diff --git a/.git-hooks/pre-commit b/.git-hooks/pre-commit new file mode 100755 index 000000000..a42d56744 --- /dev/null +++ b/.git-hooks/pre-commit @@ -0,0 +1,13 @@ +#!/bin/bash +# +files_to_check="docs/src/recipes/*.ipynb" +files_to_check+=" docs/src/user_guide.ipynb" +files_to_check+=" docs/src/tutorials/getting_started.ipynb" + + +for path in `git diff --name-only --staged $files_to_check` +do + echo "Pre-commit: Clearing outputs for $path" + jupyter nbconvert --clear-output "$path" --to notebook --inplace + git add $path +done diff --git a/.github/workflows/test_notebooks.yaml b/.github/workflows/test_notebooks.yaml index 958d7c477..37e61278b 100644 --- a/.github/workflows/test_notebooks.yaml +++ b/.github/workflows/test_notebooks.yaml @@ -48,5 +48,11 @@ jobs: - name: Install notebook dependencies run: poetry run pip install -r ./docs/src/tutorials/requirements.txt - - name: Execute all notebooks - run: poetry run ./tools/run_notebooks.sh `ls docs/src/*.ipynb` `ls docs/src/tutorials/*.ipynb` + - name: Execute User Guide + run: poetry run ./tools/run_notebooks.sh `ls docs/src/*.ipynb` + + - name: Execute all recipes + run: poetry run ./tools/run_notebooks.sh `ls docs/src/recipes/*.ipynb` + + - name: Execute all tutorials + run: poetry run ./tools/run_notebooks.sh `ls docs/src/tutorials/*.ipynb` diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 000000000..4ddae93cb --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,21 @@ +# Changelog + +## HEAD (might become 0.1.3) + +This is the first operational version of Temporian for users. The list whole and +detailed list of features is too long to be listed. The top features are: + +### Feature + +- Pypi release. +- 72 operators. +- Execution in eager, compiled mode, and graph mode. +- IO Support for Pandas, CSV, Numpy and TensorFlow datasets. +- Static and interactive plotting. +- Documentation (3 minutes intro, user guide and API references). +- 5 tutorials. + +### Fix + +- Proper error message when using distributed training on more than 2^31 + (i.e., ~2B) examples while compiling YDF with 32-bits example index. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 691cfeb2d..7e7c9acfe 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -29,6 +29,14 @@ To create a new release, follow these steps: ### Environment Setup +After cloning the repository, please manually install the git hooks: + +```shell +git clone git@github.com:google/temporian.git + +cp .git-hooks/* .git/hooks +``` + Install [Poetry](https://python-poetry.org/), which we use to manage Python dependencies and virtual environments. Temporian requires Python `3.9.0` or greater. We recommend using [PyEnv](https://github.com/pyenv/pyenv#installation) to install and manage multiple Python versions. Once PyEnv is available, install a supported Python version (e.g. 3.9.6) by running: diff --git a/docs/.readthedocs.yaml b/docs/.readthedocs.yaml index fa73e12e7..c75ae4b88 100644 --- a/docs/.readthedocs.yaml +++ b/docs/.readthedocs.yaml @@ -28,7 +28,10 @@ build: pre_build: - tools/run_notebooks.sh docs/src/user_guide.ipynb + - tools/run_notebooks.sh $(ls docs/src/recipes/*.ipynb) - tools/run_notebooks.sh docs/src/tutorials/getting_started.ipynb + # These are too slow + # - tools/run_notebooks.sh docs/src/tutorials/*.ipynb mkdocs: configuration: docs/mkdocs.yml diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 856764673..981aeb297 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -55,8 +55,10 @@ nav: - Home: index.md - 3 minutes to Temporian: 3_minutes.md - User Guide: user_guide.ipynb + - Recipes: recipes/ - Tutorials: tutorials/ - API Reference: reference/ # generated by gen-files + literate-nav + - Changelog: CHANGELOG.md # Plugins plugins: diff --git a/docs/src/CHANGELOG.md b/docs/src/CHANGELOG.md new file mode 120000 index 000000000..699cc9e7b --- /dev/null +++ b/docs/src/CHANGELOG.md @@ -0,0 +1 @@ +../../CHANGELOG.md \ No newline at end of file diff --git a/docs/src/recipes/aggregate_duplicated.ipynb b/docs/src/recipes/aggregate_duplicated.ipynb new file mode 100644 index 000000000..5b826ac60 --- /dev/null +++ b/docs/src/recipes/aggregate_duplicated.ipynb @@ -0,0 +1,159 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c74f7111-1b6c-4454-9770-3f67eeadaca6", + "metadata": {}, + "source": [ + "# Unify events with identical timestamps\n", + "\n", + "This recipe shows how to avoid having duplicated timestamps in an `EventSet`. Events with identical timestamps are aggregated with a moving window operation (e.g: sum, average, max, min), preserving the original timestamp values (which may be non-uniform).\n", + "\n", + "\n", + "For example, assume we've asynchronous sensor measurements, potentially from different sources. If there are two measurements at the same exact timestamp, we want to unify them and take their average value." + ] + }, + { + "cell_type": "markdown", + "id": "c63a41a5-bd95-4588-bad3-83691bd0acd0", + "metadata": {}, + "source": [ + "## Example data\n", + "\n", + "Let's define some events with non-uniform timestamps to illustrate the use case. Some of the timestamps are repeated, those are the ones that we'll unify.\n", + "\n", + "But, we've to be careful because there are events very close in time, but not actually duplicated. We don't want to interfere with those." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5e57e74-ca80-4292-834a-e7cfd9185b2f", + "metadata": {}, + "outputs": [], + "source": [ + "import temporian as tp\n", + "\n", + "sensor_evset = tp.event_set(timestamps=[1.1, 2.01, 2.02, 2.02, 3.5, 3.51, 3.51, 4.5, 5.0],\n", + " features={\"y\": [1., 2., 3., 4., 5., 6., 7., 8., 9.],\n", + " \"z\": [10., 20., 30., 40., 50., 60., 70., 80., 90.]\n", + " }\n", + " )\n", + "sensor_evset.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "4b875a15-cd14-49bd-a83e-301f9c7aef17", + "metadata": {}, + "source": [ + "## Solution\n", + "\n", + "In order to unify only the events with the exact same timestamp, we need to:\n", + "1. Get the list of unique timestamps.\n", + "2. Aggregate events at the exact same timestamp, making sure the moving window doesn't overlap with nearby measurements.\n", + "\n", + "### 1. Get unique timestamps\n", + "\n", + "The first step is to create a new sampling removing the duplicated timestamps at `2.02` and `3.51`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "037b1aa0-c808-4905-81bf-e182b221468e", + "metadata": {}, + "outputs": [], + "source": [ + "# Remove duplicated timestamps\n", + "unique_t = sensor_evset.unique_timestamps()\n", + "unique_t" + ] + }, + { + "cell_type": "markdown", + "id": "bea5a3e8-2975-4cda-a479-ba9efa219339", + "metadata": {}, + "source": [ + "### 2. Moving window with shortest length\n", + "\n", + "To create a moving window that doesn't overlap with two different timestamps at any point, it must be smaller than the smallest possible step. But we want a solution that works for any resolution, from daily sales to nano-second sensor measurements.\n", + "\n", + "In `tp.duration.shortest`, we've defined the shortest possible interval that can be represented with a `float64` timestamp at maximum resolution:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c64fbeba-5efc-437e-baac-9b38a536fd9d", + "metadata": {}, + "outputs": [], + "source": [ + "shortest_length = tp.duration.shortest\n", + "shortest_length" + ] + }, + { + "cell_type": "markdown", + "id": "81d58e94-bd23-4d9e-bb53-88c299af65f5", + "metadata": {}, + "source": [ + "Pretty small, right? Since null durations are not allowed, this is as close to zero as we can get. It's guaranteed that you'll never overlap two different timestamps using this.\n", + "\n", + "Now we just need to run the aggregation function that we need, providing this small number as `window_length` and the unique timestamps as `sampling`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "813f1562-8c95-4b22-a40f-90b7ee751e61", + "metadata": {}, + "outputs": [], + "source": [ + "unified_evset = sensor_evset.simple_moving_average(window_length=shortest_length, sampling=unique_t)\n", + "unified_evset" + ] + }, + { + "cell_type": "markdown", + "id": "72e0d5a5-e2e7-406c-84c6-dd8ac717128d", + "metadata": {}, + "source": [ + "Of course, instead of the average value, other moving window operations like `moving_min` or `moving_max` could make more sense depending on the use case. If multiple measurements are expected at each timestamp, you could also want the moving standard deviation to get a confidence interval.\n", + "\n", + "Also, keep in mind that this exact procedure would work well in an `EventSet` with multiple indexes, removing the duplicated timestamps in each index separately.\n", + "\n", + "But let's keep the example simple for now 🙂" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3a9c6e6-5cba-4950-900f-1878e87a98be", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/src/recipes/aggregate_index.ipynb b/docs/src/recipes/aggregate_index.ipynb new file mode 100644 index 000000000..71e810c64 --- /dev/null +++ b/docs/src/recipes/aggregate_index.ipynb @@ -0,0 +1,151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c74f7111-1b6c-4454-9770-3f67eeadaca6", + "metadata": {}, + "source": [ + "# Aggregate events from different indexes\n", + "\n", + "This recipe applies when you have events indexed by one or more features, and you want to drop some index levels and unify the events with the same timestamps.\n", + "\n", + "In this example, we aggregate daily sales by store and product, into daily revenue for each individual store (i.e., the total sales for each day)." + ] + }, + { + "cell_type": "markdown", + "id": "369d0511-8898-4135-b2bc-69f0ad5a2bd9", + "metadata": {}, + "source": [ + "## Example data\n", + "\n", + "Let's define 2 stores, each one with 2 products. The product IDs might be the same across stores or not.\n", + "\n", + "For each store/product, we'll create the sales (in USD) for the same 3 days (1, 2 and 3 of January, 2020)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "07b25e47-8b27-4dab-baa3-c9b75745372f", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import temporian as tp\n", + "\n", + "\n", + "sales_data = pd.DataFrame(\n", + " data=[\n", + " # date, store ID (1), product ID, sales (USD) \n", + " [\"2020-01-01\", \"store_1\", \"product_1\", 300.0],\n", + " [\"2020-01-02\", \"store_1\", \"product_1\", 450.0],\n", + " [\"2020-01-03\", \"store_1\", \"product_1\", 600.0],\n", + " [\"2020-01-01\", \"store_1\", \"product_2\", 100.0],\n", + " [\"2020-01-02\", \"store_1\", \"product_2\", 250.0],\n", + " [\"2020-01-03\", \"store_1\", \"product_2\", 100.0],\n", + " # date, store ID (2), product ID, sales (USD) \n", + " [\"2020-01-01\", \"store_2\", \"product_1\", 900.0],\n", + " [\"2020-01-02\", \"store_2\", \"product_1\", 750.0],\n", + " [\"2020-01-03\", \"store_2\", \"product_1\", 750.0],\n", + " [\"2020-01-01\", \"store_2\", \"product_3\", 20.0],\n", + " [\"2020-01-02\", \"store_2\", \"product_3\", 40.0],\n", + " [\"2020-01-03\", \"store_2\", \"product_3\", 30.0],\n", + " ],\n", + " columns=[\n", + " \"timestamp\",\n", + " \"store_id\",\n", + " \"product_id\",\n", + " \"sales_usd\",\n", + " ],\n", + ")\n", + "\n", + "# Load data indexed by store/product\n", + "sales_evset = tp.from_pandas(sales_data, indexes=[\"store_id\", \"product_id\"])\n", + "sales_evset.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "278d15bf-a58e-4ad0-a44c-272ff449150c", + "metadata": {}, + "source": [ + "## Solution\n", + "We want to aggregate all product sales per store, so this is what we can do:\n", + "1. Drop the `product_id` index, and ignore it.\n", + "2. Unify sales with the same timestamp and same store, adding them up.\n", + "\n", + "### 1. Drop index\n", + "\n", + "We don't care about the different `product_id` that we're adding up in each store." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b54eb39-7e38-479b-b926-40b12dd48a92", + "metadata": {}, + "outputs": [], + "source": [ + "store_sales = sales_evset.drop_index(\"product_id\")\n", + "\n", + "store_sales[\"sales_usd\"].plot()" + ] + }, + { + "cell_type": "markdown", + "id": "d810d461-631d-4453-9ae1-0352024427a7", + "metadata": {}, + "source": [ + "As you can see, now we've each timestamp duplicated, one for each product.\n", + "\n", + "### 2. Unify events\n", + "\n", + "We want to unify the events with the same timestamps, adding up their sales." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f28343c-d3f1-48df-8f1e-0fd4ee58505e", + "metadata": {}, + "outputs": [], + "source": [ + "unique_days = store_sales.unique_timestamps()\n", + "\n", + "store_daily_sales = store_sales[\"sales_usd\"].moving_sum(window_length=tp.duration.days(1), sampling=unique_days)\n", + "\n", + "store_daily_sales.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3a9c6e6-5cba-4950-900f-1878e87a98be", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/src/recipes/aggregate_interval.ipynb b/docs/src/recipes/aggregate_interval.ipynb new file mode 100644 index 000000000..01ef94859 --- /dev/null +++ b/docs/src/recipes/aggregate_interval.ipynb @@ -0,0 +1,171 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c74f7111-1b6c-4454-9770-3f67eeadaca6", + "metadata": {}, + "source": [ + "# Aggregate events at a fixed interval\n", + "\n", + "This recipe aggregates possibly non-uniformly sampled events into fixed-length intervals (e.g., seconds, hours, days, or weeks). In other words, it converts the event features into time series.\n", + "\n", + "For example, suppose we have the sales log from a store, where each sold item is represented by an event. Let's assume each sale event has a date-time, the sale price and the unit cost of the product. We want to calculate total daily sales, with one single event at `00:00` each day." + ] + }, + { + "cell_type": "markdown", + "id": "de274e0e-ab5a-46a0-b4b9-f1026b43076c", + "metadata": {}, + "source": [ + "## Example data\n", + "\n", + "Let's create some sale events with **non-uniform sampling** and the mentioned features." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10a56d43-d011-4e72-aed4-8d460d58c337", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import temporian as tp\n", + "\n", + "sales_data = pd.DataFrame(\n", + " data=[\n", + " # sale timestamp, price, cost\n", + " [\"2020-01-01 13:04\", 3.0, 1.0],\n", + " [\"2020-01-01 13:04\", 5.0, 2.0], # duplicated timestamp\n", + " [\"2020-01-02 15:24\", 7.0, 3.0],\n", + " [\"2020-01-03 13:45\", 3.0, 1.0],\n", + " [\"2020-01-03 16:10\", 7.0, 3.0],\n", + " [\"2020-01-03 17:30\", 10.0, 5.0],\n", + " [\"2020-01-06 10:10\", 4.0, 2.0],\n", + " [\"2020-01-06 19:35\", 3.0, 1.0],\n", + " ],\n", + " columns=[\n", + " \"timestamp\",\n", + " \"unit_price\",\n", + " \"unit_cost\",\n", + " ],\n", + ")\n", + "\n", + "sales_evset = tp.from_pandas(sales_data)\n", + "sales_evset.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "c8f6189a-ca80-4869-b8a6-35356ce42c02", + "metadata": {}, + "source": [ + "## Solution\n", + "We want to calculate total daily sales. So this is what we can do:\n", + "1. Create a uniform sampling with one tick per day (could be any other interval), at time `00:00:00`.\n", + "1. Add up all sales that happened between `00:00:01` from the previous day, and the current tick at `00:00:00`.\n", + "\n", + "### 1. Create uniform sampling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e930b36-10f6-487f-8466-278a1a08956b", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the time span to cover: one week\n", + "time_span = tp.event_set(timestamps=[\"2020-01-01 00:00\", \"2020-01-07 00:00\"])\n", + "\n", + "# Create daily ticks at 00:00\n", + "interval = tp.duration.days(1)\n", + "ticks = time_span.tick(interval)\n", + "\n", + "ticks" + ] + }, + { + "cell_type": "markdown", + "id": "aad79b2d-3fea-4e11-8d68-1e5d78f3b655", + "metadata": {}, + "source": [ + "### 2. Aggregate the events\n", + "\n", + "Now we can aggregate the events between ticks, in this case by running a moving sum over the specified `sampling=ticks`, with the `window_length` equal to the interval between ticks.\n", + "\n", + "Note that all moving window operators support the `sampling` argument, so any other kind of aggregation could be used depending on the use case (e.g: moving average, max, min)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d92dff9-b4b6-42d1-8848-b37ff768ef7e", + "metadata": {}, + "outputs": [], + "source": [ + "# Provide uniform ticks as sampling\n", + "moving_sum = sales_evset.moving_sum(window_length=interval, sampling=ticks)\n", + "\n", + "moving_sum" + ] + }, + { + "cell_type": "markdown", + "id": "706f50a9-72ab-4673-aa38-8c3dacdcf49d", + "metadata": {}, + "source": [ + "## (Optional) Rename and plot\n", + "\n", + "Finally, we can rename features to match their actual meaning after aggregation.\n", + "\n", + "In this case we also calculate and plot the daily profit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "664550bb-12ac-43c2-8216-4308843178b5", + "metadata": {}, + "outputs": [], + "source": [ + "# Rename aggregated features\n", + "daily_sales = moving_sum.rename({\"unit_price\": \"daily_revenue\", \"unit_cost\": \"daily_cost\"})\n", + "\n", + "# Profit = revenue - cost\n", + "daily_profit = (daily_sales[\"daily_revenue\"] - daily_sales[\"daily_cost\"]).rename(\"daily_profit\")\n", + "\n", + "daily_profit.plot()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3a9c6e6-5cba-4950-900f-1878e87a98be", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/src/recipes/index.md b/docs/src/recipes/index.md new file mode 100644 index 000000000..7103f9329 --- /dev/null +++ b/docs/src/recipes/index.md @@ -0,0 +1,19 @@ +# Recipes + +Recipes are short and self-contained examples showing how to use Temporian +in typical use cases. + +## Aggregate events by timestamp + +| Recipe | +| ----------------------------------------------------------------------------------------------------- | +| [Aggregate events by fixed-length intervals (turn events into time-series)](aggregate_interval.ipynb) | +| [Aggregate events from different indexes](aggregate_index.ipynb) | +| [Unify events with identical timestamps](aggregate_duplicated.ipynb) | + +## Split EventSets + +| Recipe | +| --------------------------------------------------------- | +| [Split data at a given timestamp](split_timestamp.ipynb) | +| [Split data by fraction of samples](split_fraction.ipynb) | diff --git a/docs/src/recipes/split_fraction.ipynb b/docs/src/recipes/split_fraction.ipynb new file mode 100644 index 000000000..ce43720d9 --- /dev/null +++ b/docs/src/recipes/split_fraction.ipynb @@ -0,0 +1,189 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "96d71ce2-2e81-45ef-a494-0a07a03554bc", + "metadata": {}, + "source": [ + "# Split data by fraction\n", + "\n", + "This recipe can be used to split an `EventSet` in two or more subsets, each with a specified fraction of the total number of data points.\n", + "\n", + "For example, to train a machine learning forecasting model, the data usually needs to be split into train, validation and test `EventSets`. In this case we'll use `60%` of the data for training, `20%` for validation, and `20%` for test." + ] + }, + { + "cell_type": "markdown", + "id": "972cd99f-6c1e-4655-8bfe-767e547d6014", + "metadata": {}, + "source": [ + "## Example data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28abb293-e542-4540-9c65-3cf1c89c4c16", + "metadata": {}, + "outputs": [], + "source": [ + "import temporian as tp\n", + "import numpy as np\n", + "\n", + "T = 10\n", + "t = np.arange(0, T, 0.1)\n", + "signal_evset = tp.event_set(timestamps=t, features={\"signal\": np.sin(t)})\n", + "\n", + "signal_evset.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "f04cc1b4-ac53-4f41-ad59-9f98fa0f0210", + "metadata": {}, + "source": [ + "## Solution\n", + "\n", + "We want to split this into 3 separate `EventSets` as follows:\n", + "* **Train** data: `60%` of the events, at the beginning of the series.\n", + "* **Validation**: `20%` of the events, following training data.\n", + "* **Test**: Remaining `20%` of the events.\n", + "\n", + "The proposed steps are:\n", + "1. Get the total number of events and calculate split limits.\n", + "2. Get each event's position in the `EventSet`.\n", + "3. Split comparing each event's position to the split limits." + ] + }, + { + "cell_type": "markdown", + "id": "587487f8-8bbe-424a-a707-fd8ca9c57301", + "metadata": {}, + "source": [ + "### 1. Calculate split limits" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69e85cea-9814-40cc-8ffd-dfc87a5e8716", + "metadata": {}, + "outputs": [], + "source": [ + "n_events = len(signal_evset.get_index_value(()))\n", + "\n", + "train_until = int(n_events * 0.6)\n", + "val_until = train_until + int(n_events * 0.2)" + ] + }, + { + "cell_type": "markdown", + "id": "d9e540ec-3b8b-4ba0-a727-23c71ca56cd0", + "metadata": {}, + "source": [ + "### 2. Get each event's position\n", + "\n", + "The `enumerate()` operator creates a single-feature `EventSet` with the position of each event, keeping the indexes and samplings compatible with the original `EventSet`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92545fdc-ee14-4ea3-aa85-02b6ef252a56", + "metadata": {}, + "outputs": [], + "source": [ + "sample_positions = signal_evset.enumerate()" + ] + }, + { + "cell_type": "markdown", + "id": "58d52487-3006-4bf9-9c4c-dad3d47db300", + "metadata": {}, + "source": [ + "### 3. Split based on positions\n", + "\n", + "Now we compare the `sample_positions` limits of each subset. This will create boolean `EventSets` that can be passed directly to the `filter()` operator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d1afd17-ccfa-4bd8-b0bf-406fa098aad6", + "metadata": {}, + "outputs": [], + "source": [ + "train_evset = signal_evset.filter(sample_positions <= train_until)\n", + "val_evset = signal_evset.filter((sample_positions > train_until) & (sample_positions <= val_until))\n", + "test_evset = signal_evset.filter(sample_positions > val_until)" + ] + }, + { + "cell_type": "markdown", + "id": "09775af5-c18e-4edf-b7dd-038772a63f50", + "metadata": {}, + "source": [ + "## Check results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03af0fc5-9fe4-48b2-80c3-587dec522bbc", + "metadata": {}, + "outputs": [], + "source": [ + "train_evset.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a26cf689-cf12-40de-93b9-c0b13e0b27d5", + "metadata": {}, + "outputs": [], + "source": [ + "val_evset.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "668e85b3-f318-4669-8b23-1441b3182ad9", + "metadata": {}, + "outputs": [], + "source": [ + "test_evset.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6349e115-f4f8-4ada-82c7-dbd0ee695e31", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/src/recipes/split_timestamp.ipynb b/docs/src/recipes/split_timestamp.ipynb new file mode 100644 index 000000000..5160d36b0 --- /dev/null +++ b/docs/src/recipes/split_timestamp.ipynb @@ -0,0 +1,211 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "96d71ce2-2e81-45ef-a494-0a07a03554bc", + "metadata": {}, + "source": [ + "# Split data at a given timestamp\n", + "\n", + "This recipe can be used to split an `EventSet` in two or more subsets at fixed timestamps.\n", + "\n", + "This exact same procedure applies to multi-index data or the default single empty index.\n", + "\n", + "For example, to train a machine learning forecasting model, the data usually needs to be split into train, validation and test `EventSets` at some fixed timestamps in that respective order." + ] + }, + { + "cell_type": "markdown", + "id": "972cd99f-6c1e-4655-8bfe-767e547d6014", + "metadata": {}, + "source": [ + "## Example data\n", + "\n", + "In this toy example we'll use two separate indexes, but this also applies to any number of indexes as mentioned above.\n", + "\n", + "The second index has 2 more data points from the previous year. Both of them finish at the same date." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28abb293-e542-4540-9c65-3cf1c89c4c16", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import temporian as tp\n", + "\n", + "sample_data = pd.DataFrame(\n", + " data=[\n", + " # date, index=1, feature\n", + " [\"2020-01-01\", 1, 1.0],\n", + " [\"2020-02-01\", 1, 2.0],\n", + " [\"2020-03-01\", 1, 3.0],\n", + " [\"2020-04-01\", 1, 4.0],\n", + " [\"2020-05-01\", 1, 5.0],\n", + " [\"2020-06-01\", 1, 6.0],\n", + " # date, index=2, feature\n", + " [\"2019-11-01\", 2, -20.0],\n", + " [\"2019-12-01\", 2, -10.0],\n", + " [\"2020-01-01\", 2, 10.0],\n", + " [\"2020-02-01\", 2, 20.0],\n", + " [\"2020-03-01\", 2, 30.0],\n", + " [\"2020-04-01\", 2, 40.0],\n", + " [\"2020-05-01\", 2, 50.0],\n", + " [\"2020-06-01\", 2, 60.0],\n", + " ],\n", + " columns=[\n", + " \"timestamp\",\n", + " \"idx\",\n", + " \"feature\",\n", + " ],\n", + ")\n", + "\n", + "sample_evset = tp.from_pandas(sample_data, indexes=[\"idx\"])\n", + "sample_evset" + ] + }, + { + "cell_type": "markdown", + "id": "f04cc1b4-ac53-4f41-ad59-9f98fa0f0210", + "metadata": {}, + "source": [ + "## Solution\n", + "\n", + "We want to split this into 3 separate `EventSets` as follows:\n", + "* **Train** data: all data points until `2020-03-01` (including it)\n", + "* **Validation**: after training and until `2020-05-01` (including it)\n", + "* **Test**: data after `2020-05-01` (not including it)\n", + "\n", + "So the proposed steps are:\n", + "1. Convert the timestamps of events into a feature.\n", + "2. Filter train/validation/test by comparing the timestamps feature to the defined boundaries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69e85cea-9814-40cc-8ffd-dfc87a5e8716", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "# Define boundaries for train/validation/test\n", + "train_until = datetime(2020, 3, 1).timestamp()\n", + "val_until = datetime(2020, 5, 1).timestamp()" + ] + }, + { + "cell_type": "markdown", + "id": "d9e540ec-3b8b-4ba0-a727-23c71ca56cd0", + "metadata": {}, + "source": [ + "### 1. Convert the timestamps into a feature\n", + "\n", + "The `timestamps()` operator creates a single-feature `EventSet` with the unix timestamp of each event, keeping the indexes and samplings compatible with the original `EventSet`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92545fdc-ee14-4ea3-aa85-02b6ef252a56", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the data timestamps as a feature\n", + "sample_timestamps = sample_evset.timestamps()" + ] + }, + { + "cell_type": "markdown", + "id": "58d52487-3006-4bf9-9c4c-dad3d47db300", + "metadata": {}, + "source": [ + "### 2. Split based on timestamps\n", + "\n", + "Now we compare the timestamps feature to the boundary timestamps of each subset. This will create boolean `EventSets` that can be passed directly to the `filter()` operator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d1afd17-ccfa-4bd8-b0bf-406fa098aad6", + "metadata": {}, + "outputs": [], + "source": [ + "train_evset = sample_evset.filter(sample_timestamps <= train_until)\n", + "val_evset = sample_evset.filter((sample_timestamps > train_until) & (sample_timestamps <= val_until))\n", + "test_evset = sample_evset.filter(sample_timestamps > val_until)" + ] + }, + { + "cell_type": "markdown", + "id": "09775af5-c18e-4edf-b7dd-038772a63f50", + "metadata": {}, + "source": [ + "## Check results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03af0fc5-9fe4-48b2-80c3-587dec522bbc", + "metadata": {}, + "outputs": [], + "source": [ + "train_evset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a26cf689-cf12-40de-93b9-c0b13e0b27d5", + "metadata": {}, + "outputs": [], + "source": [ + "val_evset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "668e85b3-f318-4669-8b23-1441b3182ad9", + "metadata": {}, + "outputs": [], + "source": [ + "test_evset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6349e115-f4f8-4ada-82c7-dbd0ee695e31", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/src/reference/index.md b/docs/src/reference/index.md index 3723dc814..188b96918 100644 --- a/docs/src/reference/index.md +++ b/docs/src/reference/index.md @@ -38,30 +38,31 @@ Check the index on the left for a more detailed description of any symbol. ## Operators -| Symbols | Description | -| ---------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | -| [`tp.combine()`][temporian.combine] | Combines events from [`EventSets`][temporian.EventSet] with different samplings. | -| [`tp.glue()`][temporian.glue] | Concatenates features from [`EventSets`][temporian.EventSet] with the same sampling. | -| [`EventSet.add_index()`][temporian.EventSet.add_index] | Adds indexes to an [`EventSet`][temporian.EventSet]. | -| [`EventSet.begin()`][temporian.EventSet.begin] | Generates a single timestamp at the beginning of the input. | -| [`EventSet.cast()`][temporian.EventSet.cast] | Casts the dtype of features. | -| [`EventSet.drop_index()`][temporian.EventSet.drop_index] | Removes indexes from an [`EventSet`][temporian.EventSet]. | -| [`EventSet.end()`][temporian.EventSet.end] | Generates a single timestamp at the end of the input. | -| [`EventSet.enumerate()`][temporian.EventSet.enumerate] | Creates an ordinal feature enumerating the events according to their timestamp. | -| [`EventSet.filter()`][temporian.EventSet.filter] | Filters out events in an [`EventSet`][temporian.EventSet] for which a condition is false. | -| [`EventSet.join()`][temporian.EventSet.join] | Join [`EventSets`][temporian.EventSet] with different samplings but the same index together. | -| [`EventSet.lag()`][temporian.EventSet.lag] | Adds a delay to an [`EventSet`][temporian.EventSet]'s timestamps. | -| [`EventSet.leak()`][temporian.EventSet.leak] | Subtracts a duration from an [`EventSet`][temporian.EventSet]'s timestamps. | -| [`EventSet.prefix()`][temporian.EventSet.prefix] | Adds a prefix to the names of the features in an [`EventSet`][temporian.EventSet]. | -| [`EventSet.propagate()`][temporian.EventSet.propagate] | Propagates feature values over a sub index. | -| [`EventSet.rename()`][temporian.EventSet.rename] | Renames an [`EventSet`][temporian.EventSet]'s features and index. | -| [`EventSet.resample()`][temporian.EventSet.resample] | Resamples an [`EventSet`][temporian.EventSet] at each timestamp of another [`EventSet`][temporian.EventSet]. | -| [`EventSet.select()`][temporian.EventSet.select] | Selects a subset of features from an [`EventSet`][temporian.EventSet]. | -| [`EventSet.set_index()`][temporian.EventSet.set_index] | Replaces the indexes in an [`EventSet`][temporian.EventSet]. | -| [`EventSet.since_last()`][temporian.EventSet.since_last] | Computes the amount of time since the last distinct timestamp. | -| [`EventSet.tick()`][temporian.EventSet.tick] | Generates timestamps at regular intervals in the range of a guide. | -| [`EventSet.timestamps()`][temporian.EventSet.timestamps] | Creates a feature from the events timestamps (`float64`). | -| [`EventSet.unique_timestamps()`][temporian.EventSet.unique_timestamps] | Removes events with duplicated timestamps from an [`EventSet`][temporian.EventSet]. | +| Symbols | Description | +| ---------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | +| [`tp.combine()`][temporian.combine] | Combines events from [`EventSets`][temporian.EventSet] with different samplings. | +| [`tp.glue()`][temporian.glue] | Concatenates features from [`EventSets`][temporian.EventSet] with the same sampling. | +| [`EventSet.add_index()`][temporian.EventSet.add_index] | Adds indexes to an [`EventSet`][temporian.EventSet]. | +| [`EventSet.begin()`][temporian.EventSet.begin] | Generates a single timestamp at the beginning of the input. | +| [`EventSet.cast()`][temporian.EventSet.cast] | Casts the dtype of features. | +| [`EventSet.drop_index()`][temporian.EventSet.drop_index] | Removes indexes from an [`EventSet`][temporian.EventSet]. | +| [`EventSet.end()`][temporian.EventSet.end] | Generates a single timestamp at the end of the input. | +| [`EventSet.enumerate()`][temporian.EventSet.enumerate] | Creates an ordinal feature enumerating the events according to their timestamp. | +| [`EventSet.experimental_fast_fourier_transform()`][temporian.EventSet.experimental_fast_fourier_transform] | Apply a Fast Fourier Transform. | +| [`EventSet.filter()`][temporian.EventSet.filter] | Filters out events in an [`EventSet`][temporian.EventSet] for which a condition is false. | +| [`EventSet.join()`][temporian.EventSet.join] | Join [`EventSets`][temporian.EventSet] with different samplings but the same index together. | +| [`EventSet.lag()`][temporian.EventSet.lag] | Adds a delay to an [`EventSet`][temporian.EventSet]'s timestamps. | +| [`EventSet.leak()`][temporian.EventSet.leak] | Subtracts a duration from an [`EventSet`][temporian.EventSet]'s timestamps. | +| [`EventSet.prefix()`][temporian.EventSet.prefix] | Adds a prefix to the names of the features in an [`EventSet`][temporian.EventSet]. | +| [`EventSet.propagate()`][temporian.EventSet.propagate] | Propagates feature values over a sub index. | +| [`EventSet.rename()`][temporian.EventSet.rename] | Renames an [`EventSet`][temporian.EventSet]'s features and index. | +| [`EventSet.resample()`][temporian.EventSet.resample] | Resamples an [`EventSet`][temporian.EventSet] at each timestamp of another [`EventSet`][temporian.EventSet]. | +| [`EventSet.select()`][temporian.EventSet.select] | Selects a subset of features from an [`EventSet`][temporian.EventSet]. | +| [`EventSet.set_index()`][temporian.EventSet.set_index] | Replaces the indexes in an [`EventSet`][temporian.EventSet]. | +| [`EventSet.since_last()`][temporian.EventSet.since_last] | Computes the amount of time since the last distinct timestamp. | +| [`EventSet.tick()`][temporian.EventSet.tick] | Generates timestamps at regular intervals in the range of a guide. | +| [`EventSet.timestamps()`][temporian.EventSet.timestamps] | Creates a feature from the events timestamps (`float64`). | +| [`EventSet.unique_timestamps()`][temporian.EventSet.unique_timestamps] | Removes events with duplicated timestamps from an [`EventSet`][temporian.EventSet]. | ### Binary operators diff --git a/docs/src/reference/temporian/operators/experimental_fast_fourier_transform.md b/docs/src/reference/temporian/operators/experimental_fast_fourier_transform.md new file mode 100644 index 000000000..21a684844 --- /dev/null +++ b/docs/src/reference/temporian/operators/experimental_fast_fourier_transform.md @@ -0,0 +1 @@ +::: temporian.EventSet.experimental_fast_fourier_transform diff --git a/docs/src/tutorials/getting_started.ipynb b/docs/src/tutorials/getting_started.ipynb index 929eef0f4..b9245e9ec 100644 --- a/docs/src/tutorials/getting_started.ipynb +++ b/docs/src/tutorials/getting_started.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "3aebee14", "metadata": { "execution": { @@ -32,36 +32,7 @@ "shell.execute_reply": "2023-07-24T16:46:41.979153Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: temporian in /Users/ian/temporian (0.1.2)\r\n", - "Requirement already satisfied: absl-py<2.0.0,>=1.3.0 in /Users/ian/temporian/.venv/lib/python3.9/site-packages (from temporian) (1.4.0)\r\n", - "Requirement already satisfied: matplotlib<4.0.0,>=3.7.1 in /Users/ian/temporian/.venv/lib/python3.9/site-packages (from temporian) (3.7.2)\r\n", - "Requirement already satisfied: pandas<2.0.0,>=1.5.2 in /Users/ian/temporian/.venv/lib/python3.9/site-packages (from temporian) (1.5.3)\r\n", - "Requirement already satisfied: protobuf<5.0.0,>=4.21.12 in /Users/ian/temporian/.venv/lib/python3.9/site-packages (from temporian) (4.23.4)\r\n", - "Requirement already satisfied: contourpy>=1.0.1 in 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satisfied: six>=1.5 in /Users/ian/temporian/.venv/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib<4.0.0,>=3.7.1->temporian) (1.16.0)\r\n", - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "# Skip this cell if you are running the notebook locally and have already installed temporian.\n", "%pip install temporian" @@ -69,16 +40,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "c71dc843", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T16:46:41.983821Z", - "iopub.status.busy": "2023-07-24T16:46:41.983534Z", - "iopub.status.idle": "2023-07-24T16:46:43.078329Z", - "shell.execute_reply": "2023-07-24T16:46:43.077853Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import temporian as tp\n", @@ -102,161 +66,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "bb47d14c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T16:46:43.081497Z", - "iopub.status.busy": "2023-07-24T16:46:43.081244Z", - "iopub.status.idle": "2023-07-24T16:46:43.100311Z", - "shell.execute_reply": "2023-07-24T16:46:43.099894Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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timestampnoisetrendseasonsignal
00.00.0912990.0000.0000000.091299
10.1-0.0026160.0010.0199920.018375
20.20.0574130.0020.0399330.099347
30.3-0.0708480.0030.059775-0.008073
40.4-0.1870950.0040.079468-0.103627
..................
99599.5-0.1372260.995-0.1971820.660592
99699.60.0284860.996-0.1795420.844944
99799.7-0.0346140.997-0.1614530.800933
99899.80.0358020.998-0.1429600.890842
99999.9-0.1445020.999-0.1241100.730388
\n", - "

1000 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " timestamp noise trend season signal\n", - "0 0.0 0.091299 0.000 0.000000 0.091299\n", - "1 0.1 -0.002616 0.001 0.019992 0.018375\n", - "2 0.2 0.057413 0.002 0.039933 0.099347\n", - "3 0.3 -0.070848 0.003 0.059775 -0.008073\n", - "4 0.4 -0.187095 0.004 0.079468 -0.103627\n", - ".. ... ... ... ... ...\n", - "995 99.5 -0.137226 0.995 -0.197182 0.660592\n", - "996 99.6 0.028486 0.996 -0.179542 0.844944\n", - "997 99.7 -0.034614 0.997 -0.161453 0.800933\n", - "998 99.8 0.035802 0.998 -0.142960 0.890842\n", - "999 99.9 -0.144502 0.999 -0.124110 0.730388\n", - "\n", - "[1000 rows x 5 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Generate a synthetic dataset\n", "timestamps = np.arange(0, 100, 0.1)\n", @@ -296,37 +109,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "b4a2782d", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T16:46:43.102798Z", - "iopub.status.busy": "2023-07-24T16:46:43.102616Z", - "iopub.status.idle": "2023-07-24T16:46:43.108855Z", - "shell.execute_reply": "2023-07-24T16:46:43.108446Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "indexes: []\n", - "features: [('noise', float64), ('trend', float64), ('season', float64), ('signal', float64)]\n", - "events:\n", - " (1000 events):\n", - " timestamps: [ 0. 0.1 0.2 ... 99.7 99.8 99.9]\n", - " 'noise': [ 0.0913 -0.0026 0.0574 ... -0.0346 0.0358 -0.1445]\n", - " 'trend': [0. 0.001 0.002 ... 0.997 0.998 0.999]\n", - " 'season': [ 0. 0.02 0.0399 ... -0.1615 -0.143 -0.1241]\n", - " 'signal': [0.0913 0.0184 0.0993 ... 0.8009 0.8908 0.7304]\n", - "memory usage: 40.8 kB" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "# Convert the DataFrame into a Temporian EventSet\n", "signals_evset = tp.from_pandas(raw_data)\n", @@ -336,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "e0494b95", "metadata": { "execution": { @@ -346,18 +132,7 @@ "shell.execute_reply": "2023-07-24T16:46:43.915211Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the dataset\n", "_ = signals_evset.plot()" @@ -387,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "34498512", "metadata": { "execution": { @@ -397,18 +172,7 @@ "shell.execute_reply": "2023-07-24T16:46:44.031965Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "signal = signals_evset[\"signal\"]\n", "trend = signal.simple_moving_average(tp.duration.minutes(1))\n", @@ -417,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "8b16c4e8-d4c6-4e4b-897e-f43468c530e7", "metadata": { "execution": { @@ -427,18 +191,7 @@ "shell.execute_reply": "2023-07-24T16:46:44.147980Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "trend_prefix = trend.prefix(\"trend_\")\n", "trend_prefix.plot()" @@ -504,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "f252fba3-cbff-4d7f-9751-db1e2555457e", "metadata": { "execution": { @@ -514,18 +256,7 @@ "shell.execute_reply": "2023-07-24T16:46:44.378757Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "detrend_rename = detrend.rename(\"detrend\")\n", "detrend_rename.plot()" @@ -550,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "d257c190-244c-49e1-927c-54c9b7ca3d50", "metadata": { "execution": { @@ -560,18 +291,7 @@ "shell.execute_reply": "2023-07-24T16:46:45.076975Z" } }, - "outputs": [ - { - "data": { - "image/png": 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WsThNWlrLLWSzWapsBix6zZzHzke5VYdSObN0/ysT7RYWvQbjRNBfZTPw/NFxvvrIIR7Y2cerE6MLBcuXogrqP/3pT9PY2IhCoWDPnj1zHnfPPffQ1tZGS0sLt956K6nUyi3/aW9vL/QSBAVCaF/aCP1LG6F/6bLctP+vPx3lhWPj9PmiXNTmYsAXYzyclANwgFQmS5VNz/i0jPETh4Z51zl18viscCItl26/2u3jyEiITFbiXdvr+JdHDvJMxxhffvggAJ/4+WsM+GN89v49S/eLLhPm0v+Xr85uoBeMp1Aqcpn5dCbLPc938XTHGE8dHuWGzTXUOY3ysbUOQ97jd2ypoWs8wh3Xrgbg6nUVfO2mjVRYdQRiKeyGXHBZYdVj0anxhBNsrrMTjKcJJzK0lpsZOsEs79mOMX61s/+s/gbFxslMIu/b0cvDewfRqGYPyxxGDW0VZtrb26my688oUw9QbtGf8rFVNj0P7hng6SOjPHFwhM4xMclguVNUQf1NN93E888/T0NDw5zHdHV18cUvfpHnnnuOY8eOMTIywve+970lXOXScuTIkUIvQVAghPaljdC/tBH65/PJX+xi52lkkjJZiWOjoUVc0eKx3LTf2ePlO0/nMvPrq214o0n80SSRE8quq+0G+n1TAd7THWO8ZWMV0YnjwvGpoL7fFyU64aR+RXs5Bo2KzrEILx7zMBqKc3goxI4uL/45RrCtZGbTP5OV6PFEZ7QvQC5TX+c0Mh5O8nKnl+s3VfPzV3qxGTRyyfwkP/nIuaimZXKVSgXfee822iay9wqFAoUil9HXqBRypr7cosOsU6NRKWlwmuR551V2PSPBOM8dHZPXNhZOlFyv9qfv2z3v60eGQ+zvD+S1M0zn6+/chFWv4ciRI9TYzzxTf8vFTad8bIVVj1WvZluDg9d6vIyHhdHlcqeogvpLL72U2traeY/59a9/zfXXX09lZSUKhYKPf/zj3HfffUu0QoFAIBAIBEuNL5Lkf58+fsrH93mjfOc0jhfkE0tm2NfvZyQYp9Kq5+OXtdDnjVJm0hKMpfFFk0ST+f3zbRVmjk7MLc9mJSKJNHajFoNGRTiRJpLM4JeD+hiRZJrJiuRyq55Xurzcemkz//jbA9Q4DLzS6RWO+hN4wgnSWYlwfKYzfiieotllYjyU4A/7h3jf+Q2UmbR85qpVM4416U4tA6xUKnCbdTgmgtAWt5kquwGTTk1DmZFah5HRYBy3WcdYKMG3/nKMwYlWibFQouR8EgZP0ibSNR7h0FAQm2H2ufMt7imfg4tbXZzX5DyjdVy7oeqUj9VrVPzso+dxYYuLC1tcjIUS3PHbfSTSwiNhuVJUQf2p0Nvbm5fJb2xsnHemZyKRIBgM5n0VE8utDE+wdAjtSxuhf2kj9J8incmecjAyiTeaJBhbnqPhTsZy0P74WJhvPNHBK11ezm1ycnGri3++fp3cr+uPpvJmlgM0uUxyCW8glsJl1gGwqsLCrh4faqVCztQPB+J549Eay4zs6vXxwQsaGA7GuG5DFa90eYgk0vP2Iq9EZtN/sm89lJhZuRCMpWl2mxkKxhnwx2goM/HDD22nxm44q3VU2vRyxviK1eVcva4Ss05No8tEQ5kRnVqJ25IL6kdDCXnG/VgogTdSOpsxqUz2pFluhQIOD4fkTZK5aG9vp9ltzvM9WEzWVFl566Zq/uWGdYyHE7xwzEOPJ7ok7y04fVZcUH+63HnnndhsNvmrrq6u0Es6LZZbGZ5g6RDalzZC/9KmlPUfCcb58Qtd8uNgPI3NoEGhgGOjIfb2+fnWX44SiKbwR5N5x07ijyaLdtzWctA+GEvxareXR/cNcVm7G4VCwdXrKidelWbN1OvUKlKZLNmshCeSoMycy0pW2/V0jISosusJRJNIkoRapcgr328sM1Fm0qJWKfnRh87lpm21dHuiNLlMhCeC/xePjZeE0/ps+o8EE7jMWkLTMvXffeY4I8E4oXiKJpeJn7/cw8WtZQu2jv9695a8Un2A955XT2OZiXqnEatBg9uiYzSUwBdNcnyiSsMTSWI+w57wYiSSSBNOpGdtjfjPJzv4xUQrhEqpmLP8fpJCfPZVSgVlZh3hRJqhQIxjo6K3frmy4oL6+vp6enp65Mfd3d3U19fPefwdd9xBIBCQv/r6+pZimQKBQCAQCM6AV7q8vNrtkx/7okkcxlwA8fNXenlk3yB/PDDMSCjO4weHeXiW+efeSErOCgtOn2A8l2mXkGaYb0lS7msy2M6NtssFf3qNisv+4yn6fTFcplym3mXR0eOJUms3Eoil8EaS1DmMRBNpFBMxY6PLRKPLBIDboqPWYUShgPXVNnyRnI4P7OzjeImaeQ0H47S4zXJQn0xnufuZ4/R5owTjaTbX2bl5ex0fvbh5wd5zuqHeJBe2utCqldSXGeWe/aFAnPYKC53jESKJNNmshFqpnDXIXYlMahJO5G9yJdNZ9vX72T8QoMVtptpumLP8fjkwEkywtsrK0ZHS/IwVAysuqL/xxht5+OGHGR4eRpIkvvvd7/Lud797zuN1Oh1WqzXvq5hYDmV4gsIgtC9thP6lTSnrv6vHh29aL7U/msRu1FJjN/DkoREOD4c4OhLGH03xp9dH0apn3ur4o0mC8eIM6guhfedYOG8MWTCW5sMXNfLZq2auRa1S4DLrZAO8SDKNWacC4KZttbRXWNnfH8BlyQUwbrOObk+EGoeBQCzFsdEwa6usE0FQLqpvcZv5/DWr5ffQqpWsrbJSbTfI/xa80VRJ9GrPpv9oME5ruVmeOPD0kVEqrHq8kdy/81qHgQ9f1DQjs75YNJaZ+MwbV6FQKPCEE2yqs/Pc0THe8Z0XUSjAbdHO6gj/x/1DHBgILMkal4JgPDVrUH98LMxwIE5jmYl/fdt6/vqKFqpt+lMqvy8UBo2Sy9vL2dfv5/mj4yTSGblF5nd7Bkhlsrx03JP3Pf/wm32FWGrJUlRB/W233UZtbS39/f1cffXVtLa2AnDLLbfw8MMPA9Dc3MxXvvIVLrroIlpbW3G73dx2222FXPaishzK8ASFQWhf2gj9Fx9JkuTZzsuNUtN/b5+fQCzFD57r5NBgEL1GJb/mj6awGzVU2w0oFQriqQwWvRpfNEkmK1Fh1c8I4L2R5JIFOAtNIbR/aM8gv987JD8OxFKsqbLSXjmzt9dp0lLjMMg3/N5IUnZJf8OaCi5sKWNvf4CyiUx9mVlL13iEGrsBfzTFC8c9XNTmIpxIY9TmdFYpFbSWm/Pe56cfORenSSOb5fmjSYZKwFV9Nv2HA7mgPjzhMbCr1891G6rwRZOE4+kZLveLzXS9bAYNFVY9H7moiY21Nvkz+dOXuoklM0STaT5x72v0eaPs7vPzs5d6TvLTlz8D/hiHh4P86yOvc3g4iFqpyBvx+C+/P8TLXR4qbXqUSgU6tYpVFRYqrPOPnCvked9l1rGu2spYOMHdzx7nD/uG+NFEa9M3/3SUI8Mh/veZfPPRP70+Koz1lpCiamq5++67Z33+Bz/4Qd7jW2+9lVtvvXUpllRwlMqi2pcRLCBC+9JG6L/4HBkJ8dtdA5zb6KTJbcpzIJ6PHV1e1lVbT9u87XRY6fqPhuKUmXSE42n6fFF+9nIPl65y8+j+Ia7fVM1zR8flY33RFA6jljKTlrVVViptesqtegb9MYxaFQ1lJn78QjdNLhNv3VQtf4/boiOVyc45G3q5Ugjtu8YjeY+D8RTWOcZqOYxadGqV3N7QOR6h2W2SX69xGNjX76fM3AZAmUnHUCBOjcPA60NBxsMJPn1lK9FkBpNONet7AJSZdThMWnwTpmuheLokMvWz6e+LJmkoM7K7188/PniA8XCCD1/UyN6+AFlJQl3Af+Nui44Kq463bKzmL4dHeHT/MFetreA7Tx9nV68PBaBRKfnRC92E4imOjoaJpzJ5G3fFxms9Pob8MXzRJN3jESqs+jy/gz5vlBePjXPF6nL5uc+8sQ2FYv6NxkKe96tsehpdJh6+/WI++uNX6fVGOTAQIJuVGPDFeLnTw3BgyuU/ncniieTGFzaUmeb5yYKForiuZIIZNDcvXH+UoLgQ2pc2Qv/FxxNOMhZK8HKnh+c6xk75+x7Y2UfnWOTkB54FK13/T9y7i9t+9hp7+/385MVuOsfCPLirn+1NTj50UW7W8qTruT+axG7QsL7Gxq2XNvORi5q4cWsNHSNh3BYdjWVGfvJiNw/s7JODPl8kSYPTWJRmeYXQPpJI5/2tgrEUVsPsm1ZOk5baaZn6rrEITa5pQb3dwGgogXvC/V6rVmLWqXEYtfR4ojiMOUO8aDKDUTv/xpjDqMU3MavebtTImfrjY+HTGnFYTMymvySBVa9hd6+fJw4Ok8pkJ8rvZ5a4LzVui47KiQz0JW1uPn1lG+uqbXzg/Ab29Pl5tdvHTdtqGQ8n8MdSrK+xMhYq/LrPBn80iS+awh9L0e2JUm3Xy5n6TFbCG03ywnGP/HcBThrQQ2HP+1+4bq3suq9VKzk2GmYsnGQ8nEBC4qXjHoanVcp4o0kkCQb9y3Oj7akjoxN+HysHEdQXOceOHSv0EgQFQmhf2gj9F45XOj2zPu+PphgPJxgPJ9jbP3ef54nlhd5IEs8i30yvZP29kST1TiOSlHNJ3z8QwKLXsKPLy4YaGwAGrYrYRGtErvxei0mnZluDg/oyI3UOI8dGQ7gtOtorLbxrex1/84Y2fvJiN13jEVKZLC6zjoODwVl7e5czS629JEkogAqrTt4UCcbTWA2zZ+pzmVk92YlNl67xCM3TqlwmR6k5TFOmYC6zFrNOzTvPqeWrb1sP5LK3Ju382VqHMZepT6azuM06/BOl+MOBOEeGi2tE8akyl/4WvYYDgwEcJi12gwanUYs3kqLQccv7zm9gXXXuc6tR5Uz0ANbX2Dg4GGD/QIDzmp2yiZ7TqC0KE8tkOjvna75IbvJGIJqixxOh0maQM/XDwTjnNjoZCyWosp3eWMFCnvene5PU2A0cGgyyvtrKK11eNtba2dHlpcZhlDcvxkIJmlwmhqZl7wvBXBMx/u3Rw3hW2GhFEdQXOWp1UXVQCBYQoX1pI/RfGFKZLJ/79d5ZX/NFk3jCSULxtGzGFU9l+O2ufvmYZDrLNd98Ls/J2RPJfd90Iok06czcN4Gny0rWf3evjy31dhSK3I3hUCBOlU3PmiqrHNQ7jFp51rU/lsRhyg8w7UYtHSNhyi161lXb+Pw1q9nW4GBHl5f3/eAVxiNJbAYN333mOA/tHgBy5aLPdowRiKbyMk7LjaXW3hNJ4jRpaXGb5TL8UDyNeY4s+vWbqnnLxir58VAgRtW0jKTdqKHGbshre3CZdVj0am65pFluWzHrVBhOkqmvcxroGo/gj+XWODmyPhBLrdj++un6J9NZQvEUOo0Ss16NP5riY5c0c1GrC5tBQ48nctIxaYvN6korhlk2Z0w6NYP+OHVOAzq1CoUiN6/dViRB/W0/2znna/5YEl80SSiey9RX2abK73s8Ec5pdGDRqSm36k7rPZfLeb/WYUCnUXF+cxkP7Ozj/GYnoUSacxsd8uduLJRgY61txudwqcdOfub+3bxwbKpdKxhP4YskOTZhVriSEEF9kTPfuD7BykZov/KJpzJz3twI/ReGUDxNnzeGZ5ZsbSCWkoN5i15DMJ5z5r7rTx3yMc90jMmO3ZP4Ikk54Jzknue78sawnS0rWf/XenxsrXdgNWjoGo9wUWsZLW4zd928mfqJMVoOkxb/RNm1L5rCfsIoKLtRQyCW65ufRKFQcO8t5/Ef79xENithNWjYPxBgV29OlycPjfDNP3Xw58Mj/PHAEMuVpda+ezxCo8tErcNIv28y6yahnMNoUKFQyKXEe/v8JNLZvGMVCgV/fUVL3ve4LLoZZm5GrXrennrIfS4jybRsxieRqyzwR1MMB+MrcszddP1/9Vofdz15lHKLHotejUIB12+u5p3n1KFUKujzReWS6eXIXTdv5ktvWQtMjUK0GTTLPqhPprN0e6Jzvu6P5lzvLfrc71Jly3l87O8P0OuJUu808Z7z6k/bN2C5nPfrnEbqHAYub3fzSqeXc5vKcJl1NLvNchA/Hk6ysdbOoD8/U/+Jn79GagE3uE9GMJbmh893yY8f3DXA/3lgDy6zluEV5sEhgvoip7Ozs9BLEBSI+bQPRFMcGw0t4WoEZ8qAP8ZoKM7133qeJw4O5732TMcYv9rZN+v3ic/+whCMpTBpVTy6f4jOE27+fZEkBq0KCWgrN3NsNEzXeIRwPE2PJ5ex/OOBIf7mDW3ct6OXbz81VRo5fkL5/UgwTjSZP6f4bFip+qcyWXb3+llTZcVt0XFoKMRHLmriTesqqHMa5WDRYdTgjSSJpzKyTtPRqHJ92pN925PoNSrOb3byuavbsRk0tLjNRBIZJEniN7v60aqVDAXihOMLp9VCs9TaD/hj1NgN1DkN9HnnDmRORJLgX//wet4ouknee15D3uN3bqul0pbv/G3SqU7aUw+wtsrKC8c8OIw5l/WhQJxALEUwluIvr48uur/FUjNd/6MjYR4/OEyFVY9Zq6baZsgLFC16DW3lp2bwWQiaXCb5Mz25GVQMQX0onpp1I3j665AzhQSoshn4za5+vvTwAdlI9R/fvOa033e5nPfbyi1sbXBg0Wu4qLWM1nIzt17SRKVNz9BEED8Wys21f+LQCP/3kUPy9w4v4fk1k5Uw6VTyZt/kunZ0eblydXmesd9KYEGC+mAwOO+XYPHQ6U6vdEewcphP+119Pn67a2AJVyM4HUaCcR7aPUAmK/GJe19jb1+ACque0RPMgUaDcYLTLn7Td5sn9f9/j76+NIteAdz78sxRSYFYigtbXXzjyQ7u29Gb95o/lqJhIjPcOi2of/e59Xz0Jzt57miuVPtN6yr4xSu97O8PkEhnqLTp8Z5Qfj8aShBZwLLDlXTu/92eqXPVYweGuWZ9JSqlArdZR+domK31jhnuyU6Tlj5flK89doSbt9fN+nPtRk1epn4ShULBZavcWA1qNtTYaHKZ6BqPkJVyQVCfN0oosXyD+qXWfiQYp8Kqp85hpM936kF9KJ6mrcLM+omWifm4vL18RtbSpFWftKce4MrV5fxyRy8Ok5a1VRZeHwoSiKWoLzPxcpdHDr5C8RQP7u4/yU9b3qQz2Tz9+7xRNCoFlTYdSqWCj17clHe806ilrXz5Zuqn4zRpsBrU2I0auQonGE/RMbL8EhSheJpgPD1nxlmSIJLMUGXTY9SqqLLpGQslqHcayUgSdRPXldNluZz368uMfPyyXLXNd9+/jRq7gdsua6HWYaB3YuMv5xmg5w+fupjOadMzvJFk3iSAxWTQH6PabsCkUxOduP6OhRJ89Yb1vHVTtcjUz4bdbsfhcGC32+WvyccOh2Mh3kIwB5WVlYVegqBAzKe9P5pkJFhc5k+lxDf/dJSfvdxD51iY7vEIw4EYa6qsssnTJCPBRN5s219MCzon9X+2Y0zegRbMTSYr5WXSJwnGU1zYUsYzf3eFnNF77MAQvZ4ogViKJpeZMpOW1nIzxyeC+lsvaebvrm7n0GBu07rKZuDHH95OOpvFG0nS4jbJjtyTjIUSRBcwUFwp535JkvjS7w7KfZZPHxnjzRty/dhuiw69VjVrmXe5Rc/PXurhnEYH10+MqTsRl1mH06Sd9TWArfUOPnBBA2uqLDzbMUaFVY/bomNvf2DWm850JrukZaNzsVTaS5LE/a/2MhxIUGnT5wVbp4JOo2Rznf2M39+kU8/ai30i2xocOIxaHEYta6utclC/usKCUaOSzbB2dvt4ZO9UW8Wx0fCyP3emM1ke2TcoP77jt/uJa6z8dlc/kiQhAVsbHFRYclUOHzkhqP/AhQ1ytni5U2bW4TBq8zL1z3WM84Pnlkd2ejqT5wffHEZrCgVY9bkNijKzliq7nk11dr5w3RruuPb0M/STLMfzvk499RltLTdzdKIVbTgYw23RUW7VowCyWYlsVsIbSRKML24lxqQuXeO5yRtlJi2D/hhPHBzGG03yti01rKm0rjjfjQUJ6rPZLJlMhmw2K39NPs5kltYQodTo6ZmZeRKUBvNp74ukGA2trJPVSmLQH+OSNhcdI2GC8TTHxyKsrbLMCARHgnFC8ZzBWjyVodcbpd8X5XvPHpf1Hw8nSS6DQGO544smGQrEZ5j0BGNpbAYNNoOGrCQhSRL3vtzL5369F0mSKLfqKDPraCzLZXMDsRROk5Z11Vb6fFEmpxBd2OoCclkIl1kH5AcLYwucqV8p5/5QIk0glmJfvx8ATyQhZ9fdZh1lcwTlF7SU8dhnLuXNG6rmHAX1tZs2opqj7xtyZnptFRbaKiw8cWiEJpeRapue0WCc8CwbMD96oZtfvNI7y09aWpZK+0AsxX/96SgjwTiVVn2uPJqc18d0J+y5MGnVbKm3n/H7m3Qq2TRvPhQKBf/xzk2c2+SkvdLK4eEQgViS9koLF7e55PFoO7q9jE/c7CfSGd79vZdnnHOXG0dGQnz/2amgtnM8wm9eOcZXfn+Iu5/txKxTc/sVrWypnz2B9paN1fN+BpYTZSYtTlN+UH9kOMj+gSC/2zOwrEzNQvEUBo0Kb3T2oF6ScucXu0FLldWA26zjJx85l3KLfkabyemw3M/7OrWK1MTmZzSZkT+/1XYDA/4YwXiKdFY6aVD/x/1DPLr/zHxNhgIxPnP/HgC6PTk/EKdJy65eHz9+sRtJklApFTnflWX++T9dRE99kWMwFMcOrGDhmdT+1W4vTx0ZzXstl6lfPhdAwRSjoTgusw6XWcfR0RAKBezq9bGmyoovmuR//nyU+MSorlyZWor/+4fX2d3rJ5nOcmgwyP8+fRyVVockSQRjKSIJsXl6MiaN63pP6AkOxFJY9Tl36HqnkT5vDJ1aSYvbxGgoQZlJi8usRatWMhJKyE7SlVY9BweDlJmmyiElKfc+J2aHJUkikc4Qm9ZTf7YZwpVy7h8OxNlSb+e1Xh9joUTe39Nt0U1skJwZp2oQ1lZuZkeXlyaXmSqbgYYyY55Wkzx/bJzXemaaHf759RF2dnvPeJ2ny1Jp3+uNMhiIMxZOyBlzjUrJ0ZEwdY6Tlw9/4bo1tLjPvJ/7+k01tFeemob1ZUZsBg3miTLbZDrLWzZW8bFLW+RM/eGhIM6Jz++j+4fQqZUzTLyWG7t6fHnZxGgyw++PhPmHa1cTT2W4YrWbZrf5lCoaljtuy1SmPjgR1B8dDVNl0/O1x46wu/fUjUafPzo+Y9ToQhKMp2koM8ptVtFkWp55nspkUSgUOIwabEYN33jXJhQKhXydORuK4bxfadXz6P4hzmmY2mhaVWnhmY4x+rwx7EYNwdj8VWujoQQjwTgP7x2cd3TgbBwZDtE/0SbUORaheSKoPzAQzDPNnGszuJhZ0KD+6NGjXHvttVRXV+N0OuUvweLhcrkKvQRBgZjUfl9/gJ+82J33mi+aQrkCT1jFyK9f6ycQS/GXwyPEUxm+8XgHV6x24zJrOToapnmin7fOYSQYS/Pk6yMcHZm68GSyEsOBuJzJ7BgJ0VZu4XW/kngqSzKTJbKM+38LRTCe4vd7B+VRc+PhBK3lZro9kRnH2SZu9NfV2Hjx+DgWvZptDU4UCgUXtbq4cnU5AF+/aSNfv2kTAGqVEm8kmZd10WtUDPpj8mityZs8fzRFtd0gZ+oDsRR/+6vZx+idKivl3D8ciPOmtZXs7fPzWo+X7Y1TN4IVNv1ZZXpPFYteQ7lFR5PLSJVNT7XdwIl7Lp5wQnbUP5EjIyGOLGHf71Jp3+uN0uwyyQEWQLPbxNNHRmd4HMzGdGPDM6G90nJGgZBBoyKSyFBu1dNabiaWzFU7qZQKVEoF2azE00fGeO/59cu+/HZXr59mt4l4KkM8laGxzEgWBRe3uvjMG1fx9i21hV7ignH1ukrevqUGvUZFIp0hGE8RT2XY3uik2W2Sy7qnM9fm6C929DC6iC2IwXiKhjKjvGH0tceO8Mlf7CKWzHDp157CZtBMZOo1Z9w/PxvFcN7fUGvjW385JrdRAZzb6OT3ewf50QtdNLlMeW2FsxGMpfBHUzy4q39GW+J8/MfjRzg4GMQbSSJJEv2+nMmny6zlwGCAaDLDtOmz/OfNm0/311vWLGhQf+utt/KhD30Ih8PBM888w0033cTnPve5hXwLwQn09c3ujC1Y+UxqPxqMMxpM5Dmx+mMpKm16OeMrKBw/famb7vEIn/rFbv72V3s5r9nJWzZWU2bWcXQkxLpqGy6zbqJvWGLQH+PwcL7BqDeS5MBgEKcpN3v74jYX+7qG5QAjsoCu6iuFY6NhfvpSNx/+8auMhuJ4I0m21tvp9UTpGo8wFIiRyeYqHSYDh/XVNh4/OEytwzjRp6uh2m6geSLbuKrCklfKWmHVUzUtqHeZtezp81NtN7Cx1s7LXR4AxsIJGspMck/9WCjOsx3jZ5WtXynn/uFgnCaXkXgqy6vdPs6ZFtRb9Rr+9k3tS7KON62rpM5ppLXCzOXt5ZwYi3aMhFlbZaXcomP0hCqoQDQll3gvBUulfa83yhvWlFMxbcZ8W4WZPx0epaFs4QKVhWZVhQXPCdMnusYjNLvN2I1a/LEUgViKtVVWhpa5+3UglmJ9tY0Bf4x+X5Rah4Hbt9upLZI++dNBr1HJFQfeSJJr7noWk07Ne8+v5+s3bZoR1EuSxPvv2TFjagmAJ5xcVAf9UDxNY5mJQX+M8XCCoUAMi17Ny10eWsvNbG90cEFz2SlXmpwqxXDef/f2ev74N5fI103IbdDddfNmnu4Yo6nMlGcAPBvBeGpipGzqtPrvH9k3yM9f7mFbg4OxUIKsJKFWKXGadBwaDHJBc1leJZ3NcPbVE8uJBQ3qg8EgN998M0qlkg0bNnD33Xfz0EMPLeRbCE7AbF6+o0oEi8uk9iPBONeur+Tg4FQgGEvmLjiLuVMtODnRZJrDQyE6x8OsrbaiUSp4+5YaINc/2DkWYW21lcqJm+ZEOovdqKVjJEQiPdW36okkODgQoMVtomMkxJoqK5GMikAshVKBKL+fhXA8zXUbqvj7q9v5+mNH8ISTnNPo5Plj43zml7t5aPcgH/3Jq3SORbAacn1/bRVmXu32Uesw0Fhm5F+uXz/ve9Q6DFTkBfU6nj4yxoYaGzedU8t3n+nk2Y4xRoJxmsqMsvvueDiJP5pk4CxKf1fKuX8kkHNWb3abeLZjjGZXYX6vf75+HTq1inKLnpu21eZl6r/40AHGwgnKrTrWVFlnZOV90STj84y3WmiWSvs+b5Rr1lfSOm0kWlu5hb19fuoXMPu40KyttubdrEsSvD4cYnWlBbdFR+dYGLNOLff5LmeUitxYtAFfjD5vjHqnkTesKV+RpcPTCcXTfPVt6/nmzZux6jVU2vRyRVomKzESjPPo/mHGwwmOjYZJpDN5Y9M8kcUO6lM0lJn49lPH+OHzXaQzEmuqrDxxcIS3bqzm3efWc0FL2SlVtJwOxXDeVykVqFUzw8sqmx6VUnFKmfpAbDKon6njiWal+/sDPHFwGEmSMGjVxFIZttQ76ByPoJ7YhHeatCTSWS5uc806EWWlsKBBvUaTO4laLBa6u7tJJBKMj48v5FsITsBqtRZ6CYICMal9OJGmvdIyozew0qZfceM6io09fX4219vZ1x9gS72Db757i3wz5rLoSGclNtba2FiXG/kUSaS5sKWMrvEog/64nCEzatV0jkdoLbfIGwHBZO7CV2HVi/L7WQjF01j0GtbX2MhkJfYPBFhXbeXDFzXy8cta2Nnt5ZVOLy8cG5cDAI1KSbPbRK0jVzZcf5Js5GWr3LRP69t2WXRU2vToNSpq7AY+fFEjP3yhix8818W1G6rkoN4TTnJes5PdvX4ePzh8RtnCYj33j4bi3P/qlNncUDBOpU3PtgYHDWXGWZ3uC0kqk+XR/UOMhxK4zXoayoz0ePJ9GfzRFOOhUy8RPVuWQvt7nu9id6+frfUO/um6KbfuFrcZrVpJtX35ZorXVFnygnqFQsHBgQCrK624Jzbe1tfYqLLpGfIv/2tkzcTmQ58vSq3TWLSf/dPh5u11XLbKnRccKhUK0pksfzk8yhcfOsALx8e55ZJmuj0RhvxxHj80LB/rjSTltpGFrFg8OhIiEEsRiqdpdptoKDPJ54O2cgt/en2EJvfCBvLTKWbtFQoFm2rtNLpMJ+2pD8bShOIpfJHkjGP//Y+HOTgYkB93jod5pcuLL5piXbWV/3r3FmodBl487pE3H8tMWoxaFW/eUMWb11exUlnQoP7SSy/F4/Fw++23s23bNpqamrjhhhsW8i0EJzA4OHjygwQrkuna1zhmZhyq7QbZLERQGJ47Os7bNtewrz+QV8IKYNGp0aqUtLrN8ogbu1FLncNIOpvl2Y4xzm92IklQYdWhVirkjFm1TY83lBu5Vm03EBXl9zMIxVOY9bkM/OWry3n84DBlJh2Xt5dz7YYqxsIJLm93E0tlMEybj31pm/uUb8pu2FyTF9y4zLo8c6Ar2sv5+6tXc26Tk9WVFtm4yRNJcHGrmyPDIZ4/Os7h4Vzm93TK8Yv13P/CsXEe2ZdzNZYkia6xCG6zjkva3PLc4+WAQpFb33g4gSeSpM8XxWXR0lBmpM8bRZIkHj+YCyIyWWlJR90thvYnTkt5+sgo//jmNTnH+2lZYYNWxXu21y1rR/Uau4F/ffsG+fFlq1z86rV+WsvNlFt1PLCzj8vb3Vj0mmW9IZrOZFEqFNQ6jPT7ovR5o9Q5jEX72T8dbrmkeUa2t95ppM8X46HdA3giSQb9MS5qndwEj9HvixFNpslkJcLx3FSNsVCCW3+6c8HW9aMXu7nnuU5C8RRt5WZ++9cXEoil0KiUtFWYGQslaHItXlBf7Np/5o1tbGtwEIqnSGey9E0Y1/5yR29eC2k6myWVkQgl0gTjqbzXhoJxuqbNvQ/F03SNR3Iz6W16Ll3lpsVt5jcTn3nIldnXOYy4zDo21NqW6LddehY0qP/6179OWVkZf/VXf8WePXt48sknueuuuxbyLQQnUMy7doKzY7r2NfZced4kkgStbjPHZjGWESwNqUyW/f0B3ri2nIODASqs+SVfCoUCl1mLY1p/l92Q6+G+tM3Nt586xgUtLnQaJQ6jlgqrnha3CZdZi0KhQKNWy0F9WJTfzyCcSGOZCOrPb3YSiqfzeunOby7joxc3UTExqmuSz13dTs0ZZiEvaXPxictb855bW23lk1e05r3HeDjJ6ioL4+EE3khS7tG+/b7d8jEHBgIzxu9Np1jP/a90euVZ5z9+sZtr1leiVimxGTSc07h8jHUNWjV/OTxKryc3tnB/fwC3WUetw0ifL0q/L8Y3njhSkLUthvZv//aL/M+fjwI5jwCHUculq9yzHvuVG+ZvSyk0CoUibxP1/Rc08ttPXIhWraTBaeItG6tZXTn/31CSJHo9hd0UjyQymHVquTpkwB+j1mEo2s/+2dJWYWZfv59UJsuaqlyFVIUlN4JywB+jrdzM0ZEwvmiSarueYDzFcCDOK13es87WTzqwDwfi7O7zMxxMYNFr0KiUpLNZKm16yi06auyGOcdwLgTFrv36mpyHUCie5sBgkH98cD8Ajx0c5ukjY/JxkpTzhqqw6PFHU9zw7Rdk49mxUIIeT5REOsNTR0YJxlN0j0cYCsSpmrh2r6+x8eAnL+Sd59QBoFQq+MoN65b4t116FnykXTabZXBwEEmSsFqt9PYWfqbrSsZoXL59bYLFxWg0Ek6kMWrV2Awa/BOlZsl0FrVKQbPblLebOckTB4fzxnpArvd7trnMgjPjwECAK7/xNG9YU47LpCOVkeS++elsrLWjmZaNcJi0VNn1vG1LDRtrbdgMGiw6DU6zllqHgRa3GddEP5hKleupr7bpRaZ+gp+91C3PdA7G07IBXrlFzxvXlOfN1r7j2tWc0+jkP965acHeX69RzRhnNxvj4QSrKix4I7le7JFggnQmy65p49J++lL3vI7qxXruHwzEqS8zEkmk2dXr5x1bawq9pFkx69R88aEDPHVkjFa3mSMjIRxGLXqNingqy8udHsYnxllN7tec7ZjCU2Whtc9kJVZXWtjbnytp3TfgZ+MKy2Y1TmRPN9Ta+NJb18rPKxQKeUIG5CoWstlchvBfHjm45OucTjiZxqxXY5oY0xdPZdFrVEX72T9b2srN/Pq1frY2ONha72BtlRWlUoEEDPrjXNFeTsdICG8kSaPLRCCWYjgYp8ZuYGd3/ji8WDLDc0fHZn+jE3jpuIe/+/XUtJJ3b6/n1S6vfD1pcpmosuU2h7/3gW2L6newErTXqnMbIf2+KLt7/fijSaKJDM8fy2/XliSJhjIjXeMRBvwx9g/kzk/JdJYeT4RvPNHB3c8cJxRPk5Ek+rzRPOPacos+r6Lo/OaypfkFC8iCBvU//vGPsdvtbNiwgW3btrFt2zbOOeechXwLjh49yoUXXsiqVavYvn07Bw/OPOk+/fTTGAwGNm/eLH/FYsvbDOVMGR4ePvlBC0y/Lzrj5kW4rC8O890kDg8PMxqMU2HVoVAoyEoS/+/R1yfmZOvQa1Szzvf832eOc2Q4hC+S5IFXc06qTx4a4fd7i7usqxBks9IMJ2yAZ4+O8e83buTDFzWhVCpwm3Uzyu8BvvPerXmPP/2GNjbX2nGatPzgg9sBMOvVOI1afvDBc3BbdDROGO9k07kMb5VNLzZkJnjqyBivdHkJJ3L9eJOZeoDvfyD/WjR543VBy9Jf6L3hJG6zjqwkEU1mGAnGiSRy/50s4x4LJRieZ9xWIc79Z0sonsJm0NDiMtE5FiGSSGPWqU/+jQXAqlcTjKd5/tgYm+rsGLWqvH7/l457aCgzkkhnkCQw6dTyyMLFZqG1D8fTEz3ouevNvv4Am+rsC/oeyxW3RZc3ueArDx/i6GgYfyRVkPPqSDDOHb/dx7HREOF4GtPE50PB1P1AMX72F4K2cgvPHxvnnAYHb9lYzW0T7TpOk5adPV4ua3dzdDTMeDhXAh+MpRkOxnnLxipe68kP6o+OhvjNa/2n9L7ff66TcDx3TTHp1FyzvpI3rq2QX29ymeTRpuuqF3czbCVp3++L8dZNVfzl8CgWvRpvJCln4xWKXMl8Q5mRo6MhLlvl5slDI8CUyXD3eASTVk0onptk8XTH2LL2+lgKFjSo/+pXv8qrr76Kx+NhbGyMsbExRkdHF/ItuO222/jYxz5GR0cHn//85/nQhz4063Ht7e3s2bNH/jIYVqbQDofj5ActMLf8ZGee0zrAV35/kO5ZssKCs+PTv9wzq4nWZx/Yw2NdKUaCibxg8ecv93BsNCyPu1GrFCTTWZLpLNf+13M8uLuf0WACXzTJ53+zjwd29hGIpgjGUngjS2f0tFLY2+/nK78/NOP5XT0+tk3rra6w6ii3znRcPdEUzGbQzHjOolfjNGmx6DXoNSrumpirWmU3cmw0TJXdQHSW8vvP/HJ3XgZqpTN5M9BeaabXEyUczw8Yl4tb9Od/vQ9PJCFneZwmLWOhBOFkmqyUu6mH3Bi8E/ucp1OIc//Z0jkWocVtoqXcLFcLLRddTsSsU3PTtloODgbZVGfPc0x+45oKKm161lVb6fFEMenUuC06WbvFZqG1D8RSWKcZyx0eDi34KK7lSrVNz+C0a+yhoSDDwTi+aFI2tgT41c4+7nz09UVdSzYrsaPLy3Agzt6+AOFpm16VNr1s/leMn/2FwGbUUGM3sL7GhlatlP8e122sYkeXl3ManHSNR/BGkjS7zQRiKUYCcTbW2mdMp+jxROXqxuFAXL5WZrJS3rGSJKEgF2R2jUdocplQKRX8z3u2yMf81XkNXLO+cpF/+xwrRft0NpdZf8fWWv78+ig2o4b1NVYODAbka3kuqDfRMRLmytW5Kox4KjcVKJxIc/3maiDXU3/j1lrOb3bSvIh+BsXAggb1LpeL9vbFmyk7OjrKzp07ed/73gfAjTfeSF9fH8eOHVu091zuTE4cWCoC0RRqlYL7X82flemLpOjyiKB+odnX7+e+HX15N/eRRJpoIsOeoQijoTjlE0H9569ZzbvPreelznHqJhw/651Ger1R+nxRmt0mxkNJvviWNfijKTJZiXedU8fTHaME42n8URHUny4vHBuXJwxMmqBNZlp16inzta/dtCnv8engNGlljQG5nGxVhRmFQkGzy5SXUXr84DAjwTjPdIyxt99/Ru9ZjPR4o9Q7jTQ4TfR6I7L7/XIik5V44fg4nokNNEnKlSJOGjtpVUoGJ9y4g7H0vEHiUp/7T5XRYDyvwuj5o+Ps7PYCcGw0TGu5mRZ3LqhfnuF8jmvWV/LXV7SgUSnZXGvHZZ4K6v/qvHr+/prVlFv0dIyEcBg1NE1UH0wSTqQXrSd7IbR/rccnG/0F4/lBfTiekltXVjpVdoPsgB9OpBkNxhkJ5IL66efVP70+wuvDoQVtsfjz6yM8dSSX+MpmJT72s9cYCsQ4p9HJaCiRF9Q3uUyyk/dy/ewvBb+45Xz0mvxr6cWtLi5udeXKujNZvJEkdY6cgexIMM6aKsuMpEWvNyp7e/zbH1/n9aFcompHl1f2loDchpfNmPt793iiNM4yEcWsU5/x9f10WSnaN5aZeKXLy9Z6B/sHAtROeAn979PHue3e1zDr1NiNGhrLTIyFEtTYDWSyEp5IkjKzlvdf0MAb11SgUOTuia9cXc5fX9466yi9UmJBf/u3ve1tfPOb32R0dJRgMCh/LRR9fX1UVVWhVk+UIykU1NfXz9q3f/z4cbZu3cr27dv5zne+M+fPTCQSeWtdyPUuBQtdCXEydvZ4eee2Onq9+TcrwXiq4KYyKw1vJMm2egfHR8Pc/ospA61Xu71sb3KSTubmXFdMZJDWVFlpcpl44ZiHuolMvcuswxtJ0uOJcH5zGbde2kxbhQXfxAXu8tVunu0YJxRP440s3kzXlcJPXuzmj/uH5Me7e/0YtbmL+ad+sZtdvT56PNEZ7rdnk/V673kNXNrmmvH8hRUSP/jgOThN2rye+uePjrOvP4BRq+apw0t7figkBwdzI+vqy4x0e6KkMtm8HvrlgARct6GKOyecuc16NY6JG8ZwIjciadAfI5OVJjK/c88+X+pz/6nylUcO5Y1829Xr48BEL+SxsVxQ3+w2cXAwiF67NDfCZ0JbhYVyi54NNTbWVFn43JtmJizcFh0dI2HsRi2rKiwcHZ3yQHjmyBjvuvslXuvxLvjazlb7bFbim3/q4IFX+zg+Fs4FLgYNOrWKUDxVUjfG1Ta9XA13