl/L3aGxnHl91X7+KLWEaRXEkaUmjgb/HHOdj2Bunzdik6yxgaSgcfi51YbeN3pXAD2i/c5NcbMWWtimlDmivCnN30RI1dZAqbP30MuAtYIS34ZSEorn6fe2nTw/0OgZVvHQYPm+9PuqkY85a8YdtPkzvb1fq8xXiZDvGfTbYpO2YNpYttKOAFzJMT8Qc54fAUGzHJrcBlwEfy+O+hwDPxxwH7Fh1+4ATgVeA/wW+jE2AXwiFw5fkcb9KFbNTPNqvlqipg1Rix1A7DWjwOJZS0d/rAACMMXsat9drb58qK+1MxFvxQHnlI0d/7I3zXvvfgBTJ90a3Key5JtjfPkPxj0HhA76Y8vr0btrPZPf5qynTznNfJy8cx8cc50ehcPjpbopBKc/VBSN9sDdEPudRCCVTolYyb7SLWrE9Ph6DHR9iN/Aa9gcsAezyLrReq5UiGEu4aVdDtLWxpcbrOFTx0hI17y0fOeO4pWNP6/WDJ3twT2AStsrfC8BC4DlgY8GjKH7pd/e/61a9VKrXqQtG/MC/gc/j3U0cLVFTB1TPnf+numDkwSkfmX7TsBMOm/L0Vx7e17K/edmo2eFhm56OXeQL+Lac8t0LThaffN7rWHuRI7DJ8NFeBrHlhXWNXu5f9QBaolYUtteMyEc7JHWooe4j1W5gtfscxFY3HIfe/E16CtsuTn8/VG/0eWxvsF4qme8aTdRyZ1b85oUHVvzmhT9hx6g5auNCJwRUt7a2svDzDz42ZOqo//UF/RO2vLQ+5C8L7D7snCl7R58+8UTRC7nO2uPlzlsa4m/sXLWlmBrTqyLkebGvAuDVUScf+643fr9PoBeXgBfNb0k/YGratAYgBuzA3u0+Fjt0QCk6Bzgz5jhPAjeEwuEmj+NRKp+KoVRdS9TUwWrjUVMXjCwHfpZlkXdtfeXAudvSEB/j/Pk1Vj+yfNWx155eVjOy37hCxNnLeHrHJFAZPBxY5mUMqicomovnktbqCwSN+LaJSfTWRG1fxVGT+nkdRBsqsTUhkvZgmwyMAcZ7ElHHNQGfAF7Gjkk3EzgV22FCR9oqH+U+h4C7ASc5I+Y45wC3AJeFwuE3ux6yUoVTF4wI8Cmv40BL1FQ69+Ts8ImRiLdOevWHi145cX5twufPX9/KXjDG8Nbi1c/vim5pMHCgKMEY+6dxJxi7bCqf38c7qyRnib3EFZ+ATxCfIPLO32ISpjF03pE7yvpWeNLrojGm1V8ebPFi36rn0BK14hBsbdrvM4le1yOvr0/1skEfu3B3zazjR1QcMeFIr+PpgL7Y2idge0veA7wNVGNL33zYHpTHYKtPeqEV2AzEsV3+X5nWCcizwF1ue7NjsF39z0qZ/3Hgb8D12I5GHGy79aHYxG4Y8EjMceqAR4CPAhdgj83fYo5zSSgc7vVtK1WvEgCmeR0EJVSiJukX1CqzumBkPPZLuFO3z0PnH7V47FmRk/IbVf7tWLl5uWlNJBq3799f//aepn0bdkmgIpBAhJ3RLWNMSyJUyHhqRvd/+rjrzjjFq+qjJmG2L/zCgwO1xwiVTeWt1z826NyTh7kvD7oX4f5x8DT7wr6Ug8+rDCeZZD3zUrad9seh09LWSJ0u4rays8/i3kARcfds3C2mPRv3TZkD92uMSbtnYxIGYxKYhDvPGEjY19jpkEgY47NBiIht8ic+JG2a/QowSEocB90jamlobNl/7iUnSe+4AZmoOHLi4kFXfqCmz1knTe3l1eebsVUmd2K7u+8uX8IO7L0ZmySeDazqSKIUcxwftsTtO9jqnxNC4bDT9lrvjOnmA94FbMFWCX0P8M1QOLy3g+9DKU/VBSOfBn7icRiX1Maj93scQ0H0hh+0gqiNR1fXBSMxINyZ9Tc/v5axZ0XyHFXXtDa3NO5bv2vNjpWbtzVs3dda/9aeQP3G3V4NXpjRvg27Tnn9p08vnPTB4yZXDKwa1v4a+ZNoSazfvvytWKAiOK2lId63kPtWPcfgUTUVNTX+YhprqiTtf2lVdG0v+U3re+6sp0Z97/rZXsdRIGXY0qiFedrecuy4p8M4MND1I6Fw+Ptpyz3Q0Q2HwuEE8POY4/wGWx0yluN6Blt692jK5Gc6un+lvFQXjPiAucB3vY4F2xa2JPSKH7VCcE/QTg+o2rhzf0HH90m0JhLxvU3by/tXHlSv3iSMWfPoG89sWbre37Bl33EcGBemaO14Y/PsJTf9nZrR/Z6ZcNHUQf0nDClIzK//7OndO1duKZWLJdVZrQmtluCxRGNT07qP3+h1GHkhZcHYiG9+aabXcXhgArZt2F5gNLZ9V0fsBz4WCof/GHOcsdhEaCt2MOpsbcs7JOY4Qexv5pHuY3TMce4LhcPxfGxfqSL3YbwvSQPb2+yTXgdRKJqo5ag2Hk3UBSP3AVd1Zv1Ec2uHErWdq7as2PRUbEvFwCpfw9Z9ib0bdlWa1oS//8Qh+1obWxh89MiqIdNGTwlUBt9pOG8Shh0rNr/+1uLYju3L3hpvWs3YisHVzw2IDGuqf2s3DZv39m1pjA80rcarkeS7ZN+G3Se/8v2FLTNuPmdj5ZCaUd29v/HvOXLXzpWPZ5w3alZ4yZal6yfH65t79wC7qn2JhNcRlCzTHG/Zevd9S7b/8sHxtLYWV5WFTvBVVa6Y+Oz/TfCVl3nVZstLo9wHQAuwiIPbg2XyY+ANbEL2cko1xD9j24RdHwqHd+cxxs8Bd6ZNuynmON8G7sFWb/SHwuH6PO5TKU/VBSPlwPuxpWnFQICxdcHI7tp4dLvXwXQ3baPWAXXByB+ASzq7frBP+Utjz4w09Z80dFjNqH4h8R3c7qC1uaVp84vrX1v76Aqadu4/lnYaS5YPqHxh2PFjm9b/Z9V4hAQiLaYl0VN61+q0quF9Fp/wtbO7vb3f8l8++/LWVzYe0mh29OkTnw2/7+ijW+qb31x1/8utW1/acGx3x6KK1+if3LSwz+kzteTVA7Hzr3qmKbqmqKprd0XVCUctOuw3t7eXnJSKZ4C2/rdXh8Lh72WaEXOc04DnQuHw/nwE4rZNuw/4YBuLxbGdorRiq3F+NhQOr8jH/pXySl0wMgKoozg6EEm3EbgWWANsrI1H13kbTvfQRK0D6oKRDwC/wvZa1VV7/BWBDcHqsr1Vw/o27l27o1+8vvlwbH191YYxZ0x6Kvy+o0/t7v3Ub9q9eNPTMd/GRc5MgGHTxy4dc8akxprR/d+5eGhpiL+xeN4j4xPNrZXdHY8qTqN/fOPCPmeeqImaBxpXxlavfv/nK0kkhnsdS540+PrV/LfPnBN2jrz9mlI/pxYC2Y7BD0Lh8BcLFUjMcQZzoBOSXL0FHKbVIlVPVReMTAb+CYz1OpYcrAPG1cajvS6p0UQtR3XBSDXwKraUa5y30ZSuvuMHPnXs1ad3e5KWZIxp3f/23ueadje0DIgMPUVEDvmhXnX/y89tWuTMKFRMqriM+uG8hX3fdXKpX1R7ZvdfH1+66bo7jvM6jjxLjPjWl17q/76zjvc6EI/sBv4LZHr/O4BwKBzeVciAYo4zH9v0oSPV3d/GDkuwBfh6KBz+d3fEplR3qAtGJmLbjeajcKI7JbA9qj7eGxM1baOWu/3YsVH6eB1IqfJXBJYf87lZJxRynyLirx7R96TqEdk7fRx67OimTYva7aFZ9VbaRs1T/c4//Tj/gL6v7/nHUzv2/uPpoYn9DVO8jikPfPXPvFzf/31neR2HV94EjgVex/7mjnOnx4GPeJCk9Qf+CrwI/AIYlOOqw93HJOBfMcd5HjvMz3wgChAKh3WsTlV06oKRv2Gr8jrA0R6H0x4fcHNtPPofrwPpDlqi1gF1wcgy4Aiv4yhVJ9x49vqqYX3GeB1HOpMwW569sc7XvLux072Cqp5r5J3XLexXO1tL1IqAibe0/nfOR15t3b6rR7cbDQwb9OKEx+85Tvz+Xj2AWhsMtsOAFmAFcBS27dcHQuHwQ8mF3PHJjgbWhcLhnfkMIOY4A4CvAWe6+8/n/8K4j18C1+hYasoL9Qvm1QAt1XPnNz533GlTtr226ePALuB2YD12iIue4tbaePRmr4PoDh2pb13S6oIRwd4ZUx6QgG9N5dCaokvSAMQnQ4+95vTVOiR2iWpp9ToC5ZJgwF913BE9/qJ32A1z/SWcpMGBpOh5bJJksCVpD6Ut9yls9/vdUdPiR8CXsYlgvv8XyQGwrwQuzvO2lWpT/YJ5Ur9g3mXYmyCb6hfM+8WEi6YuAK4GvgH8np6VpIHt+bVX0kQtd2PJvbqDyrNhx49dK1K81y0VA6qmn3Dj2Uu8jkMVntZKKDK94f+R0LH5XH5gG/DVUDj8++TEmOMMc9uM/QB4KBQO/7OrO4o5zmExx/ltyqQRXd1mjr4ecxy9tlAFUz13vgHOw45XOAD4RLCmLLUg4iJPAuuam+qCkTPrgpG5dcHIT+uCkV5T+00Ttdz1tsbqPUaguuzViZdMO9HrONpTNbTPzBNuOnuJvzyQly6hVQ/RGxKDXsT09BxHZFvF4eGedje7u8zAdipyf9r0K4GvAtuBP3Z1J241x0eBZSmTm7q63RytQZtUqAJyS9MuSJ3mC/gHeBROvgwB/oUd3P5TwI/dmnA9nnYmkrteM1ZPTyI+2Tjj5nPG+4P+HjFsQdXQPjOnfmH200u/8x8vBxVfDjS7f0vKI/m6lQMXIQbbYxIp8zPdwMn2hSccaE+SymR5bm89ydMybe3bZPk7W5xt/i1lAe0MoJj09MTZmAG+mqp8DtLc44XC4Vjy75jjzAVuxPakeHwoHN7YlW3HHKcv8BdgInBvyqxC/OasAmaFwuEeftKWtpjjVAGHAxUc/LtjMrz2Yc+t5COY+rpl607576mXVmKvzyVtO8nf69TXqb/t6X8nf88DyUf5wKrEzK+/+4sicvD5LQwSn7SahGlz/N4eZA5wBtDje1rVRC0HblZ+vtdxlCKTMH0CFcHsXS4WoS0vbfC6pDqA3qEtmER9w/Nex6BS9fhrXn/TqjVvBQYPGOh1IB77EvBAKBzekJwQc5yxwPexF7evdTZJc0vQ3gtciO3WuxxYFgqH33bnjwdO61r4ORkOhLG9XKoewh0AfQ5wGTATiJCnGmr+Qf120bEhIDqktTG+B/v5OYiISFnfiu1NuxqGdte+C2wrveDHADRRy9UU7JepKrCRs8KviE9meR1HRwybPtZsePK/cdOSOOTLsEB6xZeTUp3T82u7lIXHlnrVx5XAz0LhcGPMcQLYpgfHA9OxJQprga90ZsMxxwliu8YfkjYrNTG+jsI0DekLLIs5ztxQOHxvu0srT7k3Cq4APko3jafbsPSN14Buu+YZdOSIVWRpylM+oGpnL0rUBgFX1QUj8dp4dJHXwXSFJmq5Ge91ACVH2D5k6uhlkz4wrcd1e14zqt/Js+56386dKza/VjN2QL83//RKYsvS9YXsLlwTtULq6VXtepsi7nQoF1JRviowZMAkr+Pw0BvAGaFwuNF9fRs2cUr6P+DToXC4s9VDE0BVhulDYo7zQWzCNreT2+6McmybO03UipRbAvsDbAlat37BVE6bMkHKgnHTHM/7jd5AddnuyZceP0qy9MxWMbh6757V2/O9W6/4sB2mPAZoolYCYu0vovJo98xbz22tGFDV45K0JBEZMPDw4bMBplxxQnN8X9PyndEtea2OWDO6fyxy6XGbfAG/b+nt/z4+0ZLoEe34eh2jiXFR6eEDkJvGppEbPjd/4ajvXXeir7ysFD/T56VUQTyPg5M0gIc7k6TFHGcgcAMwG6jOsEgQmwR64fiY4/hC4XDPPnl7oZjjnIMdAmJkIfbXvHbTWtMc75Z9DZ9x2Bvi92XtmK1ycHVjtnk9xGoOFKwsBT5aG4++7mE8eeF1W5qeorn9RVS+DD1uzCsVA6p6S/E7IlI2+cPT8/4FOK728PV9xgw4pXpE35OOvfaMF1JmaeKgSlcPT9SAmn3/eXb2qukXb4hv2rLZ62AK7DlgbcxxDo85zm0cmjg9xqE9QOYqCFyDrT5ZbFZrklZcYo7TJ+Y4C7C9gRYkSQNoXv9Wt11vbnzyzRnNextfzja/vF+lF+fg43nYxt+AY7FNlI4DTgFm9oYkDbRELVdTvA6ghOyZePHUqV4HkW+BqrJ+vjJ/Q6K5tbKt5apG9F074sRx6956ZvVhzXsa+wf7lO8edMSIDaY1IRufcmYOnDLs9eoR/faJ32eqR/StSK5XPbLvjMM/OmPhinufP8UktC5eQenhLi6B3vGzZprjoTdPvyLh79/n1eDYEXsqj5hohn3tU6f28oGwd2B7arsX29FGql3AR7uQ0GzF9nZb3vnwuo12SFREYo4zC7gHD5q9VJ9w1DCpKG80jU0V7S/dMSZhfK3NrfFs8wPVZV78mG3A9rp6QXsLpngE2IStpry0Nh79dsq8l/IYW1HoHb9o3e9wrwMoEc3H33DmjmBN+TivA8k3f5l/wpgzJj2z9tEVJwOMmj1hyfAZh7XUv72nNV7fLHvX7KjZ9ebWMdOvP7NM/L5Tx5z+ThOVvsAYgAkXT90nIkdl2r6IBIYeN2Z2zZj+S1794aKapl0NBXlfCvD18EZRvU6vSpx9rbv2HtO6ay+Nr62iZcfuhaPu+MopEgz0li60073bfWTyh1A4vKkL264kQ293ReJFrwNQVsxxTgKewKMaZ76qyskDLztv8fZfPnBSvrc9as6EJRUDqzJeQwAEq8oK/Vu2DHiQQ9uExoDDgL8Cz2Bv3pwL/B37BX9+bTxaMiXQmqjlRkvUCkB8sq1mVP9xXsfRXca9+/Ap1SP7LVz98LIJEy+eOhOgz9jcx5gUkZr2lqka2mdm5dCa5ZqoFZBPa5AXlZ4+4HUb9v7z6dkrH3tml39Q/1j/C86oH3zVh473VVW2WUrfi/yli+sfQ/E293ih/UVUd4s5zgjgt3h8ngRGDs3r2JxDpo1+eexZkfqaMf1nikjW6/5AZbCQ7/tB4GL3cW7K9E8Cv8IOWn0N9rNxIfbzf31vqc7YEZqo5UZL1ArAJMzI5j2NO8r6VvTK8YPEJwOHThs9e8jUUd08mK2W8BRSth60lEd6fhu1thnTv3XbzmO3//IBtv/qwS0TnvhNfXDYoMFeh9XNDPBkF7fxoTzEkW+twAPAK14HUspijlOJHbfvq2TuaKag9j2+pMvXQMNOGPvikGmj66uH9y2vGFw9XUTaLYX3lxe0pP6l2njU1AUjq7BDHlwKnAVEa+PRRF0wci22i/3DgURtPHpOAWMrKpqo5Wai1wGUilfvfmpFeb/Kln7hQcHRp0081l8WyHs9ba+JSL/u3X7vqvtV9HyapxUTU0ptBhNm6Pq5Nz0T+svdvT1RE+wgwJ3qXCXmOKcBn81rRF33LPCB1AG9VWHFHEeAS4DbgbEehwOAMaZZgoEu3W3yBf2Nkz88/VgR6VAJmb88UKheZndgS9SojUdfwd6ouLcuGJkOOO4yfwJ+784vaZqotaMuGKkEcq+fprqkfuPuk+s37mbHG2/z1rNrlky/4axj/OWBUqnaky8ldKVaBHpxVbseqZQSNaBp5eqTN3x+/sKRt189w1dZ0etubLm20slOQGKOMxL4dX7DyYtva5LmnZjjzADuArJ2V++FHb966Ll9Tzx/ale2IQFfnE60x/QF/YVqw/lQbTwaTZ9YG4++kPJ3pwa0742Ktb52MUnveUoVSLC6PO4r82uS1lFawFNQpqW1l9e1U8Vu72PPzI5Ov3jrvqeWLvM6lm5QB0RC4fC6jq4Yc5wxwEJsxwTFZB92qIGsYo5TrB2f9Hgxx/kKsIQiS9KMMXu33X3fcV3dTmtDvA8Js6Wj6/kCvq72iJrrb+HfurifkqKJWvtGeB1AqQpWlyW0+U9n6DFTJaxUvzNaWses/+SNR/x31odfXH/VrQt70RhsN4TC4Z2dXHcmMCGfweTBS8ApoXD4kLE1Y44zJOY4V8Yc59/A/pjjvBJznKvcKnoqf+7Gdi5zLNDZcyv/Eon6RFNzl0vFx593xNPi93X42lX8vq7cGG8EbnX/3pMyfTO2t8akW7Hd66scadXH9mmJmkcGTB5aqPrSvYr4tOqjUiVKWrZsP37f49t58/ElTUOv+djiQZ+4KO/dfBfQs6FwuNO9vIXC4T/FHGcJNmHz0lbg49j3sy19ZsxxaoEvAKcDqR06HINNKgYA8wsQZ0kIhcP1wGsxx+kHvEWRNG8Rv3+4r6K8PlHf0KUOTUbNntCpNqvik64kagIsx96IeBb4jDv9M8DD2PP3udp49MEu7KMkaYla+0Z5HUBJErYPPXZMsd0J7RlENFErpBJrE1XstBT+HeVb7vjVCfsWvdiTu7N+Ig/bKIbqoA+EwuG/ZUnSKrDdkZ/FwUlaqq/FHGdcN8ZXcmKOUwW8SpH16j3ki5cv7cr6Uy4/YWGgIji5UytLp3q83IPtvTQKRIAfARuxHYR8rjYefbA2Ho3XxqNf0SStczRRa19R1WEuFSNPDi0v7185xOs4eiLRRK2wNFErKqa3d8/fMYH1n75lsGlt7akn6bA8bKPDbdu6QcZqqDHHKQP+CAxtZ/0K4Jv5DqrEDaL42i5SNn5051cWzJBpo8Z1enWRKnLvNXorcDn2M/o0cBU26R1aG49+qzYenVYbj/64s7GoA7TqYxvqghEfcKbXcZSixp37e+qFhefEp4laIZVUd/A9QWtCi9RStSb6id/fU49Jl2q0xBznLODmPMXSFXvTJ8Qc52zs+FHvzXEbPbkKa9EJhcPrY46zAehCZpR/vurKTn1WK4fWbDju2jN2+wL+I7qyf395YF9rY0ufdhZLAB+sjUcfd1/PqQtGAkBjbTzapRJBdSgtUWvb0YCW6njA59dTs9M0USsszdSKimnV8RLSVLZs2XFIlbse4o0urn8r2asTFtKdMcd5NOY458Ucxx9znI9gO1j4YAe2cVjMcfp2U3ylqs2eNwvNGNOw6bo7QoGRQ97u6LqRDx0XC1QGu5SkAQQqgvtzWGxvSpIGQG082qJJWvfQq+G2vcfrAEqVXvt2nlZ9LDDNC4pLorWnlh51F2lcGXvL6yA6YTVwYxe38Zt8BJIn52A7VdgJ3Evnrr+m5jWiEhVzHF/McY7Adt5STILjH/jBzvDff14vlRUNua40YMqwZf0mDM7LudHnsIHtfR8OLB4AACAASURBVFfsAR50S9BUAeiBbtuFXgegVEeJT69TC0qPd1HY0xhc9+qmwWuarrg93lq7dZEYDBjBGNuOUAQQgyAgxtjnAxvw+bCvzYF2h8bY1+J757WYhJ2eSNhtug9jt39g2jubTz0/THK6jQHkoM5/jBHBgI2dd2LPeOPKpG0bG6dPDpneb1BrsKaDx7MI/DUUDudyd78tvwNuB9qrylVIXYnlWGBRvgIpJTHH8QEXAx/GViMtip4eU4lIwN+35kiAPmeduHTPw0/kNKbasOljt4vIkXmJof0aOVfVxqP35WNfKjeaqGVRF4x8Cr175R0tpOg0LY0sMO1lsCj4fSa4Y3/FJDMsPJxhYa/DKSqrq/YvGkmHa1N5aR/Q5Y4IQuHw3pjj3Anc0uWIisOxXgfQE8UcZwawADvcQY+Q2Lc/5+vzysE1easd17ynsa0u+t+kyKqLlgJN1DKoC0ZOJw8/EqrzNNnoAj10hdWqvQwWg+qylhEj+9Uv3Li7Rse+TLO1vnLqKxsHLzpi+I5pQX+imEqXsvlRKBx28rSt72HHcuoN7c17fKLmDkcwBTgSOMp9noStCtrqPlrc5zh2nLO1wBrsMAdrctyPYEvOZgLfALoyRljBte7Yk3O85QMquzxIdtKYMyZt3u1sS+/efwvwE+CntfHo1nztS+VGE7XMzqM4GiCXLk02ukKLeApJ7yoUhdaENG3cXT3d6ziKk/Rds7PvrIpgyzOTh+462etocrA2XxtyS9W+AfwwX9v00JSY41TloUpoQcUc51jgq9ikbCKd7x9hfsxxfgj8hQPJXj/sb54v5dkHzADGdy1y77Ts2JXTmGbik9byvhV567ly8NEjZx/+8ZkLV/72hRMSza3JZPHrtfHoT/K1D9UxmqhlNs3rAJRe+3aWSRhN1ApIx+0qDn6fKT8nsm77E86olU0tgR5f8pBvlcH4C5Ehu3pKF++Ned7eAuBL9OALd5cP2yRjsdeBdFCA/LT5LwOucR+9WuuO3Tn18Nl/4pCV4vd1ubfHVEOnjZ695pHl6/Zv3jsWW5L5y3xuX3WM9vqYpi4YGQqc6nUcpU7LKLpAD15hJfSAF4uKYOug/pVNh4xZpWD6mC39RHpMafuMfG4sFA43A3fkc5seutTrADqhBWjyOoiewhjTmti3P6cqyq3x1u4ocPlBsKbsf4GHgBtq49HmbtiHypEmaoc6Hz0u3tNr304zRkvUCsm0xBMkEq0YY/S89dbuxrI1m/dWaY2IDPY2BXd7HUMHdMdwAi91wza98JGeNp5aKBx+CagBPuJ1LD2CMTtzXXTg5GHd0Wbsg9O+dNpNc3580bW18egfumH7qgO06uOhOjIApeomxvSYO7+qxI2rfEuqH7/nnTattl91EimPpPRz2mDvNCcfBiS5jkHcZzvN7SfeXU8w7uZSp6e8luTk1L7a3b+N3cIh05N/v7Neppg7xfjLtm876cKxpqyifz62l83WfZVvgYzrzn30VLsbyhsYsM/rMHL1mZjjfDcUDuezCuSKPG7LSzXY9lnPeR1IR4TC4ZaY48z0Oo6ewLS07gMGt7dc+YDKzaNPn9jucp0wDHi+eu78WDdsW3WQJmop6oKRWopvAMTSpCUTnafdxXvKHT3LT24dEpUf/NJk+DPDZ6GHfTykpWnUkEW/N4mKmuf3hab5GoeNPwp/oPzA/Ph+X/P+na3l1YMBxCRapLWlAcC/f/fW1qp+QxNllYPaO7dj2/tqbYgsVu/oO37KsB31Qb/JqZMCjw3DtifLW3IVCod3xxxnEzAyX9v0yH9C4XCPStJSPABc5XUQRS/ektMNinHvPnxloCI4O1+7bdnfvHfT4tXL/EG/r9+EIT/tCV8UpUATtYNplZli0cMuRIuJHTFXqeIiIP7GfSf0e+Mp+r7xVEO8/7BFu44+42h8/vJBS/68zN+47wS3NHKf2EGBk9cJgwFaKvs8t2vqWSNbq/uPybT9FZsHPL0/HjixQG+nxzHImJ37K5YN7dOQl4FxC+CGmONcEQqH89lbzwp6fqK2x+sAuuApYCMwyutAilnLzt05VVNu3tOYl97J316y5sVty96q3/7apokmYZLfoY/X3pqPrauu0kTtYN8GzgTydodCqcLTEjVV3AQqy3ZtnjVk0e/jQKvACe50wSZphwg07J0x6NmHDMjbrdX9Yo1DDks0DRs3qqXPoPEA/93WfzSIDqvShpc3DWk4dfzGzVVlrcO8jiUHZ3TDNoPdsM1C63HV0WKO4wcuAW5Gk7R2BUcMGYdPnnY7qjpQ5f0AAVj9yHLZu2HX3/uFBg3wlwf8/vKAP1ARDPorAmX+8mAwUBGo8AX95eITwSd+8YlP7LNfRPz4xLfusZVL1v1z5YhES+L4tDAuqwtGvlYbj2q3xh7TRC1FbTzaUheM/A/wMjDU63hKmQ5N1XlaoqZ6CrEXzjlfPNtEzgwP1O8aXlO/i+o1r+5pqRn4zO6j5ow9a9K6mqdXj1xS3xzUdjBZNMQD0x9bNTYR8Jk3KoMtu/pVNMWnDNs5qbqsZYTXsWXweD5L02KOk+zavqf5PXAvNtE5FbjP23A65U7gC14H0VM0/XftmyTMKbksu+2VjWx7ZWNXdpdtXMUxwI/rgpFXgXtq41HttdMjmqilqY1HN9UFI/cDn/U6lpKmqUbn+USPXgEZLcL0jEDf4L4dJ/eJLlnUOu1dp84JbyyvW3FYq5astUV8LQk5fG9TGXubyti6r/LlsyPrhvh8RXc9kO9eKscDPaq3ROwxuD4UDq8HHvM6mI6KOU4V8Gk0SeuQrXfdm1PX/N1sL7bTmtWapHmr2L6Yi8XhXgegNNfoLNHORArL6MnqtfIdm2YNWvzQkpY+A5siiRnxVf4jzjDi0w9CDpqaOWb1p297vCxgguL3gU8Qnw/ch53mQ/w+wedDAn4kEECCAZGAXwgGRPx+Oy8YEAkGfFIWFPt30G+/jgR8PtujaMIYYxKGVmM7jQr4kGDQj7tc8uvLV121mXA4n2+1J7ZBv8ZN0nqUmONMAq7G9qLd05JjT5l4fN2+hS8c7XEYrwEn18ajPaab2N5ME7U0dcHIALTnR89pzcfO02qjhabHuxgEGvbMDDTsYQ5r2Dz48+t2BYaO9TqmHsEf8K36f/bOO76Ns/7j7+ekk7fjDCfOaIaVRGn2TrM7oANRCoXSUgoUKOT3YzdAaRv2j7AKbWlLIcwyCl20tMV0UNo6ezZ7KImcHSdecbztk+75/XFyojh2vGTdSX7er5cs+e655z46SXfP977f5/u94e6Rw7/9MVNrahhrt5woHpp0xwdj2d/sWHYWBySwyW4RnaEoGBTAh4DfoQy0LlH95oZDgJ3nrp8DDykjzTmoVMYXU8OFtY8UdqCKNncdVYMuvig7zVFICIeEx2O3jkSicfSk0Qee3Dq27JbPrbZbSxRrY9VRUTCYA3wmVv3FgUPA5/O93u12C+kIRcGguygY/AiwHXgGZaR1mbJfPdVqVts4sRO4x28EujXpTRFblKHWAr8RMIDjduvo9ajBb9dRHrW4og63sxDgurX04TqXNNS8is4gBOU3/88VEgy7pUTIi2FfXwV6tNh6DLkdGJ3v9f7KbiHtUbtiWdbxTas+AezDSnoyyWZJCY1Z37C/cV9Rvo0SDgIDCnTfNQW6b7SNOhRRKEOtdf5tt4Dejhr8dh2pPGpxRn1bHYg0cSmvWmfRPe6TX3tktzFg8CZpf2TJN2PRSVEweCtwXyz6igOF+V7v32NcOy6m1K5Y5qpdsezdtSuW/RU4rVeWfAaI6WTC3krVK6uKbZbwAeAk8Aawv0D33WKzHgXKUGuLR1E+HZtRoY9dR31144o63I7DTch7beXfVtqtIxGpueLaqUW/fmvW0R8+HZSa66SNUq4r0H13dqeDomDQj+XpSZQsoA/YLaAtalcsG1e7YtmPgMNYGSg/CqSpG1Wxo+zXTznBi9X8WxHAXwt0X66dYhQqmUir+I3AngLddyXwZ2CEzXJ6J+rU32WUR02hgBGN+2ZlhitP1bhyYhlC12toGDtlTMOYyavSAluH2CjjdwW6b4ffCLzT2Q2LgkEP8AiJdUPaUXODalcs64+VufETwKxWGyk7LSZIKQ2juPQA1vxEgfW91bAMp+b/PcAgoBhIxwpTllFto7dxR9p7gCy6VkbGg/X5Pxq9sED3pQGfBsYB3/cbgZIu9K3oIMpQawO/EVhZoPsWYoVBmlg/huZnuPiH0fzjaHnnTmL9QGSLZbSyrLn/jj7aag8X/yhFy3V5c0eWD792XP/zCmRLTVbCZCEEovlZnM+gbC0XkfXnWkReYz2f6zGqX9n81OoZXoLU3FoGsK219Z2ktePQVZr1VnP+xNfcX/RrrcWy6HXNy6M/q+j+o5d5WlkX/X2K/g6c+y7IsKmunHFEZdl0JgLSbyt9qOS/OR/edih1QiIWOrad+nHTSQtstVOCC7gJ6LShBvwPYOd8n86yPN/rjcU1r1sUBYNDgOuz96yengafpZ2C9BmHtvkyjuwM0n6orJRCtHbda4E4P84SzW3FuR4ALRJxI4RENL+2HlIgz11/o8c75/cpkHBehxQibL0WZlQbM7LPSDuphdOyK89Mu26i9KT2zFxH03RjhBZz6TFKE3CGTs4FnHHPNYWZQ/uMRQhrjCrQQ3VNh9d84+WOlAH4bIHu+4ffCJzzrvuNQD1WMezJWF639/iNQKgzmhQdRxlql6YRmGi3iJ7CnaqvSs/NvMJuHQnIUexNn9seO+wW0MtQlppDcREe6at/p/BQ6gS7pSQkGZvfckLI4LXAdzqzQVEwOBf4ds/I6REeAb5lx46LgkEdmAvcEHlMAQinpK+hHSMNQEAuZrhD4XGOCPVocbbuqCatupy+77y65syMGyZJPSXmWS3DFVXlwIB2mjV71DqFK8XtES5t8AXLPO6s9EFZR+tOV7c3lpkIrKKVeYh+I7CjQPcVAJ/D+g4reoBECgmwg+S+ujvirKmIOaq0gUJxjuGNgekDjJNFdutINNxlxSWeE0V2F94FmF2g+zpUrLooGMwoCgYfAtYA/dtr7xD+ANyd7/XG7YZPUTCoFwWD7ykKBv8ClAGFwL1EjDQAIU11HWmBXlMxf2Dhk9kDVj+zMdYhn8bpssqYdhiFaYQv8nZqumtUztjcjiYvyYqEO7bGv4A7u6pN0T7Ko3ZpTgAvAzfaLaQnEEKdh5MRNUdNoTiPgKzMcGWwTLdzqlVikffYfYXZb78wXzhjjKABfyrQfbP9RqChtQaRQeQdqZPG3uEZMSSDlqGSVqy+jITlSyEECAGakELTrCA6lyaR0qpDKeX5uVeaZoXLNV8vhUAIwaBlS0YIl6u9+Y8GEIo8TCCMFb5mRJ43A5+NV5bHomBwHvBxrKLUiWLIOg6toWZ6SsnhrY2DRnXoBkJHMIpLeqzAdLjpYkMNKPa+f7I73BDacnrT0RmX2LwEGIiVBfJhrN/WBizv3x1YRtrAAt3n8huBcIylK3DGSdix+I3A/gLddwdQyoXzhZIDNZxPTtScqbgi1B0PRyPBKNOHqIQinSC78MXxDjHSmpkEvFag+5b5jcBqgALd1w/wA+8HrgMyGnbup2Hn/rgIyv3qJze5MtIasY6THnl4ol6LqNdt4QWmFwWDL2PdFF6d7/XGfK5PUTDoBpYD93R4I3UdaRMBbld9dX0s+zSOn45pf9Fobq21a9RzrhT3D0fdOHGEJyftm8f+E3hvi/U7ge9jGfS/BoYBP4taXwEUAVuBV5SR1nM46UTsSPxGoKpA970BvMduLTFHjS+TE+VRiytCUz8kJyNATzNrampciVLv2EbCYZm5+c3tSNOJc7NnAasKdF89VkKn/tiYdl/z6IOIzVzlMcDSyKOsKBj8LrAiVgZbUTD4buAnQMy8PwrQGutialAbx0/1XKH51m8mPg+UpfZL/2X++yaOKF5dVBWqN7KB/2IVXS/zGwGzQPc9GWnfCPwR+C1Q5DcCPRaqqbgQZah1jOdJQkNNKJ9aUqKyECoUF3LDmb/UbMi8dlMgfUbrKcZ7ORlbV+7s+68/nfGcPJyql56YbbeeNmg2ytIiD7vpievnAOAx4EtFweBhrJplf873etdcaqOiYHA4sAA4jZUVsCbS1/eAd/WAzl5P+rE9sxuGjCkKZfbLj8VNb+NESY9duN2p7tbG+jVYIbA5Qggta3jfVWcCJUHgfr8RqI1q9z3gfuC48prZgzLUOsZauwX0CMoRkKyoDzaOSGUZO540s3bq4qoX6hu1tG2HU8f32lT9orGhMWPbqr1aXU0oY9uqWtfZCuE+U6J7ThTNEc5PLua081pP6inFStJQASwsCgavB17CmtMmsEImZwJXAVfTSka+7iLduhqUXwIBqf03vJgfSsva0JDnNRoHjRwWyuw3sqv9GafLe8Y7LMCd7ml5Y2MrsB04i/W96jNv16qLEosU6L5RWNkcf+83Akd6RJ+iXZSh1jH62S2gR3DaZU8RG5TZEF/U8U4IBKRpMtyrP628X963PnvtK4vt1pEk9OR3aX7kEc03gWNADlYdz55FZQ/uEO766jmZh7aRdnzfwbJFt5UitA6VKmhJ+MzZlG7IqAUyWluRO23YO3qGZzrQgJXd8zXgmYwlyw3gQKRZXfQ2BbpPw7oR8BTQBxhfoPsa/Ebg5Zb9RzKybvMbgV59bu1JlKHWMZIytlvlQOgy6sApzqEcaonDhPqN1UVpnaoVm1RkbnxjhN0auoHTfmh26LksbntS57UOUzti0tqa0TOnIoQGrMTycA7tTB/C42nPg1mCFdqqY43dmx8VwLNY4cADgZNRj2KkLMEyxKoylixv6oiWyNy0MDD+EplW3wdkY9VQ+xnWFCFFD6AMtXYo0H0C64uoUCgUFyMdN4BUtEFauMatyXDYFC4nFHKOP269gVDP5SzoYZz2O0vuG3bKo9YhwqmZxTWjZ85BiOZzyiKsMgxrsYpTdygsdcgP7845csfXQ1w8Lq8Cfgg84jcCPZYZsiV+I7ClnSb/AsZhlXs43vOKei/KUGufOcDldotQKDqMurzGFeVRSxz6hkvnDW8M9Np5agd/v2bk0J9+YWXG9jWL7NbSBZz2Q3OanpgiUIZae5huT1X5nPeVIsTgFqtcwLzI6y1Y8z9bjcyqXbt1d/F3H6s2jhbP5vw80UbgLSxj6Gm/ESi7lI5IMpncfK+3PeMqZviNgAnsidf+ejPKUGsfdadAkWCo62tcUZZaQmGIlOSridlBZEpaavGXH5g8+lPz2m+saIfk/tlLRHK/wW5iuj1VpQtvK8HlntxO0+Zi0keBQ8AErIycSNPk5L0P1odKyq8AirEMs38B/22RebFVioLBAcAyoDrf6/12UTCYATTme72homBwLPAl4Nv5Xm9FV96jwhkoQ60d/EbgeIHuO45V7E+hAIdbQr7bZ1Qb1Q2rgIjSc3IF4tzoQnDxSOOCZe0Vcj6xOlh1au3hPq300drr5v+jl8lOvo7+X7R4bn4tW7Rrq5+OtoneT8t9CkDsWrFWd6Xp64UQEgHWs0BoyMhoR0YaR46/OLe1sKreCbC2lSFTi6yTzQ2sT0HItAEZ4Ql3zVWJILrJmPptpSdSYp4kL2EQjfWtzjlJABx13pUyyV1OQhlqbVE9ZtaquuETfAhtdCc2Gx55hLCSeiwWmsbIZx46ePDKjy8BtrZMyFEUDLqw5iUOxZqDlhp5pAFjgdnA48CZomBwQ+T/M0XB4ENYiWe+le/1nunOe1XYjzLU2qFA9y1CGWmKBCJtQMactAGtJoCKKZ6s1FVcnJms12HUNmHUdmiOdreoO119oP1WivbIDZ3Q7dZgF6K+tm7ojz8fBPLs1tIFHGUXCStxRPKS5G+vO6SePHhZ3fCJ6V3c3M357/IKPW/AVy7f9+/FwKSiYHACMB3Lu1YD3AeMbKOfauAXwKeA9wG7I+3/nu/1qlT6SYQy1C5BJJHIcrt1KBROpD2PmyLmqOMdA6pc/RI2m0Z3GfWV9+7Wy4sT9eaKs77/wvF157qHSPLYzm7gaqrri5SVCJHZhc1PAPnAi8CrWJ6vAV3oJwP4MFYdtKnAjnyvV31mSYgy1C7N9cACu0UoFE5EaMpQiy/qGhwLgqkTuzK4SnzCIdNdcSrfbhndwGHnmyR3Oamsj63S2G/IrrOTrpJoWlfrfKwHhmAlG1kE9O3ANoexEncUA+8A64B9+V5v3LJAKuxDGWptECn4p7xpCkVbqMt4XJEyye/gx4n5VQXuEx5veb0rq7/dWuKGaTLwTz9ZLaRMxGyPzfTOkgp2oXIktUpDnrdc6ildmSu8CsvY6gfMBUy46JxeAmzDCmPchZV85ARWun8DOKa8Zr0PZai1zc0kaaFrhSImqMtFXFF2cWxIlfVTZh9/8u3n9NsGCU0IIYSmaUIT2rlntyaEJjTNpWnCJTThFqAhBEKgIdAEQhOacAkhXEKgCSGs5J8SKaUMm6Y0TNM0TFOGpClNKWVYWvX2rF+NlJhShqUpw6Ypzahn05RmWJpShkNmOBwyQ6FQ2AyHzFA4bMrmfYRDYTMUCsuQYUozbCKReFJ0MWHWyJm67k5v3seAvz+8Ouf1p7K1mrNeYd29T1QkTvsJCIfpiR17gAoRDqkzfCtIV5eGzSuBdCxP2BeBB4GngTuw6qRtjjxO5Hu9sigYTAcysbI5Kq9ZL0cZaq1QoPtcwP/ZrUOhcDJm2FQX8ngiMO2WkCxo9VXD6re9eWJz6YBYZdFsNiQE1nXVjZWZLa6cPFy25cobpw5Nz0rNE0ZTU9+CPw/UGuvHxltHD2AAvbasQpzxYE35WGu3ECeSvW/d5PDhnQcrZvk9aK7hHdhkLxDECln8c77X+6eiYDANaMj3eje2tkG+11sH1MVOtSKRUaE0rXMrVsV1hULRBtJUsTHxJVlv4MefsTlVo28YfrxlaYnu4IgPp6Guacbrz22urSyvKZKeFE8oJzdZ6ic5MezREZ95DzAa2EeSVx/oKprR0FevLhstwkZlBzfxAP8ByomMufO93noVwqjoKMpQa50dqMAuheLSKEMt3qjjHUMWDC6Z2jel8R27dcQaM2x633pxq7vh7396TS8vHmy3nuQlqZOJZCM0db65BP3X/zMv5VRRYQeaeoG/RR5PFwWDqT2rTJFsJPOJpsv4jcAuYJ/dOhQKJyPDKhJPkdh8dEzQEMhk8TqdQ4bNPuNf+PliETJG2K0laRFJPXWkHGkqj9olcDXWDeyzu3ChXnFyZSc2+yDwpZ7SpEhOlKHWNr8Bau0WoVA4FVNNUYsvgrDdEpKNG0cen/OXa1aG+ngat9qtJZakN1UX6WaTunPfkwiRrHPmVgOThDLU2kVIqfV959VFqScPFCJlR+szrutRUYqkQxlqbeA3Ag8DlwP/sFuLQuFIpAp9jCdCCHW8e4DctMaB35qxPQ1ktd1aYkWf+rIauzXEGCcaDcloqDVgFU8G6chj7jgEkL139cLUU8GOJF+pxqqjplB0GGWoXQK/ETjmNwIfAj5ltxaFo1ADZlAX8nijsj72GJP6V44b26dqu906YkWdJzvFbg0xxlnnXCEQQuh2y+gBdmKlhQenHXMHEk7NOFU1ds7KkqvvrGkYPLq9DLIm8MN8r7ejnjeFAlDp+TuE3wj8sUD3DQO+b7cWhSNQA2YLdSGPI+68XLNq3LyVICQCIUGCABFlL4vmDPFwwccT5fwUZlggBOe2P7cdLV4LEBF7vHmZlFhbyvP9ShMhpYgsa/GdsJaL5hXn9V3YTlhFyiJ9Smu7SDMZdUdAykhXkX6lKS5dmDfqPQBCyuY+m3d2Tsl9g43QYyvF61sC8tpLdJgQnE3rP65eT69KM+qy7daSjAjd3URyetTOp4QX6j5cW5h6ytkq39xtjYNGTUeIjtYnrMWaUqNQdAplqHWcH2AVLLzXbiEK21EGCheOnxU9j3vYYFE/bFwiFy12NC7gyxNgx96GXY//uVzW1ctJQlAuJTqQWAaPENkvTb6r8NYtj8SqTpwiCpHiaST5DLUjwGXN/6iLXNuUzb25SHrSOvvb+kW+15t0iYsUPY8y1DqI3wjIAt13P5AC3G23HoWtqGsYwKVdGYpYo5K3xIXJl6dO/PWPhrJzX8Ou0SM9o14rrNn2/CtVc0mwqQKV6QM8ptBMTZoJpTsREG5XyG4NPcBRYOG5/3rxnFiJkI25w7ebKekNrrqzmfqZ4nH1wyduDKdlhRoGjuwj9ZQJXej2qZgLVfQKlKHWCaKMtTuBvjbLUSjsRnnU4og01YA7nkwalzoRYMr41LwXXq0qlZJBdmvqDBUZeXNXe/2rFx18eYHdWpIOtyvZMrBWAfl2i3AK5Ve8f104s++8yL8hzPAGNNf8bnZb2l1dit6JMtQ6id8INBTovr8Dn7Nbi0JhK1J5FuOKcmDGnVOlxonvP1SSIiUD7dbSFTaMui5vQvHGY/1rT1/WfmtFRxEuV7LNU9ZoceOtZvTMobWjpu0CQNA8uVRGnsWFzSVE5m6/tndYXVi6UiMTaJsn0mqRVtbr8zNEo/sRAC7M2tz0mlNl9RkDQ9LVBwgLpEQiBdKMTDA1z0uhpesvWidAZEKqlPLcJFnRPFH2/CRda96v6KPXH1uUWX51ZFsT+A+a64bOHc6L2AWUd7MPRS9FGWpd4y2UoaZQKI9aPDmXAEMRDxqbzIZ7lp/OATLs1tJVQi7P6N8s+H7V9bv/unHa8VWz7dbTDRz13RcuLZk8amXAHuDC+a+aa7jUXJ3urE5knEWIPm23kA390xs2ltelRe1PNg7MrF+fldKkjehbPSI71ZjSFKquWn1o8M56w51tIvSwqY3rtJguUG26GqJsqu/S/SRyQeAj+V5vMn1nFHFEhdJ0jaDdAhS24qhBg11IU7l44okMh9X3Lo78/DflG0lgKKEEDQAAIABJREFUI+0cQmS/OuGOWf+Y9r+Fja7UWrvldJEmuwVcQPJ41DYCabQ00rrHJRwAUnr7V22cO/LUrGF9qgvT9NAmlzAP9E9v2DB3xKnFkwZXLMxONYYDeNxm9tVjTsz3jz8yaWBm/ekY6mtJCfDJyDOmFNGF4ofR9fFeI/BvYFK+17urWwoVvRplqHWNhe03USQxykABZa7GG/Wtiyt339V/Rka62GG3jpgghNg/aNriZ2Z+6Z0ETdbqqNpTQtMS/ddYDxQCs4n9zYiLDLXLcqoLU9yhLTOGlbwzaXD5Ircm02ZeVrr4Ot/RWTdOODxmYX7xIiHAKC4taa3D6UNLp3tc4Z6qc/jdpTeJJ4A/AJhSpEetO4LlcewKrwDvzfd667snT9HbUYZa1/DbLSAWSOUQ6SrqwAGaS0vIEV/CkqAj7EQlLVXLeHz5kMnTJ6YW2q0lVhzvO3rhz9796MEtw6/ckGBfJ2d5sBIvI+IGYCWWwbsWKAZ6qnTDOUNNIE9MH1qyecaw0sU3jDs647Kc2hltbWTW1dcfvOoTOcfv/lGhbDIuyKqpu8yshaNOxjKB25+BV4FHgN89+KL0AGHglDxf8Bvgq8DHgb91sv8w8HC+15to3xOFA1GGWtfQ7RYQE9QppKuoIweReeKKuKFurMQdIQSfurXvZKDGbi2xIuTyjH59/O1zfnH1z7ZXp/TpqrcgntQDp+wW0YJE+jFuBeZghTfWAfPooQyP1ilKuEA29kltXHPjhEODh/etmdmRbRv2FR0FXNWvrFocmHXL4dp12/ZEr/cYtWl5jy97K/vN5ze5Kku7k5jjLPDJpTeJG4CvAF8DzgDLgDwQfUyTZkOxH1b44n3ALUB7IZh1wAPAqHyvN2lu8CjsRRlqXeMxuwXEBDXQ7irquIHy8MQfdbxtIDvL1Xf5PYMSwaDpFPWerCm/m//dk3br6ABpOM2jlhicBg4Ag6OWXSLJR/eRENKEGbxm9PHTV40+MV8THR9jHvvMt8NYdeeRjU2jj37y/suPfOLelWZdfT3AmV/8fm+fN/9x1eDH75/l/czicN+Xn1jXSXlPA78Afr70JtH8fboH+CEQHe5IY9hVGfWvF3gq3+t9DhhPJESyDc4C38n3eo91UptC0SbKUOsaLwGJ/0NUA21FN1AOnjijDrhtXDZEH/mDewYevnZR5src/q4NduuJFQ2ejMm1nswzdutoh1VYg2UnIYAGu0W0QwUwBsiL1w4FuG8cfzg/K5IQpKOY9Q0NZl1DyxIYom7DjkWB2R8urXpl1dYzT/5r8rkV0hxoDMhLpXPkAmuAHwA8+KIcAPyotYYNxgWGGsAAgHyvtyLf6/00MAXLaHsX0Dx3zgA+qOakKWKNMtS6gN8IhIDH7dbRXaSapNZV1HED1GGIM0LdV7GT4UM8I++4OWfR5z7er5/dWmLJwdwpB+3W0A6ZQLXdIpoRHt0YdP+SA0AlsB54B2v+13pbhV3IdiCWc7o6hBCRimSdoLHo2LH9V9x2HCkHtNogFB5+4u4fTUPKnOjFOa8/XZ16YMdBd/mpjhaSvhp4JvIMVkjtp4F9LRvWG+6Woc5ZRcHgubl3+V7vjnyvd2++1/tfrNBSgC/le72d9fIpFO2i6qh1nb12C+guZlNIjbS7hjpuoApexxtNqPAvB2CESKp6SIbL4/T3Mw1YDSywW0gzqZfn98XyVLX0Vu0DqrCyKcYTieUtygNGY6WGj5snravIUNg8fMtXqmVj0/jObpuxc92ijPvWISFUesfX1p55/13zIqtquDAhyAW7BLY8+KJcGGlTD1xUn63W0Ft6xfKAZ4EPRC8sCgYvB24ElgIrOvseFIqOoDxqXedKuwV0lxOFwSv2/XVzoaqH1WnU8bJQLp54okKVHUE4LJPKYF7rfY9HAvV6etVji3+86ffzvrVm67CFG2uclWhkDhBqt1UckE2GLtyutipBj8PyZMU7/G0VliE7Cjhqw/47hXG6vKzq9TXb9k2+qdSsre+0kRaNAHfuX382a8xHJhf1f+qR7wCXY9WHa43dS28SlcA1WMbXPixv6AXUNblbKwfx/qJgcGqLZSeBRfle70Mqw6Oip1CGWtf5NvAvu0V0E8+p9YcXr7n3pZ11JdUn7BaTQKgTMip0Nu5IqQw1BxAOJ1dii9qUPtMfu/KnWx696oGK6rR+s0qyL5v/6sSPzX70yp+m/3PKXYUO+ZEfw0ERQEXv/0KeNOXZNlaPATbFUY4BzI28NoFaei71fpcJlVSUQSQN/9V3Np740vKpmOagWPQtQNeMpvwBzz1+7+hPzM4A3myj6W8jz28Bf156k9hGK5kc65rcbX3tXy4KBs8lZ8n3es/me7172mirUMQEx5z4Eg2/Eagu0H3vB36K5fZOWEJ1xuSN33+teqR//JoR118+X6i5MIqO4JARXK9B2cWOIGwml0cNoCY15+L6VkKkn8oekWKDnNbIx5oLNM0uAVp6al32+67emTZpbEPatMs9QhNzL9E8H8tTMwAr6UgmMLaHpO0Gmj09OpZHyVHUbd2z/8hHvjbEM3r4Ws2jm4TDPRXGmlb85Qcew0ry0ZKTwG8ir3cCTz74olyHlXb/AhpC7rY8psOA9UXB4HbgI/leb20MNCsUl0QZat3AbwTCwFcLdN9J4Gd26+kmWYcL9sw/teHIhulfvWqsJys17hOREwg1YgZlOMQZqTxqjiDZPGptImX1Z1Z/d6ZDvnRnsWpaxYWB99y1qu/t/iHC5UqpfP4/ReEzVVr/z95yudC0OR3sYljkEc02YBBWuvwGoAgYysUp8xuwClQPxJpvNaudfTm+xt+pbz9aBoxtOnh0XruNu4GRO3Rjg3diW0W1szhf5mER8EngPcAOYHJ0w6aw5rnEboZHHkOB/d0SrFB0AGWoxQC/Efh5ge47g+VWT+hw0oay2jlr7/9Xqe/2mZsGzx3Z3gWit9I7BmoKZ6EMY0cQCiWfR61VhMiq1zPKM5uq+tus5DiWkTYiXjs06xuElpriBeh76w0tDa6uMhU4iDWfrD9WevdqYBfW/Dst8sjlfOjiqg706+hkMCUP/2l144EjPe0Jfa1xyKhdRx544QsyJbUtL3AWlvF7fOlN4p+RZf948EV5EitU8ly6/5CpdST1vzLUFHEhoY0KJ+E3An/AcqE7/u5Wu0hyA09unrX5x2+sDtUbif9+FD2CMhvijkOcG70bw5COHhjHinnBgtUOMNIADtOiIHFP07D7QFYPdT0aWIhlpIFlPEzEMuImR15Hz9uaBhRieeNaYgJHsDEctJmq/6zdtn/BR9+pXbftgvlaofLKM+Urnp4EZPTg7mXV/Pf87PAjr9x6CSOtmXO12h58UWY9+KJ8A2v6yqHoRqYUl/KoNTO081IVis6jDLUY4jcCzwMTgFfs1hILao5XLljzjZfOlu8q3mG3FoehbBRQHp54Y/YOR47TaeoFHjVX2Di06MCLTkiHvx4rk2Fc0DLTa3I+fMPGYb/8tlMKbGdiedemYBls0RzA8jJmx1tUS8p/9feacNmZ6ceWfCc1VHG2EkBKydFP3b8HeVFoZ6x5uvjuB+/n4lDT1vjOgy9KPfJ6BVb2xwW0mNcXNkW/strUvWfqUg6cbfAcqm7Uj9U2uU/VG66yxpB2xgiL6rBJTBKhKBTtoUIfY4zfCBwt0H1+4Ang4zbL6TbSlEN3/nrNkH7jBxVO/Oy8+Zrbpb4zylADQJrqMMQTKaW6seYAZLLXD5TSvH7Pk2UijgZSG1QAI4lTPTDh0Y2xG56uFS5XvGugdQSBZbD9F2vclkMcQ0EvhVnf0NAYPJYHIJuM/APzPmJq2Rk7RYqnKVx6Zn4cJJQCbwPHCYf2IEQ1musxrBIFaZE2FViG2a3AL4HPYiWnuZVWHRZi4OpDQwZevPwc9wOFS8fE6B0oFJdAXfh7AL8RkFjp+xvs1hIjRMWe04tX3/PSwZoTlYftFuMAknug1lHUUYgv4aR35CQKSR2Cmtl4duvkE2vtnJ9sYKVM14hj0eacW67fIlwup3tJBnPew5ZjsxYr1f41n9wnG5tGRy3WzKraSeHSM20l9Yg1t/k+NG6k70Pjcny3Tvz62Fsn1gH/BG7AqikH1hzHr2MVTl8NsPQm8QDwbixDrxRrvuDjHdznn7GybSoUPY4y1HoIvxE4ghV/ftJuLbHCbAqP2/yjNwYVvbRrtSqhpVDEGeVRcwRaUptpjmAL1rWz5w0RIfC+/vsNYzc+s2vQN//HiZ60aGpwUBSUlJLy3z67JVxxtmUR6HiTC9wL3Aj0E1L+1PehcV9eepMoBO4C9gLFkbYfB6598EWZAbD0JvEm1hy/2VjTVl4DKtvZ34vA0KU3CUcXFVckD+rC34P4jUAI+MslmpzFKuTZnjF3Jmaiuk/a0df3Ldjw3Vc3NNU0tndCS1aUlQqk5Wa2VWtGoUhahEhuj1pNSp8pq7zvXWOjhEZinDwk5yP+DVpWRnXL5UMfvLfQM3zwHFd25kQhhNPHQ7vpuVpsnab4vgcLy371lBPmMbZkALCqQPc94vvQuD6+D427aulNYgjWsVsLfBT4n+bGS28SJ5beJA4vvUkcwzL6LnWD4OvAt7BCKRWKuOD0E1MyMLqN5W9j1fK4DngA2Awsjyy7GSjBCgFZCvwf8GRPC+0MDeW1c9be96+G0m3HW8tGpegF6JkeZajFE+XFdgRCiKQ21BDCvXrM++Y/P3VJoaHpjXHeexlwWSw7vOx3P1iZd/+SwTm3XLer5boU30i9tW0cykggaLcIANlkhM6+/JZjjMZWGA58EXgWOFWg+wK+D42bihU2+j3arsn3BywvXGv8Fvg50G/pTeJgjPUqFG2iDLUepED3jcCa8PtnLO9ZNBv9RmAHVix+BfAqllH3MPAdYBPWSSMda+LqR+Iku+NImbf7d+un7Hh8VaEZCofslqNQJDOq4LUzcLmS26PWTCBvxuKfv+uR8n2DpsXzZtxuID9WnY34y09XZsyfNlfo7uEDl945fNA3/3dltn/xFnR3KGPhjJ2eEUNitq840Dx/rspWFUDJz/+4jrA52G4dnWAs8Enfh8YNW3qT+C7wkzbauYHbAQiHpX7q6Mm0PZv2Za16eYvn2MGHR9594xSs0FyFIm44Jt45GfEbgSMFum+23wjIAt3nw0rb35xJ6+oC3ZeCVTCxtVo1U+Kls5uIij2nF6+556V90756VWrm0JyRdgtSKJISZag5gqTP+hiF1FxDXpj2v0OyGs5s/vzb984UPfvW6wEdTZPC7TJkk+EBcOVknc379ue3lTz0xJjU8d7TtavfGWvW1p+ryyU8elPG3Kn7Bt5zV60+eEDfwOwPjyEUdrn6ZlfqwwdnCCF0AOF2D+13x41DueNGhoTDp4XLNakn30wP4cVK07+4vYY9gQyH5cl7frayqqBwnh377wIhzo9zbwRuLNB9a3zwE4zAy62014GvZr/1Qv6Ap35xu15+6oPAEOBRrAin+qU3CVVbVhFXhEoKET8KdF9/YC7wI+Cs3wgsKNB9C4BV9iqLGfUjbrh8y8j3jF+Q5NFB20kcQ7rHOP72gfUHn9t+hd06egue/MuOev+9YrjdOno7b6+r3fiHp884PfFEbJHSGFm+b11p5uCMuYdeNWYeeeuKHjDaioBw2swJ4eG/X57SePDoCc+wQX0OXvPJUWPXPVUpdPcwAGmaZeW/e25f+a+fmm7WNaSP+MtPV6bPmriouROzoTFY/ca641nvnneZluJJJI9ZR6nFqqEW1yQe0jQpuuGz65uOnEykc/5jQArwmVbW3ec3Aj9ubaMC3efFuomuARuwpqSM9huBPa21Vyh6EmWo2UCB7nNjzUMbhRUWeRjw2KkplqTnZa2d/rWrp7hT9Yz2WyckylADjr25f13w+R1z7dbRW/CMGnbM+8pvYjp/R9F53lxTs+GJZyvn2K3DTi6r2L/yjo0/W9R+y05RnfuVj6+W4XBW7uc/ei5JhTTNSqFpFyV4kFI2VfzxhQ397nz/RKFpfWOsxenswfIUxW2e2JE771tZt357rD/zePB5LGPt+1gFxMEKsV3oNwIXJWor0H2DgJ9iZYgsB6b7jcDRlu0Uinih5qjZQCQb5ELgx8A4LNf6s7aKiiF1p6rnrfnGy2Vni8r3261F0XPIsLrLE1dU6KMjUHXe4VjfMVf8c8pnCssyBsdsAJv75Y9v77/k1uujjTSA1ow0ACGEp/+nbl7YC400gPHEca5a7frtexLUSAOrwHV/4M6oZYHWjLQIN2MZaQAfU0aawm6UoWYfKZHnOr8RKAe+SRKlfZdhc8TWB98aefD57SuV1zY5UZ9rnBFCVbxWOAMhPHsHz1r8uwXfSWm/cfukjPcWDfjf2xYkfUbN2DIDWIMVMtqjVL26qgwhEjkl/TKsZG33Y90UX99aowLdNx3LmwZWQjeVOERhO8pQs4/msMDfFei+q/xGYD/Q2uTWRMZz/M0DizZ879UNRl2T7ZmqFDHGVJZaXFEFrx2BMiXOI6QZk/T9jfsOjQpXVu2IRV+9CAHMx6obthOr/lzMsy9LKWk8cASkzI5133Hmx8AXsI7R5pYrI/PSnscKj9wD3Oo3AiVxVahQtIK68NvHH4FKYCLwZoHuey6y7L9A2E5hsaahrHbO2ntfrjoTKEmWibhqqIbyqMUbGQ6r753CUZiae/iRfmP3drcfkeJpKH30ybpYaOqFZAOTKp97bWvZb59dGYsOpWnSdPz0KYCatzbuaNh1YCjJkSV8AFadvlmtrPNjlVM6ASz1G4FD8RSmULRFMvzwEhK/EXijQPf9EsslD/BB4H1YKWCTrpCwNOWw7Y+uDA2eN7Jw7G0zFgtNjTkTHmWnxRfTTLrzgiLx+fuspblff/3zYZcMd/j76R7Yr6zfnTfvTZviI23KuNHC7RoMJFI2QccQrq6tOXrnfdsadh9c4B7Yf/OAz9zS6T5C5ZVnzv7zv/tyPnz9pMrnXttZ+uATg2Uo1Fe/bPB642hxon8uZ7CyZM7Gumr9F/hMge57x28E3ohqNw6oBm7zG4HV8ZepULSO8qjZS8tQRx2rwHWy4i5ee3jxum8WbGk8W19mt5huoKxMRdyRYVOdrx2AciRfyLAzB/donTDSAIY9+q29/T9188L0GRMWRow0RReofnvjjv1zb6ts2H1wAUCopHxy5fOvb2qtrTRN6nfuP9jauoa9RSdKHvj9Fftn3eIp+cnv5kojNBJJnyQw0gD6As9xPlX/HuC9RI21CnTfYOBTwEeUkaZwGsqjZi+X2y3ADpqqGmas+2ZB6fhPXbF14LRh0+zW0wWUoQZIFfsYV2Q4rAw1B9CbCl63i5SNV+5/YVBnT4j60IGZ7bdStIaUkrpNu/aW/fLJ0roNOxZy4fXIU3z/w5PLfv3M+rxlSzIyF8+aBNAQOHS4eNnDxxt2HZiT/Z5FhVnvnp9d8ZcXa4zi0tRQacVgQuGJzdvH/Q31DGeBV4HrgT5YCUJ+DXwPaPAbgQAQiGrfD8uI2x5nnQpFuyhDzV5exCqm2Ptq8khy9/x+/YDTEwcXTvjM3AWaS0uksC5lqIEKfYw3YRX66ASkys9/HiFSXpi6pOaLb99zwWJ9+OATI5740UlXTlZWw96i0tM//u3ghp37RzevL/rAFy4bU/iXCqFp/eKuOUEJn62uKv7Oo1ur31iXTyh8OW3f6E0xjp684tiS76APHbgRl8s0jhZPBUYCVP175eKqf8dkKpvTqQf+iZU45DTwDNbUkrsKdF8/vxGIzmK5BziINT9NoXAU6g6tjUTqeLwbeNtmKXYhyncVL15778t760tr1AkywVAOtfgiw50LL1P0DMqjdiE1qTkzVnnfu8bQ9MaQ5jZcOVlnR/3jkbP6kIGztPS0cekzJiwc9ezDo8dueGbn4B8tLRRpqfWyriENUOUmOsiZZ17ZuP+K28LVr65eTCjc4aL3xomS2ZHwxdQelOdE+mDVTfsE8ChwCngd6ybrH4BfFOi+c95DvxGQwM2RZ4XCUShDzWb8RqAauBFYZ7cWuwjVGxM3fO/VrBMrgxvs1tJB1O8GlEct3qg5ao5Afe0vZn3+9Xn78mbsfGrmV9blLb97mysrY3zLNq4+mZNyPvCuxb4tz9WN3fhMtdC0AXZoTTSajp8+VfbYky6k7I2FvWPFx4CPYOUFcAHvB4ZHN/AbgZ026FIo2kWou+LOoED3pQGfxkoRWw7kRF73KrJH9Vs15UuL57h0l2Nj5fee6bN36ZrZ3bloCkDIyHPUclOAiZAShEAipGUUNrfTov7XuPB1y/5b/t/cNqRr5nFaH28KEFrklNBSY9RrIZDw8TH7d9825vDkqP1Z68X5bdo5Dq1pkEjCUX2d7xcwQ2Y5pgxF7QOEaH7/Qlh9ntdjvdJACETkOFkekci+pTy3RETrbbdalmjx7kRkI+tYCwAR/dk17//c5yVD5g7h0sYBJkgTiSnPvX8pz/9vrQNMkZ6KkNIFaEjpAulG4uKCTLGyFe1CRpZ3MGxXhBBtejyaj59ARr8n2fy61Q65+Hsa9TlctExamiHSb/Sjtb6jn1v0c8F+XFjlT2RUf9GeypZ9hbE8P7L5dVF19u7PFV6xuI332XuRUiKE8JhN+7725aGM86aMs1tSolP222fXlv78j3NR4faxoAr4ENbY6jXgWuAVoNFvBGJef06hiBVqjppD8BuBeqysRI8BFOg+DSu++kY7dcWbqkMVC9d846X9M75+TWrG4Ozh7W8Rf0KmwETk9UTfElofZ8cOj2G68mPSk6YdEJrIjUlf0Vzi7bt0V07M92cTwroZEfEqiKi/lyDc1fFEZ2/ISQe4jmwX0Co6Yd1uDY5EWDc3mjRPfl6uu9puOYmKlJLQqbKSxuCxkvLfPJONMtJiRTrWze/mY/oUcAgYWaD7DGCy3wjst1GfQtEqylBzKH4jYBbovtuBEiDNbj3xxGwKj920/PWa0R+csn7YVWOcmB5YXTgVil6KM81H5zBkkHtzdqY2124dicrRO+9bWbdhxyJgoN1akox9wJeBBqyafWGg+aZlCvAB4Cf2SFMo2kYZas5mOL3MSIsi8+A/tl9R8s7xVVO+tMhRoZAu0bMuL4VCoUhUFs3J6ESIrQLAOFlyuvrtjYfq39nTWLdhxzy79SQpzSUIUmk9Y+Z7UIaawoEoQ82hFOi+TOAWu3XYTdWh8oVrvvHSvhn3XJOekeeMUEhNqGQiCkVvRbScnahoRgIiM0Nza1oXDpE0Zf91z69HaNLI6m805Hn7CTNsIk1TSNPENGXzM1JKgZTnq4/L82lopURIUyJNiQTp1jXp9rikS3ebbl2Xbo9H6p4U0+VJky53CporRWquVDTt4vO6NK35wu1OWe0c4eramoo/v7i98eCRUN2mXVnhsjPTgUEx3Ymis7zUStp+hcJ2lKHmQAp033VY8dNJMx+nO5hN4XGbfvB69ehbpq4ftni07aGQLqGy7ykUvRW3MJWhFkETnDIlOSOH6Ztvf39O/z88c6Zy4ez0roU9Ck2IcEh3NdbNdNdWknYqGGO1l0ZaSWOaIo8QkC4iae2l9X8IaEKIKqm5q9BcjVIIE6GZCGFK61lKTZMITUqhSSu9kCalJkBzS6m5IGQgDxxw5+SENWame5g1u1EIsb7xbH1TqN6QQtMQLiGEQCIRCGRbtwc0lyaESxNCs9IYaS5NS8vNzJNShqQpQ9VHK482nqlNM2qbNKO2SQvVNrlNI6yZYVOTIVMzw6ZmhkxNhk2X2RR2h42wbhph3WwKe8JNoVTTMFNk2HRJKV3ILtygEHQtVlggNberUWgiFEnTJEV0T5aSSNorgdBESGjCRCKklJo0pUs0JxGyUjxJQApNnEssJIQwEVbCItMIlWcMzXnWbAr3BZShpnAUylBzJidQRlpLsg4+u+2K0i3HVk754qK5mu6ybUK/pkIfm1HHoeuEgEpge4vloo3XYA0kzVaWi3YezVkLWxK9ruWjrfCgXo/HqHeNKN+7W0iJOFdVTV5wUpBCCGuEeP64i+iPILJNZI1A0DwOFqJ5uQSBlJHtorxHrXDe4yNkZE/NS0yhaaZwaabQRGS8KkWkb7BGtUJKKaQpRWSk26yhIn1g37KsoSPa2u3V09lVXtLg/tjsE/UjhM6M24TgUPGaSNLa5qysIKJdbFJa1oj1fpsTyQJCmOGUtt9kzyKsDKCptFJzTFhjJTeQipTZImxA2Oj6zvqnX7QoLTez6/21zom+Y3Ovh9jle5KW1zKEVTi6taypAEKcz8SLlLLZ8G0+f8H5LMRhIITERKADHiAlsn28ar8ZwBKsm+MPAPG9Q6BQtIMy1JzJbuA0KhTiIs4WlS9ac+/L+2bcc01G+qCsDhf+jCUq9NHCCXkBE5gqrPTQTmW33QKcSrarYfTtmx7qFTWtTKEV/+xdjxphl97qjTHt9Ik+35+0t7+n0cznULzVKdrhMDA0lh0KK7OnHnl0dBsPlgF2iUbd09VNdOA3wCIgtXbFstyMJctLbVWkUEShBpwOxG8EJPCG3TqcSrgxNG7j/73Wx64C2S4h1e9G0V2c/h1S3tI28GSl9kVQYreOeKBJc/AX3r5nT2vrfDmVByb1P1PncZmxKfehiBUSy0jr7RQBLwEdKRUxCKsc0j7A25OiFIrO4vTBQm9mOfCO3SIcTPaBZ7bO2frw2ytNozsxKJ1HGWqKGOD075Ay1C6Bnply3G4N8aI0a+jZlsvS3KHa8obUPjNyy2Mer6foFqXAAWAkMN9eKbaTD7wPq7h1W9wPrMaal/hv4FNAXc9LUyg6jtMHC70WvxHYC8wB7sWKoVa0wtmDZYvW3PvywbqS6hPx2mdpQ2pVvPalSFqcbgipa8MlyBjSp8ZuDT2NhLqiARMK/zb7a4taruuf0lixYvHakv6pjTPs0KZok8MIG5MOAAAgAElEQVTAWLtFOIyDbSzfBdwD/A/nb4zPBBpqVyxzRIZphQLUHDVH4zcCIeAnBbqvAiuGWtEK4cbQ5Ru//1rV2NumbxyyIH92t/oyhfls0ch1b53Io8bQU2YPLK3bVdHX/ZMrNvv6pTb1bwhpDcs2TM+IlXZFr8XphpDTDUlbyRk9wFUZSN7ox7DQTvxz6pLS/YOmLW5t/cna9GHPBUcevXNcW2NgRRzYhjXX1Q1chhXq1+rn1YsJ0XpSkmIs75kBnMxYsvz7ALUrlpUDNwB9ge/GSaNCcUmEvFQmKYVjKNB9vwQ+Z7cOp5MzJnfl5M8vmKe5XV26CbG/MvvgF1dfMbrl8nE5lat+NGfL9L8dzH/n2eCohd1Xmvh8wndg1e1jDqlj0TXqcXYx+4PARb8DhUXlgdK9235RmJRZMSXU/XrR8srK9NwhbbW5/rLjm+6esmdWPHX1ciqAPcA8rJs86wHbS9UkCL8GhgHvxQoLvTZjyfLDbTWuXbEsB7gVeD1jyXKVIkdhO8qjljh8BZiIlZlI0QaVB0oXrbn35T0z73t3v7T+GXmd3b4h7Go1zHRfZc7CD7x2DYAyTM6jvC5dRx27BCbzspykDY0yhXbmli2PHtGkeVSTYaFJU9OkqQlpakJKTWCK8TePb7CmQSl6mFqsdPE+YC6wE6hBzT/rDBpwF7AAyATm1K5YVgJciTWP7Qjw74wly8MAGUuWVwIralcsuwJULlOF/SiPWgJRoPtygQ3AKLu1OB5B2cTPzjs5YNKQyZ3ZTEq4860FG07Vpc/pKWnJwsfHHlz90bFFC+zWkaA0ArbVjOoAB4AxdotwMoVf+scJacqYpj9PBDTddaDvuIEnfR+dOcWTmaLqffYcFUAfrHqL/W3WksiEubjWW8vz733AGWAWVmKRr2E5MlJUqn6F3Th9noQiCr8RKAWmA0/arcXxSAbsWrF2wv5ntq7szM0IIcAlpNl+S4WiWzjdo6bu4LWDnplyym4NdiBcoumyq8cOVEZaj7MLy8BQRlr3aGmkwcU3ybYAvwI+Dfwv8K6MJcursELUFQpbUYZaguE3ApXAncAxm6UkAq6TK4OLNi1/fW24MdThE65LKDezosdxuqGmbla0Q3peVq3dGuJJxpDsNYNmDy+c+wP/qJwxuUk5P89maoE1WOGNh7BC9RTxoY4Lz8mDI8+1tSuWXbpYt0LRwyhDLQGJZIP8DnDabi2JQN2p6nlr7n35WN3p6g7VPpqXV6IGqR1AWbNJjfoNtEPW8L5ON7ZjijvdEx66yDvEnaqn260lCdkFnMKaezYJa3qDGp/1PCbwX2Bpi+XZABlLlkug03PdFYpYok4ECYrfCPwRGALcbbeWRMA0wmM3/t9rmac3H223iPiiwad63bwTRdxx+iDf6fpsp8+o/tl2a4gn/cYNcmUM6ZO0SVTiiAmUR15vBvZiJQrz2qao99AyskYDrgFuxip1cD2wAngiqk1e7Ypl6uaEwjZU1scExm8ETODhAt2XB3zDbj0JQM7eJzZOq9hzunDcx2YuFqL1sejOin6nUQlbFD1LGGeff3W7BTid7FH9e8sNHTnr/ncfNeqa+rs8bicnwHEyJrAWyMUqe9EfqMYqsKyIHSeBF4AbgeibCpuAP2LNRVtP6zeiPp2xZPlrwGstlhdnLFle1wNaFYoOoTxqycF9wBeA/9gtJAEQpzceWbzhu69uCDUYrc4xmTOwdDjIpngLU/QqnB5aqAy1dtCzUgYgqLBbR0/i6ZO6Occ3cFXNybPlOaNzx9mtJ0E5jhXauAArzX5zcoss2xQlL0OAacAHsUrp/BLr+P8Ty1P2EK0baY9nLFn+XBt9hnpAp0LRYZShlgT4jYD0G4FfAu/BukunaIeG8to5a+59+XTNybOHW64bnFE/ZFZu2TobZCUUao5atwjbLUDRPYQQaG5XefstExejtqn/oBmXpQ2aOXy63VoSmMNAp8rEKLrFPCwP2tNYN5w+C/woY8lyE6sO7VTgi5H1O7CSs33xEv21WltVoYgXTg69UXSefNRdug4jQ2b+5h/+p3rs7TM2DZk3alb0unl5JWmbSnPtkqZIfpzuUXO6PkcgTZnU19D03Mzi3GnDlJHRdRpQc8/sYgiWkfZZ4Be1K5ZlAF/MWLJ8O7AdeKy9DmpXLBsCeICynhSqUFwK5VFLLmqArXaLSDCy9v9ty6xdv11bKM3zafmvHHpqop2iEgLZxiQ/RUdwukNSGWodQcqkDhGtLa6at+/JzVvs1pFgHMUqn7MPy1AbfOnmnae+vPZUqK6puubk2cN1p6uP1ZfXFjdW1pcZtU1VZtgMd6Z2aC/hC1gp+Nv9vdauWOauXbFsUu2KZcuBz2N9hgqFbST13cDeht8InASmF+i+EcD7gYdtlpQwlG0/uXjdtwo2z7r/2rHudE/2MwdHvYOqY3NJpPONDYWiR5Eyua+hembKNj3do27IdI4jWPOjYoZphI3y3cW7T6wqqqo+UjEg3BAaBaRx6QgaA6gVmqgVmmgQbq1Bc2mGcGlhTXeFXB5XaNhVYzyDW0STJCk7MpYs//KlGtSuWDYZSAUqMpYs34lVz06hsJ2kvsj0VvxG4AjwiwLd92VU9sIO03S2Yeba+18+Om3pVSWvHB2aYbceRVLj9MGv0/U5hCT1qAkqNLdWMeVLi3Iyh/SZarecBKPb3mgpJXWnq4+e3nj0aNn2E66Girr+phHu7OegAznSlDnSlBAyL5oYW7L1eOHxtw+uGTBliBzln5AMNybXARuwMmu+N2r57kttFAmLrM1YsnxHD2pTKLqEUC7y5KRA9w3CKqCp6Dx1P7nu12dNocU8ZCWZ+OiY4OqP+4LJcHG3g2qcPZ80iJpb0y5vf+G5KiLFcZMJd5q+KnVABkIIEAgQVqCzABASKYU05bl1CGuVNM+NJwQi8r+UQoalNdawVp8bdwghJCCkKc80VTWMpHk6hiAshJAIYQqBiSakEMJEIBFCWjIEwiXCmlszNbfL1Nya1HSXRBM0r0dYe7SCtAVCi2ynCeu9RZ6FJkAIhl05uilreF9P1KEQtB850LJNPZAeWRZ9w6Mj/Yg9T2ysLX3n2GRpyiHttI8l5tjbZ2xpOVc7gfkykIP1WawEmoA+wHXAX4ETGUuWV9onT6HoOMqjlrzMsVtAApMupTyrfArtoo5Q13H6/GA1R61jeNpvklA0AZ5QvTGt5ljlSWBsnPZbDwSwMvLZRvbIfiuzhvddZMe+TSNs7H9m69qSzUevJv7fK+3A01tzh8xLmgCcHwNvYM0VdAEf4XzmzU8DXwX+Yo80haJzKEMteXkZ2IwqqNklFh948dDbvpuVR+0SaEK547uB0w019dm2Q8QrlGq3jhgisULHNKzCzPGsm5YGVMVxf61Svqu479BF8Xckm2EzvPnHb2yqO129OO47jyBcoqH66JlgxEtKVKooITQhhEtzC7fm0lyaW7iES7g0t9CsNcKtpWguzUnntDSsotet8QCwuXbFMpGxZLk6zykcjzLUkhS/EZAFum+o3ToSlbmHXp23e8jsNaVZw+bbrcWpKHdat3C138RW1ACmHcyQ2URyedRWRp5jmggjkTgbLPNKKaUQ8c1ou+OXq9bUna62xZPXjNkUHrflp//t6uZV4+6YuT/vipF23hiuiTzy2li/BvgF1rntoDLSFImCk+6AKGLPJrsFJDJ3rfn+/H41p1Th6zbQhLrOdQOnhxYqO7wdTCOcTGm7JZbROdBGDbYncAo3htJlyCyK934bK+sTPSlN9r6/bp65+/frC6PmKcabGqzwxtuB9wFXA7OAy4HBGUuWLwBOAC9lLFmuilgrEgblUUtSCnSfDtgWRpEsXBN4NuPZGV+0W4YjUVXUukXIbgHtoGEZk+pmXhuYTeEmuzXEkDJgKDDcRg12GonnqCupPpU5NCeu8Y+zv3Xd3KOv71t79D+BPuGG0IR47juWlG49vnjTybNrZ973rtma2xWv8WU98BDwQCRByN9ba1S7YlkuEMhYsjyZfreKXoC6CCcvYeDDwGt2C0lkRpfunJxdX6E8k62goeaodYOWmbKdxnCszJQr22vYWzFDSWWopWLvHLEwlqFoO+W7T8X9vCaEYMR1l8+b/+MbfRmDs9fGe/+xpO509bx1ywp2GTWNZ3t4VyHgV4A3Y8nyZe1lccxYsrw0Y8ny8h7WpFDEHOVRS1L8RsAEXi/QfYXAE1gXwlORxxmslNJTgVuwJt4q2mD60beNwjE3nZSaK57pkhXJjdM9ap7IYxFQyMXe+UagAui1CXeSzKN2Fpho4/6LgWE27v8cZdtP5I24Np55VM6juV3umfe9e17JlmNbpCnl4Vf2hBvKahMug7NR2zR17bKCwzO/cU1lxpA+I2LcvQm8AnwlY8nygzHuW6FwHKqOWi+nQPf1xYrhrsWK8RbA9cAXAJ+N0hxFrSer4pUJH9t5YOCU+QihbnAAd12+f9Ut3sO9NvFANynBIaFeHWQzlnHpxgo1MoFpwDYsY67XUXW44uA7P3tztN06ukkR1nnfAGbYqGM7MMXG/Z9HIBc/8sFaIUSm3VLqTlcf2/h/rw0lcaOfKid+Zu6RAVOGxuqzrQM+B/xZJQNR9BbUgLOX4zcCZ4CWoRaPFei+3wL3AveTXJnNukRGU3W/D219fPEr4z+6ctvwxb1yYHox6jrZDZyeTKQlbWVzWwRswd5Bvi2YRsJ71Mqw6kyNxv6wwxqb938eiTBqGg94slKn2S0lfVDWZQOmDCks234yUeeb5+z67brMEddfvnrUeycs6GZf+4GbMpYs3xcLYQpFopCod2kUPYzfCDT6jcD3sO5yltmtxwkUZw8Pbr9sYbwKwDoeoSy17uD0OWqdoQY4iRVWXWuzlrgRbgol8me4HtiHFdJqt5EGDgsFrjpUYXtNt2Z8H505DSvUOFFxH3l174Idj6/qTkbIfcCVykhT9EaUoaa4JH4jsA9YigOKkdpFvTu96u8z7175xNxlg6TQ2qrR0htReR+7TjIZuYuBIVj1iyRWApJNWGF1q4BdUW33Y82RBdgTR40xJ9wUdpRx0QWc5NV1VHr6sp0n0+3W0Iye7snuNz5vvd06ukvFntOLN3zvlQ3hxlB9Jzc9iTUfrbgndCkUTkcZar2cAt03qUD33Vig+7Ij/2cU6L4LQh39RuAvWEkD7gDetEGmbZRmDjny8DUPuQ4PuHwRDpiz4CRUev5u4aRBcizJxAqHnAXkYxVPHo5VbHYj4AX6Ys2JSuhaRmZTOJE9agOB+XaLiCLLbgHRVOw5NdJuDdFM/tyCxf0m5BXaraO7NJTXzVl7/7+ONJypK+nEZk9w8fQMhaLXoAy1XkaB7vtkge5bW6D7jhTovqlY4Up/A8oLdN/rwO9pJTmA3wjU+Y3Ak34jcA0wAJgH3AxciZUtbD5WOu+kod6dXvXc9M+fQgjbC7E6EaHS83eH3nTssrHOD7MBV2SZjjU3agOwo0X7g4Dj538laOjjVqwEMNmc/yycgKMiFZrONuRKKS+Z7j3eTPzsvIVCEyfs1tFdwo2hceu/84p5tqh8fweam8CvMpYsT6qxhULRGVQykd7HS8CdWGFJI/xGYFvEm+YGwpG0/pfEbwTKgXUtlxfovluBf5EkNwB2DJu/uzI9d67dOhRJSbJ61DpDBjCHC++Wr8S6UbQSy5jwcb58SBnWTSI4f/5p/n2eijx3dMBfD1RihWJmAdMjy0NYxmMaVm2x8W11YDaFE/Ez7I/lTUu1W0gUNUCu3SJaYtQ0HvZkpU61W0czmkvTpn55cc32X67aZzaF7akfECtMmbf1wbeyxn1s1ua8OSOaExWFsMpERD9+k7Fk+XG7ZCoUTkAZar2MiJG1uMUySQzCkPxG4JUC3fcX4BPd7csJnE3rn9ChWT2NinxUxIipWMZROue9+c3Pq7CMikwsw60eCGAZV5djGWwSKzOtEVne0gN+GMuQa8IK4S7mfB3JwZHt92HNwx3JhSGB64E+kX1dQLgplIiGWjFWKKqTOIEDS8HUHKs822+8oxx99PEO8M374Xvrgi/sWFm87vAYTJnIdQwz9v1107r/b+/e45uq7/+Bv5L0pC1pKQVKrzQtt5SCVMULOKR4m7qIIqjbT3QKXjIZbMqmm9av8+t3GV+dOvky0ToEnaJzIgNrGBvDAVYRBZFxaxB6AWopben9Qk6T/P74pFhCW9I2yTlJX8/HIw9Kcs7n8+41530+n8/7k3S58RaIpKyFJfeJzsVEjfyt4yJgE8QF1iUI0Q21h7RUq2qBO4WVUC9E4U+DIEbWutJ5n77dEBUKo/Hdhb33iHcdxGhcCkTyVuX5d0qnY8Z6naMB0N0IxRSI4idfQ6yvO7OWyuVwhtpF5dcA2jz/qmakCN8Vl1GVU0WVktoSNQCIiJIGmf7f5OkZN2ZX7Xj670dc7a7RSsfUB/8E8JTZYd+hdCBEahcWU9RIVe6AKDoy0yzbp0Pcid6iaER91Ko3hOIaFAoN/NnqvYvw3b5f3RkCMRpnAHAKYn+3C/rZbzxEYuOEGOErAdDslENq6uNufLeGWE1JGqDS0vO1BytVPVoVOSQ6YeKDV7QiBNZzdlIP4FqzbL/eLDNJI/IFEzXyK7Nsr/YUHTnt+X8ZgGsA/AKh9YaCkmHjQ+2OOYUOJmp9MxK+FcFIhv8TkiEQI3yZAI4OvyD5NM4uoKTmxG0SRMKpxo2T1VTU5IzmEw2Zbrdb1fsCDs1Omph4Wfp2iJFStauDSNI2Kx0IUSjh1EcKOE+Bkhc9VSXfhthEW9UcusiWqtjURKXjoLDFRC20jY83JY6/5InrShtLTxUNzhg6QhcVEeloPN20d3lhk9zsUNuo1XZ0P71UaarZs+wsbsDZKpdEDNJPVDqUnoz/8WW54350cdt/Xi7cVn+k+pyKzSrRCuB6s2zfqXQgRKGGI2rUbzbJpLNJpjk2ydTjhH6zbN8HUaJ7CdR99xkrr3jyP+26yHFKx0Fhi4laGIhJictIviLzUkNKnDFqqCFpsHHoGCkmskXpuLogQSRrx5QOpAvDlA6gOy0nG1W5fs6bTh8RddEjM6YnX5G5TelYuvFjs2z/QukgiEIREzXqN7NsdwLYDOD3Nsn0uU0ydbuGxCzbHWbZ/gTEOhLV7Qnj0EW2LL3q+d21hsQp5z+aqM9UfaOC+kWNBVGNEFMM1VjJVrVrweoOVysdQq8kTclQX/UTYJNZtq9ROgiiUMVEjfzCLNvrALwAUWZ7tg/HfwoxFefrAIfWK63SoMYWfWx/iw8QnQ9H1MKXGte2JkH8zKktKeqoyqlKp/afUO1oX1diUuNGDhmboJZRtXYAywH8SulAiEIZEzXyG7Ns/9os239ulu3P+Xh8OcTi/E2Bjcx3cW21icObvuUUDQo0NV7Mkx+43W41jqgBYguCL5UOwkuV0gH0pKGkZozb7Q6ZrTR0kRHRk3467QqtpDusdCwQNRA+MMv23UoHQhTKmKiRosyyvQnAXAChNceE1DnBK3Rw6mO4UmcK3gbxfm9UOhAvjec/RDmudpfe5XCqIenxmTZCF3Hli7PGaHTaUoVDKQFwg8IxEIU8JmqkOLNsrwLwtNJxdDhlSFLbxYwqaVV6RUqkJLfbrcb3VRnAYagvUVN9WfmWk40hdxNRo9Hge8/OTEi/PusTjVajxFpwJ4DfmGX7Ywr0TRRWWJ6f+s0mmWIAXGqW7f/uRzNrAPwfVHDz4Cfbnog4lHjx55Wxaaf3pl4xHRoNx47I3ziiFqYioqQaANsgxtY06N8YW8f5Gnw3hq3xes3dzcfeIiF+7hT/G9uJ6v+21hZVumNHxisdRq9FREmGUTMnXmm8Pqvty99t2tFW3Rzo7Rm+AFAIUbTmfc86dCLqJyZq1G9m2d5kk0yX2SSTzizb/9XHNiptkmkbgBn+ja734tpqEy8t25wIAPtTLj/q0kSkKx2TGqn+CotIAXLT6QQAlyodh5f9AGoA7IK6YotUOoDzqd5bkZB+XZbSYfSZTh8RNdg4tK2t2u97dzsBOABEe/6/ySzbn/R3J0QDnZrurFFo+wOAB22S6e5+tHHaX8H4gxtAhKvd7+9uYYOZWn9wRC1MuV2qnPo4AUAagESlA/Gi+qGqxtJTY91ud0i/D4yZk3MBNKj0Y5P7ANzu+RcAtgB41Y/tE5GHGt9QKASZZbsDwJ0ADtskU18v4VUzwusG8MbUJwodEVHjlY6FwhIX+IUrjWq/txUA1DY7IFXpAM7H7XLraosqdykdR3/oB0cNHX5BSpEfm8yCeL8uAHAfgGvNsv24H9snIg/VXBhT6DPL9nYA2/vRxPsAPgXwBAL7s3kaYhpQDYBTnT6u2Z12peFj023XaOFytkmGaQGMIfSp9XI0NPCrF6Y0Go1av7cXQ4yATFQ6EI9qAMOVDsIX9nd2maY88wOXRqMJ2ZvbsenxEdX/+dZfzUUAmGmW7T/2V4PhaNeuXSMiIiJWQPzOhezPTohyAdjX3t5+/+TJk08qHUx/MFEj1TDL9nybZJoBYBaAST6eVg+xF0+153FW4tXN/1vMsv2ci6lpM7emQazhGNGvT2SAcHHuI9G51Juo7QSQAeAIgNHKhgIAqESIJGqna1sTXe2uIzpJp4avW5/EjIwf3IfTvvH8O9br+R0AnulfROEvIiJiRVJS0viEhIRarVar1r8LYcnlcmmqqqqyT5w4sQLAzUrH0x9M1EhtvoBYpPwJRALW06PaM+XSX2aASZrPXHzbITqHRqtR6/rDqRAzHr6ndCAeDUoH0BuH3t1Vm3X3pc0ajcagdCx9MTQ78QLT3Eu+LF6/N0JuOn2R18sd1UI7KwcQB2AVgMcg1qD9C2LT9ONd3eykc0xkkqYMrVbrTkhIqD9x4oRaZhD0GRM1UhWzbG+BmKKjhOMQb0JqqoqmWm5w2wIibxqdakfU6gAMBnAUYmRNaf68yRZwlV8cvSThwrStwyel5CodS19oNBokT824NOlyo/uzvI++khtPd36flSHWmr0FoBbAiwD+BGAcgAMA3jLL9v1BDzr0aXuTpNU3yrqNmyvjq2tPS8PjI+UbrkmsjYuVnIEMMJx5vvYhP+U05D8BIn8pLMjdUliQexmAFIg3rb8BCOlqX4HkVuvlKJGCNFrVJmrDAYwHUAZ1VB0NuRs9J3aUxSkdQ39ptBpNwkVpTV5P6yG2SjgCUYnzfyCmN5aaZXsNk7TAW76qOGn2vZ9PWvb6EeO7a4+nLHv9iHH2vZ9PWr6qOMlffezevTvqwgsvzNLr9Rc/9dRT3VaALSoq0k+aNCnLaDRONJvNo9ra2jQA0NraqjGbzaPS09MnTpo0Kctut+v7E093/XjbsWNH9IUXXpg1ZsyYCePGjctuaWnRAMAnn3wyaNy4cdnp6ekT77333pEulxr+rPkfEzUiL4UFuRWFBbkrCwtyZ0Nc3NwA4I8AShUNTGXUejVKpCj1rlEDxAV5GsTGxEqLPv8h6jLYGN/odrtDfoQj8wfZObqoiANeT18H4CcQSw8AYBDUVyU0LC1fVZz0ztpjqacdrrOuyU87XNp31h5L9VeyNmLEiPalS5cetVgsPW7VsHjx4rSFCxdWlpWV7YuLi2tfunTpcABYunTp8Li4uPajR4/uW7hwYeXixYvT+hNPd/10Jssy7r777sxXXnml7PDhw/u3bdtm1+v1bgBYsGCBcfny5WWlpaX7iouLo9asWdOXdZiqx0SNqAeFBblthQW5/ygsyF0EYBRE9aZfQ1zohOftGx+53SF3Q5wo4DTqHik6BLH2aLrSgQAYpnQAvVX84b4rv9125FOl4+gvKSYyTqPTyl5PTwFwDYDvA5gMIMks20P+c1W7+kZZ98FH5ck9HfPBR+XJDY1yv6/XU1NT23Nzc1skSer2ZpLL5cL27dtj582bVwsA8+fPrykoKBgCAB999NGQ+fPn1wDAvHnzaj/77LPYvo5i9dRPZ2vXro0bP35869SpU1sBICkpyRkREYGysjKpqalJe+211zZrtVrMnTu3Zt26darfl7EvmKgR+aiwINddWJC7v7Ag99nCgtwrIQqP3AXgLxDVJwcUNQ8bEClGvfuo7QJggHrK8/d4cao6GrgnPnjF1pTpo6cqHYo/DM4YWuf11BqIgjMHzbL9K7NsH9A3IoNl4+bKeO+RNG+nHS7txo8rg5KEVFZWRsTGxjolSQIAZGRkOCorK/We1/SZmZkOAJAkCTExMc7Kyso+1broqZ/O7HZ7pEajwbRp08ZmZ2ePf/LJJxMBoKysTEpOTj5zs8FoNDoqKiqkvsSidiwmQtRHhQW5NQBWA1g9beZWCcAVAG7yPLKUjC0YWEyEqAtqTdPECFY5gGylA4Go+BhS05QSclK/DtVCIl1JviIztv5w9d+dp9vHQtxofA3Arz37oVKQVNee9im5qD7lCEoS4u5i8XnH3pA9vebPfjprb2/XfPnllzE7d+48GBMT47ryyivHXXrppS1Dhgw5ZwqyJkwvSZioEflBYUGuDGCr5/HotJlbx0Ds3XELgGkIw9FrFRQTafE8ADHdzDsid6fn3F6ve7/m/bzW6zlXNx+ji+M74kGn57We5zRdHEvhRL3XCjEQCVKM0oFAbK8SUolaTHp8WBWWSshJvTjhhVQZwFtOh/M3gxf973GlYxqIhsdHek9B7fq4oXqfjutsyZIlCW+++WYCAGzcuPGbjIyM87aRlJTU3tjYqJNlGZIkobS0VD9ixAjZ85qjpKREP3r0aFmWZTQ1NelGjBhxVsK0aNGi1E2bNsUBQFFR0VnrIKdNmza2urpaysnJaX733XfLuuuns7S0NMeUKVMak5OT2wHguuuuq9+5c+eg+++//1TnEbSysjJ9UlJSr79GoYAXDEQBUFiQe7iwIPfFwoLcXABJAOYD+BBAm7KR+Y/yeRq+ggPTNfMAACAASURBVCj2MhxitGC41yMBYnrqCACJEN+HjkcyRHXPFACpnR5pAEZ28Vy652EEkOl5jPI8Rnd6ruOR4Xl0nJfmaSvF03/Y3Jmns13w0LSx0KBG6Ti81EL8yk5SOhCPkJsqnjw1IyT3T+tCO74rGCIBiNHpdVUKxjOg3XBNYm2kXtvjNNNIvdZ1w9WJtb1t+/HHH68qKio6UFRUdMCXJA0AtFotpkyZ0rhq1ap4AFi5cuWwm266qQ4AzGZz3cqVK4cBwKpVq+KnTp3aqNWenUYsW7asvKNP77YLCwu/KSoqOvDee++V9dRPZ7feemvDwYMHoxsbG7WyLOPTTz+NnTBhQpvRaJQNBoNr8+bNBpfLhdWrVw+75ZZbzjk/HDBRIwqwwoLcqsKC3FWFBbm3QCQQtwJ4E8ApZSPrHxUUE+EaClKdqPhBI66w3gRdtLRP6Vg8ygEcg7hxoRanlQ6gN4bnpH6tj43y3iQ6VMkQPw+bASwBsMRgsYbU9yOcxMVKzjk3pVb0dMycm1IrBsdK/X6/O3r0aERiYuKk1157LfEPf/hDcmJi4qRTp05pASA3N3dMaWmpBAAvvPDC8WXLliWlp6dPrK2tjfj5z39eDQA///nPq2trayPS09MnLlu2LOn555/v1yhsd/2sXr067uGHH04BgISEBOfChQsrL7roovHZ2dkTJk2a1PKjH/2oHgCWL19e9pOf/CTDaDROzMjIOH377beH3A0gX2i6midKRIE3bebWCIhpkbM8D6OyEfXO3eMOF941rniaj4fXQoyAdWR3vvzrPSURXs+dhCiOcB1E2XEi1agvrjm0+8V/p0L8jCplH8RoczyAKAXj8LYDwOVKB+GrkdeO+2z0rElXKB2Hn5yCGNE0AvihwWJdo3A8YWvPnj2lOTk51b4cu3xVcdIHH5Undy4sEqnXuubclFqxYN6oE4GLMrzt2bNneE5OTobScfQH16gRKaSwILcdwBYAW6bN3PoIgBx8l7TlKBhaIHxhsFhvCETDzfl5cRDVN/8YiPaJ+iJu1LBx+tjIrxyNpy9WMIxaiLVgakrSADHlLmTUHaoaoXQMfhQF4GGIG13/VDgW8lgwb9SJu24beXLjx5Xx1acc0vChevmGqxNr/TGSRqGNiRqRChQW5LoBfO15PD1t5tZMiEIkswBcCRVOU3a7e1ViKWCVxAwWaz2Al5vz8yZCbNhKpDi32w2Nrue1JwF0FGI9bMcaSbUJqRHwxmO1o083tH0VOThKyaTbXyQAzwHIMVisnFKlIoNjJdcdt6SpbX0rKUx1F39EBBQW5JYUFuS+VFiQOwOiEMY8AOuhomIkvXyHD0bJ58cAlAWhHyKfOE+3KzWSlQhRWVGt06nVNsLXMzc037z3VbhcL7UAyDJYrCeVDoSIzi9c/vAQha3CgtzqwoLcNwoLcmdBRcVI1JaoGSzWRgD3AWgNdF9E56PRaHDF724yGZIHf6ZA93sAfE+Bfn01SOkAekuKiXQoHYOfxEHM1CCiEMBEjSiEFBbkNhcW5K4rLMi9F+Ku+VUAlgEI+h44vaz6GJT9TQwW62aIEvgLIC5WiRSjlXTShPunpgMI5h5cHaPuvS7nHURq2MutV4ZNSA6XG0AHAfxN6SCIyDdco0YUoryKkTwM4FIAcyD29QK+27zZ+wGI0vbdvd7d46xzmtojqiEqOfpiZx8/zV4zWKx1AF5pzs97DcAKAPcGq28ib4MSY9OCXFQkCqL8fXyQ+uuLkEvUYo3xIRezl1YAfwVgNVisDUoHQ+dqdbh1B44hvrkNkiEKcvZI1EbrNc7zn0nhjIkaURgoLMh1QZS83qF0LGphsFidzfl59wFwAHhQ6Xho4Er/fpbj8AdBGeBtBfAFlN0SwBdaiJG/kFmrJhki1bQPXV/UQVQAPap0IHSubfvdSV8XI7nd9d1Mt08PYOSFo9wV0ydo/FKe3+VyYf78+SM//vjjuKioKNfKlStLp02b1uJ9XFtbm2bevHnp27dvj9VoNO7f/OY35ffee29da2ur5rbbbsvcu3fvoCFDhrS///77xSaTqc9TgouKivR33HHHqPr6+oiJEye2fPDBByVRUVFnrar429/+NvjJJ59MlWVZI0mSe8mSJcdvvvnmRgD45JNPBt13330ZbW1t2quvvrp+5cqVx7w34A4H4fcZERF5GCxWF0QlyKVKx0IDV9pVY6fEjBxSGISuoiH2ILwkCH31V5PSAQwwyQCOcHNr9dm235208zBSOydpANDugnbnYaRu2+9O8kc/77//flxxcXFUaWnpvldeeaVswYIFXVaEffzxx5MTEhLk0tLSfYcPH95//fXXNwHA0qVLh8fFxbUfPXp038KFCysXL16c1p94Fi9enLZw4cLKsrKyfXFxce1Lly4d7n3MiBEjZJvNdvjQoUMH3njjjZL7778/s+O1BQsWGJcvX15WWlq6r7i4OGrNmjWD+xOPWjFRI6KwZrBY3QaL9WEAU8B9g0ghWXdf2q+LmjB0zp18Ndv5v5vgdruDUb02EBYCuAzAE0oHQmdrdbh1Xxcjuadjvi5GcpvD3e/r9fXr1w+ZO3dujVarxTXXXNPc0NAQUVZWds6ehu++++7w3/72tycAQKfTITk5uR0APvrooyHz58+vAYB58+bVfvbZZ7EuV992IHG5XNi+fXvsvHnzagFg/vz5NQUFBUO8j/ve977XmpGRIQPA5MmT2xwOh7a1tVVTVlYmNTU1aa+99tpmrVaLuXPn1qxbt07N0737jIkaEQ0IBot1B4AbAGxSOhYaeCIHR8VDq6kIQldD0OuirIpQzVYjvmg50Zje3uzYr3QcfVRusFi/NFisQSnqRL47cAzx3iNp3tpd0B441v81pxUVFVJGRsaZqYrJyckO70SturpaBwCLFy9Oyc7OHn/jjTeOOnbsWAQAVFZW6jMzMx0AIEkSYmJinJWVlX1aQlVZWRkRGxvrlCTRfUZGhqOysrLH/RXffPPN+Ozs7Jbo6Gh3WVmZlJycfObn2Wg0OioqKs5JOsMBEzUiGjA8G7zeA8CmdCw0sNQfqS6Fy50YhK7SILbuqAFQ3elR1enjGs8xnR/1ENMR2wD0VMCgEWK9U3/1Zgre1wAaunmcgvicKgF8C6AcwDEApQAOAyiCqHR4CMARz+Mbz6PYc9wxz7kncPbX7KzH/pWfq7mSZk/GN+fnXaR0EHSu5jb4lFw0+XhcT9zuc+/faDRnV2+WZVlTWVkpTZs2renAgQMHL7/88uZFixaN7OH8Pt0U6m1bO3fujHrqqadS//SnP5X1cH5fQlE9FhMhogHFYLFWNOfnzQTwNICnFA6HBojT9W1tCM7N0aEBbr8IosJsf/k6ulMD4AIAOj/02S91h6qmNh2vK4lJG5J5/qNVJRrAwxA3qUhFDFG+/R7E+HhcZ0uWLEl48803EwBg48aN36SkpMilpaVnRq0qKir06enpZ7WbmJjYHhUV5br77rvrAOCuu+469fbbbw8HgKSkJEdJSYl+9OjRsizLaGpq0o0YMeKsmzqLFi1K3bRpUxwAFBUVHej82rRp08ZWV1dLOTk5ze+++25ZY2OjTpZlSJKE0tJS/YgRI7r8HI8cOSLddtttY15//fWSCRMmnAaAjIwMufMIWllZmT4pKSksR4w5okZEA45nZO23AO4C8ArE3XWigKkvrg6pqX7daAUQ66e2fL2oGgpAiU3DuxLZUHqqSukguuKSnXL51sM7yv5Z9Jmjoe2U18tHAXyiRFzUs+yRqI3QoseFXhFauLJH9n5fxMcff7yqqKjoQFFR0YGMjAz55ptvrlu9evUwl8uFzZs3G2JjY51Go/Gs30PP+rV6m80WCwAbNmwYPHbs2FYAMJvNdStXrhwGAKtWrYqfOnVqo3eVxWXLlpV39OkdT2Fh4TdFRUUH3nvvvTKtVospU6Y0rlq1Kh4AVq5cOeymm246Z6S+urpa94Mf/GDs008/ffz73//+mf0ojUajbDAYXJs3bza4XC6sXr162C233OKPkX7V0XQ1fEhENJA05+fFAXgbwE1Kx0Lhp73F0Vj42IduiPLooewQgHF+autTAN/zes4OwNTN8dsBTPVT330mGfRfX/zLqxMiBuljdHpdtCZCq1fDlKsDq3ZsPbnrWK7nvw1Rww0HkqdkOIdOSE6OHm54MSJa+qfBYuUNqSDas2dPaU5OTvX5juuo+tjd65eMQbk/SvS7XC7cc8896Vu2bBkcHR3tWrFiRen06dNbACArKyu7I7k6dOiQ/s4778xsaGjQDRs2rP3Pf/5z6dixYx0tLS2aOXPmZO7fv39QXFyc87333juSnZ3d5/L8Bw4c0P/whz8cXV9fr5swYULL2rVrS6Kjo92rV6+O+/LLLw0vvfTSt4899ljysmXLkoxG45mp0ps3bz6Umpravm3btkH33XdfZltbm+aqq65qeOONN456J4579uwZnpOTk9HXGNWAiRoREYDm/DwtxIXg3+G/UQMa4FxOl+ur5z/+rOlY3TSlY/GDAwCy/dTWNgDTvZ6bDeBCdD8l+QuI6oVq4gLQAg3aNBrNaY1Wc1oToW3VSrrTusgIh04f4YyIlpySQe+WYvTQD47WRQ6J1kuxkXrJEBklGfTREYMkgxQTGa/Vafs8vfPL3/3z0+ZvG7wT3w43mWU71+UGma+JGtD1PmoRWrguHAW/7aM2EIVDosY1akREOLPn2qfN+XlTAHwIYLTCIVEYOLnr2O6mY3XdXUCHGhOAkwBG+KEt77vE7QA2A1gH4HGgy+IJbq/jWyCKpAwHEOeHmPpCCyAGbsS43W64XW6g3QVnWzvkxl5vWdao0WoaNTpti1bStur0Ead1kRGOiEGSM2KQ3iXFREIy6LVR8YMkKTZS0sdGRcebRmQDQPr3s6IOvvFFd+2O7MfnR0EwfYLmxGVj3ScPHEN8UxukmCjI2SNRG6XX9K3+PYUNJmpERJ0YLNYDzfl5qyDWsBH1S9PxuhaITajDgQ6iuIcWIjnyp0KzbG8AAJtkakTXRVE672NWAuBnECNwD6DrRK0FwHGI6ZptABxQ9/TTWLfLHet2OeGSnWhvOc8yPg2qr/jdTbX62Kh4p8PZ0wX9BX6NkgIiSq9xXTwaNUrHQerCYiJEROfq9tY0UW+4ZGe4rS8YD5H8+KIZwD/Q/b5unZOLv3f6uLv95vZATJkEgAJP23sB7INIxPZBrH37BEA+gAkAroZI5oZATK1c62Ps6ufG8IbSU8cA4MTnpT0Vq7nLJpl4vUcUgjiiRkR0ri0A3gJwt8JxUIj7trB4otIx9EETgAchEqlxAPIARHZ6fSKA/wCY1Om5RyC2vCgDsAPiZscOiHVtowAshtj3rBhiJGwDgGsh1qndjbP3NjRDTD/uaL8VwEeeti8C8D8A3oEYSYsC8H8AHoVICEsgRjANECNq0RAjSvcCuBHovmhDKLK/s6u9cnTZ1qajtWk9HNYOIAPia09EIYTFRIiIutCcn2eAuDicoXAoFMJKbPsLy/5+MJQKibRBFJ/Y3PGETTLlAFgK4HKIxAg4uxjIPyGSpmqzbG/pT+c2yaQBkATxe/c6gK8APGSW7Xttkkljlu1um2SSALSbZXuvL2BskikTwA0A7vD0sRpiE2wjRGL3UH/iV7HbzbJ9jdJBDCS9KSZCgcFiIkREYcpgsTY35+fNgSieoPhmuxSapJjIUHqfdQOY1TlJAwCzbN8DYIYnQboSYu/BBojCH80ALGbZ3uzdWG95krQ0iPVvewEMMcv2M+W/OxIzs2zv88a2ZtleAuAVm2R6FcD1AH4K4BiAyRAjeeHqSgBM1FTM6XTqGhsb451Op6TT6eTY2NhanU7nPP+ZFM44Z5mIqBsGi/UUxGaxRH0yKDE2lLZ60ECMLp3DJpluB/BriGmPmyFGaG41y/a7/JGkASIRM8v2Y2bZvtss2/d1TtL8zdPXRgC3AigCMBfALTh7CmZfdWy8q6YpS3fZJFPk+Q8jJdTU1CQdLSubdKqmxlhfV5dyqqbGeLSsbFJNTU2Sv/pwuVy49957R6anp08cN25cdmFh4SDvYxobG7UzZswYk5mZOWHMmDETFixYcGaqcGtrq8ZsNo9KT0+fOGnSpCy73a4Ppf5DFRM1IqJuNOfnRQPIVDoOCl3lWw+H2tSnbTbJtNommX5tk0w/skmm2TbJlAfgXQDPAPgxgL+bZXubTTLpbJJpgrLh9o9ZtrebZXuhWbbXQySquwD8Gd0XNOlJNUQBk6EQfzeiIfadexFAr2v1+9lQALMUjoG6UFNTk1RfV5fqdrvPuiZ3u93a+rq6VH8la++//35ccXFxVGlp6b5XXnmlbMGCBeldHfeLX/yisqSkZP++ffsO7NixI+avf/3rYABYunTp8Li4uPajR4/uW7hwYeXixYt7Whepuv5DFRM1IqLuteG7u+NEvbG77nDVbTV7K65UOpBeSgZwJ4AlEMnZBxBbVegAbIJYi7bDc+whABttkslfm2Aryizb1wN4FUACxNehN2oBrABw0DNaV2qW7afNsv2gWbb/AsBYAH8CoORUtvsV7Ju64HQ6dQ319T3+rDXU1yc7nc5+X6+vX79+yNy5c2u0Wi2uueaa5oaGhoiysrKz9iuMjY11zZw5sxEAoqKi3JMmTWo5duyYHgA++uijIfPnz68BgHnz5tV+9tlnsS6X79u8Kd1/qGKiRkTUDYPF6oaY5qWUfhVmIMW4Afzs65e2NiG83mevgyju8VObZLoEoppjWN3VNsv2Coi1ai/4eIoToiDJjyAS1y7XJHqmdD4Isb2B3Q+h9sU1NskUrVDf1IXGxsZ475E0b263W9vU2Bjf374qKiqkjIyMM9OJk5OTHd6JUmfV1dW6TZs2DbnxxhsbAKCyslKfmZnpAABJkhATE+OsrKz0eQ2u0v2HqnB6AyEiCoR7AGxXoN/XIYoq/BBi3YwTwE6I4g0HvY7tc3EF6lZ/1hf9dcvCNfEI/Kbp6wHMw3d7iwXLUwC+9Hz8MYBrgtx/oA2CGB3raT/FFohtAUZ71un90yzbV52v0IlZtn8DUcBECTKAbi+MKficTqdP3492H4/rSVdV3jUaTZfHyrKM2bNnj3rwwQcrs7OzHT2c7/PfSaX7D1VM1IiIemCwWJsh9m0KtP0Q+1XNhqhA91uDxdpqsFj/arBYbwIQa7BYLwUQB1HQIRPATACLAEwBkAvgmyDEOVCUQEz9A8QIyBUA/tfHc2dmmLO1EJURn4aYLng7xIjUpRDT4EZAlLd/CWJ/MF+VArBCfO9nQyRKgwC8AeAXAL7tRVv98SGAe8yy/RoAq4LUZ7C0QEz13Of1fBlE0ZHHARjNsv3nZtnem+8dAMBTVfP1fkfZe3qIBJRUQqfT+XSTLcLH4zpbsmRJQlZWVnZWVlZ2aWmplJKSIpeWlp4pwFFRUaFPT0/vst0777wzY9SoUW1PPfXUyY7nkpKSHCUlJXpAJFJNTU26ESNGnDWVd9GiRakdfXq3GYz+wxH3USMiOo/m/Dw9xGjWBQHqog7AOIPFWtWfRprz8+4G8BzEPlTUf8shRo4OGizWHc35eXnoeZRsK4CvPR+XAljqmT7bI09Z+osgEq85ALLOc8pzZtn+K8+50WbZ3ur5eBBEQYtAT287COBNiGqJ//JX1Uc1sUmmFIjNvtsAVEJ8XZv6sndbD31cBrEm7iJ/tekDB8S2B61B7HNA8mUfNafTqTtaVjapp+mPGo3GlW407tHpdP1akPWXv/wlbvny5SO2bNnyzb///W/Dww8/nL53717v2Rn42c9+lmK326M3bNhwRKf7bmeaJUuWJOzduzf6nXfeOfraa6/Fr1u3Ln7Dhg0+b6KuRP/hsI8aEzUiIh805+fFQFSDuzUAzS80WKwv+6Oh5vy8qwH8EWItDPXPqwaL9SEAaM7PGwYx6hkHMf10mOeYSojRrNcNFqtfRjRtkmk8xM/ZtQBMAFI6vdwMMYq2AGIE7SGIi/2VAPaZZbvLJplKITZw7q8KnFtUowbAZQBOmGV7i00yac2yPfxX9AeITTI9BuDZIHf7S7Ns93UNHvWRrxted1R97O71uCFDyocNG3aiv/G4XC7cc8896Vu2bBkcHR3tWrFiRen06dNbACArKyu7qKjowJEjR6QxY8ZMyszMbNPr9W4AePDBB08uXry4uqWlRTNnzpzM/fv3D4qLi3O+9957RzqmJaq1fyZqREQDSHN+nhbAbQDug5jG1vUE+94pAzDaYLH6bQpHc37eMwDuBpDhrzYHqGMAFhgs1o+a8/MyIEaqIiGqIgJi7eJHBos1oGsEbZIpFmK65CAAX3TsL2aTTHEQI6j3A1holu2veJ6fDZFAXgWxxnJMH7ptBDANYqPkxRCFQzqcAvArACuZpPWPTTJNAfAvAIYgdlsNYJRZtjcGsc8Bx9dEDRDJWkN9fXLnkTWNRuMaHBdX4Y8kbaBiokZENEA15+dZIEYy+mu9wWL1+/5Gzfl5GgAXA/gMYm0K9c1DBov1Vc/X8yGIEbU/GyzWcoXjOqOnUS2bZNJBTNu9sA9NNwN4C2Id5IUAYiFG8zr81izb/6sP7VInNsmUCmAZAjNa350nzbLdGsT+BpzeJGoA4HQ6tU2NjfHtTqcUodPJMbGxtf2d7jjQMVEjIhqgPOvW/gtiZKGvFbnqAVxtsFi/8ltgXprz8xZBbLgb9mWMAyjZ86g2WKzHlA7GV561b9EAbgLw3wBaIUblxkAUy/DWinPXt9UDSDTL9tOeNn8HUUyj47WjEAVMLABOAnB1HEu+s0mmSIj96XKC1OUnZtk+PUh9DUi9TdTI/8IhUWPVRyKiPjBYrA6DxfpfENUX7wKQD7GGqb0XzbwfyCQNAAwW6zKI0ZDCQPYTpo5D7Kk1A8AloZSkeWggCqI8CVES/i2Iqn+/8zpuN4BHICpSPgWxV1qHx3H29g+vQSR6HaOLFwC4HqJ4ymcAttokE68tesmT3F4G8TUMhmk2ydSXUVYiCiKOqBER+YlnetxyAD/x4fBKAH8C8JQvlQH7y7O+rmNjXupZNYATAOZDrPPaYLBYvUu1hwSbZDIAuARiT74LIW4sLATwM4hRNgC4zizb/9XpnEgAn3uOf8Es23/ZTdsjIb5GT3u9tABi/RpH1nrJs+7wOICYIHS3ySzbvx+EfgYkjqgpLxxG1JioERH5WXN+XjKAqRAXxFd1cci3AC42WKyVQY5rLEThgvRg9huCfgOxv9l4AF8aLNawWSfiGe2aClGm/X4ABwC86p1U2STTxxA/p0+ZZXu3JbA90ytfhxhRewTAEIgiK/eaZfuhQHwO4c4mmZIhfkdTIP6GXB3A7m4wy/Z/BLD9AYuJmvKYqBERUbea8/OGQ1zETgWQ4HlaBpBrsFi3KxRTMsQde05P61opxNQ+dzglaL1lk0yRvo6I2SRTDIB5EPsB3g4xXdJulu29mQZMXbBJplkA/hbALvZCjKgG9abRQNDbRM3d1qJrP7Q73t3SKGkGxcoR4y6q1UQNCvsNnQMpHBI1vlETEQWIwWKtNlistwBIBDAXopT6g0olaZ6YKiAKQFDXXjJYrM6BnKQBZ9ZM+SoKwFGzbH8LwByzbN/PJM1v/gGRTAXKBQAsNsnEyrAKcny+Manl7ecmObZvMMp7PklxbN9gbHn7uUmOzzcm9afdNWvWDM7IyJiYnp4+8Yknnuiyraeffjpx9OjRE8aNG5c9derUcYcOHTrzs7Bs2bJhRqNxotFonLhs2bJhXZ3f3/4BYMWKFfGjR4+eMGbMmAkzZ87M9Ff/4YAjakREA0xzft5vIKb3+WMfuHBRBlGK/+9KBxJqbJIpgolZ4NgkUyKArRCbnwdCOYA7zLI9WIVMBgRfR9Qcn29Mkvd80u2G11LOleX6KTf0ei+19vZ2ZGZmTvzHP/5xaNSoUXJOTs74d955p3jy5MltnY8rKCiInTFjRnNsbKzr2WefTdi2bVuszWYrrqys1E2ePDl7165dB7RaLS666KLs3bt3H0hISPBplM/X/vfu3Rt5xx13jN62bZs9ISHBWV5eHpGamtre3/4BjqgREVEIMlis/w1gk9JxqIQTwPMAJjBJ6xsmaQF3EsAHAWw/FcD1Nsk0OIB9UBfcbS06ed/nyT0dI+/7PNl9uqXX1+tbtmwxGI3G09nZ2Y6oqCj37NmzT61Zs2aI93EzZ85sjI2NdQHAtGnTmioqKvQAsG7durjp06c3JCYmOhMSEpzTp09vWLt2bZy/+3/55ZcTHnjggZMdCVhqamq7P/oPF0zUiIgGpj8pHYCfOTz/ek9Z7GraSClESfo/QyRojxos1uYAxkbUHxoEvlqrC4DTJpm432IQtR/aHQ+n3PO1uFPWttt3x/e27WPHjulTU1M7/i4iLS3NUV5e3uMU1/z8/IRrr722HgDKy8ultLS0M+enpqY6ysvLfd4z1Nf+Dx8+HHno0KGoiy++OCsnJydrzZo1g/3Rf7jgLyQR0cD0IcSd+hFKB+IHNQDmANgCoBnAPohy9DUQe1N1TPFcCuB9AAcB1A30dWgUGsyy3WWTTGsBdLlNgp9UAHgbonDJnwPYD3Xibmn0KfHw9bizzuliaZNGo+l2vdPy5cuH7tmzZ1B+fr69h/P93r/T6dQcOXIkcvv27faSkhIpNzc3a8aMGfv723+44IgaEdEAZLBYHRAbOYdDCfVhALIB3AhxsfkygGkA3oBISC0QZc43AthvsFhPMUmjEBPoQgozALQCuNUmmSYHuC/y0AyKlc9/lO/HdZaenn7WCNbx48f1KSkpXbazbt262Oeffz55w4YNh6Ojo90AkJaWJh8/fvzM+eXl5eec//HHHxuysrKys7KyslevXn3WtERf+09OTnbMnDmzLjIy0p2VleUYNWpU2/79+yN96X8gYKJGRDRAGSzWgwBWoOvpX38U3AAAEGZJREFUgaHmGYj9pn5psFhXGyzWkxBbI/w/AGsBrDBYrBsNFmudkkES9dGYALd/NURl2ssA5HAKZHBEjLuoFjqp55tGOskVYbqotrdt5+bmNpeWlkYVFRXp29raNGvXrh06Z86cc/7+ffrpp9GLFi0yrl+//nDH+jAAmDVrVv3WrVsHV1VV6aqqqnRbt24dPGvWrPrO51599dXNRUVFB4qKig7MnTv3rNd87X/27Nl1W7ZsiQWAioqKiJKSkiiTyXTal/4HAv4iEhENbCsAWAGE4tz/BogpWyaIqY7TAPy1OT/vboPFWgtRKMRpsFjbemiDKBQEOlFLhPj92Q1gFsSm5QcD3OeAp4ka5JQmTqnoserjxCkVmshBvZ4BIEkSXnjhhaM33HDDOKfTiTvvvLP6kksuaQOAhx9+OOXSSy9tnjt3bv2jjz46sqWlRXf77bePBoCUlBTHxx9/fDgxMdH56KOPfjt58uTxAPDYY499m5iY6HPFRV/7nz17dsPGjRsHjx49eoJOp3M/88wzx5KSkpwA0J/+wwXL8xMRDXDN+XkfA7hK6Tj64D8AnoRYe5YJ4FMAqwD8hcVBKFzYJNOFEAlUoL0MoBrAKrNsLwtCf2GtNxteOz7fmCTv+zz5rMIiOsklTZxS0ZfS/CSEQ3l+jqgREdFDAIqUDqIPJgHYCeAXEOtrpkKMonH9GYWTh4LUzw8BPMAkLfj0U244IV00/WS7fXe8u6VR0gyKlSNMF9X2ZSSNwgsTNSKiAc5gsdqb8/NuAbBe6Vh8dBLAtwDGef7vgLiY/W+DxfqVYlER+ZlNMsUBuCtI3Q0HkANgXZD6o040kYNc0qTv1SgdB6kLi4kQEREMFuuHECNUf1M6lh7YANwDoAnAfgA3AXgLouLjHCZpFIZmAxgUxP4etEkmQxD7I6IeMFEjIiIAgMFi3QvgTgAHlI7FixvArwH8DMB4AFkADgO4HIDFYLH+3mCxtvdwPlGoujzI/aUgeCN4RHQeTNSIiOgMT4XEuRAjVmryJIAjEHulGQFcCuBFg8V6RNGoiALrEgAnAPwKYopvMPyK5fmJ1IGJGhERncVgsX4NMcVQLTQAYiBK8d8C4HYAt3s27SYKSzbJFAkxHbkRwL8gyuYfhSigE0iZEPsPUhA5aup0xUvfGH7w188lFy99Y7ijpk6ndEykPCZqRETUla+gbCXIvQBeAvAEgEcAlACYA7Hf23KDxdqiYGxEwZAK8fM+FsA7ENtRvAnglSD0/YBNMjFRCJKDj/8+aXNm7qSDv1xiLH7h9ZSDv1xi3JyZO+ng479P6k+7a9asGZyRkTExPT194hNPPNFtWytWrIgfPXr0hDFjxkyYOXNmZsfzy5YtG2Y0GicajcaJy5YtG9bb/m+//faMoUOH5owdO3ZCd8e4XC7ce++9I9PT0yeOGzcuu7Cw8MyazP72Hw6YqBER0TkMFqsbwHwAXyoUwgUAHgawyWCxvgSxNq0CQLvBYq1XKCaioDHL9mKImxVHAdjMsr0cwG8AXBmE7q8E8OMg9DPgHXz890nFz69IdbW2nXVN7mpt0xY/vyK1r8lae3s7HnnkkfQNGzYcOnTo0P4PPvhg6K5du6K8j9u7d2/kCy+8kPz5558XHT58eP+rr756DAAqKyt1zz77bMoXX3xxcOfOnQefffbZlKqqql4l7/Pnz6/+8MMPv+npmPfffz+uuLg4qrS0dN8rr7xStmDBgnR/9R8OmKgREVGXDBbrdgBmiNEspaxvzs+bD1FQJNdgsZYqGAtRsP0vgJ8AeMsmmTQAXgRwb5D6/p1NMklB6mtActTU6Upffju5p2NKX3472VFb3+vr9S1bthiMRuPp7OxsR1RUlHv27Nmn1qxZM8T7uJdffjnhgQceOJmQkOAEgNTU1HYAWLduXdz06dMbEhMTnQkJCc7p06c3rF27Nq43Mdx4441NCQkJPRZ6Wr9+/ZC5c+fWaLVaXHPNNc0NDQ0RZWVlkj/6DwdM1IiIqFsGi7UKoqDB3QA2KhCCBGAMgPsBvKtA/0SKMcv2SrNs/7tZtn8NUUjn4SB2nwTge0Hsb8A5/va6eO+RNG+u1jZt+Vvr4nvb9rFjx/Spqaln1vGmpaU5ysvL9d7HHT58OPLQoUNRF198cVZOTk7WmjVrBgNAeXm5lJaWdub81NRUR3l5ud8T94qKCikjI+NMP8nJyY6ysjIpWP2rHav6EBFRjwwW6ykAbzfn522FmIYVTO0ACgBEsHgIDUQ2yaQFkAbgaQAyxM2LYLkVwJYg9jegnK446dP3ss3H4zpzu93nPKfRaM550ul0ao4cORK5fft2e0lJiZSbm5s1Y8aM/d2c39sw+hpnt88PNBxRIyIiXx0HsD7Ifa6BqEBXGOR+idRCA+AjAG0Avg1y33fZJNM565rIPyKTR8i+HBfl43GdpaennzWCdvz4cX1KSso57SQnJztmzpxZFxkZ6c7KynKMGjWqbf/+/ZFpaWny8ePHz5xfXl5+zvkff/yxISsrKzsrKyt79erVfZqWmJKSIpeWlp7pp6KiQp+eni770v9AwESNiIh6Y1IA2y4G0NzpYwA4BOBfnuImRAOOWbY7AVwPcc1mDHL3QwHcFuQ+B4y0u2bVaqOjXD0do42OcqXePau2t23n5uY2l5aWRhUVFenb2to0a9euHTpnzpw67+Nmz55dt2XLllgAqKioiCgpKYkymUynZ82aVb9169bBVVVVuqqqKt3WrVsHz5o166xCTldffXVzUVHRgaKiogNz587tU5Gnm2++uW716tXDXC4XNm/ebIiNjXUajUbZl/4HAiZqRETkq2EQF26BkgBxUfg+gMUAXgBQDTGaQDRgmWV7BYD/Uaj7nyjUb9jTDxvizPjpXRU9HZPx07sq9PFxPSZzXZEkCS+88MLRG264YdzYsWMnzJo169Qll1zSBgAPP/xwSscI2OzZsxuGDh3aPnr06Am5ubnjnnnmmWNJSUnOxMRE56OPPvrt5MmTx0+ePHn8Y4899m1iYqKzNzHMnDkzc9q0aVklJSWRiYmJk/7whz8MB4Dnnnsu4bnnnksAgDvuuKPeaDSeNhqNEx966CHjyy+/XAYA/ug/HGi6mgNKRETUleb8PCOAwwjMGucyANsAbAXgANAA4GuDxVoWgL6IQopNMs0E8CqAlCB3vQ3AI2bZ/lWQ+w1pe/bsKc3Jyan25diDj/8+qfTlt5M7FxbRRke5Mn56V8X4JY+eCFyU4W3Pnj3Dc3JyMpSOoz+YqBERUa805+cVATAFoasLATQbLNbDQeiLSPVskumnAP4Y5G6dEBVXf2yW7bxo9FFvEjUAcNTWa8vfWhffVnFSikoeIafePau2LyNp9J1wSNRY9ZGIiHqrTxuw9tIug8W6pzk/78Ig9EUUKt4E8FsA5+yHFUA6AHdBTEF+L4j9Dij6+DhX5s/uqVE6DlIXrlEjIiKfNefnaQF8HoSuBgWhD6KQYpbtTQBe8/y3qdNLtQBOBbj7X3o23SaiIGGiRkREPjNYrC4AD0BcGAZKCYCJno/P2aCVaIB7AcBuiLL9rwB4DsC/EfhRtnTwurE3XC6Xi4mtQjxf+5CfOspfOCIi6hWDxXoMwBQEbk+nofju/ckQoD6IQpJZtp8EcAeAVgDJEBthX47AX9ONgPi9J9/sq6qqimOyFnwul0tTVVUVB2Cf0rH0F9eoERFRrxks1kPN+XnPA3gxAM3HAXgbwI8ARAagfaKQZpbth22SyQjgaoiNsIN1Q+NmAJ8Gqa+Q1t7efv+JEydWnDhxYiI4MBJsLgD72tvb71c6kP5i1UciIuqT5vy8IQCOIzAXiTKAwQBuAfCBwWJtD0AfRCHNJpkMAFIB2Ds9XQsgPkBd/o9Ztj8VoLaJyAszfCIi6hODxVoHUYUuECQAlwCwARgVoD6IQt1giN+PY57/v+N5LlBYlZAoiJioERFRf7wbwLavM1isTQAqA9gHUcgyy/YKiGUsawD8DcBNEOX0AyXQlSWJqBMmakRE1GcGi7UQgVmnBgBFnn8TmvPzAnnxSRTKSgH8EsAWBHY0DWCiRhRULCZCRET99S5EGf0LAOT6sd2LPW0fA5AD4Cs/tk0UFsyyvaOy3f/ZJFMpgPUB7I5TH4mCiCNqRETULwaLdafBYl0E4CoAJohkrc4PTf+yOT9vkcFiPQ2gxQ/tEYW7LyEK8QQKR9SIgoiJGhER+YXBYnUbLNZDBot1G4AZANYB+KifzXYUEonuZztEYc+zZi2Q60aPB7BtIvLCqY9EROR3Bot1D4BbAaA5P288gGkAfg+xR1pvZDfn52kAOPwbIVHYKg1QuyfNsp0j20RBxBE1IiIKKIPFetBgsf4JYs3ZG708/fsAHgfQxIIiRD4J1FrO0gC1S0Td4IbXREQUNJ7RsWyIEbIMiNL7bgDJACYAuAzAj7xO+wbAFQaLtTp4kRKFLptkygfwoJ+bfd8s2+/wc5tE1AMmakREpBqeRG4VgHs8T7UBeBVAMYA/GixWvmkRnYdNMs0E8KGfm33aLNv/289tElEPmKgREZGqeKY4zgPwWwB3GyzWTQqHRBRybJJJAmAAcAkAC4BZ6F9tgkvMsn2XP2IjIt8wUSMiIlXyjK6Bo2hE/WeTTEkAHgHwWB9O/xZAmlm283eRKIiYqBERERENADbJpAGwA8ClvTz1YbNsXxqAkIioB6z6SERERDQwZKH3SdqHAP4vALEQ0XlwRI2IiIhogLBJpi/Qu2Qtxyzb/xOoeIioexxRIyIiIho4Xu3FsacB7A9UIETUMyZqRERERAPHewDqfDw2EsCYAMZCRD1gokZEREQ0QJhlezOA8QCeAlB1nsM/AxAX8KCIqEtM1IiIiIgGCJtkygFgA/BnADMAtHZzaC2AlwBcHJzIiMhbfzY+JCIiIqLQchTABQC+BLABQA3EyNoFAJoArARQCkAGkADgT4pESUSs+khEREQ0kNgk09UAxkKsVfubWbY7bJIpGoDGLNtbPMfcAeCgWbbvVTBUogGNiRoRERERnWGTTBcCqDTL9gqlYyEayJioEREREREAwCaZIgFozbK9u7VrRBQkTNSIiIiIiIhUhlUfiYiIiIiIVIaJGhERERERkcowUSMiIiIiIlIZJmpEREREREQqw0SNiIiIiIhIZZioERERERERqQwTNSIiIiIiIpVhokZERERERKQyTNSIiIiIiIhUhokaERERERGRyjBRIyIiIiIiUhkmakRERERERCrDRI2IiIiIiEhlmKgRERERERGpDBM1IiIiIiIilWGiRkREREREpDJM1IiIiIiIiFSGiRoREREREZHKMFEjIiIiIiJSGSZqREREREREKsNEjYiIiIiISGWYqBEREREREakMEzUiIiIiIiKVYaJGRERERESkMkzUiIiIiIiIVIaJGhERERERkcowUSMiIiIiIlIZJmpEREREREQqw0SNiIiIiIhIZZioERERERERqQwTNSIiIiIiIpVhokZERERERKQyTNSIiIiIiIhUhokaERERERGRyjBRIyIiIiIiUhkmakRERERERCrDRI2IiIiIiEhlmKgRERERERGpDBM1IiIiIiIilfn/JS+0hldOA3gAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "complete_table[\"tau_ln\"] =tau_wr.tau_ln\n", - "fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (15,8))\n", - "ln_map = complete_table.plot(ax=ax, column=\"tau_ln\", cmap='coolwarm', scheme='equal_interval',legend=True)\n", - "leg = ax.get_legend()\n", - "leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))\n", - "ln_map.set_title(\"Neighbor set LIMA for U.S. states 1929-2009\",fontdict={\"fontsize\":20})\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Therefore, Arizona, North Dakota, and Missouri exchanged ranks with most of their neighbors over 1929-2009 while California, Virginia etc. barely exchanged ranks with their neighbors." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let see whether neighbor set LIMA statistics are siginificant for these \"extreme\" states:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.463, 0.256, 0.165, 0.101, 0.316, 0.336, 0.237, 0.614, 0.292,\n", - " 0.325, 0.33 , 0.675, 0.06 , 0.541, 0.412, 0.032, 0.594, 0.791,\n", - " 0.575, 0.049, 0.209, 0.48 , 0.488, 0.457, 0.605, 0.409, 0.259,\n", - " 0.018, 0.022, 0.405, 0.016, 0.25 , 0.001, 0.001, 0.045, 0.521,\n", - " 0.167, 0.363, 0.635, 0.478, 0.417, 0.247, 0.282, 0.423, 0.578,\n", - " 0.17 , 0.1 , 0.625])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_wr.tau_ln_pvalues" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , 0. , 0. , -0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , -0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , -0.09090909, -0.5 , 0. ,\n", - " 0.09090909, 0. , 0.63636364, -1. , -1. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sig_wr = tau_wr.tau_ln * (tau_wr.tau_ln_pvalues<0.05)\n", - "sig_wr" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "complete_table[\"sig_wr\"] =sig_wr\n", - "fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (15,8))\n", - "complete_table[complete_table[\"sig_wr\"] == 0].plot(ax=ax, color='white',edgecolor='black')\n", - "sig_ln_map = complete_table[complete_table[\"sig_wr\"] != 0].plot(ax=ax,column=\"sig_wr\",cmap='coolwarm',scheme='equal_interval',legend=True)\n", - "leg = ax.get_legend()\n", - "leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))\n", - "sig_ln_map.set_title(\"Significant Neighbor set LIMA for U.S. states 1929-2009\",fontdict={\"fontsize\":20})\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Thus, Arizona and Missouri have significant and negative neighbor set LIMA values, and can be considered as hotspots of rank exchanges. This means that Arizona (or Missouri) tended to exchange ranks with its neighbors than with others over 1929-2009. On the contrary, Virgina has significant and large positive neighbor set LIMA value indicating that it tended to exchange ranks with its nonneighbors than with neighbors." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Neighborhood set LIMA\n", - "\n", - "Neighborhood set LIMA extends neighbor set LIMA $\\tilde{\\tau}_{r}$ to consider the concordance relationships between any two spatial units in the subset which is composed of the focal unit $r$ and its neighbors." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "giddy.rank.Tau_Local_Neighborhood(self, x, y, w, permutations=0)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_wwr = giddy.rank.Tau_Local_Neighborhood(complete_table[\"1929\"],complete_table[\"2009\"],w,999) \n", - "tau_wwr" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.66666667, 0.8 , 0.86666667, -0.14285714, -0.14285714,\n", - " 0.8 , 0.4 , 0.8 , 0.86666667, -0.14285714,\n", - " 0.66666667, 0.86666667, -0.14285714, 0.86666667, -0.14285714,\n", - " 0. , 0. , 0.86666667, 0.86666667, 0. ,\n", - " 0.4 , 0.66666667, 0.8 , 0.66666667, 0.4 ,\n", - " 0.4 , 0. , 0.54545455, 0. , 0.8 ,\n", - " 0.54545455, -0.14285714, 0.54545455, -0.14285714, 0. ,\n", - " 0. , 0.54545455, 0.54545455, 0. , 0. ,\n", - " 0.54545455, 0.54545455, 0.54545455, 0.54545455, 0.54545455,\n", - " 0.54545455, 0.54545455, 0.4 ])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_wwr.tau_lnhood" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "complete_table[\"tau_lnhood\"] =tau_wwr.tau_lnhood\n", - "fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (15,8))\n", - "ln_map = complete_table.plot(ax=ax, column=\"tau_lnhood\", cmap='coolwarm', scheme='equal_interval',legend=True)\n", - "leg = ax.get_legend()\n", - "leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))\n", - "ln_map.set_title(\"Neighborhood set LIMA for U.S. states 1929-2009\",fontdict={\"fontsize\":20})\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.585, 0.278, 0.104, 0.032, 0.024, 0.295, 0.43 , 0.225, 0.167,\n", - " 0.02 , 0.548, 0.116, 0.023, 0.158, 0.017, 0.016, 0.075, 0.225,\n", - " 0.168, 0.027, 0.505, 0.66 , 0.146, 0.605, 0.614, 0.37 , 0.08 ,\n", - " 0.505, 0.059, 0.358, 0.541, 0.025, 0.185, 0.017, 0.225, 0.151,\n", - " 0.541, 0.527, 0.191, 0.12 , 0.519, 0.427, 0.526, 0.442, 0.453,\n", - " 0.528, 0.478, 0.617])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tau_wwr.tau_lnhood_pvalues" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0. , 0. , 0. , -0.14285714, -0.14285714,\n", - " 0. , 0. , 0. , 0. , -0.14285714,\n", - " 0. , 0. , -0.14285714, 0. , -0.14285714,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , -0.14285714, 0. , -0.14285714, 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. , 0. , 0. ,\n", - " 0. , 0. , 0. ])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sig_lnhood = tau_wwr.tau_lnhood * (tau_wwr.tau_lnhood_pvalues<0.05)\n", - "sig_lnhood" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "complete_table[\"sig_lnhood\"] =sig_lnhood\n", - "fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (15,8))\n", - "complete_table[complete_table[\"sig_lnhood\"] == 0].plot(ax=ax, color='white',edgecolor='black')\n", - "sig_ln_map = complete_table[complete_table[\"sig_lnhood\"] != 0].plot(ax=ax,column=\"sig_lnhood\",categorical=True,legend=True)\n", - "leg = ax.get_legend()\n", - "leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))\n", - "sig_ln_map.set_title(\"Significant Neighborhood set LIMA for U.S. states 1929-2009\",fontdict={\"fontsize\":20})\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Theta statistic of exchange mobility" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "* theta statistic" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "* Rey, Sergio J., and Myrna L. Sastré-Gutiérrez. 2010. “[Interregional Inequality Dynamics in Mexico](http://www.tandfonline.com/doi/abs/10.1080/17421772.2010.493955).” Spatial Economic Analysis 5 (3). Taylor & Francis: 277–98.\n", - "* Rey, Sergio J. 2014. “[Fast Algorithms for a Space-Time Concordance Measure](https://link.springer.com/article/10.1007/s00180-013-0461-2).” Computational Statistics 29 (3-4). Springer: 799–811.\n", - "* Rey, Sergio J. 2016. “[Space--Time Patterns of Rank Concordance: Local Indicators of Mobility Association with Application to Spatial Income Inequality Dynamics](http://www.tandfonline.com/doi/abs/10.1080/24694452.2016.1151336?journalCode=raag21).” Annals of the Association of American Geographers. Association of American Geographers 106 (4): 788–803." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docsrc/RankBasedMethods.nblink b/docs/RankBasedMethods.nblink similarity index 100% rename from docsrc/RankBasedMethods.nblink rename to docs/RankBasedMethods.nblink diff --git a/docs/RankMarkov.html b/docs/RankMarkov.html deleted file mode 100644 index fcc09b4..0000000 --- a/docs/RankMarkov.html +++ /dev/null @@ -1,1515 +0,0 @@ - - - - - - - - Full Rank Markov and Geographic Rank Markov — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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This page was generated from RankMarkov.ipynb. -Interactive online version: -Binder badge

