def map_generator(map_data,key,title):
### This function makes a colored map for the data given, white outlines for countries without data
geo_data = [{'name': 'countries',
'url': 'world-countries.topo.json',
'feature': 'world-countries'},
{'name': 'countries_outline',
'url': 'world-countries.topo.json',
'feature': 'world-countries'}
]
mapx = vincent.Map(data=map_data, geo_data=geo_data, projection='mercator', scale=150,
data_bind=key, data_key='iso3',
map_key={'countries': 'id'}, brew='Purples')
del mapx.marks[1].properties.update
mapx.marks[1].properties.enter.stroke.value = '#000'
mapx.legend(title=title)
return mapx
def visualizePandasData(stateData, thresholds, file_name, color_option):
state_topo = 'https://raw.githubusercontent.com/wrobstory/vincent_map_data/master/us_states.topo.json'
geo_data = [{
'name': 'states',
'url': state_topo,
'feature': 'us_states.geo'
}]
vis = vincent.Map(data=stateData,
geo_data=geo_data,
scale=1000,
projection='albersUsa',
data_bind=MappingHandlerKeys.DATA_BIND_KEY,
data_key=MappingHandlerKeys.DATA_KEY,
map_key={'states': 'properties.NAME'},
brew=color_option)
vis.scales[0].type = 'threshold'
vis.scales[0].domain = thresholds
vis.legend(title=MappingHandlerKeys.MAP_TITLE_KEY)
vis.to_json(file_name)
import pickle, vincent
import pandas as pd
with open('db.p', 'rb') as f:
df = pickle.load(f)
world_topo = r'world-countries.topo.json'
geo_data = [{
'name': 'countries',
'url': world_topo,
'feature': 'world-countries'
}]
vis = vincent.Map(geo_data=geo_data, scale=200)
if(count < num_projects):
pbar.update(count)
else:
to_add = num_projects - (count - 50)
pbar.update(to_add)
pbar.finish()
geo_data = [{'name': 'countries',
'url': 'https://raw.githubusercontent.com/wrobstory/vincent_map_data/master/world-countries.topo.json',
'feature': 'world-countries'}]
country_dict[donating_org] = addNonDonatedCountries(country_dict[donating_org], get_id)
receiving_df = pd.DataFrame(list(country_dict[donating_org].iteritems()), columns=['iso_a3', 'total_received'])
merged = pd.merge(receiving_df, country_df, on='iso_a3', how='inner')
# Uses vincent to create a map in vega format
vis = vincent.Map(data=merged, geo_data=geo_data, scale=1000, projection='patterson',
data_bind='total_received', data_key='iso_a3',
map_key={'countries': 'id'})
vis.legend(title="Donation Amount")
json_file_name = donating_org.replace(' ', '_') + '_donations.json'
png_file_name = donating_org.replace(' ', '_') + '_donations.png'
vis.to_json(json_file_name)
# Transforms the vega json into a donor map image using the vg2png command line function
os.system("vg2png " + json_file_name + " " + png_file_name)
import vincent world_countries = r'world-countries.json' world = vincent.Map(width=1200, height=1000) world.geo_data(projection='winkel3', scale=200, world=world_countries) world.to_json(path)
# -*- coding: utf-8 -*-
'''
Builds a Vega grammar specification from vincent.Map(), performs shapefile
conversion with the Ogre web tool: http://ogre.adc4gis.com/
'''
import vincent
#Add Oceans
vis = vincent.Map(width=1200, height=800)
vis.geo_data(scale=200, spatial_convert=True, ocean=r'data/ne_110m_ocean.zip')
vis += ('#75b9e7', 'marks', 0, 'properties', 'enter', 'fill', 'value')
vis.update_vis(padding={'bottom': 20, 'left': 50, 'right': 20, 'top': 50})
