Make flake8 linting

Add gitignore

.gitignore

@@ -0,0 +1,2 @@
+tests/__pycache__/*
+*.pyc

src/graph_laplacian.py

@@ -2,12 +2,13 @@
 import numpy as np
 import scipy
 import torch
-from scipy.sparse import lil_matrix, block_diag,csr_array,diags,csr_matrix
+from scipy.sparse import lil_matrix, block_diag, csr_array, diags, csr_matrix
 
 
 def create_laplacian_matrix(nx, ny=None):
     """
-    Helper method to create the laplacian matrix for the laplacian regularization
+    Helper method to create the laplacian matrix for the laplacian
+    regularization
 
     Parameters
     ----------
@@ -24,23 +25,23 @@ def create_laplacian_matrix(nx, ny=None):
     """
     if ny is None:
         ny = nx
-    assert(nx>1)
+    assert (nx > 1)
-    assert(ny>1)
+    assert (ny > 1)
-    #Blocks corresponding to the corner of the image (linking row elements)
+    # Blocks corresponding to the corner of the image (linking row elements)
-    top_block=lil_matrix((ny,ny),dtype=np.float32)
+    top_block = lil_matrix((ny, ny), dtype=np.float32)
-    top_block.setdiag([2]+[3]*(ny-2)+[2])
+    top_block.setdiag([2] + [3] * (ny - 2) + [2])
-    top_block.setdiag(-1,k=1)
+    top_block.setdiag(-1, k=1)
-    top_block.setdiag(-1,k=-1)
+    top_block.setdiag(-1, k=-1)
-    #Blocks corresponding to the middle of the image (linking row elements)
+    # Blocks corresponding to the middle of the image (linking row elements)
-    mid_block=lil_matrix((ny,ny),dtype=np.float32)
+    mid_block = lil_matrix((ny, ny), dtype=np.float32)
-    mid_block.setdiag([3]+[4]*(ny-2)+[3])
+    mid_block.setdiag([3] + [4]*(ny - 2) + [3])
-    mid_block.setdiag(-1,k=1)
+    mid_block.setdiag(-1, k=1)
-    mid_block.setdiag(-1,k=-1)
+    mid_block.setdiag(-1, k=-1)
-    #Construction of the diagonal of blocks
+    # Construction of the diagonal of blocks
-    list_blocks=[top_block]+[mid_block]*(nx-2)+[top_block]
+    list_blocks = [top_block] + [mid_block]*(nx-2) + [top_block]
-    blocks=block_diag(list_blocks)
+    blocks = block_diag(list_blocks)
-    #Diagonals linking different rows
+    # Diagonals linking different rows
-    blocks.setdiag(-1,k=ny)
+    blocks.setdiag(-1, k=ny)
     return blocks
 
 
@@ -64,7 +65,7 @@ def delete_from_csr(mat, row_indices=[], col_indices=[]):
         row_mask[rows] = False
         col_mask = np.ones(mat.shape[1], dtype=bool)
         col_mask[cols] = False
-        return mat[row_mask][:,col_mask]
+        return mat[row_mask][:, col_mask]
     elif len(rows) > 0:
         mask = np.ones(mat.shape[0], dtype=bool)
         mask[rows] = False
@@ -72,11 +73,12 @@ def delete_from_csr(mat, row_indices=[], col_indices=[]):
     elif len(cols) > 0:
         mask = np.ones(mat.shape[1], dtype=bool)
         mask[cols] = False
-        return mat[:,mask]
+        return mat[:, mask]
     else:
         return mat
 
-def remove_vertex(L,lst_rows=[],lst_cols=[]):
+
+def remove_vertex(L, lst_rows=[], lst_cols=[]):
     """
     Function that removes a vertex and adjust the graph laplacian matrix.
     """
@@ -85,10 +87,10 @@ def remove_vertex(L,lst_rows=[],lst_cols=[]):
     L_cut = L_cut - diags(L_cut.sum(axis=1).A1)
     assert (L_cut.sum(axis=1).A1 == np.zeros(L_cut.shape[0])).all()
 
-
     return L_cut
 
-def laplacian(Y,nx,ny=None):
+
+def laplacian(Y, nx, ny=None):
     """
     Function that removes the vertices corresponding to Nan locations of tensor Y.
     Args:
@@ -96,16 +98,17 @@ def laplacian(Y,nx,ny=None):
         - nx,ny: dimensions of the image (Int64)
     """
     # find Nan indices
-    Nan_indices = torch.where(torch.isnan(Y.ravel())==True)[0]
+    Nan_indices = torch.where(torch.isnan(Y.ravel()) is True)[0]
 
     # get list of indices
     list_idx = list(Nan_indices.detach().numpy())
 
     # create Laplacian
     L = create_laplacian_matrix(nx, ny=ny)
-    L = remove_vertex(L,list_idx,list_idx)
+    L = remove_vertex(L, list_idx, list_idx)
     return L
 
+
 def unnormalized_laplacian(y, nx, ny=None, method='inverse'):
     """Construct numpy array with non zeros weights and non zeros indices.
 
