# Source code for elephant.spade

```
"""
SPADE :cite:`spade-Torre2013_132,spade-Quaglio2017_41,spade-Stella2019_104022`
is the combination of a mining technique and multiple statistical tests to
detect and assess the statistical significance of repeated occurrences of spike
sequences (spatio-temporal patterns, STP).
.. autosummary::
:toctree: _toctree/spade
spade
concepts_mining
pvalue_spectrum
test_signature_significance
approximate_stability
pattern_set_reduction
concept_output_to_patterns
Visualization
-------------
Visualization of SPADE analysis is covered in Viziphant:
https://viziphant.readthedocs.io/en/latest/modules.html
Notes
-----
This modules relies on the C++ implementation of the fp-growth algorithm developed by
Forian Porrmann (available at https://github.com/fporrmann/FPG). The module replaces
a more generic implementation of the algorithm by Christian Borgelt
(http://www.borgelt.net/pyfim.html) that was used in previous versions of Elephant.
If the module (fim.so) is not available in a precompiled format (currently Linux/Windows) or cannot
be compiled on a given system during install, SPADE will make use of a pure Python implementation
of the fast fca algorithm contained in `elephant/spade_src/fast_fca.py`, which is
significantly slower.
See Also
--------
elephant.cell_assembly_detection.cell_assembly_detection : another synchronous
patterns detection
Examples
--------
Given a list of Neo Spiketrain objects, assumed to be recorded in parallel, the
SPADE analysis can be applied as demonstrated in this short toy example of 10
artificial spike trains of exhibiting fully synchronous events of order 10.
>>> import quantities as pq
>>> import numpy as np
>>> from elephant.spike_train_generation import compound_poisson_process
>>> from elephant.spade import spade
Generate correlated spiketrains.
>>> np.random.seed(30)
>>> spiketrains = compound_poisson_process(rate=15*pq.Hz,
... amplitude_distribution=[0, 0.95, 0, 0, 0, 0, 0.05], t_stop=5*pq.s)
Mining patterns with SPADE using a `bin_size` of 1 ms and a window length of 1
bin (i.e., detecting only synchronous patterns).
>>> patterns = spade(spiketrains, bin_size=10 * pq.ms, winlen=1,
... dither=5 * pq.ms, min_spikes=6, n_surr=10,
... psr_param=[0, 0, 3])['patterns']
>>> patterns[0]
{'itemset': (4, 3, 0, 2, 5, 1),
'windows_ids': (9,
16,
55,
91,
...,
393,
456,
467),
'neurons': [4, 3, 0, 2, 5, 1],
'lags': array([0., 0., 0., 0., 0.]) * ms,
'times': array([ 90., 160., 550., 910., 930., 1420., 1480., 1650., 2570.,
3130., 3430., 3480., 3610., 3800., 3830., 3930., 4560., 4670.]) * ms,
'signature': (6, 18),
'pvalue': 0.0}
Refer to Viziphant documentation to check how to visualzie such patterns.
:copyright: Copyright 2014-2022 by the Elephant team, see `doc/authors.rst`.
:license: BSD, see LICENSE.txt for details.
"""
from __future__ import division, print_function, unicode_literals
import operator
import time
import warnings
from collections import defaultdict
from functools import reduce
from itertools import chain, combinations
import neo
from neo.core.spiketrainlist import SpikeTrainList
import numpy as np
import quantities as pq
from scipy import sparse
import elephant.conversion as conv
import elephant.spike_train_surrogates as surr
from elephant.spade_src import fast_fca
from elephant.utils import deprecated_alias
__all__ = [
"spade",
"concepts_mining",
"pvalue_spectrum",
"test_signature_significance",
"approximate_stability",
"pattern_set_reduction",
"concept_output_to_patterns"
]
warnings.simplefilter('once', UserWarning)
try:
from mpi4py import MPI # for parallelized routines
HAVE_MPI = True
except ImportError: # pragma: no cover
HAVE_MPI = False
try:
from elephant.spade_src import fim
HAVE_FIM = True
except ImportError: # pragma: no cover
HAVE_FIM = False
[docs]@deprecated_alias(binsize='bin_size')
def spade(spiketrains, bin_size, winlen, min_spikes=2, min_occ=2,
max_spikes=None, max_occ=None, min_neu=1, approx_stab_pars=None,
n_surr=0, dither=15 * pq.ms, spectrum='#',
alpha=None, stat_corr='fdr_bh', surr_method='dither_spikes',
psr_param=None, output_format='patterns', **surr_kwargs):
r"""
Perform the SPADE :cite:`spade-Torre2013_132`,
:cite:`spade-Quaglio2017_41`, :cite:`spade-Stella2019_104022` analysis for
the parallel input `spiketrains`. They are discretized with a temporal
resolution equal to `bin_size` in a sliding window of `winlen*bin_size`.
First, spike patterns are mined from the `spiketrains` using a technique
called frequent itemset mining (FIM) or formal concept analysis (FCA). In
this framework, a particular spatio-temporal spike pattern is called a
"concept". It is then possible to compute the stability and the p-value
of all pattern candidates. In a final step, concepts are filtered
according to a stability threshold and a significance level `alpha`.
Parameters
----------
spiketrains : list of neo.SpikeTrain
List containing the parallel spike trains to analyze
bin_size : pq.Quantity
The time precision used to discretize the spiketrains (binning).
winlen : int
The size (number of bins) of the sliding window used for the analysis.
The maximal length of a pattern (delay between first and last spike) is
then given by winlen*bin_size
min_spikes : int, optional
Minimum number of spikes of a sequence to be considered a pattern.
Default: 2
min_occ : int, optional
Minimum number of occurrences of a sequence to be considered as a
pattern.
Default: 2
max_spikes : int, optional
Maximum number of spikes of a sequence to be considered a pattern. If
None no maximal number of spikes is considered.
Default: None
max_occ : int, optional
Maximum number of occurrences of a sequence to be considered as a
pattern. If None, no maximal number of occurrences is considered.
Default: None
min_neu : int, optional
Minimum number of neurons in a sequence to considered a pattern.
Default: 1
approx_stab_pars : dict or None, optional
Parameter values for approximate stability computation.
'n_subsets': int
Number of subsets of a concept used to approximate its stability.
If `n_subsets` is 0, it is calculated according to to the formula
given in Babin, Kuznetsov (2012), proposition 6:
.. math::
n_{\text{subset}} = \frac{1}{2 \cdot \epsilon^2}
\ln{\left( \frac{2}{\delta} \right)} +1
'delta' : float, optional
delta: probability with at least :math:`1-\delta`
'epsilon' : float, optional
epsilon: absolute error
'stability_thresh' : None or list of float
List containing the stability thresholds used to filter the
concepts.
If `stability_thresh` is None, then the concepts are not filtered.
Otherwise, only concepts with
* intensional stability is greater than `stability_thresh[0]` or
* extensional stability is greater than `stability_thresh[1]`
are further analyzed.
n_surr : int, optional
Number of surrogates to generate to compute the p-value spectrum.
This number should be large (`n_surr >= 1000` is recommended for 100
spike trains in `spiketrains`). If `n_surr == 0`, then the p-value
spectrum is not computed.
Default: 0
dither : pq.Quantity, optional
Amount of spike time dithering for creating the surrogates for
filtering the pattern spectrum. A spike at time `t` is placed randomly
within `[t-dither, t+dither]` (see also
:func:`elephant.spike_train_surrogates.dither_spikes`).
Default: 15*pq.ms
spectrum : {'#', '3d#'}, optional
Define the signature of the patterns.
'#': pattern spectrum using the as signature the pair:
(number of spikes, number of occurrences)
'3d#': pattern spectrum using the as signature the triplets:
(number of spikes, number of occurrence, difference between last
and first spike of the pattern)
Default: '#'
alpha : float, optional
The significance level of the hypothesis tests performed.
If `alpha is None`, all the concepts are returned.
If `0.<alpha<1.`, the concepts are filtered according to their
signature in the p-value spectrum.
Default: None
stat_corr : str
Method used for multiple testing.
See: :func:`test_signature_significance`
Default: 'fdr_bh'
surr_method : str
Method to generate surrogates. You can use every method defined in
:func:`elephant.spike_train_surrogates.surrogates`.
Default: 'dither_spikes'
psr_param : None or list of int or tuple of int
This list contains parameters used in the pattern spectrum filtering:
`psr_param[0]`: correction parameter for subset filtering
(see `h_subset_filtering` in :func:`pattern_set_reduction`).
`psr_param[1]`: correction parameter for superset filtering
(see `k_superset_filtering` in :func:`pattern_set_reduction`).
`psr_param[2]`: correction parameter for covered-spikes criterion
(see `l_covered_spikes` in :func:`pattern_set_reduction`).
output_format : {'concepts', 'patterns'}
Distinguish the format of the output (see Returns).
Default: 'patterns'
surr_kwargs
Keyword arguments that are passed to the surrogate methods.
Returns
-------
output : dict
'patterns':
If `output_format` is 'patterns', see the return of
:func:`concept_output_to_patterns`
If `output_format` is 'concepts', then `output['patterns']` is a
tuple of patterns which in turn are tuples of
1. spikes in the pattern
2. occurrences of the pattern
For details see :func:`concepts_mining`.
if stability is calculated, there are also:
3. intensional stability
4. extensional stability
For details see :func:`approximate_stability`.
'pvalue_spectrum' (only if `n_surr > 0`):
A list of signatures in tuples format:
* size
* number of occurrences
* duration (only if `spectrum=='3d#'`)
* p-value
'non_sgnf_sgnt': list
Non significant signatures of 'pvalue_spectrum'.
Notes
-----
If detected, this function will use MPI to parallelize the analysis.
