WIP HBP-75 clean tvb-scripts

tvb_scripts/datatypes/base.py

@@ -1,7 +1,9 @@
 from copy import deepcopy
-from tvb_scripts.utils.log_error_utils import warning
+
+from tvb.basic.neotraits.api import HasTraits
+
 from tvb_scripts.utils.data_structures_utils import labels_to_inds
-from tvb.basic.neotraits.api import HasTraits, Attr
+from tvb_scripts.utils.log_error_utils import warning
 
 
 class BaseModel(HasTraits):
@@ -40,4 +42,3 @@ def from_tvb_file(cls, filepath, **kwargs):
     @staticmethod
     def labels2inds(all_labels, labels):
         return labels_to_inds(all_labels, labels)
-

tvb_scripts/datatypes/connectivity.py

@@ -1,8 +1,9 @@
 # coding=utf-8
 
-from tvb_scripts.datatypes.base import BaseModel
 from tvb.datatypes.connectivity import Connectivity as TVBConnectivity
 
+from tvb_scripts.datatypes.base import BaseModel
+
 
 class ConnectivityH5Field(object):
     WEIGHTS = "weights"
@@ -30,4 +31,4 @@ def centers(self):
 
     # A usefull method for addressing subsets of the connectome by label:
     def get_regions_inds_by_labels(self, labels):
-        return  self.labels2inds(self.region_labels, labels)
+        return self.labels2inds(self.region_labels, labels)

tvb_scripts/datatypes/head.py

@@ -1,17 +1,17 @@
 # coding=utf-8
 
-from six import string_types
 from collections import OrderedDict
 
-from tvb_scripts.utils.log_error_utils import initialize_logger, raise_value_error, warning
-from tvb_scripts.utils.data_structures_utils import isequal_string, is_integer
-from tvb_scripts.datatypes.connectivity import Connectivity
-from tvb_scripts.datatypes.sensors import SensorTypes, SensorTypesNames, SensorTypesToProjectionDict
-
-from tvb.datatypes.surfaces import CorticalSurface
+from six import string_types
 from tvb.datatypes.cortex import Cortex
-from tvb.datatypes.sensors import Sensors
 from tvb.datatypes.projections import ProjectionMatrix
+from tvb.datatypes.sensors import Sensors
+from tvb.datatypes.surfaces import CorticalSurface
+
+from tvb_scripts.datatypes.connectivity import Connectivity
+from tvb_scripts.datatypes.sensors import SensorTypes, SensorTypesNames, SensorTypesToProjectionDict
+from tvb_scripts.utils.data_structures_utils import isequal_string, is_integer
+from tvb_scripts.utils.log_error_utils import initialize_logger, raise_value_error, warning
 
 
 class Head(object):

tvb_scripts/datatypes/sensors.py

@@ -2,21 +2,20 @@
 from enum import Enum
 
 import numpy as np
-
-from tvb_scripts.utils.log_error_utils import warning
-from tvb_scripts.utils.data_structures_utils import ensure_list, \
-    labels_to_inds, monopolar_to_bipolar, split_string_text_numbers
-from tvb_scripts.datatypes.base import BaseModel
-
 from tvb.basic.neotraits.api import Attr, NArray
+from tvb.datatypes.projections import \
+    ProjectionSurfaceEEG, ProjectionSurfaceMEG, ProjectionSurfaceSEEG
+from tvb.datatypes.sensors import EEG_POLYMORPHIC_IDENTITY, MEG_POLYMORPHIC_IDENTITY, \
+    INTERNAL_POLYMORPHIC_IDENTITY
 from tvb.datatypes.sensors import Sensors as TVBSensors
 from tvb.datatypes.sensors import SensorsEEG as TVBSensorsEEG
-from tvb.datatypes.sensors import SensorsMEG as TVBSensorsMEG
 from tvb.datatypes.sensors import SensorsInternal as TVBSensorsInternal
-from tvb.datatypes.sensors import EEG_POLYMORPHIC_IDENTITY, MEG_POLYMORPHIC_IDENTITY, \
-    INTERNAL_POLYMORPHIC_IDENTITY
-from tvb.datatypes.projections import \
-    ProjectionSurfaceEEG, ProjectionSurfaceMEG, ProjectionSurfaceSEEG
+from tvb.datatypes.sensors import SensorsMEG as TVBSensorsMEG
+
+from tvb_scripts.datatypes.base import BaseModel
+from tvb_scripts.utils.data_structures_utils import ensure_list, \
+    labels_to_inds, monopolar_to_bipolar, split_string_text_numbers
+from tvb_scripts.utils.log_error_utils import warning
 
