def test_ndim_tuple(self, datadir):
file1 = datadir('RGB.BRDF.tif')
file2 = datadir('RGB.BRDF.tif')
files = (file1, file2)
r = rpy.Raster(files, path=None)
r.to_array()
r2 = rpy.Raster(files, path=None)
r2.to_array(flatten=False)
r3 = rpy.Raster(files, path=None)
r3.to_array(flatten=False, quantification_factor=10000)
r4 = rpy.Raster(files, path=None)
r4.to_array(band=1)
assert r.array[0].ndim == 2
assert r.array[0].shape[0] == 3
assert r.array[0].shape[1] == 62328
assert r2.array[0].shape[0] == 3
assert r2.array[0].shape[1] == 196
assert r2.array[0].shape[2] == 318
assert r.array[1].ndim == 2
assert r.array[1].shape[0] == 3
assert r.array[1].shape[1] == 62328
assert r2.array[1].shape[0] == 3
assert r2.array[1].shape[1] == 196
assert r2.array[1].shape[2] == 318
def calculate_temperature(tif_path, tif_file):
grid = rpy.Raster(tif_file, tif_path)
grid.to_array()
grid.reshape()
#print(grid.array[0])
for i in range(0, len(grid.array)):
for j in range(0, len(grid.array[i])):
print("id: " + str(i) + " " + str(j))
value = float(grid.array[i][j])
#print(value)
toa = const_radiance_mul_b10 * value + const_radiance_add_b10
print("Излучение: " + str(toa))
# ЕБУЧИЕ КОМПЛЕКСНЫЕ ЧИСЛА
foo = math.log((const_k1_b10 / toa) + 1)
#print("TEST: " + str(math.log10((774.8853 / 9.192913599999999) + 1) ))
bt = (const_k2_b10 / foo) - const_celsius
print("Температура: " + str(bt))
grid.array[i][j] = bt
print("----------------")
print("Максимальная температура: " + str(max(grid.array.flatten())))
print("Минимальная температура: " + str(min(grid.array.flatten())))
#print(len(grid.array))
return grid.array
def test_no_data(self, datadir):
file1 = datadir('RGB.BRDF.tif')
ras = rpy.Raster(file1, path=None)
ras.to_array()
ras.set_nodata(0)
assert ras.nodata == 0
def test_dstack2(self, datadir):
file1 = datadir('RGB.BRDF.tif')
file2 = datadir('RGB.BRDF.tif')
files = (file1, file2)
ras = rpy.Raster(files, path=None)
with pytest.raises(AssertionError):
ras.dstack()
def test_set_nodata(self, datadir):
file1 = datadir('RGB.BRDF.tif')
file2 = datadir('RGB.BRDF.tif')
files = (file1, file2)
ras = rpy.Raster(files, path=None)
with pytest.raises(AssertionError):
ras.set_nodata(-99999)
def test_select_int_tuple(self, datadir):
file1 = datadir('RGB.BRDF.tif')
file2 = datadir('RGB.BRDF.tif')
files = (file1, file2)
r = rpy.Raster(files, path=None)
r.to_array(band=1)
assert r.array[0].shape[0] == 62328
assert r.array[1].shape[0] == 62328
def test_value_tuple_select_tuple(self, datadir):
file1 = datadir('RGB.BRDF.tif')
file2 = datadir('RGB.BRDF.tif')
files = (file1, file2)
r = rpy.Raster(files, path=None)
r.to_array(band=(1, 3))
assert allclose(r.array[0].mean(), -16.39290269043384)
assert allclose(r.array[1].mean(), -16.39290269043384)
def test_value_tuple_select_int(self, datadir):
file1 = datadir('RGB.BRDF.tif')
file2 = datadir('RGB.BRDF.tif')
files = (file1, file2)
r = rpy.Raster(files, path=None)
r.to_array(band=1)
assert allclose(r.array[0].mean(), -16.961706109713138)
assert allclose(r.array[1].mean(), -16.961706109713138)
def test_value_tuple(self, datadir):
file1 = datadir('RGB.BRDF.tif')
file2 = datadir('RGB.BRDF.tif')
files = (file1, file2)
r = rpy.Raster(files, path=None)
r.to_array()
assert allclose(r.array[0].mean(), -16.48756920623467)
assert allclose(r.array[1].mean(), -16.48756920623467)
def test_select_tuple_tuple(self, datadir):
file1 = datadir('RGB.BRDF.tif')
file2 = datadir('RGB.BRDF.tif')
files = (file1, file2)
r = rpy.Raster(files, path=None)
r.to_array(band=(1, 3))
