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bennyistanto/OSS_ZonalStats_regular_CHIRPS.ipynb

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Zonal Statistics using rasterstats on CHIRPS and admin boundaries, then join the csv's into single csv file.
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OSS_ZonalStats_regular_CHIRPS.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "d4c9c49e",
"metadata": {},
"source": [
"# Zonal Statistics based on raster value using admin boundaries"
]
},
{
"cell_type": "markdown",
"id": "ec4fce24",
"metadata": {},
"source": [
"## Admin1"
]
},
{
"cell_type": "markdown",
"id": "f9dc44b6",
"metadata": {},
"source": [
"**1. Zonal Statistics**\n",
"\n",
"This process required GeoTIFFs in a folder as input and shapefile as boundary zone. This utilize `rasterstats` library, and the process and output are similar to ArcPy Zonal Statistics (`categorical=False`), if the value is `True` then the result will produce simlar output like ArcPy Zonal Histogram."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "6d009a38",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" adm1_src adm1_name adm0_src adm0_name \\\n",
"0 LB1 Beirut LBN Lebanon \n",
"1 LB2 Bekaa LBN Lebanon \n",
"2 LB3 Mount Lebanon LBN Lebanon \n",
"3 LB4 El Nabatieh LBN Lebanon \n",
"4 LB5 North LBN Lebanon \n",
"\n",
" geometry \n",
"0 MULTIPOLYGON (((35.48944 33.86795, 35.48880 33... \n",
"1 POLYGON ((35.77209 33.41926, 35.77188 33.41959... \n",
"2 MULTIPOLYGON (((35.43331 33.57270, 35.43321 33... \n",
"3 POLYGON ((35.35334 33.05768, 35.35267 33.05801... \n",
"4 MULTIPOLYGON (((35.72782 34.33613, 35.72824 34... \n"
]
},
{
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_198101.csv\n"
]
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202108.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202112.csv\n"
]
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]
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]
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]
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202208.csv\n"
]
},
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"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202209.csv\n"
]
},
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"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202210.csv\n"
]
},
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"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202211.csv\n"
]
},
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"output_type": "stream",
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202212.csv\n"
]
},
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"name": "stderr",
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202301.csv\n"
]
},
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]
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202303.csv\n"
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202304.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202305.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202306.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202307.csv\n"
]
},
{
"name": "stderr",
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]
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"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202308.csv\n"
]
},
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"name": "stderr",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/wld_cli_chirps_precip_month1_202309.csv\n",
"Zonal Histogram processing complete!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import os\n",
"import geopandas as gpd\n",
"from rasterstats import zonal_stats\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"\n",
"# Define input, zone, and output locations\n",
"input_folder = \"/mnt/x/Temp/CHIRPS/Monthly/Month1\"\n",
"zonefieldVector = \"/mnt/x/Temp/modis/lbn/bnd/lbn_bnd_adm1_edge.shp\"\n",
"output_folder = \"/mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1\"\n",
"selected_fields = ['adm1_src', 'adm1_name', 'adm0_src', 'adm0_name', 'geometry']\n",
