def array_image_src(request):
res_x = 1000
res_y = 500
x_range = (50, 80)
y_range = (-40, -30)
pixsize_x = (x_range[1] - x_range[0]) / res_x
pixsize_y = (y_range[1] - y_range[0]) / res_y
A = Affine(pixsize_x, 0, x_range[0],
0, -pixsize_y, y_range[1])
lons = np.array([(x, 0) * A for x in np.arange(res_x)])[:, 0]
lats = np.array([(0, y) * A for y in np.arange(res_y)])[:, 1]
channel_1 = lons[:, np.newaxis] * np.ones((res_x, res_y))
channel_2 = lats[np.newaxis, :] * np.ones((res_x, res_y))
data = np.concatenate((channel_1[:, :, np.newaxis],
channel_2[:, :, np.newaxis]), axis=2)
masked_array = np.ma.MaskedArray(data=data, mask=False)
im_src = geoio.ArrayImageSource(masked_array,
(x_range[0], y_range[0]),
crs,
(pixsize_x, pixsize_y))
return im_src
def test_array_image_src():
res_x = 1000
res_y = 500
data = np.transpose(np.mgrid[0:res_x, 0:res_y], axes=(1, 2, 0))
masked_array = np.ma.array(data=data, mask=False)
pix_size = (1., 1.)
origin = (0., 0.)
src = geoio.ArrayImageSource(masked_array, origin, crs, pix_size)
x_min = 0
x_max = 10
y_min = 1
y_max = 20
data_new = src.data(x_min, x_max, y_min, y_max)
data_orig = masked_array[x_min:x_max, :][:, y_min:y_max]
assert np.all(data_new.data == data_orig.data)
assert np.all(data_new.mask == data_orig.mask)
def make_image(pix_size_single, origin_point,
is_flipped, num_chunks, chunk_position):
data = np.random.rand(32, 24, 1)
masked_array = np.ma.array(data=data, mask=False)
pix_size = (pix_size_single, pix_size_single)
if is_flipped:
pix_size = (pix_size_single, -1 * pix_size_single)
# origin needs to change as well
origin = (origin_point, origin_point)
src = geoio.ArrayImageSource(masked_array, origin, crs, pix_size)
if num_chunks > 1:
if chunk_position == 'start':
chunk_index = 0
elif chunk_position == 'end':
chunk_index = num_chunks - 1
else:
chunk_index = round(num_chunks/2)
ispec = Image(src, chunk_idx=chunk_index, nchunks=num_chunks)
else:
ispec = Image(src)
return ispec