diff --git a/Use_cases/Enhanced_Combined_Drought_Index/00_EnhancedDroughtIndex.ipynb b/Use_cases/Enhanced_Combined_Drought_Index/00_EnhancedDroughtIndex.ipynb new file mode 100644 index 00000000..1990a13c --- /dev/null +++ b/Use_cases/Enhanced_Combined_Drought_Index/00_EnhancedDroughtIndex.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1ee47cf-3544-4245-9884-92989e9f8877", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Enhanced Combined Drought Index" + ] + }, + { + "cell_type": "markdown", + "id": "4d6ce758-4950-4cea-96e6-ee58a302108a", + "metadata": {}, + "source": [ + "## Background " + ] + }, + { + "cell_type": "markdown", + "id": "1d1c29f8-816c-414b-8d63-efd95a7a647c", + "metadata": {}, + "source": [ + "Drought is an extended period, during which fresh water availability and accessibility for the ecosystem at a given place is below normal due to unfavourable spatial and temporal distribution of rainfall, temperature, soil moisture and wind characteristics [(Balint et al., 2013)](https://doi.org/10.1016/B978-0-444-59559-1.00023-2). Severe droughts can affect large populations, threatening people's livelihoods and resulting in economic loss [(Enenkel et al., 2016)](https://doi.org/10.3390/rs8040340)." + ] + }, + { + "cell_type": "markdown", + "id": "d93fb910-bc48-4a5b-a885-131251436d75", + "metadata": {}, + "source": [ + "The Enhanced Combined Drought Index (ECDI) provides timely and reliable detection of drought events with regard to their spatio-temporal extent and severity. The Enhanced Drought Index is a combination of the following:\n", + "\n", + "- **Vegetation** component which considers NDVI anomalies and their persistence. The index based on the vegetation component is named as **Vegetation Drought Index (VDI)**.\n", + "- **Precipitation** component, which considers rainfall deficits and dryness persistence. The index calculated using the precipitation component is referred as the **Precipitation Drought Index (PDI)**.\n", + "- **Temperature** component, which considers temperature excesses and persistence of high temperatures. The index based on temperature is named as **Temperature Drought Index (TDI)**.\n", + "- **Soil moisture** component, which considers soil moisture deficits and deficit persistence. The index based on soil moisture is named as the **Soil Moisture Drought Index (SMDI)**.\n", + "\n", + "The index uses **satellite-derived** rainfall, soil moisture, land surface temperature, and vegetation status as input datasets [(Enenkel et al., 2016)](https://doi.org/10.3390/rs8040340)." + ] + }, + { + "cell_type": "markdown", + "id": "6fa3a6ca-552c-44ee-a3cc-c77d1c2470f0", + "metadata": {}, + "source": [ + "### Enhanced Drought Index Formulae" + ] + }, + { + "cell_type": "markdown", + "id": "ecca9fa6-651b-415b-9552-b13e0e96ffbd", + "metadata": {}, + "source": [ + "Each drought index can be expressed as:\n", + "\n", + "$\\text{Drought Index} = \\frac{\\text{Actual Average for Interest Period}}{\\text{Long Term Average for Interest Period}} * \\sqrt{\\frac{\\text{Actual Length of Continuous Deficit or Excess in the Interest Period}}{\\text{Long Term Average of Continuous Deficit or Excess in the Interest Period}}}$" + ] + }, + { + "cell_type": "markdown", + "id": "7fcf3b69-9a11-4ce8-ab76-34f1dfc54ccf", + "metadata": {}, + "source": [ + "Each drought index is calculated similarly. The equation below illustrates the calculation of the ECDI precipitation component:\n", + "\n", + "\\begin{equation}\n", + "\\text{PDI}_{y,d} = \\frac{\n", + "\\frac{1}{\\text{IP}} \\sum_{j=0}^{\\text{IP} - 1} P^*_{y,(d-j)}}{\\frac{1}{n}\\sum_{k=1}^n[\\frac{1}{\\text{IP}} \\sum_{j=0}^{\\text{IP} - 1} P^*_{(d-j), k}]} * \\sqrt{\\frac{(\\text{RL}^*)P^*_{d, y}}{\\frac{1}{n}\\sum_{k=1}^{n}(\\text{RL}^*)P^*_{d, k}}}\n", + "\\end{equation}\n", + "\n", + "- $\\text{PDI}_{y,d}$ is the Precipitation Drought Index for year $\\text{y}$ and time unit (dekad/month) $\\text{d}$\n", + "\n", + "- $P^*$ is the modified dekadal/monthly precipitation average \n", + "\n", + "- $\\text{RL}*$ is the modified run length parameter \n", + "\n", + "- $\\text{RL*}(P*)$ (run length) is the maximum number of successive dekads/months below the long-term average rainfall in the interest period\n", + "> **Note**: For temperature, run length is the maximum number of successive dekads/months above the long-term average temperature in the interest period\n", + "\n", + "- $\\text{IP}$ is the interest period (e.g. 3, 4, 5, . . . dekads/months) (longer IPs detect more severe drought events). IP is flexible defines to what extent past observations are considered.\n", + "\n", + "- $n$ is the number of years where relevant data are available,\n", + "\n", + "- $j$ is the summation running parameter covering the Interest Period\n", + "\n", + "- $k$ is the summation parameter covering the years where relevant data are available\n", + "\n", + "- $d$ time unit (dekad or month) \n", + "\n", + "- $y$ year" + ] + }, + { + "cell_type": "markdown", + "id": "ef502771-249d-4f52-bc39-7f49491e3fe2", + "metadata": {}, + "source": [ + "The raw time series of temperature and precipitation as well as the run length are modified to adjust the range of all variables and to avoid a division by zero.\n", + "\n", + "\\begin{equation}\n", + "T^* = (T_{max} + 1) - T\n", + "\\end{equation}\n", + "\n", + "\\begin{equation}\n", + "P^* = P + 1\n", + "\\end{equation}\n", + "\n", + "\\begin{equation}\n", + "\\text{RL}^* = (\\text{RL}_{max} + 1) - \\text{RL}\n", + "\\end{equation}\n", + "\n", + "\\begin{equation}\n", + "\\text{NDVI}^* = \\text{NDVI} - (\\text{NDVI}_{min} -0.01)\n", + "\\end{equation}\n", + "\n", + "\n", + "- $T^*$ is the modified dekadal/monthly temperature average \n", + "- $P^*$ is the modified dekadal/monthly precipitation average \n", + "- $\\text{RL}^*$ is the modified run length" + ] + }, + { + "cell_type": "markdown", + "id": "67e6e6b9-a159-4fcc-b976-3ab9db86922e", + "metadata": {}, + "source": [ + "All the individual drought indices differ in range. To improve their interpretability and visual comparability a simple scaling factor is introduced.\n", + "\n", + "\\begin{equation}\n", + "PDI_{scaled} = \\frac{(PDI - PDI_{min})}{(PDI_{max} - PDI_{min})}\n", + "\\end{equation}" + ] + }, + { + "cell_type": "markdown", + "id": "e6caab44-32ee-4d7d-87b8-2486108f5cf7", + "metadata": {}, + "source": [ + "\n", + "The **weight** of each individual drought index is automatically calculated for every grid point (pixel) with respect to its capability to reflect the future vegetation status (NDVI) and multiplied by the respective individual index to calculate the ECDI. In the case of data gaps in one input dataset, the weights are automatically redistributed to other available variables.\n", + "\n", + "\\begin{equation}\n", + "ECDI = \\sum_{i-1}^{n}w_{i} * \\text{DI}_{i}\n", + "\\end{equation}\n", + "\n", + "- $ECDI$ Enhanced Combined Drought Index \n", + "\n", + "- $w$ Weight for each individual drought index (e.g., rainfall)\n", + "\n", + "- $\\text{DI}$ Individual drought index \n", + "\n", + "- $n$ number of drought indices used to calculate the ECDI \n", + "\n", + "- $i$ running parameter covering the number of drought indices\n", + "\n", + "\\begin{equation}\n", + "w_{i} = \\frac{\\frac{lag^*_{i}}{\\sum_{j=1}^{n} lag^*_{j}} + \\frac{corr^*_{i}}{\\sum_{j=1}^{n} corr^*_{j}}}{2}\n", + "\\end{equation}\n", + "\n", + "- $w$ weight for the respective drought index \n", + "\n", + "- $lag^*$ modified time lag for the respective parameter \n", + "\n", + "- $corr^*$ modified correlation coefficient for the respective parameter \n", + "\n", + "- $i$ index for the respective parameter/drought index \n", + "\n", + "- $j$ running parameter covering all parameters used for the ECDI calculation\n", + "\n", + "- $n$ number of individual drought indices used for the ECDI calculation" + ] + }, + { + "cell_type": "markdown", + "id": "9568d111-65c6-4dc8-a713-21a41c739e3c", + "metadata": {}, + "source": [ + "There are five primary notebooks in this notebook series, each represent a critical step in a ECDI workflow. \n", + "\n", + "1. The `Vegetation Drought Index notebook` calculates the Vegetation Drought Index using Landsat Surface Reflectance Collections 5, 7, 8, and 9.\n", + " \n", + "2. The `Precipitation Drought Index notebook` calculates the Precipitation Drought Index using CHIRPS.\n", + "\n", + "3. The `Temperature Drought Index notebook` calculates the Temperature Drought Index using Landsat Surface Temperature Collections 5, 7, 8, and 9\n", + "\n", + "4. The `Soil Moisture Drought Index`notebook calculates the Soil Moisture Drought Index using WAPOR.\n", + "\n", + "5. The `Enhanced Combined Drought Index` notebook combines the indices calculated from Step 1 to 4 to compute the ECDI results" + ] + }, + { + "cell_type": "markdown", + "id": "c78f1f54-fea9-41b0-ba00-cb4f8724a5a1", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "## Getting Started\n", + "\n", + "To begin, work through the notebooks in this workflow, starting with Notebook 1 below:\n", + "\n", + "1. [VegetationDroughtIndex](01_VegetationDroughtIndex.ipynb)\n", + "2. [PrecipitationDroughtIndex](02_PrecipitationDroughtIndex.ipynb)\n", + "3. [TemperatureDroughtIndex](03_TemperatureDroughtIndex.ipynb)\n", + "4. [SoilMoistureDroughtIndex](04_SoilMoistureDroughtIndex.ipynb)\n", + "5. [EnhancedCombinedDroughtIndex](05_EnhancedCombinedDroughtIndex.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "2792a22b-6593-42ea-a5c4-a2c3c7704063", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Additional information\n", + "\n", + " License The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n", + "\n", + "Digital Earth Africa data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n", + "\n", + " Contact If you need assistance, please post a question on the [DE Africa Slack channel](https://digitalearthafrica.slack.com/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n", + "\n", + "If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/digitalearthafrica/deafrica-sandbox-notebooks).\n", + "\n", + " Compatible datacube version " + ] + } + ], + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Use_cases/Enhanced_Combined_Drought_Index/01_VegetationDroughtIndex.ipynb b/Use_cases/Enhanced_Combined_Drought_Index/01_VegetationDroughtIndex.ipynb new file mode 100644 index 00000000..b93cadf0 --- /dev/null +++ b/Use_cases/Enhanced_Combined_Drought_Index/01_VegetationDroughtIndex.ipynb @@ -0,0 +1,6368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1ee47cf-3544-4245-9884-92989e9f8877", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Enhanced Combined Drought Index - Vegetation Drought Index\n", + "\n", + "* **Products used:** \n", + "[ls5_sr](https://explorer.digitalearth.africa/products/ls5_sr),\n", + "[ls7_sr](https://explorer.digitalearth.africa/products/ls7_sr),\n", + "[ls8_sr](https://explorer.digitalearth.africa/products/ls8_sr),\n", + "[ls9_sr](https://explorer.digitalearth.africa/products/ls9_st)" + ] + }, + { + "cell_type": "raw", + "id": "5f36c0bc-9aba-4e41-9698-96cc57458962", + "metadata": {}, + "source": [ + "**Keywords**: :index:`data used; landsat 9`, :index:`data used; landsat 8`, :index:`data used; landsat 7`, :index:`data used; landsat 5`, :index:`surface reflectance`, :index:`band index; NDVI`; " + ] + }, + { + "cell_type": "markdown", + "id": "4d6ce758-4950-4cea-96e6-ee58a302108a", + "metadata": {}, + "source": [ + "## Background " + ] + }, + { + "cell_type": "markdown", + "id": "1d1c29f8-816c-414b-8d63-efd95a7a647c", + "metadata": {}, + "source": [ + "Drought is an extended period during which fresh water availability and accessibility for the ecosystem at a given place is below normal, due to unfavourable spatial and temporal distribution of rainfall, temperature, soil moisture and wind characteristics [(Balint et al., 2013)](https://doi.org/10.1016/B978-0-444-59559-1.00023-2). Severe droughts can affect large populations, threatening people’s livelihoods and resulting economic loss [(Enenkel et al., 2016)](https://doi.org/10.3390/rs8040340)." + ] + }, + { + "cell_type": "markdown", + "id": "48d311ac-d044-47a6-b4db-420c76c297fb", + "metadata": {}, + "source": [ + "The **Enhanced Drought Index** is a combination of the following components: Vegetation, Precipitation, Temperature and Soil moisture.\n", + "\n", + "This notebook will focus on the **Vegetation component**, which considers Normalized Difference Vegetation Index (NDVI) deficits and the persistence of these deficits. The index based on the vegetation component is called the Vegetation Drought Index (VDI). The VDI is a remote sensing-based indicator designed to assess the impact of drought on vegetation health. The VDI provides a way to monitor the health of vegetation under drought stress over large areas and is useful in agricultural, forestry, and environmental monitoring." + ] + }, + { + "cell_type": "markdown", + "id": "4630c991-7329-4ef8-8d34-e4dd5a2ef280", + "metadata": {}, + "source": [ + "\n", + "The notebook outlines:\n", + "\n", + "***\n", + "1. Loading Python and DE Africa Packages\n", + "2. Setting dask cluster\n", + "3. Loading area on interest\n", + "5. Load Landsat Surface reflectance and Calculate the Normalized Difference Vegetation Index (NDVI)\n", + "6. Resampling of data to dekadal (10-day) intervals\n", + "7. Calculate the Vegetation Drought Index\n", + "8. Export the result as zip" + ] + }, + { + "cell_type": "markdown", + "id": "ec6d4e07-18c3-4830-b669-77e01b021a53", + "metadata": {}, + "source": [ + "## Getting started\n", + "\n", + "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell. " + ] + }, + { + "cell_type": "markdown", + "id": "3251008f-3b84-41eb-876d-d68059b0c6de", + "metadata": {}, + "source": [ + "### Load packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "73f5d02f-aee0-452f-81b3-ff22d921457f", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "\n", + "import dask\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import pandas as pd\n", + "import rioxarray\n", + "import toolz\n", + "import xarray as xr\n", + "import datacube\n", + "from deafrica_tools.bandindices import calculate_indices\n", + "from deafrica_tools.dask import create_local_dask_cluster\n", + "from deafrica_tools.datahandling import load_ard\n", + "from dekad import group_by_dekad, bin_by_interest_period, get_dekad_no_in_year, get_no_data_mask, max_consecutive_ones\n", + "from odc.geo.geobox import GeoBox\n", + "from odc.geo.geom import Geometry\n", + "from odc.geo.xr import rasterize\n", + "from pyarrow import Table\n", + "from pyarrow.parquet import write_table" + ] + }, + { + "cell_type": "markdown", + "id": "1cebec83-8c5b-4b87-a652-f7ab00d33ced", + "metadata": {}, + "source": [ + "### Connect to the datacube\n", + "\n", + "Connect to the datacube so we can access DE Africa data.\n", + "The `app` parameter is a unique name for the analysis which is based on the notebook file name." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2ee46b55-a5c7-4404-991f-02d3e1b234df", + "metadata": {}, + "outputs": [], + "source": [ + "# Connect to the datacube\n", + "dc = datacube.Datacube(app=\"VegetationDroughtIndex\")" + ] + }, + { + "cell_type": "markdown", + "id": "8143eda0-f831-4f4c-bb2b-675587cfb09c", + "metadata": {}, + "source": [ + "### Set up a Dask cluster\n", + "\n", + "Dask can be used to better manage memory use and conduct the analysis in parallel. \n", + "For an introduction to using Dask with Digital Earth Africa, see the [Dask notebook](../../Beginners_guide/08_Parallel_processing_with_dask.ipynb).\n", + "\n", + ">**Note**: We recommend opening the Dask processing window to view the different computations that are being executed; to do this, see the *Dask dashboard in DE Africa* section of the [Dask notebook](../../Beginners_guide/08_Parallel_processing_with_dask.ipynb).\n", + "\n", + "To use Dask, set up the local computing cluster using the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e7bf7690-e8b4-468a-a9e6-9632953dd918", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/distributed/node.py:187: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 45637 instead\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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It defines to what extent past observations are considered. Longer IPs detect more severe drought events. For example, if the IP=3 dekads, then the drought index (say PDI) of 0.35 for dekad 2 of 2006 implies actual drought for dekad 36 of 2005, dekad 1 of 2006 and dekad 2 of 2006.\n", + "* `output_dir` : The directory in which to store results from the analysis.\n", + "\n", + "**If running the notebook for the first time**, keep the default settings below.\n", + "This will demonstrate how the analysis works and provide meaningful results." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cc3c12d5-0a20-48db-8044-070ee03b3ddb", + "metadata": {}, + "outputs": [], + "source": [ + "resolution = (-5000, 5000)\n", + "output_crs = \"EPSG:6933\"\n", + "dask_chunks = dict(x=100,y=100)\n", + "time_range = (\"2014\",\"2025\")\n", + "output_dir = \"results\"\n", + "\n", + "# Corresponding to the six-months Standardized Precipitation Evapotranspiration Index\n", + "ip = 18\n", + "\n", + "# Create the outpur directory if it does not exist.\n", + "os.makedirs(output_dir, exist_ok=True)\n", + "\n", + "# Create the output folder for the selected country\n", + "output_path = os.path.join(output_dir, \"VDI_Amhara.parquet\")" + ] + }, + { + "cell_type": "markdown", + "id": "f962ad84-2bff-46b3-a058-e1eeb979f4f5", + "metadata": {}, + "source": [ + "Below, a vector dataset is called and read which contains the extents of level 1 administrative boundaries in Ethiopia. In the default case, the regional state of Amhara is northern Ethiopia is the area of interest. This can be replaced with any administrative boundary, such as those available on the [GADM database](https://gadm.org/data.html). Larger areas have a larger processing and memory burden and may require additional resources (larger instances) to run effectively." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "60ab10bc-8e2b-45ec-a512-08ee27eecf77", + "metadata": {}, + "outputs": [], + "source": [ + "# Read GADM Ethiopia level 1 file\n", + "eth = gpd.read_file('data/gadm41_ETH_1.json')\n", + "\n", + "# Select the country of interest by providing the name below\n", + "idx = eth[eth['NAME_1'] == 'Amhara'].index[0]\n", + "geopolygon = Geometry(geom=eth.iloc[idx].geometry, crs=eth.crs)" + ] + }, + { + "cell_type": "markdown", + "id": "316d2648-b4b8-4338-acb2-8bf368cb220b", + "metadata": {}, + "source": [ + "### Load Landsat surface reflectance data\n", + "Load the Landsat surface reflectance data from the datacube using the analysis parameters set in the previous section and the measurements used to calculate the **NDVI** spectral index." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "529949fc-f949-4159-8a95-30cf2c844646", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/deafrica_tools/datahandling.py:244: UserWarning: Setting 'min_gooddata' percentage to > 0.0 will cause dask arrays to compute when loading pixel-quality data to calculate 'good pixel' percentage. This can slow the return of your dataset.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using pixel quality parameters for USGS Collection 2\n", + "Finding datasets\n", + " ls8_sr\n", + " ls9_sr\n", + "Counting good quality pixels for each time step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/distributed/client.py:3361: UserWarning: Sending large graph of size 15.97 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n", + "/opt/venv/lib/python3.12/site-packages/rasterio/warp.py:387: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n", + " dest = _reproject(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtering to 4449 out of 4538 time steps with at least 90.0% good quality pixels\n", + "Applying pixel quality/cloud mask\n", + "Re-scaling Landsat C2 data\n", + "Returning 4449 time steps as a dask array\n", + "CPU times: user 55.2 s, sys: 2.32 s, total: 57.6 s\n", + "Wall time: 6min 14s\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "Dimensions:      (time: 4449, y: 128, x: 96)\n",