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Full Rank Markov and Geographic Rank Markov

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Author: Wei Kang weikang9009@gmail.com

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import libpysal as ps
-import numpy as np
-import matplotlib.pyplot as plt
-%matplotlib inline
-import seaborn as sns
-import pandas as pd
-import geopandas as gpd
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Full Rank Markov

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from giddy.markov import FullRank_Markov
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income_table = pd.read_csv(ps.examples.get_path("usjoin.csv"))
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NameSTATE_FIPS19291930193119321933193419351936...2000200120022003200420052006200720082009
0Alabama1323267224162166211217251...23471244672516126065276652909730634319883281932274
1Arizona4600520429321308362416462...25578262322646927106287533067132552334703344532077
2Arkansas5310228215157157187207247...22257235322392925074264652751229041310703180031493
3California6991887749580546603660771...32275327503290033801356633746340169419434237740902
4Colorado8634578471354353368444542...32949342283396334092355433738839662411654171940093
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5 rows × 83 columns

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[4]:
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pci = income_table[list(map(str,range(1929,2010)))].values
-pci
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[4]:
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-array([[  323,   267,   224, ..., 31988, 32819, 32274],
-       [  600,   520,   429, ..., 33470, 33445, 32077],
-       [  310,   228,   215, ..., 31070, 31800, 31493],
-       ...,
-       [  460,   408,   356, ..., 29769, 31265, 31843],
-       [  673,   588,   469, ..., 35839, 36594, 35676],
-       [  675,   585,   476, ..., 43453, 45177, 42504]])
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[5]:
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m = FullRank_Markov(pci)
-m.ranks
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[5]:
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-array([[45, 45, 44, ..., 41, 40, 39],
-       [24, 25, 25, ..., 36, 38, 41],
-       [46, 47, 45, ..., 43, 43, 43],
-       ...,
-       [34, 34, 34, ..., 47, 46, 42],
-       [17, 17, 22, ..., 25, 26, 25],
-       [16, 18, 19, ...,  6,  6,  7]])
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[6]:
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m.transitions
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[6]:
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-array([[66.,  5.,  5., ...,  0.,  0.,  0.],
-       [ 8., 51.,  9., ...,  0.,  0.,  0.],
-       [ 2., 13., 44., ...,  0.,  0.,  0.],
-       ...,
-       [ 0.,  0.,  0., ..., 40., 17.,  0.],
-       [ 0.,  0.,  0., ..., 15., 54.,  2.],
-       [ 0.,  0.,  0., ...,  2.,  1., 77.]])
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Full rank Markov transition probability matrix

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[7]:
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m.p
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[7]:
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-array([[0.825 , 0.0625, 0.0625, ..., 0.    , 0.    , 0.    ],
-       [0.1   , 0.6375, 0.1125, ..., 0.    , 0.    , 0.    ],
-       [0.025 , 0.1625, 0.55  , ..., 0.    , 0.    , 0.    ],
-       ...,
-       [0.    , 0.    , 0.    , ..., 0.5   , 0.2125, 0.    ],
-       [0.    , 0.    , 0.    , ..., 0.1875, 0.675 , 0.025 ],
-       [0.    , 0.    , 0.    , ..., 0.025 , 0.0125, 0.9625]])
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Full rank mean first passage times

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[8]:
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m.mfpt
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[8]:
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-array([[  48.        ,   87.96280048,   68.1089084 , ...,  443.76689275,
-         518.31000749, 1628.59025557],
-       [ 225.92564594,   48.        ,   78.75804364, ...,  440.0173313 ,
-         514.56045127, 1624.84070661],
-       [ 271.55443692,  102.484092  ,   48.        , ...,  438.93288204,
-         513.47599512, 1623.75624059],
-       ...,
-       [ 727.11189921,  570.15910508,  546.61934646, ...,   48.        ,
-         117.41906375, 1278.96860316],
-       [ 730.40467469,  573.45179415,  549.91216045, ...,   49.70722573,
-          48.        , 1202.06279368],
-       [ 754.8761577 ,  597.92333477,  574.38361779, ...,   43.23574191,
-         104.9460425 ,   48.        ]])
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[9]:
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m.sojourn_time
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[9]:
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-array([ 5.71428571,  2.75862069,  2.22222222,  1.77777778,  1.66666667,
-        1.73913043,  1.53846154,  1.53846154,  1.53846154,  1.42857143,
-        1.42857143,  1.56862745,  1.53846154,  1.40350877,  1.29032258,
-        1.21212121,  1.31147541,  1.37931034,  1.29032258,  1.25      ,
-        1.15942029,  1.12676056,  1.25      ,  1.17647059,  1.19402985,
-        1.08108108,  1.19402985,  1.25      ,  1.25      ,  1.14285714,
-        1.33333333,  1.26984127,  1.25      ,  1.37931034,  1.42857143,
-        1.31147541,  1.26984127,  1.25      ,  1.31147541,  1.25      ,
-        1.19402985,  1.25      ,  1.53846154,  1.6       ,  1.86046512,
-        2.        ,  3.07692308, 26.66666667])
-
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[10]:
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df_fullrank = pd.DataFrame(np.c_[m.p.diagonal(),m.sojourn_time], columns=["Staying Probability","Sojourn Time"], index = np.arange(m.p.shape[0])+1)
-df_fullrank.head()
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[10]:
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Staying ProbabilitySojourn Time
10.82505.714286
20.63752.758621
30.55002.222222
40.43751.777778
50.40001.666667
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[11]:
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df_fullrank.plot(subplots=True, layout=(1,2), figsize=(15,5))
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[11]:
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-array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a2ad213c8>,
-        <matplotlib.axes._subplots.AxesSubplot object at 0x1a2ad64668>]],
-      dtype=object)
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-_images/RankMarkov_14_1.png -
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[12]:
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sns.distplot(m.mfpt.flatten(),kde=False)
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-/Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
-  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
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[12]:
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-<matplotlib.axes._subplots.AxesSubplot at 0x1a2ae70908>
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-_images/RankMarkov_15_2.png -
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Geographic Rank Markov

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[13]:
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from giddy.markov import GeoRank_Markov, Markov, sojourn_time
-gm = GeoRank_Markov(pci)
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[21]:
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gm.transitions
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[21]:
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-array([[38.,  0.,  8., ...,  0.,  0.,  0.],
-       [ 0., 15.,  0., ...,  0.,  1.,  0.],
-       [ 6.,  0., 44., ...,  5.,  0.,  0.],
-       ...,
-       [ 2.,  0.,  5., ..., 34.,  0.,  0.],
-       [ 0.,  0.,  0., ...,  0., 18.,  2.],
-       [ 0.,  0.,  0., ...,  0.,  3., 14.]])
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[24]:
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gm.p
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[24]:
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-array([[0.475 , 0.    , 0.1   , ..., 0.    , 0.    , 0.    ],
-       [0.    , 0.1875, 0.    , ..., 0.    , 0.0125, 0.    ],
-       [0.075 , 0.    , 0.55  , ..., 0.0625, 0.    , 0.    ],
-       ...,
-       [0.025 , 0.    , 0.0625, ..., 0.425 , 0.    , 0.    ],
-       [0.    , 0.    , 0.    , ..., 0.    , 0.225 , 0.025 ],
-       [0.    , 0.    , 0.    , ..., 0.    , 0.0375, 0.175 ]])
-
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[28]:
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gm.sojourn_time[:10]
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[28]:
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-array([1.9047619 , 1.23076923, 2.22222222, 1.73913043, 1.15942029,
-       3.80952381, 1.70212766, 1.25      , 1.31147541, 1.11111111])
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[14]:
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gm.sojourn_time
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[14]:
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-array([ 1.9047619 ,  1.23076923,  2.22222222,  1.73913043,  1.15942029,
-        3.80952381,  1.70212766,  1.25      ,  1.31147541,  1.11111111,
-        1.73913043,  1.37931034,  1.17647059,  1.21212121,  1.33333333,
-        1.37931034,  1.09589041,  2.10526316,  2.        ,  1.45454545,
-        1.26984127, 26.66666667,  1.19402985,  1.23076923,  1.09589041,
-        1.56862745,  1.26984127,  2.42424242,  1.50943396,  2.        ,
-        1.29032258,  1.09589041,  1.6       ,  1.42857143,  1.25      ,
-        1.45454545,  1.29032258,  1.6       ,  1.17647059,  1.56862745,
-        1.25      ,  1.37931034,  1.45454545,  1.42857143,  1.29032258,
-        1.73913043,  1.29032258,  1.21212121])
-
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[15]:
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gm.mfpt
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[15]:
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-array([[ 48.        ,  63.35532038,  92.75274652, ...,  82.47515731,
-         71.01114491,  68.65737127],
-       [108.25928005,  48.        , 127.99032986, ...,  92.03098299,
-         63.36652935,  61.82733039],
-       [ 76.96801786,  64.7713783 ,  48.        , ...,  73.84595169,
-         72.24682723,  69.77497173],
-       ...,
-       [ 93.3107474 ,  62.47670463, 105.80634118, ...,  48.        ,
-         69.30121319,  67.08838421],
-       [113.65278078,  61.1987031 , 133.57991745, ...,  96.0103924 ,
-         48.        ,  56.74165107],
-       [114.71894813,  63.4019776 , 134.73381719, ...,  97.287895  ,
-         61.45565054,  48.        ]])
-
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[16]:
-
-
-
income_table["geo_sojourn_time"] = gm.sojourn_time
-i = 0
-for state in income_table["Name"]:
-    income_table["geo_mfpt_to_" + state] = gm.mfpt[:,i]
-    income_table["geo_mfpt_from_" + state] = gm.mfpt[i,:]
-    i = i + 1
-income_table.head()
-
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[16]:
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
NameSTATE_FIPS19291930193119321933193419351936...geo_mfpt_to_Virginiageo_mfpt_from_Virginiageo_mfpt_to_Washingtongeo_mfpt_from_Washingtongeo_mfpt_to_West Virginiageo_mfpt_from_West Virginiageo_mfpt_to_Wisconsingeo_mfpt_from_Wisconsingeo_mfpt_to_Wyominggeo_mfpt_from_Wyoming
0Alabama1323267224162166211217251...72.186055109.82853282.994754118.76998482.47515793.31074771.011145113.65278168.657371114.718948
1Arizona4600520429321308362416462...67.54444760.83880776.09089566.72926292.03098362.47670563.36652961.19870361.82733063.401978
2Arkansas5310228215157157187207247...73.650943129.53369184.071211138.69251373.845952105.80634172.246827133.57991769.774972134.733817
3California6991887749580546603660771...71.377700111.64488462.23041797.908341104.922271121.67024369.368408110.66838859.998457105.965215
4Colorado8634578471354353368444542...69.62717957.10633966.35393052.22923098.79763666.46439860.76258952.32456555.55902053.872702
-

5 rows × 180 columns

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[17]:
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geo_table = gpd.read_file(ps.examples.get_path('us48.shp'))
-# income_table = pd.read_csv(libpysal.examples.get_path("usjoin.csv"))
-complete_table = geo_table.merge(income_table,left_on='STATE_NAME',right_on='Name')
-complete_table.head()
-
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[17]:
-
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
AREAPERIMETERSTATE_STATE_IDSTATE_NAMESTATE_FIPS_xSUB_REGIONSTATE_ABBRgeometryName...geo_mfpt_to_Virginiageo_mfpt_from_Virginiageo_mfpt_to_Washingtongeo_mfpt_from_Washingtongeo_mfpt_to_West Virginiageo_mfpt_from_West Virginiageo_mfpt_to_Wisconsingeo_mfpt_from_Wisconsingeo_mfpt_to_Wyominggeo_mfpt_from_Wyoming
020.75034.95611Washington53PacificWA(POLYGON ((-122.400749206543 48.22539520263672...Washington...71.66305573.75680448.00000048.000000101.59240081.69258665.21912470.70122653.12617764.476985
145.13234.52722Montana30MtnMTPOLYGON ((-111.4746322631836 44.70223999023438...Montana...69.91893159.06789776.18408864.71082390.78185058.79520163.45524858.97552260.88195460.553000
29.57118.89933Maine23N EngME(POLYGON ((-69.77778625488281 44.0740737915039...Maine...69.43186253.87283677.51238162.86237887.73476054.24482366.25780756.90574161.97850658.336426
321.87421.35344North Dakota38W N CenNDPOLYGON ((-98.73005676269531 45.93829727172852...North Dakota...69.44169056.52634776.65964662.82366885.03121849.51124067.36271858.71745864.38638259.728719
422.59822.74655South Dakota46W N CenSDPOLYGON ((-102.7879333496094 42.99532318115234...South Dakota...68.22989461.54820978.88630468.79408388.19265955.75410966.18769463.80235964.33631165.070022
-

5 rows × 189 columns

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[18]:
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complete_table.columns
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[18]:
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-Index(['AREA', 'PERIMETER', 'STATE_', 'STATE_ID', 'STATE_NAME', 'STATE_FIPS_x',
-       'SUB_REGION', 'STATE_ABBR', 'geometry', 'Name',
-       ...
-       'geo_mfpt_to_Virginia', 'geo_mfpt_from_Virginia',
-       'geo_mfpt_to_Washington', 'geo_mfpt_from_Washington',
-       'geo_mfpt_to_West Virginia', 'geo_mfpt_from_West Virginia',
-       'geo_mfpt_to_Wisconsin', 'geo_mfpt_from_Wisconsin',
-       'geo_mfpt_to_Wyoming', 'geo_mfpt_from_Wyoming'],
-      dtype='object', length=189)
-
-
-

Visualizing mean first passage time from/to California/Mississippi:

-
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[19]:
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-
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fig, axes = plt.subplots(nrows=2, ncols=2,figsize = (15,7))
-target_states = ["California","Mississippi"]
-directions = ["from","to"]
-for i, direction in enumerate(directions):
-    for j, target in enumerate(target_states):
-        ax = axes[i,j]
-        col = direction+"_"+target
-        complete_table.plot(ax=ax,column = "geo_mfpt_"+ col,cmap='OrRd',
-                    scheme='quantiles', legend=True)
-        ax.set_title("Mean First Passage Time "+direction+" "+target)
-        ax.axis('off')
-        leg = ax.get_legend()
-        leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))
-plt.tight_layout()
-
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-/Users/weikang/anaconda3/lib/python3.6/site-packages/pysal/__init__.py:65: VisibleDeprecationWarning: PySAL's API will be changed on 2018-12-31. The last release made with this API is version 1.14.4. A preview of the next API version is provided in the `pysal` 2.0 prelease candidate. The API changes and a guide on how to change imports is provided at https://migrating.pysal.org
-  ), VisibleDeprecationWarning)
-/Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
-  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
-
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-_images/RankMarkov_27_1.png -
-
-

Visualizing sojourn time for each US state:

-
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[20]:
-
-
-
fig, axes = plt.subplots(nrows=1, ncols=2,figsize = (15,7))
-schemes = ["Quantiles","Equal_Interval"]
-for i, scheme in enumerate(schemes):
-    ax = axes[i]
-    complete_table.plot(ax=ax,column = "geo_sojourn_time",cmap='OrRd',
-                scheme=scheme, legend=True)
-    ax.set_title("Rank Sojourn Time ("+scheme+")")
-    ax.axis('off')
-    leg = ax.get_legend()
-    leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))
-plt.tight_layout()
-
-
-
-
-
-
-
-
-/Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
-  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
-
-
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-
-_images/RankMarkov_29_1.png -
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- - -
- -
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- Back to top - -
- -

- -

-

- © Copyright 2018-, pysal developers.
- Created using Sphinx 5.0.2.
-

-
-
- - \ No newline at end of file diff --git a/docs/RankMarkov.ipynb b/docs/RankMarkov.ipynb deleted file mode 100644 index fb31a69..0000000 --- a/docs/RankMarkov.ipynb +++ /dev/null @@ -1,1348 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Full Rank Markov and Geographic Rank Markov \n", - "\n", - "**Author: Wei Kang **" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import libpysal as ps\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import seaborn as sns\n", - "import pandas as pd\n", - "import geopandas as gpd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Full Rank Markov" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from giddy.markov import FullRank_Markov" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameSTATE_FIPS19291930193119321933193419351936...2000200120022003200420052006200720082009
0Alabama1323267224162166211217251...23471244672516126065276652909730634319883281932274
1Arizona4600520429321308362416462...25578262322646927106287533067132552334703344532077
2Arkansas5310228215157157187207247...22257235322392925074264652751229041310703180031493
3California6991887749580546603660771...32275327503290033801356633746340169419434237740902
4Colorado8634578471354353368444542...32949342283396334092355433738839662411654171940093
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5 rows × 83 columns

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" - ], - "text/plain": [ - " Name STATE_FIPS 1929 1930 1931 1932 1933 1934 1935 1936 \\\n", - "0 Alabama 1 323 267 224 162 166 211 217 251 \n", - "1 Arizona 4 600 520 429 321 308 362 416 462 \n", - "2 Arkansas 5 310 228 215 157 157 187 207 247 \n", - "3 California 6 991 887 749 580 546 603 660 771 \n", - "4 Colorado 8 634 578 471 354 353 368 444 542 \n", - "\n", - " ... 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 \n", - "0 ... 23471 24467 25161 26065 27665 29097 30634 31988 32819 32274 \n", - "1 ... 25578 26232 26469 27106 28753 30671 32552 33470 33445 32077 \n", - "2 ... 22257 23532 23929 25074 26465 27512 29041 31070 31800 31493 \n", - "3 ... 32275 32750 32900 33801 35663 37463 40169 41943 42377 40902 \n", - "4 ... 32949 34228 33963 34092 35543 37388 39662 41165 41719 40093 \n", - "\n", - "[5 rows x 83 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "income_table = pd.read_csv(ps.examples.get_path(\"usjoin.csv\"))\n", - "income_table.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 323, 267, 224, ..., 31988, 32819, 32274],\n", - " [ 600, 520, 429, ..., 33470, 33445, 32077],\n", - " [ 310, 228, 215, ..., 31070, 31800, 31493],\n", - " ...,\n", - " [ 460, 408, 356, ..., 29769, 31265, 31843],\n", - " [ 673, 588, 469, ..., 35839, 36594, 35676],\n", - " [ 675, 585, 476, ..., 43453, 45177, 42504]])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pci = income_table[list(map(str,range(1929,2010)))].values\n", - "pci" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[45, 45, 44, ..., 41, 40, 39],\n", - " [24, 25, 25, ..., 36, 38, 41],\n", - " [46, 47, 45, ..., 43, 43, 43],\n", - " ...,\n", - " [34, 34, 34, ..., 47, 46, 42],\n", - " [17, 17, 22, ..., 25, 26, 25],\n", - " [16, 18, 19, ..., 6, 6, 7]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = FullRank_Markov(pci)\n", - "m.ranks" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[66., 5., 5., ..., 0., 0., 0.],\n", - " [ 8., 51., 9., ..., 0., 0., 0.],\n", - " [ 2., 13., 44., ..., 0., 0., 0.],\n", - " ...,\n", - " [ 0., 0., 0., ..., 40., 17., 0.],\n", - " [ 0., 0., 0., ..., 15., 54., 2.],\n", - " [ 0., 0., 0., ..., 2., 1., 77.]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.transitions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Full rank Markov transition probability matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.825 , 0.0625, 0.0625, ..., 0. , 0. , 0. ],\n", - 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" 117.41906375, 1278.96860316],\n", - " [ 730.40467469, 573.45179415, 549.91216045, ..., 49.70722573,\n", - " 48. , 1202.06279368],\n", - " [ 754.8761577 , 597.92333477, 574.38361779, ..., 43.23574191,\n", - " 104.9460425 , 48. ]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.mfpt" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 5.71428571, 2.75862069, 2.22222222, 1.77777778, 1.66666667,\n", - " 1.73913043, 1.53846154, 1.53846154, 1.53846154, 1.42857143,\n", - " 1.42857143, 1.56862745, 1.53846154, 1.40350877, 1.29032258,\n", - " 1.21212121, 1.31147541, 1.37931034, 1.29032258, 1.25 ,\n", - " 1.15942029, 1.12676056, 1.25 , 1.17647059, 1.19402985,\n", - " 1.08108108, 1.19402985, 1.25 , 1.25 , 1.14285714,\n", - " 1.33333333, 1.26984127, 1.25 , 1.37931034, 1.42857143,\n", - " 1.31147541, 1.26984127, 1.25 , 1.31147541, 1.25 ,\n", - " 1.19402985, 1.25 , 1.53846154, 1.6 , 1.86046512,\n", - " 2. , 3.07692308, 26.66666667])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m.sojourn_time" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Staying ProbabilitySojourn Time
10.82505.714286
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" - ], - "text/plain": [ - " Staying Probability Sojourn Time\n", - "1 0.8250 5.714286\n", - "2 0.6375 2.758621\n", - "3 0.5500 2.222222\n", - "4 0.4375 1.777778\n", - "5 0.4000 1.666667" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_fullrank = pd.DataFrame(np.c_[m.p.diagonal(),m.sojourn_time], columns=[\"Staying Probability\",\"Sojourn Time\"], index = np.arange(m.p.shape[0])+1)\n", - "df_fullrank.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[,\n", - " ]],\n", - " dtype=object)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_fullrank.plot(subplots=True, layout=(1,2), figsize=(15,5))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", - " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.distplot(m.mfpt.flatten(),kde=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Geographic Rank Markov" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from giddy.markov import GeoRank_Markov, Markov, sojourn_time\n", - "gm = GeoRank_Markov(pci)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[38., 0., 8., ..., 0., 0., 0.],\n", - " [ 0., 15., 0., ..., 0., 1., 0.],\n", - " [ 6., 0., 44., ..., 5., 0., 0.],\n", - " ...,\n", - " [ 2., 0., 5., ..., 34., 0., 0.],\n", - " [ 0., 0., 0., ..., 0., 18., 2.],\n", - " [ 0., 0., 0., ..., 0., 3., 14.]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gm.transitions" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.475 , 0. , 0.1 , ..., 0. , 0. , 0. ],\n", - " [0. , 0.1875, 0. , ..., 0. , 0.0125, 0. ],\n", - " [0.075 , 0. , 0.55 , ..., 0.0625, 0. , 0. ],\n", - " ...,\n", - " [0.025 , 0. , 0.0625, ..., 0.425 , 0. , 0. ],\n", - " [0. , 0. , 0. , ..., 0. , 0.225 , 0.025 ],\n", - " [0. , 0. , 0. , ..., 0. , 0.0375, 0.175 ]])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gm.p" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.9047619 , 1.23076923, 2.22222222, 1.73913043, 1.15942029,\n", - " 3.80952381, 1.70212766, 1.25 , 1.31147541, 1.11111111])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gm.sojourn_time[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1.9047619 , 1.23076923, 2.22222222, 1.73913043, 1.15942029,\n", - " 3.80952381, 1.70212766, 1.25 , 1.31147541, 1.11111111,\n", - " 1.73913043, 1.37931034, 1.17647059, 1.21212121, 1.33333333,\n", - " 1.37931034, 1.09589041, 2.10526316, 2. , 1.45454545,\n", - " 1.26984127, 26.66666667, 1.19402985, 1.23076923, 1.09589041,\n", - " 1.56862745, 1.26984127, 2.42424242, 1.50943396, 2. ,\n", - " 1.29032258, 1.09589041, 1.6 , 1.42857143, 1.25 ,\n", - " 1.45454545, 1.29032258, 1.6 , 1.17647059, 1.56862745,\n", - " 1.25 , 1.37931034, 1.45454545, 1.42857143, 1.29032258,\n", - " 1.73913043, 1.29032258, 1.21212121])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gm.sojourn_time" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 48. , 63.35532038, 92.75274652, ..., 82.47515731,\n", - " 71.01114491, 68.65737127],\n", - " [108.25928005, 48. , 127.99032986, ..., 92.03098299,\n", - " 63.36652935, 61.82733039],\n", - " [ 76.96801786, 64.7713783 , 48. , ..., 73.84595169,\n", - " 72.24682723, 69.77497173],\n", - " ...,\n", - " [ 93.3107474 , 62.47670463, 105.80634118, ..., 48. ,\n", - " 69.30121319, 67.08838421],\n", - " [113.65278078, 61.1987031 , 133.57991745, ..., 96.0103924 ,\n", - " 48. , 56.74165107],\n", - " [114.71894813, 63.4019776 , 134.73381719, ..., 97.287895 ,\n", - " 61.45565054, 48. ]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gm.mfpt" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1Arizona4600520429321308362416462...67.54444760.83880776.09089566.72926292.03098362.47670563.36652961.19870361.82733063.401978
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5 rows × 180 columns

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" - ], - "text/plain": [ - " Name STATE_FIPS 1929 1930 1931 1932 1933 1934 1935 1936 \\\n", - "0 Alabama 1 323 267 224 162 166 211 217 251 \n", - "1 Arizona 4 600 520 429 321 308 362 416 462 \n", - "2 Arkansas 5 310 228 215 157 157 187 207 247 \n", - "3 California 6 991 887 749 580 546 603 660 771 \n", - "4 Colorado 8 634 578 471 354 353 368 444 542 \n", - "\n", - " ... geo_mfpt_to_Virginia geo_mfpt_from_Virginia \\\n", - "0 ... 72.186055 109.828532 \n", - "1 ... 67.544447 60.838807 \n", - "2 ... 73.650943 129.533691 \n", - "3 ... 71.377700 111.644884 \n", - "4 ... 69.627179 57.106339 \n", - "\n", - " geo_mfpt_to_Washington geo_mfpt_from_Washington \\\n", - "0 82.994754 118.769984 \n", - "1 76.090895 66.729262 \n", - "2 84.071211 138.692513 \n", - "3 62.230417 97.908341 \n", - "4 66.353930 52.229230 \n", - "\n", - " geo_mfpt_to_West Virginia geo_mfpt_from_West Virginia \\\n", - "0 82.475157 93.310747 \n", - "1 92.030983 62.476705 \n", - "2 73.845952 105.806341 \n", - "3 104.922271 121.670243 \n", - "4 98.797636 66.464398 \n", - "\n", - " geo_mfpt_to_Wisconsin geo_mfpt_from_Wisconsin geo_mfpt_to_Wyoming \\\n", - "0 71.011145 113.652781 68.657371 \n", - "1 63.366529 61.198703 61.827330 \n", - "2 72.246827 133.579917 69.774972 \n", - "3 69.368408 110.668388 59.998457 \n", - "4 60.762589 52.324565 55.559020 \n", - "\n", - " geo_mfpt_from_Wyoming \n", - "0 114.718948 \n", - "1 63.401978 \n", - "2 134.733817 \n", - "3 105.965215 \n", - "4 53.872702 \n", - "\n", - "[5 rows x 180 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "income_table[\"geo_sojourn_time\"] = gm.sojourn_time\n", - "i = 0\n", - "for state in income_table[\"Name\"]:\n", - " income_table[\"geo_mfpt_to_\" + state] = gm.mfpt[:,i]\n", - " income_table[\"geo_mfpt_from_\" + state] = gm.mfpt[i,:]\n", - " i = i + 1\n", - "income_table.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AREAPERIMETERSTATE_STATE_IDSTATE_NAMESTATE_FIPS_xSUB_REGIONSTATE_ABBRgeometryName...geo_mfpt_to_Virginiageo_mfpt_from_Virginiageo_mfpt_to_Washingtongeo_mfpt_from_Washingtongeo_mfpt_to_West Virginiageo_mfpt_from_West Virginiageo_mfpt_to_Wisconsingeo_mfpt_from_Wisconsingeo_mfpt_to_Wyominggeo_mfpt_from_Wyoming
020.75034.95611Washington53PacificWA(POLYGON ((-122.400749206543 48.22539520263672...Washington...71.66305573.75680448.00000048.000000101.59240081.69258665.21912470.70122653.12617764.476985
145.13234.52722Montana30MtnMTPOLYGON ((-111.4746322631836 44.70223999023438...Montana...69.91893159.06789776.18408864.71082390.78185058.79520163.45524858.97552260.88195460.553000
29.57118.89933Maine23N EngME(POLYGON ((-69.77778625488281 44.0740737915039...Maine...69.43186253.87283677.51238162.86237887.73476054.24482366.25780756.90574161.97850658.336426
321.87421.35344North Dakota38W N CenNDPOLYGON ((-98.73005676269531 45.93829727172852...North Dakota...69.44169056.52634776.65964662.82366885.03121849.51124067.36271858.71745864.38638259.728719
422.59822.74655South Dakota46W N CenSDPOLYGON ((-102.7879333496094 42.99532318115234...South Dakota...68.22989461.54820978.88630468.79408388.19265955.75410966.18769463.80235964.33631165.070022
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5 rows × 189 columns

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" - ], - "text/plain": [ - " AREA PERIMETER STATE_ STATE_ID STATE_NAME STATE_FIPS_x SUB_REGION \\\n", - "0 20.750 34.956 1 1 Washington 53 Pacific \n", - "1 45.132 34.527 2 2 Montana 30 Mtn \n", - "2 9.571 18.899 3 3 Maine 23 N Eng \n", - "3 21.874 21.353 4 4 North Dakota 38 W N Cen \n", - "4 22.598 22.746 5 5 South Dakota 46 W N Cen \n", - "\n", - " STATE_ABBR geometry Name \\\n", - "0 WA (POLYGON ((-122.400749206543 48.22539520263672... Washington \n", - "1 MT POLYGON ((-111.4746322631836 44.70223999023438... Montana \n", - "2 ME (POLYGON ((-69.77778625488281 44.0740737915039... Maine \n", - "3 ND POLYGON ((-98.73005676269531 45.93829727172852... North Dakota \n", - "4 SD POLYGON ((-102.7879333496094 42.99532318115234... South Dakota \n", - "\n", - " ... geo_mfpt_to_Virginia geo_mfpt_from_Virginia \\\n", - "0 ... 71.663055 73.756804 \n", - "1 ... 69.918931 59.067897 \n", - "2 ... 69.431862 53.872836 \n", - "3 ... 69.441690 56.526347 \n", - "4 ... 68.229894 61.548209 \n", - "\n", - " geo_mfpt_to_Washington geo_mfpt_from_Washington \\\n", - "0 48.000000 48.000000 \n", - "1 76.184088 64.710823 \n", - "2 77.512381 62.862378 \n", - "3 76.659646 62.823668 \n", - "4 78.886304 68.794083 \n", - "\n", - " geo_mfpt_to_West Virginia geo_mfpt_from_West Virginia \\\n", - "0 101.592400 81.692586 \n", - "1 90.781850 58.795201 \n", - "2 87.734760 54.244823 \n", - "3 85.031218 49.511240 \n", - "4 88.192659 55.754109 \n", - "\n", - " geo_mfpt_to_Wisconsin geo_mfpt_from_Wisconsin geo_mfpt_to_Wyoming \\\n", - "0 65.219124 70.701226 53.126177 \n", - "1 63.455248 58.975522 60.881954 \n", - "2 66.257807 56.905741 61.978506 \n", - "3 67.362718 58.717458 64.386382 \n", - "4 66.187694 63.802359 64.336311 \n", - "\n", - " geo_mfpt_from_Wyoming \n", - "0 64.476985 \n", - "1 60.553000 \n", - "2 58.336426 \n", - "3 59.728719 \n", - "4 65.070022 \n", - "\n", - "[5 rows x 189 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_table = gpd.read_file(ps.examples.get_path('us48.shp'))\n", - "# income_table = pd.read_csv(libpysal.examples.get_path(\"usjoin.csv\"))\n", - "complete_table = geo_table.merge(income_table,left_on='STATE_NAME',right_on='Name')\n", - "complete_table.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['AREA', 'PERIMETER', 'STATE_', 'STATE_ID', 'STATE_NAME', 'STATE_FIPS_x',\n", - " 'SUB_REGION', 'STATE_ABBR', 'geometry', 'Name',\n", - " ...\n", - " 'geo_mfpt_to_Virginia', 'geo_mfpt_from_Virginia',\n", - " 'geo_mfpt_to_Washington', 'geo_mfpt_from_Washington',\n", - " 'geo_mfpt_to_West Virginia', 'geo_mfpt_from_West Virginia',\n", - " 'geo_mfpt_to_Wisconsin', 'geo_mfpt_from_Wisconsin',\n", - " 'geo_mfpt_to_Wyoming', 'geo_mfpt_from_Wyoming'],\n", - " dtype='object', length=189)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "complete_table.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Visualizing mean first passage time from/to California/Mississippi:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/weikang/anaconda3/lib/python3.6/site-packages/pysal/__init__.py:65: VisibleDeprecationWarning: PySAL's API will be changed on 2018-12-31. The last release made with this API is version 1.14.4. A preview of the next API version is provided in the `pysal` 2.0 prelease candidate. The API changes and a guide on how to change imports is provided at https://migrating.pysal.org\n", - " ), VisibleDeprecationWarning)\n", - "/Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", - " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(nrows=2, ncols=2,figsize = (15,7))\n", - "target_states = [\"California\",\"Mississippi\"]\n", - "directions = [\"from\",\"to\"]\n", - "for i, direction in enumerate(directions):\n", - " for j, target in enumerate(target_states):\n", - " ax = axes[i,j]\n", - " col = direction+\"_\"+target\n", - " complete_table.plot(ax=ax,column = \"geo_mfpt_\"+ col,cmap='OrRd', \n", - " scheme='quantiles', legend=True)\n", - " ax.set_title(\"Mean First Passage Time \"+direction+\" \"+target)\n", - " ax.axis('off')\n", - " leg = ax.get_legend()\n", - " leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Visualizing sojourn time for each US state:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", - " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(nrows=1, ncols=2,figsize = (15,7))\n", - "schemes = [\"Quantiles\",\"Equal_Interval\"]\n", - "for i, scheme in enumerate(schemes):\n", - " ax = axes[i]\n", - " complete_table.plot(ax=ax,column = \"geo_sojourn_time\",cmap='OrRd', \n", - " scheme=scheme, legend=True)\n", - " ax.set_title(\"Rank Sojourn Time (\"+scheme+\")\")\n", - " ax.axis('off')\n", - " leg = ax.get_legend()\n", - " leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))\n", - "plt.tight_layout()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docsrc/RankMarkov.nblink b/docs/RankMarkov.nblink similarity index 100% rename from docsrc/RankMarkov.nblink rename to docs/RankMarkov.nblink diff --git a/docs/Sequence.html b/docs/Sequence.html deleted file mode 100644 index e052af8..0000000 --- a/docs/Sequence.html +++ /dev/null @@ -1,786 +0,0 @@ - - - - - - - - Alignment-based sequence methods — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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This page was generated from Sequence.ipynb. -Interactive online version: -Binder badge

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Alignment-based sequence methods

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This notebook introduces the alignment-based sequence methods (operationalized by the Optimal Matching (OM) algorithm), which was originally developed for matching protein and DNA sequences in biology and used extensively for analyzing strings in computer science and recently widely applied to explore the neighborhood change.

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It generally works by finding the minimum cost for aligning one sequence to match another using a combination of operations including substitution, insertion, deletion and transposition. The cost of each operation can be parameterized diferently and may be theory-driven or data-driven. The minimum cost is considered as the distance between the two sequences.

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The sequence module in giddy provides a suite of alignment-based sequence methods.

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Author: Wei Kang weikang9009@gmail.com

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import numpy as np
-import pandas as pd
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import libpysal
-import mapclassify as mc
-f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
-pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
-q5 = np.array([mc.Quantiles(y,k=5).yb for y in pci]).transpose()
-q5
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-/Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
-  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
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-array([[0, 0, 0, ..., 0, 0, 0],
-       [2, 2, 2, ..., 1, 1, 0],
-       [0, 0, 0, ..., 0, 0, 0],
-       ...,
-       [1, 1, 1, ..., 0, 0, 0],
-       [3, 3, 2, ..., 2, 2, 2],
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q5.shape
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-(48, 81)
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Import Sequence class from giddy.sequence:

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from giddy.sequence import Sequence
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“hamming”

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  • substitution cost = 1

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  • insertion/deletion cost = \(\infty\)

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seq_hamming = Sequence(q5, dist_type="hamming")
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-<giddy.sequence.Sequence at 0x7ff498df1160>
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seq_hamming.seq_dis_mat #pairwise sequence distance matrix
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-array([[ 0., 75.,  7., ..., 21., 81., 78.],
-       [75.,  0., 80., ..., 79., 57., 73.],
-       [ 7., 80.,  0., ..., 14., 81., 81.],
-       ...,
-       [21., 79., 14., ...,  0., 81., 81.],
-       [81., 57., 81., ..., 81.,  0., 51.],
-       [78., 73., 81., ..., 81., 51.,  0.]])
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“interval”

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Assuming there are \(k\) states in the sequences and they are ordinal/continuous.

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  • substitution cost = differences between states

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seq_interval = Sequence(q5, dist_type="interval")
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-<giddy.sequence.Sequence at 0x7ff451d8d160>
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seq_interval.seq_dis_mat
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-array([[  0., 123.,   7., ...,  21., 190., 225.],
-       [123.,   0., 130., ..., 116.,  69., 108.],
-       [  7., 130.,   0., ...,  14., 197., 232.],
-       ...,
-       [ 21., 116.,  14., ...,   0., 183., 218.],
-       [190.,  69., 197., ..., 183.,   0.,  61.],
-       [225., 108., 232., ..., 218.,  61.,   0.]])
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“arbitrary”

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  • substitution cost = 0.5

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seq_arbitrary = Sequence(q5, dist_type="arbitrary")
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seq_arbitrary.seq_dis_mat
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-array([[ 0. , 37.5,  3.5, ..., 10.5, 40.5, 39. ],
-       [37.5,  0. , 40. , ..., 39.5, 28.5, 36.5],
-       [ 3.5, 40. ,  0. , ...,  7. , 40.5, 40.5],
-       ...,
-       [10.5, 39.5,  7. , ...,  0. , 40.5, 40.5],
-       [40.5, 28.5, 40.5, ..., 40.5,  0. , 25.5],
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“markov”

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  • substitution cost = \(1-\frac{p_{ij}+p_{ji}}{2}\) where \(p_{ij}\) is the empirical rate of transitioning from state \(i\) to \(j\)

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seq_markov.seq_dis_mat
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-array([[ 0.        , 72.31052406,  6.34073233, ..., 19.02219698,
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-       [72.31052406,  0.        , 77.05042347, ..., 74.77437281,
-        50.75696949, 65.9128181 ],
-       [ 6.34073233, 77.05042347,  0.        , ..., 12.68146465,
-        80.97128589, 80.51785856],
-       ...,
-       [19.02219698, 74.77437281, 12.68146465, ...,  0.        ,
-        80.10306616, 80.46369148],
-       [80.2334688 , 50.75696949, 80.97128589, ..., 80.10306616,
-         0.        , 41.57088046],
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“tran”

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Biemann, T. (2011). A Transition-Oriented Approach to Optimal Matching. Sociological Methodology, 41(1), 195–221. https://doi.org/10.1111/j.1467-9531.2011.01235.x

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-array([[ 0., 23.,  8., ..., 12., 24., 21.],
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seq_tran.seq_dis_mat
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-array([[  0., 220.,  25., ...,  55., 220., 220.],
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-       [ 25., 241.,   0., ...,  44., 241., 241.],
-       ...,
-       [ 55., 199.,  44., ...,   0., 207., 220.],
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- © Copyright 2018-, pysal developers.
- Created using Sphinx 5.0.2.
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- - \ No newline at end of file diff --git a/docs/Sequence.ipynb b/docs/Sequence.ipynb deleted file mode 100644 index f0a3cf9..0000000 --- a/docs/Sequence.ipynb +++ /dev/null @@ -1,450 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Alignment-based sequence methods\n", - "\n", - "This notebook introduces the alignment-based sequence methods (operationalized by the Optimal Matching (OM) algorithm), which was originally developed for matching protein and DNA sequences in biology and used extensively for analyzing strings in computer science and recently widely applied to explore the neighborhood change. \n", - "\n", - "It generally works by finding the minimum cost for aligning one sequence to match another using a combination of operations including substitution, insertion, deletion and transposition. The cost of each operation can be parameterized diferently and may be theory-driven or data-driven. The minimum cost is considered as the distance between the two sequences.\n", - "\n", - "The `sequence` module in `giddy` provides a suite of alignment-based sequence methods. \n", - "\n", - "**Author: Wei Kang **" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/weikang/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", - " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[0, 0, 0, ..., 0, 0, 0],\n", - " [2, 2, 2, ..., 1, 1, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [1, 1, 1, ..., 0, 0, 0],\n", - " [3, 3, 2, ..., 2, 2, 2],\n", - " [3, 3, 3, ..., 4, 4, 4]])" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import libpysal\n", - "import mapclassify as mc\n", - "f = libpysal.io.open(libpysal.examples.get_path(\"usjoin.csv\"))\n", - "pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])\n", - "q5 = np.array([mc.Quantiles(y,k=5).yb for y in pci]).transpose()\n", - "q5" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(48, 81)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "q5.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import `Sequence` class from `giddy.sequence`:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from giddy.sequence import Sequence" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### \"hamming\" \n", - "\n", - "* substitution cost = 1\n", - "* insertion/deletion cost = $\\infty$" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_hamming = Sequence(q5, dist_type=\"hamming\")\n", - "seq_hamming" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 75., 7., ..., 21., 81., 78.],\n", - " [75., 0., 80., ..., 79., 57., 73.],\n", - " [ 7., 80., 0., ..., 14., 81., 81.],\n", - " ...,\n", - " [21., 79., 14., ..., 0., 81., 81.],\n", - " [81., 57., 81., ..., 81., 0., 51.],\n", - " [78., 73., 81., ..., 81., 51., 0.]])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_hamming.seq_dis_mat #pairwise sequence distance matrix " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### \"interval\" \n", - "\n", - "Assuming there are $k$ states in the sequences and they are ordinal/continuous.\n", - "\n", - "* substitution cost = differences between states \n", - "* insertion/deletion cost = $k-1$" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_interval = Sequence(q5, dist_type=\"interval\")\n", - "seq_interval" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 123., 7., ..., 21., 190., 225.],\n", - " [123., 0., 130., ..., 116., 69., 108.],\n", - " [ 7., 130., 0., ..., 14., 197., 232.],\n", - " ...,\n", - " [ 21., 116., 14., ..., 0., 183., 218.],\n", - " [190., 69., 197., ..., 183., 0., 61.],\n", - " [225., 108., 232., ..., 218., 61., 0.]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_interval.seq_dis_mat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### \"arbitrary\"\n", - "\n", - "* substitution cost = 0.5\n", - "* insertion/deletion cost = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_arbitrary = Sequence(q5, dist_type=\"arbitrary\")\n", - "seq_arbitrary" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0. , 37.5, 3.5, ..., 10.5, 40.5, 39. ],\n", - " [37.5, 0. , 40. , ..., 39.5, 28.5, 36.5],\n", - " [ 3.5, 40. , 0. , ..., 7. , 40.5, 40.5],\n", - " ...,\n", - " [10.5, 39.5, 7. , ..., 0. , 40.5, 40.5],\n", - " [40.5, 28.5, 40.5, ..., 40.5, 0. , 25.5],\n", - " [39. , 36.5, 40.5, ..., 40.5, 25.5, 0. ]])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_arbitrary.seq_dis_mat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### \"markov\"\n", - "\n", - "* substitution cost = $1-\\frac{p_{ij}+p_{ji}}{2}$ where $p_{ij}$ is the empirical rate of transitioning from state $i$ to $j$\n", - "* insertion/deletion cost = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_markov = Sequence(q5, dist_type=\"markov\")\n", - "seq_markov" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0. , 72.31052406, 6.34073233, ..., 19.02219698,\n", - " 80.2334688 , 77.48002783],\n", - " [72.31052406, 0. , 77.05042347, ..., 74.77437281,\n", - " 50.75696949, 65.9128181 ],\n", - " [ 6.34073233, 77.05042347, 0. , ..., 12.68146465,\n", - " 80.97128589, 80.51785856],\n", - " ...,\n", - " [19.02219698, 74.77437281, 12.68146465, ..., 0. ,\n", - " 80.10306616, 80.46369148],\n", - " [80.2334688 , 50.75696949, 80.97128589, ..., 80.10306616,\n", - " 0. , 41.57088046],\n", - " [77.48002783, 65.9128181 , 80.51785856, ..., 80.46369148,\n", - " 41.57088046, 0. ]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_markov.seq_dis_mat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### \"tran\"\n", - "\n", - "Biemann, T. (2011). A Transition-Oriented Approach to Optimal Matching. Sociological Methodology, 41(1), 195–221. https://doi.org/10.1111/j.1467-9531.2011.01235.x\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_tran = Sequence(q5, dist_type=\"tran\")\n", - "seq_tran" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 23., 8., ..., 12., 24., 21.],\n", - " [23., 0., 17., ..., 16., 28., 22.],\n", - " [ 8., 17., 0., ..., 4., 18., 16.],\n", - " ...,\n", - " [12., 16., 4., ..., 0., 21., 15.],\n", - " [24., 28., 18., ..., 21., 0., 23.],\n", - " [21., 22., 16., ..., 15., 23., 0.]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_tran.seq_dis_mat" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 220., 25., ..., 55., 220., 220.],\n", - " [220., 0., 241., ..., 199., 93., 123.],\n", - " [ 25., 241., 0., ..., 44., 241., 241.],\n", - " ...,\n", - " [ 55., 199., 44., ..., 0., 207., 220.],\n", - " [220., 93., 241., ..., 207., 0., 84.],\n", - " [220., 123., 241., ..., 220., 84., 0.]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "seq_tran.seq_dis_mat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - 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    -
    -
    - -

    Source code for giddy.directional

    -"""
-Directional Analysis of Dynamic LISAs
-
-"""
-__author__ = "Sergio J. Rey <sjsrey@gmail.com>"
-
-__all__ = ["Rose"]
-
-import warnings
-import numpy as np
-from libpysal import weights
-from libpysal.common import requires as _requires
-
-_POS8 = np.array([1, 1, 0, 0, 1, 1, 0, 0])
-_POS4 = np.array([1, 0, 1, 0])
-_NEG8 = 1 - _POS8
-_NEG4 = 1 - _POS4
-
-
-
    [docs]class Rose(object):
-    """
-    Rose diagram based inference for directional LISAs.
-
-    For n units with LISA values at two points in time, the Rose class provides
-    the LISA vectors, their visualization, and computationally based inference.
-
-    Parameters
-    ----------
-    Y : array (n,2)
-        Columns correspond to end-point time periods to calculate LISA vectors for n object.
-    w : PySAL W
-        Spatial weights object.
-    k : int
-        Number of circular sectors in rose diagram.
-
-    Attributes
-    ----------
-    cuts : (k, 1) ndarray
-        Radian cuts for rose diagram (circular histogram).
-    counts: (k, 1) ndarray
-        Number of vectors contained in each sector.
-    r : (n, 1) ndarray
-        Vector lengths.
-    theta : (n,1) ndarray
-        Signed radians for observed LISA vectors.
-
-    If self.permute is called the following attributes are available:
-
-    alternative : string
-        Form of the specified alternative hypothesis ['two-sided'(default) |
-        'positive' | 'negative']
-    counts_perm : (permutations, k) ndarray
-        Counts obtained for each sector for every permutation
-    expected_perm : (k, 1) ndarray
-        Average number of counts for each sector taken over all permutations.
-    p : (k, 1) ndarray
-        Psuedo p-values for the observed sector counts under the specified alternative.
-    larger_perm : (k, 1) ndarray
-        Number of times realized counts are as large as observed sector count.
-    smaller_perm : (k, 1) ndarray
-        Number of times realized counts are as small as observed sector count.
-
-    """
-
-
    [docs]    def __init__(self, Y, w, k=8):
-        """
-        Calculation of rose diagram for local indicators of spatial
-        association.
-
-        Parameters
-        ----------
-        Y :  (n, 2) ndarray
-            Variable observed on n spatial units over 2 time periods
-        w : W
-            Spatial weights object.
-        k : int
-            number of circular sectors in rose diagram (the default is 8).
-
-
-        Notes
-        -----
-        Based on :cite:`Rey2011`.
-
-        Examples
-        --------
-        Constructing data for illustration of directional LISA analytics.
-        Data is for the 48 lower US states over the period 1969-2009 and
-        includes per capita income normalized to the national average.
-
-        Load comma delimited data file in and convert to a numpy array
-
-        >>> import libpysal
-        >>> from giddy.directional import Rose
-        >>> import numpy as np
-        >>> import matplotlib.pyplot as plt
-        >>> file_path = libpysal.examples.get_path("spi_download.csv")
-        >>> f=open(file_path,'r')
-        >>> lines=f.readlines()
-        >>> f.close()
-        >>> lines=[line.strip().split(",") for line in lines]
-        >>> names=[line[2] for line in lines[1:-5]]
-        >>> data=np.array([list(map(int,line[3:])) for line in lines[1:-5]])
-
-        Bottom of the file has regional data which we don't need for this
-        example so we will subset only those records that match a state name
-
-        >>> sids=list(range(60))
-        >>> out=['"United States 3/"',
-        ...      '"Alaska 3/"',
-        ...      '"District of Columbia"',
-        ...      '"Hawaii 3/"',
-        ...      '"New England"',
-        ...      '"Mideast"',
-        ...      '"Great Lakes"',
-        ...      '"Plains"',
-        ...      '"Southeast"',
-        ...      '"Southwest"',
-        ...      '"Rocky Mountain"',
-        ...      '"Far West 3/"']
-        >>> snames=[name for name in names if name not in out]
-        >>> sids=[names.index(name) for name in snames]
-        >>> states=data[sids,:]
-        >>> us=data[0]
-        >>> years=np.arange(1969,2009)
-
-        Now we convert state incomes to express them relative to the national
-        average
-
-        >>> rel=states/(us*1.)
-
-        Create our contiguity matrix from an external GAL file and row
-        standardize the resulting weights
-
-        >>> gal=libpysal.io.open(libpysal.examples.get_path('states48.gal'))
-        >>> w=gal.read()
-        >>> w.transform='r'
-
-        Take the first and last year of our income data as the interval to do
-        the directional directional analysis
-
-        >>> Y=rel[:,[0,-1]]
-
-        Set the random seed generator which is used in the permutation based
-        inference for the rose diagram so that we can replicate our example
-        results
-
-        >>> np.random.seed(100)
-
-        Call the rose function to construct the directional histogram for the
-        dynamic LISA statistics. We will use four circular sectors for our
-        histogram
-
-        >>> r4=Rose(Y,w,k=4)
-
-        What are the cut-offs for our histogram - in radians
-
-        >>> r4.cuts
-        array([0.        , 1.57079633, 3.14159265, 4.71238898, 6.28318531])
-
-        We can test whether these counts are different than what would be
-        expected if there was no association between the movement of the
-        focal unit and its spatial lag.
-
-        To do so we call the `permute` method of the object
-
-        >>> r4.permute()
-
-        and then inspect the `p` attibute:
-
-        >>> r4.p
-        array([0.04, 0.  , 0.02, 0.  ])
-
-        Repeat the exercise but now for 8 rather than 4 sectors
-
-        >>> r8 = Rose(Y, w, k=8)
-        >>> r8.permute()
-        >>> r8.p
-        array([0.86, 0.08, 0.16, 0.  , 0.02, 0.2 , 0.56, 0.  ])
-
-        The default is a two-sided alternative. There is an option for a
-        directional alternative reflecting positive co-movement of the focal
-        series with its spatial lag. In this case the number of vectors in
-        quadrants I and III should be much larger than expected, while the
-        counts of vectors falling in quadrants II and IV should be much lower
-        than expected.
-
-        >>> r8.permute(alternative='positive')
-        >>> r8.p
-        array([0.51, 0.04, 0.28, 0.02, 0.01, 0.14, 0.57, 0.03])
-
-        Finally, there is a second directional alternative for examining the
-        hypothesis that the focal unit and its lag move in opposite directions.
-
-        >>> r8.permute(alternative='negative')
-        >>> r8.p
-        array([0.69, 0.99, 0.92, 1.  , 1.  , 0.97, 0.74, 1.  ])
-
-        We can call the plot method to visualize directional LISAs as a
-        rose diagram conditional on the starting relative income:
-
-        >>> fig1, _ = r8.plot(attribute=Y[:,0])
-        >>> plt.show()
-        """
-
-        self.Y = Y
-        self.w = w
-        self.k = k
-        self.sw = 2 * np.pi / self.k
-        self.cuts = np.arange(0.0, 2 * np.pi + self.sw, self.sw)
-        observed = self._calc(Y, w, k)
-        self.theta = observed["theta"]
-        self.bins = observed["bins"]
-        self.counts = observed["counts"]
-        self.r = observed["r"]
-        self.lag = observed["lag"]
-        self._dx = observed["dx"]
-        self._dy = observed["dy"]
    
-
-
    [docs]    def permute(self, permutations=99, alternative="two.sided"):
-        """
-        Generate ransom spatial permutations for inference on LISA vectors.
-
-        Parameters
-        ----------
-        permutations : int, optional
-            Number of random permutations of observations.
-        alternative : string, optional
-            Type of alternative to form in generating p-values.
-            Options are: `two-sided` which tests for difference between observed
-            counts and those obtained from the permutation distribution;
-            `positive` which tests the alternative that the focal unit and its
-            lag move in the same direction over time; `negative` which tests
-            that the focal unit and its lag move in opposite directions over
-            the interval.
-        """
-        rY = self.Y.copy()
-        idxs = np.arange(len(rY))
-        counts = np.zeros((permutations, len(self.counts)))
-        for m in range(permutations):
-            np.random.shuffle(idxs)
-            res = self._calc(rY[idxs, :], self.w, self.k)
-            counts[m] = res["counts"]
-        self.counts_perm = counts
-        self.larger_perm = np.array(
-            [(counts[:, i] >= self.counts[i]).sum() for i in range(self.k)]
-        )
-        self.smaller_perm = np.array(
-            [(counts[:, i] <= self.counts[i]).sum() for i in range(self.k)]
-        )
-        self.expected_perm = counts.mean(axis=0)
-        self.alternative = alternative
-
-        # pvalue logic
-        # if P is the proportion that are as large for a one sided test (larger
-        # than), then
-        # p=P.
-        #
-        # For a two-tailed test, if P < .5, p = 2 * P, else, p = 2(1-P)
-        # Source: Rayner, J. C. W., O. Thas, and D. J. Best. 2009. "Appendix B:
-        # Parametric Bootstrap P-Values." In Smooth Tests of Goodness of Fit,
-        # 247. John Wiley and Sons.
-        # Note that the larger and smaller counts would be complements (except
-        # for the shared equality, for
-        # a given bin in the circular histogram. So we only need one of them.
-
-        # We report two-sided p-values for each bin as the default
-        # since a priori there could # be different alternatives for each bin
-        # depending on the problem at hand.
-
-        alt = alternative.upper()
-        if alt == "TWO.SIDED":
-            P = (self.larger_perm + 1) / (permutations + 1.0)
-            mask = P < 0.5
-            self.p = mask * 2 * P + (1 - mask) * 2 * (1 - P)
-        elif alt == "POSITIVE":
-            # NE, SW sectors are higher, NW, SE are lower
-            POS = _POS8
-            if self.k == 4:
-                POS = _POS4
-            L = (self.larger_perm + 1) / (permutations + 1.0)
-            S = (self.smaller_perm + 1) / (permutations + 1.0)
-            P = POS * L + (1 - POS) * S
-            self.p = P
-        elif alt == "NEGATIVE":
-            # NE, SW sectors are lower, NW, SE are higher
-            NEG = _NEG8
-            if self.k == 4:
-                NEG = _NEG4
-            L = (self.larger_perm + 1) / (permutations + 1.0)
-            S = (self.smaller_perm + 1) / (permutations + 1.0)
-            P = NEG * L + (1 - NEG) * S
-            self.p = P
-        else:
-            print(("Bad option for alternative: %s." % alternative))
    
-
-    def _calc(self, Y, w, k):
-        wY = weights.lag_spatial(w, Y)
-        dx = Y[:, -1] - Y[:, 0]
-        dy = wY[:, -1] - wY[:, 0]
-        self.wY = wY
-        self.Y = Y
-        r = np.sqrt(dx * dx + dy * dy)
-        theta = np.arctan2(dy, dx)
-        neg = theta < 0.0
-        utheta = theta * (1 - neg) + neg * (2 * np.pi + theta)
-        counts, bins = np.histogram(utheta, self.cuts)
-        results = {}
-        results["counts"] = counts
-        results["theta"] = theta
-        results["bins"] = bins
-        results["r"] = r
-        results["lag"] = wY
-        results["dx"] = dx
-        results["dy"] = dy
-        return results
-
-
    [docs]    @_requires("splot")
-    def plot(self, attribute=None, ax=None, **kwargs):
-        """
-        Plot the rose diagram.
-
-        Parameters
-        ----------
-        attribute : (n,) ndarray, optional
-            Variable to specify colors of the colorbars.
-        ax : Matplotlib Axes instance, optional
-            If given, the figure will be created inside this axis.
-            Default =None. Note, this axis should have a polar projection.
-        **kwargs : keyword arguments, optional
-            Keywords used for creating and designing the plot.
-            Note: 'c' and 'color' cannot be passed when attribute is not None
-
-        Returns
-        -------
-        fig : Matplotlib Figure instance
-            Moran scatterplot figure
-        ax : matplotlib Axes instance
-            Axes in which the figure is plotted
-
-        """
-
-        from splot.giddy import dynamic_lisa_rose
-
-        fig, ax = dynamic_lisa_rose(self, attribute=attribute, ax=ax, **kwargs)
-        return fig, ax
    
-
-
    [docs]    def plot_origin(self):  # TODO add attribute option to color vectors
-        """
-        Plot vectors of positional transition of LISA values starting
-        from the same origin.
-        """
-        import matplotlib.cm as cm
-        import matplotlib.pyplot as plt
-
-        ax = plt.subplot(111)
-        xlim = [self._dx.min(), self._dx.max()]
-        ylim = [self._dy.min(), self._dy.max()]
-        for x, y in zip(self._dx, self._dy):
-            xs = [0, x]
-            ys = [0, y]
-            plt.plot(xs, ys, "-b")  # TODO change this to scale with attribute
-        plt.axis("equal")
-        plt.xlim(xlim)
-        plt.ylim(ylim)
    
-
-
    [docs]    @_requires("splot")
-    def plot_vectors(self, arrows=True):
-        """
-        Plot vectors of positional transition of LISA values
-        within quadrant in scatterplot in a polar plot.
-
-        Parameters
-        ----------
-        ax : Matplotlib Axes instance, optional
-            If given, the figure will be created inside this axis.
-            Default =None.
-        arrows : boolean, optional
-            If True show arrowheads of vectors. Default =True
-        **kwargs : keyword arguments, optional
-            Keywords used for creating and designing the plot.
-            Note: 'c' and 'color' cannot be passed when attribute is not None
-
-        Returns
-        -------
-        fig : Matplotlib Figure instance
-            Moran scatterplot figure
-        ax : matplotlib Axes instance
-            Axes in which the figure is plotted
-
-        """
-
-        from splot.giddy import dynamic_lisa_vectors
-
-        fig, ax = dynamic_lisa_vectors(self, arrows=arrows)
-        return fig, ax
    
