import pandas as pd
import os
from mapboxgl.utils import *
from mapboxgl.viz import *
# Load data from sample csv
data_url = 'https://raw.githubusercontent.com/mapbox/mapboxgl-jupyter/master/examples/points.csv'
df = pd.read_csv(data_url)
# Must be a public token, starting with `pk`
token = os.getenv('MAPBOX_ACCESS_TOKEN')
# Create a geojson Feature Collection export from a Pandas dataframe
points = df_to_geojson(df,
                       properties=['Avg Medicare Payments', 'Avg Covered Charges', 'date'],
                       lat='lat', lon='lon', precision=3)
# Generate data breaks and color stops from colorBrewer
color_breaks = [0,10,100,1000,10000]
color_stops = create_color_stops(color_breaks, colors='YlGnBu')
# Create the viz from the dataframe
viz = CircleViz(points,
                access_token=token,
                height='400px',
                color_property='Avg Medicare Payments',
                color_stops=color_stops,
                label_property='Avg Medicare Payments',
                stroke_color='black',
                center=(-95, 40),
                zoom=3,
                below_layer='waterway-label')
viz.show()

import pandas as pd
import os
from mapboxgl.utils import *
from mapboxgl.viz import *
# Load data from sample csv
data_url = 'https://raw.githubusercontent.com/mapbox/mapboxgl-jupyter/master/examples/points.csv'
df = pd.read_csv(data_url)
# Must be a public token, starting with `pk`
token = os.getenv('MAPBOX_ACCESS_TOKEN')
# Create a geojson Feature Collection export from a Pandas dataframe
points = df_to_geojson(df,
                       properties=['Avg Medicare Payments', 'Avg Covered Charges', 'date'],
                       lat='lat', lon='lon', precision=3)
#Create a clustered circle map
color_stops = create_color_stops([1,10,50,100], colors='BrBG')
viz = ClusteredCircleViz(points,
                         access_token=token,
                         color_stops=color_stops,
                         radius_stops=[[1,5], [10, 10], [50, 15], [100, 20]],
                         radius_default=2,
                         cluster_maxzoom=10,
                         cluster_radius=30,
                         label_size=12,
                         opacity=0.9,
                         center=(-95, 40),
                         zoom=3)
viz.show()

import pandas as pd
import os
from mapboxgl.utils import *
from mapboxgl.viz import *
# Load data from sample csv
data_url = 'https://raw.githubusercontent.com/mapbox/mapboxgl-jupyter/master/examples/points.csv'
df = pd.read_csv(data_url)
# Must be a public token, starting with `pk`
token = os.getenv('MAPBOX_ACCESS_TOKEN')
# Create a geojson Feature Collection export from a Pandas dataframe
points = df_to_geojson(df,
                       properties=['Avg Medicare Payments', 'Avg Covered Charges', 'date'],
                       lat='lat', lon='lon', precision=3)
# Generate color stops from colorBrewer
measure_color = 'Avg Covered Charges'
color_breaks = [round(df[measure_color].quantile(q=x*0.1), 2) for x in range(2, 10)]
color_stops = create_color_stops(color_breaks, colors='Blues')
# Generate radius breaks from data domain and circle-radius range
measure_radius = 'Avg Medicare Payments'
radius_breaks = [round(df[measure_radius].quantile(q=x*0.1), 2) for x in range(2,10)]
radius_stops = create_radius_stops(radius_breaks, 0.5, 10)
# Create the viz
viz = GraduatedCircleViz(points,
                         access_token=token,
                         color_property='Avg Covered Charges',
                         color_stops=color_stops,
                         radius_property='Avg Medicare Payments',
                         stroke_color='black',
                         stroke_width=0.5,
                         radius_stops=radius_stops,
                         center=(-95, 40),
                         zoom=3,
                         below_layer='waterway-label')
viz.show()

