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plot_functions.py
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plot_functions.py
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'''
Retrieve the relevant data from the database
to be displayed in a table as results.
'''
import sqlite3
import os
import csv
import json
import requests
import re
import datetime
from datetime import timedelta
import time
import importlib
import pipeline
import process_data
from scipy import stats
import pandas as pd
import numpy as np
from geopandas import GeoDataFrame
import geopandas as gpd
import json
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.ioff()
from matplotlib import colors
from matplotlib.patches import RegularPolygon
import seaborn as sns
import shapely
from shapely.geometry import Point
from shapely.geometry import Polygon
from shapely import wkt
import plotly.express as px
import plotly.graph_objects as go
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import warnings
from pandas.core.common import SettingWithCopyWarning
warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
import sys
DATA_DIR = os.path.dirname(__file__)
#DATABASE_FILENAME = os.path.join(DATA_DIR, 'db.sqlite3')
OUTDIR = os.path.join(DATA_DIR, 'data')
INDIR = os.path.join(OUTDIR, 'downloaded')
NEIGH_DIR = os.path.join(OUTDIR, 'neighs')
TEMP_DIR = '/Users/ldinh/Documents/GitHub/Air-Quality-Tool/data/points/'
STATIC = os.path.join(DATA_DIR, 'static/graphs')
def unload_geo(filename):
df = pd.read_csv(os.path.join(OUTDIR, filename))
df['geometry'] = df['geometry'].apply(wkt.loads)
df = gpd.GeoDataFrame(df, geometry='geometry')
df.crs = "EPSG:4326"
return df
def plot_summary_by_neighborhood(df, timevar):
by_neigh = df[[timevar] + ['pri_neigh', 'sum', 'count']].groupby([timevar] + ['pri_neigh']).sum().reset_index()
if timevar:
by_neigh['timevar_count'] = 1
else:
by_neigh['timevar_count'] = by_neigh['count']
by_neigh = by_neigh.groupby(['pri_neigh']).sum().reset_index()
by_neigh['mean'] = by_neigh['sum']/by_neigh['count']
by_neigh = by_neigh.sort_values(by='mean')
by_neigh = by_neigh.set_index('pri_neigh')
y1 = df['thirtymins'].dt.year.min()
y2 = df['thirtymins'].dt.year.max()
unit_dict = {'Daily': 'days', 'MonthYear': 'months', 'Year': 'years'}
fig = plt.figure(figsize=(20, 10))
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
width = .35
by_neigh['mean'].plot(kind='bar', color='red', ax=ax1, width=width, position=0)
by_neigh['timevar_count'].plot(kind='bar', color='green', ax=ax2, width=width, position=1)
ax1.set_ylabel('Average PM2.5')
ax1.set_xlabel('Neighborhood')
ax1.set_xlim(-1, len(by_neigh))
if timevar:
ax2.set_ylabel('Number of ' + unit_dict[timevar] + ' with measurements ' + '(' + str(y1) + '-' + str(y2) + ')')
else:
ax2.set_ylabel('Number of measurements ' + '(' + str(y1) + '-' + str(y2) + ')')
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
if timevar:
ax2.legend(lines1 + lines2, ['Average PM2.5', 'Number of ' + unit_dict[timevar] + ' with measurements ' + '(' + str(y1) + '-' + str(y2) + ')'], loc='upper center')
else:
ax2.legend(lines1 + lines2, ['Average PM2.5', 'Number of measurements ' + '(' + str(y1) + '-' + str(y2) + ')'], loc='upper center')
fig.savefig('static/graphs/neighborhood_summary_{}.png'.format(timevar), transparent=False, bbox_inches='tight')
def plot_avg_figures(combined, var_list, label_list, scat_x, scat_y, line_ax1, line_ax2, f):
years = [x for x in list(combined.index.year.unique()) if x > 2017]
for y in years:
temp = combined.dropna(0)
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year.isin(years))]
#temp = temp.resample('D').asfreq()
vmin = min(temp[var_list].min())
vmax = max(temp[var_list].max())
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year == y)]
