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def generate_id(self, agreement_id, types, values):
"""
Generate id for the condition.
:param agreement_id: id of the agreement, hex str
:param types: list of types
:param values: list of values
:return: id, str
"""
values_hash = utils.generate_multi_valu... |
def _fulfill(self, *args, **kwargs):
"""
Fulfill the condition.
:param args:
:param kwargs:
:return: true if the condition was successfully fulfilled, bool
"""
tx_hash = self.send_transaction('fulfill', args, **kwargs)
receipt = self.get_tx_receipt(tx_has... |
def subscribe_condition_fulfilled(self, agreement_id, timeout, callback, args,
timeout_callback=None, wait=False):
"""
Subscribe to the condition fullfilled event.
:param agreement_id: id of the agreement, hex str
:param timeout:
:param call... |
def transact_with_contract_function(
address,
web3,
function_name=None,
transaction=None,
contract_abi=None,
fn_abi=None,
*args,
**kwargs):
"""
Helper function for interacting with a contract function by sending a
transaction. This is copied fr... |
def transact(self, transaction=None):
"""
Customize calling smart contract transaction functions to use `personal_sendTransaction`
instead of `eth_sendTransaction` and to estimate gas limit. This function
is largely copied from web3 ContractFunction with important addition.
Note... |
def fulfill(self,
agreement_id,
amount,
receiver_address,
sender_address,
lock_condition_id,
release_condition_id,
account):
"""
Fulfill the escrow reward condition.
:param agreement_... |
def hash_values(self, amount, receiver_address, sender_address, lock_condition_id,
release_condition_id):
"""
Hash the values of the escrow reward condition.
:param amount: Amount of tokens, int
:param receiver_address: ethereum address of the receiver, hex str
... |
def as_dictionary(self):
"""
Return the parameter as a dictionary.
:return: dict
"""
return {
"name": self.name,
"type": self.type,
"value": remove_0x_prefix(self.value) if self.type == 'bytes32' else self.value
} |
def init_from_condition_json(self, condition_json):
"""
Init the condition values from a condition json.
:param condition_json: dict
"""
self.name = condition_json['name']
self.timelock = condition_json['timelock']
self.timeout = condition_json['timeout']
... |
def as_dictionary(self):
"""
Return the condition as a dictionary.
:return: dict
"""
condition_dict = {
"name": self.name,
"timelock": self.timelock,
"timeout": self.timeout,
"contractName": self.contract_name,
"functio... |
def update_value(self, name, value):
"""
Update value in the array of values.
:param name: Key of the value, str
:param value: New value, str
:return: None
"""
if name not in {'id', self.SERVICE_ENDPOINT, self.CONSUME_ENDPOINT, 'type'}:
self._values[n... |
def as_text(self, is_pretty=False):
"""Return the service as a JSON string."""
values = {
'type': self._type,
self.SERVICE_ENDPOINT: self._service_endpoint,
}
if self._consume_endpoint is not None:
values[self.CONSUME_ENDPOINT] = self._consume_endpoint... |
def as_dictionary(self):
"""Return the service as a python dictionary."""
values = {
'type': self._type,
self.SERVICE_ENDPOINT: self._service_endpoint,
}
if self._consume_endpoint is not None:
values[self.CONSUME_ENDPOINT] = self._consume_endpoint
... |
def from_json(cls, service_dict):
"""Create a service object from a JSON string."""
sd = service_dict.copy()
service_endpoint = sd.get(cls.SERVICE_ENDPOINT)
if not service_endpoint:
logger.error(
'Service definition in DDO document is missing the "serviceEndpo... |
def fulfill(self, agreement_id, document_id, grantee_address, account):
"""
Fulfill the access secret store condition.
:param agreement_id: id of the agreement, hex str
:param document_id: refers to the DID in which secret store will issue the decryption
keys, DID
:param... |
def get_purchased_assets_by_address(self, address, from_block=0, to_block='latest'):
"""
Get the list of the assets dids consumed for an address.
