/usr/lib/python2.7/dist-packages/dipy/segment/clustering.py is in python-dipy 0.10.1-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 | import operator
import numpy as np
from abc import ABCMeta, abstractmethod
from dipy.segment.metric import Metric
from dipy.segment.metric import ResampleFeature
from dipy.segment.metric import AveragePointwiseEuclideanMetric
class Identity:
""" Provides identity indexing functionality.
This can replace any class supporting indexing used for referencing
(e.g. list, tuple). Indexing an instance of this class will return the
index provided instead of the element. It does not support slicing.
"""
def __getitem__(self, idx):
return idx
class Cluster(object):
""" Provides functionalities for interacting with a cluster.
Useful container to retrieve index of elements grouped together. If
a reference to the data is provided to `cluster_map`, elements will
be returned instead of their index when possible.
Parameters
----------
cluster_map : `ClusterMap` object
Reference to the set of clusters this cluster is being part of.
id : int
Id of this cluster in its associated `cluster_map` object.
refdata : list (optional)
Actual elements that clustered indices refer to.
Notes
-----
A cluster does not contain actual data but instead knows how to
retrieve them using its `ClusterMap` object.
"""
def __init__(self, id=0, indices=None, refdata=Identity()):
self.id = id
self.refdata = refdata
self.indices = indices if indices is not None else []
def __len__(self):
return len(self.indices)
def __getitem__(self, idx):
""" Gets element(s) through indexing.
If a reference to the data was provided (via refdata property)
elements will be returned instead of their index.
Parameters
----------
idx : int, slice or list
Index of the element(s) to get.
Returns
-------
`Cluster` object(s)
When `idx` is a int, returns a single element.
When `idx` is either a slice or a list, returns a list of elements.
"""
if isinstance(idx, int) or isinstance(idx, np.integer):
return self.refdata[self.indices[idx]]
elif type(idx) is slice:
return [self.refdata[i] for i in self.indices[idx]]
elif type(idx) is list:
return [self[i] for i in idx]
raise TypeError("Index must be a int or a slice! Not " + str(type(idx)))
def __iter__(self):
return (self[i] for i in range(len(self)))
def __str__(self):
return "[" + ", ".join(map(str, self.indices)) + "]"
def __repr__(self):
return "Cluster(" + str(self) + ")"
def __eq__(self, other):
return isinstance(other, Cluster) and self.indices == other.indices
def __ne__(self, other):
return not self == other
def __cmp__(self, other):
raise TypeError("Cannot compare Cluster objects.")
def assign(self, *indices):
""" Assigns indices to this cluster.
Parameters
----------
*indices : list of indices
Indices to add to this cluster.
"""
self.indices += indices
class ClusterCentroid(Cluster):
""" Provides functionalities for interacting with a cluster.
Useful container to retrieve the indices of elements grouped together and
the cluster's centroid. If a reference to the data is provided to
`cluster_map`, elements will be returned instead of their index when
possible.
Parameters
----------
cluster_map : `ClusterMapCentroid` object
Reference to the set of clusters this cluster is being part of.
id : int
Id of this cluster in its associated `cluster_map` object.
refdata : list (optional)
Actual elements that clustered indices refer to.
Notes
-----
A cluster does not contain actual data but instead knows how to
retrieve them using its `ClusterMapCentroid` object.
"""
def __init__(self, centroid, id=0, indices=None, refdata=Identity()):
super(ClusterCentroid, self).__init__(id, indices, refdata)
self.centroid = centroid.copy()
self.new_centroid = centroid.copy()
def __eq__(self, other):
return isinstance(other, ClusterCentroid) \
and np.all(self.centroid == other.centroid) \
and super(ClusterCentroid, self).__eq__(other)
def assign(self, id_datum, features):
""" Assigns a data point to this cluster.
Parameters
----------
id_datum : int
Index of the data point to add to this cluster.
features : 2D array
Data point's features to modify this cluster's centroid.
"""
N = len(self)
self.new_centroid = ((self.new_centroid * N) + features) / (N+1.)
super(ClusterCentroid, self).assign(id_datum)
def update(self):
""" Update centroid of this cluster.
Returns
-------
converged : bool
Tells if the centroid has moved.
"""
converged = np.equal(self.centroid, self.new_centroid)
self.centroid = self.new_centroid.copy()
return converged
class ClusterMap(object):
""" Provides functionalities for interacting with clustering outputs.
Useful container to create, remove, retrieve and filter clusters.
