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| import streamlit as st | |
| import datetime | |
| import numpy as np | |
| today = datetime.date.today() | |
| def plot(df, n=10): | |
| import plotly.express as px | |
| # Order: prev pred, future pred, real | |
| line_colors = ['tomato', 'steelblue', 'limegreen'] | |
| # Extract the x-axis values (dates) from the dataframe | |
| x_values = df["date"] | |
| # Define your colors, labels, and ranges | |
| colors = ['green', 'yellow', 'orange', 'red', 'purple', 'darkred'] | |
| labels = ['Good', 'Moderate', 'Unhealthy for Some', 'Unhealthy', 'Very Unhealthy', 'Hazardous'] | |
| ranges = [(1, 49), (50, 99), (100, 149), (150, 199), (200, 299), (300, 500)] # Avoid 0 for log scale | |
| # Create the Plotly Express line chart for actual pm25 | |
| fig = px.line( | |
| df.iloc[:-n], # Exclude the last n points | |
| x="date", | |
| y="pm25", | |
| markers=True, | |
| line_shape='linear' | |
| ) | |
| fig.update_traces(line=dict(color=line_colors[2]), name='Actual', showlegend=True) | |
| # Add the predicted pm25 line in two segments | |
| fig.add_scatter( | |
| x=df["date"][:-n], | |
| y=df["predicted_pm25"][:-n], | |
| mode='lines+markers', | |
| name='Past prediction', | |
| line=dict(color=line_colors[0], width=3), | |
| marker=dict(size=10) | |
| ) | |
| fig.add_scatter( | |
| x=df["date"][-n:], | |
| y=df["predicted_pm25"][-n:], | |
| mode='lines+markers', | |
| name='Future prediction', | |
| line=dict(color=line_colors[1], width=3, dash='dot'), | |
| marker=dict(size=10) | |
| ) | |
| # Add a dotted line connecting past predicted pm25 to future predicted pm25 | |
| fig.add_scatter( | |
| x=[df["date"].iloc[-n-1], df["date"].iloc[-n]], | |
| y=[df["predicted_pm25"].iloc[-n-1], df["predicted_pm25"].iloc[-n]], | |
| mode='lines', | |
| name='Connecting Line', | |
| line=dict(color=line_colors[1], width=3, dash='dot'), | |
| showlegend=False # Remove from legend | |
| ) | |
| # Add background color rectangles using `shapes` | |
| shapes = [] | |
| for i, (start, end) in enumerate(ranges): | |
| shapes.append( | |
| dict( | |
| type="rect", # Add a rectangle | |
| xref="paper", # Extend the rectangle across the entire x-axis | |
| yref="y", # Anchor the rectangle to the y-axis | |
| x0=0, # Start from the left (x0 in paper coordinates) | |
| x1=1, # End at the right (x1 in paper coordinates) | |
| y0=start, # Start of the y-range | |
| y1=end, # End of the y-range | |
| fillcolor=colors[i], # Background color | |
| opacity=0.2, # Transparency level | |
| layer="below", # Place behind the data | |
| line_width=0 # No border | |
| ) | |
| ) | |
| label_font_size = 26 | |
| ticks_font_size = 20 | |
| # Update the layout and traces for customization | |
| fig.update_traces( | |
| marker=dict(size=10), # Increase marker size | |
| line=dict(width=3) # Make the line thicker | |
| ) | |
| # x range start BEFORE today | |
| k = 4 | |
| fig.update_layout( | |
| shapes=shapes, # Add the background rectangles | |
| xaxis=dict( | |
| range=[x_values.iloc[-n-k], x_values.iloc[-1]], # Dynamically set the range | |
| title=dict( | |
| text="Date", # Set x-axis label | |
| font=dict(size=label_font_size) # Increase font size for the x-axis label | |
| ), | |
| tickfont=dict(size=ticks_font_size) # Increase font size for x-axis numbers | |
| ), | |
| yaxis=dict( | |
| title=dict( | |
| text="log PM2.5", # Set y-axis label | |
| font=dict(size=label_font_size) # Increase font size for the y-axis label | |
| ), | |
| type="log", # Set y-axis to logarithmic scale | |
| fixedrange=True, # Disable vertical panning/zooming | |
| tickfont=dict(size=ticks_font_size), # Increase font size for y-axis numbers | |
| range=[1, np.log10(500)] # Set y-axis range to be positive | |
| ), | |
| autosize=True, | |
| width=2100, | |
| height=750, | |
| hoverlabel=dict( | |
| font_size=20 # Increase hover label font size | |
| ) | |
| ) | |
| return fig |