 
 class SensorTypes(Enum):
@@ -28,7 +27,6 @@ class SensorTypes(Enum):
 
 SensorTypesNames = [getattr(SensorTypes, stype).value for stype in SensorTypes.__members__]
 
-
 SensorTypesToProjectionDict = {"EEG": ProjectionSurfaceEEG,
                                "MEG": ProjectionSurfaceMEG,
                                "SEEG": ProjectionSurfaceSEEG,
@@ -51,8 +49,8 @@ class Sensors(TVBSensors, BaseModel):
 
     def configure(self, remove_leading_zeros_from_labels=False):
         if len(self.labels) > 0:
-                if remove_leading_zeros_from_labels:
-                    self.remove_leading_zeros_from_labels()
+            if remove_leading_zeros_from_labels:
+                self.remove_leading_zeros_from_labels()
         self.configure()
 
     def sensor_label_to_index(self, labels):
@@ -86,7 +84,7 @@ def get_bipolar_sensors(self, sensors_inds=None):
 
 
 class SensorsEEG(Sensors, TVBSensorsEEG):
-   pass
+    pass
 
 
 class SensorsMEG(Sensors, TVBSensorsMEG):

tvb_scripts/datatypes/surface.py

@@ -1,17 +1,17 @@
 # coding=utf-8
 
 import numpy as np
-
-from tvb_scripts.datatypes.base import BaseModel
 from tvb.basic.neotraits.api import NArray, Attr
-from tvb.datatypes.surfaces import Surface as TVBSurface
-from tvb.datatypes.surfaces import WhiteMatterSurface as TVBWhiteMatterSurface
-from tvb.datatypes.surfaces import CorticalSurface as TVBCorticalSurface
-from tvb.datatypes.surfaces import SkinAir as TVBSkinAir
 from tvb.datatypes.surfaces import BrainSkull as TVBBrainSkull
-from tvb.datatypes.surfaces import SkullSkin as TVBSkullSkin
+from tvb.datatypes.surfaces import CorticalSurface as TVBCorticalSurface
 from tvb.datatypes.surfaces import EEGCap as TVBEEGCap
 from tvb.datatypes.surfaces import FaceSurface as TVBFaceSurface
+from tvb.datatypes.surfaces import SkinAir as TVBSkinAir
+from tvb.datatypes.surfaces import SkullSkin as TVBSkullSkin
+from tvb.datatypes.surfaces import Surface as TVBSurface
+from tvb.datatypes.surfaces import WhiteMatterSurface as TVBWhiteMatterSurface
+
+from tvb_scripts.datatypes.base import BaseModel
 
 
 class SurfaceH5Field(object):
@@ -23,7 +23,6 @@ class SurfaceH5Field(object):
 
 
 class Surface(TVBSurface, BaseModel):
-
     vox2ras = NArray(
         dtype=np.float,
         label="vox2ras", default=None, required=False,
@@ -52,9 +51,9 @@ def get_vertex_areas(self):
         return vertex_areas
 
