def _init_viz_schema(self):
self.state_counties = {
code: county for code, county in counties.items() if county["state"] == "wa"
}
self.county_xs = [county["lons"] for county in self.state_counties.values()]
self.county_ys = [county["lats"] for county in self.state_counties.values()]
self.county_names = [county['name'] for county in self.state_counties.values()]
self.county_rates = []
self.rate_categories = []
for county_name in self.county_names:
cfg = self.county_data.get(county_name, {'Confirmed_Cases': 0})
val = cfg['Confirmed_Cases']
self.county_rates.append(val)
if val is 0:
self.rate_categories.append('OK')
elif 0 < val <= 10:
self.rate_categories.append('Arriving')
elif 10 < val <= 50:
self.rate_categories.append('Warn')
else:
self.rate_categories.append('Severe')
def plot(location, predictions,state_name):
from bokeh.sampledata.us_counties import data as counties
palette.reverse()
counties = {code: county for code, county in counties.items() if county["state"] in [state_name]}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]#Make sure names match with data
county_rates = [unemployment[county_id] for county_id in counties]#These would be the predictions
#color_mapper = LogColorMapper(palette=palette)
color_mapper = LinearColorMapper(palette=palette,low=25, high=75)
data=dict(x=county_xs,y=county_ys,name=county_names,rate=predictions,)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(title="Vote Preference Predictions", tools=TOOLS, plot_width=900,
#x_axis_location=None, y_axis_location=None,tooltips=[("Name", "@name"), ("Unemployment rate)", "@rate%"), ("(Long, Lat)", "($x, $y)")])
x_axis_location=None, y_axis_location=None,tooltips=[("Name", "@name"), ("Yes vote (percent)", "@rate%")])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x', 'y', source=data,fill_color={'field': 'rate', 'transform': color_mapper},fill_alpha=1.0, line_color="white", line_width=0.5)
script, div = components(p)
return script, div
def bokeh_chloropleth(state, values):
"""Creates a Bokeh chloropleth for a given state's counties
given values.
Args:
state (str): The state to analyze.
values (list): A list of county values.
"""
# Define county shapes.
counties = {
code: county
for code, county in counties.items() if county["state"] == state
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
# Define county values
# county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
data = dict(
x=county_xs,
y=county_ys,
name=county_names,
value=values,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(title="Washington State Cannabis Cultivators",
tools=TOOLS,
x_axis_location=None,
y_axis_location=None,
tooltips=[("Name", "@name"), ("Number", "@value%"),
("(Long, Lat)", "($x, $y)")])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x',
'y',
source=data,
fill_color={
'field': 'value',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5,
plot_width=800,
plot_height=500)
show(p)
return p
def plot(location, predictions, state_name):
from bokeh.sampledata.us_counties import data as counties
#print(counties.keys())
counties = {
code: county
for code, county in counties.items()
if county["state"] in [state_name]
}
print(len(counties))
#print(type(counties))
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()
] #Make sure names match with data
print(county_names)
color_mapper = LinearColorMapper(palette=palette, low=25, high=75)
data = dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=predictions,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(title="Vote Preference Predictions",
tools=TOOLS,
plot_width=900,
x_axis_location=None,
y_axis_location=None,
tooltips=[("Name", "@name"), ("Yes vote (percent)", "@rate%")])
color_bar = ColorBar(color_mapper=color_mapper,
ticker=LogTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
p.add_layout(color_bar, 'right')
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x',
'y',
source=data,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=1.0,
line_color="white",
line_width=0.5)
script, div = components(p)
return script, div
def create_plot(ridb):
or_counties = {
code: county
for code, county in counties.items() if county["state"] == "or"
}
county_xs = [county["lons"] for county in or_counties.values()]
county_ys = [county["lats"] for county in or_counties.values()]
county_names = [county['name'] for county in or_counties.values()]
lat = ridb['FacilityLatitude']
lon = ridb['FacilityLongitude']
source = ColumnDataSource(
data=dict(x=county_xs, y=county_ys, name=ridb['FacilityName']))
TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
ridb = ridb.assign(Secret="TRILLIUM")
df_source = ColumnDataSource(
data=ridb[['FacilityName', 'FacilityPhone', 'Secret']])
p = figure(title="Oregon Counties", tools=TOOLS)
p.circle(lon,
lat,
size=8,
color='navy',
alpha=1,
source=df_source,
name='geo_points')
p.patches('x',
'y',
source=source,
fill_color='green',
fill_alpha=0.7,