ZDjIBS0uhgJx/NFUXgVUJith1auJp2a2tN31ZMcZBfujoYTs/u2NJtnd62PQH2dTrZ3RUDyvPeWiVhcXteauA8v1s78UzDZmVKNScs+Hcu1qJp2a546Os6bKiiSRM82zGeQW0cl7n8kJFgAdI2HGQgk++fNd7O7zyRl8IK8a0h9L5RnbFoKVov15TU5SmSwqpYJVFWZqHAa2NjjoGo8QT2WwGTQ4jDmPIa1aOWFom6tkc5l1vPe8BvQaFRa9hmAsvexGoRaKBT1zf+ELX+Czn/0slZWVOBwO7HZ7QUpFtm7dSn9/P7t27eLBBx/ku9/9Lg888MCsx955553YbDb5q66ubolXe3aUlS1tP+hrPT7OaXRg0Kjy+ujDifSMQF9wZngjSY6OhDg8HGR1lYVvv3crqyrM3PtyDy8eG+elTg8XtpRR5TCx/4RRafVOI/v6/XKm3mHS4o0k6R6f2mF2GLWMhxNyr7c/miQUT4lM/Rx85pe7iacyxJIZvv9cJ32+KF/5/UG6xiMolQp0ahV93igHB4McGAhwbDREW/nCla6qlIpZS5QnP/smnZrItJtPbzTJ/oEAl7W7Z7TJrGQ6RsK0V1ponDY3eLlh0qpZX2PjvAnDnjKTjjKzDqVSQTCeoq3CwoA/hi+apNllmvczudTn/lPFF0nSOT5lxDnojzEaSjDoj9ExHKKxzIRRq6bfF6XcMrMlZblx70fPQ61Ssr5mZq9suUXHXw6P0FZhZlVFznl7krFQnDetq5DHFS4kZ6v9nn4/m2rtvO+CBp7rGCM4EdQ7TBp29foXdSTXcqPabpCrY/b2BXjjmvJp5ff5PfVWg4ZgfObm96929tHvixGIpuQg/VSIpzL4Iil+t2eAPm8UTyTJ0dEQG2ptcqZ+sqd+TZV16ryxTD/7y4G2cgvBWEq+L5KkqTa3WDLDdf/9HIFobtSdy6wjncmZrg0H4zxxaJhf7ewnHJ/SfTQUp9yiQ5IgGCt8BctK0X57k1P2B7qgxUVruRmNSskf/+YSbru0hUqbgY9f1sLmWjtWvYYKq55Km56DA4G8ViiHUYtX3LvKLGhQn81m5a9MJiP/d6Goq6tjaGiIdDr3gZMkid7eXurr6/OOs1qt2Gy5C3BtbS3vec97eO6552b9mXfccQeBQED+6uvrm/W4UuOxA0OzXpy6PRFa3GbqnPkz0O1GLf2+KLFkhscODOV9jzDROz2e6Rjld3sG6RgO0T4xdudNayv5+uNH2NHtpWc8V0pfbtayp8+f16td7zSiU6vkkSpOoxZfNJepnzyB2gwausYjlJm0crAYjKfz+gcFU7xw3MO+/gB7+vyc11SGJ5Lk8FCIb/6pg+2NDtwWLT9+sZv/c9UqDg4EOToSpmViNupSoFMr5dJ/yAVV+/v9stv+UjlyF4rJ81D3RL9juUXHWCjOcmzVdpq0ea7iLouWMlPuq98bZU2Vhe7xCGOhBOXW3I1ksenni6byytCHAnFGggm++NABah0GuXS2wqqn3DLTPHK5YZinmqDcoufYaK7f027UEpiW4RsLJ9ha76DPu/xMevf3B9jW4GBrnYNdvf5cT71ejdOk48Xj47Qt4fmr0FROy9S/eHyct26qZjycwBfNmaIl0hniqZwZolWvIRibGdSH4mle6vTwcpeHP+wfOmUvk3gqiy+a5P5X+3i124vLrKPHE8Vm0JBIZQjH03lmn4KTc26Tk786LxcTBOMpKqa5od+3oxetWikHgQqFgm5PhDVVVg4OBmgrt6BRKVAo4Nev9fOmu55h37TESTCewmYQeiwELrOO731gGwAfvbiJbQ25caYKhYKL21x8/LJmTDo1SqVCvkY2OE08+fponhmew6hBLbL0MkVVY1VeXs7WrVu59957AfjNb35DbW0tra35c4GHhobIZnMlUqFQiEceeYQtW7bM+HkAOp0Oq9Wa91VMeDyeRfm5j+4f5i+zlO4mJkaqNJyQDdOqFKQzEo/uH+Lnr+S3Q7zjOy8W3Y1pITk2GmYoEJdLVSE3//q3f30hw4GcO7ZOrcKkSMgj7SapthtoKDPKwfpkpn7AH5dHfaiUCsbDybwe0VgyjUGr4r4dvYRmyUSUKpFEGrVSwavdXvq8UbY22PGGk6SzWX6/d5ALW1xUWg08dmCYN2+oZDQU5+jolG6LyeRnX6FQMP3jFUtl2Nef280uP8HZeaVxbDTEtf/1HH3eKJFErodeqVSQlSCZWX7nnH++fh0NZVNZ0FqHkSqbgTKzlm5PlGaXieFgnNFQgnKLnkQ6yxv/85lZP5OLde4/W5QK6JxmmqpSKuSKg+nzzVvLzUWRqZ+PKrued2ytlc/BSgVyQDcWygX10ze/z5bJ6+jpan/i9Xdff4D1NTZsRg2BWGoiWNFQZtLyxMERzml0LtialzuTU2LGwwmM2tz4OEmS8EeTVNn0/HbXAPe+3INKqcBqUOdl6p/tGGM0FKetwszLnR484STBWIp/++Pr9EzzGHql08PdzxwHyEuWJNIZ/NEkY6EELx33cF6zM28kZmRapn46y/Wzvxy4oKWMGzbnxmTe+Y6N/N+Jc45CkWsFumZ9FZ5wAoVCgVWv5rUeHxe3udjbF+Ca9ZX8v4nWqEODQa5aW8Ef9g1RYdWhUirwRZIFz9SvJO3n8yGYXp14+5WtKJUKGsqMBGMpLmtzy685TFqx8TWNBQ3qlUolKpUq78vpdHLdddfR3d29IO9x9913c/fdd7Nq1Sr+7d/+jR/96EcA3HLLLTz88MNALtjfsGEDmzZt4vzzz+eqq67iwx/+8IK8/3KjvLz8tL/nL4dHTnrMeDjBq91TvYCjobjcFwnQUGaUg/rJGwa3Rcf3n+vMCzD6fVEODQXxRUWgeKp0jkXwRZOMBBNytlWhUNBUZpLnIgO01bhmjEnTqpV866+mxqQ5jBr80SRZScrrk9SoFJSZp24eJCl3Q/rzV3pmjH4pZXo8Ua7bUMXePj+93iibau34orkSvPecW8/aKitVNj0uiw6jVo3dqKXXG5VdeReT6Z99iVxFzKA/hsOoJRhP4TbraKuw0DGtJHil8dDuQb5y/TruerIj7/kLW8pIZ2b2vhYa1QkZhbdurOLNGyopM+lkF/V11TZ+t3sAt0XHX1/ewmevaueHz3fP+Flncu5fbCRJosKqZ2RiFN+kc3QinUVzQp/2G1ZXsKmuuGehu8w6OQiAnJlZ10TrgTeSosZhILaAFVB//fNdwOlr/8tX+3j+6NQMaG8kIZewVlhzvgBWg0b255jNDGyls6vHJ4+3lKRcG1ytI2c0u7c/gMOoncjUT5Vm/3ZXPzu7fbS4zQSiKTzhBMF4iuFgggF/jGQ6y9ceO0zHaJgHdvaxt8/PO7/7kly9mMvUp/DHUrza7eP8JifVtqksZHiOkY/L8bO/HGktN8ul91a9hkF/jHqnkc6xCE6TBqtBwytdXi5udXF4OEiN3SBvaI2G4ly9rpJDQ0HKLXpMOjVDgXjBzVdLUfu3bsq53F+6ys33PrAtr3/eadIWfKNlObGg2xv/8i//Qjqd5tZbbwXgnnvuIZFIUFFRwW233cbjjz9+1u/R3t7OSy+9NOP5H/zgB/L/33777dx+++1n/V7FQCp1+sHyV35/iCtXV8x4/q4nO/g/V60im5UwaFSE4mmyWQmlUsEje4f48+ER+YLT4DTx69f6+eojh9hSb0enUXHnOzbQ7Ynyfx+ZGvG1o8tLi9tErzeatwMtmJtEOivPpJ0eBCiVCpKZLAbNxCgsg5KKWcakTc8SO01aRkOJGcGE3ailbCJTb9SpGQ8lsOjV+KMpdnb7OKfRybMdY7x5Q9Ui/IbLn2xWIiNJ9HgirKq00DkeocsToaHMKGvwrxM389V2A+c25rxD7rp585JVpUz/7Bs0Kv54YIh9/QGUilxJablVh1at5OhoiItnMdordr7++GE6xyL87ZtW8dCeQaqnlVleu6FqUXqZF5rJbESZWUuvN4JJp+aGzdU8vHeQK1eXo9eokCSJ99+zg1c6PSQzWS6ZyFKcybl/sYmlMhi1KrmVZ9I5ums8wtrq/Cq4lfhvcl21jQMDQVrLLTPO32eLJEm83OlBkqRT1n4slODXr/UTTaZJZ7Jc3OYikc7kGUhurXfwjSc7+OxVqygzabmguWzZjhlcTIaDcdldvsZh4IVj41y2qpxeT5S9/X7esrF6Rk9913iEzrEwNoMGbySJJ5IkGEsTiKUYDSbwRpI8d3ScK1aX86kr2/jXR1/HG01yfCzMumob8VSG0VCCWoeB3b1+Ll3lptaRW4NBq2Y0lJg1qF+On/3ljtOkZSycwGnS8Gq3D7dFh0al5M+HR/jqDesx69RyWbdCoSCSSLO+2obNoMFt0WHSqTgynESvKWyBcylrr9eoZkw+sBs1IlM/jQX91/nQQw/xz//8z9TU1FBTU8OXvvQlHn/8cT796U8zMDCwkG8lmMDnO72saiSRpscTzTOAeaXTQziR5r//cpQ+b5QBf4wah4G3bqrm3x8/jCRJvNrtZTyUpGFiB7/arieZztLsNvHMkTGsenUum+wyybPRAV7t9vGOrbXCRO8UmXQDzc4RGMaSaTk7r0tHeP/5jfP+PKtew+tDuR3o6TiMGlzmyb57DaFECrtRI+9M/3H/EP/v0dfleaGlxp8Pj/I/fz5KtydKY5mJNVUWDgwEsOg1+CJJeUMEclnhv53mjL1UN8TTP/uNLiN/2DfMgYGc632V1YDbomN1pYVDp2CWd+K4n2Lg9aEQ//u+bSgUCq5ZV0mLe2ozq8Zu4Os3bSzg6k4Pl1lHrzeKWadmTZWVz1+zWr55USgUrK+x8fe/2cerXVPVU6d77l8KfNEUDqMWrSrn89DtiVJjN1Bu0cnXjpXM+ppcb+509FoVD7w606vnwd39p5XFj6Uy+KIpgvH0KWs/4I+xr9+PN5JkcKJ6YiSQoGpaNnhrg4OxUAKbQcPmejt/88ZVp7ymlYJJp6ZzLELlxMbgWzdVT2TJVfR6owSiKcpMWqx6tdxTL0kSPd4ox8ci2Ay5tp/hQJxwIk0gmmQkGGc8nGA4GGcsFOfCljIC0RTv2FIjGyom0lm8kQRrqqzYDBrqHEauWJ3LxDa5TOzo8mLSzSxRXo6f/eWO06ylrdyC3ailYySE26zDPlFRZ9KpKbfoqbbn9LfqNQTjOUf1f7puDXqNCpNOTTSZLviGl9A+H6dJW/DqieXEggb1oVCIsbEx+fHY2BihUC5bslJmKy43KisrT+v4yZnag/44Y6EEw4E433iig+c6xmhxm3lk35A8S/imbbU4jFr+7tf7iKcyXLO+ksYJV1y1Ssn/vm8b12+q5vXhYF75i9uiYzyc6+MdDcY5r8l5Wo6wpUQ2K+VldnOGdkYsek2ew+ck5RMOoAC11VVcs35+/ZVKBd5IckY5pcOolXvqHSYtRq0ah0nLhhobVTY933+uk6vXVbK7z3+Wv2FxMh5O8MShEfb2+WktN3NuUxl2Y24TxKBR5fkRKJWKGbvHS8H0z36Ty8yLx8fxRVM4TVree349LrOOcque4RPmhs/Gu783s/ppufJKp4fnj47nGZi9e3sdH7ywMe+4Qt98nQ5lZi3xVHbW/lmAt26qYnOdnQH/1Nz6yspKsllpVjfupeS//jQ109kXSeIw5cYQDQfifPupY9y4tZZyi56mspXvqN7kMrO7189fDo/IRo2fuKyF3+0dyNs4C8RSfOl3B0+r1WmyhW04ED/l674nnMATTuKLJhmeCOqHAjHZXwWg1W3GadJi1KowatUl5Xw/icusY/9AQG5321bv4O1bajHpclMaNtTacJi0WA0a+nwxvvvMccbDSWodBo6PhbEZNZRbchtzSkWurH4kmGA8nPsaCSZwmrT8582beO95DfLow0Qqg8Oopdyi47bLmvPKim+7tBmXRYdJO/OccLr3fYKcqWV7Ze7f+pHhEG6LHrtRS3tFblLNqkqLfG9ln2a+9s5zchOxTFr1srimCO3zaXWb+dilzYVexrJhQWsWPvvZz7Jp0yauvfZaAB5//HH+6Z/+iXA4zEUXXbSQbyWYIBo9vWB5JBCn1mGgzxvly8930uI2c2AwwIvHPbx9Sw17+nxY9Go5s/vxy1o4MBAglsqwsdaGWpm/D2TRaxgNJvLKX8otekZDCbmUqd5p5Nev9Z/lb7oy+f2+QZ45MsY33rUJhUIhb6goFIpZ+7KrrHr5huxUtbcbtfJmzCQfuLBRzpzlegXVXLu+ilqHgbdsrOKVLi9GrYqnj4yxrWHpx1IWGm8kSSYrUWnT4zRp2dbg4LoNuYup06TFbS58K8l0/ZvdJuqdRmodRhxGrWwUBMgj3o6MhNhcZ5/hwzASjNMxEiaeyhRkc2I+RoNxbEZNnqHOq91eHt47yFs3VsvPKZUKtEXsgFtmym0SmWe5gYdcWfc3b97MLT/ZKT8XjUY5HlTw210D/HuBqhL80STffuoYn7i8JecqHUniNGqw6tW8eNxDpVVPo8vEhy9qzHMsXqmolAo+cGEjv9rZj2NiE3B9jY3LVrk5PBzkwpZcy8FDuwd4//kN7OjyzNmGIElSXhDhiySxGTQMB+MY9KfmqD8ZVFr0ahKpLIeHgwwF4lRN00KpVPDVG9Yvi4ClULgtOrrGI/I1V6lU8A/Xrubel3uA3Pm1bKJ396XjHuxGDVvrHWxvdPKrnf3YDBoqrXrCiTSSlNukGw3F8YSTOIxahgJx1CqlXHL/tccDBOMp4ukMlTY9LrOO953fkLcmk07N7z45+33z6d73CeCGzdUoFbkkx2go5ykRSaZZNRHU//e7N8ufAbtBQ/kJ10mTTo11GZR5C+3zUauU8ghnwQJn6m+77TaeeOIJNm/ezObNm3nsscf4+Mc/jtls5lvf+tZCvpVggmDw9OZQDwXibGtw8MDOPi5tc7Ojy8v6ahsvHh+nscxEOiMxHk7kZSLX19jY3uhEp1bN2iNYbtXllb+4Jxy3JUlCmnj8+nCI42O5krN/f+ywyNxPMBpM0OWJyBmbyaC+2qaftVx1e5OT9dU5c6lT1d5p1Mrj7CbZXGeXAyXHRPnS5jo7LrMOtUrJRa0uWsrNdE1zsS4lfJEk/3LDej53da6s3qxT87FLW4BcGd9sVRRLzXT9W8vN3LC5hha3CacpfzPootYyXjg+zm9e6+e//3z0xB/Dq91eTFpV3jiu5cLPXu6RDb46RkKkM1mGg3ECsRSrq4prUsl8OIwaFApmLbWd5MSg61evDdDvi/FyV+HckA8OBtFpcr4NoXgKXzSXqa+2G/jz66Osr8lp1Ow2L7sNo8Xi+k3VfO2mjXmVI6srrbw+FJKve51jYd51Th0H5miNSWey3H7f7rzn/NEUa6osDAdic577+31RPvyjHXzkx6/yyZ/vYjycJBhPo1DkzDQ//KNX2dPnz/OfALhuY2l6p0ziMucctE/8jJl0KpwmLdeur2J1lQWrQc3BwQBjoQTd4xG21jsIJ9JYDRoqbXocJi1ZScKoVRNPZfBEEqw7wUtCr1Fx9bpKvvy7gyTTWcotutO+npzufZ8ANColKqUCuzF3fXRbdFzU4uLWS3JZ3una2ycqL6Zj1qmwLoEB7skQ2gvmY8EdH9avX8+nPvUpPvWpT7F+/fqTf4PgrKiurj75QeRcVBPpDMPBOFvrHTx5aIQ3rKngwhYXb99aw/GxiNxPNB5O4DqNi0yt3YjVMD1Tr2M0FCeUSMu99t96zxb+6cEDPH5wmF/u6OXoaIhP/mIXt/9i1+n9wisMXzTJdRuq2DNR5n5sNEyr28JfndfAtetn3mhdva6Stomd5VPV/s0bKqlxzJ0lcxpnHwli1WsIJ9KzfMfKxxtNsqbKMqtJUbVNn9eTWiim62/Va/jE5S1sqbfnjUwD2FznYH9/gHRWYigQl52XJ9nZ7eOSNjf+ZTihIpxIc3Q0TDSZ5q9/vosvPHiA0WCCr96wnnNWUAWJWqWkyqrPm1AxGwqFgmxWIpuVuHd/kAF/DINGxaB/aWahx1MZ9vX75ccHBgI575XHjvCfT3bkyu+NuaD+hWPjtFeunI2X08Gi17C+ZsrZf3WVhR8+3yUH6iPBBFV2PRqVIs/fJpJII0kSY+EEe09ofcqdk6wMBxJznvsfPzjCBy9s5Icf2k5Wkhjwx3BbdCTSWVKZLMl0licODstlxoIcbotuRgUT5Equy0w6Lmp1UWUzYNVryEqgUyvp98doLDPhMGpymXqbHpdJSzKTxaJXI0ngCSdZW22dEbS/97x6uTffZZ79vefjVK/9gploVEosejVuS85I1jGLgbPNqJ1hQpzL1Bc+qBfaC+ZjQYL697znPQBs2bKFrVu3zvgSLB6numv3i1d6+OmLPQwHckF9pU1Pi9vE569t5+Zz6jDrpkruPeGkbKJ2KtQ6DFh0Uye7CquekWCCkUCciombhzqnkX+4djW7en18+a3r6BqPkkxniaeW39ipxeTOP76eNzfcF01y6Sq3PC4wGE9jM2rQqpUndU4+Ve0/dFHTjHFS06l1GOadqz5XP/b//PkoI8H4rK8VK5Ik8bOXewjG0nNewD9xeSub6uxLu7BZmE3/a9ZXcekqd95zFVYdfb4oWpWS9koL3Z786ot+X5SNdbaTZuofOGEs1lIQSaTpGAlxaDDI27fUMBqKk8lKvGld5aw3Y8VMk/vkvcxui46xcAJfNIkvmmZvn5+bttXycufSZOv7fVF+9lKP/PjgYJCbttWyu8fHkD/O2ESVV7VdTzydYVXF3OeVUsJt1lHvNFJm0hJOpEllsujUKi5scfHCsSnt/vPJDnb3+RkKxBkKxPPGMvong/rg3Jn6AV9M3tRrKDOxq8dHe4UZpUJBtc3Axy5tZigYP+0gcqXjMuvkfvrpmHXqvNGveo2KGruBCqueXk+EGoeBMrMOm0FDlU1PmVmLRZcL8hWKXGvTumrbjKzv9Kzwp9/Qxqba0xvtKLK1Z0eN3YBJO3fl0CWtLq7bmB88m3TqvORVoRDaC+ZjQYL6z33ucwB885vf5K677uKuu+7iS1/6Eh/5yEe46667FuItBHMQDs8/gzqTlej3RRkKxHlk/xBHhkO0V1r4r3dvQaFQoFOrUCoVrK2y4jLrMOpyY82m97CejCvXlNNeaZEf1zmM9Hlz71k17UK5qc7OHdeuYX2NjZeOe6hzGMkVBZYOx0bCDAWmsmo5EzsT/liKpw6PzpoZnouTaX+qNLpMfOCCxllfc5m1jIdnd0Y/MhJi0B/jbx/YC+QMzJ48NLIgayoUwViaH7/QBUh5pkXTWcgxVWfDqeqvUChQK5U0uky0us2y8/IkWSlXrXGyoL7fF8UbXVqX/Egiw0gwzt7+ABtqbLlM9RKNDFxq/vd92056TK3DwNGRMCPBBHq1gl29Pq7bWHVKQf3Zzkv/3Z4BQvE0nmmGb5FEmk21dv77r7aQzGTp8USpLzPiMuloKzdjnMMjoNRQKBTc97HzuajVxc7uqQkGV64u5y+Hp86Z3kiSHk+EkUAcg0YlG9tmsxK+SIq2cjNjoSThcBh/NJkX9EO+CV6zO+elUe804jRpuePNq/nQRY3UOgzzbvKWIi1u84yedsiNez1xFO8/X7+OMrOOY2NheYqMzaChxW3mC29ei9WgxmbQsKXewXNHx9ne6ODClrIZP3syrtdrVKftZ7BQ1/5S5R/fvGbev7nDpJ0xMcikVS8Ll3WhvWA+FuTMvm1b7mbkzjvvZNOmTWzbto3PfOYz/Md//Ad//vOfF+ItBHNQV1c37+s7u73c+cfDDAfiXNLq4iMXN6JVK2eYn/30o+eiVCpwmbX4TvPG/cIWV55Rhc2oIRhL5Vx6ZylTrnUYeLnTQ/MpZKZWGp5IMi9Tn85IaNVKml1mXu7y8LXTMLw6mfYLQZPLLDv1nog/mmIslODPEzele/v9/KnIg3pPJEGPJ0oqs/wDx9PRf32NldZyM20VZo6O5m4K9vb5eeHYOMoJU0b/ST73Y+EEseTStmMk0hl0ahWvdHrYWJubzLBcNlUWmlMp7XzXOXX87zPHGPTH2Fpnw2HUUmUzMBJM8Gq3V56mMel0Pkk8leG2e187q/X9+MVuIokMnnAi73mVUsEV7bkxXNFkBrNOjVKp4GcfPe+s3m8lcl6Tk5c6PXJAUec05mnliSTp8eQ2xLc2OOj3xXil08MV33iapztGKTPpkCSJuro6fvRCNy8ez9/MSaazsndBzl9Di8uiw2HUYjdq0alVfOevTr55VGoYtCrObXLOeL7BaeRN6/Ldxq9aW5FraUhlUSgUvHlD1URmXoHNqMGq12A3anjzhipUSgVVNgPv2Fq7oOtdimv/SubEarZTocVt4sqJcYOFRGgvmI8F3a4dGRnBbrfz6KOPcsMNN3D06FEefPDBhXwLwQmMj88sh73n+S7Z4OyVLi89ngjJdJbPXd3ONbP0aQPyjYDbolswM5Beb5TaWXq59RoVRq1qRlD/2IEhRldYOfeJmRTvCUH9JF9661ruuHbNaZlJzab9QvOWjVX8z5+P0eOJkMlKshsw5FoHjo9F8EdThOIp+rwxDg8Xd2nY5NipWOrssppLweno/8ELGrl6XQUtbrNsWPmzl3v4ztPHqLbrsRk0J83Uj4WSRM8y23smfOSiJtZV27AbtbSWm0u6dNht0bGtwclTR0ZZ59bIXhnVdgMfuGcHY+EE3Z4onzzBq2TQHzvrc+toMEE4kZIrd+KpDFr11C2ETq0kNe18V8o6zcWaKisvHfdQNi37q1Iq5etEIpWhxxNlOBjn3MZcUP9Mxxh/+6Z2dvf6cUyYYI6PjxOIpfKqvk6kyWXGZc6NLp1unrnhNEu9SxmHSctlswSA5RadPEHgAxc05lU+WA25oL7JZeKON6+e9+efadHRUlz7BfmUmXWc3zyz4mKpEdoL5mNBg/pUKndT+Oyzz3LVVVeh0WhQqUrD8bZQxGIzL+qPHRiiazzM4weHebXbK4/WORVcZh1u89k7e1v0al7q9LB6Wln+dOqcRlrcU/2WY6EE9zzfxT8+uP+s37vQ9HmjjE9ksz7841fzXoulMrMG9WfCbNovNHVOI39/TTv37eijYyTEv//xsHzj7o+meH0oF8SPBOMMB+PUOowElqHh2on8bs9A3uOH9w4yFIjhiSRZV2PDeRqfmUJxOvqXW/VY9BpMOjWecIKfv9LDeDjBoD9OvdOIzaih3xfj+892zvkzxsOJJQnqY8kMf/ervfLji9tc/M0b24DcaLcTJzmUGhtrbPzl8ChtdiWfvyYXNPzDtbnS6txM7Dj7+v155nmD/visbTRPHR6d831GgnFemVbW74sm8USSeCIJuRpgumFklc0wo1RZkI9KqaDcossz4WooM/LEoREe3T+EWacmGEtNTKlx0ueNMuiPcdkqN29cUy63Z8ViMfzRJEPTsvzxVAadZuqWzmnS8tk3tXNuk5Nr1ovZ1guJ26KbUZ49SaVVT4Ult6H19i0Lm6GfZCmu/YLlidBeMB8LGtSvX7+ea6+9lkceeYQrr7ySaDRa0rNPl4KGhvw+sGQ6y+HhEMOBBN9+6hht5Ra5x+5UcFt0p2WSN+e6ykyUW3Rzujl/8S1rJ8xjcm7OD+0e4JNXtMqPi5nHDw6zq8dHOpNlR5dX/n0S6QxVNj1jEwF/NitxNh+PE7VfLDbX2TkwEODF47mWiUlXZqNWxetDQeqdRoYDCTJZiU11Ng4OBZZkXWfDv//xMJFEml5PlKePjPLr1/r53Z5BvJEk5zY6isKE7Uz1//GHz8Vu0PLXl7dyXpOTOqcRm0HDs0fHeGT/0Jzf548m85y6F4tALMXv9w3OcOkHOLfJyS2XNC36GpYzG2ptDAXibFrVIDusT87J9oSTjATjXLu+iicODsvfM+iPEYyl8s6t2azElx8+OOf77Ojy8osdvUDOQDKWyjA80esdTWYY9MfkiSkA1XY9DWJe8Ek5v7ksbwZ2a7mZu57s4PljuQycRG6k5tpqK13jkZx5qkHDDz64HYVCgVatpLKmDn8sxZA/jiRJPH1kNOdhc0K722Wr3LjMOlrLZ99cF5wZqyosXDBLnzzAu7bXcWGr66Q/Q6dRkUifmVHwUl37BcsPob1gPhbUxebHP/4xjz32GJs2bcJoNDIwMMCdd965kG8hOIHh4WEslqkL9sHBAOc1ldHni1Jl0/Olt67lO08fO2VjnNWVFtIL0E+8vsY2b8/85gn3cINWRTydodcb5doNlVj0akKJ3E1MseKPpnBbMoyGEiTSWcbDCcqteryRJO0VFjlTH4rP7bB+Kpyo/WKhUCh41/Y6vvDgfv77PVu4b0cfq6usVNr0vHjcwzXrKxkMxFAA9U5TbhZzy6Iv64wIJ9Lo1EqGg3GOjYbp9kT44/5hJEnixeMezmtyckV7OfYiyNSfqf56jUqeS11l0+OcmK3cORah0qpHkqQZm7GSJKFRKZckUx9OpFApFLzS5QVm7nqV+kZxhVVPhVVHOuwFpkqDnSYt3kguqH/Lxir+sH+ID028NuCP0ezOGXJOZtM9kSR9vtwUkull9JP0eqPs689t0MVTWSQpl/GvLzPhCScZPCGI3FhrX5JNn2Lnvec15Jk9tpab8cdS9HmjWPRqrlpbQTKdxWbQEIqnZvx7d5q0HOkeQKlQ4IkkeOrIKH/3q33c+Y4NNLlKu4plqWhymc76b22ZqMo4E5bq2i9YfgjtBfOxoJl6vV7P2972NpqacpmUmpoarrnmmoV8C8EJJBL5pdw/famHD1zQwK4eHzX2XNakqcw067iW2aiyGbhqbcVZr+uqtRXcsLnmpMeZtCoiiQzDwTiVVn0uqI8v//Lt+fBFkyRSWYYmslqDEyWSnnCSKpueZDrLsdEQ3mjyrDLCJ2q/mLx1YxV/84Y2Ll/lZn2Nlbue7KDWYUStVLCxxsahwSBlZi31TiN93uVbHvbffz7KQ7sHcjfGI6GcEVWXhxq7AZdJy9GRENV2A2url/987YXQv85pxKRTY9ap0aqVbG2w45ulfSKSzOC26M7aQf1UCMbTXNbuZkfX0oxpK0b+9W0bkNL5OpWZtYyHEwwH46yqsOR5JAz6Y2yoseWZ3A36Y0gSM0z1JumdcE4fDcXlYH04GKPBaWQ8kmDIH6NqWqb+3CYnl7cX3khquWPQqjBNm3LSXmnhE5e14IsmcRi1vH1LLTdvrwdyM7VPNIYsM+sYC8ZQKnI92T99qYeL21z8ft8QW+rtS/mrCM4Ci16NbpbNtFNhKa/9guWF0F4wH2KuSZHT3Nws///BwQA2g4aLW10cGgxS58xlUd6wpmLOkWWFxqhVE02myWYl1ColVr2GYKy4sz3+WIpYKsNQIMbmOjtDE72t3kgSp0nLcDDOjf/7EocGgzSUnXm56nTtFxuFQsEtlzSjUCh4x9Zanj86PjHOR8eGGhsP7h7g4jY3dU4Dvd7ooq3jd3sG8sy4Tpd+X5Rnj45zSZuboyMhBvwxLmlzs67aypYGB08dGcO5AO0nS8FC6q9QKHjHlhrWVdvoGg9zcDCQ540wHkpQ7zQuaKb+648flv//6SOjdIzkpiyE4mnWVlnp9kRRr1Cn+7PljWsrZujvMuvwRJKMBhNUWPU4jVo84Vz/uzeSpK3CnDeObtAfo73CwoB/9k248XCCq9ZWsLcvQDSZQamAIX+cxjIjnnCS7omgX3B2WPUaPnJx04ShXf65p6XcnNd/D1Bm0mJ0TE0baHKZuKjFxbMdY7RXiAxesWDWadCdhjHudJby2i9YXgjtBfMhgvoip6enhx5Pzul+JBintdyMUqnArFdT68jdcGnVSmzG5VnObtKpCCfS8rT6lZCpD0RTxCf6T7c22Hn6yBj3PN+FJ5LAadbx5g1VvP/8Br72+GGuXnfmBka9vb0LuOpTx2bQEE2lcRi1lJm1rKu28akrW7l+UzUWvYZw4uSbMgP+mNybfzr8bs8gvkiSZDorm/SdDiPBBM8fHeOC5jK6xiOMBhP8w7WreeumarbW58qHLbrimK290Pr/240baXKZ6ByLcO/LvfzqtT75tfFwgrqzCOpnc17/5Y4+pIky5D/uH+aBV3PvF4qnqLYbGA7E8zKagnxO1L/MlAvi46kMBq2KLfV2dvb4uPWnOykzaykz6fBMM8sb8Mc4p9EhG+oF4yl+9nKP/PmVmJx1HiGWyuAy6xgMxGgoMzEaiuOJJHAtgKmqIEe90zjD1HZ1pUWuuJvEadLS0ZfzS1hVYea959WzodbG2irrnB42guWHWa9Grz6zoL5Q135B4RHaC+ZDXAGKmJ+/0sNr/WHed88rxFMZfJGUfFNQYdXPOk5uuWHUqhkOxOUeeqtBQzCeu6l8udPDi8emxndksxLdE6P6ljP+WJJYKsOgP862Bge/3tXPzm4vnnCSMpOWT17Rytu31uRccs9i7FM6XbiKhtWVVuxGDbde0ozNqOGWS6Z2j08lt/qXw6M8dIID/akQiqfwRVPs7vXxw+e7Tvv7bQYNyXSWWqcBUBBPZai2G7AbtbRXWKhzGoumZ3sx9G92mzg+FmE0GOeZjjH5+fFwArdFB5y+34YkSdz03ZfkAB5yOnoiSXmE4GgozpGREJIkEY6nseg1xFMZzDoxPWUuTtTfbtTmtU5cva6SH7/QjdWg4Ws3baLMrMUTmV5+H2d7o1M+z37/2U66xiJ8/tf7+NhPd6JRKahzGOn1RokmM1RY9cRTWS5rd/P7vYNFMSGimKh3Gmdk6q/bUMUHL8w3xiozaRnwJzDr1HzlhvW0lltor7DwtZs2LuVyBWeJRafOm1ZwOhTy2i8oLEJ7wXwUXVB/9OhRLrzwQlatWsX27ds5eHB299577rmHtrY2WlpauPXWW+VxeyuJtVVWDgY09HljHBsN44smsU9k5NdVW4siqDfpVHSMhOXxMFa9Rs7U/+XwKLunZXOHg3G++sihQizztAjH08RTWYaDMbbUOVhbZSWZztLrnSpXbXGb+dlHzzur92ltbV2I5Z4Ra6usOE1a3rqpesZrBq2KyLRs/d4+PwcG8h3xXx8Kcnzs9DdoQvE0/miS3X1+oqc5Sz6aTGPQqmitsFBtM3Bek5N0dqqUX61S8sMPbj/tNRWKxdC/2WWmcyyMBGxrcMij/8ZCk0H9/Pzp0MiM5+Kp3L/9Q9MqK0aCCRQKGArESWWyqFVKWtxmOscjhOJpzDo1botOZOrn4UT9VUoFA76YHBiWW/Vo1UquXZ8zRXSZdezo8jLoj/G1xw7zxKHhnMZ7B/n9viEG/DE+/YZWLmlz8YXr1nD7FW1U2fQM+nM99RVWHQaNCpdZh92gZWuDY8l/55XMO8+p403r8v1s1ColRm3+Z6Dcqmc0qckz81QqFdSJVoii4mwy9YW89gsKi9BeMB9FF9TfdtttfOxjH6Ojo4PPf/7zfOhDH5pxTFdXF1/84hd57rnnOHbsGCMjI3zve99b+sUuMuuqbfxh/xDnNjo5OhoiEEvJQf3/fdt6LGfhrL5UGLVqjgwH5Q0Ii37KEfbAQCCv3zMYT3F8LEznWJh+3+L1bZ8tWrWSeCpDMp3FYdLym09cCDAxAsqQd9zZ0Nk590zxxeZjlzZz2Sr3rK/VOY30+6Z0e6nTw6MnjEob8sdQnUFCPBRP44um2NfvJ3GaQf2AL0atw8AtFzdRbTdwxeryGT2ojUXkHr0Y+mvVSmKpDCqlgtuvaOWXO3Il8WPhJO6TlFoP+GP88+9nbrIGYinays385fWpmegjwTgtbjOD/hidYxFa3GbWVVs5PBQiFE9h0aupshlEUD8Ps+mflSRuu2xq9MR/vmsTV67O9V83u02c0+Dgsw/soXMswn+9ezO1DgN/+NTFjIUSBKIpbAYN7z63noYyExtqbahVSrKSRCyZodyqx6zP6fH317Tz5g1VS/OLlgg2g2ZGAD8bLW4Thwb9WIt4QowAzGeRqS/ktV9QWIT2gvkoqqB+dHSUnTt38r73vQ+AG2+8kb6+Po4dO5Z33K9//Wuuv/56KisrUSgUfPzjH+e+++4rxJIXFa1aSYNdw03n1NIxEpbdc6F4xj6ZtCr29gdoqzADufL7UDxNJpsboTUanCoXDURT9Hqj/OTFbh47MDzXjywoqUwWk06dN2Nbq1ain5jtfKKT8dmQzZ65YdzZYtKp5+zfrHca88zyhgNxdvf65ceZrIRSocBh1OKbZtx1KoQT6Yl56fMH9JmsJJd7B+MpHtjZR78vRq3DyFs3VaNVK2ktN/PP1687rfdfTiyW/najlhq7AbVKidOkJZbMMB7O9U9LJ1Tf93mjHBvNGdw92zGGP5rKK7OHXFB/ebubA4O5ag1vJMmgP2ciORyMs6PLw5Z6O2uqrLw+FCQ4Meqxxq7HLIL6OZlN/3tvOY/WcrP8uMysk885Rq2aD13UxI1ba/nYZc1sa3CiUChoq7DIms123VAqcp+7SuuUHs1u84xSccHSoFAo2FChxy6C+qLGbdGddKN0Lgp57RcUFqG9YD6KKqjv6+ujqqoKtTp3Y6FQKKivr59hHNHb20tDw1QfWmNj45zmEolEgmAwmPdVTPzHzefw1o3VdI6F8UVTM4x2ljtGnZqu8Qit5bmMqUWvJhhPTTxnzpvnG4ynsRo0/On1UQ4Phwq15HkJxlJU2fTETsgi1zgMWPQLG6C0t7cv6M9bKOocRno8Ee747T4gF9SrVQrSmSx3P3OcHk+E+jIjqyoteSXZJ2My8Oj3xU5ahfKFB/fz21250vE/7Bvi4T2DDAZiVJ3gYVAsm1+zsVj6r660yFMZqu16BvwxPOEEZbNMBfiXRw5xz/PdADx/bJzzm8vwnzASLxBL4TLrSGVy+n3jiSP88tU+NtfZGfTHeaZjnEvb3LSWmzk6GiKcSGPWq2koM53VyMeVzmz6n4px3TvPqWNr/amXzpdb9XSPR6mw6sQmyzLho1esZX2NrdDLEJwFa6qsfOTipjP63uV67RcsPkJ7wXwUVVC/GNx5553YbDb5q66urtBLOi1S3n4MWhXJdJZ4MoP+DMu5CoVJq6LCqpsyytPnMvUD/tiMcUnBWIotdXbWVFlkg63lxJ9fH+FT9+2eMJQ6Iai3G2hymef4zjPjyJEjC/rzFop6p5Gnj4zxy1f7GAslSGaybKmz81Knh689foRXurysrbJy3YYq7n+17+Q/cIJYKkOVTc/+gQB18/hFdI9HCMZTPLRnAEmSePLQCApFro+70nbmxoTLjcXS//pN1Vy1NtfbW2M3MOCPyZUzk3sgiXSGD/1oB+c3l9HviyJJEtFEmlUVZoZPcLoPxFJYDRq0qlxbykgwwWs9PrbU23m504NOrcSgVaHXqEiks3L5/Vs2VvEWUeI9Jwupv9WgmbOcu85hpGMkRLlFVE4sF0yxEc5tchZ6GYICsVyv/YLFR2gvmI+iigDr6uoYGhqS3R8lSaK3t5f6+vq84+rr6+np6ZEfd3d3zzhmkjvuuINAICB/9fWdepCxnDBq1cRSmaLLPBq1alZN62uezNSPBONUWHXyY8iVUV+5upx3bK1FqchlfpcLkiTx3WeOU+804jLryGTzS5C3NTi4uNVVoNUtLVV2PTu6vLxpbQXPdoyhIDdX+98fO0ylVc+vX+tnTZVVNnYaDeUHgT97qZt9/f4ZPzcUT1PnNLJ/IDDvfOw/Hx7l3dvraSjL9fYrAJ1ayWgwTrlVjOA6GXVOozwOs8ZhZMA3fY65gmxWYkeXlwtbyvjoxU2srrTwWo8Pk05NpU3PyCxBvc2godltoms8giRJXNLmosVt5rZLm/NaICosevq8sYkNBAVKMad+Sai2zz2Jo95p5PBwEKtBzT9cu3qJVyYQCAQCgeBUKKqgvry8nK1bt3LvvfcC8Jvf/Iba2toZbpA33ngjDz/8MMPDw7lg67vf5d3vfvesP1On02G1WvO+ionJUpyWcvOyzF6fjHKrjjeumXL81WtyVQcjgTgVVj3VdsPUHOVYmk11dt68oYrGMiPdnuVjlueNJGksM/GP163hxq21ZKX80u71NTYublvYoH65lmFpVErKrTo+88ZVPHloBKVSwYYaG95wkg9d2MjePr/sodBWbj4haITjYxH6vDH+609H854PxdPUOgx4I8l5nZ739vnZXG+nymbg+FgYs16NRqVkMBCnzLRygvql0D9Xfj/1OdNrlMTTGZ46PMYV7TkDtitWl3P3s52sqrBQbtHT44nmGVxOBvWt5WaOjYYB+OlHzkWvUXHthqo8V/2L2lyMhqZ8NARzs5D6V9sNVM1RxVLvzJ1rDRo1m+rsC/aegjNnuZ77BUuD0L90EdoL5qOognqAu+++m7vvvptVq1bxb//2b/zoRz8C4JZbbuHhhx8GoLm5ma985StcdNFFtLa24na7ue222wq57EVjshSnvcIil7AXEy6zjg9e2Jj3nCTlxtdVWPU0u0wcH82NPgvEUlgneqnLrTo84eVz8z8cjFNl02PVa6i06Ykk0pi0iztjezmXYX3xLWtZU2VFpVRQYdWhUCi46+bN3LitltZyM7qJUT5lZh2ecP5m1Fg4gTeS4MHd/QRiU/3ZoXgKp1GLWaemzjF3UB+M5/6dVFj1HBgIUG7R4bboGA3GF9SosNAshf61diNHhkPoNTm9TFo1779nB0dGgrIh2zkNuVnn7ZUWKqw6vvvMcX78Qpf8MyaD+tWVVvb0+dGqlXNWFF3YUobVIEq8T4WF1P+adZVzutnXO41kshLGRT6fCU6d5XzuFyw+Qv/SRWgvmI+iu3tqb2/npZdemvH8D37wg7zHt956K7feeutSLavgrKowF51J3lxI5Pqf3RYda6qs/PHAENdtrCIYT8kbFw6jFl90+VQmDAfiVEzLdE2afZUqV6+rBODWS5vpGs9lZ89rLgPghx+amgVfZtbiieQ2Z8bDCQ4MBBgPJfBEkoyHkwz4Yph1akLxFOFEGotejcuspcquZ7IUfHqJ9mgoLjsKV1h1/OnQCNsaHCTSGXRnOUKwFLEa1Fj0Gt66KRfwWfRq3r29jneeM+U9olUruXJ1OWsnNnEm++YnCU4E9dV2A88dHePClrkrVlxmHV+9Yf3i/UKCWSmbx2DPZtRg0atFUC8QCAQCwTKmdKOOFcJkKU6z28z/uWpVgVezMLSWm/nT6yNoVEraKsz8959zQWEwlpId5J0mbV7gIEkSmaw065i12ISB4EL7DRweDqJVKWl2mxkKxKm2TwX1yXR20WdsF0MZ1uY6O5tPKNmttk+Z3LnMWrkk+/GDw7zS6c0ZJfpiRJJp+nxRRkNxfrtrgGvWV2LWa/jE5S1oVDlztVgqk/d37vNG5VnzFdacqd61GypJpLKUz9EzXKwshf6TFRaT/N017XKVxXTuetdmlEoFmazEDz54Dt956rj8WnDCKE+lVOC26E5qVnhRiXhPnC1L+fmvdxoxiKB+2VAM537B4iH0L12E9oL5EKmrImeyFEelVNBeaTnJ0cXBljq7nBXSqVWkJgzxstJU0O4w5Wfqn+4Y474dU2ML9/cH5BFo33rqKK90eRd8nf/2x8P85fAokMvUV1qnglW9RoVlkYP6lVCGVWaaKr9//ug4vmgSh0nDkZEQbeVm+n0xjo2G2dHlZTQYx6JXc/P2nOmlUaOaMa9+NJigfKJHu9yiY8Afy80DtuqoXGFBfSH0ny2gB+RqCZVSwbaGKVfuXk+UQCwlfxa21TtWnA6FYin1f9c5dRi1IgewXFgJ537BmSP0L12E9oL5EEF9kaNUrjwJN9fbqZmWzbUbtfhPKLV3GrV5xoD9vhjjE8FhJivxqft2cdvPXmM0GKd7PMr4PP33k8H/qfLw3kGOjYbIZCW6Pbl+/+FgPC8DadCoFj1TvxK0nyy/T2eyJNJZlAoFVr2GAV+MLXUO+n1Rjo6E+eCFjfz8ld68jRKDVkXshKB+ZMKLAcBm0KBVKym36FlbZeXKNeVL+rstNsWg/788cojdfX456P/IxU28aV3FSb5LcCospf4fvLBxRflRFDvF8NkXLB5C/9JFaC+YD/Gvo8hpbm4u9BIWnCqbgf969xb5cYvbxPGxMNNjb4dJi29aUD8ajMuj73q9Ua5YXc6nrmzjrj8dpccbkY9NzTIG79O/3EM6k6VnIkCfj0xW4rP37+GfHz7EF65bw5A/N77LF0niME4ZFeo0ykWf6bwStDfr1ITjaXq9UZpcJtwWHS6zDr1GxYZaG4P+GGPhBLdd2ozbosNhmvKNMGpVRFPpvJ83GkrIY+sUipxJX7lVR4VVL7u1rxSWs/5GnZpwIg1I/O6TF8nP241akfFdIJaz/oLFRWhf2gj9SxehvWA+RFBf5Bw7dqzQS1gUJt22IecXcHQkzPSWeOu0+fWQy9BOOqUfGQ7RXmFhfY2VoUCM8VASTySJJEnc8pOdAEQSaeKpXJb35U4PO7q8fOep4zx9ZJT9/YG8tXjCCfm9xkIJtjU4sBtzbt4ScHAwMMPRW69RLXpQvxK0n/ybHR+L0OI201hmxG3RUWbWUmM3kMpIpCfM8H720fNYUzU1ctKonVl+PxJMUGGZqpg4t7Fs0dsgCsVy1r9iYtoAMO/4QcGZs5z1FywuQvvSRuhfugjtBfMhgvoiR61emQHLdFrcZv6wfyjPM2AyGLz35R4gNyc+FM9lbY8Mh2ivtKBQKFArFTSUGfFGkoyGEvT7cjO3H947yHeePo4kSQSiKZ58fYRgPMXBwSBPHxmV36fXE+Wt//M8D+4aAGAoEOPiVhff+qutAGhVSr76yCH+39s35K3ZoFEtuvv9StL+2GiYFreJdTU2Gl0mnCYtbouOf3vHBt65rRZgRvmvQauWy+9/9lI38VSGQCyJfVrFxDfetWnBDRKXC8tZ/wqrns6xyKK3oJQyy1l/weIitC9thP6li9BeMB8iqC9y6uvrC72ERaehzMhLxz2c0+jMe94XTfHlhw8ST2WQpKne+I7REKsqchsAa6ttbKl34Ikk6fFEiSRyQWAwluKh3QNEkhkyksSTh0YIxdMEYyn29vvpGo/w+V/v4/h4mEva3HjCCb737HEODAbzeudby82859z6vLJwAL1GuegBzUrR3qJX8+LxcVrLzVzRXs71m6ppdpmpsOopt+p566bqWb/PqFURSaSRJIlvPXWMR/YNAazYIP5ElrP+1XYDL3d6qLIZTn6w4IxYzvoLFhehfWkj9C9dhPaC+RBBfZHT2dlZ6CUsOnqNilqHgW0Njrzn1UoFrW6zPBJtknhyaszZm9ZWcNXaChKpDD2eyESfLwTjKdrKzbx83MPqSguD/hhKpYJgPMWAP84dv93HsbEwQ/4462ttjIWTPH/Mw6P7hvIClb990ypu2FwzY82GJSi/Xynaf+TiJvYPBHBO2xj54lvW4LbMPTsbckH9gcEgLx73cOXqch7Y2Uc6e3qmh8XMctb/wpYyHtozSI1dON0vFstZf8HiIrQvbYT+pYvQXjAfoo6jyNHp5g98Vgrf/8A5WPWavOeuXV+JUatmX3+upz2ZnmmCt77GJv9/rzeKzaBBkiSCsTRrq60cHg6yuc5OLJlBq1IQjKW5tM3FZavcPLRngAODAd65rZbnOsYYCyU4Mhzk/759vfwz58oKf/TiZqoWOaBZKdpvrLXzww9tz/tbnkq23aBR8aPnu7hvRy9fuX4dH7igkZ3dCz+6cLmynPV3mLQ0u0xU20WmfrFYzvoLFhehfWkj9C9dhPaC+RCZ+iKnsrKy0EtYEtoqLDOe+9BFTWxtsHP/zj4aXSYgV4I/VzzY44nSXmkhmswQjKdoLTdzeDhEY5mJf3zzGgAS6Qx3vHkNF7a6qHMY2dHlpd5pJJOVKLfo0KiUpzRnu77MiEa1uB+vlaT91nrHyQ86AZdFx/svaOCrN6zjohYXa6qsvP+CxoVf3DJluev/ictb2DBtU02wsCx3/QWLh9C+tBH6ly5Ce8F8iKC+yOnp6Sn0EgpKY5mJWruBT13ZikqpYDycxGbQznKkgrFQggqrnkgiTSSRocVtpmMkhM2o4Y1rZ87OrnMa6fNGcZq0xNMZTDoVt1zStGzMv0pd+631Dv7+mtVcs74Km1Fz8m9YYSx3/a9YXU75KWyACc6M5a6/YPEQ2pc2Qv/SRWgvmA8R1Bc5BkNpl7eqVUq+/d6tGLVqrAYNx8fCs/Zir6608LdvWoVFryY0MT+70pZz6LYbcgGhVq0kMa2Ev85poMKqR6FQkM5IlFv0/N3Vq5fqVzsppa59qSP0L22E/qWL0L60EfqXLkJ7wXyIoL7IcblchV7CssFm0HBsdPag/nNXt3NOoxOTVk1kwizPadSiUIDdmMvsW3SavLnndQ4jVRNO9y6LLs/1fjkgtC9thP6ljdC/dBHalzZC/9JFaC+YDxHUFzl9fX2FXsKywarPBfXl87imm3Qqwok0kgRKpYJyi16ea241qPNK690Wndxr7zbrTqmXfikR2pc2Qv/SRuhfugjtSxuhf+kitBfMhwjqixyz2VzoJSwbKm06Xu70zDsKzaxTE46n5cflVp1cfm/Ra7Dqp4J6hULBpjo7kOuvbygzLs7CzxChfWkj9C9thP6li9C+tBH6ly5Ce8F8iKC+yLFarYVewrLh7VtqAebNqJv1agKxFCplziL/yvZy2WTNos/15c/GRy5qZMsZOLQvJkL70kboX9oI/UsXoX1pI/QvXYT2gvkomqA+m83yqU99ipaWFlpbW/nWt74157GNjY20t7ezefNmNm/ezP3337+EK11aBgcHC72EZYNWreR3t18kj7ebDZNOzXAgjmVi5v2n3tCGTq0CcuX7Vv3sQf2pzE1faoT2pY3Qv7QR+pcuQvvSRuhfugjtBfOxPGZznQL33nsvhw4doqOjg0AgwJYtW7jiiitYt27drMfff//9bN68eWkXWQDErl0+kwH6XJh1agYDcayGmf/0c5n6ovlICO1LHKF/aSP0L12E9qWN0L90EdoL5qNoMvX3338/t956KyqVCqfTyc0338x9991X6GUVHKNxefV5L3dMWjX9vqicqZ/Opjo7V64uL8CqzgyhfWkj9C9thP6li9C+tBH6ly5Ce8F8FE1Q39vbS0NDg/y4sbGR3t7eOY//wAc+wIYNG/joRz/K2NjYnMclEgmCwWDeVzExPDxc6CUUFRa9mpeOe7i83T3jtQqrntWVxbMLKrQvbYT+pY3Qv3QR2pc2Qv/SRWgvmI9lE9RfcMEFuFyuWb9Od4TDs88+y759+9i1axcul4sPfvCDcx575513YrPZ5K+6urqz/VWWFIdjeZm3LXdsRg1vWFPO1mVmencmCO1LG6F/aSP0L12E9qWN0L90EdoL5mPZNBC/9NJL875eX19PT08PF1xwAQDd3d3U19fPeSyARqPhM5/5DKtWrZrz595xxx189rOflR8Hg8GiCuw1mtmN3QSzY9VruPv95xR6GQuC0L60EfqXNkL/0kVoX9oI/UsXob1gPpZNpv5kvPOd7+T73/8+mUwGr9fL/fffz8033zzjuEgkgt/vlx/fd999bNmyZc6fq9PpsFqteV/FxOjoaKGXICgQQvvSRuhf2gj9SxehfWkj9C9dhPaC+Vg2mfqT8f73v59XX32VtrY2FAoFn/3sZ9mwYQMADz/8MA8//DA/+MEPGBkZ4cYbbySTySBJEs3Nzfz0pz8t8OoXj7KyskIvQVAghPaljdC/tBH6ly5C+9JG6F+6CO0F81E0Qb1KpeLb3/72rK9df/31XH/99QA0Nzeze/fupVyaQCAQCAQCgUAgEAgEBaFogvqlQpIkgKJxwe/p6cFkMhV6GYICILQvbYT+pY3Qv3QR2pc2Qv/SRWhfmkzGpJMx6lyIoP4EQqEQQFGZ5QkEAoFAIBAIBAKBYGUSCoWw2Wxzvq6QThb2lxjZbJbBwUEsFgsKhaLQy5mXSaf+vr6+ojP4E5wdQvvSRuhf2gj9SxehfWkj9C9dhPaliyRJhEIhqqurUSrn9rgXmfoTUCqV1NbWFnoZp0UxuvYLFgahfWkj9C9thP6li9C+tBH6ly5C+9Jkvgz9JEUz0k4gEAgEAoFAIBAIBAJBPiKoFwgEAoFAIBAIBAKBoEgRQX0Ro9Pp+PKXv4xOpyv0UgRLjNC+tBH6lzZC/9JFaF/aCP1LF6G94GQIozyBQCAQCAQCgUAgEAiKFJGpFwgEAoFAIBAIBAKBoEgRQb1AIBAIBAKBQCAQCARFigjqi5SjR49y4YUXsmrVKrZv387BgwcLvSTBIvLpT3+axsZGFAoFe/bskZ9vbGykvb2dzZs3s3nzZu6///7CLVKwaLzpTW9i48aNbN68mUsuuYTdu3cD4jxQSvzoRz9CoVDw0EMPAXD55ZfT1NQkf/bvuuuuwi5QsCgkEgluv/122tra2LBhA+973/sA8dkvBTwej/z53rx5M6tWrUKtVuP1esXnvwR47LHHOOecc9i4cSPnn38+e/fuBWB0dJRrrrmGtrY21q9fz7PPPlvglQqWDZKgKLniiiukH/3oR5IkSdKvfvUr6ZxzzinsggSLyjPPPCP19fVJDQ0N0u7du+XnT3wsWJn4fD75/3/7299KGzdulCRJnAdKha6uLumCCy6Qzj//fOnBBx+UJEmSLrvsMvn/BSuXz3zmM9Ltt98uZbNZSZIkaWhoSJIk8dkvRb7+9a9Lb3nLWyRJEp//lY7X65WcTqd04MABSZIk6dlnn5XWrVsnSZIkffjDH5a+/OUvS5IkSTt27JBqamqkZDJZqKUKlhEiU1+EjI6OsnPnTnnH/sYbb6Svr49jx44VeGWCxeLSSy+ltra20MsQFAi73S7/fyAQQKFQiPNAiZDNZrnlllv4n//5H+F6XGJEIhHuuece/vVf/xWFQgFAZWWl+OyXKPfccw8f/ehHC70MwRJw/PhxysrKWLduHQCXXHIJvb297Nq1iwceeICPf/zjAGzfvp3q6mqeeeaZ/8/efYfHVZ55H//OaJqkmVHv1bZk417ABIwNxCYhBDABFkhxwFlaSEiySzYhpEIKhuybZJfNhpIQskBCSOgthIRqwIANBvciybJ6l6Zr6nn/ONaxx5Jl2ZY0Mzr357rmQlM08ww/n9Hc52mJbK5IElLUp6Dm5mZKSkowmUwAGAwGKisraWpqSnDLRCJceeWVzJ8/n6uvvpru7u5EN0dMkCuvvJKKigp+8IMf8NBDD8nngE788pe/5IwzzuDkk08edt93vvMd5s+fzxVXXEFDQ0MCWicmUn19Pbm5udx+++2ccsoprFixgpdfflmOfR16++236e/v54ILLtBuk+N/6qqtraW3t5e3334bgGeeeQaPx8O+ffsIh8MUFxdrj62urpZjXwBS1AuR0t544w22bNnCBx98QH5+PldddVWimyQmyIMPPkhzczM//elPufnmmxPdHDEJtm3bxuOPP873v//9Yfc99NBD7Nq1iy1btrBixYq4L/tiaohEIuzfv585c+awadMm7rrrLq644goikUiimyYm2f3338+VV16pnciR439qy8rK4rHHHuOWW27h5JNP5qWXXmLOnDl4vd5EN00kMdmnPgV1dXVRU1NDX18fJpMJRVEoKSnhzTffpKamJtHNExOourqap556ikWLFg27r729nZkzZ+LxeCa/YWJSpaen09jYSG1trXwOTGF33303P/7xj7Vh9x0dHTidTm677TZuuOGGuMfabDZaW1vJy8tLRFPFBOjp6aGoqIhQKERaWhqgDrf91re+xTXXXCPHvk54vV5KSkrYuHEjJ5100oiPkeN/agsGgxQXF7Nx40YWLlxIfX291lt/6qmncvvtt3POOeckuJUi0aSnPgUVFhayZMkSHn74YQAef/xxysvL5Y+5zvh8PgYGBrTrjzzyCIsXL05cg8SEGBgYoK2tTbv+1FNPkZeXJ58DOnDDDTfQ3t5OY2MjjY2NnHbaadx3331ce+21dHZ2ao97/PHHKSoqki/0U0x+fj6rVq3i73//OwD79u1j3759nHHGGXLs68ijjz7KwoULtYI+EonI8a8D7e3t2s8/+clPWLlyJTU1NVx22WXcc889AGzcuJHW1lbOOuusRDVTJBFTohsgjs+9997L2rVruf3223E6nTzwwAOJbpKYQNdffz3PP/88HR0dnHvuuTgcDl566SUuvfRSotEoiqIwffp0HnzwwUQ3VYwzl8vFZZddRiAQwGg0UlBQwHPPPYfBYJDPAZ0KBoOcf/75BINBjEYj+fn5PPPMM4lulpgA99xzD1dffTU333wzRqORe++9l7KyMjn2deT+++/n2muv1a7L8a8PP/zhD1m/fj2RSITTTz+d+++/H4A777yTL37xi9TW1mKxWHj44Ycxm80Jbq1IBjL8XgghhBBCCCGESFEy/F4IIYQQQgghhEhRUtQLIYQQQgghhBApSop6IYQQQgghhBAiRUlRL4QQQgghhBBCpCgp6oUQQgghhBBCiBQlRb0QQgghhBBCCJGipKgXQgghhBBCCCFSlBT1QgghhBBCCCFEipKiXgghhBBCCCGESFFS1AshhBBCCCGEEClKinohhBBCCCGEECJFSVEvhBBCCCGEEEKkKCnqhRBCCCGEEEKIFCVFvRBCCCGEEEIIkaKkqBdCCCGEEEIIIVKUFPVCCCGEEEIIIUSKMiW6AckmFovR1taGw+HAYDAkujlCCCGEEEIIIXRIURQ8Hg+lpaUYjUfuj5ei/jBtbW1UVFQkuhlCCCGEEEIIIQTNzc2Ul5cf8X4p6g/jcDgA9X+c0+lMcGuOrru7m4KCgkQ3QySAZK9vkr++Sf76Jdnrm+SvX5K9PrndbioqKrQa9UikqD/M0JB7p9OZEkV9U1NTSrRTjD/JXt8kf32T/PVLstc3yV+/JHt9O9q0cFkoL8Xl5eUlugkiQSR7fZP89U3y1y/JXt8kf/2S7MVopKdeCCGEEEIIIUTyiMUgEoBwAMJ+CA8e+G/gkNsPuQzdFhmEWBSUmPrfaAgiQYgG1fuiYcAA9gJY/T+JfpfjRor6FNfb20tJSUmimyESQLLXN8lf3yR//ZLs9U3y168pmX2gH/obD1z2q//1tIOigMEIlgwwpYP58EsGmGzqf9OzwVly8DaTDYxpYEgDoxHSrGAautjAaIIpuMOZFPUprrCwMNFNEAki2eub5K9vkr9+Sfb6JvnrV0pnH+iHtg+hbTN0bIGQX709PRtyqtVL+VKYfxk4StRiXBwTKepTXDgcTnQTRIJI9vom+eub5K9fkr2+Sf76lVLZh/zQ9DbUvwpdOyE9B0oXQ+VpcOq1YB19JXdx7KSoT3H9/f2UlZUluhkiASR7fZP89U3y1y/JXt8kf/1K6uxjMejcBvWvQNM76vD3qmWweA0UnDQlh7snGynqU1xxcXGimyASRLLXN8lf3yR//ZLs9U3y16+ky97dDg2vQsNr4O+FonkwYyV87MtgtiW6dbojRX2K8/v9iW6CSBDJXt8kf32T/PVLstc3yV+/Ep59JASN69Xe+K6d4CiG6R+HT/5MXUleJJQU9SnO7XYnugkiQSR7fZP89U3y1y/JXt8kf/1KSPYhP9S/DDufg0AfVK+QIfVJSor6FFdaWproJogEkez1TfLXN8lfvyR7fZP89WvSsh90w96XYNfz6r7vNavgnFvVbeNE0pKiPsW53W5yc3MT3QyRAJK9vkn++ib565dkr2+Sv35NaPb+Ptj9Aux5Ub1eey58+v9BZt7EvJ4Yd0m5CeDevXtZtmwZM2fOZOnSpWzfvn3Ex23dupWzzz6b2bNnM3v2bJ544gntvvvvv5/a2lpmzJjBtddem1rbQBwDr9eb6CaIBJHs9U3y1zfJX78ke32T/PVr3LP3dMB7v4VHPgfPfxPSrHDR/8IVD8OSL0pBn2IMiqIoiW7E4VauXMmVV17J2rVreeyxx7jzzjvZuHFj3GP8fj/z5s3jwQcfZPny5USjUfr6+igoKGDfvn2cccYZfPDBBxQVFXHRRRdx7rnn8tWvfvWor+12u8nKysLlcuF0OifqLY4bl8tFVlZWopshEkCy1zfJX98kf/2S7PVN8tevccm+fz/sfBb2vQ7puTD7ApixCiwZ49NIMe7GWpsmXU99V1cXmzZtYs2aNQBceumlNDc3U1dXF/e4P/3pT5x22mksX74cgLS0NAoK1JUXH3vsMVavXk1xcTEGg4Evf/nLPPLII5P7RiZJT09PopsgEkSy1zfJX98kf/2S7PVN8tev486+Zy+s/wU8dAm88Z+QP1Ptjb/kXph9oRT0U0TSzalvbm6mpKQEk0ltmsFgoLKykqamJmpqarTH7dixA6vVygUXXEBLSwsLFizgF7/4BQUFBTQ1NVFVVaU9trq6mqamphFfLxgMEgwGteuptqpoIBBIdBNEgkj2+ib565vkr1+Svb5J/vo15uyjYWjaAHv+Dp3bIa9G7ZFf9g1IS7rST4yTlE02Eonwz3/+k3feeYfS0lK++93vcsMNN/DYY48d0/OsW7eO2267bYJaOfEOPXkh9EWy1zfJX98kf/2S7PVN8tevUbP3dkHdP6HuZQi6ofI0WPQFKJwtW8/pRNIV9RUVFbS3txOJRDCZTCiKQlNTE5WVlXGPq6ys5OMf/zhlZWUArFmzhnPPPVe7r76+XntsY2PjsN8fcsstt3DTTTdp191uNxUVFeP9tiZMR0cHDocj0c0QCSDZ65vkr2+Sv35J9vom+etXXPbRMLRshPpXoPUDyMyHmk/AeT+XBe50KumK+sLCQpYsWcLDDz/M2rVrefzxxykvL48beg9w+eWXc//99+N2u3E6nbzwwgssXLgQUOfhL1++nFtvvZWioiLuuecePvvZz474elarFavVOuHva6IcOnVA6Itkr2+Sv75J/vol2eub5K9DgX7o3k3Gzlegzg39+0BRoPwUdeu5s28BY1qiWykSLClXv9+9ezdr166lt7cXp9PJAw88wPz587nmmmtYvXo1q1evBuChhx7izjvvxGg0UlZWxn333af1sv/2t7/ljjvuAODss8/mnnvuwWw2H/W1U231e7/fT0aGLHChR5K9vkn++ib565dkr2+S/xQVDqgr0w/sh/5G6K1Xf1ZiYMuGglkEs6ZjLV8I2VUyN15HxlqbJmVRn0ipVtTv2rWLk046KdHNEAkg2eub5K9vkr9+Sfb6JvknEUWBaAhCPvUS9kPICyH/gesHbg8duD3sP/jYkA8ig2rRDmCyQXYl5FRDThXkTleL90N64CV7fRprbSqneVJcJBJJdBNEgkj2+ib565vkr1+Svb5J/idAUdSh7K5m8HRAYAACfeDrgUGXusBc2B//+Fh05IXmhvpE08xgsYMlU90azmIHc8aB65mQkQdZFQcec+B284H7zOnHtIidZC9GIz31h0m1nvrBwUFsNluimyESQLLXN8lf3yR//ZLs9U3yP4rwIHTvhL4GGGhWC3h328Hi3JYN2RXgKIH0bEjPVReZs2WDzakW5BxSaCfRMHfJXp+kp14nGhoamDNnTqKbIRJAstc3yV/fJH/9kuz1TfI/hKJA105ofBPaPwRfN6RZoHAO5M2A8qUw7xJwlCZVcX68JHsxmtT/F65zsVgs0U0QCSLZ65vkr2+Sv35J9vqm6/wVRV35fd8bsG89DA6o+7BXr4C5nwF7YaJbOKF0nb04Khl+f5hUG34fDofHtKq/mHoke32T/PVN8tcvyV7fdJW/rxea34WOLdC5XZ3vnl0F089SC/mM3ES3cFLpKnuhGWttapzENokJsHv37kQ3QSSIZK9vkr++Sf76Jdnr25TOPxaF5vfg1dvh4Uvhb98GTzvUfAIuvhfWPA4X/BLmXKS7gh6mePbihMnweyGEEEIIIcTkG3TBnr+rl0EXlJ2sFvFn3Ry3nZsQYnQy/P4wMvxepArJXt8kf32T/PVLste3KZG/vw92PQ97XlRXpJ/5KZh5HmTmJbplSW1KZC+OmQy/1wkZiqNfkr2+Sf76Jvnrl2SvbymZv6JAz15452545HPw/DfBZIPP3A1XPAyL10hBPwYpmb2YNDL8XgghhBBCCDF+omFoeB12v6CuWJ9XC7WfgCVXgSUj0a0TYsqR4feHkeH3IlVI9vom+eub5K9fkr2+JXX+0Qg0vgHbnwR3G0w/G2Z9Wt0zXpywpM5eTBgZfq8TMhRHvyR7fZP89U3y1y/JXt+SLv9oBPZvgL/dDH/8F2j/CJbfpK5Uv+xrUtCPo6TLXiQVGX6f4oxGOS+jV5K9vkn++ib565dkr29Jkb+/D+r+CXv/AYF+KF8KS66EormJbtmUlhTZi6Qlw+8Pk2rD7wcHB7HZbIluhkgAyV7fJH99k/z1S7LXt4TkryjQuR32/h2a3gGLXZ0fX/MJsBdMblt0TI59fUrp4fd79+5l2bJlzJw5k6VLl7J9+/YjPlZRFFauXEl2drZ2W2NjI2lpaSxatEi71NfXT0LLJ19dXV2imyASRLLXN8lf3yR//ZLs9W3S8g8PqnvHP/tv8PAl8NEjao/8Z/8Elz0Aiz4vBf0kk2NfjCYph99ff/31XHfddaxdu5bHHnuMtWvXsnHjxhEf+6tf/YoZM2bwwQcfxN3ucDj48MMPJ6G1iWUyJWWEYhJI9vom+eub5K9fkr2+TVj+igKuZqh/BepfhcggTDsLzvgG5E6bmNcUx0SOfTGapBt+39XVRU1NDX19fZhMJhRFoaSkhDfffJOampq4x27fvp0bbriBBx54gJNPPpmBgQFA7alftGiRdv1YpNrwe7/fT0aGbA2iR5K9vkn++ib565dkr2/jkr+iqKvTt38IbR9C1w6IBCGrTF2xfvrHIT37xBsrxpUc+/qUdMPvV65cOabHNTc3U1JSop2NMhgMVFZW0tTUFPe4cDjMtddey7333ktaWtqw5/H5fCxdupQlS5bw4x//mGg0OuLrBYNB3G533CWVNDQ0JLoJIkEke32T/PVN8tcvyV7fjjn/SBB66mDX8/DKz+DPX4A/XQ6v3wneTph1HvzL72HNY3Dhf8Pci6WgT1Jy7IvRjOs4jtEK4r17947nS3HbbbdxySWXMHv2bBobG+PuKykpobW1lcLCQvr6+rjiiiv4xS9+wbe//e1hz7Nu3Tpuu+22cW3bZLJarYlugkgQyV7fJH99k/z1S7LXN6vVCrGoWqwbjODtAHc79OyBsB8G3dBXD4EB9RfSzJBdBfk1agF/5n+ASf4NpSI59sVoxnX4vdFoxGAwcOhTDl03GAxH7C0/1FiH369YsYKmpiYMBgORSIS2tjYqKyvZuHEjBQXxC3c88sgj/OlPf+LZZ58d9nrBYJBgMKhdd7vdVFRUpMzwe4/Hg8PhSHQzRAJI9vom+eub5K9fkn2KiEbA16UW10EPeNrA0wnRIGAAowlCXgi6IRpWC3UlCkpMvZjSDxTpA+pzHRCJRDCZLWCyqI+zF4OjGPJngtUOFoc6Bz4jN1HvXEwQOfb1aazD78e1p76kpISPPvqI/Pz8YfdVVFSM6TkKCwtZsmQJDz/8MGvXruXxxx+nvLx82Hz69evXaz8PzaEf6rHv6uoiJycHs9lMMBjkiSeeYPHixSO+ntVqTekzX/v372fevHmJboZIAMle3yR/fZP89UuyTwLhAPTWQX8j+LqhfQv4e9TiHIP6GGMaZBaALQusDnCWQcFMMNnUYjwWUW+3ONQC3ZCm9rwb09TniATAnAG2bPX+A3Zt2yb565Qc+2I041rUL1u2jC1btow4f37hwoVjfp57772XtWvXcvvtt+N0OnnggQcAuOaaa1i9ejWrV68e9ffffPNNfvjDH5KWlkYkEmHlypV873vfO7Y3kyLS09MT3QSRIJK9vkn++ib565dkP8n69sH+t9TCfaBJ7U1Ps0DBLMipBnsRrPimWsCbJ34PcclfvyR7MZqkW/0+0VJt9XuXy0VWVlaimyESQLLXN8lf3yR//ZLsJ1B4EHa/AO0fqfPSIyGwF0LNOVA8H3KmgXHS1pgekeSvX5K9PiVk+L2YfM3NzXKA65Rkr2+Sv75J/vol2Y+TSAj698G2x6Fzu7ovu8kGM1bCgisgd/qk9LwfK8lfvyR7MZpxLeqnTZuGwWA44v2yFcP4s9vtiW6CSBDJXt8kf32T/PVLsj8Bg27Y/iTsfUmd155VDvP+BZbflJQF/Egkf/2S7MVoxrWof+655wD485//TGNjI9dffz0Av/3tb6mqqhrPlxIHpMIUATExJHt9k/z1TfLXL8n+GCgKdGyB3X+Dlk3qVm5zPgOX3AeWzES37rhI/vol2YvRjGtRP3fuXABefPFFNm7cqN1+xhlncOqpp/LjH/94PF9OAG1tbeTmyrYleiTZ65vkr2+Sv35J9kehKNCzF5reVnvlixcc2J/92wmfDz8eJH/9kuzFaCZkTr3L5cLn85GZqZ4F9fl8uFyuiXgp3ZOzdvol2eub5K9vkr9+SfYjGHRB80bY+hfw90JeLVQtg8sfAtvU+v8l+euXZC9GMyFF/ec//3lOO+00Lr/8cgD++te/smbNmol4Kd3LyMhIdBNEgkj2+ib565vkr1+S/QGxKOx8Vu2NV6JQeTqs+hFklSW6ZRNK8tcvyV6MZkKK+ltvvZWlS5fyyiuvAHDnnXdy3nnnTcRL6V5HRwf5+fmJboZIAMle3yR/fZP89UvX2SsKtG2GD/8E3bvgpPPh/F9CZl6iWzZpdJ2/zkn2YjQTtqXd+eefz/nnnz9RTy8OyMnJSXQTRIJI9vom+eub5K9fusy+rwF2PgcNr0LhHDj1WsifCaPsuDRV6TJ/AUj2YnQTUtQ3NjZy5513Ul9fTyQS0W4f6rkX48dsNie6CSJBJHt9k/z1TfLXL91k7+mADx6E/W9DTjXMvgA+9mUwWRLdsoTSTf5iGMlejGZCivrLL7+cVatWceONN5KWljYRLyEO6OrqorCwMNHNEAkg2eub5K9vkr9+TdnsY1Ho2Ar73oDmd8FogpPXwor/mBKr1o+XKZu/OCrJXoxmQor6wcFB1q1bNxFPLQ6Tl6efeWQinmSvb5K/vkn++jWlsh90qz3xbR+o/y1fCtPPUofXm9MT3bqkNKXyF8dEshejmZCift68eTQ1NVFZWTkRTy+EEEIIIVJNeFDtjW99H+r+CZZMqDoDaj4BZ90MRhndKYQQx2NCivru7m4WLlzI6aefjs1m025/4oknJuLldK23t5eSkpJEN0MkgGSvb5K/vkn++pUy2Q+6oHMHdG6Drh0w0AwmKxQvgJIF8Nk/qtfFMUmZ/MW4k+zFaCakqF+zZo3sSz9JZG6Nfkn2+ib565vkr19JmX00rBbu9a9AyyaIhsDqhKK56mXWeeAs0+Vq9eMtKfMXk0KyF6OZkKL+qquuAiAYDGK1HvtZ2L1793LVVVfR09NDVlYWf/jDH5g7d27cYzZs2MANN9wAQDgcZvny5dx1113a691///3ccccdxGIxVq5cyW9+85spuWpkOBxOdBNEgkj2+ib565vkr18JzT48CN4OcLVA1061F97Vqi5qV3gSTP84fOwGMNuO/lziuMixr1+SvRjNhBT1W7du5XOf+xwDAwO0tLTw/vvv8+ijj/Lzn/98TL9//fXXc91117F27Voee+wx1q5dy8aNG+Mes3DhQjZu3IjZbCYWi3HppZfym9/8hn//939n3759/OAHP+CDDz6gqKiIiy66iPvuu4+vfvWrE/F2E6q/v5+ysrJEN0MkgGSvb5K/vkn++nVM2SuK2osOauFtMKjXI4MHL+EAeDvB16PeFwtDoF/dUs7bCUHPgSczqMPl7UXgLIXC2dIDnwBy7OuXZC9GMyFF/de+9jXuuecevva1rwGwZMkSrrzyyjEV9V1dXWzatImXXnoJgEsvvZQbb7yRuro6ampqtMdlZGRoP4dCIQKBAIYDf1Qee+wxVq9eTXFxMQBf/vKXuf3226dkUT/0HlNKLCqL4YyDlMxejBvJX98k/0kWjYCvG3xd4D1wOfTnkPfAA4+nuFWO6fdqw2HYMtLIwyM8T5pJvT0WBSUGaWYw2dTedNOBi70IMgvU+8zpkF0FM4vV260OKdqTiBz7+iXZi9FMSFHv9XpZvny5dt1gMGCxWMb0u83NzZSUlGAymbTfrayspKmpKa6oB2hsbOSiiy6ivr6e888/n6985SsANDU1UVVVpT2uurqapqamEV8vGAwSDAa16263e2xvMkn4/f5EN+GgaATefwB2vwD2YljyRejbB1v/Gr8YjsGo9g6kWcGWBRf+l2xdcxySKnsx6SR/fZP8x1nID3310FsPvXXQ16D2XA8Vs0YTZOSpRa69UL3kTj/4s8U+OYVvLEZn034qq6dN/GuJpCTHvn5J9mI0E1LUm0wmwuGw1nPe3NxMWtr498xWV1fz0Ucf4fV6WbNmDU888QSf/exnj+k51q1bx2233TbubZssSXMSovV9ePnHMPcS+MLj0L8Ptj0BzhL43J+PPL+u/lX4y1XwyZ9A/kzpDTgGSZO9SAjJX98k/+OkKOqw8o6t0LkVOrapvezmdMirgdwZUL0cllwFmfnJ9zfJaMTt9SW6FSKB5NjXL8lejGZCivobb7yRz3zmM3R3d/P973+fhx9+eMzz6SsqKmhvbycSiWAymVAU5ah73tvtdj772c/yxz/+kc9+9rNUVlZSX1+v3d/Y2HjE37/lllu46aabtOtut5uKiooxvtPEKy0tTWwDeuuh8U3Y+Qz8ywOQkavenjcDzvrW0X9/xsfVuXmbfq/2jmQWwKfWQXrOxLZ7Ckh49iKhJH99k/zHIBZTe9/bNkPHFuipAyUKjmJ1W7WqM2DptWBzJrqlx0Sy1zfJX78kezGaCdvSbvr06Tz99NOEQiEefvjhuOH4oyksLGTJkiU8/PDDrF27lscff5zy8vJhQ+/r6uqoqqrCbDYTCoV48sknWbBgAaDOw1++fDm33norRUVF3HPPPUfswbdarce1Qn+ycLvd5ObmTv4L+/vg9TvVOYazPg2X/PZgQX+sCmbBeXeqP7dthsevgSv+KKvnHkXCshdJQfLXN8l/BINuaNkIze9C+xaIRdTe97KTYdEa9ee0CfnaM6kke32T/PVLshejGfe/btFolPnz57Njxw6WLVt2XM9x7733snbtWm6//XacTicPPPAAANdccw2rV69m9erVvPLKK9x1112kpaURiURYtWoVP/jBDwCYPn06t912G2eccQYAZ599Ntdff/34vMEk4/V6j/6g8dbfCE99FVZ+D6qOL+MjKl2sbofz5PVw0f+C1T6+zz+FJCR7kTQkf33Tff6RoLqdWttmaPtQHVJvtUP5Uqj9JKz4DzCNbS2fVKP77HVO8tcvyV6MxqAoijLeT3rWWWfxt7/9LW6F+lThdrvJysrC5XLhdCb/kDyXy0VWVtbkvWA0DH+8DFbfBdlHnhJxwhrfVEcCLL0G5lw0ca+TwiY9e5FUJH9901X+sSj07IHWD9T1Wwaa1MVXi+aqJ4JLFqnrt+iErrIXw0j++iXZ69NYa9MJGYdWU1PDGWecwWWXXYbdfrCn9etf//pEvJyu9fT0TO4B/vqdsHjNxBb0oC5UVHEaPH+Tukfu4jUT+3opaNKzF0lF8te3KZt/LKYutNqxRS3iu3eri9Xl10LpEjjjG+rfn2RbwG4STdnsxZhI/vol2YvRTEhR39HRwaJFi9i7d692W3d3txT1EyAQCEzOC8Wi8M7dMOiC+f8yOa+ZZoIL/guevA5yqtVCX2gmLXuRlCT/KSQcgKAXPO3qMPJBFwRd6n8H3QeuuyE0tOq5gQKPG94d4Yy9waBur2Z1HLzYska+WJ1gyZz8AllRwN8L/fthoFGd0tW/X33vKOrnffECWPg5dc0V4/jvnpPK5NjXN8lfvyR7MZoJKerb29t5/vnn425bsmTJRLyU7lVVVU38i4QD8PSN6vz5T9058a93KKNRLez//Dn1i15W+eS+fhKblOxF0pL8U0w4oA4h796t7hrSv08t1hVFXRTU4lBXZXeWgC0bsqvUotvmHLEAN3g84HAMf51YDEIe9SRB0KOeDAgeODHgboWuHQdOFhw4YRAaYXu0NNOBwj9b/a+zVD1RYLJCmgXMGWqbTemAop70jUUgGlK3hwv5IexXXz/Qf8hlQH0cqNvFZVdBThVULoOFn1f3gDcaJyiAqUOOfX2T/PVLshejGdeiPhQKMTg4SDQaxePxMDRd3+Vy4fPJvqoToaOjA8dIX+zGS+d2ePE7cMa/Qc2qiXud0VjtcOFd8MT16iiBJVfJFz8mIXuR1CT/JOZug/1vq5+fPXvUYtdkg/yZUHASzPwk5Ew7/h1DGCV/o/FgT/zxioQOnggIDKgnA0I+9XokCJGAepIi7AcMYDSpvelpFrX4t2SohX9WORTPh/RcdZtSW9aUWH0+0eTY1zfJX78kezGacf3rum7dOm677TYMBkPcnA+n08k3v/nN8XwpcUAwGJy4J9/5LGx+WN1/PjN/4l5nLHKnwZVPq/vZ//FSOPNb47/yfoqZ0OxF0pP8k0Q0rM79blyvrsQeDYG9UO19nnuxOhfcnD7uLzuh+ZssYMo/5HP/5Il7LXHM5NjXN8lfvyR7MZoJWf3+hhtu4O677x7vp50Uqbb6vd/vn5hdBvb+A7b8BT7zG0gzj//zn4hBF7zwLaj5BCy4LNGtSZgJy16kBMk/QYIeaHoX9r+p9sQbjOoK7NXLoewUdVj6JJD89Uuy1zfJX78ke30aa206IWOYU7WgT0VNTU3j/6SBAXjrv+GiXydfQQ/qEM7P3A27noXGtxLdmoSZkOxFypD8J0nIr57kfOn78PC/wNNfVYfUz70YPvdn+PyjcPZ31KJ+kgp6kPz1TLLXN8lfvyR7MRqZ3JbiIpHI+D2Zvw8+/KNaKK/6obooUrIypsFFv4FH16hDXfNrE92iSTeu2YuUI/lPkJAPWjZB83vqnugGI1SfcWAl9tlJs56H5K9fkr2+Sf76JdmL0UzI8PtUlmrD7wcHB7HZxqF3KBqGRz4HCz8LeTVQuujEn3MyeDrg8Wvgsj8kft7/JBu37EVKOqH8FQWUmHqJRdRV0E1WdYX1JClaJ42rBZreUYv4vnp1gbeyk6HyNChZNKm978dCjn/9kuz1TfLXL8len8Zam0pPfYpraGhgzpw5J/YkigLrfwHzL5u8PejHi6MYzrsT/roWLvxvyJuR6BZNmnHJXqSsYflHw+qq6wP71S3TeuvU/b9jEeDQfcgV9brBqBbwhjR127RIUN0GTYkd8jjU6S7ZlWrBb7Wrq7bnVENWReqtZB4NQ+c2tYBvfg8GB8BZphbwH7secqdP/p7tx0mOf/2S7PVN8tcvyV6MJsW+kYnDxWKxoz/oaJ6+UV1dfsHlJ/5ciVA0Fy65D568Xp3jaslMdIsmxbhkL1KTomDp2w3r/wbNG9VC3GhS9xPPrlRPblWdoe4BfqLTaAL9MNB0YM9zD3TthN1/U3u4lShgUIt9Z6la6BfMgsI5yTFyxtejFu8t70HnDvVERvE8KD9V/bxLz0l0C4+bHP/6Jdnrm+SvX5K9GI0Mvz9Mqg2/D4fDmM0nsJjdjqeh/SN1Dn2qa3gdtv4FLvrfRLdkUpxw9iK1BD3qv/G9L4GrmWjBbNJmnaf2Mid6QctBN3ja1eK/e5da+Hu71K3cCmeDo0Qt8h0lkF0FGXnjN8w/EgJPGww0q6MTuneroxWiYfV1KpZCxcfUEw3GtPF5zSQgx79+Sfb6Jvnrl2SvT2OtTaWoP0yqFfXbtm1j3rx5x/fLzRvhrf+Cf/l9ci+Kdyze/JU6hPjj35vyc4NPKHuR/BRFLVL3vgT71qsF6fSzofaTkFOVGvmHfGqR7e0CX/eBwn+/uijnoX96TBZ1eL8t6+DF6lR71oNuCHnV4zrkPTBiwK0W7XBghEIZZJWp64EUnKROD5hCBfxIUiJ/MSEke32T/PVLstcnmVMvRheNwKs/VReYmyoFPcDyf4dNv1dXxT//F+AsSXSLhBi7kB8a34S6f0DPXnVXh9pPwin/qvZ6pxpLJpQtOfrjwoNqoT7ohqALBg9cFEUt0K0OsNjVYf5Wp/qzyTLhzRdCCCGESAVJ2VO/d+9errrqKnp6esjKyuIPf/gDc+fOjXtMY2Mja9euZfPmzUybNo0PP/xQu++1117jvPPOY9asWdptGzZsID396F+KU62n/riH4rxzt/rlePEXxr9RyaBrF7x4M3zsBpj1qUS3ZkLIMKwpIhqG+ldhy58hHIDqFVD7CbXXeZRF2yR/fZP89Uuy1zfJX78ke31K6Z7666+/nuuuu461a9fy2GOPsXbtWjZu3Bj3GKfTyU9/+lNcLhff+973hj3HrFmz4gr9qWr37t3HPhSntx7qX4HPPToxjUoGhSfB5/8Cz98EbR/Aim9OrREJHGf2RxOLQc8eaH4XWjaqQ6YxoK2YPszQOUHDwZ8VRR36nFMNRfOgZIG6gFp69vi2NZUpiroX+pZH1W3UZqyEc29Xd3MYownJX6QMyV+/JHt9k/z1S7IXo0m6or6rq4tNmzbx0ksvAXDppZdy4403UldXR01Njfa43Nxcli9fzmuvvZaglqYoRYHnvwmf+c2Un3OOyQqrfw3bn4DH/hUu/V1qDmGeaK5WqH8Z9vwdoiHIn6kuLPbx7x3/9IVoRN1Orf1D2PYEuJrVodUGg7pQWl4N2AvV1dmL5qfe1mjHq7cetvwFmjZA+Smw9Br1BJQQQgghhBDHKem+STc3N1NSUoLJpDbNYDBQWVlJU1NTXFF/NPX19SxZsoS0tDS+9KUv8ZWvfGXExwWDQYLBoHbd7Xaf2BuYZIdOMRiTjq3qFnDO0olpULIxGGDepZCeq+5lv/rXYC9IdKvGxTFnf6jwIOx4Crb+VV2RvOoMuOS3YMkYn8almSC/Rr3M/5eDt8eiB/ZRbwBfF+x8Dl75KdiL1WkSFadNmXw0A83qLhMNr0JWOSy4As66+YRPqp1Q/iLlSf76Jdnrm+SvX5K9GE3SFfXjYcmSJbS0tJCVlUVLSwuf/vSnyc/P5/LLh+/Dvm7dOm677bYEtHJ8HPNQnO1PwtyLJ65ByWrGx9XtrR77krrav70w0S06YcecvaKow+rfu09dkG3Wp+DyB9XFzCaLMQ1yp6uXQw00q4vD/eOH6pD/rHKYtkIdMZBVPnntGw9Br9oT3/Caur2bvRjmXASnXjeui7vJMDx9k/z1S7LXN8lfvyR7MZqkK+orKipob28nEolgMplQFIWmpiYqKyvH/ByHLiJQXl7O5z73OdavXz9iUX/LLbdw0003adfdbjcVFRUn9iYmkfFYevvcbepw6KmwJ/3xKFkA5/0cnr4RPvvHxO/tfYLGnH3Ir/bKb3tcneP+yZ8m30iN7Ap1hfdT/lW93tcA+zfA6z9X/93aC6FqmTqqIKscsivBZBt1IblJE/Kp8+Mb31SPL5NNbeuiL6h7tE9QG4/p2BdTjuSvX5K9vkn++iXZi9EkXVFfWFjIkiVLePjhh1m7di2PP/445eXlxzT0vr29naKiIoxGIx6Ph+eee46rr756xMdarVas1tRdQG369OlHfxCovbR/+zacuy45CqFEKZoDiz4PT1ynbnmXkZvoFh23o2bf/B68fZea/axPT36v/IkY6s0f2p3B0wn731K3eWt4Ve3ZD7rVXvCTzlcXmhuvqQOjicXUhe06tkDzRujdC+YMdX78zHPVYfWTtD7AmI99MSVJ/vol2eub5K9fkr0YTVJuabd7927Wrl1Lb28vTqeTBx54gPnz53PNNdewevVqVq9ejd/vZ+bMmQSDQVwuF4WFhXzxi19k3bp1/PrXv+buu+/GZDIRiUS47LLL+NGPfoRhDMVsqm1pt23btrENxdn6mLpY2fJ/n/hGpYLW9+Gft8I5t41tH+0kdMTsB13qezOa4cz/mBJTDY7I1QK7/6ZuCQdQcap6ya5SV5I3pp3Y8wc96r+Vpnehc6u6FkFejTrqo3zpUbedm0hjPvbFlCT565dkr2+Sv35J9vo01to0KYv6REq1on7Xrl2cdNJRVs9WFHj4Evjsn2T190MFBuDxq2H2hbDkqpQbwTAse283vPMbdTHEZV+D6WclrnGJEB5U1wxofV89geXpBCUGKOp6CrnTwJatHg9KVP0vqPfHIup+8ZEgDDRBoF+9y2qHkkVQeToUzwezLTHvbQRjOvbFlCX565dkr2+Sv35J9vokRf1xSrWi3u/3k5ExyrDjaATe+pXa4/iJH09ew1JFNAIbfwv71qv/f/LHPs0j0bTsIyF4/Q7o2gWnf0VdyT7FTlBMKEUBf686T3/QBQbjwcuQNDMYTZBmUefrp8C0jKMe+2JKk/z1S7LXN8lfvyR7fRprbZp0c+rFsWloaBh9KM6u59QeyFU/mrxGpZI0E5x2A8w6D579BpxzK5QuTnSrxqShoYF5Th+8djuccrV+F0A8GoMBMvPVyxRy1GNfTGmSv35J9vom+euXZC9GI0V9ijvqIn9bHoWL7znxucVTXU41XPp7+Pst6p72H78FbFmJbtWRNb5J9YY7oHKhur/8VJ43L0aUygt8ihMn+euXZK9vkr9+SfZiNFLUpzJ3O8W59iPfv2895ExL7uI0mWTmwSX3QcPr8OgaOPlLMPfi5BnKHg7A3pdg88NQNBdW/xqKqhPdKpEgxcXFiW6CSCDJX78ke32T/PVLshejkQ0PU9mu5+j94NmR71MUWP8LOPvmyW3TVDD9LPjCY+rWZY+ugd76xLYn5Ic3/wv+dAV4u+Die+GcW2ns9ia2XSKh9u/fn+gmiASS/PVLstc3yV+/JHsxGumpT2X5tdg7Xx9+eywGjW9Axcekl/54maxw5regbx+89H0oWQhnfEO9fbLEYrD1r7D5ITj1Wlj2FBgPnodLT5edDPRM8tc3yV+/JHt9k/z1S7IXo5Ge+lSWV4sj1DH89he/A2/8PzjlXye/TVNN7jS44mEomAV//Beof+WQrdAmSCQIH/4JHr4YfN2w5gmYc1FcQQ+Qnz+1Fn4Tx0by1zfJX78ke32T/PVLshejkaI+lTlKCHY3wuPXqivcg1pw9uyBtc+BoyihzZsyDAa1qP7sn6DxTXjoM/D6f6q9+OMlFoW2D+HVdfDHy9Q8P/dnWHYjmCwj/kpzc/P4vb5IOZK/vkn++iXZ65vkr1+SvRiNDL9PZUYjlqgX9vwdmt+F6uXQs1ftVRbjz+pQt42LRWH/W/DmL8HdDjPPVRfUO5Yt0xQFQl5ofR8+fAT8PVA8H2asgrO/M6bF+ez2URZJFFOe5K9vkr9+Sfb6Jvnrl2QvRiNFfYoz5M+EZV+Gvf9Qi/q9L0HtJxLdrKnNmAbTzlQv4UH1//kL/wEhHzjLIBZWt8jLq4HuPRANqkW8ElNHUSgxwACWTCg8CVb9ALLKj7kZTqdz3N+aSB2Sv75J/vol2eub5K9fkr0YjRT1KW7vnG8wb958dcg2qMPDl16T2EbpidkGc1arl1gUPB1gMELXDvXnmlVgTgcMau97TvWB6yeura2N3NzccXkukXokf32T/PVLstc3yV+/JHsxGinqU5wzK1stFo0mCPRDmkktNMXkM6ZBVpn6s7Nkwl9Oztjqm+Svb5K/fkn2+ib565dkL0YjC+WluIyMDPWHvBmw6fcw/eyEtkdMHi17oUuSv75J/vol2eub5K9fkr0YjRT1Ka6j48CWdkXz4J27oUbm0+uFlr3QJclf3yR//ZLs9U3y1y/JXowmKYv6vXv3smzZMmbOnMnSpUvZvn37iI+7//77qa2tZcaMGVx77bWEw+Ex3TeV5OTkqD8UzYX0XMipSmyDxKTRshe6JPnrm+SvX5K9vkn++iXZi9EkZVF//fXXc91117Fnzx5uvvlm1q5dO+wx+/bt4wc/+AHr16+nrq6Ozs5O7rvvvqPeN9WYzWb1h8LZcMEvE9sYMam07IUuSf76Jvnrl2Svb5K/fkn2YjRJV9R3dXWxadMm1qxZA8Cll15Kc3MzdXV1cY977LHHWL16NcXFxRgMBr785S/zyCOPHPW+qaarq0v9Ic2sbmkndEPLXuiS5K9vkr9+Sfb6Jvnrl2QvRpN0RX1zczMlJSWYTOrC/AaDgcrKSpqamuIe19TURFXVwaHm1dXV2mNGu+9wwWAQt9sdd0kleXl5iW6CSBDJXt8kf32T/PVLstc3yV+/JHsxmqQr6ifbunXryMrK0i4VFRWJbpIQQgghhBBCCDEmSbdPfUVFBe3t7UQiEUwmE4qi0NTURGVlZdzjKisrqa+v1643NjZqjxntvsPdcsst3HTTTdp1l8tFZWVlyvTY79+/n8zMzEQ3QySAZK9vkr++Sf76Jdnrm+SvX5K9Pg3VpIqijP5AJQmdddZZygMPPKAoiqL89a9/VU4++eRhj6mvr1dKSkqU9vZ2JRaLKRdeeKHyP//zP0e972iam5sVQC5ykYtc5CIXuchFLnKRi1zkIpeEX5qbm0etYQ2KcrSyf/Lt3r2btWvX0tvbi9Pp5IEHHmD+/Plcc801rF69mtWrVwPw29/+ljvuuAOAs88+m3vuuUdbGXK0+0YTi8Voa2vD4XBgMBgm6B2OD7fbTUVFBc3NzTidzkQ3R0wiyV7fJH99k/z1S7LXN8lfvyR7/VIUBY/HQ2lpKUbjkWfOJ2VRL8bG7XaTlZWFy+WSA1xnJHt9k/z1TfLXL8le3yR//ZLsxdHofqE8IYQQQgghhBAiVUlRL4QQQgghhBBCpCgp6lOY1WrlRz/6EVarNdFNEZNMstc3yV/fJH/9kuz1TfLXL8leHI3MqRdCCCGEEEIIIVKU9NQLIYQQQgghhBApSop6IYQQQgghhBAiRUlRL4QQQgghhBBCpCgp6lPU3r17WbZsGTNnzmTp0qVs37490U0SE+jrX/861dXVGAwGPvzwQ+326upqZs2axaJFi1i0aBGPPvpo4hopJswnP/lJFixYwKJFi1ixYgWbN28G5HNATx544AEMBgNPPfUUAGeffTbTpk3Tjv1f/epXiW2gmBDBYJAbb7yR2tpa5s+fz5o1awA59vWgt7dXO74XLVrEzJkzMZlM9PX1yfGvAy+++CKnnHIKCxYs4LTTTuOjjz4CoKuri0996lPU1tYyb9483njjjQS3VCQNRaSkj3/848oDDzygKIqi/PWvf1VOOeWUxDZITKjXX39daW5uVqqqqpTNmzdrtx9+XUxN/f392s9PPPGEsmDBAkVR5HNAL/bt26ecfvrpymmnnaY8+eSTiqIoyllnnaX9LKauf/u3f1NuvPFGJRaLKYqiKO3t7YqiyLGvR//5n/+pXHDBBYqiyPE/1fX19Sm5ubnKtm3bFEVRlDfeeEOZO3euoiiK8qUvfUn50Y9+pCiKorz33ntKWVmZEgqFEtVUkUSkpz4FdXV1sWnTJu2M/aWXXkpzczN1dXUJbpmYKGeeeSbl5eWJboZIkOzsbO1nl8uFwWCQzwGdiMViXHPNNfzP//yPbGWkMz6fj/vvv5+f/exnGAwGAIqLi+XY16n777+fq6++OtHNEJOgvr6evLw85s6dC8CKFStoamrigw8+4C9/+Qtf/vKXAVi6dCmlpaW8/vrriWyuSBJS1Keg5uZmSkpKMJlMABgMBiorK2lqakpwy0QiXHnllcyfP5+rr76a7u7uRDdHTJArr7ySiooKfvCDH/DQQw/J54BO/PKXv+SMM87g5JNPHnbfd77zHebPn88VV1xBQ0NDAlonJlJ9fT25ubncfvvtnHLKKaxYsYKXX35Zjn0devvtt+nv7+eCCy7QbpPjf+qqra2lt7eXt99+G4BnnnkGj8fDvn37CIfDFBcXa4+trq6WY18AUtQLkdLeeOMNtmzZwgcffEB+fj5XXXVVopskJsiDDz5Ic3MzP/3pT7n55psT3RwxCbZt28bjjz/O97///WH3PfTQQ+zatYstW7awYsWKuC/7YmqIRCLs37+fOXPmsGnTJu666y6uuOIKIpFIopsmJtn999/PlVdeqZ3IkeN/asvKyuKxxx7jlltu4eSTT+all15izpw5eL3eRDdNJDGDoihKohshjk1XVxc1NTX09fVhMplQFIWSkhLefPNNampqEt08MYGqq6t56qmnWLRo0bD72tvbmTlzJh6PZ/IbJiZVeno6jY2N1NbWyufAFHb33Xfz4x//WBt239HRgdPp5LbbbuOGG26Ie6zNZqO1tZW8vLxENFVMgJ6eHoqKigiFQqSlpQHqcNtvfetbXHPNNXLs64TX66WkpISNGzdy0kknjfgYOf6ntmAwSHFxMRs3bmThwoXU19drvfWnnnoqt99+O+ecc06CWykSTXrqU1BhYSFLlizh4YcfBuDxxx+nvLxc/pjrjM/nY2BgQLv+yCOPsHjx4sQ1SEyIgYEB2tratOtPPfUUeXl58jmgAzfccAPt7e00NjbS2NjIaaedxn333ce1115LZ2en9rjHH3+coqIi+UI/xeTn57Nq1Sr+/ve/A7Bv3z727dvHGWecIce+jjz66KMsXLhQK+gjkYgc/zrQ3t6u/fyTn/yElStXUlNTw2WXXcY999wDwMaNG2ltbeWss85KVDNFEjElugHi+Nx7772sXbuW22+/HafTyQMPPJDoJokJdP311/P888/T0dHBueeei8Ph4KWXXuLSSy8lGo2iKArTp0/nwQcfTHRTxThzuVxcdtllBAIBjEYjBQUFPPfccxgMBvkc0KlgMMj5559PMBjEaDSSn5/PM888k+hmiQlwzz33cPXVV3PzzTdjNBq59957KSsrk2NfR+6//36uvfZa7boc//rwwx/+kPXr1xOJRDj99NO5//77Abjzzjv54he/SG1tLRaLhYcffhiz2Zzg1opkIMPvhRBCCCGEEEKIFCXD74UQQgghhBBCiBQlRb0QQgghhBBCCJGipKgXQgghhBBCCCFSlBT1QgghhBBCCCFEipKiXgghhBBCCCGESFFS1AshhBBCCCGEEClKinohhBBCCCGEECJFSVEvhBBCCCGEEEKkKCnqhRBCCCGEEEKIFCVFvRBCCCGEEEIIkaKkqBdCCCGEEEIIIVKUFPVCCCGEEEIIIUSKkqJeCCGEEEIIIYRIUVLUCyGEEEIIIYQQKUqKeiGEEEIIIYQQIkWZEt2AZBOLxWhra8PhcGAwGBLdHCGEEEIIIYQQOqQoCh6Ph9LSUozGI/fHS1F/mLa2NioqKhLdDCGEEEIIIYQQgubmZsrLy494vxT1h3E4HID6P87pdCa4NUfX1tZGaWlpopshEkCy1zfJX98kf/2S7PVN8tcvyV6f3G43FRUVWo16JFLUH2ZoyL3T6UyJon5wcDAl2inGn2Svb5K/vkn++iXZ65vkr1+Svb4dbVq4LJSX4rq6uhLdBJEgkr2+Sf76Jvnrl2Svb5K/fkn2YjRS1Ke4vLy8RDdBJIhkr2+Sv75J/vol2eub5K9fY8m+b7BvEloikpEU9UIIIYQQQgiR4n701o/wh/2JboZIACnqU1xvb2+imyASRLLXN8lf3yR//ZLs9U3y16+xZO8OufGGvZPQGpFspKhPcYWFhYlugkgQyV7fJH99k/z1KxWz/6DzAx7c/mCimzElpGL+YnyMJXsp6vVLivoUFw6HE90EkSCSvb5J/vom+etXKmbvDrnpHZQe5vGQivmL8TGW7N0hN76Qb9jtclJt6pOiPsX19/cnugkiQSR7fZP89U3y169UzD4QCeAKuhLdjCkhFfMX42Ms2XtCHjxhz7Dbn6p/ipgSm4hmiSQhRX2KKy4uTnQTRIJI9vom+eub5K9fqZh9IBLAHXInuhlTwmTl/1rza9T114358YqioCjKxDVIHDH7Vm8rb7S8QTgWJhAJ4AsP76nv8HUQjskoj6ks5Yr6vXv3smzZMmbOnMnSpUvZvn37sMfEYjFuuukm5syZw4IFC/j4xz9OXd3YP5hSid8vK1zqlWSvb5K/vkn++pWK2UtP/fiZqPwP7cVtcjfx4w0/Zlf/rjH//u+3/Z4dvTsmomkCiMQiPNvw7Ij3be/ZzuvNr+MJeXBanHhD8XPq/WE/npCHYDQ4GU0VCZJyRf3111/Pddddx549e7j55ptZu3btsMc888wzvPXWW3z00Uds2bKFVatW8d3vfnfyGzsJ3G45861Xkr2+Sf76JvnrV7JmH41Fj1jUSVE/fiYq/x+89QNtNMXe/r0sLlw8rDgctV0ht4zGmEDekJdXWl8Z8b52Xzut3lY8IQ8lmSXDFsrr8HcAEIqGJrydInFSqqjv6upi06ZNrFmzBoBLL72U5ubmYb3wBoOBYDDI4OAgiqLgdrspLy9PRJMnXGlpaaKbIBJEstc3yV/fJH/9StbsuwPdrHt33Yj3+cN+WZF7nExU/p2+TtxBtSjvDnRTnVV9TJkFIgHpCZ5AvoiPaFp0xPs6fB30BHpwB92U2IcX9Z2+TowGoxT1U1xKFfXNzc2UlJRgMpkAtXivrKykqakp7nEXXnghZ599NsXFxZSUlPDyyy/z4x//eMTnDAaDuN3uuEsqSbX2ivEj2eub5K9vkr9+JWv23f5udvfvJhobXngEIgEsaZYEtGrqmaj8+4P9WjHYE+ih2lmNJ6QuuBZTYnzvze+N+vv+sJ/ByOCEtE2AL+w74kiIDl8Heel5DAQHKM0sHbb6fYevg9LMUjnpMsWlVFE/Vps2bWLbtm20trbS1tbGqlWr+PKXvzziY9etW0dWVpZ2qaiomOTWnhivV85865Vkr2+Sv75J/vE6fZ2JbsKkSdbsuwJd2NJs7HPtG3ZfIBLAaXGO2lO4vXf4GkliuInKf2BwQCviewI9TMuapg2/94Q8fNT90ai/H4gECEQCE9I2oZ40GWkBPIBwLEy5vZwGV8OIw+87/Z1UOCqkp36KS6mivqKigvb2diKRCKCutNnU1ERlZWXc4x588EFWrlxJdnY2RqORq666ildffXXE57zllltwuVzapbm5ecLfx3hKtZMQYvxI9vom+eub5B/vqhev4tn6kReRmmqSNftufzcrylewvXc7zzc8T6u3VbsvEAlQnFk86pzr/7fx/8nq6WMwEfkrikJ/sF8rGvsG+6hwVGjF4UBwgHZv+7At0Q7NKxAJMBiVnvqJ4g17GVSO/P+3zFHGjt4dIw6/7/B1UOmslKJ+ikupor6wsJAlS5bw8MMPA/D4449TXl5OTU1N3OOmT5/OK6+8Qiik/uN97rnnmDdv3ojPabVacTqdcZdU0tPTk+gmiASR7PVN8tc3yT/ejOwZ/L3x72N+fDgaZnPX5gls0cRJtuxfbXqV3kAvXf4uPlbyMfa797O9dztd/i7tMYFIgOKM4rjF8mJKjJ+98zOe3PskwIirc7/U+NIxLdamBxORvy/sQ0HReuqjShSnxYk/oq60PxAcIBQL0TfYF/d7V/7tSi3TQCQgw+8nkC/swx/2oygKG9o20OHr4M+7/kyLpwVLmoXTS07njZY3KEgvIBgJEogE2NixEYDewV5KMktk+P0Ul1JFPcC9997Lvffey8yZM7njjjt44IEHALjmmmt45plnAPjqV7/KtGnTWLhwIQsWLODll1/m7rvvTmSzJ0wgIEOd9Eqy1zfJX98k/4NiSgyj4di+zrT72nlsz2MT1KKJlWzZP7r7UW5/93a6A91Mz5qOJ+TBG/LGFXiKopBjy4kr6jd3baY/2M+e/j0Acftrb+newkfdH/Fi44s8VffUpL6fZDcR+fcH+ym3l8f18BoMBu1nV9BFYXoh7d72uN/rCfRw90fq92vpqZ9Y/rCfNEOaVqzv6N3BM/XP8O+v/TtXz7+a2XmzuX7B9ZTZywDY59rHI7seAdTjL92UTigaGrbmxdN1T0/6exETw5ToBhyrWbNmsWHDhmG3/+53v9N+tlqt/Pa3v53MZiVMVVVVopsgEkSy1zfJX9/0nL8r6GJD2wY+Ne1TgPplN8OUgT98cP9uRVHiipLDecKeuMenkmTKfuiESo4th/c63uM/TvkPvGEvwWhwWK9tji2H/sF+7fr7ne9zxawreGjHQ8DBoj4vPY+tPVtJN6UTjob5x/5/YDVZ+UzNZ/CH/WRZsyb1PSab48l/W8825uWPPGIV1Pn0FY4KvCGvmulQn9+B0fUDwQFOyjuJdl878wvmA+oWhrU5tQwEB9jbv3dYT70r6KLL30VtTu0xt1cM5wv7KMwoxBv2EogE6PR3kmvL5derfq09Zu28tdrP3f5umtwHFxK3pFnwhr3c9NpN/PfK/9Zuf2jHQ1xUc9GkvAcxsVKup17E6+joSHQTRIJI9vom+U+8SCxCTyC5hjoP0XP+23u383rL69p1b9iLw+LAZDSxsWMjrzW/xs83/pz+wX4CkcCIw/K9Ie8RF51KdsmQfd9gHy80vECrp5VyRzkXTL+AQCSA3WzHG/aOOJS+1F5Km69Nu761ZysLCxYSVdSew0N76rv93dqia78793e82vQqG9o2cNcHdwFw69u3EowGtZ5IPTnW/MPRML96/1ejPqY/2E+5oxxP2MP33vwepfb4bfMGBgeYnTubdp/aU68oCr6ID7vZztq5a3m24VnSjGlxRf2uvl28sO+FY2qrGO6VplfwhX14w16y07K1on53325ybDkj/k6MGF2BLtq8bfjDftJN6VjTrPQN9g1bkLLD3yFz7acIKepTXDAo82P0SrLXN8l/4u3t38vvt/2ezV2bk251dT3nv7d/b9w8a2/IS6Y5k1xbLv/Y/w/e73yfLT1b6A/2s6lj04jD7L0hL75Iahb1yZB9u6+dp+qeYkffDmbnzmZhwULWzl1LmjENRVHwhrzDVkIvyyzTFs9TFIVILIIlzYLRYCQSixCIBLTh392BbvWkgAHMRjNGg5EOXwdvtr6Joiisb13PPtc+Xtz34qS/90Q71vz9EX/ctIfDvdr0Kg/ueJAKRwUdvg5iSoxbPnZL3GMGggPMyZtDi6cFgHXvraNhoIFMcyaFGYX0D/aTnpYeN/zeG/bGjcwQx6bT18me/j08W/8s7d52/GE/2aZsfCF1bv1H3R9RlFE04u9mmjLZ59pHqb2UHb07KM4sxpJmoT/YT6e/U/v8DMfCuIIuvGF1hIasX5HapKhPcdOnT090E0SCjJZ9/UA9Lzbq78uOnsixP/G8YS/uoJs3Wt7gvY73xvx77pB7wlfx1nP+e/r3xBWMQz31+en5vN/5Po3uRva79+MNednQvmHEgsYdcqfs8PtkyN4f9rOzbyfvd77PwsKFGAwGvjD7C9r93rB32Pzq4sxiOnxqL7Mv7MNpURcmLs0spcndRFSJapn0BHrUXt8Dh1GmOZO6gToqHBVs7tpMl7+Lnb078YQ9k/Buk8ux5h+IBBgIDhzx/r/t+xudvk4qHZXs7ttNuaP84J0G9QSMK+iK66l/q/Ut9rn2YbfYcVqcuIIubCZb3HHpD/vpHew9praKg97reI+XGl+iw9eBN6yOLJpWME3tqY8GaPO2UZQ5clFf6axkU8cmlhQtYVPnJooyirAYLbiCLkwGEw2uBnxhH70BNR9fyMfGjo38YfsfJvEdivEmRX2Ka2pqOvqDxJQ0WvYtnhZ29u6cxNaIySbH/vh5tWnkLU+9IS/ukBtX0DXi3ttH8j8f/A/73GN//PHQW/4v7nuRdm87v9j0C1o8LaSb0rX7vGG1pz4vPY92XzsDgwNa71ODq4HCjMJhzzf0JTkVJUP2vrCPwcgg23q2MT1reJHpDXsJRuJ7lM1pZiIxdUtid8itzY0vs5exd2AvZqM5rqf+0KHcRZlFfNT9EWvmrOGuzXdR6ahkR+8ObbV2PTnW/P1h/4hbCXpDXr67/rv4Ij5+s+o3nFpyKk2eJsrtB4v6DFMG/oifgeAAObYcYkqMvsE+2rxttHhbsJvtmIwmIrEI6ab0uMy8Ye+w1fLF2HUHumn2NNPh78AdcuOL+DAPmtWdChSFgoyCI/bUVzmrqBuoY2HBQv6x/x8UZxZrw+9n5s6kfqCe+7fez1N1T2E0GPGGvXT6O0c9+SOSnxT1KS4SiSS6CSJBRsveE/bIh3OK8IV9hGNh/rDtD+zu2x13nz/s186kH06O/fERiUX45fu/HPE+b/hgUd/obhzzc7pCLtzBI+/HPR6mev7vtr+LK+iiJ9DDpo5NrG9dz0c9H7GpYxOnFJ8S91hvyIvdbCc/PZ8qRxUOi4MqZxXekBezwUymOXPYsFJvyDtsFehUkQzZ+8N+FhYsZGnx0hHvN2AgEFV7bQ8ftTKU7VBPfbYtmw5fBwXpBXEnWg7t6S/OKKbZ3cxZ5WfRG+jl9NLT2dW3S5fDhY+U/02v3TTi7f6In0AkoK1x8Frza/jCPt7vfB+T0cTlMy+nwllBuimddFN6XE+9w+LAE/IQioawmWxkWbN4o+UNFhctpsWjFvWgZpVhzkDhYNa+sE+G35+ALn8X+9376RvswxvyqotEpmVpJ7KKMoqOWNRXOivJseVwZvmZfGPJNzir/CwsaRYGBgeYnz+fBlcDLZ4WNnVsoiRT3de+09eJK+ii09dJOBqezLcqxokU9SmupqYm0U0QCTKUvT/sHzaM1BuSuWzJzB1ys61nG4qi8I1Xv8Hmzs08Xf80u/vji/p32t/huYbnRnwOOfbHhz/ip8nTNOJCQZ6QB3fITSga0r4QK4qiDSEeMrQX8OG/N5Gmev7/++H/8p3132FP/x6ea3iOVm8r77S9w9z8uXxt8dfiHnvo8PuqrCqqnFXMyZtD72Av5jQzRZlFbO3ZGrdvuifswWFxTPbbGheJyP6NljfY2r1Vu+6L+Lh81uVcv+D6YY81GAxkmjO1nvpwLIw5zQxAljWL6/9xPW3eNq2nPsuSRYevg/yMfPUkZzSMw+xQe30PbGBQlFlEfkY+BoOB7532PT5R9Ql29+/GZDQRU2IT/O6Ty5Hy39u/d8Tbh74fDJ1o/M2Hv+Gj7o94r+M9rpp7FWdVnKU91m62U+Go0K4XZxazz7WPsKIWeTOyZ3D/1vu5cPqFalFvUYv6aCxKhikDgMf2PEZMieEL+3SXzXjq8ncRVaKU2cvwhDwoikJtZa124mtp8VJK7CUj/m6Vo4rC9EIcFgdnlp+JOc2sFvXBAU7KPYn97v10B7r5sPtDqpxV+MI+raf+ge0P8H7X+5P5VsU4kaI+xTU0NCS6CSJBhrJ/af9L/GnXn+LukwVqktvdH97Nd9/8rnYmvnewlzl5c4YNJR1avXskcuyPD3/YT0yJjdgT7wv71C9TKJgMJsLRMG2+Nr7/1vfjfv/Gl28kHDvYszEZRf1Uzt8b8lKUUUSaIQ13yE2juxFFUXi1+VWmZU3THjfUAzy0UF5tTi1XzrmSS2ov4ZzKc2j1tJJtzaYoo4ifb/w5P9/4c+13PSEPuem5KbnqcyKy/7DrQzZ2Hjx55Q/7ybHlkGHOGPbYoVETQz3tgUhAmy6xZvYaLqq5iHpX/cGi3ppFp7+TfJta1PcEeih3lKu/f6DjtzijmOKMYgBOKzmNaVnTCEQCWkECk7OWRTIYKX9FUWjzto1YRPsjfnKsObiCLvoH+/GFfWzr2Ub9QP2wqRO5tlwK0gu065+p+Qzr3lvHnNw5ACzIX8BFNRcxO2+2Nvwe1AyHMv7d1t/xXsd7+MI+cqw5w3ZBEEcWjob5276/ARCKhqh2VjMzZ6a2dsRA54D285cXflkb7XK4bFs2N50SP3LDmmalP9iP0+IkqkTJNGeSYcqg0lGJN+yl299NmiGNnkDPMU03E8lDivoUF4vJWVC9Gsq+f7Cfd9vfjbvPG/LiCh15tVuRWE2eJs4sO5NWbyt9gT56A71UOiqHzfHtD8YX9be+fav281D+Lze9PCltngpGmt/pD/spSC/gjZY3qOuvi7vPE/ZgNqo9jKX2Utp97bR729nRs0MrHt5pfwdzmplmd7P2e+6Qe9jw+7r+OgYGB8btvUzlz/66gTptb2t30E3DQAPZ1myMBqNW1Keb0/FH1B7IoV73dFM6c/LmUJtTS5lDXWk925pNcUYx4VgYu9lOw4BaEHlDXgozClNyXn0ism/3tccdH/6wX+uZPZxW1B+YXz0QHNCKj9l5s6l2VtPgatBuc1qd6vD7DHX4fYu3hWlZ0+LmZ5c7yjm99HTteo4tBwMGqp3V2hD8X276pbY6+1Q2Uv7esJdQLBT3/2yIL+yjOLOYgeAA73a8y5o5a3iu4TmmZU3DYDDEPfaOFXeQZkzTruen53P9guu5dsG1AJxacirXzL+GbGs2fYN9Wk99ji2HdFM6iqKQZc3imbpn8IV9VDgqpIPhGNy58U5+u/W32uKe5Y5yanNqtRP+mWmZuINuDBhGexqAYVNjhobfZ5ozSTelk2HKYHr2dCqdlfhCPqJKFJPRRE+gh0ZX47i/NzHxpKhPcbNmzUp0E0SCDGXfP9hPhy9+n1Fv2Is1zZqopolRDPWkZNuyafW2EoqFaPI0aXOAD9U/2B83teLDrg+1n4fyf3D7gxPf6CniG698Y9htvrCP+fnzuW/LfcOmOvhCPrKt2URiEUrtpbR6W2n3tWM1WWnyqItVvdX6Fp+d9Vm2925nR+8O7TkP76n/+/6/s6t/17i9l6n82b+nfw+12bWkGdLoG+wj3ZROcWYx07Omaz2LDrND+6LrC/vINGfGPYfD7KDF20KWNYsZ2TP43Emf46Kai7RdQSKxCNnW7JQs6hORvScUv06LL+wbsZce0KZCDPXUt/va4/Y9z0/Pp2GgYdjw+7z0PHxhH1t7trIgfwHBaDBu2P6X5n1Jew6z0Uyls5KCjAKt59IT8uhitfWR8h8qnEca2eWP+CnJLMEVcvFO2zucXX42DouDNXPWDHtsdVb1sNvOn35+3MKUANnWbACtpz7bmk26KZ3+wX5Oyj0JT8iDL+yj3FE+LJNwNJyyO09MtE5fJ58/6fNsaN+AAQMXTr+QVZWrtBFjp8w+hd5Ab9zaBWNlTbNqax9Mz5pOmaOML8z+AjXZNdoClaDuNNHmbRvPtyUmiRT1KW737t1Hf5CYkoay7xvs47SS09jTv0e7zxPykGXJ0lYaFomz370/Lof97v1UOipxWpy0eNVepbqBOiqcFXjD8Yt3DQQHtC9pkViEDn8H4ViYNm+bln/vYK8uhpyeqHA0POKK9L6IjyVFS3j0gke1Qn2IJ+yh1F6KJc2i9dR3+DpYVbmK3239HY2uRrr8XZxZfiZ3f3Q3f9z5R0BdMXqkqRSHrwZ+IqbyZ/+uvl3MzJ2J0+qk2dPMgoIFlNpL+ckZP9EWhrJb7NpJME/Ig8McPz8+05xJi6eFbGs21VnVfGH2F1hYsJC3297m0mcuJayEyTBl8MTeJ4aNdEp2ico+zZCmfT75IsNPpAwZKuqH/r23e9spyTw49zcvPY99rn1kWdSi3ml10jvYqy2Ut71nO3Pz5zIYGRxWTB7qxsU3agu5gfrvQA+9wiPl3x9U3/fQ6JVD+cN+SuwluINuOnwdlNhL+MOn/kCZvey422Az2Ug3pWv/BnKsak89BnWqhIJCNBalIKNg2GKvb7e9zV/3/PW4X3uqO730dP6444/k2HLUnnRHJW2+NuwWO80NzXjCHtIMaUd/osNY0iyA+vdpYcFC5uTN4RNVnyAvPY+B4AAmo0lboyIUC3HXB3eN91sTE0yKeiFSnCvkojanlm5/t3abP+KnOLN4wuf1iqP7xaZf0ORp4odv/RB/2M//fvi/LCtdhtPqpMXTQn56PvUD9VQ4KvCGvHxn/Xe0L6muoAt/xM8jux6hydOkfeG94707tOf3hDyEYqk3L3iyuUIuXEHXsOGpvrAPh8XBtKxp2tzPuz64i81dmwmEAxRlFJFlzaI0s5Q2bxvtvnYurb0Ui9GizTGekT0Db8iLJ+QhGA1SkFEw7NhzBV3D9u0Ww0ViEVo8LZRmlpJtzabZ08ynpn2KU4pOodReqg0XdlgcWu+SL+wj03JYT/2B+4d6FAGMBiNfmvcllpUuo93bToY5gzdb3+SDzg8AdbGvDW0bCEQC8tl5iJgSw4CBckc5rd5WYPTh9w6LgxxbjnYys8PXEVfUD/XiO63q8Huz0YwtTV1ZPRQN4Y/4yTRnEogERh1x9qnqT2E3Hzy54w17teJWb/oH+8mx5sT11L/V+haekEfrqd/eu10r5IemFZ2IbGu2ttjk0PD79LR0ijKLSDelx83lP7Sw7w/2y1Z3oyizl/G1xV/jhoU3AJBuSqeuv07LLhAJkG4+8smuIzm0qD+j7Aw+UfUJQB1tsa1nG5XOSrKsWTjMDhYVLOK1ltfG5w2JSSNFfYqbykMwxei07BUozCikZ7An7v5cW+64zuEVx2dP/x76B/t5vuF5vvvmdzmn8hxWlK/AaXHS6m2lyllFTInhMDsIRAI0e5rj5oWGo2G2dG9hV686dLvN28Y77e9QXVMNHNwvWoxuqEg7dAV0OFCcHBhGbMRINBZlR+8Ofr3514A67DfLkkWpXS3quwPdzMyZycW1F9MTUI+5THMmj1zwCIqi4Al5KM0sHd5TH+wf1wWjptJn/9N1T2s/b2jbwOmlp2MwGMiyZtHkVtefmJs/N+537Ga7ttWWP+wfVqRY0iyYjWZtiPeQVZWruLjmYjLNmWSaM9nv3s/eAXXV8Pc73+eej+7hjZY3eL7h+Ql6tydusrPvG+wjNz2XwoxC7fjxR/xHHH6/pHAJiwoWadfbfG1xq3Tnp+cDaD31cHChtd39u1lSuASAYDR41GlkDosjbvi9HorFIw2/L7OXxQ1r/82Hv6HJ00QgHKA4s5iXGl/iktpLxq0d2dZs7d/AmeVnclbFWdhMNooziinMKKQ30EuWNQtX0MX33/q+NopiaME+cWSnlpyqHTMGg4FgNEi5vZxZs2bF7TRwLIaOpcOP20xzJh91f8TcvLlkWbPIz8jnhkU3UJNVI9MkUowU9SluKg/BFKM7NPv89PxhQ9yybdm67bVIFu6Qm05fJ23eNmZkz6DZ08wnqz8JqF9oW72tVDurybXlaj2QvYO92rD8ocVwPCGPtjp7i7eFmuwannr/KSKxCMFo8Igr5OtZJBaJ23rOHXTjtDjp9HfGPc4X9mlfkErsJbT72jEZTeSl5+EJe3BanGRZs3BanNrq2mnGNPLT8+NGxwz1orhDbnJtucOmvow0SuBETKXP/v/64L+0Yd3vtL/DWeXqFltZ1iwCkYA2r/pQQz3xzzU8xzlV54z4vA6LI66nfsi0rGl885RvkmnOpMJRoeXywr4XCMfC9AR6kvqYmuzsO3wdFGcUq//mA+q/eUVRMBpG/go5PXs607MPrqo+1Is8JNuajcPsiMs1y5pFhimD25bdxjXzrwEgzZiGzWQbtW2H9tQPvdZUN1L+fYN9lNpLtX+3PYEedvTuUPc3j/hZkL+Av17412Enx07EBdMv0E6m5dhyyE/Px2ayUZRZRGFGIZY0C9nWbAaCA/QP9tPuawfUaWXy3SReJBYZdfs/u9lOuaOc3bt3q7tOHEdRP5TVSEV9OBZmbt5csq3Z2u4HObYcXZwkm0qkqBcixSko5NnytF7DAzeSa8sdVuiLybWnbw9z8+eyz7WPxYWL+fP5f9a+CDutTjp9nVQ5q8i15QJqlv6wf9gKzp6Qh0ZXIw6LgxZPCycXnUxHsENb5Eu2DBpuT/8ernjuCl5vfh1Qi+3anNphRf3QUF+AKmcVdQN1mIwmarNrcYfUEwFOixODwUDfYB/ljnIA8mx5NLobtSHEQ9xB94j7n/cPjm9P/VQRjqpF9NB6Bk1uddFIUE98Hf7/d4jD4uDXm3/Nzt6dXDjjwhEfYzfbRyzqDQYDJxedTKYpk+lZ08kwZ+AL++jwd5Bry6VvsC+pi/rJ1u3vJj8jn8KMwrgTWcfi0FXWD93FYEiWRe2pHxqlAZCelj6mnvqhaRi5tlytqG/xtPCX3X85rramooHgAGX2Mu3f7Ya2DZxcfLI6/D7sJ9OSSVFm0bi+5pVzrxx224qyFZTaSynMKCTTnEmWNWvEon5odXe9ONqoySf2PsHfG/8et/PAoRwWh3biOC8977iG3xsNRmxpNmxp8SfKTEYTRRlFlNnLyLXlauuWDH0WitRhmugXWLlyJa+88spEv4xuTaUhmOLYzJo1i1A0hNloJi89b9iHb0lmCbv6xm+1bXHsdvTu4LSS06gbqOOk3JPieqacFicKSlxR7wl5mJU7i2ZPs7byczgaxh1yE4gEKM1UV2BfULCAdkO7VtTL8Pvh/GE/a2av4en6p8lLz8MdclOTXUOnr5OBQXVRILvFHrdy+jTnNN5pf4f89Hxqc2pZ37qesyvOJqKove53nnkn5Xa1qDenmenydzE3L77nyx1Si/rDtxwKRUfebup4TZXP/qHV7ff276XaWU2MmPbFNtuafcR9mBcVLuK/Pv5f2tZ3I3FanCOeYNHutzqpyakhHAtTP1CP2WgmzZBGp69zxJMByWKys+8P9mv7l78VeAtgTFtqAXz15a/GDcUf8sPTfxh33Wl1DlsUz2qyjrpQHqjFjjvkJhwLk23N1qa9dPo72dm3c0xtTDUj5d832Mf8/PnaQnkNrgZOLjpZm1N/tP+P4+WimosAKEgvINOcSbY1G3fQjSfk0UZOuYKuYYXlVHfz+pu59xP3HvH+Zk9z3MKDh6vJrqEwo5CCWQXkbcs7rp56UEdwHr6NIajHo8Fg4ILpF2jHdm66FPWpZlx66t1u9xEve/fuHY+XEEcwlYZgiuF+vOHHRzyjvXv3bnVYoy0HS5qFcDQMHNgyzQClmaXamXGRGBs7N3JO1Tk0uBq0wn3IUOE3M2cmX1/yde32k3JPosvfRV+gT/udqBKl0d1Iqb2UFk8LVc4qGjsb8YV9GA1G6VUcgT/ix2lx8q1TvsXDOx/GHXQzM2cmrd5Wfvbuz3hh3ws8tucxXEEXmaYDPfVZVWzs2KgV9XaznQxzhlZYVjgq4r4QmY1mcmwHhxXbTDa6/d3DCtFAJECWNUtbKC+mxGhwNZzQ+5sqn/39wX5OLjqZvQN76Q50a0M/4cB6BofNiR/itDhHLehB7Uk8Us8XwClFp/CleV+i3F7Oh10fUpBeQK4tl7qBuqQ+piY7+77BPvJseeRn5NMVUOfUj2VLrbASxmgwanucH2pWbnxhOi1r2rCsbWm2o/bUl2SW0OppxRvyxp3A8YV99AWmZkEyUv7ukJuijCI6fB383/b/o9nTzJzcOXhCHqKx6LgsjHcsijKKtP3Q+4J9lNhLtG3SwtGwtmibXsSNpBxBm7eN/e792haBh/vZ8p9hMprYvXs3eenHX9QfOg3mUGeWnwmovfZDn5lDPfXhWPi4XktMvnEp6rOzs8nJySE7O1u7DF1vaxvfvQ737t3LsmXLmDlzJkuXLmX79u1HfKyiKKxcuZLs7OxxbUMyMRplBsVU9m77u2xo3zDs9qfrnmYgos5LO7So2NW3S1uVuDizOG5OsZg8g5FBXtz3ImajmZLMEprdzXE5gToUzmFxkGXN0oYb2y12CtMLiSpRdVuvnJkA2qrGZfYyOvwdVDmr8EV9+MLqPuqyqvpwgUiADHMGJfYSQtEQLd4WZuXOIqpE8YQ87O3fy/9t/z929u7U5hiWZJZQ76qnIL2A0sxS/u3kfxv1NfLS8+JO1mRZs9jv3k+2NZtsW3Zcz1RRZpG2xVffYB+3v3v7Cb2/qfLZ3xfo45SiU6gfqFf3pz+kUB9apPB4nTftvFHvNxgMmI1myuxlvN/5PiWZJeTactXFxUYo6ve59rHPNXxbxMk2mdk/3/A8fYN95NhycJgdcfPXjyYQDlDtrB7TY7+2+GvDPiNtpqMX9RnmDAKRwMhF/RTtZRwxf0X9+7Gnbw/PNTzHYGSQwoxCbRHByVZqL+Vf5/0rBoOBgcEB5uTNodXbyjvt7wDq9oh6KRYVRTnqv8WeQI9a1FtGLuqHTiYbjUZqsmuocFQcV1sOP8ZGk2fL4+22t7n17Vt5uelldvZOzZEvU8m4/GUoKSmhs7OTWCymXaLRKLFYjNLS0vF4Cc3111/Pddddx549e7j55ptZu3btER/7q1/9ihkzZozr6yeb6dOnH/1BIiWFY2GcFif/3P9Pmj3N2u2KovDYnsf4v87/U1clPlBUhGIhvvTil+gP9pNtzVaHbo/wR/PpuqfZ3Tc1evmS1QedH/DY3se4uOZiHBYHMWIjniHPseXEDbfLNGeSl55Hmb2M5/c9z/z8+cSIqXkeKD6MGClIL8BoM+IL+8hLzxvX/c+nCn/44JDTRQWLeKf9HbKt2dy27DZ+c85v2Duwlw5fBzv7dmoZGA1GyuxlFGQUYDAYtJMqRzLUszvEaXHyfuf7TM+ezoqyFaxvXQ+o8+mLM4q1ky/uoJv6gfoTen9T5bO/L6gu8DUYGWR3325m5Rzswc1Pz+fzsz8/4W0od5Tzftf7FGcWk2vLJc+WN+J+30/XPc0/9/9zwttzNJOVvT/s556P7tFGhA0VFoqijGn4vc1kY0b28X8Hs6XZjrpQHqhD91u8LTgsDhQUFEXRivrNXZun3NoyR8o/3ZROo7uR+oF6YkoMu8U+bBeOyZJmTNNGY0SVKCWZJWzp3sJP3/kpoP7t08u8+lAsdNSTYenmdFq8LUfsqR8yffp0lhYvZVnZsuNqy8+W/2zMj8215fJ68+vs7NvJ682vn/DoMjHxxqWoX7ZsGVu2bBnxvoULF47HSwDQ1dXFpk2bWLNmDQCXXnopzc3N1NXVDXvs9u3beeqpp/jOd74zbq+fjEZ672Jq6PB2sLBwIVXOKm7bcJt2e7uvnZk5MwkFQnGrCpfbyyl3lNMw0EBhRuGw5xtajfvJuifjThIAtHpbaXI3TeC7mZpcQRdP7H1i2O1berZw46IbWVG+AqPBSJYla9jwe1AXZjt0BWmH2UGeLY/lZctZ37Ke2pxarGlWnFYnubZcSu2lOK3qom0+n9pTn2fLIxBN3qHCiRKIBLQhirPzZrO3f6/W62s0GAlHw6woX0FMicX1Bk7LmhY3BHw0RRlFw4aLt3pbKcooYlnpMp6rf471LesZCA6oPfUHFspzh9z0BHpO6EvtVPnsHyoYbSYbH3V/xMzcgydSTEYTS4uXTngbCtIL8IV9alF/YOu2odX4D7Wzb6e2/d2hPuj8YFJPlE5W9l3+Llq9reo6EeaDveD9wf4jTos4lC3NRm326FMkRv39MfTUA8zOnc3Gjo3YzXZthX5f2Icr5OKZ+mfY07/nuNuQjI6Uf4Ypg0Z3I/Pz55Ofno/T4sQT8hDjyKuqT4YsaxY5thz+45T/YHHhYm39g5F2Kniq7im2dI9cT6SqQDjAYHRwxNXtn61/lk0dm8gwZZBmSDvinPohJ3rsD20nORa5tlwiSoTC9EI2d23WzUmYVDYuRf1f//pXVq5cOeJ9zz333Hi8BADNzc2UlJRgMqnr+xkMBiorK2lqii9GwuEw1157Lffeey9paUeeTwcQDAaHrQOQSob+X4ipY1vPNp7c+yRNniYqHBV8bfHXyLJk4Qv7UBSFLT1bmJc/D6PBSN9gH9m2bAC+f9r3WVa6jO2927Wi3m62q4sIRcNc+OSFNLub2dO3B0/Iw0uNL3H1368G1C+lQ8PixNht6d7Ca82vDbt9V98uTso9Sbuebcsecdjbr87+Vdz1EnsJpfZSTi0+lVWVqzAZTWSY1DndQ0X90AJeBoMBX9hHfnq+LJR3gD/sR1HUub7+iF9bIXh27mxtusOQmpwazp9+PpnmzLh58lfNvWrYytxH8tXFX2V23mztepY1ixnZMzAYDNgtdm5cfCMP7niQl5te5pSiU7QRFa6gizJ7GXUDx/8FLZU/+w/9cjs02qgmu4Z9rn1HXBhvIqUZ0yizl1GcWUyeLY88W96wxwydJBpanPJQ23q2sbt/8or6ycq+y99FVInS7e/WjpEMUwb7XPvGtJL6WRVnxW1td6ysadYx9dQvKlzEq82v4rA4qHJWsd+9H1/Yh8PsYE//Htyh1PpedzSH5h9TYgxGBjGnmUk3pROIBDhv2nnMyp1FpjmTbn/3pC2SdyTZ1mxyrDl8evqnWVK4BKfFSV56Ht96/Vt0+jrxhryseWEN3f5uGlwNSTEaZjwNTeU5/O90TInxl91/4a7Nd1GUUUSuLXfUhT1hcj/3M82ZXFxzMfPy52knyURymxqT8g5z2223cckllzB79uyjPnbdunVkZWVpl4qK45unkiiVlZWJboIYZ7v7dtPgamC/ez+VDjXfhQUL+fYb3+bJuifZ3rOd+fnzKcouotHVqPUAGwwGCtIL2NazTSvqizKL6PJ10envpDCjkB+/82Mun3U5vrCPp+qeIi89j/7BfnVOYnjscyX1KhwNx21Ltq1n27C5coqiEIqG4r6MDvWaHO7wxYKumX8N1VnVZJgzuH2FOuc63ZSOw+Lg09M+TZ4tT+shy0jP0HrqpahX3f7u7fzbq/8GxPfU2y125uXNi1s07SsLv8KZ5WcO6wleWLBwTIUEMGzxqSxLVtyQ/aXFS1k9YzW7+3ZzavGpB4ffh9wsKVxC/UA9922577h6eVP1s79hoIF1767TrvcP9mtFfXVWdcLadXLRyRRlFFGSWTJiIVrXX8fMnJkjzgX2hX2T2os1Wdl3+juZlTMrblG8Unspm7s2a9tejeaS2ktOqKBMN6WPaZX0BfkLCEVD2C12Kh2VNHua8Ya9VDgq2NG7Q8smHAuzvefI6zClikPzf77hef5v+/+Ra8vV/l9fOONCPn/S5zEZTTR7mim1j+802GM11FMPcErxKdTm1PLZWZ/l0pmXsrt/N3v695Bjy+GhnQ/hDXl5v/P9hLZ3vA0V9YdP6XEH3VQ6K7l12a2cU3UOeba8o/bUT+bnvsFg4Ien/5DZebM5vfR03EE3v9v6uxFHMYnkMC5F/bRp05g+ffoRL+OloqKC9vZ2IhF1GLGiKDQ1NQ37R/7666/zP//zP1RXV7N8+XLcbjfV1dV0dw/fX/WWW27B5XJpl+bm5mGPSWYNDTLHZapp9bbSN9hHi7dF2xP7lOJT2Nu/l3ZfO+2+dkrtpaQF0qgbqIubq12QUcD23u0UZKhDgp0WJ56wupXMWRVn8dtP/pZPVH0CT9iD0WDk5MKT+aj7I/xhf8Lm3iW7Q3t57tlyD3/b9zft+q6+XVrh+Piex+nyd+EKuob1yv+/s/7fqKtwjybDnIHD4mDtvLU4LA7thE0wEMQVcqlz6mX/c0AdFmw1WfGFfXFz6gF++8nfxj22KLMIs9E8bLTEiVhUuIjLZl4Wd9t5087jF2f/gjRjmtZD7Qq6mJ03mzZvG13+Lrr8Xcf8Wqn62b+tdxuN7kbtenegm2xrNosLF3PB9AsS1q7blt2GJc1CuaOcLy/8ctx9/YP9DAQHyLHlUOWsGjZVyRP2MBAcmLS2Tlb23YFuFhcujvsbU2ov5YPOD8ZU1J+oj5V8jErn0YsYg8HAmjlrKLWXUuGsoMndhD/sp8JRQZ4tT/sM39O/h99v+/1EN3vCHZr/fvd+NrRvIMeWQ4Y5A7vZHjf6KBwLa9twJkqlo5LizGJA3UHk2gXXYk4zMzNnJvtc+9jVt4tzKs/BFXThDXnJS8+bMkO93+98X+vhDoQPTpP7e+Pfafe1k2fLY3rWdE4uOpnc9NyjzqlPxOf+yoqVfPOUb+IKuvjbvr8ddSV/kTjjUtQ/99xzPPvss3zhC1/gjDPO4MEHH+TBBx9kxYoV2vz38VBYWMiSJUt4+OGHAXj88ccpLy+npqYm7nHr169n//79NDY28uabb+J0OmlsbKSgYPg8SavVitPpjLukEqv16PPNRGpp8bbgCrro8ndpX5zm5M3hD5/6A/2D/QxGBskwZ5Cbnku9qz6ugCzMKKRvsI/CdLXwc5gdeEIeOv2dFGeof1QzzZnaoi2LChexuWsz/ogU9UfylX9+BX/YTzgW5rn653AH3fxp55/whryEYiEsaRYUReG3W3/Lzt6dtHpbKbOXxT3HSPPpxyrdlK718ttMNu5ccSegnrBp87aRl56X1NtvTRZFURfIKsoootPfqa1+P+TQnw810p69xyvHljOsl9dkNA1b48IdclPpqMQdcuMOubWC8FgKjlT97N/Zu1N7v5s6NlGQXoDJaKIgo4Bzq89NbOOO4BuvfoOB4ADZ1mxK7aXa1lxDJrunfrKy7/J3sbhocdzfmHJ7OR92fzjiui3j7WMlHxvzHODPnfQ55ubNpcJRQZOnSeupX1Cw4GBR37cnLqdUXR3/0Pxbva3s6N1BjjUHs9E8rFfeYXEM+3s02a6ef7W2w8uhpmdNp8HVwO5+dSSTK+giEAlQai9N+SkTe/v3srFjI0/sfYKGgQYsRktcT/3jex5na8/WuO8GRRlFR12rIhGf+waDgWxrNp6why5/F22+8d3VTIyfcSnq586dy9y5c3nxxRd56KGHWL58OcuXL+cPf/gDf/vb347+BMfg3nvv5d5772XmzJnccccdPPDAAwBcc801PPPMM+P6WqmguLg40U0Q42xozuZQ8T4kLz0v7ktIZX4lkVgkbgh3QXoB6aZ0bQiXw6IW9R2+Du1Mud1i14baz8ieQaOrEX/YL8Pvj6DN20bdQB27encxK3cWnrCHN1vf5B/7/0GZvQxLmoXNXZvJs+XR6G6kxdsyrl+iMs2ZcUP3h3r8i7OL2efad8Th999d/11tfrkeDM3NLswopNPXqc6pT/Bc0sMNrRjuCrqocFTgCrpwh9z0DfahKArPNzyvPfb3235Pu7f9iM+Vqp/9TZ4mijKKiCkxnmt4bliveLKJxCI0uhpxh9w4rU4KMwqHjazwhDyTWtRPRvbRWJQufxfLSpfxuZM+p91e5ijDE/KMaU59IqSb0glGgwxGBllUuIhVlatwB9UCcVffLu3vq6IoXPW3q1LuZLYr6KKo6OD/e3fITZWzStud4Op5V8c93mlxJryoP5L89Hy6/d20e9spzizWFvPNNGfiDw/ffSKV1A3Usa1nG76wj05/J7npuXEn37v8Xezo3UFu+sGi/utLvs70rNFHNyfqc99oMBKMBPGGvLR6WxPSBnF04zqn3uVy4fMdXETG5/Phco3vH7pZs2axYcMG9uzZw6ZNm5g/fz4Av/vd71i9evWwx1dXVzMwMDCubUgm+/fvT3QTxDg70lZB1jQr4ejBuZxhV3jYNmkFGQUUZRRpvY92ix1vyEuHr0P7EuawOOjyd5FhysBkNBFTYvgj/mPaf1gvwrEw3rCX3f276fB3MCt3Fu6gG1fIxSO7HmFe/jxybbm8sO8FvjD7C+x37x+xp/5EXFxzMaeXnj7s9rJYGSvKVlDhqIjbp35bzzY8IQ8b2jcM2+VgKtvv3k91VjVFGUV0+bsYjAyOaT5uIrhDbgoyCghFQ3iC6tDtUCxEt//gFLG6/jo6/B1HfI5U/OwfGk2Rn55Pb6AXV9A14qJ0yUJRFFxBF/3BfnoDvWRbs7V/X4cKRUOTuq7FZGT/H6//B9t6tuG0OFlUuEi7vSSzBGuadcQtOpPNosJFfKLqE1qvb6u3VSuidvbtpNnTnPS99ZFYhKfrntaur3tvHW/vfpu3294G1O8LNdk12miKT0//dNzvz8mbk/A59UdiMBhwh9ysrFwZN2Iq05yZ8p0M3rBXu3T6O9VtMg85UdEV6GJbz7a4nnqz0XzUkWOJ/NzvHexlZu7MUU82i8Qa16L+85//PKeddho/+clP+MlPfsKyZcvGdfi9GC49Pbl6osSJGYwMDls87VBhJUyaQe2pLcwsHDas25pm5cq5V2rXh+bUdwe6teGSFqOFbn83TuvB3l9/xD/ivsx61+Pv4ZSiU9jdt5sufxc12TV4Qh6saVZ29e1ifv58cmw5vNP+DsvLl6tbQHlatbUQxoPdYh/x38Tpxadzw6IbyDBnxBUUL+x7gT396g4HH3V/NG7tSHaN7kaqndVaT6qiKMe9jsFEiikxvGGvNpomqkS1aTX9wX5tEbaB4MCoBUcqfvb3B9VF8UoyS2jztRGOhTGnmY/+iwlgTjMTjoW1qQKN7kayrdnqSBB/J5FYhO+9+T3gyCdiJ8pEZH/4Xu6hWIhHzn9k2OMsaRaWFi8d12kr4y0/PV+b92tJsxCKhtQTSihaVv/c/09WVq5M+j3smzxNPLbnMe16l7+L5nAzX/3nV9navRWz0czqGauPuHXgzafePOp3ikT731X/y2dP+ixw8Diym+0j7jKRSrwhL96QF1/IR6evk1zbwZ56f9hPni2PuoG6Y56al8jPfVuajcWFi6WnPomNa1F/6623cscddzAwMMDAwAB33nknP/zhD8fzJcRh8vPHvufkeBkaIiVOXIevgxZPi3a9zdtGmb0Ma5p1xLlV/rBfOyM/vXj6iNukHbpQl91sxxPyEIlFtJW6h86OHzqk2x/2j2k/YL3p8HewuHAx7b52Ov2dTMuahjfsJcOUwbKyZVQ7q8m15RKNRXFanCgodAW6xrzP+YkYOvZtaba4nnpPyEOLp4XZubP5sOvDCW9Hsmj1qidTijOL6fR3xq3YnSwUFK77x3X0D/ZjNKh/frOt2QwEB7QvfEN7N7uCLgYGB474XIn47D9RQ59vpfbSpO/tSTel87N3f8ae/j3Y0mw0DDSQZc0iz5ZH72Avja5GtvZsTUjbJiL7y5+7nL/s/gugrtZtTbOSlz7yKIpfr/z1uL/+eKp0VA5bPLQ/2E+ONQdLmoVgNEiDq4HTSk5L+p76vf176Q4cHMHTG+jlHdc7XFx7MT955ycUZxZzeunpR8wq2R36PWfoMzvDPPLWkclmtFX6D++pz7Xlah0nXf4uFhYsJKpEj7moT+TnvtPqZE7eHD7q/ohfvv9LPCGPdlLsjZY3AIaN+Dx8UVExscZ9S7vzzz+fX/ziF/ziF7/gvPPOG++nF4dJxGr9N/zzhqQ/u50q3mp9ix+89QNt7vPQfOwcW86IhWGWJUv74+3t8vL1JV8f9fkdFseIw+pNRlPcH9OYEsNkMHHne3dKtqhb1/nDfjp9nRRnFmPAoP0cU2IYMHD3qrtJM6aRY8vR9io/o/QMnBbnpPQQDx37VpM1rqfeG/LS4Gpgdt7sUYdvTxVDx06Hr4PijGLy0vPihrEnEwWFuv46rXBXUDAZTURiES3DoeNvIDhAf7D/iM+VrDu1HL6Ow9AWnaCeeCm1l1JqL0363p50Uzr/2P8PNndtZlrWNNp8bWSYMkgzpqEoCjv7dsbNo5/Mk0jjnb2iKNTm1GpDuhtdjUzLmnbE1Ka/ywABAABJREFUxyfjCJhDVTorh20N1uRuospZRZY1C1fQRTgWpjizOOn/3u3p30MkFtGOq1xbLpu6NvHxio9z3yfu44tzvpjgFo6fNEMaBoMhZXrq7/rgriPe5w158YQ8BCIBOn2dcQvadvm7qM2pJdOcecxFfSI/97OsWRRlFLGibAV7+vfwWvNrPFn3JAC/ev9XdPg6+OHb8R25333zuwloqX6Na1Hf2NjIDTfcwCc/+UlWrlypXcTEsdtH3/5iJENn1I5HJBZha89WNnZsjLt9Q9sG2Sv7OHjDXkLRENt71b1z27xtlNpLybXlatvSHSrHlqP9EXA6nHF7Yo/EYXHQE+gZNvzObraPeIb8/c73j2vP7KnmjdY3eGD7A+quAZnFlNnL2NW3S9tNwGFxaMNPKxwVnFx0MgCfn/15frb8Z5PSxqFj32w0E1UO7hvrCXlocDWQbc2e9GHBk80dcrPmhTVEY1FcQRdZ1izMRvOwfcSTRTga5szyMzlvmnrCO82Qpi3mF4gEyLHmaMOGjQajVvyP5Hg++yfDuvfWxRVK77S/o40YGfp8K81UV5BP5n+f6aZ0IrEI23q2MSN7BhmmjLgh5zv7duKwOLRtCk1G06T9uxvv7IPRIJmmTO1zpN5Vz4ysGeP6GpOp0lE5bGuw/e79VDorybJk0e3vxmK0kGfLS/qe+n2ufczPn48v7MMX9lGQXkCeNY/ZebPJtmUn7Xz545FlzdK25Ev2oj4cDdM7eOQTQt6wF3/YT6Y5E0/Yo/bUh/2EoiG6Al0UZhQyO3f2MU+NSOTnfpWjinJHOTedchNmg5lOfyf1A/UA9AR62Nm7c9hJsmZPc9xaUGJijWtRf/nll5Odnc2NN97IN7/5Te0iJs7xbMH3q/ePf1/m+oF6ziw/k3c73o27/bmG52jyyDCbY+UOuflYyce0D8ZWbyvldnUI8Uh7y+bacrWe+rFkn25KZ797/7A9he0Wuzb8Pt2crp5BNkCnv5M9/XuA4T1ueuIOutncuZlWbytFGUXMyp1FVIliMBgYjA7GnRCZmzeXL8z+wqS3MS7/Q6LyhX3sc+0jy5qFNc06pbe7e6ftHRQUXtj3AnBwezqbKX5KQrKwmqzUZNdw4+IbAfVL7NBxOBgdpMxeRu9gL9FYlLz0vFGL+mTdfrXd2x7XC9872KvNSx8afl+QUUCDqwG7JTlPTABkmDJYUbaCXX27mJE9g2xrtnafJc3Cpo5NzM2biyvowmgwaj3Ak2G8s/eFfWrP9oHPkYaBhlF76pNdhaOChYULtetGg5EGVwNVziqyrdl81P0Rlc5Kcm25oxZmySAUDVFmL6Mn0EO3v5uCjAK+Pv/rY97qL5U4LU7sFnvc8PtWbyuvNL2S4JYNd7RtgL0hL0aDUfvcyLXl8nrL69z+7u20e9spyijiN+f85phfN5Gf+zcsuiFuEeBOXyf73fsJR9W1R3b07Yg7STa00GiyH2NTybgW9YODg6xbt47Vq1dz/vnnaxcxcdrajm2/yFA0RKOrUetdOJI/7fwTdf11w27f2rOVc6vOpcMXP6zXF/Yl7ZDXZPLnXX+O+0PgDXlZULCAfa59wMHhqZ+p+Qxnlp857Pfn58+nJqsGGFv2BoOB/mD/sKI+05ypFaYOszpEP9OcycKChezp30OTu4kfvPWD436fqW5oxfsGVwMVjgpm5czSpkPYzfa4RQYT5dD8Dx36a7fYafY0k23NpsxeNmxP7ankzdY3uX357by0/6W4Xt+Ti04mCafUk56WHlcsOS1OnFYnBoMBX9hHuaOcnkAPnpCHMnvZqF8aj/Wzf7K4Q27afQfny/cGehkYHKDF00Kzt5nCjEJMRpO273uyKs4s5qKaiwjHwszImhF3Iu+7H/suv171a3JtubR6W7Fb7GRbs0c9CTOexjv7oR7FIS2eFiocFeP6GpMpw5zBDQtv0K7X5tTy4r4XqXBUkGXNYlPnJqZnTSc3PTfpe+oNGLSF/4YWvK1RahLdrAnhtDqxm+1xw++3dG/h5aaXE9yy4QKRAO6Q+4idHzElRjAa1LYSzrPl8WHXh7zb/i6bOjcxL3/ecW25miyf+1nWLOoG6rCb7XQFurCmWdnesz2ugPeGvUSVqBT1k2hci/p58+bR1CS9tZPpWM/a9Q32EVEiR+1R+LDrQ7b0bBl2+57+PczKnUWaIS3uw8wf9g/b5kcMt7FjY9zJD0/Iw9y8udooh2A0iM1kw2gwjri68KqqVdTkqH/Qx5p9hilD+8MyxGF2kGU5UNRbHJjTzNjNdhYVLqI/2M8bLW/wTvs7Rz35M1V5Qh4WFy7m/GnnYzAYqMmp4eLaiwH1/9fQ/7tEOjR/BYU2bxvrW9ZjTbOSbkrXivpkn7t8vG59+1ZsJhvVWdWEo+G4/X6XFi8l3Zx8q8Nn27KZkX1wWPNQT73T4qTL30W5o1wtgsdQ8CZrT7037I076dsb6KU/2M8d791Bni0Pk9EEqAsEJnNRf3HtxZxZfibZ1mxqsmviivr89HwKMwrJtmbT4mnBbrYPO4E2tH3fRBiP7Nu8bTQMqGsd+CI+MswZ2n2+iC+pR1Ecqy8v+DIX1Vykncx+p/0dzq44G2ualVA0lOjmHVVeeh49gR66/OoirMl67J+oLIv6eXjo8PtmTzMNAw20e9uTahi3P+xX10IZZURYJBbBaXHiMDu0EZafqPoE1c7q496RIFmyL8gooN3XzrSsaWzt3srMnJns6N2Bw+zQcnIFXViMFvoCyX3ibCoZ16K+u7ubhQsX8ulPf5pLLrlEu4iJk5GRcfQHHaIn0IMBw4hnpw/98t/ma9OGYR+q09dJUWaRuv/5IfuI+iNS1I+FO+TWhqKC+gU415ZLMBrUtuEaq7Fm77A4hhX1Z1WcRYm9BFB7dtNN6TgtTqocVUzLmsajux9lVeUq9vbvHXN7phJPyMNNJ9/EZ2o+A6hbBa6esRpQe1eToRg5NH+nxcn6lvW83vI6Bgzk2nLJtmVT5iiL211hKukOdPPdj6mL8JxSfArFGQf/jddm1/LvS/49UU07opuX3hw3B/bQor7T38n0rOm0eFriivojnZQ51s/+yRJTYnE99QaDQTuJfOh6E6X20hF370g2Ndk1lDvK+dbSbw27L8eWQ4u3hUxzJmX2Mlq8B4+1t9re4tJnLx02qm08jEf29225j9s23EYoGtKG3xsMhil5ItecZuYri74CqEPz/3Xev464u0yy0nrqDwy/T9Zj/0Rl2bJwWBxxRX2TuwmjwcidG+9Mqi1ah6a1jTaaKtOcqU51tDrJteVyUu5J/Ou8f+X6hdcf9+smS/b56flkmjOZlTuLN1re4KTck+gd7GVW7iytvnAFXVRlVSVtT/2Gtg1T7vNuXIv6NWvW8F//9V9cccUVXHTRRdpFTJyOjmP7wtAT6KHKWRVX1O/u200kFuHqv1+tFZUOi2PEYiCqRDEbzdpe0EPCsbAU9WPgCXniivqhvbSNBiPfffO73HzqzWN+rrFmb7fYhw2/P7f6XG24pdPiJMOUwSerP8kpxafwjSXf4EvzvsTysuVs6tw05vZMJZ6QR11oboTREg6LIym+EB6af0lmCe+0v8M+1z5sJpta1B/oqT+00JgK+gb71HnMh/z5unD6hXyy+pPadYPBwPTs6Ylo3qgOXzV8SeESFhcuxmlVe+rtZjsKCr2DvWRZswhEA3zuuc+NuLXdsX72T5bCjMK4QjbNkDbiF6dSe2lSnBw7mt+c8xsMBsOIw9GzrFk0DDSQY8sZNiqm299Nub18QgqRE83eFXTRN9jHJ6o+wTvt76hFvSkTu9lOp6+TTFPm0Z8kRdXm1HLN/GvG9NihRSsTJRwLYzKayE/PpzvQrS2wlqzH/olaWbGSC2ZcoG7TemDh5YHgADNzZ/J229tJNerMH/FjNppHLertFru2KHFeeh63nXEb2bbsY17x/lDJkn1BegGFGYUsLlzMa82vcVLuSaQZ0qjJrtHqi4HgANOzpiftFJe7PrgLd9Cd6GaMK9N4PtlVV10FQDAYxGqVPa8nQ07O2Ho6dvftxm6x0xPoYWbOTHoHe7lvy31UOCp4qu4pvrb4a7R6W9nv3k9uei5Z1izcQXW+kMGgbud1aNFemF5Ip79TG0qaYzu4anNMiWl7MIt47pB7xKkPPzr9R2Rbs7WhqWMx1uxXVqwkP+PIi+rYzerCNCflnqTddkntJTS7m3mr7a0xt2eq2O/ejzfsPeLw05k5M5NixeFD8y/OLObPu/9Mri2XKmcVMSWm7cnc7D76FjivNb/G2RVnT1xjx9Hf9v2NZk9z3OiToswiiiga5beSkzaVpt3J1u6t2Ew2ZuXO4rXm11hRtoJ/X/LvtPvaeab+Ga6ce2Xc7471+J9MMSWGJc2irQIfjamLS7qD7mGjhT5V/amkODl2NKPNe822ZvNay2t8YfYXKLWX0u49OELBFXRRk10zIYXhiWa/o3cHS4uXMjtvNpu7NlNuLyfDnIHD4mBn307KHcMXaZ2qLGkWQtGQNhzaE/JgN9sJRAL8ZMNP+O+V/52wtgUiAdJN6RRnFtPp7yQai1KQXoApZ1y/uieNw4ekb+zYiIJCTXYNZ5afeUzrwzy590nOrT43blrJePKH/RRlFI1a1DssDuxmO5+e9mmMBuNRdysai2T53C/IUIv6amc15jQzs3JnUZBRQH56flxP/fSs6cNWxK8fqI+bhjbRHtj2ABfOuDBucUlFUdjv3k9fsI9sW/aktWWijWvltXXrVubNm8eMGWpY77//Pt/+9rfH8yXEYcxm87Dbdvftxh/2a9cjsQivt7zOi/te1Ir615tfZ3ffbt5ue5tdfbvY1rONkswSNndtpsXTQrm9nNqcWq2n9pXmV/jTrj9pH7qH99SbDCaiSpRmT/OwBdaGtjPSo59v/HncdV/YN2JRn5+ef0wFPYyc/UjWzFmD2XjkxzosDjJMw//wFWUW0enrPKY2pbpgNMgP3voB0Vj0iHlcVHNRUqwMfWj+JZklmI1mcm25OCwO7jjzDjLMGZiM6nF5tCFm695dN9HNPS6Hr0EB0D/Yz7P1z1LprExQq8bf0Jz6dFM6y0qX4Qv7WFa6jLn5czm74mw2tG/AE/LEFYhjPf4n09CCa0OLFg5NI4gpsWFF/YzsGSm/gvfQSIOTck+KO5kB4Aq5qM2pnZARbCeafYNLXd2+JruGvf17teH3Q0X9oStcT3W5tvjF8ta9u45GdyOekCfhO2gMRgaxmWw4LOpitoFIgAxzRlIe++OtK9DFD976AXaznUtrL+XbS7894qizVm8r0Vh02O3rW9dPaA+xP+KnKLMId0jt6d3eu10rXvf079HWKcq0ZHLV3KvG7XWTJfuZOTO5uOZiDAYDHyv5GKWZpVw287K4xSeHeup7B3vjduFZ9+66SV0f4Y2WN3i67mnt+vbe7bzb8S6esGfKzfcf16L+a1/7Gvfccw8FBeoq0UuWLOH5558fz5cQh+nqGv6F4e6P7mZX3y4A9vbv5Wfv/ozeQC8fdH2gFfUvN73M52d/nrr+OqJKlO2921lVuYqPuj9Si3pHOV+a+yV+t/V3RGNRtnRvYXPXZm0Y9+FFPai9NH9v/HvcfEpFUfiP1/9DV9ujPd/wPJs61JMh/9j/D+12RVFIN6XHDb8/ESNlfzyOVNQf/iVVDzwhT8qsFn9o/iWZJVQ7qynJLFEXPjzkJE6ls5JmTzNvt7494jDu3kAvbb62pFwwakPbBrZ0qwt2tnhaUBSFvsE+Ms2ZVDqmTlGfZc2i099Juimdk4tO5pdn/1IbKWIymkg3pXP/1vt5vuHg39PxOv7H01AvZ5ohjXBM3cc5Pz2fbFv2sClAU0Feeh7Ly5Zr0yoUDi6ONxAcoDa7dkJ66k80+32ufUzPmk6WNQtPyKMV9U6Lk/c739dGkOjB4dva7Xfvp2+wD2/Yqw0BT5ShnvrDJeOxP96CkSDfXvptbl9+OzaTjYL0gmHfnSKxCDf88wb2Dgxf+8cdck/oXveH99Q/tucxbnrtJjwhD5977nPqKv4Hht+Pp2TJPtOcyaLCRQCsW76OgowCrltwHXm2PO3khivootxRznvt7/HTd36q/a475MYf8Y/0tBMi3ZTOex3vadff73ifX276JdXOavqDk7NjyWQZ16Le6/WyfPly7brBYMBiOb4VHsXY5OXlxV2PKTE2dW7SttbZ2LGRVk8rPYEeApEADa4GanNqURSFBfkLKLGXsLxsOdt7tzMnbw69g730DfaRZ8sj25bNp6o/xX1b76M/2M+snFlab0tRRhFd/i7ebn2bvf17STOksapyFY/ufjRuCFW7r51Of6d2NlMPOnwdDAQHCEQCdPg6tGLJH/FTZi/DFVJ76mNKDIZP2R6zw7M/XtOyprGycuUx/976lvVxI0KmAnfITXegm7CS/CczDs2/zFHGpTMvVYt6syPucXPz5rKtZxtP1D3BE3VPDHue3f27MWCIW/gyWQQiAVq8LYSjYa7++9X86oNfMRAc4Jr518RNF0l1TouTgeAANpNtxPsXFy7mkV2PxA1jHK/j/0QdOhLLE/bgsDjITc+lf7Cff+7/JycXnUyONWdYT/1UkJ+ezx0r7tCuH9rr6wq6qHBWjOve9Q/veBg49ux39e2KO1nZ4evQ8jAZTbhDbq2nflvPNmblzBq3Nie7Q4sQRVHY595H/2A/npAnrnfxtebXeHLvkxPenj39e7j46YvZ1LFJ66kH9fv00NalyXLsT6Tp2dM5vfR07YTZoevbRGIRmt3N/HP/P7GmWenwdRBTYrzY+KL2GE/IM6GF41BPvSvoIhwN0z/YT6Wzkm0921hcuJiZOTOZnz9/3EeUJWP2h64VU5xZTJtP/axxhVxkWbP4x7/8I+5z0B1yxx1bEymmxEgzpGExHqxLBoID1A3UsaRoifTUj8ZkMhEOh7WDr7m5mbS0tKP8lhhPe/v3kmfLoy/Yx3fWf4fXW14nHAsTjAa5cPqFXD7rcooyi7hgxgWY08x8svqTXDj9QuoH6inIKABF/TIyNKzwMzWfQVEUyu3lzC+YT0mmumJ6fkY+77a/yzMNz/DCvhfIMGdw6cxL+esFf8VkODhseUfvDnJtubpaRM8b9hKKhrQvd0P/dQfdVDgOfsnzhX3jfhb3eGRZs1hStGTE+8xG8xF7cF/a/xKt3lZ29u4EIBxV/52lMk/IQ0yJ4QtN3Bn+iZBuSmf1jNWU2ktxWuO3vKnNqaVuoA5f2MfbbW8P+909fXuozalNyvc8GB2k1dtKk6eJT1Z/koaBBsLRsPY5NlUMZXak+dvLSpdx3rTzRuz1TeTqvdFYlG+/cXCKnTfkxWFxkGfLo9XbypbuLZxRegZl9jKqnFUJa+dEOnT9mGpnNY3uRgBC0RDWtPFdW2ikk3JHMxgZ5O22t7URLxC/7k21s5odvTvIMGfgtDgpd5Qf8eTSVHT4cGGjwXiwp/6Q4ffP1j/Lk3UTX9TX9ddR5ayi1dsa11NfmF6I05Ic25lNhl+c9Ythn4cmg4lwLMzbbW/zqw9+xdaerVww/QI6fZ30D/bzf9v+T3usOzg5PfV/2vUn/rD9D0RjUaqcVXzQ9QGrqlaxZs4alpctZ3pW8i3YOpFKMw+uLdI32EeONWfYArGuoGvSOoT6BvvIS8/DnHbwu6w75OaKWVdweunp9AWlqD+iG2+8kc985jN0d3fz/e9/nxUrVsic+gnW23uw56Y30Mt/bvxPPn/S5+kf7Gdn7076B/u1Vc4vrr2YT1V/CrPRzI9O/xGgLlb0sZKPAepqlqD+YRtawMhgMPCVRV/h+6d9nyvnXMmqylWAWuw9e/GzfGXhV9jv3q8N3x5acGJoCOKO3h2cVX7WsHmxU5k35CUYDdIb6MVitGhD+9whN7m2XO2DZWio6vE6NPuJMtq8em/Iizvk5ifv/ARQi/xHdz064W0aD4efJe4N9BKNRXEH3ZTby1Nij+aR8v9MzWc4p+qcuNsqHZXsd+/HaDBSkF4wbNRM3UAdCwsW4ouM/gWow9ehjQCaLIORQXU/bVfDlP5yNPRl/UjF1IzsGfzo9B/FZdfb28uO3h389weJW8ir3ddOu68db0gd5eENe7Gb7eSl5/F+5/vMyZuDwWDgugXXTerCSIlS7axmv3v/hDy3oig0u/8/e/cdX1dd/w/8de7eK8nNnk3S3YbSQgdlT8GKv7L0y6hQKFVERQURkKG2DEUZCgVrFapYoIAMBVRkWaAtXXQnafa6Se7N3fue3x+XnPY2yU3a3ptzbz7v5+ORB737k7w49973+aw28Dw/5vf+Dk8HfrX1V3CH3MLn0JG9v0D8pN/O3p3QyuI99VMtU9PS/kx1ZE99i6sFdXl1cAQc8IQSh9+HYiEYlcaUnkTb1rNNGOnC8zwe2vwQ7AE7qoxVwmi/wcK2UFcY73jB+Hz2i224nWcKdYXo9nbjv23/RY+3B23uNswtmIseXw/6/H0Jc+7TPvw+4kOBpgCdnk5hVf4ibRG2dm9N2F411TI9e6lEihjix4g75Ba+SymkCgSjQURjUXjCnnEbft/t7Ua+Nh8GhUH4/BwIDuB7c76Hmbkzx/07TbqlfEu7u+66C9/85jcRCoWwfv16XHHFFal8CXIUq9Uq/LthoAGnl5yOhUUL0eXtQrWpGs9/5XnoFLqkPagyiQy5qlxh0SJXyDXsqsQKqWLIGbdcdS5aXC3CiQMA0Mg0QtHU7GrGqYWnosfHzoJr3rAX4Vg4/uFsqhKG9wz+XQORAO753z1whVzQK/SjPNvIjsw+XQq1hcJQqqO5w/Ht+ZqcTQAAm8827Ny2TLTy3ysT1nl4bNtj+KzrM7hDbky2TD6hXMbLcPkrpIohiyJq5Bo4g06opCoU6YrQ4U7cFmggOIASfYlQmI3k3eZ3sc227cQbfgwCkQDCsTAODcQX9pJwEkT5oYsiZTujIv5+q5aOvNL6kcNvASAnNwdd3q6EHtjx1uxqhllpRqu7FaFoSHhPy1XnYodth7AN3HBf0CeiCmMFmp3NACAsFqiQKnDAfmDIfbf1bDumNUv8ET8C0QDcYfeY3/sHAgPCUPLBwnVwnYNBNeYaYfj9lJwpuHrq1WNu00Rw5JSJFlcL6qx1GAgOwB12D/nepJFphu1hfGbXM8f12i2uFjQONAKI5/tB+wfoD/RjkmnS0KJeWyh0vIzHZ38mKtIWocPTgQ53B/I0efBH/CjRlaDb241+fz8GggPwhr3x0XZhL3xhHzwhDx7c/ODoTz5Gr9a/iv32/fBH/Cg3lOOHc38onDAr1hdjV++utE41yobslVIlOj2dCVv3DW75ObgGwWg99W3utjHt3DOcaCyKz7o+A/DlVCNNAQwKA9rcbfj11l8jGAlCJVPBrDRn7HZ7xytlRX00GsW0adOwcOFCPPTQQ3j44YcT5teT9AiHD38pcIfi8xnNKjMO2A/AqrFCKVUiX5M/apFSa6mN9xZx8WFLYy1qNHINHAEH1PLDX0aNSqOw+EQ4GkaJvgS9foZ66sOHe+prTDXCyp+DX3idISf+1/E/fNr5KabmHH+vyJHZp8sZJWfg2V3PCgus7e7bLdzmCXnQ6emEJxxfldfms+HQwKG0tykVmpxNGAgOYFfvLqzfux4trhZ82v0pXCEXJlsmZ8Uwx2PJX8JJUKwrRomuZNiFAHVy3ahnzp0hJ4KR8Z1eEYlFIJPI0OhsRJWpCnnqPEi5iTelSy6VQy/XH9MOGHdtvQt9vj7UD9SP2xB8nucTRrk0O5uxuGQx/rrvr3hyx5PwhL7sqVflYEfvjgm1Q8FYlOpL0eZuSzhheH75+bj9w9sTpjHxPI+ffvxTNDgaxvzcg2te2P32MR/7zqATzpAT7pD78HQwvz3hy3alsRIyTiYMvz+Rz6RslKPOEYqynb07sbh4cXz4fciTeNKFj3/fGe598pX6V45r7YRQNHQ414AdPd54j/NgUR+IBKCSxkdVnFt+Lr5e/XUA4/PZn4mK9cXY3rMdRboiVBgqhMUdXSEX+gP9KNOXod3dDm/YC4vKAm/Yi15/L95vez9lbfi853O8fPBl+MI+5Gny8H9T/w+BSAByqRzFumKEYiFhmmo6ZEP2Zfoy/L3h75hjPTyts1hXjA53B1whF9Qy9ajfN7Z2b8WWni3H9fpd3i6s/WKt8O8CbQH0Cj3qHfX4ou8L4X4auWbcv9OkW8qKeqlUiry8PPh86R1SUV9fj4ULF6K2thbz5s3Dnj17hr3f2rVrUVNTg0mTJuHGG2/MigPheDgch4eOuENuGBQGaOVaNLuaExa1G23roCfOfgIcx0Ev18MVch3TPvNauTZh9XSTypSwSqlVPXSl/InMG/YKc+przDV4u+ltPLbtMSGfm2bdhG/N+Bae2/scTik45bhf58js06XCWIHLai/De23voc/fhzs+vEO4zRP2CENNHQEH+vx9UMlUos7xHU00FkUkFoEj4EC7ux2t7lZs7dkKjVyDekc93CE3Tik4BddOu3b0JxPZseRfYahAka4IRboiYYhiMBoUzpZr5dpRe+qdQacoayZcVHkRgPgQ9SJdEcyqzNinN9Xytfmj9mhLOSmisSiisSh2O3aj19+LScZJaHMfX4/GsWr3tOPRrY8Kl5tdzTi95HS8degt2Hw24cRyjjonvlDclz31rFBIFXAEHbj2n9dCKYvPp7+w8kIsLl6cMDR4Z+9OqKQqYZeasfCEPJByUtgD9jEf+66QC66gC76wTxhmag8kFvVKqRJTLFOO6TN/ItHJdcJ6Ip2eTtSaa+EL+4TpcTE+JnymaWXaYYd0e8Ne7LfvxwH7gWMqIEOxENwhN9Z+sRZd3i6EYiE0O5tRYaiAM+hM6KlXSpXCUObx+OzPRMW6Yvy37b+YbJmMSaZJqDJWCe+Zff4+zM6bjXZPO1whFwq0BfBF4v/fd3g6UtYjOxAcQJu7Dc6QU/jeG4qGYFFZkKPKgVlpHnaka6pkQ/ZTLFOwqXMTTis+3LFbbijHfZvuwz+b/olCbeGoPfW+iA/esBevN75+zNvftXvahY7Ebm83CrWFMCgMaHI2CfP9J6qUvotXV1dj0aJFWLVqFR5//HHhJ5VWrFiBm266CQcPHsQdd9yBZcuWDblPU1MT7rnnHnz00UdoaGhAT08Pnnnm+IZHZbqCggJh8avBL1Qcx0EtVcOqiQ/TydfmI0eVfMXMwR4io9J4zCvV52nyEobfm5QmOAPxs9Y8eOSqcxMWeGp3t0/ordIGF8rrD/SjxlyDTZ2bYPPZhBEQ55Wfh7n5c5GnyTuhN/+CgvFZTXph8UJ83PExDtoPotPTKfRIhKIhtLnboJAo4Ag44I/4UWOuQbt76F6ymWLNrjX4uONjqGQqtHva41s99myDSWmCWqZGj68HZpU5K3oYjyX/kwtOxhTLFBTrioWe+pcPvoy1u9dCLVNDJ9eNuvq9K+gal6I+xsewrefwMP9Lqi7Bw6c/DCD+pW6iFvVj2aLPrDLDEfxyZe6YHw0DDVhcslhYrDLdfGFfwgnbHm8PTik4BVNzpsIT8sDms8GqsSJXnQuVVCUMF2bJny78E75W/TVhsVngy20ljxhK+knXJ1g+a3nSov7onl932I0iXREcAUfSYz8UDcHms4HneTiDTuHzPMpH8U7zO+gP9CNHnfh94KlznzqWX3FCGSwKOz2dKNAWCJc9YQ9yVDl4p/kdvN74OoCRe+qjfBT77fvR4mpBvaN+zEVIOBaGJ+zBJ12fYG//XpiVZth8NqhkKoSjYQSigWHX2Rivz/5MU6wrxj77Pkw2T8bZZWfjisnx6b0cONh8NszOmx3vDQ7Gi3pv2AtHwIEplinY27834bnCsfCw02KG0+XpwhPbnxAun1Z8Gj7v+RxyaXyqW74m/h2b4zhcP+P6tE43yobsL6y8EM9/5fmExWznF87HI2c8gk+7PhVOuCTjDXvhCXvwr+Z/CTtGjcWevj3ocHckFPX52vho5WZXM3r9vcKcfwC4bvp1x/jbZbaUFvWxWAx1dXWor6/H9u3bsX37duzYsSNlz2+z2bB161ZcfXV8ztfSpUvR1taGhobEIWwvv/wylixZgoKC+Bv0zTffjBdeeCFl7cgkXq8Xd318V/zM8pfbCQHxL3+D+wKfVnya8OY3GqPSCI186J7lyeSqcxN76pUmOIIOhGNhyCQyyKVyaGQavFr/Kniexw8/+CEO2A+g29uNekd2zME+Fr6wD6FYCHa/HTWmGuRr84UvWoML3Uy2TMYvF/3yxF4nzaNiBhkUBoRjYWzv3Y7FJYuFIfh6hR6trlaUG8uF4Ytl+rJx6zU8Hp2eTmyzbcOM3Blod7ejz9+HYl0xKgwVqDZVY4dtR1YMvQeOLf8LKy5EnbUO+dp8dHu7AcSHEX7U/hGsGiu0cu2oZ86dofHpqXeH3Fi9efWwt83Om42FRQvT3gYxPHrmo6PeJ0eVgz5/nzC9aXffbpxWfBr22veO8sgTt273OmEa0ZHMKjP+eMEfAcR7y/LUedDINJhsmczMXPqj/b+a/4db59wqXC7Vl6LV3Sr87fr9/ZhjnZPQe3+kcCyM7//3+wnXeUIelBnK0B/oH/HY7/B0YOW/V+J3O36Hlf9eOWTrqAc3P4h9/fuGnOQfXOCWZfvt+zE9d7pw2RPyIFeTi3Z3OxocDVDL1EPeJ/9x6B/o8HRgimUK9tv3wxlywhfx4def/zrhJM5Bx0F83PHxkNcMReM99a6gC/vs+zDFMiXh+9dI+9SP12d/ptEr9DAqjag110Ir1wqjUWsttfio4yPMts5Ghyc+xLtQWwhv2At70I4FRQuGnEBrcbZg/b71Y3rdDQc2oN4Rn+bEgcNFlRcl7PJUoC0QRr8sm7EsNb/sCLI1e47jMMk0Cbv7dseL+lG+bwyuieAOu49ppfyff/pzbOnZArPSjGA0iGA0CLVMDYMy3lNfpi9L2JVkbsHc4/6dMlFKi/pHHnkE69atS/h5+OGHU/b8bW1tKCwshEwWP5g4jkNZWRlaW1sT7tfa2ory8sPb51RUVAy5z6BgMAiXy5Xwky0+bP8Q/+v4H/r8fWhxtQg99UD8i9ZgT71Sqhxz75ZJaRIWbRqrPHVewgeRSRkffj8QGBBe95en/RL/avkXXq5/GQOBAdh8Ntzx4R34zee/OabXygYcxyEYDcIf8cOqseLh0x8GBw7dvm5hVVQJJ0G1ufqEXmc8/1+9uOpiPLfnOVxWexk+6/oMoWgIOaocdHo7UWmoFIZ2WjWZPdWiz9+HXb27hGF6ff4+nFN+DmrMNagx1+Cg42BWLJIHHF/+cokcnrAHH7Z/iEAkIPw/qpVr0evvTbp7gTPoTFgNOl38ET8OOg4O+0FeqCsUduuYaI5ehHQ4M3Jn4H8d/8NAcACFykJEYhFUm6rRNNCU9HExPoY/fPGHE2rfe63vxYv64ND/71QyFThwCMfCkEvl4DgOa85bc0Kvl80knCTh5GCZoQz/bPonbvvvbQDiQ3gHPxujsaELPzoCjiHvo+6wG2X6MjgCjhGP/Q/aPsB106/D/Qvvh0KqgD1gh0lpQpSPIhANwBV0YWvP1lFH7rHIHrALfxcJJ4E9GL9s89nQ4GyAXqGPzwU+4n3pf53/w/7+/SjUFgrFuTfshSsYn+PN8zy2dG/B3v69eOCTB+AL+/DQ5oeENRdC0RC8YS8GggPY178PU3OmJoyiGKmoz6bvqal208ybhuxOM79wPnq8PZhkmoQubxfcIbdQODoCDlSbqoescm7z28a8Ov4++774rhP++CiXXHUu7l14r3B7qaE0rYvjHSmbszcqjdAr9CjQjN5T7wv74Al74A65j2ml/P5AP/7T8h/UWesSdt3SK/To9HRiRu6MrOm4OR4pLerPP//8MV2XSVavXg2j0Sj8lJZmzxzAHHUO/tr1V8yxzkGzqzmhqP9a9deEnvpjYVQaE4YNjsVky2ThBAIQP+vvCDgS5u4Nbo3X6mrFD+b+AN3ebhiUhgm56JVSqhQWReI4DidZTwKP+AJTxzoKIpmioqKUPddozi07F4tLFmNR0SL4Ij68fPBlWDVWcOBQZapCm7sNWrk27UX949seP6HC0hv2Ym//XtSYatDr64U75MYNM27AWaVnodpUDYVUkfK9pdPlePN/9MxHUe+ox5JJSzDVMhX5mnzo5Drs6t2Fd1reGfFx7tDQ1aDTwRfxQcbJEhZlJHFnlZ6Fzd2b0eHpwLTcachV50ImkSHKR/HigRcRjoYRioawpTtxgSFX0IUP2j44odceXI17sLc5Eosk9MRzHJewM8GRU7JYV6ApwEHHQbhCLvA8j0AkALVMjRpTTcKOIS8ffBmdnk44Ao4hi8t6Qh6UG8phD9hHPPb7/H3C575VY0XjQCNK9CUIRUMwK804t/xcNAw0wKK2DPt4VkklUvT6e4XvKzPzZmJf/z5o5VrYfDY0DjRCr9BDK0+cU9/l7UKntxM6uQ4cOLhCLqEYcQQc8IQ9WLNzDZxBJ+YVzMM7ze9gw4ENwtSKcCwMT8iDaCyKJmcTplqmJqx3MFJRP56f/Znm2ulD17upy6tDqaEUcokcMT4mzKkfHH5fbigXVl0fZPPZhHVkWlwtwpTQGB9LmH/P8zwUEgWAeN6Di+ANbu8MAEtrluL0ktNT+4uOINuzrzJVjWn4vS/igzfkhSfkGXLy5ciFSIH4qMM3Gt8Az/MwK83QyDUoN5Sjy9slTC02KAzgwWNW3qxjrnGySUqK+lAoBJfLhWg0CrfbLfR4t7W1wetN3T6RpaWl6OrqQiQSARAPtrW1FWVliXMRy8rK0NJyeK/Y5ubmIfcZdOedd8LpdAo/bW2ZO3T4aNMs0xCLxXBx1cVodjYn7Ht+SdUlwnyfY2FUGI95nveSSUswLWeacDlfkw+bz4b+QH/CB9SM3Bn44dwfosJQgT39e2BVZ/7WHMcqHAtDI9MMWTNAK9cO2Rv9RI3nGVuZRIZfnfErSCVSXDvtWvyv83/QK/QwKAyoNFRiR+8O5GvzYdVY07rTwX77frhDboSj4WNaZGrQ4P/buepccByHCB8vTDiOQ5m+bNQFJTPJ8eZvVBpxw8wbcGHlhTi77GzUWmqhkWuwt39v0kVkpBLpuBT1/rAfJ1lPwq6+XQCbo7dHxHEc5ljnYGv3VpQry5Grif//WqQrEva47vB04Ndbf53wuD5/3zGvlXK0geAAfBFfwl6/R44AM6vMzC62NhqpRIpHz3wUs/JmJWwRWmetw3bbduHy3v69aHI2CTumHPll1hPyoFRfCnvAPuKxbw/YhZ7eAm0BDjgOoFhXDK1cizn5c3DVlKuglCoTpsuR+Bf+FleL8H1lQeECyDgZVDIVev29GAgMCEX9kcVIl6cLXd4uaOVa8IivYeCL+IS1J1whF+xBOwaCA7io8iI8t/c5yCQyYV/zUDQEe9COEn0JePA4yXqSMMdXI9eg398/7Jz6bO6tTQe5VI51F6wTLvf7+5GjyhEK9HL98EX94PH1zK5nhJ17dvXuStii0BP2CCMDur3dw/bISzjJuE01yvbsT84/GRXGijENv/dH/MMOv39i+xM45Dy801Kvrxf1A/XwhD2oNFbiByf/AHnqPOzp2yOs66JX6CGXyLGgcAHm5M/BRJWST+DVq1fDZDJh9+7dMBqNMJlMMJlMmDlzpjD/PRWsVivmzJmD9evj82A2btyIkpISVFcnDmNeunQpXn/9dXR3d4PneTz99NO46qqrhn1OpVIJg8GQ8JMtOI7D9yq+hwsqLkCTqwk8z49pCGcypfrShHllx2Nw39cjh7MdKU+dh129u1CoS9+2H2Lxhrwwq8xDip9cdW7Ke648nuQLm6VLka4IB+wH4kW90oBKYyW29WzD5TWXI0edk7Ao4kgGeyiO1eCb/D77Pryw/9jWyYjEIpByUhTrimFRWVBrrk1og1QixY/n/viY2ySWVOR/YeWFqDJWQafQIRQLgeO4YXcviMaiUMvU49ZTX2OumfCr1B6vSmMldth2oEhWhGXTlwEAVs5eiSunXAlXyAVHwIF99n0JC631B/qHLeo3dWwa8XWanE14u+lt4bI/4o/3PoY8h4eiHvH+nqPKSRixRRKdXnI6plqmYn//4ZORs/NmY4dth3DZHXKjx9cDe8AOq9oqDB91Bp1wh93I1+TDH/HD4/GgwdEwZDE9e8AOszJ+oqVQW4hgNAiLygKdQodvTPlGvEdTX8rsWgcjMSqNaHY2CyepplimYGrOVGHx1EpjJfQKPTQyDbxhL8LRMGJ8DM6QE12eLmjkGsglcvQH+hGIBOI99UEHnEEnHAEHBoIDqDXXIhKL4KzSs4S1FMKxMJxBJyqNlVBJVchV52J6Tvz7V446B4ech4btqRfrsz+TDZ6wz1Hn4IP2D4StGX1hH4xKIyKxSML9bT6b0PnS5m7DQHAA/2z6JxoHGhO+FwwEBoSe3cEdjMSU7dmvnL0SVcYq+CN+RGIRYeHefzb9M+EzKhKLgAcf76mPJHYO9/h60OHuEC77I350e7uF6RGXVl+KPE0edvXtEk7CGBQGWFQWlBpKx21UhRhSUtTfe++9iMViuOmmmxCLxYSfgYEB3HPPPal4CcGaNWuwZs0a1NbW4sEHH8S6dfGzc8uXL8frr8dXKK2qqsL999+PRYsWobq6Gnl5eVixYkVK25Ep5tfOh1FpPK4CaTilhlJcUnXJCT2HhJOABz9kP9xBZpUZ7Z72IXt5buvZlrLfQwzukBvvt78Pi8qSsCcxEC/qU713qVhTRWSSeA+GTq6DUWFEuaEcT579pDD87egPz+G83/b+mBepOZI/4oc35EXjQOMxD8MffMMv0ZXAorZgdt7sIT3z55SfM8KjM08q81dIFFBJVajLqxt26x93yI08dV5K59Tf9fFdwr9frX9VGDLuj/hRrCtGl7cra6ZCjKdKYyUanY2YUjoF8wvnA4h/kc1T58WL+qAD0yzTsLV7q/CYPn/fsHPhH9n6yIivc8BxAB+0x4fs8zyPYDQIe8AOo9IIb9grHE+DctRU1I9mSs4U7OnfA+7LIShmlTlh1wl32I0ebw/sfjtqLbXo9fdiS/cWXPbGZfhfx/+EHsPS0lK80/LOkCkqMT4mnNgf3MbJqDQKhQjHcWNakJE1RqURHZ4OYaSjVCLFE2c/AZVUBXfIjRJ9ibBdcJOzCfd9ch/6/f2oNFSi09sJrVwLs8qMHm8PpJwUgUggvvZByIWBwIBQGN406yZcXHWxsENMKBqCUqqEUWnEFMuUhE6Zy2ouQ4OjYdie+myaJjreSnQlUElVCSdDOI4Dj/iQ7fs23YdAJIBeX6/w/tXubocj6MCDmx/Ezt6dCSNkHEGHcLLHF/GldArl8ZgI2WtkGvjCPhxwHMDDW+Lrrr3T/E7CCU4g/vdWyVTwh/34y76/CNc7g070+HoS7tfj7UnoSJxsngyej49+AeLbjRbritP8m4kvpWPlnnrqKXz++ed4/vnnAQADAwPo6kptb8vkyZPxySef4ODBg9i6dStmzpwJAPjDH/6AJUuWCPe78cYb0djYiMbGRqxduxZy+bEPRc8GfX3xXlGFVIFANP2LWB2LVncrivVDDyIJJxlS5Mb4GB7e8jB+u+2349jC1PrV1l/hdzt+B5PSNGRLm3QU9YPZi6HcUA69Qo+LKi+CRq7BwuJjW5G8xdVyXFvf+SLx1VDrB+qHnDgZTV+gD7nqXKxevBoWlQVz8ufgvPLzjrkNmSKV+XMchzprfF5it7cbff6+hCkkzpATVo31mP/myXza9anw7529O/FR+0cA4hkPbq2pkg79Qsu6MkMZJJwEUU/iAmsGhQGuYLyn/qyys+LTF77U7++HVq5NOCnjC/vQ6modMj9xkM1rw0HHQQDx/bQHn8eqscIZcsa3Rjuip75MX4YqY1XKfs+JqMZUg529OxMW+uLACRn4wj70+HrgCDow2TwZvb54Ub98xnLs6d8jFJ19fX3whDxJ994eLOoNCkPC4p+Vxso0/XbZy6g0QiFRDFkjQiVTCWvFGBQGqOVq7LPvQ5+/D13eLky2TEaXpws6uQ5mlVnoadQr9PGiPuiKd3AE7FBIFfjqpK+i1lx7ePh9LL6/uUlpwu/O/V1Cm6pMVXj87Mchlwz97irmZ3+mKzeUY1HxIgDxv++Rn2MNjgZsrN8IV8iFKB+FTCKDP+KHP+JHn68PjoAD77W9J7zfAfFpRoM99d6wV/SpKxMhe7k03vnT5+vD1p6tiPEx9Pp78UXfFwn384Q8yNfkwxl04rFtjwlZDhb1vrAPr9a/KrxvHrldZ446B7856zcJQ+1/fWbitLSJKKVF/e9//3tcf/31uO+++wAA/f39+OY3v5nKlyBH8fvj87QnWyan9Av3idLJdTjoOIgi7fCLelg11oQid1PnJpxbfm5G73GeTIyPodfXi2XTlw27JsHpJafj4qqLU/qag9mLoUxfBq1ci6unDZ1eI+EkCb31be62IUOpW12twtZqx8If8cMT8qDN1TamEQGDj/ln0z/R54sX9YNfqA0KQ8ozGU+pzv/35/4eBZoCdHm7sPaLtXiv9T3hNmcwXtQf7/D7dbvXJVwOR8Ow+WzCXLlef6+wYJg/HF9QMhAZfo9m1imlSpTqSyENJ061MigNwvD7k6wnocV1eF2ZvkAfKo2VCcMb+/39CMVCCT3FR7L5bAhF41+KB08G2AN25Gvz4Qq6EuZvA8DiksW4oOKCVP6qE45CqoA37E1YqMmoNGIgOIBAJACdXId+fz/sATtqzfGe+n5/P04vOR1mpVko6v1+vzBaYhDP88IIAADI0+ShzBBfJ2S4EXPkMKPCOOzigSqZCgaFAacXn44qUxW0Mi0aHA0YCA7Ei3rzZDiCDmjkGlhUFqF32KA0wBlywhVyoURfImxBCRxewLDX14twNAyLypIwmuJIi0sWD9teMT/7M9255efi2mnxxfTK9GX4dt23AcRPnv2j6R84teBUYX49Bw5t7jZMy5mGQ85DmJE7A1aNFRJI8Lf9f8NFGy/CAfuBhJ56sRcAnUjZ2/w2WFQW1DvqoZPrEtZJ4sBBLpEjX5OPDk8H/BE/9vXvAxD/vm3z2bBuzzr8vfHv8EV84MCh19eb9L0um9ZMOl4pLeqfeeYZfPrpp8K89EmTJqG3N32LZhEIW/fNyMmsbRoKtAXQK/Qjzt372qSvCXu28zyPL/q+wKkFp0Ij1wzp5c4GzqATFpUF35jyDVw15SpE+WjC725UGlM+NPXIbRvH2+LixSNuyZejzknoQfqo/SO82/Juwn3sATuUsmMfWj24eNSRq2wP538d/xOKms1dm/FG4xtDehazXarzl0vkKNQVosvTBZvPlrCvsjPohFlpHna+/Wh4nseaXWsSekwGv+T2Bw4XJRqZBp6QB76ID2qZGjqFbtj5pAS485Q7UVWR2CtuUMSLenvADqvGmvD37vf3x4v6I4bg9wf6IeEkI66BYfPZcGrhqWhxtggnjO0BO/I1+XCFXOj391OxeBxqzDUJf7ciXRH+su8vWLd7nTB1zRFwoMYc36GjP9CPXE0uHjr9IWF4dllZGTzhxJ56V8gFg/Lwd4DBxU1PLTwVV00Zfk0hEmdUGoW1CI6kksaL+oXFC1GgLYBGrkEoFoIv7EOvrxdlhjLIJDJo5VpYVBYYFAZE+SiknBQ8H184r8JQkTDiSMJJcO20a/HI1kcQiUVgVpmPeXFiMT/7M52EkwjHyV3z78KsvFnxG7j4CMFZebPgDrnBgYNWrsUB+wHMyJ2BxoFGLChagO/M/g4AoNPbicUli/FZ12fxk3BcfM0ksYffT6Ts+3x9uLDiQmy3bYdcIkc0Fk34jqGVa5GvjRf1s3Jn4fOezwHEd9jq9fVip20ndHId/BE/yg3lOOg4mHCimUUpLeqVSiXU6sQvYYN7ypP06O6O93bOzJ15+M0rAxRoCzAjZ8aIt18x+QrIJDJhGE6/vx95mjxhIZps4wg4YFFZIOEkUEqVCEQDaS9IBrMXw8LihSMOtT16Wzt7wC4M4z2SUqo85p7fcDQcPyEwylzrNbvW4KUDLwEAPur4SNja5sjVurNdOvIv1hajw9OBYDQIm88mfMA6Q84xffEcbrTQ4ImYA/YDwnWOgAMyTgZ7wA5fOF7EV5ur0eRsim/9KNMgR5VDRf0IFhUvGpL/YFE/OAdUKVUKO264gvEewyN76vv8fag0VCb09h7JF/FhRu4MHHIeQiAaEPIaLOqHWxeFjG6qZSpMKpNwuUhbhDcPvSnMER3c/rREX4JObycisUh81eaiBQAAtUyNls4W+MI+YS/0f7X8a8jChQBoN4IxMiqNw342qGSqhKkLGpkGHDjka/PR5+9DnjovPtdeFp9Tb1AYEIqFhBEVrpAL5YbyIe+dZ5WdJRybOrnumLfYEvOzP1tp5Vr0B+LfM7u8XdApdEJRPyt3FhoGGmBVW4V1dQYCA5hXMA87enfArDRDJVXBEXSI/pk0UbKP8lH0+nuxoGgBdvTugE6hQ7mhHC2uFmE6klauRb4mH+2edpxWfBr22/eD53lIIEGruxUnWU8CBw6+sA+TTJOwuXvziKODWZHSd/y8vDwcPHhQ6KH805/+NOJWciQ1gsF4UWRSmXDjrBtFbs1hZ5aciSXVS0a9n1KqhD/qF3p9NHLNqPtXZqKjt+8b3Ic4nQazzzRW9dCi/sihwINFXJG2SJhbOBaRWAQGpQFNzqakw6j29e/DjNwZOOA4gBgfQ7unHQaFYcgWXNkuHfkX6OLD74H4lJG/N/wdQLyn/shewOFEYhHc+O7Q9yBv2IsibRG29WwTrnMEHagwVqDf349WdyvK9GUo05ehxd0i/P+Ro86h4fdJHJ2/QRmfU+8JeaCX61FlrMIh5yG83fQ2+vx9wjoFg/r9/ai11KIvcLinvsPTkTDHfnB70mAkCJPKhH5/P/K18TmOgUhA9F6rbHRp9aX4SuVXhMvFumK4Qi50+7ohl8hxdunZuKDighF3mzAoDLD77JBJZHAFXdjRuwMPfvYgOjwdw263RUZXqC0cdqi7SqpKeN+TSqQoN5RDJ9eh29eNXHUujEojtAotzEozDEoDNDKNcFy4Qi5UGCuGFO0SToIv123DOWXnYJJx0jG1N1M/+zOZXq5HJBaBXqFHu7sdJqUJWrkW++37MdkyGb6wL6GX1xl0Yo51DoLR+HufSqZCv79f9Pe8iZJ9vjYfe/v3YkbODOzu2408dR5m5c3CDtsOfNH3BeRSuVDUd3o6UaIvgTfshTfshVYRHxkzeMz6Ij4sn7kcG5dsnFDf8Y5HSov63/72t/i///s/7N+/H6WlpXjkkUfw2GOPpfIlyFGqqjJzYaJCXeGYVppUyVQIRoIIx8JQSBWj9tQfvYVPpnAEHAlz8vwRf9qL+kzNPk+Th15fL/68588A4n8bnVyHSCyCX376S7S6W1GqL0WlsXLYHvyR+CN+WDVWNLmakk5l2GbbhgWFC1BhqECXtwsyLj5a6MjtniaCdOQ/uHsBBw5XTr4SbzW9BSDe0ztaT329ox6t7tYh13vDXpxSeAr22fcJ1zkCDkwyTRJGcdSYa1BuKEebqy2hp54WyhvZ0fkP9tQD8UW+pudMx67eXXil/hWsXrw64XYgPs9+snmy0FP/dvPbuPlfN+Ojjo+wp28PgCOK+lgQZpUZgWgAJ+WdhM3dm+mEy3HSyDUJ83KL9cU4yXoSXEEX9Ao9ltYuxWW1l434eL1CD0uhJb7AHni8Uv8KCnQF2G7bjhpzzXj8ChOOUWkcdtcftUw9ZFrjd076DoxKI9pcbTAqjTAqjNDKtCjVl+LiyouhkWviO8MojTg0cAgVhoqk751z8uckjNwYi0z97M9keoUehdpC6OV6tHvaYVQaoZPr0DDQgAJtAUwqk1DUSzgJwrGwsKuIUWmESqqCJ+wZduHC8TRRsp9inoJub7dQvOeqczEzdyae3PEkbnv/NmhkGmGRysGtOYEvRw0qjPhO3XcwLWcagHgnmklpEn0URSZISVHf2tqK1tZWKBQKvPjii9i2bRveffddvPHGG5BKT2zfdJJca+vQL9HZRCVVJazIrJVrhZ76GB9DNHZ47nSvrxf3f3L/uLdxJEe2b7iCMd1fejM1+3xNPg44DuCJ7U8gHA0jEotgkmkSDjgO4KWDL2Ff/z6UG8pxdtnZeLf53SGPr3fUYyAwMOR6f8SPPHUemgaahPUYhtM40Bift6q2oNPTCbVMDalECnvAPmpvczZJV/4xxGBSmSCTyKCWqRGNRYUP0qO5Qi5hTvau3l3whX1DVlP3Rrwo1BYKixO93vg69vXvwyTTJPT7+7G1eyvmWOegVF+KVndr/ISYPN5TTx/SIzs6f61cC0/YI2zddGrhqXi1/lWUG8pRYawQVscf1O/vx2TLZLx16C38ec+fsdO2Ew+e/iB+/unP8cyuZ1CoLUSeJg82f7yn3qw0Q8bJUGooRbOzGdWm4dfUIMemWFeMh0+Pb+t05FBvAMIe80cyKA1oaG8AEN/nvN/fj9OKT8O/Wv5FmaRYsa4YZ5acmXDdhRUXCqNeOI6DQRnf6m5wFxiNTAOdQodTCuInMqflTMOSScOPWhw8Vo9Vpn72ZzKj0ohiXTF0Ch3a3PETMoO5ySQymJQm5KriIwB1Cp3QuXTz7Jshl8ihlqlTuqXr8Zoo2U+2TBZGXFYZq5CjzkGxrhgn55+MUwtPhVauhU6hg0VtgVwiF3rgB4IDMCqNOLXwVEg4iTC6d6T1u1iTkgnvJ598svAH7e/vh0KhAACEQiHk5OTAZrMlezg5AZHI2FYAz1QqmSphKz6NXCOsiP3WobcQ5aO4tPpSAIAn7EGHpwPukBtSTir6MKg/7/kz8jR5uKTqEjgCDszMmyncppAq0l6QZGr2eZo8bOrcBKVUiQOO+DzqOdY5eOnAS5BL5Piw/UNcN/06mFVmcODgDCbO13635V2clHcS5hXMg1x6+Ky4L+xDnjoP7rAbVvXIPfU9vh7ka/JhVprR5GyCUWlEJBbBAfuBCTXHNF35l+hKhDzMSjMGggNDMhr07K5nIZfIceucW7GrbxdOsp4Ed9id0LvlCyeuGLy5azM2dW7Cnafeia3dW2Hz25CvzQcQP0mglWuhkWlwWvFpx7UwHyuOzl/CSWAP2FGsjY+Q0sg1wpcfIP6lttnVDG/Yi6d2PIUP2z/EzbNvRuNAIxoHGhGMBlGuL8dfv/LXhEVMPSEPgtF4T/3ge9qi4kWYbJ48jr/txDbYI3V0AV+mL8NAcCDhOr1CD9dA/OQMz/M4tehUFGgL4Al7mB96mmomlWnY7VqNCqMwb/6HJ/8w4f1t8P3rlIJToJfroZVrU77eUaZ+9mcyvUIPjVyTMPw+HAujVB/f992kPNxTr5VrhcV4r5h8BYD4d9XRFugdDxMl+1pzrdDTXpdXhwpDBTiOw8OnP4x/t/wbe/v34tpp10Ir1woLUR45QmaQXqFHk7NJrF8j46TkG25vby9sNhu+9a1v4emnn4bX64XX68WaNWtwww03pOIlyAiqq7P7zLxKqoI9YBf2/tTIDhf19QP16PH2CPf1RXzo9HTipYMv4e3mt0Vp75HeaX4HHe74nPD+QD8sysPD7xUSRdqHDmdq9malGT2+HiyZtAQ7e3cCAOYWzMU/mv6B00tOx6bOTcIHabmhXJjDPWggMABXyIXvvvfdhF5ff8Qv7Cc8Uk89z/PxrZ04DiaVCc2uZphVZpiUpoSTRxNBuvIv1ZcKJ01y1bno8/fBE/YIX2IHdXo6ccB+QJhCMRAcQJWpCo6AI+F+npAHWrkWcokcoWgIzqATff4+TDJNwiddn6BAkzgP2B/xQyVToUBbgCId24veJDNc/o6AA1+pOjxf+7aTb8PConhRUqwrRigawgOfPIBGZyN+vujnyFPn4a8X/xUDwQHh5MuRx9bgyfpgNAiT0iSMPvr27G/jrLKz0vnrMWdwL/QjlRpKh6wfolfoocvRAVz8i/E5ZeegylhFvfTjyKQ0He5lNFUl9BIO9tQX6grxjanfSPo8R25BeCwy9bM/k11QcQEurrwYerkePb4eoae+RFcCALh93u1CR9FwixeqZKqMGDk2UbLXyDV4YNEDAIArp1yJGbmHF9Y+u+xsXD/jehiVRsgkMmhkGpiUJuRr8nHQcTDhfXJw9XsSl9Juq3feeQc33ngjJBIJJBIJli9fjrffFr/4msgOHTokdhNOiEqmQoenQzhDeuRCeU3OpoQtr3xhHwaCA9hu256w8Np484Q8aHO3waKyoNsXX4n06JXVlVIl1PL0fgBkavYcx8GqtuKy2suwuWszZBIZjEojyg3luLDyQkg4iTA/yqg0JgwJBiDs79vmbkuYAzy4ToFOoRtxTn2vv1e4zaK0oMnZBJPSBJPSlFFbPqZCuvI/v/x8nFF6BoDDRT14JHxxdYfc+PEHP8aP5/0YHDiEY2Fw4GBRWYYU9d6IF1q5FgXaw4vwTbFMQaG2EHedehd+cspPhPvma/LR5emaUCMq0mW4/C+suBCnFZ8mXJ6aM1X4ompUGvHAogcQioawuHgxFhQtAMdxmGSaFD8ZBn7YIYwcOASigYR9uOVSOWWUYlaNdcjw+3PKzsGVk69MuM6gMGB/x36oZWr8aN6PUKIvQaWxEj+a+6PxbC7TjuzVPdqRayYsn7k8La+fqZ/9mWxwqL1OoUOMj8GkNKFQWygUk1Nzpibcd0hRL1WJPjoUYCN7CSdJGLWUq8mFSqZCviYf/237L6pMh9cV0Cv0GXGyJVOkdL+5UCiEAwcOYPLk+LC8gwcPTpiVGjNVLJbdw1NVUhXa3e3CWW+NTCMUBf6IP2EPZV/YB4PCgHpHvaiLlVzx5hW4tPpSfGPKN/DigRcBxLfyOnIOvVwqT3tPfSZnf3HVxagyViEcCwtDqx8+/WFYNVaUG8qF4uHoxbuA+KJsrpALzpBTWLU7EovEF1CTa4T9gCWcBJFYBDLJ4bcxm+/wUG6TyoQmZxMuqboE4Vh4wu2pna78C3WHtykTivoj8DyPL/q+wPkV56PGXIPJlsn4rOszGJVGmJSmIUW9L+xDgbYAJfoSYWTLs+c/C7VMLQwNHzQzdyb+0/qftPxeE81w+d8659ZRH/ezBT87pi9BGrkGdr89oaeepN4pBacMyUUukQ/5rDMoDOgN9sKsPXwSWSaRYbKFpkOMlxx1DvI1+cPedkbJGUOmUYwkhuN7D8/kz/5Mp5VrwYET5tgPDgE/0nA99WqZGlqZdsh9xxuL2f947o8BxFfMt6gsmJ4zXbiNivpEKS3qH3zwQSxatAizZ88GAOzatQt//OMfU/kS5CiDJ1CylVKmRKu7FWeUxHsGB3vqw9EwtDItwtGwcF9v2Itacy3UMvWw+2GPh2A0iC5PF1488CL+ufSf2HBgA3p9vTh6FJ1CohCmFKRLJmc/WFycV34e2j3tAIBKYyUA4LGzDu+IYVQahSLwgP0A3mt7T5jD7Qq6YPPZ4A658Xbz2zg5/2SoZWpcO+1acBwHpSy+z/2RRf1AcEBYsNCsNKPT0wmT0oRILHLMKwxnuvHIP0+Th20924QFneRSOUKxEHb27sSCwvi+2XXWOrzR+AYqjBUwq8zo9feiz98nnKjzhr3QyDQo0hYJ/y+MtBr0jNwZ9AE9Rseb/0jzrkcaCmzVWNHmbsOsvFmUTRqNdd51qb4UHdEOlMpL09wiMpLZebOHLQYBoMJYMabnUMqUwy4IOxaZ/Nmf6SScZNii/UhahXbI9wWVLDN66lnMvs5aByB+4vPoHT50cl1G5JIpUjp+bsmSJdi3bx++//3v4/vf/z727duHr371q6l8CXKUAwcOiN2EE6KSqdA40Chsf6eVa+EL+9DmaUOJviThvr6ID5MtkzHZMhkcx4myiJYj4MB55efh+hnXCz0ov9vxO3yn7jsJ91NKlWnv1cqG7C+ZdAmunXZtwnWDPelAYk/91p6tqHfUgwePDk8HVDIV+vx9aHG14KP2j4Th91+v+TqA+CiPo/dxdgQcwoexSWUCDx4mpQkWlWXC9dSPR/65qlz0+nuFgk8lVeHz7s+xtXur8KV2dt5svN/2Pkr1pTArzdhwYAOe2/Oc8BzecHz4fYWxAvvs+5IeF1XGKtpre4xSmb9BaRgy9HuQVWNN2NeZiMuoNMLj91AWIuI4Dgqp4oSeQy1TD+kMGKts+OzPZBa1JWmnS5m+DLXm2oTrVFJV2jtqxoLl7DVyzZCtsnUKHZ1sPkJKe+oBIC8vjwp5MmYqqQotrhZhQazBfer7/f2waqwJc+e9YS/OKj0LddY6PLLlEfR4exKGCo/EHrBDr9CPOGS/1dWKMkPZiI/v8HRAxsmQr81Hf6AfFcYKfHPqNwHET0IMBAcwxTIl4TFyqZzeaBAfPppsj97BrYEA4Iu+L+AKumBQGNDmbkOVsQq9/l4EIgEYFAYcsB/A/ML5wmMVUgWCkcSifiA4IJzJlUvk0Mv1MKlMKNIW0fDU45CjzkHDQIOwcJ5CqsCTO57EFZOvEL7U6hV6FOuLhVXzDzoOJgyPGyzqi3XF2GHbgVMKThnx9aQSKdactya9vxQZIkeVM+JJ0jxNHtrcbVDL1Lh/YeZsKcqyGl3NkIUrSXZRy9RQSE7sxAA5PlMtU5NugTbcqJlM6akniQwKQ0acbMkUtNJNlsv2oTgqmQo8eOSp4ysua+Qa+CP++L7vX26hNLgavi/ig06ug1KqRJ46L2ERvWSe3/s8tvdsH/H2n3z0E3R7u/H7Hb/Hxx0fY1//PuG2Hm8PbnjnBvyn9T/geR52vz1h+Ook0yRhy5MjjUdPfbZnD8TfkJ1BJ4B4QT54MsTms6HaVI1eXy86PB24fub1eKPxjYQTJSP11A8OvwfivfUmpSklPSuZZjzy18g1MCvNWFK9RLi8bPoyYZvJQVfWXokqUxUsKgvmWOcImQKHi3qpRAqlVDnqtltKqTLlv8dElMr8c9Q5I+aSr8lHu6cdCqmCRlFkiPNqz5twI49Yo5FpjvszaSJ89ovpkTMeOebHqGXqjCjqKftEOjn11B+Jivosl+1DcVRSFQo0BZBKpAAO99QPBAaEbWP6/fHi3Rf2CSvK6xX6IQusHWnwMUB88bRk9+3ydmFP3x60u9txwH4Am7s3C7e1udswPWc6nCEnVn22Cvvs+xK+TN0480Zhy6iE30umSvvwyGzPHogP+3WFXPCGvdDL9ZBxMhgUBnDg4kW9vxfOoBPnlp2LOflzEnr9B+fUH8kZdCbMlSvQFkzYs7jjlf8jZzwijJD47knfxXnl5w25z5VTroRBYYBRacQz5z+T0Os7WNQD8S24qBhJjVTmX6QrQqF2+FFPeeo8BKPBtC/8ScauMliJiyovErsZ5ASoZerjXvB3Inz2Z5tSfSkWFy8WuxmU/VEqjZW4fsb1YjcjY1BRT0SlkqkS5sioZCoEIgE4gvEeV6vGKmwb5wv7hNVH9Qo93CG38LhmZzO29WwTLl/9j6txx4d3wOazCYutAfEiHYhvyTU4AsAb9uJz2+fwRXzwRXxoGGhAn78P33vve7AH7KgyVcEVdKHd045POj9JKEpGGsJ1S90tKNOPPKSfxKmk8bw7PZ0o0hUhX5sPo9IIg9KAAm0BBoIDwlZbj575aMIiKUqpcsje846gI2GBm0fPeDTpMDtybNQy9ah/zyN72pudzfBH/MJ1M3JnjFg8EvGcW3buiEXi4BaRShmNoMgk9L6W3dQy9YQbPTaR5ahzMK9gntjNIEeRSqRjmobLiqwp6mOxGL773e9i0qRJqK6uxpNPPjnqY9atWweO4/Daa6+lv4EiyfahOCqpSphPD8RXJuXBx1cxV5lRYagQ5tV7I15h+JNBYUgo6vfb92NH7w4A8YJ9Vt4sfHPqN7Fm5xqhp57nedz98d0AgHeb38Vze59DNBaFP+LH5z2fwxf2wRf2oXGgEY9seQRtnjbYA3ZUGirhDDoxEBjAzt6dyFENvz/tkTRyTdq/dGV79kD8iykHDl3eLhRpi1CgLYBJaYJeoYdRacT8wvmQfPk2dfS+2EqpUtgF4S/7/oJQNIRwNJxQVE60Fe+PlA35P/r5o/ii7wvhWLi89vKEfdTJ8Utl/hzHjfh+pZAqYFKaaFpEBsmGY58kp5Yff0895c8uyp4kkzVF/fr167F3714cPHgQmzdvxiOPPII9e/aMeP/m5mY8++yzmD9//oj3mQiyfShOmaFs2L2VHQFHvKg3VqDJ2QQg3lM/OJT66OH3jqAD3rAXQHxhu2JdMWblzkKXtwv+iB/OoFP4AeLz899teRfeiBcGhQH77fsRjoXhi/gQiUWgkqlQbaxGm7sNZYYyuMNuKKQKhGPhUecEj5dsz/5IHZ4OFOoKUawrhkllEoZyXz/jevz01J8O+5jBXn4gvm7C/zr+J2y9xoJMzp/jOERiEfA8j0fPfDTheuphTI3xzN+qsVJRn0Ey+dgnY0PD78nxoOxJMllT1G/YsAE33ngjpFIpLBYLrrzySrzwwgvD3jcWi2H58uV44oknoFRO7C8iEknWRDgsCScR9rMexIOHJ+yBRqZBib4E7e743tY8zwtz748efj8QGBCG07e721GqLwXHcQjFQrBqrHCFXOjx9cAf8QOInyCQcTIcsB9AtakaKqlK2E7vgooLsGz6Mlg1VhywH4i3j4+PDijWFSddzX08ZXv2R+ryxHvqzy8/H5dOuhQGhQEmpQkSTjLi0CqFVIFgNAhPyIMCbQHebXl3nFstrkzO36Q0CSfQaMhieoxn/oXaQirqM0gmH/tkbE5k+D3lzy7KniSTNf93tLa2ory8XLhcUVGB1tbWYe/76KOPYtGiRTj55JNHfd5gMAiXy5Xwk02qqqrEbkLKFeuK0eXtAsdxkEvkiPCRIfc5uqgfrqceiH8ZrTZVwx1yo9ffKxT13ogXUyxT0DDQgGpTNYp0ReDAIRAJ4PoZ16PSWAmrxor9jv0wq8wIRoOQS+W4d8G9Q4aBi2WiZJ+jzsHm7s0o0hVBKpFCLpVjxewVyNfkJ32cSqbCps5NePPQm1hUtAgmpQlRPjpOrRZfJudvUpngCDjEbsaENp75P3z6wzT/N4Nk8rFPxuZEinrKn12UPUkmM6oTAAsWLEBubu6wP21tbWN+nt27d2Pjxo24++67x3T/1atXw2g0Cj+lpaXH+yuIoqGhQewmpNxk82RhHjUAKCSKIaucGxSGhOH3A4EBeMIeAPHF8Er0JQCAalM1KgwV8IV9sPlswsJqvrAPJfoStLnbUKAtwIrZK4Sh24PDg60aKyKxCJRSJXwRHywqCxYULUjfL36MJkr2l9dejnZPe8J2MVXGKmFUxkiUUiXePPQmntzxJGrNtbh93u1YfdrqdDc3Y2Ry/malGR2ejozYAmiiGs/8KcfMksnHPhkbrVx73EU95c8uyp4kIxO7AYM++eSTpLeXlZWhpaUFCxbEi6rm5maUlQ1dXfyjjz5Cc3Mzamriq2R3d3fjpptuQldXF1auXDnk/nfeeSduu+024bLL5cqqwl4my5gIU2ayZXLCAmdFuiJ0ejoT7qOWqeEP+4XLvogPHOLFuM1nE3p5zyk7B6FYCFt7tqLH1wOdXIcYH4M/4keJrgRf9H6B0uJSXFhxId5sfDPhNawaq7DSvV6hT9gqLRNMlOyn507H3fPHdhLuSEqpEqcVn4YzS8/ErLxZ4DhuQi+Md7RMzt+kNKF+oB456tEXlSTHJ5PzJ+lF2We/6TnTUWU8vl5Xyp9dlD1JJmN66kdz+eWX49lnn0U0GoXdbseGDRtw5ZVXDrnfypUr0dXVhebmZjQ3N2P+/Pl45plnhi3oAUCpVMJgMCT8ZJPhTmxku2pTNc4rO7wXdrGuGO3u9oRF0AZ703+15Vfxyzi8+NaRc+9L9CXCB2evrxel+lIEo0H4wj4U64vR5m4T9tCWclKE+bDwPHmaPKGoNyqNGbNA3qCJlP2FFRce82Nm5c3CD+b8AEsmLcm4bMZDJudfbarGps5NQ9bLIKmTyfmT9KLss59MIoNeoT+ux1L+7KLsSTJZU9Rfc801mDJlCmpqajBv3jzcdtttmDlzJgDg9ddfx/Lly0VuoTgOHTokdhNSTiVT4coph0/YFOuKsd22XdgveZAr5ML6feuHDM0fbgV0Dhz6/H0o0ZfAH/EjEosgX5OPdk+7UNRr5JqE3n+rxoqTrCcBiPc8mpWZVThOxOyPhV6hR6khe0bVpFom5z81Zyr29+8f0/aP5Phkcv4kvSh7tlH+7KLsSTJZM45DKpXid7/73bC3LVmyBEuWLBn2tvfffz+NrRLfRF/dH4gX9Y9vfxz/r+b/JVwfiAagkWnQ5hp9zQUePELREArkBcI2aIML4OnkOgDxOW5HLoKnlCrxw7k/BAAYFcaMG9rNQvZkZJmcv0wiw8y8mTT8Po0yOX+SXpQ92yh/dlH2JJmsKerJ8AoKCsRuQtoV6Ypw0HEQ1abqhOv1Cj3Orzgf+x3743Psv+yBH251epVMhfNLzkfDQAP8ET948FBKldDKtUJRr5FpRlwQ6rLay6BT6FL/y50AFrInI8v0/C+rvWzIMUtSJ9PzJ+lD2bON8mcXZU+SyZrh92R4LS0tYjch7TRyDSwqy5AC4eLKi3F+xfn4qP0j5GnyAADukBsGxdB1EW6edTMuqrxIKP4HWVQWYfi9Wq6GRjZ8UZ+nyYNapk7Vr5QSLGRPRpbp+Z9Xfh4KtPQFJF0yPX+SPpQ92yh/dlH2JBkq6rOcWp1ZhWa6LK1ZOmQo79drvo5acy3eaX4Hl9VeBrlUjl5/77Cr1FebqyGXyKGWxov6wYX1zCrz4Tn1Mo3w72zASvZkeJQ/2yh/dlH2bKP82UXZk2SoqM9yublsrC5965xbh70+R5WDny/6OSqNldDINOj0dMKoNI74PEf31FvVVqGQ18q1Gdcbnwwr2ZPhUf5so/zZRdmzjfJnF2VPkqGiPsu1tY2+SNxExnEcvjrpqwAAnUI3elEvV8MX9glb4q1avEqYR59sTn0mYj171lH+bKP82UXZs43yZxdlT5Khoj7L6XSZtXibmMbSU6+SqtAf6BeK9yN75jXy7Bp+T9mzjfJnG+XPLsqebZQ/uyh7kgwV9VnOYBi6KByrtHItOr2dw86pH6SWqWEP2IddEG9azjQsKlqUxhamFmXPNsqfbZQ/uyh7tlH+7KLsSTJU1Ge5zs5OsZuQMaZapuKzrs+S99TLVOj39w9b1Fs1Vky2TE5nE1OKsmcb5c82yp9dlD3bKH92UfYkGSrqsxydtTtsYfFCfGvGt1CoLRzxPhqZJl7UZ9Hc+ZFQ9myj/NlG+bOLsmcb5c8uyp4kQ0V9ltNosr84TaXlM5eP2lO/vXc7asw149iq9KDs2Ub5s43yZxdlzzbKn12UPUmGivos193dLXYTsopapkYgEsiqufMjoezZRvmzjfJnF2XPNsqfXZQ9SYaK+ixnNpvFbkJW0Sl0uKDiggkx/J6yZxvlzzbKn12UPdsof3ZR9iQZKuqznFwuF7sJWcWgMOCXp/1S7GakBGXPNsqfbZQ/uyh7tlH+7KLsSTJU1Gc5m80mdhOISCh7tlH+bKP82UXZs43yZxdlT5Khoj7L5eTkiN0EIhLKnm2UP9sof3ZR9myj/NlF2ZNkqKgnhBBCCCGEEEKylEzsBmQanucBAC6XS+SWjE1LSwu0Wq3YzSAioOzZRvmzjfJnF2XPNsqfXZQ9mwZr0sEadSRU1B/F7XYDAEpLS0VuCSGEEEIIIYQQ1rndbhiNxhFv5/jRyn7GxGIxdHZ2Qq/Xg+M4sZuTlMvlQmlpKdra2mAwGMRuDhlHlD3bKH+2Uf7souzZRvmzi7JnF8/zcLvdKCoqgkQy8sx56qk/ikQiQUlJidjNOCYGg4EOcEZR9myj/NlG+bOLsmcb5c8uyp5NyXroB9FCeYQQQgghhBBCSJaiop4QQgghhBBCCMlSVNRnMaVSiXvvvRdKpVLsppBxRtmzjfJnG+XPLsqebZQ/uyh7MhpaKI8QQgghhBBCCMlS1FNPCCGEEEIIIYRkKSrqCSGEEEIIIYSQLEVFfZaqr6/HwoULUVtbi3nz5mHPnj1iN4mk0a233oqKigpwHIcdO3YI11dUVGDy5Mmoq6tDXV0dNmzYIF4jSdqcf/75mDVrFurq6rB48WJs374dAL0PsGTdunXgOA6vvfYaAODMM89EZWWlcOz/5je/EbeBJC2CwSBuueUW1NTUYObMmbj66qsB0LHPgv7+fuH4rqurQ21tLWQyGex2Ox3/DHj77bcxd+5czJo1C/Pnz8fOnTsBADabDRdeeCFqamowY8YMfPjhhyK3lGQMnmSls846i1+3bh3P8zz/0ksv8XPnzhW3QSStPvjgA76trY0vLy/nt2/fLlx/9GUyMTkcDuHfr7zyCj9r1iye5+l9gBVNTU38ggUL+Pnz5/Ovvvoqz/M8f8YZZwj/JhPX97//ff6WW27hY7EYz/M839XVxfM8HfsseuSRR/hLLrmE53k6/ic6u93OWywWfvfu3TzP8/yHH37IT58+ned5nv/Wt77F33vvvTzP8/zmzZv54uJiPhQKidVUkkGopz4L2Ww2bN26VThjv3TpUrS1taGhoUHklpF0Of3001FSUiJ2M4hITCaT8G+n0wmO4+h9gBGxWAzLly/HE088QaseM8br9WLt2rX45S9/CY7jAAAFBQV07DNq7dq1uOGGG8RuBhkHjY2NyMnJwfTp0wEAixcvRmtrK7Zt24YXX3wRN998MwBg3rx5KCoqwgcffCBmc0mGoKI+C7W1taGwsBAymQwAwHEcysrK0NraKnLLiBiuvfZazJw5EzfccAN6e3vFbg5Jk2uvvRalpaW455578Pzzz9P7ACMeffRRLFq0CCeffPKQ237yk59g5syZuPLKK3Ho0CERWkfSqbGxERaLBatWrcLcuXOxePFi/Oc//6Fjn0GbNm2Cw+HAJZdcIlxHx//EVVNTg/7+fmzatAkA8Prrr8PtdqOpqQnhcBgFBQXCfSsqKujYJwCoqCckq3344YfYtWsXtm3bhtzcXFx33XViN4mkyXPPPYe2tjb84he/wB133CF2c8g42L17NzZu3Ii77757yG3PP/889u/fj127dmHx4sUJX/bJxBCJRNDS0oJp06Zh69atePzxx3HllVciEomI3TQyztauXYtrr71WOJFDx//EZjQa8fLLL+POO+/EySefjHfffRfTpk2Dx+MRu2kkg9E+9VnIZrOhuroadrsdMpkMPM+jsLAQH3/8Maqrq8VuHkmjiooKvPbaa6irqxtyW1dXF2pra+F2u8e/YWRcqdVqNDc3o6amht4HJrCnnnoKDzzwgDDsvru7GwaDAffffz9WrlyZcF+VSoWOjg7k5OSI0VSSBn19fcjPz0coFIJUKgUQH2774x//GMuXL6djnxEejweFhYXYsmULpkyZMux96Pif2ILBIAoKCrBlyxbMnj0bjY2NQm/9KaecglWrVuHcc88VuZVEbNRTn4WsVivmzJmD9evXAwA2btyIkpIS+jBnjNfrxcDAgHD5hRdewEknnSReg0haDAwMoLOzU7j82muvIScnh94HGLBy5Up0dXWhubkZzc3NmD9/Pp555hnceOON6OnpEe63ceNG5Ofn0xf6CSY3NxfnnHMO3nnnHQBAU1MTmpqasGjRIjr2GbJhwwbMnj1bKOgjkQgd/wzo6uoS/v3zn/8cZ599Nqqrq3H55Zfj6aefBgBs2bIFHR0dOOOMM8RqJskgMrEbQI7PmjVrsGzZMqxatQoGgwHr1q0Tu0kkjVasWIG33noL3d3duOCCC6DX6/Huu+9i6dKliEaj4HkeVVVVeO6558RuKkkxp9OJyy+/HH6/HxKJBHl5eXjzzTfBcRy9DzAqGAzi4osvRjAYhEQiQW5uLl5//XWxm0XS4Omnn8YNN9yAO+64AxKJBGvWrEFxcTEd+wxZu3YtbrzxRuEyHf9s+NnPfoaPPvoIkUgECxYswNq1awEADz30EK655hrU1NRAoVBg/fr1kMvlIreWZAIafk8IIYQQQgghhGQpGn5PCCGEEEIIIYRkKSrqCSGEEEIIIYSQLEVFPSGEEEIIIYQQkqWoqCeEEEIIIYQQQrIUFfWEEEIIIYQQQkiWoqKeEEIIIYQQQgjJUlTUE0IIIYQQQgghWYqKekIIIYQQQgghJEtRUU8IIYQQQgghhGQpKuoJIYQQQgghhJAsRUU9IYQQQgghhBCSpaioJ4QQQgghhBBCshQV9YQQQgghhBBCSJaiop4QQgghhBBCCMlSVNQTQgghhBBCCCFZSiZ2AzJNLBZDZ2cn9Ho9OI4TuzmEEEIIIYQQQhjE8zzcbjeKioogkYzcH09F/VE6OztRWloqdjMIIYQQQgghhBC0tbWhpKRkxNupqD+KXq8HEP/DGQwGkVszus7OThQVFYndDCICyp5tlD/bKH92UfZso/zZRdmzyeVyobS0VKhRR0JF/VEGh9wbDIasKOoDgUBWtJOkHmXPNsqfbZQ/uyh7tlH+7KLs2TbatPCsWyivvr4eCxcuRG1tLebNm4c9e/YMuc8nn3yCuro61NXVYfr06VixYgWCwaAIrU0/m80mdhOISCh7tlH+bKP82UXZs43yZxdlT5LJuqJ+xYoVuOmmm3Dw4EHccccdWLZs2ZD7zJ49G1u2bMGOHTvwxRdfwGaz4fe///34N3Yc5OTkiN0EIhLKnm2UP9sof3ZR9myj/NlF2ZNksmr4vc1mw9atW/Huu+8CAJYuXYpbbrkFDQ0NqK6uFu6n0WiEf4dCIfj9flrJnhBCCCGEkAkuFgoh3N4BxKIIHDgAPhCAzJqPSG8v5EVFkGg1ACeBavo0IBxGqKUFEbsDUYcDMa8XErUKkX47ZFYrJBo1pCYTVNOmIebxIOr2gJPL4d+xAzG3CzGfD5BIITUaIC8qAh8MIupyQ6JWIdzdg3BnJ0KHGiGz5kNRWQlFZQWUVVWQFxaOy9+CD4cR7upCqLkZ4c4ucColZDk5kOh0kGi0AIcv/yZagOcBjkPEZkO4tRWywkIgEkG4sxOcSg2p0QBZbi74WAxSnQ6cXJ48h2AQfCiEcGcn+Egk/rduawenUEC7YD6kNJUgpbKqqG9ra0NhYSFksnizOY5DWVkZWltbE4p6AGhubsbXvvY1NDY24uKLL8a3v/3tYZ8zGAwmDM13uVzp+wXSoL+/H4Xj9MZAMgtlzzbKn22UP7soe7ZN5Pz5WAyhxkbIi4sh0WjAx2IIt7YisP8AIOEgz8+HxGBAzOlExO5A6FAjAvv2IxYMADE+XpR+WZgqyssBiQTK2hpItFqEWlogy81BYM9uxPwB8AE/+p54ApDJoKysgNSSA6nJBIlWCz4UhKKiAhFbDyL9fYhs2Yq+p56GVK+DRKtDLBCA+qQ6yCyWeCEfjSHqdML3+TZIVEpIDAZE+vsgy82DetZMKCorEentRaipCaGGBrjffhvhjg5Ic3PBh8LgAwHIiwqhnDIFUrMZqtpayMvKhnRIDmYf9cRPPHBSKfhoFIF9++H77FNEevsQ6e9HzO2OP4DjwMmkkBUUQlFRDnlhEfhQCKHmFsS8XsS8XoCPIdzZhZjXCx48AEBuzYe8tBSRt98BJ5dDZrUiFvAj5vEi0tcLTiJFzOsBH4l+GVz8cZBKIdXr488dCkKiVIFTKCAvKgInkwIyGRSlZYi6nOi+7z7EfH6A58GDB4f478qDhzy/AHw0Ak4iAWQycDJ5vB15eeCUCkg0GihKSyHLzYWivBx8OIyo04nowACiDgeibg8kGg2khi/b4veDD4cR8/mgmTMH8uJi8NEoOKl0XP6/Hi9ZVdQfi4qKCuzcuRMejwdXX301XnnlFVx11VVD7rd69Wrcf//9IrQwNaxWq9hNICKh7NlG+bON8mcXZc+2bM0/6nIhsHcv/F98gcDeveADQUAigUStRtTtAgcOPB+DsrIq3rMbDgMcB3lpCVSTJwPg4G1sRHTACanZBKnZAtWMmTBdeSUkGk28gE2yh7fYFKWlUJSWAqefDiC+93jM5QKnVoOTy+MFf3MzIv39sD/3vNC7zclkkKhVkOj0UB06hDa9HhK1ClGPB5xECkgkUFRWQHf6GZBZ8yDLy4NEqxVlhDIfiSDqdkOiVkOiUiW9r/nyy4d/jmgUkb4+cDJZvOAPh8FHIsKIAz4cBu/3w7dtGyJd3Qh3dAByGaQmE2QmE6RmMyQ6PWIeD2JeT/xvoVbH/44qFfrWPIOIzQZIJJBazCj6xS/S8acQRVYV9aWlpejq6kIkEoFMJgPP82htbUVZWdmIj9HpdLjqqqvwl7/8Zdii/s4778Rtt90mXB7cNiBbhMNhsZtARELZs43yZxvlzy7Knm2ZlH/M60WwoQGhtvYviygvogMOROx2RPvt8TtxHBCLQaLVQjV9OtSzZ8P8jW9AqtOBj0YR8/kgHWWrromI4zhIjUbhsrKqCsqqqiH343keMa8P0b5exORyFBcXj2czjwknk0FmNp/Yc0ilkOfnD3vbcH+fY2VcsuSEnyNTZVVRb7VaMWfOHKxfvx7Lli3Dxo0bUVJSMmTofUNDA8rLyyGXyxEKhfDqq69i1qxZwz6nUqmEUqkcj+anhcPhyOgDnKQPZc82yp9tlD+7KHu2jTV/nuePqbc23N2NwN69CB06hFBbOyJ9fUA0Gi/KOQ58JBzvGY4/OQCAU6uhrKmGoqwMsnwrpDodpCYTpDk5kBqNow5v5r4crk1GxnEcpDotpDotHLt307FPRpRVRT0ArFmzBsuWLcOqVatgMBiwbt06AMDy5cuxZMkSLFmyBO+99x4ef/xxSKVSRCIRnHPOObjnnntEbnl6FBQUiN0EIhLKnm2UP9sof3ZR9mwrKCgAH40i6nIh6nAA4BBua4Xr7XcQHRgAYrEvi/AIIOHAcV8OSee4eDE+3BD1WAxSsxnqk06CevZsGC65JD5/+cuinI/FMnpoOyvo2CfJcDw/uLoBAeLD741GI5xOJwxZsCrjaNMPyMRF2bON8mcb5c8uyn7i4XkegZ07wcd48KEQ5EWFiAUCCOzeg6jLCYlSiYjDgcDOXfAH/FCrNZCazZCaTADPQ15YAP0FF0CepOgb/LpPu0FlLzr22TTW2jTreupJomxbrZ+kDmXPNsqfbZQ/uyj7zBcLhcCHQojYbJDq9fEF4nbvRqTHhqjTCdXUKfBv3wGejwHRGGJeL9Qnz4HUaAInl8P1z39CotVCPWsm5EVFiPm8UFRWIffmm7Fn717UzphxzG2iYj770bFPkqGiPssVFRWJ3QQiEsqebZQ/2yh/dlH24uG/HNqOWCw+/H1gAABg/+MfER1wgg+HITWZEO7shCwvD7L8fETtdsgK8qE/5xzIrPmQqFUI7N8Py3XXQaJWx5/3y1XOx4LyZxdlT5Khoj7LuVwuWCwWsZtBREDZs43yZxvlzy7KPnV4nkekpwecTAY+FAIARN3xVdxDTYfAKVWI2u3wbd16eC/0aAScUgWJTgeZxYLogAM5K26GorQEnFyOcFcXZFZr0kXitKecknB5rAU9QPmzjLInyVBRn+U8Ho/YTSAioezZRvmzjfJnF2U/NnwshqjTiUhvL6J2B/hwGKG2VoSamuN7W8di4KNRyAvy4yvFy+UAAKlOD4lWC0VVJWI+H2SFBSh66EGhV3008sLCdP5alD/DKHuSDBX1Wa60tFTsJhCRUPZso/zZRvmzi7JPFOntRczrRWDfPgQOHgRiPMIdHYi6nJBZciDLy4PUYonvf11WCt2iRZAXFx9T73gmofzZRdmTZLLzHY0I+vr6YDQaxW4GEQFlzzbKn22UP7tYzz7S3w/vp5/C9+mnCHd1Q2a1QqLTQlk1CYbzzgOkUkgNhrT3mIuF9fxZRtmTZKioz3J+v1/sJhCRUPZso/zZRvmzi4Xs+XA4/g+pFIE9exCsb4B30yZEBwYgtZihPXU+cleuhJzBhcNYyJ8Mj7InyVBRn+XKy8vFbgIRCWXPNsqfbZQ/u7I1+8F90iPd3Qg1N0NZWwv/9u0INh5CsLEBiPGI+XzgpBJEXW6A58HJpFDNng1l1SQU3H0XJEYj81uzZWv+5MRR9iQZKuqzXHd3N/R6vdjNICKg7NlG+bON8mdXJmcfbGiA8+9/R9TlhnrWLHBKJcKdnfBv2xbftk0uhywvD/KSEgxsfAWak+dAM28uTFdcDk4mg0SrBaJRQCKJ/4D2Vz9aJudP0ouyJ8lQUZ/lgsGg2E0gIqHs2Ub5s43yZ1cmZc/zPDzvvYfA/v3wb9sORXk5jF+/FDKLBf4vdoOPRKCeOQM5139r7AvTfVnMk+FlUv5kfFH2JBmOHxwPRQDE94A0Go1wOp0wGAxiN2dUPp8PGo1G7GYQEVD2bKP82Ub5sysd2fOxGPhQCBKVashtkf5+OP/+OgL79kGi00JutYKPxRDYsxd8MAjNqadCM28u1DNmCNvCkfShY59dlD2bxlqbUk99lmttbcWUKVPEbgYRAWXPNsqfbZQ/u8aSPc/ziPT2wvfZZoSamhB1OhHu7ITUYkbM4wUfCg3eMf7DceCUSvCBQLynPBYDOA7gOEgNBujPOxemyy9DzONBxGYDH4kgd8WKrN0WLpvRsc8uyp4kQ+/GWS4SiYjdBCISyp5tlD/bKH/28LEYgvUNiBysR1ivR9hmQ8TWi+jAADi5HNGBAfh37ULM5wUnkUJqMkG7cAF0Z50FqdkEeX4+ogMDkBgMkCiVx9UGqV4/YbeKyxZ07LOLsifJ0PD7o2Tb8PtAIADVMMPlyMRH2bON8mcb5c+OWCAAz3//C8ffNkA9cwYiPCDx+yCzWiHLs0JqNoOPhCHV6aCaNQtSnU7sJpM0omOfXZQ9mybs8Pv6+npcd9116Ovrg9FoxJ/+9CdMnz494T7vvfcefvKTn8Dj8YDjOFx88cV48MEHIZmAi68cOnQI06ZNE7sZRASUPdsof7ZR/hNP1ONBpKsLweZm+D75BDGvD1GPBwCgPfVUlK55GhKVCnv37qXsGUbHPrsoe5JM1hX1K1aswE033YRly5bh5ZdfxrJly7Bly5aE+5jNZvztb39DVVUVAoEAzj33XDz33HNYtmyZOI1Oo1gsJnYTiEgoe7ZR/myj/LMTHw7Dt2ULfJ9vAx8OI3ioEYjGs5RotZAXFkBeXALLsmWQGo3glMohi9dR9myj/NlF2ZNksmr4vc1mQ3V1Nex2O2QyGXieR2FhIT7++GNUV1eP+LhbbrkFubm5uO+++0Z9jWwbfh8OhyGn1WaZRNmzjfJnG+Wf+cLd3fDv3IXggf0I1jfEF6eTy6CeNRvaBfPBSaVQVFdDolAc2/NS9kyj/NlF2bNpQg6/b2trQ2FhIWRfrrbKcRzKysrQ2to6YlHf3d2Nl19+GW+++eawtweDwYR9H10uV+obnkYHDhzAjBkzxG4GEQFlzzbKn22Uf2aI+f1w/+c9uP/1L/CD3yU4DnwkApk1D5qTToLurLORc9NNw24Xdzwoe7ZR/uyi7EkyWVXUHyuXy4WvfvWruP322zF37txh77N69Wrcf//949wyQgghhGQDPhJBzOtFpLcXwfr6+E9DI2LBACQqNTSnnoLCX/6CFqgjhBAimgk7/N7tduOCCy7AV77yFdx9990jPudwPfWlpaU0/J5kPMqebZQ/2yj/4xcLhRDt64N/zx7woRD4QBBSixmhlhaEOzvBSaQINTfH92nneUAmhVSrhTQnF8qaGihra6CcNCllPe/HirJnG+XPLsqeTRNy+L3VasWcOXOwfv16LFu2DBs3bkRJScmQgt7j8eDCCy/EhRdemLSgBwClUgnlce7XmgloKA67KHu2Uf5so/yHx0ejCHd1IWKzQWoyIdTUBP/u3Qg1HhKGx3MKOSRGI9SzZoFTKCDR6xDp7oaypga6M84AHwpDWVsDjuNE/m2GR9mzjfJnF2VPksmqoh4A1qxZg2XLlmHVqlUwGAxYt24dAGD58uVYsmQJlixZgsceewybN2+G1+vFK6+8AgC4/PLLcdddd4nZdEIIIYQcJz4SQaitDaHGRgQbGhGx94OTycFJOIQ7OxHz+gCJBPLCQsjy8xF1OKCoqID+3HOhvLkakiw+gU8IIYQkk1XD78cDrX5PsgVlzzbKn20s5B/u7IT3s83wfvQRYl4vIJVCUVoKRfUkKCdVQ5abAz4SBWJRyAoKIdVpxW7yuGAhezIyyp9dlD2bJuTwezIUDcVhF2XPNsqfbRMpf57nEXO5ELHZEO6xwbdlCwJffAF5cTHUJ89BwX33QpoFJ9nHy0TKnhw7yp9dlD1Jhor6LCeRSMRuAhEJZc82yp9tmZr/4ArxQLynPdzVjUhfH/hoBJxUhpjXCz4YAKQycFIp+GgUMbcbstwcyKxWyPKs0J9zNvK+dyu4DP0dxZap2ZPxQfmzi7InydDw+6Nk2/D7QCAAlUgr8BJxUfZso/zZlu78Y34/PB9+hHBHBxSVFUA0Cj4chm/LVvDhEPhwBFGnM746PMcJK8VLLWaoamsBALKiIsgLiyDLywUnk4GPRCDRaiFRKsHHYuDDEQA8bQV3jOjYZxvlzy7Knk00/J4RDQ0NNBSHUZQ92yh/tp1I/lGPF+GOdgT27YPMYgEnk0FRXY1Idzc8H3+MwK4vAIkEusWnQTVtGoKHGsHJ5QDPw3L9t+ILzslkkBoM4KTS42oDBwC0cN1xoWOfbZQ/uyh7kgwV9VlOJqMIWUXZs43yZ9to+UfdboSamhBqbka4sxO+7dvBxUtpSHQ6yIsKoZw8BaHWNvDhMJxvvQWZ1Qrdaach98Yb40X8l7TzT03r70KODR37bKP82UXZk2Ro+P1Rsm34vc/ng0ajEbsZRASUPdsof7YN5h/u6kKwoRHRgQFAwiHmdsP93nuQ6nRQTJoERUUFZHl50Jx88nH3qpPMQsc+2yh/dlH2bBq34ff9/f340Y9+hJaWFlx66aW49dZbhduWLl2KjRs3nuhLkCQOHTpEQ3EYRdmzjfJnTywUgn/rVkR6e9H+3nvQe7yQFRZANXkKpCYTEOMhtVhQ8thjkKjVYjeXpAkd+2yj/NlF2ZNkTriov/nmm1FZWYlLLrkETz75JD744AO8+OKLkEqlOHToUCraSJJQ0pxEZlH2bKP82RCx2+H54EN4PvwAvD8Azbx5kBXkQ37ZZShbvFjs5hER0LHPNsqfXZQ9SeaEi/r6+nq89NJLAICvf/3rWLlyJS699FK88sorJ9w4MrqCggKxm0BEQtmzjfKfeGJeL4KNjQg1N8P97/+AD4UgNRqgPe00FN57b7w3/ksSt1u8hhJR0bHPNsqfXZQ9SeaEi/pgMCj8WyKRYM2aNbj11ltx6aWXIhQKnejTk1G0tLTQUBxGUfZso/yzH8/z8O/YAc9/30dg/z5IlCooJ0+GoqwUBfffB5nZPOJjKX92UfZso/zZRdmTZE64qC8vL8emTZuwcOFC4brHH38ct956K95+++0TfXoyCjXNm2QWZc82yj97hdo7YF+3DqG2Vqhqa6G/6CLkfe/WY1rIjvJnF2XPNsqfXZQ9SeaEi/p169ZBOswXkccffxxXXXXViT49GUVubq7YTSAioezZRvlnBz4WQ+jQIbj/8x5ChxoRsTsgy8uD5er/g2ratON+XsqfXZQ92yh/dlH2JJkTLuoLCwtHvO3I3nuSHm1tbTAajWI3g4iAsmcb5Z+Z+EgEzr//HYG9+xDu6ADPx6CsrIL2tNNgvvIKSIxGcBx3wq9D+bOLsmcb5c8uyp4kc8JFfWVlZdIvKLQCfnrpdDqxmzAEz/Mp+dJKksvE7Mn4ofzFFQsGEentRaipGf4dO+DftSs+fJ7joDvjDFiWXQd5UVHa9oan/NlF2bON8mcXZU+SOeGi/s033wQA/O1vf0NzczNWrFgBAHj22WdRXl5+ok9PRmEwGMb9NfloFI6/voCoww4+FoN24UIEvtgN3+bN4MEj5vWi4Kc/PaGhpWR0YmRPMgflPz74aBTBAwcQdbnBKRQIHjwI93v/gUSphMyaD0V5GbSnLULud74NTiIZt3ZR/uyi7NlG+bOLsifJnHBRP336dADA22+/jS1btgjXL1q0CKeccgoeeOCBE32JBPX19bjuuuvQ19cHo9GIP/3pT0IbBjU3N2PZsmXYvn07KisrsWPHjpS2IZN0dnbCYrGM2+vxPI+ue34Gdd1s6M89F5BI0P/HP0I1eTJKnn4KHMch6nSi/dbvoeTJJyDV68etbawZ7+xJZqH8UyPqciGwbz8C+/YisGcvYh4PAICTy8FHo0AkAuXkyZBazOCDIShKS1D65JPgFApR2035s4uyZxvlzy7KniRzwkX9IKfTCa/XC61WCwDwer1wOp2penrBihUrcNNNN2HZsmV4+eWXsWzZsoSTCUD8TNYvfvELOJ1O3HXXXSlvQyYZ77N2AxtehKKsDOYrrhCuK3744YT7SI1G5H3/e+i8/Q7kfnslwp1d0C5aCCkNG0opOmPLNsp/dNGBAfi2bUO4rQ3hzi6EWlqAwalBHAfEYpBotVBNnQLl1KkwXXoppCYTeJ4HwmGA48DJ5eL+EiOg/NlF2bON8mcXZU+SSVlR/81vfhPz58/HFV8Wey+99BKuvvrqVD09AMBms2Hr1q149913AQBLly7FLbfcgoaGBlRXVwv3s1gsOO200/D++++n9PUzkUajSftrxLxeuP/7PtzvvAOpyYiC++4bvV0nnQRuxU3wvP8BpDkWdPzgNqhmTEfODcsh1WnT3mYWjEf2JHNR/onCXV1w/+vfCHd0IObzIdLfD06hgHb+qVDW1kK7eDEU5eVjmuPOcRwgck/8aCh/dlH2bKP82UXZk2RSVtTfd999mDdvHt577z0AwEMPPYSLLrooVU8PIL7qY2FhIWSyeLM5jkNZWRlaW1sTivpjEQwGEQwGhcsulyslbR0v3d3dadniIhYIoP/ZP8C/fTukJiPUdXUoXL0aEq1mzIvgqevqoK6rAwCYv/ENeD/+GO233ALt/PmwXHsNJPTmdELSlT3JDqzlz/M8EIkAUikiPT3wbf0cvs2bEentBc/HILPkQH/+edCdvhicWgOp0QCJSiV2s9OGtfzJYZQ92yh/dlH2JJmUFfUAcPHFF+Piiy9O5VOm3erVq3H//feL3YzjZjabU/Zc/j17EGpuhuErX0Hn7XfA+LUlyL3lOylZyZ7jOOgWL4Z24UJ4/vtftH//+7D+8IdQTZ6cgpazKZXZk+wzkfMP7NuHcFcXoo4BRN0uhDs6EWpsAKfWALEYZFYr1HV1yP3OtyHLz2dyt42JnD9JjrJnG+XPLsqeJJOyor65uRkPPfQQGhsbEYlEhOsHe+5TobS0FF1dXYhEIpDJZOB5Hq2trSgrKzvu57zzzjtx2223CZddLhdKS0tT0dxxIU/BfM9QayuCjY0YePElSNRquP/1b2hOngP9OeekoIWJOKkU+nPPhaJqEhx//StiPh+KVv0y5a/DglRkT7JXNufP8zwCu3cj5vEgFgjA9dY/EHO7wcdi4CQSyEtKoKythdSgh7yoENqFC6GsqWGyeB9JNudPTgxlzzbKn12UPUkmZUX9FVdcgXPOOQe33HILpGnal9dqtWLOnDlYv349li1bho0bN6KkpOS4h94DgFKphFKpTGErx5fNZoPVaj3uxwcPHUL3/Q9AWVuLokceBsdxCLW3p70HXVlViYK770L3qlXw79wJ9ezZaX29iehEsyfZLVPzjwWDQCwGTqkEJ5Eg6vHCt2UzAnv2QmbNQ2DvXoTbO6CaOgVSkwmxYBAFd98lLFAX6e2FPAN/r0yTqfmT9KPs2Ub5s4uyJ8mkrKgPBAJYvXp1qp5uRGvWrMGyZcuwatUqGAwGrFu3DgCwfPlyLFmyBEuWLIHP50NtbS2CwSCcTidKSkpwzTXXjEv7xltOTs5xP9bxt7/B+8mnKHpwNeSFhcL14zkkPueG5ei6526UPvXUmBawIoedSPYk+2VK/jG/H6533kHwwEHEAn6E29oh0esQ83oh1ekQHXBCd9ZZ0J1xOkLNzTAu+Ro0c04a9rk4jqOCfowyJX8y/ih7tlH+7KLsSTIpK+pnzJhxwkPhx2Ly5Mn45JNPhlz/hz/8Qfi3RqNBe3t7WtuR7fxf7Ib3k09R/NvfiDqkVZ5vheH889Hz0EPIv/12cLKULvNACEmx6MAAIv39CLW0ItzWCs9HH8Pwla/AdNlSxLxeqGfNEu4b7uyErKAAnEQCAFDPnClWswkhhBBCJqyUVVC9vb2YPXs2FixYANURKw6/8sorqXoJMoz+/n4UHtHLPhaBAwdge+QRlDz+WEbMUTVddhmcb7yJtptXIv8nd0B5AtMpWHI82ZOJYzzy5yMRBHbvRqilBaGWVvi2b4PcaoXUZIa8pAScWo3SZ58Z8X1EXlSU1vaxjI5/dlH2bKP82UXZk2RSVtRfffXVKd+XnozuWOfW8JEIuh/4OYoffRRSkyk9jToOxq9eAs0p89B5x09Q+ItfQFFSLHaTMh7Nq2JbuvKPL2K3B9EBB/qfeRaaU06BclIVdGediZybV0CS4fu3s4KOf3ZR9myj/NlF2ZNkUlbUX3fddQDi+75n88Jz2SYcDh/T/X3btkF32iLI8zPvjUGen4+Cu+9C7+OPofjhh8VuTsY71uzJxJLq/PlwGI6//hWeDz6Aato0cBoNin/zKGS0J25GouOfXZQ92yh/dlH2JBlJqp7oiy++wIwZMzBp0iQAwOeff47bb789VU9PRuBwOI7p/p73P4DurLPS1JoTp6yuhlSng2/7drGbkvGONXsysaQifz4Wg/eTT9B1zz1ovekmSAxGlK5ZA+uPfoS8b3+bCvoMRsc/uyh7tlH+7KLsSTIpK+q/+93v4umnn0ZeXh4AYM6cOXjrrbdS9fRkBAUFBWO+b2DvXoSamqAcx9Xtj0fuLbeg7/dPged5sZuS0Y4lezLxnEj+ofZ2dN59N9pWroRv2zbkrLgZxb/+NUxfvxQc7YObFej4ZxdlzzbKn12UPUkmZcPvPR4PTjvtNOEyx3FQ0NzLtPP5fGO6X9hmQ8/Dj6BE5NXux0JmsUA7/1Q4//53mC69VOzmZKyxZk8mpmPNn49G4fv8czhffx28P4Ccm26CanJtmlpH0o2Of3ZR9myj/NlF2ZNkUlbUy2QyhMNhoWBsa2uDlPYdTzuXyzWm+/U/vQb5d9yeUYvjJWO55hp0/uROyHJyoVt82ugPYNBYsycT01jz58NhOF9/Hc7X34DmlHnIueEGKCsr09w6km50/LOLsmcb5c8uyp4kk7Ki/pZbbsGll16K3t5e3H333Vi/fj0epsXO0q5olC2j+EgE/l27EGpvg2rq1HFq1YnjFAoUrl6FthU3Qz1rJqRGo9hNyjijZU8mttHyD3d1YeDljfB9/jkMX7kIZc8+A45GT00YdPyzi7JnG+XPLsqeJJPSLe2qqqrw97//HaFQCOvXr08Yjk/Sw+VywWKxjHi758MP4diwAZYs3G5QolQid+VK9H05yoAkGi17MrGNlH+woQH9zz4LPhyB6aorkfvtleBo1NSEQ8c/uyh7tlH+7KLsSTIpKeqj0ShmzpyJvXv3YuHChal4SjJGHo8n6e3ON95A0apVkOXkjFOLUkt76ilw/OUvCHd1QV5YKHZzMspo2ZOJbTB/PhpFqKkJ/l1fwL9zJ6L2flhvvx2K0lKRW0jSiY5/dlH2bKP82UXZk2RSUtRLpVLk5eXB5/NBo9Gk4inJGJUm+eIePHQInFyetQX9oJzrvwX7n59D/k/uELspGSVZ9mTiK7Fa4fz73zHw8kaoZsyAetZM5Nx4I+TFRRm/GCY5cXT8s4uyZxvlzy7KniSTsuH31dXVWLRoES6//HLodDrh+ltvvTVVL0GG0dfXB+MI8817f/vYhBi2rq6rQ9+aZxDp7YXsyy0TSfLsycQV6e2F/bnnYN++HfmXfBWla/8ACc2VZw4d/+yi7NlG+bOLsifJpKyo7+7uRl1dHerr64Xrent7qahPM7/fP+Q6PhRC2GaDRKeDvLhYhFalXu63V8L2q1+hcNUqmh/8peGyJxNXqK0Nvb/5LQDAdMUVsF1wAcwzZojbKCIaOv7ZRdmzjfJnF2VPkklZUd/V1YW33nor4bo5c+ak6unJCMrLy4dcZ/v1r+H+939QuGqVCC1KD/XMmQjOX4COH9yG4l89Qqt4Y/jsycTDR6OwP/88fJ9tRv6dP4GirAwAUO52i9wyIiY6/tlF2bON8mcXZU+SkZzoE4RCIbhcLkSjUbjdbrhcLrhcLrS1tcHr9aaijQnq6+uxcOFC1NbWYt68edizZ8+w91u7di1qamowadIk3HjjjQiHwylvSybo7u4ecl2ouQXFv/0NNPPmitCi9DF9/VIYv34p2n9wG6JOp9jNEd1w2ZOJI9zZiZ7VD6Lt5pWQ6nQo+f3vhIIeoPxZR/mzi7JnG+XPLsqeJHPCPfWrV6/G/fffD47jEuZ5GAwG/PCHPzzRpx9ixYoVuOmmm7Bs2TK8/PLLWLZsGbZs2ZJwn6amJtxzzz3Ytm0b8vPz8bWvfQ3PPPMMvvOd76S8PWILBoMJl/lYDDx4qGfOFKlF6aU/6yzIcvPQ8cMfoeSJxyFRq8VukmiOzj7Voh4vgvUHEXO5EHV7wEkl4JQqSFRKcCoVonY7IJUi5nZDotWCk8sRdTgg0RugPqkOMrM5re2biGKBAAZefBHu//4X8qIimJYuRf4II57SnT/JbJQ/uyh7tlH+7KLsSTIcz/N8Kp5o5cqVeOqpp1LxVCOy2Wyorq6G3W6HTCYDz/MoLCzExx9/jOrqauF+jzzyCBobG/H0008DAP7xj39g1apV+Pjjj0d9DZfLBaPRCKfTCYPBkLbfJVWO3nEg1NYG+5/+jIJ77haxVenn/s9/ENi7D3nfvUXspogmHbtNRD0euN9+G54PPgAAqKZPh9RohESnA2IxxAJB8MEAYoEgpCYjEItBotcj5vOBD4chM5sRHRiAb8tWRN1uqKZPg3bhQqjr6mgxtyQiDgf6n16DYHMTjEuWwHDhhaOuHUG7jbCN8mcXZc82yp9dlD2bxlqbpmxOfboLegBoa2tDYWEhZLJ4szmOQ1lZGVpbWxOK+tbW1oR5JxUVFWhtbR32OYPBYMKZL5fLlabWp15g7160OJ2YumDBEdftg2rqFBFbNT50Z5+NgVdeZXr/+tbWVkyZkpqs/bt2wb5+PWIeLwwXXYTC1Q9CqtMe9/OZv/EN8NEoAnv3wvu/TbA/9xz4UAjKyiqoT6qDuq4O8oKClLQ9W/HRKNzvvgvn62+Ak8thvvr/kH/KKWN+fCrzJ9mH8mcXZc82yp9dlD1JJmVFfbYanD6QjYL19Yh1daH7X/9G/k/vBCeTIfDFLhi+ukTspqUdx3HI+96t6P3tb1H00ENiN0cUkUjkhJ/Du2kT7H9+DjJrHqw//BHk+dYUtCyOk0qhnjlTmArCx2IINTXBv2MH+n73O4RtNki1Ohgv/Rq0ixczs7d6LBiE89XX4Hr7bejPPgvFv/4VJMdx5j0V+ZPsRfmzi7JnG+XPLsqeJJNVRX1paSm6uroQiUSE4fetra0oO2LxKAAoKytDY2OjcLm5uXnIfQbdeeeduO2224TLLpcLpaWl6fkFUkw1fTqMn2+D49VXYbz0a1DPmoVgQyOU1ZPEbtq4UNXWQqLVwrd9OzQnnSR2c8bdkaNTjlXE4YDtV7+CRKtF8aO/hkR7/L3yY8VJJFBOmgTlpEkwLV0qtGNgwwbY//RnGL+2BLpzzoFUp0t7W8QQttkw8OJL8G/7HIYlS1D27DPg5PLjfr4TyZ9kP8qfXZQ92yh/dlH2JJkTXv1+PFmtVsyZMwfr168HAGzcuBElJSVD/idfunQpXn/9dXR3d4PneTz99NO46qqrhn1OpVIJg8GQ8JMtFJWVcP3739DOmwvvJ5+Cj0QAjmNqH/fcW25B35O/Ax+Lid2UcXfo0KFjfkwsGETPw4+g6667Yb78chT89KfjUtCPRGY2I/fmm1Hy5BPgIxF0/PCH6Pjx7XC/91/woZBo7UoVnucx8OpraFtxM2wPPQzN3JNRunYtTJdeekIFPXB8+ZOJg/JnF2XPNsqfXZQ9SSareuoBYM2aNVi2bBlWrVoFg8GAdevWAQCWL1+OJUuWYMmSJaiqqsL999+PRYsWAQDOPPNMrFixQsxmpwUnlQJWKyzf+hYcL/wNwYYGKBk7iyezWKA74wwMvPwyzFdcIXZzxlXsGE9kBOvr0bN6NczXXIP823+cplYdH4lGA9PSpTAtXYqI3Q7nq6+hfcMGSE1GGC75KrQLF2TVyapwRwcGXn0N/m2fQ3v66Sh+/DFIlMqUvsax5k8mFsqfXZQ92yh/dlH2JJmUrX4/UWTb6vfODz6E/tRT0P6978F48cWAVBr/L0PiPbw/guWaq6GZO1fs5oybcDgM+Rh6e/lIBI6/vgDv5s9QeO+9kOXljUPrUiPc3Q3nG2/A98knUFRXQ1FeDtWUKZDl5UGWlyf6loZ8NIpwRwfCXd3wbd2C4MF6xPw+yHJyYfp/X4d6zpy0nYwYa/5kYqL82UXZs43yZxdlz6ax1qZU1B8l24r63bt3Y8aMGei886eAVALLNddANXmy2M0ad1GPBx23fg+la//AzIJrg9mPhI/F4Fj/F7jfew+Gr1wE02WXgZNk1YwbAc/zCNbXI9LVhcC+/YjY+xFqaoZqymRYrr123E5URD0ehNva4NuyBf4vdiPqcEBRUQFZvhWauXOhmjJl3E40jJY/mdgof3ZR9myj/NlF2bNp3Le0I+JS1tSg/w9/QMHPfiZ2U0Qh1emgXbgA7n/9C4bzzxe7OaKLhULouvOnUJ88B2V/XJu1xfwgjuOgqq0FamuhO+MM4Xrf55+j+xe/BCeXw/jVS6BdtAicLHVva1GPB4Hde+DfsQP+7dvBqVRQVFRAPXs2DF/9KmRmc8peixBCCCGEkONBPfVHybae+sGhOJ6PPkbPqlWY9M9/iN0k0cR8PrR95zso+8Mfsmr+9fEaaRgWz/Pouuce6M86C/pzzhGhZeMv3GOD68034f3f/yDLy4O8pATy4mJo5s2FvKRk1NEbfCyGqNMJPhxGqLkZwYYGeN5/HxKtFuoZM6CeNQvq2bPBKRTj9BuNjobhsY3yZxdlzzbKn12UPZuop54RBw4cwIwZM6CsrYVq2jSxmyMqiUYD45Kvoe+pp5F3y3fEbk7aDWZ/pJjfj+777oe6bjYzBT0AyPOtyLnheuTccD0i/f0Id3Yh3NGO/rVrEenuAXgekMsgUSgR8/sBjotfN3hOUyKB1GAAZFIoKyshLytDyZNPQpJBRfzRhsufsIPyZxdlzzbKn12UPUmGivoJQp5vRdHDD4ndDNGZvn4pelavhv2552C59lqxmzOuQm1t6L73XuTcdBO08+eL3RzRyHJyIMvJgXrmDBguvFC4ng+FwIfD4DQaZtZdIIQQQgghEx8Nvz9Ktg6/J4fxPI/exx4DJ5Eg97vfnbAF3JHZez/bjP41a1D48wcgLy4WuWVkPNCxzzbKn12UPdsof3ZR9mwaa22a3atnERw4cEDsJmQcjuNg/f73ITEYYPvVr8RuTtoMZh84cAD9a/+Akt//jgp6htCxzzbKn12UPdsof3ZR9iQZKuqznCTLVzVPp5xlyyBRa2B79DfgIxGxm5NyEokE4Y4O9KxajaKHHoJEpRK7SWQc0bHPNsqfXZQ92yh/dlH2JBn6vyPLVVVVid2EjJZ3y3cgLymG7dePit2UlCvs70fnT+9C4S9/QVurMYiOfbZR/uyi7NlG+bOLsifJUFGf5RoaGsRuQsYzX3EFEIvB9fY7YjclZRwbXkTnX/6Ckt/9DoqSErGbQ0RAxz7bKH92UfZso/zZRdmTZKioz3IyGW1gMBbWH/0QzjfegPu998RuygnheR69v/89Qs3NkNx6K6Q6rdhNIiKhY59tlD+7KHu2Uf7souxJMlTUZ7mysjKxm5AVOLkcJb95FAMbXkS4s1Ps5hyXSF8fOn98OyRKFfLvuB3lFRViN4mIiI59tlH+7KLs2Ub5s4uyJ8lQUZ/lDh06JHYTsganUMB6+4+zcn59qKUFHT/8EXJuuB45N1wPgLJnHeXPNsqfXZQ92yh/dlH2JBkq6rOcUqkUuwlZRTlpEmT5+XC//77YTRmzwMGD6Lr3PhQ9/BBUU6cK11P2bKP82Ub5s4uyZxvlzy7KniSTNUV9LBbDd7/7XUyaNAnV1dV48sknR7zvrbfeioqKCnAchx07doxfI0VQUFAgdhOyTt4