-
    - -
    - -
    -
    -
    -
    -

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    -

    - © Copyright 2018-, pysal developers.
    - Created using Sphinx 5.0.2.
    -

    -
    -
    - - \ No newline at end of file diff --git a/docs/_modules/giddy/ergodic.html b/docs/_modules/giddy/ergodic.html deleted file mode 100644 index 6e96fe5..0000000 --- a/docs/_modules/giddy/ergodic.html +++ /dev/null @@ -1,553 +0,0 @@ - - - - - - - giddy.ergodic — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    -
    - -

    Source code for giddy.ergodic

    -"""
-Summary measures for ergodic Markov chains.
-"""
-__author__ = "Sergio J. Rey <sjsrey@gmail.com>, Wei Kang <weikang9009@gmail.com>"
-
-__all__ = ["steady_state", "var_mfpt_ergodic", "mfpt"]
-
-import numpy as np
-import numpy.linalg as la
-import quantecon as qe
-from warnings import warn
-from .util import fill_empty_diagonals
-
-
-def _steady_state_ergodic(P):
-    """
-    Calculates the steady state probability vector for a regular Markov
-    transition matrix P.
-
-    Parameters
-    ----------
-    P        : array
-               (k, k), an ergodic Markov transition probability matrix.
-
-    Returns
-    -------
-             : array
-               (k, ), steady state distribution.
-
-    Examples
-    --------
-    Taken from :cite:`Kemeny1967`. Land of Oz example where the states are
-    Rain, Nice and Snow, so there is 25 percent chance that if it
-    rained in Oz today, it will snow tomorrow, while if it snowed today in
-    Oz there is a 50 percent chance of snow again tomorrow and a 25
-    percent chance of a nice day (nice, like when the witch with the monkeys
-    is melting).
-
-    >>> import numpy as np
-    >>> import giddy
-    >>> p=np.array([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
-    >>> giddy.ergodic._steady_state_ergodic(p)
-    array([0.4, 0.2, 0.4])
-
-    Thus, the long run distribution for Oz is to have 40 percent of the
-    days classified as Rain, 20 percent as Nice, and 40 percent as Snow
-    (states are mutually exclusive).
-
-    """
-
-    v, d = la.eig(np.transpose(P))
-    d = np.array(d)
-
-    # for a regular P maximum eigenvalue will be 1
-    mv = max(v)
-    # find its position
-    i = v.tolist().index(mv)
-
-    row = abs(d[:, i])
-
-    # normalize eigenvector corresponding to the eigenvalue 1
-    return row / sum(row)
-
-
-
    [docs]def steady_state(P, fill_empty_classes=False):
-    """
-    Generalized function for calculating the steady state distribution
-    for a regular or reducible Markov transition matrix P.
-
-    Parameters
-    ----------
-    P        : array
-               (k, k), an ergodic or non-ergodic Markov transition probability
-               matrix.
-    fill_empty_classes: bool, optional
-                        If True, assign 1 to diagonal elements which fall in rows full
-                        of 0s to ensure the transition probability matrix is a
-                        stochastic one. Default is False.
-
-    Returns
-    -------
-             : array
-               If the Markov chain is irreducible, meaning that
-               there is only one communicating class, there is one unique
-               steady state distribution towards which the system is
-               converging in the long run. Then steady_state is the
-               same as _steady_state_ergodic (k, ).
-               If the Markov chain is reducible, but only has 1 recurrent
-               class, there will be one steady state distribution as well.
-               If the Markov chain is reducible and there are multiple
-               recurrent classes (num_rclasses), the system could be trapped
-               in any one of  these recurrent classes. Then, there will be
-               `num_rclasses` steady state distributions. The returned array
-               will of (num_rclasses, k) dimension.
-
-    Examples
-    --------
-
-    >>> import numpy as np
-    >>> from giddy.ergodic import steady_state
-
-    Irreducible Markov chain
-
-    >>> p = np.array([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
-    >>> steady_state(p)
-    array([0.4, 0.2, 0.4])
-
-    Reducible Markov chain: two communicating classes
-
-    >>> p = np.array([[.5, .5, 0],[.2,0.8,0],[0,0,1]])
-    >>> steady_state(p)
-    array([[0.28571429, 0.71428571, 0.        ],
-           [0.        , 0.        , 1.        ]])
-
-    Reducible Markov chain: two communicating classes
-
-    >>> p = np.array([[.5, .5, 0],[.2,0.8,0],[0,0,0]])
-    >>> steady_state(p, fill_empty_classes = True)
-    array([[0.28571429, 0.71428571, 0.        ],
-           [0.        , 0.        , 1.        ]])
-
-    >>> steady_state(p, fill_empty_classes = False)
-    Traceback (most recent call last):
-        ...
-    ValueError: Input transition probability matrix has 1 rows full of 0s. Please set fill_empty_classes=True to set diagonal elements for these rows to be 1 to make sure the matrix is stochastic.
-
-    """
-
-    P = np.asarray(P)
-    rows0 = (P.sum(axis=1) == 0).sum()
-    if rows0 > 0:
-        if fill_empty_classes:
-            P = fill_empty_diagonals(P)
-        else:
-            raise ValueError(
-                "Input transition probability matrix has "
-                "%d rows full of 0s. Please set "
-                "fill_empty_classes=True to set diagonal "
-                "elements for these rows to be 1 to make "
-                "sure the matrix is stochastic." % rows0
-            )
-    mc = qe.MarkovChain(P)
-    num_classes = mc.num_communication_classes
-    if num_classes == 1:
-        return mc.stationary_distributions[0]
-    else:
-        return mc.stationary_distributions
    
-
-
-def _fmpt_ergodic(P):
-    warn('_fmpt_ergodic is deprecated. It will be replaced in giddy 2.5 with _mfpt_',
-         DeprecationWarning, stacklevel=2)
-    return _mfpt_ergodic(P)
-
-
-def _mfpt_ergodic(P):
-    """
-    Calculates the matrix of mean first passage times for an ergodic transition
-    probability matrix.
-
-    Parameters
-    ----------
-    P    : array
-           (k, k), an ergodic Markov transition probability matrix.
-
-    Returns
-    -------
-    M    : array
-           (k, k), elements are the expected value for the number of intervals
-           required for a chain starting in state i to first enter state j.
-           If i=j then this is the recurrence time.
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> import giddy
-    >>> p=np.array([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
-    >>> fm = giddy.ergodic._mfpt_ergodic(p)
-    >>> fm
-    array([[2.5       , 4.        , 3.33333333],
-           [2.66666667, 5.        , 2.66666667],
-           [3.33333333, 4.        , 2.5       ]])
-
-    Thus, if it is raining today in Oz we can expect a nice day to come
-    along in another 4 days, on average, and snow to hit in 3.33 days. We can
-    expect another rainy day in 2.5 days. If it is nice today in Oz, we would
-    experience a change in the weather (either rain or snow) in 2.67 days from
-    today. (That wicked witch can only die once so I reckon that is the
-    ultimate absorbing state).
-
-    Notes
-    -----
-    Uses formulation (and examples on p. 218) in :cite:`Kemeny1967`.
-
-    """
-
-    P = np.asarray(P)
-    k = P.shape[0]
-    A = np.zeros_like(P)
-    ss = _steady_state_ergodic(P)
-    for i in range(k):
-        A[:, i] = ss
-    A = A.transpose()
-    I = np.identity(k)
-    Z = la.inv(I - P + A)
-    E = np.ones_like(Z)
-    A_diag = np.diag(A)
-    A_diag = A_diag + (A_diag == 0)
-    D = np.diag(1.0 / A_diag)
-    Zdg = np.diag(np.diag(Z))
-    M = (I - Z + E.dot(Zdg)).dot(D)
-    return M
-
-
-
    [docs]def fmpt(P, fill_empty_classes=False):
-    warn('fmpt is deprecated. It will be replaced in giddy 2.5 with mfpt',
-         DeprecationWarning, stacklevel=2)
-    return mfpt(P, fill_empty_classes)
    
-
-
-
    [docs]def mfpt(P, fill_empty_classes=False):
-    """
-    Generalized function for calculating mean first passage times for an
-    ergodic or non-ergodic transition probability matrix.
-
-    Parameters
-    ----------
-    P        : array
-               (k, k), an ergodic/non-ergodic Markov transition probability
-               matrix.
-    fill_empty_classes: bool, optional
-                        If True, assign 1 to diagonal elements which fall in rows full
-                        of 0s to ensure the transition probability matrix is a
-                        stochastic one. Default is False.
-
-    Returns
-    -------
-    mfpt_all : array
-               (k, k), elements are the expected value for the number of
-               intervals required for a chain starting in state i to first
-               enter state j. If i=j then this is the recurrence time.
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from giddy.ergodic import mfpt
-    >>> np.set_printoptions(suppress=True) #prevent scientific format
-
-    Irreducible Markov chain
-
-    >>> p = np.array([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
-    >>> fm = mfpt(p)
-    >>> fm
-    array([[2.5       , 4.        , 3.33333333],
-           [2.66666667, 5.        , 2.66666667],
-           [3.33333333, 4.        , 2.5       ]])
-
-    Thus, if it is raining today in Oz we can expect a nice day to come
-    along in another 4 days, on average, and snow to hit in 3.33 days. We can
-    expect another rainy day in 2.5 days. If it is nice today in Oz, we would
-    experience a change in the weather (either rain or snow) in 2.67 days from
-    today.
-
-    Reducible Markov chain: two communicating classes (this is an
-    artificial example)
-
-    >>> p = np.array([[.5, .5, 0],[.2,0.8,0],[0,0,1]])
-    >>> mfpt(p)
-    array([[3.5, 2. , inf],
-           [5. , 1.4, inf],
-           [inf, inf, 1. ]])
-
-    Thus, if it is raining today in Oz we can expect a nice day to come
-    along in another 2 days, on average, and should not expect snow to hit.
-    We can expect another rainy day in 3.5 days. If it is nice today in Oz,
-    we should expect a rainy day in 5 days.
-
-
-    >>> p = np.array([[.5, .5, 0],[.2,0.8,0],[0,0,0]])
-    >>> mfpt(p, fill_empty_classes=True)
-    array([[3.5, 2. , inf],
-           [5. , 1.4, inf],
-           [inf, inf, 1. ]])
-
-    >>> p = np.array([[.5, .5, 0],[.2,0.8,0],[0,0,0]])
-    >>> mfpt(p, fill_empty_classes=False)
-    Traceback (most recent call last):
-        ...
-    ValueError: Input transition probability matrix has 1 rows full of 0s. Please set fill_empty_classes=True to set diagonal elements for these rows to be 1 to make sure the matrix is stochastic.
-    """
-
-    P = np.asarray(P)
-    rows0 = (P.sum(axis=1) == 0).sum()
-    if rows0 > 0:
-        if fill_empty_classes:
-            P = fill_empty_diagonals(P)
-        else:
-            raise ValueError(
-                "Input transition probability matrix has "
-                "%d rows full of 0s. Please set "
-                "fill_empty_classes=True to set diagonal "
-                "elements for these rows to be 1 to make "
-                "sure the matrix is stochastic." % rows0
-            )
-    mc = qe.MarkovChain(P)
-    num_classes = mc.num_communication_classes
-    if num_classes == 1:
-        mfpt_all = _mfpt_ergodic(P)
-    else:  # deal with non-ergodic Markov chains
-        k = P.shape[0]
-        mfpt_all = np.zeros((k, k))
-        for desti in range(k):
-            b = np.ones(k - 1)
-            p_sub = np.delete(np.delete(P, desti, 0), desti, 1)
-            p_calc = np.eye(k - 1) - p_sub
-            m = np.full(k - 1, np.inf)
-            row0 = (p_calc != 0).sum(axis=1)
-            none0 = np.arange(k - 1)
-            try:
-                m[none0] = np.linalg.solve(p_calc, b)
-            except np.linalg.LinAlgError as err:
-                if "Singular matrix" in str(err):
-                    if (row0 == 0).sum() > 0:
-                        index0 = set(np.argwhere(row0 == 0).flatten())
-                        x = (p_calc[:, list(index0)] != 0).sum(axis=1)
-                        setx = set(np.argwhere(x).flatten())
-                        while not setx.issubset(index0):
-                            index0 = index0.union(setx)
-                            x = (p_calc[:, list(index0)] != 0).sum(axis=1)
-                            setx = set(np.argwhere(x).flatten())
-                        none0 = np.asarray(list(set(none0).difference(index0)))
-                        if len(none0) >= 1:
-                            p_calc = p_calc[none0, :][:, none0]
-                            b = b[none0]
-                            m[none0] = np.linalg.solve(p_calc, b)
-            recc = (
-                np.nan_to_num(
-                    (np.delete(P, desti, 1)[desti] * m), 0, posinf=np.inf
-                ).sum()
-                + 1
-            )
-            mfpt_all[:, desti] = np.insert(m, desti, recc)
-            mfpt_all = np.where(mfpt_all < -1e16, np.inf, mfpt_all)
-            mfpt_all = np.where(mfpt_all > 1e16, np.inf, mfpt_all)
-    return mfpt_all
    
-
-
-
    [docs]def var_fmpt_ergodic(p):
-    warn('var_fmpt_ergodic is deprecated. It will be replaced in giddy 2.5 with var_fmpt_ergodic',
-         DeprecationWarning, stacklevel=2)
-    return var_mfpt_ergodic(p)
    
-
-
-
    [docs]def var_mfpt_ergodic(p):
-    """
-    Variances of mean first passage times for an ergodic transition
-    probability matrix.
-
-    Parameters
-    ----------
-    P      : array
-             (k, k), an ergodic Markov transition probability matrix.
-
-    Returns
-    -------
-           : array
-             (k, k), elements are the variances for the number of intervals
-             required for a chain starting in state i to first enter state j.
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from giddy.ergodic import var_mfpt_ergodic
-    >>> p=np.array([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
-    >>> vfm=var_mfpt_ergodic(p)
-    >>> vfm
-    array([[ 5.58333333, 12.        ,  6.88888889],
-           [ 6.22222222, 12.        ,  6.22222222],
-           [ 6.88888889, 12.        ,  5.58333333]])
-
-    Notes
-    -----
-    Uses formulation (and examples on p. 83) in :cite:`Kemeny1967`.
-
-
-    """
-
-    P = np.asarray(p)
-    k = P.shape[0]
-    A = _steady_state_ergodic(P)
-    A = np.tile(A, (k, 1))
-    I = np.identity(k)
-    Z = la.inv(I - P + A)
-    E = np.ones_like(Z)
-    D = np.diag(1.0 / np.diag(A))
-    Zdg = np.diag(np.diag(Z))
-    M = (I - Z + E.dot(Zdg)).dot(D)
-    ZM = Z.dot(M)
-    ZMdg = np.diag(np.diag(ZM))
-    W = M.dot(2 * Zdg.dot(D) - I) + 2 * (ZM - E.dot(ZMdg))
-    return np.array(W - np.multiply(M, M))
    
-
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    - - \ No newline at end of file diff --git a/docs/_modules/giddy/markov.html b/docs/_modules/giddy/markov.html deleted file mode 100644 index 15b1f6a..0000000 --- a/docs/_modules/giddy/markov.html +++ /dev/null @@ -1,2377 +0,0 @@ - - - - - - - giddy.markov — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    -
    - -

    Source code for giddy.markov

    -"""
-Markov based methods for spatial dynamics.
-"""
-__author__ = "Sergio J. Rey <sjsrey@gmail.com>, Wei Kang <weikang9009@gmail.com>"
-
-__all__ = [
-    "Markov",
-    "LISA_Markov",
-    "Spatial_Markov",
-    "kullback",
-    "prais",
-    "homogeneity",
-    "FullRank_Markov",
-    "sojourn_time",
-    "GeoRank_Markov",
-]
-
-import numpy as np
-from .ergodic import steady_state, mfpt
-from .util import fill_empty_diagonals
-from .components import Graph
-from warnings import warn
-from scipy import stats
-from scipy.stats import rankdata
-from operator import gt
-from libpysal import weights
-from esda.moran import Moran_Local
-import mapclassify as mc
-import itertools
-import quantecon as qe
-
-# TT predefine LISA transitions
-# TT[i,j] is the transition type from i to j
-# i = quadrant in period 0
-# j = quadrant in period 1
-# uses one offset so first row and col of TT are ignored
-TT = np.zeros((5, 5), int)
-c = 1
-for i in range(1, 5):
-    for j in range(1, 5):
-        TT[i, j] = c
-        c += 1
-
-# MOVE_TYPES is a dictionary that returns the move type of a LISA transition
-# filtered on the significance of the LISA end points
-# True indicates significant LISA in a particular period
-# e.g. a key of (1, 3, True, False) indicates a significant LISA located in
-# quadrant 1 in period 0 moved to quadrant 3 in period 1 but was not
-# significant in quadrant 3.
-
-MOVE_TYPES = {}
-c = 1
-cases = (True, False)
-sig_keys = [(i, j) for i in cases for j in cases]
-
-for i, sig_key in enumerate(sig_keys):
-    c = 1 + i * 16
-    for i in range(1, 5):
-        for j in range(1, 5):
-            key = (i, j, sig_key[0], sig_key[1])
-            MOVE_TYPES[key] = c
-            c += 1
-
-
-
    [docs]class Markov(object):
-    """
-    Classic Markov Chain estimation.
-
-    Parameters
-    ----------
-    class_ids    : array
-                   (n, t), one row per observation, one column recording the
-                   state of each observation, with as many columns as time
-                   periods.
-    classes      : array
-                   (k, 1), all different classes (bins) of the matrix.
-    fill_empty_classes: bool
-                        If True, assign 1 to diagonal elements which fall in rows
-                        full of 0s to ensure p is a stochastic transition
-                        probability matrix (each row sums up to 1).
-    summary      : bool
-                   If True, print out the summary of the Markov Chain during
-                   initialization. Default is True.
-
-    Attributes
-    ----------
-    k            : int
-                   Number of Markov states.
-    p            : array
-                   (k, k), transition probability matrix.
-    num_cclasses : int
-                   Number of communicating classes.
-    cclasses_indices : list
-                       List of indices within each communicating classes.
-    num_rclasses : int
-                   Number of recurrent classes.
-    rclasses_indices : list
-                       List of indices within each recurrent classes.
-    num_astates  : int
-                   Number of absorbing states.
-    astates_indices  : list
-                       List of indices of absorbing states.
-    steady_state : array
-                   Steady state distributions. If the Markov chain only has
-                   one recurrent class (num_rclasses=1), it will converge to
-                   an unique distribution in the long run, and thus steady_state
-                   is of (k, ) dimension; if the Markov chain has multiple
-                   recurrent classes (num_rclasses>1), there will be
-                   (num_rclasses) steady state distributions and steady_state
-                   will be of (num_rclasses, k) dimension.
-    transitions  : array
-                   (k, k), count of transitions between each state i and j.
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from giddy.markov import Markov
-    >>> c = [['b','a','c'],['c','c','a'],['c','b','c']]
-    >>> c.extend([['a','a','b'], ['a','b','c']])
-    >>> c = np.array(c)
-    >>> m = Markov(c)
-    The Markov Chain is irreducible and is composed by:
-    1 Recurrent class (indices):
-    [0 1 2]
-    0 Transient classes.
-    The Markov Chain has 0 absorbing states.
-    >>> m.classes.tolist()
-    ['a', 'b', 'c']
-    >>> m.p
-    array([[0.25      , 0.5       , 0.25      ],
-           [0.33333333, 0.        , 0.66666667],
-           [0.33333333, 0.33333333, 0.33333333]])
-    >>> m.steady_state
-    array([0.30769231, 0.28846154, 0.40384615])
-
-    Reducible Markov chain
-
-    >>> c = [['b','a','a'],['c','c','a'],['c','b','c']]
-    >>> m = Markov(c)
-    The Markov Chain is reducible and is composed by:
-    1 Recurrent class (indices):
-    [0]
-    1 Transient class (indices):
-    [1 2]
-    The Markov Chain has 1 absorbing state (index):
-    [0]
-
-    US nominal per capita income 48 states 81 years 1929-2009
-
-    >>> import libpysal
-    >>> import mapclassify as mc
-    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
-    >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
-
-    set classes to quintiles for each year
-
-    >>> q5 = np.array([mc.Quantiles(y).yb for y in pci]).transpose()
-    >>> m = Markov(q5)
-    The Markov Chain is irreducible and is composed by:
-    1 Recurrent class (indices):
-    [0 1 2 3 4]
-    0 Transient classes.
-    The Markov Chain has 0 absorbing states.
-    >>> m.transitions
-    array([[729.,  71.,   1.,   0.,   0.],
-           [ 72., 567.,  80.,   3.,   0.],
-           [  0.,  81., 631.,  86.,   2.],
-           [  0.,   3.,  86., 573.,  56.],
-           [  0.,   0.,   1.,  57., 741.]])
-    >>> m.p
-    array([[0.91011236, 0.0886392 , 0.00124844, 0.        , 0.        ],
-           [0.09972299, 0.78531856, 0.11080332, 0.00415512, 0.        ],
-           [0.        , 0.10125   , 0.78875   , 0.1075    , 0.0025    ],
-           [0.        , 0.00417827, 0.11977716, 0.79805014, 0.07799443],
-           [0.        , 0.        , 0.00125156, 0.07133917, 0.92740926]])
-    >>> m.steady_state
-    array([0.20774716, 0.18725774, 0.20740537, 0.18821787, 0.20937187])
-
-    Relative incomes
-
-    >>> pci = pci.transpose()
-    >>> rpci = pci/(pci.mean(axis=0))
-    >>> rq = mc.Quantiles(rpci.flatten()).yb.reshape(pci.shape)
-    >>> mq = Markov(rq)
-    The Markov Chain is irreducible and is composed by:
-    1 Recurrent class (indices):
-    [0 1 2 3 4]
-    0 Transient classes.
-    The Markov Chain has 0 absorbing states.
-    >>> mq.transitions
-    array([[707.,  58.,   7.,   1.,   0.],
-           [ 50., 629.,  80.,   1.,   1.],
-           [  4.,  79., 610.,  73.,   2.],
-           [  0.,   7.,  72., 650.,  37.],
-           [  0.,   0.,   0.,  48., 724.]])
-    >>> mq.steady_state
-    array([0.17957376, 0.21631443, 0.21499942, 0.21134662, 0.17776576])
-
-    """
-
-
    [docs]    def __init__(self, class_ids, classes=None, fill_empty_classes=False, summary=True):
-        if classes is not None:
-            self.classes = classes
-        else:
-            self.classes = np.unique(class_ids)
-
-        class_ids = np.array(class_ids)
-        n, t = class_ids.shape
-        k = len(self.classes)
-        self.k = k
-        js = list(range(t - 1))
-
-        classIds = self.classes.tolist()
-        transitions = np.zeros((k, k))
-        for state_0 in js:
-            state_1 = state_0 + 1
-            state_0 = class_ids[:, state_0]
-            state_1 = class_ids[:, state_1]
-            initial = np.unique(state_0)
-            for i in initial:
-                ending = state_1[state_0 == i]
-                uending = np.unique(ending)
-                row = classIds.index(i)
-                for j in uending:
-                    col = classIds.index(j)
-                    transitions[row, col] += sum(ending == j)
-        self.transitions = transitions
-        row_sum = transitions.sum(axis=1)
-        self.p = np.dot(np.diag(1 / (row_sum + (row_sum == 0))), transitions)
-
-        if fill_empty_classes:
-            self.p = fill_empty_diagonals(self.p)
-
-        p_temp = self.p
-        if (p_temp.sum(axis=1) == 0).sum() > 0:
-            p_temp = fill_empty_diagonals(p_temp)
-        markovchain = qe.MarkovChain(p_temp)
-        self.num_cclasses = markovchain.num_communication_classes
-        self.num_rclasses = markovchain.num_recurrent_classes
-
-        self.cclasses_indices = markovchain.communication_classes_indices
-        self.rclasses_indices = markovchain.recurrent_classes_indices
-        transient = set(list(map(tuple, self.cclasses_indices))).difference(
-            set(list(map(tuple, self.rclasses_indices)))
-        )
-        self.num_tclasses = len(transient)
-        if len(transient):
-            self.tclasses_indices = [np.asarray(i) for i in transient]
-        else:
-            self.tclasses_indices = None
-        self.astates_indices = list(np.argwhere(np.diag(p_temp) == 1))
-        self.num_astates = len(self.astates_indices)
-
-        if summary:
-            if markovchain.is_irreducible:
-                print("The Markov Chain is irreducible and is composed by:")
-            else:
-                print("The Markov Chain is reducible and is composed by:")
-            if self.num_rclasses == 1:
-                print("1 Recurrent class (indices):")
-            else:
-                print("{0} Recurrent classes (indices):".format(self.num_rclasses))
-            print(*self.rclasses_indices, sep=", ")
-            if self.num_tclasses == 0:
-                print("0 Transient classes.")
-            else:
-                if self.num_tclasses == 1:
-                    print("1 Transient class (indices):")
-                else:
-                    print("{0} Transient classes (indices):".format(self.num_tclasses))
-                print(*self.tclasses_indices, sep=", ")
-            if self.num_astates == 0:
-                print("The Markov Chain has 0 absorbing states.")
-            else:
-                if self.num_astates == 1:
-                    print("The Markov Chain has 1 absorbing state (index):")
-                else:
-                    print(
-                        "The Markov Chain has {0} absorbing states (indices):".format(
-                            self.num_astates
-                        )
-                    )
-                print(*self.astates_indices, sep=", ")
    
-
-    @property
-    def mfpt(self):
-        warn('self._mfpt is deprecated. Please use self._mfpt')
-        if not hasattr(self, "_mfpt"):
-            self._mfpt = mfpt(self.p, fill_empty_classes=True)
-        return self._mfpt
-
-    @property
-    def mfpt(self):
-        if not hasattr(self, "_mfpt"):
-            self._mfpt = mfpt(self.p, fill_empty_classes=True)
-        return self._mfpt
-
-    @property
-    def steady_state(self):
-        if not hasattr(self, "_steady_state"):
-            self._steady_state = steady_state(self.p, fill_empty_classes=True)
-        return self._steady_state
-
-    @property
-    def sojourn_time(self):
-        if not hasattr(self, "_st"):
-            self._st = sojourn_time(self.p)
-        return self._st
    
-
-
-
    [docs]class Spatial_Markov(object):
-    """
-    Markov transitions conditioned on the value of the spatial lag.
-
-    Parameters
-    ----------
-    y               : array
-                      (n, t), one row per observation, one column per state of
-                      each observation, with as many columns as time periods.
-    w               : W
-                      spatial weights object.
-    k               : integer, optional
-                      number of classes (quantiles) for input time series y.
-                      Default is 4. If discrete=True, k is determined
-                      endogenously.
-    m               : integer, optional
-                      number of classes (quantiles) for the spatial lags of
-                      regional time series. Default is 4. If discrete=True,
-                      m is determined endogenously.
-    permutations    : int, optional
-                      number of permutations for use in randomization based
-                      inference (the default is 0).
-    fixed           : bool, optional
-                      If true, discretization are taken over the entire n*t
-                      pooled series and cutoffs can be user-defined. If
-                      cutoffs and lag_cutoffs are not given, quantiles are
-                      used. If false, quantiles are taken each time period
-                      over n. Default is True.
-    discrete        : bool, optional
-                      If true, categorical spatial lags which are most common
-                      categories of neighboring observations serve as the
-                      conditioning and fixed is ignored; if false, weighted
-                      averages of neighboring observations are used. Default is
-                      false.
-    cutoffs         : array, optional
-                      users can specify the discretization cutoffs for
-                      continuous time series. Default is None, meaning that
-                      quantiles will be used for the discretization.
-    lag_cutoffs     : array, optional
-                      users can specify the discretization cutoffs for the
-                      spatial lags of continuous time series. Default is
-                      None, meaning that quantiles will be used for the
-                      discretization.
-    variable_name   : string
-                      name of variable.
-    fill_empty_classes: bool
-                        If True, assign 1 to diagonal elements which fall in rows
-                        full of 0s to ensure each conditional transition
-                        probability matrix is a stochastic matrix (each row
-                        sums up to 1). In other words, the probability of
-                        staying at that state is 1.
-
-    Attributes
-    ----------
-    class_ids       : array
-                      (n, t), discretized series if y is continuous. Otherwise
-                      it is identical to y.
-    classes         : array
-                      (k, 1), all different classes (bins).
-    lclass_ids      : array
-                      (n, t), spatial lag series.
-    lclasses        : array
-                      (k, 1), all different classes (bins) for
-                      spatial lags.
-    p               : array
-                      (k, k), transition probability matrix for a-spatial
-                      Markov.
-    s               : array
-                      (k, ), steady state distribution for a-spatial Markov.
-    f               : array
-                      (k, k), first mean passage times for a-spatial Markov.
-    transitions     : array
-                      (k, k), counts of transitions between each state i and j
-                      for a-spatial Markov.
-    T               : array
-                      (m, k, k), counts of transitions for each conditional
-                      Markov.  T[0] is the matrix of transitions for
-                      observations with lags in the 0th quantile; T[m-1] is the
-                      transitions for the observations with lags in the m-1th.
-    P               : array
-                      (m, k, k), transition probability matrix for spatial
-                      Markov first dimension is the conditioned on the lag.
-    S               : arraylike
-                      (m, k), steady state distributions for spatial Markov.
-                      Each row is a conditional steady state distribution.
-                      If one (or more) spatially conditional Markov chain is
-                      reducible (having more than 1 steady state distribution),
-                      this attribute is an array of m arrays of varying
-                      dimensions.
-    F               : array
-                      (m, k, k),first mean passage times.
-                      First dimension is conditioned on the spatial lag.
-    shtest          : list
-                      (k elements), each element of the list is a tuple for a
-                      multinomial difference test between the steady state
-                      distribution from a conditional distribution versus the
-                      overall steady state distribution: first element of the
-                      tuple is the chi2 value, second its p-value and the third
-                      the degrees of freedom.
-    chi2            : list
-                      (k elements), each element of the list is a tuple for a
-                      chi-squared test of the difference between the
-                      conditional transition matrix against the overall
-                      transition matrix: first element of the tuple is the chi2
-                      value, second its p-value and the third the degrees of
-                      freedom.
-    x2              : float
-                      sum of the chi2 values for each of the conditional tests.
-                      Has an asymptotic chi2 distribution with k(k-1)(k-1)
-                      degrees of freedom. Under the null that transition
-                      probabilities are spatially homogeneous.
-                      (see chi2 above)
-    x2_dof          : int
-                      degrees of freedom for homogeneity test.
-    x2_pvalue       : float
-                      pvalue for homogeneity test based on analytic.
-                      distribution
-    x2_rpvalue      : float
-                      (if permutations>0)
-                      pseudo p-value for x2 based on random spatial
-                      permutations of the rows of the original transitions.
-    x2_realizations : array
-                      (permutations,1), the values of x2 for the random
-                      permutations.
-    Q               : float
-                      Chi-square test of homogeneity across lag classes based
-                      on :cite:`Bickenbach2003`.
-    Q_p_value       : float
-                      p-value for Q.
-    LR              : float
-                      Likelihood ratio statistic for homogeneity across lag
-                      classes based on :cite:`Bickenbach2003`.
-    LR_p_value      : float
-                      p-value for LR.
-    dof_hom         : int
-                      degrees of freedom for LR and Q, corrected for 0 cells.
-
-    Notes
-    -----
-    Based on :cite:`Rey2001`.
-
-    The shtest and chi2 tests should be used with caution as they are based on
-    classic theory assuming random transitions. The x2 based test is
-    preferable since it simulates the randomness under the null. It is an
-    experimental test requiring further analysis.
-
-    Examples
-    --------
-    >>> import libpysal
-    >>> from giddy.markov import Spatial_Markov
-    >>> import numpy as np
-    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
-    >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
-    >>> pci = pci.transpose()
-    >>> rpci = pci/(pci.mean(axis=0))
-    >>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
-    >>> w.transform = 'r'
-
-    Now we create a `Spatial_Markov` instance for the continuous relative per
-    capita income time series for 48 US lower states 1929-2009. The current
-    implementation allows users to classify the continuous incomes in a more
-    flexible way.
-
-    (1) Global quintiles to discretize the income data (k=5), and global
-    quintiles to discretize the spatial lags of incomes (m=5).
-
-    >>> sm = Spatial_Markov(rpci, w, fixed=True, k=5, m=5, variable_name='rpci')
-
-    We can examine the cutoffs for the incomes and cutoffs for the spatial lags
-
-    >>> sm.cutoffs
-    array([0.83999133, 0.94707545, 1.03242697, 1.14911154])
-    >>> sm.lag_cutoffs
-    array([0.88973386, 0.95891917, 1.01469758, 1.1183566 ])
-
-    Obviously, they are slightly different.
-
-    We now look at the estimated spatially lag conditioned transition
-    probability matrices.
-
-    >>> for p in sm.P:
-    ...     print(p)
-    [[0.96341463 0.0304878  0.00609756 0.         0.        ]
-     [0.06040268 0.83221477 0.10738255 0.         0.        ]
-     [0.         0.14       0.74       0.12       0.        ]
-     [0.         0.03571429 0.32142857 0.57142857 0.07142857]
-     [0.         0.         0.         0.16666667 0.83333333]]
-    [[0.79831933 0.16806723 0.03361345 0.         0.        ]
-     [0.0754717  0.88207547 0.04245283 0.         0.        ]
-     [0.00537634 0.06989247 0.8655914  0.05913978 0.        ]
-     [0.         0.         0.06372549 0.90196078 0.03431373]
-     [0.         0.         0.         0.19444444 0.80555556]]
-    [[0.84693878 0.15306122 0.         0.         0.        ]
-     [0.08133971 0.78947368 0.1291866  0.         0.        ]
-     [0.00518135 0.0984456  0.79274611 0.0984456  0.00518135]
-     [0.         0.         0.09411765 0.87058824 0.03529412]
-     [0.         0.         0.         0.10204082 0.89795918]]
-    [[0.8852459  0.09836066 0.         0.01639344 0.        ]
-     [0.03875969 0.81395349 0.13953488 0.         0.00775194]
-     [0.0049505  0.09405941 0.77722772 0.11881188 0.0049505 ]
-     [0.         0.02339181 0.12865497 0.75438596 0.09356725]
-     [0.         0.         0.         0.09661836 0.90338164]]
-    [[0.33333333 0.66666667 0.         0.         0.        ]
-     [0.0483871  0.77419355 0.16129032 0.01612903 0.        ]
-     [0.01149425 0.16091954 0.74712644 0.08045977 0.        ]
-     [0.         0.01036269 0.06217617 0.89637306 0.03108808]
-     [0.         0.         0.         0.02352941 0.97647059]]
-
-
-    The probability of a poor state remaining poor is 0.963 if their
-    neighbors are in the 1st quintile and 0.798 if their neighbors are
-    in the 2nd quintile. The probability of a rich economy remaining
-    rich is 0.976 if their neighbors are in the 5th quintile, but if their
-    neighbors are in the 4th quintile this drops to 0.903.
-
-    The global transition probability matrix is estimated:
-
-    >>> print(sm.p)
-    [[0.91461837 0.07503234 0.00905563 0.00129366 0.        ]
-     [0.06570302 0.82654402 0.10512484 0.00131406 0.00131406]
-     [0.00520833 0.10286458 0.79427083 0.09505208 0.00260417]
-     [0.         0.00913838 0.09399478 0.84856397 0.04830287]
-     [0.         0.         0.         0.06217617 0.93782383]]
-
-    The Q and likelihood ratio statistics are both significant indicating
-    the dynamics are not homogeneous across the lag classes:
-
-    >>> "%.3f"%sm.LR
-    '170.659'
-    >>> "%.3f"%sm.Q
-    '200.624'
-    >>> "%.3f"%sm.LR_p_value
-    '0.000'
-    >>> "%.3f"%sm.Q_p_value
-    '0.000'
-    >>> sm.dof_hom
-    60
-
-    The long run distribution for states with poor (rich) neighbors has
-    0.435 (0.018) of the values in the first quintile, 0.263 (0.200) in
-    the second quintile, 0.204 (0.190) in the third, 0.0684 (0.255) in the
-    fourth and 0.029 (0.337) in the fifth quintile.
-
-    >>> sm.S
-    array([[0.43509425, 0.2635327 , 0.20363044, 0.06841983, 0.02932278],
-           [0.13391287, 0.33993305, 0.25153036, 0.23343016, 0.04119356],
-           [0.12124869, 0.21137444, 0.2635101 , 0.29013417, 0.1137326 ],
-           [0.0776413 , 0.19748806, 0.25352636, 0.22480415, 0.24654013],
-           [0.01776781, 0.19964349, 0.19009833, 0.25524697, 0.3372434 ]])
-
-    States with incomes in the first quintile with neighbors in the
-    first quintile return to the first quartile after 2.298 years, after
-    leaving the first quintile. They enter the fourth quintile after
-    80.810 years after leaving the first quintile, on average.
-    Poor states within neighbors in the fourth quintile return to the
-    first quintile, on average, after 12.88 years, and would enter the
-    fourth quintile after 28.473 years.
-
-    >>> for f in sm.F:
-    ...     print(f)
-    [[  2.29835259  28.95614035  46.14285714  80.80952381 279.42857143]
-     [ 33.86549708   3.79459555  22.57142857  57.23809524 255.85714286]
-     [ 43.60233918   9.73684211   4.91085714  34.66666667 233.28571429]
-     [ 46.62865497  12.76315789   6.25714286  14.61564626 198.61904762]
-     [ 52.62865497  18.76315789  12.25714286   6.          34.1031746 ]]
-    [[  7.46754205   9.70574606  25.76785714  74.53116883 194.23446197]
-     [ 27.76691978   2.94175577  24.97142857  73.73474026 193.4380334 ]
-     [ 53.57477715  28.48447637   3.97566318  48.76331169 168.46660482]
-     [ 72.03631562  46.94601483  18.46153846   4.28393653 119.70329314]
-     [ 77.17917276  52.08887197  23.6043956    5.14285714  24.27564033]]
-    [[  8.24751154   6.53333333  18.38765432  40.70864198 112.76732026]
-     [ 47.35040872   4.73094099  11.85432099  34.17530864 106.23398693]
-     [ 69.42288828  24.76666667   3.794921    22.32098765  94.37966594]
-     [ 83.72288828  39.06666667  14.3          3.44668119  76.36702977]
-     [ 93.52288828  48.86666667  24.1          9.8          8.79255406]]
-    [[ 12.87974382  13.34847151  19.83446328  28.47257282  55.82395142]
-     [ 99.46114206   5.06359731  10.54545198  23.05133495  49.68944423]
-     [117.76777159  23.03735526   3.94436301  15.0843986   43.57927247]
-     [127.89752089  32.4393006   14.56853107   4.44831643  31.63099455]
-     [138.24752089  42.7893006   24.91853107  10.35         4.05613474]]
-    [[ 56.2815534    1.5         10.57236842  27.02173913 110.54347826]
-     [ 82.9223301    5.00892857   9.07236842  25.52173913 109.04347826]
-     [ 97.17718447  19.53125      5.26043557  21.42391304 104.94565217]
-     [127.1407767   48.74107143  33.29605263   3.91777427  83.52173913]
-     [169.6407767   91.24107143  75.79605263  42.5          2.96521739]]
-
-    (2) Global quintiles to discretize the income data (k=5), and global
-    quartiles to discretize the spatial lags of incomes (m=4).
-
-    >>> sm = Spatial_Markov(rpci, w, fixed=True, k=5, m=4, variable_name='rpci')
-
-    We can also examine the cutoffs for the incomes and cutoffs for the spatial
-    lags:
-
-    >>> sm.cutoffs
-    array([0.83999133, 0.94707545, 1.03242697, 1.14911154])
-    >>> sm.lag_cutoffs
-    array([0.91440247, 0.98583079, 1.08698351])
-
-    We now look at the estimated spatially lag conditioned transition
-    probability matrices.
-
-    >>> for p in sm.P:
-    ...     print(p)
-    [[0.95708955 0.03544776 0.00746269 0.         0.        ]
-     [0.05825243 0.83980583 0.10194175 0.         0.        ]
-     [0.         0.1294964  0.76258993 0.10791367 0.        ]
-     [0.         0.01538462 0.18461538 0.72307692 0.07692308]
-     [0.         0.         0.         0.14285714 0.85714286]]
-    [[0.7421875  0.234375   0.0234375  0.         0.        ]
-     [0.08550186 0.85130112 0.06319703 0.         0.        ]
-     [0.00865801 0.06926407 0.86147186 0.05627706 0.004329  ]
-     [0.         0.         0.05363985 0.92337165 0.02298851]
-     [0.         0.         0.         0.13432836 0.86567164]]
-    [[0.95145631 0.04854369 0.         0.         0.        ]
-     [0.06       0.79       0.145      0.         0.005     ]
-     [0.00358423 0.10394265 0.7921147  0.09677419 0.00358423]
-     [0.         0.01630435 0.13586957 0.75543478 0.0923913 ]
-     [0.         0.         0.         0.10204082 0.89795918]]
-    [[0.16666667 0.66666667 0.         0.16666667 0.        ]
-     [0.03488372 0.80232558 0.15116279 0.01162791 0.        ]
-     [0.00840336 0.13445378 0.70588235 0.1512605  0.        ]
-     [0.         0.01171875 0.08203125 0.87109375 0.03515625]
-     [0.         0.         0.         0.03434343 0.96565657]]
-
-    We now obtain 4 (5,5) spatial lag conditioned transition probability
-    matrices instead of 5 as in case (1).
-
-    The Q and likelihood ratio statistics are still both significant.
-
-    >>> "%.3f"%sm.LR
-    '172.105'
-    >>> "%.3f"%sm.Q
-    '321.128'
-    >>> "%.3f"%sm.LR_p_value
-    '0.000'
-    >>> "%.3f"%sm.Q_p_value
-    '0.000'
-    >>> sm.dof_hom
-    45
-
-    (3) We can also set the cutoffs for relative incomes and their
-    spatial lags manually.
-    For example, we want the defining cutoffs to be [0.8, 0.9, 1, 1.2],
-    meaning that relative incomes:
-
-    * class 0: smaller than 0.8
-
-    * class 1: between 0.8 and 0.9
-
-    * class 2: between 0.9 and 1.0
-
-    * class 3: between 1.0 and 1.2
-
-    * class 4: larger than 1.2
-
-    >>> cc = np.array([0.8, 0.9, 1, 1.2])
-    >>> sm = Spatial_Markov(rpci, w, cutoffs=cc, lag_cutoffs=cc, variable_name='rpci')
-    >>> sm.cutoffs
-    array([0.8, 0.9, 1. , 1.2])
-    >>> sm.k
-    5
-    >>> sm.lag_cutoffs
-    array([0.8, 0.9, 1. , 1.2])
-    >>> sm.m
-    5
-    >>> for p in sm.P:
-    ...     print(p)
-    [[0.96703297 0.03296703 0.         0.         0.        ]
-     [0.10638298 0.68085106 0.21276596 0.         0.        ]
-     [0.         0.14285714 0.7755102  0.08163265 0.        ]
-     [0.         0.         0.5        0.5        0.        ]
-     [0.         0.         0.         0.         0.        ]]
-    [[0.88636364 0.10606061 0.00757576 0.         0.        ]
-     [0.04402516 0.89308176 0.06289308 0.         0.        ]
-     [0.         0.05882353 0.8627451  0.07843137 0.        ]
-     [0.         0.         0.13846154 0.86153846 0.        ]
-     [0.         0.         0.         0.         1.        ]]
-    [[0.78082192 0.17808219 0.02739726 0.01369863 0.        ]
-     [0.03488372 0.90406977 0.05813953 0.00290698 0.        ]
-     [0.         0.05919003 0.84735202 0.09034268 0.00311526]
-     [0.         0.         0.05811623 0.92985972 0.01202405]
-     [0.         0.         0.         0.14285714 0.85714286]]
-    [[0.82692308 0.15384615 0.         0.01923077 0.        ]
-     [0.0703125  0.7890625  0.125      0.015625   0.        ]
-     [0.00295858 0.06213018 0.82248521 0.10946746 0.00295858]
-     [0.         0.00185529 0.07606679 0.88497217 0.03710575]
-     [0.         0.         0.         0.07803468 0.92196532]]
-    [[0.         0.         0.         0.         0.        ]
-     [0.         0.         0.         0.         0.        ]
-     [0.         0.06666667 0.9        0.03333333 0.        ]
-     [0.         0.         0.05660377 0.90566038 0.03773585]
-     [0.         0.         0.         0.03932584 0.96067416]]
-
-    (3.1) As we can see from the above estimated conditional transition
-    probability matrices, some rows are full of zeros and this violate the
-    requirement that each row of a transition probability matrix sums to 1.
-    We can easily adjust this assigning `fill_empty_classes = True` when initializing
-    `Spatial_Markov`.
-
-    >>> sm = Spatial_Markov(rpci, w, cutoffs=cc, lag_cutoffs=cc, fill_empty_classes=True)
-    >>> for p in sm.P:
-    ...     print(p)
-    [[0.96703297 0.03296703 0.         0.         0.        ]
-     [0.10638298 0.68085106 0.21276596 0.         0.        ]
-     [0.         0.14285714 0.7755102  0.08163265 0.        ]
-     [0.         0.         0.5        0.5        0.        ]
-     [0.         0.         0.         0.         1.        ]]
-    [[0.88636364 0.10606061 0.00757576 0.         0.        ]
-     [0.04402516 0.89308176 0.06289308 0.         0.        ]
-     [0.         0.05882353 0.8627451  0.07843137 0.        ]
-     [0.         0.         0.13846154 0.86153846 0.        ]
-     [0.         0.         0.         0.         1.        ]]
-    [[0.78082192 0.17808219 0.02739726 0.01369863 0.        ]
-     [0.03488372 0.90406977 0.05813953 0.00290698 0.        ]
-     [0.         0.05919003 0.84735202 0.09034268 0.00311526]
-     [0.         0.         0.05811623 0.92985972 0.01202405]
-     [0.         0.         0.         0.14285714 0.85714286]]
-    [[0.82692308 0.15384615 0.         0.01923077 0.        ]
-     [0.0703125  0.7890625  0.125      0.015625   0.        ]
-     [0.00295858 0.06213018 0.82248521 0.10946746 0.00295858]
-     [0.         0.00185529 0.07606679 0.88497217 0.03710575]
-     [0.         0.         0.         0.07803468 0.92196532]]
-    [[1.         0.         0.         0.         0.        ]
-     [0.         1.         0.         0.         0.        ]
-     [0.         0.06666667 0.9        0.03333333 0.        ]
-     [0.         0.         0.05660377 0.90566038 0.03773585]
-     [0.         0.         0.         0.03932584 0.96067416]]
-    >>> sm.S[0]
-    array([[0.54148249, 0.16780007, 0.24991499, 0.04080245, 0.        ],
-           [0.        , 0.        , 0.        , 0.        , 1.        ]])
-    >>> sm.S[2]
-    array([0.03607655, 0.22667277, 0.25883041, 0.43607249, 0.04234777])
-
-    (4) `Spatial_Markov` also accepts discrete time series and calculates
-    categorical spatial lags on which several transition probability matrices
-    are conditioned.
-    Let's still use the US state income time series to demonstrate. We first
-    discretize them into categories and then pass them to `Spatial_Markov`.
-
-    >>> import mapclassify as mc
-    >>> y = mc.Quantiles(rpci.flatten(), k=5).yb.reshape(rpci.shape)
-    >>> np.random.seed(5)
-    >>> sm = Spatial_Markov(y, w, discrete=True, variable_name='discretized rpci')
-    >>> sm.k
-    5
-    >>> sm.m
-    5
-    >>> for p in sm.P:
-    ...     print(p)
-    [[0.94787645 0.04440154 0.00772201 0.         0.        ]
-     [0.08333333 0.81060606 0.10606061 0.         0.        ]
-     [0.         0.12765957 0.79787234 0.07446809 0.        ]
-     [0.         0.02777778 0.22222222 0.66666667 0.08333333]
-     [0.         0.         0.         0.33333333 0.66666667]]
-    [[0.888      0.096      0.016      0.         0.        ]
-     [0.06049822 0.84341637 0.09608541 0.         0.        ]
-     [0.00666667 0.10666667 0.81333333 0.07333333 0.        ]
-     [0.         0.         0.08527132 0.86821705 0.04651163]
-     [0.         0.         0.         0.10204082 0.89795918]]
-    [[0.65217391 0.32608696 0.02173913 0.         0.        ]
-     [0.07446809 0.80851064 0.11170213 0.         0.00531915]
-     [0.01071429 0.1        0.76428571 0.11785714 0.00714286]
-     [0.         0.00552486 0.09392265 0.86187845 0.03867403]
-     [0.         0.         0.         0.13157895 0.86842105]]
-    [[0.91935484 0.06451613 0.         0.01612903 0.        ]
-     [0.06796117 0.90291262 0.02912621 0.         0.        ]
-     [0.         0.05755396 0.87769784 0.0647482  0.        ]
-     [0.         0.02150538 0.10752688 0.80107527 0.06989247]
-     [0.         0.         0.         0.08064516 0.91935484]]
-    [[0.81818182 0.18181818 0.         0.         0.        ]
-     [0.01754386 0.70175439 0.26315789 0.01754386 0.        ]
-     [0.         0.14285714 0.73333333 0.12380952 0.        ]
-     [0.         0.0042735  0.06837607 0.89316239 0.03418803]
-     [0.         0.         0.         0.03891051 0.96108949]]
-
-    """
-
-
    [docs]    def __init__(
-        self,
-        y,
-        w,
-        k=4,
-        m=4,
-        permutations=0,
-        fixed=True,
-        discrete=False,
-        cutoffs=None,
-        lag_cutoffs=None,
-        variable_name=None,
-        fill_empty_classes=False,
-    ):
-
-        y = np.asarray(y)
-        self.fixed = fixed
-        self.discrete = discrete
-        self.cutoffs = cutoffs
-        self.m = m
-        self.lag_cutoffs = lag_cutoffs
-        self.variable_name = variable_name
-
-        if discrete:
-            merged = list(itertools.chain.from_iterable(y))
-            classes = np.unique(merged)
-            self.classes = classes
-            self.k = len(classes)
-            self.m = self.k
-            label_dict = dict(zip(classes, range(self.k)))
-            y_int = []
-            for yi in y:
-                y_int.append(list(map(label_dict.get, yi)))
-            self.class_ids = np.array(y_int)
-            self.lclass_ids = self.class_ids
-        else:
-            self.class_ids, self.cutoffs, self.k = self._maybe_classify(
-                y, k=k, cutoffs=self.cutoffs
-            )
-            self.classes = np.arange(self.k)
-
-        classic = Markov(
-            self.class_ids, fill_empty_classes=fill_empty_classes, summary=False
-        )
-        self.p = classic.p
-        self.transitions = classic.transitions
-        self.T, self.P = self._calc(y, w, fill_empty_classes=fill_empty_classes)
-
-        if permutations:
-            nrp = np.random.permutation
-            counter = 0
-            x2_realizations = np.zeros((permutations, 1))
-            for perm in range(permutations):
-                T, P = self._calc(nrp(y), w)
-                x2 = [chi2(T[i], self.transitions)[0] for i in range(self.k)]
-                x2s = sum(x2)
-                x2_realizations[perm] = x2s
-                if x2s >= self.x2:
-                    counter += 1
-            self.x2_rpvalue = (counter + 1.0) / (permutations + 1.0)
-            self.x2_realizations = x2_realizations
    
-
-    @property
-    def s(self):
-        if not hasattr(self, "_s"):
-            self._s = steady_state(self.p)
-        return self._s
-
-    @property
-    def S(self):
-        if not hasattr(self, "_S"):
-            _S = []
-            for i, p in enumerate(self.P):
-                _S.append(steady_state(p))
-            # if np.array(_S).dtype is np.dtype('O'):
-            self._S = np.asarray(_S)
-        return self._S
-
-    @property
-    def f(self):
-        if not hasattr(self, "_f"):
-            self._f = mfpt(self.p)
-        return self._f
-
-    @property
-    def F(self):
-        if not hasattr(self, "_F"):
-            F = np.zeros_like(self.P)
-            for i, p in enumerate(self.P):
-                F[i] = mfpt(np.asarray(p))
-            self._F = np.asarray(F)
-        return self._F
-
-    # bickenbach and bode tests
-    @property
-    def ht(self):
-        if not hasattr(self, "_ht"):
-            self._ht = homogeneity(self.T)
-        return self._ht
-
-    @property
-    def Q(self):
-        if not hasattr(self, "_Q"):
-            self._Q = self.ht.Q
-        return self._Q
-
-    @property
-    def Q_p_value(self):
-        self._Q_p_value = self.ht.Q_p_value
-        return self._Q_p_value
-
-    @property
-    def LR(self):
-        self._LR = self.ht.LR
-        return self._LR
-
-    @property
-    def LR_p_value(self):
-        self._LR_p_value = self.ht.LR_p_value
-        return self._LR_p_value
-
-    @property
-    def dof_hom(self):
-        self._dof_hom = self.ht.dof
-        return self._dof_hom
-
-    # shtests
-    @property
-    def shtest(self):
-        if not hasattr(self, "_shtest"):
-            self._shtest = self._mn_test()
-        return self._shtest
-
-    @property
-    def chi2(self):
-        if not hasattr(self, "_chi2"):
-            self._chi2 = self._chi2_test()
-        return self._chi2
-
-    @property
-    def x2(self):
-        if not hasattr(self, "_x2"):
-            self._x2 = sum([c[0] for c in self.chi2])
-        return self._x2
-
-    @property
-    def x2_pvalue(self):
-        if not hasattr(self, "_x2_pvalue"):
-            self._x2_pvalue = 1 - stats.chi2.cdf(self.x2, self.x2_dof)
-        return self._x2_pvalue
-
-    @property
-    def x2_dof(self):
-        if not hasattr(self, "_x2_dof"):
-            k = self.k
-            self._x2_dof = k * (k - 1) * (k - 1)
-        return self._x2_dof
-
-    def _calc(self, y, w, fill_empty_classes=False):
-        """Helper to estimate spatial lag conditioned Markov transition
-        probability matrices based on maximum likelihood techniques.
-
-        If fill_empty_classes=True, assign 1 to diagonal elements which fall in rows
-        full of 0s to ensure each conditional transition probability matrix
-        is a stochastic matrix (each row sums up to 1).
-
-        """
-        if self.discrete:
-            self.lclass_ids = weights.lag_categorical(w, self.class_ids, ties="tryself")
-        else:
-            ly = weights.lag_spatial(w, y)
-            self.lclass_ids, self.lag_cutoffs, self.m = self._maybe_classify(
-                ly, self.m, self.lag_cutoffs
-            )
-            self.lclasses = np.arange(self.m)
-
-        T = np.zeros((self.m, self.k, self.k))
-        n, t = y.shape
-        for t1 in range(t - 1):
-            t2 = t1 + 1
-            for i in range(n):
-                T[
-                    self.lclass_ids[i, t1], self.class_ids[i, t1], self.class_ids[i, t2]
-                ] += 1
-
-        P = np.zeros_like(T)
-        for i, mat in enumerate(T):
-            row_sum = mat.sum(axis=1)
-            row_sum = row_sum + (row_sum == 0)
-            p_i = np.array(np.diag(1.0 / row_sum)).dot(np.array(mat))
-            P[i] = p_i
-
-        if fill_empty_classes:
-            P = fill_empty_diagonals(P)
-        return T, P
-
-    def _mn_test(self):
-        """
-        helper to calculate tests of differences between steady state
-        distributions from the conditional and overall distributions.
-        """
-        n0, n1, n2 = self.T.shape
-        rn = list(range(n0))
-        mat = [self._ssmnp_test(self.s, self.S[i], self.T[i].sum()) for i in rn]
-        return mat
-
-    def _ssmnp_test(self, p1, p2, nt):
-        """
-        Steady state multinomial probability difference test.
-
-        Arguments
-        ---------
-        p1       :  array
-                    (k, ), first steady state probability distribution.
-        p1       :  array
-                    (k, ), second steady state probability distribution.
-        nt       :  int
-                    number of transitions to base the test on.
-
-        Returns
-        -------
-        tuple
-                   (3 elements)
-                   (chi2 value, pvalue, degrees of freedom)
-
-        """
-
-        o = nt * p2
-        e = nt * p1
-        d = np.multiply((o - e), (o - e))
-        d = d / e
-        chi2 = d.sum()
-        pvalue = 1 - stats.chi2.cdf(chi2, self.k - 1)
-        return (chi2, pvalue, self.k - 1)
-
-    def _chi2_test(self):
-        """
-        helper to calculate tests of differences between the conditional
-        transition matrices and the overall transitions matrix.
-        """
-        n0, n1, n2 = self.T.shape
-        rn = list(range(n0))
-        mat = [chi2(self.T[i], self.transitions) for i in rn]
-        return mat
-
-
    [docs]    def summary(self, file_name=None):
-        """
-        A summary method to call the Markov homogeneity test to test for
-        temporally lagged spatial dependence.
-
-        To learn more about the properties of the tests, refer to
-        :cite:`Rey2016a` and :cite:`Kang2018`.
-        """
-
-        class_names = ["C%d" % i for i in range(self.k)]
-        regime_names = ["LAG%d" % i for i in range(self.k)]
-        ht = homogeneity(self.T, class_names=class_names, regime_names=regime_names)
-        title = "Spatial Markov Test"
-        if self.variable_name:
-            title = title + ": " + self.variable_name
-        if file_name:
-            ht.summary(file_name=file_name, title=title)
-        else:
-            ht.summary(title=title)
    
-
-    def _maybe_classify(self, y, k, cutoffs):
-        """Helper method for classifying continuous data."""
-
-        rows, cols = y.shape
-        if cutoffs is None:
-            if self.fixed:
-                mcyb = mc.Quantiles(y.flatten(), k=k)
-                yb = mcyb.yb.reshape(y.shape)
-                cutoffs = mcyb.bins
-                k = len(cutoffs)
-                return yb, cutoffs[:-1], k
-            else:
-                yb = np.array(
-                    [mc.Quantiles(y[:, i], k=k).yb for i in np.arange(cols)]
-                ).transpose()
-                return yb, None, k
-        else:
-            cutoffs = list(cutoffs) + [np.inf]
-            cutoffs = np.array(cutoffs)
-            yb = mc.UserDefined(y.flatten(), np.array(cutoffs)).yb.reshape(y.shape)
-            k = len(cutoffs)
-            return yb, cutoffs[:-1], k
    
-
-
-def chi2(T1, T2):
-    """
-    chi-squared test of difference between two transition matrices.
-
-    Parameters
-    ----------
-    T1    : array
-            (k, k), matrix of transitions (counts).
-    T2    : array
-            (k, k), matrix of transitions (counts) to use to form the
-            probabilities under the null.
-
-    Returns
-    -------
-          : tuple
-            (3 elements).
-            (chi2 value, pvalue, degrees of freedom).
-
-    Examples
-    --------
-    >>> import libpysal
-    >>> from giddy.markov import Spatial_Markov, chi2
-    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
-    >>> years = list(range(1929, 2010))
-    >>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
-    >>> rpci = pci/(pci.mean(axis=0))
-    >>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
-    >>> w.transform='r'
-    >>> sm = Spatial_Markov(rpci, w, fixed=True)
-    >>> T1 = sm.T[0]
-    >>> T1
-    array([[562.,  22.,   1.,   0.],
-           [ 12., 201.,  22.,   0.],
-           [  0.,  17.,  97.,   4.],
-           [  0.,   0.,   3.,  19.]])
-    >>> T2 = sm.transitions
-    >>> T2
-    array([[884.,  77.,   4.,   0.],
-           [ 68., 794.,  87.,   3.],
-           [  1.,  92., 815.,  51.],
-           [  1.,   0.,  60., 903.]])
-    >>> chi2(T1,T2)
-    (23.39728441473295, 0.005363116704861337, 9)
-
-    Notes
-    -----
-    Second matrix is used to form the probabilities under the null.
-    Marginal sums from first matrix are distributed across these probabilities
-    under the null. In other words the observed transitions are taken from T1
-    while the expected transitions are formed as follows
-
-    .. math::
-
-            E_{i,j} = \sum_j T1_{i,j} * T2_{i,j}/\sum_j T2_{i,j}
-
-    Degrees of freedom corrected for any rows in either T1 or T2 that have
-    zero total transitions.
-    """
-    rs2 = T2.sum(axis=1)
-    rs1 = T1.sum(axis=1)
-    rs2nz = rs2 > 0
-    rs1nz = rs1 > 0
-    dof1 = sum(rs1nz)
-    dof2 = sum(rs2nz)
-    rs2 = rs2 + (rs2 == 0)
-    dof = (dof1 - 1) * (dof2 - 1)
-    p = np.diag(1 / rs2).dot(np.array(T2))
-    E = np.diag(rs1).dot(np.array(p))
-    num = T1 - E
-    num = np.multiply(num, num)
-    E = E + (E == 0)
-    chi2 = num / E
-    chi2 = chi2.sum()
-    pvalue = 1 - stats.chi2.cdf(chi2, dof)
-    return chi2, pvalue, dof
-
-
-
    [docs]class LISA_Markov(Markov):
-    """
-    Markov for Local Indicators of Spatial Association
-
-    Parameters
-    ----------
-    y                  : array
-                         (n, t), n cross-sectional units observed over t time
-                         periods.
-    w                  : W
-                         spatial weights object.
-    permutations       : int, optional
-                         number of permutations used to determine LISA
-                         significance (the default is 0).
-    significance_level : float, optional
-                         significance level (two-sided) for filtering
-                         significant LISA endpoints in a transition (the
-                         default is 0.05).
-    geoda_quads        : bool
-                         If True use GeoDa scheme: HH=1, LL=2, LH=3, HL=4.
-                         If False use PySAL Scheme: HH=1, LH=2, LL=3, HL=4.
-                         (the default is False).
-
-    Attributes
-    ----------
-    chi_2        : tuple
-                   (3 elements)
-                   (chi square test statistic, p-value, degrees of freedom) for
-                   test that dynamics of y are independent of dynamics of wy.
-    classes      : array
-                   (4, 1)
-                   1=HH, 2=LH, 3=LL, 4=HL (own, lag)
-                   1=HH, 2=LL, 3=LH, 4=HL (own, lag) (if geoda_quads=True)
-    expected_t   : array
-                   (4, 4), expected number of transitions under the null that
-                   dynamics of y are independent of dynamics of wy.
-    move_types   : matrix
-                   (n, t-1), integer values indicating which type of LISA
-                   transition occurred (q1 is quadrant in period 1, q2 is
-                   quadrant in period 2).
-
-                   .. table:: Move Types
-                      :widths: auto
-
-                      ==  ==     =========
-                      q1  q2     move_type
-                      ==  ==     =========
-                      1   1      1
-                      1   2      2
-                      1   3      3
-                      1   4      4
-                      2   1      5
-                      2   2      6
-                      2   3      7
-                      2   4      8
-                      3   1      9
-                      3   2      10
-                      3   3      11
-                      3   4      12
-                      4   1      13
-                      4   2      14
-                      4   3      15
-                      4   4      16
-                      ==  ==     =========
-
-    p            : array
-                   (k, k), transition probability matrix.
-    p_values     : matrix
-                   (n, t), LISA p-values for each end point (if permutations >
-                   0).
-    significant_moves : matrix
-                        (n, t-1), integer values indicating the type and
-                        significance of a LISA transition. st = 1 if
-                        significant in period t, else st=0 (if permutations >
-                        0).
-
-                        .. table:: Significant Moves1
-
-                           ===============  ===================
-                           (s1,s2)          move_type
-                           ===============  ===================
-                           (1,1)            [1, 16]
-                           (1,0)            [17, 32]
-                           (0,1)            [33, 48]
-                           (0,0)            [49, 64]
-                           ===============  ===================
-
-                        .. table:: Significant Moves2
-
-                           == ==  ==  ==  =========
-                           q1 q2  s1  s2  move_type
-                           == ==  ==  ==  =========
-                           1  1   1   1   1
-                           1  2   1   1   2
-                           1  3   1   1   3
-                           1  4   1   1   4
-                           2  1   1   1   5
-                           2  2   1   1   6
-                           2  3   1   1   7
-                           2  4   1   1   8
-                           3  1   1   1   9
-                           3  2   1   1   10
-                           3  3   1   1   11
-                           3  4   1   1   12
-                           4  1   1   1   13
-                           4  2   1   1   14
-                           4  3   1   1   15
-                           4  4   1   1   16
-                           1  1   1   0   17
-                           1  2   1   0   18
-                           .  .   .   .    .
-                           .  .   .   .    .
-                           4  3   1   0   31
-                           4  4   1   0   32
-                           1  1   0   1   33
-                           1  2   0   1   34
-                           .  .   .   .    .
-                           .  .   .   .    .
-                           4  3   0   1   47
-                           4  4   0   1   48
-                           1  1   0   0   49
-                           1  2   0   0   50
-                           .  .   .   .    .
-                           .  .   .   .    .
-                           4  3   0   0   63
-                           4  4   0   0   64
-                           == ==  ==  ==  =========
-
-    steady_state : array
-                   (k, ), ergodic distribution.
-    transitions  : array
-                   (4, 4), count of transitions between each state i and j.
-    spillover    : array
-                   (n, 1) binary array, locations that were not part of a
-                   cluster in period 1 but joined a prexisting cluster in
-                   period 2.
-
-    Examples
-    --------
-    >>> import libpysal
-    >>> import numpy as np
-    >>> from giddy.markov import LISA_Markov
-    >>> np.set_printoptions(suppress=True) #prevent scientific format
-    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
-    >>> years = list(range(1929, 2010))
-    >>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
-    >>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
-    >>> lm = LISA_Markov(pci,w)
-    >>> lm.classes
-    array([1, 2, 3, 4])
-    >>> lm.steady_state
-    array([0.28561505, 0.14190226, 0.40493672, 0.16754598])
-    >>> lm.transitions
-    array([[1087.,   44.,    4.,   34.],
-           [  41.,  470.,   36.,    1.],
-           [   5.,   34., 1422.,   39.],
-           [  30.,    1.,   40.,  552.]])
-    >>> lm.p
-    array([[0.92985458, 0.03763901, 0.00342173, 0.02908469],
-           [0.07481752, 0.85766423, 0.06569343, 0.00182482],
-           [0.00333333, 0.02266667, 0.948     , 0.026     ],
-           [0.04815409, 0.00160514, 0.06420546, 0.88603531]])
-    >>> lm.move_types[0,:3]
-    array([11, 11, 11])
-    >>> lm.move_types[0,-3:]
-    array([11, 11, 11])
-
-    Now consider only moves with one, or both, of the LISA end points being
-    significant
-
-    >>> np.random.seed(10)
-    >>> lm_random = LISA_Markov(pci, w, permutations=99)
-    >>> lm_random.significant_moves[0, :3]
-    array([11, 11, 11])
-    >>> lm_random.significant_moves[0,-3:]
-    array([59, 43, 27])
-
-
-    Any value less than 49 indicates at least one of the LISA end points was
-    significant. So for example, the first spatial unit experienced a
-    transition of type 11 (LL, LL)  during the first three and last tree
-    intervals (according to lm.move_types), however, the last three of these
-    transitions involved insignificant LISAS in both the start and ending year
-    of each transition.
-
-    Test whether the moves of y are independent of the moves of wy
-
-    >>> "Chi2: %8.3f, p: %5.2f, dof: %d" % lm.chi_2
-    'Chi2: 1058.208, p:  0.00, dof: 9'
-
-    Actual transitions of LISAs
-
-    >>> lm.transitions
-    array([[1087.,   44.,    4.,   34.],
-           [  41.,  470.,   36.,    1.],
-           [   5.,   34., 1422.,   39.],
-           [  30.,    1.,   40.,  552.]])
-
-    Expected transitions of LISAs under the null y and wy are moving
-    independently of one another
-
-    >>> lm.expected_t
-    array([[1123.2809778 ,   11.53773565,    0.34752216,   33.83376439],
-           [   3.50272664,  528.47388155,   15.917888  ,    0.10550381],
-           [   0.15387808,   23.21635562, 1466.90710117,    9.72266513],
-           [   9.60775143,    0.09868563,    6.23537392,  607.05818902]])
-
-    If the LISA classes are to be defined according to GeoDa, the `geoda_quad`
-    option has to be set to true
-
-    >>> lm.q[0:5,0]
-    array([3, 2, 3, 1, 4])
-    >>> lm = LISA_Markov(pci,w, geoda_quads=True)
-    >>> lm.q[0:5,0]
-    array([2, 3, 2, 1, 4])
-
-    """
-
-
    [docs]    def __init__(
-        self, y, w, permutations=0, significance_level=0.05, geoda_quads=False
-    ):
-        y = y.transpose()
-        pml = Moran_Local
-        gq = geoda_quads
-        ml = [pml(yi, w, permutations=permutations, geoda_quads=gq) for yi in y]
-        q = np.array([mli.q for mli in ml]).transpose()
-        classes = np.arange(1, 5)  # no guarantee all 4 quadrants are visited
-        Markov.__init__(self, q, classes, summary=False)
-        self.q = q
-        self.w = w
-        n, k = q.shape
-        k -= 1
-        self.significance_level = significance_level
-        move_types = np.zeros((n, k), int)
-        sm = np.zeros((n, k), int)
-        self.significance_level = significance_level
-        if permutations > 0:
-            p = np.array([mli.p_z_sim for mli in ml]).transpose()
-            self.p_values = p
-            pb = p <= significance_level
-        else:
-            pb = np.zeros_like(y.T)
-        for t in range(k):
-            origin = q[:, t]
-            dest = q[:, t + 1]
-            p_origin = pb[:, t]
-            p_dest = pb[:, t + 1]
-            for r in range(n):
-                move_types[r, t] = TT[origin[r], dest[r]]
-                key = (origin[r], dest[r], p_origin[r], p_dest[r])
-                sm[r, t] = MOVE_TYPES[key]
-        if permutations > 0:
-            self.significant_moves = sm
-        self.move_types = move_types
-
-        # null of own and lag moves being independent
-
-        ybar = y.mean(axis=0)
-        r = y / ybar
-        ylag = np.array([weights.lag_spatial(w, yt) for yt in y])
-        rlag = ylag / ybar
-        rc = r < 1.0
-        rlagc = rlag < 1.0
-        markov_y = Markov(rc, summary=False)
-        markov_ylag = Markov(rlagc, summary=False)
-        A = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]])
-
-        kp = A.dot(np.kron(markov_y.p, markov_ylag.p)).dot(A.T)
-        trans = self.transitions.sum(axis=1)
-        t1 = np.diag(trans).dot(kp)
-        t2 = self.transitions
-        self.chi_2 = chi2(t2, t1)
-        self.expected_t = t1
-        self.permutations = permutations
    
-
-
    [docs]    def spillover(self, quadrant=1, neighbors_on=False):
-        """
-        Detect spillover locations for diffusion in LISA Markov.
-
-        Parameters
-        ----------
-        quadrant     : int
-                       which quadrant in the scatterplot should form the core
-                       of a cluster.
-        neighbors_on : binary
-                       If false, then only the 1st order neighbors of a core
-                       location are included in the cluster.
-                       If true, neighbors of cluster core 1st order neighbors
-                       are included in the cluster.
-
-        Returns
-        -------
-        results      : dictionary
-                       two keys - values pairs:
-                       'components' - array (n, t)
-                       values are integer ids (starting at 1) indicating which
-                       component/cluster observation i in period t belonged to.
-                       'spillover' - array (n, t-1)
-                       binary values indicating if the location was a
-                       spill-over location that became a new member of a
-                       previously existing cluster.
-
-        Examples
-        --------
-        >>> import libpysal
-        >>> from giddy.markov import LISA_Markov
-        >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
-        >>> years = list(range(1929, 2010))
-        >>> pci = np.array([f.by_col[str(y)] for y in years]).transpose()
-        >>> w = libpysal.io.open(libpysal.examples.get_path("states48.gal")).read()
-        >>> np.random.seed(10)
-        >>> lm_random = LISA_Markov(pci, w, permutations=99)
-        >>> r = lm_random.spillover()
-        >>> (r['components'][:, 12] > 0).sum()
-        17
-        >>> (r['components'][:, 13]>0).sum()
-        23
-        >>> (r['spill_over'][:,12]>0).sum()
-        6
-
-        Including neighbors of core neighbors
-        >>> rn = lm_random.spillover(neighbors_on=True)
-        >>> (rn['components'][:, 12] > 0).sum()
-        26
-        >>> (rn["components"][:, 13] > 0).sum()
-        34
-        >>> (rn["spill_over"][:, 12] > 0).sum()
-        8
-
-        """
-        n, k = self.q.shape
-        if self.permutations:
-            spill_over = np.zeros((n, k - 1))
-            components = np.zeros((n, k))
-            i2id = {}  # handle string keys
-            for key in list(self.w.neighbors.keys()):
-                idx = self.w.id2i[key]
-                i2id[idx] = key
-            sig_lisas = (self.q == quadrant) * (
-                self.p_values <= self.significance_level
-            )
-            sig_ids = [np.nonzero(sig_lisas[:, i])[0].tolist() for i in range(k)]
-
-            neighbors = self.w.neighbors
-            for t in range(k - 1):
-                s1 = sig_ids[t]
-                s2 = sig_ids[t + 1]
-                g1 = Graph(undirected=True)
-                for i in s1:
-                    for neighbor in neighbors[i2id[i]]:
-                        g1.add_edge(i2id[i], neighbor, 1.0)
-                        if neighbors_on:
-                            for nn in neighbors[neighbor]:
-                                g1.add_edge(neighbor, nn, 1.0)
-                components1 = g1.connected_components(op=gt)
-                components1 = [list(c.nodes) for c in components1]
-                g2 = Graph(undirected=True)
-                for i in s2:
-                    for neighbor in neighbors[i2id[i]]:
-                        g2.add_edge(i2id[i], neighbor, 1.0)
-                        if neighbors_on:
-                            for nn in neighbors[neighbor]:
-                                g2.add_edge(neighbor, nn, 1.0)
-                components2 = g2.connected_components(op=gt)
-                components2 = [list(c.nodes) for c in components2]
-                c2 = []
-                c1 = []
-                for c in components2:
-                    c2.extend(c)
-                for c in components1:
-                    c1.extend(c)
-
-                new_ids = [j for j in c2 if j not in c1]
-                spill_ids = []
-                for j in new_ids:
-                    # find j's component in period 2
-                    cj = [c for c in components2 if j in c][0]
-                    # for members of j's component in period 2, check if they
-                    # belonged to any components in period 1
-                    for i in cj:
-                        if i in c1:
-                            spill_ids.append(j)
-                            break
-                for spill_id in spill_ids:
-                    id = self.w.id2i[spill_id]
-                    spill_over[id, t] = 1
-                for c, component in enumerate(components1):
-                    for i in component:
-                        ii = self.w.id2i[i]
-                        components[ii, t] = c + 1
-            results = {}
-            results["components"] = components
-            results["spill_over"] = spill_over
-            return results
-
-        else:
-            return None
    
-
-
-
    [docs]def kullback(F):
-    """
-    Kullback information based test of Markov Homogeneity.
-
-    Parameters
-    ----------
-    F : array
-        (s, r, r), values are transitions (not probabilities) for
-        s strata, r initial states, r terminal states.
-
-    Returns
-    -------
-    Results : dictionary
-              (key - value)
-
-              Conditional homogeneity - (float) test statistic for homogeneity
-              of transition probabilities across strata.
-
-              Conditional homogeneity pvalue - (float) p-value for test
-              statistic.
-
-              Conditional homogeneity dof - (int) degrees of freedom =
-              r(s-1)(r-1).
-
-    Notes
-    -----
-    Based on :cite:`Kullback1962`.
-    Example below is taken from Table 9.2 .
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from giddy.markov import kullback
-    >>> s1 = np.array([
-    ...         [ 22, 11, 24,  2,  2,  7],
-    ...         [ 5, 23, 15,  3, 42,  6],
-    ...         [ 4, 21, 190, 25, 20, 34],
-    ...         [0, 2, 14, 56, 14, 28],
-    ...         [32, 15, 20, 10, 56, 14],
-    ...         [5, 22, 31, 18, 13, 134]
-    ...     ])
-    >>> s2 = np.array([
-    ...     [3, 6, 9, 3, 0, 8],
-    ...     [1, 9, 3, 12, 27, 5],
-    ...     [2, 9, 208, 32, 5, 18],
-    ...     [0, 14, 32, 108, 40, 40],
-    ...     [22, 14, 9, 26, 224, 14],
-    ...     [1, 5, 13, 53, 13, 116]
-    ...     ])
-    >>>
-    >>> F = np.array([s1, s2])
-    >>> res = kullback(F)
-    >>> "%8.3f"%res['Conditional homogeneity']
-    ' 160.961'
-    >>> "%d"%res['Conditional homogeneity dof']
-    '30'
-    >>> "%3.1f"%res['Conditional homogeneity pvalue']
-    '0.0'
-
-    """
-
-    F1 = F == 0
-    F1 = F + F1
-    FLF = F * np.log(F1)
-    T1 = 2 * FLF.sum()
-
-    FdJK = F.sum(axis=0)
-    FdJK1 = FdJK + (FdJK == 0)
-    FdJKLFdJK = FdJK * np.log(FdJK1)
-    T2 = 2 * FdJKLFdJK.sum()
-
-    FdJd = F.sum(axis=0).sum(axis=1)
-    FdJd1 = FdJd + (FdJd == 0)
-    T3 = 2 * (FdJd * np.log(FdJd1)).sum()
-
-    FIJd = F[:, :].sum(axis=1)
-    FIJd1 = FIJd + (FIJd == 0)
-    T4 = 2 * (FIJd * np.log(FIJd1)).sum()
-
-    T6 = F.sum()
-    T6 = 2 * T6 * np.log(T6)
-
-    s, r, r1 = F.shape
-    chom = T1 - T4 - T2 + T3
-    cdof = r * (s - 1) * (r - 1)
-    results = {}
-    results["Conditional homogeneity"] = chom
-    results["Conditional homogeneity dof"] = cdof
-    results["Conditional homogeneity pvalue"] = 1 - stats.chi2.cdf(chom, cdof)
-    return results
    
-
-
-
    [docs]def prais(pmat):
-    """
-    Prais conditional mobility measure.
-
-    Parameters
-    ----------
-    pmat : matrix
-           (k, k), Markov probability transition matrix.
-
-    Returns
-    -------
-    pr   : matrix
-           (1, k), conditional mobility measures for each of the k classes.
-
-    Notes
-    -----
-    Prais' conditional mobility measure for a class is defined as:
-
-    .. math::
-
-            pr_i = 1 -  p_{i,i}
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> import libpysal
-    >>> from giddy.markov import Markov,prais
-    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
-    >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
-    >>> q5 = np.array([mc.Quantiles(y).yb for y in pci]).transpose()
-    >>> m = Markov(q5, summary=False)
-    >>> m.transitions
-    array([[729.,  71.,   1.,   0.,   0.],
-           [ 72., 567.,  80.,   3.,   0.],
-           [  0.,  81., 631.,  86.,   2.],
-           [  0.,   3.,  86., 573.,  56.],
-           [  0.,   0.,   1.,  57., 741.]])
-    >>> m.p
-    array([[0.91011236, 0.0886392 , 0.00124844, 0.        , 0.        ],
-           [0.09972299, 0.78531856, 0.11080332, 0.00415512, 0.        ],
-           [0.        , 0.10125   , 0.78875   , 0.1075    , 0.0025    ],
-           [0.        , 0.00417827, 0.11977716, 0.79805014, 0.07799443],
-           [0.        , 0.        , 0.00125156, 0.07133917, 0.92740926]])
-    >>> prais(m.p)
-    array([0.08988764, 0.21468144, 0.21125   , 0.20194986, 0.07259074])
-
-    """
-    pmat = np.array(pmat)
-    pr = 1 - np.diag(pmat)
-    return pr
    
-
-
-
    [docs]def homogeneity(
-    transition_matrices,
-    regime_names=[],
-    class_names=[],
-    title="Markov Homogeneity Test",
-):
-    """
-    Test for homogeneity of Markov transition probabilities across regimes.
-
-    Parameters
-    ----------
-    transition_matrices : list
-                          of transition matrices for regimes, all matrices must
-                          have same size (r, c). r is the number of rows in the
-                          transition matrix and c is the number of columns in
-                          the transition matrix.
-    regime_names        : sequence
-                          Labels for the regimes.
-    class_names         : sequence
-                          Labels for the classes/states of the Markov chain.
-    title               : string
-                          name of test.
-
-    Returns
-    -------
-                        : implicit
-                          an instance of Homogeneity_Results.
-    """
-
-    return Homogeneity_Results(
-        transition_matrices,
-        regime_names=regime_names,
-        class_names=class_names,
-        title=title,
-    )
    
-
-
-class Homogeneity_Results:
-    """
-    Wrapper class to present homogeneity results.
-
-    Parameters
-    ----------
-    transition_matrices : list
-                          of transition matrices for regimes, all matrices must
-                          have same size (r, c). r is the number of rows in
-                          the transition matrix and c is the number of columns
-                          in the transition matrix.
-    regime_names        : sequence
-                          Labels for the regimes.
-    class_names         : sequence
-                          Labels for the classes/states of the Markov chain.
-    title               : string
-                          Title of the table.
-
-    Attributes
-    -----------
-
-    Notes
-    -----
-    Degrees of freedom adjustment follow the approach in :cite:`Bickenbach2003`.
-
-    Examples
-    --------
-    See Spatial_Markov above.
-
-    """
-
-    def __init__(
-        self,
-        transition_matrices,
-        regime_names=[],
-        class_names=[],
-        title="Markov Homogeneity Test",
-    ):
-        self._homogeneity(transition_matrices)
-        self.regime_names = regime_names
-        self.class_names = class_names
-        self.title = title
-
-    def _homogeneity(self, transition_matrices):
-        # form null transition probability matrix
-        M = np.array(transition_matrices)
-        m, r, k = M.shape
-        self.k = k
-        B = np.zeros((r, m))
-        T = M.sum(axis=0)
-        self.t_total = T.sum()
-        n_i = T.sum(axis=1)
-        A_i = (T > 0).sum(axis=1)
-        A_im = np.zeros((r, m))
-        p_ij = np.dot(np.diag(1.0 / (n_i + (n_i == 0) * 1.0)), T)
-        den = p_ij + 1.0 * (p_ij == 0)
-        b_i = np.zeros_like(A_i)
-        p_ijm = np.zeros_like(M)
-        # get dimensions
-        m, n_rows, n_cols = M.shape
-        m = 0
-        Q = 0.0
-        LR = 0.0
-        lr_table = np.zeros_like(M)
-        q_table = np.zeros_like(M)
-
-        for nijm in M:
-            nim = nijm.sum(axis=1)
-            B[:, m] = 1.0 * (nim > 0)
-            b_i = b_i + 1.0 * (nim > 0)
-            p_ijm[m] = np.dot(np.diag(1.0 / (nim + (nim == 0) * 1.0)), nijm)
-            num = (p_ijm[m] - p_ij) ** 2
-            ratio = num / den
-            qijm = np.dot(np.diag(nim), ratio)
-            q_table[m] = qijm
-            Q = Q + qijm.sum()
-            # only use nonzero pijm in lr test
-            mask = (nijm > 0) * (p_ij > 0)
-            A_im[:, m] = (nijm > 0).sum(axis=1)
-            unmask = 1.0 * (mask == 0)
-            ratio = (mask * p_ijm[m] + unmask) / (mask * p_ij + unmask)
-            lr = nijm * np.log(ratio)
-            LR = LR + lr.sum()
-            lr_table[m] = 2 * lr
-            m += 1
-        # b_i is the number of regimes that have non-zero observations in row i
-        # A_i is the number of non-zero elements in row i of the aggregated
-        # transition matrix
-        self.dof = int(((b_i - 1) * (A_i - 1)).sum())
-        self.Q = Q
-        self.Q_p_value = 1 - stats.chi2.cdf(self.Q, self.dof)
-        self.LR = LR * 2.0
-        self.LR_p_value = 1 - stats.chi2.cdf(self.LR, self.dof)
-        self.A = A_i
-        self.A_im = A_im
-        self.B = B
-        self.b_i = b_i
-        self.LR_table = lr_table
-        self.Q_table = q_table
-        self.m = m
-        self.p_h0 = p_ij
-        self.p_h1 = p_ijm
-
-    def summary(self, file_name=None, title="Markov Homogeneity Test"):
-        regime_names = ["%d" % i for i in range(self.m)]
-        if self.regime_names:
-            regime_names = self.regime_names
-        cols = ["P(%s)" % str(regime) for regime in regime_names]
-        if not self.class_names:
-            self.class_names = list(range(self.k))
-
-        max_col = max([len(col) for col in cols])
-        col_width = max([5, max_col])  # probabilities have 5 chars
-        n_tabs = self.k
-        width = n_tabs * 4 + (self.k + 1) * col_width
-        lead = "-" * width
-        head = title.center(width)
-        contents = [lead, head, lead]
-        l = "Number of regimes: %d" % int(self.m)
-        k = "Number of classes: %d" % int(self.k)
-        r = "Regime names: "
-        r += ", ".join(regime_names)
-        t = "Number of transitions: %d" % int(self.t_total)
-        contents.append(k)
-        contents.append(t)
-        contents.append(l)
-        contents.append(r)
-        contents.append(lead)
-        h = "%7s %20s %20s" % ("Test", "LR", "Chi-2")
-        contents.append(h)
-        stat = "%7s %20.3f %20.3f" % ("Stat.", self.LR, self.Q)
-        contents.append(stat)
-        stat = "%7s %20d %20d" % ("DOF", self.dof, self.dof)
-        contents.append(stat)
-        stat = "%7s %20.3f %20.3f" % ("p-value", self.LR_p_value, self.Q_p_value)
-        contents.append(stat)
-        print(("\n".join(contents)))
-        print(lead)
-
-        cols = ["P(%s)" % str(regime) for regime in self.regime_names]
-        if not self.class_names:
-            self.class_names = list(range(self.k))
-        cols.extend(["%s" % str(cname) for cname in self.class_names])
-
-        max_col = max([len(col) for col in cols])
-        col_width = max([5, max_col])  # probabilities have 5 chars
-        p0 = []
-        line0 = ["{s: <{w}}".format(s="P(H0)", w=col_width)]
-        line0.extend(
-            (["{s: >{w}}".format(s=cname, w=col_width) for cname in self.class_names])
-        )
-        print(("    ".join(line0)))
-        p0.append("&".join(line0))
-        for i, row in enumerate(self.p_h0):
-            line = ["%*s" % (col_width, str(self.class_names[i]))]
-            line.extend(["%*.3f" % (col_width, v) for v in row])
-            print(("    ".join(line)))
-            p0.append("&".join(line))
-        pmats = [p0]
-
-        print(lead)
-        for r, p1 in enumerate(self.p_h1):
-            p0 = []
-            line0 = ["{s: <{w}}".format(s="P(%s)" % regime_names[r], w=col_width)]
-            line0.extend(
-                (
-                    [
-                        "{s: >{w}}".format(s=cname, w=col_width)
-                        for cname in self.class_names
-                    ]
-                )
-            )
-            print(("    ".join(line0)))
-            p0.append("&".join(line0))
-            for i, row in enumerate(p1):
-                line = ["%*s" % (col_width, str(self.class_names[i]))]
-                line.extend(["%*.3f" % (col_width, v) for v in row])
-                print(("    ".join(line)))
-                p0.append("&".join(line))
-            pmats.append(p0)
-            print(lead)
-
-        if file_name:
-            k = self.k
-            ks = str(k + 1)
-            with open(file_name, "w") as f:
-                c = []
-                fmt = "r" * (k + 1)
-                s = "\\begin{tabular}{|%s|}\\hline\n" % fmt
-                s += "\\multicolumn{%s}{|c|}{%s}" % (ks, title)
-                c.append(s)
-                s = "Number of classes: %d" % int(self.k)
-                c.append("\\hline\\multicolumn{%s}{|l|}{%s}" % (ks, s))
-                s = "Number of transitions: %d" % int(self.t_total)
-                c.append("\\multicolumn{%s}{|l|}{%s}" % (ks, s))
-                s = "Number of regimes: %d" % int(self.m)
-                c.append("\\multicolumn{%s}{|l|}{%s}" % (ks, s))
-                s = "Regime names: "
-                s += ", ".join(regime_names)
-                c.append("\\multicolumn{%s}{|l|}{%s}" % (ks, s))
-                s = "\\hline\\multicolumn{2}{|l}{%s}" % ("Test")
-                s += "&\\multicolumn{2}{r}{LR}&\\multicolumn{2}{r|}{Q}"
-                c.append(s)
-                s = "Stat."
-                s = "\\multicolumn{2}{|l}{%s}" % (s)
-                s += "&\\multicolumn{2}{r}{%.3f}" % self.LR
-                s += "&\\multicolumn{2}{r|}{%.3f}" % self.Q
-                c.append(s)
-                s = "\\multicolumn{2}{|l}{%s}" % ("DOF")
-                s += "&\\multicolumn{2}{r}{%d}" % int(self.dof)
-                s += "&\\multicolumn{2}{r|}{%d}" % int(self.dof)
-                c.append(s)
-                s = "\\multicolumn{2}{|l}{%s}" % ("p-value")
-                s += "&\\multicolumn{2}{r}{%.3f}" % self.LR_p_value
-                s += "&\\multicolumn{2}{r|}{%.3f}" % self.Q_p_value
-                c.append(s)
-                s1 = "\\\\\n".join(c)
-                s1 += "\\\\\n"
-                c = []
-                for mat in pmats:
-                    c.append("\\hline\n")
-                    for row in mat:
-                        c.append(row + "\\\\\n")
-                c.append("\\hline\n")
-                c.append("\\end{tabular}")
-                s2 = "".join(c)
-                f.write(s1 + s2)
-
-
-
    [docs]class FullRank_Markov(Markov):
-    """
-    Full Rank Markov in which ranks are considered as Markov states rather
-    than quantiles or other discretized classes. This is one way to avoid
-    issues associated with discretization.
-
-    Parameters
-    ----------
-    y            : array
-                   (n, t) with t>>n, one row per observation (n total),
-                   one column recording the value of each observation,
-                   with as many columns as time periods.
-    fill_empty_classes: bool
-                        If True, assign 1 to diagonal elements which fall in rows
-                        full of 0s to ensure p is a stochastic transition
-                        probability matrix (each row sums up to 1).
-    summary      : bool
-                   If True, print out the summary of the Markov Chain during
-                   initialization. Default is True.
-
-    Attributes
-    ----------
-    ranks        : array
-                   ranks of the original y array (by columns): higher values
-                   rank higher, e.g. the largest value in a column ranks 1.
-    p            : array
-                   (n, n), transition probability matrix for Full
-                   Rank Markov.
-    steady_state : array
-                   (n, ), ergodic distribution.
-    transitions  : array
-                   (n, n), count of transitions between each rank i and j
-    mfpt         : array
-                   (n, n), mean first passage times.
-    sojourn_time : array
-                   (n, ), sojourn times.
-
-    Notes
-    -----
-    Refer to :cite:`Rey2014a` Equation (11) for details. Ties are resolved by
-    assigning distinct ranks, corresponding to the order that the values occur
-    in each cross section.
-
-    Examples
-    --------
-    US nominal per capita income 48 states 81 years 1929-2009
-
-    >>> from giddy.markov import FullRank_Markov
-    >>> import libpysal as ps
-    >>> import numpy as np
-    >>> f = ps.io.open(ps.examples.get_path("usjoin.csv"))
-    >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)]).transpose()
-    >>> m = FullRank_Markov(pci)
-    The Markov Chain is irreducible and is composed by:
-    1 Recurrent class (indices):
-    [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
-     24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47]
-    0 Transient classes.
-    The Markov Chain has 0 absorbing states.
-    >>> m.transitions
-    array([[66.,  5.,  5., ...,  0.,  0.,  0.],
-           [ 8., 51.,  9., ...,  0.,  0.,  0.],
-           [ 2., 13., 44., ...,  0.,  0.,  0.],
-           ...,
-           [ 0.,  0.,  0., ..., 40., 17.,  0.],
-           [ 0.,  0.,  0., ..., 15., 54.,  2.],
-           [ 0.,  0.,  0., ...,  2.,  1., 77.]])
-    >>> m.p[0, :5]
-    array([0.825 , 0.0625, 0.0625, 0.025 , 0.025 ])
-    >>> m.mfpt[0, :5]
-    array([48.        , 87.96280048, 68.1089084 , 58.83306575, 41.77250827])
-    >>> m.sojourn_time[:5]
-    array([5.71428571, 2.75862069, 2.22222222, 1.77777778, 1.66666667])
-
-    """
-
-
    [docs]    def __init__(self, y, fill_empty_classes=False, summary=True):
-
-        y = np.asarray(y)
-        # resolve ties: All values are given a distinct rank, corresponding
-        # to the order that the values occur in each cross section.
-        r_asc = np.array([rankdata(col, method="ordinal") for col in y.T]).T
-        # ranks by high (1) to low (n)
-        self.ranks = r_asc.shape[0] - r_asc + 1
-        super(FullRank_Markov, self).__init__(
-            self.ranks, fill_empty_classes=fill_empty_classes, summary=summary
-        )
    
-
-
-
    [docs]def sojourn_time(p, summary=True):
-    """
-    Calculate sojourn time based on a given transition probability matrix.
-
-    Parameters
-    ----------
-    p        : array
-               (k, k), a Markov transition probability matrix.
-    summary  : bool
-               If True and the Markov Chain has absorbing states whose
-               sojourn time is infinitely large, print out the information
-               about the absorbing states. Default is True.
-    Returns
-    -------
-             : array
-               (k, ), sojourn times. Each element is the expected time a Markov
-               chain spends in each state before leaving that state.
-
-    Notes
-    -----
-    Refer to :cite:`Ibe2009` for more details on sojourn times for Markov
-    chains.
-
-    Examples
-    --------
-    >>> from giddy.markov import sojourn_time
-    >>> import numpy as np
-    >>> p = np.array([[.5, .25, .25], [.5, 0, .5], [.25, .25, .5]])
-    >>> sojourn_time(p)
-    array([2., 1., 2.])
-
-    Non-ergodic Markov Chains with rows full of 0
-
-    >>> p = np.array([[.5, .25, .25], [.5, 0, .5],[ 0, 0, 0]])
-    >>> sojourn_time(p)
-    Sojourn times are infinite for absorbing states! In this Markov Chain, states [2] are absorbing states.
-    array([ 2.,  1., inf])
-    """
-
-    p = np.asarray(p)
-    if (p.sum(axis=1) == 0).sum() > 0:
-        p = fill_empty_diagonals(p)
-
-    markovchain = qe.MarkovChain(p)
-    pii = p.diagonal()
-
-    if not (1 - pii).all():
-        absorbing_states = np.where(pii == 1)[0]
-        non_absorbing_states = np.where(pii != 1)[0]
-        st = np.full(len(pii), np.inf)
-        if summary:
-            print(
-                "Sojourn times are infinite for absorbing states! In this "
-                "Markov Chain, states {} are absorbing states.".format(
-                    list(absorbing_states)
-                )
-            )
-        st[non_absorbing_states] = 1 / (1 - pii[non_absorbing_states])
-    else:
-        st = 1 / (1 - pii)
-    return st
    
-
-
-
    [docs]class GeoRank_Markov(Markov):
-    """
-    Geographic Rank Markov.
-    Geographic units are considered as Markov states.
-
-    Parameters
-    ----------
-    y            : array
-                   (n, t) with t>>n, one row per observation (n total),
-                   one column recording the value of each observation,
-                   with as many columns as time periods.
-    fill_empty_classes: bool
-                        If True, assign 1 to diagonal elements which fall in rows
-                        full of 0s to ensure p is a stochastic transition
-                        probability matrix (each row sums up to 1).
-    summary      : bool
-                   If True, print out the summary of the Markov Chain during
-                   initialization. Default is True.
-
-    Attributes
-    ----------
-    p            : array
-                   (n, n), transition probability matrix for
-                   geographic rank Markov.
-    steady_state : array
-                   (n, ), ergodic distribution.
-    transitions  : array
-                   (n, n), count of rank transitions between each
-                   geographic unit i and j.
-    mfpt         : array
-                   (n, n), mean first passage times.
-    sojourn_time : array
-                   (n, ), sojourn times.
-
-    Notes
-    -----
-    Refer to :cite:`Rey2014a` Equation (13)-(16) for details. Ties are
-    resolved by assigning distinct ranks, corresponding to the order
-    that the values occur in each cross section.
-
-    Examples
-    --------
-    US nominal per capita income 48 states 81 years 1929-2009
-
-    >>> from giddy.markov import GeoRank_Markov
-    >>> import libpysal as ps
-    >>> import numpy as np
-    >>> f = ps.io.open(ps.examples.get_path("usjoin.csv"))
-    >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)]).transpose()
-    >>> m = GeoRank_Markov(pci)
-    The Markov Chain is irreducible and is composed by:
-    1 Recurrent class (indices):
-    [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
-     24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47]
-    0 Transient classes.
-    The Markov Chain has 0 absorbing states.
-    >>> m.transitions
-    array([[38.,  0.,  8., ...,  0.,  0.,  0.],
-           [ 0., 15.,  0., ...,  0.,  1.,  0.],
-           [ 6.,  0., 44., ...,  5.,  0.,  0.],
-           ...,
-           [ 2.,  0.,  5., ..., 34.,  0.,  0.],
-           [ 0.,  0.,  0., ...,  0., 18.,  2.],
-           [ 0.,  0.,  0., ...,  0.,  3., 14.]])
-    >>> m.p
-    array([[0.475 , 0.    , 0.1   , ..., 0.    , 0.    , 0.    ],
-           [0.    , 0.1875, 0.    , ..., 0.    , 0.0125, 0.    ],
-           [0.075 , 0.    , 0.55  , ..., 0.0625, 0.    , 0.    ],
-           ...,
-           [0.025 , 0.    , 0.0625, ..., 0.425 , 0.    , 0.    ],
-           [0.    , 0.    , 0.    , ..., 0.    , 0.225 , 0.025 ],
-           [0.    , 0.    , 0.    , ..., 0.    , 0.0375, 0.175 ]])
-    >>> m.mfpt
-    array([[ 48.        ,  63.35532038,  92.75274652, ...,  82.47515731,
-             71.01114491,  68.65737127],
-           [108.25928005,  48.        , 127.99032986, ...,  92.03098299,
-             63.36652935,  61.82733039],
-           [ 76.96801786,  64.7713783 ,  48.        , ...,  73.84595169,
-             72.24682723,  69.77497173],
-           ...,
-           [ 93.3107474 ,  62.47670463, 105.80634118, ...,  48.        ,
-             69.30121319,  67.08838421],
-           [113.65278078,  61.1987031 , 133.57991745, ...,  96.0103924 ,
-             48.        ,  56.74165107],
-           [114.71894813,  63.4019776 , 134.73381719, ...,  97.287895  ,
-             61.45565054,  48.        ]])
-    >>> m.sojourn_time
-    array([ 1.9047619 ,  1.23076923,  2.22222222,  1.73913043,  1.15942029,
-            3.80952381,  1.70212766,  1.25      ,  1.31147541,  1.11111111,
-            1.73913043,  1.37931034,  1.17647059,  1.21212121,  1.33333333,
-            1.37931034,  1.09589041,  2.10526316,  2.        ,  1.45454545,
-            1.26984127, 26.66666667,  1.19402985,  1.23076923,  1.09589041,
-            1.56862745,  1.26984127,  2.42424242,  1.50943396,  2.        ,
-            1.29032258,  1.09589041,  1.6       ,  1.42857143,  1.25      ,
-            1.45454545,  1.29032258,  1.6       ,  1.17647059,  1.56862745,
-            1.25      ,  1.37931034,  1.45454545,  1.42857143,  1.29032258,
-            1.73913043,  1.29032258,  1.21212121])
-
-    """
-
-
    [docs]    def __init__(self, y, fill_empty_classes=False, summary=True):
-        y = np.asarray(y)
-        n = y.shape[0]
-        # resolve ties: All values are given a distinct rank, corresponding
-        # to the order that the values occur in each cross section.
-        ranks = np.array([rankdata(col, method="ordinal") for col in y.T]).T
-        geo_ranks = np.argsort(ranks, axis=0) + 1
-        super(GeoRank_Markov, self).__init__(
-            geo_ranks, fill_empty_classes=fill_empty_classes, summary=summary
-        )
    