#Add Land
vis.geo_data(spatial_convert=True, world=r'data/ne_110m_land.zip')
vis += ('1a252f', 'marks', 1, 'properties', 'enter', 'fill', 'value')
#Add Lakes
vis.geo_data(spatial_convert=True, lakes=r'data/ne_50m_lakes.zip')
vis += ('#3498db', 'marks', 2, 'properties', 'enter', 'fill', 'value')
vis.to_json(path, html=True)
'Venezuela'
]),
'country_alpha3':
np.array([
'AIA', 'ATG', 'ARG', 'ABW', 'BHS', 'BRB', 'BLZ', 'BMU', 'BOL', 'VGB',
'CAN', 'CYM', 'CHL', 'COL', 'CRI', 'CUB', 'CUW', 'DMA', 'DOM', 'ECU',
'SLV', 'FLK', 'GRL', 'GRD', 'GTM', 'GUY', 'HTI', 'HND', 'JAM', 'MEX',
'MSR', 'NIC', 'PAN', 'PRY', 'PER', 'PRI', 'BLM', 'KNA', 'LCA', 'MAF',
'SPM', 'VCT', 'SXM', 'SUR', 'TTO', 'TCA', 'USA', 'VIR', 'URY', 'VEN'
])
})
country_data_tmp.head()
#map drawing bit Input[2] from your notebook
#Note the changes in variable names
world_topo = r'world-countries.topo.json'
geo_data = [{
'name': 'countries',
'url': world_topo,
'feature': 'world-countries'
}]
vis = vi.Map(data=country_data_tmp,
geo_data=geo_data,
scale=1100,
data_key='country_alpha3',
map_key={'countries': 'id'})
vis.display()
import json, vincent
import pandas as pd
with open('us_states.topo.json', 'r') as f:
get_id = json.load(f)
df = pd.read_csv('prevalence.csv',na_values=[' '])
geo_data = [{'name':'states','url':'us_states.topo.json','feature':'us_states.geo'}]
vis = vincent.Map(data=df,geo_data=geo_data,scale=1000,projection='albersUsa', data_bind='Prevalence',
data_key='Prevalence', map_key={'states':'properties.NAME'})
vis.display()
vis.to_json('vega.json', html_out=True,html_path='vega_tmp.html')
dict_values = country_dict[donor]
sorted_x = sorted(dict_values.items(), key=operator.itemgetter(1), reverse=True)
geo_data = [{'name': 'countries',
'url': 'https://raw.githubusercontent.com/wingmanzz/Python-Visualizations/master/world-countries.topo.json',
'feature': 'world-countries'}]
country_dict[donating_org] = addNonFlowCountries(country_dict[donating_org], get_id)
receiving_df = pd.DataFrame(list(country_dict[donating_org].iteritems()), columns=['iso_a3', 'total_received'])
merged = pd.merge(receiving_df, country_df, on='iso_a3', how='inner')
# Uses vincent to create a map in vega format
vis = vincent.Map(data=merged, geo_data=geo_data, brew="Greens", projection='times', scale=250,
data_bind='total_received', data_key='iso_a3',
map_key={'countries': 'id'})
vis.padding = {'top': 210, 'left': 80, 'bottom': 250, 'right': 80}
json_file_name = donating_org.replace(' ', '_') + '_donations.json'
png_file_name = donating_org.replace(' ', '_') + '_donations.png'
vis.to_json(json_file_name)
# Transforms the vega json into a donor map image using the vg2png command line function
os.system("vg2png " + json_file_name + " " + png_file_name)
donating_org = donating_org.replace("'","")
#sets title
os.system("convert " + png_file_name + " -pointsize 26 -gravity north -annotate +10+33 'Distribution of " + donating_org + "'\\''" + "s Official Development Assistance (ODA), " + str(start_year) + "-" + str(end_year) + "' " + png_file_name)
#sets 'top 10 partner countries' text
!curl -v -X HEAD http://mashupguide.net/wwod14/world-countries.json
# <codecell>
# https://vincent.readthedocs.org/en/latest/quickstart.html
#world_topo = r'https://raw.github.com/trifacta/vega/gh-pages/data/world-countries.json'
