comments cedric + pep8

Author: rflamary

benchmarks/__init__.py

@@ -2,4 +2,4 @@
 from . import sinkhorn_knopp
 from . import emd
 
-__all__= ["benchmark", "sinkhorn_knopp", "emd"]
+__all__ = ["benchmark", "sinkhorn_knopp", "emd"]

benchmarks/emd.py

@@ -34,7 +34,7 @@ def setup(n_samples):
         warmup_runs=warmup_runs
     )
     print(convert_to_html_table(
-        results, 
+        results,
         param_name="Sample size",
         main_title=f"EMD - Averaged on {n_runs} runs"
     ))

benchmarks/sinkhorn_knopp.py

@@ -36,7 +36,7 @@ def setup(n_samples):
         warmup_runs=warmup_runs
     )
     print(convert_to_html_table(
-        results, 
+        results,
         param_name="Sample size",
         main_title=f"Sinkhorn Knopp - Averaged on {n_runs} runs"
     ))

docs/nb_run_conv

@@ -17,22 +17,24 @@ import subprocess
 
 import os
 
-cache_file='cache_nbrun'
+cache_file = 'cache_nbrun'
+
+path_doc = 'source/auto_examples/'
+path_nb = '../notebooks/'
 
-path_doc='source/auto_examples/'
-path_nb='../notebooks/'
 
 def load_json(fname):
     try:
-        f=open(fname)
-        nb=json.load(f)
+        f = open(fname)
+        nb = json.load(f)
         f.close()
-    except (OSError, IOError) :
-        nb={}
+    except (OSError, IOError):
+        nb = {}
     return nb
 
-def save_json(fname,nb):
-    f=open(fname,'w')
+
+def save_json(fname, nb):
+    f = open(fname, 'w')
     f.write(json.dumps(nb))
     f.close()
 
@@ -44,39 +46,36 @@ def md5(fname):
             hash_md5.update(chunk)
     return hash_md5.hexdigest()
 
-def to_update(fname,cache):
+
+def to_update(fname, cache):
     if fname in cache:
-        if md5(path_doc+fname)==cache[fname]:
-            res=False
+        if md5(path_doc + fname) == cache[fname]:
+            res = False
         else:
-            res=True
+            res = True
     else:
-        res=True
-    
+        res = True
+
     return res
 
-def update(fname,cache):
-    
+
+def update(fname, cache):
+
     # jupyter nbconvert --to notebook --execute mynotebook.ipynb --output targte
-    subprocess.check_call(['cp',path_doc+fname,path_nb])
-    print(' '.join(['jupyter','nbconvert','--to','notebook','--ExecutePreprocessor.timeout=600','--execute',path_nb+fname,'--inplace']))
-    subprocess.check_call(['jupyter','nbconvert','--to','notebook','--ExecutePreprocessor.timeout=600','--execute',path_nb+fname,'--inplace'])
-    cache[fname]=md5(path_doc+fname)
-    
+    subprocess.check_call(['cp', path_doc + fname, path_nb])
+    print(' '.join(['jupyter', 'nbconvert', '--to', 'notebook', '--ExecutePreprocessor.timeout=600', '--execute', path_nb + fname, '--inplace']))
+    subprocess.check_call(['jupyter', 'nbconvert', '--to', 'notebook', '--ExecutePreprocessor.timeout=600', '--execute', path_nb + fname, '--inplace'])
+    cache[fname] = md5(path_doc + fname)
 
 
-cache=load_json(cache_file)
+cache = load_json(cache_file)
 
-lst_file=glob.glob(path_doc+'*.ipynb')
+lst_file = glob.glob(path_doc + '*.ipynb')
 
-lst_file=[os.path.basename(name) for name in lst_file]
+lst_file = [os.path.basename(name) for name in lst_file]
 
 for fname in lst_file:
-    if to_update(fname,cache):
+    if to_update(fname, cache):
         print('Updating file: {}'.format(fname))
-        update(fname,cache)
-        save_json(cache_file,cache)
-
-
-
-
+        update(fname, cache)
+        save_json(cache_file, cache)

docs/rtd/conf.py

@@ -3,4 +3,4 @@
 source_parsers = {'.md': CommonMarkParser}
 
 source_suffix = ['.md']
-master_doc = 'index'
+master_doc = 'index'

docs/source/conf.py

@@ -22,16 +22,12 @@
     print("warning sphinx-gallery not installed")
 
 
-    
-
-
-
 # !!!! allow readthedoc compilation
 try:
     from unittest.mock import MagicMock
 except ImportError:
     from mock import Mock as MagicMock
-    ## check whether in the source directory...
+    # check whether in the source directory...
 #
 
 
@@ -42,7 +38,7 @@ def __getattr__(cls, name):
         return MagicMock()
 