"\n",
"# Ensure that the output folder exists\n",
"if not os.path.exists(output_folder):\n",
" os.makedirs(output_folder)\n",
"\n",
"# Read the vector data using Geopandas\n",
"vector_data_full = gpd.read_file(zonefieldVector)\n",
"vector_data = vector_data_full[selected_fields].sort_values(by='adm1_src')\n",
"vector_data = vector_data.reset_index(drop=True)\n",
"\n",
"# Check the GeoDataFrame structure\n",
"print(vector_data.head())\n",
"\n",
"# List all .tif files\n",
"tif_files = [f for f in os.listdir(input_folder) if f.endswith('.tif')]\n",
"\n",
"# Initialize tqdm progress bar\n",
"pbar = tqdm(total=len(tif_files), desc='Processing', unit='file')\n",
"\n",
"# Loop through each file in the input folder and compute Zonal Histogram\n",
"for filename in tif_files:\n",
" input_file_path = os.path.join(input_folder, filename)\n",
" output_file_path = os.path.join(output_folder, os.path.splitext(filename)[0] + \".csv\")\n",
"\n",
" # Compute Zonal Histogram using rasterstats\n",
" stats = zonal_stats(vector_data, input_file_path, categorical=False, all_touched=True)\n",
"\n",
" # Convert the stats to a pandas DataFrame and sort columns\n",
" stats_df = pd.DataFrame(stats).sort_index(axis=1)\n",
"\n",
" # Make a copy of vector_data without the 'geometry' column for merging\n",
" vector_data_no_geom = vector_data.drop(columns=['geometry']).copy()\n",
"\n",
" # Merge the statistics back with the vector data without geometry\n",
" vector_data_stats = vector_data_no_geom.join(stats_df)\n",
"\n",
" # Add an 'ID' column based on the new sorted index\n",
" vector_data_stats.reset_index(inplace=True)\n",
" vector_data_stats.rename(columns={'index': 'ID'}, inplace=True)\n",
"\n",
" # Save to CSV\n",
" vector_data_stats.to_csv(output_file_path, index=False)\n",
" print(f\"Output file created: {output_file_path}\")\n",
"\n",
" # Update progress bar\n",
" pbar.update(1)\n",
"\n",
"# Close progress bar\n",
"pbar.close()\n",
"print(\"Zonal Histogram processing complete!\")\n"
]
},
{
"cell_type": "markdown",
"id": "15875f2b",
"metadata": {},
"source": [
"**2. Compile each statistical value into single csv**\n",
"\n",
"Grab each statsitical value (`max`, `mean`, and `min`) from each file (1 file for 1 date), compile into single csv for each stats, put the date as column name.\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "a7033e23",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data compilation complete!\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"output_folder = \"/mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1\"\n",
"csv_files = [f for f in os.listdir(output_folder) if f.endswith('.csv')]\n",
"\n",
"# Initialize dictionaries for storing data\n",
"max_data = {}\n",
"mean_data = {}\n",
"min_data = {}\n",
"\n",
"# Process each CSV file\n",
"for file in csv_files:\n",
" file_path = os.path.join(output_folder, file)\n",
" df = pd.read_csv(file_path)\n",
" \n",
" # Extract year-month from filename\n",
" yyyymm = file.split('_')[-1][:6]\n",
"\n",
" # Update the dictionaries with data from each file\n",
" for index, row in df.iterrows():\n",
" key = (row['ID'], row['adm1_src'], row['adm1_name'], row['adm0_src'], row['adm0_name'])\n",
" if key not in max_data:\n",
" max_data[key] = {}\n",
" mean_data[key] = {}\n",
" min_data[key] = {}\n",
" \n",
" max_data[key][yyyymm] = row['max']\n",
" mean_data[key][yyyymm] = row['mean']\n",
" min_data[key][yyyymm] = row['min']\n",
"\n",
"# Function to convert dictionary to DataFrame and save as CSV\n",
"def dict_to_csv(data_dict, filename):\n",
" # Convert dictionary to DataFrame\n",
" df = pd.DataFrame.from_dict({(i,j,k,l,m): data_dict[(i,j,k,l,m)] \n",
" for i,j,k,l,m in sorted(data_dict)},\n",
" orient='index')\n",
" df.reset_index(inplace=True)\n",
" df.columns = ['ID', 'adm1_src', 'adm1_name', 'adm0_src', 'adm0_name'] + sorted(df.columns[5:])\n",
" df.to_csv(os.path.join(output_folder, filename), index=False)\n",
"\n",
"# Save the data to CSV files\n",
"dict_to_csv(max_data, 'lbn_cli_precip_monthly_chirps_1981_2023_adm1_max.csv')\n",
