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2,700
BlueBrain/NeuroM
neurom/view/_dendrogram.py
_vertical_segment
def _vertical_segment(old_offs, new_offs, spacing, radii): '''Vertices for a vertical rectangle ''' return np.array(((new_offs[0] - radii[0], old_offs[1] + spacing[1]), (new_offs[0] - radii[1], new_offs[1]), (new_offs[0] + radii[1], new_offs[1]), (new_offs[0] + radii[0], old_offs[1] + spacing[1])))
python
def _vertical_segment(old_offs, new_offs, spacing, radii): '''Vertices for a vertical rectangle ''' return np.array(((new_offs[0] - radii[0], old_offs[1] + spacing[1]), (new_offs[0] - radii[1], new_offs[1]), (new_offs[0] + radii[1], new_offs[1]), (new_offs[0] + radii[0], old_offs[1] + spacing[1])))
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Vertices for a vertical rectangle
[ "Vertices", "for", "a", "vertical", "rectangle" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/view/_dendrogram.py#L80-L86
2,701
BlueBrain/NeuroM
neurom/view/_dendrogram.py
_horizontal_segment
def _horizontal_segment(old_offs, new_offs, spacing, diameter): '''Vertices of a horizontal rectangle ''' return np.array(((old_offs[0], old_offs[1] + spacing[1]), (new_offs[0], old_offs[1] + spacing[1]), (new_offs[0], old_offs[1] + spacing[1] - diameter), (old_offs[0], old_offs[1] + spacing[1] - diameter)))
python
def _horizontal_segment(old_offs, new_offs, spacing, diameter): '''Vertices of a horizontal rectangle ''' return np.array(((old_offs[0], old_offs[1] + spacing[1]), (new_offs[0], old_offs[1] + spacing[1]), (new_offs[0], old_offs[1] + spacing[1] - diameter), (old_offs[0], old_offs[1] + spacing[1] - diameter)))
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Vertices of a horizontal rectangle
[ "Vertices", "of", "a", "horizontal", "rectangle" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/view/_dendrogram.py#L89-L95
2,702
BlueBrain/NeuroM
neurom/view/_dendrogram.py
_spacingx
def _spacingx(node, max_dims, xoffset, xspace): '''Determine the spacing of the current node depending on the number of the leaves of the tree ''' x_spacing = _n_terminations(node) * xspace if x_spacing > max_dims[0]: max_dims[0] = x_spacing return xoffset - x_spacing / 2.
python
def _spacingx(node, max_dims, xoffset, xspace): '''Determine the spacing of the current node depending on the number of the leaves of the tree ''' x_spacing = _n_terminations(node) * xspace if x_spacing > max_dims[0]: max_dims[0] = x_spacing return xoffset - x_spacing / 2.
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Determine the spacing of the current node depending on the number of the leaves of the tree
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/view/_dendrogram.py#L98-L107
2,703
BlueBrain/NeuroM
neurom/view/_dendrogram.py
_update_offsets
def _update_offsets(start_x, spacing, terminations, offsets, length): '''Update the offsets ''' return (start_x + spacing[0] * terminations / 2., offsets[1] + spacing[1] * 2. + length)
python
def _update_offsets(start_x, spacing, terminations, offsets, length): '''Update the offsets ''' return (start_x + spacing[0] * terminations / 2., offsets[1] + spacing[1] * 2. + length)
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Update the offsets
[ "Update", "the", "offsets" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/view/_dendrogram.py#L110-L114
2,704
BlueBrain/NeuroM
neurom/view/_dendrogram.py
_max_diameter
def _max_diameter(tree): '''Find max diameter in tree ''' return 2. * max(max(node.points[:, COLS.R]) for node in tree.ipreorder())
python
def _max_diameter(tree): '''Find max diameter in tree ''' return 2. * max(max(node.points[:, COLS.R]) for node in tree.ipreorder())
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Find max diameter in tree
[ "Find", "max", "diameter", "in", "tree" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/view/_dendrogram.py#L117-L120
2,705
BlueBrain/NeuroM
neurom/view/_dendrogram.py
Dendrogram._generate_dendro
def _generate_dendro(self, current_section, spacing, offsets): '''Recursive function for dendrogram line computations ''' max_dims = self._max_dims start_x = _spacingx(current_section, max_dims, offsets[0], spacing[0]) for child in current_section.children: segments = child.points # number of leaves in child terminations = _n_terminations(child) # segement lengths seg_lengths = np.linalg.norm(np.subtract(segments[:-1, COLS.XYZ], segments[1:, COLS.XYZ]), axis=1) # segment radii radii = np.vstack((segments[:-1, COLS.R], segments[1:, COLS.R])).T \ if self._show_diameters else np.zeros((seg_lengths.shape[0], 2)) y_offset = offsets[1] for i, slen in enumerate(seg_lengths): # offset update for the vertical segments new_offsets = _update_offsets(start_x, spacing, terminations, (offsets[0], y_offset), slen) # segments are drawn vertically, thus only y_offset changes from init offsets self._rectangles[self._n] = _vertical_segment((offsets[0], y_offset), new_offsets, spacing, radii[i, :]) self._n += 1 y_offset = new_offsets[1] if y_offset + spacing[1] * 2 + sum(seg_lengths) > max_dims[1]: max_dims[1] = y_offset + spacing[1] * 2. + sum(seg_lengths) self._max_dims = max_dims # recursive call to self. self._generate_dendro(child, spacing, new_offsets) # update the starting position for the next child start_x += terminations * spacing[0] # write the horizontal lines only for bifurcations, where the are actual horizontal # lines and not zero ones if offsets[0] != new_offsets[0]: # horizontal segment. Thickness is either 0 if show_diameters is false # or 1. if show_diameters is true self._rectangles[self._n] = _horizontal_segment(offsets, new_offsets, spacing, 0.) self._n += 1
python
def _generate_dendro(self, current_section, spacing, offsets): '''Recursive function for dendrogram line computations ''' max_dims = self._max_dims start_x = _spacingx(current_section, max_dims, offsets[0], spacing[0]) for child in current_section.children: segments = child.points # number of leaves in child terminations = _n_terminations(child) # segement lengths seg_lengths = np.linalg.norm(np.subtract(segments[:-1, COLS.XYZ], segments[1:, COLS.XYZ]), axis=1) # segment radii radii = np.vstack((segments[:-1, COLS.R], segments[1:, COLS.R])).T \ if self._show_diameters else np.zeros((seg_lengths.shape[0], 2)) y_offset = offsets[1] for i, slen in enumerate(seg_lengths): # offset update for the vertical segments new_offsets = _update_offsets(start_x, spacing, terminations, (offsets[0], y_offset), slen) # segments are drawn vertically, thus only y_offset changes from init offsets self._rectangles[self._n] = _vertical_segment((offsets[0], y_offset), new_offsets, spacing, radii[i, :]) self._n += 1 y_offset = new_offsets[1] if y_offset + spacing[1] * 2 + sum(seg_lengths) > max_dims[1]: max_dims[1] = y_offset + spacing[1] * 2. + sum(seg_lengths) self._max_dims = max_dims # recursive call to self. self._generate_dendro(child, spacing, new_offsets) # update the starting position for the next child start_x += terminations * spacing[0] # write the horizontal lines only for bifurcations, where the are actual horizontal # lines and not zero ones if offsets[0] != new_offsets[0]: # horizontal segment. Thickness is either 0 if show_diameters is false # or 1. if show_diameters is true self._rectangles[self._n] = _horizontal_segment(offsets, new_offsets, spacing, 0.) self._n += 1
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Recursive function for dendrogram line computations
[ "Recursive", "function", "for", "dendrogram", "line", "computations" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/view/_dendrogram.py#L219-L270
2,706
BlueBrain/NeuroM
neurom/view/_dendrogram.py
Dendrogram.types
def types(self): ''' Returns an iterator over the types of the neurites in the object. If the object is a tree, then one value is returned. ''' neurites = self._obj.neurites if hasattr(self._obj, 'neurites') else (self._obj,) return (neu.type for neu in neurites)
python
def types(self): ''' Returns an iterator over the types of the neurites in the object. If the object is a tree, then one value is returned. ''' neurites = self._obj.neurites if hasattr(self._obj, 'neurites') else (self._obj,) return (neu.type for neu in neurites)
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Returns an iterator over the types of the neurites in the object. If the object is a tree, then one value is returned.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/view/_dendrogram.py#L292-L297
2,707
BlueBrain/NeuroM
neurom/fst/__init__.py
register_neurite_feature
def register_neurite_feature(name, func): '''Register a feature to be applied to neurites Parameters: name: name of the feature, used for access via get() function. func: single parameter function of a neurite. ''' if name in NEURITEFEATURES: raise NeuroMError('Attempt to hide registered feature %s' % name) def _fun(neurites, neurite_type=_ntype.all): '''Wrap neurite function from outer scope and map into list''' return list(func(n) for n in _ineurites(neurites, filt=_is_type(neurite_type))) NEURONFEATURES[name] = _fun
python
def register_neurite_feature(name, func): '''Register a feature to be applied to neurites Parameters: name: name of the feature, used for access via get() function. func: single parameter function of a neurite. ''' if name in NEURITEFEATURES: raise NeuroMError('Attempt to hide registered feature %s' % name) def _fun(neurites, neurite_type=_ntype.all): '''Wrap neurite function from outer scope and map into list''' return list(func(n) for n in _ineurites(neurites, filt=_is_type(neurite_type))) NEURONFEATURES[name] = _fun
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Register a feature to be applied to neurites Parameters: name: name of the feature, used for access via get() function. func: single parameter function of a neurite.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/__init__.py#L108-L122
2,708
BlueBrain/NeuroM
neurom/fst/__init__.py
get
def get(feature, obj, **kwargs): '''Obtain a feature from a set of morphology objects Parameters: feature(string): feature to extract obj: a neuron, population or neurite tree **kwargs: parameters to forward to underlying worker functions Returns: features as a 1D or 2D numpy array. ''' feature = (NEURITEFEATURES[feature] if feature in NEURITEFEATURES else NEURONFEATURES[feature]) return _np.array(list(feature(obj, **kwargs)))
python
def get(feature, obj, **kwargs): '''Obtain a feature from a set of morphology objects Parameters: feature(string): feature to extract obj: a neuron, population or neurite tree **kwargs: parameters to forward to underlying worker functions Returns: features as a 1D or 2D numpy array. ''' feature = (NEURITEFEATURES[feature] if feature in NEURITEFEATURES else NEURONFEATURES[feature]) return _np.array(list(feature(obj, **kwargs)))
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Obtain a feature from a set of morphology objects Parameters: feature(string): feature to extract obj: a neuron, population or neurite tree **kwargs: parameters to forward to underlying worker functions Returns: features as a 1D or 2D numpy array.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/__init__.py#L125-L141
2,709
BlueBrain/NeuroM
neurom/fst/__init__.py
_get_doc
def _get_doc(): '''Get a description of all the known available features''' def get_docstring(func): '''extract doctstring, if possible''' docstring = ':\n' if func.__doc__: docstring += _indent(func.__doc__, 2) return docstring ret = ['\nNeurite features (neurite, neuron, neuron population):'] ret.extend(_INDENT + '- ' + feature + get_docstring(func) for feature, func in sorted(NEURITEFEATURES.items())) ret.append('\nNeuron features (neuron, neuron population):') ret.extend(_INDENT + '- ' + feature + get_docstring(func) for feature, func in sorted(NEURONFEATURES.items())) return '\n'.join(ret)
python
def _get_doc(): '''Get a description of all the known available features''' def get_docstring(func): '''extract doctstring, if possible''' docstring = ':\n' if func.__doc__: docstring += _indent(func.__doc__, 2) return docstring ret = ['\nNeurite features (neurite, neuron, neuron population):'] ret.extend(_INDENT + '- ' + feature + get_docstring(func) for feature, func in sorted(NEURITEFEATURES.items())) ret.append('\nNeuron features (neuron, neuron population):') ret.extend(_INDENT + '- ' + feature + get_docstring(func) for feature, func in sorted(NEURONFEATURES.items())) return '\n'.join(ret)
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Get a description of all the known available features
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/__init__.py#L154-L171
2,710
BlueBrain/NeuroM
neurom/io/hdf5.py
read
def read(filename, remove_duplicates=False, data_wrapper=DataWrapper): '''Read a file and return a `data_wrapper'd` data * Tries to guess the format and the H5 version. * Unpacks the first block it finds out of ('repaired', 'unraveled', 'raw') Parameters: remove_duplicates: boolean, If True removes duplicate points from the beginning of each section. ''' with h5py.File(filename, mode='r') as h5file: version = get_version(h5file) if version == 'H5V1': points, groups = _unpack_v1(h5file) elif version == 'H5V2': stg = next(s for s in ('repaired', 'unraveled', 'raw') if s in h5file['neuron1']) points, groups = _unpack_v2(h5file, stage=stg) if remove_duplicates: points, groups = _remove_duplicate_points(points, groups) neuron_builder = BlockNeuronBuilder() points[:, POINT_DIAMETER] /= 2 # Store radius, not diameter for id_, row in enumerate(zip_longest(groups, groups[1:, GPFIRST], fillvalue=len(points))): (point_start, section_type, parent_id), point_end = row neuron_builder.add_section(id_, int(parent_id), int(section_type), points[point_start:point_end]) return neuron_builder.get_datawrapper(version, data_wrapper=data_wrapper)
python
def read(filename, remove_duplicates=False, data_wrapper=DataWrapper): '''Read a file and return a `data_wrapper'd` data * Tries to guess the format and the H5 version. * Unpacks the first block it finds out of ('repaired', 'unraveled', 'raw') Parameters: remove_duplicates: boolean, If True removes duplicate points from the beginning of each section. ''' with h5py.File(filename, mode='r') as h5file: version = get_version(h5file) if version == 'H5V1': points, groups = _unpack_v1(h5file) elif version == 'H5V2': stg = next(s for s in ('repaired', 'unraveled', 'raw') if s in h5file['neuron1']) points, groups = _unpack_v2(h5file, stage=stg) if remove_duplicates: points, groups = _remove_duplicate_points(points, groups) neuron_builder = BlockNeuronBuilder() points[:, POINT_DIAMETER] /= 2 # Store radius, not diameter for id_, row in enumerate(zip_longest(groups, groups[1:, GPFIRST], fillvalue=len(points))): (point_start, section_type, parent_id), point_end = row neuron_builder.add_section(id_, int(parent_id), int(section_type), points[point_start:point_end]) return neuron_builder.get_datawrapper(version, data_wrapper=data_wrapper)
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Read a file and return a `data_wrapper'd` data * Tries to guess the format and the H5 version. * Unpacks the first block it finds out of ('repaired', 'unraveled', 'raw') Parameters: remove_duplicates: boolean, If True removes duplicate points from the beginning of each section.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/hdf5.py#L67-L98
2,711
BlueBrain/NeuroM
neurom/io/hdf5.py
_remove_duplicate_points
def _remove_duplicate_points(points, groups): ''' Removes the duplicate points from the beginning of a section, if they are present in points-groups representation. Returns: points, groups with unique points. ''' group_initial_ids = groups[:, GPFIRST] to_be_reduced = np.zeros(len(group_initial_ids)) to_be_removed = [] for ig, g in enumerate(groups): iid, typ, pid = g[GPFIRST], g[GTYPE], g[GPID] # Remove first point from sections that are # not the root section, a soma, or a child of a soma if pid != -1 and typ != 1 and groups[pid][GTYPE] != 1: # Remove duplicate from list of points to_be_removed.append(iid) # Reduce the id of the following sections # in groups structure by one to_be_reduced[ig + 1:] += 1 groups[:, GPFIRST] = groups[:, GPFIRST] - to_be_reduced points = np.delete(points, to_be_removed, axis=0) return points, groups
python
def _remove_duplicate_points(points, groups): ''' Removes the duplicate points from the beginning of a section, if they are present in points-groups representation. Returns: points, groups with unique points. ''' group_initial_ids = groups[:, GPFIRST] to_be_reduced = np.zeros(len(group_initial_ids)) to_be_removed = [] for ig, g in enumerate(groups): iid, typ, pid = g[GPFIRST], g[GTYPE], g[GPID] # Remove first point from sections that are # not the root section, a soma, or a child of a soma if pid != -1 and typ != 1 and groups[pid][GTYPE] != 1: # Remove duplicate from list of points to_be_removed.append(iid) # Reduce the id of the following sections # in groups structure by one to_be_reduced[ig + 1:] += 1 groups[:, GPFIRST] = groups[:, GPFIRST] - to_be_reduced points = np.delete(points, to_be_removed, axis=0) return points, groups
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Removes the duplicate points from the beginning of a section, if they are present in points-groups representation. Returns: points, groups with unique points.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/hdf5.py#L101-L129
2,712
BlueBrain/NeuroM
neurom/io/hdf5.py
_unpack_v1
def _unpack_v1(h5file): '''Unpack groups from HDF5 v1 file''' points = np.array(h5file['points']) groups = np.array(h5file['structure']) return points, groups
python
def _unpack_v1(h5file): '''Unpack groups from HDF5 v1 file''' points = np.array(h5file['points']) groups = np.array(h5file['structure']) return points, groups
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Unpack groups from HDF5 v1 file
[ "Unpack", "groups", "from", "HDF5", "v1", "file" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/hdf5.py#L132-L136
2,713
BlueBrain/NeuroM
neurom/io/hdf5.py
_unpack_v2
def _unpack_v2(h5file, stage): '''Unpack groups from HDF5 v2 file''' points = np.array(h5file['neuron1/%s/points' % stage]) # from documentation: The /neuron1/structure/unraveled reuses /neuron1/structure/raw groups_stage = stage if stage != 'unraveled' else 'raw' groups = np.array(h5file['neuron1/structure/%s' % groups_stage]) stypes = np.array(h5file['neuron1/structure/sectiontype']) groups = np.hstack([groups, stypes]) groups[:, [1, 2]] = groups[:, [2, 1]] return points, groups
python
def _unpack_v2(h5file, stage): '''Unpack groups from HDF5 v2 file''' points = np.array(h5file['neuron1/%s/points' % stage]) # from documentation: The /neuron1/structure/unraveled reuses /neuron1/structure/raw groups_stage = stage if stage != 'unraveled' else 'raw' groups = np.array(h5file['neuron1/structure/%s' % groups_stage]) stypes = np.array(h5file['neuron1/structure/sectiontype']) groups = np.hstack([groups, stypes]) groups[:, [1, 2]] = groups[:, [2, 1]] return points, groups
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Unpack groups from HDF5 v2 file
[ "Unpack", "groups", "from", "HDF5", "v2", "file" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/hdf5.py#L139-L148
2,714
BlueBrain/NeuroM
neurom/stats.py
fit_results_to_dict
def fit_results_to_dict(fit_results, min_bound=None, max_bound=None): '''Create a JSON-comparable dict from a FitResults object Parameters: fit_results (FitResults): object containing fit parameters,\ errors and type min_bound: optional min value to add to dictionary if min isn't\ a fit parameter. max_bound: optional max value to add to dictionary if max isn't\ a fit parameter. Returns: JSON-compatible dictionary with fit results Note: Supported fit types: 'norm', 'expon', 'uniform' ''' type_map = {'norm': 'normal', 'expon': 'exponential', 'uniform': 'uniform'} param_map = {'uniform': lambda p: [('min', p[0]), ('max', p[0] + p[1])], 'norm': lambda p: [('mu', p[0]), ('sigma', p[1])], 'expon': lambda p: [('lambda', 1.0 / p[1])]} d = OrderedDict({'type': type_map[fit_results.type]}) d.update(param_map[fit_results.type](fit_results.params)) if min_bound is not None and 'min' not in d: d['min'] = min_bound if max_bound is not None and 'max' not in d: d['max'] = max_bound return d
python
def fit_results_to_dict(fit_results, min_bound=None, max_bound=None): '''Create a JSON-comparable dict from a FitResults object Parameters: fit_results (FitResults): object containing fit parameters,\ errors and type min_bound: optional min value to add to dictionary if min isn't\ a fit parameter. max_bound: optional max value to add to dictionary if max isn't\ a fit parameter. Returns: JSON-compatible dictionary with fit results Note: Supported fit types: 'norm', 'expon', 'uniform' ''' type_map = {'norm': 'normal', 'expon': 'exponential', 'uniform': 'uniform'} param_map = {'uniform': lambda p: [('min', p[0]), ('max', p[0] + p[1])], 'norm': lambda p: [('mu', p[0]), ('sigma', p[1])], 'expon': lambda p: [('lambda', 1.0 / p[1])]} d = OrderedDict({'type': type_map[fit_results.type]}) d.update(param_map[fit_results.type](fit_results.params)) if min_bound is not None and 'min' not in d: d['min'] = min_bound if max_bound is not None and 'max' not in d: d['max'] = max_bound return d