Examples
--------
The following example applies SPADE to `spiketrains` (list of
`neo.SpikeTrain`).
>>> from elephant.spade import spade
>>> import quantities as pq
>>> bin_size = 3 * pq.ms # time resolution to discretize the spiketrains
>>> winlen = 10 # maximal pattern length in bins (i.e., sliding window)
>>> result_spade = spade(spiketrains, bin_size, winlen)
"""
if HAVE_MPI: # pragma: no cover
comm = MPI.COMM_WORLD # create MPI communicator
rank = comm.Get_rank() # get rank of current MPI task
else:
rank = 0
compute_stability = _check_input(
spiketrains=spiketrains, bin_size=bin_size, winlen=winlen,
min_spikes=min_spikes, min_occ=min_occ,
max_spikes=max_spikes, max_occ=max_occ, min_neu=min_neu,
approx_stab_pars=approx_stab_pars,
n_surr=n_surr, dither=dither, spectrum=spectrum,
alpha=alpha, stat_corr=stat_corr, surr_method=surr_method,
psr_param=psr_param, output_format=output_format)
time_mining = time.time()
if rank == 0 or compute_stability:
# Mine the spiketrains for extraction of concepts
concepts, rel_matrix = concepts_mining(
spiketrains, bin_size, winlen, min_spikes=min_spikes,
min_occ=min_occ, max_spikes=max_spikes, max_occ=max_occ,
min_neu=min_neu, report='a')
time_mining = time.time() - time_mining
print("Time for data mining: {}".format(time_mining))
# Decide if compute the approximated stability
if compute_stability:
if 'stability_thresh' in approx_stab_pars.keys():
stability_thresh = approx_stab_pars.pop('stability_thresh')
else:
stability_thresh = None
# Computing the approximated stability of all the concepts
time_stability = time.time()
concepts = approximate_stability(
concepts, rel_matrix, **approx_stab_pars)
time_stability = time.time() - time_stability
print("Time for stability computation: {}".format(time_stability))
# Filtering the concepts using stability thresholds
if stability_thresh is not None:
concepts = [concept for concept in concepts
if _stability_filter(concept, stability_thresh)]
output = {}
pv_spec = None # initialize pv_spec to None
# Decide whether compute pvalue spectrum
if n_surr > 0:
# Compute pvalue spectrum
time_pvalue_spectrum = time.time()
pv_spec = pvalue_spectrum(
spiketrains, bin_size, winlen, dither=dither, n_surr=n_surr,
min_spikes=min_spikes, min_occ=min_occ, max_spikes=max_spikes,
max_occ=max_occ, min_neu=min_neu, spectrum=spectrum,
surr_method=surr_method, **surr_kwargs)
time_pvalue_spectrum = time.time() - time_pvalue_spectrum
print("Time for pvalue spectrum computation: {}".format(
time_pvalue_spectrum))
# Storing pvalue spectrum
output['pvalue_spectrum'] = pv_spec
# rank!=0 returning None
if rank != 0:
warnings.warn('Returning None because executed on a process != 0')
return None
# Initialize non-significant signatures as empty list:
ns_signatures = []
# Decide whether filter concepts with psf
if n_surr > 0:
if len(pv_spec) > 0 and alpha is not None:
# Computing non-significant entries of the spectrum applying
# the statistical correction
ns_signatures = test_signature_significance(
pv_spec, concepts, alpha, winlen, corr=stat_corr,
report='non_significant', spectrum=spectrum)
# Storing non-significant entries of the pvalue spectrum
output['non_sgnf_sgnt'] = ns_signatures
# Filter concepts with pvalue spectrum (psf)
if len(ns_signatures) > 0:
concepts = [concept for concept in concepts
if _pattern_spectrum_filter(concept, ns_signatures,
spectrum, winlen)]
# Decide whether to filter concepts using psr
if psr_param is not None:
# Filter using conditional tests (psr)
concepts = pattern_set_reduction(
concepts, ns_signatures, winlen=winlen, spectrum=spectrum,
h_subset_filtering=psr_param[0], k_superset_filtering=psr_param[1],
l_covered_spikes=psr_param[2], min_spikes=min_spikes,
min_occ=min_occ)
# Storing patterns for output format concepts
if output_format == 'concepts':
output['patterns'] = concepts
else: # output_format == 'patterns':
# Transforming concepts to dictionary containing pattern's infos
output['patterns'] = concept_output_to_patterns(
concepts, winlen, bin_size, pv_spec, spectrum,
spiketrains[0].t_start)
return output
def _check_input(
spiketrains, bin_size, winlen, min_spikes=2, min_occ=2,
max_spikes=None, max_occ=None, min_neu=1, approx_stab_pars=None,
n_surr=0, dither=15 * pq.ms, spectrum='#',
alpha=None, stat_corr='fdr_bh', surr_method='dither_spikes',
psr_param=None, output_format='patterns'):
"""
Checks all input given to SPADE
Parameters
----------
see :`func`:`spade`
Returns
-------
compute_stability: bool
if the stability calculation is used
"""
# Check spiketrains
if not all([isinstance(elem, neo.SpikeTrain) for elem in spiketrains]):
raise TypeError(
'spiketrains must be a list of SpikeTrains')
# Check that all spiketrains have same t_start and same t_stop
if not all([spiketrain.t_start == spiketrains[0].t_start
for spiketrain in spiketrains]) or \
not all([spiketrain.t_stop == spiketrains[0].t_stop
for spiketrain in spiketrains]):
raise ValueError(
'All spiketrains must have the same t_start and t_stop')
# Check bin_size
if not isinstance(bin_size, pq.Quantity):
raise TypeError('bin_size must be a pq.Quantity')
# Check winlen
if not isinstance(winlen, int):
raise TypeError('winlen must be an integer')
# Check min_spikes
if not isinstance(min_spikes, int):
raise TypeError('min_spikes must be an integer')
# Check min_occ
if not isinstance(min_occ, int):
raise TypeError('min_occ must be an integer')
# Check max_spikes
if not (isinstance(max_spikes, int) or max_spikes is None):
raise TypeError('max_spikes must be an integer or None')
# Check max_occ
if not (isinstance(max_occ, int) or max_occ is None):
raise TypeError('max_occ must be an integer or None')
# Check min_neu
if not isinstance(min_neu, int):
raise TypeError('min_neu must be an integer')
# Check approx_stab_pars
compute_stability = False
if isinstance(approx_stab_pars, dict):
if 'n_subsets' in approx_stab_pars.keys() or\
('epsilon' in approx_stab_pars.keys() and
'delta' in approx_stab_pars.keys()):
compute_stability = True
else:
raise ValueError(
'for approximate stability computation you need to '
'pass n_subsets or epsilon and delta.')
# Check n_surr
if not isinstance(n_surr, int):
raise TypeError('n_surr must be an integer')
# Check dither
if not isinstance(dither, pq.Quantity):
raise TypeError('dither must be a pq.Quantity')
# Check spectrum
if spectrum not in ('#', '3d#'):
raise ValueError("spectrum must be '#' or '3d#'")
# Check alpha
if isinstance(alpha, (int, float)):
# Check redundant use of alpha
if 0. < alpha < 1. and n_surr == 0:
warnings.warn('0.<alpha<1. but p-value spectrum has not been '
'computed (n_surr==0)')
elif alpha is not None:
raise TypeError('alpha must be an integer, a float or None')
# Check stat_corr:
if stat_corr not in \
('bonferroni', 'sidak', 'holm-sidak', 'holm',
'simes-hochberg', 'hommel', 'fdr_bh', 'fdr_by',
'fdr_tsbh', 'fdr_tsbky', '', 'no'):
raise ValueError("Parameter stat_corr not recognized")
# Check surr_method
if surr_method not in surr.SURR_METHODS:
raise ValueError(
'specified surr_method (=%s) not valid' % surr_method)
# Check psr_param
if psr_param is not None:
if not isinstance(psr_param, (list, tuple)):
raise TypeError('psr_param must be None or a list or tuple of '
'integer')
if not all(isinstance(param, int) for param in psr_param):
raise TypeError('elements of psr_param must be integers')
# Check output_format
if output_format not in ('concepts', 'patterns'):
raise ValueError("The output_format value has to be"
"'patterns' or 'concepts'")
return compute_stability
[docs]@deprecated_alias(binsize='bin_size')
def concepts_mining(spiketrains, bin_size, winlen, min_spikes=2, min_occ=2,
max_spikes=None, max_occ=None, min_neu=1, report='a'):
"""
Find pattern candidates extracting all the concepts of the context, formed
by the objects defined as all windows of length `winlen*bin_size` slided
along the discretized `spiketrains` and the attributes as the spikes
occurring in each of the windows. Hence, the output are all the repeated
sequences of spikes with maximal length `winlen`, which are not trivially
explained by the same number of occurrences of a superset of spikes.
Parameters
----------
spiketrains : list of neo.SpikeTrain or conv.BinnedSpikeTrain
Either list of the spiketrains to analyze or
BinningSpikeTrain object containing the binned spiketrains to analyze
bin_size : pq.Quantity
The time precision used to discretize the `spiketrains` (clipping).
winlen : int
The size (number of bins) of the sliding window used for the analysis.
The maximal length of a pattern (delay between first and last spike) is
then given by `winlen*bin_size`
min_spikes : int, optional
Minimum number of spikes of a sequence to be considered a pattern.
Default: 2
min_occ : int, optional
Minimum number of occurrences of a sequence to be considered as a
pattern.
Default: 2
max_spikes : int, optional
Maximum number of spikes of a sequence to be considered a pattern. If
None no maximal number of spikes is considered.
Default: None
max_occ : int, optional
Maximum number of occurrences of a sequence to be considered as a
pattern. If None, no maximal number of occurrences is considered.