If `refdata` is given, elements will be returned instead of their
index when using `Cluster` objects.
Parameters
----------
refdata : list
Actual elements that clustered indices refer to.
"""
def __init__(self, refdata=Identity()):
self._clusters = []
self.refdata = refdata
@property
def clusters(self):
return self._clusters
@property
def refdata(self):
return self._refdata
@refdata.setter
def refdata(self, value):
if value is None:
value = Identity()
self._refdata = value
for cluster in self.clusters:
cluster.refdata = self._refdata
def __len__(self):
return len(self.clusters)
def __getitem__(self, idx):
""" Gets cluster(s) through indexing.
Parameters
----------
idx : int, slice, list or boolean array
Index of the element(s) to get.
Returns
-------
`Cluster` object(s)
When `idx` is a int, returns a single `Cluster` object.
When `idx`is either a slice, list or boolean array, returns
a list of `Cluster` objects.
"""
if isinstance(idx, np.ndarray) and idx.dtype == np.bool:
return [self.clusters[i] for i, take_it in enumerate(idx) if take_it]
elif type(idx) is slice:
return [self.clusters[i] for i in range(*idx.indices(len(self)))]
elif type(idx) is list:
return [self.clusters[i] for i in idx]
return self.clusters[idx]
def __iter__(self):
return iter(self.clusters)
def __str__(self):
return "[" + ", ".join(map(str, self)) + "]"
def __repr__(self):
return "ClusterMap(" + str(self) + ")"
def _richcmp(self, other, op):
""" Compares this cluster map with another cluster map or an integer.
Two `ClusterMap` objects are equal if they contain the same clusters.
When comparing a `ClusterMap` object with an integer, the comparison
will be performed on the size of the clusters instead.
Parameters
----------
other : `ClusterMap` object or int
Object to compare to.
op : rich comparison operators (see module `operator`)
Valid operators are: lt, le, eq, ne, gt or ge.
Returns
-------
bool or 1D array (bool)
When comparing to another `ClusterMap` object, it returns whether
the two `ClusterMap` objects contain the same clusters or not.
When comparing to an integer the comparison is performed on the
clusters sizes, it returns an array of boolean.
"""
if isinstance(other, ClusterMap):
if op is operator.eq:
return isinstance(other, ClusterMap) \
and len(self) == len(other) \
and self.clusters == other.clusters
elif op is operator.ne:
return not self == other
raise NotImplementedError("Can only check if two ClusterMap instances are equal or not.")
elif isinstance(other, int):
return np.array([op(len(cluster), other) for cluster in self])
raise NotImplementedError("ClusterMap only supports comparison with a int or another instance of Clustermap.")
def __eq__(self, other):
return self._richcmp(other, operator.eq)
def __ne__(self, other):
return self._richcmp(other, operator.ne)
def __lt__(self, other):
return self._richcmp(other, operator.lt)
def __le__(self, other):
return self._richcmp(other, operator.le)
def __gt__(self, other):
return self._richcmp(other, operator.gt)
def __ge__(self, other):
return self._richcmp(other, operator.ge)
def add_cluster(self, *clusters):
""" Adds one or multiple clusters to this cluster map.
Parameters
----------
*clusters : `Cluster` object, ...
Cluster(s) to be added in this cluster map.
"""
for cluster in clusters:
self.clusters.append(cluster)
cluster.refdata = self.refdata
def remove_cluster(self, *clusters):
""" Remove one or multiple clusters from this cluster map.
Parameters
----------
*clusters : `Cluster` object, ...
Cluster(s) to be removed from this cluster map.
"""
for cluster in clusters:
self.clusters.remove(cluster)
def clear(self):
""" Remove all clusters from this cluster map. """
del self.clusters[:]
def size(self):
""" Gets number of clusters contained in this cluster map. """
return len(self)
def clusters_sizes(self):
""" Gets the size of every cluster contained in this cluster map.
Returns
-------
list of int
Sizes of every cluster in this cluster map.
"""
return list(map(len, self))
def get_large_clusters(self, min_size):
""" Gets clusters which contains at least `min_size` elements.
Parameters
----------
min_size : int
Minimum number of elements a cluster needs to have to be selected.
Returns
-------
list of `Cluster` objects
Clusters having at least `min_size` elements.
"""
return self[self >= min_size]
def get_small_clusters(self, max_size):
""" Gets clusters which contains at most `max_size` elements.
Parameters
----------
max_size : int
Maximum number of elements a cluster can have to be selected.
Returns
-------
list of `Cluster` objects
Clusters having at most `max_size` elements.