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Create a JSON-comparable dict from a FitResults object Parameters: fit_results (FitResults): object containing fit parameters,\ errors and type min_bound: optional min value to add to dictionary if min isn't\ a fit parameter. max_bound: optional max value to add to dictionary if max isn't\ a fit parameter. Returns: JSON-compatible dictionary with fit results Note: Supported fit types: 'norm', 'expon', 'uniform'
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/stats.py#L60-L91
2,715
BlueBrain/NeuroM
neurom/stats.py
fit
def fit(data, distribution='norm'): '''Calculate the parameters of a fit of a distribution to a data set Parameters: data: array of data points to be fitted Options: distribution (str): type of distribution to fit. Default 'norm'. Returns: FitResults object with fitted parameters, errors and distribution type Note: Uses Kolmogorov-Smirnov test to estimate distance and p-value. ''' params = getattr(_st, distribution).fit(data) return FitResults(params, _st.kstest(data, distribution, params), distribution)
python
def fit(data, distribution='norm'): '''Calculate the parameters of a fit of a distribution to a data set Parameters: data: array of data points to be fitted Options: distribution (str): type of distribution to fit. Default 'norm'. Returns: FitResults object with fitted parameters, errors and distribution type Note: Uses Kolmogorov-Smirnov test to estimate distance and p-value. ''' params = getattr(_st, distribution).fit(data) return FitResults(params, _st.kstest(data, distribution, params), distribution)
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Calculate the parameters of a fit of a distribution to a data set Parameters: data: array of data points to be fitted Options: distribution (str): type of distribution to fit. Default 'norm'. Returns: FitResults object with fitted parameters, errors and distribution type Note: Uses Kolmogorov-Smirnov test to estimate distance and p-value.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/stats.py#L94-L110
2,716
BlueBrain/NeuroM
neurom/stats.py
optimal_distribution
def optimal_distribution(data, distr_to_check=('norm', 'expon', 'uniform')): '''Calculate the parameters of a fit of different distributions to a data set and returns the distribution of the minimal ks-distance. Parameters: data: array of data points to be fitted Options: distr_to_check: tuple of distributions to be checked Returns: FitResults object with fitted parameters, errors and distribution type\ of the fit with the smallest fit distance Note: Uses Kolmogorov-Smirnov test to estimate distance and p-value. ''' fit_results = [fit(data, d) for d in distr_to_check] return min(fit_results, key=lambda fit: fit.errs[0])
python
def optimal_distribution(data, distr_to_check=('norm', 'expon', 'uniform')): '''Calculate the parameters of a fit of different distributions to a data set and returns the distribution of the minimal ks-distance. Parameters: data: array of data points to be fitted Options: distr_to_check: tuple of distributions to be checked Returns: FitResults object with fitted parameters, errors and distribution type\ of the fit with the smallest fit distance Note: Uses Kolmogorov-Smirnov test to estimate distance and p-value. ''' fit_results = [fit(data, d) for d in distr_to_check] return min(fit_results, key=lambda fit: fit.errs[0])
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Calculate the parameters of a fit of different distributions to a data set and returns the distribution of the minimal ks-distance. Parameters: data: array of data points to be fitted Options: distr_to_check: tuple of distributions to be checked Returns: FitResults object with fitted parameters, errors and distribution type\ of the fit with the smallest fit distance Note: Uses Kolmogorov-Smirnov test to estimate distance and p-value.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/stats.py#L113-L131
2,717
BlueBrain/NeuroM
neurom/stats.py
scalar_stats
def scalar_stats(data, functions=('min', 'max', 'mean', 'std')): '''Calculate the stats from the given numpy functions Parameters: data: array of data points to be used for the stats Options: functions: tuple of numpy stat functions to apply on data Returns: Dictionary with the name of the function as key and the result as the respective value ''' stats = {} for func in functions: stats[func] = getattr(np, func)(data) return stats
python
def scalar_stats(data, functions=('min', 'max', 'mean', 'std')): '''Calculate the stats from the given numpy functions Parameters: data: array of data points to be used for the stats Options: functions: tuple of numpy stat functions to apply on data Returns: Dictionary with the name of the function as key and the result as the respective value ''' stats = {} for func in functions: stats[func] = getattr(np, func)(data) return stats
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Calculate the stats from the given numpy functions Parameters: data: array of data points to be used for the stats Options: functions: tuple of numpy stat functions to apply on data Returns: Dictionary with the name of the function as key and the result as the respective value
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/stats.py#L134-L152
2,718
BlueBrain/NeuroM
neurom/stats.py
total_score
def total_score(paired_dats, p=2, test=StatTests.ks): '''Calculates the p-norm of the distances that have been calculated from the statistical test that has been applied on all the paired datasets. Parameters: paired_dats: a list of tuples or where each tuple contains the paired data lists from two datasets Options: p : integer that defines the order of p-norm test: Stat_tests\ Defines the statistical test to be used, based\ on the scipy available modules.\ Accepted tests: ks_2samp, wilcoxon, ttest Returns: A float corresponding to the p-norm of the distances that have been calculated. 0 corresponds to high similarity while 1 to low. ''' scores = np.array([compare_two(fL1, fL2, test=test).dist for fL1, fL2 in paired_dats]) return np.linalg.norm(scores, p)
python
def total_score(paired_dats, p=2, test=StatTests.ks): '''Calculates the p-norm of the distances that have been calculated from the statistical test that has been applied on all the paired datasets. Parameters: paired_dats: a list of tuples or where each tuple contains the paired data lists from two datasets Options: p : integer that defines the order of p-norm test: Stat_tests\ Defines the statistical test to be used, based\ on the scipy available modules.\ Accepted tests: ks_2samp, wilcoxon, ttest Returns: A float corresponding to the p-norm of the distances that have been calculated. 0 corresponds to high similarity while 1 to low. ''' scores = np.array([compare_two(fL1, fL2, test=test).dist for fL1, fL2 in paired_dats]) return np.linalg.norm(scores, p)
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Calculates the p-norm of the distances that have been calculated from the statistical test that has been applied on all the paired datasets. Parameters: paired_dats: a list of tuples or where each tuple contains the paired data lists from two datasets Options: p : integer that defines the order of p-norm test: Stat_tests\ Defines the statistical test to be used, based\ on the scipy available modules.\ Accepted tests: ks_2samp, wilcoxon, ttest Returns: A float corresponding to the p-norm of the distances that have been calculated. 0 corresponds to high similarity while 1 to low.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/stats.py#L182-L202
2,719
BlueBrain/NeuroM
neurom/core/_neuron.py
iter_neurites
def iter_neurites(obj, mapfun=None, filt=None, neurite_order=NeuriteIter.FileOrder): '''Iterator to a neurite, neuron or neuron population Applies optional neurite filter and mapping functions. Parameters: obj: a neurite, neuron or neuron population. mapfun: optional neurite mapping function. filt: optional neurite filter function. neurite_order (NeuriteIter): order upon which neurites should be iterated - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical Examples: Get the number of points in each neurite in a neuron population >>> from neurom.core import iter_neurites >>> n_points = [n for n in iter_neurites(pop, lambda x : len(x.points))] Get the number of points in each axon in a neuron population >>> import neurom as nm >>> from neurom.core import iter_neurites >>> filter = lambda n : n.type == nm.AXON >>> mapping = lambda n : len(n.points) >>> n_points = [n for n in iter_neurites(pop, mapping, filter)] ''' neurites = ((obj,) if isinstance(obj, Neurite) else obj.neurites if hasattr(obj, 'neurites') else obj) if neurite_order == NeuriteIter.NRN: last_position = max(NRN_ORDER.values()) + 1 neurites = sorted(neurites, key=lambda neurite: NRN_ORDER.get(neurite.type, last_position)) neurite_iter = iter(neurites) if filt is None else filter(filt, neurites) return neurite_iter if mapfun is None else map(mapfun, neurite_iter)
python
def iter_neurites(obj, mapfun=None, filt=None, neurite_order=NeuriteIter.FileOrder): '''Iterator to a neurite, neuron or neuron population Applies optional neurite filter and mapping functions. Parameters: obj: a neurite, neuron or neuron population. mapfun: optional neurite mapping function. filt: optional neurite filter function. neurite_order (NeuriteIter): order upon which neurites should be iterated - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical Examples: Get the number of points in each neurite in a neuron population >>> from neurom.core import iter_neurites >>> n_points = [n for n in iter_neurites(pop, lambda x : len(x.points))] Get the number of points in each axon in a neuron population >>> import neurom as nm >>> from neurom.core import iter_neurites >>> filter = lambda n : n.type == nm.AXON >>> mapping = lambda n : len(n.points) >>> n_points = [n for n in iter_neurites(pop, mapping, filter)] ''' neurites = ((obj,) if isinstance(obj, Neurite) else obj.neurites if hasattr(obj, 'neurites') else obj) if neurite_order == NeuriteIter.NRN: last_position = max(NRN_ORDER.values()) + 1 neurites = sorted(neurites, key=lambda neurite: NRN_ORDER.get(neurite.type, last_position)) neurite_iter = iter(neurites) if filt is None else filter(filt, neurites) return neurite_iter if mapfun is None else map(mapfun, neurite_iter)
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Iterator to a neurite, neuron or neuron population Applies optional neurite filter and mapping functions. Parameters: obj: a neurite, neuron or neuron population. mapfun: optional neurite mapping function. filt: optional neurite filter function. neurite_order (NeuriteIter): order upon which neurites should be iterated - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical Examples: Get the number of points in each neurite in a neuron population >>> from neurom.core import iter_neurites >>> n_points = [n for n in iter_neurites(pop, lambda x : len(x.points))] Get the number of points in each axon in a neuron population >>> import neurom as nm >>> from neurom.core import iter_neurites >>> filter = lambda n : n.type == nm.AXON >>> mapping = lambda n : len(n.points) >>> n_points = [n for n in iter_neurites(pop, mapping, filter)]
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/core/_neuron.py#L54-L90
2,720
BlueBrain/NeuroM
neurom/core/_neuron.py
iter_sections
def iter_sections(neurites, iterator_type=Tree.ipreorder, neurite_filter=None, neurite_order=NeuriteIter.FileOrder): '''Iterator to the sections in a neurite, neuron or neuron population. Parameters: neurites: neuron, population, neurite, or iterable containing neurite objects iterator_type: section iteration order within a given neurite. Must be one of: Tree.ipreorder: Depth-first pre-order iteration of tree nodes Tree.ipreorder: Depth-first post-order iteration of tree nodes Tree.iupstream: Iterate from a tree node to the root nodes Tree.ibifurcation_point: Iterator to bifurcation points Tree.ileaf: Iterator to all leaves of a tree neurite_filter: optional top level filter on properties of neurite neurite objects. neurite_order (NeuriteIter): order upon which neurites should be iterated - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical Examples: Get the number of points in each section of all the axons in a neuron population >>> import neurom as nm >>> from neurom.core import ites_sections >>> filter = lambda n : n.type == nm.AXON >>> n_points = [len(s.points) for s in iter_sections(pop, neurite_filter=filter)] ''' return chain.from_iterable( iterator_type(neurite.root_node) for neurite in iter_neurites(neurites, filt=neurite_filter, neurite_order=neurite_order))
python
def iter_sections(neurites, iterator_type=Tree.ipreorder, neurite_filter=None, neurite_order=NeuriteIter.FileOrder): '''Iterator to the sections in a neurite, neuron or neuron population. Parameters: neurites: neuron, population, neurite, or iterable containing neurite objects iterator_type: section iteration order within a given neurite. Must be one of: Tree.ipreorder: Depth-first pre-order iteration of tree nodes Tree.ipreorder: Depth-first post-order iteration of tree nodes Tree.iupstream: Iterate from a tree node to the root nodes Tree.ibifurcation_point: Iterator to bifurcation points Tree.ileaf: Iterator to all leaves of a tree neurite_filter: optional top level filter on properties of neurite neurite objects. neurite_order (NeuriteIter): order upon which neurites should be iterated - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical Examples: Get the number of points in each section of all the axons in a neuron population >>> import neurom as nm >>> from neurom.core import ites_sections >>> filter = lambda n : n.type == nm.AXON >>> n_points = [len(s.points) for s in iter_sections(pop, neurite_filter=filter)] ''' return chain.from_iterable( iterator_type(neurite.root_node) for neurite in iter_neurites(neurites, filt=neurite_filter, neurite_order=neurite_order))
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Iterator to the sections in a neurite, neuron or neuron population. Parameters: neurites: neuron, population, neurite, or iterable containing neurite objects iterator_type: section iteration order within a given neurite. Must be one of: Tree.ipreorder: Depth-first pre-order iteration of tree nodes Tree.ipreorder: Depth-first post-order iteration of tree nodes Tree.iupstream: Iterate from a tree node to the root nodes Tree.ibifurcation_point: Iterator to bifurcation points Tree.ileaf: Iterator to all leaves of a tree neurite_filter: optional top level filter on properties of neurite neurite objects. neurite_order (NeuriteIter): order upon which neurites should be iterated - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical Examples: Get the number of points in each section of all the axons in a neuron population >>> import neurom as nm >>> from neurom.core import ites_sections >>> filter = lambda n : n.type == nm.AXON >>> n_points = [len(s.points) for s in iter_sections(pop, neurite_filter=filter)]
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/core/_neuron.py#L93-L126
2,721
BlueBrain/NeuroM
neurom/core/_neuron.py
iter_segments
def iter_segments(obj, neurite_filter=None, neurite_order=NeuriteIter.FileOrder): '''Return an iterator to the segments in a collection of neurites Parameters: obj: neuron, population, neurite, section, or iterable containing neurite objects neurite_filter: optional top level filter on properties of neurite neurite objects neurite_order: order upon which neurite should be iterated. Values: - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical Note: This is a convenience function provided for generic access to neuron segments. It may have a performance overhead WRT custom-made segment analysis functions that leverage numpy and section-wise iteration. ''' sections = iter((obj,) if isinstance(obj, Section) else iter_sections(obj, neurite_filter=neurite_filter, neurite_order=neurite_order)) return chain.from_iterable(zip(sec.points[:-1], sec.points[1:]) for sec in sections)
python
def iter_segments(obj, neurite_filter=None, neurite_order=NeuriteIter.FileOrder): '''Return an iterator to the segments in a collection of neurites Parameters: obj: neuron, population, neurite, section, or iterable containing neurite objects neurite_filter: optional top level filter on properties of neurite neurite objects neurite_order: order upon which neurite should be iterated. Values: - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical Note: This is a convenience function provided for generic access to neuron segments. It may have a performance overhead WRT custom-made segment analysis functions that leverage numpy and section-wise iteration. ''' sections = iter((obj,) if isinstance(obj, Section) else iter_sections(obj, neurite_filter=neurite_filter, neurite_order=neurite_order)) return chain.from_iterable(zip(sec.points[:-1], sec.points[1:]) for sec in sections)
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Return an iterator to the segments in a collection of neurites Parameters: obj: neuron, population, neurite, section, or iterable containing neurite objects neurite_filter: optional top level filter on properties of neurite neurite objects neurite_order: order upon which neurite should be iterated. Values: - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical Note: This is a convenience function provided for generic access to neuron segments. It may have a performance overhead WRT custom-made segment analysis functions that leverage numpy and section-wise iteration.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/core/_neuron.py#L129-L150
2,722
BlueBrain/NeuroM
neurom/core/_neuron.py
graft_neuron
def graft_neuron(root_section): '''Returns a neuron starting at root_section''' assert isinstance(root_section, Section) return Neuron(soma=Soma(root_section.points[:1]), neurites=[Neurite(root_section)])
python
def graft_neuron(root_section): '''Returns a neuron starting at root_section''' assert isinstance(root_section, Section) return Neuron(soma=Soma(root_section.points[:1]), neurites=[Neurite(root_section)])
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Returns a neuron starting at root_section
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/core/_neuron.py#L153-L156
2,723
BlueBrain/NeuroM
neurom/core/_neuron.py
Neurite.points
def points(self): '''Return unordered array with all the points in this neurite''' # add all points in a section except the first one, which is a duplicate _pts = [v for s in self.root_node.ipreorder() for v in s.points[1:, COLS.XYZR]] # except for the very first point, which is not a duplicate _pts.insert(0, self.root_node.points[0][COLS.XYZR]) return np.array(_pts)
python
def points(self): '''Return unordered array with all the points in this neurite''' # add all points in a section except the first one, which is a duplicate _pts = [v for s in self.root_node.ipreorder() for v in s.points[1:, COLS.XYZR]] # except for the very first point, which is not a duplicate _pts.insert(0, self.root_node.points[0][COLS.XYZR]) return np.array(_pts)
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Return unordered array with all the points in this neurite
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/core/_neuron.py#L211-L218
2,724
BlueBrain/NeuroM
neurom/core/_neuron.py
Neurite.transform
def transform(self, trans): '''Return a copy of this neurite with a 3D transformation applied''' clone = deepcopy(self) for n in clone.iter_sections(): n.points[:, 0:3] = trans(n.points[:, 0:3]) return clone
python
def transform(self, trans): '''Return a copy of this neurite with a 3D transformation applied''' clone = deepcopy(self) for n in clone.iter_sections(): n.points[:, 0:3] = trans(n.points[:, 0:3]) return clone
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Return a copy of this neurite with a 3D transformation applied
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/core/_neuron.py#L247-L253
2,725
BlueBrain/NeuroM
neurom/core/_neuron.py
Neurite.iter_sections
