diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb deleted file mode 100644 index 8bbc87b..0000000 --- a/notebooks/demo.ipynb +++ /dev/null @@ -1,686 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "70a32352-80c9-40b7-8f68-1aeecfc52658", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], - "source": [ - "# Import to be able to import python package from src\n", - "import sys\n", - "sys.path.insert(0, '../src')\n", - "\n", - "import pandas as pd\n", - "import ontime as on\n", - "\n", - "from darts.datasets import EnergyDataset" - ] - }, - { - "cell_type": "markdown", - "id": "9f94ac2b-bc8a-4757-affb-6e570a024804", - "metadata": {}, - "source": [ - "# **onTime** Common Context Demo" - ] - }, - { - "cell_type": "markdown", - "id": "520ed047-e840-4bc3-8b0e-32c1a9e0fda3", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e75060cc-c514-4210-b359-585f4f51e873", - "metadata": {}, - "outputs": [], - "source": [ - "ts = EnergyDataset().load()" - ] - }, - { - "cell_type": "markdown", - "id": "e766b6d8-985a-44ae-9d52-a74ee7511561", - "metadata": {}, - "source": [ - "## Process the data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4d355f16-5c6d-477a-802c-9b1dbf3718f0", - "metadata": {}, - "outputs": [], - "source": [ - "df = ts.pd_dataframe()\n", - "df = df.interpolate()\n", - "cols = ['generation biomass', 'generation solar', 'generation nuclear']\n", - "df = df[cols]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c1cca8db-e15f-4e40-936b-8ef18e7a63c3", - "metadata": {}, - "outputs": [], - "source": [ - "ts = on.TimeSeries.from_dataframe(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "ebe23c8b-82ca-4ba4-aba7-969ee926e802", - "metadata": {}, - "outputs": [], - "source": [ - "ts_uni = ts['generation solar'].slice(pd.Timestamp('2015'), pd.Timestamp('2016'))\n", - "ts_multi = ts.slice(pd.Timestamp('2015'), pd.Timestamp('2016'))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "ef3cde59-c483-407e-821b-dfb566ca51f5", - "metadata": {}, - "outputs": [], - "source": [ - "train, test = ts_uni.split_after(pd.Timestamp('2015-09-01'))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "26560ca6-f072-4e06-bec7-2e2516f998c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "5c1c15b8-a54b-400d-8a6f-323c66d6fd2e", - "metadata": {}, - "source": [ - "---\n", - "## Context" - ] - }, - { - "cell_type": "markdown", - "id": "3cb667de-932c-400b-a062-1b652c67b828", - "metadata": {}, - "source": [ - "### Profiler" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "06d4cfb7-132b-4dd4-9a0d-273a8dc0c234", - "metadata": {}, - "outputs": [], - "source": [ - "profiler = on.context.common.Profiler()" - ] - }, - { - "cell_type": "markdown", - "id": "2f16554a-3511-498b-a052-aceabd381d7c", - "metadata": {}, - "source": [ - "#### Daily aggregation" - ] - }, - { - "cell_type": "markdown", - "id": "d54a3472-5359-4f8e-9f56-b63d9719bdab", - "metadata": {}, - "source": [ - "What does the common day looks like ?" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "4d79ec2f-86a9-4265-89c9-deabc1afea30", - "metadata": {}, - "outputs": [], - "source": [ - "day_mean = profiler.profile(ts_uni, profiler.Period.DAILY, profiler.Aggregation.MEAN)\n", - "day_median = profiler.profile(ts_uni, profiler.Period.DAILY, profiler.Aggregation.MEDIAN)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "64bc19dd-217e-4d35-9269-cd27847b397f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "day_mean.plot();\n", - "day_median.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "f6f51cbe-7535-49c2-88b0-430b7747995e", - "metadata": {}, - "source": [ - "#### Weekly aggreagation" - ] - }, - { - "cell_type": "markdown", - "id": "d582ef08-0ab7-4719-8ba7-7c3a6ca39c9e", - "metadata": {}, - "source": [ - "What does the common weeks looks like ?" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "e1b23b1e-6af9-45b1-9bc2-53ae65d73556", - "metadata": {}, - "outputs": [], - "source": [ - "week_mean = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEAN)\n", - "week_median = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEDIAN)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "b6090438-5348-45be-b355-3a14ac287ec4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "week_mean.plot();\n", - "week_median.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "813f08f7-51aa-4413-92da-8455bc854aed", - "metadata": {}, - "source": [ - "### Generic Predictor" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e5df7b11-4c2d-4b26-9b9c-f1bf521b6300", - "metadata": {}, - "outputs": [], - "source": [ - "model = on.context.common.GenericPredictor()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "402c4d6e-dd2c-4920-9736-eee98fa3ab32", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit(train)" - ] - }, - { - "cell_type": "markdown", - "id": "eac89dd3-f5ac-42f4-9db0-29e07328151b", - "metadata": {}, - "source": [ - "What does the future looks like ?" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "33c52d1f-6983-4edb-945b-9578d954f738", - "metadata": {}, - "outputs": [], - "source": [ - "pred = model.predict(48)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c10dd2d7-7a48-4343-b18a-152175dcd799", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.plots.prediction(train[-96:], pred, test[:48])" - ] - }, - { - "cell_type": "markdown", - "id": "1eacbd84-bb31-48b1-bd5b-6ab7db392204", - "metadata": {}, - "source": [ - "## Generic Detector" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9715fd4d-24d8-4d95-a77a-3c347916f2aa", - "metadata": {}, - "outputs": [], - "source": [ - "model = on.context.common.GenericDetector()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "56eae67d-ae1a-438c-bbd4-e1b52c93c2c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit(train)" - ] - }, - { - "cell_type": "markdown", - "id": "cad58ac3-bc19-4281-8975-d31b13e5b7f5", - "metadata": {}, - "source": [ - "Does the current signal has problem ? " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3fcf48b4-6d60-411d-88e5-191d575967a7", - "metadata": {}, - "outputs": [], - "source": [ - "detected_test = model.detect(test)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "317ef652-cfa1-4d85-b873-a1944c13fefc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.LayerChart(...)" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.plots.anomalies(test[:72], detected_test[:72])" - ] - }, - { - "cell_type": "markdown", - "id": "2eef8370-d1d7-47dc-ac68-91fa9d375646", - "metadata": {}, - "source": [ - "What if we want to have an idea about the future problems ?" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "0e31b255-cf60-4470-910e-31fe826b9782", - "metadata": {}, - "outputs": [], - "source": [ - "predetected = model.predetect(72)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "fe406b99-9025-4553-8c3d-0237a670e513", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.LayerChart(...)" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.plots.anomalies(test[:72], predetected[:72])" - ] - } - ], - "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": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/docs/0.1-time-series-custom-class.ipynb b/notebooks/docs/0.1-time-series-custom-class.ipynb deleted file mode 100644 index 90a644e..0000000 --- a/notebooks/docs/0.1-time-series-custom-class.ipynb +++ /dev/null @@ -1,799 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "670316b8-460c-4009-a5da-94278f4ac9a9", - "metadata": {}, - "source": [ - "# Time Series, Custom Class" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "52af59bb-083c-46c6-989a-bd4c65137a1a", - "metadata": {}, - "outputs": [], - "source": [ - "# Import to be able to import python package from src\n", - "import sys\n", - "sys.path.insert(0, '../src')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d6fc731f-3f50-4e9a-a24c-b2ab01d4fa31", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import ontime as on" - ] - }, - { - "cell_type": "markdown", - "id": "831f1944-599b-4761-a071-2a682346610a", - "metadata": {}, - "source": [ - "---\n", - "## Generation of random time series" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ef3e03e1-c247-4b5a-a27a-a13361e673b0", - "metadata": {}, - "outputs": [], - "source": [ - "ts = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "01962643-33af-4adf-8bfa-7d0163e4e41c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "    static_covariates:  None\n",
-       "    hierarchy:          None
" - ], - "text/plain": [ - "\n", - "array([[[ 0.2723568 ]],\n", - "\n", - " [[-1.5569272 ]],\n", - "\n", - " [[ 0.00838568]],\n", - "\n", - " [[-2.1595223 ]],\n", - "\n", - " [[-2.14411014]]])\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n", - " * component (component) object 'random_walk'\n", - "Dimensions without coordinates: sample\n", - "Attributes:\n", - " static_covariates: None\n", - " hierarchy: None" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ts[0:5]" - ] - }, - { - "cell_type": "markdown", - "id": "0cbd8da5-81fd-4b2d-8b7d-8394ad87348b", - "metadata": {}, - "source": [ - "---\n", - "## Use `TimeSeries` object" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1cbbd4f4-035d-43fa-93a7-6801b944835f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ts.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "06a8a724-7142-4077-b8b6-afafa8950d7b", - "metadata": {}, - "source": [ - "---\n", - "## Custom Class Creation" - ] - }, - { - "cell_type": "markdown", - "id": "91a1218b-8e8f-488e-94e4-875ea28477d8", - "metadata": {}, - "source": [ - "### Create custom model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "42e2f870-fd46-4100-9fb0-180a283e3d1e", - "metadata": {}, - "outputs": [], - "source": [ - "from ontime.abstract import AbstractBaseModel\n", - "\n", - "class MyModel(AbstractBaseModel):\n", - "\n", - " def __init__(self):\n", - " super().__init__()\n", - "\n", - " def fit(self, series):\n", - " super().fit(series)\n", - " print('I am fitted')\n", - "\n", - " def predict(self, n):\n", - " super().predict(n)\n", - " print('I predicted')\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "bd48fbd8-f40b-4db8-a1b2-21e2ea3aa2ff", - "metadata": {}, - "source": [ - "Load custom model in OnTime" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "372be43f-b104-4dfd-a26f-0e70c23f0f2d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['arima', 'catboost', 'TCN']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.models.get_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2b8c0f2b-31b6-481c-8e58-d087eb3d1e30", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['arima', 'catboost', 'TCN', 'my_model']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.models.load('my_model', MyModel) \n", - "on.models.get_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "cce99e86-5cce-4d2e-811a-4938fa637b83", - "metadata": {}, - "outputs": [], - "source": [ - "m = on.models.my_model()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "aa9eb39f-f636-4bfa-a783-787096d52f3e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I am fitted\n" - ] - } - ], - "source": [ - "m.fit(ts)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "47ef7030-f5cd-4d50-a74f-0a204eb47686", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I predicted\n" - ] - } - ], - "source": [ - "m.predict(5)" - ] - }, - { - "cell_type": "markdown", - "id": "cd126529-3b5e-4a22-a871-60d221b6df6d", - "metadata": {}, - "source": [ - "### Create custom detector" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "40a0dd53-0fe9-40a7-86d9-ee551e4c5e6e", - "metadata": {}, - "outputs": [], - "source": [ - "from ontime.abstract import AbstractBaseDetector\n", - "\n", - "class MyDetector(AbstractBaseDetector):\n", - "\n", - " def __init__(self):\n", - " super().__init__()\n", - "\n", - " def detect(self, ts):\n", - " print('I detected')\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "55bc256f-0ca3-4087-b7a9-bf563f26ffe7", - "metadata": {}, - "source": [ - "Load custom detector in OnTime" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6a8dd074-6350-4c3a-a8a7-7d901b790f95", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['threshold', 'quantile']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.detectors.get_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "91bdf719-f451-4d07-aaed-c2f5f7b4fa1b", - "metadata": {}, - "outputs": [], - "source": [ - "on.detectors.load('my_detector', MyDetector)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "bb636aa5-f155-46e4-8038-027d5f5db78a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['threshold', 'quantile', 'my_detector']" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.detectors.get_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f16ac090-142d-4d1b-8351-40b443275c72", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I detected\n" - ] - } - ], - "source": [ - "on.detectors.my_detector().detect(ts)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74a24176-7c9b-4790-9e5c-ac71e8517872", - "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": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/docs/0.2-detectors-generators.ipynb b/notebooks/docs/0.2-detectors-generators.ipynb deleted file mode 100644 index 8957294..0000000 --- a/notebooks/docs/0.2-detectors-generators.ipynb +++ /dev/null @@ -1,720 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "41296cc6-9d84-47c5-8a92-2d292f6f3c4a", - "metadata": {}, - "source": [ - "# Detectors, Generators" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe", - "metadata": {}, - "outputs": [], - "source": [ - "# Import to be able to import python package from src\n", - "import sys\n", - "sys.path.insert(0, '../src')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import ontime as on" - ] - }, - { - "cell_type": "markdown", - "id": "e24da8ab-6a83-4c2f-9ff0-c633d4693a91", - "metadata": {}, - "source": [ - "---\n", - "## Generation of random time series" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e9a96d79-0423-4d79-b01d-726193216238", - "metadata": {}, - "outputs": [], - "source": [ - "ts = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d463df9c-4f02-4c1e-b1a5-7162b9ea8c63", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "\n",
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-       "\n",
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-       "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n",
-       "  * component  (component) object 'random_walk'\n",
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"cell_type": "code", - "execution_count": 5, - "id": "8310ade1-a382-4d2a-b139-0331b3b8ebed", - "metadata": {}, - "outputs": [], - "source": [ - "td = on.detectors.threshold(low_threshold=-2)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5b3d020e-18cc-47f2-a553-eb00ff972ef3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "td.detect(ts).plot();" - ] - }, - { - "cell_type": "markdown", - "id": "ffbed9d6-d331-4708-8d50-25882c85e60d", - "metadata": {}, - "source": [ - "### Quantile" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "04f2a0c4-5744-46bf-b622-9abaaaf6b35c", - "metadata": {}, - "outputs": [], - "source": [ - "td = on.detectors.quantile(low_quantile=0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "02f12ec0-d1cc-41db-ba53-c53a98f6d8f3", - "metadata": {}, - "outputs": [], - "source": [ - "td.fit(ts)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d640d149-f0eb-4d19-9e2b-10926d6fa26f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "on.generators.constant().generate(1, pd.Timestamp('2022-01-01'), pd.Timestamp('2022-12-31')).plot();" - ] - }, - { - "cell_type": "markdown", - "id": "a389170d-8cce-4cd2-8bd2-a6bf2403213c", - "metadata": {}, - "source": [ - "### Gaussian Noise" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ae82840b-2bf5-4d9b-936c-7355cd7da95d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "on.generators.gaussian().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot();" - ] - }, - { - "cell_type": "markdown", - "id": "bb922dbb-03eb-4888-ae64-e7b297dcb9ba", - "metadata": {}, - "source": [ - "### Random Walk" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9802955b-6791-43bb-80d4-74ccb4119d70", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
+       "array([[[0.40508181]],\n",
+       "\n",
+       "       [[1.43521041]],\n",
+       "\n",
+       "       [[1.17597071]],\n",
+       "\n",
+       "       [[0.97419216]],\n",
+       "\n",
+       "       [[0.1523104 ]]])\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n",
+       "  * component  (component) object 'random_walk'\n",
+       "Dimensions without coordinates: sample\n",
+       "Attributes:\n",
+       "    static_covariates:  None\n",
+       "    hierarchy:          None
" + ], + "text/plain": [ + "\n", + "array([[[0.40508181]],\n", + "\n", + " [[1.43521041]],\n", + "\n", + " [[1.17597071]],\n", + "\n", + " [[0.97419216]],\n", + "\n", + " [[0.1523104 ]]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n", + " * component (component) object 'random_walk'\n", + "Dimensions without coordinates: sample\n", + "Attributes:\n", + " static_covariates: None\n", + " hierarchy: None" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts[0:5]" + ] + }, + { + "cell_type": "markdown", + "id": "0cbd8da5-81fd-4b2d-8b7d-8394ad87348b", + "metadata": {}, + "source": [ + "---\n", + "## Use `TimeSeries` object" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1cbbd4f4-035d-43fa-93a7-6801b944835f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ts.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "06a8a724-7142-4077-b8b6-afafa8950d7b", + "metadata": {}, + "source": [ + "---\n", + "## Custom Class Creation" + ] + }, + { + "cell_type": "markdown", + "id": "cd126529-3b5e-4a22-a871-60d221b6df6d", + "metadata": {}, + "source": [ + "### Create custom detector" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "40a0dd53-0fe9-40a7-86d9-ee551e4c5e6e", + "metadata": {}, + "outputs": [], + "source": [ + "from ontime.core.detector.abstract_detector import AbstractDetector\n", + "\n", + "class MyDetector(AbstractDetector):\n", + "\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " def detect(self, ts):\n", + " print('I detected')\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "55bc256f-0ca3-4087-b7a9-bf563f26ffe7", + "metadata": {}, + "source": [ + "Load custom detector in OnTime" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6a8dd074-6350-4c3a-a8a7-7d901b790f95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['threshold', 'quantile']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "on.detectors.get_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "91bdf719-f451-4d07-aaed-c2f5f7b4fa1b", + "metadata": {}, + "outputs": [], + "source": [ + "on.detectors.load('my_detector', MyDetector)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bb636aa5-f155-46e4-8038-027d5f5db78a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['threshold', 'quantile', 'my_detector']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "on.detectors.get_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f16ac090-142d-4d1b-8351-40b443275c72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I detected\n" + ] + } + ], + "source": [ + "on.detectors.my_detector().detect(ts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74a24176-7c9b-4790-9e5c-ac71e8517872", + "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": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/docs/0_core/0.2-detectors-generators.ipynb b/notebooks/docs/0_core/0.2-detectors-generators.ipynb new file mode 100644 index 0000000..80c641f --- /dev/null +++ b/notebooks/docs/0_core/0.2-detectors-generators.ipynb @@ -0,0 +1,720 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "41296cc6-9d84-47c5-8a92-2d292f6f3c4a", + "metadata": {}, + "source": [ + "# Detectors, Generators" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe", + "metadata": {}, + "outputs": [], + "source": [ + "# Import to be able to import python package from src\n", + "import sys\n", + "sys.path.insert(0, '../src')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", + "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", + "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from tqdm.autonotebook import tqdm\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import ontime as on" + ] + }, + { + "cell_type": "markdown", + "id": "e24da8ab-6a83-4c2f-9ff0-c633d4693a91", + "metadata": {}, + "source": [ + "---\n", + "## Generation of random time series" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e9a96d79-0423-4d79-b01d-726193216238", + "metadata": {}, + "outputs": [], + "source": [ + "ts = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d463df9c-4f02-4c1e-b1a5-7162b9ea8c63", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
+       "array([[[-0.15813833]],\n",
+       "\n",
+       "       [[ 0.430772  ]],\n",
+       "\n",
+       "       [[ 0.86925141]],\n",
+       "\n",
+       "       [[-0.93593666]],\n",
+       "\n",
+       "       [[-2.10009435]]])\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n",
+       "  * component  (component) object 'random_walk'\n",
+       "Dimensions without coordinates: sample\n",
+       "Attributes:\n",
+       "    static_covariates:  None\n",
+       "    hierarchy:          None
" + ], + "text/plain": [ + "\n", + "array([[[-0.15813833]],\n", + "\n", + " [[ 0.430772 ]],\n", + "\n", + " [[ 0.86925141]],\n", + "\n", + " [[-0.93593666]],\n", + "\n", + " [[-2.10009435]]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n", + " * component (component) object 'random_walk'\n", + "Dimensions without coordinates: sample\n", + "Attributes:\n", + " static_covariates: None\n", + " hierarchy: None" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts[0:5]" + ] + }, + { + "cell_type": "markdown", + "id": "2e4f348e-e7f7-4ed6-9f5a-25504e729529", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "851d573e-f47d-4055-9021-f9ef1002694d", + "metadata": {}, + "source": [ + "## Detectors" + ] + }, + { + "cell_type": "markdown", + "id": "5af625dd-ba6b-4f3b-9f42-462fe8918c5a", + "metadata": {}, + "source": [ + "### Threshold" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8310ade1-a382-4d2a-b139-0331b3b8ebed", + "metadata": {}, + "outputs": [], + "source": [ + "td = on.detectors.threshold(low_threshold=-2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5b3d020e-18cc-47f2-a553-eb00ff972ef3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "td.detect(ts).plot();" + ] + }, + { + "cell_type": "markdown", + "id": "ffbed9d6-d331-4708-8d50-25882c85e60d", + "metadata": {}, + "source": [ + "### Quantile" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "04f2a0c4-5744-46bf-b622-9abaaaf6b35c", + "metadata": {}, + "outputs": [], + "source": [ + "td = on.detectors.quantile(low_quantile=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02f12ec0-d1cc-41db-ba53-c53a98f6d8f3", + "metadata": {}, + "outputs": [], + "source": [ + "td.fit(ts)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d640d149-f0eb-4d19-9e2b-10926d6fa26f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "td.detect(ts).plot();" + ] + }, + { + "cell_type": "markdown", + "id": "047ee4b8-3f9c-4cca-8fe3-53200301a013", + "metadata": {}, + "source": [ + "---\n", + "## Generators\n", + "\n", + "### Constant" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b6723710-34ae-4d60-b120-5dce3df07989", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "on.generators.constant().generate(1, pd.Timestamp('2022-01-01'), pd.Timestamp('2022-12-31')).plot();" + ] + }, + { + "cell_type": "markdown", + "id": "a389170d-8cce-4cd2-8bd2-a6bf2403213c", + "metadata": {}, + "source": [ + "### Gaussian Noise" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ae82840b-2bf5-4d9b-936c-7355cd7da95d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "on.generators.gaussian().