     def add_vertices_and_triangles(self, new_vertices, new_triangles,
-                                   new_vertex_normals=np.array([]),  new_triangle_normals=np.array([])):
+                                   new_vertex_normals=np.array([]), new_triangle_normals=np.array([])):
         self.triangles = np.array(self.triangles.tolist() +
-                                       (new_triangles + self.number_of_vertices).tolist())
+                                  (new_triangles + self.number_of_vertices).tolist())
         self.vertices = np.array(self.vertices.tolist() + new_vertices.tolist())
         self.vertex_normals = np.array(self.vertex_normals.tolist() + new_vertex_normals.tolist())
         self.triangle_normals = np.array(self.triangle_normals.tolist() + new_triangle_normals.tolist())

tvb_scripts/datatypes/time_series.py

@@ -1,22 +1,23 @@
 # -*- coding: utf-8 -*-
 
-from six import string_types
-from enum import Enum
 from copy import deepcopy
+from enum import Enum
 
 import numpy
-from tvb_scripts.utils.log_error_utils import initialize_logger, warning
-from tvb_scripts.utils.data_structures_utils import ensure_list, is_integer, monopolar_to_bipolar
+from six import string_types
 from tvb.basic.neotraits.api import List, Attr
 from tvb.basic.profile import TvbProfile
+from tvb.datatypes.sensors import Sensors, SensorsEEG, SensorsMEG, SensorsInternal
 from tvb.datatypes.time_series import TimeSeries as TimeSeriesTVB
-from tvb.datatypes.time_series import TimeSeriesRegion as TimeSeriesRegionTVB
 from tvb.datatypes.time_series import TimeSeriesEEG as TimeSeriesEEGTVB
 from tvb.datatypes.time_series import TimeSeriesMEG as TimeSeriesMEGTVB
+from tvb.datatypes.time_series import TimeSeriesRegion as TimeSeriesRegionTVB
 from tvb.datatypes.time_series import TimeSeriesSEEG as TimeSeriesSEEGTVB
 from tvb.datatypes.time_series import TimeSeriesSurface as TimeSeriesSurfaceTVB
 from tvb.datatypes.time_series import TimeSeriesVolume as TimeSeriesVolumeTVB
-from tvb.datatypes.sensors import Sensors, SensorsEEG, SensorsMEG, SensorsInternal
+
+from tvb_scripts.utils.data_structures_utils import ensure_list, is_integer, monopolar_to_bipolar
+from tvb_scripts.utils.log_error_utils import initialize_logger, warning
 
 TvbProfile.set_profile(TvbProfile.LIBRARY_PROFILE)
 
@@ -252,16 +253,16 @@ def slice_data_across_dimension_by_index(self, indices, dimension, **kwargs):
     def slice_data_across_dimension_by_label(self, labels, dimension, **kwargs):
         dim_index = self.get_dimension_index(dimension)
         return self.slice_data_across_dimension_by_index(
-                    self._get_index_of_label(labels,
-                                             self.get_dimension_name(dim_index)),
-                    dim_index, **kwargs)
+            self._get_index_of_label(labels,
+                                     self.get_dimension_name(dim_index)),
+            dim_index, **kwargs)
 
     def slice_data_across_dimension_by_slice(self, slice_arg, dimension, **kwargs):
         dim_index = self.get_dimension_index(dimension)
         return self.slice_data_across_dimension_by_index(
-                    self._slice_to_indices(
-                        self._process_slice(slice_arg, dim_index), dim_index),
-                    dim_index, **kwargs)
+            self._slice_to_indices(
+                self._process_slice(slice_arg, dim_index), dim_index),
+            dim_index, **kwargs)
 
     def _index_or_label_or_slice(self, inputs):
         inputs = ensure_list(inputs)
@@ -589,7 +590,6 @@ def SEEGsensor_labels(self):
                   TimeSeriesMEG.__name__: TimeSeriesMEG,
                   TimeSeriesSEEG.__name__: TimeSeriesSEEG}
 