def iter_sections(self, order=Tree.ipreorder, neurite_order=NeuriteIter.FileOrder): '''iteration over section nodes Parameters: order: section iteration order within a given neurite. Must be one of: Tree.ipreorder: Depth-first pre-order iteration of tree nodes Tree.ipreorder: Depth-first post-order iteration of tree nodes Tree.iupstream: Iterate from a tree node to the root nodes Tree.ibifurcation_point: Iterator to bifurcation points Tree.ileaf: Iterator to all leaves of a tree neurite_order: order upon which neurites should be iterated. Values: - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical ''' return iter_sections(self, iterator_type=order, neurite_order=neurite_order)
python
def iter_sections(self, order=Tree.ipreorder, neurite_order=NeuriteIter.FileOrder): '''iteration over section nodes Parameters: order: section iteration order within a given neurite. Must be one of: Tree.ipreorder: Depth-first pre-order iteration of tree nodes Tree.ipreorder: Depth-first post-order iteration of tree nodes Tree.iupstream: Iterate from a tree node to the root nodes Tree.ibifurcation_point: Iterator to bifurcation points Tree.ileaf: Iterator to all leaves of a tree neurite_order: order upon which neurites should be iterated. Values: - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical ''' return iter_sections(self, iterator_type=order, neurite_order=neurite_order)
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iteration over section nodes Parameters: order: section iteration order within a given neurite. Must be one of: Tree.ipreorder: Depth-first pre-order iteration of tree nodes Tree.ipreorder: Depth-first post-order iteration of tree nodes Tree.iupstream: Iterate from a tree node to the root nodes Tree.ibifurcation_point: Iterator to bifurcation points Tree.ileaf: Iterator to all leaves of a tree neurite_order: order upon which neurites should be iterated. Values: - NeuriteIter.FileOrder: order of appearance in the file - NeuriteIter.NRN: NRN simulator order: soma -> axon -> basal -> apical
[ "iteration", "over", "section", "nodes" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/core/_neuron.py#L255-L270
2,726
BlueBrain/NeuroM
neurom/apps/morph_stats.py
eval_stats
def eval_stats(values, mode): '''Extract a summary statistic from an array of list of values Parameters: values: numpy array of values mode: summary stat to extract. One of ['min', 'max', 'median', 'mean', 'std', 'raw'] Note: fails silently if values is empty, and None is returned ''' if mode == 'raw': return values.tolist() if mode == 'total': mode = 'sum' try: return getattr(np, mode)(values, axis=0) except ValueError: pass return None
python
def eval_stats(values, mode): '''Extract a summary statistic from an array of list of values Parameters: values: numpy array of values mode: summary stat to extract. One of ['min', 'max', 'median', 'mean', 'std', 'raw'] Note: fails silently if values is empty, and None is returned ''' if mode == 'raw': return values.tolist() if mode == 'total': mode = 'sum' try: return getattr(np, mode)(values, axis=0) except ValueError: pass return None
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Extract a summary statistic from an array of list of values Parameters: values: numpy array of values mode: summary stat to extract. One of ['min', 'max', 'median', 'mean', 'std', 'raw'] Note: fails silently if values is empty, and None is returned
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/apps/morph_stats.py#L40-L59
2,727
BlueBrain/NeuroM
neurom/apps/morph_stats.py
_stat_name
def _stat_name(feat_name, stat_mode): '''Set stat name based on feature name and stat mode''' if feat_name[-1] == 's': feat_name = feat_name[:-1] if feat_name == 'soma_radii': feat_name = 'soma_radius' if stat_mode == 'raw': return feat_name return '%s_%s' % (stat_mode, feat_name)
python
def _stat_name(feat_name, stat_mode): '''Set stat name based on feature name and stat mode''' if feat_name[-1] == 's': feat_name = feat_name[:-1] if feat_name == 'soma_radii': feat_name = 'soma_radius' if stat_mode == 'raw': return feat_name return '%s_%s' % (stat_mode, feat_name)
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Set stat name based on feature name and stat mode
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/apps/morph_stats.py#L62-L71
2,728
BlueBrain/NeuroM
neurom/apps/morph_stats.py
extract_stats
def extract_stats(neurons, config): '''Extract stats from neurons''' stats = defaultdict(dict) for ns, modes in config['neurite'].items(): for n in config['neurite_type']: n = _NEURITE_MAP[n] for mode in modes: stat_name = _stat_name(ns, mode) stat = eval_stats(nm.get(ns, neurons, neurite_type=n), mode) if stat is None or not stat.shape: stats[n.name][stat_name] = stat else: assert stat.shape in ((3, ), ), \ 'Statistic must create a 1x3 result' for i, suffix in enumerate('XYZ'): compound_stat_name = stat_name + '_' + suffix stats[n.name][compound_stat_name] = stat[i] for ns, modes in config['neuron'].items(): for mode in modes: stat_name = _stat_name(ns, mode) stats[stat_name] = eval_stats(nm.get(ns, neurons), mode) return stats
python
def extract_stats(neurons, config): '''Extract stats from neurons''' stats = defaultdict(dict) for ns, modes in config['neurite'].items(): for n in config['neurite_type']: n = _NEURITE_MAP[n] for mode in modes: stat_name = _stat_name(ns, mode) stat = eval_stats(nm.get(ns, neurons, neurite_type=n), mode) if stat is None or not stat.shape: stats[n.name][stat_name] = stat else: assert stat.shape in ((3, ), ), \ 'Statistic must create a 1x3 result' for i, suffix in enumerate('XYZ'): compound_stat_name = stat_name + '_' + suffix stats[n.name][compound_stat_name] = stat[i] for ns, modes in config['neuron'].items(): for mode in modes: stat_name = _stat_name(ns, mode) stats[stat_name] = eval_stats(nm.get(ns, neurons), mode) return stats
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Extract stats from neurons
[ "Extract", "stats", "from", "neurons" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/apps/morph_stats.py#L74-L100
2,729
BlueBrain/NeuroM
neurom/apps/morph_stats.py
get_header
def get_header(results): '''Extracts the headers, using the first value in the dict as the template''' ret = ['name', ] values = next(iter(results.values())) for k, v in values.items(): if isinstance(v, dict): for metric in v.keys(): ret.append('%s:%s' % (k, metric)) else: ret.append(k) return ret
python
def get_header(results): '''Extracts the headers, using the first value in the dict as the template''' ret = ['name', ] values = next(iter(results.values())) for k, v in values.items(): if isinstance(v, dict): for metric in v.keys(): ret.append('%s:%s' % (k, metric)) else: ret.append(k) return ret
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Extracts the headers, using the first value in the dict as the template
[ "Extracts", "the", "headers", "using", "the", "first", "value", "in", "the", "dict", "as", "the", "template" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/apps/morph_stats.py#L103-L113
2,730
BlueBrain/NeuroM
neurom/apps/morph_stats.py
generate_flattened_dict
def generate_flattened_dict(headers, results): '''extract from results the fields in the headers list''' for name, values in results.items(): row = [] for header in headers: if header == 'name': row.append(name) elif ':' in header: neurite_type, metric = header.split(':') row.append(values[neurite_type][metric]) else: row.append(values[header]) yield row
python
def generate_flattened_dict(headers, results): '''extract from results the fields in the headers list''' for name, values in results.items(): row = [] for header in headers: if header == 'name': row.append(name) elif ':' in header: neurite_type, metric = header.split(':') row.append(values[neurite_type][metric]) else: row.append(values[header]) yield row
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extract from results the fields in the headers list
[ "extract", "from", "results", "the", "fields", "in", "the", "headers", "list" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/apps/morph_stats.py#L116-L128
2,731
BlueBrain/NeuroM
neurom/core/tree.py
Tree.add_child
def add_child(self, tree): '''Add a child to the list of this tree's children This tree becomes the added tree's parent ''' tree.parent = self self.children.append(tree) return tree
python
def add_child(self, tree): '''Add a child to the list of this tree's children This tree becomes the added tree's parent ''' tree.parent = self self.children.append(tree) return tree
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Add a child to the list of this tree's children This tree becomes the added tree's parent
[ "Add", "a", "child", "to", "the", "list", "of", "this", "tree", "s", "children" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/core/tree.py#L41-L48
2,732
BlueBrain/NeuroM
neurom/core/tree.py
Tree.ipreorder
def ipreorder(self): '''Depth-first pre-order iteration of tree nodes''' children = deque((self, )) while children: cur_node = children.pop() children.extend(reversed(cur_node.children)) yield cur_node
python
def ipreorder(self): '''Depth-first pre-order iteration of tree nodes''' children = deque((self, )) while children: cur_node = children.pop() children.extend(reversed(cur_node.children)) yield cur_node
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Depth-first pre-order iteration of tree nodes
[ "Depth", "-", "first", "pre", "-", "order", "iteration", "of", "tree", "nodes" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/core/tree.py#L66-L72
2,733
BlueBrain/NeuroM
neurom/core/tree.py
Tree.ipostorder
def ipostorder(self): '''Depth-first post-order iteration of tree nodes''' children = [self, ] seen = set() while children: cur_node = children[-1] if cur_node not in seen: seen.add(cur_node) children.extend(reversed(cur_node.children)) else: children.pop() yield cur_node
python
def ipostorder(self): '''Depth-first post-order iteration of tree nodes''' children = [self, ] seen = set() while children: cur_node = children[-1] if cur_node not in seen: seen.add(cur_node) children.extend(reversed(cur_node.children)) else: children.pop() yield cur_node
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Depth-first post-order iteration of tree nodes
[ "Depth", "-", "first", "post", "-", "order", "iteration", "of", "tree", "nodes" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/core/tree.py#L74-L85
2,734
BlueBrain/NeuroM
neurom/utils.py
deprecated
def deprecated(fun_name=None, msg=""): '''Issue a deprecation warning for a function''' def _deprecated(fun): '''Issue a deprecation warning for a function''' @wraps(fun) def _wrapper(*args, **kwargs): '''Issue deprecation warning and forward arguments to fun''' name = fun_name if fun_name is not None else fun.__name__ _warn_deprecated('Call to deprecated function %s. %s' % (name, msg)) return fun(*args, **kwargs) return _wrapper return _deprecated
python
def deprecated(fun_name=None, msg=""): '''Issue a deprecation warning for a function''' def _deprecated(fun): '''Issue a deprecation warning for a function''' @wraps(fun) def _wrapper(*args, **kwargs): '''Issue deprecation warning and forward arguments to fun''' name = fun_name if fun_name is not None else fun.__name__ _warn_deprecated('Call to deprecated function %s. %s' % (name, msg)) return fun(*args, **kwargs) return _wrapper return _deprecated
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Issue a deprecation warning for a function
[ "Issue", "a", "deprecation", "warning", "for", "a", "function" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/utils.py#L86-L99
2,735
BlueBrain/NeuroM
neurom/check/__init__.py
check_wrapper
def check_wrapper(fun): '''Decorate a checking function''' @wraps(fun) def _wrapper(*args, **kwargs): '''Sets the title property of the result of running a checker''' title = fun.__name__.replace('_', ' ').capitalize() result = fun(*args, **kwargs) result.title = title return result return _wrapper
python
def check_wrapper(fun): '''Decorate a checking function''' @wraps(fun) def _wrapper(*args, **kwargs): '''Sets the title property of the result of running a checker''' title = fun.__name__.replace('_', ' ').capitalize() result = fun(*args, **kwargs) result.title = title return result return _wrapper
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Decorate a checking function
[ "Decorate", "a", "checking", "function" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/__init__.py#L34-L44
2,736
BlueBrain/NeuroM
neurom/check/runner.py
CheckRunner.run
def run(self, path): '''Test a bunch of files and return a summary JSON report''' SEPARATOR = '=' * 40 summary = {} res = True for _f in utils.get_files_by_path(path): L.info(SEPARATOR) status, summ = self._check_file(_f) res &= status if summ is not None: summary.update(summ) L.info(SEPARATOR) status = 'PASS' if res else 'FAIL' return {'files': summary, 'STATUS': status}
python
def run(self, path): '''Test a bunch of files and return a summary JSON report''' SEPARATOR = '=' * 40 summary = {} res = True for _f in utils.get_files_by_path(path): L.info(SEPARATOR) status, summ = self._check_file(_f) res &= status if summ is not None: summary.update(summ) L.info(SEPARATOR) status = 'PASS' if res else 'FAIL' return {'files': summary, 'STATUS': status}
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Test a bunch of files and return a summary JSON report
[ "Test", "a", "bunch", "of", "files", "and", "return", "a", "summary", "JSON", "report" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/runner.py#L53-L71
2,737
BlueBrain/NeuroM
neurom/check/runner.py
CheckRunner._do_check
def _do_check(self, obj, check_module, check_str): '''Run a check function on obj''' opts = self._config['options'] if check_str in opts: fargs = opts[check_str] if isinstance(fargs, list): out = check_wrapper(getattr(check_module, check_str))(obj, *fargs) else: out = check_wrapper(getattr(check_module, check_str))(obj, fargs) else: out = check_wrapper(getattr(check_module, check_str))(obj) try: if out.info: L.debug('%s: %d failing ids detected: %s', out.title, len(out.info), out.info) except TypeError: # pragma: no cover pass return out
python
def _do_check(self, obj, check_module, check_str): '''Run a check function on obj''' opts = self._config['options'] if check_str in opts: fargs = opts[check_str] if isinstance(fargs, list): out = check_wrapper(getattr(check_module, check_str))(obj, *fargs) else: out = check_wrapper(getattr(check_module, check_str))(obj, fargs) else: out = check_wrapper(getattr(check_module, check_str))(obj) try: if out.info: L.debug('%s: %d failing ids detected: %s', out.title, len(out.info), out.info) except TypeError: # pragma: no cover pass return out
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Run a check function on obj
[ "Run", "a", "check", "function", "on", "obj" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/runner.py#L73-L92
2,738
BlueBrain/NeuroM
neurom/check/runner.py
CheckRunner._check_loop
def _check_loop(self, obj, check_mod_str): '''Run all the checks in a check_module''' check_module = self._check_modules[check_mod_str] checks = self._config['checks'][check_mod_str] result = True summary = OrderedDict() for check in checks: ok = self._do_check(obj, check_module, check) summary[ok.title] = ok.status result &= ok.status return result, summary
python
def _check_loop(self, obj, check_mod_str): '''Run all the checks in a check_module''' check_module = self._check_modules[check_mod_str] checks = self._config['checks'][check_mod_str] result = True summary = OrderedDict() for check in checks: ok = self._do_check(obj, check_module, check) summary[ok.title] = ok.status result &= ok.status return result, summary
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Run all the checks in a check_module
[ "Run", "all", "the", "checks", "in", "a", "check_module" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/runner.py#L94-L105
2,739
BlueBrain/NeuroM
neurom/check/runner.py
CheckRunner._check_file
def _check_file(self, f): '''Run tests on a morphology file''' L.info('File: %s', f) full_result = True full_summary = OrderedDict() try: data = load_data(f) except Exception as e: # pylint: disable=W0703 L.error('Failed to load data... skipping tests for this file') L.error(e.args) return False, {f: OrderedDict([('ALL', False)])} try: result, summary = self._check_loop(data, 'structural_checks') full_result &= result full_summary.update(summary) nrn = fst_core.FstNeuron(data) result, summary = self._check_loop(nrn, 'neuron_checks') full_result &= result full_summary.update(summary) except Exception as e: # pylint: disable=W0703 L.error('Check failed: %s', str(type(e)) + str(e.args)) full_result = False full_summary['ALL'] = full_result for m, s in full_summary.items(): self._log_msg(m, s) return full_result, {f: full_summary}
python
def _check_file(self, f): '''Run tests on a morphology file''' L.info('File: %s', f) full_result = True full_summary = OrderedDict() try: data = load_data(f) except Exception as e: # pylint: disable=W0703 L.error('Failed to load data... skipping tests for this file') L.error(e.args) return False, {f: OrderedDict([('ALL', False)])} try: result, summary = self._check_loop(data, 'structural_checks') full_result &= result full_summary.update(summary) nrn = fst_core.FstNeuron(data) result, summary = self._check_loop(nrn, 'neuron_checks') full_result &= result full_summary.update(summary) except Exception as e: # pylint: disable=W0703 L.error('Check failed: %s', str(type(e)) + str(e.args)) full_result = False full_summary['ALL'] = full_result for m, s in full_summary.items(): self._log_msg(m, s) return full_result, {f: full_summary}
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Run tests on a morphology file
[ "Run", "tests", "on", "a", "morphology", "file" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/runner.py#L107-L138
2,740
BlueBrain/NeuroM
neurom/check/runner.py
CheckRunner._log_msg
def _log_msg(self, msg, ok): '''Helper to log message to the right level''' if self._config['color']: CGREEN, CRED, CEND = '\033[92m', '\033[91m', '\033[0m' else: CGREEN = CRED = CEND = '' LOG_LEVELS = {False: logging.ERROR, True: logging.INFO} # pylint: disable=logging-not-lazy L.log(LOG_LEVELS[ok], '%35s %s' + CEND, msg, CGREEN + 'PASS' if ok else CRED + 'FAIL')
python
def _log_msg(self, msg, ok): '''Helper to log message to the right level''' if self._config['color']: CGREEN, CRED, CEND = '\033[92m', '\033[91m', '\033[0m' else: CGREEN = CRED = CEND = '' LOG_LEVELS = {False: logging.ERROR, True: logging.INFO} # pylint: disable=logging-not-lazy L.log(LOG_LEVELS[ok], '%35s %s' + CEND, msg, CGREEN + 'PASS' if ok else CRED + 'FAIL')
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Helper to log message to the right level
[ "Helper", "to", "log", "message", "to", "the", "right", "level" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/runner.py#L140-L151
2,741
BlueBrain/NeuroM
neurom/check/runner.py
CheckRunner._sanitize_config
def _sanitize_config(config): '''check that the config has the correct keys, add missing keys if necessary''' if 'checks' in config: checks = config['checks'] if 'structural_checks' not in checks: checks['structural_checks'] = [] if 'neuron_checks' not in checks: checks['neuron_checks'] = [] else: raise ConfigError('Need to have "checks" in the config') if 'options' not in config: L.debug('Using default options') config['options'] = {} if 'color' not in config: config['color'] = False return config
python
def _sanitize_config(config): '''check that the config has the correct keys, add missing keys if necessary''' if 'checks' in config: checks = config['checks'] if 'structural_checks' not in checks: checks['structural_checks'] = [] if 'neuron_checks' not in checks: checks['neuron_checks'] = [] else: raise ConfigError('Need to have "checks" in the config') if 'options' not in config: L.debug('Using default options') config['options'] = {} if 'color' not in config: config['color'] = False return config
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check that the config has the correct keys, add missing keys if necessary
[ "check", "that", "the", "config", "has", "the", "correct", "keys", "add", "missing", "keys", "if", "necessary" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/runner.py#L154-L172
2,742
BlueBrain/NeuroM
neurom/io/swc.py
read
def read(filename, data_wrapper=DataWrapper): '''Read an SWC file and return a tuple of data, format.''' data = np.loadtxt(filename) if len(np.shape(data)) == 1: data = np.reshape(data, (1, -1)) data = data[:, [X, Y, Z, R, TYPE, ID, P]] return data_wrapper(data, 'SWC', None)
python
def read(filename, data_wrapper=DataWrapper): '''Read an SWC file and return a tuple of data, format.''' data = np.loadtxt(filename) if len(np.shape(data)) == 1: data = np.reshape(data, (1, -1)) data = data[:, [X, Y, Z, R, TYPE, ID, P]] return data_wrapper(data, 'SWC', None)
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Read an SWC file and return a tuple of data, format.