"dict_to_csv(mean_data, 'lbn_cli_precip_monthly_chirps_1981_2023_adm1_mean.csv')\n",
"dict_to_csv(min_data, 'lbn_cli_precip_monthly_chirps_1981_2023_adm1_min.csv')\n",
"\n",
"print(\"Data compilation complete!\")\n"
]
},
{
"cell_type": "markdown",
"id": "682da4bc",
"metadata": {},
"source": [
"**3. Calculate annual from monthly timeseries**\n",
"\n",
"From the monthly timeseries data, will calculate the annual `SUM`, to get annual value. We use `SUM` because the data is precipitation."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "26b65c74",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Annual sum values saved to /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/lbn_cli_precip_annual_chirps_1981_2023_adm1_max.csv\n",
"Annual sum values saved to /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/lbn_cli_precip_annual_chirps_1981_2023_adm1_mean.csv\n",
"Annual sum values saved to /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1/lbn_cli_precip_annual_chirps_1981_2023_adm1_min.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"from pathlib import Path\n",
"\n",
"# Define the output folder where the merged CSV files are stored\n",
"output_folder = Path(\"/mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm1\")\n",
"\n",
"def calculate_annual_sum(file_path):\n",
" # Read the CSV file\n",
" df = pd.read_csv(file_path)\n",
"\n",
" # Get all yyyymmdd columns for monthly data (considering years starting from 1981)\n",
" monthly_columns = [col for col in df.columns if col[:4].isdigit() and 1981 <= int(col[:4]) <= 2099]\n",
"\n",
" # Calculate the annual mean for each year present in the columns\n",
" # Assuming the format is YYYYMMDD\n",
" years = sorted(set(col[:4] for col in monthly_columns))\n",
" for year in years:\n",
" # Calculate the mean of all columns for the specific year\n",
" year_cols = [col for col in monthly_columns if col.startswith(year)]\n",
" if year_cols: # Check if there are columns for the year\n",
" df[f'{year}_annual'] = df[year_cols].sum(axis=1)\n",
"\n",
" # Keep the location, ID and annual mean columns\n",
" annual_columns = ['ID', 'adm1_src', 'adm1_name', 'adm0_src', 'adm0_name'] + \\\n",
" [f'{year}_annual' for year in years]\n",
" annual_df = df[annual_columns]\n",
"\n",
" # Construct the new filename\n",
" new_filename = file_path.stem.replace('monthly', 'annual') + '.csv'\n",
" annual_file_path = file_path.parent / new_filename\n",
" annual_df.to_csv(annual_file_path, index=False)\n",
"\n",
" print(f\"Annual sum values saved to {annual_file_path}\")\n",
"\n",
"# Call the function for both SOS and EOS CSV files\n",
"calculate_annual_sum(output_folder / \"lbn_cli_precip_monthly_chirps_1981_2023_adm1_max.csv\")\n",
"calculate_annual_sum(output_folder / \"lbn_cli_precip_monthly_chirps_1981_2023_adm1_mean.csv\")\n",
"calculate_annual_sum(output_folder / \"lbn_cli_precip_monthly_chirps_1981_2023_adm1_min.csv\")\n"
]
},
{
"cell_type": "markdown",
"id": "6f694071",
"metadata": {},
"source": [
"## Admin2"
]
},
{
"cell_type": "markdown",
"id": "4a5d568b",
"metadata": {},
"source": [
"**1. Zonal Statistics**\n",
"\n",
"This process required GeoTIFFs in a folder as input and shapefile as boundary zone. This utilize `rasterstats` library, and the process and output are similar to ArcPy Zonal Statistics (`categorical=False`), if the value is `True` then the result will produce simlar output like ArcPy Zonal Histogram."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "030945d2",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" adm2_src adm2_name adm1_src adm1_name adm0_src adm0_name \\\n",
"0 LB11 Beirut LB1 Beirut LBN Lebanon \n",
"1 LB21 Baalbek LB8 Baalbek-El Hermel LBN Lebanon \n",
"2 LB22 El Hermel LB8 Baalbek-El Hermel LBN Lebanon \n",
"3 LB23 Rachaya LB2 Bekaa LBN Lebanon \n",
"4 LB24 West Bekaa LB2 Bekaa LBN Lebanon \n",
"\n",
" geometry \n",
"0 MULTIPOLYGON (((35.49097 33.86795, 35.49000 33... \n",
"1 POLYGON ((36.07106 33.82234, 36.07109 33.82252... \n",
"2 POLYGON ((36.29382 34.25967, 36.29345 34.25975... \n",
"3 POLYGON ((35.77274 33.41771, 35.77209 33.41926... \n",