Default: None
min_neu : int, optional
Minimum number of neurons in a sequence to considered a pattern.
Default: 1
report : {'a', '#', '3d#'}, optional
Indicates the output of the function.
'a':
All the mined patterns
'#':
Pattern spectrum using as signature the pair:
(number of spikes, number of occurrence)
'3d#':
Pattern spectrum using as signature the triplets:
(number of spikes, number of occurrence, difference between the
times of the last and the first spike of the pattern)
Default: 'a'
Returns
-------
mining_results : np.ndarray
If report == 'a':
Numpy array of all the pattern candidates (concepts) found in the
`spiketrains`. Each pattern is represented as a tuple containing
(spike IDs, discrete times (window position) of the occurrences
of the pattern). The spike IDs are defined as:
`spike_id=neuron_id*bin_id` with `neuron_id` in
`[0, len(spiketrains)]` and `bin_id` in `[0, winlen]`.
If report == '#':
The pattern spectrum is represented as a numpy array of triplets
(pattern size, number of occurrences, number of patterns).
If report == '3d#':
The pattern spectrum is represented as a numpy array of
quadruplets (pattern size, number of occurrences, difference
between last and first spike of the pattern, number of patterns)
rel_matrix : sparse.coo_matrix
A binary matrix of shape (number of windows, winlen*len(spiketrains)).
Each row corresponds to a window (order
according to their position in time). Each column corresponds to one
bin and one neuron and it is 0 if no spikes or 1 if one or more spikes
occurred in that bin for that particular neuron. For example, the entry
[0,0] of this matrix corresponds to the first bin of the first window
position for the first neuron, the entry `[0,winlen]` to the first
bin of the first window position for the second neuron.
"""
if report not in ('a', '#', '3d#'):
raise ValueError(
"report has to assume of the following values:" +
" 'a', '#' and '3d#,' got {} instead".format(report))
# if spiketrains is list of neo.SpikeTrain convert to conv.BinnedSpikeTrain
if isinstance(spiketrains, (list, SpikeTrainList)) and \
isinstance(spiketrains[0], neo.SpikeTrain):
spiketrains = conv.BinnedSpikeTrain(
spiketrains, bin_size=bin_size, tolerance=None)
if not isinstance(spiketrains, conv.BinnedSpikeTrain):
raise TypeError(
'spiketrains must be either a list of neo.SpikeTrain or '
'a conv.BinnedSpikeTrain object')
# Clipping the spiketrains and (binary matrix)
binary_matrix = spiketrains.to_sparse_bool_array().tocoo(copy=False)
# Computing the context and the binary matrix encoding the relation between
# objects (window positions) and attributes (spikes,
# indexed with a number equal to neuron idx*winlen+bin idx)
context, transactions, rel_matrix = _build_context(binary_matrix, winlen)
# By default, set the maximum pattern size to the maximum number of
# spikes in a window
if max_spikes is None:
max_spikes = binary_matrix.shape[0] * winlen
# By default, set maximum number of occurrences to number of non-empty
# windows
if max_occ is None:
max_occ = int(np.sum(np.sum(rel_matrix, axis=1) > 0))
# Check if fim.so available and use it
if HAVE_FIM:
# Return the output
mining_results = _fpgrowth(
transactions,
rel_matrix=rel_matrix,
min_c=min_occ,
min_z=min_spikes,
max_z=max_spikes,
max_c=max_occ,
winlen=winlen,
min_neu=min_neu,
report=report)
return mining_results, rel_matrix
# Otherwise use fast_fca python implementation
warnings.warn(
'Optimized C implementation of FCA (fim.so/fim.pyd) not found ' +
'in elephant/spade_src folder, or not compatible with this ' +
'Python version. You are using the pure Python implementation ' +
'of fast fca.')
# Return output
mining_results = _fast_fca(
context,
min_c=min_occ,
min_z=min_spikes,
max_z=max_spikes,
max_c=max_occ,
winlen=winlen,
min_neu=min_neu,
report=report)
return mining_results, rel_matrix
def _build_context(binary_matrix, winlen):
"""
Building the context given a matrix (number of trains x number of bins) of
binned spike trains
Parameters
----------
binary_matrix : sparse.coo_matrix
Binary matrix containing the binned spike trains
winlen : int
Length of the bin_size used to bin the spiketrains
Returns
-------
context : list of tuple
List of tuples containing one object (window position idx) and one of
the correspondent spikes idx (bin idx * neuron idx)
transactions : list
List of all transactions, each element of the list contains the
attributes of the corresponding object.
rel_matrix : sparse.coo_matrix
A binary matrix with shape (number of windows,
winlen*len(spiketrains)). Each row corresponds to a window (order
according to their position in time).
Each column corresponds to one bin and one neuron and it is 0 if no
spikes or 1 if one or more spikes occurred in that bin for that
particular neuron.
E.g. the entry [0,0] of this matrix corresponds to the first bin of the
first window position for the first neuron, the entry [0,winlen] to the
first bin of the first window position for the second neuron.
"""
# Initialization of the outputs
context = []
transactions = []
num_neurons, num_bins = binary_matrix.shape
indices = np.argsort(binary_matrix.col)
binary_matrix.row = binary_matrix.row[indices]
binary_matrix.col = binary_matrix.col[indices]
# out of all window positions
# get all non-empty first bins
unique_cols, unique_col_idx = np.unique(
binary_matrix.col, return_index=True)
unique_col_idx = np.concatenate((unique_col_idx, [len(binary_matrix.col)]))
windows_row = []
windows_col = []
# all non-empty bins are starting positions for windows
for idx, window_idx in enumerate(unique_cols):
# find the end of the current window in unique_cols
end_of_window = np.searchsorted(unique_cols, window_idx + winlen)
# loop over all non-empty bins in the current window
for rel_idx, col in enumerate(unique_cols[idx:end_of_window]):
# get all occurrences of the current col in binary_matrix.col
spike_indices_in_window = np.arange(
unique_col_idx[idx + rel_idx],
unique_col_idx[idx + rel_idx + 1])
# get the binary_matrix.row entries matching the current col
# prepare the row of rel_matrix matching the current window
# spikes are indexed as (neuron_id * winlen + bin_id)
windows_col.extend(
binary_matrix.row[spike_indices_in_window] * winlen
+ (col - window_idx))
windows_row.extend([window_idx] * len(spike_indices_in_window))
# Shape of the rel_matrix:
# (total number of bins,
# number of bins in one window * number of neurons)
rel_matrix = sparse.coo_matrix(
(np.ones((len(windows_col)), dtype=bool),
(windows_row, windows_col)),
shape=(num_bins, winlen * num_neurons),
dtype=bool).A
# Array containing all the possible attributes (each spike is indexed by
# a number equal to neu idx*winlen + bin_idx)
attributes = np.array(
[s * winlen + t for s in range(binary_matrix.shape[0])
for t in range(winlen)])
# Building context and rel_matrix
# Looping all the window positions w
for window in unique_cols:
# spikes in the current window
times = rel_matrix[window]
current_transactions = attributes[times]
# adding to the context the window positions and the correspondent
# attributes (spike idx) (fast_fca input)
context.extend(
(window, transaction) for transaction in current_transactions)
# appending to the transactions spike idx (fast_fca input) of the
# current window (fpgrowth input)
transactions.append(list(current_transactions))
# Return context and rel_matrix
return context, transactions, rel_matrix
def _fpgrowth(transactions, min_c=2, min_z=2, max_z=None,
max_c=None, rel_matrix=None, winlen=1, min_neu=1,
target='c', report='a'):
"""
Find frequent item sets with the fpgrowth algorithm.
Parameters
----------
transactions: tuple
Transactions database to mine.
The database must be an iterable of transactions;
each transaction must be an iterable of items;
each item must be a hashable object.
If the database is a dictionary, the transactions are
the keys, the values their (integer) multiplicities.
target: str
type of frequent item sets to find
s/a sets/all all frequent item sets
c closed closed frequent item sets
m maximal maximal frequent item sets
g gens generators
Default:'c'
min_c: int
minimum support of an item set
Default: 2
min_z: int
minimum number of items per item set
Default: 2
max_z: None/int
maximum number of items per item set. If max_c==None no maximal
size required
Default: None
max_c: None/int
maximum support per item set. If max_c==None no maximal
support required
Default: None
report: str
'a': all the mined patterns
'#': pattern spectrum using as signature the pair:
(number of spikes, number of occurrence)
'3d#': pattern spectrum using as signature the triplets:
(number of spikes, number of occurrence, difference between the
times of the last and the first spike of the pattern)
Default: 'a'
rel_matrix : None or sparse.coo_matrix
A binary matrix with shape (number of windows,
winlen*len(spiketrains)). Each row corresponds to a window (order
according to their position in time).
Each column corresponds to one bin and one neuron and it is 0 if no
spikes or 1 if one or more spikes occurred in that bin for that
particular neuron.
E.g. the entry [0,0] of this matrix corresponds to the first bin of the
first window position for the first neuron, the entry [0,winlen] to the
first bin of the first window position for the second neuron.
If == None only the closed frequent itemsets (intent) are returned and
not which the index of their occurrences (extent)
Default: None
The following parameters are specific to Massive parallel SpikeTrains
winlen: int
The size (number of bins) of the sliding window used for the
analysis. The maximal length of a pattern (delay between first and
last spike) is then given by winlen*bin_size
Default: 1
min_neu: int
Minimum number of neurons in a sequence to considered a
potential pattern.
Default: 1
Returns
-------
If report == 'a':
numpy array of all the pattern candidates (concepts) found in the
spiketrains.
