Merge pull request #98 from NeuralEnsemble/fix/update-py3

fix: update for py3

neuroml/arraymorph_load_time_benchmark.py

@@ -1,5 +1,10 @@
+import numpy as np
+import neuroml
+import neuroml.arraymorph as am
+
+
 class Benchmark:
-    def __init__(num_segments):
+    def __init__(self, num_segments):
         self.num_segments = num_segments
 
     def set_up(self):

neuroml/benchmarks/arraymorph_benchmarks.py

@@ -1,15 +1,16 @@
 """
 Benchmarks for reading and writing arraymorphs.
 """
+from __future__ import print_function
 import numpy as np
 from neuroml import arraymorph as am
 import neuroml
 import neuroml.writers
 import tempfile
 from matplotlib import pyplot as plt
-import time                                                
+import time
 import os
-from os import path
+
 
 def timeit(method):
 
@@ -18,12 +19,12 @@ def timed(*args, **kw):
         result = method(*args, **kw)
         te = time.time()
 
-        print '%r (%r, %r) %2.2f sec' % \
-              (method.__name__, args, kw, te-ts)
+        print('%r (%r, %r) %2.2f sec' % (method.__name__, args, kw, te-ts))
         return te-ts
 
     return timed
 
+
 class AMWriteBenchmark(object):
     """
     TODO: Get rid of methods which are not used
@@ -38,11 +39,11 @@ def __init__(self,num_segments=1e6):
         d = np.linspace(1,0.01,num_vertices)
 
         vertices = np.array([x,y,z,d]).T
-        
+
         connectivity = range(-1,num_segments)
 
-        big_arraymorph = am.ArrayMorphology(vertices = vertices,
-                                            connectivity = connectivity)
+        big_arraymorph = am.ArrayMorphology(vertices=vertices,
+                                            connectivity=connectivity)
 
         self.big_arraymorph = big_arraymorph
 
@@ -57,10 +58,10 @@ def __init__(self,num_segments=1e6):
         self.__write_time = None
 
         self.num_segments = num_segments
-        
+
     @property
     def write_time(self):
-        if self.__write_time == None:
+        if self.__write_time is None:
             print("Benchmark has not been executed")
         else:
             return self.__write_time
@@ -77,7 +78,6 @@ def run_json(self):
     def run_hdf5(self):
         self.test_write_big_arraymorph_hdf5()
 
-
     def test_write_big_arraymorph_json(self):
         writer_method = neuroml.writers.JSONWriter.write
         fh,filename = tempfile.mkstemp()
@@ -87,10 +87,10 @@ def test_write_big_arraymorph_json(self):
         except:
             self.fail("Exception raised!")
 
-        print 'JSON Number of segments:'
-        print self.num_segments
-        print 'JSON size in bytes:'
-        print self.file_size(filename)
+        print('JSON Number of segments:')
+        print(self.num_segments)
+        print('JSON size in bytes:')
+        print(self.file_size(filename))
 
         os.close(fh)
 
@@ -103,17 +103,16 @@ def test_write_big_arraymorph_neuroml(self):
         except:
             self.fail("Exception raised!")
 
-        print 'NeuroML (XML) Number of segments:'
-        print self.num_segments
-        print 'NeuroML (XML) size in bytes:'
-        print self.file_size(filename)
+        print('NeuroML (XML) Number of segments:')
+        print(self.num_segments)
+        print('NeuroML (XML) size in bytes:')
+        print(self.file_size(filename))
 
         os.close(fh)
 
     def file_size(self,path):
         return os.path.getsize(path)
 
-
     def test_write_big_arraymorph_hdf5(self):
         writer_method = neuroml.writers.ArrayMorphWriter.write
         fh,filename = tempfile.mkstemp()
@@ -123,10 +122,10 @@ def test_write_big_arraymorph_hdf5(self):
         except:
             self.fail("Exception raised!")
 
-        print 'HDF5 Number of segments:'
-        print self.num_segments
-        print 'HDF5 size in bytes:'
-        print self.file_size(filename)
+        print('HDF5 Number of segments:')
+        print(self.num_segments)
+        print('HDF5 size in bytes:')
+        print(self.file_size(filename))
 
         os.close(fh)
 
@@ -148,10 +147,9 @@ def test_write_expected(self):
         physical_masks_equal = np.testing.assert_array_equal(array_morph.physical_mask,self.big_arraymorph.physical_mask)
         vertices_equal = np.testing.assert_array_equal(array_morph.vertices,self.big_arraymorph.vertices)
 
-
-        self.assertEqual(connectivity_equal,None) #None when equal
-        self.assertEqual(physical_masks_equal,None) #None when equal
-        self.assertEqual(vertices_equal,None) #None when equal        
+        self.assertEqual(connectivity_equal,None) # None when equal
+        self.assertEqual(physical_masks_equal,None) # None when equal
+        self.assertEqual(vertices_equal,None)# None when equal
 
     def test_write_multiple_morphologies(self):
         filename = tempfile.mkstemp()[1]
@@ -173,9 +171,6 @@ def test_write_multiple_morphologies(self):
         self.assertIsInstance(document,neuroml.NeuroMLDocument)
 
