From e703d4fb7f205fe86e3d29dabaa7a454350e2ea5 Mon Sep 17 00:00:00 2001 From: MacPingu Date: Tue, 21 Jun 2022 19:32:31 +0200 Subject: [PATCH] Update notebook cmip6 decadal (#103) * update cmip6 decadal notebooks * update decadal notebooks --- notebooks/demo/demo-intake-catalog.ipynb | 2356 ++++++++++++++++- notebooks/demo/demo-rooki-cmip6-decadal.ipynb | 517 +++- 2 files changed, 2730 insertions(+), 143 deletions(-) diff --git a/notebooks/demo/demo-intake-catalog.ipynb b/notebooks/demo/demo-intake-catalog.ipynb index 07b6131..e948f50 100644 --- a/notebooks/demo/demo-intake-catalog.ipynb +++ b/notebooks/demo/demo-intake-catalog.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "demographic-regulation", "metadata": {}, "outputs": [], @@ -21,35 +21,6 @@ "import intake" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "textile-fortune", - "metadata": {}, - "outputs": [], - "source": [ - "# filter catalog\n", - "def filter_by_time(df, collection, time=None):\n", - " # a common search we do in rook\n", - " start = end = None\n", - " if time:\n", - " if \"/\" in time:\n", - " start, end = time.split(\"/\")\n", - " start = start.strip()\n", - " end = end.strip()\n", - " else:\n", - " start = time.strip()\n", - " \n", - " start = start or \"1800-01-01\"\n", - " end = end or \"2500-12-31\"\n", - " \n", - " sdf = df.fillna({'start_time': '1000-01-01T12:00:00', 'end_time': '3000-12-31T12:00:00'})\n", - "\n", - " result = sdf.loc[(sdf.ds_id == collection) & (sdf.end_time >= start) & (sdf.start_time <= end)]\n", - " return list(result.path.sort_values().to_dict().values())\n", - " " - ] - }, { "cell_type": "markdown", "id": "fewer-robert", @@ -60,13 +31,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "becoming-theater", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['c3s-cmip5',\n", + " 'c3s-cmip5-daily-pressure-level',\n", + " 'c3s-cmip5-daily-single-level',\n", + " 'c3s-cmip5-monthly-pressure-level',\n", + " 'c3s-cmip5-monthly-single-level',\n", + " 'c3s-cmip6',\n", + " 'c3s-cmip6-decadal',\n", + " 'c3s-cmip6-decadal-dwd',\n", + " 'c3s-cordex']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cat_url = \"https://raw.githubusercontent.com/cp4cds/c3s_34g_manifests/master/intake/catalogs/c3s.yaml\"\n", - "# cat_url = \"https://github.com/cehbrecht/c3s_34g_manifests/raw/fix-intake-catalog/intake/catalogs/c3s.yaml\"\n", + "\n", "cat = intake.open_catalog(cat_url)\n", "list(cat)" ] @@ -84,47 +74,368 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "dc65a830-2602-4afb-84dd-74c5130db310", - "metadata": {}, - "outputs": [], - "source": [ - "print(cat['c3s-cmip6'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "ceramic-today", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ds_idpathsizemip_eraactivity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelversionstart_timeend_timebboxlevel
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1c3s-cmip6.ScenarioMIP.EC-Earth-Consortium.EC-E...ScenarioMIP/EC-Earth-Consortium/EC-Earth3-Veg-...1691093c3s-cmip6ScenarioMIPEC-Earth-ConsortiumEC-Earth3-Veg-LRssp585r1i1p1f1SImonsithickgnv202012012091-01-16T12:00:002091-12-16T12:00:000.05, -78.58, 359.99, 89.74NaN
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3c3s-cmip6.ScenarioMIP.EC-Earth-Consortium.EC-E...ScenarioMIP/EC-Earth-Consortium/EC-Earth3-CC/s...167116577c3s-cmip6ScenarioMIPEC-Earth-ConsortiumEC-Earth3-CCssp245r1i1p1f1dayhussgrv202101132066-01-01T12:00:002066-12-31T12:00:000.00, -89.46, 359.30, 89.462.00
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" + ], + "text/plain": [ + " ds_id \\\n", + "48328 c3s-cmip6.CMIP.MIROC.MIROC6.historical.r1i1p1f... \n", + "49075 c3s-cmip6.CMIP.MIROC.MIROC6.historical.r1i1p1f... \n", + "50196 c3s-cmip6.CMIP.MIROC.MIROC6.historical.r1i1p1f... \n", + "52562 c3s-cmip6.CMIP.MIROC.MIROC6.historical.r1i1p1f... \n", + "54490 c3s-cmip6.CMIP.MIROC.MIROC6.historical.r1i1p1f... \n", + "\n", + " path size \\\n", + "48328 CMIP/MIROC/MIROC6/historical/r1i1p1f1/day/tas/... 268131835 \n", + "49075 CMIP/MIROC/MIROC6/historical/r1i1p1f1/day/tas/... 268260608 \n", + "50196 CMIP/MIROC/MIROC6/historical/r1i1p1f1/day/tas/... 268187514 \n", + "52562 CMIP/MIROC/MIROC6/historical/r1i1p1f1/day/tas/... 268278571 \n", + "54490 CMIP/MIROC/MIROC6/historical/r1i1p1f1/day/tas/... 268214955 \n", + "\n", + " mip_era activity_id institution_id source_id experiment_id member_id \\\n", + "48328 c3s-cmip6 CMIP MIROC MIROC6 historical r1i1p1f1 \n", + "49075 c3s-cmip6 CMIP MIROC MIROC6 historical r1i1p1f1 \n", + "50196 c3s-cmip6 CMIP MIROC MIROC6 historical r1i1p1f1 \n", + "52562 c3s-cmip6 CMIP MIROC MIROC6 historical r1i1p1f1 \n", + "54490 c3s-cmip6 CMIP MIROC MIROC6 historical r1i1p1f1 \n", + "\n", + " table_id variable_id grid_label version start_time \\\n", + "48328 day tas gn v20191016 1850-01-01T12:00:00 \n", + "49075 day tas gn v20191016 1860-01-01T12:00:00 \n", + "50196 day tas gn v20191016 1870-01-01T12:00:00 \n", + "52562 day tas gn v20191016 1880-01-01T12:00:00 \n", + "54490 day tas gn v20191016 1890-01-01T12:00:00 \n", + "\n", + " end_time bbox level \n", + "48328 1859-12-31T12:00:00 0.00, -88.93, 358.59, 88.93 2.00 \n", + "49075 1869-12-31T12:00:00 0.00, -88.93, 358.59, 88.93 2.00 \n", + "50196 1879-12-31T12:00:00 0.00, -88.93, 358.59, 88.93 2.00 \n", + "52562 1889-12-31T12:00:00 0.00, -88.93, 358.59, 88.93 2.00 \n", + "54490 1899-12-31T12:00:00 0.00, -88.93, 358.59, 88.93 2.00 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = df_cmip6.loc[\n", " (df_cmip6.variable_id==\"tas\") \n", @@ -152,38 +650,878 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "maritime-association", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array(['c3s-cmip6.CMIP.MIROC.MIROC6.historical.r1i1p1f1.day.tas.gn.v20191016'],\n", + " dtype=object)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.ds_id.unique()" ] }, { "cell_type": "markdown", - "id": "159209e3-bb6d-4280-8c44-240de51d28bf", + "id": "2967e076-00f6-4353-a287-5301b51f0dc3", "metadata": {}, "source": [ - "## Load Catalog for C3S-CORDEX" + "## Load catalog for c3s-cmip6-decadal" ] }, { "cell_type": "code", - "execution_count": null, - "id": "b8c69d80-3e2c-4892-9707-8882ed4d1aaa", + "execution_count": 6, + "id": "0e1b6e47-c67b-4d7b-8aa1-efaae72a6ee3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ds_idpathsizemip_eraactivity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelversionstart_timeend_timebboxlevel
0c3s-cmip6-decadal.DCPP.MPI-M.MPI-ESM1-2-HR.dcp...DCPP/MPI-M/MPI-ESM1-2-HR/dcppA-hindcast/s2016-...492479692c3s-cmip6-decadalDCPPMPI-MMPI-ESM1-2-HRdcppA-hindcasts2016-r8i1p1f1daytasmaxgnv202101112016-11-01T12:00:002026-12-31T12:00:000.00, -89.28, 359.06, 89.282.00
1c3s-cmip6-decadal.DCPP.MPI-M.MPI-ESM1-2-HR.dcp...DCPP/MPI-M/MPI-ESM1-2-HR/dcppA-hindcast/s2009-...737204109c3s-cmip6-decadalDCPPMPI-MMPI-ESM1-2-HRdcppA-hindcasts2009-r8i1p1f1dayprgnv202101072009-11-01T12:00:002019-12-31T12:00:000.00, -89.28, 359.06, 89.28NaN
2c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dc...DCPP/MOHC/HadGEM3-GC31-MM/dcppA-hindcast/s1987...953384c3s-cmip6-decadalDCPPMOHCHadGEM3-GC31-MMdcppA-hindcasts1987-r3i1p1f2Amonprgnv202004171987-11-16T00:00:001987-12-16T00:00:000.42, -89.72, 359.58, 89.72NaN
3c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dc...DCPP/MOHC/HadGEM3-GC31-MM/dcppA-hindcast/s1995...603757c3s-cmip6-decadalDCPPMOHCHadGEM3-GC31-MMdcppA-hindcasts1995-r6i1p1f2Amontasgnv202004171995-11-16T00:00:001995-12-16T00:00:000.42, -89.72, 359.58, 89.721.50
4c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dc...DCPP/MOHC/HadGEM3-GC31-MM/dcppA-hindcast/s2004...14824913c3s-cmip6-decadalDCPPMOHCHadGEM3-GC31-MMdcppA-hindcasts2004-r10i1p1f2AERdayzg500gnv202004172004-11-01T12:00:002004-12-30T12:00:000.42, -89.72, 359.58, 89.7250000.00
......................................................
102225c3s-cmip6-decadal.DCPP.DWD.MPI-ESM1-2-LR.dcppA...DCPP/DWD/MPI-ESM1-2-LR/dcppA-hindcast/s1995-r1...4119531c3s-cmip6-decadalDCPPDWDMPI-ESM1-2-LRdcppA-hindcasts1995-r16i1p1f1Amontasgnv202201261995-11-30T18:00:002005-12-31T18:00:000.00, -88.57, 358.12, 88.572.00
102226c3s-cmip6-decadal.DCPP.DWD.MPI-ESM1-2-LR.dcppA...DCPP/DWD/MPI-ESM1-2-LR/dcppA-hindcast/s1973-r1...6635233c3s-cmip6-decadalDCPPDWDMPI-ESM1-2-LRdcppA-hindcasts1973-r16i1p1f1Amonprgnv202201261973-11-30T18:00:001983-12-31T18:00:000.00, -88.57, 358.12, 88.57NaN
102227c3s-cmip6-decadal.DCPP.DWD.MPI-ESM1-2-LR.dcppA...DCPP/DWD/MPI-ESM1-2-LR/dcppA-hindcast/s2014-r8...4114536c3s-cmip6-decadalDCPPDWDMPI-ESM1-2-LRdcppA-hindcasts2014-r8i1p1f1Amontasgnv202201262014-11-30T18:00:002024-12-31T18:00:000.00, -88.57, 358.12, 88.572.00
102228c3s-cmip6-decadal.DCPP.DWD.MPI-ESM1-2-LR.dcppA...DCPP/DWD/MPI-ESM1-2-LR/dcppA-hindcast/s2006-r1...6641928c3s-cmip6-decadalDCPPDWDMPI-ESM1-2-LRdcppA-hindcasts2006-r14i1p1f1Amonprgnv202201262006-11-30T18:00:002016-12-31T18:00:000.00, -88.57, 358.12, 88.57NaN
102229c3s-cmip6-decadal.DCPP.DWD.MPI-ESM1-2-LR.dcppA...DCPP/DWD/MPI-ESM1-2-LR/dcppA-hindcast/s2012-r1...138029115c3s-cmip6-decadalDCPPDWDMPI-ESM1-2-LRdcppA-hindcasts2012-r10i1p1f1daytasmaxgnv202201262012-11-01T09:00:002022-12-31T09:00:000.00, -88.57, 358.12, 88.572.00
\n", + "

102230 rows × 17 columns

\n", + "