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot();" + ] + }, + { + "cell_type": "markdown", + "id": "bb922dbb-03eb-4888-ae64-e7b297dcb9ba", + "metadata": {}, + "source": [ + "### Random Walk" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9802955b-6791-43bb-80d4-74ccb4119d70", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb55754b-215a-457e-9577-2571c6d81c6d", + "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": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/docs/0.3-processing.ipynb b/notebooks/docs/0_core/0.3-processing.ipynb similarity index 56% rename from notebooks/docs/0.3-processing.ipynb rename to notebooks/docs/0_core/0.3-processing.ipynb index 2dafe10..b7e4cf6 100644 --- a/notebooks/docs/0.3-processing.ipynb +++ b/notebooks/docs/0_core/0.3-processing.ipynb @@ -499,7 +499,7 @@ "Dimensions without coordinates: sample\n", "Attributes:\n", " static_covariates: None\n", - " hierarchy: None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", @@ -1042,7 +1042,7 @@ "Dimensions without coordinates: sample\n", "Attributes:\n", " static_covariates: None\n", - " hierarchy: None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", @@ -1211,7 +1211,7 @@ }, { "data": { - "image/png": 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", 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", 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WrBlCQ0PRsWNH1K9f36hxTJkyBUFBQZg6dSpSUlIQFBSEoKAg0Q1oa2uLbdu2wdbWFk2bNsXAgQPxzjvvvLLZWR82NjZYu3Ytzpw5g4CAAIwdOzZfMw6aNGmCZcuWYcGCBahXrx7++eefHJ+T7t27Y8GCBZgzZw78/f2xZMkSrFq1SmeMhym0atUKSqVS5z6tW7fO8Vy3bt3w8ccfY9q0aahfvz6OHj2KyZMn630fT09P7N+/HxcvXsSAAQM4rdTEVCoVJk6cKB4PHjzY4GsEBASgfv36On/4+Pr6Yvjw4fj+++8xefLkXMfSDRkyRDy/Y8cOnD17Nh/vgCydQpLymKtVhMg9xsQSjRgxAtHR0Th06BAA1pGhWF/6Y10ZTq46W79+Pfr06QNA3R176tQps95/0aJFGD16NABgzJgx+Omnn157Dj9fhpG7vvgTImHOnDk4f/48YmJi8NNPP+GXX37J119DRFR4aa8v891335n9i2vQoEFwcHAAoF6jh8vdFz5MTEg4efIk2rdvjzp16uDnn3/Gjz/+iOHDhwMA/P394e7ujoCAALi7u8PNzU389/vvv8sc+atNnz5dJ1bt/4y1l5C/v3+u13d3dy+0Gym+6j1b+ueBCubMmTNi5eGAgAB07NjR7DEUK1YMHTp0AKBeDt/Ug/TJ/DhKjATt6X8v27FjBzIyMnDnzh1UrFgxx9oklmrUqFGi2fllzs7ORrnHjh07cp0RolKpcp0WWRi86j0Dlv15oIJZvHixKH/wwQcmHRyfl549e4o1lsLDw9G4cWNZ4iDTYGJCeqlcuTJUKhXs7e2tqp/Ww8MDHh4eJr3HqxZ60vTTFkZc3KroUSqVogXQzc0NAwYMkC2W0NBQ2NraQqlUIjw8HDNmzJAtSSLjs45vFyIiktXJkyfFBpEdOnQwy6KQr1K6dGmxFk5MTAyioqJki4WMj4kJERG9lvaaIV26dJExErWePXuKcnh4uIyRkLExMSEiotfSTkyMNXC8ILT3R2JiUrgwMSEiojzduXNHrIhcv359sZmknCpWrIgmTZoAAC5cuICYmBiZIyJjMUlismHDBgwYMACNGzfGkiVLdF7bunUrQkJC0KpVK3z11Vfc34KIyMLt2LFDlC2hG0dDuztH320MyPKZJDEpXbo03nvvPbRt21bn+ZiYGMybNw+zZ8/G9u3bkZSUhOXLl5siBCIiMhJLG1+i0aNHD1Fmd07hYZLEpHXr1mjVqhWKFSum8/yuXbvQtm1bsTjTsGHDuAkTEZEFy8jIEBs+lilTBg0bNpQ5ov/n6+uLunXrAlDvqq3ZWJCsm1nXMYmLi0OjRo3EY19fX9y7dw+pqalwcXHJcXxmZiYyMzN1nrOzsxPLERuTZlljLm/8aqwjw7C+9Me6Mpy56uzEiRN48eIFAPVOwua4pyG6du2KCxcuAAAiIiLQt2/fHMfw82UYU9WXvutfmTUxSUtL05n77ubmBgCvTExWrVqFZcuW6TwXFhb2ypU8jSEhIcFk1y4sWEeGYX3pj3VlOFPX2b///ivKfn5+FrdoYPXq1UV59+7dYkBsbvj5Moyx68vHx0ev48yamDg7O4vMGwBSUlIAINekBACGDh2aY3VBU7aYJCQkwMvLy2pWNTU31pFhWF/6Y10Zzlx1dufOHVFu3bq1xa36261bNygUCkiShKioqFzj4+fLMHLXl1kTk6pVq+pM6YqNjYWnp+crExMHBweTJCF5sbGx4Qf3NVhHhmF96Y91ZThT19nFixdFuW7duhb38/Hw8IC/vz+ioqIQGRmJ1NRU0Rr/Mn6+DCNXfZnkjtnZ2cjIyIBKpYJSqURGRgaUSiU6deqE/fv348qVK0hJScHKlSstaoQ3ERH9P5VKJRITHx8fuLu7yxxR7po1awZAvZ8Pdxu2fiZJTFasWIHmzZtj8+bNWLlyJZo3b44dO3bA19cXY8eOxbhx4xASEoIyZcrg3XffNUUIRERUQDdu3BDd75rZL5aoRYsWosyZntbPJF05I0eOxMiRI3N9LTQ0FKGhoaa4LRERGZFmtgtg2YlJSEgI7O3tkZWVhd9//x0zZsyAnZ1ZRyqQEbGzjYiIcnXp0iVRrlOnjoyR5K1UqVJiWMC9e/ewb98+mSOigmBiQkREubpx44Yo+/r6yhjJ673zzjuivHbtWhkjoYJiYkJERLnSTkyqVKkiXyB6CAkJgZOTEwDg4MGDMkdDBcHEhIiIcnXz5k0AQPHixVGyZEl5g3kNR0dHsVx+XFwcEhMTZY6I8ouJCRER5ZCdnY34+HgA+q/YKbfmzZuL8pEjR2SMhAqCiQkREeVw+/ZtKJVKAJbfjaOhPW348OHDMkZCBcHEhIiIctB04wDW02LStGlTUeY4E+vFxISIiHLQHvhqLYmJh4cHAgMDAQDnzp1jd46VYmJCREQ5WGNiAgAfffSRKH/11VdQqVQyRkP5wcSEiIhy0O7KsZYxJgAwYMAAscPwnj170LVrV6SlpckcFRmCiQkREeVw/fp1UbamFhN7e3vMmTMHCoUCALBz507MnTtX55iHDx/i22+/Rb9+/XDu3Dk5wqQ8cDMBIiLSIUkSrly5AgDw9vaGq6urzBEZpnfv3ti9ezc6d+4MpVKJ6dOnIyQkBJUrV8bdu3fRsGFD3L17FwBw4sQJxMTEwNbWVuaoSYMtJkREpOPevXt49uwZAKBWrVoyR5M/7du3x6BBgwAAGRkZaNiwIcaNG4eePXuKpARQd1nt2LFDrjApF0xMiIhIR3R0tChba2ICABMnToS9vb14vGDBApw4cSLHcQsXLjRnWPQaTEyIiEiHphsHAGrWrCljJAXj6+uLvXv3YsCAAToJCgD8+uuvYuzM7t27dQb7krw4xoSIiHRoJybW3GICAG+88QZatGiBfv36YevWrZAkCe3atUPfvn2RkJCAiRMnAgB+++03TJ48WeZoCWCLCRFRofX8+XNMmTIFf/zxh0HnFZauHG3+/v5YvHgxli5dir59+wIABg0aJGbv/Prrr5AkSc4Q6X/YYkJEVAipVCr07dsXu3btAqBeFbVTp056natpMSlVqhTKlCljshjl5uXlhbZt22Lfvn2IiYnByZMn0bhxY7nDKvLYYkJEVAj99NNPIikB1CuiZmZmvva858+f486dOwCse3yJvsLCwkSZS9hbBiYmRESFjFKpxIwZM3Seu3btGlatWvXac69evSrKhaUbJy9NmjQR5ZMnT8oYCWkwMSEiKmQiIiJw7949AECFChXE84sXL37tOIrCNPBVH/7+/nB2dgYAnDp1SuZoCGBiQkRU6Pz555+iPG/ePDFu4vz5869tFSgsU4X1ZWdnh6CgIABAXFwcHj58KHNExMSEiKgQycrKwsaNGwEArq6uCA0NxciRI8XrK1euzPP8wjgj53UaNWokyqdPn5YxEgKYmBARFSqnTp3CkydPAABdunSBi4sL+vTpIxYYO3z4cJ7na1pMnJ2dxS69hV3Dhg1FmeNM5MfEhIioENmzZ48od+zYEYC65aROnToA1IlHcnJyrudmZmYiJiYGAFCjRg3Y2BSNrwjtFhOOM5Ff0fjUEREVEXv37hXlN998U5Q1rQKSJOHs2bO5nnv16lUolUoARWN8iUa1atVQsmRJAOrEhAutyYuJCRFRIZGcnIzjx48DAPz8/ODt7S1e0+6ueNU4Cu1ungYNGpgoSsujUChE/SQlJSEhIUHmiIo2JiZERIXEgQMHkJ2dDQBo3769zmvaicmruisiIiJEuVWrViaI0HLpUz9kHkxMiIgKiVd14wBA7dq1xXodx44dy9FdIUmSSEyKFSsmptAWFRwAazmYmBARFRKaga+2trZo06aNzmt2dnZo0aIFACA+Pj7H8usxMTFiUbbmzZvDzq5obaXGKcOWg4kJEVEhcOfOHTHVt1GjRihevHiOY9555x1Rfnl5eu3WljfeeMNEUVqu8uXLo1y5cgCAixcvyhxN0cbEhIioEMirG0ejZ8+ecHd3BwCsW7cOKSkp4rXff/9dlDt37myiKC2bv78/AODBgwd48OCB0a/P2T76YWJCRFQIHDx4UJTbtWuX6zEuLi7o378/AODFixdYv349APVS7JquHX9/f9SrV8/E0VqmgIAAUb506ZJRr71r1y5UqVIFDRo0wOPHj4167cKGiQkRUSFw7NgxAOqxJNrjJV42bNgwUdYsT6/dWjJo0CAoFAoTRWnZNC0mABAVFWWUa0ZFRaF3797o3Lkz4uPjcfbsWcydO9co1y6smJgQEVm5J0+eiPElQUFBYvZNbho2bIjatWsDUK9bcvXqVaxZs0a8/vbbb5s2WAumnZgYo8VEpVKha9euYu8ijf/+97949uxZga9fWDExISKycidOnBDlpk2b5nmsQqHQaTVp2bIlrl27BgBo3bo1vLy8TBOkFTB2i8mxY8dw69atHM8/f/4cixYtKvD1CysmJkREVk7TjQO8PjEBgOHDh6N8+fIAoDPIc+DAgcYPzoqUKFECFStWBKBuMSnoYFXtlpIVK1bg2rVrYv+h+fPnIzU1tUDXL6yYmBARWTlDE5PixYvn+Ivd0dERvXr1Mnps1kYzAPbJkydITEzM93UkSUJ4eDgA9bifHj16oHr16ggLCwOgTghXrFhR8IALISYmRERWTKVSia6c8uXL6+yPk5fu3bvj559/Ro0aNeDs7Ixp06ahRIkSJozUOhhrnMnFixdFN067du3EJoETJkwQx8ydO1dsIUD/j4kJEZEVu3z5Mp4/fw5A3VpiyIyakSNHIjo6Gi9evMD48eNNFaJVMdaU4UOHDolyx44dRblevXro1KkTAODWrVvYvHlzvu9RWDExISKyYoZ24+SmqE4Pzo2xBsBq79TcsmVLndfGjh0ryvPnz8/3PQorJiZERFbs+PHjopzfxIT+n2YqNVCwFhPNgnWurq4IDAzUea19+/YiATp69Cg3DXwJExMiIiumSUzs7e3RoEEDmaOxfm5ubqhSpQqA/M/MiY+PR0JCAgCgSZMmOTZEVCgUbDXJAxMTIiIrlZqaiujoaABAYGAgnJycZI6ocNC0ZiQnJ4sEQ1+SJGHBggXisWZH55cNGDAAZcqUAQCsX78ed+7cyWe0hQ8TEyIiKxUbGyvK2oM2qWDq1KkjyqdOnTLo3BUrVmDevHnicbdu3XI9zsnJCSNHjgQAKJVKMbWYmJgQEVkt7cSkVq1aMkZSuGi3cvz7778GnavdWrJw4ULUr1//lcf27dtXlP/++2+D7lOYMTEhIrJS2olJzZo1ZYykcGnZsiVsbW0BGJaYREdHi5k8zZo1w/vvv5/n8f7+/qhatSoAICIiAk+ePMlnxDlduXIF69atw/Hjx6FSqYx2XXNgYkJEZKViYmJEmS0mxuPu7o6GDRsCUK8Tc+/ePb3OW79+vSj37t37tccrFArR1ZOdnY3t27cbHKtKpcKECRPQqVMnnDt3DgCwdetW1KtXD/369UPTpk0xevRog68rJyYmRERWKi4uDgDg4OAgZpKQcbRt21aU9+/fr9c5GzZsEGV9EhMA6NGjhyj//vvvekb3/5YuXYoZM2Zg9+7daNWqFcaMGYNevXohKytLHLNs2TKDB/HKiYkJEZEVys7Oxs2bNwEAfn5+OaakUsG0adNGlLVXcX2Va9eu4cKFCwDUU4T13aW5RYsWIqn8559/DJqdk5CQgM8++0w8Tk5OxsKFC3WSEkA9uPa///2v3teVGxMTIiIrFBsbK76A2I1jfI0bNxY7AWsWS8uLdmuJZqM+fdjY2GDw4MEA1N0yv/zyi97nrlixAikpKbm+1q9fP9y5cwcODg4A1C0rmZmZel9bTkxMiIiskPbAVz8/PxkjKZyKFSuGevXqAVAvTf/s2bM8j9ceX2LoLs1DhgzJ9Tqvs2XLFlGOj4/HsmXLEBwcjE8++QS//fYbKlSogJ49ewIAnj59itOnTxsUl1yYmBARWSHtMQOVK1eWMZLCq3nz5gDUi6ZpL/3/soSEBERGRgIAGjVqZPDPo0qVKmJacWRkJJKSkl57Tnx8vBjs2qBBA3h5eWH48OE4deoU5syZI7r23nzzTXGOoVOf5cLEhIjICmknJt7e3jJGUnhpEhMg7+6cs2fPirJ2ImCIDh06iPLevXtfe7x2a8lbb731yuO0x8ocOHAgX7GZmyyJyXvvvYdmzZqhZcuWaNmyJT788EM5wiAislrx8fGirO9ASzKMdmJy8ODBVx53/vx5UX55wz59dezYUZR379792uM3btwoynklJj4+PiJxPXLkCDIyMvIVnznJ1mIyadIkHDp0CIcOHcKPP/4oVxhERFZJu8WEiYlpeHl5oVq1agDUX+rJycm5HqfpxgHyn5g0a9YMrq6uANSJiVKpfOWxiYmJiIiIAABUr15dZwn9lykUCrRu3RoAkJaWlmeXlKWw6PllmZmZOUYR29nZiVHGxqRZGc/aVsgzJ9aRYVhf+mNdGU7TYlKyZEm4urqy7vJQkM9Xx44dsWjRImRnZ2Pfvn257n2jSUxcXV3h4+OTr/vY2dmhffv22Lx5M+7fv49du3ahc+fOANRjXBQKhTh2/fr1YtfjPn36QJKkPHdBbteuHX799VcAwLZt29CyZcs8YzHVv0fNLKfXkS0xmTdvHubNmwc/Pz+MHTsW1atXz3HMqlWrsGzZMp3nwsLC0KdPH5PFZU2L0MiFdWQY1pf+WFf6USqVYr0LT09P3Lp1S+aIrEN+Pl9BQUGivGHDBjFTR+P58+e4ceMGAKBGjRoF+gyHhIRg8+bNAICuXbuicuXKSExMhEqlwrBhwzB+/HgAEEkGoF4H5XU//4CAANjY2EClUiE8PPy1S+VrGPvfo4+Pj17HKaS80iwTiYqKQtWqVWFjY4N169Zh7dq12LBhg2jG0jB3i0lCQgK8vLz0zuqKGtaRYVhf+mNdGebu3bui+6ZLly46AyEpp4J8vlJSUlC6dGlkZWXBx8dHZxsAQL34mqarZNSoUVi4cGG+48zKykLlypVfOStn9+7d8Pb2FuvW+Pv7i0XdXqdVq1Y4fPgwAPWePrk1BmiY6t+jRbeYaG/PPXjwYGzZsgUXL15EkyZNdI5zcHAwSRKSFxsbG/5ifA3WkWFYX/pjXenn9u3bouzt7c0601N+Pl/u7u5o1qwZIiIicOPGDSQmJqJixYride3EIDAwsEA/C0dHRwwbNgzff/89AMDJyQnp6eni9TFjxqBr167i8dChQ/W+X2hoqEhMdu7ciRo1arz2HLn+PVrEp5n/qIiI9Kc9I4dThU2vRYsWonz06FGd14wxI0fbpEmT8O2332Lp0qV49OgRlEqlmB10/fp1zJ8/H4C692DgwIF6X1d7OvKpU6cKHKcpmT0jSE5OxvHjx5GZmYmsrCz8/vvveP78uU4rChERvZr2mALOyDG9Zs2aifLL65loBr4qFAqjfI+5uLhg4sSJGDFiBFxcXGBjY4MlS5bk2Aupa9euKFeunN7XrV27Nuzt7QHoJlOWyOyJSXZ2NhYuXIg333wTHTt2xKFDh7BgwQK4ubmZOxQiIqt09epVUdZMZyXTadq0qShrt5hkZ2cjKioKgHpbgJfHSRqLv78/Pv30U/G4atWqmDdvnkHXcHBwEGNToqOjdbqILI3Zx5iULFkSv/32m7lvS0RUaFy6dEmUuYGf6ZUsWRK1a9fG5cuXce7cOaSmpsLFxQVXr14VC5a9PFvH2KZOnYrU1FSkpaVh+vTpKF26tMHXCAwMxIULF6BUKnHp0iU0aNDABJEWHAd3EBFZEUmScPnyZQBAhQoVUKxYMZkjKho04zyys7OxadMmAMZZWE1fTk5OWLBgAZYuXZqvpATQTZ4suTuHiQkRkRW5c+cOnj9/DgB5Tvkk4xowYIAoz549G5Ik6ezWa+rExBiYmBARkdFpWksAJibm9MYbb6Bhw4YA1F/qS5cuxZo1awCoZ8gEBwfLGZ5etBMT7dYeS8PEhIjIijAxkYdCocAXX3whHo8aNQoPHz4EoF6RvEyZMnKFprfSpUuLNVjOnz+f5zL2cmJiQkRkRbQHvjIxMa+ePXti5MiROZ7/+OOPzR9MPmlaTZ49e6azHo4lYWJCRGRFtFtMfH19ZYyk6FEoFFi0aBG+/PJLscRF9+7d0ahRI5kj05/2WBhL7c5hYkJEZCUkSRItJl5eXlz/SQY2Njb47rvvkJSUhKioKKxbt07ukAxiDQNgZdtdmIiIDJOYmIhnz54BUK/kSfJxcXGBv7+/3GEYzBoSE7aYEBFZCe1uHCYmlB++vr5wcXEBwK4cIiIqIO2Br0xMKD9sbW1Rp04dAEBcXJxYE8eSMDEhIrISbDEhY9Duzrl48aKMkeSOiQkRkZXgHjlkDJY+M4eJCRGRFVCpVCIxqVSpEooXLy5zRGStLH0ALBMTIiIrcO3aNTx9+hQAEBQUJG8wZNU0Y0wAJiZERJRPR48eFWXNTrdE+VGsWDGxON/FixehVCpljkgXExMiIitw7NgxUW7WrJmMkVBhoOnOSUtLw/Xr12WORhcTEyIiK6BpMbGWnWzJslnyOBMmJkREFu7JkydiqnD9+vXh7Owsc0Rk7Sx5Zg4TEyIiC3fmzBlRbtKkiYyRUGHBFhMiIso37S8OzsghY/Dy8kKJEiUAMDEhIiIDaTe1a/+lS5RfCoVCdOfcvXsXDx48kDcgLUxMiIgsnOYvWjs7Oy5FT0Zjqd05TEyIiCxYRkYGrly5AkC9DL2jo6PMEVFhoT0AlokJERHp5fLly8jOzgbAbhwyLu0VYKOiomSMRBcTEyIiC8bxJWQqtWrVgkKhAKC7QaTcmJgQEVmww4cPi3KDBg1kjIQKGxcXF1SrVg2AOjFRqVQyR6TGxISIyIJFREQAABwcHLiGCRmdv78/ACA1NRU3b96UN5j/YWJCRGShbt++jdjYWABA48aNueIrGV1AQIAoW8o4EyYmREQWStNaAgCtWrWSMRIqrLQTE0sZZ8LEhIjIQh08eFCUmZiQKbDFhIiI9Hbu3DkA6lU6Ob6ETMHPzw92dnYAmJgQEVEelEql+KKoVq0a3NzcZI6ICiMHBwf4+fkBAKKjo8WaOXJiYkJEZIHi4uKQlpYGAKhbt67M0VBhpunOyczMRExMjMzRMDEhIrJIFy5cEGXtFTqJjE0zZRiwjO4cJiZERBbo4sWLoswWEzIlSxsAy8SEiMgCabeYMDEhU7K0KcNMTIiILJCmxcTFxQVVq1aVORoqzKpVqyZ2rWaLCRER5ZCeno64uDgA6o3WbGz4q5pMx9bWFrVq1QIAXL9+Henp6bLGw087EZGFiYmJERuqab4wiExJ052jVCpx9epVWWNhYkJEZGGio6NFuUaNGjJGQkWFJY0zYWJCRGRhtP9irVmzpoyRUFGhPWWYiQkREenQbjFhYkLmYElThpmYEBFZGE1iolAo4OvrK3M0VBR4e3uLbQ/YYkJERIIkSSIx8fHxgZOTk8wRUVFgY2MjunNu3LiB1NRU+WKR7c5ERJRDYmIiUlJSALAbh8xLe5zJ9evXZYvDTrY7ExFRDpyRQ3IJCAiAo6MjatWqJetaJmwxISKyIBz4SnIZNWoUUlJScObMGTRu3Fi2ONhiQkRkQThVmOTi7OwMAGJxP7mwxYSIyIKwK4eKOiYmREQWRJOYlChRAmXLlpU5GiLzY2JCRGQhUlNTER8fD0DdjaNQKGSOiMj8mJgQEVmIa9euiTK7caioYmJCRGQhOCOHSKbE5MmTJ/joo4/QokUL9OzZEydPnpQjDCIii3Lx4kVRrl27toyREMlHlsRk5syZKFWqFPbu3YuPPvoIEyZMwLNnz+QIhYjIYpw/f16U69WrJ2MkRPIxe2KSmpqKAwcOYOTIkXByckKrVq1QrVo1REREmDsUIiKLcuHCBQBA8eLF4e3tLXM0RPIw+wJr8fHxcHFxQbly5cRzvr6+iIuLy3FsZmYmMjMzdZ6zs7ODg4OD0ePSLCgj98Iylox1ZBjWl/5YV8Djx4+RkJAAAKhbty4kSYIkSa88nnWmP9aVYUxVXzY2+rWFmD0xSUtLg6urq85zrq6uuXblrFq1CsuWLdN5LiwsDH369DFZfJpfDPRqrCPDsL70V5Tr6sSJE6JcpUoV3Lp1S6/zinKdGYp1ZRhj15ePj49ex5k9MXF2dsaLFy90nnvx4gVcXFxyHDt06FAMGDBA5zlTtpgkJCTAy8tL76yuqGEdGYb1pT/WFbBlyxZRbt68OSpXrpzn8awz/bGuDCN3fZk9MfH29kZqairu378vVjWMjY1Fly5dchzr4OBgkiQkLzY2NvzgvgbryDCsL/0V5bqKjIwU5cDAQL3roSjXmaFYV4aRq77MfkcXFxe0atUKS5YsQXp6Og4dOoSYmBi0atXK3KEQEVmMAwcOAAAcHR05I4eKNFlSx/Hjx+PBgwdo164d5s+fj+nTp6N48eJyhEJEJLsbN27g5s2bANTdOE5OTvIGRCQjs3flAEDJkiXx448/ynFrIiKLs3//flFu27atjJEQyY+dbUREMmNiQvT/mJgQEcnsyJEjANRLJwQHB8scDZG8mJgQEckoLS1NrFkSEBAAe3t7mSMikhcTEyIiGcXExIiyn5+fjJEQWQYmJkREMrp27ZooV69eXcZIiCwDExMiIhlpJyZsMSFiYkJEJCsmJkS6mJgQEcmIXTlEupiYEBHJSJOYVKhQAW5ubjJHQyQ/JiZERDJ5/PgxHj58CIDdOEQaTEyIiGRy8eJFUa5Vq5aMkRBZDiYmREQyiYyMFOWgoCD5AiGyIExMiIhkcv78eVGuV6+ejJEQWQ4mJkREMtG0mNjY2CAgIEDeYIgsBBMTIiIZZGVl4dKlSwCAGjVqwMXFReaIiCwDExMiIhlER0cjMzMTABAYGChvMEQWhIkJEZEMzp49K8ocX0L0/5iYEBHJ4PTp06LcoEEDGSMhsixMTIiIZHDq1ClRDg4OljESIsvCxISIyMwyMzPFjJzq1aujRIkSssZDZEmYmBARmdmlS5eQkZEBAGjYsKHM0RBZFiYmRERmpt2Nw8SESBcTEyIiM4uIiBBlji8h0sXEhIjIjNLT07F161YAQMmSJdG4cWOZIyKyLExMiIjM6J9//kFycjIA4K233oK9vb3MERFZFiYmRERmtG7dOlEOCwuTMRIiy8TEhIjITK5evSoSkxIlSqBdu3YyR0RkeZiYEBGZyZdffgmlUgkAGDduHBwdHWWOiMjyMDH5n6ysLMTGxiI7O1vuUIioEIqOjkZ4eDgAoHz58hg3bpzMERFZJiYmAD755BO4ubmhffv2uHbtmtzhEFEhtGTJElH+9NNP4erqKmM0RJaLiQmA4sWLi5aS6OhomaMhosImNTUVq1evBgA4OjpiyJAhssZDZMmYmACoWbOmKF+9elXGSIiosJEkCSNGjMDTp08BAH379oWHh4e8QRFZMCYmAGrVqiXKV65ckTESIipsFixYgD/++AMA4OLigi+++ELmiIgsGxMTqHf3VCgUANhiQkTGExUVhfHjx4vHa9asQe3atWWMiMjyMTEB4OTkBB8fHwDqMSaSJMkcEREVBh9//LHYRXjcuHHo0aOHzBERWT4mJv+jGWeSkpKCO3fuyBwNEVm7q1evYt++fQCAqlWr4rvvvpM5IiLrwMTkf7QHwHJmDhEV1NKlS0X5/fffh5OTk4zREFkPJib/U6NGDVHmAFgiKojk5GSsWrUKgHp68ODBg2WOiMh6MDH5H39/f1E+d+6cjJEQkbX76aef8OTJEwDq6cGlS5eWOSIi68HE5H+CgoLE9uNHjx6VORoislbPnz/HnDlzAAA2NjaYOHGizBERWRcmJv/j5OQkpvFdvXoVjx8/ljkiIrJGa9asEa0lAwcOhJ+fn8wREVkXJiZa6tevL8rHjx8HoN7cT6VSyRUSEVkRSZKwbNky8Zgb9REZjomJFu3E5NixY7h58yb8/f3h4eGBy5cvyxgZEVmDM2fOIDIyEgDQsGFD1KtXT96AiKwQExMtQUFBorx582Z07twZ169fx7Nnz7BgwQIZIyMia7B27VpRHjFihIyREFkvJiZaKlSoIFpNoqKidNYz2bhxI1eEJaI87dixA4B60GuvXr1kjobIOjExecnkyZNzff7Ro0e4ePGimaMhImtx69YtsQZS48aNuYMwUT4xMXlJaGgoGjZsCACwt7fHm2++KV7T/DVERPSynTt3inJISIiMkRBZNyYmL1EoFAgPD8fEiRNx8OBBLFmyRLy2adMmGSMjIku2e/duUe7cubOMkRBZNzu5A7BElSpVwrfffiseBwYGIjIyEidPnkRsbCyqVasmY3REZGlUKhUOHToEAPDw8NAZSE9EhmGLiR7efvttUf7zzz9ljISILFF0dDQePXoEAGjRogVsbPirlSi/+K9HD3379hXl33//nbNzqMCePHkiVgcl66dpLQGAli1byhgJkfVjYqIHb29v8csmOjoa58+flzkislZHjx5Fq1atUKpUKXh4eKBJkyZiQS6yXocPHxblFi1ayBgJkfVjYqIn7e6cP/74Q8ZIyFr9+OOPaNmyJQ4ePCha3U6cOIE2bdrg9OnTMkdHBaFJTJydnXVWkCYiwzEx0VPv3r1hZ6ceK/znn39y/xwySEREBMaOHSs+N5UrV0a5cuUAAE+fPkXXrl1x584dOUOkfEpOTsbNmzcBqLe1cHBwkDcgIitn9sQkODgYLVq0QMuWLdGyZUusXLnS3CHkS+nSpdGxY0cAwO3bt3H27FmZIyJrkZaWhoEDB4qkZNy4cYiJiUFMTIxo9k9KSkLv3r2Z8Fqh69evizJ3EiYqOFlaTDZu3IhDhw7h0KFDGDZsmBwh5Iv22gRHjhyRMRIyVHZ2NrKysmS5d3h4OG7fvg0AaNOmDWbNmgU7Ozu4ublh06ZNqFy5MgD1jtZ//fWXLDFS/mknJtWrV5cxEqLCwaLXMcnMzERmZqbOc3Z2diZpKtX8pZrXX6xNmjQR5SNHjuCDDz4wehyWTJ86skT79+/H4MGDkZ6ejkOHDqFmzZpmua+mnlasWCGemzp1KhQKhXjNw8MDS5YsQadOnQAA06ZNQ8+ePUW3YVFhrZ8tALh27Zoo+/r6mu09WHOdmRvryjCmqi99p9ErJDPPfQ0ODkbp0qWhUCjQuHFjfPzxxyhRokSuxy5ZsgTLli3TeS4sLAx9+vQxQ6Q5ZWdnIygoCC9evICnpyeOHDkChUIhSyykn8OHD+Odd94Rj8PCwjBz5kyz3f/GjRto164dAMDHxwd79+7N8ZmRJAn9+/fHyZMnAQDLly9H27ZtzRYjFcynn36K8PBwAMD27dtRq1YtmSMiskw+Pj56HWf2xOTcuXOoU6cOkpOTMXPmTGRkZGD+/Pm5HmvuFpOEhAR4eXnlmdV16NAB+/btA6D+0vH29jZ6LJZK3zqyJK1atdKZyunm5oa7d+/C1dXV5PdWqVQICQnBnj17AAAzZszAZ599luuxmzZtQu/evQEAY8eOxZw5c0wenyWxxs+WRosWLXDs2DEA6oGwLi4uZrmvNdeZubGuDGOq+tL3WkZtL3733XdfucbHsGHD8P7774ulmkuWLIlPP/0UISEhyMjIgKOjY45zHBwczD7C3cbGJs/Ka968uUhMjh8/jipVqpgpMsvxujqyFFeuXNFJSgAgJSUFmzdvxqBBg0x+/y1btoikxNPTE6NGjXplvbVp0wYKhQKSJOHAgQNWUb+mYC2fLW2aMSYVK1aEm5ub2e9vjXUmF9aVYeSqL6MmJtp96frQvGFrWklVs/MwAFy6dEnGSCgvt27dQo8ePcTjsLAwrF+/HgCwYcMGkycmiYmJeO+998TjefPmoXjx4q883sPDA/Xq1UNkZCQiIyPx5MkTlCxZ0qQxUsE9ffoUDx8+BMCBr0TGYtZUKDY2FteuXYNSqcTz588xd+5cNG7cGE5OTuYMo0C0f/loj8Yny5GSkoIWLVrg6tWrANQtb4sWLYKHhwcA9eqrpkyGVSoVBg8ejAcPHgAAQkJC0K9fv9ee16ZNGwDqRP3gwYMmi4+MJyYmRpSZmBAZh1kTk8ePH2P8+PFo1aoVwsLCYGNjg6+++sqcIRSYj4+PaOlhYmKZVq5cKabnAsCvv/6K0qVLi1lVDx8+RGxsrMnuP2fOHNGFU7ZsWaxcuVKvQdKtW7cW5YiICFOFR0akWVgN0H9gHxHlzaxzEhs2bChGr1srBwcHVKlSBXFxcbh+/TokSeLMnNdITEzEhg0b4Ovrq7MWjClkZ2fjhx9+EI9PnDiBRo0aAQCaNm2KHTt2AFC3mvj6+hr13iqVCl9++aWY9aNQKDB37lyUKVNGr/O191g5evSoUWMj07h165Yoa9ajIaKC4SigfNA02SYnJ4vmespJpVLhm2++QeXKlfHhhx8iJCQEX3/9tUnXEpg7dy5u3LgBAOjYsaNISgB1YqKhmUVhTN98843OVOQvv/wSzZs31/t8Dw8PscbK2bNnkZ6ebvQYybiYmBAZHxOTfND+S5vdObl7/PgxevfujSlTpuisuDp16lT4+fmZZEn/c+fOYdKkSQDUrRWTJ0/Web1Ro0aiG87Yicm2bdswbdo0ce8FCxbkq5uyWbNmAICsrCycOXPGmCGSCWh35TAxITIOJib5wAGweUtKSkKDBg2wadMmAMjR1RUbG4u+ffvmWKOmoH7++WdkZ2cDACZMmJCjtaJYsWLw9/cHAERFRSEtLa3A95QkCZGRkejfv794bsaMGfjwww/z1cWnSUwAdudYA02Lib29PcqXLy9zNESFAxOTfGBi8mqSJGHYsGHiL8mSJUtiy5YtkCQJO3fuFDvqxsTEYOnSpUa997///gtA/SXx5Zdf5npMgwYNAABKpRIXL14s0P1UKhXeeustBAUFISUlBQDQtWvXVy6ipg/t7iYmJpZPk5h4eXnB1tZW5miICgcmJvmgnZhER0fLGInl+eOPP8QA03LlyuH8+fPo2rUrAKBTp07Ytm2bOPbbb7812sZ6d+7cEUlio0aNXrmya/369UW5oN1J69atw9atW8VjDw8PLF26tECDoWvWrCm2aDD1tGYqmGfPnuHZs2cA2I1DZExMTPKhatWqcHZ2BgBERkbKG4wFkSRJZ/Dn8uXL4eXlpXNMcHAwunfvDkDd5fPPP/8Y5d4HDhwQZc16ILkxVmKSnp6OCRMmiMcdOnTAgQMHCtycb2NjI1pN7t+/j7i4uAJdj0yHA1+JTIOJST7Y2toiMDAQABAXF4enT5/KGo+l2L9/v+geadKkiWgpednQoUNF+bfffjPKvTXdOEDeiUm9evVEi0ZBEpOtW7eKL6aOHTti9+7dqFOnTr6vp43jTKyDdmJSFLemIDIVJib5pP2XN1tN1H7++WdRHjdu3CuP69SpE0qXLg0A2Lx5s2gOLwhNYuLg4KAzTuNlbm5uqFGjBgDg4sWL+R6Aq92Fk9d7zQ9TT2sm4+CMHCLTYGKST8Ycq1BYHD9+HADg7u6us0/NyxwcHNCnTx8AQEZGRoGXX4+PjxddHk2aNBHdbK+i+dllZmbiwoULBt8vOzsb27dvB6Ce6aO9YqsxaE9rZouJ5WJXDpFpMDHJJyYmuh4+fCiWgQ8MDISdXd6LCr/55puiXNDERN9uHA3N0vRA/r74jx07hsePHwNQd+MYewfsYsWKoW7dugDUrTrPnz836vXJOJiYEJkGE5N8ql27tvhCYmKiXtxMIygo6LXHay+/XtDERN+Br7nd+8iRIwbfT7sbp1u3bgafrw/NOBOVSoWTJ0+a5B5UMJrERKFQoFKlSjJHQ1R4MDHJJwcHBzHYMTo6Gi9evJA5InkZmpiUKVMGtWvXBgCcOXNGrANiKEmSRIuJk5OTTmvIq9SpUwdubm4AgMOHDxs8JVeTmNjY2CAkJMTAiPXD9UwsnyYxqVixotFbzYiKMiYmBaDpzpEkCefPn5c5GnlpDwDWJzEBgDfeeAOAerGz/H75nj17VnxBNG/eHI6Ojq89x87OTiQwd+/e1WmSf52YmBixdk2zZs1QqlSpfET9etozczgA1vKkpqbi/v37ANiNQ2RsTEwKgONM/p+mxcTBwQG1atXS6xztQaPa3SOG+P3330W5b9++ep+nvVy9Id052nGGhobqfZ6hfHx8xCq5x44dM+nGh2S4+Ph4UWZiQmRcTEwKgImJ2osXL3D16lUAQEBAAOzt7fU6r3PnzqKF46+//hL73OhLqVRi7dq1ANQJUe/evfU+N7+JSXh4uCibanwJoB63oGk1efbsGa5cuWKye5HhOPCVyHSYmBRAnTp1xP4YRTkxuXDhghinoW83DqCeVtylSxcA6lVOIyIiDLrvv//+i8TERABASEgISpYsqfe5TZo0EVNy9U1Mrl27hsOHDwNQD37WrIdiKhxnYrmYmBCZDhOTAnB2dhbdFpcuXUJ6errMEckjP+NLNPr16yfK48ePx5MnT/Q+V7sbZ8CAAQbd9+Upufos8rZ69WpRHjZsWIH2xNGHvivAPnr0CBcvXizyA7DNiau+EpkOE5MC0nTnZGdnIyoqSuZo5GHojBxtXbp0QcWKFQEAp0+fxptvvolHjx699ry0tDRs3LgRgLrl5VXL3+dF050jSZJYHO5VsrKy8MsvvwBQD54dOHCgwfczVIMGDUS3WG4DYJOSktCnTx+ULl0adevWhb+/v1hfhUyLLSZEpsPEpIC0v4iL6tL0msREoVCIVgh9ubi4YNeuXShbtiwAdZdYmTJlMGjQoFe2YkiShFmzZiE5ORkA0KtXLzg5ORkctyHjTDZt2oS7d+8CALp27SoGppqSk5MTGjRoAAC4evUqHj58KF7LzMxE69atsX79evHcrVu3MH/+fJPHRRA7WSsUCiYmREbGxKSAtBMT7ZaDoiIrK0ts3Fe9enWxPoghAgICEBERIXbmlSQJa9aswfDhw3NdY2Tu3LmYNm2aeDxkyJB8xd6yZUtR3rVrV57H/vTTT6L8wQcf5Ot++aE9zkS7VWfTpk1i2rK2H3/80aDuMDKcJElisHflypVfuwUCERmGiUkB1atXT5SLYmJy9epVZGRkADC8G0dbzZo1cejQIZ0pvxs2bMCyZct0jnvy5Am+/vpr8fibb74R66EYqlKlSuLnd+rUKdy5cyfX47Zt2yYGvfr7++u1uqyxaI8zWb58uShrb5i4f/9+DB8+HADw/Plz0eVEppGUlCRa80w9AJqoKGJiUkAlSpSAj48PAOD8+fNQKpUyR2Re2pvgaSdp+VGtWjWsXbsWmzZtEs999NFHYuzO48ePERYWJrpwhg8fjkmTJhXont27dxflLVu25Hj9zp07GDx4sHj86aefmnzQq7ZOnTrB09MTAPD3339j27ZtOHfunFiGv0aNGmjdujU+/vhjcY52/ZHxaVpLACYmRKbAxMQINC0FqampiImJkTka89J04wAQS/QXVPfu3TF69GgAQHp6Ovr27YvY2FgEBgZi3759ANQDUCdOnGiUe2lor1Gi8eWXX4oBpd27d9dJUszBzc0Nc+fOFY+HDx+us17LqFGjoFAoULt2bfj5+QFQL7P/4MEDs8ZZlDAxITItJiZGEBgYKMpFrTvHFIkJAMyZM0cMpL18+TJ8fX2RkJAgXp86dapRpmnWq1dPtHjt3bsXly9fFq9dunQJv/32GwCgZMmSWLFihVlbSzT69+8vuo+SkpIQFxcHQB37f/7zHwDqQZg9evQAoN74L7fWHzIO7cREkwwSkfEwMTEC7ZkoRW2FTk1i4u7uDm9vb6Nd18nJCWvXroWLi4vO846Ojvj3338L3IWjoVAoMGbMGPG4Q4cO2LRpEyIjIzFp0iQx+Hb8+PHw8PAwyj3zE+PChQt1VtR1dXXF77//rrM3kCYxAcBxJibEFhMi02JiYgTav5xymylRWD179kzsGRIQEGD01oRatWph1apVOju3zp8/X2ePHWMYMWIESpQoAUA9pqRnz54ICgrC5s2bAQDly5fXSV7kUKtWLfz4449wdXXFm2++iZMnT8Lf31/nmEaNGqFmzZoAgEOHDuH06dNyhFroaRITFxcXsQYPERkPExMjqFatGuzs7ADo/jVV2GkvKGfMbhxtffr0QWxsLL755hssXrwYo0aNMvo9ihUrhgkTJrzy9SlTpuRouZHDqFGjkJycjD179qB27do5XlcoFBg7dqx4zDVNjC81NVV0pdWsWVNsa0BExsN/VUZgb2+PatWqAVAnJkVlJ1hTjS95WaVKlTBp0iQx0NMUPvvsM5w8eRKLFy/G4MGDxRdOtWrV8O6775rknvnxuvc/aNAglCpVCoB6dk5+t0l48uQJlEolnj17xoG0WqKiosS/74LOQiOi3DExMRJNE3p6errOluiFmbkSE3NQKBRo2LAhRo0ahdWrV+PAgQP48MMPsXXrVr13S7YEzs7OYnn+tLQ0gzf/S01NxbBhw+Dh4QFnZ2eUKFECZcuWRa1atcQU5aLs/PnzoszEhMg0mJgYiSYxAYrOOJPClJi8rGXLlliwYIHYpNGatG/fXpT37Nmj93kqlQohISFYtWoVAPWqvhrR0dFo06YNfHx8MG3atFxX5C0KmJgQmR4TEyMpaomJJEkiMalYsSJKliwpc0Sk8eabb4ryP//8o/d5mzdvRkREhHjs4eGBunXrokKFCuK5mzdv4quvvsLff/9tnGCtDBMTItNjYmIkRS0xuXPnDp4+fQqg8LWWWLty5cqJL81z584hNjb2teeoVCp89dVX4vGWLVvw6NEjnD9/HjExMTpL4wPA119/XeRaTSRJEisde3l5MRknMhEmJkZSvXp1US4Kq78W5m6cwkCzpokkSTozdV5lx44d4ku3UaNGYpwKoB63sn//fmzbtk1Mqz537hx27Nhh/MAtWFxcHJ4/fw6ArSVEpsTExEg8PDxQvHhxANDrL1Rrx8TEso0bN07s1rx161adLprcLFiwQJQnTpyYY/aPo6MjunTpgtWrV+d6TlHw77//inKjRo1kjISocGNiYiQKhQK+vr4AgPj4eGRmZsockWkxMbFsxYoVw/fffy8e//7776889tKlS9i7dy8A9fRo7daSl4WGhoqp8Xv27NFZwr+w09QRoDvAmIiMi4mJEWl+YatUKty8eVPeYExMs6qovb29Vc5cKQrCwsLEwnCbN29GdnZ2rsf9+OOPojxmzJg8Fw2zsbHBBx98IB4vXrzYSNFaNpVKJTaQLF68OIKDg2WOiKjwYmJiRJrEBCjc3TnPnj0TA3wDAwN19mshy+Hi4oKQkBAAwIMHD3Do0KEcxzx+/FhsVOjm5oahQ4e+9rpDhgyBk5MTAGDjxo1FYkHB8+fP4+HDhwCANm3aiJWeicj4mJgYkaYrByjcA2C192BhX7tl6927tygvXbo0x+vLly9HWloaAGDo0KFinFReihcvLroyEhMTceLECSNF+2oJCQno168fevTogS+++ALHjx83+T21aXfjaE/HJiLjY2JiREWlxeTUqVOizMTEsnXt2lUsUb927VqdL3RJkrBixQrxWLuL5nW0dzLWbHZoKpIkYeDAgVi3bh02b96MWbNmoWnTpmbdC4iJCZH5MDExoqKSmJw8eVKUmZhYNldXV3z99dfi8fjx40X57NmzuHbtGgCgVatWOlPeXyc0NFSMRTF1YvLHH3/g4MGDOZ4fP368iN+U0tPTxf0rVaoEPz8/k9+TqChjYmJEFSpUEH3v169flzka08jKyhL7r7i7u/OXtBV47733xM8pIiIC0dHRSE1NxYcffiiOefvttw26ZunSpcWia9euXcPt27eNF7AWpVKJyZMni8dLliwRrTWZmZl6rdFSUEePHhWbIbZv395kG0kSkRoTEyOysbERK8Bev3493zu7WrI9e/aI3WbbtWvHbd+tgJ2dHUaNGiUet2zZEr6+viLBtLe31xmLoq+2bduKsvYaH8a0e/du3LhxA4A6KXjvvffw22+/wcvLCwCwc+dOJCUlmeTeGuzGITIvfqsYWd26dQGopxcWxjUefvnlF1EePXq0jJGQIQYNGiR2SX748CESExPFa/3794eHh4fB12zTpo0o79+/v+BB5kJ7OvKYMWMAqLunBg4cCEA9/mTLli0mubeG9uJ02skYEZkGExMj0yQmgO6GX5YsPT1dzMzIy+LFi8XA11q1avGXtBUpXbo0+vTpo/Ncy5YtsXHjRp0BsIZo0qSJmCpuihaTy5cvY/v27QDUe9N06dJFvNazZ09RDg8PN/q9NdLS0sRnvnr16vD09DTZvYhIjZPxjUx7Dw3N3iOW7MCBA+jbty9SU1OxZ88eNGnSJNfj9u3bpzMmIbdly8my/fjjj6hevToePXqE+vXrY9CgQbC1tc339ZycnNC8eXPs378ft27dwvXr1w0aQPs6kydPFhsFjhkzRifWBg0awMvLCwkJCdi3bx8ePXokZh8Z06lTp5CVlQVAncgRkemxxcTItFtMLD0x2bdvHzp06ID79+8jJSUFgwcPznVczNOnTzFkyBCxkNbnn3+OAQMGmDtcKiAPDw9MnToVP/74I4YMGVKgpESjU6dOorxx48YCX0/j1KlToiXE09NTdONoKBQKhIWFAVAPyF61apXR7q3t8OHDotyiRQuT3IOIdDExMbKyZcuiXLlyANRdOZa6NXxGRgZGjRol/hoE1LMrPvjgA52Ys7KyMGDAADHrolmzZvjuu+/MHi9ZJu1Bs+vXrzfadSdOnCjKkydPFkvraxs5cqQoL1q0CEql0mj319BeLZeJCZF5MDExAU2ryaNHj0w+YyC/fvzxR7E6balSpcTsmuXLl8PGxgYhISG4cuUKhg8fLra3L168OGbOnMmZOCT4+PigQYMGANTrosTFxRX4mv/++y/27Nkjrj98+PBcj/Pz80OHDh0AADdu3MCmTZsKfG9tmZmZosWkXLlyOis7E5Hp8BvGBLTX9jDGL2pjS0tLw6xZswCopzjv3bsXa9as0Uk4du7cidq1a+PXX38FADg4OGDjxo2oWLGiLDGT5dJuNdmwYUOBriVJEr788kvx+Ouvv4aDg8Mrj//oo49E+YsvvkBGRkaB7q/t2LFjSElJAcD1S4jMiYmJCVStWlWUNWswWJJff/1VbEjWt29fBAYGon///ti0aRNatmwJV1dXneNtbGzw559/6kwPJdIwZmKydetWsWx+QEAA+vfvn+fxnTt3Fp/LuLg4nVVuC2rXrl2i3LFjR6Ndl4jyxsTEBHx8fETZ0lpMsrKyMG/ePPH4k08+EeVu3brh4MGDuH//Pr755htUrlwZTk5OWL58uc70TCJtvr6+CAwMBKAetHrz5s18XeflVV6//fbb1w7QVSgUmD9/vmjtmz59OtatW5ev+79s9+7doqzpMiIi02NiYgKW3GLy3//+V+wv0rp1azE+QJuLiwsmTZqEmzdvIiUlBUOHDjV3mGRlNDNkgPzPztm6dSuioqIAqNdI6datm17n1atXD7NnzxaPP/zwQ6SmpuYrBo0HDx7g3LlzAICgoCCULVu2QNcjIv0xMTEBS20xSUxMxNSpUwGo/9LUjDPJizGmlFLhV9DZOenp6Tq7BU+fPt2gMR1jx44Ve+jcv38fy5cvNzgGbZrl+gH11gtEZD5MTEzA3d1dLPZkSS0mn332GZKTkwEAI0aMQMOGDWWOiAoLPz8/MRvtxIkTiI+Pz/N4SZLEujgAMHPmTCQkJABQ70dj6HgmhUKBadOmicezZ89GZmamQdfQduTIEVFu3rx5vq9DRIYzemIyffp0dO/eHcHBwTh9+rTOayqVCnPnzkXr1q3RoUMH/P7778a+vcXQtJokJCQU6BeksRw+fFjUt4eHB6ZPny5zRFTY6NOdk52djc8//xzly5eHo6Mj3nrrLfz222+YOXMmAPWGgz/88EO+7l+3bl2EhoYCAG7fvl2gpeq1ExPNLspEZB5GT0z8/PwwadKkXKeVbty4EWfOnEF4eDiWL1+ONWvW4OTJk8YOwSJoxplIkvTavx7NYe7cuaI8ffp0kyzfTUWbdnfO2rVrc7wuSRLGjBmD2bNnIykpCdnZ2diyZQveeecdMc137Nix8Pf3z3cMY8eOFeWffvopX9dIT08Xf1T5+vpyfAmRmRl9rxzNLyc7u5yX3rFjBwYOHAgPDw94eHige/fu2L59Oxo1apTrtTIzM3O0NtjZ2eW5rkF+aZqVtZuXC6JKlSqiHBMTozMg1twSExOxdetWAED58uUxdOjQfL1PY9dRYVfU6svPzw/16tXD+fPncfLkSZw7d05n76hVq1ZhyZIl4rGNjY1O3TRu3BhTpkwpUH298cYbCAgIQFRUFI4ePYrTp0+jfv36Bl3jxIkT4vdOs2bNLPbnV9Q+XwXBujKMqepL38U5zbqJX1xcnM4mX76+vjp7Ubxs1apVWLZsmc5zYWFhOXZJNSZNP3dBubu7i/KZM2dQo0YNo1w3P7SX6+7Rowfu3LlToOsZq46KiqJUX7169RK7as+ePVtsX/DgwQOMGzdOHDd37lyEhIRgw4YNWLduHSpUqIBZs2bhwYMHBY6hf//+Ykn7hg0bomrVqmjevDnee+89vRYI1AwQB4DatWvj1q1bBY7JlIrS56ugWFeGMXZ9aU8MyYtCMtFmLr169cKECRMQHBwsnmvUqBE2b96MChUqAACOHz+O2bNnv7I/2twtJgkJCfDy8jLKkut79uwRG5x99tlnmDFjRoGvmR8pKSnw8/MTS+Nfv3493603xq6jwq4o1ldycjIqVaqElJQU2Nvb46uvvkLPnj0xePBgnDhxAgAwYMAAsaKwhjHr6sWLF/D29sbTp091ni9btiz27dsHT09PeHh4iOefPHmCP//8E7t27cK+ffvERpZeXl64cuUKnJ2dCxSPqRTFz1d+sa4MY6r6MkmLybvvviv+GnrZsGHD8P777+d5vrOzM168eCEev3jxItfNuTQcHBxMkoTkxcbGxig/iGrVqonyzZs3ZfvHsGDBApGU9O7d2yj7fRirjoqKolRfxYsXx6hRozBnzhxkZWXhyy+/1FlivkyZMjoLor3MGHVVrFgxvPvuuzrjqgD1NOI6derA0dERw4YNQ3R0NJKSknDt2jVkZ2fnuM6MGTNyrIJsiYrS56ugWFeGkau+DEpMVqxYUaCbVa1aFTExMaI7JzY2VtaxF6bk7e0t+tDlWsskKioK33//PQD1eiTcFZjMQfM5mzt3rs5O1Z6enti1axfKlClj8hg++OADLFu2DM+fP8egQYMQEREhBqFnZGRg8eLFuZ7n7u6OChUqoFu3bujXr5/J4ySinIyeCmVlZSEjIwOSJCE7O1uUAfW+Fr/99huePHmChIQEbN68GV26dDF2CBbBwcEBlSpVAiDPWiZpaWkICwtDWloaAGD06NE6mwsSmYqDgwNmz56NyMhIvP3222jatCk++eQTnD59WmcwrClVrlwZ586dw+HDh/Hrr7/i77//Rs2aNV95fL9+/RAZGYmnT5/iypUr3EWbSEZGH/w6evRonD17FgAwZswYAMCWLVtQoUIF9O7dGwkJCejRowfs7e0xePDgV87IKQyqVq2K+Ph4PH78GM+ePUPx4sXNdu+VK1ciOjoaABAYGCjWiSAyl7p168q6VlHVqlVFi2xgYCCuXLmC5ORkNG/eHBcvXkT79u3RrVs3+Pr6ivFgRCQ/oycmS5cufeVrNjY2+OSTT3Q2jivMfHx8cODAAQDqVhPNRmemlp2djTlz5ojHK1euhJOTk1nuTWTJihUrhuPHj+PGjRuoXbu2QcveE5F5sK3ShOTazG/z5s1ih9cOHTogKCjIbPcmsnQuLi7w9/dnUkJkoZiYmJBcm/lt2rRJlItK6xQRERUOTExMSLvFJDY21iz3VCqV2L17NwD1DANDN0MjIiKSExMTE9Je5fbatWtmuefp06fx6NEjAOpdWu3t7c1yXyIiImNgYmJCpUuXFpvlaWbImNrOnTtFmTMNiIjI2jAxMTHNHjl37txBSkqKSe+VmZmJVatWicdMTIiIyNowMTEx7c37TN2ds3r1arG6ZUhICLy8vEx6PyIiImNjYmJi2qtNmrI7R5IkzJo1SzzW3iGViIjIWjAxMTHtFpOrV6+a7D7nz58XM39at25dqFfUJSKiwouJiYlpJyambDHZvHmzKPfp08dk9yEiIjIlJiYmVq1aNdjZqVf+v3Llisnuo52YdOvWzWT3ISIiMiUmJiZmb28vxplcuXIFGRkZRr/HjRs3cP78eQBAo0aNULFiRaPfg4iIyByYmJiBZq+a7OxsREVFGf36a9euFeUePXoY/fpERETmwsTEDLQ30Tt37pzRr//HH3+Icr9+/Yx+fSIiInNhYmIGpkxMoqKiRCtM06ZNUaVKFaNen4iIyJyYmJhBYGCgKBs7Mdm4caMo9+/f36jXJiIiMjcmJmZQokQJ+Pj4AFCvN6JUKo127W3btokyx5cQEZG1Y2JiJvXq1QMApKam4tatW0a5ZmJiIk6fPg1A3SpTqVIlo1yXiIhILkxMzMQUK8Bu375dlENDQ41yTSIiIjkxMTETUyQmmzZtEuWuXbsa5ZpERERyYmJiJsbeZfjhw4f4559/AABeXl4IDg4u8DWJiIjkxsTETIzdYrJ+/XpkZ2cDUM/GsbHhj5KIiKwfv83MpFSpUihVqhQA4yQma9asEeW33367wNcjIiKyBExMzMjPzw8AcOfOHaSkpOT7OpcvX8bRo0cBAP7+/qhbt65R4iMiIpIbExMzMtY4k6VLl4ry8OHDoVAoChQXERGRpWBiYkaaXYYB4NKlS/m6Rnp6On799VcAgKOjI9555x2jxEZERGQJmJiYkWaRNUC9Amx+7N69G0+ePAEAhIWFwcPDwyixERERWQImJmZkjMSEe+MQEVFhxsTEjDw9PVGmTBkAQGRkJCRJMuj8jIwMbNmyBQDg7u6Odu3aGT1GIiIiOTExMSOFQiF2Gn748CESExMNOn/fvn149uwZAKBbt25wdHQ0dohERESyYmJiZgXpztHuxunVq5fRYiIiIrIUTEzMTDsxiYyM1Pu8rKwsbN68GQDg6uqKjh07GjkyIiIi+TExMTNNVw5gWIvJgQMH8PjxYwDqDfucnZ2NHRoREZHsmJiYWY0aNeDg4ADAsMSE3ThERFQUMDExM3t7e/j7+wNQr/6ampr62nOUSiXCw8MBAM7OzujcubNJYyQiIpILExMZaLpzVCoVoqKiXnv8oUOH8ODBAwBA586d4ebmZsrwiIiIZMPERAaGzsxhNw4RERUVTExkYMgAWEmSsGnTJgCAg4MDunbtasrQiIiIZMXERAZ169YV5ddNGT537hzu3LkDAGjXrh3c3d1NGRoREZGsmJjIoGTJkvD29gYAXLhwASqV6pXHbt26VZRDQ0NNHhsREZGcmJjIRNOdk5ycjJs3b77yOO3EhN04RERU2DExkYk+K8DevXsXZ86cAaBOZLy8vMwRGhERkWyYmMhEn5k527ZtE2V24xARUVHAxEQm+szM4fgSIiIqapiYyMTHx0cslJZbV05qair27t0LAPD09ESDBg3MGR4REZEsmJjIxMbGRnTn3Lp1C48ePdJ5PTw8HOnp6QDUg15tbPijIiKiwo/fdjJq2LChKJ88eVKUMzMzMXXqVPH47bffNmtcREREcmFiIqPGjRuLsnZisnLlSsTFxQEA3nzzTbRp08bssREREcmBiYmMtBOTEydOiPKyZctE+fvvvzdrTERERHJiYiKjKlWqoHTp0gDULSaSJOHSpUs4e/YsACA4OBjBwcFyhkhERGRWTExkpFAoRKvJo0ePEBMTg19//VW8/s4778gVGhERkSyYmMisRYsWovz111/jp59+AgDY2dmhX79+coVFREQkCyYmMnv77behUCgAAGvWrEFaWhoAYNSoUShTpoycoREREZmd0ROT6dOno3v37ggODsbp06d1XluyZAkaN26Mli1biv+KOm9vb3Tq1EnnOT8/P8ycOVOmiIiIiORj9MTEz88PkyZNQsWKFXN9vWvXrjh06JD4j9StIxpVqlTBpk2b4OLiImNERERE8rAz9gV79+6tvrCd0S9daIWGhmLevHm4e/cuxo8fj1KlSskdEhERkSzMnj3s27cPBw4cQLly5TB8+HC0bdv2lcdmZmYiMzNT5zk7Ozs4ODgYPS6VSqXzf3P76KOPcsRiaeSuI2vD+tIf68pwrDP9sa4MY6r60ndrFbMmJu3bt0evXr1QokQJnDp1CuPHj0fZsmUREBCQ6/GrVq3SWWwMAMLCwtCnTx+TxZiQkGCyaxcWrCPDsL70x7oyHOtMf6wrwxi7vnx8fPQ6zqDE5N1338X58+dzfW3YsGF4//338zy/atWqoty0aVN07NgRERERr0xMhg4digEDBugGbMIWk4SEBHh5eXHDvFdgHRmG9aU/1pXhWGf6Y10ZRu76MigxWbFihVFv/ro37ODgYJIkJC82Njb84L4G68gwrC/9sa4MxzrTH+vKMHLVl9HvmJWVhYyMDEiShOzsbFEGgIiICKSkpEClUuHUqVPYuXOnzgJjREREVLQZfYzJ6NGjxV4vY8aMAQBs2bIFFSpUwK5duzBt2jQolUpUqFABEydORL169YwdAhEREVkpoycmS5cufeVr3CmXiIiI8sLONiIiIrIYTEyIiIjIYjAxISIiIovBxISIiIgsBhMTIiIishhMTIiIiMhiMDEhIiIii8HEhIiIiCyGQtKsF09EREQkM7aYEBERkcVgYkJEREQWg4kJERERWQwmJkRERGQxmJgQERGRxWBiQkRERBaDiQkRERFZDCYmREREZDGYmBAREZHFYGJCREREFoOJCVEBcVcH/WRnZ8sdAhFZASYmJDx+/FjuEKzKhg0bAAAKhULmSCzfmjVr8MMPPyAjI0PuUKxGSkqK3CEQyaLQJyZ79+7FhAkTEBUVBQBQqVQyR2R5duzYgZ49e2L69OmYN28enj9/LndIFm379u0ICQnBzp07kZKSws9UHnbs2IHOnTtjwYIFuHr1KhwdHVlfr7Fr1y5069YNkydPxvz58/Hw4UO5Q7Joe/fuxYgRI3D8+HEA/B2fF2v5PrSTOwBTycrKwl9//YVffvkF3t7e2LNnDwICAmBjU+hzMb2lpKRg/vz5OH36NMaOHYuqVatiyJAhqFmzJkJCQiBJElsDtCQnJ2P69Ok4cuQIvv/+ezRv3lzukCzWvXv3MG7cOLx48QLffPMNqlWrhn79+uHp06coUaKE3OFZrJMnT2L58uWYMGECSpQogUWLFmHRokUYPHgwKleuLHd4FkWpVGLr1q1Yvnw5vLy8sHHjRjRp0gQ2Njb83fUSa/s+tMyojECSJJQqVQpff/01wsLCcO/ePRw4cEC8RuouiAYNGmDz5s1o3bo1SpQoAXd3d9y9e1e8Tv9PpVIhIyMDgwYNQvPmzZGdnY0jR47g9u3bcodmcWxtbdGtWzf8/fffCA4OxtOnT+Hj44MrV67IHZpFUiqVAIALFy6gcePGaNq0KWrVqoURI0bg1q1bCA8PlzlCy+Tp6YnPP/8cI0eOREZGBjZu3AiAv+NfZm3fh4UqMYmIiMC9e/eQnp4OBwcHNGrUCE2aNEGTJk3g5eWFiIgIJCcnQ6FQWOQPwxy068jV1RVt2rSBQqHAnj170LFjR5QqVQqSJOHo0aNITEyUO1zZaeorLS0NxYsXR4cOHRAbG4tx48ahS5cuWL9+PQYPHozVq1fjwYMHcocrK+26KlOmDPr16ydeK1WqFO7fvy++gC21CdncNHWWlZUFAHj69CliY2PF67Vr18bDhw9x9uxZnDlzRq4wLcaTJ09E2dbWFnXq1MEbb7yBgIAANG/eHP/88w+ePHkCGxubIv8Zs+bvQ4VkaRHlw+XLl/HZZ5/B1dUVpUuXhqOjI+bPn69zzPHjx7F161YEBgYiLCwMKpXKYpuxTOF1dXT8+HFUqFAB3t7euHLlCtatW4eyZcviP//5T5FsOXm5vhwcHPDDDz9ApVJh1qxZuHv3Lj744ANUr14d+/btw/bt29GmTRuEhobKHbrZve6zpVQqYWtriy+//BLOzs6YPHmyjNFahpfrzN7eHgsWLMDTp0/RsWNHfPbZZ+jYsSMiIyMRHh4Ob29vVKxYEX369JE7dFmcPn0aU6ZMQVBQEMaPH49ixYrlOCYuLg4rVqxAhQoVMHr06CL3O16jMHwfWk4kBXDo0CF06NABf/31F6ZOnYqbN29i4cKFePr0qTgmMDAQ1atXx9mzZ3Hv3j3Y2NjgxYsX8gVtZq+qI81MnCZNmsDb2xvZ2dmoVasWypcvj5iYGKSnp8scuTxerq9bt25hwYIFUCqVGD58OCZMmIDq1atDqVSiXbt2cHd3x+XLlwFYZtOoKb3u35+mz79atWqQJAlpaWnyBmwBXq6z+Ph4LFiwACVKlMDUqVPxzz//YMyYMZg7dy4GDx4MpVIpBqUXtc9XTEwMVq5ciaZNm+L69eu4cOFCrnXg7e2NVq1a4ezZs7hx4wZsbGyK5ED+wvB9WCgSkwMHDqBChQoAgHLlymHSpEk4deoUzp07J5rznJyc0KRJE5QuXRp//fUXvvrqK/zyyy+iCbWwe1UdnT9/XqfJ085OPR7axcUFtra2cHZ2liVeueVWX2fPnsXhw4dRqlQplC9fHoC6ORkASpYsKVqWiloL0+v+/SkUCigUCri5uSEmJgbOzs5F7sv1Za/6fB04cAAhISFYtGgRJkyYgM2bNyMwMBD29vZwcHAAUPQ+X76+vggNDcXkyZPRvHlzbNiwAY8ePcpxnJ2dHQIDA9GgQQMsXboU06ZNw+zZs4vcH1eF4fvQqhMTTX91s2bNdPpfGzRoAH9/f+zfv1/nr7OaNWsiLi4Ov/32Gx49eoQBAwbA3t7e7HGbkz51lJqaCgBijMQff/yBdevWoUOHDuYPWGZ51VdAQAD2798v/rLQ/DX2559/4t9//0W7du3MH7CM9P33p0lC2rZti1u3buH69etF7stV43Wfr7179yIlJQV2dnaoXr06AGDVqlU4fPgwmjVrJkvMctJ8dtq3bw8AeO+995CYmIiDBw/mumBf2bJlcfv2bezduxfPnj3DJ598AicnJ7PGLJfC9H1o1YmJ5q/V2rVrIysrCydPnhSvDRo0CAcPHsT9+/cBAM+ePcPkyZNx8+ZN/PLLL/jxxx9RvHhxWeI2J33qSJOQHD16FL169cK2bdswffp08cugKDGkvo4cOYKuXbti69at+Oabb9CgQQNZYpaLvv/+NEnIo0eP0KdPH3h4eMgSryV4XZ0dOnRIfL7i4uLw2WefYfv27ZgyZQp8fX1liVlOms+OnZ0dsrOz4ezsjLCwMGzZsgUJCQk6rb2ZmZmYOXMmzpw5g9WrV2P+/PlFamp6Yfo+tPjEJCkpCeHh4TlGpEuSJJqdatWqhXLlymH37t0ii/b09ET16tVx6tQpAICrqyuGDx+O7du3o3bt2uZ9EyZW0DrSfIDbtWuHCRMm4I8//kDdunXN+ybMyFj11aJFC1FfderUMe+bMJOC1tXp06fFOTVr1sTo0aNRqlQp870BGRjrd1blypUxatQobNiwodB+voC860u7VUTTzdyrVy84ODhgz549sLGxEd069vb2ePfdd7F79274+/ub7w2Y0b1797B69WocOHBAZxXlwvZ9aNGJycKFC9GnTx9cuHABU6ZMwQ8//CBWQVQoFKLZycHBAW3atMGDBw+wcOFCAOrFw2xsbBAcHAxA/aEujAsUGaOOGjZsCABwc3MT9VVYGbO+ihUrVqgXWTNGXRW1ViRj/s5ycHBAtWrV5HkjZvK6+tIkI5q1gjRftJ9++in27NmDMWPGoFOnTrh27RoUCgVKly4tzxsxgwULFqBfv364d+8efv75Z8yePRvPnj0DUAi/DyULtWnTJuk///mPdPv2bUmSJOn8+fNSnz59pGvXroljNm7cKAUHB0s///yzlJWVJUVGRkodOnSQxo0bJ7Vu3Vr64osvpLS0NLnegsmxjgzD+tKfMetKpVLJ9TbMip8vw+hbX40aNZJ++uknnXM3b94sBQcHS59//rk4vzDbunWrNHHiRCkhIUGSJEn6999/pd69e0vPnj0Tx2zYsKHQfLYsKjHJysoS5ejoaGnr1q2SJElSRkaGJEmSNHjwYCk8PFySJEmKj4+X3nnnHenYsWM610hMTJROnTolnTt3zjxBmxnryDCsL/2xrgzHOjOMMerr5MmT0sCBA3M8X9ho19Xjx4+l5ORkSZIk6cyZM1JoaKj01ltvSWfPnpUkSf0ZGjRoUKH5bFnEAmtPnjzBwoULoVAo4Ovrix49eoipcRpZWVkYOXIkxo4dm6O/VZIkqFQqMfinMGIdGYb1pT/WleFYZ4Zhfekvr7q6desWfvrpJ1SvXh0tWrTAwYMHoVAo0K9fPzHQtzDUlexjTLZt24Z+/fqJ6XHbtm3DzJkzAaiXrZbUrTp49OgR0tPT4e7urrMGglKphEKhsOofwuuwjgzD+tIf68pwrDPDsL70l1ddAepF5GbNmoWRI0fC398fDRs2RFxcnBhkXljqStbdhVNSUnDz5k2MHj0a3bp1AwDUrVsXEydOxOPHj+Hh4SGWyr1y5QpsbW3FgJ3o6Gh4enoW+ulgrCPDsL70x7oyHOvMMKwv/eVVV0+ePEHJkiUBqFdSzszMhIODA+rWrYspU6agTZs2AGD1CYmG2ROTpKQkKBQKlC1bFs7OzmjTpg0qVaokXn/27BmKFy8OFxcXABDr98fExKBr165ISkrCBx98AFdXV8yePdvc4ZsF68gwrC/9sa4MxzozDOtLf/rWlWYFbs26LpquncuXL6NSpUpiMb7CwmyJSVZWFqZOnYrIyEiUKVMGLVu2RNeuXcV8c0mSoFAo4OjoCBcXFzFNTJIkKJVKXLp0CSdOnMCiRYswaNAgDB8+3Fyhmw3ryDCsL/2xrgzHOjMM60t/+a0rAHj8+DEiIiLEFhmjRo0qdIvvmW2Mya5du/Ds2TNs2bIFgwYNwu3btzF9+vQcx+3btw8VKlQQPwjNXPa7d++iY8eO2LlzZ6H9wLKODMP60h/rynCsM8OwvvSX37oCAA8PD8TFxcHNzQ1bt25F3759zRm6WZg0MUlPTxeDmGJiYuDu7g47Ozu0a9cO7777Lm7evIn169cDUGeQkiTh0qVLYo+WXbt2YcOGDQCA1atXY9q0aaL5r7BgHRmG9aU/1pXhWGeGYX3pzxh1FR4eDgD48MMP8cUXX8DNzU2eN2NiJunKiY+Px5w5c+Di4gJnZ2d8/vnnKFasGGxtbZGcnIxixYrBy8sL7777LhYtWiSWGE5NTUWJEiXw9OlTfPTRR7h48SI+//xzACh0H1bWkWFYX/pjXRmOdWYY1pf+TFFXlrLZnqkYvcVk8+bNGDVqFPz8/DBw4EBcvXoVK1asgK+vL06dOoWkpCRxbOvWrVG1alVs3LgRgHrTqkOHDuGbb76Br68v9u/fj06dOhk7RNmxjgzD+tIf68pwrDPDsL70x7rKH6MnJnfv3sV7772HMWPGICAgADNmzMDatWvRvHlzuLu7Y/v27Xj69CkAddbn6emJzMxMdTA2NhgxYgT+/vtvfPDBB8YOzWKwjgzD+tIf68pwrDPDsL70x7rKH6N35WiaoQB1P5mtrS18fHyQnZ2N4cOHY/78+ahcuTI6d+4MFxcXPH36VGy3XLNmTYvc6dDYWEeGYX3pj3VlONaZYVhf+mNd5Y/RE5Ny5coBUE93sre3x8OHD6FQKODg4ICgoCB069YNu3fvxv79+5GdnY27d++KKVKa+eyFHevIMKwv/bGuDMc6MwzrS3+sq/wx2TommoVgTp48CR8fH7EiXa9evdCiRQscOXIEycnJGDJkiKlCsHisI8OwvvTHujIc68wwrC/9sa4MY7LERKlUwtbWFteuXUP79u0BAH/99RdSUlIwbNgw9OrVy1S3thqsI8OwvvTHujIc68wwrC/9sa4MY7K2IltbW2RnZyM9PR1JSUkYMWIEfvnlFwQEBJjqllaHdWQY1pf+WFeGY50ZhvWlP9aVYUy6JH1cXByOHz+O69ev4+2338Y777xjyttZJdaRYVhf+mNdGY51ZhjWl/5YV/pTSNr7SxtZdnY21q1bh969e8PR0dFUt7FqrCPDsL70x7oyHOvMMKwv/bGu9GfSxISIiIjIEEV3PhIRERFZHCYmREREZDGYmBAREZHFYGJCREREFoOJCREREVkMJiZERERkMZiYEBERkcVgYkJEREQWg4kJEZnU6dOnERwcjODgYNy9e1fucIjIwjExISKjmTZtGoKDg/Hee++J59zc3BAQEICAgAA4ODjIGB0RWQOTbuJHRFSzZk2sXr1a7jCIyEpwrxwiMorQ0FAkJibmeP7nn3/GqFGjAABbtmxBhQoVMG3aNGzbtg3ly5fHyJEjsXjxYqSkpKBbt24YPXo0Fi5ciC1btsDNzQ1Dhw5F7969xfUePHiARYsW4dixY3j69CnKlSuH0NBQDBkyBHZ2/FuLyNrxXzERGUWNGjWQlpaGp0+fwtXVFT4+PgCA6OjoV57z8OFDzJgxA6VLl8aLFy/w559/4vjx47h//z7c3NyQlJSEWbNmoUGDBvDx8cHTp08xZMgQJCUliXvExcXh559/xp07dzB16lRzvV0iMhGOMSEio5gzZw5atGgBQJ2krF69GqtXr0bNmjVfeU5WVhb++9//Ijw8HOXKlQMAJCQk4M8//8T69evh6OgIlUqFM2fOAAD++usvJCUloVSpUti8eTP+/PNPzJw5EwCwbds2JCQkmPhdEpGpscWEiGTj7u6OwMBAAICnpyeSkpJQrVo1VKhQAQBQsmRJ3Lt3D48fPwYAXLp0CQDw6NEjtG/fXudakiQhKioKXl5e5nsDRGR0TEyISDaurq6ibGtrm+M5hUIBQJ10vHyepqtIm5OTkynCJCIzYmJCREajSQzS09NNcv3atWvjyJEjsLW1xfTp00XLyosXL/Dvv/+iTZs2JrkvEZkPExMiMpoqVaoAAC5fvoy+ffvC2dkZI0aMMNr1+/Tpg7///hv3799Hr1694OPjgxcvXiApKQnZ2dno2rWr0e5FRPLg4FciMppu3bqhbdu2cHNzQ2xsLKKioqBSqYx2/ZIlS2LVqlUIDQ1F8eLFERsbi4yMDAQFBWHcuHFGuw8RyYfrmBAREZHFYIsJERERWQwmJkRERGQxmJgQERGRxWBiQkRERBaDiQkRERFZDCYmREREZDGYmBAREZHFYGJCREREFoOJCREREVkMJiZERERkMZiYEBERkcX4PyqdK0FficmiAAAAAElFTkSuQmCC", + "image/png": 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", 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", 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j1ltvxVtvvQWgZr5H//798cADD5g8nvFutkRE5HwMHuRUlZWV0s60jRs3BlATFHQ6HQD7gofaGS3G6tevj4SEBEyePBk//fQT/vzzT4SEhOCrr75CZmamYg0R+ZogRETkGtyrhZzq/PnzqK6uBgA0adIEAKDT6RAWFoaioiJNgsfBgwelyx06dAAA+Pn5oV+/ftL1Op0OKSkpit9j8CAicj1WPMipjOd3iMThFnvmeMiDgjxAyPdv6dixo8XHkE9KlQcZIiJyDQYPcip58JBP+hSDhxYVj8mTJ0uXR44cafEx5MGDFQ8iItfjUAs5lfzMEXnwEM9ssTV4vPHGG5g2bZr0szx4TJo0CdXV1ejUqRNatmxp8XE41EJE5F4MHuRU8uDRsGFD6bJY8aioqEBlZSUuXbqE+vXrm+y7AtSEE3noAJQBIiEhAe+9955N7eFQCxGRe3GohZxKHjzS0tKky/JTaidOnIiGDRuid+/eqquJqgUEeYCwB4daiIjci8GDnEoMHklJSYot7OWLiImnuG7dulV1NVG1CajmTqe1JjQ0FP7+NYW+2lQ8tmzZgoULF0oLmhERkW041EJOU1paiqysLADKYRZAWfGQKyoqMtlrRW0eiKPBQ6fTISoqCrm5uQ5XPAoKCjBgwAAUFxejuLgYs2bNcuhxiIh8ESse5DTiwmGAafCQVzzk1EKGlsED+Hu4xdHgceDAAakKM3v2bIfbQUTkixg8SDMVFRUYNWoUhg8fjqKiIrNntADmKx5qQxfOCh7Xrl1zaIfaixcvKn4WqzpERGQdh1pIM4sWLcJXX30FoGaPlFatWkm32TPUYst1tQke4u9WV1ejuLjYbFvMMd5cbtOmTRg+fLjD7SEi8iWseJBmNm7cKF1evHgxzpw5I/1s61CLWsVDbXJpQECAg62s/Zkt586dU/y8adMmh9tCRORrGDxIM/JJodevX8eXX34p/dy0aVPFfR2tePj5+eGnn36qVTtru5aHcfCQBy4iIrKMwYM0Exoaqvj50qVLAIABAwZoNsdjxYoVis3fHFHb1UuNh1oOHjyI0tLSWrWJiMhXMHiQZtRCQ0REBD755BOT6x09q8Xe+RhqajPUIgiCScWjuroaBw4cqHW7iIh8AYMHaUZt2KJ///5ISUkxud7Pz0/1MazN8dA6eNg71JKbm4uSkhKT6/fs2VPbZhER+QQGD9LM9evXTa7r0qWL6n2rq6tVr3dFxaM2Qy3yYZbmzZtLlxk8iIhsw+BBmlGrHtxyyy2q973jjjuQmpoKAHjzzTel663N8XD3UIt8mOX222+HTqcDAOzevbvW7SIi8gUMHqQZteDRoUMH1fsGBwfj6NGjyMzMxIMPPihdb63iYW5uiD3kwSM3N9eu3z19+rR0uXnz5sjIyABQs5ppRUVFrdtGRFTXMXiQZtSCh3xjOGOhoaFISUlRVDFcUfFo0qSJdPngwYN2/e7Jkyely+np6WjdujUAoLKyEpmZmbVuGxFRXcfgQZoxnuPx4Ycf2vR7ERER0mW1ioc4uVSv11sMMrZq0KAB4uPjAQB//fWXXcumnzp1SrqclpaGmJgY6We1thMRkRKDB2lCEASp4tGqVSscO3YMTz75pE2/GxgYKG1Vf/jwYezcuVMRBsQDenh4uDSnojZ0Oh3at28PALh69SouX75s8++KwSM6OhrR0dGKCgyDBxGRdQwepImSkhLpTJV69eqhefPmNocEnU4nVT1ycnLQuXNnrFy5UrpdHjy00q5dO+nyX3/9ZdPvVFRUSDvuNmnSBDqdjsGDiMhODB6kCfkwiyMbuBmHilGjRkmXnRE8xIoHYHvwOHv2LAwGAwCgcePGJm1i8CAiso7BgzQhn1gaGRlp9+/L53kAUCxBLs7x0OKMFpEjwUM+v0OcoMrgQURkHwYP0oQ8eGhR8RBVVFSgsrLS4n0c0axZM2leiXwXXUsYPIiIao/BgzRR26EW44oHUDNvROtTaUV+fn7Seh62LpsuP5VWLXionQpMRERKDB6kidoOtaiFilOnTjkteAB/ByRbVy+Vr1rasGFDkzax4kFEZB2DB2mitkMtamtpnDx50qnBQ17xsGUtD3GBML1ejwYNGpi0icGDiMg6u4NHRUUFXn/9ddx5553o0aMHxowZg/3790u3L168GLfffjt69+6NOXPm2LU4E3mv2g61ZGVlmVx38uRJxc60Wk4uBf5uZ3V1teJ5zBFPpU1OTpbmhzB4EBHZx+7gUV1djeTkZCxcuBC///47Ro4cieeffx4lJSXYvHkzli9fjsWLF2PZsmXYunUrvvvuO2e0mzxMbSseaot4uariAVif51FaWoqrV68CqFmxVK1NDB5ERNbZHTxCQkIwfvx41K9fH3q9Hv3790dAQADOnTuHdevW4Z577kFKSgri4+Px0EMPYd26dc5oN3mY2s7xuO2220yuO3DgAPLz82v1uJbIA5K1eR4XLlyQLsuDh7Xl3omISMm/tg9w/vx5XL9+HampqThz5gz69+8v3da0aVPFKYjGKioqTHb09Pf3R2BgYG2bZUJc+En8P5mqTR/J/50TExPtfow333wTBw8eRGJiIk6dOoUzZ85g27Zt2LRpk3SfpKQkTf/95MEjPz/f4mOfPXtWupyamirdNzQ0VLq+qKiIry8z+P6zH/vMduwr+zirv/R622oZtQoeZWVlePXVVzFmzBiEh4ejpKREMQ4fFhamWAjK2KJFi/Dpp58qrhs+fDhGjBhRm2ZZxB1ErXOkj3bt2gXg7/1U5GeA2GrVqlUAgHnz5uHf//43AGD27NnS7YGBgQ49ri2OHz8uTRhVI19kLCwsTOqj7Oxs+Pv7o6qqCrm5uU5rX13B95/92Ge2Y1/ZR+v+atSokU33czh4VFVVYcqUKUhNTcX48eMB1Hz7k0/SKy4uRkhIiNnHGDt2LB588EFlg5xY8cjMzERqaqrNqczXONpHOTk50hyNDh062PziM+fpp5/GzJkzTSYmd+zYEenp6bV6bDn5YwUGBlp87JKSEunyjTfeiNTUVGRmZiItLQ3h4eEoKChARUWFpu2rS/j+sx/7zHbsK/u4u78cCh4GgwGvvvoqdDodXnvtNWkzsEaNGuHkyZPo0aMHgJryu7jQkprAwECnhAxL9Ho9X5hW2NtH+/btky536NCh1v3bsGFDdOvWTTHMAtTMrdDy306+pX1hYaHFx5bP8WjYsKF0X71eLwWPoqIivras4PvPfuwz27Gv7OOu/nLoGd9++23k5ubi3XfflU4rBICBAwdi5cqVuHDhAnJzc/H1119j4MCBmjWWPJN8GEK+B0ptdOjQQfFzXFycxeqZI+yZXCqeSgvUzPGQE89s4eRSIiLr7K54XL58GatWrUJQUBBuv/126fq5c+eiW7duGDZsGB5++GEYDAYMGTIEd999t6YNJs+zZ88e6bJWwaNNmzaKn40P9lqw53Race5GaGgoYmNjFcNA8uAhCIJUASQiIlN2B4+kpCRpIqGasWPHYuzYsbVqFHmXgwcPAqgZOsvIyNDkMVu3bq34OSUlRZPHlbO14mEwGKSzWho1agSdTqcIHuIptQaDAWVlZZpXZsiz5ObmIjY2lgGTyEEcDKNaE2dGp6WlISAgQJPHNA4e7qx4XLp0STrtu3Hjxia3cxEx3zFnzhzEx8dj5MiR7m4Kkddi8KBaKSoqkpZLt3Q6qr2MFwtzRsVDHjwsVTxOnz4tXWbw8G0TJ04EAHzzzTcoLy93b2OIvBSDB9XKxYsXpcvOCAciZ5S15eGGwYPsJd+fiIhsx+BBtSI/zVTLigdQs5icqFOnTpo+NgD4+flJ8zMsDbUweJAaBg8ix9R6yXTybfKKh9bBY+7cuSgrK0Pjxo0VZ1BpKSoqCoWFhZpVPAoLCzVtH3kua2dCEZE6Bg+qFXnFQ+uhlvr162P16tWaPqax6OhoXLhwweaKR8OGDU1uZ8XDNzF4EDmGQy1UK86seLiCOMG0pKTEZMNCkRg8kpKSFJvCieQ71LL8XncZvz74b03kGAYPqhVvDx6xsbHS5by8PJPb58+fj+zsbADqwyxAzaqqopycHI1bSJ5Cvg8VwIoHkaMYPKhWxKEWvV6P+vXru7k19pMHj9zcXADA9u3bMXjwYLz44ot46qmnpNvvuusu1cdISEiQLl+9etVJLSVnOX/+PD766CNkZWVZvB+DB5E2OMeDakWseNSvX1+xb4+3kFcrxIpHr169UFZWhjVr1ki3jR49GpMmTVJ9jMTEROmyLcHjxIkTyM7ORteuXbn6pQcYNmwYdu7cidWrV+OHH34wez/j+TscaiFyDCse5LDKykppGMIbh1kAZfAQKx5lZWUm9xs6dKjZVVntqXhcunQJbdu2xW233Yb//e9/jjSZNCQIAnbu3AkA+PHHHy3e1zh4sOJB5BgGD3JYZmamtGeJM5Y0dwXj4JGfn696P0vDSHFxcVLlwlrw+OCDD6QVL0ePHm1vc0ljxlUL+R48xjjUQqQN76uNk9tVVFTgu+++w7Fjx6TrmjVr5sYWOU4+x+PQoUNmN3hLSkoy+xh+fn6IjY1Fbm6u1eAhP1iVlpba2VrSmvG/1/Xr1xWbB8pxqIVIGwweZJdjx45h+PDhOHDggOL6pk2buqlFtSOveMyaNcvs/erVq2fxcRISEmwKHvJhHO5i637GZyHl5OTYHDxY8SByDIdayC4TJ040CR0A0KRJEze0pvbkwcOc+Ph4BAYGWryPOM+jsLDQ4uZh8uARHBxsYyvJWdSChznGQy2seBA5hsGD7CJft0OuLlQ8zLHlNGFbJ5jK55AweLif8b+VOMFYDSseRNpg8CC7GH/rA4CgoCCvPatFPsfDHEvzO0S2Bo9Lly5Jl3kqrfvZU/Fg8CDSBoMH2UUteKSmpkKv986XUkhIiNW5FloGj8uXL0uXLW1MR65h/G/FoRYi5/POowU51VtvvYWRI0eqruSotglaSUmJK5rlNNaGW7QaaqmoqFDcVlRUhMrKShtbSc5Qm4rH9evXYTAYnNIuorqMZ7WQwoEDBzB16lQANRMl165dK90mCIJqyJCv3OmNIiMjLd5uy98nDx5XrlxRvY+42JpcQUGB4nfJtWozx0MQBBQVFVl9/RCREisepHD8+HHp8vfff6+4rbS0VFpgKS0tDUFBQQCA6dOnu66BTiA/oHTo0MHk7/bz87P6GLZUPOTzO0TmFiwj5ygtLcX69eulNVRqc1YLwOEWIkcweJBCWFiY2dvkH7xt27bFoUOHsHv3bvTr188VTXMa+STB5s2bY+DAgYrJsvYOtZg7eMnnd4gYPFxr9OjR6Nu3L+6//34A9s3xUBtm5ART1/v444/x8MMPIzMz091NIQcxeJCC8T4l8iWk5cEjLCwMTZo0QYcOHVzWNmeRf2sVF49asWIFoqKi0LFjRwwdOtTqY8jL7YWFhar3YfBwvxUrVgAAVq9eDcA0aNgz1AIACxYssLjMOmnr7NmzePzxx/Gf//yHWw54MQYPUjD+cJV/MMtvs1QZ8TYPPfSQdLlXr14AgC5duuDKlSvYuXOn2c3h5CIiIqTL5oLHmTNnTK5j8HCd6upqxc8VFRUmFQt7h1o+/PBDfPvtt9o0kKySDwVv2LDBfQ2hWmHwIAXjyaPyg6X8gzc8PNxlbXK2d999F/3798eECRMwYsQI6frAwECb19qwJXjs2rXL5DoGD9cx7mu1ScA5OTlmKxhi8I6MjMSHH34oXf/zzz9r2EqyxNoKwuQdGDxIwfhbnbngUZcqHsnJyfjxxx+xYMEChxf1CgoKkiojasGjurqawcPNjIdR1CpQ1dXVZudtiMEjLCwMDz/8sPRa2bt3r7YNJbO4sWLdwOBBCr4YPLQiVj3UgsexY8ek6+Pj46XrGTxcx3gYRb67slxeXp7q9eLrPzw8HOHh4dI2AQcOHEBVVZV0P67t4TxqpzST92HwIAXj4HH69Gnpcl2d46EVS8Fj586d0mX5WUAMHq5jKXjIT5k2928ivv7FYcZ27doBqJmQfeLECVy9ehWtWrVCw4YNce7cOS2bTv+f8eeTuWFN8mwMHqRgKXiw4mGZeEBS+zDcsWOHdLl///7SZQYP17EUPNq0aSNdVlvK/sqVK9Iqs9HR0QCAG2+8Ubp97969mDx5Mo4cOYLMzEzMmzdPw5aTyLjiobYoH3k+Bg9SMA4eO3bsQEVFhcltdWlyqVbEikdJSYnJGRSHDh2SLvft21e6zA9O17EUPNq2bStdVguDf/75p3S5Y8eOAP6ueADAX3/9hUWLFkk/y4Mmacc4eJhbJZg8G4MHKaiVMjdt2mRyGysepuRntpg7LTk0NBRJSUlIS0sDUHPA4n4trmE8uVR+aqa14LF9+3bpcpcuXQAoKx7GFQ4uLOYcrHjUDQwepKC2F4u4XwuDh2WWTqkVg4c4sbRr164Aambp86wI1zC3RkdwcDCaNGki/Wxr8EhOToa/f812V8bvm6NHj5pUvaj2jL8YseLhnRg8SEH+jUKccLdmzRqT2xg8TJkLHoIgmA0eALBlyxYXtdC3mQseDRo0QExMjPSzcfCorq6Whk5SUlKk5fT1er3ZDQTLyspw9uxZDVpNchxqqRsYPEhB/OaWmpoqfbM7deoUrl+/zoqHFeaCx7Vr16Rvv2rBY+vWrS5qoW8zFzxSUlKkCaOAafA4cOCAdMAT3xMiS/v4HD582MGWkjnGweP48eOsLHkhBg9SkK9VkJycLF2fn5/PyaVWmAse8gOeGDzatGkj9SErHs4lCAK+/vprbNu2TfV244qHeFaLIAg4e/Ysvv76a+m2Hj16KH6XwcO1jIdavv76a3Tr1k2aAE/ewd/dDSDPYTAYpDd2RESEybdAVjwskwcPcRGqw4cPY9myZdL1YvDw9/dH27ZtsW3bNly6dAkVFRVcDtpJNmzYoNiPx1hKSooieHzzzTe4cOECTp06haysLOl6Pz8/xZL6AFCvXj2zj3vgwIFatJrUqG3Ut337dnz55Ze4/fbb3dAicgQrHiSRT5CLiIgw+RbI4GGZPHiMGDECnTp1wk033YTXX39dul6+aqk82HEhJOf5/fffLd7eoEEDREZGKpbL37JliyJ0AMAdd9xhMqfDuOLRs2dPBAcHAwA2b95stW379+/HG2+8gVOnTlm9L6kHDwCYPn26YvVY8mwMHiSRH/zCw8NNKh7yN31oaKgrm+YV5MEDAHbv3m1ytoM8eERGRkqXefqlcwiCYHUV0eTkZOj1esXrXY3aNuzGwaNRo0a45ZZbAADnzp1T3Q9G7r777sO0adPQsWNH7Nmzx+J9yXzwOHXqlHTaP3k+Bg+SyN/UlioeISEh0Ov50jFmHDzUyINHVFSUdPn69etOaZMvmz9/PiIjI/Gf//zH4v2SkpIAQPF6B4Bx48bhyJEjeOihh/Diiy9i2LBhJr9rPNSSlJSEXr16ST9bqrZUVlbi6NGjAGqC5/DhwzlR0grxM6h+/frIycnBl19+Kd124sQJdzWL7MSjB0mMKx7GpxjKJ56SKVuCR1xcnHRZHjxY8dDeU089ZfINedasWSb3E6sWxsFjypQpyMjIwJdffonp06erhm3jikdycjJ69+4t/WwpeBgvaHb69GmT4R1Sku+XExcXh+bNm0u3cX8c78HgQRLjioe5yaWc36HO3oqHfKiFFQ9tqR3A+/Xrh7Fjx5pcL4YH46GWRo0aWX0eteBx0003SUOR69evN7tbrdrpvcZhhJSMN+pr1qyZdBvXTfEeDB4ksVTxkA+1MHios6USZG6ohRUPbe3evdvkuhYtWpi8diMiIqTr5P8eoaGh0qqklqgNtQQGBqJPnz4AagKQuXVaGDzsU1lZKZ02K/6bxcTEIDY2FgArHt6EwYMk8uBhXPHIycmRJkoyeKizd6iFFQ/nUQseGRkZ8Pf3V5y2LM7vAJRrRJhbkdSYPKwAkNa+kZ92Kz+dWk4teJhb5IzMb1LZtGlTAMDly5dRVlbm8naR/Rg8SCIfajGueKxevVq6nJ6e7tJ2eQvj4PHHH3/gp59+UlwXFBQkXWbFw3nUgoe4MZ/8tFn5UIl8+e2EhASbnkf+WPLHGzRokBRwVqxYoTrcwoqHfYw/n0Ri8BAEAUOGDMGRI0dc3jayD4MHSSzN8ZCfFnrvvfe6sllewzh4ZGRkICMjw+z9WfFwHuPgkZiYiJ49ewIAysvLpevlwSM1NVW6LN/y3poHHngAAHDzzTdLYSMqKgp9+/YFUPNNXG2dDgYP+5jbK0oMHgDwyy+/4KmnnnJpu8h+DB4kMQ4eAQEBJsMqISEhuOuuu1zdNK8QEBCg+DkhIQEpKSnSz23atFHczoqHcxQUFODixYsAgO7du2P//v04evSo6hwc+VDLO++8g/DwcMTHx+PNN9+0+fkWLFiAFStWSJspisQKC6AeLDnUYh9rQy2i33//HYIguKxdZD8GD5IYTy4FTE8xvPPOO3k6rQWdO3cGAPTp0wc6nQ56vR6//PILHn30USxZskRxX1Y8nEO+yVtSUhLatm1r8joWySserVq1wsWLF3HhwgWb53gANSF96NChJsMz8tBuvMcIwIqHvcwNtbRo0cLkvtnZ2S5pEzmGwYMkxhUPwPQUwwEDBriySV5n+fLlmDdvHr766ivputtvvx2ffvopKx4uIg9xxpM/jRkHksjISMU8nNqQBw/jFWwBBg97mQseN910Ex588EHFfU+ePOmydpH9GDxIYkvFQ1wOmtSlpaXhySeftLhrqYgVD+eQhzh5H6txZvXOkYoHh1rMM7dXlE6nw3/+8x9MnTpVuo6rmHo2Bg+SGJ9OC5hWPNTKmuSY4OBgaTIiKx7akYc4a8FDfnqz1mwNHgkJCVLAZ8XDPPnnk9op/fKz7Rg8PBuDB0nUJm/J3+wpKSnco0Vj4oGRFQ/tWAsen332GYCalUnly5trzdbgER8fLy0sx+BhXl5ennRZXDRMrmHDhtJlBg/PxqMISdS+URw7dky6ztKpoeQYcQ4CKx7asRY8HnnkERw+fBgHDhxQLCamNfkOzsbBo7S0VLouPj5eqrwUFBRwe3cz5MNQ8hWARSkpKfDz8wPAOR6ejsGDJOLkrbCwMKmyMX78eOl2tX0uqHbkFQ+eAqgNWyaXtmzZ0ukr8FqqeMgrG/LgASi/2dPfrAWPgIAAqepx4sQJvp88mPXNCMhniBUP+UJYzz77LE6fPo2UlBSMHDnSXU2rs8QDY1VVFUpLSxXfkskx9kwudSZLZ7WcP39euhwfHy/tQQLUhBJ7Tuf1FcZhTU2jRo1w6tQpFBcXo6CgwOxp1OReDB4kESse8uARFxeHL7/80l1NqvOMz2xh8Kg9eyaXOpOliod8Kf2bbroJR48elX7mmS3q5P1iblKw/LOruLiYwcNDcaiFANTscyBWPLhAmOtwLQ/teUPwWLt2rXT5zjvvVHyDv3r1qvMb54XE4BEREWF2bo61Cb3kGRg8CEDN/hXipDZbdlklbcgPjAwe2rBnATFnMncQPHXqFPbs2QMA6NixI5KTk6VdbQHg0qVLrmukF5GfBWQOg4d3cCh4rFixAg8++CBuvvlmfPzxx9L1u3btwk033YTbbrtN+u+vv/7SrLHkPOY2YCLnkp8WyEmF2vCUiofaWS179uzBbbfdJl1/5513AgAaNGggXSfuM0N/q66ult4fDB7ez6E5HvHx8Xjsscfw448/mtzWoEEDrFq1qrbtIhdTWzyMnE++v4d8W3ZynFg50uv1bp0zY3wQ3LFjB/r27SsFo4iICIwZMwYAg4c1+fn50lkqDB7ez6GKR8+ePdGjRw8eoOoQc/sgkHPJgwfH9rUhHtgjIyOh0+nc1o7g4GDp+UtKSjB+/HipbZ07d8aff/6JRo0aAYBiqMWXgkdBQQGeffZZfPDBBxbvZ+1UWpG1/XHIM2h+Vkt2djb69u2L8PBwDBw4EOPGjZMWdTFWUVGhOI0MAPz9/Z2yqI/BYFD8n/62Y8cOPPvss9LP4eHh7CcrtHo9yT9Es7Oz62y/u/L9Jw8e7u7PsLAwFBUV4fLly7hw4QKAmoX41q9fj7CwMKl94eHhCAsLQ3FxMS5evAiDweATn1mvvfaaFDo6duyILl26qN5PHspjY2NN+kT8OSQkRLqusLCwTvddbTjrtWXrytaaBo+GDRtiyZIlSEtLw9mzZzFlyhSEhITgoYceUr3/okWL8OmnnyquGz58OEaMGKFlsxQyMzOd9tje6s4771TMLzAYDDh37pwbW+Q9avt6qqyslC6fOXOmzve7K95/4lBLSEiI2/szODgYRUVFUugAgA4dOiAnJ8fktNnExEScOXMGFy5cULS7Ln9mzZkzR7q8dOlSJCUlqd7vyJEj0mV/f3+z/65lZWXS5czMTLf/+3s6rV9bYgXPGk2Dh3zPgcaNG+ORRx7BN998YzZ4jB071mQ7Y2dWPDIzM5Gamsr9RmQEQTCZ1NigQQPFhktkSqvXU3BwsHS5tLS0zva7q95/lZWV0sEnPj7e7f0ZGRlpEjBuvfVW1XY1bNgQZ86ckdafCA8P96nPrPDwcLP/XvIhsyZNmpjcT/76EgUHB7v9399Tuft46NQFxKz9QYGBgU7dK0GNXq/3iTexrdQmYEVERLCPbFTb15PxHI+63u/Ofv/J5ypFRka6vT/VJrfeeOONqu2STzC9fPmytBO0r3xmlZaWmv075V+OEhMTzd5PPj+tpKTEJ/qtNtz12nLoGauqqlBeXg6DwYDq6mqUl5ejuroau3btQlZWFoCaJYEXLlyI7t27a9pg0pba2hGcXOo6/v7+0im1nFxae55yKq1I7dT0Nm3aqN7X189ssfQ3OzK5lGe1eC6HKh4LFy5UzM34/PPPMW3aNFy7dg2vvvoqCgsLERsbi4EDB5odZiHPUFBQYHIdz1ZyrcTEROTl5fF0Wg14yuJhIuPgkZaWhujoaNX7yoOHLywiZrwLr3wejDH5e8PccukAg4e3cCh4TJgwARMmTFC9jUHDu6gFD1Y8XCshIQFHjx5FcXExSktLFTPzyT6eXvFo27at2fv6WsXDuMJnaaLjmTNnpMtpaWlm78fg4R04AObj1IZaWPFwLflOpBxuqR3569kTXsfGweOGG24we9969epJl32h+iUOy4suXLiADz74QPFvWFxcjKNHj+LEiRMAat4rlgIl1/HwDgwePo4VD/fztdVLMzMz8d577+H48eOaP7b8YOYJW8vbU/GQH1DlKwnXVcbBAwCeffZZvPTSSwBqTo3t2LEjWrZsKd23adOmFh+TFQ/vwODh49SCh/wUT3I+X1u99IEHHsCLL76Inj17avaYP/zwA9q1a4fJkydL16WkpGj2+I4yPqvFUsXD14LH5cuXVa//6KOPAADr1q3DsWPHFLcxeNQNTj2dljyf8VBLXFyc4lx4cj75N3NfqHhs3rwZQM2Bx2AwaHI638CBA02u84TgIT8QBgQEoHnz5mbvKx8aks9VqavUKh5yK1euNLnOWvAQl6kXBIHBw4Ox4uHj5BWPUaNGYcmSJS5fW8XX+VrFQ86ZO/LKJ2u6izx4tGrVCgEBAWbvKw8evlDxMBc8wsPDUVZWhtWrV5vcZi146HQ6qcrE4OG5GDx8nLzi8dxzz1l9Y5P2YmJipMtqk33rMmdVeAIDAy2u9+Aq8uBhaX4HUFMREYc5fbniUVRUhDVr1qiGL1s+n8Q+Z/DwXAwePk5e8TC3vgA5l3xsv64fcMStzUXOCh4NGjRw6860InkVw1rwAP5+LfhaxePgwYNo1qyZ9PM333yj+jsMHnUDg4ePkwcPT1hwyRf5UvCQb4oHOC94eML8DgC44447EBcXh5iYGNx///1W7y8GlbrwOhBXtDZHHGYLDQ1F69at0a9fP+m2devWqf6OvDpojhg8eDqt52Lw8HHy0r4nLLjki+SBry4ccETXrl3Dhg0bFGGjtLRUcR8tgodxFQXwjPkdQE0AyszMRFZWlsWFr0Ri8CgsLFT9u6xZunQpnnnmGbevfHry5EmkpKSgSZMmyM/PV72P+Nkjvv7lk6zF10mrVq2wdOlS3Hjjjfjiiy9sem558NB623fSBoOHjxMrHuHh4fD350lO7iAPfHVljocgCOjatSt69eqFKVOmSNcbfwvVIngYV1EAz6l4AEBISIjNE7bF10JlZSXKy8vtep7MzEyMHDkSH374ISZNmmR3O7X0/PPPIycnB+fOncMbb7yheh/xs0cc4pUvoCbq3r077rvvPuzduxejR4+26bnl82qMgy55BgYPH2f85ifXCwsLk+Yj1JWKR35+Pg4dOgQAmDlzpjTebnwg0OIsHrWSelJSUq0f1x1qc2bLzp07pctLly7VrE2OOHr0qHT58OHDJrdXVVVJOwmrVTxEjmwyyrU8PB+Dh48Tv2EzeLiPXq+vU2P7gGnlZtWqVQCcU/FQCx6ODFN4gtrM91FbDNBd5KsfiwFDTv63iZ89asHjtttus/u5GTw8H4OHD6uoqJA+tDmx1L3EA05dCR7Gf8dXX30FwDlzPNSCh5arorpSbSoeZ8+e1bg1jrMWPOTB1FzFo3Hjxg4NmTF4eD4GDx8mf/Oz4uFedS14GFc8NmzYAEEQXFLxmD17Njp27Fjrx3WH2lQ85Du4Au6t+siXilcLHmqn8RvP8XBkmMX4uRk8PBODhw9T+9ZB7iEecIqLiy2egugtjA+aFRUVKCwsdHrFY+LEiXjuuedq/ZjuUpuKh3HwcOdEZfnEWFsrHhEREQgKCpKudzR4sOLh+Rg8fBiDh+eoaxuEqX1bz8/PN6lOFBQUoKKiolbPJX9M403ZvI2WFQ937vsj/2zJz8+HIAjIz8/HuHHj8Morr6hWPHQ6nWK4xZH5HYBymMcbg8f+/fulidl1FYOHD5Mf4OTftMj16toiYmrftgsKClRPbzS3S6mt6lLwcLTiUVZWZrJ2h6cEj8rKSuTk5OCLL77AokWL8NZbb2HZsmXS7fIvPZ06dQIAtG7dGk2aNHHoua3NL/FkO3fuxI033oi2bdti//797m6O0zB4+DAGD89R1xYRU/sb8vLyVCeCvv7667V6LgYP9Ymlrg4eU6ZMQY8ePXD06FGTf/9Lly7h/Pnz0s/ff/+9dFk+v+yTTz7BJ598grVr1zq85L03B4+XXnoJQM38HHevxeJMDB4+jMHDc9S1RcTsqXgsWrQIW7dutfmxjx07hnHjxuG7774DULeCh6NDbsbDLIBrg8dff/2F6dOnY+PGjbjvvvtM/v0vXbqkuE4eTOShOz4+HuPHj0fDhg0dbos3B4+ysjLpcnZ2thtb4lwMHj6MwcNz1LWhFnMVD3nwkJ/yunnzZpsf+6GHHsKiRYswZMgQlJaW1qngIX8f2vI6+OWXX9C3b198+OGHJre5MnicPHlSurx//35UVVUpbr948aLZQK31/LLaTNB1N/nEWG8LTfbgGtk+TP7CZvBwr7oWPNQOMvn5+YryeY8ePbBhwwYAwIULF2x+7F27dkmXz58/X6eCh70VD/nGasY86RuzccVDTutT+b254iFfWt8bJ8baihUPH8aKh+eoa8HD3Fkt8oqHfBt0e4KH3Llz5+pU8LCn4mHttGt5xeP8+fP46KOPnBZGzG0EJzp58qTLKh7eHDzk/ehtbbcHg4cPY/DwHN4WPNasWYPExEQ88cQT0nVbtmxB9+7dsWDBAptOp23SpIlUAbl48aJD7Zg8eTKmTZsm/eztwcOeioe1PpMHj3vuuQdPPvkkhg8fXrsGmpGTk2Px9r/++svsku5aVzy8eahF3o8lJSVe8VngCAYPH8bg4Tm8KXgYDAYMHjwYV69exYIFC6QDSrdu3bBp0yY88cQTqgci48mlkZGR0mqVtlY8jFfj3Lt3r+Jnbw8e9lQ81CaUyh06dAjHjh0DABw8eBAAsGnTJpM+00Jubq7F248cOaJabdHr9YoKhRa8ueJh/L45d+6cm1riXAwePkwePLR+85N9vCl4iPMyRGrfZMUgERsbi+DgYACmp9OGhoZKe3FcvnxZdXt7Y9YOJN4ePAICAhATEwMAyMrKsnjf06dPq15/yy23AKgJA3feeSeKi4sVi7R9+umnGrX2b9YqHtXV1aqv66ioKIdPmzXHOHjs378fI0eOxMqVKzV9Hq1VV1cjLy9PcZ0n7b+jJQYPH8aKh+fwpuBhfOAqLCw0Gb8Xw0hUVBTi4uKk6+QVj5CQECl4CIJg9UArf1xzvD14AJD65OLFizAYDGbvp1bx0Ol0WLJkCVq0aAEAOHXqFHbv3q24z1dffaX5xEVzwaNVq1YWf88ZKybLzwwpLCxE7969sXTpUgwdOtSjtyMQV3iVY8WD6hwGD8/hTQuIrVu3TvFzYWEhTpw4oXrfyMhIxMfHAzCd4yGveAC2zfOwNomxLgWPiooKk2/AcmoVj+joaKSnp2Po0KHSdcePH1fc5/r161i+fLlGra1hbqhFbZfghIQE6bIzNqfU6/VS+CgqKlK0zZOHXtTCGyseVOeIwcPf31+xORO5njx4WDrYuFtFRYVJMCoqKrIYPMSKR2VlJa5evSrdFhISggYNGkg/9+zZE1u2bLH4/L5U8QAsD7eoVTzEYZrk5GTpOnGeh9wnn3xSmyaaMFfx6N69u8lQSs+ePaUwKlZmtCZ+kTKeXOrJoV6tD60NYXkrBg8fJr4pIyIiNB9nJftERERI5/DLD86eRu3Ab6niERUVJR1kACAzMxNAzVwGf39/xUG2vLwcjz/+uN3PLxcSEmLxdm9ga/BQq3jYGjy2bduGI0eO1KaZCuYqHg0aNFCESwBITEzEmjVrMHXqVLz//vuatUFOnOdhvH+NJwcPtfd9XV3Lg8HDh8mDB7mXfGdOd27uZY3aUIetQy3y3xcDgvwgC/x99oU51oKHv7/3r4ko7xNzG+iVlpaqhhJrwUMezPbt21frtgI1lSxz/y6RkZFo1KiR4rqoqCh06dIFr7/+usm/v1bE4GG8gqonn16rVt1Q29uoLmDw8DIXLlzA0qVLVfe8sBeDh2cRg0dOTo7FSYXu5EjFo23btibXi0MixgcloOZbniAIqh+61oJHXSA/GJtb8OvUqVOq14vBIykpSbpOPsdDvgeKtfkytrI0NBgVFaUaPJzN3GeaJ1c81IIHKx5k0YwZM5CRkaHYdVFrVVVV6NOnD0aOHIl+/fqZpHl7VFdXSx/sDB6eQQwe1dXVmh0UtKbWLmtzPHr37m1yvfjNOz09HS+//LLitsuXL6NLly6Ij4/H+vXrzT7/unXrMHv2bHv/BI9ny1CL8YRRkRg86tevr3p7WlqadFmruUTmhlnS0tLQoEEDkw3fXBE8zC0PoHXFo6CgQLPHZMWD7FJVVYXJkyfj2LFjuOuuu5z2PD/99JP0gbN582ZMnz7d4cfiPi2eRz7b31OHW9QqDvn5+WYPYtHR0WjWrJmi9A8oJ4G+9dZbihVQP/74Y+zYsQOlpaUYN26c2eePjo52WqnenWwZajEX9MTgERgYqHg9ieTBQ6twq3bADAkJwfLly+Hv729S8XDGmSzGzAUPLSsee/bsQUJCAho2bKjJvCy1U2dZ8SCzXPXtdNGiRYqfp0+f7nDVg6fSeh6x4gG4d4KpIAj44osvsHjxYpN1BdRe6+YOjkDN36TT6dCrVy/F9caTQOV/u7zKIU5GFflC8IiMjJTek45WPACYhD3AOcFDXvG47777MGXKFOzatQudO3cGYDqcVleGWoYMGYKqqirk5eVh8eLFtXqs69evS6epR0dHS/+OrHiQWcZvYOMPay3k5eVh9erViusKCwutLlVsDoOH55EffN1Z8Vi3bh3GjBmDsWPH4ueff1bcplbxsBQ8xG/dxsFDvgun/H6Achl04/sZBw/5XIa6RL6iq/zz5NKlS8jLy9MkeGg11CIPyb1798Y777yjWDisrg61yENxbT/zly1bJs3be+CBB6SqECseZJbxG9jcLoy1sW/fPtUlpR398GDw8DyeEjxeeOEF6fK7776ruE3tW7KlUz7FQDFs2DDFwcC4oqM2LADA5FRM4+CRnJwsPa7xsIw3EwNVWVmZNCz6xhtvoEGDBoiLi8PmzZsBmM7lkB/UXVXxkP/7q80tMa5KuTN4aFXxMK40BwQE1OrxvvzyS+ny2LFjpQXQLAWPoqIixVL43oTBQwPGB39bln62l7nSOysedYf84OvOoRb5mL3xeLy9Qy3i3xQREYHPPvtMut64AmIueJSVlUmXq6urpXUZgoKCEBISAn9/f2zfvh1z587FjBkzzLbD28j7Q/z3kPefqHnz5oqf9fq/P9LVgkdiYqK0WKBWFQ/5v79aBcrPz0/xsyvmeJj7TNOq4mG8BkptH1c85TkpKQkdO3aU5kCVlJSoVlN2796NhIQENGnSxClfdJ2NwUMDxh/Go0ePxvz58zV9DvnBoFmzZtJlBo+6w1MqHvIDkvGKtmpDLZYOYPIDaOfOnfHzzz/j1VdfxZtvvmn2fsaPLQgCKioqcOONN0pDDPJvza1bt8YzzzwjrZBaF8jXPsnJyUFeXp7JfBfAtN/k37zVgkdERIQ0HOOMioe5oS/5/il1YXLprl27NH1csaoVExMDnU6n6C/50gkzZ85E69at0alTJ5SVleHChQu1nl/iDgweGjD+4N25cyeeeuop7N+/X7PnkAcP+TLDDB51hycED+PJbMbtsOdgFRgYaPLa6tOnD9544w2TA6a54FFeXo7S0lLs3r0bhw4dkq6vbWnb08mDx9WrV81+ltx6663S8ucJCQno16+fdJt8WEUUHh6O2NhYANoFD3nFQ/4alvvjjz/Qr18/fPzxxy5Z1t7ZwWPnzp2aPa58zRoxcMj7SLztzz//xKRJk3D48GHF73vqGXCWeP8yfx7A3Bt48+bNuOGGGzR5DnnpvUWLFli7di0Ay8FDEAScPHkSjRo1MlnRUd5m+c6o5D6eMNRiXEI2XsBKrHjo9XrExsZa3EtCPKPFFnFxcdDpdKpl5by8PJPNvTp06GDT43or44qHfHn0WbNmYceOHbh27RpGjx6NuLg4tG3bFo0bN1YcsNSCh7ziUVxcjIqKCpMJvMb27duHLVu24KGHHlL9rBCDR3x8vNnH6tixI3766SeLz6MlZw21XLhwAadPnzZZYbc2waO0tFR63YvBQ17xKC4uRlxcnGLulZwn7+1kDoOHBsz9w9erV0+z55B/wGdkZEiXLQWPF154AbNnz8a9996Lb7/9VnGb/IPMeNY5uUdoaCjCwsJQXFzstm8x8qoCUFNGLy0txeLFixEUFCQtsx0dHY3IyEiLwcNcFUONv78/YmNjVV/PeXl5JpWYurhwmJy873JzcxXfcm+55RZMnDhRcf8uXbqYPIZx8AgODpb6WZSfn2/2c+rKlSsoLi5Gt27dUFRUhEOHDmHevHmK+wiCIA21eNIZRuYqYrUJCNevX0fbtm3Nrt7rKPkEUrFSY1zxOHz4MLZu3ar6+2p79ng6DrVowFzFo7y8XLPncGSoRfxwXrlypclmSSdPnpQuN23aVKNWUm25cr+W8+fPY8iQIZg2bZp0nfE3uby8PIwaNQpPPvkkHnnkEWk2f0xMjNUhOnuCB2B+y/K8vDzFOPesWbPQuHFjux7b2xhXPMShFp1OhzZt2tj0GJGRkYq5MOJBTX7KrbkvTb/88guSk5PRuHFj6d9Fbd5aXl6edGaFJwUPeR+9+OKLUtiqTUBYv3692SX7axNo5MHDXMXD3l2KPR2DhwbMvXm12E9FJJbeAwICFBUKW+d4iIvTiMSVD0NDQz3qA8PXiRMk8/Pznb5fy4QJE/Ddd9/hjTfekLajP3v2rMn9jKtlQE3Fw1rwsHcSoTyoy7+xGlc8XDFHwN3kwSM7O1sKhE2bNlUclKyRVz3Ugoe5L03Lli1DdXW14jq1DfisndHiLo0bN8b//vc/zJo1C2+++ab0Wq1NQFBbzkBUm8eVB2614FFSUmLx8c+ePWvyb+XpGDw0YC54aLnqnFjxiI+PV8zeNxc8jMfKxTkhQM056GJKbtq0qc3j8OR84kHBYDA4fSfNH3/8UbosztI3tymZscjISLMT+ETBwcF2tUdeebvnnnuky8bBw3jV07pIHjyOHj0qnVZsfPqsNfLgIfah8VCLGrUAWlVVZVLF9dTgAdSsLDpx4kQEBARIc1Nq856yVIXUuuIhD9fFxcUWH7+ystKkou3pGDw0YO7Nq1XFQxAEqeKRkJCA0NBQ6UPdXOgxLlv/8ssv0odXZmamlN45zOJZ5FUCV24UV15ejurqarMfrq1bt0a7du2kn/fu3Wu14mF8Kq418+bNQ3R0NIYMGYLhw4dL1xsPtfhaxePo0aPSZXuHr+Sn1IqfFbYMtajtGwLUTK6U8+TgIScGj9LSUouVC3NKSkosrlfjzKEW44rH6NGjFfP8gJp9YxYtWqQaGD0Rg0ctCYLg9KEW+Qp14geSWPUwV/EwHossKSmRhlc4v8Nz2VIGt+bKlSvo27cvRo0aZfNePpMnT0ZMTIziICc3fPhwPPLII9LPt956q9XgYW/Fo1+/fsjNzcX//vc/xYHXF4dagoKCpP6Vvw7k/WILeXVDfC1Yq3gYDAYpeLRp0wb/+Mc/pNvOnz+vuK+3BA/5a9Xeqsd3332H2NhYvPPOO2bvU1hY6PCy6dYmlxYXFyvaPHToUBw5cgRz586VrhsyZAjGjRuHAQMGOGXLDq0xeNTCiRMn0KhRI7Plaa2Ch3xiqfiNRx481F5oapOgxA9v+c6WDB6eRR48zE1ks+all17C+vXr8dVXX2HlypU2/56lD+S77roLjz32GG6//XYkJCTglVde0Tx4AH+vvCk/OBpXPHxhqAVQDxn2Bg/560ntOrUvTVlZWdIXnYYNGyqGa4yDhy2Lh3kC+WnA9gaPIUOGmD1RQJy8W1lZ6fDJBPbO8RDfd8ab7wE11TFnD9FqgcGjFj777DNFSbJXr15YsmSJ9LNWwUO+poNxxaOiokJ1PX+1g5Y41MKKh+eqbcVDEAR8/vnn0s+rVq0CULPg0U033YSRI0faNGnVeCGo9u3bIzAwEL/88guys7PRpUsX1Tke4pkP/v7+eOaZZ+xuv8g4ePhaxQPQJniorV4qnyOmdjq0/DMtPT1dETy+/fZbKWwIgoDff/9dui01NdWutrmSPCRruUNt27ZtLT6uIAg4evSoxcqjvXM8xBClti8OYPs8LXdi8KgFcX19UUxMDFq3bi39rNXkUvmHg3HwANSHW9SChxiE5Kdf1fXTEr1NbYOH8YqKe/fuxeHDh9G5c2fs2rULS5culTYYszSpuEePHtJiUOPGjVPsASL+ntq36UceeQSrVq3C1q1ba3UgYsUDqkvA2xs8hg0bJm20t3TpUgDKA5baaZryeQLGFY81a9agTZs2KCwsxA8//CCt63LTTTepLljmKeQVDy2Dh/xvvnTpEpYuXapY2n7y5Mlo2bIlBg0aZPYxbDmdVi14qL3/gL+Dx65du/DAAw+4dOE2W3EBsVowXnjnwIEDig9FVwy1ADXBIz09XfE7lioeFy9eBFBzAFH7RkTuU9vgsWzZMsXPR44cwfjx4xXXnT17Ft27d0dQUJBiEza5li1bYuzYsdixY4fJYlUitQWqAgMDcffdd9vdbmMhISEICgpCeXk5cnNzFWV8VjxsFxISgqNHj+LSpUvSGTHy4KF2NoSl4AHUfN7s2LED7733nnTdyy+/7NFnxzkjeGRkZCge98knn8S2bdvQrFkzHD16FDqdTuqjH3/8EVVVVaqnJFureJSUlCiGT8TnlIdzOTF43HzzzTAYDPjxxx89bnVTVjxqwXgs7b777nN68BADh7VyqaWKhzgzvX79+nV+zwtvU5vgkZubi08//dTkeuMVD8Xgaemsk3r16mHAgAGYNm2a2W3MjcOutdNr7aHT6aSQnZ2d7ZNDLWpnsNgbPICafxf5abhBQUHS54famRryoZaGDRuqHuD+/PNPbNy4EUDNcO3gwYPtbpcryV/D9uzmqjZEEhkZiccffxxLly5VBI9t27YBqJlDV1hYqBjSBszP2VKbXGpLxSMqKko17GVnZyM7O1saUs3Pz7d5krmrMHjUgjx4tG7dGo8++qjiQ1Gr4CF/0YlvIGvlUnMVj8rKSun+YgmWPEdtgseMGTOk14qlYQ4xeFhadMiW5f6dGTyAv1+fV65cUbwHfGWoRW3owpHgoUasIF26dMlkcvqpU6eky+np6dDpdLjxxhsV9/noo4+kA9tdd92lGIrzRI4GD7VKwb333ouPPvoIN954o9l9roqKiqRF+Sw9lnhfkbmKh/j69/PzkyZt6/V61eGW7OxsbNiwQXGd+DfPnz8f06dPx4IFC1Tb4iqe/WrxcPLgsWPHDqSnpys+FLWa46FWipOXntXKpeYqHllZWdIHTUpKiibtI+3I1/Gw96wWcVJpYGAgFi1aZPZ+Fy9ehCAIqpOSRbYEj8TEREXVxFnBQxAExX4UvlLxMF4sTK/Xa7alvDjEWlFRoQi4x48flyaMxsfHS0FnxowZiq0a5Ot5yHfE9VTy4GHP+0qtmiwP7FoED3vmeERGRiqqHGrVqOzsbMWkX+DvLzGzZs3ClClT8H//93+qbXEVBo9aEIOHXq+XAkdQUJD0wtCq4mEteKiVS81VPMRvuwArHp7I0YqHIAjSh+QNN9yAm266yex9L168iPLycovn+9sSPHQ6nSK82rOUty3kr09x3oGfn5/PDA8aLxIVGxsLPz8/TR7b3BeX1157TTqwTpw4Ufos69evH44cOWJyCnVgYCC6d++uSZucydGKh9ou0fIvlOaCR2FhoWbBQz7Hw/j5zAUP44qHeDwQqyvWToV3NgaPWpC/GMQ3qE6nk0phnhY8SktLFd9UWPHwPPIPSHuCh7juAlAzFBEREWG2MnDhwgWL1Q7A9HRac+Slf0dWhLRELRiHhIR49CRGLaWlpSkqSloNswDK02zFz4+KigosX75ceq5nn31W8Ts6nU5R9QBqFpLTOnA6g7xSVNvgId+e3twBPCcnB0eOHFFcZ0/wMHc6rXHwUBtq2bJli8kZl+JniXjMYvDwYub+EcUXjVbBQ56wbQ0eam8uVjw8n5+fnxQ+7Ake8rNTxIOzpfP8rc3sN/dNzpgt+wY5Su2MK18ZZgFqKqnyPjA3ydcRap8f8kmIXbp0UT04GQePBx54QLM2OZOjFQ/5UEufPn2wfPly3HrrrdJ15t4nakuXm3s/q00ulQ/ZX7t2TTqW2FLxUAtL4qaT4nNpPSxqLwaPWjAXPMQXjTPneISEhEhvJnuGWljx8HzitxhHg4dYcTO3kqTBYFDMmTDWu3dvm6sKzgwe5ioevkR+YNFyRUp5oBGHWuQHZHMhx/g1NXr0aM3a5EzGwUNeIbREfhB/7rnnMGzYMMXt5sK92l43tkwuFV/f8uF7+ckDxscac6fUGisoKFAcR7yy4rFixQo8+OCDuPnmm/Hxxx8rbluzZg0GDhyIHj164PXXX9e8/Oop5OnRXPAQU+qxY8fQrl07jBo1yqF19OUvGPk3PvFD4PLlyyaPa26ohRUPzycPHra+XmwJHvIPqePHj6s+zvLly03WArFEHjyMNyasLbXXpy9VPABlKV1toqOjjCsex48fV5TnzQWPvn37Spfnzp1r90aA7iL/e9auXYuQkBAsXLjQ6u+praEk17JlS9Wqhz3BQ/x8Dw0NVZwdJM6zkn9m21LxEN1yyy3S5fz8fNWzZ9zFoeARHx+Pxx57DL1791Zcf/LkScycORPvvfcevv/+e2RnZ+Ozzz7TpKGeRv6PaC14DB48GPv27cNXX30lnettD/GFqdfrFW908cPDeIEZwLaKB4OHZxIPNlVVVVbnYohsCR7t27eXLqsFj7i4OAwbNkx1xUxzunXrJl22tDqjIxg8lCsLa/l+lb82PvnkE7Ro0UKxFoe5s2f69u2L+fPnY86cOXjqqac0a4+zBQUFKT47DQYDHn30UatDjmrbVcj5+fkphl5EjgQP4zBwxx13mNzX1uARExODl156Sfo5Pz9fcYxwd8XDoZVLe/bsCQAms3Z//PFH9O7dW1o2fNy4cXjttdfwxBNPqD5ORUWFScnL399fWqpZS+I557bsU2ELeVkyPDxc8bhi8CgrK0N1dbXiQ/7ixYt2t0H+whQEQfoWLC/zXbx4URq3EwRBCh7R0dHS5ZKSEik9R0dHIyQkRNEWrfuoLnNmX8m/nd1yyy3YvHmz1Q8K+bBeUFAQDAaDSRm4ffv2+PXXXwFAdRfagIAAu/8ecVfMEydOYPbs2WZ/35H+Cg0NRWRkpMkaHr7y+jQYDHjsscewfPlyXL9+HXPmzNHsb5eftaS2em1kZKTZ55owYYKijZ7AltdXVFQUrly5orjuww8/RJs2bXDt2jWMHDnSZD0S+f3j4uJUH79bt2748ccfFdeZCx4GgwHbtm3D7NmzMXbsWNxxxx2KeRfyxx88eLDJehsRERGK+5irTD3zzDOKoJqfn29yzAK0//ezdT0XTZdMP336NDp37iz93LRpU2RlZaGkpET1m8qiRYtMVlocPnw4RowYoWWzFOTr6NeGfJEdvV6veKHJO1/cF0N0+vRp1RelJeIHb3BwsOJ35Qm5VatWuPHGGzFq1Cj0799fOiUuLi5OCh5Xr16VdpdMTk422w6t+sgXOKOv5N/MDh48iFmzZuHhhx+2+Dvy/XcqKytx7tw5k9NOGzZsKF3eu3evyWO88MILdr82AeCVV16RLlv7fXv7KzExURE8dDqdQ230VrGxsdi4cSOKi4uRmJio6d/epk0bHDx4UPW2qqoqr+xnS68vtWOQfD2L69evY+DAgYrbxflzAQEByM/PV60kq220KR8eEWVlZeHcuXNSlXDFihU4ffq0VD0PCAhQ9Hnjxo0RHh6uqK5XV1cr7mO8CKC/vz9SU1MxZMgQxe9dvHhRccwSv7xq/fmltmOuGk2DR2lpqeJgKKYqc8Fj7NixePDBB5UNcmLFIzMzE6mpqZqssidPwvXr11es4igvUx46dEjxe4IgmKz4aI243XJkZKTid43P89+3bx8OHz6sWMMhPT1desFdvHhRmrXeokULk3Zo3Ud1mTP76tFHH5U29AJqPrCsvWbkwSMhIQHp6emKDQuBmhUXn376aekxRTfddBNGjRqFxx9/XLN1Iow52l/p6emKpadjY2Ptfv94K7HPMjIynPJ+fOONN3Dvvfeq3taoUSOv6mdbXl/x8fGqZ5uIlixZYlKdF6sE8fHxiuAuZ8uaN0BN5dq4T1NSUqQh+ZiYGJPbBw8ejP/+97/Sz2lpaYr7GJ9llJeXh5CQEOj1ekWFo7y8XHEMFicXu+uzXtPgERISohiTFhOXuXHZwMBAp4QMS/R6vSYdLf87IyMjFY8p/3vXr1+v+L2cnBy7n9/c5CO10w0rKysxefJk6Wf5i1R+XnmTJk3MtkOrPvIFzuirPn36ICsrSxoqOXnypNXnMF7HQ6/Xm8wJSEpKQr169Uy2zX7yyScxZswYbRpvhb39ZfxBbPwe8AXOej8OGTIEnTt3xo4dO0xui4mJ8cp+ttRX1lZ9LS0thU6nw6FDh5CamoqwsDApoCcnJ5t93NDQUHz77bd4/fXXsX//frOPrzbHQz6HJCwszOQ5Jk2apAge4ntbZDzvRD4kK+7lIggCrl27phiOFe/nrs96TZ+xcePGim8np06dQv369evkhDBLE3Xkp/yJY+oitXOsLamurpbGYI0nH7Vv31467XHQoEFSP2/fvl26j7k9O+ST1sjz1KtXT5o4Zu4MFDnjdTwA9dNpjasggGdP2DR+nXpyW72NTqfD6tWrMWDAAJPbtFwzxFNY+5uOHTuGzz//HG3btkWrVq2QmZkpDWVYm9h77733mt1+XqyI5Ofnm5wiLx/qUDvTpEOHDoo1N4wXDLN0Vot8iX3jyaVeuY5HVVUVysvLYTAYUF1djfLyclRXV+OOO+7Ab7/9hiNHjqCoqAiff/457rzzTq3b7BEsBQ/5h6PxrGl7g4fa4mGijIwMrF69GgsWLMC3336rOg+gfv36qlUlBg/PJ+7VkZmZaXVNGLWzWuLj46UPHrGk3qZNG5PfdfepdZYYjxn72joezlavXj188sknJtf7QvDo0qWL4ufi4mI8+uijAGrWNlm1apV0my1rHpk7mItzQKqqqkyGeqZMmSJdVqtgAzU7AScmJqJp06YmZ7rIg4i/v+kAhvzUfPmcD68MHgsXLkTXrl2xatUqfP755+jatSvWrVuHpk2b4vnnn8cLL7yAgQMHIiEhAY888ojWbfYItlY8jGkZPICanSEnTJiAgIAAtGrVyuT2xMRE1fYweHi+Zs2aSZeNt9g2phY89Ho9NmzYgOnTp2PevHkA1IOHJ1cRjIOHJ7fVW6mdPu0LwePzzz9Hu3btzN7f3jWP1F6boaGhisqjcfVS3FMlODjYZIl6UatWrZCVlYWjR4+aVDwCAwOxcOFC3H777di0aZPJ74pfPAoKCrz/dNoJEyYoTqmSGzRokObn83sieSXD+NxqLYOH2qql5qi9ORITExEcHKyYaKTX671q4pivku9OeuLECdxwww1m76sWPADgxhtvVGxprjbU4k0VDwYP7YWEhCAkJESxxYMvBI+WLVvir7/+wv/+9z/VSbbyBdVsqXjo9XqTs1Di4uJsWrjvlVdeUf3iKNLpdGYnfo8bNw7jxo1TvU0MKtXV1YoVrt0dPLxv9pCHcFXFw9yqpWrU3hwJCQkm7UlNTXX5pF6yn7ziYW2eh7ngYczb5ngYnzHAoRbnkFc9xANoXWPuM89c1UMePGxdvM2432JjYxUrnhqf5Siy9KWiNuQVEvl8Enf/+zJ4OMje4CGWvEpKSuzaw8Weioda8BArHnIcZvEO8oqH8W6TxuTfVi0Fj6ioKJPhFk+ueBjvGcPg4Rzy4CHfbbsuMbcKcMOGDRWr+opOnDghXbZ1XyvjY0FcXJxi7oa5s15s3Q3aXvIzecQ1nABWPLyWrZNLgZpvEPKlpe2petgTPBITExXlOH9/f2mFUrm0tDSbn5/cp0WLFtK/5549e1Tvc/36dTzwwAN4+eWXpessBQ8A6N69u+JnTw4exqwtcU2OkZ+W6Y2n0dpCvu+RfJE+nU6nWF5cTW0qHvLfNfcFQm0fGC3IA6V84TFWPLyUPRWPpk2bKk5rtSd4WJtcKufn56dI1wkJCdDr9SYHInM7KpJnCQ0NRdu2bQHUlGjVNmH717/+hSVLliius1YVuO2220yex1vIFz4j7cgPUOIig3XNk08+Ka3mu3LlSsVt9957L1q2bKn6e1FRUTYfqI3vZ1zxMMdZFQ/5v6s8tLPi4WU+++wzPPfcc4rlZ60Fj5tvvlmRaJ1V8QCUyVycTGUcPGxdaY/cT9yCwGAwqFY9jD9AAesVD28LHq+//rp0+e6773ZjS+ou+QGzrgaPBg0a4NChQ9i+fbvJ2iV+fn5Yu3atYssP+e/ZSm3bemu/Hxwc7LSqo9oZSzqdzu3veU1XLq3rdu/ejfHjxyuui4iIMFkRz/iDf+rUqYpNhJwZPORn2IgfIMZBiMHDe3Tu3FlaZ2HHjh0mwyRq25JbCx7GH4SeXlqfMmUKysvLER0djT59+ri7OXWSfA2Iuho8AOWEbWONGzfG9OnT0atXL8X1ts7vAEwrHjfffDMSExOh1+vNbsiWkJDgtDk1asFDbYVUV2PwsIPaQjstW7Y0edHIzxx499130bRpU0XFIycnx+bntOesFkA5c1tcRpsVD+8l/wamtrS1I8EDAFavXo1XX33V7M7RniQwMBBvvfWWu5tRp/lK8LBGbUjFnqFp4y95/fr1g7+/P+rXr49Lly6p/o6zhlkA9eDh7mEWgMHDLsY7zQKmG7UBNWcjbNy4Ebm5uVJpWH5ak/GyuZbYW/FQCx6seHivli1bIjQ0FCUlJdiyZQsMBoPi24qjwcNX1tsh28iDh6dsde8Oap+xlpYlN7Z7927pclpamvTZm5ycbDZ4OGtiKaAePNw9sRTgHA+bHT9+HIcPHza5Xi14ADXj6EOGDJGqIa4KHh06dJAud+3aFYDpgYiTS72Hv78/evToAaBmGectW7Yobnc0eBDJyRevMh7O8yVqn7HGq4VaIg/zEydOlC5bmufh6uDhCRUPBg8bGW/2JjIXPIzJ54EUFBTY/Lz2nNUCAM8//zw6d+6Mpk2bYtasWQBMKx72JHhyvwceeEC6LN+pElAPGQweZK9x48ahd+/eaNasGRYuXOju5riNWjXAnuDx5JNPonfv3hgzZgyeeeYZ6XpLwcOZQy1qn/WeEDw41GIjcxNCbQ0erqp4hIaG4s8//4QgCFK1xfhA5O6JRWSfIUOGSEtaL1u2DHPnzpVOC1SreHCRLbJXQEAAfv31V8Xnhi9S+4w1PnnAkpSUFNUvqZZOqXVmxcPf3x9RUVGKLTM41OJF5P9wck2aNLHp9+UvXkeDhz2nQMk/PNQOTuQ9wsPDpdP/8vLyFBvGGR8kdDqdFEqI7OXLoQOomSNnvCeKPRUPc9SGPETOrkAYP7d8sTh3YfCwkbngYeueJ/7+/lLStGeoxd6Khxr5Ph7kneQL0MmDa3l5ueJ+wcHBPn/wIHKUTqczqQhoETx69+4tvS+/+eYbxW3O3jfLOHio7VDtagweNpIHjxdeeAHNmzfHsmXL7HoM8QVsT8VD3BzMz8/P4TeAvO0c//dO8rHavLw86bJxqOS/L1HtGH/B0yJ4NG/eHPv27cO2bdswYsQIxW3Gu5trjcHDi8kP3lOnTsWxY8cwfPhwux5DfAHbWvG4evWqdCZNx44dHR67l7fd2S9ycg5zc4SMgweH1Yhqxzh42DPHw5K2bduiS5cuAP6eJJ6cnIzBgwdr8vjmMHh4MfHgrdPpHB6TE1/AZWVlNg1/bNy4UbosnlLpCPkLT21bdPJ8tgYP+UZYRGQ/Z1Q8jI0cORJHjx7F0aNHnV6lNA4e9iwB7ywMHjYSg0dERITDZ4XYe2bLH3/8IV2uTfCYOnUqYmJiEB4ejs8++8zhxyH3sXWoRVw0jogcI5/jodfrnXaWWIsWLVxyaqt8cTjAMyYQ83RaG4nDI+LGa44wXssjKSnJ4v3Fioder0e3bt0cft6kpCRkZmYC8K4t0Olv5kKr8eRS45+JyD7yz8i6cIaYfEdnTznVnhUPG4kVj9oED3srHuLE0hYtWtTqeYGaNxNDh/eydaiFwYOodupa8JBPZp06daobW/I3VjxsUFFRIX3Auyp4VFVVobS01OT3yDfZOtRSXV3tsjYR1UXyORfOPtXVFQYNGoRp06ahtLQUkyZNcndzADB42ER+VoiWQy2WyNfv8ISV5si9bK14EFHtyHfnrQvBQ6/X47XXXnN3MxQ41GIDrYKHPRWPoqIi6bInrK1P7hUQECAFUPG1IwgCgweRxuQTtOvCUIsnYvCwgTuCR2FhoXSZFQ8CTBeg4xksRNqrrKyULjN4OAeDhw3cMdQir3gweBDwd/DIy8uDIAicSErkBPLgUReGWjwRg4cN5MGjNqvYmat4/Otf/8Ltt9+O9evXS9fJKx4caiHg7wmmFRUVKC0tVR1mceYW20S+gEMtzsfgYQNnDLXk5uYCAE6dOoWpU6fi119/Rd++ffHFF18AYMWDTBkHV+PgERsbi++++87VzSKqU1555RXp8ttvv+3GltRdDB420Cp4JCYmSqvIiQt67dmzR3GfV199FQAnl5Ip41Nq5cHjoYceQlZWlrQXBBE5pnfv3vjmm2+wZMkS3Hnnne5uTp3E02ltoFXw8Pf3R0pKCs6ePYuzZ88CAA4cOKC4T2ZmJoqLizm5lEwYVzzkw34hISEsCxNpQKfTmewgS9pixcMGWgUPAEhPTwdQc+A4duwYtm7danKfkydPcqiFTFgaanH2RlNERFphxcMGzggeAJCRkaF6nxMnTnByKZmQD7Xk5OQoggiDBxF5C1Y8bOCs4GEOKx6kJjk5Wbp88eJFxem0DB5E5C0YPGwgnoECKL91OqJhw4aq1zdp0kS6fOLECQYPMpGamipdzszMVAy1BAUFuaNJRER2Y/CwQU5ODoCaxWRqu8OruYrHI488Il3mUAupsRQ8WPEgIm/B4GEDseIRHx8PnU5Xq8dSCx49e/bEE088IS3+xIoHqYmLi5MCBoMHEXkrBg8rBEGQgkdcXFytH0/+rRUANm/ejN9//x3R0dFo2rQpACArKwvbtm2T7sOKBwE1p/mlpKQAYPAgIu/F4GFFSUmJNIlPi+BhPBbfoUMH6XLXrl2ly1euXAFQc7AJCQmp9fNS3SAG18LCQuk1AjB4EJH3YPCwQpzfAdQMtWjh9ddfh06nwzPPPKMIFS+//DI6deqkuG9YWBj0ev4zUQ15xezkyZPSZQYPIvIWPKJZIT+jRYuKBwBMnToV169fx9y5cxXXR0dH47333lNcx2EWkpMHjxMnTkiXGTyIyFsweFghDx5aVTwA8xNGW7VqZdP9yDeZCx48nZaIvAWDhxXyoRatKh6WJCYmKgIOKx4kJ04uBcA5HkTklRg8rHDGUIs14tktAFBZWemS5yTvkJaWpno9gwcReQsGDyucNdRiifzgcv78eZc8J3mHli1bKvZoETF4EJG3YPCwwtVDLYBykTH5PjFE/v7+GDBggMn1DB5E5C0YPKxwx1BL27Ztpcv169d3yXOS9xg8eLDJdZxcSkTegsHDCncMtdx3331o27YtAgMDsXjxYpc8J3mPO+64A/7+/tLPKSkpSEpKcmOLiIhsx+BhhTjU4ufnh6ioKJc8Z2BgIPbu3YsrV66gf//+LnlO8h5RUVF44YUX4O/vj3vvvRdbt25VBBEiIk/G4GGFfJ+W2m4QZw+9Xu+yoEPeZ/r06SgpKcG3335rsv8PEZEnY/CwIi8vDwAQGxvr5pYQKQUEBLi7CUREdmPwsKCqqgqFhYUAoHoKIxEREdmHwcMC+ams0dHR7msIERFRHcHgYUF+fr50mRUPIiKi2mPwsKCgoEC6zIoHERFR7TF4WMCKBxERkbYYPCxgxYOIiEhbDB4WsOJBRESkLQYPC1jxICIi0pZT1ll+7LHHcPDgQfj5+QEA2rdvj7lz5zrjqZyKFQ8iIiJtOW2Dh1deeQUDBw501sO7BCseRERE2uJQiwWseBAREWnLaRWPmTNnYubMmWjevDmef/55NGvWzOQ+FRUVqKioUDbI3x+BgYGat8dgMCj+bwt58IiMjLTrd72RI33kq9hX9mF/2Y99Zjv2lX2c1V96vW21DJ0gCIKmzwzg4MGDaNy4MfR6Pb755hssXboUK1asQFhYmOJ+H3/8MT799FPFdcOHD8eIESO0bpJD7rnnHuzbtw86nQ4nTpywuVOJiIh8TaNGjWy6n1OCh7GhQ4fin//8J7p06aK43tUVj8zMTKSmptocIDIyMnDixAlERUVJu9TWZY70ka9iX9mH/WU/9pnt2Ff2cVZ/2fpYThtqkTPXmMDAQKeEDGttMdeeH3/8Ef/+97/x9NNP4+6775Yml8bExPjUi9lSH5ES+8o+7C/7sc9sx76yj7v6S/PgUVhYiEOHDqFDhw7Q6XRYtmwZrl+/jjZt2mj9VJobMGAAAGD9+vUwGAzSHA+e0UJERKQNzYNHVVUV5s2bh3PnzsHf3x/NmzfHnDlzEB4ervVTOVVJSQmqqqoA8IwWIiIirWgePGJiYvDll19q/bBOJ4YM0eXLl6XLDB5ERETa4GDY/ydfLAwATpw4IV1m8CAiItKGSyaXerrly5dj7969iuuOHDkiXU5KSnJxi4iIiOomnw8eW7duVV03hMGDiIhIez4/1PL222+rXi8PHsnJya5qDhERUZ3m88FD3EHXGCseRERE2vP54GFu8RT5SqUMHkRERNrw+eBhyyY59evXd0FLiIiI6j6fDx5Xr161eHt8fLzLl3UnIiKqq3w+eGRnZ1u8ncMsRERE2vH54HHlyhWLtzN4EBERaceng0dJSQmKioos3ofBg4iISDs+HTysDbMAXMODiIhISwweVrDiQUREpB0GDyvatGnjgpYQERH5BgYPCyIiItC1a1cXtYaIiKjuY/Cw4NZbb+UaHkRERBry6eBx+fJl6fKbb76JV199VXE7qx1ERETa8nd3A9zpxIkT0uXHHnsMCQkJCAoKwiuvvIKIiAhMmDDBja0jIiKqe3w6eBw7dgwAEBMTg/j4eADApEmTkJaWhrZt2yIxMdGdzSMiIqpzfDZ4FBcXIzMzEwDQokUL6HQ6AEBwcDBGjRrlzqYRERHVWT47x0M+zNKiRQs3toSIiMh3+GzwEIdZAAYPIiIiV2HwAIMHERGRq/hs8Dh69Kh0OSMjw40tISIi8h0+GzzEOR56vR5NmjRxc2uIiIh8g88GD3HV0sTERAQFBbm5NURERL7BZ4NHbm4uACA2NtbNLSEiIvIdPhk8ysrKUFJSAgCIi4tzc2uIiIh8h08Gj7y8POkygwcREZHr+GTwEIdZAA61EBERuZLPBw9WPIiIiFzHJ4MHh1qIiIjcwyeDByseRERE7uHzwYNzPIiIiFzHJ4MHh1qIiIjcwyeDB4daiIiI3MPngweHWoiIiFzHJ4MHh1qIiIjcwyeDh1jxCAsL4wZxRERELuTTwYPVDiIiItfyueAhCAJ3piUiInITnwseRUVFqKqqAsCKBxERkav5XPAoKCiQLsfExLivIURERD7Ip4NHdHS029pBRETkixg8iIiIyGUYPIiIiMhlGDyIiIjIZXwueFy7dk26zOBBRETkWj4XPFjxICIich8GDyIiInIZBg8iIiJyGQYPIiIichmfCx6cXEpEROQ+PhU8Ll68iMzMTACAv78/QkND3dwiIiIi3+Lv7ga4yg8//IC77rpL+jk6Oho6nc6NLSIiIvI9PlPxeOKJJxQ/c5iFiIjI9XwmeIhDLCIGDyIiItdzSvDIz8/Hc889h27duuHee+/Fjh07nPE0dtHrlX8qgwcREZHrOSV4TJ8+HXFxcVi/fj2ee+45vPTSS4qzSVytoqICgiAorouKinJTa4iIiHyX5pNLS0pKsGHDBnz33XcIDg5Gjx490KRJE/zxxx8YPHiw4r4VFRWoqKhQNsjfH4GBgZq26cyZMybBo6SkBAaDQdPn8XZif7BfrGNf2Yf9ZT/2me3YV/ZxVn8ZjyyYo3nwOH/+PEJDQ1GvXj3puqZNm+L06dMm9120aBE+/fRTxXXDhw/HiBEjNG3Ttm3bTK47ceIEzp07p+nz1BXG82HIPPaVfdhf9mOf2Y59ZR+t+6tRo0Y23U/z4FFaWoqwsDDFdWFhYapDLWPHjsWDDz6obJATKh5FRUUm1/Xo0QPp6emaPo+3MxgMyMzMRGpqqs3J1Vexr+zD/rIf+8x27Cv7uLu/NA8eISEhKC4uVlxXXFysulhXYGCg5iFDzZkzZ6TL/v7+aNWqFaZNm8YXqBl6vZ59YyP2lX3YX/Zjn9mOfWUfd/WX5s+YlpaGkpISXLlyRbru1KlTaNy4sdZPZbNTp05Jl0+cOIF9+/YhNTXVbe0hIiLyVZoHj9DQUPTo0QMff/wxysrKsGnTJpw8eRI9evTQ+qlsJgaPgIAANGjQwG3tICIi8nVOWTJ9ypQpmDZtGvr06YN69erh7bffduvpq/feey+aNWuGoqIi+Pn5ua0dREREvs4pwSMmJgZz5851xkM75PXXX4fBYOBZLERERG7GWThERETkMgweRERE5DIMHkREROQyDB5ERETkMgweRERE5DIMHkREROQyDB5ERETkMgweRERE5DIMHkREROQyDB5ERETkMgweRERE5DIMHkREROQyDB5ERETkMgweRERE5DI6QRAEdzeCiIiIfAMrHkREROQyDB5ERETkMgweRERE5DIMHkREROQyDB5ERETkMgweRERE5DIMHkREROQyDB5ERETkMgweRERE5DIMHkREROQyDB5EVnBXAdtUVVW5uwlE5AUYPHxIXl6eu5vgVVasWAEA0Ol0bm6J5/vqq68we/ZslJeXu7spXqOoqMjdTSByC68PHuvXr8dLL72EgwcPAgAMBoObW+R51q1bh3vvvRdvv/02Zs6cievXr7u7SR7t+++/x8CBA/HDDz+gqKiIrykL1q1bhwEDBmDOnDk4duwYgoKC2F9W/Pjjjxg8eDBeffVVzJo1Czk5Oe5uksdav349xo8fj+3btwPg57s13nI89Hd3AxxVWVmJZcuW4YsvvkBaWhp++eUXtGnTBnq912cpzRQVFWHWrFnYtWsXnn/+eTRu3BhjxoxBRkYGBg4cCEEQ+G1eprCwEG+//Ta2bNmCd955B127dnV3kzxWVlYWXnjhBRQXF+Nf//oXmjRpgvvvvx8FBQWIjo52d/M81o4dO/DZZ5/hpZdeQnR0NObPn4/58+fj4YcfRnp6urub5zGqq6uxZs0afPbZZ0hNTcW3336LLl26QK/X83NLhbcdDz2zVTYQBAFxcXF44403MHz4cGRlZWHDhg3SbVQzRNCxY0esWrUKPXv2RHR0NCIjI3Hp0iXpdvqbwWBAeXk5Ro0aha5du6KqqgpbtmzBhQsX3N00j+Pn54fBgwfju+++Q6dOnVBQUIBGjRrhyJEj7m6aR6qurgYA7N+/HzfffDNuueUWtGzZEuPHj8e5c+ewcuVKN7fQ89SvXx8vvvgiJkyYgPLycnz77bcA+PmuxtuOh14VPP744w9kZWWhrKwMgYGB6Ny5M7p06YIuXbogNTUVf/zxBwoLC6HT6Tyys11B3kdhYWHo1asXdDodfvnlF/Tv3x9xcXEQBAFbt27F5cuX3d1ctxP7q7S0FFFRUejXrx9OnTqFF154AXfeeSeWL1+Ohx9+GIsXL8bVq1fd3Vy3kvdVQkIC7r//fum2uLg4XLlyRTrAemqJ19XEPqusrAQAFBQU4NSpU9LtrVq1Qk5ODvbs2YPdu3e7q5keIT8/X7rs5+eHtm3bonv37mjTpg26du2Kn3/+Gfn5+dDr9Xx9wbuPhzrB01qk4vDhw/jnP/+JsLAwxMfHIygoCLNmzVLcZ/v27VizZg3atWuH4cOHw2AweGyZyRms9dH27duRnJyMtLQ0HDlyBN988w0SExPxxBNP+GTlw7i/AgMDMXv2bBgMBsyYMQOXLl3CM888g2bNmuHXX3/F999/j169emHQoEHubrrLWXttVVdXw8/PDy+//DJCQkLw6quvurG1nsG4zwICAjBnzhwUFBSgf//++Oc//4n+/ftj7969WLlyJdLS0tCgQQOMGDHC3U13uV27dmHq1Klo3749pkyZgoiICJP7nD59GgsXLkRycjKeeuopn/t8l6sLx0PPaYkFmzZtQr9+/bBs2TJMmzYNZ8+exbx581BQUCDdp127dmjWrBn27NmDrKws6PV6FBcXu6/RLmauj8QzWbp06YK0tDRUVVWhZcuWSEpKwsmTJ1FWVubmlruHcX+dO3cOc+bMQXV1NR599FG89NJLaNasGaqrq9GnTx9ERkbi8OHDADyzdOlM1t5/4rh7kyZNIAgCSktL3dtgD2DcZ+fPn8ecOXMQHR2NadOm4eeff8bTTz+Nf//733j44YdRXV0tTfr2pdfXyZMn8fnnn+OWW27BiRMnsH//ftW/Py0tDT169MCePXtw5swZ6PV6n50kXxeOh14RPDZs2IDk5GQAQL169fDKK69g586d+Ouvv6SSW3BwMLp06YL4+HgsW7YMr7/+Or744gupxFnXmeujffv2KcqS/v4184lDQ0Ph5+eHkJAQt7TX3dT6a8+ePdi8eTPi4uKQlJQEoKbkCwAxMTFSZcjXKkTW3n86nQ46nQ7h4eE4efIkQkJCfOrgqcbc62vDhg0YOHAg5s+fj5deegmrVq1Cu3btEBAQgMDAQAC+9fpq2rQpBg0ahFdffRVdu3bFihUrkJuba3I/f39/tGvXDh07dsQnn3yC1157De+9955PfnGqC8dDjw4e4njxrbfeqhj/7NixI1q3bo3ffvtN8e0qIyMDp0+fxpdffonc3Fw8+OCDCAgIcHm7XcmWPiopKQEAaY7Cf//7X3zzzTfo16+f6xvsZpb6q02bNvjtt9+kbwbiN6olS5bg999/R58+fVzfYDey9f0nhozevXvj3LlzOHHihE8dPOWsvb7Wr1+PoqIi+Pv7o1mzZgCARYsWYfPmzbj11lvd0mZ3EV83ffv2BQA89thjuHz5MjZu3Ki6GF1iYiIuXLiA9evX49q1a5g0aRKCg4Nd2mZ3qkvHQ48OHuK3zVatWqGyshI7duyQbhs1ahQ2btyIK1euAACuXbuGV199FWfPnsUXX3yBuXPnIioqyi3tdiVb+kgMHFu3bsXQoUOxdu1avP3229Ib3pfY019btmzBXXfdhTVr1uBf//oXOnbs6JY2u4ut7z8xZOTm5mLEiBGIjY11S3s9gbU+27Rpk/T6On36NP75z3/i+++/x9SpU9G0aVO3tNldxNeNv78/qqqqEBISguHDh2P16tXIzMxUVGorKiowffp07N69G4sXL8asWbN87rTtunQ8dHvwyM7OxsqVK01mdAuCIJWFWrZsiXr16uGnn36SknD9+vXRrFkz7Ny5EwAQFhaGRx99FN9//z1atWrl2j/CyWrbR+ILtE+fPnjppZfw3//+FzfccINr/wgX0qq/unXrJvVX27ZtXftHuEht+2rXrl3S72RkZOCpp55CXFyc6/4AN9DqMys9PR2PP/44VqxY4ZOvL3lVQxwCHjp0KAIDA/HLL79Ar9dLwy4BAQF45JFH8NNPP6F169au+wNcLCsrC4sXL8aGDRsUqwDXteOhW4PHvHnzMGLECOzfvx9Tp07F7NmzpVX8dDqdVBYKDAxEr169cPXqVcybNw9AzeJYer0enTp1AlDzwq2LC/Bo0Uc33XQTACA8PFzqr7pKy/6KiIio04uIadFXvlYF0vIzKzAwEE2aNHHPH+IC1vpKDBviOjniQfQf//gHfvnlFzz99NO44447cPz4ceh0OsTHx7vnD3GROXPm4P7770dWVhYWLFiA9957D9euXQNQB4+Hgpv873//E5544gnhwoULgiAIwr59+4QRI0YIx48fl+7z7bffCp06dRIWLFggVFZWCnv37hX69esnvPDCC0LPnj2FyZMnC6Wlpe76E5yOfWQf9pfttOwrg8Hgrj/Dpfj6sp2tfdW5c2fhgw8+UPzuqlWrhE6dOgkvvvii9Pt13Zo1a4T/+7//EzIzMwVBEITff/9dGDZsmHDt2jXpPitWrKgzry2XBo/Kykrp8tGjR4U1a9YIgiAI5eXlgiAIwsMPPyysXLlSEARBOH/+vDB69Ghh27Ztise4fPmysHPnTuGvv/5yTaNdjH1kH/aX7dhX9mOf2U6LvtqxY4fw0EMPmVxfF8n7Ky8vTygsLBQEQRB2794tDBo0SLj77ruFPXv2CIJQ8xoaNWpUnXltuWQBsfz8fMybNw86nQ5NmzbFPffcI506JqqsrMSECRPw/PPPm4x3CoIAg8EgTa6pi9hH9mF/2Y59ZT/2me3YV/ax1F/nzp3DBx98gGbNmqFbt27YuHEjdDod7r//fmkybV3oL6fP8Vi7di3uv/9+6fSxtWvXYvr06QBqllUWaqouyM3NRVlZGSIjIxVrAFRXV0On03l1J1vDPrIP+8t27Cv7sc9sx76yj6X+AmoWSpsxYwYmTJiA1q1b46abbsLp06elSdx1pb+cujttUVERzp49i6eeegqDBw8GANxwww34v//7P+Tl5SE2NlZayvXIkSPw8/OTJsQcPXoU9evXr/OnTLGP7MP+sh37yn7sM9uxr+xjqb/y8/MRExMDoGYl4IqKCgQGBuKGG27A1KlT0atXLwDw+sAh0jx4ZGdnQ6fTITExESEhIejVqxdSUlKk269du4aoqCiEhoYCgLR+/MmTJ3HXXXchOzsbzzzzDMLCwvDee+9p3TyPwD6yD/vLduwr+7HPbMe+so+t/SWuIC2ubSIOvRw+fBgpKSnSYnN1hWbBo7KyEtOmTcPevXuRkJCA2267DXfddZd0zrUgCNDpdAgKCkJoaKh0KpUgCKiursahQ4fw559/Yv78+Rg1ahQeffRRrZrmMdhH9mF/2Y59ZT/2me3YV/ZxtL8AIC8vD3/88Ye0hcPjjz9e5xaX02yOx48//ohr165h9erVGDVqFC5cuIC3337b5H6//vorkpOTpY4Wz+e+dOkS+vfvjx9++KHOvijZR/Zhf9mOfWU/9pnt2Ff2cbS/ACA2NhanT59GeHg41qxZg/vuu8+VTXeN2pwSIz+Hf+bMmcKUKVMEQRAEg8EgnD9/Xhg0aJCwbNkyQRBqTqkyGAzC2LFjhZ07dwqCIAg//PCDsHz5ckEQBKG4uLg2TfFY7CP7sL9sx76yH/vMduwr+2jRX99++60gCIJQUVHhhr/AdRwaajl//jzef/99hIaGIiQkBC+++CIiIiLg5+eHwsJCREREIDU1FY888gjmz58vLYNbUlKC6OhoFBQU4LnnnsOBAwfw4osvAoA0JlhXsI/sw/6yHfvKfuwz27Gv7OOM/vKUzdycxe6hllWrVuHxxx9H8+bN8dBDD+HYsWNYuHAhmjZtip07dyI7O1u6b8+ePdG4cWN8++23AGo2Rdq0aRP+9a9/oWnTpvjtt99wxx13aPfXeAj2kX3YX7ZjX9mPfWY79pV92F+OsTt4XLp0CY899hiefvpptGnTBu+++y6WLl2Krl27IjIyEt9//z0KCgoA1KS2+vXro6KioubJ9HqMHz8e3333HZ555hlN/xBPwj6yD/vLduwr+7HPbMe+sg/7yzF2D7WIZSKgZuaun58fGjVqhKqqKjz66KOYNWsW0tPTMWDAAISGhqKgoEDajjcjI8Mjd8rTGvvIPuwv27Gv7Mc+sx37yj7sL8fYHTzq1asHoOZ0oICAAOTk5ECn0yEwMBDt27fH4MGD8dNPP+G3335DVVUVLl26JJ1CJJ7TXdexj+zD/rId+8p+7DPbsa/sw/5yjMPreIgLnezYsQONGjWSVlQbOnQounXrhi1btqCwsBBjxozRpKHeiH1kH/aX7dhX9mOf2Y59ZR/2l30cDh7V1dXw8/PD8ePH0bdvXwDAsmXLUFRUhHHjxmHo0KGaNdJbsY/sw/6yHfvKfuwz27Gv7MP+so/DtR4/Pz9UVVWhrKwM2dnZGD9+PL744gu0adNGy/Z5NfaRfdhftmNf2Y99Zjv2lX3YX/ap1ZLpp0+fxvbt23HixAk88MADGD16tFbtqjPYR/Zhf9mOfWU/9pnt2Ff2YX/ZTicIsj2K7VRVVYVvvvkGw4YNQ1BQkJbtqjPYR/Zhf9mOfWU/9pnt2Ff2YX/ZrlbBg4iIiMgevns+DxEREbkcgwcRERG5DIMHERERuQyDBxEREbkMgwcRERG5DIMHERERuQyDBxEREbkMgwcRERG5DIMHEdXKrl270KlTJ3Tq1AmXLl1yd3OIyMMxeBCRzV577TV06tQJjz32mHRdeHg42rRpgzZt2iAwMNCNrSMib1CrTeKIiDIyMrB48WJ3N4OIvAT3aiEimwwaNAiXL182uX7BggV4/PHHAQCrV69GcnIyXnvtNaxduxZJSUmYMGECPvroIxQVFWHw4MF46qmnMG/ePKxevRrh4eEYO3Yshg0bJj3e1atXMX/+fGzbtg0FBQWoV68eBg0ahDFjxsDfn9+ViLwd38VEZJMWLVqgtLQUBQUFCAsLQ6NGjQAAR48eNfs7OTk5ePfddxEfH4/i4mIsWbIE27dvx5UrVxAeHo7s7GzMmDEDHTt2RKNGjVBQUIAxY8YgOztbeo7Tp09jwYIFuHjxIqZNm+aqP5eInIRzPIjIJu+//z66desGoCaELF68GIsXL0ZGRobZ36msrMSHH36IlStXol69egCAzMxMLFmyBMuXL0dQUBAMBgN2794NAFi2bBmys7MRFxeHVatWYcmSJZg+fToAYO3atcjMzHTyX0lEzsaKBxE5TWRkJNq1awcAqF+/PrKzs9GkSRMkJycDAGJiYpCVlYW8vDwAwKFDhwAAubm56Nu3r+KxBEHAwYMHkZqa6ro/gIg0x+BBRE4TFhYmXfbz8zO5TqfTAagJFca/Jw7lyAUHBzujmUTkQgweRGQz8cBfVlbmlMdv1aoVtmzZAj8/P7z99ttSZaS4uBi///47evXq5ZTnJSLXYfAgIps1bNgQAHD48GHcd999CAkJwfjx4zV7/BEjRuC7777DlStXMHToUDRq1AjFxcXIzs5GVVUV7rrrLs2ei4jcg5NLichmgwcPRu/evREeHo5Tp07h4MGDMBgMmj1+TEwMFi1ahEGDBiEqKgqnTp1CeXk52rdvjxdeeEGz5yEi9+E6HkREROQyrHgQERGRyzB4EBERkcsweBAREZHLMHgQERGRyzB4EBERkcsweBAREZHLMHgQERGRyzB4EBERkcsweBAREZHLMHgQERGRyzB4EBERkcv8PyFCX47nZGxqAAAAAElFTkSuQmCC", "text/plain": [ "
    " ] @@ -1743,148 +1743,148 @@ " fill: currentColor;\n", "}\n", "
    <TimeSeries (DataArray) (time: 365, component: 1, sample: 1)>\n",
-       "array([[[ 0.22856328]],\n",
+       "array([[[-0.28446992]],\n",
        "\n",
-       "       [[ 0.37364648]],\n",
+       "       [[ 0.40206202]],\n",
        "\n",
-       "       [[ 1.53911985]],\n",
+       "       [[-0.08163155]],\n",
        "\n",
-       "       [[ 2.95379204]],\n",
+       "       [[ 1.408035  ]],\n",
        "\n",
-       "       [[ 3.28662792]],\n",
+       "       [[ 2.67956629]],\n",
        "\n",
-       "       [[ 3.79643527]],\n",
+       "       [[ 3.28924893]],\n",
        "\n",
-       "       [[ 5.97261116]],\n",
+       "       [[ 4.32680234]],\n",
        "\n",
-       "       [[ 7.0681775 ]],\n",
+       "       [[ 3.39678829]],\n",
        "\n",
-       "       [[ 7.0533794 ]],\n",
+       "       [[ 4.72305147]],\n",
        "\n",
-       "       [[ 7.21773104]],\n",
+       "       [[ 3.25746454]],\n",
        "\n",
        "...\n",
        "\n",
-       "       [[17.86891974]],\n",
+       "       [[ 9.68434639]],\n",
        "\n",
-       "       [[15.91575202]],\n",
+       "       [[11.07561827]],\n",
        "\n",
-       "       [[16.09807771]],\n",
+       "       [[10.03762309]],\n",
        "\n",
-       "       [[16.59950989]],\n",
+       "       [[10.2329436 ]],\n",
        "\n",
-       "       [[16.4253566 ]],\n",
+       "       [[10.42700157]],\n",
        "\n",
-       "       [[17.34655627]],\n",
+       "       [[10.7945289 ]],\n",
        "\n",
-       "       [[17.56370215]],\n",
+       "       [[ 9.36551048]],\n",
        "\n",
-       "       [[18.88084012]],\n",
+       "       [[ 8.11774274]],\n",
        "\n",
-       "       [[17.02758259]],\n",
+       "       [[ 8.65613076]],\n",
        "\n",
-       "       [[16.20917557]]])\n",
+       "       [[10.548976  ]]])\n",
        "Coordinates:\n",
        "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-12-31\n",
        "  * component  (component) object 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", - "array([[[ 0.22856328]],\n", + "array([[[-0.28446992]],\n", "\n", - " [[ 0.37364648]],\n", + " [[ 0.40206202]],\n", "\n", - " [[ 1.53911985]],\n", + " [[-0.08163155]],\n", "\n", - " [[ 2.95379204]],\n", + " [[ 1.408035 ]],\n", "\n", - " [[ 3.28662792]],\n", + " [[ 2.67956629]],\n", "\n", - " [[ 3.79643527]],\n", + " [[ 3.28924893]],\n", "\n", - " [[ 5.97261116]],\n", + " [[ 4.32680234]],\n", "\n", - " [[ 7.0681775 ]],\n", + " [[ 3.39678829]],\n", "\n", - " [[ 7.0533794 ]],\n", + " [[ 4.72305147]],\n", "\n", - " [[ 7.21773104]],\n", + " [[ 3.25746454]],\n", "\n", "...\n", "\n", - " [[17.86891974]],\n", + " [[ 9.68434639]],\n", "\n", - " [[15.91575202]],\n", + " [[11.07561827]],\n", "\n", - " [[16.09807771]],\n", + " [[10.03762309]],\n", "\n", - " [[16.59950989]],\n", + " [[10.2329436 ]],\n", "\n", - " [[16.4253566 ]],\n", + " [[10.42700157]],\n", "\n", - " [[17.34655627]],\n", + " [[10.7945289 ]],\n", "\n", - " [[17.56370215]],\n", + " [[ 9.36551048]],\n", "\n", - " [[18.88084012]],\n", + " [[ 8.11774274]],\n", "\n", - " [[17.02758259]],\n", + " [[ 8.65613076]],\n", "\n", - " [[16.20917557]]])\n", + " [[10.548976 ]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-12-31\n", " * component (component) object 'random_walk'\n", @@ -1941,7 +1941,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 17, "id": "f5244eb1-4811-4c6d-baf3-33c52da1c824", "metadata": {}, "outputs": [], @@ -1960,30 +1960,30 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "id": "99fa151a-1fe4-4aea-95c0-8a6029e34c51", "metadata": {}, "outputs": [], "source": [ - "ts_corr = on.processors.correlation.process(ts, '1D')" + "correlation = on.processors.correlation('1D')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "943287fb-a54d-4c3c-a797-ce44057241da", + "metadata": {}, + "outputs": [], + "source": [ + "ts_corr = correlation.process(ts)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "id": "dea59b2d-2ce0-4916-a7a6-b6778bdbedfd", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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", diff --git a/notebooks/docs/0.4-modelling-libraries.ipynb b/notebooks/docs/0_core/0.4-modelling.ipynb similarity index 83% rename from notebooks/docs/0.4-modelling-libraries.ipynb rename to notebooks/docs/0_core/0.4-modelling.ipynb index 3ef4a0b..95d13e1 100644 --- a/notebooks/docs/0.4-modelling-libraries.ipynb +++ b/notebooks/docs/0_core/0.4-modelling.ipynb @@ -74,15 +74,13 @@ }, { "cell_type": "markdown", - "id": "58cf0f06-a12e-4e60-905c-eca5f7b734f9", + "id": "c91c7618-9742-4a87-91d7-468f91c222ec", "metadata": {}, "source": [ - "- [x] Darts\n", - "- [x] Scikit-learn API compatible regressor\n", - "- [ ] GluonTS\n", - "- [ ] Kats\n", - "- [ ] Custom PyTorch\n", - "- [ ] Custom TensorFlow" + "Here are a few examples to add models to onTime with : \n", + "\n", + "- Darts models\n", + "- Sklearn models\n" ] }, { @@ -90,7 +88,7 @@ "id": "8e991124-59fd-4bde-84d0-c1622e37173a", "metadata": {}, "source": [ - "## Darts models" + "### Darts models" ] }, { @@ -105,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "eaec176b-c27c-4f8b-a4b1-967c258bd944", "metadata": {}, "outputs": [ @@ -138,7 +136,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 46.29it/s, train_loss=4.480]" + "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 43.73it/s, train_loss=16.60]" ] }, { @@ -152,7 +150,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 46.16it/s, train_loss=4.480]\n" + "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 43.60it/s, train_loss=16.60]\n" ] }, { @@ -169,7 +167,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Predicting DataLoader 0: 100%|██████████████████████████████████| 1/1 [00:00<00:00, 124.78it/s]\n" + "Predicting DataLoader 0: 100%|███████████████████████████████████| 1/1 [00:00<00:00, 2.98it/s]\n" ] }, { @@ -539,46 +537,46 @@ " fill: currentColor;\n", "}\n", "
    <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
-       "array([[[-9.234826 ]],\n",
+       "array([[[10.742026]],\n",
        "\n",
-       "       [[-9.625329 ]],\n",
+       "       [[10.184119]],\n",
        "\n",
-       "       [[-8.548808 ]],\n",
+       "       [[10.689295]],\n",
        "\n",
-       "       [[-9.272842 ]],\n",
+       "       [[10.624429]],\n",
        "\n",
-       "       [[-9.6081705]]], dtype=float32)\n",
+       "       [[10.057509]]], dtype=float32)\n",
        "Coordinates:\n",
        "  * time       (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n",