 
-
-
-
 def benchmark_arraymorph_writer():
     """
     TODO: Add NeuroML Document benchmark
@@ -197,12 +192,12 @@ def benchmark_arraymorph_writer():
         neuroml_num_segments_list = []
 
         for i in range(30):
-            print "test %d" % (i)
+            print("test %d" % (i))
 
             neuroml_num_segments_factor = 4e2
             json_num_segments_factor = 4e2
             hdf5_num_segments_factor = 4e2
-            
+
             neuroml_num_segments = i * neuroml_num_segments_factor
             json_num_segments = i * json_num_segments_factor
             hdf5_num_segments = i * hdf5_num_segments_factor
@@ -231,7 +226,6 @@ def benchmark_arraymorph_writer():
         np.savetxt("json_results.csv", json_results, delimiter=",")
         np.savetxt("hdf5_results.csv", hdf5_results, delimiter=",")
 
-
     neuroml_runtimes_averaged = np.mean(neuroml_results,axis=0)
     json_runtimes_averaged = np.mean(json_results,axis=0)
     hdf5_runtimes_averaged = np.mean(hdf5_results,axis=0)
@@ -244,44 +238,43 @@ def benchmark_arraymorph_writer():
     json_num_segments_list = np.array(json_num_segments_list)
     hdf5_num_segments_list = np.array(hdf5_num_segments_list)
 
-
     plt_neuroml = plt.errorbar(neuroml_num_segments_list,
-                 neuroml_runtimes_averaged,
-                 yerr=hdf5_errors,
-                 marker='o',
-                 color='k',
-                 ecolor='k',
-                 markerfacecolor='r',
-                 label="series 2",
-                 capsize=5,)
+                               neuroml_runtimes_averaged,
+                               yerr=hdf5_errors,
+                               marker='o',
+                               color='k',
+                               ecolor='k',
+                               markerfacecolor='r',
+                               label="series 2",
+                               capsize=5,)
     plt.title("ArrayMorph write to disk benchmark (NeuroML (XML) serialization)")
     plt.xlabel("Number of segments in morphology (Units of 1000 segments)")
     plt.ylabel("Time to write to disk (s)")
 
     plt_hdf5 = plt.errorbar(hdf5_num_segments_list,
-                 hdf5_runtimes_averaged,
-                 yerr=hdf5_errors,
-                 marker='o',
-                 color='k',
-                 ecolor='k',
-                 markerfacecolor='g',
-                 label="series 2",
-                 capsize=5,)
+                            hdf5_runtimes_averaged,
+                            yerr=hdf5_errors,
+                            marker='o',
+                            color='k',
+                            ecolor='k',
+                            markerfacecolor='g',
+                            label="series 2",
+                            capsize=5,)
     plt.title("ArrayMorph write to disk benchmark (HDF5 serialization)")
     plt.xlabel("Number of segments in morphology (Units of 1000 segments)")
     plt.ylabel("Time to write to disk (s)")
 
 #    plt.show()
 
     plt_json = plt.errorbar(json_num_segments_list,
-                 json_runtimes_averaged,
-                 yerr=json_errors,
-                 marker='o',
-                 color='k',
-                 ecolor='k',
-                 markerfacecolor='b',
-                 label="series 2",
-                 capsize=5,)
+                            json_runtimes_averaged,
+                            yerr=json_errors,
+                            marker='o',
+                            color='k',
+                            ecolor='k',
+                            markerfacecolor='b',
+                            label="series 2",
+                            capsize=5,)
 
     plt.title("ArrayMorph write to disk benchmarks for JSON, HDF5 and NeuroML serialization formats")
     plt.xlabel("Number of segments in morphology")
@@ -293,6 +286,7 @@ def benchmark_arraymorph_writer():
     plt.xscale('log')
     plt.show()
 
+
 #prototype:
 if __name__ == "__main__":
     benchmark_arraymorph_writer()

neuroml/loaders.py

@@ -114,7 +114,8 @@ def load_swc_single(cls,  src, name=None):
         if len(id_to_index_dict) != len(index_to_id):
             s =  "Internal Error Loading SWC: Index and ID map are different lengths."
             s += " [ID:%d, Index:%d]"%( len(index_to_id), len(id_to_index_dict) )
-            raise MorphologyImportError(s)
+            # TODO: this is undefined!!
+            raise MorphologyImportError(s)  # noqa: F821
 
         # Vertices and section types are easy:
         vertices =  d[ ['x','y','z','r'] ]

neuroml/nml/nml.py

@@ -35,7 +35,7 @@
 
 Validate_simpletypes_ = True
 if sys.version_info.major == 2:
-    BaseStrType_ = basestring
+    BaseStrType_ = basestring  # noqa: F821
 else:
     BaseStrType_ = str
 
@@ -415,7 +415,7 @@ def gds_encode(instring):
         def convert_unicode(instring):
             if isinstance(instring, str):
                 result = quote_xml(instring)
-            elif sys.version_info.major == 2 and isinstance(instring, unicode):
+            elif sys.version_info.major == 2 and isinstance(instring, unicode): # noqa: F821
                 result = quote_xml(instring).encode('utf8')
             else:
                 result = GeneratedsSuper.gds_encode(str(instring))