line_color="black",
line_width=0.5)
hover = HoverTool(names=['geo_points'])
hover.tooltips = [
(c, '@' + c)
for c in ridb[['FacilityName', 'FacilityPhone', 'Secret']].columns
]
p.add_tools(hover)
return p
def bokehPlot(p):
from bokeh.io import show
from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis6 as palette
try:
from bokeh.sampledata.unemployment import data as unemployment
from bokeh.sampledata.us_counties import data as counties
except:
import bokeh
bokeh.sampledata.download()
from bokeh.sampledata.unemployment import data as unemployment
from bokeh.sampledata.us_counties import data as counties
palette = tuple(reversed(palette))
counties = {
code: county
for code, county in counties.items() if county["state"] == "tx"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
data = dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
)
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x',
'y',
source=data,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
def gen_texas():
from bokeh.io import show
from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
palette.reverse()
counties = {
code: county for code, county in counties.items() if county["state"] == "tx"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
data=dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
title="Texas Unemployment, 2009", tools=TOOLS,
x_axis_location=None, y_axis_location=None,
tooltips=[
("Name", "@name"), ("Unemployment rate)", "@rate%"), ("(Long, Lat)", "($x, $y)")
])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x', 'y', source=data,
fill_color={'field': 'rate', 'transform': color_mapper},
fill_alpha=0.7, line_color="white", line_width=0.5)
show(p)
def add_location_to_dfm(dfm, state="ca"):
""" Given dataframe with County field adds county boundaries in order to
be used in the plotting routine """
# Build County boundary DataFrame from bokeh county data
from bokeh.sampledata.us_counties import data as counties
counties = {
code: county
for code, county in counties.items() if county["state"] == state
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
location_df = pd.DataFrame({
'County': county_names,
'x': county_xs,
'y': county_ys
})
return dfm.merge(location_df, on='County', how='left')
def create_plot(ridb) :
or_counties = {
code: county for code, county in counties.items() if county["state"] == "or"
}
county_xs = [county["lons"] for county in or_counties.values()]
county_ys = [county["lats"] for county in or_counties.values()]
county_names = [county['name'] for county in or_counties.values()]
lat = ridb['FacilityLatitude']
lon = ridb['FacilityLongitude']
source = ColumnDataSource(data=dict(
x=county_xs,
y=county_ys,
name=ridb['FacilityName']
))
TOOLS="pan,wheel_zoom,box_zoom,reset,save"
ridb = ridb.assign(Secret = "TRILLIUM")
df_source = ColumnDataSource(data=ridb[['FacilityName','FacilityPhone','Secret']])
p = figure(title="Oregon Counties", tools=TOOLS)
p.circle(lon, lat, size=8, color='navy', alpha=1, source=df_source, name='geo_points')
p.patches('x', 'y', source=source,
fill_color='green', fill_alpha=0.7,
line_color="black", line_width=0.5)
hover = HoverTool(names=['geo_points'])
hover.tooltips = [(c, '@' + c) for c in ridb[['FacilityName','FacilityPhone','Secret']].columns]
p.add_tools(hover)
return p
def create_plot(ridb, list_to_display) :
ridb_show = ridb[list_to_display]
or_counties = {
code: county for code, county in counties.items() if county["state"] == "or"
}
county_xs = [county["lons"] for county in or_counties.values()]
county_ys = [county["lats"] for county in or_counties.values()]
county_names = [county['name'] for county in or_counties.values()]
lat = ridb['FacilityLatitude']
lon = ridb['FacilityLongitude']
source = ColumnDataSource(data=dict(
x=county_xs,
y=county_ys,
))
TOOLS="pan,wheel_zoom,box_zoom,reset,save"
df_source = ColumnDataSource(data=ridb_show)
p = figure(title="Camping in Oregon!", tools=TOOLS)
p.circle(lon, lat, size=8, color='navy', alpha=1, source=df_source, name='geo_points')
p.patches('x', 'y', source=source,
fill_color='green', fill_alpha=0.7,
line_color="black", line_width=0.5)
hover = HoverTool(names=['geo_points'])
for c in ridb_show.columns:
print(c)
hover.tooltips = [(c, '@' + c) for c in ridb_show.columns]
p.add_tools(hover)
return p
x=latLong_recovered[0],
y=latLong_recovered[1],
size=10 *
np.log10(total.loc[total['status'] == 'recovered'][latestDateCol])))
latLong_deaths = merc_from_arrays(
total.loc[total['status'] == 'deaths']['Lat'],
total.loc[total['status'] == 'deaths']['Long'])
map_source_deaths = ColumnDataSource(
data=dict(x=latLong_deaths[0],
y=latLong_deaths[1],
size=10 *
np.log10(total.loc[total['status'] == 'deaths'][latestDateCol])))
#################################################### Choropleth
counties = {code: county for code, county in counties.items()}
US_county_confirmed = US_total.loc[US_total['status'] ==
'confirmed']['Province/State']
