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oceanprotocol/squid-py | squid_py/keeper/conditions/condition_base.py | ConditionBase.generate_id | 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... | python | 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... | [
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oceanprotocol/squid-py | squid_py/keeper/conditions/condition_base.py | ConditionBase._fulfill | 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... | python | 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)
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oceanprotocol/squid-py | squid_py/keeper/conditions/condition_base.py | ConditionBase.subscribe_condition_fulfilled | 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... | python | 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:
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oceanprotocol/squid-py | squid_py/keeper/web3/contract.py | transact_with_contract_function | 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... | python | def transact_with_contract_function(
address,
web3,
function_name=None,
transaction=None,
contract_abi=None,
fn_abi=None,
*args,
**kwargs):
"""
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oceanprotocol/squid-py | squid_py/keeper/web3/contract.py | SquidContractFunction.transact | 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... | python | 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.
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oceanprotocol/squid-py | squid_py/keeper/conditions/escrow_reward.py | EscrowRewardCondition.fulfill | def fulfill(self,
agreement_id,
amount,
receiver_address,
sender_address,
lock_condition_id,
release_condition_id,
account):
"""
Fulfill the escrow reward condition.
:param agreement_... | python | def fulfill(self,
agreement_id,
amount,
receiver_address,
sender_address,
lock_condition_id,
release_condition_id,
account):
"""
Fulfill the escrow reward condition.
:param agreement_... | [
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oceanprotocol/squid-py | squid_py/keeper/conditions/escrow_reward.py | EscrowRewardCondition.hash_values | 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
... | python | def hash_values(self, amount, receiver_address, sender_address, lock_condition_id,
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"""
Hash the values of the escrow reward condition.
:param amount: Amount of tokens, int
:param receiver_address: ethereum address of the receiver, hex str
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oceanprotocol/squid-py | squid_py/agreements/service_agreement_condition.py | Parameter.as_dictionary | 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
} | python | 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
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oceanprotocol/squid-py | squid_py/agreements/service_agreement_condition.py | ServiceAgreementCondition.init_from_condition_json | 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']
... | python | 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']
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oceanprotocol/squid-py | squid_py/agreements/service_agreement_condition.py | ServiceAgreementCondition.as_dictionary | def as_dictionary(self):
"""
Return the condition as a dictionary.
:return: dict
"""
condition_dict = {
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"timelock": self.timelock,
"timeout": self.timeout,
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oceanprotocol/squid-py | squid_py/ddo/service.py | Service.update_value | 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... | python | 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'}:
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oceanprotocol/squid-py | squid_py/ddo/service.py | Service.as_text | def as_text(self, is_pretty=False):
"""Return the service as a JSON string."""
values = {
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self.SERVICE_ENDPOINT: self._service_endpoint,
}
if self._consume_endpoint is not None:
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"""Return the service as a JSON string."""
values = {
'type': self._type,
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oceanprotocol/squid-py | squid_py/ddo/service.py | Service.as_dictionary | def as_dictionary(self):
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}
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oceanprotocol/squid-py | squid_py/ddo/service.py | Service.from_json | 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(
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"""Create a service object from a JSON string."""
sd = service_dict.copy()
service_endpoint = sd.get(cls.SERVICE_ENDPOINT)
if not service_endpoint:
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oceanprotocol/squid-py | squid_py/keeper/conditions/access.py | AccessSecretStoreCondition.fulfill | 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... | python | 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
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oceanprotocol/squid-py | squid_py/keeper/conditions/access.py | AccessSecretStoreCondition.get_purchased_assets_by_address | 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... | python | 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
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oceanprotocol/squid-py | squid_py/keeper/web3_provider.py | Web3Provider.get_web3 | def get_web3():
"""Return the web3 instance to interact with the ethereum client."""
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"""Return the web3 instance to interact with the ethereum client."""
if Web3Provider._web3 is None:
config = ConfigProvider.get_config()
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BlueBrain/NeuroM | neurom/core/_soma.py | _get_type | 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... | python | 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:
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BlueBrain/NeuroM | neurom/core/_soma.py | make_soma | 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... | python | def make_soma(points, soma_check=None, soma_class=SOMA_CONTOUR):
'''Make a soma object from a set of points
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BlueBrain/NeuroM | neurom/core/_soma.py | SomaSimpleContour.center | 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) | python | 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) | [
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BlueBrain/NeuroM | neurom/fst/sectionfunc.py | section_tortuosity | 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 ... | python | def section_tortuosity(section):
'''Tortuosity of a section
The tortuosity is defined as the ratio of the path length of a section
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The path length is the sum of distances between consecutive points.