:param address: is the address of the granted user, hex-str
:param from_block: block to start to listen
:param to_block: block to sto... |
def get_web3():
"""Return the web3 instance to interact with the ethereum client."""
if Web3Provider._web3 is None:
config = ConfigProvider.get_config()
provider = (
config.web3_provider
if config.web3_provider
else CustomHTTPProvid... |
def _get_type(points, soma_class):
'''get the type of the soma
Args:
points: Soma points
soma_class(str): one of 'contour' or 'cylinder' to specify the type
'''
assert soma_class in (SOMA_CONTOUR, SOMA_CYLINDER)
npoints = len(points)
if soma_class == SOMA_CONTOUR:
retur... |
def make_soma(points, soma_check=None, soma_class=SOMA_CONTOUR):
'''Make a soma object from a set of points
Infers the soma type (SomaSinglePoint, SomaSimpleContour)
from the points and the 'soma_class'
Parameters:
points: collection of points forming a soma.
soma_check: optional valid... |
def center(self):
'''Obtain the center from the average of all points'''
points = np.array(self._points)
return np.mean(points[:, COLS.XYZ], axis=0) |
def section_tortuosity(section):
'''Tortuosity of a section
The tortuosity is defined as the ratio of the path length of a section
and the euclidian distnce between its end points.
The path length is the sum of distances between consecutive points.
If the section contains less than 2 points, the ... |
def section_end_distance(section):
'''End to end distance of a section
The end to end distance of a section is defined as
the euclidian distnce between its end points.
If the section contains less than 2 points, the value 0 is returned.
'''
pts = section.points
return 0 if len(pts) < 2 els... |
def section_meander_angles(section):
'''Inter-segment opening angles in a section'''
p = section.points
return [mm.angle_3points(p[i - 1], p[i - 2], p[i])
for i in range(2, len(p))] |
def strahler_order(section):
'''Branching order of a tree section
The strahler order is the inverse of the branch order,
since this is computed from the tips of the tree
towards the root.
This implementation is a translation of the three steps described in
Wikipedia (https://en.wikipedia.org/w... |
def figure_naming(pretitle='', posttitle='', prefile='', postfile=''):
"""
Helper function to define the strings that handle pre-post conventions
for viewing - plotting title and saving options.
Args:
pretitle(str): String to include before the general title of the figure.
posttitle(str... |
def get_figure(new_fig=True, subplot='111', params=None):
"""
Function to be used for viewing - plotting,
to initialize the matplotlib figure - axes.
Args:
new_fig(bool): Defines if a new figure will be created, if false current figure is used
subplot (tuple or matplolib subplot specifi... |
def save_plot(fig, prefile='', postfile='', output_path='./', output_name='Figure',
output_format='png', dpi=300, transparent=False, **_):
"""Generates a figure file in the selected directory.
Args:
fig: matplotlib figure
prefile(str): Include before the general filename of the fi... |
def plot_style(fig, ax, # pylint: disable=too-many-arguments, too-many-locals
# plot_title
pretitle='',
title='Figure',
posttitle='',
title_fontsize=14,
title_arg=None,
# plot_labels
label_fontsize=1... |
def plot_title(ax, pretitle='', title='Figure', posttitle='', title_fontsize=14, title_arg=None):
"""Set title options of a matplotlib plot
Args:
ax: matplotlib axes
pretitle(str): String to include before the general title of the figure
posttitle (str): String to include after the gene... |
def plot_labels(ax, label_fontsize=14,
xlabel=None, xlabel_arg=None,
ylabel=None, ylabel_arg=None,
zlabel=None, zlabel_arg=None):
"""Sets the labels options of a matplotlib plot
Args:
ax: matplotlib axes
label_fontsize(int): Size of the labels' fo... |
def plot_ticks(ax, tick_fontsize=12,
xticks=None, xticks_args=None,
yticks=None, yticks_args=None,
zticks=None, zticks_args=None):
"""Function that defines the labels options of a matplotlib plot.