t34Hz73+H428bxG7KqPxffAHbgw+h+NFfQ56fn3AbZc82yp9tlD+7KHu2Uf7souxJMllT1K9fvx579+7FwYMHsXnzZjzyyCPYs2fPsPe97LLL8PHHH6O8vHycWzn+WlpaxG5C1pFoNCj+9a8R7uiAff1fxG7OiPw7d6L38SdQ/NhvIbNYhtxO2bON8mcb5c8uyp5tlD+7KHuSTNYU9Rs2bMCNN94IqVQKi8WCK6+8Ei+88MKw9z399NNRwsg2X2q1WuwmZCVOIkHebT9A8MAB2H71K/DhsNhNSuDbsgW9TzyJ4t88CqleP+x9KHu2Uf5so/zZRdmzjfJnF2VPksmaor61tTWh572iogKtra0n/LzBYBAulyvhJ5vk5uaK3YSsxXEcCn/+AFQzZ6Ft5bcRrK8Xu0kAAPtf/oKBja+g+Le/gVSnG/F+lD3bKH+2Uf7souzZRvmzi7InyWRMUb9gwQLk5uYO+9PW1pa21129ejWMRqPwU1pamrbXSod0/m1YYbjgfBT+4ufo/uUqhHtsorWDj0TQ+/jjCLd3oHD1qqQFPUDZs47yZxvlzy7Knm2UP7soe5JMxhT1n3zyCfr6+ob9KS0tRVlZWcJckubm5pTs13jnnXfC6XQKP9l2wOhGKfzI2MgLClDws5+h66c/heOFF8Dz/Li+vu/zz9F6441QVFQg/47bwXHcqI+h7NlG+bON8mcXZc82yp9dlD1JJmOK+tFcfvnlePbZZxGNRmG327FhwwZceeWVJ/y8SqUSBoMh4SebZFt7M5myqhKlzz4DPhxB+3duQfBQU9pfkw+F0P2LX8L55pso+c1vYFyyZMyPpezZRvmzjfJnF2XPNsqfXZQ9SSZrivprrrkGU6ZMQU1NDebNm4fbbrsNM2fOBAC8/vrrWL58uXDfFStWoKSkBO3t7bjgggtQXV0tVrPTrrOzU+wmTCicRALLtdcg/86fwPbQQ3C++Vbaeu2D9fVo/+6t0Jx6CgrvvRdSk+mYHk/Zs43yZxvlzy7Knm2UP7soe5IMx4/3OOMM53K5YDQa4XQ6s+KMWGtra0qmIZCh+FAI/X9cB+9nnyL3ppugXbDghJ8z6nTC+dZb8Pz3fSjKymD+v/+DsqryuJ6Lsmcb5c82yp9dlD3bKH92UfZsGmttKhvHNpE00Gg0YjdhwuIUCuTevAKWZdeh6+574N20CTk33TTiFnPJRPr74fngQzhffx3mq65Cye+ehEShOKH2UfZso/zZRvmzi7JnG+XPLsqeJENFfZbr7u6mLS7STKJSoeiRh+H77DN0fO/7kJeUQLtwIbTzTx12yPzg4Bfe50P/n/4E//YdkJrN0C5aiNLf/w6SFL0pU/Zso/zZRvmzi7JnG+XPLsqeJENFfZYzm81iN4EJHMdBO38+tPPnI9TeAe8nm9D9818g6nZBNW0atKecgpjPh3BnFzzvvw8+GgUnl8Ny7TXIvflmcFJpyttE2bON8mcb5c8uyp5tlD+7KHuSDBX1WU4ul4vdBOYoSoqhuPxymC+/HHwshsDu3fBt2Qqp0QDFpCoUL/nqMS96dzwoe7ZR/myj/NlF2bON8mcXZU+SoaI+y9lsNlitVrGbwSxOIoF61iyoZ80a99em7NlG+bON8mcXZc82yp9dlD1JJmu2tCPDy8nJEbsJRCSUPdsof7ZR/uyi7NlG+bOLsifJUFFPCCGEEEIIIYRkKRp+f5TBlctdLpfILRmblpYWaLVasZtBREDZs43yZxvlzy7Knm2UP7soezYN1qSDNepIqKg/itvtBgCUlpaK3BJCCCGEEEIIIaxzu90wGo0j3s7xo5X9jInFYujs7IRerwfHcWI3JymXy4XS0lK0tbXBYDCI3Rwyjih7tlH+bKP82UXZs43yZxdlzy6e5+F2u1FUVASJZOSZ89RTfxSJRIKSkhKxm3FMDAYDHeCMouzZRvmzjfJnF2XPNsqfXZQ9m5L10A+ihfIIIYQQQgghhJAsRUU9IYQQQgghhBCSpaioz2JKpRL33nsvlEql2E0h44yyZxvlzzbKn12UPdsof3ZR9mQ0tFAeIYQQQgghhBCSpainnhBCCCGEEEIIyVJU1BNCCCGEEEIIIVmKinpCCCGEEEIIISRLUVGfperr67Fw4ULU1tZi3rx52LNnj9hNIml06623oqKiAhzHYceOHcL1FRUVmDx5Murq6lBXV4cNGzaI10iSNueffz5mzZqFuro6LF68GNu3bwdA7wMsWbduHTiOw2uvvQYAOPPMM1FZWSkc+7/5zW/EbSBJi2AwiFtuuQU1NTWYOXMmrr76agB07LOgv79fOL7r6upQW1sLmUwGu91Oxz8D3n77bcydOxezZs3C/PnzsXPnTgCAzWbDhRdeiJqaGsyYMQMffvihyC0lGYMnWemss87i161bx/M8z7/00kv83LlzxW0QSasPPviAb2tr48vLy/nt27cL1x99mUxMDodD+Pcrr7zCz5o1i+d5eh9gRVNTE79gwQJ+/vz5/KuvvsrzPM+fccYZwr/JxPX973+fv+WWW/hYLMbzPM93dXXxPE/HPoseeeQR/pJLLuF5no7/ic5ut/MWi4XfvXs3z/M8/+GHH/LTp0/neZ7nv/Wtb/H33nsvz/M8v3nzZr64uJgPhUJiNZVkEOqpz0I2mw1bt24VztgvXboUbW1taGhoELllJF1OP/10lJSUiN0MIhKTyST82+l0guM4eh9gRCwWw/Lly/HEE0/QVkaM8Xq9WLt2LX75y1+C4zgAQEFBAR37jFq7di1uuOEGsZtBxkFjYyNycnIwffp0AMDixYvR2tqKbdu24cUXX8TNN98MAJg3bx6KiorwwQcfiNlckiGoqM9CbW1tKCwshEwmAwBwHIeysjK0traK3DIihmuvvRYzZ87EDTfcgN7eXrGbQ9Lk2muvRWlpKe655x48//zz9D7AiEcffRSLFi3CySefPOS2n/zkJ5g5cyauvPJKHDp0SITWkXRqbGyExWLBqlWrMHfuXCxevBj/+c9/6Nhn0KZNm+BwOHDJJZcI19HxP3HV1NSgv78fmzZtAgC8/vrrcLvdaGpqQjgcRkFBgXDfiooKOvYJACrqCclqH374IXbt2oVt27YhNzcX1113ndhNImny3HPPoa2tDb/4xS9wxx13iN0cMg52796NjRs34u677x5y2/PPP4/9+/dj165dWLx4ccKXfTIxRCIRtLS0YNq0adi6dSsef/xxXHnllYhEImI3jYyztWvX4tprrxVO5NDxP7EZjUa8/PLLuPPOO3HyySfj3XffxbRp0+DxeMRuGslgHM/zvNiNIMfGZrOhuroadrsdMpkMPM+jsLAQH3/8Maqrq8VuHkmjiooKvPbaa6irqxtyW1dXF2pra+F2u8e/YWRcqdVqNDc3o6amht4HJrCnnnoKDzzwgDDsvru7GwaDAffffz9WrlyZcF+VSoWOjg7k5OSI0VSSBn19fcjPz0coFIJUKgUQH2774x//GMuXL6djnxEejweFhYXYsmULpkyZMux96Pif2ILBIAoKCrBlyxbMnj0bjY2NQm/9KaecglWrVuHcc88VuZVEbNRTn4WsVivmzJmD9evXAwA2btyIkpIS+jBnjNfrxcDAgHD5hRdewEknnSReg0haDAwMoLOzU7j82muvIScnh94HGLBy5Up0dXWhubkZzc3NmD9/Pp555hnceOON6OnpEe63ceNG5Ofn0xf6CSY3NxfnnHMO3nnnHQBAU1MTmpqasGjRIjr2GbJhwwbMnj1bKOgjkQgd/wzo6uoS/v3zn/8cZ599Nqqrq3H55Zfj6aefBgBs2bIFHR0dOOOMM8RqJskgMrEbQI7PmjVrsGzZMqxatQoGgwHr1q0Tu0kkjVasWIG33noL3d3duOCCC6DX6/Huu+9i6dKliEaj4HkeVVVVeO6558RuKkkxp9OJyy+/HH6/HxKJBHl5eXjzzTfBcRy9DzAqGAzi4osvRjAYhEQiQW5uLl5//XWxm0XS4Omnn8YNN9yAO+64AxKJBGvWrEFxcTEd+wxZu3YtbrzxRuEyHf9s+NnPfoaPPvoIkUgECxYswNq1awEADz30EK655hrU1NRAoVBg/fr1kMvlIreWZAIafk8IIYQQQgghhGQpGn5PCCGEEEIIIYRkKSrqCSGEEEIIIYSQLEVFPSGEEEIIIYQQkqWoqCeEEEIIIYQQQrIUFfWEEEIIIYQQQkiWoqKeEEIIIYQQQgjJUlTUE0IIIYQQQgghWYqKekIIIYQQQgghJEtRUU8IIYQQQgghhGQpKuoJIYQQQgghhJAsRUU9IYQQQgghhBCSpaioJ4QQQgghhBBCshQV9YQQQgghhBBCSJaiop4QQgghhBBCCMlSVNQTQgghhBBCCCFZSiZ2AzJNLBZDZ2cn9Ho9OI4TuzmEEEIIIYQQQhjE8zzcbjeKioogkYzcH09F/VE6OztRWloqdjMIIYQQQgghhBC0tbWhpKRkxNupqD+KXq8HEP/DGQwGkVszus7OThQVFYndDCICyp5tlD/bKH92UfZso/zZRdmzyeVyobS0VKhRR0JF/VEGh9wbDIasKOoDgUBWtJOkHmXPNsqfbZQ/uyh7tlH+7KLs2TbatHBaKC/L2Ww2sZtARELZs43yZxvlzy7Knm2UP7soe5IMFfVZLicnR+wmEJEkyz4aiSHoC49ja8h4o2OfbZQ/uyh7tlH+7KLsSTJU1BMyAXU1OvHF+x1iN4MQQgghhBCSZlTUZ7n+/n6xm0BEkiz7kD+CAPXUT2h07LON8mcXZc82yp9dlD1Jhor6LGe1WsVuAhHJYPaObi/6OzwJt4WDUQR9ETGaRcYJHftso/zZlY3Z97V7cHBLt9jNmBCyMX+SGpQ9SYZWv89y4TD1xrJqMPvO+gG47QHkFOuE20L+CIJe+n9jIqNjn22UP7uyMXvvQBCOLp/YzZgQsjF/khqUPUmGeuqznMPhELsJRCSD2Qd9Edha3Am3UU999uF5HjzPj/n+dOyzjfJnVzZmHwlFEfTTZ1IqZGP+JDXGkv1//rwX0XBsHFpDMg0V9VmuoKBA7CYQkQxmH/CE4er1JxSEoUCEivoMFovx4GPxvLa81QRXvx8v3P8Zmnb2jfk56NhnG+XPrmzMPhKKIkSfSSmRjfmT1BhL9m57EKEgHWssoqI+y/l8NJyNVYPZB3xh5Jbq4eoLCLeFA9Fj6vUl42vLm034y72fIuSPoOFzG+ydXugsqiHbEHYcdGDfps5hn4OOfbZR/uzKxuwj4Rj11KdINuZPUmMs2YcDEYQD0XFoDck0VNRnOZfLJXYTiEgGsw96I7CW6+G2Hy7qQ8EoFCqpWE0jo7B3elEyxQyPIwi/O4SANwx9jgqhoz6IvQNB+N3Dz6GjY59tlD+7sjH7SCiGEBX1KZGN+ZPUGEv2QX8E4SAV9Syioj7LFRUVid0EIpLB7GMxHlqjAgHP4eIv7I9AqZEjGqV5VZkoGolBrVfA4wgg4I0g4AlDb1EN+SAOeCMIh4b/cKZjP/2ikRhcfX6xmzEsyj/Rh387OGSky0SVqdlHozE07ewd9rZImObUp0qm5k/SbyzZhwLRIR0EALDtnZZ0NIlkECrq/z97/xkdWXpdB8P75lS5kBuhAzpMT/eknjwchiFFMc1wXo5EUhQtUTKHFC3qsz6vd1nWZ0mWrHeZlLlky5RkcUjK9GvSIhUoidHDGVHkcEhOzt3TGY2cCoWKN6fvx1N1URdVQAPdBaAKXXstrIXKF3Vwn/ucc/bZu83Rqdheu6iNvaBwMMpWcNt1fUgxvjPD2ALQilZY70B3wIkMeJFFOWfC93wUMzpiabGOMmdqNhxrpTBz8kczwe+dc3/rsTyr4tUfTCO/oLVcQtKJfxiz5/P46dcv7PRhbAtaNfZq3sQLjzZOHBzLg2201jnUrtiu+C9Nl0MMwA52HhuJva07sBvM1J9/fqEzlrnL0Unq2xzlcvnyT+qgbTE/VlhzEa6NvRThoZfDXSpBZjtieS2AJ//mHAqLetDBys6UkR5QwEsMSjmyYcovaoimpQadehtOTaf+9E/ngt+r8Z84ld3qP+GahW06sHQHZ5+dx8yZ1lKc7qz9YUTTIrSidfknVuD7/qae30po1djrJRvLc2rDa5ZjuaCZzpazGdiO+Buqje/+91cxP1bY8GtmL+ShFswtPKoO1op9cUnH+KtLcF0Pju01pN+rBQue00nqdzM6K2ybY2hoaKcPoYMtxI//9jzyC42FUYaGhoLNkxjhQvR7ABBlFsY1QkdtZeTmVJiag3/+8hksXCrimW+O4cAtPeAl0qkXZBb5BZ3Q7w0Hy7Mrm2JTdYKk3vd9qLmVrkn13H9xjc5YBxvH0nTjjRKhMTqwNAf5zMbFqbZj7KWz9q/A83xQ1OZeU86ZePofL27NAW0xWjX2etGC5/oo5+oTO8fywPJ04PrRwZVjq+L//P8ZD8a9Fi4VkeiVNiW4NvX6MgqLHRG/rYLnerAWpIaPZaZKmHx9GbbhghWYurh5rge9ZHVGMnc5Okl9m2NpaeMWWB20H0pZA3MXG1fKl5aWgo2SFOEadOq5Tqd+h+G5HnILGvSyBUt38L0vnMS9HziEZJ8CQWJRXjYQ65JgqDakKAfbdPGTvzsfiONV6feZqRIsw4Wu2rBNF5nJUnDuG2qncHM18D0f//y/Tjd8zDZdWDrp1hczG5+tf+rrF1HYRBHgSnAtr/2GauPcs/PBbcd0wQmbEwa12lhMqlVjr5Us9B+IY3lWrUsgHNuFHOXX/c7PPTe/5mMdrGCr4j9/sQCzcj3RyxZi3TKsTYxMOLYXGhfroLmwDBcXXlhs+Jiat1DOGbB0B5GEUHee6SUb8IlOTAe7F52kvs2h660p4tTB1cOxXMS7JcxdyNc9phZM6LoOQ7UhKhw4kambV5SiPPRSe9JLdwsKGR2CzKGUNTB0NIWBQwmk90QAALzEQs2biKVFCAoLhqXhuj70so1ytSNPUXBsDyd/OI3MRBGe4yMzVcKz374UnPum5sDrVN+vGI7tIb+gNaQM24YLUycCX+X8xmmlhmrDULe2oHYtr/1L02VMnloOblsGSeopmkJ+QUNmqoSzT8/BNom1Z6PCl2W4a4pQtjpaLfamZsP3feglC3sOJbE8q+KV709haWqFAeNYHuQ4v642xcknZjozvxvAVsXfqBSNAZIExrrC4q16ef39hGO5cOzOtWirYJvumkUWtWCinDNhGQ6UBF/3vOpYhGt3zq/djE5S3+YYGRnZ6UPooMnQihaWpksoZg307o/D1Jw6AahvffYV6NPE11xUOFA13NPqpkhJCFA3kYh00Hzk5jQMHIijsKgj0SvjbR85GjzGSyxKORPRtAhR4YL79bJVR1+1DBfFJZLol3MG5i7kMTw0DIBc6DsbqSuHbbqwTbehdaBlOLANB7V5hu/7dYr45ZxR97qttu+6ltf+/Lwa2rTapgNOZCEoHM4/v4DxV5dw9tkFaEUL+QUNP/jymbr3sA0HTpt26lst9j/4yhmcfXoeetFGejBCmEmmC9eu7dR7kGN86LzwPR8//N9n8PqPZwGQdW51J3FxotihDK/CVsXf1JxANd0oW4h3ScFtz/Xw6CMn1329Y4Vj3kFz4VguGIpr+JiWN8GwFCzdhdKgU68VLDAs3WkA7HJ0kvo2x/x8h6622zB7Po+xl5dQyhqIpUW86xM3IDurBo/blot4j4SLLy/CKNsQZJY8QFEo5wy4jgeGpaHEeagNZhs72D4sTBSx53AShSUdohy+GPMiC8d0EeuSQkm9pTn1SaLuBIlkedkEyzO4cHIKvu/DMTsbqauBY7kAhYazoNWEHwAYhoZreygtG/jhX50NnuN5Pr7+mRfCDgeGsyna6pXgWl77c/NaaNNqmy54kYEU4bAwXkRxSUcpa8A2Hcyez0Mr1q+DluG2Lf2+1WJvGy7OPD2Hct5EvJvMYduGA8cOf7+CzIU69QvjRagFC7l5cn1zawS+9JIFrWjhpccnMVXDyuhg6+JvanbA+COdeim4vRG2kmN7nQLzFsI2XRhaY7aEodqQ4wL0kgUlXp/UqwXSQOjQ73c3Okl9m8M0O0nbbkM5Z8BUbRSXdETTIgBAjnKBUnN2uoyuwQgAD4bqQIyQhNBzPfzNp56HpZMNrhIXoLapuvNuwdJUiST1ixpEhQ09xktkBjiaFoMYOpaLaFpEedmsSxKLSzpYgUE5Z2LoaArZGQ2e48Pz/M5GaoNoNLNrmy7iXRIuvpzB/KWwfoVtuGBYcpmMJAWU8wa0goXlmRVacXa6jPKyGer0W7oLS9/ahPFaW/sLGR2O7SK/oKGYNYK4ACv0ezHCYXG8CK1goZjVYRku5i401iSxDAd2m87/tkrsXceDY7tgeQaDh1OYu5CHEucrDJf6YuNqR5aFS0Vcd3c/Ssvk73EsNxD4uvTqEiZPZeFYHs4+M98R2KvBVsSfjKnUdOpVG9H0Cv3e1BxoeXPd8QjH8uDa7VkoawfYpgvHbLxm+SDXqOU5lST1q4TytKJVSeo759FuRiepb3Ps379/pw+hgyajvGzC0OxKp54onXYNRfHiYxOYfD2LzGQJ3UNRyIpCZuorHWBBYsGwpFsvRTkwHA2vU5XdERiqjee+cwmCzEFUOBSXDAhKuFNfnQFO9Su4435yHtumi66hKMo5A3aN+JdluChmDUQSAkrLBuLdEuKRZLDh6nTqN4aXHpusu8+2XHQNRnDqiRmMvxIWoLJMF4LMwnP9YJxFzZswVCeY054+k8PgkSTyNZ3+qrheLbSiFbInvFrs9rW/+v36vg/HcvHK96cwf7GAb/3py1DzJih6ZeTINhxwAgMpwoOiKJi6A98j55NeJiNKq5MR23Dbln7fCrHPL2j48d+eR25eQ7JPxt4b0mBYGpzIktnfBmNBSjw8ErYwXsTAwUSQCDqWF+gc6CUreH3PSAyPfuEkDNVGdqY17fy2E1sRf9twQVEIrim+50OQ2OC2pTtwbK9uXXvmm2NBwaUzU7+1sE0Xvru2zUckKZKkfg36fTTV6dTvdnSS+jbH5GT9JrWD9kY5b8DSXWhFC3KcBwB0D0dw8oczyEyWkJ1VkR6MwHYNFJf0oMt73y9dh73Hu5CdKUOOCWu+f3am3Lb+zK2IRp2L+bECMpMlXHd3PwSFhet4IYo9AFAUBV5kIMgsEr0yAJLoxyrdEb1kQarE1rU9FJd00i3OkbGMzPwyLJNssDobqcvDtT2Uskbd/bbpou9AHA/91gmUlo1VjzlQEgIoqtqpN1HOm+gfjQfJxcJ4EYfv6MPCpSLGXs4AIJvb1fT7U0/OYP5SsWl/z25f+7/9Z6/gJ393HguXinj+u+Mo5wzk5jWk90Twpl84HHouod+zkCIcYl0SpCiPRA+hgdMMRRLNVZ0r23TadoPbCrE3NQfZ6TKWZ1WkBhSk90Rw7M17QNMUfB8NO/VKMpzUm5oTWhcda2UkQi/awetvfvswPNfH3IU8Xn6c/O0//rvzcF0PM+dyW/2nthy2Iv6GZiOSDCeDVCWWALFXZXkaaj68d3jth9O49CophjpWh36/laiOg/m+j6nXl1FaNvD8d8cJi4+jMXAwgclTy5BjPBzbg+/5wSifVrQQTQltu+Z1sDG0XVJ//vx53H333Th06BBuu+02nDp1as3n+r6P++67D4lEYvsOcJvhOB3Lst2G6kbG1Fbm5fv2x/HOXzsOU3WglyzIUR6MABQW9eA5LM9AivLIzqiQYpWNEkXBq1TRq8nnS49PYnGiecnFtYxiVseTXztXd//SVBk3v30EQ9elwDA0oQYr9QI3gsyCE1do+bzIQozw8H0gO60GSvmCwkEv2VASAso5E7EuCZa+QsVbPbvaQT1M3YGp1c/5Vu3QugajQRdq6sxyQAVW4gJ4iQ2+e61gYvj6NH74v89i6vQyXNtFejCClx6fxNhLJKnnRLauo2VqzU0id/Pa73k+pAiH/CJx+Cgs6VDzJqbP5BDvltC7LwaExlMq9Psoh1i3iFiXiPSeCAzVBsNQkOM81MLqkZbN2+C1Cloh9rblIjurYmGsgJ6RGCiKwi1vrwi4+T5s06lL8KJJMUgyXNcDw5KuI8MxgeBnlT2hlay6c1UtWJituMGMv0J0Z577zqUt/CtbE1cS/8uJDZqqg2hKXFMLxNQdpPqVOvHd9J4ITj4xTRg1dkffZSthmy4ohhRPZi/kkZ0u49IrGXzjT17CTW8bRu/eGPbd2AU5RppBuXkNT39jDAAZz+QEtiF7c6v1XzrYPrRdUv/xj38cH/vYx3Du3Dn81m/9Fj7ykY+s+dz/+l//Kw4cOLB9B7cDGB0d3elD6GALUVW1pxka3cNRGJoNz/HAcDR6B7qQX9SCTj1AZu+zM2XIUT64rZcseK6Hv/ujF+C6HqZeX67rWvme35lZvAIsTZbrursAsdxK71GC24LMrgga1kBJCKBraMScwECKcoilRVx4cRFdQxH4vh8UBCIJAUaZzDpKvEI6lBLb2UhtANUkezVLxbbqk7sX/s8E/vl/nYbvA4LCQqgk9WqlU3/kzj6ceOcI8guEch/vluC5HizDCVgZ1qpzzNTspsZpN6/9payBaGX0yNQcFJcMcCKD6bMkqa+imqTbhgtOZJDqU3D07gHsv7EbQ0dTKC8b4GUWcozHs9+6FBq/sAxy7nhtuO61Quwd04Vju1gYLyLeI9U9bjWaqVdYGGUyVmHpTrAmRtNicC7VCuXVvp4TGOQXNQgyh0JGQ3FJx/KsWnctuxaw2fj7no/vfX595XpDsxFJibANwhKk2XB6YGo2kv1KYI0GkAaEFOEwcDCBiZNZuKuE8vSS1WkgNBGO5UKJETaFbblQCybkGI9f/I93oW9/HADwto8cDZh/WskKsdMYlljkrtaOudz/Rgftg7ZK6hcXF/H888/jwx/+MADgoYcewtTUFC5cuFD33FOnTuEf//Ef8e/+3b/b7sPcVoyNje30IXRwFfA9P9Q9cm0PNNP4tBQUFqbqAJVEv6wXUMzoEKSVZJF06lfo98Sr3oZWtFHM6PinL70OXmJhGQ7GX13C4/+DMF3GXs7g3HMLW/Vn7hqoBTO0qclMlxp6YDsWoQNXUZ2tX407HgjPRnIiEfsaOJTEpVcySA9EQNNUpaPPIJIkRQA5yqOwXIJtEqHETlLfGLWFKstwQLNUXVLvmB5YniT1FE3BdT1wAhPQHHmJBS+xiCQEqHkLlka+81S/Ar1E3osXWTz4/70Fvk8+J5IUGnfqmxin3bz25+ZVJPvIxtTSHRQWNfAiC5ajEask9SzPwKkI3dmmA14kcdpzOIk9h5NIDSgo50wIMgclxmPyVBaTp7JBx9I2HMhxvqk6B9uFVog90aKIorvSpQ+Booi9WW1X0PdDz7N0B3zl2hVJCMgvamBYOuSTXpsgyjEeS5MlHLq9FyefmAHD0sjNq3XFs2sBm42/bbnrjtyZuoNiRke0ktT/wx+/iP03doWfo1U69VW/c8cLdF8O3NKD6bM50AwNt+Z8ys6UA/ZSB1eOQkaD5/mkU897hAVjeShkdHAiG2oM1EIvWijnjGCPSbM0sUj+u3DOlM/o6wogdtA+aKukfmpqCv39/WBZciGgKArDw8N180W2bePhhx/GI488AoZZn15nmiaKxWLop53geZ3NfDvjwouLePF7E8Htct6EkqzMw6/aKDFM2GOUEcgiXVsEkGIkia/SrziRgW040Iomjr1pD66/dw/ufO9+2KaLkz+aqYjfuBVqcn1y2gEw/tqKeNoLj05g4mQ2uJ2bVYOEcHGiCLtiL8dw4aU1mhbBifVr0Z5DydBtJS4gkhDQPxpHolcGw9FgeQa8xEJUOCgJAYLCVmYdfdgW6ZR05hjrsTyr4m8//XyQXJu6g0SPDK2wqlNvukFsIgkhsIFUEgK0ggVeJCwLlmcCQS+KoiBGOOjllXOGOFIQ5fvqTGMtCPW/eXHazWt/fkFDok8GTVMwVBucyEBJCEQgspLU8yIT0EYts55twQkMyjkDgsRCjgnoH01g5HgXps+QGWzbdCFH+ba0tduJ2OcXtVBiaJsuDt3eixvePFj/ZN8HTVNBwWR1wjB7IU9cWipJvSCz0AoWpCi3ItTm+6EimBwjo2VH7urHmafm0bs/huVZ9ZqkDm82/o5VL3BXi5NPTOOnf38R0bSIwpKO/gNxHL6zP/QcU3OQ3hNBueJU8IMvn8HyXBmcSK5NpkZm7p2aQo5teQ2L3h1sDs99exzFjA7bcMHLFBlVsVwsTZeDvV4dfB9a0YJRtmGoNgSFA1NJ6vOr7FtrRSk7aG+0VVK/UfzBH/wB3ve+9+G666677HM/9alPIR6PBz9DQ0PbcITNw+HDhy//pA5aFkbZxsknZoKORjlnIJIUwEuEhr0e9o0O19mkyVE+6OwCpINoGS7UvIlIUsDg4SSh2JlE6bZrKIrlWbVy0W+/ze124OlvjMEo23BdD2MvZWDpDs4/twC7ovTLVhL4n/79BSxOFFHOE5X6Wrzj48fqu1kNcOu79iLZp0CJC3j3v7oRAMDxNHiRzORHkmKwEVYiEdimA6nTqW8IUyczoo//j1NwbBeW7iDRKwfd9Sps0wVXKcxEKjRghqWQ6JXhVRSgq9+5VrKDTrEU5VHKGmBXJZOWsdKBDB2PZjd1pn43r/3ZWRWpPgW8zKKUNdA1GEUkKeCWd4wgmiI2n7Xid1X6fS14kUVp2YAgs+geieK2d+/D3uNpTJ7MVhhSpOjZjp36nYj9xRcXQwVOx3KR7JORGlDqn0xRZCyo8v/uOiuFTl5m8Y3/8hLKeTNgmfESC61oQoyQpN73fLAcHe7Ux3nQLAVR4dC7P4b+Awksz12b9PvNxt+x3JCV4GrMjxXRPxpHNE3U0yPJ8PXL930yUz+gBF71C+NFMhYjMOBlFpbmgGHpgD0DEAZNJ6m/csxeyOPM03NQC2ZAue8ZSMEyKkn9VGnNpF6McFieV5HsV7A0WYYS5ytJvQ29ZAdFHtf2YBtuIGzZEVFub7RVUj80NIS5ublAJMT3fUxOTmJ4eDj0vCeeeAJ/+qd/ir179+INb3gDisUi9u7di0ymngb027/92ygUCsHP1NTUtvwtzcLZs2d3+hA6uApYhoP0YAQLlRmncs5ENCVClLlgLr4Wpu4E/uYLSzN1lG4pykGK8YHdU7WbpRYsKHGh5j6yEeoeiiIzWaqoDl97HY+NQCuYyM2rWJoqo2soAstwce65BcxfKJDkjSLdxMxkGaWsgfKyiUhSDL0Hs8ZIxXqobqyqnfqRY2mIES7YCKvlMmzDhRjhOkJ5DeBYLgYOJbDvpm6ce3aBJPU9MrSihexsGYWMDiA8Ux/vkrAwXiTq6b0yeJHByPE0Dt7WCwB46y9fhzf8/EEAAMczKOfMunPQNpzQ6EUVzabf79a13/N8qDmDsFIkkpj37o0ilpaw93hXwEziRCbo6tprderzJgSZdBJ798WQ6JWxOFHCV37vKSIcxTM4/9wi5scae9m3KnYi9mrBQqGmw1dbDFsNTmAq61JFyNNaGXE5dFsfRo6nyUhFTVKvFogAbDUBVRJCXae+eg17y4ePYO8NXcjNE8r+taYHs1b811pfbIuw8VbDUG18409eAgC84+FjGDyURDlrIJJauX5xPEnUTY1YQ8L3YRmk21vOEa2LKouQ5elQ4dI2XBhqZ19xpcgvaMhOE7ciy3Bgmy40q8IIdHx4rh84JK1GNC1hcbyInr0xzF8qQI4JoBmqMsJJmDe+7wdFF9t0MXcxj9d+OL2df2IHTUZbJfU9PT245ZZb8JWvfAUA8PWvfx2Dg4N1oiFPPvkkJiYmMD4+jh//+MeIxWIYHx9Hd3d33XsKgoBYLBb66aCDrcLCpWJIhdYyXHQPR1FcImImpWUDkaQIQSHJ+WowLA1JIfezIl2XUPASi2hqpcpe9QzWCmaw+HPCivds11AES1Nl2JZ7Tc4mXg5E/MxFbkGDVjCR6ldgGcSD/Oyz8+gajECQWFx8cREHb+1BcUknbIvU2paCmwXLM+BFFnc8sB+cwIQE9xzLgxjhO536BnAsknQcvLUXl17OwNJdJHplFDM6fvr1C5g4uYQXHh2HqdpBwhHvkTB/sQApyiPZJ5MulMgGhZRUvxJiXNimU3cOmtpK4S18PF7HTmgNnH1mPvh9/mIe/aMJAISWXVo2cMNbhjB6oif0Gl5gV+j3hlOXYHIiA8/xIcgr8aEoCje/fRh7j3ehlCUJyeSpbKCoDiDwSe8gDC1vIr+oB7cdy6tjqVTBi8SJpbouWYYTFF1GjqXJNS+jB8UvQWKhFUyIFfq9VrQQSYmrZuoFKJVrmBInv7u2h1iX2IlZBY9+obHgmWN5cG0viMfUmWXYlov5sQJiaRHX3zsAlmdA0RRYng7YMEDFycNwKoUZGjRLY2G8iGSvjNKyGcTVdYgWieeuFFhs0+106q8CeslCadmEmjdJd950wct0wE6RY/yanfpYWsTyrIqe4ShmzuUhx3kwHA1Ds5HokVHI6Hj58anAjrB63jUq/nTQPmirpB4AHnnkETzyyCM4dOgQPv3pT+NLX/oSAOCjH/0ovvnNb+7w0W0/djMFczfi1R9MBTO7AGDrDroGIygukc1SOUdo8qLCNlysxQgXqN0fu/EIBq9LhR6nKArv+Pjx4DaZqXdDnXpOZGBpNiiaQjQlQi2YcCzvmqQxXg56yUbPSBS5ORVa0UKiR4alk4r5xZcy6BqMQFQ4TJ/JYfRED+nU5+s79VcDlqeDJJGXWBy+ow8AEEtEYah2Z6Z+DVQ3oVV6aCGjIzWgBJvQ/IKOs0/PIzujBvfFuiTMXypCjnKIpiVcf++edT9DVMICiJzAQM2bdfT7qvheM+O0m9b+Z74xFsxdT53OYeRYGgCh0KsFC5zIBOyjKqprm2U4oCiq7nGGocGwdJ3rxP6bujF6ay9YngHLM8jOlJGbJx3o0rKBRx85ifmxAsZfXUKrYidi77p+uAu7TqeeF9nQuqQVrCAhBwApwqFQI/Ja7dRLER624SK/qCE9oAQaFgBJUvbe0FXzHuT9Yt1ScO069eTMNZFErhX/KvtoNao2gdWE7blvXUJmooSFS0Vc/8Y92Ht85XvlRHZVUs8EVG2KohDrknDmqTkMH00Td4nK2EstGyP4XMuFeQ3EY6ugFSwUl3Sy5zAc2JaHkdGhoCkjx/lAFHk1oikyqjdwMIHevVHsOZQMZuq7BiMoLGrILaiYPZcLtCy0IhGC7aB90XZJ/eHDh/HUU0/h3LlzeP7553H8OElgvvjFL+KBBx6oe/7evXuRz+e3+Si3D7uVgrlbYRnh2TbLcJHeE0GxYjui5k0ocR6H7+gLXWirEOWVpP7C2Hnc/DPDdc+pJu9AdaaeeNtXZ/Q5gYFaEQAjgmvk4nstCg5dDmrBRP9oAoWMTpL6XikQQoPnIz0YgaCwmB8roG9/HIZqV+j3ze/UAwBNU4GAkW6pKOfNEM21gxXYlhtsMrsGo5i7mIcgsXjjLxzGu//VDcgvaihkdCzNlMHylXlfkQXD0RAjRJti/0317K5aiBE+ZCkpyCyKWSLOxvF0ID5pqkRpvdopc20v1J2+EuyWtd/zfJRyBtQ8meUkyvdkTluQWTAs1VCPghcZWKaDCy8s4sDNjeO0mtlSRc/eKEaOpUnBR2IDnYVLr2Sglyzk5rU1E6RWwE7FnqKowAKw0chDFdyqTr1aMKHU6IyIER6FjL4ilCfVCOVVZoW7hqIhzRJeYkNFNnKecogkhODaNXehgHIu7KO+G7FW/NUapfNakLWQhqWTEaDcvIbsTJlYr1YEPqvgRWZFrBfA4KEkzj23EIw4JHokLE2Vsf/mbpRyJjihcm1iqWC9/cGXT5NZbdMFzVxeS6aDxtBKFlzHQzQtBkzKhcxcMCqZ6o/U6fdUEU2LkGM8Yl0S7vq/RiHHeDAMBVOzkR6MIL+oo5wzMT9WrLgeONAKFkzNxqs/mEJuXt22v7OD5qHtkvoOOmhnWKtU5i3DQSwtBt0F3/dBMzQEmWu4YRIU0gHZKKrz877nB7OoVUVpvpbGbbptqQK91dCKFqIpEZ7rQ69QQl3HA0VTuPGtQ1DiAsSKqmx1Q6MWzFBX6moxfDSFnpH6sSBWoKHmDIhKRyivEaqdegBIDSjITpcDMTWaoaEVLPSMROGs6jgmuiXIsY2dY1I03KnnJRaLE0UkemX0jyYwe4HMapsacaSoxkkvWzj15ExT/s52h6U5gA/kF8gm0rG8YO3jJTZk2VkLTmTx0mOTmD2Xx4FbetZ4DhOi31fBMDQZZ+GZwGPd931Mnc5BkImXekenYgXVRDGaFFBeJgVo23TXod8THYNqsZEUq1eSDynCoZQ1VgRdJQaGZpNOvekiO6uSTr1T7yRSi+6hSDBiBpDr6bXaGXZsMkJXS38PHrM8KHEBpuZgcbKEA7d0Y/ZCHhRVr/cyfH06tB4OH0vj7FPzOPYmUlAZPJLEm37hEESFQ3nZCNZUQeaCAszk68uYOr1c0XxpT9vInUT1fHNMF/FuiTAEK4UrhqOC//d7P3AwVFSuRSQpIL0nXLBhONKpj6Yqe07fRzlnIJqWgk69bRJV/exMJ6lvRzQlqc9ms/iVX/kV3HffffjsZz8beuyhhx5qxkd0sAZ2EwXzWoBlOKGZpWqyTQEoLum4XE27eyiKRC/xb95I7DmBhd2gA28ZTmizbFveage9axqLE0VcfHERWsEiFW6ORmnZgBzj4fs+KAq488EDAMhIRLKfdBYjKRGlZSNkM3i1SO+JNOz87xnphVqwEEkKDTv1z377UtOOoR3h1HTq0wMKfB+h//lYl4h9N3WD5egQdTveI0FqIFLZCFKUDyX1gsRieVZFJCViz6EkZs4R+zRTc6DEhcDuydQdFBavrhO8W9Z+Q7WR6JWRX9ThuV4oFoLMNnQSAIiF4K3v2ou3/crRNTvGvMisWRQAAFZgEO+SIEd5ovrs+2B5BoZqhVS8Ww3bHXtTdSDKLOSEAK3CaiCaFY3XOU4kDAhUkhM1b63q1HPwPD+ILc2sWHe6DrGyIxaS6yf17/pXN4SEXy3DhXENWLM2in+VAdgogXYsF0pCgKnbmLuYx4ETPZg5m6vTqQAQCIFWQdMUHvw3NwcjMfFuGQMHkxAUUkzhK+eeKLOkiOr7kKI8Lr64CNtyKwlkhwW4UcxfKuCrf/DMSiEtJSLRKwcjJqOH9gVORes56tAMjZ/51aOh+xiWJhahlesiw9KIpETE0sQRydRssAIDvWQHI6EdtBeasvP8tV/7NXR3d+PXf/3X8Q//8A946KGH4Lrkn25sbKwZH9HBGtgtFMzditUUJtKpr7/AsQKDJ//mPO794KF13+/ALT1B9XUjseelyjzcqsWfZujQZtl1PKIi3IC6dy0iv6hh4lQWakVgMNEjY3lOBcuR77NW3VyMcEhVkvo3/cJh/Ny/u3VbjlHYU8Yv/N4dEFdZ2uUXNDiWi9M/nYVR3v0b3Fr89OsXgmJGVSgPIJ7zUpQLJQhD16Ww/8buOkHKez9wKCicXQ4n3jGC7pFocJuXWMhxATRNIdkvY+ZsDj/+2/MwVLtCv68kH7obKBpfKXbL2m+oNvr2xZCf11BcMhDvkoLHBHntTn00JeLAzY079FUoCWHdpFCJ8+geiSHWJaKQ0UHRFBiGQjlvtnR3cbtjrxZMyAkBUoSDXqqwymrYX6sxeCSJwSPJ0OtXJ/UAQoKSgkSSwkJGD9TXHdsDy64dP5Yjo0nVwrVtOETde5ejUfyrbL9GxahqUm/pLhYvFdG7N4ajbxi47HhRFbEuqS6BrLIswp16BobqID2gwNCI9kw0JVwTOgfNwouPTqB3f5zofFAUUgMKuoej5Frh+5iZn9jw97k6ZjRDwdQcotMjsuBlFvFuCdFKUl99janZKHSS+rZEU5L68+fP4z//5/+Mhx56CN///vfR1dWFBx98ELbdOZG3GjTdmaBoNZRzZrDo/vP/Oh16zDLcQHQGQJBsv/1Xr8e7/9UNiKUlbBQbiT3HMygsGZBXed5zQoMOFkXhib862/EpBbHiyUyWUMwaiCREJPvkYANrm25odGHPoSROvHMkuH0l9nVXAoYhmymWY0JU4VM/nsXSdBmm5iAzXdqWY2kV5Ba0wGGiln5PUWRconaTc/zNg0j0yjj+psHQe/Aiu24HpBaiwoGu7SxLLFJ9cvCZ7///3YZyzsDZp+cxfH06KL6Ymg1BYa9qbnu3rP2GaqN7JIZiVsfynIpk/0pBhZfW7tRvBD/78LF1H+8ZieHYG/cgkhSxOF6EHBcgRDjk57WW7tRvd+y1it2cFOUD/YH1EEtLRGytch6Zqh3SNhAjlZElbiWp5yUWHM9g+LoU7n4fYUH5nr9uUQaoCLkZK9aG10IC2Sj+VauyRk4AtumRTr1mw7Y88CKLO997oE7YbjNgOQYMRwcz9YJCijKe50OujFq4Dvl9dUyKSzoWLhWv+LN3M3wf2HssjZmzOdA0hevv3YORY2kypkRR4CvjQVcChqUJC0YgY0eRpIgb7xtCok8OjV/yEgstv/u1KXYjmnJlMM2V4NM0jUceeQT79u3Dgw8+CMvqJAhbif379+/0IXSwChdfWsTcxQJ830d+YWXT7vs+2MpM02qsVm7eCDYSe4qmoObN4CJbBV+lR4Lsu6oiOJnJEnJznVkqS3eh5k1oBSJ6l+iTAzeC1QURmqYa+pJvNarxZzk61Km3DAda0QJNU1iaLG/7ce0ofJ90hkp2iH4PACfesbfhS25+e73Y5JVCUNhgFAMgif2BW3qgFS10D0WCMQnLcNAzEkNhUUcho12RJdduWftNlegNeJ5fEclbSeqlKI/BI6l1Xr0+NnpeRpICFsaLUOJknKKwpDfs1Pu+3xKe6Nsde0O1IUY4yLGNJfWNUFsoYxga0XTYIUSQiBvBG95/MIgbyzOXT+oFJkhIXNcPadbsVjSKv6HakKN8qBg1ez5H7OhsF0qcx+J4EYmejTcOLgdBZoPRF0HmiKMER0OO88G+QlS4Op2DzGQJ02eXm3Ycuw39owmc/NFMYEPMi6QALMd5jB48AMe+MgHC6rnE8QxS/QriXRL23tAFKcLBrDgikbiR4sLYy5mm/l0dbD2aktSPjIzgpz/9aei+z372szhw4ADOnDnTjI/oYA1cuHBhpw+hg1WwDTdQfjXUFcEl1/Ygx/im+YBuNPa+74eojwCZta8mprWbotKygdyC1pTja2dYhoO+/fFAgCvVp6B/NA6AdGMbKWpvN6rxZ1Yl9bbhoriko380gaVrqFNfnceWY2Q+2rZXOvXbhaEjqTpHiv03d+Ntv3IUNENXR4xh6S66h6IoZDScenIWS1ObL77slrXfUB2ICguGoZGdLgfK9wDZfN7wlsF1Xt0cVDv1SpxQzDm+sf3g6Z/OXbVrQTOw3bGvJvVSlINe3FzS/PQ3LjbsCA9fHy7W8BJXd76Sbv765zAvsgH7TZTZYH7bdT2ohd3ZbWwUf1Ozib5KTTHq2W9fQiGjwzYJ/f7CixkcvL23acchyFxgaZfoITRulqMhx0hxzKgwNEzdwaVXlwLnBFN3OnP260CO8XjHx47hropuD8PRyGd0RBICLly4EGKhbQZVFiHL0xg90YMjdxMnHU5gsDynIZIUSaGGp0EzFJ78m3PN+6M62BY0ZcfzpS99CaOjo3X3f/azn8WTTz7ZjI/oYA2w7M4nFx2EYZsuPNcLKFJGmVy8TN0h1iSVTr3n+VclTrfR2PNived91bsbQKAezPI0lISA/LwGz/ORv4aTe9twcdf/dQDXVS56vEToigD5vq6GEtwsVONPMxTcGsVjy3BQyOiId0nhUY9djnLORCQlQo4LUAtmXad+O0DRVN1nMgwd8n0GiLZGvEeCoTrE5vIKKMO7Ze03VBuCwiHeLWFxsrRhkcJmIpIUUFwyIFc69Uqisfjk3MUClmd3nsm03bE3VBuizEGK8oFQ3kaRnS43HIO49/1h/RjSqV+V1HM0mHVm6gFCv7dNF74f7gpnp8t4+fHJTR1ru6BR/I2yE/q/tS0XcxcLsA0HjuWheyiK9/7mTejbF2/acUiRFZ2SwSMpjFyfBsPRpDgW42HpRJDX1By89oOpgOVhqteuS8FlUan8JvuU0D7Dd31EkiJYloXneld0baNZsuHkBAYURQWjY5zAYuFSAakBBYJEzvOfffgYuoei8NzWHUPqoB5NSer7+/vR09NYsObuu+9uxkd0sAaGh5tHHe2gObBNF67jwVBtUNSKgI1tuIgkxEAcyzbdQGTmSrDR2PMiE7ITqt5X7dTzIgOKAniBweCRFIpZHYvjRTzx1d0hxHUlsAwHgsw19sdex2ZrO1GN/+pjtHQHxYwOQdn5Y9xOFJb0kJK5Y60vsrWTMHUH0aQIS3dCNpcXX1zc8Hu069qvFS2cfGI6uG2oNkSFQ6JXhrwDCT1AaN5ihCTzosKtaRtaXjYa6iBkZ8vbqha9nbF/9QdTlU49G2J1bQSW7oTcIdZD77543XNZ/vKdekFmCSPO8iDIbFDgtI3dO1/fKP6GZiOSFINO/fyFQmWNcWFbLgSZbWiNejW475eO1F1/qvR7OcqD5RkICmFSGKoDtTKnbep2wzHEDtYGJzKIJAUMDw8Ty2Nu83vHagFmdUGAE4nifXpPBLzMQowSzQtB4TpxajM0Zcezb98+7N+/f82fDrYOHXeB1oNtOHAdH0bZRrRLCjYWluFAUNgaCq5zVbPYG409JzJQEuHN8g33DQYzjbxIZhk5iUWiR4LreJg+s4z8ghbQ5a41WIYbUmauBS8yIaG8ncJa8XdtD/mMDkFmQdEU3Guk0l7M6IimRcjxlbnfK9Gq2A5YGmHtmJoTbHgB4KWazmIxq6/bJWnXtX9xooip07ngdpXaneiVQ/P0241IUoASFyBGuIbez5buEKcJpz4mM2dzmL9U2I7DBLB9sbdNFyefmCGWdspKkXOjLikUBcR7NhbTG986BEFeldSz9GVn6qNJEeVlA5bhgKu5npLxt92ZkDSKv6lW6ffk/zMzXcKeI0lYpkNYS2vYPl4N4t31sR29tRfRpAg5xhNGYKVTb2o21EKlU685u7bgcqXIL2goLRt1TkVV8CKLSFLA2NgYxEj9qMpGEHTmVyX1LEeDokA69TIbFFerIxQdtA+asjP99re/DQD42te+hvHxcXz84x8HAHzhC1/AyMjIei/t4CohCPX+1ZfD4kSx6RXbDlZQpd/rZRvJXjmg4ZuVJN5zPPz07y/g8J19wTzalWCjse/dF6+z7eoaXLHh4gTSgeFFFrG0hD2Hknj1B9M4cHM3cvMq0gORKz7GdoPn+Xjsi6fg2u6atM/+A3EkNrhR3UqE4l+zyeYlFpmpMgSZdB61glVH/96NKOdN9IzEIMgstEJrC7SaugM5Rizuqp16b9UM8EuPTeL6e/ega7Dx+Xcla38rYHlORbnSsXMdD3rRAsvR6DsQ37CV4Fbg6D0DkCIcfN9H3/44xl/Lhh7PzWtI9StYnCjB8/yQ64FluKHbW43tir1WtFBaNhDvkUMFaNtwQwn0WmA4+qpiynCXT+opmoIfHBMDVETVbevKxlraAY3ib6gO+kfJTL3n+ShnDaQHIrB0d1tZS1WbPKmS1AsyB1O3w516zbmq0cN2hGWs38QZeyUDXmTXZKbwIkNm3ksCxEh9t30joCgKrMAENPza+5P9CgSJWNxV9z6icuVK+x3sDJqS1F9//fUAgEcffRTPPfdccP8999yD22+/Hf/xP/7HZnxMBw3Q19e36dc88Vdn8fO/fdsVf6ZjuyErmg7CsE0Xrk069YleGWrBJGI1Oun+qkUL8z+aQd++OCLJK0+2Nhr7qtjKWuArs4w3vXUInMBg4FCC+EPLHBYuFa+ppN42HOTmVcTS4pq2Zvtu3Ji371ZjdfyLSzqWpstgeQY0Q0GQSGW/nDOviaRey5uQ4zyx42lxW0bbdAPVaNtyYWgOHNsLbaCq3a21cCVrfytgeVYNNvQnfzSD6+4ZAEUR1eXV2h/bieNvJoJ8SlzA9ffuCSX1vu/D1G0IModoUkB52UCsa0VF3DbdbU1Stiv2eomMsZiqHWK9lPPmhmLFcgwSvVeuts5yNFj28nsNUeZQzOrgBQbwffi+HwjV6iULvMxum9XodqBR/F3Hg6hwmLuQxw++fBqm5mDfzd3ITBKx1O1mLcnRSqdeYGBqZLa+mtTXrn/XCh77y1N4z6/fuObjat6EyTlrjmTe8d794AQGfX19KF7MXvEeXJQbW7b+zK8eBQDsPd618txOp77t0NRVrlAoQFVXRGRUVUWhsH2UtGsRExMTm3q+5/lXrW7+rc++Ar3c2pvmnYRluHBdMlOf6JVx/rkFvPz4ZNCp33djF657wwBO/mg6UFS/Emw29muBE1iwPKHJUTQFhqVx/b17kOyTr8pHux1hGcTKrh3aCLXxF2QOk68vY/osoTWLCgdBZhFJCCjnjJ06xC2HY7v4wf8mDitayYYU5ddULm8VPP4/TpEksLLJ5kUWpmbDtT04lhfY25maQ7yn10Czzv/thlGuWNi5HpamShg+euWWdduF//O510hiIrOIpiWUsuFzyjbcbZ093a7Ya0ULcowPbezlOI/sdHlDSf3Q0dSG6feNwHCXt7QDCG144VIRnMhWxPKcYKb+xccmsTC2u/aha8Wf5WnkF7WATSIqHGxj83aZzUAkJaDvQBwUTcEo20gNKLvWjWAjuBx7TM2bKGb1NYsdew4lAZDYi8qV0e8B1I24VFHL3qxCjHSS+nZDU5P6D33oQ7jzzjvxh3/4h/jDP/xD3H333fjwhz/czI/oYBUkaXNVcKNswzbcwGZts/A8H4uTJSxOhK2yNjpjdy0gEMqr0O8XLhWhlywyQy+xuOP+/RgYTWBhvITUVXTBNxv7tcCLTN2MFUA6Vlr+2roIEyq009CjutVQG385zmP+YgGlJR0MQ1WSeg5KUgy6I7sRi+NFXHh+EZmpEuCvUKJbwUt8LUydXkaxpljGiQyhyFYKEdVuvaXbMNbp1Dfr/N9O+L4PVDryWpGIZbWCPsV68H0f2Zly0G2UY1ydArxtONvqNLFdsdeKFnpGoiELz0hCwOJkKfDQXg/H3rin4bVlo2A3QL8HgPRgBDPn8oSinBZRzOqBoF8xo8PUW3893wzWij/LMyguGcjPa/A9cm2vCvNuN3iRxY33DQEg19VEr4z8goZnvkX0ABiWvuJ9aDvicjR2rWihlDUuy2CQJAndw1Gk91zZ3nEzdryiwiEzWcaz376E5Tn1mi7KtAuamtT//u//Pj796U8jn88jn8/jj/7oj/B7v/d7zfyIDlahq6vr8k+qQa2lyHqYPptrSGHNz2voHopgcbwYuv/Jvz6Pcq5zwgMgM22OD71Cv5djPBzHg162ArumnpEYBg4mrmoOc7OxXwucyDas+goKC+MaUz61DBegEPJ9b1XUxl+JC5i7mEdhyQAnMhAVFoJCOvWl5d3bqZ85l8c9PzeK0z+eDd2vJISWtPNzHY+IEVWcCXxU3At8PygkBRocl+nUN+v8306YqgOpYhmn5k34nt/ytGjbdKEWLJga8dyWYnzdtdEyXZjb+P+2XbHXiha6R2IhVfpIUsTieHFbRiUY7vLq9wCw52ACmYliRRdGRGnZCLzZl6ZLLbkWXA3Wij/L0yhkdCQHFIgRFrzIkmvaDjddGI6GGOFQyhq4+MIi4PvEv/4a2V94nn/Z9YHlGRSzxmXFk7u6utAzEkPf/itjeR59w8CGnysqHC69ksH0mWWcfXoO8xd3F+NlN6LpV9N3v/vd+OM//mP88R//Md75znc2++07WIWpqam6+5791tiaHuNayQIrMJel1Jx/fgHzDShrmckirrt7IJjTqsJQ7V3dEdwMaIaC63hwbRdyjMc9PzcKCoBasAIV+khSwDs/Xu/duxk0iv2VIN4tYfC6egpsNdnYjbBXdeIt3YHv+7AMB9GUeFVWg9uF2vgrCQFqwQLDUuBFFr3744Qq3CXWUYV3CzKTJUy9vozDd/Qht6CFZkZ798XAsK03QkEzFPr2xfGuT9wAgHSrqp0Zx/bASyvCRK7jrdupb9b5v50o500oCSFI6lsdvufD1By4tge1QGaz5RgPfVVS77l+oDq+Hdiu2OvVTn0oqReQmSptS1J/3d39G+pIsjyD4WNpcCKDaIqsebbpIpoSUcwaQVLvWC7GX1va6sPecqyOv+t6oBkKHM/A0h0MH00hmhLJaM8qPYSdgCCTsYif/dgx9I8m4Lo+sUtrULScfD2LQubqRkRbDY7lrsv+8z0fDEvDtS6vNXC15/7hOzauxyFGOJRzJizdQXZG3dbCZQdXhqYm9ePj4/jEJz6Bt7/97bjvvvuCnw62DpHIygXP9314rodTP55FadUc7Zmn5zB5Kgu9aIUU2ddCKWs0LAwszajo3RerszqzTbeOknitgmZpeI4HUBQomsKh2/sAisyV1XY86KvsUNXG/mogx3iMXJ/e9OvGXsq0bQfkO3/2Suj2T//+AuYu5GHpDpJ9ylVZDW4XauOvxHlEUyLkKBGKu+vBA2A5BgxDb8iWsB0v1s98cwx3PzRK/HQlFkp8RRG6b1/8itSBtxosSyPWLQXChYLEQpDI/5pre4gkBegqWUcFmVtXvbtZ5/9Ww9KdgApdzhmIJAWi9dDiST3L07BNNxArLGZ0CBIHOSZAK1nwPR/f/1+nd+TYtiv2esnCwMEEbvnZFRejSFKAbbiQY1uvwB/rkjYsqHb7e/ahb18c0XQlqbdcRNMiYl1SsL5lZ1SceWp+Kw95W1Ab/5lzOYy9lIEos8Gad/QNAzj+pkEwHI3SshFaG3cCgkQ0Xvr2xdE9HAVf0T546fEJWLoD1/Hw3Hcuwfd9zJ7P49Ir7V94qYVjefBcv6FF6df+8Bl85y9eDaw0L9dQ2M51X1Q4SFEO3cNRzI8VrhlmRTujqTvX97///XjrW9+KT37yk2CY1ttQ7UbEYivWdIsTJXz3L14FJzBB0l7M6jj/3AIcy0NhkXhXp/qVUKfedTwwLI0fffUs3vgLhwGQjVh+sT6pVyubMgqkiFBV0XQst657cS3C83ywHE28jGu63BRFujlrKapfCWpjv1WgWTr4/1iN8deWEO+VIFoclLgQ6Co082/cKuQWNFi6A61oITevorikY36sCEFmkeyXYbdBklsbfzkuINYlQY7zdZtgXmSJF7jSWCAHAL79py/joX9765Yd65aAQkBB7DsQD3VCkn0ybnv3vp06sjVBszTi3SvzsLzEEkZBjqyh0ZQIo2zDsV0ocX7dTdR2nP9XgtM/ncXorb3BLPW55xbAiwwO3d4HNW9CSYpQEgJmzuUu8047C5aj8dhfnsLoiR6wAoNChlw/GY6GUbZRzOp1Y2jbhe2I/eNfOoXFyRJ4kUWqf2WrqCQEUBTp4rUSkn0KAKJRUc6ZoGgK6QEF3YORoPicm1dh6e0v/FUb/4XxIpamyogkBTJGRwGxtBg0DbSCBSW5s0k9X+nUA0D3UBSZqRL239SN575bRnamDF5mceapOUhRHrbhYnlWxU1vG97RY24mqtoBju2Br2nmuI6HeI+MeJdERhQU7rJFrO1c9xmWxr0fOIRS1sDCpWLbNnGuJTS1U28YBj71qU/hgQcewLvf/e7gp4Otw+zsyiypXrJw/E2DuOP+/TA1B+OvLmH6TA7zY0SobWmqBL1kI9lPbNYe/9IpnHlqDv/8v06jtGzg9Z/MwbFd+J4PKcI1nKm3TRe8yIKrzmpV4FidTj1Avh9R4eC64e6ooHBBt6pZqI39VkFuMD9ahWW4sHQXj33xFABg/LVsW3RBfJ/YDZZyBrIzZVx6dQme52NhvAjLcNEzEkXXUL0SbKuhNv7RlIDb3rMXSsXSrRbpPQqW51S8/pNZFLP1bgae6yEzWW5pcbla5Bc0FJf0kB7F6Ile7LtpxWqQoil0D7deDOPdUsi3W5BZ8BILVmCgl23SqS8TATk5IaxL2dyO8/9KMHEyGxr5MMpWUJwo50xEKvT7Uta4arbSVoLlGcyPFZCZLCHeLaGcN8HyNGiagu8Ta76d8kHfjtibmoOffbh+RIwXWSR65avSg9lKMCwNz/UC/YqBQ8lQUl9bKHvyr8+1hX7KatTGX82ZmB8rEEV0joYU4ULnFcvTiO5wUi9IbCDQ1rM3ijvfux+JXhl7j3ehsKQjN6fh8J39WJ5VYRnEz343iS9X/8dWj+gYqg1RZnH7/ftw/M2DECP1RfnV2O51/+CtvegeimLwSAqm7uDHf3N+Q+y/DnYGTb2iHjt2DJOTk818yw4ug9qqnWU4iHWJiKZJt+eJr53Fq/88DdtwoJdsOLYHrWQh2afgwguLEGQu2LRkp8twHQ/Lsyq0kgU5TrrxVfi+HzqRV88VOrbX6dSDWBuJCkc69TUdaynCQWpyZ2M7KrZKnF9z9tU2ieJz1faulNWRnS5v+TFdDabP5lDI6PBcH+VlE3rZRna6DE5g4Vguod/3KoFndSujNv40Q6NvXxxKXKjbFKT6FSzPqpg8lcWF5xfr3qeQ0eE6Xp3OQKti4lQWLz8+CSWxslGNJAWkr8JJYrtwxwP7Q1RYXiRiVrzEQitaiKZFaEWSBIuXUSlu1U69ZbjQalSSSZGCJL9qZaZekFkiCtXCyvcsR8MyXCxNl5HokcBydIiFtDyn7tjmditi/70vngzWct8jrLLevY0/p8roa3UMH03jyF19QQMiv6AHyaWlOzj55ExbWnbVxl/Nm4HdIs3QiKbDyvicwEJJitt9iCHc/PZhdA2S9ZmiKEgRoscQ75ZQyOhYnlPRtz9GRnUMF4k+ec1mQruhkFlxYqgt0o69nIFWsCBGOLA8AznGQ4pwlx3924l1f+hoCre/Zx8s3cH0uVxnr9/CaGpSn8lkcOONN+Jd73oX3ve+9wU/HWwdZHml62PpLniJ0JyMsg1BYuH7PjiBged6iCQELE2VkOiRMXexgKP39KOQ0VFY1LE0XUayX0FmsoRS1kA0LUKK8igukYv8xMksXnx0IkhUV3dwBbne5udaw9TpZTz6+dcgKhw8J7zZkyI85ERzq+W1sd8qxHtkTJ1ebviYbbjQihbUggnP86GX7IYjG62ESy9nMPX6MqJpEeWcAb1kIb+gIZoUIMcFFDI6eKk9Rocaxf/grb3YezysjxDvlisbCw8zZ+spz7k5DTRLNZ1J0gxcenUpWIOqMFQbF17KIJre2Y1qMyBGOGI/KJKkPtYlQc2RTXqVceE6jTuJ23H+Xwlsw4Fa48lslG0YqoOl6RKKWQNShANFUbAMd1P2StsNlmfQtz+G7EwZ8W450D6oYnlWRWpACeKznRZdWxH7wqKOZyt2Y+U8YVSshcHDyaZ/fjPBiWzQnecEJljbXHdllGziVBai3H4+3AuXihD4lbXPdTyk+pWA3n77/eGxI15iENnhTr0SFxqycuLdEgqLOnLzKnr3xmAZZL5eUvi2sJVdD1rRQiGj4YX/M47cnAqKCgv0nnpyFpmpUkiEUo7VM+1WY6fWfV4iIox60eo4XbUwmprUf/jDH8af/Mmf4AMf+ADe+973Bj/NxPnz53H33Xfj0KFDuO2223Dq1KmGz/vLv/xLHDx4EAcOHMDDDz8M226vhXujmJ9foTubmh0k9fmMhp6RGB76tycAioIP4A3vP4if+61bIUU5CBKL9EAEnusj2S8jO1PG8NEUSeqXDURTIo6+YQCv/XAaAJnbmjmXC7qAq5N6TmDgmC7xsZ8IzxleeiWz9V9EC+DF702A5WkIClvXwZGiHJQN+PpuBrWx3yqMnuhBIaMjM1mC5/l45ptjwWOW4RK7NJ90PbSSFVgmtir0koWl6TK6h6IoLRvQyzb6DsQR65aQ7JWxOF687EW1VdAo/mKE+NPXgijg66Dpynm6arO0PKeieyjakpuohbECllaxP0yVqJHH0u3n074aB2/twYFbuoNOPcszoGgKesmCIHNwbA9f+d2nGib223H+Xwksww2xe6qd+qe/MYZkrxwocctR7rJshJ0Ey9MYOZaGqTmI90ihAkT/aBye5yOWlqCXLDAMBUFitk1Iqtmx9z2f2EBWOtqFRQ3xnvY9v2JpMbgWVdkVnusFv/u+j7nzeey/ufuyosE7Dcd28aOvnQtuv/xPk7h0egYXXlhhXSV6pSA5XC16G0kI6xZodhKCzMHSHZiqDUHmAhkiTmRassi8GcxdyGP81SyxxCxW1vMa+r1espCdLoe0bu58cD+Sfesn7Tu17tMMDc8lVs3l/O501NkNaGpS/8u//Mv45V/+ZXzwgx8Mfv/lX/7lZn4EPv7xj+NjH/sYzp07h9/6rd/CRz7ykbrnXLp0Cb/7u7+LJ598EhcuXMDCwgI+//nPN/U4WgXJZDKwl7N0F4LEgpdZLM+qkOM8URmVWTimC0HmiFK0wuGmtw0RIZk9EQxel8LSdBldQxFoRStQae/bH0duQYNaMLE8o6KcMwPKayOvXgCYHyvgue+MB7d938cTf3V2V81HNYLreOBFFje+dZh8R6v+3v7ROIaPbl5hfj0kk1vfLaEoCkfu7MPU6WWUsjpO1fiB26YTeKAbZRtGyYYc49fsLLYC9LKNpakSuoYiKOdMGCUL+27sRnowgkQv6WjzQusmGrXYaPw5noFtuKBoCkpSgFoIV9kLixq6hqItuYlyHA/lHJmv/NtPP4+Jk1kYqo2h61K7olNPMzRohiZJfcECy9FI9slYuFSAILMYPdGDQ3f0YfpMPcNiO87/K4HvI/Q/RlFkPpsC8KYPrdC2CQ2/tcTWajF6ogdH3zAAigISPVKo2HfL20fwsx89BkFmUc6Z4ASm4lawPUl9s2NvWy44fmU7mF/UEe9pTSbIRhBNi3XuF+W8ScTkBAaO5aGUM9E9HIXe4kl9YVHHQo0go1a04OsCHvvLU8jOlEHRFA7e2otUv9Lw9W/71etb0gmkCkO1ceCWnpU7KIo4Txitdz3aDGzThW0S5w+tSGj2tYVzrWghO1uuc0S6nNDwTq77hkr2eO1gR3qtoqlJ/WuvvYZjx47hwIEDAIAXXngB//bf/tumvf/i4iKef/55fPjDHwYAPPTQQ5iamsKFCxdCz/u7v/s7PPDAA+jr6wNFUfi1X/s1fPWrX23acbQSWJbFd/78lWAemJdY0DQFS3eC2U05zocoPjRN4dZ3EYrWsTftwXV39aOwqBEFc8+HqTtBV+KO+/fjJ397Ho7tIb1HCbrNcowPKuHVhN3ziM2ZWeOtTOjZVkhUbzfC1BwICot9N3Th6D0Ddfbu8W656cJdHLc9G+K+A3HMjxWQm9dgaU6wYfc9MkfP8jQMzYbnekj0KcFcZivCsTxkZ1UkemQYqg3b8nD9vQMYOpJCokcGw9JguNYV76rFZuJvWy7kGI9IQqyjzhmag2hKuOwmSita225951oe1LwBrWgh3i3h9Z/MwjZd3PdLR9Az0npCeFcKQWKgl0hS3zMSw8UXM+gZieL6e/fg2Bv34OKL9VoI23X+bxZEh2Ol4EszdN16CCCYrW9VxLtlSBEeSkJAoldp6MsuyCxKywY4kYWSqC+YbRWaHXvH8sDWaHEUl3TEutq3aBZNiXXaItWxQkFiYWoOfN+HHOVbnn6fm9dglFfOJ71kYeGChv03deGJvzoLJSFg+Pp0w/9PAC0raFjFOz52HNffuyd0HyewbaPxshYsw4VluCSpL1gQlZVOve/5cCwX2Rl10y4SO7nu2yYREu7Q71sXTd29/sZv/AY+97nPobubqBDfcsst+M53vtO095+amkJ/fz9YlmwEKIrC8PBwnTjf5OQkRkZWfFX37t27poCfaZooFouhn3aBY7mYODcHtWghv6iTZLzSTRAULuiqyzEBUrTxgp/qV5Dok+EDwUXB0laS+u7hKEaOd6FrKIKuwQjkmqS+nDfx+k9mMXexAIahcPzNezD2UibU0cjOlMHy9K4X1ljtQe/YXqjzsRVYXKzf6G8FWI6B7wOZyRKGr08FYni8xKKUNRDvlgIKYzQlory8ddSsV74/dVVMAFEh/5tihAN8ADW2jPFuCYLSuknGamwm/pGkWPkR6i/Ivg9eZC/bqT//3ELDmfythGO7KC2bKGR0pPco8Cqx50W2LawTNwrSqTfB8gz23diFn/t3tyLeTTql0ZQItWBBK1ohfYHtOv83A9/3wfIM3FWz5Z7r1c3Udg1FEOtqfYp3774Y5BiPt37kaN1jgsyhvGyCExlEUgLKuZW1L7+o4dxzW0OVbXbsbdMJLAgBoFwZwWtXRNMNkvplA7E0GaPQyxZomoIY4Vqefp9fUEO0bV5iMXeuiAO39OCOB/Zj/43d67y69dFo3r9WB6FdYZtE9M8yHGhFM9SpNzWHWEuX17eabYSdXPcFmUX3MEnqPbd1GZnXMpqadZTLZbzhDW8IblMUBZ5v7hxxs/GpT30K8Xg8+BkaGtrpQ9owLr6UwcUf6Ri+LoXcvArbdIMLmahwoU69FF174WAYYoNSTfxN3Qkl5ofv6MNdDx7A8TcPBhcQKcIhM1nC0lQZE68tgRUY7D3ehV/4/TtCqvnZGRUDB5O7Rsl0LRjaqqTecsFsMeUtnW4unX897LuxC6/+YBoHb+vF0nQZrutBjJANbaxLCmZJ5Ri/pYKJ02dzQZdls56pvucDFIVIQoAY4cjcZc2GjuFoHLipfTZIm4l/rIsk9EpCaEid4wTmsp2RqojRdsKxPeglC4VFHYkqHXgXjvLwEgutZIPhaDAsXbfRE2QWL35vAmMvr+iTbOf5v1G4zooQGUAYImzF111a1Uk8dFsf+vbHt/sQN42qrVujjicvsSjlDPAiU8eCyUyW8PQ/jmH2fPMLYc2IvWU4QZfaNr1QEly1rm1XRNMiBo+EacrFjE469TKH7EwZ0ZRIRIVbvVO/oCE1oMCxXbiuBznGg2FopAYU7DmcxNDR1E4fYnNREXd22iCpH391ac3HbMOFVaXfF8L0e61ooavC3NxsUr+T6z4vsUj0kPGwRz9/EuWcgdy8CgB4/rvj8FwPM+fC691WrH8drI2mJvUsy8K27aCDMjU1BYZpXmIzNDSEubk5OA7ZzPu+j8nJSQwPD4eeNzw8jImJieD2+Ph43XOq+O3f/m0UCoXgZ2pqqmnHu9U4dHsvBJnB0TcMIDevka5jZeMhKiyUBNlE9e2P14mnrIYcF4IuplnTqa+FIHPBbBbN0Pjg79yOG+4bRCFjBPdzqxLZ3JyKPYcTuz+pb9CpZ9uExr0RjJ7oAcvR2HtDF6bP5jB7Ng8lwcPzfMS7Jah5EwxHQ45xWxprxyJ0ttychp/+/YXLv6AG1bGSSFKAqHDoGo7WKVq3i1XTZrH3hi70j8ZJp76S1M+czZEkkaLA8pfvjJja9if1rk2SxKpwF0VTu9IjlxdZ+J6/5prRuzeGU0/OQC+1dgJi6S54ceUaYJRtiBEOnMA0XSh0u7AeI0SQWcyezyPZq5BOfQ1LydQc9I/GUVpuTarqy/80hR9+5QwAsq5W6fe7Qf+G5Rjc9LaVPZ8U4TBzLo9YWgQvsVicKCHeI5FOfYsn9abmkHGxim6NFOOR6BdXipy7CBxPxt/apVP/4mMTaz5mmS5sw4VjeWSmXuFgVxgXWtFELC1CUNhg390OECQWcozH8TcPwnV8TJ/JBYKN556dRzFr4JXvh3Oon/79xZ041GsWTc06PvnJT+LBBx9EJpPB7/zO7+Dee+9t6kx9T08PbrnlFnzlK18BAHz961/H4OAgRkdHQ8976KGH8M1vfhPz8/PwfR+f+9zn8MEPfrDhewqCgFgsFvppF1AUhUM/I2PwuhTy82rIF/2GtwwFdPpoSrxsR+Se940SkQ6aWFux3MaKMXKURymrh6jmNEvDtcnipZdtpAciLa+KfrUw1HBS7zlbn9Rns9ktff9a8CKLD/zO7eB4Bne+dz/OPTcPQWTBiQzi3RJy8yqkKA8pym/pqIVjebBNB/lFDY69uQRTL1mQojyuu2cAcoxH93AU4joMllbHZuLfty+OeLcMJS5AqyT1l15ZwoUXFiFFOKI2fJmZekt3gvN6u+D7AEVTyC1oiHVJkNewRmp3VJlRawla9e6LoXdvLDRbu53n/0Zhmw54kSVFIosIREkRDoLCrTnz284QFQ7Z6TL6DsQhR3loNUUXS3eQ7JW35Np3tbH3fR9zF/KQYjyWpkuE5cczYDkatukGzYHdghPv3IvColZxB2Ex8doSekZiwXx9K4OiyJiAXrKhFS3IUR5H3qmEGDG7BbzEghNZcAIDq8WTes/11h3dIEJ5hD1rag5EhV3p1JcsyDEe6YHIpq9nO7nui5Wx3pveNgyartj2LZKRML1kY3lWrftOilkDboeqv21ouqXdv//3/x4f+tCHYFkWvvKVr+D9739/Mz8CjzzyCB555BEcOnQIn/70p/GlL30JAPDRj34U3/zmNwEA+/fvxx/8wR/gnnvuwejoKLq7u/Hxj3+8qcfRKujp6SEXJt0J0VJHjqU3tVhUKVyCzG6K1syJDLFiqqHuCTILo0Ysb7X93W6BpTuwDPJdmaoTmsemGXrLFWd7enou/6Qmolq0iHVJyM6o4EQWgsQi1i1h4mQWPSOxDdPvXdu7og6JYxPxmfyCtukEUy/ZkCIcDt/RB4al0TUYwd3vG738C1sUVxJ/hqNhmS58z0duQcX8WAFyjN9QZ8TUt79TD5DxH5anwYssGZ1oo87GRlFljKzZqd8Xw7s+cQOMGnX1np4euI4XmuPeCbz6g+ngd8twwYlMMKt8/rkFjBzvgiCzkOOtaat1NeAlFvEeCXKMJ4lwzTXY1GzEe+QtYVdc7dq/PKuiayiKPYeSyEyWSfIhMuBFBvkFbdfFKt4t4Rf/8C5QFAWhkjj27ovVxaxVIUU56GUrKEz39ffu9CFtCXiRBS8wIfp9IaOH7PtaBY7lrbtXtg0HFIWAuSRFeCzPqnj9J7PQi4Rx8e5fv2HTn7vd+75a3PW+A0j0rjBEtJKFQkaD5/kwtEpSv2pfZ2nOtrmCdAA0bXfkui6OHz+O119/HXfffXez3rYOhw8fxlNPPVV3/xe/+MXQ7YcffhgPP/zwlh1Hq8C2yQkkx/im0PwEiYNllDb8/Co1sZZ2L8gcTK1GfX+L56x3Ci9/fwqRpICj9wzUdeoZltpyFfVq7LcbgsyinCc2TrzEIt4tgxMYHLqjt+K8cPkK+/hrS1ieU3Hbu/dt6rMdy4NtuCgsahtOME3Nxus/nkOsWwxpS1AUFfyPtiOuNP6jJ3rw3c+9FsRPjpOk3tQdzJzNYc/hxpY5pubAtbd+A+z7PvILGpJ9SnC8oyfIRiaSFEIFw90CTiLrJ7OGuCZFUeAlNiROZNs2Fi4Vce65Bbz5QzszNuK5Hp7/7iXc8JZBckyGA05kAIoIk+UXNPTujWHs5UxDQax2hxLnceTO/oaPmZqDRK+MqdeXm/65V7v25xc0pPplJHplnH9uAakBBRzPgJOIHW40tftiVd2jpAcjuPfnD7aV0KYU4aGXbPi+DynK7di1f6vBSywc2w0VmTOTJUy+ng2uAa0C23LXdXVyHR+e5xPB6okSRIXD5KksludUxHskjN7ac0W6FTsZ+1oGLycwKGZ0UDQVdOezs+VQUu/YLlzHg162diVTqxXRtKyDYRh0d3dD07RmvWUHG0AuR0QoBo+k6tRerwS8zG76faQoH3qNIIcpbWKUg1HTrXjp8cnAe7od5qbWwuSpLLQCKVYYqh2yJmFYuk5foNmoxn67QVEU5Chf6eyQGfUP/PvbwWzAY7WKYta4IluU6ky9WrSI8N0aWJouB+yQ2fN5TJ/NXZHSbCvjSuN/9J4B3Pv+g7j1XXuR6lfI+cszWJoq46XHG7uEABX6vbP156tluPjBl880fCzWJe7KzQHD0OBFZlP2U0uLy9DLFpZny1t4ZOujnDOhl+ygY2UZRGBNinBYHC8ivYcUZm6/fx+6h3aPBWEVgszh1nftDW7zEhtYupq6Q5xBtmBm+2rX/kJGR7xbQqJHQmFRC2bqeZFFdqaMSLJ9le8vByUurFm4bFWIUcJ80YokOdqpa/9Wg5dY8BX6fXVvWMoaAcW7lUBGAd11NV5skwgK0wwFMcJCLVjwXB9G2bnihkKrxF6K8ihkdERTIgqLGmJpEcuzKjzPD3Q5qnmA2eK6FbsJTW0ljo6O4p577sF/+k//CZ/97GeDnw62Dn19fQCAoetSTfFtFmR2097BcowHW9NhEmWysfE8HxRFNqwUTeHcs/NwXQ8vPTaBQkbDY188hce+ePKqj3knYJRt0DQV+BKbqg1RXkkY6W3o1FdjvxOIpkXwIoMb7hsEw9Iht4SNoLRsXJH1nWN5wcgD1ikgPPPNMZx5eg4AMHM+D0t3iADkLkrqryb+sS4JXYNR7DmUQLJPBicyyEwW1/XZJkn91nfqHcvF0nS5YdFm4GAyJIC1myDHhcsXxWoeP/eYBr1kIzen7Zi4WWFJBycwKFXOZdsgQnlShMfiRCmwRWN2oQ5CI8S6JBSXyHfhWERRfr3i42ZRPT+vdu0vZHTEumSwPAPH9ioz9aSwtDBeRLJv94mwrQWKphrac7m2h3/+8ukdOKKaY3A90DRV6dRb0Es25Bi/o9f+rYQgkREQtsaNpbRswNQcvPDoOPILrdM0rM7Hr9eYYlgKvMCCl1iICmmEHHvTHowcu3IF+1aJvRTlYJsu4j0y5i4WkOpXkJ/XkOiRgyKvqTmV0ZHWTOp3o+huU6+0nufhpptuwvnz5/HSSy/hpZdewssvv9zMj+hgFarMCDnGb5rK3AiCzNYpgl8OUpQLzY9X6fe1fvc/+9HrcebpeZz84QwiSRFa0QoWxXbDi9+bwNSZZRy6oy/oBruuH0riGYYOFTq2AjvJiol1ieAEFgdvrZ/to6jwYjl3IY+FS8XQc8rLxhUlIq7jwdScdTua1U7Z7Lk8ACA3p0FQSKGptvDS7mhG/K+/dw+6BqPgBAZ6yV53/peiqU2LE14JqmyMQqZxd2Yz3ex2wnq2o1VUzy3f85Gb16GXLMjxndMsKWZ0DBxMYOJkFqeenIFlOOBEFmKEw9JUCZE29jq/EsS6RBSzK/+3zaZ4P/4/TgHY/LmfnSmHtBe0ghm4EVA0BUt3wFWSj8xkCcl+pXkH3eIgtnYrzMLnv3sJasGEZThQ8zs7NuhaXsVVhpzjepHM1O9WRmzfgQQGDiXB8XQg3FrOGYh1iTjz1DxyLZTUV4sO683VcwITaFWIEQ5dgxFcd3c/jr95zxV/bqvEXorykCp/09TpZaQGFHiej2S/ErCTrCpbqUWT+u994WTb5iFroamKQ5/5zGfQ1dUVum9paW0fxw6uHsVi8fJP2gQEmQO/ycSnEf0+v6iF5swpmsLNbx/GhRcWcft79iE7WyZdgjY7oXzfxzPfGsPAaAJv+8hRTJzMwnW8uvlumqU37CBwpWh27DeDnpFY0IVbDVEhVMEqTXpxogTHdtG7b8VZwvN80CwN3/c3vPH1PR+swKC0pJONzRqJzPSZHPYeT1c87W3QNACKgqGFxQzbHc2Mf7UopyQEeK5XJ7Lp+z4YltoWoTzH8qDEeWSmNq7tsRtwx/37L/scQeFgqjZoloateyhmDew5lMTynLot+hB6ycKFFxZx/M1khr64RD7/lX+ewsjxNBI9MhI9DHiZRW5BQzR9jSX1aQmLE/X/t5tZ59ZDlQWw2XN/8tQyomkRoydIPHwgULiPpkTk5jUcOEHEKOW4sOWjY62Eqq1d9Xo1d7GA4evTEGRuW8aN1oNje+B4GlKk0u30fQgyi+Klnbv2byXi3VLwe7Xm73s+UgMK5i4WoO6wKGgtHIvYrQbMwQbgBDJKIMgcpCiH+/7FdVe9Duzkvq8WUpSHHOPRuzeG753P48idRIA4lhZhlB3Eu0mnPrZFI0ibwVrrb1XYL7KL1rumthLf/va3b+i+DpqHgYGBpr6fIG2efp/eEwltKKud+tVz5kNHUnjLLx5BJCViaapMXtNGYjUAoZcKEgu9ZBMBFABnnprDwVvDIi4MS225pV2zY78ZHL6jL5Sk10KK8SEbJ0O1kZ9fqS5XO/Ri5f9ko3AcD1KEQ2FJX7ermVvQkOpXEE2KUPNWsHk1VXvT/9utjGbGn6YpSDEeXYORht16x/LIJncbOvW25aJ3XxzLs+qWf1YrYSNzvtXNfXVGcWmqhD2HEtv2XWklC4vjK5vK4pKOPYcTMHUHRuW4BIWDFOEAH2sW/nYroqs69QDp1i1N1esebHaj63s+1JwJ3/M3fO6rBRPPfHMMluEEn+dVKN1VJHplZCZL4HgGvMQgdQ116YGVInQVxSUDpurANh041s5acTmWC4ZngmuYD8L+2Mlr/3bimW+OwQeQGojgurv6UdqEDs/Zp+eCbvpWwDFdyDF+XStYXiRK/nsOJ0FRVEg5/krRKrGXIhykGA8lIUCMcEj0KZDjfIX5UtUVsRHvlus69esVQrYCP/zKmbrRDd/zUczqIZbObkBTsg7LslAsFuG6LkqlEorFIorFIqampqCq19bGbLvR7Kpd93AUR+9prOa7Fg7f0Yfu4ZV5/iod1CjbEBp0/eUY6cLJibDg1TPfGsPiRGtUIdeCodrYf3MP3vzhFbXpCy8s4tAd4TknhqXXVLJuFlqlYrsa8qouuqHayNcI3ZiVsYxIStiUHZdreZBjRJxlPbG0wqKGRK8MQWFRzOrE+5anYajOlrMnthPNjv/oLT1Q4nzDuXpLdyDH+aZ26msvsrVzx47lIdYlbom/d7tDiQsoLukwNQcsT6GUNdCzN4blua2/ztqWC9f2QorPtuUi1a/ghrcMwnP9wH+56gd+JerO7YxIUkQ5Z4Z8mUdv7cXjX3q9zqv5u//9VRSXNi4AZltElMvUnQ2f+1rRQiGjwzbcYGNtqE6o2NNS7hgAAIWSSURBVJ7olVDIEG2EauftWkK1Uw+QgnNp2YCh2bAMN7TenXtuHqeenNny4/E9HwvjRdiWC8f2Qs2BaimmVa/9zURxScf0mWWk+hQcur0Xt7xjBGqDpN4o2w1H+cZPZrf0GmJbLuQ4H9DvXdsLjsN1PVAUsXzmRAb3PNQ869xWiX2yX8GBm0kzq3cvsTM+eFtvxc6UfO+m2ph+/9hfntrWefbcgoYLLywEt2fO5nDxpQwRLdxlIn5NyTo+9alPIZFI4OTJk4jH40gkEkgkEjh+/Dg+/OEPN+MjOlgD5XJzlY85gUG8++qqiXKMh1aw6mzeqhAjHEpLRqi777oeZs/l8fQ3xq7qs7cahmpDjnLo2xcHQKqVqX6lTghqO+j3zY59s1Cd/6tWyU3VBsNS8H0fz393HKVlA7G0hESvjOxMfTLi+37Di7RtuZCiPEpZA1J07aReL9uVpIJDcUmvsE+4YNZ+t6DZ8X/jBw9BjgtQCxZOPjGNpemV9zc1B3KUb2qn/vv/7+vB7y9/fwrjr5JRLcdySXKUN8Gw14bA2kYxemsPzjw1B1OzISXJhjGSFBpudmvh+z6mz1ydtdpjXzwFx/Jg1syQUiCjG3c9eAAA2WCLEQ4sx4QKvdcKaJqCqdn4+h+9EGiq7L+pG4NHkqEYaUULmcnSphgW1Y6godobPvct3YFlkJ8gqV/lAlLtHnICg67BKE68c2TDx7QbUNup14oWEfpVbdiGG+rUj720hLPPzG/58UydWcajn38NU6eW4dYk9Qyzwq5o1Wt/s3HiHXtx90OjoCiq4qoUvob7vo9v//krDc8jS3e21F3JqTQZqkXOp75xEc99Zxyu6+FvP/U8eHFFyb+ZaJXYiwoXCP699SNHEU2JuOvBAyGNClN3EEuLmDmfx8v/tOKsY5TtbR295QQWcxcKwe3MVAkvPDqOZK+865T5m7Jj+g//4T/A8zx87GMfg+d5wU8+n8fv/u7vNuMjOlgDQ0NDO30IdWBYGp7r1dHvq6BpCoLCBvR1AJg5k8PI8TSYypx1K+HCC4uYPZ8HQDZUtQrqvftiOHJXPbOBYbdeKK8VYw8Q+r1WsPD3n3kBAGBbHqJdErSihRe+N4H8goZoSsTeY11BIleLlx+fwvxYfTXasVzIUR6e66+b1AOEoigqHIoZI3B08NzW+r+6WmxF/JWEAK1gEm/gU9ngflN3IMWuvFPf6JzOL+rB/fkFDXMX8wDIZklUWFias+XnULtBiQtgWBq5eQ29IwlIET6YFTz90zm4rgfP8+s2uY7l4fnvjl/VZ2tFC47trisM5Xt+QO1+z6/feFWf16543/99Aofv6AsJzkZTIkrZFVbSxMklHL6zb1MMiypl1VSdDZ/7lu4EiX21I7W62B5NimAFJhB6bSf/9magtlNfyhroHonBUMl3VlvEdB0ygrTV+xO9ZKNvfxyGZsO23EDvRE4Iwd6jVa/9zYSocBg4lAhur/6/9D0fcxfyKGaNgBlYm/RbugPH3LrxCcdyocSF4LzUixamzyyjtGQEzBeuQr9vJlox9rVrHWnqkAKmpRFW0Jt+4XCwhwYqsdmm0Rbf90HTpNFWhak7WJouo3sk2unUr4e/+Iu/wAsvvIAvf/nLAIB8Po+5ublmfkQHq9DKQoTlZRORRGPxJjnGB+q7ALA4WUL/gQQYll6zG+hY7pZXyldfsItLOp76hwvITJYwfWYZat4KbYiuv3dPw44Uuw0+9a0aeznKY3GyiKWpcpAAdO2J4NLLGTimi7nzeUTTIsQIB9fx6uar9DJhebz6g6nQ/Y7lgRPJ5lOO8itKOjUwNTug/Aoyi8KSDkEmXfvd5FEPbE38lTiPct6EVrIxP7ZS2bZ0B6LMNvrKN4RHPx+2rnQdr1KtJ+e6VrSCRNSpbGRXU087IEjviWD+UgFsxA2KW0qcxw++fBpG2UYpa+DJvzkXeo1eskK0+SuBqZJ4Vc9pz/ODWd8qav89ttrSs5Vx/RsHQtaL0ZQY2P4BQH5Bx8Fbe5FbJ6lfXTyxTRdSjIeh2hs+96tJvWuvMCyqbIoqKJpC797oNZfMVxGI0AEoZnV0D0dJQm26cOzwObPW/iQ3f2XjL5OnspiqYdA88dWzMMo2Ej0yTM0J1O8Bco7LlfO9Va/9zcR7Pnljw/Ed3/eRnSnjR187h5lzeRy+vRdawYRtuvjeF1auM5bhbulMvW2R87G8bKC4pMMyXPQMx7AwXsQNb96D69+4B0fvGcDAwURTP7fVYx9NiyhWCpim7oCXWIwcS4f2DqbubFun3jZccCIbcmWyNAd7DibQuzfWSerXw3//7/8dv/qrv4rf//3fBwBks1l86EMfauZHdLAKur7xmbxtBUUhn9EQq1EzrQVJ6lcSfqNsQ4pw4AR6zYVYL9s49+xCw8euBk//40UYZRunnpzB6Z/OYeLkSoeynDPQuzcGU7Nx8kezmLuQb8g+WI3b3rMX8Z7Gf3uz0Kqxl2M8Zs/noSSEQMG8b38cp386h2S/gumzuUAVO9knhza7AFnwLd3B6z+ZC/kHk2SPBicwkGKNY1DOmYE4l6hwKGR0CApLfnaRSB6wNfGPpshMsO8T27TqBtbUbfBX+P35no+J17KhLn9VIFGvzN5Vr/ie5wdJPUVhV2kgNAuxLgmZiRL4mB9YIx1/yyAO39EHS3dgajYWxouhmUVDtUO0+SuBqTmkU18pwtValgLYlWyYKwXLMSFRrGhaRCGjBwmcodlI9Mpr+jd7rof/88hrofssw0U0JcJQ7XXP/eKSjsnXs7AMB6bursTd9zH2cqbhWNx7PnltsioAhIS9lmdU9O+PB/R71wn/P6+1P3nsL0+FdEE2inLeDBgcjuVi+kwOetlCvEeCqdlwbDcobCoJISjiteq1v5lo1OEWFQ6m6mDy1DKys2UUFnX0jyagFi3oJQul5ZURF9Kp33r6/Ws/JPtGAIikBCyOFxHvkdE1GIGocE2/hrV67HmRDcYe9JLVsJmy1aMRtVALJpSEEDouU3fwM//yegxdl+oI5a2Hz3/+83j66acRixGhlQMHDiCTyTTzIzpYhZGR1px/k6Jk8V3LU/ptv3I9eKlSPXO9oHvA8cyaJ7ttuijnDIy9nMH02VzTjnXs5QzyixrmLxWhlywsTZNEdH6sAEN1EOuSYFY2y/NjhQ15nQsyt+Wdj1aNvSCzMEo2Dt7ag8wk+S67hiLIzqrYeyxNrK6SJPHmJRaWHo63pTlBclK76XVsDyzPQJBZiDIHiqZCST9QHY9Y6dSXKjP1osyFxiZ2A7Yi/mKEg1GyQAHYd1M3zj1HimiW5mxI9KzcYLbbsYnlYy0lPFDHVR2SfFIU4t0yihkdtumB5WmIEW7LxSbbEfFuCbkFDXsPDGLvcWIh2zUYRaxbgqW7wXdb2wXWS3ZD2vzYy2tfn9W8GaJMOrYLU3WCJFEvWxAjK2wrMcIFncQOwoimRJz+ySyeqWjGVJ04artHtTB1B2ohLPJl6U6Q1K917mcmS3jiq2exPKviO3/+KizdDnWWf/J355GbV4k7QQ2u5eJZ1SYSALIzZQwcTMDUCP1+NVNorf2JUbbritMbgef6Ia0ErWDCUB3Eu+VKEc0L6PfDR9PYf3M3gNa99m81lKSAct7A3MU8GJaGodlI9SuBhlM5ZwRsS1N3tlb93nKhxHnYlgutIi4bSYpYGC+uK+R7tWiX2Pu+D8/1A10clqdhmy5c14NjedtGv1cLFpQ4D15kYFVcWizdgRTlidZSp1O/NgRBgCSFu5Msu7u6Y62G+fmtF265EihxIeQ5uhrVRa9KszV1B4LEghWYYA7Ktb0QRcexXJRzJuYvFjalkr+W8FoVhmpjaboMxyTCOPl5DXMX8vjeF07CUG2yWdYcmJqD3LwGMdIa/9OtGnuKpiBFORy8rRfzFwugGQoMS6NnJIq9N6ShxPiAUsiLbF2yEYg76S70kgVTs5GZLMGxXHA8g57hKCiaAsPRdZ0UU3OCoougcHBsr0K/ZyHusk79VsSfoqiAQn3o9t5AMdbUnYZOFrVwXQ/f/YtX6+63TReiwgUFHoAkNUqch1G2oeZJJT2aJnPHjk069URwrZPUr0asWwJ8IF8OC9+RAhmZnR6+LhUanzDKFizdqVsHn/vOpTU/Z2G8iPPPrzCjXMeHXrbBMISCTATXVs6pqsVRB/WQohysSjLo+4QBw/Kkm1/rAjFzLgdTs2GU7Trlbtt0EUuLMFV7zXN/9kIeN711GDe9bRi8xMLQyHnrgxTXiksGFi4VN8Q2u1ZA0xR8n6xfoMh1xfcJbZeXWBSX9CBGrMA0TEZs00VuXsPSdGndQtlqeC4ZP5u7kCeFN8NFedlAokciSb21MoIkx/hgT9Wq1/6tRiRJrhG+R3R1nIoCvVa0oJfIeJCpOfA9H47lwTYJs+gnX7/QtGM4+aMZLE4U4ZguYl0S7rh/P7SKDWwkKRC75jXGTpuBdog9LzDIzWlQkivfA3EGMWBXmjiXo98vTZeDBttm4dhuML6p5k3IcR6cxCI7U8YTXzsL3yfnvaiwHfr9euju7sa5c+eCDuX//J//E8PDw5d5VQdXA9PcuG/ndkJJCEgNXN7vliRmHuCT+UxOYILq6tln50Mz9I5FRJoWJ0soLm28Kv7i9yawML52EcA2XSxWLGRsy0VuQcNLj08ikhRglO2gU89LpGLeKrPZrRp7gFCEu4bIbGI1mX7XJ25A7954iJYqyCxMnSyqasHExZcWK6wIktjrRRvzl4p48XsTZIPD03j7R48BILoFq4XbTG3Fi54Xib+vILNI9iuBUutuwVbF33N9CDKhDVaZNlal6LYe8vNaQzs823TRPxoPRjEAwNAcQj9WLZSyOmJpsTKLpwdjFpLCX9Nz2WtBkFiICgePsuvuN3UHpuqg/2ACubmVZFEvE62J2vPFtT0UFtemcuolK0hmPNeD7xH7HznGw9Qd6GUbUm2nXul06tcCRVF4168dR3owAjVvApU9UtdQNFTsuvhSBsuzKgyVFGdqu/i24SCaFmGozprnvlG2IUbJ9UmOcigs6lASAimICgx698WwOFlqmWtYK2FpqoyuwUhw2zJdSFEOk68vByN5LM80TEZ8z0duXkVhUd/UfL3rkE79i9+bQG5eBUVTKGYNiBGOdDXtFaG8WrTytX8rEUkKmLtQQKxLQrxHghTlA1p1dWyhnDNgmS5EhYVjkcbAxGvNm0NfnCji/HMLsG0PcpzHzW8fhud4oGkKkaQA1/FCo6XNRjvEPtol4cKLi+gdWbHHjCQFlJdNWIYDmqUuy6JYnChiceLKkvryshkU17SCBSUugBcZFJcMsv5WQDP0FY3NtDKaumP6kz/5E/ziL/4izpw5g6GhIXzmM5/Bf/tv/62ZH9HBKuzfv3+nD6Eh9t3QhUO39132eSxHk8p3ZZPD8kwwB1Vc0qGXVjautkmE0rSCGToxL4dyzqzzyVyeU5Ff0CozvF5Qea0KQQkSi3iPjMKiBinKwfeJLUY0LbaM93Krxh4A3vMbN4KmKQwfTQddIVHhwHB0aH6zln4/d6GA8VeX4DoeyjkTDENDK1lQc4QGXKsEDAA0Vy9aZNbM+VZtcASZgxzjMXgktdV/9rZiq+IfSQgBk4bhSJHN1EnXajWqhTYAWJoqwdLqKd626SLZJwfnrOt6MFUb8R4ZRpmo0Kb3REinftmAbXngeAZitPnziLsFfftjOHjoQOg+XmIDlfOe4SiK2ZWEXS/bAT2/9r5GYmBVaEUr8FGvMmKMsh14M692N+ndF0P/aLxpf+Nuw+CRFFL9SmgMpXs4Gip22Qah3RuqDQoIXbcsw60k9faa575e0aYBiFp6bl5FNCWAYQnzZfRET0XFvTWuYa2EUtZYYRf6PmyDUHT1koXCUkXNfBX9fuzlDErLBpL9CnLzWkUAzMNz37mEcm6l8WDqTqDQXgvP9WCZLkzNQXZGRbxbgmO6QWNsLbHQVr72byUiSQGTp5eR7JOR6JER71phgxplG91DUZI46g6UhADbdGGoDvKLetPsWPXSCrOzKoZsGQ4EhYUc48GwdN14SzPRDrGPd0s4/9xCyL0gmhTx4mMTmLuQhxITLku/dywXtuFi6vRy3Zjl5VDOGUHuoBZMKHEBQoV1s5ncoR3RlKR+cnISk5OT4Hkef/M3f4MXX3wRjz32GL71rW+BYTqbsq3E5OTk5Z+0A1ASAiLJy1crmVWJWW2nvrhkrAhpoZIc9MqIpsVN2croJStIPKpezXMX8njth9NBB2N5VoXvA47pIjWg4NCdfZBjPJbnNIgKB9/zQVHA3e8brVN83im0auwBBIWPQ7f3YqQy91tFbWJepQwDwNJ0CVrBAi+yKC8biPdI0EsWynkT8R4JS5Ol0AancafeCc3OCzIbzNjvNmxV/CMpMaBRS1Hi4WzVMCBq8dLjk3j1B9MACF0u1i3VdbIcixQEqqfs0/9wEROnskj0yDDKFhYuFdG7N4ZYlX5fKd5IEa5jabcG3vmJG+riXz2XTNWBHOfDQnklC/FuKTTqYqhkbV1d8KxCK1qQonylY1gRTKx06qu+57VJfddgFH37O0n9ekj1KyEbu0SvHGJLWIYLrWgRhli3FFz/DJUUYJS4ANtwMDk5Ccd267pMRskK2BNKXEA5Z0KOkS7VLW8fweE7idUezXTOq9UwtRW7WoqmgqKVVrRQWNTAiwwRyqtJ6ideW0J2poxEjwytaAUCYMWsEdJEmDyZxY//9jwAhDqUpFNPdCqWZ8tIDSihc8qx3Ia6Iq187d9KRBIistNlJPsUjFyfXrET9n3oJRvdw1GU85WkPl5N6m3wIoPlK3QoAEjxJWDU+D7SAxFkJksBk0xQiLsOzdDoHo5s6R6xHWJ/5K5+fPD3bkeiZ4WVOXAogf7RBMZfy0JJ8Jel39smYc6efGJmUyKvpmajnDOD8SWtUKHfi9Wk3go5J514597N/XEtjqas7CdOnMCtt96KW2+9FaOjo7j55ptxyy234ODBg7j11lub8REdrAHHaW/lRpalYeoOGIYsghy/ctGszklV4VguEpUKLUXVi6TV4sXvTWDmbA6+7wcXW9/38cw3iVCRbboYf20JtuEi0SuDYWnwEgvbcnHfL12HoSMpyDEeuXkVosIF3fvREz1b+G1sDu0Q+0hSxMBoYs3Hq5RhAKSwAoATGZRyBqFnlyyoOQPH3zSI888vhgoCDEvDWd2pV8PJpxzjd+1c9lbFP9WvBB0rKcJBL1mwK6MPtfBcD9NncliaLgMgvvO9+2KBsn0VtlFhWFQupFrRwuSpZcR7pArNmHjZCjIXdLpYnkbvvhi6h+rtIjsg84Cr4y/UzNRXhTqra6RetklSX2MfqZfIZnctBXa9aKHvQByFjBYUz/SyDTkm1NDvOzTuzSA1oGBpuhyMtdA0FdpgWroDrWjCUG0k+xToJRuzF/L42h8+i+kzuWAEzHEcvPxPU5i7WAi9v+v6KxZoCQEcz4CXGPAimd+XIjy6hiLoIAyKIudDdSyheziKpakyOJ4hYyiLOniRrZupVwsW1LwZKLVbFasu23BhajY818PT37gIU3cwez6P0rKBf/zjF4OmhOd4sIxKp35WJUl9TRGazNTXN8ba4dq/FRAUFgxHI9EnQ4xwwSifFOWRnSmjayiC8rJRSep5OCZhhQ0cTGB5phx6L6Ns4+V/2liCPHU6hxceHQ9u77uxC2rRChgVSowP/nfe8xs3Xf0fug7aIfY0TYFZVTgUFQ6HbuvF4mQJcqXgsh5qNRE2o5T/3b94DcuzKrn+eT5s0wUvsoE+hhjhAmYwgF1XiG7KbjeTyWBxcRG/8iu/gs997nNQVRWqquKRRx7Bv/yX/7IZH9HBGhgdHd3pQ7gqsDwNNWcG1Wly0XSDx+yaTahjuRg+msZ19/QjmhQaKm0DRIho4mQWE6eyePZbl6CX7GBO2wxEOjxIUR6ZqRISPVIgbOK5ftBllmOk28WwNEDhskJh2412jz1Q6S5qK77XABnJsA2XdD9KNrSSjdETPegaioRsbgI9hhoYNUJ5APC2jxzdtf7LWxX/0RM92H8TUVkWI3yQ9NV+j57r4dt/9gpu+pnhlYq770OK8DC0cJJoV9gwDEcHdH3LIDP182MFyKvsCaud+q7BKLqHO0n9Wlgdf05kYFUszASJrXijkzXS93xiB1XbqS/bSPYrMEqNk3rX8ZAeUFBY1OHYZGa0OlNv6Q7Ky8aWCkLtRsgxHrk5tY71Uk3yXNsjnXrVRrKPFDVnzuZw7E17sHCpAE5gAYrC6OgoLN0JMdlWQ4nz4CUGgsSCqxkZu/+TN23J39bO4CUWxaweJNR9B+LwQa5FWtFCOWeAl+rp92plFJANknrSXXQskqhbuovZ83mYmo3RW3tw6ZUMMpOloFnhuj5s0wFFEfp/ql8JOUqsRb/fDdf+KwFFUegZjtapy/cdiGP6bA5dg1GUcyYswyWJo0Xo9737YihkwvohxaweGn1ZD2efmYdre3BtDwxLo380jmgNE1WOC8Ee9nLaM1eLdo59VQxXifGXpd/bpltJ6t1APHsj0IoWLr6cQddQJMRCEyQGhSUD6T2RXT1+1NQW1ve+9z08/PDDoGkaNE3jox/9KB599NFmfkQHqzA2NrbTh3BVYFgG5bwRLIjkoumR+elVFWrb9BBJCugajEKM8qEOE6HcGJXfHcTSIu54YD+yM2W4lWq4XrKCBMS2CJW/sKgHom6rIcX4FSV1qfVo3O0ee2ClU28ZDvjKzCIvseAEBvFuiSzKFRHFd/7a8VCXiWHrZ+pt0wUnrvzfxLrWdmBod2xH/Kv0+9XIzWvoGopi3w1dRGVbtYGKhoG5yvfVNklSH0mIKFdEwpJ9CiJJAYdu78U9Dx0MnstyDBHSaZERl1bG6vgLEgvTcILzJd4jIb+o4Z++9DoYjnRsa+n3etkiiWNNYrg0XQpRuuWYAK1okTnsilIwmaknyUur6Iu0CyiKOIHUjghVvbd93wcvsdBLdk1ST2w9993QBVAU6dT7PsbGxgi1eA2WBUA69YLMgZdY8GK4GNpBGNV522rhvndvDKJMusJ6yYYSE8AFnfqapD5PknquwmIyDQeOSTqMpIlQaSZoDvYeS+Pkj2bBsHSwV/FcotYeqVi89u6N4fCdRIuI5WkYqt1wBGk3XPuvFA/85k11hfr+A3GwHE2slDV71Uw9ETtebZ2rFa3ATvBysAwHoCioBRNyXADN0Hjf/30ieDzZL2/bXqOdY8+wNKIpAXJCuKxQnm2RpN42Lm9NqJetwFLSsVyUsyR5r3UQ4UQWjumiazCy5YWXnURTV3fLsnD27Nng9rlz59pCqbGd4Xnb4/W4VWB5GuVlM5gDrF40S0sGomkxRJOpdvyACtW0pos/cy6PCy8sAiDCeEpSAMMSKr8UIfR5vWQFdG3HchFJkaohL7H42Yevrzs2OcYHxQa+IrjWSmj32AMVDQXTDWzNlDgPQSJUKUEmqrbVP5MX2dDFvLZT/+O/Ox/8P+zWzvxqbEf8q/T7KiiqMl84VQq66N1DUUy9vgwpSuYKzdWd+oqgkJIUoFbYNW/7yHXgBAY3vW141Vy2gvy8hg4uj9Xx5yUWdk3S3rsvhrkLeRiqjXd87FhIvwIgVPpknxIUR888NYfHvngKU6eXg3NJjhG7KLdiDVll0BQyGq6Ns6z5SPaHKdZKQsDZZ+bxyvenyCyu78OsxqZE5uujaRGDR5KB57PneUHCUsXq+XopykOO80Fi38HaINRcI4gLL7J484ePVJJ6C7FuCbzIguNpWIaDzGQpuPaoBQucwIJhqWCPQZJ6OxiHMXUHyX4Fru1i8LpUwDL0HOIokeiRQNMUlKSAkeuJS4sgc4QF0IB+vxuu/VeKRt9HoldG3/54cO03dQdyjMxtmyo5f2r3iwDRWqqyLpbn1KBB4Pt+iI3h+37wvmrBgpIge9Xa69ah2/oweDjZxL9ybbR77GNdEhmNuEyi7pgunEq33llFv9dLVmi9m7tQwPnnFuB5PpL9CiJpEUpcQCGjBzlDtQDdNRgB3+nUbwyf/vSncc899+Ctb30r3vrWt+Kee+7BH/3RHzXzIzpYhcOHD+/0IVwVGI5GMatDjpOFskpvM1QLUsWap0pNdMyV7j0nMoHfJUA69dWFuJwzgso3y9GI98iVWUUbrrVCvyf0VAOcwKwkgn5tl4oPLvKCxLUcZafdYw8g2MRWk3o5LkCQSWeJl1mceOcIIonGNlm1nfqxlzKYOr3c8Hm7FdsRf2kVI4blaLiOj8xEKZh37xqO4NIrGURTIgSZhaGG/dCrYpSRpEA69QB6RmINiy/pwWiokNfB2lgdf4al4bo+qt9893AUF15YDKxFa50mAMAo2Uj1K5g+vYwzT89habqMN//iEfzk6xfw1//Ps0SsMMpDK5FOvaiwAAX07o9h/NUsIilxu/7UXYVUvxIqEEeSAi68sLhi00pRcB0P0bQIrWAGTJf7P3ljcM4cOnSorlNv6k5gHwqQudZ3fvw4Bo8kcfQNA9vzx7UpBJkUvGoFBAdGE2A5Br7nB6MMLM8gO6PihUcnoBUtJHplqAUTnEBDVDhoBQs0Q1WSSWclqVfJSMyBm8loUzWpd10P8InQWqJPDs0hD4zGMXeh0FAobzdc+5sJiqLw7l+/IbhtVPQ+fM+HodmIpaW6pF4rriT1Lz02gdwCEdJbmirjmW+tdMNtY6WZpObNLbWr2wjaPfbpgQiiKXFD9PvquN7qTv3z3x0PtHwAssdQCyYsjWgpvOPhY5CiHLIzZciVETFBYsFyNIaOpnDwRG/z/7AWQVOT+gceeACnT5/Gb/7mb+I3f/M3cfr0adx///3N/IgOVqGWGdGOYDkaxSUjWCir6veGSmajBZkNhLccywUrVNRGawTWAEK5r1b+yjkTkcqJnOiVKwJRhH7vuWTLa5suIikhSOqrx1IrvCYqHG57D7EPEWS25bx92z32tSAVcAFKggcvkc6SILEYOpLCmz98pOFr2Eqn3rFcyDEe4682z4u2HbAd8RcjHLS8GdDhGZbG6Z/OYX6sEIjpdQ9FMXEyi0hShKBwmDi5hJceXxEgsk0XrMAgmhJRyOjr5uxdg5GO4v0G0Sj+xSUdsTSJC8PQiHdLgRBQLbvpzNNzWBgvItEnY/y1LBbGijBVG6kBBXc8sB8f/N07cPv9+yDFOOjVTr3CgWVpMAyhUFZFqjrYHA7e1ot9N644gihJEYvjRWjFFVajqTuQo3xgg0ZRVKCozQoMTp86G+rUkzltK9Q9BMj5yrB0Z0ziMqgyw1aD5YiArhQjfuicwCA3r8LSbah5E8l+JRDKExQuiAcnMDA10qGvduNZnsHdD42iZ290hX7v+BAUss+57d37Qp89eqIHsS4RLFu/Hu6ma3+zUC14iVEeU6eXkR4ko3qW7kKKcoEtZxVacYW5WVo2YaoOChkNuQU1xHgyNDsollmGs+Osl3aP/T0/N4r0YCRI1N2KmOv8WCFkr1rtC9gVq+larLaIdCwPWsGCXrYgRnh0D0chRXksTZehxCq5hUhsPUWFCwrduxFN3z11d3fj/vvvx/3334+urq7Lv2CD8DwPv/Ebv4EDBw5gdHQUf/Znf3bZ13zpS18CRVH4x3/8x6YdRwfNBcPRKGT0gNJU9amv+o3X0n9r6fe8GKbfm5oTzEepeUK/B4BknwKlwgLQihakGA/PJYlgJEno90FSLzAh4TWKotB/gGyIh4+mkN7TUQ3eElBUUAHfcziJoeuS4EU25DffCAxLwbZc5Bd17DmUhO8jKNp00ByIEQ6ZqVJAV2M4GmMvLeKenzsYJBmiQpTroynCsrj06hLK2RWP5mqnMdErY+FSITRPvBqRpNARx7sKeK6P0RPdwe3b3rMPg0cILVSK8jj/3AJe+f4Uzj49j+6RKKIpEXe8dz/0sgVTJxvW/Td1gxMYJPsUMAwN3/NJp17mgq7h6K296Nu3u1SDtwtyjA91+yIJAYk+GUbZBicweOsvXYcH/j83gaIpNFrNeJGFaxHhQlNzkF/U8L0vnoRWtCDHOsKFVwJhjfE6ppLUDx9NIdErg+UZ5Oc1mJoDtWAiVXEoYAWmYmlGBe9nakQrpjrnXUU0KYY69aJCrnWrnXVohsYHfvf2lrHQbRdEkgKxUa0Wsmro8wBJHn3Ph160AiZmedmAqTv4zp+/ivy8BsusZYGu2OQSEddO0flqQNEU2edbLnLzKn70tXMAgFf/eQqZyRqHAt+HbXmgGAq26QZ21ACJSW0R1K506g3VCWKa7FNgGw66R8h+gmHpgMG7m9E2/51f+cpX8Prrr+PcuXN49tln8ZnPfAanTp1a8/nj4+P4whe+gDvvvHMbj3L70e5UHIalA09RAMQH1iLzaILMQkkIgd+rbXrgKpZmnMSElPFNdTX9nrzfodt7ceiOvoqPqYVYWoRje/B9QI7yMFQ7SOo5nglmFlejfzQRvGeroN1jX4sq/T6WlpDsU7D3hq7LblAZjsFz3x7HE391FoleGff90nW49wMH133NbsJ2xJ9haBx9wx7c9u69AMg84/E3D2LgYCL0vO7hKCIpEaLCgWFoGDW2dtWkXlQ4FDL6uowXiqLw9n9Zr2/RQT0axX/01h70HUgEt/v2xYPNbTQl4hd+7w6ce3YeyV4Zb/nFI2BYGre+cy/cypq4lkChY3sQZDZYfw/f0YfefbHm/1HXIKJpEaMnemAZLvhKN6lq3dkoGrzIYGhwL0n6PR8XXliEbbjIzamI9+xeYdCtBC+xIZ2DKliOBi8yGD6aRjQlghMYODYR3tUKFvm+KVTWN8Iug+8HozCW7iCalkKCrrzEIjevYvzVJXgOcaVYS7hrtS1YFbvp2t9sRJMi+qs2uqsaAq7r4bufe42I81a0XnzPRzlvwtRsFJcMjL+WDc3U19rkOtbKHnSnsBtiz3I0HMuDmjcxX7Hl1Eo2lla5EdgG0UawTRff/39PB2N9VqVT7/s+MlOlCv3eglG2An2uSFLAA//65pDWwbWwP2ybpP6v//qv8fDDD4NhGKRSKXzgAx/AV7/61YbP9TwPH/3oR/Gnf/qnEITWSsSajXan4rA8A0Fmg00M6dR7lU49h3i3hMIiEc5ybTdQ7iX0+5r5UG3Fy5JU67jg/aqbWr1kI1Izy8PLbHBBJs+lQz7orY52j30VUpTD/FghYFQAJGmota9rBIalYJRtWIaDZJ8MmqaQ6Ll2KMHbFf8b3jIYULpve8/ewO6uFvf90hHEuyVIEQ43vGUwbEVprjBslLjQcPNci2tF6PBq0Sj+d9y/f13nAIqmcN09Axi9bdVM4WW+c6J+z3WU07cAosLhtnfvA8vRIes5gFCJV6+DvMTi/JnKzC9FYe58Hvtv7sbUmVxnJOIKIUiNx+sYLjy6UE3oRIWFXrYhRSu0fJ4JBAldxwPDrfjWx7rEOsr2e379Rpx9Zh6e55PXbVKEd7dc+7cCQ0dTgYaEqdmhuE68moVT8T6nAICioJdtKHEe5ZwJiiZWdxRF7DuzM+VgLwqsjJLtJHZD7KvXeL1ko5DRYelkfLZ2Th4gNsdyTIBaMFHOEacJgMRBK1gYfy2Ln379AhzLg+d40Mv2uk2DnpHdX4humyv05OQkRkZGgtt79+7F5ORkw+f+l//yX3DPPffgxIkTDR+vhWmaKBaLoZ8Otg8sR4e8jhmWDmxeBJlFvEdGfnHFX7S6GKym39umC7eGel2XGFBEvIaX2GD2nqaJBVfVAo0TmMsmkh00H0fu7EdhSd90QYXhaIwcS+NdnzgeUKw62FoIMteQDirIHCiK0OrufPBA6LGq+j0AJPvkltOmuNZw7I17MFDtZFXhrz22wgoMTNWGqLANlac7aA7I3Hb4+42mxEAwtgpCv69qwzgQoxwSvTJmzuYCnYsONgcpyjcUfqzO1FdBsxRhUygcjJIFKcIR//oKw4KXWICigj2FqbuIdUl18/pynA/muTmR2dUWW9uNaEpEqp/MTB9/0yDe+Asrne2Z8zkMX5+CbbjwQfaAhYyOrsEo8gsaBg8nSWPA93HyRzP47udew8KlYhA/23J3vFO/m6CXLQwcTGBxoggpwgVJexWcwECO8SjnDPAig4Vxkp8JMgutaOHUkzOgKBIXOS6guKTX6Ypca2iZpP6uu+5CV1dXw5+pqakNv8/Jkyfx9a9/Hb/zO7+zoed/6lOfQjweD36Ghoau9E/YEbQ7FYfh6FCHtgpTsyEoHOJdEopLlaS+JlFfbd90Ofi+DyUpNBTDW+nUM201L9Xusa+ifzSOm39meNOv690bw63v2ot4t7wmTXE3o6XjXzlX58cKxHeeJbe7BiOhIl4HV46mxn+dTr0c5VFaNiDIXFutj+0GudL1rUU0JQZ00ip4iUFvdz8A0jkePppGvFsiTIo1xsc6WB/RlIi73zdadz9h+q0kcRRFIdmvgBUYlJYNSFFiwcryDMRKp57l6GBPYekOYmmxLmmnKCoopA0eTm56bKKl1/4WwsHbekONmnKOiBuaugOqUnzJzatID0aQX9TRszeGN37wEAAyt73veBfmLxWCQrRtuDvO5txNsddLNoaPppCZKgMVMVDPXdmfcwILOc5DzZkYPJLC4jih57M8U3GdYMBwDBzLQ7xbQnZGveabBi1zBXjqqaewtLTU8GdoaAjDw8OYmJgInj8+Po7h4fpE4Mknn8T4+DgOHjyIvXv34umnn8bHPvYx/MVf/EXDz/3t3/5tFAqF4GczBYRWQLtTcVZ36gHAB8h8ocCEvMhDr+Np2Ja34qHt++t2m3iBwaHbeoli+qqkvrpIcwLTVlXYdo99FRRF4cQ79m76dYLMXdN003aI/wuPTmBxohQwZ66/dw9GjqV3+Kh2B5oZf6HiNtEIUoxHadkkY1Id+v2WQYrxQYe3ij2HEhg6mgrdx4ssJsemyeOHkxg+mkK8W7qmRo+2CwxLg1t1XrzlXxwhHvIFC5zAgJeIKn6sW8TRewaCfQQvMsSNoltqKMJXxd7jXZu2SWuHtb/VwAkMjLINOcpDzZuEYSEwWJ5T0TUYQWFRgxThCEWbomBqNnr3xZCZKFVGRGmYqg1O2Nk1cLfE3vd9aCULffvjyEwUIUgsEr0y8gs6mZ2nKHACDTnGo5Qz0bsvhuKSHvjT6yUb/RX9GMdy0X8gDoahEO3a/WJ466FtrtA///M/jy984QtwXRfLy8v467/+a3zgAx+oe94nPvEJzM3NYXx8HOPj47jzzjvx+c9/Hp/4xCcavq8gCIjFYqGfdgJNt00IGyKaFnH8zYOh+ygAvucHNF+KouB54aS9miT8w395KfRaf43k/q73jWLPoSRYngl5XkoRLjxT30b0+3aPfQdXh1aPv+/78H0ft9+/YtVE0VRnZr5JaGb8xQgHYQ2tAznGo7xM6I/7b+5p+JwOrh6RhABxVfIX65LQuze8J+FFFrbugxMY3PS2YUhRHvEeGXe/Lzz20sHVI7VHwU1vDbM30wMRYg9ZYQoKlaSe5Rj0H4iDrYzx9e6LY2G8iPRABAOHEk09rlZf+1sR1dFLTiQsC0Eie7/crIpUvwLLcCFWWTG+D1Nz0LsvVhEJ5cBydGBNuJPYLbGXFA75eQ3dQ1FkpsoQoxy6BiPITJVQyhpgWAqcwECJkU69khAqIpUOeInQ8vdUzivHcnH0DQN4x8ePt1VjbivQNv8d/+Jf/AscOXIEBw8exG233YZ/82/+DY4fPw4A+OY3v4mPfvSjO3yEO4P9+/fv9CFcFViOqROviHVLgY8rAKKAv2rWBgD0koXcnBp4w4Ki4NorIjW1iKbEipUGDbfG8/JNHzocUB45ngHXRvTSdo99B1eHVo4/JzCwDRcUgBve0l4jTe2CZsa/6t/bCFX6Pc3SuOEtgw2f08HV48hdfdhb41+/FjiJgcBEQrRimqY6lqtbAIZpbIMlyGwwa3/He/eHYsFxJKnv2x8Dw5JO48Fbe+ve43LilOuhldf+VoUgMZDjAniBRTlHmEecwGB5XkUkJRCxxOo8NkXBczxE0yJYgYGgsGA4Bqbu7PiIy26JfbJfQW5BA8PRoBkKUoRD11AEL//TJL79Z6+Q/bjAQo4LxFJV4UixRXcgSBxuf8++QDvBdfzATvJaR9tkMAzD4M///M8xNjaGixcv4l//638dPPbAAw/gi1/8YsPX/fCHP8SDDz64TUe5/bhw4cJOH0LTkd4TCS2c0ZSAwqIGetXctGO5EGQWy7PloHpq6k6dKE0tWI6BbbrB9bT2gs2JZBFpF+zG2HewcbRy/EWFg1HjzdxB89HM+EvrJPVSjNBVO9T7rQXN0Os6F1ShxAXMjWc7oq47CF5iAwHDrsFoiH3ECjQ4kUF6MLJpWv1G0cprf6uCE1lEkgI4iSHMowrDwnV8sBwDXmYhRaquSUR7iaIodA9FwHJ03ejmTmG3xD7VrwQjX/FuCVKUR7JXhpo3ia1nhfEiKkSct8oksyp7/D2Hk6BoChxPV/b0naQeANong+mgIVh294UwvScCoWaDGU2LmD2fRzQdrpjzEouRwQgWLpGZJ6dgwdScOvuYWjDrUKgGDyfr/LdbGbsx9h1sHK0cf0FmoZfshkr5HTQHzYx//2gimFVcDTnGw/ex4x2qDggiSQFGwesk9TsIQWbrBAyrqLroMAyN93zyxi35/FZe+1sVvMiCSiLUqfc9H9EkKbyI8kqnnhMYFJcIW/Q9n7yROLusElneKeyW2Cf7laAwFu+RIUV50AyND/7uHTj/3ALUvImRYykkemVwPA1R5sBwDNS8FbaZFFnYGX2tj7nm0LlKtzkaiQW2O1IDCvYeXxHTiiZFzJzLI9YVVojlRRb7b+zGqR/PoH80AZajoRWtde1hOJ6BXrYa0uxphm4ry6bdGPsONo5Wjr8gsyhm9GteiXYr0cz4p/qVNenb1Y1XJ6lvDVAUhfRApJPU7yAEmYUYbby2cQIbxCaSXKdTv46w7+XQymt/q0JJ8Ej2KuBEBuWcEdgZV60MpSgfXK84gQHDrtgnA6QhtBEmzVZjt8Q+lhaD0bwjd/Whf38cACki77uxC/2jcQwcTEKO8cRdQuEgx3nkF7QQG5cXCfu2A4LOVbrNMTY2ttOH0HRwPBOaw42mRcxfKtR58L7pQ4fRP5qAmjex78YucAJTUTVdp1PP0zDKdlsJ4q2F3Rj7DjaOVo6/qHDIL2rXvGfsVmK74s9yROGb6dDvWwZ0xO4k9TuIeLeMgYry9mocvae/zrGgIa6CLtzKa3+rYv9N3dh/czd4kYFesisChyyilRHMt3/0+qBwyYtsnWMByzEtsW/cLbGnaAqjJ4jwanogEtorxLok7LuxO7jNiWQ8Qo7xWJ5XQ3t8TmQ7jMAadK7SbQ5B2P2ez3JcADzUJfXRlAgpyuG+X7oOvEiq49UK7FpguUpSvwsUMq+F2HewNlo5/ok+GbPn852kfguxnfGXY3ynU99C6DsYQWpA2enDuGYR75aw/+buho+xPKHeXw4MS8OxrqzD2Mprf6uiOnNd7bwLMod4t4T+g/HQ/QDp1Iur9pEsT7eEkPK1GHslLoCmKcgxHpdezqD/QDx4jBeZXbGfbxZ2/j+0g6tCX1/fTh/CloOmKURSAqKpehVaiqICZVlWIPM269HvWZ6BVrJ2RZfjWoh9B2ujlePfNRjBwqViIDzUQfOxnfGXolynU99CuO7ECPYcSu70YXTw/2/vzuOjKg/9j39ny2Sdyb5nEiALSzbCIvvqLoKKiK0ItCLqlfqzVqu0tdZbC/Z6u9m+vKCluFAVC4iKVqGo4G7Yd0hClslO1sk6yWSe3x/R0UAISUhyzjPzfb9e85KZOcw88cOZyTPnzDmXweCl7TwFbz+o+bVf7b793c/oq0dwlB9GdHOaToNRd8GWep1Bq4rJoye2n7tsFIDOD5cjh5vhF/jdBxte3npVfNiiFvw/IbnCwkKlhzAkpt2WfMlfKnuz+73eoEV5Xj3C4gIGeohDzlPaU/fU3F9v0CEo0pffqR9EQ9l/5OQo6LmlXjXUvO5T7+h6uUW/O+zffxqtBnqjrsffEw3eOtfR1r+lN2hVsTHIE9t/ezytmJQgzLg9uct9Bm6p78I9DqPowXx8fC69kBsYln7p8/f6+BtwztrQ8+7335z+IvJ7u+/IylPaU/fU3j9iuBk+Ad0fIZou31D2Hz01esieiy5N7es+XZreSwttPz8oY//LY/S+xKS+my31eoM6Jo+e3N7gpYPhvAYGow56bql34aRecqGhl57seopRU6JQV9nSZdec8+kNneeP7WkXfVmwvWdTe/8pN4/gLtuDSO39afCwvfz0Bi10uv4d4Iv9L49fkHePR7K3jA5Bh6Pr6et0BnV8p57tu/Ly1qviwxa1UP5fKF0Wq9Wq9BBUQ6vTYurCxB53+fUOMGDKzYlDOKrBw/aeTe399V4618GJaOCpvT8NHraXn95L1+8t9ex/eTLmxPZ4v8Gou+D3SL1Bq4qj37N9V14+F26992Sc1EvO37/7cwtT93Q6be9ONyMBtvds7O/Z2N9zsb389AYttP3cUs/+lyd5Yt8PNqfzUseB8ti+K/8gb0QnByo9DNWQfx9kD2cymZQeAimE7T0b+3s29vdcbC+/zlPf9W9Sz/5DLyTGXxVnc2H7rrz9DK4zYBG31EuvtLRU6SGQQtjes7G/Z2N/z8X28jNcxoHy2H/oGbx0MIf5Kj0MtqcecVIvOX5q57nY3rOxv2djf8/F9vLTGfp/Sjv291xsTz3hpF5yvr7Kf3JIymB7z8b+no39PRfby6/zlHb92/2e/T0X21NPOKmXXHl5udJDIIWwvWdjf8/G/p6L7eWn99JB18/d79nfc7E99YSTeskFBQUpPQRSCNt7Nvb3bOzvudheft5+ehh9+3esavb3XGxPPeHR7yVnMCh/NE5SBtt7Nvb3bOzvudhefqGxAQiNDejX32V/z8X21BNuqZdcZWWl0kMghbC9Z2N/z8b+novtPRv7ey62p55wUi+5kJAQpYdACmF7z8b+no39PRfbezb291xsTz3hpJ6IiIiIiIhIUvxO/XmEEAAAm82m8Eh6p7CwEH5+fkoPgxTA9p6N/T0b+3sutvds7O+52N4zfTsn/XaOejGc1J+noaEBABAXF6fwSIiIiIiIiMjTNTQ0wGw2X/R+jbjUtN/DOJ1OlJaWIiAgABqNRunh9MhmsyEuLg5WqxUmk0np4dAQYnvPxv6ejf09F9t7Nvb3XGzvuYQQaGhoQHR0NLTai39znlvqz6PVahEbG6v0MPrEZDJxBfdQbO/Z2N+zsb/nYnvPxv6ei+09U09b6L/FA+URERERERERSYqTeiIiIiIiIiJJcVIvMaPRiCeeeAJGo1HpodAQY3vPxv6ejf09F9t7Nvb3XGxPl8ID5RERERERERFJilvqiYiIiIiIiCTFST0RERERERGRpDipl1ROTg6mTJmC5ORkTJgwAcePH1d6SDSIHnjgASQkJECj0eDQoUOu2xMSEpCSkoLMzExkZmZi8+bNyg2SBs3VV1+N9PR0ZGZmYvr06Th48CAAvg54ko0bN0Kj0WD79u0AgFmzZmHYsGGudf9Pf/qTsgOkQWG327Fq1SokJSUhLS0NS5YsAcB13xNUV1e71u/MzEwkJydDr9ejpqaG678HeP/99zF+/Hikp6dj0qRJOHz4MACgsrIS1157LZKSkpCamoq9e/cqPFJSDUFSmj17tti4caMQQoh//etfYvz48coOiAbVnj17hNVqFfHx8eLgwYOu28+/Tu6ptrbW9edt27aJ9PR0IQRfBzxFfn6+mDx5spg0aZJ48803hRBCzJw50/Vncl8PPvigWLVqlXA6nUIIIcrKyoQQXPc90TPPPCPmzZsnhOD67+5qampEcHCwOHbsmBBCiL1794oxY8YIIYT40Y9+JJ544gkhhBBff/21iImJEW1tbUoNlVSEW+olVFlZiX379rk+sV+4cCGsVityc3MVHhkNlhkzZiA2NlbpYZBCAgMDXX+ur6+HRqPh64CHcDqdWLFiBf7617/yqMcepqmpCRs2bMDvfvc7aDQaAEBkZCTXfQ+1YcMG3HXXXUoPg4ZAXl4eQkJCMGbMGADA9OnTUVRUhAMHDuCNN97AvffeCwCYMGECoqOjsWfPHiWHSyrBSb2ErFYroqKioNfrAQAajQYWiwVFRUUKj4yUsHTpUqSlpeGuu+7CuXPnlB4ODZKlS5ciLi4Ojz/+OF555RW+DniIP/7xj5g6dSrGjRt3wX2PPfYY0tLSsHjxYpw9e1aB0dFgysvLQ3BwMNasWYPx48dj+vTp2L17N9d9D/T555+jtrYW8+bNc93G9d99JSUlobq6Gp9//jkA4O2330ZDQwPy8/PR3t6OyMhI17IJCQlc9wkAJ/VEUtu7dy+OHDmCAwcOIDQ0FMuWLVN6SDRIXn75ZVitVjz11FN49NFHlR4ODYFjx45h69at+NWvfnXBfa+88gpOnTqFI0eOYPr06V1+2Sf34HA4UFhYiNGjR2Pfvn149tlnsXjxYjgcDqWHRkNsw4YNWLp0qeuDHK7/7s1sNmPLli1YvXo1xo0bh507d2L06NFobGxUemikYjxPvYQqKyuRmJiImpoa6PV6CCEQFRWFTz/9FImJiUoPjwZRQkICtm/fjszMzAvuKysrQ3JyMhoaGoZ+YDSkfHx8UFBQgKSkJL4OuLH/+7//w3//93+7drsvLy+HyWTCk08+ifvuu6/Lst7e3igpKUFISIgSQ6VBUFVVhYiICLS1tUGn0wHo3