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BlueBrain/NeuroM | neurom/fst/sectionfunc.py | section_end_distance | 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... | python | 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.
'''
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BlueBrain/NeuroM | neurom/fst/sectionfunc.py | section_meander_angles | 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))] | python | 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])
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BlueBrain/NeuroM | neurom/fst/sectionfunc.py | strahler_order | def strahler_order(section):
'''Branching order of a tree section
The strahler order is the inverse of the branch order,
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This implementation is a translation of the three steps described in
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'''Branching order of a tree section
The strahler order is the inverse of the branch order,
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BlueBrain/NeuroM | neurom/view/common.py | figure_naming | 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... | python | def figure_naming(pretitle='', posttitle='', prefile='', postfile=''):
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Helper function to define the strings that handle pre-post conventions
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pretitle(str): String to include before the general title of the figure.
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BlueBrain/NeuroM | neurom/view/common.py | get_figure | 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
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"""
Function to be used for viewing - plotting,
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BlueBrain/NeuroM | neurom/view/common.py | save_plot | 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
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BlueBrain/NeuroM | neurom/view/common.py | plot_style | 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... | python | def plot_style(fig, ax, # pylint: disable=too-many-arguments, too-many-locals
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pretitle='',
title='Figure',
posttitle='',
title_fontsize=14,
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BlueBrain/NeuroM | neurom/view/common.py | plot_title | def plot_title(ax, pretitle='', title='Figure', posttitle='', title_fontsize=14, title_arg=None):
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Args:
ax: matplotlib axes
pretitle(str): String to include before the general title of the figure
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BlueBrain/NeuroM | neurom/view/common.py | plot_labels | 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... | python | def plot_labels(ax, label_fontsize=14,
xlabel=None, xlabel_arg=None,
ylabel=None, ylabel_arg=None,
zlabel=None, zlabel_arg=None):
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BlueBrain/NeuroM | neurom/view/common.py | plot_ticks | 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... | python | def plot_ticks(ax, tick_fontsize=12,
xticks=None, xticks_args=None,
yticks=None, yticks_args=None,
zticks=None, zticks_args=None):
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BlueBrain/NeuroM | neurom/view/common.py | update_plot_limits | def update_plot_limits(ax, white_space):
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ax: matplotlib axes
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Note: This relies on ax.dataLim (in 2d) and ax.[xy, zz]_dataLim being set in 3d
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Args:
ax: matplotlib axes
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ax: matplotlib axes
no_legend (bool): Defines the presence of a legend in the figure
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Function that defines the legend options
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ax: matplotlib axes
no_legend (bool): Defines the presence of a legend in the figure
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BlueBrain/NeuroM | neurom/view/common.py | _get_normals | 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 | python | 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):
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BlueBrain/NeuroM | neurom/view/common.py | generate_cylindrical_points | def generate_cylindrical_points(start, end, start_radius, end_radius,
linspace_count=_LINSPACE_COUNT):
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Based on: http://stackoverflow.com/a/32383775
'''
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BlueBrain/NeuroM | neurom/view/common.py | project_cylinder_onto_2d | def project_cylinder_onto_2d(ax, plane,
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ax: matplotlib axes
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start, end, start_radius, end_radius,
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BlueBrain/NeuroM | neurom/view/common.py | plot_cylinder | def plot_cylinder(ax, start, end, start_radius, end_radius,
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assert not np.all(start == end), 'Cylinder must have length'
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... | python | def plot_cylinder(ax, start, end, start_radius, end_radius,
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assert not np.all(start == end), 'Cylinder must have length'
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BlueBrain/NeuroM | neurom/view/common.py | plot_sphere | def plot_sphere(ax, center, radius, color='black', alpha=1., linspace_count=_LINSPACE_COUNT):
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u = np.linspace(0, 2 * np.pi, linspace_count)
v = np.linspace(0, np.pi, linspace_count)
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x = center[0] + radius * np.outer(np.c... | python | 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)
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BlueBrain/NeuroM | neurom/check/structural_checks.py | has_sequential_ids | 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... | python | 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]
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BlueBrain/NeuroM | neurom/check/structural_checks.py | no_missing_parents | 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
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Point's parent ID must exist and parent must be declared
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Returns:
CheckResult with result and list of IDs that have no parent
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db = data_wrapper.data_block
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BlueBrain/NeuroM | neurom/check/structural_checks.py | is_single_tree | 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:
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'''
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bad_ids = db[db[:, COLS.P] == -1][1:,... | python | 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:
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BlueBrain/NeuroM | neurom/check/structural_checks.py | has_soma_points | 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
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'''Checks if the TYPE column of raw data block has an element of type soma
Returns:
CheckResult with result
'''
db = data_wrapper.data_block
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BlueBrain/NeuroM | neurom/check/structural_checks.py | has_all_finite_radius_neurites | 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... | python | 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
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BlueBrain/NeuroM | neurom/check/structural_checks.py | has_valid_soma | 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:
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'''Check if a data block has a valid soma
Returns:
CheckResult with result
'''
try:
make_soma(data_wrapper.soma_points())
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BlueBrain/NeuroM | doc/source/conf.py | _pelita_member_filter | 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
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"""
Filter a list of autodoc items for which to generate documentation.
