6Generate product_gridded0.5°.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "brutal-infection",
   "metadata": {},
   "source": [
    "### 基于冰川尺度统计结果给予格网产品赋值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "promising-links",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import proplot as pplt\n",
    "import geopandas as gpd\n",
    "import rioxarray as rioxr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2989569c-6a4c-4575-9707-5521c7b5d10b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pth='D:/Key_works/20231212dh_calculate/xnhl_test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "little-peninsula",
   "metadata": {},
   "outputs": [],
   "source": [
    "gla_pth=pth+'/shp/gla_cgi1_basin.gpkg'\n",
    "df_gla=gpd.read_file(gla_pth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "graphic-excess",
   "metadata": {},
   "outputs": [],
   "source": [
    "pth1=pth+'\\\\dh-effe-stat.csv'\n",
    "#pth2='F:\\\\User1\\\\Python_code\\\\20210612_glacier_dh_testcode\\\\TerraSAR\\\\dh-effe-stat-terrasar.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "promotional-chance",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_effe_dxt=pd.read_csv(pth1,index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "22abe028-8c76-41bb-93ce-2053f8841bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>group_id</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean_fill</th>\n",
       "      <th>median_fill</th>\n",
       "      <th>Glc_Area</th>\n",
       "      <th>Mean_Elev</th>\n",
       "      <th>Mean_Slp</th>\n",
       "      <th>Mean_Asp</th>\n",
       "      <th>MA_Elev</th>\n",
       "      <th>basin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G101096E33298N</th>\n",
       "      <td>g0018</td>\n",
       "      <td>-2.247736</td>\n",
       "      <td>-4.501035</td>\n",
       "      <td>-2.532606</td>\n",
       "      <td>-4.124494</td>\n",
       "      <td>2.930313e+05</td>\n",
       "      <td>4942.3</td>\n",
       "      <td>39.4</td>\n",
       "      <td>315.8</td>\n",
       "      <td>4939.9</td>\n",
       "      <td>Yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G101108E33292N</th>\n",
       "      <td>g0018</td>\n",
       "      <td>1.705512</td>\n",
       "      <td>1.456494</td>\n",
       "      <td>1.766449</td>\n",
       "      <td>1.840311</td>\n",
       "      <td>1.968628e+06</td>\n",
       "      <td>4977.4</td>\n",
       "      <td>26.6</td>\n",
       "      <td>40.8</td>\n",
       "      <td>4998.3</td>\n",
       "      <td>Yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G101093E33294N</th>\n",
       "      <td>g0018</td>\n",
       "      <td>-6.184100</td>\n",
       "      <td>-3.108037</td>\n",
       "      <td>-5.655661</td>\n",
       "      <td>-3.263412</td>\n",
       "      <td>3.890786e+05</td>\n",
       "      <td>5065.6</td>\n",
       "      <td>33.5</td>\n",
       "      <td>292.3</td>\n",
       "      <td>5061.8</td>\n",
       "      <td>Yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G101093E33290N</th>\n",
       "      <td>g0018</td>\n",
       "      <td>-12.320572</td>\n",
       "      <td>-8.514097</td>\n",
       "      <td>-11.220770</td>\n",
       "      <td>-9.178255</td>\n",
       "      <td>2.439229e+05</td>\n",
       "      <td>5122.8</td>\n",
       "      <td>37.7</td>\n",
       "      <td>293.8</td>\n",
       "      <td>5125.4</td>\n",
       "      <td>Yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G101093E33286N</th>\n",
       "      <td>g0018</td>\n",
       "      <td>-5.804585</td>\n",
       "      <td>-6.774280</td>\n",
       "      <td>-5.146242</td>\n",
       "      <td>-5.558762</td>\n",
       "      <td>5.173286e+05</td>\n",
       "      <td>5035.6</td>\n",
       "      <td>30.3</td>\n",
       "      <td>228.8</td>\n",
       "      <td>5025.2</td>\n",
       "      <td>Yellow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               group_id       mean    median  mean_fill  median_fill  \\\n",
       "G101096E33298N    g0018  -2.247736 -4.501035  -2.532606    -4.124494   \n",
       "G101108E33292N    g0018   1.705512  1.456494   1.766449     1.840311   \n",
