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November 15, 2023 00:44
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Zonal Statistics using rasterstats on ERA5-Land temperature and admin boundaries, then join the csv's into single csv file.
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"id": "e34d15eb", | |
"metadata": {}, | |
"source": [ | |
"# Zonal Statistics based on raster value using admin boundaries" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "71c6466c", | |
"metadata": {}, | |
"source": [ | |
"## Admin1" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "4a4dc1d7", | |
"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." | |
] | |
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"cell_type": "code", | |
"execution_count": 1, | |
"id": "4c31e360", | |
"metadata": {}, | |
"outputs": [ | |
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"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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"Processing: 0%| | 0/513 [00:00<?, ?file/s]/home/bennyistanto/anaconda3/envs/gis/lib/python3.10/site-packages/rasterstats/io.py:328: NodataWarning: Setting nodata to -999; specify nodata explicitly\n", | |
" warnings.warn(\n", | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_19810101.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_19810201.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_19810301.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_19810401.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_19810501.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_19810601.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_19810701.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20220901.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20221001.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20221101.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20221201.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20230101.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20230201.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20230301.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20230501.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20230601.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20230701.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20230801.csv\n" | |
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"text": [ | |
"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm1/wld_cli_era5land_monthly_tmean_20230901.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/era5land/tmean_monthly\"\n", | |
"zonefieldVector = \"/mnt/x/Temp/modis/lbn/bnd/lbn_bnd_adm1_edge.shp\"\n", | |
"output_folder = \"/mnt/x/Temp/era5land/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": "c8fb173a", | |
"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": 2, | |
"id": "98290b92", | |
"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/era5land/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", | |
" yyyymmdd = file.split('_')[-1][:8]\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][yyyymmdd] = row['max']\n", | |
" mean_data[key][yyyymmdd] = row['mean']\n", | |
" min_data[key][yyyymmdd] = 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_tmean_monthly_era5land_1981_2023_adm1_max.csv')\n", | |
"dict_to_csv(mean_data, 'lbn_cli_tmean_monthly_era5land_1981_2023_adm1_mean.csv')\n", | |
"dict_to_csv(min_data, 'lbn_cli_tmean_monthly_era5land_1981_2023_adm1_min.csv')\n", | |
"\n", | |
"print(\"Data compilation complete!\")\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "9f06c5ba", | |
"metadata": {}, | |
"source": [ | |
"**3. Calculate annual from monthly timeseries**\n", | |
"\n", | |
"From the monthly timeseries data, will calculate the annual `MEAN`, to get annual value. We use `MEAN` because the data is air temperature." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"id": "9f201df5", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Annual mean values saved to /mnt/x/Temp/era5land/zonal/lbn/adm1/lbn_cli_tmean_annual_era5land_1981_2023_adm1_max.csv\n", | |
"Annual mean values saved to /mnt/x/Temp/era5land/zonal/lbn/adm1/lbn_cli_tmean_annual_era5land_1981_2023_adm1_mean.csv\n", | |
"Annual mean values saved to /mnt/x/Temp/era5land/zonal/lbn/adm1/lbn_cli_tmean_annual_era5land_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/era5land/zonal/lbn/adm1\")\n", | |
"\n", | |
"def calculate_annual_mean(file_path):\n", | |
" # Read the CSV file\n", | |
" df = pd.read_csv(file_path)\n", | |
"\n", | |