Args:
ax: matplotlib axes
tick_fontsize (int): Define... |
def update_plot_limits(ax, white_space):
"""Sets the limit options of a matplotlib plot.
Args:
ax: matplotlib axes
white_space(float): whitespace added to surround the tight limit of the data
Note: This relies on ax.dataLim (in 2d) and ax.[xy, zz]_dataLim being set in 3d
"""
if ha... |
def plot_legend(ax, no_legend=True, legend_arg=None):
"""
Function that defines the legend options
of a matplotlib plot.
Args:
ax: matplotlib axes
no_legend (bool): Defines the presence of a legend in the figure
legend_arg (dict): Addition arguments for matplotlib.legend() call
... |
def _get_normals(v):
'''get two vectors that form a basis w/ v
Note: returned vectors are unit
'''
not_v = np.array([1, 0, 0])
if np.all(np.abs(v) == not_v):
not_v = np.array([0, 1, 0])
n1 = np.cross(v, not_v)
n1 /= norm(n1)
n2 = np.cross(v, n1)
return n1, n2 |
def generate_cylindrical_points(start, end, start_radius, end_radius,
linspace_count=_LINSPACE_COUNT):
'''Generate a 3d mesh of a cylinder with start and end points, and varying radius
Based on: http://stackoverflow.com/a/32383775
'''
v = end - start
length = norm(v)... |
def project_cylinder_onto_2d(ax, plane,
start, end, start_radius, end_radius,
color='black', alpha=1.):
'''take cylinder defined by start/end, and project it onto the plane
Args:
ax: matplotlib axes
plane(tuple of int): where x, y, z = 0... |
def plot_cylinder(ax, start, end, start_radius, end_radius,
color='black', alpha=1., linspace_count=_LINSPACE_COUNT):
'''plot a 3d cylinder'''
assert not np.all(start == end), 'Cylinder must have length'
x, y, z = generate_cylindrical_points(start, end, start_radius, end_radius,
... |
def plot_sphere(ax, center, radius, color='black', alpha=1., linspace_count=_LINSPACE_COUNT):
""" Plots a 3d sphere, given the center and the radius. """
u = np.linspace(0, 2 * np.pi, linspace_count)
v = np.linspace(0, np.pi, linspace_count)
sin_v = np.sin(v)
x = center[0] + radius * np.outer(np.c... |
def has_sequential_ids(data_wrapper):
'''Check that IDs are increasing and consecutive
returns tuple (bool, list of IDs that are not consecutive
with their predecessor)
'''
db = data_wrapper.data_block
ids = db[:, COLS.ID]
steps = ids[np.where(np.diff(ids) != 1)[0] + 1].astype(int)
retu... |
def no_missing_parents(data_wrapper):
'''Check that all points have existing parents
Point's parent ID must exist and parent must be declared
before child.
Returns:
CheckResult with result and list of IDs that have no parent
'''
db = data_wrapper.data_block
ids = np.setdiff1d(db[:, ... |
def is_single_tree(data_wrapper):
'''Check that data forms a single tree
Only the first point has ID of -1.
Returns:
CheckResult with result and list of IDs
Note:
This assumes no_missing_parents passed.
'''
db = data_wrapper.data_block
bad_ids = db[db[:, COLS.P] == -1][1:,... |
def has_soma_points(data_wrapper):
'''Checks if the TYPE column of raw data block has an element of type soma
Returns:
CheckResult with result
'''
db = data_wrapper.data_block
return CheckResult(POINT_TYPE.SOMA in db[:, COLS.TYPE], None) |
def has_all_finite_radius_neurites(data_wrapper, threshold=0.0):
'''Check that all points with neurite type have a finite radius
Returns:
CheckResult with result and list of IDs of neurite points with zero radius
'''
db = data_wrapper.data_block
neurite_ids = np.in1d(db[:, COLS.TYPE], POINT... |
def has_valid_soma(data_wrapper):
'''Check if a data block has a valid soma
Returns:
CheckResult with result
'''
try:
make_soma(data_wrapper.soma_points())
return CheckResult(True)
except SomaError:
return CheckResult(False) |
def _pelita_member_filter(parent_name, item_names):
"""
Filter a list of autodoc items for which to generate documentation.
