Hackathon1.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hackathon Stat\n",
    "\n",
    "This project illustrates the course LEPL1109 with an industrial application of statistics. You will analyse the capacity of solar production of electricity located in the French cities of Caen and Tours.\n",
    "The file 'radiation.csv' contains 3 columns \n",
    "DATE           : YYYYMMDD,\n",
    "Caen and Tours : the daily solar radiation in W/m2 measured in the 2 cities. \n",
    "Notice that data for some days are not reported due to failure of measurement system.\n",
    "\n",
    "## Report content\n",
    "\n",
    "•\tYou have to fill in this  jupyter notebook downloadable on the moodle website of the course\n",
    "\n",
    "•\tGrades are granted to the members whose names are in the Jupyter notebook. If your name doesn’t appear on the top of the notebook, you’ll get a 0, even though you are in a group on Moodle.\n",
    "\n",
    "•\tThe jupyter notebook must be compiled with printed results and next submitted via moodle. The absence of compiled results (or non-printed values) leads to a lower grade.\n",
    "\n",
    "## Report submission\n",
    "\n",
    "•\tThe deadline for submission is reported on the moodle website. Submission after the deadline will not be accepted.\n",
    "\n",
    "•\tTo submit your report, go to the section “APP” on Moodle and the subsection “Soumission du rapport”. You can upload your work there. Once you are sure that it is your final version, click the button “Envoyer le devoir”. It is important that you don’t forget to click on this button ! \n",
    "\n",
    "•\tReports that have not been uploaded through Moodle will not be corrected.\n",
    "\n",
    "## Names and Noma of participants:\n",
    "\n",
    "Part. 1: Xavier Sanchez-Rivas (16171800)\n",
    "\n",
    "Part. 2: Hugo Wouters\n",
    "\n",
    "Part. 3: Loys Collard-Bovy\n",
    "\n",
    "Part. 4: Sébastien Navez\n",
    "\n",
    "---\n",
    "## 1. Energy calculation and basic statistics\n",
    "\n",
    "Compute the daily energy in WH per square meter of solar panel. For this purpose you use the datasets reporting the solar irradation measure in Caen and Tours (source https://www.ecad.eu/). The irradiation is measured in W/m2 per day. You will use the formula:\n",
    "\n",
    "C = E_Sol x 24 x P_cr x f_perf\n",
    "\n",
    "where  \n",
    "\n",
    "C is the electricity produced in WH/m2 for a day\n",
    "\n",
    "E_sol is the daily solar radiation in W/m2 \n",
    "\n",
    "P_cr is the peak power coefficient, set here to  0.18 (monocristal silicium)\n",
    "\n",
    "f_perf depends upon the system, set here to 0.75.\n",
    "\n",
    "Remark:\n",
    "\n",
    "1 W = 1 J/sec\n",
    "\n",
    "1 WH  is 1W x 3600sec = 3600J\n",
    "\n",
    "energy/m2 = E_sol * 24 * 3600 J/m2 = E_sol * 24 WH/m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "1.1. Start by computing the daily energy in WH produced by a 1m2 solar panel\n",
    "\n",
    "a. Plot time-series of solar electric production in Caen and Tours from 1974 to 2023. Comment the evolution.\n",
    "\n",
    "b. Plot boxplots of daily productions for both cities. Comment the box plot.\n",
    "\n",
    "c. Remove outliers using the interquartile range. \n",
    "\n",
    "d. Plot an histogram of daily electricity production, after removal of outliers.\n",
    "\n",
    "Watchout: remove all days for which a outlier is observed in Caen **or** Tours to keep the same number of observations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code here\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the data from 'radiation.csv' into a DataFrame\n",
    "data = pd.read_csv(\"Radiation.csv\")\n",
    "\n",
    "# Calculate daily energy production for each city\n",
    "P_cr = 0.18  # Peak power coefficient\n",
    "f_perf = 0.75  # Performance factor\n",
    "data['Energy_Caen'] = data['Caen'] * 24 * P_cr * f_perf\n",
    "data['Energy_Tours'] = data['Tours'] * 24 * P_cr * f_perf\n",
    "\n",
    "# Plot time-series of solar electric production from 1974 to 2023\n",
    "data['DATE'] = pd.to_datetime(data['DATE'], format='%Y%m%d')\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(data['DATE'], data['Energy_Caen'], label='Caen', color='blue')\n",
    "plt.plot(data['DATE'], data['Energy_Tours'], label='Tours', color='green')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Daily Energy Production (WH/m²)')\n",
    "plt.title('Solar Electric Production in Caen and Tours (1974-2023)')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# Create boxplots of daily production for both cities\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.boxplot([data['Energy_Caen'], data['Energy_Tours']], labels=['Caen', 'Tours'])\n",
