HI,
Can i be able to save panel plot/holoview plot/bokeh plot in geotiff ?
I know we can save the plot in .html but i want to save my plot in geotiff format,
Kindly provide me link or example where i can learn how to save the plot in geotiff format with best result.
Thank you.
Hi @sameerCoder
I don’t know anything about the geotiff format. But if I did I would need you to be a bit more specific in your question in order to be able to answer.
I would like to know
- Is it a Holoviews plot you want to save? Or anything you can create in Panel?
- Do you need to save it directly to geotiff?
- Could you provide a small, reproducibile code example of something you would like to save in geotiff?
If saving to tiff is good enough (sorry for my ignorance) then there are online converters like https://convertio.co/html-tiff/
Hi,
Thanks for quick reply
I want geotiff final format,
As ask kindly find the minimum example below
def plot():
import numpy as np
import pandas as pd
import geoviews as gv
import holoviews as hv
import panel as pn
from holoviews.operation.datashader import rasterize
hv.extension("bokeh")
from bokeh.models.callbacks import CustomJS
# Some points defining a triangulation over (roughly) Britain.
xy = np.asarray([
[-0.101, 0.872], [-0.080, 0.883], [-0.069, 0.888], [-0.054, 0.890],
[-0.045, 0.897], [-0.057, 0.895], [-0.073, 0.900], [-0.087, 0.898],
[-0.090, 0.904], [-0.069, 0.907], [-0.069, 0.921], [-0.080, 0.919],
[-0.073, 0.928], [-0.052, 0.930], [-0.048, 0.942], [-0.062, 0.949],
[-0.054, 0.958], [-0.069, 0.954], [-0.087, 0.952], [-0.087, 0.959],
[-0.080, 0.966], [-0.085, 0.973], [-0.087, 0.965], [-0.097, 0.965],
[-0.097, 0.975], [-0.092, 0.984], [-0.101, 0.980], [-0.108, 0.980],
[-0.104, 0.987], [-0.102, 0.993], [-0.115, 1.001], [-0.099, 0.996],
[-0.101, 1.007], [-0.090, 1.010], [-0.087, 1.021], [-0.069, 1.021],
[-0.052, 1.022], [-0.052, 1.017], [-0.069, 1.010], [-0.064, 1.005],
[-0.048, 1.005], [-0.031, 1.005], [-0.031, 0.996], [-0.040, 0.987],
[-0.045, 0.980], [-0.052, 0.975], [-0.040, 0.973], [-0.026, 0.968],
[-0.020, 0.954], [-0.006, 0.947], [0.003, 0.935], [0.006, 0.926],
[0.005, 0.921], [0.022, 0.923], [0.033, 0.912], [0.029, 0.905],
[0.017, 0.900], [0.012, 0.895], [0.027, 0.893], [0.019, 0.886],
[0.001, 0.883], [-0.012, 0.884], [-0.029, 0.883], [-0.038, 0.879],
[-0.057, 0.881], [-0.062, 0.876], [-0.078, 0.876], [-0.087, 0.872],
[-0.030, 0.907], [-0.007, 0.905], [-0.057, 0.916], [-0.025, 0.933],
[-0.077, 0.990], [-0.059, 0.993]])
# Make lats + lons
x = abs(xy[:, 0] * 180 / 3.14159)
