utils.py

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