import pandas as pd
import os
from mapboxgl.utils import *
from mapboxgl.viz import *
# Load data from sample csv
data_url = 'https://raw.githubusercontent.com/mapbox/mapboxgl-jupyter/master/examples/points.csv'
df = pd.read_csv(data_url)
# Must be a public token, starting with `pk`
token = os.getenv('MAPBOX_ACCESS_TOKEN')
# Create a geojson Feature Collection export from a Pandas dataframe
points = df_to_geojson(df,
                       properties=['Avg Medicare Payments', 'Avg Covered Charges', 'date'],
                       lat='lat', lon='lon', precision=3)
#Create a heatmap
heatmap_color_stops = create_color_stops([0.01,0.25,0.5,0.75,1], colors='RdPu')
heatmap_radius_stops = [[0,1], [15, 40]] #increase radius with zoom
color_breaks = [0,10,100,1000,10000]
color_stops = create_color_stops(color_breaks, colors='Spectral')
heatmap_weight_stops = create_weight_stops(color_breaks)
#Create a heatmap
viz = HeatmapViz(points,
                 access_token=token,
                 weight_property='Avg Medicare Payments',
                 weight_stops=heatmap_weight_stops,
                 color_stops=heatmap_color_stops,
                 radius_stops=heatmap_radius_stops,
                 opacity=0.9,
                 center=(-95, 40),
                 zoom=3,
                 below_layer='waterway-label')
viz.show()

import os
from mapboxgl.viz import *
from mapboxgl.utils import *
# Must be a public token, starting with `pk`
token = os.getenv('MAPBOX_ACCESS_TOKEN')
# Create Choropleth with GeoJSON Source
viz = ChoroplethViz('https://raw.githubusercontent.com/mapbox/mapboxgl-jupyter/master/examples/data/us-states.geojson',
                     color_property='density',
                     color_stops=create_color_stops([0, 50, 100, 500, 1500], colors='YlOrRd'),
                     color_function_type='interpolate',
                     line_stroke='--',
                     line_color='rgb(128,0,38)',
                     line_width=1,
                     line_opacity=0.9,
                     opacity=0.8,
                     center=(-96, 37.8),
                     zoom=3,
                     below_layer='waterway-label')
viz.show()

from mapboxgl.viz import ImageViz
img_url = 'https://raw.githubusercontent.com/mapbox/mapboxgl-jupyter/master/examples/mosaic.jpg'
# Coordinates must be an array in the form of [UL, UR, LR, LL]
coordinates = [[-123.40515640309, 38.534294809274336],
               [-115.92938988349292, 38.534294809274336],
               [-115.92938988349292, 32.08296982365502],
               [-123.40515640309, 32.08296982365502]]
# Create the viz
viz = ImageViz(img_url, coordinates, access_token=token,
               height='600px',
               center=(-119, 35),
               zoom=5,
               below_layer='waterway-label')
viz.show()

from rio_glui.server import TileServer
from rio_glui.raster import RasterTiles
from mapboxgl.viz import RasterTilesViz
file = 'myfile.tif'
# Create raster tile object
# More info: https://github.com/mapbox/rio-glui/blob/master/rio_glui/raster.py#L16-L44
raster = RasterTiles(file, indexes=(2,1,3))
# Create local tile server
# More info: https://github.com/mapbox/rio-glui/blob/master/rio_glui/server.py#L21-L56
ts = TileServer(raster)
# Start tile server
ts.start()
# Initialize RasterTiles Viz by passing our local tile server url `ts.get_tiles_url`
viz = RasterTilesViz(ts.get_tiles_url(),
                     tiles_bounds=ts.get_bounds(),
                     center=ts.get_center(),
                     height='1000px',
                     zoom=13)
viz.show()

import random
import os
from mapboxgl.viz import LinestringViz
from mapboxgl.utils import create_color_stops
# Must be a public token, starting with `pk`
token = os.getenv('MAPBOX_ACCESS_TOKEN')
# JSON join-data object
data = [{"elevation": x, "weight": random.randint(0,100)} for x in range(0, 21000, 10)]
viz = LinestringViz(data,
                    vector_url='mapbox://mapbox.mapbox-terrain-v2',
                    vector_layer_name='contour',
                    vector_join_property='ele',
                    data_join_property='elevation',
                    color_property='elevation',
                    color_stops=create_color_stops([0, 25, 50, 75, 100], colors='YlOrRd'),
                    line_stroke='-',
                    line_width_default=2,
                    opacity=0.8,
                    center=(-122.48, 37.83),
                    zoom=16,
                    below_layer='waterway-label')
viz.show()