# CORS enabled
#world_topo = r'http://mashupguide.net/wwod14/world-countries.topo.json'
world_topo = "files/vincent_map_data/world-countries.topo.json"
geo_data = [{'name': 'countries',
'url': world_topo,
'feature': 'world-countries'}]
vis = vincent.Map(geo_data=geo_data, scale=200)
vis
# <codecell>
vis.display()
# <codecell>
vis.to_json('world_map.json', html_out=True, html_path='vincent_world_map.html')
# <markdowncell>
# When I load vincent_world_map.html -- I don't see a map. I see in JS console: "Uncaught TypeError: Cannot read property 'world-countries' of undefined "
# <codecell>
#Grab the State Names and load them into a dataframe, also not neccesary but good practice :)
geometries = get_id['objects']['us_states.geo']['geometries']
state_names = [x['properties']['NAME'] for x in geometries]
state_df = pd.DataFrame({'NAME': state_names}, dtype=str)
#Read into Dataframe, cast to string for consistency
df = pd.read_csv('state_data.csv', delimiter=',')
df['NAME'] = df['State'].astype(str)
#Perform an inner join, pad NA's with data from nearest county
merged = pd.merge(df, state_df, on='NAME', how='inner')
merged = merged.fillna(method='pad')
state_topo = r'us_states.topo.json'
geo_data = [{'name': 'states', 'url': state_topo, 'feature': 'us_states.geo'}]
#create Map of states color coded based upon mean tweet sentiment
vis = vince.Map(data=merged,
geo_data=geo_data,
scale=1100,
projection='albersUsa',
data_bind='Mean Sentiment',
data_key='NAME',
map_key={'states': 'properties.NAME'})
vis.marks[0].properties.enter.stroke_opacity = vince.ValueRef(value=0.5)
vis.rebind(column='Mean Sentiment', brew='YlGnBu')
vis.scales['color'].type = 'threshold'
vis.scales['color'].domain = [-1.0, -0.48, 0.04, 0.56, 1.08, 1.6, 2.11]
vis.legend(title='States by Mean Sentiment')
vis.to_json('vega_state_map.json')
import vincent
data = pd.read_csv(
"c:/Users/DCUK/.PyCharmCE2018.1/PycharmProjects/Aspnet_user/country_map.csv"
)
col = ["country", "z"]
data = data[col]
world_countries = r'world-countries.json'
geo_data = [{
'name': 'countries',
'url': world_countries,
'feature': 'world-countries'
}]
vis = vincent.Map(data=data,
geo_data=geo_data,
scale=1100,
data_bind='z',
data_key='country',
map_key={'countries': 'id'})
from IPython.display import display
vis.display()
print(vis)
vis.to_json(
"c:/Users/DCUK/.PyCharmCE2018.1/PycharmProjects/Aspnet_user/map.json")
world = vincent.Map(width=1200, height=1000)
world.geo_data(projection='winkel3', scale=200, world=world_countries)
import json
def data_map():
return_dict = {}
income = request.args.get("income")
housing = request.args.get("housing")
diversity = request.args.get("diversity")
urbanization = request.args.get("urbanization")
state = request.args.get("state")
statename = request.args.get("statename").replace(" ","_")
global df_final
fit = []
fit.append(float(income)/100)
fit.append(float(housing)/100)
fit.append(float(diversity)/100)
fit.append(float(urbanization)/100)
df_final['fit'] = df_final.apply(lambda x: abs(fit[0]-x['diversity_index'])+abs(fit[1]-x['housing_index'])+abs(fit[2]-x['income_index'])+abs(fit[3]-x['urban_index']),axis=1)
if state == 'ZZ':
zip_topo = r'/data/zips'
state_topo = r'/data/states'
geo_data = [{'name': 'states',
'url': state_topo,
'feature': 'us_states.geo'},
{'name': 'zip_codes',