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+       "  * time         (time) datetime64[ns] 36kB 2014-01-02T07:40:34.913504 ... 20...\n",
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+       "    spatial_ref  int32 4B 6933\n",
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+       "    nir          (time, y, x) float32 219MB dask.array<chunksize=(349, 128, 96), meta=np.ndarray>\n",
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" + ], + "text/plain": [ + " Size: 437MB\n", + "Dimensions: (time: 4449, y: 128, x: 96)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 36kB 2014-01-02T07:40:34.913504 ... 20...\n", + " * y (y) float64 1kB 1.742e+06 1.738e+06 ... 1.112e+06 1.108e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.872e+06 3.878e+06\n", + " spatial_ref int32 4B 6933\n", + "Data variables:\n", + " nir (time, y, x) float32 219MB dask.array\n", + " red (time, y, x) float32 219MB dask.array\n", + "Attributes:\n", + " crs: EPSG:6933\n", + " grid_mapping: spatial_ref" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "# Load the Landsat data\n", + "ds_ls_sr = load_ard(dc=dc,\n", + " products=[\"ls8_sr\", \"ls9_sr\"],\n", + " measurements=[\"nir\", \"red\"],\n", + " min_gooddata=0.9,\n", + " time=time_range,\n", + " dask_chunks=dict(time=366),\n", + " geopolygon=geopolygon,\n", + " resolution=resolution,\n", + " output_crs=output_crs,\n", + " )\n", + "\n", + "ds_ls_sr" + ] + }, + { + "cell_type": "markdown", + "id": "62356df7-3131-489c-a7a1-c020467cac62", + "metadata": {}, + "source": [ + "### Mask the Landsat data\n", + "\n", + "The cell below masks the Landsat reflectance data to our country of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c39b35e0-3366-4059-91ad-c069e70071d9", + "metadata": {}, + "outputs": [], + "source": [ + "# Rasterize the country geopolygon and mask the loaded Landsat data.\n", + "country_mask = rasterize(poly=geopolygon, how=ds_ls_sr.odc.geobox)\n", + "ds_ls_sr = ds_ls_sr.where(country_mask)" + ] + }, + { + "cell_type": "markdown", + "id": "b5a54033-d0f4-4fef-84dc-b668c603f104", + "metadata": {}, + "source": [ + "### Calculate NDVI index\n", + "\n", + "The `calculate_indices` function from Digital Earth Africa tools is used to calculate NDVI from the `nir` and `red` bands from the Landsat data." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5cde119c-4e3a-4330-89e7-54abd54723f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dropping bands ['nir', 'red']\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "Coordinates:\n",
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" + ], + "text/plain": [ + " Size: 219MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 36kB 2014-01-02T07:40:34.913504 ... 20...\n", + " * y (y) float64 1kB 1.742e+06 1.738e+06 ... 1.112e+06 1.108e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.872e+06 3.878e+06\n", + " spatial_ref int32 4B 6933" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate the NDVI spectral index using the calculate indices.\n", + "ds_ndvi = calculate_indices(ds_ls_sr, index=['NDVI'], satellite_mission='ls', drop=True)\n", + "# Convert to DataArray\n", + "da_ndvi = ds_ndvi[\"NDVI\"]\n", + "\n", + "da_ndvi" + ] + }, + { + "cell_type": "markdown", + "id": "1539c002-4c33-4a0b-a342-d35cbcb8ad78", + "metadata": {}, + "source": [ + "## Resample data\n", + "\n", + "Resample the NDVI `da_ndvi` timeseries into **dekadal** (10-day) timesteps." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f44b1c9d-6213-4620-8b5f-7028dbb09968", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/distributed/client.py:3361: UserWarning: Sending large graph of size 17.15 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 7s, sys: 4.74 s, total: 1min 12s\n", + "Wall time: 20min 11s\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "    spatial_ref  int32 4B 6933\n",
+       "  * dekad        (dekad) datetime64[ns] 3kB 2014-01-01 2014-01-11 ... 2025-05-11
" + ], + "text/plain": [ + " Size: 40MB\n", + "array([[[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + "...\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]]])\n", + "Coordinates:\n", + " * y (y) float64 1kB 1.742e+06 1.738e+06 ... 1.112e+06 1.108e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.872e+06 3.878e+06\n", + " spatial_ref int32 4B 6933\n", + " * dekad (dekad) datetime64[ns] 3kB 2014-01-01 2014-01-11 ... 2025-05-11" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Group the interest periods by year by getting the dekad number in the year for the end dekad of the interest period. \n", + "grouped_by_year = toolz.groupby(lambda dekad: get_dekad_no_in_year(date=dekad), interest_periods)\n", + "\n", + "#Get the long term average NDVI for each interest period.\n", + "long_term_avg_ndvi = {}\n", + "for dekad_no_in_year, interest_periods_list in grouped_by_year.items():\n", + " long_term_avg_for_period = xr.concat([da_ndvi_binned_by_interest_period[i] for i in interest_periods_list], dim=\"dekad\").mean(dim=\"dekad\")\n", + " long_term_avg_ndvi[dekad_no_in_year] = long_term_avg_for_period\n", + " \n", + "da_ndvi_long_term_avg_for_ip = xr.concat([long_term_avg_ndvi[get_dekad_no_in_year(i)].assign_coords(dekad=i).expand_dims({\"dekad\": 1}) for i in interest_periods], dim=\"dekad\")\n", + "\n", + "assert all(da_ndvi_actual_avg_for_ip.dekad.values == da_ndvi_long_term_avg_for_ip.dekad.values)\n", + "\n", + "da_ndvi_long_term_avg_for_ip" + ] + }, + { + "cell_type": "markdown", + "id": "bcadc8fc-327a-410f-8b4a-fff945618c47", + "metadata": {}, + "source": [ + "## Get the actual length of continous deficit in each interest period\n", + "\n", + "For NDVI, the run length is the number of successive dekads during an interest period that are below the long-term average for the same period. We can expect this cell to take around 6 minutes to run for the default area." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0b321647-3ca9-4015-85d1-9f4125957136", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 2s, sys: 347 ms, total: 1min 2s\n", + "Wall time: 1min 2s\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "    spatial_ref  int32 4B 6933\n",
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NDVI
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" + ], + "text/plain": [ + " NDVI\n", + "dekad y x \n", + "2014-01-01 1742500.0 3402500.0 NaN\n", + " 3407500.0 NaN\n", + " 3412500.0 NaN\n", + " 3417500.0 NaN\n", + " 3422500.0 NaN\n", + "... ...\n", + "2025-05-11 1107500.0 3857500.0 NaN\n", + " 3862500.0 NaN\n", + " 3867500.0 NaN\n", + " 3872500.0 NaN\n", + " 3877500.0 NaN\n", + "\n", + "[5038080 rows x 1 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert to DataFrame.\n", + "df = VDI_scaled.to_dataframe().drop(columns=\"spatial_ref\")\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8cbea8bb-da57-4815-85d3-ecf04e7f2fa8", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the DataFrame to an Arrow table\n", + "table = Table.from_pandas(df)\n", + "\n", + "# Get the tables existing metadata\n", + "existing_meta = table.schema.metadata\n", + "\n", + "# Dump the crs of the DataArray as new metadata to JSON.\n", + "meta_json = json.dumps(dict(crs=str(VDI_scaled.rio.crs)))\n", + "\n", + "# Combine the metadata\n", + "combined_meta = {b\"xarray.metadata\": meta_json.encode(),**existing_meta}\n", + "\n", + "# Replace the metadata.\n", + "table = table.replace_schema_metadata(combined_meta)" + ] + }, + { + "cell_type": "markdown", + "id": "09cc5de7-e60c-4c2f-9dd6-30c69700a22f", + "metadata": {}, + "source": [ + "### Export the results in a Zip file" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4089e0d3-ef95-49fb-b7b1-4c83058215dc", + "metadata": {}, + "outputs": [], + "source": [ + "# Write the table\n", + "write_table(table, output_path, compression=\"GZIP\")" + ] + }, + { + "cell_type": "markdown", + "id": "c60c91c1-21d1-4e5b-8e52-501d2de3c61d", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Additional information\n", + "\n", + " License The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n", + "\n", + "Digital Earth Africa data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n", + "\n", + " Contact If you need assistance, please post a question on the [DE Africa Slack channel](https://digitalearthafrica.slack.com/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n", + "\n", + "If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/digitalearthafrica/deafrica-sandbox-notebooks).\n", + "\n", + " Compatible datacube version :" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "17fb2ac4-635a-44df-87da-baa06d51d46f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.8.20\n" + ] + } + ], + "source": [ + "print(datacube.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "c68985c8-0798-4840-9062-810d739400d4", + "metadata": {}, + "source": [ + "**Last Tested:**" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f51a8dc7-94d3-44f1-b947-8c2d9b72fb97", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2025-05-19'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime\n", + "datetime.today().strftime('%Y-%m-%d')" + ] + } + ], + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Use_cases/Enhanced_Combined_Drought_Index/02_PrecipitationDroughtIndex.ipynb b/Use_cases/Enhanced_Combined_Drought_Index/02_PrecipitationDroughtIndex.ipynb new file mode 100644 index 00000000..3d5a8f42 --- /dev/null +++ b/Use_cases/Enhanced_Combined_Drought_Index/02_PrecipitationDroughtIndex.ipynb @@ -0,0 +1,4945 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1ee47cf-3544-4245-9884-92989e9f8877", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Enhanced Combined Drought Index - Precipitation Drought Index\n", + "\n", + "* **Products used:** \n", + "[rainfall_chirps_daily](https://explorer.digitalearth.africa/products/rainfall_chirps_daily)" + ] + }, + { + "cell_type": "raw", + "id": "5f36c0bc-9aba-4e41-9698-96cc57458962", + "metadata": {}, + "source": [ + "**Keywords**: :index:`data used; CHIRPS`, :index:`climate`, :index:`rainfall`;" + ] + }, + { + "cell_type": "markdown", + "id": "4d6ce758-4950-4cea-96e6-ee58a302108a", + "metadata": {}, + "source": [ + "## Background " + ] + }, + { + "cell_type": "markdown", + "id": "1d1c29f8-816c-414b-8d63-efd95a7a647c", + "metadata": {}, + "source": [ + "Drought is an extended period, during which, fresh water availability and accessibility for the ecosystem at a given time and place is below normal, due to unfavourable spatial and temporal distribution of rainfall, temperature, soil moisture and wind characteristics [(Balint et al., 2013)](https://doi.org/10.1016/B978-0-444-59559-1.00023-2). Severe droughts can affect large populations, leading to a long-term threat to people’s livelihoods and result in tremendous economic loss [(Enenkel et al., 2016)](https://doi.org/10.3390/rs8040340)." + ] + }, + { + "cell_type": "markdown", + "id": "48d311ac-d044-47a6-b4db-420c76c297fb", + "metadata": {}, + "source": [ + "The **Enhanced Drought Index** is a combination of the following components: Vegetation, Precipitation, Temperature and Soil moisture.\n", + "\n", + "This notebook will focus on the **Precipitation component**, component, which considers rainfall deficits and dryness persistence. The index calculated using the precipitation component is referred as the Precipitation Drought Index (PDI).\" The Precipitation Drought Index (PDI) is a commonly used indicator for assessing drought conditions based on precipitation deficits. It measures the difference between observed precipitation and expected or normal precipitation over a specific time period, typically in relation to the long-term average." + ] + }, + { + "cell_type": "markdown", + "id": "4630c991-7329-4ef8-8d34-e4dd5a2ef280", + "metadata": {}, + "source": [ + "The notebook outlines:\n", + "\n", + "***\n", + "1. Loading Python and DE Africa Packages\n", + "2. Setting dask cluster\n", + "3. Loading area on interest\n", + "5. Load CHIRPS data\n", + "6. Resampling of data to dekadal (10-day) intervals\n", + "7. Calculate the Vegetation Drought Index\n", + "8. Export the result as zip" + ] + }, + { + "cell_type": "markdown", + "id": "ec6d4e07-18c3-4830-b669-77e01b021a53", + "metadata": {}, + "source": [ + "## Getting started\n", + "\n", + "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell. " + ] + }, + { + "cell_type": "markdown", + "id": "3251008f-3b84-41eb-876d-d68059b0c6de", + "metadata": {}, + "source": [ + "### Load packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "73f5d02f-aee0-452f-81b3-ff22d921457f", + "metadata": {}, + "outputs": [], + "source": [ + "# import calendar\n", + "import json\n", + "import os\n", + "# from datetime import datetime, timedelta\n", + "\n", + "import dask\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import pandas as pd\n", + "import rioxarray\n", + "import toolz\n", + "import xarray as xr\n", + "import datacube\n", + "from deafrica_tools.bandindices import calculate_indices\n", + "from deafrica_tools.dask import create_local_dask_cluster\n", + "from deafrica_tools.datahandling import load_ard\n", + "from dekad import group_by_dekad, bin_by_interest_period, get_dekad_no_in_year, get_no_data_mask, max_consecutive_ones\n", + "from odc.geo.geobox import GeoBox\n", + "from odc.geo.geom import Geometry\n", + "from odc.geo.xr import rasterize\n", + "from pyarrow import Table\n", + "from pyarrow.parquet import write_table" + ] + }, + { + "cell_type": "markdown", + "id": "f685410a-57bd-4139-bde3-d0617617ef1b", + "metadata": {}, + "source": [ + "### Connect to the datacube\n", + "\n", + "Connect to the datacube so we can access DE Africa data.\n", + "The `app` parameter is a unique name for the analysis which is based on the notebook file name." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "12ea4b92-d8c6-48e4-b418-57aa395b2945", + "metadata": {}, + "outputs": [], + "source": [ + "# Connect to the datacube\n", + "dc = datacube.Datacube(app=\"PrecipitationDroughtIndex\")" + ] + }, + { + "cell_type": "markdown", + "id": "8143eda0-f831-4f4c-bb2b-675587cfb09c", + "metadata": {}, + "source": [ + "### Set up a Dask cluster\n", + "\n", + "Dask can be used to better manage memory use and conduct the analysis in parallel. \n", + "For an introduction to using Dask with Digital Earth Africa, see the [Dask notebook](../../Beginners_guide/08_Parallel_processing_with_dask.ipynb).\n", + "\n", + ">**Note**: We recommend opening the Dask processing window to view the different computations that are being executed; to do this, see the *Dask dashboard in DE Africa* section of the [Dask notebook](../../Beginners_guide/08_Parallel_processing_with_dask.ipynb).\n", + "\n", + "To use Dask, set up the local computing cluster using the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e7bf7690-e8b4-468a-a9e6-9632953dd918", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "create_local_dask_cluster()" + ] + }, + { + "cell_type": "markdown", + "id": "50542e91-46c5-4de9-823a-88c505db1a40", + "metadata": { + "tags": [] + }, + "source": [ + "### Analysis parameters\n", + "\n", + "The following cell sets important parameters for the analysis:\n", + "\n", + "* `resolution`: The x and y cell resolution of the satellite data in metres (spatial resolution). We'll use 5,000 m, which is approximately equal to the default CHIRPS resolution.\n", + "* `output_crs` : The coordinate reference system that the loaded data is to be reprojected to.\n", + "* `dask_chunks`: the size of the dask chunks, dask breaks data into manageable chunks that can be easily stored in memory, e.g. `dict(x=1000,y=1000)`\n", + "* `time_range` : Time range to load data for.\n", + "* `ip` : The interest period to use to calculate the drought indices e.g. (3, 4, 5 dekads). It defines to what extent past observations are considered. Longer IPs detect more severe drought events. For example, if the IP=3 dekads, then the drought index (say PDI) of 0.35 for dekad 2 of 2006 implies actual drought for dekad 36 of 2005, dekad 1 of 2006 and dekad 2 of 2006.\n", + "* `output_dir` : The directory in which to store results from the analysis.\n", + "\n", + "**If running the notebook for the first time**, keep the default settings below.\n", + "This will demonstrate how the analysis works and provide meaningful results." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cc3c12d5-0a20-48db-8044-070ee03b3ddb", + "metadata": {}, + "outputs": [], + "source": [ + "resolution = (-5000, 5000)\n", + "output_crs = \"EPSG:6933\"\n", + "dask_chunks = dict(time=366)\n", + "time_range = (\"2014\",\"2025\")\n", + "output_dir = \"results\"\n", + "# Corresponding to the six-months Standardized Precipitation Evapotranspiration Index\n", + "ip = 18\n", + "\n", + "# Create the outpur directory if it does not exist.\n", + "os.makedirs(output_dir, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "id": "78afba48-334b-4dc8-a121-92b764b54dd1", + "metadata": {}, + "source": [ + "Below, a vector dataset is called and read which contains the extents of level 1 administrative boundaries in Ethiopia. In the default case, the regional state of Amhara is northern Ethiopia is the area of interest. This can be replaced with any administrative boundary, such as those available on the [GADM database](https://gadm.org/data.html). Larger areas have a larger processing and memory burden and may require additional resources (larger instances) to run effectively." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9abcafc5-e427-4097-8301-3482d76ad303", + "metadata": {}, + "outputs": [], + "source": [ + "# Read GADM Ethiopia level 1 file\n", + "eth = gpd.read_file('data/gadm41_ETH_1.json')\n", + "\n", + "# Select the country of interest by providing the name below\n", + "idx = eth[eth['NAME_1'] == 'Amhara'].index[0]\n", + "geopolygon = Geometry(geom=eth.iloc[idx].geometry, crs=eth.crs)\n", + "\n", + "# Create the output path\n", + "output_path = os.path.join(output_dir, f\"PDI_Amhara.parquet\")" + ] + }, + { + "cell_type": "markdown", + "id": "316d2648-b4b8-4338-acb2-8bf368cb220b", + "metadata": {}, + "source": [ + "## Load CHIRPS data\n", + "Load the CHIRPS daily data from the datacube using the analysis parameters set in the previous section." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d1e0b41f-bf8f-4af5-bdff-e60b75d9b0c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 44.7 s, sys: 3.7 s, total: 48.4 s\n", + "Wall time: 44.5 s\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "Data variables:\n",
