def main():
column_short = sys.argv[1]
column_long = sys.argv[2]
config_file = "keplergl_config.json"
data_id = "buildings"
config = read_configuration(config_file,
label="Buildings",
data_id=data_id,
color_column=column_long)
print("Using configuration (JSON syntax):")
print(json.dumps(config, indent=2))
kepler = KeplerGl(config=config)
kepler.add_data(data=open("buildings.geojson").read(), name=data_id)
output = f"keplergl_{column_short}.html"
kepler.save_to_html(file_name=output)
name = column_long.replace("_", " ")
# Add map title and creator
with InPlace(output) as file:
for line in file:
line = line.replace(
"<title>Kepler.gl</title>",
f"<title>{name} – Campus Research Reopening Map"
" by NCSU CGA</title>",
)
line = line.replace("Kepler.gl Jupyter",
"Kepler.gl Map by NCSU CGA")
file.write(line)
def write_html(geojson_file, data_id, output_html, config, title):
"""Write Kepler.gl HTML and update its content"""
# Lazy load non-standard dependencies to play nicely in cases
# when only interface description is requested and the module
# actually does not run.
try:
# pylint: disable=import-outside-toplevel
from keplergl import KeplerGl
from in_place import InPlace
except ImportError as error:
gs.fatal(
_("Missing mandatory keplergl or in_place dependencies: {error}").
format(error=error))
# Useful to examine the resulting configuration
# print("Using configuration (JSON syntax):")
# print(json.dumps(config, indent=2))
kepler = KeplerGl(config=config)
kepler.add_data(data=open(geojson_file).read(), name=data_id)
kepler.save_to_html(file_name=output_html)
# Add map title and creator
with InPlace(output_html) as file:
for line in file:
line = line.replace(
"<title>Kepler.gl</title>",
f"<title>{title} – GRASS GIS Kepler.gl</title>",
)
line = line.replace("Kepler.gl Jupyter", title)
file.write(line)
def create_map(self, filename='airport_map'):
flight_map = KeplerGl(height=500, width=800)
flight_feature_collection = {
'type': 'FeatureCollection',
'features': self.flight_list
}
large_airport_feature_collection = {
'type':
'FeatureCollection',
'features': [
airport for airport in self.airport_list
if airport['properties']['facility_type'] == 'large_airport'
]
}
medium_airport_feature_collection = {
'type':
'FeatureCollection',
'features': [
airport for airport in self.airport_list
if airport['properties']['facility_type'] == 'medium_airport'
]
}
small_airport_feature_collection = {
'type':
'FeatureCollection',
'features': [
airport for airport in self.airport_list
if airport['properties']['facility_type'] == 'small_airport'
]
}
flight_map.add_data(data=flight_feature_collection, name='flights')
flight_map.add_data(data=large_airport_feature_collection,
name='large_airports')
flight_map.add_data(data=medium_airport_feature_collection,
name='medium_airports')
flight_map.add_data(data=small_airport_feature_collection,
name='small_airports')
flight_map.save_to_html(file_name=f'{filename}.html')
return flight_map
def showTrajectoriesFromZones(origin_id, dest_id, direct):
int_shapefile = gpd.read_file(direct + "TAZ/InternalCentroidZones.shp") # change ./.... to rootdir for DB
ext_shapefile = gpd.read_file(direct + "TAZ/ExternalCentroidZones.shp") # change ./.... to rootdir for DB
df = pd.read_csv("trajectories_condensed.csv")
gdf_origins = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['Origin X'], df['Origin Y']))
df = pd.read_csv("trajectories_condensed.csv")
gdf_dests = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df['Dest X'], df['Dest Y']))
int_trajectories_origins = clusterByZone(gdf_origins, int_shapefile, merge=False)
int_trajectories_dests = clusterByZone(gdf_dests, int_shapefile, merge=False)
matching_trajectories = trajectoriesFromZones(int_trajectories_origins, int_trajectories_dests, origin_id, dest_id)
df = pd.read_csv("trajectories_condensed.csv")
chosen_zones_map = KeplerGl(height=500)
int_zones = clusterByZone(gdf_origins, int_shapefile, merge=True)
chosen_zones_map.add_data(data=df, name='Trajectories')
chosen_zones_map.add_data(data=matching_trajectories, name='Matching Trajectories')
chosen_zones_map.add_data(data=int_zones, name='Internal Zones')
chosen_zones_map.save_to_html(file_name="chosen_zones_map.html")
return chosen_zones_map
class Visualize:
"""Quickly visualize data in browser over Mapbox tiles with the help of the AMAZING kepler.gl.