N32kUcewYoVK7jue4jGxkZERUUhOzsbI0eO7HYZrv/uzW63IzIyEtnZ2cjIyEBeXp5ra/3EiROxZs0aXHnllQqPkpTGLfUSCg8PR1ZWFjZt2gQA2Lp1K2JjY/lm7mGamppQV1fnuv7aa69h7Nixyg2IBkVdXR1KS0td17dv346QkBC+DniA++67D2VlZSgoKEBBQQEmTZqE559/HnfffTcqKipcy23duhURERH8hd7NhIaGYu7cufjggw8AAPn5+cjPz8fUqVO57nuQzZs3IyMjwzWhdzgcXP89QFlZmevPv/3tbzFnzhwkJiZi0aJFWLduHQAgOzsbJSUlmDlzplLDJBXRKz0A6p/169dj+fLlWLNmDUwmEzZu3Kj0kGgQ3XPPPXj33XdRXl6Oa665BgEBAdi5cycWLlyIjo4OCCEwfPhwvPzyy0oPlQZYfX09Fi1ahJaWFmi1WoSFhWHHjh3QaDR8HfBQdrsdN9xwA+x2O7RaLUJDQ/H2228rPSwaBOvWrcNdd92FRx99FFqtFuvXr0dMTAzXfQ+yYcMG3H333a7rXP89w69//Wt88skncDgcmDx5MjZs2AAA+P3vf48777wTSUlJ8PLywqZNm2AwGBQeLakBd78nIiIiIiIikhR3vyciIiIiIiKSFCf1RERERERERJLipJ6IiIiIiIhIUpzUExEREREREUmKk3oiIiIiIiIiSXFST0RERERERCQpTuqJiIiIiIiIJMVJPREREREREZGkOKknIiIiIiIikhQn9URERERERESS4qSeiIiIiIiISFKc1BMRERERERFJipN6IiIiIiIiIklxUk9EREREREQkKU7qiYiIiIiIiCTFST0RERERERGRpPRKD0BtnE4nSktLERAQAI1Go/RwiIiIiIiIyAMJIdDQ0IDo6GhotRffHs9J/XlKS0sRFxen9DCIiIiIiIiIYLVaERsbe9H7Oak/T0BAAIDO/3Emk0nh0VzauXPnEBYWpvQw6BLYSR5sJQd2kgM7yYOt5MBOcmAneai9lc1mQ1xcnGuOejGc1J/n213uTSaTFJP6oqIiKcbp6dhJHmwlB3aSAzvJg63kwE5yYCd5yNLqUl8L54HyJBcSEqL0EKgX2EkebCUHdpIDO8mDreTATnJgJ3m4SytuqSeSkBAC7a0tOPyf92E7V4mw+GFImTwNJadP4MyXn6HFVo/AiCikX3kd/AKDUHL6BAxGb0Qlp8DgZVR6+ERERERENEA4qZdcdXU1oqKilB4GXcJAdGpvs6Pk5HGc+eoz1FdWQG8wIG3utUiaMBlleWfw7rPPICoxBVMXL4F/UAiqrYX4ctvraGtpxvCsCbA3N+PLba8j8+rrkTxp2gD9ZO7HXdYpIQTszU1orK5CQ001GqqroNFq4ONvgre/P4KiYuAXGKT0MPvNXTq5O3aSB1vJgZ3kwE7ycJdWGiGEUHoQamKz2WA2m1FfXy/F9ysqKysRHh6u9DDoEi6nU3trKz7f8iqqi4tgSc3A8KwJCI6++NEve9LhaMenr7+CxppqxI4ag5FTZ8Lo69evx3JXsq5Tzo4OlJ4+idz9X6G6uAharRZGP38EBIfAPyQU/sEhgBBobWxES4MNNSXFaKqvhSkkDEFR0WisrYat6hw6HA5EDBsBZ4cToXEWaHU6tNvt8PLxga8pEL5mM3zNgdBotdAbvKDV6RT5eWXt5GnYSR5sJQd2kgM7yUPtrXo7N+WWesm1t7crPQTqhf52qq+swM71z2L8jbdg5pIfX/Y4dHoDZi75Meory1Gel4t3/vQ0EtLHYux1N0KnN1z247sDGdapdnsr6srLkH9oP4pPHoNGo4EQAtFJIzFmxhyEWhIueUCVb9VXVqC+shzDx12BgNBQaLVaVOafhVanQ3VxETocDui9jGhtakRNaQlabHVoqquDEAIttjqkzroKlrTMId/qL0MnYieZsJUc2EkO7CQPd2nFSb3kamtrERMTo/Qw6BL606nibC72bPoHrr3vQZjCBvYTRHN4JMzhkUieNBWnPtuDrWuewA0PPCL17tgDRa3rlBACFWdzkf3WFggIBEXFICZlNCbceAs02v4f89QcHgFzeESX26KSUgAAEcMTe/y7rU2NyNv3FXaufxbx6VkYe+28Xn+YcLnU2om6Yid5sJUc2EkO7CQPd2nF3e/PI9vu91VVVQgNDVV6GHQJfenU2tiIr7a/gcaaasxevhK+JvMgjw6oKirA3ldfROKEyYhPy4A5PHLQn1Ot1LZOtbW24Ovt/0J5Xg4iho1A2pxrEBipru9+CSGwb8ebOHvgaxh9/eBrMsMUGo6o5JEIiopGQEjYgE/21daJusdO8mArObCTHNhJHmpvJfXu9zk5OVi2bBmqqqpgNpvx4osvYsyYMV2WKSgowPLly3Hw4EEMGzYMhw4d6nL/0aNH8ZOf/AQVFRUAgN/97ne45ZZbhupHGDLNzc1KD4F6obedSk6dwGdvbMLkW3+AuNFpgzyq74RaEjD51h+goboKe//5IsIsCbji5tsuawuwrNSyTjXUVCH/wD6c/mIvJty4ENNuX6r0kC5Ko9Fgwo23YPy8mwEh0GyrR11FOUrPnETOV5+htqwUiRMnI+PK6wbsO/hq6UQ9Yyd5sJUc2EkO7CQPd2mlykn9Pffcg5UrV2L58uXYsmULli9fjuzs7C7LmEwmPPXUU6ivr8cvf/nLLvc1NzdjwYIFePnllzFt2jR0dHSgpqZmKH+EIWOz2ZQeAvVCbzod2vkeSs+cxIKHfwWjr+8QjKqrqMQURCWmIGniFBz5z7+x49lncM09P4GXz9CPRUlKr1NOZwf2v/sWSk4dR8qUGbj5sSehN8hxvAONRgNoNPALDIJfYBBiUkYB6PyZju/Zja1rfo3kSdOQdMWUy94DRelO1DvsJA+2kgM7yYGd5OEurVS3+31lZSUSExNRU1MDvV4PIQSioqLw6aefIjHxwu93fvzxx3jwwQe7bKn/+9//jg8//BCvvvpqn59ftt3va2pqEBwcrPQw6BIu1Skn+wvkH9yHq+5eNWTfSb4U6/EjOPTBu5j34KMetcVeyXWqprQYH730ApInTUXqrKtU829hoLTbW3H2wD7kZn+BtpZm+JoDMfW2JZ1H5u8jvvbJgZ3kwVZyYCc5sJM81N5K2t3vrVYroqKioNd3Dk2j0cBisaCoqKjbSX13Tpw4AaPRiHnz5qG4uBjp6en4wx/+gLCwsAuWtdvtsNvtruuyfVpjs9lU/Q+ROvXUqeJsLo59uBPzf/YLVU3i4sako6G6Ctt+/yTC44dh2u1LPWJyr8Q61d5mx6EP3kXp6RO46u5VMIVe+FrlDgxGb6RMnoaUydMAdP7b/+jlv0Oj0SBl8jQkZI6DwcvYq8fia58c2EkebCUHdpIDO8nDXVq55W/oDocD//nPf7B+/XocPHgQMTExuO+++7pddu3atTCbza5LXFzcEI/28jQ2Nio9BOqFi3WqKS3G3n/+A9fe/5AqTyk3esYcLFz9JIKiY/HWH36H+spypYc06IZ6nSo8cghvrv0N/AODMP9nv3TbCX13IoYn4sYHH8XsZXejsbYG//7bH7B17RP4YN1fYKuq7PHv8rVPDuwkD7aSAzvJgZ3k4S6tVLelPi4uDmVlZXA4HK7d74uKimCxWHr9GBaLBbNnz3adnmDJkiW45pprul129erVeOihh1zXbTabVBN7mcbqybrrdPZANva/9xauW/UwfPwDFBhV76XOuhKxI8dg5/pncfU9D7j10fGHap1yOjuQ/dZWVBcX4ebVv+n1Fmp35BcYhLHXzMPYa+YBAKqLi/DJqy+hraUZqbOvwohxV1xwgD2+9smBneTBVnJgJzmwkzzcpZXqttSHh4cjKysLmzZtAgBs3boVsbGxvd71HgBuu+02ZGdnu3alf++995CRkdHtskajESaTqctFJlVVVUoPgXrh+52E04mPXnwehUcO4uaf/xr+QXLs8hMYGYVr7nsQO5//Gxzt7UoPZ9AM9jrVVFeLU5/twfb/+S38AoNw3aqfefSEvjshsRbc8MAjuOGBR1BfUY6tax7H4V3vwdHW5lqGr31yYCd5sJUc2EkO7CQPd2mlui31ALB+/XosX74ca9asgclkwsaNGwEAK1aswPz58zF//nw0NzcjOTkZdrsd9fX1iI2NxZ133om1a9fCYrHgF7/4BaZMmQKtVouYmBg8//zzCv9Ug6OlpUXpIVAvfL/TJ6+/jJA4C9LnXqvgiPrHFBqOsdfMw/vP/Qnjb7gJEcMT3e579oO1TpXlnsb+d9+CRqNBQkYWrlxxv0ftat8fXj6+GH/jLci6YQGO79mNt/7wOwRGRCJ11lVobuZrnwz4HiUPtpIDO8mBneThLq1Ud/R7pcl29PuGhgYEBKh71236rtPB999Bc30dpi6+U+khXZby3DPI3fclSs+cwpRbf4jY0alKD2nADOQ61d5mR8HhAzj20S4ERkRh3A03cSJ/maqKCnBsz25U5OchJCYOGVdei7D4YUoPiy6C71HyYCs5sJMc2Ekeam8l7dHvqW/Ky8tV/Q+ROpWXl6Ps+GGcK8zHVSt/ovRwLltkYjIiE5Nhb27G+8/9CU6nE5bUdKWHNSAGYp0STif2v7sdBUcOYnjWRFz7Xz9V/XETZBFqScCsO+9CTk4OQv198elrL8PHZEJU0khEjkhCUHQMtFrdpR+IhgTfo+TBVnJgJzmwkzzcpRUn9ZL7/un4SL3KzpxE3eljmPf/HlXVaesul9HXF9ev+hm2P/NbBMfESnN8gJ5c7jpVXWzFp6+/hPj0LCz8xX+7VW81sdvtCEpKwryfPob6inKUn83B0Y92obasBBqNBnN+dA9MoeFKD9Pj8T1KHmwlB3aSAzvJw11acff788i2+31zczN8fX2VHgb14FxRAT566QXc/MjjMHh7Kz2cQVFZcBZfbf8XZvxwmfRHxu/vOlVXUY4vt70OCIFJt9yOwMioQRgdfaunTnUV5fhw4zpEDBuBrOsXwCdA/a/l7orvUfJgKzmwkxzYSR5qb8Xd7z1EUVERRo4cqfQw6CKa6mrx8UvPY+T1C912Qg8A4QnDkTJ5Gj7/16sw+voh6/oFMIdHSLmVuq/rlO1cJfZs+gd0BgMmLrgVoXHxgzg6+lZPnQIjInHzo0+g6Nhh/Ofvz8HRZodwOjFs7HiMmTkXXj7qffN2N3yPkgdbyYGd5MBO8nCXVpzUS87hcCg9BOrBZ5tfwYw7foxzza1KD2XQJV8xFclXTEX+of34+q1/ob21FVetXAUvbx+lh9YnfVmnCo4cRPbbW3HVivu5ZX6IXaqTRqNBfFom4tMyAQAdDgdyvv4cO/7yP/ALDEZQVDQ0Gg18zYGIHZUKc3jEEIza8/A9Sh5sJQd2kgM7ycNdWnH3+/PItvt9a2srvN14C7DMio4dwclPP8I19/4/j+yUf3Afzh7ch7k/vlfpofRJb1rVlJbg6+3/gt7LgJl33gWD0bPaqsHlrFP1lRVorKmGcDrRWFeDgkMH0Gyrg0ajgU5vQPiwEfA1B6KuvBSTF/7ArfeyGWye+NonK7aSAzvJgZ3kofZWvZ2bclJ/Htkm9SdOnMDo0aOVHgadp+jYERx8/x1ct+oheHn7eGynnc//FebwSIyZOVeag+j11KpzMv8GhBCYeNMihMTEDfHo6FuDtU61t9lReTYPDTVV8PLxQfbbW+Hl44vkK6YiZcoM6PR6Kb9WohRPfe2TEVvJgZ3kwE7yUHsrfqfeQzidTqWHQOdpb7Pjy22v46afP+7a9dxTO8398X0oOnoIuzc8B72XEclXTEXixMmqnhSd36qlwYa8/V/j7IGvYfAyYuLNt3EyrwKDtU4ZvIyIGfndm/vwsRPgaGvDiU8+wlvP/Bbt9lakTJ6OsdfeOCjP72489bVPRmwlB3aSAzvJw11acUv9eWTbUt/e3g6DwaD0MOgbQgh8uHEdEjKyMGLcFa7b2QlottXj6Ic7YT1+BGlzrkbypGmqnNw3NzSgIu8MqqyFKDl1HDqDFxLHTcSwrAnw9vNXenj0DaXWKSEEvnrzDZSeOQmNVvv9OxA+LBGm0DAER8eiofocrCeO4oqbF8MUGtbjY3Y4HNBoNNDqdIM8+qHH1z55sJUc2EkO7CQPtbfi7vf9JNuk/tixY0hNTVV6GPSNox/uRFNtDSYtvL3L7ez0nXZ7K/a/+xYqzubgiptuQ2RistJDAgCU557Byc/2oOjMKaROmYFQSzyik0by+9QqpbZ1qr3NjppiK+rPVaC2tATe/v6IHJGM7He2oa25CUFRMQiJs+DsgWxkXHU99F5eaLe3ouTkcZSfzYVWq8XkW38AH5MZ3v4BaKqtwYlPPsL4G26Ct39AjxP+xppqaHU6+JoDAQDC6URFfh4qzubC2z8AI8ZNhN7Ly3Vfh8Phuu7s6AA0gFZ74eMLIdDR3u5a9pL/D+ytOFdYgPyD2ag4mwuNVgubzYZREyejub4WtnPn0OFohxACeoMXYkenwsc/AMUnj6Oh+hw0Wi18TGbEjhyDttYWtDU3Q6vXIzAiEn6BwV32oKCBp7Z1irrHTnJgJ3movRV3vycaYu32Vpz67GMs/OVvlR6KqhmM3ph0y2I0VFdh7z83IjxhOKKTRyEgJBSmsPAhH0+VtRCfv/FPmMLCMHrGHISNm6zqF3dSJ4OXERHDExExPLHL7fP+388hhEBtWQkqC87iyrv+C9nvbIO3vz98/E0YPu4KTPvBMtSVl+Lkp3tgb2pEU30dnB0OpM2+Grs3rkd7awt0egO8vL1hqzqHiOEjEDE8CS22etSWl6G+ogwA4B8cAqfTicaaakQMT0RU0kg01dbgnT8/DXzv83u9wQuO9jZAo4FWq4UQgBDO75b5Zg8a0dEBncELHY52eHn7ICg6Fs11tfAOCICPfwBKc06ho729c1mnE3qjEaFxCUjIGIcpty2BRqPBoQMHEOylR0BICAJCw6H/ZmtIW0szSk6dQGtTIyYsuBWBEZEAgOoSK2qKrQgIDYOXjw8cbW2or6xAafYX+OrNzTCFhaOhpho6vR6ps6+Cf1AIQmIt0On56wwREXkubqk/j2xb6tW+y4gn+eyNTQiNS0DK5GkX3MdO3RNOJ05/8Qnqz1Wi5OQxZF2/AAkZWYPyXN/fItneZof1+BEc+2gXvHx8MXnhD1ynNGMrOXhap/bWVrTbW+FrDkRZzmnUVZbDJ8AEc3gEAsMjAY0GjTXVAICAkNABf357czNqS4vhFxSM1qZGNNfVITIxCUZfv57HPYCd2tvsaLHVwz84BPbmZhzd/QHszU2ozM9z7ckQOSIZGVdd59prgXrP09YpWbGTHNhJHmpvxd3v+0m2Sb3adxnxFEc/3Inq4kLMvHNFt98TZ6dLa2+z492//A+E0wmjnz8mzF+IMEtCnx+nw+FA3r4vYas6B9u5SrQ2NqCpvu6b084JCKcTOr0BUUkpGDNz7gW//LOVHNhJDkPZSQiBomOHceC9t6DRamH09YMpNAzx6WMRM3KMKo/hoSZcp+TATnJgJ3movRV3vycaIhVnc5F/aB9ufPAx/tJ4GQxeRtz0yOMQQqCxthr/+ftzEE4nQuPiMXLqTOi9jPAPDnadUQDoPPhewaH9sJ2rhMHbBy0NNpTlnETihMkITxiBxAmT4RMQAC8fX7YhcnMajQbxaZmIT8uE09kBe3MzbJUVOPPVZ/jqzTcwbOwE2JsbodXqEDEiCdFJKfDy8VV62NIQQqCmpPO4Ed5+ATCFhUtzqlIiInenyi31OTk5WLZsGaqqqmA2m/Hiiy9izJgxFyy3YcMGPP3003A6nZgzZw6ee+45GAwGOJ1OPPzww3j//feh1+sREhKCF154AYmJid08W1eybalX+y4j7k4Iga1rfo0bHngEPgEX//fCTv1XcOQgik8cg6O9DXXlZXB2OAB0/r83+vphRNYEBEZGo93eCoO3NyKHJ3U9KnkfsZUc2EkOaulkb25Gyanj8DGZ4HR0oDzvDKwnjsEnIAAjp8xEQ/U5ePn6IjQuAXn7v0JTbQ1MYeFot9vhaLOjrrwM7fZWzF620vVVnaHU3tqKlgbbBccdEUKgoboK9qZGhMRZUHziGM4VFqCloR5anQ4+ASb4BJjgaGtDVXERmmproDMYoNVq0eFwQDidaLbVQ6fXIyA0HFqtFl4+vmisrYZGo4G9qQkdjnZotFo42toQEmtBUFQM7E2NqCktRrOtHhoAGq0WfkHBiE4ehdiRY1BbXoqgyGgERkYN+f8rd6eWdYp6xk7yUHsrqXe/nzNnDpYuXYrly5djy5Yt+P3vf4/s7Owuy+Tn52Pq1Kk4cOAAIiIisGDBAlxzzTW4//77sX37dqxduxaffvopDAYDnnrqKRw5cgRvvPHGJZ9btkm92ncZcXc52V+gpqQYV9y0qMfl2EkebCUHdpKD2jvVlJYg56vPYA6PgKOtDZWFZxGdNBKhlgQ01tbAYPSG3ssLAcEhaGttxedvbEJrUyOMvn4ICA1DRd4ZWFIzodFq4eXtg9ryUlQXFyFyRBKm3HYHNBotnB0daKqrRWtjA8Lih11yr6F2eytKz5zC6c/3oqmuFkDnpNknwIzG2moYjEYERkZDOJ2oLDgLc3gEvP38UXE2p3NSPToNviYznB0daGmwoaXBBp1ej5DYePgHh3SegcDphFavh3A64WsyQzid+PrTvUhJGYm2lmb4BQYBALx8fWHwMsLR3g4IcdEzITidHWiqrUXxqeMoOXkcgZFRqC0rQWNNNYQQiEpMgbPDgYbqaoTFJ8AvKBhanQ4ajQaHd/0bOoMBEAJOpxOxI8cgMDIK1cVF8A8ORW1ZCby8faAzGGBvakRU0kiExMbB1xwEo2/nnhaOtrYuY+twtKP09En4BJjgHWAChID45uLt7w+DlxFluWdQcPgA2u2t0BsMiE8bi4DQMFQXFyE+PRM6vTp/yVf7OkWd2Ekeam8l7e73lZWV2LdvH3bu3AkAWLhwIVatWoXc3NwuW9q3bNmC+fPnIzKy84i59957L9asWYP777+/89Nlux2tra3Q6/Ww2WyIjY1V5OcZbNrL2CJJl0c4nTj0wbtY8PAvL7ksO8mDreTATnJQe6fg6BhccfNt3d4XGhff5bqvGbj+Jw8DAFqbGlFfUY6pty2B9dhh6L45RWFcajqCo2NxYu+HeOt/fwcIAa1ODx+TCUZfP+zZ9A+ExsXD1xwIg5cXcrK/wLjrb0J7mx2tjQ2oyMtFs60OcaPTMP7GhQiOjrlgXO32VtRXlMPpdGLGHT8akK8WabRaBISGu85CcD79JbZiabU6BISEYtTUmRg1dWaX+5wdHSjLOQ2NVgNzeCSqrIVoqq1BW0sLmuvrsOCRX8HgZexc1tkB6/GjaKypRkLGODRUn0PK5Olob22B0+mEt38AynNPo/jkUdjOnYOjzQ4BQKvTwdnRAY1GAyEENBoNYlJGo7WpEW3NzYAG3/x/0qClwQZHextCYy1InDgZXj6+aG9tReGRg2j4/BwCQsOxb8ebSMjIgnA6EZ4wHPEZY7s97aMS1L5OUSd2koe7tFLdpN5qtSIqKgr6b05Po9FoYLFYUFRU1GVSX1RUhPj4795wExISUFRUBAC48cYb8dFHHyEyMhIBAQGIiYnBnj17un0+u90Ou93uum6z2Qbjxxo0w4cPV3oIHis3+0skpI/t8h3vi2EnebCVHNhJDu7aydvPH97fnL4wIXPcBfePmTkXY2bOveB24XSipqykcwt6fT0WPPwrHHjvbZjDI2AOj0DihEkICO757AUGozdC+3EQ0UsZrFZanQ4xI0e7rn+7F0C3y2p1iE/L/N4tIy9YJmLYiB6fTzid/foKVuSIJNefx12/AKVnTkKr16P09Ensf+8txI5KhV9gEHzNgTB+8xWF1qYmjBg3cVDOOHEx7rpOuRt2koe7tFLdpH4g7Nu3D8eOHUNJSQlMJhMee+wx3Hvvvdi0adMFy65duxZPPvmkAqMcGLm5uareZcRdtbe24uAHO3Dzz3/dq+XZSR5sJQd2kgM7daXRahESE9fltimLfqjQaLpyl1aXc0yVb+m9vGBJzQAAxI4cg4yrrkdNiRVNdbVorKlGTYsVPiYz/MyB+GLLq2isqe583vP3mhACBqM3opJS4BcYhOCYOARGRkGDzuV0XoY+7wHgLp3cHTvJw11aqW5SHxcXh7KyMjgcDuj1+s5T1BQVwWKxdFnOYrEgLy/Pdb2goMC1zMsvv4w5c+YgMDAQALBs2TJcffXV3T7f6tWr8dBDD7mu22w2xMXFdbusGn27RwMNrY9efgGTblkMg7d3r5ZnJ3mwlRzYSQ7sJA+2ujijry+iklK6vS/piikA4Nrt/3z25iaU5+agub4Wpz7bg4bqqu/ua2p0fQhhDo9EUHQMGqurUFVchJCYOARFxSA+fSxMoWGuv8NOcmAnebhLK9X9FOHh4cjKysKmTZuwfPlybN26FbGxsRccuX7hwoWYNm0afvOb3yAiIgLr1q3D7bffDqBzN4r33nsPDz/8MLy8vLBjx46LfgJjNBphNBoH/ecaLOd/2EGDr/jkMej0Bten+L3BTvJgKzmwkxzYSR5sdXkudmwDo68f4tMzAQCjLvJ3hRCoqyhDbVkJYlJGY0psHGpKilFbVoJPXn0RRl9fpM66CgGhYWguOovPjuyHKSwMppBwaPU61JQUo+JsDhprazo/JBACPgEmBEXHIjgmFsFRsfD294evyYzW5ibYGxth9POD0c+vy54CQgjYm5vg7OiAr8n83e1OJ+orK1BXUQZbVSX0hs6DEnr7ByBudBoM3t4QQqDk1HFYTxyF0dcfRl9fONrsSJkyA95+/gPy/1gmXJ/k4S6tVHn0+9OnT2P58uWorq6GyWTCxo0bkZaWhhUrVmD+/PmYP38+AOCFF17A008/DQCYNWsW1q1bB4PBALvdjlWrVrmOfh8ZGYl169b16jsTPPo99cTp7MDWNU9g3oOPwsc/oNd/j53kwVZyYCc5sJM82Eq9ynPPIO/A12isqYHwC8DYqdNhq6qE7dw5dDjaERwTh/CEYTCFdp7yUAiBlgYbakqsqCktRk1pCexNTWiqrYbBxxf+QcGwNzWhtakBwun87ok0Ghh9fOHs6EBbSzO8/QPQ2tQIjabzIIeBkVEICA1DR1sboNGgub4Oefu/7lyusQHRySORkDEO7S3NaGttgRACRz/ciaikkfDy8UHyFVMVOSWkErg+yUPtraQ+pZ2SZJvU5+TkICkp6dIL0oA48p/34XQ6kXn19X36e+wkD7aSAzvJgZ3kwVZyGKpOHQ4H2ltbYfTz6/EMC0IItNtbL3rQYCEErMePosPRjqMffgBfUyBGTp2B6JRRrr0E7M3NaG9tga85EFqdOs4ycLm4PslD7a2kPaUd9c23p/SjwddYW4MzX32GW1b/ps9/l53kwVZyYCc5sJM82EoOQ9VJp9dD53/p3eY1Gk2PZwHSaDSwpKYDAIZljkNVUQHOfPUZst/eirgx6Sg6eghevn6uMwoIIaDV6RA3Oh0NNVUICA5BypQZOFeYj8CISARFXXiaRzXi+iQPd2nFSb3kCgsLVb3LiLsQTid2Pf9XzF52d7/OVctO8mArObCTHNhJHmwlB9k7hVoSEGpJQF15GerPVSB97jXw8vHtskxbSzOKTx1HQkYW6irK8PFLLyBieCLOfPkZnB0ORCWNRHB0LBxtdmi0WrQ02FB84ii8fP3gHxiEyoKzGDPzSlhS0wfkbAj9IXsnT+Iurfo8qbdarbjvvvtQXFyMQ4cO4dChQ/joo4/w05/+dDDGR5fg43Ppc6TT5Tuy+wMkZI5DSGz/DqbBTvJgKzmwkxzYSR5sJQd36RQYGYXAyKhu7/Py8cXwsRMAACGxcRgxbqLrviprIWznKlGWexoGozeE0wmDtzem3LYEzfV1aKg+h5QpM3Ds413IfmcrzGERGDH+Cnj7ByAk1gKjr2+3zznQ3KWTJ3CXVn3+Tv3111+PH/7wh3jmmWdw+PBhOBwOjB07FkePHh2sMQ4p2b5TX19fD7PZfOkFqd8aqqvwwbq/4JbVv+nXVnqAnWTCVnJgJzmwkzzYSg7s1Df1leXI258Ne3MjKvPPQu/lhaCoaEQlpsDo5w9zeAT8AoMG/nnZSRpqbzVo36mvrKzEkiVL8Ic//KHzAfR6tzm/n4ysVquq/yHKrsPRjp3P/xVzf3xvvyf0ADvJhK3kwE5yYCd5sJUc2KlvzOGRyLruRtf1hpoqNNXUoOT0CbS3tqK6xAp7UyNM4ZEYkTUBCZnjejwwYG+xkzzcpVWfZ+N6vR7f37hfW1sLHkBfOf69OIgJ9d/HL29AxpXXXfaBWdhJHmwlB3aSAzvJg63kwE6XJyA4FAHBoYhMTHbdJoRAfWUFTn76Efa9ux0hMXGIT89E3Oi0C77v31vsJA93adXnSf2iRYtwzz33wGaz4e9//zvWrVuHFStWDMbYqBdk+IqArE5+8hG8fHyQOGHSZT8WO8mDreTATnJgJ3mwlRzYaeBpNBoERkRi8sIfQAiB2rISFB45iHf+/HsER8fCLzAIRl8/JE+eBh//gF49JjvJw11a9XlS/7Of/QyvvfYa6uvrsXPnTjz00EP44Q9/OBhjo14oLS1FcHCw63qHwwEdvw5x2aqLrTj95aeY/7NfDMjjnd+J1Iut5MBOcmAnebCVHNhpcGk0GgRHxyI4OhaZ18xDVVEB7C3NaK6rxa71f0WHox1CCIRZEpB0xVSExQ/r9vdudpKHu7Tq1+zvBz/4AW6//XYAGJDvnVD/ff/TJevxIyg8ehjTbr9TwRG5h882v4I5y++5rO/Rf5+7fAroCdhKDuwkB3aSB1vJgZ2GjkajQVj8MNf15EnTAHTurl+Rl4Ocrz7Dl9s2AxAYM3MuQuPi4RcYBIO3DzStLSg6dgRGX1/UVZRjeNZ4GIzeAID2Njv2vbMNPgFmJKSPRUBoKHR6AwqPHkJjTTWComJgq6qEzmBA6emTqC4uwuSFP0BY/DDovbw6x+B0Kna6PnfjLutUnyf1RUVFuPvuu7Fnzx4AwOzZs7F+/XpYLP071RddHt/vnZqjsuAsqqwFyg3GTVhPHEVgZBRMYeED9pi+Q3QKFbp8bCUHdpIDO8mDreTATsrTaDSITEx2fS+/qa4WOV99jpJTx3GuqBAajQY+QcEIj4uHvakR/sGh2PGX//nuAYRA6pyrIZxOHPj322isqUaHox1h8cMQGBmNgsP7YQ6PRFtLM0ZOmQH/4BBkv70F2e9sRUd7O5xOJ9paWhA3Og2xI0cjKDoW5vAItDQ2QKfT9fs4AJ7KXdapPk/qly5dihtuuAFvvPEGhBD4+9//jqVLl+Ljjz8ehOHRpZSXlyM0NBQAcK6wAM22eoVHJL/DO9/DrGV3D+hjfr8TqRtbyYGd5MBO8mArObCT+vgFBiHzmhsAAE5nBxx2O87knUVqaqprmcyrr+/27yZfMbVXzzFr6Xe/lwohoNFocPqLT1FTVoIzX32Gxtoa6PR6tLU0Y/SMuWiorkJHexuabfVoqqtDVGIyopJSYD1xDPFpGagsOIvR0+fA200OEnc53GWd6vOk/ty5c3jkkUdc1x9++GG8+OKLAzkm6oOgoO/Ordlsq4OfOQiO9nboDQYFRyWvprpaCAD+QQP73ZrvdyJ1Yys5sJMc2EkebCUHdlI3rbZzS/lgdvr2q88pk6ddcJ/tXCVKTh2HJTUdei8jvP394WsOQunpkyg5fRxxo9OQf2g/gqJi8NYfnoJfYDD0Bi/4B4egraUZ3v7+iB2VhtA4C3xMZo/4mrW7rFN9ntQnJibizJkzSE7u3OXkzJkzSEpKGvCBUe8IhwOOtjZo9TpoNBoERUWjrrwUoXHxSg9NSsc+2oW0WVcO+OMa+CGLNNhKDuwkB3aSB1vJgZ3koFQnU1h4t18ftaSmw5Ka7vozACRNnAyNVgtHWxsaa6rh5e2Dpvo6lJ05iROffIiWb/b+dbS1IdSSAJ1ej+b6um/2FNBCo9VCq+38r9HXF2EJwxE5PAnm8Aipvu/vLutUnyf1jY2NyMjIwJQpUwAAX3zxBaZMmYJbbrkFALBt27aBHSH1aN+7b2J4yijEjhqDoKhoBEfHoqbEykl9PwinE0XHDmHiglsH/LErKysRHj5w39GnwcNWcmAnObCTPNhKDuwkBxk6GX39AABe3j7wNZkBAIGRUYhJGdVlOSEEakqKIYQTfoFB0Gi0cDo7IJxOCCHg7OiAvbkJFXk5OPDvt1FdXIiQuHjovYwIiopGSEwcznz1OYTTCUtqBtpaWxAUEQXfwEAYff1g8PYesANT94cMrXqjX9+pX7p0aZfrpJzW6nOoyNNDbzAgYlgizBFRKDx6UOlhSanw6CHEjckYlE8XQ0JCBvwxaXCwlRzYSQ7sJA+2kgM7ycGdOmk0GoTExl1iqTCEWRIAdJ5eu6HqHDoc7SjPy0Fu9pcYM3MuNACKjh+Ft78/zh7MRkuDDfbmZrS3tsDpdH73UEIAGg1C4+IRM3I0LGMyXEf9Hwzu0qrPk/ply5YB6PzUBhicU9rl5ORg2bJlqKqqgtlsxosvvogxY8ZcsNyGDRvw9NNPw+l0Ys6cOXjuuedcu1D0dJ+7+PbTsYbqcyg8eggz7vgRDN7e2L/jTaWHJp0OhwPZ72zDDQ88cumFiYiIiIjoAjq9HoGRUQCAkNiuZ0cL/WbifykdDgeqi4tgPX4Uh3e+B4PRGyPGTYQlLRNevr5ob23FmS8/g1ang19gEPzMgXA6O6DTG2D084e9uembDyMs0On7dQZ36fT5pywrK8Ndd92Fjz76CAAwd+5cvPDCC4iKihqwQd1zzz1YuXIlli9fji1btmD58uXIzs7uskx+fj4ef/xxHDhwABEREViwYAGef/553H///T3e504aqqug8/GFTqdDY20N/AI7D/TQbm9VeGTyOfjvtzFm5lzX7kcDrbq6ekDXERo8bCUHdpIDO8mDreTATnJgp8uj0+sRnjAc4QnDMe6GBWi21ePsgWx88upLaLe3QqvTIemKKdBqdWisqUZlfh40Wi06HA7Ymxrh5eML4exAdYm182sCTidiRqViWOY4+AUGwensgH9QCDQajdu06vOkfuXKlZg2bRpeffVVAMC6deuwcuVKvPPOOwMyoMrKSuzbtw87d+4EACxcuBCrVq1Cbm4uEhMTXctt2bIF8+fPR2RkJADg3nvvxZo1a3D//ff3eJ87qcjLQWzyKOicHWhtanTd7hcUgsaaavgHu8fuJIPN0daG/EP7cesvfztoz+EO39XxFGwlB3aSAzvJg63kwE5yYKeB5WsyI3XWlUjt58GsOxwOFJ88htOf70WzzQYAEMKJa+970G1a9XlSb7Vau0zgH3vsMWRmZg7YgKxWK6KioqD/ZlcJjUYDi8WCoqKiLpP6oqIixMd/dzC4hIQEFBUVXfK+89ntdtjtdtd12zehZWDw9kbIsBGIio6Gs6PDdXtUYjLK83KQyEl9r5z45EOMnjFnUI/U2d7ePmiPTQOLreTATnJgJ3mwlRzYSQ7spC46vR7xaZmIT8u84D53adXnWYwQAuXl5a7r5eXlru/Xy2jt2rUwm82uS1zcpQ4EoR4JGVlw6L1gDo9EUFSM6/bIxGSU5Z5WcGTyEE4nTn++FyOnzhjU56mtrR3Ux6eBw1ZyYCc5sJM82EoO7CQHdpKHu7Tq85b6hx9+GGPHjsV1110HAHj//ffxzDPPDNiA4uLiUFZWBofDAb1eDyEEioqKYLF0PdCCxWJBXl6e63pBQYFrmZ7uO9/q1avx0EMPua7bbDapJvbffsXg+0JiLagutiowGvnk7v8KCZnjodMP7kEUu+tE6sRWcmAnObCTPNhKDuwkB3aSh7u06vOW+jvvvBO7du1CVlYWsrKysGvXLtxxxx0DNqDw8HBkZWVh06ZNAICtW7ciNja2y673QOd37d9++23XngLr1q3D7bfffsn7zmc0GmEymbpcZNLc3HzBbTq9HsLZ0c3S9H1CCBze+R7S51476M/VXSdSJ7aSAzvJgZ3kwVZyYCc5sJM83KVVv47xn5qaitTU1IEei8v69euxfPlyrFmzBiaTCRs3bgQArFixAvPnz8f8+fMxfPhwPPnkk5g6dSoAYNasWbjnnnsAoMf73M3FjgHg7eePlgYbfALk+pBiKJ358jPEp2XC6Os76M8l07EaPB1byYGd5MBO8mArObCTHNhJHu7SSiN6+YX4sWPH9nhO+gMHDgzYoJRks9lgNptRX18vxVb7mpoaBAcHX3B79ttbETkiCXFj0hUYlfoJIbDld4/jpp8/DoOXcdCf72KdSH3YSg7sJAd2kgdbyYGd5MBO8lB7q97OTXu9pf7Pf/7zBbfV1taiuLgYaWlp/RokXT6bzdbtP8QwSwLOFRUg1JLArfXdKM87g/CE4UMyoQcu3onUh63kwE5yYCd5sJUc2EkO7CQPd2nV60n9zJkzAQDXXnstXn/9dej1etcu+EuXLnXdT0OrsbGx29tD44fh38/9Ccf37MaStX/ucS8LT3R09wcYf+PCIXu+i3Ui9WErObCTHNhJHmwlB3aSAzvJw11a9flAeRUVFQgMDMR7772HBQsWICcnB2+++eZgjI164WJH6vcPCsadT/8FsSPHoLq4aIhHpW7leTlwtLcjODrm0gsPEJnOqODp2EoO7CQHdpIHW8mBneTATvJwl1Z9ntS3t7cDAPbu3YurrroKBoMBOp1uwAdGvVNVVXXR+/yDQzBi/CTk7ftqCEekbq2Njdj7z42YtXTFkD5vT51IXdhKDuwkB3aSB1vJgZ3kwE7ycJdWfZ7Up6am4rrrrsOOHTswZ84cNDc3c9duBbW0tPR4f+yoMbCeOIpeHg/RrXU4HHjvr89g5pIfw9dkHtLnvlQnUg+2kgM7yYGd5MFWcmAnObCTPNylVZ9Paffiiy/i/fffR0ZGBnx9fVFSUoK1a9cOxtioF+Lj43u8X6vTIXzYCJScPoHYkWOGaFTqtO+dbRg1bRYihicO+XNfqhOpB1vJgZ3kwE7yYCs5sJMc2Eke7tKqz1vqvb29cdNNN2HYsGEAgJiYGFx77bUDPjDqnfLy8ksuM/baeTj4/o4hGI165e77ClXWQoycNkuR5+9NJ1IHtpIDO8mBneTBVnJgJzmwkzzcpVWfJ/WkLna7/ZLLBASHAuj8PrknqiktwdEPP8C1//WgYl8V6U0nUge2kgM7yYGd5MFWcmAnObCTPNylFSf1khs+fHivlhsxbiLy9nvmAfO+3v4vzPjhj6DTGxQbQ287kfLYSg7sJAd2kgdbyYGd5MBO8nCXVpzUS66oqHenqxueNQG52V+irdU9DgbRW/WVFehwtCMkVtnTVfS2EymPreTATnJgJ3mwlRzYSQ7sJA93acVJveQcDkevlvP280dUUgpef+JRNNXVDvKo1OPQzneRdd18pYfR606kPLaSAzvJgZ3kwVZyYCc5sJM83KUVJ/WSS0zs/ZHcJy64FZNuvg252V8M4ojUo721FVVFBYhKSlF6KH3qRMpiKzmwkxzYSR5sJQd2kgM7ycNdWnFSL7mzZ8/2aflhmeORf2j/II1GXY7s/gCps69SehgA+t6JlMNWcmAnObCTPNhKDuwkB3aSh7u04qReck6ns0/LG7y94eXtg9qykkEakTo42ttx9sBXSLpiitJDAdD3TqQctpIDO8mBneTBVnJgJzmwkzzcpZVGCCGUHoSa2Gw2mM1m1NfXw2QyKT2cS2pvb4fB0LejutuqKrHr+b/h5kefgFanG6SRKWv/u2/Bx2TC6OmzlR4KgP51ImWwlRzYSQ7sJA+2kgM7yYGd5KH2Vr2dm6pqS73T6cRPfvITjBgxAomJifjb3/520WVzcnIwZcoUJCcnY8KECTh+/Ljrvvfeew9ZWVnIzMxEamoqXnrppaEYviJOnz7d579jCg1HyuTpOPTBjkEYkfJaGhuQf2gfRk2dqfRQXPrTiZTBVnJgJzmwkzzYSg7sJAd2koe7tFLVpH7Tpk04ceIEzpw5g6+//hrPPPNMl8n6991zzz1YuXIlzpw5g0cffRTLly8HAAghsGTJErz44os4dOgQduzYgXvuuQcNDQ1D+JOo35iZc5F/+ACabfVKD2VAtTY14r1nn8H0HyyDRquqf95EREREREQDTlWzns2bN+Puu++GTqdDcHAwFi9ejNdee+2C5SorK7Fv3z4sWbIEALBw4UJYrVbk5uYCADQaDerq6gB07rIQEhICo9E4ZD/HUEpJ6d+R3TVaLUZPn438g/sGeETK2rvpH7jilsWIGK6uI1n2txMNPbaSAzvJgZ3kwVZyYCc5sJM83KWVqib1RUVFiI+Pd11PSEhAUVHRBctZrVZERUVBr9cD6JzEWywWFBUVQaPRYPPmzbjlllsQHx+PadOm4aWXXoKXl1e3z2m322Gz2bpcZHI5u4zEp49F4ZGDAzgaZZXn5QAAYkeOUXgkF3KXXXs8AVvJgZ3kwE7yYCs5sJMc2Eke7tJqSCf1kydPRmhoaLcXq9U6IM/hcDjw1FNPYdu2bSgsLMTu3btx5513oqqqqtvl165dC7PZ7LrExcUNyDhk4Gsyo6XBBuEmR33c/+52TL71h0oPg4iIiIiIaMgM6aT+iy++QFVVVbeXuLg4WCwWFBYWupYvKCiAxWK54HHi4uJQVlYGh8MBoPN79EVFRbBYLDh06BBKS0sxY8YMAMCECRMQGxuLgwe73yK9evVq1NfXuy4D9eHCULncXUbCh41A4bHDAzQa5TTW1sDpdCIgJFTpoXTLXXbt8QRsJQd2kgM7yYOt5MBOcmAnebhLK1Xtfr9o0SK88MIL6OjoQE1NDTZv3ozFixdfsFx4eDiysrKwadMmAMDWrVsRGxuLxMRE14T/5MmTAIDc3Fzk5eVdNJjRaITJZOpykcnl7jIyccEi7N/xJqqshZdeWMUO7/o30udco/QwLspddu3xBGwlB3aSAzvJg63kwE5yYCd5uEsrVU3q77zzTowcORJJSUmYMGECHnroIaSlpQEA3n77baxYscK17Pr167F+/XokJyfj6aefxsaNGwEAEREReP7553HbbbchIyMDN998M/72t791u8XfHWgv8wjvRl9fXHPv/8Pef26EEGKARjW07M3NKMs5BUtahtJDuajL7URDh63kwE5yYCd5sJUc2EkO7CQPd2mlEbLO5AaJzWaD2WxGfX29FFvtW1tb4e3tfdmP8/VbW2AKC8fIKTMGYFRDa++rLyImZTRGjJuo9FAuaqA60eBjKzmwkxzYSR5sJQd2kgM7yUPtrXo7N3WPjyY82Len8btcWdcvwJH/vI/21tYBebyhIITAvne2wdnRgeFZE5QeTo8GqhMNPraSAzvJgZ3kwVZyYCc5sJM83KUVJ/WS+/a0fpf9OAYDJs5fiPf/78+wNzcPyGMOJmdHB9599hlotDrMvONH0Gg0Sg+pRwPViQYfW8mBneTATvJgKzmwkxzYSR7u0oqTeskN5LECEjLHYex1N2Lnur8M2GMOlo9f+TuSJk7BuBsWQCPBd2Hc9ZgO7oit5MBOcmAnebCVHNhJDuwkD3dppf7ZEPXo7NmzA/p4sSPHIDgmFrv/8X/Y/+5bA/rYA6XibC7aW+1ImTxN6aH02kB3osHDVnJgJzmwkzzYSg7sJAd2koe7tOKkXnJGo3HAH/OKmxcjaeIUVBcXouTUCbQ2NuLN3z+JxprqAX+u/vhy2+uYetsdSg+jTwajEw0OtpIDO8mBneTBVnJgJzmwkzzcpZV7fInAg0VGRg74Y+q9vGBJzUB4wgi886c1gEaLMTPn4t/P/QkLVz8JrU434M/ZW+cK8+EXFAL/4BDFxtAfg9GJBgdbyYGd5MBO8mArObCTHNhJHu7SilvqJVdYWDhoj+3t74+bH/0NJt2yGKOnz0bKpGk4vnf3oD1fbxz8YAfGXjNP0TH0x2B2ooHFVnJgJzmwkzzYSg7sJAd2koe7tOKkXnI+Pj6D+vh6Ly/EjU4DAIyZdSVOfvIxWhobBvU5u9NUV4t3n30GvqZAhMTGDfnzX67B7kQDh63kwE5yYCd5sJUc2EkO7CQPd2nFSb3kQkNDh+y5dHo9Zi1dgX//9X/R2tQ4ZM9bV16Gd//yP5h40yJMu/3OIXvegTSUnejysJUc2EkO7CQPtpIDO8mBneThLq04qZec1Wod0ucLTxiOyYt+iLf/sAYlp08O+vPZqs5h1wt/xXWrfoYwS8KgP99gGepO1H9sJQd2kgM7yYOt5MBOcmAnebhLK07qJefv7z/kzxmVmIL5D/0Cxz7ahY9ffgHt9tZBeZ6zB7Pxwbq/4MoV9yMgRO5P0ZToRP3DVnJgJzmwkzzYSg7sJAd2koe7tOLR7yVnMpkUeV5vf39cc+8DKDi0H9v/579hSRuLUdNmwRQadtmP7ezowGebX4G9pQU3//zX0Ht5DcCIlaVUJ+o7tpIDO8mBneTBVnJgJzmwkzzcpRW31EuutLRU0edPyByHm37+a0QlJuP95/6Eprrafj1Ou70VjrY2VJdYsf2Z3yI0fhiuvOs+t5jQA8p3ot5jKzmwkxzYSR5sJQd2kgM7ycNdWnFLveTU8OmSwegNS2oGfExm7P7H/+HGn66GRqPp1d8VQuCjl55HQ1UVAMDHZMLsZXcjKCpmMIc85NTQiXqHreTATnJgJ3mwlRzYSQ7sJA93acVJveR8fX2VHoJLmCUBYfHDcOrTjzFq+uxe/Z0vt72OoKgYzFl+zyCPTllq6kQ9Yys5sJMc2EkebCUHdpIDO8nDXVqpavd7p9OJn/zkJxgxYgQSExPxt7/97aLLPvDAA0hISIBGo8GhQ4e63Ge327Fq1SokJSUhLS0NS5YsGeSRK6e8vFzpIXQxccEinPxsD4qOHUFTXS2cHR2wNzfj2Ee7IJzOLstWFRWg2lqEsdfMU2i0Q0dtneji2EoO7CQHdpIHW8mBneTATvJwl1aq2lK/adMmnDhxAmfOnEF9fT3Gjh2L2bNnY8yYMRcse+utt+LnP/85pk2bdsF9jz32GDQaDc6cOQONRuM2sboTFBSk9BC60On1uObe/4eD7+/Aqc8+RmNNNewtLbCkZuDtP65BfNpYONrb4B8cgiO7/o3rfvIzpYc8JNTWiS6OreTATnJgJ3mwlRzYSQ7sJA93aaWqSf3mzZtx9913Q6fTITg4GIsXL8Zrr72Gp5566oJlZ8yY0e1jNDU1YcOGDSguLnZ9rzsyMnJQx60kg8Gg9BAu4BcYhGm333nB7fWV5agutkJnMKCmxIp5P30MviazAiMcemrsRN1jKzmwkxzYSR5sJQd2kgM7ycNdWqlq9/uioiLEx8e7rickJKCoqKhPj5GXl4fg4GCsWbMG48ePx/Tp07F79+6LLm+322Gz2bpcZFJZWan0EHrNHB6J4VkTEJ+WibHX3ugxE3pArk6ejq3kwE5yYCd5sJUc2EkO7CQPd2k1pJP6yZMnIzQ0tNuL1WodkOdwOBwoLCzE6NGjsW/fPjz77LNYvHgxKioqul1+7dq1MJvNrktcXNyAjGOohISEKD0E6gV2kgdbyYGd5MBO8mArObCTHNhJHu7Sakgn9V988QWqqqq6vcTFxcFisaCwsNC1fEFBASwWS5+ew2KxQKvV4o477gAAjB07FsOGDcPRo0e7XX716tWor693XQbqwwUiIiIiIiKiwaaq79QvWrQIL7zwAhYtWoT6+nps3rwZO3bs6NNjhIaGYu7cufjggw9w/fXXIz8/H/n5+Rg1alS3yxuNRhiNRtd1IQQASLMbfmFhIfz8/JQeBl0CO8mDreTATnJgJ3mwlRzYSQ7sJA+1t/p2TvrtHPWihIo4HA7xX//1X2LYsGFi+PDh4s9//rPrvrfeekvcddddrusrV64UMTExQqfTifDwcDFixAjXfXl5eWLWrFkiNTVVpKeniy1btvR6DFarVQDghRdeeOGFF1544YUXXnjhhRfFL1artcc5rEaIS037PYvT6URpaSkCAgJcR89XK5vNhri4OFitVphMJqWHQxfBTvJgKzmwkxzYSR5sJQd2kgM7yUOGVkIINDQ0IDo6Glrtxb85r6rd79VAq9UiNjZW6WH0iclkUu0/RPoOO8mDreTATnJgJ3mwlRzYSQ7sJA+1tzKbzZdcRlWntCMiIiIiIiKi3uOknoiIiIiIiEhSnNRLzGg04oknnuhy9H5SH3aSB1vJgZ3kwE7yYCs5sJMc2Eke7tSKB8ojIiIiIiIikhS31BMRERERERFJipN6IiIiIiIiIklxUk9EREREREQkKU7qJdHa2oqbbroJycnJyMjIwFVXXYXc3FwAQGVlJa699lokJSUhNTUVe/fuVXi0nu3qq69Geno6MjMzMX36dBw8eBAAkJOTgylTpiA5ORkTJkzA8ePHFR4pAcDGjRuh0Wiwfft2AFyf1CghIQEpKSnIzMxEZmYmNm/eDIDrlNrY7XasWrUKSUlJSEtLw5IlSwCwk5pUV1e71qPMzEwkJydDr9ejpqaGr30q9N577yErKwuZmZlITU3FSy+9BIDvU2rz/vvvY/z48UhPT8ekSZNw+PBhAOyktAceeAAJCQnQaDQ4dOiQ6/ae3pOkfr8SJIWWlhbx7rvvCqfTKYQQ4q9//auYOXOmEEKIH/3oR+KJJ54QQgjx9ddfi5iYGNHW1qbQSKm2ttb1523bton09HQhhBCzZ88WGzduFEII8a9//UuMHz9egdHR9+Xn54vJkyeLSZMmiTfffFMIwfVJjeLj48XBgwcvuJ3rlLo8+OCDYtWqVa73qbKyMiEEO6nZM888I+bNmyeE4Guf2jidThEUFCQOHz4shOh8vzIajcJms7GVitTU1Ijg4GBx7NgxIYQQe/fuFWPGjBFCcJ1S2p49e4TVar3gd4ie3pNkfr/ipF5S2dnZIj4+XgghhJ+fn+uXJyGEmDBhgti1a5dCI6Pv27hxo8jIyBAVFRUiICBAtLe3CyE636wjIiJETk6OwiP0XB0dHWLu3Lli3759YubMma5JPdcn9eluUs91Sl0aGxtFQECAqK+v73I7O6nbyJEj+dqnUk6nUwQHB4s9e/YIIYQ4fPiwiI6OFna7na1UJDs7WyQlJXW5LSAgQOzfv5+dVOL7v0P09J4k+/sVd7+X1F/+8hcsWLAA1dXVaG9vR2RkpOu+hIQEFBUVKTg6Wrp0KeLi4vD444/jlVdegdVqRVRUFPR6PQBAo9HAYrGwk4L++Mc/YurUqRg3bpzrNq5P6rV06VKkpaXhrrvuwrlz57hOqUxeXh6Cg4OxZs0ajB8/HtOnT8fu3bvZScU+//xz1NbWYt68eXztUyGNRoPNmzfjlltuQXx8PKZNm4aXXnoJDQ0NbKUiSUlJqK6uxueffw4AePvtt9HQ0ID8/Hx2UqGe3pNkf7/ipF5Ca9asQW5uLtauXav0UOgiXn75ZVitVjz11FN49NFHlR4OnefYsWPYunUrfvWrXyk9FOqFvXv34siRIzhw4ABCQ0OxbNkypYdE53E4HCgsLMTo0aOxb98+PPvss1i8eDEcDofSQ6OL2LBhA5YuXer6BZbUxeFw4KmnnsK2bdtQWFiI3bt348477+Q6pTJmsxlbtmzB6tWrMW7cOOzcuROjR49GY2Oj0kMjD8NXcsn87//+L7Zt24b//Oc/8PX1ha+vL/R6PcrLy12fBhYUFMBisSg8UgKAZcuW4d5770VsbCzKysrgcDig1+shhEBRURE7KeSTTz5BQUEBkpKSAADl5eVYuXIlnnzySa5PKvTt/3+DwYAHH3wQycnJiIuL4zqlIhaLBVqtFnfccQcAYOzYsRg2bBgKCwvZSYUaGxvxxhtvIDs7GwAQEhLC1z6VOXToEEpLSzFjxgwAwIQJExAbG4sjR46wlcrMnj0bs2fPBtB5wNDIyEhMnTqVnVSop98dTCaT1O9X3FIvkT/+8Y947bXXsGvXLgQGBrpuX7RoEdatWwcAyM7ORklJCWbOnKnQKD1bXV0dSktLXde3b9+OkJAQhIeHIysrC5s2bQIAbN26FbGxsUhMTFRqqB7tvvvuQ1lZGQoKClBQUIBJkybh+eefx3333cf1SWWamppQV1fnuv7aa69h7NixXKdUJjQ0FHPnzsUHH3wAAMjPz0d+fj6mTp3KTiq0efNmZGRkYOTIka7b+NqnLt9OPk6ePAkAyM3NRV5eHlJSUthKZcrKylx//u1vf4s5c+YgMTGRnVSop98dpP+9QtFv9FOvWa1WAUAMHz5cZGRkiIyMDDFx4kQhhBDl5eXiqquuEomJiWL06NHiww8/VHi0nqugoEBMmDBBpKamivT0dDF37lzXwTlOnTolJk2aJJKSksS4cePEkSNHlB0suXz/QHlcn9QlLy9PZGZmirS0NJGamirmz58v8vPzhRBcp9QmLy9PzJo1y/X6t2XLFiEEO6nR5MmTxT/+8Y8ut/G1T31effVV1/qUmpoq/vnPfwoh2EptVqxYIVJSUsSIESPEkiVLXGdBYidlrVy5UsTExAidTifCw8PFiBEjhBA9vyfJ/H6lEUIIpT9YICIiIiIiIqK+4+73RERERERERJLipJ6IiIiIiIhIUpzUExEREREREUmKk3oiIiIiIiIiSXFST0RERERERCQpTuqJiIiIiIiIJMVJPREREREREZGkOKknIiIiIiIikhQn9URERERERESS4qSeiIiIiIiISFKc1BMRERERERFJ6v8D7Bsbu4lanNAAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Add some operations\n", "trend2 = detrend.simple_moving_average(25) # duration in seconds\n", @@ -604,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "1ef53eb7", "metadata": { "execution": { @@ -614,175 +334,7 @@ "shell.execute_reply": "2023-07-24T16:46:45.089615Z" } }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " timestamp noise trend season signal device_id\n", - "0 0.0 0.091299 0.000 0.000000 0.091299 Device 1\n", - "1 0.1 -0.002616 0.001 0.019992 0.018375 Device 1\n", - "2 0.2 0.057413 0.002 0.039933 0.099347 Device 1\n", - "3 0.3 -0.070848 0.003 0.059775 -0.008073 Device 1\n", - "4 0.4 -0.187095 0.004 0.079468 -0.103627 Device 1\n", - ".. ... ... ... ... ... ...\n", - "995 99.5 -0.137226 8.955 -0.197182 9.615592 Device 2\n", - "996 99.6 0.028486 8.964 -0.179542 9.808944 Device 2\n", - "997 99.7 -0.034614 8.973 -0.161453 9.773933 Device 2\n", - "998 99.8 0.035802 8.982 -0.142960 9.872842 Device 2\n", - "999 99.9 -0.144502 8.991 -0.124110 9.721388 Device 2\n", - "\n", - "[1600 rows x 6 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Reuse raw_data to create one with two signals with overlapping timestamps\n", "new_data = raw_data.copy()\n", @@ -998,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "c7ba2e0d-0271-44c9-a82f-6ea64e898b77", "metadata": { "execution": { @@ -1008,40 +404,7 @@ "shell.execute_reply": "2023-07-24T16:46:45.114355Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Feature \"device_id\" is an array of numpy.object_ and was casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n" - ] - }, - { - "data": { - "text/plain": [ - "indexes: [('device_id', str_)]\n", - "features: [('noise', float64), ('trend', float64), ('season', float64), ('signal', float64)]\n", - "events:\n", - " device_id=b'Device 1' (900 events):\n", - " timestamps: [ 0. 0.1 0.2 ... 89.7 89.8 89.9]\n", - " 'noise': [ 0.0913 -0.0026 0.0574 ... 0.0849 -0.1479 -0.0568]\n", - " 'trend': [0. 0.001 0.002 ... 0.897 0.898 0.899]\n", - " 'season': [0. 0.02 0.0399 ... 0.3051 0.3177 0.3294]\n", - " 'signal': [0.0913 0.0184 0.0993 ... 1.2871 1.0678 1.1716]\n", - " device_id=b'Device 2' (700 events):\n", - " timestamps: [30. 30.1 30.2 ... 99.7 99.8 99.9]\n", - " 'noise': [ 0.1521 -0.0129 0.0074 ... -0.0346 0.0358 -0.1445]\n", - " 'trend': [2.7 2.709 2.718 ... 8.973 8.982 8.991]\n", - " 'season': [ 0.2601 0.2446 0.2285 ... -0.1615 -0.143 -0.1241]\n", - " 'signal': [3.4122 3.2417 3.2558 ... 9.7739 9.8728 9.7214]\n", - "memory usage: 65.5 kB" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "devices_evset = tp.from_pandas(data_2_devices, indexes=[\"device_id\"])\n", "devices_evset" @@ -1060,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "5e3cc024-37c0-4dd4-8423-e3616b92313f", "metadata": { "execution": { @@ -1070,18 +433,7 @@ "shell.execute_reply": "2023-07-24T16:46:45.389467Z" } }, - "outputs": [ - { - "data": { - "image/png": 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78PgXrQCAUDCAaJwUyptOaJoGRQE5Kib14aHL69jnTCoxPjtiQ22BOqvmPTHqMwAej3xMXEG05xaiP3cQ7bmDaM8dRHvuyCTtW61+3LiqFP/Z34fGbhee/ap91DF8HjXKA/zi111Y+/vPEE/QCEbj6BtRTM8diOLjgwM40OvGrc/txJfNgwhF4wjH4uxGgNMfAU3TcAdSq6dPB5mk/2TY3eUct71gMBpHJJZgP4dQNI7ntnQAAF7c1gWdXIS7zqhCZY4SlTlKGBViVOUoU84x16zEO429aLH6YFCIsbBAg9d39uCuMypRpJNhr8UFY9Koz1VLwedRUEmE+PyIFe/t70eL1Qde0os/VLxtJIO+8KjHCMeGpml4QjEoJUIAQFWOEvkaKfu8USlmcuvVkqya99nzl2QxZWVlXA/hpIVozy1Ef+4g2nMH0Z47iPbcke7aR+MJfPeFneh1BdFq8+Gi+nw0DfhgcQRT8ufjCRoURSFHJU7xAPvDMXx0sB/n1ebiSL8XrkAU9hGttfb1uPHhwX54QzFct6IE7+3vw/NbO/HUpnY8vrENAPDT1xuxv8eDO17aBad/ettypbv+U+WDAwPY1+0a8zlnIAqVRIDepNG/YW8v/vRJM7yhKLZ3OHDT6lIsKdGxxxtVEiws1KSc43unVeDN3T348ZnV4PEoCPg8fPGTU7GsTI8CrQztg36opAIAQKFOBo1UCJVUgD1dLty5rgKfHbZCpZDDqBiupD+S6/6+bUxj/2QkEktMaJPjzpd2o9Puh0EhGvN5k1KChQVqUBSVVfM+7Yz673//+ygpKQFFUdizZ8+4xz399NOorKxEeXk5brnlFkSj079rmS60tLRwPYSTFqI9txD9uYNozx1Ee+7gQvvf/Ocwfvn2QdYbO1EGPKFjhn9nGuk+77d3OKCWivCjV/bCGYigQCuFOxiBzRuCJxhjj/OGolBJBSgzKNBqG65o/u6+Ppxfm4clxTrs7HTAHYwiGI0jEGFe22rzwRuKwReKYU6uEj88owpH+r042OfBZ4et8Iai+LrNjrZBH9psfljHMABPhHTXf6pYnAE4x4lscPojmJenQq+L2Xx5e28vvreuAjc9uwMX1xek5MEDwGUNBbisoSDlMbGAj2dvWIplZXr2MbmYMeILtFJoZEL2PAVaKVRSIVQSIZQSIeblqfB1ux38WBBFeiav/j/7+hBM1mOIJ2g0DXjZVnonO43dLjz95eiomJHQNI0trXbs6HCyaQ9HU2qQ4/5vzAeQXfM+7Yz6Sy+9FF9++SWKi4vHPaa9vR333XcfNm3ahJaWFgwMDOCJJ56YxVHOLgKBgOshnLQQ7bmF6M8dRHvuINpzBxfat9p8CEZjky6UtbHJho8PDczQqGafdJ33/9zSAZqm8ckhK65fWYJvLSlgvfE0DQz6IvCGho0uVyAKjVSE2gJ1iof400NWnFNjRkOxFjs7nXAFoqAosN76tqRR7wlFoZQIYFSKMegLIxCO4da1ZfjuC7uwrEyPA70e9LqDY3p1T4R01f9E6XYE4BrHqHcFopifp0afO4g9FhfmmJW4qC4f8/NVuGRR/qjjJUI+BPyJm04FWmmKYVmTr8byMh1UUiGqzUrkqiU41OeFRiZAnkaCXlcIL27rwoFeNwAm9D4ap2FxZFZ7xJnCFYiO+1kOMeAJIxxLYGenE/pxjHo+j0KRXgYgu+Z92hn1a9euRUFBwTGPee2113DBBRfAbDaDoijcfvvteOmll8Y9PhwOw+PxpNwyiaKiIq6HcNJCtOcWoj93EO25g2jPHTOtfSgaRyiaWhWdpmnMMavQNuhnw2wdI0Krjz5+CJsvnHJcppOu8/7JTW3Y3+PBkX4vqnIUuKguH7+9pJZ93uYNwxsa9tQPVdmel6fCgd7h681oPAGZSACtXAR3MAp3MIpCrQx2/3CfcyGfgjcUg1LM5ALzKApiAR9nzMtBvkaKm1eX4uNDAyjWyWD1Tm/LvHTV/0RxB6NsK7khhu47AxFUm5Xod4fw/NZOXL28GFq5CPd/Y/4oL/1UyNNIMcesTLl/9oJcqJNGPUVRMKnEKM01Ik8tRa8rCIsjgP09jFHf4wqiRE8q4w8x1md5NAd63Vg/LwfbOxzjht+PJJvmfdoZ9ROhq6srxZNfUlKCrq6ucY9/8MEHoVar2VthYeFsDHPaaGtr43oIJy1Ee24h+nMH0Z47iPbcMdPaP/1lO95p7Bv1eJlRjnabH9c/sx2BSAxXPLGFGY/Nh2uf3jbmuayeMGsQZgPpOu/jcRo/e6MR6+aYQFEUKIpCoS7p5eNTGPCGwBth/7mCUWhkQshEAgSjcby3rw/hWDylNzmPouAKRFBmlMOezBFO0IwH0RuKQSlhvIfFejmqchQAgN9cUotlZXoEwnEsL9OneOp/9MreE/4701X/E8EfjiFXLU3x7sbiCXzj0S/hCkTgCkRgVIrRYvNBJxehWC+f1veXCPn43aW1ox5fV23ETatLAQBVJiViPifyNFJYnAFoZCJ2M6jXFcSyUj0sDmLUA0NG/die+oc/asI3//YV/vxJM86vzYXVGx43/H4k2TTvM9Konyz33nsv3G43e7NYLFwPaVKIxceflISZgWjPLUR/7iDacwfRnjtmWvsvmmwpHtYhz3ypQY79PW5sbh3Eqzu60WrzI5Gg8dzWTtj94TELZdl84WkvlsYl6TDvY0dVqw/H4qgpUEMlEeKqpaM9egaFGFZPmK1eDgx76gEgVy3Br987hH3dbkiEw23QdHIR2u1+lBkUsPsj8IVjbJs0XzgKRdKoX12px4pyQ8p7lhnlWF6mZ3PqaZrGhwf6j9lrfSKkg/7TjcUZQE2BGq7g8Pfk3X19CEYSsHrDcPij0MpEeOt7q/Bf586dkTGM5fEX8HnsfFhdaUCZUQazWoLGbjfm56nQ7wnhwfcOoWPQj2VlOnSNY9SHonG0D/rHfC4bcQejcB1VX2BobTzU58FLty7HRfX5WFVhYFNYjkc2zfuMNOqLiorQ2dnJ3u/o6Dhm+IRYLIZKpUq5ZRJms5nrIZy0EO25hejPHUR77iDac8d0a59IMO3H3MEodnU54Q/HUjysQ22X8tRSfN5kw5nzzHhiYxvUUiGcgQharD6sLDeg2zk6pzYUiZ+wIZdOpMO8v3/DAeyxuNj7rkAUeoUYL926PKU3+RAmpQQ0kOKFd/oj0MgYo/5XF9Xg7vVV2NxqR65awh4zlEvNeOoj+OKIDauSxvtIT/1pc3Kwony4ABsAXLuiGKsqDKxR7wvH4A3H0Oc6sXD8dNB/urE4gijRyxEfUYTyvX19uH5lMayeMJyBCHQyEVTJ1mdc8I2FeVg9vwRCPg+D3jAKtDLo5SJ02gN4aZsFi4q06HUF0Z9suxeNJ/DqDgvCsTj2WFy461+7s7o6vjvAhNz/Y3MH3MHoqI23Zzd3YHPLIGIJGmIBHzesKoVEyEeZQQ6d/Pjh99k07zPSqL/kkkuwYcMG9Pf3g6ZpPPbYY7jiiiu4HtaMMXIDgzC7EO25hejPHUR77hhL+0c/ZdosfXp4YNIXcFZvCJ4QqZ48EU503g9VsKdpGhZHAFvb7Xjg7QN4p7EXv3z7IL69rDjFqB/0hWFQiMDjUTApxbhxdQmcgQjOmGtC26AfBoUY1WYldnY68cau7pT3moaU37QiHdac/b0efH7Eyt53+BmjbzxMKvEob2DTgBcVJiZkns+jUGZUYHPrIMzq4T7ZuRopDvd52PD79w/046z5ORAL+PCGYhALRm8gDHH2glwYlWIEwkwev80bhkjAG9ebO1HSQf/pxuIIoFDH6P7Grm60D/oRjiVQrJfD6g3BFYhAI+fOoB9iSHuzWoJCnRSPXFGP31xSg0FfGHkaKYxKMW59bgdomsb2dgfe3deHX71ziK3nsKXVzvFfMDP4wjHc+a/d6HYGsbHJBs+IKJgh9lpc2NHpxNHL4WPXNEA4gaKG2TTv086ov+2221BQUIDu7m6cddZZqKioAADcfPPN2LBhAwCml+YvfvELrFq1ChUVFTAajbjtttu4HPaMIpVKj38QYUYg2nML0Z87iPbcMZb2W9sc6HWF8PsPmtBq803qfG/t7sGnh6zHP5BwQvM+FI3jjIe+wC/ePoBuZxA/eW0v9ve4sb3DgW3tDvz9+iW4alkR264KAAZH5H3etLoUtQUaPPStOlSbVdhrcSFPI8EcsxK/fu8QXt3RjQ17e9nXZptzjus1JxZPQCcTYu8IT73DH4H2GN4+k1LMbMpQFOtB7HEFka8Z/ltK9XLs6nTBrEr11Fu9YZQbFdjR6UQkFodeIYZKKpj0BpzNG8bCAjVr1Ieicfz5k+ZJnQPgXv+ZwOIMoFDL1D94b18/Hvm4CSV6OXJUjP6+cAxKMffVz4e0z9NI2fFqZCI8fk0DRAIefnfpQiwq0qJ90I9PD1txz9lz0OcOweYN49LFBdjW4eBy+DPGoDcMmzeMQV8Y/Z4QPKEY9ApRSvFQmy+ML5psMKlSN9dy1RObz9k079POqH/88cfR3d2NWCyGgYEBtn/gU089hQsuuIA97pZbbkFraytaW1vx9NNPQyjkfqdtpjAYDMc/iDAjEO25hejPHUT72WXQF8bBZHGksbS3eZmLGps3jK9axvfKJBJ0SnstgGnx0++Z3krZ2cqJzPuv2x24alkRuuwB9LlD2N3lwu4uF5aUMDmxY4WCDvoirFF/2eJCCPk8nL3ADINChL3dbuRppKg2K1FboMFzNy3Fy9u70O0MoHnAC4mID4qi0GL1wpoFny/Xa06HPYByowIy8bBh7fBHoDuGJ7dQJ0OxTg6lRAhvKIZ4ggYvWUxvCLVMCKVEAHNK+L0UIgEPJqUYayoN+N2lCwEASokQ/nBs1PscC5svjEXFWtaot3nD+Ojg5Fsdcq3/TNDrCiIvucESSySwtc2ORcVamJRiDHhCbGtCrhnS/sbVpageUS3/1GoT+/8V5Xp81WrHkQFvsqI+DZsvjLlmFdsWMdMYr7PHEIM+xqi3+yLod4dA0zR0cjHcybz6eIKGRMCHMxBBkW5qRQ6zad6nnVFPGE2mFfbLJoj23EL05w6i/eyyq9OJpzYxVXjH0n7QF0an3Y95eSpsa2e8MjRN448fHkk57u3GXtz31v6UxwY8ITYfcyQWRyCrczGnwonM+41NNqytMoIG0OcOolAnw5EBL85eYMbCAs2Yr7F5Q2MWczIqxWjsdiFPI4VSIsRT1y2GgM/DjatK8Z3nd+H7/9oDo0IMjUyIRz5uxsvbh8f92s5u+MMxbGyyZdTny/Wac6jPg7m5KszJUaJ5gImGcQYi0B4j/L4qR4nvn14BpUQAbyiGTrsfJWNUUC81yFNz6jUSqKVCUBSFH51ZzYYUK8UCyEQT8xwXaKVosfpg84axqEjLVkgfWisATOrz51r/6cQbisLqDSEWpyESMEXp5GIBbltbjuWlOphUYrQP+iFLAy89MKz9oiJtSkHFkSwv1ePhj5pwYV0+KIoCTTMbONVmZca2tvzlOwfRcYxCfzZvGA5/GAPJ4qKReAIamZCtgN9p96NIL8O8XBWKk33nJ0s2zXti1GcACoWC6yGctBDtuYXof+KEonG0WEeHazv9ETQNeMd9HdF+dnEGIviqdRA0TY/SPhZPgM+jsKfLhQqjAv5IDDRNw+oN4y+ftqDXxRRRo2kar+3sxoAnjGg8gUCE8fh5QrExe1r/5LW9CEWzp9DadDDVeZ9I0Djc70GlSQG1VIjmAR8uqsvDHLMS6+fm4KdnV7PHSkR8NgR/vLZLRoUYnfYACjSpoaGnz83B23euxulzTDAqxdDLRTjY68HOLicAoNsZwM//vR/NVh9+/8ERBCLH9oSlE1yvOUf6vag2K1FhUqA1uWYynvpjF9uiKAoqiQD7etz49XuHsLBQPeqYH66vQs6I8HulWJASoj+ESiqEYoKG5lkLzPjgQD9s3jCK9TL4k993R7Ka/tdtdvzmP4cndC6Ae/2nk3ca+/DY58OtyjQyIapzlLhxdSlMKglkIgH297hRkz/6s+KCiWivlgnx+U9OxaUNBQAAmViAHmcQZpXkuB7vdMXuC6NvjA3nIQZ9YZiUErQM+DAvTwVPMAqtTIhNzTZ8dtiKpgEvqnKUuKShAPVFmimNIZvmPTHqM4BMq9afTWS79o9+Ovm8u9kk2/WfSV7dwew+X/v0Nty/Yf+o57e02fHuiH7Z0XgixaszpP1YHl7C1BmvWrndH4FZLcUDGw7gkDPVu+YIRDAvT4XdyRzrYp0MXY4ADvZ5cGq1Ea/t7EaXPYAOewBlBjlqC9S49Z878MxXHQAAIY9CeAzjvccVHBWqf7Iz0TXnaA/o67u6sX5uDiiKQq5agl1dTpxXm4cHLpgPHo9K8b4aFeKkByqCxm43KnNGX1QOee9zxzD8AOCO0ypw9fJiaOUiLCzUQCURwu4L49mvOnDV0iJ02v3oGPSPav+UznC93nc6AijWyxijPlm3wuk/tqd+CJVUiKe/bMP1K0txwcK8Uc+vqjCAP6KZPUVRePyahlHHKSUCtvL98VhaosPWNjus3jCMCjFkIj584RjsvgiqzSq8vMOCVtuwFzSRoFOqwI/6G7Lo93Zfjxsbm23QJD87jVSEqhxlyjEqiRC1aWLUT1T7kVX6c9USDHhD4PG4Tx+YKs5AdMwN5yFsvgjm5CpxqN+LBflqKCVCaKQiPPZFG/76WQte3m7BumoT1lWbJpxDfzTZNO+JUZ8B9Pb2Hv8gwoyQ7do/u7mDrdacjmS7/tPNnz5uxuWPb0EiQePhj5rg9EdgVksgGaOScrczAFdgOGTv2a86sKl5kL0/pP1dL+9O6zmSaVzz9NdjPu70R3DdimII+Ty8t4fZkIknaNA0jUFvBHPMKlgcAZjVEizIV2NfjxsHez24ZU0Zmq0+/HzDfnTa/Sg3KbBujgnROI0eV3Dc8Nt4gkafiyk8RBhmomvOT15rRLeTCXemaRpv7u7Bt5cXA2CKXe21uJCrlsCklIx6rVEpxiOfNOF7L+zCPWfPGbNCs1oqhEEhHtdrKxHyoZYKUaCV4pQqI1ZV6PF1uwNtg36cU5OL3V0ueMOxlO94usPVeh+KxhFP0PCHmfaCxXo52/vbEYhOqC2WUiJAhz2AleX6Cedoj/TcD6GSCKGcYHs1AZ+HxcU6fH7ECq1MhLpCDfZaXLD7I1hUpMEH+/tT8vNf2NaF13aOH2qcSb+34diwZzoaT7A1BIaKFfa5glhYoGEr31+1rAinVhtTzpGvlWJ+mhj1U9HerJKMuT68usPCpmilO65ABFZPeNznbd4w5uaq0Gn3ozpHCbVUCLVMiBK9DJc0FGBNpTGlVsVUyKR5fzyIUZ8BZNMuUqaRzdqHonEM+iIY9I2/oHJNNus/3URiCeyxOFGsl7GVYre02VGkY/LMjjbuup3BFC9ejyuIPncQh/o88IairPY2bxi+CDH8pgNvKIodHU5EYqO95g5/FKfNMeG/z5sLa5D5rB7+qAmXP7EVh/s9yFGJYVCIkauWoKaAMeoP93tRU6DGX66sB4+i0OUIoFAnw/IyPf5x41Km3VE4BlUyd3fk5ozVG0IsQcM3yaJc2c5E15yDvR58fsQGAGix+jDHrGKN8zyNBGIhf9zc2DPm5uAbC/Pw4i3LUFMwtlFBURSuWlp43HGcV5OLi+rzsTBp0NE0jTKDHBubbNDKhHAHRnvqvaEonv2qfUJ/52zC1Xr/9Jft2LC3h70vEvAQS35XAuEYZGP0pz8apUSIFeX6E/aaTsZTDwC3n1qGM+bmgMej0FCsw44OJ+w+Jsc+TyNNGfvHBwdS2t799v3DaBvRSSNTfm9j8QQu/utm9n77oB//9+5B7Ohw4Lw/fwm7LwwBn4dvLMxFXaEGALPRdvT38clrF49qj8YVU9E+Vy2BMZm6w+NRbBTGZ0es+OhgP7qdgTF/a9KJWILGwDGKfNp9YcwxK6GUCJCrlkAlFWJergrfO60CVy4two2rS094DJky7ycCMeozAJlsasUfCCdOtmi/s9M5KszW5g2DopiWL+lKtug/G2xrd2BZmR5mlQT7e92gKAobm2wo0sugkAjgDkaxqdnGHt/nDsEXimFnpxNft9kxkKys/o/NHXhqUzurvd0fgSeDQnjTmX53CHGaRtvg6BoH7mAUKgljfMvFQgQiMbTafLhqaRH+tc0Cg0KMHLUEuWopKowK7OhwwuYNseGYcrEAB3s9KE5u4vB5jBFv9YRgUjG51/YRxZR6nEEYlWISfn8UE1lzaJqGQizAVy1MZMtnR6xYN2fYC5inkaa0LzuaarMS66pNx/Xo3n1m9TGfB8Ceo9KkxMbmQeRppNDIhLD5wlhYqBkz/P6dxj489kXbmBE4LVbfMQtXzSRcrfdNA1582WyHfITXU8TnIRSNI0FPrDr6vFwVvr206ITHUqCToXacjZ6xEAv4+M0ltQCABfkq7O91w+6PYGmpDlcvZyJ/IjGmvkY4FkevizGgWm0+vLLdgkN9w3VVMuX3drfFhUP9HrZmSPOAD85AFH//qh3XrSzBzf/cgbm5KpxabUqpHn804226ccFUtM9RS9g0Ha1MCGcyKicQiaNpwIfvvbALW9sm178+nqDH3AicCWiaRo5SggHv+I6leIJGrloKg0KMarMSZ87PQaFOhnXH+FwnS6bM+4lAjPoMoL+/n+shnLRki/bPbu7AS9u6Uh4b9IVRYVSg2xnkaFTHJ1v0n26aBrywOALodgbgC8dg84bx96/accbcHJjVUuy1uLEgT4VNzYMo1slgUIixo8OJ374/XDRp6IJ+j8WFrW0OWEf0g91tcaHd0otYPAFXIApPkHhzp0LTgDfFE97vCaEmX40j/WMVKKRZL1+RgsaeLhcisQTWzTFhV5cTBoUY36zPh1EphoDPw6UNBfjDZQvZV5ca5NjUPIh8bWpe4YCHKTSUo5ak1EfocQUxx6yEb0T4/R8+SK2kfzIykTXHHYyiQCdFOJZAJJbAtnYHlpbq2OfzNFKUGGb3QpHPo6CUCDAnVwWKolCil2NhgYZt/TSS9/f347zaXBxItlAcycYmGzaO2PybTbha793BKLZ3OFCkG/7uzMlVYnuHA4oJhsKXGORYWXHirbHyNVJ8c1HBlF4rFvART9BwBSIo0Epx3coS5KiY1m1ftzlw5jwzu0H74tdduGt9FTrswxs4mfJ7u7HJhtUVBrQl6wW0WH24YkkhGrvduHJpIV6/fSW+f1oFx6OcHFPRfl6uCteuKAEA6BVi2H0R9LmDyFUznRUkQj729bgndc6dnU787YuWSY9lKvgjcRiVYgRG/EZ+78VdeGWHBTRN43sv7kIoxhyjV4ihkYmm1ZgfIlPm/USYsFHv8XiOeSPMHFqtlushnLRki/b+cAyfH7Gx+WYA46mvL9KwbXDSkWzRf7p55qt2bGoexLNfdeCBDQdw18u78b115agwKZCrlqCxm+mN3eMKokgvg1EpxvZOB5r6fQhG4imh+FZPCJ0OP6RCPmy+MBI0UF+ogZuWwpncsSfe3Knxm/8cxnee34n/93kraJpGvzuEU6qMaBrwIhyLY3/ygutoj+mqCiM2Ng+Cx6OglgqxqEgLo1KE61aWsCHeVy4tQoF22HAsNzJttMQj6idIRHwc6vOgUCvF8jIdPjw4fPHS7WSMem/SqE8kaDyxsS0r+p2fCBNZc7ocARTpZFhcosXnR6wQCXgpuqskQvz5ivqZHOaYrCzXoy7ZOm9dtRF1RRq29dMQoWgcMhEf6+fljGm8D/rC6HFxs9HLxXofT9AQ8CiUGORsqhIA1Bao8drOblSPUcQwnanKUaLDHmCjC8xqKfo9IezoTN14arb6cPZ8c0pURqb83u7vcePCuny0JcfeYvPhupUluHt9FSiKAo9HQTBGnYp0ZiraS4R8zMtjQsf1chEe/M8hXPnEVtQXavH90yvwyBV1ONA72qjvcwdxyz93jHnOPncQzllqj8cUoRSCBtgQfH84hrf39qLHFYTNG0aJXg6zSoIz5+XM2DgyZd5PhAnPeo1GA61WC41Gw96G7meTIOmIUJgeOT8nI9miPQVgfp4qZVd+0BdBfZE2rT312aL/dELTNLa1O2BxBtDjCkIu4uP2U8rRUMxcsJnVEjR2u7GoWAuZiI8cpQQGhRjb2x1YUqrF/l53SpsmqzeMPlcIEiGPbW+Wr5HCHoyzx5FialODRwH/vHEp4okEXtzWhQFPCCvLDfiqxY5739iHX759ENs7HPjvo/rKL8hX46OD/Wwo/a+/uWDM3tcjKTMo2KJQQ+SqJHhtZzeWl+uxokyPxm43ntrUBoc/AosjgGqzCp7kho3dH4FEyEsplngyMpE1Z8ioP7XKhN++fxirxvDQcmFU3HVGFZujf/eZ1SjUSuEKDl+g/9eb+9DrCiJPI0V9kQaN3a5R5xj0MesBF3Cx3g/VoVhRpkfliOroNfkavL+/P+WxTGBxcWqf81y1BH3uEA71eTHHrARFUbD7wslCjKkpOZnye8ujKFSYFGw9AH84hrwTiHBIB05Ue71ChH53CO/9YA0uXpSPCpMSuWppSkvLFisTIfbAhgMY9IURiMQQisbxwIYD7DFWT3jWet67AlFoZCIkaBrn/GkTmge80EiFWJCvxr/39OL82lz838U1kIr4uDpZhHQmyJR5PxEm/KuTSCQQj8eRSCTY29D9eDwz+yNmClarleshnLRkg/ahaBwSIR9lRgUbrgYMVxW1HSOfiWuyQf/p5siAF0tLdeh2BhGJJfCLCxdgTeVwPm+uWgK7P4J8jRTXrywBj0fBqBRjX48bF9XlY3eXE0f6vSgzKsDjUXAGIojGE9DJRfCFYlBJBMjXStHc64Ddz/Q/Jjn1U4eiKHz31Aq8t68PFgcTOfGHy2px1nwz8rVSvPh1F7Z3OFIqoNsHbSgzKlBuYryEFSblcY3ECpMCVx6V05urkUItFbK5+r+6aAGMSjG+/9JuOAMRVOUo2PSAfncI31iYh69aBhGOxfHIx03TrERmMNaa88aubnw9Ijd1yKifm6uELxw7Zt4ul6ikw4XyQtE43trdg15XCHkaKcQCPqLx0Tn1Dn+Us8ic2V7vNzbZ8IN/7UalSYnbTynDoqJhB5VRydShqM4wo76hWIvcEdXAzWoJOgf9oMBsNOWoxHh7by+WlepG1QrIlN9bigLKjEyHgniCRgZ3dGM5Ue3rCrV44IL5kIkEKb8lBoUYVm8InlAUF/91Mw72eiDg85IRYz5YHAH8e08PG73X7wmxGz2uQGRGDPzdXU602XxwBRlPfYlejjWVBnxwoB9Fejlq89V4dYcFc8yzU8AuU+b9RMis+JSTFL1ez/UQTlqyQftuZwAFWilKDcNtegDGI2NUihEfp+VVOpAN+k83r+3oxrcWF6bkoY2EyaXjIUclwU/PngMAMChE4FEUzqnJxVctdry3vw9nzs+BWiqEOxhFjloCs0oCPo9CnkaKPI0U3rgQDn8EpXo5Cb+fIkNfLR6PwrpqEz442A+TUowKkxJnzTdjTaUBOzudWF1hSGmbpdfrccWSQiwp0Y1z5tFIRXxcWJef8lipQYYz55vZ+4U6GS6sy8dliwvwvxctgFIiZMPv+9xBLMhXwxWMwuoJ48MDwy2iejkKx+aCsdac13Z2Y3MrY9T7wzF82TyIMqMCFEXhhZuXIX+cXvJco5YK2fD7HlcQgUgce7tdyNcwRp9UyEcgEsPBXg/+vYep/k7TNHgTbMk23czkeh+NJ/Dazm62uBoAvH+gHw9cMB8X1uWNWQzvjtMqUajLrCJaWrkIj13dwN6vSba/XJBs3ZankeK5rZ04ZwGzLkhFfLbtXSb83jKpShRUybXL4gikpCFlKieqfalBjuVlo89RW6DGvm43ntvSibMWmPHQR01YUqzFHLMKh/s8aBv0Ixan2YjNfk+IbYH7dmMfHv+i9YTGNRav7+rGL94+CGfSU//ABfNx5dIifHBgAMU6GWoK1OiwBzAnd3Y21DJh3k+USRv1zc3NOOecc5CXlwedTsfeCARCemJxBlGglaLMIE/x1A/6wtDLRUz10THyaBMJGqEoicJJFwZ9Yby+sxttg37UF2nhS7YqOxqKopCnlsKgGDYSjQoxivUyKMQCzM9T4UCvB+VGBbQyIYQ8Hkr0MphUTCXdXI0UuWoJBrwR2H0RlBjkJPx+gnhCUfY7E0/QKe2tzppvhpDPS/GinFptwp2nVeCUaiNbxXiI0+fmoMJ0Yvm866pNuHFVyajHL6zLh0nJ9DgeKpTX7wnBrJaASv5/qCvG4X4v/vRxM/vaza2D2NI6uYrKmYw7EIVMJMDhfqZ20J8/acbtp5SzmzAVpvT15IoFfMQSCdz7RiMsjgDMKgm2tNqRq2Y2IcpNCrRYfdiwtxdfj+hrTVHD7bGyhV5XEP/Y3IHfvT9cDLLbGcSiIm1K1fuRXLWsCPwMdAOLBMNrjFwswBPXLsZdZ1QCANZUGvDbS2qhT7ZCK9HL0Gkfv67Oi193jfscF3iT0WQAUxzycL/3hNfJbKa2QIPGbje2ttlx33nz8NkRK5aX6zE3V4nD/V60D/pxTo0ZeywuAEAoEodczBj13Y4APj9iG9UOFxjdIvd4dAz62fD/HmcQDcVavLy9CxoZcw0z16zCvh43ivUy5GukWFNpYDu7ECbOpI36W265Bddffz20Wi2++OILXHrppfjxj388E2MjJLHbT54LqHQjG7TvdgRQoGOKpdl8YXQlf8DDsQQkQj4WJHdyj+bFbV14cmMbAMbLMXSR9+GBfli9s5NzmQ36Txev7uhGs9WHn53DeN91chEKtWN7CH9wRmVKuLZOLmLzDW9aXYq711cBADQyEUwqMVaVG1BXqIFRKUaeWgKJkA9/KAy7P2nUk/D7CfH794/g0sc2w+IIwBOMpvRALtTJcP835qUcr5OLcNniQqyrNuF764arNU/XvKco6pjtuJQSAbxh5rPtd4eQq5ZAJhagzeZDOJqAOxiFOxhNifDZY3FhV5dzWsaXjhyt/cZmG86cl4Nwst9zh90/pkcsXel1hfCv7RZsabNjVYUBOzodyEtGFlTlKNA04MP+HjesnjBomgZFATkq8ayt8SOZyfXe4Y/gtDkm9LkZj2SvK4g89fhtB7ONoXWgtkCDxSMigIr1crbWzpD+/+9zxjsbisbx4rbOWR7paPzhGFvQzx2MspvZ5UYFPjzQnxVG/UzN/TlmJT49bEWeWgq1TIhb15ahyqREoVaG9kE/2m1+XFSfj71Jox5gNksisQQszgBWlOuxv2d0MfS7X9k7YadPNJ7AnS/txudHbMl0CQq3ri1Dx2AAWhmzOaqWCZGnlqBYLwdFUfjnjUun5e+fCNl0nTlpo97j8eDyyy8Hj8dDTU0NHn/8cbz11lvTOqjm5masXLkSVVVVWLJkCQ4cODDqmM8//xxSqRR1dXXsLRjMzhBBk4nbfL1oPIG7/rV71M6cxRHA+/uzpxXEWHCt/bEIx5gcybFosfrw9t5eROMJdDuDKNRKQVEUgpE4znpkI1yBCPt51uar0Ziswh2MxPHPLR2IxRN4ZYcFA94Qmge8+ObfNuPZzR0AmIvc5oHRfbZngnTWfyaIxRP4xduj1zsA2NJmx4/PrEJVMsezQCtFwTihoUeHYQv4PNx+SjkApvXNUA6+RiZEjkqClRUGLMhX48K6PDavVCQSweEPJ8PvR3vqo/EEPjk0MLU/NEvpcgRw3YoSNHa74QpGoTkqkuL82rxxXzvSgz9b814i5CMSS+DjgwPodQWRq2KiNPZYXKgpULObE+0jCmz2OIPodqZvx4wT5Wjt91pcWFSsYXNTY3E6xROa7tAAvlGbh3cb+7C6Uo9EgqmUDTAtsZ7Y2IrKHAVommYNpjyNlJOUi5mc9w5/BCaVmN2c3t6RWg3+ZKXUMGzUm0wm+MMxPPTREYRjcQz6wintTGPxBL5osmHQN7t1eP77zX24/fmdcPoj8ISGjfp5eSp8cKAflVlg1M/U3JcI+aBBY90c5jf/nrPngMdjOgTkaZi1flmpHq02H3tNqJOL4ApEEI4msKxUx0YpjWRbu2PC9Zg2t9pRmoz4ax/0o8Qgh0TIx9+vX4Jq83Ck0/dOq2AjDI+1GT3dZNN15qR/mYaqBCqVSnR0dCAcDmNwcHqr5d5222249dZb0dTUhHvuuQfXX3/9mMdVV1djz5497E0qTc+8thMlGuXWS7a93YHtHU583pTa/qbZ6sWHB7LbqOda+2NxsNeD/3pzH+t5GMnTX7bhs8NWfHJoAN3OIPI1jPF3bo0Zly0uwK4uJ/vDOCdXiSPJRftgnxvv7O3D9c9sxw2rSjDojWBruwN3nlaBz49YQdM0nIHorBXXS2f9Z4Kv2x14Y9fojRqLI4A8tSTF+76m0oiG4hPrPKKViZCjGg77ri3QQJu84BfzKezsdKHEIGMrpA+xx+JCtzOIe15vTGmTeDLjDjAXm3PMKhzp98AViLChhZNlNud9LEHjZ2/sw+dNNqikAuSqJdjd5cLiEqYzhicURSAcYwvqDXjCaV1cc7Ic3U7Q6QthZ+dwKHr7oB+lBgUWJNNWMo2z55tx+ynl6HYGsarcgFyNhE0LKTMq8O/vrcbPz2ciSGzeMIwKMcqN8lEbt7PxPZ/OeU/TNDa3DLKGit0fYTczAGB3lwv1RaRzU7Fehs5BZpMuGo1ij8UFPo9CjzOIQV8kZe1vGvDhoY+a8EQygm+6OdLvZdt8AsCGvb3odQXhCcVw5rwc9LiCcI+IgJqXq4JIwGNTCTKZmVzzb1lThtUjiukOcd3KEggFFPg8CjyKwqAvArVMCJ2c6YpAAzCpJLAetd77wjH0uIJsQb3vvbiLfc4djOLn/07t5mJxBLCoSANPMIqDfR7My2UK4FWblSmdGr69rHhWjfkhsuk6c9JG/dq1a2G323HHHXegoaEBpaWluPDCC6dtQFarFTt27MDVV18NALjkkktgsVjQ0tIy5XOGw2F4PJ6UWybhdHIb6vjJYSv+dEUdXtluSXnc6Y+iKZkjk61wrf2x2N3lwndPLcet/9yJn73eyD4eTzBFT65YWoT2wQBC0TikImbhvGZFCVZXGPCfff2oMDK722IBH7FkFeTD/V7csrYMz9+8DBfXFyAaT6DfHUShToaqHCYHyxWIzFpoZjrrf6LQNI3/e/cgIrHhi+X39vWh1CCHOxBFq82H+5M/jjs6HaNCftdWGVFuPDEPxSlVRlwyThugW+sV+Pf3VkEpESI64oI+HIvjZ683otcVhFjAP+lboA2xo9OBJSVaVJgUaLb64ApEoZaJjv/CMZjNeW9xBPCtxQX43qkVoCgKuWopmga8aCjSotsZgDsYxfx8NRv+StM0sind+oZnt6fc/7plAP/axvzWJRI0aDDhqAvy1djaaodCMnb+dbrygzMqMcesRHGybsYdI9I8AKZQGkVRUMuEaLb6YFSKUVugwd6jUrLuenkPrnn66xk17qdz3r/T2IeHP27CQx8xXRyY9pxitl5Ah92PEn3mF1g7UYwKJi0PYPTf0eHE2fPN6HIEYPOG4Q3F2I2RAW8Ip88xjVsRfVOzDdYx6vNMlAO97pTaDp8cGsCOTicoAPlaKbqdqUZ9qUGO82pzp/x+6cRMrvkX1uVDMUbdiDlmFf5+3RIAQGWOEq/ssGCOWQmtTISOQT9UEgFyVGK25tLQdUCL1QeTUgy7LwynP4L39vUhHIvjoQ+PYGubPSWUH2BqV8zLU8MTimLAHWLTf9KFbLrOnLRR//vf/x56vR5XXXUV9uzZg48++ggPP/zwtA3IYrEgNzcXAgEzASmKQlFREbq6RhfraG1txaJFi7BkyRL87W9/G/ecDz74INRqNXsrLCyctvHOBmaz+fgHzSDNVh8airVsTuEQzkAEFkcw6wrqjIRr7cdidzKfdW+3C9csL8Hbd66GMxDB50es2NRsw44OB5aU6FBikLEX4iOpzFHio0MDY+ahHU72sh1JXzLXtiZfjWarD+7g7Hnq01H/E+H5rZ1sQZoPDgzgpW0WWL0h/Pzf+xGMxNHlCOCUKiM6HX78+ZNm7La4QNM09nV72P7T04lcLBjXy1FRnD9mmLHNG0aH3Y9uZwA3rynFf/b3Tfu4MpE9FhfqC7WQivgIxxJsu56pMJvzXiLkY0W5HresLQMA5GkkkIkEmGNWJcPvY6gr1KTk1fMoZMW67w/H2BZ+Q/goxngIx+LY0mZHcdLwm5enwkeHBtK20v2x4PEoPH/TMgDAZYvHvv7J10jx7r4+VOYok73NUyPAQtE4KkwKdDlmLvViuuY9TdN48esuvHDzcuzvcSMaT8Dpj0AnF0IvF6HfE4KAx+PEK5hujNTAbDZjb7cL59TkwpI06sUCHtvr3OYJo9KkgHucGisfHOjH243M70Gn3T/pYmqBSBw2bxh7LS5YHAF0O4PY0joInVyEfI1slKeez6Pwq4tqpvJnpx1cXeuYVExdibpCNZ7Y2IZLGwqhV4iwt9uNwmQtJqsnjFg8gcsf34JDfR40D3ixolyPQV8Ye7pdkAr5aB7w4c+ftuDZrzpG2QrdTqaSvTcUgzMQSen0kg5k03XmlBLDEokEent7QdM0VCrVmAb3TLNo0SJ0d3dj165dePPNN/HYY4/hlVdeGfPYe++9F263m71ZLJYxj0tXAoGZz198ZbsFPWPk0PnDMSjFAlAUBZVEwPa8BQBXIIq5uUpYHAFs73DgUF9qBITdF570op5uzIb2kyEYiePOl3YDYMKc1Emj4eL6fPz83wfw0cEBHOrzYGGhBkaFGB12P2RH7dAWaqUIROIpRr2AzxRG6XEFR120ugPMj6hJKYbVE4JMJBgVjjVTpJv+J8rm1kG829gLAHhzdzduWFWCAU8YHx0cwEMfHcGSEh2K9TJsa3eAR1FoKNai1eZHh92PUr18Vsc6nvY2bxihaAJ7LC40FGtnbS6kI05/hF03mwa8qMwZin7hoc8dgkY6tYuX2Zz3JQZ5ShqHWS1BjkoMs1qCPjfT37ihWIuDfR54QlEoJQLkqCTotPtHha5nGlZvGHGaTgk1b7P54AxE8PFBK/78STPWz8sBAMhEAvAoKmPbZx2vNVu+Ror9PW6sqTCAoiiIBbyUQlg0DVQkq+XPFNM17zvsAVTmKCAS8FBfpMUeiwv2pKfepJLgs8NWzJuldlmZwFBrw+Y+J/RyEcqNzObNoC+MMqMc7mAUPa4gBpIdMsa7rnP4I/iqhYnc+p+39sMdjCIYmXgHnWDSqP/o4AA+O2KFTMTHp4etKDHIka+VoifpqVdlWLTMROD6WmdRsRbXrSiGTi6CTi7G23t7sbLcALGAj2g8gZe2dYGiKPR7Qmi2+rCiTI9BXwR7LS6cMTcHX7YM4oy5JnTY/cjXSGH1hnDenzfhDx8cQTASh0oiTKZvTn2ze6bgWvvpZNJG/bPPPguNRoOamho0NDSgoaEBixcvnrYBFRYWoq+vD7EYk79H0zS6urpQVFSUcpxKpYJazXiuCgoKcOWVV2LTpk1jnlMsFkOlUqXcMonZSBd4Y3c3/rNvtMetxepjjb8qszIl3N4ZiGBZqR5HBrz433cOYmtbagXJO17cjeYZvACYDdItVeNgnwc9riAGfeGUdh9nzTfjPz9Yg15XCB32AEr0MlAUBX8khoKjKqQL+DzMNStRPMJINCrFGExuwoxswyUTC+CPxEBRFEwqJkxPmWyD9cjHTWO2wptO0k3/EyUQieNwP/MdisQSKNbL0esKwqQU4+9fdeDcGjOKdDI8t7UTZ8zNwbJSHba22UEBKZ/LbDBS+5HXcFZvGCV6Gba02pGvkUI2os/xycZfPm3BHS/ugsMfQSTZTQIAVpTr8dbuninn1M/mvH/0ynrIRMMXyQa5GGurjBAJeIglmOJpK8r12NftRrcjiDyNFIU6Ga5/Zjv+8unU0+LSAZs3jNp8NQ6O2JC2OALIVUtwoNeN+86fh5XlBva5BXkq5I/TcSLTWZCvxh3rKth1ZkGems1vDkXjEAt5qDAq0GKb/t90ezL8e6rzfmThVwDY2mbHimS60ilVRnxxxMakw0iFyFGJ8eLXXTilOnuKY50o+VqmMOKru/vx7eXFKNBKYXEEYfOGUW5UoM3mx09e3QurN8x6dkcy5EyIxBKQifhwBSIY8ITg8Edww7Pb2M2/aDyBSCyBc/60Cb2uIHzhWEonjUAkDpsvjF5XELu7XFBJhFBKhCjWy9joEc8IZ0Y2wfW1jkkpwd1nVgMAlpfp8O87VmFFOfMdogFsbXPg28uKYPOE0Wn3o6FYC7svgoO9HpwxLwdftQzi3JpcPHvDUsjFAhzo8eD82jzs7XalvI/DH4FmimlpMwXX2k8nkzbq//d//xfbt2+H3W6HzWaDzWaD1WqdtgGZTCYsWrQIzz//PADg9ddfR0FBASoqUvPA+vr6kEgwIR5erxfvvPMO6uvrp20c6URe3vgVk49FKBrHXz8b+6IrFk+wC20iQYPPo0b1Hv7t+4dxuN/DVqeszlHiSL8XiQQNf5gJo7mgLg//2NyBSpMSPc5hT787EEVjt2tGd/Vng6lqP1Ps63ahzCDHF0dsKRVfKYqCXCxAgqaZVj1Jb3uRTobCMTxLL96yPCW02qiQoLHbDfNRLX5Se51L0O0Msq97a3cPPjs8fd/9sUg3/U+EUDQOiYCPEr0ch/o8kAj5yFGJsa/HjQX5avzvhQtQYVKiSC9DjzOINVUGLC/T4/MjVtZbOJuM1F4rF8HmDeP1nd2wesNYXKJDvycEnVyEBfnqlOJGJwM0TeNgrwcddj/++9y5+M1/DqW0r7uoPh8WRzDlsckwm/P+6BBkHo/C/d+Yz973h2NQiARYUqLDb94/jAqTApcvLsTbd6zGkQEPOu3+lJoLmYTNG8YpVUYcHFEATygSI18rxVctg6NSlG5cXYq6Qs0sj3J2WJCvTgnNX1yiw/YOxuBi2r9JZ8xTf8Oz20HT9KTnvScURSJB4/4NB1LSQ75us7OV7Wvy1djf6wZNM9c5OUoJBn1h1Gfp5zgVctUS9LpCsIZ4WFighkTIRyDKVL8vMyjQbPWiacAHqzcE4xjpWh8fHGB/A+bmqtBq86HfHWJTNIeiua5/Zhv63Ew04M//vR/vNvbiryM2BoPROAa9YbiCUezucqJAK0W1WYkSvZzt1OE+qlVotpBO1zpiAR+GEZ8zBaY4XrFeDpsvjFicRq5GCpsvjEg8gTKDHNvaHSgxyFFtVsKgEGOPxYUCLbMBPPJaMx27h6ST9ifKpJU1GAyorq6eibGwPP7443j88cdRVVWF3/zmN3jmmWcAADfffDM2bNgAgDH2a2pqsHDhQixfvhzr16/HDTfcMKPj4oqp7iJZHAG80zh2vuurO7vx/75gepF2OgKoNCkhFwvY1hU9riCe/aoDz2/tQlUypLQqWUjjyie34hdvH0AklkC5UYEXb1mO/7t4AXpH5OB93mTFRfX5s9b6bKZItx28fT0enFeTi08PW8fMiedRQCSeYNtjlejlKNSN9izJjwrJN6nE2NRsQ3VOakiiUSlGrpp5vUoqQJc9AI1MhEAkjqocJTY1DyKRoPHtp7aOm2d3IqSb/lOh0+5H84CXjXo5ba4JT2xsQ4VJgRyVBHu6XMjTSHHVMiYayagQ49a1ZVBJhNDIRHjquiW4YmnRcd5l+hmp/bxcFba02ZluC64glpRokadh2iQuLNCgsfvkMuq/bBnEHz88gh+dWYXFJTo0W32YkzscAaaSCPE/58+FUTm1qszpMu8FPAqROBO9c82KYtyxrgIX1+dDKxdBLRPi9lPK8dyWTlz5xFZ02kfX70h3bN4QGkp0sCTzxKPxBOhEHAVaGfyReEplZoDpDpGNBsVY1Bdp2Potva4QcjVS6BViOMcpkgZMvc5Cpz0Amy886Xn/p4+bsdvixIAnhM4Ruf7uYJStFcLjUdBIhWwV9yK9DN9YmDfrkU/pTK6a8dTHYjF2k6/CqMDhfi/0ChGaBnxwByMY8IQhEvDA51EpG3mH+j3odzNRe0U6GQ73e+EJxWD3RWD3h9HtDCAQiWFnpxM9ziCWleqQq5bima864AsPF+ILRmJI0DSo5JgKdTLcd948zE2urTSQtUZ9uqz5Y6GTi2BQiNkUTBqAXMTHwV43inUyFGilCMcSbIqgQSnC3m4X8rVSrK00pH0dknTWfrJM2qi/6KKL8Mgjj8Bqtc5YNfnq6mps2bIFTU1N2LFjB2pqmEIYTz31FC644AIAwB133IEDBw5g7969OHDgAB544IGsLXri803NMO5yBEYVK/n8iBXPb+1k+5jH4gk0drtQk6/G/d+Yh/979xA+ONCP9/f346dnV+PIgJcN0y7QSvGzs+fghZuXYdCX+sMuEfIRjg4v8l+3O3DdyhI0Z3h1/KlqP1M4/GHUF2mxsdk2plGfq5amXFh9b10FVo0IHx0Pk1KMTc2DqDanpqYYFWLWe09RFPzhGDQyIdRSIdbNMSEci+O5rZ2IxBJ4Y1f3Cf51o0k3/afCpuZB/OXTFhzs86DKrMSKMj0+PjSAyhwlcpQS7OtxI3dEhARFUfjp2XM4HDHDSO3n5anw/JZOyER8bG2zM4UYk+tCXaEmpWLxsXhqU1vGenUBxkM/4Alhe4cTd55eifl5TArYT86sxrqjwnm/vax4lFE4UdJl3ptUYrYopk4uwtJSXcrvbG2BBru6nLB6w2idgbDs6ebo30OrN4wclRhDj7y3rw/lGj7yNdKU/sknIxIhk0sbTzDRX/kaZo1SS4W48omto/Kqv2oZxG3P7Zz0+4RjcbiDUXTZA5Oe9/3uEPrcIVi9YXQmPfWxeAJ8Xuql7ZpKIztvq3KU+J/z5k56nNlMvkaKFqsPQmp4bb5+ZQkEPAoqqRAtVi8airVsjQWtTIQuRwB97iAT9UnT6HEFmcLWOhm2tztQqJOixxVEIsFUP+93h5haLElj77qVJVhepke5SYHe5IZAIBKHXCwARTG/OYU6GcxqCfjJDZjT5pjw+REbpFNcV9OZdFnzxyJHJcHCQjWMSjEsziAkQqbIZCiaQF0Rs9GZr5Gy7XANcjH2Jj316+eZcedplQCYa5t4GtbZSmftJ8ukjfr//u//xt133w2z2QytVguNRgOtlvT6nEmmWq2/yxGAViaC1RvG81s78ev3DuGTQ1bs6nSiyxHAeTW52NrmwJF+L+bmqqBXiPHktYtxqM+DDw/048qlRfj9pbWs15eiKKysMLC9so/13bR6QqgwKtiqqZkKl50S3tjVjaaB4U2RYNJzVKiTIRCJp+TED1Gkk7HVmgHGIz8Rj4RJKUGXIzCq8v0cswoLCzTsfY1MCK1MiHl5Kiwv0+OBC+ZjY5MNj13dgE9nIBQ/0zpVjIXVG0Zjtwtv7OrGumojU228TI+qHAVUUgFo0Gw0RDoxUvt5eSps63DgyqVF2NfjRrFejv939SIAzBwTC3lw+iPwhWMY9I1dOC8aT+BPHzcf09OXToxVDKrZ6sOtz+3Ewd7hXrsAsLLCMK1GYLrM+1y1FILjrB//d3EN/ue8uWizjfbU//q9QzM1tClx23M70T0iTWyoL7tUxEevK4jXdnbjB2fNx/w8Fc6YS3Kul5fpcdljm/HUl21sStcjV9RjTq4SbSPC3WmaxmNftCIYjSEQmVx9jUEfUw27yxGY9LzvczPGIp+i0GEPIJGg0ecOsRsQQ6ypMqREoWWrA2iq5Gok2NbhQFWejn2sSC/DP25cCpVEgKYBH9ZWGdl8eq1chGe/6sDrO7vRPujHslI9mq0+aKRCxqjvcGJergotVh/m5TFdNPrdIRgUImxptSNPw6RyPHDBfCwsULMt0ALROPI1UhiVYnz/tEqsKk9t43r18mI8fk1DVn5+6bLmj8X6eTk4fW4OJEI+Oux+5CWvVwwKERYWaEBRFP74rYXs8QalCP5IHAa5GHwexdZAUKZpgcN01n6yTNqoTyQS7C0ej7P/EmaOwcGp9YHutAdwSrURHxzox7Z2xnhvG2Qq+8YTNObmqtDp8DO78MniPxIhH3edUYWXb1sBiZCPC+vyxzy3Xi5C5CiPm1jIYyud0jQT9sajKDbXLZ6g8a9ts98p4USYqvbTwfv7+/E/b+1nL5IO9jGGRIFWilKDfMy8pIocBSpNkzcujEomtEp7VKuRmgI1zl4w3O7DpJRAIxPh+6dXotQgR4FWhqevXwK9Qgy1VAhvaHpD8LnUf7qweUP4yVlz8JOzqqFMFjf80xX1mGNWgaIo5KgkyNOMLj7ENSO1V0mEWFSkwcX1+VBLReDzKIgFw96SM+fl4KODA3h7by++8/zOMaui7+hwIk7To6J80pXvvbiLNeyHenNbHAFYHEwo6UzmBabLvM9RSaCSHvtCbG6uCgvy1Sk5zXstLgQjcTyxsS3lcS5xBSLocQaxs9PJfq7OQBRamQglehne3N2D9fNy4HLYUWZU4OL6Ao5HzD23nVKO17+zEmfMzUGJYXgTeVGRFrs6nez3wuoNo0Arxdnzzfiyefy5+05j76guOzZvGIuKtEy19WPM+0gsgac2teHmf+zAj1/di15XEJ5QDG2DfpQbFehxBfGtx7dgj8WFoqM2vE1KCf73ogVTkeCkQC8XocXqg1qQuiGTp5FCJRXCHYxiXbUJC5NtVfVyET45NIAeVwgH+zyYn6eCwx+BTiGCRiaEJxjF3FwVmq0+1BVqGE+9J4QlJTrs7HSm/N6tLDfg2c0d+PeeHoSjCRTqpMhVS6GWCVkH0khWVRw/8jATSZc1fywWFmrYEHoeRbEbfLeuLUdpcl1YXja8AaOXi1GgkY5yKKkkQujSrEgekN7aT5b0qlZAGJNgcHSruWMx1HO31xXEqnID/vpZC25eU4r183JQpJOBz+OBR1HI1UjQ5wrBE4pNukVIuUkxKq8pTy1FrzuIaDzBhkv97Jw5+NU7B/Hp4QH85dNm/OHDI4gnaPz41b1p58UZi8lqP50kaBpnzsvBjmSxov09bixIFrH561WLxnzNqVVGXLO8eNLvZVCIUoz38TAqxdCOsyjPzVWxld2nCy71ny4GfRGcvcCMhuJhL4hUNGwQ56olowoUpgNHa//q7StRZlRg7hitoE6pMuLLlkHs63Fjfp4a7x/oH3XM50esOK8mF44M8dTv6HCi28lUaD79oS/wq3cOossRwA9Or8R5tbkz+t7pMu9z1ZIJ5a+aVRI2pxYAfvjyHhzodaMmX40Px5gLswHTenHY4bC9w4krlxVhc+sgLntsC2iaZrt9lOjleH1XN+oLtWmjfbowlA40stvKomIt3tjVg/UPb0QiQcPiCKBAK8MZ83LwwYGBlNfHEzS7yffJISv2dLlSnrd5w2go1qLLHjim9p8eHoA7GMXj1zTgtDkmfHigH2aVBI3drmT7xSA67H68ssOCEn1mth3kCoqiYFKKoRePTo1SSQQQ8ilU5yjxo2R1dK1MBAGfB5s3hFabH5UmJTQyIfRyEROCr5dhjlmFVqsPVTlKDPrC6HMzRn0sTsMgH641UqiT4VcXLcChPubawaSUpKSjnSxkyrpjVIhZo/682twxoyYMSvGYXUJUUmHaVb4HMkf7iTBpo57H44HP56fcdDodzjvvPHR0dMzAEAnFxZMz0s55ZBMCkRjiCRqVOQrIRALU5Ktx2eIC/OjMapQZ5SjQSpGvYYxwCpMPRys3KkYZd0wblEBK9fUKkwJ//fYivLevHwqxAOfX5mFHhwN8ikJbBuRgTlb7qfLhgX5saraNenx5mZ5tFbivh7lIBjBuqC9FUVMqACTg8/DLC4/vyZibqxqz8B7znBKH+savr9E84MW/9/SwF9MTYbb0nwm6nQF8mSwkyD/GZ/LktYtTvN7pwtHa83kU+DwKz96wdNSxGpkIrmAUva4grllRzG5EjaTF6sOqCgPs/uP3tW+xevH81s6pD34a8ISiaOx2Y3u7A9evLEGrzYcuRwCnVhvx7WUzOy/TZd4zRv3xL8J4PIrNS08kaFicTJHWq5YVjWp1Olv8a1sX2zMbALa123HJogJsbBpEhz0Ahz/CjrnMyLSWnJOrTBvt05k8tQSBSAyLirRo7HHD4gygUCdDrloKVyCSEoL/xq5uvLWnBwBT0+Do2guDvjAqTAq4gtFjat9q8+OUKiP4PAqLirTY1DyIQp0U/e4wTCox9HIx7jt/HjY1D46ZmkY4NrlqKRqqS0Y9rpIIYVJKUq4rdHIRFpdokaCB9kE/Sgwy5Kml0CeN9SKdDHNzlXAEIjAoRIglaPS7Q1haqkOeRjLqGkWTjAYAaFy8KH/GN03TkUxZd0wq8XEL3+UoxfjuqRWjHldJBNDJ06/IYaZoPxEmbdT/8pe/xH333Yeuri50dXXh/vvvx3e+8x2cddZZuO2222ZijCc9/f0T83TstbjgDkThDETwu/ePQMjnocwgx8OX14GiKLZNxYJ8NapylFBLhXAFophKelJ9kQZnHNVmqzJHiRarD532QEpet0TIxx8uW4ib15ShwqTAhr29mJ+vOmZOfrowUe1PlCP9XhzuG/ZyM54NCnNzVdjf68G/9/TA6g2ntBnhgquWFWHOUcX0hpibqzq2UW/1YXeXCx8eHMCGvb0AgJe2dWFPMp9uLGZL/5mgsduNt/b0HPf7pZSk348cML72421QlOhloGmgVC9HxxiV0GkwkR4TCb9vHvBxGrYdjsWRp5GisduFLW12rCjXQysXYX+Pm92wnEnSZd6XGRUTLiomFwvQ7QwwvayVEry7rw/VZiU0MhGs3tBxX+/wR9gosxNhr8WFfd1uuINR9HuG37d90I9KkwK//uYCXLuiGDs6ndAn041KDQrUFmgg5PPSRvt0hqIovPW9VbhmRTE+PjgAiyOIwqRn7qz5ZnwwIjqjxxXEgWTLQAGPl2LU231hpq5BskvEkPZjtc3rGPSzob45KjEO93thVklhUIhgUkrwx28txAUL85CvkY678UwYnztPqwACozdjVVLhqEiymgI1GxHoC0WhlDDH6JItcP/v4hoU6WRQS4XQK8SYl6fCp4etqDYrcfUYkYRMiD/zu6CSCCETpWfu9UySKevO1cuLUZkzukjzSAR8HtvjfiQqiXBUimc6kCnaT4RJG/VvvfUWHnjgAeTn5yM/Px8///nP8cEHH+D73/8+enp6ZmKMJz3h8NierZEXQKFoHFc//TWarV5ctawIPIrCr79ZAwGfN6qv7jdqc3HFkkJQFMXkQU3hS2ZQiHFKlTHlsWqzEof7veh0pBr1I6kwKfDevj7MzxvbMEw3xtN+uhnwhlJyDZ2BCLQyYdIroYHDH8GT1zbMylimilklQavND1dgbKPN5g2jzx1Ei9WHI8kw/a1t9pQLwKOZLf1nggFPCPu63WC6vGYek9V+eZkeNflq8HgUKAy3t/rzJ834us3OhGcqRHBMwFPf6w7BF5pcwa3pxBNkvJDbOxzY0+VClUmJmnw1+twhtnDoTJJO8/7o9pfj8dOzqnHP643otPtx1nwzbN4wyg0KnDE3B89v6cS/k95afziGXV2jjYenv2w7Zj72RNnf60Zjj4sx6kekBAzVeTltTg7KjHJ8eGAAVcniaTq5CH+/fgmA9NI+naEoCrX5asZTnwy/B4DT5pqwacTnaPWG0TTghT8cg0klZr/Xv//gMK58cis2NtlgVIrBoygEgiEEI3H86NW9o97PPuJahaIolBnlyFUzodo5KjEMCjEoisJLtyxPy8indGdlhQGx6OjfbrGAh5+dk9qNxaAQo75IC41MiFhynT+3JhcLkt1AdMkwfJ1MBINChKuWFiGWYFrt3rymbNR7HN096WQkU9adJSW6KXd1WVmhx9pK4/EPnGUyRfuJMOmrE6/XC5ttOEzYZrPB62Uu0IXC9PQ4ZTplZaMXQQD43gu72f66m1sHIeLz8MlhK8oMCvz8G/PGNdZHhmgrxAKYp6nytkEhht0Xxv5uN3uxdDRDYXZHe3s3tw7intca2cI76cJ42p8I4Vgch/tTPdpWTzjlAtQ6wntx1xlVuGFVadpfqFAUhR+fWY1bn9uJSCwBmzeMe15rZJ+3ecPo94TR6wqiKzlv3cEo68UZi5nQfzoZK41gyJgd8IQRiMbSMtxsIkxW+7Pnm3HHaUzIXblJgRarD75wDG/t7sHDHzeh0qSEXi6G3RdJyXUeiz4Xk8s+23Q7A6BpGp5QFFqZEA9cMB/3nFMNHo9CTb4aBWPkCc4E6T7vx6JQJ0N9oRYfHhzAwkI16go1UMuEOLWaqbfwxi7GqN/b7cI9rzWO+u60D/pTPOtTxReKwRWIphj14Vg8ZTOmRC/HJ4cHUjxOiuTmRSZqzxU8HoUCrRSN3W4Ykl5ag0IMhz+CRIJGJJbAYLIlYpcjgCIdY/gnEjQO93nxxDWLsavLCYOCMf405gLY/WF0jhHpA6SmCc7LVcGsliBXI0WOatiTXETy6afMWHOfoigsKdGNcTTTCm9oM6ehWMteswzBbOSKUaiTsZtmxyYzN8Cng5Nh3SnQylCoS7/vZzZpP2mj/u6778bChQtx00034aabbkJ9fT1++MMfwufzYdWqVTMxxpOerq7RFeND0Ti+ahlEpz2A+/+9H3/4oAk3rynDBwf6x/WSj0WeRoK8aSxKwqMo9HtC7EJ/NHq5CKdUGZO9SCkkEjSCkTj+8kkLqsxKPMdxHu3RjKX9VEgkaDz8URMA4ECvB3/7rDXl+XiCTunfbfOGYVJyG2o/FZaW6nBxfT7e2tODz45Y8dGhAbgDTEX8QV8YoGkMeMLwhWNMrjlFwSAfPzx3uvSfKW5/frgvcygaR4vVh5v+sR0A09bxtGoTTMrMLPozWe15PIrdwb9gYR5+859DuPqpr3H3mVVosfpQlaOAViaEwx/BhY9+dcx+9X2e0KRbY00H//3mfuzrccMTjEIlEaK2QMMWOJyfp8aVS4tmZRzpPu/HY3mZHv/e04MCrQyv3LYCAOPpf+O7q9jWeN2OIPg8Cl+3O1Je2zEYSNnYHOLeN/axG2Vj0TzgxW/fP8ze94VjcPojiCVoOJNRQxZHMCUku8QghysQHXPzOVO154r183KQoOkUg1sq5OOZzR14clMb4gkaRgXTt7pYL0OuRoJeNxOVVmKQ4zeX1EIs4MOkkqCxqQt2XwSuQDQl4ssdjI5qh3Xn6ZVYVWHA/5w3d1wnAmFyTHbu52mkKDOMX7/g6uXFkCeLws5PevGPTQbkZM4QZN3hjmzSftJG/W233YYPP/wQdXV1qKurw/vvv4/bb78dCoUCjz766EyM8aQnFht9cbujw4n5eSpYnAF0OQL45YXzsX5eDtps/kntVOdrZNOaI1qkl2FN5fgtRyiKwjPJHVuFmA9fJIaDfR6sqtBjXbURPc70qkI5lvZTwRWMsgWjOu3+cT0RQ1i9YbYnbKbxzUX5eHNXD97b14cfnF6J9w/0AWDCJ41KMdMdgaLQ6w4iVyPBwkLNuN766dJ/JghF4/jw4ACs3hC8oSgue2wLtrbZ0WL1oc3mgycUxfp55uPmn6UrJ6L9gnw1nrlhKV69fQXOr83D1cuLsSBfDQGfhy5HAE1W76iCWSMJR+PgcdCL2BeO4d97euEJxdjeukNIReO3+Jxu0nneH4tFxRq4AlEUaKVjtvyjaaaI3g9Or2Tragw9LhTwxjTqPz40gF7X+L8L+3rceLexj73vDcXgDETBo8DWbekY9Ke0ZFOIBWz49tFkqvZcsbJcj0saUtv/zc1V4e9ftqNpgIninJenwv/7ohXVZhVqCzT45JCVrQ/zrcVMj2izSgKrLwy7n4lS67QH8EWTDQ9sOIBWmw9lhtR1VCEWgM+jTsr865lisnP/nAVmfHPR+GviNxcVTLgI88lrzjOQdYc7skn7Ka2GCxYswIIFpOfnbFFRkVpFkqZpvLrTgutWlmCPxQWxgI/FJTrE4gloZUIYJ1FM7da1ZRBPY7/lG1eVQiM7drjx0CKvlAjhDcXQZvOhzKiATi6CY5x8bK44WvupMugLIxBhQo47BpliUkPEE0xbJRGfh0AkBplIAKs3NG7IW7ojFvDx47Oq8cjHTbhscQHufnkvEjQTrWBWS9Bq80GjkmCPxYUKowKVJgUO9Hqwrto06lzTpf9087v3D+Oi+nyopUI0WtwwqcQ42McUNPzVRQvw8nYLAGD1MTa40p3p0H4o5PmuM6rYx2IJplXjoT7PuEUXuUIjFaJpwIuFhZqUFl6zTbrO++MhEwlwUX3+mL9BGpkIzkAUXY4Abl5ThpeS3xGASVWZl6uEzZua2+gLxzDoC6PD7h83bLNpwAezWsIa7v5wDK5ABBQFtkhlh92Pubmpc+3Fm5ePaXBkqvZcIRbwcfsp5SmPLSzUoN8TQq8rCAGPwo2rSnH9yhII+DwoxHzc/cpenFuTWuE8RyWGJarDoC+ChiItjgx48eYuptDollY7lpZm5u9hJjHZuT+d7cn4PGrMjcCTBbLucEc2aT/hb9CVV14JAKivr8eiRYtG3QgzR1tbW8r913Z2Y45ZhXVzTNjUbGM9EAI+D7+5pHZS7emkIv6UWqCNR6FONuFq3kqJAN5QFG2DfpQZ5VBJhPAE02vH7Gjtp8qgLwx/Mpy4yxHAvFwVG1446GOq2udpJPjW41vw/v5+tNv8x20bks40FGvxzxuXQiYS4K/fXoSPDjK9i80qCfLUUhTpZPjH5g4sLNSgIkeBZuvY/e2nS/8h9h6j0v5keGlbFzY22XBuTS72WFxos/nxjdpcuINRnFJlxMFjdAHIFKZb+yHMKgluWFXK9iU+2jsbiydAUdSUunIcj067H/t73Oz9A71uOP2pG4lmFWMgqqTceQBnSvvZ4A+XLRzzNyVXzfSy94ZiUEuFyNdI2Zow7cnK5kdH2XcM+lGTrz5mJ4Q2mw/XryzBxmRLUF84hkg8AZpmCnD5wzG02nxs5fQhjq7ozZ4vg7VPF9ZWGvDAN+YjnqChkYnA41EQJDf4ygyKlEr2Q+SoJDjSNQC7L4KGYi3+9HEzrlhaiBKDHO829qGhWMvFn3JSweXc10iFkE2xAFs2QNYd7sgm7Sd81fLjH/8YAPDII4+wjzmdTnR3d6OmpmbaB0YYJpFIIBCJgQIFqYiPxm43bj+1HAqxgKkwbBz+cTxrvpnDkU6OIU99p92PEr2c6XWcZn3uEonpKdw36IsgmPTUu4NRLCzQ4D/7+6GXi2BOVu8tNchhUIjxwtedEPF5s9I6ayYZ2lwa6m8+5KkPxxJYVKyFSSVBfZEWNE2P8tANcbT+4VgcB3s9qC+a2gXeff/ejw13rAZN0+h2BqdUtIVpvZXA23t7cfeZ1fjn5g7wKOCaFcVw+qOgKApmlQTB4xSDS3ema+4fzZPXLoaQT+GpTW2IxhM4508b8flP1kEtZTYDrcl6EgMnWDTNF46xxc+G+LJlEAPuEBbkM/mdT2xsQ7lRge+fXol4gskLLjHIsavTiZVjtOSZLWZKey7JUUtSPtM1lQZ80WSDVibCY1+04mfnzMHWttQ8+/ZBP9ZVm9BmGzbqu+wBdDr8WJOsohyNJ7C4RIs/ftDE3ucli8HmqiXocwfR7QxOeD3NRu1nG4qiIBJQKDcqIBOlGmo8HoW6Iu2YRr3dH4PcF8Y5Nbl4bWc3zq/NA59HweoJn9Re3NmCy7mvlgqPWWcl2yHrDndkk/YTXiUbGph2Wg8++CAWLlyIhoYG3HXXXfjDH/6ATz75ZMYGeLLT2O3CZrsEf/+yHf/azhRzsPvDbH/dQp0M5abMzNkd8tSHookxW2R8fHAAB3rdKY+12XzJHu6zQ3V19bScxz4i/B4ASgwy/Pb9w/jkkBV97hByVBJcWJeP204ph0YmGpWjmOksyFNDLRNiUbEWayoNWFdtYvvcMl7ZsTd0jtZ/V6cLv3v/yJTGQNM0Wq3M/OmwB/Bfb+6b0nnabD6cPd+Mvd1ulBvlEAt52G1xodqswhnzcgAAa6qMMGdoTYQhpmvuH41UxIeAz0MsQaNj0A+jUowXvx4uVNPnDrEG2FQ3+QKRGC7+61ejXm9xBNFhD7D3nYEovm63I56g4Q1FoZYKUaKXY2+3m91k4IKZ0p5LzCoJ2gf9kAiZy47T5pjwn/19+Nf2Lrx6+wqsqjBAKuKnFEhsH/RjTaUBXY4AXtluAU3TuPOlXfjXNgv++OER3PHiLkhFfBgVYth8wxuDwUgcKokQa6uM+MunLaMMyGORjdpzRVWOcsyCrw9+swYVxtTrFr1chJhACrs/ggqTAm/fuRp8HoXT5phwz9nkM5kNuJz7GpkQEtHJ66kn6w53ZJP2k976HBgYgEajwXvvvYcLL7wQzc3NePPNN2dibAQwvd8/auzCjk4nGrsZAzcSGzaCV5TrUW7MXKPeHWQKGg0xMuT2jd3d2NmZ2s/41+8dQsdxisxNJ0eOTM2APJpBXxgKsQC+cAxysQAVJgXWz82BzRdGi9WHyhEbM7+/tBbnLMiciIuJsLRUB7NKgnKjAsvKRntAc1TilDoDAPDXz1pw+PDhlMd2djrQYfcfsxr2eISiCfgjcbiDUWxqtsE/xZZpbTY/zpiXA51chFy1FOfW5KJpwJviFT5zXg5uWlM6pfOnC9M198ejSCfDx4esuGl1KXZ0ONje5X3uIHLVEkhFguNGO7y6wzJmG0y7L4JWm49dM4ewOANwJMPte1xB5GukWFyswx6LE85AFBqZECUGGQZ9Yag4NOpnWnsuMKskeP7rTqxIfv8lQj5WlhswL1fF/p7lqiR47PNWOP0R/PDlPXh5uwUVJgW6HAH86ZNmDHjCmJurwh8uW4g8jRT3nD0Hd55WyUYFDW3iyMV8qKVCrK4woNXmwylVE++NnI3ac8VF9Xm4eNHoDep8jXRUigaPR8Hp8SY7TwhYz7xMJEAlqW4/K3A599UyEWTCk7foIVl3uCObtJ+0UR+NMu2pNm7ciPXr10MoFILPn97dtebmZqxcuRJVVVVYsmQJDhw4MOZxTz/9NCorK1FeXo5bbrmFHVs2IRbwIeIzRdScYxSRu/ecuZx6lE4ElUSIfd2elBBoiqIQiyeQSNBoHvCNyqV0+CPY3+vB01+2p12o/rGw+yLJnNIgDAoR5uep8btLawGAKRg2ooiTRMifVF2ETGBFuR4/XF817vMVJiWaB1KroT/+RSu6PamGd2O3O9nlYfzK6ePhCjLfH5svjK1tdogFU1u3Wm3MJsyjV9WDz6NwxtycUVXRJUI+ctWZnT4x09QXafDqDgvmmFV45Io6/O2zFgBAnyuEXLWU2QQLjb/xEorG8cCGA2P2Nnf4IzinJhdv7u5JeTwYibOFQXd0OLCkRIulpTrs7HTCFYhAK2M89QA4LZSXjeRpJNDKRLhqWTH72O2nlONHZw57Sa5ZUYwyowLX/n0b8jQSvHTLcmhkIrx863Ksm2PE1+12FOllkIr4uHJpEQp1MrYAXo5KjAEPszGolYmglgpBURQeu7oBaydh1BOmD5lIMKnrkwqdCO2D/qz7/SMcH7VUOCpVg0AgTI5JG/ULFizAOeecg3feeQennXYaAoHAtC/At912G2699VY0NTXhnnvuwfXXXz/qmPb2dtx3333YtGkTWlpaMDAwgCeeeGJax5EuXLi4DCvL9VBJhGzP72xAKRFgc+sgFhZo2Mc0UiHcwSgO93txSpUR3Ue1uPOH4/i6zY7f/ufwMYsnTRfTFn7vj6BAK0WvK8Re5FAUBZVEgAFPaFTubzYyVAl9LCpNTLG8QV8YNE3DFYhAIuTDEmM8NNHkRk8knsCyUj12T6HgnSsQhVTIh83LpEIc7wKi1ebD4IiQ3u+9uAv2ZCXuIr0MK8uZyvYSIR//de7cSY8n3ZnpkLT6Qi3aBv2oMCmglAjZImm9SU+9UiKA96hoiic2tuJQsgjhJ4esyFFJYHGMbnfm8EewvEzPto60+8L4qmUQAHPx6ApE8MGBfpxSZcTCQg32WFxwBaJQy0SQiwXI10jZMHEuyKZwwCH0CjFeu30F+CM8tEdXvC7Wy3FRfT7uPrMKt59SzrZn1SvEKDUo8EWTDUXj1MGoMCnZgpsamQjqZKHDAq3smGvP0WSj9pnCjactgJQYdpzB5dyfa1aipmAiveyzE7LucEc2aT/pq5Znn30Wt912Gz777DPIZDI4nU48+OCD0zYgq9WKHTt24OqrrwYAXHLJJbBYLGhpaUk57rXXXsMFF1wAs9kMiqJw++2346WXXhrznOFwGB6PJ+WWScyVenDdyhLUFqjxdfvUPYzphlIixOF+L2pHLORaOdP2aLfFiaWlulH583kaCd7b14f6Is2o0PyZ4ETDchIJGru6nEgkaKikQvS5gymeizKjAnr5xFsQZiuVOQo0W3246dntONDrQac9gIsX5ePTfV1wB5ge8M1WH6pylFhSosVnh62Tfg9XIIpykxyN3e7jdhYIx+K49/V9+O1/mPD/A71ufHrIik5HANE4nTXfwWMx0yFpxXoZVpTpIU9uaEmFTD71gCcEs1qS4qn3hWN48esufNFkw7+2Mfn3Hx7sx01rSmFxBkad2+6PQC8XQcjnIRyLY1eXC//z1n5IRXwU6+XY1u6AgMeDXiGGQiyAPxyHMxCBJvnd/P7pFZx6C7MpHHAkE9V0XbVpVBeVUoMMG5sGUawbOz++0qRA04APAAWtTDjl9Ils1T4TiDt72Ag2wuzD5dyvzFFi+RipeScLZN3hjmzSftJGvUQiwUUXXYTSUiZfND8/H2efffa0DchisSA3NxcCAXOhR1EUioqK0NXVlXJcV1cXiouHw/hKSkpGHTPEgw8+CLVazd4KCwunbbyzwVAhsbpCDT45ZIVxjMIzmYhSIoBSLGDDXQGmWIozEEGXI4DiZEX8oYqo0XgCQj4PZUYFfnp2NZuDm8488PYB/PLtgxj0hSETCdDnDqWE9ZYbFZiXl169urnArJLgYK8HkTiNf+/pQacjgOocJYR8Cq/t6kar1YePDw2gIVk1v0gvw6ZkC6uR7Ot2sznTR+MORlBpUmJLmx2VJgUoavxCbK/u6MaVywpBgynW9eqObtywqgQ9ztFeYcLUoCgKL9y8jL1fapCjfdDP1gwZqkEBAH/+pBnhWBxPXrsYbYN+ROMJtovE0dE8AODwh6GTizDHrERTvw+ddj9UUiEKtTLUFKjw1KZ23Li6NOW9d3U5oU32Xb58SdEM//WEyVKil2PQFx7XU1+Zo0BjtwsyER/lJsWUOlsQuGd+3snrrSUQCIQT4aToEXLvvffC7XazN4vFwvWQJsVQaMj8PDU2NttgUIg4HtH0oJIKUVekSSmYo5OJ4PRH0O0MokArRYFWyl60uwJRaGUiPHvDEjQU60b1t54JTiQsZ6ht2j9vWopzanIhE/HRNyL8HgDOmp+DW9aUTcdQM5ohD95Nq5n+5W02H4r1Mly3thp//PAIbl5Thmc3d7C9iq9cUoTPDo826t/c3YM9Fid2dzkROqrImisQRYVJgR0dDlSYlMcsxPZl8yDWzzNjWakOh/s86HEFsbrSgP09btbwy3ZmIyRt5He/1CBHm82PoX0WhUQAbyiGeILGoT4Prl9ZAplIgFOqjHhjVzcMCjGzPjgCoyJ67D7GU78gX43GHhcsjgB+ccF8XNpQgNPm5OCV21egrlDDHn/m/Bz8e3cvNLL0yKPPpnDA6aJQJ4NeLoJ6nM/IrJKgxeqDQiLAuTW5WFKim9L7EO25g2jPLUR/7iDac0c2aZ92Rn1hYSH6+voQizEeGpqm0dXVhaKiVM9JUVEROjs72fsdHR2jjhlCLBZDpVKl3DKJodAQqYgPrUwEgyI7PPUKsQBPXrs45TGNTARngOnpLhcLUJ2jxOFkDq0zEIFOIWLDMvmz0Nf+RMJynMlNCJVEiNtPKWeMek8o5aJUwOeRHMIk59aYcWq1EefV5uIfmztQpJPDGLfjtrXlOLfGDIVYwM59s1qCfs9oD22vK4gBTxjPfNWB/T2plc9dQcaoD0TiqMxRQCHmj1mILRJLIBpPQCEWoEArQ7czCJqmUaSTYWu7A4W6k6MA3myHpJUZ5Wga8LI510qxAP/Z34cbnt2OFeV6duPngoV5eOijJtQVaqCWCmH1hnHunzelrAV2fwR6hRgLCzXY3eVCjyuEBXkqVIzT/nNpiQ4iAS9tjPpsCgecLoR8Hu45Z864zzO90XlQnmB9EqI9dxDtuYXozx1Ee+7IJu3Tzqg3mUxYtGgRnn/+eQDA66+/joKCAlRUVKQcd8kll2DDhg3o7+8HTdN47LHHcMUVV3Ax5BmHxxv+mBYWarIm/B7AqP705UY5mgZ8GPLfNRRr2dx5hz8C3QgvqVIihOcY1bGng5HaT5ZOux/F+uEQULlYgD5XMGO7Fcw0t64th0EhxmUNBagp0MCgEEEo4OMHZ1SiwqTAg9+sYY+VCPkIR4dbmTUPeGFxBNDnDqLfHUKvK4jWZIX8odZ1rkAUJXo5tDIhTEpxSnj3SHZ3OVFfpAEAFGilbM62WSXBoT4PCrQnR1jvicz9qVBuUuDLlkGcPtcEgPl+t9n8ePhbC3H72nL2OJNKgjlmFeqLNKAoCnZ/BIO+CJwjioi6AlFopELkqCQY8IQQTyQgOEaxNAGfh/86d27abJjOtvaZwrcWHzt1rtKkOOGio0R77iDacwvRnzuI9tyRTdqn5V/y+OOP4/HHH0dVVRV+85vf4JlnngEA3HzzzdiwYQMAoKysDL/4xS+watUqVFRUwGg04rbbbuNy2DNGWdlwePaNq0rYEORspMKUzItMXpiVGxWsceb0R6CVDxv1JqUYNm9qCP7RIdcj8YVjbH7+RBmp/UR4b18f+x5djkBK/qdMxEe/O0SM+uMg4PPwzxuXgqIoVn+KokYV0RlZc+vRz1rwwYF+xGkaVm8IrmAUrTam8vm3n/oaTQNeuIMRaOVC3HVGFSiKgkIsHNOobxrwYn4+k9eZq5bgcJ8XKqkQAj4PRoUYhdqTw1M/2bl/oqgkQrz53VVsPvuyMh2ev3kZ9ArxqJ7Wj13dwObe3nN2Na5aWojulIJ5NPuaOWYlYonjR/Rc0lAwapORK2Zb+2yh0qSEQnJiRj3RnjuI9txC9OcOoj13ZJP2aWnUV1dXY8uWLWhqasKOHTtQU8N46J566ilccMEF7HG33HILWltb0draiqeffhpCYXYaSyMr/1fmKKHJ4pxeiqJQqJOxhhMv2fIoFI3D7o9AJx/+jI1KMaye4ZZjDn8Ed7+yJ+X+RwcH2LDcv3/Zjk8ODUxqPEd3XTgWnXY/7n5lD9qSxmSXPcC2ZAKYnr3ecIwY9ZPgWPpTFIV4goYvHEO3M4jGbjfMKikGPGEUaKXoSBZdC0XjeHZzB9OyTCrEdStLADA522OF33e7guz8E/B5sPnCKEhWy8/XSlFwkhTgmszcnwmEfN6435WRKSunVptQpJeztTc2NtkwMivn1GpTSsRMJsC19pnKKdXGlFoJU4Fozx1Ee24h+nMH0Z47skn7tDTqCakMdQI4WTh7vjmlInxNvhr7e9xw+iPQjWj/ZlSKYfUOG/UWRyDFyP/0sBWv7LDghme3o9Pux5EBLwZGPD8W1qM8/xPR/vWd3djX7cbDHzXh1rXlONzP1AA42lMvF/EhEvDSxhuYCRxLf4NCjEFfGJ8fseKyhgIc7vegQCtFnzuEfI0UsQSNw/0erJ+Xg0A4hmarL6UVnULMH9UHHQC6nUHkjWh5l6MSIz9p5N91RiXMKsk0/oXpSyatO/kaKXqcQURiCfzs9cYUz/zKcj3+69y5HI5u8mSS9ulEVY4S9UUnFslGtOcOoj23EP25g2jPHdmkPTHqM4DxCgBmK2fON+P82jz2/vx8NQ70euAIpObUm5SSFCPc4gyktDPb3eXE/d+Yhx+tr8ZL2yxotfow4GGOb+x2jRmqf+3T20DTNF74minCaMzNP+54v2iy4WdvNEIlFeLcGjMO9XlB0zR63UHoR6QLyMQC4qWfJMea+2aVBP3uEBq73VhUrEWOSoJctQQiPoU8jRRCPoXNrXbUFmjwq4trcGqVMeX1CrGQzbcfSSgSh0w0vMgXaGVsHv3KcgNbyC3byaR1h+mSEYDFGcC5Nbl45vol7HMURaV8nplAJmmfbRDtuYNozy1Ef+4g2nNHNmlPjPoMoK2tjeshcMr8XBUO9LrRaQ/ArB72kppUTPh9IMIYZhZHMKVwXo8riHyNFPPzVNjf44ZKImQ99f/v81a0DzJh8i9+3YVAJAaaptFm82NHpxPPbemEOxjFdU9/nTIWdyCKB987hB0dDvYxfziGH55RhR+cXokyA1MD4IENB3BuTS5bsRtgPPWqE8z3PNk41tzPVUvQ5w6h1epDmUGOOWYlcjVSmFQS5GkkuHVtOR7/ohW1BWooxAL8z/nzUl6vkAwXykskaHhD0bHeBpcsKsCCk7B3ciatO0Nzod3mR4lBPioHP9PIJO2zDaI9dxDtuYXozx1Ee+7IJu2JUZ8BiMXpUZGZK0wqCdpsfkiT4evs40oxjgx4cd6fvwQAdDsDyFVLEIsnEIjEIBXyQVEUeDwKcjEf9cUauAKMJ//IgBeeIGPEvb6rGy9ts8AbjiGWSOCV7RY4AxE4/BEcsoXhD8cw4AnhcL8HnzdZYfWGsa3DgS47ExlAAzhjXg70CjFEAh4GPEwxvG8vK075O4infvIca+6b1RJ0OfxI0DQEfB4uX1KEVeV65KjEyFNL0VCsxZf3nIacccLlFWKmDzoAfHxoAN99YRdC0TgkR7UYXFGuH7c3djaTSeuOgM9DLEGjw+5HmUHO9XBOmEzSPtsg2nMH0Z5biP7cQbTnjmzSnrgNMwCz2cz1EDhHLhbglMqjw6cF2NnphEYqRL87hAFPGNVmJVzBKHqcQVSbleyxp1aboJYK0TLgQygaR6c9AHfSqFdJBPj08ADWVRtRU6DBu/v6oJWJ4PBHoJWJ8PzWTnx8aAClBjnyNTKcMTcHX7fb8fqubtA0DeVR3vefnz9vzGJNMiGfGPWT5Fhzf2mpDg9/3ISaZKX6oR7kN6wqRX4yJ15+jPZWQy3t3MEoPj1shUoqxPNbO9nXnuxk2rqjl4uwqXkQ59bkcj2UEybTtM8miPbcQbTnFqI/dxDtuSObtCee+gygs7OT6yFwzrUrirF+Xk7KYxRFYW2lEd9ZV4E9FidomoZBIYbDH0GPK5jST/yKJYU4ZwHzxW21+VCsl7Gh+nweBYNCjA67H6vK9UjQNGoL1Oh1BbGyUIznv+7EY1c3wOYNo9Phx5JSLQY8IVicAby+qwfF+lTP4OIS3Zg9sZUSwXH7LBNSOdbclwj5+Pn580YZceVGxYSKESokAry1uwcX//UrdDuDePCbNTjY58H8EUUaT2Yybd25pKEAX7fbs6KQYaZpn00Q7bmDaM8tRH/uINpzRzZpTzz1GYBUSjyHp8/NGfPxx65pwJF+Lx75uAkiAQ86uZAx6p1BzM8fNs6GctvFQh4au91YUqyDOxhFPEEzbfS0MuzpcqHEIMd/nTsXLVYf2mx+LCpQ4XffrmGNRFcgCpNSglichisQRaFOiuIJtjgT8Hk4Jwu8iLPJ8eb+iVS6VkkE0MhE+N0ltTjU74FKIsRD36qb8vmyjUxbd5aV6nDr2vKMz6cHMk/7bIJozx1Ee24h+nMH0Z47skl74qnPAAwGA9dDSGsqTQrEEjQeuGA+dPIRnnrNaGPbpJTglR0WrKkywBOMwh1kepcX6WTYbXHBoBDh2hUl0MhEaBv0oShHxxr0JqVkVDG1SxYVoLbg5CuiNlvM5NxXSoR4587VqClQkwiKMci0dYeiKNy9vorrYUwLmaZ9NkG05w6iPbcQ/bmDaM8d2aQ9MeozAIvFwvUQ0hoej8KT1y5GjkrCeur73MGUSvlDLCrW4ubVZajN18AdjMIZiEAnF6FAJ8UeiwsGBVMwQysTotXmQ8g9yL62wqRgi64J+TwI+RQuW1yIyhzlqPchTA8zPfdPlvZ0U4GsO9xBtOcOoj13EO25hejPHUR77sgm7Un4fQagUCi4HkLGoJOLsb/Hg1icTqmUP8QFC/MAAK5ABJ5QFM5kMbxCrQzeUAx61qgXod3mR65+OFy+IkfBFtczqyXEIJwFyNznDqI9dxDtuYNozx1Ee24h+nMH0Z47skl7YtRnACoVKdw1UXTJqvXHQykRwhuKJSvcC5GrlkDAo6CXiwAAGpkQ/kgc+cbhnO1TKo1YXqoHwPTFFo+xaUCYXsjc5w6iPXcQ7bmDaM8dRHtuIfpzB9GeO7JJe2KVZAC9vb1cDyFjyNVI0DTghVR07OrnfB6FRIIpdqeViyDg81CVo2Tz57UyEVQSAaz9fexreDyKPe8p1UasrTKOeW7C9EHmPncQ7bmDaM8dRHvuINpzC9GfO4j23JFN2hNPfQaQTbtIM42Qz8PvLq1Fs9V33GMpCnAEIig1Mi3p/t/Vi9jndHIRdHLRuNrPMZPPZDYgc587iPbcQbTnDqI9dxDtuYXozx1Ee+7IJu3TylOfSCRw5513ory8HBUVFXj00UfHPbakpATV1dWoq6tDXV0dXn755Vkc6ewik02sZRqBoUArw7pq04SOHcqpB5DSb14jE0IrFxHtOYbozx1Ee+4g2nMH0Z47iPbcQvTnDqI9d2ST9mll1D///PM4ePAgmpqasG3bNvz+97/HgQMHxj3+5Zdfxp49e7Bnzx5cfvnlszjS2aW/v5/rIWQtQzn1R6MQC3B+bR7RnmOI/txBtOcOoj13EO25g2jPLUR/7iDac0c2aZ9WRv3LL7+MW265BXw+HzqdDpdffjleeumlEz5vOByGx+NJuWUSWq32+AcRJg2fR6HPHWI99SOhKAo3rS4l2nMM0Z87iPbcQbTnDqI9dxDtuYXozx1Ee+7IJu3Tyqjv6upCcXExe7+kpARdXV3jHn/ttdeipqYGN910E2w227jHPfjgg1Cr1eytsLBwWsc90wiFoz3JhBOn1KDAD9dXgXeM1nREe24h+nMH0Z47iPbcQbTnDqI9txD9uYNozx3ZpP2sGvUrVqyAwWAY82axWCZ1ro0bN6KxsRG7du2CwWDAddddN+6x9957L9xuN3ub7HtxjdVq5XoIWcnPzpmDhuJj79AR7bmF6M8dRHvuINpzB9GeO4j23EL05w6iPXdkk/azWv1+y5Ytx3y+qKgInZ2dWLFiBQCgo6MDRUVF4x4LMDssd911F6qqqsY9r1gshlgsnuKouUev13M9hJMWoj23EP25g2jPHUR77iDacwfRnluI/txBtOeObNI+rcLvL7vsMjz55JOIx+NwOBx4+eWXxyyA5/f74XK52PsvvfQS6uvrZ3GkBAKBQCAQCAQCgUAgcE9a9am/5pprsH37dlRWVoKiKNx9992oqakBAGzYsAEbNmzAU089hYGBAVxyySWIx+OgaRplZWX45z//OeH3oWkaADKmYF5nZyfkcvnxDyRMO0R7biH6cwfRnjuI9txBtOcOoj23EP25g2jPHZmg/ZC9OmS/jgdFH++ILKS7uzvjiuURCAQCgUAgEAgEAuHkw2KxoKCgYNznT0qjPpFIoLe3F0qlEhQ1fuXzdMDj8aCwsBAWiwUqlYrr4ZxUEO25hejPHUR77iDacwfRnjuI9txC9OcOoj13ZIr2NE3D6/UiLy8PPN74mfNpFX4/W/B4vGPudKQjKpUqrSdcNkO05xaiP3cQ7bmDaM8dRHvuINpzC9GfO4j23JEJ2qvV6uMek1aF8ggEAoFAIBAIBAKBQCBMHGLUEwgEAoFAIBAIBAKBkKEQoz7NEYvFuP/++yEWi7keykkH0Z5biP7cQbTnDqI9dxDtuYNozy1Ef+4g2nNHtml/UhbKIxAIBAKBQCAQCAQCIRsgnnoCgUAgEAgEAoFAIBAyFGLUEwgEAoFAIBAIBAKBkKEQo55AIBAIBAKBQCAQCIQMhRj1aUxzczNWrlyJqqoqLFmyBAcOHOB6SFnL97//fZSUlICiKOzZs4d9nHwGM08oFMJFF12EqqoqLFy4EOvXr0dLSwsAwGq14uyzz0ZlZSUWLFiAjRs3cjza7OTMM89EbW0t6urqsGbNGuzevRsAmf+zyTPPPAOKovDWW28BIHN/NigpKUF1dTXq6upQV1eHl19+GQCZ97NBOBzGHXfcgcrKStTU1ODqq68GQLSfDex2Ozvn6+rqUFVVBYFAAIfDQdadWeC9997DokWLUFdXhwULFuAf//gHALLmzwbvv/8+Fi9ejNraWixfvhx79+4FkEXa04S0Zd26dfQzzzxD0zRNv/rqq/TixYu5HVAW88UXX9AWi4UuLi6md+/ezT5OPoOZJxgM0u+++y6dSCRomqbpv/zlL/Qpp5xC0zRN33DDDfT9999P0zRNb9u2jc7Pz6cjkQhHI81enE4n+/833niDrq2tpWmazP/Zor29nV6xYgW9fPly+s0336Rpmsz92eDo9X4IMu9nnrvuuou+44472HW/r6+PpmmiPRf8/ve/p88//3yapsm6M9MkEglaq9XSe/fupWmaWfvFYjHt8XiI9jOMw+GgdTodvX//fpqmaXrjxo30/PnzaZrOnnlPjPo0ZWBggFYqlXQ0GqVpmlkIcnJy6ObmZo5Hlt2MvMgjnwE3bN++nS4uLqZpmqblcjl7sUfTNL1kyRL6o48+4mhkJwfPPPMMvXDhQjL/Z4l4PE6ffvrp9I4dO+hTTjmFNerJ3J95xjLqybyfeXw+H61UKmm3253yONGeG+bMmUPWnVkikUjQOp2O/uKLL2iapum9e/fSeXl5dDgcJtrPMNu3b6crKytTHlMqlfTOnTuzRnsSfp+mWCwW5ObmQiAQAAAoikJRURG6uro4HtnJA/kMuOFPf/oTLrzwQtjtdkSjUZjNZva5kpISov8Mce2116KwsBD33XcfnnvuOTL/Z4mHHnoIq1atQkNDA/sYmfuzx7XXXouamhrcdNNNsNlsZN7PAq2trdDpdPj1r3+NxYsXY82aNfjkk0+I9hywefNmOJ1OnH/++WTdmQUoisLLL7+Mb37zmyguLsbq1avxj3/8A16vl2g/w1RWVsJut2Pz5s0AgA0bNsDr9aK9vT1rtCdGPYFASBt+/etfo6WlBQ8++CDXQznp+Oc//wmLxYJf/epXuOeee7gezknB/v378frrr+N//ud/uB7KScnGjRvR2NiIXbt2wWAw4LrrruN6SCcFsVgMnZ2dmDdvHnbs2IE///nPuPzyyxGLxbge2knH008/jWuvvZbdSCHMLLFYDL/61a/wxhtvoLOzE5988gmuueYaMvdnAbVajddeew333nsvGhoa8OGHH2LevHnw+XxcD23aIN/iNKWwsBB9fX2IxWIQCASgaRpdXV0oKiriemgnDeQzmF3+8Ic/4I033sDHH38MmUwGmUwGgUCA/v5+dge1o6OD6D/DXHfddbj99ttRUFBA5v8Ms2nTJnR0dKCyshIA0N/fj1tvvRW/+MUvyNyfBYb0FAqFuOuuu1BVVUXW/VmgqKgIPB4P3/72twEA9fX1KC0tRWdnJ9F+FvH5fHjllVewfft2AIBeryfrzgyzZ88e9Pb2Yu3atQCAJUuWoKCgAI2NjUT7WWDdunVYt24dAKZYp9lsxqpVq7JGe+KpT1NMJhMWLVqE559/HgDw+uuvo6CgABUVFRyP7OSBfAazx0MPPYSXXnoJH330ETQaDfv4ZZddhsceewwAsH37dvT09OCUU07haJTZicvlQm9vL3v/rbfegl6vJ/N/FvjOd76Dvr4+dHR0oKOjA8uXL8cTTzyB73znO2TuzzB+vx8ul4u9/9JLL6G+vp7M+1nAYDDg9NNPxwcffAAAaG9vR3t7O1atWkW0n0VefvllLFy4EHPmzGEfI+vOzDK0aXjo0CEAQEtLC1pbW1FdXU20nwX6+vrY///v//4vTjvtNFRUVGSP9pxm9BOOyeHDh+nly5fTlZWVdENDA93Y2Mj1kLKWW2+9lc7Pz6f5fD5tMpno8vJymqbJZzAbWCwWGgBdVlZGL1y4kF64cCG9dOlSmqZpur+/n16/fj1dUVFBz5s3j/700085Hm320dHRQS9ZsoResGABXVtbS59++uls8TAy/2eXkYXyyNyfWVpbW+m6ujq6pqaGXrBgAX3BBRfQ7e3tNE2TeT8btLa20qeeeiq77rz22ms0TRPtZ5MVK1bQf//731MeI+vOzPPiiy+y837BggX0Cy+8QNM00X42uPnmm+nq6mq6vLycvvrqq9nOP9miPUXTNM31xgKBQCAQCAQCgUAgEAiEyUPC7wkEAoFAIBAIBAKBQMhQiFFPIBAIBAKBQCAQCARChkKMegKBQCAQCAQCgUAgEDIUYtQTCAQCgUAgEAgEAoGQoRCjnkAgEAgEAoFAIBAIhAyFGPUEAoFAIBAIBAKBQCBkKMSoJxAIBAKBQCAQCAQCIUMhRj2BQCAQCAQCgUAgEAgZCjHqCQQCgUAgEAgEAoFAyFCIUU8gEAgEAoFAIBAIBEKGQox6AoFAIBAIBAKBQCAQMhRi1BMIBAKBQCAQCAQCgZChEKOeQCAQCAQCgUAgEAiEDIUY9QQCgUAgEAgEAoFAIGQoxKgnEAgEAoFAIBAIBAIhQyFGPYFAIBAIBAKBQCAQCBkKMeoJBAKBQCAQCAQCgUDIUIhRTyAQCATCLGAwGNDR0THl12/YsAE//OEPp29ASX7+85/jhRdeGPO5Rx99FNdff/2kzkdRFFwu15jPlZSUsP8/9dRTUVpairq6OlRUVGD9+vV49913J/VeY3HuuefiyJEjJ3weANi3bx/Wrl2LOXPmYMGCBbjxxhsRDAbZ50f+PQTC/2/vvsOjqvI/jr9nJsmk9x6SUELoISEJRZqoKDbEShUpKuqqa2f9rQXZXVF37WUVQUAQBQFBhRW7oCAECCXUFNJJD+llyv39ERmNoSpkTpLv63nyPOTOnXvPzCd3hu89554rhBD2IkW9EEII0QaMHTuWl19++bxvd+7cuUyePPm8b/dsvPzyy+zevZu0tDT+/ve/M3PmTNasWfOntrlhwwZ69OhxXtrn7OzMG2+8waFDh9izZw81NTU8//zz52XbQgghxPkiRb0QQghxAXz66af06tWLmJgYHnvssWaPpaamcvXVV5OYmEhMTAxvvPEGAP/617+49957betVV1fj6+tLcXExixcvZty4cbbHFi1aRGxsLP379ychIcE2CmDjxo0MGzaM+Ph4Bg4cyHfffXfadk6bNo1XXnkFgKqqKsaPH0+PHj0YNmwY+/bt+0Ov/T//+Q9xcXFER0c3GwUQEBBwyudcfPHFzJkzh3nz5jXbzsCBAxkwYABjxowhKyuL2tpa/Pz8KCgosK03Z84c2yiGzp07s3v3bgDy8vK46aab6NevHzExMTz55JO213nHHXcwcOBAYmJiuPPOO2lsbGzRpu7duxMTEwOAwWAgMTGx2WiL070eIYQQorU42LsBQgghRHtTVFTE9OnT2bx5M71792b+/PmUlpYCYLFYmDhxIsuWLaNnz57U1tYyePBgBg0axNSpU4mPj+fFF1/EaDTy8ccfM2rUqBbF4/fff8/cuXPZsmULISEh1NbWApCRkcGcOXPYuHEjnp6epKWlMXz4cDIzMzEajWds99y5czEajRw6dIjKykpbuwAOHz7M+PHjT/q8uLg4Fi1aZPtdp9ORnJxMRkYGCQkJDB06lM6dO5OUlHTa/Q8aNIiHHnoIgOXLl3P48GG2bt2KwWBg6dKl3HPPPaxfv54bb7yRZcuW8cgjj6BpGkuWLOHTTz9tsb0pU6Zw+eWXs2rVKgCKi4sBePjhhxk+fDjvvvsumqZxxx138Oqrr/Loo4+esm01NTUsWLCg2UmHM70eIYQQojVIUS+EEEKcZz///DMxMTH07t0bgJkzZ3LfffcBTcXx/v37mTBhgm39qqoqDhw4QGJiInFxcXz66afcfPPNLF68+KSF5vr167n11lsJCQkBwNXVFYAvvviCtLQ0RowYYVtXr9eTnZ1N9+7dz9jub775hpdffhmdToeXlxeTJk0iPT0dgB49eth6wM/k9ttvB6Br166MGDGCTZs2ndX155qm2f69du1akpKSiI+PB5pOhpwwffp0br/9dh555BG+//57/Pz86NevX7NtVVdX8+OPP7Jx40bbshMnR9auXcvWrVt56aWXAKirq8NgMJyyXY2NjYwfP57LL7+c66+//oyvQwghhGhNUtQLIYQQF5hOp7P9W9M0fH19T1kgz5gxg0WLFhEfH09aWhpjxow56/1omsbo0aNZvnz5n20y0Lzd59JTf7rtnE5SUhJ9+/YFml7L448/zp133tlivSFDhmC1Wtm+fTuLFy9m+vTpZ7X9EzRNY/Xq1URHR59xXZPJxPjx4wkJCeHVV189p/0IIYQQrUGuqRdCCCHOsyFDhrB3714OHToEwHvvvWe7ZrtHjx54eno2K4LT0tIoKysDYNy4cSQlJTFv3jymTJmCg0PL8+/XXnsty5Yt49ixYwDU1tZSW1vLFVdcwddff83evXtt627fvv2s233ZZZexaNEiNE2jsrKSDz/80PbYiZ76k/38vqA/8XtmZiabN29m+PDhZ9z35s2bmTNnDrNnz7a9D2+//bbtfTGZTCQnJ9vWnz59Oq+//jrr169n0qRJLbbn7u7OiBEjePHFF23LTgy/HzduHM8//zxmsxmA8vJy0tLSWmzDbDYzYcIEfH19mT9//lmfnBBCCCFak/TUCyGEEOdZQEAA7733Htdffz1OTk6MGTMGPz8/ABwcHPj888954IEHePnll7FYLPj7+9t6141GI7fccgtvvfUWBw8ePOn2R4wYwdNPP80VV1yBTqfDycmJVatWERUVxfLly5k1axa1tbU0NjYSFxd31j33Tz75JLfffjs9e/YkICCAYcOG0dDQcM6v32KxEBcXR01NDa+99toph94/+OCDzJkzh5qaGiIjI3n33Xe55pprAJg8eTKlpaWMGjUKaCqwZ8yYQVxcHAC33norERER3Hjjjfj4+Jx0+0uXLuW+++6jT58+ODo6ct111/HMM8/w8ssv87e//Y3Y2Fj0ej0ODg688MILREVFNXv+ihUrWLNmDTExMbb9Dh06lDfffPOc3xMhhBDiQtFpv72ATQghhBBCCCGEEG2GDL8XQgghhBBCCCHaKCnqhRBCiHZuw4YNxMbGtvhZsWKFvZsmhBBCiD9Jht8LIYQQQgghhBBtlPTUCyGEEEIIIYQQbZQU9UIIIYQQQgghRBul/C3t7r//fj799FOysrJITk4mNjYWgNTUVG677TZKSkrw8vJi8eLF9OnT56y2abVayc/Px8PDQ+45K4QQQgghhBBCOZqmUVVVRWhoKHr9qfvjlS/qb7rpJh577DGGDRvWbPmsWbO48847mTZtGqtWrWLatGkkJSWd1Tbz8/MJDw+/EM0VQgghhBBCCCHOm5ycHDp16nTKx9vMRHmdO3dm7dq1xMbGUlRURFRUFGVlZTg4OKBpGiEhIfz4449ERUW1eG5DQwMNDQ223ysqKoiIiCAnJwdPT8/WfBnKOnLkCNHR0fZuRocnOahBclCD5KAGycH+JAM1SA5qkBzUIDm0jsrKSsLDwzl+/DheXl6nXE/5nvqTycnJISQkBAeHpubrdDoiIiLIzs4+aVE/b948nnnmmRbLPT09paj/hbOzs7wXCpAc1CA5qEFyUIPkYH+SgRokBzVIDmqQHFrXmS4Z7xAT5T3++ONUVFTYfnJycuzdJOWc7GSIaH2SgxokBzVIDmqQHOxPMlCD5KAGyUENbTWH3PJaiqsazrxiG9Mmi/rw8HCOHTuG2WwGmiYQyM7OJiIi4qTrG41GW6+89M6fXEZGhr2bIJAcVCE5qEFyUIPkYH+SgRokBzW01RzMFitmi9XezThvVM0hJa+C2kZzs2WapvHupgxW7czl4ZV7eHzNXqzWNnEF+llrk0V9YGAgAwYMYNmyZQCsXr2aTp06tdkzRiqwWtvPh0xbJjmoQXJQg+SgBsnB/iQDNUgOalAxh6p6E/d8sJNTTVWmaRp/Wb6LD5NOP1q43mS5EM27IFTMYenPWczflMHkBdvIO15HVmkN24+W8cORYvKO11FvsvDW5AGMiA5g+fZsezf3vFJ+orxZs2axfv16CgoK8PPzw8PDg7S0NA4fPsy0adMoLS3F09OTRYsW0a9fv7PaZmVlJV5eXlRUVEiv/S9MJhOOjo72bkaHJzmoQXJQg+SgBsnB/iQDNUgOalAlh28PFRLTyRt/dyP//T6dbw8Vct8l3RkRHdBi3be+T8Ns0UgvrubVCXH8lFbCkK5+6PW/XiddWFnP1a9tZsP9wwn0dGZreimf7c3n2ev78cG2LIZ09aNrgDuapilxS25751DTYOa1b1N58LJonB0NbEkvYUVSDq+Mj+XAsUre35JFQWU9Br2O0uoGlt0+CA/npvZarRqV9Sa8XZ3s1v6zdbZ1q/JF/YUgRX1LKSkp9O3b197N6PAkBzVIDmqQHNQgOdifZKAGyUENFzqH4qoGnB31tgLwZDanFvP+1iyq683cGN+JT5JzeW1CHLNX7+PdqfGs2pnLj2klPHdDDEmZZXy+N5/nb4zhtkVJzB3bh3s/3EWghzPPjO3D4i2Z7MutwNnJwGW9AimuamDiwAge+XgPXQPc8HB2pLCynqp6M41mKz6ujrw8PtZW2Fc3mLFqGp6nae+5yCmrpZOPyxlPHLTm8WCyWHE0NA0wX7Mrl/V7j2G2aiR29iG9uIar+4WwZGsm829NwMXJAMD9HyYzpJsf18eFkVZUTd+wU88cr7KzrVvb5Oz3QgghhBBCCHG+/XvjIXzcnHj8yl4AfLYnn0t7BfLD4WI+TMrBoAMfNydemxBHdYOZ7UfLeOLq3vi5Gxka5cf9H+2mrtHC9KGdmbVsJ0EeRv4xri86nY5wHxfe+C6NJ67uTYiXM0+tS2FwVz/euTWe3bnHuTg6gCkLt7Ezq5x5N/TDz93Iox/v4aVbYm3F6n+/T+eBFbvJLK0lJsyLI4VVhHg58+wN/fjn+oM8PDoaP3ej7fX8tmc/73gd1fVmugW48c6mDGaN6IqD4dersStqTYx/ZysPXd6Dm+Jb3hP9cEEVTg56uvi7nfX7abVqvPV9Gvde0p2fM0oJ83Yh3Ne1xXoNZguHjlXRP9wb+LWQL6qq55rXfuT9mQPxdnFi3e58Xh4fS2l1A92DPPjqQCHZZbX85+b+tvcI4JXxsbaREG21oD8X0lMvPfWA/YfQiCaSgxokBzVIDmqQHOxPMlCD5KCG852D2WLlWEU94b6uVDeYuf/DZCxWjTcnD8Cg03HT21vwdXPC0aBn/q3xzYrgk9maXkpUoDsBHsYWj63bnccLXxxm82Ojmg29/62s0hoCPIy4Op2879Vq1dicVsLwKH/25B6ni78br3ydyqGCSq7qF8Jne/IxOhi4ql8IvUI8eHDFbv56WXeujQll5pId1Jss9Aj24Hitic7+bjw0+td7zT//xSGGdPVjyZZMdDod3YPcefTyHra2Tlu0nRAvZ+bdEHPWOWzLKOXOpTtZNnMQz3y2n36dvHj8yl6s2pnLFX2CbCcg3vvxKB8lZXPPxVFsSS8hraiacXFhWK0aLk4GvjpQiKbB41f1JCrQ44z7bS9k+P1pSFHfkgwpU4PkoAbJQQ2SgxokB/uTDNQgOajhfOUw97MD9AzxILWwiq0Zpay9Zyird+XiaNDj7Gggu6yWcB9XymsbiQ7yoIu/20kL9XNRUt3ApiPF3DCgZS/4n3G8tpHvDxczLi6MepMFJ4OeB1fuJq+8jvlTE/h4Rw7r9x1jyuBIEiJ9WLs7n4dGR/P3T/bRxd+NsbGhrN6ZR3pxNf+5uT+mX2bpX5ucx8c7c3HQ60js7Eu92cKRgiremhxP+pGDeIV2JdDTiNHBQFlNI299l8akQRF0DXC3te2xVXu4JiaUZzcc5PI+wezOOU5CpA/1Jgu7c47z2JiedA90Z9bSnbx9azxvfZfG9XFhdA1wZ8qCbTgYdCy4LQGjg+FUL79dk+H3QgghhBBCiA6voKKez/bkAzCqZwCbjpTg7uxAenE1zo4GJiRG8MZ3aWw/WsbC2xIxOui5+4OdbNxfwIKpCc2Gs/8Z/u7G817QA3i7OjEuLgwAZ8em4ve5G2IorWnA182JWSO7ceeIrrZh+Cd65/85ri/Lfs7ixY1HGNbdn7tGdgWwXb9+c0I4NyeE02C28O6mDKYO6crXBwv5fG8+PmYTz6zcjZODnmtiQtmaXsoVfYJ547s0NA3qGi3odNBotjIiOoCvDxZy6+BIgjyNrNudz4o7B1PVYObuZTvRoePeS6JwNzrw2Jiettc1Li6UHZnlHbagPxfSUy899YAMKVOF5KAGyUENkoMaJAf7kwzUIDmo4VxyOHE9+QMfJTM2NhQdOjanlhDi5cyMYV0w/DKsvMFsYdbSnfzr+n6EebsAUFrdwOItmTx8eY8L9lraotpGM/+3Zh8Hj1Wy4LZEQr1dWPhjBg56PTOGdQGaJtvzcnVEs0JVg4lOPr9eQ2+yWKlpMNtmnrdYNfQ6Tjoxn6ZpNJitthMVHZEMvz8NKepbkiFlapAc1CA5qEFyUIPkYH+SgRokBzWcbQ77ciu498NdvDYhjvmbM3hz0oBWaF3HsW3XXgYNiLF3M9o9GX4vzolef/pJP0TrkBzUIDmoQXJQg+Rgf5KBGiQH+yqoqOfLAwUkeJ06h1U7c9m4vwAdUFlv4rUJccxcsoNXxse2Wjs7Cg9nKSNVIj310lMPQH19Pc7OzvZuRocnOahBclCD5KAGycH+JAM1SA72ceLWZu/9eJT3fjrKF/cOxt3t1+Hchwoq+efnB3lwdDTzN6Xz1uR4rJqGVdMwOhhIL66mi5/bKWebF3+MHA+t42zrVjnlKABIS0uzdxMEkoMqJAc1SA5qkBzsTzJQg+TQeirrTbz5XdP7PWtp04R1P6WVcHN8OKt/2k9ZTSMWq0ZxVQNzPzvAPRd349GP9/DE1b0x6HU4GvS2ydW6BbhLQX8ByPGgFhk3IQBwcJA/BRVIDmqQHNQgOahBcrA/yUANkkPr+XhHLit35BAb7o2Lo4GFm48S5OXMzQmdmPD2Ub7N3Y0GuDkZeOLq3vQO9eSbh0eedLI1cWHI8aAWSUMAEBERYe8mCCQHVUgOapAc1CA52J9koAbJ4c/JP17HM5/t551bE065jqZpaBp8e6iQNyYOYNKCn1k8PZGCigZcjQZCvV34z00xJHYLalHAS0HfuuR4UIsMvxcAZGRk2LsJAslBFZKDGiQHNUgO9icZtL6CivoWyySHP+ezPfmUVDeyJa0Ei7VpSq+0oipmLk7i75/so6rexN3LdjF9cRLXxITSr5MXT13TmwERPlwdE8KoHoEAuNaXSAGvADke1CI99QIAo9Fo7yYIJAdVSA5qkBzUIDnYn2TQuswWK9e8vpkvHxyJr5uTbbnRaORoSQ2Rvq5yjfZZajBbcDLo0el0/JReyju3xjP53W04Oxm4ul8wW9JLmXdDPzJLarn29R95cHQ018WG2Z5/c0J4i23K8aAGyUEtUtQLAIKDg+3dBIHkoArJQQ2SgxokB/uTDM5Og9nC6p151DaauX14V9tys8XKlvRSRkQHnPR572/NJKu0ljBvF0b1DCT/eB0BHs58e6iIm+I72dYLDAripgU7GJ8Y3mz7v1XXaCE5u5yLovzP74trI4qq6vEwOuLiZCC7tJa/rkjGw9kRX1dHIn1d8Xc3svqei3BzMjDvf4cY3TuIEC8XQrxc+Oy+YXg4O55xH3I8qEFyUIsMvxcAZGVl2bsJAslBFZKDGiQHNUgO9icZtFRS3cCin442W/bil0cwWawcOFbJoYJK2/IVO3J46asjLNicwYk7OW/LKGXljhzKahrZnFrC5EERRAW6839r9rFudx7PXt+X7w4XseznLKa+t52VSTl8vv0w18SEsiu7nMMFVdSbLLzzQ7ptKDnAez8d5alP95NZUtOsbZqmcaigkqO/LN+WUcrDK/ewN/f4BXqHmuSW12K2WC/oPk544YtD3Ls8meXbs7FYNR7/ZC9vThrA+zMG8uiYnjw4OhoAd6MDOp2O/7uqF5MHRdqefzYFPcjxoArJQS1tuqd+w4YNPPHEE1itVsxmM48++ii33XabvZvVJrm4uNi7CQLJQRWSgxokBzVIDvYnGbT04pdHOJBfwZV9Q8gtr2XTkWIqak3831W9OFpSwxvfpvHiLf2pqDOxfu8xVt01hHc2ZfCX5bvQocPL1ZGy6kaSjpYxcWA4XQPc6RrgTm2jmZe/SuWFm3yobUhlR2YZ792WwNzPD/DDweN8eFdf9Dod//fJPmI6eXHseD13L9uJTtd067QDxypZNC2Rez7YhberIxMSI0js4sPLX6Wi00FOWS2PXtGDhT8e5f5Lu/PshoMsmznINpy/3mShuKqBcF9X2/3ZT6Wu0UKdyWK7RKCkugE3JwdcnJpu5Wa1aty+ZAf3jIpibP9QrFaN+ZszGNs/lFDvk/9NfXe4CL1Ox8hfRjV8daCQ4d39MVmsHCqowt/dyOxVe3n3tgTQ4JtDhXi5OBLgYaSsppH3ZwzkLx/sokeQBxd187ftJ+wU+/sj5HhQg+SgFp124pRlG6NpGn5+fnz//ffExMSQmZlJz549KS4uxsPD47TPraysxMvLi4qKCjw9PVupxWqrqKjAy8vL3s3o8CQHNUgOapAc1CA52J9k0DSsu6LWxOItmZTVNOLh7MD4xAhe/uoIHs4OzBzWhX6dvGz3Jn/l6yPszjmOo0HP3Rd3Y0CEDwBZpTV4Ojvi4+bEsYo67luezIpZQzD85hr5sppGfN2cOFJYRZi3C25GByxWjbVJGdw4qBsA7/yQzk/ppSyZnkhaUTUh3i7syTlOoIeR7kEemH7pHX/92zTyyuu4Pi6MYd39Kaqs586lO+kV4sm8G/rx3+/T6RHsztAof2obLMxevReTxYqr0YGs0hpuiOvEjGFdAKiqN/HVgULG9A1m2c9ZbEkvpa7Rwr9v6k+ItzPTFyWh1+t4+Zb++Lkb2bi/gH25FRwqqGTBbYl8uD2boyU17M09zvM3xhDp50ZpdQOFlQ30DvUkp6yW//tkH3qdjsXTE/lwew4b9h0jNtyb4qoGnB315B2vY2SPQGobzBwtqaF/uDcFFfWs3Z3Hx3cNIdDDmb8s30WDycqcsb3p5ON63v8W5HhQg+TQOs62bm3TPfU6nY7jx48DTS/Yz8/vpJM2NDQ00NDQYPu9srKyxTodXU5OjhyYCpAc1CA5qEFyUIPkYH/tPYNnNxxkSFc/RvVsmt28wWzh4x25TfcodzLw1nfpmCxWfN2cmDwoks7+rjgZ9DgY9FzSM5BJgyJwdjQ02+YDl0WfdF+Rfm62f4d4ubDq7otarHOi5zs66NdOIoNeRw+3Otvvdwzvyq1DItHpdHT/Zb2hv7mO/kQP+0Ojm7cj0NOZ8YnhDOziC8D4xHAmvfsz/u6Z+Lk7ceuQSIZF+ZNdVkuErysvfXWEF788TFyENwt/PMroXkFMfHcbl/UMZNG0RI5V1PPYqr0Y9DpuTuhEj2AP7l2ezNQhkbz301EWTR/Iv9Yf5J0f0tmcWsKSGQNJL67m9W/TGNjFl40pBVTVm1k4LYF/fH6AZ6/vxwfbsvnn+oPUNJh5f8ZA7vswmWAvZ568pjcAFqvGzW9voXugBxMHNt3W7N5Lomyv+ZIegazckXNBCnpo/8dDWyE5qKXN9tQDfP3110yYMAE3NzfKy8tZs2YNl112WYv15syZwzPPPNNiufTU/yozM5POnTvbuxkdnuSgBslBDZKDGiQH+2urGdQ2mrFYtdNeK91otnLbe9vR6eC9aYlsTi3hvR+Pcv2AMPbmHseqwdQhkfQMtv//1y5UDlmlNUT4up7yNm2LfzrK0ZIa/u/qXraRCL+laRoZJTV09XdDp9ORd7yOlUk5TB0SiZ+7kcLKepKzjzO4qy/erk0nLB5auRuA/9zUn28PFbF8ezY9gj2YPaYnxyrqeOeHDJ68pjcGvY56kwUHvQ6H31wKsCW9hKhAdwI9nFu0p7bRTG55XbOTIudTWz0e2hvJoXWcbU99my3qzWYzl112GXPnzmXEiBEkJSUxduxY9u3bh79/8xlHT9ZTHx4eLkX9b5SVleHr62vvZnR4koMaJAc1SA5qkBzsr61m8OjHe/B1d+LxK3vZlmmaxsb9hTjodVzWO4hvDxWSUVxDuK8rH2zLZlAXX2YM7WK7LlwlbTWHk6k3WXA06DHodWiaxn0fJvOv6/vh5XJ2k9XZU3vKoS2THFrH2Rb1bXb2+927d5Ofn8+IESMASExMpFOnTiQnJ7dY12g04unp2exHNJefn2/vJggkB1VIDmqQHNQgOdhfW8xgV3Y5DgY9+/MqsVo1th8to95k4cEVu0kvrua9n4423YJuVx7XxIRyRZ/gpknWRkUpWdBD28zhVJwdDbZ5BHQ6HW9MGtAmCnpoXzm0ZZKDWtrsNfXh4eEcO3aMgwcP0qtXL9LS0khPT6dHjx72blqbJCc61CA5qEFyUIPkoAbJwf7aWgYWq8bLXx3h1QlxLPs5i8dW76XOZCG3vI47h3fl6pgQgj2duXXhdoZH+RPs1XIIt4raWg7tleSgBslBLW22qA8KCmL+/Pnccsst6PV6rFYrb7zxBhEREfZuWpvk6nphJjMR50ZyUIPkoAbJQQ2Sg/21lQySMst4/n+H6BvmxejeQfi6OXF9XBivfJ3K6xPibLdtA7guNhQNuHFAmP0afI7aSg7tneSgBslBLW22qAeYOHEiEydOtHcz2oWCgoIWcxGI1ic5qEFyUIPkoAbJwf7snUFdo6XZkPicslo+3pnLpIERvPTVYcwWDU8XR9KLq3lz8gDW7c5j0i+zoof7uvLiLf1bbNPBoOem+E6t9hrOB3vnIJpIDmqQHNRyQYv6M906ToZtqMPHx8feTRBIDqqQHNQgOahBcrA/e2VgsWo897+DbE4t4f2ZAwn0cMZksfL3tSlc3S+YR1ftYfaYnoR5u1DdYCbAw4izo4E7R3SzS3svNDkW1CA5qEFyUMsFLeq9vb3R6Zpm1TzhxO86nQ6LxXIhdy/OgaNj25gcpb2THNQgOahBclCD5GB/9spgw75jeLs68frEOGav2st/p8Tz8tdHmJgYzpX9Qhif+Osljz6/3Nu9PZNjQQ2SgxokB7Vc0NnvrVYrFosFq9Vq+znxuxT0aikqKrJ3EwSSgyokBzVIDmqQHOyvNTKwWjV2ZZezN/e4bdnXBwsZnxhO9yAPZgzrwpQF26hpMHNlv5AL3h4VybGgBslBDZKDWtr0NfXi/PHz87N3EwSSgyokBzVIDmr4szlYrVqzCdL+rIo6U7Nbb1msGhV1Jnx/6Sn+IqWAuAhv6k0WvkgpYNbI5kPBT4wWbEvO17FQb7JgdNA3e/1rduWS2NmXrw4Ukl5cTW55HU9e05sIX1cq60z4uxsBGN49AF83J7oFuJ+XtrRF8pmkBslBDZKDWlqlqE9NTeX+++9nz5491NfX25aXlZW1xu6FEEIIYScT3/2Zp6/tQ+/Qk8+jo2kamoat8P/2UCGdfFyJDvKwrfPBtixG9w6iuKqBh1fuIdTbhYo6E3cM78rW9BLyjtdjtlpxMzrg5eLI8u3Z6ACz1cq1/UMJ9XYB4LvDRby/JZP3piXaCtuKWhNVDSY6+bT/mZxf+OIwPYLdGdY9gOf/d4i7L+7G2t35rNyRg5uTAwtuSyDveB1PrE1hVI9ABndt/p/2PqFedmq5EEKI02mVov6OO+7g7rvvZu7cuXz00Ue8/vrrdO7cuTV2Lc5SaWkpISEdczidSiQHNUgOapAc1HCqHE7Ml3O6Xu/843U4Oxp48cvDLLgtodm69SYLr3+bSkpeJfUmC/8c15fuQR4s3ZqFr5vRNmN6UmYZi37KxKpBdb2Zp67pzUVR/pgsVu5bnkx0sAfPXNcXi1Uj/3gd4b6ubDpSTG2jmRAvF97+IZ251/UF4IOfs+kT6sUH27KZMjgSgAU/ZrA5tYRXxsfywsZDvHRLLM6OTTO9HymsYvm2bJ6+tjc6nY6qehPvbj5K/vE6nr8xBsN5HIHwe1arhlXTcDDoKS0tJSgo+KQjHk6MPGgwW1i9M49Qb2eGdPNjXXI+wV7OjIgOsK17tKSaQwWVJGWWExfhzW3vbefju4ag1+lwNDT14HfyceUvo6LYfrSMWxLCL9jra4vkM0kNkoMaJAe1XNBr6k+orKxk/Pjx6PV6+vXrxzvvvMPatWtbY9fiLAUGBtq7CQLJQRWSgxokBzWcLAerVeOuZTuZsTiJtKJqNE3jpS8PN5sYF+DL/QVMGRzJ4K5+vPFtGharRnWDGYBHPt5DbLgPS2YM5I1JA3hyXQrZpbW4OztS02CmpLqB7w8X8crXR/jg9kH8mFrMzqwy4js3zbjsaNDz3ykDeGh0NAAGvY5w36be9hHRAYzpG0L/cG98XJ24a+lOFmzOIMjTyEOjo9mTc5wFmzMwW6zsyi7nvkuimLEkib5hXizZkgnAj6klzNtwEBcnA2t351FYWc/dy3YRF+FNQqQPb/+QftL363BBle19KK5q4Kl1Kc0eL6qq566lO3ls1Z4W7xc0newAeH9rJjf8dwtb00sJCAjg5ne2cqSwip8zSnn04z229f+2eh9rk/NYujWLkuoGUgurmb4oicp6E2t25fL4mr0s+ukoFquGXqfjkp6BWK0a04d2YdNjo4j0cyPc15VgL2fbNhM7+/KXUVEEeBhP8VfRMclnkhokBzVIDmpplZ76E7Mjenh4kJmZSXBwMCUlJa2xa3GWTCaTvZsgkBxUITmoQXJQw4kcVu3M5bM9+Xg4O2B0MDAiOoBRPQJ5at1+7hzRlYU/HuW6uLBm11xvTi3hzckDuKxXIO9symDaou3UNVq4Z1Q3fFydGN07CIAADyN/GRXFtMXbeeCyaII8jNzzwS4SIn1YMDURFycDVq1pVIDR4df7pZ/NtfEPjo6mqLKeLemljO0fil6v44WbYliw+SgT5v/MFX2CubRXEBf3CESvg2mLksgqq6W4qoH/TonHoNcxY3ESXx0oZM7YPkQFuqNpGg+t3NN0kiHS17avdbvz+O/36UwZHMmUwZEs35bNd4eLqG4wk1VaQ4SvKwt/PMqMYV346kABKXmV9Ov065D2tKIqJr67jQ/vGMzXB4tYOnMQ932YzJAwZwLcjaxIyqGoqgFHg45vDxUS6edGvdnC6l25ACyZPhC9XscdI7oCTSdfcsvr+Mf6A8R08qJrgBszhnbBZLUC2EYkiLMjn0lqkBzUIDmopVWK+hEjRlBaWsq9995LfHw8Tk5OTJgwoTV2Lc5SeXk5YWFh9m5Ghyc5qEFyUIPkYF9Wq8bEd3+mj49GkbmQrgHuvDctkZpGM4eOVZHY2QedTkeknyvPfLafp6/tw7cHi2xF/eqduUQHe9gKx7tGduOukd1IK6piwvyf+fTeYc32N7x700mCUT0C8HB2ZOWsIc0eHxkdQKPZ+odeS6CnM+Pifv1b0umaCt+4CG+ig5uu3T8xlP6dW+MpqW4gyNMZR0PTgMalMwc1255Op+OZ6/pw5/s7mD81AQe9jifX7sfb1ZHP7hvGXUt34uvmxPbMUu4c0Y3vDhWxZEsmni6OWKwafxvTE183Rxb+mIk+CYZF+TOsuz9PrdvP21MG8OCK3Qzp5oeXiyNPX9ubW/77I98+eil/+WAXXq6O/GtcDI98vIdDBZW8c2sCTgY9tSZzi+H5er2OCD9Xru4Xwn82HmHCwHD0eh1GvRTzf4R8JqlBclCD5KAWnXaysV8XUE5ODhUVFfTt27c1d9tMZWUlXl5eVFRU4Ol58ol7OpqSkhL8/f3t3YwOT3JQg+SgBsmhdVisGptTixnRPaBZUfhTWgk7s8oJdLZyUc9wIvxOPpFcUWU9y37O4p5RUfzlg12MjQ3lsz35uBsdePGW2JNed15vspxzL/HvJ9RTwU9pJfxwpBgdMLCLL5f2ahp5UFlvYtWOXCL9XOkT6sW4N3/inlHdiPRzw8XRwMAuTb37UxZs4+qYED7bk0+QpzMTB0YwsIsvCzZncFmvIDr7uwFQUFhEcFAg6/ceI9DTSGLnpudX1ZvwcD7zvaKrG8zE/+MrvnhgBF1+2aY4d/KZpAbJQQ2SQ+s427q11W5pZ7VaKSgoQNM0PD09yc7OJiIiorV2L86gtrbW3k0QSA6qkBzUIDm0jue/OERlnYmlW7N4dWIc7sam/xqs2ZXHQ5dHY6ksPmVBD0294A9d3gNoGs69N7eCt6fE42A49bQ9f2TYt06nQ7U70Q2N8mf59mwMOp2toAfwdHZkxrAutt/H9A3mloTwFq976cyB6HQ6Ovu5sSOzzFbs3z68a7P1Ghua7hx0dUzzSanOpqAHcDc6MPe6PkT6tv8Z/i8k+UxSg+SgBslBLa1S1C9evJj7778fR0dH9PqmL3mdTkdRUVFr7F6chcrKSns3QSA5qEJyUIPkcOGcmDH96wOFWK0az90YQ1JmGbNX7+X1CXGU1zZSUddImLcLKblnn8OrE2JPW8y3R/+4ri9nGjwwZ2yfky4/MSfAkG5+DOl26ns+n49jYXyidKT8WfKZpAbJQQ2Sg1papaj/xz/+QVJSEj169GiN3Yk/IDQ01N5NEEgOqpAc1CA5XBj1JguTF2xjTJ9gvjtcxHvTEoGmGc9zy2t5dNVeKutNPDS66Tv7XHLoaAU9gK+b0wXfhxwLapAc1CA5qEFyUEurfPv6+/tLQa84OdumBslBDZKDGiSH86u20cyaXbk88vEe7r+0O3UmC3df3K3ZkPDr4zpxeZ8gxsWG0Tu06do9ycH+JAM1SA5qkBzUIDmopVV66seNG8crr7zCpEmTcHb+9T6oMkmdOqqrq+3dBIHkoArJQQ2Swx9TVtPIh9uziQp054o+wQCUVjfwwIrdXBMTwtQhnRnYxZeR0QEnff6J55wgOdifZKAGyUENkoMaJAe1tMrs9yeuo4em68dOXMtnsVj+1HYbGhp4+OGH2bhxI87OzvTv359ly5ad8Xky+31LFRUVeHl5nXlFcUFJDmqQHNQgOfwxb3ybir+7ka8OFPLsDf1wdjTwlw928cQ1vegZfO7feZKD/UkGapAc1CA5qEFyaB1KzX5vtf6x+8qeyd/+9jd0Oh1HjhxBp9NRUFBwQfbTEZSUlMiBqQDJQQ2Sgxokh9NLyavgtW9SabQ0fcfec3EUiZ19+DmjjPdnDKRXiCdzPt1PZb2J2WN6/qGCHiQHFUgGapAc1CA5qEFyUEur3dLufKupqWHhwoXk5ubaZo8NDg4+6boNDQ00NDTYfpdrQFqqq6uzdxMEkoMqJIc/p9Fsxcnhz0/ZIjk0qag14eJkaPaebk0vZf6mdP5zc3/83I1U1pt47n+HeHdzBgMifdDrdfQP92ZQF1+u7BdCkKfzafZwepKD/UkGapAc1CA5qEFyUEurTJSn1+sxGAzNfnx9fbn66qvJzMz8Q9tMT0/H19eXZ599loSEBIYPH84333xz0nXnzZuHl5eX7Sc8PPxPvJr2KTIy0t5NEEgOqpAc/ris0hqufHUTVuufv7KrI+fQYLbw7IaDaJrG39fu4x+fH6DBbGF/fgUVtSZe/zaV/06Jx8/dCDTdF/3Z6/txx/Cu3Dr41/dt2tAuf6qgh46dgyokAzVIDmqQHNQgOailVYr6uXPn8uSTT5KdnU12djZPP/00d999N1dccQWzZs36Q9s0m81kZWXRu3dvduzYwWuvvcb48eMpLCxsse7jjz9ORUWF7ScnJ+fPvqR2Ry5dUIPkoIaOmENydjkPrtjNyh1Nn48VtSY0TSPveB0peRUAHKuo4++f7GNHZhnQdK/z31v0UybRQR5sSS+los5Eo/nXy6/MFivfHy6iwWwh73gdPxwpPm2bthzIorbRfMrHK2pNvPDFoXN+rRdScVUD6cXnNnnQkcIqPknOZcHmDP6z8TDJ2eWs3JHLjswy3v4hAycHPUYHPRPn/8zCzUe56e0tPDQ6utms9ScM7OJLgIfxfL0coGMeD6qRDNQgOahBclCD5KCWVhl+v3btWnbs2GH7/amnniIhIYEdO3Ywf/78P7TNiIgI9Ho9kydPBiAuLo4uXbqwb98+goKCmq1rNBoxGs/vf3Lam99eniDsR3JQQ3vO4eCxSkK9XfBycWy2/L/fp/PkNb157ZtUiqsa+PJAIeE+LpRWN+LooOftKQP460e7mT2mJ698fQQ3JwdqTRZ0QBd/N8pqGvF2dSSvvI5nb+jH/R8mowGOBh2zx/Skk48r9y7fRXSQB5tTS8guq6Wu0XLKGdif+Ww/OcdKWLxnB09c04s+oU3X7ZXXNOLzy33BV+zI5qe0En5MLWFYd/8L+badtTe/S2Nv7nE+uH0wLk4ti+7f++pAIWt25TKmbzC9Qz3xdHbkP18eprbBwtKZg7j85U3MnxpPjyAP6kwWPJwdyTteR5i3Syu8mibt+XhoKyQDNUgOapAc1CA5qKVVivqqqiqKi4sJCGj6z1txcTFVVVUAODo6nu6pp+Tv78+ll17Kxo0bueqqqzh69ChHjx6lV69e563dHUnXrl3t3QSB5KCK9pJDo9mKhoaDXs/a5DwKKuv57lARA7v48tiYnrb1jpbU4O3qSLivK3Ov68vr36by4R2DyCiuwd/dyAfbsvjLB7uYOawL8ZE+vDV5AHWNFgI9ndE0jfTiagLcnSmubsDZUU+QpzPXxYZxZd9g9DodD63cTWW9ibnX9bVN3jaoiy9HS2rIKK6ma4A7AB9sy2J/fiVX9wvByUHPa1MG0ogD93ywi4dGR5OSV8HCn47y3A0xDO7qx3eHinl/xiBmLdtBzxAP/N1/PXmraRoPrdxDUVU918WGcUvC6S+7yiypoZOPCw6GMw9gW5ucx/Du/rah7wAmi5U6k4Xsslr+elk0//nyME9e0xuAbw42jSC7tFfTCecTJyZKqhtY9NNR3puW2KzX/Y1JA8grr8PN6MCXD47Azdj0Ve3xS9tas6CH9nM8tGWSgRokBzVIDmqQHNTSKkX9Qw89RP/+/bnyyisB2LhxI0888QTV1dUMHTr0D2/37bffZubMmcyePRu9Xs8777xDWFjY+Wp2h5KdnU3Pnj3PvKK4oCQHNbS1HDKKq9mXV8GACB86+bjwr/UHmTAwnHkbDlHbaMHLxZFBXX3pHerJXSO7MXNJEhV1JqxWjT25x3l3cwaPX9l0QtTFyWAr+PuGNfWO33ZRZ+Z+doDLezcVpR7Ojng4N52Q1el0RAV6AODl+utJ2kmDImz/fmvKAI6W1NhmX58ztg8A2zJK+ffGw+RX1DMxMZwvUgroFeLJM5/t5+NZF5GdnU7Pnj154aYYlv2cTbcAN9bfP5xZ7+9k3e48rukfgperI3Ov68t9y5P5x7i+RAU2nSD4/kgx4T4uvHhzf+7/KJns0lp83JyYdlFn9DpsE6yaLFYeWLEbZwcDhZX1zBzehYujA2yPA3x7qJAjhdXcNbIbhwoqWbUzl8/25PP2rfGUVjfywIpk3JwcyK+o56+XdmdkdAArk3LIKasl3NeVpT9nYbFqmCwan+7Jo+6XTAorG5gztk+LYfTuRgd6BDe9pycKentqa8dDeyQZqEFyUIPkoAbJQS2tcp96gJSUFL777jsARo0aRd++fVtjtycl96lvKSUlxa6ZiCaSgxraSg6apvHZ3mOsTc7j2v4hrNmVx9QhnfkxtZiMkhpmDO1CTCcv8o/X06/Tr7ed2ZJWwhvfpRHs6UyvEE+u7R9KsNefm0ztj7BaNf77QzpTBkfy8ldHGBsbSt9QL/blVRAf6XPKHJIyy3DQ64iL8LEtK6qsZ/bqvRj0eqyaRml1A8tuH4SHsyP1Jgv78yt/uXY9Dx0Q6OnMY1f04OuDhbgbHbg5IZyKWhOLt2RyuLCSf1zXl8/3HmN8Yjh3Lt1JV3836hotpBVX89bkAezJOc4nyXmYrRqzx/QgKtCDRrMVR4MOnU7HwWOVvLspgxnDurBqZy7TLurM2t15TEiMINjLmZS8CoI8nc/79e8XQls5HtozyUANkoMaJAc1SA6t42zr1lYr6lUiRX1L9fX1ODu3/n/qRXOSgxpUzGFXdjn9O3lj0Df1IB8qqOSfnx9kSDc/7hjeFScHPYt+OsqinzJZf/8wW096W3auOVitGlZNw6DX0WixYnRoeU17Zb0JD6MD6cU1vPDFIWoazSydMQi9/tee+R9TS3jtm1Qu6RXIVwcKGdLVj4cvjya1qJogT2fbfAQ7MsvILa9jXNzJR4h9tD2bhT8e5c3JA4gO8jjHV68OFY+HjkYyUIPkoAbJQQ2SQ+tQoqifOHEiH374IXFxcc2GMp6wa9euC7Xr05KivqUDBw7Qu3dvezejw5Mc1KBaDruyy3luwyFcnAz8/epepBVVs2pnLi/cFNPsOnKzxcr+/Er6h3vbr7Hn0YXOQdOahsT/9v7vv/f1gULiIrybXT/f0ah2PHREkoEaJAc1SA5qkBxax9nWrRf0Yr1HHnkEgFdeecW2rLy8nNzcXPr163chdy3OkdVqPfNK4oKTHNRgzxwq6kzNZqbPLKnhpS+P8M6t8TRarMzbcBA/dyPv3BqP4+8mdXMw6NtNQQ8XPgedToeTQ8sTzr91We+g0z7eEcjnkv1JBmqQHNQgOahBclBLqwy/HzNmDB999BEODg62ay+mTp3K3LlzL/SuT0p66lsymUx/+E4E4vyRHNRgjxwazBbmfLqflLxKHrmiByOjA3h/aya7ssqZNrQLse2oWD9bcjyoQXKwP8lADZKDGiQHNUgOreNs69Yz37vnPCgsLMTb25sNGzZw3XXXkZqayieffNIauxZn6fDhw/ZugkByUMWFzsFq1Siqqm/2+2Or9nJJzyA+vmsIH23PZtXOXL46UMhLt8R2yIIe5HhQheRgf5KBGiQHNUgOapAc1NIqRb3JZAJg06ZNjB49GkdHRwyGlhMYCSFEe5J/vI4vUgpaLP9gWxaT3t1GVb2JRz/ew/TFSYyMDmB07yCcHQ28OiGOreml3HdJ92YTuAkhhBBCCPF7rTL8fsKECVRUVHDw4EEOHDgAwNChQ0lOTr7Quz4pGX7fkgyhUYPkoIbzlcOcT/dztKSG6+PCGBcXhtWqse1oGQs2ZzD1os48vS6F+y7pzo3xnc5Dq9sfOR7UIDnYn2SgBslBDZKDGiSH1qHU8PvFixcza9YsvvvuO1xdXSkvL2fevHmtsWtxlmQIjRokBzWcaw5V9SYq6kzNllXUmcguq+W9aYl8kVLAtoxS7nh/B1vSS5h3Yz9GRgfw4i2xUtCfhhwPapAc7E8yUIPkoAbJQQ2Sg1ou6Oz3Jzg7OzNu3Djb72FhYYSFnfy+vkKICy+rtIZIP7eTPtZgtuBk0J/0NpSipYKKeh5csRuAoVF+JGcf59WJcczflM6UwREY9Drm3dCPKQu38dQ1vRnU1c/23PhIHzu1WgghhBBCtBetMvxeNTL8viUZQqOG85mDpmmU1jQ2u485QN7xOi5/6QfW3TuMqED3Fs+bvmg7U4d0ZlTPQNuyRrP1tPfybm9+n0NBRT3BXs4s2JxBhK8rl/cJBpruCz99cRJzr+uLj6sjOzLLcTUaeOWrVLoFujHvhhh7vYR2QT6X1CA52J9koAbJQQ2Sgxokh9ah1PB7oT4ZQqOGU+Xwv33HSM4up6S6gVsXbuNwQZXtsdLqBpb9nEVdo4W6RgsAhZX1zFicxEMr9/DUuhRuXbiNpMwyABZszmDejTG88vWRFvv5MbUEo4OB7w4X2ZZpmsbMJUk88FEyFbUmLFaN9348yonzgccq6tiWUdpsO5/uyWf1ztw/92bYSU2DuVkO1Q1mrnptM/UmC5tSS1i8JROTpenerAt+PMq1/UPp4u+Gt6sTl/UO4qJu/tx1cVeevraPvV5CuyGfS2qQHOxPMlCD5KAGyUENkoNaWmX4vVCfXi/nd+yh3mShss5EoKczcPIcNE1j6c9ZOBj0dA90Z9LACJ7/4hC3DolkSFc/Hv54D6N6BPKX5buoqjcxZXAkXx8s4m9X9qJHsAfJ2eUEeToze/VeHr68B3nldTx9bR/yyuuYvmg7bkYHNMDfzYm04mremhzPvct32fa/Jb2U/p28Gdbdn3n/O8jQKH9W7sjBz92J62LDWLMrj7XJebw3LZFwX1fqGi18tD0bJwc9fcI8qWu0EB3k0bSfX04EnGpov6Zpf2jYv6Zp1JusuDid/K4aVfUmPJzPfDY5tbCKuz/YRS8fPf/pacHoYCAps4xADyNLtmQS6uVMfKQPs1fvJczbhZLqRmaN6NpiO5f0DDrn1yBaks8lNUgO9icZqEFyUIPkoAbJQS0y/F6G3wNQX1+Ps7OzvZvR4SzdmskH27IZFuXPscp67h4WQd8Ifwor63ntm1TcnR3oE+rF0eIaHB10bD9axuLpA2kwW3jikxTKaxuZOqQzI6IDgKbh4DOX7KB3qCezx/Rsvq+fs/jhcBEv3hyLl2tTgdtgtmC2aGhAQUUd3QLc0el0PLUuhSBPZ344XEx1g5kPbh+Ej5sTj63aw6GCKpbdPoi7l+3krUnxPLAimaev7cPs1Xu5uEcgKXkVXNs/lOggd177JpUIPzcOHqvExdHA8ToTHkYHXp8YZ7tVW0ZxNdUNZtyMDsxaupNRPQK495LueLk4NivyCyrqcTTo8Pvd5QQA7/yQzk/ppbw/Y2CLx9KLqxn/zlbmT01gQMSv17B/f7iIQwVVhHg5c7zWRH5FHcnZx3ljYhzb04v5NrWUF2/uz7z/HeLSnoHc/v4OXr4llkt7BXLgWCU5ZbVc0SdY5h64gORzSQ2Sg/1JBmqQHNQgOahBcmgdZ1u3SlEvRT0AKSkp9O3b197NaHeWbMlkfGI4zo4n70G+7b3tvD0lniOFVfh7GHlg6VYmDYtmw74C/jIqCrPFyktfHeGV8bH4uDlR02DG29XptPusN1lw0OtwMPzxM6hb0ktYv/cYz4ztg16nsxXgRVX17Mut4NJeQfyUVsInyXmYLVZemRCHyWJlS3opIV7ORAd5tNhmQUU9bkYDn+05xv78Cq6JCWVwV19mLtmByWKlrtHC65PiOFpcw39/SAegk48r827oR6PZyrRF2/F0diTMx4VHr+iBs6OBrNIaUvIq+XRPHl0D3Lmomx9Du/mj1+vIKq1hT24FH+/I4Ymre/Pk2hQcDDpGRAfQK8STdbvzuCUhnGMVdXi5OBLu44qPmxP+7kZSUlLYVeVOaXUju3OOs2haIgt+zGDqkM6nzFKcf/K5pAbJwf4kAzVIDmqQHNQgObSOs61b28Xw+0WLFjFjxgw++eSTZrPsi7Pn4NAu/hTspqbBjEXTcNDrcHE0oNPp2JlVxlcHCtl+tIzJgyPYn1dJVb2JGwZ04sWvjtC/kxcRvq64OBnoH+4NwJxLQ9iYW8PoXkHE/rJs+R2Dbfs5U0EPnJei86Ju/lzUzb/F8kAPZy7t1XRWdmiUP/M3ZXDDgKY7WTga9Iz8ZcTAyQR7NT1v4sBwtqa7sim1mJe/OsKIaH9mDOtCRnENIV4uhHi5cFFU074fW7WHg8cq+fpAIbckhDMuLozvDhUxc0kSXi6OOBn0xHf25V/X98PJQc/9Hyaz+KdMzFYNPzcnhkf78+gVPegR7MHiGYkYHQw8tmovX6QU8MHtg3Aznvzv3sHBgalDOjP3swO4Ozug1+u4c0S3P/WeinMnn0tqkBzsTzJQg+SgBslBDZKDWtp8T31mZiaTJk1C0zRmz559VkW99NS3VFtbi6urq72b0SZV1JmYOP9nooPcMVs1qhvMhHi5cKyijhdujCGtuJr9eZVEBbqjofHh9hweGh3N//Yd4+qYUHoE/9qr3dZyqKo34erkgEH/x4agHyqopLOf2ylPROQdr+OeZTsZ1TOQv17a3TbUXdM0iqsabHMRnIvaRjN55XV0P8loAts6v+RgsWpU1ZvO6mSKOP/a2vHQXkkO9icZqEFyUIPkoAbJoXV0iJ56q9XK7bffzuuvv87DDz98yvUaGhpoaGiw/V5ZWdkazWtTMjIyZAjNWdqdc5yKOhNDu/nhYNDz9YFCZg7rwo3xnWzrpORVcLigikBPZwI9nZv1ep+YRK1XSMsDs63lcDaTz51Oz+DTn1QL83Zh3b3DWizX6XR/qKAHcHVyOG1BD7/mYNDrpKC3o7Z2PLRXkoP9SQZqkBzUIDmoQXJQS5uetvCll15i6NChxMfHn3a9efPm4eXlZfsJDw9vpRa2HUZjy8nHREsLNmfwwc9Z7Mwq58l1+9E0ja8OFHJZ7+aznfcN82pW5J8tyUENkoMaJAc1SA72JxmoQXJQg+SgBslBLW22pz4lJYXVq1ezadOmM677+OOP89BDD9l+r6yslML+d4KDg+3dBOVomsbunOO4GR2ICnBn1c5cCivreeGmGHQ6HUt/zmLmkh04GfR4ufy5HusTJAc1SA5qkBzUIDnYn2SgBslBDZKDGiQHtbTZon7z5s1kZmbSvXt3AAoKCrjzzjs5duwYd999d7N1jUajnE06g6ysLBlC84v843Us2ZrJ4YIq2yzuKXkV+Lg58fqEONt13bcOjmRs/1BqG83nbd+SgxokBzVIDmqQHOxPMlCD5KAGyUENkoNa2mxRf/fddzcr3i+++GIeeOABmf3+D3JxcbF3E+zuuf8dwuigZ/vRMmZf2ZO/XtodV6emQ8Rq1dDpaHFPci8Xx/PWSw+SgyokBzVIDmqQHOxPMlCD5KAGyUENkoNa2mxRL84vf/+Wty9rb+pNFh75eA//uK4v3q6OvPTVEfblVeCg13F9XCdMFivDu/tzc0InOvk0n81T/wdndz9XHSGHtkByUIPkoAbJwf4kAzVIDmqQHNQgOail3RT133//vb2b0CoKKuoJ9DCe9yIzJycHLy+v87pN1WxNL8Xd6MCspTtxdNBxRZ9gFk8fSEpeBU+tS2HJjIF/ejb3P6sj5NAWSA5qkBzUIDnYn2SgBslBDZKDGiQHtbSbor49s1g1233AZy3byd/G9GRIN7/zug93d/fzuj1V/GX5LlwdDcy+sidfHyzkrpHd6Ozv1mydvmFerLlnqJ1a2Fx7zaGtkRzUIDmoQXKwP8lADZKDGiQHNUgOamnTt7TrCA4eq+Sf6w8AcKigEjcnA18fLDztc0qrG855P56ep79fuGrqTRYsVq3Zsi9SjrE5tRhNa1peVtOIDpgyOJK/rd5Ldllti4JeNW0th/ZKclCD5KAGycH+JAM1SA5qkBzUIDmoRYp6xfUK8aSi1kRKXgUf/JzN36/uxZHCKlvhesLG/QWk5FWwJb2EyQu28dCK3VTVm856P/n5+QAUVdazJa3knNu5ckcONQ3nbxZ44LTbe3DFbl744hDQNInd/E3p/JRWyq6s49y1bCeV9SY27i9gTN9g+od7c0WfYOIjfc5r+y6EEzkI+5Ic1CA5qEFysD/JQA2SgxokBzVIDmqR4fdtwN+u7MmT61LoEexJn1AvugW4k1ZUTfcgD8prGvF0cWTxT5kAGPQ6Vtw5hPSSau5etos5Y/sQFdg0POZIYRWdfFxsM7r/lqenJwfym0YFmK0aiV18cTSc3TmfvON1PLvhIL6uTlzWO+i8vOZGs5Xr3/qJzn5uPD22D2HeLpgtVt7ZlIG70YEu/m5U1Jl45rP9pBfXcHF0AHOv64NOpyM5u5y7ljYV9itnDQHg5oTw89KuC03OeqpBclCD5KAGycH+JAM1SA5qkBzUIDmoRaf9vsu3A6isrMTLy4uKioo2+QeZVlTFM58dYEg3P1btzGVYlD8Rvq70C/Mit7yOG+M7AZBTVsuLXx4m1NuFcF9XfjhcTFWDiVsSwrkuNgxo6pn/YFs2ccFOvLO1gLcmD2Dd7jx83Jw4XFDFLQnhZxyy/viafQyN8iPpaBnPXNfyfpVWq4YGtnkB8o7X8eTaFAZ28eWukd1Ous3/7TtGUVUDl/QM5LFVe3nq2t5sTS+lqt6MTgczh3XBwaAjtbCaSD/XFhPcmSxWDuRX0j/c+xzfXfsqKSmR2UQVIDmoQXJQg+Rgf5KBGiQHNUgOapAcWsfZ1q3SU98GRQV6MHtMTzalFrPh/uE8979DjE8Mx8PZkUG/WS/c15VXJsSxbnceSZllvDV5AFZNY+7nB/h0dz46HTg56Lk5IZxlmw7w2Jj++Lg5cWN8J654eRP3XtKdx9fs44WbYqioM/H3T/bRO9SLPqGeXB8XhpvRgaMlNdQ1mrm6XwgrknJs+7ZaNT7bm8+enAoOF1ZitcJVMSEM7uLLU+v286/r+/LCF4dZvi2b7LJaZo/pgU6nQ9M0Gi1W1u3O5/kbY/BydeS1iXE889l+KupMLJk+sNnM/33DTj7rpqNB3+YKeoCCggL5gFSA5KAGyUENkoP9SQZqkBzUIDmoQXJQi/TUt8Ge+gshLy+PsLAw2+9mixUHg57843U8umoPZovG21Piqao3sz2zjM2pxbwyPpaHV+7hnlFRRAW689iqPfTr5E1aYRUZJTVc3COQkdEBdPV3w6o1Ffnr9xbwj3F9CPFyoaCinvX7jlHTYCbY05lbEsN5+4d0fkorYVAXX+69pLutPZqmYbZqZ31JQFv1+xyEfUgOapAc1CA52J9koAbJQQ2Sgxokh9YhPfXinDg6Nh++7vBL8Rzq7cLbU+IprzHh4+aEj5sTEX6u1DSYuXXhdgZE+tiu2Z88KJKU/ApmDuuKn7sTbsZf/7z06Lg+rhPXx3WyLQv2cmbmsC5YrBq3L0kiv6KOwwVVvD9jIDqdrll7dDodjobmy9qj3+cg7ENyUIPkoAbJwf4kAzVIDmqQHNQgOailfXd7irNWVFR0ysc8nB2J8HNttuy2izqz7PZBPDQ62rasf7g3kwdFEuHn2qygPxODXse7UxPoHeLJP8b1bVHQdySny0G0HslBDZKDGiQH+5MM1CA5qEFyUIPkoBbpqRcA+Pn52XX/DgY9l/cJtmsbVGDvHEQTyUENkoMaJAf7kwzUIDmoQXJQg+SgFumpF0IIIYQQQggh2qgO2VN/Ym7AyspKO7dEHVlZWbi5nf7WdeLCkxzUIDmoQXJQg+Rgf5KBGiQHNUgOapAcWseJevVMc9t3yKK+qqoKgPDwcDu3RAghhBBCCCGEOLWqqiq8vE5+K2/ooLe0s1qt5Ofn4+Hh0aEnZTuhsrKS8PBwcnJy5BZ/diQ5qEFyUIPkoAbJwf4kAzVIDmqQHNQgObQeTdOoqqoiNDQUvf7UV853yJ56vV5Pp06dzrxiB+Pp6SkHpgIkBzVIDmqQHNQgOdifZKAGyUENkoMaJIfWcboe+hNkojwhhBBCCCGEEKKNkqJeCCGEEEIIIYRoo6SoFxiNRp5++mmMRqO9m9KhSQ5qkBzUIDmoQXKwP8lADZKDGiQHNUgO6umQE+UJIYQQQgghhBDtgfTUCyGEEEIIIYQQbZQU9UIIIYQQQgghRBslRb0QQgghhBBCCNFGSVEvhBBCCCGEEEK0UVLUdzCXX345MTExxMbGMnz4cJKTk6mvr2fcuHFER0fTv39/Ro8eTVpamr2b2iEsWrQInU7H2rVrASgqKmLMmDF0796dvn37smnTJvs2sIP4fQ7bt29n8ODBxMXF0atXL1544QX7NrCd69y5Mz169CA2NpbY2FhWrFgBQENDA/feey/du3enX79+TJkyxc4tbd/O9H7//jgR519paantOIiNjSU6OhoHBwfKysqYPn267Xt66NChJCUl2bu57dqGDRsYMGAAsbGx9O3blyVLlgDyPd3aTpWDfE+3ri+++IKEhARiYmIYPHgwe/bsAUDTNObMmUN0dDT9+vVj1KhRdm5pB6aJDqW8vNz27zVr1mgxMTFaXV2dtn79es1qtWqapmmvv/66NnLkSPs0sAM5evSoNmTIEG3w4MHaJ598ommapk2fPl17+umnNU3TtO3bt2thYWFaY2Oj/RrZAZwsh/79+2vr1q3TNE3TSktLtYCAAG3//v12bGX7FhkZqSUnJ7dY/sADD2j33nuv7bPp2LFjrdyyjuV07/fJjhNx4f373//WrrnmGk3TNG3dunWayWTSNE3TPvvsMy0yMtKOLWvfrFar5uPjo+3Zs0fTtKa/f6PRqFVWVsr3dCs6XQ7yPd16ysrKNF9fXy0lJUXTNE3btGmT1qdPH03TNO2VV17Rrr/+eq2hoUHTNPmetifpqe9gvL29bf+uqKhAp9Ph7OzMVVddhU6nA2Dw4MFkZmbap4EdhNVq5fbbb+f1119vdo/PlStXctdddwGQmJhIaGgoP/zwg72a2e6dKgedTsfx48cBqKmpwcnJCV9fXzu1smOqqalh4cKF/Otf/7J9NgUHB9u5Ve3X6d7vUx0n4sJbuHAhM2fOBGDs2LE4ODgATd/TeXl5mM1mezavXfvt90BlZSV+fn4YjUb5nm5lp8pBvqdbT3p6On5+fvTp0weA4cOHk52dza5du/j3v//Nc889h5OTEyDf0/YkRX0HNHXqVMLDw3nyySdZunRpi8dfffVVrrvuOju0rON46aWXGDp0KPHx8bZlpaWlmEymZh+InTt3Jjs72x5N7BBOlgM0DTN+8skniYiIIDo6mmeffVa+qC6wqVOn0q9fP2bOnElxcTHp6en4+vry7LPPkpCQwPDhw/nmm2/s3cx263Tv96mOE3FhbdmyhfLycq655poWj7366qtcddVVtiJfnF86nY4VK1Zwww03EBkZybBhw1iyZAlVVVXyPd2KTpWDk5OTfE+3ou7du1NaWsqWLVsA+PTTT6mqqiIlJYXCwkLWrVvHoEGDGDRokO3yOdH65NugA3r//fcBWLJkCbNnz2bDhg22x5599lnS0tLkP88XUEpKCqtXr5br8OzsdDk899xzzJs3j0mTJpGRkcHIkSNJSEigd+/edmhp+7dp0yYiIiIwmUw88cQT3Hbbbfzzn/8kKyuL3r1789xzz5GcnMzo0aPZv38/QUFB9m5yu2M2m0/6fq9evVo+r+xk4cKFTJ06tUXhvmzZMlauXCmZXEBms5l//vOfrFmzhhEjRpCUlMTYsWPZvXu3vZvWoZwqh3379sn3dCvy8vJi1apVPP7441RXVzNkyBDb+2w2m6mrq2Pbtm1kZmZy0UUX0bNnT/r372/nVndA9h7/L+zL2dlZKykp0TSt6dq9+Pj4Ztfdi/Pvrbfe0oKDg7XIyEgtMjJSMxqNWkBAgPbWW29prq6uza5HSkxM1L766is7trb9OlUOc+fO1ZycnJqte9NNN2nz58+3U0s7lvz8fM3d3V0rLi7W9Hq9ZjabbY8lJCTI8XCBnOr9fu211075eSUunKqqKs3d3V07ePBgs+UfffSRFhUVpWVlZdmpZR1DUlKS1r1792bLEhIStC+//FK+p1vRqXJYuXKlfE/bUX19vebt7a2lpqZq7u7uWnp6uu2xm266SXv33Xft2LqOS4r6DqS8vFzLy8uz/f7JJ59oYWFhmtVq1V588UVtwIABWllZmR1b2DGNHDnSNvHUbbfd1mwCntDQUJmAp5WcyMFsNms+Pj7aN998o2laU7ETHh6ubdmyxc4tbJ+qq6ubnUh88cUXteHDh2uapmmjR4/W1q9fr2mapmVkZGh+fn5abm6uPZrZIZzN+/3bzytx4SxYsEAbOnRos2UrVqzQoqKitMzMTDu1quMoKCjQ3N3dtQMHDmiapmmpqamaj4+PlpWVJd/TrehUOWRmZsr3dCvLz8+3/fvvf/+7dsMNN2iapml33HGH9uabb2qa1jRhYUREhLZt2za7tLGjk+H3HUhFRQU333wzdXV16PV6AgIC+Pzzz8nLy+Phhx+ma9eutltRGI1Gtm3bZucWdzzPP/88t956K927d8fJyYlly5bh6Oho72Z1KAaDgZUrV/Loo49iNpsxmUw88MADDBkyxN5Na5cKCwu58cYbsVgsaJpG165dbZcIvf3228ycOZPZs2ej1+t55513CAsLs3OL2y95v9WxcOFC7rjjjmbLJk+eTHBwcLM5b7755hv8/Pxau3ntXlBQEPPnz+eWW25Br9djtVp54403iIiIkO/pVnSqHCIjI+V7upU99dRTbN68GbPZzJAhQ1i4cCEA8+bNY/r06bz11lsAzJ49m4EDB9qzqR2WTtM0zd6NEEIIIYQQQgghxLmT2e+FEEIIIYQQQog2Sop6IYQQQgghhBCijZKiXgghhBBCCCGEaKOkqBdCCCGEEEIIIdooKeqFEEIIIYQQQog2Sop6IYQQQgghhBCijZKiXgghhBBCCCGEaKOkqBdCCCGEEEIIIdooKeqFEEIIIYQQQog2Sop6IYQQQgghhBCijfp/ufWWqEwaCXwAAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "signal_i = devices_evset[\"signal\"]\n", "trend_i = signal_i.simple_moving_average(tp.duration.seconds(20))\n", diff --git a/docs/src/tutorials/m5_competition.ipynb b/docs/src/tutorials/m5_competition.ipynb index 3d07f4bba..3c8f8d533 100644 --- a/docs/src/tutorials/m5_competition.ipynb +++ b/docs/src/tutorials/m5_competition.ipynb @@ -6,7 +6,7 @@ "id": "98a0068b-af99-47d9-a6b2-9b36debd2880", "metadata": {}, "source": [ - "# M5 Sales Forecasting Competition\n", + "# M5 Competition\n", "\n", "## Introduction\n", "\n", @@ -136,12 +136,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "total 879848\n", - "-rw-r--r--@ 1 ian staff 101K Aug 18 11:22 calendar.csv\n", - "-rw-r--r--@ 1 ian staff 116M Aug 18 11:22 sales_train_evaluation.csv\n", - "-rw-r--r--@ 1 ian staff 114M Aug 18 11:22 sales_train_validation.csv\n", - "-rw-r--r--@ 1 ian staff 5.0M Aug 18 11:22 sample_submission.csv\n", - "-rw-r--r--@ 1 ian staff 194M Aug 18 11:22 sell_prices.csv\n" + "total 430M\n", + "-rw-r----- 1 gbm primarygroup 102K Aug 18 21:48 calendar.csv\n", + "-rw-r----- 1 gbm primarygroup 117M Aug 18 21:48 sales_train_evaluation.csv\n", + "-rw-r----- 1 gbm primarygroup 115M Aug 18 21:48 sales_train_validation.csv\n", + "-rw-r----- 1 gbm primarygroup 5.0M Aug 18 21:48 sample_submission.csv\n", + "-rw-r----- 1 gbm primarygroup 194M Aug 18 21:48 sell_prices.csv\n" ] } ], @@ -829,8 +829,285 @@ }, { "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; 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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
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    • \\n\"+\n \"
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\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -839,7 +1116,7 @@ "data": { "text/html": [ "\n", - "
\n" + "
\n" ] }, "metadata": {}, @@ -847,7 +1124,32 @@ }, { "data": { - "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"5c8fa614-9fc9-4525-8a7c-03433f847146\":{\"version\":\"3.1.1\",\"title\":\"Bokeh 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+ " const render_items = [{\"docid\":\"a080d758-2e00-4fab-b825-1066c4799484\",\"roots\":{\"p1560\":\"b857ad2f-86ad-4af8-a9fe-e48cd339deff\"},\"root_ids\":[\"p1560\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { @@ -859,7 +1161,7 @@ } ], "source": [ - "sales_evset.plot(max_num_plots=4, interactive=True, width_px=500)" + "sales_evset.plot(max_num_plots=4, interactive=True, width_px=600)" ] }, { @@ -1596,7 +1898,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Selected product (selected_index): (b'FOODS_3_714', b'FOODS_3', b'FOODS', b'WI_3', b'WI')\n" + "Selected product (selected_index): (b'FOODS_3_694', b'FOODS_3', b'FOODS', b'WI_2', b'WI')\n" ] } ], @@ -1629,7 +1931,7 @@ " # Make the plot interactive.\n", " \"interactive\": True,\n", " # Make sure the toolbar is visible\n", - " \"width_px\": 500,\n", + " \"width_px\": 600,\n", " # Only plot the data for the year 2015.\n", " \"min_time\": datetime(2015, 1, 1),\n", " \"max_time\": datetime(2015, 12, 31),\n", @@ -1680,7 +1982,7 @@ "\n", "# Prediction horizon. In the M5 competition, participants should forecast the next 28 days.\n", "# You can predict less days to make the training section of this notebook run faster.\n", - "horizons = list(range(1, 14))\n", + "horizons = list(range(1, 28))\n", "\n", "# For each of the horizons.\n", "for horizon in horizons:\n", @@ -1716,13 +2018,290 @@ "name": "stdout", "output_type": "stream", "text": [ - "The number of plots (13) is larger than \"options.max_num_plots=4\". Only the first plots will be printed.\n" + "The number of plots (27) is larger than \"options.max_num_plots=4\". 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\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -1731,7 +2310,7 @@ "data": { "text/html": [ "\n", - "
\n" + "
\n" ] }, "metadata": {}, @@ -1739,12 +2318,37 @@ }, { "data": { - "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"bb18ff9a-be4c-4fb5-8f26-f8f085ad8fd1\":{\"version\":\"3.1.1\",\"title\":\"Bokeh 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+ " const render_items = [{\"docid\":\"de453ab9-c30d-44ca-bcf4-cf811458adeb\",\"roots\":{\"p3037\":\"b21b973c-8415-421e-bc43-438d2749ba7b\"},\"root_ids\":[\"p3037\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p3041" + "id": "p3037" } }, "output_type": "display_data" @@ -1802,8 +2406,285 @@ }, { "data": { - 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(!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -1812,7 +2693,7 @@ "data": { "text/html": [ "\n", - "
\n" + "
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+ " const render_items = [{\"docid\":\"fe1897c3-a6c9-4bf4-ac00-c8b4a8700605\",\"roots\":{\"p4670\":\"a831705f-b7b9-4395-8134-9597644be3a1\"},\"root_ids\":[\"p4670\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p4678" + "id": "p4670" } }, "output_type": "display_data" @@ -1892,8 +2798,285 @@ }, { "data": { - 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(!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -1902,7 +3085,7 @@ "data": { "text/html": [ "\n", - "
\n" + "
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+ " const render_items = [{\"docid\":\"192ae905-6ddc-450a-aa50-788adfe45e29\",\"roots\":{\"p6591\":\"a367d764-0e3c-4ab6-b35b-3cca180fe144\"},\"root_ids\":[\"p6591\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p6604" + "id": "p6591" } }, "output_type": "display_data" @@ -1960,56 +3168,358 @@ "outputs": [ { "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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"f7ba5b0c-9d86-4b08-825a-6bb565de23ac", - "metadata": {}, - "source": [ - "### Sales aggregated per department\n", - "\n", - "When predicting the sales of a product, the sales of the other products in the same department can help forecasting.\n", - "\n", - "Let's first group all the sales per department in each store, by removing the `item_id` feature from our EventSet's index." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "48a0b18c-0eb8-4ebc-85d1-a928ac7355d5", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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+ " const render_items = [{\"docid\":\"97172bdc-7f9c-4349-ae2e-eea385b6ed94\",\"roots\":{\"p8621\":\"fdaf7267-aa15-431e-9658-0c40c9fc68ec\"},\"root_ids\":[\"p8621\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p8621" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "feature_calendar.plot(**plot_options, max_num_plots=5)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f7ba5b0c-9d86-4b08-825a-6bb565de23ac", + "metadata": {}, + "source": [ + "### Sales aggregated per department\n", + "\n", + "When predicting the sales of a product, the sales of the other products in the same department can help forecasting.\n", + "\n", + "Let's first group all the sales per department in each store, by removing the `item_id` feature from our EventSet's index." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "48a0b18c-0eb8-4ebc-85d1-a928ac7355d5", "metadata": {}, "outputs": [], "source": [ @@ -2072,7 +3582,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -2082,7 +3592,7 @@ } ], "source": [ - "sum28_sales_per_dept.plot(max_num_plots=3)" + "sum28_sales_per_dept.plot(max_num_plots=3, width_px=600)" ] }, { @@ -2123,7 +3633,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -2133,7 +3643,7 @@ } ], "source": [ - "sum28_sales_per_dept.plot(max_num_plots=3)" + "sum28_sales_per_dept.plot(max_num_plots=3, width_px=600)" ] }, { @@ -2192,8 +3702,285 @@ }, { "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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+ " const render_items = [{\"docid\":\"0b61f21a-270c-4de3-a7ec-1d23bb7876d0\",\"roots\":{\"p10373\":\"f6c65aac-2e6a-40e8-b028-24ca0f8948af\"},\"root_ids\":[\"p10373\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "id": "p10392" + "id": "p10373" } }, "output_type": "display_data" @@ -2246,7 +4058,7 @@ { "data": { "text/plain": [ - "features: [('f_sales', int64), ('f_sales_lag_1_d', int64), ('f_sales_lag_2_d', int64), ('f_sales_lag_3_d', int64), ('f_sales_lag_7_d', int64), ('f_sales_lag_14_d', int64), ('f_sma_7_sales', float32), ('f_sd_7_sales', float32), ('f_sum_7_sales', float32), ('f_sma_14_sales', float32), ('f_sd_14_sales', float32), ('f_sum_14_sales', float32), ('f_sma_28_sales', float32), ('f_sd_28_sales', float32), ('f_sum_28_sales', float32), ('f_sma_84_sales', float32), ('f_sd_84_sales', float32), ('f_sum_84_sales', float32), ('f_calendar_day_of_month', int32), ('f_calendar_day_of_week', int32), ('f_calendar_month', int32), ('f_sum28_per_dep_sales', int64), ('f_event_name_1', str_), ('f_event_type_1', str_), ('f_event_name_2', str_), ('f_event_type_2', str_), ('f_snap_CA', int64), ('f_snap_TX', int64), ('f_snap_WI', int64), ('f_leak5_event_name_1', str_), ('f_leak5_event_type_1', str_), ('f_leak5_event_name_2', str_), ('f_leak5_event_type_2', str_), ('f_leak5_snap_CA', int64), ('f_leak5_snap_TX', int64), ('f_leak5_snap_WI', int64), ('day', int64), ('label_horizon_1_days', int64), ('label_horizon_2_days', int64), ('label_horizon_3_days', int64), ('label_horizon_4_days', int64), ('label_horizon_5_days', int64), ('label_horizon_6_days', int64), ('label_horizon_7_days', int64), ('label_horizon_8_days', int64), ('label_horizon_9_days', int64), ('label_horizon_10_days', int64), ('label_horizon_11_days', int64), ('label_horizon_12_days', int64), ('label_horizon_13_days', int64)]\n", + "features: [('f_sales', int64), ('f_sales_lag_1_d', int64), ('f_sales_lag_2_d', int64), ('f_sales_lag_3_d', int64), ('f_sales_lag_7_d', int64), ('f_sales_lag_14_d', int64), ('f_sma_7_sales', float32), ('f_sd_7_sales', float32), ('f_sum_7_sales', float32), ('f_sma_14_sales', float32), ('f_sd_14_sales', float32), ('f_sum_14_sales', float32), ('f_sma_28_sales', float32), ('f_sd_28_sales', float32), ('f_sum_28_sales', float32), ('f_sma_84_sales', float32), ('f_sd_84_sales', float32), ('f_sum_84_sales', float32), ('f_calendar_day_of_month', int32), ('f_calendar_day_of_week', int32), ('f_calendar_month', int32), ('f_sum28_per_dep_sales', int64), ('f_event_name_1', str_), ('f_event_type_1', str_), ('f_event_name_2', str_), ('f_event_type_2', str_), ('f_snap_CA', int64), ('f_snap_TX', int64), ('f_snap_WI', int64), ('f_leak5_event_name_1', str_), ('f_leak5_event_type_1', str_), ('f_leak5_event_name_2', str_), ('f_leak5_event_type_2', str_), ('f_leak5_snap_CA', int64), ('f_leak5_snap_TX', int64), ('f_leak5_snap_WI', int64), ('day', int64), ('label_horizon_1_days', int64), ('label_horizon_2_days', int64), ('label_horizon_3_days', int64), ('label_horizon_4_days', int64), ('label_horizon_5_days', int64), ('label_horizon_6_days', int64), ('label_horizon_7_days', int64), ('label_horizon_8_days', int64), ('label_horizon_9_days', int64), ('label_horizon_10_days', int64), ('label_horizon_11_days', int64), ('label_horizon_12_days', int64), ('label_horizon_13_days', int64), ('label_horizon_14_days', int64), ('label_horizon_15_days', int64), ('label_horizon_16_days', int64), ('label_horizon_17_days', int64), ('label_horizon_18_days', int64), ('label_horizon_19_days', int64), ('label_horizon_20_days', int64), ('label_horizon_21_days', int64), ('label_horizon_22_days', int64), ('label_horizon_23_days', int64), ('label_horizon_24_days', int64), ('label_horizon_25_days', int64), ('label_horizon_26_days', int64), ('label_horizon_27_days', int64)]\n", "indexes: [('item_id', str_), ('dept_id', str_), ('cat_id', str_), ('store_id', str_), ('state_id', str_)]\n", "is_unix_timestamp: True" ] @@ -2337,179 +4149,179 @@ " f_sales_lag_3_d\n", " f_sales_lag_7_d\n", " ...\n", - " label_horizon_5_days\n", - " label_horizon_6_days\n", - " label_horizon_7_days\n", - " label_horizon_8_days\n", - " label_horizon_9_days\n", - " label_horizon_10_days\n", - " label_horizon_11_days\n", - " label_horizon_12_days\n", - " label_horizon_13_days\n", + " label_horizon_19_days\n", + " label_horizon_20_days\n", + " label_horizon_21_days\n", + " label_horizon_22_days\n", + " label_horizon_23_days\n", + " label_horizon_24_days\n", + " label_horizon_25_days\n", + " label_horizon_26_days\n", + " label_horizon_27_days\n", " timestamp\n", " \n", " \n", " \n", " \n", " 0\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 22\n", - " 20\n", - " 36\n", - " 26\n", - " 20\n", + " 24\n", + " 29\n", + " 33\n", + " 31\n", + " 38\n", " ...\n", " 31\n", - " 19\n", - " 21\n", - " 13\n", - " 18\n", - " 25\n", - " 42\n", + " 31\n", + " 34\n", + " 24\n", + " 36\n", + " 30\n", + " 44\n", " 37\n", - " 27\n", + " 42\n", " 1.461542e+09\n", " \n", " \n", " 1\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", " WI_1\n", " WI\n", - " 17\n", - " 15\n", - " 30\n", + " 13\n", + " 24\n", + " 28\n", " 18\n", - " 16\n", + " 27\n", " ...\n", - " 12\n", - " 13\n", - " 21\n", - " 16\n", - " 13\n", + " 10\n", + " 27\n", + " 20\n", + " 20\n", " 16\n", - " 18\n", - " 26\n", - " 28\n", + " 22\n", + " 31\n", + " 30\n", + " 32\n", " 1.461542e+09\n", " \n", " \n", " 2\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", " TX_3\n", " TX\n", - " 32\n", - " 19\n", - " 30\n", + " 21\n", " 17\n", - " 13\n", + " 31\n", + " 20\n", + " 16\n", " ...\n", - " 34\n", - " 38\n", - " 25\n", - " 27\n", + " 21\n", + " 26\n", + " 17\n", + " 35\n", " 19\n", - " 24\n", - " 8\n", - " 27\n", - " 31\n", + " 23\n", + " 22\n", + " 14\n", + " 21\n", " 1.461542e+09\n", " \n", " \n", " 3\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " CA_4\n", - " CA\n", - " 6\n", - " 8\n", - " 5\n", - " 10\n", - " 8\n", + " TX_2\n", + " TX\n", + " 26\n", + " 21\n", + " 17\n", + " 22\n", + " 9\n", " ...\n", - " 10\n", + " 26\n", + " 33\n", " 12\n", - " 9\n", - " 4\n", - " 5\n", - " 5\n", - " 5\n", - " 7\n", - " 9\n", + " 19\n", + " 13\n", + " 11\n", + " 18\n", + " 38\n", + " 25\n", " 1.461542e+09\n", " \n", " \n", " 4\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " CA_1\n", - " CA\n", - " 10\n", - " 27\n", - " 22\n", - " 18\n", - " 15\n", - " ...\n", - " 38\n", - " 44\n", - " 21\n", - " 16\n", - " 13\n", - " 20\n", - " 16\n", - " 23\n", + " WI_3\n", + " WI\n", + " 42\n", + " 48\n", + " 46\n", + " 46\n", + " 54\n", + " ...\n", + " 64\n", + " 57\n", + " 57\n", " 37\n", + " 30\n", + " 45\n", + " 51\n", + " 46\n", + " 48\n", " 1.461542e+09\n", " \n", " \n", "\n", - "

5 rows × 56 columns

\n", + "

5 rows × 70 columns

\n", "" ], "text/plain": [ " item_id dept_id cat_id store_id state_id f_sales f_sales_lag_1_d \\\n", - "0 FOODS_3_714 FOODS_3 FOODS WI_3 WI 22 20 \n", - "1 FOODS_3_714 FOODS_3 FOODS WI_1 WI 17 15 \n", - "2 FOODS_3_714 FOODS_3 FOODS TX_3 TX 32 19 \n", - "3 FOODS_3_714 FOODS_3 FOODS CA_4 CA 6 8 \n", - "4 FOODS_3_714 FOODS_3 FOODS CA_1 CA 10 27 \n", + "0 FOODS_3_694 FOODS_3 FOODS WI_2 WI 24 29 \n", + "1 FOODS_3_694 FOODS_3 FOODS WI_1 WI 13 24 \n", + "2 FOODS_3_694 FOODS_3 FOODS TX_3 TX 21 17 \n", + "3 FOODS_3_694 FOODS_3 FOODS TX_2 TX 26 21 \n", + "4 FOODS_3_694 FOODS_3 FOODS WI_3 WI 42 48 \n", "\n", " f_sales_lag_2_d f_sales_lag_3_d f_sales_lag_7_d ... \\\n", - "0 36 26 20 ... \n", - "1 30 18 16 ... \n", - "2 30 17 13 ... \n", - "3 5 10 8 ... \n", - "4 22 18 15 ... \n", - "\n", - " label_horizon_5_days label_horizon_6_days label_horizon_7_days \\\n", - "0 31 19 21 \n", - "1 12 13 21 \n", - "2 34 38 25 \n", - "3 10 12 9 \n", - "4 38 44 21 \n", - "\n", - " label_horizon_8_days label_horizon_9_days label_horizon_10_days \\\n", - "0 13 18 25 \n", - "1 16 13 16 \n", - "2 27 19 24 \n", - "3 4 5 5 \n", - "4 16 13 20 \n", - "\n", - " label_horizon_11_days label_horizon_12_days label_horizon_13_days \\\n", - "0 42 37 27 \n", - "1 18 26 28 \n", - "2 8 27 31 \n", - "3 5 7 9 \n", - "4 16 23 37 \n", + "0 33 31 38 ... \n", + "1 28 18 27 ... \n", + "2 31 20 16 ... \n", + "3 17 22 9 ... \n", + "4 46 46 54 ... \n", + "\n", + " label_horizon_19_days label_horizon_20_days label_horizon_21_days \\\n", + "0 31 31 34 \n", + "1 10 27 20 \n", + "2 21 26 17 \n", + "3 26 33 12 \n", + "4 64 57 57 \n", + "\n", + " label_horizon_22_days label_horizon_23_days label_horizon_24_days \\\n", + "0 24 36 30 \n", + "1 20 16 22 \n", + "2 35 19 23 \n", + "3 19 13 11 \n", + "4 37 30 45 \n", + "\n", + " label_horizon_25_days label_horizon_26_days label_horizon_27_days \\\n", + "0 44 37 42 \n", + "1 31 30 32 \n", + "2 22 14 21 \n", + "3 18 38 25 \n", + "4 51 46 48 \n", "\n", " timestamp \n", "0 1.461542e+09 \n", @@ -2518,7 +4330,7 @@ "3 1.461542e+09 \n", "4 1.461542e+09 \n", "\n", - "[5 rows x 56 columns]" + "[5 rows x 70 columns]" ] }, "execution_count": 43, @@ -2579,179 +4391,179 @@ " f_sales_lag_3_d\n", " f_sales_lag_7_d\n", " ...\n", - " label_horizon_5_days\n", - " label_horizon_6_days\n", - " label_horizon_7_days\n", - " label_horizon_8_days\n", - " label_horizon_9_days\n", - " label_horizon_10_days\n", - " label_horizon_11_days\n", - " label_horizon_12_days\n", - " label_horizon_13_days\n", + " label_horizon_19_days\n", + " label_horizon_20_days\n", + " label_horizon_21_days\n", + " label_horizon_22_days\n", + " label_horizon_23_days\n", + " label_horizon_24_days\n", + " label_horizon_25_days\n", + " label_horizon_26_days\n", + " label_horizon_27_days\n", " timestamp\n", " \n", " \n", " \n", " \n", " 0\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", + " 48\n", + " 47\n", " 31\n", - " 37\n", - " 36\n", - " 29\n", - " 14\n", + " 46\n", + " 38\n", " ...\n", " 34\n", - " 63\n", - " 47\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 32\n", + " 29\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", " 1.298765e+09\n", " \n", " \n", " 1\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 43\n", + " 30\n", + " 48\n", + " 47\n", " 31\n", - " 37\n", " 36\n", - " 14\n", " ...\n", - " 63\n", - " 47\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 32\n", + " 29\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", - " 47\n", + " 36\n", " 1.298851e+09\n", " \n", " \n", " 2\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 15\n", - " 43\n", - " 31\n", - " 37\n", - " 33\n", - " ...\n", + " 42\n", + " 30\n", + " 48\n", " 47\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 29\n", + " ...\n", + " 29\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", - " 47\n", - " 38\n", + " 36\n", + " 15\n", " 1.298938e+09\n", " \n", " \n", " 3\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 34\n", - " 15\n", - " 43\n", - " 31\n", - " 27\n", + " 39\n", + " 42\n", + " 30\n", + " 48\n", + " 44\n", " ...\n", - " 25\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 32\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", - " 47\n", - " 38\n", - " 22\n", + " 36\n", + " 15\n", + " 26\n", " 1.299024e+09\n", " \n", " \n", " 4\n", - " FOODS_3_714\n", + " FOODS_3_694\n", " FOODS_3\n", " FOODS\n", - " WI_3\n", + " WI_2\n", " WI\n", - " 32\n", - " 34\n", - " 15\n", - " 43\n", - " 29\n", + " 25\n", + " 39\n", + " 42\n", + " 30\n", + " 46\n", " ...\n", - " 27\n", - " 27\n", - " 27\n", - " 49\n", + " 18\n", + " 24\n", + " 41\n", + " 30\n", " 44\n", - " 47\n", - " 38\n", - " 22\n", - " 38\n", + " 36\n", + " 15\n", + " 26\n", + " 33\n", " 1.299110e+09\n", " \n", " \n", "\n", - "

