elon_py/pkg/dash/func/render.py

from datetime import datetime, timedelta
from dash.dependencies import Input, Output
from pkg.dash.app_init import app
from pkg.config import render_data
from pkg.tool import aggregate_data, minutes_to_time, get_tweets_since_last_friday
from dash import dcc
import plotly.graph_objs as go
import pandas as pd

@app.callback(
    [Output('tabs-content', 'children'),
     Output('multi-day-warning', 'children'),
     Output('multi-tweet-summary', 'children')],
    [Input('tabs', 'value'),
     Input('multi-interval-picker', 'value'),
     Input('days-display-picker', 'value')]
)
def render_tab_content(tab, interval, days_to_display):
    warning = ""
    available_dates = sorted(render_data.global_agg_df['date'].unique(), reverse=True)
    selected_dates = available_dates[:days_to_display] if available_dates else [datetime.now().date()]
    if not available_dates:
        warning = "No data available. Showing today’s date with zero tweets."

    multi_data_agg = render_data.global_agg_df[render_data.global_agg_df['date'].isin(selected_dates)].copy()
    if multi_data_agg.empty:
        multi_data_agg = pd.DataFrame({
            'date': selected_dates,
            'minute_of_day': [0] * len(selected_dates),
        })
    tweet_count_total = multi_data_agg.get('tweet_count', pd.Series([0] * len(multi_data_agg))).sum()

    multi_data_raw = render_data.global_df[render_data.global_df['date'].isin(selected_dates)].copy()
    if multi_data_raw.empty:
        tweet_count_total = 0

    agg_data = aggregate_data(multi_data_agg, interval)

    if tab == 'heatmap':
        pivot_data = agg_data.pivot(index='date', columns='interval_group', values='tweet_count').fillna(0)
        pivot_data.index = pivot_data.index.astype(str)
        fig = go.Figure(data=go.Heatmap(
            z=pivot_data.values,
            x=[minutes_to_time(m) for m in pivot_data.columns],
            y=pivot_data.index,
            colorscale='Viridis',
            hoverongaps=False,
            hovertemplate='%{y} %{x} EST<br>Tweets: %{z}<extra></extra>'
        ))

        for i, date_str in enumerate(pivot_data.index):
            date = datetime.strptime(date_str, '%Y-%m-%d').date()
            if date.weekday() == 4:
                prev_date = date - timedelta(days=1)
                if str(prev_date) in pivot_data.index:
                    y_position = i / len(pivot_data.index)
                    fig.add_hline(
                        y=1 - y_position,
                        line_dash="dash",
                        line_color="white",
                        xref="x",
                        yref="paper"
                    )
        fig.update_layout(
            title=f'Tweet Heatmap (Interval: {interval} minutes, EST, {len(selected_dates)} days)',
            xaxis_title='Time of Day (HH:MM EST)',
            yaxis_title='Date',
            height=max(400, len(selected_dates) * 20),
            yaxis=dict(autorange='reversed')
        )

    elif tab == 'one_day_heatmap':
        one_day_data = agg_data.groupby('interval_group')['tweet_count'].sum().reset_index()

        hours = list(range(24))
        intervals_per_hour = 60 // interval
        z_values = [[0] * intervals_per_hour for _ in range(24)]

        for _, row in one_day_data.iterrows():
            minute = row['interval_group']
            hour = int(minute // 60)
            interval_idx = int((minute % 60) // interval)
            if hour < 24:
                z_values[hour][interval_idx] = row['tweet_count']

        x_labels = [f"{i * interval:02d}" for i in range(intervals_per_hour)]

        rate_values = [[z_values[h][i] / tweet_count_total if tweet_count_total > 0 else 0
                        for i in range(intervals_per_hour)]
                       for h in range(24)]

        fig = go.Figure(data=go.Heatmap(
            z=z_values,
            x=x_labels,
            y=[f"{h:02d}" for h in hours],
            colorscale='Viridis',
            hoverongaps=False,
            customdata=rate_values,
            hovertemplate='%{y}:%{x} EST<br>Tweets: %{z}<br>Rate: %{customdata:.2%}<extra></extra>'
        ))

        fig.update_layout(
            title=f'One-Day Heatmap Tweet Frequency (Interval: {interval} minutes, EST, {len(selected_dates)} days)',
            xaxis_title='Minutes',
            yaxis_title='Hour of Day',
            height=600,
            yaxis=dict(autorange='reversed')
        )

    summary = f"Total tweets: {get_tweets_since_last_friday()}"
    return dcc.Graph(figure=fig), warning, summary