"""
def __init__(self,
data=None,
names=None,
read_only=False,
api_key=None,
style=None):
"""Visualize data using kepler.gl
Args:
data Optional[Union[List[]]]:
either None, a List of data objects, or a single data object. If
data is not None, then Visualize(data) will perform all steps,
including rendering and opening a browser.
"""
super(Visualize, self).__init__()
if api_key is not None:
self.MAPBOX_API_KEY = api_key
else:
self.MAPBOX_API_KEY = os.getenv('MAPBOX_API_KEY')
msg = 'Warning: api_key not provided and MAPBOX_API_KEY '
msg += 'environment variable not set.\nMap may not display.'
if self.MAPBOX_API_KEY is None:
print(msg)
config = self.config(style=style)
self.map = KeplerGl(config=config)
if data is not None:
self.add_data(data=data, names=names)
self.html_path = self.render(read_only=read_only)
def config(self, style=None):
"""Load kepler.gl config and insert Mapbox API Key"""
config_file = resource_filename('keplergl_cli', 'keplergl_config.json')
# First load config file as string, replace {MAPBOX_API_KEY} with the
# actual api key, then parse as JSON
with open(config_file) as f:
text = f.read()
text = text.replace('{MAPBOX_API_KEY}', self.MAPBOX_API_KEY)
keplergl_config = json.loads(text)
# If style_url is not None, replace existing value
standard_styles = [
'streets',
'outdoors',
'light',
'dark',
'satellite',
'satellite-streets',
]
if style is not None:
style = style.lower()
if style in standard_styles:
# Just change the name of the mapStyle.StyleType key
keplergl_config['config']['config']['mapStyle'][
'styleType'] = style
else:
# Add a new style with that url
d = {
'accessToken': self.MAPBOX_API_KEY,
'custom': True,
'id': 'custom',
'label': 'Custom map style',
'url': style
}
keplergl_config['config']['config']['mapStyle']['mapStyles'][
'custom'] = d
keplergl_config['config']['config']['mapStyle'][
'styleType'] = 'custom'
# Remove map state in the hope that it'll auto-center based on data
# keplergl_config['config']['config'].pop('mapState')
return keplergl_config['config']
def add_data(self, data, names=None):
"""Add data to kepler map
Data should be either GeoJSON or GeoDataFrame. Kepler isn't aware of the
geojson or shapely package, so if I supply an object from one of these
libraries, first convert it to a GeoJSON dict.
"""
# Make `data` iterable
if not isinstance(data, list):
data = [data]
# Make `names` iterable and of the same length as `data`
if isinstance(names, list):
# Already iterable, make sure they're the same length
msg = 'data and names are iterables of different length'
assert len(data) == len(names), msg
else:
# `names` not iterable, make sure it's the same length as `data`
name_stub = 'data' if names is None else names
names = [f'{name_stub}_{x}' for x in range(len(data))]
for datum, name in zip(data, names):
if any(isinstance(datum, c) for c in SHAPELY_GEOJSON_CLASSES):
datum = dict(mapping(datum))
self.map.add_data(data=datum, name=name)
def render(self, open_browser=True, read_only=False):
"""Export kepler.gl map to HTML file and open in Chrome
"""
# Generate path to a temporary file
path = os.path.join(tempfile.mkdtemp(), 'vis.html')
self.map.save_to_html(file_name=path, read_only=read_only)
# Open saved HTML file in new tab in default browser
webbrowser.open_new_tab('file://' + path)
return path
from keplergl import KeplerGl
import ast
with open("df_freq.pkl", "rb") as f:
df = pickle.load(f)
df = df[["Latitude", "Longitude", "Ryear", "Cluster_ID", "Topic_ID", "freq_words"]]
df["Timestamp"] = df["Ryear"].apply(lambda x: datetime.timestamp(datetime(year=x, month=1, day=1)))
df = df.drop_duplicates()
cluster_topic_data = {}
