def test_designmat(tmpdir):
mat1 = Design_Matrix({'X':[1,4,2,7,5,9,2,1,3,2],'Y':[3,0,0,6,9,9,10,10,1,10],'Z':[2,2,2,2,7,0,1,3,3,2],'intercept':[1,1,1,1,1,1,1,1,1,1]},TR=2.0,hasIntercept=True)
mat2 = Design_Matrix({'X':[9,9,2,7,5,0,1,1,1,2],'Y':[3,3,3,6,9,0,1,10,1,10],'Z':[2,6,3,2,7,0,1,7,8,8],'intercept':[1,1,1,1,1,1,1,1,1,1]},TR=2.0,hasIntercept=True)
#appending
assert mat1.append(mat1,axis=1).shape == (mat1.shape[0],mat1.shape[1]+mat2.shape[1])
assert mat1.append(mat2,axis=0).shape == (mat1.shape[0]+mat2.shape[0],mat1.shape[1]+1)
#convolution doesn't affect intercept
assert all(mat1.convolve().iloc[:,-1] == mat1.iloc[:,-1])
#but it still works
assert (mat1.convolve().iloc[:,:3].values != mat1.iloc[:,:3].values).any()
#Test vifs
expectedVifs = np.array([ 1.03984251, 1.02889877, 1.02261945])
assert np.allclose(expectedVifs,mat1.vif())
#poly
mat1.addpoly(order=4).shape[1] == mat1.shape[1]+4
mat1.addpoly(order=4,include_lower=False).shape[1] == mat1.shape[1]+1
#zscore
z = mat1.zscore(colNames=['X','Z'])
assert (z['Y'] == mat1['Y']).all()
assert z.shape == mat1.shape
def test_designmat(tmpdir):
mat1 = Design_Matrix({
'X':[1, 4, 2, 7, 5, 9, 2, 1, 3, 2],
'Y':[3, 0, 0, 6, 9, 9, 10, 10, 1, 10],
'Z':[2, 2, 2, 2, 7, 0, 1, 3, 3, 2],
'intercept':[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
},
sampling_rate=2.0,polys=['intercept'])
mat2 = Design_Matrix({
'X':[9, 9, 2, 7, 5, 0, 1, 1, 1, 2],
'Y':[3, 3, 3, 6, 9, 0, 1, 10, 1, 10],
'Z':[2, 6, 3, 2, 7, 0, 1, 7, 8, 8],
'intercept':[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
},
sampling_rate=2.0, polys=['intercept'])
# Appending
# Basic horz cat
new_mat = mat1.append(mat1,axis=1)
assert new_mat.shape == (mat1.shape[0], mat1.shape[1] + mat2.shape[1])
both_cols = list(mat1.columns) + list(mat1.columns)
assert all(new_mat.columns == both_cols)
# Basic vert cat
new_mat = mat1.append(mat1,axis=0)
assert new_mat.shape == (mat1.shape[0]*2, mat1.shape[1]+1)
# Advanced vert cat
new_mat = mat1.append(mat1,axis=0,keep_separate=False)
assert new_mat.shape == (mat1.shape[0]*2,mat1.shape[1])
# More advanced vert cat
new_mat = mat1.append(mat1,axis=0,add_poly=2)
assert new_mat.shape == (mat1.shape[0]*2, 9)
#convolution doesn't affect intercept
assert all(mat1.convolve().iloc[:, -1] == mat1.iloc[:, -1])
#but it still works
assert (mat1.convolve().iloc[:, :3].values != mat1.iloc[:, :3].values).any()
#Test vifs
expectedVifs = np.array([ 1.03984251, 1.02889877, 1.02261945])
assert np.allclose(expectedVifs,mat1.vif())
#poly
mat1.add_poly(order=4).shape[1] == mat1.shape[1]+4
mat1.add_poly(order=4, include_lower=False).shape[1] == mat1.shape[1]+1
#zscore
z = mat1.zscore(columns=['X', 'Z'])
assert (z['Y'] == mat1['Y']).all()
assert z.shape == mat1.shape
# clean
mat = Design_Matrix({
'X':[1, 4, 2, 7, 5, 9, 2, 1, 3, 2],
'A':[1, 4, 2, 7, 5, 9, 2, 1, 3, 2],
'Y':[3, 0, 0, 6, 9, 9, 10, 10, 1, 10],
'Z':[2, 2, 2, 2, 7, 0, 1, 3, 3, 2],
'C':[1, 4, 2, 7, 5, 9, 2, 1, 3, 2],
'intercept':[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
},
sampling_rate=2.0,polys=['intercept'])
mat = mat[['X','A','Y','Z','C','intercept']]
assert all(mat.clean().columns == ['X','Y','Z','intercept'])
# replace data
mat = Design_Matrix({
'X':[1, 4, 2, 7, 5, 9, 2, 1, 3, 2],
'A':[1, 4, 2, 7, 5, 9, 2, 1, 3, 2],
'Y':[3, 0, 0, 6, 9, 9, 10, 10, 1, 10],
'Z':[2, 2, 2, 2, 7, 0, 1, 3, 3, 2],
'C':[1, 4, 2, 7, 5, 9, 2, 1, 3, 2]
},
sampling_rate=2.0)
mat = mat.replace_data(np.ones((mat.shape[0],mat.shape[1]-1)),column_names=['a','b','c','d'])
assert(np.allclose(mat.values,1))
assert(all(mat.columns == ['a','b','c','d']))
#DCT basis_mat
mat = Design_Matrix(np.random.randint(2,size=(500,3)),sampling_rate=2.0)
mat = mat.add_dct_basis()
assert len(mat.polys) == 11
assert mat.shape[1] == 14
#Up and down sampling
mat = Design_Matrix(np.random.randint(2,size=(500,4)),sampling_rate=2.0,columns=['a','b','c','d'])
target = 1
assert mat.upsample(target).shape[0] == mat.shape[0]*2 - target*2
target = 4
assert mat.downsample(target).shape[0] == mat.shape[0]/2