assert r.array[0].ndim == 2
assert r.array[1].ndim == 2
# Here is an example of some basic features that rasterpy provides. Three bands are read from an image. Then we do some
# raster calculation on it. This new bands are then written to a new multi band TIFF.
# At first import rasterpy.
import rasterpy as rpy
import numpy as np
# After that we define a path where our test tif file is located.
path = "C:\Users\ibari\Dropbox\GitHub\\rasterpy\\tests\data"
# Then we open the grid and read it to an multidimensional array:
grid = rpy.Raster('RGB.BRF.tif', path)
# The quantification factor is a factor which scales reflectance value between 0 and 1. For sentinel 2 the factor is
# 10000
grid.to_array(flatten=False, quantification_factor=10000)
# By default the loaded grid is flatten. The reason is as following: With a flatten 2 dimensional array the calculations
# based on the array are much easier. But in our case this is not necessary:
print(grid.array)
# Now we convert the whole file from a Bidirectional Reflectance Factor (BRF) into a Bidirectional Reflectance
# Distribution Function (BRDF):
grid.convert(system='BRF', to='BRDF', output_unit='dB')
print(grid.array)
# After that we can write the converted data as a multi band tiff:
grid.write(data=grid.array, filename='RGB.BRDF.tif', path=path)
# We can also convert the arrays from a BRF or BRDF into Backscatter coefficients (BSC). For this we need the
# inclination and viewing angles:
# Here is an example of some basic features that rasterpy provides. Three bands are read from an image and averaged to
# produce something like a panchromatic band. This new band is then written to a new single band TIFF.
# At first import rasterpy.
import rasterpy as rpy
import numpy as np
# After that we define a path where our test tif file is located.
path = "C:\Users\ibari\Dropbox\GitHub\\rasterpy\\tests\data"
# Then we open the grid and read it to an multidimensional array:
grid = rpy.Raster('RGB.byte.tif', path)
grid.to_array()
# By default the loaded grid is flatten. The reason is as following: With a flatten 2 dimensional array the calculations
# based on the array are much easier:
print(grid.array)
# We can reshape the array to their original shapes with:
grid.reshape()
print(grid.array)
# And we can also flatten it again with:
grid.flatten()
print(grid.array)
# Now average each pixels of the RGB bands:
pan = np.mean(grid.array, axis=0)
# After that we can write the reshaped pan array as a tiff:
grid.write(data=pan.reshape(grid.rows, grid.cols),
def test_select_int(self, datadir):
file1 = datadir('RGB.BRDF.tif')
r = rpy.Raster(file1, path=None)
r.to_array(band=1)
assert r.array.shape[0] == 62328
def test_value(self, datadir):
file1 = datadir('RGB.BRDF.tif')
r = rpy.Raster(file1, path=None)
r.to_array()
assert allclose(r.array.mean(), -16.48756920623467)
def test_select_tuple(self, datadir):
file1 = datadir('RGB.BRDF.tif')
r = rpy.Raster(file1, path=None)
r.to_array(band=(1, 3))
assert r.array.ndim == 2
def test_flatten(self, datadir):
file1 = datadir('RGB.BRDF.tif')
ras = rpy.Raster(file1, path=None)
with pytest.raises(AssertionError):
ras.flatten()