cell_s = len(temp.index.unique())
figure, axes = plt.subplots(figsize=(4, 0.25*cell_s))
sns.heatmap(temp[var_list], annot=True, linewidths=.5, ax=axes, cmap="Reds", vmin=vmin, vmax=vmax, fmt='.1f', cbar_kws={"shrink": 0.5})
axes.axes.set_title("Daily Avg PM2.5: Summer " + str(y), fontsize=10, y=1.01)
axes.axes.set_xlabel("Source", labelpad=15, rotation=0)
axes.axes.set_xticklabels(labels=label_list, rotation=0)
axes.axes.set_ylabel("Date", labelpad=20, rotation=0)
axes.axes.set_yticklabels(labels=temp.index.strftime('%Y-%m-%d').sort_values().unique())
figure.savefig('static/graphs/{}/Heatmap_Daily_Avg_PM25_Summer_{}.png'.format(f, y), transparent=False, bbox_inches='tight')
#plt.yticks(rotation=0)
for y in years:
temp = combined.dropna(0)
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year.isin(years))]
#temp = temp.resample('D').sum().fillna(0)
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year == y)]
figure = pipeline.plot_2_axis(temp, temp.index, {'ax1': line_ax1, 'ax2': line_ax2},
'Daily Avg PM2.5', '', title='Summer ' + str(y), figsize=(12,6))
figure.savefig('static/graphs/{}/Timeseries_Daily_Avg_PM25_Summer_{}.png'.format(f, y), transparent=False, bbox_inches='tight')
for y in years:
temp = combined.dropna(0)
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year.isin(years))]
#temp = temp.resample('D').sum().fillna(0)
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year == y)]
for v1 in scat_y:
figure = pipeline.plot_scatter(temp, scat_x, v1, hue=None, title='Daily Avg PM2.5: Summer ' + str(y), figsize=(8,8))
figure.savefig('static/graphs/{}/Scatter_Daily_Avg_PM25_{}_vs_{}_by_{}.png'.format(f, scat_x, v1, y), transparent=False, bbox_inches='tight')
for v1 in scat_y:
for v2 in [('Year', (7,7)), (None, (8,8))]:
temp = combined.dropna(0)
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year.isin(years))]
figure = pipeline.plot_scatter(temp, scat_x, v1, hue=v2[0], title='Daily Avg PM2.5: All years', figsize=v2[1])
figure.savefig('static/graphs/{}/Scatter_Daily_Avg_PM25_{}_vs_{}_by_{}.png'.format(f, scat_x, v1, v2[0]), transparent=False, bbox_inches='tight')
for y in years:
temp = combined.dropna(0)
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year.isin([2018, 2019, 2020]))]
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year == y)]
temp = pd.melt(temp, id_vars=['Daily'], value_vars=var_list)
figure, ax = plt.subplots(figsize=(5, 10))
sns.boxplot(y="value", x="variable", data=temp,
whis=[0, 100], palette="vlag", ax=ax)
sns.swarmplot(y="value", x="variable", data=temp,
size=2, color=".3", linewidth=0)
ax.yaxis.grid(True)
ax.set(xlabel="Summer " + str(y))
ax.set_xticklabels(labels=label_list)
ax.set(ylabel="Daily Avg PM2.5")
sns.despine(trim=True, left=True)
figure.savefig('static/graphs/{}/Boxplot_Daily_Avg_PM25_Summer_{}.png'.format(f, y), transparent=False, bbox_inches='tight')
temp = combined.dropna(0)
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year.isin(years))]
temp = pd.melt(temp, id_vars=['Daily'], value_vars=var_list)
fig, ax = plt.subplots(figsize=(5, 10))
sns.boxplot(y="value", x="variable", data=temp,
whis=[0, 100], palette="vlag", ax=ax)
sns.swarmplot(y="value", x="variable", data=temp,
size=2, color=".3", linewidth=0)
ax.yaxis.grid(True)
ax.set(xlabel="All years")
ax.set_xticklabels(labels=label_list)
ax.set(ylabel="Daily Avg PM2.5")
sns.despine(trim=True, left=True)
fig.savefig('static/graphs/{}/Boxplot_Daily_Avg_PM25_All_Summers.png'.format(f), transparent=False, bbox_inches='tight')
def plot_harmful_figures(combined, harmful, harmful_lab, not_harmful, not_harmful_lab, f):
years = [x for x in list(combined.index.year.unique()) if x > 2017]
for y in years:
temp = combined.dropna(0)
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year.isin(years))]
