From 067871784a843c74647e453fabd081902d1a44a3 Mon Sep 17 00:00:00 2001 From: f82c1c4a1227cdba8ff3317d228324d6 Date: Thu, 4 Apr 2024 21:10:43 +0000 Subject: [PATCH] resultb --- module3/exo3/module3_exo3_exercice.ipynb | 72 ++++++++++++++++++++---- 1 file changed, 61 insertions(+), 11 deletions(-) diff --git a/module3/exo3/module3_exo3_exercice.ipynb b/module3/exo3/module3_exo3_exercice.ipynb index 512a7bb..e5b8a41 100644 --- a/module3/exo3/module3_exo3_exercice.ipynb +++ b/module3/exo3/module3_exo3_exercice.ipynb @@ -249,6 +249,7 @@ } ], "source": [ + "# Contribution lente\n", "# Calcula el promedio anual de concentraciones de CO2\n", "annual_mean_co2 = data.groupby('Year')['Concentration'].mean()\n", "\n", @@ -261,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -278,7 +279,7 @@ } ], "source": [ - "# Grafica el promedio anual de concentraciones de CO2\n", + "# # Contribution lente. Grafica el promedio anual de concentraciones de CO2\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(annual_mean_co2.index, annual_mean_co2.values, marker='o', linestyle='-')\n", "plt.title('Promedio anual de concentraciones de CO2')\n", @@ -391,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -401,7 +402,6 @@ "Regression coefficient: 0.03169965286205089\n", "Intercept: 305.8667902052942\n", "R-squared (R²): 0.9813151125069396\n", - "It is considered a linear model, or line, that verifies the linear function Y=M.X + H with M: Regression coefficient, H: Intercept, and R-squared (R²): proportion of the total variability of the variable dependent that is explained by the model\n", "Predicted CO2 concentration in 2025: 416.3083807766795 ppm\n" ] } @@ -427,7 +427,6 @@ "print(\"Regression coefficient:\", coeficiente)\n", "print(\"Intercept:\", intercepto)\n", "print(\"R-squared (R²):\", r_cuadrado)\n", - "print(\"It is considered a linear model, or line, that verifies the linear function Y=M.X + H with M: Regression coefficient, H: Intercept, and R-squared (R²): proportion of the total variability of the variable dependent that is explained by the model\")\n", "\n", "# Prédire la concentration de CO2 en 2025\n", "weeks_in_2025 = (2025 - data['Date'].dt.year.min()) * 52\n", @@ -436,6 +435,54 @@ "print(\"Predicted CO2 concentration in 2025:\", predicted_CO2_2025[0][0], \"ppm\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here above, it is considered a linear model, or line, that verifies the linear function Y=M.X + H with M: Regression coefficient, H: Intercept, and R-squared (R²): proportion of the total variability of the variable dependent that is explained by the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pour amelliorer le document un plot est plus utile que les paramètres de la régression." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Visualizar la evolución temporal de la concentración de CO2\n", + "plt.figure(figsize=(12, 8)) # Aumentar el tamaño de la figura\n", + "plt.plot(data['Date'], data['Concentration'], label='Concentración de CO2', color='blue')\n", + "plt.xlabel('Fecha')\n", + "plt.ylabel('Concentración de CO2 (ppm)')\n", + "plt.title('Evolución temporal de la concentración de CO2')# Graficar el modelo de regresión lineal y la proyección\n", + "\n", + "plt.plot(data['Date'], model.predict(X), label='Modelo de regresión lineal', color='red')\n", + "plt.plot(pd.to_datetime(['2025-01-01']), predicted_CO2_2025, marker='o', markersize=8, label='Proyección a 2025', color='green')\n", + "\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": 19, @@ -481,32 +528,35 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Dominant oscillation frequency: 0.019356759976176297 cycles/week\n", + "Dominant oscillation frequency: 51.661538461538456 week/cycles\n", "Maximum oscillation amplitude in CO2 anomalies: 3207.2901558554163 ppm\n" ] } ], "source": [ "# Oscillation périodique. Calculer la transformée de Fourier de la série chronologique des anomalies CO2\n", - "co2_anomaly_fft = np.fft.fft(data['Oscilation'])\n", + "co2_oscilation_fft = np.fft.fft(data['Oscilation'])\n", "\n", "# Calculer les fréquences correspondant aux composantes de Fourier\n", "n = len(data)\n", "frequencies = np.fft.fftfreq(n, d=1) # Fréquences en cycles par semaine\n", "\n", "# Trouver la fréquence et l'amplitude maximales\n", - "max_freq_index = np.argmax(np.abs(co2_anomaly_fft))\n", + "max_freq_index = np.argmax(np.abs(co2_oscilation_fft))\n", "max_freq = frequencies[max_freq_index]\n", - "max_amplitude = np.abs(co2_anomaly_fft[max_freq_index])\n", + "max_amplitude = np.abs(co2_oscilation_fft[max_freq_index])\n", + "\n", + "# Calcula la frecuencia inversa en semanas por ciclo\n", + "max_freq_week_cycles = 1 / max_freq\n", "\n", - "print(\"Dominant oscillation frequency:\", max_freq, \"cycles/week\")\n", + "print(\"Dominant oscillation frequency:\", max_freq_week_cycles, \"week/cycles\")\n", "print(\"Maximum oscillation amplitude in CO2 anomalies:\", max_amplitude, \"ppm\")" ] }, -- 2.18.1