add correlation matrix

torave · May 24, 2025 · 4dafb49 · 4dafb49
1 parent e37df08
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diff --git a/notebooks/notebook_compare_one_day_data.ipynb b/notebooks/notebook_compare_one_day_data.ipynb
@@ -491,6 +491,72 @@
     "# Show the plot\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Korrelasjonsmatrise\n",
+    "Ved hjelp av Seaborn har vi visualisert korrelasjons koeffisienten mellom ulike variabler. Det viser sammenhengen mellom variablene, og hvordan de påvirker hverandre. Sammenhengen kan forklares slik:\n",
+    "- +1 - Sterk positiv sammenheng\n",
+    "- 0 - Ingen sammenheng\n",
+    "- -1 - Sterk negativ sammenheng\n",
+    "\n",
+    "For å lese av grafen, finner man en variabel vertikalt og en horisontal variabel. Der de møtes i diagrammet er korrelasjons koeffisienten mellom disse.\n",
+    "\n",
+    "Her har vi laget en korrelasjonsmatrsie for hver av stedene, slik at man kan sammeligne om et sted har sterkere eller svakere sammenhenger mellom de ulike variablene.\n",
+    "\n",
+    "Grafen lagres i mappen `../data/figures/output_fig_compare_one_day`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Where the figure should be saved when exported\n",
+    "output_folder = \"../data/figures/output_fig_compare_one_day\"\n",
+    "\n",
+    "# Creates the folder if it does not exist\n",
+    "os.makedirs(output_folder, exist_ok=True)\n",
+    "\n",
+    "cities = [\"city_1\", \"city_2\"]\n",
+    "\n",
+    "# The columns we want to include in the correlation matrix\n",
+    "columns_needed = ['main.temp', 'main.pressure', 'main.humidity', 'wind.speed', 'wind.gust', 'clouds.all', 'rain.1h', 'snow.1h']\n",
+    "\n",
+    "# Two horisontally plots (1 row, 2 columns), width and height of the figure\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))  # Adjust figsize as needed\n",
+    "\n",
+    "# Loops through both cities, the enumerate make sure we get both the city and the index of the city\n",
+    "for i, city in enumerate(cities):\n",
+    "    # Stores the data for the right city in cities\n",
+    "    city_df = both_cities_df[both_cities_df[\"city\"] == city]\n",
+    "    city_name = city_df['city_name'].iloc[0]\n",
+    "\n",
+    "    df_selected = city_df[columns_needed]\n",
+    "\n",
+    "    # Calculates the correlation\n",
+    "    corr_matrix = df_selected.corr()\n",
+    "\n",
+    "    # Makes a seaborn heatmat, with the values in the rectangel and 2 decimals\n",
+    "    sns.heatmap(corr_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\", ax=axes[i])\n",
+    "\n",
+    "    # Add a title, with the city_name\n",
+    "    axes[i].set_title(f\"Correlation Matrix - {city_name}\")\n",
+    "\n",
+    "# Save the plot to the 'data/figures/output_fig_compare_one_day' folder\n",
+    "plot_path = os.path.join(output_folder, f\"correlation_matrix_{city_1}_{city_2}.png\")\n",
+    "plt.savefig(plot_path)  # Save the plot as a PNG file\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {

diff --git a/notebooks/notebook_compare_one_week_data.ipynb b/notebooks/notebook_compare_one_week_data.ipynb
@@ -523,6 +523,74 @@
     "# Show the plot\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5390c2d",
+   "metadata": {},
+   "source": [
+    "### Korrelasjonsmatrise\n",
+    "Ved hjelp av Seaborn har vi visualisert korrelasjons koeffisienten mellom ulike variabler. Det viser sammenhengen mellom variablene, og hvordan de påvirker hverandre. Sammenhengen kan forklares slik:\n",
+    "- +1 - Sterk positiv sammenheng\n",
+    "- 0 - Ingen sammenheng\n",
+    "- -1 - Sterk negativ sammenheng\n",
+    "\n",
+    "For å lese av grafen, finner man en variabel vertikalt og en horisontal variabel. Der de møtes i diagrammet er korrelasjons koeffisienten mellom disse.\n",
+    "\n",