Each pattern is represented as a tuple containing
(spike IDs, discrete times (window position)
of the occurrences of the pattern). The spike IDs are defined as:
spike_id=neuron_id*bin_id; with neuron_id in [0, len(spiketrains)] and
bin_id in [0, winlen].
If report == '#':
The pattern spectrum is represented as a numpy array of triplets each
formed by:
(pattern size, number of occurrences, number of patterns)
If report == '3d#':
The pattern spectrum is represented as a numpy array of quadruplets
each formed by:
(pattern size, number of occurrences, difference between last
and first spike of the pattern, number of patterns)
"""
if min_neu < 1:
raise ValueError('min_neu must be an integer >=1')
# By default, set the maximum pattern size to the number of spiketrains
if max_z is None:
max_z = max(max(map(len, transactions)), min_z + 1)
# By default set maximum number of data to number of bins
if max_c is None:
max_c = len(transactions)
# Initializing outputs
concepts = []
if report == '#':
spec_matrix = np.zeros((max_z + 1, max_c + 1))
if report == '3d#':
spec_matrix = np.zeros((max_z + 1, max_c + 1, winlen))
spectrum = []
# check whether all transactions are identical
# in that case FIM would not find anything,
# so we need to create the output manually
# for optimal performance,
# we do the check sequentially and immediately break
# once we find a second unique transaction
first_transaction = transactions[0]
for transaction in transactions[1:]:
if transaction != first_transaction:
# Mining the spiketrains with fpgrowth algorithm
fpgrowth_output = fim.fpgrowth(
tracts=transactions,
target=target,
supp=-min_c,
zmin=min_z,
zmax=max_z,
report='a',
algo='s',
winlen=winlen,
threads=0,
verbose=4)
break
else:
fpgrowth_output = [(tuple(transactions[0]), len(transactions))]
# Applying min/max conditions and computing extent (window positions)
# fpgrowth_output = [concept for concept in fpgrowth_output
# if _fpgrowth_filter(concept, winlen, max_c, min_neu)]
# filter out subsets of patterns that are found as a side-effect
# of using the moving window strategy
fpgrowth_output = _filter_for_moving_window_subsets(
fpgrowth_output, winlen)
for (intent, supp) in fpgrowth_output:
if report == 'a':
if rel_matrix is not None:
# Computing the extent of the concept (patterns
# occurrences), checking in rel_matrix in which windows
# the intent occurred
extent = tuple(
np.nonzero(
np.all(rel_matrix[:, intent], axis=1)
)[0])
concepts.append((intent, extent))
# Computing 2d spectrum
elif report == '#':
spec_matrix[len(intent) - 1, supp - 1] += 1
# Computing 3d spectrum
elif report == '3d#':
spec_matrix[len(intent) - 1, supp - 1, max(
np.array(intent) % winlen)] += 1
del fpgrowth_output
if report == 'a':
return concepts
if report == '#':
for (size, occurrences) in np.transpose(np.where(spec_matrix != 0)):
spectrum.append(
(size + 1, occurrences + 1,
int(spec_matrix[size, occurrences])))
elif report == '3d#':
for (size, occurrences, duration) in\
np.transpose(np.where(spec_matrix != 0)):
spectrum.append(
(size + 1, occurrences + 1, duration,
int(spec_matrix[size, occurrences, duration])))
del spec_matrix
if len(spectrum) > 0:
spectrum = np.array(spectrum)
elif report == '#':
spectrum = np.zeros(shape=(0, 3))
elif report == '3d#':
spectrum = np.zeros(shape=(0, 4))
return spectrum
# def _fpgrowth_filter(concept, winlen, max_c, min_neu):
# """
# Filter for selecting closed frequent items set with a minimum number of
# neurons and a maximum number of occurrences and first spike in the first
# bin position
# """
# intent = np.array(concept[0])
# keep_concept = (min(intent % winlen) == 0
# and concept[1] <= max_c
# and np.unique(intent // winlen).shape[0] >= min_neu
# )
# return keep_concept
def _rereference_to_last_spike(transactions, winlen):
"""
Converts transactions from the default format
neu_idx * winlen + bin_idx (relative to window start)
into the format
neu_idx * winlen + bin_idx (relative to last spike)
"""
len_transactions = len(transactions)
neurons = np.zeros(len_transactions, dtype=int)
bins = np.zeros(len_transactions, dtype=int)
# extract neuron and bin indices
for idx, attribute in enumerate(transactions):
neurons[idx] = attribute // winlen
bins[idx] = attribute % winlen
# rereference bins to last spike
bins = bins.max() - bins
# calculate converted transactions
converted_transactions = neurons * winlen + bins
return converted_transactions
def _filter_for_moving_window_subsets(concepts, winlen):
"""
Since we're using a moving window subpatterns starting from
subsequent spikes after the first pattern spike will also be found.
This filter removes them if they do not occur on their own in
addition to the occurrences explained by their superset.
Uses a reverse map with a set representation.
"""
# don't do anything if the input list is empty
if len(concepts) == 0:
return concepts
# don't do anything if winlen is one
if winlen == 1:
return concepts
if hasattr(concepts[0], 'intent'):
# fca format
# sort the concepts by (decreasing) support
concepts.sort(key=lambda c: -len(c.extent))
support = np.array([len(c.extent) for c in concepts])
# convert transactions relative to last pattern spike
converted_transactions = [_rereference_to_last_spike(concept.intent,
winlen=winlen)
for concept in concepts]
else:
# fim.fpgrowth format
# sort the concepts by (decreasing) support
concepts.sort(key=lambda concept: -concept[1])
support = np.array([concept[1] for concept in concepts])
# convert transactions relative to last pattern spike
converted_transactions = [_rereference_to_last_spike(concept[0],
winlen=winlen)
for concept in concepts]
output = []
for current_support in np.unique(support):
support_indices = np.nonzero(support == current_support)[0]
# construct reverse map
reverse_map = defaultdict(set)
for map_idx, i in enumerate(support_indices):
for window_bin in converted_transactions[i]:
reverse_map[window_bin].add(map_idx)
for i in support_indices:
intersection = reduce(
operator.and_,
(reverse_map[window_bin]
for window_bin in converted_transactions[i]))
if len(intersection) == 1:
output.append(concepts[i])
return output
def _fast_fca(context, min_c=2, min_z=2, max_z=None,
max_c=None, report='a', winlen=1, min_neu=1):
"""
Find concepts of the context with the fast-fca algorithm.
Parameters
----------
context : list
List of tuples containing one object and one the correspondent
attribute
min_c: int
minimum support of an item set
Default: 2
min_z: int
minimum number of items per item set
Default: 2
max_z: None/int
maximum number of items per item set. If max_c==None no maximal
size required
Default: None
max_c: None/int
maximum support per item set. If max_c==None no maximal
support required
Default: None
report: str
'a': all the mined patterns
'#': pattern spectrum using as signature the pair:
(number of spikes, number of occurrence)
'3d#': pattern spectrum using as signature the triplets:
(number of spikes, number of occurrence, difference between the
times of the last and the first spike of the pattern)
Default: 'a'
The following parameters are specific to Massive parallel SpikeTrains
winlen: int
The size (number of bins) of the sliding window used for the
analysis. The maximal length of a pattern (delay between first and
last spike) is then given by winlen*bin_size
Default: 1
min_neu: int
Minimum number of neurons in a sequence to considered a
potential pattern.
Default: 1
Returns
-------
If report == 'a':
All the pattern candidates (concepts) found in the spiketrains. Each
pattern is represented as a tuple containing
(spike IDs, discrete times (window position)
of the occurrences of the pattern). The spike IDs are defined as:
spike_id=neuron_id*bin_id; with neuron_id in [0, len(spiketrains)] and
bin_id in [0, winlen].
If report == '#':
The pattern spectrum is represented as a list of triplets each
formed by:
(pattern size, number of occurrences, number of patterns)
If report == '3d#':
The pattern spectrum is represented as a list of quadruplets each
formed by:
(pattern size, number of occurrences, difference between last
and first spike of the pattern, number of patterns)
"""
# Initializing outputs
concepts = []
# Check parameters
if min_neu < 1:
raise ValueError('min_neu must be an integer >=1')
# By default set maximum number of attributes
if max_z is None:
max_z = len(context)
# By default set maximum number of data to number of bins
if max_c is None:
max_c = len(context)
if report == '#':
spec_matrix = np.zeros((max_z, max_c))
elif report == '3d#':
spec_matrix = np.zeros((max_z, max_c, winlen))
spectrum = []
# Mining the spiketrains with fast fca algorithm
fca_out = fast_fca.FormalConcepts(context)
fca_out.computeLattice()
fca_concepts = fca_out.concepts
fca_concepts = [concept for concept in fca_concepts
if _fca_filter(concept, winlen, min_c, min_z, max_c, max_z,
min_neu)]
fca_concepts = _filter_for_moving_window_subsets(fca_concepts, winlen)
# Applying min/max conditions
for fca_concept in fca_concepts:
intent = tuple(fca_concept.intent)
extent = tuple(fca_concept.extent)
concepts.append((intent, extent))
# computing spectrum
if report == '#':
spec_matrix[len(intent) - 1, len(extent) - 1] += 1
elif report == '3d#':
spec_matrix[len(intent) - 1, len(extent) - 1, max(
np.array(intent) % winlen)] += 1
if report == 'a':
return concepts
del concepts
# returning spectrum
if report == '#':
for (size, occurrence) in np.transpose(np.where(spec_matrix != 0)):
spectrum.append(
(size + 1, occurrence + 1, int(spec_matrix[size, occurrence])))
if report == '3d#':
for (size, occurrence, duration) in\
np.transpose(np.where(spec_matrix != 0)):
spectrum.append(
(size + 1, occurrence + 1, duration,
int(spec_matrix[size, occurrence, duration])))
del spec_matrix
if len(spectrum) > 0:
spectrum = np.array(spectrum)
elif report == '#':
spectrum = np.zeros(shape=(0, 3))
elif report == '3d#':
spectrum = np.zeros(shape=(0, 4))
return spectrum
def _fca_filter(concept, winlen, min_c, min_z, max_c, max_z, min_neu):
"""
Filter to select concepts with minimum/maximum number of spikes and
occurrences and first spike in the first bin position
"""
intent = tuple(concept.intent)
extent = tuple(concept.extent)
keep_concepts = \
min_z <= len(intent) <= max_z and \
min_c <= len(extent) <= max_c and \
len(np.unique(np.array(intent) // winlen)) >= min_neu and \
min(np.array(intent) % winlen) == 0
return keep_concepts
[docs]@deprecated_alias(binsize='bin_size')
def pvalue_spectrum(
spiketrains, bin_size, winlen, dither, n_surr, min_spikes=2, min_occ=2,
max_spikes=None, max_occ=None, min_neu=1, spectrum='#',
surr_method='dither_spikes', **surr_kwargs):
"""
Compute the p-value spectrum of pattern signatures extracted from
surrogates of parallel spike trains, under the null hypothesis of
independent spiking.
