# This file will generate 3 maps - poverty rate, unemployment rate, and food stamp/SNAP usage rate

# It's set up for New York but that can easily be changed

# The counties are filtered by a list, then the dataframe is filtered by the same list

# The script can be adapted to map different parameters from our dataframe too

# the first time you run this, and after importing bokeh, you'll have to run bokeh.sampledata.download() to get the county boundaries

from bokeh.io import show, output_file

from bokeh.models import LinearColorMapper, ColorBar, Label, WheelZoomTool, HoverTool

from bokeh.models.tickers import ContinuousTicker, SingleIntervalTicker

from bokeh.palettes import viridis

from bokeh.plotting import figure

from bokeh.sampledata.us_counties import data as counties

from bokeh.models.widgets import Panel, Tabs

import pandas as pd

import tkinter as tk

from tkinter import filedialog

root = tk.Tk()

root.withdraw()

# getting the csv file

print("Choose a csv file.")

file_input = filedialog.askopenfilename()

print("Selected: " + file_input)

# Naming

print("Title the maps: ")

input_state = input("Name of the state > ")

input_year = input("Year of the map > ")

desired_filename = input("Output filename > ")

parsed_filename = desired_filename + ".html"

print(parsed_filename)

output_file(parsed_filename, title=input_state + input_year)

# parsing name inputs

povertytitle = input_state + " Poverty Rate, " + input_year

fsratetitle = input_state + " Food Stamp/SNAP use, " + input_year

unemptitle = input_state + " Unemployment Rate, " + input_year

# creating dataframes and filter to stats we'll use

datafile = file_input

df_pcts = pd.read_csv(datafile)

df_stats_unfiltered = df_pcts[['totalpopE', 'NAME', 'pctpoverty', 'pctfoodstamps', 'unemploymentrate']]

# listing the counties we want the map to show

county_filter = ['Sullivan County, New York', 'Ulster County, New York', 'Dutchess County, New York', 'Orange County, New York', 'Putnam County, New York', 'Rockland County, New York', 'Westchester County, New York', 'Bronx County, New York', 'New York County, New York', 'Queens County, New York', 'Kings County, New York', 'Richmond County, New York', 'Nassau County, New York', 'Suffolk County, New York']

df_filter = df_stats_unfiltered['NAME'].isin(county_filter)

df_stats = df_stats_unfiltered[df_filter]

# filtering counties

counties = {

code: county for code, county in counties.items() if county["state"] == "ny" and (county["detailed name"] in county_filter)

}

# getting x/y coords from the lon/lat in dataframe county

county_xs = [county["lons"] for county in counties.values()]

county_ys = [county["lats"] for county in counties.values()]

# getting the data

county_pop = df_stats['totalpopE']

county_names = df_stats['NAME']

county_poverty = df_stats['pctpoverty']

county_fsrate = df_stats['pctfoodstamps']

county_unemprate = df_stats['unemploymentrate']

# x/y and label for stadium

barclays_y = 40.6826 #long/lat for stadium

barclays_x = -73.9754

barclays_label1 = Label(x=-74.52, y=barclays_y, text='Barclays Center', border_line_color=None, background_fill_color='white', background_fill_alpha=0.7, text_font_size='9pt')

barclays_label2 = Label(x=-74.52, y=barclays_y, text='Barclays Center', border_line_color=None, background_fill_color='white', background_fill_alpha=0.7, text_font_size='9pt')

barclays_label3 = Label(x=-74.52, y=barclays_y, text='Barclays Center', border_line_color=None, background_fill_color='white', background_fill_alpha=0.7, text_font_size='9pt')

# setting up color mapping to data

pov_palette = viridis(11)

unemp_palette = viridis(14)

fs_palette = viridis(13)

pov_palette.reverse()

unemp_palette.reverse()

fs_palette.reverse()

pov_color_mapper = LinearColorMapper(palette=pov_palette, low=0, high=0.11)

unemp_color_mapper = LinearColorMapper(palette=unemp_palette, low=0, high=0.07)

fs_color_mapper = LinearColorMapper(palette=fs_palette, low=0, high=0.13)

# assigning data to dictionary in order to feed them into bokeh

data=dict(

)

# assigning plot tools

TOOLS = "pan,wheel_zoom,reset,save"

# generating the poverty plot

poverty = figure(

title=povertytitle, tools=TOOLS, toolbar_location=None,

x_axis_location=None, y_axis_location=None)

# using the patches glyph on poverty

pov_patches = poverty.patches('x', 'y', source=data,

fill_color={'field': 'poverty', 'transform': pov_color_mapper},

fill_alpha=1, line_color="white", line_width=0.5)

pov_hover = HoverTool(tooltips=[

("Name", "@name"), ("Population", "@totpop"), ("Poverty %", "@poverty{1.11%}"), ("(Long, Lat)", "($x, $y)")