+       "    rainfall     (time, y, x) float32 197MB dask.array<chunksize=(366, 128, 96), meta=np.ndarray>\n",
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+       "    spatial_ref  int32 4B 6933\n",
+       "  * dekad        (dekad) datetime64[ns] 3kB 2014-01-01 2014-01-11 ... 2025-04-21
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actual_run_length\n", + " modified_run_length = modified_run_length.where(~no_data_mask)\n", + " rf_run_length.append(modified_run_length.assign_coords(dekad=interest_period).expand_dims({\"dekad\": 1}))\n", + "\n", + "da_rf_actual_run_length = xr.concat(rf_run_length, dim=\"dekad\")\n", + "da_rf_actual_run_length" + ] + }, + { + "cell_type": "markdown", + "id": "4d76d733-2c91-46f7-9cfb-a4c73b86eb31", + "metadata": {}, + "source": [ + "### Get the long term average of continous deficit in each interest period" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8ae1de4-9dd2-4a48-8b75-73be3ca12064", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the long term average rainfall run length for each interest period \n", + "long_term_avg_run_lenth_rf = {}\n", + "for dekad_no_in_year, interest_periods_list in grouped_by_year.items():\n", + " long_term_avg_for_period = da_rf_actual_run_length.sel(dekad=interest_periods_list).mean(dim=\"dekad\")\n", + " long_term_avg_run_lenth_rf[dekad_no_in_year] = long_term_avg_for_period\n", + " \n", + "da_rf_long_term_avg_run_length_for_ip = xr.concat([long_term_avg_run_lenth_rf[get_dekad_no_in_year(i)].assign_coords(dekad=i).expand_dims({\"dekad\": 1}) for i in interest_periods], dim=\"dekad\")\n", + "da_rf_long_term_avg_run_length_for_ip" + ] + }, + { + "cell_type": "markdown", + "id": "44145483-0499-4869-af45-50f6e3dd2252", + "metadata": {}, + "source": [ + "### Calculate the scaled Precipitation Drought Index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9aa98e76-b31c-4ee7-902f-2b675a6ddf6a", + "metadata": {}, + "outputs": [], + "source": [ + "PDI = (da_rf_actual_avg_for_ip / da_rf_long_term_avg_for_ip) * np.sqrt((da_rf_actual_run_length / da_rf_long_term_avg_run_length_for_ip))\n", + "PDI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46da40ce-e163-4f74-9857-5b84aac53a1b", + "metadata": {}, + "outputs": [], + "source": [ + "PDI_scaled = (PDI - PDI.min()) / (PDI.max() - PDI.min())\n", + "PDI_scaled" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4caa6f2b-9d5f-4870-b595-ea96b9db1500", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert to DataFrame.\n", + "df = PDI_scaled.to_dataframe().drop(columns=\"spatial_ref\")\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cbea8bb-da57-4815-85d3-ecf04e7f2fa8", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the DataFrame to an Arrow table\n", + "table = Table.from_pandas(df)\n", + "\n", + "# Get the tables existing metadata\n", + "existing_meta = table.schema.metadata\n", + "\n", + "# Dump the crs of the DataArray as new metadata to JSON.\n", + "meta_json = json.dumps(dict(crs=str(PDI_scaled.rio.crs)))\n", + "\n", + "# Combine the metadata\n", + "combined_meta = {\n", + "b\"xarray.metadata\": meta_json.encode(),\n", + "**existing_meta,\n", + "}\n", + "\n", + "# Replace the metadata.\n", + "table = table.replace_schema_metadata(combined_meta)" + ] + }, + { + "cell_type": "markdown", + "id": "a822f6ac-e558-43f3-a022-1b472c2f5a2b", + "metadata": {}, + "source": [ + "### Export the results in a Zip file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4089e0d3-ef95-49fb-b7b1-4c83058215dc", + "metadata": {}, + "outputs": [], + "source": [ + "# Write the table\n", + "write_table(table, output_path, compression=\"GZIP\")" + ] + }, + { + "cell_type": "markdown", + "id": "9ce478df-88b0-46dc-885c-7e121da898fe", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Additional information\n", + "\n", + " License The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n", + "\n", + "Digital Earth Africa data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n", + "\n", + " Contact If you need assistance, please post a question on the [DE Africa Slack channel](https://digitalearthafrica.slack.com/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n", + "\n", + "If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/digitalearthafrica/deafrica-sandbox-notebooks).\n", + "\n", + " Compatible datacube version " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cde1dedb-48d0-43bc-ac3e-1a0042d18a33", + "metadata": {}, + "outputs": [], + "source": [ + "print(datacube.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "7f902691-ac90-4283-9967-1f95e1626444", + "metadata": {}, + "source": [ + "**Last Tested:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "463813d9-8d84-43cf-8f5f-d19048ade5c5", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "datetime.today().strftime('%Y-%m-%d')" + ] + } + ], + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Use_cases/Enhanced_Combined_Drought_Index/03_TemperatureDroughtIndex.ipynb b/Use_cases/Enhanced_Combined_Drought_Index/03_TemperatureDroughtIndex.ipynb new file mode 100644 index 00000000..bd514986 --- /dev/null +++ b/Use_cases/Enhanced_Combined_Drought_Index/03_TemperatureDroughtIndex.ipynb @@ -0,0 +1,6264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1ee47cf-3544-4245-9884-92989e9f8877", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Enhanced Combined Drought Index - Temperature Drought Index\n", + "\n", + "* **Products used:** \n", + "[ls5_st](https://explorer.digitalearth.africa/products/ls5_st),\n", + "[ls7_st](https://explorer.digitalearth.africa/products/ls7_st),\n", + "[ls8_st](https://explorer.digitalearth.africa/products/ls8_st),\n", + "[ls9_st](https://explorer.digitalearth.africa/products/ls9_st)" + ] + }, + { + "cell_type": "raw", + "id": "5f36c0bc-9aba-4e41-9698-96cc57458962", + "metadata": {}, + "source": [ + "**Keywords**: :index:`data used; landsat 9`, :index:`data used; landsat 8`, :index:`data used; landsat 7`, :index:`data used; landsat 5`, :index:`surface temperature`" + ] + }, + { + "cell_type": "markdown", + "id": "4d6ce758-4950-4cea-96e6-ee58a302108a", + "metadata": {}, + "source": [ + "## Background " + ] + }, + { + "cell_type": "markdown", + "id": "1d1c29f8-816c-414b-8d63-efd95a7a647c", + "metadata": {}, + "source": [ + "Drought is an extended period, during which, fresh water availability and accessibility for the ecosystem at a given time and place is below normal, due to unfavourable spatial and temporal distribution of rainfall, temperature, soil moisture and wind characteristics [(Balint et al., 2013)](https://doi.org/10.1016/B978-0-444-59559-1.00023-2). Severe droughts can affect large populations, leading to a long-term threat to people’s livelihoods and result in tremendous economic loss [(Enenkel et al., 2016)](https://doi.org/10.3390/rs8040340)." + ] + }, + { + "cell_type": "markdown", + "id": "ae8fe6cb-a893-4ea7-b84f-fbf52df8433a", + "metadata": {}, + "source": [ + "The **Enhanced Drought Index** is a combination of the following components: Vegetation, Precipitation, Temperature and Soil moisture.\n", + "\n", + "This notebook will focus on the **Temperature** component, which considers temperature excesses and persistence of high temperatures. The index based on temperature is named as **Temperature Drought Index (TDI)** The Temperature Drought Index (TDI) is used to assess the combined impact of temperature and precipitation deficits on drought conditions. It is particularly useful for understanding the role of temperature in exacerbating drought effects, as high temperatures can increase evapotranspiration (the combined loss of water through evaporation and plant transpiration), which in turn can worsen drought conditions." + ] + }, + { + "cell_type": "markdown", + "id": "4630c991-7329-4ef8-8d34-e4dd5a2ef280", + "metadata": {}, + "source": [ + "The notebook outlines:\n", + "\n", + "***\n", + "1. Loading Python and DE Africa Packages\n", + "2. Setting dask cluster\n", + "3. Loading area on interest\n", + "5. Load Landsat Surface temperature\n", + "6. Resampling of data to dekadal (10-day) intervals\n", + "7. Calculate the Vegetation Drought Index\n", + "8. Export the result as zip" + ] + }, + { + "cell_type": "markdown", + "id": "ec6d4e07-18c3-4830-b669-77e01b021a53", + "metadata": {}, + "source": [ + "## Getting started\n", + "\n", + "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell. " + ] + }, + { + "cell_type": "markdown", + "id": "3251008f-3b84-41eb-876d-d68059b0c6de", + "metadata": {}, + "source": [ + "### Load packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "73f5d02f-aee0-452f-81b3-ff22d921457f", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "from datetime import datetime, timedelta\n", + "\n", + "import dask\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import pandas as pd\n", + "import rioxarray\n", + "import toolz\n", + "import xarray as xr\n", + "import datacube\n", + "from deafrica_tools.bandindices import calculate_indices\n", + "from deafrica_tools.dask import create_local_dask_cluster\n", + "from deafrica_tools.datahandling import load_ard\n", + "from dekad import group_by_dekad, bin_by_interest_period, get_dekad_no_in_year, get_no_data_mask, max_consecutive_ones\n", + "from odc.geo.geobox import GeoBox\n", + "from odc.geo.geom import Geometry\n", + "from odc.geo.xr import rasterize\n", + "from pyarrow import Table\n", + "from pyarrow.parquet import write_table" + ] + }, + { + "cell_type": "markdown", + "id": "c94e1c3a-8046-46e9-95a3-b1bac3dd5d5d", + "metadata": {}, + "source": [ + "### Connect to the datacube\n", + "\n", + "Connect to the datacube so we can access DE Africa data.\n", + "The `app` parameter is a unique name for the analysis which is based on the notebook file name." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b44604c6-daa7-4f93-857b-9da7f4fe2109", + "metadata": {}, + "outputs": [], + "source": [ + "# Connect to the datacube\n", + "dc = datacube.Datacube(app=\"TemperatureDroughtIndex\")" + ] + }, + { + "cell_type": "markdown", + "id": "8143eda0-f831-4f4c-bb2b-675587cfb09c", + "metadata": {}, + "source": [ + "### Set up a Dask cluster\n", + "\n", + "Dask can be used to better manage memory use and conduct the analysis in parallel. \n", + "For an introduction to using Dask with Digital Earth Africa, see the [Dask notebook](../../Beginners_guide/08_Parallel_processing_with_dask.ipynb).\n", + "\n", + ">**Note**: We recommend opening the Dask processing window to view the different computations that are being executed; to do this, see the *Dask dashboard in DE Africa* section of the [Dask notebook](../../Beginners_guide/08_Parallel_processing_with_dask.ipynb).\n", + "\n", + "To use Dask, set up the local computing cluster using the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e7bf7690-e8b4-468a-a9e6-9632953dd918", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/distributed/node.py:187: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 44891 instead\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "create_local_dask_cluster()" + ] + }, + { + "cell_type": "markdown", + "id": "50542e91-46c5-4de9-823a-88c505db1a40", + "metadata": {}, + "source": [ + "### Analysis parameters\n", + "\n", + "The following cell sets important parameters for the analysis:\n", + "\n", + "* `resolution`: The x and y cell resolution of the satellite data in metres (spatial resolution). We'll use 5,000 m, which is approximately equal to the default CHIRPS resolution.\n", + "* `output_crs` : The coordinate reference system that the loaded data is to be reprojected to.\n", + "* `dask_chunks`: the size of the dask chunks, dask breaks data into manageable chunks that can be easily stored in memory, e.g. `dict(x=1000,y=1000)`\n", + "* `time_range` : Time range to load data for.\n", + "* `ip` : The interest period to use to calculate the drought indices e.g. (3, 4, 5 dekads). It defines to what extent past observations are considered. Longer IPs detect more severe drought events. For example, if the IP=3 dekads, then the drought index (say PDI) of 0.35 for dekad 2 of 2006 implies actual drought for dekad 36 of 2005, dekad 1 of 2006 and dekad 2 of 2006.\n", + "* `output_dir` : The directory in which to store results from the analysis.\n", + "\n", + "**If running the notebook for the first time**, keep the default settings below.\n", + "This will demonstrate how the analysis works and provide meaningful results." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cc3c12d5-0a20-48db-8044-070ee03b3ddb", + "metadata": {}, + "outputs": [], + "source": [ + "resolution = (-5000, 5000)\n", + "output_crs = \"EPSG:6933\"\n", + "dask_chunks = dict(time=366)\n", + "time_range = (\"2014\",\"2025\")\n", + "output_dir = \"results\"\n", + "# Corresponding to the six-months Standardized Precipitation Evapotranspiration Index\n", + "ip = 18\n", + "\n", + "# Create the outpur directory if it does not exist.\n", + "os.makedirs(output_dir, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "id": "6315035e-a255-4ec8-8923-6386b357401d", + "metadata": {}, + "source": [ + "Below, a vector dataset is called and read which contains the extents of level 1 administrative boundaries in Ethiopia. In the default case, the regional state of Amhara is northern Ethiopia is the area of interest. This can be replaced with any administrative boundary, such as those available on the [GADM database](https://gadm.org/data.html). Larger areas have a larger processing and memory burden and may require additional resources (larger instances) to run effectively." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cf3acd61-bdb2-40cb-a793-4b930509a443", + "metadata": {}, + "outputs": [], + "source": [ + "# Read GADM Ethiopia level 1 file\n", + "eth = gpd.read_file('data/gadm41_ETH_1.json')\n", + "\n", + "# Select the country of interest by providing the name below\n", + "idx = eth[eth['NAME_1'] == 'Amhara'].index[0]\n", + "geopolygon = Geometry(geom=eth.iloc[idx].geometry, crs=eth.crs)\n", + "\n", + "# Create the output path\n", + "output_path = os.path.join(output_dir, f\"TDI_Amhara.parquet\")" + ] + }, + { + "cell_type": "markdown", + "id": "316d2648-b4b8-4338-acb2-8bf368cb220b", + "metadata": {}, + "source": [ + "## Load Landsat surface temperature data\n", + "Load the Landsat surface temperature data from the datacube using the analysis parameters set in the previous section." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d1e0b41f-bf8f-4af5-bdff-e60b75d9b0c5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/deafrica_tools/datahandling.py:244: UserWarning: Setting 'min_gooddata' percentage to > 0.0 will cause dask arrays to compute when loading pixel-quality data to calculate 'good pixel' percentage. This can slow the return of your dataset.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using pixel quality parameters for USGS Collection 2\n", + "Finding datasets\n", + " ls8_st\n", + " ls9_st\n", + "Counting good quality pixels for each time step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/distributed/client.py:3361: UserWarning: Sending large graph of size 16.34 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtering to 4449 out of 4538 time steps with at least 90.0% good quality pixels\n", + "Applying pixel quality/cloud mask\n", + "Re-scaling Landsat C2 data\n", + "Returning 4449 time steps as a dask array\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "    spatial_ref          int32 4B 6933\n",
+       "Data variables:\n",
+       "    surface_temperature  (time, y, x) float32 219MB dask.array<chunksize=(349, 128, 96), meta=np.ndarray>\n",
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" + ], + "text/plain": [ + " Size: 219MB\n", + "Dimensions: (time: 4449, y: 128, x: 96)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 36kB 2014-01-02T07:40:34.91350...\n", + " * y (y) float64 1kB 1.742e+06 1.738e+06 ... 1.108e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.878e+06\n", + " spatial_ref int32 4B 6933\n", + "Data variables:\n", + " surface_temperature (time, y, x) float32 219MB dask.array\n", + "Attributes:\n", + " crs: EPSG:6933\n", + " grid_mapping: spatial_ref" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the Landsat data\n", + "ds_ls_st = load_ard(dc=dc,\n", + " products=[\"ls8_st\", \"ls9_st\"],\n", + " measurements=[\"surface_temperature\"],\n", + " min_gooddata=0.9,\n", + " time=time_range,\n", + " dask_chunks=dict(time=366),\n", + " geopolygon=geopolygon,\n", + " resolution=resolution,\n", + " output_crs=output_crs,\n", + " )\n", + " \n", + "ds_ls_st" + ] + }, + { + "cell_type": "markdown", + "id": "f3173f13-6e2b-462a-8cd0-fe92ba25a449", + "metadata": {}, + "source": [ + "### Masked the landsat data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c956459c-613f-4278-8b08-23ad0328f3e2", + "metadata": {}, + "outputs": [], + "source": [ + "# Rasterize the country geopolygon and mask the loaded Landsat data.\n", + "country_mask = rasterize(poly=geopolygon, how=ds_ls_st.odc.geobox)\n", + "ds_ls_st = ds_ls_st.where(country_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5cde119c-4e3a-4330-89e7-54abd54723f1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'surface_temperature' (time: 4449, y: 128, x: 96)> Size: 219MB\n",
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+       "Coordinates:\n",