* n_surr surrogates are obtained from each spike train by spike dithering
* pattern candidates (concepts) are collected from each surrogate data
* the signatures (number of spikes, number of occurrences) of all patterns
are computed, and their occurrence probability estimated by their
occurrence frequency (p-value spectrum)
Parameters
----------
spiketrains : list of neo.SpikeTrain
List containing the parallel spike trains to analyze
bin_size : pq.Quantity
The time precision used to discretize the `spiketrains` (binning).
winlen : int
The size (number of bins) of the sliding window used for the analysis.
The maximal length of a pattern (delay between first and last spike) is
then given by `winlen*bin_size`
dither : pq.Quantity
Amount of spike time dithering for creating the surrogates for
filtering the pattern spectrum. A spike at time t is placed randomly
within `[t-dither, t+dither]` (see also
:func:`elephant.spike_train_surrogates.dither_spikes`).
Default: 15*pq.s
n_surr : int
Number of surrogates to generate to compute the p-value spectrum.
This number should be large (`n_surr>=1000` is recommended for 100
spike trains in spiketrains). If `n_surr` is 0, then the p-value
spectrum is not computed.
Default: 0
min_spikes : int, optional
Minimum number of spikes of a sequence to be considered a pattern.
Default: 2
min_occ : int, optional
Minimum number of occurrences of a sequence to be considered as a
pattern.
Default: 2
max_spikes : int, optional
Maximum number of spikes of a sequence to be considered a pattern. If
None no maximal number of spikes is considered.
Default: None
max_occ : int, optional
Maximum number of occurrences of a sequence to be considered as a
pattern. If None, no maximal number of occurrences is considered.
Default: None
min_neu : int, optional
Minimum number of neurons in a sequence to considered a pattern.
Default: 1
spectrum : {'#', '3d#'}, optional
Defines the signature of the patterns.
'#': pattern spectrum using the as signature the pair:
(number of spikes, number of occurrence)
'3d#': pattern spectrum using the as signature the triplets:
(number of spikes, number of occurrence, difference between last
and first spike of the pattern)
Default: '#'
surr_method : str
Method that is used to generate the surrogates. You can use every
method defined in
:func:`elephant.spike_train_surrogates.dither_spikes`.
Default: 'dither_spikes'
surr_kwargs
Keyword arguments that are passed to the surrogate methods.
Returns
-------
pv_spec : list
if spectrum == '#':
A list of triplets (z,c,p), where (z,c) is a pattern signature
and p is the corresponding p-value (fraction of surrogates
containing signatures (z*,c*)>=(z,c)).
if spectrum == '3d#':
A list of triplets (z,c,l,p), where (z,c,l) is a pattern signature
and p is the corresponding p-value (fraction of surrogates
containing signatures (z*,c*,l*)>=(z,c,l)).
Signatures whose empirical p-value is 0 are not listed.
"""
# Initializing variables for parallel computing
if HAVE_MPI: # pragma: no cover
comm = MPI.COMM_WORLD # create MPI communicator
rank = comm.Get_rank() # get rank of current MPI task
size = comm.Get_size() # get tot number of MPI tasks
else:
rank = 0
size = 1
# Check on number of surrogates
if n_surr <= 0:
raise ValueError('n_surr has to be >0')
if surr_method not in surr.SURR_METHODS:
raise ValueError(
'specified surr_method (=%s) not valid' % surr_method)
if spectrum not in ('#', '3d#'):
raise ValueError("Invalid spectrum: '{}'".format(spectrum))
len_partition = n_surr // size # length of each MPI task
len_remainder = n_surr % size
add_remainder = rank < len_remainder
if max_spikes is None:
# if max_spikes not defined, set it to the number of spiketrains times
# number of bins per window.
max_spikes = len(spiketrains) * winlen
if spectrum == '#':
max_occs = np.empty(shape=(len_partition + add_remainder,
max_spikes - min_spikes + 1),
dtype=np.uint16)
else: # spectrum == '3d#':
max_occs = np.empty(shape=(len_partition + add_remainder,
max_spikes - min_spikes + 1, winlen),
dtype=np.uint16)
for surr_id, binned_surrogates in _generate_binned_surrogates(
spiketrains, bin_size=bin_size, dither=dither,
surr_method=surr_method, n_surrogates=len_partition+add_remainder,
**surr_kwargs):
# Find all pattern signatures in the current surrogate data set
surr_concepts = concepts_mining(
binned_surrogates, bin_size, winlen, min_spikes=min_spikes,
max_spikes=max_spikes, min_occ=min_occ, max_occ=max_occ,
min_neu=min_neu, report=spectrum)[0]
# The last entry of the signature is the number of times the
# signature appeared. This entry is not needed here.
surr_concepts = surr_concepts[:, :-1]
max_occs[surr_id] = _get_max_occ(
surr_concepts, min_spikes, max_spikes, winlen, spectrum)
# Collecting results on the first PCU
if size != 1:
max_occs = comm.gather(max_occs, root=0)
if rank != 0: # pragma: no cover
return []
# The gather operator gives a list out. This is rearranged as a 2 resp.
# 3 dimensional numpy-array.
max_occs = np.vstack(max_occs)
# Compute the p-value spectrum, and return it
return _get_pvalue_spec(max_occs, min_spikes, max_spikes, min_occ,
n_surr, winlen, spectrum)
def _generate_binned_surrogates(
spiketrains, bin_size, dither, surr_method, n_surrogates,
**surr_kwargs):
if surr_method == 'bin_shuffling':
binned_spiketrains = [
conv.BinnedSpikeTrain(
spiketrain, bin_size=bin_size, tolerance=None)
for spiketrain in spiketrains]
max_displacement = int(dither.rescale(pq.ms).magnitude /
bin_size.rescale(pq.ms).magnitude)
elif surr_method in ('joint_isi_dithering', 'isi_dithering'):
isi_dithering = surr_method == 'isi_dithering'
joint_isi_instances = \
[surr.JointISI(spiketrain, dither=dither,
isi_dithering=isi_dithering, **surr_kwargs)
for spiketrain in spiketrains]
for surr_id in range(n_surrogates):
if surr_method == 'bin_shuffling':
binned_surrogates = \
[surr.bin_shuffling(binned_spiketrain,
max_displacement=max_displacement,
**surr_kwargs)[0]
for binned_spiketrain in binned_spiketrains]
binned_surrogates = np.array(
[binned_surrogate.to_bool_array()[0]
for binned_surrogate in binned_surrogates])
binned_surrogates = conv.BinnedSpikeTrain(
binned_surrogates,
bin_size=bin_size,
t_start=spiketrains[0].t_start,
t_stop=spiketrains[0].t_stop,
tolerance=None)
elif surr_method in ('joint_isi_dithering', 'isi_dithering'):
surrs = [instance.dithering()[0]
for instance in joint_isi_instances]
elif surr_method == 'dither_spikes_with_refractory_period':
# The initial refractory period is set to the bin size in order to
# prevent that spikes fall into the same bin, if the spike trains
# are sparse (min(ISI)>bin size).
surrs = \
[surr.dither_spikes(
spiketrain, dither=dither, n_surrogates=1,
refractory_period=bin_size, **surr_kwargs)[0]
for spiketrain in spiketrains]
else:
surrs = \
[surr.surrogates(
spiketrain, n_surrogates=1, method=surr_method,
dt=dither, **surr_kwargs)[0]
for spiketrain in spiketrains]
if surr_method != 'bin_shuffling':
binned_surrogates = conv.BinnedSpikeTrain(
surrs, bin_size=bin_size, tolerance=None)
yield surr_id, binned_surrogates
def _get_pvalue_spec(max_occs, min_spikes, max_spikes, min_occ, n_surr, winlen,
spectrum):
"""
This function converts the list of maximal occurrences into the
corresponding p-value spectrum.