US_county_recovered = US_total.loc[US_total['status'] ==
'recovered']['Province/State']
US_county_deaths = US_total.loc[US_total['status'] ==
'deaths']['Province/State']
# Join county patches to case count data by joining on county key
print(f'---------------US_county_confirmed, {US_county_confirmed}')
county_df = pd.DataFrame.from_dict(counties, orient='index')
county_df['state'] = county_df['state'].str.upper()
county_df['Province/State'] = county_df['name'] + ', ' + county_df['state']
county_join_df = US_total.join(county_df.set_index('Province/State'),
on='Province/State')
def plot_map(pollutant, date, all_pollutants_df):
'''
Output a map of all the counties and display the pollutant levels on a specific day
'''
from bokeh.sampledata.us_counties import data as counties
#The Bokeh counties data has 58 counties listed in CA, but EPA never has all 58 counties
#for the pollutants. Here I am copying all the lat/long data for the counties that I have======
#from original BOKEH SAMPLE
counties = {
code: county
for code, county in counties.items() if county["state"] == "ca"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
my_counties = all_pollutants_df[pollutant].columns
#Change Log/Lat to use my_counties
county_xs_new = []
county_ys_new = []
for c in my_counties:
county_xs_new.append(county_xs[county_names.index(c)])
county_ys_new.append(county_ys[county_names.index(c)])
#Plot =========
from bokeh.io import show, export_png
# export_png is dependent on selenium. run:
# conda install selenium phantomjs pillow
from bokeh.models import LinearColorMapper, BasicTicker, ColorBar
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
color_mapper = LinearColorMapper(palette=palette)
data = dict(
x=county_xs_new,
y=county_ys_new,
name=my_counties,
rate=list(all_pollutants_df[pollutant].loc[date]),
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
pollutant_info = {
'co': ['ppm', 'Carbon monoxide'],
'no2': ['ppb', 'Nitrogen dioxide (NO2)'],
'ozone': ['ppm', 'Ozone'],
'pb': ['ug/m3', 'Lead'],
'pm2_5': ['ug/m3', 'PM2.5'],
'pm10': ['ug/m3', 'PM10'],
'so2': ['ppb', 'Sulfur dioxide']
}
p = figure(title="California %s, %s" %
(pollutant_info[pollutant][1], date),
tools=TOOLS,
x_axis_location=None,
y_axis_location=None,
tooltips=[("Name", "@name"),
("%s" % pollutant_info[pollutant][1],
"@rate %s" % pollutant_info[pollutant][0]),
("(Long, Lat)", "($x, $y)")])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x',
'y',
source=data,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=BasicTicker(),
label_standoff=12,
border_line_color=None,
location=(0, 0))
p.add_layout(color_bar, 'right')
#export_png(p,'Results/CA_ozone_%s.png'%t.date())
show(p)
def map():
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
palette.reverse()
counties = {
code: county
for code, county in counties.items() if county["state"] == "tx"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
source = ColumnDataSource(data=dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
))
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(title="Texas Unemployment, 2009",
tools=TOOLS,
x_axis_location=None,
y_axis_location=None)
p.grid.grid_line_color = None
p.patches('x',
'y',
source=source,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [
("Name", "@name"),
("Unemployment rate)", "@rate%"),
("(Long, Lat)", "($x, $y)"),
]
#show(p)
# grab the static resources
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
# render template
script, div = components(p)
html = render_template(
'index.html',
plot_script=script,
plot_div=div,
js_resources=js_resources,
css_resources=css_resources,
)
return encode_utf8(html)
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
from bokeh.models.widgets import Panel, Tabs
from bokeh.io import output_file, show
from bokeh.models import LogColorMapper, LogTicker, ColorBar
from bokeh.models import ColumnDataSource, HoverTool
import bokeh.palettes as bp
from bokeh.palettes import Viridis6 as myPalette # @UnresolvedImport
EXCLUDED = ("ak", "hi", "pr", "gu", "vi", "mp", "as")
uscountiesdf = pd.DataFrame(us_counties_data)
print(uscountiesdf)
us_states = us_states.data.copy()
counties = {
code: county
for code, county in us_counties_data.items()
if county["state"] not in EXCLUDED
}
unemployment = unemployment.data
del us_states["HI"]
del us_states["AK"]
state_xs = [us_states[code]["lons"] for code in us_states]
state_ys = [us_states[code]["lats"] for code in us_states]
county_xs = [
us_counties_data[code]["lons"] for code in us_counties_data
if us_counties_data[code]["state"] not in EXCLUDED
]
county_ys = [
x=latLong_recovered[0],
y=latLong_recovered[1],
size=10 * np.log10(total.loc[total['status'] == 'recovered'][dates[
timeslider.value]].replace(to_replace=0, value=1)))
map_source_deaths.data = dict(
x=latLong_deaths[0],
y=latLong_deaths[1],
size=10 * np.log10(total.loc[total['status'] == 'deaths'][dates[