" # Get all yyyymm 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 YYYYMM\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].mean(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 mean values saved to {annual_file_path}\")\n", | |
"\n", | |
"# Call the function for both SOS and EOS CSV files\n", | |
"calculate_annual_mean(output_folder / \"lbn_cli_tmean_monthly_era5land_1981_2023_adm1_max.csv\")\n", | |
"calculate_annual_mean(output_folder / \"lbn_cli_tmean_monthly_era5land_1981_2023_adm1_mean.csv\")\n", | |
"calculate_annual_mean(output_folder / \"lbn_cli_tmean_monthly_era5land_1981_2023_adm1_min.csv\")\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "199a37c1", | |
"metadata": {}, | |
"source": [ | |
"## Admin2" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "08f05214", | |
"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": 3, | |
"id": "722aaa01", | |
"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/era5land/zonal/lbn/adm2/wld_cli_era5land_monthly_tmean_19810101.csv\n" | |
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"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm2/wld_cli_era5land_monthly_tmean_19810201.csv\n" | |
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"output_type": "stream", | |
"text": [ | |
"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm2/wld_cli_era5land_monthly_tmean_19810301.csv\n" | |
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"output_type": "stream", | |
"text": [ | |
"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm2/wld_cli_era5land_monthly_tmean_19810401.csv\n" | |
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"text": [ | |
"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm2/wld_cli_era5land_monthly_tmean_19810501.csv\n" | |
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"output_type": "stream", | |
"text": [ | |
"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm2/wld_cli_era5land_monthly_tmean_19810601.csv\n" | |
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] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm2/wld_cli_era5land_monthly_tmean_20230701.csv\n" | |
] | |
}, | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"\r", | |
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] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm2/wld_cli_era5land_monthly_tmean_20230801.csv\n" | |
] | |
}, | |
{ | |
"name": "stderr", | |
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] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm2/wld_cli_era5land_monthly_tmean_20230901.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/era5land/tmean_monthly\"\n", | |
"zonefieldVector = \"/mnt/x/Temp/modis/lbn/bnd/lbn_bnd_adm2_edge.shp\"\n", | |
"output_folder = \"/mnt/x/Temp/era5land/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": "323da09b", | |
"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." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"id": "8c05760d", | |
"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/era5land/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", | |
" yyyymmdd = file.split('_')[-1][:8]\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][yyyymmdd] = row['max']\n", | |
" mean_data[key][yyyymmdd] = row['mean']\n", | |
" min_data[key][yyyymmdd] = 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_tmean_monthly_era5land_1981_2023_adm2_max.csv')\n", | |
"dict_to_csv(mean_data, 'lbn_cli_tmean_monthly_era5land_1981_2023_adm2_mean.csv')\n", | |
"dict_to_csv(min_data, 'lbn_cli_tmean_monthly_era5land_1981_2023_adm2_min.csv')\n", | |
"\n", | |
"print(\"Data compilation complete!\")\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "4cca313a", | |
"metadata": {}, | |
"source": [ | |
"**3. Calculate annual from monthly timeseries**\n", | |
"\n", | |
"From the monthly timeseries data, will calculate the annual `MEAN`, to get annual value. We use `MEAN` because the data is air temperature." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"id": "5c52de7a", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Annual mean values saved to /mnt/x/Temp/era5land/zonal/lbn/adm2/lbn_cli_tmean_annual_era5land_1981_2023_adm2_max.csv\n", | |
"Annual mean values saved to /mnt/x/Temp/era5land/zonal/lbn/adm2/lbn_cli_tmean_annual_era5land_1981_2023_adm2_mean.csv\n", | |
"Annual mean values saved to /mnt/x/Temp/era5land/zonal/lbn/adm2/lbn_cli_tmean_annual_era5land_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/era5land/zonal/lbn/adm2\")\n", | |
"\n", | |
"def calculate_annual_mean(file_path):\n", | |
" # Read the CSV file\n", | |
" df = pd.read_csv(file_path)\n", | |
"\n", | |