[ "Read", "an", "SWC", "file", "and", "return", "a", "tuple", "of", "data", "format", "." ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/swc.py#L47-L53
2,743
BlueBrain/NeuroM
neurom/io/datawrapper.py
_merge_sections
def _merge_sections(sec_a, sec_b): '''Merge two sections Merges sec_a into sec_b and sets sec_a attributes to default ''' sec_b.ids = list(sec_a.ids) + list(sec_b.ids[1:]) sec_b.ntype = sec_a.ntype sec_b.pid = sec_a.pid sec_a.ids = [] sec_a.pid = -1 sec_a.ntype = 0
python
def _merge_sections(sec_a, sec_b): '''Merge two sections Merges sec_a into sec_b and sets sec_a attributes to default ''' sec_b.ids = list(sec_a.ids) + list(sec_b.ids[1:]) sec_b.ntype = sec_a.ntype sec_b.pid = sec_a.pid sec_a.ids = [] sec_a.pid = -1 sec_a.ntype = 0
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Merge two sections Merges sec_a into sec_b and sets sec_a attributes to default
[ "Merge", "two", "sections" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/datawrapper.py#L89-L100
2,744
BlueBrain/NeuroM
neurom/io/datawrapper.py
_section_end_points
def _section_end_points(structure_block, id_map): '''Get the section end-points''' soma_idx = structure_block[:, TYPE] == POINT_TYPE.SOMA soma_ids = structure_block[soma_idx, ID] neurite_idx = structure_block[:, TYPE] != POINT_TYPE.SOMA neurite_rows = structure_block[neurite_idx, :] soma_end_pts = set(id_map[id_] for id_ in soma_ids[np.in1d(soma_ids, neurite_rows[:, PID])]) # end points have either no children or more than one # ie: leaf or multifurcation nodes n_children = defaultdict(int) for row in structure_block: n_children[row[PID]] += 1 end_pts = set(i for i, row in enumerate(structure_block) if n_children[row[ID]] != 1) return end_pts.union(soma_end_pts)
python
def _section_end_points(structure_block, id_map): '''Get the section end-points''' soma_idx = structure_block[:, TYPE] == POINT_TYPE.SOMA soma_ids = structure_block[soma_idx, ID] neurite_idx = structure_block[:, TYPE] != POINT_TYPE.SOMA neurite_rows = structure_block[neurite_idx, :] soma_end_pts = set(id_map[id_] for id_ in soma_ids[np.in1d(soma_ids, neurite_rows[:, PID])]) # end points have either no children or more than one # ie: leaf or multifurcation nodes n_children = defaultdict(int) for row in structure_block: n_children[row[PID]] += 1 end_pts = set(i for i, row in enumerate(structure_block) if n_children[row[ID]] != 1) return end_pts.union(soma_end_pts)
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Get the section end-points
[ "Get", "the", "section", "end", "-", "points" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/datawrapper.py#L103-L120
2,745
BlueBrain/NeuroM
neurom/io/datawrapper.py
_extract_sections
def _extract_sections(data_block): '''Make a list of sections from an SWC-style data wrapper block''' structure_block = data_block[:, COLS.TYPE:COLS.COL_COUNT].astype(np.int) # SWC ID -> structure_block position id_map = {-1: -1} for i, row in enumerate(structure_block): id_map[row[ID]] = i # end points have either no children, more than one, or are the start # of a new gap sec_end_pts = _section_end_points(structure_block, id_map) # a 'gap' is when a section has part of it's segments interleaved # with those of another section gap_sections = set() sections = [] def new_section(): '''new_section''' sections.append(DataBlockSection()) return sections[-1] curr_section = new_section() parent_section = {-1: -1} for row in structure_block: row_id = id_map[row[ID]] parent_id = id_map[row[PID]] if not curr_section.ids: # first in section point is parent curr_section.ids.append(parent_id) curr_section.ntype = row[TYPE] gap = parent_id != curr_section.ids[-1] # If parent is not the previous point, create a section end-point. # Else add the point to this section if gap: sec_end_pts.add(row_id) else: curr_section.ids.append(row_id) if row_id in sec_end_pts: parent_section[curr_section.ids[-1]] = len(sections) - 1 # Parent-child discontinuity section if gap: curr_section = new_section() curr_section.ids.extend((parent_id, row_id)) curr_section.ntype = row[TYPE] gap_sections.add(len(sections) - 2) elif row_id != len(data_block) - 1: # avoid creating an extra DataBlockSection for last row if it's a leaf curr_section = new_section() for sec in sections: # get the section parent ID from the id of the first point. if sec.ids: sec.pid = parent_section[sec.ids[0]] # join gap sections and "disable" first half if sec.pid in gap_sections: _merge_sections(sections[sec.pid], sec) # TODO find a way to remove empty sections. Currently they are # required to maintain tree integrity. return sections
python
def _extract_sections(data_block): '''Make a list of sections from an SWC-style data wrapper block''' structure_block = data_block[:, COLS.TYPE:COLS.COL_COUNT].astype(np.int) # SWC ID -> structure_block position id_map = {-1: -1} for i, row in enumerate(structure_block): id_map[row[ID]] = i # end points have either no children, more than one, or are the start # of a new gap sec_end_pts = _section_end_points(structure_block, id_map) # a 'gap' is when a section has part of it's segments interleaved # with those of another section gap_sections = set() sections = [] def new_section(): '''new_section''' sections.append(DataBlockSection()) return sections[-1] curr_section = new_section() parent_section = {-1: -1} for row in structure_block: row_id = id_map[row[ID]] parent_id = id_map[row[PID]] if not curr_section.ids: # first in section point is parent curr_section.ids.append(parent_id) curr_section.ntype = row[TYPE] gap = parent_id != curr_section.ids[-1] # If parent is not the previous point, create a section end-point. # Else add the point to this section if gap: sec_end_pts.add(row_id) else: curr_section.ids.append(row_id) if row_id in sec_end_pts: parent_section[curr_section.ids[-1]] = len(sections) - 1 # Parent-child discontinuity section if gap: curr_section = new_section() curr_section.ids.extend((parent_id, row_id)) curr_section.ntype = row[TYPE] gap_sections.add(len(sections) - 2) elif row_id != len(data_block) - 1: # avoid creating an extra DataBlockSection for last row if it's a leaf curr_section = new_section() for sec in sections: # get the section parent ID from the id of the first point. if sec.ids: sec.pid = parent_section[sec.ids[0]] # join gap sections and "disable" first half if sec.pid in gap_sections: _merge_sections(sections[sec.pid], sec) # TODO find a way to remove empty sections. Currently they are # required to maintain tree integrity. return sections
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Make a list of sections from an SWC-style data wrapper block
[ "Make", "a", "list", "of", "sections", "from", "an", "SWC", "-", "style", "data", "wrapper", "block" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/datawrapper.py#L142-L210
2,746
BlueBrain/NeuroM
neurom/io/datawrapper.py
DataWrapper.neurite_root_section_ids
def neurite_root_section_ids(self): '''Get the section IDs of the intitial neurite sections''' sec = self.sections return [i for i, ss in enumerate(sec) if ss.pid > -1 and (sec[ss.pid].ntype == POINT_TYPE.SOMA and ss.ntype != POINT_TYPE.SOMA)]
python
def neurite_root_section_ids(self): '''Get the section IDs of the intitial neurite sections''' sec = self.sections return [i for i, ss in enumerate(sec) if ss.pid > -1 and (sec[ss.pid].ntype == POINT_TYPE.SOMA and ss.ntype != POINT_TYPE.SOMA)]
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Get the section IDs of the intitial neurite sections
[ "Get", "the", "section", "IDs", "of", "the", "intitial", "neurite", "sections" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/datawrapper.py#L76-L81
2,747
BlueBrain/NeuroM
neurom/io/datawrapper.py
DataWrapper.soma_points
def soma_points(self): '''Get the soma points''' db = self.data_block return db[db[:, COLS.TYPE] == POINT_TYPE.SOMA]
python
def soma_points(self): '''Get the soma points''' db = self.data_block return db[db[:, COLS.TYPE] == POINT_TYPE.SOMA]
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Get the soma points
[ "Get", "the", "soma", "points" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/datawrapper.py#L83-L86
2,748
BlueBrain/NeuroM
neurom/io/datawrapper.py
BlockNeuronBuilder.add_section
def add_section(self, id_, parent_id, section_type, points): '''add a section Args: id_(int): identifying number of the section parent_id(int): identifying number of the parent of this section section_type(int): the section type as defined by POINT_TYPE points is an array of [X, Y, Z, R] ''' # L.debug('Adding section %d, with parent %d, of type: %d with count: %d', # id_, parent_id, section_type, len(points)) assert id_ not in self.sections, 'id %s already exists in sections' % id_ self.sections[id_] = BlockNeuronBuilder.BlockSection(parent_id, section_type, points)
python
def add_section(self, id_, parent_id, section_type, points): '''add a section Args: id_(int): identifying number of the section parent_id(int): identifying number of the parent of this section section_type(int): the section type as defined by POINT_TYPE points is an array of [X, Y, Z, R] ''' # L.debug('Adding section %d, with parent %d, of type: %d with count: %d', # id_, parent_id, section_type, len(points)) assert id_ not in self.sections, 'id %s already exists in sections' % id_ self.sections[id_] = BlockNeuronBuilder.BlockSection(parent_id, section_type, points)
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add a section Args: id_(int): identifying number of the section parent_id(int): identifying number of the parent of this section section_type(int): the section type as defined by POINT_TYPE points is an array of [X, Y, Z, R]
[ "add", "a", "section" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/datawrapper.py#L234-L246
2,749
BlueBrain/NeuroM
neurom/io/datawrapper.py
BlockNeuronBuilder._make_datablock
def _make_datablock(self): '''Make a data_block and sections list as required by DataWrapper''' section_ids = sorted(self.sections) # create all insertion id's, this needs to be done ahead of time # as some of the children may have a lower id than their parents id_to_insert_id = {} row_count = 0 for section_id in section_ids: row_count += len(self.sections[section_id].points) id_to_insert_id[section_id] = row_count - 1 datablock = np.empty((row_count, COLS.COL_COUNT), dtype=np.float) datablock[:, COLS.ID] = np.arange(len(datablock)) datablock[:, COLS.P] = datablock[:, COLS.ID] - 1 sections = [] insert_index = 0 for id_ in section_ids: sec = self.sections[id_] points, section_type, parent_id = sec.points, sec.section_type, sec.parent_id idx = slice(insert_index, insert_index + len(points)) datablock[idx, COLS.XYZR] = points datablock[idx, COLS.TYPE] = section_type datablock[idx.start, COLS.P] = id_to_insert_id.get(parent_id, ROOT_ID) sections.append(DataBlockSection(idx, section_type, parent_id)) insert_index = idx.stop return datablock, sections
python
def _make_datablock(self): '''Make a data_block and sections list as required by DataWrapper''' section_ids = sorted(self.sections) # create all insertion id's, this needs to be done ahead of time # as some of the children may have a lower id than their parents id_to_insert_id = {} row_count = 0 for section_id in section_ids: row_count += len(self.sections[section_id].points) id_to_insert_id[section_id] = row_count - 1 datablock = np.empty((row_count, COLS.COL_COUNT), dtype=np.float) datablock[:, COLS.ID] = np.arange(len(datablock)) datablock[:, COLS.P] = datablock[:, COLS.ID] - 1 sections = [] insert_index = 0 for id_ in section_ids: sec = self.sections[id_] points, section_type, parent_id = sec.points, sec.section_type, sec.parent_id idx = slice(insert_index, insert_index + len(points)) datablock[idx, COLS.XYZR] = points datablock[idx, COLS.TYPE] = section_type datablock[idx.start, COLS.P] = id_to_insert_id.get(parent_id, ROOT_ID) sections.append(DataBlockSection(idx, section_type, parent_id)) insert_index = idx.stop return datablock, sections
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Make a data_block and sections list as required by DataWrapper
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/datawrapper.py#L248-L277
2,750
BlueBrain/NeuroM
neurom/io/datawrapper.py
BlockNeuronBuilder._check_consistency
def _check_consistency(self): '''see if the sections have obvious errors''' type_count = defaultdict(int) for _, section in sorted(self.sections.items()): type_count[section.section_type] += 1 if type_count[POINT_TYPE.SOMA] != 1: L.info('Have %d somas, expected 1', type_count[POINT_TYPE.SOMA])
python
def _check_consistency(self): '''see if the sections have obvious errors''' type_count = defaultdict(int) for _, section in sorted(self.sections.items()): type_count[section.section_type] += 1 if type_count[POINT_TYPE.SOMA] != 1: L.info('Have %d somas, expected 1', type_count[POINT_TYPE.SOMA])
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see if the sections have obvious errors
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/datawrapper.py#L279-L286
2,751
BlueBrain/NeuroM
neurom/io/datawrapper.py
BlockNeuronBuilder.get_datawrapper
def get_datawrapper(self, file_format='BlockNeuronBuilder', data_wrapper=DataWrapper): '''returns a DataWrapper''' self._check_consistency() datablock, sections = self._make_datablock() return data_wrapper(datablock, file_format, sections)
python
def get_datawrapper(self, file_format='BlockNeuronBuilder', data_wrapper=DataWrapper): '''returns a DataWrapper''' self._check_consistency() datablock, sections = self._make_datablock() return data_wrapper(datablock, file_format, sections)
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returns a DataWrapper
[ "returns", "a", "DataWrapper" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/datawrapper.py#L288-L292
2,752
BlueBrain/NeuroM
neurom/io/utils.py
_is_morphology_file
def _is_morphology_file(filepath): """ Check if `filepath` is a file with one of morphology file extensions. """ return ( os.path.isfile(filepath) and os.path.splitext(filepath)[1].lower() in ('.swc', '.h5', '.asc') )
python
def _is_morphology_file(filepath): """ Check if `filepath` is a file with one of morphology file extensions. """ return ( os.path.isfile(filepath) and os.path.splitext(filepath)[1].lower() in ('.swc', '.h5', '.asc') )
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Check if `filepath` is a file with one of morphology file extensions.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/utils.py#L50-L55
2,753
BlueBrain/NeuroM
neurom/io/utils.py
get_morph_files
def get_morph_files(directory): '''Get a list of all morphology files in a directory Returns: list with all files with extensions '.swc' , 'h5' or '.asc' (case insensitive) ''' lsdir = (os.path.join(directory, m) for m in os.listdir(directory)) return list(filter(_is_morphology_file, lsdir))
python
def get_morph_files(directory): '''Get a list of all morphology files in a directory Returns: list with all files with extensions '.swc' , 'h5' or '.asc' (case insensitive) ''' lsdir = (os.path.join(directory, m) for m in os.listdir(directory)) return list(filter(_is_morphology_file, lsdir))
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Get a list of all morphology files in a directory Returns: list with all files with extensions '.swc' , 'h5' or '.asc' (case insensitive)
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/utils.py#L92-L99
2,754
BlueBrain/NeuroM
neurom/io/utils.py
get_files_by_path
def get_files_by_path(path): '''Get a file or set of files from a file path Return list of files with path ''' if os.path.isfile(path): return [path] if os.path.isdir(path): return get_morph_files(path) raise IOError('Invalid data path %s' % path)
python
def get_files_by_path(path): '''Get a file or set of files from a file path Return list of files with path ''' if os.path.isfile(path): return [path] if os.path.isdir(path): return get_morph_files(path) raise IOError('Invalid data path %s' % path)
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Get a file or set of files from a file path Return list of files with path
[ "Get", "a", "file", "or", "set", "of", "files", "from", "a", "file", "path" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/utils.py#L102-L112
2,755
BlueBrain/NeuroM
neurom/io/utils.py
load_neuron
def load_neuron(handle, reader=None): '''Build section trees from an h5 or swc file''' rdw = load_data(handle, reader) if isinstance(handle, StringType): name = os.path.splitext(os.path.basename(handle))[0] else: name = None return FstNeuron(rdw, name)
python
def load_neuron(handle, reader=None): '''Build section trees from an h5 or swc file''' rdw = load_data(handle, reader) if isinstance(handle, StringType): name = os.path.splitext(os.path.basename(handle))[0] else: name = None return FstNeuron(rdw, name)
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Build section trees from an h5 or swc file