" + ], + "text/plain": [ + " ds_id \\\n", + "0 c3s-cmip6-decadal.DCPP.MPI-M.MPI-ESM1-2-HR.dcp... \n", + "1 c3s-cmip6-decadal.DCPP.MPI-M.MPI-ESM1-2-HR.dcp... \n", + "2 c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dc... \n", + "3 c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dc... \n", + "4 c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dc... \n", + "... ... \n", + "102225 c3s-cmip6-decadal.DCPP.DWD.MPI-ESM1-2-LR.dcppA... \n", + "102226 c3s-cmip6-decadal.DCPP.DWD.MPI-ESM1-2-LR.dcppA... \n", + "102227 c3s-cmip6-decadal.DCPP.DWD.MPI-ESM1-2-LR.dcppA... \n", + "102228 c3s-cmip6-decadal.DCPP.DWD.MPI-ESM1-2-LR.dcppA... \n", + "102229 c3s-cmip6-decadal.DCPP.DWD.MPI-ESM1-2-LR.dcppA... \n", + "\n", + " path size \\\n", + "0 DCPP/MPI-M/MPI-ESM1-2-HR/dcppA-hindcast/s2016-... 492479692 \n", + "1 DCPP/MPI-M/MPI-ESM1-2-HR/dcppA-hindcast/s2009-... 737204109 \n", + "2 DCPP/MOHC/HadGEM3-GC31-MM/dcppA-hindcast/s1987... 953384 \n", + "3 DCPP/MOHC/HadGEM3-GC31-MM/dcppA-hindcast/s1995... 603757 \n", + "4 DCPP/MOHC/HadGEM3-GC31-MM/dcppA-hindcast/s2004... 14824913 \n", + "... ... ... \n", + "102225 DCPP/DWD/MPI-ESM1-2-LR/dcppA-hindcast/s1995-r1... 4119531 \n", + "102226 DCPP/DWD/MPI-ESM1-2-LR/dcppA-hindcast/s1973-r1... 6635233 \n", + "102227 DCPP/DWD/MPI-ESM1-2-LR/dcppA-hindcast/s2014-r8... 4114536 \n", + "102228 DCPP/DWD/MPI-ESM1-2-LR/dcppA-hindcast/s2006-r1... 6641928 \n", + "102229 DCPP/DWD/MPI-ESM1-2-LR/dcppA-hindcast/s2012-r1... 138029115 \n", + "\n", + " mip_era activity_id institution_id source_id \\\n", + "0 c3s-cmip6-decadal DCPP MPI-M MPI-ESM1-2-HR \n", + "1 c3s-cmip6-decadal DCPP MPI-M MPI-ESM1-2-HR \n", + "2 c3s-cmip6-decadal DCPP MOHC HadGEM3-GC31-MM \n", + "3 c3s-cmip6-decadal DCPP MOHC HadGEM3-GC31-MM \n", + "4 c3s-cmip6-decadal DCPP MOHC HadGEM3-GC31-MM \n", + "... ... ... ... ... \n", + "102225 c3s-cmip6-decadal DCPP DWD MPI-ESM1-2-LR \n", + "102226 c3s-cmip6-decadal DCPP DWD MPI-ESM1-2-LR \n", + "102227 c3s-cmip6-decadal DCPP DWD MPI-ESM1-2-LR \n", + "102228 c3s-cmip6-decadal DCPP DWD MPI-ESM1-2-LR \n", + "102229 c3s-cmip6-decadal DCPP DWD MPI-ESM1-2-LR \n", + "\n", + " experiment_id member_id table_id variable_id grid_label \\\n", + "0 dcppA-hindcast s2016-r8i1p1f1 day tasmax gn \n", + "1 dcppA-hindcast s2009-r8i1p1f1 day pr gn \n", + "2 dcppA-hindcast s1987-r3i1p1f2 Amon pr gn \n", + "3 dcppA-hindcast s1995-r6i1p1f2 Amon tas gn \n", + "4 dcppA-hindcast s2004-r10i1p1f2 AERday zg500 gn \n", + "... ... ... ... ... ... \n", + "102225 dcppA-hindcast s1995-r16i1p1f1 Amon tas gn \n", + "102226 dcppA-hindcast s1973-r16i1p1f1 Amon pr gn \n", + "102227 dcppA-hindcast s2014-r8i1p1f1 Amon tas gn \n", + "102228 dcppA-hindcast s2006-r14i1p1f1 Amon pr gn \n", + "102229 dcppA-hindcast s2012-r10i1p1f1 day tasmax gn \n", + "\n", + " version start_time end_time \\\n", + "0 v20210111 2016-11-01T12:00:00 2026-12-31T12:00:00 \n", + "1 v20210107 2009-11-01T12:00:00 2019-12-31T12:00:00 \n", + "2 v20200417 1987-11-16T00:00:00 1987-12-16T00:00:00 \n", + "3 v20200417 1995-11-16T00:00:00 1995-12-16T00:00:00 \n", + "4 v20200417 2004-11-01T12:00:00 2004-12-30T12:00:00 \n", + "... ... ... ... \n", + "102225 v20220126 1995-11-30T18:00:00 2005-12-31T18:00:00 \n", + "102226 v20220126 1973-11-30T18:00:00 1983-12-31T18:00:00 \n", + "102227 v20220126 2014-11-30T18:00:00 2024-12-31T18:00:00 \n", + "102228 v20220126 2006-11-30T18:00:00 2016-12-31T18:00:00 \n", + "102229 v20220126 2012-11-01T09:00:00 2022-12-31T09:00:00 \n", + "\n", + " bbox level \n", + "0 0.00, -89.28, 359.06, 89.28 2.00 \n", + "1 0.00, -89.28, 359.06, 89.28 NaN \n", + "2 0.42, -89.72, 359.58, 89.72 NaN \n", + "3 0.42, -89.72, 359.58, 89.72 1.50 \n", + "4 0.42, -89.72, 359.58, 89.72 50000.00 \n", + "... ... ... \n", + "102225 0.00, -88.57, 358.12, 88.57 2.00 \n", + "102226 0.00, -88.57, 358.12, 88.57 NaN \n", + "102227 0.00, -88.57, 358.12, 88.57 2.00 \n", + "102228 0.00, -88.57, 358.12, 88.57 NaN \n", + "102229 0.00, -88.57, 358.12, 88.57 2.00 \n", + "\n", + "[102230 rows x 17 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_cmip6_decadal = cat['c3s-cmip6-decadal'].read()\n", + "df_cmip6_decadal" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9f40de66-b9ad-4062-885d-543d30574f37", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ds_id 10\n", + "path 10\n", + "size 10\n", + "mip_era 1\n", + "activity_id 1\n", + "institution_id 1\n", + "source_id 1\n", + "experiment_id 1\n", + "member_id 10\n", + "table_id 1\n", + "variable_id 1\n", + "grid_label 1\n", + "version 1\n", + "start_time 1\n", + "end_time 1\n", + "bbox 1\n", + "level 1\n", + "dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "print(cat['c3s-cordex'])" + "df = df_cmip6_decadal.loc[\n", + " (df_cmip6_decadal.variable_id==\"tas\") \n", + " & (df_cmip6_decadal.experiment_id==\"dcppA-hindcast\")\n", + " & (df_cmip6_decadal.table_id==\"Amon\")\n", + " & (df_cmip6_decadal.institution_id==\"MOHC\")\n", + " & (df_cmip6_decadal.start_time==\"1995-11-16T00:00:00\")\n", + "]\n", + "df.nunique()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, + "id": "0208c132-dc03-46d9-b2bd-56f4296b02ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r10i1p1f2.Amon.tas.gn.v20200417',\n", + " 'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r1i1p1f2.Amon.tas.gn.v20200417',\n", + " 'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r2i1p1f2.Amon.tas.gn.v20200417',\n", + " 'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r3i1p1f2.Amon.tas.gn.v20200417',\n", + " 'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r4i1p1f2.Amon.tas.gn.v20200417',\n", + " 'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r5i1p1f2.Amon.tas.gn.v20200417',\n", + " 'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r6i1p1f2.Amon.tas.gn.v20200417',\n", + " 'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r7i1p1f2.Amon.tas.gn.v20200417',\n", + " 'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r8i1p1f2.Amon.tas.gn.v20200417',\n", + " 'c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r9i1p1f2.Amon.tas.gn.v20200417']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_ids = list(df.ds_id)\n", + "ds_ids.sort()\n", + "ds_ids" + ] + }, + { + "cell_type": "markdown", + "id": "159209e3-bb6d-4280-8c44-240de51d28bf", + "metadata": {}, + "source": [ + "## Load Catalog for C3S-CORDEX" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "d04937d7-744b-45db-876a-ae487751b0ab", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ds_idpathsizeprojectproductdomaininstitutedriving_modelexperimentensemblercm_namercm_versiontime_frequencyvariableversionstart_timeend_timebboxlevelexperiment_id
0c3s-cordex.output.AFR-22.ICTP.MOHC-HadGEM2-ES....output/AFR-22/ICTP/MOHC-HadGEM2-ES/rcp85/r1i1p...21597412c3s-cordexoutputAFR-22ICTPMOHC-HadGEM2-ESrcp85r1i1p1ICTP-RegCM4-7v0dayuasv202010152005-12-01T12:00:002005-12-30T12:00:00-41.54, -49.92, 65.54, 47.2810.00NaN
1c3s-cordex.output.AFR-22.ICTP.MPI-M-MPI-ESM-MR...output/AFR-22/ICTP/MPI-M-MPI-ESM-MR/rcp26/r1i1...19464039c3s-cordexoutputAFR-22ICTPMPI-M-MPI-ESM-MRrcp26r1i1p1ICTP-RegCM4-7v0dayhursv202010152005-12-01T12:00:002005-12-31T12:00:00-41.54, -49.92, 65.54, 47.282.00NaN
2c3s-cordex.output.AFR-22.ICTP.MOHC-HadGEM2-ES....output/AFR-22/ICTP/MOHC-HadGEM2-ES/rcp26/r1i1p...14791855c3s-cordexoutputAFR-22ICTPMOHC-HadGEM2-ESrcp26r1i1p1ICTP-RegCM4-7v0daytasv202010152005-12-01T12:00:002005-12-30T12:00:00-41.54, -49.92, 65.54, 47.282.00NaN
3c3s-cordex.output.AFR-22.ICTP.MPI-M-MPI-ESM-MR...output/AFR-22/ICTP/MPI-M-MPI-ESM-MR/historical...236295975c3s-cordexoutputAFR-22ICTPMPI-M-MPI-ESM-MRhistoricalr1i1p1ICTP-RegCM4-7v0dayrsdsv202010151970-01-01T12:00:001970-12-31T12:00:00-41.54, -49.92, 65.54, 47.28NaNNaN
4c3s-cordex.output.AFR-22.ICTP.MOHC-HadGEM2-ES....output/AFR-22/ICTP/MOHC-HadGEM2-ES/rcp85/r1i1p...18728887c3s-cordexoutputAFR-22ICTPMOHC-HadGEM2-ESrcp85r1i1p1ICTP-RegCM4-7v0dayhursv202010152005-12-01T12:00:002005-12-30T12:00:00-41.54, -49.92, 65.54, 47.282.00NaN
...............................................................
566767c3s-cordex.output.EUR-11.CNRM.CNRM-CERFACS-CNR...output/EUR-11/CNRM/CNRM-CERFACS-CNRM-CM5/rcp85...1129745566c3s-cordexoutputEUR-11CNRMCNRM-CERFACS-CNRM-CM5NaNr1i1p1CNRM-ALADIN63v2daysfcWindv201904192091-01-01T12:00:002095-12-31T12:00:00-51.56, 20.98, 72.56, 73.97NaNrcp85
566768c3s-cordex.output.EUR-11.ICTP.MPI-M-MPI-ESM-LR...output/EUR-11/ICTP/MPI-M-MPI-ESM-LR/rcp26/r1i1...1448576089c3s-cordexoutputEUR-11ICTPMPI-M-MPI-ESM-LRNaNr1i1p1ICTP-RegCM4-6v13hrrsusv201905022012-01-01T01:30:002012-12-31T22:30:00-62.74, 14.77, 82.24, 76.33NaNrcp26
566769c3s-cordex.output.EUR-11.ICTP.ICHEC-EC-EARTH.r...output/EUR-11/ICTP/ICHEC-EC-EARTH/rcp85/r12i1p...294010575c3s-cordexoutputEUR-11ICTPICHEC-EC-EARTHNaNr12i1p1ICTP-RegCM4-6v1dayrsusv201905022009-01-01T12:00:002009-12-31T12:00:00-62.74, 14.77, 82.24, 76.33NaNrcp85
566770c3s-cordex.output.EUR-11.CNRM.CNRM-CERFACS-CNR...output/EUR-11/CNRM/CNRM-CERFACS-CNRM-CM5/rcp85...1129918622c3s-cordexoutputEUR-11CNRMCNRM-CERFACS-CNRM-CM5NaNr1i1p1CNRM-ALADIN63v2daysfcWindv201904192096-01-01T12:00:002100-12-31T12:00:00-51.56, 20.98, 72.56, 73.97NaNrcp85
566771c3s-cordex.output.EUR-11.ICTP.MPI-M-MPI-ESM-LR...output/EUR-11/ICTP/MPI-M-MPI-ESM-LR/rcp26/r1i1...1445865590c3s-cordexoutputEUR-11ICTPMPI-M-MPI-ESM-LRNaNr1i1p1ICTP-RegCM4-6v13hrrsusv201905022013-01-01T01:30:002013-12-31T22:30:00-62.74, 14.77, 82.24, 76.33NaNrcp26
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566772 rows × 20 columns