-
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    - - \ No newline at end of file diff --git a/docs/_modules/giddy/mobility.html b/docs/_modules/giddy/mobility.html deleted file mode 100644 index 271b52c..0000000 --- a/docs/_modules/giddy/mobility.html +++ /dev/null @@ -1,288 +0,0 @@ - - - - - - - giddy.mobility — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    -
    - -

    Source code for giddy.mobility

    -"""
-Income mobility measures.
-"""
-
-__author__ = "Wei Kang <weikang9009@gmail.com>, Sergio J. Rey <sjsrey@gmail.com>"
-
-__all__ = ["markov_mobility"]
-
-import numpy as np
-import numpy.linalg as la
-
-
-
    [docs]def markov_mobility(p, measure="P", ini=None):
-    """
-    Markov-based mobility index.
-
-    Parameters
-    ----------
-    p       : array
-              (k, k), Markov transition probability matrix.
-    measure : string
-              If measure= "P",
-              :math:`M_{P} = \\frac{m-\sum_{i=1}^m P_{ii}}{m-1}`;
-              if measure = "D",
-              :math:`M_{D} = 1 - |\det(P)|`,
-              where :math:`\det(P)` is the determinant of :math:`P`;
-              if measure = "L2",
-              :math:`M_{L2} = 1  - |\lambda_2|`,
-              where :math:`\lambda_2` is the second largest eigenvalue of
-              :math:`P`;
-              if measure = "B1",
-              :math:`M_{B1} = \\frac{m-m \sum_{i=1}^m \pi_i P_{ii}}{m-1}`,
-              where :math:`\pi` is the initial income distribution;
-              if measure == "B2",
-              :math:`M_{B2} = \\frac{1}{m-1} \sum_{i=1}^m \sum_{
-              j=1}^m \pi_i P_{ij} |i-j|`,
-              where :math:`\pi` is the initial income distribution.
-    ini     : array
-              (k,), initial distribution. Need to be specified if
-              measure = "B1" or "B2". If not,
-              the initial distribution would be treated as a uniform
-              distribution.
-
-    Returns
-    -------
-    mobi    : float
-              Mobility value.
-
-    Notes
-    -----
-    The mobility indices are based on :cite:`Formby:2004fk`.
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> import libpysal
-    >>> import mapclassify as mc
-    >>> from giddy.markov import Markov
-    >>> from giddy.mobility import markov_mobility
-    >>> f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
-    >>> pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
-    >>> q5 = np.array([mc.Quantiles(y).yb for y in pci]).transpose()
-    >>> m = Markov(q5)
-    The Markov Chain is irreducible and is composed by:
-    1 Recurrent class (indices):
-    [0 1 2 3 4]
-    0 Transient classes.
-    The Markov Chain has 0 absorbing states.
-    >>> m.p
-    array([[0.91011236, 0.0886392 , 0.00124844, 0.        , 0.        ],
-           [0.09972299, 0.78531856, 0.11080332, 0.00415512, 0.        ],
-           [0.        , 0.10125   , 0.78875   , 0.1075    , 0.0025    ],
-           [0.        , 0.00417827, 0.11977716, 0.79805014, 0.07799443],
-           [0.        , 0.        , 0.00125156, 0.07133917, 0.92740926]])
-
-    (1) Estimate Shorrock1 mobility index:
-
-    >>> mobi_1 = markov_mobility(m.p, measure="P")
-    >>> print("{:.5f}".format(mobi_1))
-    0.19759
-
-    (2) Estimate Shorrock2 mobility index:
-
-    >>> mobi_2 = markov_mobility(m.p, measure="D")
-    >>> print("{:.5f}".format(mobi_2))
-    0.60685
-
-    (3) Estimate Sommers and Conlisk mobility index:
-
-    >>> mobi_3 = markov_mobility(m.p, measure="L2")
-    >>> print("{:.5f}".format(mobi_3))
-    0.03978
-
-    (4) Estimate Bartholomew1 mobility index (note that the initial
-    distribution should be given):
-
-    >>> ini = np.array([0.1,0.2,0.2,0.4,0.1])
-    >>> mobi_4 = markov_mobility(m.p, measure = "B1", ini=ini)
-    >>> print("{:.5f}".format(mobi_4))
-    0.22777
-
-    (5) Estimate Bartholomew2 mobility index (note that the initial
-    distribution should be given):
-
-    >>> ini = np.array([0.1,0.2,0.2,0.4,0.1])
-    >>> mobi_5 = markov_mobility(m.p, measure = "B2", ini=ini)
-    >>> print("{:.5f}".format(mobi_5))
-    0.04637
-
-    """
-
-    p = np.array(p)
-    k = p.shape[1]
-    if measure == "P":
-        t = np.trace(p)
-        mobi = (k - t) / (k - 1)
-    elif measure == "D":
-        mobi = 1 - abs(la.det(p))
-    elif measure == "L2":
-        w, v = la.eig(p)
-        eigen_value_abs = abs(w)
-        mobi = 1 - np.sort(eigen_value_abs)[-2]
-    elif measure == "B1":
-        if ini is None:
-            ini = 1.0 / k * np.ones(k)
-        mobi = (k - k * np.sum(ini * np.diag(p))) / (k - 1)
-    elif measure == "B2":
-        mobi = 0
-        if ini is None:
-            ini = 1.0 / k * np.ones(k)
-        for i in range(k):
-            for j in range(k):
-                mobi = mobi + ini[i] * p[i, j] * abs(i - j)
-        mobi = mobi / (k - 1)
-
-    return mobi
    
-
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    - Created using Sphinx 5.0.2.
    -

    -
    -
    - - \ No newline at end of file diff --git a/docs/_modules/giddy/rank.html b/docs/_modules/giddy/rank.html deleted file mode 100644 index cb88f4d..0000000 --- a/docs/_modules/giddy/rank.html +++ /dev/null @@ -1,1086 +0,0 @@ - - - - - - - giddy.rank — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    -
    - -

    Source code for giddy.rank

    -"""
-Rank and spatial rank mobility measures.
-"""
-__author__ = "Sergio J. Rey <sjsrey@gmail.com>, Wei Kang <weikang9009@gmail.com>"
-
-__all__ = [
-    "SpatialTau",
-    "Tau",
-    "Theta",
-    "Tau_Local",
-    "Tau_Local_Neighbor",
-    "Tau_Local_Neighborhood",
-    "Tau_Regional",
-]
-
-from scipy.stats.mstats import rankdata
-from scipy.special import erfc
-import numpy as np
-import scipy as sp
-from libpysal import weights
-
-
-
    [docs]class Theta:
-    """
-    Regime mobility measure. :cite:`Rey2004a`
-
-    For sequence of time periods Theta measures the extent to which rank
-    changes for a variable measured over n locations are in the same direction
-    within mutually exclusive and exhaustive partitions (regimes) of the n locations.
-
-    Theta is defined as the sum of the absolute sum of rank changes within
-    the regimes over the sum of all absolute rank changes.
-
-    Parameters
-    ----------
-    y            : array
-                   (n, k) with k>=2, successive columns of y are later moments
-                   in time (years, months, etc).
-    regime       : array
-                   (n, ), values corresponding to which regime each observation
-                   belongs to.
-    permutations : int
-                   number of random spatial permutations to generate for
-                   computationally based inference.
-
-    Attributes
-    ----------
-    ranks        : array
-                   ranks of the original y array (by columns).
-    regimes      : array
-                   the original regimes array.
-    total        : array
-                   (k-1, ), the total number of rank changes for each of the
-                   k periods.
-    max_total    : int
-                   the theoretical maximum number of rank changes for n
-                   observations.
-    theta        : array
-                   (k-1,), the theta statistic for each of the k-1 intervals.
-    permutations : int
-                   the number of permutations.
-    pvalue_left  : float
-                   p-value for test that observed theta is significantly lower
-                   than its expectation under complete spatial randomness.
-    pvalue_right : float
-                   p-value for test that observed theta is significantly
-                   greater than its expectation under complete spatial randomness.
-
-    Examples
-    --------
-    >>> import libpysal as ps
-    >>> from giddy.rank import Theta
-    >>> import numpy as np
-    >>> f=ps.io.open(ps.examples.get_path("mexico.csv"))
-    >>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
-    >>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))
-    >>> regime=np.array(f.by_col['esquivel99'])
-    >>> np.random.seed(10)
-    >>> t=Theta(y,regime,999)
-    >>> t.theta
-    array([[0.41538462, 0.28070175, 0.61363636, 0.62222222, 0.33333333,
-            0.47222222]])
-    >>> t.pvalue_left
-    array([0.307, 0.077, 0.823, 0.552, 0.045, 0.735])
-    >>> t.total
-    array([130., 114.,  88.,  90.,  90.,  72.])
-    >>> t.max_total
-    512
-
-    """
-
-
    [docs]    def __init__(self, y, regime, permutations=999):
-        ranks = rankdata(y, axis=0)
-        self.ranks = ranks
-        n, k = y.shape
-        ranks_d = ranks[:, list(range(1, k))] - ranks[:, list(range(k - 1))]
-        self.ranks_d = ranks_d
-        regimes = sp.unique(regime)
-        self.regimes = regimes
-        self.total = sum(abs(ranks_d))
-        self.max_total = sum([abs(i - n + i - 1) for i in range(1, n + 1)])
-        self._calc(regime)
-        self.theta = self._calc(regime)
-        self.permutations = permutations
-        if permutations:
-            np.perm = np.random.permutation
-            sim = np.array([self._calc(np.perm(regime)) for i in range(permutations)])
-            self.theta.shape = (1, len(self.theta))
-            sim = np.concatenate((self.theta, sim))
-            self.sim = sim
-            den = permutations + 1.0
-            self.pvalue_left = (sim <= sim[0]).sum(axis=0) / den
-            self.pvalue_right = (sim > sim[0]).sum(axis=0) / den
-            self.z = (sim[0] - sim.mean(axis=0)) / sim.std(axis=0)
    
-
-    def _calc(self, regime):
-        within = [abs(sum(self.ranks_d[regime == reg])) for reg in self.regimes]
-        return np.array(sum(within) / self.total)
    
-
-
-
    [docs]class Tau:
-    """
-    Kendall's Tau is based on a comparison of the number of pairs of n
-    observations that have concordant ranks between two variables.
-
-    Parameters
-    ----------
-    x            : array
-                   (n, ), first variable.
-    y            : array
-                   (n, ), second variable.
-
-    Attributes
-    ----------
-    tau          : float
-                   The classic Tau statistic.
-    tau_p        : float
-                   asymptotic p-value.
-
-    Notes
-    -----
-    Modification of algorithm suggested by :cite:`Christensen2005`.PySAL/giddy
-    implementation uses a list based representation of a binary tree for
-    the accumulation of the concordance measures. Ties are handled by this
-    implementation (in other words, if there are ties in either x, or y, or
-    both, the calculation returns Tau_b, if no ties classic Tau is returned.)
-
-    Examples
-    --------
-    >>> from scipy.stats import kendalltau
-    >>> from giddy.rank import Tau
-    >>> x1 = [12, 2, 1, 12, 2]
-    >>> x2 = [1, 4, 7, 1, 0]
-    >>> kt = Tau(x1,x2)
-    >>> kt.tau
-    -0.47140452079103173
-    >>> kt.tau_p
-    0.24821309157521476
-    >>> tau, p = kendalltau(x1,x2)
-    >>> tau
-    -0.4714045207910316
-    >>> p
-    0.2827454599327748
-
-    """
-
-
    [docs]    def __init__(self, x, y):
-        res = self._calc(x, y)
-        self.tau = res[0]
-        self.tau_p = res[1]
-        self.concordant = res[2]
-        self.discordant = res[3]
-        self.extraX = res[4]
-        self.extraY = res[5]
    
-
-    def _calc(self, x, y):
-        """
-        List based implementation of binary tree algorithm for concordance
-        measure after :cite:`Christensen2005`.
-
-        """
-        x = np.array(x)
-        y = np.array(y)
-        n = len(y)
-        perm = list(range(n))
-        perm.sort(key=lambda a: (x[a], y[a]))
-        vals = y[perm]
-        ExtraY = 0
-        ExtraX = 0
-        ACount = 0
-        BCount = 0
-        CCount = 0
-        DCount = 0
-        ECount = 1
-        DCount = 0
-        Concordant = 0
-        Discordant = 0
-        # ids for left child
-        li = [None] * (n - 1)
-        # ids for right child
-        ri = [None] * (n - 1)
-        # number of left descendants for a node
-        ld = np.zeros(n)
-        # number of values equal to value i
-        nequal = np.zeros(n)
-
-        for i in range(1, n):
-            NumBefore = 0
-            NumEqual = 1
-            root = 0
-            x0 = x[perm[i - 1]]
-            y0 = y[perm[i - 1]]
-            x1 = x[perm[i]]
-            y1 = y[perm[i]]
-            if x0 != x1:
-                DCount = 0
-                ECount = 1
-            else:
-                if y0 == y1:
-                    ECount += 1
-                else:
-                    DCount += ECount
-                    ECount = 1
-            root = 0
-            inserting = True
-            while inserting:
-                current = y[perm[i]]
-                if current > y[perm[root]]:
-                    # right branch
-                    NumBefore += 1 + ld[root] + nequal[root]
-                    if ri[root] is None:
-                        # insert as right child to root
-                        ri[root] = i
-                        inserting = False
-                    else:
-                        root = ri[root]
-                elif current < y[perm[root]]:
-                    # increment number of left descendants
-                    ld[root] += 1
-                    if li[root] is None:
-                        # insert as left child to root
-                        li[root] = i
-                        inserting = False
-                    else:
-                        root = li[root]
-                elif current == y[perm[root]]:
-                    NumBefore += ld[root]
-                    NumEqual += nequal[root] + 1
-                    nequal[root] += 1
-                    inserting = False
-
-            ACount = NumBefore - DCount
-            BCount = NumEqual - ECount
-            CCount = i - (ACount + BCount + DCount + ECount - 1)
-            ExtraY += DCount
-            ExtraX += BCount
-            Concordant += ACount
-            Discordant += CCount
-
-        cd = Concordant + Discordant
-        num = Concordant - Discordant
-        tau = num / np.sqrt((cd + ExtraX) * (cd + ExtraY))
-        v = (4.0 * n + 10) / (9.0 * n * (n - 1))
-        z = tau / np.sqrt(v)
-        pval = erfc(np.abs(z) / 1.4142136)  # follow scipy
-        return tau, pval, Concordant, Discordant, ExtraX, ExtraY
    
-
-
-
    [docs]class SpatialTau(object):
-    """
-    Spatial version of Kendall's rank correlation statistic.
-
-    Kendall's Tau is based on a comparison of the number of pairs of n
-    observations that have concordant ranks between two variables. The spatial
-    Tau decomposes these pairs into those that are spatial neighbors and those
-    that are not, and examines whether the rank correlation is different
-    between the two sets relative to what would be expected under spatial randomness.
-
-    Parameters
-    ----------
-    x             : array
-                    (n, ), first variable.
-    y             : array
-                    (n, ), second variable.
-    w             : W
-                    spatial weights object.
-    permutations  : int
-                    number of random spatial permutations for computationally
-                    based inference.
-
-    Attributes
-    ----------
-    tau                : float
-                         The classic Tau statistic.
-    tau_spatial        : float
-                         Value of Tau for pairs that are spatial neighbors.
-    taus               : array
-                         (permtuations, 1), values of simulated tau_spatial values
-                         under random spatial permutations in both periods. (Same
-                         permutation used for start and ending period).
-    pairs_spatial      : int
-                         Number of spatial pairs.
-    concordant         : float
-                         Number of concordant pairs.
-    concordant_spatial : float
-                         Number of concordant pairs that are spatial neighbors.
-    extraX             : float
-                         Number of extra X pairs.
-    extraY             : float
-                         Number of extra Y pairs.
-    discordant         : float
-                         Number of discordant pairs.
-    discordant_spatial : float
-                         Number of discordant pairs that are spatial neighbors.
-    taus               : float
-                         spatial tau values for permuted samples (if permutations>0).
-    tau_spatial_psim   : float
-                         one-sided pseudo p-value for observed tau_spatial under the null
-                         of spatial randomness of rank exchanges (if permutations>0).
-
-    Notes
-    -----
-    Algorithm has two stages. The first calculates classic Tau using a list
-    based implementation of the algorithm from :cite:`Christensen2005`. Second
-    stage calculates concordance measures for neighboring pairs of locations
-    using a modification of the algorithm from :cite:`Press2007`. See
-    :cite:`Rey2014` for details.
-
-    Examples
-    --------
-    >>> import libpysal as ps
-    >>> import numpy as np
-    >>> from giddy.rank import SpatialTau
-    >>> f=ps.io.open(ps.examples.get_path("mexico.csv"))
-    >>> vnames=["pcgdp%d"%dec for dec in range(1940,2010,10)]
-    >>> y=np.transpose(np.array([f.by_col[v] for v in vnames]))
-    >>> regime=np.array(f.by_col['esquivel99'])
-    >>> w=ps.weights.block_weights(regime)
-    >>> np.random.seed(12345)
-    >>> res=[SpatialTau(y[:,i],y[:,i+1],w,99) for i in range(6)]
-    >>> for r in res:
-    ...     ev = r.taus.mean()
-    ...     "%8.3f %8.3f %8.3f"%(r.tau_spatial, ev, r.tau_spatial_psim)
-    ...
-    '   0.397    0.659    0.010'
-    '   0.492    0.706    0.010'
-    '   0.651    0.772    0.020'
-    '   0.714    0.752    0.210'
-    '   0.683    0.705    0.270'
-    '   0.810    0.819    0.280'
-    """
-
-
    [docs]    def __init__(self, x, y, w, permutations=0):
-
-        w.transform = "b"
-        self.n = len(x)
-        res = Tau(x, y)
-        self.tau = res.tau
-        self.tau_p = res.tau_p
-        self.concordant = res.concordant
-        self.discordant = res.discordant
-        self.extraX = res.extraX
-        self.extraY = res.extraY
-        res = self._calc(x, y, w)
-        self.tau_spatial = res[0]
-        self.pairs_spatial = int(w.s0 / 2.0)
-        self.concordant_spatial = res[1]
-        self.discordant_spatial = res[2]
-
-        if permutations > 0:
-            taus = np.zeros(permutations)
-            ids = np.arange(self.n)
-            for r in range(permutations):
-                rids = np.random.permutation(ids)
-                taus[r] = self._calc(x[rids], y[rids], w)[0]
-            self.taus = taus
-            self.tau_spatial_psim = pseudop(taus, self.tau_spatial, permutations)
    
-
-    def _calc(self, x, y, w):
-        n1 = n2 = iS = gc = 0
-        ijs = {}
-        for i in w.id_order:
-            xi = x[i]
-            yi = y[i]
-            for j in w.neighbors[i]:
-                if i < j:
-                    ijs[(i, j)] = (i, j)
-                    xj = x[j]
-                    yj = y[j]
-                    dx = xi - xj
-                    dy = yi - yj
-                    dxdy = dx * dy
-                    if dxdy != 0:
-                        n1 += 1
-                        n2 += 1
-                        if dxdy > 0.0:
-                            gc += 1
-                            iS += 1
-                        else:
-                            iS -= 1
-                    else:
-                        if dx != 0.0:
-                            n1 += 1
-                        if dy != 0.0:
-                            n2 += 1
-        tau_g = iS / (np.sqrt(n1) * np.sqrt(n2))
-        gd = gc - iS
-        return [tau_g, gc, gd]
    
-
-
-def pseudop(sim, observed, nperm):
-    above = sim >= observed
-    larger = above.sum()
-    psim = (larger + 1.0) / (nperm + 1.0)
-    if psim > 0.5:
-        psim = (nperm - larger + 1.0) / (nperm + 1.0)
-    return psim
-
-
-
    [docs]class Tau_Local:
-    """
-    Local version of the classic Tau.
-
-    Decomposition of the classic Tau into local components.
-
-    Parameters
-    ----------
-    x             : array
-                    (n, ), first variable.
-    y             : array
-                    (n, ), second variable.
-
-    Attributes
-    ----------
-    n             : int
-                    number of observations.
-    tau           : float
-                    The classic Tau statistic.
-    tau_local     : array
-                    (n, ), local concordance (local version of the
-                    classic tau).
-    S             : array
-                    (n ,n), concordance matrix, s_{i,j}=1 if
-                    observation i and j are concordant, s_{i,j}=-1
-                    if observation i and j are discordant, and
-                    s_{i,j}=0 otherwise.
-
-    Notes
-    -----
-    The equation for calculating local concordance statistic can be
-    found in :cite:`Rey2016` Equation (9).
-
-    Examples
-    --------
-    >>> import libpysal as ps
-    >>> import numpy as np
-    >>> from giddy.rank import Tau_Local,Tau
-    >>> np.random.seed(10)
-    >>> f = ps.io.open(ps.examples.get_path("mexico.csv"))
-    >>> vnames = ["pcgdp%d"%dec for dec in range(1940, 2010, 10)]
-    >>> y = np.transpose(np.array([f.by_col[v] for v in vnames]))
-    >>> r = y / y.mean(axis=0)
-    >>> tau_local = Tau_Local(r[:,0],r[:,1])
-    >>> tau_local.tau_local
-    array([-0.03225806,  0.93548387,  0.80645161,  0.74193548,  0.93548387,
-            0.74193548,  0.67741935,  0.41935484,  1.        ,  0.5483871 ,
-            0.74193548,  0.93548387,  0.67741935,  0.74193548,  0.80645161,
-            0.74193548,  0.5483871 ,  0.67741935,  0.74193548,  0.74193548,
-            0.5483871 , -0.16129032,  0.93548387,  0.61290323,  0.67741935,
-            0.48387097,  0.93548387,  0.61290323,  0.74193548,  0.41935484,
-            0.61290323,  0.61290323])
-    >>> tau_local.tau
-    0.6612903225806451
-    >>> tau_classic = Tau(r[:,0],r[:,1])
-    >>> tau_classic.tau
-    0.6612903225806451
-
-    """
-
-
    [docs]    def __init__(self, x, y):
-
-        self.n = len(x)
-        x = np.asarray(x)
-        y = np.asarray(y)
-        xx = x.reshape(self.n, 1)
-        yy = y.reshape(self.n, 1)
-
-        C = (xx - xx.T) * (yy - yy.T)
-        self.S = -1 * (C < 0) + 1 * (C > 0)
-
-        self.tau = self.S.sum() * 1.0 / (self.n * (self.n - 1))
-        si = self.S.sum(axis=1)
-
-        self.tau_local = si * 1.0 / (self.n - 1)
    
-
-
-
    [docs]class Tau_Local_Neighbor:
-    """
-    Neighbor set LIMA.
-
-    Local concordance relationships between a focal unit and its
-    neighbors. A decomposition of local Tau into neighbor and
-    non-neighbor components.
-
-    Parameters
-    ----------
-    x              : array
-                     (n, ), first variable.
-    y              : array
-                     (n, ), second variable.
-    w              : W
-                     spatial weights object.
-    permutations   : int
-                     number of random spatial permutations for
-                     computationally based inference.
-
-    Attributes
-    ----------
-    n              : int
-                     number of observations.
-    tau_local       : array
-                     (n, ), local concordance (local version of the
-                     classic tau).
-    S              : array
-                     (n ,n), concordance matrix, s_{i,j}=1 if
-                     observation i and j are concordant, s_{i,
-                     j}=-1 if observation i and j are discordant,
-                     and s_{i,j}=0 otherwise.
-    tau_ln         : array
-                     (n, ), observed neighbor set LIMA values.
-    tau_ln_weights : array
-                     (n, ), weights for neighbor set LIMA at each
-                     location. GIMA is the weighted average of
-                     neighbor set LIMA.
-    tau_ln_sim     : array
-                     (n, permutations), neighbor set LIMA values for
-                     permuted samples (if permutations>0).
-    tau_ln_pvalues : array
-                     (n, ), one-sided pseudo p-values for observed neighbor
-                     set LIMA values under the null that concordance
-                     relationship between the focal state and itsn
-                     eighbors is not different from what could be
-                     expected from randomly distributed rank changes.
-    sign           : array
-                     (n, ), values indicate concordant or
-                     disconcordant: 1 concordant, -1 disconcordant
-
-    Notes
-    -----
-    The equation for calculating neighbor set LIMA statistic can be
-    found in :cite:`Rey2016` Equation (16).
-
-    Examples
-    --------
-    >>> import libpysal as ps
-    >>> import numpy as np
-    >>> from giddy.rank import Tau_Local_Neighbor, SpatialTau
-    >>> np.random.seed(10)
-    >>> f = ps.io.open(ps.examples.get_path("mexico.csv"))
-    >>> vnames = ["pcgdp%d"%dec for dec in range(1940, 2010, 10)]
-    >>> y = np.transpose(np.array([f.by_col[v] for v in vnames]))
-    >>> r = y / y.mean(axis=0)
-    >>> regime = np.array(f.by_col['esquivel99'])
-    >>> w = ps.weights.block_weights(regime)
-    >>> res = Tau_Local_Neighbor(r[:,0], r[:,1], w, permutations=999)
-    >>> res.tau_ln
-    array([-0.2       ,  1.        ,  1.        ,  1.        ,  0.33333333,
-            0.6       ,  0.6       , -0.5       ,  1.        ,  1.        ,
-            0.2       ,  0.33333333,  0.33333333,  0.5       ,  1.        ,
-            1.        ,  1.        ,  0.        ,  0.6       , -0.33333333,
-           -0.33333333, -0.6       ,  1.        ,  0.2       ,  0.        ,
-            0.2       ,  1.        ,  0.6       ,  0.33333333,  0.5       ,
-            0.5       , -0.2       ])
-    >>> res.tau_ln_weights
-    array([0.03968254, 0.03968254, 0.03174603, 0.03174603, 0.02380952,
-           0.03968254, 0.03968254, 0.03174603, 0.00793651, 0.03968254,
-           0.03968254, 0.02380952, 0.02380952, 0.03174603, 0.00793651,
-           0.02380952, 0.02380952, 0.03174603, 0.03968254, 0.02380952,
-           0.02380952, 0.03968254, 0.03174603, 0.03968254, 0.03174603,
-           0.03968254, 0.03174603, 0.03968254, 0.02380952, 0.03174603,
-           0.03174603, 0.03968254])
-    >>> res.tau_ln_pvalues
-    array([0.541, 0.852, 0.668, 0.568, 0.11 , 0.539, 0.609, 0.058, 1.   ,
-           0.255, 0.125, 0.087, 0.393, 0.433, 0.908, 0.657, 0.447, 0.128,
-           0.531, 0.033, 0.12 , 0.271, 0.868, 0.234, 0.124, 0.387, 0.859,
-           0.697, 0.349, 0.664, 0.596, 0.041])
-    >>> res.sign
-    array([-1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
-            1,  1, -1, -1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1])
-    >>> (res.tau_ln * res.tau_ln_weights).sum() #global spatial tau
-    0.39682539682539675
-    >>> res1 = SpatialTau(r[:,0],r[:,1],w,permutations=999)
-    >>> res1.tau_spatial
-    0.3968253968253968
-
-    """
-
-
    [docs]    def __init__(self, x, y, w, permutations=0):
-
-        x = np.asarray(x)
-        y = np.asarray(y)
-        self.n = len(x)
-
-        w.transform = "b"
-        self.tau_ln, self.tau_ln_weights = self._calc(x, y, w)
-
-        concor_sign = np.ones(self.n)
-        concor_sign[self.tau_ln < 0] = -1
-        self.sign = concor_sign.astype(int)
-
-        if permutations > 0:
-            tau_ln_sim = np.zeros((self.n, permutations))
-            tau_ln_pvalues = np.zeros(self.n)
-            for i in range(self.n):
-                obs_i = self.tau_ln[i]  # observed value i LIMA statistic
-                yr = np.zeros_like(y)
-                xr = np.zeros_like(y)
-                rids = np.arange(self.n)
-                rids = np.delete(rids, i)
-                for j in range(permutations):
-                    pids = np.random.permutation(rids)
-                    xr[i] = x[i]
-                    xr[rids] = x[pids]
-                    yr[i] = y[i]
-                    yr[rids] = y[pids]
-                    tau_ln_sim[i, j] = self._calc(xr, yr, w, i)
-                larger = (tau_ln_sim[i] >= obs_i).sum()
-                smaller = (tau_ln_sim[i] <= obs_i).sum()
-                tau_ln_pvalues[i] = (np.min([larger, smaller]) + 1.0) / (
-                    1 + permutations
-                )
-            self.tau_ln_sim = tau_ln_sim
-            self.tau_ln_pvalues = tau_ln_pvalues
    
-
-    def _calc_r(self, xi, yi, xj, yj, w):
-        dx = xi - xj
-        dy = yi - yj
-        dxdy = dx * dy
-        if dxdy != 0:
-            if dxdy > 0.0:
-                return 1
-            else:
-                return -1
-        else:
-            return 0
-
-    def _calc(self, x, y, w, i=None):
-        if i is not None:
-            iS_local = 0
-            for j in w.neighbors[i]:
-                iS_local += self._calc_r(x[i], y[i], x[j], y[j], w)
-            tau_ln = iS_local * 1.0 / w.cardinalities[i]
-            return tau_ln
-        else:
-            tau_ln = np.zeros(self.n)
-            tau_ln_weights = np.zeros(self.n)
-            for i in w.id_order:
-                iS_local = 0
-                for j in w.neighbors[i]:
-                    iS_local += self._calc_r(x[i], y[i], x[j], y[j], w)
-                tau_ln[i] = iS_local * 1.0 / w.cardinalities[i]
-                tau_ln_weights[i] = w.cardinalities[i] * 1.0 / w.s0
-            return tau_ln, tau_ln_weights
    
-
-
-
    [docs]class Tau_Local_Neighborhood:
-    """
-    Neighborhood set LIMA.
-
-    An extension of neighbor set LIMA. Consider local concordance
-    relationships for a subset of states, defined as the focal state
-    and its neighbors.
-
-    Parameters
-    ----------
-    x                  : array
-                         (n, ), first variable.
-    y                  : array
-                         (n, ), second variable.
-    w                  : W
-                         spatial weights object.
-    permutations       : int
-                         number of random spatial permutations for
-                         computationally based inference.
-
-    Attributes
-    ----------
-    n                  : int
-                         number of observations.
-    tau_local          : array
-                         (n, ), local concordance (local version of the
-                         classic tau).
-    S                  : array
-                         (n ,n), concordance matrix, s_{i,j}=1 if
-                         observation i and j are concordant, s_{i,
-                         j}=-1 if observation i and j are discordant,
-                         and s_{i,j}=0 otherwise.
-    tau_lnhood         : array
-                         (n, ), observed neighborhood set LIMA values.
-    tau_lnhood_sim     : array
-                         (n, permutations), neighborhood set LIMA
-                         values for permuted samples (if
-                         permutations>0).
-    tau_lnhood_pvalues : array
-                         (n, 1), one-sided pseudo p-values for observed
-                         neighborhood set LIMA values under the null
-                         that the concordance relationships for a
-                         subset of states, defined as the focal state
-                         and its neighbors, is different from what
-                         would be expected from randomly distributed
-                         rank changes.
-    sign              :  array
-                         (n, ), values indicate concordant or
-                         disconcordant: 1 concordant, -1 disconcordant
-
-    Notes
-    -----
-    The equation for calculating neighborhood set LIMA statistic can
-    be found in :cite:`Rey2016` Equation (22).
-
-    Examples
-    --------
-    >>> import libpysal as ps
-    >>> from giddy.rank import Tau_Local_Neighborhood
-    >>> import numpy as np
-    >>> np.random.seed(10)
-    >>> f = ps.io.open(ps.examples.get_path("mexico.csv"))
-    >>> vnames = ["pcgdp%d"%dec for dec in range(1940, 2010, 10)]
-    >>> y = np.transpose(np.array([f.by_col[v] for v in vnames]))
-    >>> r = y / y.mean(axis=0)
-    >>> regime = np.array(f.by_col['esquivel99'])
-    >>> w = ps.weights.block_weights(regime)
-    >>> res = Tau_Local_Neighborhood(r[:,0],r[:,1],w,permutations=999)
-    >>> res.tau_lnhood
-    array([0.06666667, 0.6       , 0.2       , 0.8       , 0.33333333,
-           0.6       , 0.6       , 0.2       , 1.        , 0.06666667,
-           0.06666667, 0.33333333, 0.33333333, 0.2       , 1.        ,
-           0.33333333, 0.33333333, 0.2       , 0.6       , 0.33333333,
-           0.33333333, 0.06666667, 0.8       , 0.06666667, 0.2       ,
-           0.6       , 0.8       , 0.6       , 0.33333333, 0.8       ,
-           0.8       , 0.06666667])
-    >>> res.tau_lnhood_pvalues
-    array([0.106, 0.33 , 0.107, 0.535, 0.137, 0.414, 0.432, 0.169, 1.   ,
-           0.03 , 0.019, 0.146, 0.249, 0.1  , 0.908, 0.225, 0.311, 0.125,
-           0.399, 0.215, 0.334, 0.115, 0.669, 0.045, 0.11 , 0.525, 0.655,
-           0.466, 0.236, 0.413, 0.504, 0.038])
-    >>> res.sign
-    array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-           1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
-
-    """
-
-
    [docs]    def __init__(self, x, y, w, permutations=0):
-
-        x = np.asarray(x)
-        y = np.asarray(y)
-        res = Tau_Local(x, y)
-        self.n = res.n
-        self.S = res.S
-        self.tau_local = res.tau_local
-
-        w.transform = "b"
-        tau_lnhood = np.zeros(self.n)
-        for i in range(self.n):
-            neighbors_i = [i]
-            neighbors_i.extend(w.neighbors[i])
-            n_i = len(neighbors_i)
-            sh_i = self.S[neighbors_i, :][:, neighbors_i]
-            # Neighborhood set LIMA
-            tau_lnhood[i] = sh_i.sum() * 1.0 / (n_i * (n_i - 1))
-        self.tau_lnhood = tau_lnhood
-
-        concor_sign = np.ones(self.n)
-        concor_sign[self.tau_lnhood < 0] = -1
-        self.sign = concor_sign.astype(int)
-
-        if permutations > 0:
-            tau_lnhood_sim = np.zeros((self.n, permutations))
-            tau_lnhood_pvalues = np.zeros(self.n)
-            for i in range(self.n):
-                obs_i = self.tau_lnhood[i]
-                rids = list(range(self.n))
-                rids.remove(i)
-                larger = 0
-                for j in range(permutations):
-                    np.random.shuffle(rids)
-                    neighbors_i = [i]
-                    neighbors_i.extend(rids[: len(w.neighbors[i])])
-                    n_i = len(neighbors_i)
-                    neighbors_i_second = neighbors_i
-                    sh_i = self.S[neighbors_i, :][:, neighbors_i_second]
-                    tau_lnhood_sim[i, j] = sh_i.sum() * 1.0 / (n_i * (n_i - 1))
-
-                larger = (tau_lnhood_sim[i] >= obs_i).sum()
-                smaller = (tau_lnhood_sim[i] <= obs_i).sum()
-                tau_lnhood_pvalues[i] = (np.min([larger, smaller]) + 1.0) / (
-                    1 + permutations
-                )
-
-            self.tau_lnhood_sim = tau_lnhood_sim
-            self.tau_lnhood_pvalues = tau_lnhood_pvalues
    
-
-
-
    [docs]class Tau_Regional:
-    """
-    Inter and intraregional decomposition of the classic Tau.
-
-    Parameters
-    ----------
-    x               : array
-                      (n, ), first variable.
-    y               : array
-                      (n, ), second variable.
-    regimes         : array
-                      (n, ), ids of which regime an observation belongs to.
-    permutations    : int
-                      number of random spatial permutations for
-                      computationally based inference.
-
-    Attributes
-    ----------
-    n               : int
-                      number of observations.
-    S               : array
-                      (n ,n), concordance matrix, s_{i,j}=1 if
-                      observation i and j are concordant, s_{i,
-                      j}=-1 if observation i and j are discordant,
-                      and s_{i,j}=0 otherwise.
-    tau_reg         : array
-                      (k, k), observed concordance matrix with
-                      diagonal elements measuring concordance
-                      between units within a regime and the
-                      off-diagonal elements denoting concordance
-                      between observations from a specific
-                      pair of different regimes.
-    tau_reg_sim     : array
-                      (permutations, k, k), concordance matrices for
-                      permuted samples (if permutations>0).
-    tau_reg_pvalues : array
-                      (k, k), one-sided pseudo p-values for observed
-                      concordance matrix under the null that income
-                      mobility were random in its spatial distribution.
-
-    Notes
-    -----
-    The equation for calculating inter and intraregional Tau
-    statistic can be found in :cite:`Rey2016` Equation (27).
-
-    Examples
-    --------
-    >>> import libpysal as ps
-    >>> import numpy as np
-    >>> from giddy.rank import Tau_Regional
-    >>> np.random.seed(10)
-    >>> f = ps.io.open(ps.examples.get_path("mexico.csv"))
-    >>> vnames = ["pcgdp%d"%dec for dec in range(1940, 2010, 10)]
-    >>> y = np.transpose(np.array([f.by_col[v] for v in vnames]))
-    >>> r = y / y.mean(axis=0)
-    >>> regime = np.array(f.by_col['esquivel99'])
-    >>> res = Tau_Regional(y[:,0],y[:,-1],regime,permutations=999)
-    >>> res.tau_reg
-    array([[1.        , 0.25      , 0.5       , 0.6       , 0.83333333,
-            0.6       , 1.        ],
-           [0.25      , 0.33333333, 0.5       , 0.3       , 0.91666667,
-            0.4       , 0.75      ],
-           [0.5       , 0.5       , 0.6       , 0.4       , 0.38888889,
-            0.53333333, 0.83333333],
-           [0.6       , 0.3       , 0.4       , 0.2       , 0.4       ,
-            0.28      , 0.8       ],
-           [0.83333333, 0.91666667, 0.38888889, 0.4       , 0.6       ,
-            0.73333333, 1.        ],
-           [0.6       , 0.4       , 0.53333333, 0.28      , 0.73333333,
-            0.8       , 0.8       ],
-           [1.        , 0.75      , 0.83333333, 0.8       , 1.        ,
-            0.8       , 0.33333333]])
-    >>> res.tau_reg_pvalues
-    array([[0.782, 0.227, 0.464, 0.638, 0.294, 0.627, 0.201],
-           [0.227, 0.352, 0.391, 0.14 , 0.048, 0.252, 0.327],
-           [0.464, 0.391, 0.587, 0.198, 0.107, 0.423, 0.124],
-           [0.638, 0.14 , 0.198, 0.141, 0.184, 0.089, 0.217],
-           [0.294, 0.048, 0.107, 0.184, 0.583, 0.25 , 0.005],
-           [0.627, 0.252, 0.423, 0.089, 0.25 , 0.38 , 0.227],
-           [0.201, 0.327, 0.124, 0.217, 0.005, 0.227, 0.322]])
-
-    """
-
-
    [docs]    def __init__(self, x, y, regime, permutations=0):
-
-        x = np.asarray(x)
-        y = np.asarray(y)
-        res = Tau_Local(x, y)
-        self.n = res.n
-        self.S = res.S
-
-        reg = np.array(regime).flatten()
-        ur = np.unique(reg).tolist()
-        k = len(ur)
-        P = np.zeros((k, self.n))
-        for i, r in enumerate(reg):
-            P[ur.index(r), i] = 1  # construct P matrix
-
-        w = weights.block_weights(regime)
-        w.transform = "b"
-        W = w.full()[0]
-        WH = np.ones((self.n, self.n)) - np.eye(self.n) - W
-
-        # inter and intraregional decomposition of Tau for the observed value
-
-        self.tau_reg = self._calc(W, WH, P, self.S)
-
-        if permutations > 0:
-            tau_reg_sim = np.zeros((permutations, k, k))
-            larger = np.zeros((k, k))
-            smaller = np.zeros((k, k))
-            rids = np.arange(len(x))
-            for i in range(permutations):
-                np.random.shuffle(rids)
-                res = Tau_Local(x[rids], y[rids])
-                tau_reg_sim[i] = self._calc(W, WH, P, res.S)
-                larger += np.greater_equal(tau_reg_sim[i], self.tau_reg)
-                smaller += np.less_equal(tau_reg_sim[i], self.tau_reg)
-
-            m = np.less(smaller, larger)
-            pvalues = (1 + m * smaller + (1 - m) * larger) / (1.0 + permutations)
-            self.tau_reg_sim = tau_reg_sim
-            self.tau_reg_pvalues = pvalues
    
-
-    def _calc(self, W, WH, P, S):
-
-        nomi = np.dot(P, np.dot(S, P.T))
-        denomi = np.dot(P, np.dot(W, P.T)) + np.dot(P, np.dot(WH, P.T))
-        T = nomi / denomi
-
-        return T
    
-
    - -
    - -
    -
    -
    -
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    - Created using Sphinx 5.0.2.
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    - - \ No newline at end of file diff --git a/docs/_modules/giddy/sequence.html b/docs/_modules/giddy/sequence.html deleted file mode 100644 index 0d3ff8f..0000000 --- a/docs/_modules/giddy/sequence.html +++ /dev/null @@ -1,487 +0,0 @@ - - - - - - - giddy.sequence — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    -
    - -

    Source code for giddy.sequence

    -"""
-Sequence analysis methods.
-"""
-
-__author__ = "Wei Kang <weikang9009@gmail.com>, Sergio J. Rey <sjsrey@gmail.com>"
-
-__all__ = ["Sequence"]
-
-import itertools
-import numpy as np
-import scipy.spatial.distance as d
-from .markov import Markov
-
-
-
    [docs]class Sequence(object):
-    """
-    Pairwise sequence analysis.
-
-    Dynamic programming if optimal matching.
-
-    Parameters
-    ----------
-    y               : array
-                      one row per sequence of neighborhood types for each
-                      spatial unit. Sequences could be of varying lengths.
-    subs_mat        : array
-                      (k,k), substitution cost matrix. Should be hollow (
-                      0 cost between the same type), symmetric and non-negative.
-    dist_type       : string
-                      "hamming": hamming distance (substitution only
-                      and its cost is constant 1) from sklearn.metrics;
-                      "markov": utilize empirical transition
-                      probabilities to define substitution costs;
-                      "interval": differences between states are used
-                      to define substitution costs, and indel=k-1;
-                      "arbitrary": arbitrary distance if there is not a
-                      strong theory guidance: substitution=0.5, indel=1.
-                      "tran": transition-oriented optimal matching. Sequence of
-                      transitions. Based on :cite:`Biemann:2011`.
-    indel           : float
-                      insertion/deletion cost.
-    cluster_type    : string
-                      cluster algorithm (specification) used to generate
-                      neighborhood types, such as "ward", "kmeans", etc.
-
-    Attributes
-    ----------
-    seq_dis_mat     : array
-                      (n,n), distance/dissimilarity matrix for each pair of
-                      sequences
-    classes         : array
-                      (k, ), unique classes
-    k               : int
-                      number of unique classes
-    label_dict      : dict
-                      dictionary - {input label: int value between 0 and k-1 (k
-                      is the number of unique classes for the pooled data)}
-
-    Examples
-    --------
-    >>> import numpy as np
-
-    1. Testing on unequal string sequences
-    1.1 substitution cost matrix and indel cost are not given, and will be
-    generated based on the distance type "interval"
-
-    >>> seq1 = 'ACGGTAG'
-    >>> seq2 = 'CCTAAG'
-    >>> seq3 = 'CCTAAGC'
-    >>> seqAna = Sequence([seq1,seq2,seq3],dist_type="interval")
-    >>> seqAna.k
-    4
-    >>> seqAna.classes
-    array(['A', 'C', 'G', 'T'], dtype='<U1')
-    >>> seqAna.subs_mat
-    array([[0., 1., 2., 3.],
-           [1., 0., 1., 2.],
-           [2., 1., 0., 1.],
-           [3., 2., 1., 0.]])
-    >>> seqAna.seq_dis_mat
-    array([[ 0.,  7., 10.],
-           [ 7.,  0.,  3.],
-           [10.,  3.,  0.]])
-
-    1.2 User-defined substitution cost matrix and indel cost
-
-    >>> subs_mat = np.array([[0, 0.76, 0.29, 0.05],[0.30, 0, 0.40, 0.60],[0.16, 0.61, 0, 0.26],[0.38, 0.20, 0.12, 0]])
-    >>> indel = subs_mat.max()
-    >>> seqAna = Sequence([seq1,seq2,seq3], subs_mat=subs_mat, indel=indel)
-    >>> seqAna.seq_dis_mat
-    array([[0.  , 1.94, 2.46],
-           [1.94, 0.  , 0.76],
-           [2.46, 0.76, 0.  ]])
-
-    1.3 Calculating "hamming" distance will fail on unequal sequences
-
-    >>> seqAna = Sequence([seq1,seq2,seq3], dist_type="hamming")
-    Traceback (most recent call last):
-    ValueError: hamming distance cannot be calculated for sequences of unequal lengths!
-
-
-    2. Testing on equal string sequences
-
-    >>> seq1 = 'ACGGTAG'
-    >>> seq2 = 'CCTAAGA'
-    >>> seq3 = 'CCTAAGC'
-
-    2.1  Calculating "hamming" distance
-
-    >>> seqAna = Sequence([seq1,seq2,seq3], dist_type="hamming")
-    >>> seqAna.seq_dis_mat
-    array([[0., 6., 6.],
-           [6., 0., 1.],
-           [6., 1., 0.]])
-
-    2.2 User-defined substitution cost matrix and indel cost (distance
-    between different types is always 1 and indel cost is 2) - give the same
-    sequence distance matrix as "hamming" distance
-
-    >>> subs_mat = np.array([[0., 1., 1., 1.],[1., 0., 1., 1.],[1., 1., 0., 1.],[1., 1., 1., 0.]])
-    >>> indel = 2
-    >>> seqAna = Sequence([seq1,seq2,seq3], subs_mat=subs_mat, indel=indel)
-    >>> seqAna.seq_dis_mat
-    array([[0., 6., 6.],
-           [6., 0., 1.],
-           [6., 1., 0.]])
-
-    2.3 User-defined substitution cost matrix and indel cost (distance
-    between different types is always 1 and indel cost is 1) - give a
-    slightly different sequence distance matrix from "hamming" distance since
-    insertion and deletion is happening
-
-    >>> subs_mat = np.array([[0., 1., 1., 1.],[1., 0., 1., 1.],[1., 1., 0.,1.],[1., 1., 1., 0.]])
-    >>> indel = 1
-    >>> seqAna = Sequence([seq1,seq2,seq3], subs_mat=subs_mat, indel=indel)
-    >>> seqAna.seq_dis_mat
-    array([[0., 5., 5.],
-           [5., 0., 1.],
-           [5., 1., 0.]])
-
-    3. Not passing proper parameters will raise an error
-
-    >>> seqAna = Sequence([seq1,seq2,seq3])
-    Traceback (most recent call last):
-    ValueError: Please specify a proper `dist_type` or `subs_mat` and `indel` to proceed!
-
-    >>> seqAna = Sequence([seq1,seq2,seq3], subs_mat=subs_mat)
-    Traceback (most recent call last):
-    ValueError: Please specify a proper `dist_type` or `subs_mat` and `indel` to proceed!
-
-    >>> seqAna = Sequence([seq1,seq2,seq3], indel=indel)
-    Traceback (most recent call last):
-    ValueError: Please specify a proper `dist_type` or `subs_mat` and `indel` to proceed!
-    """
-
-
    [docs]    def __init__(self, y, subs_mat=None, dist_type=None, indel=None, cluster_type=None):
-
-        y = np.asarray(y)
-        merged = list(itertools.chain.from_iterable(y))
-        self.classes = np.unique(merged)
-        self.k = len(self.classes)
-        self.n = len(y)
-        self.indel = indel
-        self.subs_mat = subs_mat
-        self.cluster_type = cluster_type
-        self.label_dict = dict(zip(self.classes, range(self.k)))
-
-        y_int = []
-        for yi in y:
-            y_int.append(list(map(self.label_dict.get, yi)))
-        y_int = np.array(y_int)
-
-        if subs_mat is None or indel is None:
-            if dist_type is None:
-                raise ValueError(
-                    "Please specify a proper `dist_type` or "
-                    "`subs_mat` and `indel` to proceed!"
-                )
-            else:
-                if dist_type.lower() == "interval":
-                    self.indel = self.k - 1
-                    self.subs_mat = np.zeros((self.k, self.k))
-                    for i in range(0, self.k - 1):
-                        for j in range(i + 1, self.k):
-                            self.subs_mat[i, j] = j - i
-                            self.subs_mat[j, i] = j - i
-                    self._om_dist(y_int)
-
-                elif dist_type.lower() == "hamming":
-                    if len(y_int.shape) != 2:
-                        raise ValueError(
-                            "hamming distance cannot be calculated for "
-                            "sequences of unequal lengths!"
-                        )
-                    hamming_dist = d.pdist(y_int, metric="hamming") * y_int.shape[1]
-                    self.seq_dis_mat = d.squareform(hamming_dist)
-
-                elif dist_type.lower() == "arbitrary":
-                    self.indel = 1
-                    mat = np.ones((self.k, self.k)) * 0.5
-                    np.fill_diagonal(mat, 0)
-                    self.subs_mat = mat
-                    self._om_dist(y_int)
-
-                elif dist_type.lower() == "markov":
-                    p = Markov(y_int).p
-                    self.indel = 1
-                    mat = (2 - (p + p.T)) / 2
-                    np.fill_diagonal(mat, 0)
-                    self.subs_mat = mat
-                    self._om_dist(y_int)
-
-                elif dist_type.lower() == "tran":  # sequence of transitions
-                    self.indel = 2
-                    y_uni = np.unique(y_int)
-                    dict_trans_state = {}
-                    trans_list = []
-                    for i, tran in enumerate(itertools.product([-1], y_uni)):
-                        trans_list.append(tran)
-                        dict_trans_state[tran] = i
-                    for i, tran in enumerate(itertools.product(y_uni, y_uni)):
-                        trans_list.append(tran)
-                        dict_trans_state[tran] = i + len(y_uni)
-                    subs_mat = np.ones((self.k * (self.k + 1), self.k * (self.k + 1)))
-                    np.fill_diagonal(subs_mat, 0)
-                    for row in range(self.k**2):
-                        row_index = row + self.k
-                        row_tran = trans_list[row_index]
-                        for col in range(self.k**2):
-                            col_index = col + self.k
-                            col_tran = trans_list[col_index]
-                            subs_mat[row_index, col_index] = abs(
-                                int(row_tran[0] == row_tran[1])
-                                - int(col_tran[0] == col_tran[1])
-                            )
-
-                    self.dict_trans_state = dict_trans_state
-                    self.subs_mat = subs_mat
-
-                    # Transform sequences of states into sequences of transitions.
-                    y_int_ext = np.insert(y_int, 0, -1, axis=1)
-                    y_tran_index = np.zeros_like(y_int)
-                    y_tran = []
-                    for i in range(y_int.shape[1]):
-                        y_tran.append(list(zip(y_int_ext[:, i], y_int_ext[:, i + 1])))
-                    for i in range(y_int.shape[0]):
-                        for j in range(y_int.shape[1]):
-                            y_tran_index[i, j] = dict_trans_state[y_tran[j][i]]
-                    self._om_dist(y_tran_index)
-
-        else:
-            self._om_dist(y_int)
    
-
-    def _om_pair_dist(self, seq1, seq2):
-        """
-        Method for calculating the optimal matching distance between a pair of
-        sequences given a substitution cost matrix and an indel cost.
-
-        Arguments
-        ---------
-        seq1            : array
-                          (t1, ), the first sequence
-        seq2            : array
-                          (t2, ), the second sequence
-
-        Returns
-        -------
-        D               : array
-                          (t2+1, t1+1), score matrix: D[i+1,j+1] is the best
-                          score for aligning the substring, seq1[0:j] and seq2[0:i],
-                          and D[t2+1, t1+1] (or D[-1,-1]) is the global optimal score.
-
-        """
-
-        t1 = len(seq1)
-        t2 = len(seq2)
-
-        D = np.zeros((t2 + 1, t1 + 1))
-        for j in range(1, t1 + 1):
-            D[0, j] = self.indel * j
-        for i in range(1, t2 + 1):
-            D[i, 0] = self.indel * i
-
-        for i in range(1, t2 + 1):
-            for j in range(1, t1 + 1):
-                gaps = D[i, j - 1] + self.indel
-                gapt = D[i - 1, j] + self.indel
-                match = D[i - 1, j - 1] + self.subs_mat[seq1[j - 1], seq2[i - 1]]
-                D[i, j] = min(match, gaps, gapt)
-        return D
-
-    def _om_dist(self, y_int):
-        """
-        Method for calculating optimal matching distances between all
-        sequence pairs.
-
-        Arguments
-        ---------
-        y_int           : array
-                          Encoded longitudinal data ready for optimal matching.
-
-        Note
-        ----
-        This method is optimized to calculate the distance between unique
-        sequences only in order to save computation time.
-
-        """
-        y_str = []
-        for i in y_int:
-            y_str.append("".join(list(map(str, i))))
-
-        moves_str, unique_indices = np.unique(y_str, axis=0, return_index=True)
-        moves_int = y_int[unique_indices]
-        uni_num = len(moves_str)
-        dict_move_index = dict(zip(map(tuple, moves_int), range(uni_num)))
-
-        # dict_move_index = dict(zip(map(tuple, moves_str), range(uni_num)))
-
-        # y_uni = moves_int
-        uni_seq_dis_mat = np.zeros((uni_num, uni_num))
-        for pair in itertools.combinations(range(uni_num), 2):
-            seq1 = moves_int[pair[0]]
-            seq2 = moves_int[pair[1]]
-            uni_seq_dis_mat[pair[0], pair[1]] = self._om_pair_dist(seq1, seq2)[-1, -1]
-        uni_seq_dis_mat = uni_seq_dis_mat + uni_seq_dis_mat.transpose()
-
-        seq_dis_mat = np.zeros((self.n, self.n))
-        for pair in itertools.combinations(range(self.n), 2):
-            seq1 = y_int[pair[0]]
-            seq2 = y_int[pair[1]]
-            seq_dis_mat[pair[0], pair[1]] = uni_seq_dis_mat[
-                dict_move_index[tuple(seq1)], dict_move_index[tuple(seq2)]
-            ]
-
-        self.seq_dis_mat = seq_dis_mat + seq_dis_mat.transpose()
    
-
    - -
    - -
    -
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    -

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    - © Copyright 2018-, pysal developers.
    - Created using Sphinx 5.0.2.
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    - - \ No newline at end of file diff --git a/docs/_modules/giddy/util.html b/docs/_modules/giddy/util.html deleted file mode 100644 index 48bc75f..0000000 --- a/docs/_modules/giddy/util.html +++ /dev/null @@ -1,337 +0,0 @@ - - - - - - - giddy.util — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    -
    - -

    Source code for giddy.util

    -"""
-Utilities for the spatial dynamics module.
-"""
-
-__all__ = ["shuffle_matrix", "get_lower", "fill_empty_diagonals"]
-
-import numpy as np
-import copy
-
-
-
    [docs]def shuffle_matrix(X, ids):
-    """
-    Random permutation of rows and columns of a matrix
-
-    Parameters
-    ----------
-    X   : array
-          (k, k), array to be permutated.
-    ids : array
-          range (k, ).
-
-    Returns
-    -------
-    X   : array
-          (k, k) with rows and columns randomly shuffled.
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from giddy.util import shuffle_matrix
-    >>> X=np.arange(16)
-    >>> X.shape=(4,4)
-    >>> np.random.seed(10)
-    >>> shuffle_matrix(X,list(range(4)))
-    array([[10,  8, 11,  9],
-           [ 2,  0,  3,  1],
-           [14, 12, 15, 13],
-           [ 6,  4,  7,  5]])
-
-    """
-    np.random.shuffle(ids)
-    return X[ids, :][:, ids]
    
-
-
-
    [docs]def get_lower(matrix):
-    """
-    Flattens the lower part of an n x n matrix into an n*(n-1)/2 x 1 vector.
-
-    Parameters
-    ----------
-    matrix  : array
-              (n, n) numpy array, a distance matrix.
-
-    Returns
-    -------
-    lowvec  : array
-              numpy array, the lower half of the distance matrix flattened into
-              a vector of length n*(n-1)/2.
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from giddy.util import get_lower
-    >>> test = np.array([[0,1,2,3],[1,0,1,2],[2,1,0,1],[4,2,1,0]])
-    >>> lower = get_lower(test)
-    >>> lower
-    array([[1],
-           [2],
-           [1],
-           [4],
-           [2],
-           [1]])
-
-    """
-    n = matrix.shape[0]
-    lowvec = matrix[np.tril_indices(n, k=-1)].reshape(-1, 1)
-    return lowvec
    
-
-
-
    [docs]def fill_empty_diagonals(p):
-    """
-    Assign 1 to diagonal elements which fall in rows full of 0s to ensure
-    the transition probability matrix is a stochastic one. Currently
-    implemented for two- and three-dimensional transition probability
-    matrices.
-
-    Parameters
-    ----------
-    p        : array
-               (k, k), an ergodic/non-ergodic Markov transition probability
-               matrix.
-
-    Returns
-    -------
-    p_temp   : array
-               Matrix without rows full of 0 transition probabilities.
-               (stochastic matrix)
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from giddy.util import fill_empty_diagonals
-    >>> p2 = np.array([[.5, .5, 0], [.3, .7, 0], [0, 0, 0]])
-    >>> fill_empty_diagonals(p2)
-    array([[0.5, 0.5, 0. ],
-           [0.3, 0.7, 0. ],
-           [0. , 0. , 1. ]])
-
-    >>> p3 = np.array([[[0.5, 0.5, 0. ], [0.3, 0.7, 0. ], [0. , 0. , 0. ]],
-    ...  [[0. , 0. , 0. ], [0.3, 0.7, 0. ], [0. , 0. , 0. ]]])
-    >>> p_new = fill_empty_diagonals(p3)
-    >>> p_new[1]
-    array([[1. , 0. , 0. ],
-           [0.3, 0.7, 0. ],
-           [0. , 0. , 1. ]])
-    """
-
-    p_temp = np.asarray(p)
-    if len(p_temp.shape) == 3:
-        return _fill_empty_diagonal_3d(p_temp)
-    elif len(p_temp.shape) == 2:
-        return _fill_empty_diagonal_2d(p_temp)
-    else:
-        raise NotImplementedError(
-            "Filling empty diagonals is " "only implemented for 2/3d matrices."
-        )
    
-
-
-def _fill_empty_diagonal_2d(p):
-    """
-    Assign 1 to diagonal elements which fall in rows full of 0s to ensure
-    the transition probability matrix is a stochastic one.
-
-    Parameters
-    ----------
-    p        : array
-               (k, k), an ergodic/non-ergodic Markov transition probability
-               matrix.
-
-    Returns
-    -------
-    p_temp   : array
-               Matrix without rows full of 0 transition probabilities.
-               (stochastic matrix)
-
-    """
-
-    p_temp = copy.copy(p)
-    p0 = p_temp.sum(axis=1) == 0
-    if p0.sum() > 0:
-        row_zero_i = np.where(p0)
-        for row in row_zero_i:
-            p_temp[row, row] = 1
-    return p_temp
-
-
-def _fill_empty_diagonal_3d(p):
-    """
-    Assign 1 to diagonal elements which fall in rows full of 0s to ensure
-    the conditional transition probability matrices is are stochastic matrices.
-    Staying probabilities are 1.
-
-    Parameters
-    ----------
-    p        : array
-               (m, k, k), m ergodic/non-ergodic Markov transition probability
-               matrices.
-
-    Returns
-    -------
-    p_temp   : array
-               Matrices without rows full of 0 transition probabilities.
-               (stochastic matrices)
-
-    """
-
-    p_temp = copy.copy(p)
-    p0 = p_temp.sum(axis=2) == 0
-    if p0.sum() > 0:
-        rows, cols = np.where(p0)
-        row_zero_i = list(zip(rows, cols))
-        for row in row_zero_i:
-            i, j = row
-            p_temp[i, j, j] = 1
-    return p_temp
-
    - -
    - -
    -
    -
    -
    -

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    - - -

    -

    - © Copyright 2018-, pysal developers.
    - Created using Sphinx 5.0.2.
    -

    -
    -
    - - \ No newline at end of file diff --git a/docs/_modules/index.html b/docs/_modules/index.html deleted file mode 100644 index dd90598..0000000 --- a/docs/_modules/index.html +++ /dev/null @@ -1,159 +0,0 @@ - - - - - - - Overview: module code — giddy v2.3.4 Manual - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    -
    - -