"4 POLYGON ((35.68421 33.45926, 35.68343 33.45815... \n"
]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202112.csv\n"
]
},
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]
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202203.csv\n"
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202204.csv\n"
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202206.csv\n"
]
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202208.csv\n"
]
},
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"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202209.csv\n"
]
},
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"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202210.csv\n"
]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202211.csv\n"
]
},
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"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202212.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202301.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202302.csv\n"
]
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202303.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202304.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202305.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202306.csv\n"
]
},
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"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202307.csv\n"
]
},
{
"name": "stderr",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202308.csv\n"
]
},
{
"name": "stderr",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/wld_cli_chirps_precip_month1_202309.csv\n",
"Zonal Histogram processing complete!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import os\n",
"import geopandas as gpd\n",
"from rasterstats import zonal_stats\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"\n",
"# Define input, zone, and output locations\n",
"input_folder = \"/mnt/x/Temp/CHIRPS/Monthly/Month1\"\n",
"zonefieldVector = \"/mnt/x/Temp/modis/lbn/bnd/lbn_bnd_adm2_edge.shp\"\n",
"output_folder = \"/mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2\"\n",
"selected_fields = ['adm2_src', 'adm2_name', 'adm1_src', 'adm1_name', 'adm0_src', 'adm0_name', 'geometry']\n",
"\n",
"# Ensure that the output folder exists\n",
"if not os.path.exists(output_folder):\n",
" os.makedirs(output_folder)\n",
"\n",
"# Read the vector data using Geopandas\n",
"vector_data_full = gpd.read_file(zonefieldVector)\n",
"vector_data = vector_data_full[selected_fields].sort_values(by='adm2_src')\n",
"vector_data = vector_data.reset_index(drop=True)\n",
"\n",
"# Check the GeoDataFrame structure\n",
"print(vector_data.head())\n",
"\n",
"# List all .tif files\n",
"tif_files = [f for f in os.listdir(input_folder) if f.endswith('.tif')]\n",
"\n",
"# Initialize tqdm progress bar\n",
"pbar = tqdm(total=len(tif_files), desc='Processing', unit='file')\n",
"\n",
"# Loop through each file in the input folder and compute Zonal Histogram\n",
"for filename in tif_files:\n",
" input_file_path = os.path.join(input_folder, filename)\n",
" output_file_path = os.path.join(output_folder, os.path.splitext(filename)[0] + \".csv\")\n",
"\n",
" # Compute Zonal Histogram using rasterstats\n",
" stats = zonal_stats(vector_data, input_file_path, categorical=False, all_touched=True)\n",
"\n",
" # Convert the stats to a pandas DataFrame and sort columns\n",
" stats_df = pd.DataFrame(stats).sort_index(axis=1)\n",
"\n",
" # Make a copy of vector_data without the 'geometry' column for merging\n",
" vector_data_no_geom = vector_data.drop(columns=['geometry']).copy()\n",
"\n",
" # Merge the statistics back with the vector data without geometry\n",
" vector_data_stats = vector_data_no_geom.join(stats_df)\n",
"\n",
" # Add an 'ID' column based on the new sorted index\n",
" vector_data_stats.reset_index(inplace=True)\n",
" vector_data_stats.rename(columns={'index': 'ID'}, inplace=True)\n",
"\n",
" # Save to CSV\n",
" vector_data_stats.to_csv(output_file_path, index=False)\n",
" print(f\"Output file created: {output_file_path}\")\n",
"\n",
" # Update progress bar\n",
" pbar.update(1)\n",
"\n",