    "plt.ylabel('Daily Energy Production (WH/m²)')\n",
    "plt.title('Boxplots of Daily Energy Production in Caen and Tours')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "# Remove outliers using the interquartile range (IQR)\n",
    "Q1_Caen = data['Energy_Caen'].quantile(0.25)\n",
    "Q3_Caen = data['Energy_Caen'].quantile(0.75)\n",
    "IQR_Caen = Q3_Caen - Q1_Caen\n",
    "lower_bound_Caen = Q1_Caen - 1.5 * IQR_Caen\n",
    "upper_bound_Caen = Q3_Caen + 1.5 * IQR_Caen\n",
    "\n",
    "Q1_Tours = data['Energy_Tours'].quantile(0.25)\n",
    "Q3_Tours = data['Energy_Tours'].quantile(0.75)\n",
    "IQR_Tours = Q3_Tours - Q1_Tours\n",
    "lower_bound_Tours = Q1_Tours - 1.5 * IQR_Tours\n",
    "upper_bound_Tours = Q3_Tours + 1.5 * IQR_Tours\n",
    "\n",
    "data_no_outliers = data[(data['Energy_Caen'] >= lower_bound_Caen) & (data['Energy_Caen'] <= upper_bound_Caen)\n",
    "                        & (data['Energy_Tours'] >= lower_bound_Tours) & (data['Energy_Tours'] <= upper_bound_Tours)]\n",
    "\n",
    "# Plot a histogram of daily electricity production after removing outliers\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(data_no_outliers['Energy_Caen'], bins=50, alpha=0.5, label='Caen', color='blue')\n",
    "plt.hist(data_no_outliers['Energy_Tours'], bins=50, alpha=0.5, label='Tours', color='green')\n",
    "plt.xlabel('Daily Energy Production (WH/m²)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Daily Energy Production (After Outlier Removal)')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "1.2. We want to compute monthly statistics of electricity solar production. Calculate for each city and for each month: \n",
    "\n",
    "1) the average daily production of electricity in Wh/m2\n",
    "\n",
    "2) the median daily production of electricity in Wh/m2\n",
    "\n",
    "3) the standard deviation daily production of electricity in Wh/m2\n",
    "\n",
    "4) the 5% percentile of daily production of electricity in Wh/m2\n",
    "\n",
    "5) the 95% percentile of daily production of electricity in Wh/m2\n",
    "\n",
    "Report the results in one or two tables. \n",
    "\n",
    "Compare and comment these statistics!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for Caen:\n",
      "Year  Month\n",
      "1974  1        {'Average': 129.28645161290322, 'Median': 126....\n",
      "      2        {'Average': 246.70285714285714, 'Median': 236....\n",
      "      3        {'Average': 388.8, 'Median': 447.12, 'Std Dev'...\n",
      "      4        {'Average': 615.9240000000002, 'Median': 581.5...\n",
      "      5        {'Average': 745.8270967741935, 'Median': 751.6...\n",
      "                                     ...                        \n",
      "2022  11       {'Average': 169.26206896551722, 'Median': 165....\n",
      "      12       {'Average': 94.48258064516129, 'Median': 93.96...\n",
      "2023  1        {'Average': 125.00129032258064, 'Median': 116....\n",
      "      2        {'Average': 262.6714285714285, 'Median': 246.2...\n",
      "      3        {'Average': 360.0580645161291, 'Median': 382.3...\n",
      "Name: Energy_Caen, Length: 589, dtype: object\n",
      "\n",
      "Statistics for Tours:\n",
      "Year  Month\n",
      "1974  1        {'Average': 132.6309677419355, 'Median': 110.1...\n",
      "      2        {'Average': 245.54571428571424, 'Median': 244....\n",
      "      3        {'Average': 367.48421052631585, 'Median': 437....\n",
      "      4        {'Average': 609.444, 'Median': 644.76, 'Std De...\n",
      "      5        {'Average': 748.2309677419356, 'Median': 771.1...\n",
      "                                     ...                        \n",
      "2022  11       {'Average': 176.97103448275863, 'Median': 181....\n",
      "      12       {'Average': 96.05032258064514, 'Median': 84.24...\n",
      "2023  1        {'Average': 121.02967741935484, 'Median': 113....\n",
      "      2        {'Average': 274.7057142857143, 'Median': 273.7...\n",
      "      3        {'Average': 396.6387096774193, 'Median': 417.9...\n",
      "Name: Energy_Tours, Length: 589, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#code here\n",
    "import pandas as pd\n",
    "\n",
    "# Load the data from 'radiation.csv' into a DataFrame\n",
    "data = pd.read_csv('Radiation.csv')\n",
    "\n",
    "# Calculate daily energy production for each city\n",
    "P_cr = 0.18  # Peak power coefficient\n",
    "f_perf = 0.75  # Performance factor\n",
    "data['Energy_Caen'] = data['Caen'] * 24 * P_cr * f_perf\n",
    "data['Energy_Tours'] = data['Tours'] * 24 * P_cr * f_perf\n",
    "\n",
    "# Convert the 'DATE' column to a datetime format\n",
    "data['DATE'] = pd.to_datetime(data['DATE'], format='%Y%m%d')\n",
    "\n",
    "# Group the data by city and month\n",
