fmda/OLD/explore_data.ipynb

   1 {
   2  "cells": [
   3   {
   4    "cell_type": "code",
   5    "execution_count": null,
   6    "id": "6d28f8ee-2537-4dbb-872f-863cb8fb0381",
   7    "metadata": {
   8     "tags": []
   9    },
  10    "outputs": [],
  11    "source": [
  12     "import reproducibility\n",
  13     "import pickle, os, math\n",
  14     "import numpy as np\n",
  15     "import matplotlib.pyplot as plt\n",
  16     "from data_funcs import raws_data, synthetic_data, plot_data, check_data, rmse_data, plot_one\n",
  17     "%matplotlib inline"
  18    ]
  19   },
  20   {
  21    "cell_type": "code",
  22    "execution_count": null,
  23    "id": "68d5eee1-63f9-4ef9-a2f4-c1549d6909a0",
  24    "metadata": {
  25     "tags": []
  26    },
  27    "outputs": [],
  28    "source": [
  29     "filename = 'data/testing_dict_CO_202206.pickle'\n",
  30     "with open(filename, 'rb') as handle:\n",
  31     "    test_dict = pickle.load(handle)"
  32    ]
  33   },
  34   {
  35    "cell_type": "code",
  36    "execution_count": null,
  37    "id": "65d182ba-d9af-4ed3-ad50-b3f22e430929",
  38    "metadata": {
  39     "tags": []
  40    },
  41    "outputs": [],
  42    "source": [
  43     "for case in test_dict:\n",
  44     "    test_dict[case]['case'] = case\n",
  45     "    test_dict[case]['filename'] = filename"
  46    ]
  47   },
  48   {
  49    "cell_type": "code",
  50    "execution_count": null,
  51    "id": "2ca2b65e-37e3-4f4f-8f1a-d7b7a6a1bc23",
  52    "metadata": {
  53     "tags": []
  54    },
  55    "outputs": [],
  56    "source": [
  57     "def plt_rain_fmd(rain,fmd):\n",
  58     "    plt.figure(figsize=(16,4))\n",
  59     "    plt.scatter(rain,fmd)\n",
  60     "    plt.xlabel('Rain (mm/h)')\n",
  61     "    plt.ylabel('FMC change (%/h)')\n",
  62     "    plt.show()\n",
  63     "    "
  64    ]
  65   },
  66   {
  67    "cell_type": "code",
  68    "execution_count": null,
  69    "id": "89e2983e-be46-4dc6-89f7-a7c41bdecb22",
  70    "metadata": {
  71     "tags": []
  72    },
  73    "outputs": [],
  74    "source": [
  75     "def rain_to_fmd(dat):\n",
  76     "    rain = dat['rain']\n",
  77     "    fm = dat['fm'] \n",
  78     "    hmin=1\n",
  79     "    hmax=len(rain)\n",
  80     "    dat['fmg']=np.zeros(hmax)\n",
  81     "    dat['fmr']=np.zeros(hmax)\n",
  82     "    rain_out=[]\n",
  83     "    fmd_out=[]\n",
  84     "    for i in range(hmin,hmax):\n",
  85     "        if rain[i]>0.0:\n",
  86     "            fmd = fm[i] - fm[i-1]\n",
  87     "            dat['fmg'][i] = fmd.copy()           # fm gradient in past hour\n",
  88     "            dat['fmr'][i] = fmd/rain[i] # increase in fm per mm of rain\n",
  89     "            rain_out.append(rain[i])\n",
  90     "            fmd_out.append(fmd)\n",
  91     "    plt.figure(figsize=(16,4))\n",
  92     "    plot_one(hmin,hmax,dat,'fmg',linestyle='-',c='r',label='FM change in 1h')\n",
  93     "    plot_one(hmin,hmax,dat,'rain',linestyle='--',c='r',label='rain (mm/h)')\n",
  94     "    plot_one(hmin,hmax,dat,'fmr',linestyle='-',c='k',label='FM change/mm rain')\n",
  95     "    plt.title(case + '-' + dat['title'])\n",
  96     "    plt.xlabel('Time (hours)')\n",
  97     "    plt.ylabel('FMC (%) change/rain mm')\n",
  98     "    plt.legend()\n",
  99     "    plt.show()\n",
 100     "    # print('rain',rain_out)\n",
 101     "    # print('fmd',fmd_out)\n",
 102     "    print(\"rain\",\" \".join(f\"{num:.2f}\" for num in rain_out))\n",
 103     "    print(\"fmd \",\" \".join(f\"{num:.2f}\" for num in fmd_out))\n",
 104     " \n",
 105     "    return rain_out,fmd_out\n"
 106    ]
 107   },
 108   {
 109    "cell_type": "code",
 110    "execution_count": null,
 111    "id": "f23e84a7-31a6-4640-9a5f-c4a5f77eb581",
 112    "metadata": {
 113     "tags": []
 114    },
 115    "outputs": [],
 116    "source": [
 117     "rain_all=[]\n",
 118     "fmd_all=[]\n",
 119     "for case in test_dict:\n",
 120     "    case_data = test_dict[case]\n",
 121     "    if 'Synth' not in case_data['title']: \n",
 122     "        print('\\n***** ',case,'*****\\n')\n",
 123     "        plot_data(case_data)\n",
 124     "        plt.show()\n",
 125     "        rain_out,fmd_out = rain_to_fmd(case_data)\n",
 126     "        plt_rain_fmd(rain_out,fmd_out)\n",
 127     "        rain_all.extend(rain_out)\n",
 128     "        fmd_all.extend(fmd_out)\n",
 129     "        check_data(case_data)\n",
 130     "plt_rain_fmd(rain_all,fmd_all)\n"
 131    ]
 132   },
 133   {
 134    "cell_type": "code",
 135    "execution_count": null,
 136    "id": "5499085a-eb3e-4ec5-95df-2e0021fde003",
 137    "metadata": {
 138     "tags": []
 139    },
 140    "outputs": [],
 141    "source": [
 142     "plot_data(test_dict['case'])"
 143    ]
 144   },
 145   {
 146    "cell_type": "code",
 147    "execution_count": null,
 148    "id": "89f72f45-abf4-49d8-9623-843684c3cf33",
 149    "metadata": {
 150     "tags": []
 151    },
 152    "outputs": [],
 153    "source": [
 154     "test_dict.keys()"
 155    ]
 156   },
 157   {
 158    "cell_type": "code",
 159    "execution_count": null,
 160    "id": "87ddb08f-902e-4e2f-8d97-ec0aa66f1a6e",
 161    "metadata": {},
 162    "outputs": [],
 163    "source": []
 164   }
 165  ],
 166  "metadata": {
 167   "kernelspec": {
 168    "display_name": "Python 3 (ipykernel)",
 169    "language": "python",
 170    "name": "python3"
 171   },
 172   "language_info": {
 173    "codemirror_mode": {
 174     "name": "ipython",
 175     "version": 3
 176    },
 177    "file_extension": ".py",
 178    "mimetype": "text/x-python",
 179    "name": "python",
 180    "nbconvert_exporter": "python",
 181    "pygments_lexer": "ipython3",
 182    "version": "3.9.12"
 183   }
 184  },
 185  "nbformat": 4,
 186  "nbformat_minor": 5
 187 }