Parameters
----------
max_occs: np.ndarray
min_spikes: int
max_spikes: int
min_occ: int
n_surr: int
winlen: int
spectrum: {'#', '3d#'}
Returns
-------
if spectrum == '#':
List[List]:
each entry has the form: [pattern_size, pattern_occ, p_value]
if spectrum == '3d#':
List[List]:
each entry has the form:
[pattern_size, pattern_occ, pattern_dur, p_value]
"""
if spectrum not in ('#', '3d#'):
raise ValueError("Invalid spectrum: '{}'".format(spectrum))
pv_spec = []
if spectrum == '#':
max_occs = np.expand_dims(max_occs, axis=2)
winlen = 1
for size_id, pt_size in enumerate(range(min_spikes, max_spikes + 1)):
for dur in range(winlen):
max_occs_size_dur = max_occs[:, size_id, dur]
counts, occs = np.histogram(
max_occs_size_dur,
bins=np.arange(min_occ,
np.max(max_occs_size_dur) + 2))
occs = occs[:-1].astype(np.uint16)
pvalues = np.cumsum(counts[::-1])[::-1] / n_surr
for occ_id, occ in enumerate(occs):
if spectrum == '#':
pv_spec.append([pt_size, occ, pvalues[occ_id]])
else: # spectrum == '3d#':
pv_spec.append([pt_size, occ, dur, pvalues[occ_id]])
return pv_spec
def _get_max_occ(surr_concepts, min_spikes, max_spikes, winlen, spectrum):
"""
This function takes from a list of surrogate_concepts those concepts which
have the highest occurrence for a given pattern size and duration.
Parameters
----------
surr_concepts: List[List]
min_spikes: int
max_spikes: int
winlen: int
spectrum: {'#', '3d#'}
Returns
-------
np.ndarray
Two-dimensional array. Each element corresponds to a highest occurrence
for a specific pattern size (which range from min_spikes to max_spikes)
and pattern duration (which range from 0 to winlen-1).
The first axis corresponds to the pattern size the second to the
duration.
"""
if spectrum == '#':
winlen = 1
max_occ = np.zeros(shape=(max_spikes - min_spikes + 1, winlen))
for size_id, pt_size in enumerate(range(min_spikes, max_spikes + 1)):
concepts_for_size = surr_concepts[
surr_concepts[:, 0] == pt_size][:, 1:]
for dur in range(winlen):
if spectrum == '#':
occs = concepts_for_size[:, 0]
else: # spectrum == '3d#':
occs = concepts_for_size[concepts_for_size[:, 1] == dur][:, 0]
max_occ[size_id, dur] = np.max(occs, initial=0)
for pt_size in range(max_spikes - 1, min_spikes - 1, -1):
size_id = pt_size - min_spikes
max_occ[size_id] = np.max(max_occ[size_id:size_id + 2], axis=0)
if spectrum == '#':
max_occ = np.squeeze(max_occ, axis=1)
return max_occ
def _stability_filter(concept, stability_thresh):
"""Criteria by which to filter concepts from the lattice"""
# stabilities larger then stability_thresh
keep_concept = \
concept[2] > stability_thresh[0]\
or concept[3] > stability_thresh[1]
return keep_concept
def _mask_pvalue_spectrum(pv_spec, concepts, spectrum, winlen):
"""
The function filters the pvalue spectrum based on the number of
the statistical tests to be done. Only the entries of the pvalue spectrum
that coincide with concepts found in the original data are kept.
Moreover, entries of the pvalue spectrum with a value of 1 (all surrogates
datasets containing at least one mined pattern with that signature)
are discarded as well and considered trivial.
Parameters
----------
pv_spec: List[List]
concepts: List[Tuple]
spectrum: {'#', '3d#'}
winlen: int
Returns
-------
mask : np.array
An array of boolean values, indicating if a signature of p-value
spectrum is also in the mined concepts of the original data.
"""
if spectrum == '#':
signatures = {(len(concept[0]), len(concept[1]))
for concept in concepts}
else: # spectrum == '3d#':
# third entry of signatures is the duration, fixed as the maximum lag
signatures = {(len(concept[0]), len(concept[1]),
max(np.array(concept[0]) % winlen))
for concept in concepts}
mask = np.zeros(len(pv_spec), dtype=bool)
for index, pv_entry in enumerate(pv_spec):
if tuple(pv_entry[:-1]) in signatures \
and not np.isclose(pv_entry[-1], [1]):
# select the highest number of occurrences for size and duration
mask[index] = True
if mask[index-1]:
if spectrum == '#':
size = pv_spec[index][0]
prev_size = pv_spec[index-1][0]
if prev_size == size:
mask[index-1] = False
else:
size, duration = pv_spec[index][[0, 2]]
prev_size, prev_duration = pv_spec[index-1][[0, 2]]
if prev_size == size and duration == prev_duration:
mask[index-1] = False
return mask
[docs]def test_signature_significance(pv_spec, concepts, alpha, winlen,
corr='fdr_bh', report='spectrum',
spectrum='#'):
"""
Compute the significance spectrum of a pattern spectrum.
Given pvalue_spectrum `pv_spec` as a list of triplets (z,c,p), where z is
pattern size, c is pattern support and p is the p-value of the signature
(z,c), this routine assesses the significance of (z,c) using the
confidence level alpha.
Bonferroni or FDR statistical corrections can be applied.
Parameters
----------
pv_spec : list
A list of triplets (z,c,p), where z is pattern size, c is pattern
support and p is the p-value of signature (z,c)
concepts : list of tuple
Output of the concepts mining for the original data.
alpha : float
Significance level of the statistical test
winlen : int
Size (number of bins) of the sliding window used for the analysis
corr : str, optional
Method used for testing and adjustment of pvalues.
Can be either the full name or initial letters.
Available methods are:
'bonferroni' : one-step correction
'sidak' : one-step correction
'holm-sidak' : step down method using Sidak adjustments
'holm' : step-down method using Bonferroni adjustments
'simes-hochberg' : step-up method (independent)
'hommel' : closed method based on Simes tests (non-negative)
'fdr_bh' : Benjamini/Hochberg (non-negative)
'fdr_by' : Benjamini/Yekutieli (negative)
'fdr_tsbh' : two stage fdr correction (non-negative)
'fdr_tsbky' : two stage fdr correction (non-negative)
'' or 'no': no statistical correction
For further description see:
https://www.statsmodels.org/stable/generated/statsmodels.stats.multitest.multipletests.html
Default: 'fdr_bh'
report : {'spectrum', 'significant', 'non_significant'}, optional
Format to be returned for the significance spectrum:
'spectrum': list of triplets (z,c,b), where b is a boolean specifying
whether signature (z,c) is significant (True) or not
(False)
'significant': list containing only the significant signatures (z,c) of
pvalue_spectrum
'non_significant': list containing only the non-significant signatures
spectrum : {'#', '3d#'}, optional
Defines the signature of the patterns.
'#': pattern spectrum using the as signature the pair:
(number of spikes, number of occurrence)
'3d#': pattern spectrum using the as signature the triplets:
(number of spikes, number of occurrence, difference between last
and first spike of the pattern)
Default: '#'
Returns
-------
sig_spectrum : list
Significant signatures of pvalue_spectrum, in the format specified
by `report`
"""
# If alpha == 1 all signatures are significant
if alpha == 1:
return []
if spectrum not in ('#', '3d#'):
raise ValueError("spectrum must be either '#' or '3d#', "
"got {} instead".format(spectrum))
if report not in ('spectrum', 'significant', 'non_significant'):
raise ValueError("report must be either 'spectrum'," +
" 'significant' or 'non_significant'," +
"got {} instead".format(report))
if corr not in ('bonferroni', 'sidak', 'holm-sidak', 'holm',
'simes-hochberg', 'hommel', 'fdr_bh', 'fdr_by',
'fdr_tsbh', 'fdr_tsbky', '', 'no'):
raise ValueError("Parameter corr not recognized")
pv_spec = np.array(pv_spec)
mask = _mask_pvalue_spectrum(pv_spec, concepts, spectrum, winlen)
pvalues = pv_spec[:, -1]
pvalues_totest = pvalues[mask]
# Initialize test array to False
tests = [False] * len(pvalues)
if len(pvalues_totest) > 0:
# Compute significance for only the non trivial tests
if corr in ['', 'no']: # ...without statistical correction
tests_selected = pvalues_totest <= alpha
else:
try:
import statsmodels.stats.multitest as sm
except ModuleNotFoundError:
raise ModuleNotFoundError(
"Please run 'pip install statsmodels' if you "
"want to use multiple testing correction")
tests_selected = sm.multipletests(pvalues_totest, alpha=alpha,
method=corr)[0]
# assign each corrected pvalue to its corresponding entry
# this breaks
for index, value in zip(mask.nonzero()[0], tests_selected):
tests[index] = value
# Return the specified results:
if spectrum == '#':
if report == 'spectrum':
sig_spectrum = [(size, occ, test)
for (size, occ, pv), test in zip(pv_spec, tests)]
elif report == 'significant':
sig_spectrum = [(size, occ) for ((size, occ, pv), test)
in zip(pv_spec, tests) if test]
else: # report == 'non_significant'
sig_spectrum = [(size, occ)
for ((size, occ, pv), test) in zip(pv_spec, tests)
if not test]
else: # spectrum == '3d#'
if report == 'spectrum':
sig_spectrum =\
[(size, occ, l, test)
for (size, occ, l, pv), test in zip(pv_spec, tests)]
elif report == 'significant':
sig_spectrum = [(size, occ, l) for ((size, occ, l, pv), test)
in zip(pv_spec, tests) if test]
else: # report == 'non_significant'
sig_spectrum =\
[(size, occ, l)
for ((size, occ, l, pv), test) in zip(pv_spec, tests)
if not test]
return sig_spectrum
def _pattern_spectrum_filter(concept, ns_signatures, spectrum, winlen):
"""
Filter for significant concepts
"""
if spectrum == '#':
keep_concept = (len(concept[0]), len(concept[1])) not in ns_signatures
else: # spectrum == '3d#':
# duration is fixed as the maximum lag
duration = max(np.array(concept[0]) % winlen)
keep_concept = (len(concept[0]), len(concept[1]),
duration) not in ns_signatures
return keep_concept
[docs]def approximate_stability(concepts, rel_matrix, n_subsets=0,
delta=0., epsilon=0.):
r"""
Approximate the stability of concepts. Uses the algorithm described
in Babin, Kuznetsov (2012): Approximating Concept Stability
Parameters
----------
concepts : list
All the pattern candidates (concepts) found in the spiketrains. Each
pattern is represented as a tuple containing (spike IDs,
discrete times (window position)
of the occurrences of the pattern). The spike IDs are defined as:
`spike_id=neuron_id*bin_id` with `neuron_id` in `[0, len(spiketrains)]`
and `bin_id` in `[0, winlen]`.
rel_matrix : sparse.coo_matrix
A binary matrix with shape (number of windows,
winlen*len(spiketrains)). Each row corresponds to a window (order
according to their position in time).