#temp = temp.resample('D').asfreq()
vmin = min(temp[harmful].min())
vmax = max(temp[harmful].max())
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year == y)]
cell_s = len(temp.index.unique())
figure, axes = plt.subplots(figsize=(4, 0.25*cell_s))
sns.heatmap(temp[harmful], annot=True, linewidths=.5, ax=axes, cmap="Reds", vmin=vmin, vmax=0.1, fmt='.1%', cbar_kws={"shrink": 0.5})
axes.axes.set_title("PM2.5 Measurements >=35 (%): Summer " + str(y), fontsize=10, y=1.01)
axes.axes.set_xlabel("Source", labelpad=15, rotation=0)
axes.axes.set_xticklabels(labels=harmful_lab, rotation=0)
axes.axes.set_ylabel("Date", labelpad=20, rotation=0)
axes.axes.set_yticklabels(labels=temp.index.strftime('%Y-%m-%d').sort_values().unique())
figure.savefig('static/graphs/{}/Heatmap_Daily_Harmful_PM25_Summer_{}.png'.format(f, y), transparent=False, bbox_inches='tight')
#plt.yticks(rotation=0)
for y in years:
temp = combined.dropna(0)
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year.isin(years))]
#temp = temp.resample('D').asfreq()
vmin = min(temp[not_harmful].min())
vmax = max(temp[not_harmful].max())
temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year == y)]
cell_s = len(temp.index.unique())
figure, axes = plt.subplots(figsize=(4, 0.25*cell_s))
sns.heatmap(temp[not_harmful], annot=True, linewidths=.5, ax=axes, cmap="Greens", vmin=vmin, vmax=vmax, fmt='.1%', cbar_kws={"shrink": 0.5})
axes.axes.set_title("PM2.5 Measurements <=12 (%): Summer " + str(y), fontsize=10, y=1.01)
axes.axes.set_xlabel("Source", labelpad=15, rotation=0)
axes.axes.set_xticklabels(labels=not_harmful_lab, rotation=0)
axes.axes.set_ylabel("Date", labelpad=20, rotation=0)
axes.axes.set_yticklabels(labels=temp.index.strftime('%Y-%m-%d').sort_values().unique())
figure.savefig('static/graphs/{}/Heatmap_Daily_Low_PM25_Summer_{}.png'.format(f, y), transparent=False, bbox_inches='tight')
#plt.yticks(rotation=0)
#for y in [2018, 2019, 2020]:
#temp = combined.dropna(0)
#temp = temp.resample('D').sum().fillna(0)
#temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year == y)]
#figure = process_data.plot_2_axis(temp, temp.index, {'ax1': [('AQ_harmful', 'green', '-'), ('EPA_harmful', 'coral', '--'), ('PA_harmful', 'yellow', '--')], 'ax2': None},
#'PM2.5 Measurements >=35 (%)', '', title='Summer ' + str(y), figsize=(16,8))
#figure.savefig('static/graphs/{}/Timeseries_Daily_Harmful_PM25_Summer_{}.png'.format(f, y))
#for y in [2018, 2019, 2020]:
#temp = combined.dropna(0)
#temp = temp.resample('D').sum().fillna(0)
#temp = temp[temp.index.month.isin([4,5,6,7,8,9]) & (temp.index.year == y)]
#figure = process_data.plot_2_axis(temp, temp.index, {'ax1': [('AQ_not_harmful', 'green', '-'), ('EPA_not_harmful', 'coral', '--'), ('PA_not_harmful', 'yellow', '--')], 'ax2': None},
#'PM2.5 Measurements <=12 (%)', '', title='Summer ' + str(y), figsize=(16,8))
#figure.savefig('static/graphs/{}/Timeseries_Daily_Low_PM25_Summer_{}.png'.format(f, y))
def plot_daily_maps(epa, purpleair, aq_hex, days):
fig = px.choropleth_mapbox(aq_hex, geojson=aq_hex, color="Average PM2.5", opacity=0.8,
locations="id", featureidkey="properties.id",
animation_frame='Date',
range_color=[0,30],
color_continuous_scale='YlOrRd', zoom=13,
category_orders={'Yearly': ['All', '2016', '2017', '2018', '2019', '2020'],
'Hourly': ['All', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11',
'12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24'],
'Monthly': ['All', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12'],
'MonthYear': ['All'],
'Date': days,
'Average PM2.5 Group': ["0-6", "6-12", "12-22", "22-35", "35+"]},
hover_name='Source',
hover_data={'Average PM2.5': True, 'id': False, 'Date': False},
center={"lat": 41.8781, "lon": -87.6298})