    All modules for which code is available

    - - -
    - -
    -
    -
    -
    -

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    - - -

    -

    - © Copyright 2018-, pysal developers.
    - Created using Sphinx 5.0.2.
    -

    -
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    - - \ No newline at end of file diff --git a/docs/_sources/DirectionalLISA.nblink.txt b/docs/_sources/DirectionalLISA.nblink.txt deleted file mode 100644 index 1635fb2..0000000 --- a/docs/_sources/DirectionalLISA.nblink.txt +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../notebooks/DirectionalLISA.ipynb" -} \ No newline at end of file diff --git a/docs/_sources/MarkovBasedMethods.nblink.txt b/docs/_sources/MarkovBasedMethods.nblink.txt deleted file mode 100644 index 372bd7a..0000000 --- a/docs/_sources/MarkovBasedMethods.nblink.txt +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../notebooks/MarkovBasedMethods.ipynb" -} \ No newline at end of file diff --git a/docs/_sources/MobilityMeasures.nblink.txt b/docs/_sources/MobilityMeasures.nblink.txt deleted file mode 100644 index a48705a..0000000 --- a/docs/_sources/MobilityMeasures.nblink.txt +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../notebooks/MobilityMeasures.ipynb" -} \ No newline at end of file diff --git a/docs/_sources/RankBasedMethods.nblink.txt b/docs/_sources/RankBasedMethods.nblink.txt deleted file mode 100644 index 2843be1..0000000 --- a/docs/_sources/RankBasedMethods.nblink.txt +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../notebooks/RankBasedMethods.ipynb" -} \ No newline at end of file diff --git a/docs/_sources/RankMarkov.nblink.txt b/docs/_sources/RankMarkov.nblink.txt deleted file mode 100644 index 7a27421..0000000 --- a/docs/_sources/RankMarkov.nblink.txt +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../notebooks/RankMarkov.ipynb" -} \ No newline at end of file diff --git a/docs/_sources/Sequence.nblink.txt b/docs/_sources/Sequence.nblink.txt deleted file mode 100644 index 1f2005e..0000000 --- a/docs/_sources/Sequence.nblink.txt +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../notebooks/Sequence.ipynb" -} \ No newline at end of file diff --git a/docs/_sources/api.rst.txt b/docs/_sources/api.rst.txt deleted file mode 100644 index 1bd2f8a..0000000 --- a/docs/_sources/api.rst.txt +++ /dev/null @@ -1,85 +0,0 @@ -.. _api_ref: - -.. currentmodule:: giddy - -API reference -============= - -.. _markov_api: - -Markov Methods --------------- - -.. autosummary:: - :toctree: generated/ - - giddy.markov.Markov - giddy.markov.Spatial_Markov - giddy.markov.LISA_Markov - giddy.markov.FullRank_Markov - giddy.markov.GeoRank_Markov - giddy.markov.kullback - giddy.markov.prais - giddy.markov.homogeneity - giddy.markov.sojourn_time - giddy.ergodic.steady_state - giddy.ergodic.fmpt - giddy.ergodic.var_fmpt_ergodic - - -.. _directional_api: - -Directional LISA ----------------- - -.. autosummary:: - :toctree: generated/ - - giddy.directional.Rose - - -.. _mobility_api: - -Economic Mobility Indices -------------------------- -.. autosummary:: - :toctree: generated/ - - giddy.mobility.markov_mobility - -.. _rank_api: - -Exchange Mobility Methods -------------------------- -.. autosummary:: - :toctree: generated/ - - giddy.rank.Theta - giddy.rank.Tau - giddy.rank.SpatialTau - giddy.rank.Tau_Local - giddy.rank.Tau_Local_Neighbor - giddy.rank.Tau_Local_Neighborhood - giddy.rank.Tau_Regional - -.. _sequence_api: - -Alignment-based Sequence Methods --------------------------------- - -.. autosummary:: - :toctree: generated/ - - giddy.sequence.Sequence - -.. _utility_api: - -Utility Functions ------------------ - -.. autosummary:: - :toctree: generated/ - - giddy.util.shuffle_matrix - giddy.util.get_lower - giddy.util.fill_empty_diagonals diff --git a/docs/_sources/generated/giddy.directional.Rose.rst.txt b/docs/_sources/generated/giddy.directional.Rose.rst.txt deleted file mode 100644 index ce4a363..0000000 --- a/docs/_sources/generated/giddy.directional.Rose.rst.txt +++ /dev/null @@ -1,26 +0,0 @@ -giddy.directional.Rose -====================== - -.. currentmodule:: giddy.directional - -.. autoclass:: Rose - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Rose.__init__ - ~Rose.permute - ~Rose.plot - ~Rose.plot_origin - ~Rose.plot_vectors - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.ergodic.fmpt.rst.txt b/docs/_sources/generated/giddy.ergodic.fmpt.rst.txt deleted file mode 100644 index 84447cf..0000000 --- a/docs/_sources/generated/giddy.ergodic.fmpt.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.ergodic.fmpt -================== - -.. currentmodule:: giddy.ergodic - -.. autofunction:: fmpt \ No newline at end of file diff --git a/docs/_sources/generated/giddy.ergodic.mfpt.rst.txt b/docs/_sources/generated/giddy.ergodic.mfpt.rst.txt deleted file mode 100644 index 2395811..0000000 --- a/docs/_sources/generated/giddy.ergodic.mfpt.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.ergodic.mfpt -================== - -.. currentmodule:: giddy.ergodic - -.. autofunction:: mfpt \ No newline at end of file diff --git a/docs/_sources/generated/giddy.ergodic.steady_state.rst.txt b/docs/_sources/generated/giddy.ergodic.steady_state.rst.txt deleted file mode 100644 index 01a5d26..0000000 --- a/docs/_sources/generated/giddy.ergodic.steady_state.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.ergodic.steady\_state -=========================== - -.. currentmodule:: giddy.ergodic - -.. autofunction:: steady_state \ No newline at end of file diff --git a/docs/_sources/generated/giddy.ergodic.var_fmpt_ergodic.rst.txt b/docs/_sources/generated/giddy.ergodic.var_fmpt_ergodic.rst.txt deleted file mode 100644 index c7694b3..0000000 --- a/docs/_sources/generated/giddy.ergodic.var_fmpt_ergodic.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.ergodic.var\_fmpt\_ergodic -================================ - -.. currentmodule:: giddy.ergodic - -.. autofunction:: var_fmpt_ergodic \ No newline at end of file diff --git a/docs/_sources/generated/giddy.ergodic.var_mfpt_ergodic.rst.txt b/docs/_sources/generated/giddy.ergodic.var_mfpt_ergodic.rst.txt deleted file mode 100644 index 67c5966..0000000 --- a/docs/_sources/generated/giddy.ergodic.var_mfpt_ergodic.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.ergodic.var\_mfpt\_ergodic -================================ - -.. currentmodule:: giddy.ergodic - -.. autofunction:: var_mfpt_ergodic \ No newline at end of file diff --git a/docs/_sources/generated/giddy.markov.FullRank_Markov.rst.txt b/docs/_sources/generated/giddy.markov.FullRank_Markov.rst.txt deleted file mode 100644 index 2ba4ed0..0000000 --- a/docs/_sources/generated/giddy.markov.FullRank_Markov.rst.txt +++ /dev/null @@ -1,30 +0,0 @@ -giddy.markov.FullRank\_Markov -============================= - -.. currentmodule:: giddy.markov - -.. autoclass:: FullRank_Markov - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~FullRank_Markov.__init__ - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~FullRank_Markov.mfpt - ~FullRank_Markov.sojourn_time - ~FullRank_Markov.steady_state - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.markov.GeoRank_Markov.rst.txt b/docs/_sources/generated/giddy.markov.GeoRank_Markov.rst.txt deleted file mode 100644 index dadebca..0000000 --- a/docs/_sources/generated/giddy.markov.GeoRank_Markov.rst.txt +++ /dev/null @@ -1,30 +0,0 @@ -giddy.markov.GeoRank\_Markov -============================ - -.. currentmodule:: giddy.markov - -.. autoclass:: GeoRank_Markov - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~GeoRank_Markov.__init__ - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~GeoRank_Markov.mfpt - ~GeoRank_Markov.sojourn_time - ~GeoRank_Markov.steady_state - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.markov.LISA_Markov.rst.txt b/docs/_sources/generated/giddy.markov.LISA_Markov.rst.txt deleted file mode 100644 index e36c907..0000000 --- a/docs/_sources/generated/giddy.markov.LISA_Markov.rst.txt +++ /dev/null @@ -1,31 +0,0 @@ -giddy.markov.LISA\_Markov -========================= - -.. currentmodule:: giddy.markov - -.. autoclass:: LISA_Markov - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~LISA_Markov.__init__ - ~LISA_Markov.spillover - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~LISA_Markov.fmpt - ~LISA_Markov.sojourn_time - ~LISA_Markov.steady_state - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.markov.Markov.rst.txt b/docs/_sources/generated/giddy.markov.Markov.rst.txt deleted file mode 100644 index 7ba70c6..0000000 --- a/docs/_sources/generated/giddy.markov.Markov.rst.txt +++ /dev/null @@ -1,30 +0,0 @@ -giddy.markov.Markov -=================== - -.. currentmodule:: giddy.markov - -.. autoclass:: Markov - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Markov.__init__ - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~Markov.fmpt - ~Markov.sojourn_time - ~Markov.steady_state - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.markov.Spatial_Markov.rst.txt b/docs/_sources/generated/giddy.markov.Spatial_Markov.rst.txt deleted file mode 100644 index 930540d..0000000 --- a/docs/_sources/generated/giddy.markov.Spatial_Markov.rst.txt +++ /dev/null @@ -1,43 +0,0 @@ -giddy.markov.Spatial\_Markov -============================ - -.. currentmodule:: giddy.markov - -.. autoclass:: Spatial_Markov - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Spatial_Markov.__init__ - ~Spatial_Markov.summary - - - - - - .. rubric:: Attributes - - .. autosummary:: - - ~Spatial_Markov.F - ~Spatial_Markov.LR - ~Spatial_Markov.LR_p_value - ~Spatial_Markov.Q - ~Spatial_Markov.Q_p_value - ~Spatial_Markov.S - ~Spatial_Markov.chi2 - ~Spatial_Markov.dof_hom - ~Spatial_Markov.f - ~Spatial_Markov.ht - ~Spatial_Markov.s - ~Spatial_Markov.shtest - ~Spatial_Markov.x2 - ~Spatial_Markov.x2_dof - ~Spatial_Markov.x2_pvalue - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.markov.homogeneity.rst.txt b/docs/_sources/generated/giddy.markov.homogeneity.rst.txt deleted file mode 100644 index 6458e39..0000000 --- a/docs/_sources/generated/giddy.markov.homogeneity.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.markov.homogeneity -======================== - -.. currentmodule:: giddy.markov - -.. autofunction:: homogeneity \ No newline at end of file diff --git a/docs/_sources/generated/giddy.markov.kullback.rst.txt b/docs/_sources/generated/giddy.markov.kullback.rst.txt deleted file mode 100644 index 25fd4ae..0000000 --- a/docs/_sources/generated/giddy.markov.kullback.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.markov.kullback -===================== - -.. currentmodule:: giddy.markov - -.. autofunction:: kullback \ No newline at end of file diff --git a/docs/_sources/generated/giddy.markov.prais.rst.txt b/docs/_sources/generated/giddy.markov.prais.rst.txt deleted file mode 100644 index 5639f37..0000000 --- a/docs/_sources/generated/giddy.markov.prais.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.markov.prais -================== - -.. currentmodule:: giddy.markov - -.. autofunction:: prais \ No newline at end of file diff --git a/docs/_sources/generated/giddy.markov.sojourn_time.rst.txt b/docs/_sources/generated/giddy.markov.sojourn_time.rst.txt deleted file mode 100644 index c733e78..0000000 --- a/docs/_sources/generated/giddy.markov.sojourn_time.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.markov.sojourn\_time -========================== - -.. currentmodule:: giddy.markov - -.. autofunction:: sojourn_time \ No newline at end of file diff --git a/docs/_sources/generated/giddy.mobility.markov_mobility.rst.txt b/docs/_sources/generated/giddy.mobility.markov_mobility.rst.txt deleted file mode 100644 index da38b5c..0000000 --- a/docs/_sources/generated/giddy.mobility.markov_mobility.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.mobility.markov\_mobility -=============================== - -.. currentmodule:: giddy.mobility - -.. autofunction:: markov_mobility \ No newline at end of file diff --git a/docs/_sources/generated/giddy.rank.SpatialTau.rst.txt b/docs/_sources/generated/giddy.rank.SpatialTau.rst.txt deleted file mode 100644 index f0313f2..0000000 --- a/docs/_sources/generated/giddy.rank.SpatialTau.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.SpatialTau -===================== - -.. currentmodule:: giddy.rank - -.. autoclass:: SpatialTau - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~SpatialTau.__init__ - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.rank.Tau.rst.txt b/docs/_sources/generated/giddy.rank.Tau.rst.txt deleted file mode 100644 index 181a8f4..0000000 --- a/docs/_sources/generated/giddy.rank.Tau.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Tau -============== - -.. currentmodule:: giddy.rank - -.. autoclass:: Tau - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Tau.__init__ - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.rank.Tau_Local.rst.txt b/docs/_sources/generated/giddy.rank.Tau_Local.rst.txt deleted file mode 100644 index 01347ea..0000000 --- a/docs/_sources/generated/giddy.rank.Tau_Local.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Tau\_Local -===================== - -.. currentmodule:: giddy.rank - -.. autoclass:: Tau_Local - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Tau_Local.__init__ - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.rank.Tau_Local_Neighbor.rst.txt b/docs/_sources/generated/giddy.rank.Tau_Local_Neighbor.rst.txt deleted file mode 100644 index 6a9d2b2..0000000 --- a/docs/_sources/generated/giddy.rank.Tau_Local_Neighbor.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Tau\_Local\_Neighbor -=============================== - -.. currentmodule:: giddy.rank - -.. autoclass:: Tau_Local_Neighbor - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Tau_Local_Neighbor.__init__ - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.rank.Tau_Local_Neighborhood.rst.txt b/docs/_sources/generated/giddy.rank.Tau_Local_Neighborhood.rst.txt deleted file mode 100644 index ff3b6a2..0000000 --- a/docs/_sources/generated/giddy.rank.Tau_Local_Neighborhood.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Tau\_Local\_Neighborhood -=================================== - -.. currentmodule:: giddy.rank - -.. autoclass:: Tau_Local_Neighborhood - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Tau_Local_Neighborhood.__init__ - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.rank.Tau_Regional.rst.txt b/docs/_sources/generated/giddy.rank.Tau_Regional.rst.txt deleted file mode 100644 index 3896417..0000000 --- a/docs/_sources/generated/giddy.rank.Tau_Regional.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Tau\_Regional -======================== - -.. currentmodule:: giddy.rank - -.. autoclass:: Tau_Regional - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Tau_Regional.__init__ - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.rank.Theta.rst.txt b/docs/_sources/generated/giddy.rank.Theta.rst.txt deleted file mode 100644 index e84caba..0000000 --- a/docs/_sources/generated/giddy.rank.Theta.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -giddy.rank.Theta -================ - -.. currentmodule:: giddy.rank - -.. autoclass:: Theta - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Theta.__init__ - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.sequence.Sequence.rst.txt b/docs/_sources/generated/giddy.sequence.Sequence.rst.txt deleted file mode 100644 index e368ce5..0000000 --- a/docs/_sources/generated/giddy.sequence.Sequence.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -giddy.sequence.Sequence -======================= - -.. currentmodule:: giddy.sequence - -.. autoclass:: Sequence - - - .. automethod:: __init__ - - - .. rubric:: Methods - - .. autosummary:: - - ~Sequence.__init__ - - - - - - \ No newline at end of file diff --git a/docs/_sources/generated/giddy.util.fill_empty_diagonals.rst.txt b/docs/_sources/generated/giddy.util.fill_empty_diagonals.rst.txt deleted file mode 100644 index 5daece7..0000000 --- a/docs/_sources/generated/giddy.util.fill_empty_diagonals.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.util.fill\_empty\_diagonals -================================= - -.. currentmodule:: giddy.util - -.. autofunction:: fill_empty_diagonals \ No newline at end of file diff --git a/docs/_sources/generated/giddy.util.get_lower.rst.txt b/docs/_sources/generated/giddy.util.get_lower.rst.txt deleted file mode 100644 index 8ed893e..0000000 --- a/docs/_sources/generated/giddy.util.get_lower.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.util.get\_lower -===================== - -.. currentmodule:: giddy.util - -.. autofunction:: get_lower \ No newline at end of file diff --git a/docs/_sources/generated/giddy.util.shuffle_matrix.rst.txt b/docs/_sources/generated/giddy.util.shuffle_matrix.rst.txt deleted file mode 100644 index d691283..0000000 --- a/docs/_sources/generated/giddy.util.shuffle_matrix.rst.txt +++ /dev/null @@ -1,6 +0,0 @@ -giddy.util.shuffle\_matrix -========================== - -.. currentmodule:: giddy.util - -.. autofunction:: shuffle_matrix \ No newline at end of file diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt deleted file mode 100644 index 8e21b62..0000000 --- a/docs/_sources/index.rst.txt +++ /dev/null @@ -1,95 +0,0 @@ -.. giddy documentation master file, created by - sphinx-quickstart on Wed Jun 6 15:54:22 2018. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -GeospatIal Distribution DYnamics (GIDDY) -======================================== - -Giddy is an open-source python library for the analysis of dynamics of -longitudinal spatial data. Originating from the spatial dynamics module -in `PySAL`_ (Python Spatial Analysis Library), it is under active development -for the inclusion of many newly proposed analytics that consider the -role of space in the evolution of distributions over time and has -several new features including inter- and intra-regional decomposition -of mobility association and local measures of exchange mobility in -addition to space-time LISA and spatial markov methods. - - -.. raw:: html - -
    -
    -
    -
    -
    - - -
    -
    Rose diagram (directional LISAs)
    -
    -     -
    -
    - - -
    -
    Spatial Markov Chain
    -
    -     -
    -
    - - -
    -
    Neighbor Set Local Indicator of Mobility Association (LIMA) -
    -
    -     -
    -
    -
    -
    -
    - -Citing `giddy` --------------- - -If you use PySAL-giddy in a scientific publication, we would appreciate using the following citation: - -Bibtex entry:: - - @software{wei_kang_2020_3887455, - author = {Wei Kang and - Sergio Rey and - Philip Stephens and - Nicholas Malizia and - James Gaboardi and - Stefanie Lumnitz and - Levi John Wolf and - Charles Schmidt and - Jay Laura and - Eli Knaap}, - title = {pysal/giddy: Release v2.3.3}, - month = jun, - year = 2020, - publisher = {Zenodo}, - version = {v2.3.3}, - doi = {10.5281/zenodo.3887455}, - url = {https://doi.org/10.5281/zenodo.3887455} - } - -.. toctree:: - :hidden: - :maxdepth: 3 - :caption: Contents: - - Installation - Tutorial - API - References - - - -.. _PySAL: https://github.com/pysal/pysal \ No newline at end of file diff --git a/docs/_sources/installation.rst.txt b/docs/_sources/installation.rst.txt deleted file mode 100644 index f8804de..0000000 --- a/docs/_sources/installation.rst.txt +++ /dev/null @@ -1,50 +0,0 @@ -.. Installation - -Installation -============ - -giddy currently supports python `3.6`_, `3.7`_, and `3.8`_. -Please make sure that you are operating in a python 3 environment. - -Installing released version ---------------------------- - -giddy is available on the `Python Package Index`_. Therefore, you can either -install directly with `pip` from the command line:: - - pip install -U giddy - - -or download the source distribution (.tar.gz) and decompress it to your selected -destination. Open a command shell and navigate to the decompressed folder. -Type:: - - pip install . - -You may also install the latest stable giddy via `conda-forge`_ channel by -running:: - - conda install --channel conda-forge giddy - -Installing development version ------------------------------- - -Potentially, you might want to use the newest features in the development -version of giddy on github - `pysal/giddy`_ while have not been incorporated -in the Pypi released version. You can achieve that by installing `pysal/giddy`_ -by running the following from a command shell:: - - pip install git+https://github.com/pysal/giddy.git - -You can also `fork`_ the `pysal/giddy`_ repo and create a local clone of -your fork. By making changes -to your local clone and submitting a pull request to `pysal/giddy`_, you can -contribute to the giddy development. - -.. _3.6: https://docs.python.org/3.6/ -.. _3.7: https://docs.python.org/3.7/ -.. _3.8: https://docs.python.org/3.8/ -.. _Python Package Index: https://pypi.org/project/giddy/ -.. _pysal/giddy: https://github.com/pysal/giddy -.. _fork: https://help.github.com/articles/fork-a-repo/ -.. _conda-forge: https://github.com/conda-forge/giddy-feedstock diff --git a/docs/_sources/references.rst.txt b/docs/_sources/references.rst.txt deleted file mode 100644 index 09d2529..0000000 --- a/docs/_sources/references.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -.. reference for the docs - -References -========== - -.. bibliography:: _static/references.bib - :cited: diff --git a/docs/_sources/tutorial.rst.txt b/docs/_sources/tutorial.rst.txt deleted file mode 100644 index 36dd221..0000000 --- a/docs/_sources/tutorial.rst.txt +++ /dev/null @@ -1,14 +0,0 @@ -Tutorial -======== - -.. toctree:: - :maxdepth: 1 - :caption: Contents: - - MarkovBasedMethods.ipynb - RankMarkov.ipynb - MobilityMeasures.ipynb - DirectionalLISA.ipynb - RankBasedMethods.ipynb - Sequence.ipynb - diff --git a/docs/_static/_sphinx_javascript_frameworks_compat.js b/docs/_static/_sphinx_javascript_frameworks_compat.js deleted file mode 100644 index 8549469..0000000 --- a/docs/_static/_sphinx_javascript_frameworks_compat.js +++ /dev/null @@ -1,134 +0,0 @@ -/* - * _sphinx_javascript_frameworks_compat.js - * ~~~~~~~~~~ - * - * Compatability shim for jQuery and underscores.js. - * - * WILL BE REMOVED IN Sphinx 6.0 - * xref RemovedInSphinx60Warning - * - */ - -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x - } - return decodeURIComponent(x.replace(/\+/g, ' ')); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var 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- width: 844px; - margin-left: 0; -} - -.table td.span12, -.table th.span12 { - float: none; - width: 924px; - margin-left: 0; -} - -.table tbody tr.success > td { - background-color: #dff0d8; -} - -.table tbody tr.error > td { - background-color: #f2dede; -} - -.table tbody tr.warning > td { - background-color: #fcf8e3; -} - -.table tbody tr.info > td { - background-color: #d9edf7; -} - -.table-hover tbody tr.success:hover > td { - background-color: #d0e9c6; -} - -.table-hover tbody tr.error:hover > td { - background-color: #ebcccc; -} - -.table-hover tbody tr.warning:hover > td { - background-color: #faf2cc; -} - -.table-hover tbody tr.info:hover > td { - background-color: #c4e3f3; -} - -[class^="icon-"], -[class*=" icon-"] { - display: inline-block; - width: 14px; - height: 14px; - margin-top: 1px; - *margin-right: .3em; - line-height: 14px; - vertical-align: text-top; - background-image: url("../img/glyphicons-halflings.png"); - background-position: 14px 14px; - background-repeat: no-repeat; -} - -/* White icons with optional class, or on hover/focus/active states of certain elements */ - -.icon-white, -.nav-pills > .active > a > [class^="icon-"], -.nav-pills > .active > a > [class*=" icon-"], -.nav-list > .active > a > [class^="icon-"], -.nav-list > .active > a > [class*=" icon-"], -.navbar-inverse .nav > .active > a > [class^="icon-"], -.navbar-inverse .nav > .active > a > [class*=" icon-"], -.dropdown-menu > li > a:hover > [class^="icon-"], -.dropdown-menu > li > a:focus > [class^="icon-"], -.dropdown-menu > li > a:hover > [class*=" icon-"], -.dropdown-menu > li > a:focus > [class*=" icon-"], -.dropdown-menu > .active > a > [class^="icon-"], -.dropdown-menu > .active > a > [class*=" icon-"], -.dropdown-submenu:hover > a > [class^="icon-"], -.dropdown-submenu:focus > a > [class^="icon-"], -.dropdown-submenu:hover > a > [class*=" icon-"], -.dropdown-submenu:focus > a > [class*=" icon-"] { - background-image: url("../img/glyphicons-halflings-white.png"); -} - 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-} - -.icon-trash { - background-position: -456px 0; -} - -.icon-home { - background-position: 0 -24px; -} - -.icon-file { - background-position: -24px -24px; -} - -.icon-time { - background-position: -48px -24px; -} - -.icon-road { - background-position: -72px -24px; -} - -.icon-download-alt { - background-position: -96px -24px; -} - -.icon-download { - background-position: -120px -24px; -} - -.icon-upload { - background-position: -144px -24px; -} - -.icon-inbox { - background-position: -168px -24px; -} - -.icon-play-circle { - background-position: -192px -24px; -} - -.icon-repeat { - background-position: -216px -24px; -} - -.icon-refresh { - background-position: -240px -24px; -} - -.icon-list-alt { - background-position: -264px -24px; -} - -.icon-lock { - background-position: -287px -24px; -} - -.icon-flag { - background-position: -312px -24px; -} - -.icon-headphones { - background-position: -336px -24px; -} - -.icon-volume-off { - background-position: -360px -24px; -} - 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-216px -72px; -} - -.icon-backward { - background-position: -240px -72px; -} - -.icon-play { - background-position: -264px -72px; -} - -.icon-pause { - background-position: -288px -72px; -} - -.icon-stop { - background-position: -312px -72px; -} - -.icon-forward { - background-position: -336px -72px; -} - -.icon-fast-forward { - background-position: -360px -72px; -} - -.icon-step-forward { - background-position: -384px -72px; -} - -.icon-eject { - background-position: -408px -72px; -} - -.icon-chevron-left { - background-position: -432px -72px; -} - -.icon-chevron-right { - background-position: -456px -72px; -} - -.icon-plus-sign { - background-position: 0 -96px; -} - -.icon-minus-sign { - background-position: -24px -96px; -} - -.icon-remove-sign { - background-position: -48px -96px; -} - -.icon-ok-sign { - background-position: -72px -96px; -} - -.icon-question-sign { - background-position: -96px -96px; -} - -.icon-info-sign { - background-position: -120px -96px; -} - -.icon-screenshot { - background-position: -144px -96px; -} - -.icon-remove-circle { - background-position: -168px -96px; -} - -.icon-ok-circle { - background-position: -192px -96px; -} - -.icon-ban-circle { - background-position: -216px -96px; -} - -.icon-arrow-left { - background-position: -240px -96px; -} - -.icon-arrow-right { - background-position: -264px -96px; -} - -.icon-arrow-up { - background-position: -289px -96px; -} - -.icon-arrow-down { - background-position: -312px -96px; -} - -.icon-share-alt { - background-position: -336px -96px; -} - -.icon-resize-full { - background-position: -360px -96px; -} - -.icon-resize-small { - background-position: -384px -96px; -} - -.icon-plus { - background-position: -408px -96px; -} - -.icon-minus { - background-position: -433px -96px; -} - -.icon-asterisk { - background-position: -456px -96px; -} - -.icon-exclamation-sign { - background-position: 0 -120px; -} - -.icon-gift { - background-position: -24px -120px; -} - -.icon-leaf { - background-position: 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-.icon-resize-vertical { - background-position: -432px -119px; -} - -.icon-resize-horizontal { - background-position: -456px -118px; -} - -.icon-hdd { - background-position: 0 -144px; -} - -.icon-bullhorn { - background-position: -24px -144px; -} - -.icon-bell { - background-position: -48px -144px; -} - -.icon-certificate { - background-position: -72px -144px; -} - -.icon-thumbs-up { - background-position: -96px -144px; -} - -.icon-thumbs-down { - background-position: -120px -144px; -} - -.icon-hand-right { - background-position: -144px -144px; -} - -.icon-hand-left { - background-position: -168px -144px; -} - -.icon-hand-up { - background-position: -192px -144px; -} - -.icon-hand-down { - background-position: -216px -144px; -} - -.icon-circle-arrow-right { - background-position: -240px -144px; -} - -.icon-circle-arrow-left { - background-position: -264px -144px; -} - -.icon-circle-arrow-up { - background-position: -288px -144px; -} - -.icon-circle-arrow-down { - background-position: -312px -144px; -} - -.icon-globe { - background-position: -336px -144px; -} - -.icon-wrench { - background-position: -360px -144px; -} - -.icon-tasks { - background-position: -384px -144px; -} - -.icon-filter { - background-position: -408px -144px; -} - -.icon-briefcase { - background-position: -432px -144px; -} - -.icon-fullscreen { - background-position: -456px -144px; -} - -.dropup, -.dropdown { - position: relative; -} - -.dropdown-toggle { - *margin-bottom: -3px; -} - -.dropdown-toggle:active, -.open .dropdown-toggle { - outline: 0; -} - -.caret { - display: inline-block; - width: 0; - height: 0; - vertical-align: top; - border-top: 4px solid #000000; - border-right: 4px solid transparent; - border-left: 4px solid transparent; - content: ""; -} - -.dropdown .caret { - margin-top: 8px; - margin-left: 2px; -} - -.dropdown-menu { - position: absolute; - top: 100%; - left: 0; - z-index: 1000; - display: none; - float: left; - min-width: 160px; - padding: 5px 0; - margin: 2px 0 0; - list-style: none; - background-color: #ffffff; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.2); - *border-right-width: 2px; - *border-bottom-width: 2px; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; - -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - -webkit-background-clip: padding-box; - -moz-background-clip: padding; - background-clip: padding-box; -} - -.dropdown-menu.pull-right { - right: 0; - left: auto; -} - -.dropdown-menu .divider { - *width: 100%; - height: 1px; - margin: 9px 1px; - *margin: -5px 0 5px; - overflow: hidden; - background-color: #e5e5e5; - border-bottom: 1px solid #ffffff; -} - -.dropdown-menu > li > a { - display: block; - padding: 3px 20px; - clear: both; - font-weight: normal; - line-height: 20px; - color: #333333; - white-space: nowrap; -} - -.dropdown-menu > li > a:hover, -.dropdown-menu > li > a:focus, -.dropdown-submenu:hover > a, -.dropdown-submenu:focus > a { - color: #ffffff; - text-decoration: none; - background-color: #0081c2; - background-image: -moz-linear-gradient(top, #0088cc, #0077b3); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); - background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); - background-image: -o-linear-gradient(top, #0088cc, #0077b3); - background-image: linear-gradient(to bottom, #0088cc, #0077b3); - background-repeat: repeat-x; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); -} - -.dropdown-menu > .active > a, -.dropdown-menu > .active > a:hover, -.dropdown-menu > .active > a:focus { - color: #ffffff; - text-decoration: none; - background-color: #0081c2; - background-image: -moz-linear-gradient(top, #0088cc, #0077b3); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3)); - background-image: -webkit-linear-gradient(top, #0088cc, #0077b3); - background-image: -o-linear-gradient(top, #0088cc, #0077b3); - background-image: linear-gradient(to bottom, #0088cc, #0077b3); - background-repeat: repeat-x; - outline: 0; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0); -} - -.dropdown-menu > .disabled > a, -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - color: #999999; -} - -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - text-decoration: none; - cursor: default; - background-color: transparent; - background-image: none; - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); -} - -.open { - *z-index: 1000; -} - -.open > .dropdown-menu { - display: block; -} - -.dropdown-backdrop { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 990; -} - -.pull-right > .dropdown-menu { - right: 0; - left: auto; -} - -.dropup .caret, -.navbar-fixed-bottom .dropdown .caret { - border-top: 0; - border-bottom: 4px solid #000000; - content: ""; -} - -.dropup .dropdown-menu, -.navbar-fixed-bottom .dropdown .dropdown-menu { - top: auto; - bottom: 100%; - margin-bottom: 1px; -} - -.dropdown-submenu { - position: relative; -} - -.dropdown-submenu > .dropdown-menu { - top: 0; - left: 100%; - margin-top: -6px; - margin-left: -1px; - -webkit-border-radius: 0 6px 6px 6px; - -moz-border-radius: 0 6px 6px 6px; - border-radius: 0 6px 6px 6px; -} - -.dropdown-submenu:hover > .dropdown-menu { - display: block; -} - -.dropup .dropdown-submenu > .dropdown-menu { - top: auto; - bottom: 0; - margin-top: 0; - margin-bottom: -2px; - -webkit-border-radius: 5px 5px 5px 0; - -moz-border-radius: 5px 5px 5px 0; - border-radius: 5px 5px 5px 0; -} - -.dropdown-submenu > a:after { - display: block; - float: right; - width: 0; - height: 0; - margin-top: 5px; - margin-right: -10px; - border-color: transparent; - border-left-color: #cccccc; - border-style: solid; - border-width: 5px 0 5px 5px; - content: " "; -} - -.dropdown-submenu:hover > a:after { - border-left-color: #ffffff; -} - -.dropdown-submenu.pull-left { - float: none; -} - -.dropdown-submenu.pull-left > .dropdown-menu { - left: -100%; - margin-left: 10px; - -webkit-border-radius: 6px 0 6px 6px; - -moz-border-radius: 6px 0 6px 6px; - border-radius: 6px 0 6px 6px; -} - -.dropdown .dropdown-menu .nav-header { - padding-right: 20px; - padding-left: 20px; -} - -.typeahead { - z-index: 1051; - margin-top: 2px; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; -} - -.well { - min-height: 20px; - padding: 19px; - margin-bottom: 20px; - background-color: #f5f5f5; - border: 1px solid #e3e3e3; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); -} - -.well blockquote { - border-color: #ddd; - border-color: rgba(0, 0, 0, 0.15); -} - -.well-large { - padding: 24px; - -webkit-border-radius: 6px; - -moz-border-radius: 6px; - border-radius: 6px; -} - -.well-small { - padding: 9px; - -webkit-border-radius: 3px; - -moz-border-radius: 3px; - border-radius: 3px; -} - -.fade { - opacity: 0; - -webkit-transition: opacity 0.15s linear; - -moz-transition: opacity 0.15s linear; - -o-transition: opacity 0.15s linear; - transition: opacity 0.15s linear; -} - -.fade.in { - opacity: 1; -} - -.collapse { - position: relative; - height: 0; - overflow: hidden; - -webkit-transition: height 0.35s ease; - -moz-transition: height 0.35s ease; - -o-transition: height 0.35s ease; - transition: height 0.35s ease; -} - -.collapse.in { - height: auto; -} - -.close { - float: right; - font-size: 20px; - font-weight: bold; - line-height: 20px; - color: #000000; - text-shadow: 0 1px 0 #ffffff; - opacity: 0.2; - filter: alpha(opacity=20); -} - -.close:hover, -.close:focus { - color: #000000; - text-decoration: none; - cursor: pointer; - opacity: 0.4; - filter: alpha(opacity=40); -} - -button.close { - padding: 0; - cursor: pointer; - background: transparent; - border: 0; - -webkit-appearance: none; -} - -.btn { - display: inline-block; - *display: inline; - padding: 4px 12px; - margin-bottom: 0; - *margin-left: .3em; - font-size: 14px; - line-height: 20px; - color: #333333; - text-align: center; - text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75); - vertical-align: middle; - cursor: pointer; - background-color: #f5f5f5; - *background-color: #e6e6e6; - background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6)); - background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6); - background-image: -o-linear-gradient(top, #ffffff, #e6e6e6); - background-image: linear-gradient(to bottom, #ffffff, #e6e6e6); - background-repeat: repeat-x; - border: 1px solid #cccccc; - *border: 0; - border-color: #e6e6e6 #e6e6e6 #bfbfbf; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - border-bottom-color: #b3b3b3; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0); - filter: progid:DXImageTransform.Microsoft.gradient(enabled=false); - *zoom: 1; - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn:hover, -.btn:focus, -.btn:active, -.btn.active, -.btn.disabled, -.btn[disabled] { - color: #333333; - background-color: #e6e6e6; - *background-color: #d9d9d9; -} - -.btn:active, -.btn.active { - background-color: #cccccc \9; -} - -.btn:first-child { - *margin-left: 0; -} - -.btn:hover, -.btn:focus { - color: #333333; - text-decoration: none; - background-position: 0 -15px; - -webkit-transition: background-position 0.1s linear; - -moz-transition: background-position 0.1s linear; - -o-transition: background-position 0.1s linear; - transition: background-position 0.1s linear; -} - -.btn:focus { - outline: thin dotted #333; - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} - -.btn.active, -.btn:active { - background-image: none; - outline: 0; - -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn.disabled, -.btn[disabled] { - cursor: default; - background-image: none; - opacity: 0.65; - filter: alpha(opacity=65); - -webkit-box-shadow: none; - -moz-box-shadow: none; - box-shadow: none; -} - -.btn-large { 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.btn-large { - font-size: 17.5px; -} - -.btn-group > .btn:first-child { - margin-left: 0; - -webkit-border-bottom-left-radius: 4px; - border-bottom-left-radius: 4px; - -webkit-border-top-left-radius: 4px; - border-top-left-radius: 4px; - -moz-border-radius-bottomleft: 4px; - -moz-border-radius-topleft: 4px; -} - -.btn-group > .btn:last-child, -.btn-group > .dropdown-toggle { - -webkit-border-top-right-radius: 4px; - border-top-right-radius: 4px; - -webkit-border-bottom-right-radius: 4px; - border-bottom-right-radius: 4px; - -moz-border-radius-topright: 4px; - -moz-border-radius-bottomright: 4px; -} - -.btn-group > .btn.large:first-child { - margin-left: 0; - -webkit-border-bottom-left-radius: 6px; - border-bottom-left-radius: 6px; - -webkit-border-top-left-radius: 6px; - border-top-left-radius: 6px; - -moz-border-radius-bottomleft: 6px; - -moz-border-radius-topleft: 6px; -} - -.btn-group > .btn.large:last-child, -.btn-group > .large.dropdown-toggle { - -webkit-border-top-right-radius: 6px; - border-top-right-radius: 6px; - -webkit-border-bottom-right-radius: 6px; - border-bottom-right-radius: 6px; - -moz-border-radius-topright: 6px; - -moz-border-radius-bottomright: 6px; -} - -.btn-group > .btn:hover, -.btn-group > .btn:focus, -.btn-group > .btn:active, -.btn-group > .btn.active { - z-index: 2; -} - -.btn-group .dropdown-toggle:active, -.btn-group.open .dropdown-toggle { - outline: 0; -} - -.btn-group > .btn + .dropdown-toggle { - *padding-top: 5px; - padding-right: 8px; - *padding-bottom: 5px; - padding-left: 8px; - -webkit-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn-group > .btn-mini + .dropdown-toggle { - *padding-top: 2px; - padding-right: 5px; - *padding-bottom: 2px; - padding-left: 5px; -} - -.btn-group > .btn-small + .dropdown-toggle { - *padding-top: 5px; - *padding-bottom: 4px; -} - -.btn-group > .btn-large + .dropdown-toggle { - *padding-top: 7px; - padding-right: 12px; - *padding-bottom: 7px; - padding-left: 12px; -} - -.btn-group.open .dropdown-toggle { - background-image: none; - -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); - -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05); -} - -.btn-group.open .btn.dropdown-toggle { - background-color: #e6e6e6; -} - -.btn-group.open .btn-primary.dropdown-toggle { - background-color: #0044cc; -} - -.btn-group.open .btn-warning.dropdown-toggle { - background-color: #f89406; -} - -.btn-group.open .btn-danger.dropdown-toggle { - background-color: #bd362f; -} - -.btn-group.open .btn-success.dropdown-toggle { - background-color: #51a351; -} - -.btn-group.open .btn-info.dropdown-toggle { - background-color: #2f96b4; -} - -.btn-group.open .btn-inverse.dropdown-toggle { - background-color: #222222; -} - -.btn .caret { - margin-top: 8px; - margin-left: 0; -} - -.btn-large .caret { - margin-top: 6px; -} - -.btn-large .caret { - border-top-width: 5px; - border-right-width: 5px; - border-left-width: 5px; -} - -.btn-mini .caret, -.btn-small .caret { - margin-top: 8px; -} - -.dropup .btn-large .caret { - border-bottom-width: 5px; -} - -.btn-primary .caret, -.btn-warning .caret, -.btn-danger .caret, -.btn-info .caret, -.btn-success .caret, -.btn-inverse .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; -} - -.btn-group-vertical { - display: inline-block; - *display: inline; - /* IE7 inline-block hack */ - - *zoom: 1; -} - -.btn-group-vertical > .btn { - display: block; - float: none; - max-width: 100%; - -webkit-border-radius: 0; - 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.dropdown-toggle:focus .caret { - border-top-color: #005580; - border-bottom-color: #005580; -} - -/* move down carets for tabs */ - -.nav-tabs .dropdown-toggle .caret { - margin-top: 8px; -} - -.nav .active .dropdown-toggle .caret { - border-top-color: #fff; - border-bottom-color: #fff; -} - -.nav-tabs .active .dropdown-toggle .caret { - border-top-color: #555555; - border-bottom-color: #555555; -} - -.nav > .dropdown.active > a:hover, -.nav > .dropdown.active > a:focus { - cursor: pointer; -} - -.nav-tabs .open .dropdown-toggle, -.nav-pills .open .dropdown-toggle, -.nav > li.dropdown.open.active > a:hover, -.nav > li.dropdown.open.active > a:focus { - color: #ffffff; - background-color: #999999; - border-color: #999999; -} - -.nav li.dropdown.open .caret, -.nav li.dropdown.open.active .caret, -.nav li.dropdown.open a:hover .caret, -.nav li.dropdown.open a:focus .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; - opacity: 1; - filter: alpha(opacity=100); -} - 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> .nav-tabs > .active > a, -.tabs-below > .nav-tabs > .active > a:hover, -.tabs-below > .nav-tabs > .active > a:focus { - border-color: transparent #ddd #ddd #ddd; -} - -.tabs-left > .nav-tabs > li, -.tabs-right > .nav-tabs > li { - float: none; -} - -.tabs-left > .nav-tabs > li > a, -.tabs-right > .nav-tabs > li > a { - min-width: 74px; - margin-right: 0; - margin-bottom: 3px; -} - -.tabs-left > .nav-tabs { - float: left; - margin-right: 19px; - border-right: 1px solid #ddd; -} - -.tabs-left > .nav-tabs > li > a { - margin-right: -1px; - -webkit-border-radius: 4px 0 0 4px; - -moz-border-radius: 4px 0 0 4px; - border-radius: 4px 0 0 4px; -} - -.tabs-left > .nav-tabs > li > a:hover, -.tabs-left > .nav-tabs > li > a:focus { - border-color: #eeeeee #dddddd #eeeeee #eeeeee; -} - -.tabs-left > .nav-tabs .active > a, -.tabs-left > .nav-tabs .active > a:hover, -.tabs-left > .nav-tabs .active > a:focus { - border-color: #ddd transparent #ddd #ddd; - *border-right-color: #ffffff; -} - -.tabs-right > .nav-tabs { - float: right; - margin-left: 19px; - border-left: 1px solid #ddd; -} - -.tabs-right > .nav-tabs > li > a { - margin-left: -1px; - -webkit-border-radius: 0 4px 4px 0; - -moz-border-radius: 0 4px 4px 0; - border-radius: 0 4px 4px 0; -} - -.tabs-right > .nav-tabs > li > a:hover, -.tabs-right > .nav-tabs > li > a:focus { - border-color: #eeeeee #eeeeee #eeeeee #dddddd; -} - -.tabs-right > .nav-tabs .active > a, -.tabs-right > .nav-tabs .active > a:hover, -.tabs-right > .nav-tabs .active > a:focus { - border-color: #ddd #ddd #ddd transparent; - *border-left-color: #ffffff; -} - -.nav > .disabled > a { - color: #999999; -} - -.nav > .disabled > a:hover, -.nav > .disabled > a:focus { - text-decoration: none; - cursor: default; - background-color: transparent; -} - -.navbar { - *position: relative; - *z-index: 2; - margin-bottom: 20px; - overflow: visible; -} - -.navbar-inner { - min-height: 40px; - padding-right: 20px; - padding-left: 20px; - background-color: #fafafa; - background-image: -moz-linear-gradient(top, #ffffff, #f2f2f2); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f2f2f2)); - background-image: -webkit-linear-gradient(top, #ffffff, #f2f2f2); - background-image: -o-linear-gradient(top, #ffffff, #f2f2f2); - background-image: linear-gradient(to bottom, #ffffff, #f2f2f2); - background-repeat: repeat-x; - border: 1px solid #d4d4d4; - -webkit-border-radius: 4px; - -moz-border-radius: 4px; - border-radius: 4px; - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0); - *zoom: 1; - -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); - -moz-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); - box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065); -} - -.navbar-inner:before, -.navbar-inner:after { - display: table; - line-height: 0; - content: ""; -} - -.navbar-inner:after { - clear: both; -} - -.navbar .container { - width: auto; -} - -.nav-collapse.collapse { - height: auto; - overflow: visible; -} - -.navbar .brand { - display: block; - float: left; - padding: 10px 20px 10px; - margin-left: -20px; - font-size: 20px; - font-weight: 200; - color: #777777; - text-shadow: 0 1px 0 #ffffff; -} - -.navbar .brand:hover, -.navbar .brand:focus { - text-decoration: none; -} - -.navbar-text { - margin-bottom: 0; - line-height: 40px; - color: #777777; -} - -.navbar-link { - color: #777777; -} - -.navbar-link:hover, -.navbar-link:focus { - color: #333333; -} - -.navbar .divider-vertical { - height: 40px; - margin: 0 9px; - border-right: 1px solid #ffffff; - border-left: 1px solid #f2f2f2; -} - -.navbar .btn, -.navbar .btn-group { - margin-top: 5px; -} - -.navbar .btn-group .btn, -.navbar .input-prepend .btn, -.navbar .input-append .btn, -.navbar .input-prepend .btn-group, -.navbar .input-append .btn-group { - margin-top: 0; -} - -.navbar-form { - margin-bottom: 0; - *zoom: 1; -} - -.navbar-form:before, -.navbar-form:after { - display: table; - line-height: 0; - content: ""; -} - -.navbar-form:after { - clear: both; -} - -.navbar-form input, -.navbar-form select, -.navbar-form .radio, -.navbar-form .checkbox { - margin-top: 5px; -} - -.navbar-form input, -.navbar-form select, -.navbar-form .btn { - display: inline-block; - margin-bottom: 0; -} - -.navbar-form input[type="image"], -.navbar-form input[type="checkbox"], -.navbar-form input[type="radio"] { - margin-top: 3px; -} - -.navbar-form .input-append, -.navbar-form .input-prepend { - margin-top: 5px; - white-space: nowrap; -} - -.navbar-form .input-append input, -.navbar-form .input-prepend input { - margin-top: 0; -} - -.navbar-search { - position: relative; - float: left; - margin-top: 5px; - margin-bottom: 0; -} - -.navbar-search .search-query { - padding: 4px 14px; - margin-bottom: 0; - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; - font-size: 13px; - font-weight: normal; - line-height: 1; - -webkit-border-radius: 15px; - -moz-border-radius: 15px; - border-radius: 15px; -} - -.navbar-static-top { - position: static; - margin-bottom: 0; -} - -.navbar-static-top .navbar-inner { - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.navbar-fixed-top, -.navbar-fixed-bottom { - position: fixed; - right: 0; - left: 0; - z-index: 1030; - margin-bottom: 0; -} - -.navbar-fixed-top .navbar-inner, -.navbar-static-top .navbar-inner { - border-width: 0 0 1px; -} - -.navbar-fixed-bottom .navbar-inner { - border-width: 1px 0 0; -} - -.navbar-fixed-top .navbar-inner, -.navbar-fixed-bottom .navbar-inner { - padding-right: 0; - padding-left: 0; - -webkit-border-radius: 0; - -moz-border-radius: 0; - border-radius: 0; -} - -.navbar-static-top .container, -.navbar-fixed-top .container, -.navbar-fixed-bottom .container { - width: 940px; -} - -.navbar-fixed-top { - top: 0; -} - -.navbar-fixed-top .navbar-inner, -.navbar-static-top .navbar-inner { - -webkit-box-shadow: 0 1px 10px rgba(0, 0, 0, 0.1); - -moz-box-shadow: 0 1px 10px 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text-decoration: none; - background-color: #e5e5e5; - -webkit-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); - -moz-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); - box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125); -} - -.navbar .btn-navbar { - display: none; - float: right; - padding: 7px 10px; - margin-right: 5px; - margin-left: 5px; - color: #ffffff; - text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25); - background-color: #ededed; - *background-color: #e5e5e5; - background-image: -moz-linear-gradient(top, #f2f2f2, #e5e5e5); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5)); - background-image: -webkit-linear-gradient(top, #f2f2f2, #e5e5e5); - background-image: -o-linear-gradient(top, #f2f2f2, #e5e5e5); - background-image: linear-gradient(to bottom, #f2f2f2, #e5e5e5); - background-repeat: repeat-x; - border-color: #e5e5e5 #e5e5e5 #bfbfbf; - border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25); - filter: 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border-radius: 1px; - -webkit-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); - -moz-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); - box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25); -} - -.btn-navbar .icon-bar + .icon-bar { - margin-top: 3px; -} - -.navbar .nav > li > .dropdown-menu:before { - position: absolute; - top: -7px; - left: 9px; - display: inline-block; - border-right: 7px solid transparent; - border-bottom: 7px solid #ccc; - border-left: 7px solid transparent; - border-bottom-color: rgba(0, 0, 0, 0.2); - content: ''; -} - -.navbar .nav > li > .dropdown-menu:after { - position: absolute; - top: -6px; - left: 10px; - display: inline-block; - border-right: 6px solid transparent; - border-bottom: 6px solid #ffffff; - border-left: 6px solid transparent; - content: ''; -} - -.navbar-fixed-bottom .nav > li > .dropdown-menu:before { - top: auto; - bottom: -7px; - border-top: 7px solid #ccc; - border-bottom: 0; - border-top-color: rgba(0, 0, 0, 0.2); -} - -.navbar-fixed-bottom .nav > li > .dropdown-menu:after { - top: auto; - bottom: -6px; - border-top: 6px solid #ffffff; - border-bottom: 0; -} - -.navbar .nav li.dropdown > a:hover .caret, -.navbar .nav li.dropdown > a:focus .caret { - border-top-color: #333333; - border-bottom-color: #333333; -} - -.navbar .nav li.dropdown.open > .dropdown-toggle, -.navbar .nav li.dropdown.active > .dropdown-toggle, -.navbar .nav li.dropdown.open.active > .dropdown-toggle { - color: #555555; - background-color: #e5e5e5; -} - -.navbar .nav li.dropdown > .dropdown-toggle .caret { - border-top-color: #777777; - border-bottom-color: #777777; -} - -.navbar .nav li.dropdown.open > .dropdown-toggle .caret, -.navbar .nav li.dropdown.active > .dropdown-toggle .caret, -.navbar .nav li.dropdown.open.active > .dropdown-toggle .caret { - border-top-color: #555555; - border-bottom-color: #555555; -} - -.navbar .pull-right > li > .dropdown-menu, -.navbar .nav > li > .dropdown-menu.pull-right { - right: 0; - left: auto; -} - -.navbar .pull-right > li > .dropdown-menu:before, -.navbar .nav > li > .dropdown-menu.pull-right:before { - right: 12px; - left: auto; -} - -.navbar .pull-right > li > .dropdown-menu:after, -.navbar .nav > li > .dropdown-menu.pull-right:after { - right: 13px; - left: auto; -} - -.navbar .pull-right > li > .dropdown-menu .dropdown-menu, -.navbar .nav > li > .dropdown-menu.pull-right .dropdown-menu { - right: 100%; - left: auto; - margin-right: -1px; - margin-left: 0; - -webkit-border-radius: 6px 0 6px 6px; - -moz-border-radius: 6px 0 6px 6px; - border-radius: 6px 0 6px 6px; -} - -.navbar-inverse .navbar-inner { - background-color: #1b1b1b; - background-image: -moz-linear-gradient(top, #222222, #111111); - background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#222222), to(#111111)); - background-image: -webkit-linear-gradient(top, #222222, #111111); - background-image: -o-linear-gradient(top, #222222, #111111); - background-image: linear-gradient(to bottom, #222222, #111111); - background-repeat: 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#ffffff; -} - -.navbar-inverse .divider-vertical { - border-right-color: #222222; - border-left-color: #111111; -} - -.navbar-inverse .nav li.dropdown.open > .dropdown-toggle, -.navbar-inverse .nav li.dropdown.active > .dropdown-toggle, -.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle { - color: #ffffff; - background-color: #111111; -} - -.navbar-inverse .nav li.dropdown > a:hover .caret, -.navbar-inverse .nav li.dropdown > a:focus .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; -} - -.navbar-inverse .nav li.dropdown > .dropdown-toggle .caret { - border-top-color: #999999; - border-bottom-color: #999999; -} - -.navbar-inverse .nav li.dropdown.open > .dropdown-toggle .caret, -.navbar-inverse .nav li.dropdown.active > .dropdown-toggle .caret, -.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle .caret { - border-top-color: #ffffff; - border-bottom-color: #ffffff; -} - -.navbar-inverse .navbar-search .search-query { - color: #ffffff; - background-color: #515151; - border-color: #111111; - -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); - -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); - box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15); - -webkit-transition: none; - -moz-transition: none; - -o-transition: none; - transition: none; -} - -.navbar-inverse .navbar-search .search-query:-moz-placeholder { - color: #cccccc; -} - -.navbar-inverse .navbar-search .search-query:-ms-input-placeholder { - color: #cccccc; -} - -.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder { - color: #cccccc; -} - -.navbar-inverse .navbar-search .search-query:focus, -.navbar-inverse .navbar-search .search-query.focused { - padding: 5px 15px; - color: #333333; - text-shadow: 0 1px 0 #ffffff; - background-color: #ffffff; - border: 0; - outline: 0; - -webkit-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15); - 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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* CSS TRANSITION SUPPORT (http://www.modernizr.com/) - * ======================================================= */ - - $(function () { - - $.support.transition = (function () { - - var transitionEnd = (function () { - - var el = document.createElement('bootstrap') - , transEndEventNames = { - 'WebkitTransition' : 'webkitTransitionEnd' - , 'MozTransition' : 'transitionend' - , 'OTransition' : 'oTransitionEnd otransitionend' - , 'transition' : 'transitionend' - } - , name - - for (name in transEndEventNames){ - if (el.style[name] !== undefined) { - return transEndEventNames[name] - } - } - - }()) - - return transitionEnd && { - end: transitionEnd - } - - })() - - }) - -}(window.$jqTheme || window.jQuery); -/* ========================================================== - * bootstrap-alert.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#alerts - * ========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* ALERT CLASS DEFINITION - * ====================== */ - - var dismiss = '[data-dismiss="alert"]' - , Alert = function (el) { - $(el).on('click', dismiss, this.close) - } - - Alert.prototype.close = function (e) { - var $this = $(this) - , selector = $this.attr('data-target') - , $parent - - if (!selector) { - selector = $this.attr('href') - selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - $parent = $(selector) - - e && e.preventDefault() - - $parent.length || ($parent = $this.hasClass('alert') ? $this : $this.parent()) - - $parent.trigger(e = $.Event('close')) - - if (e.isDefaultPrevented()) return - - $parent.removeClass('in') - - function removeElement() { - $parent - .trigger('closed') - .remove() - } - - $.support.transition && $parent.hasClass('fade') ? - $parent.on($.support.transition.end, removeElement) : - removeElement() - } - - - /* ALERT PLUGIN DEFINITION - * ======================= */ - - var old = $.fn.alert - - $.fn.alert = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('alert') - if (!data) $this.data('alert', (data = new Alert(this))) - if (typeof option == 'string') data[option].call($this) - }) - } - - $.fn.alert.Constructor = Alert - - - /* ALERT NO CONFLICT - * ================= */ - - $.fn.alert.noConflict = function () { - $.fn.alert = old - return this - } - - - /* ALERT DATA-API - * ============== */ - - $(document).on('click.alert.data-api', dismiss, Alert.prototype.close) - -}(window.$jqTheme || window.jQuery); -/* ============================================================ - * bootstrap-button.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#buttons - * ============================================================ - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* BUTTON PUBLIC CLASS DEFINITION - * ============================== */ - - var Button = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.button.defaults, options) - } - - Button.prototype.setState = function (state) { - var d = 'disabled' - , $el = this.$element - , data = $el.data() - , val = $el.is('input') ? 'val' : 'html' - - state = state + 'Text' - data.resetText || $el.data('resetText', $el[val]()) - - $el[val](data[state] || this.options[state]) - - // push to event loop to allow forms to submit - setTimeout(function () { - state == 'loadingText' ? - $el.addClass(d).attr(d, d) : - $el.removeClass(d).removeAttr(d) - }, 0) - } - - Button.prototype.toggle = function () { - var $parent = this.$element.closest('[data-toggle="buttons-radio"]') - - $parent && $parent - .find('.active') - .removeClass('active') - - this.$element.toggleClass('active') - } - - - /* BUTTON PLUGIN DEFINITION - * ======================== */ - - var old = $.fn.button - - $.fn.button = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('button') - , options = typeof option == 'object' && option - if (!data) $this.data('button', (data = new Button(this, options))) - if (option == 'toggle') data.toggle() - else if (option) data.setState(option) - }) - } - - $.fn.button.defaults = { - loadingText: 'loading...' - } - - $.fn.button.Constructor = Button - - - /* BUTTON NO CONFLICT - * ================== */ - - $.fn.button.noConflict = function () { - $.fn.button = old - return this - } - - - /* BUTTON DATA-API - * =============== */ - - $(document).on('click.button.data-api', '[data-toggle^=button]', function (e) { - var $btn = $(e.target) - if (!$btn.hasClass('btn')) $btn = $btn.closest('.btn') - $btn.button('toggle') - }) - -}(window.$jqTheme || window.jQuery); -/* ========================================================== - * bootstrap-carousel.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#carousel - * ========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* CAROUSEL CLASS DEFINITION - * ========================= */ - - var Carousel = function (element, options) { - this.$element = $(element) - this.$indicators = this.$element.find('.carousel-indicators') - this.options = options - this.options.pause == 'hover' && this.$element - .on('mouseenter', $.proxy(this.pause, this)) - .on('mouseleave', $.proxy(this.cycle, this)) - } - - Carousel.prototype = { - - cycle: function (e) { - if (!e) this.paused = false - if (this.interval) clearInterval(this.interval); - this.options.interval - && !this.paused - && (this.interval = setInterval($.proxy(this.next, this), this.options.interval)) - return this - } - - , getActiveIndex: function () { - this.$active = this.$element.find('.item.active') - this.$items = this.$active.parent().children() - return this.$items.index(this.$active) - } - - , to: function (pos) { - var activeIndex = this.getActiveIndex() - , that = this - - if (pos > (this.$items.length - 1) || pos < 0) return - - if (this.sliding) { - return this.$element.one('slid', function () { - that.to(pos) - }) - } - - if (activeIndex == pos) { - return this.pause().cycle() - } - - return this.slide(pos > activeIndex ? 'next' : 'prev', $(this.$items[pos])) - } - - , pause: function (e) { - if (!e) this.paused = true - if (this.$element.find('.next, .prev').length && $.support.transition.end) { - this.$element.trigger($.support.transition.end) - this.cycle(true) - } - clearInterval(this.interval) - this.interval = null - return this - } - - , next: function () { - if (this.sliding) return - return this.slide('next') - } - - , prev: function () { - if (this.sliding) return - return this.slide('prev') - } - - , slide: function (type, next) { - var $active = this.$element.find('.item.active') - , $next = next || $active[type]() - , isCycling = this.interval - , direction = type == 'next' ? 'left' : 'right' - , fallback = type == 'next' ? 'first' : 'last' - , that = this - , e - - this.sliding = true - - isCycling && this.pause() - - $next = $next.length ? $next : this.$element.find('.item')[fallback]() - - e = $.Event('slide', { - relatedTarget: $next[0] - , direction: direction - }) - - if ($next.hasClass('active')) return - - if (this.$indicators.length) { - this.$indicators.find('.active').removeClass('active') - this.$element.one('slid', function () { - var $nextIndicator = $(that.$indicators.children()[that.getActiveIndex()]) - $nextIndicator && $nextIndicator.addClass('active') - }) - } - - if ($.support.transition && this.$element.hasClass('slide')) { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $next.addClass(type) - $next[0].offsetWidth // force reflow - $active.addClass(direction) - $next.addClass(direction) - this.$element.one($.support.transition.end, function () { - $next.removeClass([type, direction].join(' ')).addClass('active') - $active.removeClass(['active', direction].join(' ')) - that.sliding = false - setTimeout(function () { that.$element.trigger('slid') }, 0) - }) - } else { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $active.removeClass('active') - $next.addClass('active') - this.sliding = false - this.$element.trigger('slid') - } - - isCycling && this.cycle() - - return this - } - - } - - - /* CAROUSEL PLUGIN DEFINITION - * ========================== */ - - var old = $.fn.carousel - - $.fn.carousel = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('carousel') - , options = $.extend({}, $.fn.carousel.defaults, typeof option == 'object' && option) - , action = typeof option == 'string' ? option : options.slide - if (!data) $this.data('carousel', (data = new Carousel(this, options))) - if (typeof option == 'number') data.to(option) - else if (action) data[action]() - else if (options.interval) data.pause().cycle() - }) - } - - $.fn.carousel.defaults = { - interval: 5000 - , pause: 'hover' - } - - $.fn.carousel.Constructor = Carousel - - - /* CAROUSEL NO CONFLICT - * ==================== */ - - $.fn.carousel.noConflict = function () { - $.fn.carousel = old - return this - } - - /* CAROUSEL DATA-API - * ================= */ - - $(document).on('click.carousel.data-api', '[data-slide], [data-slide-to]', function (e) { - var $this = $(this), href - , $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 - , options = $.extend({}, $target.data(), $this.data()) - , slideIndex - - $target.carousel(options) - - if (slideIndex = $this.attr('data-slide-to')) { - $target.data('carousel').pause().to(slideIndex).cycle() - } - - e.preventDefault() - }) - -}(window.$jqTheme || window.jQuery); -/* ============================================================= - * bootstrap-collapse.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#collapse - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* COLLAPSE PUBLIC CLASS DEFINITION - * ================================ */ - - var Collapse = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.collapse.defaults, options) - - if (this.options.parent) { - this.$parent = $(this.options.parent) - } - - this.options.toggle && this.toggle() - } - - Collapse.prototype = { - - constructor: Collapse - - , dimension: function () { - var hasWidth = this.$element.hasClass('width') - return hasWidth ? 'width' : 'height' - } - - , show: function () { - var dimension - , scroll - , actives - , hasData - - if (this.transitioning || this.$element.hasClass('in')) return - - dimension = this.dimension() - scroll = $.camelCase(['scroll', dimension].join('-')) - actives = this.$parent && this.$parent.find('> .accordion-group > .in') - - if (actives && actives.length) { - hasData = actives.data('collapse') - if (hasData && hasData.transitioning) return - actives.collapse('hide') - hasData || actives.data('collapse', null) - } - - this.$element[dimension](0) - this.transition('addClass', $.Event('show'), 'shown') - $.support.transition && this.$element[dimension](this.$element[0][scroll]) - } - - , hide: function () { - var dimension - if (this.transitioning || !this.$element.hasClass('in')) return - dimension = this.dimension() - this.reset(this.$element[dimension]()) - this.transition('removeClass', $.Event('hide'), 'hidden') - this.$element[dimension](0) - } - - , reset: function (size) { - var dimension = this.dimension() - - this.$element - .removeClass('collapse') - [dimension](size || 'auto') - [0].offsetWidth - - this.$element[size !== null ? 'addClass' : 'removeClass']('collapse') - - return this - } - - , transition: function (method, startEvent, completeEvent) { - var that = this - , complete = function () { - if (startEvent.type == 'show') that.reset() - that.transitioning = 0 - that.$element.trigger(completeEvent) - } - - this.$element.trigger(startEvent) - - if (startEvent.isDefaultPrevented()) return - - this.transitioning = 1 - - this.$element[method]('in') - - $.support.transition && this.$element.hasClass('collapse') ? - this.$element.one($.support.transition.end, complete) : - complete() - } - - , toggle: function () { - this[this.$element.hasClass('in') ? 'hide' : 'show']() - } - - } - - - /* COLLAPSE PLUGIN DEFINITION - * ========================== */ - - var old = $.fn.collapse - - $.fn.collapse = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('collapse') - , options = $.extend({}, $.fn.collapse.defaults, $this.data(), typeof option == 'object' && option) - if (!data) $this.data('collapse', (data = new Collapse(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.collapse.defaults = { - toggle: true - } - - $.fn.collapse.Constructor = Collapse - - - /* COLLAPSE NO CONFLICT - * ==================== */ - - $.fn.collapse.noConflict = function () { - $.fn.collapse = old - return this - } - - - /* COLLAPSE DATA-API - * ================= */ - - $(document).on('click.collapse.data-api', '[data-toggle=collapse]', function (e) { - var $this = $(this), href - , target = $this.attr('data-target') - || e.preventDefault() - || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') //strip for ie7 - , option = $(target).data('collapse') ? 'toggle' : $this.data() - $this[$(target).hasClass('in') ? 'addClass' : 'removeClass']('collapsed') - $(target).collapse(option) - }) - -}(window.$jqTheme || window.jQuery); -/* ============================================================ - * bootstrap-dropdown.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#dropdowns - * ============================================================ - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* DROPDOWN CLASS DEFINITION - * ========================= */ - - var toggle = '[data-toggle=dropdown]' - , Dropdown = function (element) { - var $el = $(element).on('click.dropdown.data-api', this.toggle) - $('html').on('click.dropdown.data-api', function () { - $el.parent().removeClass('open') - }) - } - - Dropdown.prototype = { - - constructor: Dropdown - - , toggle: function (e) { - var $this = $(this) - , $parent - , isActive - - if ($this.is('.disabled, :disabled')) return - - $parent = getParent($this) - - isActive = $parent.hasClass('open') - - clearMenus() - - if (!isActive) { - if ('ontouchstart' in document.documentElement) { - // if mobile we we use a backdrop because click events don't delegate - $('
    ').insertBefore($(this)).on('click', clearMenus) - } - $parent.toggleClass('open') - } - - $this.focus() - - return false - } - - , keydown: function (e) { - var $this - , $items - , $active - , $parent - , isActive - , index - - if (!/(38|40|27)/.test(e.keyCode)) return - - $this = $(this) - - e.preventDefault() - e.stopPropagation() - - if ($this.is('.disabled, :disabled')) return - - $parent = getParent($this) - - isActive = $parent.hasClass('open') - - if (!isActive || (isActive && e.keyCode == 27)) { - if (e.which == 27) $parent.find(toggle).focus() - return $this.click() - } - - $items = $('[role=menu] li:not(.divider):visible a', $parent) - - if (!$items.length) return - - index = $items.index($items.filter(':focus')) - - if (e.keyCode == 38 && index > 0) index-- // up - if (e.keyCode == 40 && index < $items.length - 1) index++ // down - if (!~index) index = 0 - - $items - .eq(index) - .focus() - } - - } - - function clearMenus() { - $('.dropdown-backdrop').remove() - $(toggle).each(function () { - getParent($(this)).removeClass('open') - }) - } - - function getParent($this) { - var selector = $this.attr('data-target') - , $parent - - if (!selector) { - selector = $this.attr('href') - selector = selector && /#/.test(selector) && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - $parent = selector && $(selector) - - if (!$parent || !$parent.length) $parent = $this.parent() - - return $parent - } - - - /* DROPDOWN PLUGIN DEFINITION - * ========================== */ - - var old = $.fn.dropdown - - $.fn.dropdown = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('dropdown') - if (!data) $this.data('dropdown', (data = new Dropdown(this))) - if (typeof option == 'string') data[option].call($this) - }) - } - - $.fn.dropdown.Constructor = Dropdown - - - /* DROPDOWN NO CONFLICT - * ==================== */ - - $.fn.dropdown.noConflict = function () { - $.fn.dropdown = old - return this - } - - - /* APPLY TO STANDARD DROPDOWN ELEMENTS - * =================================== */ - - $(document) - .on('click.dropdown.data-api', clearMenus) - .on('click.dropdown.data-api', '.dropdown form', function (e) { e.stopPropagation() }) - .on('click.dropdown.data-api' , toggle, Dropdown.prototype.toggle) - .on('keydown.dropdown.data-api', toggle + ', [role=menu]' , Dropdown.prototype.keydown) - -}(window.$jqTheme || window.jQuery); -/* ========================================================= - * bootstrap-modal.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#modals - * ========================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================= */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* MODAL CLASS DEFINITION - * ====================== */ - - var Modal = function (element, options) { - this.options = options - this.$element = $(element) - .delegate('[data-dismiss="modal"]', 'click.dismiss.modal', $.proxy(this.hide, this)) - this.options.remote && this.$element.find('.modal-body').load(this.options.remote) - } - - Modal.prototype = { - - constructor: Modal - - , toggle: function () { - return this[!this.isShown ? 'show' : 'hide']() - } - - , show: function () { - var that = this - , e = $.Event('show') - - this.$element.trigger(e) - - if (this.isShown || e.isDefaultPrevented()) return - - this.isShown = true - - this.escape() - - this.backdrop(function () { - var transition = $.support.transition && that.$element.hasClass('fade') - - if (!that.$element.parent().length) { - that.$element.appendTo(document.body) //don't move modals dom position - } - - that.$element.show() - - if (transition) { - that.$element[0].offsetWidth // force reflow - } - - that.$element - .addClass('in') - .attr('aria-hidden', false) - - that.enforceFocus() - - transition ? - that.$element.one($.support.transition.end, function () { that.$element.focus().trigger('shown') }) : - that.$element.focus().trigger('shown') - - }) - } - - , hide: function (e) { - e && e.preventDefault() - - var that = this - - e = $.Event('hide') - - this.$element.trigger(e) - - if (!this.isShown || e.isDefaultPrevented()) return - - this.isShown = false - - this.escape() - - $(document).off('focusin.modal') - - this.$element - .removeClass('in') - .attr('aria-hidden', true) - - $.support.transition && this.$element.hasClass('fade') ? - this.hideWithTransition() : - this.hideModal() - } - - , enforceFocus: function () { - var that = this - $(document).on('focusin.modal', function (e) { - if (that.$element[0] !== e.target && !that.$element.has(e.target).length) { - that.$element.focus() - } - }) - } - - , escape: function () { - var that = this - if (this.isShown && this.options.keyboard) { - this.$element.on('keyup.dismiss.modal', function ( e ) { - e.which == 27 && that.hide() - }) - } else if (!this.isShown) { - this.$element.off('keyup.dismiss.modal') - } - } - - , hideWithTransition: function () { - var that = this - , timeout = setTimeout(function () { - that.$element.off($.support.transition.end) - that.hideModal() - }, 500) - - this.$element.one($.support.transition.end, function () { - clearTimeout(timeout) - that.hideModal() - }) - } - - , hideModal: function () { - var that = this - this.$element.hide() - this.backdrop(function () { - that.removeBackdrop() - that.$element.trigger('hidden') - }) - } - - , removeBackdrop: function () { - this.$backdrop && this.$backdrop.remove() - this.$backdrop = null - } - - , backdrop: function (callback) { - var that = this - , animate = this.$element.hasClass('fade') ? 'fade' : '' - - if (this.isShown && this.options.backdrop) { - var doAnimate = $.support.transition && animate - - this.$backdrop = $('
    ') - .appendTo(document.body) - - this.$backdrop.click( - this.options.backdrop == 'static' ? - $.proxy(this.$element[0].focus, this.$element[0]) - : $.proxy(this.hide, this) - ) - - if (doAnimate) this.$backdrop[0].offsetWidth // force reflow - - this.$backdrop.addClass('in') - - if (!callback) return - - doAnimate ? - this.$backdrop.one($.support.transition.end, callback) : - callback() - - } else if (!this.isShown && this.$backdrop) { - this.$backdrop.removeClass('in') - - $.support.transition && this.$element.hasClass('fade')? - this.$backdrop.one($.support.transition.end, callback) : - callback() - - } else if (callback) { - callback() - } - } - } - - - /* MODAL PLUGIN DEFINITION - * ======================= */ - - var old = $.fn.modal - - $.fn.modal = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('modal') - , options = $.extend({}, $.fn.modal.defaults, $this.data(), typeof option == 'object' && option) - if (!data) $this.data('modal', (data = new Modal(this, options))) - if (typeof option == 'string') data[option]() - else if (options.show) data.show() - }) - } - - $.fn.modal.defaults = { - backdrop: true - , keyboard: true - , show: true - } - - $.fn.modal.Constructor = Modal - - - /* MODAL NO CONFLICT - * ================= */ - - $.fn.modal.noConflict = function () { - $.fn.modal = old - return this - } - - - /* MODAL DATA-API - * ============== */ - - $(document).on('click.modal.data-api', '[data-toggle="modal"]', function (e) { - var $this = $(this) - , href = $this.attr('href') - , $target = $($this.attr('data-target') || (href && href.replace(/.*(?=#[^\s]+$)/, ''))) //strip for ie7 - , option = $target.data('modal') ? 'toggle' : $.extend({ remote:!/#/.test(href) && href }, $target.data(), $this.data()) - - e.preventDefault() - - $target - .modal(option) - .one('hide', function () { - $this.focus() - }) - }) - -}(window.$jqTheme || window.jQuery); -/* =========================================================== - * bootstrap-tooltip.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#tooltips - * Inspired by the original jQuery.tipsy by Jason Frame - * =========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* TOOLTIP PUBLIC CLASS DEFINITION - * =============================== */ - - var Tooltip = function (element, options) { - this.init('tooltip', element, options) - } - - Tooltip.prototype = { - - constructor: Tooltip - - , init: function (type, element, options) { - var eventIn - , eventOut - , triggers - , trigger - , i - - this.type = type - this.$element = $(element) - this.options = this.getOptions(options) - this.enabled = true - - triggers = this.options.trigger.split(' ') - - for (i = triggers.length; i--;) { - trigger = triggers[i] - if (trigger == 'click') { - this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this)) - } else if (trigger != 'manual') { - eventIn = trigger == 'hover' ? 'mouseenter' : 'focus' - eventOut = trigger == 'hover' ? 'mouseleave' : 'blur' - this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this)) - this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this)) - } - } - - this.options.selector ? - (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) : - this.fixTitle() - } - - , getOptions: function (options) { - options = $.extend({}, $.fn[this.type].defaults, this.$element.data(), options) - - if (options.delay && typeof options.delay == 'number') { - options.delay = { - show: options.delay - , hide: options.delay - } - } - - return options - } - - , enter: function (e) { - var defaults = $.fn[this.type].defaults - , options = {} - , self - - this._options && $.each(this._options, function (key, value) { - if (defaults[key] != value) options[key] = value - }, this) - - self = $(e.currentTarget)[this.type](options).data(this.type) - - if (!self.options.delay || !self.options.delay.show) return self.show() - - clearTimeout(this.timeout) - self.hoverState = 'in' - this.timeout = setTimeout(function() { - if (self.hoverState == 'in') self.show() - }, self.options.delay.show) - } - - , leave: function (e) { - var self = $(e.currentTarget)[this.type](this._options).data(this.type) - - if (this.timeout) clearTimeout(this.timeout) - if (!self.options.delay || !self.options.delay.hide) return self.hide() - - self.hoverState = 'out' - this.timeout = setTimeout(function() { - if (self.hoverState == 'out') self.hide() - }, self.options.delay.hide) - } - - , show: function () { - var $tip - , pos - , actualWidth - , actualHeight - , placement - , tp - , e = $.Event('show') - - if (this.hasContent() && this.enabled) { - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - $tip = this.tip() - this.setContent() - - if (this.options.animation) { - $tip.addClass('fade') - } - - placement = typeof this.options.placement == 'function' ? - this.options.placement.call(this, $tip[0], this.$element[0]) : - this.options.placement - - $tip - .detach() - .css({ top: 0, left: 0, display: 'block' }) - - this.options.container ? $tip.appendTo(this.options.container) : $tip.insertAfter(this.$element) - - pos = this.getPosition() - - actualWidth = $tip[0].offsetWidth - actualHeight = $tip[0].offsetHeight - - switch (placement) { - case 'bottom': - tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2} - break - case 'top': - tp = {top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2} - break - case 'left': - tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth} - break - case 'right': - tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width} - break - } - - this.applyPlacement(tp, placement) - this.$element.trigger('shown') - } - } - - , applyPlacement: function(offset, placement){ - var $tip = this.tip() - , width = $tip[0].offsetWidth - , height = $tip[0].offsetHeight - , actualWidth - , actualHeight - , delta - , replace - - $tip - .offset(offset) - .addClass(placement) - .addClass('in') - - actualWidth = $tip[0].offsetWidth - actualHeight = $tip[0].offsetHeight - - if (placement == 'top' && actualHeight != height) { - offset.top = offset.top + height - actualHeight - replace = true - } - - if (placement == 'bottom' || placement == 'top') { - delta = 0 - - if (offset.left < 0){ - delta = offset.left * -2 - offset.left = 0 - $tip.offset(offset) - actualWidth = $tip[0].offsetWidth - actualHeight = $tip[0].offsetHeight - } - - this.replaceArrow(delta - width + actualWidth, actualWidth, 'left') - } else { - this.replaceArrow(actualHeight - height, actualHeight, 'top') - } - - if (replace) $tip.offset(offset) - } - - , replaceArrow: function(delta, dimension, position){ - this - .arrow() - .css(position, delta ? (50 * (1 - delta / dimension) + "%") : '') - } - - , setContent: function () { - var $tip = this.tip() - , title = this.getTitle() - - $tip.find('.tooltip-inner')[this.options.html ? 'html' : 'text'](title) - $tip.removeClass('fade in top bottom left right') - } - - , hide: function () { - var that = this - , $tip = this.tip() - , e = $.Event('hide') - - this.$element.trigger(e) - if (e.isDefaultPrevented()) return - - $tip.removeClass('in') - - function removeWithAnimation() { - var timeout = setTimeout(function () { - $tip.off($.support.transition.end).detach() - }, 500) - - $tip.one($.support.transition.end, function () { - clearTimeout(timeout) - $tip.detach() - }) - } - - $.support.transition && this.$tip.hasClass('fade') ? - removeWithAnimation() : - $tip.detach() - - this.$element.trigger('hidden') - - return this - } - - , fixTitle: function () { - var $e = this.$element - if ($e.attr('title') || typeof($e.attr('data-original-title')) != 'string') { - $e.attr('data-original-title', $e.attr('title') || '').attr('title', '') - } - } - - , hasContent: function () { - return this.getTitle() - } - - , getPosition: function () { - var el = this.$element[0] - return $.extend({}, (typeof el.getBoundingClientRect == 'function') ? el.getBoundingClientRect() : { - width: el.offsetWidth - , height: el.offsetHeight - }, this.$element.offset()) - } - - , getTitle: function () { - var title - , $e = this.$element - , o = this.options - - title = $e.attr('data-original-title') - || (typeof o.title == 'function' ? o.title.call($e[0]) : o.title) - - return title - } - - , tip: function () { - return this.$tip = this.$tip || $(this.options.template) - } - - , arrow: function(){ - return this.$arrow = this.$arrow || this.tip().find(".tooltip-arrow") - } - - , validate: function () { - if (!this.$element[0].parentNode) { - this.hide() - this.$element = null - this.options = null - } - } - - , enable: function () { - this.enabled = true - } - - , disable: function () { - this.enabled = false - } - - , toggleEnabled: function () { - this.enabled = !this.enabled - } - - , toggle: function (e) { - var self = e ? $(e.currentTarget)[this.type](this._options).data(this.type) : this - self.tip().hasClass('in') ? self.hide() : self.show() - } - - , destroy: function () { - this.hide().$element.off('.' + this.type).removeData(this.type) - } - - } - - - /* TOOLTIP PLUGIN DEFINITION - * ========================= */ - - var old = $.fn.tooltip - - $.fn.tooltip = function ( option ) { - return this.each(function () { - var $this = $(this) - , data = $this.data('tooltip') - , options = typeof option == 'object' && option - if (!data) $this.data('tooltip', (data = new Tooltip(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.tooltip.Constructor = Tooltip - - $.fn.tooltip.defaults = { - animation: true - , placement: 'top' - , selector: false - , template: '
    ' - , trigger: 'hover focus' - , title: '' - , delay: 0 - , html: false - , container: false - } - - - /* TOOLTIP NO CONFLICT - * =================== */ - - $.fn.tooltip.noConflict = function () { - $.fn.tooltip = old - return this - } - -}(window.$jqTheme || window.jQuery); -/* =========================================================== - * bootstrap-popover.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#popovers - * =========================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * =========================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* POPOVER PUBLIC CLASS DEFINITION - * =============================== */ - - var Popover = function (element, options) { - this.init('popover', element, options) - } - - - /* NOTE: POPOVER EXTENDS BOOTSTRAP-TOOLTIP.js - ========================================== */ - - Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype, { - - constructor: Popover - - , setContent: function () { - var $tip = this.tip() - , title = this.getTitle() - , content = this.getContent() - - $tip.find('.popover-title')[this.options.html ? 'html' : 'text'](title) - $tip.find('.popover-content')[this.options.html ? 'html' : 'text'](content) - - $tip.removeClass('fade top bottom left right in') - } - - , hasContent: function () { - return this.getTitle() || this.getContent() - } - - , getContent: function () { - var content - , $e = this.$element - , o = this.options - - content = (typeof o.content == 'function' ? o.content.call($e[0]) : o.content) - || $e.attr('data-content') - - return content - } - - , tip: function () { - if (!this.$tip) { - this.$tip = $(this.options.template) - } - return this.$tip - } - - , destroy: function () { - this.hide().$element.off('.' + this.type).removeData(this.type) - } - - }) - - - /* POPOVER PLUGIN DEFINITION - * ======================= */ - - var old = $.fn.popover - - $.fn.popover = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('popover') - , options = typeof option == 'object' && option - if (!data) $this.data('popover', (data = new Popover(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.popover.Constructor = Popover - - $.fn.popover.defaults = $.extend({} , $.fn.tooltip.defaults, { - placement: 'right' - , trigger: 'click' - , content: '' - , template: '