-
 if __name__ == "__main__":
     kwargs = {"data": numpy.ones((4, 2, 10, 1)), "start_time": 0.0,
               "labels_dimensions": {LABELS_ORDERING[1]: ["x", "y"]}}

tvb_scripts/datatypes/time_series_xarray.py

@@ -35,11 +35,13 @@
 
 """
 from copy import deepcopy
-from six import string_types
-import xarray as xr
+
 import numpy as np
-from tvb.datatypes import sensors, surfaces, volumes, region_mapping, connectivity
+import xarray as xr
+from six import string_types
 from tvb.basic.neotraits.api import HasTraits, Attr, List, narray_summary_info
+from tvb.datatypes import sensors, surfaces, volumes, region_mapping, connectivity
+
 from tvb_scripts.datatypes.time_series import TimeSeries as TimeSeriesTVB
 from tvb_scripts.utils.data_structures_utils import is_integer
 
@@ -358,7 +360,7 @@ def duplicate(self, **kwargs):
 
     def _assert_array_indices(self, slice_tuple):
         if is_integer(slice_tuple) or isinstance(slice_tuple, string_types):
-            return ([slice_tuple], )
+            return ([slice_tuple],)
         else:
             if isinstance(slice_tuple, slice):
                 slice_tuple = (slice_tuple,)
@@ -547,9 +549,9 @@ def plot_timeseries(self, **kwargs):
                 outputs.append(self[:, var].plot_timeseries(**kwargs))
             return outputs
         if np.any([s < 2 for s in self.shape[1:]]):
-            if self.shape[1] == 1:   # only one variable
+            if self.shape[1] == 1:  # only one variable
                 figname = kwargs.pop("figname", "%s" % (self.title + "Time Series")) + ": " \
-                              + self.labels_dimensions[self.labels_ordering[1]][0]
+                          + self.labels_dimensions[self.labels_ordering[1]][0]
                 kwargs["figname"] = figname
             return self.plot_line(**kwargs)
         else:

tvb_scripts/io/edf.py

@@ -2,9 +2,9 @@
 
 import numpy as np
 
+from tvb_scripts.datatypes.time_series import TimeSeries, TimeSeriesDimensions
 from tvb_scripts.utils.log_error_utils import initialize_logger
 from tvb_scripts.utils.data_structures_utils import ensure_string
-from tvb_scripts.model.timeseries import Timeseries, TimeseriesDimensions
 
 
 def read_edf_with_mne(path, exclude_channels):
@@ -79,5 +79,5 @@ def read_edf_to_Timeseries(path, sensors, rois_selection=None, label_strip_fun=N
     data, times, rois, rois_inds, rois_lbls = \
         read_edf(path, sensors, rois_selection, label_strip_fun, time_unit)
 
-    return Timeseries(data, time=times, labels_dimensions={TimeseriesDimensions.SPACE.value: rois_lbls},
+    return TimeSeries(data, time=times, labels_dimensions={TimeSeriesDimensions.SPACE.value: rois_lbls},
                       sample_period=np.mean(np.diff(times)), sample_period_unit=time_unit, **kwargs)

tvb_scripts/io/h5_reader_base.py

@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
 
 import os
+
 import h5py
 
-from tvb_scripts.utils.log_error_utils import initialize_logger
 from tvb_scripts.io.h5_writer import H5Writer
+from tvb_scripts.utils.log_error_utils import initialize_logger
 
 
 class H5ReaderBase(object):
@@ -33,6 +34,7 @@ def _log_success(self, name, path=None):
 
 
 class H5GroupHandlers(object):
+    H5_SUBTYPE_ATTRIBUTE = H5Writer().H5_SUBTYPE_ATTRIBUTE
 
     def read_dictionary_from_group(self, group, type=None):
         dictionary = dict()
@@ -41,6 +43,6 @@ def read_dictionary_from_group(self, group, type=None):
         for attr in group.attrs.keys():
             dictionary.update({attr: group.attrs[attr]})
         if type is None:
-            type = group.attrs[H5_SUBTYPE_ATTRIBUTE]
+            type = group.attrs[self.H5_SUBTYPE_ATTRIBUTE]
         else:
             return dictionary