5 rows × 56 columns

\n", + "

5 rows × 70 columns

\n", "" ], "text/plain": [ " item_id dept_id cat_id store_id state_id f_sales f_sales_lag_1_d \\\n", - "0 FOODS_3_714 FOODS_3 FOODS WI_3 WI 31 37 \n", - "1 FOODS_3_714 FOODS_3 FOODS WI_3 WI 43 31 \n", - "2 FOODS_3_714 FOODS_3 FOODS WI_3 WI 15 43 \n", - "3 FOODS_3_714 FOODS_3 FOODS WI_3 WI 34 15 \n", - "4 FOODS_3_714 FOODS_3 FOODS WI_3 WI 32 34 \n", + "0 FOODS_3_694 FOODS_3 FOODS WI_2 WI 48 47 \n", + "1 FOODS_3_694 FOODS_3 FOODS WI_2 WI 30 48 \n", + "2 FOODS_3_694 FOODS_3 FOODS WI_2 WI 42 30 \n", + "3 FOODS_3_694 FOODS_3 FOODS WI_2 WI 39 42 \n", + "4 FOODS_3_694 FOODS_3 FOODS WI_2 WI 25 39 \n", "\n", " f_sales_lag_2_d f_sales_lag_3_d f_sales_lag_7_d ... \\\n", - "0 36 29 14 ... \n", - "1 37 36 14 ... \n", - "2 31 37 33 ... \n", - "3 43 31 27 ... \n", - "4 15 43 29 ... \n", - "\n", - " label_horizon_5_days label_horizon_6_days label_horizon_7_days \\\n", - "0 34 63 47 \n", - "1 63 47 25 \n", - "2 47 25 27 \n", - "3 25 27 27 \n", - "4 27 27 27 \n", - "\n", - " label_horizon_8_days label_horizon_9_days label_horizon_10_days \\\n", - "0 25 27 27 \n", - "1 27 27 27 \n", - "2 27 27 49 \n", - "3 27 49 44 \n", - "4 49 44 47 \n", - "\n", - " label_horizon_11_days label_horizon_12_days label_horizon_13_days \\\n", - "0 27 49 44 \n", - "1 49 44 47 \n", - "2 44 47 38 \n", - "3 47 38 22 \n", - "4 38 22 38 \n", + "0 31 46 38 ... \n", + "1 47 31 36 ... \n", + "2 48 47 29 ... \n", + "3 30 48 44 ... \n", + "4 42 30 46 ... \n", + "\n", + " label_horizon_19_days label_horizon_20_days label_horizon_21_days \\\n", + "0 34 32 29 \n", + "1 32 29 32 \n", + "2 29 32 18 \n", + "3 32 18 24 \n", + "4 18 24 41 \n", + "\n", + " label_horizon_22_days label_horizon_23_days label_horizon_24_days \\\n", + "0 32 18 24 \n", + "1 18 24 41 \n", + "2 24 41 30 \n", + "3 41 30 44 \n", + "4 30 44 36 \n", + "\n", + " label_horizon_25_days label_horizon_26_days label_horizon_27_days \\\n", + "0 41 30 44 \n", + "1 30 44 36 \n", + "2 44 36 15 \n", + "3 36 15 26 \n", + "4 15 26 33 \n", "\n", " timestamp \n", "0 1.298765e+09 \n", @@ -2760,7 +4572,7 @@ "3 1.299024e+09 \n", "4 1.299110e+09 \n", "\n", - "[5 rows x 56 columns]" + "[5 rows x 70 columns]" ] }, "execution_count": 44, @@ -2806,19 +4618,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "==> tmp/temporian_m5/train.csv <==\n", - "item_id,dept_id,cat_id,store_id,state_id,f_sales,f_sales_lag_1_d,f_sales_lag_2_d,f_sales_lag_3_d,f_sales_lag_7_d,f_sales_lag_14_d,f_sma_7_sales,f_sd_7_sales,f_sum_7_sales,f_sma_14_sales,f_sd_14_sales,f_sum_14_sales,f_sma_28_sales,f_sd_28_sales,f_sum_28_sales,f_sma_84_sales,f_sd_84_sales,f_sum_84_sales,f_calendar_day_of_month,f_calendar_day_of_week,f_calendar_month,f_sum28_per_dep_sales,f_event_name_1,f_event_type_1,f_event_name_2,f_event_type_2,f_snap_CA,f_snap_TX,f_snap_WI,f_leak5_event_name_1,f_leak5_event_type_1,f_leak5_event_name_2,f_leak5_event_type_2,f_leak5_snap_CA,f_leak5_snap_TX,f_leak5_snap_WI,day,label_horizon_1_days,label_horizon_2_days,label_horizon_3_days,label_horizon_4_days,label_horizon_5_days,label_horizon_6_days,label_horizon_7_days,label_horizon_8_days,label_horizon_9_days,label_horizon_10_days,label_horizon_11_days,label_horizon_12_days,label_horizon_13_days,timestamp\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,31,37,36,29,14,52,29.571428,7.168525,207.0,27.714285,9.779195,388.0,30.964285,10.942976,867.0,31.733334,11.017965,952.0,27,6,2,9092,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,0,0,30,43,15,34,32,34,63,47,25,27,27,27,49,44,1298764800.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,43,31,37,36,14,45,33.714287,5.0345745,236.0,27.571428,9.537252,386.0,31.5,11.149952,882.0,32.096775,11.020083,995.0,28,0,2,8983,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,1,1,31,15,34,32,34,63,47,25,27,27,27,49,44,47,1298851200.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,15,43,31,37,33,37,31.142857,8.288177,218.0,26.0,9.665846,364.0,31.071428,11.538533,870.0,31.5625,11.247048,1010.0,1,1,3,8891,no_event,no_event,no_event,no_event,1,1,0,no_event,no_event,no_event,no_event,1,1,1,32,34,32,34,63,47,25,27,27,27,49,44,47,38,1298937600.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,34,15,43,31,27,24,32.142857,8.14912,225.0,26.714285,9.859213,374.0,31.464285,11.443907,881.0,31.636364,11.083206,1044.0,2,2,3,8929,no_event,no_event,no_event,no_event,1,0,1,no_event,no_event,no_event,no_event,1,1,0,33,32,34,63,47,25,27,27,27,49,44,47,38,22,1299024000.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,32,34,15,43,29,14,32.57143,8.0508585,228.0,28.0,9.273619,392.0,31.464285,11.443907,881.0,31.647058,10.919174,1076.0,3,3,3,8743,no_event,no_event,no_event,no_event,1,1,1,no_event,no_event,no_event,no_event,1,0,1,34,34,63,47,25,27,27,27,49,44,47,38,22,38,1299110400.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,34,32,34,15,36,15,32.285713,7.959079,226.0,29.357143,8.640495,411.0,31.5,11.450203,882.0,31.714285,10.769193,1110.0,4,4,3,8582,no_event,no_event,no_event,no_event,1,0,0,LentStart,Religious,no_event,no_event,1,1,1,35,63,47,25,27,27,27,49,44,47,38,22,38,35,1299196800.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,63,34,32,34,37,32,36.0,13.458932,252.0,31.571428,12.251614,442.0,31.892857,12.303714,893.0,32.583332,11.797775,1173.0,5,5,3,8273,no_event,no_event,no_event,no_event,1,1,1,no_event,no_event,no_event,no_event,1,0,0,36,47,25,27,27,27,49,44,47,38,22,38,35,29,1299283200.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,47,63,34,32,31,14,38.285713,13.770718,268.0,33.92857,11.810796,475.0,32.392857,12.618975,907.0,32.972973,11.869759,1220.0,6,6,3,7988,no_event,no_event,no_event,no_event,1,1,1,no_event,no_event,no_event,no_event,0,1,1,37,25,27,27,27,49,44,47,38,22,38,35,29,34,1299369600.0\n", - "FOODS_3_714,FOODS_3,FOODS,WI_3,WI,25,47,63,34,43,14,35.714287,14.319959,250.0,34.714287,10.779799,486.0,32.107143,12.692347,899.0,32.763157,11.781864,1245.0,7,0,3,7831,no_event,no_event,no_event,no_event,1,1,0,no_event,no_event,no_event,no_event,0,1,1,38,27,27,27,49,44,47,38,22,38,35,29,34,26,1299456000.0\n", - "head: -n: No such file or directory\n", - "head: 5: No such file or directory\n" + "item_id,dept_id,cat_id,store_id,state_id,f_sales,f_sales_lag_1_d,f_sales_lag_2_d,f_sales_lag_3_d,f_sales_lag_7_d,f_sales_lag_14_d,f_sma_7_sales,f_sd_7_sales,f_sum_7_sales,f_sma_14_sales,f_sd_14_sales,f_sum_14_sales,f_sma_28_sales,f_sd_28_sales,f_sum_28_sales,f_sma_84_sales,f_sd_84_sales,f_sum_84_sales,f_calendar_day_of_month,f_calendar_day_of_week,f_calendar_month,f_sum28_per_dep_sales,f_event_name_1,f_event_type_1,f_event_name_2,f_event_type_2,f_snap_CA,f_snap_TX,f_snap_WI,f_leak5_event_name_1,f_leak5_event_type_1,f_leak5_event_name_2,f_leak5_event_type_2,f_leak5_snap_CA,f_leak5_snap_TX,f_leak5_snap_WI,day,label_horizon_1_days,label_horizon_2_days,label_horizon_3_days,label_horizon_4_days,label_horizon_5_days,label_horizon_6_days,label_horizon_7_days,label_horizon_8_days,label_horizon_9_days,label_horizon_10_days,label_horizon_11_days,label_horizon_12_days,label_horizon_13_days,label_horizon_14_days,label_horizon_15_days,label_horizon_16_days,label_horizon_17_days,label_horizon_18_days,label_horizon_19_days,label_horizon_20_days,label_horizon_21_days,label_horizon_22_days,label_horizon_23_days,label_horizon_24_days,label_horizon_25_days,label_horizon_26_days,label_horizon_27_days,timestamp\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,48,47,31,46,38,53,40.142857,7.3955307,281.0,42.357143,7.393116,593.0,42.82143,11.796045,1199.0,43.333332,12.070166,1300.0,27,6,2,5313,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,0,0,30,30,42,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,1298764800.0\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,30,48,47,31,36,41,39.285713,8.136589,275.0,41.57143,8.0508585,582.0,42.607143,11.971213,1193.0,42.903225,12.10533,1330.0,28,0,2,5298,no_event,no_event,no_event,no_event,0,0,0,no_event,no_event,no_event,no_event,1,1,1,31,42,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,1298851200.0\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,42,30,48,47,29,45,41.142857,6.9781,288.0,41.357143,7.9964914,579.0,42.392857,11.926379,1187.0,42.875,11.91572,1372.0,1,1,3,5254,no_event,no_event,no_event,no_event,1,1,0,no_event,no_event,no_event,no_event,1,1,1,32,39,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,15,1298937600.0\n", + "FOODS_3_694,FOODS_3,FOODS,WI_2,WI,39,42,30,48,44,34,40.42857,6.9045978,283.0,41.714287,7.768329,584.0,42.67857,11.744462,1195.0,42.757576,11.752576,1411.0,2,2,3,5319,no_event,no_event,no_event,no_event,1,0,1,no_event,no_event,no_event,no_event,1,1,0,33,25,27,43,34,29,29,19,7,42,30,32,46,27,33,36,34,32,29,32,18,24,41,30,44,36,15,26,1299024000.0\n" ] } ], @@ -2839,7 +4643,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "18d6d3b1-32d0-4d4c-b829-814277353ad2", "metadata": {}, "outputs": [ @@ -2852,6 +4656,18 @@ "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-22 17:19:07.938517: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2023-08-22 17:19:07.941786: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-08-22 17:19:08.021257: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n", + "2023-08-22 17:19:08.022287: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-08-22 17:19:09.593394: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n" + ] } ], "source": [ @@ -2863,10 +4679,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "f6666033-3849-4418-bfc8-5a791221e5d1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label_names: ['label_horizon_1_days', 'label_horizon_2_days', 'label_horizon_3_days', 'label_horizon_4_days', 'label_horizon_5_days', 'label_horizon_6_days', 'label_horizon_7_days', 'label_horizon_8_days', 'label_horizon_9_days', 'label_horizon_10_days', 'label_horizon_11_days', 'label_horizon_12_days', 'label_horizon_13_days', 'label_horizon_14_days', 'label_horizon_15_days', 'label_horizon_16_days', 'label_horizon_17_days', 'label_horizon_18_days', 'label_horizon_19_days', 'label_horizon_20_days', 'label_horizon_21_days', 'label_horizon_22_days', 'label_horizon_23_days', 'label_horizon_24_days', 'label_horizon_25_days', 'label_horizon_26_days', 'label_horizon_27_days']\n", + "feature_names: ['f_sales', 'f_sales_lag_1_d', 'f_sales_lag_2_d', 'f_sales_lag_3_d', 'f_sales_lag_7_d', 'f_sales_lag_14_d', 'f_sma_7_sales', 'f_sd_7_sales', 'f_sum_7_sales', 'f_sma_14_sales', 'f_sd_14_sales', 'f_sum_14_sales', 'f_sma_28_sales', 'f_sd_28_sales', 'f_sum_28_sales', 'f_sma_84_sales', 'f_sd_84_sales', 'f_sum_84_sales', 'f_calendar_day_of_month', 'f_calendar_day_of_week', 'f_calendar_month', 'f_sum28_per_dep_sales', 'f_event_name_1', 'f_event_type_1', 'f_event_name_2', 'f_event_type_2', 'f_snap_CA', 'f_snap_TX', 'f_snap_WI', 'f_leak5_event_name_1', 'f_leak5_event_type_1', 'f_leak5_event_name_2', 'f_leak5_event_type_2', 'f_leak5_snap_CA', 'f_leak5_snap_TX', 'f_leak5_snap_WI', 'dept_id', 'cat_id', 'store_id', 'state_id']\n" + ] + } + ], "source": [ "# The name of the labels and features.\n", "label_names = [x for x in test_dataset.columns if x.startswith(\"label_\")]\n", @@ -2893,7 +4718,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "6a46a286-1825-49ec-a147-f539987da590", "metadata": {}, "outputs": [], @@ -2919,29 +4744,1107 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "4a7580bf-bd81-4806-9dde-1465b0a6fb00", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Use /tmp/tmpfn9n19rs as temporary training directory\n", + "Reading training dataset...\n", + "Training tensor examples:\n", + "Features: {'f_sales': , 'f_sales_lag_1_d': , 'f_sales_lag_2_d': , 'f_sales_lag_3_d': , 'f_sales_lag_7_d': , 'f_sales_lag_14_d': , 'f_sma_7_sales': , 'f_sd_7_sales': , 'f_sum_7_sales': , 'f_sma_14_sales': , 'f_sd_14_sales': , 'f_sum_14_sales': , 'f_sma_28_sales': , 'f_sd_28_sales': , 'f_sum_28_sales': , 'f_sma_84_sales': , 'f_sd_84_sales': , 'f_sum_84_sales': , 'f_calendar_day_of_month': , 'f_calendar_day_of_week': , 'f_calendar_month': , 'f_sum28_per_dep_sales': , 'f_event_name_1': , 'f_event_type_1': , 'f_event_name_2': , 'f_event_type_2': , 'f_snap_CA': , 'f_snap_TX': , 'f_snap_WI': , 'f_leak5_event_name_1': , 'f_leak5_event_type_1': , 'f_leak5_event_name_2': , 'f_leak5_event_type_2': , 'f_leak5_snap_CA': , 'f_leak5_snap_TX': , 'f_leak5_snap_WI': , 'dept_id': , 'cat_id': , 'store_id': , 'state_id': }\n", + "Label: {'label_horizon_1_days': , 'label_horizon_2_days': , 'label_horizon_3_days': , 'label_horizon_4_days': , 'label_horizon_5_days': , 'label_horizon_6_days': , 'label_horizon_7_days': , 'label_horizon_8_days': , 'label_horizon_9_days': , 'label_horizon_10_days': , 'label_horizon_11_days': , 'label_horizon_12_days': , 'label_horizon_13_days': , 'label_horizon_14_days': , 'label_horizon_15_days': , 'label_horizon_16_days': , 'label_horizon_17_days': , 'label_horizon_18_days': , 'label_horizon_19_days': , 'label_horizon_20_days': , 'label_horizon_21_days': , 'label_horizon_22_days': , 'label_horizon_23_days': , 'label_horizon_24_days': , 'label_horizon_25_days': , 'label_horizon_26_days': , 'label_horizon_27_days': }\n", + "Weights: None\n", + "Normalized tensor features:\n", + " {'f_sales': SemanticTensor(semantic=, tensor=), 'f_sales_lag_1_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_2_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_3_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_7_d': SemanticTensor(semantic=, tensor=), 'f_sales_lag_14_d': SemanticTensor(semantic=, tensor=), 'f_sma_7_sales': SemanticTensor(semantic=, tensor=), 'f_sd_7_sales': SemanticTensor(semantic=, tensor=), 'f_sum_7_sales': SemanticTensor(semantic=, tensor=), 'f_sma_14_sales': SemanticTensor(semantic=, tensor=), 'f_sd_14_sales': SemanticTensor(semantic=, tensor=), 'f_sum_14_sales': SemanticTensor(semantic=, tensor=), 'f_sma_28_sales': SemanticTensor(semantic=, tensor=), 'f_sd_28_sales': SemanticTensor(semantic=, tensor=), 'f_sum_28_sales': SemanticTensor(semantic=, tensor=), 'f_sma_84_sales': SemanticTensor(semantic=, tensor=), 'f_sd_84_sales': SemanticTensor(semantic=, tensor=), 'f_sum_84_sales': SemanticTensor(semantic=, tensor=), 'f_calendar_day_of_month': SemanticTensor(semantic=, tensor=), 'f_calendar_day_of_week': SemanticTensor(semantic=, tensor=), 'f_calendar_month': SemanticTensor(semantic=, tensor=), 'f_sum28_per_dep_sales': SemanticTensor(semantic=, tensor=), 'f_event_name_1': SemanticTensor(semantic=, tensor=), 'f_event_type_1': SemanticTensor(semantic=, tensor=), 'f_event_name_2': SemanticTensor(semantic=, tensor=), 'f_event_type_2': SemanticTensor(semantic=, tensor=), 'f_snap_CA': SemanticTensor(semantic=, tensor=), 'f_snap_TX': SemanticTensor(semantic=, tensor=), 'f_snap_WI': SemanticTensor(semantic=, tensor=), 'f_leak5_event_name_1': SemanticTensor(semantic=, tensor=), 'f_leak5_event_type_1': SemanticTensor(semantic=, tensor=), 'f_leak5_event_name_2': SemanticTensor(semantic=, tensor=), 'f_leak5_event_type_2': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_CA': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_TX': SemanticTensor(semantic=, tensor=), 'f_leak5_snap_WI': SemanticTensor(semantic=, tensor=), 'dept_id': SemanticTensor(semantic=, tensor=), 'cat_id': SemanticTensor(semantic=, tensor=), 'store_id': SemanticTensor(semantic=, tensor=), 'state_id': SemanticTensor(semantic=, tensor=)}\n", + "Training dataset read in 0:00:15.636382. Found 150720 examples.\n", + "Training model...\n", + "Standard output detected as not visible to the user e.g. running in a notebook. Creating a training log redirection. If training gets stuck, try calling tfdf.keras.set_training_logs_redirection(False).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO 23-08-22 17:19:27.7980 CEST kernel.cc:773] Start Yggdrasil model training\n", + "[INFO 23-08-22 17:19:27.7980 CEST kernel.cc:774] Collect training examples\n", + "[INFO 23-08-22 17:19:27.7981 CEST kernel.cc:787] Dataspec guide:\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_1_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_2_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_3_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_4_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_5_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_6_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_7_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_8_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_9_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_10_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_11_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_12_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_13_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_14_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_15_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_16_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_17_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_18_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_19_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_20_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_21_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_22_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_23_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_24_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_25_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_26_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "column_guides {\n", + " column_name_pattern: \"^label_horizon_27_days$\"\n", + " type: NUMERICAL\n", + "}\n", + "default_column_guide {\n", + " categorial {\n", + " max_vocab_count: 2000\n", + " }\n", + " discretized_numerical {\n", + " maximum_num_bins: 255\n", + " }\n", + "}\n", + "ignore_columns_without_guides: false\n", + "detect_numerical_as_discretized_numerical: false\n", + "\n", + "[INFO 23-08-22 17:19:27.8163 CEST kernel.cc:393] Number of batches: 1508\n", + "[INFO 23-08-22 17:19:27.8163 CEST kernel.cc:394] Number of examples: 150720\n", + "[INFO 23-08-22 17:19:28.0986 CEST kernel.cc:794] Training dataset:\n", + "Number of records: 150720\n", + "Number of columns: 67\n", + "\n", + "Number of columns by type:\n", + "\tNUMERICAL: 55 (82.0896%)\n", + "\tCATEGORICAL: 12 (17.9104%)\n", + "\n", + "Columns:\n", + "\n", + "NUMERICAL: 55 (82.0896%)\n", + "\t2: \"f_calendar_day_of_month\" NUMERICAL mean:15.7107 min:1 max:31 sd:8.79464\n", + "\t3: \"f_calendar_day_of_week\" NUMERICAL mean:3.00159 min:0 max:6 sd:2.00066\n", + "\t4: \"f_calendar_month\" NUMERICAL mean:6.42622 min:1 max:12 sd:3.44017\n", + "\t13: \"f_leak5_snap_CA\" NUMERICAL mean:0.327495 min:0 max:1 sd:0.469299\n", + "\t14: \"f_leak5_snap_TX\" NUMERICAL mean:0.328025 min:0 max:1 sd:0.469494\n", + "\t15: \"f_leak5_snap_WI\" NUMERICAL mean:0.328025 min:0 max:1 sd:0.469494\n", + "\t16: \"f_sales\" NUMERICAL mean:29.5872 min:0 max:763 sd:31.5277\n", + "\t17: \"f_sales_lag_14_d\" NUMERICAL mean:29.5573 min:0 max:763 sd:31.5369\n", + "\t18: \"f_sales_lag_1_d\" NUMERICAL mean:29.5861 min:0 max:763 sd:31.5303\n", + "\t19: \"f_sales_lag_2_d\" NUMERICAL mean:29.5797 min:0 max:763 sd:31.5265\n", + "\t20: \"f_sales_lag_3_d\" NUMERICAL mean:29.5746 min:0 max:763 sd:31.5255\n", + "\t21: \"f_sales_lag_7_d\" NUMERICAL mean:29.5707 min:0 max:763 sd:31.5324\n", + "\t22: \"f_sd_14_sales\" NUMERICAL mean:10.0323 min:0 max:203.329 sd:10.8368\n", + "\t23: \"f_sd_28_sales\" NUMERICAL mean:10.8908 min:0 max:192.891 sd:11.4905\n", + "\t24: \"f_sd_7_sales\" NUMERICAL mean:9.12405 min:0 max:235.264 sd:10.209\n", + "\t25: \"f_sd_84_sales\" NUMERICAL mean:12.2336 min:0 max:173.639 sd:12.8959\n", + "\t26: \"f_sma_14_sales\" NUMERICAL mean:29.5678 min:0 max:468.643 sd:27.8563\n", + "\t27: \"f_sma_28_sales\" NUMERICAL mean:29.5606 min:0 max:442.75 sd:27.2844\n", + "\t28: \"f_sma_7_sales\" NUMERICAL mean:29.5773 min:0 max:602.857 sd:28.3994\n", + "\t29: \"f_sma_84_sales\" NUMERICAL mean:29.5427 min:0 max:279.786 sd:26.147\n", + "\t30: \"f_snap_CA\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", + "\t31: \"f_snap_TX\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", + "\t32: \"f_snap_WI\" NUMERICAL mean:0.329087 min:0 max:1 sd:0.469882\n", + "\t33: \"f_sum28_per_dep_sales\" NUMERICAL mean:6621.58 min:1333 max:19255 sd:2895.7\n", + "\t34: \"f_sum_14_sales\" NUMERICAL mean:413.949 min:0 max:6561 sd:389.988\n", + "\t35: \"f_sum_28_sales\" NUMERICAL mean:827.698 min:0 max:12397 sd:763.964\n", + "\t36: \"f_sum_7_sales\" NUMERICAL mean:207.041 min:0 max:4220 sd:198.796\n", + "\t37: \"f_sum_84_sales\" NUMERICAL mean:2460.49 min:0 max:23502 sd:2194.56\n", + "\t38: \"label_horizon_10_days\" NUMERICAL mean:29.6007 min:0 max:763 sd:31.5209\n", + "\t39: \"label_horizon_11_days\" NUMERICAL mean:29.6038 min:0 max:763 sd:31.5202\n", + "\t40: \"label_horizon_12_days\" NUMERICAL mean:29.6097 min:0 max:763 sd:31.5218\n", + "\t41: \"label_horizon_13_days\" NUMERICAL mean:29.6176 min:0 max:763 sd:31.5275\n", + "\t42: \"label_horizon_14_days\" NUMERICAL mean:29.6194 min:0 max:763 sd:31.5271\n", + "\t43: \"label_horizon_15_days\" NUMERICAL mean:29.6189 min:0 max:763 sd:31.5249\n", + "\t44: \"label_horizon_16_days\" NUMERICAL mean:29.6204 min:0 max:763 sd:31.5241\n", + "\t45: \"label_horizon_17_days\" NUMERICAL mean:29.6229 min:0 max:763 sd:31.5229\n", + "\t46: \"label_horizon_18_days\" NUMERICAL mean:29.6257 min:0 max:763 sd:31.5219\n", + "\t47: \"label_horizon_19_days\" NUMERICAL mean:29.6304 min:0 max:763 sd:31.523\n", + "\t48: \"label_horizon_1_days\" NUMERICAL mean:29.5853 min:0 max:763 sd:31.5245\n", + "\t49: \"label_horizon_20_days\" NUMERICAL mean:29.6377 min:0 max:763 sd:31.5259\n", + "\t50: \"label_horizon_21_days\" NUMERICAL mean:29.6423 min:0 max:763 sd:31.5277\n", + "\t51: \"label_horizon_22_days\" NUMERICAL mean:29.6404 min:0 max:763 sd:31.5232\n", + "\t52: \"label_horizon_23_days\" NUMERICAL mean:29.6433 min:0 max:763 sd:31.5218\n", + "\t53: \"label_horizon_24_days\" NUMERICAL mean:29.6473 min:0 max:763 sd:31.5202\n", + "\t54: \"label_horizon_25_days\" NUMERICAL mean:29.6505 min:0 max:763 sd:31.518\n", + "\t55: \"label_horizon_26_days\" NUMERICAL mean:29.6582 min:0 max:763 sd:31.5185\n", + "\t56: \"label_horizon_27_days\" NUMERICAL mean:29.6661 min:0 max:763 sd:31.5204\n", + "\t57: \"label_horizon_2_days\" NUMERICAL mean:29.5869 min:0 max:763 sd:31.5242\n", + "\t58: \"label_horizon_3_days\" NUMERICAL mean:29.5863 min:0 max:763 sd:31.5234\n", + "\t59: \"label_horizon_4_days\" NUMERICAL mean:29.5873 min:0 max:763 sd:31.5224\n", + "\t60: \"label_horizon_5_days\" NUMERICAL mean:29.5916 min:0 max:763 sd:31.525\n", + "\t61: \"label_horizon_6_days\" NUMERICAL mean:29.5976 min:0 max:763 sd:31.5282\n", + "\t62: \"label_horizon_7_days\" NUMERICAL mean:29.5993 min:0 max:763 sd:31.5276\n", + "\t63: \"label_horizon_8_days\" NUMERICAL mean:29.5965 min:0 max:763 sd:31.5239\n", + "\t64: \"label_horizon_9_days\" NUMERICAL mean:29.5992 min:0 max:763 sd:31.5224\n", + "\n", + "CATEGORICAL: 12 (17.9104%)\n", + "\t0: \"cat_id\" CATEGORICAL has-dict vocab-size:2 zero-ood-items most-frequent:\"FOODS\" 150720 (100%)\n", + "\t1: \"dept_id\" CATEGORICAL has-dict vocab-size:2 zero-ood-items most-frequent:\"FOODS_3\" 150720 (100%)\n", + "\t5: \"f_event_name_1\" CATEGORICAL has-dict vocab-size:32 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t6: \"f_event_name_2\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t7: \"f_event_type_1\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t8: \"f_event_type_2\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t9: \"f_leak5_event_name_1\" CATEGORICAL has-dict vocab-size:32 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t10: \"f_leak5_event_name_2\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t11: \"f_leak5_event_type_1\" CATEGORICAL has-dict vocab-size:6 zero-ood-items most-frequent:\"no_event\" 138640 (91.9851%)\n", + "\t12: \"f_leak5_event_type_2\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"no_event\" 150400 (99.7877%)\n", + "\t65: \"state_id\" CATEGORICAL has-dict vocab-size:4 zero-ood-items most-frequent:\"CA\" 60288 (40%)\n", + "\t66: \"store_id\" CATEGORICAL has-dict vocab-size:11 zero-ood-items most-frequent:\"CA_1\" 15072 (10%)\n", + "\n", + "Terminology:\n", + "\tnas: Number of non-available (i.e. missing) values.\n", + "\tood: Out of dictionary.\n", + "\tmanually-defined: Attribute which type is manually defined by the user i.e. the type was not automatically inferred.\n", + "\ttokenized: The attribute value is obtained through tokenization.\n", + "\thas-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.\n", + "\tvocab-size: Number of unique values.\n", + "\n", + "[INFO 23-08-22 17:19:28.1055 CEST kernel.cc:810] Configure learner\n", + "[INFO 23-08-22 17:19:28.1057 CEST kernel.cc:824] Training config:\n", + "learner: \"MULTITASKER\"\n", + "features: \"^cat_id$\"\n", + "features: \"^dept_id$\"\n", + "features: \"^f_calendar_day_of_month$\"\n", + "features: \"^f_calendar_day_of_week$\"\n", + "features: \"^f_calendar_month$\"\n", + "features: \"^f_event_name_1$\"\n", + "features: \"^f_event_name_2$\"\n", + "features: \"^f_event_type_1$\"\n", + "features: \"^f_event_type_2$\"\n", + "features: \"^f_leak5_event_name_1$\"\n", + "features: \"^f_leak5_event_name_2$\"\n", + "features: \"^f_leak5_event_type_1$\"\n", + "features: \"^f_leak5_event_type_2$\"\n", + "features: \"^f_leak5_snap_CA$\"\n", + "features: \"^f_leak5_snap_TX$\"\n", + "features: \"^f_leak5_snap_WI$\"\n", + "features: \"^f_sales$\"\n", + "features: \"^f_sales_lag_14_d$\"\n", + "features: \"^f_sales_lag_1_d$\"\n", + "features: \"^f_sales_lag_2_d$\"\n", + "features: \"^f_sales_lag_3_d$\"\n", + "features: \"^f_sales_lag_7_d$\"\n", + "features: \"^f_sd_14_sales$\"\n", + "features: \"^f_sd_28_sales$\"\n", + "features: \"^f_sd_7_sales$\"\n", + "features: \"^f_sd_84_sales$\"\n", + "features: \"^f_sma_14_sales$\"\n", + "features: \"^f_sma_28_sales$\"\n", + "features: \"^f_sma_7_sales$\"\n", + "features: \"^f_sma_84_sales$\"\n", + "features: \"^f_snap_CA$\"\n", + "features: \"^f_snap_TX$\"\n", + "features: \"^f_snap_WI$\"\n", + "features: \"^f_sum28_per_dep_sales$\"\n", + "features: \"^f_sum_14_sales$\"\n", + "features: \"^f_sum_28_sales$\"\n", + "features: \"^f_sum_7_sales$\"\n", + "features: \"^f_sum_84_sales$\"\n", + "features: \"^state_id$\"\n", + "features: \"^store_id$\"\n", + "label: \"^label_horizon_1_days$\"\n", + "task: REGRESSION\n", + "metadata {\n", + " framework: \"TF Keras\"\n", + "}\n", + "[yggdrasil_decision_forests.model.multitasker.proto.multitasker_config] {\n", + " base_learner {\n", + " learner: \"GRADIENT_BOOSTED_TREES\"\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_1_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_2_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_3_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_4_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_5_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_6_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_7_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_8_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_9_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_10_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_11_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_12_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_13_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_14_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_15_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_16_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_17_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_18_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_19_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_20_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_21_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_22_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_23_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_24_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_25_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_26_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + " subtasks {\n", + " primary: true\n", + " train_config {\n", + " label: \"^label_horizon_27_days$\"\n", + " task: REGRESSION\n", + " }\n", + " }\n", + "}\n", + "\n", + "[INFO 23-08-22 17:19:28.1066 CEST kernel.cc:827] Deployment config:\n", + "cache_path: \"/tmp/tmpfn9n19rs/working_cache\"\n", + "num_threads: 8\n", + "try_resume_training: true\n", + "\n", + "[INFO 23-08-22 17:19:28.1118 CEST kernel.cc:889] Train model\n", + "[INFO 23-08-22 17:19:28.1495 CEST multitasker.cc:286] Train multitasker primary tasks with 27 model(s)\n", + "[WARNING[ 23-08-22 17:19:28.1574 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING[[WARNING[WARNINGWARNINGWARNING 23-08-22 17:19:28.1575 CEST gradient_boosted_trees.cc: 23-08-22 17:19:28.1575 CEST gradient_boosted_trees.cc:1818 23-08-22 17:19:28.1575 CEST gradient_boosted_trees.cc] 1818\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "] 23-08-22 17:19:28.1575 CEST gradient_boosted_trees.cc:[WARNING 23-08-22 17:19:28.1576 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1576 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + " 23-08-22 17:19:28.1574 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1577 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[WARNING 23-08-22 17:19:28.1580 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1581 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1581 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "\"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1581 CEST gradient_boosted_trees.cc:1829] [WARNING 23-08-22 17:19:28.1582 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1582 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1582 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[WARNING 23-08-22 17:19:28.1584 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1584 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1584 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "\"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1598 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1600 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1600 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + ":1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1602 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:19:28.1602 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:19:32.0507 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.0614 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.1276 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.1455 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.4159 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.4160 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.5365 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.5365 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.6083 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.6084 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.8171 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:32.8182 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.8183 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:32.9276 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:32.9277 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:33.1349 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:19:33.1350 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:19:33.1581 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:34.0283 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:34.1116 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:34.3145 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:34.8990 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:35.1325 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.267696 train-rmse:29.267696 valid-loss:29.130789 valid-rmse:29.130789\n", + "[INFO 23-08-22 17:19:35.1814 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:35.3696 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:19:35.9690 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.335985 train-rmse:29.335985 valid-loss:29.099661 valid-rmse:29.099661\n", + "[INFO 23-08-22 17:19:37.9991 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.214186 train-rmse:29.214186 valid-loss:29.899080 valid-rmse:29.899080\n", + "[INFO 23-08-22 17:19:38.5385 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.303213 train-rmse:29.303213 valid-loss:29.393198 valid-rmse:29.393198\n", + "[INFO 23-08-22 17:19:38.5435 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.399338 train-rmse:29.399338 valid-loss:29.028122 valid-rmse:29.028122\n", + "[INFO 23-08-22 17:19:38.6758 CEST gradient_boosted_trees.cc:1544] \tnum-trees:2 train-loss:27.390989 train-rmse:27.390989 valid-loss:27.127483 valid-rmse:27.127483\n", + "[INFO 23-08-22 17:19:38.9145 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.124109 train-rmse:29.124109 valid-loss:29.133936 valid-rmse:29.133936\n", + "[INFO 23-08-22 17:19:39.5908 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.295769 train-rmse:29.295769 valid-loss:28.769020 valid-rmse:28.769020\n", + "[INFO 23-08-22 17:19:39.7044 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.323404 train-rmse:29.323404 valid-loss:28.357626 valid-rmse:28.357626\n", + "[INFO 23-08-22 17:20:09.1838 CEST gradient_boosted_trees.cc:1544] \tnum-trees:15 train-loss:16.286356 train-rmse:16.286356 valid-loss:17.388578 valid-rmse:17.388578\n", + "[INFO 23-08-22 17:20:39.5899 CEST gradient_boosted_trees.cc:1544] \tnum-trees:26 train-loss:15.550566 train-rmse:15.550566 valid-loss:15.613172 valid-rmse:15.613172\n", + "[INFO 23-08-22 17:21:09.7584 CEST gradient_boosted_trees.cc:1544] \tnum-trees:36 train-loss:12.604913 train-rmse:12.604913 valid-loss:12.728271 valid-rmse:12.728271\n", + "[INFO 23-08-22 17:21:29.1166 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.866138 train-rmse:13.866138 valid-loss:15.359539 valid-rmse:15.359539\n", + "[INFO 23-08-22 17:21:29.1166 CEST gradient_boosted_trees.cc:247] Truncates the model to 48 tree(s) i.e. 48 iteration(s).\n", + "[INFO 23-08-22 17:21:29.1167 CEST gradient_boosted_trees.cc:310] Final model num-trees:48 valid-loss:15.355445 valid-rmse:15.355445\n", + "[WARNING 23-08-22 17:21:29.1246 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:29.1246 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:29.1246 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:30.4713 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:30.4714 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:30.6207 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:30.8606 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.575471 train-rmse:13.575471 valid-loss:15.029240 valid-rmse:15.029240\n", + "[INFO 23-08-22 17:21:30.8838 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:21:30.8877 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.029240 valid-rmse:15.029240\n", + "[WARNING 23-08-22 17:21:30.9208 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:30.9309 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:30.9312 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:31.0555 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.423209 train-rmse:13.423209 valid-loss:13.453820 valid-rmse:13.453820\n", + "[INFO 23-08-22 17:21:31.0684 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:21:31.0688 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.453820 valid-rmse:13.453820\n", + "[WARNING 23-08-22 17:21:31.0988 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.0991 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.0993 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:31.5115 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.071945 train-rmse:14.071945 valid-loss:14.601532 valid-rmse:14.601532\n", + "[INFO 23-08-22 17:21:31.5116 CEST gradient_boosted_trees.cc:247] Truncates the model to 47 tree(s) i.e. 47 iteration(s).\n", + "[INFO 23-08-22 17:21:31.5116 CEST gradient_boosted_trees.cc:310] Final model num-trees:47 valid-loss:14.593384 valid-rmse:14.593384\n", + "[WARNING 23-08-22 17:21:31.5663 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.5663 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.5663 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:31.7412 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.070064 train-rmse:13.070064 valid-loss:13.403740 valid-rmse:13.403740\n", + "[INFO 23-08-22 17:21:31.7490 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:21:31.7490 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.403740 valid-rmse:13.403740\n", + "[WARNING 23-08-22 17:21:31.8163 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.8618 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:31.8654 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:32.1903 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.487211 train-rmse:29.487211 valid-loss:28.607134 valid-rmse:28.607134\n", + "[INFO 23-08-22 17:21:33.1783 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:33.1786 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:33.3258 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:33.4076 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:12.181689 train-rmse:12.181689 valid-loss:12.686768 valid-rmse:12.686768\n", + "[INFO 23-08-22 17:21:33.4866 CEST gradient_boosted_trees.cc:247] Truncates the model to 41 tree(s) i.e. 41 iteration(s).\n", + "[INFO 23-08-22 17:21:33.4872 CEST gradient_boosted_trees.cc:310] Final model num-trees:41 valid-loss:12.684937 valid-rmse:12.684937\n", + "[WARNING 23-08-22 17:21:33.5223 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:33.5226 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:33.5228 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:34.5676 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:34.5677 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:34.7348 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:34.8318 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.497025 train-rmse:29.497025 valid-loss:28.542063 valid-rmse:28.542063\n", + "[INFO 23-08-22 17:21:35.1272 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:35.1273 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:35.2377 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:35.8849 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:35.9068 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:36.0690 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:36.0691 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:36.1502 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:36.6343 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:36.8570 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.453743 train-rmse:29.453743 valid-loss:29.037130 valid-rmse:29.037130\n", + "[INFO 23-08-22 17:21:37.0224 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:13.252879 train-rmse:13.252879 valid-loss:13.566219 valid-rmse:13.566219\n", + "[INFO 23-08-22 17:21:37.0225 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:21:37.0225 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:13.566219 valid-rmse:13.566219\n", + "[WARNING 23-08-22 17:21:37.0245 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:37.0245 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:37.0245 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:37.1079 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.470354 train-rmse:29.470354 valid-loss:28.912182 valid-rmse:28.912182\n", + "[INFO 23-08-22 17:21:37.2099 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.514459 train-rmse:14.514459 valid-loss:14.994228 valid-rmse:14.994228\n", + "[INFO 23-08-22 17:21:37.2436 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:21:37.2473 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:14.994228 valid-rmse:14.994228\n", + "[WARNING 23-08-22 17:21:37.3063 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:37.3167 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:21:37.3167 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:21:37.8581 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.474899 train-rmse:29.474899 valid-loss:28.634254 valid-rmse:28.634254\n", + "[INFO 23-08-22 17:21:38.7341 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.421598 train-rmse:29.421598 valid-loss:29.626032 valid-rmse:29.626032\n", + "[INFO 23-08-22 17:21:39.3875 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:39.3893 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:39.5529 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:39.8978 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:21:39.8978 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:21:40.0404 CEST gradient_boosted_trees.cc:1544] \tnum-trees:6 train-loss:22.339996 train-rmse:22.339996 valid-loss:21.602955 valid-rmse:21.602955\n", + "[INFO 23-08-22 17:21:40.3224 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:21:41.1769 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.545298 train-rmse:29.545298 valid-loss:29.298836 valid-rmse:29.298836\n", + "[INFO 23-08-22 17:21:43.4812 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.541935 train-rmse:29.541935 valid-loss:28.897583 valid-rmse:28.897583\n", + "[INFO 23-08-22 17:22:10.7016 CEST gradient_boosted_trees.cc:1544] \tnum-trees:13 train-loss:18.213362 train-rmse:18.213362 valid-loss:18.072496 valid-rmse:18.072496\n", + "[INFO 23-08-22 17:22:41.1286 CEST gradient_boosted_trees.cc:1544] \tnum-trees:23 train-loss:16.149427 train-rmse:16.149427 valid-loss:16.552889 valid-rmse:16.552889\n", + "[INFO 23-08-22 17:23:11.2261 CEST gradient_boosted_trees.cc:1544] \tnum-trees:41 train-loss:15.091777 train-rmse:15.091777 valid-loss:15.722352 valid-rmse:15.722352\n", + "[INFO 23-08-22 17:23:37.3905 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.713874 train-rmse:14.713874 valid-loss:15.431265 valid-rmse:15.431265\n", + "[INFO 23-08-22 17:23:37.4181 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:37.4187 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.431265 valid-rmse:15.431265\n", + "[WARNING 23-08-22 17:23:37.5522 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:37.5523 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:37.5524 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:41.2318 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.750937 train-rmse:14.750937 valid-loss:15.528153 valid-rmse:15.528153\n", + "[INFO 23-08-22 17:23:41.2319 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:41.2319 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.528153 valid-rmse:15.528153\n", + "[WARNING 23-08-22 17:23:41.2358 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:41.2358 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:41.2358 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:41.6881 CEST gradient_boosted_trees.cc:1544] \tnum-trees:46 train-loss:15.416377 train-rmse:15.416377 valid-loss:15.507325 valid-rmse:15.507325\n", + "[INFO 23-08-22 17:23:41.9143 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.019258 train-rmse:15.019258 valid-loss:15.995070 valid-rmse:15.995070\n", + "[INFO 23-08-22 17:23:41.9144 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:41.9144 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.995070 valid-rmse:15.995070\n", + "[WARNING 23-08-22 17:23:41.9572 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:41.9962 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:41.9966 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:42.3678 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.769040 train-rmse:14.769040 valid-loss:15.246088 valid-rmse:15.246088\n", + "[INFO 23-08-22 17:23:42.3678 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:42.3679 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.246088 valid-rmse:15.246088\n", + "[WARNING 23-08-22 17:23:42.4095 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:42.4095 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:42.4095 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:44.4584 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:44.4585 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:45.0821 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:45.3081 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:45.3082 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:45.5279 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:45.7003 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.393883 train-rmse:15.393883 valid-loss:15.816501 valid-rmse:15.816501\n", + "[INFO 23-08-22 17:23:45.7003 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:45.7004 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.816501 valid-rmse:15.816501\n", + "[WARNING 23-08-22 17:23:45.7447 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:45.7448 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:45.7448 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:47.3018 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.870501 train-rmse:14.870501 valid-loss:15.861364 valid-rmse:15.861364\n", + "[INFO 23-08-22 17:23:47.3018 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:47.3019 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.861364 valid-rmse:15.861364\n", + "[WARNING 23-08-22 17:23:47.4206 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:47.4727 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:47.4728 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:47.6738 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:47.6739 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:47.9977 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:48.3165 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.561636 train-rmse:29.561636 valid-loss:28.802879 valid-rmse:28.802879\n", + "[INFO 23-08-22 17:23:48.4621 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.545330 train-rmse:29.545330 valid-loss:28.827353 valid-rmse:28.827353\n", + "[INFO 23-08-22 17:23:50.4980 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:50.5432 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:50.5479 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:50.5480 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:50.6301 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:14.620991 train-rmse:14.620991 valid-loss:15.723470 valid-rmse:15.723470\n", + "[INFO 23-08-22 17:23:50.6547 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:50.6548 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.723470 valid-rmse:15.723470\n", + "[WARNING 23-08-22 17:23:50.6926 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:50.6927 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:50.6927 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:51.0291 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:51.1478 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.277184 train-rmse:15.277184 valid-loss:15.470344 valid-rmse:15.470344\n", + "[INFO 23-08-22 17:23:51.1479 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:23:51.1479 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:15.470344 valid-rmse:15.470344\n", + "[WARNING 23-08-22 17:23:51.1515 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:51.1515 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:23:51.1516 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:23:51.4062 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:51.4393 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.519100 train-rmse:29.519100 valid-loss:28.915615 valid-rmse:28.915615\n", + "[INFO 23-08-22 17:23:51.4788 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:51.4788 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:51.6864 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:53.6416 CEST [INFO 23-08-22 17:23:53.6617 CEST gradient_boosted_trees.cc:gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.555222 train-rmse:29.555222 valid-loss:29.081779 valid-rmse:29.081779\n", + "1542] \tnum-trees:1 train-loss:29.580265 train-rmse:29.580265 valid-loss:28.985928 valid-rmse:28.985928\n", + "[INFO 23-08-22 17:23:53.8442 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:53.8442 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:53.9488 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:53.9779 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.485874 train-rmse:29.485874 valid-loss:29.859213 valid-rmse:29.859213\n", + "[INFO 23-08-22 17:23:54.5426 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:23:54.5427 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:23:54.6690 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:23:55.6092 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.552290 train-rmse:29.552290 valid-loss:29.436209 valid-rmse:29.436209\n", + "[INFO 23-08-22 17:23:57.1400 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.592987 train-rmse:29.592987 valid-loss:29.220953 valid-rmse:29.220953\n", + "[INFO 23-08-22 17:24:11.9398 CEST gradient_boosted_trees.cc:1544] \tnum-trees:12 train-loss:19.211472 train-rmse:19.211472 valid-loss:19.920101 valid-rmse:19.920101\n", + "[INFO 23-08-22 17:24:42.3389 CEST gradient_boosted_trees.cc:1544] \tnum-trees:22 train-loss:16.972153 train-rmse:16.972153 valid-loss:17.439720 valid-rmse:17.439720\n", + "[INFO 23-08-22 17:25:12.4270 CEST gradient_boosted_trees.cc:1544] \tnum-trees:32 train-loss:16.314236 train-rmse:16.314236 valid-loss:16.762869 valid-rmse:16.762869\n", + "[INFO 23-08-22 17:25:42.6012 CEST gradient_boosted_trees.cc:1544] \tnum-trees:48 train-loss:15.584856 train-rmse:15.584856 valid-loss:16.379702 valid-rmse:16.379702\n", + "[INFO 23-08-22 17:25:43.3731 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.488959 train-rmse:15.488959 valid-loss:16.052216 valid-rmse:16.052216\n", + "[INFO 23-08-22 17:25:43.3966 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:43.3967 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.052216 valid-rmse:16.052216\n", + "[WARNING 23-08-22 17:25:43.4003 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:43.4004 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:43.4004 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:25:47.8493 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.795024 train-rmse:15.795024 valid-loss:17.031176 valid-rmse:17.031176\n", + "[INFO 23-08-22 17:25:47.8494 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:47.8495 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.031176 valid-rmse:17.031176\n", + "[WARNING 23-08-22 17:25:47.9558 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:47.9558 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:47.9559 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:25:48.0492 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.498506 train-rmse:15.498506 valid-loss:16.925005 valid-rmse:16.925005\n", + "[INFO 23-08-22 17:25:48.0493 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:48.0493 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.925005 valid-rmse:16.925005\n", + "[WARNING 23-08-22 17:25:48.1019 CEST gradient_boosted_trees.cc:1818] \"goss_alpha\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:48.1020 CEST gradient_boosted_trees.cc:1829] \"goss_beta\" set but \"sampling_method\" not equal to \"GOSS\".\n", + "[WARNING 23-08-22 17:25:48.1020 CEST gradient_boosted_trees.cc:1843] \"selective_gradient_boosting_ratio\" set but \"sampling_method\" not equal to \"SELGB\".\n", + "[INFO 23-08-22 17:25:49.0721 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.436479 train-rmse:15.436479 valid-loss:16.489502 valid-rmse:16.489502\n", + "[INFO 23-08-22 17:25:49.0858 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:49.0859 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.489502 valid-rmse:16.489502\n", + "[INFO 23-08-22 17:25:49.3451 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.940226 train-rmse:15.940226 valid-loss:16.628620 valid-rmse:16.628620\n", + "[INFO 23-08-22 17:25:49.3730 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:49.3731 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.628620 valid-rmse:16.628620\n", + "[INFO 23-08-22 17:25:49.5604 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.528732 train-rmse:15.528732 valid-loss:16.360813 valid-rmse:16.360813\n", + "[INFO 23-08-22 17:25:49.5826 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:49.5827 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.360813 valid-rmse:16.360813\n", + "[INFO 23-08-22 17:25:49.7487 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:25:49.7488 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:25:49.8992 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.585465 train-rmse:15.585465 valid-loss:16.446827 valid-rmse:16.446827\n", + "[INFO 23-08-22 17:25:49.9236 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:49.9237 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.446827 valid-rmse:16.446827\n", + "[INFO 23-08-22 17:25:50.0453 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:25:51.9348 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:25:51.9349 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + "[INFO 23-08-22 17:25:52.1247 CEST gradient_boosted_trees.cc[INFO 23-08-22 17:25:52.1449 CEST gradient_boosted_trees.cc:459] Default loss set to SQUARED_ERROR\n", + "[INFO 23-08-22 17:25:52.1450 CEST gradient_boosted_trees.cc:1085] Training gradient boosted tree on 150720 example(s) and 40 feature(s).\n", + ":1542] \tnum-trees:1 train-loss:29.473177 train-rmse:29.473177 valid-loss:30.028440 valid-rmse:30.028440\n", + "[INFO 23-08-22 17:25:52.1676 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:25:52.8560 CEST gradient_boosted_trees.cc:1128] 135737 examples used for training and 14983 examples used for validation\n", + "[INFO 23-08-22 17:25:53.4240 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.958979 train-rmse:15.958979 valid-loss:17.016514 valid-rmse:17.016514\n", + "[INFO 23-08-22 17:25:53.4504 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:25:53.4529 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.016514 valid-rmse:17.016514\n", + "[INFO 23-08-22 17:25:54.2969 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.512590 train-rmse:29.512590 valid-loss:29.749025 valid-rmse:29.749025\n", + "[INFO 23-08-22 17:25:55.0639 CEST gradient_boosted_trees.cc:1542] \tnum-trees:1 train-loss:29.433599 train-rmse:29.433599 valid-loss:30.823900 valid-rmse:30.823900\n", + "[INFO 23-08-22 17:26:12.6080 CEST gradient_boosted_trees.cc:1544] \tnum-trees:14 train-loss:18.923498 train-rmse:18.923498 valid-loss:19.393936 valid-rmse:19.393936\n", + "[INFO 23-08-22 17:26:42.7529 CEST gradient_boosted_trees.cc:1544] \tnum-trees:46 train-loss:16.113173 train-rmse:16.113173 valid-loss:17.555851 valid-rmse:17.555851\n", + "[INFO 23-08-22 17:26:45.2334 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.972644 train-rmse:15.972644 valid-loss:17.476349 valid-rmse:17.476349\n", + "[INFO 23-08-22 17:26:45.2351 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:26:45.2352 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:17.476349 valid-rmse:17.476349\n", + "[INFO 23-08-22 17:26:46.1924 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.949216 train-rmse:15.949216 valid-loss:16.998175 valid-rmse:16.998175\n", + "[INFO 23-08-22 17:26:46.1925 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:26:46.1925 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:16.998175 valid-rmse:16.998175\n", + "[INFO 23-08-22 17:26:46.2895 CEST gradient_boosted_trees.cc:1542] \tnum-trees:50 train-loss:15.933821 train-rmse:15.933821 valid-loss:18.116308 valid-rmse:18.116308\n", + "[INFO 23-08-22 17:26:46.2895 CEST gradient_boosted_trees.cc:247] Truncates the model to 50 tree(s) i.e. 50 iteration(s).