    ' - }) - - - /* POPOVER NO CONFLICT - * =================== */ - - $.fn.popover.noConflict = function () { - $.fn.popover = old - return this - } - -}(window.$jqTheme || window.jQuery); -/* ============================================================= - * bootstrap-scrollspy.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#scrollspy - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* SCROLLSPY CLASS DEFINITION - * ========================== */ - - function ScrollSpy(element, options) { - var process = $.proxy(this.process, this) - , $element = $(element).is('body') ? $(window) : $(element) - , href - this.options = $.extend({}, $.fn.scrollspy.defaults, options) - this.$scrollElement = $element.on('scroll.scroll-spy.data-api', process) - this.selector = (this.options.target - || ((href = $(element).attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7 - || '') + ' .nav li > a' - this.$body = $('body') - this.refresh() - this.process() - } - - ScrollSpy.prototype = { - - constructor: ScrollSpy - - , refresh: function () { - var self = this - , $targets - - this.offsets = $([]) - this.targets = $([]) - - $targets = this.$body - .find(this.selector) - .map(function () { - var $el = $(this) - , href = $el.data('target') || $el.attr('href') - , $href = /^#\w/.test(href) && $(href) - return ( $href - && $href.length - && [[ $href.position().top + (!$.isWindow(self.$scrollElement.get(0)) && self.$scrollElement.scrollTop()), href ]] ) || null - }) - .sort(function (a, b) { return a[0] - b[0] }) - .each(function () { - self.offsets.push(this[0]) - self.targets.push(this[1]) - }) - } - - , process: function () { - var scrollTop = this.$scrollElement.scrollTop() + this.options.offset - , scrollHeight = this.$scrollElement[0].scrollHeight || this.$body[0].scrollHeight - , maxScroll = scrollHeight - this.$scrollElement.height() - , offsets = this.offsets - , targets = this.targets - , activeTarget = this.activeTarget - , i - - if (scrollTop >= maxScroll) { - return activeTarget != (i = targets.last()[0]) - && this.activate ( i ) - } - - for (i = offsets.length; i--;) { - activeTarget != targets[i] - && scrollTop >= offsets[i] - && (!offsets[i + 1] || scrollTop <= offsets[i + 1]) - && this.activate( targets[i] ) - } - } - - , activate: function (target) { - var active - , selector - - this.activeTarget = target - - $(this.selector) - .parent('.active') - .removeClass('active') - - selector = this.selector - + '[data-target="' + target + '"],' - + this.selector + '[href="' + target + '"]' - - active = $(selector) - .parent('li') - .addClass('active') - - if (active.parent('.dropdown-menu').length) { - active = active.closest('li.dropdown').addClass('active') - } - - active.trigger('activate') - } - - } - - - /* SCROLLSPY PLUGIN DEFINITION - * =========================== */ - - var old = $.fn.scrollspy - - $.fn.scrollspy = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('scrollspy') - , options = typeof option == 'object' && option - if (!data) $this.data('scrollspy', (data = new ScrollSpy(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.scrollspy.Constructor = ScrollSpy - - $.fn.scrollspy.defaults = { - offset: 10 - } - - - /* SCROLLSPY NO CONFLICT - * ===================== */ - - $.fn.scrollspy.noConflict = function () { - $.fn.scrollspy = old - return this - } - - - /* SCROLLSPY DATA-API - * ================== */ - - $(window).on('load', function () { - $('[data-spy="scroll"]').each(function () { - var $spy = $(this) - $spy.scrollspy($spy.data()) - }) - }) - -}(window.$jqTheme || window.jQuery); -/* ======================================================== - * bootstrap-tab.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#tabs - * ======================================================== - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ======================================================== */ - - -!function ($) { - - "use strict"; // jshint ;_; - - - /* TAB CLASS DEFINITION - * ==================== */ - - var Tab = function (element) { - this.element = $(element) - } - - Tab.prototype = { - - constructor: Tab - - , show: function () { - var $this = this.element - , $ul = $this.closest('ul:not(.dropdown-menu)') - , selector = $this.attr('data-target') - , previous - , $target - , e - - if (!selector) { - selector = $this.attr('href') - selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7 - } - - if ( $this.parent('li').hasClass('active') ) return - - previous = $ul.find('.active:last a')[0] - - e = $.Event('show', { - relatedTarget: previous - }) - - $this.trigger(e) - - if (e.isDefaultPrevented()) return - - $target = $(selector) - - this.activate($this.parent('li'), $ul) - this.activate($target, $target.parent(), function () { - $this.trigger({ - type: 'shown' - , relatedTarget: previous - }) - }) - } - - , activate: function ( element, container, callback) { - var $active = container.find('> .active') - , transition = callback - && $.support.transition - && $active.hasClass('fade') - - function next() { - $active - .removeClass('active') - .find('> .dropdown-menu > .active') - .removeClass('active') - - element.addClass('active') - - if (transition) { - element[0].offsetWidth // reflow for transition - element.addClass('in') - } else { - element.removeClass('fade') - } - - if ( element.parent('.dropdown-menu') ) { - element.closest('li.dropdown').addClass('active') - } - - callback && callback() - } - - transition ? - $active.one($.support.transition.end, next) : - next() - - $active.removeClass('in') - } - } - - - /* TAB PLUGIN DEFINITION - * ===================== */ - - var old = $.fn.tab - - $.fn.tab = function ( option ) { - return this.each(function () { - var $this = $(this) - , data = $this.data('tab') - if (!data) $this.data('tab', (data = new Tab(this))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.tab.Constructor = Tab - - - /* TAB NO CONFLICT - * =============== */ - - $.fn.tab.noConflict = function () { - $.fn.tab = old - return this - } - - - /* TAB DATA-API - * ============ */ - - $(document).on('click.tab.data-api', '[data-toggle="tab"], [data-toggle="pill"]', function (e) { - e.preventDefault() - $(this).tab('show') - }) - -}(window.$jqTheme || window.jQuery); -/* ============================================================= - * bootstrap-typeahead.js v2.3.2 - * http://twitter.github.com/bootstrap/javascript.html#typeahead - * ============================================================= - * Copyright 2012 Twitter, Inc. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * ============================================================ */ - - -!function($){ - - "use strict"; // jshint ;_; - - - /* TYPEAHEAD PUBLIC CLASS DEFINITION - * ================================= */ - - var Typeahead = function (element, options) { - this.$element = $(element) - this.options = $.extend({}, $.fn.typeahead.defaults, options) - this.matcher = this.options.matcher || this.matcher - this.sorter = this.options.sorter || this.sorter - this.highlighter = this.options.highlighter || this.highlighter - this.updater = this.options.updater || this.updater - this.source = this.options.source - this.$menu = $(this.options.menu) - this.shown = false - this.listen() - } - - Typeahead.prototype = { - - constructor: Typeahead - - , select: function () { - var val = this.$menu.find('.active').attr('data-value') - this.$element - .val(this.updater(val)) - .change() - return this.hide() - } - - , updater: function (item) { - return item - } - - , show: function () { - var pos = $.extend({}, this.$element.position(), { - height: this.$element[0].offsetHeight - }) - - this.$menu - .insertAfter(this.$element) - .css({ - top: pos.top + pos.height - , left: pos.left - }) - .show() - - this.shown = true - return this - } - - , hide: function () { - this.$menu.hide() - this.shown = false - return this - } - - , lookup: function (event) { - var items - - this.query = this.$element.val() - - if (!this.query || this.query.length < this.options.minLength) { - return this.shown ? this.hide() : this - } - - items = $.isFunction(this.source) ? this.source(this.query, $.proxy(this.process, this)) : this.source - - return items ? this.process(items) : this - } - - , process: function (items) { - var that = this - - items = $.grep(items, function (item) { - return that.matcher(item) - }) - - items = this.sorter(items) - - if (!items.length) { - return this.shown ? this.hide() : this - } - - return this.render(items.slice(0, this.options.items)).show() - } - - , matcher: function (item) { - return ~item.toLowerCase().indexOf(this.query.toLowerCase()) - } - - , sorter: function (items) { - var beginswith = [] - , caseSensitive = [] - , caseInsensitive = [] - , item - - while (item = items.shift()) { - if (!item.toLowerCase().indexOf(this.query.toLowerCase())) beginswith.push(item) - else if (~item.indexOf(this.query)) caseSensitive.push(item) - else caseInsensitive.push(item) - } - - return beginswith.concat(caseSensitive, caseInsensitive) - } - - , highlighter: function (item) { - var query = this.query.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g, '\\$&') - return item.replace(new RegExp('(' + query + ')', 'ig'), function ($1, match) { - return '' + match + '' - }) - } - - , render: function (items) { - var that = this - - items = $(items).map(function (i, item) { - i = $(that.options.item).attr('data-value', item) - i.find('a').html(that.highlighter(item)) - return i[0] - }) - - items.first().addClass('active') - this.$menu.html(items) - return this - } - - , next: function (event) { - var active = this.$menu.find('.active').removeClass('active') - , next = active.next() - - if (!next.length) { - next = $(this.$menu.find('li')[0]) - } - - next.addClass('active') - } - - , prev: function (event) { - var active = this.$menu.find('.active').removeClass('active') - , prev = active.prev() - - if (!prev.length) { - prev = this.$menu.find('li').last() - } - - prev.addClass('active') - } - - , listen: function () { - this.$element - .on('focus', $.proxy(this.focus, this)) - .on('blur', $.proxy(this.blur, this)) - .on('keypress', $.proxy(this.keypress, this)) - .on('keyup', $.proxy(this.keyup, this)) - - if (this.eventSupported('keydown')) { - this.$element.on('keydown', $.proxy(this.keydown, this)) - } - - this.$menu - .on('click', $.proxy(this.click, this)) - .on('mouseenter', 'li', $.proxy(this.mouseenter, this)) - .on('mouseleave', 'li', $.proxy(this.mouseleave, this)) - } - - , eventSupported: function(eventName) { - var isSupported = eventName in this.$element - if (!isSupported) { - this.$element.setAttribute(eventName, 'return;') - isSupported = typeof this.$element[eventName] === 'function' - } - return isSupported - } - - , move: function (e) { - if (!this.shown) return - - switch(e.keyCode) { - case 9: // tab - case 13: // enter - case 27: // escape - e.preventDefault() - break - - case 38: // up arrow - e.preventDefault() - this.prev() - break - - case 40: // down arrow - e.preventDefault() - this.next() - break - } - - e.stopPropagation() - } - - , keydown: function (e) { - this.suppressKeyPressRepeat = ~$.inArray(e.keyCode, [40,38,9,13,27]) - this.move(e) - } - - , keypress: function (e) { - if (this.suppressKeyPressRepeat) return - this.move(e) - } - - , keyup: function (e) { - switch(e.keyCode) { - case 40: // down arrow - case 38: // up arrow - case 16: // shift - case 17: // ctrl - case 18: // alt - break - - case 9: // tab - case 13: // enter - if (!this.shown) return - this.select() - break - - case 27: // escape - if (!this.shown) return - this.hide() - break - - default: - this.lookup() - } - - e.stopPropagation() - e.preventDefault() - } - - , focus: function (e) { - this.focused = true - } - - , blur: function (e) { - this.focused = false - if (!this.mousedover && this.shown) this.hide() - } - - , click: function (e) { - e.stopPropagation() - e.preventDefault() - this.select() - this.$element.focus() - } - - , mouseenter: function (e) { - this.mousedover = true - this.$menu.find('.active').removeClass('active') - $(e.currentTarget).addClass('active') - } - - , mouseleave: function (e) { - this.mousedover = false - if (!this.focused && this.shown) this.hide() - } - - } - - - /* TYPEAHEAD PLUGIN DEFINITION - * =========================== */ - - var old = $.fn.typeahead - - $.fn.typeahead = function (option) { - return this.each(function () { - var $this = $(this) - , data = $this.data('typeahead') - , options = typeof option == 'object' && option - if (!data) $this.data('typeahead', (data = new Typeahead(this, options))) - if (typeof option == 'string') data[option]() - }) - } - - $.fn.typeahead.defaults = { - source: [] - , items: 8 - , menu: '
      ' - , item: '
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progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #dbdbdb;\n text-shadow: 0 1px 0 #fff;\n border-color: #ccc;\n}\n.btn-default:hover,\n.btn-default:focus {\n background-color: #e0e0e0;\n background-position: 0 -15px;\n}\n.btn-default:active,\n.btn-default.active {\n background-color: #e0e0e0;\n border-color: #dbdbdb;\n}\n.btn-default.disabled,\n.btn-default[disabled],\nfieldset[disabled] .btn-default,\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus,\n.btn-default.disabled:active,\n.btn-default[disabled]:active,\nfieldset[disabled] .btn-default:active,\n.btn-default.disabled.active,\n.btn-default[disabled].active,\nfieldset[disabled] .btn-default.active {\n background-color: #e0e0e0;\n background-image: none;\n}\n.btn-primary {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #265a88 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #265a88 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #245580;\n}\n.btn-primary:hover,\n.btn-primary:focus {\n background-color: #265a88;\n background-position: 0 -15px;\n}\n.btn-primary:active,\n.btn-primary.active {\n background-color: #265a88;\n border-color: #245580;\n}\n.btn-primary.disabled,\n.btn-primary[disabled],\nfieldset[disabled] .btn-primary,\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus,\n.btn-primary.disabled:active,\n.btn-primary[disabled]:active,\nfieldset[disabled] .btn-primary:active,\n.btn-primary.disabled.active,\n.btn-primary[disabled].active,\nfieldset[disabled] .btn-primary.active {\n background-color: #265a88;\n background-image: none;\n}\n.btn-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%);\n background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #3e8f3e;\n}\n.btn-success:hover,\n.btn-success:focus {\n background-color: #419641;\n background-position: 0 -15px;\n}\n.btn-success:active,\n.btn-success.active {\n background-color: #419641;\n border-color: #3e8f3e;\n}\n.btn-success.disabled,\n.btn-success[disabled],\nfieldset[disabled] .btn-success,\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus,\n.btn-success.disabled:active,\n.btn-success[disabled]:active,\nfieldset[disabled] .btn-success:active,\n.btn-success.disabled.active,\n.btn-success[disabled].active,\nfieldset[disabled] .btn-success.active {\n background-color: #419641;\n background-image: none;\n}\n.btn-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n background-color: #2aabd2;\n background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n background-color: #2aabd2;\n border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info[disabled],\nfieldset[disabled] .btn-info,\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus,\n.btn-info.disabled:active,\n.btn-info[disabled]:active,\nfieldset[disabled] .btn-info:active,\n.btn-info.disabled.active,\n.btn-info[disabled].active,\nfieldset[disabled] .btn-info.active {\n background-color: #2aabd2;\n background-image: none;\n}\n.btn-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n background-color: #eb9316;\n background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n background-color: #eb9316;\n border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning[disabled],\nfieldset[disabled] .btn-warning,\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus,\n.btn-warning.disabled:active,\n.btn-warning[disabled]:active,\nfieldset[disabled] .btn-warning:active,\n.btn-warning.disabled.active,\n.btn-warning[disabled].active,\nfieldset[disabled] .btn-warning.active {\n background-color: #eb9316;\n background-image: none;\n}\n.btn-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n background-color: #c12e2a;\n background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n background-color: #c12e2a;\n border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger[disabled],\nfieldset[disabled] .btn-danger,\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus,\n.btn-danger.disabled:active,\n.btn-danger[disabled]:active,\nfieldset[disabled] .btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n background-color: #c12e2a;\n 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progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n background-color: #2e6da4;\n}\n.navbar-default {\n background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n@media (max-width: 767px) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n }\n}\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n background-repeat: repeat-x;\n border-color: #b2dba1;\n}\n.alert-info {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #9acfea;\n}\n.alert-warning {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #f5e79e;\n}\n.alert-danger {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dca7a7;\n}\n.progress {\n background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 #286090;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n background-repeat: repeat-x;\n border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n text-shadow: none;\n}\n.panel {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-primary > .panel-heading {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-success > .panel-heading {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-info > .panel-heading {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-warning > .panel-heading {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-danger > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n background-repeat: repeat-x;\n}\n.well {\n background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dcdcdc;\n -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","// stylelint-disable selector-no-qualifying-type, selector-max-compound-selectors\n\n/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n\n//\n// Load core variables and mixins\n// --------------------------------------------------\n\n@import \"variables.less\";\n@import \"mixins.less\";\n\n\n//\n// Buttons\n// --------------------------------------------------\n\n// Common styles\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, .2);\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);\n .box-shadow(@shadow);\n\n // Reset the shadow\n &:active,\n &.active {\n .box-shadow(inset 0 3px 5px rgba(0, 0, 0, .125));\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n .box-shadow(none);\n }\n\n .badge {\n text-shadow: none;\n }\n}\n\n// Mixin for generating new styles\n.btn-styles(@btn-color: #555) {\n #gradient > .vertical(@start-color: @btn-color; @end-color: darken(@btn-color, 12%));\n .reset-filter(); // Disable gradients for IE9 because filter bleeds through rounded corners; see https://github.com/twbs/bootstrap/issues/10620\n background-repeat: repeat-x;\n border-color: darken(@btn-color, 14%);\n\n &:hover,\n &:focus {\n background-color: darken(@btn-color, 12%);\n background-position: 0 -15px;\n }\n\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n border-color: darken(@btn-color, 14%);\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n &,\n &:hover,\n &:focus,\n &.focus,\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n background-image: none;\n }\n }\n}\n\n// Common styles\n.btn {\n // Remove the gradient for the pressed/active state\n &:active,\n &.active {\n background-image: none;\n }\n}\n\n// Apply the mixin to the buttons\n.btn-default {\n .btn-styles(@btn-default-bg);\n text-shadow: 0 1px 0 #fff;\n border-color: #ccc;\n}\n.btn-primary { .btn-styles(@btn-primary-bg); }\n.btn-success { .btn-styles(@btn-success-bg); }\n.btn-info { .btn-styles(@btn-info-bg); }\n.btn-warning { .btn-styles(@btn-warning-bg); }\n.btn-danger { .btn-styles(@btn-danger-bg); }\n\n\n//\n// Images\n// --------------------------------------------------\n\n.thumbnail,\n.img-thumbnail {\n .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n\n\n//\n// Dropdowns\n// --------------------------------------------------\n\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-hover-bg; @end-color: darken(@dropdown-link-hover-bg, 5%));\n background-color: darken(@dropdown-link-hover-bg, 5%);\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n background-color: darken(@dropdown-link-active-bg, 5%);\n}\n\n\n//\n// Navbar\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n #gradient > .vertical(@start-color: lighten(@navbar-default-bg, 10%); @end-color: @navbar-default-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered\n border-radius: @navbar-border-radius;\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);\n .box-shadow(@shadow);\n\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: darken(@navbar-default-link-active-bg, 5%); @end-color: darken(@navbar-default-link-active-bg, 2%));\n .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .075));\n }\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, .25);\n}\n\n// Inverted navbar\n.navbar-inverse {\n #gradient > .vertical(@start-color: lighten(@navbar-inverse-bg, 10%); @end-color: @navbar-inverse-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n border-radius: @navbar-border-radius;\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .25));\n }\n\n .navbar-brand,\n .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, .25);\n }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a {\n &,\n &:hover,\n &:focus {\n color: #fff;\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n }\n }\n}\n\n\n//\n// Alerts\n// --------------------------------------------------\n\n// Common styles\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, .2);\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);\n .box-shadow(@shadow);\n}\n\n// Mixin for generating new styles\n.alert-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 7.5%));\n border-color: darken(@color, 15%);\n}\n\n// Apply the mixin to the alerts\n.alert-success { .alert-styles(@alert-success-bg); }\n.alert-info { .alert-styles(@alert-info-bg); }\n.alert-warning { .alert-styles(@alert-warning-bg); }\n.alert-danger { .alert-styles(@alert-danger-bg); }\n\n\n//\n// Progress bars\n// --------------------------------------------------\n\n// Give the progress background some depth\n.progress {\n #gradient > .vertical(@start-color: darken(@progress-bg, 4%); @end-color: @progress-bg)\n}\n\n// Mixin for generating new styles\n.progress-bar-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 10%));\n}\n\n// Apply the mixin to the progress bars\n.progress-bar { .progress-bar-styles(@progress-bar-bg); }\n.progress-bar-success { .progress-bar-styles(@progress-bar-success-bg); }\n.progress-bar-info { .progress-bar-styles(@progress-bar-info-bg); }\n.progress-bar-warning { .progress-bar-styles(@progress-bar-warning-bg); }\n.progress-bar-danger { .progress-bar-styles(@progress-bar-danger-bg); }\n\n// Reset the striped class because our mixins don't do multiple gradients and\n// the above custom styles override the new `.progress-bar-striped` in v3.2.0.\n.progress-bar-striped {\n #gradient > .striped();\n}\n\n\n//\n// List groups\n// --------------------------------------------------\n\n.list-group {\n border-radius: @border-radius-base;\n .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 darken(@list-group-active-bg, 10%);\n #gradient > .vertical(@start-color: @list-group-active-bg; @end-color: darken(@list-group-active-bg, 7.5%));\n border-color: darken(@list-group-active-border, 7.5%);\n\n .badge {\n text-shadow: none;\n }\n}\n\n\n//\n// Panels\n// --------------------------------------------------\n\n// Common styles\n.panel {\n .box-shadow(0 1px 2px rgba(0, 0, 0, .05));\n}\n\n// Mixin for generating new styles\n.panel-heading-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 5%));\n}\n\n// Apply the mixin to the panel headings only\n.panel-default > .panel-heading { .panel-heading-styles(@panel-default-heading-bg); }\n.panel-primary > .panel-heading { .panel-heading-styles(@panel-primary-heading-bg); }\n.panel-success > .panel-heading { .panel-heading-styles(@panel-success-heading-bg); }\n.panel-info > .panel-heading { .panel-heading-styles(@panel-info-heading-bg); }\n.panel-warning > .panel-heading { .panel-heading-styles(@panel-warning-heading-bg); }\n.panel-danger > .panel-heading { .panel-heading-styles(@panel-danger-heading-bg); }\n\n\n//\n// Wells\n// --------------------------------------------------\n\n.well {\n #gradient > .vertical(@start-color: darken(@well-bg, 5%); @end-color: @well-bg);\n border-color: darken(@well-bg, 10%);\n @shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);\n .box-shadow(@shadow);\n}\n","// stylelint-disable indentation, property-no-vendor-prefix, selector-no-vendor-prefix\n\n// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n -webkit-animation: @animation;\n -o-animation: @animation;\n animation: @animation;\n}\n.animation-name(@name) {\n -webkit-animation-name: @name;\n animation-name: @name;\n}\n.animation-duration(@duration) {\n -webkit-animation-duration: @duration;\n animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n -webkit-animation-timing-function: @timing-function;\n animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n -webkit-animation-delay: @delay;\n animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n -webkit-animation-iteration-count: @iteration-count;\n animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n -webkit-animation-direction: @direction;\n animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n -webkit-animation-fill-mode: @fill-mode;\n animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n -webkit-backface-visibility: @visibility;\n -moz-backface-visibility: @visibility;\n backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n -webkit-box-sizing: @boxmodel;\n -moz-box-sizing: @boxmodel;\n box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n -webkit-column-count: @column-count;\n -moz-column-count: @column-count;\n column-count: @column-count;\n -webkit-column-gap: @column-gap;\n -moz-column-gap: @column-gap;\n column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n -webkit-hyphens: @mode;\n -moz-hyphens: @mode;\n -ms-hyphens: @mode; // IE10+\n -o-hyphens: @mode;\n hyphens: @mode;\n word-wrap: break-word;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n // Firefox\n &::-moz-placeholder {\n color: @color;\n opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n }\n &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n &::-webkit-input-placeholder { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n -webkit-transform: scale(@ratio);\n -ms-transform: scale(@ratio); // IE9 only\n -o-transform: scale(@ratio);\n transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n -webkit-transform: scale(@ratioX, @ratioY);\n -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n -o-transform: scale(@ratioX, @ratioY);\n transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n -webkit-transform: scaleX(@ratio);\n -ms-transform: scaleX(@ratio); // IE9 only\n -o-transform: scaleX(@ratio);\n transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n -webkit-transform: scaleY(@ratio);\n -ms-transform: scaleY(@ratio); // IE9 only\n -o-transform: scaleY(@ratio);\n transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n -webkit-transform: skewX(@x) skewY(@y);\n -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n -o-transform: skewX(@x) skewY(@y);\n transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n -webkit-transform: translate(@x, @y);\n -ms-transform: translate(@x, @y); // IE9 only\n -o-transform: translate(@x, @y);\n transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n -webkit-transform: translate3d(@x, @y, @z);\n transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n -webkit-transform: rotate(@degrees);\n -ms-transform: rotate(@degrees); // IE9 only\n -o-transform: rotate(@degrees);\n transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n -webkit-transform: rotateX(@degrees);\n -ms-transform: rotateX(@degrees); // IE9 only\n -o-transform: rotateX(@degrees);\n transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n -webkit-transform: rotateY(@degrees);\n -ms-transform: rotateY(@degrees); // IE9 only\n -o-transform: rotateY(@degrees);\n transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n -webkit-perspective: @perspective;\n -moz-perspective: @perspective;\n perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n -webkit-perspective-origin: @perspective;\n -moz-perspective-origin: @perspective;\n perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n -webkit-transform-origin: @origin;\n -moz-transform-origin: @origin;\n -ms-transform-origin: @origin; // IE9 only\n transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n -webkit-transition: @transition;\n -o-transition: @transition;\n transition: @transition;\n}\n.transition-property(@transition-property) {\n -webkit-transition-property: @transition-property;\n transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n -webkit-transition-delay: @transition-delay;\n transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n -webkit-transition-duration: @transition-duration;\n transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n -webkit-transition-timing-function: @timing-function;\n transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n -webkit-transition: -webkit-transform @transition;\n -moz-transition: -moz-transform @transition;\n -o-transition: -o-transform @transition;\n transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n -webkit-user-select: @select;\n -moz-user-select: @select;\n -ms-user-select: @select; // IE10+\n user-select: @select;\n}\n","// stylelint-disable value-no-vendor-prefix, selector-max-id\n\n#gradient {\n\n // Horizontal gradient, from left to right\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down\n background-repeat: repeat-x;\n }\n\n // Vertical gradient, from top to bottom\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down\n background-repeat: repeat-x;\n }\n\n .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n }\n .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n background-repeat: no-repeat;\n }\n .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n background-repeat: no-repeat;\n }\n .radial(@inner-color: #555; @outer-color: #333) {\n background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n background-image: radial-gradient(circle, @inner-color, @outer-color);\n background-repeat: no-repeat;\n }\n .striped(@color: rgba(255, 255, 255, .15); @angle: 45deg) {\n background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]} \ No newline at end of file diff --git a/docs/_static/bootstrap-3.4.1/css/bootstrap-theme.min.css b/docs/_static/bootstrap-3.4.1/css/bootstrap-theme.min.css deleted file mode 100644 index 2a69f48..0000000 --- a/docs/_static/bootstrap-3.4.1/css/bootstrap-theme.min.css +++ /dev/null @@ -1,6 +0,0 @@ -/*! - * Bootstrap v3.4.1 (https://getbootstrap.com/) - * Copyright 2011-2019 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 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background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n background-color: #2aabd2;\n background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n background-color: #2aabd2;\n border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info[disabled],\nfieldset[disabled] .btn-info,\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus,\n.btn-info.disabled:active,\n.btn-info[disabled]:active,\nfieldset[disabled] .btn-info:active,\n.btn-info.disabled.active,\n.btn-info[disabled].active,\nfieldset[disabled] .btn-info.active {\n background-color: #2aabd2;\n background-image: none;\n}\n.btn-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n background-color: #eb9316;\n background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n background-color: #eb9316;\n border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning[disabled],\nfieldset[disabled] .btn-warning,\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus,\n.btn-warning.disabled:active,\n.btn-warning[disabled]:active,\nfieldset[disabled] .btn-warning:active,\n.btn-warning.disabled.active,\n.btn-warning[disabled].active,\nfieldset[disabled] .btn-warning.active {\n background-color: #eb9316;\n background-image: none;\n}\n.btn-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n background-color: #c12e2a;\n background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n background-color: #c12e2a;\n border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger[disabled],\nfieldset[disabled] .btn-danger,\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus,\n.btn-danger.disabled:active,\n.btn-danger[disabled]:active,\nfieldset[disabled] .btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n background-color: #c12e2a;\n background-image: none;\n}\n.thumbnail,\n.img-thumbnail {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-repeat: repeat-x;\n background-color: #e8e8e8;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n background-color: #2e6da4;\n}\n.navbar-default {\n background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n@media (max-width: 767px) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n }\n}\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n background-repeat: repeat-x;\n border-color: #b2dba1;\n}\n.alert-info {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #9acfea;\n}\n.alert-warning {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #f5e79e;\n}\n.alert-danger {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dca7a7;\n}\n.progress {\n background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 #286090;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n background-repeat: repeat-x;\n border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n text-shadow: none;\n}\n.panel {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-primary > .panel-heading {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-success > .panel-heading {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-info > .panel-heading {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-warning > .panel-heading {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-danger > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n background-repeat: repeat-x;\n}\n.well {\n background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dcdcdc;\n -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2);\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.btn-default:active,\n.btn-primary:active,\n.btn-success:active,\n.btn-info:active,\n.btn-warning:active,\n.btn-danger:active,\n.btn-default.active,\n.btn-primary.active,\n.btn-success.active,\n.btn-info.active,\n.btn-warning.active,\n.btn-danger.active {\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-default.disabled,\n.btn-primary.disabled,\n.btn-success.disabled,\n.btn-info.disabled,\n.btn-warning.disabled,\n.btn-danger.disabled,\n.btn-default[disabled],\n.btn-primary[disabled],\n.btn-success[disabled],\n.btn-info[disabled],\n.btn-warning[disabled],\n.btn-danger[disabled],\nfieldset[disabled] .btn-default,\nfieldset[disabled] .btn-primary,\nfieldset[disabled] .btn-success,\nfieldset[disabled] .btn-info,\nfieldset[disabled] .btn-warning,\nfieldset[disabled] .btn-danger {\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn-default .badge,\n.btn-primary .badge,\n.btn-success .badge,\n.btn-info .badge,\n.btn-warning .badge,\n.btn-danger .badge {\n text-shadow: none;\n}\n.btn:active,\n.btn.active {\n background-image: none;\n}\n.btn-default {\n background-image: -webkit-linear-gradient(top, #fff 0%, #e0e0e0 100%);\n background-image: -o-linear-gradient(top, #fff 0%, #e0e0e0 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#e0e0e0));\n background-image: linear-gradient(to bottom, #fff 0%, #e0e0e0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #dbdbdb;\n text-shadow: 0 1px 0 #fff;\n border-color: #ccc;\n}\n.btn-default:hover,\n.btn-default:focus {\n background-color: #e0e0e0;\n background-position: 0 -15px;\n}\n.btn-default:active,\n.btn-default.active {\n background-color: #e0e0e0;\n border-color: #dbdbdb;\n}\n.btn-default.disabled,\n.btn-default[disabled],\nfieldset[disabled] .btn-default,\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus,\n.btn-default.disabled:active,\n.btn-default[disabled]:active,\nfieldset[disabled] .btn-default:active,\n.btn-default.disabled.active,\n.btn-default[disabled].active,\nfieldset[disabled] .btn-default.active {\n background-color: #e0e0e0;\n background-image: none;\n}\n.btn-primary {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #265a88 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#265a88));\n background-image: linear-gradient(to bottom, #337ab7 0%, #265a88 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #245580;\n}\n.btn-primary:hover,\n.btn-primary:focus {\n background-color: #265a88;\n background-position: 0 -15px;\n}\n.btn-primary:active,\n.btn-primary.active {\n background-color: #265a88;\n border-color: #245580;\n}\n.btn-primary.disabled,\n.btn-primary[disabled],\nfieldset[disabled] .btn-primary,\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus,\n.btn-primary.disabled:active,\n.btn-primary[disabled]:active,\nfieldset[disabled] .btn-primary:active,\n.btn-primary.disabled.active,\n.btn-primary[disabled].active,\nfieldset[disabled] .btn-primary.active {\n background-color: #265a88;\n background-image: none;\n}\n.btn-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#419641));\n background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #3e8f3e;\n}\n.btn-success:hover,\n.btn-success:focus {\n background-color: #419641;\n background-position: 0 -15px;\n}\n.btn-success:active,\n.btn-success.active {\n background-color: #419641;\n border-color: #3e8f3e;\n}\n.btn-success.disabled,\n.btn-success[disabled],\nfieldset[disabled] .btn-success,\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus,\n.btn-success.disabled:active,\n.btn-success[disabled]:active,\nfieldset[disabled] .btn-success:active,\n.btn-success.disabled.active,\n.btn-success[disabled].active,\nfieldset[disabled] .btn-success.active {\n background-color: #419641;\n background-image: none;\n}\n.btn-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#2aabd2));\n background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n background-color: #2aabd2;\n background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n background-color: #2aabd2;\n border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info[disabled],\nfieldset[disabled] .btn-info,\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus,\n.btn-info.disabled:active,\n.btn-info[disabled]:active,\nfieldset[disabled] .btn-info:active,\n.btn-info.disabled.active,\n.btn-info[disabled].active,\nfieldset[disabled] .btn-info.active {\n background-color: #2aabd2;\n background-image: none;\n}\n.btn-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#eb9316));\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n background-color: #eb9316;\n background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n background-color: #eb9316;\n border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning[disabled],\nfieldset[disabled] .btn-warning,\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus,\n.btn-warning.disabled:active,\n.btn-warning[disabled]:active,\nfieldset[disabled] .btn-warning:active,\n.btn-warning.disabled.active,\n.btn-warning[disabled].active,\nfieldset[disabled] .btn-warning.active {\n background-color: #eb9316;\n background-image: none;\n}\n.btn-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c12e2a));\n background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n background-repeat: repeat-x;\n border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n background-color: #c12e2a;\n background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n background-color: #c12e2a;\n border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger[disabled],\nfieldset[disabled] .btn-danger,\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus,\n.btn-danger.disabled:active,\n.btn-danger[disabled]:active,\nfieldset[disabled] .btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n background-color: #c12e2a;\n background-image: none;\n}\n.thumbnail,\n.img-thumbnail {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-repeat: repeat-x;\n background-color: #e8e8e8;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n background-color: #2e6da4;\n}\n.navbar-default {\n background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#ffffff), to(#f8f8f8));\n background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#dbdbdb), to(#e2e2e2));\n background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#3c3c3c), to(#222));\n background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n background-repeat: repeat-x;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#080808), to(#0f0f0f));\n background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n background-repeat: repeat-x;\n -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n@media (max-width: 767px) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n }\n}\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#c8e5bc));\n background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n background-repeat: repeat-x;\n border-color: #b2dba1;\n}\n.alert-info {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#b9def0));\n background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #9acfea;\n}\n.alert-warning {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#f8efc0));\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n background-repeat: repeat-x;\n border-color: #f5e79e;\n}\n.alert-danger {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#e7c3c3));\n background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dca7a7;\n}\n.progress {\n background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#ebebeb), to(#f5f5f5));\n background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#286090));\n background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-success {\n background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#449d44));\n background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-info {\n background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#31b0d5));\n background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-warning {\n background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#ec971f));\n background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-danger {\n background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c9302c));\n background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n background-repeat: repeat-x;\n}\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 #286090;\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2b669a));\n background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n background-repeat: repeat-x;\n border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n text-shadow: none;\n}\n.panel {\n -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));\n background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-primary > .panel-heading {\n background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));\n background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-success > .panel-heading {\n background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#d0e9c6));\n background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-info > .panel-heading {\n background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#c4e3f3));\n background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-warning > .panel-heading {\n background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#faf2cc));\n background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n background-repeat: repeat-x;\n}\n.panel-danger > .panel-heading {\n background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#ebcccc));\n background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n background-repeat: repeat-x;\n}\n.well {\n background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n background-image: -webkit-gradient(linear, left top, left bottom, from(#e8e8e8), to(#f5f5f5));\n background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n background-repeat: repeat-x;\n border-color: #dcdcdc;\n -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","// stylelint-disable selector-no-qualifying-type, selector-max-compound-selectors\n\n/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n\n//\n// Load core variables and mixins\n// --------------------------------------------------\n\n@import \"variables.less\";\n@import \"mixins.less\";\n\n\n//\n// Buttons\n// --------------------------------------------------\n\n// Common styles\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, .2);\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);\n .box-shadow(@shadow);\n\n // Reset the shadow\n &:active,\n &.active {\n .box-shadow(inset 0 3px 5px rgba(0, 0, 0, .125));\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n .box-shadow(none);\n }\n\n .badge {\n text-shadow: none;\n }\n}\n\n// Mixin for generating new styles\n.btn-styles(@btn-color: #555) {\n #gradient > .vertical(@start-color: @btn-color; @end-color: darken(@btn-color, 12%));\n .reset-filter(); // Disable gradients for IE9 because filter bleeds through rounded corners; see https://github.com/twbs/bootstrap/issues/10620\n background-repeat: repeat-x;\n border-color: darken(@btn-color, 14%);\n\n &:hover,\n &:focus {\n background-color: darken(@btn-color, 12%);\n background-position: 0 -15px;\n }\n\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n border-color: darken(@btn-color, 14%);\n }\n\n &.disabled,\n &[disabled],\n fieldset[disabled] & {\n &,\n &:hover,\n &:focus,\n &.focus,\n &:active,\n &.active {\n background-color: darken(@btn-color, 12%);\n background-image: none;\n }\n }\n}\n\n// Common styles\n.btn {\n // Remove the gradient for the pressed/active state\n &:active,\n &.active {\n background-image: none;\n }\n}\n\n// Apply the mixin to the buttons\n.btn-default {\n .btn-styles(@btn-default-bg);\n text-shadow: 0 1px 0 #fff;\n border-color: #ccc;\n}\n.btn-primary { .btn-styles(@btn-primary-bg); }\n.btn-success { .btn-styles(@btn-success-bg); }\n.btn-info { .btn-styles(@btn-info-bg); }\n.btn-warning { .btn-styles(@btn-warning-bg); }\n.btn-danger { .btn-styles(@btn-danger-bg); }\n\n\n//\n// Images\n// --------------------------------------------------\n\n.thumbnail,\n.img-thumbnail {\n .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n\n\n//\n// Dropdowns\n// --------------------------------------------------\n\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-hover-bg; @end-color: darken(@dropdown-link-hover-bg, 5%));\n background-color: darken(@dropdown-link-hover-bg, 5%);\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n background-color: darken(@dropdown-link-active-bg, 5%);\n}\n\n\n//\n// Navbar\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n #gradient > .vertical(@start-color: lighten(@navbar-default-bg, 10%); @end-color: @navbar-default-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered\n border-radius: @navbar-border-radius;\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);\n .box-shadow(@shadow);\n\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: darken(@navbar-default-link-active-bg, 5%); @end-color: darken(@navbar-default-link-active-bg, 2%));\n .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .075));\n }\n}\n.navbar-brand,\n.navbar-nav > li > a {\n text-shadow: 0 1px 0 rgba(255, 255, 255, .25);\n}\n\n// Inverted navbar\n.navbar-inverse {\n #gradient > .vertical(@start-color: lighten(@navbar-inverse-bg, 10%); @end-color: @navbar-inverse-bg);\n .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n border-radius: @navbar-border-radius;\n .navbar-nav > .open > a,\n .navbar-nav > .active > a {\n #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .25));\n }\n\n .navbar-brand,\n .navbar-nav > li > a {\n text-shadow: 0 -1px 0 rgba(0, 0, 0, .25);\n }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n .navbar .navbar-nav .open .dropdown-menu > .active > a {\n &,\n &:hover,\n &:focus {\n color: #fff;\n #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n }\n }\n}\n\n\n//\n// Alerts\n// --------------------------------------------------\n\n// Common styles\n.alert {\n text-shadow: 0 1px 0 rgba(255, 255, 255, .2);\n @shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);\n .box-shadow(@shadow);\n}\n\n// Mixin for generating new styles\n.alert-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 7.5%));\n border-color: darken(@color, 15%);\n}\n\n// Apply the mixin to the alerts\n.alert-success { .alert-styles(@alert-success-bg); }\n.alert-info { .alert-styles(@alert-info-bg); }\n.alert-warning { .alert-styles(@alert-warning-bg); }\n.alert-danger { .alert-styles(@alert-danger-bg); }\n\n\n//\n// Progress bars\n// --------------------------------------------------\n\n// Give the progress background some depth\n.progress {\n #gradient > .vertical(@start-color: darken(@progress-bg, 4%); @end-color: @progress-bg)\n}\n\n// Mixin for generating new styles\n.progress-bar-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 10%));\n}\n\n// Apply the mixin to the progress bars\n.progress-bar { .progress-bar-styles(@progress-bar-bg); }\n.progress-bar-success { .progress-bar-styles(@progress-bar-success-bg); }\n.progress-bar-info { .progress-bar-styles(@progress-bar-info-bg); }\n.progress-bar-warning { .progress-bar-styles(@progress-bar-warning-bg); }\n.progress-bar-danger { .progress-bar-styles(@progress-bar-danger-bg); }\n\n// Reset the striped class because our mixins don't do multiple gradients and\n// the above custom styles override the new `.progress-bar-striped` in v3.2.0.\n.progress-bar-striped {\n #gradient > .striped();\n}\n\n\n//\n// List groups\n// --------------------------------------------------\n\n.list-group {\n border-radius: @border-radius-base;\n .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n text-shadow: 0 -1px 0 darken(@list-group-active-bg, 10%);\n #gradient > .vertical(@start-color: @list-group-active-bg; @end-color: darken(@list-group-active-bg, 7.5%));\n border-color: darken(@list-group-active-border, 7.5%);\n\n .badge {\n text-shadow: none;\n }\n}\n\n\n//\n// Panels\n// --------------------------------------------------\n\n// Common styles\n.panel {\n .box-shadow(0 1px 2px rgba(0, 0, 0, .05));\n}\n\n// Mixin for generating new styles\n.panel-heading-styles(@color) {\n #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 5%));\n}\n\n// Apply the mixin to the panel headings only\n.panel-default > .panel-heading { .panel-heading-styles(@panel-default-heading-bg); }\n.panel-primary > .panel-heading { .panel-heading-styles(@panel-primary-heading-bg); }\n.panel-success > .panel-heading { .panel-heading-styles(@panel-success-heading-bg); }\n.panel-info > .panel-heading { .panel-heading-styles(@panel-info-heading-bg); }\n.panel-warning > .panel-heading { .panel-heading-styles(@panel-warning-heading-bg); }\n.panel-danger > .panel-heading { .panel-heading-styles(@panel-danger-heading-bg); }\n\n\n//\n// Wells\n// --------------------------------------------------\n\n.well {\n #gradient > .vertical(@start-color: darken(@well-bg, 5%); @end-color: @well-bg);\n border-color: darken(@well-bg, 10%);\n @shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);\n .box-shadow(@shadow);\n}\n","// stylelint-disable indentation, property-no-vendor-prefix, selector-no-vendor-prefix\n\n// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n -webkit-animation: @animation;\n -o-animation: @animation;\n animation: @animation;\n}\n.animation-name(@name) {\n -webkit-animation-name: @name;\n animation-name: @name;\n}\n.animation-duration(@duration) {\n -webkit-animation-duration: @duration;\n animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n -webkit-animation-timing-function: @timing-function;\n animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n -webkit-animation-delay: @delay;\n animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n -webkit-animation-iteration-count: @iteration-count;\n animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n -webkit-animation-direction: @direction;\n animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n -webkit-animation-fill-mode: @fill-mode;\n animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n -webkit-backface-visibility: @visibility;\n -moz-backface-visibility: @visibility;\n backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n -webkit-box-sizing: @boxmodel;\n -moz-box-sizing: @boxmodel;\n box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n -webkit-column-count: @column-count;\n -moz-column-count: @column-count;\n column-count: @column-count;\n -webkit-column-gap: @column-gap;\n -moz-column-gap: @column-gap;\n column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n -webkit-hyphens: @mode;\n -moz-hyphens: @mode;\n -ms-hyphens: @mode; // IE10+\n -o-hyphens: @mode;\n hyphens: @mode;\n word-wrap: break-word;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n // Firefox\n &::-moz-placeholder {\n color: @color;\n opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n }\n &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n &::-webkit-input-placeholder { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n -webkit-transform: scale(@ratio);\n -ms-transform: scale(@ratio); // IE9 only\n -o-transform: scale(@ratio);\n transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n -webkit-transform: scale(@ratioX, @ratioY);\n -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n -o-transform: scale(@ratioX, @ratioY);\n transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n -webkit-transform: scaleX(@ratio);\n -ms-transform: scaleX(@ratio); // IE9 only\n -o-transform: scaleX(@ratio);\n transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n -webkit-transform: scaleY(@ratio);\n -ms-transform: scaleY(@ratio); // IE9 only\n -o-transform: scaleY(@ratio);\n transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n -webkit-transform: skewX(@x) skewY(@y);\n -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n -o-transform: skewX(@x) skewY(@y);\n transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n -webkit-transform: translate(@x, @y);\n -ms-transform: translate(@x, @y); // IE9 only\n -o-transform: translate(@x, @y);\n transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n -webkit-transform: translate3d(@x, @y, @z);\n transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n -webkit-transform: rotate(@degrees);\n -ms-transform: rotate(@degrees); // IE9 only\n -o-transform: rotate(@degrees);\n transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n -webkit-transform: rotateX(@degrees);\n -ms-transform: rotateX(@degrees); // IE9 only\n -o-transform: rotateX(@degrees);\n transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n -webkit-transform: rotateY(@degrees);\n -ms-transform: rotateY(@degrees); // IE9 only\n -o-transform: rotateY(@degrees);\n transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n -webkit-perspective: @perspective;\n -moz-perspective: @perspective;\n perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n -webkit-perspective-origin: @perspective;\n -moz-perspective-origin: @perspective;\n perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n -webkit-transform-origin: @origin;\n -moz-transform-origin: @origin;\n -ms-transform-origin: @origin; // IE9 only\n transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n -webkit-transition: @transition;\n -o-transition: @transition;\n transition: @transition;\n}\n.transition-property(@transition-property) {\n -webkit-transition-property: @transition-property;\n transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n -webkit-transition-delay: @transition-delay;\n transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n -webkit-transition-duration: @transition-duration;\n transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n -webkit-transition-timing-function: @timing-function;\n transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n -webkit-transition: -webkit-transform @transition;\n -moz-transition: -moz-transform @transition;\n -o-transition: -o-transform @transition;\n transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n -webkit-user-select: @select;\n -moz-user-select: @select;\n -ms-user-select: @select; // IE10+\n user-select: @select;\n}\n","// stylelint-disable value-no-vendor-prefix, selector-max-id\n\n#gradient {\n\n // Horizontal gradient, from left to right\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down\n background-repeat: repeat-x;\n }\n\n // Vertical gradient, from top to bottom\n //\n // Creates two color stops, start and end, by specifying a color and position for each color stop.\n // Color stops are not available in IE9 and below.\n .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent); // Opera 12\n background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down\n background-repeat: repeat-x;\n }\n\n .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n background-repeat: repeat-x;\n }\n .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n background-repeat: no-repeat;\n }\n .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n background-repeat: no-repeat;\n }\n .radial(@inner-color: #555; @outer-color: #333) {\n background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n background-image: radial-gradient(circle, @inner-color, @outer-color);\n background-repeat: no-repeat;\n }\n .striped(@color: rgba(255, 255, 255, .15); @angle: 45deg) {\n background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]} \ No newline at end of file diff --git a/docs/_static/bootstrap-3.4.1/css/bootstrap.css b/docs/_static/bootstrap-3.4.1/css/bootstrap.css deleted file mode 100644 index fcab415..0000000 --- a/docs/_static/bootstrap-3.4.1/css/bootstrap.css +++ /dev/null @@ -1,6834 +0,0 @@ -/*! - * Bootstrap v3.4.1 (https://getbootstrap.com/) - * Copyright 2011-2019 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - */ -/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */ -html { - font-family: sans-serif; - -ms-text-size-adjust: 100%; - -webkit-text-size-adjust: 100%; -} -body { - margin: 0; -} -article, -aside, -details, -figcaption, -figure, -footer, -header, -hgroup, -main, -menu, -nav, -section, -summary { - display: block; -} -audio, -canvas, -progress, -video { - display: inline-block; - vertical-align: baseline; -} -audio:not([controls]) { - display: none; - height: 0; -} -[hidden], -template { - display: none; -} -a { - background-color: transparent; -} -a:active, -a:hover { - outline: 0; -} -abbr[title] { - border-bottom: none; - text-decoration: underline; - -webkit-text-decoration: underline dotted; - -moz-text-decoration: underline dotted; - text-decoration: underline dotted; -} -b, -strong { - font-weight: bold; -} -dfn { - font-style: italic; -} -h1 { - font-size: 2em; - margin: 0.67em 0; -} -mark { - background: #ff0; - color: #000; -} -small { - font-size: 80%; -} -sub, -sup { - font-size: 75%; - line-height: 0; - position: relative; - vertical-align: baseline; -} -sup { - top: -0.5em; -} -sub { - bottom: -0.25em; -} -img { - border: 0; -} -svg:not(:root) { - overflow: hidden; -} -figure { - margin: 1em 40px; -} -hr { - -webkit-box-sizing: content-box; - -moz-box-sizing: content-box; - box-sizing: content-box; - height: 0; -} -pre { - overflow: auto; -} -code, -kbd, -pre, -samp { - font-family: monospace, monospace; - font-size: 1em; -} -button, -input, -optgroup, -select, -textarea { - color: inherit; - font: inherit; - margin: 0; -} -button { - overflow: visible; -} -button, -select { - text-transform: none; -} -button, -html input[type="button"], -input[type="reset"], -input[type="submit"] { - -webkit-appearance: button; - cursor: pointer; -} -button[disabled], -html input[disabled] { - cursor: default; -} -button::-moz-focus-inner, -input::-moz-focus-inner { - border: 0; - padding: 0; -} -input { - line-height: normal; -} -input[type="checkbox"], -input[type="radio"] { - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - padding: 0; -} -input[type="number"]::-webkit-inner-spin-button, -input[type="number"]::-webkit-outer-spin-button { - height: auto; -} -input[type="search"] { - -webkit-appearance: textfield; - -webkit-box-sizing: content-box; - -moz-box-sizing: content-box; - box-sizing: content-box; -} -input[type="search"]::-webkit-search-cancel-button, -input[type="search"]::-webkit-search-decoration { - -webkit-appearance: none; -} -fieldset { - border: 1px solid #c0c0c0; - margin: 0 2px; - padding: 0.35em 0.625em 0.75em; -} -legend { - border: 0; - padding: 0; -} -textarea { - overflow: auto; -} -optgroup { - font-weight: bold; -} -table { - border-collapse: collapse; - border-spacing: 0; -} -td, -th { - padding: 0; -} -/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */ -@media print { - *, - *:before, - *:after { - color: #000 !important; - text-shadow: none !important; - background: transparent !important; - -webkit-box-shadow: none !important; - box-shadow: none !important; - } - a, - a:visited { - text-decoration: underline; - } - a[href]:after { - content: " (" attr(href) ")"; - } - abbr[title]:after { - content: " (" attr(title) ")"; - } - a[href^="#"]:after, - a[href^="javascript:"]:after { - content: ""; - } - pre, - blockquote { - border: 1px solid #999; - page-break-inside: avoid; - } - thead { - display: table-header-group; - } - tr, - img { - page-break-inside: avoid; - } - img { - max-width: 100% !important; - } - p, - h2, - h3 { - orphans: 3; - widows: 3; - } - h2, - h3 { - page-break-after: avoid; - } - .navbar { - display: none; - } - .btn > .caret, - .dropup > .btn > .caret { - border-top-color: #000 !important; - } - .label { - border: 1px solid #000; - 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background-color: #e4b9b9; -} -.page-header { - padding-bottom: 9px; - margin: 40px 0 20px; - border-bottom: 1px solid #eeeeee; -} -ul, -ol { - margin-top: 0; - margin-bottom: 10px; -} -ul ul, -ol ul, -ul ol, -ol ol { - margin-bottom: 0; -} -.list-unstyled { - padding-left: 0; - list-style: none; -} -.list-inline { - padding-left: 0; - list-style: none; - margin-left: -5px; -} -.list-inline > li { - display: inline-block; - padding-right: 5px; - padding-left: 5px; -} -dl { - margin-top: 0; - margin-bottom: 20px; -} -dt, -dd { - line-height: 1.42857143; -} -dt { - font-weight: 700; -} -dd { - margin-left: 0; -} -@media (min-width: 768px) { - .dl-horizontal dt { - float: left; - width: 160px; - clear: left; - text-align: right; - overflow: hidden; - text-overflow: ellipsis; - white-space: nowrap; - } - .dl-horizontal dd { - margin-left: 180px; - } -} -abbr[title], -abbr[data-original-title] { - cursor: help; -} -.initialism { - font-size: 90%; - text-transform: uppercase; -} -blockquote { - padding: 10px 20px; - margin: 0 0 20px; - font-size: 17.5px; - border-left: 5px solid #eeeeee; -} -blockquote p:last-child, -blockquote ul:last-child, -blockquote ol:last-child { - margin-bottom: 0; -} -blockquote footer, -blockquote small, -blockquote .small { - display: block; - font-size: 80%; - line-height: 1.42857143; - color: #777777; -} -blockquote footer:before, -blockquote small:before, -blockquote .small:before { - content: "\2014 \00A0"; -} -.blockquote-reverse, -blockquote.pull-right { - padding-right: 15px; - padding-left: 0; - text-align: right; - border-right: 5px solid #eeeeee; - border-left: 0; -} -.blockquote-reverse footer:before, -blockquote.pull-right footer:before, -.blockquote-reverse small:before, -blockquote.pull-right small:before, -.blockquote-reverse .small:before, -blockquote.pull-right .small:before { - content: ""; -} -.blockquote-reverse footer:after, -blockquote.pull-right footer:after, -.blockquote-reverse small:after, -blockquote.pull-right small:after, -.blockquote-reverse .small:after, -blockquote.pull-right .small:after { - content: "\00A0 \2014"; -} -address { - margin-bottom: 20px; - font-style: normal; - line-height: 1.42857143; -} -code, -kbd, -pre, -samp { - font-family: Menlo, Monaco, Consolas, "Courier New", monospace; -} -code { - padding: 2px 4px; - font-size: 90%; - color: #c7254e; - background-color: #f9f2f4; - border-radius: 4px; -} -kbd { - padding: 2px 4px; - font-size: 90%; - color: #fff; - background-color: #333; - border-radius: 3px; - -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); - box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); -} -kbd kbd { - padding: 0; - font-size: 100%; - font-weight: 700; - -webkit-box-shadow: none; - box-shadow: none; -} -pre { - display: block; - padding: 9.5px; - margin: 0 0 10px; - font-size: 13px; - line-height: 1.42857143; - color: #333333; - word-break: break-all; - word-wrap: break-word; - background-color: #f5f5f5; - border: 1px solid #ccc; - border-radius: 4px; -} -pre code { - padding: 0; - font-size: inherit; - color: inherit; - white-space: pre-wrap; - background-color: transparent; - border-radius: 0; -} -.pre-scrollable { - max-height: 340px; - overflow-y: scroll; -} -.container { - padding-right: 15px; - padding-left: 15px; - margin-right: auto; - margin-left: auto; -} -@media (min-width: 768px) { - .container { - width: 750px; - } -} -@media (min-width: 992px) { - .container { - width: 970px; - } -} -@media (min-width: 1200px) { - .container { - width: 1170px; - } -} -.container-fluid { - padding-right: 15px; - padding-left: 15px; - margin-right: auto; - margin-left: auto; -} -.row { - margin-right: -15px; - margin-left: -15px; -} -.row-no-gutters { - margin-right: 0; - margin-left: 0; -} -.row-no-gutters [class*="col-"] { - padding-right: 0; - padding-left: 0; -} -.col-xs-1, -.col-sm-1, -.col-md-1, -.col-lg-1, -.col-xs-2, -.col-sm-2, -.col-md-2, -.col-lg-2, -.col-xs-3, -.col-sm-3, -.col-md-3, -.col-lg-3, -.col-xs-4, -.col-sm-4, -.col-md-4, -.col-lg-4, -.col-xs-5, -.col-sm-5, -.col-md-5, -.col-lg-5, -.col-xs-6, -.col-sm-6, -.col-md-6, -.col-lg-6, -.col-xs-7, -.col-sm-7, -.col-md-7, -.col-lg-7, -.col-xs-8, -.col-sm-8, -.col-md-8, -.col-lg-8, -.col-xs-9, -.col-sm-9, -.col-md-9, -.col-lg-9, -.col-xs-10, -.col-sm-10, -.col-md-10, -.col-lg-10, -.col-xs-11, -.col-sm-11, -.col-md-11, -.col-lg-11, -.col-xs-12, -.col-sm-12, -.col-md-12, -.col-lg-12 { - position: relative; - min-height: 1px; - padding-right: 15px; - padding-left: 15px; -} -.col-xs-1, -.col-xs-2, -.col-xs-3, -.col-xs-4, -.col-xs-5, -.col-xs-6, -.col-xs-7, -.col-xs-8, -.col-xs-9, -.col-xs-10, -.col-xs-11, -.col-xs-12 { - float: left; -} -.col-xs-12 { - width: 100%; -} -.col-xs-11 { - width: 91.66666667%; -} -.col-xs-10 { - width: 83.33333333%; -} -.col-xs-9 { - width: 75%; -} -.col-xs-8 { - width: 66.66666667%; -} -.col-xs-7 { - width: 58.33333333%; -} -.col-xs-6 { - width: 50%; -} -.col-xs-5 { - width: 41.66666667%; -} -.col-xs-4 { - width: 33.33333333%; -} -.col-xs-3 { - width: 25%; -} -.col-xs-2 { - width: 16.66666667%; -} -.col-xs-1 { - width: 8.33333333%; -} -.col-xs-pull-12 { - right: 100%; -} -.col-xs-pull-11 { - right: 91.66666667%; -} -.col-xs-pull-10 { - right: 83.33333333%; -} -.col-xs-pull-9 { - right: 75%; -} -.col-xs-pull-8 { - right: 66.66666667%; -} -.col-xs-pull-7 { - right: 58.33333333%; -} -.col-xs-pull-6 { - right: 50%; -} -.col-xs-pull-5 { - right: 41.66666667%; -} -.col-xs-pull-4 { - right: 33.33333333%; -} -.col-xs-pull-3 { - right: 25%; -} -.col-xs-pull-2 { - right: 16.66666667%; -} -.col-xs-pull-1 { - right: 8.33333333%; -} -.col-xs-pull-0 { - right: auto; -} -.col-xs-push-12 { - left: 100%; -} -.col-xs-push-11 { - left: 91.66666667%; -} -.col-xs-push-10 { - left: 83.33333333%; -} -.col-xs-push-9 { - left: 75%; -} -.col-xs-push-8 { - left: 66.66666667%; -} -.col-xs-push-7 { - left: 58.33333333%; -} -.col-xs-push-6 { - left: 50%; -} -.col-xs-push-5 { - left: 41.66666667%; -} -.col-xs-push-4 { - left: 33.33333333%; -} -.col-xs-push-3 { - left: 25%; -} -.col-xs-push-2 { - left: 16.66666667%; -} -.col-xs-push-1 { - left: 8.33333333%; -} -.col-xs-push-0 { - left: auto; -} -.col-xs-offset-12 { - margin-left: 100%; -} -.col-xs-offset-11 { - margin-left: 91.66666667%; -} -.col-xs-offset-10 { - margin-left: 83.33333333%; -} -.col-xs-offset-9 { - margin-left: 75%; -} -.col-xs-offset-8 { - margin-left: 66.66666667%; -} -.col-xs-offset-7 { - margin-left: 58.33333333%; -} -.col-xs-offset-6 { - margin-left: 50%; -} -.col-xs-offset-5 { - margin-left: 41.66666667%; -} -.col-xs-offset-4 { - margin-left: 33.33333333%; -} -.col-xs-offset-3 { - margin-left: 25%; -} -.col-xs-offset-2 { - margin-left: 16.66666667%; -} -.col-xs-offset-1 { - margin-left: 8.33333333%; -} -.col-xs-offset-0 { - margin-left: 0%; -} -@media (min-width: 768px) { - .col-sm-1, - .col-sm-2, - .col-sm-3, - .col-sm-4, - .col-sm-5, - .col-sm-6, - .col-sm-7, - .col-sm-8, - .col-sm-9, - .col-sm-10, - .col-sm-11, - .col-sm-12 { - float: left; - } - .col-sm-12 { - width: 100%; - } - .col-sm-11 { - width: 91.66666667%; - } - .col-sm-10 { - width: 83.33333333%; - } - .col-sm-9 { - width: 75%; - } - .col-sm-8 { - width: 66.66666667%; - } - .col-sm-7 { - width: 58.33333333%; - } - .col-sm-6 { - width: 50%; - } - .col-sm-5 { - width: 41.66666667%; - } - .col-sm-4 { - width: 33.33333333%; - } - .col-sm-3 { - width: 25%; - } - .col-sm-2 { - width: 16.66666667%; - } - .col-sm-1 { - width: 8.33333333%; - } - .col-sm-pull-12 { - right: 100%; - } - .col-sm-pull-11 { - right: 91.66666667%; - } - .col-sm-pull-10 { - right: 83.33333333%; - } - .col-sm-pull-9 { - right: 75%; - } - .col-sm-pull-8 { - right: 66.66666667%; - } - .col-sm-pull-7 { - right: 58.33333333%; - } - .col-sm-pull-6 { - right: 50%; - } - .col-sm-pull-5 { - right: 41.66666667%; - } - .col-sm-pull-4 { - right: 33.33333333%; - } - .col-sm-pull-3 { - right: 25%; - } - .col-sm-pull-2 { - right: 16.66666667%; - } - .col-sm-pull-1 { - right: 8.33333333%; - } - .col-sm-pull-0 { - right: auto; - } - .col-sm-push-12 { - left: 100%; - } - .col-sm-push-11 { - left: 91.66666667%; - } - .col-sm-push-10 { - left: 83.33333333%; - } - .col-sm-push-9 { - left: 75%; - } - .col-sm-push-8 { - left: 66.66666667%; - } - .col-sm-push-7 { - left: 58.33333333%; - } - .col-sm-push-6 { - left: 50%; - } - .col-sm-push-5 { - left: 41.66666667%; - } - .col-sm-push-4 { - left: 33.33333333%; - } - .col-sm-push-3 { - left: 25%; - } - .col-sm-push-2 { - left: 16.66666667%; - } - .col-sm-push-1 { - left: 8.33333333%; - } - .col-sm-push-0 { - left: auto; - } - .col-sm-offset-12 { - margin-left: 100%; - } - .col-sm-offset-11 { - margin-left: 91.66666667%; - } - .col-sm-offset-10 { - margin-left: 83.33333333%; - } - .col-sm-offset-9 { - margin-left: 75%; - } - .col-sm-offset-8 { - margin-left: 66.66666667%; - } - .col-sm-offset-7 { - margin-left: 58.33333333%; - } - .col-sm-offset-6 { - margin-left: 50%; - } - .col-sm-offset-5 { - margin-left: 41.66666667%; - } - .col-sm-offset-4 { - margin-left: 33.33333333%; - } - .col-sm-offset-3 { - margin-left: 25%; - } - .col-sm-offset-2 { - margin-left: 16.66666667%; - } - .col-sm-offset-1 { - margin-left: 8.33333333%; - } - .col-sm-offset-0 { - margin-left: 0%; - } -} -@media (min-width: 992px) { - .col-md-1, - .col-md-2, - .col-md-3, - .col-md-4, - .col-md-5, - .col-md-6, - .col-md-7, - .col-md-8, - .col-md-9, - .col-md-10, - .col-md-11, - .col-md-12 { - float: left; - } - .col-md-12 { - width: 100%; - } - .col-md-11 { - width: 91.66666667%; - } - .col-md-10 { - width: 83.33333333%; - } - .col-md-9 { - width: 75%; - } - .col-md-8 { - width: 66.66666667%; - } - .col-md-7 { - width: 58.33333333%; - } - .col-md-6 { - width: 50%; - } - .col-md-5 { - width: 41.66666667%; - } - .col-md-4 { - width: 33.33333333%; - } - .col-md-3 { - width: 25%; - } - .col-md-2 { - width: 16.66666667%; - } - .col-md-1 { - width: 8.33333333%; - } - .col-md-pull-12 { - right: 100%; - } - .col-md-pull-11 { - right: 91.66666667%; - } - .col-md-pull-10 { - right: 83.33333333%; - } - .col-md-pull-9 { - right: 75%; - } - .col-md-pull-8 { - right: 66.66666667%; - } - .col-md-pull-7 { - right: 58.33333333%; - } - .col-md-pull-6 { - right: 50%; - } - .col-md-pull-5 { - right: 41.66666667%; - } - .col-md-pull-4 { - right: 33.33333333%; - } - .col-md-pull-3 { - right: 25%; - } - .col-md-pull-2 { - right: 16.66666667%; - } - .col-md-pull-1 { - right: 8.33333333%; - } - .col-md-pull-0 { - right: auto; - } - .col-md-push-12 { - left: 100%; - } - .col-md-push-11 { - left: 91.66666667%; - } - .col-md-push-10 { - left: 83.33333333%; - } - .col-md-push-9 { - left: 75%; - } - .col-md-push-8 { - left: 66.66666667%; - } - .col-md-push-7 { - left: 58.33333333%; - } - .col-md-push-6 { - left: 50%; - } - .col-md-push-5 { - left: 41.66666667%; - } - .col-md-push-4 { - left: 33.33333333%; - } - .col-md-push-3 { - left: 25%; - } - .col-md-push-2 { - left: 16.66666667%; - } - .col-md-push-1 { - left: 8.33333333%; - } - .col-md-push-0 { - left: auto; - } - .col-md-offset-12 { - margin-left: 100%; - } - .col-md-offset-11 { - margin-left: 91.66666667%; - } - .col-md-offset-10 { - margin-left: 83.33333333%; - } - .col-md-offset-9 { - margin-left: 75%; - } - .col-md-offset-8 { - margin-left: 66.66666667%; - } - .col-md-offset-7 { - margin-left: 58.33333333%; - } - .col-md-offset-6 { - margin-left: 50%; - } - .col-md-offset-5 { - margin-left: 41.66666667%; - } - .col-md-offset-4 { - margin-left: 33.33333333%; - } - .col-md-offset-3 { - margin-left: 25%; - } - .col-md-offset-2 { - margin-left: 16.66666667%; - } - .col-md-offset-1 { - margin-left: 8.33333333%; - } - .col-md-offset-0 { - margin-left: 0%; - } -} -@media (min-width: 1200px) { - .col-lg-1, - .col-lg-2, - .col-lg-3, - .col-lg-4, - .col-lg-5, - .col-lg-6, - .col-lg-7, - .col-lg-8, - .col-lg-9, - .col-lg-10, - .col-lg-11, - .col-lg-12 { - float: left; - } - .col-lg-12 { - width: 100%; - } - .col-lg-11 { - width: 91.66666667%; - } - .col-lg-10 { - width: 83.33333333%; - } - .col-lg-9 { - width: 75%; - } - .col-lg-8 { - width: 66.66666667%; - } - .col-lg-7 { - width: 58.33333333%; - } - .col-lg-6 { - width: 50%; - } - .col-lg-5 { - width: 41.66666667%; - } - .col-lg-4 { - width: 33.33333333%; - } - .col-lg-3 { - width: 25%; - } - .col-lg-2 { - width: 16.66666667%; - } - .col-lg-1 { - width: 8.33333333%; - } - .col-lg-pull-12 { - right: 100%; - } - .col-lg-pull-11 { - right: 91.66666667%; - } - .col-lg-pull-10 { - right: 83.33333333%; - } - .col-lg-pull-9 { - right: 75%; - } - .col-lg-pull-8 { - right: 66.66666667%; - } - .col-lg-pull-7 { - right: 58.33333333%; - } - .col-lg-pull-6 { - right: 50%; - } - .col-lg-pull-5 { - right: 41.66666667%; - } - .col-lg-pull-4 { - right: 33.33333333%; - } - .col-lg-pull-3 { - right: 25%; - } - .col-lg-pull-2 { - right: 16.66666667%; - } - .col-lg-pull-1 { - right: 8.33333333%; - } - .col-lg-pull-0 { - right: auto; - } - .col-lg-push-12 { - left: 100%; - } - .col-lg-push-11 { - left: 91.66666667%; - } - .col-lg-push-10 { - left: 83.33333333%; - } - .col-lg-push-9 { - left: 75%; - } - .col-lg-push-8 { - left: 66.66666667%; - } - .col-lg-push-7 { - left: 58.33333333%; - } - .col-lg-push-6 { - left: 50%; - } - .col-lg-push-5 { - left: 41.66666667%; - } - .col-lg-push-4 { - left: 33.33333333%; - } - .col-lg-push-3 { - left: 25%; - } - .col-lg-push-2 { - left: 16.66666667%; - } - .col-lg-push-1 { - left: 8.33333333%; - } - .col-lg-push-0 { - left: auto; - } - .col-lg-offset-12 { - margin-left: 100%; - } - .col-lg-offset-11 { - margin-left: 91.66666667%; - } - .col-lg-offset-10 { - margin-left: 83.33333333%; - } - .col-lg-offset-9 { - margin-left: 75%; - } - .col-lg-offset-8 { - margin-left: 66.66666667%; - } - .col-lg-offset-7 { - margin-left: 58.33333333%; - } - .col-lg-offset-6 { - margin-left: 50%; - } - .col-lg-offset-5 { - margin-left: 41.66666667%; - } - .col-lg-offset-4 { - margin-left: 33.33333333%; - } - .col-lg-offset-3 { - margin-left: 25%; - } - .col-lg-offset-2 { - margin-left: 16.66666667%; - } - .col-lg-offset-1 { - margin-left: 8.33333333%; - } - .col-lg-offset-0 { - margin-left: 0%; - } -} -table { - background-color: transparent; -} -table col[class*="col-"] { - position: static; - display: table-column; - float: none; -} -table td[class*="col-"], -table th[class*="col-"] { - position: static; - display: table-cell; - float: none; -} -caption { - padding-top: 8px; - padding-bottom: 8px; - color: #777777; - text-align: left; -} -th { - text-align: left; -} -.table { - width: 100%; - max-width: 100%; - margin-bottom: 20px; -} -.table > thead > tr > th, -.table > tbody > tr > th, -.table > tfoot > tr > th, -.table > thead > tr > td, -.table > tbody > tr > td, -.table > tfoot > tr > td { - padding: 8px; - line-height: 1.42857143; - vertical-align: top; - border-top: 1px solid #ddd; -} -.table > thead > tr > th { - vertical-align: bottom; - border-bottom: 2px solid #ddd; -} -.table > caption + thead > tr:first-child > th, -.table > colgroup + thead > tr:first-child > th, -.table > thead:first-child > tr:first-child > th, -.table > caption + thead > tr:first-child > td, -.table > colgroup + thead > tr:first-child > td, -.table > thead:first-child > tr:first-child > td { - border-top: 0; -} -.table > tbody + tbody { - border-top: 2px solid #ddd; -} -.table .table { - background-color: #fff; -} -.table-condensed > thead > tr > th, -.table-condensed > tbody > tr > th, -.table-condensed > tfoot > tr > th, -.table-condensed > thead > tr > td, -.table-condensed > tbody > tr > td, -.table-condensed > tfoot > tr > td { - padding: 5px; -} -.table-bordered { - border: 1px solid #ddd; -} -.table-bordered > thead > tr > th, -.table-bordered > tbody > tr > th, -.table-bordered > tfoot > tr > th, -.table-bordered > thead > tr > td, -.table-bordered > tbody > tr > td, -.table-bordered > tfoot > tr > td { - border: 1px solid #ddd; -} -.table-bordered > thead > tr > th, -.table-bordered > thead > tr > td { - border-bottom-width: 2px; -} -.table-striped > tbody > tr:nth-of-type(odd) { - background-color: #f9f9f9; -} -.table-hover > tbody > tr:hover { - background-color: #f5f5f5; -} -.table > thead > tr > td.active, -.table > tbody > tr > td.active, -.table > tfoot > tr > td.active, -.table > thead > tr > th.active, -.table > tbody > tr > th.active, -.table > tfoot > tr > th.active, -.table > thead > tr.active > td, -.table > tbody > tr.active > td, -.table > tfoot > tr.active > td, -.table > thead > tr.active > th, -.table > tbody > tr.active > th, -.table > tfoot > tr.active > th { - background-color: #f5f5f5; -} -.table-hover > tbody > tr > td.active:hover, -.table-hover > tbody > tr > th.active:hover, -.table-hover > tbody > tr.active:hover > td, -.table-hover > tbody > tr:hover > .active, -.table-hover > tbody > tr.active:hover > th { - background-color: #e8e8e8; -} -.table > thead > tr > td.success, -.table > tbody > tr > td.success, -.table > tfoot > tr > td.success, -.table > thead > tr > th.success, -.table > tbody > tr > th.success, -.table > tfoot > tr > th.success, -.table > thead > tr.success > td, -.table > tbody > tr.success > td, -.table > tfoot > tr.success > td, -.table > thead > tr.success > th, -.table > tbody > tr.success > th, -.table > tfoot > tr.success > th { - background-color: #dff0d8; -} -.table-hover > tbody > tr > td.success:hover, -.table-hover > tbody > tr > th.success:hover, -.table-hover > tbody > tr.success:hover > td, -.table-hover > tbody > tr:hover > .success, -.table-hover > tbody > tr.success:hover > th { - background-color: #d0e9c6; -} -.table > thead > tr > td.info, -.table > tbody > tr > td.info, -.table > tfoot > tr > td.info, -.table > thead > tr > th.info, -.table > tbody > tr > th.info, -.table > tfoot > tr > th.info, -.table > thead > tr.info > td, -.table > tbody > tr.info > td, -.table > tfoot > tr.info > td, -.table > thead > tr.info > th, -.table > tbody > tr.info > th, -.table > tfoot > tr.info > th { - background-color: #d9edf7; -} -.table-hover > tbody > tr > td.info:hover, -.table-hover > tbody > tr > th.info:hover, -.table-hover > tbody > tr.info:hover > td, -.table-hover > tbody > tr:hover > .info, -.table-hover > tbody > tr.info:hover > th { - background-color: #c4e3f3; -} -.table > thead > tr > td.warning, -.table > tbody > tr > td.warning, -.table > tfoot > tr > td.warning, -.table > thead > tr > th.warning, -.table > tbody > tr > th.warning, -.table > tfoot > tr > th.warning, -.table > thead > tr.warning > td, -.table > tbody > tr.warning > td, -.table > tfoot > tr.warning > td, -.table > thead > tr.warning > th, -.table > tbody > tr.warning > th, -.table > tfoot > tr.warning > th { - background-color: #fcf8e3; -} -.table-hover > tbody > tr > td.warning:hover, -.table-hover > tbody > tr > th.warning:hover, -.table-hover > tbody > tr.warning:hover > td, -.table-hover > tbody > tr:hover > .warning, -.table-hover > tbody > tr.warning:hover > th { - background-color: #faf2cc; -} -.table > thead > tr > td.danger, -.table > tbody > tr > td.danger, -.table > tfoot > tr > td.danger, -.table > thead > tr > th.danger, -.table > tbody > tr > th.danger, -.table > tfoot > tr > th.danger, -.table > thead > tr.danger > td, -.table > tbody > tr.danger > td, -.table > tfoot > tr.danger > td, -.table > thead > tr.danger > th, -.table > tbody > tr.danger > th, -.table > tfoot > tr.danger > th { - background-color: #f2dede; -} -.table-hover > tbody > tr > td.danger:hover, -.table-hover > tbody > tr > th.danger:hover, -.table-hover > tbody > tr.danger:hover > td, -.table-hover > tbody > tr:hover > .danger, -.table-hover > tbody > tr.danger:hover > th { - background-color: #ebcccc; -} -.table-responsive { - min-height: 0.01%; - overflow-x: auto; -} -@media screen and (max-width: 767px) { - .table-responsive { - width: 100%; - margin-bottom: 15px; - overflow-y: hidden; - -ms-overflow-style: -ms-autohiding-scrollbar; - border: 1px solid #ddd; - } - .table-responsive > .table { - margin-bottom: 0; - } - .table-responsive > .table > thead > tr > th, - .table-responsive > .table > tbody > tr > th, - .table-responsive > .table > tfoot > tr > th, - .table-responsive > .table > thead > tr > td, - .table-responsive > .table > tbody > tr > td, - .table-responsive > .table > tfoot > tr > td { - white-space: nowrap; - } - .table-responsive > .table-bordered { - border: 0; - } - .table-responsive > .table-bordered > thead > tr > th:first-child, - .table-responsive > .table-bordered > tbody > tr > th:first-child, - .table-responsive > .table-bordered > tfoot > tr > th:first-child, - .table-responsive > .table-bordered > thead > tr > td:first-child, - .table-responsive > .table-bordered > tbody > tr > td:first-child, - .table-responsive > .table-bordered > tfoot > tr > td:first-child { - border-left: 0; - } - .table-responsive > .table-bordered > thead > tr > th:last-child, - .table-responsive > .table-bordered > tbody > tr > th:last-child, - .table-responsive > .table-bordered > tfoot > tr > th:last-child, - .table-responsive > .table-bordered > thead > tr > td:last-child, - .table-responsive > .table-bordered > tbody > tr > td:last-child, - .table-responsive > .table-bordered > tfoot > tr > td:last-child { - border-right: 0; - } - .table-responsive > .table-bordered > tbody > tr:last-child > th, - .table-responsive > .table-bordered > tfoot > tr:last-child > th, - .table-responsive > .table-bordered > tbody > tr:last-child > td, - .table-responsive > .table-bordered > tfoot > tr:last-child > td { - border-bottom: 0; - } -} -fieldset { - min-width: 0; - padding: 0; - margin: 0; - border: 0; -} -legend { - display: block; - width: 100%; - padding: 0; - margin-bottom: 20px; - font-size: 21px; - line-height: inherit; - color: #333333; - border: 0; - border-bottom: 1px solid #e5e5e5; -} -label { - display: inline-block; - max-width: 100%; - margin-bottom: 5px; - font-weight: 700; -} -input[type="search"] { - -webkit-box-sizing: border-box; - -moz-box-sizing: border-box; - box-sizing: border-box; - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; -} -input[type="radio"], -input[type="checkbox"] { - margin: 4px 0 0; - margin-top: 1px \9; - line-height: normal; -} -input[type="radio"][disabled], -input[type="checkbox"][disabled], -input[type="radio"].disabled, -input[type="checkbox"].disabled, -fieldset[disabled] input[type="radio"], -fieldset[disabled] input[type="checkbox"] { - cursor: not-allowed; -} -input[type="file"] { - display: block; -} -input[type="range"] { - display: block; - width: 100%; -} -select[multiple], -select[size] { - height: auto; -} -input[type="file"]:focus, -input[type="radio"]:focus, -input[type="checkbox"]:focus { - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -output { - display: block; - padding-top: 7px; - font-size: 14px; - line-height: 1.42857143; - color: #555555; -} -.form-control { - display: block; - width: 100%; - height: 34px; - padding: 6px 12px; - font-size: 14px; - line-height: 1.42857143; - color: #555555; - background-color: #fff; - background-image: none; - border: 1px solid #ccc; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; - -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; - -webkit-transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s; - transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s; - transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; - transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s, -webkit-box-shadow ease-in-out .15s; -} -.form-control:focus { - border-color: #66afe9; - outline: 0; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6); -} -.form-control::-moz-placeholder { - color: #999; - opacity: 1; -} -.form-control:-ms-input-placeholder { - color: #999; -} -.form-control::-webkit-input-placeholder { - color: #999; -} -.form-control::-ms-expand { - background-color: transparent; - border: 0; -} -.form-control[disabled], -.form-control[readonly], -fieldset[disabled] .form-control { - background-color: #eeeeee; - opacity: 1; -} -.form-control[disabled], -fieldset[disabled] .form-control { - cursor: not-allowed; -} -textarea.form-control { - height: auto; -} -@media screen and (-webkit-min-device-pixel-ratio: 0) { - input[type="date"].form-control, - input[type="time"].form-control, - input[type="datetime-local"].form-control, - input[type="month"].form-control { - line-height: 34px; - } - input[type="date"].input-sm, - input[type="time"].input-sm, - input[type="datetime-local"].input-sm, - input[type="month"].input-sm, - .input-group-sm input[type="date"], - .input-group-sm input[type="time"], - .input-group-sm input[type="datetime-local"], - .input-group-sm input[type="month"] { - line-height: 30px; - } - input[type="date"].input-lg, - input[type="time"].input-lg, - input[type="datetime-local"].input-lg, - input[type="month"].input-lg, - .input-group-lg input[type="date"], - .input-group-lg input[type="time"], - .input-group-lg input[type="datetime-local"], - .input-group-lg input[type="month"] { - line-height: 46px; - } -} -.form-group { - margin-bottom: 15px; -} -.radio, -.checkbox { - position: relative; - display: block; - margin-top: 10px; - margin-bottom: 10px; -} -.radio.disabled label, -.checkbox.disabled label, -fieldset[disabled] .radio label, -fieldset[disabled] .checkbox label { - cursor: not-allowed; -} -.radio label, -.checkbox label { - min-height: 20px; - padding-left: 20px; - margin-bottom: 0; - font-weight: 400; - cursor: pointer; -} -.radio input[type="radio"], -.radio-inline input[type="radio"], -.checkbox input[type="checkbox"], -.checkbox-inline input[type="checkbox"] { - position: absolute; - margin-top: 4px \9; - margin-left: -20px; -} -.radio + .radio, -.checkbox + .checkbox { - margin-top: -5px; -} -.radio-inline, -.checkbox-inline { - position: relative; - display: inline-block; - padding-left: 20px; - margin-bottom: 0; - font-weight: 400; - vertical-align: middle; - cursor: pointer; -} -.radio-inline.disabled, -.checkbox-inline.disabled, -fieldset[disabled] .radio-inline, -fieldset[disabled] .checkbox-inline { - cursor: not-allowed; -} -.radio-inline + .radio-inline, -.checkbox-inline + .checkbox-inline { - margin-top: 0; - margin-left: 10px; -} -.form-control-static { - min-height: 34px; - padding-top: 7px; - padding-bottom: 7px; - margin-bottom: 0; -} -.form-control-static.input-lg, -.form-control-static.input-sm { - padding-right: 0; - padding-left: 0; -} -.input-sm { - height: 30px; - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -select.input-sm { - height: 30px; - line-height: 30px; -} -textarea.input-sm, -select[multiple].input-sm { - height: auto; -} -.form-group-sm .form-control { - height: 30px; - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -.form-group-sm select.form-control { - height: 30px; - line-height: 30px; -} -.form-group-sm textarea.form-control, -.form-group-sm select[multiple].form-control { - height: auto; -} -.form-group-sm .form-control-static { - height: 30px; - min-height: 32px; - padding: 6px 10px; - font-size: 12px; - line-height: 1.5; -} -.input-lg { - height: 46px; - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -select.input-lg { - height: 46px; - line-height: 46px; -} -textarea.input-lg, -select[multiple].input-lg { - height: auto; -} -.form-group-lg .form-control { - height: 46px; - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -.form-group-lg select.form-control { - height: 46px; - line-height: 46px; -} -.form-group-lg textarea.form-control, -.form-group-lg select[multiple].form-control { - height: auto; -} -.form-group-lg .form-control-static { - height: 46px; - min-height: 38px; - padding: 11px 16px; - font-size: 18px; - line-height: 1.3333333; -} -.has-feedback { - position: relative; -} -.has-feedback .form-control { - padding-right: 42.5px; -} -.form-control-feedback { - position: absolute; - top: 0; - right: 0; - z-index: 2; - display: block; - width: 34px; - height: 34px; - line-height: 34px; - text-align: center; - pointer-events: none; -} -.input-lg + .form-control-feedback, -.input-group-lg + .form-control-feedback, -.form-group-lg .form-control + .form-control-feedback { - width: 46px; - height: 46px; - line-height: 46px; -} -.input-sm + .form-control-feedback, -.input-group-sm + .form-control-feedback, -.form-group-sm .form-control + .form-control-feedback { - width: 30px; - height: 30px; - line-height: 30px; -} -.has-success .help-block, -.has-success .control-label, -.has-success .radio, -.has-success .checkbox, -.has-success .radio-inline, -.has-success .checkbox-inline, -.has-success.radio label, -.has-success.checkbox label, -.has-success.radio-inline label, -.has-success.checkbox-inline label { - color: #3c763d; -} -.has-success .form-control { - border-color: #3c763d; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} -.has-success .form-control:focus { - border-color: #2b542c; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; -} -.has-success .input-group-addon { - color: #3c763d; - background-color: #dff0d8; - border-color: #3c763d; -} -.has-success .form-control-feedback { - color: #3c763d; -} -.has-warning .help-block, -.has-warning .control-label, -.has-warning .radio, -.has-warning .checkbox, -.has-warning .radio-inline, -.has-warning .checkbox-inline, -.has-warning.radio label, -.has-warning.checkbox label, -.has-warning.radio-inline label, -.has-warning.checkbox-inline label { - color: #8a6d3b; -} -.has-warning .form-control { - border-color: #8a6d3b; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} -.has-warning .form-control:focus { - border-color: #66512c; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; -} -.has-warning .input-group-addon { - color: #8a6d3b; - background-color: #fcf8e3; - border-color: #8a6d3b; -} -.has-warning .form-control-feedback { - color: #8a6d3b; -} -.has-error .help-block, -.has-error .control-label, -.has-error .radio, -.has-error .checkbox, -.has-error .radio-inline, -.has-error .checkbox-inline, -.has-error.radio label, -.has-error.checkbox label, -.has-error.radio-inline label, -.has-error.checkbox-inline label { - color: #a94442; -} -.has-error .form-control { - border-color: #a94442; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -} -.has-error .form-control:focus { - border-color: #843534; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; -} -.has-error .input-group-addon { - color: #a94442; - background-color: #f2dede; - border-color: #a94442; -} -.has-error .form-control-feedback { - color: #a94442; -} -.has-feedback label ~ .form-control-feedback { - top: 25px; -} -.has-feedback label.sr-only ~ .form-control-feedback { - top: 0; -} -.help-block { - display: block; - margin-top: 5px; - margin-bottom: 10px; - color: #737373; -} -@media (min-width: 768px) { - .form-inline .form-group { - display: inline-block; - margin-bottom: 0; - vertical-align: middle; - } - .form-inline .form-control { - display: inline-block; - width: auto; - vertical-align: middle; - } - .form-inline .form-control-static { - display: inline-block; - } - .form-inline .input-group { - display: inline-table; - vertical-align: middle; - } - .form-inline .input-group .input-group-addon, - .form-inline .input-group .input-group-btn, - .form-inline .input-group .form-control { - width: auto; - } - .form-inline .input-group > .form-control { - width: 100%; - } - .form-inline .control-label { - margin-bottom: 0; - vertical-align: middle; - } - .form-inline .radio, - .form-inline .checkbox { - display: inline-block; - margin-top: 0; - margin-bottom: 0; - vertical-align: middle; - } - .form-inline .radio label, - .form-inline .checkbox label { - padding-left: 0; - } - .form-inline .radio input[type="radio"], - .form-inline .checkbox input[type="checkbox"] { - position: relative; - margin-left: 0; - } - .form-inline .has-feedback .form-control-feedback { - top: 0; - } -} -.form-horizontal .radio, -.form-horizontal .checkbox, -.form-horizontal .radio-inline, -.form-horizontal .checkbox-inline { - padding-top: 7px; - margin-top: 0; - margin-bottom: 0; -} -.form-horizontal .radio, -.form-horizontal .checkbox { - min-height: 27px; -} -.form-horizontal .form-group { - margin-right: -15px; - margin-left: -15px; -} -@media (min-width: 768px) { - .form-horizontal .control-label { - padding-top: 7px; - margin-bottom: 0; - text-align: right; - } -} -.form-horizontal .has-feedback .form-control-feedback { - right: 15px; -} -@media (min-width: 768px) { - .form-horizontal .form-group-lg .control-label { - padding-top: 11px; - font-size: 18px; - } -} -@media (min-width: 768px) { - .form-horizontal .form-group-sm .control-label { - padding-top: 6px; - font-size: 12px; - } -} -.btn { - display: inline-block; - margin-bottom: 0; - font-weight: normal; - text-align: center; - white-space: nowrap; - vertical-align: middle; - -ms-touch-action: manipulation; - touch-action: manipulation; - cursor: pointer; - background-image: none; - border: 1px solid transparent; - padding: 6px 12px; - font-size: 14px; - line-height: 1.42857143; - border-radius: 4px; - -webkit-user-select: none; - -moz-user-select: none; - -ms-user-select: none; - user-select: none; -} -.btn:focus, -.btn:active:focus, -.btn.active:focus, -.btn.focus, -.btn:active.focus, -.btn.active.focus { - outline: 5px auto -webkit-focus-ring-color; - outline-offset: -2px; -} -.btn:hover, -.btn:focus, -.btn.focus { - color: #333; - text-decoration: none; -} -.btn:active, -.btn.active { - background-image: none; - outline: 0; - -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); - box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); -} -.btn.disabled, -.btn[disabled], -fieldset[disabled] .btn { - cursor: not-allowed; - filter: alpha(opacity=65); - opacity: 0.65; - -webkit-box-shadow: none; - box-shadow: none; -} -a.btn.disabled, -fieldset[disabled] a.btn { - pointer-events: none; -} -.btn-default { - color: #333; - background-color: #fff; - border-color: #ccc; -} -.btn-default:focus, -.btn-default.focus { - color: #333; - background-color: #e6e6e6; - border-color: #8c8c8c; -} -.btn-default:hover { - color: #333; - background-color: #e6e6e6; - border-color: #adadad; -} -.btn-default:active, -.btn-default.active, -.open > .dropdown-toggle.btn-default { - color: #333; - background-color: #e6e6e6; - background-image: none; - border-color: #adadad; -} -.btn-default:active:hover, -.btn-default.active:hover, -.open > .dropdown-toggle.btn-default:hover, -.btn-default:active:focus, -.btn-default.active:focus, -.open > .dropdown-toggle.btn-default:focus, -.btn-default:active.focus, -.btn-default.active.focus, -.open > .dropdown-toggle.btn-default.focus { - color: #333; - background-color: #d4d4d4; - border-color: #8c8c8c; -} -.btn-default.disabled:hover, -.btn-default[disabled]:hover, -fieldset[disabled] .btn-default:hover, -.btn-default.disabled:focus, -.btn-default[disabled]:focus, -fieldset[disabled] .btn-default:focus, -.btn-default.disabled.focus, -.btn-default[disabled].focus, -fieldset[disabled] .btn-default.focus { - background-color: #fff; - border-color: #ccc; -} -.btn-default .badge { - color: #fff; - background-color: #333; -} -.btn-primary { - color: #fff; - background-color: #337ab7; - border-color: #2e6da4; -} -.btn-primary:focus, -.btn-primary.focus { - color: #fff; - background-color: #286090; - border-color: #122b40; -} -.btn-primary:hover { - color: #fff; - background-color: #286090; - border-color: #204d74; -} -.btn-primary:active, -.btn-primary.active, -.open > .dropdown-toggle.btn-primary { - color: #fff; - background-color: #286090; - background-image: none; - border-color: #204d74; -} -.btn-primary:active:hover, -.btn-primary.active:hover, -.open > .dropdown-toggle.btn-primary:hover, -.btn-primary:active:focus, -.btn-primary.active:focus, -.open > .dropdown-toggle.btn-primary:focus, -.btn-primary:active.focus, -.btn-primary.active.focus, -.open > .dropdown-toggle.btn-primary.focus { - color: #fff; - background-color: #204d74; - border-color: #122b40; -} -.btn-primary.disabled:hover, -.btn-primary[disabled]:hover, -fieldset[disabled] .btn-primary:hover, -.btn-primary.disabled:focus, -.btn-primary[disabled]:focus, -fieldset[disabled] .btn-primary:focus, -.btn-primary.disabled.focus, -.btn-primary[disabled].focus, -fieldset[disabled] .btn-primary.focus { - background-color: #337ab7; - border-color: #2e6da4; -} -.btn-primary .badge { - color: #337ab7; - background-color: #fff; -} -.btn-success { - color: #fff; - background-color: #5cb85c; - border-color: #4cae4c; -} -.btn-success:focus, -.btn-success.focus { - color: #fff; - background-color: #449d44; - border-color: #255625; -} -.btn-success:hover { - color: #fff; - background-color: #449d44; - border-color: #398439; -} -.btn-success:active, -.btn-success.active, -.open > .dropdown-toggle.btn-success { - color: #fff; - background-color: #449d44; - background-image: none; - border-color: #398439; -} -.btn-success:active:hover, -.btn-success.active:hover, -.open > .dropdown-toggle.btn-success:hover, -.btn-success:active:focus, -.btn-success.active:focus, -.open > .dropdown-toggle.btn-success:focus, -.btn-success:active.focus, -.btn-success.active.focus, -.open > .dropdown-toggle.btn-success.focus { - color: #fff; - background-color: #398439; - border-color: #255625; -} -.btn-success.disabled:hover, -.btn-success[disabled]:hover, -fieldset[disabled] .btn-success:hover, -.btn-success.disabled:focus, -.btn-success[disabled]:focus, -fieldset[disabled] .btn-success:focus, -.btn-success.disabled.focus, -.btn-success[disabled].focus, -fieldset[disabled] .btn-success.focus { - background-color: #5cb85c; - border-color: #4cae4c; -} -.btn-success .badge { - color: #5cb85c; - background-color: #fff; -} -.btn-info { - color: #fff; - background-color: #5bc0de; - border-color: #46b8da; -} -.btn-info:focus, -.btn-info.focus { - color: #fff; - background-color: #31b0d5; - border-color: #1b6d85; -} -.btn-info:hover { - color: #fff; - background-color: #31b0d5; - border-color: #269abc; -} -.btn-info:active, -.btn-info.active, -.open > .dropdown-toggle.btn-info { - color: #fff; - background-color: #31b0d5; - background-image: none; - border-color: #269abc; -} -.btn-info:active:hover, -.btn-info.active:hover, -.open > .dropdown-toggle.btn-info:hover, -.btn-info:active:focus, -.btn-info.active:focus, -.open > .dropdown-toggle.btn-info:focus, -.btn-info:active.focus, -.btn-info.active.focus, -.open > .dropdown-toggle.btn-info.focus { - color: #fff; - background-color: #269abc; - border-color: #1b6d85; -} -.btn-info.disabled:hover, -.btn-info[disabled]:hover, -fieldset[disabled] .btn-info:hover, -.btn-info.disabled:focus, -.btn-info[disabled]:focus, -fieldset[disabled] .btn-info:focus, -.btn-info.disabled.focus, -.btn-info[disabled].focus, -fieldset[disabled] .btn-info.focus { - background-color: #5bc0de; - border-color: #46b8da; -} -.btn-info .badge { - color: #5bc0de; - background-color: #fff; -} -.btn-warning { - color: #fff; - background-color: #f0ad4e; - border-color: #eea236; -} -.btn-warning:focus, -.btn-warning.focus { - color: #fff; - background-color: #ec971f; - border-color: #985f0d; -} -.btn-warning:hover { - color: #fff; - background-color: #ec971f; - border-color: #d58512; -} -.btn-warning:active, -.btn-warning.active, -.open > .dropdown-toggle.btn-warning { - color: #fff; - background-color: #ec971f; - background-image: none; - border-color: #d58512; -} -.btn-warning:active:hover, -.btn-warning.active:hover, -.open > .dropdown-toggle.btn-warning:hover, -.btn-warning:active:focus, -.btn-warning.active:focus, -.open > .dropdown-toggle.btn-warning:focus, -.btn-warning:active.focus, -.btn-warning.active.focus, -.open > .dropdown-toggle.btn-warning.focus { - color: #fff; - background-color: #d58512; - border-color: #985f0d; -} -.btn-warning.disabled:hover, -.btn-warning[disabled]:hover, -fieldset[disabled] .btn-warning:hover, -.btn-warning.disabled:focus, -.btn-warning[disabled]:focus, -fieldset[disabled] .btn-warning:focus, -.btn-warning.disabled.focus, -.btn-warning[disabled].focus, -fieldset[disabled] .btn-warning.focus { - background-color: #f0ad4e; - border-color: #eea236; -} -.btn-warning .badge { - color: #f0ad4e; - background-color: #fff; -} -.btn-danger { - color: #fff; - background-color: #d9534f; - border-color: #d43f3a; -} -.btn-danger:focus, -.btn-danger.focus { - color: #fff; - background-color: #c9302c; - border-color: #761c19; -} -.btn-danger:hover { - color: #fff; - background-color: #c9302c; - border-color: #ac2925; -} -.btn-danger:active, -.btn-danger.active, -.open > .dropdown-toggle.btn-danger { - color: #fff; - background-color: #c9302c; - background-image: none; - border-color: #ac2925; -} -.btn-danger:active:hover, -.btn-danger.active:hover, -.open > .dropdown-toggle.btn-danger:hover, -.btn-danger:active:focus, -.btn-danger.active:focus, -.open > .dropdown-toggle.btn-danger:focus, -.btn-danger:active.focus, -.btn-danger.active.focus, -.open > .dropdown-toggle.btn-danger.focus { - color: #fff; - background-color: #ac2925; - border-color: #761c19; -} -.btn-danger.disabled:hover, -.btn-danger[disabled]:hover, -fieldset[disabled] .btn-danger:hover, -.btn-danger.disabled:focus, -.btn-danger[disabled]:focus, -fieldset[disabled] .btn-danger:focus, -.btn-danger.disabled.focus, -.btn-danger[disabled].focus, -fieldset[disabled] .btn-danger.focus { - background-color: #d9534f; - border-color: #d43f3a; -} -.btn-danger .badge { - color: #d9534f; - background-color: #fff; -} -.btn-link { - font-weight: 400; - color: #337ab7; - border-radius: 0; -} -.btn-link, -.btn-link:active, -.btn-link.active, -.btn-link[disabled], -fieldset[disabled] .btn-link { - background-color: transparent; - -webkit-box-shadow: none; - box-shadow: none; -} -.btn-link, -.btn-link:hover, -.btn-link:focus, -.btn-link:active { - border-color: transparent; -} -.btn-link:hover, -.btn-link:focus { - color: #23527c; - text-decoration: underline; - background-color: transparent; -} -.btn-link[disabled]:hover, -fieldset[disabled] .btn-link:hover, -.btn-link[disabled]:focus, -fieldset[disabled] .btn-link:focus { - color: #777777; - text-decoration: none; -} -.btn-lg, -.btn-group-lg > .btn { - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -.btn-sm, -.btn-group-sm > .btn { - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -.btn-xs, -.btn-group-xs > .btn { - padding: 1px 5px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -.btn-block { - display: block; - width: 100%; -} -.btn-block + .btn-block { - margin-top: 5px; -} -input[type="submit"].btn-block, -input[type="reset"].btn-block, -input[type="button"].btn-block { - width: 100%; -} -.fade { - opacity: 0; - -webkit-transition: opacity 0.15s linear; - -o-transition: opacity 0.15s linear; - transition: opacity 0.15s linear; -} -.fade.in { - opacity: 1; -} -.collapse { - display: none; -} -.collapse.in { - display: block; -} -tr.collapse.in { - display: table-row; -} -tbody.collapse.in { - display: table-row-group; -} -.collapsing { - position: relative; - height: 0; - overflow: hidden; - -webkit-transition-property: height, visibility; - -o-transition-property: height, visibility; - transition-property: height, visibility; - -webkit-transition-duration: 0.35s; - -o-transition-duration: 0.35s; - transition-duration: 0.35s; - -webkit-transition-timing-function: ease; - -o-transition-timing-function: ease; - transition-timing-function: ease; -} -.caret { - display: inline-block; - width: 0; - height: 0; - margin-left: 2px; - vertical-align: middle; - border-top: 4px dashed; - border-top: 4px solid \9; - border-right: 4px solid transparent; - border-left: 4px solid transparent; -} -.dropup, -.dropdown { - position: relative; -} -.dropdown-toggle:focus { - outline: 0; -} -.dropdown-menu { - position: absolute; - top: 100%; - left: 0; - z-index: 1000; - display: none; - float: left; - min-width: 160px; - padding: 5px 0; - margin: 2px 0 0; - font-size: 14px; - text-align: left; - list-style: none; - background-color: #fff; - background-clip: padding-box; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.15); - border-radius: 4px; - -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); - box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); -} -.dropdown-menu.pull-right { - right: 0; - left: auto; -} -.dropdown-menu .divider { - height: 1px; - margin: 9px 0; - overflow: hidden; - background-color: #e5e5e5; -} -.dropdown-menu > li > a { - display: block; - padding: 3px 20px; - clear: both; - font-weight: 400; - line-height: 1.42857143; - color: #333333; - white-space: nowrap; -} -.dropdown-menu > li > a:hover, -.dropdown-menu > li > a:focus { - color: #262626; - text-decoration: none; - background-color: #f5f5f5; -} -.dropdown-menu > .active > a, -.dropdown-menu > .active > a:hover, -.dropdown-menu > .active > a:focus { - color: #fff; - text-decoration: none; - background-color: #337ab7; - outline: 0; -} -.dropdown-menu > .disabled > a, -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - color: #777777; -} -.dropdown-menu > .disabled > a:hover, -.dropdown-menu > .disabled > a:focus { - text-decoration: none; - cursor: not-allowed; - background-color: transparent; - background-image: none; - filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); -} -.open > .dropdown-menu { - display: block; -} -.open > a { - outline: 0; -} -.dropdown-menu-right { - right: 0; - left: auto; -} -.dropdown-menu-left { - right: auto; - left: 0; -} -.dropdown-header { - display: block; - padding: 3px 20px; - font-size: 12px; - line-height: 1.42857143; - color: #777777; - white-space: nowrap; -} -.dropdown-backdrop { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 990; -} -.pull-right > .dropdown-menu { - right: 0; - left: auto; -} -.dropup .caret, -.navbar-fixed-bottom .dropdown .caret { - content: ""; - border-top: 0; - border-bottom: 4px dashed; - border-bottom: 4px solid \9; -} -.dropup .dropdown-menu, -.navbar-fixed-bottom .dropdown .dropdown-menu { - top: auto; - bottom: 100%; - margin-bottom: 2px; -} -@media (min-width: 768px) { - .navbar-right .dropdown-menu { - right: 0; - left: auto; - } - .navbar-right .dropdown-menu-left { - right: auto; - left: 0; - } -} -.btn-group, -.btn-group-vertical { - position: relative; - display: inline-block; - vertical-align: middle; -} -.btn-group > .btn, -.btn-group-vertical > .btn { - position: relative; - float: left; -} -.btn-group > .btn:hover, -.btn-group-vertical > .btn:hover, -.btn-group > .btn:focus, -.btn-group-vertical > .btn:focus, -.btn-group > .btn:active, -.btn-group-vertical > .btn:active, -.btn-group > .btn.active, -.btn-group-vertical > .btn.active { - z-index: 2; -} -.btn-group .btn + .btn, -.btn-group .btn + .btn-group, -.btn-group .btn-group + .btn, -.btn-group .btn-group + .btn-group { - margin-left: -1px; -} -.btn-toolbar { - margin-left: -5px; -} -.btn-toolbar .btn, -.btn-toolbar .btn-group, -.btn-toolbar .input-group { - float: left; -} -.btn-toolbar > .btn, -.btn-toolbar > .btn-group, -.btn-toolbar > .input-group { - margin-left: 5px; -} -.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { - border-radius: 0; -} -.btn-group > .btn:first-child { - margin-left: 0; -} -.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} -.btn-group > .btn:last-child:not(:first-child), -.btn-group > .dropdown-toggle:not(:first-child) { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group > .btn-group { - float: left; -} -.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { - border-radius: 0; -} -.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, -.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} -.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group .dropdown-toggle:active, -.btn-group.open .dropdown-toggle { - outline: 0; -} -.btn-group > .btn + .dropdown-toggle { - padding-right: 8px; - padding-left: 8px; -} -.btn-group > .btn-lg + .dropdown-toggle { - padding-right: 12px; - padding-left: 12px; -} -.btn-group.open .dropdown-toggle { - -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); - box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); -} -.btn-group.open .dropdown-toggle.btn-link { - -webkit-box-shadow: none; - box-shadow: none; -} -.btn .caret { - margin-left: 0; -} -.btn-lg .caret { - border-width: 5px 5px 0; - border-bottom-width: 0; -} -.dropup .btn-lg .caret { - border-width: 0 5px 5px; -} -.btn-group-vertical > .btn, -.btn-group-vertical > .btn-group, -.btn-group-vertical > .btn-group > .btn { - display: block; - float: none; - width: 100%; - max-width: 100%; -} -.btn-group-vertical > .btn-group > .btn { - float: none; -} -.btn-group-vertical > .btn + .btn, -.btn-group-vertical > .btn + .btn-group, -.btn-group-vertical > .btn-group + .btn, -.btn-group-vertical > .btn-group + .btn-group { - margin-top: -1px; - margin-left: 0; -} -.btn-group-vertical > .btn:not(:first-child):not(:last-child) { - border-radius: 0; -} -.btn-group-vertical > .btn:first-child:not(:last-child) { - border-top-left-radius: 4px; - border-top-right-radius: 4px; - border-bottom-right-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group-vertical > .btn:last-child:not(:first-child) { - border-top-left-radius: 0; - border-top-right-radius: 0; - border-bottom-right-radius: 4px; - border-bottom-left-radius: 4px; -} -.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { - border-radius: 0; -} -.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, -.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { - border-bottom-right-radius: 0; - border-bottom-left-radius: 0; -} -.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { - border-top-left-radius: 0; - border-top-right-radius: 0; -} -.btn-group-justified { - display: table; - width: 100%; - table-layout: fixed; - border-collapse: separate; -} -.btn-group-justified > .btn, -.btn-group-justified > .btn-group { - display: table-cell; - float: none; - width: 1%; -} -.btn-group-justified > .btn-group .btn { - width: 100%; -} -.btn-group-justified > .btn-group .dropdown-menu { - left: auto; -} -[data-toggle="buttons"] > .btn input[type="radio"], -[data-toggle="buttons"] > .btn-group > .btn input[type="radio"], -[data-toggle="buttons"] > .btn input[type="checkbox"], -[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { - position: absolute; - clip: rect(0, 0, 0, 0); - pointer-events: none; -} -.input-group { - position: relative; - display: table; - border-collapse: separate; -} -.input-group[class*="col-"] { - float: none; - padding-right: 0; - padding-left: 0; -} -.input-group .form-control { - position: relative; - z-index: 2; - float: left; - width: 100%; - margin-bottom: 0; -} -.input-group .form-control:focus { - z-index: 3; -} -.input-group-lg > .form-control, -.input-group-lg > .input-group-addon, -.input-group-lg > .input-group-btn > .btn { - height: 46px; - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; - border-radius: 6px; -} -select.input-group-lg > .form-control, -select.input-group-lg > .input-group-addon, -select.input-group-lg > .input-group-btn > .btn { - height: 46px; - line-height: 46px; -} -textarea.input-group-lg > .form-control, -textarea.input-group-lg > .input-group-addon, -textarea.input-group-lg > .input-group-btn > .btn, -select[multiple].input-group-lg > .form-control, -select[multiple].input-group-lg > .input-group-addon, -select[multiple].input-group-lg > .input-group-btn > .btn { - height: auto; -} -.input-group-sm > .form-control, -.input-group-sm > .input-group-addon, -.input-group-sm > .input-group-btn > .btn { - height: 30px; - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; - border-radius: 3px; -} -select.input-group-sm > .form-control, -select.input-group-sm > .input-group-addon, -select.input-group-sm > .input-group-btn > .btn { - height: 30px; - line-height: 30px; -} -textarea.input-group-sm > .form-control, -textarea.input-group-sm > .input-group-addon, -textarea.input-group-sm > .input-group-btn > .btn, -select[multiple].input-group-sm > .form-control, -select[multiple].input-group-sm > .input-group-addon, -select[multiple].input-group-sm > .input-group-btn > .btn { - height: auto; -} -.input-group-addon, -.input-group-btn, -.input-group .form-control { - display: table-cell; -} -.input-group-addon:not(:first-child):not(:last-child), -.input-group-btn:not(:first-child):not(:last-child), -.input-group .form-control:not(:first-child):not(:last-child) { - border-radius: 0; -} -.input-group-addon, -.input-group-btn { - width: 1%; - white-space: nowrap; - vertical-align: middle; -} -.input-group-addon { - padding: 6px 12px; - font-size: 14px; - font-weight: 400; - line-height: 1; - color: #555555; - text-align: center; - background-color: #eeeeee; - border: 1px solid #ccc; - border-radius: 4px; -} -.input-group-addon.input-sm { - padding: 5px 10px; - font-size: 12px; - border-radius: 3px; -} -.input-group-addon.input-lg { - padding: 10px 16px; - font-size: 18px; - border-radius: 6px; -} -.input-group-addon input[type="radio"], -.input-group-addon input[type="checkbox"] { - margin-top: 0; -} -.input-group .form-control:first-child, -.input-group-addon:first-child, -.input-group-btn:first-child > .btn, -.input-group-btn:first-child > .btn-group > .btn, -.input-group-btn:first-child > .dropdown-toggle, -.