Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80f8560c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "\n",
    "\n",
    "path = os.getcwd()\n",
    "\n",
    "#run it on machine\n",
    "observed_path = os.path.join(path, \"C:\\\\Users\\\\1989s\\\\source\\\\repos\\\\SVS\\\\modelCsharp\\\\TestModel\\\\Observed\\\\SoilMineralN.xlsx\")\n",
    "\n",
    "\n",
    "observed_data = pd.read_excel(observed_path,index_col=[0,1])\n",
    "\n",
    "observed_data.sort_index(axis=0,inplace=True)\n",
    "\n",
    "\n",
    "tests = []\n",
    "test_name = []\n",
    "for file in os.listdir(\"C:\\\\Users\\\\1989s\\\\source\\\\repos\\\\SVS\\\\modelCsharp\\\\ConsoleAppSVS-test\\\\bin\\\\Debug\\\\net6.0\\\\OutputFiles\"):\n",
    "    \n",
    "    if file.endswith('.csv'):\n",
    "        tests.append(file)       \n",
    "        test_name.append(os.path.splitext(file)[0])\n",
    "\n",
    "Alltests =[]\n",
    "for t in tests[:]:  \n",
    "   \n",
    "    testframe = pd.read_csv(\"C:\\\\Users\\\\1989s\\\\source\\\\repos\\\\SVS\\\\modelCsharp\\\\ConsoleAppSVS-test\\\\bin\\\\Debug\\\\net6.0\\\\OutputFiles\\\\\"+t,index_col=0,dayfirst=True)  \n",
    "    \n",
    "    Alltests.append(testframe)   \n",
    "\n",
    "AllData = pd.concat(Alltests,axis=1,keys=test_name)\n",
    "\n",
    "observed_test = observed_data.columns.get_level_values(0).drop_duplicates()\n",
    "AllData.sort_index(axis=0,inplace=True)\n",
    "\n",
    "AllData.index = pd.to_datetime(AllData.index)\n",
    "\n",
    "tests = AllData.columns.get_level_values(0).drop_duplicates()\n",
    "colors = pd.Series(['r','b','g'])\n",
    "\n",
    "#should we change it ?\n",
    "start = dt.datetime.date(AllData['8Wheat'].dropna().index.min())\n",
    "end = dt.datetime.date(AllData['8Wheat'].dropna().index.max())\n",
    "\n",
    "def makeplot(Data,color):\n",
    "    plt.plot(Data,color=color)\n",
    "    \n",
    "def make_observed(observed):\n",
    "    plt.plot(observed.index,observed.loc[:,'0-30cm'],'*',color='g')     \n",
    "        \n",
    "Graph = plt.figure(figsize=(10,10))\n",
    "pos = 1\n",
    "row_num=len(tests)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0621596f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# AllData.SoilMineralN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d92fc8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# AllData.loc[:,(t,'SoilMineralN')].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ca1e0e45",
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in tests:\n",
    "    start = dt.datetime.date(AllData[t].dropna().index.min())\n",
    "    end = dt.datetime.date(AllData[t].dropna().index.max())\n",
    "    datefilter = []\n",
    "    observed_data_currentTest = observed_data.loc[int(t[0]),:]\n",
    "    for d in observed_data_currentTest.index:\n",
    "        ret = False\n",
    "        if ((d >= pd.Timestamp(start)) and (d<=pd.Timestamp(end))):\n",
    "            ret = True\n",
    "        datefilter.append(ret)\n",
    "        \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d8324dac",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'Timestamp' object cannot be interpreted as an integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[24], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m dt\u001b[38;5;241m.\u001b[39mdatetime(observed_data_currentTest\u001b[38;5;241m.\u001b[39mindex[\u001b[38;5;241m0\u001b[39m])\n",
      "\u001b[1;31mTypeError\u001b[0m: 'Timestamp' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "dt.datetime(observed_data_currentTest.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ff88db9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-08-28', '2020-10-22', '2020-11-20', '2020-12-18',\n",
       "               '2021-01-21', '2021-02-19', '2021-03-15', '2021-04-12',\n",
       "               '2021-05-13', '2021-06-16', '2021-07-12', '2021-08-12',\n",
       "               '2021-09-15', '2021-10-20', '2021-11-17', '2021-12-20',\n",
       "               '2022-01-26', '2022-03-03', '2022-04-05', '2022-05-12',\n",
       "               '2022-06-08', '2022-08-25', '2022-09-29', '2022-11-03',\n",
       "               '2022-12-05', '2023-01-25', '2023-03-02', '2023-04-04',\n",
       "               '2023-05-17', '2023-06-27', '2023-08-08'],\n",
       "              dtype='datetime64[ns]', name='Date', freq=None)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "observed_data_currentTest.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b2b95dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    color = 'b'\n",
    "    Graph.add_subplot(row_num,2,pos)\n",
    "    Data = AllData.loc[:,(t,'SoilMineralN')].sort_index()\n",
    "    plt.xticks(rotation = 45)    \n",
    "    plt.title(t)\n",
    "    makeplot(Data,color)\n",
    "#     make_observed(observed_data[datefilter])\n",
    "#     Graph.tight_layout(pad=1.5)\n",
    "    pos+=1\n",
    "    \n",
    "    Graph.add_subplot(row_num,2,pos)\n",
    "#     plt.xticks(rotation = 45)  \n",
    "#     plt.title(\"CropN\")\n",
    "#     Data = AllData.loc[:,(t,'CropN')].sort_index()\n",
    "#     makeplot(Data,color)\n",
    "#     make_observed(observed_data[datefilter])\n",
    "    pos+=1\n",
    "\n",
    "# plt.savefig('testplot.png')\n",
    "\n",
    "# doc = aw.Document()\n",
    "# builder = aw.DocumentBuilder(doc)\n",
    "# builder.insert_image(\"testplot.png\")\n",
    "# doc.save(\"index.html\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# shutil.rmtree(path+\"\\\\OutputFiles\")\n",
    "# shutil.rmtree(path+\"\\\\NitrogenApplied\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4775c9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87ffe50a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}