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filtered_names = []
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BlueBrain/NeuroM | examples/boxplot.py | boxplot | 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... | python | 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
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neurons : li... | [
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_axon | 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... | python | 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
'''
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_apical_dendrite | 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... | python | def has_apical_dendrite(neuron, min_number=1, treefun=_read_neurite_type):
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neuron(Neuron): The neuron object to test
min_number: minimum number of apical dendrites required
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_basal_dendrite | 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'... | python | def has_basal_dendrite(neuron, min_number=1, treefun=_read_neurite_type):
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Arguments:
neuron(Neuron): The neuron object to test
min_number: minimum number of basal dendrites required
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_no_flat_neurites | 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... | python | 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' \
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_all_nonzero_segment_lengths | 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... | python | 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
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C... | [
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_all_nonzero_section_lengths | def has_all_nonzero_section_lengths(neuron, threshold=0.0):
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neuron(Neuron): The neuron object to test
threshold(float): value above which a section length is considered
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Returns:
C... | python | def has_all_nonzero_section_lengths(neuron, threshold=0.0):
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Arguments:
neuron(Neuron): The neuron object to test
threshold(float): value above which a section length is considered
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_all_nonzero_neurite_radii | def has_all_nonzero_neurite_radii(neuron, threshold=0.0):
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neuron(Neuron): The neuron object to test
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neuron(Neuron): The neuron object to test
threshold: value above which a radius is considered to be non-zero
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_no_jumps | 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
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axis(str): one of x/y/z, whic... | python | def has_no_jumps(neuron, max_distance=30.0, axis='z'):
'''Check if there are jumps (large movements in the `axis`)
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neuron(Neuron): The neuron object to test
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_no_fat_ends | 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... | python | def has_no_fat_ends(neuron, multiple_of_mean=2.0, final_point_count=5):
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neuron(Neuron): The neuron object to test
multiple_of_mean(float): how many times larger the final radius
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_no_narrow_start | 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... | python | def has_no_narrow_start(neuron, frac=0.9):
'''Check if neurites have a narrow start
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neuron(Neuron): The neuron object to test
frac(float): Ratio that the second point must be smaller than the first
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_no_dangling_branch | 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... | python | 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()
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BlueBrain/NeuroM | neurom/check/neuron_checks.py | has_no_narrow_neurite_section | def has_no_narrow_neurite_section(neuron,
neurite_filter,
radius_threshold=0.05,
considered_section_min_length=50):
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Arguments:
neuron(Neuron): ... | python | def has_no_narrow_neurite_section(neuron,
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BlueBrain/NeuroM | examples/synthesis_json.py | extract_data | def extract_data(neurons, feature, params=None):
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Normal distribution params (mean, std)
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Normal distribution params (mean, std)
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BlueBrain/NeuroM | examples/synthesis_json.py | transform_header | 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 | python | 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)
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BlueBrain/NeuroM | examples/synthesis_json.py | transform_package | 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)... | python | 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
'''
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'''Parse command line arguments'''
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BlueBrain/NeuroM | neurom/view/plotly.py | draw | 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
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'''Draw the morphology using in the given plane
plane (str): a string representing the 2D plane (example: 'xy')
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BlueBrain/NeuroM | neurom/view/plotly.py | _plot_neuron3d | def _plot_neuron3d(neuron, inline, **kwargs):
'''
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'''
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BlueBrain/NeuroM | neurom/view/plotly.py | _make_trace | 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... | python | 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]
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BlueBrain/NeuroM | neurom/view/plotly.py | get_figure | 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)'
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... | python | def get_figure(neuron, plane, title):
'''Returns the plotly figure containing the neuron'''
data = list(_make_trace(neuron, plane))
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gridcolor='rgb(255, 255, 255)',
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BlueBrain/NeuroM | neurom/geom/transform.py | rotate | 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... | python | 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).