'url': zip_topo,
'feature': 'zip_codes_for_the_usa'}]
vis = vincent.Map(data=df_final, geo_data=geo_data, scale=800, projection='albersUsa',
data_bind='fit', data_key='zip5',brew='YlOrRd',
map_key={'zip_codes': 'properties.zip'})
del vis.marks[0].properties.update
vis.marks[1].properties.enter.stroke_opacity = ValueRef(value=0.05)
vis.marks[0].properties.enter.stroke.value = '#C0C0C0'
vis.legend(title='Preferred ZipCode')
return_dict[0] = json.loads(vis.to_json())
ziplist = json.loads(df_final[['ZCTA5','ZIPName','fit']].sort(['fit']).reset_index().head(5).to_json())
table_data = []
for i in range (5):
dict_row = {}
dict_row['index'] = i
dict_row['ZCTA5'] = ziplist['ZCTA5'][str(i)]
dict_row['ZIPName'] = ziplist['ZIPName'][str(i)]
table_data.append(dict_row)
return_dict[1] = table_data
#with open ('data.json','w') as outfile:
# json.dump(lst,outfile)
return json.dumps(return_dict)
else:
zip_topo = r'/data/state_map?state='+statename
feature_name = statename+r'.geo'
global land_area
rank = int(land_area[statename])
if rank > 0 and rank <=1:
scale = 700
elif rank >1 and rank <=3:
scale = 2500
elif rank >2 and rank <=19:
scale = 3000
elif rank >19 and rank <=26:
scale = 4000
elif rank >26 and rank <=39:
scale = 4500
elif rank >39 and rank <=40:
scale = 5000
elif rank >40 and rank <=48:
scale = 6000
else:
scale = 23000
geo_data = [{'name': 'state',
'url': zip_topo,
'feature': feature_name},
{'name': 'zip_codes',
'url': zip_topo,
'feature': feature_name}]
vis = vincent.Map(data=df_final[df_final['State']==state],geo_data=geo_data, scale=scale, projection='equirectangular',
data_bind='fit', data_key='zip5',brew='YlOrRd',
map_key={'zip_codes': 'id'})
del vis.marks[0].properties.update
#vis.marks[0].properties.enter.stroke.value = '#C0C0C0'
vis.marks[1].properties.enter.stroke_opacity = ValueRef(value=0.5)
#vis.legend(title='Preferred ZipCode')
return_dict[0] = json.loads(vis.to_json())
ziplist = json.loads(df_final[['ZCTA5','ZIPName','fit']][df_final['State']==state].sort(['fit']).reset_index().head(5).to_json())
table_data = []
for i in range (5):
dict_row = {}
dict_row['index'] = i
dict_row['ZCTA5'] = ziplist['ZCTA5'][str(i)]
dict_row['ZIPName'] = ziplist['ZIPName'][str(i)]
table_data.append(dict_row)
return_dict[1] = table_data
return json.dumps(return_dict)
# -*- coding: utf-8 -*-
'''
Builds a Vega grammar specification from vincent.Map()
'''
import vincent
import pandas as pd
state_geo = r'data/us-states.json'
county_geo = r'data/us-counties.json'
world_countries = r'data/world-countries.json'
path = r'vega.json'
#Simple map of the US
states = vincent.Map(width=1000, height=800)
states.geo_data(projection='albersUsa', scale=1000, states=state_geo)
states.to_json(path, html=True)
#Simple map of the world
world = vincent.Map(width=1200, height=1000)
world.geo_data(projection='winkel3', scale=200, world=world_countries)
world.to_json(path)
#Map with both US States and Counties
vis = vincent.Map(width=1000, height=800)
#Add our county data first
vis.geo_data(projection='albersUsa', scale=1000, counties=county_geo)
vis += ('2B4ECF', 'marks', 0, 'properties', 'enter', 'stroke', 'value')
#The projection and scale will stay fixed unless you 'reset' the map
vis.geo_data(states=state_geo)
def setup(self):
'''Setup method'''
self.testmap = vincent.Map()