        "  * component  (component) object 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", - "array([[[-9.234826 ]],\n", + "array([[[10.742026]],\n", "\n", - " [[-9.625329 ]],\n", + " [[10.184119]],\n", "\n", - " [[-8.548808 ]],\n", + " [[10.689295]],\n", "\n", - " [[-9.272842 ]],\n", + " [[10.624429]],\n", "\n", - " [[-9.6081705]]], dtype=float32)\n", + " [[10.057509]]], dtype=float32)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n", " * component (component) object 'random_walk'\n", @@ -588,7 +586,7 @@ " hierarchy: None" ] }, - "execution_count": 15, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -609,12 +607,12 @@ "id": "2534d1df-b474-4b09-a471-a66cfa211880", "metadata": {}, "source": [ - "## Scikit-learn API compatible models" + "### Scikit-learn API compatible models" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "73778d5b-e8d1-4df9-b0dd-b877e2670323", "metadata": {}, "outputs": [], @@ -624,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "f712c297-579a-4ede-88a6-198ed7b17ca0", "metadata": {}, "outputs": [ @@ -1003,46 +1001,46 @@ " fill: currentColor;\n", "}\n", "
    <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
-       "array([[[-17.22121839]],\n",
+       "array([[[15.82046499]],\n",
        "\n",
-       "       [[-17.54466988]],\n",
+       "       [[16.25745354]],\n",
        "\n",
-       "       [[-18.1406066 ]],\n",
+       "       [[16.78803853]],\n",
        "\n",
-       "       [[-18.56771941]],\n",
+       "       [[17.49676382]],\n",
        "\n",
-       "       [[-18.52810896]]])\n",
+       "       [[18.3747027 ]]])\n",
        "Coordinates:\n",
        "  * time       (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n",
        "  * component  (component) object 'pred'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", - "array([[[-17.22121839]],\n", + "array([[[15.82046499]],\n", "\n", - " [[-17.54466988]],\n", + " [[16.25745354]],\n", "\n", - " [[-18.1406066 ]],\n", + " [[16.78803853]],\n", "\n", - " [[-18.56771941]],\n", + " [[17.49676382]],\n", "\n", - " [[-18.52810896]]])\n", + " [[18.3747027 ]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n", " * component (component) object 'pred'\n", @@ -1052,7 +1050,7 @@ " hierarchy: None" ] }, - "execution_count": 14, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1063,6 +1061,14 @@ "model.fit(ts)\n", "model.predict(5)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad2ca1b9-288f-4b73-b9d1-d135b01d09e6", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1086,4 +1092,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/docs/0.6-plots.ipynb b/notebooks/docs/0_core/0.5-plots.ipynb similarity index 100% rename from notebooks/docs/0.6-plots.ipynb rename to notebooks/docs/0_core/0.5-plots.ipynb diff --git a/notebooks/docs/0.7-anomaly-frequency.ipynb b/notebooks/docs/0_core/0.6-anomaly-frequency.ipynb similarity index 100% rename from notebooks/docs/0.7-anomaly-frequency.ipynb rename to notebooks/docs/0_core/0.6-anomaly-frequency.ipynb diff --git a/notebooks/docs/1_module/1.0-preprocessing-common.ipynb b/notebooks/docs/1_module/1.0-preprocessing-common.ipynb new file mode 100644 index 0000000..50e53d6 --- /dev/null +++ b/notebooks/docs/1_module/1.0-preprocessing-common.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "41296cc6-9d84-47c5-8a92-2d292f6f3c4a", + "metadata": {}, + "source": [ + "# Module - Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe", + "metadata": {}, + "outputs": [], + "source": [ + "# Import to be able to import python package from src\n", + "import sys\n", + "sys.path.insert(0, '../src')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from darts.datasets import EnergyDataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4733b4e6-71a2-42b2-93fd-a5615b84ac1a", + "metadata": {}, + "outputs": [], + "source": [ + "import ontime as on" + ] + }, + { + "cell_type": "markdown", + "id": "e24da8ab-6a83-4c2f-9ff0-c633d4693a91", + "metadata": {}, + "source": [ + "---\n", + "## Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e9a96d79-0423-4d79-b01d-726193216238", + "metadata": {}, + "outputs": [], + "source": [ + "ts = EnergyDataset().load()\n", + "ts = ts.astype(np.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "1d4bec6b-eedb-4a88-ba68-dbeae5f0644e", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "c2c873dd-8643-40cd-895b-fddd7a515c6d", + "metadata": {}, + "source": [ + "## Common Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a630af5c-687e-48e2-a6d4-5a8cb1d1ec66", + "metadata": {}, + "outputs": [], + "source": [ + "from ontime.module import preprocessing" + ] + }, + { + "cell_type": "markdown", + "id": "9b508ee5-7c7e-4793-904e-45a40df354db", + "metadata": {}, + "source": [ + "### Normalize" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a4b12f07-8a97-403a-a554-89e166574120", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:479: RuntimeWarning: All-NaN slice encountered\n", + " data_min = np.nanmin(X, axis=0)\n", + "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:480: RuntimeWarning: All-NaN slice encountered\n", + " data_max = np.nanmax(X, axis=0)\n" + ] + } + ], + "source": [ + "ts_t = preprocessing.common.normalize(ts)" + ] + }, + { + "cell_type": "markdown", + "id": "42428ed1-7556-4341-9675-bad6dca0ecac", + "metadata": {}, + "source": [ + "### Train test split (for time series)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8b67892d-db8c-4f12-93b6-147016da4186", + "metadata": {}, + "outputs": [], + "source": [ + "train, test = preprocessing.common.train_test_split(ts_t, train_split=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "498b0e13-04bc-45ee-ab1a-3996fbfd1df2", + "metadata": {}, + "source": [ + "### Split time series in chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "500e954a-82d6-4eff-bbdd-0b889c2a10f8", + "metadata": {}, + "outputs": [], + "source": [ + "train_list = preprocessing.common.split_by_length(train, 6)\n", + "test_list = preprocessing.common.split_by_length(test, 6)" + ] + }, + { + "cell_type": "markdown", + "id": "b4a88496-6b33-4bff-abb7-1d5ff4c81597", + "metadata": {}, + "source": [ + "### Split in X and y" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f7897c44-71ba-4752-86c6-547387245ae4", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, y_train = preprocessing.common.split_inputs_from_targets(train_list, 4, 2)\n", + "X_test, y_test = preprocessing.common.split_inputs_from_targets(test_list, 4, 2)" + ] + }, + { + "cell_type": "markdown", + "id": "9626370a-e4ba-4421-b40b-d6e7c5787beb", + "metadata": {}, + "source": [ + "### Transform in generic data type " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a4ab9cfa-289d-4d8e-be40-d5d4247f5ab5", + "metadata": {}, + "outputs": [], + "source": [ + "X_train = preprocessing.common.timeseries_list_to_numpy(X_train)\n", + "y_train = preprocessing.common.timeseries_list_to_numpy(y_train)\n", + "X_test = preprocessing.common.timeseries_list_to_numpy(X_test)\n", + "y_test = preprocessing.common.timeseries_list_to_numpy(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1b0a2843-6d02-4b08-96f8-91712e521bf5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4675, 4, 28)\n", + "(4675, 2, 28)\n", + "(1168, 4, 28)\n", + "(1168, 2, 28)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(y_train.shape)\n", + "print(X_test.shape)\n", + "print(y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54b0dfbd-be2f-4a3e-b152-f0bab31bb372", + "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": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/docs/0.5-context.ipynb b/notebooks/docs/2_context/2.0-context-common.ipynb similarity index 95% rename from notebooks/docs/0.5-context.ipynb rename to notebooks/docs/2_context/2.0-context-common.ipynb index 334fba8..0b99a4f 100644 --- a/notebooks/docs/0.5-context.ipynb +++ b/notebooks/docs/2_context/2.0-context-common.ipynb @@ -5,12 +5,12 @@ "id": "9f94ac2b-bc8a-4757-affb-6e570a024804", "metadata": {}, "source": [ - "# Context" + "# Context - Common" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "id": "54e70524-472a-49e4-bca9-32e57a1c4313", "metadata": {}, "outputs": [], @@ -22,27 +22,25 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "70a32352-80c9-40b7-8f68-1aeecfc52658", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", - "import ontime as on\n", "from darts.datasets import EnergyDataset" ] }, + { + "cell_type": "code", + "execution_count": 9, + "id": "24fa5881-61b9-4ca0-9987-a6945136a29d", + "metadata": {}, + "outputs": [], + "source": [ + "import ontime as on" + ] + }, { "cell_type": "markdown", "id": "43dac0e6-ae1e-4bbd-8537-2f2ed1262c76", @@ -61,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "id": "e75060cc-c514-4210-b359-585f4f51e873", "metadata": {}, "outputs": [], @@ -79,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "id": "4d355f16-5c6d-477a-802c-9b1dbf3718f0", "metadata": {}, "outputs": [], @@ -92,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "id": "c1cca8db-e15f-4e40-936b-8ef18e7a63c3", "metadata": {}, "outputs": [], @@ -102,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "id": "ebe23c8b-82ca-4ba4-aba7-969ee926e802", "metadata": {}, "outputs": [], @@ -113,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "id": "7732e3a2-753a-4f3c-aa92-766e8fc70adc", "metadata": {}, "outputs": [], @@ -129,22 +127,40 @@ "---" ] }, + { + "cell_type": "markdown", + "id": "df7fffe4-ce41-47be-a304-793e84ff1c9c", + "metadata": {}, + "source": [ + "## Common Context" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "54d07f6a-c1c1-45dc-91f4-8c92f1f89ed8", + "metadata": {}, + "outputs": [], + "source": [ + "from ontime.context import common" + ] + }, { "cell_type": "markdown", "id": "0850a3ae-ce2a-4e8f-98d0-dfad92bc5c72", "metadata": {}, "source": [ - "## Profiler" + "### Profiler" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "id": "38a123fd-37cb-4206-b0dc-fb8ecd597b46", "metadata": {}, "outputs": [], "source": [ - "profiler = on.context.common.Profiler()" + "profiler = common.Profiler()" ] }, { @@ -152,12 +168,12 @@ "id": "7706081d-0a79-410e-ae65-480de017c194", "metadata": {}, "source": [ - "### Daily Aggregation" + "#### Daily Aggregation" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "id": "9e34e371-9d34-41e8-8145-116c6bc463d3", "metadata": {}, "outputs": [], @@ -168,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "id": "fc72abf9-f1d9-4757-80c5-ecffa37d756f", "metadata": {}, "outputs": [ @@ -193,12 +209,12 @@ "id": "628fc1ce-1631-4844-bd51-649fff52a5e8", "metadata": {}, "source": [ - "### Weekly Aggregation" + "#### Weekly Aggregation" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "id": "e26c11bc-e46d-45ab-aab6-4e80c4288ca0", "metadata": {}, "outputs": [], @@ -209,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "id": "4409d4ee-aca9-4bae-b1d1-03283554bbbe", "metadata": {}, "outputs": [ @@ -234,32 +250,32 @@ "id": "813f08f7-51aa-4413-92da-8455bc854aed", "metadata": {}, "source": [ - "## Generic Predictor" + "### Generic Predictor" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "id": "e5df7b11-4c2d-4b26-9b9c-f1bf521b6300", "metadata": {}, "outputs": [], "source": [ - "model = on.context.common.GenericPredictor()" + "model = common.GenericPredictor()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "id": "402c4d6e-dd2c-4920-9736-eee98fa3ab32", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -278,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "id": "33c52d1f-6983-4edb-945b-9578d954f738", "metadata": {}, "outputs": [], @@ -288,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 25, "id": "c10dd2d7-7a48-4343-b18a-152175dcd799", "metadata": {}, "outputs": [ @@ -297,23 +313,23 @@ "text/html": [ "\n", "\n", - "
    \n", + "
    \n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 11, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -380,32 +396,32 @@ "id": "1eacbd84-bb31-48b1-bd5b-6ab7db392204", "metadata": {}, "source": [ - "## Generic Detector" + "### Generic Detector" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "id": "9715fd4d-24d8-4d95-a77a-3c347916f2aa", "metadata": {}, "outputs": [], "source": [ - "model = on.context.common.GenericDetector()" + "model = common.GenericDetector()" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 28, "id": "56eae67d-ae1a-438c-bbd4-e1b52c93c2c2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -424,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 29, "id": "3fcf48b4-6d60-411d-88e5-191d575967a7", "metadata": {}, "outputs": [], @@ -434,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 30, "id": "317ef652-cfa1-4d85-b873-a1944c13fefc", "metadata": {}, "outputs": [ @@ -443,23 +459,23 @@ "text/html": [ "\n", "\n", - "
    \n", + "
    \n", "" ], "text/plain": [ "alt.LayerChart(...)" ] }, - "execution_count": 38, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -531,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 31, "id": "0e31b255-cf60-4470-910e-31fe826b9782", "metadata": {}, "outputs": [], @@ -541,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 32, "id": "fe406b99-9025-4553-8c3d-0237a670e513", "metadata": {}, "outputs": [ @@ -550,23 +566,23 @@ "text/html": [ "\n", "\n", - "
    \n", + "
    \n", "" ], "text/plain": [ "alt.LayerChart(...)" ] }, - "execution_count": 40, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -627,6 +643,14 @@ "source": [ "on.plots.anomalies(test[:72], predetected[:72])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23994cad-3e1f-49f2-90c5-08080409c2a6", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/getting-started.ipynb b/notebooks/getting-started.ipynb index 10e19c3..2f54b7b 100644 --- a/notebooks/getting-started.ipynb +++ b/notebooks/getting-started.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 41, "id": "70a32352-80c9-40b7-8f68-1aeecfc52658", "metadata": {}, "outputs": [], @@ -13,95 +13,68 @@ ] }, { - "cell_type": "markdown", - "id": "9f94ac2b-bc8a-4757-affb-6e570a024804", + "cell_type": "code", + "execution_count": 51, + "id": "f8a26d78-229f-47f7-9f66-d0c245dbc096", "metadata": {}, + "outputs": [], "source": [ - "# **onTime Demo**" + "import ontime as on" ] }, { "cell_type": "markdown", - "id": "9f48dcdc-2c09-48bf-8e0e-a9e8f6cd84bf", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "id": "9f94ac2b-bc8a-4757-affb-6e570a024804", + "metadata": {}, "source": [ - "---\n", - "## Scenario\n", - "\n", - "1. Creation of the model\n", - "2. Packaging of the model\n", - "3. Add the model to the library\n", - "4. Use the model with other tools from the library\n", - "\n", - "## Structure\n", - "\n", - " .\n", - " └── ontime\n", - " ├── abstract <- Used today\n", - " ├── config\n", - " ├── detectors <- Used today\n", - " ├── generators <- Used today\n", - " ├── metrics\n", - " ├── models <- Used today\n", - " ├── plots \n", - " ├── processors\n", - " ├── time_series <- Used today\n", - " └── utils\n" + "# **onTime** — Getting Started" ] }, { "cell_type": "markdown", - "id": "8a308508-779c-432f-a610-9b2d9984357e", + "id": "19665f45-64ac-47bc-a2d8-951a282764c0", "metadata": {}, "source": [ "---\n", - "## Creation of the model" + "## Structure of the Library" ] }, { "cell_type": "markdown", - "id": "bde92f83-48bb-4f8b-a4fa-cc68985b1c0c", - "metadata": {}, - "source": [ - "Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "379906f3-4248-4d1d-92c1-c3906d79f72b", + "id": "56de274f-bce1-4252-bd44-f639c22eac6e", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], "source": [ - "import pandas as pd\n", - "import ontime as on" + "The library is divided in three parts : \n", + "\n", + "1. `core` for all basic features\n", + "2. `module` for all features using core features. E.g. benchmarking, ml preprocessing, etc.\n", + "3. `context` for all features related to the usage of onTime in an applied scenario" ] }, { "cell_type": "markdown", - "id": "021b7dd0-d8a7-49d3-bee5-521e2963bc94", + "id": "c0271c7d-d9b4-414e-b7be-83adeafcc741", "metadata": {}, "source": [ - "Generate some fake data" + "## `core` Features\n", + "\n", + "This is a low level API. Most objects and functions are accessible in the base object : \n", + " \n", + " ontime\n", + " ├── detectors\n", + " ├── generators\n", + " ├── Model\n", + " ├── plots\n", + " ├── processors\n", + " └── TimeSeries\n", + "\n", + "For instance : " ] }, { "cell_type": "code", - "execution_count": 67, - "id": "2c459a9e-4747-454c-8bbd-2c9bfaad1c19", + "execution_count": 52, + "id": "bcbdae2b-2833-43d6-9bf7-16caef87cf75", "metadata": {}, "outputs": [ { @@ -471,46 +444,46 @@ " fill: currentColor;\n", "}\n", "
    <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
-       "array([[[-0.58126402]],\n",
+       "array([[[-0.07710256]],\n",
        "\n",
-       "       [[-1.36677965]],\n",
+       "       [[-0.30611734]],\n",
        "\n",
-       "       [[-1.98731443]],\n",
+       "       [[-0.42833724]],\n",
        "\n",
-       "       [[-3.63736368]],\n",
+       "       [[-0.49277018]],\n",
        "\n",
-       "       [[-4.18556985]]])\n",
+       "       [[ 1.13635256]]])\n",
        "Coordinates:\n",
        "  * time       (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n",
        "  * component  (component) object 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", - "array([[[-0.58126402]],\n", + "array([[[-0.07710256]],\n", "\n", - " [[-1.36677965]],\n", + " [[-0.30611734]],\n", "\n", - " [[-1.98731443]],\n", + " [[-0.42833724]],\n", "\n", - " [[-3.63736368]],\n", + " [[-0.49277018]],\n", "\n", - " [[-4.18556985]]])\n", + " [[ 1.13635256]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n", " * component (component) object 'random_walk'\n", @@ -520,27 +493,27 @@ " hierarchy: None" ] }, - "execution_count": 67, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ts = on.generators.random().generate(start=pd.Timestamp('01-01-2023'), end=pd.Timestamp('12-31-2023'))\n", + "ts = on.generators.random_walk().generate(start=pd.Timestamp('01-01-2023'), end=pd.Timestamp('12-31-2023'))\n", "ts[0:5]" ] }, { "cell_type": "code", - "execution_count": 68, - "id": "cffaf9a3-1a28-4e04-b22e-57e6b10a6d1c", + "execution_count": 54, + "id": "6b9959a6-489a-4c80-a53e-e65cad57fe62", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -548,47 +521,160 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(6, 2))\n", - "ts.plot(ax=ax);" + "ts.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "2487b267-ec4a-49bc-9bda-33e4bc39194c", + "metadata": {}, + "source": [ + "## `module` and `context` features" ] }, { "cell_type": "markdown", - "id": "d61a953b-aceb-4145-9526-b784b73946d8", + "id": "0904039a-4cb4-4795-9656-8c48a919dfee", "metadata": {}, "source": [ - "Split in train and test" + "High level API with various features. Let's load some data for an example :" ] }, { "cell_type": "code", - "execution_count": 69, - "id": "b1b9e04b-fcc8-402c-8e8c-6feb67d2fd08", + "execution_count": 61, + "id": "4c906526-20c4-47b5-8023-8216c6af18d1", + "metadata": {}, + "outputs": [], + "source": [ + "from darts.datasets import EnergyDataset\n", + "ts = EnergyDataset().load()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "15724cb7-d0d6-40ab-a240-57df7e8f3c21", + "metadata": {}, + "outputs": [], + "source": [ + "df = ts.pd_dataframe()\n", + "df = df.interpolate()\n", + "cols = ['generation biomass', 'generation solar', 'generation nuclear']\n", + "df = df[cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "401cf66e-f0eb-48e0-ac7c-adf2bef92a3c", + "metadata": {}, + "outputs": [], + "source": [ + "ts = on.TimeSeries.from_dataframe(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "3013d9e8-d7cd-4caf-be3f-607eab84647e", + "metadata": {}, + "outputs": [], + "source": [ + "ts_uni = ts['generation solar'].slice(pd.Timestamp('2015'), pd.Timestamp('2016'))\n", + "ts_multi = ts.slice(pd.Timestamp('2015'), pd.Timestamp('2016'))" + ] + }, + { + "cell_type": "markdown", + "id": "a1a81ffb-dca7-4ee5-93e9-836223ae6956", + "metadata": {}, + "source": [ + "### `module` Features" + ] + }, + { + "cell_type": "markdown", + "id": "adb031b1-74bc-4ed2-ae54-bc16681f0363", + "metadata": {}, + "source": [ + "High level API with features related to data processing, ML/AI, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "79fd0990-dac4-461e-98b0-29f1e579b11a", + "metadata": {}, + "outputs": [], + "source": [ + "train, test = on.module.preprocessing.common.train_test_split(ts_uni, test_split=0.3)" + ] + }, + { + "cell_type": "markdown", + "id": "0641d9e6-c5b8-49a1-a3db-9df1927d62be", + "metadata": {}, + "source": [ + "### `context` Features" + ] + }, + { + "cell_type": "markdown", + "id": "2982c551-1607-40c8-911d-154a3493e4b1", + "metadata": {}, + "source": [ + "High level API with features related to a physical machine or process." + ] + }, + { + "cell_type": "markdown", + "id": "f7fe172e-588a-4f7e-aa0e-1cdc07dd8aca", + "metadata": {}, + "source": [ + "#### Profiler" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "ad40696c-c5fa-4b5f-8f49-92c4fc0a9941", "metadata": {}, "outputs": [], "source": [ - "train, test = ts.split_after(pd.Timestamp('09-30-2023'))" + "profiler = on.context.common.Profiler()" ] }, { "cell_type": "markdown", - "id": "58a033a7-fa9d-417a-9796-5ea4c587c981", + "id": "0a304607-7ccb-4d2b-9796-dd80bce76b6c", + "metadata": {}, + "source": [ + "What does the common week looks like ?" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "bac9e224-c12a-4d7a-b7ca-db2a9acff278", "metadata": {}, + "outputs": [], "source": [ - "Make a plot" + "week_mean = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEAN)\n", + "week_median = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEDIAN)" ] }, { "cell_type": "code", "execution_count": 70, - "id": "a3c4a5e4-6b63-4b15-b101-73a83dc16802", + "id": "e8474db3-5e1e-4d08-bdc1-30f88bfd5f28", "metadata": {}, "outputs": [ { "data": { - "image/png": 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wO9fwFW/zdi9aFnkOBDEHusQBADwoRpvLPQb8Kps/3gAgcSCCe0MvblhB6DnQTO86ZswY7N27F7VqqSNahg0bJtpes2bNEvfLz8+Pn/Zo7KqRRPlC1xz28uY54MSBj48Phg4dCjs7O52eA+HwXEUSB/oOKwDSQwtZ2SyiNC6NB/FAWkbF8R6kZ7K49HKaYoMgoLKH+IEsHFZIkvEcNK+n+yH+oJiRLrmZD+Q5sEA4cZCSkiL7NgaIPQeenp5wdHTk17mCSRz9+vUTrTs4OJS4XzY2NnxZ6YiIiHKb3IYwPLpiUO7evVtuBEJBQQHvFRMmJPPy8oKzs7No39q1awNQPxi52Qzh4eEV5neh77ACUJT5FQBOnToFALgTIz2F70aUQbpXLjhxDVCp1Mtdm2pvF8UcpBcZixsGcLADOr0ifvtvEARsmVPUEFOMk1fokRBCMQcWiDDuQFcmQqHnwMPDg0+XrFQqMXbsWNG+NjY2WLx4Mb+uOcygL1zth/z8fERFVaBfMSFLZmYmxo0bx6/PmzcPN27cQKdOnfi2xo0b4/lzA5WNMyIJCQlQvbxb+/n58e0Mw2h5D15//XV+mfMe5OTkaHnxrBWht6g4z0Hnzp355WPHjgEA/hGUZKgXULT8bwWaEXpEUHGxazNtD4DUsALLskhMUy+7uwAeroxoaMHRXjzU8CBetydGrpojeQ4sELnAP5VKhVOnTvEVFTnPgUKhgKurKwYNGoS9e/fi/PnzksmNPv30U8yaNQuffPIJ3nrrrVL1TThnWTNnAlE+YVkWUVFRpa6XsWrVKkRGFg2cNm3aFI0aNRJNq01KStJK521KIiMj8eWXXxZbr0QYbyAUB4DaO8fh7OzMew4AoG3btvzyyZMny9pdi2f//v2iwm7FeQ5q1qzJF3g7c+YMcnNz8U900UOri+CtWZ8EP9bCuZdpHxQKoEMT7e1iz4H6/wfxQMLLd8aQGur/6whq591/Io5dKG5YIeW5tHjwqyzZbHJIHAiQm7GwevVqdOjQAU2bNkVKSgrvOXB3d4dCoQDDMOjZs6eo1LIQpVKJb775BsuWLSvVsAJQ5DkA1OmYifLPzJkzUatWLfTt27dUnxdOj504cSKfE6BXr16YNm0av81cufXT09PRrl07fPvttxgxYoTOfXWJA+FvUSiSAaBjx478slQSJWvin3/+EeV9sLOzg5ubm45PqD0vnPcgOzsbFy5cEHkO2jYqemtOz6o4MQepL8sse7sBbs7angNP16IhA85zcOxq0fbOr6o3vi7wOrRqAFRyB5xfjjI/iFfHNjx+Jm1XqVLPbs7S/TEHJA4ECD0HwhvqF198AUB9k1q/fj3vORC+0Rgb8hxYH9999x0AYN++faWaWSAMnP3yyy9Fs2CE14u5ZrjMmzeP/x2Fh4cjK0s6vRzLsnxBMkBbHEydOpVfXrBggWhb3bp1+QJD+/btwxtvvCGqwWBNCG0EqIcU9Jn5JBxmOnv2LG6+HJWs4qnOCshRkTwHmS9LTTjLvKsplQw81ZPXBOKg6CHf6RX1/5P6Ac3rqUXBwvEMGIZBjZf19CIfA8FvsQh+S53oSJNkiamObUNL8WWMBIkDAUJ35eXLl/llYdGSQ4cO8Z4DzVzmxqRWrVr8jAXyHJR/cnNzReuleesVVgMV1gYBxGPR5vAc5ObmYs2aNaK2KlWqYMqUKXxymLi4OGzcuBE//vgjfvtNXevW0dFRqz7J8OHDsWDBAqxbt05rG8MwIu/BwYMHsXz5ciN8I/MjLPAGQO/ET8KgxGvXb+HtLuqHW4cmgLsg1lOfokLWQlaO+n9nR/l9uKGFpHS1gD3+0nPg4gg0q6dedrRncOkXBeJ3MHiljlqoCeMOUp4D+QXAsK9ZLN/KovEoFVZtU1//Up6D7i0sw2sAkDgQIRy/5CJ7c3JyROr8yJEj/BixKcWBra0tH89w7949nbMpCMtHM6iUK6RTEjjPgYuLi9ZwlbnFQXJyspYAevHiBVavXs3HQAwcOBCjR4/Ghx9+yO+zdu1aLc+Bvb09ZsyYgTFjxkieS/hmDFivZ034wgJA73tA3bp1YWdnBwD45+ZVrPxIgWMrFdg6TwF3l6L99KkbYA2oVCxecOJAxygvF5SYnqku0sTFG7zWELC1ET/EbQTrDYKkj/fpjyxuRgEfrmRRUMBKBiR2kx6ZNgskDgRUrlyZd8deuXIFmZmZiIiIkE2DaUpxABS5ivPy8mjGQjlHGEgIlM5zwIkDTa8BYH5xkJ4unx7u8ePHKCwsxIULF0Tt9vb2pQrYfffdd9GjRw9+XRiLYS08f/5cK8fJ3Llz9fqsjY0NH7MUGRmJFy9e8NtEngMziIPTN1h88ZMKj02YFT5boFld9PAcAID/oKJngHAoRopBHYt/+49Plp6tUDdAu81ckDjQoEOHDgCAwsJCnD9/Hv/++6/svlwyFlMhHEemoYXyjaY4iIqKwurVq7F+/Xq9Zi+oVCp+uq0wkJZDLn7GVOiaPpmVlSWZn6FevXqyFQZ14eTkhH379uHVV18FoC7EZG05D65eLYqG69KlC7Zt24aZM2fq/fnGjRsDUF83wgqNdrYMHO3Vy2kSbm5jUljIYsCXLBb/AfSbZbrzZhaNEuv0HLg5S7cHVdP98G9at/g+xD4V5zlwcwbWf8GUOHuuMSFxoIGwTvzZs2d1PoS5H5ypMMaMBZZlzRbNXpHRHD8GgClTpmDs2LH4888/i/18eno6LyKkPAe2trZ8u6WJg5SUFMnxcuH1XRq4hEiFhYV4+LCUdXMtFOF01LfffhsDBgzgY5D0QXivatmyJT7//HN+nfMemNpzkJZZlH3wWiQQFWdjkvNy8QaA7pgDf5kphUE+uo/PMAxmDte9z50YdSwCoI7/SNvHYHQvyxEGAIkDLYTBO/PmzcO8efNk9zW1ODDGjIVevXrBx8cHP/zwg0GOR+iHpudAyPvvv1/s54XBiFKeAwB8FH9CQoLJSxoXJw6kMjdqTlMsKcLU5NY27MblWAHAp2YvCZr3qvDwohrNXNxBehaQm8fiZhSL/IKyXy/HrrD4+wQLlUr6WKkanootJ13LfE59yBJ4DnQNK4zqyaCSu3Z7ceIAAKa/w2BoZ2B0T+kYhAlLi2zi5QaL8hhwkDjQoEaNGpK5CJycnNCkSRNRW/369U3UKzW1a9eGjY1aXf/xxx9a07pKSnJyMvbv3w8A+Oijj8rcP0J/dIkDLlOgLoTTGOXEARd3kJOTg4wM0/qMheJg/Pjxovn4qampRvUcANYXdyCsWKkZsKkPbdq0QdOmRRmPhH8fj5fi4HkWMGIBi8ajWHT9hMWQOSp8van40sNS3LjPouunLAZ/xWKbTG4qTXGw54KT9I4GRt9hhXqBDJ6FMVpJiYKKKbgEAK5ODLbMVWD9dEWx6ZC9daeqMBskDjRQKpWSD/1ffvlFVCIWUAdQmRI7OzvRdMtZs2ZplWLVJDk5GZcvX5b8gZe3qn3lmZycHEybNg3ffPMNnj17ptP2+sQcCMWB1LACYN6gRGFAYseOHXHu3Dl+Xc5zUFZxYM2eA11JovTBzs4O4eHhiIuLQ3p6umjmgzAo8U91hmWcugH8dRyYs4EVJf8RkpvHYt5GFu8vU+G5RgKlpVtYvn7Dx6v08xw8TbPh0xMbE32HFQD1G319jdAyKW+CLip76N6uy3thTkgcSKApAn755Re88847JRrjMxaTJ08WreuKcn/x4gVCQkLQvHlzrFu3Tmu75g1anzdWonSsWrUKS5YswezZs0WBZK1atdLaV5+/gz7DCuYUB8I3Uzc3N1HCMKmYAx8fH9Gbf2kQioP79++X6ViWBicONAu9lQSGYVCtWjW4ublBoSi69QunM0rxv8PaD/f8ArVnYO5GFj/tBDbtF28XPvgzXkASTXEAAAt/A65FGHcITDysULw7v57GDIKSDgFULkZMNK5leUMKgBnEQbNmzdC2bVu0a9cO7dq1w4YNG0zdhWLRfIPhirtMnDiRb/vpp59M2ieOyZMni3LIc/kYpDh69CifHW/8+PFa2zXfXoUPHMKwLFmyhF9ev349v9ynTx+tfQsKCop15ZbUc2DqLIm6xEFqaqpImL7//vvYs2dPqWYqCPH39+crOJ49e9ZqxC7Lsry9SuM1KA53mah8jm0ngZxc8fW4/SRw5mbRenSceLuwroCKheT1nCqRdGnl30CLCSzikownEPQdVuCo5F62h7dmOWgh301g8HZX2c1mxSyeg23btuH06dM4ffo0Ro8ebY4u6ERTHHCBUi1atMDmzZuxaNEis/a7TZs2/Bju6dOnZR8kwsyOUmiKg379+iEsLMwwnSREyL1tyIkDYVlwKfTxHAhLH5ckej8tLQ1Dhw7FlClTRA/YO3fuoFevXlixYkWxxxCKA3d3dzg6OvJvvELPgUKhwA8//CAaDy8tSqWSr9iYmJgoCrorzwgTShlDHHgU4zl4ngUcuSJuuxMrvucIUy+/yGHxr+Byy8pWz+vXRMpzAAAFhcAFI+axEg0r6CEOGgviP9uUIr2x3LDCdxMYfPEOo5VQyVIwzdyRUpKXl4e8vDxRm42NDZ/tyxBwNz/hTVAYc8BFfHPb3333Xa3PmhqGYdC6dWscOHAAT58+xb179ySrQQoL1gDa/RUGOQHAuXPn0L9/f/4t01revAyJ1PVSWrikWw4ODsjJyRFtq1KlCsLCwrRKfLMsi4yMDNHf1svLS7I/NWrU4JcjIyP17vO0adOwdetWAOqpvYMGDQIATJo0CadOncK+ffvQq1cvkRsfENtGGHPg4uIClUoFT09PZGdnIzU1lRe0Pj4+YBjGYNdajx49sHPnTgDAnj17ZIuhmYPSXjvCmQp+fn4G/126acQBTnwTWPEB8NthYOwidVtMvHjWgWZsQEpG0fe6GQVodvFmFAsfL7GgkCtZDAC5+fKzHMqK0HPg5FD8eXq/BgzsANx+APz0acn/ft4ywwq9Wxd/bkPebziEQ0q6MIs4GDFiBBiGQcuWLfHxxx/LZhrcuHEj1q5dK2obPHgwhgwZYvA+CX+ACoUCEyZMwIkTJ/Dtt98iNtbyCp2HhITgwIEDANRlXKWCIzUTOEVERIj2kxqXValUCAsLQ+fOnUU2IcSU1DZSP+6GDRvi4cOHsLW11RIHBQUFGDx4MMLDw2Fvb4/NmzcjLy8P4eHhOHbsmMhblJ2dLXmNCkX07du39b6Ohb+5/fv38w9Y4RDW/v37Jb0egNo2whiH9PR0xMbGwsVF/YqamJjIB116eXkZ9PfVqFEjfnnnzp0W6Zks6bVz7do1ftnZ2dng96PCPFcAXvx67apJiHuSBTbPEYC6JHT0ozTExhYJvodxlQAUjUckJOYgNlb9Nz960QWAeKjr9NUU1K0idhU8ivcCID19MfphMmJjy1bsYec5Z3z7hyfe7piBTwcW9f1JvDsADwBAVsYzxMbq9rACwBLBZVRS8xfm2AMomv/YsfELdG+aDSdk6n0sQ96LhS8NujC5OFi7di1CQ0ORkZGBRYsWYd68ebKFUkaNGoV33nlH1GYMz8GjR4/g7+8vUlSaRWMsjXbt2mHp0qUA1G5HqWyNwjSpgNrtKtxPznV9//59dO7cWcsmhPz1UhxSMxCaN2+OwMBA2WGhrKwsHD16FPXr18fXX38tuY9SqUTTpk0lg2UDAgLg6uqKjIwMxMXFlSqjZ7Vq1SQ/l5qaqtUutI0wQ2GDBg1ga2uLqlWrIiIiQuQNDAoKMmim0cDAQNSqVQv379/Hw4cPTZ7FVBeluXZyc3N5Lw6gtqWhv1OQv3i9d/tKCKxeCfUEz/JCxgOBgR78erZGWYfsfAe+X8kSAYibD3vhZqwXRvUA+rVTt+ULLvuWDVhcvFPkXlfaeSMwUDqWRl8+fVkl/McwD+w874FK7sDc0YCN4D2qRkAVGPsS0ZQe+5c6wc7WCZoCSorS3m8MgUHFwZgxY0SZvISMHj0a77//Pl55RV3r0tPTE1OnTkXPnj2Rm5sr+eZrZ2dnUCGgC4VCUa4ehMKEMdeuXUNsbKyWItSMCH/y5IloKqTcdLpz585h/Pjx5c4mpqQktsnLy+MreQqpU6cOFAqFTpfhjz/+KKoboEmNGjV0TqmtWbMmrl+/jpiYGBQWFpZ4xo1KpZL8njdu3JD9/gqFgo85cHR05Pvn5eWltW9oaKjBr7FKlSrh/v37eP78OViWLXOgo6EpybXz8ccf4+DBg/y6MR4Sni4sAEHtAH91Gt/KHkXtqZlid3RSuviaFW6Pide+np8kqf+F/wsM6KDeLy2zaL/VnzDoPa0ACanqR1J6lv7ubynUAY1F3+lxovpfv5niJEZuTgwUCuOO+Vf1EvfFwb7k38sc92KDnm39+vW4fPmy5D+prG/clzV19jZrICgoiBdO+/btQ3BwsNbMD01xIAxKy8vLk8xvDwCXLl3SivUgSo/cNEJOqOm6/iMjIyUfqhxSsSZCuLgAfVMKa3o4uMBHlUolCqq8fv26zuNw4kCY/Eg4Y4HDEIGImgiHKXUVgCoPnD9/XrRuDE9IocaznPs7ewmS82jGByRpmFUYXPjg5cQYGyUwtrd4v6cpQPbLmQ/cZ5RKoEktYO0nRfejtDKWjz6rI/1LjGDiTnF5DgyBp2kSPxock0qRqKgoREREoLCwEM+fP8f333+Pli1bSmYkJHRjY2Mj8gIA6rcMIZriQDhupasWfE5Ojs6CU0TJkJtGyD3Y/f39JbcD6oeyrmx/xYkDYapducRALMti4cKFaNiwodZwGjdlMi0tTSRiHjx4oHNGBfdQFooDKZFjDHGgOW2yPCO8dkaOHFnmRFFSdH4VcH0ZlPjfWUUC0FMwi0FYJIhlWdE6ALzIAfJejhNw0xgDqgLvddd+K3/28k/CiQNPF4BhADenouurrOLgzE39Xjj1ma1QVhQKBgPV9fwwteRFR82GScVBSkoKpk+fjg4dOmDw4MFQKBQ6axcQuqlXr55oXZgit6CgQMszIBQHxaWXpeyJhkNOiHEzYTZs2ACGYeDs7IyHDx8iISEBo0aN4vfTLNUrRFMgaqJPYqDvv/8eM2fOxJ07d/Dhhx+KtnHiQJhXgUPOe8CyLO85cHcvCtXWFAfe3t5GeRMWeg6KmxJqyRQUFPB2b9GiBTZu3GiUHPwergzu/JfBuTUM3u1W1G5jw/AJkoSeg6xsIFfCsZiaAaRmsEh/+WCvUU099a+FRsJZXhy83I97s3ZzKnJhlFUcnL5Z/D6A6bITbp3HIOJ/DBZPssxpi1KYNCCxefPm2L59uylPadVoTiUDisaInz59quWuFrqVhWmXq1SpgmfPnsHT05N/0zJ10hxrRk4ccDf6Nm3aICYmBi4uLvwDVJjASJcXpzjPgVBA/vrrr5g0aZLoAZOamopZs+Tr5eoSB+Hh4ejYsSNYlkV0dDQUCgVYlkVWVhZ/7Qk9B5wY4mjatKlxHnYCcVCePQeJiYm8HYXXgzGoXoVB9Sra7V6uQHqmWBxoDilwpGaIcwjUqKZ+az64FBgyh8Xhlxmbn6aqyzWna4gDF0fDiAOWZXHrgX77msJzAKjtUFveQWiRULRZOUYqIQqXSU3qgXTjxg3+ZiMUB2FhYUhLS8O2bdv4NhIHhkMfWwYEBIjerKtUKbpTCx9wV6+KE90XJw5ee+01Pnj1woULWuWgIyMjdcaXFCcOAOCTTz5BrVq1EBwcjFGjRone1oXiYPDgwaKqp61bt9bZ99IiHFYoz54D4XWjKaxMBRd3oM5joL536BIHwsyINaqphZ+HK4MBHYpE4LNUcXloThwoFYDby9mRaWWoE5byvKgcspDOr4rXFQrA3jTx7uUSEgflmGHDhmmlzuXGlaUK28TFxSEmJgYAcPNmkd+tYcOGcHd3F2XU0xWTQJQMqb9Fz549dX5G6k1RqVRqVQYV/s2ksLGx4ae8AsDChQtFHqXi5k+npKRApVJpJdQC1OKAZVls3LiRbzt16hTmzJnDrwvFgZubG06fPo05c+Zg7NixRqsEai3DCsJAVnOJA65ioEqlzpQI6BYHt6KLrq0aguqFVQWxqE9TxQGMwoA9LltjWTwHTyWcRT5ewKzhYi+Vi6Nllkq2FEgclGMqVaqEmJgY0c343Llz+Oijj7B48WK+TTj8cObMGahUKty6dYvfxiWnEXoiTF2ox5oRxgy0atUKTZo0wc8//6zzM0LPAYeHhwcYhuEDT8eMGaPX9KYePXqgZcuWANSiUBgBr5klUxOVSoW0tDRJz0FMTAyuX78uSpUMAJs2beKXhTEHAODg4IC5c+di7dq1ssnPyoq1BCRakucAKApKFIqDmgLn5W+HWczbVLQuFAdVBOLgWSqLpylF68IqhyURB7cfsJi9ToVDl8SZBhMEqZqnvgWcWsXg6joG7RqLz0UJYHVD4qCc4+LiIkoRO3PmTPzwww84e/Ys3zZixAh++b333sPmzZv5BEmhoUXJwp2cnPgbKw0rGI47d+4AUN/gz58/j2vXrumcoQBIew64v83y5cvx5MkTreyhuhBOJRbOSNAn81pSUpJIHAiHBoReA00cHByMks20OKzFcyD8DRo75kAOb4npjElpRW21BY6rP44ULXu6AiGCtCuanoNIgSat6Vv09s6Jg5w87WJPmgz+isU3/wW6T2Ux+KuifYWeg2reDNo1ZlCtkrqGwZBORdsyi0+MWKEhcWAFSAUmChk2bBhsbIpiT4UpZYXiAChyUz99+pRqKxiAxMRE3iVfkmlocp4DDl9f3xK5RIcMGcLHNOzYsYMXh8V5DgBtcdC7d9Hk9VWrVkl+pkmTJoiPj0fbtm317qOhsBbPgSUMK4hyHbwcCkhKFyRMkhjVcrQHLv7MwMWp6PoUew6AyMfCpEtF24Tlo4VxCZqkZ7K4K0g9vP0UkPyyXwkCr4SPxuzZd16nYQR9IXFgBQQFBck+KJydnREcHKxVwIejffv2onVOHOTl5Um6komScffuXX5ZmNWyOCpVqqT1N5VKIqQvDg4OGDhwIAB1Wm2uLoec50AoRI4dOyaKORgxYoRkX5YsWQKlUomaNWti7969Rhs2KA5r9ByYTRy4Fl2DyS+HEx4Lwk9qV9e+73w7jkFtf3G7qxPg8DL472mK2HMgFBjCCpFyQwvRcSzW79VuvxurnqmQkFwkPDTFwWshQPOXE3imDJA+PqGGxIEV4ODgIBu1XrNmTTAMgw0bNmD69OmibQEBAejcubOoTRjgps9bJaGb27eLas+WRBwolUqtUsxlEQcA+OqKAPD3338DKPoba+Yg4EofA8Ds2bOxd2/R3bh69eoYO3as1vEnTpyI69ev48aNG/D19S1TX8uCtXgOLGJYQTBGn/JcPQVx/0X1uqM90DpE+zN922i3MQzDew+epRWJA6VSHJvgXlTLiRcHuXks9p1XP/QTklmEjGDx2WrtIYcr94Dm41ks/qOoraqGOGAYBoeXMTj9I4MVH5AXQRckDqwEuWlhXIa8KlWqYOHChaKH/9ChQ7UC2oTb9Um3S+jm8uXL/HJJxAGg/UAoqzjo1KkTf4zdu3cjPz+fT3almYxIrp6Dp6cnbGxsMHnyZFFm0+DgYDg5OcHZ2RmOjibKLCODcIbEgQMH8N///teMvSkdp0+fxokTJwCovX9c0LCp0QxIvHinKIlR9xZA/cCi6YcA0LgWUNNP+qHLiYPENODfl7eWIB/A1kY75gAoEgczfmHR6wsW7aaw2H0OyM6V7uvXm1lc0cgXpuk5AAB3FwZtGzFQKkkc6ILEgZUgJw40cyGsW7cOSqUS1apVk5xKJoxfWLhwIdVYKAP79u0T1bsoaepbQ4sDW1tbdOigzuOamZmJc+fO8bUUqlevjpkzZwIAevXqhSFDhvBF0oRUrlwZgFpM7Nq1i38Q6yoOZWpsbGzg6lo0P27EiBGIiIgwY49KRnJyMt544w1+vbjpqsbEVzBT+sAl9UOao19bBk4ODM78yOCzocDIHsDmmfIPXC4okWWLHvCaMQtCcXD/MXDgIovlL4tS3n8CXLgtH6SoWf/BRll+6xpYAiQOrAR9E8p0794dDx48wD///COZRKlfv358St7Lly9j/fr1Bu1nRYFlWZHrvVevXlo5KYojODhYtG6IMXxhxsSOHTvyy/7+/vjmm28QFRWFXbt2wdnZGVeuXNGqiioc6ujWrRvu3buHXbt2YcmSJWXumyHRtFVxhaIsia1bt4rKrQunKpuaugHqugcAcOF20du8Ugn0fnnLCa3JYOlkBTbOUKBxLXlxUF8iU7aWOBA8zKesYNHjc7EY2HlG+xi6BICxKy5aMyQOrATNOgscUmPD/v7+sg8qZ2dnrF69ml+XK8FN6ObBgwd8Iil/f39s2bKlxMcYMEAcMVVWzwEgf52EhoaCYRgEBwfzJY4ZhtGKe9Bc9/HxQd++fc0+lKCJZqBlTk6OzJ6Wx6+//sovX758GW+//bbZ+uLkwKCmRPjIyg8YeLuX7MHb6RXt/esHitsquWvtIkLTOwAADWtotwFAQaF0O6EfJA6sBIVCgcmTJwMAOnTogHXr1mH37t1o1KhRiY8lrOSXkpKiY09CjitXrvDLo0ePLtWYcZcuXUTrtra2Ze6XlDiYOHGiKBeGkOLEQXnBkNdxYWEhfv31V+zfv99gx+SIjo7mk1SFhoYapWplSdF8+H4xDJg8oORv5O0aF3khAHX64jc1ZrpW8SjZMWePABoYvnYXARIHVsXKlStx5coVHDx4EGPGjBHNRy8Jwsh1EgelQxiIWNobvK2tLdq0KQr9Li6fhT7UrVtXtF6zZk389NNPsm/+mmKAizmwdCZNmiRaT05Oltmz5GzduhXvvfceevbsyWcaNRQ7duzgl4cNG2bQY5eWEA1x0FHCA6APrk4MhLXgmtQCfCuJj1VFT+eYmzNw6HsGX41gEBos3Z8l5agCoiVC4sCKUCqVePXVV2Fvb1+m47i4uPBJk0gclAyWZfHtt9+K0leX5e3v77//RteuXTFixAiDJBTSHIsv7pjl1XPwzTffYOjQofx6Wa7jvXv3YsaMGXwJdGE57ZUrV5a+kwKePXuGsWPHYurUqXyb5rCSuWhYQ/yQfa1kcbUiZg0vWv5sqPbDW1/PQesQ4PXmDGxsGIRqaOZK7sCZ1Qw+e6v0/SRMXLKZKB8wDAN3d3ckJyeTOCgh165dw5dffsmv+/j4lGnOv4+PDw4fPmyIrkkSEiIxUV1AeRUHXl5eWLBgAV+FUp/reOfOnVizZg0aNWqETz75BH5+fnjy5AnvgcvOzsaKFSv4GR4A9KptoQ+rV68WBf/Wr1+/2IqbpqJBkHjd3aX0b+SfDGFw7xEL/yrAW120tzs7Ak4OwAuNEJFm9YDLgsrlwtwIoeK4XbxaB2gTSl6DskKeA0IS7g2TxEHJ4Kpicrz22mtm6ok8n332Gb/cp08fnfuWV3EAQBR0W9ywQnZ2Nt577z0cPnwY33//PZo3b45nz55hz549/D6cl0AoDrjgzbKiOdWyX79+BjmuIWgYBNQLUC9/N6FsD11vdwZ/fa3AsikK2ZkElT3E6yd+YLBrgXjf6pWL1r3cxNv8ysfIl8VDngNCEk4cZGRkID8/3yDBcBUBzvXM8fXXX5upJ/J88cUXyMvLQ7NmzbRiEDQprzEHgDoZklKpRGFhYbEi99ChQ8jIKKojHB8fj3HjxmklhwIgKnmdn59vkL5mZopzBUvNMjIXNjYMwn9R5xloXKv4/ctKFQ8gNkG87luJwS+fA+OXqG3fVcdIHc1eNAwkDghJhKV2U1NTJQsBEdoIaxDs3bu3WLe9OahcuTJ++OEHvfYtz54DhmHg5eWFxMREJCcnQ6VSITExUTIV8bZt27TawsLCRNNHNctPA+K/d1kQ1oBISUkxyLRVQ+LixKBJbdOcSzMokUuBPK4PgyAfQKkAWjQQK4A6/kDEy9mrDuLUHEQpoWEFQhJh4BoNLeiP8GFRnt6y5dD8DuVJHABFM2+Sk5PRtWtX+Pj4iEpWA+oHc1hYGAC1t+Hjjz/mtwlrM6hUKhQUFIg+ayhxwJ1HWDa9oqIpDoRJjl5vzqBzU23XwPov1G1KJfDRIHIdGAISB4QkwrckEgf6IxxWsAZxoCkGhHULygOcOMjIyMDx48cBgM8HwjFt2jSkp6tLDvbv319yKIE7BleLgsPQ4qCiCwNAe8aCPqXJ2zZi8M8mBv/+ql0RkigdJA4ISYztObh//z5mzZpVrtLa6oO1eQ40xYE+N2pLoriU1VeuXMHatWsBqKfwfv311zrLIwurbAIkDoxBpRJmXuQICWZQS6KENFE6SBwQkhjbczBp0iQsWLAAbdu2FQV4lRVDHqs0cA8LrkJheaek9SAsDc1S1BzcNb106VK+7ZtvvkFAQECJxEFaWlqZgxJzc3ORnZ0NwDD1M8o7NoaZAEKUERIHhCTG9hwcOXIEAJCVlYW4uDiDHHPp0qXw8fExa7EoThxYg9cAABwdHdGuXTsA4imQ5QU5cRMZGYmHDx/ir7/+AqD+e40fPx6AdjVMIZriACh79kVhMCJ5DghLgcQBIYkpAxKvXr1qkON89913ePbsGRYtWmSQ45WUwsJCJCUlAbAecQCop/ldunRJlPWxvCDnOYiMjMT+/fv5nAWTJk3iU0iXxHMAqOMUyjK8IAx6JHEA9G9ftLxgPA0TmAsSB4QkxhxW0HTDGkIcsCzL32QfP35sluGFlJQU/rzWNPXTwcEBzZs3N1g2QFMi5zmIiIjghRwgTnHt4eGhVaqaQ1gzg+PChQu816E0kDgQE+jD4OBSBj98xODjwebuTcWl/P3aCZNgTHEgvBkChhEHL168gEqlAqDOdvf8uURtVyNjbcGI1sDrr78u2R4ZGSn7UGYYRqf3QIqdO3dqZcfUF+GwAsUcqOnWgsEHAxk42pPnwFyQOCAkMeawgqY4OHjwIP9gLy2aGebi4+PLdLzSYG3TGK2BWrVq4e+//9bK8KlLHAAoNpi0Ro0aWm0rVqwAoL72SiJOyXNAWCIkDghJXFxc+GVDv4VrioPc3Fx07doVOTk5Mp8oHmHqW8D44mDu3LkYPHiwKJhS6DmwpmGF8s7AgQMRERGBkydPIjhYXaXn7t27okBCzYeycOjLz89PtG3SpEl80iQh//nPf7Bs2TIEBQWhbt26ePr0qV79I3FAWCIkDghJbGxs+LcnLkGModAUBwBw/PhxrFu3rtTH1BQHCQkJMnuWnTNnzmD+/Pn4+++/MXPmTL5d+DAgz4FlERQUhPbt26NRo0YA1MNQV65c4bfrEgfVqlUTbevVqxdCQkJw/PhxhIWF8VU48/Pz8dlnnyEvLw8JCQn4/fff9eobiQPCEiFxQMjCZcMztDgQjrG2bduWX16yZEmp54ybcljh1KlT/PLmzZv55QcPHvDLcln2CPPCiQNAHbgKALa2tvxMBQ5hQKvm1MYOHToAADp27Ig+ffpg+vTpqF69uta5hBUddSEUBxRzQFgKJA4IWbigRGMOK4wdOxY9evQAADx8+BC7du0q1TFNOawgHD4QDr9ER0fzyzVr1jTa+YnSIxQHHJ6enlqZH1etWsUvz5s3Dy1atAAADBkyRPQ3B9TxCdu2bYOvr6+o/dSpU3oJa8pzQFgiJA4IWThxkJGRIaphX1Y03ajvv/8+v37x4sVSHdOU4kA4BU74VslFq9va2mqNUxOWgZw40KR3797YsmUL9u7di6ZNm2Lfvn3Ytm0bn2pZkxYtWuDmzZtYvnw5WrZsCQAoKCjAwYMHi+0TDSsQlgiVbCZkERbZycjIMJjLU9ONKnzL/ueff0p1TFMOKwjFATePnmVZ3nMQFBQEpZJywFoiwcHBcHJywosXL/g2qQeyQqHA0KFD+XVvb28MGDBA57G9vb3x8ccfo3bt2ujduzcA9TTdIUOG6PyccDYQiQPCUjC452DBggXo168fmjVrppUwRKVS4fvvv0fHjh3RrVs3/O9//zP06QkDIhQHhow70HxT8vX15W+KpRUHpvIcsCwrqsyXm5sLQD3UkJWVBYCGFCwZpVKJhg0bitoM/UAOCQnhl8+cOYPjx4/rjKUR1uNwcnIyaF8IorQYXBzUqVMHX375paRbddu2bbhy5Qq2b9+OdevW4bfffsOlS5cM3QXCQAgTIekbd/Ds2TOsWbMGc+fOlc05rznGyjAMQkNDAQBxcXEICAjAsWPHStRXU4mDxMREkZeCO68wAQ43XY6wTIwtDvz9/fmZPmfPnkXnzp3x+eefIzU1FZ9//jm+/fZb0f5cfgya/kpYEgYfVhg0aJD6wDbah963bx/effddeHl5wcvLC/369cPevXv5YB9N8vLykJeXJ+6wjY1satPSwCXfKWsSHmuCs4XQc5CamlqsjZ48eYLQ0FDey/Ds2TP8+OOPWvsJPQfu7u5QqVQICQnhZwE8evQI8+bNQ8eOHfXus6Z4SUtLQ1ZWllYUelmJiIgQrWdkZEClUuH+/ft8W40aNSrk9VRefkt16tQRrXPXoCGpX7++yHO6cuVKREVFYc+ePdi2bRt69+6Nxo0bo7CwkBfRlStXtnjbGYvycu2YGmPYRd806CaNOYiOjkbt2rX59Vq1auHMmTOy+2/cuFErAGjw4MHFjuGVhkePHhn8mNZEZGQkWJaFl5eXVrQ2x4YNG0TDDwcOHEBsbKzWflw+ABsbGyQmJiIpKUky0jsmJkYrilwOKU/B1atXJaeYlQXNobLnz58jNjZWNGfe1dVV8ntXFCz9t6RZjEmhUBj87xUQEKB1rQinNp49exYeHh5ISkrip026uLhU6OsGsPxrx1wY0i5S2T2lMKk4yM7OFqUldXZ2FgUGaTJq1Ci88847ojZjeA4ePXoEf3//cllYxhhwNhE+WMPCwhAWFgZfX1/cuXMHrq6uWp8LDw8XrT948ACOjo68u3TTpk1IS0vjXfGenp4ICgoCAHTu3FnreI6OjjrL5xaHQqEweL4BLsZAuO7t7Y2dO3fybe3bt6+QeQ7Ky2+JK0HNERQUZPC/V/PmzbF9+3bZ7Q4ODggMDBQNUQUEBFTI6wYoP9eOqTGnXUokDsaMGYMbN25Ibhs9erRoSpoUjo6OfNAWAGRlZekMwLGzszOoENCFQqGgi1ID4ewELl1sXFwcDh48qOW9ycnJESUH4njttdfwv//9DwkJCRgzZoxom6enJ2/zVq1aYfz48fjll1/47ffu3dPKTieH5mwFQO2hMPTfVFg/gWPJkiX8G1+3bt1Qv359g56zvGHpvyWh9xJQexIM3d9atWrp3J6SkgKFQqE1LdaS7WYKLP3aMRfmsEuJxMH69evLdLLg4GDcv3+f/3FGRUVR8JYFIwxIFHLt2jUMGTIEx44dw+bNmzFlyhSkpaUhOzsbgHpKFzeOGhMTgyFDhqBSpUpaxxEGYDEMg//85z945ZVXMGnSJADq/Pf6xh1IiQNjBCVKpWUWDn0tXrzY4OckDItmESZjZCVs3749HB0d+d+EJtzvgyp5EpaKwaVIfn4+cnNzwbIsCgoK+GUA6NGjB3799Vekpqbi0aNH2LlzJ3r16mXoLhAGQk4cnD9/HgDQpUsX/Pe//0W/fv3w66+/8tu//vpr0f5PnjyR9DiNGjVKq0341n337l29+6o5WwEwjjiQ8hxwMRR16tRB48aNDX5OwrjoGtosLVWqVMHp06fx66+/Sl4TnDgQXk80W4GwJAwuDiZPnow2bdrg4cOHmDJlCtq0acPfpAcNGoSmTZuif//+GD16NIYNGyY7U4EwP8LZCkLCw8NFRYbi4uLw559/AlC7aEeNGlVs0GjHjh0lxUGDBg345Tt37ujdV1OJA12V9po2bWrw8xHGgZtOaGtriy5duhjlHE2bNsW7776LLVu2YPr06Thw4AC/jRtOIM8BYakYPCBROGasiUKhwGeffYbPPvvM0KcljICc5+DFixfYtm2bqI2bcjp69Gg4Ojpiy5YtqF69OpYtWybab/bs2UhNTcXs2bMlZyJUrlyZH5YoiTjghhXs7Oz4vpA4IOT49NNP4eXlhXr16ukd11Ja6tWrh4ULF4qGGMhzQFg6lD6ZkEVOHABqD5EUI0eOBKCOIWjVqpVom729PebMmVNsauGQkBCcPHkS8fHxSEpKkoxX0ITzHPj7+yM2NhYFBQUGL9ucn58vCiDThMRB+cHBwQETJ0406Tnt7e3h7OyMrKws8hwQFg+FhRKyaIqDuXPn6swmxzCMKBJccz5tnTp19Ko5IByjvXnzZrH7syzLiwN3d3d++qOhPQfCG7kUr7zyikHPR1gf3O9HynNA4oCwJEgcELLY29uL1t98803MnTtXdv9q1aqJpp5qzkTRd4qfsHKePuIgJyeHrxrp6urKu4mfPXtm0GqSwiEFYb4OQD09TpenhSCAopkRycnJUKlUvOB0cXExeDZPgigLJA6sjKCgIKxYscIgx9KMCQgNDcWECRNkvQcBAQGidc399BUHJfUcCIMRheJApVJJzi4oLcJhCs258jSkQOgDl51RpVIhLS0Njx8/BgD4+PiYs1sEoQWJAwugY8eO+Pjjjw1yrPDwcIwfP94gxwLUQwm2traYP38+lEol7O3tZWciaIoDTXGhmdNejoYNG/IJP+SSbgkRvtFXrlxZFGBmyKEF4Xk0Ky+++uqrBjsPYb0IcyrcuXOHD6QtLmkSQZgaEgflAC5nhD5UrlzZoGVf58yZg6ysLHz55Zd821tvvSW5r6Y4ACCqw6BvTm9HR0deSNy+fRs5OTk69xeWUPb19RW9hRlLHJDngCgNwroOFy5c4JdJHBCWBokDMzNy5EicPHkSK1euBMMwYBgGmzZtAsMw2L9/P5o2bQp7e3ucOXMGUVFRePPNN1G1alW4uLigefPmOHLkiOh4msMKDMNg3bp16N+/P5ycnFC7dm0+FbK+aGaU69Chg2SOAilxsG3bNri5uaFPnz5asxd00bJlSwDq2gW///67zn2F4sDPz89ongPhschzQJQGoefg888/55c1xSZBmBurn8rYrFmzYqe0FRYW6hVFXxJ8fHy0qrJJsXLlSkRERCAkJITPLHj79m0AwPTp07F06VIEBwfD09MTjx49Qs+ePfHtt9/C3t4e//3vf9GnTx/cu3dP8sHMMW/ePCxevBhLlizBqlWr8M477yA2NlarOp2+MAyDDRs24OeffxYFLUr1oVu3bkhOTpYs4a2LSZMmYfPmzQDUNT0qV66M3r17S+ZGiIuL45f9/Pz4jJyAYcUBNz4MiJM11axZ0ygpeAnrQy5ehzwHhKVh9eIgISFB9GZpabi7u8POzg5OTk68O/zff/8FoE5D/Prrr/P7enl5iYL15s+fjx07diAsLAxTpkyRPcfIkSPx9ttvAwAWLFiAH374AZcuXcIbb7xRpr5rFsWSEyglFQaA2nPQtm1bvqR33759cfToUcnqjZrDCsLa54bMdcCVTWUYBo0bN4afnx+ePHmCnj17GuwchHXTvn17yXYSB4SlYfXiQJ8oYGN5DspKs2bNROuZmZmYO3cu9u7di/j4eBQUFCA7OxsPHz7UeRzh1EBnZ2e4ubkZNIqfQ5f3ojQsXLhQVF73xIkTxYoDPz8/UXyGIT0HnDioVKkSHB0dcfLkSZw/fx79+vUz2DkI6yYwMBAJCQkIDAwUlf/mSpcThKVg9eKgONe+SqVCbGwsAgMDLa5UqOZc+qlTp+Lw4cNYunQpatWqBUdHRwwaNIhPFyyHZswAwzCit+uyMHHiRPz8888IDQ0t9TCFHG3btsX169fRpEkTAEXDLZpwwwpKpRJVqlQR5TYwhDi4fv06fvrpJ/5YXExDzZo1tWIPCKI4KleujGXLlomyjJqqND1B6ItlPQ0rKHZ2dnol6zl79ixGjhyJ/v37IzQ0FD4+PoiJiTF+B3WwbNkyhIWF4ciRI5LxAGUlJCQEDg4OAOTFAec58PHxgVKphJ2dHZ9y2RDiYNy4caKaIcbOxU9YP2+++Sa/3LdvXzP2hCCkIXFgAQQFBeHixYuIiYlBUlKS7Ft97dq1sX37dly/fh03btzAsGHDDOYBKC2Ojo7o06eP0YrGKJVK1KtXDwAQGRmpNa0xPz+fHyLx8/Pj27lhnfj4eFGAYmnQ9D6ROCDKip+fHzZu3IgBAwbgm2++MXd3CEILEgcWwNSpU6FUKtGgQQNUrlxZNoZg2bJl8PT0ROvWrdGnTx907969Qkyha9iwIQD1ENC9e/dE24QPf19fX76de4Dn5eUhNTXVoP0hcUAYgpEjR2Lbtm0IDQ01d1cIQgurjzkoD9SpUwfnz58XtXHVDYUEBQXh2LFjojbN6oiawwxSb81paWml6qe54MQBoB5aaNy4MfLz82Fra6sVjMihmeugtPEQwqAxqWMTBEFYI+Q5ICweTXHwv//9D25ubnjvvfdw//59fltgYCC/LHyAl2U64/Pnz7XaSBwQBGHtkDggLJ6QkBB++fbt23j33XeRk5ODX3/9Ffv27eO3CQs7+fv788sRERGlPreUOKAiOQRBWDskDgiLJygoiK8XoTljYcuWLfyyUBy88sor/HJ4eHipz52eni5ab9eunSi2gSAIwhohcUBYPAqFgn/wC4cRhNjb24sSyTRp0oRPbFUWcSD0HIwYMQJHjx41ypRNgiAIS4LEAVEuEMYdSFGnTh1RlksnJyd+OOLOnTvIysoq1XmF4kDzHARBENYKiQOiXFCcOOByIQhp0aIFAPUUyKtXr5bqvMJhBTc3t1IdgyAIorxB4oAoF5RGHDRv3pxfvnjxYqnOK/QckDggCKKiQOKAKBcIZyxwCLMyCoUAR+vWrfnlo0ePluq8QnHg7u5eqmMQBEGUNygJElEuCAgIQGhoKP755x++LTw8HGvXroWDgwN69+6t9ZkGDRrA19cXcXFxOHnyJHJycvg6DfpCwwoEQVREyHNAlAsYhsG3334ravP398f8+fMxa9YsyRkEDMOgW7duAIDs7GycO3euxOelYQWCICoiJA4sgI4dO+Ljjz822PFGjhyJfv36Gex4lkLv3r3RvXt3AMDQoUP1mlLIiQMAOHToUInPScMKBEFURGhYgSg3MAyDHTt24MaNG2jWrJlen+nQoQO/fOPGjRKfk4YVCIKoiJDnwMyMHDkSJ0+exMqVK8EwDBiGQUxMDG7duoUePXrAxcUFVatWxfDhw5GUlMR/7u+//0ZoaCgcHR3h7e2Nrl27IisrC3PnzsXmzZuxa9cu/ngnTpww3xc0MI6OjmjVqhVsbPTTtdWqVeOzK0ZHR5f4fDSsQBBERcTqPQfNxqmQkKJjBxYoLPSDUgmAURnsvD5ewOW1xWuvlStXIiIiAiEhIfj6668BALa2tmjRogXGjh2L5cuXIzs7G1988QWGDBmCY8eOIT4+Hm+//TYWL16M/v37IyMjA6dPnwbLspg6dSru3r2L58+fY+PGjQBQ6oqE1gDDMAgODsatW7cQExODwsLCEiUy4sSBnZ0dHBwcoFIZ7hohCIKwVKxeHCSkAE8Si9vLfGZwd3eHnZ0dnJyc+II+33zzDV555RUsWLCA32/Dhg3w9/dHREQEMjMzUVBQgAEDBvCVCIU14R0dHZGbm0sFgl7CiYO8vDzExcWJijJxnDlzBsuWLcOYMWPQq1cvvp0bViCvAUEQFQmrFwc+xb00s0BhYQGUShvAgCnziz2vDm7cuIHjx4/DxcVFa1tUVBS6deuGLl26IDQ0FN27d0e3bt0waNAgeHp6lqHH1ktwcDC/HB0dLSkOhgwZgvj4eOzYsQOFhYVQKBSIjY3FgwcPAJA4IAiiYmH14qA4175KpUJs7BMEBgZCobCMEIzMzEz06dMHixYt0tpWrVo1KJVKHD58GOfOncOhQ4ewatUqzJo1CxcvXkSNGjXM0GPLRlMcCIMUAaCgoADx8fH8+v379+Ht7Y1GjRrxbTRTgSCIioTVi4PygJ2dHQoLC/n1V199Fdu2bUNQUJBs4B3DMGjTpg3atGmDr776CoGBgdixYwc+/fRTreNVdDTFAQDcunULrq6uCAwM5L0DHFeuXIFSqRQFI1aqVMk0nSUIgrAALONVuYITFBSEixcvIiYmBklJSZg8eTJSUlLw9ttvIzw8HFFRUTh48CBGjRqFwsJCXLx4EQsWLMDly5fx8OFDbN++HYmJiXxZ46CgINy8eRP37t1DUlIS8vPzzfwNzYumODh06BBCQ0PRoEED3Lt3D//++69o/8uXL+PMmTOitmnTppmkrwRBEJaAwT0HCxYswKVLl/D48WP8/PPPovno//nPf7BhwwbY2dnxbadPnzZ0F8odU6dOxYgRI9CgQQNkZ2fjwYMHOHv2LL744gt069YNubm5CAwMxBtvvAGFQgE3NzecOnUKK1aswPPnzxEYGIjvv/8ePXr0AACMGzcOJ06cQLNmzZCZmYnjx4+jY8eO5v2SZiQoKIhfPn78OC8GXrx4gQ8//BCvv/66aP/Lly8jMzMTgNpDk5KSAg8PD1N1lyAIwuwYXBzUqVMH3bp1w/z58yW39+7dG7Nnzzb0acs1derUwfnz57Xat2/fLrl//fr1ceDAAdnjVa5cuVTZAK0VR0dHdO3aFUeOHEF8fLwovuDQoUNaUz0vX76M3NxcAOpqkCQMCIKoaBhcHAwaNEh9YD2T1OgiLy8PeXl5ojYbGxuR56GscPPWaf56EdZok02bNqFVq1Z4/Pix1rYtW7aI1l+8eMEvt27dWmQHa7SNoSDb6IbsIw/ZRhpj2EXfwHuTByQePXoUJ06cQNWqVTF27Fh07txZdt+NGzdi7dq1orbBgwdjyJAhBu/Xo0ePDH7M8o612aR///5YtWpViT5Tt25dxMbGarVbm20MCdlGN2Qfecg20hjSLvrOaGNYlmUNdlYBAwcOxIwZM0QxB9HR0XBzc4OHhwfCw8Mxffp0rF69GiEhIZLHMJXn4NGjR/D397eYqYzmxlptcuLECXTp0oVfr1atGhISEsD9BLy8vJCSIk6nGRkZKQpotFbbGAKyjW7IPvKQbaQxhl2M4jkYM2aMbPGa0aNH4/3339f5eeFN9rXXXkP37t1x8uRJWXFgZ2dnUCGgC4VCQRelBtZmk9atW4vWFy1ahDp16iAsLAzu7u548803Ua9ePX67j48PatasKVn90dpsY0jINroh+8hDtpHGHHYpkThYv369QU9OFwFhShwcHKBQKPjxu/r166NZs2Zo2bIlv09wcDCfC6Ft27Z6lYUmCIKwNgz+dM7Pz0dubi5YlkVBQQG/DAAnT55EZmYmVCoVwsPDsX//frRt29bQXSAIWfbs2QNnZ2e88cYbePXVV7W2C4fB2rRpY8quEQRBWAwGD0icPHkyrl69CgCYMmUKACAsLAy+vr44cOAA5s6di8LCQvj6+mLWrFlo3LixobtAELL06NEDqampsLW1ldz+xhtvYOvWrVAqlXzeCIIgiIqGwcXBL7/8Irtt4cKFhj4dQZQYOWEAAO+99x6cnJzg5+eHunXrmrBXBEEQlgPVViAIAUqlEkOHDjV3NwiCIMwKRQQSBEEQBCGCxAFBEARBECJIHBAEQRAEIYLEAUEQBEEQIoyWPpkgCIIgiPIJeQ4IgiAIghBB4oAgCIIgCBEkDgiCIAiCEEHigCAIgiAIESQOCIIgCIIQQeKAIAiCIAgRJA4IgiAIghBB4oAgCIIgCBEkDgiCIAiCEEHigCAIgiAIESQOCIIgCIIQQeKAICSgkiPSFBQUmLsLBEGYABIHFZCUlBRzd8Fi+fvvvwEADMOYuSeWx2+//YYVK1YgNzfX3F2xSDIzM83dBYIwGFYtDo4cOYIZM2bg1q1bAACVSmXmHpmXffv2YcCAAViwYAGWLVuG58+fm7tLFsPevXvRs2dP7N+/H5mZmRX+WhGyb98+9OjRAytXrsS9e/dgb29P9hFw4MAB9O3bF7Nnz8by5cuRlJRk7i5ZDEeOHMG4ceNw4cIFAHQPFmLpzycbc3fAGOTn52Pr1q3YvHkzAgICcPjwYYSEhEChsGotJEtmZiaWL1+Oy5cv45NPPkFwcDBGjhyJevXqoWfPnmBZtsK+KWdkZGDBggU4e/YsFi5ciDZt2pi7SxZDQkICPv30U2RlZWH+/PmoWbMm3nrrLaSlpcHDw8Pc3bMILl26hHXr1mHGjBnw8PDAmjVrsGbNGowYMQKBgYHm7p7ZKCwsxO7du7Fu3Tr4+/tj27ZtaNWqFRQKRYW+3wDl5/lkWb0xECzLwtvbG19//TUGDx6MhIQEnDhxgt9W0WAYBk2bNsXOnTvRsWNHeHh4wM3NDXFxcfz2iopKpUJubi6GDx+ONm3aoKCgAGfPnsXjx4/N3TWzo1Qq0bdvX+zatQvNmjVDWloaatSogbt375q7a2ansLAQAHDz5k20bNkSr732GurXr49x48YhNjYW27dvN3MPzY+Pjw+mTZuGCRMmIDc3F9u2bQNQMe/BQsrL88lqxMHJkyeRkJCAnJwc2NnZoUWLFmjVqhVatWoFf39/nDx5EhkZGWAYxqL+AMZCaA9nZ2d06tQJDMPg8OHD6N69O7y9vcGyLM6dO4f4+Hhzd9ekcLbJzs6Gu7s7unXrhqioKHz66afo1asX/vrrL4wYMQKbNm1CYmKiubtrUoS2qVy5Mt566y1+m7e3N549e8Y/GC3NDWoKOPvk5+cDANLS0hAVFcVvb9CgAZKSknD16lVcuXLFXN00C6mpqfyyUqlEaGgo2rdvj5CQELRp0waHDh1CamoqFApFhbt2yuPziWEtpSel5M6dO/j888/h7OyMSpUqwd7eHsuXLxftc+HCBezevRtNmjTB4MGDoVKpLM6FYyiKs8eFCxfg6+uLgIAA3L17F3/++SeqVKmCSZMmWb0HQdM2dnZ2WLFiBVQqFRYvXoy4uDh88MEHqF27No4ePYq9e/eiU6dO6NOnj7m7bnSKu24KCwuhVCoxc+ZMODo6Yvbs2WbsrenRtI+trS1WrlyJtLQ0dO/eHZ9//jm6d++O69evY/v27QgICICfnx+GDBli7q4bncuXL+Orr77CK6+8gunTp8PV1VVrn+joaKxfvx6+vr6YPHmyVd+DhZTn55P5e1BGTp8+jW7dumHr1q2YM2cOYmJisHr1aqSlpfH7NGnSBLVr18bVq1eRkJAAhUKBrKws83XaiMjZg5uh0KpVKwQEBKCgoAD169dHtWrVcP/+feTk5Ji558ZH0zaxsbFYuXIlCgsLMXbsWMyYMQO1a9dGYWEhunTpAjc3N9y5cweAZbn7jEFxvyNurLhmzZpgWRbZ2dnm7bCJ0bTPw4cPsXLlSnh4eGDOnDk4dOgQpkyZgu+//x4jRoxAYWEhH/BrzdfO/fv3sWHDBrz22muIjIzEzZs3Jb9vQEAAOnTogKtXr+LBgwdQKBQVIiC6PD+fyr04OHHiBHx9fQEAVatWxZdffonw8HBcu3aNd105ODigVatWqFSpErZu3Yp58+Zh8+bNvGvQmpCzx40bN0SuPBsbdSyqk5MTlEolHB0dzdJfUyJlm6tXr+LMmTPw9vZGtWrVAKhdogDg6enJe1Os3atS3O+IYRgwDAMXFxfcv38fjo6OVv3Q00Tu2jlx4gR69uyJNWvWYMaMGdi5cyeaNGkCW1tb2NnZAbDua6dWrVro06cPZs+ejTZt2uDvv/9GcnKy1n42NjZo0qQJmjZtil9++QVz587FkiVLrP6lpDw/n8qtOODGPVu3bi0a22vatCkaNmyIY8eOid5u6tWrh+joaPz6669ITk7GO++8A1tbW5P321joY48XL14AAD+O/vvvv+PPP/9Et27dTN9hE6LLNiEhITh27Biv1Lm3mT/++APHjx9Hly5dTN9hE6Lv74gTAp07d0ZsbCwiIyOt+qHHUdy1c+TIEWRmZsLGxga1a9cGAGzcuBFnzpxB69atzdJnU8FdE6+//joAYPz48YiPj8epU6ckk2VVqVIFjx8/xpEjR5Ceno7PPvsMDg4OJu2zqbCG51O5FQfc212DBg2Qn5+PS5cu8duGDx+OU6dO4dmzZwCA9PR0zJ49GzExMdi8eTN++OEHuLu7m6XfxkIfe3Ci4Ny5cxg4cCD27NmDBQsW8D9ua6Uktjl79ix69+6N3bt3Y/78+WjatKlZ+mwq9P0dcUIgOTkZQ4YMgZeXl1n6a2qKs8/p06f5ayc6Ohqff/459u7di6+++gq1atUyS59NBXdN2NjYoKCgAI6Ojhg8eDDCwsLw6NEjkacyLy8PixYtwpUrV7Bp0yYsX77caqbDckJI+H2t4flk0eIgISEBmzZtwokTJ0RZ2ViW5V0u9evXR9WqVXHw4EH+j+Tj44PatWsjPDwcAODs7IyxY8di7969aNCggem/iIEoqz24C7RLly6YMWMGfv/9dzRq1Mj0X8QIGMo2bdu25W0TGhpq+i9iBMpqm8uXL/OfqVevHiZPngxvb2/TfgkjYqj7TGBgICZOnIi///67Qlw7Qu8AN0w5cOBA2NnZ4fDhw1AoFPwQg62tLcaMGYODBw+iYcOGpv0SRoBlWWRlZeHLL7/ks6oKgwit4flkseJg5cqVeOutt5CQkICff/4ZS5YsQXp6OgC1YuVcLnZ2dujUqRMSExOxevVqAOqkPwqFAs2aNQOgvnDLe0ISQ9ijefPmAAAXFxfeNtaAIW3j6upqVYmQDGEba/aeGPI+Y2dnh5o1a5rnixiB4mzDCQIuJwj38Js6dSoOHz6MKVOm4I033kBERAQYhkGlSpXM80WMAMMwSEpKwvHjxxEeHs7n/uCGE6zh+WSRGRL37NmDxMRE/Pbbb6hevTpOnDiB1atXi8Y4t23bhu+++w5jx47FmDFjYGdnh2nTpuHhw4e4evUqWrZsyQeClHfIHvKQbeQxpG2sMasdXTvy6GubxYsXY/jw4ZgyZQovFu7evYuYmBgEBwdj586d8PPzM9fXMCoxMTGoWbMmAgMDcfDgQdSvX58fTti+fTsWLlxYvq8b1kLIz8/nl1NSUtiMjAyWZVn2ypUrbJ8+fdg333yTvXr1KsuyLBsfH88OHz6cPX/+vOgY8fHxbHh4OHvt2jWT9dtYkD3kIdvIQ7bRDdlHHkPY5tKlS+y7776r1V7eEdqGW46MjGQXL17M7tixg506dSp74sQJlmVZNjo6mn3vvffK/XVj9iRIqampvCKtVasW+vfvz08Bio2NxapVq1C7dm20bdsWp06dAsMweOutt/hgFpZloVKpeMVW3iF7yEO2kYdsoxuyjzxkG3k0bTNgwAB+yGD37t24desWPv74Y/z0008AgKFDh8Ld3R0uLi4AyrdtzBpzsGfPHrz11lv8NKA9e/Zg0aJF/PaAgAAsXrwYEyZMQMOGDdG8eXNER0fzAVKFhYVgGKZcGl4Ksoc8ZBt5yDa6IfvIQ7aRR8o23333Hb89KCgICoUCjo6OaNGiBS5evIjhw4fj+PHjAKzANuZyWWRkZLCrVq1id+3axbfdvXuXHTBgAJuSksKyLMuqVCqWZVk2NzeX/79Hjx7svn37TN9hI0P2kIdsIw/ZRjdkH3nINvLosk1ycjLLsiy7e/dudtGiRezNmzfZ/v37s3379mWnTJnCxsfHsyxbZLvyikkDEp8+fQqGYVClShU4OjqiU6dOqF69Or89PT0d7u7ufLY+LviFc3HduXMH1atX55ONlHfIHvKQbeQh2+iG7CMP2UYefW3j5OQEAKhduzbmzZuH06dPY/LkyfDx8cFvv/2Gs2fPYuDAgeU+gNck4iA/Px9z5szB9evXUblyZbRr1w69e/fm57uyLyOh7e3t4eTkxEe9AkBKSgpOnjzJp7mdOHFiuU8uQvaQh2wjD9lGN2Qfecg28pTUNtwwgbe3N5YtW4YmTZrwxaacnJxQp04ds30XQ2KSmIMDBw4gPT0dYWFhGD58OB4/fowFCxZo7Xf06FH4+vqKLkwvLy9ER0fDxcUFu3fvxtChQ03RZaNC9pCHbCMP2UY3ZB95yDbylNQ2XEBipUqV0K5dO7i6uvLZEa1FGABGFAc5OTl87u379+/Dzc0NNjY26NKlC8aMGYOYmBj89ddfANTKjWVZ3L59m8/zf+DAAWzfvh0A8OGHH+KLL77gI0DLI2QPecg28pBtdEP2kYdsI48hbLNz507+eJZQYtnQGHxY4eHDh1i6dCmcnJzg6OiIadOmwdXVFUqlEhkZGXB1dYW/vz/GjBmDNWvW8Ok2X7x4AQ8PD6SlpeGjjz7CP//8g2nTpgGA2QtQlAWyhzxkG3nINroh+8hDtpHHGLaxVgwqd3bu3ImJEyeiTp06ePfdd3Hv3j2sX78etWrVQnh4OJ4+fcrv27FjRwQHB2Pbtm0A1EVLTp8+jfnz56NWrVo4duwY3njjDUN2z+SQPeQh28hDttEN2Uceso08ZJuSYVBxEBcXh/Hjx2PKlCkICQnBd999hy1btqBNmzZwc3PD3r17kZaWBkCtRH18fJCXl6fuiEKBcePGYdeuXfjggw8M2S2zQfaQh2wjD9lGN2Qfecg28pBtSoZBhxU4FwygHqdRKpWoUaMGCgoKMHbsWCxfvhyBgYHo0aMHnJyckJaWxpemrFevnkVVpDIEZA95yDbykG10Q/aRh2wjD9mmZBhUHFStWhWAeuqHra0tkpKSwDAM7Ozs8Morr6Bv3744ePAgjh07hoKCAsTFxfHTRawxoIPsIQ/ZRh6yjW7IPvKQbeQh25QMo+Q54JI/XLp0CTVq1ODnhQ4cOBBt27bF2bNnkZGRgZEjRxrj9BYH2UMeso08ZBvdkH3kIdvIQ7bRD6OIg8LCQiiVSkREROD1118HAGzduhWZmZkYPXo0Bg4caIzTWixkD3nINvKQbXRD9pGHbCMP2UY/jOIrUSqVKCgoQE5ODp4+fYpx48Zh8+bNCAkJMcbpLB6yhzxkG3nINroh+8hDtpGHbKMfRkufHB0djQsXLiAyMhLDhg3De++9Z6xTlQvIHvKQbeQh2+iG7CMP2UYesk3xMCyXJsrAFBQU4M8//8SgQYNgb29vjFOUK8ge8pBt5CHb6IbsIw/ZRh6yTfEYTRwQBEEQBFE+qXjzMwiCIAiC0AmJA4IgCIIgRJA4IAiCIAhCBIkDgiAIgiBEkDggCIIgCEIEiQOCIAiCIESQOCAIgiAIQgSJA4IgdHL58mU0a9YMzZo1Q1xcnLm7QxCECSBxQBAEz9y5c9GsWTOMHz+eb3NxcUFISAhCQkJgZ2dnxt4RBGEqjFZbgSAI66BevXrYtGmTubtBEIQJofTJBEEAAPr06YP4+Hit9p9//hkTJ04EAISFhcHX1xdz587Fnj17UK1aNUyYMAE//fQTMjMz0bdvX0yePBmrV69GWFgYXFxcMGrUKAwaNIg/XmJiItasWYPz588jLS0NVatWRZ8+fTBy5EjY2ND7CkFYAvRLJAgCAFC3bl1kZ2cjLS0Nzs7OqFGjBgDg33//lf1MUlISvvvuO1SqVAlZWVn4448/cOHCBTx79gwuLi54+vQpFi9ejKZNm6JGjRpIS0vDyJEj8fTpU/4c0dHR+Pnnn/HkyRPMmTPHVF+XIAgdUMwBQRAAgKVLl6Jt27YA1EJh06ZN2LRpE+rVqyf7mfz8fPz444/Yvn07qlatCgB49OgR/vjjD/z111+wt7eHSqXClStXAABbt27F06dP4e3tjZ07d+KPP/7AokWLAAB79uzBo0ePjPwtCYLQB/IcEARRatzc3NCkSRMAgI+PD54+fYqaNWvC19cXAODp6YmEhASkpKQAAG7fvg0ASE5Oxuuvvy46FsuyuHXrFvz9/U33BQiCkITEAUEQpcbZ2ZlfViqVWm0MwwBQP/g1P8cNWwhxcHAwRjcJgighJA4IguDhHs45OTlGOX6DBg1w9uxZKJVKLFiwgPcwZGVl4fjx4+jUqZNRzksQRMkgcUAQBE9QUBAA4M6dOxg6dCgcHR0xbtw4gx1/yJAh2LVrF549e4aBAweiRo0ayMrKwtOnT1FQUIDevXsb7FwEQZQeCkgkCIKnb9++6Ny5M1xcXBAVFYVbt25BpVIZ7Pienp7YuHEj+vTpA3d3d0RFRSE3NxevvPIKPv30U4OdhyCIskF5DgiCIAiCEEGeA4IgCIIgRJA4IAiCIAhCBIkDgiAIgiBEkDggCIIgCEIEiQOCIAiCIESQOCAIgiAIQgSJA4IgCIIgRJA4IAiCIAhCBIkDgiAIgiBEkDggCIIgCEIEiQOCIAiCIET8H7XcSjYowAq9AAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -596,1247 +682,407 @@ } ], "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 2))\n", - "train.plot(ax=ax, label='train')\n", - "test.plot(ax=ax, label='test')\n", - "ax.legend();" + "week_mean.plot();\n", + "week_median.plot();" ] }, { "cell_type": "markdown", - "id": "3e3abf2b-3966-47d0-a83a-9a29771ee643", + "id": "3abbc2e7-a631-40b0-878c-36a552f95dc8", + "metadata": {}, + "source": [ + "#### Generic Predictor" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "c775b740-fe78-4c0c-87fe-a51af3d42993", "metadata": {}, + "outputs": [], "source": [ - "Create the model" + "model = on.context.common.GenericPredictor()" ] }, { "cell_type": "code", - "execution_count": 71, - "id": "e58247b7-6a10-428b-a0ab-aebb39b02124", + "execution_count": 80, + "id": "a633dce3-27d8-4deb-bb0f-d40e3af96f8a", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from skforecast.ForecasterAutoreg import ForecasterAutoreg \n", - "from sklearn.neural_network import MLPRegressor\n", - "\n", - "model = ForecasterAutoreg(\n", - " regressor = MLPRegressor(),\n", - " lags = 30\n", - " )\n", - "model.fit(y=train.pd_series())" + "model.fit(train)" ] }, { - "cell_type": "code", - "execution_count": 72, - "id": "c33947fa-700a-4a0c-9486-5e7bc55b83bc", + "cell_type": "markdown", + "id": "6943aa60-c757-4f54-bf63-854beecfab80", "metadata": {}, - "outputs": [], "source": [ - "preds = model.predict(steps=7)" + "What does the future looks like ?" ] }, { - "cell_type": "markdown", - "id": "221ebcea-6cda-4560-98fa-cc553cb439ad", + "cell_type": "code", + "execution_count": 82, + "id": "04dfe189-f736-4fe3-b27c-467c154b2c42", "metadata": {}, + "outputs": [], "source": [ - "Plot the prediction" + "pred = model.predict(48)" ] }, { "cell_type": "code", - "execution_count": 73, - "id": "d3e70c93-e129-4f4f-874e-e7c7f60cd704", + "execution_count": 83, + "id": "67da9001-1e2a-4578-9436-8db8151190dc", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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    " + "alt.Chart(...)" ] }, + "execution_count": 83, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "fig, ax = plt.subplots(figsize=(6, 2))\n", - "train.split_after(pd.Timestamp('09-01-2023'))[1].plot(ax=ax, label='train')\n", - "test.split_after(pd.Timestamp('11-01-2023'))[0].plot(ax=ax, label='test')\n", - "preds.plot(ax=ax, label='preds')\n", - "ax.legend();" + "on.plots.prediction(train[-96:], pred, test[:48])" ] }, { "cell_type": "markdown", - "id": "ab33b163-4161-4fe7-b674-986a5f2a580d", + "id": "dc965c95-1b5c-43e4-9c4b-57ff73c275c1", "metadata": {}, "source": [ - "---\n", - "## Packaging of the model" + "## Generic Detector" ] }, { "cell_type": "code", - "execution_count": 74, - "id": "29975ac3-7189-4399-a20f-4ccdde603ef0", + "execution_count": 84, + "id": "9751b373-97d3-45e9-9969-2b4ba224f815", "metadata": {}, "outputs": [], "source": [ - "from ontime.abstract import AbstractBaseModel\n", - "from skforecast.ForecasterAutoreg import ForecasterAutoreg\n", - "from sklearn.neural_network import MLPRegressor\n", - "\n", - "\n", - "class MyPrivateModel(AbstractBaseModel):\n", - " \"\"\"\n", - " Model to predict 14 days of activity given a training on 7 days.\n", - " \"\"\"\n", - "\n", - " def __init__(self):\n", - " super().__init__()\n", - "\n", - " def fit(self, series):\n", - " super().fit(series)\n", - " self.model = ForecasterAutoreg(\n", - " regressor = MLPRegressor(),\n", - " lags = 30\n", - " )\n", - " self.model.fit(y=series.pd_series())\n", - "\n", - " def predict(self):\n", - " horizon = 7\n", - " super().predict(horizon)\n", - " predictions = self.model.predict(steps=horizon)\n", - " return on.TimeSeries.from_series(predictions)\n", - "\n" + "model = on.context.common.GenericDetector()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "89be0c48-0ab6-42b4-ab64-611b95d2a76f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(train)" ] }, { "cell_type": "markdown", - "id": "27102eef-6797-4679-90c7-2db2a6a5e157", + "id": "06be738b-8bfd-4c95-8dec-ed52803e5ff9", "metadata": {}, "source": [ - "Try the model for fun" + "Does the current signal has problem ? " ] }, { "cell_type": "code", - "execution_count": 75, - "id": "11837b8a-0667-4271-9b66-c4fef3d19c86", + "execution_count": 87, + "id": "4650f34a-9cdb-4ea6-9dfe-2c66109b2627", "metadata": {}, "outputs": [], "source": [ - "model = MyPrivateModel()" + "detected_test = model.detect(test)" ] }, { "cell_type": "code", - "execution_count": 76, - "id": "b5e1ec63-aa13-4659-a3a1-f3c6d00f33b2", + "execution_count": 90, + "id": "49e09caa-37f1-4201-b79a-4d1d65d86a8c", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] + "data": { + "text/html": [ + "\n", + "\n", + "