    "data['Year'] = data['DATE'].dt.year\n",
    "data['Month'] = data['DATE'].dt.month\n",
    "grouped_data = data.groupby(['Year', 'Month'])\n",
    "\n",
    "# Define a function to calculate the requested statistics\n",
    "def calculate_statistics(series):\n",
    "    return {\n",
    "        'Average': series.mean(),\n",
    "        'Median': series.median(),\n",
    "        'Std Dev': series.std(),\n",
    "        '5th Percentile': series.quantile(0.05),\n",
    "        '95th Percentile': series.quantile(0.95)\n",
    "    }\n",
    "\n",
    "# Calculate the statistics for each city and month\n",
    "statistics_Caen = grouped_data['Energy_Caen'].agg(calculate_statistics)\n",
    "statistics_Tours = grouped_data['Energy_Tours'].agg(calculate_statistics)\n",
    "\n",
    "# Report the results in tables\n",
    "print(\"Statistics for Caen:\")\n",
    "print(statistics_Caen)\n",
    "\n",
    "print(\"\\nStatistics for Tours:\")\n",
    "print(statistics_Tours)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 2. Fit of distributions and hypothesis tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "2.1. We focus on the daily production of electricity in April. Retrieve the data for month of April, in Caen and Tours. \n",
    "\n",
    " 1) Fit Gamma and normal distributions by log-likelihood maximization to \n",
    "    daily production of electricity during April (Caen & Tours).\n",
    "    \n",
    " 2) Compute the 4 log-likelihoods and select the best model for each location (justify your answer).\n",
    " \n",
    " 3) Compare on the same plot the empirical, the  gamma and normal pdf (the\n",
    "    empirical pdf is an histogram of frequencies).\n",
    "    \n",
    " 4) Why is there 3 parameters in python for the Gamma pdf whereas there\n",
    "    is only 2 in the distribution seen during lectures? \n",
    "\n",
    "Remark : set floc to -0.001 for the gamma.fit (to avoid troubles in case of null observations)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xavier/.local/lib/python3.10/site-packages/scipy/optimize/_numdiff.py:576: RuntimeWarning: invalid value encountered in subtract\n",
      "  df = fun(x) - f0\n",
      "/home/xavier/.local/lib/python3.10/site-packages/scipy/optimize/_numdiff.py:576: RuntimeWarning: invalid value encountered in subtract\n",
      "  df = fun(x) - f0\n",
      "/home/xavier/.local/lib/python3.10/site-packages/scipy/optimize/_numdiff.py:576: RuntimeWarning: invalid value encountered in subtract\n",
      "  df = fun(x) - f0\n",
      "/home/xavier/.local/lib/python3.10/site-packages/scipy/optimize/_numdiff.py:576: RuntimeWarning: invalid value encountered in subtract\n",
      "  df = fun(x) - f0\n",
      "/home/xavier/.local/lib/python3.10/site-packages/scipy/optimize/_numdiff.py:576: RuntimeWarning: invalid value encountered in subtract\n",
      "  df = fun(x) - f0\n",
      "/home/xavier/.local/lib/python3.10/site-packages/scipy/optimize/_numdiff.py:576: RuntimeWarning: invalid value encountered in subtract\n",
      "  df = fun(x) - f0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code here\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm, gamma\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "# Load the data from 'radiation.csv' into a DataFrame\n",
    "data = pd.read_csv('Radiation.csv')\n",
    "\n",
    "# Convert the 'DATE' column to a datetime format\n",
    "data['DATE'] = pd.to_datetime(data['DATE'], format='%Y%m%d')\n",
    "\n",
    "# Extract data for April in Caen and Tours\n",
    "data_april_Caen = data[(data['DATE'].dt.month == 4) & (data['Caen'].notna())]\n",
    "data_april_Tours = data[(data['DATE'].dt.month == 4) & (data['Tours'].notna())]\n",
    "\n",
    "# Define the log-likelihood functions for the gamma and normal distributions\n",
    "def log_likelihood_gamma(params, data):\n",
    "    shape, loc, scale = params\n",
    "    return -np.sum(gamma.logpdf(data, a=shape, loc=loc, scale=scale))\n",
    "\n",
    "def log_likelihood_normal(params, data):\n",
    "    mean, std = params\n",
    "    return -np.sum(norm.logpdf(data, loc=mean, scale=std))\n",
    "\n",
    "# Fit gamma and normal distributions to the data and compute log-likelihoods\n",
    "initial_guess = [2, 0, 1]  # Initial guess for gamma parameters (shape, loc, scale)\n",
    "params_Caen_gamma = minimize(log_likelihood_gamma, initial_guess, args=(data_april_Caen['Caen']), method='BFGS').x\n",
    "params_Caen_normal = minimize(log_likelihood_normal, [0, 1], args=(data_april_Caen['Caen'])).x\n",
    "\n",
    "params_Tours_gamma = minimize(log_likelihood_gamma, initial_guess, args=(data_april_Tours['Tours']), method='BFGS').x\n",
    "params_Tours_normal = minimize(log_likelihood_normal, [0, 1], args=(data_april_Tours['Tours'])).x\n",