+       "  * time         (time) datetime64[ns] 36kB 2014-01-02T07:40:34.913504 ... 20...\n",
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" + ], + "text/plain": [ + " Size: 219MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 36kB 2014-01-02T07:40:34.913504 ... 20...\n", + " * y (y) float64 1kB 1.742e+06 1.738e+06 ... 1.112e+06 1.108e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.872e+06 3.878e+06\n", + " spatial_ref int32 4B 6933" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert from Kelvins to Celsius\n", + "da_ls_st = ds_ls_st[\"surface_temperature\"] - 273.15\n", + "da_ls_st" + ] + }, + { + "cell_type": "markdown", + "id": "1539c002-4c33-4a0b-a342-d35cbcb8ad78", + "metadata": {}, + "source": [ + "### Resample data\n", + "\n", + "Resample the surface temperature `da_ls_st` timeseries into **dekadal** (10-day) timesteps." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f44b1c9d-6213-4620-8b5f-7028dbb09968", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/distributed/client.py:3361: UserWarning: Sending large graph of size 17.07 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 52.5 s, sys: 3.1 s, total: 55.6 s\n", + "Wall time: 12min 56s\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'surface_temperature' (dekad: 410, y: 128, x: 96)> Size: 40MB\n",
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+       "    spatial_ref  int32 4B 6933\n",
+       "  * dekad        (dekad) datetime64[ns] 3kB 2014-01-01 2014-01-11 ... 2025-05-11
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<xarray.DataArray 'surface_temperature' (dekad: 410, y: 128, x: 96)> Size: 40MB\n",
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surface_temperature
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" + ], + "text/plain": [ + " surface_temperature\n", + "dekad y x \n", + "2014-01-01 1742500.0 3402500.0 NaN\n", + " 3407500.0 NaN\n", + " 3412500.0 NaN\n", + " 3417500.0 NaN\n", + " 3422500.0 NaN\n", + "... ...\n", + "2025-05-11 1107500.0 3857500.0 NaN\n", + " 3862500.0 NaN\n", + " 3867500.0 NaN\n", + " 3872500.0 NaN\n", + " 3877500.0 NaN\n", + "\n", + "[5038080 rows x 1 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert to DataFrame.\n", + "df = TDI_scaled.to_dataframe().drop(columns=\"spatial_ref\")\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8cbea8bb-da57-4815-85d3-ecf04e7f2fa8", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the DataFrame to an Arrow table\n", + "table = Table.from_pandas(df)\n", + "\n", + "# Get the tables existing metadata\n", + "existing_meta = table.schema.metadata\n", + "\n", + "# Dump the crs of the DataArray as new metadata to JSON.\n", + "meta_json = json.dumps(dict(crs=str(TDI_scaled.rio.crs)))\n", + "\n", + "# Combine the metadata\n", + "combined_meta = {\n", + "b\"xarray.metadata\": meta_json.encode(),\n", + "**existing_meta,\n", + "}\n", + "\n", + "# Replace the metadata.\n", + "table = table.replace_schema_metadata(combined_meta)" + ] + }, + { + "cell_type": "markdown", + "id": "db8612a8-496e-4a12-bb11-4be9b10e3852", + "metadata": {}, + "source": [ + "### Export the results in a Zip file" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4089e0d3-ef95-49fb-b7b1-4c83058215dc", + "metadata": {}, + "outputs": [], + "source": [ + "# Write the table\n", + "write_table(table, output_path, compression=\"GZIP\")" + ] + }, + { + "cell_type": "markdown", + "id": "daaa05d2-e814-474d-b834-641f3ae74467", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Additional information\n", + "\n", + " License The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n", + "\n", + "Digital Earth Africa data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n", + "\n", + " Contact If you need assistance, please post a question on the [DE Africa Slack channel](https://digitalearthafrica.slack.com/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n", + "\n", + "If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/digitalearthafrica/deafrica-sandbox-notebooks).\n", + "\n", + " Compatible datacube version " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "f6dd02e3-e322-43d9-9ff0-148a2977dfb1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.8.20\n" + ] + } + ], + "source": [ + "print(datacube.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "f7d20e3e-806a-4247-be05-4089eacef7dd", + "metadata": {}, + "source": [ + "**Last Tested:**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "40491836-fc4e-4422-9bd8-a8ef1a33e292", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2025-05-19'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime\n", + "datetime.today().strftime('%Y-%m-%d')" + ] + } + ], + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Use_cases/Enhanced_Combined_Drought_Index/04_SoilMoistureDroughtIndex.ipynb b/Use_cases/Enhanced_Combined_Drought_Index/04_SoilMoistureDroughtIndex.ipynb new file mode 100644 index 00000000..524814af --- /dev/null +++ b/Use_cases/Enhanced_Combined_Drought_Index/04_SoilMoistureDroughtIndex.ipynb @@ -0,0 +1,6838 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1ee47cf-3544-4245-9884-92989e9f8877", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Enhanced Combined Drought Index - Soil Moisture Drought Index\n" + ] + }, + { + "cell_type": "raw", + "id": "5f36c0bc-9aba-4e41-9698-96cc57458962", + "metadata": {}, + "source": [ + "**Keywords**: :index:`data used; WAPOR`, :index:`climate`, :index:`soil moisture`;" + ] + }, + { + "cell_type": "markdown", + "id": "4d6ce758-4950-4cea-96e6-ee58a302108a", + "metadata": {}, + "source": [ + "## Background " + ] + }, + { + "cell_type": "markdown", + "id": "1d1c29f8-816c-414b-8d63-efd95a7a647c", + "metadata": {}, + "source": [ + "Drought is an extended period, during which, fresh water availability and accessibility for the ecosystem at a given time and place is below normal, due to unfavourable spatial and temporal distribution of rainfall, temperature, soil moisture and wind characteristics [(Balint et al., 2013)](https://doi.org/10.1016/B978-0-444-59559-1.00023-2). Severe droughts can affect large populations, leading to a long-term threat to people’s livelihoods and result in tremendous economic loss [(Enenkel et al., 2016)](https://doi.org/10.3390/rs8040340)." + ] + }, + { + "cell_type": "markdown", + "id": "b20be23e-140e-4755-9324-eb1926bb72a6", + "metadata": {}, + "source": [ + "The **Enhanced Drought Index** is a combination of the following components: Vegetation, Precipitation, Temperature and Soil moisture.\n", + "\n", + "This notebook will focus on the **Soil moisture** component, which considers soil moisture deficits and deficit persistence. The index based on soil moisture is named as the Soil Moisture Drought Index (SMDI). The Soil Moisture Drought Index (SMDI) is used to assess drought conditions based on soil moisture levels.SMDI is an essential for monitoring drought conditions, especially in areas where soil moisture directly affects agriculture and natural resources. Droughts can have serious impacts on agriculture, water resources, and ecosystems, and soil moisture is a key factor in understanding drought severity, as it influences crop growth and the availability of water in the soil." + ] + }, + { + "cell_type": "markdown", + "id": "4630c991-7329-4ef8-8d34-e4dd5a2ef280", + "metadata": {}, + "source": [ + "\n", + "The notebook outlines:\n", + "\n", + "***\n", + "1. Loading Python and DE Africa Packages\n", + "2. Setting dask cluster\n", + "3. Loading area on interest\n", + "5. Load the WAPOR soil moisture dataset\n", + "6. Resampling of data to dekadal (10-day) intervals\n", + "7. Calculate the Soil Moisture Drought Index\n", + "8. Export the result as zip" + ] + }, + { + "cell_type": "markdown", + "id": "ec6d4e07-18c3-4830-b669-77e01b021a53", + "metadata": {}, + "source": [ + "## Getting started\n", + "\n", + "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell. " + ] + }, + { + "cell_type": "markdown", + "id": "3251008f-3b84-41eb-876d-d68059b0c6de", + "metadata": {}, + "source": [ + "### Load packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "73f5d02f-aee0-452f-81b3-ff22d921457f", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "\n", + "import dask\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import pandas as pd\n", + "import rioxarray\n", + "import toolz\n", + "import xarray as xr\n", + "import datacube\n", + "from deafrica_tools.bandindices import calculate_indices\n", + "from deafrica_tools.dask import create_local_dask_cluster\n", + "from deafrica_tools.datahandling import load_ard\n", + "from dekad import group_by_dekad, bin_by_interest_period, get_dekad_no_in_year, get_no_data_mask, max_consecutive_ones\n", + "from odc.geo.geobox import GeoBox\n", + "from odc.geo.geom import Geometry\n", + "from odc.geo.xr import rasterize\n", + "from pyarrow import Table\n", + "from pyarrow.parquet import write_table" + ] + }, + { + "cell_type": "markdown", + "id": "78798cea-a53f-48f6-bb69-037f79208a40", + "metadata": {}, + "source": [ + "### Connect to the datacube\n", + "\n", + "Connect to the datacube so we can access DE Africa data.\n", + "The `app` parameter is a unique name for the analysis which is based on the notebook file name." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "667f2e3b-1f3e-4c59-a73f-f5e769fc4943", + "metadata": {}, + "outputs": [], + "source": [ + "# Connect to the datacube\n", + "dc = datacube.Datacube(app=\"VegetationDroughtIndex\")" + ] + }, + { + "cell_type": "markdown", + "id": "8143eda0-f831-4f4c-bb2b-675587cfb09c", + "metadata": {}, + "source": [ + "### Set up a Dask cluster\n", + "\n", + "Dask can be used to better manage memory use and conduct the analysis in parallel. \n", + "For an introduction to using Dask with Digital Earth Africa, see the [Dask notebook](../../Beginners_guide/08_Parallel_processing_with_dask.ipynb).\n", + "\n", + ">**Note**: We recommend opening the Dask processing window to view the different computations that are being executed; to do this, see the *Dask dashboard in DE Africa* section of the [Dask notebook](../../Beginners_guide/08_Parallel_processing_with_dask.ipynb).\n", + "\n", + "To use Dask, set up the local computing cluster using the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e7bf7690-e8b4-468a-a9e6-9632953dd918", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/venv/lib/python3.12/site-packages/distributed/node.py:187: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 42469 instead\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "create_local_dask_cluster()" + ] + }, + { + "cell_type": "markdown", + "id": "50542e91-46c5-4de9-823a-88c505db1a40", + "metadata": {}, + "source": [ + "### Analysis parameters\n", + "\n", + "The following cell sets important parameters for the analysis:\n", + "\n", + "* `resolution`: The x and y cell resolution of the satellite data in metres (spatial resolution). We'll use 5,000 m, which is approximately equal to the default CHIRPS resolution.\n", + "* `output_crs` : The coordinate reference system that the loaded data is to be reprojected to.\n", + "* `dask_chunks`: the size of the dask chunks, dask breaks data into manageable chunks that can be easily stored in memory, e.g. `dict(x=1000,y=1000)`\n", + "* `time_range` : Time range to load data for.\n", + "* `ip` : The interest period to use to calculate the drought indices e.g. (3, 4, 5 dekads). It defines to what extent past observations are considered. Longer IPs detect more severe drought events. For example, if the IP=3 dekads, then the drought index (say PDI) of 0.35 for dekad 2 of 2006 implies actual drought for dekad 36 of 2005, dekad 1 of 2006 and dekad 2 of 2006.\n", + "* `output_dir` : The directory in which to store results from the analysis.\n", + "\n", + "**If running the notebook for the first time**, keep the default settings below.\n", + "This will demonstrate how the analysis works and provide meaningful results." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cc3c12d5-0a20-48db-8044-070ee03b3ddb", + "metadata": {}, + "outputs": [], + "source": [ + "resolution = (-5000, 5000)\n", + "output_crs = \"EPSG:6933\"\n", + "dask_chunks = dict(x=100,y=100)\n", + "time_range = (\"2014\",\"2025\")\n", + "output_dir = \"results\"\n", + "\n", + "# Corresponding to the six-months Standardized Precipitation Evapotranspiration Index\n", + "ip = 18\n", + "\n", + "# Create the outpur directory if it does not exist.\n", + "os.makedirs(output_dir, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2264008d-b8fe-447c-8f84-576a430d330e", + "metadata": {}, + "source": [ + "Below, a vector dataset is called and read which contains the extents of level 1 administrative boundaries in Ethiopia. In the default case, the regional state of Amhara is northern Ethiopia is the area of interest. This can be replaced with any administrative boundary, such as those available on the [GADM database](https://gadm.org/data.html). Larger areas have a larger processing and memory burden and may require additional resources (larger instances) to run effectively." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2498fe63-719b-4d52-8283-7ebfd433f64b", + "metadata": {}, + "outputs": [], + "source": [ + "# Read GADM Ethiopia level 1 file\n", + "eth = gpd.read_file('data/gadm41_ETH_1.json')\n", + "\n", + "# Select the country of interest by providing the name below\n", + "idx = eth[eth['NAME_1'] == 'Amhara'].index[0]\n", + "geopolygon = Geometry(geom=eth.iloc[idx].geometry, crs=eth.crs)\n", + "\n", + "# Create the output path\n", + "output_path = os.path.join(output_dir, \"SMDI_Amhara.parquet\")" + ] + }, + { + "cell_type": "markdown", + "id": "316d2648-b4b8-4338-acb2-8bf368cb220b", + "metadata": {}, + "source": [ + "### Load WaPOR soil moisture data\n", + "Load the WaPOR soil moisture data using the analysis parameters set in the previous section." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "161b8c5f-5c49-46c1-b13a-f8df74b5b4be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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namedescriptionlicensedefault_crsdefault_resolution
name
wapor_soil_moisturewapor_soil_moistureWaPOR v3 Level 2 100m dekadal relative root zo...CC-BY-SA-4.0EPSG:4326(-0.0009765625, 0.0009765625)
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" + ], + "text/plain": [ + " name \\\n", + "name \n", + "wapor_soil_moisture wapor_soil_moisture \n", + "\n", + " description \\\n", + "name \n", + "wapor_soil_moisture WaPOR v3 Level 2 100m dekadal relative root zo... \n", + "\n", + " license default_crs default_resolution \n", + "name \n", + "wapor_soil_moisture CC-BY-SA-4.0 EPSG:4326 (-0.0009765625, 0.0009765625) " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# List all available WAPOR products.\n", + "products_df =dc.list_products()\n", + "products_df[products_df.name.str.contains(\"wapor\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "64fcf007-1729-48c1-b977-18b492c62582", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 13MB\n",
+       "Dimensions:                 (time: 260, y: 128, x: 96)\n",
+       "Coordinates:\n",
+       "  * time                    (time) datetime64[ns] 2kB 2018-01-05T23:59:59.500...\n",
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+       "  * x                       (x) float64 768B 3.402e+06 3.408e+06 ... 3.878e+06\n",
+       "    spatial_ref             int32 4B 6933\n",
+       "Data variables:\n",
+       "    relative_soil_moisture  (time, y, x) float32 13MB 320.0 300.0 ... 494.0\n",
+       "Attributes:\n",
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" + ], + "text/plain": [ + " Size: 13MB\n", + "Dimensions: (time: 260, y: 128, x: 96)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2kB 2018-01-05T23:59:59.500...\n", + " * y (y) float64 1kB 1.742e+06 1.738e+06 ... 1.108e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.878e+06\n", + " spatial_ref int32 4B 6933\n", + "Data variables:\n", + " relative_soil_moisture (time, y, x) float32 13MB 320.0 300.0 ... 494.0\n", + "Attributes:\n", + " crs: EPSG:6933\n", + " grid_mapping: spatial_ref" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "ds_sm = dc.load(\n", + " product=\"wapor_soil_moisture\",\n", + " measurements=[\"relative_soil_moisture\"],\n", + " time=time_range,\n", + " geopolygon=geopolygon,\n", + " resolution=resolution,\n", + " output_crs=output_crs,\n", + ")\n", + "\n", + "# Mask no data values\n", + "ds_sm = ds_sm.where(ds_sm != -9999.0)\n", + "\n", + "ds_sm" + ] + }, + { + "cell_type": "markdown", + "id": "cd2fe825-6416-4eb9-b4eb-f3a30c2bef1b", + "metadata": {}, + "source": [ + "### Mask the soil moisture data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b2d19516-9cce-4bbe-b308-014e2bfe1280", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 13MB\n",
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+       "  * y                       (y) float64 1kB 1.742e+06 1.738e+06 ... 1.108e+06\n",
+       "  * x                       (x) float64 768B 3.402e+06 3.408e+06 ... 3.878e+06\n",
+       "    spatial_ref             int32 4B 6933\n",
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+       "    relative_soil_moisture  (time, y, x) float32 13MB nan nan nan ... nan nan\n",
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" + ], + "text/plain": [ + " Size: 13MB\n", + "Dimensions: (time: 260, y: 128, x: 96)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2kB 2018-01-05T23:59:59.500...\n", + " * y (y) float64 1kB 1.742e+06 1.738e+06 ... 1.108e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.878e+06\n", + " spatial_ref int32 4B 6933\n", + "Data variables:\n", + " relative_soil_moisture (time, y, x) float32 13MB nan nan nan ... nan nan\n", + "Attributes:\n", + " crs: EPSG:6933\n", + " grid_mapping: spatial_ref" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Rasterize the country geopolygon and mask the loaded Landsat data.\n", + "country_mask = rasterize(poly=geopolygon, how=ds_sm.odc.geobox)\n", + "ds_sm = ds_sm.where(country_mask)\n", + "ds_sm" + ] + }, + { + "cell_type": "markdown", + "id": "42c40979-e266-4626-9bc3-e0772aaec02f", + "metadata": {}, + "source": [ + "### Resample data\n", + "\n", + "Resample the soil moisture `ds_sm` timeseries into **dekadal** (10-day) timesteps." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7296ef6f-7ceb-4c07-8b55-bebb03532a62", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    spatial_ref  int32 4B 6933\n",
+       "  * dekad        (dekad) datetime64[ns] 2kB 2018-01-01 2018-01-11 ... 2025-03-11
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<xarray.DataArray 'relative_soil_moisture' (dekad: 260, y: 128, x: 96)> Size: 13MB\n",
