pyhrf.parcellation.
Ant
(a_id, greed, graph, labels, path_marks, site_marks, pressures, world, verbosity=0)¶Bases: pyhrf.parcellation.Talker
action
(time)¶fix_explosion
()¶to_conquer
()¶to_patrol
()¶pyhrf.parcellation.
Talker
(talker_string_id, verbosity=0)¶verbose
(level, msg)¶verbose_array
(level, array)¶pyhrf.parcellation.
Visit_graph_noeud
(noeud, graphe, Visited=None)¶pyhrf.parcellation.
World
(graph, nb_ants, greed=0.05, time_min=100, time_max=None, tolerance=1, verbosity=0, stop_when_all_controlled=False)¶Bases: pyhrf.parcellation.Talker
balanced
()¶force_end
()¶get_final_labels
()¶resolve
()¶site_taken
(site)¶pyhrf.parcellation.
init_edge_data
(g, init_value=0)¶pyhrf.parcellation.
make_parcellation_cubed_blobs_from_file
(parcellation_file, output_path, roi_ids=None, bg_parcel=0, skip_existing=False)¶pyhrf.parcellation.
make_parcellation_from_files
(betaFiles, maskFile, outFile, nparcels, method, dry=False, spatial_weight=10.0)¶pyhrf.parcellation.
make_parcellation_surf_from_files
(beta_files, mesh_file, parcellation_file, nbparcel, method, mu=10.0, verbose=0)¶pyhrf.parcellation.
parcellate_balanced_vol
(mask, nb_parcels)¶Parameters: |
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Returns: | a 3D array of integers |
Return type: |
|
pyhrf.parcellation.
parcellate_voronoi_vol
(mask, nb_parcels, seeds=None)¶Produce a parcellation from a Voronoi diagram built on random seeds. The number of seeds is equal to the nb of parcels. Seed are randomly placed within the mask, expect on edge positions
Parameters: |
|
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Returns: | a 3D array of integers - |
Return type: |
|
pyhrf.parcellation.
parcellation_dist
(p1, p2, mask=None)¶Compute the distance between the two parcellation p1 and p2 as the minimum number of positions to remove in order to obtain equal partitions. “mask” may be a binary mask to limit the distance computation to some specific positions. Important convention: parcel label 0 is treated as background and corresponding positions are discarded if no mask is provided.
Returns: | (distance value, parcellation overlap) |
---|
pyhrf.parcellation.
parcellation_for_jde
(fmri_data, avg_parcel_size=250, output_dir=None, method='gkm', glm_drift='Cosine', glm_hfcut=128)¶method: gkm, ward, ward_and_gkm
pyhrf.parcellation.
parcellation_report
(d)¶pyhrf.parcellation.
parcellation_ward_spatial
(func_data, n_clusters, graph=None)¶Make parcellation based upon ward hierarchical clustering from scikit-learn
Parameters: |
|
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Returns: | |
Return type: | parcellation labels |
pyhrf.parcellation.
permutation
(x)¶Randomly permute a sequence, or return a permuted range.
If x is a multi-dimensional array, it is only shuffled along its first index.
Parameters: | x (int or array_like) – If x is an integer, randomly permute np.arange(x) .
If x is an array, make a copy and shuffle the elements
randomly. |
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Returns: | out – Permuted sequence or array range. |
Return type: | ndarray |
Examples
>>> np.random.permutation(10)
array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])
>>> np.random.permutation([1, 4, 9, 12, 15])
array([15, 1, 9, 4, 12])
>>> arr = np.arange(9).reshape((3, 3))
>>> np.random.permutation(arr)
array([[6, 7, 8],
[0, 1, 2],
[3, 4, 5]])
pyhrf.parcellation.
rand
(d0, d1, ..., dn)¶Random values in a given shape.
Create an array of the given shape and populate it with
random samples from a uniform distribution
over [0, 1)
.
Parameters: | d1, .., dn (d0,) – The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned. |
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Returns: | out – Random values. |
Return type: | ndarray, shape (d0, d1, ..., dn) |
See also
random()
Notes
This is a convenience function. If you want an interface that takes a shape-tuple as the first argument, refer to np.random.random_sample .
Examples
>>> np.random.rand(3,2)
array([[ 0.14022471, 0.96360618], #random
[ 0.37601032, 0.25528411], #random
[ 0.49313049, 0.94909878]]) #random
pyhrf.parcellation.
randint
(low, high=None, size=None, dtype='l')¶Return random integers from low (inclusive) to high (exclusive).
Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then results are from [0, low).
Parameters: |
|
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Returns: | out – size-shaped array of random integers from the appropriate distribution, or a single such random int if size not provided. |
Return type: | int or ndarray of ints |
See also
random.random_integers()
Examples
>>> np.random.randint(2, size=10)
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
>>> np.random.randint(1, size=10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
Generate a 2 x 4 array of ints between 0 and 4, inclusive:
>>> np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1],
[3, 2, 2, 0]])
pyhrf.parcellation.
random_pick
(a)¶pyhrf.parcellation.
round_nb_parcels
(n)¶pyhrf.parcellation.
split_big_parcels
(parcel_file, output_file, max_size=400)¶pyhrf.parcellation.
split_parcel
(labels, graphs, id_parcel, n_parcels, inplace=False, verbosity=0, balance_tolerance='exact')¶balance_tolerance : exact or draft