def test_raster_math_mean(self):
for is_3d in [True, False]:
rM = processing.RasterMath(raster,
return_3d=is_3d,
verbose=is_3d,
in_image_mask=mask,
n_jobs=is_3d + 1)
if is_3d is False:
# test without compression with reading/writing pixel per pixel, very slow...
rM.custom_block_size(10, 10) # to have full masked block
rM.add_function(np.mean,
'/tmp/mean.tif',
axis=is_3d + 1,
dtype=np.int16)
rM.run()
else:
# test using default block size and high compressio of raster
rM.add_function(np.mean,
'/tmp/mean.tif',
axis=is_3d + 1,
dtype=np.int16,
compress='high')
rM.run()
assert (gdal.Open('/tmp/mean.tif').RasterCount == 1)
assert (gdal.Open('/tmp/mean.tif').RasterXSize == rM.n_columns)
assert (gdal.Open('/tmp/mean.tif').RasterYSize == rM.n_lines)
os.remove('/tmp/mean.tif')
def test_mask(self):
for is_3d in [True, False]:
mask = '/tmp/mask.tif'
processing.image_mask_from_vector(vector, raster, mask)
mask_src = gdal.Open(mask)
raster_src = gdal.Open(raster)
mask_proj = osr.SpatialReference(wkt=mask_src.GetProjection())
raster_proj = osr.SpatialReference(wkt=raster_src.GetProjection())
assert (raster_proj.GetAttrValue('projcs') ==
mask_proj.GetAttrValue('projcs'))
assert (mask_src.RasterCount == 1)
assert (mask_src.RasterXSize == raster_src.RasterXSize)
assert (mask_src.RasterYSize == raster_src.RasterYSize)
rM_band = processing.RasterMath(raster, return_3d=is_3d)
for idx, band in enumerate(rM_band.read_band_per_band()):
pass
rM_band.add_function(np.mean,
axis=is_3d + 1,
out_image='/tmp/mean.tif')
rM_band.run()
self.assertRaises(MemoryError, rM_band.run, '1K')
assert (idx + 1 == rM_band.n_bands)
x = rM_band.get_random_block()
assert (x.ndim == is_3d + 2)
os.remove('/tmp/mean.tif')
def test_3d(self):
rM_3d = processing.RasterMath(raster, return_3d=True)
self.assertRaises(ValueError, rM_3d.get_block, 100)
assert (rM_3d.get_random_block().ndim == 3)
for block in rM.read_block_per_block():
pass
for band in rM.read_band_per_band():
pass
rM.custom_block_size(128, 256)
assert (rM.y_block_size == 256)
assert (rM.x_block_size == 128)
rM.custom_block_size(-1, -1)
assert (rM.x_block_size == rM.n_columns)
assert (rM.y_block_size == rM.n_lines)
rM.custom_block_size(1 / 2, 1 / 3)
assert (rM.x_block_size == np.ceil(1 / 2 * rM.n_columns))
assert (rM.y_block_size == np.ceil(1 / 3 * rM.n_lines))
rM.add_image(raster)
self.assertRaises(ValueError, rM.add_image, '/tmp/100x100size.tif')
return_x = lambda x: x[0].astype(np.int16)
rM.add_function(return_x, '/tmp/test_double.tif')
rM.run()
os.remove('/tmp/test_double.tif')
assert (np.all(rM.get_random_block(random_state=12)) == np.all(
rM.get_random_block(random_state=12)))
def test_readPerBand(self):
for is_3d in [True,False]:
rM_band = processing.RasterMath(raster,return_3d=is_3d,in_image_mask=mask)
for idx,band in enumerate(rM_band.read_band_per_band()):
print(band.ndim)
if is_3d is True:
assert(band.ndim == 2)
else:
assert(band.ndim == 2)
del rM_band
def test_mask(self) :
for is_3d in [True, False]:
mask = '/tmp/mask.tif'
processing.image_mask_from_vector(vector,raster,mask)
mask_src = gdal.Open(mask)
raster_src = gdal.Open(raster)
assert(mask_src.GetProjection() == raster_src.GetProjection())
assert(mask_src.RasterCount == 1)
assert(mask_src.RasterXSize == raster_src.RasterXSize)
assert(mask_src.RasterYSize == raster_src.RasterYSize)
rM_band = processing.RasterMath(raster,return_3d=is_3d)
for idx,band in enumerate(rM_band.read_band_per_band()):
pass
rM_band.add_function(np.mean,axis=1,out_image='/tmp/mean.tif')
rM_band.run()
assert(idx+1 == rM_band.n_bands)
x = rM_band.get_random_block()
assert(x.ndim == is_3d+2)
os.remove('/tmp/mean.tif')
def test_3d(self) :
rM_3d = processing.RasterMath(raster,return_3d=True)
self.assertRaises(ValueError,rM_3d.get_block,100)
assert(rM_3d.get_random_block().ndim == 3)
for block in rM.read_block_per_block():
pass
for band in rM.read_band_per_band():
pass
rM.custom_block_size(128,256)
assert(rM.y_block_size==256)
assert(rM.x_block_size==128)
rM.custom_block_size(-1,-1)
assert(rM.x_block_size == rM.n_columns)
assert(rM.y_block_size == rM.n_lines)
rM.custom_block_size(1/2,1/3)
assert(rM.x_block_size == np.ceil(1/2*rM.n_columns))
assert(rM.y_block_size == np.ceil(1/3*rM.n_lines))
rM.add_image(raster)
assert(np.all(rM.get_random_block(random_state=12))== np.all(rM.get_random_block(random_state=12)))
# -*- coding: utf-8 -*-
import unittest
from shutil import copyfile
import numpy as np
from museotoolbox import processing
from museotoolbox.datasets import load_historical_data
from osgeo import gdal
import os
raster,vector = load_historical_data()
rM = processing.RasterMath(raster)
mask = processing.image_mask_from_vector(vector,raster,'/tmp/mask.tif')
class TestRaster(unittest.TestCase):
def test_convert_datatype(self):
self._assert_np_gdt(np.dtype('uint8').name,gdal.GDT_Byte)
self._assert_np_gdt(np.dtype('int16').name,gdal.GDT_Int16)
self._assert_np_gdt(np.dtype('uint16').name,gdal.GDT_UInt16)
self._assert_np_gdt(np.dtype('int32').name,gdal.GDT_Int32)
self._assert_np_gdt(np.dtype('uint32').name,gdal.GDT_UInt32)
self._assert_np_gdt(np.dtype('int64').name,gdal.GDT_Int32)
self._assert_np_gdt(np.dtype('uint64').name,gdal.GDT_Int32)
self._assert_np_gdt(np.dtype('uint16').name,gdal.GDT_UInt16)
self._assert_np_gdt(np.dtype('float32').name,gdal.GDT_Float32)
self._assert_np_gdt(np.dtype('float64').name,gdal.GDT_Float64)
self._assert_np_gdt(gdal.GDT_Byte,np.uint8)
def test_get_parameter(self):
rM = processing.RasterMath(raster)
assert (isinstance(rM.get_raster_parameters(), dict))
rM.custom_raster_parameters(['TILED=NO'])
assert (rM.get_raster_parameters() == ['TILED=NO'])
