# -*- coding: utf-8 -*- """ Created on 2016.4.25 @author: gongming.wei.shanghai@gmail.com A simple demo to show heat flow map of dwelling time, density and path flow """ from bokeh.plotting import figure, show, output_file, ColumnDataSource,vplot from bokeh.models import Range1d, HoverTool, CustomJS import numpy as np #random import pandas as pd from collections import OrderedDict # Calculate the area of polygon def PolyArea(x,y): return 0.5*np.abs(np.dot(x,np.roll(y,1))-np.dot(y,np.roll(x,1))) # Construct the zone data, and lats, lons represents y an x position on the boundary in the shop #shop_area={} booth_area={} guest_area={} # booth area booth_area['kid']={'lon':[0,5,5,0],'lat':[5,5,20,20],'name':'booth_kid','center':[1,12],'count':'N.A.'} booth_area['men']={'lon':[10,30,30,10],'lat':[16,16,20,20],'name':'booth_men','center':[17,17.5],'count':'N.A.'} booth_area['lady']={'lon':[40,45,45,30,30,40],'lat':[0,0,20,20,16,16],'name':'booth_lady','center':[35,17.5],'count':'N.A.'} booth_area['new']={'lon':[13,27,27,13],'lat':[5,5,8,8],'name':'booth_new','center':[18,6],'count':'N.A.'} booth_area['counter']={'lon':[10,30,30,10],'lat':[11,11,13,13],'name':'booth_counter','center':[16,11.5],'count':'N.A.'} #booth_area['rest_a']={'lon':[32,34,34,32],'lat':[0,0,8,8]} booth_area['onpromotion']={'lon':[0,10,10,5,5,0],'lat':[0,0,2,2,5,5],'name':'booth_on_promo','center':[1,0.5],'count':'N.A.'} # guest area guest_area['kid']={'lon':[5,10,10,5],'lat':[5,5,20,20],'name':'kid','center':[7,12],'count':50} guest_area['men']={'lon':[10,30,30,10],'lat':[13,13,16,16],'name':'men','center':[20,14],'count':10} guest_area['lady']={'lon':[30,40,40,30],'lat':[0,0,16,16],'name':'lady','center':[34,8],'count':70} guest_area['new']={'lon':[10,30,30,27,27,13,13,10],'lat':[2,2,8,8,5,5,8,8],'name':'new','center':[20,3],'count':40} guest_area['counter']={'lon':[10,30,30,10],'lat':[8,8,11,11],'name':'counter','center':[20,9],'count':25} #guest_area['rest_a']={'lon':[28,32,32,28],'lat':[0,0,8,8]} guest_area['onpromotion']={'lon':[5,10,10,5],'lat':[2,2,5,5],'name':'onpromotion','center':[7,4],'count':10} guest_area['door']={'lon':[10,30,30,10],'lat':[0,0,2,2],'name':'door','center':[20,0],'count':10} # Very important to keep the fixed order of the dict booth_area=OrderedDict(sorted(booth_area.items())) guest_area=OrderedDict(sorted(guest_area.items())) # Calculate the area of each guest area. For calculation of footage per square meter for code in guest_area: guest_area[code]['area']=PolyArea(guest_area[code]['lon'],guest_area[code]['lat']) booth_xs=[booth_area[code]['lon'] for code in booth_area] # if booth_area[code]["area_id"] not in EXCLUDED] booth_ys=[booth_area[code]['lat'] for code in booth_area] # if booth_area[code]["area_id"] not in EXCLUDED] booth_name=[booth_area[code]['name'] for code in booth_area] guest_xs=[guest_area[code]['lon'] for code in guest_area] # if guest_area[code]["area_id"] not in EXCLUDED] guest_ys=[guest_area[code]['lat'] for code in guest_area] # if guest_area[code]["area_id"] not in EXCLUDED] guest_name=[guest_area[code]['name'] for code in guest_area] colors = ["#F1EEF6", "#D4B9DA", "#C994C7", "#DF65B0", "#DD1C77", "#980043"] guest_colors=[] #guest_count= np.rint(np.random.random((1,len(guest_area)))*50).astype(int) # guest_count_per_area=([guest_area[code]['count']/guest_area[code]['area'] for code in guest_area]) tmpidx=(5*np.array(guest_count_per_area)/max(guest_count_per_area)).astype(int) thiscolors=[colors[idx] for idx in tmpidx] booth_nr=len(booth_area) guest_nr=len(guest_area) # Load test data from excel file testdata=pd.read_csv('static\\data\\ShopData\\shop demo data.csv',sep=',',keep_date_col=True,index_col=0, parse_dates=True) # reorder the data so that we can create the the same order as here testdata.sort_index(axis=1, inplace=True) timelabel=testdata.index pcdata=np.hstack((np.zeros((len(timelabel),booth_nr)),testdata.values)) # Array of test data with zeo value filled in booth area # Smart way to combine the two patches area data_source = ColumnDataSource(data=dict( x=booth_xs+guest_xs, # area borders y=booth_ys+guest_ys, # area borders linecolor=["white"]*booth_nr+["#884444"]*guest_nr, # line color linewidth=[2]*booth_nr+[0.5]*guest_nr, fillcolor=["grey"]*booth_nr+thiscolors, # fill color fillalpha=[0.5]*booth_nr+[0.7]*guest_nr, guestcount=["N.A."]*booth_nr+[guest_area[code]['count'] for code in guest_area], name=booth_name+guest_name, # Area name to be referred for the 2nd Chart rate=["N.A."]*booth_nr+guest_count_per_area, pcdata=pcdata.T.tolist() # updated on Apr. 26 we have to do this transform !!! )) data_source_2 = ColumnDataSource(data=dict( x=timelabel, y=pcdata[:,booth_nr] # )) TOOLS="pan,wheel_zoom,box_zoom,reset,hover,save" pheatmap = figure(title="heatmap over location", toolbar_location="left", plot_width=900, plot_height=600, tools=TOOLS) # Split patches to pheatmap.patches('x','y',source=data_source, fill_color='fillcolor', fill_alpha='fillalpha', line_color='linecolor', line_width='linewidth') #p.patches(booth_xs, booth_ys, fill_color="grey", fill_alpha=0.5, # line_color="#884444", line_width=2, line_alpha=0.3) pheatmap.xaxis.visible = False pheatmap.yaxis.visible = False pheatmap.xgrid.grid_line_color = None pheatmap.ygrid.grid_line_color = None pheatmap.text([booth_area[code]['center'][0] for code in booth_area],[booth_area[code]['center'][1] for code in booth_area],[booth_area[code]['name'] for code in booth_area],text_color='white',text_font_size='22pt') hover=pheatmap.select_one(HoverTool) # define the callback to update the plot data for plinedaily update_ts = CustomJS(args=dict(sourcedata=data_source,sourcedata2=data_source_2), code=""" // get data source from callback args var data = sourcedata.get('data'); var data2=sourcedata2.get('data') var names = data['name']; var id=names.indexof("@name") // Get the index of current area to pick up var tmpdata=data2['y'] // output the data for curve plot var datlen=tmpdata.length var tmppcdata=data['pcdata'] for (var i = 0; i