#!/usr/bin/env python
# coding: utf-8

# In[58]:


import IPython.core.display as di

# Hide code by default when the notebook is exported as HTML
di.display_html('<script>jQuery(function() {if (jQuery("body.notebook_app").length == 0) { jQuery(".input_area").toggle(); jQuery(".prompt").toggle();}});</script>', raw=True)

# Extend width of page
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# In[57]:


import pandas as pd
import altair as alt

df = pd.read_csv('output/pm2_5_hourly_summary.csv')


# In[39]:


# Filter to Zavala, Douglass, Mesita & Hawkins 

includeKeywords = ["Zavala", "Douglass", "Mesita", "Hawkins"]

dsa_str = '|'.join(includeKeywords)

mask = df.stack().str.contains(dsa_str).any(level=0)

df_masked = df[mask]


### --- Build hurly mean and median charts
source = df_masked

selection = alt.selection_multi(fields=['column_label'], bind='legend')

# set colorscale
domain = ['ry/Zavala 2 B (undefined) (31.771832 -106.447092) Primary ',
          'ry/Zavala E.S (outside) (31.771649 -106.447023) Primary ', 
          'ry/Zavala E.S B (undefined) (31.771649 -106.447023) Primary ',
          'ry/Zavala 2 (outside) (31.771832 -106.447092) Primary ',
          'ry/Mesita E.S (outside) (31.783542 -106.50389) Primary ',
          'ry/Mesita E.S B (undefined) (31.783542 -106.50389) Primary ',
          'ry/Mesita E.S (outside) (31.783542 -106.50389) Primary ',
          'ry/Hawkins E.S B (undefined) (31.777423 -106.419229) Primary ',
          'ry/Hawkins E.S (outside) (31.777423 -106.419229) Primary ',
          'ry/Douglass E.S. B (undefined) (31.766496 -106.464951) Primary ',
          'ry/Douglass E.S. (outside) (31.766496 -106.464951) Primary '
         ]

range_ = ['rgb(57, 59, 121)', 'rgb(82, 84, 163)', 'rgb(107, 110, 207)', 'rgb(156, 158, 222)', 
          'rgb(99, 121, 57)', 'rgb(140, 162, 82)',
          'rgb(132, 60, 57)', 'rgb(173, 73, 74)', 
          'rgb(140, 109, 49)', 'rgb(189, 158, 57)']

chart_mean_all = alt.Chart(source).mark_line().encode(
    alt.X('hour'),
    alt.Y('mean_pm2_5'),
    alt.Color('column_label', scale=alt.Scale(domain=domain, range=range_)),
    opacity=alt.condition(selection, alt.value(1), alt.value(0.1))
).add_selection(
    selection
)


chart_median_all = alt.Chart(source).mark_line().encode(
    alt.X('hour'),
    alt.Y('median_pm2_5'),
    alt.Color('column_label', scale=alt.Scale(domain=domain, range=range_)),
    opacity=alt.condition(selection, alt.value(1), alt.value(0.1))
).add_selection(
    selection
)


# Build faceted hourly charts

source_melt = pd.melt(df_masked, id_vars=['column_label', 'hour'], value_vars=['mean_pm2_5', 'median_pm2_5'])

facet_all = alt.Chart(source_melt).mark_line().encode(
    alt.X('hour', scale=alt.Scale(zero=False)),
    y='value',
    color='variable',
    facet=alt.Facet('column_label:O', columns=2),
).properties(
    width=200,
    height=100,
)


# In[40]:


# Adjust filter to exclude Zavala 2 (outside) 

includeKeywords = ["Zavala E.S B", "Zavala E.S", "Zavala 2 B", "Douglass", "Mesita", "Hawkins"]

dsa_str = '|'.join(includeKeywords)

mask = df.stack().str.contains(dsa_str).any(level=0)

df_filtered = df[mask]



## Hourly mean and median charts without Zavala 2 (outside)
source = df_filtered

selection = alt.selection_multi(fields=['column_label'], bind='legend')

# set colorscale
domain = ['ry/Zavala 2 B (undefined) (31.771832 -106.447092) Primary ',
          'ry/Zavala E.S (outside) (31.771649 -106.447023) Primary ', 
          'ry/Zavala E.S B (undefined) (31.771649 -106.447023) Primary ',
          'ry/Mesita E.S (outside) (31.783542 -106.50389) Primary ',
          'ry/Mesita E.S B (undefined) (31.783542 -106.50389) Primary ',
          'ry/Mesita E.S (outside) (31.783542 -106.50389) Primary ',
          'ry/Hawkins E.S B (undefined) (31.777423 -106.419229) Primary ',
          'ry/Hawkins E.S (outside) (31.777423 -106.419229) Primary ',
          'ry/Douglass E.S. B (undefined) (31.766496 -106.464951) Primary ',
          'ry/Douglass E.S. (outside) (31.766496 -106.464951) Primary '
         ]

range_ = ['rgb(57, 59, 121)', 'rgb(82, 84, 163)', 'rgb(107, 110, 207)', 
          'rgb(99, 121, 57)', 'rgb(140, 162, 82)',
          'rgb(132, 60, 57)', 'rgb(173, 73, 74)', 
          'rgb(140, 109, 49)', 'rgb(189, 158, 57)']

chart_mean_filtered = alt.Chart(source).mark_line().encode(
    alt.X('hour'),
    alt.Y('mean_pm2_5'),
    alt.Color('column_label', scale=alt.Scale(domain=domain, range=range_)),
    opacity=alt.condition(selection, alt.value(1), alt.value(0.1))
).add_selection(
    selection
)


chart_median_filtered = alt.Chart(source).mark_line().encode(
    alt.X('hour'),
    alt.Y('median_pm2_5'),
    alt.Color('column_label', scale=alt.Scale(domain=domain, range=range_)),
    opacity=alt.condition(selection, alt.value(1), alt.value(0.1))
).add_selection(
    selection
)



# Build faceted chart

# Build faceted hourly median charts

source_melt_filtered = pd.melt(df_filtered, id_vars=['column_label', 'hour'], value_vars=['mean_pm2_5', 'median_pm2_5'])

facet_filtered = alt.Chart(source_melt_filtered).mark_line().encode(
    alt.X('hour', scale=alt.Scale(zero=False)),
    y='value',
    color='variable',
    facet=alt.Facet('column_label:O', columns=2),
).properties(
    width=200,
    height=100,
)


# In[61]:


# Mean and median pm2.5 by hour for all Zavala, Hawkins, Douglass, and Mesita primary sensors
print('1. Mean and median pm2.5 by hour for all Zavala, Hawkins, Douglass, and Mesita monitors')

chart_mean_all | chart_median_all


# In[60]:


# Mean and median pm2.5 by hour (excluding Zavala 2 (outside))
print('2. Mean and median pm2.5 by hour (excluding Zavala 2 (outside))')

chart_mean_filtered | chart_median_filtered


# In[59]:


# Mean and median pm2.5 by hour (excluding Zavala 2 (outside))
print('3. Mean and median pm2.5 by hour (excluding Zavala 2 (outside))')
facet_filtered