# start with the setup

# supress warnings about future deprecations
# import warnings
# warnings.simplefilter(action='ignore', category=FutureWarning)
import panel as pn
import vega_datasets
import pandas as pd
import altair as alt
# import numpy as np
# import pprint
import datetime as dt
from vega_datasets import data
# import matplotlib.pyplot as plt

# Solve a javascript error by explicitly setting the renderer
# alt.renderers.enable('jupyterlab')

#load data 
# df1=pd.read_csv("https://raw.githubusercontent.com/dallascard/SI649_public/main/altair_hw3/approval_polllist.csv")
df2=pd.read_csv("https://raw.githubusercontent.com/dallascard/SI649_public/main/altair_hw3/approval_topline.csv")

df2['timestamp']=pd.to_datetime(df2['timestamp'])
df2=pd.melt(df2, id_vars=['president', 'subgroup', 'timestamp'], value_vars=['approve','disapprove']).rename(columns={'variable':'choice', 'value':'rate'})



## Viz 4
# Import panel and vega datasets

# import panel as pn
# import vega_datasets

# Enable Panel extensions
pn.extension(design='bootstrap')
pn.extension('vega')

template = pn.template.BootstrapTemplate(
    title='SI649 Altair Assignment 3 (ritaycw)',
)

# Define a function to create and return a plot

def create_plot(subgroup, date_range, moving_av_window):

    # Apply any required transformations to the data in pandas
    approve_data = df2[df2['choice']=='approve']
    filtered_data = approve_data[approve_data['subgroup'] == subgroup]
    filtered_data = filtered_data[(filtered_data['timestamp'].dt.date >= date_range[0]) & \
                                  (filtered_data['timestamp'].dt.date <= date_range[1])]
    filtered_data['mov_avg'] = filtered_data['rate'].rolling(window=moving_av_window).mean().shift(-moving_av_window//2)

    # Line chart
    smoothed_line = alt.Chart(filtered_data).mark_line(color='red', size=2).encode(
        x='timestamp:T',
        y='mov_avg:Q'
    )

    # Scatter plot with individual polls
    scatter4 = alt.Chart(filtered_data).mark_point(size=2, opacity=0.7, color='grey').encode(
        x='timestamp:T',
        y=alt.Y('rate:Q', title='approve', scale=alt.Scale(domain=[30, 60])),
    )

    # Put them togetehr
    plot = (scatter4+smoothed_line).encode(
        y=alt.Y(axis=alt.Axis(title='approve, mov_avg'))
        )

    # Return the combined chart
    return pn.pane.Vega(plot)

# Create the selection widget
dropdown = pn.widgets.Select(options=['All polls', 'Adults', 'Voters'], name='Select')

# Create the slider for the date range
date_range_slider = pn.widgets.DateRangeSlider(name='Date Range Slider', 
                                               start=df2['timestamp'].dt.date.min(), 
                                               end=df2['timestamp'].dt.date.max(),
                                               value=(df2['timestamp'].dt.date.min(), df2['timestamp'].dt.date.max()),
                                               step=1)

# Create the slider for the moving average window
window_size_slider = pn.widgets.IntSlider(name='Moving average window', start=1, end=100, value=1)

# Bind the widgets to the create_plot function
final = pn.Row(pn.bind(create_plot, subgroup=dropdown, 
                            date_range=date_range_slider, 
                            moving_av_window=window_size_slider))

# window_size_slider

# Combine everything in a Panel Column to create an app
maincol = pn.Column()
maincol.append("# Visualization 4: Interactive smoothing (hosted on Huggingface using Panel)")
maincol.append(final)
maincol.append(dropdown)
maincol.append(date_range_slider)
maincol.append(window_size_slider)


# set the app to be servable
template.main.append(maincol)
template.servable(title="SI649 Altair Assignment 3 (ritaycw)")