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 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 _log_bokeh_figure():
palette2 = tuple(reversed(pltt))
counties = {
code: county
for code, county in cnts.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=palette2)
chart_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, $y)")])
p.grid.grid_line_color = None
p.hover.point_policy = "follow_mouse"
p.patches('x',
'y',
source=chart_data,
fill_color={
'field': 'rate',
'transform': color_mapper
},
fill_alpha=0.7,
line_color="white",
line_width=0.5)
# Log bokeh figure as interactive chart in the experiment' artifacts tab.
log_chart(name='bokeh_figure', chart=p)
def gen_texas():
from bokeh.io import show
from bokeh.models import LogColorMapper
from bokeh.palettes import Viridis6 as palette
from bokeh.plotting import figure
from bokeh.sampledata.us_counties import data as counties
from bokeh.sampledata.unemployment import data as unemployment
palette.reverse()
counties = {
code: county for code, county in counties.items() if county["state"] == "tx"
}
county_xs = [county["lons"] for county in counties.values()]
county_ys = [county["lats"] for county in counties.values()]
county_names = [county['name'] for county in counties.values()]
county_rates = [unemployment[county_id] for county_id in counties]
color_mapper = LogColorMapper(palette=palette)
data=dict(
x=county_xs,
y=county_ys,
name=county_names,
rate=county_rates,
)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
title="Texas Unemployment, 2009", tools=TOOLS,
x_axis_location=None, y_axis_location=None,
tooltips=[
("Name", "@name"), ("Unemployment rate)", "@rate%"), ("(Long, Lat)", "($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 gen_fake_rows():
for year in range(1990, 2031):
for county in counties.values():
yield {
'year': year,
'total_population': int(random.random() * 100000),
'energy_use': random.random() * 100000,
'energy_cost': random.random(),
'solar_installed': random.random() * 100000,
'county': county['name']
}
def assignCountyColors(dic, valid=True):
EXCLUDED = ("ak", "hi", "pr", "gu", "vi", "mp", "as", "wa", "nv", "ca",
"or", "id", "mt", "wy", "ut", "az", "nm", "co", "sd", "nd",
"mn", "ne", "ia", "wi", "il", "mi")
county_xs = [
np.array(county["lons"]) for county in counties.values()
if county['state'] not in EXCLUDED
]
county_ys = [
np.array(county["lats"]) for county in counties.values()
if county['state'] not in EXCLUDED
]
county_names = [
county['name'] for county in counties.values()
if county['state'] not in EXCLUDED
]
county_damages = []
county_colors = []
for county in counties.values():
if county['state'] in EXCLUDED:
continue
if not valid:
county_damages.append(0)
county_colors.append('white')
else:
try:
name = county['name']
state = abbrevstostate[county['state'].upper()]
county_damages.append(dic[state][name])
county_colors.append(getCountyColors(dic[state][name]))
except:
county_damages.append(0)
county_colors.append('white')
newdf = pd.DataFrame(
dict(x=county_xs,
y=county_ys,
color=county_colors,
damages=county_damages,
names=county_names))
newdf.fillna(0, inplace=True)
return ColumnDataSource(newdf)
def parse_data(source_data, data_start_date, data_end_date):
"""Parses the previously downloaded data based on the provided fields
Args:
source_data: the JSON data that was downloaded
data_start_date: the first date given in the data
data_end_date: the last date given in the data
Returns:
source: a subset of data that is stored/displayed in the plot
dates_source: the full dataset placed into a ColumnDataSource
"""
global counties
county_xs = [county["lons"] for county in counties.values() if county["state"] == "ny"]
county_ys = [county["lats"] for county in counties.values() if county["state"] == "ny"]
county_names = [county['name'] for county in counties.values() if county["state"] == "ny"]
county_new_positives = [int(county['new_positives']) for county in source_data if county['test_date'] == data_end_date]
county_cumulative_number_of_positives = [int(county['cumulative_number_of_positives']) for county in source_data if county['test_date'] == data_end_date]
county_total_number_of_tests = [int(county['total_number_of_tests']) for county in source_data if county['test_date'] == data_end_date]
county_cumulative_number_of_tests = [int(county['cumulative_number_of_tests']) for county in source_data if county['test_date'] == data_end_date]
total_data = {}
for county in source_data:
date = county['test_date'].split("T")[0]
if date not in total_data:
total_data[date] = [[], [], [], []]
total_data[date][0].append(int(county['new_positives']))
total_data[date][1].append(int(county['cumulative_number_of_positives']))
total_data[date][2].append(int(county['total_number_of_tests']))
total_data[date][3].append(int(county['cumulative_number_of_tests']))
data=dict(x=county_xs,
y=county_ys,
name=county_names,
new_positives=county_new_positives,
cumulative_number_of_positives=county_cumulative_number_of_positives,
total_number_of_tests=county_total_number_of_tests,
cumulative_number_of_tests=county_cumulative_number_of_tests)
source = ColumnDataSource(data=data )