timeslider.value]].replace(to_replace=0, value=1)))
print(f'Current date: {dates[timeslider.value]}')
#################################################### MAP PLOT ####################################################
# Choropleth
counties = {code: county for code, county in counties.items()}
county_lons = [county["lons"] for county in counties.values()]
county_lats = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
US_latLong_confirmed = merc_from_arrays(
total.loc[total['status'] == 'confirmed']['Lat'],
total.loc[total['status'] == 'confirmed']['Long'])
US_map_source_confirmed = ColumnDataSource(data=dict(
x=latLong_confirmed[0],
y=latLong_confirmed[1],
size=10 *
np.log10(total.loc[total['status'] == 'confirmed'][latestDateCol])))
US_latLong_recovered = merc_from_arrays(
def show_analytics():
st.title("Land analytics :seedling:")
st.text(
"Welcome to the analytics page, here you can easily view all your data"
)
crops = {
'Peppers': 150,
'Tomatoes': 300,
'Beet': 100,
'Rice': 63,
}
data = pd.Series(crops).reset_index(name='value').rename(
columns={'index': 'crop'})
data['angle'] = data['value'] / data['value'].sum() * 2 * np.pi
data['color'] = Category20c[len(crops)]
p = figure(plot_height=350,
title="Pie Chart",
toolbar_location=None,
tools="hover",
tooltips="@crop: @value",
x_range=(-0.5, 1.0))
p.wedge(x=0,
y=1,
radius=0.3,
start_angle=cumsum('angle', include_zero=True),
end_angle=cumsum('angle'),
line_color="white",
fill_color='color',
legend_field='crop',
source=data)
p.axis.axis_label = None
p.axis.visible = False
p.grid.grid_line_color = None
st.markdown("## Crop diversity")
st.bokeh_chart(p, use_container_width=True)
st.markdown("## Land Nitrogen status")
n = 500
x = 2 + 2 * np.random.uniform(-5, 5, n)
y = 2 + 2 * np.random.uniform(-5, 5, n)
p = figure(title="Farm hexagons",
match_aspect=True,
tools="wheel_zoom,reset",
background_fill_color='#440154')
p.grid.visible = False
r, bins = p.hexbin(x, y, size=0.5, hover_color="pink", hover_alpha=0.8)
p.circle(x, y, color="white", size=1)
p.add_tools(
HoverTool(tooltips=[("amount", "@c"), ("(q,r)", "(@q, @r)")],
mode="mouse",
point_policy="follow_mouse",
renderers=[r]))
st.bokeh_chart(p)
st.markdown("## Soil humidity")
palette = tuple(reversed(Viridis6))
counties = {
code: county
for code, county in us_counties.items() if county["state"] == "wy"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
crops = [np.random.choice(list(crops.keys())) for _ in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
data = dict(
x=county_xs,
y=county_ys,
name=crops,
rate=county_rates,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(title="Soil humidity in your field",
tools=TOOLS,
x_axis_location=None,
y_axis_location=None,
tooltips=[("Crop", "@name"), ("Soil humidity", "@rate%"),
("(Long, Lat)", "($x, $y)")])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x',
'y',
source=data,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
st.bokeh_chart(p)
import pandas as pd
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('st',
metavar='N',
type=str,
help='an integer for the accumulator')
args = parser.parse_args()
palette.reverse()
counties = {
code: county
for code, county in counties.items() if county["state"] == args.st.lower()
}
us_election1 = pd.read_csv('../../data/xaa.csv',
error_bad_lines=False,
sep=';')
us_election2 = pd.read_csv('../../data/xab.csv',
error_bad_lines=False,
sep=';')
us_election3 = pd.read_csv('../../data/xac.csv',
error_bad_lines=False,
sep=';')
us_election4 = pd.read_csv('../../data/xad.csv',
error_bad_lines=False,
sep=';')
frames = [us_election1, us_election2, us_election3, us_election4]
from bokeh.io import show
from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
palette = tuple(reversed(palette))
counties = {
code: county for code, county in counties.items() if county["state"] == "tx"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
data=dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
def getCountyDict(counties):
countydict = {}
for code, county in counties.items():
if counties[code]["state"] == "ia":
countydict[code] = county
return countydict
def plot_counties(df, variable_to_distribute, variables_to_display, state=None, logcolor=False):
"""Plots the distribution of a given variable across the given sttate
Params
------
df
df is a data frame containing the county level data
variable_to_distribute
variable_to_distribute is the variable that you want to see across the state
variables_to_display
Variables to display on hovering over each county
output: Bokeh plotting object
"""
from bokeh.sampledata.us_counties import data as counties
counties = {
code: county for code, county in counties.items()
if county["state"] == state.lower()
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
if variable_to_distribute in variables_to_display:
variables_to_display.remove(variable_to_distribute)
colors = palettes.RdBu11 #(n_colors)
