{ "cells": [ { "cell_type": "code", "execution_count": 1, "outputs": [], "source": [ "import numpy as np\n", "import netCDF4 as nc" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 10, "outputs": [], "source": [ "from osgeo import gdal, osr, ogr" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 3, "outputs": [ { "data": { "text/plain": "\nroot group (NETCDF4 data model, file format HDF5):\n title: Himawari-08 AHI equal latitude-longitude map data\n id: H08_20221107_1800_RFL020_FLDK.02401_02401.nc\n date_created: 2022-11-07T18:25:18Z\n pixel_number: 2401\n line_number: 2401\n upper_left_latitude: 60.0\n upper_left_longitude: 80.0\n grid_interval: 0.05\n band_number: 6\n algorithm_version: 0201\n Ancillary meteorological data: JMA forcast\n Ancillary ozone data: JMA objective analysis\n BRDF correction: on (Morel and Maritorena 2001)\n dimensions(sizes): latitude(2401), longitude(2401), band(6), time(1), geometry(17)\n variables(dimensions): float32 latitude(latitude), float32 longitude(longitude), int32 band_id(band), float64 start_time(time), float64 end_time(time), float64 geometry_parameters(geometry), int16 TAOT_02(latitude, longitude), int16 TAAE(latitude, longitude), int16 PAR(latitude, longitude), int16 SWR(latitude, longitude), int16 UVA(latitude, longitude), int16 UVB(latitude, longitude), uint8 QA_flag(latitude, longitude)\n groups: " }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = r\"D:\\Datasets\\Himawari\\pub\\L2_PAR\\20221107\\18\\H08_20221107_1800_RFL020_FLDK.02401_02401.nc\"\n", "nc_data = nc.Dataset(data)\n", "nc_data" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 4, "outputs": [ { "data": { "text/plain": "['latitude',\n 'longitude',\n 'band_id',\n 'start_time',\n 'end_time',\n 'geometry_parameters',\n 'TAOT_02',\n 'TAAE',\n 'PAR',\n 'SWR',\n 'UVA',\n 'UVB',\n 'QA_flag']" }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(nc_data.variables.keys())" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 5, "outputs": [ { "data": { "text/plain": "\nint16 PAR(latitude, longitude)\n long_name: Photosynthetically active radiation\n units: umol/m^2/s\n scale_factor: 0.1\n add_offset: 0.0\n valid_min: 0\n valid_max: 25000\n missing_value: -32768\nunlimited dimensions: \ncurrent shape = (2401, 2401)\nfilling on, default _FillValue of -32767 used" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nc_data['PAR']" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 6, "outputs": [ { "data": { "text/plain": "\nfloat32 latitude(latitude)\n long_name: latitude\n units: degrees_north\nunlimited dimensions: \ncurrent shape = (2401,)\nfilling on, default _FillValue of 9.969209968386869e+36 used" }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nc_data['latitude']" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 7, "outputs": [ { "data": { "text/plain": "array([[ 0. , 0. , 0. , ..., 0. , 0. ,\n 0. ],\n [ 0. , 0. , 0. , ..., 0. , 0. ,\n 0. ],\n [ 0. , 0. , 0. , ..., 0. , 0. ,\n 0. ],\n ...,\n [ 0. , 0. , 0. , ..., 181.6 , 139.7 ,\n 144.40001],\n [ 0. , 0. , 0. , ..., 201.6 , 318.6 ,\n 169.7 ],\n [ 0. , 0. , 0. , ..., 240.8 , 338.9 ,\n 340.1 ]], dtype=float32)" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "par = np.asarray(nc_data['PAR'][:])\n", "par" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 14, "outputs": [], "source": [ "import pandas as pd" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 42, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2401 2401\n" ] } ], "source": [ "lat = list(map(lambda x: round(x, 2), np.asarray(nc_data['latitude'][:])))\n", "lon = list(map(lambda x: round(x, 2), np.asarray(nc_data['longitude'][:])))\n", "print(len(lat), len(lon))\n", "latMin, latMax, lonMin, lonMax = min(lat), max(lat), min(lon), max(lon)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 9, "outputs": [], "source": [ "# 分辨率\n", "lat_Res = (latMax - latMin) / (lat.shape[0]-1)\n", "lon_Res = (lonMax - lonMin) / (lon.shape[0]-1)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 53, "outputs": [], "source": [ "cols = [str(x) for x in lat]\n", "rows = [str(x) for x in lon]" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 54, "outputs": [], "source": [ "par_df = pd.DataFrame.from_records(par)\n", "par_df.columns = cols\n", "par_df.index = rows" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 58, "outputs": [ { "data": { "text/plain": " 60.0 59.95 59.9 59.85 59.8 59.75 59.7 59.65 59.6 59.55 ... \\\n199.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n199.85 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n199.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n199.95 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n\n -59.55 -59.6 -59.65 -59.7 -59.75 \\\n199.8 160.699997 160.900009 155.400009 155.500000 142.100006 \n199.85 153.800003 174.500000 147.300003 139.400009 139.600006 \n199.9 164.199997 166.800003 151.000000 153.800003 153.900009 \n199.95 152.900009 159.800003 184.300003 164.000000 164.199997 \n200.0 149.199997 148.400009 148.600006 152.300003 152.800003 \n\n -59.8 -59.85 -59.9 -59.95 -60.0 \n199.8 143.199997 143.699997 138.100006 138.300003 139.600006 \n199.85 144.199997 144.199997 160.199997 142.199997 143.699997 \n199.9 169.000000 169.300003 181.600006 139.699997 144.400009 \n199.95 167.100006 167.600006 201.600006 318.600006 169.699997 \n200.0 159.699997 240.699997 240.800003 338.899994 340.100006 \n\n[5 rows x 2401 columns]", "text/html": "
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" }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "par_df[par_df.index=='120.85']" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }