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bennyistanto/nc2h3_zonalstats.ipynb

Created March 12, 2025 00:17
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NetCDF to H3 grid zonal statistics
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nc2h3_zonalstats.ipynb
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# NetCDF to H3 grid zonal statistics, save table to Parquet"
],
"metadata": {
"id": "3Yc4zDnWtOAh"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "6qmGRxhn6t0I"
},
"source": [
"## Connect to Google Drive"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9f4Vtg70NZfd",
"outputId": "eb6ffe78-6021-413d-a0ad-9fb1234b24f9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"import os\n",
"\n",
"# Check if the drive is mounted\n",
"if os.path.exists('/content/drive'):\n",
" # Try to unmount\n",
" try:\n",
" drive.flush_and_unmount()\n",
" print(\"Successfully unmounted\")\n",
" except:\n",
" print(\"Unmount failed, the drive might not be mounted or busy\")\n",
"\n",
"# Mount the drive\n",
"drive.mount('/content/drive')\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MRfVeBNM6yVZ"
},
"source": [
"## Test directory access"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_QLdYGDqygOV",
"outputId": "46a7a977-4a9e-4c6b-ec2b-5171409aaa34"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"total 8\n",
"drwx------ 2 root root 4096 Feb 6 12:14 input\n",
"drwx------ 2 root root 4096 Mar 11 00:54 output\n"
]
}
],
"source": [
"# Import the library\n",
"import os\n",
"\n",
"# Main directory on Google Drive\n",
"main_dir = '/content/drive/MyDrive/wbg/s2s'\n",
"\n",
"!ls -l {main_dir}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Oiu21pWK627k"
},
"source": [
"## Install additional packages which not available in Colab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JgzM7rxPOIBZ",
"outputId": "4412de33-cf2b-4fc7-da6f-c35f99579fe0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting h3\n",
" Downloading h3-4.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (18 kB)\n",
"Collecting h3ronpy\n",
" Downloading h3ronpy-0.22.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.8 kB)\n",
"Collecting affine\n",
" Downloading affine-2.4.0-py3-none-any.whl.metadata (4.0 kB)\n",
"Requirement already satisfied: pyarrow in /usr/local/lib/python3.11/dist-packages (18.1.0)\n",
"Collecting fastparquet\n",
" Downloading fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from h3ronpy) (1.26.4)\n",
"Collecting arro3-core>=0.4 (from h3ronpy)\n",
" Downloading arro3_core-0.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (885 bytes)\n",
"Requirement already satisfied: pandas>=1.5.0 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.2.2)\n",
"Requirement already satisfied: cramjam>=2.3 in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2.9.1)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from fastparquet) (2024.10.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from fastparquet) (24.2)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2025.1)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.5.0->fastparquet) (2025.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5.0->fastparquet) (1.17.0)\n",
"Downloading h3-4.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading h3ronpy-0.22.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.4 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m95.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading affine-2.4.0-py3-none-any.whl (15 kB)\n",
"Downloading fastparquet-2024.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.8 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m74.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading arro3_core-0.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.5 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m70.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: h3, arro3-core, affine, h3ronpy, fastparquet\n",