clusters = np.unique(df["Cluster_ID"])
for cluster in clusters:
df2 = df.loc[df["Cluster_ID"] == cluster]
topicids = np.unique(df2["Topic_ID"])
cluster_topic_data[cluster] = {}
for topicid in topicids:
s = f"topic_{topicid}"
df3 = df2.loc[df2["Topic_ID"] == topicid]
cluster_topic_data[cluster][s] = df3
for cluster in clusters:
with open('./config/config_'+str(cluster)+'.txt', "r") as f:
config = f.read()
x = ast.literal_eval(config)
w = KeplerGl(data=cluster_topic_data[cluster], config=x)
file_name = f"./kepler/map_{cluster}.html"
w.save_to_html(file_name=file_name)
from keplergl import KeplerGl # df needs to contain 'latitude' and 'longitude' columns map_ = KeplerGl(height=700) map_.add_data(df, 'Data') map_.save_to_html(file_name='try.html')
import pandas as pd
import os
from six.moves import urllib
import pandas as pd # importing the Pandas Library as 'pd'
from keplergl import KeplerGl # importing KeplerGl
import geopandas as gpd # importing geopandas as 'gpd'
# datasheet
dataset = pd.read_csv("poluition_map.csv",
encoding='utf8',
delimiter=',',
engine='python')
dataset.head()
#Create a basemap
map = KeplerGl(height=600, width=800)
# Create a geodataframe
gdf = gpd.GeoDataFrame(dataset,
geometry=gpd.points_from_xy(dataset.latitude,
dataset.longitude))
#make sure that your latitude and longitude are named as they are in your csv
map.add_data(data=gdf, name="IQA") # add geoenabled dataframe to map
map.save_to_html(file_name='GeoViz.html')
map
class RastaKepler(Visualize):
"""Inheriting from the `Visualize` and adding option to pass the output
map to save map at custom location alongisde the option to pass
a custom config file.
"""
def __init__(
self,
data=None,
names=None,
read_only=False,
api_key=None,
style=None,
config_file=None,
output_map=None,
):
if api_key is not None:
self.MAPBOX_API_KEY = api_key
else:
self.MAPBOX_API_KEY = os.getenv("MAPBOX_API_KEY")
msg = "Warning: api_key not provided and MAPBOX_API_KEY "
msg += "environment variable not set.\nMap may not display."
if self.MAPBOX_API_KEY is None:
print(msg)
if config_file is None:
self.config_file = resource_filename("rasta",
"keplergl_config.json")
else:
self.config_file = config_file
print(self.config_file)
if output_map is not None:
self.path = output_map + "_vis.html"
else:
self.path = os.path.join(tempfile.mkdtemp(), "defaultmap_vis.html")
config = self.config(style=style)
self.map = KeplerGl(config=config)
if data is not None:
self.add_data(data=data, names=names)
self.html_path = self.render(read_only=read_only)
def config(self, style=None):
"""Load kepler.gl config and insert Mapbox API Key"""
# config_file = resource_filename('keplergl_cli', 'keplergl_config.json')
# First load config file as string, replace {MAPBOX_API_KEY} with the
# actual api key, then parse as JSON
with open(self.config_file) as f:
text = f.read()
text = text.replace("{MAPBOX_API_KEY}", self.MAPBOX_API_KEY)
keplergl_config = json.loads(text)
# If style_url is not None, replace existing value
standard_styles = [
"streets",
"outdoors",
"light",
"dark",
"satellite",
"satellite-streets",
]
if style is not None:
style = style.lower()
if style in standard_styles:
# Just change the name of the mapStyle.StyleType key
keplergl_config["config"]["config"]["mapStyle"][
"styleType"] = style
else:
# Add a new style with that url
d = {
"accessToken": self.MAPBOX_API_KEY,
"custom": True,
"id": "custom",
"label": "Custom map style",
"url": style,
}
keplergl_config["config"]["config"]["mapStyle"]["mapStyles"][
"custom"] = d
keplergl_config["config"]["config"]["mapStyle"][
"styleType"] = "custom"
