318 lines
14 KiB
Plaintext
318 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import netCDF4 as nc"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"outputs": [],
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"source": [
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"from osgeo import gdal, osr, ogr"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [
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{
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"data": {
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"text/plain": "<class 'netCDF4._netCDF4.Dataset'>\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: "
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data = r\"D:\\Datasets\\Himawari\\pub\\L2_PAR\\20221107\\18\\H08_20221107_1800_RFL020_FLDK.02401_02401.nc\"\n",
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"nc_data = nc.Dataset(data)\n",
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"nc_data"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"outputs": [
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{
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"data": {
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"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']"
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"list(nc_data.variables.keys())"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"outputs": [
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{
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"data": {
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"text/plain": "<class 'netCDF4._netCDF4.Variable'>\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"
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"nc_data['PAR']"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"outputs": [
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{
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"data": {
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"text/plain": "<class 'netCDF4._netCDF4.Variable'>\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"
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"nc_data['latitude']"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"outputs": [
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{
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"data": {
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"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)"
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"par = np.asarray(nc_data['PAR'][:])\n",
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"par"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"outputs": [],
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"source": [
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"import pandas as pd"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2401 2401\n"
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]
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}
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],
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"source": [
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"lat = list(map(lambda x: round(x, 2), np.asarray(nc_data['latitude'][:])))\n",
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"lon = list(map(lambda x: round(x, 2), np.asarray(nc_data['longitude'][:])))\n",
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"print(len(lat), len(lon))\n",
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"latMin, latMax, lonMin, lonMax = min(lat), max(lat), min(lon), max(lon)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"outputs": [],
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"source": [
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"# 分辨率\n",
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"lat_Res = (latMax - latMin) / (lat.shape[0]-1)\n",
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"lon_Res = (lonMax - lonMin) / (lon.shape[0]-1)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"outputs": [],
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"source": [
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"cols = [str(x) for x in lat]\n",
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"rows = [str(x) for x in lon]"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"outputs": [],
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"source": [
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"par_df = pd.DataFrame.from_records(par)\n",
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"par_df.columns = cols\n",
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"par_df.index = rows"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"outputs": [
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{
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"data": {
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"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]",
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"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>60.0</th>\n <th>59.95</th>\n <th>59.9</th>\n <th>59.85</th>\n <th>59.8</th>\n <th>59.75</th>\n <th>59.7</th>\n <th>59.65</th>\n <th>59.6</th>\n <th>59.55</th>\n <th>...</th>\n <th>-59.55</th>\n <th>-59.6</th>\n <th>-59.65</th>\n <th>-59.7</th>\n <th>-59.75</th>\n <th>-59.8</th>\n <th>-59.85</th>\n <th>-59.9</th>\n <th>-59.95</th>\n <th>-60.0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>199.8</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>160.699997</td>\n <td>160.900009</td>\n <td>155.400009</td>\n <td>155.500000</td>\n <td>142.100006</td>\n <td>143.199997</td>\n <td>143.699997</td>\n <td>138.100006</td>\n <td>138.300003</td>\n <td>139.600006</td>\n </tr>\n <tr>\n <th>199.85</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>153.800003</td>\n <td>174.500000</td>\n <td>147.300003</td>\n <td>139.400009</td>\n <td>139.600006</td>\n <td>144.199997</td>\n <td>144.199997</td>\n <td>160.199997</td>\n <td>142.199997</td>\n <td>143.699997</td>\n </tr>\n <tr>\n <th>199.9</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>164.199997</td>\n <td>166.800003</td>\n <td>151.000000</td>\n <td>153.800003</td>\n <td>153.900009</td>\n <td>169.000000</td>\n <td>169.300003</td>\n <td>181.600006</td>\n <td>139.699997</td>\n <td>144.400009</td>\n </tr>\n <tr>\n <th>199.95</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>152.900009</td>\n <td>159.800003</td>\n <td>184.300003</td>\n <td>164.000000</td>\n <td>164.199997</td>\n <td>167.100006</td>\n <td>167.600006</td>\n <td>201.600006</td>\n <td>318.600006</td>\n <td>169.699997</td>\n </tr>\n <tr>\n <th>200.0</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>149.199997</td>\n <td>148.400009</td>\n <td>148.600006</td>\n <td>152.300003</td>\n <td>152.800003</td>\n <td>159.699997</td>\n <td>240.699997</td>\n <td>240.800003</td>\n <td>338.899994</td>\n <td>340.100006</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 2401 columns</p>\n</div>"
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},
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"execution_count": 58,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"par_df.tail()"
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],
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"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
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"name": "#%%\n"
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||
}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"outputs": [
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{
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"data": {
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"text/plain": " 60.0 59.95 59.9 59.85 59.8 59.75 59.7 59.65 59.6 59.55 ... \\\n120.85 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 -59.8 -59.85 -59.9 \\\n120.85 12.5 27.4 53.100002 57.200001 11.1 11.900001 15.6 90.0 \n\n -59.95 -60.0 \n120.85 91.599998 94.900002 \n\n[1 rows x 2401 columns]",
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"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>60.0</th>\n <th>59.95</th>\n <th>59.9</th>\n <th>59.85</th>\n <th>59.8</th>\n <th>59.75</th>\n <th>59.7</th>\n <th>59.65</th>\n <th>59.6</th>\n <th>59.55</th>\n <th>...</th>\n <th>-59.55</th>\n <th>-59.6</th>\n <th>-59.65</th>\n <th>-59.7</th>\n <th>-59.75</th>\n <th>-59.8</th>\n <th>-59.85</th>\n <th>-59.9</th>\n <th>-59.95</th>\n <th>-60.0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>120.85</th>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>12.5</td>\n <td>27.4</td>\n <td>53.100002</td>\n <td>57.200001</td>\n <td>11.1</td>\n <td>11.900001</td>\n <td>15.6</td>\n <td>90.0</td>\n <td>91.599998</td>\n <td>94.900002</td>\n </tr>\n </tbody>\n</table>\n<p>1 rows × 2401 columns</p>\n</div>"
|
||
},
|
||
"execution_count": 68,
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||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"par_df[par_df.index=='120.85']"
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||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
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||
}
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||
}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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||
"collapsed": false,
|
||
"pycharm": {
|
||
"name": "#%%\n"
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}
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||
}
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||
}
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],
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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} |