def test_nans(self):
# Test that less than 2 texp will yield a NaN for u_I_mean
data = {'temporal': {'texp': [0, 1]}, 'spatial': {}}
r = Results1288(data)
self.assertIs(r.u_I_mean, np.nan)
# Test that a negative slope for t vs s2_ydark will yield Nan for
# u_I_var
data['temporal']['s2_ydark'] = [1, 0]
r = Results1288(data)
self.assertIs(r.u_I_var, np.nan)
# Test that a negative s2y_dark will yield a Nan for DSNU1288
data['spatial'] = {
'avg_var_dark': 0.,
'var_mean_dark': 1.,
'L_dark': 3
}
r = Results1288(data)
self.assertIs(r.DSNU1288, np.nan)
self.assertIs(r.DSNU1288_DN(), np.nan)
del r
def _init(pixel_area=0, **kwargs):
# create dataset
dataset = DatasetGenerator(**kwargs)
# parse dataset
parser = ParseEmvaDescriptorFile(dataset.descriptor_path)
# load image data
loader = LoadImageData(parser.images)
# create data
data = Data1288(loader.data)
# Make results object
px = pixel_area
if pixel_area == 0:
px = dataset.cam.pixel_area
results = Results1288(data.data, pixel_area=px)
return dataset, parser, loader, data, results
def __init__(self, fname):
parser = ParseEmvaDescriptorFile(fname)
imgs = LoadImageData(parser.images)
dat = Data1288(imgs.data)
self._results = Results1288(dat.data)