class MappedArrayData(MappedType):
"""
Array that will be Mapped as a table in DB.
"""
title = basic.String
label_x, label_y = basic.String, basic.String
aggregation_functions = basic.JSONType(required=False)
dimensions_labels = basic.JSONType(required=False)
nr_dimensions, length_1d, length_2d, length_3d, length_4d = [basic.Integer] * 5
array_data = Array()
__generate_table__ = True
@property
def display_name(self):
"""
Overwrite from superclass and add title field
"""
previous = super(MappedArrayData, self).display_name
if previous is None:
return str(self.title)
return str(self.title) + " " + previous
@property
def shape(self):
"""
Shape for current wrapped NumPy array.
"""
return self.array_data.shape
class Internal_Class(MappedType):
""" Dummy persisted class"""
x = 5
z = basic.Integer()
j = basic.JSONType()
class In_Internal_Class(object):
"""Internal of Internal class"""
t = numpy.array(range(10))
@property
def y(self):
return self.x
class Connectivity(MappedType):
# data
region_labels = arrays.StringArray(
label="Region labels",
doc="""Short strings, 'labels', for the regions represented by the connectivity matrix.""")
weights = arrays.FloatArray(
label="Connection strengths",
stored_metadata=[key for key in MappedType.DEFAULT_WITH_ZERO_METADATA],
doc="""Matrix of values representing the strength of connections between regions, arbitrary units.""")
undirected = basic.Integer(
default=0, required=False,
doc="1, when the weights matrix is square and symmetric over the main diagonal, 0 when directed graph.")
tract_lengths = arrays.FloatArray(
label="Tract lengths",
stored_metadata=[key for key in MappedType.DEFAULT_WITH_ZERO_METADATA],
doc="""The length of myelinated fibre tracts between regions.
If not provided Euclidean distance between region centres is used.""")
speed = arrays.FloatArray(
label="Conduction speed",
default=numpy.array([3.0]), file_storage=core.FILE_STORAGE_NONE,
doc="""A single number or matrix of conduction speeds for the myelinated fibre tracts between regions.""")
centres = arrays.PositionArray(
label="Region centres",
doc="An array specifying the location of the centre of each region.")
cortical = arrays.BoolArray(
label="Cortical",
required=False,
doc="""A boolean vector specifying whether or not a region is part of the cortex.""")
hemispheres = arrays.BoolArray(
label="Hemispheres (True for Right and False for Left Hemisphere",
required=False,
doc="""A boolean vector specifying whether or not a region is part of the right hemisphere""")
orientations = arrays.OrientationArray(
label="Average region orientation",
required=False,
doc="""Unit vectors of the average orientation of the regions represented in the connectivity matrix.
NOTE: Unknown data should be zeros.""")
areas = arrays.FloatArray(
label="Area of regions",
required=False,
doc="""Estimated area represented by the regions in the connectivity matrix.
NOTE: Unknown data should be zeros.""")
idelays = arrays.IndexArray(
label="Conduction delay indices",
required=False, file_storage=core.FILE_STORAGE_NONE,
doc="An array of time delays between regions in integration steps.")
delays = arrays.FloatArray(
label="Conduction delay",
file_storage=core.FILE_STORAGE_NONE, required=False,
doc="""Matrix of time delays between regions in physical units, setting conduction speed automatically
combines with tract lengths to update this matrix, i.e. don't try and change it manually.""")
number_of_regions = basic.Integer(
label="Number of regions",
doc="""The number of regions represented in this Connectivity """)
number_of_connections = basic.Integer(
label="Number of connections",
doc="""The number of non-zero entries represented in this Connectivity """)
# Original Connectivity, from which current connectivity was edited.
parent_connectivity = basic.String(required=False)
# In case of edited Connectivity, this are the nodes left in interest area,
# the rest were part of a lesion, so they were removed.
saved_selection = basic.JSONType(required=False)
# framework
@property
def display_name(self):
"""
Overwrite from superclass and add number of regions field (as title on DataStructure tree)
"""
previous = "Connectivity"
return previous + " " + str(self.number_of_regions)
def branch_connectivity(self, new_weights, interest_areas, storage_path, new_tracts=None):
"""
Generate new Connectivity based on current one, by changing weights (e.g. simulate lesion).
The returned connectivity has the same number of nodes. The edges of unselected nodes will have weight 0.
:param new_weights: weights matrix for the new connectivity
:param interest_areas: ndarray of the selected node id's
:param new_tracts: tracts matrix for the new connectivity
"""
if new_tracts is None:
new_tracts = self.tract_lengths
for i in xrange(len(self.weights)):
for j in xrange(len(self.weights)):
if i not in interest_areas or j not in interest_areas:
new_weights[i][j] = 0
final_conn = self.__class__()
final_conn.parent_connectivity = self.gid
final_conn.storage_path = storage_path
final_conn.weights = new_weights
final_conn.centres = self.centres
final_conn.region_labels = self.region_labels
final_conn.orientations = self.orientations
final_conn.cortical = self.cortical
final_conn.hemispheres = self.hemispheres
final_conn.areas = self.areas
final_conn.tract_lengths = new_tracts
final_conn.saved_selection = interest_areas.tolist()
final_conn.subject = self.subject
return final_conn
def cut_connectivity(self, new_weights, interest_areas, storage_path, new_tracts=None):
"""
Generate new Connectivity object based on current one, by removing nodes (e.g. simulate lesion).
Only the selected nodes will get used in the result. The order of the indices in interest_areas matters.
If indices are not sorted then the nodes will be permuted accordingly.
:param new_weights: weights matrix for the new connectivity
:param interest_areas: ndarray with the selected node id's.