       "G101093E33294N    g0018  -6.184100 -3.108037  -5.655661    -3.263412   \n",
       "G101093E33290N    g0018 -12.320572 -8.514097 -11.220770    -9.178255   \n",
       "G101093E33286N    g0018  -5.804585 -6.774280  -5.146242    -5.558762   \n",
       "\n",
       "                    Glc_Area  Mean_Elev  Mean_Slp  Mean_Asp  MA_Elev   basin  \n",
       "G101096E33298N  2.930313e+05     4942.3      39.4     315.8   4939.9  Yellow  \n",
       "G101108E33292N  1.968628e+06     4977.4      26.6      40.8   4998.3  Yellow  \n",
       "G101093E33294N  3.890786e+05     5065.6      33.5     292.3   5061.8  Yellow  \n",
       "G101093E33290N  2.439229e+05     5122.8      37.7     293.8   5125.4  Yellow  \n",
       "G101093E33286N  5.173286e+05     5035.6      30.3     228.8   5025.2  Yellow  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_effe_dxt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "nearby-litigation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-9.524962689799008"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_effe_dxt['mean'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "metallic-nicaragua",
   "metadata": {},
   "outputs": [],
   "source": [
    "dxt_ix=pth+'/shp/dxt_group_xn.shp'\n",
    "df_dxt=gpd.read_file(dxt_ix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hollow-dominican",
   "metadata": {},
   "source": [
    "##### 获取西南河流地形图group 信息  下次不运行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "stuffed-onion",
   "metadata": {},
   "outputs": [],
   "source": [
    "####选取研究区内的dh\n",
    "group_lst=[]\n",
    "for i in df_dxt.reg_group.values:\n",
    "    if i is not None:\n",
    "        ist=i.split('+')\n",
    "        for j in ist:\n",
    "            group_lst.append(j)\n",
    "group_lst=list(set(group_lst))# 去除重复元素 保证标识的唯一性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "excess-sweden",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(group_lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fifteen-clinton",
   "metadata": {},
   "source": [
    "#### 将生成的tif文件在qgis中合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "august-brunei",
   "metadata": {},
   "outputs": [],
   "source": [
    "###生成tif后，在arcgis里合并输出为'dh1970_group/dh1970_group.tif' ##最好为int型 不然存储过大"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "searching-binary",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "#### 生成0.1°格网  根据审稿意见，弃用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bdd3bfb6-a022-45c4-a304-170c7b7c8070",
   "metadata": {},
   "outputs": [],
   "source": [
    "pth_dh=pth+'/gla_level_dh_final/'+'xnhl_dh_all_effe.tif'\n",
    "pth_yr=pth+'/gla_level_dh_final/'+'xnhl_dh_all_effe_yr.tif'\n",
    "pth_err=pth+'/gla_level_dh_final/'+'xnhl_dh_all_effe_err.tif'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a9a13265-f079-431b-b500-4d3b1e8ba78e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dh_all=xr.open_dataset(pth_dh,engine='rasterio')\n",
    "dh_all_prj0=dh_all.rio.reproject('epsg:4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bb736523-3104-4b12-bc49-bcf99f806b8a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 13GB\n",
       "Dimensions:      (band: 1, x: 77914, y: 42152)\n",
       "Coordinates:\n",
       "  * band         (band) int32 4B 1\n",
       "  * x            (x) float64 623kB -2.439e+06 -2.439e+06 ... -1.02e+05 -1.02e+05\n",
       "  * y            (y) float64 337kB 4.122e+06 4.122e+06 ... 2.858e+06 2.858e+06\n",
       "    spatial_ref  int32 4B ...\n",
       "Data variables:\n",
       "    band_data    (band, y, x) float32 13GB nan nan nan nan ... nan nan nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dc42ceed-abf6-4ce2-96f6-48357905052b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dc42ceed-abf6-4ce2-96f6-48357905052b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>x</span>: 77914</li><li><span class='xr-has-index'>y</span>: 42152</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-12e7c70a-6c7f-4c36-aa76-9aedd2263e77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-12e7c70a-6c7f-4c36-aa76-9aedd2263e77' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-641da5f1-04f7-4a07-908b-9105202e2b3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-641da5f1-04f7-4a07-908b-9105202e2b3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fc1deb7-ff4a-4032-af08-bedba54d765d' class='xr-var-data-in' type='checkbox'><label