rainfall_runoff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80d8be04",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#load data\n",
    "import os\n",
    "root_path=r'C:\\F\\all_basin\\camel_monthly\\02'\n",
    "files=os.listdir(root_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "884f9478",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['01013500monthly.csv',\n",
       " '01022500monthly.csv',\n",
       " '01030500monthly.csv',\n",
       " '01031500monthly.csv',\n",
       " '01047000monthly.csv',\n",
       " '01052500monthly.csv',\n",
       " '01054200monthly.csv',\n",
       " '01055000monthly.csv',\n",
       " '01057000monthly.csv',\n",
       " '01073000monthly.csv',\n",
       " '01078000monthly.csv',\n",
       " '01118300monthly.csv',\n",
       " '01121000monthly.csv',\n",
       " '01123000monthly.csv',\n",
       " '01134500monthly.csv',\n",
       " '01137500monthly.csv',\n",
       " '01139000monthly.csv',\n",
       " '01139800monthly.csv',\n",
       " '01142500monthly.csv',\n",
       " '01144000monthly.csv',\n",
       " '01162500monthly.csv',\n",
       " '01169000monthly.csv',\n",
       " '01170100monthly.csv',\n",
       " '01181000monthly.csv',\n",
       " '01187300monthly.csv',\n",
       " '01333000monthly.csv',\n",
       " '01350000monthly.csv',\n",
       " '01350140monthly.csv',\n",
       " '01365000monthly.csv',\n",
       " '01411300monthly.csv',\n",
       " '01413500monthly.csv',\n",
       " '01414500monthly.csv',\n",
       " '01415000monthly.csv',\n",
       " '01423000monthly.csv',\n",
       " '01435000monthly.csv',\n",
       " '01439500monthly.csv',\n",
       " '01440000monthly.csv',\n",
       " '01440400monthly.csv',\n",
       " '01451800monthly.csv',\n",
       " '01466500monthly.csv',\n",
       " '01484100monthly.csv',\n",
       " '01485500monthly.csv',\n",
       " '01486000monthly.csv',\n",
       " '01487000monthly.csv',\n",
       " '01491000monthly.csv',\n",
       " '01510000monthly.csv',\n",
       " '01516500monthly.csv',\n",
       " '01532000monthly.csv',\n",
       " '01539000monthly.csv',\n",
       " '01542810monthly.csv',\n",
       " '01543000monthly.csv',\n",
       " '01543500monthly.csv',\n",
       " '01544500monthly.csv',\n",
       " '01545600monthly.csv',\n",
       " '01547700monthly.csv',\n",
       " '01548500monthly.csv',\n",
       " '01549500monthly.csv',\n",
       " '01550000monthly.csv',\n",
       " '01552000monthly.csv',\n",
       " '01552500monthly.csv',\n",
       " '01557500monthly.csv',\n",
       " '01567500monthly.csv',\n",
       " '01568000monthly.csv',\n",
       " '01580000monthly.csv',\n",
       " '01583500monthly.csv',\n",
       " '01591400monthly.csv',\n",
       " '01596500monthly.csv',\n",
       " '01605500monthly.csv',\n",
       " '01606500monthly.csv',\n",
       " '01613050monthly.csv',\n",
       " '01620500monthly.csv',\n",
       " '01632000monthly.csv',\n",
       " '01632900monthly.csv',\n",
       " '01634500monthly.csv',\n",
       " '01638480monthly.csv',\n",
       " '01639500monthly.csv',\n",
       " '01644000monthly.csv',\n",
       " '01658500monthly.csv',\n",
       " '01664000monthly.csv',\n",
       " '01666500monthly.csv',\n",
       " '01667500monthly.csv',\n",
       " '01669000monthly.csv',\n",
       " '01669520monthly.csv',\n",
       " '02011400monthly.csv',\n",
       " '02011460monthly.csv',\n",
       " '02013000monthly.csv',\n",
       " '02014000monthly.csv',\n",
       " '02015700monthly.csv',\n",
       " '02016000monthly.csv',\n",
       " '02017500monthly.csv',\n",
       " '02018000monthly.csv',\n",
       " '02027000monthly.csv',\n",
       " '02027500monthly.csv',\n",
       " '02028500monthly.csv',\n",
       " '02038850monthly.csv',\n",
       " '04296000monthly.csv']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "588b64a3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>PRCP</th>\n",
       "      <th>PET</th>\n",
       "      <th>OBS_RUN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-10-01</th>\n",
       "      <td>134.47</td>\n",
       "      <td>29.4733</td>\n",
       "      <td>55.0656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-11-01</th>\n",
       "      <td>153.95</td>\n",
       "      <td>11.1180</td>\n",
       "      <td>76.9592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-01</th>\n",
       "      <td>79.54</td>\n",
       "      <td>3.2931</td>\n",
       "      <td>82.5545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>64.15</td>\n",
       "      <td>5.3490</td>\n",
       "      <td>28.9961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-01</th>\n",
       "      <td>88.29</td>\n",
