from bokeh.io import show, output_notebook, output_file

from bokeh.models import (

GeoJSONDataSource,

HoverTool,

LinearColorMapper

)

from bokeh.plotting import figure

from bokeh.palettes import Viridis6

from bokeh.tile_providers import CARTODBPOSITRON

from bokeh.models.widgets import CheckboxGroup, Slider, RangeSlider, Tabs

import json

import collections

import bokeh

from bokeh.io import show, output_notebook, push_notebook

from bokeh.plotting import figure

from bokeh.models import CategoricalColorMapper, HoverTool, ColumnDataSource, Panel

from bokeh.models.widgets import CheckboxGroup, Slider, RangeSlider, Tabs

from bokeh.layouts import column, row, WidgetBox

from bokeh.palettes import Category20_16

from bokeh.application.handlers import FunctionHandler

from bokeh.application import Application

import pandas as pd

import numpy as np

from numpy.random import random

from bokeh.core.properties import Instance, Dict, JSON, Any

from bokeh import events

from bokeh.models import (Select, Column, Row, ColumnDataSource, HoverTool,

Range1d, LinearAxis, GeoJSONDataSource, ColumnarDataSource)

from bokeh.plotting import figure

from bokeh.io import curdoc

# import os

# import datetime

from collections import OrderedDict

from bokeh.models.widgets import Select

from numpy.random import random, normal, lognormal

from bokeh import events

from bokeh.models import (Select, Column, Row, ColumnDataSource, HoverTool,

Range1d, LinearAxis, GeoJSONDataSource, ColumnarDataSource)

from bokeh.models import CheckboxGroup, RadioGroup, Toggle

from bokeh.palettes import Blues9, OrRd9

from bokeh.models import (

BasicTicker, ColumnDataSource, ColorBar,

ColorMapper, CustomJS, Div,

HBar, HoverTool, LinearColorMapper,

LogColorMapper, MultiSelect, PrintfTickFormatter,

Select

)

from bokeh.themes import Theme

import yaml

from bokeh.tile_providers import get_provider, Vendors

from bokeh.io import curdoc

#

# initialize the default view of hood2vec.

#

with open('chi-zip-code-tabulation-areas-2012.geojson','rt') as json_file: # load geojson files as the background

chi_json = json.load(json_file)

zipcodes = collections.defaultdict()

# record zipcodes in geojson into zipcodes list

for zip_idx in range(len(chi_json['features'])):

zipcodes[chi_json['features'][zip_idx]['properties']['ZIP']] = zip_idx

chi_zipcode_to_dist = pd.read_pickle('chi_zipcode_to_dist_'+ 'midday') # load parameters for default view

# chi_zipcode_to_dist stores the content of hood2vec statistical file. It is a dictionary, where keys are zip codes and values are closest zip codes with corresponding distances to key.

# pick up zip codes existing in both geojson file and hood2vec statistical file

shared_zips = collections.defaultdict(int)

for zipcode in chi_zipcode_to_dist:

shared_zips[zipcode] +=1

for zipcode in zipcodes:

shared_zips[zipcode] +=1

zipcodes_sorted = []

for zipcode in shared_zips:

if shared_zips[zipcode] == 2:

zipcodes_sorted.append(zipcode)

# sort the shared zip codes for users to select easily (left part of visualization)

zipcodes_sorted.sort()

initial_zipcodes = zipcodes_sorted[0]

initial_num = 3

initial_period = 'midday'

zipcodes_to_plot = initial_zipcodes

num_neighbor = initial_num

period_str = initial_period

with open('chi-zip-code-tabulation-areas-2012.geojson','rt') as json_file: # load geojson files as the background

chi_json = json.load(json_file)

chi_json['features'][zipcodes[zipcodes_to_plot]]['properties']['color_type'] = 2 # mark the target area a dark color "2"

chi_zipcode_to_dist = pd.read_pickle('chi_zipcode_to_dist_'+ period_str)

# if a zip code exists in both geojson file and hood2vec statistical file, then "YES", we can visualize it. Otherwise, "NO"

for zipcode in zipcodes:

if shared_zips[zipcode] == 2:

chi_json['features'][zipcodes[zipcode]]['properties']['has_data'] = 'YES'

else:

chi_json['features'][zipcodes[zipcode]]['properties']['has_data'] = 'NO'

# if closest zip codes are also in geojson file, we can visualize it. We set them to a lighter color "1"

distances = chi_zipcode_to_dist[zipcodes_to_plot]

num_neighbor_count = 0

for neighbor_idx in range(len(distances)):

if shared_zips[distances[neighbor_idx][0]] == 2:

num_neighbor_count +=1

chi_json['features'][zipcodes[distances[neighbor_idx][0]]]['properties']['color_type'] = 1

if num_neighbor_count == num_neighbor:

break

# convert json to string

geojson = json.dumps(chi_json)

geo_source = GeoJSONDataSource(geojson = geojson)

#

# interactive view of hood2vec.

#

def make_map(zipcodes_to_plot = '60608', num_neighbor = 3, period_str = 'midday', cate= []):

return p

zipcode_selection = Select(options=zipcodes_sorted, value = initial_zipcodes)

num_selection = Select(options=['3','4','5'], value = str(initial_num))

period_selection = Select(options=['overnight',\

'night'], value = initial_period)

category_selection = CheckboxGroup(labels=[' category'], active = [])

# if active = [0], it means there are a total of 1 option, and it is checked;

# if active = [0,1], it means there are a total of 2 options, and they are both checked;

# webpage modules

div_title = Div(text=\

)

div_zipcode = Div(text=\

"<br>Move cursor on the map for the data availability of a zip code. "+\

"""For a zip code with "Has Data: Yes", you can select that <b>zip code</b> from this menu: """

)

div_num = Div(text=\

"<br>For the selected zip code, you can select the <b>number</b> of nearest neighbors "+\

"to be visualized under <tt>hood2vec</tt> metric:"

)

div_period = Div(text=\

"<br>You can select a <b>period</b> "+\

"to visualize period-dependent nearest neighbors of selected zip code:"

)

div_cate = Div(text=\

)

# pass user's selected parameters to backend

def update_plot(attr, old, new):

cate = category_selection.active)

# user's actions to update parameters

zipcode_selection.on_change('value', update_plot)

num_selection.on_change('value', update_plot)

period_selection.on_change('value', update_plot)

category_selection.on_change('active', update_plot)

# webpage layout

layout = row(Column(div_title,div_zipcode,zipcode_selection,div_num,num_selection,\

div_period,period_selection, div_cate, category_selection), make_map())

# doc.add_root(layout)

curdoc().add_root(layout)