for='data-4fc1deb7-ff4a-4032-af08-bedba54d765d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.439e+06 -2.439e+06 ... -1.02e+05</div><input id='attrs-56d871ae-8f98-46f2-8294-45901123f504' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56d871ae-8f98-46f2-8294-45901123f504' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d968a56-0062-48b7-8694-09e15457e7d9' class='xr-var-data-in' type='checkbox'><label for='data-8d968a56-0062-48b7-8694-09e15457e7d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2439355.350904, -2439325.350904, -2439295.350904, ...,  -102025.350904,\n",
       "        -101995.350904,  -101965.350904])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.122e+06 4.122e+06 ... 2.858e+06</div><input id='attrs-110bf9e8-e402-4311-8ca2-d9bcd77e8769' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-110bf9e8-e402-4311-8ca2-d9bcd77e8769' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccb2a8cb-c494-4865-8930-40f447a5ed86' class='xr-var-data-in' type='checkbox'><label for='data-ccb2a8cb-c494-4865-8930-40f447a5ed86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([4122095.936844, 4122065.936844, 4122035.936844, ..., 2857625.936844,\n",
       "       2857595.936844, 2857565.936844])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1f7c1c5-c863-43f9-938b-f881af57f02c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1f7c1c5-c863-43f9-938b-f881af57f02c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70eec178-ed01-43bc-b363-5c7073458cb8' class='xr-var-data-in' type='checkbox'><label for='data-70eec178-ed01-43bc-b363-5c7073458cb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;Albers_Conic_Equal_Area&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Albers_Conic_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,0],PARAMETER[&quot;longitude_of_center&quot;,105],PARAMETER[&quot;standard_parallel_1&quot;,25],PARAMETER[&quot;standard_parallel_2&quot;,47],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>Albers_Conic_Equal_Area</dd><dt><span>grid_mapping_name :</span></dt><dd>albers_conical_equal_area</dd><dt><span>standard_parallel :</span></dt><dd>(25.0, 47.0)</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>105.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;Albers_Conic_Equal_Area&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Albers_Conic_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,0],PARAMETER[&quot;longitude_of_center&quot;,105],PARAMETER[&quot;standard_parallel_1&quot;,25],PARAMETER[&quot;standard_parallel_2&quot;,47],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-2439370.35090433 30.0 0.0 4122110.936844331 0.0 -30.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf11bd2d-a256-4ff6-8fe4-4cf3787a56f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf11bd2d-a256-4ff6-8fe4-4cf3787a56f0' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>band_data</span></div><div class='xr-var-dims'>(band, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-3fe9d7d0-d4a5-40ee-878a-0da377ea2926' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fe9d7d0-d4a5-40ee-878a-0da377ea2926' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24928e8b-d284-4912-aa56-47a685ab6146' class='xr-var-data-in' type='checkbox'><label for='data-24928e8b-d284-4912-aa56-47a685ab6146' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>RepresentationType :</span></dt><dd>ATHEMATIC</dd><dt><span>STATISTICS_COVARIANCES :</span></dt><dd>468.6640099848793</dd><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>198.37173461914</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>-7.4333146896726</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>-199.9942779541</dd><dt><span>STATISTICS_SKIPFACTORX :</span></dt><dd>1</dd><dt><span>STATISTICS_SKIPFACTORY :</span></dt><dd>1</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>21.648649149194</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, ..., nan, nan],\n",
       "        [nan, nan, ..., nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, ..., nan, nan],\n",