Include only imports that come from the documented module or its
submodules.
"""
filtered_names = []
if parent_name not in sys.modules:
return item_names
... |
def boxplot(neurons, feature, new_fig=True, subplot=False):
'''
Plot a histogram of the selected feature for the population of neurons.
Plots x-axis versus y-axis on a scatter|histogram|binned values plot.
More information about the plot and how it works.
Parameters
----------
neurons : li... |
def has_axon(neuron, treefun=_read_neurite_type):
'''Check if a neuron has an axon
Arguments:
neuron(Neuron): The neuron object to test
treefun: Optional function to calculate the tree type of
neuron's neurites
Returns:
CheckResult with result
'''
return CheckResult... |
def has_apical_dendrite(neuron, min_number=1, treefun=_read_neurite_type):
'''Check if a neuron has apical dendrites
Arguments:
neuron(Neuron): The neuron object to test
min_number: minimum number of apical dendrites required
treefun: Optional function to calculate the tree type of neur... |
def has_basal_dendrite(neuron, min_number=1, treefun=_read_neurite_type):
'''Check if a neuron has basal dendrites
Arguments:
neuron(Neuron): The neuron object to test
min_number: minimum number of basal dendrites required
treefun: Optional function to calculate the tree type of neuron'... |
def has_no_flat_neurites(neuron, tol=0.1, method='ratio'):
'''Check that a neuron has no flat neurites
Arguments:
neuron(Neuron): The neuron object to test
tol(float): tolerance
method(string): way of determining flatness, 'tolerance', 'ratio' \
as described in :meth:`neurom.che... |
def has_all_nonzero_segment_lengths(neuron, threshold=0.0):
'''Check presence of neuron segments with length not above threshold
Arguments:
neuron(Neuron): The neuron object to test
threshold(float): value above which a segment length is considered to
be non-zero
Returns:
C... |
def has_all_nonzero_section_lengths(neuron, threshold=0.0):
'''Check presence of neuron sections with length not above threshold
Arguments:
neuron(Neuron): The neuron object to test
threshold(float): value above which a section length is considered
to be non-zero
Returns:
C... |
def has_all_nonzero_neurite_radii(neuron, threshold=0.0):
'''Check presence of neurite points with radius not above threshold
Arguments:
neuron(Neuron): The neuron object to test
threshold: value above which a radius is considered to be non-zero
Returns:
CheckResult with result inc... |
def has_no_jumps(neuron, max_distance=30.0, axis='z'):
'''Check if there are jumps (large movements in the `axis`)
Arguments:
neuron(Neuron): The neuron object to test
max_distance(float): value above which consecutive z-values are
considered a jump
axis(str): one of x/y/z, whic... |
def has_no_fat_ends(neuron, multiple_of_mean=2.0, final_point_count=5):
'''Check if leaf points are too large
Arguments:
neuron(Neuron): The neuron object to test
multiple_of_mean(float): how many times larger the final radius
has to be compared to the mean of the final points
f... |
def has_no_narrow_start(neuron, frac=0.9):
'''Check if neurites have a narrow start
Arguments:
neuron(Neuron): The neuron object to test
frac(float): Ratio that the second point must be smaller than the first
Returns:
CheckResult with a list of all first segments of neurites with a... |
def has_no_dangling_branch(neuron):
'''Check if the neuron has dangling neurites'''
soma_center = neuron.soma.points[:, COLS.XYZ].mean(axis=0)
recentered_soma = neuron.soma.points[:, COLS.XYZ] - soma_center
radius = np.linalg.norm(recentered_soma, axis=1)
soma_max_radius = radius.max()
def is_d... |
def has_no_narrow_neurite_section(neuron,
neurite_filter,
radius_threshold=0.05,
considered_section_min_length=50):
'''Check if the neuron has dendrites with narrow sections
Arguments:
neuron(Neuron): ... |
def extract_data(neurons, feature, params=None):
'''Extracts feature from a list of neurons
and transforms the fitted distribution in the correct format.
