Set the zoom level of the bokeh map when using a tile provider
I followed the example here: http://docs.bokeh.org/en/latest/docs/user_guide/geo.html#tile-providers
I got a basemap loading a GeoJSON file with a polygon list (already projected for Web Mercator EPSG: 3857) so I could use it 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 unable to set the default zoom level for the tiles. I thought it might have been a tool tweak ( http://docs.bokeh.org/en/latest/docs/user_guide/tools.html ), but there I can't find a default for the zoom capabilities.
How can I set the default zoom level of tiles?
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Old question, but answer in case anyone has the same problem. Set the range for your map, so you can zoom in to the desired area when loading. Below is an example from Papua New Guinea
p = figure(title="PNG Highlands Earthquake 7.5 Affected Villages",y_range=(-4.31509, -7.0341),x_range=( 141.26667, 145.56598))
p.xaxis.axis_label = 'longitude'
p.yaxis.axis_label = 'latitude'
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The notion of zoom level only applies to GMapPlot
, and only because Google has very tight control over the presentation of the maps, and this is the API they provide. All other Bokeh charts have explicitly configurable properties x_range
and y_range
. You can set start
and end
these ranges as you wish, and the graph will display the corresponding area defined by these boundaries.
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I ran into this problem myself and found a good solution that should work in most cases. This requires making sure the data and x_range / y_range are projected correctly (I've used Proj
and transform
from pyproj
, but I'm sure there are other packages out there 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()
Reading the data (I took a few extra steps to try and clear the coordinates, as I am working with an extremely confusing dataset that contains NaN
paginated text in coordinate columns as well):
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)]
Redesign 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)
Redesign x_range, y_range. This is very important because these values set the extent of the map bokeh
- the coordinates of these values must match the projection. To make sure you have the correct coordinates, I suggest using http://bboxfinder.com to create a bounding rectangular AOI and get the correct min / max and min / max coordinates (making sure EPSG:3857 - WGS 84/Pseudo-Mercator is selected
). Using this method, simply copy coodinates next to the "box" - they're fine minx
, miny
, maxx
, maxy
and then you need to be reordered as a 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)
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