" # Get all yyyymm 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 YYYYMM\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].mean(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 mean values saved to {annual_file_path}\")\n", | |
"\n", | |
"# Call the function for both SOS and EOS CSV files\n", | |
"calculate_annual_mean(output_folder / \"lbn_cli_tmean_monthly_era5land_1981_2023_adm2_max.csv\")\n", | |
"calculate_annual_mean(output_folder / \"lbn_cli_tmean_monthly_era5land_1981_2023_adm2_mean.csv\")\n", | |
"calculate_annual_mean(output_folder / \"lbn_cli_tmean_monthly_era5land_1981_2023_adm2_min.csv\")\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "90470ce0", | |
"metadata": {}, | |
"source": [ | |
"## Admin3" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "846bf8da", | |
"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": 3, | |
"id": "b904a89a", | |
"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" | |
] | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm3/wld_cli_era5land_monthly_tmean_19810101.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm3/wld_cli_era5land_monthly_tmean_19810201.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm3/wld_cli_era5land_monthly_tmean_19810301.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm3/wld_cli_era5land_monthly_tmean_19810401.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm3/wld_cli_era5land_monthly_tmean_19810501.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm3/wld_cli_era5land_monthly_tmean_19810601.csv\n" | |
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"Output file created: /mnt/x/Temp/era5land/zonal/lbn/adm3/wld_cli_era5land_monthly_tmean_19810701.csv\n" | |
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"Zonal Histogram processing complete!\n" | |
] | |
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} | |
], | |
"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/era5land/tmean_monthly\"\n", | |
"zonefieldVector = \"/mnt/x/Temp/modis/lbn/bnd/lbn_bnd_adm3_edge.shp\"\n", | |
"output_folder = \"/mnt/x/Temp/era5land/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": "bf813491", | |
"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." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"id": "a0453734", | |
"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/era5land/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", | |
" yyyymmdd = file.split('_')[-1][:8]\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][yyyymmdd] = row['max']\n", | |
" mean_data[key][yyyymmdd] = row['mean']\n", | |
" min_data[key][yyyymmdd] = 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_tmean_monthly_era5land_1981_2023_adm3_max.csv')\n", | |
"dict_to_csv(mean_data, 'lbn_cli_tmean_monthly_era5land_1981_2023_adm3_mean.csv')\n", | |
"dict_to_csv(min_data, 'lbn_cli_tmean_monthly_era5land_1981_2023_adm3_min.csv')\n", | |
"\n", | |
"print(\"Data compilation complete!\")\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "a5ed606b", | |
"metadata": {}, | |
"source": [ | |
"**3. Calculate annual from monthly timeseries**\n", | |
"\n", | |
"From the monthly timeseries data, will calculate the annual `MEAN`, to get annual value. We use `MEAN` because the data is air temperature." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"id": "2327fdde", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Annual mean values saved to /mnt/x/Temp/era5land/zonal/lbn/adm3/lbn_cli_tmean_annual_era5land_1981_2023_adm3_max.csv\n", | |
"Annual mean values saved to /mnt/x/Temp/era5land/zonal/lbn/adm3/lbn_cli_tmean_annual_era5land_1981_2023_adm3_mean.csv\n", | |
"Annual mean values saved to /mnt/x/Temp/era5land/zonal/lbn/adm3/lbn_cli_tmean_annual_era5land_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/era5land/zonal/lbn/adm3\")\n", | |
"\n", | |
"def calculate_annual_mean(file_path):\n", | |
" # Read the CSV file\n", | |
" df = pd.read_csv(file_path)\n", | |
"\n", | |
" # Get all yyyymm 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 YYYYMM\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].mean(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 mean values saved to {annual_file_path}\")\n", | |
"\n", | |
"# Call the function for both SOS and EOS CSV files\n", | |
"calculate_annual_mean(output_folder / \"lbn_cli_tmean_monthly_era5land_1981_2023_adm3_max.csv\")\n", | |
"calculate_annual_mean(output_folder / \"lbn_cli_tmean_monthly_era5land_1981_2023_adm3_mean.csv\")\n", | |
"calculate_annual_mean(output_folder / \"lbn_cli_tmean_monthly_era5land_1981_2023_adm3_min.csv\")\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "fd192575", | |
"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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