def test_generate_terrain():
csv = ds.Canvas(x_range=X_RANGE,
y_range=Y_RANGE,
plot_width=W,
plot_height=H)
terrain = geo.generate_terrain(csv)
assert terrain is not None
from __future__ import absolute_import
import datashader as ds
import xarray as xr
import numpy as np
import pytest
from datashader import geo
W = 25
H = 30
X_RANGE = (0, 500)
Y_RANGE = (0, 500)
csv = ds.Canvas(x_range=X_RANGE, y_range=Y_RANGE, plot_width=W, plot_height=H)
terrain = geo.generate_terrain(csv)
def _raster_aggregate_default():
return terrain
#
def _do_sparse_array(data_array):
import random
indx = list(zip(*np.where(data_array)))
pos = random.sample(range(data_array.size), data_array.size // 2)
indx = np.asarray(indx)[pos]
r = indx[:, 0]
c = indx[:, 1]
data_half = data_array.copy()
def test_generate_terrain():
terrain = geo.generate_terrain(20, 20)
assert terrain is not None
import datashader as ds
import xarray as xr
import numpy as np
import pytest
from datashader import geo
W = 25
H = 30
csv = ds.Canvas(plot_width=W, plot_height=H)
terrain = geo.generate_terrain(W, H)
def _raster_aggregate_default():
return terrain
#
def _do_sparse_array(data_array):
import random
indx = list(zip(*np.where(data_array)))
pos = random.sample(range(data_array.size), data_array.size//2)
indx = np.asarray(indx)[pos]
r = indx[:,0]
c = indx[:,1]
data_half = data_array.copy()
data_half[r,c] = 0
return data_half
def _do_gaussian_array():
_x = np.linspace(0, 50, 101)
_y = _x.copy()
_mean = 25
def test_generate_terrain():
csv = ds.Canvas(x_range=X_RANGE, y_range=Y_RANGE,
plot_width=W, plot_height=H)
terrain = geo.generate_terrain(csv)
assert terrain is not None
import datashader as ds
import xarray as xr
import numpy as np
import pytest
from datashader import geo
W = 25
H = 30
X_RANGE = (0, 500)
Y_RANGE = (0, 500)
csv = ds.Canvas(x_range=X_RANGE, y_range=Y_RANGE,
plot_width=W, plot_height=H)
terrain = geo.generate_terrain(csv)
def _raster_aggregate_default():
return terrain
#
def _do_sparse_array(data_array):
import random
indx = list(zip(*np.where(data_array)))
pos = random.sample(range(data_array.size), data_array.size//2)
indx = np.asarray(indx)[pos]
r = indx[:,0]
c = indx[:,1]
data_half = data_array.copy()
data_half[r,c] = 0
return data_half