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), -.input-group-btn:last-child > .btn-group:not(:last-child) > .btn { - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} -.input-group-addon:first-child { - border-right: 0; -} -.input-group .form-control:last-child, -.input-group-addon:last-child, -.input-group-btn:last-child > .btn, -.input-group-btn:last-child > .btn-group > .btn, -.input-group-btn:last-child > .dropdown-toggle, -.input-group-btn:first-child > .btn:not(:first-child), -.input-group-btn:first-child > .btn-group:not(:first-child) > .btn { - border-top-left-radius: 0; - border-bottom-left-radius: 0; -} -.input-group-addon:last-child { - border-left: 0; -} -.input-group-btn { - position: relative; - font-size: 0; - white-space: nowrap; -} -.input-group-btn > .btn { - position: relative; -} -.input-group-btn > .btn + .btn { - margin-left: -1px; -} -.input-group-btn > .btn:hover, -.input-group-btn > .btn:focus, -.input-group-btn > .btn:active { - z-index: 2; -} -.input-group-btn:first-child > .btn, -.input-group-btn:first-child > .btn-group { - margin-right: -1px; -} -.input-group-btn:last-child > .btn, -.input-group-btn:last-child > .btn-group { - z-index: 2; - margin-left: -1px; -} -.nav { - padding-left: 0; - margin-bottom: 0; - list-style: none; -} -.nav > li { - position: relative; - display: block; -} -.nav > li > a { - position: relative; - display: block; - padding: 10px 15px; -} -.nav > li > a:hover, -.nav > li > a:focus { - text-decoration: none; - background-color: #eeeeee; -} -.nav > li.disabled > a { - color: #777777; -} -.nav > li.disabled > a:hover, -.nav > li.disabled > a:focus { - color: #777777; - text-decoration: none; - cursor: not-allowed; - background-color: transparent; -} -.nav .open > a, -.nav .open > a:hover, -.nav .open > a:focus { - background-color: #eeeeee; - border-color: #337ab7; -} -.nav .nav-divider { - height: 1px; - margin: 9px 0; - overflow: hidden; - background-color: #e5e5e5; -} -.nav > li > a > img { - max-width: none; -} -.nav-tabs { - border-bottom: 1px solid #ddd; -} -.nav-tabs > li { - float: left; - margin-bottom: -1px; -} -.nav-tabs > li > a { - margin-right: 2px; - line-height: 1.42857143; - border: 1px solid transparent; - border-radius: 4px 4px 0 0; -} -.nav-tabs > li > a:hover { - border-color: #eeeeee #eeeeee #ddd; -} -.nav-tabs > li.active > a, -.nav-tabs > li.active > a:hover, -.nav-tabs > li.active > a:focus { - color: #555555; - cursor: default; - background-color: #fff; - border: 1px solid #ddd; - border-bottom-color: transparent; -} -.nav-tabs.nav-justified { - width: 100%; - border-bottom: 0; -} -.nav-tabs.nav-justified > li { - float: none; -} -.nav-tabs.nav-justified > li > a { - margin-bottom: 5px; - text-align: center; -} -.nav-tabs.nav-justified > .dropdown .dropdown-menu { - top: auto; - left: auto; -} -@media (min-width: 768px) { - .nav-tabs.nav-justified > li { - display: table-cell; - width: 1%; - } - .nav-tabs.nav-justified > li > a { - margin-bottom: 0; - } -} -.nav-tabs.nav-justified > li > a { - margin-right: 0; - border-radius: 4px; -} -.nav-tabs.nav-justified > .active > a, -.nav-tabs.nav-justified > .active > a:hover, -.nav-tabs.nav-justified > .active > a:focus { - border: 1px solid #ddd; -} -@media (min-width: 768px) { - .nav-tabs.nav-justified > li > a { - border-bottom: 1px solid #ddd; - border-radius: 4px 4px 0 0; - } - .nav-tabs.nav-justified > .active > a, - .nav-tabs.nav-justified > .active > a:hover, - .nav-tabs.nav-justified > .active > a:focus { - border-bottom-color: #fff; - } -} -.nav-pills > li { - float: left; -} -.nav-pills > li > a { - border-radius: 4px; -} -.nav-pills > li + li { - margin-left: 2px; -} -.nav-pills > li.active > a, -.nav-pills > li.active > a:hover, -.nav-pills > li.active > a:focus { - color: #fff; - background-color: #337ab7; -} -.nav-stacked > li { - float: none; -} -.nav-stacked > li + li { - margin-top: 2px; - margin-left: 0; -} -.nav-justified { - width: 100%; -} -.nav-justified > li { - float: none; -} -.nav-justified > li > a { - margin-bottom: 5px; - text-align: center; -} -.nav-justified > .dropdown .dropdown-menu { - top: auto; - left: auto; -} -@media (min-width: 768px) { - .nav-justified > li { - display: table-cell; - width: 1%; - } - .nav-justified > li > a { - margin-bottom: 0; - } -} -.nav-tabs-justified { - border-bottom: 0; -} -.nav-tabs-justified > li > a { - margin-right: 0; - border-radius: 4px; -} -.nav-tabs-justified > .active > a, -.nav-tabs-justified > .active > a:hover, -.nav-tabs-justified > .active > a:focus { - border: 1px solid #ddd; -} -@media (min-width: 768px) { - .nav-tabs-justified > li > a { - border-bottom: 1px solid #ddd; - border-radius: 4px 4px 0 0; - } - .nav-tabs-justified > .active > a, - .nav-tabs-justified > .active > a:hover, - .nav-tabs-justified > .active > a:focus { - border-bottom-color: #fff; - } -} -.tab-content > .tab-pane { - display: none; -} -.tab-content > .active { - display: block; -} -.nav-tabs .dropdown-menu { - margin-top: -1px; - border-top-left-radius: 0; - border-top-right-radius: 0; -} -.navbar { - position: relative; - min-height: 50px; - margin-bottom: 20px; - border: 1px solid transparent; -} -@media (min-width: 768px) { - .navbar { - border-radius: 4px; - } -} -@media (min-width: 768px) { - .navbar-header { - float: left; - } -} -.navbar-collapse { - padding-right: 15px; - padding-left: 15px; - overflow-x: visible; - border-top: 1px solid transparent; - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); - -webkit-overflow-scrolling: touch; -} -.navbar-collapse.in { - overflow-y: auto; -} -@media (min-width: 768px) { - .navbar-collapse { - width: auto; - border-top: 0; - -webkit-box-shadow: none; - box-shadow: none; - } - .navbar-collapse.collapse { - display: block !important; - height: auto !important; - padding-bottom: 0; - overflow: visible !important; - } - .navbar-collapse.in { - overflow-y: visible; - } - .navbar-fixed-top .navbar-collapse, - .navbar-static-top .navbar-collapse, - .navbar-fixed-bottom .navbar-collapse { - padding-right: 0; - padding-left: 0; - } -} -.navbar-fixed-top, -.navbar-fixed-bottom { - position: fixed; - right: 0; - left: 0; - z-index: 1030; -} -.navbar-fixed-top .navbar-collapse, -.navbar-fixed-bottom .navbar-collapse { - max-height: 340px; -} -@media (max-device-width: 480px) and (orientation: landscape) { - .navbar-fixed-top .navbar-collapse, - .navbar-fixed-bottom .navbar-collapse { - max-height: 200px; - } -} -@media (min-width: 768px) { - .navbar-fixed-top, - .navbar-fixed-bottom { - border-radius: 0; - } -} -.navbar-fixed-top { - top: 0; - border-width: 0 0 1px; -} -.navbar-fixed-bottom { - bottom: 0; - margin-bottom: 0; - border-width: 1px 0 0; -} -.container > .navbar-header, -.container-fluid > .navbar-header, -.container > .navbar-collapse, -.container-fluid > .navbar-collapse { - margin-right: -15px; - margin-left: -15px; -} -@media (min-width: 768px) { - .container > .navbar-header, - .container-fluid > .navbar-header, - .container > .navbar-collapse, - .container-fluid > .navbar-collapse { - margin-right: 0; - margin-left: 0; - } -} -.navbar-static-top { - z-index: 1000; - border-width: 0 0 1px; -} -@media (min-width: 768px) { - .navbar-static-top { - border-radius: 0; - } -} -.navbar-brand { - float: left; - height: 50px; - padding: 15px 15px; - font-size: 18px; - line-height: 20px; -} -.navbar-brand:hover, -.navbar-brand:focus { - text-decoration: none; -} -.navbar-brand > img { - display: block; -} -@media (min-width: 768px) { - .navbar > .container .navbar-brand, - .navbar > .container-fluid .navbar-brand { - margin-left: -15px; - } -} -.navbar-toggle { - position: relative; - float: right; - padding: 9px 10px; - margin-right: 15px; - margin-top: 8px; - margin-bottom: 8px; - background-color: transparent; - background-image: none; - border: 1px solid transparent; - border-radius: 4px; -} -.navbar-toggle:focus { - outline: 0; -} -.navbar-toggle .icon-bar { - display: block; - width: 22px; - height: 2px; - border-radius: 1px; -} -.navbar-toggle .icon-bar + .icon-bar { - margin-top: 4px; -} -@media (min-width: 768px) { - .navbar-toggle { - display: none; - } -} -.navbar-nav { - margin: 7.5px -15px; -} -.navbar-nav > li > a { - padding-top: 10px; - padding-bottom: 10px; - line-height: 20px; -} -@media (max-width: 767px) { - .navbar-nav .open .dropdown-menu { - position: static; - float: none; - width: auto; - margin-top: 0; - background-color: transparent; - border: 0; - -webkit-box-shadow: none; - box-shadow: none; - } - .navbar-nav .open .dropdown-menu > li > a, - .navbar-nav .open .dropdown-menu .dropdown-header { - padding: 5px 15px 5px 25px; - } - .navbar-nav .open .dropdown-menu > li > a { - line-height: 20px; - } - .navbar-nav .open .dropdown-menu > li > a:hover, - .navbar-nav .open .dropdown-menu > li > a:focus { - background-image: none; - } -} -@media (min-width: 768px) { - .navbar-nav { - float: left; - margin: 0; - } - .navbar-nav > li { - float: left; - } - .navbar-nav > li > a { - padding-top: 15px; - padding-bottom: 15px; - } -} -.navbar-form { - padding: 10px 15px; - margin-right: -15px; - margin-left: -15px; - border-top: 1px solid transparent; - border-bottom: 1px solid transparent; - -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); - box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); - margin-top: 8px; - margin-bottom: 8px; -} -@media (min-width: 768px) { - .navbar-form .form-group { - display: inline-block; - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .form-control { - display: inline-block; - width: auto; - vertical-align: middle; - } - .navbar-form .form-control-static { - display: inline-block; - } - .navbar-form .input-group { - display: inline-table; - vertical-align: middle; - } - .navbar-form .input-group .input-group-addon, - .navbar-form .input-group .input-group-btn, - .navbar-form .input-group .form-control { - width: auto; - } - .navbar-form .input-group > .form-control { - width: 100%; - } - .navbar-form .control-label { - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .radio, - .navbar-form .checkbox { - display: inline-block; - margin-top: 0; - margin-bottom: 0; - vertical-align: middle; - } - .navbar-form .radio label, - .navbar-form .checkbox label { - padding-left: 0; - } - .navbar-form .radio input[type="radio"], - .navbar-form .checkbox input[type="checkbox"] { - position: relative; - margin-left: 0; - } - .navbar-form .has-feedback .form-control-feedback { - top: 0; - } -} -@media (max-width: 767px) { - .navbar-form .form-group { - margin-bottom: 5px; - } - .navbar-form .form-group:last-child { - margin-bottom: 0; - } -} -@media (min-width: 768px) { - .navbar-form { - width: auto; - padding-top: 0; - padding-bottom: 0; - margin-right: 0; - margin-left: 0; - border: 0; - -webkit-box-shadow: none; - box-shadow: none; - } -} -.navbar-nav > li > .dropdown-menu { - margin-top: 0; - border-top-left-radius: 0; - border-top-right-radius: 0; -} -.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { - margin-bottom: 0; - border-top-left-radius: 4px; - border-top-right-radius: 4px; - border-bottom-right-radius: 0; - border-bottom-left-radius: 0; -} -.navbar-btn { - margin-top: 8px; - margin-bottom: 8px; -} -.navbar-btn.btn-sm { - margin-top: 10px; - margin-bottom: 10px; -} -.navbar-btn.btn-xs { - margin-top: 14px; - margin-bottom: 14px; -} -.navbar-text { - margin-top: 15px; - margin-bottom: 15px; -} -@media (min-width: 768px) { - .navbar-text { - float: left; - margin-right: 15px; - margin-left: 15px; - } -} -@media (min-width: 768px) { - .navbar-left { - float: left !important; - } - .navbar-right { - float: right !important; - margin-right: -15px; - } - .navbar-right ~ .navbar-right { - margin-right: 0; - } -} -.navbar-default { - background-color: #f8f8f8; - border-color: #e7e7e7; -} -.navbar-default .navbar-brand { - color: #777; -} -.navbar-default .navbar-brand:hover, -.navbar-default .navbar-brand:focus { - color: #5e5e5e; - background-color: transparent; -} -.navbar-default .navbar-text { - color: #777; -} -.navbar-default .navbar-nav > li > a { - color: #777; -} -.navbar-default .navbar-nav > li > a:hover, -.navbar-default .navbar-nav > li > a:focus { - color: #333; - background-color: transparent; -} -.navbar-default .navbar-nav > .active > a, -.navbar-default .navbar-nav > .active > a:hover, -.navbar-default .navbar-nav > .active > a:focus { - color: #555; - background-color: #e7e7e7; -} -.navbar-default .navbar-nav > .disabled > a, -.navbar-default .navbar-nav > .disabled > a:hover, -.navbar-default .navbar-nav > .disabled > a:focus { - color: #ccc; - background-color: transparent; -} -.navbar-default .navbar-nav > .open > a, -.navbar-default .navbar-nav > .open > a:hover, -.navbar-default .navbar-nav > .open > a:focus { - color: #555; - background-color: #e7e7e7; -} -@media (max-width: 767px) { - .navbar-default .navbar-nav .open .dropdown-menu > li > a { - color: #777; - } - .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, - .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { - color: #333; - background-color: transparent; - } - .navbar-default .navbar-nav .open .dropdown-menu > .active > a, - .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, - .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { - color: #555; - background-color: #e7e7e7; - } - .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, - .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, - .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { - color: #ccc; - background-color: transparent; - } -} -.navbar-default .navbar-toggle { - border-color: #ddd; -} -.navbar-default .navbar-toggle:hover, -.navbar-default .navbar-toggle:focus { - background-color: #ddd; -} -.navbar-default .navbar-toggle .icon-bar { - background-color: #888; -} -.navbar-default .navbar-collapse, -.navbar-default .navbar-form { - border-color: #e7e7e7; -} -.navbar-default .navbar-link { - color: #777; -} -.navbar-default .navbar-link:hover { - color: #333; -} -.navbar-default .btn-link { - color: #777; -} -.navbar-default .btn-link:hover, -.navbar-default .btn-link:focus { - color: #333; -} -.navbar-default .btn-link[disabled]:hover, -fieldset[disabled] .navbar-default .btn-link:hover, -.navbar-default .btn-link[disabled]:focus, -fieldset[disabled] .navbar-default .btn-link:focus { - color: #ccc; -} -.navbar-inverse { - background-color: #222; - border-color: #080808; -} -.navbar-inverse .navbar-brand { - color: #9d9d9d; -} -.navbar-inverse .navbar-brand:hover, -.navbar-inverse .navbar-brand:focus { - color: #fff; - background-color: transparent; -} -.navbar-inverse .navbar-text { - color: #9d9d9d; -} -.navbar-inverse .navbar-nav > li > a { - color: #9d9d9d; -} -.navbar-inverse .navbar-nav > li > a:hover, -.navbar-inverse .navbar-nav > li > a:focus { - color: #fff; - background-color: transparent; -} -.navbar-inverse .navbar-nav > .active > a, -.navbar-inverse .navbar-nav > .active > a:hover, -.navbar-inverse .navbar-nav > .active > a:focus { - color: #fff; - background-color: #080808; -} -.navbar-inverse .navbar-nav > .disabled > a, -.navbar-inverse .navbar-nav > .disabled > a:hover, -.navbar-inverse .navbar-nav > .disabled > a:focus { - color: #444; - background-color: transparent; -} -.navbar-inverse .navbar-nav > .open > a, -.navbar-inverse .navbar-nav > .open > a:hover, -.navbar-inverse .navbar-nav > .open > a:focus { - color: #fff; - background-color: #080808; -} -@media (max-width: 767px) { - .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { - border-color: #080808; - } - .navbar-inverse .navbar-nav .open .dropdown-menu .divider { - background-color: #080808; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { - color: #9d9d9d; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, - .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { - color: #fff; - background-color: transparent; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, - .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, - .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { - color: #fff; - background-color: #080808; - } - .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, - .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, - .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { - color: #444; - background-color: transparent; - } -} -.navbar-inverse .navbar-toggle { - border-color: #333; -} -.navbar-inverse .navbar-toggle:hover, -.navbar-inverse .navbar-toggle:focus { - background-color: #333; -} -.navbar-inverse .navbar-toggle .icon-bar { - background-color: #fff; -} -.navbar-inverse .navbar-collapse, -.navbar-inverse .navbar-form { - border-color: #101010; -} -.navbar-inverse .navbar-link { - color: #9d9d9d; -} -.navbar-inverse .navbar-link:hover { - color: #fff; -} -.navbar-inverse .btn-link { - color: #9d9d9d; -} -.navbar-inverse .btn-link:hover, -.navbar-inverse .btn-link:focus { - color: #fff; -} -.navbar-inverse .btn-link[disabled]:hover, -fieldset[disabled] .navbar-inverse .btn-link:hover, -.navbar-inverse .btn-link[disabled]:focus, -fieldset[disabled] .navbar-inverse .btn-link:focus { - color: #444; -} -.breadcrumb { - padding: 8px 15px; - margin-bottom: 20px; - list-style: none; - background-color: #f5f5f5; - border-radius: 4px; -} -.breadcrumb > li { - display: inline-block; -} -.breadcrumb > li + li:before { - padding: 0 5px; - color: #ccc; - content: "/\00a0"; -} -.breadcrumb > .active { - color: #777777; -} -.pagination { - display: inline-block; - padding-left: 0; - margin: 20px 0; - border-radius: 4px; -} -.pagination > li { - display: inline; -} -.pagination > li > a, -.pagination > li > span { - position: relative; - float: left; - padding: 6px 12px; - margin-left: -1px; - line-height: 1.42857143; - color: #337ab7; - text-decoration: none; - background-color: #fff; - border: 1px solid #ddd; -} -.pagination > li > a:hover, -.pagination > li > span:hover, -.pagination > li > a:focus, -.pagination > li > span:focus { - z-index: 2; - color: #23527c; - background-color: #eeeeee; - border-color: #ddd; -} -.pagination > li:first-child > a, -.pagination > li:first-child > span { - margin-left: 0; - border-top-left-radius: 4px; - border-bottom-left-radius: 4px; -} -.pagination > li:last-child > a, -.pagination > li:last-child > span { - border-top-right-radius: 4px; - border-bottom-right-radius: 4px; -} -.pagination > .active > a, -.pagination > .active > span, -.pagination > .active > a:hover, -.pagination > .active > span:hover, -.pagination > .active > a:focus, -.pagination > .active > span:focus { - z-index: 3; - color: #fff; - cursor: default; - background-color: #337ab7; - border-color: #337ab7; -} -.pagination > .disabled > span, -.pagination > .disabled > span:hover, -.pagination > .disabled > span:focus, -.pagination > .disabled > a, -.pagination > .disabled > a:hover, -.pagination > .disabled > a:focus { - color: #777777; - cursor: not-allowed; - background-color: #fff; - border-color: #ddd; -} -.pagination-lg > li > a, -.pagination-lg > li > span { - padding: 10px 16px; - font-size: 18px; - line-height: 1.3333333; -} -.pagination-lg > li:first-child > a, -.pagination-lg > li:first-child > span { - border-top-left-radius: 6px; - border-bottom-left-radius: 6px; -} -.pagination-lg > li:last-child > a, -.pagination-lg > li:last-child > span { - border-top-right-radius: 6px; - border-bottom-right-radius: 6px; -} -.pagination-sm > li > a, -.pagination-sm > li > span { - padding: 5px 10px; - font-size: 12px; - line-height: 1.5; -} -.pagination-sm > li:first-child > a, -.pagination-sm > li:first-child > span { - border-top-left-radius: 3px; - border-bottom-left-radius: 3px; -} -.pagination-sm > li:last-child > a, -.pagination-sm > li:last-child > span { - border-top-right-radius: 3px; - border-bottom-right-radius: 3px; -} -.pager { - padding-left: 0; - margin: 20px 0; - text-align: center; - list-style: none; -} -.pager li { - display: inline; -} -.pager li > a, -.pager li > span { - display: inline-block; - padding: 5px 14px; - background-color: #fff; - border: 1px solid #ddd; - border-radius: 15px; -} -.pager li > a:hover, -.pager li > a:focus { - text-decoration: none; - background-color: #eeeeee; -} -.pager .next > a, -.pager .next > span { - float: right; -} -.pager .previous > a, -.pager .previous > span { - float: left; -} -.pager .disabled > a, -.pager .disabled > a:hover, -.pager .disabled > a:focus, -.pager .disabled > span { - color: #777777; - cursor: not-allowed; - background-color: #fff; -} -.label { - display: inline; - padding: 0.2em 0.6em 0.3em; - font-size: 75%; - font-weight: 700; - line-height: 1; - color: #fff; - text-align: center; - white-space: nowrap; - vertical-align: baseline; - border-radius: 0.25em; -} -a.label:hover, -a.label:focus { - color: #fff; - text-decoration: none; - cursor: pointer; -} -.label:empty { - display: none; -} -.btn .label { - position: relative; - top: -1px; -} -.label-default { - background-color: #777777; -} -.label-default[href]:hover, -.label-default[href]:focus { - background-color: #5e5e5e; -} -.label-primary { - background-color: #337ab7; -} -.label-primary[href]:hover, -.label-primary[href]:focus { - background-color: #286090; -} -.label-success { - background-color: #5cb85c; -} -.label-success[href]:hover, -.label-success[href]:focus { - background-color: #449d44; -} -.label-info { - background-color: #5bc0de; -} -.label-info[href]:hover, -.label-info[href]:focus { - background-color: #31b0d5; -} -.label-warning { - background-color: #f0ad4e; -} -.label-warning[href]:hover, -.label-warning[href]:focus { - background-color: #ec971f; -} -.label-danger { - background-color: #d9534f; -} -.label-danger[href]:hover, -.label-danger[href]:focus { - background-color: #c9302c; -} -.badge { - display: inline-block; - min-width: 10px; - padding: 3px 7px; - font-size: 12px; - font-weight: bold; - line-height: 1; - color: #fff; - text-align: center; - white-space: nowrap; - vertical-align: middle; - background-color: #777777; - border-radius: 10px; -} -.badge:empty { - display: none; -} -.btn .badge { - position: relative; - top: -1px; -} -.btn-xs .badge, -.btn-group-xs > .btn .badge { - top: 0; - padding: 1px 5px; -} -a.badge:hover, -a.badge:focus { - color: #fff; - text-decoration: none; - cursor: pointer; -} -.list-group-item.active > .badge, -.nav-pills > .active > a > .badge { - color: #337ab7; - background-color: #fff; -} -.list-group-item > .badge { - float: right; -} -.list-group-item > .badge + .badge { - margin-right: 5px; -} -.nav-pills > li > a > .badge { - margin-left: 3px; -} -.jumbotron { - padding-top: 30px; - padding-bottom: 30px; - margin-bottom: 30px; - color: inherit; - background-color: #eeeeee; -} -.jumbotron h1, -.jumbotron .h1 { - color: inherit; -} -.jumbotron p { - margin-bottom: 15px; - font-size: 21px; - font-weight: 200; -} -.jumbotron > hr { - border-top-color: #d5d5d5; -} -.container .jumbotron, -.container-fluid .jumbotron { - padding-right: 15px; - padding-left: 15px; - border-radius: 6px; -} -.jumbotron .container { - max-width: 100%; -} -@media screen and (min-width: 768px) { - .jumbotron { - padding-top: 48px; - padding-bottom: 48px; - } - .container .jumbotron, - .container-fluid .jumbotron { - padding-right: 60px; - padding-left: 60px; - } - .jumbotron h1, - .jumbotron .h1 { - font-size: 63px; - } -} -.thumbnail { - display: block; - padding: 4px; - margin-bottom: 20px; - line-height: 1.42857143; - background-color: #fff; - border: 1px solid #ddd; - border-radius: 4px; - -webkit-transition: border 0.2s ease-in-out; - -o-transition: border 0.2s ease-in-out; - transition: border 0.2s ease-in-out; -} -.thumbnail > img, -.thumbnail a > img { - margin-right: auto; - margin-left: auto; -} -a.thumbnail:hover, -a.thumbnail:focus, -a.thumbnail.active { - border-color: #337ab7; -} -.thumbnail .caption { - padding: 9px; - color: #333333; -} -.alert { - padding: 15px; - margin-bottom: 20px; - border: 1px solid transparent; - border-radius: 4px; -} -.alert h4 { - margin-top: 0; - color: inherit; -} -.alert .alert-link { - font-weight: bold; -} -.alert > p, -.alert > ul { - margin-bottom: 0; -} -.alert > p + p { - margin-top: 5px; -} -.alert-dismissable, -.alert-dismissible { - padding-right: 35px; -} -.alert-dismissable .close, -.alert-dismissible .close { - position: relative; - top: -2px; - right: -21px; - color: inherit; -} -.alert-success { - color: #3c763d; - background-color: #dff0d8; - border-color: #d6e9c6; -} -.alert-success hr { - border-top-color: 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.table-bordered > tfoot > tr > th:last-child, -.panel > .table-bordered > thead > tr > td:last-child, -.panel > .table-responsive > .table-bordered > thead > tr > td:last-child, -.panel > .table-bordered > tbody > tr > td:last-child, -.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, -.panel > .table-bordered > tfoot > tr > td:last-child, -.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { - border-right: 0; -} -.panel > .table-bordered > thead > tr:first-child > td, -.panel > .table-responsive > .table-bordered > thead > tr:first-child > td, -.panel > .table-bordered > tbody > tr:first-child > td, -.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, -.panel > .table-bordered > thead > tr:first-child > th, -.panel > .table-responsive > .table-bordered > thead > tr:first-child > th, -.panel > .table-bordered > tbody > tr:first-child > th, -.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { - border-bottom: 0; -} -.panel > .table-bordered > tbody > tr:last-child > td, -.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, -.panel > .table-bordered > tfoot > tr:last-child > td, -.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, -.panel > .table-bordered > tbody > tr:last-child > th, -.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, -.panel > .table-bordered > tfoot > tr:last-child > th, -.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { - border-bottom: 0; -} -.panel > .table-responsive { - margin-bottom: 0; - border: 0; -} -.panel-group { - margin-bottom: 20px; -} -.panel-group .panel { - margin-bottom: 0; - border-radius: 4px; -} -.panel-group .panel + .panel { - margin-top: 5px; -} -.panel-group .panel-heading { - border-bottom: 0; -} -.panel-group .panel-heading + .panel-collapse > .panel-body, -.panel-group .panel-heading + .panel-collapse > .list-group { - border-top: 1px solid #ddd; -} -.panel-group .panel-footer { - border-top: 0; -} -.panel-group .panel-footer + .panel-collapse .panel-body { - border-bottom: 1px solid #ddd; -} -.panel-default { - border-color: #ddd; -} -.panel-default > .panel-heading { - color: #333333; - background-color: #f5f5f5; - border-color: #ddd; -} -.panel-default > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #ddd; -} -.panel-default > .panel-heading .badge { - color: #f5f5f5; - background-color: #333333; -} -.panel-default > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #ddd; -} -.panel-primary { - border-color: #337ab7; -} -.panel-primary > .panel-heading { - color: #fff; - background-color: #337ab7; - border-color: #337ab7; -} -.panel-primary > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #337ab7; -} -.panel-primary > .panel-heading .badge { - color: #337ab7; - background-color: #fff; -} -.panel-primary > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #337ab7; -} -.panel-success { - border-color: #d6e9c6; -} -.panel-success > .panel-heading { - color: #3c763d; - background-color: #dff0d8; - border-color: #d6e9c6; -} -.panel-success > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #d6e9c6; -} -.panel-success > .panel-heading .badge { - color: #dff0d8; - background-color: #3c763d; -} -.panel-success > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #d6e9c6; -} -.panel-info { - border-color: #bce8f1; -} -.panel-info > .panel-heading { - color: #31708f; - background-color: #d9edf7; - border-color: #bce8f1; -} -.panel-info > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #bce8f1; -} -.panel-info > .panel-heading .badge { - color: #d9edf7; - background-color: #31708f; -} -.panel-info > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #bce8f1; -} -.panel-warning { - border-color: #faebcc; -} -.panel-warning > .panel-heading { - color: #8a6d3b; - background-color: #fcf8e3; - border-color: #faebcc; -} -.panel-warning > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #faebcc; -} -.panel-warning > .panel-heading .badge { - color: #fcf8e3; - background-color: #8a6d3b; -} -.panel-warning > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #faebcc; -} -.panel-danger { - border-color: #ebccd1; -} -.panel-danger > .panel-heading { - color: #a94442; - background-color: #f2dede; - border-color: #ebccd1; -} -.panel-danger > .panel-heading + .panel-collapse > .panel-body { - border-top-color: #ebccd1; -} -.panel-danger > .panel-heading .badge { - color: #f2dede; - background-color: #a94442; -} -.panel-danger > .panel-footer + .panel-collapse > .panel-body { - border-bottom-color: #ebccd1; -} -.embed-responsive { - position: relative; - display: block; - height: 0; - padding: 0; - overflow: hidden; -} -.embed-responsive .embed-responsive-item, -.embed-responsive iframe, -.embed-responsive embed, -.embed-responsive object, -.embed-responsive video { - position: absolute; - top: 0; - bottom: 0; - left: 0; - width: 100%; - height: 100%; - border: 0; -} -.embed-responsive-16by9 { - padding-bottom: 56.25%; -} -.embed-responsive-4by3 { - padding-bottom: 75%; -} -.well { - min-height: 20px; - padding: 19px; - margin-bottom: 20px; - background-color: #f5f5f5; - border: 1px solid #e3e3e3; - border-radius: 4px; - -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); - box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); -} -.well blockquote { - border-color: #ddd; - border-color: rgba(0, 0, 0, 0.15); -} -.well-lg { - padding: 24px; - border-radius: 6px; -} -.well-sm { - padding: 9px; - border-radius: 3px; -} -.close { - float: right; - font-size: 21px; - font-weight: bold; - line-height: 1; - color: #000; - text-shadow: 0 1px 0 #fff; - filter: alpha(opacity=20); - opacity: 0.2; -} -.close:hover, -.close:focus { - color: #000; - text-decoration: none; - cursor: pointer; - filter: alpha(opacity=50); - opacity: 0.5; -} -button.close { - padding: 0; - cursor: pointer; - background: transparent; - border: 0; - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; -} -.modal-open { - overflow: hidden; -} -.modal { - position: fixed; - top: 0; - right: 0; - bottom: 0; - left: 0; - z-index: 1050; - display: none; - overflow: hidden; - -webkit-overflow-scrolling: touch; - outline: 0; -} -.modal.fade .modal-dialog { - -webkit-transform: translate(0, -25%); - -ms-transform: translate(0, -25%); - -o-transform: translate(0, -25%); - transform: translate(0, -25%); - -webkit-transition: -webkit-transform 0.3s ease-out; - -o-transition: -o-transform 0.3s ease-out; - transition: -webkit-transform 0.3s ease-out; - transition: transform 0.3s ease-out; - transition: transform 0.3s ease-out, -webkit-transform 0.3s ease-out, -o-transform 0.3s ease-out; -} -.modal.in .modal-dialog { - -webkit-transform: translate(0, 0); - -ms-transform: 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text-align: start; - text-decoration: none; - text-shadow: none; - text-transform: none; - letter-spacing: normal; - word-break: normal; - word-spacing: normal; - word-wrap: normal; - white-space: normal; - font-size: 12px; - filter: alpha(opacity=0); - opacity: 0; -} -.tooltip.in { - filter: alpha(opacity=90); - opacity: 0.9; -} -.tooltip.top { - padding: 5px 0; - margin-top: -3px; -} -.tooltip.right { - padding: 0 5px; - margin-left: 3px; -} -.tooltip.bottom { - padding: 5px 0; - margin-top: 3px; -} -.tooltip.left { - padding: 0 5px; - margin-left: -3px; -} -.tooltip.top .tooltip-arrow { - bottom: 0; - left: 50%; - margin-left: -5px; - border-width: 5px 5px 0; - border-top-color: #000; -} -.tooltip.top-left .tooltip-arrow { - right: 5px; - bottom: 0; - margin-bottom: -5px; - border-width: 5px 5px 0; - border-top-color: #000; -} -.tooltip.top-right .tooltip-arrow { - bottom: 0; - left: 5px; - margin-bottom: -5px; - border-width: 5px 5px 0; - border-top-color: #000; -} -.tooltip.right .tooltip-arrow { - top: 50%; - left: 0; - margin-top: -5px; - border-width: 5px 5px 5px 0; - border-right-color: #000; -} -.tooltip.left .tooltip-arrow { - top: 50%; - right: 0; - margin-top: -5px; - border-width: 5px 0 5px 5px; - border-left-color: #000; -} -.tooltip.bottom .tooltip-arrow { - top: 0; - left: 50%; - margin-left: -5px; - border-width: 0 5px 5px; - border-bottom-color: #000; -} -.tooltip.bottom-left .tooltip-arrow { - top: 0; - right: 5px; - margin-top: -5px; - border-width: 0 5px 5px; - border-bottom-color: #000; -} -.tooltip.bottom-right .tooltip-arrow { - top: 0; - left: 5px; - margin-top: -5px; - border-width: 0 5px 5px; - border-bottom-color: #000; -} -.tooltip-inner { - max-width: 200px; - padding: 3px 8px; - color: #fff; - text-align: center; - background-color: #000; - border-radius: 4px; -} -.tooltip-arrow { - position: absolute; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; -} -.popover { - position: absolute; - top: 0; - left: 0; - z-index: 1060; - display: none; - max-width: 276px; - padding: 1px; - font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; - font-style: normal; - font-weight: 400; - line-height: 1.42857143; - line-break: auto; - text-align: left; - text-align: start; - text-decoration: none; - text-shadow: none; - text-transform: none; - letter-spacing: normal; - word-break: normal; - word-spacing: normal; - word-wrap: normal; - white-space: normal; - font-size: 14px; - background-color: #fff; - background-clip: padding-box; - border: 1px solid #ccc; - border: 1px solid rgba(0, 0, 0, 0.2); - border-radius: 6px; - -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); - box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); -} -.popover.top { - margin-top: -10px; -} -.popover.right { - margin-left: 10px; -} -.popover.bottom { - margin-top: 10px; -} -.popover.left { - margin-left: -10px; -} -.popover > .arrow { - border-width: 11px; -} -.popover > .arrow, -.popover > .arrow:after { - position: absolute; - display: block; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; -} -.popover > .arrow:after { - content: ""; - border-width: 10px; -} -.popover.top > .arrow { - bottom: -11px; - left: 50%; - margin-left: -11px; - border-top-color: #999999; - border-top-color: rgba(0, 0, 0, 0.25); - border-bottom-width: 0; -} -.popover.top > .arrow:after { - bottom: 1px; - margin-left: -10px; - content: " "; - border-top-color: #fff; - border-bottom-width: 0; -} -.popover.right > .arrow { - top: 50%; - left: -11px; - margin-top: -11px; - border-right-color: #999999; - border-right-color: rgba(0, 0, 0, 0.25); - border-left-width: 0; -} -.popover.right > .arrow:after { - bottom: -10px; - left: 1px; - content: " "; - border-right-color: #fff; - border-left-width: 0; -} -.popover.bottom > .arrow { - top: -11px; - left: 50%; - margin-left: -11px; - border-top-width: 0; - border-bottom-color: #999999; - border-bottom-color: rgba(0, 0, 0, 0.25); -} -.popover.bottom > .arrow:after { - top: 1px; - margin-left: -10px; - content: " "; - border-top-width: 0; - border-bottom-color: #fff; -} -.popover.left > .arrow { - top: 50%; - right: -11px; - margin-top: -11px; - border-right-width: 0; - border-left-color: #999999; - border-left-color: rgba(0, 0, 0, 0.25); -} -.popover.left > .arrow:after { - right: 1px; - bottom: -10px; - content: " "; - border-right-width: 0; - border-left-color: #fff; -} -.popover-title { - padding: 8px 14px; - margin: 0; - font-size: 14px; - background-color: #f7f7f7; - border-bottom: 1px solid #ebebeb; - border-radius: 5px 5px 0 0; -} -.popover-content { - padding: 9px 14px; -} -.carousel { - position: relative; -} -.carousel-inner { - position: relative; - width: 100%; - overflow: hidden; -} -.carousel-inner > .item { - position: relative; - display: none; - -webkit-transition: 0.6s ease-in-out left; - -o-transition: 0.6s ease-in-out left; - transition: 0.6s ease-in-out left; -} -.carousel-inner > .item > img, -.carousel-inner > .item > a > img { - line-height: 1; -} -@media all and (transform-3d), (-webkit-transform-3d) { - .carousel-inner > .item { - -webkit-transition: -webkit-transform 0.6s ease-in-out; - -o-transition: -o-transform 0.6s ease-in-out; - transition: -webkit-transform 0.6s ease-in-out; - transition: transform 0.6s ease-in-out; - transition: transform 0.6s ease-in-out, -webkit-transform 0.6s ease-in-out, -o-transform 0.6s ease-in-out; - -webkit-backface-visibility: hidden; - backface-visibility: hidden; - -webkit-perspective: 1000px; - perspective: 1000px; - } - .carousel-inner > .item.next, - .carousel-inner > .item.active.right { - -webkit-transform: translate3d(100%, 0, 0); - transform: translate3d(100%, 0, 0); - left: 0; - } - .carousel-inner > .item.prev, - .carousel-inner > .item.active.left { - -webkit-transform: translate3d(-100%, 0, 0); - transform: translate3d(-100%, 0, 0); - left: 0; - } - .carousel-inner > .item.next.left, - .carousel-inner > .item.prev.right, - .carousel-inner > .item.active { - -webkit-transform: translate3d(0, 0, 0); - transform: translate3d(0, 0, 0); - left: 0; - } -} -.carousel-inner > .active, -.carousel-inner > .next, -.carousel-inner > .prev { - display: block; -} -.carousel-inner > .active { - left: 0; -} -.carousel-inner > .next, -.carousel-inner > .prev { - position: absolute; - top: 0; - width: 100%; -} -.carousel-inner > .next { - left: 100%; -} -.carousel-inner > .prev { - left: -100%; -} -.carousel-inner > .next.left, -.carousel-inner > .prev.right { - left: 0; -} -.carousel-inner > .active.left { - left: -100%; -} -.carousel-inner > .active.right { - left: 100%; -} -.carousel-control { - position: absolute; - top: 0; - bottom: 0; - left: 0; - width: 15%; - font-size: 20px; - color: #fff; - text-align: center; - text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); - background-color: rgba(0, 0, 0, 0); - filter: alpha(opacity=50); - opacity: 0.5; -} -.carousel-control.left { - background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); - background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); - background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, 0.5)), to(rgba(0, 0, 0, 0.0001))); - background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); - filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); - background-repeat: repeat-x; -} -.carousel-control.right { - right: 0; - left: auto; - background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); - background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); - background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, 0.0001)), to(rgba(0, 0, 0, 0.5))); - background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); - filter: 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.icon-next:before { - content: "\203a"; -} -.carousel-indicators { - position: absolute; - bottom: 10px; - left: 50%; - z-index: 15; - width: 60%; - padding-left: 0; - margin-left: -30%; - text-align: center; - list-style: none; -} -.carousel-indicators li { - display: inline-block; - width: 10px; - height: 10px; - margin: 1px; - text-indent: -999px; - cursor: pointer; - background-color: #000 \9; - background-color: rgba(0, 0, 0, 0); - border: 1px solid #fff; - border-radius: 10px; -} -.carousel-indicators .active { - width: 12px; - height: 12px; - margin: 0; - background-color: #fff; -} -.carousel-caption { - position: absolute; - right: 15%; - bottom: 20px; - left: 15%; - z-index: 10; - padding-top: 20px; - padding-bottom: 20px; - color: #fff; - text-align: center; - text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); -} -.carousel-caption .btn { - text-shadow: none; -} -@media screen and (min-width: 768px) { - .carousel-control .glyphicon-chevron-left, - .carousel-control 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0.67em 0;\n}\nmark {\n background: #ff0;\n color: #000;\n}\nsmall {\n font-size: 80%;\n}\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\nsup {\n top: -0.5em;\n}\nsub {\n bottom: -0.25em;\n}\nimg {\n border: 0;\n}\nsvg:not(:root) {\n overflow: hidden;\n}\nfigure {\n margin: 1em 40px;\n}\nhr {\n box-sizing: content-box;\n height: 0;\n}\npre {\n overflow: auto;\n}\ncode,\nkbd,\npre,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n color: inherit;\n font: inherit;\n margin: 0;\n}\nbutton {\n overflow: visible;\n}\nbutton,\nselect {\n text-transform: none;\n}\nbutton,\nhtml input[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button;\n cursor: pointer;\n}\nbutton[disabled],\nhtml input[disabled] {\n cursor: default;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\ninput {\n line-height: normal;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box;\n padding: 0;\n}\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\ninput[type=\"search\"] {\n -webkit-appearance: textfield;\n box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\nfieldset {\n border: 1px solid #c0c0c0;\n margin: 0 2px;\n padding: 0.35em 0.625em 0.75em;\n}\nlegend {\n border: 0;\n padding: 0;\n}\ntextarea {\n overflow: auto;\n}\noptgroup {\n font-weight: bold;\n}\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\ntd,\nth {\n padding: 0;\n}\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n@media print {\n *,\n *:before,\n *:after {\n color: #000 !important;\n text-shadow: none !important;\n background: transparent !important;\n box-shadow: none !important;\n }\n a,\n a:visited {\n text-decoration: underline;\n }\n a[href]:after {\n content: \" (\" attr(href) \")\";\n }\n abbr[title]:after {\n content: \" (\" attr(title) \")\";\n }\n a[href^=\"#\"]:after,\n a[href^=\"javascript:\"]:after {\n content: \"\";\n }\n pre,\n blockquote {\n border: 1px solid #999;\n page-break-inside: avoid;\n }\n thead {\n display: table-header-group;\n }\n tr,\n img {\n page-break-inside: avoid;\n }\n img {\n max-width: 100% !important;\n }\n p,\n h2,\n h3 {\n orphans: 3;\n widows: 3;\n }\n h2,\n h3 {\n page-break-after: avoid;\n }\n .navbar {\n display: none;\n }\n .btn > .caret,\n .dropup > .btn > .caret {\n border-top-color: #000 !important;\n }\n .label {\n border: 1px solid #000;\n }\n .table {\n border-collapse: collapse !important;\n }\n .table td,\n .table th {\n background-color: #fff !important;\n }\n .table-bordered th,\n .table-bordered td {\n border: 1px solid #ddd !important;\n }\n}\n@font-face {\n font-family: \"Glyphicons Halflings\";\n src: url(\"../fonts/glyphicons-halflings-regular.eot\");\n src: url(\"../fonts/glyphicons-halflings-regular.eot?#iefix\") format(\"embedded-opentype\"), url(\"../fonts/glyphicons-halflings-regular.woff2\") format(\"woff2\"), url(\"../fonts/glyphicons-halflings-regular.woff\") format(\"woff\"), url(\"../fonts/glyphicons-halflings-regular.ttf\") format(\"truetype\"), url(\"../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular\") format(\"svg\");\n}\n.glyphicon {\n position: relative;\n top: 1px;\n display: inline-block;\n font-family: \"Glyphicons Halflings\";\n font-style: normal;\n font-weight: 400;\n line-height: 1;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n.glyphicon-asterisk:before {\n content: \"\\002a\";\n}\n.glyphicon-plus:before {\n content: \"\\002b\";\n}\n.glyphicon-euro:before,\n.glyphicon-eur:before {\n content: \"\\20ac\";\n}\n.glyphicon-minus:before {\n content: \"\\2212\";\n}\n.glyphicon-cloud:before {\n content: \"\\2601\";\n}\n.glyphicon-envelope:before {\n content: \"\\2709\";\n}\n.glyphicon-pencil:before {\n content: \"\\270f\";\n}\n.glyphicon-glass:before {\n content: \"\\e001\";\n}\n.glyphicon-music:before {\n content: \"\\e002\";\n}\n.glyphicon-search:before {\n content: \"\\e003\";\n}\n.glyphicon-heart:before {\n content: \"\\e005\";\n}\n.glyphicon-star:before {\n content: \"\\e006\";\n}\n.glyphicon-star-empty:before {\n content: \"\\e007\";\n}\n.glyphicon-user:before {\n content: \"\\e008\";\n}\n.glyphicon-film:before {\n content: \"\\e009\";\n}\n.glyphicon-th-large:before {\n content: \"\\e010\";\n}\n.glyphicon-th:before {\n content: \"\\e011\";\n}\n.glyphicon-th-list:before {\n content: \"\\e012\";\n}\n.glyphicon-ok:before {\n content: \"\\e013\";\n}\n.glyphicon-remove:before {\n content: \"\\e014\";\n}\n.glyphicon-zoom-in:before {\n content: \"\\e015\";\n}\n.glyphicon-zoom-out:before {\n content: \"\\e016\";\n}\n.glyphicon-off:before {\n content: \"\\e017\";\n}\n.glyphicon-signal:before {\n content: \"\\e018\";\n}\n.glyphicon-cog:before {\n content: \"\\e019\";\n}\n.glyphicon-trash:before {\n content: \"\\e020\";\n}\n.glyphicon-home:before {\n content: \"\\e021\";\n}\n.glyphicon-file:before {\n content: \"\\e022\";\n}\n.glyphicon-time:before {\n content: \"\\e023\";\n}\n.glyphicon-road:before {\n content: \"\\e024\";\n}\n.glyphicon-download-alt:before {\n content: \"\\e025\";\n}\n.glyphicon-download:before {\n content: \"\\e026\";\n}\n.glyphicon-upload:before {\n content: \"\\e027\";\n}\n.glyphicon-inbox:before {\n content: \"\\e028\";\n}\n.glyphicon-play-circle:before {\n content: \"\\e029\";\n}\n.glyphicon-repeat:before {\n content: \"\\e030\";\n}\n.glyphicon-refresh:before {\n content: \"\\e031\";\n}\n.glyphicon-list-alt:before {\n content: \"\\e032\";\n}\n.glyphicon-lock:before {\n content: \"\\e033\";\n}\n.glyphicon-flag:before {\n content: \"\\e034\";\n}\n.glyphicon-headphones:before {\n content: \"\\e035\";\n}\n.glyphicon-volume-off:before {\n content: \"\\e036\";\n}\n.glyphicon-volume-down:before {\n content: \"\\e037\";\n}\n.glyphicon-volume-up:before {\n content: \"\\e038\";\n}\n.glyphicon-qrcode:before {\n content: \"\\e039\";\n}\n.glyphicon-barcode:before {\n content: \"\\e040\";\n}\n.glyphicon-tag:before {\n content: \"\\e041\";\n}\n.glyphicon-tags:before {\n content: \"\\e042\";\n}\n.glyphicon-book:before {\n content: \"\\e043\";\n}\n.glyphicon-bookmark:before {\n content: \"\\e044\";\n}\n.glyphicon-print:before {\n content: \"\\e045\";\n}\n.glyphicon-camera:before {\n content: \"\\e046\";\n}\n.glyphicon-font:before {\n content: \"\\e047\";\n}\n.glyphicon-bold:before {\n content: \"\\e048\";\n}\n.glyphicon-italic:before {\n content: \"\\e049\";\n}\n.glyphicon-text-height:before {\n content: \"\\e050\";\n}\n.glyphicon-text-width:before {\n content: \"\\e051\";\n}\n.glyphicon-align-left:before {\n content: \"\\e052\";\n}\n.glyphicon-align-center:before {\n content: \"\\e053\";\n}\n.glyphicon-align-right:before {\n content: \"\\e054\";\n}\n.glyphicon-align-justify:before {\n content: \"\\e055\";\n}\n.glyphicon-list:before {\n content: \"\\e056\";\n}\n.glyphicon-indent-left:before {\n content: \"\\e057\";\n}\n.glyphicon-indent-right:before {\n content: \"\\e058\";\n}\n.glyphicon-facetime-video:before {\n content: \"\\e059\";\n}\n.glyphicon-picture:before {\n content: \"\\e060\";\n}\n.glyphicon-map-marker:before {\n content: \"\\e062\";\n}\n.glyphicon-adjust:before {\n content: \"\\e063\";\n}\n.glyphicon-tint:before {\n content: \"\\e064\";\n}\n.glyphicon-edit:before {\n content: \"\\e065\";\n}\n.glyphicon-share:before {\n content: \"\\e066\";\n}\n.glyphicon-check:before {\n content: \"\\e067\";\n}\n.glyphicon-move:before {\n content: \"\\e068\";\n}\n.glyphicon-step-backward:before {\n content: \"\\e069\";\n}\n.glyphicon-fast-backward:before {\n content: \"\\e070\";\n}\n.glyphicon-backward:before {\n content: \"\\e071\";\n}\n.glyphicon-play:before {\n content: \"\\e072\";\n}\n.glyphicon-pause:before {\n content: \"\\e073\";\n}\n.glyphicon-stop:before {\n content: \"\\e074\";\n}\n.glyphicon-forward:before {\n content: \"\\e075\";\n}\n.glyphicon-fast-forward:before {\n content: \"\\e076\";\n}\n.glyphicon-step-forward:before {\n content: \"\\e077\";\n}\n.glyphicon-eject:before {\n content: \"\\e078\";\n}\n.glyphicon-chevron-left:before {\n content: \"\\e079\";\n}\n.glyphicon-chevron-right:before {\n content: \"\\e080\";\n}\n.glyphicon-plus-sign:before {\n content: \"\\e081\";\n}\n.glyphicon-minus-sign:before {\n content: \"\\e082\";\n}\n.glyphicon-remove-sign:before {\n content: \"\\e083\";\n}\n.glyphicon-ok-sign:before {\n content: \"\\e084\";\n}\n.glyphicon-question-sign:before {\n content: \"\\e085\";\n}\n.glyphicon-info-sign:before {\n content: \"\\e086\";\n}\n.glyphicon-screenshot:before {\n content: \"\\e087\";\n}\n.glyphicon-remove-circle:before {\n content: \"\\e088\";\n}\n.glyphicon-ok-circle:before {\n content: \"\\e089\";\n}\n.glyphicon-ban-circle:before {\n content: \"\\e090\";\n}\n.glyphicon-arrow-left:before {\n content: \"\\e091\";\n}\n.glyphicon-arrow-right:before {\n content: \"\\e092\";\n}\n.glyphicon-arrow-up:before {\n content: \"\\e093\";\n}\n.glyphicon-arrow-down:before {\n content: \"\\e094\";\n}\n.glyphicon-share-alt:before {\n content: \"\\e095\";\n}\n.glyphicon-resize-full:before {\n content: \"\\e096\";\n}\n.glyphicon-resize-small:before {\n content: \"\\e097\";\n}\n.glyphicon-exclamation-sign:before {\n content: \"\\e101\";\n}\n.glyphicon-gift:before {\n content: \"\\e102\";\n}\n.glyphicon-leaf:before {\n content: \"\\e103\";\n}\n.glyphicon-fire:before {\n content: \"\\e104\";\n}\n.glyphicon-eye-open:before {\n content: \"\\e105\";\n}\n.glyphicon-eye-close:before {\n content: \"\\e106\";\n}\n.glyphicon-warning-sign:before {\n content: \"\\e107\";\n}\n.glyphicon-plane:before {\n content: \"\\e108\";\n}\n.glyphicon-calendar:before {\n content: \"\\e109\";\n}\n.glyphicon-random:before {\n content: \"\\e110\";\n}\n.glyphicon-comment:before {\n content: \"\\e111\";\n}\n.glyphicon-magnet:before {\n content: \"\\e112\";\n}\n.glyphicon-chevron-up:before {\n content: \"\\e113\";\n}\n.glyphicon-chevron-down:before {\n content: \"\\e114\";\n}\n.glyphicon-retweet:before {\n content: \"\\e115\";\n}\n.glyphicon-shopping-cart:before {\n content: \"\\e116\";\n}\n.glyphicon-folder-close:before {\n content: \"\\e117\";\n}\n.glyphicon-folder-open:before {\n content: \"\\e118\";\n}\n.glyphicon-resize-vertical:before {\n content: \"\\e119\";\n}\n.glyphicon-resize-horizontal:before {\n content: \"\\e120\";\n}\n.glyphicon-hdd:before {\n content: \"\\e121\";\n}\n.glyphicon-bullhorn:before {\n content: \"\\e122\";\n}\n.glyphicon-bell:before {\n content: \"\\e123\";\n}\n.glyphicon-certificate:before {\n content: \"\\e124\";\n}\n.glyphicon-thumbs-up:before {\n content: \"\\e125\";\n}\n.glyphicon-thumbs-down:before {\n content: \"\\e126\";\n}\n.glyphicon-hand-right:before {\n content: \"\\e127\";\n}\n.glyphicon-hand-left:before {\n content: \"\\e128\";\n}\n.glyphicon-hand-up:before {\n content: \"\\e129\";\n}\n.glyphicon-hand-down:before {\n content: \"\\e130\";\n}\n.glyphicon-circle-arrow-right:before {\n content: \"\\e131\";\n}\n.glyphicon-circle-arrow-left:before {\n content: \"\\e132\";\n}\n.glyphicon-circle-arrow-up:before {\n content: \"\\e133\";\n}\n.glyphicon-circle-arrow-down:before {\n content: \"\\e134\";\n}\n.glyphicon-globe:before {\n content: \"\\e135\";\n}\n.glyphicon-wrench:before {\n content: \"\\e136\";\n}\n.glyphicon-tasks:before {\n content: \"\\e137\";\n}\n.glyphicon-filter:before {\n content: \"\\e138\";\n}\n.glyphicon-briefcase:before {\n content: \"\\e139\";\n}\n.glyphicon-fullscreen:before {\n content: \"\\e140\";\n}\n.glyphicon-dashboard:before {\n content: \"\\e141\";\n}\n.glyphicon-paperclip:before {\n content: \"\\e142\";\n}\n.glyphicon-heart-empty:before {\n content: \"\\e143\";\n}\n.glyphicon-link:before {\n content: \"\\e144\";\n}\n.glyphicon-phone:before {\n content: \"\\e145\";\n}\n.glyphicon-pushpin:before {\n content: \"\\e146\";\n}\n.glyphicon-usd:before {\n content: \"\\e148\";\n}\n.glyphicon-gbp:before {\n content: \"\\e149\";\n}\n.glyphicon-sort:before {\n content: \"\\e150\";\n}\n.glyphicon-sort-by-alphabet:before {\n content: \"\\e151\";\n}\n.glyphicon-sort-by-alphabet-alt:before {\n content: \"\\e152\";\n}\n.glyphicon-sort-by-order:before {\n content: \"\\e153\";\n}\n.glyphicon-sort-by-order-alt:before {\n content: \"\\e154\";\n}\n.glyphicon-sort-by-attributes:before {\n content: \"\\e155\";\n}\n.glyphicon-sort-by-attributes-alt:before {\n content: \"\\e156\";\n}\n.glyphicon-unchecked:before {\n content: \"\\e157\";\n}\n.glyphicon-expand:before {\n content: \"\\e158\";\n}\n.glyphicon-collapse-down:before {\n content: \"\\e159\";\n}\n.glyphicon-collapse-up:before {\n content: \"\\e160\";\n}\n.glyphicon-log-in:before {\n content: \"\\e161\";\n}\n.glyphicon-flash:before {\n content: \"\\e162\";\n}\n.glyphicon-log-out:before {\n content: \"\\e163\";\n}\n.glyphicon-new-window:before {\n content: \"\\e164\";\n}\n.glyphicon-record:before {\n content: \"\\e165\";\n}\n.glyphicon-save:before {\n content: \"\\e166\";\n}\n.glyphicon-open:before {\n content: \"\\e167\";\n}\n.glyphicon-saved:before {\n content: \"\\e168\";\n}\n.glyphicon-import:before {\n content: \"\\e169\";\n}\n.glyphicon-export:before {\n content: \"\\e170\";\n}\n.glyphicon-send:before {\n content: \"\\e171\";\n}\n.glyphicon-floppy-disk:before {\n content: \"\\e172\";\n}\n.glyphicon-floppy-saved:before {\n content: \"\\e173\";\n}\n.glyphicon-floppy-remove:before {\n content: \"\\e174\";\n}\n.glyphicon-floppy-save:before {\n content: \"\\e175\";\n}\n.glyphicon-floppy-open:before {\n content: \"\\e176\";\n}\n.glyphicon-credit-card:before {\n content: \"\\e177\";\n}\n.glyphicon-transfer:before {\n content: \"\\e178\";\n}\n.glyphicon-cutlery:before {\n content: \"\\e179\";\n}\n.glyphicon-header:before {\n content: \"\\e180\";\n}\n.glyphicon-compressed:before {\n content: \"\\e181\";\n}\n.glyphicon-earphone:before {\n content: \"\\e182\";\n}\n.glyphicon-phone-alt:before {\n content: \"\\e183\";\n}\n.glyphicon-tower:before {\n content: \"\\e184\";\n}\n.glyphicon-stats:before {\n content: \"\\e185\";\n}\n.glyphicon-sd-video:before {\n content: \"\\e186\";\n}\n.glyphicon-hd-video:before {\n content: \"\\e187\";\n}\n.glyphicon-subtitles:before {\n content: \"\\e188\";\n}\n.glyphicon-sound-stereo:before {\n content: \"\\e189\";\n}\n.glyphicon-sound-dolby:before {\n content: \"\\e190\";\n}\n.glyphicon-sound-5-1:before {\n content: \"\\e191\";\n}\n.glyphicon-sound-6-1:before {\n content: \"\\e192\";\n}\n.glyphicon-sound-7-1:before {\n content: \"\\e193\";\n}\n.glyphicon-copyright-mark:before {\n content: \"\\e194\";\n}\n.glyphicon-registration-mark:before {\n content: \"\\e195\";\n}\n.glyphicon-cloud-download:before {\n content: \"\\e197\";\n}\n.glyphicon-cloud-upload:before {\n content: \"\\e198\";\n}\n.glyphicon-tree-conifer:before {\n content: \"\\e199\";\n}\n.glyphicon-tree-deciduous:before {\n content: \"\\e200\";\n}\n.glyphicon-cd:before {\n content: \"\\e201\";\n}\n.glyphicon-save-file:before {\n content: \"\\e202\";\n}\n.glyphicon-open-file:before {\n content: \"\\e203\";\n}\n.glyphicon-level-up:before {\n content: \"\\e204\";\n}\n.glyphicon-copy:before {\n content: \"\\e205\";\n}\n.glyphicon-paste:before {\n content: \"\\e206\";\n}\n.glyphicon-alert:before {\n content: \"\\e209\";\n}\n.glyphicon-equalizer:before {\n content: \"\\e210\";\n}\n.glyphicon-king:before {\n content: \"\\e211\";\n}\n.glyphicon-queen:before {\n content: \"\\e212\";\n}\n.glyphicon-pawn:before {\n content: \"\\e213\";\n}\n.glyphicon-bishop:before {\n content: \"\\e214\";\n}\n.glyphicon-knight:before {\n content: \"\\e215\";\n}\n.glyphicon-baby-formula:before {\n content: \"\\e216\";\n}\n.glyphicon-tent:before {\n content: \"\\26fa\";\n}\n.glyphicon-blackboard:before {\n content: \"\\e218\";\n}\n.glyphicon-bed:before {\n content: \"\\e219\";\n}\n.glyphicon-apple:before {\n content: \"\\f8ff\";\n}\n.glyphicon-erase:before {\n content: \"\\e221\";\n}\n.glyphicon-hourglass:before {\n content: \"\\231b\";\n}\n.glyphicon-lamp:before {\n content: \"\\e223\";\n}\n.glyphicon-duplicate:before {\n content: \"\\e224\";\n}\n.glyphicon-piggy-bank:before {\n content: \"\\e225\";\n}\n.glyphicon-scissors:before {\n content: \"\\e226\";\n}\n.glyphicon-bitcoin:before {\n content: \"\\e227\";\n}\n.glyphicon-btc:before {\n content: \"\\e227\";\n}\n.glyphicon-xbt:before {\n content: \"\\e227\";\n}\n.glyphicon-yen:before {\n content: \"\\00a5\";\n}\n.glyphicon-jpy:before {\n content: \"\\00a5\";\n}\n.glyphicon-ruble:before {\n content: \"\\20bd\";\n}\n.glyphicon-rub:before {\n content: \"\\20bd\";\n}\n.glyphicon-scale:before {\n content: \"\\e230\";\n}\n.glyphicon-ice-lolly:before {\n content: \"\\e231\";\n}\n.glyphicon-ice-lolly-tasted:before {\n content: \"\\e232\";\n}\n.glyphicon-education:before {\n content: \"\\e233\";\n}\n.glyphicon-option-horizontal:before {\n content: \"\\e234\";\n}\n.glyphicon-option-vertical:before {\n content: \"\\e235\";\n}\n.glyphicon-menu-hamburger:before {\n content: \"\\e236\";\n}\n.glyphicon-modal-window:before {\n content: \"\\e237\";\n}\n.glyphicon-oil:before {\n content: \"\\e238\";\n}\n.glyphicon-grain:before {\n content: \"\\e239\";\n}\n.glyphicon-sunglasses:before {\n content: \"\\e240\";\n}\n.glyphicon-text-size:before {\n content: \"\\e241\";\n}\n.glyphicon-text-color:before {\n content: \"\\e242\";\n}\n.glyphicon-text-background:before {\n content: \"\\e243\";\n}\n.glyphicon-object-align-top:before {\n content: \"\\e244\";\n}\n.glyphicon-object-align-bottom:before {\n content: \"\\e245\";\n}\n.glyphicon-object-align-horizontal:before {\n content: \"\\e246\";\n}\n.glyphicon-object-align-left:before {\n content: \"\\e247\";\n}\n.glyphicon-object-align-vertical:before {\n content: \"\\e248\";\n}\n.glyphicon-object-align-right:before {\n content: \"\\e249\";\n}\n.glyphicon-triangle-right:before {\n content: \"\\e250\";\n}\n.glyphicon-triangle-left:before {\n content: \"\\e251\";\n}\n.glyphicon-triangle-bottom:before {\n content: \"\\e252\";\n}\n.glyphicon-triangle-top:before {\n content: \"\\e253\";\n}\n.glyphicon-console:before {\n content: \"\\e254\";\n}\n.glyphicon-superscript:before {\n content: \"\\e255\";\n}\n.glyphicon-subscript:before {\n content: \"\\e256\";\n}\n.glyphicon-menu-left:before {\n content: \"\\e257\";\n}\n.glyphicon-menu-right:before {\n content: \"\\e258\";\n}\n.glyphicon-menu-down:before {\n content: \"\\e259\";\n}\n.glyphicon-menu-up:before {\n content: \"\\e260\";\n}\n* {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\n*:before,\n*:after {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n}\nhtml {\n font-size: 10px;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\nbody {\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-size: 14px;\n line-height: 1.42857143;\n color: #333333;\n background-color: #fff;\n}\ninput,\nbutton,\nselect,\ntextarea {\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\na {\n color: #337ab7;\n text-decoration: none;\n}\na:hover,\na:focus {\n color: #23527c;\n text-decoration: underline;\n}\na:focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\nfigure {\n margin: 0;\n}\nimg {\n vertical-align: middle;\n}\n.img-responsive,\n.thumbnail > img,\n.thumbnail a > img,\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n display: block;\n max-width: 100%;\n height: auto;\n}\n.img-rounded {\n border-radius: 6px;\n}\n.img-thumbnail {\n padding: 4px;\n line-height: 1.42857143;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 4px;\n -webkit-transition: all 0.2s ease-in-out;\n -o-transition: all 0.2s ease-in-out;\n transition: all 0.2s ease-in-out;\n display: inline-block;\n max-width: 100%;\n height: auto;\n}\n.img-circle {\n border-radius: 50%;\n}\nhr {\n margin-top: 20px;\n margin-bottom: 20px;\n border: 0;\n border-top: 1px solid #eeeeee;\n}\n.sr-only {\n position: absolute;\n width: 1px;\n height: 1px;\n padding: 0;\n margin: -1px;\n overflow: hidden;\n clip: rect(0, 0, 0, 0);\n border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n position: static;\n width: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n clip: auto;\n}\n[role=\"button\"] {\n cursor: pointer;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\n.h1,\n.h2,\n.h3,\n.h4,\n.h5,\n.h6 {\n font-family: inherit;\n font-weight: 500;\n line-height: 1.1;\n color: inherit;\n}\nh1 small,\nh2 small,\nh3 small,\nh4 small,\nh5 small,\nh6 small,\n.h1 small,\n.h2 small,\n.h3 small,\n.h4 small,\n.h5 small,\n.h6 small,\nh1 .small,\nh2 .small,\nh3 .small,\nh4 .small,\nh5 .small,\nh6 .small,\n.h1 .small,\n.h2 .small,\n.h3 .small,\n.h4 .small,\n.h5 .small,\n.h6 .small {\n font-weight: 400;\n line-height: 1;\n color: #777777;\n}\nh1,\n.h1,\nh2,\n.h2,\nh3,\n.h3 {\n margin-top: 20px;\n margin-bottom: 10px;\n}\nh1 small,\n.h1 small,\nh2 small,\n.h2 small,\nh3 small,\n.h3 small,\nh1 .small,\n.h1 .small,\nh2 .small,\n.h2 .small,\nh3 .small,\n.h3 .small {\n font-size: 65%;\n}\nh4,\n.h4,\nh5,\n.h5,\nh6,\n.h6 {\n margin-top: 10px;\n margin-bottom: 10px;\n}\nh4 small,\n.h4 small,\nh5 small,\n.h5 small,\nh6 small,\n.h6 small,\nh4 .small,\n.h4 .small,\nh5 .small,\n.h5 .small,\nh6 .small,\n.h6 .small {\n font-size: 75%;\n}\nh1,\n.h1 {\n font-size: 36px;\n}\nh2,\n.h2 {\n font-size: 30px;\n}\nh3,\n.h3 {\n font-size: 24px;\n}\nh4,\n.h4 {\n font-size: 18px;\n}\nh5,\n.h5 {\n font-size: 14px;\n}\nh6,\n.h6 {\n font-size: 12px;\n}\np {\n margin: 0 0 10px;\n}\n.lead {\n margin-bottom: 20px;\n font-size: 16px;\n font-weight: 300;\n line-height: 1.4;\n}\n@media (min-width: 768px) {\n .lead {\n font-size: 21px;\n }\n}\nsmall,\n.small {\n font-size: 85%;\n}\nmark,\n.mark {\n padding: 0.2em;\n background-color: #fcf8e3;\n}\n.text-left {\n text-align: left;\n}\n.text-right {\n text-align: right;\n}\n.text-center {\n text-align: center;\n}\n.text-justify {\n text-align: justify;\n}\n.text-nowrap {\n white-space: nowrap;\n}\n.text-lowercase {\n text-transform: lowercase;\n}\n.text-uppercase {\n text-transform: uppercase;\n}\n.text-capitalize {\n text-transform: capitalize;\n}\n.text-muted {\n color: #777777;\n}\n.text-primary {\n color: #337ab7;\n}\na.text-primary:hover,\na.text-primary:focus {\n color: #286090;\n}\n.text-success {\n color: #3c763d;\n}\na.text-success:hover,\na.text-success:focus {\n color: #2b542c;\n}\n.text-info {\n color: #31708f;\n}\na.text-info:hover,\na.text-info:focus {\n color: #245269;\n}\n.text-warning {\n color: #8a6d3b;\n}\na.text-warning:hover,\na.text-warning:focus {\n color: #66512c;\n}\n.text-danger {\n color: #a94442;\n}\na.text-danger:hover,\na.text-danger:focus {\n color: #843534;\n}\n.bg-primary {\n color: #fff;\n background-color: #337ab7;\n}\na.bg-primary:hover,\na.bg-primary:focus {\n background-color: #286090;\n}\n.bg-success {\n background-color: #dff0d8;\n}\na.bg-success:hover,\na.bg-success:focus {\n background-color: #c1e2b3;\n}\n.bg-info {\n background-color: #d9edf7;\n}\na.bg-info:hover,\na.bg-info:focus {\n background-color: #afd9ee;\n}\n.bg-warning {\n background-color: #fcf8e3;\n}\na.bg-warning:hover,\na.bg-warning:focus {\n background-color: #f7ecb5;\n}\n.bg-danger {\n background-color: #f2dede;\n}\na.bg-danger:hover,\na.bg-danger:focus {\n background-color: #e4b9b9;\n}\n.page-header {\n padding-bottom: 9px;\n margin: 40px 0 20px;\n border-bottom: 1px solid #eeeeee;\n}\nul,\nol {\n margin-top: 0;\n margin-bottom: 10px;\n}\nul ul,\nol ul,\nul ol,\nol ol {\n margin-bottom: 0;\n}\n.list-unstyled {\n padding-left: 0;\n list-style: none;\n}\n.list-inline {\n padding-left: 0;\n list-style: none;\n margin-left: -5px;\n}\n.list-inline > li {\n display: inline-block;\n padding-right: 5px;\n padding-left: 5px;\n}\ndl {\n margin-top: 0;\n margin-bottom: 20px;\n}\ndt,\ndd {\n line-height: 1.42857143;\n}\ndt {\n font-weight: 700;\n}\ndd {\n margin-left: 0;\n}\n@media (min-width: 768px) {\n .dl-horizontal dt {\n float: left;\n width: 160px;\n clear: left;\n text-align: right;\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n }\n .dl-horizontal dd {\n margin-left: 180px;\n }\n}\nabbr[title],\nabbr[data-original-title] {\n cursor: help;\n}\n.initialism {\n font-size: 90%;\n text-transform: uppercase;\n}\nblockquote {\n padding: 10px 20px;\n margin: 0 0 20px;\n font-size: 17.5px;\n border-left: 5px solid #eeeeee;\n}\nblockquote p:last-child,\nblockquote ul:last-child,\nblockquote ol:last-child {\n margin-bottom: 0;\n}\nblockquote footer,\nblockquote small,\nblockquote .small {\n display: block;\n font-size: 80%;\n line-height: 1.42857143;\n color: #777777;\n}\nblockquote footer:before,\nblockquote small:before,\nblockquote .small:before {\n content: \"\\2014 \\00A0\";\n}\n.blockquote-reverse,\nblockquote.pull-right {\n padding-right: 15px;\n padding-left: 0;\n text-align: right;\n border-right: 5px solid #eeeeee;\n border-left: 0;\n}\n.blockquote-reverse footer:before,\nblockquote.pull-right footer:before,\n.blockquote-reverse small:before,\nblockquote.pull-right small:before,\n.blockquote-reverse .small:before,\nblockquote.pull-right .small:before {\n content: \"\";\n}\n.blockquote-reverse footer:after,\nblockquote.pull-right footer:after,\n.blockquote-reverse small:after,\nblockquote.pull-right small:after,\n.blockquote-reverse .small:after,\nblockquote.pull-right .small:after {\n content: \"\\00A0 \\2014\";\n}\naddress {\n margin-bottom: 20px;\n font-style: normal;\n line-height: 1.42857143;\n}\ncode,\nkbd,\npre,\nsamp {\n font-family: Menlo, Monaco, Consolas, \"Courier New\", monospace;\n}\ncode {\n padding: 2px 4px;\n font-size: 90%;\n color: #c7254e;\n background-color: #f9f2f4;\n border-radius: 4px;\n}\nkbd {\n padding: 2px 4px;\n font-size: 90%;\n color: #fff;\n background-color: #333;\n border-radius: 3px;\n box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\nkbd kbd {\n padding: 0;\n font-size: 100%;\n font-weight: 700;\n box-shadow: none;\n}\npre {\n display: block;\n padding: 9.5px;\n margin: 0 0 10px;\n font-size: 13px;\n line-height: 1.42857143;\n color: #333333;\n word-break: break-all;\n word-wrap: break-word;\n background-color: #f5f5f5;\n border: 1px solid #ccc;\n border-radius: 4px;\n}\npre code {\n padding: 0;\n font-size: inherit;\n color: inherit;\n white-space: pre-wrap;\n background-color: transparent;\n border-radius: 0;\n}\n.pre-scrollable {\n max-height: 340px;\n overflow-y: scroll;\n}\n.container {\n padding-right: 15px;\n padding-left: 15px;\n margin-right: auto;\n margin-left: auto;\n}\n@media (min-width: 768px) {\n .container {\n width: 750px;\n }\n}\n@media (min-width: 992px) {\n .container {\n width: 970px;\n }\n}\n@media (min-width: 1200px) {\n .container {\n width: 1170px;\n }\n}\n.container-fluid {\n padding-right: 15px;\n padding-left: 15px;\n margin-right: auto;\n margin-left: auto;\n}\n.row {\n margin-right: -15px;\n margin-left: -15px;\n}\n.row-no-gutters {\n margin-right: 0;\n margin-left: 0;\n}\n.row-no-gutters [class*=\"col-\"] {\n padding-right: 0;\n padding-left: 0;\n}\n.col-xs-1,\n.col-sm-1,\n.col-md-1,\n.col-lg-1,\n.col-xs-2,\n.col-sm-2,\n.col-md-2,\n.col-lg-2,\n.col-xs-3,\n.col-sm-3,\n.col-md-3,\n.col-lg-3,\n.col-xs-4,\n.col-sm-4,\n.col-md-4,\n.col-lg-4,\n.col-xs-5,\n.col-sm-5,\n.col-md-5,\n.col-lg-5,\n.col-xs-6,\n.col-sm-6,\n.col-md-6,\n.col-lg-6,\n.col-xs-7,\n.col-sm-7,\n.col-md-7,\n.col-lg-7,\n.col-xs-8,\n.col-sm-8,\n.col-md-8,\n.col-lg-8,\n.col-xs-9,\n.col-sm-9,\n.col-md-9,\n.col-lg-9,\n.col-xs-10,\n.col-sm-10,\n.col-md-10,\n.col-lg-10,\n.col-xs-11,\n.col-sm-11,\n.col-md-11,\n.col-lg-11,\n.col-xs-12,\n.col-sm-12,\n.col-md-12,\n.col-lg-12 {\n position: relative;\n min-height: 1px;\n padding-right: 15px;\n padding-left: 15px;\n}\n.col-xs-1,\n.col-xs-2,\n.col-xs-3,\n.col-xs-4,\n.col-xs-5,\n.col-xs-6,\n.col-xs-7,\n.col-xs-8,\n.col-xs-9,\n.col-xs-10,\n.col-xs-11,\n.col-xs-12 {\n float: left;\n}\n.col-xs-12 {\n width: 100%;\n}\n.col-xs-11 {\n width: 91.66666667%;\n}\n.col-xs-10 {\n width: 83.33333333%;\n}\n.col-xs-9 {\n width: 75%;\n}\n.col-xs-8 {\n width: 66.66666667%;\n}\n.col-xs-7 {\n width: 58.33333333%;\n}\n.col-xs-6 {\n width: 50%;\n}\n.col-xs-5 {\n width: 41.66666667%;\n}\n.col-xs-4 {\n width: 33.33333333%;\n}\n.col-xs-3 {\n width: 25%;\n}\n.col-xs-2 {\n width: 16.66666667%;\n}\n.col-xs-1 {\n width: 8.33333333%;\n}\n.col-xs-pull-12 {\n right: 100%;\n}\n.col-xs-pull-11 {\n right: 91.66666667%;\n}\n.col-xs-pull-10 {\n right: 83.33333333%;\n}\n.col-xs-pull-9 {\n right: 75%;\n}\n.col-xs-pull-8 {\n right: 66.66666667%;\n}\n.col-xs-pull-7 {\n right: 58.33333333%;\n}\n.col-xs-pull-6 {\n right: 50%;\n}\n.col-xs-pull-5 {\n right: 41.66666667%;\n}\n.col-xs-pull-4 {\n right: 33.33333333%;\n}\n.col-xs-pull-3 {\n right: 25%;\n}\n.col-xs-pull-2 {\n right: 16.66666667%;\n}\n.col-xs-pull-1 {\n right: 8.33333333%;\n}\n.col-xs-pull-0 {\n right: auto;\n}\n.col-xs-push-12 {\n left: 100%;\n}\n.col-xs-push-11 {\n left: 91.66666667%;\n}\n.col-xs-push-10 {\n left: 83.33333333%;\n}\n.col-xs-push-9 {\n left: 75%;\n}\n.col-xs-push-8 {\n left: 66.66666667%;\n}\n.col-xs-push-7 {\n left: 58.33333333%;\n}\n.col-xs-push-6 {\n left: 50%;\n}\n.col-xs-push-5 {\n left: 41.66666667%;\n}\n.col-xs-push-4 {\n left: 33.33333333%;\n}\n.col-xs-push-3 {\n left: 25%;\n}\n.col-xs-push-2 {\n left: 16.66666667%;\n}\n.col-xs-push-1 {\n left: 8.33333333%;\n}\n.col-xs-push-0 {\n left: auto;\n}\n.col-xs-offset-12 {\n margin-left: 100%;\n}\n.col-xs-offset-11 {\n margin-left: 91.66666667%;\n}\n.col-xs-offset-10 {\n margin-left: 83.33333333%;\n}\n.col-xs-offset-9 {\n margin-left: 75%;\n}\n.col-xs-offset-8 {\n margin-left: 66.66666667%;\n}\n.col-xs-offset-7 {\n margin-left: 58.33333333%;\n}\n.col-xs-offset-6 {\n margin-left: 50%;\n}\n.col-xs-offset-5 {\n margin-left: 41.66666667%;\n}\n.col-xs-offset-4 {\n margin-left: 33.33333333%;\n}\n.col-xs-offset-3 {\n margin-left: 25%;\n}\n.col-xs-offset-2 {\n margin-left: 16.66666667%;\n}\n.col-xs-offset-1 {\n margin-left: 8.33333333%;\n}\n.col-xs-offset-0 {\n margin-left: 0%;\n}\n@media (min-width: 768px) {\n .col-sm-1,\n .col-sm-2,\n .col-sm-3,\n .col-sm-4,\n .col-sm-5,\n .col-sm-6,\n .col-sm-7,\n .col-sm-8,\n .col-sm-9,\n .col-sm-10,\n .col-sm-11,\n .col-sm-12 {\n float: left;\n }\n .col-sm-12 {\n width: 100%;\n }\n .col-sm-11 {\n width: 91.66666667%;\n }\n .col-sm-10 {\n width: 83.33333333%;\n }\n .col-sm-9 {\n width: 75%;\n }\n .col-sm-8 {\n width: 66.66666667%;\n }\n .col-sm-7 {\n width: 58.33333333%;\n }\n .col-sm-6 {\n width: 50%;\n }\n .col-sm-5 {\n width: 41.66666667%;\n }\n .col-sm-4 {\n width: 33.33333333%;\n }\n .col-sm-3 {\n width: 25%;\n }\n .col-sm-2 {\n width: 16.66666667%;\n }\n .col-sm-1 {\n width: 8.33333333%;\n }\n .col-sm-pull-12 {\n right: 100%;\n }\n .col-sm-pull-11 {\n right: 91.66666667%;\n }\n .col-sm-pull-10 {\n right: 83.33333333%;\n }\n .col-sm-pull-9 {\n right: 75%;\n }\n .col-sm-pull-8 {\n right: 66.66666667%;\n }\n .col-sm-pull-7 {\n right: 58.33333333%;\n }\n .col-sm-pull-6 {\n right: 50%;\n }\n .col-sm-pull-5 {\n right: 41.66666667%;\n }\n .col-sm-pull-4 {\n right: 33.33333333%;\n }\n .col-sm-pull-3 {\n right: 25%;\n }\n .col-sm-pull-2 {\n right: 16.66666667%;\n }\n .col-sm-pull-1 {\n right: 8.33333333%;\n }\n .col-sm-pull-0 {\n right: auto;\n }\n .col-sm-push-12 {\n left: 100%;\n }\n .col-sm-push-11 {\n left: 91.66666667%;\n }\n .col-sm-push-10 {\n left: 83.33333333%;\n }\n .col-sm-push-9 {\n left: 75%;\n }\n .col-sm-push-8 {\n left: 66.66666667%;\n }\n .col-sm-push-7 {\n left: 58.33333333%;\n }\n .col-sm-push-6 {\n left: 50%;\n }\n .col-sm-push-5 {\n left: 41.66666667%;\n }\n .col-sm-push-4 {\n left: 33.33333333%;\n }\n .col-sm-push-3 {\n left: 25%;\n }\n .col-sm-push-2 {\n left: 16.66666667%;\n }\n .col-sm-push-1 {\n left: 8.33333333%;\n }\n .col-sm-push-0 {\n left: auto;\n }\n .col-sm-offset-12 {\n margin-left: 100%;\n }\n .col-sm-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-sm-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-sm-offset-9 {\n margin-left: 75%;\n }\n .col-sm-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-sm-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-sm-offset-6 {\n margin-left: 50%;\n }\n .col-sm-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-sm-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-sm-offset-3 {\n margin-left: 25%;\n }\n .col-sm-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-sm-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-sm-offset-0 {\n margin-left: 0%;\n }\n}\n@media (min-width: 992px) {\n .col-md-1,\n .col-md-2,\n .col-md-3,\n .col-md-4,\n .col-md-5,\n .col-md-6,\n .col-md-7,\n .col-md-8,\n .col-md-9,\n .col-md-10,\n .col-md-11,\n .col-md-12 {\n float: left;\n }\n .col-md-12 {\n width: 100%;\n }\n .col-md-11 {\n width: 91.66666667%;\n }\n .col-md-10 {\n width: 83.33333333%;\n }\n .col-md-9 {\n width: 75%;\n }\n .col-md-8 {\n width: 66.66666667%;\n }\n .col-md-7 {\n width: 58.33333333%;\n }\n .col-md-6 {\n width: 50%;\n }\n .col-md-5 {\n width: 41.66666667%;\n }\n .col-md-4 {\n width: 33.33333333%;\n }\n .col-md-3 {\n width: 25%;\n }\n .col-md-2 {\n width: 16.66666667%;\n }\n .col-md-1 {\n width: 8.33333333%;\n }\n .col-md-pull-12 {\n right: 100%;\n }\n .col-md-pull-11 {\n right: 91.66666667%;\n }\n .col-md-pull-10 {\n right: 83.33333333%;\n }\n .col-md-pull-9 {\n right: 75%;\n }\n .col-md-pull-8 {\n right: 66.66666667%;\n }\n .col-md-pull-7 {\n right: 58.33333333%;\n }\n .col-md-pull-6 {\n right: 50%;\n }\n .col-md-pull-5 {\n right: 41.66666667%;\n }\n .col-md-pull-4 {\n right: 33.33333333%;\n }\n .col-md-pull-3 {\n right: 25%;\n }\n .col-md-pull-2 {\n right: 16.66666667%;\n }\n .col-md-pull-1 {\n right: 8.33333333%;\n }\n .col-md-pull-0 {\n right: auto;\n }\n .col-md-push-12 {\n left: 100%;\n }\n .col-md-push-11 {\n left: 91.66666667%;\n }\n .col-md-push-10 {\n left: 83.33333333%;\n }\n .col-md-push-9 {\n left: 75%;\n }\n .col-md-push-8 {\n left: 66.66666667%;\n }\n .col-md-push-7 {\n left: 58.33333333%;\n }\n .col-md-push-6 {\n left: 50%;\n }\n .col-md-push-5 {\n left: 41.66666667%;\n }\n .col-md-push-4 {\n left: 33.33333333%;\n }\n .col-md-push-3 {\n left: 25%;\n }\n .col-md-push-2 {\n left: 16.66666667%;\n }\n .col-md-push-1 {\n left: 8.33333333%;\n }\n .col-md-push-0 {\n left: auto;\n }\n .col-md-offset-12 {\n margin-left: 100%;\n }\n .col-md-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-md-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-md-offset-9 {\n margin-left: 75%;\n }\n .col-md-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-md-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-md-offset-6 {\n margin-left: 50%;\n }\n .col-md-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-md-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-md-offset-3 {\n margin-left: 25%;\n }\n .col-md-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-md-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-md-offset-0 {\n margin-left: 0%;\n }\n}\n@media (min-width: 1200px) {\n .col-lg-1,\n .col-lg-2,\n .col-lg-3,\n .col-lg-4,\n .col-lg-5,\n .col-lg-6,\n .col-lg-7,\n .col-lg-8,\n .col-lg-9,\n .col-lg-10,\n .col-lg-11,\n .col-lg-12 {\n float: left;\n }\n .col-lg-12 {\n width: 100%;\n }\n .col-lg-11 {\n width: 91.66666667%;\n }\n .col-lg-10 {\n width: 83.33333333%;\n }\n .col-lg-9 {\n width: 75%;\n }\n .col-lg-8 {\n width: 66.66666667%;\n }\n .col-lg-7 {\n width: 58.33333333%;\n }\n .col-lg-6 {\n width: 50%;\n }\n .col-lg-5 {\n width: 41.66666667%;\n }\n .col-lg-4 {\n width: 33.33333333%;\n }\n .col-lg-3 {\n width: 25%;\n }\n .col-lg-2 {\n width: 16.66666667%;\n }\n .col-lg-1 {\n width: 8.33333333%;\n }\n .col-lg-pull-12 {\n right: 100%;\n }\n .col-lg-pull-11 {\n right: 91.66666667%;\n }\n .col-lg-pull-10 {\n right: 83.33333333%;\n }\n .col-lg-pull-9 {\n right: 75%;\n }\n .col-lg-pull-8 {\n right: 66.66666667%;\n }\n .col-lg-pull-7 {\n right: 58.33333333%;\n }\n .col-lg-pull-6 {\n right: 50%;\n }\n .col-lg-pull-5 {\n right: 41.66666667%;\n }\n .col-lg-pull-4 {\n right: 33.33333333%;\n }\n .col-lg-pull-3 {\n right: 25%;\n }\n .col-lg-pull-2 {\n right: 16.66666667%;\n }\n .col-lg-pull-1 {\n right: 8.33333333%;\n }\n .col-lg-pull-0 {\n right: auto;\n }\n .col-lg-push-12 {\n left: 100%;\n }\n .col-lg-push-11 {\n left: 91.66666667%;\n }\n .col-lg-push-10 {\n left: 83.33333333%;\n }\n .col-lg-push-9 {\n left: 75%;\n }\n .col-lg-push-8 {\n left: 66.66666667%;\n }\n .col-lg-push-7 {\n left: 58.33333333%;\n }\n .col-lg-push-6 {\n left: 50%;\n }\n .col-lg-push-5 {\n left: 41.66666667%;\n }\n .col-lg-push-4 {\n left: 33.33333333%;\n }\n .col-lg-push-3 {\n left: 25%;\n }\n .col-lg-push-2 {\n left: 16.66666667%;\n }\n .col-lg-push-1 {\n left: 8.33333333%;\n }\n .col-lg-push-0 {\n left: auto;\n }\n .col-lg-offset-12 {\n margin-left: 100%;\n }\n .col-lg-offset-11 {\n margin-left: 91.66666667%;\n }\n .col-lg-offset-10 {\n margin-left: 83.33333333%;\n }\n .col-lg-offset-9 {\n margin-left: 75%;\n }\n .col-lg-offset-8 {\n margin-left: 66.66666667%;\n }\n .col-lg-offset-7 {\n margin-left: 58.33333333%;\n }\n .col-lg-offset-6 {\n margin-left: 50%;\n }\n .col-lg-offset-5 {\n margin-left: 41.66666667%;\n }\n .col-lg-offset-4 {\n margin-left: 33.33333333%;\n }\n .col-lg-offset-3 {\n margin-left: 25%;\n }\n .col-lg-offset-2 {\n margin-left: 16.66666667%;\n }\n .col-lg-offset-1 {\n margin-left: 8.33333333%;\n }\n .col-lg-offset-0 {\n margin-left: 0%;\n }\n}\ntable {\n background-color: transparent;\n}\ntable col[class*=\"col-\"] {\n position: static;\n display: table-column;\n float: none;\n}\ntable td[class*=\"col-\"],\ntable th[class*=\"col-\"] {\n position: static;\n display: table-cell;\n float: none;\n}\ncaption {\n padding-top: 8px;\n padding-bottom: 8px;\n color: #777777;\n text-align: left;\n}\nth {\n text-align: left;\n}\n.table {\n width: 100%;\n max-width: 100%;\n margin-bottom: 20px;\n}\n.table > thead > tr > th,\n.table > tbody > tr > th,\n.table > tfoot > tr > th,\n.table > thead > tr > td,\n.table > tbody > tr > td,\n.table > tfoot > tr > td {\n padding: 8px;\n line-height: 1.42857143;\n vertical-align: top;\n border-top: 1px solid #ddd;\n}\n.table > thead > tr > th {\n vertical-align: bottom;\n border-bottom: 2px solid #ddd;\n}\n.table > caption + thead > tr:first-child > th,\n.table > colgroup + thead > tr:first-child > th,\n.table > thead:first-child > tr:first-child > th,\n.table > caption + thead > tr:first-child > td,\n.table > colgroup + thead > tr:first-child > td,\n.table > thead:first-child > tr:first-child > td {\n border-top: 0;\n}\n.table > tbody + tbody {\n border-top: 2px solid #ddd;\n}\n.table .table {\n background-color: #fff;\n}\n.table-condensed > thead > tr > th,\n.table-condensed > tbody > tr > th,\n.table-condensed > tfoot > tr > th,\n.table-condensed > thead > tr > td,\n.table-condensed > tbody > tr > td,\n.table-condensed > tfoot > tr > td {\n padding: 5px;\n}\n.table-bordered {\n border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > tbody > tr > th,\n.table-bordered > tfoot > tr > th,\n.table-bordered > thead > tr > td,\n.table-bordered > tbody > tr > td,\n.table-bordered > tfoot > tr > td {\n border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > thead > tr > td {\n border-bottom-width: 2px;\n}\n.table-striped > tbody > tr:nth-of-type(odd) {\n background-color: #f9f9f9;\n}\n.table-hover > tbody > tr:hover {\n background-color: #f5f5f5;\n}\n.table > thead > tr > td.active,\n.table > tbody > tr > td.active,\n.table > tfoot > tr > td.active,\n.table > thead > tr > th.active,\n.table > tbody > tr > th.active,\n.table > tfoot > tr > th.active,\n.table > thead > tr.active > td,\n.table > tbody > tr.active > td,\n.table > tfoot > tr.active > td,\n.table > thead > tr.active > th,\n.table > tbody > tr.active > th,\n.table > tfoot > tr.active > th {\n background-color: #f5f5f5;\n}\n.table-hover > tbody > tr > td.active:hover,\n.table-hover > tbody > tr > th.active:hover,\n.table-hover > tbody > tr.active:hover > td,\n.table-hover > tbody > tr:hover > .active,\n.table-hover > tbody > tr.active:hover > th {\n background-color: #e8e8e8;\n}\n.table > thead > tr > td.success,\n.table > tbody > tr > td.success,\n.table > tfoot > tr > td.success,\n.table > thead > tr > th.success,\n.table > tbody > tr > th.success,\n.table > tfoot > tr > th.success,\n.table > thead > tr.success > td,\n.table > tbody > tr.success > td,\n.table > tfoot > tr.success > td,\n.table > thead > tr.success > th,\n.table > tbody > tr.success > th,\n.table > tfoot > tr.success > th {\n background-color: #dff0d8;\n}\n.table-hover > tbody > tr > td.success:hover,\n.table-hover > tbody > tr > th.success:hover,\n.table-hover > tbody > tr.success:hover > td,\n.table-hover > tbody > tr:hover > .success,\n.table-hover > tbody > tr.success:hover > th {\n background-color: #d0e9c6;\n}\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n background-color: #d9edf7;\n}\n.table-hover > tbody > tr > td.info:hover,\n.table-hover > tbody > tr > th.info:hover,\n.table-hover > tbody > tr.info:hover > td,\n.table-hover > tbody > tr:hover > .info,\n.table-hover > tbody > tr.info:hover > th {\n background-color: #c4e3f3;\n}\n.table > thead > tr > td.warning,\n.table > tbody > tr > td.warning,\n.table > tfoot > tr > td.warning,\n.table > thead > tr > th.warning,\n.table > tbody > tr > th.warning,\n.table > tfoot > tr > th.warning,\n.table > thead > tr.warning > td,\n.table > tbody > tr.warning > td,\n.table > tfoot > tr.warning > td,\n.table > thead > tr.warning > th,\n.table > tbody > tr.warning > th,\n.table > tfoot > tr.warning > th {\n background-color: #fcf8e3;\n}\n.table-hover > tbody > tr > td.warning:hover,\n.table-hover > tbody > tr > th.warning:hover,\n.table-hover > tbody > tr.warning:hover > td,\n.table-hover > tbody > tr:hover > .warning,\n.table-hover > tbody > tr.warning:hover > th {\n background-color: #faf2cc;\n}\n.table > thead > tr > td.danger,\n.table > tbody > tr > td.danger,\n.table > tfoot > tr > td.danger,\n.table > thead > tr > th.danger,\n.table > tbody > tr > th.danger,\n.table > tfoot > tr > th.danger,\n.table > thead > tr.danger > td,\n.table > tbody > tr.danger > td,\n.table > tfoot > tr.danger > td,\n.table > thead > tr.danger > th,\n.table > tbody > tr.danger > th,\n.table > tfoot > tr.danger > th {\n background-color: #f2dede;\n}\n.table-hover > tbody > tr > td.danger:hover,\n.table-hover > tbody > tr > th.danger:hover,\n.table-hover > tbody > tr.danger:hover > td,\n.table-hover > tbody > tr:hover > .danger,\n.table-hover > tbody > tr.danger:hover > th {\n background-color: #ebcccc;\n}\n.table-responsive {\n min-height: 0.01%;\n overflow-x: auto;\n}\n@media screen and (max-width: 767px) {\n .table-responsive {\n width: 100%;\n margin-bottom: 15px;\n overflow-y: hidden;\n -ms-overflow-style: -ms-autohiding-scrollbar;\n border: 1px solid #ddd;\n }\n .table-responsive > .table {\n margin-bottom: 0;\n }\n .table-responsive > .table > thead > tr > th,\n .table-responsive > .table > tbody > tr > th,\n .table-responsive > .table > tfoot > tr > th,\n .table-responsive > .table > thead > tr > td,\n .table-responsive > .table > tbody > tr > td,\n .table-responsive > .table > tfoot > tr > td {\n white-space: nowrap;\n }\n .table-responsive > .table-bordered {\n border: 0;\n }\n .table-responsive > .table-bordered > thead > tr > th:first-child,\n .table-responsive > .table-bordered > tbody > tr > th:first-child,\n .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n .table-responsive > .table-bordered > thead > tr > td:first-child,\n .table-responsive > .table-bordered > tbody > tr > td:first-child,\n .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n border-left: 0;\n }\n .table-responsive > .table-bordered > thead > tr > th:last-child,\n .table-responsive > .table-bordered > tbody > tr > th:last-child,\n .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n .table-responsive > .table-bordered > thead > tr > td:last-child,\n .table-responsive > .table-bordered > tbody > tr > td:last-child,\n .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n border-right: 0;\n }\n .table-responsive > .table-bordered > tbody > tr:last-child > th,\n .table-responsive > .table-bordered > tfoot > tr:last-child > th,\n .table-responsive > .table-bordered > tbody > tr:last-child > td,\n .table-responsive > .table-bordered > tfoot > tr:last-child > td {\n border-bottom: 0;\n }\n}\nfieldset {\n min-width: 0;\n padding: 0;\n margin: 0;\n border: 0;\n}\nlegend {\n display: block;\n width: 100%;\n padding: 0;\n margin-bottom: 20px;\n font-size: 21px;\n line-height: inherit;\n color: #333333;\n border: 0;\n border-bottom: 1px solid #e5e5e5;\n}\nlabel {\n display: inline-block;\n max-width: 100%;\n margin-bottom: 5px;\n font-weight: 700;\n}\ninput[type=\"search\"] {\n -webkit-box-sizing: border-box;\n -moz-box-sizing: border-box;\n box-sizing: border-box;\n -webkit-appearance: none;\n appearance: none;\n}\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n margin: 4px 0 0;\n margin-top: 1px \\9;\n line-height: normal;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled],\ninput[type=\"radio\"].disabled,\ninput[type=\"checkbox\"].disabled,\nfieldset[disabled] input[type=\"radio\"],\nfieldset[disabled] input[type=\"checkbox\"] {\n cursor: not-allowed;\n}\ninput[type=\"file\"] {\n display: block;\n}\ninput[type=\"range\"] {\n display: block;\n width: 100%;\n}\nselect[multiple],\nselect[size] {\n height: auto;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\noutput {\n display: block;\n padding-top: 7px;\n font-size: 14px;\n line-height: 1.42857143;\n color: #555555;\n}\n.form-control {\n display: block;\n width: 100%;\n height: 34px;\n padding: 6px 12px;\n font-size: 14px;\n line-height: 1.42857143;\n color: #555555;\n background-color: #fff;\n background-image: none;\n border: 1px solid #ccc;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n}\n.form-control:focus {\n border-color: #66afe9;\n outline: 0;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);\n}\n.form-control::-moz-placeholder {\n color: #999;\n opacity: 1;\n}\n.form-control:-ms-input-placeholder {\n color: #999;\n}\n.form-control::-webkit-input-placeholder {\n color: #999;\n}\n.form-control::-ms-expand {\n background-color: transparent;\n border: 0;\n}\n.form-control[disabled],\n.form-control[readonly],\nfieldset[disabled] .form-control {\n background-color: #eeeeee;\n opacity: 1;\n}\n.form-control[disabled],\nfieldset[disabled] .form-control {\n cursor: not-allowed;\n}\ntextarea.form-control {\n height: auto;\n}\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n input[type=\"date\"].form-control,\n input[type=\"time\"].form-control,\n input[type=\"datetime-local\"].form-control,\n input[type=\"month\"].form-control {\n line-height: 34px;\n }\n input[type=\"date\"].input-sm,\n input[type=\"time\"].input-sm,\n input[type=\"datetime-local\"].input-sm,\n input[type=\"month\"].input-sm,\n .input-group-sm input[type=\"date\"],\n .input-group-sm input[type=\"time\"],\n .input-group-sm input[type=\"datetime-local\"],\n .input-group-sm input[type=\"month\"] {\n line-height: 30px;\n }\n input[type=\"date\"].input-lg,\n input[type=\"time\"].input-lg,\n input[type=\"datetime-local\"].input-lg,\n input[type=\"month\"].input-lg,\n .input-group-lg input[type=\"date\"],\n .input-group-lg input[type=\"time\"],\n .input-group-lg input[type=\"datetime-local\"],\n .input-group-lg input[type=\"month\"] {\n line-height: 46px;\n }\n}\n.form-group {\n margin-bottom: 15px;\n}\n.radio,\n.checkbox {\n position: relative;\n display: block;\n margin-top: 10px;\n margin-bottom: 10px;\n}\n.radio.disabled label,\n.checkbox.disabled label,\nfieldset[disabled] .radio label,\nfieldset[disabled] .checkbox label {\n cursor: not-allowed;\n}\n.radio label,\n.checkbox label {\n min-height: 20px;\n padding-left: 20px;\n margin-bottom: 0;\n font-weight: 400;\n cursor: pointer;\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n position: absolute;\n margin-top: 4px \\9;\n margin-left: -20px;\n}\n.radio + .radio,\n.checkbox + .checkbox {\n margin-top: -5px;\n}\n.radio-inline,\n.checkbox-inline {\n position: relative;\n display: inline-block;\n padding-left: 20px;\n margin-bottom: 0;\n font-weight: 400;\n vertical-align: middle;\n cursor: pointer;\n}\n.radio-inline.disabled,\n.checkbox-inline.disabled,\nfieldset[disabled] .radio-inline,\nfieldset[disabled] .checkbox-inline {\n cursor: not-allowed;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n margin-top: 0;\n margin-left: 10px;\n}\n.form-control-static {\n min-height: 34px;\n padding-top: 7px;\n padding-bottom: 7px;\n margin-bottom: 0;\n}\n.form-control-static.input-lg,\n.form-control-static.input-sm {\n padding-right: 0;\n padding-left: 0;\n}\n.input-sm {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\nselect.input-sm {\n height: 30px;\n line-height: 30px;\n}\ntextarea.input-sm,\nselect[multiple].input-sm {\n height: auto;\n}\n.form-group-sm .form-control {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.form-group-sm select.form-control {\n height: 30px;\n line-height: 30px;\n}\n.form-group-sm textarea.form-control,\n.form-group-sm select[multiple].form-control {\n height: auto;\n}\n.form-group-sm .form-control-static {\n height: 30px;\n min-height: 32px;\n padding: 6px 10px;\n font-size: 12px;\n line-height: 1.5;\n}\n.input-lg {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\nselect.input-lg {\n height: 46px;\n line-height: 46px;\n}\ntextarea.input-lg,\nselect[multiple].input-lg {\n height: auto;\n}\n.form-group-lg .form-control {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\n.form-group-lg select.form-control {\n height: 46px;\n line-height: 46px;\n}\n.form-group-lg textarea.form-control,\n.form-group-lg select[multiple].form-control {\n height: auto;\n}\n.form-group-lg .form-control-static {\n height: 46px;\n min-height: 38px;\n padding: 11px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n}\n.has-feedback {\n position: relative;\n}\n.has-feedback .form-control {\n padding-right: 42.5px;\n}\n.form-control-feedback {\n position: absolute;\n top: 0;\n right: 0;\n z-index: 2;\n display: block;\n width: 34px;\n height: 34px;\n line-height: 34px;\n text-align: center;\n pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n width: 46px;\n height: 46px;\n line-height: 46px;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n width: 30px;\n height: 30px;\n line-height: 30px;\n}\n.has-success .help-block,\n.has-success .control-label,\n.has-success .radio,\n.has-success .checkbox,\n.has-success .radio-inline,\n.has-success .checkbox-inline,\n.has-success.radio label,\n.has-success.checkbox label,\n.has-success.radio-inline label,\n.has-success.checkbox-inline label {\n color: #3c763d;\n}\n.has-success .form-control {\n border-color: #3c763d;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-success .form-control:focus {\n border-color: #2b542c;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n}\n.has-success .input-group-addon {\n color: #3c763d;\n background-color: #dff0d8;\n border-color: #3c763d;\n}\n.has-success .form-control-feedback {\n color: #3c763d;\n}\n.has-warning .help-block,\n.has-warning .control-label,\n.has-warning .radio,\n.has-warning .checkbox,\n.has-warning .radio-inline,\n.has-warning .checkbox-inline,\n.has-warning.radio label,\n.has-warning.checkbox label,\n.has-warning.radio-inline label,\n.has-warning.checkbox-inline label {\n color: #8a6d3b;\n}\n.has-warning .form-control {\n border-color: #8a6d3b;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-warning .form-control:focus {\n border-color: #66512c;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n}\n.has-warning .input-group-addon {\n color: #8a6d3b;\n background-color: #fcf8e3;\n border-color: #8a6d3b;\n}\n.has-warning .form-control-feedback {\n color: #8a6d3b;\n}\n.has-error .help-block,\n.has-error .control-label,\n.has-error .radio,\n.has-error .checkbox,\n.has-error .radio-inline,\n.has-error .checkbox-inline,\n.has-error.radio label,\n.has-error.checkbox label,\n.has-error.radio-inline label,\n.has-error.checkbox-inline label {\n color: #a94442;\n}\n.has-error .form-control {\n border-color: #a94442;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-error .form-control:focus {\n border-color: #843534;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n}\n.has-error .input-group-addon {\n color: #a94442;\n background-color: #f2dede;\n border-color: #a94442;\n}\n.has-error .form-control-feedback {\n color: #a94442;\n}\n.has-feedback label ~ .form-control-feedback {\n top: 25px;\n}\n.has-feedback label.sr-only ~ .form-control-feedback {\n top: 0;\n}\n.help-block {\n display: block;\n margin-top: 5px;\n margin-bottom: 10px;\n color: #737373;\n}\n@media (min-width: 768px) {\n .form-inline .form-group {\n display: inline-block;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .form-control {\n display: inline-block;\n width: auto;\n vertical-align: middle;\n }\n .form-inline .form-control-static {\n display: inline-block;\n }\n .form-inline .input-group {\n display: inline-table;\n vertical-align: middle;\n }\n .form-inline .input-group .input-group-addon,\n .form-inline .input-group .input-group-btn,\n .form-inline .input-group .form-control {\n width: auto;\n }\n .form-inline .input-group > .form-control {\n width: 100%;\n }\n .form-inline .control-label {\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .radio,\n .form-inline .checkbox {\n display: inline-block;\n margin-top: 0;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .form-inline .radio label,\n .form-inline .checkbox label {\n padding-left: 0;\n }\n .form-inline .radio input[type=\"radio\"],\n .form-inline .checkbox input[type=\"checkbox\"] {\n position: relative;\n margin-left: 0;\n }\n .form-inline .has-feedback .form-control-feedback {\n top: 0;\n }\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox,\n.form-horizontal .radio-inline,\n.form-horizontal .checkbox-inline {\n padding-top: 7px;\n margin-top: 0;\n margin-bottom: 0;\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox {\n min-height: 27px;\n}\n.form-horizontal .form-group {\n margin-right: -15px;\n margin-left: -15px;\n}\n@media (min-width: 768px) {\n .form-horizontal .control-label {\n padding-top: 7px;\n margin-bottom: 0;\n text-align: right;\n }\n}\n.form-horizontal .has-feedback .form-control-feedback {\n right: 15px;\n}\n@media (min-width: 768px) {\n .form-horizontal .form-group-lg .control-label {\n padding-top: 11px;\n font-size: 18px;\n }\n}\n@media (min-width: 768px) {\n .form-horizontal .form-group-sm .control-label {\n padding-top: 6px;\n font-size: 12px;\n }\n}\n.btn {\n display: inline-block;\n margin-bottom: 0;\n font-weight: normal;\n text-align: center;\n white-space: nowrap;\n vertical-align: middle;\n touch-action: manipulation;\n cursor: pointer;\n background-image: none;\n border: 1px solid transparent;\n padding: 6px 12px;\n font-size: 14px;\n line-height: 1.42857143;\n border-radius: 4px;\n -webkit-user-select: none;\n -moz-user-select: none;\n -ms-user-select: none;\n user-select: none;\n}\n.btn:focus,\n.btn:active:focus,\n.btn.active:focus,\n.btn.focus,\n.btn:active.focus,\n.btn.active.focus {\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n.btn:hover,\n.btn:focus,\n.btn.focus {\n color: #333;\n text-decoration: none;\n}\n.btn:active,\n.btn.active {\n background-image: none;\n outline: 0;\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn.disabled,\n.btn[disabled],\nfieldset[disabled] .btn {\n cursor: not-allowed;\n filter: alpha(opacity=65);\n opacity: 0.65;\n -webkit-box-shadow: none;\n box-shadow: none;\n}\na.btn.disabled,\nfieldset[disabled] a.btn {\n pointer-events: none;\n}\n.btn-default {\n color: #333;\n background-color: #fff;\n border-color: #ccc;\n}\n.btn-default:focus,\n.btn-default.focus {\n color: #333;\n background-color: #e6e6e6;\n border-color: #8c8c8c;\n}\n.btn-default:hover {\n color: #333;\n background-color: #e6e6e6;\n border-color: #adadad;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n color: #333;\n background-color: #e6e6e6;\n background-image: none;\n border-color: #adadad;\n}\n.btn-default:active:hover,\n.btn-default.active:hover,\n.open > .dropdown-toggle.btn-default:hover,\n.btn-default:active:focus,\n.btn-default.active:focus,\n.open > .dropdown-toggle.btn-default:focus,\n.btn-default:active.focus,\n.btn-default.active.focus,\n.open > .dropdown-toggle.btn-default.focus {\n color: #333;\n background-color: #d4d4d4;\n border-color: #8c8c8c;\n}\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus {\n background-color: #fff;\n border-color: #ccc;\n}\n.btn-default .badge {\n color: #fff;\n background-color: #333;\n}\n.btn-primary {\n color: #fff;\n background-color: #337ab7;\n border-color: #2e6da4;\n}\n.btn-primary:focus,\n.btn-primary.focus {\n color: #fff;\n background-color: #286090;\n border-color: #122b40;\n}\n.btn-primary:hover {\n color: #fff;\n background-color: #286090;\n border-color: #204d74;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n color: #fff;\n background-color: #286090;\n background-image: none;\n border-color: #204d74;\n}\n.btn-primary:active:hover,\n.btn-primary.active:hover,\n.open > .dropdown-toggle.btn-primary:hover,\n.btn-primary:active:focus,\n.btn-primary.active:focus,\n.open > .dropdown-toggle.btn-primary:focus,\n.btn-primary:active.focus,\n.btn-primary.active.focus,\n.open > .dropdown-toggle.btn-primary.focus {\n color: #fff;\n background-color: #204d74;\n border-color: #122b40;\n}\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus {\n background-color: #337ab7;\n border-color: #2e6da4;\n}\n.btn-primary .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.btn-success {\n color: #fff;\n background-color: #5cb85c;\n border-color: #4cae4c;\n}\n.btn-success:focus,\n.btn-success.focus {\n color: #fff;\n background-color: #449d44;\n border-color: #255625;\n}\n.btn-success:hover {\n color: #fff;\n background-color: #449d44;\n border-color: #398439;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n color: #fff;\n background-color: #449d44;\n background-image: none;\n border-color: #398439;\n}\n.btn-success:active:hover,\n.btn-success.active:hover,\n.open > .dropdown-toggle.btn-success:hover,\n.btn-success:active:focus,\n.btn-success.active:focus,\n.open > .dropdown-toggle.btn-success:focus,\n.btn-success:active.focus,\n.btn-success.active.focus,\n.open > .dropdown-toggle.btn-success.focus {\n color: #fff;\n background-color: #398439;\n border-color: #255625;\n}\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus {\n background-color: #5cb85c;\n border-color: #4cae4c;\n}\n.btn-success .badge {\n color: #5cb85c;\n background-color: #fff;\n}\n.btn-info {\n color: #fff;\n background-color: #5bc0de;\n border-color: #46b8da;\n}\n.btn-info:focus,\n.btn-info.focus {\n color: #fff;\n background-color: #31b0d5;\n border-color: #1b6d85;\n}\n.btn-info:hover {\n color: #fff;\n background-color: #31b0d5;\n border-color: #269abc;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n color: #fff;\n background-color: #31b0d5;\n background-image: none;\n border-color: #269abc;\n}\n.btn-info:active:hover,\n.btn-info.active:hover,\n.open > .dropdown-toggle.btn-info:hover,\n.btn-info:active:focus,\n.btn-info.active:focus,\n.open > .dropdown-toggle.btn-info:focus,\n.btn-info:active.focus,\n.btn-info.active.focus,\n.open > .dropdown-toggle.btn-info.focus {\n color: #fff;\n background-color: #269abc;\n border-color: #1b6d85;\n}\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus {\n background-color: #5bc0de;\n border-color: #46b8da;\n}\n.btn-info .badge {\n color: #5bc0de;\n background-color: #fff;\n}\n.btn-warning {\n color: #fff;\n background-color: #f0ad4e;\n border-color: #eea236;\n}\n.btn-warning:focus,\n.btn-warning.focus {\n color: #fff;\n background-color: #ec971f;\n border-color: #985f0d;\n}\n.btn-warning:hover {\n color: #fff;\n background-color: #ec971f;\n border-color: #d58512;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n color: #fff;\n background-color: #ec971f;\n background-image: none;\n border-color: #d58512;\n}\n.btn-warning:active:hover,\n.btn-warning.active:hover,\n.open > .dropdown-toggle.btn-warning:hover,\n.btn-warning:active:focus,\n.btn-warning.active:focus,\n.open > .dropdown-toggle.btn-warning:focus,\n.btn-warning:active.focus,\n.btn-warning.active.focus,\n.open > .dropdown-toggle.btn-warning.focus {\n color: #fff;\n background-color: #d58512;\n border-color: #985f0d;\n}\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus {\n background-color: #f0ad4e;\n border-color: #eea236;\n}\n.btn-warning .badge {\n color: #f0ad4e;\n background-color: #fff;\n}\n.btn-danger {\n color: #fff;\n background-color: #d9534f;\n border-color: #d43f3a;\n}\n.btn-danger:focus,\n.btn-danger.focus {\n color: #fff;\n background-color: #c9302c;\n border-color: #761c19;\n}\n.btn-danger:hover {\n color: #fff;\n background-color: #c9302c;\n border-color: #ac2925;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n color: #fff;\n background-color: #c9302c;\n background-image: none;\n border-color: #ac2925;\n}\n.btn-danger:active:hover,\n.btn-danger.active:hover,\n.open > .dropdown-toggle.btn-danger:hover,\n.btn-danger:active:focus,\n.btn-danger.active:focus,\n.open > .dropdown-toggle.btn-danger:focus,\n.btn-danger:active.focus,\n.btn-danger.active.focus,\n.open > .dropdown-toggle.btn-danger.focus {\n color: #fff;\n background-color: #ac2925;\n border-color: #761c19;\n}\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus {\n background-color: #d9534f;\n border-color: #d43f3a;\n}\n.btn-danger .badge {\n color: #d9534f;\n background-color: #fff;\n}\n.btn-link {\n font-weight: 400;\n color: #337ab7;\n border-radius: 0;\n}\n.btn-link,\n.btn-link:active,\n.btn-link.active,\n.btn-link[disabled],\nfieldset[disabled] .btn-link {\n background-color: transparent;\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn-link,\n.btn-link:hover,\n.btn-link:focus,\n.btn-link:active {\n border-color: transparent;\n}\n.btn-link:hover,\n.btn-link:focus {\n color: #23527c;\n text-decoration: underline;\n background-color: transparent;\n}\n.btn-link[disabled]:hover,\nfieldset[disabled] .btn-link:hover,\n.btn-link[disabled]:focus,\nfieldset[disabled] .btn-link:focus {\n color: #777777;\n text-decoration: none;\n}\n.btn-lg,\n.btn-group-lg > .btn {\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\n.btn-sm,\n.btn-group-sm > .btn {\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.btn-xs,\n.btn-group-xs > .btn {\n padding: 1px 5px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\n.btn-block {\n display: block;\n width: 100%;\n}\n.btn-block + .btn-block {\n margin-top: 5px;\n}\ninput[type=\"submit\"].btn-block,\ninput[type=\"reset\"].btn-block,\ninput[type=\"button\"].btn-block {\n width: 100%;\n}\n.fade {\n opacity: 0;\n -webkit-transition: opacity 0.15s linear;\n -o-transition: opacity 0.15s linear;\n transition: opacity 0.15s linear;\n}\n.fade.in {\n opacity: 1;\n}\n.collapse {\n display: none;\n}\n.collapse.in {\n display: block;\n}\ntr.collapse.in {\n display: table-row;\n}\ntbody.collapse.in {\n display: table-row-group;\n}\n.collapsing {\n position: relative;\n height: 0;\n overflow: hidden;\n -webkit-transition-property: height, visibility;\n transition-property: height, visibility;\n -webkit-transition-duration: 0.35s;\n transition-duration: 0.35s;\n -webkit-transition-timing-function: ease;\n transition-timing-function: ease;\n}\n.caret {\n display: inline-block;\n width: 0;\n height: 0;\n margin-left: 2px;\n vertical-align: middle;\n border-top: 4px dashed;\n border-top: 4px solid \\9;\n border-right: 4px solid transparent;\n border-left: 4px solid transparent;\n}\n.dropup,\n.dropdown {\n position: relative;\n}\n.dropdown-toggle:focus {\n outline: 0;\n}\n.dropdown-menu {\n position: absolute;\n top: 100%;\n left: 0;\n z-index: 1000;\n display: none;\n float: left;\n min-width: 160px;\n padding: 5px 0;\n margin: 2px 0 0;\n font-size: 14px;\n text-align: left;\n list-style: none;\n background-color: #fff;\n background-clip: padding-box;\n border: 1px solid #ccc;\n border: 1px solid rgba(0, 0, 0, 0.15);\n border-radius: 4px;\n -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n}\n.dropdown-menu.pull-right {\n right: 0;\n left: auto;\n}\n.dropdown-menu .divider {\n height: 1px;\n margin: 9px 0;\n overflow: hidden;\n background-color: #e5e5e5;\n}\n.dropdown-menu > li > a {\n display: block;\n padding: 3px 20px;\n clear: both;\n font-weight: 400;\n line-height: 1.42857143;\n color: #333333;\n white-space: nowrap;\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n color: #262626;\n text-decoration: none;\n background-color: #f5f5f5;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n color: #fff;\n text-decoration: none;\n background-color: #337ab7;\n outline: 0;\n}\n.dropdown-menu > .disabled > a,\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n color: #777777;\n}\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n text-decoration: none;\n cursor: not-allowed;\n background-color: transparent;\n background-image: none;\n filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n}\n.open > .dropdown-menu {\n display: block;\n}\n.open > a {\n outline: 0;\n}\n.dropdown-menu-right {\n right: 0;\n left: auto;\n}\n.dropdown-menu-left {\n right: auto;\n left: 0;\n}\n.dropdown-header {\n display: block;\n padding: 3px 20px;\n font-size: 12px;\n line-height: 1.42857143;\n color: #777777;\n white-space: nowrap;\n}\n.dropdown-backdrop {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 990;\n}\n.pull-right > .dropdown-menu {\n right: 0;\n left: auto;\n}\n.dropup .caret,\n.navbar-fixed-bottom .dropdown .caret {\n content: \"\";\n border-top: 0;\n border-bottom: 4px dashed;\n border-bottom: 4px solid \\9;\n}\n.dropup .dropdown-menu,\n.navbar-fixed-bottom .dropdown .dropdown-menu {\n top: auto;\n bottom: 100%;\n margin-bottom: 2px;\n}\n@media (min-width: 768px) {\n .navbar-right .dropdown-menu {\n right: 0;\n left: auto;\n }\n .navbar-right .dropdown-menu-left {\n right: auto;\n left: 0;\n }\n}\n.btn-group,\n.btn-group-vertical {\n position: relative;\n display: inline-block;\n vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n position: relative;\n float: left;\n}\n.btn-group > .btn:hover,\n.btn-group-vertical > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group-vertical > .btn:focus,\n.btn-group > .btn:active,\n.btn-group-vertical > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn.active {\n z-index: 2;\n}\n.btn-group .btn + .btn,\n.btn-group .btn + .btn-group,\n.btn-group .btn-group + .btn,\n.btn-group .btn-group + .btn-group {\n margin-left: -1px;\n}\n.btn-toolbar {\n margin-left: -5px;\n}\n.btn-toolbar .btn,\n.btn-toolbar .btn-group,\n.btn-toolbar .input-group {\n float: left;\n}\n.btn-toolbar > .btn,\n.btn-toolbar > .btn-group,\n.btn-toolbar > .input-group {\n margin-left: 5px;\n}\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n border-radius: 0;\n}\n.btn-group > .btn:first-child {\n margin-left: 0;\n}\n.