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" + ], + "text/plain": [ + " ds_id \\\n", + "0 c3s-cordex.output.AFR-22.ICTP.MOHC-HadGEM2-ES.... \n", + "1 c3s-cordex.output.AFR-22.ICTP.MPI-M-MPI-ESM-MR... \n", + "2 c3s-cordex.output.AFR-22.ICTP.MOHC-HadGEM2-ES.... \n", + "3 c3s-cordex.output.AFR-22.ICTP.MPI-M-MPI-ESM-MR... \n", + "4 c3s-cordex.output.AFR-22.ICTP.MOHC-HadGEM2-ES.... \n", + "... ... \n", + "566767 c3s-cordex.output.EUR-11.CNRM.CNRM-CERFACS-CNR... \n", + "566768 c3s-cordex.output.EUR-11.ICTP.MPI-M-MPI-ESM-LR... \n", + "566769 c3s-cordex.output.EUR-11.ICTP.ICHEC-EC-EARTH.r... \n", + "566770 c3s-cordex.output.EUR-11.CNRM.CNRM-CERFACS-CNR... \n", + "566771 c3s-cordex.output.EUR-11.ICTP.MPI-M-MPI-ESM-LR... \n", + "\n", + " path size \\\n", + "0 output/AFR-22/ICTP/MOHC-HadGEM2-ES/rcp85/r1i1p... 21597412 \n", + "1 output/AFR-22/ICTP/MPI-M-MPI-ESM-MR/rcp26/r1i1... 19464039 \n", + "2 output/AFR-22/ICTP/MOHC-HadGEM2-ES/rcp26/r1i1p... 14791855 \n", + "3 output/AFR-22/ICTP/MPI-M-MPI-ESM-MR/historical... 236295975 \n", + "4 output/AFR-22/ICTP/MOHC-HadGEM2-ES/rcp85/r1i1p... 18728887 \n", + "... ... ... \n", + "566767 output/EUR-11/CNRM/CNRM-CERFACS-CNRM-CM5/rcp85... 1129745566 \n", + "566768 output/EUR-11/ICTP/MPI-M-MPI-ESM-LR/rcp26/r1i1... 1448576089 \n", + "566769 output/EUR-11/ICTP/ICHEC-EC-EARTH/rcp85/r12i1p... 294010575 \n", + "566770 output/EUR-11/CNRM/CNRM-CERFACS-CNRM-CM5/rcp85... 1129918622 \n", + "566771 output/EUR-11/ICTP/MPI-M-MPI-ESM-LR/rcp26/r1i1... 1445865590 \n", + "\n", + " project product domain institute driving_model \\\n", + "0 c3s-cordex output AFR-22 ICTP MOHC-HadGEM2-ES \n", + "1 c3s-cordex output AFR-22 ICTP MPI-M-MPI-ESM-MR \n", + "2 c3s-cordex output AFR-22 ICTP MOHC-HadGEM2-ES \n", + "3 c3s-cordex output AFR-22 ICTP MPI-M-MPI-ESM-MR \n", + "4 c3s-cordex output AFR-22 ICTP MOHC-HadGEM2-ES \n", + "... ... ... ... ... ... \n", + "566767 c3s-cordex output EUR-11 CNRM CNRM-CERFACS-CNRM-CM5 \n", + "566768 c3s-cordex output EUR-11 ICTP MPI-M-MPI-ESM-LR \n", + "566769 c3s-cordex output EUR-11 ICTP ICHEC-EC-EARTH \n", + "566770 c3s-cordex output EUR-11 CNRM CNRM-CERFACS-CNRM-CM5 \n", + "566771 c3s-cordex output EUR-11 ICTP MPI-M-MPI-ESM-LR \n", + "\n", + " experiment ensemble rcm_name rcm_version time_frequency \\\n", + "0 rcp85 r1i1p1 ICTP-RegCM4-7 v0 day \n", + "1 rcp26 r1i1p1 ICTP-RegCM4-7 v0 day \n", + "2 rcp26 r1i1p1 ICTP-RegCM4-7 v0 day \n", + "3 historical r1i1p1 ICTP-RegCM4-7 v0 day \n", + "4 rcp85 r1i1p1 ICTP-RegCM4-7 v0 day \n", + "... ... ... ... ... ... \n", + "566767 NaN r1i1p1 CNRM-ALADIN63 v2 day \n", + "566768 NaN r1i1p1 ICTP-RegCM4-6 v1 3hr \n", + "566769 NaN r12i1p1 ICTP-RegCM4-6 v1 day \n", + "566770 NaN r1i1p1 CNRM-ALADIN63 v2 day \n", + "566771 NaN r1i1p1 ICTP-RegCM4-6 v1 3hr \n", + "\n", + " variable version start_time end_time \\\n", + "0 uas v20201015 2005-12-01T12:00:00 2005-12-30T12:00:00 \n", + "1 hurs v20201015 2005-12-01T12:00:00 2005-12-31T12:00:00 \n", + "2 tas v20201015 2005-12-01T12:00:00 2005-12-30T12:00:00 \n", + "3 rsds v20201015 1970-01-01T12:00:00 1970-12-31T12:00:00 \n", + "4 hurs v20201015 2005-12-01T12:00:00 2005-12-30T12:00:00 \n", + "... ... ... ... ... \n", + "566767 sfcWind v20190419 2091-01-01T12:00:00 2095-12-31T12:00:00 \n", + "566768 rsus v20190502 2012-01-01T01:30:00 2012-12-31T22:30:00 \n", + "566769 rsus v20190502 2009-01-01T12:00:00 2009-12-31T12:00:00 \n", + "566770 sfcWind v20190419 2096-01-01T12:00:00 2100-12-31T12:00:00 \n", + "566771 rsus v20190502 2013-01-01T01:30:00 2013-12-31T22:30:00 \n", + "\n", + " bbox level experiment_id \n", + "0 -41.54, -49.92, 65.54, 47.28 10.00 NaN \n", + "1 -41.54, -49.92, 65.54, 47.28 2.00 NaN \n", + "2 -41.54, -49.92, 65.54, 47.28 2.00 NaN \n", + "3 -41.54, -49.92, 65.54, 47.28 NaN NaN \n", + "4 -41.54, -49.92, 65.54, 47.28 2.00 NaN \n", + "... ... ... ... \n", + "566767 -51.56, 20.98, 72.56, 73.97 NaN rcp85 \n", + "566768 -62.74, 14.77, 82.24, 76.33 NaN rcp26 \n", + "566769 -62.74, 14.77, 82.24, 76.33 NaN rcp85 \n", + "566770 -51.56, 20.98, 72.56, 73.97 NaN rcp85 \n", + "566771 -62.74, 14.77, 82.24, 76.33 NaN rcp26 \n", + "\n", + "[566772 rows x 20 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_cordex = cat['c3s-cordex'].read()\n", "df_cordex" @@ -191,10 +1529,222 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "46ec77bb-ce85-4b52-96bb-3e5bb81dabf6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ds_idpathsizeprojectproductdomaininstitutedriving_modelexperimentensemblercm_namercm_versiontime_frequencyvariableversionstart_timeend_timebboxlevelexperiment_id
107046c3s-cordex.output.EUR-11.CLMcom.MOHC-HadGEM2-E...output/EUR-11/CLMcom/MOHC-HadGEM2-ES/rcp85/r1i...25751621c3s-cordexoutputEUR-11CLMcomMOHC-HadGEM2-ESrcp85r1i1p1CLMcom-CCLM4-8-17v1montasv201503202006-01-16T00:00:002010-12-16T00:00:00-44.59, 21.99, 64.96, 72.582.00NaN
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107138c3s-cordex.output.EUR-11.CLMcom.MOHC-HadGEM2-E...output/EUR-11/CLMcom/MOHC-HadGEM2-ES/rcp85/r1i...47688544c3s-cordexoutputEUR-11CLMcomMOHC-HadGEM2-ESrcp85r1i1p1CLMcom-CCLM4-8-17v1montasv201503202021-01-16T00:00:002030-12-16T00:00:00-44.59, 21.99, 64.96, 72.582.00NaN
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107220c3s-cordex.output.EUR-11.CLMcom.MOHC-HadGEM2-E...output/EUR-11/CLMcom/MOHC-HadGEM2-ES/rcp85/r1i...47661324c3s-cordexoutputEUR-11CLMcomMOHC-HadGEM2-ESrcp85r1i1p1CLMcom-CCLM4-8-17v1montasv201503202041-01-16T00:00:002050-12-16T00:00:00-44.59, 21.99, 64.96, 72.582.00NaN
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" + ], + "text/plain": [ + " ds_id \\\n", + "107046 c3s-cordex.output.EUR-11.CLMcom.MOHC-HadGEM2-E... \n", + "107097 c3s-cordex.output.EUR-11.CLMcom.MOHC-HadGEM2-E... \n", + "107138 c3s-cordex.output.EUR-11.CLMcom.MOHC-HadGEM2-E... \n", + "107177 c3s-cordex.output.EUR-11.CLMcom.MOHC-HadGEM2-E... \n", + "107220 c3s-cordex.output.EUR-11.CLMcom.MOHC-HadGEM2-E... \n", + "\n", + " path size \\\n", + "107046 output/EUR-11/CLMcom/MOHC-HadGEM2-ES/rcp85/r1i... 25751621 \n", + "107097 output/EUR-11/CLMcom/MOHC-HadGEM2-ES/rcp85/r1i... 47693820 \n", + "107138 output/EUR-11/CLMcom/MOHC-HadGEM2-ES/rcp85/r1i... 47688544 \n", + "107177 output/EUR-11/CLMcom/MOHC-HadGEM2-ES/rcp85/r1i... 47704709 \n", + "107220 output/EUR-11/CLMcom/MOHC-HadGEM2-ES/rcp85/r1i... 47661324 \n", + "\n", + " project product domain institute driving_model experiment \\\n", + "107046 c3s-cordex output EUR-11 CLMcom MOHC-HadGEM2-ES rcp85 \n", + "107097 c3s-cordex output EUR-11 CLMcom MOHC-HadGEM2-ES rcp85 \n", + "107138 c3s-cordex output EUR-11 CLMcom MOHC-HadGEM2-ES rcp85 \n", + "107177 c3s-cordex output EUR-11 CLMcom MOHC-HadGEM2-ES rcp85 \n", + "107220 c3s-cordex output EUR-11 CLMcom MOHC-HadGEM2-ES rcp85 \n", + "\n", + " ensemble rcm_name rcm_version time_frequency variable \\\n", + "107046 r1i1p1 CLMcom-CCLM4-8-17 v1 mon tas \n", + "107097 r1i1p1 CLMcom-CCLM4-8-17 v1 mon tas \n", + "107138 r1i1p1 CLMcom-CCLM4-8-17 v1 mon tas \n", + "107177 r1i1p1 CLMcom-CCLM4-8-17 v1 mon tas \n", + "107220 r1i1p1 CLMcom-CCLM4-8-17 v1 mon tas \n", + "\n", + " version start_time end_time \\\n", + "107046 v20150320 2006-01-16T00:00:00 2010-12-16T00:00:00 \n", + "107097 v20150320 2011-01-16T00:00:00 2020-12-16T00:00:00 \n", + "107138 v20150320 2021-01-16T00:00:00 2030-12-16T00:00:00 \n", + "107177 v20150320 2031-01-16T00:00:00 2040-12-16T00:00:00 \n", + "107220 v20150320 2041-01-16T00:00:00 2050-12-16T00:00:00 \n", + "\n", + " bbox level experiment_id \n", + "107046 -44.59, 21.99, 64.96, 72.58 2.00 NaN \n", + "107097 -44.59, 21.99, 64.96, 72.58 2.00 NaN \n", + "107138 -44.59, 21.99, 64.96, 72.58 2.00 NaN \n", + "107177 -44.59, 21.99, 64.96, 72.58 2.00 NaN \n", + "107220 -44.59, 21.99, 64.96, 72.58 2.00 NaN " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = df_cordex.loc[\n", " (df_cordex.variable==\"tas\") \n", @@ -209,29 +1759,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "8f5fb385-f19b-4263-8b6a-ba71316307e8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'c3s-cordex.output.EUR-11.CLMcom.MOHC-HadGEM2-ES.rcp85.r1i1p1.CLMcom-CCLM4-8-17.v1.mon.tas.v20150320'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "collection = df.ds_id.unique()[0]\n", "collection" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "86e9b6df-777d-44a8-9a17-3326f4821651", - "metadata": {}, - "outputs": [], - "source": [ - "result = filter_by_time(\n", - " df_cordex, \n", - " collection=collection,\n", - " time=\"2006-01-01/2006-12-31\")\n", - "result" - ] - }, { "cell_type": "markdown", "id": "b706e525-4a39-4eaf-b3ed-a3a86ffba534", @@ -242,20 +1789,437 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "8d339ff3-9d95-414d-8e97-638e2d03fadb", - "metadata": {}, - "outputs": [], - "source": [ - "print(cat['c3s-cmip5'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "34858887-9592-4e73-8cb6-30e0bed63671", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ds_idpathsizeprojectproductinstitutemodelexperimenttime_frequencyrealm...versionstart_timeend_timebboxlevelunitsvariable_namedescriptionurllabel
0c3s-cmip5.output1.BCC.bcc-csm1-1-m.amip.mon.at...output1/BCC/bcc-csm1-1-m/amip/mon/atmos/Amon/r...NaNc3s-cmip5output1BCCbcc-csm1-1-mamipmonatmos...v201812011979-01-01T12:00:002008-12-31T12:00:00NaNNaNKNear-surface (2m) air temperature (tas)Temperature of the air near the surface.http://data.mips.copernicus-climate.eu/thredds...cmip5-monthly-single-level
1c3s-cmip5.output1.BCC.bcc-csm1-1-m.amip.mon.at...output1/BCC/bcc-csm1-1-m/amip/mon/atmos/Amon/r...NaNc3s-cmip5output1BCCbcc-csm1-1-mamipmonatmos...v201812011979-01-01T12:00:002008-12-31T12:00:00NaNNaNKNear-surface (2m) air temperature (tas)Temperature of the air near the surface.http://data.mips.copernicus-climate.eu/thredds...cmip5-monthly-single-level
2c3s-cmip5.output1.BCC.bcc-csm1-1-m.amip.mon.at...output1/BCC/bcc-csm1-1-m/amip/mon/atmos/Amon/r...NaNc3s-cmip5output1BCCbcc-csm1-1-mamipmonatmos...v201812011979-01-01T12:00:002008-12-31T12:00:00NaNNaNKNear-surface (2m) air temperature (tas)Temperature of the air near the surface.http://data.mips.copernicus-climate.eu/thredds...cmip5-monthly-single-level
3c3s-cmip5.output1.BCC.bcc-csm1-1-m.historical....output1/BCC/bcc-csm1-1-m/historical/mon/atmos/...NaNc3s-cmip5output1BCCbcc-csm1-1-mhistoricalmonatmos...v201812011850-01-01T12:00:002012-12-31T12:00:00NaNNaNKNear-surface (2m) air temperature (tas)Temperature of the air near the surface.http://data.mips.copernicus-climate.eu/thredds...cmip5-monthly-single-level
4c3s-cmip5.output1.BCC.bcc-csm1-1-m.historical....output1/BCC/bcc-csm1-1-m/historical/mon/atmos/...NaNc3s-cmip5output1BCCbcc-csm1-1-mhistoricalmonatmos...v201812011850-01-01T12:00:002012-12-31T12:00:00NaNNaNKNear-surface (2m) air temperature (tas)Temperature of the air near the surface.http://data.mips.copernicus-climate.eu/thredds...cmip5-monthly-single-level
..................................................................