 
-MOCK_MODULES = [ 'cupy']
+MOCK_MODULES = ['cupy']
 # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers',
 sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
 # !!!!
@@ -357,12 +353,12 @@ def __getattr__(cls, name):
 sphinx_gallery_conf = {
     'examples_dirs': ['../../examples', '../../examples/da'],
     'gallery_dirs': 'auto_examples',
-    'filename_pattern': 'plot_', #(?!barycenter_fgw)
-    'nested_sections' : False,
-    'backreferences_dir':  'gen_modules/backreferences',
-	'inspect_global_variables'  : True,
-    'doc_module'          : ('ot','numpy','scipy','pylab'),
+    'filename_pattern': 'plot_',  # (?!barycenter_fgw)
+    'nested_sections': False,
+    'backreferences_dir': 'gen_modules/backreferences',
+    'inspect_global_variables': True,
+    'doc_module': ('ot', 'numpy', 'scipy', 'pylab'),
     'matplotlib_animations': True,
     'reference_url': {
-	'ot': None}
+        'ot': None}
 }

ot/__init__.py

@@ -68,7 +68,7 @@
            'sinkhorn_unbalanced2', 'sliced_wasserstein_distance', 'sliced_wasserstein_sphere',
            'gromov_wasserstein', 'gromov_wasserstein2', 'gromov_barycenters', 'fused_gromov_wasserstein',
            'fused_gromov_wasserstein2', 'max_sliced_wasserstein_distance', 'weak_optimal_transport',
-           'factored_optimal_transport', 'solve', 'solve_gromov','solve_sample', 
+           'factored_optimal_transport', 'solve', 'solve_gromov', 'solve_sample',
            'smooth', 'stochastic', 'unbalanced', 'partial', 'regpath', 'solvers',
            'binary_search_circle', 'wasserstein_circle',
            'semidiscrete_wasserstein2_unif_circle', 'sliced_wasserstein_sphere_unif',

ot/da.py

@@ -1055,7 +1055,8 @@ class SinkhornTransport(BaseTransport):
         The ground metric for the Wasserstein problem
     norm : string, optional (default=None)
         If given, normalize the ground metric to avoid numerical errors that
-        can occur with large metric values.
+        can occur with large metric values. Accepted values are  'median',
+        'max', 'log' and 'loglog'.
     distribution_estimation : callable, optional (defaults to the uniform)
         The kind of distribution estimation to employ
     out_of_sample_map : string, optional (default="continuous")

ot/gnn/__init__.py

@@ -17,8 +17,8 @@
 # All submodules and packages
 
 
-from ._utils import (FGW_distance_to_templates,wasserstein_distance_to_templates)
+from ._utils import (FGW_distance_to_templates, wasserstein_distance_to_templates)
 
-from ._layers import (TFGWPooling,TWPooling)
+from ._layers import (TFGWPooling, TWPooling)
 
-__all__ = [ 'FGW_distance_to_templates', 'wasserstein_distance_to_templates','TFGWPooling','TWPooling']
+__all__ = ['FGW_distance_to_templates', 'wasserstein_distance_to_templates', 'TFGWPooling', 'TWPooling']

ot/lp/__init__.py

@@ -21,8 +21,8 @@
 
 # import compiled emd
 from .emd_wrap import emd_c, check_result, emd_1d_sorted
-from .solver_1d import (emd_1d, emd2_1d, wasserstein_1d, 
-                        binary_search_circle, wasserstein_circle, 
+from .solver_1d import (emd_1d, emd2_1d, wasserstein_1d,
+                        binary_search_circle, wasserstein_circle,
                         semidiscrete_wasserstein2_unif_circle)
 
 from ..utils import dist, list_to_array
@@ -262,7 +262,7 @@ def emd(a, b, M, numItermax=100000, log=False, center_dual=True, numThreads=1, c
     check_marginals: bool, optional (default=True)
         If True, checks that the marginals mass are equal. If False, skips the
         check.
-    
+
 
     Returns
     -------
@@ -341,8 +341,8 @@ def emd(a, b, M, numItermax=100000, log=False, center_dual=True, numThreads=1, c
     # ensure that same mass
     if check_marginals:
         np.testing.assert_almost_equal(a.sum(0),
-                                    b.sum(0), err_msg='a and b vector must have the same sum',
-                                    decimal=6)
+                                       b.sum(0), err_msg='a and b vector must have the same sum',
+                                       decimal=6)
     b = b * a.sum() / b.sum()
 
     asel = a != 0
@@ -440,8 +440,8 @@ def emd2(a, b, M, processes=1,
     check_marginals: bool, optional (default=True)
         If True, checks that the marginals mass are equal. If False, skips the
         check.
-        
-    
+
+
     Returns
     -------
     W: float, array-like
@@ -506,16 +506,15 @@ def emd2(a, b, M, processes=1,
     b = np.asarray(b, dtype=np.float64)
     M = np.asarray(M, dtype=np.float64, order='C')
 
-
     assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \
         "Dimension mismatch, check dimensions of M with a and b"
 
     # ensure that same mass
     if check_marginals:
         np.testing.assert_almost_equal(a.sum(0),
-                                    b.sum(0,keepdims=True), err_msg='a and b vector must have the same sum',
-                                    decimal=6)
-    b = b * a.sum(0) / b.sum(0,keepdims=True)
+                                       b.sum(0, keepdims=True), err_msg='a and b vector must have the same sum',
+                                       decimal=6)
+    b = b * a.sum(0) / b.sum(0, keepdims=True)
 
     asel = a != 0