Returns the optimal distribution and corresponding parameters.
Normal distribution params (mean, std)
Exponential distribution params ... |
def transform_header(mtype_name):
'''Add header to json output to wrap around distribution data.
'''
head_dict = OrderedDict()
head_dict["m-type"] = mtype_name
head_dict["components"] = defaultdict(OrderedDict)
return head_dict |
def transform_package(mtype, files, components):
'''Put together header and list of data into one json output.
feature_list contains all the information about the data to be extracted:
features, feature_names, feature_components, feature_min, feature_max
'''
data_dict = transform_header(mtype)... |
def parse_args():
'''Parse command line arguments'''
parser = argparse.ArgumentParser(
description='Morphology fit distribution extractor',
epilog='Note: Outputs json of the optimal distribution \
and corresponding parameters.')
parser.add_argument('datapaths',
... |
def draw(obj, plane='3d', inline=False, **kwargs):
'''Draw the morphology using in the given plane
plane (str): a string representing the 2D plane (example: 'xy')
or '3d', '3D' for a 3D view
inline (bool): must be set to True for interactive ipython notebook plotting
'''
if plane.... |
def _plot_neuron3d(neuron, inline, **kwargs):
'''
Generates a figure of the neuron,
that contains a soma and a list of trees.
'''
return _plotly(neuron, plane='3d', title='neuron-3D', inline=inline, **kwargs) |
def _make_trace(neuron, plane):
'''Create the trace to be plotted'''
for neurite in iter_neurites(neuron):
segments = list(iter_segments(neurite))
segs = [(s[0][COLS.XYZ], s[1][COLS.XYZ]) for s in segments]
coords = dict(x=list(chain.from_iterable((p1[0], p2[0], None) for p1, p2 in seg... |
def get_figure(neuron, plane, title):
'''Returns the plotly figure containing the neuron'''
data = list(_make_trace(neuron, plane))
axis = dict(
gridcolor='rgb(255, 255, 255)',
zerolinecolor='rgb(255, 255, 255)',
showbackground=True,
backgroundcolor='rgb(230, 230,230)'
)
... |
def rotate(obj, axis, angle, origin=None):
'''
Rotation around unit vector following the right hand rule
Parameters:
obj : obj to be rotated (e.g. neurite, neuron).
Must implement a transform method.
axis : unit vector for the axis of rotation
angle : rotation angle in r... |
def _sin(x):
'''sine with case for pi multiples'''
return 0. if np.isclose(np.mod(x, np.pi), 0.) else np.sin(x) |
def _rodrigues_to_dcm(axis, angle):
'''
Generates transformation matrix from unit vector
and rotation angle. The rotation is applied in the direction
of the axis which is a unit vector following the right hand rule.