dates_source = ColumnDataSource(data=total_data)
return source, dates_source
heading.append(head)
identity.append(ident)
origin.append(org)
destination.append(dest)
groundspeed.append(gs)
altitude.append(alt + "00")
common_aircraft.append('-')
county_count_graph.append('-')
#if(state_id!=''):
# aircraft[(int(state_id), int(county_id))] = (rate)
# destination[(int(state_id), int(county_id))] = (destination1)
#print(county_xs)
for county in counties.values():
county_xs.append(county["lons"])
county_ys.append(county["lats"])
county_names.append(county['name'])
county_rate.append('-')
identity.append('-')
heading.append('-')
altitude.append('-')
groundspeed.append('-')
origin.append('-')
destination.append('-')
##Aircraft counting
#Make county list, init county_count and county_count_aircraft_type
for county in counties.values():
county_list.append(county['name'])
HoverTool, # hovertool helps the user to hover over the map and give details
LogColorMapper # fills the color
)
from bokeh.palettes import Viridis6 as palette # imports different palettes
from bokeh.plotting import figure # import different figures
from bokeh.sampledata.us_counties import data as counties # importing data of counties
from bokeh.sampledata.unemployment import data as unemployment # importing data of unemployment within counties
palette.reverse() # reverses colors of the palette
counties = {
code: county for code, county in counties.items() if county["state"] == "nj"
} # specify the coude for county state Nj
county_xs = [county["lons"] for county in counties.values()] # plotting x axis
county_ys = [county["lats"] for county in counties.values()] # plotting y axis
county_names = [county['name'] for county in counties.values()] # assigning names of county data
county_rates = [unemployment[county_id] for county_id in counties] # assigning unemployment rate of county
color_mapper = LogColorMapper(palette=palette) # using the color mapper funtion to fill the map according to the rates
source = ColumnDataSource(data=dict(
x=county_xs, # assigining x axis
y=county_ys, # assigining y axis
name=county_names, # naming name
rate=county_rates, # naming rate
))
TOOLS = "pan,wheel_zoom,reset,hover,save" # tools funtions
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 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
# External imports
import pandas as pd
from bokeh.sampledata.us_counties import data as counties
# Internal imports
from logistics import geocode_addresses
from bokeh_chloropleth import bokeh_chloropleth
# Washington counties
state = "wa"
counties = {
code: county
for code, county in counties.items() if county["state"] == state
}
county_names = [county['name'] for county in counties.values()]
# Read in licensees.
licenses = pd.read_excel("./data/active_licenses.xlsx")
# Get counties.
licenses = licenses
licenses["street"] = licenses["address1"]
licenses["state"] = licenses["state_code"]
licenses["zip"] = licenses["postal_code"]
# sample = geocode_addresses(licenses)
# Create Bokeh chloropleth
from random import randint
values = [randint(0, 100) for p in range(0, len(counties))]
bokeh_chloropleth(state, values)
with open('noah_data.csv', newline='') as csvfile:
next(csvfile)
datareader = csv.reader(csvfile)
for row in datareader:
if float(row[7]) > 0: #only keep wells with positive values
data.append(row)
#lat = 3, lon = 4, depth = 7
counties = {
code: county
for code, county in counties.items()
if county["state"] == "ca" #Select State
}
cname = 'Los Angeles' #Name of county
county_xs = [
county["lons"] for county in counties.values() if county['name'] == cname
]
county_ys = [
county["lats"] for county in counties.values() if county['name'] == cname
]
colors = ["#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", "#980043"
] #we can set colors here for different types, not currently used
county_names = [
county['name'] for county in counties.values() if county['name'] == cname
]
print(county_names)
#This block of code assumes we look at one country a time. Fundamentally we can do more, so this picks out the first one.
xc = county_xs[0]
yc = county_ys[0]
x_well = [float(well[4]) for well in data] #pulls out longitude of wells
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
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
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,box_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},
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
#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, 2021",
tools=TOOLS,
x_axis_location=None,
y_axis_location=None)
############################################################################
#
# 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
bedford_x = [-79.5436,-79.54339,-79.54295,-79.5434,-79.54201,-79.54353,-79.55541,-79.55612,-79.54206,-79.54129,-79.53956,-79.53926,-79.53926,-79.53683,-79.52744,-79.51268,-79.49368,-79.48793,-79.49904,-79.50356,-79.51611,-79.52537,-79.53285,-79.53989,-79.54553,-79.54386]
for i in range(0, len(locs.x)):
if locs.x[i][0] == bedford_x[0]:
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)
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)