# Remove map state in the hope that it'll auto-center based on data
# keplergl_config['config']['config'].pop('mapState')
return keplergl_config["config"]
def render(self, open_browser=True, read_only=False):
"""Export kepler.gl map to HTML file and open in Chrome"""
self.map.save_to_html(file_name=self.path, read_only=read_only)
# Open saved HTML file in new tab in default browser
if open_browser:
webbrowser.open_new_tab("file://" + self.path)
return self.path
def generate_vis():
config = {
"version": "v1",
"config": {
"visState": {
"filters": [],
"layers": [
{
"id": "rfnq31gn",
"type": "grid",
"config": {
"dataId": "income",
"label": "Point",
"color": [
255,
203,
153
],
"columns": {
"lat": "Lat",
"lng": "Lon"
},
"isVisible": True,
"visConfig": {
"opacity": 0.89,
"worldUnitSize": 15,
"colorRange": {
"name": "Global Warming",
"type": "sequential",
"category": "Uber",
"colors": [
"#5A1846",
"#900C3F",
"#C70039",
"#E3611C",
"#F1920E",
"#FFC300"
]
},
"coverage": 1,
"sizeRange": [
0,
1000
],
"percentile": [
0,
100
],
"elevationPercentile": [
0,
100
],
"elevationScale": 5,
"colorAggregation": "average",
"sizeAggregation": "average",
"enable3d": True
},
"hidden": False,
"textLabel": [
{
"field": None,
"color": [
255,
255,
255
],
"size": 18,
"offset": [
0,
0
],
"anchor": "start",
"alignment": "center"
}
]
},
"visualChannels": {
"colorField": {
"name": "Median",
"type": "integer"
},
"colorScale": "quantile",
"sizeField": {
"name": "Median",
"type": "integer"
},
"sizeScale": "linear"
}
}
],
"interactionConfig": {
"tooltip": {
"fieldsToShow": {
"97ap1ofch": [
{
"name": "id",
"format": None
},
{
"name": "State_Code",
"format": None
},
{
"name": "State_Name",
"format": None
},
{
"name": "State_ab",
"format": None
},
{
"name": "County",
"format": None
}
]
},
"compareMode": False,
"compareType": "absolute",
"enabled": True
},
"brush": {
"size": 0.5,
"enabled": False
},
"geocoder": {
"enabled": False
},
"coordinate": {
"enabled": False
}
},
"layerBlending": "normal",
"splitMaps": [],
"animationConfig": {
"currentTime": None,
"speed": 1
}
},
"mapState": {
"bearing": 24,
"dragRotate": True,
"latitude": 30.067203168518454,
"longitude": -128.1037388392268,
"pitch": 50,
"zoom": 3.379836309981588,
"isSplit": False
},
"mapStyle": {
"styleType": "dark",
"topLayerGroups": {},
"visibleLayerGroups": {
"label": True,
"road": True,
"border": False,
"building": True,
"water": True,
"land": True,
"3d building": False
},
"threeDBuildingColor": [
9.665468314072013,
17.18305478057247,
31.1442867897876
],
"mapStyles": {}
}
}
}
m = KeplerGl()
m.config = config
m.add_data(data=import_data(), name="income")
m.save_to_html(file_name="index.html")
"pk.eyJ1IjoianVzdHN0YW4iLCJhIjoiY2tlc3hncWJ4MWh3cTJ4cXY5ZmsxdmoyYiJ9.LHycdQ3Il_MDxeW8JfiNWw",
"custom": True,
"icon":
"https://api.mapbox.com/styles/v1/mapbox/satellite-streets-v11/static/-122.3391,37.7922,9,0,0/400x300?access_token=pk.eyJ1IjoianVzdHN0YW4iLCJhIjoiY2tlc3hncWJ4MWh3cTJ4cXY5ZmsxdmoyYiJ9.LHycdQ3Il_MDxeW8JfiNWw&logo=false&attribution=false",
"id": "satellite-streets",
"label": "Mapbox Satellite Streets",
"url": "mapbox://styles/mapbox/satellite-streets-v11"
}
}
}
}
}
## 1st
df = pd.read_csv("data/olist_orders_geo.csv")
map_1 = KeplerGl(height=600, width=800, data={"data_1": df}, config=config)
map_1.add_data(data=df, name='data_1')
map_1.data
print(df.head())
gdf = gpd.GeoDataFrame(df,
geometry=gpd.points_from_xy(df.cust_lng, df.cust_lat))
viz = Visualize(gdf, api_key=MAPBOX_API_KEY)
viz.render(open_browser=True, read_only=False)
map_1.save_to_html(data={'data_1': df},
config=config,
file_name='olist-order-geo-config.html')