[ "Build", "section", "trees", "from", "an", "h5", "or", "swc", "file" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/utils.py#L115-L122
2,756
BlueBrain/NeuroM
neurom/io/utils.py
load_neurons
def load_neurons(neurons, neuron_loader=load_neuron, name=None, population_class=Population, ignored_exceptions=()): '''Create a population object from all morphologies in a directory\ of from morphologies in a list of file names Parameters: neurons: directory path or list of neuron file paths neuron_loader: function taking a filename and returning a neuron population_class: class representing populations name (str): optional name of population. By default 'Population' or\ filepath basename depending on whether neurons is list or\ directory path respectively. Returns: neuron population object ''' if isinstance(neurons, (list, tuple)): files = neurons name = name if name is not None else 'Population' elif isinstance(neurons, StringType): files = get_files_by_path(neurons) name = name if name is not None else os.path.basename(neurons) ignored_exceptions = tuple(ignored_exceptions) pop = [] for f in files: try: pop.append(neuron_loader(f)) except NeuroMError as e: if isinstance(e, ignored_exceptions): L.info('Ignoring exception "%s" for file %s', e, os.path.basename(f)) continue raise return population_class(pop, name=name)
python
def load_neurons(neurons, neuron_loader=load_neuron, name=None, population_class=Population, ignored_exceptions=()): '''Create a population object from all morphologies in a directory\ of from morphologies in a list of file names Parameters: neurons: directory path or list of neuron file paths neuron_loader: function taking a filename and returning a neuron population_class: class representing populations name (str): optional name of population. By default 'Population' or\ filepath basename depending on whether neurons is list or\ directory path respectively. Returns: neuron population object ''' if isinstance(neurons, (list, tuple)): files = neurons name = name if name is not None else 'Population' elif isinstance(neurons, StringType): files = get_files_by_path(neurons) name = name if name is not None else os.path.basename(neurons) ignored_exceptions = tuple(ignored_exceptions) pop = [] for f in files: try: pop.append(neuron_loader(f)) except NeuroMError as e: if isinstance(e, ignored_exceptions): L.info('Ignoring exception "%s" for file %s', e, os.path.basename(f)) continue raise return population_class(pop, name=name)
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Create a population object from all morphologies in a directory\ of from morphologies in a list of file names Parameters: neurons: directory path or list of neuron file paths neuron_loader: function taking a filename and returning a neuron population_class: class representing populations name (str): optional name of population. By default 'Population' or\ filepath basename depending on whether neurons is list or\ directory path respectively. Returns: neuron population object
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/utils.py#L125-L164
2,757
BlueBrain/NeuroM
neurom/io/utils.py
_get_file
def _get_file(handle): '''Returns the filename of the file to read If handle is a stream, a temp file is written on disk first and its filename is returned''' if not isinstance(handle, IOBase): return handle fd, temp_file = tempfile.mkstemp(str(uuid.uuid4()), prefix='neurom-') os.close(fd) with open(temp_file, 'w') as fd: handle.seek(0) shutil.copyfileobj(handle, fd) return temp_file
python
def _get_file(handle): '''Returns the filename of the file to read If handle is a stream, a temp file is written on disk first and its filename is returned''' if not isinstance(handle, IOBase): return handle fd, temp_file = tempfile.mkstemp(str(uuid.uuid4()), prefix='neurom-') os.close(fd) with open(temp_file, 'w') as fd: handle.seek(0) shutil.copyfileobj(handle, fd) return temp_file
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Returns the filename of the file to read If handle is a stream, a temp file is written on disk first and its filename is returned
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/utils.py#L167-L180
2,758
BlueBrain/NeuroM
neurom/io/utils.py
load_data
def load_data(handle, reader=None): '''Unpack data into a raw data wrapper''' if not reader: reader = os.path.splitext(handle)[1][1:].lower() if reader not in _READERS: raise NeuroMError('Do not have a loader for "%s" extension' % reader) filename = _get_file(handle) try: return _READERS[reader](filename) except Exception as e: L.exception('Error reading file %s, using "%s" loader', filename, reader) raise RawDataError('Error reading file %s:\n%s' % (filename, str(e)))
python
def load_data(handle, reader=None): '''Unpack data into a raw data wrapper''' if not reader: reader = os.path.splitext(handle)[1][1:].lower() if reader not in _READERS: raise NeuroMError('Do not have a loader for "%s" extension' % reader) filename = _get_file(handle) try: return _READERS[reader](filename) except Exception as e: L.exception('Error reading file %s, using "%s" loader', filename, reader) raise RawDataError('Error reading file %s:\n%s' % (filename, str(e)))
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Unpack data into a raw data wrapper
[ "Unpack", "data", "into", "a", "raw", "data", "wrapper" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/utils.py#L183-L196
2,759
BlueBrain/NeuroM
neurom/io/utils.py
_load_h5
def _load_h5(filename): '''Delay loading of h5py until it is needed''' from neurom.io import hdf5 return hdf5.read(filename, remove_duplicates=False, data_wrapper=DataWrapper)
python
def _load_h5(filename): '''Delay loading of h5py until it is needed''' from neurom.io import hdf5 return hdf5.read(filename, remove_duplicates=False, data_wrapper=DataWrapper)
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Delay loading of h5py until it is needed
[ "Delay", "loading", "of", "h5py", "until", "it", "is", "needed" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/utils.py#L199-L204
2,760
BlueBrain/NeuroM
neurom/io/utils.py
NeuronLoader._filepath
def _filepath(self, name): """ File path to `name` morphology file. """ if self.file_ext is None: candidates = glob.glob(os.path.join(self.directory, name + ".*")) try: return next(filter(_is_morphology_file, candidates)) except StopIteration: raise NeuroMError("Can not find morphology file for '%s' " % name) else: return os.path.join(self.directory, name + self.file_ext)
python
def _filepath(self, name): """ File path to `name` morphology file. """ if self.file_ext is None: candidates = glob.glob(os.path.join(self.directory, name + ".*")) try: return next(filter(_is_morphology_file, candidates)) except StopIteration: raise NeuroMError("Can not find morphology file for '%s' " % name) else: return os.path.join(self.directory, name + self.file_ext)
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File path to `name` morphology file.
[ "File", "path", "to", "name", "morphology", "file", "." ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/utils.py#L75-L84
2,761
BlueBrain/NeuroM
neurom/viewer.py
draw
def draw(obj, mode='2d', **kwargs): '''Draw a morphology object Parameters: obj: morphology object to be drawn (neuron, tree, soma). mode (Optional[str]): drawing mode ('2d', '3d', 'dendrogram'). Defaults to '2d'. **kwargs: keyword arguments for underlying neurom.view.view functions. Raises: InvalidDrawModeError if mode is not valid NotDrawableError if obj is not drawable NotDrawableError if obj type and mode combination is not drawable Examples: >>> nrn = ... # load a neuron >>> fig, _ = viewer.draw(nrn) # 2d plot >>> fig.show() >>> fig3d, _ = viewer.draw(nrn, mode='3d') # 3d plot >>> fig3d.show() >>> fig, _ = viewer.draw(nrn.neurites[0]) # 2d plot of neurite tree >>> dend, _ = viewer.draw(nrn, mode='dendrogram') ''' if mode not in MODES: raise InvalidDrawModeError('Invalid drawing mode %s' % mode) if mode in ('2d', 'dendrogram'): fig, ax = common.get_figure() else: fig, ax = common.get_figure(params={'projection': '3d'}) if isinstance(obj, Neuron): tag = 'neuron' elif isinstance(obj, (Tree, Neurite)): tag = 'tree' elif isinstance(obj, Soma): tag = 'soma' else: raise NotDrawableError('draw not implemented for %s' % obj.__class__) viewer = '%s_%s' % (tag, mode) try: plotter = _VIEWERS[viewer] except KeyError: raise NotDrawableError('No drawer for class %s, mode=%s' % (obj.__class__, mode)) output_path = kwargs.pop('output_path', None) plotter(ax, obj, **kwargs) if mode != 'dendrogram': common.plot_style(fig=fig, ax=ax, **kwargs) if output_path: common.save_plot(fig=fig, output_path=output_path, **kwargs) return fig, ax
python
def draw(obj, mode='2d', **kwargs): '''Draw a morphology object Parameters: obj: morphology object to be drawn (neuron, tree, soma). mode (Optional[str]): drawing mode ('2d', '3d', 'dendrogram'). Defaults to '2d'. **kwargs: keyword arguments for underlying neurom.view.view functions. Raises: InvalidDrawModeError if mode is not valid NotDrawableError if obj is not drawable NotDrawableError if obj type and mode combination is not drawable Examples: >>> nrn = ... # load a neuron >>> fig, _ = viewer.draw(nrn) # 2d plot >>> fig.show() >>> fig3d, _ = viewer.draw(nrn, mode='3d') # 3d plot >>> fig3d.show() >>> fig, _ = viewer.draw(nrn.neurites[0]) # 2d plot of neurite tree >>> dend, _ = viewer.draw(nrn, mode='dendrogram') ''' if mode not in MODES: raise InvalidDrawModeError('Invalid drawing mode %s' % mode) if mode in ('2d', 'dendrogram'): fig, ax = common.get_figure() else: fig, ax = common.get_figure(params={'projection': '3d'}) if isinstance(obj, Neuron): tag = 'neuron' elif isinstance(obj, (Tree, Neurite)): tag = 'tree' elif isinstance(obj, Soma): tag = 'soma' else: raise NotDrawableError('draw not implemented for %s' % obj.__class__) viewer = '%s_%s' % (tag, mode) try: plotter = _VIEWERS[viewer] except KeyError: raise NotDrawableError('No drawer for class %s, mode=%s' % (obj.__class__, mode)) output_path = kwargs.pop('output_path', None) plotter(ax, obj, **kwargs) if mode != 'dendrogram': common.plot_style(fig=fig, ax=ax, **kwargs) if output_path: common.save_plot(fig=fig, output_path=output_path, **kwargs) return fig, ax
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Draw a morphology object Parameters: obj: morphology object to be drawn (neuron, tree, soma). mode (Optional[str]): drawing mode ('2d', '3d', 'dendrogram'). Defaults to '2d'. **kwargs: keyword arguments for underlying neurom.view.view functions. Raises: InvalidDrawModeError if mode is not valid NotDrawableError if obj is not drawable NotDrawableError if obj type and mode combination is not drawable Examples: >>> nrn = ... # load a neuron >>> fig, _ = viewer.draw(nrn) # 2d plot >>> fig.show() >>> fig3d, _ = viewer.draw(nrn, mode='3d') # 3d plot >>> fig3d.show() >>> fig, _ = viewer.draw(nrn.neurites[0]) # 2d plot of neurite tree >>> dend, _ = viewer.draw(nrn, mode='dendrogram')
[ "Draw", "a", "morphology", "object" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/viewer.py#L77-L134
2,762
BlueBrain/NeuroM
examples/histogram.py
population_feature_values
def population_feature_values(pops, feature): '''Extracts feature values per population ''' pops_feature_values = [] for pop in pops: feature_values = [getattr(neu, 'get_' + feature)() for neu in pop.neurons] # ugly hack to chain in case of list of lists if any([isinstance(p, (list, np.ndarray)) for p in feature_values]): feature_values = list(chain(*feature_values)) pops_feature_values.append(feature_values) return pops_feature_values
python
def population_feature_values(pops, feature): '''Extracts feature values per population ''' pops_feature_values = [] for pop in pops: feature_values = [getattr(neu, 'get_' + feature)() for neu in pop.neurons] # ugly hack to chain in case of list of lists if any([isinstance(p, (list, np.ndarray)) for p in feature_values]): feature_values = list(chain(*feature_values)) pops_feature_values.append(feature_values) return pops_feature_values
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Extracts feature values per population
[ "Extracts", "feature", "values", "per", "population" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/histogram.py#L96-L112
2,763
BlueBrain/NeuroM
examples/section_ids.py
get_segment
def get_segment(neuron, section_id, segment_id): '''Get a segment given a section and segment id Returns: array of two [x, y, z, r] points defining segment ''' sec = neuron.sections[section_id] return sec.points[segment_id:segment_id + 2][:, COLS.XYZR]
python
def get_segment(neuron, section_id, segment_id): '''Get a segment given a section and segment id Returns: array of two [x, y, z, r] points defining segment ''' sec = neuron.sections[section_id] return sec.points[segment_id:segment_id + 2][:, COLS.XYZR]
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Get a segment given a section and segment id Returns: array of two [x, y, z, r] points defining segment
[ "Get", "a", "segment", "given", "a", "section", "and", "segment", "id" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/section_ids.py#L37-L44
2,764
BlueBrain/NeuroM
examples/extract_distribution.py
extract_data
def extract_data(data_path, feature): '''Loads a list of neurons, extracts feature and transforms the fitted distribution in the correct format. Returns the optimal distribution, corresponding parameters, minimun and maximum values. ''' population = nm.load_neurons(data_path) feature_data = [nm.get(feature, n) for n in population] feature_data = list(chain(*feature_data)) return stats.optimal_distribution(feature_data)
python
def extract_data(data_path, feature): '''Loads a list of neurons, extracts feature and transforms the fitted distribution in the correct format. Returns the optimal distribution, corresponding parameters, minimun and maximum values. ''' population = nm.load_neurons(data_path) feature_data = [nm.get(feature, n) for n in population] feature_data = list(chain(*feature_data)) return stats.optimal_distribution(feature_data)
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Loads a list of neurons, extracts feature and transforms the fitted distribution in the correct format. Returns the optimal distribution, corresponding parameters, minimun and maximum values.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/extract_distribution.py#L59-L70
2,765
BlueBrain/NeuroM
neurom/fst/_bifurcationfunc.py
bifurcation_partition
def bifurcation_partition(bif_point): '''Calculate the partition at a bifurcation point We first ensure that the input point has only two children. The number of nodes in each child tree is counted. The partition is defined as the ratio of the largest number to the smallest number.''' assert len(bif_point.children) == 2, 'A bifurcation point must have exactly 2 children' n = float(sum(1 for _ in bif_point.children[0].ipreorder())) m = float(sum(1 for _ in bif_point.children[1].ipreorder())) return max(n, m) / min(n, m)
python
def bifurcation_partition(bif_point): '''Calculate the partition at a bifurcation point We first ensure that the input point has only two children. The number of nodes in each child tree is counted. The partition is defined as the ratio of the largest number to the smallest number.''' assert len(bif_point.children) == 2, 'A bifurcation point must have exactly 2 children' n = float(sum(1 for _ in bif_point.children[0].ipreorder())) m = float(sum(1 for _ in bif_point.children[1].ipreorder())) return max(n, m) / min(n, m)
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Calculate the partition at a bifurcation point We first ensure that the input point has only two children. The number of nodes in each child tree is counted. The partition is defined as the ratio of the largest number to the smallest number.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_bifurcationfunc.py#L80-L91
2,766
BlueBrain/NeuroM
neurom/fst/_bifurcationfunc.py
partition_pair
def partition_pair(bif_point): '''Calculate the partition pairs at a bifurcation point The number of nodes in each child tree is counted. The partition pairs is the number of bifurcations in the two daughter subtrees at each branch point.''' n = float(sum(1 for _ in bif_point.children[0].ipreorder())) m = float(sum(1 for _ in bif_point.children[1].ipreorder())) return (n, m)
python
def partition_pair(bif_point): '''Calculate the partition pairs at a bifurcation point The number of nodes in each child tree is counted. The partition pairs is the number of bifurcations in the two daughter subtrees at each branch point.''' n = float(sum(1 for _ in bif_point.children[0].ipreorder())) m = float(sum(1 for _ in bif_point.children[1].ipreorder())) return (n, m)
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Calculate the partition pairs at a bifurcation point The number of nodes in each child tree is counted. The partition pairs is the number of bifurcations in the two daughter subtrees at each branch point.