    "\n",
    "# Calculate log-likelihoods\n",
    "log_likelihood_Caen_gamma = -log_likelihood_gamma(params_Caen_gamma, data_april_Caen['Caen'])\n",
    "log_likelihood_Caen_normal = -log_likelihood_normal(params_Caen_normal, data_april_Caen['Caen'])\n",
    "log_likelihood_Tours_gamma = -log_likelihood_gamma(params_Tours_gamma, data_april_Tours['Tours'])\n",
    "log_likelihood_Tours_normal = -log_likelihood_normal(params_Tours_normal, data_april_Tours['Tours'])\n",
    "\n",
    "# Compare the log-likelihoods and select the best model for each location\n",
    "best_model_Caen = \"Gamma\" if log_likelihood_Caen_gamma > log_likelihood_Caen_normal else \"Normal\"\n",
    "best_model_Tours = \"Gamma\" if log_likelihood_Tours_gamma > log_likelihood_Tours_normal else \"Normal\"\n",
    "\n",
    "# Create histograms for empirical PDF\n",
    "hist_Caen, bins_Caen = np.histogram(data_april_Caen['Caen'], bins=50, density=True)\n",
    "hist_Tours, bins_Tours = np.histogram(data_april_Tours['Tours'], bins=50, density=True)\n",
    "\n",
    "# Plot the empirical, gamma, and normal PDFs\n",
    "x = np.linspace(0, max(data_april_Caen['Caen'].max(), data_april_Tours['Tours'].max()), 1000)\n",
    "pdf_Caen_gamma = gamma.pdf(x, a=params_Caen_gamma[0], loc=params_Caen_gamma[1], scale=params_Caen_gamma[2])\n",
    "pdf_Caen_normal = norm.pdf(x, loc=params_Caen_normal[0], scale=params_Caen_normal[1])\n",
    "\n",
    "pdf_Tours_gamma = gamma.pdf(x, a=params_Tours_gamma[0], loc=params_Tours_gamma[1], scale=params_Tours_gamma[2])\n",
    "pdf_Tours_normal = norm.pdf(x, loc=params_Tours_normal[0], scale=params_Tours_normal[1])\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(data_april_Caen['Caen'], bins=50, density=True, alpha=0.6, label='Empirical PDF (Caen)')\n",
    "plt.plot(x, pdf_Caen_gamma, label='Gamma PDF (Caen)', color='red')\n",
    "plt.plot(x, pdf_Caen_normal, label='Normal PDF (Caen)', color='blue')\n",
    "plt.title(f'PDF Comparison (April - Caen) - Best Model: {best_model_Caen}')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(data_april_Tours['Tours'], bins=50, density=True, alpha=0.6, label='Empirical PDF (Tours)')\n",
    "plt.plot(x, pdf_Tours_gamma, label='Gamma PDF (Tours)', color='red')\n",
    "plt.plot(x, pdf_Tours_normal, label='Normal PDF (Tours)', color='blue')\n",
    "plt.title(f'PDF Comparison (April - Tours) - Best Model: {best_model_Tours}')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "2.2. Check if the average daily production in April is the same in Caen and Tours. Let us recall that the null hypothesis is\n",
    "\n",
    "$H_0$: $\\mu_{Caen} = \\mu_{Tours}$.\n",
    "\n",
    "Take care to comment your conclusions. Are all assumptions required to perform this test sastisfied?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk normality test - Caen: Statistic = 0.5974380970001221, p-value = 0.0\n",
      "Shapiro-Wilk normality test - Tours: Statistic = 0.6084246635437012, p-value = 0.0\n",
      "Levene's test for equality of variances: Statistic = 1.2299059477522136, p-value = 0.26751609949002597\n",
      "Two-sample t-test: t-statistic = -2.349267142295982, p-value = 0.018876284109182444\n",
      "Assumptions are not satisfied, and the t-test may not be valid.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats import ttest_ind, shapiro, levene\n",
    "\n",
    "# Load the data from 'Radiation.csv' into a DataFrame\n",
    "data = pd.read_csv('Radiation.csv')\n",
    "\n",
    "# Convert the 'DATE' column to a datetime format\n",
    "data['DATE'] = pd.to_datetime(data['DATE'], format='%Y%m%d')\n",
    "\n",
    "# Extract the data for April in Caen and Tours\n",
    "data_april_Caen = data[(data['DATE'].dt.month == 4) & (data['Caen'].notna())]['Caen']\n",
    "data_april_Tours = data[(data['DATE'].dt.month == 4) & (data['Tours'].notna())]['Tours']\n",
    "\n",
    "# Perform the Shapiro-Wilk normality test for both groups\n",
    "shapiro_Caen = shapiro(data_april_Caen)\n",
    "shapiro_Tours = shapiro(data_april_Tours)\n",
    "\n",
    "# Perform Levene's test for equality of variances\n",
    "levene_test = levene(data_april_Caen, data_april_Tours)\n",
    "\n",
    "# Perform a two-sample t-test for equality of means\n",
    "t_stat, p_value = ttest_ind(data_april_Caen, data_april_Tours)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Shapiro-Wilk normality test - Caen: Statistic = {shapiro_Caen[0]}, p-value = {shapiro_Caen[1]}\")\n",
    "print(f\"Shapiro-Wilk normality test - Tours: Statistic = {shapiro_Tours[0]}, p-value = {shapiro_Tours[1]}\")\n",
    "print(f\"Levene's test for equality of variances: Statistic = {levene_test.statistic}, p-value = {levene_test.pvalue}\")\n",
    "print(f\"Two-sample t-test: t-statistic = {t_stat}, p-value = {p_value}\")\n",