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+       "    spatial_ref  int32 4B 6933\n",
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<xarray.DataArray 'relative_soil_moisture' (dekad: 260, y: 128, x: 96)> Size: 26MB\n",
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" + ], + "text/plain": [ + " relative_soil_moisture\n", + "dekad y x \n", + "2018-01-01 1742500.0 3402500.0 NaN\n", + " 3407500.0 NaN\n", + " 3412500.0 NaN\n", + " 3417500.0 NaN\n", + " 3422500.0 NaN\n", + "... ...\n", + "2025-03-11 1107500.0 3857500.0 NaN\n", + " 3862500.0 NaN\n", + " 3867500.0 NaN\n", + " 3872500.0 NaN\n", + " 3877500.0 NaN\n", + "\n", + "[3194880 rows x 1 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert to DataFrame.\n", + "df = SMDI_scaled.to_dataframe().drop(columns=\"spatial_ref\")\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "8cbea8bb-da57-4815-85d3-ecf04e7f2fa8", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the DataFrame to an Arrow table\n", + "table = Table.from_pandas(df)\n", + "\n", + "# Get the tables existing metadata\n", + "existing_meta = table.schema.metadata\n", + "\n", + "# Dump the crs of the DataArray as new metadata to JSON.\n", + "meta_json = json.dumps(dict(crs=str(SMDI_scaled.rio.crs)))\n", + "\n", + "# Combine the metadata\n", + "combined_meta = {\n", + "b\"xarray.metadata\": meta_json.encode(),\n", + "**existing_meta,\n", + "}\n", + "\n", + "# Replace the metadata.\n", + "table = table.replace_schema_metadata(combined_meta)" + ] + }, + { + "cell_type": "markdown", + "id": "eed49c65-f85f-4a29-a0b7-42fd7470b1f6", + "metadata": {}, + "source": [ + "### Export the results in a Zip file" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "4089e0d3-ef95-49fb-b7b1-4c83058215dc", + "metadata": {}, + "outputs": [], + "source": [ + "# Write the table\n", + "write_table(table, output_path, compression=\"GZIP\")" + ] + }, + { + "cell_type": "markdown", + "id": "7e9e0e15-e198-4c73-98df-f64600837c6c", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Additional information\n", + "\n", + " License The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n", + "\n", + "Digital Earth Africa data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n", + "\n", + " Contact If you need assistance, please post a question on the [DE Africa Slack channel](https://digitalearthafrica.slack.com/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n", + "\n", + "If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/digitalearthafrica/deafrica-sandbox-notebooks).\n", + "\n", + " Compatible datacube version :" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "fc3485ff-1566-4bda-992e-b25c66304799", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.8.20\n" + ] + } + ], + "source": [ + "print(datacube.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "d5c2520f-9842-443d-ac8b-6b181d6f2545", + "metadata": {}, + "source": [ + "**Last Tested:**" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "114cacb9-9850-450e-b772-6dae78d01c96", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2025-05-19'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime\n", + "datetime.today().strftime('%Y-%m-%d')" + ] + } + ], + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Use_cases/Enhanced_Combined_Drought_Index/05_EnhancedCombinedDroughtIndex.ipynb b/Use_cases/Enhanced_Combined_Drought_Index/05_EnhancedCombinedDroughtIndex.ipynb new file mode 100644 index 00000000..55c910e6 --- /dev/null +++ b/Use_cases/Enhanced_Combined_Drought_Index/05_EnhancedCombinedDroughtIndex.ipynb @@ -0,0 +1,3689 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "65609557-cb58-4c09-960a-5626912862f9", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Calculate the Enhanced Combined Drought Index" + ] + }, + { + "cell_type": "markdown", + "id": "8f5574dd-2771-4ec4-8303-4b03f6502f17", + "metadata": {}, + "source": [ + "## Background \n", + "\n", + "Drought is an extended period, during which, fresh water availability and accessibility for the ecosystem at a given time and place is below normal, due to unfavourable spatial and temporal distribution of rainfall, temperature, soil moisture and wind characteristics [(Balint et al., 2013)](https://doi.org/10.1016/B978-0-444-59559-1.00023-2). Severe droughts can affect large populations, leading to a long-term threat to people’s livelihoods and result in tremendous economic loss [(Enenkel et al., 2016)](https://doi.org/10.3390/rs8040340)." + ] + }, + { + "cell_type": "markdown", + "id": "7e5ddc51-27a5-473c-966a-3321f9aef411", + "metadata": {}, + "source": [ + "The Enhanced Combined Drought(ECDI) Index aims at the timely and reliable detection of drought events with regard to their spatio-temporal extent and severity. The Enhanced Drought Index is a combination of the following:\n", + "\n", + "- **Vegetation** component which considers NDVI deficits and deficit persistence. The index based on the vegetation component is named as **Vegetation Drought Index (VDI)**.\n", + "- **Precipitation** component, which considers rainfall deficits and dryness persistence. The index calculated using the precipitation component is referred as the **Precipitation Drought Index (PDI)**.\n", + "- **Temperature** component, which considers temperature excesses and persistence of high temperatures. The index based on temperature is named as **Temperature Drought Index (TDI)**.\n", + "- **Soil moisture** component, which considers soil moisture deficits and deficit persistence. The index based on soil moisture is named as the **Soil Moisture Drought Index (SMDI)**.\n", + "\n", + "The index uses **satellite-derived** rainfall, soil moisture, land surface temperature, and vegetation status as input datasets [(Enenkel et al., 2016)](https://doi.org/10.3390/rs8040340)" + ] + }, + { + "cell_type": "markdown", + "id": "39869edb-9cb6-4da6-b155-da10a3ca5e82", + "metadata": {}, + "source": [ + "\n", + "The notebook outlines:\n", + "\n", + "***\n", + "1. Loading Python and DE Africa Packages\n", + "2. Load drought indices data\n", + "3. Calculate the weight for each drought index\n", + "4. Calculate the Enhanced Combined Drought Index\n", + "5. Drought Risk Warning Levels.\n", + "7. Calculate the Soil Moisture Drought Index\n", + "8. Export the result as zip" + ] + }, + { + "cell_type": "markdown", + "id": "a7cd34ba-6709-4e2e-8911-c7e2aeda90d1", + "metadata": {}, + "source": [ + "## Getting started\n", + "\n", + "To run this analysis, run all the cells in the notebook, starting with the \"Load packages\" cell. " + ] + }, + { + "cell_type": "markdown", + "id": "5812b146-5eb8-4f15-9f94-3d9f6116e63e", + "metadata": {}, + "source": [ + "### Load packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "674b8333-61f4-4c58-aa18-e682a3fe36b8", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "from datetime import datetime\n", + "\n", + "import geopandas as gpd\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import rioxarray\n", + "import xarray as xr\n", + "from deafrica_tools.spatial import add_geobox\n", + "from dekad import get_correlation, get_max_value\n", + "from odc.geo.geom import Geometry\n", + "from odc.geo.xr import rasterize\n", + "from pyarrow.parquet import read_table" + ] + }, + { + "cell_type": "markdown", + "id": "a95be322-012b-451d-8411-bdc55fedafbf", + "metadata": {}, + "source": [ + "### Define Area of Interest" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2f41cd6f-db8c-40d4-ba35-96585418485b", + "metadata": {}, + "outputs": [], + "source": [ + "# Read GADM Ethiopia level 1 file\n", + "eth = gpd.read_file('data/gadm41_ETH_1.json')\n", + "\n", + "# Select the country of interest by providing the name below\n", + "idx = eth[eth['NAME_1'] == 'Amhara'].index[0]\n", + "geopolygon = Geometry(geom=eth.iloc[idx].geometry, crs=eth.crs)" + ] + }, + { + "cell_type": "markdown", + "id": "2933dbfd-bc85-41fe-8e98-1a09046d3c12", + "metadata": {}, + "source": [ + "### Load the drought indices data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ddaa3ff9-b0ec-44df-b14e-3dc39ae7d69b", + "metadata": {}, + "outputs": [], + "source": [ + "PARQUET_META_KEY = b\"xarray.metadata\"\n", + "output_dir = \"results\"" + ] + }, + { + "cell_type": "markdown", + "id": "d8b3a165-7a74-4a70-b5ea-03b70104d2a3", + "metadata": {}, + "source": [ + "### Load the results of Vegetation Drought Index (VDI) " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e301e7bf-92a3-49bf-b97b-93ab2881490c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    relative_soil_moisture  (dekad, y, x) float64 40MB nan nan nan ... nan nan
" + ], + "text/plain": [ + " Size: 40MB\n", + "Dimensions: (dekad: 410, y: 128, x: 96)\n", + "Coordinates:\n", + " * dekad (dekad) datetime64[ns] 3kB 2014-01-01 ... 2025-05-11\n", + " * y (y) float64 1kB 1.108e+06 1.112e+06 ... 1.742e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.878e+06\n", + " spatial_ref int32 4B 6933\n", + "Data variables:\n", + " relative_soil_moisture (dekad, y, x) float64 40MB nan nan nan ... nan nan" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SMDI_path = os.path.join(output_dir, \"SMDI_Amhara.parquet\")\n", + "\n", + "SMDI_df = read_table(SMDI_path)\n", + "attrs = json.loads(SMDI_df.schema.metadata[PARQUET_META_KEY])\n", + "\n", + "SMDI_df = SMDI_df.to_pandas()\n", + "SMDI_df.attrs = attrs\n", + "\n", + "SMDI = SMDI_df.to_xarray()\n", + "SMDI = add_geobox(SMDI, crs=SMDI_df.attrs[\"crs\"])\n", + "\n", + "SMDI = SMDI.reindex_like(VDI)\n", + "\n", + "# Ensure the timeseries matches the vegetation timeseries.\n", + "SMDI" + ] + }, + { + "cell_type": "markdown", + "id": "60c089e0-fb92-408e-8ed5-e41cdb67fc11", + "metadata": {}, + "source": [ + "### Load the results of Temperature Drought Index (TDI)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fbbf58df-fd63-4de1-8515-18d38ceaedf2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 40MB\n",
+       "Dimensions:              (dekad: 410, y: 128, x: 96)\n",
+       "Coordinates:\n",
+       "  * dekad                (dekad) datetime64[ns] 3kB 2014-01-01 ... 2025-05-11\n",
+       "  * y                    (y) float64 1kB 1.108e+06 1.112e+06 ... 1.742e+06\n",
+       "  * x                    (x) float64 768B 3.402e+06 3.408e+06 ... 3.878e+06\n",
+       "    spatial_ref          int32 4B 6933\n",
+       "Data variables:\n",
+       "    surface_temperature  (dekad, y, x) float64 40MB nan nan nan ... nan nan nan
" + ], + "text/plain": [ + " Size: 40MB\n", + "Dimensions: (dekad: 410, y: 128, x: 96)\n", + "Coordinates:\n", + " * dekad (dekad) datetime64[ns] 3kB 2014-01-01 ... 2025-05-11\n", + " * y (y) float64 1kB 1.108e+06 1.112e+06 ... 1.742e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.878e+06\n", + " spatial_ref int32 4B 6933\n", + "Data variables:\n", + " surface_temperature (dekad, y, x) float64 40MB nan nan nan ... nan nan nan" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TDI_path = os.path.join(output_dir, \"TDI_Amhara.parquet\")\n", + "\n", + "TDI_df = read_table(TDI_path)\n", + "attrs = json.loads(TDI_df.schema.metadata[PARQUET_META_KEY])\n", + "\n", + "TDI_df = TDI_df.to_pandas()\n", + "TDI_df.attrs = attrs\n", + "\n", + "TDI = TDI_df.to_xarray()\n", + "TDI = add_geobox(TDI, crs=TDI_df.attrs[\"crs\"])\n", + "\n", + "# Ensure the timeseries matches the vegetation timeseries.\n", + "TDI = TDI.reindex_like(VDI)\n", + "\n", + "TDI" + ] + }, + { + "cell_type": "markdown", + "id": "b3de2f27-9abc-40a0-900e-ffbc97a7b443", + "metadata": {}, + "source": [ + "### Load the results of Precipitation Drought Index (PDI)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b3454d61-ce85-4e64-9117-dd8e01017de7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 40MB\n",
+       "Dimensions:      (dekad: 410, y: 128, x: 96)\n",
+       "Coordinates:\n",
+       "  * dekad        (dekad) datetime64[ns] 3kB 2014-01-01 2014-01-11 ... 2025-05-11\n",
+       "  * y            (y) float64 1kB 1.108e+06 1.112e+06 ... 1.738e+06 1.742e+06\n",
+       "  * x            (x) float64 768B 3.402e+06 3.408e+06 ... 3.872e+06 3.878e+06\n",
+       "    spatial_ref  int32 4B 6933\n",
+       "Data variables:\n",
+       "    rainfall     (dekad, y, x) float64 40MB nan nan nan nan ... nan nan nan nan
" + ], + "text/plain": [ + " Size: 40MB\n", + "Dimensions: (dekad: 410, y: 128, x: 96)\n", + "Coordinates:\n", + " * dekad (dekad) datetime64[ns] 3kB 2014-01-01 2014-01-11 ... 2025-05-11\n", + " * y (y) float64 1kB 1.108e+06 1.112e+06 ... 1.738e+06 1.742e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.872e+06 3.878e+06\n", + " spatial_ref int32 4B 6933\n", + "Data variables:\n", + " rainfall (dekad, y, x) float64 40MB nan nan nan nan ... nan nan nan nan" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PDI_path = os.path.join(output_dir, \"PDI_Amhara.parquet\")\n", + "\n", + "PDI_df = read_table(PDI_path)\n", + "attrs = json.loads(PDI_df.schema.metadata[PARQUET_META_KEY])\n", + "\n", + "PDI_df = PDI_df.to_pandas()\n", + "PDI_df.attrs = attrs\n", + "\n", + "PDI = PDI_df.to_xarray()\n", + "PDI = add_geobox(PDI, crs=PDI_df.attrs[\"crs\"])\n", + "\n", + "# Ensure the timeseries matches the vegetation timeseries.\n", + "PDI = PDI.reindex_like(VDI)\n", + "\n", + "PDI" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1302d145-0d7c-467e-acbb-a3f9f06b0be2", + "metadata": {}, + "outputs": [], + "source": [ + "# If any index is missing data, set to None e.g.\n", + "# drought_indices = dict(rainfall=PDI.rainfall, temperature=TDI.surface_temperature, soil_moisture=None, vegetation=VDI.NDVI)\n", + "drought_indices = dict(rainfall=PDI.rainfall, temperature=TDI.surface_temperature, soil_moisture=SMDI.relative_soil_moisture, vegetation=VDI.NDVI)" + ] + }, + { + "cell_type": "markdown", + "id": "506244e0-bff7-4896-9b14-3f3ebefb4016", + "metadata": {}, + "source": [ + "### Calculate the weight for each drought index" + ] + }, + { + "cell_type": "markdown", + "id": "d6478d52-2933-4e2e-a79a-e002595ab1f9", + "metadata": {}, + "source": [ + "The weight $w$ of each individual drought index is automatically calculated for every grid point (pixel) with respect to its capability to reflect the future vegetation status (NDVI). In the case of data gaps in one input dataset, the weights are automatically redistributed to other available variables.\n", + "\n", + "\\begin{equation}\n", + "w_{i} = \\frac{\\frac{lag^*_{i}}{\\sum_{j=1}^{n} lag^*_{j}} + \\frac{corr^*_{i}}{\\sum_{j=1}^{n} corr^*_{j}}}{2}\n", + "\\end{equation}\n", + "\n", + "- $w$ weight for the respective drought index \n", + "\n", + "- $lag^*$ modified time lag for the respective parameter \n", + "\n", + "- $corr^*$ modified correlation coefficient for the respective parameter \n", + "\n", + "- $i$ index for the respective parameter/drought index \n", + "\n", + "- $j$ running parameter covering all parameters used for the ECDI calculation\n", + "\n", + "- $n$ number of individual drought indices used for the ECDI calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "edd82aee-58bd-43ee-9107-481889f9aa6e", + "metadata": {}, + "outputs": [], + "source": [ + "correlation = {}\n", + "lag = {}\n", + "for k in drought_indices.keys():\n", + " if drought_indices[k] is not None:\n", + " calc_corr = get_correlation(reference_drought_index=VDI.NDVI, comparison_drought_index=drought_indices[k])\n", + " correlation[k] = calc_corr[\"corr\"]\n", + " lag[k] = calc_corr[\"lag\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "44434968-e60d-4095-9f57-ad91ea6660f8", + "metadata": {}, + "outputs": [], + "source": [ + "weights = {}\n", + "for i in drought_indices.keys():\n", + " if drought_indices[i] is not None:\n", + " weights[i]=((lag[i]/np.nansum([i.values for i in lag.values()], axis=0) + (correlation[i]/np.nansum([i.values for i in correlation.values()], axis=0))))/2" + ] + }, + { + "cell_type": "markdown", + "id": "a9deed6d-0150-4eea-8772-cc62e6886af1", + "metadata": {}, + "source": [ + "### Calculate the Enhanced Combined Drought Index" + ] + }, + { + "cell_type": "markdown", + "id": "d0425176-f0cd-41f5-997a-bd7a327e365d", + "metadata": {}, + "source": [ + "The weight of every individual index is multiplied by the respective individual index to calculate the ECDI. The sum of all weights per decade is one.\n", + "\n", + "\\begin{equation}\n", + "ECDI = \\sum_{i-1}^{n}w_{i} * \\text{DI}_{i}\n", + "\\end{equation}\n", + "\n", + "- $ECDI$ Enhanced Combined Drought Index \n", + "\n", + "- $w$ Weight for each individual drought index (e.g., rainfall)\n", + "\n", + "- $\\text{DI}$ Individual drought index \n", + "\n", + "- $n$ number of drought indices used to calculate the ECDI \n", + "\n", + "- $i$ running parameter covering the number of drought indices" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "81cc9241-306b-4751-a476-6db03d353bd9", + "metadata": {}, + "outputs": [], + "source": [ + "ECDI_np = np.nansum([weights[i]*drought_indices[i] for i in weights.keys()], axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "684db4a3-bca5-4dc2-a6b9-1d5cb6af2e65", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert NumPy array to xarray DataArray\n", + "ECDI = xr.DataArray(\n", + " ECDI_np,\n", + " dims=(\"y\", \"x\", \"dekad\"), # Use the same dimension names\n", + " coords={dim: VDI.coords[dim] for dim in VDI[\"NDVI\"].dims}, # Use the same coordinates\n", + ")\n", + "ECDI = add_geobox(ECDI, VDI.odc.geobox.crs)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "57f252e1-a680-43fd-9f0b-9d6604434098", + "metadata": {}, + "outputs": [], + "source": [ + "# Rasterize the country geopolygon and mask the data.\n", + "country_mask = rasterize(poly=geopolygon, how=ECDI.odc.geobox)\n", + "ECDI = ECDI.where(country_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0b915666-2d8e-4a1b-ad14-b9d6d44ed614", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the most recent dekad\n", + "ECDI.sel(dekad=\"2020-12-21\").plot()" + ] + }, + { + "cell_type": "markdown", + "id": "398f36f2-4b8d-4225-8a9e-bf5bb3288bfa", + "metadata": {}, + "source": [ + "### Drought Risk Warning Levels." + ] + }, + { + "cell_type": "markdown", + "id": "84a67e96-54c7-4e53-aae4-25a7e7057ac8", + "metadata": {}, + "source": [ + "The ECDI-based drought risk warning levels (WL) are calculated as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "dd9a7d65-690f-4bdb-a26c-dd6062596bd4", + "metadata": {}, + "source": [ + "\\begin{equation}\n", + "\\text{Warning Levels}= \\frac{\\text{Current decadal ECDI value} - \\text{Average ECDI value per decade}}{\\text{Standard deviation of the decadal ECDI value}}\n", + "\\end{equation}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "beb789d5-0739-434a-a3d3-efc130f23ba3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (y: 128, x: 96, dekad: 252)> Size: 25MB\n",