fig2 = px.choropleth_mapbox(epa, geojson=epa, color='Average PM2.5', opacity=0.5,
locations="site_number", featureidkey="properties.site_number",
#size='count',
#hover_name="value_represented",
hover_name='Source',
hover_data={'Average PM2.5': True, 'Date': False, 'site_number': False, 'EPA Station': True},
range_color=[0,30],
color_continuous_scale='YlOrRd', zoom=13,
animation_frame='Date')
fig2.update_traces(marker_line_width=1)
fig3 = px.scatter_mapbox(purpleair, lat='lat', lon='lon', color='Average PM2.5', opacity=0.8,
#size=12,
hover_name='Source',
hover_data={'Average PM2.5': True, 'Date': False, 'lat': False, 'lon': False},
range_color=[0,30],
color_continuous_scale='YlOrRd', zoom=13,
animation_frame='Date')
fig3.update_traces(marker=dict(size=15, symbol='circle'))
fig.add_trace(fig2.data[0], secondary_y=False)
fig.add_trace(fig3.data[0], secondary_y=False)
for i, frame in enumerate(fig.frames):
fig.frames[i].data += (fig2.frames[i].data[0],)
fig.frames[i].data += (fig3.frames[i].data[0],)
fig.update_layout(mapbox_style="carto-positron")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0}, legend=dict(yanchor="bottom", xanchor="left"), width=1200, height=600)
return fig
def plot_neigh(aq_points_neigh_agg, timevar):
lat_cent = round(aq_points_neigh_agg['lat_r'].median(), 4)
lon_cent = round(aq_points_neigh_agg['lng_r'].median(), 4)
fig = px.scatter_mapbox(aq_points_neigh_agg,
lat='lat_r', lon='lng_r',
color='Average PM2.5 Group', #size='count',
#color_discrete_sequence=['yellow'],
#color_continuous_scale=px.colors.cyclical.IceFire,
color_discrete_map = {'0-6': "#31a354",
'6-12': "#bae4b3",
'12-22': "#fc8d59",
'22-35': "#e34a33",
'35+': "#b30000"},
zoom=13,
animation_frame=timevar,
#hover_name='Average PM2.5 Group',
hover_data={'mean': True, 'time': True, 'Average PM2.5 Group': False, 'lat_r': False, 'lng_r': False, timevar: False},
category_orders={'Yearly': ['All', '2016', '2017', '2018', '2019', '2020'],
'Hourly': ['All', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11',
'12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24'],
'Monthly': ['All', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12'],
'MonthYear': ['All'],
'Average PM2.5 Group': ["0-6", "6-12", "12-22", "22-35", "35+"]},
center={"lat": lat_cent, "lon": lon_cent})
fig.update_layout(mapbox_style="carto-positron")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0}, legend=dict(yanchor="bottom", xanchor="left"), width=600, height=600)
return fig
def plot_neigh_block(aq_points_neigh_agg, timevar):
lat_cent = round(aq_points_neigh_agg['geometry'].centroid.y.median(), 4)
lon_cent = round(aq_points_neigh_agg['geometry'].centroid.x.median(), 4)
fig = px.choropleth_mapbox(aq_points_neigh_agg, geojson=aq_points_neigh_agg,
locations='geoid10', featureidkey="properties.geoid10",
color='Average PM2.5 Group', #size='count',
#color_discrete_sequence=['yellow'],
#color_continuous_scale=px.colors.cyclical.IceFire,
color_discrete_map = {'Low 90%': "#ffffcc",
'Top 10%': "#b30000"},
zoom=13,
animation_frame=timevar,
#hover_name='Average PM2.5 Group',
hover_data={'mean': True, 'count': True, 'geoid10': False, 'Average PM2.5 Group': False, timevar: False},
category_orders={'Yearly': ['All', '2016', '2017', '2018', '2019', '2020'],
'Hourly': ['All', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11',
'12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24'],
'Monthly': ['All', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12'],
'MonthYear': ['All'],
'Average PM2.5 Group': ['Highest 10%', 'Remaining 90%']},
center={"lat": lat_cent, "lon": lon_cent})
fig.update_layout(mapbox_style="carto-positron")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0}, legend=dict(yanchor="bottom", xanchor="left"), width=600, height=600)
return fig