# + colab={"base_uri": "https://localhost:8080/", "height": 0} colab_type="code" executionInfo={"elapsed": 32915, "status": "ok", "timestamp": 1574653714934, "user": {"displayName": "So Negishi", "photoUrl": "", "userId": "14957100293746809516"}, "user_tz": 300} id="tS9Pv_2K5-LV" outputId="35d38b54-d0b7-4ec0-8e82-56b8bb1df5a0"
airport_map = KeplerGl(height=900, width=800)
airport_map_data = airport_df[[
'name', 'latitude', 'longitude', 'facility_type'
]]
airport_map.add_data(
data=airport_map_data[airport_map_data.facility_type == 'large_airport'],
name='large_airports')
airport_map.add_data(
data=airport_map_data[airport_map_data.facility_type == 'medium_airport'],
name='medium_airports')
airport_map.add_data(
data=airport_map_data[airport_map_data.facility_type == 'small_airport'],
name='small_airports')
airport_map.save_to_html(file_name='./visualization/airport_map.html')
# airport_map
# + [markdown] colab_type="text" id="uJf8Ce225-LY"
# ## 2. Get flight info
# + colab={"base_uri": "https://localhost:8080/", "height": 0} colab_type="code" executionInfo={"elapsed": 39951, "status": "ok", "timestamp": 1574653721974, "user": {"displayName": "So Negishi", "photoUrl": "", "userId": "14957100293746809516"}, "user_tz": 300} id="IN6ZY7qS5-LY" outputId="9b70a9cf-0857-4370-f401-d81fc28e2bbb"
flight_df = pd.read_csv('./raw_data/266694930_T_ONTIME_REPORTING.csv',
header=0)
flight_df.columns = [col.lower() for col in flight_df.columns.tolist()]
# + colab={"base_uri": "https://localhost:8080/", "height": 0} colab_type="code" executionInfo={"elapsed": 41216, "status": "ok", "timestamp": 1574653723243, "user": {"displayName": "So Negishi", "photoUrl": "", "userId": "14957100293746809516"}, "user_tz": 300} id="y-u1C4OX5-La" outputId="88c5f28a-777f-4998-8550-92ff5234d177"
flight_df
# + colab={} colab_type="code" id="lW8_yzoW5-Lc"
flights = flight_df[['origin', 'dest']].to_records(index=False).tolist()
# mape = KeplerGl(height=500)
#
# In[23]:
map
# In[25]:
config = map.config
# In[29]:
map_2 = KeplerGl(height=800, data={"test": df})
map_2
# In[31]:
config_2 = map_2.config
config_2
# In[32]:
map_2.save_to_html(data={"test": df},
config=config_2,
file_name="Kepler_Baumkataster_Height.html")
# In[ ]:
import config
import configparser
import pandas as pd #importing the Pandas Library as 'pd'
from keplergl_cli import Visualize #importing KeplerGl
from keplergl import KeplerGl
import geopandas as gpd #importing geopandas as 'gpd'
#add mapbox API key
MAPBOX_API_KEY = config.MAPBOX_API
df = pd.read_csv("data/olist_orders_geo.csv")
print(df.head())
map_1 = KeplerGl(height=600, width=800)
map_1.add_data(data=df, name='data_1')
map_1.data
print(df.head())
gdf = gpd.GeoDataFrame(df,
geometry=gpd.points_from_xy(df.cust_lng, df.cust_lat))
viz = Visualize(gdf, api_key=MAPBOX_API_KEY)
viz.render(open_browser=True, read_only=False)
map_1.save_to_html(data={'data_1': df}, file_name='olist-order-geo.html')
sign.append(0)
df['Sign'] = sign
latitude_layer = []
longitude_layer = []
latitude = 41.377491
longitude = 64.585262
for i in range(len(df)):
if (df['Sign'][i] == 1):
latitude_layer.append(latitude)
longitude_layer.append(longitude)
else:
latitude_layer.append(df['Latitude'][i])
longitude_layer.append(df['Longitude'][i])
df['Latitude_Layer'] = latitude_layer
df['Longitude_Layer'] = longitude_layer
from keplergl import KeplerGl
import geopandas as gpd
map_1 = KeplerGl(height=500)
#df=pd.read_csv('df2001.csv')
df['Longitude'] = pd.to_numeric(df['Longitude'])
df['Latitude'] = pd.to_numeric(df['Latitude'])
gdf = gpd.GeoDataFrame(df,
geometry=gpd.points_from_xy(df.Longitude, df.Latitude))
map_1.add_data(data=gdf)
map_1.save_to_html(file_name='dark2009.html')