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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
# Get the screen dimensions
screen_width = st.get_option("browser.clientWidth")
screen_height = st.get_option("browser.clientHeight")
# Set the width and height to take up nearly the entire screen
fig_width = screen_width * 0.8
fig_height = screen_height * 0.65
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=fig_width,
height=fig_height,
hoverlabel=dict(
font_size=20 # Increase hover label font size
)
)
return fig |