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+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]])\n",
+       "Coordinates:\n",
+       "  * dekad        (dekad) datetime64[ns] 2kB 2014-01-01 2014-01-11 ... 2020-12-21\n",
+       "  * y            (y) float64 1kB 1.108e+06 1.112e+06 ... 1.738e+06 1.742e+06\n",
+       "  * x            (x) float64 768B 3.402e+06 3.408e+06 ... 3.872e+06 3.878e+06\n",
+       "    spatial_ref  int32 4B 6933
" + ], + "text/plain": [ + " Size: 25MB\n", + "array([[[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + "...\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]]])\n", + "Coordinates:\n", + " * dekad (dekad) datetime64[ns] 2kB 2014-01-01 2014-01-11 ... 2020-12-21\n", + " * y (y) float64 1kB 1.108e+06 1.112e+06 ... 1.738e+06 1.742e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.872e+06 3.878e+06\n", + " spatial_ref int32 4B 6933" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select the dekad to calculate the warning levels for\n", + "selected_dekad = \"2020-12-21\"\n", + "\n", + "# Select the timeseries upto the specific dekad\n", + "selected_ECDI = ECDI.sel(dekad=slice(None, selected_dekad))\n", + "selected_ECDI" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9c38844a-b999-4efc-94e9-cbe3d8e8a9bf", + "metadata": {}, + "outputs": [], + "source": [ + "selected_dekad_month = datetime.strptime(selected_dekad, \"%Y-%m-%d\").month\n", + "selected_dekad_day = datetime.strptime(selected_dekad, \"%Y-%m-%d\").day\n", + "\n", + "# Get the average ECDI value for the selected dekad \n", + "avg_ECDI = selected_ECDI.sel(dekad=(selected_ECDI.dekad.dt.month == selected_dekad_month) & (selected_ECDI.dekad.dt.day == selected_dekad_day)).mean(dim=\"dekad\")\n", + "# Get the standard deviation ECDI value for the selected dekad \n", + "std_ECDI = selected_ECDI.sel(dekad=(selected_ECDI.dekad.dt.month == selected_dekad_month) & (selected_ECDI.dekad.dt.day == selected_dekad_day)).std(dim=\"dekad\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4dab06ea-2a70-4392-86be-4500ac5f8ccf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "Coordinates:\n",
+       "    dekad        datetime64[ns] 8B 2020-12-21\n",
+       "  * y            (y) float64 1kB 1.108e+06 1.112e+06 ... 1.738e+06 1.742e+06\n",
+       "  * x            (x) float64 768B 3.402e+06 3.408e+06 ... 3.872e+06 3.878e+06\n",
+       "    spatial_ref  int32 4B 6933
" + ], + "text/plain": [ + " Size: 98kB\n", + "array([[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]])\n", + "Coordinates:\n", + " dekad datetime64[ns] 8B 2020-12-21\n", + " * y (y) float64 1kB 1.108e+06 1.112e+06 ... 1.738e+06 1.742e+06\n", + " * x (x) float64 768B 3.402e+06 3.408e+06 ... 3.872e+06 3.878e+06\n", + " spatial_ref int32 4B 6933" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "warning_levels = (selected_ECDI.sel(dekad=selected_dekad) - avg_ECDI)/ std_ECDI\n", + "warning_levels" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f37e5ea4-77b8-4ae1-b06c-9abfdc3f4a73", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jxgydPXvWOd/2ujp16uj999/XlStXtHXrVt11110KCQmRdK1waNWqlerVq6e3335bhQsXVvbs2TV79mzNnz8/1X7cFSCSnPNM/834V2CR2duaJSUlRQ6HQ6tXr06zPzd+53CtWrVUsmRJLVy4UJ06ddLnn3+uf/75R4888ohLe9K1ebc3fsXLdTeOQP9bjhw5tHnzZm3cuFErV67UF198oQULFqhhw4Zau3at2/NlFk/X2Z1169apS5cuatGihWbMmJHq9cKFCys5OVmnTp1SgQIFnMsvX76s06dPp/l1Nz/++KNatWqlSpUqadGiRanOWeHChSVJ8fHxqb5GKz4+3vmPOAsWLNDjjz/u8vp/ubdiY2P19ttva+zYsanm3X/99dcu/xAmSUeOHHH5R5jjx4/r/vvvV3h4uFatWmXaEyAAAASqTF3cNmvWTM2aNXP7elJSkoYOHaqPP/5YZ8+eVaVKlTRu3DhnwubevXs1ffp0/fzzzypXrpwk30cigLS0bNlSY8aM0UcffWRKcfvhhx9Kkpo0aeLT9vfcc4+mT5+u9evXa+fOnXruueecr9WpU0f//POPVq5cqcOHD7ukOy9evFghISFas2aNy6Ods2fP9vFIbo3rKbWHDh1yKRyuXr2qo0ePqkqVKv95H2XKlJFhGCpVqpSio6Nvun779u01depUJSYmasGCBSpZsqRq1arl0p4kFShQQI0aNfK6P1myZNF9992n++67T5MmTdLo0aM1dOhQbdy40ev2SpQoofXr1+vcuXMuhdS+ffucr/8X27ZtU5s2bVS9enUtXLgwzcL9jjvukHTtEfPmzZs7l+/YsUMpKSnO16/79ddf1bRpUxUoUECrVq1y+ceFtNq88WmEP/74Q7///rt69uwp6dr7ytskcnfeeustDR8+XP3793f5bt7rqlatmmpfN/7jxunTp3X//fcrKSlJGzZscBboAABkZJn6seSb6dOnj7755ht98skn+umnn9SuXTs1bdrU+Rjg559/rtKlS2vFihUqVaqUSpYsqe7duzNyC9PUrl1bTZs21axZs9J8bP7y5csaPHhwutqaP3++Zs2apdq1a+u+++7zqT/X53lOmjRJV65ccRm5LVmypAoXLqzXX3/dZV3p2oipw+FweWzy6NGjPk0FuJWqV6+ufPnyaebMmbp69apz+bx589L12HN6PPTQQ8qaNatiY2NTjeoZhuGSli1de3Q4KSlJc+fO1RdffKH27du7vN6kSROFhYVp9OjRunLlSqr9/ftrkm6U1mfV9UIurUdwb6Z58+ZKTk7WtGnTXJZPnjxZDofD4z8m3szevXvVokULlSxZUitWrHA76tuwYUNFRERo+vTpLsunT5+unDlzqkWLFs5lJ0+e1P33368sWbJozZo1yp8/f5pt3n777Spfvrzeffddl3t4+vTpcjgcevjhhyVdG+H99zQDXyxYsED9+vVT586dNWnSpDTXyZs3b6p9XX9S4sKFC2revLlOnDihVatWucwfBgAgI8vUI7eeHDt2TLNnz9axY8ecj7ENHjxYX3zxhWbPnq3Ro0fr8OHD+u233/Tpp5/qgw8+UHJysgYMGKCHH35YX375pZ+PAHawevVq56jWjerUqaPSpUtLkj744APdf//9euihh9SyZUvdd999Cg0N1cGDB/XJJ58oPj4+1XfdLlq0SLly5dLly5d14sQJrVmzRnFxcapatao+/fRTn/tbvHhxRUZG6ptvvlHJkiVTPeJZp04dLV68WA6HQ3Xr1nUub9GihSZNmqSmTZuqU6dOOnXqlN566y2VLVtWP/30k8/9MVtQUJCGDx+uvn37qmHDhmrfvr2OHj2qOXPmqEyZMi5BVp4cOnRII0eOTLW8WrVqatGihUaOHKkhQ4bo6NGjat26tXLnzq0jR45o6dKl6tmzp8s/WNx5550qW7ashg4dqqSkJJdHkqVr87KnT5+uxx57THfeeac6dOig/Pnz69ixY1q5cqXq1q2bqti8bsSIEdq8ebNatGihEiVK6NSpU3r77bdVrFgxl3+cSK+WLVsqJiZGQ4cO1dGjR1W1alWtXbtWn332mfr37+8cZfbWuXPn1KRJE505c0bPPfdcqq9HKlOmjGrXri3p2qPOr732mnr37q127dqpSZMm2rJliz766CONGjVKERERzu2aNm2qw4cP6/nnn9fWrVudYWmSVLBgQTVu3Nj5+/jx49WqVSvdf//96tChg37++WdNmzZN3bt3d84p9iQhIcEZCHV9bvq0adOUJ08e5cmTx/l1Vtu3b1eXLl2UL18+3XfffZo3b55LOzd+NrjTuXNnbd++XU888YT27t3r8t22uXLlUuvWrZ2///TTT8453YcOHVJCQoLz3q1atapatmx502MDACBg+C2nOcBIMpYuXer8fcWKFYYkIzQ01OUnW7ZsRvv27Q3DMIwePXoYkoz9+/c7t/v+++8NSca+ffusPgTYiKevApJkzJ4922X9ixcvGhMmTDBq1Khh5MqVywgKCjKioqKMvn37uny9yvWvern+ExISYhQrVsx44IEHjPfff9+4dOlSqr6k96uAruvYsaMhyejUqVOq1yZNmuT8yp1/e++994yoqCgjODjYKF++vDF79uxUX01jGNfei7179061/fVz9t1337ksv/41Pxs3bnQuc/dVQJ9++qnLtkeOHEnzfL/xxhtGiRIljODgYKNmzZpGXFyccddddxlNmzZ1d1qcSpQo4fa6Pvnkk871Fi9ebNxzzz3Oz5by5csbvXv3dvk8uW7o0KGGJKNs2bJu97tx40ajSZMmRnh4uBESEmKUKVPG6Natm7Fjxw7nOv8+3xs2bDAefPBBo0iRIkZQUJBRpEgRo2PHji5fg+NOWl8FZBjXvppowIABRpEiRYzs2bMbUVFRxvjx412+vskw3F/ntFy/Tu5+unbtmmqbd9991yhXrpwRFBRklClTxpg8eXKafXD3k9bXPi1dutS44447jODgYKNYsWLGyy+/bFy+fPk/H8ONX7/j7WdDWjzdg//+qh9P+0vrvAIAEMgchmFhkkoAczgcWrp0qfNftBcsWKDOnTvrl19+SRWqkitXLhUqVEjDhg1L9SjgP//8o5w5c2rt2rUu/+oPwL5SUlKUP39+PfTQQ5o5c6a/uwMAAIA08FiyG9WqVXMmbroL8qlbt66uXr2qX3/91fm43YEDByT99+AUAP5x6dIlBQcHuzyC/MEHH+jvv/92hskBAAAg8GTqkdvz58/r0KFDkq4Vs5MmTVJMTIwiIiJUvHhxPfroo4qLi9PEiRNVrVo1/fnnn9qwYYOqVKmiFi1aKCUlRTVq1FCuXLk0ZcoUpaSkqHfv3goLC9PatWv9fHQAfLFp0yYNGDBA7dq1U758+fTDDz/ovffeU4UKFfT9998rKCjI310EAABAGjJ1cbtp06ZU3xMoSV27dtWcOXN05coVjRw5Uh988IFOnDih2267TbVq1VJsbKwqV64s6dpXQfTt21dr165VaGiomjVrpokTJ7qElgCwj6NHj6pfv37avn27/v77b0VERKh58+YaO3asy/emAgAAILBk6uIWAAAAAJAx8D23AAAAAADbo7gFAAAAANhepktLTklJ0R9//KHcuXO7pKECAAAgMBiGoXPnzqlIkSLKkiXwxmIuXbqky5cv+2XfQUFBCgkJ8cu+gUCX6YrbP/74Q5GRkf7uBgAAAG7i+PHjKlasmL+74eLSpUsqlSOHTvpp/2FhYSpcuLCyZMmi3r17q3fv3n7qCRB4Ml1xmzt3bknXPizDwsL83BsAAAD8W2JioiIjI51/twWSy5cv66Sk45Ks/ksyUVJkYiJ/xwJuZLri9vqjyGFhYXwoAAAABLBAnkIWJuuLWwCeBd4kBgAAAAAAvERxCwAAAACwvUz3WDJwa7h7bMqwtBcAAABAZsXILQAAAADA9ihuAQAAAAC2R3ELAAAAALA9ilsAAAAAgO1R3AIAAAAAbI/iFgAAAABgexS3AAAAAADbo7gFAAAAANgexS0AAAAAwPYobgEAAAAAtkdxCwAAAACwPYpbAAAAAIDtUdwCAAAAAGwvm787ANiFI9bh9jVjmNutPLRo/JfuAAAAALgBI7cAAAAAANujuAUAAAAA2B7FLQAAAADA9ihuAQAAAAC2R6AUkE7uQ6MA//MceEZ4GQAAyPgYuQUAAAAA2B7FLQAAAADA9ihuAQAAAAC2R3ELAAAAALA9AqUAv3EXAET4D7xH4JlvPAVxecvzNeB9DQDArcbILQAAAADA9ihuAQAAAAC2R3ELAAAAALA95twCfuKITXs5cydhvsw9v9vMebWeZY7zCQBAoGLkFgAAAABgexS3AAAAAADbo7gFAAAAANgexS0AAAAAwPYIlAJSsSp8BvCFmfenp7bchSPZ7/3hKaTNXbCbLzwFV7nvAyFUAACYxa8jt5s3b1bLli1VpEgRORwOLVu2zOP63bp1k8PhSPVz++23W9NhAAAAAEBA8mtxe+HCBVWtWlVvvfVWutafOnWq4uPjnT/Hjx9XRESE2rVrd4t7CgAAAAAIZH59LLlZs2Zq1qxZutcPDw9XeHi48/dly5bpzJkzevzxx29F9wAAAAAANmHrObfvvfeeGjVqpBIlSrhdJykpSUlJSc7fExMTregaAAAAAMBCti1u//jjD61evVrz58/3uN6YMWMUG2tiYggyLU/BM+7CYnzZxjq+hAnBCr4FE/myH4+v3vL9+8rMECiruOtzIJxPAAAyCtt+FdDcuXOVJ08etW7d2uN6Q4YMUUJCgvPn+PHj1nQQAAAAAGAZW47cGoah999/X4899piCgoI8rhscHKzg4GCLegYAAAAA8Adbjtx+9dVXOnTokJ588kl/dwUAAAAAEAD8OnJ7/vx5HTp0yPn7kSNHtGvXLkVERKh48eIaMmSITpw4oQ8++MBlu/fee0933323KlWqZHWXAQAAAAAByK/F7Y4dOxQTE+P8feDAgZKkrl27as6cOYqPj9exY8dctklISNDixYs1depUS/uKjMddaI+7gBdfgl88bRPIATPuzw1BU+YK3NAmX0LS4D1P4WHu8D4EACBtfi1uGzRoIMNw/x/pOXPmpFoWHh6uixcv3sJeAQAAAADsxpZzbgEAAAAAuBHFLQAAAADA9ihuAQAAAAC2Z8vvuQXMEAihPWnxFDDj/z6775v3AVmBG4rjS8iPL/x/PWFHnj8jAvd9BQDArcbILQAAAADA9ihuAQAAAAC2R3ELAAAAALA95twCt5C7eaieeJqH6Ut7vuzHzPbcz8X1ZV6r2fMJmVtrJl+O0/u52ua+D+wos9xPAAB4i5FbAAAAAIDtUdwCAAAAAGyP4hYAAAAAYHsUtwAAAAAA2yNQCrARb0ObAoG54Tf+D4Dy5Vz7EpoUqMwMjfK1vYz0PvCtz2YHqwEAkDEwcgsAAAAAsD2KWwAAAACA7VHcAgAAAABsj+IWAAAAAGB7BEohg3MfQOTv8Bmzg3n82Zan9swMTTI7mMgXdgyBMpNV18CX+zOQg6bM7YN5n2vGMPfhVI7YtPfjaRsAAPyJkVsAAAAAgO1R3AIAAAAAbI/iFgAAAABgexS3AAAAAADbI1AKSKdACBKyKoTKl/14G+Zj9vm06jh9YdU58Hb/vjDz3pACI+zJCoEaduUuNMrXbQibAgDvDB8+XMuWLdOuXbss2d+mTZsUExOjM2fOKE+ePKatGygYuQUAAAAyoG7dusnhcKT6adq0abrbaNCggfr373/rOnmLNGjQwHm8wcHBKlq0qFq2bKklS5b4u2teS+81uPGYQ0JCFB0drTFjxsgw/v8fHuvUqaP4+HiFh4ffwh77D8UtAAAAkEE1bdpU8fHxLj8ff/yxqfswDENXr141tU0z9OjRQ/Hx8fr111+1ePFiVaxYUR06dFDPnj09bnflyhWLemi+68e8f/9+DRkyRK+++qpmzJjhfD0oKEiFChWSw+H9kzt2QHELAAAAZFDBwcEqVKiQy0/evHklXXvsNCgoSFu2bHGu//rrr6tAgQL63//+p27duumrr77S1KlTnSOCR48e1aZNm+RwOLR69WrdddddCg4O1tatW5WSkqIxY8aoVKlSypEjh6pWrapFixY5276+3Zo1a1StWjXlyJFDDRs21KlTp7R69WpVqFBBYWFh6tSpky5evOjc7mbtupMzZ04VKlRIxYoVU61atTRu3Di98847mjlzptavXy9JOnr0qBwOhxYsWKD69esrJCRE8+bNU0pKikaMGKFixYopODhYd9xxh7744otUx3L27Fnnsl27djnP0XUzZ85UZGSkcubMqTZt2mjSpElpPuL74YcfqmTJkgoPD1eHDh107tw5SXJ7DW52zCVKlNDjjz+uKlWqaN26dW77/dtvv6lly5bKmzevQkNDdfvtt2vVqlVptn3x4kU1a9ZMdevWdTnuQEJxCwAAANhIYmKiy09SUpJP7Vx/3PWxxx5TQkKCdu7cqVdeeUWzZs1SwYIFNXXqVNWuXds5GhgfH6/IyEjn9i+++KLGjh2rvXv3qkqVKhozZow++OADzZgxQ7/88osGDBigRx99VF999ZXLfocPH65p06bp66+/1vHjx9W+fXtNmTJF8+fP18qVK7V27Vq9+eabzvXT2256dO3aVXnz5k31ePKLL76oZ599Vnv37lWTJk00depUTZw4URMmTNBPP/2kJk2aqFWrVjp48GC69xUXF6enn35azz77rHbt2qXGjRtr1KhRqdb79ddftWzZMq1YsUIrVqzQV199pbFjx0rSTa+BO4ZhaMuWLdq3b5+CgoLcrte7d28lJSVp8+bN2r17t8aNG6dcuXKlWu/s2bNq3LixUlJStG7duoCdg0ugFJBOnoJffAmLsWobb9vylbd98OU4fWHXIDAr9u/vMCNfZbTjAQBv/bu4GTZsmIYPH57muitWrEhVrLz00kt66aWXJEkjR47UunXr1LNnT/3888/q2rWrWrVqJUkKDw9XUFCQczTw30aMGKHGjRtLkpKSkjR69GitX79etWvXliSVLl1aW7du1TvvvKP69es7txs5cqTq1q0rSXryySc1ZMgQ/frrrypdurQk6eGHH9bGjRv1wgsveNVuemTJkkXR0dGpRj/79++vhx56yPn7hAkT9MILL6hDhw6SpHHjxmnjxo2aMmWK3nrrrXTt680331SzZs00ePBgSVJ0dLS+/vprrVixwmW9lJQUzZkzR7lz55YkPfbYY9qwYYNGjRp102vwb2+//bZmzZqly5cv68qVKwoJCVG/fv3crn/s2DG1bdtWlStXliTnNbjRyZMn9cgjjygqKkrz58/3WCz7G8UtAAAAYCPHjx9XWFiY8/fg4GC368bExGj69OkuyyIiIpz/PygoSPPmzVOVKlVUokQJTZ48Od39qF69uvP/Hzp0SBcvXnQWu9ddvnxZ1apVc1lWpUoV5/8vWLCgcubM6VJUFSxYUNu3b/e63fQyDCPVnNMbjyUxMVF//PGHswC/rm7duvrxxx/TvZ/9+/erTZs2Lstq1qyZqrgtWbKks7CVpMKFC+vUqVPp3s+NOnfurKFDh+rMmTMaNmyY6tSpozp16rhdv1+/furVq5fWrl2rRo0aqW3bti7XR5IaN26smjVrasGCBcqaNatP/bIKxS0AAABgI2FhYS7FrSehoaEqW7asx3W+/vprSdLff/+tv//+W6Ghoelu+7rz589LklauXKmiRYu6rPfv4jt79uzO/+9wOFx+v74sJSXF63bTIzk5WQcPHlSNGjXcHkt6ZMlybXbnjUnEvgZReTp+b4WHhzuv98KFC1W2bFnVqlVLjRo1SnP97t27q0mTJs7HwceMGaOJEyeqb9++znVatGihxYsXa8+ePc4R3kDFnFsAAAAgk/r11181YMAAzZw5U3fffbe6du3qUlgFBQUpOTn5pu1UrFhRwcHBOnbsmMqWLevyk545ola1O3fuXJ05c0Zt27Z1u05YWJiKFCmiuLg4l+VxcXGqWLGiJCl//vySpPj4eOfr//6u2nLlyum7775zWfbv39Mjvdfg33LlyqVnn31WgwcPdinC/y0yMlJPP/20lixZokGDBmnmzJkur48dO1Zdu3bVfffdpz179njdDysxcgsAAABkUElJSTp58qTLsmzZsum2225TcnKyHn30UTVp0kSPP/64mjZtqsqVK2vixIl67rnnJF17ZHbbtm06evSocuXK5fJI841y586twYMHa8CAAUpJSdE999yjhIQExcXFKSwsTF27dvWp//+l3YsXL+rkyZO6evWqfv/9dy1dulSTJ09Wr169FBMT43G/zz33nIYNG6YyZcrojjvu0OzZs7Vr1y7NmzdPkpzF9fDhwzVq1CgdOHBAEydOdGmjb9++qlevniZNmqSWLVvqyy+/1OrVq73+Gp60rsH1keObeeqpp/Taa69p8eLFevjhh1O93r9/fzVr1kzR0dE6c+aMNm7cqAoVKqRab8KECUpOTlbDhg21adMmlS9f3qtjsArFLWACM4NsPIUPuduPL4FFVgU6mSlQ+2UlX+4BM+8bs2WWcCgzj8ff58YY5v5f/wEEni+++EKFCxd2WVauXDnt27dPo0aN0m+//eacA1q4cGG9++676tixo+6//35VrVpVgwcPVteuXVWxYkX9888/OnLkiNt9vfbaa8qfP7/GjBmjw4cPK0+ePLrzzjud4VW+8rXdmTNnaubMmQoKClK+fPl01113acGCBanmwaalX79+SkhI0KBBg3Tq1ClVrFhRy5cvV1RUlKRrjxJ//PHH6tWrl6pUqaIaNWpo5MiRateunbONunXrasaMGYqNjdXLL7+sJk2aaMCAAZo2bZpXx5/WNShZsmS6to2IiFCXLl00fPhwl8Cs65KTk9W7d2/9/vvvCgsLU9OmTd3Ou548ebJLgRsdHe3VcVjBYXgao86AEhMTFR4eroSEhHTPVYCduf+XMf//gej9NoFc3FpV4Gd2Ga24dceq1HB4j+IWVgjkv9ecfZNkdc8SJYVLAXlekD49evTQvn37XL5bGOZh5BYAAAAAboEJEyaocePGCg0N1erVqzV37ly9/fbb/u5WhkVxCwAAAAC3wPbt2/X666/r3LlzKl26tN544w11797d393KsChukUF4NzHfE6seyfXlUVF/P0bqy3Ha8RHSQJ6PbNX+rXqU2cz3jh35ewoAAODWWrhwob+7kKnwVUAAAAAAANujuAUAAAAA2B7FLQAAAADA9ihuAQAAAAC2R6AUMi0zg47MbMvM/QcCq76b1x1f9hPI59Mds78X1qogsMweqOTLNfDlO43N5Ih1H+DHd+ACAPyJkVsAAAAAgO1R3AIAAAAAbI/iFgAAAABgexS3AAAAAADbI1AKGcN8N8s7WbN7q4KO3O0nEIKWvA2O8iVIxxM7hkD5WyCc54wUDgX3YVMETQEArMDILQAAAADA9ihuAQAAAAC2R3ELAAAAALA9ilsAAAAAgO0RKIWMwZfgKHchVG74EqbkiZkhUFbt30xWBVqZ3QczeRvC5WkbM/d/sz740p63zNy/mUFonrYxm7/fo75w/xmVdtCUp20kQqgAAN5h5BYAAAAAYHsUtwAAAAAA26O4BQAAAADYHsUtAAAAAMD2CJRC5uUuhMpNiIvZ4Ttwz5eQH1/CtnzZj5nMDNXy5Tg9yUhhRr6w4/FnNJ5CqNwxhhFCBQCZGSO3AAAAAADbo7gFAAAAANgexS0AAAAAwPYchmFkqgkqiYmJCg8PV0JCgsLCwvzdHZjE3dwsX+bgmTnXzqq5m4EwP9DbuaCBME/ZjtfaTB7nkUe5ecHdXPWbtOd2Pz7MIfa2LU8C4b0TqMy8NoGA+bj2Esh/rzn7JsnqniVKCpcC8rwAgYCRWwAAAACA7VHcAgAAAABsj+IWAAAAAGB7FLcAAAAAANvL5u8OAGbwJfjEzAAgM/fv7z7DMyuutadrY8cQrMwUrJaR2PF8EhoFAJkbI7cAAAAAANujuAUAAAAA2B7FLQAAAADA9ihuAQAAAAC2R6AUMgRfgk/MDEsxMxgoEIKerDiejBa2ZeY2vtybgRDa5EsfrAjVCoRz429m3mtm7t+TQD6fAIDAxMgtAAAAAMD2KG4BAAAAALZHcQsAAAAAsD2KWwAAAACA7TkMwzD83QkrJSYmKjw8XAkJCQoLC/N3d2ASR6wjzeVmhtJYJRCCVwI16Mjf+/fUB6sCkCw7B/PdLO/kQ1s+CNT3Z0Zj1fvDF771LVP9SZOhBfLfa86+SbK6Z4mSwqWAPC9AIGDkFgAAAABgexS3AAAAAADbo7gFAAAAANgexS0AAAAAwPay+bsDyOjSDnryjfdBIVaFm1gVfuNLmJGZ+/HEzBCqQObLuTb7+lixD8dBNy94uJ6BEIZmN1aFh/nSli+fN2beA576bMV7CgBgP4zcAgAAAABsj+IWAAAAAGB7FLcAAAAAANtjzm0G54g1b86rT3Oc5rtZ3smXHrg/lkCd62f2vLBAnmfm7fkM1GsWCMycHylZd99klutjJl+um6dtvL0GZt9rvvDt/vQ+gwEAkPH5deR28+bNatmypYoUKSKHw6Fly5bddJukpCQNHTpUJUqUUHBwsEqWLKn333//1ncWAAAAABCw/Dpye+HCBVWtWlVPPPGEHnrooXRt0759e/3vf//Te++9p7Jlyyo+Pl4pKSm3uKcAAAAAgEDm1+K2WbNmatasWbrX/+KLL/TVV1/p8OHDioiIkCSVLFnyFvUOAAAAAGAXtgqUWr58uapXr67XX39dRYsWVXR0tAYPHqx//vnH7TZJSUlKTEx0+QEAAAAAZCy2CpQ6fPiwtm7dqpCQEC1dulR//fWXnnnmGZ0+fVqzZ89Oc5sxY8YoNpaUEzP4EiLi7wCkjNZnXwJmfNnGF1acN7P34e4cBHIAk7/vz0Bm1b3u7f595cv96e9QLzOD4oxhhEYBALxjq5HblJQUORwOzZs3TzVr1lTz5s01adIkzZ071+3o7ZAhQ5SQkOD8OX78uMW9BgAAAADcarYauS1cuLCKFi2q8PBw57IKFSrIMAz9/vvvioqKSrVNcHCwgoODrewmAAAAAMBithq5rVu3rv744w+dP3/euezAgQPKkiWLihUr5seeAQAAAAD8ya/F7fnz57Vr1y7t2rVLknTkyBHt2rVLx44dk3TtkeIuXbo41+/UqZPy5cunxx9/XHv27NHmzZv13HPP6YknnlCOHDn8cQgAAAAAgADg18eSd+zYoZiYGOfvAwcOlCR17dpVc+bMUXx8vLPQlaRcuXJp3bp16tu3r6pXr658+fKpffv2GjlypOV9DySOWIfX2/g7eMUXZofveNue2efGzDAjs7fxd9CSO4EQnOWOL30zez9W7N+X/Zh9bgL1c8rsa+PvexoAALvxa3HboEEDGYb7NMQ5c+akWla+fHmtW7fuFvYKAAAAAGA3tppzCwAAAABAWihuAQAAAAC2R3ELAAAAALA9W33PbWbnS3CU+7bSXu7vwCCz+RK8ktHOTUY7Hm+ZGSoWCPvxhb/vAbNDo+x479qxz74w91739N8893kdAIDMi5FbAAAAAIDtUdwCAAAAAGyP4hYAAAAAYHsUtwAAAAAA23MYhpGpUhkSExMVHh6uhIQEhYWF+bs7Xko7XMOqUBy/m+/htU7m7caX8xnQ580HVpyDzHSevQ108ncAldn8fd0C+XwGchCZVXx5HxjDMtWfLplSIP+95uzbTCksp8X7viiF95Cio6OVNWtW9e7dW71797a2E0AAIy0ZAAAAsJHvvvsu4Ip+IBDwWDIAAAAAwPYobgEAAAAAtkdxCwAAAACwPebcBhhHbNqhUdbt3/1r/g6F8RQa5e9wIn/v32xW9C2Qj98qVt033gZaedrGjsw8Z77ux117djzPmeW+AQDYDyO3AAAAAADbo7gFAAAAANgexS0AAAAAwPaYcxtgzJ6H6Mtcu4zEjnPDAnnecyD3LZC5Oze+3INm3rdWvQesmr9q1dxaK9qSzL1vzGT2/jP7f6cAAOZh5BYAAAAAYHsUtwAAAAAA26O4BQAAAADYHsUtAAAAAMD2CJTyG4d/925ywIsdWRHW4ss5C+Tz7O++BXKglb9DfuwYnuZJIF9rq9jxuvnCl8/izHIPAAC8w8gtAAAAAMD2KG4BAAAAALZHcQsAAAAAsD2KWwAAAACA7REolUkFchiHuxCRQO6zO3YN7vLlGlhx3QLh3PjCzH6beU+ZHVjk72CgjHZu7Mi697u7UEbDvA4AAGyHkVsAAAAAgO1R3AIAAAAAbI/iFgAAAABgexS3AAAAAADbI1DqFnLEugu8sC54xdv9BEJgT6AGzPiyH18CmAKBmfdaZmHmfWNXvhyPFe8ds4PQzDxOuBcIgXye/jue9v59CbTybh/p6IXJ7QGAfTByCwAAAACwPYpbAAAAAIDtUdwCAAAAAGyP4hYAAAAAYHsESmVwdgwxMTPsypdQGDP3b8fz7ysrQsrMDlyzihUBSIFwr/kSlOeOmcdj9rkx8zgzC6vOjbcBUJJ1nx3W9c3dfgiaApDxMXILAAAAALA9ilsAAAAAgO1R3AIAAAAAbI85twHG7Lk/Vsx3zEhzHX1ti3ml3vfNrsfpCzPPjVn7uBV98OUzwsz3aGaZF2/V+bSKv+cwm/vfCffzan05Tt8+C5hbCyDzYuQWAAAAAGB7FLcAAAAAANujuAUAAAAA2B7FLQAAAADA9giUMoEvX8zuvi3vt/F3+E4gBC35worzZva5sWOolzuBEIDkjh3PZyAEdJm5H3+/PwL5fAZyaJRV70N/h5e5Y8fPDgDISBi5BQAAAADYHsUtAAAAAMD2KG4BAAAAALZHcQsAAAAAsD0CpfyEYCD/syL4xJdzY9fz6U6gBr9Yycz3eyCHDJkZiOfvQCer7k9/h1N5YtU9ZeZ+/P0+8Pd9CwCZHSO3AAAAAADbo7gFAAAAANgexS0AAAAAwPYobgEAAAAAtkegVDo5Yh2mtheowRJWhWGYHfoRyGEpSJtV59+XYCCz3wfebhMIYVuZ/T0VyMdi9j3t7X6s+vwO5GvgC6uugbu/V4xhhvcdAACbYeQWAAAAAGB7FLcAAAAAANujuAUAAAAA2B7FLQAAAADA9giUMkGghkMFgkAOBOG6BS4zA2bMDHHx1F4gB67ZsQ9mhmr5+1jsyo7nLRDC2Mxk9ucXAGR0jNwCAAAAAGyP4hYAAAAAYHsUtwAAAAAA22PObToxh8WauYbIeHyZP+vLvWbm3DRPbZk5H9iq946Z712r5iP7e35kRpu76W+cT+vw32oAmRkjtwAAAAAA26O4BQAAAADYHsUtAAAAAMD2KG4BAAAAALZHoFQqDn93IGDZMYzCqqASM4OJMhp/h8WYHYBkVXCVmcwOjvKWVWFbZp5Ps6+NVe93b+81s/vl72A3AEDmxsgtAAAAAMD2KG4BAAAAALZHcQsAAAAAsD2KWwAAAACA7REoBRdmh+/4m5nBQBnt3GQ0VgUgZaRravZxmhmc5e8gMk+sCruyImjJl7YCOQgtkO8b//MUmGlY1gsAuJUYuQUAAAAA2B7FLQAAAADA9ihuAQAAAAC2R3ELAAAAALA9AqVMkJHChHwLKnEfROE+kMR9sIVVISZmBq/AvUAOevJ3YI4vzOyzmYFrntoz+7MwUK+P2f0yM4jLl/vGqvNs1b0WqPeNVTJLUB4QKBwOh5YuXarWrVtbsr8GDRrojjvu0JQpU0xd124YuQUAAAAyoD///FO9evVS8eLFFRwcrEKFCqlJkyaKi4vzd9d8smnTJjkcDjkcDmXJkkXh4eGqVq2ann/+ecXHx/u7e165fixnz55N13rXf/Lnz6/mzZtr9+7dLustWbJEr7322i3ssT1Q3AIAAAAZUNu2bbVz507NnTtXBw4c0PLly9WgQQOdPn3ar/26fPnyf9p+//79+uOPP/Tdd9/phRde0Pr161WpUqVUBZ+Z+/S3/fv3Kz4+XmvWrFFSUpJatGjhckwRERHKnTu3H3sYGChuAQAAgAzm7Nmz2rJli8aNG6eYmBiVKFFCNWvW1JAhQ9SqVSuX9bp37678+fMrLCxMDRs21I8//ihJOnDggBwOh/bt2+fS9uTJk1WmTBnn7z///LOaNWumXLlyqWDBgnrsscf0119/OV9v0KCB+vTpo/79++u2225TkyZN0rWdOwUKFFChQoUUHR2tDh06KC4uTvnz51evXr2c63Tr1k2tW7fWqFGjVKRIEZUrV06StHv3bjVs2FA5cuRQvnz51LNnT50/f96lr/3793fZX+vWrdWtWzfn7/Hx8WrRooVy5MihUqVKaf78+SpZsmSqx3z/+usvtWnTRjlz5lRUVJSWL18uSTp69KhiYmIkSXnz5pXD4XBp39Mx33nnnerfv7+OHz/ucl3+3e+3335bUVFRCgkJUcGCBfXwww+7bXvlypUKDw/XvHnzPPbBDihuAQAAABtJTEx0+UlKSkq1Tq5cuZQrVy4tW7Yszdeva9eunU6dOqXVq1fr+++/15133qn77rtPf//9t6Kjo1W9evVURc+8efPUqVMnSdeK44YNG6patWrasWOHvvjiC/3vf/9T+/btXbaZO3eugoKCFBcXpxkzZqR7u/TIkSOHnn76acXFxenUqVPO5Rs2bND+/fu1bt06rVixQhcuXFCTJk2UN29efffdd/r000+1fv169enTx6v9denSRX/88Yc2bdqkxYsX691333XZ73WxsbFq3769fvrpJzVv3lydO3fW33//rcjISC1evFjS/4/ITp06NV37TkhI0CeffCJJCgoKSnOdHTt2qF+/fhoxYoT279+vL774QvXq1Utz3fnz56tjx46aN2+eOnfunK4+BDICpUyQkYIYPB+L++Ao7/fjqa20w6Z8Oc92DCrJaKEfvgSumRmk44kdz6c7Zh+LVWFbVgTyWRVoFchhRma+P3z5jDL7c83f5xPwt8jISJffhw0bpuHDh7ssy5Ytm+bMmaMePXpoxowZuvPOO1W/fn116NBBVapUkSRt3bpV27dv16lTpxQcHCxJmjBhgpYtW6ZFixapZ8+e6ty5s6ZNm+acz3ngwAF9//33+uijjyRJ06ZNU7Vq1TR69Gjnvt9//31FRkbqwIEDio6OliRFRUXp9ddfd64zcuTIdG2XXuXLl5d0bVS0QIECkqTQ0FDNmjXLWQTOnDlTly5d0gcffKDQ0FBn/1u2bKlx48apYMGCN93Pvn37tH79en333XeqXr26JGnWrFmKiopKtW63bt3UsWNHSdLo0aP1xhtvaPv27WratKkiIiIkXRuRzZMnz033W6xYMUnShQsXJEmtWrVyHvO/HTt2TKGhoXrggQeUO3dulShRQtWqVUu13ltvvaWhQ4fq888/V/369W/aBzvw68jt5s2b1bJlSxUpUkQOh0PLli3zuP6/J1Rf/zl58qQ1HQYAAAD87Pjx40pISHD+DBkyJM312rZtqz/++EPLly9X06ZNtWnTJt15552aM2eOJOnHH3/U+fPnlS9fPudIb65cuXTkyBH9+uuvkqQOHTro6NGj+vbbbyVdG7W98847nYXVjz/+qI0bN7psf/21621I0l133eXSt/Rul16GcW3gxOH4/0GSypUru4xu7t27V1WrVnUWtpJUt25dpaSkaP/+/enaz/79+5UtWzbdeeedzmVly5ZV3rx5U617/R8RpGuFdlhYWJojvOmxZcsWff/995ozZ46io6M1Y8YMt+s2btxYJUqUUOnSpfXYY49p3rx5unjxoss6ixYt0oABA7Ru3boMU9hKfh65vXDhgqpWraonnnhCDz30ULq3279/v8LCwpy/X//XGQAAACCjCwsLc/lb2JOQkBA1btxYjRs31iuvvKLu3btr2LBh6tatm86fP6/ChQtr06ZNqba7PppYqFAhNWzYUPPnz1etWrU0f/58l7mt58+fd458/lvhwoWd///GgtKb7dJr7969kqSSJUu63Wd6ZMmSxVkoX3flyhWv25Gk7Nmzu/zucDiUkpLiU1ulSpVSnjx5VK5cOZ06dUqPPPKINm/enOa6uXPn1g8//KBNmzZp7dq1evXVVzV8+HB99913zutarVo1/fDDD3r//fdVvXp1l38UsDO/jtw2a9ZMI0eOVJs2bbza7vqE6us/WbIwdRgAAAC4mYoVKzofbb3zzjt18uRJZcuWTWXLlnX5ue2225zbdO7cWQsWLNA333yjw4cPq0OHDs7X7rzzTv3yyy8qWbJkqjY8FZe+bpeWf/75R++++67q1aun/Pnzu12vQoUK+vHHH53HL0lxcXHKkiWLM3Aqf/78Ll8rlJycrJ9//tn5e7ly5XT16lXt3LnTuezQoUM6c+aMV32+PqKcnJzs1XaS1Lt3b/38889aunSp23WyZcumRo0a6fXXX9dPP/2ko0eP6ssvv3S+XqZMGW3cuFGfffaZ+vbt63UfApUtq8I77rhDhQsXVuPGjW/6PV1JSUmpJt0DAAAAGdnp06fVsGFDffTRR/rpp5905MgRffrpp3r99df14IMPSpIaNWqk2rVrq3Xr1lq7dq2OHj2qr7/+WkOHDtWOHTucbT300EM6d+6cevXqpZiYGBUpUsT5Wu/evfX333+rY8eO+u677/Trr79qzZo1evzxxz0Wbr5uJ0mnTp3SyZMndfDgQX3yySeqW7eu/vrrL02fPt3jdp07d1ZISIi6du2qn3/+WRs3blTfvn312GOPOefbNmzYUCtXrtTKlSu1b98+9erVy+W7aMuXL69GjRqpZ8+e2r59u3bu3KmePXsqR44cXo1+lihRQg6HQytWrNCff/7pkth8Mzlz5lSPHj00bNiwVKPMkrRixQq98cYb2rVrl3777Td98MEHSklJcRbw10VHR2vjxo1avHhxqoRou7JVoFThwoU1Y8YMVa9eXUlJSZo1a5YaNGigbdu2uTz3fqMxY8YoNjajJ0+YF/QUyMwOELEiTCizhxxJ1gXM+MLMMKNAOB4r+BKaZNewq0Ddvy+sCm2y6h6A9zwHOSIjypUrl+6++25NnjxZv/76q65cuaLIyEj16NFDL730kqRrj8muWrVKQ4cO1eOPP64///xThQoVUr169VzClXLnzq2WLVtq4cKFev/99132U6RIEcXFxemFF17Q/fffr6SkJJUoUUJNmzb1+HSlr9tJ10ZPHQ6HcuXKpdKlS+v+++/XwIEDVahQIY/b5cyZU2vWrNGzzz6rGjVqKGfOnGrbtq0mTZrkXOeJJ57Qjz/+qC5duihbtmwaMGCA82t7rvvggw/05JNPql69eipUqJDGjBmjX375RSEhIR73f6OiRYsqNjZWL774oh5//HF16dLFORc6Pfr06aNJkybp008/TZUwnSdPHi1ZskTDhw/XpUuXFBUVpY8//li33357qnbKlSunL7/8Ug0aNFDWrFk1ceLEdPchEDmMtMp9P3A4HFq6dKlat27t1Xb169dX8eLF9eGHH6b5elJSkkv8eWJioiIjI5WQkOBmroIdnzcPiEtoorSvQSAXt2b2LbP8ERgIxaC/i9uM9o8fZqYIm3kN/H0/BQJ/JxJblVgN9+xW3CYmJio8PNzD32v+4+zbTCksp8X7viiF91BAnpfM7Pfff1dkZKTWr1+v++67z9/dydRsNXKblpo1a2rr1q1uXw8ODnZGmwMAAADAf/Hll1/q/Pnzqly5suLj4/X888+rZMmSbr9LFtaxfXG7a9cunxLVAAAAAMBbV65c0UsvvaTDhw8rd+7cqlOnjubNm5cqHRnW82txe/78eR06dMj5+5EjR7Rr1y5FRESoePHiGjJkiE6cOKEPPvhAkjRlyhSVKlVKt99+uy5duqRZs2bpyy+/1Nq1a03slbvHduz4uLJdpX0NjGHur4Fvj6ilvR9HbNr78eWxukB+hNTf7HpurHgcMhDmtZrJ7EfQrXqcPDOw6tr4sk0g39MAMrcmTZqoSZMm/u4G0uDX4nbHjh0uE7QHDhwoSeratavmzJmj+Ph4HTt2zPn65cuXNWjQIJ04cUI5c+ZUlSpVtH79+lSTvAEAAAAAmYtfi9sGDRqkGV993b8Tw55//nk9//zzt7hXAAAAAAC7seX33AIAAAAAcCOKWwAAAACA7dk+Ldk6nr4fzsywKXt9D12gs+p7/az6ztyMFLBi9vfCBvI18DYwx8xQHk/7CQRmBjrZMRzKl/AwX5h9T5kpkO/PQOXbOfP0twp/ewDIGBi5BQAAAADYHsUtAAAAAMD2KG4BAAAAALZHcQsAAAAAsD0CpUxBEIO/WREcFQiBPd4GE5m9H0+87YMvfTYzzMnX/Zh5Dczsm9nhP74cp1XnICMJ5OO3470WyOfTKlZ8fgNAoGLkFgAAAABgexS3AAAAAADbo7gFAAAAANgexS0AAAAAwPYIlIKN+De4y6qQIV/74I5VwUBm7t+qbbxty1N7ZgbZBHLYli/tEVYTuMy+16wKSbPre8Qs5vfL4fV+rAhyBABvMXILAAAAALA9ilsAAAAAgKV++OEH7d692/n7Z599ptatW+ull17S5cuXfWqT4hYAAAAAYKmnnnpKBw4ckCQdPnxYHTp0UM6cOfXpp5/q+eef96lNilsAAAAAgKUOHDigO+64Q5L06aefql69epo/f77mzJmjxYsX+9QmgVJAOlkVLBIIIVT+ZlVYjBVt+cLMUB4rBUIfkDarro2Z4VBmB1cFajhUIAdd8Z4GcCsZhqGUlBRJ0vr16/XAAw9IkiIjI/XXX3/51CYjtwAAAAAAS1WvXl0jR47Uhx9+qK+++kotWrSQJB05ckQFCxb0qU2KWwAAAACApaZMmaIffvhBffr00dChQ1W2bFlJ0qJFi1SnTh2f2uSxZAAAAACApapUqeKSlnzd+PHjlTVrVp/apLgF/sX9F9On/SX3GZG7+Vxmz4HzltlzUX05Tl+Y2Z67PgfqfELJ/L75Mq8zs7PqXnfHqvcU9wAA2F9ISIjP21LcAgAAAABuubx588rhSN+A0d9//+11+xS3AAAAAIBbbsqUKbe0fYpbAAAAAMAt17Vr11vaPmnJAAAAAADL/frrr3r55ZfVsWNHnTp1SpK0evVq/fLLLz61x8gtkG7ugqYkR2zacwesCmvxhS/BK2aGtQRCyJBVoTTehvkEciiOL+fTl/eBp/0E8vlxJ1CvtV1D2nxhRbCbL/sPhM9CAPCHr776Ss2aNVPdunW1efNmjRo1SgUKFNCPP/6o9957T4sWLfK6TUZuAQAAAACWevHFFzVy5EitW7dOQUFBzuUNGzbUt99+61ObFLcAAAAAAEvt3r1bbdq0SbW8QIEC+uuvv3xqk+IWAAAAAGCpPHnyKD4+PtXynTt3qmjRoj61SXELAAAAALBUhw4d9MILL+jkyZNyOBxKSUlRXFycBg8erC5duvjUJoFSgAkCISwF5vElMMcXVoXSmB3o5O02vD+sC/Px9lybfd/4+1r7e/+emB3e5e1+zA4Ccx+k6D58EQBuNHr0aPXu3VuRkZFKTk5WxYoVlZycrE6dOunll1/2qU2KWwAAAACApYKCgjRz5ky98sor+vnnn3X+/HlVq1ZNUVFRPrdJcQsAAAAAsNTWrVt1zz33qHjx4ipevLgpbTLnFgAAAABgqYYNG6pUqVJ66aWXtGfPHlPapLgFAAAAAFjqjz/+0KBBg/TVV1+pUqVKuuOOOzR+/Hj9/vvvPrfJY8lAJmV28IpVgTl25G0IlNmBVr7sx98hVHZkVRBZZmFVAJMvAvn94UvonJn7B4D0uu2229SnTx/16dNHR44c0fz58zV37lwNGTJE9erV05dfful1m4zcAgAAAAD8plSpUnrxxRc1duxYVa5cWV999ZVP7VDcAgAAAAD8Ii4uTs8884wKFy6sTp06qVKlSlq5cqVPbfFYMgAAAADAUkOGDNEnn3yiP/74Q40bN9bUqVP14IMPKmfOnD63SXELAAAAALDU5s2b9dxzz6l9+/a67bbbTGmT4hYwheHDNg7Te+HV3i0KCjEz3MSXEBergl+sbC8tVgXpmC0Qro9ZzO6Xme8dX/j7M8IXnvpsxb0WCJ9Rgfr+AIB/i4uLM71N5twCAAAAACz34Ycfqm7duipSpIh+++03SdKUKVP02Wef+dQexS0AAAAAwFLTp0/XwIED1bx5c509e1bJycmSpDx58mjKlCk+tUlxCwAAAACw1JtvvqmZM2dq6NChypo1q3N59erVtXv3bp/aZM4tkElZNdfRzPlfdp1L5u3cyYx2nJ7Y9VitYMV7x+y52mb22ap55GbP0zWzrUC+bnad5w8gcBw5ckTVqlVLtTw4OFgXLlzwqU1GbgEAAAAAlipVqpR27dqVavkXX3yhChUq+NQmI7cAAAAAAEsNHDhQvXv31qVLl2QYhrZv366PP/5YY8aM0axZs3xqk+IWAAAAAGCp7t27K0eOHHr55Zd18eJFderUSUWKFNHUqVPVoUMHn9rksWQAAAAAgOU6d+6sgwcP6vz58zp58qR+//13dezYUV9//bVP7TFyC2RS/g5RCYQgITuGavk7xMWq+wbWMPt96O/QJF/uT0/78fb8BMJ7wJfzGcif0wAyh5w5cypnzpySpIMHD+ree+91fjWQNxi5BQAAAADYHsUtAAAAAMD2KG4BAAAAALbHnFsAAAAAgCWWL1/u8fUjR4743DbFLYBUfAkR8Xcgib/374mZITNmXhtf9mN2YE4gXzd3/N1nKwLfPPF0nFbdN+74Eg7lS9CSmcwOabPqXLs/nw43Wxi3rC8A7KV169Y3XcfhcPdZ4hnFLQAAAADAEikpKbesbebcAgAAAABsj+IWAAAAAGB7FLcAAAAAANtjzi1wS/k2GT5QWRVUYhUzA2bMDNkxO2DGquMM5KAjM9kxOModX66nVaFiZrPivWN24Ju/A7rM5em/h4RNATAHI7cAAAAAANvzurjt2rWrNm/efCv6AgAAAACAT7wubhMSEtSoUSNFRUVp9OjROnHixK3oFwAAAAAgg8qbN68iIiJS/eTLl09FixZV/fr1NXv2bK/a9Lq4XbZsmU6cOKFevXppwYIFKlmypJo1a6ZFixbpypUr3jYHAAAAAMhkXn31VWXJkkUtWrRQbGysYmNj1aJFC2XJkkW9e/dWdHS0evXqpZkzZ6a7TZ8CpfLnz6+BAwdq4MCB+uGHHzR79mw99thjypUrlx599FE988wzioqK8qVpIEOxKnwH5ob8BELIjpkCuW++sCrQyd/MDifydn1/hxnZ8fPTl/MZCPezL9c6EPrtd+0lhVm8z0RJPaQaNWooa9as6t27t3r37m1xJwBzbN26VSNHjtTTTz/tsvydd97R2rVrtXjxYlWpUkVvvPGGevToka42/1OgVHx8vNatW6d169Ypa9asat68uXbv3q2KFStq8uTJ/6VpAAAAAGn47rvvtGfPHgpb2NqaNWvUqFGjVMvvu+8+rVmzRpLUvHlzHT58ON1tel3cXrlyRYsXL9YDDzygEiVK6NNPP1X//v31xx9/aO7cuVq/fr0WLlyoESNGeNs0AAAAACATiIiI0Oeff55q+eeff66IiAhJ0oULF5Q7d+50t+n1Y8mFCxdWSkqKOnbsqO3bt+uOO+5ItU5MTIzy5MnjbdMAAAAAgEzglVdeUa9evbRx40bVrFlT0rWnElatWqUZM2ZIktatW6f69eunu02vi9vJkyerXbt2CgkJcbtOnjx5dOTIEW+bBgAAAABkAj169FDFihU1bdo0LVmyRJJUrlw5ffXVV6pTp44kadCgQV616TAMwzC9pwEsMTFR4eHhSkhIUFiY1SkAyHwc/u6AW4SBWMfb8Buzr02ghu+YjXvaPTPvAavOc0a7b60ICPNVQL53Lkkaq4D8e+3//5aUrO5aYqIUHh6Y5wUIBD6lJQMAAAAA8F+kpKTo0KFDOnXqlFJSUlxeq1evntftUdwCAAAAACz17bffqlOnTvrtt9/074eJHQ6HkpOTvW6T4hYAAAAAYKmnn35a1atX18qVK1W4cGE5HP99Oh/FLXALeZrHlNHmk/mbL+fTzHlmVl1Pf8/BM3v/Vsz189Rnf8819KVvVh2PVfvns9Bc/r6nASC9Dh48qEWLFqls2bKmten199wCAAAAAPBf3H333Tp06JCpbTJyCwAAAACwVN++fTVo0CCdPHlSlStXVvbs2V1er1KlitdtUtwCAAAAACzVtm1bSdITTzzhXOZwOGQYBoFSAAAAAAB7OHLkiOltUtwCt5AxzLj5Sql4nxRHgIi5AjmAyEwZ6Vg8sSrkyKrzaWZQnS999mX/gXyv2TEEy+zzacfrBsD+SpQoYXqbFLcAAAAAgFtu+fLlatasmbJnz67ly5d7XLdVq1Zet09xCwAAAAC45Vq3bq2TJ0+qQIECat26tdv1mHMLAAAAAAhYKSkpaf5/s/A9twAAAAAA22PkFrilvA+H8tiam3APs8Nv/B2m4+/9m31uvOXvsBqz+ftey2jn0xNvPyPMDk/z5TPK36FFgXx/+DukzN+fxQAyvg0bNmjDhg06depUqpHc999/3+v2/Dpyu3nzZrVs2VJFihSRw+HQsmXL0r1tXFycsmXLpjvuuOOW9Q8AAAAAYL7Y2Fjdf//92rBhg/766y+dOXPG5ccXfh25vXDhgqpWraonnnhCDz30ULq3O3v2rLp06aL77rtP//vf/25hDwEAAAAAZpsxY4bmzJmjxx57zLQ2/VrcNmvWTM2aNfN6u6efflqdOnVS1qxZvRrtBQAAAAD43+XLl1WnTh1T27RdoNTs2bN1+PBhDRuWvokgSUlJSkxMdPkBAAAAAPhP9+7dNX/+fFPbtFWg1MGDB/Xiiy9qy5YtypYtfV0fM2aMYmNJN8Ct5n1wlC8BHmYGn/g7KCQQQlys6oOZYS2BzKoAIjNZFX7jy/G428aXgC5/n2dPzDxOmMu694eRalliYqLCx4Zb0wEAfnHp0iW9++67Wr9+vapUqaLs2bO7vD5p0iSv27RNcZucnKxOnTopNjZW0dHR6d5uyJAhGjhwoPP3xMRERUZG3oouAgAAAADS4aeffnKGA//8888urzkcvn3jiG2K23PnzmnHjh3auXOn+vTpI+naF/8ahqFs2bJp7dq1atiwYartgoODFRwcbHV3AQAAAABpSE5OVmxsrCpXrqy8efOa1q5tituwsDDt3r3bZdnbb7+tL7/8UosWLVKpUqX81DMAAAAAQHplzZpV999/v/bu3Ztxitvz58/r0KFDzt+PHDmiXbt2KSIiQsWLF9eQIUN04sQJffDBB8qSJYsqVarksn2BAgUUEhKSajkAAAAAIHBVqlRJhw8fNnWQ0q/F7Y4dOxQTE+P8/frc2K5du2rOnDmKj4/XsWPH/NU9wIUj1v2z/2aGxdiVL6Ew7gRy+E2g8iV8x9+hYr7y9l4LhD770gczr5s7Zp8bM+8pO77fzbzOvrbn7/34sn8AmdPIkSM1ePBgvfbaa7rrrrsUGhrq8npYWJjXbfq1uG3QoIEMI3VC3nVz5szxuP3w4cM1fPhwczsFAAAAALilmjdvLklq1aqVS4CUYRhyOBxKTk72uk3bzLkFAAAAAGQMGzduNL1NilsAAAAAgKXq169vepsUt0A62XW+kFXz2byds2XmXFzJ//NH/T3X0N/X05NAnusXCPNx3fHl/jDzngrk+fJmnht/s6pfVu3H/TVwPw0NQOZ28eJFHTt2TJcvX3ZZXqVKFa/borgFAAAAAFjqzz//1OOPP67Vq1en+bovc26z/NdOAQAAAADgjf79++vs2bPatm2bcuTIoS+++EJz585VVFSUli9f7lObjNwCAAAAACz15Zdf6rPPPlP16tWVJUsWlShRQo0bN1ZYWJjGjBmjFi1aeN0mI7cAAAAAAEtduHBBBQoUkCTlzZtXf/75pySpcuXK+uGHH3xqk5FbIBXHzVexETODXMxsy5dwE7NDaawK2XG3H38H3Ph7/1LgBrUFQjCRFcFiZh+LL/e6mdv4wqpr7e/QO6u477P7/7YawwibAjKjcuXKaf/+/SpZsqSqVq2qd955RyVLltSMGTNUuHBhn9qkuAUAAAAAWOrZZ59VfHy8JGnYsGFq2rSp5s2bp6CgIM2ZM8enNiluAQAAAACWOHLkiEqVKqVHH33Uueyuu+7Sb7/9pn379ql48eK67bbbfGqb4hYAAAAAYIkyZcqoRIkSiomJUcOGDdWgQQMVK1ZMOXPm1J133vmf2qa4BQAAAABY4ssvv9SmTZu0adMmffzxx7p8+bJKly6thg0bKiYmRjExMSpYsKBPbVPcAvALq4KEAjkwxx2zw7bctRcIoUlm8ncwkS/Mvm7etuULs+8bfwd0+Tskzo7vNQD4Lxo0aKAGDRpIki5duqSvv/7aWezOnTtXV65cUfny5fXLL7943TbFLQAAAADAciEhIWrYsKHuuecexcTEaPXq1XrnnXe0b98+n9qjuAUAAAAAWOby5cv69ttvtXHjRm3atEnbtm1TZGSk6tWrp2nTpql+/fo+tUtxCwAAAACwRMOGDbVt2zaVKlVK9evX11NPPaX58+f7/N22N6K4BQAAAABYYsuWLSpcuLAzKbl+/frKly+fKW1T3AIZgC+BOWaH7JgZfmMmqwJ7rGJVkI2/Q4vMZPaxmPmesirsyqrradW5CdQQKl/Oc0YLdgOAmzl79qy2bNmiTZs2ady4cerYsaOio6NVv359Z7GbP39+n9qmuAUAAAAAWCI0NFRNmzZV06ZNJUnnzp3T1q1btXHjRr3++uvq3LmzoqKi9PPPP3vddhazOwsAAAAAQHqEhoYqIiJCERERyps3r7Jly6a9e/f61BYjtwAAAAAAS6SkpGjHjh3atGmTNm7cqLi4OF24cEFFixZVTEyM3nrrLcXExPjUNsUtAAAAAMASefLk0YULF1SoUCHFxMRo8uTJatCggcqUKfOf26a4RSbmcLPc8HJ9c1kVMOOLQO6bL6zqs7fhM2YG+fjanpmBOVbdNxkt5Mfs+8DbfVgVQuXv0CQzr7XZ59PMzwgACBTjx49XTEyMoqOjTW+b4hYAAAAAYImnnnrqlrVNoBQAAAAAwPYobgEAAAAAtsdjycjE3M2t9S+z5wfCPSvmNPqyjS9zAD1tY9W8PX/fh/7evye+XDcrtgmEucX+nntu5n6YC+ueMSww/5sLIGNh5BYAAAAAYHsUtwAAAAAA26O4BQAAAADYHsUtAAAAAMD2CJQCUnGkvdSicBNfQnHMDjEJ1FAUT/2yKkzIinMTCMeZ2flyDQL1fSNZF0TmyzZmnrdAvgbuZLT3ux37DCDjYOQWAAAAAGB7FLcAAAAAANujuAUAAAAA2B7FLQAAAADA9giUAv7FzKAnM/dvZR/M3I+Z59PsgK5ADQYy+zqbHYaGtAVCGJw7ZoZDeWLmvebvc+OJvz8jApm//xsKIHNj5BYAAAAAYHsUtwAAAAAA26O4BQAAAADYHsUtAAAAAMD2CJQC0snsUB4zQ1TMDkSxIgTKlyCbQAi/sSNfzqeZ94C/Q9IC4Tqb2Qdf2grkkB9/Xx9/h2AFwjUAMqKjR4+qVKlS2rlzp+644w5/d+c/GT58uJYtW6Zdu3ZZsr9NmzYpJiZGZ86cUZ48eUxb1wqM3AIAAAAZULdu3dS6dWt/dyNgNWjQQA6HQw6HQ8HBwSpatKhatmypJUuW+LtrXmvQoIH69++frvWuH3NISIiio6M1ZswYGYbhXKdOnTqKj49XeHj4LezxrUFxCwAAAMBrycnJSklJ8Xc3/pMePXooPj5ev/76qxYvXqyKFSuqQ4cO6tmzp8ftrly5YlEPzXf9mPfv368hQ4bo1Vdf1YwZM5yvBwUFqVChQnI4HH7spW8obgEAAIBMoEGDBurXr5+ef/55RUREqFChQho+fLjLOmfPntVTTz2lggULKiQkRJUqVdKKFSskSXPmzFGePHm0fPlyVaxYUcHBwTp27JiSkpI0ePBgFS1aVKGhobr77ru1adMmZ5unT59Wx44dVbRoUeXMmVOVK1fWxx9/7LLfRYsWqXLlysqRI4fy5cunRo0a6cKFC87XZ82apQoVKigkJETly5fX22+/7bL99u3bVa1aNYWEhKh69erauXNnus5Jzpw5VahQIRUrVky1atXSuHHj9M4772jmzJlav369pGuPODscDi1YsED169dXSEiI5s2bp5SUFI0YMULFihVTcHCw7rjjDn3xxRfOtjdt2iSHw6GzZ886l+3atUsOh0NHjx51Lps5c6YiIyOVM2dOtWnTRpMmTUrzEd8PP/xQJUuWVHh4uDp06KBz585JujZC/9VXX2nq1KnOUdkb23d3zCVKlNDjjz+uKlWqaN26dW77/dtvv6lly5bKmzevQkNDdfvtt2vVqlVptn3x4kU1a9ZMdevWdTluqzDnFgAAALCRxMREl9+Dg4MVHBycrm3nzp2rgQMHatu2bfrmm2/UrVs31a1bV40bN1ZKSoqaNWumc+fO6aOPPlKZMmW0Z88eZc2a1bn9xYsXNW7cOM2aNUv58uVTgQIF1KdPH+3Zs0effPKJihQpoqVLl6pp06bavXu3oqKidOnSJd1111164YUXFBYWppUrV+qxxx5TmTJlVLNmTcXHx6tjx456/fXX1aZNG507d05btmxxPio7b948vfrqq5o2bZqqVaumnTt3qkePHgoNDVXXrl11/vx5PfDAA2rcuLE++ugjHTlyRM8++6zP57dr164aNGiQlixZokaNGjmXv/jii5o4caKziJ46daomTpyod955R9WqVdP777+vVq1a6ZdfflFUVFS69hUXF6enn35a48aNU6tWrbR+/Xq98sorqdb79ddftWzZMq1YsUJnzpxR+/btNXbsWI0aNUpTp07VgQMHVKlSJY0YMUKSlD9//pvu2zAMbd26Vfv27fPY3969e+vy5cvavHmzQkNDtWfPHuXKlSvVemfPnlWLFi2UK1curVu3Tjlz5kzXOTATxS1wC/kSpGN2uEhGCkvx1Ja/Q2nsGAoTCPdaoDL73JjZnlXn2cz9+PLeDeT3lFV99mU/Gel9CPciIyNdfh82bFiqEVh3qlSpomHDrt1EUVFRmjZtmjZs2KDGjRtr/fr12r59u/bu3avo6GhJUunSpV22v3Llit5++21VrVpVknTs2DHNnj1bx44dU5EiRSRJgwcP1hdffKHZs2dr9OjRKlq0qAYPHuxso2/fvlqzZo0WLlzoLG6vXr2qhx56SCVKlJAkVa5c2eX4Jk6cqIceekiSVKpUKe3Zs0fvvPOOunbtqvnz5yslJUXvvfeeQkJCdPvtt+v3339Xr1690ntKXWTJkkXR0dGpRj/79+/v7IMkTZgwQS+88II6dOggSRo3bpw2btyoKVOm6K233krXvt588001a9bMeX6io6P19ddfO0fLr0tJSdGcOXOUO3duSdJjjz2mDRs2aNSoUQoPD1dQUJBzRPZm3n77bc2aNUuXL1/WlStXFBISon79+rld/9ixY2rbtq3zmvz7npCkkydP6pFHHlFUVJTmz5+voKCgdB2/2ShuAQAAABs5fvy4wsLCnL+nd9RWulbc3qhw4cI6deqUpGuPzBYrVsxZ2KYlKCjIpY3du3crOTk51TZJSUnKly+fpGtzc0ePHq2FCxfqxIkTunz5spKSkpwje1WrVtV9992nypUrq0mTJrr//vv18MMPK2/evLpw4YJ+/fVXPfnkk+rRo4ez/atXrzoDj/bu3asqVaooJCTE+Xrt2rXTfU7SYhhGqjmn1atXd/7/xMRE/fHHH6pbt67LOnXr1tWPP/6Y7v3s379fbdq0cVlWs2bNVMVtyZIlnYWt5HrdvNW5c2cNHTpUZ86c0bBhw1SnTh3VqVPH7fr9+vVTr169tHbtWjVq1Eht27ZNdR81btxYNWvW1IIFC1xG+q1GcQsAAADYSFhYmEtx643s2bO7/O5wOJyhUDly5Ljp9jly5HAp+s6fP6+sWbPq+++/T1XUXH90dfz48Zo6daqmTJmiypUrKzQ0VP3799fly5clSVmzZtW6dev09ddfa+3atXrzzTc1dOhQbdu2zVkAz5w5U3fffbdL+7eqiEpOTtbBgwdVo0YNl+WhoaFetZMly7V4oxuTiH0NovJ03bwVHh6usmXLSpIWLlyosmXLqlatWi6PYN+oe/fuatKkiVauXKm1a9dqzJgxmjhxovr27etcp0WLFlq8eLH27NnjMupuNQKlAAAAAKhKlSr6/fffdeDAgXRvU61aNSUnJ+vUqVMqW7asy8/1R2Tj4uL04IMP6tFHH1XVqlVVunTpVPtwOByqW7euYmNjtXPnTgUFBWnp0qUqWLCgihQposOHD6dqv1SpUpKkChUq6KefftKlS5ec7X377bc+n4e5c+fqzJkzatu2rdt1wsLCVKRIEcXFxbksj4uLU8WKFSX9/7zX+Ph45+v//q7acuXK6bvvvnNZ9u/f0yMoKEjJycleb5crVy49++yzGjx4sEsR/m+RkZF6+umntWTJEg0aNEgzZ850eX3s2LHq2rWr7rvvPu3Zs8frfpiF4hYAAACA6tevr3r16qlt27Zat26djhw5otWrV7skAP9bdHS0OnfurC5dumjJkiU6cuSItm/frjFjxmjlypWSrs3tvT4yu3fvXj311FP63//+52xj27ZtGj16tHbs2KFjx45pyZIl+vPPP1WhQgVJUmxsrMaMGaM33nhDBw4c0O7duzV79mxNmjRJktSpUyc5HA716NFDe/bs0apVqzRhwoR0HfPFixd18uRJ/f777/r222/1wgsv6Omnn1avXr0UExPjcdvnnntO48aN04IFC7R//369+OKL2rVrlzPMqmzZsoqMjNTw4cN18OBBrVy5UhMnTnRpo2/fvlq1apUmTZqkgwcP6p133tHq1au9/hqekiVLatu2bTp69Kj++usvr0Z1n3rqKR04cECLFy9O8/X+/ftrzZo1OnLkiH744Qdt3LjReW1uNGHCBHXu3FkNGzbUvn37vOq/WXgsGfiXQA5+8XfAi5l8Oc++BHSZfSxm9tuqe82qYJ5ADQAK5OAsq4Kr/B0yZPb9aVXQklXBf97u346fhY5Y93+oG8PcjxbBeosXL9bgwYPVsWNHXbhwQWXLltXYsWM9bjN79myNHDlSgwYN0okTJ3TbbbepVq1aeuCBByRJL7/8sg4fPqwmTZooZ86c6tmzp1q3bq2EhARJ10ZBN2/erClTpigxMVElSpTQxIkT1axZM0nXHovNmTOnxo8fr+eee06hoaGqXLmy+vfvL+na6OPnn3+up59+WtWqVVPFihU1btw4jyOv182cOVMzZ85UUFCQ8uXLp7vuuksLFixINQ82Lf369VNCQoIGDRqkU6dOqWLFilq+fLkzeTh79uz6+OOP1atXL1WpUkU1atTQyJEj1a5dO2cbdevW1YwZMxQbG6uXX35ZTZo00YABAzRt2rSb7v9GgwcPVteuXVWxYkX9888/OnLkiEqWLJmubSMiItSlSxcNHz7cJTDruuTkZPXu3Vu///67wsLC1LRpU02ePDnNtiZPnqzk5GQ1bNhQmzZt8jh/+1ZwGJ7GnzOgxMREhYeHKyEhwee5CsjozPvCaqv+oPOFVcWtvws4f/9B56kP/j438I2/3x9mvg/8/Y9cN+uDO/4ubv3Njp+Fnvuc+k/RQP577f/7JlndtcREKTxcAXleYJ4ePXpo37592rJli7+7YjuM3AIAAACAn0yYMEGNGzdWaGioVq9erblz5+rtt9/2d7dsieIWAAAAAPxk+/btev3113Xu3DmVLl1ab7zxhrp37+7vbtkSxS2Qirsn9d0/ruzL416ZZQ6cHVn1qKgvbcF7Vj2OGsjvG38fT6A+3nszgTqlIBAeJ+dzCjDPwoUL/d2FDIO0ZAAAAACA7VHcAgAAAABsj+IWAAAAAGB7FLcAAAAAANsjUAowgW9fQO/99+maGYwTyCFYmf37fH3ZD+Eu7pl9bcxsL5BDqHwJU/L3+8DsbawQyPeAmQL1/APIWBi5BQAAAADYHsUtAAAAAMD2KG4BAAAAALZHcQsAAAAAsD2HYRi+JOHYVmJiosLDw5WQkKCwsDB/dweZmveBUm5bsmG4iC+sCl7xJYSKsJTMw5d7wMxtzGT2/n0JPLPqvROo712rPqOs4tv5TP2naCD/vfb/fZOs7lpiohQeroA8L0AgYOQWAAAAAGB7FLcAAAAAANujuAUAAAAA2B7FLQAAAADA9rL5uwMAcCuZHULl7/AZd6wKkQnU47eSmefAt3vNUw6kd0F1ZocZmcns96EVgXRm98vf4VC+COR7CkDGx8gtAAAAAMD2KG4BAAAAALZHcQsAAAAAsD2KWwAAAACA7REoBfiNp1AYd9IOizGGed+WI9a74JlAZ8fgFasQ1uI9s4OerLsG3n4WBO7nQCDft2aGU1kRdOUrfx8nAHiLkVsAAAAAgO1R3AIAAAAAbI/iFgAAAABge8y5BWzFl3m6blryYZ6uL3yZ2+vvuVn+3r8nzGezhufzac17xwqBfN/4Mq/U38fjS599md8dCNt42xYAWIGRWwAAAACA7VHcAgAAAABsj+IWAAAAAGB7FLcAAAAAANsjUArALeVLcJUvIVTu9+9pP6btxjKEtZiLgC5rmP0+NPP6mBkC5Uu/zD5+M/vG+wCA3fh15Hbz5s1q2bKlihQpIofDoWXLlnlcf+vWrapbt67y5cunHDlyqHz58po8ebI1nQUAAAAABCy/jtxeuHBBVatW1RNPPKGHHnropuuHhoaqT58+qlKlikJDQ7V161Y99dRTCg0NVc+ePS3oMQAAAAAgEPm1uG3WrJmaNWuW7vWrVaumatWqOX8vWbKklixZoi1btlDcAgAAAEAmZutAqZ07d+rrr79W/fr13a6TlJSkxMRElx8AAAAAQMZiy0CpYsWK6c8//9TVq1c1fPhwde/e3e26Y8aMUWysDVNjgEzMzBAqT2EtmSUshdAkX3h/DwYyX0La/B3aFMj3p7u+WfV548t+Mto1AIC02HLkdsuWLdqxY4dmzJihKVOm6OOPP3a77pAhQ5SQkOD8OX78uIU9BQAAAABYwZYjt6VKlZIkVa5cWf/73/80fPhwdezYMc11g4ODFRwcbGX3AAAAAAAWs+XI7Y1SUlKUlJTk724AAAAAAPzIryO358+f16FDh5y/HzlyRLt27VJERISKFy+uIUOG6MSJE/rggw8kSW+99ZaKFy+u8uXLS7r2PbkTJkxQv379/NJ/AAAAAEBg8Gtxu2PHDsXExDh/HzhwoCSpa9eumjNnjuLj43Xs2DHn6ykpKRoyZIiOHDmibNmyqUyZMho3bpyeeuopy/sOwB+8D8UxvQd+DmfyJRQG7mWWwBx/hxkFwnn2d6CSL/sP5Pe7t33zJSgQALzl1+K2QYMGMgz3H3Zz5sxx+b1v377q27fvLe4VAAAAAMBubD/nFgAAAAAAilsAAAAAgO1R3AIAAAAAbM+W33MLAOllxyAbT30OhOOBHbnLt/A+pM3TPejvwDWzeXs8vhyn2aFRZgZn+RIe5m4bR6z7e42wKQBmYeQWAAAAAGB7FLcAAAAAANujuAUAAAAA2B5zbgHYiPt5WcYw7+cOmjk/0Je5abAK8/l8mVvrC6vudavm9lpxPIE8T9eXvvF5B8CfGLkFAAAAANgexS0AAAAAwPYobgEAAAAAtkdxCwAAAACwPQKlAGQQ7kKD3AfpEJZiVwREuWdicNR8N8s7mbcLX5n53jU7nMlM/u8b7zUA9sLILQAAAADA9ihuAQAAAAC2R3ELAAAAALA9ilsAAAAAgO0RKAUgg/MUiOIufMeXbWAugmwcsWnfa76EKfkSTGTH8DRPx2nVeTOTHUOwAMCfGLkFAAAAANgexS0AAAAAwPYobgEAAAAAtkdxCwAAAACwPQKlAGRivoQW+bJNZg+hIhzKF8awW3+v2TE0yleBGsIUyNfAXaiZ5KnfvN8B+A8jtwAAAAAA26O4BQAAAADYHsUtAAAAAMD2KG4BAAAAALZHoBQA3HLuAlYyWtAUQTL+59295ilkKZCDjvzN3bkJ1NCqW8HdsWam+yZ8jKQQi3d66dr/1KhRQ1mzZlXv3r3Vu3dvizsBBC6KWwAAAMBGvvvuO4WFhfm7G0DA4bFkAAAAAIDtUdwCAAAAAGyP4hYAAAAAYHvMuQUAvwnkoCnCoTK7zBIY5Ol43J0DOwZH+XKcAGA3jNwCAAAAAGyP4hYAAAAAYHsUtwAAAAAA22POLQAEHE/zXc2cj8u82szCl3mydpyHadV8YHf7MfucmdmeHa8nAHiLkVsAAAAAgO1R3AIAAAAAbI/iFgAAAABgexS3AAAAAADbI1AKAGyFECj4l6dgIqsCnczky/EQ9CQZw/gsAhB4GLkFAAAAANgexS0AAAAAwPYobgEAAAAAtkdxCwAAAACwPQKlAADI4NyFFnkKgMpo4VCB0J4/ebqeGek4AWRujNwCAAAAAGyP4hYAAAAAYHsUtwAAAAAA26O4BQAAAADYHoFSAABkcMYww80rDl9a+y9dMakP5rEqaMndfjztw5dQL/ftebpu/r0GAGAWRm4BAAAAALZHcQsAAAAAsD2KWwAAAACA7VHcAgAAAABsj0ApAAAyLbPDoczkfd88BzDZLzTJzEArT9wHjgGAvTByCwAAAACwPYpbAAAAAIDtUdwCAAAAAGyPObcAAMCPrJrv6W4/3s/FdTe319McWV/mz/qyHwDIzBi5BQAAAADYHsUtAAAAAMD2KG4BAAAAALZHcQsAAAAAsD0CpQAAQCZw64Oj3K3viVXhUI5Y98dvDLMq1AsAbi1GbgEAAAAAtkdxCwAAAACwPYpbAAAAAIDtUdwCAAAAAGyPQCkAAJAJuAtNch+05G1wlKdwKHfbeAqhMjNsypewKwCwG0ZuAQAAAAC2R3ELAAAAALA9ilsAAAAAgO1R3AIAAAAAbI9AKQAAABOYHdrkS3CVh9b+S1cAwBYYuQUAAAAA2B7FLQAAAADA9ihuAQAAAAC2R3ELAAAAALA9AqUAAEAG4fB+C5/Cmbxn1X4AIDNj5BYAAAAAYHsUtwAAAAAA26O4BQAAAADYHnNuAQBABmF4v8Uw8+bpGsO8bsonnvbD3F4AmZlfR243b96sli1bqkiRInI4HFq2bJnH9ZcsWaLGjRsrf/78CgsLU+3atbVmzRprOgsAAAAACFh+LW4vXLigqlWr6q233krX+ps3b1bjxo21atUqff/994qJiVHLli21c+fOW9xTAAAAAEAg8+tjyc2aNVOzZs3Svf6UKVNcfh89erQ+++wzff7556pWrZrJvQMAAAAA2IWt59ympKTo3LlzioiIcLtOUlKSkpKSnL8nJiZa0TUAAAAAgIVsXdxOmDBB58+fV/v27d2uM2bMGMXGkq4AAADS4imEyvuwKX+zKtQKAAKRbb8KaP78+YqNjdXChQtVoEABt+sNGTJECQkJzp/jx49b2EsAAAAAgBVsOXL7ySefqHv37vr000/VqFEjj+sGBwcrODjYop4BAAAAAPzBdiO3H3/8sR5//HF9/PHHatGihb+7AwAAAAAIAH4duT1//rwOHTrk/P3IkSPatWuXIiIiVLx4cQ0ZMkQnTpzQBx98IOnao8hdu3bV1KlTdffdd+vkyZOSpBw5cig8PNwvxwAAAAAA8D+/Frc7duxQTEyM8/eBAwdKkrp27ao5c+YoPj5ex44dc77+7rvv6urVq+rdu7d69+7tXH59fQAAgEDkS9CTw6c8TE8BWQCQsfm1uG3QoIEMw/2H8L8L1k2bNt3aDgEAAAAAbMl2c24BAAAAAPg3ilsAAAAAgO1R3AIAAAAAbM+W33MLAACQmsPc1twEOrkLh/IlAMpT0JT71wiNQsa2adMmxcTE6MyZM8qTJ4+/u5Mmh8OhpUuXqnXr1jp69KhKlSqlnTt36o477nC7TYMGDXTHHXdoypQplvUzs2HkFgAAAMiAunXrJofDobFjx7osX7ZsmRwOc/8xyB927typdu3aqWDBggoJCVFUVJR69OihAwcOWNqPyMhIxcfHq1KlSpKuFecOh0Nnz551WW/JkiV67bXXLO1bZkNxCwAAAGRQISEhGjdunM6cOWNqu5cvXza1PW+tWLFCtWrVUlJSkubNm6e9e/fqo48+Unh4uF555RVL+5I1a1YVKlRI2bJ5fig2IiJCuXPntqhXmRPFLQAAAJBBNWrUSIUKFdKYMWM8rrd48WLdfvvtCg4OVsmSJTVx4kSX10uWLKnXXntNXbp0UVhYmHr27Kk5c+YoT548WrFihcqVK6ecOXPq4Ycf1sWLFzV37lyVLFlSefPmVb9+/ZScnOxs68MPP1T16tWVO3duFSpUSJ06ddKpU6fSfUwXL17U448/rubNm2v58uVq1KiRSpUqpbvvvlsTJkzQO++841z3q6++Us2aNRUcHKzChQvrxRdf1NWrV52vN2jQQP369dPzzz+viIgIFSpUSMOHD3fZ38GDB1WvXj2FhISoYsWKWrduncvrR48elcPh0K5du3T06FHFxMRIkvLmzSuHw6Fu3bo599W/f3/ndmfOnFGXLl2UN29e5cyZU82aNdPBgwedr18/v2vWrFGFChWUK1cuNW3aVPHx8c51Nm3apJo1ayo0NFR58uRR3bp19dtvv6X7XGY0FLcAAACAjSQmJrr8JCUluV03a9asGj16tN588039/vvvaa7z/fffq3379urQoYN2796t4cOH65VXXtGcOXNc1pswYYKqVq2qnTt3OkdHL168qDfeeEOffPKJvvjiC23atElt2rTRqlWrtGrVKn344Yd65513tGjRImc7V65c0WuvvaYff/xRy5Yt09GjR50FYHqsWbNGf/31l55//vk0X78+T/fEiRNq3ry5atSooR9//FHTp0/Xe++9p5EjR7qsP3fuXIWGhmrbtm16/fXXNWLECGcBm5KSooceekhBQUHatm2bZsyYoRdeeMFt3yIjI7V48WJJ0v79+xUfH6+pU6emuW63bt20Y8cOLV++XN98840Mw1Dz5s115coV5zoXL17UhAkT9OGHH2rz5s06duyYBg8eLEm6evWqWrdurfr16+unn37SN998o549e2aIR859RaAUAADI8LwNh7rZa97ypS33ffblD1dCqDKSyMhIl9+HDRuWarTxRm3atNEdd9yhYcOG6b333kv1+qRJk3Tfffc5C9bo6Gjt2bNH48ePdyk6GzZsqEGDBjl/37Jli65cuaLp06erTJkykqSHH35YH374of73v/8pV65cqlixomJiYrRx40Y98sgjkqQnnnjC2Ubp0qX1xhtvqEaNGjp//rxy5cp10+O/PrpZvnx5j+u9/fbbioyM1LRp0+RwOFS+fHn98ccfeuGFF/Tqq68qS5Zr43xVqlTRsGHX3qRRUVGaNm2aNmzYoMaNG2v9+vXat2+f1qxZoyJFikiSRo8erWbNmqW5z6xZsyoiIkKSVKBAAbeBWAcPHtTy5csVFxenOnXqSJLmzZunyMhILVu2TO3atZN07R8CZsyY4Ty/ffr00YgRIyRd+0eOhIQEPfDAA87XK1SocNPzl5ExcgsAAADYyPHjx5WQkOD8GTJkyE23GTdunObOnau9e/emem3v3r2qW7euy7K6devq4MGDLo8TV69ePdW2OXPmdBZWklSwYEGVLFnSpUgtWLCgy2PH33//vVq2bKnixYsrd+7cql+/viTp2LFjNz0OSTKM9P1jzd69e1W7dm2Xkcy6devq/PnzLqPYVapUcdmucOHCzv7u3btXkZGRzsJWkmrXrp2u/d+sb9myZdPdd9/tXJYvXz6VK1fO5Rr9+/ze2LeIiAh169ZNTZo0UcuWLTV16lSXR5YzI4pbAAAAwEbCwsJcfoKDg2+6Tb169dSkSZN0FcLuhIaGplqWPXt2l98dDkeay1JSUiRJFy5cUJMmTRQWFqZ58+bpu+++09KlSyWlP6QqOjpakrRv3z6vjyEtnvrrb2n17cbifvbs2frmm29Up04dLViwQNHR0fr222+t7mbAoLgFAAAAMoGxY8fq888/1zfffOOyvEKFCoqLi3NZFhcXp+joaGXNmtXUPuzbt0+nT5/W2LFjde+996p8+fJehUlJ0v3336/bbrtNr7/+epqvX/8KngoVKjjnsl4XFxen3Llzq1ixYunaV4UKFXT8+HGXEdGbFY9BQUGS5DLqnVa7V69e1bZt25zLTp8+rf3796tixYrp6tt11apV05AhQ/T111+rUqVKmj9/vlfbZyQUtwAAAEAmULlyZXXu3FlvvPGGy/JBgwZpw4YNeu2113TgwAHNnTtX06ZNcwYXmal48eIKCgrSm2++qcOHD2v58uVef/draGioZs2apZUrV6pVq1Zav369jh49qh07duj555/X008/LUl65plndPz4cfXt21f79u3TZ599pmHDhmngwIHO+bY306hRI0VHR6tr16768ccftWXLFg0dOtTjNiVKlJDD4dCKFSv0559/6vz586nWiYqK0oMPPqgePXpo69at+vHHH/Xoo4+qaNGievDBB9PVtyNHjmjIkCH65ptv9Ntvv2nt2rU6ePBgpp53S3ELAAACkMPDj/eMYWn/+NSz2LR/ADsYMWJEqkdu77zzTi1cuFCffPKJKlWqpFdffVUjRozwKsE4vfLnz685c+bo008/VcWKFTV27FhNmDDB63YefPBBff3118qePbs6deqk8uXLq2PHjkpISHCmIRctWlSrVq3S9u3bVbVqVT399NN68skn9fLLL6d7P1myZNHSpUv1zz//qGbNmurevbtGjRrlcZuiRYsqNjZWL774ogoWLKg+ffqkud7s2bN111136YEHHlDt2rVlGIZWrVqV6lFkd3LmzKl9+/apbdu2io6OVs+ePdW7d2899dRT6T6+jMZhpHdGdgaRmJio8PBwJSQkKCwszN/dAQAAafJUxLr708War7/wpZA1Ny3Z+7bslpYcyH+vXe+bXpQUYvHOL0kaq4A8L0AgYOQWAAAAAGB7FLcAAAAAANvL5u8OAAAApOb+MVpHbNqPH/s6h9Zbgbwfcx9lBgB7YeQWAAAAAGB7FLcAAAAAANujuAUAAAAA2B7FLQAAAADA9giUAgAAtkI4knucGwCZGSO3AAAAAADbo7gFAAAAANgexS0AAAAAwPYobgEAAAAAtkegFAAAsBVHbNrLAzlMybo+G2Y3CAC2wcgtAAAAAMD2KG4BAAAAALZHcQsAAAAAsD2KWwAAAACA7REoBQAAbMUY5ktoksP0fgAAAgsjtwAAAAAA26O4BQAAAADYHsUtAAAAAMD2KG4BAAAAALZHoBQAAMAtZgwztTUzGwOADIORWwAAAACA7VHcAgAAAABsj+IWAAAAAGB7zLkFAAAZhMPfHQAA+BEjtwAAAAAA26O4BQAAAADYHsUtAAAAAMD2KG4BAAAAALZHcQsAAAAAsD2KWwAAAACA7VHcAgAAAABsj+IWAAAAAGB7FLcAAAAAANvL5u8OAAAA3GqOWO+3MYaZ3w9vuOuzv/sFAIGKkVsAAAAAgO1R3AIAAAAAbI/iFgAAAABgexS3AAAAAADbI1AKAABkeIEawuQp6CpQ+wwAgYqRWwAAAACA7VHcAgAAAABsj+IWAAAAAGB7FLcAAAAAANsjUAoAANiMw98dMA2hUQBgHkZuAQAAAAC2R3ELAAAAALA9ilsAAAAAgO1R3AIAAAAAbI/iFgAAAABgexS3AAAAAADbo7gFAAAAANgexS0AAAAAwPay+bsDAAAA3jHcLHeY2JYv7fnSlqdtAADeYOQWAAAAAGB7FLcAAAAAANujuAUAAAAA2B7FLQAAAADA9giUAgAAuOUIjgKAW42RWwAAAACA7VHcAgAAAABsj+IWAAAAAGB7FLcAAAAAANsjUAoAAGQQZoc2EQIFAHbCyC0AAAAAwPYobgEAAAAAtkdxCwAAAACwvUw359Ywrs2fSUxM9HNPAAAAkJbrf6dd/7stICVlkn0CNpLpitvTp09LkiIjI/3cEwAAAHhy7tw5hYeH+7sbLoKCglSoUCGdnHzSL/sPCwtTzZo1lSVLFvXu3Vu9e/f2Sz+AQOQwAvqfxMx39uxZ5c2bV8eOHQu4D0tYJzExUZGRkTp+/LjCwsL83R34AfcAuAfAPRC4DMPQuXPnVKRIEWXJEniz6C5duqTLly/7Zd9BQUEKCQnxy76BQJfpRm6vf0CGh4fzHzIoLCyM+yCT4x4A9wC4BwJTIA9ChISEUGACASjw/ikMAAAAAAAvUdwCAAAAAGwv0xW3wcHBGjZsmIKDg/3dFfgR9wG4B8A9AO4BAMhYMl2gFAAAAAAg48l0I7cAAAAAgIyH4hYAAAAAYHsUtwAAAAAA26O4BQAAAADYXoYrbqdPn64qVao4v5C9du3aWr16dbq2/eSTT+RwONS6detb20ncUt7eA3PmzJHD4XD54YvZ7c2Xz4GzZ8+qd+/eKly4sIKDgxUdHa1Vq1ZZ1GOYzdt7oEGDBqk+BxwOh1q0aGFhr2EmXz4HpkyZonLlyilHjhyKjIzUgAEDdOnSJYt6DAD4r7L5uwNmK1asmMaOHauoqCgZhqG5c+fqwQcf1M6dO3X77be73e7o0aMaPHiw7r33Xgt7i1vBl3sgLCxM+/fvd/7ucDis6i5uAW/vgcuXL6tx48YqUKCAFi1apKJFi+q3335Tnjx5rO88TOHtPbBkyRJdvnzZ+fvp06dVtWpVtWvXzspuw0Te3gPz58/Xiy++qPfff1916tTRgQMH1K1bNzkcDk2aNMkPRwAA8Fam+CqgiIgIjR8/Xk8++WSarycnJ6tevXp64okntGXLFp09e1bLli2ztpO4pTzdA3PmzFH//v119uxZ6zsGy3i6B2bMmKHx48dr3759yp49ux96Byvc7L8FN5oyZYpeffVVxcfHKzQ01ILewQqe7oE+ffpo79692rBhg3PZoEGDtG3bNm3dutXKbgIAfJThHku+UXJysj755BNduHBBtWvXdrveiBEjVKBAgXT9wQN7Se89cP78eZUoUUKRkZF68MEH9csvv1jYS9xK6bkHli9frtq1a6t3794qWLCgKlWqpNGjRys5Odni3uJWSO/nwI3ee+89dejQgcI2g0jPPVCnTh19//332r59uyTp8OHDWrVqlZo3b25lVwEA/0GGeyxZknbv3q3atWvr0qVLypUrl5YuXaqKFSumue7WrVv13nvvadeuXdZ2EreUN/dAuXLl9P7776tKlSpKSEjQhAkTVKdOHf3yyy8qVqyYxT2HWby5Bw4fPqwvv/xSnTt31qpVq3To0CE988wzunLlioYNG2Zxz2EWb+6BG23fvl0///yz3nvvPQt6iVvJm3ugU6dO+uuvv3TPPffIMAxdvXpVTz/9tF566SWLew0A8FWGfCz58uXLOnbsmBISErRo0SLNmjVLX331Var/oJ07d05VqlTR22+/rWbNmkmSunXrxmPJGUB674G0XLlyRRUqVFDHjh312muvWdBb3Are3APR0dG6dOmSjhw5oqxZs0qSJk2apPHjxys+Pt7qrsMkvn4OPPXUU/rmm2/0008/WdRT3Cre3AObNm1Shw4dNHLkSN199906dOiQnn32WfXo0UOvvPKKH3oPAPBWhixu/61Ro0YqU6aM3nnnHZflu3btUrVq1Zx/zEpSSkqKJClLlizav3+/ypQpY2lfcWu4uwfcadeunbJly6aPP/74FvcMVvF0D9SvX1/Zs2fX+vXrnctWr16t5s2bKykpSUFBQVZ2FbdIej4HLly4oCJFimjEiBF69tlnLewdrODpHrj33ntVq1YtjR8/3rnso48+Us+ePXX+/HllyZKhZ3IBQIaQKT6pU1JSlJSUlGp5+fLltXv3bu3atcv506pVK8XExGjXrl2KjIz0Q29xK7i7B9KSnJys3bt3q3Dhwre4V7CSp3ugbt26OnTokPMftyTpwIEDKly4MIVtBpKez4FPP/1USUlJevTRRy3qFazk6R64ePFiqgL2+j9+Z4JxAADIEDLcnNshQ4aoWbNmKl68uM6dO6f58+dr06ZNWrNmjSSpS5cuKlq0qMaMGaOQkBBVqlTJZfvrX/3x7+WwD2/uAelaoFitWrVUtmxZnT17VuPHj9dvv/2m7t27+/Mw8B94ew/06tVL06ZN07PPPqu+ffvq4MGDGj16tPr16+fPw8B/4O09cN17772n1q1bK1++fP7oNkzk7T3QsmVLTZo0SdWqVXM+lvzKK6+oZcuWLk94AQACV4Yrbk+dOqUuXbooPj5e4eHhqlKlitasWaPGjRtLko4dO8ajRRmct/fAmTNn1KNHD508eVJ58+bVXXfdpa+//jpd83MRmLy9ByIjI7VmzRoNGDBAVapUUdGiRfXss8/qhRde8Nch4D/y5b8F+/fv19atW7V27Vp/dBkm8/YeePnll+VwOPTyyy/rxIkTyp8/v1q2bKlRo0b56xAAAF7KFHNuAQAAAAAZG0OYAAAAAADbo7gFAAAAANgexS0AAAAAwPYobgEAAAAAtkdxCwAAAACwPYpbAAAAAIDtUdwCAAAAAGyP4hYAAABe27x5s1q2bKkiRYrI4XBo2bJlXrdhGIYmTJig6OhoBQcHq2jRoho1apT5nQWQKWTzdwcAAABgPxcuXFDVqlX1xBNP6KGHHvKpjWeffVZr167VhAkTVLlyZf3999/6+++/Te4pgMzCYRiG4e9OAAAAwL4cDoeWLl2q1q1bO5clJSVp6NCh+vjjj3X27FlVqlRJ48aNU4MGDSRJe/fuVZUqVfTzzz+rXLly/uk4gAyFx5IBAABguj59+uibb77RJ598op9++knt2rVT06ZNdfDgQUnS559/rtKlS2vFihUqVaqUSpYsqe7duzNyC8BnFLcAYFN//vmnChUqpNGjRzuX/V979+7SShBAcfhgRIsEhfgAK4tAgqBiVETQIOID1MbCLqggWKmd/gGCYGNgOytN2JRraSUEAoLdgpJKMb7AFNqmiZrlFhcE8RbXJsuY39ft7BRnyzMzy1xcXKipqUm5XM7HZADq3dPTk9LptBzHUSKRUCQS0fb2tsbHx5VOpyVJd3d3enx8lOM4sm1bmUxGrutqaWnJ5/QATMU/twBgqI6ODh0fH2txcVGzs7OKxWJaXl7W5uampqam/I4HoI4VCgVVq1VFo9Ev45VKRW1tbZIkz/NUqVRk2/bnvKOjIw0NDen6+pqjygB+jHILAAabn5/X+vq6ksmkhoeHFQwGtb+/73csAHWuXC4rEAjIdV0FAoEv70KhkCSpq6tLjY2NXwpwT0+PpL87v5RbAD9FuQUAwx0cHKi3t1eO48h1XTU3N/sdCUCdi8fjqlarenl5USKR+OecsbExfXx8qFgsKhKJSJJubm4kSd3d3TXLCuD3oNwCgOGKxaJKpZI8z9PDw4P6+vr8jgSgDpTLZd3e3n4+39/f6/LyUuFwWNFoVMlkUisrK0qlUorH43p9fVUul1N/f78WFhY0PT2twcFBra2tybIseZ6njY0NzczMfDvODAD/g6uAAMBgb29vGhkZ0cDAgGKxmCzLUqFQUGdnp9/RAPxy+Xxek5OT38ZXV1eVyWT0/v6uvb092bat5+dntbe3a3R0VLu7u5+LcKVSSVtbWzo7O1MwGNTc3JxSqZTC4XCtPwfAL0C5BQCD7ezs6OTkRFdXVwqFQpqYmFBra6tOT0/9jgYAAFBTXAUEAIbK5/OyLEvZbFYtLS1qaGhQNpvV+fm5Dg8P/Y4HAABQU+zcAgAAAACMx84tAAAAAMB4lFsAAAAAgPEotwAAAAAA41FuAQAAAADGo9wCAAAAAIxHuQUAAAAAGI9yCwAAAAAwHuUWAAAAAGA8yi0AAAAAwHiUWwAAAACA8Si3AAAAAADj/QHavM8xk0GREgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "\n", + "colors = ['green', 'yellow', 'orange', 'red']\n", + "cmap = mpl.colors.ListedColormap(colors)\n", + "\n", + "bounds = [-10, 0.5, 1.5, 2.5, 10]\n", + "norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n", + "\n", + "im = warning_levels.plot(cmap=cmap, norm=norm, add_colorbar=False)\n", + "\n", + "ticks = pd.Series(bounds).rolling(2).mean()[1:].to_list()\n", + "tick_labels = [\"Normal Conditions\", \"Increased Drought Risk\", \"Severe Drought Risk\", \"Extreme Drought Risk\"]\n", + "cbar = plt.colorbar(im, ax=ax, ticks=ticks)\n", + "cbar.set_ticklabels(tick_labels)\n", + "cbar.set_label(\"Warning Levels\")\n", + "\n", + "ax.set_title(f\"ECDI Warning Levels for {selected_dekad}\")\n", + "plt.savefig(f\"results/Amhara_ecdi_warning_levels_{selected_dekad}.png\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cf7b7997-1432-4be7-ac84-18f50d5a7e16", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Additional information\n", + "\n", + " License The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n", + "\n", + "Digital Earth Africa data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n", + "\n", + " Contact If you need assistance, please post a question on the [DE Africa Slack channel](https://digitalearthafrica.slack.com/) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n", + "\n", + "If you would like to report an issue with this notebook, you can file one on [Github](https://github.com/digitalearthafrica/deafrica-sandbox-notebooks).\n", + "\n", + " Compatible datacube version :" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "86de93b6-9942-4a79-bd76-dd36cc8d8055", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.8.20\n" + ] + } + ], + "source": [ + "import datacube\n", + "print(datacube.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "3faed437-b537-4860-a9be-dd8a06553453", + "metadata": {}, + "source": [ + "**Last Tested:**" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5a8581f5-9a59-4579-9c24-5b7408bb8d02", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'2025-05-19'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datetime import datetime\n", + "datetime.today().strftime('%Y-%m-%d')" + ] + } + ], + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Use_cases/Enhanced_Combined_Drought_Index/data/gadm41_ETH_1.json b/Use_cases/Enhanced_Combined_Drought_Index/data/gadm41_ETH_1.json new file mode 100644 index 00000000..86fe2275 --- /dev/null +++ b/Use_cases/Enhanced_Combined_Drought_Index/data/gadm41_ETH_1.json @@ -0,0 +1 @@ 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diff --git a/Use_cases/Enhanced_Combined_Drought_Index/dekad.py b/Use_cases/Enhanced_Combined_Drought_Index/dekad.py new file mode 100644 index 00000000..0a3942c8 --- /dev/null +++ b/Use_cases/Enhanced_Combined_Drought_Index/dekad.py @@ -0,0 +1,299 @@ +##Resample the data timeseries into **dekadal** (10-day) timesteps. + +import calendar +from datetime import datetime, timedelta +import pandas as pd +import numpy as np +import xarray as xr + +from xarray.core.groupby import DataArrayGroupBy, DatasetGroupBy + +def get_dekad(date: np.datetime64) -> np.datetime64: + """ + Get the start date of the dekad that a date belongs to. + Every month has three dekads, such that the first two dekads + have 10 days (i.e., 1-10, 11-20), and the third is comprised of the + remaining days of the month. + + Parameters + ---------- + date : np.datetime64 + Date to check. + + Returns + ------- + np.datetime64 + Start date of the dekad. + """ + timestamp = pd.Timestamp(date) + year = timestamp.year + month = timestamp.month + + first_day = datetime(year, month, 1) + last_day = datetime(year, month, calendar.monthrange(year, month)[1]) + + d1_start_date, d2_start_date, d3_start_date = pd.date_range( + start=first_day, end=last_day, freq="10D", inclusive="left" + ) + + if d1_start_date <= timestamp < d2_start_date: + return np.datetime64(d1_start_date, "ns") + elif d2_start_date <= timestamp < d3_start_date: + return np.datetime64(d2_start_date, "ns") + else: + return np.datetime64(d3_start_date, "ns") + +def group_by_dekad( + ds: xr.DataArray | xr.Dataset, +) -> DataArrayGroupBy | DatasetGroupBy: + """ + Group a dataset or array by dekad. + + Parameters + ---------- + ds : xr.DataArray | xr.Dataset + Dataset or DataArray to group + + Returns + ------- + xr.core.groupby.DataArrayGroupBy | xr.core.groupby.DatasetGroupBy + Groupby oject + """ + group = xr.DataArray( + data=np.vectorize(get_dekad)(ds.time.values), + coords=ds.time.coords, + dims=ds.time.dims, + name="dekad", + attrs=ds.time.attrs, + ) + grouped_by_dekad = ds.groupby(group) + return grouped_by_dekad + +def get_dekad_no_in_month(date: np.datetime64) -> int: + """ + Get the number of the dekad in a month that a date belongs to. + Every month has three dekads, such that the first two dekads + have 10 days (i.e., 1-10, 11-20), and the third is comprised of the + remaining days of the month. + + Parameters + ---------- + date : np.datetime64 + Date to check. + + Returns + ------- + int + Number of the dekad in a month. + """ + timestamp = pd.Timestamp(date) + year = timestamp.year + month = timestamp.month + + first_day = datetime(year, month, 1) + last_day = datetime(year, month, calendar.monthrange(year, month)[1]) + + d1_start_date, d2_start_date, d3_start_date = pd.date_range( + start=first_day, end=last_day, freq="10D", inclusive="left" + ) + + if d1_start_date <= timestamp < d2_start_date: + return 1 + elif d2_start_date <= timestamp < d3_start_date: + return 2 + else: + return 3 + + +def get_dekad_no_in_year(date: np.datetime64) -> int: + """ + Get the number of the dekad in a year that a date belongs to. + Every month has three dekads, such that the first two dekads + have 10 days (i.e., 1-10, 11-20), and the third is comprised of the + remaining days of the month (21-last day). Every year has 36 dekads. + + Parameters + ---------- + date : np.datetime64 + Date to check. + + Returns + ------- + int + Number of the dekad in a year. + """ + dekad_no_in_month = get_dekad_no_in_month(date=date) + timestamp = pd.Timestamp(date) + month = timestamp.month + dekad_no_in_year = ((month - 1) * 3) + dekad_no_in_month + return dekad_no_in_year + + +def get_interest_period(dekad: np.datetime64, ip: int) -> list[np.datetime64]: + """ + Get all the dekads in the interest period for a dekad. + `dekad` will always be the end dekad of the interest period. + + Parameters + ---------- + dekad : np.datetime64 + Dekad to get the interest period for. + This will always be the end dekad of the interest period + ip : int + Number of dekads in an interest period. + + Returns + ------- + list[np.datetime64] + All the dekads in the interest period. + """ + year = pd.Timestamp(dekad).year + dekad_no_in_year = get_dekad_no_in_year(dekad) - (ip - 1) + while dekad_no_in_year <= 0: + year -= 1 + dekad_no_in_year += 36 + + month = (dekad_no_in_year - 1) // 3 + 1 + dekad_no_in_month = (dekad_no_in_year - 1) % 3 + 1 + if dekad_no_in_month == 1: + day = 1 + elif dekad_no_in_month == 2: + day = 11 + elif dekad_no_in_month == 3: + day = 21 + + start_dekad = np.datetime64(datetime(year, month, day), "ns") + date_range = pd.date_range( + start_dekad, dekad, freq=timedelta(days=1), inclusive="both" + ).values + interest_period_dekads = np.unique(np.vectorize(get_dekad)(date_range)) + return interest_period_dekads + + +def bin_by_interest_period( + ds: xr.Dataset | xr.DataArray, ip: int +) -> dict[np.datetime64, xr.Dataset | xr.DataArray]: + """ + Bin each dekad in the dataset by interest period. + + Parameters + ---------- + ds : xr.Dataset | xr.DataArray + Dataset to bin + ip : int + Number of dekads in an interest period. + + Returns + ------- + list[tuple[np.datetime64, np.datetime64]] + List of dekad ranges to bin + """ + start_date = ds.dekad.min().values + end_date = ds.dekad.max().values + date_range = pd.date_range( + start_date, end_date, freq=timedelta(days=1), inclusive="both" + ).values + dekads = np.unique(np.vectorize(get_dekad)(date_range)) + bins = {i: get_interest_period(dekad=i, ip=ip) for i in dekads} + binned_by_interest_period = {interest_period_label : ds.reindex(dekad=interest_period) for interest_period_label, interest_period in bins.items()} + return binned_by_interest_period + +def get_no_data_mask(arr): + """ + Check if all values in an array are NaN + """ + return np.all(np.isnan(arr)) + +# From https://www.geeksforgeeks.org/maximum-consecutive-ones-or-zeros-in-a-binary-array/ +def max_consecutive_ones(arr: np.ndarray) -> int: + """ + Get the maximum number of successive ones in an array. + + Parameters + ---------- + + arr : np.ndarray + Array to check + + Returns + ------- + int + Maximum number of consecutive ones in the input array. + + """ + + n = len(arr) + # initialize count + count = 0 + # initialize max + result = 0 + + for i in range(0, n): + # If 1 is found, increment count + # and update result if count + # becomes more. + if arr[i] == 1: + # increase count + count += 1 + result = max(result, count) + # Reset count if one is not found + else: + count = 0 + + return result + +def read_table(path: str) -> pd.DataFrame: + """Read a Parquet file with Conflux metadata. + + Arguments + --------- + path : str + Path to Parquet file. + + Returns + ------- + pd.DataFrame + DataFrame with attrs set. + """ + table = pq_read_table(path) + df = table.to_pandas() + meta_json = table.schema.metadata[PARQUET_META_KEY] + metadata = json.loads(meta_json) + for key, val in metadata.items(): + df.attrs[key] = val + return df + +def get_max_value(index, da): + return da.isel(time_lag=index) + +def get_correlation(reference_drought_index, comparison_drought_index): + # Identify pixels that are NaN across all dekads + # in the comparison index. + all_nan_mask = comparison_drought_index.isnull().all(dim='dekad') + + lags=[0, 10] + corr_list = [] + for time_lag in range(lags[0], lags[1]): + # Modify the time lag + time_lag += abs(lags[0]) + 1 + # Pearson's correlation coefficient + corr = xr.corr(reference_drought_index, comparison_drought_index.shift(dekad=time_lag), dim="dekad") + modified_corr = np.abs(corr).assign_coords(time_lag=time_lag).expand_dims({"time_lag": 1}) + corr_list.append(modified_corr) + + da_corr = xr.concat(corr_list, dim="time_lag") + + # Get the maximum modified correlation value for each pixel. + max_corr = da_corr.max(dim="time_lag", skipna=True) + + # For each pixel get the the time lag at which the maximum + # correlation value occurs. + max_time_lag = xr.apply_ufunc( + get_max_value, + # Replace NaNs with -np.inf to ensure they are ignored in the argmax calculation. + da_corr.fillna(-np.inf).argmax(dim="time_lag"), + kwargs={"da": da_corr.time_lag}, + vectorize=True, + dask="allowed",) + max_time_lag = max_time_lag.where(~all_nan_mask) + return dict(corr=max_corr, lag=max_time_lag) \ No newline at end of file