hima8_pv/读tif获得.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from osgeo import gdal"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "file_path = \"data/2022-07-22/PAR_Minutes_Download/\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "target_lon = '120.85'\n",
    "target_lat = '29.6'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "par_list = list()\n",
    "file_list = [x for x in os.listdir(file_path) if x.endswith('.nc')]\n",
    "for file in file_list:\n",
    "    file_name = f\"{file_path}{file}\"\n",
    "    date = file.split('_')[1] + ':' + file.split('_')[2]\n",
    "    nc_data = nc.Dataset(file_name)\n",
    "    lat = nc_data['latitude'][:] # 纬度\n",
    "    lon = nc_data['longitude'][:]  # 经度\n",
    "    lat_str = list(map(lambda x: str(round(x, 2)), np.asarray(nc_data['latitude'][:])))\n",
    "    lon_str = list(map(lambda x: str(round(x, 2)), np.asarray(nc_data['longitude'][:])))\n",
    "    latMin, latMax, lonMin, lonMax = min(lat), max(lat), min(lon), max(lon)\n",
    "    # 分辨率\n",
    "    lat_Res = (latMax - latMin) / (lat.shape[0]-1)\n",
    "    lon_Res = (lonMax - lonMin) / (lon.shape[0]-1)\n",
    "    lon_index = lon_str.index(target_lon)\n",
    "    lat_index = lat_str.index(target_lat)\n",
    "    par = np.asarray(nc_data['PAR'][:])\n",
    "    par_list.append([date, par[lat_index][lon_index]])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "par_df = pd.DataFrame.from_records(par_list, columns=['time', 'par'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "par_df.time = par_df.time.apply(lambda x: dt.datetime.strptime(x, '%Y%m%d:%H%M') + dt.timedelta(hours=8))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "                              time          par\ntime                                           \n2022-07-22 08:00:00  20220722:0000  1146.900024\n2022-07-22 08:10:00  20220722:0010  1217.099976\n2022-07-22 08:20:00  20220722:0020  1288.800049\n2022-07-22 08:30:00  20220722:0030  1354.800049\n2022-07-22 08:40:00  20220722:0040  1419.900024\n...                            ...          ...\n2022-07-23 07:10:00  20220722:2310   778.400024\n2022-07-23 07:20:00  20220722:2320   854.799988\n2022-07-23 07:30:00  20220722:2330   928.000000\n2022-07-23 07:40:00  20220722:2340  1011.000000\n2022-07-23 07:50:00  20220722:2350  1081.800049\n\n[141 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>time</th>\n      <th>par</th>\n    </tr>\n    <tr>\n      <th>time</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2022-07-22 08:00:00</th>\n      <td>20220722:0000</td>\n      <td>1146.900024</td>\n    </tr>\n    <tr>\n      <th>2022-07-22 08:10:00</th>\n      <td>20220722:0010</td>\n      <td>1217.099976</td>\n    </tr>\n    <tr>\n      <th>2022-07-22 08:20:00</th>\n      <td>20220722:0020</td>\n      <td>1288.800049</td>\n    </tr>\n    <tr>\n      <th>2022-07-22 08:30:00</th>\n      <td>20220722:0030</td>\n      <td>1354.800049</td>\n    </tr>\n    <tr>\n      <th>2022-07-22 08:40:00</th>\n      <td>20220722:0040</td>\n      <td>1419.900024</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2022-07-23 07:10:00</th>\n      <td>20220722:2310</td>\n      <td>778.400024</td>\n    </tr>\n    <tr>\n      <th>2022-07-23 07:20:00</th>\n      <td>20220722:2320</td>\n      <td>854.799988</td>\n    </tr>\n    <tr>\n      <th>2022-07-23 07:30:00</th>\n      <td>20220722:2330</td>\n      <td>928.000000</td>\n    </tr>\n    <tr>\n      <th>2022-07-23 07:40:00</th>\n      <td>20220722:2340</td>\n      <td>1011.000000</td>\n    </tr>\n    <tr>\n      <th>2022-07-23 07:50:00</th>\n      <td>20220722:2350</td>\n      <td>1081.800049</td>\n    </tr>\n  </tbody>\n</table>\n<p>141 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "par_df"
   ],
   "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
}