136187c3s-cmip5.output1.NOAA-GFDL.GFDL-HIRAM-C360.am...output1/NOAA-GFDL/GFDL-HIRAM-C360/amip/day/atm...NaNc3s-cmip5output1NOAA-GFDLGFDL-HIRAM-C360amipdayatmos...v201106012004-01-01T12:00:002004-12-31T12:00:00NaNNaNm.s^-1Meridional component of Wind (va)Magnitude of the meridional (northward) compon...http://data.mips.copernicus-climate.eu/thredds...cmip5-daily-pressure-level
136188c3s-cmip5.output1.NOAA-GFDL.GFDL-HIRAM-C360.am...output1/NOAA-GFDL/GFDL-HIRAM-C360/amip/day/atm...NaNc3s-cmip5output1NOAA-GFDLGFDL-HIRAM-C360amipdayatmos...v201106012005-01-01T12:00:002005-12-31T12:00:00NaNNaNm.s^-1Meridional component of Wind (va)Magnitude of the meridional (northward) compon...http://data.mips.copernicus-climate.eu/thredds...cmip5-daily-pressure-level
136189c3s-cmip5.output1.NOAA-GFDL.GFDL-HIRAM-C360.am...output1/NOAA-GFDL/GFDL-HIRAM-C360/amip/day/atm...NaNc3s-cmip5output1NOAA-GFDLGFDL-HIRAM-C360amipdayatmos...v201106012006-01-01T12:00:002006-12-31T12:00:00NaNNaNm.s^-1Meridional component of Wind (va)Magnitude of the meridional (northward) compon...http://data.mips.copernicus-climate.eu/thredds...cmip5-daily-pressure-level
136190c3s-cmip5.output1.NOAA-GFDL.GFDL-HIRAM-C360.am...output1/NOAA-GFDL/GFDL-HIRAM-C360/amip/day/atm...NaNc3s-cmip5output1NOAA-GFDLGFDL-HIRAM-C360amipdayatmos...v201106012007-01-01T12:00:002007-12-31T12:00:00NaNNaNm.s^-1Meridional component of Wind (va)Magnitude of the meridional (northward) compon...http://data.mips.copernicus-climate.eu/thredds...cmip5-daily-pressure-level
136191c3s-cmip5.output1.NOAA-GFDL.GFDL-HIRAM-C360.am...output1/NOAA-GFDL/GFDL-HIRAM-C360/amip/day/atm...NaNc3s-cmip5output1NOAA-GFDLGFDL-HIRAM-C360amipdayatmos...v201106012008-01-01T12:00:002008-12-31T12:00:00NaNNaNm.s^-1Meridional component of Wind (va)Magnitude of the meridional (northward) compon...http://data.mips.copernicus-climate.eu/thredds...cmip5-daily-pressure-level
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136192 rows × 23 columns

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" + ], + "text/plain": [ + " ds_id \\\n", + "0 c3s-cmip5.output1.BCC.bcc-csm1-1-m.amip.mon.at... \n", + "1 c3s-cmip5.output1.BCC.bcc-csm1-1-m.amip.mon.at... \n", + "2 c3s-cmip5.output1.BCC.bcc-csm1-1-m.amip.mon.at... \n", + "3 c3s-cmip5.output1.BCC.bcc-csm1-1-m.historical.... \n", + "4 c3s-cmip5.output1.BCC.bcc-csm1-1-m.historical.... \n", + "... ... \n", + "136187 c3s-cmip5.output1.NOAA-GFDL.GFDL-HIRAM-C360.am... \n", + "136188 c3s-cmip5.output1.NOAA-GFDL.GFDL-HIRAM-C360.am... \n", + "136189 c3s-cmip5.output1.NOAA-GFDL.GFDL-HIRAM-C360.am... \n", + "136190 c3s-cmip5.output1.NOAA-GFDL.GFDL-HIRAM-C360.am... \n", + "136191 c3s-cmip5.output1.NOAA-GFDL.GFDL-HIRAM-C360.am... \n", + "\n", + " path size project \\\n", + "0 output1/BCC/bcc-csm1-1-m/amip/mon/atmos/Amon/r... NaN c3s-cmip5 \n", + "1 output1/BCC/bcc-csm1-1-m/amip/mon/atmos/Amon/r... NaN c3s-cmip5 \n", + "2 output1/BCC/bcc-csm1-1-m/amip/mon/atmos/Amon/r... NaN c3s-cmip5 \n", + "3 output1/BCC/bcc-csm1-1-m/historical/mon/atmos/... NaN c3s-cmip5 \n", + "4 output1/BCC/bcc-csm1-1-m/historical/mon/atmos/... NaN c3s-cmip5 \n", + "... ... ... ... \n", + "136187 output1/NOAA-GFDL/GFDL-HIRAM-C360/amip/day/atm... NaN c3s-cmip5 \n", + "136188 output1/NOAA-GFDL/GFDL-HIRAM-C360/amip/day/atm... NaN c3s-cmip5 \n", + "136189 output1/NOAA-GFDL/GFDL-HIRAM-C360/amip/day/atm... NaN c3s-cmip5 \n", + "136190 output1/NOAA-GFDL/GFDL-HIRAM-C360/amip/day/atm... NaN c3s-cmip5 \n", + "136191 output1/NOAA-GFDL/GFDL-HIRAM-C360/amip/day/atm... NaN c3s-cmip5 \n", + "\n", + " product institute model experiment time_frequency realm \\\n", + "0 output1 BCC bcc-csm1-1-m amip mon atmos \n", + "1 output1 BCC bcc-csm1-1-m amip mon atmos \n", + "2 output1 BCC bcc-csm1-1-m amip mon atmos \n", + "3 output1 BCC bcc-csm1-1-m historical mon atmos \n", + "4 output1 BCC bcc-csm1-1-m historical mon atmos \n", + "... ... ... ... ... ... ... \n", + "136187 output1 NOAA-GFDL GFDL-HIRAM-C360 amip day atmos \n", + "136188 output1 NOAA-GFDL GFDL-HIRAM-C360 amip day atmos \n", + "136189 output1 NOAA-GFDL GFDL-HIRAM-C360 amip day atmos \n", + "136190 output1 NOAA-GFDL GFDL-HIRAM-C360 amip day atmos \n", + "136191 output1 NOAA-GFDL GFDL-HIRAM-C360 amip day atmos \n", + "\n", + " ... version start_time end_time bbox level \\\n", + "0 ... v20181201 1979-01-01T12:00:00 2008-12-31T12:00:00 NaN NaN \n", + "1 ... v20181201 1979-01-01T12:00:00 2008-12-31T12:00:00 NaN NaN \n", + "2 ... v20181201 1979-01-01T12:00:00 2008-12-31T12:00:00 NaN NaN \n", + "3 ... v20181201 1850-01-01T12:00:00 2012-12-31T12:00:00 NaN NaN \n", + "4 ... v20181201 1850-01-01T12:00:00 2012-12-31T12:00:00 NaN NaN \n", + "... ... ... ... ... ... ... \n", + "136187 ... v20110601 2004-01-01T12:00:00 2004-12-31T12:00:00 NaN NaN \n", + "136188 ... v20110601 2005-01-01T12:00:00 2005-12-31T12:00:00 NaN NaN \n", + "136189 ... v20110601 2006-01-01T12:00:00 2006-12-31T12:00:00 NaN NaN \n", + "136190 ... v20110601 2007-01-01T12:00:00 2007-12-31T12:00:00 NaN NaN \n", + "136191 ... v20110601 2008-01-01T12:00:00 2008-12-31T12:00:00 NaN NaN \n", + "\n", + " units variable_name \\\n", + "0 K Near-surface (2m) air temperature (tas) \n", + "1 K Near-surface (2m) air temperature (tas) \n", + "2 K Near-surface (2m) air temperature (tas) \n", + "3 K Near-surface (2m) air temperature (tas) \n", + "4 K Near-surface (2m) air temperature (tas) \n", + "... ... ... \n", + "136187 m.s^-1 Meridional component of Wind (va) \n", + "136188 m.s^-1 Meridional component of Wind (va) \n", + "136189 m.s^-1 Meridional component of Wind (va) \n", + "136190 m.s^-1 Meridional component of Wind (va) \n", + "136191 m.s^-1 Meridional component of Wind (va) \n", + "\n", + " description \\\n", + "0 Temperature of the air near the surface. \n", + "1 Temperature of the air near the surface. \n", + "2 Temperature of the air near the surface. \n", + "3 Temperature of the air near the surface. \n", + "4 Temperature of the air near the surface. \n", + "... ... \n", + "136187 Magnitude of the meridional (northward) compon... \n", + "136188 Magnitude of the meridional (northward) compon... \n", + "136189 Magnitude of the meridional (northward) compon... \n", + "136190 Magnitude of the meridional (northward) compon... \n", + "136191 Magnitude of the meridional (northward) compon... \n", + "\n", + " url \\\n", + "0 http://data.mips.copernicus-climate.eu/thredds... \n", + "1 http://data.mips.copernicus-climate.eu/thredds... \n", + "2 http://data.mips.copernicus-climate.eu/thredds... \n", + "3 http://data.mips.copernicus-climate.eu/thredds... \n", + "4 http://data.mips.copernicus-climate.eu/thredds... \n", + "... ... \n", + "136187 http://data.mips.copernicus-climate.eu/thredds... \n", + "136188 http://data.mips.copernicus-climate.eu/thredds... \n", + "136189 http://data.mips.copernicus-climate.eu/thredds... \n", + "136190 http://data.mips.copernicus-climate.eu/thredds... \n", + "136191 http://data.mips.copernicus-climate.eu/thredds... \n", + "\n", + " label \n", + "0 cmip5-monthly-single-level \n", + "1 cmip5-monthly-single-level \n", + "2 cmip5-monthly-single-level \n", + "3 cmip5-monthly-single-level \n", + "4 cmip5-monthly-single-level \n", + "... ... \n", + "136187 cmip5-daily-pressure-level \n", + "136188 cmip5-daily-pressure-level \n", + "136189 cmip5-daily-pressure-level \n", + "136190 cmip5-daily-pressure-level \n", + "136191 cmip5-daily-pressure-level \n", + "\n", + "[136192 rows x 23 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_cmip5 = cat['c3s-cmip5'].read()\n", "df_cmip5" @@ -263,10 +2227,149 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "b8224e1c-fb84-4c3d-b267-feb26cefcd97", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ds_idpathsizeprojectproductinstitutemodelexperimenttime_frequencyrealm...versionstart_timeend_timebboxlevelunitsvariable_namedescriptionurllabel
9c3s-cmip5.output1.BCC.bcc-csm1-1-m.rcp85.mon.a...output1/BCC/bcc-csm1-1-m/rcp85/mon/atmos/Amon/...NaNc3s-cmip5output1BCCbcc-csm1-1-mrcp85monatmos...v201812012006-01-01T12:00:002099-12-31T12:00:00NaNNaNKNear-surface (2m) air temperature (tas)Temperature of the air near the surface.http://data.mips.copernicus-climate.eu/thredds...cmip5-monthly-single-level
10c3s-cmip5.output1.BCC.bcc-csm1-1-m.rcp85.mon.a...output1/BCC/bcc-csm1-1-m/rcp85/mon/atmos/Amon/...NaNc3s-cmip5output1BCCbcc-csm1-1-mrcp85monatmos...v201812012100-01-01T12:00:002100-12-31T12:00:00NaNNaNKNear-surface (2m) air temperature (tas)Temperature of the air near the surface.http://data.mips.copernicus-climate.eu/thredds...cmip5-monthly-single-level
\n", + "

2 rows × 23 columns

\n", + "
" + ], + "text/plain": [ + " ds_id \\\n", + "9 c3s-cmip5.output1.BCC.bcc-csm1-1-m.rcp85.mon.a... \n", + "10 c3s-cmip5.output1.BCC.bcc-csm1-1-m.rcp85.mon.a... \n", + "\n", + " path size project \\\n", + "9 output1/BCC/bcc-csm1-1-m/rcp85/mon/atmos/Amon/... NaN c3s-cmip5 \n", + "10 output1/BCC/bcc-csm1-1-m/rcp85/mon/atmos/Amon/... NaN c3s-cmip5 \n", + "\n", + " product institute model experiment time_frequency realm ... \\\n", + "9 output1 BCC bcc-csm1-1-m rcp85 mon atmos ... \n", + "10 output1 BCC bcc-csm1-1-m rcp85 mon atmos ... \n", + "\n", + " version start_time end_time bbox level units \\\n", + "9 v20181201 2006-01-01T12:00:00 2099-12-31T12:00:00 NaN NaN K \n", + "10 v20181201 2100-01-01T12:00:00 2100-12-31T12:00:00 NaN NaN K \n", + "\n", + " variable_name \\\n", + "9 Near-surface (2m) air temperature (tas) \n", + "10 Near-surface (2m) air temperature (tas) \n", + "\n", + " description \\\n", + "9 Temperature of the air near the surface. \n", + "10 Temperature of the air near the surface. \n", + "\n", + " url \\\n", + "9 http://data.mips.copernicus-climate.eu/thredds... \n", + "10 http://data.mips.copernicus-climate.eu/thredds... \n", + "\n", + " label \n", + "9 cmip5-monthly-single-level \n", + "10 cmip5-monthly-single-level \n", + "\n", + "[2 rows x 23 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = df_cmip5.loc[\n", " (df_cmip5.variable==\"tas\") \n", @@ -280,28 +2383,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "85441254-938b-40e2-aa3b-9b86544275ee", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'c3s-cmip5.output1.BCC.bcc-csm1-1-m.rcp85.mon.atmos.Amon.r1i1p1.tas.v20181201'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "collection = df.ds_id.unique()[0]\n", "collection" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97971f9f-7cb4-4dc6-8c1d-a33637c095e5", - "metadata": {}, - "outputs": [], - "source": [ - "result = filter_by_time(\n", - " df_cmip5, \n", - " collection=collection,\n", - " time=\"2006-01-01/2006-12-31\")\n", - "result" - ] } ], "metadata": { @@ -320,7 +2420,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.7.12" } }, "nbformat": 4, diff --git a/notebooks/demo/demo-rooki-cmip6-decadal.ipynb b/notebooks/demo/demo-rooki-cmip6-decadal.ipynb index a4b42b2..8a057ca 100644 --- a/notebooks/demo/demo-rooki-cmip6-decadal.ipynb +++ b/notebooks/demo/demo-rooki-cmip6-decadal.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -30,16 +30,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "wf = ops.Subset(\n", " ops.Input(\n", - " 'tas', ['c3s-cmip6.DCPP.CMCC.CMCC-CM2-SR5.dcppA-hindcast.s2005-r10i1p1f1.Amon.tas.gn.v20210719']\n", - " # 'tas', ['cmip6.DCPP.CMCC.CMCC-CM2-SR5.dcppA-hindcast.s2005-r10i1p1f1.Amon.tas.gn.v20210719']\n", + " 'tas', ['c3s-cmip6-decadal.DCPP.MOHC.HadGEM3-GC31-MM.dcppA-hindcast.s1995-r1i1p1f2.Amon.tas.gn.v20200417']\n", " ),\n", - " time=\"2010-01-01/2010-12-30\",\n", + " time=\"1995-01-01/1995-12-31\",\n", ")\n", "\n", "resp = wf.orchestrate()\n", @@ -55,18 +65,472 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading to /var/folders/qb/mg0csz190wd4rxybhhnwjln80000gn/T/metalink_5bb6rumf/tas_Amon_HadGEM3-GC31-MM_dcppA-hindcast_s1995-r1i1p1f2_gn_199511-199512.nc.\n" + ] + } + ], "source": [ "dsets = resp.datasets()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:    (time: 2, bnds: 2, lat: 324, lon: 432)\n",
+       "Coordinates:\n",
+       "  * time       (time) object 1995-11-16 00:00:00 1995-12-16 00:00:00\n",
+       "  * lat        (lat) float64 -89.72 -89.17 -88.61 -88.06 ... 88.61 89.17 89.72\n",
+       "  * lon        (lon) float64 0.4167 1.25 2.083 2.917 ... 357.1 357.9 358.7 359.6\n",
+       "    height     float64 1.5\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    time_bnds  (time, bnds) object 1995-11-01 00:00:00 ... 1996-01-01 00:00:00\n",
+       "    lat_bnds   (lat, bnds) float64 -90.0 -89.44 -89.44 ... 89.44 89.44 90.0\n",
+       "    lon_bnds   (lon, bnds) float64 -1.018e-05 0.8333 0.8333 ... 359.2 360.0\n",
+       "    tas        (time, lat, lon) float32 ...\n",
+       "Attributes: (12/43)\n",
+       "    Conventions:            CF-1.7 CMIP-6.2\n",
+       "    activity_id:            DCPP\n",
+       "    branch_method:          no parent\n",
+       "    branch_time_in_child:   0.0\n",
+       "    branch_time_in_parent:  0.0\n",
+       "    creation_date:          2020-05-26T09:30:20Z\n",
+       "    ...                     ...\n",
+       "    tracking_id:            hdl:21.14100/96ad0d23-a8bc-42f4-b3e7-3dd5287f48e1\n",
+       "    variable_id:            tas\n",
+       "    variable_name:          tas\n",
+       "    variant_label:          r1i1p1f2\n",
+       "    license:                CMIP6 model data produced by Met Office Hadley Ce...\n",
+       "    cmor_version:           3.4.0
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 2, bnds: 2, lat: 324, lon: 432)\n", + "Coordinates:\n", + " * time (time) object 1995-11-16 00:00:00 1995-12-16 00:00:00\n", + " * lat (lat) float64 -89.72 -89.17 -88.61 -88.06 ... 88.61 89.17 89.72\n", + " * lon (lon) float64 0.4167 1.25 2.083 2.917 ... 357.1 357.9 358.7 359.6\n", + " height float64 ...\n", + "Dimensions without coordinates: bnds\n", + "Data variables:\n", + " time_bnds (time, bnds) object ...\n", + " lat_bnds (lat, bnds) float64 ...\n", + " lon_bnds (lon, bnds) float64 ...\n", + " tas (time, lat, lon) float32 ...\n", + "Attributes: (12/43)\n", + " Conventions: CF-1.7 CMIP-6.2\n", + " activity_id: DCPP\n", + " branch_method: no parent\n", + " branch_time_in_child: 0.0\n", + " branch_time_in_parent: 0.0\n", + " creation_date: 2020-05-26T09:30:20Z\n", + " ... ...\n", + " tracking_id: hdl:21.14100/96ad0d23-a8bc-42f4-b3e7-3dd5287f48e1\n", + " variable_id: tas\n", + " variable_name: tas\n", + " variant_label: r1i1p1f2\n", + " license: CMIP6 model data produced by Met Office Hadley Ce...\n", + " cmor_version: 3.4.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds = dsets[0]\n", "ds" @@ -81,9 +545,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'http://rook4.cloud.dkrz.de:80/outputs/rook/9a0c1ccc-f186-11ec-9d5a-fa163ed6c06f/provenance.png'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "prov_plot_url = resp.provenance_image()\n", "prov_plot_url" @@ -91,9 +566,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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IAjH4zegKnDOZTDAYDK7/O4Pu3IPqnFntDIZsmM0mx/+dgXkWScu4ikFvAjQaGZQKMQBOo7E7guJs0GrEMs6McWqVmI1Oo8lZltWZnU4uB1QqwZVxTqXyDL4LjCnpiIiIiIhud86ljr0vcRyuoLacvx1ygvzEzILOjIJ6vd2VJVDMOGhzZe12ZhF0ZiR0BhOmpcocmQgBk2MZYqPRBpNJDC70328ZlEoFNBq1WyCfxpH1TwO1Wu3KAKhSqaBUioGCGo3G9Zjzd+dzqpwITCLJGLBHRERERERERERERHcsZ+Cc80ev13sNsPMMwDPAZDLmet7id6lXcVlXMahOpRQgl9uh0QBKpR0KBRAXm7Mcl1aTs5SrRqN0ZbBTqwGFQoBKCajVOYF0/jGAjoiIiIiIijbn30Jipm33gMDwBgc6g//MZsBiscNgzMkiaDDkLC9sNOrFYECzHUajWNZkAjIzZI4AQMBsEWA222E02mAw+I8E1GhUUCoVbgF9WqhUasdP3gA/9wBAtVoNtVoNrVbLLIB3EQbsEREREREREREREVGRZLFYoNfrYTAYoNfrYbFY8jyWk63OBIMhG3p9tuM5A/R6I6xW3zdWnFnrtFpnxjk7tFo7IrRAsRjBdUNJqxEcN5gU0GoFRyCe43k1xNfz03YiIiIiIqJCJQji33CegYH5DYDzzBSo14uBgGLwn7h8sF5vg8VigsVigt5gdwsOFIMAr10FLFbAYhGg11uh19tgtdq9bk2hkDt+FNBqtdBoNNBqIx3/aqFQKHI9p82zDHBUVBQD/4o4foRARERERERERERERAXCufyre3Cd+/9zHtM7Au30jseMMJm8Z7OTyQC1WgaNRgaNWoBabXf8AMVigFLxAjRqMTOdWi2DWi2HRiOIv6vEYDuVSqyHiIiIiIiISCqFAoiOFhAd7XwklKA4MVTLbAZMJjHDn9EkZgA0GsXfDQabmCXeZHT8DmTrBKTeFBzl/Wf+EwQBGo0KGo3aEdCnhUYTkSfIz/3/7o8x2K/gMWCPiIiIiIiIiIiIiPyyWq3Izs6GXq/3+Nf5o9eLAXfO7HbODHcWizVPXXK5M6udHBo1oNHYodXaEB0vZqoTHxOXilU7stepVeISseKSsYWwA4iIiIiIiIjCSKkUM7dHRro/GkygnJj5z2iEWxCfGPRnMAAGgxV6vQ4Go07MCmgAUm8K0OsBg1GAXu874E+tVkKrVTsC+LTQaiOh1UYgIkL80Wq1ef7VarUM9AsCP9ogIiIiIiIiIiIiuovY7XZXoF1WVhZ0Ol2eQDy9PhvZ2TrHcwaYTOY89Wi1cmi1MkRoBWi1NkRE2BEXm7OErEYjQKNRupaQFR8Tb0p4EgAw3R0RERERERFRsNSODPPBLwEsBvyJwX1wLeVrMNgdAX4G6A16GAypMBiAtFTg8iUgWy8uAWw2513SNyJCDa1W4wjii0JERKQroC8yMtL1b2RkJKKioqBSqcKxC25LDNgjIiIiIiIiIiIiugMYDAZkZmZCr9cjKysr1//TodfrkJWVhfT0LNhsnh+sKxRigF10lLi0T4TWjhLF3QPtFI5lf8QMeJGRQq4lZeW3tK9ERERERERElH9ihnsgNsiAP4sFYmCfEa4Mfnq9FQaDDplZWcjMvIpsnQw3rguOMjbodFbY3T6OUCjkrmV7o6NjEBUV7fh/NKKiohAdHY3o6GhoNBpERUXdURn8GLBHREREREREREREVIQZjUZkZGQgIyMDmZmZSE9PR2ZmJjIzM5CenurIkmfweI1cLiAyUo6oSCAy0o7ISDtKl5IhMgKIiJAjOlpAZKSAyAgBERGF1DEiIiIiIiIiui0pFHB8sQ+QFuQng9WqQHY2kKWzIyvLDp3ODp3OhCydETpdKjIzZLiSLECXLQb3uZPLZYiM1KJYsWKIjo5FdHS04//RiImJQUxMDKKjoyGX3x5fKGTAHhERURgYDVlQa6IKuxkUhIz0azi8fw0y01NQp0EnlKtYp7CbRGHk7fiG+j5NuXIaMbGlodZEBv3aO+HccCf0gW4/HHdFW2HPobfT+CjsfXUnyT0f307jIBQF1b9bPSbv9OMULjabDWlpaUhNTUVqaqorMC8jIx2ZmelIT8+E2WxxlVcqZYiJkSE6CoiJsaFUdTErXmSkAlFRYgBeVJSYGY+IipasLBOiogp3yaei0Aa6u3DM0a1wJ4yzW92HO2Gf3UqnT99E6dJRiIy8s/fZtWtZ+HfNSaSk6NCpU3XUrVO6sJtERHcYuRyIjhYD/fwH+clgtyug0wG6bGdwH5CVZURG5lVkZFzBpYsCDh8GdDqbxyoCkZEaREdHISYmFjExYmBfbGws4uLiEBcXh6ioovFZFQP2iIiI8mHbhl+xZe1cXDp3CBNmXyjs5hQZRw6sw/Rv+we9T3ZtTcSJI1sBAOmpyWjf7SXUqtc27HVcuXQMC2aNwLNDfsC86cOwYNYI/DDvKrQRMcjWpWHl4m9hs1nxRP+v/G4r1H76bvsiJO39B1HRJZBy9QxKlamGx57+CApFzocA/238DauWTMCVyydQpnwtPPb0x2hw/0Nh2b47f32Tso+C2Y/BCOX4/jrjLVSq1hAWsxFf/nDAb/0GfSbeer4y9NnprscqVmmAzybuDqqdG1bPwI7NfyD54jF8O/NsUO13d+HMAezZvgRyuRItHngGJeIrSq7n7Mnd+Pt/oxFfugr02RmocU9LtOrQX/K29u38G7u2LEJmRkrA/Qbk75iP/6QbDu371+tzwz5cgvuaPCy5roIae3RrrFn+Y1Dj7m4RjnEt5ZwgpYyvOfTQvn8CzmE7Ni/Esv+NwdXkkyiVUBU9n/kcDZs+IrkNwYyPYM+BvviaD6XM2f6uN/JLyjVIOK5TfM1DvkjZL0DgMe1vPg7HeULKWHSXn+siqfvEydc1hLtgj4uTlDHpra+hzNNS+hHua+miTq/XuwLycn5uIDX1JtLTs2B3rAej0cgQEy1DsWJ2xMXaUamCDDExQHS0EjExAmKicwfi3R7fGqc7y4KFBzB6zHqcOHkD1aoWxxefd8Gjj9xT2M3yae26Uxj5wSr8/msfVK4cF3I9+w8kY8mSw1AqZej3TENUrBgr+bU/zdiJhX8cwNFjKbhw9r2Q25AfwbbBYrFh566LWLnqOFq1rIQunWt4Lbd8xTFM/nEblq84BgBo0bwiZDIBOp0JarUC7dpVweAXm6JatRJeX5+ebsC4bzZi48YzSE3Vo3LlOCgUMtxzTykAQNkyMahSJQ7jxm/Eho1noFTK0bpVJZhMVlitdlSvXgKDBzVFm9aVsWz5UUnlcvvr7yOYPWc3EhcfAgAc3P8G6tX1HaDQoOF3OHDwCkqUiMA7b7fFa6+2RESE0vX8jRvZGDNuA44dSwEApKbqERmpwmuvtsRDD9Zylft3zUlMmrwNfy49DAB4tl9DvPJyczRvVhEGgwU/TNqKbyduxpUrmShVKgpvvdHaY1tS9t2rQ1vkaX9+tyuVvzGn15sxYeJmbNl6DufPp6FChVgolTJUrVocZRJicOr0DUyb0hOLEpMw/ptN+G/7eahUcrRtUwUKhQx2u3iz9uixFNy4kY3jR97G0WMpYTuO4TjPSe2ju2COqdQ2pqUZMP7bjbBa7Rj1VdeA7Q7H+2HN2pNhfU/5UxTOr+66PzIbs2c+ifh46V/8nf/rPsyZuwdJh67g8oWRPsuFaz4ravssGIHG86LEJGzZeg4AkJyciSEvNUO7tlWCLpOZaUSFyqORnp6Twfq+BmWwd/frBdZ+f8c32PdxKNsHgGPHUvDOiBWY/MNjeG3YUrwzYgWuX/0Ify87gm8nbMbxE9dRu1Y8Pvm4I7o/VDvo7Yfy/qBb59ff9gU8zrt2X8So0etRpUpxZGQY0KplZQzo36hA6pFSxt3adafQr/8Cr+dRf2O/S7eZ+OffE17rXLqkPx55uOj+nXM3EAQgKgqIihJQupTv4D7x2tCOjEw7MjOBjAwLMjJTkZmZipRrAk6fBtLTxb8LAECpVCAurhji4kogLq64K5AvLi4OsbGxUChuTSgdA/aIiIjyoVmb3ti4+mdYrOYC3U5aajJi48oUubq8MRqyMPOHQbDDHriwm81rZmPVnxPx+Xd7IAgyXDh7EKNHtsfL7/yKeg27hLWO//3yIcpVqofiJStg0BuzsH/XcmgjYrD7vyXYsWkhdmxeiI7dhxZIP33ZsfkPrEgch4/Gb4NMJofdbsfELx7FonkfofdzYwAAq5dOxOH9a9GyfT9cv3YOG1bPwHdfPobhn61EnQYdw9IOwH/fpOyjYPZjMEI9vnu2L8XKxd/AZrMF3MbGf2bi/pa9EJ+Q8yFNvfukjT93bToNxJZ1v8Bmy8mEEswYT7l6Bn/MeR+6rFQMeOVHlEqoFlQ9F84cwOgPOmD4Z6tQvXZzmE0GfDysEUwmPdp3e0nSttp3HYz1K6dL2m/5OeYpV88g9cZl9HrmMxRzOzfdTLmAFYvHo869HSTXVVBjj26dYMZdfoQ6Fxb0HOpNOMa1lHOC1POGtzn04J5VAeewjf/8jOSLx/DiG7ORlXkDv//8Nn4c0xtfTTqAUmWqS2qD1PERzDnQH1/zoZQ529e+CkewnpRrkPxep/ibh3yRul+kjGl/83F+zxNSxqK7/FwXSd0n7rxdQziFclzcBRqT3voa6jztrx++tnWnyM7OxrVr19x+kpGSch0GgwkAIJMJiImRIy4WiIuzoWplAcWLKxAXJyAullnxqOib8fNOHD2Wgjmzn8KNG9l46+2/8WTv+Th04E1Ur+49IKuwpabqceFiOnTZppBef+ZMKka8vwKpqXpM/bGHz8Azf54f2Bhzf9kDi6Vgr3PD2Yaduy7ipxk7MGv2bsyY/rjPcg89WAv16yWgYpXRqFQpFls3v+xRx8ef/INadb7FiHfb4YvPOkMmy7nRtnzFMbwwaBGqVyuBX+Y85QpQSE3V4823/8acuXswZtSD6P5QbZQrVwwNG3+Pxo3KYe2/gwAAly9noP9zC9Gu/XRMn9oTL77QRHI5d488fA86tK+GxMWfAAC+/2ELpk/t5bW/W7aew6HD1wAAA/o3woh323k8/8+/JzBg4B/o1bMeFv7eF2q1eBtu46YzeObZBejVsy7Gjn4QarUCnTpWR8P7yuLPpYcRHa3GnFlPQhDE/aPRKPDO8LbI0pnw+RdrMOjFJh7bkrrvvMnPdoPha8zt3HURzw5YCIVchrlznkKjhmUBiEGiY8dtwAcfrULPHnUBAI/3qofSpaPQpt00NLm/PP5Z9YJHXSaTFV0fnAmLxRa24xiO81wwfXQK5phKbePiJYewYOEBLFh4wGvwpjfh2I/hfE8FUhTOr07HjqVg+Ypj+GnGDox8v73k1z3d+17M+HknzGb/fcjvfOZUGPssOTkTZcpE56uOQON51uzdmDBxM/bteR0ymYADB6+gXftp+P3XPujapabkMgDw88xdeLxXPVStUtz1WJcu3gPXw9V+X8c3lPdxKNsHgJEfrkK9eqVRoUIxzJn1JJYtP4qfZ+7EmrWn8Gy/hjh7LhU/zdiJRx6bi9Urn0enjnn/hvYl1PdHURGOMVyUTZi4OeBx3n8gGQ90+An/rHoBLZqLQf4NGn0Hvd6MIS81C2s9Usq4y8oy4YVBi2D38hGDv7F/5kwqLl1OxxefdUaZMjmfj5y/kIZx4zeiYwfpY5wKlyA4l+b1F9QnR2YmkJpmR2qqHalp6UhNTcOF86dx4IAd2dk5y+8WKxaF+PjSKF06AaVKlUJ8fDzi4+PDHsgnC2ttREREdxmZTI64kuULdBv67Az8NGFAkavLl8T5n6JsheC+cWI0ZGHhnPfRvF0fCIJ4eVKhcn3cU/8B/D5zuCvbRDjqsNvtOLB7BSIcNye1EcXQvG0fAEDj5j3w3NCpBdZPfzas/gnVareATCZmyBAEAfUbdcW+HX+7+nfmxG688dFSdH7kdfR54Ru8+8U/AASsWDw+bO0A/PdNyj4KZj9KlZ/j26r9s4grUS7gNmw2K/ZuX4rnXpmCR54c6fqpUuP+oNsrk8lRvETOuSGYMX725G588U5LxMaVwfDPVnrcjJdaz++zhqNqzaaoXrs5AECp0qDzI69hwawRMOgzJW1LJldI2m9A/o75kQPr8P7Xa/HIUx+gbefnXT/ayBjUbdgZKnWE5LoKYuzRrRXMuAtVqHPhrZhDvQnHuJZyTpBSxtccGmgOs1hMMBqz0XvgWNc567mh02CxmHDm5G7J7ZQ6PqSeAwPxNR8G6q+/fRUOUq5B8nOd4m9u8EfKfgECj+lA83F+zhNSx6K7/FwXSd0n7nJfQziFelycpIxJb30NdZ721Q9/27rd2O12pKSkYN++fVi1ahV++WUOxo8fi3HjxmHOnDlYv341rqfsR0Lpq+jY3oZ+fZV4fagKH7ynwrBX5ejfT45HuivRupUCde6RoUwCg/Wo6DOZrMjONmP82Idwb/0EtH+gKqZP6wWTyYpduy8WdvN8erxXPVw6/35IS7rt2n0RzVpORpky0Vi98vmQgvUAQC6XoXz5YiG9NlyCbUOL5hXx2qstJZWNjlYDALRaz4xYTe4vj2V/PYene9+Lr0etw+gx613PnTmTiqf7/oZKFWOx9t8XPbIJxcVpMXvmk+jb5z5X4EKUYwlChSLntlbZsjH4+acnYLfb8e57K2C32yWXyy0yUoXKleMQGanCvPn7cONGtte+/jjlP/R4TFxSPi5W6/HchQvp6Pn4PDRtUgGTvn/UFawHAG3bVMGC3/rg+x+2YsT7K12PFysmnvxjYtSuoDl3zm3EFsvZVrD7zptQthssb2Pu+nUdHuw+C0qFDDu3D3UFsgHiMRv5fnuM+qobsrNzvhxdsoSYiUmpzJtVVqWS46XBTV19yO9xDMd5LpQ+BnNMg2ljzx51fQbK+ROO90M46pCiKJxfnaZO3w6NRoEp07YHFQwntQ/5mc9C2V64ZGQY8eyABfmux994zsoyYcT7K9C3TwNXYLj4/qiGt4cvh91ul1QGAKxWG/5cehjTpvTEByPbu36a3J+/+0CB3o++jm+o7+Ngt2+327F8xTEUixHnh2LFNHj0kTrYtfsS/l46AMNeb4UJ3zyMNf+8CEEAxo3fGNT2Q31/FAXhGsNFVVaWSdJxfnv4MjRrWgEtmotZ/jUaBYa91grvjFiOzExj2OqRWsbdx5/+gzqObLS5+Rv7a9edwoa1g/HhBx3wwvP3u36KxWjQpXONoLMLU9EmCEBMDFCpooD7GsjQvp0cvXoo8OLzcrzzlgLvv6vGkMEq9H5SiSaNDdBqzuDUye1YunQJpk+fjq+//gqTJk3EwoULsH79ehw7dgw6nS5fbWLAHhERURFmNOjw49jeuJZ8ukjV5cvRpA0oFlsKZSvWCep1p4/vRGZ6CkrnympSu/4DuHT+MI4f3hy2Ogz6DJhNBm9VAACUSnXAbYXaT38M2Zk4vH8NbNacLCQXzyUhrriYyeTUsR14pLdnKu+qNZuiUrWGuJp8KmztkNI3KftISplghOv4+rNrayLOn9mPKeP7Yv2qn6DPzsh3u52ktj8r4zomfPEoSpepjqefH5fnQ3Mp9aSlJuPw/rWoVbeNR5laddvCaMjCtvXzJW0rWKEe87adn0dUTMk8j+/ethiNmj12y9pBd4dQ58JbMYf6k59xLeWcIPW84escG2gOk8nk6Pjgyx6viYwWb0A5g7CktiEc/ZXC33wYqL9A/uajUNsVTBlf8jM3SNkvTv7GdEHOx1LGorv8XhcFs0/8CcecHWhM+upruOdpf9sq6ux2Oy5fvoxNmzZh3ry5GDNmFH788UcsX/4Xzp/bhWIxF9CqhRnPPqPEW8NUePdtBZ7rL8dD3RS4v7Ec1arKEBcnQMZPY+k2JpcLeOXl5h6PFY8TgyvyexO7KLp+XYeHH52DGtVL4ptx3fP9N9PtSKWStuy2v10jkwmY/MNjKFUqCl+NWodz59IAAP2fW4jMTCO++Lyz10AsAPjsk07IdgSd+dpGpUqx0GgUSEszQKczSy7nTWwxDfo/2wh6vRk/zdiR5/lr17Jw7FgKHmhX1dEmz40Ne/Mv6HQmvPVma6/1t2xRCa1aVsLkH7fhsCOjmLMKX+Mr5/mcx4Ldd/7rlb7dcHhr+DLcuJGNr77smifA02nY6y09lksM1IanezdA7drxrt/zcxzDcZ4LpY/BHNNg26hWS3sf55bf90O46rhd6PVmbNt2HsPfbouLF9Ox5M/Dhd2kIkGnM+HJ3vNx6vTNsNTnazzv2HkBKSk6VK/u+bdL+weq4tDhq9i0+aykMoC4ZO6+/ZfxdN/fMP2nHcjIyBscFO72F9TrgqknI8MIg8EzQ/r2HRfw4UjPbHjNmlZAo4blcPLUDcnbvZ3fH+Eew0WRlOOcnJyJNWtPoW0bz+Wj27atgqwsE+bN3xu2eqSUcbd+w2mULhWFOnW8B+wBvsf+C8/fj5Il8y7RnLg4yRVMTncPlQooXUqyuKvUAAAgAElEQVRA7VoytGopR68eSrw0SI6RI1QYOkSFJ3opUa+uDrCfQFLSFixY8DvGjx+P7777FomJidi7dy8yMoL7HJNL4hIRUdBSb1zC1vXzsG39fLzz+Wr8NPE5XLl0HJ9O2Imo6BLYtXURjiZthFKpxsVzSahcvTEe6/0hFG43s6SUObB7BfbtXAaFXInTJ3agTaeBaNflRQDiUmerlk5E2Qr3ID31CsxmI/oPmQQAOHF4C6aMfwbPDZ2Kext3k7TNQH3y1xan9NQrmDPlFRw/tAklS1XC4LfmomwF8YLObrdj/arpuHDmAM6d2gNtZDE8+9IPKF22ht/+7PlvCS5fOApdVipmTXoJZcrVRLO2T/tsq81qQeL8T1C8ZHncSLmArMzrGPjqdERFl/BaV7eebwdsm1RGgw5rl0/BkOHz8cfckXmez0i76rNtV5NPAADkCpXHa4rFJQAAki8eRa26bfy2VUod16+ewaH9awAAO7f8D1eTT6F0mWp46PF3w9ZPf/yNo9adnsPcKUMx9Zt+GPTGbKRcO4PDB9Zi6LviN7fqNPC+3Jg2IibP8npSxmu4+xaMUMZcuI7v6RM7kTjvI5w9uRtVatyP/kMmIz5B/FDw6MH1MBmzsWtrInZtTcTSBV/i+ddmoF7DzpL6tXf7UuzftRyRUXEwmfRIS00Oqv216rbBH3NHIiPtKl4cNhMyed5LdSn1WC3ijYfc+7N0WTHI78TRbWj/4JCA23Lnb79JEewxT0+9gtPHd+L1kYmux/ydQ+5kt+Oc66v8js0LMfOHwYiIjMW3M89Cn52BTf/Owh9zR6JS1fvw4dgtHn33N+78tcnfePM1Fwbi63WBxqW/dt4ql88fAeD/nFC6bM2AZVTqCJ/n2EBzmDO7mLtt6+ej1zOfubKESWln+weHuB73NT6CrcebQPNhoP5uWTvX577Kz3WXlHk6UJlA2w9mbgh2v0gVzHwc7PwkZSw6heO6SOo+8XcNAUg/Lr7O5f7GZCh99TZPS+lHKNsqbHa7HWfOnEFSUhKOHj0Mvd6IqCg5qlYBOrYXUL68CqVLMQiPfLt0KQO/zNuDefP34d/VL6D/c3/g2PEU7Nn5GkqUiMD/FiVhw8bTUKsVSEq6isaNy+HjDzt4ZOWSUmb5imP4e9lRKJUy7NhxEc8PvB+DXhSX/Nx/IBkTJm5GnXtKI/lKBoxGK36cJAbcbt5yFn2e+R3Tp/bEg91qBaxPLs872OfN34cvPuvskXnO3zb98bXdQ4evYv6v+7AoMQn/rHwBU6dvx7z5exEdrcak7x9Di+YV8f4HK7H0ryMwmaz4aVovdOuas6xdSooOCxYeQJMm5dGsaQUcPnwN83/bh8VLDuGflS/glVeXYNPms6herQS+/+4RNG8mZvJ4b+RKXL2ahdkzn/TI1ibVn0sPY9nyo4iL00KvtyA52fPmjd1ux7TpO7D/QDL27LmMYsU0mPzDo6hRIyeQwN+xvXo1Cx99shoVysfi/IU0XL+uw4zpj6NEiQjJbZBSRzgUK6bBU0/Wx6TJ27Bg4QE8+GBNbN5yFrGxGnTu5Ps6qHr1EnhtqP8sfxcupMNgsKBRw7KIilLh6lVp5Xx5/dWWmDptOyZP+Q/D327rcexn/LwTgwc1hdlszfM6vd6MP5cehiAIaNa0gs/6GzUqhy1bz+G3BfvxxWfSPm9wdzDpStj2XbDyO+b0ejPm/7oPMpmArn6WllQq5Zj18xOS2jT9px14uHttlC3r+dlYqMdR6nnOl1D6GOwxzW8bgxHqfgx3HbkFOrcBvs+fUuYAqfOEu99+34+nnrwXTz1ZH6NGr8cPk7biicfrBdUvALhyJRNDXlmCjZvOoHKlOMyb29sVhJJ7Ptu3Pxnzf92LRYlJ2L3jNbz59t/4e9lRVK1SHL//2gdVq+Ys55qffQYEfv/7mvsXLzmEI0evITVVj0EvJaJWzXgMf7tNnm37q0OKEyeuA8gbaJ6QIC5hevRoiiuDnr8ybdtUwbr1p5GdbcaixCQsSkzC51+uwcwZT6BLZ/9/twe67gsk9/GVIpSx6s2cuXvw75qTAIA//ncQJ0/dQPVqJXwuUR0To0ZMjPQvl0p5f+T3eg/wf90czPvFna8xHOp7wp9duy9i2vQd0OlMOHnyhiPbWxMoFLKQ279x0xk88dR8pKTo8MHI9vjy8y4AgDVrT6Hn479g+Ftt8PFHHb2+1v04Hz4iftGgRg3PeaaGYxn2rdvO4+UhnsHkodZTs2a85G3pdCb8OOU//Db/abw3ciXC4cqVTOzYeRFLEp8NS310+5PJgJIlBZQsKaCO22IRRqMcFy/ZcPGiHucvHMby5UmwWOyIjy+OunXvRb169VCihP9rMwbsERFR0C6eS8KWtb/g6uUTWL/6JzRr0xvrV06HxWzE6qUTsWtrIkZ8tQZyuRJZmTfw5butceLwZoz4ai0EQZBUZuu6eTiwZyVeemsuBEGGv/8YhdmTh6BUQjXcc297TBnfFwNfnY4a97SExWLC5NFPudqn12dAl3kD2bpU12OBtumvT4HaAgBmkx7L/jcGT/b/GlarBV+/1w4LZo3Amx//BQBYnjgWsXFl0P/lybDZrPj6vbYYNbI9xk47DpU6wmd/WjzwDLZvXohL5w5h4KvTAAAH96zy2dZp3zyLmNjSeLT3hwCAj99ohF9nvIXBb87xWpeUtkmVOP9jPNr7Q683QgFgyri+PtumjRBT8Kde91y2ITJKzHhy/dq5gG2VUscTz36JBk26Y9v6+WjUogceeTL4m4SB+ulLoHHUvttLuHTuENYs/xGXzh1C8fgKGPHlvygR7/uPapvNiovnkvBk/68lb6cg+hasUMZcOI5vZsZ1bF03D916vIWUK6fx+6x38fX7D2D0lCNQayLR/+XJ6Df4O5w/ewBrlk3GlrW/4Puve+LzibuRUK4W/Plv42/49+9JGPHlGihVGmRl3sDIofVc+1JK+40GHbZvWgCVSovjhzfjf3NH4vq1c6hcvRF6DxyHilUaSKrHajV7bNNJoVRDoVTj5vULkrYldb9JEewx37tjKarXbu6R0cffOeROdjvOub7KN239FDb9OwuXHAFV2ogYdHl0GLZt+DVPvwONO39t8jfefM2Fgfh6XaBx6a+dt0rypaMA/J8TpJRp1aG/z3NsMHOYQZ+JFYu/wdoVU9H3xW+DaqeTv/ERTD2+BJoPA/XX377Kz3WXlHk6UBl/27fb7ZLnhlD2i1RS5+P8zk++xqJTOK6LpOyTQNcQUudsf+dyf2MylL56m6cD9SPUbRUWu92OvXv3YvPmjUhNTUfZMgq0bglUr65CqfjbN/ML3XoHk65gzi97cOLEDUz/aQee7n0vpk3fAaPRggkTN2NRYhLWrRkEpVKOGzey0aL1FGzefBbr1w6CIAiSyvwyby9WrDyGeXN7QyYT8PWodRg8JBHVqhVHh/bV8HTf3zBj+uNo1bISTCYrnngqJ9tsRoYRN25kIzVV73osUH1OmZlGjPtmI6ZM3Y6J3z7s0W9/2/TF33br10vAxYvpOH78Oj7/cg1eebkF3h/xAB56eDZeHLwIDz1YC68MaY4vPuuCR3vMwdDX/sSp4+8AALZsPYf3R67Eps1n8b+Fz6BZ0wq4cTMbS/48hOPHr+ObCZvw5hutMaB/YwwekoiOnWfg5LF3EBOjxu8LDkCrVWLT5rN4f+RKnD2XisaNymH8uO64r4H/bKm//rYPP0zahnVrBkGjUeDGjWzcU+9bj0CbMWM3oEyZGEyZ3ANWqw2t205D2/bTcer4O4iIUEo6tqVLR+GjD8Uv+N3X6Hu88dbf+GXOU5LbEKiOcGrerCImTd6GI0evuW5mV6saOLioYsVYn8+lpOgweEgitFolRn3dLd/lAKB27Xh06VwDq1YfR+LiJDz15L0AxCUSFyw8gK2bX8bsObvzvO7wkWuw2ewoX76YR0BtbtUdAVVHj17z2w5fduwQPw/I774LRX7H3KHDV2Gz2VG5cpzffSSF3W7HpUsZmDlrF7o/VDvP86EeR3f+znO+hNLH/BzTUNoYjHDsx3DU4U7Kuc3f+VOplAecA6TME2XKRHu0a9bs3ViS+CxKlIjAIw/XxpI/D2P/gWQ0uFd6dm293oxRY9Zj9NfdYLZY0abdNLwzYjmW/fWc1/ksoXQU9u1LxpkzqXj/g1V4d3g79HumIR5/ch4++Gg1fpv/dFj2mZQ5x9fc3++Zhliw8ACSDl3FT9P8L+sayvWDk3OZ74sX0z0ej3Nknzx7LhX31k8IWAYApkzugR++exT7DyRj0uRtmPvLHjzWcy727X4dtWrFwxd/132BeDu+UoQyVr0Z0L8RHu5eG/Pm70XPHnXxwUjf9xGsVhsOJl3B6ADzqTsp749S8VEhX+8BCHjdLPX9kpuvMRzqe8KXc+fS8ECHn5C0/w1UrhyHAQP/wJBXluCnGTvRpk1ljHinXUjtb9umCj79uBOGvvYnGjcq53q8XdsqaNyonM9gvdzH2Xnd4nyvOanVCqjVCly4kBa2eoLZ1ocfr8ZHH3bwGsweqj+XHkaL5hW9Zt4jcqdWA9WqylDN8d1hiwU4f8GG4yfSsWvXJqxfvx7Vq1fFAw90QLly5bzWwe9/EhFR0Oo36ooa97SEzWZFi3Z90abTQHw0fhtkcgUS53+C9t1eglwupvuPii6Bh594D8cObcK29fORkX4tYJnM9BTMmz4Mj/f7AoIgTlXturyIxi16oljxBFitZiRfPIrzp8W0xwqFCm06DXS1797GD2LKgjQ0b9sHACRt01ef5HKl37Y4yWQK9H5+LMqUr43yleqh7n0dcc7RvrSbl7F66fdo2b6fo6wc97d8HOmpV7Bv598B+yN1/8cWLwsAqFjlXlfZ8hXr4eLZAz7rCtQ2qY4lbURUdHGUr+T/W4O+2lapWkMIgoA92//0KO9ctksbER2wrVLqyC+p/cwt0Jh26jtoAipXb4zkS0dx/NAmnDyy1W+9+3cuQ1zxsmjVoX9Q2wln34IV6pgLx/FVKFToN/g71GvYBe0fHIKuj72JtJuXsfHfma4yMrkClas1wguv/4yhIxbAYjYicf4nfus1GbOxYOa76PzI61CqxD8io6JLoGadnCVwpLT//Ol9MBp0qFStIdp0HIDPJu7Gp9/uQOqNy/j6vbZIvXFJUj2pNy4DADSaqDxt1WiikJF2TdK2gtlv/oRyzHdvW4xGzfN+4zCY89ud4nabcwOV9xaQJPeSLcrfuPO3jXDNa8HwNS6Dnd8LipRzgpQygUiZw4yGLKxcMgGXLxyGLvMmpn/bH5v+nSW5nU7+xkd++yJ1Pgx2zgbyNz6ltCtQmUDbD2Zu8CWU/eKNlPk4P/OTv7EIhPe6yN8+kXINIeW43OprwNzztJR+hLqtwmA2m/Hbb/OxfPnfqFolC6+9osKgF+Ro2ULOYD0KWreuNdGqZSVYrTY80/c+PD/wfmzf9goUChk++uQfDHmpmWvpwxIlIjDyvQewcdMZzJu/D9euZQUsk5Kiw2vDluKrL7pCJhPH56AXm6JXz7ookxANs9mKo0dTsHevOD+qVHI8PzBnCfCHHqyFzLRP0bfPfQAQsD6nrCwTvpmwCYcPX8PNm9no138BZs7aBQABt+lNoO3Gx0e6MsQMe70VGjUsi+hoNXr1rIvTp2/iheeb4J57SiEqSoVHH6mD06dvIiVFBwBo1bKS60aqU5vWldGsaQUIAjBmVDc80K4qevWsix8nPYbsbDOmTt+OvfsuQ6czoVHDshg4oDH27n4du3e8hkuXM9C67VRcuuR7qaPsbDOGv7scw15vCY1G4Tp2bVpXdpW5fDkDE7/fgmf7NQQgZvV64vF6uHIlE3/9fUTysXC/0V2vXmkcOJAsuQ2B6gg35/KfFy6kuYJEQ8nkdzDpCjp1mYEWraagZZspiI5WY92aQXkyH0kt582w18XMdN99nzNvL1t+DJ06VUdkpPfsfKcdS+WVy5XpLTdnwNXpXEvrXbmSifoNJub5GT12g0e5/Ow7b6Ru1yk/Y+7o0RQAQMUK3gMJd++5hGFv/oX4hC8Rn/Alhr+73PVeBoA9ey+hRaspaNFqCpq3nIIWradg+w7fX8gJ5Tg6+TvP+RNKH4+fEF8T7DENtY3Bys9+DGcdgLRxFuj8KWUOkFLG3a7dF1GlSpzrGDozP02avE1y3wBAoZBh/NiHULt2POrXS0CnjtWxxzGfepvPEhKi0aSJuAzyV190QZ06pdCpY3W0aV0Fu/dcCts+c/L1/g9l7s8tv3U0algOgiDkWWrVucRrTLRaUhknhUKGxo3KYdbPT+CPBc/AaLTio0/+8dsGX9d9uTOAeuPt+EoR7FgNh7+XHUW5sjEY0L+RpPJS3x/5ud6Tct0s5f0SrHC+Jyb9uBXFi2tRubL4xXzn0rKDBzXFhG8ezlf7Bz7XGMWLR2D+r/tcj61afRxPPlHf52tyH+dLl8Vr36iovJkVo6JUuHotK2z1SN3Who1nUKJ4BOrX8/+5R7ASFx/icrgUEoUCqFpFhm5dFHhrmALP9FHCaDiHmTN/xs6dO72/5ha3kYiI7hByuRIyucJj6ahTx7bDaNCheEnPb//c16Q7AOBo0gZoI4sFLKPWRsFutyG+dBXX89HF4vHqe3+4fq97XyfM/+lNXLpwBE88+2WewA73jA1S2tWyfT+vfTp+ZEvAtgCAXKF0BUMAQERkLLJ14rc8Th7dBqvFjNmTX/Z4TdvOL0Cp0kIuVwbsT27e2goAI74Sl7oyGnTYtmE+zpzYBZvd5rOeQG2TwmjQ4Z+/f8DL7+TNkCS1bQlla6JNp4HY+M9MLF80Fg90G4wrl09gxZJvAAAl4ysFbKuUOvJDaj+9kTKOLGYjJo1+Eg90HYSSpSpj+rf9Me1bMeV2s7Z5vx1lsZiwPHEcXnn3d9d4lzpew9m3YIU65sJxfHMvHdym4wD8tfArnDu5x2v5xi16onGLnjh/Zr/feo8f3oy01GSUr+h581nhtmytlPan3hT/gG/etg9KlRGXboxPqIqnnhuN777sgbUrpuLxfl8ErMd57jEas/O01WTMRon4CpK3Fcp+yy3YY56tS8ORA+vRf8hkj8eDPb/dSW6nOTeUOc0bf+PO3zbCMa8Fw9+4DNe+yK8SJcUP8/ydE6SU8UfqHKbWRKFHn48BAEl7V+PHsX2wbNFYtOk0MKg2+Bsf1Wo1C7kvUufDYOdsp1DHp5R2SSkTaPvBzA3ehLpfAvE1H+dnfvI3FsN5XRRon0i5hpByXCpXb3zLrgG9zdNS+nErrzfza/fu3bhw4SxeGKhEmQQG6FH+KZVyKBQyj2UK/9t+ATqdCRVyBXQ83F3MErV+w2kUK6YJWCYqSgWbzY4qVeJcz8fHR2LRH/1cv3fuVAPD3vwLh49cw9dfds1z88k9G8XmLWcD1geIN8o+/bgTAPFmX+8+v2H02A14fuD9UCrlAbeZm5TtyuXi+9EZSAAA0Y4b60plTh+cS51ev65zBYhFROQNBpHLZVAoZK6bugDQs0ddqFRyHDx4BXUdyw/27XMfqjuW36patTjGjn4Qj/aYix+n/oevvujitT+bNp9BcnIm6uW6eahS5dyO2brtPMxmG156ebFHmRdfaAKtVilpn6xbMwiAuBTYvPl7sXPXRdhsdsltCFRHuKWni180q1kzHpUqieP6zNlUfy/xqn69BPy7+sWwlfOmW9eaqFmzJLZuO4dduy/i/sblMWXqf5j0/aM+X+O8we68yeyLcz9UqhTn8XhCQjQO7n8jT/nvvt+CN97K+XJHfvadN1K3C+R/zJUuLX6pxtdN/caNyqFRw7KYPWc3LBYbxo15EIIg4OZN8dq+UcNyrjY4Pd33N599C+U4Ovk7z/kTSh//tygJQPDHNNQ2Bis/+zGcdQDSxpm0OcX/HCC1jNOPU/7DS4ObuX7v3Kk6qlcvgfm/7sOYUd1QvLi0YEylUu6xvdhYDdLScrLg+prPAHgsNRwdrUZmphFA+PaZv/d/KHO/t77np46aNUvi+YGN8fPMXRgzdgNeGtwMx0+kYPw3GwGI51wpZbzp1bMuevWsi337Awe0e7vuk8rb8ZUimLGaXyaTFWPHbcTC3/tKzmgWzPsj1Os9KdfWz/ZrGPD9EoxwvycuXcpAdrbZ9XuNGiVRsmQkLrhlhAy1/VqtEv2fbYjJP/6H69d1KFkyEgsWHsB3Ex7xWt7bca5QXlzJIjvblKd8drbZa6B6qPVIKaPTmfD9D1vw+699/PY9WGlpBqxbfxpTJvcIa7109xEEoHo1GapXk2HzFitWrFiOWrVqISbG8/NMBuwREVHY3HAsW5qV5fkN0aiYklCpI5B287KkMpfOJcFqNcNut0MQvN8gefmd3zB1fF+sWzEVu7cm4pURC1CrbpuQ2+WLlLZ4l1P28gVxWS5/S+8F0x9/bDYrli8ai2tXTqPrY2/i+OEtOHXM97eopLQtkMT5H+O+Jt1x2bHEIQBkpl2D1WLGhTMHoFRrkFC2ZsC2PTd0KspVrIuDe1bi2KGNqNewC0qXqYETR7aibsPOWLt8SsC2BqojP6T08/rVs/jm04c8Xvf6yERJ42j+T2/CbDaiXRfxw+SPv/kPo0d2wNypr+Le+x/Kc1P6jzkj8cSzX6F02ZxvhgfaTtLe1V7bdzRpg6RjGA6BxpyvNjZs9mjYj2/J0pWhUKhgMul9lqlZpzVOH9/pt23Xr50FAMgV/i+tA7X/wtmDAACZ3HOJuFp12wIAki8ckVTPWUeggj7bc3kHi8UEk0mPhHK1EF0sXtK2vJGy39wFe57Zv3MZypSvhfiEqh6PB3t+u9MV1Tk3lPJS5B53vrYRjnktGIHGZUHsi2DFOwI9/Z0TpJTxJ9g5DADqNeyCLo+8jqULv4LNZs1XG9zHh5R68jsfhtJfIPQ5UEq71q2YFrBMoO0fObgegP+5wd88Hep+kcJ9PvbFfRz4a2duucei1GtbKQLtk+SL4hLO/q4hpMzZSqU6pL9ZQumrt3laSj/CuV9vFfFmCwP2qGCccyy55gxCcSpZMhIREUpcvpwhqUxS0lWYzVa/7/8Fv/XB031/w5Sp/2FRYhL+WNAXbdtU8VpWSn25de1SE8Neb4Uvv1oLq9UGuVzmc5urVh9Ht4dmebx+SeKzIW0XgNeyzsds9uCDzpRKOcqWjYHFYkN8vBh047xx7NSurXj+O3Lkms/+nHUE3SgVvm9kHzlyDZGRSp/LA37x5dqA+8RqtWHM2A04dfom3nqzNTZvOYf/tp8HkJPly18bAtURbs42Nbg3AXXrlAYAnDlzExaLzePGc1EgCAJef7UlXn19Kb77fis++ahjwACMe2qLQZ6XLmVApzP5zBp28tQNAED9+qFlgynMfZffMVerpnhdc/r0Tdf5IjdBEBAdrYbRaJV0Phj6SgtERCi9PhfKcfQm93nu3zUnvb73H3u0Tkh9DMcx9XYuDsTXOeyxRz2DSsKxH8N1LKSMs1DnFPc5IJgyaWkG/PPvSRw5kpKnvF5vxs8zd+Gd4eK1u9R97hTc/QjvwrXPAs0XwVxv+JLfOqZP7YV6dROwYuUxbNh4Bl271ECNGiWxZes5V3ZVKWW8adO6MnbsFDN6BnscC4OU8RyK90auxNdfdUWNGiUllQ/m/eGLlOs9KdfN4Rbu90TXLjXx2+/7sWbtKXTsUA1paQZkZRnRrWt4/k4e/GJTTPxuC+bN34fnBjSGXC5zLQedm7fj7DxfO7944GQyWaHXm70uFx1qPVLKfPjxajzc/R4cPpKzksa1azqYzVbsP5AMrUaJmjWljVN3fy87gtq14lG1avGgX0vki78vRDFgj4iIwsaZ0SHlymmvzyeUqyWpjDYiBmaTAZcvHEG5ip5/5FgsJigUKqg1kXj70xXYtn4+fp/5DsZ/0g2fT9yNMuVrh9QuX6S0JRCVOgKpNy4i9cZFxJUo7/FcVsZ1RMWUDKo/vtjtNkz4/GFERpfAkLfnSXqNlLYFcurYf1i99Duvz338RiNUrNIAn07YGbBtgiBDl0eHocujwwCIWXDeHFgR97foiajoEpLaGqiO/JDSz/dHrcfnEz2zupQsXRnXr50NOI52bV2E9t1ecj1eIr4ievT9BDMmDsT50/tQq17OH65rl09BrbptPB4DAo/X6rVbeG3fskVjAvbts4m7feyZ4AQ6jr7aCAQeI8ESBBlkcgXKVarrt1xCOfGPYl9t+2+DmCnm+rXzfs8ngdpfprz42rQbnt/W1EbEQC5XIjK6uKR6ylWsA0EQXIGETs7fy5SrJXlbvvohZb85BXueEZfZ8/wGWyjntztdUZ1zAYRlTsst97jztY1wzGtSSRmXBbEvgiXlnCCljD/BzGHuylasgxLxFSGTyfPVBvfxIaWe/M6HofY31DlQSruUKk3AMi0eeMbv9qXMDf7m6VD3i1TO+dgX93Hgr53euI9FKdd8/2fvzsPbKu807t9Hu2zLC14SxXESzGJIaBYICcQOgdCd0qGllGE6hU5LO2/3TmfaTGemhb59KS1tmUIpLW0H6JQdpmVtWUJIiB3I6sQkzr4nXpM4kTftev+QdWw5XmTHjpz4+7kuX5alR895ztGRzpF16/ekel402DZJvJcY6BwilcdluO9ZhrOufR2nU1mPkdyuo23u3Lnat2+PHn50l2bNtOrK+RYVMhUuRti558bPt3tPiZlQVlaYUpvsbKf8/rBqtzaZgY+EYDAih8OqzEyHXvvb5/XY49X61+/8VR/88MPauP4buuiikz9US6W/vsyYXqQpU3LNgEh/y1xw5VRt3PCN5G0x7Rzt29cyrOWOhmAworKyAl3U9aFjXX1r0u3Z2U7Z7Vadc4673/V5/IlqSdL+A8f7/PBSkjIy7Dp0yKdDh05oclcVkYQjR9oHfSxsNos++rFHlZ+foesSgHYAACAASURBVCceO7mSbWKbDTSGaDQ2YB8jKRaL6dnn3lN2tlMfu+5iTZyYpbKyQm3f3qyVlft0zdWlg3dymt1262X6z++/rmeerVEkEtXXvnrlgO2zshy6/mMX66WXt2rjpnqVL+h7JoA33tgpq9Uy7GBHWVlBWrbdYPtLKvvclCm5WnDlVK16Z7+efe49/f3Ns/psN5SQUmJaz6amNuXluZMqTElDfxz70/N1rr/nvjS8dRypx7T3a/FgBlqP3kZiO45EH6nsZ8M9liVuLysb+P8Gvds88ug6fedfr9I3vr4gqd3Bgyd07vn36MHfvqt//fZCWSzGkLb5SBmJbTbYMUfq/9jf1/lGf061D4vF0Le+Wa5vfbNcUrwSV/GUH+uTn7jEnI41lTb9SQRy0/E4Dkcq+/NQPPibd3XVwnO16KrUQ5RDeX6cilTOm0dSKudQQ92fb7v1UtXX+3Tr557R5/9prurqfHry8Vv6PZ8YqosvLtLCiml6+JF1crtt+sw/zO6zXX+P84zpE2QYhvnFlITE3xf12san0k8qbf70WLV+eV9Vn+sw+9L7NXuWV9Xrv9Hn7QP581+26IYbxk74FmeuaFTauSuqyqqY6uqj+uhHrzupup4kja2vLQEAzmjnlc2Xy+3RhtUvJF3fcvSwgoEOzZl3fUptpp0fL9v/l8d/oFiP6Q6b6ndpbeWzCocCeutvv5UkXXn1Z/RfP6tSLBrV1pq3zLY975fKMvsz2FhSMXnq+xSLxfTMo99Lut53okkr33x00PWxGBZFIqGT+u1tz4612lz9hqbPXGxeFwmHpB7fKu/d12BjS8V/3VOlR14IJ/1cd+MS5eRN1CMvhPXDX65PaWy9Pf77bykWi+nvP//zYY+1dx+SBlzmqa6ny+1Rybkzk37cGdkp7UdZ2QXydyZ/EHDu+ZdJkjy53W923n37SdkdrpOmVdxZWzXocvobXyrrNlIGexz7G2NfTvXxPdK0T5FwSPPKb+q3zfYtK1Vx7W2S1O/YJk+bKUlaW/Vc0n1jsaii0Ui/ffcef26eVxddskhbNi1NatfWelSRSMic5nGwfvLyi3XhjIXavvnt5HXZ/LZsNofmLvjksJclpbbdehrKczcY6NB71a/r0vnJ+/dwXkPOdmP1mDvoMc1iU8DflvTc8PvbFIsO/I3fnvvdQMtIZX9L9bjaW+/7DbZfprLtTodUXhNSaSOp3+dcqsew3hoOb9fseR9LeZz96bl/pNLPqR4PU1rfPrbVcI+BqYwrlTaDLT+VY8NAx+nh7gep6Hk87k/P/WAo5xNS8r44kudFg22TVM4hUnlcUnrP0sc+OdR17e84ncp6nM7zzVNls9l088236PrrP64DBz168KGgHvp9WFWrImpojCk2js8/MHKumF8ij8ep51+oTbo+MRXWx6+/OKU2c+fGA9jf/8EbSd/a37XrqJ55tkaBQFi/+e27kqR//MwcvVv1FUWjMS17a7fZtuf9BuuvP9u3H9H1H7tYkgZcpsfj1KyZ3qSf7GznsJc70pqa2tTQ0KpP3fg+eb0eXb2oVEuX7kpqc/Roh0KhiK6YP6Xf9Zk50ytJeva595LuG43GFInEX6ff976JisViWvK9V08awyOPrh90m6xZe1Cvv7FT1y4+37wtXh0pfjmVMQzWx0B6V4kY7D6/uLdS721u0M/vuU7FxdmyWi166DefkCR97z9eVTDY93tnny+gx5/YmLTMwV6Hh9quex1iam3rnnotK8uhL3x+roLBiNatP5xUeSnRde9l3Pvz6+RwWPXL+yr7XOYbS3dq9ZqD+vrXrtSlcyYN2NfJy4r/Hs6263t9h7bckdjnJOk3D94gm82i//iv11KegjCVffKfvvCcLBZjRB7HvvR8nevvuT/cdRzuYzrQGFMx0HqMxHYcqcei576byn423GNKz2NAqm2i0Zge+v0a/cMtJwczS0py9OEPXah9+1r00svxKtOD7TujYSS22WDP/8HONywWQ6HQyft1z2Wlcs4yVN/41ouKxeKvzafSRpLeXrlXn7ut671cGh7HoUplf+5toNfCJ57cKJfLdtK0rpVV+8zLvY8zQ31+nIpUzpuHove69N6HT/U50dcy/P6wjh7r0KYN39CPfvgB/c/vbxzy1NL99Z3wpS/O03ubG/S/f6rW4mvOO+n2gR7n4uJsXbVwmla8vTfpthVv75XDYdWNn7xkxPpJpc07VV9WLHx30s+/L7laEyd6FAvfPaywXkdHSK+9vkM3/F1qhQqA3kIhadfuqP76alj33hfWU8+ElJE5Tbff/kXNnTu3z/sQ2AMADEskElIsGkn64Doru0CfuvUu7dq6SrWblpnXL335AS245h918cxrUmpzwcULNPOyD2v9u8/rnv/6gN585dd65tF/11OPfFfzF94sSXr7jYfND3/OKZgsd2aOppTOkSRt2bhUX7klX+tW/V/K4+pvnVIZSzDQoWAgudR2MNChcCioWCymGbPfr3MvmKt3335SD/zkJq166zE9/8QP9dAvPquF135u0PXJPWeSTrQ06MDeTdq++W0FAx19jjXxzcyqZf+rQ/s3q/LNP6ruYK1OHG/UwX3vyXe88aS+Lrh4waBjGwmpjK2nv/3l51q36s/69h0vKy+/WJJS2o6D9SFJnV0fmgYDfU/nGeh6LENBf5+3D0cq+9HVH7pdq1c+rdYT3eXhN1e/oQunV5hVhWrW/01LX35AkUhIy1/7nZa/9ju99epDeux339TBfTUpLWckpLKN+msz1MexP0N9fC0Wq/ydrebzLBaL6cWn79Lf3fJ9eSdfpB21lfr+N2br9Rd/abZZ/85f5HJl6cpF/zDgWC64eIHKLrlKlW8+qrdefUjBQIf27lynHbVVaj3RrHfffvKk14j+xv/pz/1U+3atV836v5nXvbPiCZWcO1MVi08OKgzUz47aKu3fE/9Hbjgc1NKXH9D1n/4PZedOSHlZg223nkbiMX+v+nVlefI19bw5SdcP5TVkNJ7D6XYmHXMHaz952iXqaD+uV577iRrqduilZ+5SOBRUQ91O7d8dr0SSyn7X3zJS2d/6Oq6m4qT7dU3P299+eeJ444DbYihOdb9O9TVhsDb9vcYOdgzraD+uh37xWa1++ynzH8CNdTu1c2uVbrr1x0MaZyr7Ryr9nIpUjtl9bauROgYOVyrLH+pxqKdUtktCf/t0qsfjoRyfekp1XxyKgZ6fg22TVM8hBntcUnktH+wcOBX9HaeHcy401hmGodmzZ+urX/26brvtNk0qnqV3Vtv10O+D+sUvI3ruz2GtXhPR4bqYIv1/PwOQFP/wLhKJJX3IV1CQqbvv+pCqVu3Xm8u6P7i7/4EqffYf52jxNeel1KZ8wVR95MNl+svzW3TtB/6gB379jr7773/Tv333r2ZVp/95eJ35gfzkydnKyXFpTldQ6I2lO5Wb/0M993+bJWnQ/o4f9+szn31aTz61yXwd3bnziCqr9uknP/6wOcaBltmXVNYjEWDpObVbIBCWFJ9Sref2jt/Wvb0Tt/v94aTlBgIRvbe5wfz7rrvf0j9+Zo7mzyuRJN3z049o3fpD+uvftpttHn9io2bN9Jof2ve3PouuOlePPLpev31otTo6Qlq77pAqq/apubldTzy5UeULpuryuZP1xJMbdeNNj+lPj1Xrjh8u1Wc++7T+6XOXDbpNEu+R/vi/6/Xe5gY9+sf1qq1tUmNjq2rea9D55+UPOobOzvCAfTQ2tplTkYXD3dvz3v+uVMGEH2lrjynIEu06OpK/GLN//3F941sv6bv//jd98xvl+uLtl5u3LbrqXD34wN/pvc0Nunrx77R23SHztuPH/frzX7bo87c/Z1YbazkeP4Ydaxn4WJZKu77W4eDBE6qr8yXtJ1/7ygJZLIa+9pUrkyqiJabcSywr4fzz8/XCX25VZdV+/ef3X08KXr27+oA+f/v/6bZbL9VdP/pQj3WN9+HzBfoMSxxriS/r+InuZQ112/W1vkNd7kjscx0dIc1830Qtff12WSwWXXr5r7Tqnf1Jy99UU6/m5nYVFHRXukps577Cbx0dIX39my/KbrfKarWc8uOY6uvcQIazjkN5TIc6xsTzsvdr4EBG4vkwEn303ndTeX2dM3vSoMcUafBjQCptnnp6k87Jc6ugILPP7fix6+LvTe752dt93t5TR0dQHR3BXteFFAxGzMe5r+NZMBi/3PP42N4eVGdnSLFYbES22WDP/8bGtgGP/ZMmZauhoU0bN9Vrxdt71dER6vN1KZXzh1T355/9/G393583668vf07FxX1/eauvNisr92nm7Pv037+sNMfy579sUVaWs9+KZD31dd6X6vj7O18Z7H5SavvzYP20tsb3v47O5GP5X/+2Xb964B2FQhE99LvVeuh3q/Xbh1br6998UTU18WX29XgO5/kx3PO9VM6b4/0P/Hzpb11678OpnEMNtD/3tYy77n5Lb765W8/932Y9/0Ktlr65S9u3J08lPNzxJ3zqxvcpL8+tD3zg/JOqGqbyON/z04+osmqfqjfWmY/Xrx5Ypf/6j8WaMCFrRPtJpc1QpPLa8drrO5Sfn2F+qQIYTGdnTDt3RfXWioj++KeIfvrzoB5/MqQDB/M0f/4ifeMb39Att3xGXq+33z6sd955552nb8gAgLHo2Wel463SvIrUKgWsfvspvfnKg/J3tsrf2ab8whJ5cuJVM0ovuFwl02bpjZfv154da7V7+2plZuXp05/7qfnGLpU2l135CXW0H9eube9o2+YVKvKep8/+8/1yODMUjUa0avlj2rjmZbUcO6x3lj+h8sWf1Zz5H5ckHW0+oE1rX9GcedebU0gNtsyB1mmgsVSveUlLX/qVOjt8slqtmlo6R+9teE2vv3S/Av52GRaLzr/oSs2ruEktRw9r23srtHnj6/JkF+iz/3y/snMnDLo+5xRO0ca1L6v63Rd03kXzVXdwa59jzcsvlu94o7ZsXKo9O1br0itu0MUzr9bGta/oaPMBzau4SUXe85L6mnLuLM1d8Ml+xzZctZuW6cDeTfrwDd+WpJTGZrM5tH/PRj36wJfUeqJZX/nOEyo5t/ufKIZhpDTWgfrYv2ej/vp/9+jgvhq1HD0sT06h8gunyG53SYp/SPzqX36h/Xuq1XL0sPILpyjLky+Xu+83AL3XcyAD7UeSdF7ZFXJlePTyc3fr4L73tLXmLbX5juq2r/xaDmeG9u5cp//+4cd0pGmfNq19pftn3V91cF+Nbv/m/8jhcA+6nOE+hgmpbKOB2qT6OPZnuI9v8dRLdKz5gCrffFQ7tqxUbc0yTZ91ra758Jfid47FVFuzTGtWPqstm5aqoW6nzimYrOs+tSSl7XXZlTfoeEuDVrz+P1r+6u/kcmcq9xyvJk+bqfPLrtSESefLMIwBxy9JefmTNHPuR/TqX+7VjtpK7d25TieO1euL//JHOZzulLZDvJ9iXTLnA/rbn3+u/XuqVbnsj7rsyhv04Ru+bb7OprKsQbfbCD/mLz97t6acO0szL/vISeuTymv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+ "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from IPython.display import Image\n", "Image(prov_plot_url)" @@ -116,7 +603,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.1" + "version": "3.7.12" } }, "nbformat": 4,