+    "Her har vi laget en korrelasjonsmatrsie for hver av stedene, slik at man kan sammeligne om et sted har sterkere eller svakere sammenhenger mellom de ulike variablene.\n",
+    "\n",
+    "Grafen lagres i mappen `../data/figures/output_fig_compare_one_week`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55d18857",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Where the figure should be saved when exported\n",
+    "output_folder = \"../data/figures/output_fig_compare_one_week\"\n",
+    "\n",
+    "# Creates the folder if it does not exist\n",
+    "os.makedirs(output_folder, exist_ok=True)\n",
+    "\n",
+    "cities = [\"city_1\", \"city_2\"]\n",
+    "\n",
+    "# The columns we want to include in the correlation matrix\n",
+    "columns_needed = ['main.temp', 'main.pressure', 'main.humidity', 'wind.speed', 'wind.gust', 'clouds.all', 'rain.1h', 'snow.1h']\n",
+    "\n",
+    "# Two horisontally plots (1 row, 2 columns), width and height of the figure\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))  # Adjust figsize as needed\n",
+    "\n",
+    "# Loops through both cities, the enumerate make sure we get both the city and the index of the city\n",
+    "for i, city in enumerate(cities):\n",
+    "    # Stores the data for the right city in cities\n",
+    "    city_df = both_cities_df[both_cities_df[\"city\"] == city]\n",
+    "    city_name = city_df['city_name'].iloc[0]\n",
+    "\n",
+    "    df_selected = city_df[columns_needed]\n",
+    "\n",
+    "    # Calculates the correlation\n",
+    "    corr_matrix = df_selected.corr()\n",
+    "\n",
+    "    # Makes a seaborn heatmat, with the values in the rectangel and 2 decimals\n",
+    "    sns.heatmap(corr_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\", ax=axes[i])\n",
+    "\n",
+    "    # Add a title, with the city_name\n",
+    "    axes[i].set_title(f\"Correlation Matrix - {city_name}\")\n",
+    "\n",
+    "# Save the plot to the 'data/figures/output_fig_compare_one_week' folder\n",
+    "plot_path = os.path.join(output_folder, f\"correlation_matrix_{city_1}_{city_2}.png\")\n",
+    "plt.savefig(plot_path)  # Save the plot as a PNG file\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {

diff --git a/notebooks/notebook_compare_statistic_data.ipynb b/notebooks/notebook_compare_statistic_data.ipynb
@@ -361,6 +361,74 @@
     "# Show the plot\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04c13808",
+   "metadata": {},
+   "source": [
+    "### Korrelasjonsmatrise\n",
+    "Ved hjelp av Seaborn har vi visualisert korrelasjons koeffisienten mellom ulike variabler. Det viser sammenhengen mellom variablene, og hvordan de påvirker hverandre. Sammenhengen kan forklares slik:\n",
+    "- +1 - Sterk positiv sammenheng\n",
+    "- 0 - Ingen sammenheng\n",
+    "- -1 - Sterk negativ sammenheng\n",
+    "\n",
+    "For å lese av grafen, finner man en variabel vertikalt og en horisontal variabel. Der de møtes i diagrammet er korrelasjons koeffisienten mellom disse.\n",
+    "\n",
+    "Her har vi laget en korrelasjonsmatrsie for hver av stedene, slik at man kan sammeligne om et sted har sterkere eller svakere sammenhenger mellom de ulike variablene.\n",
+    "\n",
+    "Grafen lagres i mappen `../data/figures/output_fig_compare_statistic`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ceb72c4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Where the figure should be saved when exported\n",
+    "output_folder = \"../data/figures/output_fig_compare_statistic\"\n",
+    "\n",
+    "# Creates the folder if it does not exist\n",
+    "os.makedirs(output_folder, exist_ok=True)\n",
+    "\n",
+    "cities = [\"city_1\", \"city_2\"]\n",
+    "\n",
+    "# The columns we want to include in the correlation matrix\n",