       "      <td>17.7414</td>\n",
       "      <td>107.9626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-01</th>\n",
       "      <td>57.99</td>\n",
       "      <td>117.6077</td>\n",
       "      <td>17.3437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-01</th>\n",
       "      <td>46.04</td>\n",
       "      <td>72.0792</td>\n",
       "      <td>7.4688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>207.19</td>\n",
       "      <td>33.0019</td>\n",
       "      <td>48.4921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>148.48</td>\n",
       "      <td>10.6652</td>\n",
       "      <td>89.7280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01</th>\n",
       "      <td>197.77</td>\n",
       "      <td>6.3539</td>\n",
       "      <td>212.8358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>411 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              PRCP       PET   OBS_RUN\n",
       "1980-10-01  134.47   29.4733   55.0656\n",
       "1980-11-01  153.95   11.1180   76.9592\n",
       "1980-12-01   79.54    3.2931   82.5545\n",
       "1981-01-01   64.15    5.3490   28.9961\n",
       "1981-02-01   88.29   17.7414  107.9626\n",
       "...            ...       ...       ...\n",
       "2014-08-01   57.99  117.6077   17.3437\n",
       "2014-09-01   46.04   72.0792    7.4688\n",
       "2014-10-01  207.19   33.0019   48.4921\n",
       "2014-11-01  148.48   10.6652   89.7280\n",
       "2014-12-01  197.77    6.3539  212.8358\n",
       "\n",
       "[411 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_csv(os.path.join(root_path,files[1]),index_col=0)\n",
    "data.index=pd.to_datetime(data.index)\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ab098ea2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1980-10-01', '1982-06-01', '1984-02-01', '1985-10-01',\n",
       "               '1987-06-01', '1989-02-01', '1990-10-01', '1992-06-01',\n",
       "               '1994-02-01', '1995-10-01', '1997-06-01', '1999-02-01',\n",
       "               '2000-10-01', '2002-06-01', '2004-02-01', '2005-10-01',\n",
       "               '2007-06-01', '2009-02-01', '2010-10-01', '2012-06-01',\n",
       "               '2014-02-01'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precipitation.index[0:-1:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e72f13eb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-10-01    134.47\n",
       "1982-06-01    159.88\n",
       "1984-02-01    171.25\n",
       "1985-10-01     76.00\n",
       "1987-06-01    104.47\n",
       "1989-02-01     86.29\n",
       "1990-10-01    168.36\n",
       "1992-06-01    132.69\n",
       "1994-02-01     26.28\n",
       "1995-10-01    141.53\n",
       "1997-06-01     75.44\n",
       "1999-02-01    118.96\n",
       "2000-10-01    116.85\n",
       "2002-06-01     82.59\n",
       "2004-02-01     35.83\n",
       "2005-10-01    340.47\n",
       "2007-06-01     61.54\n",
       "2009-02-01     78.46\n",
       "2010-10-01    175.52\n",
       "2012-06-01    161.59\n",
       "2014-02-01    103.44\n",
       "Name: PRCP, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precipitation[0:-1:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0f3208b7",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 197.7045)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.dates as mdate\n",
    "precipitation=data['PRCP']\n",
    "fig, ax= plt.subplots( figsize=(15, 5))\n",
    "#ax.plot(precipitation.index,precipitation, color='r', linestyle='-',\n",
    "                 #label='observed', linewidth=1.5)\n",
    "ax.bar(precipitation.index[1:20],precipitation[1:20], color='r', width = 15,\n",
    "                 label='observed', #linewidth=1.5\n",
    "      )\n",
    "ax.set_xlim([precipitation.index[0], precipitation.index[20]])\n",
    "#ax.set_xticks为设置x刻度轴稀疏程度\n",
    "#ax.set_xticklabels设置x的标签\n",
    "ax.set_xticks(precipitation.index[0:-1:40], #labels=precipitation.index[0:-1:20]\n",
    "             )\n",
    "ax.set_xticklabels(precipitation.index[0:-1:40],rotation=45,fontsize='small')\n",
    "ax.xaxis.set_major_formatter(mdate.DateFormatter('%m/%Y'))\n",
    "ax.set_ylim(ymin=0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "267c9794",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1980-10-01     55.0656\n",
       "1980-11-01     76.9592\n",
       "1980-12-01     82.5545\n",
       "1981-01-01     28.9961\n",
       "1981-02-01    107.9626\n",
       "                ...   \n",
       "2014-08-01     17.3437\n",
       "2014-09-01      7.4688\n",
       "2014-10-01     48.4921\n",
       "2014-11-01     89.7280\n",