min_value = df[variable_to_distribute].min()
max_value = df[variable_to_distribute].max()
gran = (max_value - min_value) / float(len(colors))
#print variable_to_distribute,state,min_value,max_value
index_range = [min_value + x*gran for x in range(len(colors))]
county_colors = []
variable_dictionary = {}
variable_dictionary["county_names"] = [county['name'] for county in counties.values()]
variable_dictionary["x"] = county_xs
variable_dictionary["y"] = county_ys
variable_dictionary[re.sub("[^\w]","",variable_to_distribute)] = []
for vd in variables_to_display:
variable_dictionary[re.sub("[^\w]","",vd)] = []
for county_id in counties:
StateCountyID = str(county_id[0]).zfill(2) + str(county_id[1]).zfill(3)
if StateCountyID in list(df["Header-FIPSStandCtyCode"].values):
temp_var = df[df["Header-FIPSStandCtyCode"] == StateCountyID][variable_to_distribute].values[0]
# if temp_var > 0.0:
variable_dictionary[re.sub("[^\w]","",variable_to_distribute)].append(temp_var)
for vd in variables_to_display:
variable_dictionary[re.sub("[^\w]","",vd)].append(round(float(df[df["Header-FIPSStandCtyCode"] == StateCountyID][vd].values),2))
color_idx = list(temp_var - np.array(index_range)).index(min(x for x in list(temp_var - np.array(index_range)) if x >= 0))
county_colors.append(colors[color_idx])
'''
else:
variable_dictionary[re.sub("[^\w]","",variable_to_distribute)].append(0.0)
county_colors.append("#A9A9A9")
for vd in variables_to_display:
variable_dictionary[re.sub("[^\w]","",vd)].append(0.0)
'''
else:
variable_dictionary[re.sub("[^\w]","",variable_to_distribute)].append(0.0)
county_colors.append("#A9A9A9")
for vd in variables_to_display:
variable_dictionary[re.sub("[^\w]","",vd)].append(0.0)
#print temp_var,counties[county_id]["name"]
variable_dictionary["color"] = county_colors
source = ColumnDataSource(data = variable_dictionary)
TOOLS = "pan,wheel_zoom,box_zoom,reset,hover,save"
if logcolor:
mapper = LogColorMapper(palette=colors, low=min_value, high=max_value)
else:
mapper = LinearColorMapper(palette=colors, low=min_value, high=max_value)
color_bar = ColorBar(color_mapper=mapper, location=(0, 0), orientation='horizontal',
title = variable_to_distribute,ticker=FixedTicker(ticks=index_range))
p = figure(title=variable_to_distribute, toolbar_location="left",tools=TOOLS,
plot_width=1100, plot_height=700,x_axis_location=None, y_axis_location=None)
p.patches('x', 'y', source=source, fill_alpha=0.7,fill_color='color',
line_color="#884444", line_width=2)
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
tool_tips = [("County ", "@county_names")]
for key in variable_dictionary.keys():
if key not in ["x","y","color","county_names"]:
tool_tips.append((key,"@"+re.sub("[^\w]","",key) + "{1.11}"))
hover.tooltips = tool_tips
p.add_layout(color_bar, 'below')
return p
from os.path import abspath
import webbrowser
import holoviews as hv
from holoviews import opts
hv.extension('bokeh')
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
counties = [dict(county, Unemployment=unemployment[cid])
for cid, county in counties.items()
if county["state"] == "tx"]
choropleth = hv.Polygons(counties, ['lons', 'lats'],
[('detailed name', 'County'), 'Unemployment'])
choropleth.opts(opts.Polygons(logz=True,
tools=['hover'],
xaxis=None, yaxis=None,
show_grid=False,
show_frame=False,
width=500, height=500,
color_index='Unemployment',
colorbar=True, toolbar='above',
line_color='white'))
hv.save(choropleth, 'texas.html', backend='bokeh')
url = abspath('texas.html')
webbrowser.open(url)
def make_map_plot(counties=counties,
data=unemployment,
year=2018,
additional_data=True):
# examples directory: http://bokeh.pydata.org/en/latest/docs/gallery/
# example adapted from http://bokeh.pydata.org/en/latest/docs/gallery/texas.html
# changes color pallete assignments
#palette.reverse()
# grab polygons for Colorado counties only
counties = {
code: county
for code, county in counties.items() if county["state"] == "co"
}
if additional_data:
df = build_simple_model_df(save=False)
#df = pd.read_parquet('data/processed_data/simple_modeling_set.parquet', engine='fastparquet')
counties_df = pd.DataFrame.from_records(counties).T
additional_data_df = pd.merge(df,
counties_df,
how='left',
left_on='county',
right_on='name')
# subset to 2018
additional_data_df = additional_data_df[additional_data_df['year'] ==
year]
# need to convert to this format for Bokeh to render the plot
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
# This is what we will need to change to adapt the map to use different values
# county_rates = [data[county_id] for county_id in counties]
county_rates = additional_data_df['unemprate']
expected_consumers = additional_data_df['total_expected_consumers'] / 1000
# maps colors to the
color_mapper = LogColorMapper(palette=palette)
# Bokeh expects the data in a dict as follows
data = dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
customers=expected_consumers,
)