       "        [nan, nan, ..., nan, nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c7cdb132-68d2-497d-8736-a62c271416aa' class='xr-section-summary-in' type='checkbox'  ><label for='section-c7cdb132-68d2-497d-8736-a62c271416aa' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5794d794-8081-45ba-882b-1208eecb5805' class='xr-index-data-in' type='checkbox'/><label for='index-5794d794-8081-45ba-882b-1208eecb5805' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1], dtype=&#x27;int32&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0863d654-59c3-44b3-a4f6-3999e63ec96a' class='xr-index-data-in' type='checkbox'/><label for='index-0863d654-59c3-44b3-a4f6-3999e63ec96a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -2439355.35090433,   -2439325.35090433,   -2439295.35090433,\n",
       "         -2439265.35090433,   -2439235.35090433,   -2439205.35090433,\n",
       "         -2439175.35090433,   -2439145.35090433,   -2439115.35090433,\n",
       "         -2439085.35090433,\n",
       "       ...\n",
       "       -102235.35090433015, -102205.35090433015, -102175.35090433015,\n",
       "       -102145.35090433015, -102115.35090433015, -102085.35090433015,\n",
       "       -102055.35090433015, -102025.35090433015, -101995.35090433015,\n",
       "       -101965.35090433015],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=77914))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b5235d04-2b8d-4c46-8796-eb3b7f174ac9' class='xr-index-data-in' type='checkbox'/><label for='index-b5235d04-2b8d-4c46-8796-eb3b7f174ac9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4122095.936844331, 4122065.936844331, 4122035.936844331,\n",
       "       4122005.936844331, 4121975.936844331, 4121945.936844331,\n",
       "       4121915.936844331, 4121885.936844331, 4121855.936844331,\n",
       "       4121825.936844331,\n",
       "       ...\n",
       "       2857835.936844331, 2857805.936844331, 2857775.936844331,\n",
       "       2857745.936844331, 2857715.936844331, 2857685.936844331,\n",
       "       2857655.936844331, 2857625.936844331, 2857595.936844331,\n",
       "       2857565.936844331],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=42152))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-908fa3ff-40af-4e43-909b-a305a974e7c6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-908fa3ff-40af-4e43-909b-a305a974e7c6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 13GB\n",
       "Dimensions:      (band: 1, x: 77914, y: 42152)\n",
       "Coordinates:\n",
       "  * band         (band) int32 4B 1\n",
       "  * x            (x) float64 623kB -2.439e+06 -2.439e+06 ... -1.02e+05 -1.02e+05\n",
       "  * y            (y) float64 337kB 4.122e+06 4.122e+06 ... 2.858e+06 2.858e+06\n",
       "    spatial_ref  int32 4B ...\n",
       "Data variables:\n",
       "    band_data    (band, y, x) float32 13GB nan nan nan nan ... nan nan nan nan"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dh_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "funded-oregon",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dh_all_prj1=dh_all_prj0.where((dh_all_prj0>-200)&(dh_all_prj0<50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a56e807-424d-4162-9188-b5322701138e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dh_all_prj0.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "corrected-myrtle",
   "metadata": {},
   "outputs": [],
   "source": [
    "dh_year=xr.open_dataset(pth_yr,engine='rasterio')\n",
    "dh_year_prj=dh_year.rio.reproject('epsg:4326')\n",
    "#dh_year_prj=dh_year_prj.where(dh_year_prj!=dh_year_prj.attrs['_FillValue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18d67432-5505-4962-9311-83ff3dfc6330",
   "metadata": {},
   "outputs": [],
   "source": [
    "dh_year_prj=dh_year_prj.where(dh_year_prj!=1)\n",
    "dh_year_prj=dh_year_prj['band_data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "macro-breach",
   "metadata": {},
   "outputs": [],
   "source": [
    "dh_err=xr.open_dataset(pth_err,engine='rasterio')\n",
    "dh_err_prj=dh_err.rio.reproject('epsg:4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee6179e7-f304-462e-bf4a-860b8b767de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dh_err_prj=dh_err_prj['band_data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "wrong-management",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dh_err_prj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "tender-garage",