"""
return self[self <= max_size]
class ClusterMapCentroid(ClusterMap):
""" Provides functionalities for interacting with clustering outputs
that have centroids.
Allows to retrieve easely the centroid of every cluster. Also, it is
a useful container to create, remove, retrieve and filter clusters.
If `refdata` is given, elements will be returned instead of their
index when using `ClusterCentroid` objects.
Parameters
----------
refdata : list
Actual elements that clustered indices refer to.
"""
@property
def centroids(self):
return [cluster.centroid for cluster in self.clusters]
class Clustering(object):
__metaclass__ = ABCMeta
@abstractmethod
def cluster(self, data, ordering=None):
""" Clusters `data`.
Subclasses will perform their clustering algorithm here.
Parameters
----------
data : list of N-dimensional arrays
Each array represents a data point.
ordering : iterable of indices, optional
Specifies the order in which data points will be clustered.
Returns
-------
`ClusterMap` object
Result of the clustering.
"""
raise NotImplementedError("Subclass has to define method 'cluster(data, ordering)'!")
class QuickBundles(Clustering):
r""" Clusters streamlines using QuickBundles [Garyfallidis12]_.
Given a list of streamlines, the QuickBundles algorithm sequentially
assigns each streamline to its closest bundle in $\mathcal{O}(Nk)$ where
$N$ is the number of streamlines and $k$ is the final number of bundles.
If for a given streamline its closest bundle is farther than `threshold`,
a new bundle is created and the streamline is assigned to it except if the
number of bundles has already exceeded `max_nb_clusters`.
Parameters
----------
threshold : float
The maximum distance from a bundle for a streamline to be still
considered as part of it.
metric : str or `Metric` object (optional)
The distance metric to use when comparing two streamlines. By default,
the Minimum average Direct-Flip (MDF) distance [Garyfallidis12]_ is
used and streamlines are automatically resampled so they have 12 points.
max_nb_clusters : int
Limits the creation of bundles.
Examples
--------
>>> from dipy.segment.clustering import QuickBundles
>>> from dipy.data import get_data
>>> from nibabel import trackvis as tv
>>> streams, hdr = tv.read(get_data('fornix'))
>>> streamlines = [i[0] for i in streams]
>>> # Segment fornix with a treshold of 10mm and streamlines resampled to 12 points.
>>> qb = QuickBundles(threshold=10.)
>>> clusters = qb.cluster(streamlines)
>>> len(clusters)
4
>>> list(map(len, clusters))
[61, 191, 47, 1]
>>> # Resampling streamlines differently is done explicitly as follows.
>>> # Note this has an impact on the speed and the accuracy (tradeoff).
>>> from dipy.segment.metric import ResampleFeature
>>> from dipy.segment.metric import AveragePointwiseEuclideanMetric
>>> feature = ResampleFeature(nb_points=2)
>>> metric = AveragePointwiseEuclideanMetric(feature)
>>> qb = QuickBundles(threshold=10., metric=metric)
>>> clusters = qb.cluster(streamlines)
>>> len(clusters)
4
>>> list(map(len, clusters))
[58, 142, 72, 28]
References
----------
.. [Garyfallidis12] Garyfallidis E. et al., QuickBundles a method for
tractography simplification, Frontiers in Neuroscience,
vol 6, no 175, 2012.
"""
def __init__(self, threshold, metric="MDF_12points", max_nb_clusters=np.iinfo('i4').max):
self.threshold = threshold
self.max_nb_clusters = max_nb_clusters
if isinstance(metric, Metric):
self.metric = metric
elif metric == "MDF_12points":
feature = ResampleFeature(nb_points=12)
self.metric = AveragePointwiseEuclideanMetric(feature)
else:
raise ValueError("Unknown metric: {0}".format(metric))
def cluster(self, streamlines, ordering=None):
""" Clusters `streamlines` into bundles.
Performs quickbundles algorithm using predefined metric and threshold.
Parameters
----------
streamlines : list of 2D arrays
Each 2D array represents a sequence of 3D points (points, 3).
ordering : iterable of indices
Specifies the order in which data points will be clustered.
Returns
-------
`ClusterMapCentroid` object
Result of the clustering.
"""
from dipy.segment.clustering_algorithms import quickbundles
cluster_map = quickbundles(streamlines, self.metric,
threshold=self.threshold,
max_nb_clusters=self.max_nb_clusters,
ordering=ordering)
cluster_map.refdata = streamlines
return cluster_map
|