@@ -114,11 +117,11 @@ def unnormalized_laplacian(y, nx, ny=None, method='inverse'):
             - method: str indicating how to compute the weight of the unnormalized laplacian
     """
     # create laplacian matrix
-    lapl_tmp = laplacian(y,nx,ny)
+    lapl_tmp = laplacian(y, nx, ny)
     lapl_tmp.setdiag(np.zeros(nx*ny))
 
     # select the non nan indices
-    y_tmp = y[torch.isnan(y)==False]
+    y_tmp = y[torch.isnan(y) is False]
 
     # store non zero indices
     idx_rows = np.array(lapl_tmp.nonzero()[0])
@@ -129,7 +132,7 @@ def unnormalized_laplacian(y, nx, ny=None, method='inverse'):
 
     # construction of the non zeros weights
     if method == 'inverse':
-        nnz_w = 1/(np.abs(y_tmp[idx_rows] - y_tmp[idx_cols])+1e-4)
+        nnz_w = 1/(np.abs(y_tmp[idx_rows] - y_tmp[idx_cols]) + 1e-4)
     else:
         nnz_w = np.exp(-np.abs(y_tmp[idx_rows] - y_tmp[idx_cols]))
 
@@ -144,15 +147,16 @@ def unnormalized_laplacian(y, nx, ny=None, method='inverse'):
 
     return L
 
+
 def laplacian_chain(nb_vertices):
     """
     Construct the Laplacian matrix of a chain.
     """
-    L = np.zeros((nb_vertices,nb_vertices))
+    L = np.zeros((nb_vertices, nb_vertices))
 
     # First vertex
-    L[0,0] = 1
+    L[0, 0] = 1
-    L[0,1] = -1
+    L[0, 1] = -1
 
     # Toeplitz matrix
     if nb_vertices > 2:
@@ -166,14 +170,15 @@ def laplacian_chain(nb_vertices):
 
         D = scipy.linalg.toeplitz(first_col, r=first_row)
 
-        L[1:nb_vertices-1,:] = D
+        L[1:nb_vertices-1, :] = D
 
     # Last vertex
-    L[-1,-2] = -1
+    L[-1, -2] = -1
-    L[-1,-1] = 1
+    L[-1, -1] = 1
 
     return L
 
+
 def unnormalized_laplacian_chain(y, nx, method='inverse'):
     """Construct numpy array with non zeros weights and non zeros indices.
 
@@ -192,7 +197,6 @@ def unnormalized_laplacian_chain(y, nx, method='inverse'):
     idx_rows = np.array(lapl_tmp.nonzero()[0])
     idx_cols = np.array(lapl_tmp.nonzero()[1])
 
-    
     # construct the set of weights
     nnz_w = np.zeros_like(idx_rows, dtype=np.float32)
 
@@ -202,7 +206,6 @@ def unnormalized_laplacian_chain(y, nx, method='inverse'):
     else:
         nnz_w = np.exp(-np.abs(y_tmp[idx_rows] - y_tmp[idx_cols]))
 
-    
     # construct the non diagonal terms of the Laplacian
     lapl_nondiag = csr_array((nnz_w, (idx_rows, idx_cols)), shape=(lapl_tmp.shape[0], lapl_tmp.shape[0]), dtype=np.float32)
 

tests/test_background.py

@@ -1,8 +1,6 @@
 import unittest
 import torch
 import numpy as np
-
-
 import os
 import sys
 maindir = os.getcwd()
@@ -11,7 +9,6 @@ sys.path.append(main_path+"/ds4ms/code/src")
 from background import background
 
 
-
 class TestBackground(unittest.TestCase):
 
     def setUp(self):
@@ -19,7 +16,7 @@ class TestBackground(unittest.TestCase):
         self.bg.load_data(verbose=True)
         self.bg.set_grid_volume(dqx=0.03, dqy=0.03, dE=0.08)
         self.bg.set_binned_data()
-        self.bg.set_radial_bins(max_radius=6.0, n_bins=10
+        self.bg.set_radial_bins(max_radius=6.0, n_bins=10)
         self.bg.Ygrid = torch.tensor(np.random.rand(10, 10), dtype=torch.float64)
 
     def test_load_data(self):
@@ -166,12 +163,14 @@ class TestBackground(unittest.TestCase):
         alpha_range = torch.tensor([1.0])
         beta_range = torch.tensor([1.0])
         mu_range = torch.tensor([1.0])
-        result = self.bg.cross_validation(lambda_range=lambda_range, alpha_range=alpha_range, beta_range=beta_range, mu_range=mu_range, n_epochs=1, verbose=False)
+        result = self.bg.cross_validation(lambda_range=lambda_range, alpha_range=alpha_range,
+                                          beta_range=beta_range, mu_range=mu_range, n_epochs=1, verbose=False)
         self.assertIsNotNone(result)
 
     def test_compute_mask(self):
         result = self.bg.compute_mask(q=0.75, e_cut=None)
         self.assertIsNotNone(result)
 
+
 if __name__ == '__main__':
     unittest.main()

tests/test_graph_laplacian.py

@@ -2,14 +2,15 @@ import unittest
 import numpy as np
 import torch
 from scipy.sparse import lil_matrix, csr_matrix, coo_matrix
-
-
 import os
 import sys
 maindir = os.getcwd()
 main_path = maindir[:maindir.find('ds4ms/code')]
 sys.path.append(main_path+"/ds4ms/code/src")
-from graph_laplacian import create_laplacian_matrix, delete_from_csr, remove_vertex, laplacian, unnormalized_laplacian, laplacian_chain, unnormalized_laplacian_chain
+from graph_laplacian import create_laplacian_matrix, delete_from_csr, \
+    remove_vertex, laplacian, unnormalized_laplacian, laplacian_chain, \
+    unnormalized_laplacian_chain
+
 
 class TestGraphLaplacian(unittest.TestCase):
 
@@ -66,5 +67,6 @@ class TestGraphLaplacian(unittest.TestCase):
         self.assertEqual(L.shape, (nx, nx))
         self.assertIsInstance(L, csr_matrix)
 
+
 if __name__ == '__main__':
     unittest.main()