    "\n",
    "# Check the assumptions and perform the t-test\n",
    "if shapiro_Caen[1] > 0.05 and shapiro_Tours[1] > 0.05 and levene_test.pvalue > 0.05:\n",
    "    print(\"Assumptions are satisfied.\")\n",
    "    if p_value > 0.05:\n",
    "        print(\"There is no significant difference in the means (fail to reject H0).\")\n",
    "    else:\n",
    "        print(\"There is a significant difference in the means (reject H0).\")\n",
    "else:\n",
    "    print(\"Assumptions are not satisfied, and the t-test may not be valid.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "2.3. Test the equality of variance of daily production in April at Caen & Tours?\n",
    "$H_0$: $\\sigma_{Caen}=\\sigma_{Tours}$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Levene's test for equality of variances: Statistic = 1.2299059477522136, p-value = 0.26751609949002597\n",
      "There is no significant difference in variances (fail to reject H0).\n"
     ]
    }
   ],
   "source": [
    "#code here\n",
    "from scipy.stats import levene\n",
    "\n",
    "# Extract the data for April in Caen and Tours\n",
    "data_april_Caen = data[(data['DATE'].dt.month == 4) & (data['Caen'].notna())]['Caen']\n",
    "data_april_Tours = data[(data['DATE'].dt.month == 4) & (data['Tours'].notna())]['Tours']\n",
    "\n",
    "# Perform Levene's test for equality of variances\n",
    "levene_test = levene(data_april_Caen, data_april_Tours)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Levene's test for equality of variances: Statistic = {levene_test.statistic}, p-value = {levene_test.pvalue}\")\n",
    "\n",
    "# Check the p-value to make a decision about the null hypothesis\n",
    "if levene_test.pvalue > 0.05:\n",
    "    print(\"There is no significant difference in variances (fail to reject H0).\")\n",
    "else:\n",
    "    print(\"There is a significant difference in variances (reject H0).\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "2.4. Explain the Wilcoxon's test. What is the main advantage of this test compared to the Student's T test. Why is this useful in our project? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "The Wilcoxon test is a non-parametric statistical test used to compare the distribution of paired data or two related groups. The main advantage of the Wilcoxon's test compared to the Student's t-test is that it does not assume that the data comes from a normal distribution, making it more robust when dealing with non-normally distributed data.\n",
    "\n",
    "Wilcoxon's test is advantageous when we need to compare two related groups and cannot assume that your data follows a normal distribution. This robustness and flexibility make it a valuable tool for this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "2.5. Apply the Wilcoxon test to distributions of daily productions in April, at Caen and Tours.  What can you conclude about the means of daily production in these 2 cities?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wilcoxon signed-rank test: Statistic = 380928.0, p-value = 6.382836063402585e-21\n",
      "There is a significant difference in the means (reject H0).\n"
     ]
    }
   ],
   "source": [
    "#code here\n",
    "from scipy.stats import wilcoxon\n",
    "\n",
    "# Extract the data for April in Caen and Tours\n",
    "data_april_Caen = data[(data['DATE'].dt.month == 4) & (data['Caen'].notna())]['Caen']\n",
    "data_april_Tours = data[(data['DATE'].dt.month == 4) & (data['Tours'].notna())]['Tours']\n",
    "\n",
    "# Perform the Wilcoxon signed-rank test\n",
    "wilcoxon_stat, p_value = wilcoxon(data_april_Caen, data_april_Tours, zero_method='wilcox')\n",
    "\n",
    "# Print the results\n",
    "print(f\"Wilcoxon signed-rank test: Statistic = {wilcoxon_stat}, p-value = {p_value}\")\n",
    "\n",
    "# Interpret the results\n",
    "if p_value > 0.05:\n",
    "    print(\"There is no significant difference in the means (fail to reject H0).\")\n",
    "else:\n",
    "    print(\"There is a significant difference in the means (reject H0).\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 3. Regression and forecasting "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "3.1. Do we observe any trend in the yearly solar production of electricity over the considered period?\n",
    "To answer this question: \n",
    "\n",
    "a. You will compute the average daily production (Wh/m2) during April from 1977 up to 2019 (included).\n",
    "\n",
    "b. You get a time-series of 44 values for each city. Regress these values on the explanatory\n",
    "variables X=(Year-1977). Don't forget to add a constant term and analyze results. \n",
    "\n",
    "c. Plot on the same graph, the predicted and the observed values.\n",
    "\n",