Inputs :
axis : unit vector of the direction of the rotation
angle... |
def total_length(nrn_pop, neurite_type=NeuriteType.all):
'''Get the total length of all sections in the group of neurons or neurites'''
nrns = _neuronfunc.neuron_population(nrn_pop)
return list(sum(section_lengths(n, neurite_type=neurite_type)) for n in nrns) |
def n_segments(neurites, neurite_type=NeuriteType.all):
'''Number of segments in a collection of neurites'''
return sum(len(s.points) - 1
for s in iter_sections(neurites, neurite_filter=is_type(neurite_type))) |
def n_neurites(neurites, neurite_type=NeuriteType.all):
'''Number of neurites in a collection of neurites'''
return sum(1 for _ in iter_neurites(neurites, filt=is_type(neurite_type))) |
def n_sections(neurites, neurite_type=NeuriteType.all, iterator_type=Tree.ipreorder):
'''Number of sections in a collection of neurites'''
return sum(1 for _ in iter_sections(neurites,
iterator_type=iterator_type,
neurite_filter=is_... |
def n_bifurcation_points(neurites, neurite_type=NeuriteType.all):
'''number of bifurcation points in a collection of neurites'''
return n_sections(neurites, neurite_type=neurite_type, iterator_type=Tree.ibifurcation_point) |
def n_forking_points(neurites, neurite_type=NeuriteType.all):
'''number of forking points in a collection of neurites'''
return n_sections(neurites, neurite_type=neurite_type, iterator_type=Tree.iforking_point) |
def n_leaves(neurites, neurite_type=NeuriteType.all):
'''number of leaves points in a collection of neurites'''
return n_sections(neurites, neurite_type=neurite_type, iterator_type=Tree.ileaf) |
def total_area_per_neurite(neurites, neurite_type=NeuriteType.all):
'''Surface area in a collection of neurites.
The area is defined as the sum of the area of the sections.
'''
return [neurite.area for neurite in iter_neurites(neurites, filt=is_type(neurite_type))] |
def map_sections(fun, neurites, neurite_type=NeuriteType.all, iterator_type=Tree.ipreorder):
'''Map `fun` to all the sections in a collection of neurites'''
return map(fun, iter_sections(neurites,
iterator_type=iterator_type,
neurite_filter=is_... |
def section_lengths(neurites, neurite_type=NeuriteType.all):
'''section lengths in a collection of neurites'''
return map_sections(_section_length, neurites, neurite_type=neurite_type) |
def section_term_lengths(neurites, neurite_type=NeuriteType.all):
'''Termination section lengths in a collection of neurites'''
return map_sections(_section_length, neurites, neurite_type=neurite_type,
iterator_type=Tree.ileaf) |
def section_bif_lengths(neurites, neurite_type=NeuriteType.all):
'''Bifurcation section lengths in a collection of neurites'''
return map_sections(_section_length, neurites, neurite_type=neurite_type,
iterator_type=Tree.ibifurcation_point) |
def section_branch_orders(neurites, neurite_type=NeuriteType.all):
'''section branch orders in a collection of neurites'''
return map_sections(sectionfunc.branch_order, neurites, neurite_type=neurite_type) |
def section_bif_branch_orders(neurites, neurite_type=NeuriteType.all):
'''Bifurcation section branch orders in a collection of neurites'''
return map_sections(sectionfunc.branch_order, neurites, neurite_type=neurite_type,
iterator_type=Tree.ibifurcation_point) |
def section_term_branch_orders(neurites, neurite_type=NeuriteType.all):
'''Termination section branch orders in a collection of neurites'''
return map_sections(sectionfunc.branch_order, neurites, neurite_type=neurite_type,
iterator_type=Tree.ileaf) |
def section_path_lengths(neurites, neurite_type=NeuriteType.all):
'''Path lengths of a collection of neurites '''
# Calculates and stores the section lengths in one pass,
# then queries the lengths in the path length iterations.
# This avoids repeatedly calculating the lengths of the
# same sections... |
def map_neurons(fun, neurites, neurite_type):
'''Map `fun` to all the neurites in a single or collection of neurons'''
nrns = _neuronfunc.neuron_population(neurites)
return [fun(n, neurite_type=neurite_type) for n in nrns] |
def map_segments(func, neurites, neurite_type):
''' Map `func` to all the segments in a collection of neurites
`func` accepts a section and returns list of values corresponding to each segment.