:param new_tracts: tracts matrix for the new connectivity
"""
if new_tracts is None:
new_tracts = self.tract_lengths[interest_areas, :][:, interest_areas]
else:
new_tracts = new_tracts[interest_areas, :][:, interest_areas]
new_weights = new_weights[interest_areas, :][:, interest_areas]
final_conn = self.__class__()
final_conn.parent_connectivity = None
final_conn.storage_path = storage_path
final_conn.weights = new_weights
final_conn.centres = self.centres[interest_areas, :]
final_conn.region_labels = self.region_labels[interest_areas]
if self.orientations is not None and len(self.orientations):
final_conn.orientations = self.orientations[interest_areas, :]
if self.cortical is not None and len(self.cortical):
final_conn.cortical = self.cortical[interest_areas]
if self.hemispheres is not None and len(self.hemispheres):
final_conn.hemispheres = self.hemispheres[interest_areas]
if self.areas is not None and len(self.areas):
final_conn.areas = self.areas[interest_areas]
final_conn.tract_lengths = new_tracts
final_conn.saved_selection = None
final_conn.subject = self.subject
return final_conn
def _reorder_arrays(self, new_weights, interest_areas, new_tracts=None):
"""
Returns ordered versions of the parameters according to the hemisphere permutation.
"""
permutation = self.hemisphere_order_indices
inverse_permutation = numpy.argsort(permutation) # trick to invert a permutation represented as an array
interest_areas = inverse_permutation[interest_areas]
# see :meth"`ordered_weights` for why [p:][:p]
new_weights = new_weights[inverse_permutation, :][:, inverse_permutation]
if new_tracts is not None:
new_tracts = new_tracts[inverse_permutation, :][:, inverse_permutation]
return new_weights, interest_areas, new_tracts
def branch_connectivity_from_ordered_arrays(self, new_weights, interest_areas, storage_path, new_tracts=None):
"""
Similar to :meth:`branch_connectivity` but the parameters are consistent with the ordered versions of weights, tracts, labels
Used by the connectivity viewer to save a lesion.
"""
new_weights, interest_areas, new_tracts = self._reorder_arrays(new_weights, interest_areas, new_tracts)
return self.branch_connectivity(new_weights, interest_areas, storage_path, new_tracts)
def cut_new_connectivity_from_ordered_arrays(self, new_weights, interest_areas, storage_path, new_tracts=None):
"""
Similar to :meth:`cut_connectivity` but using hemisphere ordered parameters.
Used by the connectivity viewer to save a smaller connectivity.
"""
new_weights, interest_areas, new_tracts = self._reorder_arrays(new_weights, interest_areas, new_tracts)
return self.cut_connectivity(new_weights, interest_areas, storage_path, new_tracts)
@property
def saved_selection_labels(self):
"""
Taking the entity field saved_selection, convert indexes in that array
into labels.
"""
if self.saved_selection:
idxs = [int(i) for i in self.saved_selection]
result = ''
for i in idxs:
result += self.region_labels[i] + ','
return result[:-1]
else:
return ''
@staticmethod
def accepted_filters():
filters = MappedType.accepted_filters()
filters.update({'datatype_class._number_of_regions': {'type': 'int', 'display': 'No of Regions',
'operations': ['==', '<', '>']}})
return filters
def is_right_hemisphere(self, idx):
"""
:param idx: Region IDX
:return: True when hemispheres information is present and it shows that the current node is in the right
hemisphere. When hemispheres info is not present, return True for the second half of the indices and
False otherwise.
"""
if self.hemispheres is not None and self.hemispheres.size:
return self.hemispheres[idx]
return idx >= self.number_of_regions / 2
@property
def hemisphere_order_indices(self):
"""
A sequence of indices of rows/colums.
These permute rows/columns so that the first half would belong to the first hemisphere
If there is no hemisphere information returns the identity permutation
"""
if self.hemispheres is not None and self.hemispheres.size:
li, ri = [], []
for i, is_right in enumerate(self.hemispheres):
if is_right:
ri.append(i)
else:
li.append(i)
return numpy.array(li + ri)
else:
return numpy.arange(self.number_of_regions)
@property
def ordered_weights(self):
"""
This view of the weights matrix lists all left hemisphere nodes before the right ones.
It is used by viewers of the connectivity.
"""
permutation = self.hemisphere_order_indices
# how this works:
# w[permutation, :] selects all rows at the indices present in the permutation array thus permuting the rows
# [:, permutation] does the same to columns. See numpy index arrays
return self.weights[permutation, :][:, permutation]
@property
def ordered_tracts(self):
"""
Similar to :meth:`ordered_weights`
"""
permutation = self.hemisphere_order_indices
return self.tract_lengths[permutation, :][:, permutation]
@property
def ordered_labels(self):
"""
Similar to :meth:`ordered_weights`
"""
permutation = self.hemisphere_order_indices
return self.region_labels[permutation]
@property
def ordered_centres(self):
"""
Similar to :method:`ordered_weights`
"""
permutation = self.hemisphere_order_indices
return self.centres[permutation]
def get_grouped_space_labels(self):
"""
:return: A list [('left', [lh_labels)], ('right': [rh_labels])]
"""
if self.hemispheres is not None and self.hemispheres.size:
l, r = [], []
for i, (is_right, label) in enumerate(zip(self.hemispheres, self.region_labels)):
if is_right:
r.append((i, label))
else:
l.append((i, label))
return [('left', l), ('right', r)]
else:
return [('', list(enumerate(self.region_labels)))]
def get_default_selection(self):
# should this be sub-selection or all always?
sel = self.saved_selection
if sel is not None:
return sel
else:
return range(len(self.region_labels))
def get_measure_points_selection_gid(self):
"""
:return: the associated connectivity gid
"""
return self.gid
@property
def binarized_weights(self):
"""
:return: a matrix of he same size as weights, with 1 where weight > 0, and 0 in rest
"""
result = numpy.zeros_like(self.weights)
result = numpy.where(self.weights > 0, 1, result)
return result
# scientific
def configure(self):
"""
Invoke the compute methods for computable attributes that haven't been
set during initialization.
"""
super(Connectivity, self).configure()
self.number_of_regions = self.weights.shape[0]
# NOTE: In numpy 1.8 there is a function called count_zeros
self.number_of_connections = self.weights.nonzero()[0].shape[0]
self.trait["weights"].log_debug(owner=self.__class__.__name__)
self.trait["tract_lengths"].log_debug(owner=self.__class__.__name__)
self.trait["speed"].log_debug(owner=self.__class__.__name__)
self.trait["centres"].log_debug(owner=self.__class__.__name__)
self.trait["orientations"].log_debug(owner=self.__class__.__name__)
self.trait["areas"].log_debug(owner=self.__class__.__name__)
if self.tract_lengths.size == 0:
self.compute_tract_lengths()
if self.region_labels.size == 0:
self.compute_region_labels()
if self.hemispheres is None or self.hemispheres.size == 0:
self.try_compute_hemispheres()
# This can not go into compute, as it is too complex reference
# if self.delays.size == 0:
# TODO: Because delays are stored and loaded the size was never 0.0 and
# so this wasn't being run, making the conduction_speed hack on the
# simulator non-functional. Inn the longer run it'll probably be
# necessary for delays to never be stored but always calculated
# from tract-lengths and speed...
if self.speed is None: # TODO: this is a hack fix...
LOG.warning("Connectivity.speed attribute not initialized properly, setting it to 3.0...")
self.speed = numpy.array([3.0]) # F£$%^&*!!!#self.trait["speed"].value
# NOTE: Because of the conduction_speed hack for UI this must be evaluated here, even if delays
# already has a value, otherwise setting speed in the UI has no effect...
self.delays = self.tract_lengths / self.speed
self.trait["delays"].log_debug(owner=self.__class__.__name__)
if (self.weights.transpose() == self.weights).all():
self.undirected = 1
def _find_summary_info(self):
"""
Gather scientifically interesting summary information from an instance
of this dataType.
"""
summary = {"Number of regions": self.number_of_regions,
"Number of connections": self.number_of_connections,
"Undirected": self.undirected}
summary.update(self.get_info_about_array('areas',
[self.METADATA_ARRAY_MAX,
self.METADATA_ARRAY_MIN,
self.METADATA_ARRAY_MEAN]))
summary.update(self.get_info_about_array('weights',
[self.METADATA_ARRAY_MAX,
self.METADATA_ARRAY_MEAN,
self.METADATA_ARRAY_VAR,
self.METADATA_ARRAY_MIN_NON_ZERO,
self.METADATA_ARRAY_MEAN_NON_ZERO,
self.METADATA_ARRAY_VAR_NON_ZERO]))
summary.update(self.get_info_about_array('tract_lengths',
[self.METADATA_ARRAY_MAX,
self.METADATA_ARRAY_MEAN,
self.METADATA_ARRAY_VAR,
self.METADATA_ARRAY_MIN_NON_ZERO,
self.METADATA_ARRAY_MEAN_NON_ZERO,
self.METADATA_ARRAY_VAR_NON_ZERO]))
summary.update(self.get_info_about_array('tract_lengths',
[self.METADATA_ARRAY_MAX_NON_ZERO,
self.METADATA_ARRAY_MIN_NON_ZERO,
self.METADATA_ARRAY_MEAN_NON_ZERO,
self.METADATA_ARRAY_VAR_NON_ZERO],
mask_array_name='weights', key_suffix=" (connections)"))
return summary
def set_idelays(self, dt):
"""
Convert the time delays between regions in physical units into an array
of linear indices into the simulator's history attribute.
args:
``dt (float64)``: Length of integration time step...
Updates attribute:
``idelays (numpy.array)``: Transmission delay between brain regions
in integration steps.
"""
# Express delays in integration steps
self.idelays = numpy.rint(self.delays / dt).astype(numpy.int32)
self.trait["idelays"].log_debug(owner=self.__class__.__name__)
def compute_tract_lengths(self):
"""
If no tract lengths data are available, this can be used to calculate
the Euclidean distance between region centres to use as a proxy.
"""
nor = self.number_of_regions
tract_lengths = numpy.zeros((nor, nor))
# TODO: redundant by half, do half triangle then flip...
for region in range(nor):
temp = self.centres - self.centres[region, :][numpy.newaxis, :]
tract_lengths[region, :] = numpy.sqrt(numpy.sum(temp ** 2, axis=1))
self.tract_lengths = tract_lengths
self.trait["tract_lengths"].log_debug(owner=self.__class__.__name__)
def compute_region_labels(self):
""" """
labels = ["region_%03d" % n for n in range(self.number_of_regions)]
self.region_labels = numpy.array(labels, dtype="128a")
def try_compute_hemispheres(self):
"""
If all region labels are prefixed with L or R, then compute hemisphere side with that.
"""
if self.region_labels is not None and self.region_labels.size > 0:
hemispheres = []
## Check if all labels are prefixed with R / L
for label in self.region_labels:
if label is not None and label.lower().startswith('r'):
hemispheres.append(True)
elif label is not None and label.lower().startswith('l'):
hemispheres.append(False)
else:
hemispheres = None
break
## Check if all labels are sufixed with R / L
if hemispheres is None:
hemispheres = []
for label in self.region_labels:
if label is not None and label.lower().endswith('r'):
hemispheres.append(True)
elif label is not None and label.lower().endswith('l'):
hemispheres.append(False)
else:
hemispheres = None
break
if hemispheres is not None:
self.hemispheres = numpy.array(hemispheres, dtype=numpy.bool)
def transform_remove_self_connections(self):
"""
Remove the values from the main diagonal (self-connections)
"""
nor = self.number_of_regions
result = copy(self.weights)
result = result - result * numpy.eye(nor, nor)
return result
def scaled_weights(self, mode='tract'):
"""
Scale the connection strengths (weights) and return the scaled matrix.
Three simple types of scaling are supported.
The ``scaling_mode`` is one of the following:
'tract': Scale by a value such that the maximum absolute value of a single
connection is 1.0. (Global scaling)
'region': Scale by a value such that the maximum absolute value of the
cumulative input to any region is 1.0. (Global-wise scaling)
None: does nothing.
NOTE: Currently multiple 'tract' and/or 'region' scalings without
intermediate 'none' scaling mode destroy the ability to recover
the original un-scaled weights matrix.
"""
# NOTE: It is not yet clear how or if we will integrate this functinality
# into the UI. Currently the same effect can be achieved manually
# by using the coupling functions, it is just that, in certain
# situations, things are simplified by starting from a normalised
# weights matrix. However, in other situations it is not desirable
# to have a simple normalisation of this sort.
# NOTE: We should probably separate the two cases implemented here into
# 'scaling' and 'normalisation'. Normalisation implies that the norm
# of the samples is equal to 1, while here it is only scaling by a factor.
LOG.info("Starting to normalize to mode: %s" % str(mode))
normalisation_factor = None
if mode in ("tract", "edge"):
# global scaling
normalisation_factor = numpy.abs(self.weights).max()
elif mode in ("region", "node"):
# node-wise scaling
normalisation_factor = numpy.max(numpy.abs(self.weights.sum(axis=1)))
elif mode in (None, "none"):
normalisation_factor = 1.0
else:
LOG.error("Bad weights normalisation mode, must be one of:")
LOG.error("('tract', 'edge', 'region', 'node', 'none')")
raise Exception("Bad weights normalisation mode")
LOG.debug("Normalization factor is: %s" % str(normalisation_factor))
mask = self.weights != 0.0
result = copy(self.weights)
result[mask] = self.weights[mask] / normalisation_factor
return result
def transform_binarize_matrix(self):
"""
Transforms the weights matrix into a binary (unweighted) matrix
"""
LOG.info("Transforming weighted matrix into unweighted matrix")
result = copy(self.weights)
result = numpy.where(result > 0, 1, result)
return result
def switch_distribution(self, matrix='tract_lengths', mode='none', seed=42):
"""
Permutation and resampling methods for the weights and distance
(tract_lengths) matrices.
'normal' : leaves the matrix unchanged
'shuffle' : randomize the elements of the 'matrix' matrix. Fisher-Yates
algorithm.
for i from n - 1 downto 1 do
j <- random integer with 0 :math:`\leq` j :math:`\leq` i
exchange a[j] and a[i]
'mean' : sets all the values to the sample mean value.
'empirical' : uses the gaussian_kde to estimate the underlying pdf of the
values and randomly samples a new matrix.
'analytical': defined pdf. Fits the data to the distribution to get the
corresponding parameters and then randomly samples a new
matrix.
"""
# Empirical seems to fail on some scipy installations. Error is not pinned down
# so far, it seems to only happen on some machines. Most relevant related to this:
#
# http://projects.scipy.org/scipy/ticket/1735
# http://comments.gmane.org/gmane.comp.python.scientific.devel/14816
# http://permalink.gmane.org/gmane.comp.python.numeric.general/42082
numpy.random.RandomState(seed)
temp = eval("self." + matrix)
D = copy(temp)
msg = "The distribution of the %s matrix will be changed" % matrix
LOG.info(msg)
if mode == 'none':
LOG.info("Maybe not ... Doing nothing")
elif mode == 'shuffle':
for i in reversed(xrange(1, D.shape[0])):
j = int(numpy.random.rand() * (i + 1))
D[:, i], D[:, j] = D[:, j].copy(), D[:, i].copy()
D[i, :], D[j, :] = D[j, :].copy(), D[i, :].copy()
elif mode == 'mean':
D[:] = D[self.weights > 0].mean()
elif mode == 'empirical':
from scipy import stats
kernel = stats.gaussian_kde(D[D > 0].flatten())
D = kernel.resample(size=(D.shape))
if numpy.any(D < 0):
# NOTE: The KDE method is not perfect, there are still very
# small probabilities for negative values around 0.
# TODO: change the kde bandwidth method
LOG.warning("Found negative values. Setting them to 0.0")
D = numpy.where(D < 0.0, 0.0, D)
# NOTE: if we need the cdf: kernel.integrate_box_1d(lo, hi)
# TODO: make a subclass using rv_continous, might be more accurate
elif mode == 'analytical':
LOG.warning("Analytical mode has not been implemented yet.")
# NOTE: pdf name could be an argument.
D = numpy.where(temp > 0, D, 0)
# NOTE: Consider saving a copy of the original delays matrix?
# exec("self." + matrix + "[:] = D")
def motif_linear_directed(self, number_of_regions=4, max_radius=100., return_type=None):
"""
Generates a linear (open chain) unweighted directed graph with equidistant nodes.
"""
iu1 = numpy.triu_indices(number_of_regions, 1)
iu2 = numpy.triu_indices(number_of_regions, 2)
self.weights = numpy.zeros((number_of_regions, number_of_regions))
self.weights[iu1] = 1.0
self.weights[iu2] = 0.0
self.tract_lengths = max_radius * copy(self.weights)
self.number_of_regions = number_of_regions
self.create_region_labels(mode='numeric')
if return_type is not None:
return self.weights, self.tract_lengths
else:
pass
def motif_linear_undirected(self, number_of_regions=4, max_radius=42.):
"""
Generates a linear (open chain) unweighted undirected graph with equidistant nodes.
"""
self.weights, self.tract_lengths = self.motif_linear_directed(number_of_regions=number_of_regions,
max_radius=max_radius,
return_type=True)
self.weights += self.weights.T
self.tract_lengths += self.tract_lengths.T
self.number_of_regions = number_of_regions
self.create_region_labels(mode='numeric')
def motif_chain_directed(self, number_of_regions=4, max_radius=42., return_type=None):
"""
Generates a closed unweighted directed graph with equidistant nodes.
Depending on the centres it could be a box or a ring.
"""
self.weights, self.tract_lengths = self.motif_linear_directed(number_of_regions=number_of_regions,
max_radius=max_radius,
return_type=True)
self.weights[-1, 0] = 1.0
self.tract_lengths[-1, 0] = max_radius
self.number_of_regions = number_of_regions
self.create_region_labels(mode='numeric')
if return_type is not None:
return self.weights, self.tract_lengths
else:
pass
def motif_chain_undirected(self, number_of_regions=4, max_radius=42.):
"""
Generates a closed unweighted undirected graph with equidistant nodes.
Depending on the centres it could be a box or a ring.
"""
self.weights, self.tract_lengths = self.motif_chain_directed(number_of_regions=number_of_regions,
max_radius=max_radius,
return_type=True)
self.weights[0, -1] = 1.0
self.tract_lengths[0, -1] = max_radius
self.number_of_regions = number_of_regions
self.create_region_labels(mode='numeric')
def motif_all_to_all(self, number_of_regions=4, max_radius=42.):
"""
Generates an all-to-all closed unweighted undirected graph with equidistant nodes.
Self-connections are not included.
"""
diagonal_elements = numpy.diag_indices(number_of_regions)
self.weights = numpy.ones((number_of_regions, number_of_regions))
self.weights[diagonal_elements] = 0.0
self.tract_lengths = max_radius * copy(self.weights)
self.number_of_regions = number_of_regions
self.create_region_labels(mode='numeric')
def centres_spherical(self, number_of_regions=4, max_radius=42., flat=False):
"""
The nodes positions are distributed on a sphere.
See: http://mathworld.wolfram.com/SphericalCoordinates.html
If flat is true, then theta=0.0, the nodes are lying inside a circle.
r : radial
theta: azimuthal
polar: phi
"""
# azimuth
theta = numpy.random.uniform(low=-numpy.pi, high=numpy.pi, size=number_of_regions)
# side of the cube
u = numpy.random.uniform(low=0.0, high=1.0, size=number_of_regions)
if flat:
cosphi = 0.0
else:
# cos(elevation)
cosphi = numpy.random.uniform(low=-1.0, high=1.0, size=number_of_regions)
phi = numpy.arccos(cosphi)
r = max_radius * pow(u, 1 / 3.0)
# To Cartesian coordinates
x = r * numpy.sin(phi) * numpy.cos(theta)
y = r * numpy.sin(phi) * numpy.sin(theta)
z = r * numpy.cos(phi)
self.centres = numpy.array([x, y, z]).T
norm_xyz = numpy.sqrt(numpy.sum(self.centres ** 2, axis=0))
self.orientations = self.centres / norm_xyz[numpy.newaxis, :]
def centres_toroidal(self, number_of_regions=4, max_radius=77., min_radius=13., mu=numpy.pi, kappa=numpy.pi / 6):
"""
The nodes are lying on a torus.
See: http://mathworld.wolfram.com/Torus.html
"""
u = scipy.stats.vonmises.rvs(kappa, loc=mu, size=number_of_regions)
v = scipy.stats.vonmises.rvs(kappa, loc=mu, size=number_of_regions)
# To cartesian coordinates
x = (max_radius + min_radius * numpy.cos(v)) * numpy.cos(u)
y = (max_radius + min_radius * numpy.cos(v)) * numpy.sin(u)
z = min_radius * numpy.sin(v)
# Tangent vector with respect to max_radius
tx = -numpy.sin(u)
ty = -numpy.cos(u)
tz = 0
# Tangent vector with respect to min_radius
sx = -numpy.cos(u) * (-numpy.sin(v))
sy = numpy.sin(u) * (-numpy.sin(v))
sz = numpy.cos(v)
# Normal vector
nx = ty * sz - tz * sy
ny = tz * sx - tx * sz
nz = tx * sy - ty * sx
# Normalize normal vectors
norm = numpy.sqrt(nx ** 2 + ny ** 2 + nz ** 2)
nx /= norm
ny /= norm
nz /= norm
self.orientations = numpy.array([nx, ny, nz]).T
self.centres = numpy.array([x, y, z]).T
def centres_annular(self, number_of_regions=4, max_radius=77., min_radius=13., mu=numpy.pi, kappa=numpy.pi / 6):
"""
The nodes are lying inside an annulus.
"""
r = numpy.random.uniform(low=min_radius, high=max_radius, size=number_of_regions)
theta = scipy.stats.vonmises.rvs(kappa, loc=mu, size=number_of_regions)
# To cartesian coordinates
x = r * numpy.cos(theta)
y = r * numpy.sin(theta)
z = numpy.zeros(number_of_regions)
self.centres = numpy.array([x, y, z]).T
def centres_cubic(self, number_of_regions=4, max_radius=42., flat=False):
"""
The nodes are positioined in a 3D grid inside the cube centred at the origin and
with edges parallel to the axes, with an edge length of 2*max_radius.
"""
# To cartesian coordinates
x = numpy.linspace(-max_radius, max_radius, number_of_regions)
y = numpy.linspace(-max_radius, max_radius, number_of_regions)
if flat:
z = numpy.zeros(number_of_regions)
else:
z = numpy.linspace(-max_radius, max_radius, number_of_regions)
self.centres = numpy.array([x, y, z]).T
def generate_surrogate_connectivity(self, number_of_regions, motif='chain', undirected=True,
these_centres='spherical'):
"""
This one generates some defaults.
For more specific motifs, generate invoking each method separetly.
"""
# NOTE: Luckily I went for 5 motifs ...
if motif == 'chain' and undirected:
self.motif_chain_undirected(number_of_regions=number_of_regions)
elif motif == "chain" and not undirected:
self.motif_chain_directed(number_of_regions=number_of_regions)
elif motif == 'linear' and undirected:
self.motif_linear_undirected(number_of_regions=number_of_regions)
elif motif == 'linear' and not undirected:
self.motif_linear_directed(number_of_regions=number_of_regions)
else:
LOG.info("Generating all-to-all connectivity \\")
self.motif_all_to_all(number_of_regions=number_of_regions)
# centres
if these_centres in ("spherical", "annular", "toroidal", "cubic"):
eval("self.centres_" + these_centres + "(number_of_regions=number_of_regions)")
else:
raise Exception("Bad centres geometry")
def create_region_labels(self, mode="numeric"):
"""
Assumes weights already exists
"""
LOG.info("Create labels: %s" % str(mode))
if mode in ("numeric", "num"):
self.region_labels = [n for n in xrange(self.number_of_regions)]
self.region_labels = numpy.array(self.region_labels).astype(str)
elif mode in ("alphabetic", "alpha"):
import string
if self.number_of_regions < 26:
self.region_labels = numpy.array(list(map(chr, range(65, 65 + self.number_of_regions)))).astype(str)
else:
LOG.info("I'm too lazy to create several strategies to label regions. \\")
LOG.info("Please choose mode 'numeric' or set your own labels\\")
else:
LOG.error("Bad region labels mode, must be one of:")
LOG.error("('numeric', 'num', 'alphabetic', 'alpha')")
raise Exception("Bad region labels mode")
def unmapped_indices(self, region_mapping):
"""
Compute vector of indices of regions in connectivity which are not in the given
region mapping.
"""
return numpy.setdiff1d(numpy.r_[:self.number_of_regions], region_mapping)
# final
@staticmethod
def from_file(source_file="connectivity_76.zip", instance=None):
if instance is None:
result = Connectivity()
else:
result = instance
source_full_path = try_get_absolute_path("tvb_data.connectivity", source_file)
if source_file.endswith(".h5"):
reader = H5Reader(source_full_path)
result.weights = reader.read_field("weights")
result.centres = reader.read_field("centres")
result.region_labels = reader.read_field("region_labels")
result.orientations = reader.read_optional_field("orientations")
result.cortical = reader.read_optional_field("cortical")
result.hemispheres = reader.read_field("hemispheres")
result.areas = reader.read_optional_field("areas")
result.tract_lengths = reader.read_field("tract_lengths")
else:
reader = ZipReader(source_full_path)
result.weights = reader.read_array_from_file("weights")
result.centres = reader.read_array_from_file("centres", use_cols=(1, 2, 3))
result.region_labels = reader.read_array_from_file("centres", dtype=numpy.str, use_cols=(0,))
result.orientations = reader.read_optional_array_from_file("average_orientations")
result.cortical = reader.read_optional_array_from_file("cortical", dtype=numpy.bool)
result.hemispheres = reader.read_optional_array_from_file("hemispheres", dtype=numpy.bool)
result.areas = reader.read_optional_array_from_file("areas")
result.tract_lengths = reader.read_array_from_file("tract_lengths")
return result
class ConnectivityData(MappedType):
"""
This class primarily exists to bundle the long range structural connectivity
data into a single object.
"""
default = readers.File(folder_path="connectivity/o52r00_irp2008")
parcellation_mask = volumes.ParcellationMask(
label="Parcellation mask (volume)",
required=False,
doc=
"""A 3D volume mask defining the parcellation of the brain into distinct regions."""
)
region_labels = arrays.StringArray(
label="Region labels",
console_default=default.read_data(file_name="centres.txt.bz2",
usecols=(0, ),
dtype="string",
field="region_labels"),
doc=
"""Short strings, 'labels', for the regions represented by the connectivity matrix."""
)
weights = arrays.FloatArray(
label="Connection strengths",
console_default=default.read_data(file_name="weights.txt.bz2",
field="weights"),
doc=
"""Matrix of values representing the strength of connections between regions, arbitrary units."""
)
unidirectional = basic.Integer(
default=0,
required=False,
doc=
"1, when the weights matrix is square and symmetric over the main diagonal, 0 when bi-directional matrix."
)
tract_lengths = arrays.FloatArray(
label="Tract lengths",
console_default=default.read_data(file_name="tract_lengths.txt.bz2",
field="tract_lengths"),
doc="""The length of myelinated fibre tracts between regions.
If not provided Euclidean distance between region centres is used.""")
speed = arrays.FloatArray(
label="Conduction speed",
default=numpy.array([3.0]),
file_storage=core.FILE_STORAGE_NONE,
doc=
"""A single number or matrix of conduction speeds for the myelinated fibre tracts between regions."""
)
centres = arrays.PositionArray(
label="Region centres",
console_default=default.read_data(file_name="centres.txt.bz2",
usecols=(1, 2, 3),
field="centres"),
doc="An array specifying the location of the centre of each region.")
cortical = arrays.BoolArray(
label="Cortical",
console_default=default.read_data(file_name="cortical.txt.bz2",
dtype=numpy.bool,
field="cortical"),
required=False,
doc=
"""A boolean vector specifying whether or not a region is part of the cortex."""
)
hemispheres = arrays.BoolArray(
label="Hemispheres (True for Right and False for Left Hemisphere",
required=False,
doc=
"""A boolean vector specifying whether or not a region is part of the right hemisphere"""
)
orientations = arrays.OrientationArray(
label="Average region orientation",
console_default=default.read_data(
file_name="average_orientations.txt.bz2", field="orientations"),
required=False,
doc=
"""Unit vectors of the average orientation of the regions represented in the connectivity matrix.
NOTE: Unknown data should be zeros.""")
areas = arrays.FloatArray(
label="Area of regions",
console_default=default.read_data(file_name="areas.txt.bz2",
field="areas"),
required=False,
doc=
"""Estimated area represented by the regions in the connectivity matrix.
NOTE: Unknown data should be zeros.""")
idelays = arrays.IndexArray(
label="Conduction delay indices",
required=False,
file_storage=core.FILE_STORAGE_NONE,
doc="An array of time delays between regions in integration steps.")
delays = arrays.FloatArray(
label="Conduction delay",
file_storage=core.FILE_STORAGE_NONE,
required=False,
doc=
"""Matrix of time delays between regions in physical units, setting conduction speed automatically
combines with tract lengths to update this matrix, i.e. don't try and change it manually."""
)
number_of_regions = basic.Integer(
label="Number of regions",
doc="""The number of regions represented in this Connectivity """)
# ------------- FRAMEWORK ATTRIBUTES -----------------------------
# Rotation if positions are not normalized.
nose_correction = basic.JSONType(required=False)
# Original Connectivity, from which current connectivity was edited.
parent_connectivity = basic.String(required=False)
# In case of edited Connectivity, this are the nodes left in interest area,
# the rest were part of a lesion, so they were removed.
saved_selection = basic.JSONType(required=False)
class MappedArray(MappedType):
"An array stored in the database."
KEY_SIZE = "size"
KEY_OPERATION = "operation"
title = basic.String
label_x, label_y = basic.String, basic.String
aggregation_functions = basic.JSONType(required=False)
dimensions_labels = basic.JSONType(required=False)
nr_dimensions, length_1d, length_2d, length_3d, length_4d = [
basic.Integer
] * 5
array_data = Array()
__generate_table__ = True
def _find_summary_info(self):
"""
Gather scientifically interesting summary information from an instance of this datatype.
"""
summary = {"Title:": self.title, "Dimensions:": self.dimensions_labels}
summary.update(self.get_info_about_array('array_data'))
return summary
@property
def display_name(self):
"""
Overwrite from superclass and add title field
"""
previous = super(MappedArray, self).display_name
if previous is None:
return str(self.title)
return str(self.title) + " " + previous
@property
def shape(self):
"""
Shape for current wrapped NumPy array.
"""
return self.array_data.shape
def configure_chunk_safe(self):
""" Configure part which is chunk safe"""
data_shape = self.get_data_shape('array_data')
self.nr_dimensions = len(data_shape)
for i in range(min(self.nr_dimensions, 4)):
setattr(self, 'length_%dd' % (i + 1), int(data_shape[i]))
def configure(self):
"""After populating few fields, compute the rest of the fields"""
super(MappedArray, self).configure()
if not isinstance(self.array_data, numpy.ndarray):
return
self.nr_dimensions = len(self.array_data.shape)
for i in range(min(self.nr_dimensions, 4)):
setattr(self, 'length_%dd' % (i + 1), self.array_data.shape[i])
@staticmethod
def accepted_filters():
filters = MappedType.accepted_filters()
filters.update({
'datatype_class._nr_dimensions': {
'type': 'int',
'display': 'Dimensionality',
'operations': ['==', '<', '>']
},
'datatype_class._length_1d': {
'type': 'int',
'display': 'Shape 1',
'operations': ['==', '<', '>']
},
'datatype_class._length_2d': {
'type': 'int',
'display': 'Shape 2',
'operations': ['==', '<', '>']
}
})
return filters
def reduce_dimension(self, ui_selected_items):
"""
ui_selected_items is a dictionary which contains items of form:
'$name_$D : [$gid_$D_$I,..., func_$FUNC]' where '$D - int dimension', '$gid - a data type gid',
'$I - index in that dimension' and '$FUNC - an aggregation function'.
If the user didn't select anything from a certain dimension then it means that the entire
dimension is selected
"""
# The fields 'aggregation_functions' and 'dimensions' will be of form:
# - aggregation_functions = {dimension: agg_func, ...} e.g.: {0: sum, 1: average, 2: none, ...}
# - dimensions = {dimension: [list_of_indexes], ...} e.g.: {0: [0,1], 1: [5,500],...}
dimensions, aggregation_functions, required_dimension, shape_restrictions = \
self.parse_selected_items(ui_selected_items)
if required_dimension is not None:
# find the dimension of the resulted array
dim = len(self.shape)
for key in aggregation_functions.keys():
if aggregation_functions[key] != "none":
dim -= 1
for key in dimensions.keys():
if (len(dimensions[key]) == 1
and (key not in aggregation_functions
or aggregation_functions[key] == "none")):
dim -= 1
if dim != required_dimension:
self.logger.debug("Dimension for selected array is incorrect")
raise ValidationException(
"Dimension for selected array is incorrect!")
result = self.array_data
full = slice(0, None)
cut_dimensions = 0
for i in range(len(self.shape)):
if i in dimensions.keys():
my_slice = [full for _ in range(i - cut_dimensions)]
if len(dimensions[i]) == 1:
my_slice.extend(dimensions[i])
cut_dimensions += 1
else:
my_slice.append(dimensions[i])
result = result[tuple(my_slice)]
if i in aggregation_functions.keys():
if aggregation_functions[i] != "none":
result = eval("numpy." + aggregation_functions[i] +
"(result,axis=" + str(i - cut_dimensions) +
")")
cut_dimensions += 1
# check that the shape for the resulted array respects given conditions
result_shape = result.shape
for i in range(len(result_shape)):
if i in shape_restrictions:
flag = eval(
str(result_shape[i]) +
shape_restrictions[i][self.KEY_OPERATION] +
str(shape_restrictions[i][self.KEY_SIZE]))
if not flag:
msg = ("The condition is not fulfilled: dimension " +
str(i + 1) + " " +
shape_restrictions[i][self.KEY_OPERATION] + " " +
str(shape_restrictions[i][self.KEY_SIZE]) +
". The actual size of dimension " + str(i + 1) +
" is " + str(result_shape[i]) + ".")
self.logger.debug(msg)
raise ValidationException(msg)
if required_dimension is not None and 1 <= required_dimension != len(
result.shape):
self.logger.debug("Dimensions of the selected array are incorrect")
raise ValidationException(
"Dimensions of the selected array are incorrect!")
return result
def parse_selected_items(self, ui_selected_items):
"""
Used for parsing the user selected items.
ui_selected_items is a dictionary which contains items of form:
'name_D : [gid_D_I,..., func_FUNC]' where 'D - dimension', 'gid - a data type gid',
'I - index in that dimension' and 'FUNC - an aggregation function'.
"""
expected_shape_str = ''
operations_str = ''
dimensions = dict()
aggregation_functions = dict()
required_dimension = None
for key in ui_selected_items.keys():
split_array = str(key).split("_")
current_dim = split_array[len(split_array) - 1]
list_values = ui_selected_items[key]
if list_values is None or len(list_values) == 0:
list_values = []
elif not isinstance(list_values, list):
list_values = [list_values]
for item in list_values:
if str(item).startswith("expected_shape_"):
expected_shape_str = str(item).split("expected_shape_")[1]
elif str(item).startswith("operations_"):
operations_str = str(item).split("operations_")[1]
elif str(item).startswith("requiredDim_"):
required_dimension = int(
str(item).split("requiredDim_")[1])
elif str(item).startswith("func_"):
agg_func = str(item).split("func_")[1]
aggregation_functions[int(current_dim)] = agg_func
else:
str_array = str(item).split("_")
if int(str_array[1]) in dimensions:
dimensions[int(str_array[1])].append(int(str_array[2]))
else:
dimensions[int(str_array[1])] = [int(str_array[2])]
return dimensions, aggregation_functions, required_dimension, self._parse_expected_shape(
expected_shape_str, operations_str)
def _parse_expected_shape(self, expected_shape_str='', operations_str=''):
"""
If we have the inputs of form: expected_shape='x,512,x' and operations='x,<,x'.
The result will be: {1: {'size':512, 'operation':'<'}}
"""
result = {}
if len(expected_shape_str.strip()) == 0 or len(
operations_str.strip()) == 0:
return result
shape_array = str(expected_shape_str).split(",")
op_array = str(operations_str).split(",")
operations = self._get_operations()
for i in range(len(shape_array)):
if str(shape_array[i]).isdigit(
) and i < len(op_array) and op_array[i] in operations:
result[i] = {
self.KEY_SIZE: int(shape_array[i]),
self.KEY_OPERATION: operations[op_array[i]]
}
return result
def read_data_shape(self):
""" Expose shape read on field 'array_data' """
return self.get_data_shape('array_data')
def read_data_slice(self, data_slice):
""" Expose chunked-data access. """
return self.get_data('array_data', data_slice)
@staticmethod
def _get_operations():
"""Return accepted operations"""
operations = {
'<': '<',
'>': '>',
'≥': '>=',
'≤': '<=',
'==': '=='
}
return operations
class ConnectivityData(MappedType):
"""
This class primarily exists to bundle the long range structural connectivity
data into a single object.
"""
region_labels = arrays.StringArray(
label="Region labels",
doc=
"""Short strings, 'labels', for the regions represented by the connectivity matrix."""
)
weights = arrays.FloatArray(
label="Connection strengths",
stored_metadata=[key for key in MappedType.DEFAULT_WITH_ZERO_METADATA],
doc=
"""Matrix of values representing the strength of connections between regions, arbitrary units."""
)
undirected = basic.Integer(
default=0,
required=False,
doc=
"1, when the weights matrix is square and symmetric over the main diagonal, 0 when directed graph."
)
tract_lengths = arrays.FloatArray(
label="Tract lengths",
stored_metadata=[key for key in MappedType.DEFAULT_WITH_ZERO_METADATA],
doc="""The length of myelinated fibre tracts between regions.
If not provided Euclidean distance between region centres is used.""")
speed = arrays.FloatArray(
label="Conduction speed",
default=numpy.array([3.0]),
file_storage=core.FILE_STORAGE_NONE,
doc=
"""A single number or matrix of conduction speeds for the myelinated fibre tracts between regions."""
)
centres = arrays.PositionArray(
label="Region centres",
doc="An array specifying the location of the centre of each region.")
cortical = arrays.BoolArray(
label="Cortical",
required=False,
doc=
"""A boolean vector specifying whether or not a region is part of the cortex."""
)
hemispheres = arrays.BoolArray(
label="Hemispheres (True for Right and False for Left Hemisphere",
required=False,
doc=
"""A boolean vector specifying whether or not a region is part of the right hemisphere"""
)
orientations = arrays.OrientationArray(
label="Average region orientation",
required=False,
doc=
"""Unit vectors of the average orientation of the regions represented in the connectivity matrix.
NOTE: Unknown data should be zeros.""")
areas = arrays.FloatArray(
label="Area of regions",
required=False,
doc=
"""Estimated area represented by the regions in the connectivity matrix.
NOTE: Unknown data should be zeros.""")
idelays = arrays.IndexArray(
label="Conduction delay indices",
required=False,
file_storage=core.FILE_STORAGE_NONE,
doc="An array of time delays between regions in integration steps.")
delays = arrays.FloatArray(
label="Conduction delay",
file_storage=core.FILE_STORAGE_NONE,
required=False,
doc=
"""Matrix of time delays between regions in physical units, setting conduction speed automatically
combines with tract lengths to update this matrix, i.e. don't try and change it manually."""
)
number_of_regions = basic.Integer(
label="Number of regions",
doc="""The number of regions represented in this Connectivity """)
number_of_connections = basic.Integer(
label="Number of connections",
doc=
"""The number of non-zero entries represented in this Connectivity """)
# ------------- FRAMEWORK ATTRIBUTES -----------------------------
# Original Connectivity, from which current connectivity was edited.
parent_connectivity = basic.String(required=False)
# In case of edited Connectivity, this are the nodes left in interest area,
# the rest were part of a lesion, so they were removed.
saved_selection = basic.JSONType(required=False)