"Successfully installed affine-2.4.0 arro3-core-0.4.5 fastparquet-2024.11.0 h3-4.2.1 h3ronpy-0.22.0\n"
]
}
],
"source": [
"!pip install h3 h3ronpy affine pyarrow fastparquet"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HuHsQQh669lb"
},
"source": [
"## Zonalstats to H3"
]
},
{
"cell_type": "code",
"source": [
"# Script: Global SPI Data Extraction to H3 Grid\n",
"\"\"\"\n",
"Process CHIRPS3 SPI data to H3 grid format efficiently.\n",
"\n",
"Features:\n",
"- Optimized for large NetCDF files (>10GB)\n",
"- Memory-efficient using Dask chunked operations\n",
"- Progress tracking with detailed logging\n",
"- Outputs both wide and long-format Parquet files\n",
"\"\"\"\n",
"\n",
"import xarray as xr\n",
"import pandas as pd\n",
"import numpy as np\n",
"from h3ronpy.raster import nearest_h3_resolution\n",
"from h3ronpy.pandas.raster import raster_to_dataframe\n",
"from h3ronpy import cells_to_string\n",
"import dask.array as da\n",
"from datetime import datetime\n",
"import logging\n",
"from pathlib import Path\n",
"from tqdm.notebook import tqdm\n",
"import os\n",
"from affine import Affine\n",
"import psutil\n",
"import gc\n",
"\n",
"def setup_logging(log_file='spi_extraction.log'):\n",
" \"\"\"\n",
" Configure detailed logging for process tracking.\n",
" \"\"\"\n",
" logging.basicConfig(\n",
" level=logging.INFO,\n",
" format='%(asctime)s - %(levelname)s - %(message)s',\n",
" handlers=[\n",
" logging.FileHandler(log_file),\n",
" logging.StreamHandler()\n",
" ]\n",
" )\n",
"\n",
"def get_memory_usage():\n",
" \"\"\"Return current memory usage in GB.\"\"\"\n",
" process = psutil.Process(os.getpid())\n",
" return process.memory_info().rss / 1024 / 1024 / 1024\n",
"\n",
"def optimize_chunk_size(shape, mem_limit_gb=4):\n",
" \"\"\"\n",
" Calculate optimal chunk sizes based on data shape and memory limit.\n",
" \"\"\"\n",
" total_size = np.prod(shape) * 8 # 8 bytes per float64\n",
" chunk_size = int(mem_limit_gb * 1024**3)\n",
"\n",
" time_chunk = min(12, shape[0]) # Monthly data, max 1 year per chunk\n",
" spatial_chunk = int(np.sqrt((chunk_size/8) / time_chunk))\n",
"\n",
" return {\n",
" 'time': time_chunk,\n",
" 'lat': min(spatial_chunk, shape[1]),\n",
" 'lon': min(spatial_chunk, shape[2])\n",
" }\n",
"\n",
"def get_transform(ds):\n",
" \"\"\"Create affine transform from dataset coordinates.\"\"\"\n",
" lon, lat = ds.lon.values, ds.lat.values\n",
" pixel_width = (lon[-1] - lon[0]) / (len(lon) - 1)\n",
" pixel_height = (lat[-1] - lat[0]) / (len(lat) - 1)\n",
" return Affine.translation(lon[0], lat[0]) * Affine.scale(pixel_width, pixel_height)\n",
"\n",
"def process_timestep(data_slice, transform, h3_resolution):\n",
" \"\"\"Convert single timestep to H3 hexagons.\"\"\"\n",
" return raster_to_dataframe(\n",
" data_slice,\n",
" transform,\n",
" h3_resolution,\n",
" nodata_value=np.nan,\n",
" compact=False,\n",
" geo=False\n",
" )\n",
"\n",
"def extract_spi_to_h3(netcdf_path, start_date, end_date, output_dir, h3_resolution=6):\n",
" \"\"\"\n",
" Extract and aggregate SPI data to H3 grid.\n",
"\n",
" Args:\n",
" netcdf_path (str): Path to NetCDF file\n",
" start_date (str): Start date (YYYY-MM-DD)\n",
" end_date (str): End date (YYYY-MM-DD)\n",
" output_dir (str): Output directory\n",
" h3_resolution (int): H3 grid resolution\n",
"\n",
" Returns:\n",
" tuple: Path to output Parquet files (wide format, long format)\n",
" \"\"\"\n",
" ds = None\n",
" try:\n",
" # Create output directory\n",
" Path(output_dir).mkdir(parents=True, exist_ok=True)\n",
"\n",
" # Open dataset\n",
" logging.info(f\"Opening NetCDF file: {netcdf_path}\")\n",
" ds = xr.open_dataset(netcdf_path)\n",
"\n",
" # Optimize chunking\n",
" chunks = optimize_chunk_size(ds.spi_gamma_6_month.shape)\n",
" logging.info(f\"Using chunk sizes: {chunks}\")\n",
" ds = ds.chunk(chunks)\n",
"\n",
" # Get transform\n",
" transform = get_transform(ds)\n",
"\n",
" # Filter time range\n",
" start_datetime = datetime.strptime(start_date, '%Y-%m-%d')\n",