# all tools here: https://bokeh.pydata.org/en/latest/docs/user_guide/tools.html
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
# title="Colorado Unemployment, 2009", tools=TOOLS,
title=f"Colorado County Statistics {str(year)}",
tools=TOOLS,
# sizing_mode controls how the plot responds to browser resizing
sizing_mode="stretch_both",
# if you are having issues with the image fitting in the plot box,
# you may want to adjust the width policy value
width_policy="fit",
# We don't need axes for this plot
x_axis_location=None,
y_axis_location=None,
# This is the fun stuff! And so easy to implement, what do we want
# the chart to show on hover?
tooltips=[("Name", "@name"), ("Unemployment rate", "@rate%"),
("Expected Customers (k)", "@customers"),
("(Long, Lat)", "($x, $y)")])
# A grid isn't going to make this easier to read, turn off
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
# MUST set to maintain aspect ratio of state
p.match_aspect = True
# patches is how polygons are passed for visualization
p.patches('x',
'y',
source=data,
fill_color={
'field': 'customers',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
return p
from bokeh.io import show, output_file
from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
palette.reverse()
counties = {
code: county
for code, county in counties.items() if county["state"] in ["md"]
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
data = dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
def make_dataset(self):
per_capita = self.per_capita.active == 1
data_type = self.data_getter.labels[self.data_getter.active].lower()
date = self.date.value
excluded = ("ak", "hi", "pr", "gu", "vi", "mp", "as")
counties = {
abbrv: county
for abbrv, county in US_COUNTIES.items()
if county["state"] not in excluded
}
data = np.zeros(len(counties), dtype=float)
cases = np.zeros(len(counties), dtype=float)
deaths = np.zeros(len(counties), dtype=float)
cases_pc = np.zeros(len(counties), dtype=float)
deaths_pc = np.zeros(len(counties), dtype=float)
pop = np.zeros(len(counties), dtype=int)
if not per_capita:
dt_label = data_type
label = f"Total New {data_type.title()}"
else:
dt_label = f"{data_type}_pc"
label = f"New {data_type.title()} per 100,000"
subset = GH_COUNTIES_DATA.loc[GH_COUNTIES_DATA["date"] == date, :]
for i, (abbrv, county) in enumerate(counties.items()):
state_name, county_name = parse_detailed_name(
county["detailed name"])
value = subset.loc[(subset["county"] == county_name).values
& (subset["state"] == state_name).values, :, ]
if not value.empty:
dataval = value[f"avg_{dt_label}"].values[0]
if not np.isnan(dataval):
data[i] = max(0, dataval)
else:
data[i] = 0
cases[i] = value["avg_cases"].values[0]
deaths[i] = value["avg_deaths"].values[0]
cases_pc[i] = value["avg_cases_pc"].values[0]
deaths_pc[i] = value["avg_deaths_pc"].values[0]
pop[i] = population(f"{state_name}, {county_name}")
if per_capita and data_type != "deaths":
maxval = 1000
else:
maxval = GH_COUNTIES_DATA.loc[:, f"avg_{dt_label}"].max()
interp = (
compute_log_palette # if logarithmic else compute_linear_palette
)
color_data = {
"color":
[interp(PALETTE, maxval / 256, maxval, val) for val in data],
"value": data,
"cases": cases,
"deaths": deaths,
"cases_pc": cases_pc,
"deaths_pc": deaths_pc,
"population": pop,
"name": [county["detailed name"] for county in counties.values()],
"lons": [],
"lats": [],
}
for county in counties.values():
color_data["lons"].append(county["lons"])
color_data["lats"].append(county["lats"])
return label, maxval, ColumnDataSource(color_data)
print("\nPopl data at row 500:")
print(df.loc[500])
# Make dictionary of state_id, cid tuple vs popl density.
state_ids = df.state_id.tolist()
cids = df.cid.tolist()
den = df.Density.tolist()
tuple_list = tuple(zip(state_ids, cids))
popl_dens_dict = dict(zip(tuple_list, den))
# Exclude states & territories not part of conterminious US.
EXCLUDED = ('ak', 'hi', 'pr', 'gu', 'vi', 'mp', 'as')
counties = [
dict(county, Density=popl_dens_dict[cid])
for cid, county in counties.items() if county["state"] not in EXCLUDED
]
choropleth = hv.Polygons(counties, ['lons', 'lats'],
[('detailed name', 'County'), 'Density'])
choropleth.opts(
opts.Polygons(
logz=True,
tools=['hover'],
xaxis=None,
yaxis=None,
show_grid=False,
show_frame=False,
width=1100,
height=700,
from bokeh.sampledata.unemployment import data as unemployment
from bokeh.sampledata.us_counties import data as counties
except:
raise Exception(
'could not import data sets; run bokeh.sampledata.download() then re-run script.'
