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# coding: utf-8
# In[10]:
import pandas as pd
import fiona
import numpy as np
from import show, output_file
from bokeh.models import ColumnDataSource, HoverTool, LogColorMapper
from bokeh.palettes import Reds6 as palette
from bokeh.plotting import figure, save
from bokeh.resources import CDN
from shapely.geometry import Polygon, Point, MultiPoint, MultiPolygon
from shapely.prepared import prep
""" from:
converted with
ogr2ogr -lco ENCODING=UTF-8 -t_srs EPSG:4326 Stadtteil_WGS84.shp Stadtteil.shp """
def read_data(filename):
colnames =["scrape_date","scrape_time","scrape_weekday","u_id",
with open(filename,"r") as f:
#data = pd.read_csv(filename, names=colnames,nrows=1000 )
data = pd.read_csv(filename, names=colnames)
data.drop_duplicates(inplace=True, subset=['bike_id','scrape_time','lat','lon'])
data = data[data.bike_name.str.contains("BIKE")].reset_index() # Drop Stations
data.scrape_date = pd.to_datetime(data.scrape_date) # Convert to datetime object
data.scrape_time = pd.to_datetime(data.scrape_time, format="%H-%M-%S")
return data
def calc_points_per_poly(poly, points):
""" Returns number of points contained per poly """
poly = prep(poly)
return int(len(list(filter(poly.contains, points))))
# In[2]:
# Read bike location data
# bike data scraped:
data = read_data("/home/flopp/Documents/Coding/kvb/data/2017-03-01.csv")
# In[3]:
# create dataset with only one observation per hour
time = pd.DatetimeIndex(data.scrape_time)
data_hourly = data[time.minute < 1] # first obsv for each hour
time = pd.DatetimeIndex(data_hourly.scrape_time)
data_hourly['hour'] = time.hour
periods = len(data_hourly[
data_hourly.duplicated(subset='hour') == False])# number of obsv.
# In[4]:
# list of lists - one sublist per period
map_points = []
all_points = []
for i in range(periods):
map_points[i] = [Point(x,y) for x,y in
zip(data_hourly[data_hourly.hour == i].lon,
data_hourly[data_hourly.hour == i].lat)] # Points to Shapely Pts
all_points[i] = MultiPoint(map_points[i]) # all bike points
# In[22]:
# Extract features from shapefile
shp =
district_name = [ feat["properties"]["STT_NAME"] for feat in shp]
district_area = [ feat["properties"]["SHAPE_AREA"] for feat in shp]
district_x = [ [x[0] for x in feat["geometry"]["coordinates"][0]] for feat in shp]
district_y = [ [x[1] for x in feat["geometry"]["coordinates"][0]] for feat in shp]
district_xy = [ [ xy for xy in feat["geometry"]["coordinates"][0]] for feat in shp]
district_poly = [ Polygon(xy) for xy in district_xy] # coords to Polygon
# In[23]:
# calc bikes per district for each period
num_bikes = []
bikes_per_area = []
for i in range(periods):
num_bikes[i] = [ calc_points_per_poly(poly, all_points[i]) for poly in district_poly]
bikes_per_area[i] = [ x/y*10000 for x,y in zip(num_bikes[i], district_area)]
# In[7]:
# Prepare data source for plot
rate_hours = {str(i): v for i, v
in enumerate(bikes_per_area)} # from list to dict
data = dict(x=district_x, y=district_y, name=district_name,
rate=bikes_per_area[0], **rate_hours) # merge dicts
source = ColumnDataSource(data) # one col per obsv. period
# In[8]:
# prepare plotting with bokeh
custom_colors = ['#f2f2f2', '#fee5d9', '#fcbba1', '#fc9272', '#fb6a4a', '#de2d26']
color_mapper = LogColorMapper(palette=custom_colors)
TOOLS = "pan,wheel_zoom,reset,hover,save"
p = figure(
title="KVB bike density per district, Mar. 2017", 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=0.8, line_color="black", line_width=0.3)
hover = p.select_one(HoverTool)
hover.point_policy = "follow_mouse"
hover.tooltips = [("District", "@name"),("Bikes per km²", "@rate"),
("(Long, Lat)", "($x, $y)")]
# In[9]:
from bokeh.layouts import column, row, widgetbox
from bokeh.models import CustomJS, Slider, Toggle
# add slider with callback to update data source
slider = Slider(start=0, end=23, value=0, step=1, title="Hour of day")
def update(source=source, slider=slider, window=None):
""" Update the map: change the bike density measure according to slider
will be translated to JavaScript and Called in Browser """
data =
v = cb_obj.get('value')
data['rate'] = [x for x in data[v]]
slider.js_on_change('value', CustomJS.from_py_func(update))
# Add Animation: Automatically loop through data source
# add button with callback to control animation
callback = CustomJS(args=dict(p=p, source=source), code="""
var data =;
var f =;
var j = 0;
if(f == true){
mytimer = setInterval(replace_data, 500);
} else {
function replace_data() {
if(data[j] === undefined) {
p.title.text = "Bike density per district in period: " +j;
data['rate'] = data[j];
btn = Toggle(label="Play/Stop Animation", button_type="success", active=False, callback=callback)
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