   "metadata": {},
   "outputs": [],
   "source": [
    "#error=((ng_std/ng_count**0.5)**2+ng_mean**2)**0.5  ## Wu et.al TC 2018 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "suited-forth",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_pth=pth+'/shp/grid.shp'\n",
    "df_grid=gpd.read_file(grid_pth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "scientific-calibration",
   "metadata": {},
   "outputs": [],
   "source": [
    "dh_all_prj=(dh_all_prj0*1)['band_data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "rising-event",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from xdem.spatialstats import nmad\n",
    "res=0.1 ##分辨率\n",
    "mean_all=[];err_all=[];fg_all=[];yr_all=[];dh_err=[]\n",
    "for peri in df_grid['PRECTEGE'].values:\n",
    "    if peri!=0:##格网中有冰川\n",
    "        ddi=df_grid[df_grid['PRECTEGE']==peri]\n",
    "        clat=ddi.lat.values[0]\n",
    "        clon=ddi.lon.values[0]\n",
    "        ###裁剪格网###################################\n",
    "        dh_ddi=dh_all_prj.sel(y=slice(clat+res/2,clat-res/2),x=slice(clon-res/2,clon+res/2))\n",
    "        dh_ei=dh_err_prj.sel(y=slice(clat+res/2,clat-res/2),x=slice(clon-res/2,clon+res/2))\n",
    "        dh_yi=dh_year_prj.sel(y=slice(clat+res/2,clat-res/2),x=slice(clon-res/2,clon+res/2))\n",
    "        ###统计数据 计算降尺度误差\n",
    "        dh_ddi.name='tt'\n",
    "        tst=(dh_ddi.to_dataframe()['tt']).dropna()\n",
    "        count=tst.count()\n",
    "        if count==0: ##格网中有冰川  但是没有dh\n",
    "            mean_st=np.nan\n",
    "            err=np.nan\n",
    "            yr=np.nan\n",
    "            dherr=np.nan\n",
    "            gfg=-1\n",
    "        else:\n",
    "            mean_st=tst.mean()\n",
    "            std_st=nmad(tst)\n",
    "            err=(std_st/count**0.5)  ## Wu et.al TC 2018 \n",
    "            gfg=1\n",
    "            ############################\n",
    "            dherr=dh_ei.median().values\n",
    "            try:\n",
    "                yr=int(dh_yi.where(dh_yi>1).mean().values)\n",
    "            except:\n",
    "                yr=1974\n",
    "        #break\n",
    "    else: ###格网中无冰川\n",
    "        mean_st=np.nan\n",
    "        err=np.nan\n",
    "        yr=np.nan\n",
    "        dherr=np.nan\n",
    "        gfg=0\n",
    "    mean_all.append(mean_st)\n",
    "    err_all.append(err)\n",
    "    fg_all.append(gfg)\n",
    "    yr_all.append(yr)\n",
    "    dh_err.append(dherr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "mechanical-smooth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-9.409756512576276, 0.2900866095670218, 3.386099934577942)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vvv=np.array(mean_all)\n",
    "np.nanmean(vvv),np.nanmean(err_all),np.nanmedian(dh_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2688236f-e1cc-4944-adb0-c73b6b6c811c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "comparable-prince",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grid['dh']=np.array(mean_all)\n",
    "df_grid['gla_flag']=fg_all\n",
    "df_grid['dxt_year']=yr_all\n",
    "df_grid['res_error']=np.array(err_all)/(2000-np.array(yr_all))\n",
    "df_grid['dh_error']=np.array(dh_err).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "coupled-means",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_new=df_grid.drop(['GID','GLA_AREA','AREA','geometry'],axis=1)\n",
    "grid_new.columns=['gla_ratio','y','x','dh','gla_flag','dxt_year','res_error','dh_error']\n",
    "ds_grid=grid_new.set_index(['y','x']).to_xarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb426b67-1e5a-4fae-acbb-181ba6ed4172",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid['mb']=ds_grid['dh']/(2000-ds_grid['dxt_year'])*0.85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0d06a8f0-d401-42d3-b5d3-7c7a17f8f956",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Atanu Bhattacharya el,al nature communication 2021\n",
    "t=2000-ds_grid['dxt_year']\n",
    "pi=0.85;pw=1;dp=0.06\n",
    "mb_err=(((ds_grid['dh']/t)*(dp/pw))**2+((ds_grid['dh_error']/t)*(pi/pw))**2)**0.5\n",