" end_datetime = datetime.strptime(end_date, '%Y-%m-%d')\n",
" time_mask = (ds.time >= np.datetime64(start_datetime)) & \\\n",
" (ds.time <= np.datetime64(end_datetime))\n",
" ds = ds.sel(time=time_mask)\n",
"\n",
" total_steps = len(ds.time)\n",
" logging.info(f\"Processing {total_steps} timesteps\")\n",
"\n",
" # Process all timesteps\n",
" df_res = None\n",
" # Store data for long format\n",
" long_format_data = []\n",
"\n",
" pbar = tqdm(total=total_steps, desc=\"Processing timesteps\", unit=\"timestep\")\n",
"\n",
" for i, time in enumerate(ds.time.values):\n",
" mem_usage = get_memory_usage()\n",
" logging.info(f\"Memory usage before timestep {i+1}: {mem_usage:.2f}GB\")\n",
"\n",
" # Get data for timestep\n",
" data_slice = ds.spi_gamma_6_month.isel(time=i).values\n",
"\n",
" # Flip data along y-axis\n",
" data_slice_flipped = data_slice[::-1, :]\n",
"\n",
" # Convert to H3\n",
" h3_df = process_timestep(data_slice_flipped, transform, h3_resolution)\n",
"\n",
" # Format time string as YYYYMMDD with day = 01\n",
" date_str = pd.Timestamp(time).strftime('%Y%m01')\n",
" logging.info(f\"Timestep {i+1}: processed {len(h3_df)} H3 cells\")\n",
"\n",
" # Rename value column to just the date (instead of spi_YYYYMM)\n",
" h3_df.rename(columns={'value': date_str}, inplace=True)\n",
"\n",
" # Store data for long format with 'date' instead of 'timestamp'\n",
" temp_df = h3_df.copy()\n",
" temp_df['date'] = date_str\n",
" temp_df.rename(columns={date_str: 'spi'}, inplace=True)\n",
" long_format_data.append(temp_df)\n",
"\n",
" # Set cell column as index for wide format\n",
" h3_df.set_index('cell', inplace=True)\n",
"\n",
" # Add to combined result for wide format\n",
" if df_res is None:\n",
" df_res = h3_df\n",
" else:\n",
" df_res = df_res.join(h3_df, validate='one_to_one')\n",
"\n",
" pbar.update(1)\n",
" if (i + 1) % max(1, total_steps // 10) == 0:\n",
" logging.info(f\"Progress: {i+1}/{total_steps} timesteps ({((i+1)/total_steps*100):.1f}%)\")\n",
"\n",
" pbar.close()\n",
"\n",
" # Convert H3 indices to strings\n",
" logging.info(\"Converting H3 indices to strings\")\n",
" hexids_int = list(df_res.index)\n",
" hexids_arrow = cells_to_string(hexids_int)\n",
" hexids_string = hexids_arrow.to_pylist()\n",
"\n",
" # Add hex_id column with string indices\n",
" df_res.loc[:, \"hex_id\"] = hexids_string\n",
"\n",
" # Reset index\n",
" df_res.reset_index(drop=True, inplace=True)\n",
"\n",
" # Reorder columns to put hex_id at the beginning\n",
" cols = df_res.columns.tolist()\n",
" cols.remove('hex_id')\n",
" cols = ['hex_id'] + cols\n",
" df_res = df_res[cols]\n",
"\n",
" # Save wide format output\n",
" start_date_str = start_date.replace('-', '')[:8]\n",
" end_date_str = end_date.replace('-', '')[:8]\n",
" wide_output_path = Path(output_dir) / f'wld_cli_spi6_h3_res{h3_resolution}_{start_date_str}_{end_date_str}_wide.parquet'\n",
"\n",
" logging.info(f\"Saving wide format results to: {wide_output_path}\")\n",
" df_res.to_parquet(wide_output_path, index=False)\n",
" logging.info(f\"Wide format file size: {os.path.getsize(wide_output_path) / (1024**3):.2f}GB\")\n",
"\n",
" # Create and save long format output\n",
" logging.info(\"Creating long format output\")\n",
" df_long = pd.concat(long_format_data, ignore_index=True)\n",
"\n",
" # Convert cell column to string hex_id\n",
" cell_to_hexid = dict(zip(hexids_int, hexids_string))\n",
" df_long['hex_id'] = df_long['cell'].map(cell_to_hexid)\n",
"\n",
" # Drop cell column and reorder\n",
" df_long = df_long.drop('cell', axis=1)\n",
" df_long = df_long[['hex_id', 'date', 'spi']]\n",
"\n",
" long_output_path = Path(output_dir) / f'wld_cli_spi6_h3_res{h3_resolution}_{start_date_str}_{end_date_str}_long.parquet'\n",
" logging.info(f\"Saving long format results to: {long_output_path}\")\n",
" df_long.to_parquet(long_output_path, index=False)\n",
" logging.info(f\"Long format file size: {os.path.getsize(long_output_path) / (1024**3):.2f}GB\")\n",
