# start with the setup

# supress warnings about future deprecations
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

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 
df2=pd.read_csv("https://raw.githubusercontent.com/dallascard/SI649_public/main/altair_hw3/approval_topline.csv")
# Import panel and vega datasets

import panel as pn
import vega_datasets

# Enable Panel extensions
pn.extension()
maincol = pn.Column()
# Define a function to create and return a plot

df2_approve = df2[df2['choice'] == 'approve']

def create_plot(subgroup, date_range, moving_av_window):

    # Apply any required transformations to the data in pandas
    filtered_data = df2_approve[df2_approve['subgroup'] == subgroup]
    start_date, end_date = pd.to_datetime(date_range[0]), pd.to_datetime(date_range[1])
    filtered_data = filtered_data[(filtered_data['timestamp'] >= start_date) & (filtered_data['timestamp'] <= end_date)]
    filtered_data['rate_change'] = filtered_data['rate'].rolling(window=moving_av_window).mean()
    
    # Line chart
    base_line = alt.Chart(filtered_data).mark_line(color='red', size=2).encode(
        x='timestamp:T',
        y=alt.Y('rate_change:Q', scale=alt.Scale(domain=[30, 60]))
    )
    # Scatter plot with individual polls
    base_scatter = alt.Chart(filtered_data).mark_point(size=2, opacity=0.7, color="gray").encode(
        x='timestamp:T',
        y=alt.Y('rate:Q'),
    )
    
    # Put them togetehr
    plot = (base_scatter + base_line)
    # Return the combined chart
    
    return plot
    

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

# Create the slider for the date range
date_slider = pn.widgets.DateRangeSlider(
    name='Date Range Slider',
    start = pd.to_datetime('2021-01-26'), 
    end = pd.to_datetime('2023-02-14'),
    value = (pd.to_datetime('2021-01-26'), pd.to_datetime('2023-02-14'))
)

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

# Bind the widgets to the create_plot function
bound_plot=pn.bind(create_plot, subgroup=subgroup_widget, date_range=date_slider, moving_av_window=window_widget)

# Combine everything in a Panel Column to create an app
maincol.append(subgroup_widget)
maincol.append(date_slider)
maincol.append(window_widget)
maincol.append(bound_plot)

# set the app to be servable
maincol.servable()