def test_conversion_color2():
img = Image.fromGslib("tests/data/test_color.gslib")
img.exportAsPng("tests/data/test_color.png", colored=True)
img = Image.fromGslib("tests/data/test_color.gslib")
img.exportAsPpm("tests/data/test_color.ppm")
img2 = Image.fromPng("tests/data/test_color.png")
img3 = Image.fromPpm("tests/data/test_color.ppm")
assert np.alltrue((img.asArray()-img2.asArray()) < 1e8)
assert img == img3
def test_import_gslb2var():
img = Image.fromGslib("tests/data/2var.gslib")
var1 = img.asArray("V0").reshape((3, 3))
var2 = img.asArray("V1").reshape((3, 3))
expected1 = np.array([[1, 1, 1], [1, 1, 1], [0, 0, 0]])
expected2 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
assert np.alltrue(var1 == expected1)
assert np.alltrue(var2 == expected2)
def test_gslib_to_vtk():
img = Image.fromGslib("tests/data/test_img.gslib")
img.exportAsVtk("tests/data/test_img.vtk")
img2 = img.fromVtk("tests/data/test_img.vtk")
assert np.alltrue(img == img2)
def test_conversion_txt_gslib():
img = Image.fromTxt("tests/data/test_img.txt", (3, 3))
img.exportAsGslib("tests/data/test_img.gslib")
img_test = Image.fromGslib("tests/data/test_img.gslib")
assert np.alltrue(img_test == img)
def test_gslib_io():
img = Image.fromGslib("tests/data/test_color_simple.gslib")
img.exportAsGslib("tests/data/test_color_simple2.gslib")
img = Image.fromGslib("tests/data/test_color_simple.gslib")
img2 = Image.fromGslib("tests/data/test_color_simple2.gslib")
assert(img == img2)
parser = argparse.ArgumentParser()
parser.add_argument("-fun", "--fun", type=str, required=True)
parser.add_argument('-model', "--model", type=str, required=True)
parser.add_argument('-ti', "--ti", type=str, required=True)
parser.add_argument('-output', "--output", type=str, default="output.csv")
parser.add_argument("-n-samples", "--n-samples", type=int, default=100)
parser.add_argument("-n-ti", "--n-ti", type=int, default=100)
args = parser.parse_args()
model = GAN(args.model)
if ".vox" in args.ti:
ti = Image.fromVox(args.ti)
elif ".gslib" in args.ti:
ti = Image.fromGslib(args.ti)
ti.threshold(thresholds=[1], values=[0, 1])
ti = ti.asArray()
if args.fun == "conn":
stat_fun = mpstool.connectivity.get_function
elif args.fun == "vario":
stat_fun = mpstool.stats.variogram
else:
raise Exception(
"argument -fun not recognized. Should be 'conn' or 'vario' ")
# Read connectivity for the TI : min and max of values
cX_ti = []
cY_ti = []