Each column corresponds to one bin and one neuron and it is 0 if
no spikes or 1 if one or more spikes occurred in that bin for that
particular neuron. For example, the entry [0,0] of this matrix
corresponds to the first bin of the first window position for the first
neuron, the entry `[0, winlen]` to the first bin of the first window
position for the second neuron.
n_subsets : int
Number of subsets of a concept used to approximate its stability.
If `n_subsets` is 0, it is calculated according to to the formula
given in Babin, Kuznetsov (2012), proposition 6:
.. math::
n_{\text{subset}} = \frac{1}{2 \cdot \epsilon^2}
\ln{\left( \frac{2}{\delta} \right)} +1
Default: 0
delta : float, optional
delta: probability with at least :math:`1-\delta`
Default: 0.0
epsilon : float, optional
epsilon: absolute error
Default: 0.0
Returns
-------
output : list
List of all the pattern candidates (concepts) given in input, each with
the correspondent intensional and extensional stability. Each
pattern is represented as a tuple (spike IDs,
discrete times of the occurrences of the pattern, intensional
stability of the pattern, extensional stability of the pattern).
The spike IDs are defined as:
`spike_id=neuron_id*bin_id` with `neuron_id` in `[0, len(spiketrains)]`
and `bin_id` in `[0, winlen]`.
Notes
-----
If n_subset is larger than the extent all subsets are directly
calculated, otherwise for small extent size an infinite
loop can be created while doing the recursion,
since the random generation will always contain the same
numbers and the algorithm will be stuck searching for
other (random) numbers.
"""
if HAVE_MPI: # pragma: no cover
comm = MPI.COMM_WORLD # create MPI communicator
rank = comm.Get_rank() # get rank of current MPI task
size = comm.Get_size() # get tot number of MPI tasks
else:
rank = 0
size = 1
if not (isinstance(n_subsets, int) and n_subsets >= 0):
raise ValueError('n_subsets must be an integer >=0')
if n_subsets == 0 and not (isinstance(delta, float) and delta > 0. and
isinstance(epsilon, float) and epsilon > 0.):
raise ValueError('delta and epsilon must be floats > 0., '
'given that n_subsets = 0')
if len(concepts) == 0:
return []
if len(concepts) <= size:
rank_idx = [0] * (size + 1) + [len(concepts)]
else:
rank_idx = list(
range(0, len(concepts) - len(concepts) % size + 1,
len(concepts) // size)) + [len(concepts)]
# Calculate optimal n
if n_subsets == 0:
n_subsets = int(round(np.log(2. / delta) / (2 * epsilon ** 2) + 1))
if rank == 0:
concepts_on_partition = concepts[rank_idx[rank]:rank_idx[rank + 1]] + \
concepts[rank_idx[-2]:rank_idx[-1]]
else:
concepts_on_partition = concepts[rank_idx[rank]:rank_idx[rank + 1]]
output = []
for concept in concepts_on_partition:
intent, extent = np.array(concept[0]), np.array(concept[1])
stab_int = _calculate_single_stability_parameter(
intent, extent, n_subsets, rel_matrix, look_at='intent')
stab_ext = _calculate_single_stability_parameter(
intent, extent, n_subsets, rel_matrix, look_at='extent')
output.append((intent, extent, stab_int, stab_ext))
if size != 1:
recv_list = comm.gather(output, root=0)
if rank == 0:
for i in range(1, len(recv_list)):
output.extend(recv_list[i])
return output
def _calculate_single_stability_parameter(intent, extent,
n_subsets, rel_matrix,
look_at='intent'):
"""
Calculates the stability parameter for extent or intent.
For detailed describtion see approximate_stabilty
Parameters
----------
extent : np.array
2nd element of concept
intent : np.array
1st element of concept
n_subsets : int
See approximate_stabilty
rel_matrix : sparse.coo_matrix
See approximate_stabilty
look_at : {'extent', 'intent'}
whether to determine stability for extent or intent.
Default: 'intent'
Returns
-------
stability : float
Stability parameter for given extent, intent depending on which to look
"""
if look_at == 'intent':
element_1, element_2 = intent, extent
else: # look_at == 'extent':
element_1, element_2 = extent, intent
if n_subsets > 2 ** len(element_1):
subsets = chain.from_iterable(
combinations(element_1, subset_index)
for subset_index in range(len(element_1) + 1))
else:
subsets = _select_random_subsets(element_1, n_subsets)
stability = 0
excluded_subsets = []
for subset in subsets:
if any([set(subset).issubset(excluded_subset)
for excluded_subset in excluded_subsets]):
continue
# computation of the ' operator for the subset
if look_at == 'intent':
subset_prime = \
np.where(np.all(rel_matrix[:, subset], axis=1) == 1)[0]
else: # look_at == 'extent':
subset_prime = \
np.where(np.all(rel_matrix[subset, :], axis=0) == 1)[0]
# Condition holds if the closure of the subset of element_1 given in
# element_2 is equal to element_2 given in input
if set(subset_prime) == set(element_2):
stability += 1
else:
excluded_subsets.append(subset)
stability /= min(n_subsets, 2 ** len(element_1))
return stability
def _select_random_subsets(element_1, n_subsets):
"""
Creates a list of random_subsets of element_1.
Parameters
----------
element_1 : np.array
intent or extent
n_subsets : int
see approximate_stability
Returns
-------
subsets : list
each element a subset of element_1
"""
subsets_indices = [set()] * (len(element_1) + 1)
subsets = []
while len(subsets) < n_subsets:
num_indices = np.random.binomial(n=len(element_1), p=1 / 2)
random_indices = sorted(np.random.choice(
len(element_1), size=num_indices, replace=False))
random_tuple = tuple(random_indices)
if random_tuple not in subsets_indices[num_indices]:
subsets_indices[num_indices].add(random_tuple)
subsets.append(element_1[random_indices])
return subsets
[docs]def pattern_set_reduction(concepts, ns_signatures, winlen, spectrum,
h_subset_filtering=0, k_superset_filtering=0,
l_covered_spikes=0, min_spikes=2, min_occ=2):
r"""
Takes a list concepts and performs pattern set reduction (PSR).
PSR determines which patterns in concepts_psf are statistically significant
given any other pattern, on the basis of the pattern size and
occurrence count ("support"). Only significant patterns are retained.
The significance of a pattern A is evaluated through its signature
:math:`(z_a, c_A)`, where :math:`z_A = |A|` is the size and :math:`c_A` -
the support of A, by either of:
* subset filtering: any pattern B is discarded if *concepts* contains a
superset A of B such that
:math:`(z_B, c_B - c_A + h) \in \text{ns}_{\text{signatures}}`
* superset filtering: any pattern A is discarded if *concepts* contains a
subset B of A such that
:math:`(z_A - z_B + k, c_A) \in \text{ns}_{\text{signatures}}`
* covered-spikes criterion: for any two patterns A, B with
:math:`A \subset B`, B is discarded if
:math:`(z_B-l) \cdot c_B \le c_A \cdot (z_A - l)`, A is discarded
otherwise;
* combined filtering: combines the three procedures above:
takes a list concepts (see output psf function) and performs
combined filtering based on the signature (z, c) of each pattern, where
z is the pattern size and c the pattern support.
For any two patterns A and B in concepts_psf such that :math:`B \subset A`,
check:
1) :math:`(z_B, c_B - c_A + h) \in \text{ns}_{\text{signatures}}`, and
2) :math:`(z_A - z_B + k, c_A) \in \text{ns}_{\text{signatures}}`.
Then:
* if 1) and not 2): discard B
* if 2) and not 1): discard A
* if 1) and 2): discard B if
:math:`c_B \cdot (z_B - l) \le c_A \cdot (z_A - l)`,
otherwise discard A
* if neither 1) nor 2): keep both patterns
Assumptions/Approximations:
* a pair of concepts cannot cause one another to be rejected
* if two concepts overlap more than min_occ times, one of them can
account for all occurrences of the other one if it passes the
filtering
Parameters
----------
concepts : list
List of concepts, each consisting in its intent and extent
ns_signatures : list
A list of non-significant pattern signatures (z, c)
winlen : int
The size (number of bins) of the sliding window used for the analysis.
The maximal length of a pattern (delay between first and last spike) is
then given by `winlen*bin_size`.
spectrum : {'#', '3d#'}
Define the signature of the patterns.
'#': pattern spectrum using the as signature the pair:
(number of spikes, number of occurrences)
'3d#': pattern spectrum using the as signature the triplets:
(number of spikes, number of occurrence, difference between last
and first spike of the pattern)
h_subset_filtering : int, optional
Correction parameter for subset filtering
Default: 0
k_superset_filtering : int, optional
Correction parameter for superset filtering
Default: 0
l_covered_spikes : int, optional
Correction parameter for covered-spikes criterion
Default: 0
min_spikes : int, optional
Minimum pattern size
Default: 2
min_occ : int, optional
Minimum number of pattern occurrences
Default: 2
Returns
-------
tuple
A tuple containing the elements of the input argument
that are significant according to combined filtering.