"# Close progress bar\n",
"pbar.close()\n",
"print(\"Zonal Histogram processing complete!\")\n"
]
},
{
"cell_type": "markdown",
"id": "6eaea2be",
"metadata": {},
"source": [
"**2. Compile each statistical value into single csv**\n",
"\n",
"Grab each statsitical value (`max`, `mean`, and `min`) from each file (1 file for 1 date), compile into single csv for each stats, put the date as column name.\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "9c97e774",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data compilation complete!\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"output_folder = \"/mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2\"\n",
"csv_files = [f for f in os.listdir(output_folder) if f.endswith('.csv')]\n",
"\n",
"# Initialize dictionaries for storing data\n",
"max_data = {}\n",
"mean_data = {}\n",
"min_data = {}\n",
"\n",
"# Process each CSV file\n",
"for file in csv_files:\n",
" file_path = os.path.join(output_folder, file)\n",
" df = pd.read_csv(file_path)\n",
" \n",
" # Extract year-month from filename\n",
" yyyymm = file.split('_')[-1][:6]\n",
"\n",
" # Update the dictionaries with data from each file\n",
" for index, row in df.iterrows():\n",
" # Updated key with additional columns\n",
" key = (row['ID'], row['adm2_src'], row['adm2_name'], row['adm1_src'], row['adm1_name'], row['adm0_src'], row['adm0_name'])\n",
" if key not in max_data:\n",
" max_data[key] = {}\n",
" mean_data[key] = {}\n",
" min_data[key] = {}\n",
" \n",
" max_data[key][yyyymm] = row['max']\n",
" mean_data[key][yyyymm] = row['mean']\n",
" min_data[key][yyyymm] = row['min']\n",
"\n",
"# Function to convert dictionary to DataFrame and save as CSV\n",
"def dict_to_csv(data_dict, filename):\n",
" # Convert dictionary to DataFrame\n",
" df = pd.DataFrame.from_dict({(i,j,k,l,m,n,o): data_dict[(i,j,k,l,m,n,o)] \n",
" for i,j,k,l,m,n,o in sorted(data_dict)},\n",
" orient='index')\n",
" # Updated column names\n",
" df.reset_index(inplace=True)\n",
" df.columns = ['ID', 'adm2_src', 'adm2_name', 'adm1_src', 'adm1_name', 'adm0_src', 'adm0_name'] + sorted(df.columns[7:])\n",
" df.to_csv(os.path.join(output_folder, filename), index=False)\n",
"\n",
"# Save the data to CSV files\n",
"dict_to_csv(max_data, 'lbn_cli_precip_monthly_chirps_1981_2023_adm2_max.csv')\n",
"dict_to_csv(mean_data, 'lbn_cli_precip_monthly_chirps_1981_2023_adm2_mean.csv')\n",
"dict_to_csv(min_data, 'lbn_cli_precip_monthly_chirps_1981_2023_adm2_min.csv')\n",
"\n",
"print(\"Data compilation complete!\")\n"
]
},
{
"cell_type": "markdown",
"id": "dbf0831a",
"metadata": {},
"source": [
"**3. Calculate annual from monthly timeseries**\n",
"\n",
"From the monthly timeseries data, will calculate the annual `SUM`, to get annual value. We use `SUM` because the data is precipitation."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "802d5ee2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Annual sum values saved to /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/lbn_cli_precip_annual_chirps_1981_2023_adm2_max.csv\n",
"Annual sum values saved to /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/lbn_cli_precip_annual_chirps_1981_2023_adm2_mean.csv\n",
"Annual sum values saved to /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2/lbn_cli_precip_annual_chirps_1981_2023_adm2_min.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"from pathlib import Path\n",
"\n",
"# Define the output folder where the merged CSV files are stored\n",
"output_folder = Path(\"/mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm2\")\n",
"\n",
"def calculate_annual_sum(file_path):\n",
" # Read the CSV file\n",
" df = pd.read_csv(file_path)\n",
"\n",
" # Get all yyyymmdd columns for monthly data (considering years starting from 1981)\n",
" monthly_columns = [col for col in df.columns if col[:4].isdigit() and 1981 <= int(col[:4]) <= 2099]\n",
"\n",
" # Calculate the annual mean for each year present in the columns\n",
" # Assuming the format is YYYYMMDD\n",
" years = sorted(set(col[:4] for col in monthly_columns))\n",
" for year in years:\n",