'''
neurite_filter = is_type(neurite_type)
return [
s for ss in iter_sections(neurites, neuri... |
def segment_lengths(neurites, neurite_type=NeuriteType.all):
'''Lengths of the segments in a collection of neurites'''
def _seg_len(sec):
'''list of segment lengths of a section'''
return np.linalg.norm(np.diff(sec.points[:, COLS.XYZ], axis=0), axis=1)
return map_segments(_seg_len, neurites... |
def segment_volumes(neurites, neurite_type=NeuriteType.all):
'''Volumes of the segments in a collection of neurites'''
def _func(sec):
'''list of segment volumes of a section'''
return [morphmath.segment_volume(seg) for seg in zip(sec.points[:-1], sec.points[1:])]
return map_segments(_func,... |
def segment_radii(neurites, neurite_type=NeuriteType.all):
'''arithmetic mean of the radii of the points in segments in a collection of neurites'''
def _seg_radii(sec):
'''vectorized mean radii'''
pts = sec.points[:, COLS.R]
return np.divide(np.add(pts[:-1], pts[1:]), 2.0)
return ma... |
def segment_taper_rates(neurites, neurite_type=NeuriteType.all):
'''taper rates of the segments in a collection of neurites
The taper rate is defined as the absolute radii differences divided by length of the section
'''
def _seg_taper_rates(sec):
'''vectorized taper rates'''
pts = sec.... |
def segment_meander_angles(neurites, neurite_type=NeuriteType.all):
'''Inter-segment opening angles in a section'''
return list(chain.from_iterable(map_sections(
sectionfunc.section_meander_angles, neurites, neurite_type))) |
def segment_midpoints(neurites, neurite_type=NeuriteType.all):
'''Return a list of segment mid-points in a collection of neurites'''
def _seg_midpoint(sec):
'''Return the mid-points of segments in a section'''
pts = sec.points[:, COLS.XYZ]
return np.divide(np.add(pts[:-1], pts[1:]), 2.0)... |
def segment_radial_distances(neurites, neurite_type=NeuriteType.all, origin=None):
'''Lengths of the segments in a collection of neurites'''
def _seg_rd(sec, pos):
'''list of radial distances of all segments of a section'''
# TODO: remove this disable when pylint is fixed
# pylint: disab... |
def local_bifurcation_angles(neurites, neurite_type=NeuriteType.all):
'''Get a list of local bifurcation angles in a collection of neurites'''
return map_sections(_bifurcationfunc.local_bifurcation_angle,
neurites,
neurite_type=neurite_type,
... |
def remote_bifurcation_angles(neurites, neurite_type=NeuriteType.all):
'''Get a list of remote bifurcation angles in a collection of neurites'''
return map_sections(_bifurcationfunc.remote_bifurcation_angle,
neurites,
neurite_type=neurite_type,
... |
def bifurcation_partitions(neurites, neurite_type=NeuriteType.all):
'''Partition at bifurcation points of a collection of neurites'''
return map(_bifurcationfunc.bifurcation_partition,
iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
... |
def partition_asymmetries(neurites, neurite_type=NeuriteType.all):
'''Partition asymmetry at bifurcation points of a collection of neurites'''
return map(_bifurcationfunc.partition_asymmetry,
iter_sections(neurites,
iterator_type=Tree.ibifurcation_point,
... |
def partition_pairs(neurites, neurite_type=NeuriteType.all):
'''Partition pairs at bifurcation points of a collection of neurites.
Partition pait is defined as the number of bifurcations at the two
daughters of the bifurcating section'''
return map(_bifurcationfunc.partition_pair,
iter_se... |
def section_radial_distances(neurites, neurite_type=NeuriteType.all, origin=None,
iterator_type=Tree.ipreorder):
'''Section radial distances in a collection of neurites.
The iterator_type can be used to select only terminal sections (ileaf)
or only bifurcations (ibifurcation_poi... |
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