def test_get_value_01(self):
dem = Grid(infolder + "/small25.tif")
# Taking row, col in a nan position (88)
ind = 88
row, col = self.rows[ind], self.cols[ind]
computed = dem.get_value(row, col)
self.assertEqual(computed, -9999)
def test_get_value_05(self):
dem = Grid(infolder + "/small25.tif")
# Taking row, col as lists
expected = np.nansum(self.zi.tolist())
res = dem.get_value(self.rows.tolist(), self.cols.tolist())
computed = np.nansum(res)
self.assertEqual(computed, expected)
def test_set_read_array_00(self):
# Set data to an empty array and to an array with differnt dimensions
arr = np.arange(25).reshape(5,5).astype(np.int8)
dem = Grid()
res01 = dem.set_array(arr)
res02 = dem.set_array(np.arange(9).reshape(3,3).astype(np.int8))
computed = (res01, res02)
self.assertEqual(computed, (None, 0))
def test_get_value_02(self):
dem = Grid(infolder + "/small25.tif")
# Taking row, col in other position (with value)
ind = 25
row, col = self.rows[ind], self.cols[ind]
expected = self.zi[ind]
computed = dem.get_value(row, col)
self.assertEqual(computed, expected)
def test_get_nodata_pos(self):
dem = Grid(infolder + "/small25.tif")
arr = dem._array
row, col = dem.get_nodata_pos()
crow, ccol = np.where(arr == -9999)
self.assertEqual(
(np.array_equal(row, crow), np.array_equal(col, ccol)),
(True, True))
def test_values_2_nodata(self):
dem = Grid()
dem.set_array(np.arange(25).reshape((5, 5)))
dem.set_nodata(-99.)
dem.values_2_nodata([10, 11, 12, 13, 14])
row, col = dem.ind_2_cell([10, 11, 12, 13, 14])
computed = dem.get_value(row, col)
expected = np.array([-99, -99, -99, -99, -99])
res = np.array_equal(computed, expected)
self.assertEqual(res, True)
def test_save(self):
dem = Grid(infolder + "/small25.tif")
dem.save(outfolder + "/a_dummy_dem.tif")
dem2 = Grid(outfolder + "/a_dummy_dem.tif")
expected = dem.get_value([20, 30, 40, 50], [20, 30, 40, 50])
computed = dem2.get_value([20, 30, 40, 50], [20, 30, 40, 50])
self.assertEqual(np.array_equal(computed, expected), True)
def test_set_data_00(self):
arr = np.arange(9).reshape((3, 3))
arr[[1, 2], [2, 1]] = 8
dem = Grid()
dem.set_nodata(8)
dem.set_array(arr)
row, col = dem.get_nodata_pos()
computed = dem.get_value(row, col)
expected = np.array([8, 8, 8])
self.assertEqual(np.array_equal(computed, expected), True)
def test_stream_segments(self):
dem_files = ['tunez', 'tunez2', 'small25']
for filename in dem_files:
fd = Flow("data/fd_{0}.tif".format(filename))
st = Network(fd, 1000)
ssegments = st.get_stream_segments(False)
esegments = Grid("data/mlab_files/{0}_segments.tif".format(
filename)).read_array()
self.assertTrue(np.array_equal(ssegments, esegments), True)
def test_streams(self):
dem_files = ['tunez', 'tunez2', 'small25']
for filename in dem_files:
fd = Flow("data/fd_{0}.tif".format(filename))
st = Network(fd, 1000)
streams = st.get_streams()
st01 = streams.read_array()
st02 = Grid("data/str_{0}.tif".format(filename)).read_array()
self.assertTrue(np.array_equal(st01, st02), True)
def test_stream_order(self):
dem_files = ['tunez', 'tunez2', 'small25']
for filename in dem_files:
fd = Flow("data/fd_{0}.tif".format(filename))
st = Network(fd, 1000)
for kind in ['strahler', 'shreeve']:
exp_order = st.get_stream_order(kind=kind, asgrid=False)
cmp_order = Grid("data/mlab_files/{0}_{1}.tif".format(
filename, kind)).read_array()
self.assertTrue(np.array_equal(exp_order, cmp_order), True)
def test_is_inside(self):
# points 1, 2, 4, 8 --> -Fuera de ráster
# points 3, 5 --> Dentro de ráster, pero en NoData
# points 6, 7, 9 --> Dentro de ráster
puntos = np.array([[476154., 4115084.],
[472289., 4112838.],
[471317., 4114050.],
[472874., 4117717.],
[472205., 4114091.],
[470795., 4116411.],
[472257., 4115565.],
[469572., 4115376.],
[473877., 4114844.]])
x = puntos[:,0]
y = puntos[:,1]
raster = Grid(infolder + "/small25.tif")
computed = raster.is_inside(x, y)
expected = np.array([False, False, False, False, False, True, True ,False, True])
self.assertEqual(np.array_equal(computed, expected), True)
def test_save_02(self):
# Testing nodata with value of Zero
dem = Grid()
np.random.seed(1)
arr = np.random.randint(0, 100, (25, 25))
arr[np.where(arr%5==0)] = 0
dem.set_array(arr)
dem.set_nodata(0)
dem.save(outfolder + "/a_dummy_dem2.tif")
# Open with gdal
raster = gdal.Open(outfolder + "/a_dummy_dem2.tif")
banda = raster.GetRasterBand(1)
nodata = banda.GetNoDataValue()
self.assertEqual(nodata, 0)
def test_set_read_array_02(self):
# Read array (Sharing memory)
arr = np.arange(25).reshape(5,5).astype(np.int8)
dem = Grid()
dem.set_array(arr)
out_arr = dem.read_array(False)
out_arr[0, 0] = 99
computed = dem.get_value(0, 0)
expected = 99
self.assertEqual(computed, expected)
def test_get_value_04(self):
dem = Grid(infolder + "/small25.tif")
# Taking row, col as numpy arrays
expected = self.zi
computed = dem.get_value(self.rows, self.cols)
self.assertEqual(np.nansum(computed),np.nansum(expected))
streams = net.get_streams() streams.save(outfolder + "/canales_orig.tif") outlet = np.array([579213, 504282]).reshape(1, 2) basin = fd.get_drainage_basins(net.snap_points(outlet), asgrid=False) c1 = basin.max(axis=0).argmax() r1 = basin.max(axis=1).argmax() c2 = basin.shape[1] - np.fliplr(basin).max(axis=0).argmax() r2 = basin.shape[0] - np.flipud(basin).max(axis=1).argmax() basin_cl = basin[r1:r2, c1:c2] nrow = basin_cl.shape[0] ncol = basin_cl.shape[1] outgrid = Grid() outgrid._size = (ncol, nrow) outgrid._dims = (nrow, ncol) geot = net._geot ULx = geot[0] + geot[1] * c1 ULy = geot[3] + geot[5] * r1 outgrid._geot = (ULx, geot[1], 0.0, ULy, 0.0, geot[5]) outgrid._cellsize = geot[1] outgrid._proj = net._proj outgrid._ncells = basin_cl.size outgrid._nodata = 0 outgrid._array = basin_cl outgrid._tipo = str(basin.dtype) fd.get_drainage_basins(net.snap_points(outlet)).save(outfolder +
def test_get_value_03(self):
dem = Grid(infolder + "/small25.tif")
# Taking row, col outside array
row, col = 199, 133
self.assertRaises(IndexError, dem.get_value, row, col)