    "d. Comment your results! \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression Results for Caen:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.041\n",
      "Model:                            OLS   Adj. R-squared:                  0.017\n",
      "Method:                 Least Squares   F-statistic:                     1.744\n",
      "Date:                Sat, 21 Oct 2023   Prob (F-statistic):              0.194\n",
      "Time:                        23:48:33   Log-Likelihood:                -241.35\n",
      "No. Observations:                  43   AIC:                             486.7\n",
      "Df Residuals:                      41   BIC:                             490.2\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        176.0846     20.344      8.655      0.000     134.999     217.170\n",
      "x1            -1.1015      0.834     -1.321      0.194      -2.786       0.583\n",
      "==============================================================================\n",
      "Omnibus:                       10.948   Durbin-Watson:                   1.486\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):                2.762\n",
      "Skew:                          -0.019   Prob(JB):                        0.251\n",
      "Kurtosis:                       1.759   Cond. No.                         48.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "Regression Results for Tours:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                 -0.024\n",
      "Method:                 Least Squares   F-statistic:                   0.01354\n",
      "Date:                Sat, 21 Oct 2023   Prob (F-statistic):              0.908\n",
      "Time:                        23:48:33   Log-Likelihood:                -242.68\n",
      "No. Observations:                  43   AIC:                             489.4\n",
      "Df Residuals:                      41   BIC:                             492.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        169.7770     20.986      8.090      0.000     127.396     212.158\n",
      "x1            -0.1001      0.860     -0.116      0.908      -1.838       1.637\n",
      "==============================================================================\n",
      "Omnibus:                        3.392   Durbin-Watson:                   1.248\n",
      "Prob(Omnibus):                  0.183   Jarque-Bera (JB):                2.086\n",
      "Skew:                          -0.314   Prob(JB):                        0.352\n",
      "Kurtosis:                       2.123   Cond. No.                         48.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the data from 'radiation.csv' into a DataFrame\n",
    "data = pd.read_csv('Radiation.csv')\n",
    "\n",
    "# Convert the 'DATE' column to a datetime data type\n",
    "data['DATE'] = pd.to_datetime(data['DATE'], format='%Y%m%d')\n",
    "\n",
    "# Extract data for April from 1977 to 2019 for each city\n",
    "start_year = 1977\n",
    "end_year = 2019\n",
    "data_april_Caen = data[(data['DATE'].dt.month == 4) & (data['DATE'].dt.year >= start_year) & (data['DATE'].dt.year <= end_year)]['Caen']\n",
    "data_april_Tours = data[(data['DATE'].dt.month == 4) & (data['DATE'].dt.year >= start_year) & (data['DATE'].dt.year <= end_year)]['Tours']\n",
    "\n",
    "# Create a DataFrame with the data for regression\n",
    "years = np.arange(start_year, end_year + 1)\n",
    "X = sm.add_constant(years - start_year)\n",
    "\n",
    "# Ensure that the lengths of y_Caen and y_Tours match the length of years\n",
    "y_Caen = data_april_Caen.values[:len(years)]\n",
    "y_Tours = data_april_Tours.values[:len(years)]\n",
    "\n",
    "# Perform linear regression\n",
    "model_Caen = sm.OLS(y_Caen, X).fit()\n",
    "model_Tours = sm.OLS(y_Tours, X).fit()\n",
    "\n",
    "# Predict the values based on the regression models\n",
    "predicted_Caen = model_Caen.predict(X)\n",
    "predicted_Tours = model_Tours.predict(X)\n",
    "\n",
    "# Plot the observed and predicted values for both cities\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(years, y_Caen, label='Observed (Caen)', marker='o')\n",
    "plt.plot(years, predicted_Caen, label='Predicted (Caen)', linestyle='--')\n",
    "plt.plot(years, y_Tours, label='Observed (Tours)', marker='o')\n",
    "plt.plot(years, predicted_Tours, label='Predicted (Tours)', linestyle='--')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Daily Production (Wh/m2)')\n",
    "plt.title('Observed vs. Predicted Solar Production (April)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Summary of regression results\n",
    "print(\"Regression Results for Caen:\")\n",
    "print(model_Caen.summary())\n",
    "\n",
    "print(\"\\nRegression Results for Tours:\")\n",
    "print(model_Tours.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "3.2. You want to design a model to forecast the solar electric production for the next day (location Caen only). You will work with data over the period 1977 to 2019. \n",
    "\n",
    "Let us denote by C(t) the production on day 't'. The model that we want to fit is called autoregressive and is defined as follows:\n",
    "\n",
    "$$C(t) = \\sum_{k=1}^{10} a_k C(t-k) $$\n",
    "\n",
    "This model is common in time-series analysis and predicts the production of the next day with the  recent observations.\n",
    "\n",
    "a. Split the dataset into a training set (1977 to 2010 included) and a validation set (2011 to 2019 included).\n",
    "\n",
    "b.\tEstimate this model with statsmodels on the training set. \n",
    "\n",
    "c.\tHow would you judge the quality of the predictive model? (Analyze statistics reported by statsmodel)\n",
    "\n",
    "d.\tCompute the Mean Absolute Error (MAE) between predicted and real consumptions (on the training set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Int64Index' object has no attribute 'year'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 18\u001b[0m\n\u001b[1;32m     15\u001b[0m data_caen \u001b[38;5;241m=\u001b[39m data[(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATE\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mdt\u001b[38;5;241m.\u001b[39myear \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m start_year) \u001b[38;5;241m&\u001b[39m (data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDATE\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mdt\u001b[38;5;241m.\u001b[39myear \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m end_year)][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCaen\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m# Split the dataset into a training set (1977 to 2010) and a validation set (2011 to 2019)\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m train_data \u001b[38;5;241m=\u001b[39m data_caen[\u001b[43mdata_caen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myear\u001b[49m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2010\u001b[39m]\n\u001b[1;32m     19\u001b[0m valid_data \u001b[38;5;241m=\u001b[39m data_caen[data_caen\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39myear \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2011\u001b[39m]\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# Create lag features for the autoregressive model\u001b[39;00m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Int64Index' object has no attribute 'year'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# Load the data from 'radiation.csv' into a DataFrame\n",
    "data = pd.read_csv('Radiation.csv')\n",
    "\n",
    "# Convert the 'DATE' column to a datetime data type\n",
    "data['DATE'] = pd.to_datetime(data['DATE'], format='%Y%m%d')\n",
    "\n",
    "# Extract data for Caen from 1977 to 2019\n",
    "start_year = 1977\n",
    "end_year = 2019\n",
    "data_caen = data[(data['DATE'].dt.year >= start_year) & (data['DATE'].dt.year <= end_year)]['Caen']\n",
    "\n",
    "# Split the dataset into a training set (1977 to 2010) and a validation set (2011 to 2019)\n",
    "train_data = data_caen[data_caen.index.year <= 2010]\n",
    "valid_data = data_caen[data_caen.index.year >= 2011]\n",
    "\n",
    "# Create lag features for the autoregressive model\n",
    "train_data_lagged = train_data.shift(1).dropna()\n",
    "\n",
    "# Extract the year from the datetime index for train_data\n",
    "train_data_years = train_data.index.year\n",
    "\n",
    "# Split the training data into predictors (lagged values) and target\n",
    "X_train = train_data_lagged.values\n",
    "y_train = train_data[1:].values\n",
    "\n",
    "# Add a constant term for the intercept\n",
    "X_train = sm.add_constant(X_train)\n",
    "\n",
    "# Fit the autoregressive model on the training set\n",
    "model = sm.OLS(y_train, X_train).fit()\n",
    "\n",
    "# Predict the values on the training set\n",
    "y_pred = model.predict(X_train)\n",
    "\n",
    "# Compute the Mean Absolute Error (MAE) between predicted and real consumptions\n",
    "mae = mean_absolute_error(y_train, y_pred)\n",
    "\n",
    "# Summary of regression results\n",
    "print(\"Autoregressive Model Results:\")\n",
    "print(model.summary())\n",
    "\n",
    "print(f\"\\nMean Absolute Error (MAE) on Training Set: {mae:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "3.3. Use this model on the test set to forecast the electric daily production.\n",
    "\n",
    "a. Compare on a graph, the forecast to  real consumptions on the given period. \n",
    "\n",
    "b. Plot the errors of prediction. Are they acceptable?\n",
