Set the zoom level of a bokeh map when using a tile provider

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I've followed the example here: http://docs.bokeh.org/en/latest/docs/user_guide/geo.html#tile-providers

I got a basic map loading a GeoJSON file with a list of polygons (already projected to Web Mercator EPSG:3857) so then I could use STAMEN_TONER as a tile provider.

from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.tile_providers import STAMEN_TONER, STAMEN_TERRAIN
from bokeh.models import Range1d, GeoJSONDataSource

# bokeh configuration for jupyter
from bokeh.io import output_notebook
output_notebook()

# bounding box (x,y web mercator projection, not lon/lat) 
mercator_extent_x = dict(start=x_low, end=x_high, bounds=None)
mercator_extent_y = dict(start=y_low, end=y_high, bounds=None)
x_range1d = Range1d(**mercator_extent_x)
y_range1d = Range1d(**mercator_extent_y)

fig = figure(
    tools='pan, zoom_in, zoom_out, box_zoom, reset, save', 
    x_range=x_range1d, 
    y_range=y_range1d, 
    plot_width=800, 
    plot_height=600
)
fig.axis.visible = False
fig.add_tile(STAMEN_TERRAIN)

# the GeoJSON is already in x,y web mercator projection, not lon/lat 
with open('/path/to/my_polygons.geojson', 'r') as f:
    my_polygons_geo_json = GeoJSONDataSource(geojson=f.read())

fig.multi_line(
    xs='xs', 
    ys='ys', 
    line_color='black', 
    line_width=1, 
    source=my_polygons_geo_json
)
show(fig)

However I am not able to set a default zoom level for the tiles. I thought it could have been a tool setting (http://docs.bokeh.org/en/latest/docs/user_guide/tools.html) but in there I can not find a default value for the zoom capabilities.

How can I set a default value for the zoom level of the tiles?

bokeh gis python
3 Answers

I've just run into this issue myself, and found a good solution that should work under most circumstances. This requires making sure the data and the x_range/y_range to be projected properly (I used Proj and transform from pyproj but I'm sure there are other packages that will work the same).

Import modules:

import pandas as pd
import numpy as np
from pyproj import Proj, transform

import datashader as ds
from datashader import transfer_functions as tf
from datashader.bokeh_ext import InteractiveImage
from datashader.utils import export_image
from datashader.colors import colormap_select, Greys9, Hot, viridis, inferno

from IPython.core.display import HTML, display

from bokeh.plotting import figure, output_notebook, output_file, show
from bokeh.tile_providers import CARTODBPOSITRON
from bokeh.tile_providers import STAMEN_TONER
from bokeh.tile_providers import STAMEN_TERRAIN
from bokeh.embed import file_html

from functools import partial

output_notebook()

Read in data (I took a few extra steps to try and clean the coordinates since I'm working with an extremely messy dataset that contains NaN and broken text in the coordinates columns):

df = pd.read_csv('data.csv', usecols=['latitude', 'longitude'])
df.apply(lambda x: pd.to_numeric(x,errors='coerced')).dropna()
df = df.loc[(df['latitude'] > - 90) & (df['latitude'] < 90) & (df['longitude'] > -180) & (df['longitude'] < 180)]

Reproject data:

# WGS 84
inProj = Proj(init='epsg:4326')

# WGS84 Pseudo Web Mercator, projection for most WMS services
outProj = Proj(init='epsg:3857')

df['xWeb'],df['yWeb'] = transform(inProj,outProj,df['longitude'].values,df['latitude'].values)

Reproject the x_range, y_range. This is critical as these values set the extent of the bokeh map - the coordinates of these values need to match the projection. To make sure you have the correct coordinates, I suggest using http://bboxfinder.com to create a bounding box AOI and get the correct min/max and min/max coordinates (making sure EPSG:3857 - WGS 84/Pseudo-Mercator is selected). Using this method, just copy the coodinates next to "box" - these are in the order of minx,miny,maxx,maxy and should then be reordered as minx,maxx,miny,maxy (x_range = (minx,maxx))(y_range=(miny,maxy)):

world = x_range, y_range =  ((-18706892.5544, 21289852.6142), (-7631472.9040, 12797393.0236))

plot_width  = int(950)
plot_height = int(plot_width//1.2)

def base_plot(tools='pan,wheel_zoom,save,reset',plot_width=plot_width, 
plot_height=plot_height, **plot_args):
p = figure(tools=tools, plot_width=plot_width, plot_height=plot_height,
    x_range=x_range, y_range=y_range, outline_line_color=None,
    min_border=0, min_border_left=0, min_border_right=0,
    min_border_top=0, min_border_bottom=0, **plot_args)

p.axis.visible = False
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
return p

options = dict(line_color=None, fill_color='blue', size=1.5, alpha=0.25)

background = "black"
export = partial(export_image, export_path="export", background=background)
cm = partial(colormap_select, reverse=(background=="white"))

def create_image(x_range, y_range, w=plot_width, h=plot_height):
    cvs = ds.Canvas(plot_width=w, plot_height=h, x_range=x_range, y_range=y_range)
    agg = cvs.points(df, 'xWeb', 'yWeb')

    magma = ['#3B0F6F', '#8C2980', '#DD4968', '#FD9F6C', '#FBFCBF']

    img = tf.shade(agg, cmap=magma, how='eq_hist') # how='linear', 'log', 'eq_hist'
    return tf.dynspread(img, threshold=.05, max_px=15)

p = base_plot()
p.add_tile("WMS service")

#used to export image (without the WMS)
export(create_image(*world),"TweetGeos")

#call interactive image 
InteractiveImage(p, create_image)
user_76879810
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