Delete sr/1.streamlitapp.py

src/1.streamlitapp.py

@@ -1,188 +0,0 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-import bqplot as bq
-import ipywidgets as widgets
-import numpy as np
-import requests
-import os
-import seaborn as sns
-
-
-# Creating buttons for downloading and refreshing plots
-download_button = widgets.Button(description="Download Dataset")
-refresh_button = widgets.Button(description="Refresh Plots")
-
-def fetch_and_save_data(_):
-    api_url = "https://api.open-meteo.com/v1/forecast?latitude=41.88&longitude=-87.63&hourly=temperature_2m,relative_humidity_2m,precipitation,cloudcover,windspeed_10m&timezone=auto"
-    
-    try:
-        response = requests.get(api_url)
-        response.raise_for_status()
-        data = response.json()  # we need to Parse JSON
-        
-        # and then transform to csv
-        hourly = data['hourly']
-        df = pd.DataFrame(hourly)
-        df.to_csv("chicago_weather_data.csv", index=False)
-        print("Weather data Refreshed and downloaded")
-        
-    except Exception as e:
-        pass
-    
-def refresh_plots(_):
-    try:
-        # Read from the saved CSV file
-        df_raw = pd.read_csv("chicago_weather_data.csv")
-
-        df_raw['time'] = pd.to_datetime(df_raw['time'], errors='coerce')
-        for col in ['temperature_2m', 'relative_humidity_2m', 'precipitation', 'cloudcover', 'windspeed_10m']:
-            df_raw[col] = pd.to_numeric(df_raw[col], errors='coerce')
-        global df
-        df = df_raw
-
-        print("Plots refreshed")
-        
-    except Exception as e:
-        pass
-
-fetch_and_save_data(None)
-refresh_plots(None)
-
-#buttons
-print('The data and visuals has already been refreshed, but adding buttons for clarity')
-download_button.on_click(fetch_and_save_data)
-refresh_button.on_click(refresh_plots)
-widgets.HBox([download_button, refresh_button])
-
-#Plot3
-
-variable_labels = {
-    'temperature_2m': 'Temperature',
-    'relative_humidity_2m': 'Humidity',
-    'precipitation': 'Precipitation',
-    'cloudcover': 'Cloud Cover'
-}
-
-dropdown = widgets.Dropdown(
-    options=[(label, key) for key, label in variable_labels.items()],
-    value='temperature_2m',
-    description='Select Variable:',
-    style={'description_width': 'initial'}
-)
-
-x_sc = bq.DateScale()
-y_sc = bq.LinearScale()
-
-line = bq.Lines(
-    x=df['time'],
-    y=df['temperature_2m'],
-    scales={'x': x_sc, 'y': y_sc},
-    display_legend=False,
-    stroke_width=2
-)
-
-
-x_ax = bq.Axis(label='Time', scale=x_sc)
-y_ax = bq.Axis(label='Value', scale=y_sc, orientation='vertical')
-
-
-fig = bq.Figure(
-    title='Chicago Temperature Over Time',
-    marks=[line],
-    axes=[x_ax, y_ax],
-    legend_location='top-left'
-)
-
-
-def update_plot(change):
-    var = dropdown.value
-    line.y = df[var]
-    line.x = df['time']
-    fig.title = f"{variable_labels[var]} Over Time"
-
-dropdown.observe(update_plot, names='value')
-
-# Show widgets
-widgets.VBox([dropdown, fig])
-
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-
-fig = plt.figure(figsize=(12, 6))
-ax = fig.add_subplot(111, projection='3d')
-
-
-scatter = ax.scatter(df['temperature_2m'], df['precipitation'], df['windspeed_10m'],
-                    c=df['temperature_2m'], cmap='coolwarm', s=20, marker='o', alpha=0.7, edgecolors='black')
-
-ax.set_xlabel('Temperature (°C)')
-ax.set_ylabel('Precipitation')
-ax.set_zlabel('Windspeed (km/h)')
-plt.title('3D Scatterplot of Temperature, Precipitation, and Windspeed')
-
-
-cbar = plt.colorbar(scatter, ax=ax, label='Temperature (°C)')
-
-plt.show()
-
-df['time'] = pd.to_datetime(df['time'], errors='coerce')
-df['temperature_2m'] = pd.to_numeric(df['temperature_2m'], errors='coerce')
- 
-df['date_only'] = df['time'].dt.date
-
-grouped = df.groupby('date_only')['temperature_2m'].agg(['min', 'mean', 'max']).reset_index()
-
-slider = widgets.IntSlider(
-    value=0,
-    min=0,
-    max=len(grouped) - 1,
-    step=1,
-    description='Day Index:',
-    continuous_update=False
-)
-
-x_scale = bq.OrdinalScale()
-y_scale = bq.LinearScale()
-
-bar = bq.Bars(
-    x=['Min', 'Mean', 'Max'],
-    y=grouped.iloc[slider.value, 1:].tolist(),
-    scales={'x': x_scale, 'y': y_scale},
-    colors=['#76b5c5'],
-)
-
-x_axis = bq.Axis(scale=x_scale, label='Temperature Summary')
-y_axis = bq.Axis(scale=y_scale, orientation='vertical', label='Temperature (°C)')
-
-fig = bq.Figure(
-    marks=[bar],
-    axes=[x_axis, y_axis],
-    title=f"Temperature Summary for {grouped['date_only'].iloc[slider.value]}"
-)
-
-def update_bar(change):
-    i = slider.value
-    bar.y = grouped.iloc[i, 1:].tolist()
-    fig.title = f"Temperature Summary for {grouped['date_only'].iloc[i]}"
-
-slider.observe(update_bar, names='value')
-
-widgets.VBox([slider, fig])
-
-def plot_wind_boxplot(df):
-    df['date_only'] = df['time'].dt.date
-
-    plt.figure(figsize=(12, 6))
-    sns.boxplot(data=df, x='date_only', y='windspeed_10m', palette='Set3')
-
-    plt.title('Daily Wind Speed Distribution in Chicago (km/h)', fontsize=15)
-    plt.xlabel('Date', fontsize=12)
-    plt.ylabel('Wind Speed (km/h)', fontsize=12)
-    plt.xticks(rotation=45, ha='right')
-    plt.grid(axis='y', linestyle='--', alpha=0.5)
-
-    plt.tight_layout()
-    plt.show()
-
-plot_wind_boxplot(df)
-