+    "columns_needed = ['temp.mean_celsius', 'pressure.mean', 'humidity.mean', 'wind.mean', 'clouds.mean', 'precipitation.mean']\n",
+    "\n",
+    "# Two horisontally plots (1 row, 2 columns), width and height of the figure\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(14, 6))  # Adjust figsize as needed\n",
+    "\n",
+    "# Loops through both cities, the enumerate make sure we get both the city and the index of the city\n",
+    "for i, city in enumerate(cities):\n",
+    "    # Stores the data for the right city in cities\n",
+    "    city_df = both_cities_df[both_cities_df[\"city\"] == city]\n",
+    "    city_name = city_df['city_name'].iloc[0]\n",
+    "\n",
+    "    df_selected = city_df[columns_needed]\n",
+    "\n",
+    "    # Calculates the correlation\n",
+    "    corr_matrix = df_selected.corr()\n",
+    "\n",
+    "    # Makes a seaborn heatmat, with the values in the rectangel and 2 decimals\n",
+    "    sns.heatmap(corr_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\", ax=axes[i])\n",
+    "\n",
+    "    # Add a title, with the city_name\n",
+    "    axes[i].set_title(f\"Correlation Matrix - {city_name}\")\n",
+    "\n",
+    "# Save the plot to the 'data/figures/output_fig_compare_statistic' folder\n",
+    "plot_path = os.path.join(output_folder, f\"correlation_matrix_{city_1}_{city_2}.png\")\n",
+    "plt.savefig(plot_path)  # Save the plot as a PNG file\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {

diff --git a/notebooks/notebook_one_day_data.ipynb b/notebooks/notebook_one_day_data.ipynb
@@ -180,7 +180,7 @@
     "x_axis = df.index\n",
     "\n",
     "# Choose the width and height of the plot\n",
-    "plt.figure(figsize=(12, 6))\n",
+    "plt.figure(figsize=(14, 6))\n",
     "\n",
     "# Scatter plot for each temperature reading\n",
     "plt.scatter(x_axis, temp, color='tab:red', label='Temperaturmålinger', alpha=0.7)\n",
@@ -368,7 +368,7 @@
     "temp_mean = temp.mean().round(2)\n",
     "\n",
     "# Two vertically stacked axis, (2 rows, 1 column), width and height of the figure, and the axis share the same x_axis\n",
-    "fig, (ax1, ax3) = plt.subplots(2, 1,figsize=(15, 8), sharex=True)\n",
+    "fig, (ax1, ax3) = plt.subplots(2, 1,figsize=(14, 6), sharex=True)\n",
     "\n",
     "# Set the title for the diagram, above the first axis, with city_name and input_date\n",
     "ax1.set_title(f'Weather data for {city_name} ({date}) ')\n",
@@ -429,8 +429,61 @@
     "# Adjust layout\n",
     "plt.tight_layout()\n",
     "\n",
-    "# Save the plot to the 'data/figures/output_one_day' folder\n",
-    "plot_path = os.path.join(output_folder, f\"weather_data_plot{city_name}.png\")\n",
+    "# Save the plot to the 'data/figures/output_fig_one_day' folder\n",
+    "plot_path = os.path.join(output_folder, f\"weather_data_plot_{city_name}.png\")\n",
+    "plt.savefig(plot_path)  # Save the plot as a PNG file\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Korrelasjonsmatrise\n",
+    "Ved hjelp av Seaborn har vi visualisert korrelasjons koeffisienten mellom ulike variabler. Det viser sammenhengen mellom variablene, og hvordan de påvirker hverandre. Sammenhengen kan forklares slik:\n",
+    "- +1 - Sterk positiv sammenheng\n",
+    "- 0 - Ingen sammenheng\n",
+    "- -1 - Sterk negativ sammenheng\n",
+    "\n",
+    "For å lese av grafen, finner man en variabel vertikalt og en horisontal variabel. Der de møtes i diagrammet er korrelasjons koeffisienten mellom disse.\n",
+    "\n",
+    "Grafen lagres i mappen `../data/figures/output_fig_one_day`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "# Where the figure should be saved when exported\n",
+    "output_folder = \"../data/figures/output_fig_one_day\"\n",
+    "\n",
+    "# Creates the folder if it does not exist\n",
+    "os.makedirs(output_folder, exist_ok=True)\n",
+    "\n",
