import berrl as bl
# please, if possible, don't abuse this key its not difficult to get your own
apikey='your api key'
a=bl.make_points('sharks.csv')
bl.parselist(a,'sharks.geojson') # simply writes a list of lines to file name location
bl.loadparsehtml(['sharks.geojson'],apikey)
total=bl.list2df(total)
# getting the unique activities that occured
uniques=np.unique(total['Activity']).tolist()
# we now have a list with each top geohash in a aggregated table
# we can now style each color icon based on each activity being performed during the attack
count=0
filenames=[]
for unique,color in itertools.izip(uniques,colors):
count+=1
filename=str(count)+'.geojson' # generating a dummy file name
temp=total[total.Activity==unique]
if not len(temp)==0: # if dataframe is empty will not make_points
temp['color']=str(color) # setting specific color to object
a=bl.make_points(temp,list=True) # making geojson object
bl.parselist(a,filename) # writing object to file
filenames.append(filename)
# writing the squares table and setting color to red
squares['color']='red'
a=bl.make_blocks(squares,list=True)
bl.parselist(a,'squares.geojson')
# adding squares to filenames
filenames.append('squares.geojson')
#loading final html
bl.loadparsehtml(filenames,apikey,colorkey='color')
from pipeleaflet import *
import pandas as pd
import berrl as bl
import numpy as np
# loading a csv file of traffic fatalities
fatalities = pd.read_csv('wv_fatalities.csv')
# Adding the COLORKEY field by iterating through categorriclas of
# the input field in this case the field represents causes of traffic fatalities
# A field called COLORKEY if automatically added behind the scenes
# a groupby and a generator is all thatas going on
fatalities = bl.unique_groupby(fatalities, 'VAR23C')
print fatalities
# making geojson of the csv file
bl.make_points(fatalities, filename='fatalities.geojson')
# parsing / writing out the html to index.html
# we now have colorkey fields to style by
# colorkey fields are just 6-digit hex rgb strings
load(['fatalities.geojson'], colorkey='COLORKEY')
key='pk.eyJ1IjoibXVycGh5MjE0IiwiYSI6ImNpam5kb3puZzAwZ2l0aG01ZW1uMTRjbnoifQ.5Znb4MArp7v3Wwrn6WFE6A'
# reading into memory
points=pd.read_csv('points_example.csv')
line=pd.read_csv('line_example.csv')
# geohashing each table
points=bl.map_table(points,7,list=True)
line=bl.map_table(line,7,list=True)
# getting unique geohashs
uniquepoints=np.unique(points['GEOHASH']).tolist()
uniqueline=np.unique(line['GEOHASH']).tolist()
newpoints=[points.columns.values.tolist()]
# we know if a unique point is in any unique line its on the route
for row in uniquepoints:
oldrow=row
for row in uniqueline:
if row==oldrow:
temp=points[points.GEOHASH==oldrow]
temp=bl.df2list(temp)
newpoints+=temp[1:] # getting all the points within this geohashs
# making the new points, line, and blocks along line
bl.make_points(newpoints,list=True,filename='points.geojson')
bl.make_blocks('squares7.csv',filename='blocks_on_line.geojson')
bl.make_line(line,list=True,filename='line.geojson')
bl.loadparsehtml(bl.collect(),key)
import berrl as bl
# please, if possible, don't abuse this key its not difficult to get your own
apikey = 'your api key'
a = bl.make_points('sharks.csv')
bl.parselist(
a, 'sharks.geojson') # simply writes a list of lines to file name location
bl.loadparsehtml(['sharks.geojson'], apikey)
import pandas as pd
import berrl as bl
from pipeleaflet import *
data1 = pd.read_csv('polygon_example.csv')
data2 = pd.read_csv('points_example.csv')
data3 = pd.read_csv('line_example.csv')
data4 = pd.read_csv('blocks_example.csv')
bl.make_polygon(data1,filename='polygon.geojson')
bl.make_points(data2,filename='points.geojson')
bl.make_line(data3,filename='line.geojson')
dictrow1 = {'color':'#25D2EA','weight':10}
dictrow2 = {'color':'#CC33FF','radius':1}
dictrow3 = {'color':'#FFE800','weight':20}
filenames = ['polygon.geojson','points.geojson','line.geojson']
stylerows = [dictrow1,dictrow2,dictrow3,dictrow4]
load(filenames,stylerows=stylerows)