" # Calculate the mean of all columns for the specific year\n",
" year_cols = [col for col in monthly_columns if col.startswith(year)]\n",
" if year_cols: # Check if there are columns for the year\n",
" df[f'{year}_annual'] = df[year_cols].sum(axis=1)\n",
"\n",
" # Keep the location, ID and annual mean columns\n",
" annual_columns = ['ID', 'adm2_src', 'adm2_name', 'adm1_src', 'adm1_name', 'adm0_src', 'adm0_name'] + \\\n",
" [f'{year}_annual' for year in years]\n",
" annual_df = df[annual_columns]\n",
"\n",
" # Construct the new filename\n",
" new_filename = file_path.stem.replace('monthly', 'annual') + '.csv'\n",
" annual_file_path = file_path.parent / new_filename\n",
" annual_df.to_csv(annual_file_path, index=False)\n",
"\n",
" print(f\"Annual sum values saved to {annual_file_path}\")\n",
"\n",
"# Call the function for both SOS and EOS CSV files\n",
"calculate_annual_sum(output_folder / \"lbn_cli_precip_monthly_chirps_1981_2023_adm2_max.csv\")\n",
"calculate_annual_sum(output_folder / \"lbn_cli_precip_monthly_chirps_1981_2023_adm2_mean.csv\")\n",
"calculate_annual_sum(output_folder / \"lbn_cli_precip_monthly_chirps_1981_2023_adm2_min.csv\")\n"
]
},
{
"cell_type": "markdown",
"id": "36c310f3",
"metadata": {},
"source": [
"## Admin3"
]
},
{
"cell_type": "markdown",
"id": "48a96e5a",
"metadata": {},
"source": [
"**1. Zonal Statistics**\n",
"\n",
"This process required GeoTIFFs in a folder as input and shapefile as boundary zone. This utilize `rasterstats` library, and the process and output are similar to ArcPy Zonal Statistics (`categorical=False`), if the value is `True` then the result will produce simlar output like ArcPy Zonal Histogram."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "699ed768",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" adm3_src adm3_name adm2_src adm2_name adm1_src adm1_name adm0_src \\\n",
"0 LB10110 Ain el-Mreisseh LB11 Beirut LB1 Beirut LBN \n",
"1 LB10150 Ras Beyrouth LB11 Beirut LB1 Beirut LBN \n",
"2 LB10210 Moussaytbeh LB11 Beirut LB1 Beirut LBN \n",
"3 LB10250 Minet el-Hosn LB11 Beirut LB1 Beirut LBN \n",
"4 LB10310 Mazraa LB11 Beirut LB1 Beirut LBN \n",
"\n",
" adm0_name geometry \n",
"0 Lebanon POLYGON ((35.49308 33.90196, 35.49297 33.90126... \n",
"1 Lebanon MULTIPOLYGON (((35.48019 33.88269, 35.47865 33... \n",
"2 Lebanon POLYGON ((35.49327 33.86783, 35.49291 33.86785... \n",
"3 Lebanon MULTIPOLYGON (((35.49151 33.89532, 35.49102 33... \n",
"4 Lebanon POLYGON ((35.49460 33.87072, 35.49403 33.87099... \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Processing: 0%| | 1/513 [00:30<4:18:58, 30.35s/file]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/wld_cli_chirps_precip_month1_198101.csv\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"Processing: 0%|▏ | 2/513 [00:56<3:57:36, 27.90s/file]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/wld_cli_chirps_precip_month1_198102.csv\n"
]
},
{
"name": "stderr",
"output_type": "stream",
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/wld_cli_chirps_precip_month1_202210.csv\n"
]
},
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"output_type": "stream",
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/wld_cli_chirps_precip_month1_202211.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/wld_cli_chirps_precip_month1_202212.csv\n"
]
},
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/wld_cli_chirps_precip_month1_202301.csv\n"
]
},
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]
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]
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]
},
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]
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"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/wld_cli_chirps_precip_month1_202306.csv\n"
]
},
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"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/wld_cli_chirps_precip_month1_202307.csv\n"
]
},
{
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/wld_cli_chirps_precip_month1_202308.csv\n"
]
},
{