)
#
palette = tuple(reversed(palette))
# lower 48 plus dc for now
excludes = ['ak', 'hi', 'pr', 'mp', 'vi', 'as', 'gu']
counties = {
code: county
for code, county in counties.items() if county["state"] not in excludes
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
#county_rates = [unemployment[county_id] for county_id in counties]
county_rates = []
for county_id in counties:
county_rates.append(unemployment.get(county_id, np.nan))
color_mapper = LogColorMapper(palette=palette)
data = dict(
x=county_xs,
y=county_ys,
import bokeh.plotting as bk
from bokeh.resources import CDN
from bokeh.embed import components, autoload_static, autoload_server
############################################################################
#
# Data Prep
#
############################################################################
# get built-in data
bokeh.sampledata.download()
#limit by state !!! order changes with re-run of this script !!!
counties = {
code: county for code, county in counties.items() if county["state"] == "va"
}
# end !!!
county_x = [county["lons"] for county in counties.values()]
county_y = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
### experimental
locs = pd.DataFrame(county_names)
locs['x'] = county_x
locs['y'] = county_y
locs['name'] = locs[0]
del locs[0]
#delete bedford city as county equivalent and make location data frame
import os
today_date = basic.get_today_date()
csv_file_name = 'data/csv_county_summary/csv_county_summary_' + today_date + '.csv'
index_file_name = 'index.html'
readme_file_name = 'README.md'
if os.path.exists(csv_file_name):
df = pd.read_csv(csv_file_name)
else:
print('csv file not exists: ' + csv_file_name)
exit(1)
counties = {
code: county
for code, county in us_counties.items() if county["state"] == "oh"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
# county_rates = [unemployment[county_id] for county_id in counties]
county_counts = []
for index, county_name in enumerate(county_names):
county_count = df.loc[df['County'] == county_name]['Case Count'].values
if len(county_count) == 0:
county_counts.append(0)
else:
county_counts.append(county_count[0])
bokeh.sampledata.download()
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.us_states import data as states
from bokeh.sampledata.unemployment import data as unemployment
from pprint import pprint
from bokeh.models import (ColumnDataSource, LogColorMapper, LinearColorMapper,
HoverTool)
from bokeh.palettes import RdGy8 as palette
palette.reverse()
## Filter in Indiana only
counties = {
code: county
for code, county in counties.items() if county["state"] == "in"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
source = ColumnDataSource(data=dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
))
import pandas as pd
from bokeh.plotting import figure, ColumnDataSource
from bokeh.io import curdoc
from bokeh.models import HoverTool, LinearColorMapper
from bokeh.models.widgets import Slider
from bokeh.layouts import column, widgetbox
from bokeh.sampledata.us_counties import data as counties
from bokeh.palettes import PuBuGn8
#reading in GA population density data
df = pd.read_csv(".\data\GA County Population Density 1980-2017.csv")
#extracting ga county information
ga_counties = {key: info for key, info in counties.items() if info['state']=='ga'}
county_names = [county_info['name'] for county_info in ga_counties.values()]
county_lons = [county_info['lons'] for county_info in ga_counties.values()]
county_lats = [county_info['lats'] for county_info in ga_counties.values()]
#initializing ColumnDataSource to 1980 densities
source = ColumnDataSource(data=dict(
x = county_lons,
y = county_lats,
county = county_names,
density = df['1980']
))
#initializing Slider object
slider = Slider(title='Year', value=1980, start=1980, end=2017)
#updating source data on slider change
from bokeh.io import show
from bokeh.models import (ColumnDataSource, HoverTool, LogColorMapper)
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
if __name__ == "__main__":
palette.reverse()
counties = {code: county \
for code, county in counties.items() \
if county["state"] == "tx" }
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
source = ColumnDataSource(data=dict(
x=county_xs, y=county_ys, name=county_names, rate=county_rates))
p = figure(title="Texas Unemployment, 2009",
tools="pan,wheel_zoom,reset,hover,save",
x_axis_location=None,
y_axis_location=None)
p.grid.grid_line_color = None
def build_state_plot():
from bokeh.sampledata.us_counties import data as counties
print('building data sets...')
alum_points = create_dictionary_data('./local/alum_lat_lon.csv')
cm_points = create_dictionary_data('./local/cm_lat_lon.csv')
school_points = create_dictionary_data('./local/school_lat_lon.csv')
supporter_points = create_dictionary_data('./local/supporters_lat_lon.csv')
print('school data...')
cm_data, cm_counties = open_cm_data()
for cm in cm_data:
students, county = int(cm['num']), cm['county']
cm_counties[county]['num'] += students
cm_counties[county]['cms'] += 1
print('creating county info...')
