Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
def test_regrid_mean_xarray_transposed(self):
img = Image((range(10), range(5), np.arange(10) * np.arange(5)[np.newaxis].T),
datatype=['xarray'])
img.data = img.data.transpose()
regridded = regrid(img, width=2, height=2, dynamic=False)
expected = Image(([2., 7.], [0.75, 3.25], [[1, 5], [6, 22]]))
self.assertEqual(regridded, expected)
def test_operation_grid(self):
grid = GridSpace({i: Image(np.random.rand(10, 10)) for i in range(10)}, kdims=['X'])
op_grid = operation(grid, op=lambda x, k: x.clone(x.data*2))
doubled = grid.clone({k: v.clone(v.data*2, group='Operation')
for k, v in grid.items()})
self.assertEqual(op_grid, doubled)
def test_rasterize_quadmesh(self):
qmesh = QuadMesh(([0, 1], [0, 1], np.array([[0, 1], [2, 3]])))
img = rasterize(qmesh, width=3, height=3, dynamic=False, aggregator=ds.mean('z'))
image = Image(np.array([[2., 3., np.NaN], [0, 1, np.NaN], [np.NaN, np.NaN, np.NaN]]),
bounds=(-.5, -.5, 1.5, 1.5))
self.assertEqual(img, image)
def test_slice_datetime_xaxis(self):
start = np.datetime64(dt.datetime.today())
end = start+np.timedelta64(1, 's')
bounds = (start, 0, end, 10)
xs = date_range(start, end, 10)
image = Image((xs, self.ys, self.array), bounds=bounds)
sliced = image[start+np.timedelta64(530, 'ms'): start+np.timedelta64(770, 'ms')]
self.assertEqual(sliced.dimension_values(2, flat=False),
self.array[:, 5:8])
def test_image_casting(self):
img = Image([], bounds=2)
self.assertEqual(img, Image(img))
def test_canonical_vdim(self):
x = np.array([ 0. , 0.75, 1.5 ])
y = np.array([ 1.5 , 0.75, 0. ])
z = np.array([[ 0.06925999, 0.05800389, 0.05620127],
[ 0.06240918, 0.05800931, 0.04969735],
[ 0.05376789, 0.04669417, 0.03880118]])
dataset = Image((x, y, z), kdims=['x', 'y'], vdims=['z'])
canonical = np.array([[ 0.05376789, 0.04669417, 0.03880118],
[ 0.06240918, 0.05800931, 0.04969735],
[ 0.06925999, 0.05800389, 0.05620127]])
self.assertEqual(dataset.dimension_values('z', flat=False),
canonical)
def setUp(self):
if pyplot is None:
raise SkipTest("Matplotlib required to test widgets")
self.basename = 'no-file'
self.image1 = Image(np.array([[0,1],[2,3]]), label='Image1')
self.image2 = Image(np.array([[1,0],[4,-2]]), label='Image2')
self.map1 = HoloMap({1:self.image1, 2:self.image2}, label='TestMap')
self.unicode_table = ItemTable([('β','Δ1'), ('°C', '3×4')],
label='Poincaré', group='α Festkörperphysik')
self.renderer = Store.renderer.instance()
def test_reduce_x_dimension(self):
ys = np.linspace(0.5, 9.5, 10)
zs = [0., 4.5, 9., 13.5, 18., 22.5, 27., 31.5, 36., 40.5]
with DatatypeContext([self.datatype, 'dictionary' , 'dataframe'], Image):
self.assertEqual(self.image.reduce(x=np.mean),
Curve((ys, zs), kdims=['y'], vdims=['z']))
@param.output(image=hv.Image)
def output(self):
return self.get_tiff()
try:
dask_df = daskdf.from_pandas(input_frame, npartitions=multiprocessing.cpu_count()).persist()
logger.info("Converted to Dask Frame..")
num_genes, num_samples = input_frame.shape
da = dask_df.to_dask_array(True).persist()
logger.info("Converted to Dask Array..")
viridis_bad_black = copy.copy(mpl.cm.get_cmap("viridis"))
viridis_bad_black.set_bad(
(1, 0, 1)
) # It's actually pink, but it shows up as white in bokeh
# Visualize
if backend == "matplotlib":
img = hv.Image((np.arange(num_samples), np.arange(num_genes), da))
rasterized_img = rasterize(img)
rasterized_img.opts(cmap=viridis_bad_black, dpi=400, logz=True)
hv.save(rasterized_img, output_path, backend=backend)
else:
img = hv.Image((np.arange(num_samples), np.arange(num_genes), da))
rasterized_img = rasterize(img, width=width, height=height)
rasterized_img.opts(width=width, height=height, cmap=viridis_bad_black, logz=True)
hv.save(rasterized_img, output_path, backend="bokeh")
logger.info("Output visualization!", output_path=output_path)
return output_path
except Exception as e:
logger.exception(
"Unable to visualize dataframe!",
output_path=output_path,