"name": "stderr",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output file created: /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/wld_cli_chirps_precip_month1_202309.csv\n",
"Zonal Histogram processing complete!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import os\n",
"import geopandas as gpd\n",
"from rasterstats import zonal_stats\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"\n",
"# Define input, zone, and output locations\n",
"input_folder = \"/mnt/x/Temp/CHIRPS/Monthly/Month1\"\n",
"zonefieldVector = \"/mnt/x/Temp/modis/lbn/bnd/lbn_bnd_adm3_edge.shp\"\n",
"output_folder = \"/mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3\"\n",
"selected_fields = ['adm3_src', 'adm3_name', 'adm2_src', 'adm2_name', 'adm1_src', 'adm1_name', 'adm0_src', 'adm0_name', 'geometry']\n",
"\n",
"# Ensure that the output folder exists\n",
"if not os.path.exists(output_folder):\n",
" os.makedirs(output_folder)\n",
"\n",
"# Read the vector data using Geopandas\n",
"vector_data_full = gpd.read_file(zonefieldVector)\n",
"vector_data = vector_data_full[selected_fields].sort_values(by='adm3_src')\n",
"vector_data = vector_data.reset_index(drop=True)\n",
"\n",
"# Check the GeoDataFrame structure\n",
"print(vector_data.head())\n",
"\n",
"# List all .tif files\n",
"tif_files = [f for f in os.listdir(input_folder) if f.endswith('.tif')]\n",
"\n",
"# Initialize tqdm progress bar\n",
"pbar = tqdm(total=len(tif_files), desc='Processing', unit='file')\n",
"\n",
"# Loop through each file in the input folder and compute Zonal Histogram\n",
"for filename in tif_files:\n",
" input_file_path = os.path.join(input_folder, filename)\n",
" output_file_path = os.path.join(output_folder, os.path.splitext(filename)[0] + \".csv\")\n",
"\n",
" # Compute Zonal Histogram using rasterstats\n",
" stats = zonal_stats(vector_data, input_file_path, categorical=False, all_touched=True)\n",
"\n",
" # Convert the stats to a pandas DataFrame and sort columns\n",
" stats_df = pd.DataFrame(stats).sort_index(axis=1)\n",
"\n",
" # Make a copy of vector_data without the 'geometry' column for merging\n",
" vector_data_no_geom = vector_data.drop(columns=['geometry']).copy()\n",
"\n",
" # Merge the statistics back with the vector data without geometry\n",
" vector_data_stats = vector_data_no_geom.join(stats_df)\n",
"\n",
" # Add an 'ID' column based on the new sorted index\n",
" vector_data_stats.reset_index(inplace=True)\n",
" vector_data_stats.rename(columns={'index': 'ID'}, inplace=True)\n",
"\n",
" # Save to CSV\n",
" vector_data_stats.to_csv(output_file_path, index=False)\n",
" print(f\"Output file created: {output_file_path}\")\n",
"\n",
" # Update progress bar\n",
" pbar.update(1)\n",
"\n",
"# Close progress bar\n",
"pbar.close()\n",
"print(\"Zonal Histogram processing complete!\")\n"
]
},
{
"cell_type": "markdown",
"id": "b93b1191",
"metadata": {},
"source": [
"**2. Compile each statistical value into single csv**\n",
"\n",
"Grab each statsitical value (`max`, `mean`, and `min`) from each file (1 file for 1 date), compile into single csv for each stats, put the date as column name.\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "85635b1e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data compilation complete!\n"
]
}
],
"source": [
"import os\n",
"import pandas as pd\n",
"\n",
"output_folder = \"/mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3\"\n",
"csv_files = [f for f in os.listdir(output_folder) if f.endswith('.csv')]\n",
"\n",
"# Initialize dictionaries for storing data\n",
"max_data = {}\n",
"mean_data = {}\n",
"min_data = {}\n",
"\n",
"# Process each CSV file\n",
"for file in csv_files:\n",
" file_path = os.path.join(output_folder, file)\n",
" df = pd.read_csv(file_path)\n",
" \n",
" # Extract year-month from filename\n",
" yyyymm = file.split('_')[-1][:6]\n",
"\n",
" # Update the dictionaries with data from each file\n",
" for index, row in df.iterrows():\n",
" # Updated key with additional columns\n",
" key = (row['ID'], row['adm3_src'], row['adm3_name'], row['adm2_src'], row['adm2_name'], row['adm1_src'], row['adm1_name'], row['adm0_src'], row['adm0_name'])\n",