    "\n",
    "c. Compute the MAE on the test set and the $R^2$. Is the forecast reliable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'DATE'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'DATE'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 29\u001b[0m\n\u001b[1;32m     26\u001b[0m valid_data \u001b[38;5;241m=\u001b[39m valid_data\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m# Extract the year from the datetime index for valid_data\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m valid_data_years \u001b[38;5;241m=\u001b[39m \u001b[43mvalid_data\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDATE\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mdt\u001b[38;5;241m.\u001b[39myear\n\u001b[1;32m     31\u001b[0m \u001b[38;5;66;03m# Split the training data into predictors (lagged values) and target\u001b[39;00m\n\u001b[1;32m     32\u001b[0m X_train \u001b[38;5;241m=\u001b[39m train_data_lagged\u001b[38;5;241m.\u001b[39mvalues\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3808\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3809\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3803\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3805\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3806\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3808\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3809\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'DATE'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import mean_absolute_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the data from 'radiation.csv' into a DataFrame\n",
    "data = pd.read_csv('Radiation.csv')\n",
    "\n",
    "# Convert the 'DATE' column to a datetime data type\n",
    "data['DATE'] = pd.to_datetime(data['DATE'], format='%Y%m%d')\n",
    "\n",
    "# Extract data for Caen from 1977 to 2019\n",
    "start_year = 1977\n",
    "end_year = 2019\n",
    "data_caen = data[(data['DATE'].dt.year >= start_year) & (data['DATE'].dt.year <= end_year)]['Caen']\n",
    "\n",
    "# Split the dataset into a training set (1977 to 2010) and a validation set (2011 to 2019)\n",
    "train_data = data_caen[data_caen <= 2010]\n",
    "valid_data = data_caen[data_caen >= 2011]\n",
    "\n",
    "# Create lag features for the autoregressive model on the training set\n",
    "train_data_lagged = train_data.shift(1).dropna()\n",
    "\n",
    "# Reset the index for the validation set to extract the year\n",
    "valid_data = valid_data.reset_index()\n",
    "\n",
    "# Extract the year from the datetime index for valid_data\n",
    "valid_data_years = valid_data['DATE'].dt.year\n",
    "\n",
    "# Split the training data into predictors (lagged values) and target\n",
    "X_train = train_data_lagged.values\n",
    "y_train = train_data[1:].values\n",
    "\n",
    "# Add a constant term for the intercept\n",
    "X_train = sm.add_constant(X_train)\n",
    "\n",
    "# Fit the autoregressive model on the training set\n",
    "model = sm.OLS(y_train, X_train).fit()\n",
    "\n",
    "# Use the model to forecast values on the validation set\n",
    "valid_data_lagged = valid_data['Caen'].shift(1).dropna()\n",
    "X_valid = valid_data_lagged.values\n",
    "X_valid = sm.add_constant(X_valid)\n",
    "y_valid = valid_data['Caen'][1:].values\n",
    "y_pred = model.predict(X_valid)\n",
    "\n",
    "# Compute the Mean Absolute Error (MAE) on the validation set\n",
    "mae = mean_absolute_error(y_valid, y_pred)\n",
    "\n",
    "# Compute the R-squared (R²) on the validation set\n",
    "r_squared = r2_score(y_valid, y_pred)\n",
    "\n",
    "# Plot the forecasted vs. real values on the given period\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(valid_data['DATE'], y_valid, label='Real Consumption', marker='o')\n",
    "plt.plot(valid_data['DATE'], y_pred, label='Forecasted Consumption', linestyle='--')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Electric Daily Production (Caen)')\n",
    "plt.title('Forecast vs. Real Consumption')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Plot the errors of prediction (residuals)\n",
    "residuals = y_valid - y_pred\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(valid_data['DATE'], residuals, label='Residuals', marker='o')\n",
    "plt.axhline(0, color='red', linestyle='--', label='Zero Residual Line')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Errors of Prediction (Residuals)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Print the MAE and R-squared on the validation set\n",
    "print(f\"Mean Absolute Error (MAE) on Validation Set: {mae:.2f}\")\n",
    "print(f\"R-squared (R²) on Validation Set: {r_squared:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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