       "2014-12-01    212.8358\n",
       "Name: OBS_RUN, Length: 411, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discharge=data['OBS_RUN']\n",
    "discharge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "719d65d1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdate\n",
    "\n",
    "precipitation=data['PRCP']\n",
    "discharge=data['OBS_RUN']\n",
    "fig, ax= plt.subplots( figsize=(15, 5))\n",
    "#ax.plot(precipitation.index,precipitation, color='r', linestyle='-',\n",
    "                 #label='observed', linewidth=1.5)\n",
    "ax.bar(precipitation.index,precipitation, color= \"royalblue\", width = 14,\n",
    "                 label='observed', #linewidth=1.5\n",
    "      )\n",
    "ax.set_xlim([precipitation.index[0], precipitation.index[20]])\n",
    "#ax.set_xticks为设置x刻度轴稀疏程度\n",
    "#ax.set_xticklabels设置x的标签\n",
    "ax.set_xticks(precipitation.index[0:-1:30], #labels=precipitation.index[0:-1:20]\n",
    "             )\n",
    "ax.set_xticklabels(precipitation.index[0:-1:30],rotation=45,fontsize='small')\n",
    "ax.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m'))\n",
    "ax.set_ylim(ymin=0,)\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb559a36",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d9972426",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(18, 7))\n",
    "fig.tight_layout()\n",
    "gs=plt.GridSpec(10,3)\n",
    "#TEXT OF CALIBRATION RESULTS这一部分用来绘制表头\n",
    "ax0=plt.subplot(gs[0,:])\n",
    "texts='basin: basin_number & basin_information'\n",
    "ax0.text(0.5, 2.0, texts, verticalalignment='top', horizontalalignment='center', transform=ax0.transAxes,fontsize=10)\n",
    "plt.axis('off')\n",
    "#Figure of the precipitation time series\n",
    "ax=plt.subplot(gs[0:4,:])\n",
    "ax.bar(precipitation.index,precipitation, color= \"royalblue\", width = 14,\n",
    "                 label='observed', #linewidth=1.5\n",
    "      )\n",
    "ax.set_xlim([precipitation.index[0], precipitation.index[20]])\n",
    "#ax.set_xticks为设置x刻度轴稀疏程度\n",
    "#ax.set_xticklabels设置x的标签\n",
    "ax.set_xticks(precipitation.index[0:-1:30], #labels=precipitation.index[0:-1:20]\n",
    "             )\n",
    "ax.set_xticklabels(precipitation.index[0:-1:30],rotation=20,fontsize='small')\n",
    "ax.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m'))\n",
    "ax.set_ylim(ymin=0,)\n",
    "ax.invert_yaxis()\n",
    "ax.set_title('(a) precipitation time series for calibration period', )\n",
    "\n",
    "# Figure of the calibrate period river flow time series\n",
    "ax1 = plt.subplot(gs[5:10, :])\n",
    "ax1.plot(discharge.index, discharge.values, color='r', linestyle='-', label='observed',\n",
    "         linewidth=0.5)\n",
    "# ax1.plot(calibrate_simulated_flow.index, calibrate_simulated_flow.values, color='b', linestyle='--', label='simulated',\n",
    "#          linewidth=2)\n",
    "# show date\n",
    "ax1.set_xlim([discharge.index[0], discharge.index[-1]])\n",
    "ax1.set_xticks(precipitation.index[0:-1:30], #labels=precipitation.index[0:-1:20]\n",
    "             )\n",
    "ax1.set_xticklabels(precipitation.index[0:-1:30],rotation=20,fontsize='small')\n",
    "ax1.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m'))  \n",
    "ax1.set_ylim(ymin=0)\n",
    "ax1.set_title('(b) Streamflow time series for calibration period', )\n",
    "leg = ax1.legend(['Observed', 'Simulated'], fancybox=True, framealpha=0.8, prop={'size': 10}, labelspacing=0.1, loc=1)\n",
    "leg.get_frame().set_edgecolor('white')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "23312597",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "\n",
    "def format_axes(fig):\n",
    "    for i, ax in enumerate(fig.axes):\n",
    "        ax.text(0.5, 0.5, \"ax%d\" % (i+1), va=\"center\", ha=\"center\")\n",
    "        ax.tick_params(labelbottom=False, labelleft=False)\n",
    "\n",
    "fig = plt.figure(constrained_layout=True)\n",
    "\n",
    "gs = GridSpec(5, 3, figure=fig)\n",
    "ax1 = fig.add_subplot(gs[0:2, :])\n",
    "# identical to ax1 = plt.subplot(gs.new_subplotspec((0, 0), colspan=3))\n",
    "ax2 = fig.add_subplot(gs[2:5, :])\n",
    "# ax3 = fig.add_subplot(gs[1:,])\n",
    "# ax4 = fig.add_subplot(gs[-1, 0])\n",
    "#ax5 = fig.add_subplot(gs[-1, -2])\n",
    "\n",
    "fig.suptitle(\"GridSpec\")\n",
    "format_axes(fig)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75641cae",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basin: basin_number & basin_nse:3\n"
     ]
    }
   ],
   "source": [
    "nse=3\n",
    "texts = 'basin: basin_number & basin_nse:{}'.format(nse)\n",
    "print(texts)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}