" if key not in max_data:\n",
" max_data[key] = {}\n",
" mean_data[key] = {}\n",
" min_data[key] = {}\n",
" \n",
" max_data[key][yyyymm] = row['max']\n",
" mean_data[key][yyyymm] = row['mean']\n",
" min_data[key][yyyymm] = row['min']\n",
"\n",
"# Function to convert dictionary to DataFrame and save as CSV\n",
"def dict_to_csv(data_dict, filename):\n",
" # Convert dictionary to DataFrame\n",
" df = pd.DataFrame.from_dict({(i,j,k,l,m,n,o,p,q): data_dict[(i,j,k,l,m,n,o,p,q)] \n",
" for i,j,k,l,m,n,o,p,q in sorted(data_dict)},\n",
" orient='index')\n",
" # Updated column names\n",
" df.reset_index(inplace=True)\n",
" df.columns = ['ID', 'adm3_src', 'adm3_name', 'adm2_src', 'adm2_name', 'adm1_src', 'adm1_name', 'adm0_src', 'adm0_name'] + sorted(df.columns[9:])\n",
" df.to_csv(os.path.join(output_folder, filename), index=False)\n",
"\n",
"# Save the data to CSV files\n",
"dict_to_csv(max_data, 'lbn_cli_precip_monthly_chirps_1981_2023_adm3_max.csv')\n",
"dict_to_csv(mean_data, 'lbn_cli_precip_monthly_chirps_1981_2023_adm3_mean.csv')\n",
"dict_to_csv(min_data, 'lbn_cli_precip_monthly_chirps_1981_2023_adm3_min.csv')\n",
"\n",
"print(\"Data compilation complete!\")\n"
]
},
{
"cell_type": "markdown",
"id": "3ecb1fb5",
"metadata": {},
"source": [
"**3. Calculate annual from monthly timeseries**\n",
"\n",
"From the monthly timeseries data, will calculate the annual `SUM`, to get annual value. We use `SUM` because the data is precipitation."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "c621d807",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Annual sum values saved to /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/lbn_cli_precip_annual_chirps_1981_2023_adm3_max.csv\n",
"Annual sum values saved to /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/lbn_cli_precip_annual_chirps_1981_2023_adm3_mean.csv\n",
"Annual sum values saved to /mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3/lbn_cli_precip_annual_chirps_1981_2023_adm3_min.csv\n"
]
}
],
"source": [
"import pandas as pd\n",
"from pathlib import Path\n",
"\n",
"# Define the output folder where the merged CSV files are stored\n",
"output_folder = Path(\"/mnt/x/Temp/CHIRPS/Monthly/zonal/lbn/adm3\")\n",
"\n",
"def calculate_annual_sum(file_path):\n",
" # Read the CSV file\n",
" df = pd.read_csv(file_path)\n",
"\n",
" # Get all yyyymmdd columns for monthly data (considering years starting from 1981)\n",
" monthly_columns = [col for col in df.columns if col[:4].isdigit() and 1981 <= int(col[:4]) <= 2099]\n",
"\n",
" # Calculate the annual mean for each year present in the columns\n",
" # Assuming the format is YYYYMMDD\n",
" years = sorted(set(col[:4] for col in monthly_columns))\n",
" for year in years:\n",
" # Calculate the mean of all columns for the specific year\n",
" year_cols = [col for col in monthly_columns if col.startswith(year)]\n",
" if year_cols: # Check if there are columns for the year\n",
" df[f'{year}_annual'] = df[year_cols].sum(axis=1)\n",
"\n",
" # Keep the location, ID and annual mean columns\n",
" annual_columns = ['ID', 'adm3_src', 'adm3_name', 'adm2_src', 'adm2_name', 'adm1_src', 'adm1_name', 'adm0_src', 'adm0_name'] + \\\n",
" [f'{year}_annual' for year in years]\n",
" annual_df = df[annual_columns]\n",
"\n",
" # Construct the new filename\n",
" new_filename = file_path.stem.replace('monthly', 'annual') + '.csv'\n",
" annual_file_path = file_path.parent / new_filename\n",
" annual_df.to_csv(annual_file_path, index=False)\n",
"\n",
" print(f\"Annual sum values saved to {annual_file_path}\")\n",
"\n",
"# Call the function for both SOS and EOS CSV files\n",
"calculate_annual_sum(output_folder / \"lbn_cli_precip_monthly_chirps_1981_2023_adm3_max.csv\")\n",
"calculate_annual_sum(output_folder / \"lbn_cli_precip_monthly_chirps_1981_2023_adm3_mean.csv\")\n",
"calculate_annual_sum(output_folder / \"lbn_cli_precip_monthly_chirps_1981_2023_adm3_min.csv\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4725269",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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