Correlation_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3d2cdcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# some_file.py\n",
    "import sys\n",
    "# insert at 1, 0 is the script path (or '' in REPL)\n",
    "sys.path.append(r'C:\\Users\\mmazzolini\\OneDrive - Scientific Network South Tyrol\\Documents\\conda\\runoff_seasonal_forecast-master')\n",
    "\n",
    "from sf_runoff import feature_sel, loyo_cv_lc, create_it_matrix, daily_climatology\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import gaussian_kde\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.compose import TransformedTargetRegressor\n",
    "from sklearn.model_selection import GridSearchCV,TimeSeriesSplit\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from numpy.random import rand\n",
    "from numpy.random import seed\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "import pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3f55984",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'C:\\Users\\mmazzolini\\OneDrive - Scientific Network South Tyrol\\Documents\\conda\\daily_input\\old\\\\'\n",
    "# ## Feature selection\n",
    "daily_input = pd.read_csv(path+'CANDOGLIA_TOCE_2000_2019.csv', index_col=0, parse_dates=True)\n",
    "\n",
    "daily_clim = daily_climatology(daily_input)\n",
    "it_matrix = create_it_matrix(daily_input, 12)\n",
    "\n",
    "coef, p = spearmanr(it_matrix,axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd11b9c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Ex1y0', 'Ex2y0', 'Ex0y1', 'Ex1y1', 'Ex2y1', 'Ex3y1', 'Ex4y1', 'Ex5y1',\n",
       "       'Ex6y1', 'Ex7y1', 'Ex8y1', 'Ex0y2', 'Ex1y2', 'Ex2y2', 'Ex3y2', 'Ex4y2',\n",
       "       'Ex5y2', 'Ex6y2', 'Ex7y2', 'Ex8y2', 'Ex2y3', 'Ex3y3', 'Ex4y3', 'Ex5y3',\n",
       "       'Ex6y3', 'Ex7y3', 'Ex8y3', 'Ex2y4', 'Ex3y4', 'Ex4y4', 'Ex5y4', 'Ex6y4',\n",
       "       'Ex7y4', 'Ex8y4', 'Ex2y5', 'Ex3y5', 'Ex4y5', 'Ex5y5', 'Ex6y5', 'Ex7y5',\n",
       "       'Ex8y5', 'Ex2y6', 'Ex3y6', 'Ex4y6', 'Ex5y6', 'Ex6y6', 'Ex7y6', 'Ex8y6',\n",
       "       'Ex2y7', 'Ex3y7', 'Ex4y7', 'Ex5y7', 'Ex6y7', 'Ex7y7', 'Ex8y7', 'Ex9y7',\n",
       "       'Ex3y8', 'Ex4y8', 'Ex5y8', 'Ex6y8', 'Ex7y8', 'Ex8y8', 'Ex5y9', 'Ex6y9',\n",
       "       'Ex7y9', 'Ex8y9', 'Ex9y9', 'Ex6y10', 'Ex7y10', 'Ex8y10', 'Ex9y10',\n",
       "       'Ex6y11', 'Ex7y11', 'Ex8y11', 'Ex9y11'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefficients.columns[150:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a577d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff=np.reshape(coef[0,1:],(int(np.size(coef[0,1:])/12),12))\n",
    "\n",
    "a=[]\n",
    "for i in it_matrix.columns[1:]:\n",
    "    a.append((i.partition('_')[0]))\n",
    "b, indexes = np.unique(a, return_index=True)\n",
    "\n",
    "cols=[a[index] for index in sorted(indexes)]\n",
    "\n",
    "coefficients = pd.DataFrame(data=coeff.transpose(), columns=cols,index=range(-11,1))\n",
    "temp_c = coefficients[coefficients.columns[:75]]\n",
    "prec_c = coefficients[coefficients.columns[75:150]]\n",
    "evap_c = coefficients[coefficients.columns[150:]]\n",
    "pal = sns.color_palette(\"vlag\", as_cmap=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f12f7242",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20, 5))\n",
    "ax= sns.heatmap((temp_c),cmap='vlag')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d196612b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20, 5))\n",
    "ax= sns.barplot(data = pd.DataFrame(temp_c.transpose()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "95d96390",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20, 5))\n",
    "ax= sns.heatmap((prec_c),cmap='vlag')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd8ca8af",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20, 5))\n",
    "ax= sns.barplot(data = pd.DataFrame(prec_c.transpose()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84368c16",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20, 5))\n",
    "ax= sns.heatmap((evap_c),cmap='vlag')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1fc68d00",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20, 5))\n",
    "ax= sns.barplot(data = pd.DataFrame(evap_c.transpose()), palette='rocket')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "484bcc32",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### CONCLUSIONS:\n",
    "As one could expect, the closer months (up to -2) are closely related to the current variable. As you travel back in the past, the correlation decreases reaching the maximum at -6 months.\n",
    "The correlation increases again and increases with variables related to the following months on the previous year. (-11 and -10).\n",
    "Correlations in the past (both positive and negative) are lower for precipitatin, most likely due to the higher variability and smaller seasonality of this variable with respect to temperature and evapotranspiration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e06e1c0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb146d55",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}