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import matplotlib.pyplot as plt
from shiny import render, ui, reactive
import faicons as fa
#from fontawesomefree import icons
import numpy as np
from utils import conlogo, vendorlogo
from utils import create_pie_chart, create_bar_chart, create_bar_chart, create_stacked_bar_chart
import plotly.express as px
from utils import load_data, filter_trips
from shinywidgets import output_widget, render_widget
import plotly.graph_objects as go
# Load the data
zones, trips = load_data()
def create_server(app_dir):
def server(input, output, session):
@render.image
def rsglogo():
return conlogo()
@render.image
def clientlogo():
return vendorlogo()
@render.plot
def bar_chart():
# Sample data for bar chart
categories = ["Category A", "Category B", "Category C"]
values = [10, 20, 30]
return create_bar_chart(categories, values, plottitle = "Bar Chart Example")
@render.plot
def pie_chart():
# Data for pie chart
labels = ["Home-Based Work", "Home-Based Other", "Non-Home-Based"]
#labels = [f"{icons['fa-car']} Cars", f"{icons['fa-bicycle']} Bikes", f"{icons['fa-bus']} Buses"]
#labels = [f"{fa.icon_svg("briefcase")} Cars", f"{fa.icon_svg("briefcase")} Bikes", f"{fa.icon_svg("briefcase")} Buses"]
sizes = [22, 41, 37]
colors = ["#00b3b3", "#70d281", "#ff7f0e"]
explode = (0.1, 0, 0) # Explode the first slice for emphasis
return create_pie_chart(labels, sizes, plottitle ="What Types of Trips are Occuring within Napa County on a Weekday?")
@render.plot
def stacked_bar_chart():
# Data for the stacked bar chart
categories = ["Early AM", "AM Peak", "Mid-Day", "PM Peak", "Evening"]
intra_napa = [2000, 16000, 14000, 12000, 5000]
into_napa = [1000, 8000, 2000, 3000, 2000]
out_napa = [2000, 4000, 2000, 5000, 1000]
return create_stacked_bar_chart(categories, intra_napa, into_napa, out_napa)
# Reactive values to track selections
selected_origin = reactive.Value(None)
selected_destination = reactive.Value(None)
@reactive.Effect
#@reactive.event(input)
@reactive.event(input.origin_map_click, input.destination_map_click)
def update_selections():
"""Update selections when a map is clicked."""
selected_origin.set(input.origin_map_click.get("customdata"))
selected_destination.set(input.destination_map_click.get("customdata"))
@reactive.Calc
def filtered_data():
"""Filter trips based on selected origin or destination."""
return filter_trips(trips, zones, selected_origin.get(), selected_destination.get())
@output
#@render.plot
#@render.ui
@render_widget
def origin_map():
filtered_origins, _ = filtered_data()
fig = px.choropleth(
filtered_origins,
geojson=filtered_origins.geometry,
locations=filtered_origins.index,
color="trip_count",
title="Trip Origins",
custom_data=["FPID"],
#mapbox_style="open-street-map",
#zoom=7.5,
#center={"lat": filtered_origins.geometry.centroid.y.mean(), "lon": filtered_origins.geometry.centroid.x.mean()},
#opacity=0.7,
# color_continuous_scale="amp",
# labels={
# "Zone ID": "FPID",
# "Trips": "trip_count"
# }
)
fig.update_geos(fitbounds="locations", visible=False)
#fig.update_layout(coloraxis_colorbar=dict(title="Trip Count")) # Add legend
return fig
#return ui.HTML(fig.to_html())
#return ui.HTML(fig.to_html(full_html=False, include_plotlyjs="cdn"))
@output
#@render.plot
#@render.ui
@render_widget
def destination_map():
_, filtered_destinations = filtered_data()
fig = px.choropleth(
filtered_destinations,
geojson=filtered_destinations.geometry,
locations=filtered_destinations.index,
color="trip_count",
title="Trip Destinations",
custom_data=["FPID"]
)
fig.update_geos(fitbounds="locations", visible=False)
#fig.update_layout(coloraxis_colorbar=dict(title="Trip Count")) # Add legend
return fig
#return ui.HTML(fig.to_html())
#return ui.HTML(fig.to_html(full_html=False, include_plotlyjs="cdn"))
return server
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