    \n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "model.fit(train)" + "on.plots.anomalies(test[:72], detected_test[:72])" + ] + }, + { + "cell_type": "markdown", + "id": "2af5c7ba-1617-46f0-9551-67cc51ac1884", + "metadata": {}, + "source": [ + "What if we want to have an idea about the future problems ?" ] }, { "cell_type": "code", - "execution_count": 77, - "id": "42e58973-1697-4e5c-b07b-0c8a3cb700a2", + "execution_count": 91, + "id": "866533ed-bdab-4b29-bc91-50ed0043e84a", "metadata": {}, "outputs": [], "source": [ - "ts = model.predict()" + "predetected = model.predetect(72)" ] }, { "cell_type": "code", - "execution_count": 78, - "id": "7052b1b8-3a72-47ad-90bf-472d7247172e", + "execution_count": 92, + "id": "44bff6ea-f249-48b6-816f-c49e0d264520", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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"Use another tool from onTime" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "762ad088-bb06-4919-91d1-7671cf17aa40", - "metadata": {}, - "outputs": [], - "source": [ - "det = on.detectors.quantile(low_quantile=0.4, high_quantile=0.6)\n", - "det.fit(train);" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "fedb2166-276b-4af9-b867-b4a08218f4e4", - "metadata": {}, - "outputs": [], - "source": [ - "preds = model.predict()" - ] - }, - { - "cell_type": "markdown", - "id": "8a6e3e8f-aab6-40ae-a92a-bd3dae828caf", - "metadata": {}, - "source": [ - "Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "id": "097fdea6-5ae3-4622-abc2-1bc33fc190bb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(2, figsize=(6, 4))\n", - "preds.plot(ax=ax[0], label='preds')\n", - "test[0:7].plot(ax=ax[0], label='test')\n", - "det.detect(preds).plot(ax=ax[1], label='anomalies')\n", - "fig.subplots_adjust(hspace=.5);" - ] - }, - { - "cell_type": "markdown", - "id": "c9e2831e-592c-4542-b304-df9f0b9e8368", - "metadata": {}, - "source": [ - "---\n", - "## Advantages of this approach\n", - "\n", - "- possibility to share knowledge without complexity\n", - "- respect of the company privacy\n", - "- extendability of the library\n", - "- reusability\n", - "\n", - "For DiagnoBat, some of your model(s) could be added to the main library, depending on your decision.\n", - "\n", - "## Next steps\n", - "\n", - "- First predictor/detectors given our specs\n", - "- Integration of a range of plots about root cause detection\n", - "- Benchmarking of different models\n" + "on.plots.anomalies(test[:72], predetected[:72])" ] }, { diff --git a/src/ontime/__init__.py b/src/ontime/__init__.py index 1a88547..b51c3ca 100644 --- a/src/ontime/__init__.py +++ b/src/ontime/__init__.py @@ -1,10 +1 @@ -""" OnTime API Definition """ - -from .abstract import * -from .context import * -from .detectors import detectors -from .generators import generators -from .model import Model -from .plots import plots -from .processors import processors -from .time_series import TimeSeries +from .api.modular import * diff --git a/src/ontime/abstract/__init__.py b/src/ontime/abstract/__init__.py deleted file mode 100644 index cd703ea..0000000 --- a/src/ontime/abstract/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .abstract_base_detector import AbstractBaseDetector -from .abstract_base_generator import AbstractBaseGenerator -from .abstract_base_model import AbstractBaseModel -from .abstract_base_processor import AbstractBaseProcessor diff --git a/src/ontime/detectors/registry/__init__.py b/src/ontime/api/__init__.py similarity index 100% rename from src/ontime/detectors/registry/__init__.py rename to src/ontime/api/__init__.py diff --git a/src/ontime/api/modular.py b/src/ontime/api/modular.py new file mode 100644 index 0000000..c619725 --- /dev/null +++ b/src/ontime/api/modular.py @@ -0,0 +1,38 @@ +""" +onTime Modular API Definition + +The aim of the Modular API is to give building blocks to the user to build whatever is desired. +`module` and `context` are left as is. + +The core of the API is accessible through with the main object of the library `onTime`. +For instance : + + import ontime as on + on.TimeSeries() + +Features contained in `module` and `context` are accessible through the `onTime` object. +For instance : + + import ontime.module as onm + onm.preprocessing.common.my_function() + +""" + +from ..core import detectors, generators, Model, plots, processors, TimeSeries + +from .. import module +from .. import context + +__all__ = [ + # core + "detectors", + "generators", + "Model", + "plots", + "processors", + "TimeSeries", + # module + "module", + # context + "context", +] diff --git a/src/ontime/config/__init__.py b/src/ontime/config/__init__.py deleted file mode 100644 index b4ca83e..0000000 --- a/src/ontime/config/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .colors import * -from .constants import * diff --git a/src/ontime/config/colors.py b/src/ontime/config/colors.py deleted file mode 100644 index 7bee7ed..0000000 --- a/src/ontime/config/colors.py +++ /dev/null @@ -1,14 +0,0 @@ -from types import SimpleNamespace - -COLORS_DICT = { - "blue_light": "#CADEF7", - "blue": "#29335C", - "blue_dark": "#0A2342", - "green": "#297373", - "yellow": "#E8C547", - "red": "#E4572E", - "red_dark": "#92140C", - "grey": "#CDD1C4", -} - -colors = SimpleNamespace(**COLORS_DICT) diff --git a/src/ontime/config/constants.py b/src/ontime/config/constants.py deleted file mode 100644 index 6c660a8..0000000 --- a/src/ontime/config/constants.py +++ /dev/null @@ -1,25 +0,0 @@ -# Generic -DEFAULT_EXPORT_FILENAME = "export" - -# TimeSeries -TIME_SERIES_FILENAME = "data" -TIME_SERIES_EXT = "csv" - -# Component -COMPONENT_VALUES = "values" -COMPONENT_META_PREFIX = "meta_" -COMPONENT_META_LABEL_PREFIX = "label_" -COMPONENT_META_CI_LOWER = "ci_lower" -COMPONENT_META_CI_UPPER = "ci_upper" - -# Metadata -METADATA_FILENAME = "meta" -METADATA_EXT = "json" -METADATA_CLASS_LABEL = "label" - -# Model -MODEL_TYPE_UNIVARIATE = "univariate" -MODEL_TYPE_MULTIVARIATE = "multivariate" - -# Misc. IO -PICKLE_EXT = "pkl" diff --git a/src/ontime/context/__init__.py b/src/ontime/context/__init__.py index 55e5f84..e4193cf 100644 --- a/src/ontime/context/__init__.py +++ b/src/ontime/context/__init__.py @@ -1 +1 @@ -from .common import * +from . import common diff --git a/src/ontime/context/common/__init__.py b/src/ontime/context/common/__init__.py index 41de82b..c5e67c0 100644 --- a/src/ontime/context/common/__init__.py +++ b/src/ontime/context/common/__init__.py @@ -1,4 +1,6 @@ from .generic_predictor import GenericPredictor from .generic_detector import GenericDetector from .profiler import Profiler -from .anomalies_frequencies import AnomaliesFrequencies +from .anomaly_frequency import AnomalyFrequency + +__all__ = ["GenericPredictor", "GenericDetector", "Profiler", "AnomalyFrequency"] diff --git a/src/ontime/context/common/anomalies_frequencies.py b/src/ontime/context/common/anomaly_frequency.py similarity index 77% rename from src/ontime/context/common/anomalies_frequencies.py rename to src/ontime/context/common/anomaly_frequency.py index 442d402..124180b 100644 --- a/src/ontime/context/common/anomalies_frequencies.py +++ b/src/ontime/context/common/anomaly_frequency.py @@ -1,7 +1,7 @@ -from ...time_series import BinaryTimeSeries, ProbabilisticTimeSeries, TimeSeries +from ...core.time_series import BinaryTimeSeries, ProbabilisticTimeSeries, TimeSeries -class AnomaliesFrequencies: +class AnomalyFrequency: """ Class for computing the frequency of anomalies in a time window. """ @@ -13,8 +13,8 @@ def __init__(self, anomalies_ts: BinaryTimeSeries): def get_number_of_anomaly_in_window(self, window_size: str) -> TimeSeries: """ Compute the number of anomalies in a time window. - - return: TimeSeries with the number of anomalies in the window + :param window_size: str of the size of the time window + :return: TimeSeries with the number of anomalies in the window """ sum_series = self.anomalies_series.rolling(window=window_size).sum() return TimeSeries.from_series(sum_series) @@ -27,7 +27,8 @@ def get_frequency_of_anomaly_in_window( the window divided by the maximum number of anomalies in a window. So 1 mean that all samples in the window are anomalies. - return: ProbabilisticTimeSeries with the frequency of anomalies in the window + :param window_size: str of the size of the time window + :return: ProbabilisticTimeSeries with the frequency of anomalies in the window """ # Compute the maximum number of anomalies in a window max_anomalies = self.anomalies_series.rolling(window=window_size).count().max() diff --git a/src/ontime/context/common/generic_detector.py b/src/ontime/context/common/generic_detector.py index d048e3e..e49d80b 100644 --- a/src/ontime/context/common/generic_detector.py +++ b/src/ontime/context/common/generic_detector.py @@ -1,8 +1,9 @@ from darts.models import CatBoostModel from darts.utils.statistics import check_seasonality -from ...time_series import BinaryTimeSeries -from ...detectors import Quantile -from ...model import Model + +from ...core.time_series import BinaryTimeSeries +from ...core.detector import Quantile +from ...core.model import Model class GenericDetector: @@ -53,7 +54,7 @@ def detect(self, ts) -> BinaryTimeSeries: def predetect(self, n) -> BinaryTimeSeries: """ - Predict n steps into the future and detect anomalies + Predict length steps into the future and detect anomalies Can raise a ValueError if the model has not been fitted diff --git a/src/ontime/context/common/generic_predictor.py b/src/ontime/context/common/generic_predictor.py index 9ba991c..28c502a 100644 --- a/src/ontime/context/common/generic_predictor.py +++ b/src/ontime/context/common/generic_predictor.py @@ -1,6 +1,7 @@ from darts.models import CatBoostModel from darts.utils.statistics import check_seasonality -import ontime as on + +from ...core.model import Model class GenericPredictor: @@ -22,7 +23,7 @@ def fit(self, ts): lags = 12 if seasonality == 0 else seasonality # Create model - self.model = on.Model( + self.model = Model( CatBoostModel, lags=int(lags), ) @@ -31,7 +32,7 @@ def fit(self, ts): def predict(self, n): """ - Predict n steps into the future + Predict length steps into the future :param n: Int number of steps to predict :return: TimeSeries """ diff --git a/src/ontime/context/common/profiler.py b/src/ontime/context/common/profiler.py index 12ed43c..07283d4 100644 --- a/src/ontime/context/common/profiler.py +++ b/src/ontime/context/common/profiler.py @@ -1,11 +1,12 @@ -from ontime.time_series import TimeSeries from enum import Enum + import pandas as pd +from ...core.time_series import TimeSeries + class Profiler: """ - This class should not be instantiated. This class is used to make a profile from a time series. """ @@ -20,7 +21,7 @@ class Aggregation(Enum): SUM = "sum" # Define the all periods possible - # The first element is the offset alias for split_by_period from ontime.time_series + # The first element is the offset alias for split_by_period from ontime.modules.time_series # The second element is the format to make the aggregation (**Also users' format**) # The third element is the format to convert data to match with TimeSeries format and the chosen period class Period(Enum): diff --git a/src/ontime/core/__init__.py b/src/ontime/core/__init__.py new file mode 100644 index 0000000..6948a9c --- /dev/null +++ b/src/ontime/core/__init__.py @@ -0,0 +1,8 @@ +from .detector import detectors, abstract_detector +from .generator import generators, abstract_generator +from .model import Model, abstract_model +from .plot import * +from .processor import processors, abstract_processor +from .time_series import TimeSeries + +__all__ = ["detectors", "generators", "Model", "processors", "TimeSeries"] diff --git a/src/ontime/detectors/__init__.py b/src/ontime/core/detector/__init__.py similarity index 89% rename from src/ontime/detectors/__init__.py rename to src/ontime/core/detector/__init__.py index 9602986..1b6a075 100644 --- a/src/ontime/detectors/__init__.py +++ b/src/ontime/core/detector/__init__.py @@ -5,3 +5,5 @@ detectors = Detectors() detectors.load("threshold", Threshold) detectors.load("quantile", Quantile) + +__all__ = ["detectors"] diff --git a/src/ontime/abstract/abstract_base_detector.py b/src/ontime/core/detector/abstract_detector.py similarity index 76% rename from src/ontime/abstract/abstract_base_detector.py rename to src/ontime/core/detector/abstract_detector.py index 3a07fa5..14942e1 100644 --- a/src/ontime/abstract/abstract_base_detector.py +++ b/src/ontime/core/detector/abstract_detector.py @@ -3,13 +3,13 @@ from ..time_series import BinaryTimeSeries, TimeSeries -class AbstractBaseDetector(ABC): +class AbstractDetector(ABC): """Abstract class to define methods to implement for a Detector class. """ # TODO check if this must return a TimeSeries or a BinaryTimeSeries @abstractmethod - def detect(self, ts: TimeSeries) -> BinaryTimeSeries: + def detect(self, ts: TimeSeries, *args, **kwargs) -> BinaryTimeSeries: """Detect features""" raise NotImplementedError diff --git a/src/ontime/detectors/detectors.py b/src/ontime/core/detector/detectors.py similarity index 100% rename from src/ontime/detectors/detectors.py rename to src/ontime/core/detector/detectors.py diff --git a/src/ontime/generators/registry/__init__.py b/src/ontime/core/detector/registry/__init__.py similarity index 100% rename from src/ontime/generators/registry/__init__.py rename to src/ontime/core/detector/registry/__init__.py diff --git a/src/ontime/detectors/registry/quantile.py b/src/ontime/core/detector/registry/quantile.py similarity index 88% rename from src/ontime/detectors/registry/quantile.py rename to src/ontime/core/detector/registry/quantile.py index 517a7b3..a518d95 100644 --- a/src/ontime/detectors/registry/quantile.py +++ b/src/ontime/core/detector/registry/quantile.py @@ -1,9 +1,10 @@ from darts.ad.detectors.quantile_detector import QuantileDetector -from ...abstract import AbstractBaseDetector + +from ..abstract_detector import AbstractDetector from ...time_series import BinaryTimeSeries, TimeSeries -class Quantile(QuantileDetector, AbstractBaseDetector): +class Quantile(QuantileDetector, AbstractDetector): """ Wrapper around Darts QuantileDetector. """ diff --git a/src/ontime/core/detector/registry/threshold.py b/src/ontime/core/detector/registry/threshold.py new file mode 100644 index 0000000..527e508 --- /dev/null +++ b/src/ontime/core/detector/registry/threshold.py @@ -0,0 +1,38 @@ +from typing import Sequence, Union + +from darts.ad.detectors.threshold_detector import ThresholdDetector + +from ..abstract_detector import AbstractDetector +from ...time_series import TimeSeries, BinaryTimeSeries + + +class Threshold(ThresholdDetector, AbstractDetector): + """ + Wrapper around Darts ThresholdDetector. + """ + + def __init__( + self, + low_threshold: Union[int, float, Sequence[float], None] = None, + high_threshold: Union[int, float, Sequence[float], None] = None, + ): + """ + + :param low_threshold: (Sequence of) lower bounds. + If a sequence, must match the dimensionality of the series + The lower bound is included in the valid interval. So if the lower bound is 0, the value 0 is valid. + + :param high_threshold: (Sequence of) upper bounds. + If a sequence, must match the dimensionality of the series + The upper bound is included in the valid interval. So if the upper bound is 10, the value 10 is valid. + """ + super().__init__(low_threshold, high_threshold) + + def detect(self, ts: TimeSeries) -> BinaryTimeSeries: + """ + Detects anomalies in the given time series. + :param ts: TimeSeries + :return: BinaryTimeSeries + """ + ts_detected = super().detect(ts) + return BinaryTimeSeries.from_darts(ts_detected) diff --git a/src/ontime/generators/__init__.py b/src/ontime/core/generator/__init__.py similarity index 95% rename from src/ontime/generators/__init__.py rename to src/ontime/core/generator/__init__.py index e9d4dc5..67dddd4 100644 --- a/src/ontime/generators/__init__.py +++ b/src/ontime/core/generator/__init__.py @@ -13,3 +13,5 @@ generators.load("linear", Linear) generators.load("random_walk", RandomWalk) generators.load("sine", Sine) + +__all__ = ["generators"] diff --git a/src/ontime/abstract/abstract_base_generator.py b/src/ontime/core/generator/abstract_generator.py similarity index 74% rename from src/ontime/abstract/abstract_base_generator.py rename to src/ontime/core/generator/abstract_generator.py index 51b8d1d..0e20f46 100644 --- a/src/ontime/abstract/abstract_base_generator.py +++ b/src/ontime/core/generator/abstract_generator.py @@ -2,12 +2,12 @@ from typing import NoReturn -class AbstractBaseGenerator(ABC): +class AbstractGenerator(ABC): """Abstract class to define methods to implement for a Generator class. """ @abstractmethod - def generate(self, **kwargs) -> NoReturn: + def generate(self, *args, **kwargs) -> NoReturn: """Generate features""" raise NotImplementedError diff --git a/src/ontime/generators/generators.py b/src/ontime/core/generator/generators.py similarity index 100% rename from src/ontime/generators/generators.py rename to src/ontime/core/generator/generators.py diff --git a/src/ontime/model/libs/darts/__init__.py b/src/ontime/core/generator/registry/__init__.py similarity index 100% rename from src/ontime/model/libs/darts/__init__.py rename to src/ontime/core/generator/registry/__init__.py diff --git a/src/ontime/generators/registry/constant.py b/src/ontime/core/generator/registry/constant.py similarity index 92% rename from src/ontime/generators/registry/constant.py rename to src/ontime/core/generator/registry/constant.py index a050411..aca1559 100644 --- a/src/ontime/generators/registry/constant.py +++ b/src/ontime/core/generator/registry/constant.py @@ -5,10 +5,10 @@ from darts.utils.timeseries_generation import constant_timeseries from ...time_series import TimeSeries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator -class Constant(AbstractBaseGenerator): +class Constant(AbstractGenerator): """ Wrapper around Darts constant time series generator. """ diff --git a/src/ontime/generators/registry/gaussian.py b/src/ontime/core/generator/registry/gaussian.py similarity index 93% rename from src/ontime/generators/registry/gaussian.py rename to src/ontime/core/generator/registry/gaussian.py index b5e4f46..371fa07 100644 --- a/src/ontime/generators/registry/gaussian.py +++ b/src/ontime/core/generator/registry/gaussian.py @@ -4,11 +4,11 @@ import pandas as pd from darts.utils.timeseries_generation import gaussian_timeseries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator from ...time_series import TimeSeries -class Gaussian(AbstractBaseGenerator): +class Gaussian(AbstractGenerator): """ Wrapper around Darts gaussian time series generator. """ diff --git a/src/ontime/generators/registry/holiday.py b/src/ontime/core/generator/registry/holiday.py similarity index 93% rename from src/ontime/generators/registry/holiday.py rename to src/ontime/core/generator/registry/holiday.py index 59663be..1aa2b13 100644 --- a/src/ontime/generators/registry/holiday.py +++ b/src/ontime/core/generator/registry/holiday.py @@ -4,11 +4,11 @@ import pandas as pd from darts.utils.timeseries_generation import holidays_timeseries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator from ...time_series import TimeSeries -class Holiday(AbstractBaseGenerator): +class Holiday(AbstractGenerator): """ Wrapper around Darts holiday time series generator. """ diff --git a/src/ontime/generators/registry/linear.py b/src/ontime/core/generator/registry/linear.py similarity index 93% rename from src/ontime/generators/registry/linear.py rename to src/ontime/core/generator/registry/linear.py index 5327ddb..aceef5e 100644 --- a/src/ontime/generators/registry/linear.py +++ b/src/ontime/core/generator/registry/linear.py @@ -4,11 +4,11 @@ import pandas as pd from darts.utils.timeseries_generation import linear_timeseries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator from ...time_series import TimeSeries -class Linear(AbstractBaseGenerator): +class Linear(AbstractGenerator): """ Wrapper around Darts linear time series generator. """ diff --git a/src/ontime/generators/registry/random_walk.py b/src/ontime/core/generator/registry/random_walk.py similarity index 93% rename from src/ontime/generators/registry/random_walk.py rename to src/ontime/core/generator/registry/random_walk.py index 914dabc..18b26cf 100644 --- a/src/ontime/generators/registry/random_walk.py +++ b/src/ontime/core/generator/registry/random_walk.py @@ -5,10 +5,10 @@ from darts.utils.timeseries_generation import random_walk_timeseries from ...time_series import TimeSeries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator -class RandomWalk(AbstractBaseGenerator): +class RandomWalk(AbstractGenerator): """ Wrapper around Darts random walk time series generator. """ diff --git a/src/ontime/generators/registry/sine.py b/src/ontime/core/generator/registry/sine.py similarity index 94% rename from src/ontime/generators/registry/sine.py rename to src/ontime/core/generator/registry/sine.py index d9a498b..2529e30 100644 --- a/src/ontime/generators/registry/sine.py +++ b/src/ontime/core/generator/registry/sine.py @@ -4,11 +4,11 @@ import pandas as pd from darts.utils.timeseries_generation import sine_timeseries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator from ...time_series import TimeSeries -class Sine(AbstractBaseGenerator): +class Sine(AbstractGenerator): """ Wrapper around Darts sine time series generator. """ diff --git a/src/ontime/model/__init__.py b/src/ontime/core/model/__init__.py similarity index 54% rename from src/ontime/model/__init__.py rename to src/ontime/core/model/__init__.py index 3b4d86e..b7ac7f9 100644 --- a/src/ontime/model/__init__.py +++ b/src/ontime/core/model/__init__.py @@ -1 +1,3 @@ from .model import Model + +__all__ = ["Model"] diff --git a/src/ontime/abstract/abstract_base_model.py b/src/ontime/core/model/abstract_model.py similarity index 68% rename from src/ontime/abstract/abstract_base_model.py rename to src/ontime/core/model/abstract_model.py index 15d991b..37a0727 100644 --- a/src/ontime/abstract/abstract_base_model.py +++ b/src/ontime/core/model/abstract_model.py @@ -5,20 +5,20 @@ from ..time_series import TimeSeries -class AbstractBaseModel(ABC): +class AbstractModel(ABC): """Abstract class to define methods to implement for a Model class inspired by Scikit Learn API. """ - def __init__(self): + def __init__(self, *args, **kwargs): pass @abstractmethod - def fit(self, ts: TimeSeries) -> NoReturn: + def fit(self, ts: TimeSeries, *args, **kwargs) -> NoReturn: """Fit a model""" pass @abstractmethod - def predict(self, horizon: Any) -> Any: + def predict(self, horizon: Any, *args, **kwargs) -> Any: """Usage of the model to predict values""" pass diff --git a/src/ontime/model/libs/skforecast/__init__.py b/src/ontime/core/model/libs/darts/__init__.py similarity index 100% rename from src/ontime/model/libs/skforecast/__init__.py rename to src/ontime/core/model/libs/darts/__init__.py diff --git a/src/ontime/core/model/libs/darts/forecasting_model.py b/src/ontime/core/model/libs/darts/forecasting_model.py new file mode 100644 index 0000000..57c8e0e --- /dev/null +++ b/src/ontime/core/model/libs/darts/forecasting_model.py @@ -0,0 +1,39 @@ +from ...abstract_model import AbstractModel +from ....time_series import TimeSeries + + +class ForecastingModel(AbstractModel): + """ + Generic wrapper around Darts forecasting models + """ + + def __init__(self, model, **params): + """Constructor of a ForecastingModel object + + :param model: Dart's forecasting model + :param params: dict of keyword arguments for this model's constructor + """ + super().