from pathlib import Path import pandas as pd import geopandas as gpd import faicons as fa from shiny import ui from shared import app_dir import matplotlib.pyplot as plt import numpy as np #app_dir = Path(__file__).parent #tips = pd.read_csv(app_dir / "tips.csv") def load_data(): """ Load zones shapefile and trips data. Returns: zones (GeoDataFrame): GeoDataFrame of zones. trips (DataFrame): DataFrame of trip data. """ zones = gpd.read_file(app_dir / "data" / "zone" / "Napa_TBS_2024_Zone_System.shp") # Replace with your shapefile path zones = zones[zones.is_valid & ~zones.is_empty] zones = zones[["FPID", "geometry"]] # Keep only essential columns zones["geometry"] = zones["geometry"].simplify(0.001, preserve_topology=True) # Simplify geometries #trips = pd.read_csv(app_dir / "data" / "od_table.csv") # Replace with your trip data path trips = pd.read_parquet(app_dir / "data" / "od_table.parquet") # Replace with your trip data path return zones, trips def load_excel_data(SHEET_NAME): """ Load data tables from Excel Returns: data (DataFrame): DataFrame of data table. """ EXCEL_FILE = app_dir / "data" / "trips_data.xlsx" # Update with your file path data = pd.read_excel(EXCEL_FILE, sheet_name=SHEET_NAME) return data def filter_trips(trips, zones, origin_filter, destination_filter): """ Filter trips based on selected origin or destination. Args: trips (DataFrame): Trip data with origin and destination zones. zones (GeoDataFrame): GeoDataFrame of zones. origin_filter (str): Selected origin zone ID. destination_filter (str): Selected destination zone ID. Returns: filtered_origins (GeoDataFrame): Filtered origin zones with trip counts. filtered_destinations (GeoDataFrame): Filtered destination zones with trip counts. """ if origin_filter and destination_filter: filtered_trips = trips[ (trips["start_zone_id"] == origin_filter) & (trips["end_zone_id"] == destination_filter) ] elif origin_filter: filtered_trips = trips[trips["start_zone_id"] == origin_filter] elif destination_filter: filtered_trips = trips[trips["end_zone_id"] == destination_filter] else: filtered_trips = trips # Aggregate trip counts for origins and destinations filtered_origins = zones.merge( #filtered_trips.groupby("start_zone_id").size().reset_index(name="trip_count"), filtered_trips.groupby("start_zone_id")["trips"].sum().reset_index(name="trip_count"), left_on="FPID", right_on="start_zone_id", how="left" ).fillna(0) filtered_destinations = zones.merge( filtered_trips.groupby("end_zone_id")["trips"].sum().reset_index(name="trip_count"), left_on="FPID", right_on="end_zone_id", how="left" ).fillna(0) return filtered_origins, filtered_destinations def create_nav_button(button_icon, button_label_text, button_link, button_cls = "btn btn-primary"): return ui.tags.a( ui.HTML(f"{fa.icon_svg(button_icon)} {button_label_text}"), href=button_link, class_=button_cls ) def conlogo(): img = { "src": app_dir / "images" / "logo.png", "style": "width: 80%; height: auto; max-height: 150px; margin-bottom: 0px;" } return img def vendorlogo(): img = { "src": app_dir / "images" / "logo2.png", "style": "width: 80%; height: auto; max-height: 150px; margin-bottom: 0px;" } return img def create_bar_chart(categories, values, plottitle = "Bar Chart Example"): fig, ax = plt.subplots() #ax.bar(categories, values, color=["#1f77b4", "#ff7f0e", "#2ca02c"]) ax.bar(categories, values) ax.set_title(plottitle) ax.set_ylabel("Values") ax.set_xlabel("Categories") return fig def create_pie_chart(labels, sizes, plottitle): #sizes = [22, 41, 37] #colors = ["#00b3b3", "#70d281", "#ff7f0e"] #explode = (0.1, 0, 0) # Explode the first slice for emphasis fig, ax = plt.subplots() wedges, texts, autotexts = ax.pie( sizes, #explode=explode, labels=labels, autopct="%1.0f%%", startangle=90, #colors=colors, textprops=dict(color="black"), wedgeprops=dict(width=0.4) # Adjust width for the donut effect ) # Customizing the labels for text, label in zip(autotexts, labels): text.set_color("black") text.set_fontsize(12) #ax.set_title("What Types of Trips are Occuring within Napa County on a Weekday?", fontsize=16, fontweight="bold") ax.set_title(plottitle, fontsize=16, fontweight="bold") return fig def create_stacked_bar_chart(categories, intra_napa, into_napa, out_napa): # 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] bar_width = 0.5 # Width of the bars x = np.arange(len(categories)) # x-axis positions fig, ax = plt.subplots(figsize=(8, 6)) # Stacking the bars ax.bar(x, intra_napa, bar_width, label="Intra-Napa County", color="#70d281") ax.bar(x, into_napa, bar_width, bottom=intra_napa, label="Into Napa County", color="#ff7f0e") ax.bar(x, out_napa, bar_width, bottom=np.array(intra_napa) + np.array(into_napa), label="Out of Napa County", color="#00b3b3") # Customizing the plot ax.set_xticks(x) ax.set_xticklabels(categories) ax.set_ylabel("Trips") ax.set_title("Weekday Work Trip Types") ax.legend(loc="upper right") # Add value annotations for i in range(len(categories)): ax.text(x[i], intra_napa[i] / 2, f"{intra_napa[i]}", ha="center", va="center", color="white") ax.text(x[i], intra_napa[i] + into_napa[i] / 2, f"{into_napa[i]}", ha="center", va="center", color="white") ax.text(x[i], intra_napa[i] + into_napa[i] + out_napa[i] / 2, f"{out_napa[i]}", ha="center", va="center", color="white") plt.tight_layout() return fig