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axis : unit vector for the axis of rotation
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BlueBrain/NeuroM | neurom/geom/transform.py | _sin | def _sin(x):
'''sine with case for pi multiples'''
return 0. if np.isclose(np.mod(x, np.pi), 0.) else np.sin(x) | python | def _sin(x):
'''sine with case for pi multiples'''
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BlueBrain/NeuroM | neurom/geom/transform.py | _rodrigues_to_dcm | def _rodrigues_to_dcm(axis, angle):
'''
Generates transformation matrix from unit vector
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Inputs :
axis : unit vector of the direction of the rotation
angle... | python | def _rodrigues_to_dcm(axis, angle):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | total_length | 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) | python | def total_length(nrn_pop, neurite_type=NeuriteType.all):
'''Get the total length of all sections in the group of neurons or neurites'''
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | n_segments | def n_segments(neurites, neurite_type=NeuriteType.all):
'''Number of segments in a collection of neurites'''
return sum(len(s.points) - 1
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | n_neurites | 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))) | python | def n_neurites(neurites, neurite_type=NeuriteType.all):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | n_sections | 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,
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'''Number of sections in a collection of neurites'''
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | n_bifurcation_points | 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) | python | def n_bifurcation_points(neurites, neurite_type=NeuriteType.all):
'''number of bifurcation points in a collection of neurites'''
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | n_forking_points | 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) | python | def n_forking_points(neurites, neurite_type=NeuriteType.all):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | n_leaves | 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) | python | def n_leaves(neurites, neurite_type=NeuriteType.all):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | total_area_per_neurite | 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))] | python | def total_area_per_neurite(neurites, neurite_type=NeuriteType.all):
'''Surface area in a collection of neurites.
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'''
return [neurite.area for neurite in iter_neurites(neurites, filt=is_type(neurite_type))] | [
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | map_sections | 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_... | python | 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,
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | section_lengths | 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) | python | def section_lengths(neurites, neurite_type=NeuriteType.all):
'''section lengths in a collection of neurites'''
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | section_term_lengths | def section_term_lengths(neurites, neurite_type=NeuriteType.all):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | section_bif_lengths | 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) | python | def section_bif_lengths(neurites, neurite_type=NeuriteType.all):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | section_branch_orders | 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) | python | def section_branch_orders(neurites, neurite_type=NeuriteType.all):
'''section branch orders in a collection of neurites'''
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | section_bif_branch_orders | 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) | python | def section_bif_branch_orders(neurites, neurite_type=NeuriteType.all):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | section_term_branch_orders | def section_term_branch_orders(neurites, neurite_type=NeuriteType.all):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | section_path_lengths | 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,
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | map_neurons | 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)
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'''Map `fun` to all the neurites in a single or collection of neurons'''
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | map_segments | def map_segments(func, neurites, neurite_type):
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'''
neurite_filter = is_type(neurite_type)
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neurite_filter = is_type(neurite_type)
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | segment_lengths | def segment_lengths(neurites, neurite_type=NeuriteType.all):
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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)
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'''Lengths of the segments in a collection of neurites'''
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return np.linalg.norm(np.diff(sec.points[:, COLS.XYZ], axis=0), axis=1)
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | segment_volumes | def segment_volumes(neurites, neurite_type=NeuriteType.all):
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return [morphmath.segment_volume(seg) for seg in zip(sec.points[:-1], sec.points[1:])]
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | segment_radii | def segment_radii(neurites, neurite_type=NeuriteType.all):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | segment_taper_rates | def segment_taper_rates(neurites, neurite_type=NeuriteType.all):
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def _seg_taper_rates(sec):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | segment_meander_angles | def segment_meander_angles(neurites, neurite_type=NeuriteType.all):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | segment_radial_distances | def segment_radial_distances(neurites, neurite_type=NeuriteType.all, origin=None):
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def _seg_rd(sec, pos):
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | local_bifurcation_angles | 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,
... | python | 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,
... | [
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | remote_bifurcation_angles | 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,
... | python | 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,
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | bifurcation_partitions | 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,
... | python | 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,
... | [
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | partition_asymmetries | 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,
... | python | 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,
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | partition_pairs | 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... | python | 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,
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BlueBrain/NeuroM | neurom/fst/_neuritefunc.py | section_radial_distances | 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... | python | 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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