y = xy[:, 1] * 180 / 3.14159
# A selected triangulation of the points.
triangles = np.asarray([
[67, 66, 1], [65, 2, 66], [1, 66, 2], [64, 2, 65], [63, 3, 64],
[60, 59, 57], [2, 64, 3], [3, 63, 4], [0, 67, 1], [62, 4, 63],
[57, 59, 56], [59, 58, 56], [61, 60, 69], [57, 69, 60], [4, 62, 68],
[6, 5, 9], [61, 68, 62], [69, 68, 61], [9, 5, 70], [6, 8, 7],
[4, 70, 5], [8, 6, 9], [56, 69, 57], [69, 56, 52], [70, 10, 9],
[54, 53, 55], [56, 55, 53], [68, 70, 4], [52, 56, 53], [11, 10, 12],
[69, 71, 68], [68, 13, 70], [10, 70, 13], [51, 50, 52], [13, 68, 71],
[52, 71, 69], [12, 10, 13], [71, 52, 50], [71, 14, 13], [50, 49, 71],
[49, 48, 71], [14, 16, 15], [14, 71, 48], [17, 19, 18], [17, 20, 19],
[48, 16, 14], [48, 47, 16], [47, 46, 16], [16, 46, 45], [23, 22, 24],
[21, 24, 22], [17, 16, 45], [20, 17, 45], [21, 25, 24], [27, 26, 28],
[20, 72, 21], [25, 21, 72], [45, 72, 20], [25, 28, 26], [44, 73, 45],
[72, 45, 73], [28, 25, 29], [29, 25, 31], [43, 73, 44], [73, 43, 40],
[72, 73, 39], [72, 31, 25], [42, 40, 43], [31, 30, 29], [39, 73, 40],
[42, 41, 40], [72, 33, 31], [32, 31, 33], [39, 38, 72], [33, 72, 38],
[33, 38, 34], [37, 35, 38], [34, 38, 35], [35, 37, 36]])
z = np.random.uniform(0, 16, 74)
# print("x",x)
# print("y",y)
# print("z",z)
def plotthis(z, regions='w'):
if regions is 'O':
z = np.where(((x >= 0) & (x <= 4) & (y >= 50) & (y <= 56)), z, np.nan).flatten()
elif regions is 'A':
z = np.where(((x >= 3) & (x <= 4) & (y >= 54) & (y <= 57)), z, np.nan).flatten()
elif regions is 'T':
z = np.where(((x >= -2) & (x <= 3) & (y >= 50) & (y <= 57)), z, np.nan).flatten()
# else:
# # data = get_data4region(data,**odisha)
# z=z.flatten()
print("lx:", len(x), "ly:", len(y), "lz:", len(z))
print("z", z)
verts = pd.DataFrame(np.stack((x, y, z)).T, columns=['X', 'Y', 'Z'])
# #openStreet Background.
# tri_sub = cf.apply(lambda x: x - 1)
# tri_sub=tri_sub[:10]
tri = gv.TriMesh((triangles, gv.Points(verts)))
return tri
allplot = {(k, r): plotthis(k, r) for k, r in zip(z, ['O', 'A', 'T'])}
colorbar_opts = {
'major_label_overrides': {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: '10',
11: '11',
12: '12',
13: '13',
14: '>14',
15: '15'
},
'major_label_text_align': 'left', 'major_label_text_font_style': 'bold', }
logo1 = hv.RGB.load_image("https://i.ibb.co/5h74S9n/python.png")
def absolute_position(plot, element):
glyph = plot.handles['glyph']
x_range, y_range = plot.handles['x_range'], plot.handles['y_range']
glyph.dh_units = 'screen'
glyph.dw_units = 'screen'
glyph.dh = 60
glyph.dw = 90
glyph.x = x_range.start
glyph.y = y_range.start
xcode = CustomJS(code="glyph.x = cb_obj.start", args={'glyph': glyph})
plot.handles['x_range'].js_on_change('start', xcode)
ycode = CustomJS(code="glyph.y = cb_obj.start", args={'glyph': glyph})
plot.handles['y_range'].js_on_change('start', ycode)
def plot_limits(plot, element):
plot.handles['x_range'].min_interval = 100
plot.handles['x_range'].max_interval = 55000000
# 3000000
plot.handles['y_range'].min_interval = 500
plot.handles['y_range'].max_interval = 900000
opts = dict(width=700, height=700, tools=['hover', 'save', 'wheel_zoom'], active_tools=['wheel_zoom'],
hooks=[plot_limits], colorbar=True, color_levels=15,
colorbar_opts=colorbar_opts, cmap=['#000080',
'#0000cd',
'#0008ff',
'#004cff',
'#0090ff',
'#00d4ff',
'#29ffce',
'#60ff97',
'#97ff60',
'#ceff29',
'#ffe600',
'#ffa700',
'#ff2900',
'#cd0000',
'#800000',
], clim=(0, 15), title="\t\t\t\t\t\t\t\t\t Mean Period (s) ",
fontsize={'title': 18, 'xlabel': 15, 'ylabel': 15, 'ticks': 12})
hmap1 = hv.HoloMap(allplot, kdims=['Select D :', 'Select State'])
finalplot = pn.Column( rasterize(hmap1).options(**opts) *
logo1.opts(hooks=[absolute_position],apply_ranges=False))
finalplot.show()
plot()
This final plot i want in geotiff format ,
Thank you.
Hi @sameerCoder
I can see there is a question/ answer on geotiff here. Create GeoTiff using Datashader. It’s not exactly the same question you have. But maybe you can find inspiration for a solution there.
Take a look and let us know.
Right, creating a GeoTiff from datashader makes sense, but I really don’t know what it would mean to save a bokeh/matplotlib plot to datashader. The point of a GeoTiff is that it saves the coordinates, but plots have axes or may even contain multiple figures so I don’t really know what it would mean to export that to a GeoTiff.
Right; this request only seems meaningingful at the datashader level. You should be able to get the output of rasterize() as an Xarray DataArray using .data, and creating a GeoTIFF from there should be straightforward.