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group > .btn-group {\n float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n outline: 0;\n}\n.btn-group > .btn + .dropdown-toggle {\n padding-right: 8px;\n padding-left: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n padding-right: 12px;\n padding-left: 12px;\n}\n.btn-group.open .dropdown-toggle {\n -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-group.open .dropdown-toggle.btn-link {\n -webkit-box-shadow: none;\n box-shadow: none;\n}\n.btn .caret {\n margin-left: 0;\n}\n.btn-lg .caret {\n border-width: 5px 5px 0;\n border-bottom-width: 0;\n}\n.dropup .btn-lg .caret {\n border-width: 0 5px 5px;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group,\n.btn-group-vertical > .btn-group > .btn {\n display: block;\n float: none;\n width: 100%;\n max-width: 100%;\n}\n.btn-group-vertical > .btn-group > .btn {\n float: none;\n}\n.btn-group-vertical > .btn + .btn,\n.btn-group-vertical > .btn + .btn-group,\n.btn-group-vertical > .btn-group + .btn,\n.btn-group-vertical > .btn-group + .btn-group {\n margin-top: -1px;\n margin-left: 0;\n}\n.btn-group-vertical > .btn:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.btn-group-vertical > .btn:first-child:not(:last-child) {\n border-top-left-radius: 4px;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn:last-child:not(:first-child) {\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n border-bottom-right-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.btn-group-justified {\n display: table;\n width: 100%;\n table-layout: fixed;\n border-collapse: separate;\n}\n.btn-group-justified > .btn,\n.btn-group-justified > .btn-group {\n display: table-cell;\n float: none;\n width: 1%;\n}\n.btn-group-justified > .btn-group .btn {\n width: 100%;\n}\n.btn-group-justified > .btn-group .dropdown-menu {\n left: auto;\n}\n[data-toggle=\"buttons\"] > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn input[type=\"checkbox\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"checkbox\"] {\n position: absolute;\n clip: rect(0, 0, 0, 0);\n pointer-events: none;\n}\n.input-group {\n position: relative;\n display: table;\n border-collapse: separate;\n}\n.input-group[class*=\"col-\"] {\n float: none;\n padding-right: 0;\n padding-left: 0;\n}\n.input-group .form-control {\n position: relative;\n z-index: 2;\n float: left;\n width: 100%;\n margin-bottom: 0;\n}\n.input-group .form-control:focus {\n z-index: 3;\n}\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n height: 46px;\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n border-radius: 6px;\n}\nselect.input-group-lg > .form-control,\nselect.input-group-lg > .input-group-addon,\nselect.input-group-lg > .input-group-btn > .btn {\n height: 46px;\n line-height: 46px;\n}\ntextarea.input-group-lg > .form-control,\ntextarea.input-group-lg > .input-group-addon,\ntextarea.input-group-lg > .input-group-btn > .btn,\nselect[multiple].input-group-lg > .form-control,\nselect[multiple].input-group-lg > .input-group-addon,\nselect[multiple].input-group-lg > .input-group-btn > .btn {\n height: auto;\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n height: 30px;\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n border-radius: 3px;\n}\nselect.input-group-sm > .form-control,\nselect.input-group-sm > .input-group-addon,\nselect.input-group-sm > .input-group-btn > .btn {\n height: 30px;\n line-height: 30px;\n}\ntextarea.input-group-sm > .form-control,\ntextarea.input-group-sm > .input-group-addon,\ntextarea.input-group-sm > .input-group-btn > .btn,\nselect[multiple].input-group-sm > .form-control,\nselect[multiple].input-group-sm > .input-group-addon,\nselect[multiple].input-group-sm > .input-group-btn > .btn {\n height: auto;\n}\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n display: table-cell;\n}\n.input-group-addon:not(:first-child):not(:last-child),\n.input-group-btn:not(:first-child):not(:last-child),\n.input-group .form-control:not(:first-child):not(:last-child) {\n border-radius: 0;\n}\n.input-group-addon,\n.input-group-btn {\n width: 1%;\n white-space: nowrap;\n vertical-align: middle;\n}\n.input-group-addon {\n padding: 6px 12px;\n font-size: 14px;\n font-weight: 400;\n line-height: 1;\n color: #555555;\n text-align: center;\n background-color: #eeeeee;\n border: 1px solid #ccc;\n border-radius: 4px;\n}\n.input-group-addon.input-sm {\n padding: 5px 10px;\n font-size: 12px;\n border-radius: 3px;\n}\n.input-group-addon.input-lg {\n padding: 10px 16px;\n font-size: 18px;\n border-radius: 6px;\n}\n.input-group-addon input[type=\"radio\"],\n.input-group-addon input[type=\"checkbox\"] {\n margin-top: 0;\n}\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n border-top-right-radius: 0;\n border-bottom-right-radius: 0;\n}\n.input-group-addon:first-child {\n border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n border-top-left-radius: 0;\n border-bottom-left-radius: 0;\n}\n.input-group-addon:last-child {\n border-left: 0;\n}\n.input-group-btn {\n position: relative;\n font-size: 0;\n white-space: nowrap;\n}\n.input-group-btn > .btn {\n position: relative;\n}\n.input-group-btn > .btn + .btn {\n margin-left: -1px;\n}\n.input-group-btn > .btn:hover,\n.input-group-btn > .btn:focus,\n.input-group-btn > .btn:active {\n z-index: 2;\n}\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group {\n margin-right: -1px;\n}\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group {\n z-index: 2;\n margin-left: -1px;\n}\n.nav {\n padding-left: 0;\n margin-bottom: 0;\n list-style: none;\n}\n.nav > li {\n position: relative;\n display: block;\n}\n.nav > li > a {\n position: relative;\n display: block;\n padding: 10px 15px;\n}\n.nav > li > a:hover,\n.nav > li > a:focus {\n text-decoration: none;\n background-color: #eeeeee;\n}\n.nav > li.disabled > a {\n color: #777777;\n}\n.nav > li.disabled > a:hover,\n.nav > li.disabled > a:focus {\n color: #777777;\n text-decoration: none;\n cursor: not-allowed;\n background-color: transparent;\n}\n.nav .open > a,\n.nav .open > a:hover,\n.nav .open > a:focus {\n background-color: #eeeeee;\n border-color: #337ab7;\n}\n.nav .nav-divider {\n height: 1px;\n margin: 9px 0;\n overflow: hidden;\n background-color: #e5e5e5;\n}\n.nav > li > a > img {\n max-width: none;\n}\n.nav-tabs {\n border-bottom: 1px solid #ddd;\n}\n.nav-tabs > li {\n float: left;\n margin-bottom: -1px;\n}\n.nav-tabs > li > a {\n margin-right: 2px;\n line-height: 1.42857143;\n border: 1px solid transparent;\n border-radius: 4px 4px 0 0;\n}\n.nav-tabs > li > a:hover {\n border-color: #eeeeee #eeeeee #ddd;\n}\n.nav-tabs > li.active > a,\n.nav-tabs > li.active > a:hover,\n.nav-tabs > li.active > a:focus {\n color: #555555;\n cursor: default;\n background-color: #fff;\n border: 1px solid #ddd;\n border-bottom-color: transparent;\n}\n.nav-tabs.nav-justified {\n width: 100%;\n border-bottom: 0;\n}\n.nav-tabs.nav-justified > li {\n float: none;\n}\n.nav-tabs.nav-justified > li > a {\n margin-bottom: 5px;\n text-align: center;\n}\n.nav-tabs.nav-justified > .dropdown .dropdown-menu {\n top: auto;\n left: auto;\n}\n@media (min-width: 768px) {\n .nav-tabs.nav-justified > li {\n display: table-cell;\n width: 1%;\n }\n .nav-tabs.nav-justified > li > a {\n margin-bottom: 0;\n }\n}\n.nav-tabs.nav-justified > li > a {\n margin-right: 0;\n border-radius: 4px;\n}\n.nav-tabs.nav-justified > .active > a,\n.nav-tabs.nav-justified > .active > a:hover,\n.nav-tabs.nav-justified > .active > a:focus {\n border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n .nav-tabs.nav-justified > li > a {\n border-bottom: 1px solid #ddd;\n border-radius: 4px 4px 0 0;\n }\n .nav-tabs.nav-justified > .active > a,\n .nav-tabs.nav-justified > .active > a:hover,\n .nav-tabs.nav-justified > .active > a:focus {\n border-bottom-color: #fff;\n }\n}\n.nav-pills > li {\n float: left;\n}\n.nav-pills > li > a {\n border-radius: 4px;\n}\n.nav-pills > li + li {\n margin-left: 2px;\n}\n.nav-pills > li.active > a,\n.nav-pills > li.active > a:hover,\n.nav-pills > li.active > a:focus {\n color: #fff;\n background-color: #337ab7;\n}\n.nav-stacked > li {\n float: none;\n}\n.nav-stacked > li + li {\n margin-top: 2px;\n margin-left: 0;\n}\n.nav-justified {\n width: 100%;\n}\n.nav-justified > li {\n float: none;\n}\n.nav-justified > li > a {\n margin-bottom: 5px;\n text-align: center;\n}\n.nav-justified > .dropdown .dropdown-menu {\n top: auto;\n left: auto;\n}\n@media (min-width: 768px) {\n .nav-justified > li {\n display: table-cell;\n width: 1%;\n }\n .nav-justified > li > a {\n margin-bottom: 0;\n }\n}\n.nav-tabs-justified {\n border-bottom: 0;\n}\n.nav-tabs-justified > li > a {\n margin-right: 0;\n border-radius: 4px;\n}\n.nav-tabs-justified > .active > a,\n.nav-tabs-justified > .active > a:hover,\n.nav-tabs-justified > .active > a:focus {\n border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n .nav-tabs-justified > li > a {\n border-bottom: 1px solid #ddd;\n border-radius: 4px 4px 0 0;\n }\n .nav-tabs-justified > .active > a,\n .nav-tabs-justified > .active > a:hover,\n .nav-tabs-justified > .active > a:focus {\n border-bottom-color: #fff;\n }\n}\n.tab-content > .tab-pane {\n display: none;\n}\n.tab-content > .active {\n display: block;\n}\n.nav-tabs .dropdown-menu {\n margin-top: -1px;\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.navbar {\n position: relative;\n min-height: 50px;\n margin-bottom: 20px;\n border: 1px solid transparent;\n}\n@media (min-width: 768px) {\n .navbar {\n border-radius: 4px;\n }\n}\n@media (min-width: 768px) {\n .navbar-header {\n float: left;\n }\n}\n.navbar-collapse {\n padding-right: 15px;\n padding-left: 15px;\n overflow-x: visible;\n border-top: 1px solid transparent;\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);\n -webkit-overflow-scrolling: touch;\n}\n.navbar-collapse.in {\n overflow-y: auto;\n}\n@media (min-width: 768px) {\n .navbar-collapse {\n width: auto;\n border-top: 0;\n box-shadow: none;\n }\n .navbar-collapse.collapse {\n display: block !important;\n height: auto !important;\n padding-bottom: 0;\n overflow: visible !important;\n }\n .navbar-collapse.in {\n overflow-y: visible;\n }\n .navbar-fixed-top .navbar-collapse,\n .navbar-static-top .navbar-collapse,\n .navbar-fixed-bottom .navbar-collapse {\n padding-right: 0;\n padding-left: 0;\n }\n}\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n position: fixed;\n right: 0;\n left: 0;\n z-index: 1030;\n}\n.navbar-fixed-top .navbar-collapse,\n.navbar-fixed-bottom .navbar-collapse {\n max-height: 340px;\n}\n@media (max-device-width: 480px) and (orientation: landscape) {\n .navbar-fixed-top .navbar-collapse,\n .navbar-fixed-bottom .navbar-collapse {\n max-height: 200px;\n }\n}\n@media (min-width: 768px) {\n .navbar-fixed-top,\n .navbar-fixed-bottom {\n border-radius: 0;\n }\n}\n.navbar-fixed-top {\n top: 0;\n border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n bottom: 0;\n margin-bottom: 0;\n border-width: 1px 0 0;\n}\n.container > .navbar-header,\n.container-fluid > .navbar-header,\n.container > .navbar-collapse,\n.container-fluid > .navbar-collapse {\n margin-right: -15px;\n margin-left: -15px;\n}\n@media (min-width: 768px) {\n .container > .navbar-header,\n .container-fluid > .navbar-header,\n .container > .navbar-collapse,\n .container-fluid > .navbar-collapse {\n margin-right: 0;\n margin-left: 0;\n }\n}\n.navbar-static-top {\n z-index: 1000;\n border-width: 0 0 1px;\n}\n@media (min-width: 768px) {\n .navbar-static-top {\n border-radius: 0;\n }\n}\n.navbar-brand {\n float: left;\n height: 50px;\n padding: 15px 15px;\n font-size: 18px;\n line-height: 20px;\n}\n.navbar-brand:hover,\n.navbar-brand:focus {\n text-decoration: none;\n}\n.navbar-brand > img {\n display: block;\n}\n@media (min-width: 768px) {\n .navbar > .container .navbar-brand,\n .navbar > .container-fluid .navbar-brand {\n margin-left: -15px;\n }\n}\n.navbar-toggle {\n position: relative;\n float: right;\n padding: 9px 10px;\n margin-right: 15px;\n margin-top: 8px;\n margin-bottom: 8px;\n background-color: transparent;\n background-image: none;\n border: 1px solid transparent;\n border-radius: 4px;\n}\n.navbar-toggle:focus {\n outline: 0;\n}\n.navbar-toggle .icon-bar {\n display: block;\n width: 22px;\n height: 2px;\n border-radius: 1px;\n}\n.navbar-toggle .icon-bar + .icon-bar {\n margin-top: 4px;\n}\n@media (min-width: 768px) {\n .navbar-toggle {\n display: none;\n }\n}\n.navbar-nav {\n margin: 7.5px -15px;\n}\n.navbar-nav > li > a {\n padding-top: 10px;\n padding-bottom: 10px;\n line-height: 20px;\n}\n@media (max-width: 767px) {\n .navbar-nav .open .dropdown-menu {\n position: static;\n float: none;\n width: auto;\n margin-top: 0;\n background-color: transparent;\n border: 0;\n box-shadow: none;\n }\n .navbar-nav .open .dropdown-menu > li > a,\n .navbar-nav .open .dropdown-menu .dropdown-header {\n padding: 5px 15px 5px 25px;\n }\n .navbar-nav .open .dropdown-menu > li > a {\n line-height: 20px;\n }\n .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-nav .open .dropdown-menu > li > a:focus {\n background-image: none;\n }\n}\n@media (min-width: 768px) {\n .navbar-nav {\n float: left;\n margin: 0;\n }\n .navbar-nav > li {\n float: left;\n }\n .navbar-nav > li > a {\n padding-top: 15px;\n padding-bottom: 15px;\n }\n}\n.navbar-form {\n padding: 10px 15px;\n margin-right: -15px;\n margin-left: -15px;\n border-top: 1px solid transparent;\n border-bottom: 1px solid transparent;\n -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n margin-top: 8px;\n margin-bottom: 8px;\n}\n@media (min-width: 768px) {\n .navbar-form .form-group {\n display: inline-block;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .form-control {\n display: inline-block;\n width: auto;\n vertical-align: middle;\n }\n .navbar-form .form-control-static {\n display: inline-block;\n }\n .navbar-form .input-group {\n display: inline-table;\n vertical-align: middle;\n }\n .navbar-form .input-group .input-group-addon,\n .navbar-form .input-group .input-group-btn,\n .navbar-form .input-group .form-control {\n width: auto;\n }\n .navbar-form .input-group > .form-control {\n width: 100%;\n }\n .navbar-form .control-label {\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .radio,\n .navbar-form .checkbox {\n display: inline-block;\n margin-top: 0;\n margin-bottom: 0;\n vertical-align: middle;\n }\n .navbar-form .radio label,\n .navbar-form .checkbox label {\n padding-left: 0;\n }\n .navbar-form .radio input[type=\"radio\"],\n .navbar-form .checkbox input[type=\"checkbox\"] {\n position: relative;\n margin-left: 0;\n }\n .navbar-form .has-feedback .form-control-feedback {\n top: 0;\n }\n}\n@media (max-width: 767px) {\n .navbar-form .form-group {\n margin-bottom: 5px;\n }\n .navbar-form .form-group:last-child {\n margin-bottom: 0;\n }\n}\n@media (min-width: 768px) {\n .navbar-form {\n width: auto;\n padding-top: 0;\n padding-bottom: 0;\n margin-right: 0;\n margin-left: 0;\n border: 0;\n -webkit-box-shadow: none;\n box-shadow: none;\n }\n}\n.navbar-nav > li > .dropdown-menu {\n margin-top: 0;\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n margin-bottom: 0;\n border-top-left-radius: 4px;\n border-top-right-radius: 4px;\n border-bottom-right-radius: 0;\n border-bottom-left-radius: 0;\n}\n.navbar-btn {\n margin-top: 8px;\n margin-bottom: 8px;\n}\n.navbar-btn.btn-sm {\n margin-top: 10px;\n margin-bottom: 10px;\n}\n.navbar-btn.btn-xs {\n margin-top: 14px;\n margin-bottom: 14px;\n}\n.navbar-text {\n margin-top: 15px;\n margin-bottom: 15px;\n}\n@media (min-width: 768px) {\n .navbar-text {\n float: left;\n margin-right: 15px;\n margin-left: 15px;\n }\n}\n@media (min-width: 768px) {\n .navbar-left {\n float: left !important;\n }\n .navbar-right {\n float: right !important;\n margin-right: -15px;\n }\n .navbar-right ~ .navbar-right {\n margin-right: 0;\n }\n}\n.navbar-default {\n background-color: #f8f8f8;\n border-color: #e7e7e7;\n}\n.navbar-default .navbar-brand {\n color: #777;\n}\n.navbar-default .navbar-brand:hover,\n.navbar-default .navbar-brand:focus {\n color: #5e5e5e;\n background-color: transparent;\n}\n.navbar-default .navbar-text {\n color: #777;\n}\n.navbar-default .navbar-nav > li > a {\n color: #777;\n}\n.navbar-default .navbar-nav > li > a:hover,\n.navbar-default .navbar-nav > li > a:focus {\n color: #333;\n background-color: transparent;\n}\n.navbar-default .navbar-nav > .active > a,\n.navbar-default .navbar-nav > .active > a:hover,\n.navbar-default .navbar-nav > .active > a:focus {\n color: #555;\n background-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .disabled > a,\n.navbar-default .navbar-nav > .disabled > a:hover,\n.navbar-default .navbar-nav > .disabled > a:focus {\n color: #ccc;\n background-color: transparent;\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .open > a:hover,\n.navbar-default .navbar-nav > .open > a:focus {\n color: #555;\n background-color: #e7e7e7;\n}\n@media (max-width: 767px) {\n .navbar-default .navbar-nav .open .dropdown-menu > li > a {\n color: #777;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {\n color: #333;\n background-color: transparent;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a,\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #555;\n background-color: #e7e7e7;\n }\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n color: #ccc;\n background-color: transparent;\n }\n}\n.navbar-default .navbar-toggle {\n border-color: #ddd;\n}\n.navbar-default .navbar-toggle:hover,\n.navbar-default .navbar-toggle:focus {\n background-color: #ddd;\n}\n.navbar-default .navbar-toggle .icon-bar {\n background-color: #888;\n}\n.navbar-default .navbar-collapse,\n.navbar-default .navbar-form {\n border-color: #e7e7e7;\n}\n.navbar-default .navbar-link {\n color: #777;\n}\n.navbar-default .navbar-link:hover {\n color: #333;\n}\n.navbar-default .btn-link {\n color: #777;\n}\n.navbar-default .btn-link:hover,\n.navbar-default .btn-link:focus {\n color: #333;\n}\n.navbar-default .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-default .btn-link:hover,\n.navbar-default .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-default .btn-link:focus {\n color: #ccc;\n}\n.navbar-inverse {\n background-color: #222;\n border-color: #080808;\n}\n.navbar-inverse .navbar-brand {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-brand:hover,\n.navbar-inverse .navbar-brand:focus {\n color: #fff;\n background-color: transparent;\n}\n.navbar-inverse .navbar-text {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a:hover,\n.navbar-inverse .navbar-nav > li > a:focus {\n color: #fff;\n background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .active > a,\n.navbar-inverse .navbar-nav > .active > a:hover,\n.navbar-inverse .navbar-nav > .active > a:focus {\n color: #fff;\n background-color: #080808;\n}\n.navbar-inverse .navbar-nav > .disabled > a,\n.navbar-inverse .navbar-nav > .disabled > a:hover,\n.navbar-inverse .navbar-nav > .disabled > a:focus {\n color: #444;\n background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .open > a:hover,\n.navbar-inverse .navbar-nav > .open > a:focus {\n color: #fff;\n background-color: #080808;\n}\n@media (max-width: 767px) {\n .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {\n border-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu .divider {\n background-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {\n color: #9d9d9d;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {\n color: #fff;\n background-color: transparent;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {\n color: #fff;\n background-color: #080808;\n }\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n color: #444;\n background-color: transparent;\n }\n}\n.navbar-inverse .navbar-toggle {\n border-color: #333;\n}\n.navbar-inverse .navbar-toggle:hover,\n.navbar-inverse .navbar-toggle:focus {\n background-color: #333;\n}\n.navbar-inverse .navbar-toggle .icon-bar {\n background-color: #fff;\n}\n.navbar-inverse .navbar-collapse,\n.navbar-inverse .navbar-form {\n border-color: #101010;\n}\n.navbar-inverse .navbar-link {\n color: #9d9d9d;\n}\n.navbar-inverse .navbar-link:hover {\n color: #fff;\n}\n.navbar-inverse .btn-link {\n color: #9d9d9d;\n}\n.navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link:focus {\n color: #fff;\n}\n.navbar-inverse .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-inverse .btn-link:focus {\n color: #444;\n}\n.breadcrumb {\n padding: 8px 15px;\n margin-bottom: 20px;\n list-style: none;\n background-color: #f5f5f5;\n border-radius: 4px;\n}\n.breadcrumb > li {\n display: inline-block;\n}\n.breadcrumb > li + li:before {\n padding: 0 5px;\n color: #ccc;\n content: \"/\\00a0\";\n}\n.breadcrumb > .active {\n color: #777777;\n}\n.pagination {\n display: inline-block;\n padding-left: 0;\n margin: 20px 0;\n border-radius: 4px;\n}\n.pagination > li {\n display: inline;\n}\n.pagination > li > a,\n.pagination > li > span {\n position: relative;\n float: left;\n padding: 6px 12px;\n margin-left: -1px;\n line-height: 1.42857143;\n color: #337ab7;\n text-decoration: none;\n background-color: #fff;\n border: 1px solid #ddd;\n}\n.pagination > li > a:hover,\n.pagination > li > span:hover,\n.pagination > li > a:focus,\n.pagination > li > span:focus {\n z-index: 2;\n color: #23527c;\n background-color: #eeeeee;\n border-color: #ddd;\n}\n.pagination > li:first-child > a,\n.pagination > li:first-child > span {\n margin-left: 0;\n border-top-left-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.pagination > li:last-child > a,\n.pagination > li:last-child > span {\n border-top-right-radius: 4px;\n border-bottom-right-radius: 4px;\n}\n.pagination > .active > a,\n.pagination > .active > span,\n.pagination > .active > a:hover,\n.pagination > .active > span:hover,\n.pagination > .active > a:focus,\n.pagination > .active > span:focus {\n z-index: 3;\n color: #fff;\n cursor: default;\n background-color: #337ab7;\n border-color: #337ab7;\n}\n.pagination > .disabled > span,\n.pagination > .disabled > span:hover,\n.pagination > .disabled > span:focus,\n.pagination > .disabled > a,\n.pagination > .disabled > a:hover,\n.pagination > .disabled > a:focus {\n color: #777777;\n cursor: not-allowed;\n background-color: #fff;\n border-color: #ddd;\n}\n.pagination-lg > li > a,\n.pagination-lg > li > span {\n padding: 10px 16px;\n font-size: 18px;\n line-height: 1.3333333;\n}\n.pagination-lg > li:first-child > a,\n.pagination-lg > li:first-child > span {\n border-top-left-radius: 6px;\n border-bottom-left-radius: 6px;\n}\n.pagination-lg > li:last-child > a,\n.pagination-lg > li:last-child > span {\n border-top-right-radius: 6px;\n border-bottom-right-radius: 6px;\n}\n.pagination-sm > li > a,\n.pagination-sm > li > span {\n padding: 5px 10px;\n font-size: 12px;\n line-height: 1.5;\n}\n.pagination-sm > li:first-child > a,\n.pagination-sm > li:first-child > span {\n border-top-left-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.pagination-sm > li:last-child > a,\n.pagination-sm > li:last-child > span {\n border-top-right-radius: 3px;\n border-bottom-right-radius: 3px;\n}\n.pager {\n padding-left: 0;\n margin: 20px 0;\n text-align: center;\n list-style: none;\n}\n.pager li {\n display: inline;\n}\n.pager li > a,\n.pager li > span {\n display: inline-block;\n padding: 5px 14px;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 15px;\n}\n.pager li > a:hover,\n.pager li > a:focus {\n text-decoration: none;\n background-color: #eeeeee;\n}\n.pager .next > a,\n.pager .next > span {\n float: right;\n}\n.pager .previous > a,\n.pager .previous > span {\n float: left;\n}\n.pager .disabled > a,\n.pager .disabled > a:hover,\n.pager .disabled > a:focus,\n.pager .disabled > span {\n color: #777777;\n cursor: not-allowed;\n background-color: #fff;\n}\n.label {\n display: inline;\n padding: 0.2em 0.6em 0.3em;\n font-size: 75%;\n font-weight: 700;\n line-height: 1;\n color: #fff;\n text-align: center;\n white-space: nowrap;\n vertical-align: baseline;\n border-radius: 0.25em;\n}\na.label:hover,\na.label:focus {\n color: #fff;\n text-decoration: none;\n cursor: pointer;\n}\n.label:empty {\n display: none;\n}\n.btn .label {\n position: relative;\n top: -1px;\n}\n.label-default {\n background-color: #777777;\n}\n.label-default[href]:hover,\n.label-default[href]:focus {\n background-color: #5e5e5e;\n}\n.label-primary {\n background-color: #337ab7;\n}\n.label-primary[href]:hover,\n.label-primary[href]:focus {\n background-color: #286090;\n}\n.label-success {\n background-color: #5cb85c;\n}\n.label-success[href]:hover,\n.label-success[href]:focus {\n background-color: #449d44;\n}\n.label-info {\n background-color: #5bc0de;\n}\n.label-info[href]:hover,\n.label-info[href]:focus {\n background-color: #31b0d5;\n}\n.label-warning {\n background-color: #f0ad4e;\n}\n.label-warning[href]:hover,\n.label-warning[href]:focus {\n background-color: #ec971f;\n}\n.label-danger {\n background-color: #d9534f;\n}\n.label-danger[href]:hover,\n.label-danger[href]:focus {\n background-color: #c9302c;\n}\n.badge {\n display: inline-block;\n min-width: 10px;\n padding: 3px 7px;\n font-size: 12px;\n font-weight: bold;\n line-height: 1;\n color: #fff;\n text-align: center;\n white-space: nowrap;\n vertical-align: middle;\n background-color: #777777;\n border-radius: 10px;\n}\n.badge:empty {\n display: none;\n}\n.btn .badge {\n position: relative;\n top: -1px;\n}\n.btn-xs .badge,\n.btn-group-xs > .btn .badge {\n top: 0;\n padding: 1px 5px;\n}\na.badge:hover,\na.badge:focus {\n color: #fff;\n text-decoration: none;\n cursor: pointer;\n}\n.list-group-item.active > .badge,\n.nav-pills > .active > a > .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.list-group-item > .badge {\n float: right;\n}\n.list-group-item > .badge + .badge {\n margin-right: 5px;\n}\n.nav-pills > li > a > .badge {\n margin-left: 3px;\n}\n.jumbotron {\n padding-top: 30px;\n padding-bottom: 30px;\n margin-bottom: 30px;\n color: inherit;\n background-color: #eeeeee;\n}\n.jumbotron h1,\n.jumbotron .h1 {\n color: inherit;\n}\n.jumbotron p {\n margin-bottom: 15px;\n font-size: 21px;\n font-weight: 200;\n}\n.jumbotron > hr {\n border-top-color: #d5d5d5;\n}\n.container .jumbotron,\n.container-fluid .jumbotron {\n padding-right: 15px;\n padding-left: 15px;\n border-radius: 6px;\n}\n.jumbotron .container {\n max-width: 100%;\n}\n@media screen and (min-width: 768px) {\n .jumbotron {\n padding-top: 48px;\n padding-bottom: 48px;\n }\n .container .jumbotron,\n .container-fluid .jumbotron {\n padding-right: 60px;\n padding-left: 60px;\n }\n .jumbotron h1,\n .jumbotron .h1 {\n font-size: 63px;\n }\n}\n.thumbnail {\n display: block;\n padding: 4px;\n margin-bottom: 20px;\n line-height: 1.42857143;\n background-color: #fff;\n border: 1px solid #ddd;\n border-radius: 4px;\n -webkit-transition: border 0.2s ease-in-out;\n -o-transition: border 0.2s ease-in-out;\n transition: border 0.2s ease-in-out;\n}\n.thumbnail > img,\n.thumbnail a > img {\n margin-right: auto;\n margin-left: auto;\n}\na.thumbnail:hover,\na.thumbnail:focus,\na.thumbnail.active {\n border-color: #337ab7;\n}\n.thumbnail .caption {\n padding: 9px;\n color: #333333;\n}\n.alert {\n padding: 15px;\n margin-bottom: 20px;\n border: 1px solid transparent;\n border-radius: 4px;\n}\n.alert h4 {\n margin-top: 0;\n color: inherit;\n}\n.alert .alert-link {\n font-weight: bold;\n}\n.alert > p,\n.alert > ul {\n margin-bottom: 0;\n}\n.alert > p + p {\n margin-top: 5px;\n}\n.alert-dismissable,\n.alert-dismissible {\n padding-right: 35px;\n}\n.alert-dismissable .close,\n.alert-dismissible .close {\n position: relative;\n top: -2px;\n right: -21px;\n color: inherit;\n}\n.alert-success {\n color: #3c763d;\n background-color: #dff0d8;\n border-color: #d6e9c6;\n}\n.alert-success hr {\n border-top-color: #c9e2b3;\n}\n.alert-success .alert-link {\n color: #2b542c;\n}\n.alert-info {\n color: #31708f;\n background-color: #d9edf7;\n border-color: #bce8f1;\n}\n.alert-info hr {\n border-top-color: #a6e1ec;\n}\n.alert-info .alert-link {\n color: #245269;\n}\n.alert-warning {\n color: #8a6d3b;\n background-color: #fcf8e3;\n border-color: #faebcc;\n}\n.alert-warning hr {\n border-top-color: #f7e1b5;\n}\n.alert-warning .alert-link {\n color: #66512c;\n}\n.alert-danger {\n color: #a94442;\n background-color: #f2dede;\n border-color: #ebccd1;\n}\n.alert-danger hr {\n border-top-color: #e4b9c0;\n}\n.alert-danger .alert-link {\n color: #843534;\n}\n@-webkit-keyframes progress-bar-stripes {\n from {\n background-position: 40px 0;\n }\n to {\n background-position: 0 0;\n }\n}\n@keyframes progress-bar-stripes {\n from {\n background-position: 40px 0;\n }\n to {\n background-position: 0 0;\n }\n}\n.progress {\n height: 20px;\n margin-bottom: 20px;\n overflow: hidden;\n background-color: #f5f5f5;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n}\n.progress-bar {\n float: left;\n width: 0%;\n height: 100%;\n font-size: 12px;\n line-height: 20px;\n color: #fff;\n text-align: center;\n background-color: #337ab7;\n -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n -webkit-transition: width 0.6s ease;\n -o-transition: width 0.6s ease;\n transition: width 0.6s ease;\n}\n.progress-striped .progress-bar,\n.progress-bar-striped {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-size: 40px 40px;\n}\n.progress.active .progress-bar,\n.progress-bar.active {\n -webkit-animation: progress-bar-stripes 2s linear infinite;\n -o-animation: progress-bar-stripes 2s linear infinite;\n animation: progress-bar-stripes 2s linear infinite;\n}\n.progress-bar-success {\n background-color: #5cb85c;\n}\n.progress-striped .progress-bar-success {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-info {\n background-color: #5bc0de;\n}\n.progress-striped .progress-bar-info {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-warning {\n background-color: #f0ad4e;\n}\n.progress-striped .progress-bar-warning {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-danger {\n background-color: #d9534f;\n}\n.progress-striped .progress-bar-danger {\n background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.media {\n margin-top: 15px;\n}\n.media:first-child {\n margin-top: 0;\n}\n.media,\n.media-body {\n overflow: hidden;\n zoom: 1;\n}\n.media-body {\n width: 10000px;\n}\n.media-object {\n display: block;\n}\n.media-object.img-thumbnail {\n max-width: none;\n}\n.media-right,\n.media > .pull-right {\n padding-left: 10px;\n}\n.media-left,\n.media > .pull-left {\n padding-right: 10px;\n}\n.media-left,\n.media-right,\n.media-body {\n display: table-cell;\n vertical-align: top;\n}\n.media-middle {\n vertical-align: middle;\n}\n.media-bottom {\n vertical-align: bottom;\n}\n.media-heading {\n margin-top: 0;\n margin-bottom: 5px;\n}\n.media-list {\n padding-left: 0;\n list-style: none;\n}\n.list-group {\n padding-left: 0;\n margin-bottom: 20px;\n}\n.list-group-item {\n position: relative;\n display: block;\n padding: 10px 15px;\n margin-bottom: -1px;\n background-color: #fff;\n border: 1px solid #ddd;\n}\n.list-group-item:first-child {\n border-top-left-radius: 4px;\n border-top-right-radius: 4px;\n}\n.list-group-item:last-child {\n margin-bottom: 0;\n border-bottom-right-radius: 4px;\n border-bottom-left-radius: 4px;\n}\n.list-group-item.disabled,\n.list-group-item.disabled:hover,\n.list-group-item.disabled:focus {\n color: #777777;\n cursor: not-allowed;\n background-color: #eeeeee;\n}\n.list-group-item.disabled .list-group-item-heading,\n.list-group-item.disabled:hover .list-group-item-heading,\n.list-group-item.disabled:focus .list-group-item-heading {\n color: inherit;\n}\n.list-group-item.disabled .list-group-item-text,\n.list-group-item.disabled:hover .list-group-item-text,\n.list-group-item.disabled:focus .list-group-item-text {\n color: #777777;\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n z-index: 2;\n color: #fff;\n background-color: #337ab7;\n border-color: #337ab7;\n}\n.list-group-item.active .list-group-item-heading,\n.list-group-item.active:hover .list-group-item-heading,\n.list-group-item.active:focus .list-group-item-heading,\n.list-group-item.active .list-group-item-heading > small,\n.list-group-item.active:hover .list-group-item-heading > small,\n.list-group-item.active:focus .list-group-item-heading > small,\n.list-group-item.active .list-group-item-heading > .small,\n.list-group-item.active:hover .list-group-item-heading > .small,\n.list-group-item.active:focus .list-group-item-heading > .small {\n color: inherit;\n}\n.list-group-item.active .list-group-item-text,\n.list-group-item.active:hover .list-group-item-text,\n.list-group-item.active:focus .list-group-item-text {\n color: #c7ddef;\n}\na.list-group-item,\nbutton.list-group-item {\n color: #555;\n}\na.list-group-item .list-group-item-heading,\nbutton.list-group-item .list-group-item-heading {\n color: #333;\n}\na.list-group-item:hover,\nbutton.list-group-item:hover,\na.list-group-item:focus,\nbutton.list-group-item:focus {\n color: #555;\n text-decoration: none;\n background-color: #f5f5f5;\n}\nbutton.list-group-item {\n width: 100%;\n text-align: left;\n}\n.list-group-item-success {\n color: #3c763d;\n background-color: #dff0d8;\n}\na.list-group-item-success,\nbutton.list-group-item-success {\n color: #3c763d;\n}\na.list-group-item-success .list-group-item-heading,\nbutton.list-group-item-success .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-success:hover,\nbutton.list-group-item-success:hover,\na.list-group-item-success:focus,\nbutton.list-group-item-success:focus {\n color: #3c763d;\n background-color: #d0e9c6;\n}\na.list-group-item-success.active,\nbutton.list-group-item-success.active,\na.list-group-item-success.active:hover,\nbutton.list-group-item-success.active:hover,\na.list-group-item-success.active:focus,\nbutton.list-group-item-success.active:focus {\n color: #fff;\n background-color: #3c763d;\n border-color: #3c763d;\n}\n.list-group-item-info {\n color: #31708f;\n background-color: #d9edf7;\n}\na.list-group-item-info,\nbutton.list-group-item-info {\n color: #31708f;\n}\na.list-group-item-info .list-group-item-heading,\nbutton.list-group-item-info .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-info:hover,\nbutton.list-group-item-info:hover,\na.list-group-item-info:focus,\nbutton.list-group-item-info:focus {\n color: #31708f;\n background-color: #c4e3f3;\n}\na.list-group-item-info.active,\nbutton.list-group-item-info.active,\na.list-group-item-info.active:hover,\nbutton.list-group-item-info.active:hover,\na.list-group-item-info.active:focus,\nbutton.list-group-item-info.active:focus {\n color: #fff;\n background-color: #31708f;\n border-color: #31708f;\n}\n.list-group-item-warning {\n color: #8a6d3b;\n background-color: #fcf8e3;\n}\na.list-group-item-warning,\nbutton.list-group-item-warning {\n color: #8a6d3b;\n}\na.list-group-item-warning .list-group-item-heading,\nbutton.list-group-item-warning .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-warning:hover,\nbutton.list-group-item-warning:hover,\na.list-group-item-warning:focus,\nbutton.list-group-item-warning:focus {\n color: #8a6d3b;\n background-color: #faf2cc;\n}\na.list-group-item-warning.active,\nbutton.list-group-item-warning.active,\na.list-group-item-warning.active:hover,\nbutton.list-group-item-warning.active:hover,\na.list-group-item-warning.active:focus,\nbutton.list-group-item-warning.active:focus {\n color: #fff;\n background-color: #8a6d3b;\n border-color: #8a6d3b;\n}\n.list-group-item-danger {\n color: #a94442;\n background-color: #f2dede;\n}\na.list-group-item-danger,\nbutton.list-group-item-danger {\n color: #a94442;\n}\na.list-group-item-danger .list-group-item-heading,\nbutton.list-group-item-danger .list-group-item-heading {\n color: inherit;\n}\na.list-group-item-danger:hover,\nbutton.list-group-item-danger:hover,\na.list-group-item-danger:focus,\nbutton.list-group-item-danger:focus {\n color: #a94442;\n background-color: #ebcccc;\n}\na.list-group-item-danger.active,\nbutton.list-group-item-danger.active,\na.list-group-item-danger.active:hover,\nbutton.list-group-item-danger.active:hover,\na.list-group-item-danger.active:focus,\nbutton.list-group-item-danger.active:focus {\n color: #fff;\n background-color: #a94442;\n border-color: #a94442;\n}\n.list-group-item-heading {\n margin-top: 0;\n margin-bottom: 5px;\n}\n.list-group-item-text {\n margin-bottom: 0;\n line-height: 1.3;\n}\n.panel {\n margin-bottom: 20px;\n background-color: #fff;\n border: 1px solid transparent;\n border-radius: 4px;\n -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.panel-body {\n padding: 15px;\n}\n.panel-heading {\n padding: 10px 15px;\n border-bottom: 1px solid transparent;\n border-top-left-radius: 3px;\n border-top-right-radius: 3px;\n}\n.panel-heading > .dropdown .dropdown-toggle {\n color: inherit;\n}\n.panel-title {\n margin-top: 0;\n margin-bottom: 0;\n font-size: 16px;\n color: inherit;\n}\n.panel-title > a,\n.panel-title > small,\n.panel-title > .small,\n.panel-title > small > a,\n.panel-title > .small > a {\n color: inherit;\n}\n.panel-footer {\n padding: 10px 15px;\n background-color: #f5f5f5;\n border-top: 1px solid #ddd;\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .list-group,\n.panel > .panel-collapse > .list-group {\n margin-bottom: 0;\n}\n.panel > .list-group .list-group-item,\n.panel > .panel-collapse > .list-group .list-group-item {\n border-width: 1px 0;\n border-radius: 0;\n}\n.panel > .list-group:first-child .list-group-item:first-child,\n.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {\n border-top: 0;\n border-top-left-radius: 3px;\n border-top-right-radius: 3px;\n}\n.panel > .list-group:last-child .list-group-item:last-child,\n.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {\n border-bottom: 0;\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {\n border-top-left-radius: 0;\n border-top-right-radius: 0;\n}\n.panel-heading + .list-group .list-group-item:first-child {\n border-top-width: 0;\n}\n.list-group + .panel-footer {\n border-top-width: 0;\n}\n.panel > .table,\n.panel > .table-responsive > .table,\n.panel > .panel-collapse > .table {\n margin-bottom: 0;\n}\n.panel > .table caption,\n.panel > .table-responsive > .table caption,\n.panel > .panel-collapse > .table caption {\n padding-right: 15px;\n padding-left: 15px;\n}\n.panel > .table:first-child,\n.panel > .table-responsive:first-child > .table:first-child {\n border-top-left-radius: 3px;\n border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {\n border-top-left-radius: 3px;\n border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {\n border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {\n border-top-right-radius: 3px;\n}\n.panel > .table:last-child,\n.panel > .table-responsive:last-child > .table:last-child {\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {\n border-bottom-right-radius: 3px;\n border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {\n border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {\n border-bottom-right-radius: 3px;\n}\n.panel > .panel-body + .table,\n.panel > .panel-body + .table-responsive,\n.panel > .table + .panel-body,\n.panel > .table-responsive + .panel-body {\n border-top: 1px solid #ddd;\n}\n.panel > .table > tbody:first-child > tr:first-child th,\n.panel > .table > tbody:first-child > tr:first-child td {\n border-top: 0;\n}\n.panel > .table-bordered,\n.panel > .table-responsive > .table-bordered {\n border: 0;\n}\n.panel > .table-bordered > thead > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,\n.panel > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-bordered > thead > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,\n.panel > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-bordered > tfoot > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n border-left: 0;\n}\n.panel > .table-bordered > thead > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,\n.panel > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-bordered > thead > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,\n.panel > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-bordered > tfoot > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n border-right: 0;\n}\n.panel > .table-bordered > thead > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,\n.panel > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-bordered > thead > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,\n.panel > .table-bordered > tbody > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {\n border-bottom: 0;\n}\n.panel > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-bordered > tfoot > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {\n border-bottom: 0;\n}\n.panel > .table-responsive {\n margin-bottom: 0;\n border: 0;\n}\n.panel-group {\n margin-bottom: 20px;\n}\n.panel-group .panel {\n margin-bottom: 0;\n border-radius: 4px;\n}\n.panel-group .panel + .panel {\n margin-top: 5px;\n}\n.panel-group .panel-heading {\n border-bottom: 0;\n}\n.panel-group .panel-heading + .panel-collapse > .panel-body,\n.panel-group .panel-heading + .panel-collapse > .list-group {\n border-top: 1px solid #ddd;\n}\n.panel-group .panel-footer {\n border-top: 0;\n}\n.panel-group .panel-footer + .panel-collapse .panel-body {\n border-bottom: 1px solid #ddd;\n}\n.panel-default {\n border-color: #ddd;\n}\n.panel-default > .panel-heading {\n color: #333333;\n background-color: #f5f5f5;\n border-color: #ddd;\n}\n.panel-default > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #ddd;\n}\n.panel-default > .panel-heading .badge {\n color: #f5f5f5;\n background-color: #333333;\n}\n.panel-default > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #ddd;\n}\n.panel-primary {\n border-color: #337ab7;\n}\n.panel-primary > .panel-heading {\n color: #fff;\n background-color: #337ab7;\n border-color: #337ab7;\n}\n.panel-primary > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #337ab7;\n}\n.panel-primary > .panel-heading .badge {\n color: #337ab7;\n background-color: #fff;\n}\n.panel-primary > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #337ab7;\n}\n.panel-success {\n border-color: #d6e9c6;\n}\n.panel-success > .panel-heading {\n color: #3c763d;\n background-color: #dff0d8;\n border-color: #d6e9c6;\n}\n.panel-success > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #d6e9c6;\n}\n.panel-success > .panel-heading .badge {\n color: #dff0d8;\n background-color: #3c763d;\n}\n.panel-success > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #d6e9c6;\n}\n.panel-info {\n border-color: #bce8f1;\n}\n.panel-info > .panel-heading {\n color: #31708f;\n background-color: #d9edf7;\n border-color: #bce8f1;\n}\n.panel-info > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #bce8f1;\n}\n.panel-info > .panel-heading .badge {\n color: #d9edf7;\n background-color: #31708f;\n}\n.panel-info > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #bce8f1;\n}\n.panel-warning {\n border-color: #faebcc;\n}\n.panel-warning > .panel-heading {\n color: #8a6d3b;\n background-color: #fcf8e3;\n border-color: #faebcc;\n}\n.panel-warning > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #faebcc;\n}\n.panel-warning > .panel-heading .badge {\n color: #fcf8e3;\n background-color: #8a6d3b;\n}\n.panel-warning > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #faebcc;\n}\n.panel-danger {\n border-color: #ebccd1;\n}\n.panel-danger > .panel-heading {\n color: #a94442;\n background-color: #f2dede;\n border-color: #ebccd1;\n}\n.panel-danger > .panel-heading + .panel-collapse > .panel-body {\n border-top-color: #ebccd1;\n}\n.panel-danger > .panel-heading .badge {\n color: #f2dede;\n background-color: #a94442;\n}\n.panel-danger > .panel-footer + .panel-collapse > .panel-body {\n border-bottom-color: #ebccd1;\n}\n.embed-responsive {\n position: relative;\n display: block;\n height: 0;\n padding: 0;\n overflow: hidden;\n}\n.embed-responsive .embed-responsive-item,\n.embed-responsive iframe,\n.embed-responsive embed,\n.embed-responsive object,\n.embed-responsive video {\n position: absolute;\n top: 0;\n bottom: 0;\n left: 0;\n width: 100%;\n height: 100%;\n border: 0;\n}\n.embed-responsive-16by9 {\n padding-bottom: 56.25%;\n}\n.embed-responsive-4by3 {\n padding-bottom: 75%;\n}\n.well {\n min-height: 20px;\n padding: 19px;\n margin-bottom: 20px;\n background-color: #f5f5f5;\n border: 1px solid #e3e3e3;\n border-radius: 4px;\n -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.well blockquote {\n border-color: #ddd;\n border-color: rgba(0, 0, 0, 0.15);\n}\n.well-lg {\n padding: 24px;\n border-radius: 6px;\n}\n.well-sm {\n padding: 9px;\n border-radius: 3px;\n}\n.close {\n float: right;\n font-size: 21px;\n font-weight: bold;\n line-height: 1;\n color: #000;\n text-shadow: 0 1px 0 #fff;\n filter: alpha(opacity=20);\n opacity: 0.2;\n}\n.close:hover,\n.close:focus {\n color: #000;\n text-decoration: none;\n cursor: pointer;\n filter: alpha(opacity=50);\n opacity: 0.5;\n}\nbutton.close {\n padding: 0;\n cursor: pointer;\n background: transparent;\n border: 0;\n -webkit-appearance: none;\n appearance: none;\n}\n.modal-open {\n overflow: hidden;\n}\n.modal {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1050;\n display: none;\n overflow: hidden;\n -webkit-overflow-scrolling: touch;\n outline: 0;\n}\n.modal.fade .modal-dialog {\n -webkit-transform: translate(0, -25%);\n -ms-transform: translate(0, -25%);\n -o-transform: translate(0, -25%);\n transform: translate(0, -25%);\n -webkit-transition: -webkit-transform 0.3s ease-out;\n -moz-transition: -moz-transform 0.3s ease-out;\n -o-transition: -o-transform 0.3s ease-out;\n transition: transform 0.3s ease-out;\n}\n.modal.in .modal-dialog {\n -webkit-transform: translate(0, 0);\n -ms-transform: translate(0, 0);\n -o-transform: translate(0, 0);\n transform: translate(0, 0);\n}\n.modal-open .modal {\n overflow-x: hidden;\n overflow-y: auto;\n}\n.modal-dialog {\n position: relative;\n width: auto;\n margin: 10px;\n}\n.modal-content {\n position: relative;\n background-color: #fff;\n background-clip: padding-box;\n border: 1px solid #999;\n border: 1px solid rgba(0, 0, 0, 0.2);\n border-radius: 6px;\n -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n outline: 0;\n}\n.modal-backdrop {\n position: fixed;\n top: 0;\n right: 0;\n bottom: 0;\n left: 0;\n z-index: 1040;\n background-color: #000;\n}\n.modal-backdrop.fade {\n filter: alpha(opacity=0);\n opacity: 0;\n}\n.modal-backdrop.in {\n filter: alpha(opacity=50);\n opacity: 0.5;\n}\n.modal-header {\n padding: 15px;\n border-bottom: 1px solid #e5e5e5;\n}\n.modal-header .close {\n margin-top: -2px;\n}\n.modal-title {\n margin: 0;\n line-height: 1.42857143;\n}\n.modal-body {\n position: relative;\n padding: 15px;\n}\n.modal-footer {\n padding: 15px;\n text-align: right;\n border-top: 1px solid #e5e5e5;\n}\n.modal-footer .btn + .btn {\n margin-bottom: 0;\n margin-left: 5px;\n}\n.modal-footer .btn-group .btn + .btn {\n margin-left: -1px;\n}\n.modal-footer .btn-block + .btn-block {\n margin-left: 0;\n}\n.modal-scrollbar-measure {\n position: absolute;\n top: -9999px;\n width: 50px;\n height: 50px;\n overflow: scroll;\n}\n@media (min-width: 768px) {\n .modal-dialog {\n width: 600px;\n margin: 30px auto;\n }\n .modal-content {\n -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n }\n .modal-sm {\n width: 300px;\n }\n}\n@media (min-width: 992px) {\n .modal-lg {\n width: 900px;\n }\n}\n.tooltip {\n position: absolute;\n z-index: 1070;\n display: block;\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-style: normal;\n font-weight: 400;\n line-height: 1.42857143;\n line-break: auto;\n text-align: left;\n text-align: start;\n text-decoration: none;\n text-shadow: none;\n text-transform: none;\n letter-spacing: normal;\n word-break: normal;\n word-spacing: normal;\n word-wrap: normal;\n white-space: normal;\n font-size: 12px;\n filter: alpha(opacity=0);\n opacity: 0;\n}\n.tooltip.in {\n filter: alpha(opacity=90);\n opacity: 0.9;\n}\n.tooltip.top {\n padding: 5px 0;\n margin-top: -3px;\n}\n.tooltip.right {\n padding: 0 5px;\n margin-left: 3px;\n}\n.tooltip.bottom {\n padding: 5px 0;\n margin-top: 3px;\n}\n.tooltip.left {\n padding: 0 5px;\n margin-left: -3px;\n}\n.tooltip.top .tooltip-arrow {\n bottom: 0;\n left: 50%;\n margin-left: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.top-left .tooltip-arrow {\n right: 5px;\n bottom: 0;\n margin-bottom: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.top-right .tooltip-arrow {\n bottom: 0;\n left: 5px;\n margin-bottom: -5px;\n border-width: 5px 5px 0;\n border-top-color: #000;\n}\n.tooltip.right .tooltip-arrow {\n top: 50%;\n left: 0;\n margin-top: -5px;\n border-width: 5px 5px 5px 0;\n border-right-color: #000;\n}\n.tooltip.left .tooltip-arrow {\n top: 50%;\n right: 0;\n margin-top: -5px;\n border-width: 5px 0 5px 5px;\n border-left-color: #000;\n}\n.tooltip.bottom .tooltip-arrow {\n top: 0;\n left: 50%;\n margin-left: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.tooltip.bottom-left .tooltip-arrow {\n top: 0;\n right: 5px;\n margin-top: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.tooltip.bottom-right .tooltip-arrow {\n top: 0;\n left: 5px;\n margin-top: -5px;\n border-width: 0 5px 5px;\n border-bottom-color: #000;\n}\n.tooltip-inner {\n max-width: 200px;\n padding: 3px 8px;\n color: #fff;\n text-align: center;\n background-color: #000;\n border-radius: 4px;\n}\n.tooltip-arrow {\n position: absolute;\n width: 0;\n height: 0;\n border-color: transparent;\n border-style: solid;\n}\n.popover {\n position: absolute;\n top: 0;\n left: 0;\n z-index: 1060;\n display: none;\n max-width: 276px;\n padding: 1px;\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n font-style: normal;\n font-weight: 400;\n line-height: 1.42857143;\n line-break: auto;\n text-align: left;\n text-align: start;\n text-decoration: none;\n text-shadow: none;\n text-transform: none;\n letter-spacing: normal;\n word-break: normal;\n word-spacing: normal;\n word-wrap: normal;\n white-space: normal;\n font-size: 14px;\n background-color: #fff;\n background-clip: padding-box;\n border: 1px solid #ccc;\n border: 1px solid rgba(0, 0, 0, 0.2);\n border-radius: 6px;\n -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n}\n.popover.top {\n margin-top: -10px;\n}\n.popover.right {\n margin-left: 10px;\n}\n.popover.bottom {\n margin-top: 10px;\n}\n.popover.left {\n margin-left: -10px;\n}\n.popover > .arrow {\n border-width: 11px;\n}\n.popover > .arrow,\n.popover > .arrow:after {\n position: absolute;\n display: block;\n width: 0;\n height: 0;\n border-color: transparent;\n border-style: solid;\n}\n.popover > .arrow:after {\n content: \"\";\n border-width: 10px;\n}\n.popover.top > .arrow {\n bottom: -11px;\n left: 50%;\n margin-left: -11px;\n border-top-color: #999999;\n border-top-color: rgba(0, 0, 0, 0.25);\n border-bottom-width: 0;\n}\n.popover.top > .arrow:after {\n bottom: 1px;\n margin-left: -10px;\n content: \" \";\n border-top-color: #fff;\n border-bottom-width: 0;\n}\n.popover.right > .arrow {\n top: 50%;\n left: -11px;\n margin-top: -11px;\n border-right-color: #999999;\n border-right-color: rgba(0, 0, 0, 0.25);\n border-left-width: 0;\n}\n.popover.right > .arrow:after {\n bottom: -10px;\n left: 1px;\n content: \" \";\n border-right-color: #fff;\n border-left-width: 0;\n}\n.popover.bottom > .arrow {\n top: -11px;\n left: 50%;\n margin-left: -11px;\n border-top-width: 0;\n border-bottom-color: #999999;\n border-bottom-color: rgba(0, 0, 0, 0.25);\n}\n.popover.bottom > .arrow:after {\n top: 1px;\n margin-left: -10px;\n content: \" \";\n border-top-width: 0;\n border-bottom-color: #fff;\n}\n.popover.left > .arrow {\n top: 50%;\n right: -11px;\n margin-top: -11px;\n border-right-width: 0;\n border-left-color: #999999;\n border-left-color: rgba(0, 0, 0, 0.25);\n}\n.popover.left > .arrow:after {\n right: 1px;\n bottom: -10px;\n content: \" \";\n border-right-width: 0;\n border-left-color: #fff;\n}\n.popover-title {\n padding: 8px 14px;\n margin: 0;\n font-size: 14px;\n background-color: #f7f7f7;\n border-bottom: 1px solid #ebebeb;\n border-radius: 5px 5px 0 0;\n}\n.popover-content {\n padding: 9px 14px;\n}\n.carousel {\n position: relative;\n}\n.carousel-inner {\n position: relative;\n width: 100%;\n overflow: hidden;\n}\n.carousel-inner > .item {\n position: relative;\n display: none;\n -webkit-transition: 0.6s ease-in-out left;\n -o-transition: 0.6s ease-in-out left;\n transition: 0.6s ease-in-out left;\n}\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n line-height: 1;\n}\n@media all and (transform-3d), (-webkit-transform-3d) {\n .carousel-inner > .item {\n -webkit-transition: -webkit-transform 0.6s ease-in-out;\n -moz-transition: -moz-transform 0.6s ease-in-out;\n -o-transition: -o-transform 0.6s ease-in-out;\n transition: transform 0.6s ease-in-out;\n -webkit-backface-visibility: hidden;\n -moz-backface-visibility: hidden;\n backface-visibility: hidden;\n -webkit-perspective: 1000px;\n -moz-perspective: 1000px;\n perspective: 1000px;\n }\n .carousel-inner > .item.next,\n .carousel-inner > .item.active.right {\n -webkit-transform: translate3d(100%, 0, 0);\n transform: translate3d(100%, 0, 0);\n left: 0;\n }\n .carousel-inner > .item.prev,\n .carousel-inner > .item.active.left {\n -webkit-transform: translate3d(-100%, 0, 0);\n transform: translate3d(-100%, 0, 0);\n left: 0;\n }\n .carousel-inner > .item.next.left,\n .carousel-inner > .item.prev.right,\n .carousel-inner > .item.active {\n -webkit-transform: translate3d(0, 0, 0);\n transform: translate3d(0, 0, 0);\n left: 0;\n }\n}\n.carousel-inner > .active,\n.carousel-inner > .next,\n.carousel-inner > .prev {\n display: block;\n}\n.carousel-inner > .active {\n left: 0;\n}\n.carousel-inner > .next,\n.carousel-inner > .prev {\n position: absolute;\n top: 0;\n width: 100%;\n}\n.carousel-inner > .next {\n left: 100%;\n}\n.carousel-inner > .prev {\n left: -100%;\n}\n.carousel-inner > .next.left,\n.carousel-inner > .prev.right {\n left: 0;\n}\n.carousel-inner > .active.left {\n left: -100%;\n}\n.carousel-inner > .active.right {\n left: 100%;\n}\n.carousel-control {\n position: absolute;\n top: 0;\n bottom: 0;\n left: 0;\n width: 15%;\n font-size: 20px;\n color: #fff;\n text-align: center;\n text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n background-color: rgba(0, 0, 0, 0);\n filter: alpha(opacity=50);\n opacity: 0.5;\n}\n.carousel-control.left {\n background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);\n background-repeat: repeat-x;\n}\n.carousel-control.right {\n right: 0;\n left: auto;\n background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);\n background-repeat: repeat-x;\n}\n.carousel-control:hover,\n.carousel-control:focus {\n color: #fff;\n text-decoration: none;\n outline: 0;\n filter: alpha(opacity=90);\n opacity: 0.9;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-left,\n.carousel-control .glyphicon-chevron-right {\n position: absolute;\n top: 50%;\n z-index: 5;\n display: inline-block;\n margin-top: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .glyphicon-chevron-left {\n left: 50%;\n margin-left: -10px;\n}\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-right {\n right: 50%;\n margin-right: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next {\n width: 20px;\n height: 20px;\n font-family: serif;\n line-height: 1;\n}\n.carousel-control .icon-prev:before {\n content: \"\\2039\";\n}\n.carousel-control .icon-next:before {\n content: \"\\203a\";\n}\n.carousel-indicators {\n position: absolute;\n bottom: 10px;\n left: 50%;\n z-index: 15;\n width: 60%;\n padding-left: 0;\n margin-left: -30%;\n text-align: center;\n list-style: none;\n}\n.carousel-indicators li {\n display: inline-block;\n width: 10px;\n height: 10px;\n margin: 1px;\n text-indent: -999px;\n cursor: pointer;\n background-color: #000 \\9;\n background-color: rgba(0, 0, 0, 0);\n border: 1px solid #fff;\n border-radius: 10px;\n}\n.carousel-indicators .active {\n width: 12px;\n height: 12px;\n margin: 0;\n background-color: #fff;\n}\n.carousel-caption {\n position: absolute;\n right: 15%;\n bottom: 20px;\n left: 15%;\n z-index: 10;\n padding-top: 20px;\n padding-bottom: 20px;\n color: #fff;\n text-align: center;\n text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n}\n.carousel-caption .btn {\n text-shadow: none;\n}\n@media screen and (min-width: 768px) {\n .carousel-control .glyphicon-chevron-left,\n .carousel-control .glyphicon-chevron-right,\n .carousel-control .icon-prev,\n .carousel-control .icon-next {\n width: 30px;\n height: 30px;\n margin-top: -10px;\n font-size: 30px;\n }\n .carousel-control .glyphicon-chevron-left,\n .carousel-control .icon-prev {\n margin-left: -10px;\n }\n .carousel-control .glyphicon-chevron-right,\n .carousel-control .icon-next {\n margin-right: -10px;\n }\n .carousel-caption {\n right: 20%;\n left: 20%;\n padding-bottom: 30px;\n }\n .carousel-indicators {\n bottom: 20px;\n }\n}\n.clearfix:before,\n.clearfix:after,\n.dl-horizontal dd:before,\n.dl-horizontal dd:after,\n.container:before,\n.container:after,\n.container-fluid:before,\n.container-fluid:after,\n.row:before,\n.row:after,\n.form-horizontal .form-group:before,\n.form-horizontal .form-group:after,\n.btn-toolbar:before,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:before,\n.btn-group-vertical > .btn-group:after,\n.nav:before,\n.nav:after,\n.navbar:before,\n.navbar:after,\n.navbar-header:before,\n.navbar-header:after,\n.navbar-collapse:before,\n.navbar-collapse:after,\n.pager:before,\n.pager:after,\n.panel-body:before,\n.panel-body:after,\n.modal-header:before,\n.modal-header:after,\n.modal-footer:before,\n.modal-footer:after {\n display: table;\n content: \" \";\n}\n.clearfix:after,\n.dl-horizontal dd:after,\n.container:after,\n.container-fluid:after,\n.row:after,\n.form-horizontal .form-group:after,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:after,\n.nav:after,\n.navbar:after,\n.navbar-header:after,\n.navbar-collapse:after,\n.pager:after,\n.panel-body:after,\n.modal-header:after,\n.modal-footer:after {\n clear: both;\n}\n.center-block {\n display: block;\n margin-right: auto;\n margin-left: auto;\n}\n.pull-right {\n float: right !important;\n}\n.pull-left {\n float: left !important;\n}\n.hide {\n display: none !important;\n}\n.show {\n display: block !important;\n}\n.invisible {\n visibility: hidden;\n}\n.text-hide {\n font: 0/0 a;\n color: transparent;\n text-shadow: none;\n background-color: transparent;\n border: 0;\n}\n.hidden {\n display: none !important;\n}\n.affix {\n position: fixed;\n}\n@-ms-viewport {\n width: device-width;\n}\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n display: none !important;\n}\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n display: none !important;\n}\n@media (max-width: 767px) {\n .visible-xs {\n display: block !important;\n }\n table.visible-xs {\n display: table !important;\n }\n tr.visible-xs {\n display: table-row !important;\n }\n th.visible-xs,\n td.visible-xs {\n display: table-cell !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-block {\n display: block !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-inline {\n display: inline !important;\n }\n}\n@media (max-width: 767px) {\n .visible-xs-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm {\n display: block !important;\n }\n table.visible-sm {\n display: table !important;\n }\n tr.visible-sm {\n display: table-row !important;\n }\n th.visible-sm,\n td.visible-sm {\n display: table-cell !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-block {\n display: block !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-inline {\n display: inline !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .visible-sm-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md {\n display: block !important;\n }\n table.visible-md {\n display: table !important;\n }\n tr.visible-md {\n display: table-row !important;\n }\n th.visible-md,\n td.visible-md {\n display: table-cell !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-block {\n display: block !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-inline {\n display: inline !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .visible-md-inline-block {\n display: inline-block !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg {\n display: block !important;\n }\n table.visible-lg {\n display: table !important;\n }\n tr.visible-lg {\n display: table-row !important;\n }\n th.visible-lg,\n td.visible-lg {\n display: table-cell !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-block {\n display: block !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-inline {\n display: inline !important;\n }\n}\n@media (min-width: 1200px) {\n .visible-lg-inline-block {\n display: inline-block !important;\n }\n}\n@media (max-width: 767px) {\n .hidden-xs {\n display: none !important;\n }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n .hidden-sm {\n display: none !important;\n }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n .hidden-md {\n display: none !important;\n }\n}\n@media (min-width: 1200px) {\n .hidden-lg {\n display: none !important;\n }\n}\n.visible-print {\n display: none !important;\n}\n@media print {\n .visible-print {\n display: block !important;\n }\n table.visible-print {\n display: table !important;\n }\n tr.visible-print {\n display: table-row !important;\n }\n th.visible-print,\n td.visible-print {\n display: table-cell !important;\n }\n}\n.visible-print-block {\n display: none !important;\n}\n@media print {\n .visible-print-block {\n display: block !important;\n }\n}\n.visible-print-inline {\n display: none !important;\n}\n@media print {\n .visible-print-inline {\n display: inline !important;\n }\n}\n.visible-print-inline-block {\n display: none !important;\n}\n@media print {\n .visible-print-inline-block {\n display: inline-block !important;\n }\n}\n@media print {\n .hidden-print {\n display: none !important;\n }\n}\n/*# sourceMappingURL=bootstrap.css.map */","// stylelint-disable\n\n/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\n\n//\n// 1. Set default font family to sans-serif.\n// 2. Prevent iOS and IE text size adjust after device orientation change,\n// without disabling user zoom.\n//\n\nhtml {\n font-family: sans-serif; // 1\n -ms-text-size-adjust: 100%; // 2\n -webkit-text-size-adjust: 100%; // 2\n}\n\n//\n// Remove default margin.\n//\n\nbody {\n margin: 0;\n}\n\n// HTML5 display definitions\n// ==========================================================================\n\n//\n// Correct `block` display not defined for any HTML5 element in IE 8/9.\n// Correct `block` display not defined for `details` or `summary` in IE 10/11\n// and Firefox.\n// Correct `block` display not defined for `main` in IE 11.\n//\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n display: block;\n}\n\n//\n// 1. Correct `inline-block` display not defined in IE 8/9.\n// 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n//\n\naudio,\ncanvas,\nprogress,\nvideo {\n display: inline-block; // 1\n vertical-align: baseline; // 2\n}\n\n//\n// Prevent modern browsers from displaying `audio` without controls.\n// Remove excess height in iOS 5 devices.\n//\n\naudio:not([controls]) {\n display: none;\n height: 0;\n}\n\n//\n// Address `[hidden]` styling not present in IE 8/9/10.\n// Hide the `template` element in IE 8/9/10/11, Safari, and Firefox < 22.\n//\n\n[hidden],\ntemplate {\n display: none;\n}\n\n// Links\n// ==========================================================================\n\n//\n// Remove the gray background color from active links in IE 10.\n//\n\na {\n background-color: transparent;\n}\n\n//\n// Improve readability of focused elements when they are also in an\n// active/hover state.\n//\n\na:active,\na:hover {\n outline: 0;\n}\n\n// Text-level semantics\n// ==========================================================================\n\n//\n// 1. Remove the bottom border in Chrome 57- and Firefox 39-.\n// 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari.\n//\n\nabbr[title] {\n border-bottom: none; // 1\n text-decoration: underline; // 2\n text-decoration: underline dotted; // 2\n}\n\n//\n// Address style set to `bolder` in Firefox 4+, Safari, and Chrome.\n//\n\nb,\nstrong {\n font-weight: bold;\n}\n\n//\n// Address styling not present in Safari and Chrome.\n//\n\ndfn {\n font-style: italic;\n}\n\n//\n// Address variable `h1` font-size and margin within `section` and `article`\n// contexts in Firefox 4+, Safari, and Chrome.\n//\n\nh1 {\n font-size: 2em;\n margin: 0.67em 0;\n}\n\n//\n// Address styling not present in IE 8/9.\n//\n\nmark {\n background: #ff0;\n color: #000;\n}\n\n//\n// Address inconsistent and variable font size in all browsers.\n//\n\nsmall {\n font-size: 80%;\n}\n\n//\n// Prevent `sub` and `sup` affecting `line-height` in all browsers.\n//\n\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\n\nsup {\n top: -0.5em;\n}\n\nsub {\n bottom: -0.25em;\n}\n\n// Embedded content\n// ==========================================================================\n\n//\n// Remove border when inside `a` element in IE 8/9/10.\n//\n\nimg {\n border: 0;\n}\n\n//\n// Correct overflow not hidden in IE 9/10/11.\n//\n\nsvg:not(:root) {\n overflow: hidden;\n}\n\n// Grouping content\n// ==========================================================================\n\n//\n// Address margin not present in IE 8/9 and Safari.\n//\n\nfigure {\n margin: 1em 40px;\n}\n\n//\n// Address differences between Firefox and other browsers.\n//\n\nhr {\n box-sizing: content-box;\n height: 0;\n}\n\n//\n// Contain overflow in all browsers.\n//\n\npre {\n overflow: auto;\n}\n\n//\n// Address odd `em`-unit font size rendering in all browsers.\n//\n\ncode,\nkbd,\npre,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\n\n// Forms\n// ==========================================================================\n\n//\n// Known limitation: by default, Chrome and Safari on OS X allow very limited\n// styling of `select`, unless a `border` property is set.\n//\n\n//\n// 1. Correct color not being inherited.\n// Known issue: affects color of disabled elements.\n// 2. Correct font properties not being inherited.\n// 3. Address margins set differently in Firefox 4+, Safari, and Chrome.\n//\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n color: inherit; // 1\n font: inherit; // 2\n margin: 0; // 3\n}\n\n//\n// Address `overflow` set to `hidden` in IE 8/9/10/11.\n//\n\nbutton {\n overflow: visible;\n}\n\n//\n// Address inconsistent `text-transform` inheritance for `button` and `select`.\n// All other form control elements do not inherit `text-transform` values.\n// Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.\n// Correct `select` style inheritance in Firefox.\n//\n\nbutton,\nselect {\n text-transform: none;\n}\n\n//\n// 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n// and `video` controls.\n// 2. Correct inability to style clickable `input` types in iOS.\n// 3. Improve usability and consistency of cursor style between image-type\n// `input` and others.\n//\n\nbutton,\nhtml input[type=\"button\"], // 1\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button; // 2\n cursor: pointer; // 3\n}\n\n//\n// Re-set default cursor for disabled elements.\n//\n\nbutton[disabled],\nhtml input[disabled] {\n cursor: default;\n}\n\n//\n// Remove inner padding and border in Firefox 4+.\n//\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\n\n//\n// Address Firefox 4+ setting `line-height` on `input` using `!important` in\n// the UA stylesheet.\n//\n\ninput {\n line-height: normal;\n}\n\n//\n// It's recommended that you don't attempt to style these elements.\n// Firefox's implementation doesn't respect box-sizing, padding, or width.\n//\n// 1. Address box sizing set to `content-box` in IE 8/9/10.\n// 2. Remove excess padding in IE 8/9/10.\n//\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box; // 1\n padding: 0; // 2\n}\n\n//\n// Fix the cursor style for Chrome's increment/decrement buttons. For certain\n// `font-size` values of the `input`, it causes the cursor style of the\n// decrement button to change from `default` to `text`.\n//\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n//\n// 1. Address `appearance` set to `searchfield` in Safari and Chrome.\n// 2. Address `box-sizing` set to `border-box` in Safari and Chrome.\n//\n\ninput[type=\"search\"] {\n -webkit-appearance: textfield; // 1\n box-sizing: content-box; //2\n}\n\n//\n// Remove inner padding and search cancel button in Safari and Chrome on OS X.\n// Safari (but not Chrome) clips the cancel button when the search input has\n// padding (and `textfield` appearance).\n//\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n//\n// Define consistent border, margin, and padding.\n//\n\nfieldset {\n border: 1px solid #c0c0c0;\n margin: 0 2px;\n padding: 0.35em 0.625em 0.75em;\n}\n\n//\n// 1. Correct `color` not being inherited in IE 8/9/10/11.\n// 2. Remove padding so people aren't caught out if they zero out fieldsets.\n//\n\nlegend {\n border: 0; // 1\n padding: 0; // 2\n}\n\n//\n// Remove default vertical scrollbar in IE 8/9/10/11.\n//\n\ntextarea {\n overflow: auto;\n}\n\n//\n// Don't inherit the `font-weight` (applied by a rule above).\n// NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n//\n\noptgroup {\n font-weight: bold;\n}\n\n// Tables\n// ==========================================================================\n\n//\n// Remove most spacing between table cells.\n//\n\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\n\ntd,\nth {\n padding: 0;\n}\n","// stylelint-disable declaration-no-important, selector-no-qualifying-type\n\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n\n// ==========================================================================\n// Print styles.\n// Inlined to avoid the additional HTTP request: h5bp.com/r\n// ==========================================================================\n\n@media print {\n *,\n *:before,\n *:after {\n color: #000 !important; // Black prints faster: h5bp.com/s\n text-shadow: none !important;\n background: transparent !important;\n box-shadow: none !important;\n }\n\n a,\n a:visited {\n text-decoration: underline;\n }\n\n a[href]:after {\n content: \" (\" attr(href) \")\";\n }\n\n abbr[title]:after {\n content: \" (\" attr(title) \")\";\n }\n\n // Don't show links that are fragment identifiers,\n // or use the `javascript:` pseudo protocol\n a[href^=\"#\"]:after,\n a[href^=\"javascript:\"]:after {\n content: \"\";\n }\n\n pre,\n blockquote {\n border: 1px solid #999;\n page-break-inside: avoid;\n }\n\n thead {\n display: table-header-group; // h5bp.com/t\n }\n\n tr,\n img {\n page-break-inside: avoid;\n }\n\n img {\n max-width: 100% !important;\n }\n\n p,\n h2,\n h3 {\n orphans: 3;\n widows: 3;\n }\n\n h2,\n h3 {\n page-break-after: avoid;\n }\n\n // Bootstrap specific changes start\n\n // Bootstrap components\n .navbar {\n display: none;\n }\n .btn,\n .dropup > .btn {\n > .caret {\n border-top-color: #000 !important;\n }\n }\n .label {\n border: 1px solid #000;\n }\n\n .table {\n border-collapse: collapse !important;\n\n td,\n th {\n background-color: #fff !important;\n }\n }\n .table-bordered {\n th,\n td {\n border: 1px solid #ddd !important;\n }\n }\n}\n","// stylelint-disable value-list-comma-newline-after, value-list-comma-space-after, indentation, declaration-colon-newline-after, font-family-no-missing-generic-family-keyword\n\n//\n// Glyphicons for Bootstrap\n//\n// Since icons are fonts, they can be placed anywhere text is placed and are\n// thus automatically sized to match the surrounding child. To use, create an\n// inline element with the appropriate classes, like so:\n//\n// Star\n\n// Import the fonts\n@font-face {\n font-family: \"Glyphicons Halflings\";\n src: url(\"@{icon-font-path}@{icon-font-name}.eot\");\n src: url(\"@{icon-font-path}@{icon-font-name}.eot?#iefix\") format(\"embedded-opentype\"),\n url(\"@{icon-font-path}@{icon-font-name}.woff2\") format(\"woff2\"),\n url(\"@{icon-font-path}@{icon-font-name}.woff\") format(\"woff\"),\n url(\"@{icon-font-path}@{icon-font-name}.ttf\") format(\"truetype\"),\n url(\"@{icon-font-path}@{icon-font-name}.svg#@{icon-font-svg-id}\") format(\"svg\");\n}\n\n// Catchall baseclass\n.glyphicon {\n position: relative;\n top: 1px;\n display: inline-block;\n font-family: \"Glyphicons Halflings\";\n font-style: normal;\n font-weight: 400;\n line-height: 1;\n -webkit-font-smoothing: antialiased;\n -moz-osx-font-smoothing: grayscale;\n}\n\n// Individual icons\n.glyphicon-asterisk { &:before { content: \"\\002a\"; } }\n.glyphicon-plus { &:before { content: \"\\002b\"; } }\n.glyphicon-euro,\n.glyphicon-eur { &:before { content: \"\\20ac\"; } }\n.glyphicon-minus { &:before { content: \"\\2212\"; } }\n.glyphicon-cloud { &:before { content: \"\\2601\"; } }\n.glyphicon-envelope { &:before { content: \"\\2709\"; } }\n.glyphicon-pencil { &:before { content: \"\\270f\"; } }\n.glyphicon-glass { &:before { content: \"\\e001\"; } }\n.glyphicon-music { &:before { content: \"\\e002\"; } }\n.glyphicon-search { &:before { content: \"\\e003\"; } }\n.glyphicon-heart { &:before { content: \"\\e005\"; } }\n.glyphicon-star { &:before { content: \"\\e006\"; } }\n.glyphicon-star-empty { &:before { content: \"\\e007\"; } }\n.glyphicon-user { &:before { content: \"\\e008\"; } }\n.glyphicon-film { &:before { content: \"\\e009\"; } }\n.glyphicon-th-large { &:before { content: \"\\e010\"; } }\n.glyphicon-th { &:before { content: \"\\e011\"; } }\n.glyphicon-th-list { &:before { content: \"\\e012\"; } }\n.glyphicon-ok { &:before { content: \"\\e013\"; } }\n.glyphicon-remove { &:before { content: \"\\e014\"; } }\n.glyphicon-zoom-in { &:before { content: \"\\e015\"; } }\n.glyphicon-zoom-out { &:before { content: \"\\e016\"; } }\n.glyphicon-off { &:before { content: \"\\e017\"; } }\n.glyphicon-signal { &:before { content: \"\\e018\"; } }\n.glyphicon-cog { &:before { content: \"\\e019\"; } }\n.glyphicon-trash { &:before { content: \"\\e020\"; } }\n.glyphicon-home { &:before { content: \"\\e021\"; } }\n.glyphicon-file { &:before { content: \"\\e022\"; } }\n.glyphicon-time { &:before { content: \"\\e023\"; } }\n.glyphicon-road { &:before { content: \"\\e024\"; } }\n.glyphicon-download-alt { &:before { content: \"\\e025\"; } }\n.glyphicon-download { &:before { content: \"\\e026\"; } }\n.glyphicon-upload { &:before { content: \"\\e027\"; } }\n.glyphicon-inbox { &:before { content: \"\\e028\"; } }\n.glyphicon-play-circle { &:before { content: \"\\e029\"; } }\n.glyphicon-repeat { &:before { content: \"\\e030\"; } }\n.glyphicon-refresh { &:before { content: \"\\e031\"; } }\n.glyphicon-list-alt { &:before { content: \"\\e032\"; } }\n.glyphicon-lock { &:before { content: \"\\e033\"; } }\n.glyphicon-flag { &:before { content: \"\\e034\"; } }\n.glyphicon-headphones { &:before { content: \"\\e035\"; } }\n.glyphicon-volume-off { &:before { content: \"\\e036\"; } }\n.glyphicon-volume-down { &:before { content: \"\\e037\"; } }\n.glyphicon-volume-up { &:before { content: \"\\e038\"; } }\n.glyphicon-qrcode { &:before { content: \"\\e039\"; } }\n.glyphicon-barcode { &:before { content: \"\\e040\"; } }\n.glyphicon-tag { &:before { content: \"\\e041\"; } }\n.glyphicon-tags { &:before { content: \"\\e042\"; } }\n.glyphicon-book { &:before { content: \"\\e043\"; } }\n.glyphicon-bookmark { &:before { content: \"\\e044\"; } }\n.glyphicon-print { &:before { content: \"\\e045\"; } }\n.glyphicon-camera { &:before { content: \"\\e046\"; } }\n.glyphicon-font { &:before { content: \"\\e047\"; } }\n.glyphicon-bold { &:before { content: \"\\e048\"; } }\n.glyphicon-italic { &:before { content: \"\\e049\"; } }\n.glyphicon-text-height { &:before { content: \"\\e050\"; } }\n.glyphicon-text-width { &:before { content: \"\\e051\"; } }\n.glyphicon-align-left { &:before { content: \"\\e052\"; } }\n.glyphicon-align-center { &:before { content: \"\\e053\"; } }\n.glyphicon-align-right { &:before { content: \"\\e054\"; } }\n.glyphicon-align-justify { &:before { content: \"\\e055\"; } }\n.glyphicon-list { &:before { content: \"\\e056\"; } }\n.glyphicon-indent-left { &:before { content: \"\\e057\"; } }\n.glyphicon-indent-right { &:before { content: \"\\e058\"; } }\n.glyphicon-facetime-video { &:before { content: \"\\e059\"; } }\n.glyphicon-picture { &:before { content: \"\\e060\"; } }\n.glyphicon-map-marker { &:before { content: \"\\e062\"; } }\n.glyphicon-adjust { &:before { content: \"\\e063\"; } }\n.glyphicon-tint { &:before { content: \"\\e064\"; } }\n.glyphicon-edit { &:before { content: \"\\e065\"; } }\n.glyphicon-share { &:before { content: \"\\e066\"; } }\n.glyphicon-check { &:before { content: \"\\e067\"; } }\n.glyphicon-move { &:before { content: \"\\e068\"; } }\n.glyphicon-step-backward { &:before { content: \"\\e069\"; } }\n.glyphicon-fast-backward { &:before { content: \"\\e070\"; } }\n.glyphicon-backward { &:before { content: \"\\e071\"; } }\n.glyphicon-play { &:before { content: \"\\e072\"; } }\n.glyphicon-pause { &:before { content: \"\\e073\"; } }\n.glyphicon-stop { &:before { content: \"\\e074\"; } }\n.glyphicon-forward { &:before { content: \"\\e075\"; } }\n.glyphicon-fast-forward { &:before { content: \"\\e076\"; } }\n.glyphicon-step-forward { &:before { content: \"\\e077\"; } }\n.glyphicon-eject { &:before { content: \"\\e078\"; } }\n.glyphicon-chevron-left { &:before { content: \"\\e079\"; } }\n.glyphicon-chevron-right { &:before { content: \"\\e080\"; } }\n.glyphicon-plus-sign { &:before { content: \"\\e081\"; } }\n.glyphicon-minus-sign { &:before { content: \"\\e082\"; } }\n.glyphicon-remove-sign { &:before { content: \"\\e083\"; } }\n.glyphicon-ok-sign { &:before { content: \"\\e084\"; } }\n.glyphicon-question-sign { &:before { content: \"\\e085\"; } }\n.glyphicon-info-sign { &:before { content: \"\\e086\"; } }\n.glyphicon-screenshot { &:before { content: \"\\e087\"; } }\n.glyphicon-remove-circle { &:before { content: \"\\e088\"; } }\n.glyphicon-ok-circle { &:before { content: \"\\e089\"; } }\n.glyphicon-ban-circle { &:before { content: \"\\e090\"; } }\n.glyphicon-arrow-left { &:before { content: \"\\e091\"; } }\n.glyphicon-arrow-right { &:before { content: \"\\e092\"; } }\n.glyphicon-arrow-up { &:before { content: \"\\e093\"; } }\n.glyphicon-arrow-down { &:before { content: \"\\e094\"; } }\n.glyphicon-share-alt { &:before { content: \"\\e095\"; } }\n.glyphicon-resize-full { &:before { content: \"\\e096\"; } }\n.glyphicon-resize-small { &:before { content: \"\\e097\"; } }\n.glyphicon-exclamation-sign { &:before { content: \"\\e101\"; } }\n.glyphicon-gift { &:before { content: \"\\e102\"; } }\n.glyphicon-leaf { &:before { content: \"\\e103\"; } }\n.glyphicon-fire { &:before { content: \"\\e104\"; } }\n.glyphicon-eye-open { &:before { content: \"\\e105\"; } }\n.glyphicon-eye-close { &:before { content: \"\\e106\"; } }\n.glyphicon-warning-sign { &:before { content: \"\\e107\"; } }\n.glyphicon-plane { &:before { content: \"\\e108\"; } }\n.glyphicon-calendar { &:before { content: \"\\e109\"; } }\n.glyphicon-random { &:before { content: \"\\e110\"; } }\n.glyphicon-comment { &:before { content: \"\\e111\"; } }\n.glyphicon-magnet { &:before { content: \"\\e112\"; } }\n.glyphicon-chevron-up { &:before { content: \"\\e113\"; } }\n.glyphicon-chevron-down { &:before { content: \"\\e114\"; } }\n.glyphicon-retweet { &:before { content: \"\\e115\"; } }\n.glyphicon-shopping-cart { &:before { content: \"\\e116\"; } }\n.glyphicon-folder-close { &:before { content: \"\\e117\"; } }\n.glyphicon-folder-open { &:before { content: \"\\e118\"; } }\n.glyphicon-resize-vertical { &:before { content: \"\\e119\"; } }\n.glyphicon-resize-horizontal { &:before { content: \"\\e120\"; } }\n.glyphicon-hdd { &:before { content: \"\\e121\"; } }\n.glyphicon-bullhorn { &:before { content: \"\\e122\"; } }\n.glyphicon-bell { &:before { content: \"\\e123\"; } }\n.glyphicon-certificate { &:before { content: \"\\e124\"; } }\n.glyphicon-thumbs-up { &:before { content: \"\\e125\"; } }\n.glyphicon-thumbs-down { &:before { content: \"\\e126\"; } }\n.glyphicon-hand-right { &:before { content: \"\\e127\"; } }\n.glyphicon-hand-left { &:before { content: \"\\e128\"; } }\n.glyphicon-hand-up { &:before { content: \"\\e129\"; } }\n.glyphicon-hand-down { &:before { content: \"\\e130\"; } }\n.glyphicon-circle-arrow-right { &:before { content: \"\\e131\"; } }\n.glyphicon-circle-arrow-left { &:before { content: \"\\e132\"; } }\n.glyphicon-circle-arrow-up { &:before { content: \"\\e133\"; } }\n.glyphicon-circle-arrow-down { &:before { content: \"\\e134\"; } }\n.glyphicon-globe { &:before { content: \"\\e135\"; } }\n.glyphicon-wrench { &:before { content: \"\\e136\"; } }\n.glyphicon-tasks { &:before { content: \"\\e137\"; } }\n.glyphicon-filter { &:before { content: \"\\e138\"; } }\n.glyphicon-briefcase { &:before { content: \"\\e139\"; } }\n.glyphicon-fullscreen { &:before { content: \"\\e140\"; } }\n.glyphicon-dashboard { &:before { content: \"\\e141\"; } }\n.glyphicon-paperclip { &:before { content: \"\\e142\"; } }\n.glyphicon-heart-empty { &:before { content: \"\\e143\"; } }\n.glyphicon-link { &:before { content: \"\\e144\"; } }\n.glyphicon-phone { &:before { content: \"\\e145\"; } }\n.glyphicon-pushpin { &:before { content: \"\\e146\"; } }\n.glyphicon-usd { &:before { content: \"\\e148\"; } }\n.glyphicon-gbp { &:before { content: \"\\e149\"; } }\n.glyphicon-sort { &:before { content: \"\\e150\"; } }\n.glyphicon-sort-by-alphabet { &:before { content: \"\\e151\"; } }\n.glyphicon-sort-by-alphabet-alt { &:before { content: \"\\e152\"; } }\n.glyphicon-sort-by-order { &:before { content: \"\\e153\"; } }\n.glyphicon-sort-by-order-alt { &:before { content: \"\\e154\"; } }\n.glyphicon-sort-by-attributes { &:before { content: \"\\e155\"; } }\n.glyphicon-sort-by-attributes-alt { &:before { content: \"\\e156\"; } }\n.glyphicon-unchecked { &:before { content: \"\\e157\"; } }\n.glyphicon-expand { &:before { content: \"\\e158\"; } }\n.glyphicon-collapse-down { &:before { content: \"\\e159\"; } }\n.glyphicon-collapse-up { &:before { content: \"\\e160\"; } }\n.glyphicon-log-in { &:before { content: \"\\e161\"; } }\n.glyphicon-flash { &:before { content: \"\\e162\"; } }\n.glyphicon-log-out { &:before { content: \"\\e163\"; } }\n.glyphicon-new-window { &:before { content: \"\\e164\"; } }\n.glyphicon-record { &:before { content: \"\\e165\"; } }\n.glyphicon-save { &:before { content: \"\\e166\"; } }\n.glyphicon-open { &:before { content: \"\\e167\"; } }\n.glyphicon-saved { &:before { content: \"\\e168\"; } }\n.glyphicon-import { &:before { content: \"\\e169\"; } }\n.glyphicon-export { &:before { content: \"\\e170\"; } }\n.glyphicon-send { &:before { content: \"\\e171\"; } }\n.glyphicon-floppy-disk { &:before { content: \"\\e172\"; } }\n.glyphicon-floppy-saved { &:before { content: \"\\e173\"; } }\n.glyphicon-floppy-remove { &:before { content: \"\\e174\"; } }\n.glyphicon-floppy-save { &:before { content: \"\\e175\"; } }\n.glyphicon-floppy-open { &:before { content: \"\\e176\"; } }\n.glyphicon-credit-card { &:before { content: \"\\e177\"; } }\n.glyphicon-transfer { &:before { content: \"\\e178\"; } }\n.glyphicon-cutlery { &:before { content: \"\\e179\"; } }\n.glyphicon-header { &:before { content: \"\\e180\"; } }\n.glyphicon-compressed { &:before { content: \"\\e181\"; } }\n.glyphicon-earphone { &:before { content: \"\\e182\"; } }\n.glyphicon-phone-alt { &:before { content: \"\\e183\"; } }\n.glyphicon-tower { &:before { content: \"\\e184\"; } }\n.glyphicon-stats { &:before { content: \"\\e185\"; } }\n.glyphicon-sd-video { &:before { content: \"\\e186\"; } }\n.glyphicon-hd-video { &:before { content: \"\\e187\"; } }\n.glyphicon-subtitles { &:before { content: \"\\e188\"; } }\n.glyphicon-sound-stereo { &:before { content: \"\\e189\"; } }\n.glyphicon-sound-dolby { &:before { content: \"\\e190\"; } }\n.glyphicon-sound-5-1 { &:before { content: \"\\e191\"; } }\n.glyphicon-sound-6-1 { &:before { content: \"\\e192\"; } }\n.glyphicon-sound-7-1 { &:before { content: \"\\e193\"; } }\n.glyphicon-copyright-mark { &:before { content: \"\\e194\"; } }\n.glyphicon-registration-mark { &:before { content: \"\\e195\"; } }\n.glyphicon-cloud-download { &:before { content: \"\\e197\"; } }\n.glyphicon-cloud-upload { &:before { content: \"\\e198\"; } }\n.glyphicon-tree-conifer { &:before { content: \"\\e199\"; } }\n.glyphicon-tree-deciduous { &:before { content: \"\\e200\"; } }\n.glyphicon-cd { &:before { content: \"\\e201\"; } }\n.glyphicon-save-file { &:before { content: \"\\e202\"; } }\n.glyphicon-open-file { &:before { content: \"\\e203\"; } }\n.glyphicon-level-up { &:before { content: \"\\e204\"; } }\n.glyphicon-copy { &:before { content: \"\\e205\"; } }\n.glyphicon-paste { &:before { content: \"\\e206\"; } }\n// The following 2 Glyphicons are omitted for the time being because\n// they currently use Unicode codepoints that are outside the\n// Basic Multilingual Plane (BMP). Older buggy versions of WebKit can't handle\n// non-BMP codepoints in CSS string escapes, and thus can't display these two icons.\n// Notably, the bug affects some older versions of the Android Browser.\n// More info: https://github.com/twbs/bootstrap/issues/10106\n// .glyphicon-door { &:before { content: \"\\1f6aa\"; } }\n// .glyphicon-key { &:before { content: \"\\1f511\"; } }\n.glyphicon-alert { &:before { content: \"\\e209\"; } }\n.glyphicon-equalizer { &:before { content: \"\\e210\"; } }\n.glyphicon-king { &:before { content: \"\\e211\"; } }\n.glyphicon-queen { &:before { content: \"\\e212\"; } }\n.glyphicon-pawn { &:before { content: \"\\e213\"; } }\n.glyphicon-bishop { &:before { content: \"\\e214\"; } }\n.glyphicon-knight { &:before { content: \"\\e215\"; } }\n.glyphicon-baby-formula { &:before { content: \"\\e216\"; } }\n.glyphicon-tent { &:before { content: \"\\26fa\"; } }\n.glyphicon-blackboard { &:before { content: \"\\e218\"; } }\n.glyphicon-bed { &:before { content: \"\\e219\"; } }\n.glyphicon-apple { &:before { content: \"\\f8ff\"; } }\n.glyphicon-erase { &:before { content: \"\\e221\"; } }\n.glyphicon-hourglass { &:before { content: \"\\231b\"; } }\n.glyphicon-lamp { &:before { content: \"\\e223\"; } }\n.glyphicon-duplicate { &:before { content: \"\\e224\"; } }\n.glyphicon-piggy-bank { &:before { content: \"\\e225\"; } }\n.glyphicon-scissors { &:before { content: \"\\e226\"; } }\n.glyphicon-bitcoin { &:before { content: \"\\e227\"; } }\n.glyphicon-btc { &:before { content: \"\\e227\"; } }\n.glyphicon-xbt { &:before { content: \"\\e227\"; } }\n.glyphicon-yen { &:before { content: \"\\00a5\"; } }\n.glyphicon-jpy { &:before { content: \"\\00a5\"; } }\n.glyphicon-ruble { &:before { content: \"\\20bd\"; } }\n.glyphicon-rub { &:before { content: \"\\20bd\"; } }\n.glyphicon-scale { &:before { content: \"\\e230\"; } }\n.glyphicon-ice-lolly { &:before { content: \"\\e231\"; } }\n.glyphicon-ice-lolly-tasted { &:before { content: \"\\e232\"; } }\n.glyphicon-education { &:before { content: \"\\e233\"; } }\n.glyphicon-option-horizontal { &:before { content: \"\\e234\"; } }\n.glyphicon-option-vertical { &:before { content: \"\\e235\"; } }\n.glyphicon-menu-hamburger { &:before { content: \"\\e236\"; } }\n.glyphicon-modal-window { &:before { content: \"\\e237\"; } }\n.glyphicon-oil { &:before { content: \"\\e238\"; } }\n.glyphicon-grain { &:before { content: \"\\e239\"; } }\n.glyphicon-sunglasses { &:before { content: \"\\e240\"; } }\n.glyphicon-text-size { &:before { content: \"\\e241\"; } }\n.glyphicon-text-color { &:before { content: \"\\e242\"; } }\n.glyphicon-text-background { &:before { content: \"\\e243\"; } }\n.glyphicon-object-align-top { &:before { content: \"\\e244\"; } }\n.glyphicon-object-align-bottom { &:before { content: \"\\e245\"; } }\n.glyphicon-object-align-horizontal{ &:before { content: \"\\e246\"; } }\n.glyphicon-object-align-left { &:before { content: \"\\e247\"; } }\n.glyphicon-object-align-vertical { &:before { content: \"\\e248\"; } }\n.glyphicon-object-align-right { &:before { content: \"\\e249\"; } }\n.glyphicon-triangle-right { &:before { content: \"\\e250\"; } }\n.glyphicon-triangle-left { &:before { content: \"\\e251\"; } }\n.glyphicon-triangle-bottom { &:before { content: \"\\e252\"; } }\n.glyphicon-triangle-top { &:before { content: \"\\e253\"; } }\n.glyphicon-console { &:before { content: \"\\e254\"; } }\n.glyphicon-superscript { &:before { content: \"\\e255\"; } }\n.glyphicon-subscript { &:before { content: \"\\e256\"; } }\n.glyphicon-menu-left { &:before { content: \"\\e257\"; } }\n.glyphicon-menu-right { &:before { content: \"\\e258\"; } }\n.glyphicon-menu-down { &:before { content: \"\\e259\"; } }\n.glyphicon-menu-up { &:before { content: \"\\e260\"; } }\n","//\n// Scaffolding\n// --------------------------------------------------\n\n\n// Reset the box-sizing\n//\n// Heads up! This reset may cause conflicts with some third-party widgets.\n// For recommendations on resolving such conflicts, see\n// https://getbootstrap.com/docs/3.4/getting-started/#third-box-sizing\n* {\n .box-sizing(border-box);\n}\n*:before,\n*:after {\n .box-sizing(border-box);\n}\n\n\n// Body reset\n\nhtml {\n font-size: 10px;\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\n\nbody {\n font-family: @font-family-base;\n font-size: @font-size-base;\n line-height: @line-height-base;\n color: @text-color;\n background-color: @body-bg;\n}\n\n// Reset fonts for relevant elements\ninput,\nbutton,\nselect,\ntextarea {\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\n\n\n// Links\n\na {\n color: @link-color;\n text-decoration: none;\n\n &:hover,\n &:focus {\n color: @link-hover-color;\n text-decoration: @link-hover-decoration;\n }\n\n &:focus {\n .tab-focus();\n }\n}\n\n\n// Figures\n//\n// We reset this here because previously Normalize had no `figure` margins. This\n// ensures we don't break anyone's use of the element.\n\nfigure {\n margin: 0;\n}\n\n\n// Images\n\nimg {\n vertical-align: middle;\n}\n\n// Responsive images (ensure images don't scale beyond their parents)\n.img-responsive {\n .img-responsive();\n}\n\n// Rounded corners\n.img-rounded {\n border-radius: @border-radius-large;\n}\n\n// Image thumbnails\n//\n// Heads up! This is mixin-ed into thumbnails.less for `.thumbnail`.\n.img-thumbnail {\n padding: @thumbnail-padding;\n line-height: @line-height-base;\n background-color: @thumbnail-bg;\n border: 1px solid @thumbnail-border;\n border-radius: @thumbnail-border-radius;\n .transition(all .2s ease-in-out);\n\n // Keep them at most 100% wide\n .img-responsive(inline-block);\n}\n\n// Perfect circle\n.img-circle {\n border-radius: 50%; // set radius in percents\n}\n\n\n// Horizontal rules\n\nhr {\n margin-top: @line-height-computed;\n margin-bottom: @line-height-computed;\n border: 0;\n border-top: 1px solid @hr-border;\n}\n\n\n// Only display content to screen readers\n//\n// See: https://a11yproject.com/posts/how-to-hide-content\n\n.sr-only {\n position: absolute;\n width: 1px;\n height: 1px;\n padding: 0;\n margin: -1px;\n overflow: hidden;\n clip: rect(0, 0, 0, 0);\n border: 0;\n}\n\n// Use in conjunction with .sr-only to only display content when it's focused.\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n// Credit: HTML5 Boilerplate\n\n.sr-only-focusable {\n &:active,\n &:focus {\n position: static;\n width: auto;\n height: auto;\n margin: 0;\n overflow: visible;\n clip: auto;\n }\n}\n\n\n// iOS \"clickable elements\" fix for role=\"button\"\n//\n// Fixes \"clickability\" issue (and more generally, the firing of events such as focus as well)\n// for traditionally non-focusable elements with role=\"button\"\n// see https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n[role=\"button\"] {\n cursor: pointer;\n}\n","// stylelint-disable indentation, property-no-vendor-prefix, selector-no-vendor-prefix\n\n// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n -webkit-animation: @animation;\n -o-animation: @animation;\n animation: @animation;\n}\n.animation-name(@name) {\n -webkit-animation-name: @name;\n animation-name: @name;\n}\n.animation-duration(@duration) {\n -webkit-animation-duration: @duration;\n animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n -webkit-animation-timing-function: @timing-function;\n animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n -webkit-animation-delay: @delay;\n animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n -webkit-animation-iteration-count: @iteration-count;\n animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n -webkit-animation-direction: @direction;\n animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n -webkit-animation-fill-mode: @fill-mode;\n animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n -webkit-backface-visibility: @visibility;\n -moz-backface-visibility: @visibility;\n backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n -webkit-box-sizing: @boxmodel;\n -moz-box-sizing: @boxmodel;\n box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n -webkit-column-count: @column-count;\n -moz-column-count: @column-count;\n column-count: @column-count;\n -webkit-column-gap: @column-gap;\n -moz-column-gap: @column-gap;\n column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n -webkit-hyphens: @mode;\n -moz-hyphens: @mode;\n -ms-hyphens: @mode; // IE10+\n -o-hyphens: @mode;\n hyphens: @mode;\n word-wrap: break-word;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n // Firefox\n &::-moz-placeholder {\n color: @color;\n opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n }\n &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n &::-webkit-input-placeholder { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n -webkit-transform: scale(@ratio);\n -ms-transform: scale(@ratio); // IE9 only\n -o-transform: scale(@ratio);\n transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n -webkit-transform: scale(@ratioX, @ratioY);\n -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n -o-transform: scale(@ratioX, @ratioY);\n transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n -webkit-transform: scaleX(@ratio);\n -ms-transform: scaleX(@ratio); // IE9 only\n -o-transform: scaleX(@ratio);\n transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n -webkit-transform: scaleY(@ratio);\n -ms-transform: scaleY(@ratio); // IE9 only\n -o-transform: scaleY(@ratio);\n transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n -webkit-transform: skewX(@x) skewY(@y);\n -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n -o-transform: skewX(@x) skewY(@y);\n transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n -webkit-transform: translate(@x, @y);\n -ms-transform: translate(@x, @y); // IE9 only\n -o-transform: translate(@x, @y);\n transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n -webkit-transform: translate3d(@x, @y, @z);\n transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n -webkit-transform: rotate(@degrees);\n -ms-transform: rotate(@degrees); // IE9 only\n -o-transform: rotate(@degrees);\n transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n -webkit-transform: rotateX(@degrees);\n -ms-transform: rotateX(@degrees); // IE9 only\n -o-transform: rotateX(@degrees);\n transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n -webkit-transform: rotateY(@degrees);\n -ms-transform: rotateY(@degrees); // IE9 only\n -o-transform: rotateY(@degrees);\n transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n -webkit-perspective: @perspective;\n -moz-perspective: @perspective;\n perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n -webkit-perspective-origin: @perspective;\n -moz-perspective-origin: @perspective;\n perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n -webkit-transform-origin: @origin;\n -moz-transform-origin: @origin;\n -ms-transform-origin: @origin; // IE9 only\n transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n -webkit-transition: @transition;\n -o-transition: @transition;\n transition: @transition;\n}\n.transition-property(@transition-property) {\n -webkit-transition-property: @transition-property;\n transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n -webkit-transition-delay: @transition-delay;\n transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n -webkit-transition-duration: @transition-duration;\n transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n -webkit-transition-timing-function: @timing-function;\n transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n -webkit-transition: -webkit-transform @transition;\n -moz-transition: -moz-transform @transition;\n -o-transition: -o-transform @transition;\n transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n -webkit-user-select: @select;\n -moz-user-select: @select;\n -ms-user-select: @select; // IE10+\n user-select: @select;\n}\n","// WebKit-style focus\n\n.tab-focus() {\n // WebKit-specific. Other browsers will keep their default outline style.\n // (Initially tried to also force default via `outline: initial`,\n // but that seems to erroneously remove the outline in Firefox altogether.)\n outline: 5px auto -webkit-focus-ring-color;\n outline-offset: -2px;\n}\n","// stylelint-disable media-feature-name-no-vendor-prefix, media-feature-parentheses-space-inside, media-feature-name-no-unknown, indentation, at-rule-name-space-after\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n.img-responsive(@display: block) {\n display: @display;\n max-width: 100%; // Part 1: Set a maximum relative to the parent\n height: auto; // Part 2: Scale the height according to the width, otherwise you get stretching\n}\n\n\n// Retina image\n//\n// Short retina mixin for setting background-image and -size. Note that the\n// spelling of `min--moz-device-pixel-ratio` is intentional.\n.img-retina(@file-1x; @file-2x; @width-1x; @height-1x) {\n background-image: url(\"@{file-1x}\");\n\n @media\n only screen and (-webkit-min-device-pixel-ratio: 2),\n only screen and ( min--moz-device-pixel-ratio: 2),\n only screen and ( -o-min-device-pixel-ratio: 2/1),\n only screen and ( min-device-pixel-ratio: 2),\n only screen and ( min-resolution: 192dpi),\n only screen and ( min-resolution: 2dppx) {\n background-image: url(\"@{file-2x}\");\n background-size: @width-1x @height-1x;\n }\n}\n","// stylelint-disable selector-list-comma-newline-after, selector-no-qualifying-type\n\n//\n// Typography\n// --------------------------------------------------\n\n\n// Headings\n// -------------------------\n\nh1, h2, h3, h4, h5, h6,\n.h1, .h2, .h3, .h4, .h5, .h6 {\n font-family: @headings-font-family;\n font-weight: @headings-font-weight;\n line-height: @headings-line-height;\n color: @headings-color;\n\n small,\n .small {\n font-weight: 400;\n line-height: 1;\n color: @headings-small-color;\n }\n}\n\nh1, .h1,\nh2, .h2,\nh3, .h3 {\n margin-top: @line-height-computed;\n margin-bottom: (@line-height-computed / 2);\n\n small,\n .small {\n font-size: 65%;\n }\n}\nh4, .h4,\nh5, .h5,\nh6, .h6 {\n margin-top: (@line-height-computed / 2);\n margin-bottom: (@line-height-computed / 2);\n\n small,\n .small {\n font-size: 75%;\n }\n}\n\nh1, .h1 { font-size: @font-size-h1; }\nh2, .h2 { font-size: @font-size-h2; }\nh3, .h3 { font-size: @font-size-h3; }\nh4, .h4 { font-size: @font-size-h4; }\nh5, .h5 { font-size: @font-size-h5; }\nh6, .h6 { font-size: @font-size-h6; }\n\n\n// Body text\n// -------------------------\n\np {\n margin: 0 0 (@line-height-computed / 2);\n}\n\n.lead {\n margin-bottom: @line-height-computed;\n font-size: floor((@font-size-base * 1.15));\n font-weight: 300;\n line-height: 1.4;\n\n @media (min-width: @screen-sm-min) {\n font-size: (@font-size-base * 1.5);\n }\n}\n\n\n// Emphasis & misc\n// -------------------------\n\n// Ex: (12px small font / 14px base font) * 100% = about 85%\nsmall,\n.small {\n font-size: floor((100% * @font-size-small / @font-size-base));\n}\n\nmark,\n.mark {\n padding: .2em;\n background-color: @state-warning-bg;\n}\n\n// Alignment\n.text-left { text-align: left; }\n.text-right { text-align: right; }\n.text-center { text-align: center; }\n.text-justify { text-align: justify; }\n.text-nowrap { white-space: nowrap; }\n\n// Transformation\n.text-lowercase { text-transform: lowercase; }\n.text-uppercase { text-transform: uppercase; }\n.text-capitalize { text-transform: capitalize; }\n\n// Contextual colors\n.text-muted {\n color: @text-muted;\n}\n.text-primary {\n .text-emphasis-variant(@brand-primary);\n}\n.text-success {\n .text-emphasis-variant(@state-success-text);\n}\n.text-info {\n .text-emphasis-variant(@state-info-text);\n}\n.text-warning {\n .text-emphasis-variant(@state-warning-text);\n}\n.text-danger {\n .text-emphasis-variant(@state-danger-text);\n}\n\n// Contextual backgrounds\n// For now we'll leave these alongside the text classes until v4 when we can\n// safely shift things around (per SemVer rules).\n.bg-primary {\n // Given the contrast here, this is the only class to have its color inverted\n // automatically.\n color: #fff;\n .bg-variant(@brand-primary);\n}\n.bg-success {\n .bg-variant(@state-success-bg);\n}\n.bg-info {\n .bg-variant(@state-info-bg);\n}\n.bg-warning {\n .bg-variant(@state-warning-bg);\n}\n.bg-danger {\n .bg-variant(@state-danger-bg);\n}\n\n\n// Page header\n// -------------------------\n\n.page-header {\n padding-bottom: ((@line-height-computed / 2) - 1);\n margin: (@line-height-computed * 2) 0 @line-height-computed;\n border-bottom: 1px solid @page-header-border-color;\n}\n\n\n// Lists\n// -------------------------\n\n// Unordered and Ordered lists\nul,\nol {\n margin-top: 0;\n margin-bottom: (@line-height-computed / 2);\n ul,\n ol {\n margin-bottom: 0;\n }\n}\n\n// List options\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n.list-unstyled {\n padding-left: 0;\n list-style: none;\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n .list-unstyled();\n margin-left: -5px;\n\n > li {\n display: inline-block;\n padding-right: 5px;\n padding-left: 5px;\n }\n}\n\n// Description Lists\ndl {\n margin-top: 0; // Remove browser default\n margin-bottom: @line-height-computed;\n}\ndt,\ndd {\n line-height: @line-height-base;\n}\ndt {\n font-weight: 700;\n}\ndd {\n margin-left: 0; // Undo browser default\n}\n\n// Horizontal description lists\n//\n// Defaults to being stacked without any of the below styles applied, until the\n// grid breakpoint is reached (default of ~768px).\n\n.dl-horizontal {\n dd {\n &:extend(.clearfix all); // Clear the floated `dt` if an empty `dd` is present\n }\n\n @media (min-width: @dl-horizontal-breakpoint) {\n dt {\n float: left;\n width: (@dl-horizontal-offset - 20);\n clear: left;\n text-align: right;\n .text-overflow();\n }\n dd {\n margin-left: @dl-horizontal-offset;\n }\n }\n}\n\n\n// Misc\n// -------------------------\n\n// Abbreviations and acronyms\n// Add data-* attribute to help out our tooltip plugin, per https://github.com/twbs/bootstrap/issues/5257\nabbr[title],\nabbr[data-original-title] {\n cursor: help;\n}\n\n.initialism {\n font-size: 90%;\n .text-uppercase();\n}\n\n// Blockquotes\nblockquote {\n padding: (@line-height-computed / 2) @line-height-computed;\n margin: 0 0 @line-height-computed;\n font-size: @blockquote-font-size;\n border-left: 5px solid @blockquote-border-color;\n\n p,\n ul,\n ol {\n &:last-child {\n margin-bottom: 0;\n }\n }\n\n // Note: Deprecated small and .small as of v3.1.0\n // Context: https://github.com/twbs/bootstrap/issues/11660\n footer,\n small,\n .small {\n display: block;\n font-size: 80%; // back to default font-size\n line-height: @line-height-base;\n color: @blockquote-small-color;\n\n &:before {\n content: \"\\2014 \\00A0\"; // em dash, nbsp\n }\n }\n}\n\n// Opposite alignment of blockquote\n//\n// Heads up: `blockquote.pull-right` has been deprecated as of v3.1.0.\n.blockquote-reverse,\nblockquote.pull-right {\n padding-right: 15px;\n padding-left: 0;\n text-align: right;\n border-right: 5px solid @blockquote-border-color;\n border-left: 0;\n\n // Account for citation\n footer,\n small,\n .small {\n &:before { content: \"\"; }\n &:after {\n content: \"\\00A0 \\2014\"; // nbsp, em dash\n }\n }\n}\n\n// Addresses\naddress {\n margin-bottom: @line-height-computed;\n font-style: normal;\n line-height: @line-height-base;\n}\n","// Typography\n\n.text-emphasis-variant(@color) {\n color: @color;\n a&:hover,\n a&:focus {\n color: darken(@color, 10%);\n }\n}\n","// Contextual backgrounds\n\n.bg-variant(@color) {\n background-color: @color;\n a&:hover,\n a&:focus {\n background-color: darken(@color, 10%);\n }\n}\n","// Text overflow\n// Requires inline-block or block for proper styling\n\n.text-overflow() {\n overflow: hidden;\n text-overflow: ellipsis;\n white-space: nowrap;\n}\n","//\n// Code (inline and block)\n// --------------------------------------------------\n\n\n// Inline and block code styles\ncode,\nkbd,\npre,\nsamp {\n font-family: @font-family-monospace;\n}\n\n// Inline code\ncode {\n padding: 2px 4px;\n font-size: 90%;\n color: @code-color;\n background-color: @code-bg;\n border-radius: @border-radius-base;\n}\n\n// User input typically entered via keyboard\nkbd {\n padding: 2px 4px;\n font-size: 90%;\n color: @kbd-color;\n background-color: @kbd-bg;\n border-radius: @border-radius-small;\n box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25);\n\n kbd {\n padding: 0;\n font-size: 100%;\n font-weight: 700;\n box-shadow: none;\n }\n}\n\n// Blocks of code\npre {\n display: block;\n padding: ((@line-height-computed - 1) / 2);\n margin: 0 0 (@line-height-computed / 2);\n font-size: (@font-size-base - 1); // 14px to 13px\n line-height: @line-height-base;\n color: @pre-color;\n word-break: break-all;\n word-wrap: break-word;\n background-color: @pre-bg;\n border: 1px solid @pre-border-color;\n border-radius: @border-radius-base;\n\n // Account for some code outputs that place code tags in pre tags\n code {\n padding: 0;\n font-size: inherit;\n color: inherit;\n white-space: pre-wrap;\n background-color: transparent;\n border-radius: 0;\n }\n}\n\n// Enable scrollable blocks of code\n.pre-scrollable {\n max-height: @pre-scrollable-max-height;\n overflow-y: scroll;\n}\n","//\n// Grid system\n// --------------------------------------------------\n\n\n// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n.container {\n .container-fixed();\n\n @media (min-width: @screen-sm-min) {\n width: @container-sm;\n }\n @media (min-width: @screen-md-min) {\n width: @container-md;\n }\n @media (min-width: @screen-lg-min) {\n width: @container-lg;\n }\n}\n\n\n// Fluid container\n//\n// Utilizes the mixin meant for fixed width containers, but without any defined\n// width for fluid, full width layouts.\n\n.container-fluid {\n .container-fixed();\n}\n\n\n// Row\n//\n// Rows contain and clear the floats of your columns.\n\n.row {\n .make-row();\n}\n\n.row-no-gutters {\n margin-right: 0;\n margin-left: 0;\n\n [class*=\"col-\"] {\n padding-right: 0;\n padding-left: 0;\n }\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n.make-grid-columns();\n\n\n// Extra small grid\n//\n// Columns, offsets, pushes, and pulls for extra small devices like\n// smartphones.\n\n.make-grid(xs);\n\n\n// Small grid\n//\n// Columns, offsets, pushes, and pulls for the small device range, from phones\n// to tablets.\n\n@media (min-width: @screen-sm-min) {\n .make-grid(sm);\n}\n\n\n// Medium grid\n//\n// Columns, offsets, pushes, and pulls for the desktop device range.\n\n@media (min-width: @screen-md-min) {\n .make-grid(md);\n}\n\n\n// Large grid\n//\n// Columns, offsets, pushes, and pulls for the large desktop device range.\n\n@media (min-width: @screen-lg-min) {\n .make-grid(lg);\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n// Centered container element\n.container-fixed(@gutter: @grid-gutter-width) {\n padding-right: ceil((@gutter / 2));\n padding-left: floor((@gutter / 2));\n margin-right: auto;\n margin-left: auto;\n &:extend(.clearfix all);\n}\n\n// Creates a wrapper for a series of columns\n.make-row(@gutter: @grid-gutter-width) {\n margin-right: floor((@gutter / -2));\n margin-left: ceil((@gutter / -2));\n &:extend(.clearfix all);\n}\n\n// Generate the extra small columns\n.make-xs-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n float: left;\n width: percentage((@columns / @grid-columns));\n min-height: 1px;\n padding-right: (@gutter / 2);\n padding-left: (@gutter / 2);\n}\n.make-xs-column-offset(@columns) {\n margin-left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-push(@columns) {\n left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-pull(@columns) {\n right: percentage((@columns / @grid-columns));\n}\n\n// Generate the small columns\n.make-sm-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-right: (@gutter / 2);\n padding-left: (@gutter / 2);\n\n @media (min-width: @screen-sm-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-offset(@columns) {\n @media (min-width: @screen-sm-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-push(@columns) {\n @media (min-width: @screen-sm-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-sm-column-pull(@columns) {\n @media (min-width: @screen-sm-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n\n// Generate the medium columns\n.make-md-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-right: (@gutter / 2);\n padding-left: (@gutter / 2);\n\n @media (min-width: @screen-md-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-offset(@columns) {\n @media (min-width: @screen-md-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-push(@columns) {\n @media (min-width: @screen-md-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-md-column-pull(@columns) {\n @media (min-width: @screen-md-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n\n// Generate the large columns\n.make-lg-column(@columns; @gutter: @grid-gutter-width) {\n position: relative;\n min-height: 1px;\n padding-right: (@gutter / 2);\n padding-left: (@gutter / 2);\n\n @media (min-width: @screen-lg-min) {\n float: left;\n width: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-offset(@columns) {\n @media (min-width: @screen-lg-min) {\n margin-left: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-push(@columns) {\n @media (min-width: @screen-lg-min) {\n left: percentage((@columns / @grid-columns));\n }\n}\n.make-lg-column-pull(@columns) {\n @media (min-width: @screen-lg-min) {\n right: percentage((@columns / @grid-columns));\n }\n}\n","// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `@grid-columns`.\n\n.make-grid-columns() {\n // Common styles for all sizes of grid columns, widths 1-12\n .col(@index) { // initial\n @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n .col((@index + 1), @item);\n }\n .col(@index, @list) when (@index =< @grid-columns) { // general; \"=<\" isn't a typo\n @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n .col((@index + 1), ~\"@{list}, @{item}\");\n }\n .col(@index, @list) when (@index > @grid-columns) { // terminal\n @{list} {\n position: relative;\n // Prevent columns from collapsing when empty\n min-height: 1px;\n // Inner gutter via padding\n padding-right: floor((@grid-gutter-width / 2));\n padding-left: ceil((@grid-gutter-width / 2));\n }\n }\n .col(1); // kickstart it\n}\n\n.float-grid-columns(@class) {\n .col(@index) { // initial\n @item: ~\".col-@{class}-@{index}\";\n .col((@index + 1), @item);\n }\n .col(@index, @list) when (@index =< @grid-columns) { // general\n @item: ~\".col-@{class}-@{index}\";\n .col((@index + 1), ~\"@{list}, @{item}\");\n }\n .col(@index, @list) when (@index > @grid-columns) { // terminal\n @{list} {\n float: left;\n }\n }\n .col(1); // kickstart it\n}\n\n.calc-grid-column(@index, @class, @type) when (@type = width) and (@index > 0) {\n .col-@{class}-@{index} {\n width: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index > 0) {\n .col-@{class}-push-@{index} {\n left: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index = 0) {\n .col-@{class}-push-0 {\n left: auto;\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index > 0) {\n .col-@{class}-pull-@{index} {\n right: percentage((@index / @grid-columns));\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index = 0) {\n .col-@{class}-pull-0 {\n right: auto;\n }\n}\n.calc-grid-column(@index, @class, @type) when (@type = offset) {\n .col-@{class}-offset-@{index} {\n margin-left: percentage((@index / @grid-columns));\n }\n}\n\n// Basic looping in LESS\n.loop-grid-columns(@index, @class, @type) when (@index >= 0) {\n .calc-grid-column(@index, @class, @type);\n // next iteration\n .loop-grid-columns((@index - 1), @class, @type);\n}\n\n// Create grid for specific class\n.make-grid(@class) {\n .float-grid-columns(@class);\n .loop-grid-columns(@grid-columns, @class, width);\n .loop-grid-columns(@grid-columns, @class, pull);\n .loop-grid-columns(@grid-columns, @class, push);\n .loop-grid-columns(@grid-columns, @class, offset);\n}\n","// stylelint-disable selector-max-type, selector-max-compound-selectors, selector-no-qualifying-type\n\n//\n// Tables\n// --------------------------------------------------\n\n\ntable {\n background-color: @table-bg;\n\n // Table cell sizing\n //\n // Reset default table behavior\n\n col[class*=\"col-\"] {\n position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n display: table-column;\n float: none;\n }\n\n td,\n th {\n &[class*=\"col-\"] {\n position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n display: table-cell;\n float: none;\n }\n }\n}\n\ncaption {\n padding-top: @table-cell-padding;\n padding-bottom: @table-cell-padding;\n color: @text-muted;\n text-align: left;\n}\n\nth {\n text-align: left;\n}\n\n\n// Baseline styles\n\n.table {\n width: 100%;\n max-width: 100%;\n margin-bottom: @line-height-computed;\n // Cells\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n padding: @table-cell-padding;\n line-height: @line-height-base;\n vertical-align: top;\n border-top: 1px solid @table-border-color;\n }\n }\n }\n // Bottom align for column headings\n > thead > tr > th {\n vertical-align: bottom;\n border-bottom: 2px solid @table-border-color;\n }\n // Remove top border from thead by default\n > caption + thead,\n > colgroup + thead,\n > thead:first-child {\n > tr:first-child {\n > th,\n > td {\n border-top: 0;\n }\n }\n }\n // Account for multiple tbody instances\n > tbody + tbody {\n border-top: 2px solid @table-border-color;\n }\n\n // Nesting\n .table {\n background-color: @body-bg;\n }\n}\n\n\n// Condensed table w/ half padding\n\n.table-condensed {\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n padding: @table-condensed-cell-padding;\n }\n }\n }\n}\n\n\n// Bordered version\n//\n// Add borders all around the table and between all the columns.\n\n.table-bordered {\n border: 1px solid @table-border-color;\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n border: 1px solid @table-border-color;\n }\n }\n }\n > thead > tr {\n > th,\n > td {\n border-bottom-width: 2px;\n }\n }\n}\n\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n.table-striped {\n > tbody > tr:nth-of-type(odd) {\n background-color: @table-bg-accent;\n }\n}\n\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n > tbody > tr:hover {\n background-color: @table-bg-hover;\n }\n}\n\n\n// Table backgrounds\n//\n// Exact selectors below required to override `.table-striped` and prevent\n// inheritance to nested tables.\n\n// Generate the contextual variants\n.table-row-variant(active; @table-bg-active);\n.table-row-variant(success; @state-success-bg);\n.table-row-variant(info; @state-info-bg);\n.table-row-variant(warning; @state-warning-bg);\n.table-row-variant(danger; @state-danger-bg);\n\n\n// Responsive tables\n//\n// Wrap your tables in `.table-responsive` and we'll make them mobile friendly\n// by enabling horizontal scrolling. Only applies <768px. Everything above that\n// will display normally.\n\n.table-responsive {\n min-height: .01%; // Workaround for IE9 bug (see https://github.com/twbs/bootstrap/issues/14837)\n overflow-x: auto;\n\n @media screen and (max-width: @screen-xs-max) {\n width: 100%;\n margin-bottom: (@line-height-computed * .75);\n overflow-y: hidden;\n -ms-overflow-style: -ms-autohiding-scrollbar;\n border: 1px solid @table-border-color;\n\n // Tighten up spacing\n > .table {\n margin-bottom: 0;\n\n // Ensure the content doesn't wrap\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th,\n > td {\n white-space: nowrap;\n }\n }\n }\n }\n\n // Special overrides for the bordered tables\n > .table-bordered {\n border: 0;\n\n // Nuke the appropriate borders so that the parent can handle them\n > thead,\n > tbody,\n > tfoot {\n > tr {\n > th:first-child,\n > td:first-child {\n border-left: 0;\n }\n > th:last-child,\n > td:last-child {\n border-right: 0;\n }\n }\n }\n\n // Only nuke the last row's bottom-border in `tbody` and `tfoot` since\n // chances are there will be only one `tr` in a `thead` and that would\n // remove the border altogether.\n > tbody,\n > tfoot {\n > tr:last-child {\n > th,\n > td {\n border-bottom: 0;\n }\n }\n }\n\n }\n }\n}\n","// Tables\n\n.table-row-variant(@state; @background) {\n // Exact selectors below required to override `.table-striped` and prevent\n // inheritance to nested tables.\n .table > thead > tr,\n .table > tbody > tr,\n .table > tfoot > tr {\n > td.@{state},\n > th.@{state},\n &.@{state} > td,\n &.@{state} > th {\n background-color: @background;\n }\n }\n\n // Hover states for `.table-hover`\n // Note: this is not available for cells or rows within `thead` or `tfoot`.\n .table-hover > tbody > tr {\n > td.@{state}:hover,\n > th.@{state}:hover,\n &.@{state}:hover > td,\n &:hover > .@{state},\n &.@{state}:hover > th {\n background-color: darken(@background, 5%);\n }\n }\n}\n","// stylelint-disable selector-no-qualifying-type, property-no-vendor-prefix, media-feature-name-no-vendor-prefix\n\n//\n// Forms\n// --------------------------------------------------\n\n\n// Normalize non-controls\n//\n// Restyle and baseline non-control form elements.\n\nfieldset {\n // Chrome and Firefox set a `min-width: min-content;` on fieldsets,\n // so we reset that to ensure it behaves more like a standard block element.\n // See https://github.com/twbs/bootstrap/issues/12359.\n min-width: 0;\n padding: 0;\n margin: 0;\n border: 0;\n}\n\nlegend {\n display: block;\n width: 100%;\n padding: 0;\n margin-bottom: @line-height-computed;\n font-size: (@font-size-base * 1.5);\n line-height: inherit;\n color: @legend-color;\n border: 0;\n border-bottom: 1px solid @legend-border-color;\n}\n\nlabel {\n display: inline-block;\n max-width: 100%; // Force IE8 to wrap long content (see https://github.com/twbs/bootstrap/issues/13141)\n margin-bottom: 5px;\n font-weight: 700;\n}\n\n\n// Normalize form controls\n//\n// While most of our form styles require extra classes, some basic normalization\n// is required to ensure optimum display with or without those classes to better\n// address browser inconsistencies.\n\ninput[type=\"search\"] {\n // Override content-box in Normalize (* isn't specific enough)\n .box-sizing(border-box);\n\n // Search inputs in iOS\n //\n // This overrides the extra rounded corners on search inputs in iOS so that our\n // `.form-control` class can properly style them. Note that this cannot simply\n // be added to `.form-control` as it's not specific enough. For details, see\n // https://github.com/twbs/bootstrap/issues/11586.\n -webkit-appearance: none;\n appearance: none;\n}\n\n// Position radios and checkboxes better\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n margin: 4px 0 0;\n margin-top: 1px \\9; // IE8-9\n line-height: normal;\n\n // Apply same disabled cursor tweak as for inputs\n // Some special care is needed because

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