counties = {
code: county
for code, county in counties.items()
if (county['state'] == 'ms' or county['state'] == 'ar')
}
student_numbers = []
for code in counties.keys():
if counties[code]['name'].replace(
' County', '') in cm_counties.keys() and cm_counties.get(
counties[code]['name'], {
'state': ''
}).get('state', '') == counties[code]['state']:
counties[code]['students'] = cm_counties[counties[code]
['name']].get('num', 0)
student_numbers.append(cm_counties[counties[code]['name']].get(
'num', 0))
counties[code]['cms'] = cm_counties[counties[code]['name']].get(
'cms', 0)
else:
counties[code]['students'] = 0
counties[code]['cms'] = 0
max_students = max(student_numbers)
print('building plot...')
output_file('./map.html')
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [
'{} County'.format(county['name']) for county in counties.values()
]
county_place = [bool(county['students']) for county in counties.values()]
county_students = [
'{} students'.format(county['students'])
for county in counties.values()
]
county_cms = [
'{} Corps Members'.format(county['cms'])
for county in counties.values()
]
palette = [CUSTOM_COLORS['Dark Blue'], CUSTOM_COLORS['Teal']]
color_mapper = LogColorMapper(palette=palette)
source = ColumnDataSource(data=dict(x=county_xs,
y=county_ys,
county=county_names,
has_cm=county_place,
students=county_students))
TOOLS = "pan,wheel_zoom,reset,save"
p = figure(tools=TOOLS,
active_drag="pan",
active_scroll=None,
active_tap=None,
active_inspect=None,
x_axis_location=None,
y_axis_location=None,
toolbar_location="left")
p.grid.grid_line_color = None
p.sizing_mode = 'scale_width'
# p.sizing_mode = 'scale_height'
p.patches('x',
'y',
source=source,
fill_color={
'field': 'has_cm',
'transform': color_mapper
},
fill_alpha=1,
line_color="white",
line_width=2)
# school data points
source = ColumnDataSource(
dict(
x=school_points['lon'],
y=school_points['lat'],
city=school_points['city'],
name=school_points['name'],
type=school_points['type'],
cms=school_points['number'],
))
g = p.square(x="x",
y="y",
source=source,
size=8,
color=CUSTOM_COLORS['Purple'],
alpha=1,
legend='Schools')
hover1 = HoverTool(renderers=[g],
tooltips=[
("School Name", "@name"),
("Location", "@city"),
("Type of Partner", "@type"),
("Number of CMs", "@cms"),
])
p.add_tools(hover1)
# donor data points
source = ColumnDataSource(
dict(
x=supporter_points['lon'],
y=supporter_points['lat'],
city=supporter_points['city'],
name=supporter_points['name'],
donate=supporter_points['last_donate'],
))
g = p.circle(x="x",
y="y",
source=source,
size=8,
color=CUSTOM_COLORS['Blue'],
alpha=1,
legend='Supporters')
hover2 = HoverTool(renderers=[g],
tooltips=[
("Donor Name", "@name"),
("Location", "@city"),
("Latest Donation", "@donate"),
])
p.add_tools(hover2)
# alumni data points
source = ColumnDataSource(
dict(
x=alum_points['lon'],
y=alum_points['lat'],
city=alum_points['city'],
name=alum_points['name'],
corps=alum_points['corps'],
title=alum_points['title'],
employer=alum_points['employer'],
profession=alum_points['profession'],
last_survey=alum_points['last_survey'],
nps=alum_points['nps'],
))
g = p.circle(x="x",
y="y",
source=source,
size=8,
color=CUSTOM_COLORS['Red'],
alpha=1,
legend='Alumni')
hover3 = HoverTool(renderers=[g],
tooltips=[
("Alum Name", "@name"),
("Corps", "@corps"),
("Location", "@city"),
("Title", "@title"),
("Employer", "@employer"),
("profession", "@profession"),
("Last Alum Survey", "@last_survey"),
("Latest NPS", "@nps"),
])
p.add_tools(hover3)
# cm data points
source = ColumnDataSource(
dict(
x=cm_points['lon'],
y=cm_points['lat'],
city=cm_points['city'],
name=cm_points['name'],
school=cm_points['school'],
coach=cm_points['coach'],
content=cm_points['content'],
num=cm_points['num'],
yol=cm_points['yol'],
))
g = p.circle(x="x",
y="y",
source=source,
size=8,
color=CUSTOM_COLORS['Orange'],
alpha=1,
legend='Corps Members')
hover4 = HoverTool(renderers=[g],
tooltips=[
("CM Name", "@name"),
("Location", "@city"),
("School", "@school"),
("Coach", "@coach"),
("Content", "@content"),
("Number of Students", "@num"),
("Expected Growth", "@yol"),
])
p.add_tools(hover4)
p.legend.location = "top_right"
p.legend.click_policy = "hide"
p.toolbar.active_inspect = [hover2, hover3, hover4]
# p.toolbar.active_inspect = []
show(p)