[ "Calculate", "the", "partition", "pairs", "at", "a", "bifurcation", "point" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_bifurcationfunc.py#L110-L118
2,767
BlueBrain/NeuroM
neurom/io/neurolucida.py
_match_section
def _match_section(section, match): '''checks whether the `type` of section is in the `match` dictionary Works around the unknown ordering of s-expressions in each section. For instance, the `type` is the 3-rd one in for CellBodies ("CellBody" (Color Yellow) (CellBody) (Set "cell10") ) Returns: value associated with match[section_type], None if no match ''' # TODO: rewrite this so it is more clear, and handles sets & dictionaries for matching for i in range(5): if i >= len(section): return None if isinstance(section[i], StringType) and section[i] in match: return match[section[i]] return None
python
def _match_section(section, match): '''checks whether the `type` of section is in the `match` dictionary Works around the unknown ordering of s-expressions in each section. For instance, the `type` is the 3-rd one in for CellBodies ("CellBody" (Color Yellow) (CellBody) (Set "cell10") ) Returns: value associated with match[section_type], None if no match ''' # TODO: rewrite this so it is more clear, and handles sets & dictionaries for matching for i in range(5): if i >= len(section): return None if isinstance(section[i], StringType) and section[i] in match: return match[section[i]] return None
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checks whether the `type` of section is in the `match` dictionary Works around the unknown ordering of s-expressions in each section. For instance, the `type` is the 3-rd one in for CellBodies ("CellBody" (Color Yellow) (CellBody) (Set "cell10") ) Returns: value associated with match[section_type], None if no match
[ "checks", "whether", "the", "type", "of", "section", "is", "in", "the", "match", "dictionary" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/neurolucida.py#L64-L84
2,768
BlueBrain/NeuroM
neurom/io/neurolucida.py
_parse_section
def _parse_section(token_iter): '''take a stream of tokens, and create the tree structure that is defined by the s-expressions ''' sexp = [] for token in token_iter: if token == '(': new_sexp = _parse_section(token_iter) if not _match_section(new_sexp, UNWANTED_SECTIONS): sexp.append(new_sexp) elif token == ')': return sexp else: sexp.append(token) return sexp
python
def _parse_section(token_iter): '''take a stream of tokens, and create the tree structure that is defined by the s-expressions ''' sexp = [] for token in token_iter: if token == '(': new_sexp = _parse_section(token_iter) if not _match_section(new_sexp, UNWANTED_SECTIONS): sexp.append(new_sexp) elif token == ')': return sexp else: sexp.append(token) return sexp
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take a stream of tokens, and create the tree structure that is defined by the s-expressions
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/neurolucida.py#L114-L128
2,769
BlueBrain/NeuroM
neurom/io/neurolucida.py
_parse_sections
def _parse_sections(morph_fd): '''returns array of all the sections that exist The format is nested lists that correspond to the s-expressions ''' sections = [] token_iter = _get_tokens(morph_fd) for token in token_iter: if token == '(': # find top-level sections section = _parse_section(token_iter) if not _match_section(section, UNWANTED_SECTIONS): sections.append(section) return sections
python
def _parse_sections(morph_fd): '''returns array of all the sections that exist The format is nested lists that correspond to the s-expressions ''' sections = [] token_iter = _get_tokens(morph_fd) for token in token_iter: if token == '(': # find top-level sections section = _parse_section(token_iter) if not _match_section(section, UNWANTED_SECTIONS): sections.append(section) return sections
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returns array of all the sections that exist The format is nested lists that correspond to the s-expressions
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/neurolucida.py#L131-L143
2,770
BlueBrain/NeuroM
neurom/io/neurolucida.py
_flatten_subsection
def _flatten_subsection(subsection, _type, offset, parent): '''Flatten a subsection from its nested version Args: subsection: Nested subsection as produced by _parse_section, except one level in _type: type of section, ie: AXON, etc parent: first element has this as it's parent offset: position in the final array of the first element Returns: Generator of values corresponding to [X, Y, Z, R, TYPE, ID, PARENT_ID] ''' for row in subsection: # TODO: Figure out what these correspond to in neurolucida if row in ('Low', 'Generated', 'High', ): continue elif isinstance(row[0], StringType): if len(row) in (4, 5, ): if len(row) == 5: assert row[4][0] == 'S', \ 'Only known usage of a fifth member is Sn, found: %s' % row[4][0] yield (float(row[0]), float(row[1]), float(row[2]), float(row[3]) / 2., _type, offset, parent) parent = offset offset += 1 elif isinstance(row[0], list): split_parent = offset - 1 start_offset = 0 slices = [] start = 0 for i, value in enumerate(row): if value == '|': slices.append(slice(start + start_offset, i)) start = i + 1 slices.append(slice(start + start_offset, len(row))) for split_slice in slices: for _row in _flatten_subsection(row[split_slice], _type, offset, split_parent): offset += 1 yield _row
python
def _flatten_subsection(subsection, _type, offset, parent): '''Flatten a subsection from its nested version Args: subsection: Nested subsection as produced by _parse_section, except one level in _type: type of section, ie: AXON, etc parent: first element has this as it's parent offset: position in the final array of the first element Returns: Generator of values corresponding to [X, Y, Z, R, TYPE, ID, PARENT_ID] ''' for row in subsection: # TODO: Figure out what these correspond to in neurolucida if row in ('Low', 'Generated', 'High', ): continue elif isinstance(row[0], StringType): if len(row) in (4, 5, ): if len(row) == 5: assert row[4][0] == 'S', \ 'Only known usage of a fifth member is Sn, found: %s' % row[4][0] yield (float(row[0]), float(row[1]), float(row[2]), float(row[3]) / 2., _type, offset, parent) parent = offset offset += 1 elif isinstance(row[0], list): split_parent = offset - 1 start_offset = 0 slices = [] start = 0 for i, value in enumerate(row): if value == '|': slices.append(slice(start + start_offset, i)) start = i + 1 slices.append(slice(start + start_offset, len(row))) for split_slice in slices: for _row in _flatten_subsection(row[split_slice], _type, offset, split_parent): offset += 1 yield _row
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Flatten a subsection from its nested version Args: subsection: Nested subsection as produced by _parse_section, except one level in _type: type of section, ie: AXON, etc parent: first element has this as it's parent offset: position in the final array of the first element Returns: Generator of values corresponding to [X, Y, Z, R, TYPE, ID, PARENT_ID]
[ "Flatten", "a", "subsection", "from", "its", "nested", "version" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/neurolucida.py#L146-L187
2,771
BlueBrain/NeuroM
neurom/io/neurolucida.py
_extract_section
def _extract_section(section): '''Find top level sections, and get their flat contents, and append them all Returns a numpy array with the row format: [X, Y, Z, R, TYPE, ID, PARENT_ID] Note: PARENT_ID starts at -1 for soma and 0 for neurites ''' # sections with only one element will be skipped, if len(section) == 1: assert section[0] == 'Sections', \ ('Only known usage of a single Section content is "Sections", found %s' % section[0]) return None # try and detect type _type = WANTED_SECTIONS.get(section[0][0], None) start = 1 # CellBody often has [['"CellBody"'], ['CellBody'] as its first two elements if _type is None: _type = WANTED_SECTIONS.get(section[1][0], None) if _type is None: # can't determine the type return None start = 2 parent = -1 if _type == POINT_TYPE.SOMA else 0 subsection_iter = _flatten_subsection(section[start:], _type, offset=0, parent=parent) ret = np.array([row for row in subsection_iter]) return ret
python
def _extract_section(section): '''Find top level sections, and get their flat contents, and append them all Returns a numpy array with the row format: [X, Y, Z, R, TYPE, ID, PARENT_ID] Note: PARENT_ID starts at -1 for soma and 0 for neurites ''' # sections with only one element will be skipped, if len(section) == 1: assert section[0] == 'Sections', \ ('Only known usage of a single Section content is "Sections", found %s' % section[0]) return None # try and detect type _type = WANTED_SECTIONS.get(section[0][0], None) start = 1 # CellBody often has [['"CellBody"'], ['CellBody'] as its first two elements if _type is None: _type = WANTED_SECTIONS.get(section[1][0], None) if _type is None: # can't determine the type return None start = 2 parent = -1 if _type == POINT_TYPE.SOMA else 0 subsection_iter = _flatten_subsection(section[start:], _type, offset=0, parent=parent) ret = np.array([row for row in subsection_iter]) return ret
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Find top level sections, and get their flat contents, and append them all Returns a numpy array with the row format: [X, Y, Z, R, TYPE, ID, PARENT_ID] Note: PARENT_ID starts at -1 for soma and 0 for neurites
[ "Find", "top", "level", "sections", "and", "get", "their", "flat", "contents", "and", "append", "them", "all" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/neurolucida.py#L190-L222
2,772
BlueBrain/NeuroM
neurom/io/neurolucida.py
_sections_to_raw_data
def _sections_to_raw_data(sections): '''convert list of sections into the `raw_data` format used in neurom This finds the soma, and attaches the neurites ''' soma = None neurites = [] for section in sections: neurite = _extract_section(section) if neurite is None: continue elif neurite[0][COLS.TYPE] == POINT_TYPE.SOMA: assert soma is None, 'Multiple somas defined in file' soma = neurite else: neurites.append(neurite) assert soma is not None, 'Missing CellBody element (ie. soma)' total_length = len(soma) + sum(len(neurite) for neurite in neurites) ret = np.zeros((total_length, 7,), dtype=np.float64) pos = len(soma) ret[0:pos, :] = soma for neurite in neurites: end = pos + len(neurite) ret[pos:end, :] = neurite ret[pos:end, COLS.P] += pos ret[pos:end, COLS.ID] += pos # TODO: attach the neurite at the closest point on the soma ret[pos, COLS.P] = len(soma) - 1 pos = end return ret
python
def _sections_to_raw_data(sections): '''convert list of sections into the `raw_data` format used in neurom This finds the soma, and attaches the neurites ''' soma = None neurites = [] for section in sections: neurite = _extract_section(section) if neurite is None: continue elif neurite[0][COLS.TYPE] == POINT_TYPE.SOMA: assert soma is None, 'Multiple somas defined in file' soma = neurite else: neurites.append(neurite) assert soma is not None, 'Missing CellBody element (ie. soma)' total_length = len(soma) + sum(len(neurite) for neurite in neurites) ret = np.zeros((total_length, 7,), dtype=np.float64) pos = len(soma) ret[0:pos, :] = soma for neurite in neurites: end = pos + len(neurite) ret[pos:end, :] = neurite ret[pos:end, COLS.P] += pos ret[pos:end, COLS.ID] += pos # TODO: attach the neurite at the closest point on the soma ret[pos, COLS.P] = len(soma) - 1 pos = end return ret
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convert list of sections into the `raw_data` format used in neurom This finds the soma, and attaches the neurites
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/neurolucida.py#L225-L257
2,773
BlueBrain/NeuroM
neurom/io/neurolucida.py
read
def read(morph_file, data_wrapper=DataWrapper): '''return a 'raw_data' np.array with the full neuron, and the format of the file suitable to be wrapped by DataWrapper ''' msg = ('This is an experimental reader. ' 'There are no guarantees regarding ability to parse ' 'Neurolucida .asc files or correctness of output.') warnings.warn(msg) L.warning(msg) with open(morph_file, encoding='utf-8', errors='replace') as morph_fd: sections = _parse_sections(morph_fd) raw_data = _sections_to_raw_data(sections) return data_wrapper(raw_data, 'NL-ASCII')
python
def read(morph_file, data_wrapper=DataWrapper): '''return a 'raw_data' np.array with the full neuron, and the format of the file suitable to be wrapped by DataWrapper ''' msg = ('This is an experimental reader. ' 'There are no guarantees regarding ability to parse ' 'Neurolucida .asc files or correctness of output.') warnings.warn(msg) L.warning(msg) with open(morph_file, encoding='utf-8', errors='replace') as morph_fd: sections = _parse_sections(morph_fd) raw_data = _sections_to_raw_data(sections) return data_wrapper(raw_data, 'NL-ASCII')
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return a 'raw_data' np.array with the full neuron, and the format of the file suitable to be wrapped by DataWrapper
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/io/neurolucida.py#L260-L275
2,774
BlueBrain/NeuroM
examples/get_features.py
stats
def stats(data): '''Dictionary with summary stats for data Returns: dicitonary with length, mean, sum, standard deviation,\ min and max of data ''' return {'len': len(data), 'mean': np.mean(data), 'sum': np.sum(data), 'std': np.std(data), 'min': np.min(data), 'max': np.max(data)}
python
def stats(data): '''Dictionary with summary stats for data Returns: dicitonary with length, mean, sum, standard deviation,\ min and max of data ''' return {'len': len(data), 'mean': np.mean(data), 'sum': np.sum(data), 'std': np.std(data), 'min': np.min(data), 'max': np.max(data)}
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Dictionary with summary stats for data Returns: dicitonary with length, mean, sum, standard deviation,\ min and max of data
[ "Dictionary", "with", "summary", "stats", "for", "data" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/get_features.py#L43-L55
2,775
BlueBrain/NeuroM
neurom/apps/__init__.py
get_config
def get_config(config, default_config): '''Load configuration from file if in config, else use default''' if not config: logging.warning('Using default config: %s', default_config) config = default_config try: with open(config, 'r') as config_file: return yaml.load(config_file) except (yaml.reader.ReaderError, yaml.parser.ParserError, yaml.scanner.ScannerError) as e: raise ConfigError('Invalid yaml file: \n %s' % str(e))
python
def get_config(config, default_config): '''Load configuration from file if in config, else use default''' if not config: logging.warning('Using default config: %s', default_config) config = default_config try: with open(config, 'r') as config_file: return yaml.load(config_file) except (yaml.reader.ReaderError, yaml.parser.ParserError, yaml.scanner.ScannerError) as e: raise ConfigError('Invalid yaml file: \n %s' % str(e))
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Load configuration from file if in config, else use default
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/apps/__init__.py#L36-L48
2,776
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
soma_surface_area
def soma_surface_area(nrn, neurite_type=NeuriteType.soma): '''Get the surface area of a neuron's soma. Note: The surface area is calculated by assuming the soma is spherical. ''' assert neurite_type == NeuriteType.soma, 'Neurite type must be soma' return 4 * math.pi * nrn.soma.radius ** 2
python
def soma_surface_area(nrn, neurite_type=NeuriteType.soma): '''Get the surface area of a neuron's soma. Note: The surface area is calculated by assuming the soma is spherical. ''' assert neurite_type == NeuriteType.soma, 'Neurite type must be soma' return 4 * math.pi * nrn.soma.radius ** 2
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Get the surface area of a neuron's soma. Note: The surface area is calculated by assuming the soma is spherical.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L46-L53
2,777
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
soma_surface_areas
def soma_surface_areas(nrn_pop, neurite_type=NeuriteType.soma): '''Get the surface areas of the somata in a population of neurons Note: The surface area is calculated by assuming the soma is spherical. Note: If a single neuron is passed, a single element list with the surface area of its soma member is returned. ''' nrns = neuron_population(nrn_pop) assert neurite_type == NeuriteType.soma, 'Neurite type must be soma' return [soma_surface_area(n) for n in nrns]
python
def soma_surface_areas(nrn_pop, neurite_type=NeuriteType.soma): '''Get the surface areas of the somata in a population of neurons Note: The surface area is calculated by assuming the soma is spherical. Note: If a single neuron is passed, a single element list with the surface area of its soma member is returned. ''' nrns = neuron_population(nrn_pop) assert neurite_type == NeuriteType.soma, 'Neurite type must be soma' return [soma_surface_area(n) for n in nrns]
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Get the surface areas of the somata in a population of neurons Note: The surface area is calculated by assuming the soma is spherical. Note: If a single neuron is passed, a single element list with the surface area of its soma member is returned.
[ "Get", "the", "surface", "areas", "of", "the", "somata", "in", "a", "population", "of", "neurons" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L56-L67
2,778
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
soma_radii
def soma_radii(nrn_pop, neurite_type=NeuriteType.soma): ''' Get the radii of the somata of a population of neurons Note: If a single neuron is passed, a single element list with the radius of its soma member is returned. ''' assert neurite_type == NeuriteType.soma, 'Neurite type must be soma' nrns = neuron_population(nrn_pop) return [n.soma.radius for n in nrns]
python
def soma_radii(nrn_pop, neurite_type=NeuriteType.soma): ''' Get the radii of the somata of a population of neurons Note: If a single neuron is passed, a single element list with the radius of its soma member is returned. ''' assert neurite_type == NeuriteType.soma, 'Neurite type must be soma' nrns = neuron_population(nrn_pop) return [n.soma.radius for n in nrns]
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Get the radii of the somata of a population of neurons Note: If a single neuron is passed, a single element list with the radius of its soma member is returned.