__init__() + self.model = model(**params) + + def fit(self, ts, **params): + """ + Fit the model to the given time series + + :param ts: TimeSeries + :param params: dict of keyword arguments for this model's fit method + :return: self + """ + self.model.fit(ts, **params) + return self + + def predict(self, n, **params): + """ + Predict n steps into the future + + :param n: int number of steps to predict + :param params: dict of keyword arguments for this model's predict method + :return: TimeSeries + """ + pred = self.model.predict(n, **params) + return TimeSeries.from_darts(pred) diff --git a/src/ontime/core/model/libs/skforecast/__init__.py b/src/ontime/core/model/libs/skforecast/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/ontime/model/libs/skforecast/forecaster_autoreg.py b/src/ontime/core/model/libs/skforecast/forecaster_autoreg.py similarity index 81% rename from src/ontime/model/libs/skforecast/forecaster_autoreg.py rename to src/ontime/core/model/libs/skforecast/forecaster_autoreg.py index 440f779..5d60a1b 100644 --- a/src/ontime/model/libs/skforecast/forecaster_autoreg.py +++ b/src/ontime/core/model/libs/skforecast/forecaster_autoreg.py @@ -1,14 +1,14 @@ from abc import ABCMeta -from ontime.abstract.abstract_base_model import AbstractBaseModel -from ontime.time_series import TimeSeries +from ...abstract_model import AbstractModel +from ....time_series import TimeSeries from skforecast.ForecasterAutoreg import ( ForecasterAutoreg as SkForecastForecasterAutoreg, ) -class ForecasterAutoreg(AbstractBaseModel): +class ForecasterAutoreg(AbstractModel): """ Generic wrapper around SkForecast ForecasterAutoreg models """ diff --git a/src/ontime/model/model.py b/src/ontime/core/model/model.py similarity index 75% rename from src/ontime/model/model.py rename to src/ontime/core/model/model.py index 86dd05d..02d6846 100644 --- a/src/ontime/model/model.py +++ b/src/ontime/core/model/model.py @@ -1,15 +1,14 @@ from darts.models.forecasting.forecasting_model import ModelMeta -from ontime.abstract.abstract_base_model import AbstractBaseModel -from ontime.time_series import TimeSeries - +from ..time_series import TimeSeries +from .abstract_model import AbstractModel from .libs.darts.forecasting_model import ForecastingModel as DartsForecastingModel from .libs.skforecast.forecaster_autoreg import ( ForecasterAutoreg as SkForecastForecasterAutoreg, ) -class Model(AbstractBaseModel): +class Model(AbstractModel): """ Generic wrapper around all implemented time series libraries """ @@ -23,9 +22,10 @@ def __init__(self, model, **params): # scikit-learn API compatible models self.model = SkForecastForecasterAutoreg(model, **params) - def fit(self, ts, **params): + def fit(self, ts: TimeSeries, **params): """ Fit the model to the given time series + :param ts: TimeSeries :param params: Parameters to pass to the model :return: self @@ -33,11 +33,12 @@ def fit(self, ts, **params): self.model.fit(ts, **params) return self - def predict(self, n, **params): + def predict(self, n: int, **params): """ - Predict n steps into the future - :param n: Integer - :param params: Parameters to pass to the predict method + Predict length steps into the future + + :param n: int number of steps to predict + :param params: dict to pass to the predict method :return: TimeSeries """ pred = self.model.predict(n, **params) diff --git a/src/ontime/core/plot/__init__.py b/src/ontime/core/plot/__init__.py new file mode 100644 index 0000000..f6f83de --- /dev/null +++ b/src/ontime/core/plot/__init__.py @@ -0,0 +1 @@ +from . import plots diff --git a/src/ontime/plots/plots.py b/src/ontime/core/plot/plots.py similarity index 91% rename from src/ontime/plots/plots.py rename to src/ontime/core/plot/plots.py index e1b9d2b..634ef41 100644 --- a/src/ontime/plots/plots.py +++ b/src/ontime/core/plot/plots.py @@ -1,10 +1,13 @@ import pandas as pd import altair as alt +from ..time_series import TimeSeries -def line(ts): + +def line(ts: TimeSeries) -> alt.Chart: """ Standard line plot for TimeSeries + :param ts: TimeSeries :return: Altair Chart """ @@ -30,9 +33,10 @@ def line(ts): return chart -def heatmap(ts): +def heatmap(ts: TimeSeries) -> alt.Chart: """ Plot a Heatmap of a TimeSeries + :param ts: TimeSeries :return: Altair Chart """ @@ -71,9 +75,12 @@ def heatmap(ts): return chart -def prediction(train_ts, pred_ts=None, test_ts=None): +def prediction( + train_ts: TimeSeries, pred_ts: TimeSeries = None, test_ts: TimeSeries = None +) -> alt.Chart: """ Plot a prediction + :param train_ts: TimeSeries :param pred_ts: TimeSeries :param test_ts: TimeSeries @@ -125,11 +132,12 @@ def prediction(train_ts, pred_ts=None, test_ts=None): return chart -def anomalies(ts, ts_anomaly): +def anomalies(ts: TimeSeries, ts_anomaly: TimeSeries) -> alt.Chart: """ Plot Anomalies - :param ts: normal series - :param ts_anomaly: anomaly series + + :param ts: TimeSeries of the signal + :param ts_anomaly: TimeSeries of the anomalies :return: Altair Chart """ alt.data_transformers.enable("vegafusion") diff --git a/src/ontime/processors/__init__.py b/src/ontime/core/processor/__init__.py similarity index 93% rename from src/ontime/processors/__init__.py rename to src/ontime/core/processor/__init__.py index 288eefa..afbfb04 100644 --- a/src/ontime/processors/__init__.py +++ b/src/ontime/core/processor/__init__.py @@ -9,3 +9,5 @@ processors.load("mapper", Mapper) processors.load("windower", Windower) processors.load("correlation", Correlation) + +__all__ = ["processors"] diff --git a/src/ontime/abstract/abstract_base_processor.py b/src/ontime/core/processor/abstract_processor.py similarity index 69% rename from src/ontime/abstract/abstract_base_processor.py rename to src/ontime/core/processor/abstract_processor.py index 8cd9184..1fd3aef 100644 --- a/src/ontime/abstract/abstract_base_processor.py +++ b/src/ontime/core/processor/abstract_processor.py @@ -1,15 +1,14 @@ from abc import ABC, abstractmethod -from typing import NoReturn from ..time_series import TimeSeries -class AbstractBaseProcessor(ABC): +class AbstractProcessor(ABC): """Abstract class to define methods to implement for a Processor class. """ @abstractmethod - def process(self, ts: TimeSeries) -> NoReturn: + def process(self, ts: TimeSeries) -> TimeSeries: """Process time series""" raise NotImplementedError diff --git a/src/ontime/processors/processors.py b/src/ontime/core/processor/processors.py similarity index 100% rename from src/ontime/processors/processors.py rename to src/ontime/core/processor/processors.py diff --git a/src/ontime/processors/registry/correlation.py b/src/ontime/core/processor/registry/correlation.py similarity index 90% rename from src/ontime/processors/registry/correlation.py rename to src/ontime/core/processor/registry/correlation.py index 2d6ab22..2bcc0fe 100644 --- a/src/ontime/processors/registry/correlation.py +++ b/src/ontime/core/processor/registry/correlation.py @@ -7,25 +7,29 @@ import numpy as np from ...time_series import TimeSeries +from ..abstract_processor import AbstractProcessor -class Correlation: +class Correlation(AbstractProcessor): """Correlation class handles correlation computation in a TimeSeries""" - @staticmethod - def process( - ts: TimeSeries, window: Union[int, timedelta, str, BaseOffset, BaseIndexer] - ) -> TimeSeries: - """Compute correlations for a TimeSeries + def __init__(self, window: Union[int, timedelta, str, BaseOffset, BaseIndexer]): + """Constructor of a correlation processor - :param ts: TimeSeries :param window: int, timedelta, str, offset, or BaseIndexer subclass Size of the moving window as in https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html#pandas-dataframe-rolling + """ + self.window = window + + def process(self, ts: TimeSeries) -> TimeSeries: + """Compute correlations for a TimeSeries + + :param ts: TimeSeries :return: TimeSeries Each correlation is a component of the TimeSeries with a name such as 'var_a_var_b' """ df = ts.pd_dataframe() - df = Correlation.compute_correlations(df, window) + df = Correlation.compute_correlations(df, self.window) df = Correlation.pivot(df) df.columns.name = None # Otherwise, the column name is 'pair' and from_dataframe() fails in the next line return TimeSeries.from_dataframe(df) diff --git a/src/ontime/processors/registry/filler.py b/src/ontime/core/processor/registry/filler.py similarity index 95% rename from src/ontime/processors/registry/filler.py rename to src/ontime/core/processor/registry/filler.py index fc91b17..cf07820 100644 --- a/src/ontime/processors/registry/filler.py +++ b/src/ontime/core/processor/registry/filler.py @@ -2,11 +2,11 @@ MissingValuesFiller as DartsMissingValuesFiller, ) -from ...abstract import AbstractBaseProcessor +from ..abstract_processor import AbstractProcessor from ...time_series import TimeSeries -class Filler(AbstractBaseProcessor): +class Filler(AbstractProcessor): """Wrapper around Darts MissingValuesFiller. https://unit8co.github.io/darts/generated_api/darts.dataprocessing.transformers.missing_values_filler.html """ diff --git a/src/ontime/processors/registry/mapper.py b/src/ontime/core/processor/registry/mapper.py similarity index 97% rename from src/ontime/processors/registry/mapper.py rename to src/ontime/core/processor/registry/mapper.py index 8b1f4f7..bf1e29b 100644 --- a/src/ontime/processors/registry/mapper.py +++ b/src/ontime/core/processor/registry/mapper.py @@ -3,11 +3,11 @@ InvertibleMapper as DartsInvertibleMapper, ) -from ...abstract import AbstractBaseProcessor +from ..abstract_processor import AbstractProcessor from ...time_series import TimeSeries -class Mapper(AbstractBaseProcessor): +class Mapper(AbstractProcessor): """ Wrapper around Darts Mapper https://unit8co.github.io/darts/generated_api/darts.dataprocessing.transformers.mappers.html diff --git a/src/ontime/processors/registry/windower.py b/src/ontime/core/processor/registry/windower.py similarity index 97% rename from src/ontime/processors/registry/windower.py rename to src/ontime/core/processor/registry/windower.py index 4346710..3072e90 100644 --- a/src/ontime/processors/registry/windower.py +++ b/src/ontime/core/processor/registry/windower.py @@ -4,11 +4,11 @@ WindowTransformer as DartsWindowTransformer, ) -from ...abstract import AbstractBaseProcessor +from ..abstract_processor import AbstractProcessor from ...time_series import TimeSeries -class Windower(AbstractBaseProcessor): +class Windower(AbstractProcessor): """ Wrapper around Darts WindowTransformer. https://unit8co.github.io/darts/generated_api/darts.dataprocessing.transformers.window_transformer.html#window-transformer diff --git a/src/ontime/time_series/__init__.py b/src/ontime/core/time_series/__init__.py similarity index 63% rename from src/ontime/time_series/__init__.py rename to src/ontime/core/time_series/__init__.py index f073f71..b49a7b5 100644 --- a/src/ontime/time_series/__init__.py +++ b/src/ontime/core/time_series/__init__.py @@ -2,3 +2,10 @@ from .probabilistic_time_series import ProbabilisticTimeSeries from .binary_time_series import BinaryTimeSeries from .resticted_time_series import RestrictedTimeSeries + +__all__ = [ + "TimeSeries", + "ProbabilisticTimeSeries", + "BinaryTimeSeries", + "RestrictedTimeSeries", +] diff --git a/src/ontime/time_series/binary_time_series.py b/src/ontime/core/time_series/binary_time_series.py similarity index 97% rename from src/ontime/time_series/binary_time_series.py rename to src/ontime/core/time_series/binary_time_series.py index db20fac..19df797 100644 --- a/src/ontime/time_series/binary_time_series.py +++ b/src/ontime/core/time_series/binary_time_series.py @@ -1,9 +1,8 @@ -import pandas as pd - -from .resticted_time_series import RestrictedTimeSeries import xarray as xr import numpy as np +from .resticted_time_series import RestrictedTimeSeries + class BinaryTimeSeries(RestrictedTimeSeries["BinaryTimeSeries"]): def __init__(self, xa: xr.DataArray): diff --git a/src/ontime/time_series/probabilistic_time_series.py b/src/ontime/core/time_series/probabilistic_time_series.py similarity index 97% rename from src/ontime/time_series/probabilistic_time_series.py rename to src/ontime/core/time_series/probabilistic_time_series.py index 5eb717d..6296837 100644 --- a/src/ontime/time_series/probabilistic_time_series.py +++ b/src/ontime/core/time_series/probabilistic_time_series.py @@ -1,7 +1,7 @@ -from .resticted_time_series import RestrictedTimeSeries import xarray as xr import numpy as np -import pandas as pd + +from .resticted_time_series import RestrictedTimeSeries class ProbabilisticTimeSeries(RestrictedTimeSeries["ProbabilisticTimeSeries"]): diff --git a/src/ontime/time_series/resticted_time_series.py b/src/ontime/core/time_series/resticted_time_series.py similarity index 96% rename from src/ontime/time_series/resticted_time_series.py rename to src/ontime/core/time_series/resticted_time_series.py index 385c441..66f5860 100644 --- a/src/ontime/time_series/resticted_time_series.py +++ b/src/ontime/core/time_series/resticted_time_series.py @@ -8,14 +8,14 @@ Dict, Sequence, Callable, - Type, ) + +from darts import TimeSeries as DartsTimeSeries import pandas as pd -from .time_series import TimeSeries import xarray as xr import numpy as np -from darts import TimeSeries as DartsTimeSeries +from .time_series import TimeSeries T = TypeVar("T") @@ -29,7 +29,7 @@ def check(self, xa: xr.DataArray) -> bool: raise NotImplementedError @classmethod - def from_darts(cls, ts: DartsTimeSeries): + def from_darts(cls, ts: DartsTimeSeries) -> T: """ Convert a Darts TimeSeries to an OnTime TimeSeries @@ -51,7 +51,7 @@ def from_dataframe( fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, hierarchy: Optional[Dict] = None, - ): + ) -> T: ts = super().from_dataframe( df, time_col, @@ -77,7 +77,7 @@ def from_group_dataframe( fill_missing_dates: Optional[bool] = False, freq: Optional[Union[str, int]] = None, fillna_value: Optional[float] = None, - ): + ) -> T: raise NotImplementedError @classmethod @@ -88,7 +88,7 @@ def from_series( freq: Optional[Union[str, int]] = None, fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, - ): + ) -> T: ts = super().from_series( pd_series, fill_missing_dates, freq, fillna_value, static_covariates ) @@ -104,11 +104,11 @@ def from_values( fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, hierarchy: Optional[Dict] = None, - ): + ) -> T: raise NotImplementedError @classmethod - def from_pickle(cls, path: str): + def from_pickle(cls, path: str) -> T: raise NotImplementedError @classmethod @@ -122,7 +122,7 @@ def from_times_and_values( fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, hierarchy: Optional[Dict] = None, - ): + ) -> T: raise NotImplementedError @classmethod @@ -137,7 +137,7 @@ def from_csv( static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, hierarchy: Optional[Dict] = None, **kwargs, - ): + ) -> T: raise NotImplementedError @classmethod @@ -146,7 +146,7 @@ def from_json( json_str: str, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, hierarchy: Optional[Dict] = None, - ): + ) -> T: raise NotImplementedError @classmethod @@ -156,7 +156,7 @@ def from_xarray( fill_missing_dates: Optional[bool] = False, freq: Optional[Union[str, int]] = None, fillna_value: Optional[float] = None, - ) -> Type[T]: + ) -> T: cls.check(cls, xa) return super().from_xarray(xa, fill_missing_dates, freq, fillna_value) @@ -182,7 +182,7 @@ def concatenate( ignore_time_axis: bool = False, ignore_static_covariates: bool = False, drop_hierarchy: bool = True, - ) -> Type[T]: + ) -> T: # TODO : if return super().concatenate the type is TimeSeries. I choose to raise an error. See what is the best raise NotImplementedError @@ -213,7 +213,7 @@ def prepend_values(self, values: np.ndarray) -> T: """ raise NotImplementedError - def rescale_with_value(self, value_at_first_step: float) -> "TimeSeries": + def rescale_with_value(self, value_at_first_step: float) -> T: """ Rescales the time series so that the first value is equal to the given value. @@ -223,7 +223,7 @@ def rescale_with_value(self, value_at_first_step: float) -> "TimeSeries": """ raise NotImplementedError - def stack(self, other: "TimeSeries") -> "TimeSeries": + def stack(self, other: "TimeSeries") -> T: """ Stacks this time series with another one, along the time axis. @@ -233,7 +233,7 @@ def stack(self, other: "TimeSeries") -> "TimeSeries": """ return NotImplementedError - def sum(self, axis: int = 2) -> "TimeSeries": + def sum(self, axis: int = 2) -> T: """ Sums the values along the given axis. @@ -250,7 +250,7 @@ def window_transform( forecasting_safe: Optional[bool] = True, keep_non_transformed: Optional[bool] = False, include_current: Optional[bool] = True, - ): + ) -> T: raise NotImplementedError def with_values(self, values: np.ndarray) -> T: diff --git a/src/ontime/time_series/time_series.py b/src/ontime/core/time_series/time_series.py similarity index 90% rename from src/ontime/time_series/time_series.py rename to src/ontime/core/time_series/time_series.py index bc57215..abce624 100644 --- a/src/ontime/time_series/time_series.py +++ b/src/ontime/core/time_series/time_series.py @@ -5,10 +5,13 @@ import pandas as pd import xarray as xr -from ..plots import plots - class TimeSeries(DartsTimeSeries): + """ + Main class to handle time series + This is a wrapper around Darts TimeSeries, functions are added to handle various operations + """ + def __init__(self, xa: xr.DataArray): super().__init__(xa) diff --git a/src/ontime/utils/__init__.py b/src/ontime/core/utils/__init__.py similarity index 100% rename from src/ontime/utils/__init__.py rename to src/ontime/core/utils/__init__.py diff --git a/src/ontime/utils/dynamic_class.py b/src/ontime/core/utils/dynamic_class.py similarity index 86% rename from src/ontime/utils/dynamic_class.py rename to src/ontime/core/utils/dynamic_class.py index 658ac20..659093c 100644 --- a/src/ontime/utils/dynamic_class.py +++ b/src/ontime/core/utils/dynamic_class.py @@ -2,6 +2,10 @@ class DynamicClass(Registry): + """ + DynamicClass is a class that can load other classes dynamically + """ + def __init__(self): super().__init__() diff --git a/src/ontime/utils/registry.py b/src/ontime/core/utils/registry.py similarity index 78% rename from src/ontime/utils/registry.py rename to src/ontime/core/utils/registry.py index 206efb9..22014c7 100644 --- a/src/ontime/utils/registry.py +++ b/src/ontime/core/utils/registry.py @@ -1,4 +1,9 @@ class Registry: + """ + Registry class with the aim to store objects in a dictionary and retrieve them by name + + """ + def __init__(self): self.registry = {} diff --git a/src/ontime/utils/utils.py b/src/ontime/core/utils/utils.py similarity index 100% rename from src/ontime/utils/utils.py rename to src/ontime/core/utils/utils.py diff --git a/src/ontime/detectors/registry/threshold.py b/src/ontime/detectors/registry/threshold.py deleted file mode 100644 index 35a256d..0000000 --- a/src/ontime/detectors/registry/threshold.py +++ /dev/null @@ -1,22 +0,0 @@ -from darts.ad.detectors.threshold_detector import ThresholdDetector - -from ...abstract import AbstractBaseDetector -from ...time_series import TimeSeries, BinaryTimeSeries - - -class Threshold(ThresholdDetector, AbstractBaseDetector): - """ - Wrapper around Darts ThresholdDetector. - """ - - def __init__(self, low_threshold=None, high_threshold=None): - super().__init__(low_threshold, high_threshold) - - def detect(self, ts: TimeSeries) -> BinaryTimeSeries: - """ - Detects anomalies in the given time series. - :param ts: TimeSeries - :return: BinaryTimeSeries - """ - ts_detected = super().detect(ts) - return BinaryTimeSeries.from_darts(ts_detected) diff --git a/src/ontime/model/libs/darts/forecasting_model.py b/src/ontime/model/libs/darts/forecasting_model.py deleted file mode 100644 index 215e9a7..0000000 --- a/src/ontime/model/libs/darts/forecasting_model.py +++ /dev/null @@ -1,20 +0,0 @@ -from ontime.abstract.abstract_base_model import AbstractBaseModel -from ontime.time_series import TimeSeries - - -class ForecastingModel(AbstractBaseModel): - """ - Generic wrapper around Darts forecasting models - """ - - def __init__(self, model, **params): - super().__init__() - self.model = model(**params) - - def fit(self, ts, **params): - self.model.fit(ts, **params) - return self - - def predict(self, n, **params): - pred = self.model.predict(n, **params) - return TimeSeries.from_darts(pred) diff --git a/src/ontime/module/__init__.py b/src/ontime/module/__init__.py new file mode 100644 index 0000000..0fec63c --- /dev/null +++ b/src/ontime/module/__init__.py @@ -0,0 +1 @@ +from . import preprocessing diff --git a/src/ontime/module/preprocessing/__init__.py b/src/ontime/module/preprocessing/__init__.py new file mode 100644 index 0000000..e4193cf --- /dev/null +++ b/src/ontime/module/preprocessing/__init__.py @@ -0,0 +1 @@ +from . import common diff --git a/src/ontime/module/preprocessing/common.py b/src/ontime/module/preprocessing/common.py new file mode 100644 index 0000000..bca7238 --- /dev/null +++ b/src/ontime/module/preprocessing/common.py @@ -0,0 +1,144 @@ +import numpy as np +from sklearn.preprocessing import MinMaxScaler, StandardScaler +from darts.dataprocessing.transformers import Scaler + +from ...core.time_series import TimeSeries + + +def normalize( + ts: TimeSeries, type="minmax", return_transformer=False +) -> tuple | TimeSeries: + """ + Normalize a TimeSeries + + :param ts: TimeSeries to normalize + :param type: str type of normalization to apply + :param return_transformer: bool whether to return the transformer + :return: TimeSeries + """ + match type: + case "minmax": + scaler = MinMaxScaler() + case "zscore": + scaler = StandardScaler() + transformer = Scaler(scaler) + ts_transformed = transformer.fit_transform(ts) + if return_transformer: + return ts_transformed, transformer + else: + return ts_transformed + + +def train_test_split(ts: TimeSeries, test_split=None, train_split=None) -> tuple: + """ + Split a TimeSeries into train and test sets + + :param ts: TimeSeries to split + :param test_split: float, int or pd.TimeStamp + :param train_split: float, int or pd.TimeStamp + :return: tuple of TimeSeries + """ + + if train_split is not None and test_split is not None: + raise Exception( + "Only one of those two parameters can be set : train_split, test_split." + ) + + if train_split is None and test_split is None: + test_split = 0.25 + + # split time series in sub time series : train, test + if test_split is not None: + train_set, test_set = ts.split_after(1 - test_split) + + if train_split is not None: + train_set, test_set = ts.split_after(train_split) + + return train_set, test_set + + +def split_by_length(ts: TimeSeries, length: int, drop_last: bool = True) -> list: + """ + Split a TimeSeries into parts of a given length + + :param ts: TimeSeries to split + :param length: int length of each part + :param drop_last: bool, whether to drop the last part if it is shorter than n + :return: list of TimeSeries + """ + + # Get DataFrame + df = ts.pd_dataframe() + + # Calculate the total number of splits needed + total_splits = -(-len(df) // length) # Ceiling division to get the number of parts + + # Initialize a list to hold the DataFrame splits + splits_df = [] + + # Loop through the DataFrame and split it + for split in range(total_splits): + start_index = split * length + end_index = start_index + length + # Append the part to the list, using slicing with .iloc + splits_df.append(df.iloc[start_index:end_index]) + + # If the last dataframe has a different length, then drop it. + if drop_last: + last_df = splits_df[-1] + second_last = splits_df[-2] + if len(last_df) != len(second_last): + splits_df = splits_df[:-1] + + # Change the data structure from DataFrame to TimeSeries + return list(map(TimeSeries.from_dataframe, splits_df)) + + +def split_inputs_from_targets( + ts_list: list, input_length: int, target_length: int +) -> tuple: + """ + Split a list of TimeSeries into input and target TimeSeries + + :param ts_list: list of TimeSeries + :param input_length: int length of the input TimeSeries + :param target_length: int length of the target TimeSeries + :return: tuple of list of TimeSeries + """ + + # Change inner data structure to DataFrame + dfs = [ts.pd_dataframe() for ts in ts_list] + + # Create initial arrays + input_series_list = [] + target_series_list = [] + + # Iterate over each DataFrame in the list + for df in dfs: + # Check if the DataFrame is large enough to accommodate input_length and label_len + if len(df) >= input_length + target_length: + # Get the first input_length items + input_series = df.iloc[:input_length] + input_series_list.append(input_series) + # Get the last label_len items + target_series = df.iloc[-target_length:] + target_series_list.append(target_series) + else: + raise Exception( + "input_length + label_len is longer that the total length of the DataFrame" + ) + + input_ts_list = list(map(TimeSeries.from_dataframe, input_series_list)) + target_ts_list = list(map(TimeSeries.from_dataframe, target_series_list)) + + return input_ts_list, target_ts_list + + +def timeseries_list_to_numpy(ts_list: list) -> np.array: + """ + Convert a list of TimeSeries into a numpy array + + :param ts_list: list of TimeSeries + :return: np.array + """ + return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) diff --git a/src/ontime/plots/__init__.py b/src/ontime/plots/__init__.py deleted file mode 100644 index 6757fd4..0000000 --- a/src/ontime/plots/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .plots import *