    "ds_grid['mb_error']=(mb_err**2+ds_grid['res_error']**2)**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6d97dcd9-c195-413b-82ae-cae327a9712b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ds_grid['mb_error'].values[ds_grid['mb_error'].values>1]=0.26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5f0b5f78-28ea-43ca-b8de-d72ebe8bb232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(-0.2639404)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ds_grid['dh']/t).median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7005283c-1d96-4254-b1e9-1e737cd34d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(-0.30848374), array(0.10922162), array(3.38609993))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_grid['mb'].mean().values,ds_grid['mb_error'].median().values,ds_grid['dh_error'].median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aaef8602-4021-4cb2-9954-8c534c2a32c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x23f01c7cb80>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_grid['mb_error'].plot(vmax=0.3,vmin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "traditional-capture",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid['gla_flag'].attrs={'value_mean':'0->no glacier, 1->glacier&dh, -1->no dh'}\n",
    "\n",
    "ds_grid['gla_ratio'].attrs={'long_name':'Proportion of glacier area to grid area',\n",
    "                     'units':'%'}\n",
    "ds_grid['dh'].attrs={'long_name':'Elevation difference from srtm-dem and topo-dem',\n",
    "                     'units':'m'}\n",
    "ds_grid['res_error'].attrs={'long_name':'errors in resampling',\n",
    "                     'units':'m'}\n",
    "ds_grid['dxt_year'].attrs={'long_name':'Year of aerial photograph'}\n",
    "ds_grid['dh_error'].attrs={'long_name':'Elevation difference errors','units':'m'}\n",
    "ds_grid['mb_error'].attrs={'long_name':'Mass balance errors','units':'m'}\n",
    "ds_grid['mb'].attrs={'long_name':'Mass balance','units':'m'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a8a604c4-4eaa-4f14-be85-f763d6f14205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<xarray.DataArray ()> Size: 8B\n",
       " array(-0.30848374),\n",
       " <xarray.DataArray 'dh' ()> Size: 8B\n",
       " array(-9.40975651))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ds_grid['dh']/(2000-ds_grid['dxt_year'])*0.85).mean(),(ds_grid['dh']).mean() ##平均年份算下来应该是27年"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "logical-enzyme",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid=ds_grid.sortby('y',ascending=False)\n",
    "#ds_grid.attrs['transform']=(0.1, 0.0, 75, 0.0, -0.1, 40)\n",
    "#####################################\n",
    "ds_grid.attrs={'crs':'+proj=longlat +datum=WGS84 +no_defs',\n",
    "               'transform':(0.1, 0.0, 75,0.0, -0.1, 40),\n",
    "               'res': (0.1,0.1),\n",
    "               'nodatavals': 'nan','coords_format':'Grid center',\n",
    "               'created date':'2024-02','institute':'Yunnan University',\n",
    "              'authors':'Zhu Yu; Liu Shiyin*','email':'shiyin.liu@ynu.edu.cn; yuzhu@mail.ynu.edu.cn'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f5547d2c-b790-4e55-ba69-48dc255d987e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:/Key_works/20231212dh_calculate/xnhl_test'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "35ad5a4e-caee-4634-8cf1-0582f9c2a7d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid1=ds_grid.rio.write_crs('epsg:4326')\n",
    "ds_grid0=ds_grid1.rio.set_spatial_dims(x_dim='x',y_dim='y')\n",
    "ds_grid0.to_netcdf(pth+'/dh_swr_0.1deg_1970s-2000_v3.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "facial-third",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid01=ds_grid0.where(ds_grid0['dh']<0)\n",
    "ds_grid01=ds_grid01.rio.write_crs('epsg:4326')\n",
    "ds_grid01.to_netcdf(pth+'/dh_swr_0.1deg_1970s-2000_v3_le0.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "61e4f8e6-7e98-4bf9-b9a8-d86a0b7d62f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x26e013791f0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_grid0['mb'].plot(cmap='gist_heat',extend='both',vmin=-0.8,vmax=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5d46f86-8e9c-4111-bb24-215b6bc46a20",