[ "Get", "the", "radii", "of", "the", "somata", "of", "a", "population", "of", "neurons" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L70-L79
2,779
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
trunk_section_lengths
def trunk_section_lengths(nrn, neurite_type=NeuriteType.all): '''list of lengths of trunk sections of neurites in a neuron''' neurite_filter = is_type(neurite_type) return [morphmath.section_length(s.root_node.points) for s in nrn.neurites if neurite_filter(s)]
python
def trunk_section_lengths(nrn, neurite_type=NeuriteType.all): '''list of lengths of trunk sections of neurites in a neuron''' neurite_filter = is_type(neurite_type) return [morphmath.section_length(s.root_node.points) for s in nrn.neurites if neurite_filter(s)]
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list of lengths of trunk sections of neurites in a neuron
[ "list", "of", "lengths", "of", "trunk", "sections", "of", "neurites", "in", "a", "neuron" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L82-L86
2,780
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
trunk_origin_radii
def trunk_origin_radii(nrn, neurite_type=NeuriteType.all): '''radii of the trunk sections of neurites in a neuron''' neurite_filter = is_type(neurite_type) return [s.root_node.points[0][COLS.R] for s in nrn.neurites if neurite_filter(s)]
python
def trunk_origin_radii(nrn, neurite_type=NeuriteType.all): '''radii of the trunk sections of neurites in a neuron''' neurite_filter = is_type(neurite_type) return [s.root_node.points[0][COLS.R] for s in nrn.neurites if neurite_filter(s)]
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radii of the trunk sections of neurites in a neuron
[ "radii", "of", "the", "trunk", "sections", "of", "neurites", "in", "a", "neuron" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L89-L92
2,781
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
trunk_origin_azimuths
def trunk_origin_azimuths(nrn, neurite_type=NeuriteType.all): '''Get a list of all the trunk origin azimuths of a neuron or population The azimuth is defined as Angle between x-axis and the vector defined by (initial tree point - soma center) on the x-z plane. The range of the azimuth angle [-pi, pi] radians ''' neurite_filter = is_type(neurite_type) nrns = neuron_population(nrn) def _azimuth(section, soma): '''Azimuth of a section''' vector = morphmath.vector(section[0], soma.center) return np.arctan2(vector[COLS.Z], vector[COLS.X]) return [_azimuth(s.root_node.points, n.soma) for n in nrns for s in n.neurites if neurite_filter(s)]
python
def trunk_origin_azimuths(nrn, neurite_type=NeuriteType.all): '''Get a list of all the trunk origin azimuths of a neuron or population The azimuth is defined as Angle between x-axis and the vector defined by (initial tree point - soma center) on the x-z plane. The range of the azimuth angle [-pi, pi] radians ''' neurite_filter = is_type(neurite_type) nrns = neuron_population(nrn) def _azimuth(section, soma): '''Azimuth of a section''' vector = morphmath.vector(section[0], soma.center) return np.arctan2(vector[COLS.Z], vector[COLS.X]) return [_azimuth(s.root_node.points, n.soma) for n in nrns for s in n.neurites if neurite_filter(s)]
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Get a list of all the trunk origin azimuths of a neuron or population The azimuth is defined as Angle between x-axis and the vector defined by (initial tree point - soma center) on the x-z plane. The range of the azimuth angle [-pi, pi] radians
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L95-L113
2,782
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
trunk_origin_elevations
def trunk_origin_elevations(nrn, neurite_type=NeuriteType.all): '''Get a list of all the trunk origin elevations of a neuron or population The elevation is defined as the angle between x-axis and the vector defined by (initial tree point - soma center) on the x-y half-plane. The range of the elevation angle [-pi/2, pi/2] radians ''' neurite_filter = is_type(neurite_type) nrns = neuron_population(nrn) def _elevation(section, soma): '''Elevation of a section''' vector = morphmath.vector(section[0], soma.center) norm_vector = np.linalg.norm(vector) if norm_vector >= np.finfo(type(norm_vector)).eps: return np.arcsin(vector[COLS.Y] / norm_vector) raise ValueError("Norm of vector between soma center and section is almost zero.") return [_elevation(s.root_node.points, n.soma) for n in nrns for s in n.neurites if neurite_filter(s)]
python
def trunk_origin_elevations(nrn, neurite_type=NeuriteType.all): '''Get a list of all the trunk origin elevations of a neuron or population The elevation is defined as the angle between x-axis and the vector defined by (initial tree point - soma center) on the x-y half-plane. The range of the elevation angle [-pi/2, pi/2] radians ''' neurite_filter = is_type(neurite_type) nrns = neuron_population(nrn) def _elevation(section, soma): '''Elevation of a section''' vector = morphmath.vector(section[0], soma.center) norm_vector = np.linalg.norm(vector) if norm_vector >= np.finfo(type(norm_vector)).eps: return np.arcsin(vector[COLS.Y] / norm_vector) raise ValueError("Norm of vector between soma center and section is almost zero.") return [_elevation(s.root_node.points, n.soma) for n in nrns for s in n.neurites if neurite_filter(s)]
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Get a list of all the trunk origin elevations of a neuron or population The elevation is defined as the angle between x-axis and the vector defined by (initial tree point - soma center) on the x-y half-plane. The range of the elevation angle [-pi/2, pi/2] radians
[ "Get", "a", "list", "of", "all", "the", "trunk", "origin", "elevations", "of", "a", "neuron", "or", "population" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L116-L139
2,783
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
trunk_vectors
def trunk_vectors(nrn, neurite_type=NeuriteType.all): '''Calculates the vectors between all the trunks of the neuron and the soma center. ''' neurite_filter = is_type(neurite_type) nrns = neuron_population(nrn) return np.array([morphmath.vector(s.root_node.points[0], n.soma.center) for n in nrns for s in n.neurites if neurite_filter(s)])
python
def trunk_vectors(nrn, neurite_type=NeuriteType.all): '''Calculates the vectors between all the trunks of the neuron and the soma center. ''' neurite_filter = is_type(neurite_type) nrns = neuron_population(nrn) return np.array([morphmath.vector(s.root_node.points[0], n.soma.center) for n in nrns for s in n.neurites if neurite_filter(s)])
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Calculates the vectors between all the trunks of the neuron and the soma center.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L142-L151
2,784
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
trunk_angles
def trunk_angles(nrn, neurite_type=NeuriteType.all): '''Calculates the angles between all the trunks of the neuron. The angles are defined on the x-y plane and the trees are sorted from the y axis and anticlock-wise. ''' vectors = trunk_vectors(nrn, neurite_type=neurite_type) # In order to avoid the failure of the process in case the neurite_type does not exist if not vectors.size: return [] def _sort_angle(p1, p2): """Angle between p1-p2 to sort vectors""" ang1 = np.arctan2(*p1[::-1]) ang2 = np.arctan2(*p2[::-1]) return (ang1 - ang2) # Sorting angles according to x-y plane order = np.argsort(np.array([_sort_angle(i / np.linalg.norm(i), [0, 1]) for i in vectors[:, 0:2]])) ordered_vectors = vectors[order][:, [COLS.X, COLS.Y]] return [morphmath.angle_between_vectors(ordered_vectors[i], ordered_vectors[i - 1]) for i, _ in enumerate(ordered_vectors)]
python
def trunk_angles(nrn, neurite_type=NeuriteType.all): '''Calculates the angles between all the trunks of the neuron. The angles are defined on the x-y plane and the trees are sorted from the y axis and anticlock-wise. ''' vectors = trunk_vectors(nrn, neurite_type=neurite_type) # In order to avoid the failure of the process in case the neurite_type does not exist if not vectors.size: return [] def _sort_angle(p1, p2): """Angle between p1-p2 to sort vectors""" ang1 = np.arctan2(*p1[::-1]) ang2 = np.arctan2(*p2[::-1]) return (ang1 - ang2) # Sorting angles according to x-y plane order = np.argsort(np.array([_sort_angle(i / np.linalg.norm(i), [0, 1]) for i in vectors[:, 0:2]])) ordered_vectors = vectors[order][:, [COLS.X, COLS.Y]] return [morphmath.angle_between_vectors(ordered_vectors[i], ordered_vectors[i - 1]) for i, _ in enumerate(ordered_vectors)]
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Calculates the angles between all the trunks of the neuron. The angles are defined on the x-y plane and the trees are sorted from the y axis and anticlock-wise.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L154-L177
2,785
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
sholl_crossings
def sholl_crossings(neurites, center, radii): '''calculate crossings of neurites Args: nrn(morph): morphology on which to perform Sholl analysis radii(iterable of floats): radii for which crossings will be counted Returns: Array of same length as radii, with a count of the number of crossings for the respective radius ''' def _count_crossings(neurite, radius): '''count_crossings of segments in neurite with radius''' r2 = radius ** 2 count = 0 for start, end in iter_segments(neurite): start_dist2, end_dist2 = (morphmath.point_dist2(center, start), morphmath.point_dist2(center, end)) count += int(start_dist2 <= r2 <= end_dist2 or end_dist2 <= r2 <= start_dist2) return count return np.array([sum(_count_crossings(neurite, r) for neurite in iter_neurites(neurites)) for r in radii])
python
def sholl_crossings(neurites, center, radii): '''calculate crossings of neurites Args: nrn(morph): morphology on which to perform Sholl analysis radii(iterable of floats): radii for which crossings will be counted Returns: Array of same length as radii, with a count of the number of crossings for the respective radius ''' def _count_crossings(neurite, radius): '''count_crossings of segments in neurite with radius''' r2 = radius ** 2 count = 0 for start, end in iter_segments(neurite): start_dist2, end_dist2 = (morphmath.point_dist2(center, start), morphmath.point_dist2(center, end)) count += int(start_dist2 <= r2 <= end_dist2 or end_dist2 <= r2 <= start_dist2) return count return np.array([sum(_count_crossings(neurite, r) for neurite in iter_neurites(neurites)) for r in radii])
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calculate crossings of neurites Args: nrn(morph): morphology on which to perform Sholl analysis radii(iterable of floats): radii for which crossings will be counted Returns: Array of same length as radii, with a count of the number of crossings for the respective radius
[ "calculate", "crossings", "of", "neurites" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L180-L206
2,786
BlueBrain/NeuroM
neurom/fst/_neuronfunc.py
sholl_frequency
def sholl_frequency(nrn, neurite_type=NeuriteType.all, step_size=10): '''perform Sholl frequency calculations on a population of neurites Args: nrn(morph): nrn or population neurite_type(NeuriteType): which neurites to operate on step_size(float): step size between Sholl radii Note: Given a neuron, the soma center is used for the concentric circles, which range from the soma radii, and the maximum radial distance in steps of `step_size`. When a population is given, the concentric circles range from the smallest soma radius to the largest radial neurite distance. Finally, each segment of the neuron is tested, so a neurite that bends back on itself, and crosses the same Sholl radius will get counted as having crossed multiple times. ''' nrns = neuron_population(nrn) neurite_filter = is_type(neurite_type) min_soma_edge = float('Inf') max_radii = 0 neurites_list = [] for neuron in nrns: neurites_list.extend(((neurites, neuron.soma.center) for neurites in neuron.neurites if neurite_filter(neurites))) min_soma_edge = min(min_soma_edge, neuron.soma.radius) max_radii = max(max_radii, np.max(np.abs(bounding_box(neuron)))) radii = np.arange(min_soma_edge, max_radii + step_size, step_size) ret = np.zeros_like(radii) for neurites, center in neurites_list: ret += sholl_crossings(neurites, center, radii) return ret
python
def sholl_frequency(nrn, neurite_type=NeuriteType.all, step_size=10): '''perform Sholl frequency calculations on a population of neurites Args: nrn(morph): nrn or population neurite_type(NeuriteType): which neurites to operate on step_size(float): step size between Sholl radii Note: Given a neuron, the soma center is used for the concentric circles, which range from the soma radii, and the maximum radial distance in steps of `step_size`. When a population is given, the concentric circles range from the smallest soma radius to the largest radial neurite distance. Finally, each segment of the neuron is tested, so a neurite that bends back on itself, and crosses the same Sholl radius will get counted as having crossed multiple times. ''' nrns = neuron_population(nrn) neurite_filter = is_type(neurite_type) min_soma_edge = float('Inf') max_radii = 0 neurites_list = [] for neuron in nrns: neurites_list.extend(((neurites, neuron.soma.center) for neurites in neuron.neurites if neurite_filter(neurites))) min_soma_edge = min(min_soma_edge, neuron.soma.radius) max_radii = max(max_radii, np.max(np.abs(bounding_box(neuron)))) radii = np.arange(min_soma_edge, max_radii + step_size, step_size) ret = np.zeros_like(radii) for neurites, center in neurites_list: ret += sholl_crossings(neurites, center, radii) return ret
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perform Sholl frequency calculations on a population of neurites Args: nrn(morph): nrn or population neurite_type(NeuriteType): which neurites to operate on step_size(float): step size between Sholl radii Note: Given a neuron, the soma center is used for the concentric circles, which range from the soma radii, and the maximum radial distance in steps of `step_size`. When a population is given, the concentric circles range from the smallest soma radius to the largest radial neurite distance. Finally, each segment of the neuron is tested, so a neurite that bends back on itself, and crosses the same Sholl radius will get counted as having crossed multiple times.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/fst/_neuronfunc.py#L209-L245
2,787
BlueBrain/NeuroM
examples/plot_features.py
dist_points
def dist_points(bin_edges, d): """Return an array of values according to a distribution Points are calculated at the center of each bin """ bc = bin_centers(bin_edges) if d is not None: d = DISTS[d['type']](d, bc) return d, bc
python
def dist_points(bin_edges, d): """Return an array of values according to a distribution Points are calculated at the center of each bin """ bc = bin_centers(bin_edges) if d is not None: d = DISTS[d['type']](d, bc) return d, bc
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Return an array of values according to a distribution Points are calculated at the center of each bin
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/plot_features.py#L70-L78
2,788
BlueBrain/NeuroM
examples/plot_features.py
calc_limits
def calc_limits(data, dist=None, padding=0.25): """Calculate a suitable range for a histogram Returns: tuple of (min, max) """ dmin = sys.float_info.max if dist is None else dist.get('min', sys.float_info.max) dmax = sys.float_info.min if dist is None else dist.get('max', sys.float_info.min) _min = min(min(data), dmin) _max = max(max(data), dmax) padding = padding * (_max - _min) return _min - padding, _max + padding
python
def calc_limits(data, dist=None, padding=0.25): """Calculate a suitable range for a histogram Returns: tuple of (min, max) """ dmin = sys.float_info.max if dist is None else dist.get('min', sys.float_info.max) dmax = sys.float_info.min if dist is None else dist.get('max', sys.float_info.min) _min = min(min(data), dmin) _max = max(max(data), dmax) padding = padding * (_max - _min) return _min - padding, _max + padding
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Calculate a suitable range for a histogram Returns: tuple of (min, max)
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/plot_features.py#L81-L95
2,789
BlueBrain/NeuroM
examples/plot_features.py
load_neurite_features
def load_neurite_features(filepath): '''Unpack relevant data into megadict''' stuff = defaultdict(lambda: defaultdict(list)) nrns = nm.load_neurons(filepath) # unpack data into arrays for nrn in nrns: for t in NEURITES_: for feat in FEATURES: stuff[feat][str(t).split('.')[1]].extend( nm.get(feat, nrn, neurite_type=t) ) return stuff
python
def load_neurite_features(filepath): '''Unpack relevant data into megadict''' stuff = defaultdict(lambda: defaultdict(list)) nrns = nm.load_neurons(filepath) # unpack data into arrays for nrn in nrns: for t in NEURITES_: for feat in FEATURES: stuff[feat][str(t).split('.')[1]].extend( nm.get(feat, nrn, neurite_type=t) ) return stuff
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Unpack relevant data into megadict
[ "Unpack", "relevant", "data", "into", "megadict" ]
254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/plot_features.py#L112-L123
2,790
BlueBrain/NeuroM
examples/plot_features.py
main
def main(data_dir, mtype_file): # pylint: disable=too-many-locals '''Run the stuff''' # data structure to store results stuff = load_neurite_features(data_dir) sim_params = json.load(open(mtype_file)) # load histograms, distribution parameter sets and figures into arrays. # To plot figures, do # plots[i].fig.show() # To modify an axis, do # plots[i].ax.something() _plots = [] for feat, d in stuff.items(): for typ, data in d.items(): dist = sim_params['components'][typ].get(feat, None) print('Type = %s, Feature = %s, Distribution = %s' % (typ, feat, dist)) # if no data available, skip this feature if not data: print("No data found for feature %s (%s)" % (feat, typ)) continue # print 'DATA', data num_bins = 100 limits = calc_limits(data, dist) bin_edges = np.linspace(limits[0], limits[1], num_bins + 1) histo = np.histogram(data, bin_edges, normed=True) print('PLOT LIMITS:', limits) # print 'DATA:', data # print 'BIN HEIGHT', histo[0] plot = Plot(*view_utils.get_figure(new_fig=True, subplot=111)) plot.ax.set_xlim(*limits) plot.ax.bar(histo[1][:-1], histo[0], width=bin_widths(histo[1])) dp, bc = dist_points(histo[1], dist) # print 'BIN CENTERS:', bc, len(bc) if dp is not None: # print 'DIST POINTS:', dp, len(dp) plot.ax.plot(bc, dp, 'r*') plot.ax.set_title('%s (%s)' % (feat, typ)) _plots.append(plot) return _plots
python
def main(data_dir, mtype_file): # pylint: disable=too-many-locals '''Run the stuff''' # data structure to store results stuff = load_neurite_features(data_dir) sim_params = json.load(open(mtype_file)) # load histograms, distribution parameter sets and figures into arrays. # To plot figures, do # plots[i].fig.show() # To modify an axis, do # plots[i].ax.something() _plots = [] for feat, d in stuff.items(): for typ, data in d.items(): dist = sim_params['components'][typ].get(feat, None) print('Type = %s, Feature = %s, Distribution = %s' % (typ, feat, dist)) # if no data available, skip this feature if not data: print("No data found for feature %s (%s)" % (feat, typ)) continue # print 'DATA', data num_bins = 100 limits = calc_limits(data, dist) bin_edges = np.linspace(limits[0], limits[1], num_bins + 1) histo = np.histogram(data, bin_edges, normed=True) print('PLOT LIMITS:', limits) # print 'DATA:', data # print 'BIN HEIGHT', histo[0] plot = Plot(*view_utils.get_figure(new_fig=True, subplot=111)) plot.ax.set_xlim(*limits) plot.ax.bar(histo[1][:-1], histo[0], width=bin_widths(histo[1])) dp, bc = dist_points(histo[1], dist) # print 'BIN CENTERS:', bc, len(bc) if dp is not None: # print 'DIST POINTS:', dp, len(dp) plot.ax.plot(bc, dp, 'r*') plot.ax.set_title('%s (%s)' % (feat, typ)) _plots.append(plot) return _plots
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Run the stuff
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/plot_features.py#L150-L191
2,791
BlueBrain/NeuroM
examples/density_plot.py
extract_density
def extract_density(population, plane='xy', bins=100, neurite_type=NeuriteType.basal_dendrite): '''Extracts the 2d histogram of the center coordinates of segments in the selected plane. ''' segment_midpoints = get_feat('segment_midpoints', population, neurite_type=neurite_type) horiz = segment_midpoints[:, 'xyz'.index(plane[0])] vert = segment_midpoints[:, 'xyz'.index(plane[1])] return np.histogram2d(np.array(horiz), np.array(vert), bins=(bins, bins))
python
def extract_density(population, plane='xy', bins=100, neurite_type=NeuriteType.basal_dendrite): '''Extracts the 2d histogram of the center coordinates of segments in the selected plane. ''' segment_midpoints = get_feat('segment_midpoints', population, neurite_type=neurite_type) horiz = segment_midpoints[:, 'xyz'.index(plane[0])] vert = segment_midpoints[:, 'xyz'.index(plane[1])] return np.histogram2d(np.array(horiz), np.array(vert), bins=(bins, bins))
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Extracts the 2d histogram of the center coordinates of segments in the selected plane.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/density_plot.py#L39-L46
2,792
BlueBrain/NeuroM
examples/density_plot.py
plot_density
def plot_density(population, # pylint: disable=too-many-arguments, too-many-locals bins=100, new_fig=True, subplot=111, levels=None, plane='xy', colorlabel='Nodes per unit area', labelfontsize=16, color_map='Reds', no_colorbar=False, threshold=0.01, neurite_type=NeuriteType.basal_dendrite, **kwargs): '''Plots the 2d histogram of the center coordinates of segments in the selected plane. ''' fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot) H1, xedges1, yedges1 = extract_density(population, plane=plane, bins=bins, neurite_type=neurite_type) mask = H1 < threshold # mask = H1==0 H2 = np.ma.masked_array(H1, mask) getattr(plt.cm, color_map).set_bad(color='white', alpha=None) plots = ax.contourf((xedges1[:-1] + xedges1[1:]) / 2, (yedges1[:-1] + yedges1[1:]) / 2, np.transpose(H2), # / np.max(H2), cmap=getattr(plt.cm, color_map), levels=levels) if not no_colorbar: cbar = plt.colorbar(plots) cbar.ax.set_ylabel(colorlabel, fontsize=labelfontsize) kwargs['title'] = kwargs.get('title', '') kwargs['xlabel'] = kwargs.get('xlabel', plane[0]) kwargs['ylabel'] = kwargs.get('ylabel', plane[1]) return common.plot_style(fig=fig, ax=ax, **kwargs)
python
def plot_density(population, # pylint: disable=too-many-arguments, too-many-locals bins=100, new_fig=True, subplot=111, levels=None, plane='xy', colorlabel='Nodes per unit area', labelfontsize=16, color_map='Reds', no_colorbar=False, threshold=0.01, neurite_type=NeuriteType.basal_dendrite, **kwargs): '''Plots the 2d histogram of the center coordinates of segments in the selected plane. ''' fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot) H1, xedges1, yedges1 = extract_density(population, plane=plane, bins=bins, neurite_type=neurite_type) mask = H1 < threshold # mask = H1==0 H2 = np.ma.masked_array(H1, mask) getattr(plt.cm, color_map).set_bad(color='white', alpha=None) plots = ax.contourf((xedges1[:-1] + xedges1[1:]) / 2, (yedges1[:-1] + yedges1[1:]) / 2, np.transpose(H2), # / np.max(H2), cmap=getattr(plt.cm, color_map), levels=levels) if not no_colorbar: cbar = plt.colorbar(plots) cbar.ax.set_ylabel(colorlabel, fontsize=labelfontsize) kwargs['title'] = kwargs.get('title', '') kwargs['xlabel'] = kwargs.get('xlabel', plane[0]) kwargs['ylabel'] = kwargs.get('ylabel', plane[1]) return common.plot_style(fig=fig, ax=ax, **kwargs)
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Plots the 2d histogram of the center coordinates of segments in the selected plane.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/density_plot.py#L49-L80
2,793
BlueBrain/NeuroM
examples/density_plot.py
plot_neuron_on_density
def plot_neuron_on_density(population, # pylint: disable=too-many-arguments bins=100, new_fig=True, subplot=111, levels=None, plane='xy', colorlabel='Nodes per unit area', labelfontsize=16, color_map='Reds', no_colorbar=False, threshold=0.01, neurite_type=NeuriteType.basal_dendrite, **kwargs): '''Plots the 2d histogram of the center coordinates of segments in the selected plane and superimposes the view of the first neurite of the collection. ''' _, ax = common.get_figure(new_fig=new_fig) view.plot_tree(ax, population.neurites[0]) return plot_density(population, plane=plane, bins=bins, new_fig=False, subplot=subplot, colorlabel=colorlabel, labelfontsize=labelfontsize, levels=levels, color_map=color_map, no_colorbar=no_colorbar, threshold=threshold, neurite_type=neurite_type, **kwargs)
python
def plot_neuron_on_density(population, # pylint: disable=too-many-arguments bins=100, new_fig=True, subplot=111, levels=None, plane='xy', colorlabel='Nodes per unit area', labelfontsize=16, color_map='Reds', no_colorbar=False, threshold=0.01, neurite_type=NeuriteType.basal_dendrite, **kwargs): '''Plots the 2d histogram of the center coordinates of segments in the selected plane and superimposes the view of the first neurite of the collection. ''' _, ax = common.get_figure(new_fig=new_fig) view.plot_tree(ax, population.neurites[0]) return plot_density(population, plane=plane, bins=bins, new_fig=False, subplot=subplot, colorlabel=colorlabel, labelfontsize=labelfontsize, levels=levels, color_map=color_map, no_colorbar=no_colorbar, threshold=threshold, neurite_type=neurite_type, **kwargs)
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Plots the 2d histogram of the center coordinates of segments in the selected plane and superimposes the view of the first neurite of the collection.