\n", + "[INFO 23-08-22 17:26:46.2895 CEST gradient_boosted_trees.cc:310] Final model num-trees:50 valid-loss:18.116308 valid-rmse:18.116308\n", + "[INFO 23-08-22 17:26:46.2920 CEST kernel.cc:926] Export model in log directory: /tmp/tmpfn9n19rs with prefix 5b2c1467920142a0\n", + "[INFO 23-08-22 17:26:46.3781 CEST kernel.cc:944] Save model in resources\n", + "[WARNING 23-08-22 17:26:46.3801 CEST abstract_model.cc:1058] Validation evaluation not supported for MULTITASKER\n", + "[INFO 23-08-22 17:26:46.3803 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 12.6849\n", + "\n", + "RMSE: 3.56159\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3808 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 13.4037\n", + "\n", + "RMSE: 3.66111\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3813 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 13.4538\n", + "\n", + "RMSE: 3.66794\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3818 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 13.5662\n", + "\n", + "RMSE: 3.68323\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3823 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.0292\n", + "\n", + "RMSE: 3.87676\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3828 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.3554\n", + "\n", + "RMSE: 3.9186\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3833 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 14.5934\n", + "\n", + "RMSE: 3.82013\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3836 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 14.9942\n", + "\n", + "RMSE: 3.87224\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3841 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.4313\n", + "\n", + "RMSE: 3.92826\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3846 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.5282\n", + "\n", + "RMSE: 3.94058\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3850 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.7235\n", + "\n", + "RMSE: 3.96528\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3854 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.2461\n", + "\n", + "RMSE: 3.90462\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3858 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.8614\n", + "\n", + "RMSE: 3.98263\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3862 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.9951\n", + "\n", + "RMSE: 3.99938\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3865 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.4703\n", + "\n", + "RMSE: 3.93324\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3869 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 15.8165\n", + "\n", + "RMSE: 3.977\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3872 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.0522\n", + "\n", + "RMSE: 4.00652\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3876 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.4895\n", + "\n", + "RMSE: 4.06073\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3881 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.3608\n", + "\n", + "RMSE: 4.04485\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3885 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.925\n", + "\n", + "RMSE: 4.114\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3888 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.4468\n", + "\n", + "RMSE: 4.05547\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3892 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 17.0312\n", + "\n", + "RMSE: 4.12688\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3896 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.6286\n", + "\n", + "RMSE: 4.07782\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3900 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 17.0165\n", + "\n", + "RMSE: 4.12511\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3905 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 17.4763\n", + "\n", + "RMSE: 4.18047\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3909 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 16.9982\n", + "\n", + "RMSE: 4.12288\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.3912 CEST abstract_model.cc:849] Model self evaluation:\n", + "Number of predictions (with weights): 1\n", + "Task: REGRESSION\n", + "Loss (SQUARED_ERROR): 18.1163\n", + "\n", + "RMSE: 4.25633\n", + "Default RMSE: : 0\n", + "\n", + "[INFO 23-08-22 17:26:46.4543 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_0\n", + "[INFO 23-08-22 17:26:46.4598 CEST abstract_model.cc:1311] Engine \"GradientBoostedTreesQuickScorerExtended\" built\n", + "[INFO 23-08-22 17:26:46.4599 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.6703 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_1\n", + "[INFO 23-08-22 17:26:46.6773 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.7141 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_2\n", + "[INFO 23-08-22 17:26:46.7188 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.7456 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_3\n", + "[INFO 23-08-22 17:26:46.7522 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.7786 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_4\n", + "[INFO 23-08-22 17:26:46.7844 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.8141 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_5\n", + "[INFO 23-08-22 17:26:46.8192 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.8565 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_6\n", + "[INFO 23-08-22 17:26:46.8639 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.8908 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_7\n", + "[INFO 23-08-22 17:26:46.8960 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:46.9250 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_8\n", + "[INFO 23-08-22 17:26:46.9325 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 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Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_18\n", + "[INFO 23-08-22 17:26:47.2972 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.3378 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_19\n", + "[INFO 23-08-22 17:26:47.3459 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.3834 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_20\n", + "[INFO 23-08-22 17:26:47.3918 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.4245 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_21\n", + "[INFO 23-08-22 17:26:47.4294 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.4576 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_22\n", + "[INFO 23-08-22 17:26:47.4631 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.4906 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_23\n", + "[INFO 23-08-22 17:26:47.4953 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.5222 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_24\n", + "[INFO 23-08-22 17:26:47.5276 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.5570 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_25\n", + "[INFO 23-08-22 17:26:47.5627 CEST kernel.cc:1075] Use fast generic engine\n", + "[INFO 23-08-22 17:26:47.5937 CEST kernel.cc:1243] Loading model from path /tmp/tmpfn9n19rs/model/ with prefix 5b2c1467920142a0_26\n", + "[INFO 23-08-22 17:26:47.6032 CEST kernel.cc:1075] Use fast generic engine\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model trained in 0:07:19.827763\n", + "Compiling model...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:AutoGraph could not transform and will run it as-is.\n", + "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n", + "Cause: could not get source code\n", + "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING: AutoGraph could not transform and will run it as-is.\n", + "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n", + "Cause: could not get source code\n", + "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n", + "Model compiled.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "model_path = os.path.join(work_directory, \"gbm.model\")\n", - "if os.path.exists(model_path):\n", - " model = tf.keras.models.load_model(model_path)\n", - "else:\n", - " model = tfdf.keras.GradientBoostedTreesModel(\n", - " multitask=[\n", - " tfdf.keras.MultiTaskItem(x, task=tfdf.keras.Task.REGRESSION)\n", - " for x in label_names\n", - " ],\n", - " num_trees=50, # Increase the number of trees (e.g., 100) for a better model.\n", - " verbose=2, # Remove to make the cell less verbose.\n", - " num_threads=8,\n", - " )\n", - " \n", - " model.fit(tf_train_dataset)\n", - " model.save(model_path)\n" + "model = tfdf.keras.GradientBoostedTreesModel(\n", + " multitask=[\n", + " tfdf.keras.MultiTaskItem(x, task=tfdf.keras.Task.REGRESSION)\n", + " for x in label_names\n", + " ],\n", + " num_trees=40, # Increase the number of trees (e.g., 100) for a better model.\n", + " verbose=2, # Remove to make the training less verbose.\n", + ")\n", + "model.fit(tf_train_dataset)" ] }, { @@ -2955,7 +5858,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "e985dba0-dc14-4341-b0c8-ad145fef7ca3", "metadata": {}, "outputs": [], @@ -2974,7 +5877,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "068ea997-52ae-40eb-b18e-a118a3d7b026", "metadata": {}, "outputs": [], @@ -3030,10 +5933,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "978fc1ff-7914-43df-af04-e5b054a9e9f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of plots (80) is larger than \"options.max_num_plots=6\". Only the first plots will be printed.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "real_sales_evset = sales_evset[\"sales\"].filter(sales_evset[\"day\"] > 1914)\n", "\n", @@ -3047,55 +5968,11 @@ "tp.plot(\n", " [real_sales_evset, prediction_evset, past_sales_evset],\n", " max_num_plots=3*2,\n", + " width_px=600,\n", + " merge=True,\n", ")\n" ] }, - { - "cell_type": "markdown", - "id": "321bcd2c-624c-4909-a7be-3efe7ecd5f82", - "metadata": {}, - "source": [ - "#### Custom plots\n", - "\n", - "In order to show the predictions on top of real sales and compare them, we need to create our own custom plots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32bcf947-b3aa-4082-b453-3c4cacbb19da", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def convert_timestamps_to_datetimes(timestamps):\n", - " return [datetime.fromtimestamp(t, tz=timezone.utc) for t in timestamps]\n", - "\n", - "num_plot = 4\n", - "fig, axs = plt.subplots(num_plot, figsize=(10, 10))\n", - "for plot_idx, index in enumerate(real_sales_evset.data):\n", - " if plot_idx >= num_plot:\n", - " break\n", - "\n", - " ax = axs[plot_idx]\n", - " ax.set_title(index)\n", - "\n", - " # Convert timestamps to dates\n", - " dates_real = convert_timestamps_to_datetimes(real_sales_evset.data[index].timestamps)\n", - " dates_pred = convert_timestamps_to_datetimes(prediction_evset.data[index].timestamps)\n", - " dates_past = convert_timestamps_to_datetimes(past_sales_evset.data[index].timestamps)\n", - "\n", - " # Plot past sales, test ground-truth (real) and predicted sales\n", - " ax.plot(dates_past, past_sales_evset.data[index].features[0], label=\"past\")\n", - " ax.plot(dates_real, real_sales_evset.data[index].features[0], label=\"real\")\n", - " ax.plot(dates_pred, prediction_evset.data[index].features[0], label=\"pred\")\n", - " ax.legend()\n", - "\n", - "fig.tight_layout()" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -3124,7 +6001,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/docs/src/user_guide.ipynb b/docs/src/user_guide.ipynb index 5a982b510..5e0651564 100644 --- a/docs/src/user_guide.ipynb +++ b/docs/src/user_guide.ipynb @@ -72,16 +72,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "1d23b4c0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:54.721009Z", - "iopub.status.busy": "2023-07-24T15:00:54.720660Z", - "iopub.status.idle": "2023-07-24T15:00:56.481697Z", - "shell.execute_reply": "2023-07-24T15:00:56.481304Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "import temporian as tp\n", @@ -113,34 +106,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "17c712f6", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:56.484302Z", - "iopub.status.busy": "2023-07-24T15:00:56.484095Z", - "iopub.status.idle": "2023-07-24T15:00:56.488534Z", - "shell.execute_reply": "2023-07-24T15:00:56.488197Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "indexes: []\n", - "features: [('feature_1', float64), ('feature_2', str_), ('feature_3', int64)]\n", - "events:\n", - " (4 events):\n", - " timestamps: [1.6755e+09 1.6756e+09 1.6757e+09 1.6757e+09]\n", - " 'feature_1': [0.5 0.6 nan 0.9]\n", - " 'feature_2': [b'red' b'blue' b'red' b'blue']\n", - " 'feature_3': [10 -1 5 5]\n", - "memory usage: 0.8 kB\n", - "\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "print(evset)" ] @@ -159,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "e3f29b64", "metadata": { "execution": { @@ -169,18 +138,7 @@ "shell.execute_reply": "2023-07-24T15:00:57.122043Z" } }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "evset.plot()" ] @@ -220,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "f0c059c3-1412-47d5-8f21-8c034f16a551", "metadata": { "execution": { @@ -230,18 +188,7 @@ "shell.execute_reply": "2023-07-24T15:00:57.312518Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create an event set with a random walk\n", "np.random.seed(1)\n", @@ -278,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "d5405837-c801-40e9-8b41-4d4dc901d429", "metadata": { "execution": { @@ -288,18 +235,7 @@ "shell.execute_reply": "2023-07-24T15:00:57.417481Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Eager mode\n", "#\n", @@ -310,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "9c854408-0804-4435-8d44-f521ef1b379e", "metadata": { "execution": { @@ -320,18 +256,7 @@ "shell.execute_reply": "2023-07-24T15:00:57.522286Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Graph mode with @tp.compile\n", "\n", @@ -346,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "e4aa008f-aa8e-486f-a96c-854fb5b2bd83", "metadata": { "execution": { @@ -356,30 +281,7 @@ "shell.execute_reply": "2023-07-24T15:00:57.628643Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Build schedule\n", - "Run 3 operators\n", - " 1 / 3: SIMPLE_MOVING_AVERAGE [0.00008 s]\n", - " 2 / 3: SIMPLE_MOVING_AVERAGE [0.00003 s]\n", - " 3 / 3: SUBTRACTION [0.00013 s]\n", - "Execution in 0.00049 s\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Graph model without @tp.compile\n", "\n", @@ -420,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "dd4142a0-df47-4ca3-aafe-7a105b4f2deb", "metadata": { "execution": { @@ -430,36 +332,7 @@ "shell.execute_reply": "2023-07-24T15:00:57.635717Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[indexes: []\n", - "features: [('value', float64)]\n", - "events:\n", - " (1000 events):\n", - " timestamps: [ 0. 0.01 0.02 ... 9.98 9.99 10. ]\n", - " 'value': [ 0. -0.5 -1. ... 3.74 3.76 3.76]\n", - "memory usage: 8.5 kB\n", - ", indexes: []\n", - "features: [('value', float64)]\n", - "events:\n", - " (1000 events):\n", - " timestamps: [ 0. 0.01 0.02 ... 9.98 9.99 10. ]\n", - " 'value': [ 0. -0.5 -1. ... 1.04 1.09 1.14]\n", - "memory usage: 8.5 kB\n", - ", indexes: []\n", - "features: [('sub_value_value', float64)]\n", - "events:\n", - " (1000 events):\n", - " timestamps: [ 0. 0.01 0.02 ... 9.98 9.99 10. ]\n", - " 'sub_value_value': [0. 0. 0. ... 2.7 2.67 2.62]\n", - "memory usage: 8.5 kB\n", - "]\n" - ] - } - ], + "outputs": [], "source": [ "input_node = tp.input_node([(\"value\", tp.float64)])\n", "result_1_node = input_node.simple_moving_average(window_length=0.5)\n", @@ -496,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "b0b0220b-9869-454c-8f3b-afeabaca06bc", "metadata": { "execution": { @@ -534,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "945d8f39-18ad-44df-a4d2-9d8986e7825b", "metadata": { "execution": { @@ -564,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "941a5fc4-edfc-4fba-8bc8-22f3a7387e91", "metadata": { "execution": { @@ -574,16 +447,7 @@ "shell.execute_reply": "2023-07-24T15:00:57.651456Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('a_branch', float64)]\n", - "[('non_a_branch', float64)]\n" - ] - } - ], + "outputs": [], "source": [ "@tp.compile\n", "def my_function(x : tp.EventSetOrNode, a:bool) -> tp.EventSetOrNode:\n", @@ -608,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "a9ab68f3-91a6-445e-99c2-7f2379185f21", "metadata": { "execution": { @@ -618,24 +482,7 @@ "shell.execute_reply": "2023-07-24T15:00:57.658419Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "indexes: []\n", - "features: [('value', int64)]\n", - "events:\n", - " (2 events):\n", - " timestamps: [1. 2.]\n", - " 'value': [10 11]\n", - "memory usage: 0.5 kB" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "@tp.compile\n", "def my_function(x : tp.EventSetOrNode) -> tp.EventSetOrNode:\n", @@ -694,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "d4fc33b0", "metadata": { "execution": { @@ -754,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "92ec9711", "metadata": { "execution": { @@ -765,26 +612,7 @@ }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "indexes: []\n", - "features: [('feature_1', int64), ('feature_2', str_), ('calendar_day_of_week', int32)]\n", - "events:\n", - " (3 events):\n", - " timestamps: [1.6787e+09 1.6788e+09 1.6790e+09]\n", - " 'feature_1': [1 2 3]\n", - " 'feature_2': [b'a' b'b' b'c']\n", - " 'calendar_day_of_week': [0 1 4]\n", - "memory usage: 0.8 kB" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "a_evset = tp.event_set(\n", " timestamps=[\n", @@ -819,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "508c3f0c", "metadata": { "execution": { @@ -863,7 +691,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "cbf42f42", "metadata": { "execution": { @@ -873,18 +701,7 @@ "shell.execute_reply": "2023-07-24T15:00:57.884262Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "evset = tp.event_set(\n", "\ttimestamps=[1, 2, 3, 4, 5],\n", @@ -915,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "82552e04", "metadata": { "execution": { @@ -925,18 +742,7 @@ "shell.execute_reply": "2023-07-24T15:00:58.112793Z" } }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "figure = evset.plot(\n", " style=\"marker\",\n", @@ -962,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "37feddd0", "metadata": { "execution": { @@ -972,47 +778,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.004601Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - }, - { - "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", - "application/vnd.bokehjs_load.v0+json": "" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
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const render_items = [{\"docid\":\"e6067768-ffa2-4441-a891-38a24bdd414a\",\"roots\":{\"p1165\":\"bab8f4b2-aab9-4bc4-89d1-3424677589fa\"},\"root_ids\":[\"p1165\"]}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);", - "application/vnd.bokehjs_exec.v0+json": "" - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "p1165" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "!pip install bokeh -q\n", "\n", @@ -1036,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "38ff0e20", "metadata": { "execution": { @@ -1046,15 +812,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.010135Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('feature_1', float64), ('feature_2', float64)]\n" - ] - } - ], + "outputs": [], "source": [ "events = tp.event_set(\n", "\ttimestamps=[1,2,3,4,5],\n", @@ -1081,7 +839,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "838449d4", "metadata": { "execution": { @@ -1091,18 +849,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.015395Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('feature_1', float64), ('feature_2', float64)]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "node.moving_sum(window_length=10).features" ] @@ -1122,7 +869,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "b10fdec3", "metadata": { "execution": { @@ -1132,18 +879,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.020666Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('mult_feature_1_feature_2', float64)]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result = node[\"feature_1\"] * node[\"feature_2\"]\n", "result.features" @@ -1165,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "4182d3f8", "metadata": { "execution": { @@ -1175,15 +911,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.026132Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('calendar_month', int32)]\n" - ] - } - ], + "outputs": [], "source": [ "date_events = tp.event_set(\n", "\ttimestamps=[\"2020-02-15\", \"2020-06-20\"],\n", @@ -1208,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "bc36bc1f", "metadata": { "execution": { @@ -1218,15 +946,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.031429Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('renamed_1', float64)]\n" - ] - } - ], + "outputs": [], "source": [ "# Rename a single feature.\n", "renamed_f1 = node[\"feature_1\"].rename(\"renamed_1\")\n", @@ -1235,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "a44f6e7a", "metadata": { "execution": { @@ -1245,15 +965,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.044933Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('renamed_1', float64), ('renamed_2', float64)]\n" - ] - } - ], + "outputs": [], "source": [ "# Rename all features.\n", "renamed_node = node.rename(\n", @@ -1264,7 +976,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "af6103fc", "metadata": { "execution": { @@ -1274,15 +986,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.051319Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('prefixed.feature_1', float64)]\n" - ] - } - ], + "outputs": [], "source": [ "# Prefix a single feature.\n", "prefixed_f1 = node[\"feature_1\"].prefix(\"prefixed.\")\n", @@ -1291,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "3126cbed", "metadata": { "execution": { @@ -1301,15 +1005,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.055703Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('prefixed.feature_1', float64), ('prefixed.feature_2', float64)]\n" - ] - } - ], + "outputs": [], "source": [ "# Prefix all features.\n", "prefixed_node = node.prefix(\"prefixed.\")\n", @@ -1331,7 +1027,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "852abbdd", "metadata": { "execution": { @@ -1362,7 +1058,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "f7d85e33", "metadata": { "execution": { @@ -1397,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "e1276be1", "metadata": { "execution": { @@ -1437,7 +1133,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "3e3ee4c4", "metadata": { "execution": { @@ -1448,25 +1144,14 @@ }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "[('f1', float32), ('f2', float64)]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "node.features" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "6c2444ba", "metadata": { "execution": { @@ -1476,15 +1161,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.082457Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('f1', str_), ('f2', str_)]\n" - ] - } - ], + "outputs": [], "source": [ "print(node.cast(tp.str_).features)" ] @@ -1504,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "05ee00a7", "metadata": { "execution": { @@ -1514,15 +1191,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.087026Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('f1', str_), ('f2', int64)]\n" - ] - } - ], + "outputs": [], "source": [ "print(node.cast({\"f1\": tp.str_, \"f2\": tp.int64}).features)" ] @@ -1542,7 +1211,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "6deaff88", "metadata": { "execution": { @@ -1552,15 +1221,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.091498Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('f1', str_), ('f2', int64)]\n" - ] - } - ], + "outputs": [], "source": [ "print(node.cast({tp.float32: tp.str_, tp.float64: tp.int64}).features)" ] @@ -1588,7 +1249,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "2c673b17", "metadata": { "execution": { @@ -1599,23 +1260,7 @@ }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "indexes: []\n", - "features: [('add_f1_f3', int64), ('add_f2_f4', float64)]\n", - "events:\n", - " (2 events):\n", - " timestamps: [ 1. 10.]\n", - " 'add_f1_f3': [100 101]\n", - " 'add_f2_f4': [1010. 1020.]\n", - "memory usage: 0.7 kB\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "evset = tp.event_set(\n", " timestamps=[1, 10],\n", @@ -1679,7 +1324,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "3b350c4a", "metadata": { "execution": { @@ -1709,7 +1354,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "f1462eea", "metadata": { "execution": { @@ -1740,7 +1385,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "c610a0d0", "metadata": { "execution": { @@ -1750,27 +1395,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.113316Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "schema:\n", - " features: [('eq_f1_f3', bool_)]\n", - " indexes: []\n", - " is_unix_timestamp: False\n", - "\n", - "features: [Feature(id=5289595520, creator=Operator(key='EQUAL', id=66, attributes={}))]\n", - "sampling: Sampling(id=5275912800, creator=None),\n", - "name: None\n", - "creator: Operator(key='EQUAL', id=66, attributes={})\n", - "id:5289594992" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Works element-wise as expected\n", "tp.equal(node[\"f1\"], node[\"f3\"])" @@ -1778,7 +1403,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "abda404b", "metadata": { "execution": { @@ -1788,18 +1413,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.117723Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# This is just a boolean\n", "(node[\"f1\"] == node[\"f3\"])" @@ -1892,7 +1506,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "58fe7d8e", "metadata": { "execution": { @@ -1903,25 +1517,7 @@ }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "indexes: []\n", - "features: [('f1', int64), ('f2', float64), ('f3', int64), ('f4', float64)]\n", - "events:\n", - " (2 events):\n", - " timestamps: [ 1. 10.]\n", - " 'f1': [ 0 10]\n", - " 'f2': [100. 200.]\n", - " 'f3': [1000 1000]\n", - " 'f4': [10000. 10000.]\n", - "memory usage: 0.9 kB\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "node_scalar = node * 10\n", "print(node_scalar.run(evset))" @@ -1959,7 +1555,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "087b6fd6", "metadata": { "execution": { @@ -1970,24 +1566,7 @@ }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "indexes: []\n", - "features: [('x', float64)]\n", - "events:\n", - " (7 events):\n", - " timestamps: [ 0. 9. 10. 11. 19. 20. 21.]\n", - " 'x': [nan nan 1. 1. 1. 2. 2.]\n", - "memory usage: 0.6 kB" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "evset = tp.event_set(\n", " timestamps=[10, 20, 30],\n", @@ -2032,7 +1611,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "df3bbc7d", "metadata": { "execution": { @@ -2043,24 +1622,7 @@ }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "indexes: []\n", - "features: [('add_f1_f1', int64)]\n", - "events:\n", - " (3 events):\n", - " timestamps: [0. 1. 2.]\n", - " 'add_f1_f1': [10 25 34]\n", - "memory usage: 0.6 kB" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sampling_a = tp.event_set(\n", " timestamps=[0, 1, 2],\n", @@ -2134,7 +1696,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "54a71cae", "metadata": { "execution": { @@ -2145,25 +1707,7 @@ }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "indexes: [('product', int64)]\n", - "features: [('sale', float64)]\n", - "events:\n", - " product=1 (2 events):\n", - " timestamps: [1.5778e+09 1.5779e+09]\n", - " 'sale': [100. 90.]\n", - " product=2 (2 events):\n", - " timestamps: [1.5778e+09 1.5779e+09]\n", - " 'sale': [300. 400.]\n", - "memory usage: 0.9 kB\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "daily_sales = tp.event_set(\n", "\ttimestamps=[\"2020-01-01\", \"2020-01-01\", \"2020-01-02\", \"2020-01-02\"],\n", @@ -2191,7 +1735,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "2c87a680", "metadata": { "execution": { @@ -2202,27 +1746,7 @@ }, "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "text/plain": [ - "indexes: [('product', int64)]\n", - "features: [('sale', float64)]\n", - "events:\n", - " product=1 (2 events):\n", - " timestamps: [1.5778e+09 1.5779e+09]\n", - " 'sale': [100. 190.]\n", - " product=2 (2 events):\n", - " timestamps: [1.5778e+09 1.5779e+09]\n", - " 'sale': [300. 700.]\n", - "memory usage: 0.9 kB" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "a = daily_sales.node()\n", "\n", @@ -2260,7 +1784,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "8badbebf", "metadata": { "execution": { @@ -2270,24 +1794,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.151798Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "indexes: []\n", - "features: [('store', int64), ('product', int64), ('sale', float64)]\n", - "events:\n", - " (4 events):\n", - " timestamps: [1.5778e+09 1.5778e+09 1.5779e+09 1.5779e+09]\n", - " 'store': [1 1 1 2]\n", - " 'product': [1 2 1 2]\n", - " 'sale': [100. 200. 110. 300.]\n", - "memory usage: 0.9 kB\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "daily_sales = tp.event_set(\n", "\ttimestamps=[\"2020-01-01\", \"2020-01-01\", \"2020-01-02\", \"2020-01-02\"],\n", @@ -2316,7 +1823,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "479096c3", "metadata": { "execution": { @@ -2327,30 +1834,7 @@ }, "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "text/plain": [ - "indexes: [('product', int64), ('store', int64)]\n", - "features: [('sale', float64)]\n", - "events:\n", - " product=1 store=1 (2 events):\n", - " timestamps: [1.5778e+09 1.5779e+09]\n", - " 'sale': [100. 110.]\n", - " product=2 store=1 (1 events):\n", - " timestamps: [1.5778e+09]\n", - " 'sale': [200.]\n", - " product=2 store=2 (1 events):\n", - " timestamps: [1.5779e+09]\n", - " 'sale': [300.]\n", - "memory usage: 1.2 kB" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "a = daily_sales.node()\n", "b = a.add_index([\"product\", \"store\"])\n", @@ -2373,7 +1857,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "4df0d8cf", "metadata": { "execution": { @@ -2384,30 +1868,7 @@ }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "indexes: [('product', int64), ('store', int64)]\n", - "features: [('sale', float64)]\n", - "events:\n", - " product=1 store=1 (2 events):\n", - " timestamps: [1.5778e+09 1.5779e+09]\n", - " 'sale': [100. 210.]\n", - " product=2 store=1 (1 events):\n", - " timestamps: [1.5778e+09]\n", - " 'sale': [200.]\n", - " product=2 store=2 (1 events):\n", - " timestamps: [1.5779e+09]\n", - " 'sale': [300.]\n", - "memory usage: 1.2 kB" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Weekly sales by product and store\n", "c = b[\"sale\"].moving_sum(window_length=tp.duration.weeks(1))\n", @@ -2429,7 +1890,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "8d9448a2", "metadata": { "execution": { @@ -2440,27 +1901,7 @@ }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "data": { - "text/plain": [ - "indexes: [('store', int64)]\n", - "features: [('sale', float64)]\n", - "events:\n", - " store=1 (3 events):\n", - " timestamps: [1.5778e+09 1.5778e+09 1.5779e+09]\n", - " 'sale': [300. 300. 410.]\n", - " store=2 (1 events):\n", - " timestamps: [1.5779e+09]\n", - " 'sale': [300.]\n", - "memory usage: 0.9 kB" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Weekly sales by store (including all products)\n", "d = b.drop_index(\"product\")\n", @@ -2485,7 +1926,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "4e12a9e0", "metadata": { "execution": { @@ -2496,28 +1937,7 @@ }, "lines_to_next_cell": 0 }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "indexes: [('product', int64), ('store', int64)]\n", - "features: [('div_sale_sale', float64)]\n", - "events:\n", - " product=1 store=1 (2 events):\n", - " timestamps: [1.5778e+09 1.5779e+09]\n", - " 'div_sale_sale': [0.3333 0.5122]\n", - " product=2 store=1 (1 events):\n", - " timestamps: [1.5778e+09]\n", - " 'div_sale_sale': [0.6667]\n", - " product=2 store=2 (1 events):\n", - " timestamps: [1.5779e+09]\n", - " 'div_sale_sale': [1.]\n", - "memory usage: 1.2 kB\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "# Copy the content of e (indexed by (store)) into each (store, product).\n", "f = c / e.propagate(sampling=c, resample=True)\n", @@ -2552,7 +1972,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "c07453fa", "metadata": { "execution": { @@ -2581,7 +2001,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "ab15c133", "metadata": { "execution": { @@ -2591,22 +2011,14 @@ "shell.execute_reply": "2023-07-24T15:01:00.187503Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "False\n" - ] - } - ], + "outputs": [], "source": [ "print(tp.has_leak(b))" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "cd455fc1", "metadata": { "execution": { @@ -2616,15 +2028,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.191954Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - } - ], + "outputs": [], "source": [ "print(tp.has_leak(c))" ] @@ -2647,7 +2051,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "7da63117", "metadata": { "execution": { @@ -2657,18 +2061,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.198424Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "IndexData(features=[array([0.1, 0.2, 0.3])], timestamps=array([1., 2., 3.]))" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "evset = tp.event_set(\n", "\ttimestamps=[1, 2, 3, 5, 6],\n", @@ -2747,7 +2140,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "da239494", "metadata": { "execution": { @@ -2757,15 +2150,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.212298Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Feature \"f2\" is an array of numpy.object_ and was casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n" - ] - } - ], + "outputs": [], "source": [ "df = pd.DataFrame({\n", " \"timestamp\": [1, 2, 3, 5, 6],\n", @@ -2797,7 +2182,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "56a28657", "metadata": { "execution": { @@ -2807,24 +2192,7 @@ "shell.execute_reply": "2023-07-24T15:01:00.222432Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "indexes: []\n", - "features: [('f1', int32)]\n", - "events:\n", - " (3 events):\n", - " timestamps: [1. 2. 3.]\n", - " 'f1': [1 1 1]\n", - "memory usage: 0.5 kB" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Define a graph.\n", "evset = tp.event_set(\n", @@ -2871,7 +2239,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.10.4" } }, "nbformat": 4, diff --git a/poetry.lock b/poetry.lock index 09c6aced3..4d9ed260d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4762,4 +4762,4 @@ tensorflow = [] [metadata] lock-version = "2.0" python-versions = "^3.8,<3.12" -content-hash = "c8a05bfee13dc0886f76ef0b223f57a940bf69f564247e25468622ac8c0b1084" +content-hash = "c8a05bfee13dc0886f76ef0b223f57a940bf69f564247e25468622ac8c0b1084" \ No newline at end of file diff --git a/temporian/core/event_set_ops.py b/temporian/core/event_set_ops.py index d85f0a64e..6e88d6729 100644 --- a/temporian/core/event_set_ops.py +++ b/temporian/core/event_set_ops.py @@ -1093,6 +1093,68 @@ def enumerate(self: EventSetOrNode) -> EventSetOrNode: return enumerate(self) + def experimental_fast_fourier_transform( + self: EventSetOrNode, + *, + num_events: int, + hop_size: Optional[int] = None, + window: Optional[str] = None, + num_spectral_lines: Optional[int] = None, + ) -> EventSetOrNode: + """Computes the Fast Fourier Transform of an + [`EventSet`][temporian.EventSet] with a single tp.float32 feature. + + WARNING: This operator is experimental. The implementation is not yet + optimized for speed, and the operator signature might change in the + future. + + The window length is defined in number of events, instead of + timestamp duration like most other operators. The 'num_events' argument + needs to be specified by warg i.e. fast_fourier_transform(num_events=5) + instead of fast_fourier_transform(5). + + The operator returns the amplitude of each spectral line as + separate tp.float32 features named "a0", "a1", "a2", etc. By default, + `num_events // 2` spectral line are returned. + + Usage: + ```python + >>> a = tp.event_set( + ... timestamps=[1,2,3,4,5,6], + ... features={"x": [4.,3.,2.,6.,2.,1.]}, + ... ) + >>> b = a.experimental_fast_fourier_transform(num_events=4, window="hamming") + >>> b + + ``` + + Args: + num_events: Size of the FFT expressed as a number of events. + window: Optional window function applied before the FFT. if None, no + window is applied. Supported values are: "hamming". + hop_size: Step, in number of events, between consecutive outputs. + Default to num_events//2. + num_spectral_lines: Number of returned spectral lines. If set, the + operators returns the `num_spectral_lines` low frequency + spectral lines. `num_spectral_lines` should be between 1 and + `num_events`. + + Returns: + EventSet containing the amplitude of each frequency band of the + Fourier Transform. + """ + from temporian.core.operators.fast_fourier_transform import ( + fast_fourier_transform, + ) + + return fast_fourier_transform( + self, + num_events=num_events, + hop_size=hop_size, + window=window, + num_spectral_lines=num_spectral_lines, + ) + def filter( self: EventSetOrNode, condition: Optional[EventSetOrNode] = None, @@ -2155,6 +2217,7 @@ def unique_timestamps(self: EventSetOrNode) -> EventSetOrNode: applied independently for each index. Usage example: + ```python >>> a = tp.event_set(timestamps=[5, 9, 9, 16], features={'f': [1,2,3,4]}) >>> b = a.unique_timestamps() @@ -2166,6 +2229,8 @@ def unique_timestamps(self: EventSetOrNode) -> EventSetOrNode: timestamps: [ 5. 9. 16.] ... + ``` + Returns: EventSet without features with unique timestamps in the input. """ diff --git a/temporian/core/operators/BUILD b/temporian/core/operators/BUILD index 1082362e4..fb372755c 100644 --- a/temporian/core/operators/BUILD +++ b/temporian/core/operators/BUILD @@ -343,3 +343,18 @@ py_library( "//temporian/proto:core_py_proto", ], ) + +py_library( + name = "fast_fourier_transform", + srcs = ["fast_fourier_transform.py"], + srcs_version = "PY3", + deps = [ + ":base", + "//temporian/core:compilation", + "//temporian/core:operator_lib", + "//temporian/core:typing", + "//temporian/core/data:node", + "//temporian/core/data:schema", + "//temporian/proto:core_py_proto", + ], +) diff --git a/temporian/core/operators/fast_fourier_transform.py b/temporian/core/operators/fast_fourier_transform.py new file mode 100644 index 000000000..eda16d506 --- /dev/null +++ b/temporian/core/operators/fast_fourier_transform.py @@ -0,0 +1,179 @@ +# Copyright 2021 Google LLC. +# +# 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 +# +# https://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. + + +"""FFT operator class and public API function definitions.""" + +from typing import Optional +from temporian.core import operator_lib +from temporian.core.compilation import compile +from temporian.core.data.node import ( + EventSetNode, + create_node_new_features_new_sampling, +) +from temporian.core.operators.base import Operator +from temporian.core.typing import EventSetOrNode +from temporian.proto import core_pb2 as pb +from temporian.core.data.dtype import DType + + +_WINDOWS = {"hamming"} + + +class FastFourierTransform(Operator): + def __init__( + self, + input: EventSetNode, + num_events: int, + hop_size: int, + window: Optional[str] = None, + num_spectral_lines: Optional[int] = None, + ): + super().__init__() + + # Save attributes + self._num_events = num_events + self._window = window + self._num_spectral_lines = num_spectral_lines + self._hop_size = hop_size + + self.add_attribute("num_events", num_events) + self.add_attribute("hop_size", hop_size) + + if window is not None: + self.add_attribute("window", window) + if num_spectral_lines is not None: + self.add_attribute("num_spectral_lines", num_spectral_lines) + + # Check errors + if num_events <= 1: + raise ValueError( + "num_events should be strictly positive. Got" + f" {num_events} instead." + ) + if len(input.features) != 1: + raise ValueError( + "FFT input needs to be a single feature. Got" + f" {len(input.features)} features instead." + ) + if input.features[0].dtype not in [DType.FLOAT32, DType.FLOAT64]: + raise ValueError( + "FFT input feature should be tp.float32 or tp.float64. Got" + f" {input.features[0].dtype} instead." + ) + if window is not None and window not in _WINDOWS: + raise ValueError( + f"window should be None or in {_WINDOWS}. Got {window} instead." + ) + if num_spectral_lines is not None: + if num_spectral_lines <= 0: + raise ValueError( + "num_spectral_lines should be positive. Got" + f" {num_spectral_lines} instead" + ) + if num_spectral_lines > num_events // 2: + raise ValueError( + "num_spectral_lines should be less or equal to num_events" + f" // 2. Got {num_spectral_lines} instead" + ) + + self.add_input("input", input) + self.add_output( + "output", + create_node_new_features_new_sampling( + features=[ + (f"a{i}", input.schema.features[0].dtype) + for i in range(self.num_output_features) + ], + indexes=input.schema.indexes, + is_unix_timestamp=input.schema.is_unix_timestamp, + creator=self, + ), + ) + + self.check() + + @property + def num_spectral_lines(self) -> Optional[int]: + return self._num_spectral_lines + + @property + def num_output_features(self) -> int: + if self._num_spectral_lines is None: + return self._num_events // 2 + else: + return self._num_spectral_lines + + @property + def num_events(self) -> int: + return self._num_events + + @property + def window(self) -> Optional[str]: + return self._window + + @property + def hop_size(self) -> int: + return self._hop_size + + @classmethod + def build_op_definition(cls) -> pb.OperatorDef: + return pb.OperatorDef( + key="FFT", + attributes=[ + pb.OperatorDef.Attribute( + key="num_events", + type=pb.OperatorDef.Attribute.Type.INTEGER_64, + ), + pb.OperatorDef.Attribute( + key="hop_size", + type=pb.OperatorDef.Attribute.Type.INTEGER_64, + ), + pb.OperatorDef.Attribute( + key="window", + type=pb.OperatorDef.Attribute.Type.STRING, + is_optional=True, + ), + pb.OperatorDef.Attribute( + key="num_spectral_lines", + type=pb.OperatorDef.Attribute.Type.INTEGER_64, + is_optional=True, + ), + ], + inputs=[pb.OperatorDef.Input(key="input")], + outputs=[pb.OperatorDef.Output(key="output")], + ) + + +operator_lib.register_operator(FastFourierTransform) + + +@compile +def fast_fourier_transform( + input: EventSetOrNode, + *, + num_events: int, + hop_size: Optional[int] = None, + window: Optional[str] = None, + num_spectral_lines: Optional[int] = None, +) -> EventSetOrNode: + return FastFourierTransform( + input=input, + num_events=num_events, + hop_size=hop_size if hop_size is not None else num_events // 2, + window=window, + num_spectral_lines=num_spectral_lines, + ).outputs[ + "output" + ] # type: ignore diff --git a/temporian/core/operators/test/BUILD b/temporian/core/operators/test/BUILD index 87c4fd949..5bc3af1e3 100644 --- a/temporian/core/operators/test/BUILD +++ b/temporian/core/operators/test/BUILD @@ -28,3 +28,15 @@ py_test( "//temporian/core/operators:join", ], ) + +py_test( + name = "fft_test", + srcs = ["fft_test.py"], + srcs_version = "PY3", + deps = [ + # already_there/absl/testing:absltest + "//temporian/core/data:dtype", + "//temporian/core/data:node", + "//temporian/core/operators:fast_fourier_transform", + ], +) diff --git a/temporian/core/operators/test/fft_test.py b/temporian/core/operators/test/fft_test.py new file mode 100644 index 000000000..1878dd730 --- /dev/null +++ b/temporian/core/operators/test/fft_test.py @@ -0,0 +1,65 @@ +# Copyright 2021 Google LLC. +# +# 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 +# +# https://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. + + +from absl.testing import absltest + +import numpy as np + +from temporian.core.data.node import input_node +from temporian.core.data.dtype import DType +from temporian.core.operators.fast_fourier_transform import ( + fast_fourier_transform, +) + + +class FFTOperatorTest(absltest.TestCase): + def test_good(self): + input = input_node([("a", DType.FLOAT32)]) + fast_fourier_transform(input, num_events=20) + fast_fourier_transform(input, num_events=20, window="hamming") + fast_fourier_transform( + input, num_events=20, window="hamming", num_spectral_lines=10 + ) + + def test_wrong_dtype(self): + input = input_node([("a", DType.INT32)]) + with self.assertRaisesRegex(ValueError, "should be tp.float32"): + fast_fourier_transform(input, num_events=20) + + def test_wrong_features(self): + input = input_node([("a", DType.FLOAT32), ("b", DType.FLOAT32)]) + with self.assertRaisesRegex(ValueError, "to be a single feature"): + fast_fourier_transform(input, num_events=20) + + def test_wrong_num_events(self): + input = input_node([("a", DType.FLOAT32)]) + with self.assertRaisesRegex(ValueError, "should be strictly positive"): + fast_fourier_transform(input, num_events=0) + + def test_wrong_window(self): + input = input_node([("a", DType.FLOAT32)]) + with self.assertRaisesRegex(ValueError, "window should be None or"): + fast_fourier_transform(input, num_events=20, window="AAA") + + def test_wrong_num_spectral_lines(self): + input = input_node([("a", DType.FLOAT32)]) + with self.assertRaisesRegex( + ValueError, "num_spectral_lines should be less or equal" + ): + fast_fourier_transform(input, num_events=20, num_spectral_lines=15) + + +if __name__ == "__main__": + absltest.main() diff --git a/temporian/core/test/registered_operators_test.py b/temporian/core/test/registered_operators_test.py index ae6795f89..a27876b75 100644 --- a/temporian/core/test/registered_operators_test.py +++ b/temporian/core/test/registered_operators_test.py @@ -49,6 +49,7 @@ def test_base(self): "ENUMERATE", "EQUAL", "EQUAL_SCALAR", + "FFT", "FILTER", "FLOORDIV", "FLOORDIV_SCALAR", diff --git a/temporian/implementation/numpy/data/event_set.py b/temporian/implementation/numpy/data/event_set.py index 588a7c418..7de0c716c 100644 --- a/temporian/implementation/numpy/data/event_set.py +++ b/temporian/implementation/numpy/data/event_set.py @@ -133,9 +133,8 @@ def normalize_features( if feature_values.dtype == "object": feature_values = feature_values.fillna("") feature_values = feature_values.to_numpy(copy=True) - - if not isinstance(feature_values, np.ndarray): - # The data is not a np.array + elif isinstance(feature_values, (tuple, list)): + # Convert list/tuple to array # Looks for an indication of a string or non-string array. is_string = False @@ -152,6 +151,11 @@ def normalize_features( feature_values = np.array(feature_values, dtype=np.bytes_) else: feature_values = np.array(feature_values) + elif not isinstance(feature_values, np.ndarray): + raise ValueError( + "Feature values should be provided in a tuple, list, numpy array or" + f" pandas Series. Got type {type(feature_values)} instead." + ) if feature_values.dtype.type == np.string_: feature_values = feature_values.astype(np.bytes_) diff --git a/temporian/implementation/numpy/data/io.py b/temporian/implementation/numpy/data/io.py index cbaffb200..3a85fd28c 100644 --- a/temporian/implementation/numpy/data/io.py +++ b/temporian/implementation/numpy/data/io.py @@ -111,17 +111,17 @@ def event_set( if features is None: features = {} + features = { + name: normalize_features(value, name) + for name, value in features.items() + } + # Check timestamps and all features are of same length if not all(len(f) == len(timestamps) for f in features.values()): raise ValueError( "Timestamps and all features must have the same length." ) - features = { - name: normalize_features(value, name) - for name, value in features.items() - } - # Convert timestamps to expected type. timestamps, auto_is_unix_timestamp = normalize_timestamps(timestamps) diff --git a/temporian/implementation/numpy/data/test/io_test.py b/temporian/implementation/numpy/data/test/io_test.py index c18dbfeb2..3cb7f881c 100644 --- a/temporian/implementation/numpy/data/test/io_test.py +++ b/temporian/implementation/numpy/data/test/io_test.py @@ -148,6 +148,18 @@ def test_arrays_not_same_length(self): # Shouldn't raise event_set(timestamps=[1]) + def test_feature_wrong_type(self): + with self.assertRaisesRegex( + ValueError, "Feature values should be provided in a tuple, list" + ): + event_set( + timestamps=[1, 2], + features={ + # np.array({1, 2}) would produce a single-item value + "y": {1, 2}, + }, + ) + if __name__ == "__main__": absltest.main() diff --git a/temporian/implementation/numpy/operators/BUILD b/temporian/implementation/numpy/operators/BUILD index 02c258b6e..67ab51d7d 100644 --- a/temporian/implementation/numpy/operators/BUILD +++ b/temporian/implementation/numpy/operators/BUILD @@ -18,6 +18,7 @@ py_library( ":drop_index", ":end", ":enumerate", + ":fast_fourier_transform", ":filter", ":glue", ":join", @@ -352,3 +353,17 @@ py_library( "//temporian/implementation/numpy/data:event_set", ], ) + +py_library( + name = "fast_fourier_transform", + srcs = ["fast_fourier_transform.py"], + srcs_version = "PY3", + deps = [ + # already_there/numpy + ":base", + "//temporian/core/operators:fast_fourier_transform", + "//temporian/implementation/numpy:implementation_lib", + "//temporian/implementation/numpy:utils", + "//temporian/implementation/numpy/data:event_set", + ], +) diff --git a/temporian/implementation/numpy/operators/__init__.py b/temporian/implementation/numpy/operators/__init__.py index 8abc6c730..79d48c5da 100644 --- a/temporian/implementation/numpy/operators/__init__.py +++ b/temporian/implementation/numpy/operators/__init__.py @@ -61,3 +61,4 @@ from temporian.implementation.numpy.operators import tick from temporian.implementation.numpy.operators import timestamps from temporian.implementation.numpy.operators import enumerate +from temporian.implementation.numpy.operators import fast_fourier_transform diff --git a/temporian/implementation/numpy/operators/fast_fourier_transform.py b/temporian/implementation/numpy/operators/fast_fourier_transform.py new file mode 100644 index 000000000..dbbc91929 --- /dev/null +++ b/temporian/implementation/numpy/operators/fast_fourier_transform.py @@ -0,0 +1,91 @@ +# Copyright 2021 Google LLC. +# +# 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 +# +# https://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. + + +"""Implementation for the FFT operator.""" + + +from typing import Dict +import numpy as np + +from temporian.implementation.numpy.data.event_set import IndexData, EventSet +from temporian.core.operators.fast_fourier_transform import FastFourierTransform +from temporian.implementation.numpy import implementation_lib +from temporian.implementation.numpy.operators.base import OperatorImplementation + + +class FastFourierTransformNumpyImplementation(OperatorImplementation): + def __init__(self, operator: FastFourierTransform) -> None: + assert isinstance(operator, FastFourierTransform) + super().__init__(operator) + + def __call__(self, input: EventSet) -> Dict[str, EventSet]: + assert isinstance(self.operator, FastFourierTransform) + + output_schema = self.output_schema("output") + + # Create output EventSet + output_evset = EventSet(data={}, schema=output_schema) + + num_events = self.operator.num_events + num_output_features = self.operator.num_output_features + num_spectral_lines = self.operator.num_spectral_lines + hop_size = self.operator.hop_size + + if num_spectral_lines is None: + num_spectral_lines = num_output_features + else: + assert num_spectral_lines <= num_output_features + + if self.operator.window is None: + window = None + elif self.operator.window == "hamming": + window = np.hamming(num_events) + else: + raise ValueError(f"Unknown window {self.operator.window}") + + # Fill output EventSet's data + for index_key, index_data in input.data.items(): + src_values = index_data.features[0] + dst_timestamps = index_data.timestamps[(num_events - 1) :] + + # TODO: Implement in c++. + + dst_values = [] + for evt_idx in range(num_events - 1, len(src_values), hop_size): + start_idx = evt_idx - num_events + 1 + end_idx = evt_idx + 1 + selected_src_values = src_values[start_idx:end_idx] + if window is not None: + selected_src_values = selected_src_values * window + fft_res = np.fft.fft(selected_src_values)[:num_spectral_lines] + ft_ampl = np.abs(fft_res).astype(src_values.dtype) + dst_values.append(ft_ampl) + dst_values = np.stack(dst_values, axis=1) + + output_evset.set_index_value( + index_key, + IndexData( + features=dst_values, + timestamps=dst_timestamps, + schema=output_schema, + ), + ) + + return {"output": output_evset} + + +implementation_lib.register_operator_implementation( + FastFourierTransform, FastFourierTransformNumpyImplementation +) diff --git a/temporian/implementation/numpy/operators/test/BUILD b/temporian/implementation/numpy/operators/test/BUILD index 3de13d9e6..24223c9e8 100644 --- a/temporian/implementation/numpy/operators/test/BUILD +++ b/temporian/implementation/numpy/operators/test/BUILD @@ -736,4 +736,19 @@ py_test( "//temporian/implementation/numpy/operators:combine", ], ) - \ No newline at end of file + +py_test( + name = "fft_test", + srcs = ["fft_test.py"], + srcs_version = "PY3", + deps = [ + # already_there/absl/testing:absltest + ":test_util", + "//temporian/core/data:dtype", + "//temporian/core/data:node", + "//temporian/core/data:schema", + "//temporian/implementation/numpy/data:io", + "//temporian/core/operators:fast_fourier_transform", + "//temporian/implementation/numpy/operators:fast_fourier_transform", + ], +) diff --git a/temporian/implementation/numpy/operators/test/fft_test.py b/temporian/implementation/numpy/operators/test/fft_test.py new file mode 100644 index 000000000..0724a191d --- /dev/null +++ b/temporian/implementation/numpy/operators/test/fft_test.py @@ -0,0 +1,85 @@ +# Copyright 2021 Google LLC. +# +# 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 +# +# https://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. + + +from absl.testing import parameterized, absltest + +import numpy as np +from temporian.core.operators.fast_fourier_transform import FastFourierTransform +from temporian.implementation.numpy.data.io import event_set +from temporian.implementation.numpy.operators.fast_fourier_transform import ( + FastFourierTransformNumpyImplementation, +) +from temporian.implementation.numpy.operators.test.test_util import ( + assertEqualEventSet, + testOperatorAndImp, +) + + +class FastFourierTransformOperatorTest(parameterized.TestCase): + def setUp(self): + pass + + @parameterized.parameters( + {"window": None, "num_spectral_lines": None}, + {"window": "hamming", "num_spectral_lines": None}, + {"window": None, "num_spectral_lines": 1}, + ) + def test_base(self, window, num_spectral_lines): + x = [0, 1, 2, 3, 4, 5] + y = np.array([0, 1, 5, -1, 3, 1], dtype=np.float32) + + def expected(data, frequency_idx): + if window == "hamming": + data = np.hamming(len(data)) * data + else: + assert window is None + return np.abs(np.fft.fft(data)).astype(np.float32)[frequency_idx] + + evset = event_set(timestamps=x, features={"a": y}) + node = evset.node() + + if num_spectral_lines is None: + num_spectral_lines = 2 + + expected_features = {} + for i in range(num_spectral_lines): + expected_features[f"a{i}"] = [ + expected([0, 1, 5, -1], i), + expected([1, 5, -1, 3], i), + expected([5, -1, 3, 1], i), + ] + + expected_output = event_set( + timestamps=x[3:], + features=expected_features, + ) + + # Run op + op = FastFourierTransform( + input=node, + num_events=4, + hop_size=1, + window=window, + num_spectral_lines=num_spectral_lines, + ) + instance = FastFourierTransformNumpyImplementation(op) + testOperatorAndImp(self, op, instance) + output = instance.call(input=evset)["output"] + + assertEqualEventSet(self, output, expected_output) + + +if __name__ == "__main__": + absltest.main() diff --git a/temporian/implementation/numpy/test/registered_operators_test.py b/temporian/implementation/numpy/test/registered_operators_test.py index 3ad4dc90d..c627b0eda 100644 --- a/temporian/implementation/numpy/test/registered_operators_test.py +++ b/temporian/implementation/numpy/test/registered_operators_test.py @@ -47,6 +47,7 @@ def test_base(self): "ENUMERATE", "EQUAL", "EQUAL_SCALAR", + "FFT", "FILTER", "FLOORDIV", "FLOORDIV_SCALAR", diff --git a/tools/create_operator.py b/tools/create_operator.py index 48325dc5b..d5c54678e 100755 --- a/tools/create_operator.py +++ b/tools/create_operator.py @@ -368,15 +368,17 @@ def test_base(self): ) print( - """Don't forget to register the new operators in: -- The imports in the top-level init file temporian/__init__.py + """Don't forget to update the following code: +- The imports in the top-level init file temporian/__init__.py (if global) - The imports in temporian/implementation/numpy/operators/__init__.py - The "operators" py_library in temporian/implementation/numpy/operators/BUILD - The "test_base" function in temporian/core/test/registered_operators_test.py - The "test_base" function in temporian/implementation/numpy/test/registered_operators_test.py -- The PUBLIC_API_SYMBOLS set in temporian/test/public_symbols_test.py -- The docs docs/src/reference/path/to/operator.md +- The PUBLIC_API_SYMBOLS set in temporian/test/public_symbols_test.py (if global) +- The .md file in docs/src/reference/temporian/operators - The docs API ref's home page docs/reference/index.md +- The class EventSetOperations in temporian/core/event_set_ops.py (if not global) +- The unit test in temporian/core/operators/test """ )