def test_getattr(self):
# Test the
# Set up a fake dir structure to generate an aligned fixmat
img_per_cat = {1: range(1, 11), 2: range(1, 11)}
features = ['a', 'b']
path, ftrpath = test_loader.create_tmp_structure(img_per_cat,
features=features)
l = loader.LoadFromDisk(impath=path, ftrpath=ftrpath, size=(100, 100))
inp = stimuli.Categories(l, img_per_cat, features)
fm = fixmat.TestFixmatFactory(categories=[1, 2],
filenumbers=range(1, 11),
subjectindices=[1, 2, 3, 4, 5, 6],
params={
'pixels_per_degree': 10,
'image_size': [100, 100]
},
categories_obj=inp)
fm_err = fixmat.TestFixmatFactory(categories=[1, 2],
filenumbers=range(1, 11),
subjectindices=[1, 2, 3, 4, 5, 6],
params={
'pixels_per_degree': 10,
'image_size': [100, 100]
})
# Now let's check if we can access all the images
# and all the features.
fm.add_feature_values(['a', 'b'])
self.assertRaises(RuntimeError,
lambda: fm_err.add_feature_values(['a', 'b']))
for cat_mat, cat_inp in fm.by_cat():
self.assertEquals(cat_mat.category[0], cat_inp.category)
for img_mat, img_inp in cat_mat.by_filenumber():
self.assertEquals(img_mat.filenumber[0], img_inp.image)
self.assertEquals(len(fm.a), len(fm.x))
self.assertEquals(len(fm.b), len(fm.x))
# Let's also check if make_reg_data works
a, b = fm.make_reg_data(features)
self.assertEquals(a.shape[1], len(fm.x))
self.assertEquals(a.shape[0], len(features))
self.assertEquals(b.shape[1], len(fm.x))
self.assertEquals(b.shape[0], len(features))
self.assertEquals(b.sum(), a.sum())
a, b = fm.make_reg_data(features, all_controls=True)
self.assertEquals(a.shape[1], len(fm.x))
self.assertEquals(a.shape[0], len(features))
self.assertEquals(b.shape[1], len(fm.x))
self.assertEquals(b.shape[0], len(features))
self.assertEquals(b.sum(), a.sum())
test_loader.rm_tmp_structure(path)
test_loader.rm_tmp_structure(ftrpath)
def setUp(self):
self.features = range(1, 11)
self.inputs = {
'a': {
1: self.features,
2: self.features,
3: self.features
},
'b': {
10: self.features
}
}
self.test_loader = loader.TestLoader(self.inputs)
self.inp = stimuli.Categories(self.test_loader, self.inputs)
def test_fixations(self):
img_per_cat = {7: range(1, 65), 8: range(1, 65)}
l = loader.TestLoader(img_per_cat, size=(10, 10))
with NamedTemporaryFile(mode='w', prefix='fix_occ_test',
suffix='.mat') as ntf:
ntf.write(get_data('ocupy.tests', 'fixmat_demo.mat'))
ntf.seek(0)
fm = fixmat.FixmatFactory(ntf.name)
fm = fm[fm.category > 0]
inp = stimuli.FixmatStimuliFactory(fm, l)
# Now we can iterate over the input object and get fixations on each image
for cat in inp:
for img in cat:
self.assertEqual(img.fixations.filenumber[0], img.image)
self.assertEqual(img.fixations.category[0], img.category)
inp = stimuli.Categories(l, img_per_cat)
self.assertRaises(RuntimeError, lambda: inp.fixations)
self.assertRaises(RuntimeError, lambda: inp[7].fixations)
self.assertRaises(RuntimeError, lambda: inp[7][1].fixations)
def test_general(self):
#Create an input object: There are two ways, one is the
features = ['a', 'b', 'c', 'd']
img_per_cat = {7: range(1, 65), 8: range(1, 65)}
l = loader.TestLoader(img_per_cat, features, size=(10, 10))
inp = stimuli.Categories(l, img_per_cat, features)
# In this case it should have two categories (2,9) with 10 and 50 images
# Let's check this
self.assert_(7 in inp.categories())
self.assert_(8 in inp.categories())
self.assertEquals(len(inp[7].images()), 64)
self.assertEquals(len(inp[8].images()), 64)
# Good, now we can access some data
img = inp[7][16].data
self.assertTrue(img.shape[0] == 10 and img.shape[1] == 10)
# In general we can use [] to access elements in the object
# But we can also iterate trough these objects
# This should take a long time because it loads all images
# from disk
for cat in inp:
for img in cat:
img.image
img.category
img.data