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/density_plot.py#L83-L99
2,794
BlueBrain/NeuroM
neurom/check/morphtree.py
is_monotonic
def is_monotonic(neurite, tol): '''Check if neurite tree is monotonic If each child has smaller or equal diameters from its parent Args: neurite(Neurite): neurite to operate on tol(float): tolerance Returns: True if neurite monotonic ''' for node in neurite.iter_sections(): # check that points in section satisfy monotonicity sec = node.points for point_id in range(len(sec) - 1): if sec[point_id + 1][COLS.R] > sec[point_id][COLS.R] + tol: return False # Check that section boundary points satisfy monotonicity if(node.parent is not None and sec[0][COLS.R] > node.parent.points[-1][COLS.R] + tol): return False return True
python
def is_monotonic(neurite, tol): '''Check if neurite tree is monotonic If each child has smaller or equal diameters from its parent Args: neurite(Neurite): neurite to operate on tol(float): tolerance Returns: True if neurite monotonic ''' for node in neurite.iter_sections(): # check that points in section satisfy monotonicity sec = node.points for point_id in range(len(sec) - 1): if sec[point_id + 1][COLS.R] > sec[point_id][COLS.R] + tol: return False # Check that section boundary points satisfy monotonicity if(node.parent is not None and sec[0][COLS.R] > node.parent.points[-1][COLS.R] + tol): return False return True
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Check if neurite tree is monotonic If each child has smaller or equal diameters from its parent Args: neurite(Neurite): neurite to operate on tol(float): tolerance Returns: True if neurite monotonic
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/morphtree.py#L40-L64
2,795
BlueBrain/NeuroM
neurom/check/morphtree.py
is_flat
def is_flat(neurite, tol, method='tolerance'): '''Check if neurite is flat using the given method Args: neurite(Neurite): neurite to operate on tol(float): tolerance method(string): the method of flatness estimation: 'tolerance' returns true if any extent of the tree is smaller than the given tolerance 'ratio' returns true if the ratio of the smallest directions is smaller than tol. e.g. [1,2,3] -> 1/2 < tol Returns: True if neurite is flat ''' ext = principal_direction_extent(neurite.points[:, COLS.XYZ]) assert method in ('tolerance', 'ratio'), "Method must be one of 'tolerance', 'ratio'" if method == 'ratio': sorted_ext = np.sort(ext) return sorted_ext[0] / sorted_ext[1] < float(tol) return any(ext < float(tol))
python
def is_flat(neurite, tol, method='tolerance'): '''Check if neurite is flat using the given method Args: neurite(Neurite): neurite to operate on tol(float): tolerance method(string): the method of flatness estimation: 'tolerance' returns true if any extent of the tree is smaller than the given tolerance 'ratio' returns true if the ratio of the smallest directions is smaller than tol. e.g. [1,2,3] -> 1/2 < tol Returns: True if neurite is flat ''' ext = principal_direction_extent(neurite.points[:, COLS.XYZ]) assert method in ('tolerance', 'ratio'), "Method must be one of 'tolerance', 'ratio'" if method == 'ratio': sorted_ext = np.sort(ext) return sorted_ext[0] / sorted_ext[1] < float(tol) return any(ext < float(tol))
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Check if neurite is flat using the given method Args: neurite(Neurite): neurite to operate on tol(float): tolerance method(string): the method of flatness estimation: 'tolerance' returns true if any extent of the tree is smaller than the given tolerance 'ratio' returns true if the ratio of the smallest directions is smaller than tol. e.g. [1,2,3] -> 1/2 < tol Returns: True if neurite is flat
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/morphtree.py#L67-L88
2,796
BlueBrain/NeuroM
neurom/check/morphtree.py
is_back_tracking
def is_back_tracking(neurite): ''' Check if a neurite process backtracks to a previous node. Back-tracking takes place when a daughter of a branching process goes back and either overlaps with a previous point, or lies inside the cylindrical volume of the latter. Args: neurite(Neurite): neurite to operate on Returns: True Under the following scenaria: 1. A segment endpoint falls back and overlaps with a previous segment's point 2. The geometry of a segment overlaps with a previous one in the section ''' def pair(segs): ''' Pairs the input list into triplets''' return zip(segs, segs[1:]) def coords(node): ''' Returns the first three values of the tree that correspond to the x, y, z coordinates''' return node[COLS.XYZ] def max_radius(seg): ''' Returns maximum radius from the two segment endpoints''' return max(seg[0][COLS.R], seg[1][COLS.R]) def is_not_zero_seg(seg): ''' Returns True if segment has zero length''' return not np.allclose(coords(seg[0]), coords(seg[1])) def is_in_the_same_verse(seg1, seg2): ''' Checks if the vectors face the same direction. This is true if their dot product is greater than zero. ''' v1 = coords(seg2[1]) - coords(seg2[0]) v2 = coords(seg1[1]) - coords(seg1[0]) return np.dot(v1, v2) >= 0 def is_seg2_within_seg1_radius(dist, seg1, seg2): ''' Checks whether the orthogonal distance from the point at the end of seg1 to seg2 segment body is smaller than the sum of their radii ''' return dist <= max_radius(seg1) + max_radius(seg2) def is_seg1_overlapping_with_seg2(seg1, seg2): '''Checks if a segment is in proximity of another one upstream''' # get the coordinates of seg2 (from the origin) s1 = coords(seg2[0]) s2 = coords(seg2[1]) # vector of the center of seg2 (from the origin) C = 0.5 * (s1 + s2) # endpoint of seg1 (from the origin) P = coords(seg1[1]) # vector from the center C of seg2 to the endpoint P of seg1 CP = P - C # vector of seg2 S1S2 = s2 - s1 # projection of CP upon seg2 prj = mm.vector_projection(CP, S1S2) # check if the distance of the orthogonal complement of CP projection on S1S2 # (vertical distance from P to seg2) is smaller than the sum of the radii. (overlap) # If not exit early, because there is no way that backtracking can feasible if not is_seg2_within_seg1_radius(np.linalg.norm(CP - prj), seg1, seg2): return False # projection lies within the length of the cylinder. Check if the distance between # the center C of seg2 and the projection of the end point of seg1, P is smaller than # half of the others length plus a 5% tolerance return np.linalg.norm(prj) < 0.55 * np.linalg.norm(S1S2) def is_inside_cylinder(seg1, seg2): ''' Checks if seg2 approximately lies within a cylindrical volume of seg1. Two conditions must be satisfied: 1. The two segments are not facing the same direction (seg2 comes back to seg1) 2. seg2 is overlaping with seg1 ''' return not is_in_the_same_verse(seg1, seg2) and is_seg1_overlapping_with_seg2(seg1, seg2) # filter out single segment sections section_itr = (snode for snode in neurite.iter_sections() if snode.points.shape[0] > 2) for snode in section_itr: # group each section's points intro triplets segment_pairs = list(filter(is_not_zero_seg, pair(snode.points))) # filter out zero length segments for i, seg1 in enumerate(segment_pairs[1:]): # check if the end point of the segment lies within the previous # ones in the current sectionmake for seg2 in segment_pairs[0: i + 1]: if is_inside_cylinder(seg1, seg2): return True return False
python
def is_back_tracking(neurite): ''' Check if a neurite process backtracks to a previous node. Back-tracking takes place when a daughter of a branching process goes back and either overlaps with a previous point, or lies inside the cylindrical volume of the latter. Args: neurite(Neurite): neurite to operate on Returns: True Under the following scenaria: 1. A segment endpoint falls back and overlaps with a previous segment's point 2. The geometry of a segment overlaps with a previous one in the section ''' def pair(segs): ''' Pairs the input list into triplets''' return zip(segs, segs[1:]) def coords(node): ''' Returns the first three values of the tree that correspond to the x, y, z coordinates''' return node[COLS.XYZ] def max_radius(seg): ''' Returns maximum radius from the two segment endpoints''' return max(seg[0][COLS.R], seg[1][COLS.R]) def is_not_zero_seg(seg): ''' Returns True if segment has zero length''' return not np.allclose(coords(seg[0]), coords(seg[1])) def is_in_the_same_verse(seg1, seg2): ''' Checks if the vectors face the same direction. This is true if their dot product is greater than zero. ''' v1 = coords(seg2[1]) - coords(seg2[0]) v2 = coords(seg1[1]) - coords(seg1[0]) return np.dot(v1, v2) >= 0 def is_seg2_within_seg1_radius(dist, seg1, seg2): ''' Checks whether the orthogonal distance from the point at the end of seg1 to seg2 segment body is smaller than the sum of their radii ''' return dist <= max_radius(seg1) + max_radius(seg2) def is_seg1_overlapping_with_seg2(seg1, seg2): '''Checks if a segment is in proximity of another one upstream''' # get the coordinates of seg2 (from the origin) s1 = coords(seg2[0]) s2 = coords(seg2[1]) # vector of the center of seg2 (from the origin) C = 0.5 * (s1 + s2) # endpoint of seg1 (from the origin) P = coords(seg1[1]) # vector from the center C of seg2 to the endpoint P of seg1 CP = P - C # vector of seg2 S1S2 = s2 - s1 # projection of CP upon seg2 prj = mm.vector_projection(CP, S1S2) # check if the distance of the orthogonal complement of CP projection on S1S2 # (vertical distance from P to seg2) is smaller than the sum of the radii. (overlap) # If not exit early, because there is no way that backtracking can feasible if not is_seg2_within_seg1_radius(np.linalg.norm(CP - prj), seg1, seg2): return False # projection lies within the length of the cylinder. Check if the distance between # the center C of seg2 and the projection of the end point of seg1, P is smaller than # half of the others length plus a 5% tolerance return np.linalg.norm(prj) < 0.55 * np.linalg.norm(S1S2) def is_inside_cylinder(seg1, seg2): ''' Checks if seg2 approximately lies within a cylindrical volume of seg1. Two conditions must be satisfied: 1. The two segments are not facing the same direction (seg2 comes back to seg1) 2. seg2 is overlaping with seg1 ''' return not is_in_the_same_verse(seg1, seg2) and is_seg1_overlapping_with_seg2(seg1, seg2) # filter out single segment sections section_itr = (snode for snode in neurite.iter_sections() if snode.points.shape[0] > 2) for snode in section_itr: # group each section's points intro triplets segment_pairs = list(filter(is_not_zero_seg, pair(snode.points))) # filter out zero length segments for i, seg1 in enumerate(segment_pairs[1:]): # check if the end point of the segment lies within the previous # ones in the current sectionmake for seg2 in segment_pairs[0: i + 1]: if is_inside_cylinder(seg1, seg2): return True return False
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Check if a neurite process backtracks to a previous node. Back-tracking takes place when a daughter of a branching process goes back and either overlaps with a previous point, or lies inside the cylindrical volume of the latter. Args: neurite(Neurite): neurite to operate on Returns: True Under the following scenaria: 1. A segment endpoint falls back and overlaps with a previous segment's point 2. The geometry of a segment overlaps with a previous one in the section
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/morphtree.py#L91-L187
2,797
BlueBrain/NeuroM
neurom/check/morphtree.py
get_flat_neurites
def get_flat_neurites(neuron, tol=0.1, method='ratio'): '''Check if a neuron has neurites that are flat within a tolerance Args: neurite(Neurite): neurite to operate on tol(float): the tolerance or the ratio method(string): 'tolerance' or 'ratio' described in :meth:`is_flat` Returns: Bool list corresponding to the flatness check for each neurite in neuron neurites with respect to the given criteria ''' return [n for n in neuron.neurites if is_flat(n, tol, method)]
python
def get_flat_neurites(neuron, tol=0.1, method='ratio'): '''Check if a neuron has neurites that are flat within a tolerance Args: neurite(Neurite): neurite to operate on tol(float): the tolerance or the ratio method(string): 'tolerance' or 'ratio' described in :meth:`is_flat` Returns: Bool list corresponding to the flatness check for each neurite in neuron neurites with respect to the given criteria ''' return [n for n in neuron.neurites if is_flat(n, tol, method)]
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Check if a neuron has neurites that are flat within a tolerance Args: neurite(Neurite): neurite to operate on tol(float): the tolerance or the ratio method(string): 'tolerance' or 'ratio' described in :meth:`is_flat` Returns: Bool list corresponding to the flatness check for each neurite in neuron neurites with respect to the given criteria
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/morphtree.py#L190-L202
2,798
BlueBrain/NeuroM
neurom/check/morphtree.py
get_nonmonotonic_neurites
def get_nonmonotonic_neurites(neuron, tol=1e-6): '''Get neurites that are not monotonic Args: neurite(Neurite): neurite to operate on tol(float): the tolerance or the ratio Returns: list of neurites that do not satisfy monotonicity test ''' return [n for n in neuron.neurites if not is_monotonic(n, tol)]
python
def get_nonmonotonic_neurites(neuron, tol=1e-6): '''Get neurites that are not monotonic Args: neurite(Neurite): neurite to operate on tol(float): the tolerance or the ratio Returns: list of neurites that do not satisfy monotonicity test ''' return [n for n in neuron.neurites if not is_monotonic(n, tol)]
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Get neurites that are not monotonic Args: neurite(Neurite): neurite to operate on tol(float): the tolerance or the ratio Returns: list of neurites that do not satisfy monotonicity test
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/neurom/check/morphtree.py#L205-L215
2,799
BlueBrain/NeuroM
examples/radius_of_gyration.py
segment_centre_of_mass
def segment_centre_of_mass(seg): '''Calculate and return centre of mass of a segment. C, seg_volalculated as centre of mass of conical frustum''' h = mm.segment_length(seg) r0 = seg[0][COLS.R] r1 = seg[1][COLS.R] num = r0 * r0 + 2 * r0 * r1 + 3 * r1 * r1 denom = 4 * (r0 * r0 + r0 * r1 + r1 * r1) centre_of_mass_z_loc = num / denom return seg[0][COLS.XYZ] + (centre_of_mass_z_loc / h) * (seg[1][COLS.XYZ] - seg[0][COLS.XYZ])
python
def segment_centre_of_mass(seg): '''Calculate and return centre of mass of a segment. C, seg_volalculated as centre of mass of conical frustum''' h = mm.segment_length(seg) r0 = seg[0][COLS.R] r1 = seg[1][COLS.R] num = r0 * r0 + 2 * r0 * r1 + 3 * r1 * r1 denom = 4 * (r0 * r0 + r0 * r1 + r1 * r1) centre_of_mass_z_loc = num / denom return seg[0][COLS.XYZ] + (centre_of_mass_z_loc / h) * (seg[1][COLS.XYZ] - seg[0][COLS.XYZ])
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Calculate and return centre of mass of a segment. C, seg_volalculated as centre of mass of conical frustum
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254bb73535b20053d175bc4725bade662177d12b
https://github.com/BlueBrain/NeuroM/blob/254bb73535b20053d175bc4725bade662177d12b/examples/radius_of_gyration.py#L38-L48