   "metadata": {},
   "outputs": [],
   "source": [
    "from osgeo import osr\n",
    "pj=osr.SpatialReference()\n",
    "pj.SetFromUserInput(wgs84)\n",
    "strWkt=pj.ExportToProj4()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "4ad6ee0b-9faa-47fc-9d08-b57f821a9c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'+proj=longlat +datum=WGS84 +no_defs'"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strWkt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdaf2b1f-04fd-4858-b675-6e1b976e03c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "historical-electron",
   "metadata": {},
   "source": [
    "#### 生成0.5°格网"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "outside-folder",
   "metadata": {},
   "outputs": [],
   "source": [
    "#grid_pth='Shp_new/grid0_5.shp'\n",
    "grid_pth=pth+'/shp/grid0_5.shp'\n",
    "df_grid=gpd.read_file(grid_pth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "varied-palace",
   "metadata": {},
   "outputs": [],
   "source": [
    "res=0.5 ##分辨率\n",
    "mean_all=[];err_all=[];fg_all=[];yr_all=[];dh_err=[]\n",
    "for peri in df_grid['gla_ratio'].values:\n",
    "    if peri!=0:##格网中有冰川\n",
    "        ddi=df_grid[df_grid['gla_ratio']==peri]\n",
    "        clat=ddi.lat.values[0]\n",
    "        clon=ddi.lon.values[0]\n",
    "        ###裁剪格网###################################\n",
    "        dh_ddi=dh_all_prj.sel(y=slice(clat+res/2,clat-res/2),x=slice(clon-res/2,clon+res/2))\n",
    "        dh_ei=dh_err_prj.sel(y=slice(clat+res/2,clat-res/2),x=slice(clon-res/2,clon+res/2))\n",
    "        dh_yi=dh_year_prj.sel(y=slice(clat+res/2,clat-res/2),x=slice(clon-res/2,clon+res/2))\n",
    "        ###统计数据 计算降尺度误差\n",
    "        dh_ddi.name='tt'\n",
    "        tst=(dh_ddi.to_dataframe()['tt']).dropna()\n",
    "        count=tst.count()\n",
    "        if count==0: ##格网中有冰川  但是没有dh\n",
    "            mean_st=np.nan\n",
    "            err=np.nan\n",
    "            yr=np.nan\n",
    "            dherr=np.nan\n",
    "            gfg=-1\n",
    "        else:\n",
    "            mean_st=tst.mean()   ##0.5\n",
    "            std_st=nmad(tst)\n",
    "            err=(std_st/count**0.5)  ## Wu et.al TC 2018 \n",
    "            gfg=1\n",
    "            ############################\n",
    "            dherr=dh_ei.mean().values\n",
    "            try:\n",
    "                yr=int(dh_yi.where(dh_yi>1).mean().values)\n",
    "            except:\n",
    "                yr=1974\n",
    "        #break\n",
    "    else: ###格网中无冰川\n",
    "        mean_st=np.nan\n",
    "        err=np.nan\n",
    "        yr=np.nan\n",
    "        dherr=np.nan\n",
    "        gfg=0\n",
    "    mean_all.append(mean_st)\n",
    "    err_all.append(err)\n",
    "    fg_all.append(gfg)\n",
    "    yr_all.append(yr)\n",
    "    dh_err.append(dherr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "972c58fe-2012-4233-bbc3-84fbd8c6b780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-9.23290626692578, 0.10499256450557659, 3.5515574202304934)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vvv=np.array(mean_all)\n",
    "np.nanmean(vvv),np.nanmedian(err_all),np.nanmean(dh_err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "impossible-street",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grid['dh']=np.array(mean_all)\n",
    "df_grid['res_error']=err_all\n",
    "df_grid['gla_flag']=fg_all\n",
    "df_grid['year']=yr_all\n",
    "df_grid['dh_error']=np.array(dh_err).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "serial-original",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_new=df_grid.drop(['gla_area','grid_area','geometry'],axis=1)\n",
    "grid_new.columns=['gla_ratio','y','x','dh','res_error','gla_flag','dxt_year','dh_error']\n",
    "ds_grid=grid_new.set_index(['y','x']).to_xarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "065af07f-021d-4198-8f58-e877606d5866",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid['mb']=ds_grid['dh']/(2000-ds_grid['dxt_year'])*0.85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1f24f096-fbd4-4492-b949-c53f8cc6fa76",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Atanu Bhattacharya el,al nature communication 2021\n",