+    "# The columns we want to include in the correlation matrix\n",
+    "columns_needed = ['main.temp', 'main.pressure', 'main.humidity', 'wind.speed', 'wind.gust', 'clouds.all', 'rain.1h', 'snow.1h']\n",
+    "df_selected = df[columns_needed]\n",
+    "\n",
+    "# Calculates the correlation\n",
+    "corr_matrix = df_selected.corr()\n",
+    "\n",
+    "# Choose the width and height of the plot\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "\n",
+    "# Makes a seaborn heatmat, with the values in the rectangel and 2 decimals\n",
+    "sns.heatmap(corr_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
+    "\n",
+    "# Add a title, with the city_name\n",
+    "plt.title(f\"Correlation Matrix - {city_name}\")\n",
+    "\n",
+    "# Save the plot to the 'data/figures/output_fig_one_day' folder\n",
+    "plot_path = os.path.join(output_folder, f\"correlation_matrix_{city_name}.png\")\n",
     "plt.savefig(plot_path)  # Save the plot as a PNG file\n",
     "\n",
     "# Show the plot\n",
@@ -513,7 +566,7 @@
     "\n",
     "x_axis = df.index\n",
     "\n",
-    "plt.figure(figsize=(12, 6))\n",
+    "plt.figure(figsize=(14, 6))\n",
     "\n",
     "# Plot the original data\n",
     "plt.scatter(x_axis, y, color='green', label='Original data', alpha=0.6)\n",

diff --git a/notebooks/notebook_one_week_data.ipynb b/notebooks/notebook_one_week_data.ipynb
@@ -448,13 +448,67 @@
     "plt.tight_layout()\n",
     "\n",
     "# Save the plot to the 'data/figures/output_fig_one_week' folder\n",
-    "plot_path = os.path.join(output_folder, f\"weather_data_plot{city_name}.png\")\n",
+    "plot_path = os.path.join(output_folder, f\"weather_data_plot_{city_name}.png\")\n",
     "plt.savefig(plot_path)  # Save the plot as a PNG file\n",
     "\n",
     "\n",
     "# Show the plot\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Korrelasjonsmatrise\n",
+    "Ved hjelp av Seaborn har vi visualisert korrelasjons koeffisienten mellom ulike variabler. Det viser sammenhengen mellom variablene, og hvordan de påvirker hverandre. Sammenhengen kan forklares slik:\n",
+    "- +1 - Sterk positiv sammenheng\n",
+    "- 0 - Ingen sammenheng\n",
+    "- -1 - Sterk negativ sammenheng\n",
+    "\n",
+    "For å lese av grafen, finner man en variabel vertikalt og en horisontal variabel. Der de møtes i diagrammet er korrelasjons koeffisienten mellom disse.\n",
+    "\n",
+    "Grafen lagres i mappen `../data/figures/output_fig_one_week`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import os\n",
+    "\n",
+    "# Where the figure should be saved when exported\n",
+    "output_folder = \"../data/figures/output_fig_one_week\"\n",
+    "\n",
+    "# Creates the folder if it does not exist\n",
+    "os.makedirs(output_folder, exist_ok=True)\n",
+    "\n",
+    "# The columns we want to include in the correlation matrix\n",
+    "columns_needed = ['main.temp', 'main.pressure', 'main.humidity', 'wind.speed', 'wind.gust', 'clouds.all', 'rain.1h', 'snow.1h']\n",
+    "selected_df = df[columns_needed]\n",
+    "\n",
+    "# Calculates the correlation\n",
+    "corr_matrix = selected_df.corr()\n",
+    "\n",
+    "# Choose the width and height of the plot\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "\n",
+    "# Makes a seaborn heatmat, with the values in the rectangel and 2 decimals\n",
+    "sns.heatmap(corr_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
+    "\n",
+    "# Add a title, with the city_name\n",
+    "plt.title(f\"Correlation Matrix - {city_name}\")\n",
+    "\n",
+    "# Save the plot to the 'data/figures/output_fig_one_week' folder\n",
+    "plot_path = os.path.join(output_folder, f\"correlation_matrix_{city_name}.png\")\n",
+    "plt.savefig(plot_path)  # Save the plot as a PNG file\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
   }
  ],
  "metadata": {