], renderers=[pov_patches])

poverty.add_tools(pov_hover)

poverty.grid.grid_line_color = None

poverty.hover.point_policy = "follow_mouse"

poverty.circle(x=barclays_x, y=barclays_y, size=10, line_color="black", fill_color="silver", line_width=0.5)

# generating the SNAP plot

fsrate = figure(

title=fsratetitle, tools=TOOLS, toolbar_location=None,

x_axis_location=None, y_axis_location=None)

# using the patches glyph on fsrate

fs_patches = fsrate.patches('x', 'y', source=data,

fill_color={'field': 'fsrate', 'transform': fs_color_mapper},

fill_alpha=1, line_color="white", line_width=0.5)

fs_hover = HoverTool(tooltips=[

("Name", "@name"), ("Population", "@totpop"), ("SNAP %", "@fsrate{1.11%}"), ("(Long, Lat)", "($x, $y)")

], renderers=[fs_patches])

fsrate.add_tools(fs_hover)

fsrate.grid.grid_line_color = None

fsrate.hover.point_policy = "follow_mouse"

fsrate.circle(x=barclays_x, y=barclays_y, size=10, line_color="black", fill_color="silver", line_width=0.5)

# generating the unemployment plot

unemprate = figure(

title=unemptitle, tools=TOOLS, toolbar_location=None,

x_axis_location=None, y_axis_location=None)

# using the patches glyph on fsrate

un_patches = unemprate.patches('x', 'y', source=data,

fill_color={'field': 'unemprate', 'transform': unemp_color_mapper},

fill_alpha=1, line_color="white", line_width=0.5)

un_hover = HoverTool(tooltips=[

("Name", "@name"), ("Population", "@totpop"), ("Unemployment Rate", "@unemprate{1.11%}"), ("(Long, Lat)", "($x, $y)")

], renderers=[un_patches])

unemprate.add_tools(un_hover)

unemprate.grid.grid_line_color = None

unemprate.hover.point_policy = "follow_mouse"

unemprate.circle(x=barclays_x, y=barclays_y, size=10, line_color="black", fill_color="silver", line_width=0.5)

# making scroll active by default

poverty.toolbar.active_scroll = poverty.select_one(WheelZoomTool)

fsrate.toolbar.active_scroll = fsrate.select_one(WheelZoomTool)

unemprate.toolbar.active_scroll = unemprate.select_one(WheelZoomTool)

# making a legend

pov_color_bar = ColorBar(color_mapper=LinearColorMapper(palette=pov_palette, low=0, high=11), ticker=SingleIntervalTicker(interval=1), title="Population in poverty (%)",

label_standoff=12, width=220, orientation="horizontal", border_line_color=None, location=(0,0))

unemp_color_bar = ColorBar(color_mapper=LinearColorMapper(palette=unemp_palette, low=0, high=7), ticker=SingleIntervalTicker(interval=.5), title="Unemployment rate (%)",

label_standoff=12, width=220, orientation="horizontal", border_line_color=None, location=(0,0))

fs_color_bar = ColorBar(color_mapper=LinearColorMapper(palette=fs_palette, low=0, high=13), ticker=SingleIntervalTicker(interval=1), title="Food Stamps/SNAP use (%)",

label_standoff=12, width=220, orientation="horizontal", border_line_color=None, location=(0,0))

# adding the label on barclays and legend below each map

poverty.add_layout(pov_color_bar, 'below')

unemprate.add_layout(unemp_color_bar, 'below')

fsrate.add_layout(fs_color_bar, 'below')

poverty.add_layout(barclays_label1)

fsrate.add_layout(barclays_label2)

unemprate.add_layout(barclays_label3)

# linking the zoom/pan

unemprate.x_range = poverty.x_range

fsrate.x_range = poverty.x_range

unemprate.y_range = poverty.y_range

fsrate.y_range = poverty.y_range

# citing sources

citation = Label(x=0, y=0, x_units='screen', y_units='screen', text='Source: US Census American Community Survey', border_line_color=None, background_fill_color='white', background_fill_alpha=0.7)

citation2 = Label(x=0, y=0, x_units='screen', y_units='screen', text='Source: US Census American Community Survey', border_line_color=None, background_fill_color='white', background_fill_alpha=0.7)

citation3 = Label(x=0, y=0, x_units='screen', y_units='screen', text='Source: US Census American Community Survey', border_line_color=None, background_fill_color='white', background_fill_alpha=0.7)

poverty.add_layout(citation)

unemprate.add_layout(citation2)

fsrate.add_layout(citation3)

# arranging our maps in tabs

tab1 = Panel(child=poverty, title='Poverty')

tab2 = Panel(child=unemprate, title='Unemployment')

tab3 = Panel(child=fsrate, title='SNAP')

tabs = Tabs(tabs=[tab1, tab2, tab3])

show(tabs)