    "t=2000-ds_grid['dxt_year']\n",
    "pi=0.85;pw=1;dp=0.06\n",
    "mb_err=(((ds_grid['dh']/t)*(dp/pw))**2+((ds_grid['dh_error']/t)*(pi/pw))**2)**0.5\n",
    "ds_grid['mb_error']=(mb_err**2+(ds_grid['res_error']-0.1)**2)**0.5\n",
    "#ds_grid['mb_error'].values[ds_grid['mb_error'].values>1]=0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b553fde0-43d4-4290-97bd-dfc36e475275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(-0.29077758), array(0.13221596))"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_grid['mb'].mean().values,ds_grid['mb_error'].median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "343ef648-bd57-4d3e-900c-3e423e6cbac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x26e00f31f10>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_grid['mb_error'].plot(vmax=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "19186131-328f-4855-93dc-5ff1815d0d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid['gla_flag'].attrs={'value_mean':'0->no glacier, 1->glacier&dh, -1->no dh'}\n",
    "\n",
    "ds_grid['gla_ratio'].attrs={'long_name':'Proportion of glacier area to grid area',\n",
    "                     'units':'%'}\n",
    "ds_grid['dh'].attrs={'long_name':'Elevation difference from srtm-dem and topo-dem',\n",
    "                     'units':'m'}\n",
    "ds_grid['res_error'].attrs={'long_name':'errors in resampling',\n",
    "                     'units':'m'}\n",
    "ds_grid['dxt_year'].attrs={'long_name':'Year of aerial photograph'}\n",
    "ds_grid['dh_error'].attrs={'long_name':'Elevation difference errors','units':'m'}\n",
    "ds_grid['mb_error'].attrs={'long_name':'Mass balance errors','units':'m'}\n",
    "ds_grid['mb'].attrs={'long_name':'Mass balance','units':'m'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e7df05b-ede9-47bf-b7e1-c6c3cd55110d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fd395d13-0ee9-4b0c-bd6f-6ef808019658",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid=ds_grid.sortby('y',ascending=False)\n",
    "#ds_grid.attrs['transform']=(0.1, 0.0, 75, 0.0, -0.1, 40)\n",
    "#####################################\n",
    "ds_grid.attrs={'crs':'+proj=longlat +datum=WGS84 +no_defs',\n",
    "               'transform':(0.5, 0.0, 75,0.0, -0.5, 40),\n",
    "               'res': (0.5,0.5),\n",
    "               'nodatavals': 'nan','coords_format':'Grid center',\n",
    "               'created date':'2024-02','institute':'Yunnan University',\n",
    "              'authors':'Zhu Yu; Liu Shiyin*','email':'shiyin.liu@ynu.edu.cn; yuzhu@mail.ynu.edu.cn'}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "durable-lover",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<xarray.DataArray ()> Size: 8B\n",
       " array(-0.29077758),\n",
       " <xarray.DataArray 'dh' ()> Size: 8B\n",
       " array(-9.23290627))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ds_grid['dh']/(2000-ds_grid['dxt_year'])*0.85).mean(),ds_grid['dh'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "forbidden-relief",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid1=ds_grid.rio.write_crs('epsg:4326')\n",
    "ds_grid0=ds_grid1.rio.set_spatial_dims(x_dim='x',y_dim='y')\n",
    "ds_grid0.to_netcdf(pth+'/dh_swr_0.5deg_1970s-2000_v3.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "occasional-visibility",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid01=ds_grid0.where(ds_grid0['dh']<0)\n",
    "\n",
    "ds_grid01=ds_grid01.rio.write_crs('epsg:4326')\n",
    "ds_grid01.to_netcdf(pth+'/dh_swr_0.5deg_1970s-2000_v3_le0.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "worse-charleston",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x26e01045c70>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_grid0['mb'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "cdb165f2-ad04-4176-8310-506aa8844434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x23f0947c640>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_grid0['gla_flag'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f83233e7-2ea1-48b3-8d4c-9cd451350bc6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py39",
   "language": "python",
   "name": "py39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}