"\n",
" return wide_output_path, long_output_path\n",
"\n",
" except Exception as e:\n",
" logging.error(f\"Error processing data: {str(e)}\", exc_info=True)\n",
" raise\n",
" finally:\n",
" if ds is not None:\n",
" ds.close()\n",
" if 'pbar' in locals():\n",
" pbar.close()\n",
"\n",
"def main():\n",
" \"\"\"Process SPI extraction year-by-year, flushing memory after each year.\"\"\"\n",
" main_dir = '/content/drive/MyDrive/wbg/s2s'\n",
" input_dir = os.path.join(main_dir, 'input')\n",
" output_dir = os.path.join(main_dir, 'output')\n",
" os.makedirs(output_dir, exist_ok=True)\n",
"\n",
" setup_logging()\n",
"\n",
" netcdf_path = os.path.join(input_dir, 'wld_cli_chirps3_spi_gamma_6_month.nc')\n",
" h3_resolution = 6\n",
"\n",
" start_year = 1983\n",
" end_year = 2024\n",
" yearly_files = []\n",
"\n",
" # Process each year separately\n",
" for year in range(start_year, end_year + 1):\n",
" start_date = f\"{year}-01-01\"\n",
" end_date = f\"{year}-12-31\"\n",
" logging.info(f\"Processing year: {year}\")\n",
"\n",
" # Process the year\n",
" wide_path, long_path = extract_spi_to_h3(netcdf_path, start_date, end_date, output_dir, h3_resolution)\n",
" yearly_files.append((wide_path, long_path))\n",
"\n",
" # Aggressive memory cleanup\n",
" logging.info(f\"Memory usage before cleanup: {get_memory_usage():.2f}GB\")\n",
" gc.collect()\n",
" import sys\n",
" for obj in list(locals().values()) + list(globals().values()):\n",
" if isinstance(obj, (pd.DataFrame, np.ndarray)) and sys.getrefcount(obj) <= 3:\n",
" del obj\n",
" gc.collect()\n",
" logging.info(f\"Memory usage after cleanup: {get_memory_usage():.2f}GB\")\n",
"\n",
" logging.info(f\"All years ({start_year}-{end_year}) processed successfully\")\n",
" logging.info(f\"Output files: {yearly_files}\")\n",
" return yearly_files\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
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"source": [
"## Preview the output"
]
},
{
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"metadata": {
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},
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"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 3369429 entries, 0 to 3369428\n",
"Data columns (total 13 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 hex_id object \n",
" 1 20240101 float64\n",
" 2 20240201 float64\n",
" 3 20240301 float64\n",
" 4 20240401 float64\n",
" 5 20240501 float64\n",
" 6 20240601 float64\n",
" 7 20240701 float64\n",
" 8 20240801 float64\n",
" 9 20240901 float64\n",
" 10 20241001 float64\n",
" 11 20241101 float64\n",
" 12 20241201 float64\n",
"dtypes: float64(12), object(1)\n",
"memory usage: 334.2+ MB\n"
]
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"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df"
}
},
"metadata": {},
"execution_count": 23
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Replace with our actual file path\n",
"parquet_file = '/content/drive/MyDrive/wbg/s2s/output/wld_cli_spi6_h3_res6_20240101_20241231_wide.parquet'\n",
"df = pd.read_parquet(parquet_file)\n",
"\n",
"# Display the first few rows\n",
"df.info()\n",
"df.head()"
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"# Replace with our actual file path\n",
"parquet_file = '/content/drive/MyDrive/wbg/s2s/output/wld_cli_spi6_h3_res6_20240101_20241231_long.parquet'\n",
"df = pd.read_parquet(parquet_file)\n",
"\n",
"# Display the first few rows\n",
"df.info()\n",
"df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 380
},
"id": "THOWPEo9ljIV",
"outputId": "3a0f5420-b922-40b5-ffb2-0ec8bbe08f23"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 40459470 entries, 0 to 40459469\n",
"Data columns (total 3 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 hex_id object \n",
" 1 date object \n",
" 2 spi float64\n",
"dtypes: float64(1), object(2)\n",
"memory usage: 926.0+ MB\n"
]
},
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