"""
additional_measures = []
# Extracting from the extent and intent the spike and window times
for concept in concepts:
intent = concept[0]
extent = concept[1]
additional_measures.append((len(extent), len(intent)))
# by default, select all elements in conc to be returned in the output
selected = [True] * len(concepts)
# scan all conc and their subsets
for id1, id2 in combinations(range(len(concepts)), r=2):
# immediately continue if both concepts have already been rejected
if not selected[id1] and not selected[id2]:
continue
intent1, extent1 = concepts[id1][:2]
intent2, extent2 = concepts[id2][:2]
occ1, size1 = additional_measures[id1]
occ2, size2 = additional_measures[id2]
dur1 = max(np.array(intent1) % winlen)
dur2 = max(np.array(intent2) % winlen)
intent2 = set(intent2)
# Collecting all the possible distances between the windows
# of the two concepts
time_diff_all = np.array(
[w2 - w1 for w2 in extent2 for w1 in extent1])
# sort time differences by ascending absolute value
time_diff_sorting = np.argsort(np.abs(time_diff_all))
sorted_time_diff, sorted_time_diff_occ = np.unique(
time_diff_all[time_diff_sorting],
return_counts=True)
# only consider time differences that are smaller than winlen
# and that correspond to intersections that occur at least min_occ
# times
time_diff_mask = np.logical_and(
np.abs(sorted_time_diff) < winlen,
sorted_time_diff_occ >= min_occ)
# Rescaling the spike times to realign to real time
for time_diff in sorted_time_diff[time_diff_mask]:
intent1_new = [t_old - time_diff for t_old in intent1]
# from here on we will only need the intents as sets
intent1_new = set(intent1_new)
# if intent1 and intent2 are disjoint, skip this step
if len(intent1_new & intent2) == 0:
continue
# Test the case intent1 is a superset of intent2
if intent1_new.issuperset(intent2):
reject1, reject2 = _perform_combined_filtering(
occ_superset=occ1,
size_superset=size1,
dur_superset=dur1,
occ_subset=occ2,
size_subset=size2,
dur_subset=dur2,
spectrum=spectrum,
ns_signatures=ns_signatures,
h_subset_filtering=h_subset_filtering,
k_superset_filtering=k_superset_filtering,
l_covered_spikes=l_covered_spikes,
min_spikes=min_spikes,
min_occ=min_occ)
elif intent2.issuperset(intent1_new):
reject2, reject1 = _perform_combined_filtering(
occ_superset=occ2,
size_superset=size2,
dur_superset=dur2,
occ_subset=occ1,
size_subset=size1,
dur_subset=dur1,
spectrum=spectrum,
ns_signatures=ns_signatures,
h_subset_filtering=h_subset_filtering,
k_superset_filtering=k_superset_filtering,
l_covered_spikes=l_covered_spikes,
min_spikes=min_spikes,
min_occ=min_occ)
else:
# none of the intents is a superset of the other one
# we compare both concepts to the intersection
# if one of them is not significant given the
# intersection, it is rejected
inter_size = len(intent1_new & intent2)
reject1 = _superset_filter(
occ_superset=occ1,
size_superset=size1,
dur_superset=dur1,
size_subset=inter_size,
spectrum=spectrum,
ns_signatures=ns_signatures,
k_superset_filtering=k_superset_filtering,
min_spikes=min_spikes)
reject2 = _superset_filter(
occ_superset=occ2,
size_superset=size2,
dur_superset=dur2,
size_subset=inter_size,
spectrum=spectrum,
ns_signatures=ns_signatures,
k_superset_filtering=k_superset_filtering,
min_spikes=min_spikes)
# Reject accordingly:
if reject1 and reject2:
reject1, reject2 = _covered_spikes_criterion(
occ_superset=occ1,
size_superset=size1,
occ_subset=occ2,
size_subset=size2,
l_covered_spikes=l_covered_spikes)
selected[id1] &= not reject1
selected[id2] &= not reject2
# skip remaining time-shifts if both concepts have been rejected
if (not selected[id1]) and (not selected[id2]):
break
# Return the selected concepts
return [p for i, p in enumerate(concepts) if selected[i]]
def _perform_combined_filtering(occ_superset,
size_superset,
dur_superset,
occ_subset,
size_subset,
dur_subset,
spectrum,
ns_signatures,
h_subset_filtering,
k_superset_filtering,
l_covered_spikes,
min_spikes,
min_occ):
"""
perform combined filtering
(see pattern_set_reduction)
"""
reject_subset = _subset_filter(
occ_superset=occ_superset,
occ_subset=occ_subset,
size_subset=size_subset,
dur_subset=dur_subset,
spectrum=spectrum,
ns_signatures=ns_signatures,
h_subset_filtering=h_subset_filtering,
min_occ=min_occ)
reject_superset = _superset_filter(
occ_superset=occ_superset,
size_superset=size_superset,
dur_superset=dur_superset,
size_subset=size_subset,
spectrum=spectrum,
ns_signatures=ns_signatures,
k_superset_filtering=k_superset_filtering,
min_spikes=min_spikes)
# Reject the superset and/or the subset accordingly:
if reject_superset and reject_subset:
reject_superset, reject_subset = _covered_spikes_criterion(
occ_superset=occ_superset,
size_superset=size_superset,
occ_subset=occ_subset,
size_subset=size_subset,
l_covered_spikes=l_covered_spikes)
return reject_superset, reject_subset
def _subset_filter(occ_superset, occ_subset, size_subset, dur_subset, spectrum,
ns_signatures=None, h_subset_filtering=0, min_occ=2):
"""
perform subset filtering
(see pattern_set_reduction)
"""
if ns_signatures is None:
ns_signatures = []
occ_diff = occ_subset - occ_superset + h_subset_filtering
if spectrum == '#':
signature_to_test = (size_subset, occ_diff)
else: # spectrum == '3d#':
signature_to_test = (size_subset, occ_diff, dur_subset)
reject_subset = occ_diff < min_occ or signature_to_test in ns_signatures
return reject_subset
def _superset_filter(occ_superset, size_superset, dur_superset, size_subset,
spectrum, ns_signatures=None, k_superset_filtering=0,
min_spikes=2):
"""
perform superset filtering
(see pattern_set_reduction)
"""
if ns_signatures is None:
ns_signatures = []
size_diff = size_superset - size_subset + k_superset_filtering
if spectrum == '#':
signature_to_test = (size_diff, occ_superset)
else: # spectrum == '3d#':
signature_to_test = (size_diff, occ_superset, dur_superset)
reject_superset = \
size_diff < min_spikes or signature_to_test in ns_signatures
return reject_superset
def _covered_spikes_criterion(occ_superset,
size_superset,
occ_subset,
size_subset,
l_covered_spikes):
"""
evaluate covered spikes criterion
(see pattern_set_reduction)
"""
reject_superset = True
reject_subset = True
score_superset = (size_superset - l_covered_spikes) * occ_superset
score_subset = (size_subset - l_covered_spikes) * occ_subset
if score_superset >= score_subset:
reject_superset = False
else:
reject_subset = False
return reject_superset, reject_subset
[docs]@deprecated_alias(binsize='bin_size')
def concept_output_to_patterns(concepts, winlen, bin_size, pv_spec=None,
spectrum='#', t_start=0 * pq.ms):
"""
Construction of dictionaries containing all the information about a pattern
starting from a list of concepts and its associated pvalue_spectrum.
Parameters
----------
concepts : tuple
Each element of the tuple corresponds to a pattern which it turn is a
tuple of (spikes in the pattern, occurrences of the patterns)
winlen : int
Length (in bins) of the sliding window used for the analysis.
bin_size : pq.Quantity
The time precision used to discretize the `spiketrains` (binning).
pv_spec : None or tuple
Contains a tuple of signatures and the corresponding p-value. If equal
to None all p-values are set to -1.
spectrum : {'#', '3d#'}
'#': pattern spectrum using the as signature the pair:
(number of spikes, number of occurrences)
'3d#': pattern spectrum using the as signature the triplets:
(number of spikes, number of occurrence, difference between last
and first spike of the pattern)
Default: '#'
t_start : pq.Quantity
t_start of the analyzed spike trains
Returns
-------
output : list
List of dictionaries. Each dictionary corresponds to a pattern and
has the following entries:
'itemset':
A list of the spikes in the pattern, expressed in theform of
itemset, each spike is encoded by
`spiketrain_id * winlen + bin_id`.
'windows_ids':
The ids of the windows in which the pattern occurred
in discretized time (given byt the binning).
'neurons':
An array containing the idx of the neurons of the pattern.
'lags':
An array containing the lags (integers corresponding to the
number of bins) between the spikes of the patterns. The first
lag is always assumed to be 0 and corresponds to the first
spike.
'times':
An array containing the times (integers corresponding to the
bin idx) of the occurrences of the patterns.
'signature':
A tuple containing two integers (number of spikes of the
patterns, number of occurrences of the pattern).
'pvalue':
The p-value corresponding to the pattern. If `n_surr==0`,
all p-values are set to -1.
"""
if pv_spec is not None:
pvalue_dict = defaultdict(float)
# Creating a dictionary for the pvalue spectrum
for entry in pv_spec:
if spectrum == '3d#':
pvalue_dict[(entry[0], entry[1], entry[2])] = entry[-1]
if spectrum == '#':
pvalue_dict[(entry[0], entry[1])] = entry[-1]
# Initializing list containing all the patterns
t_start = t_start.rescale(bin_size.units)
output = []
for concept in concepts:
itemset, window_ids = concept[:2]
# Vocabulary for each of the patterns, containing:
# - The pattern expressed in form of Itemset, each spike in the pattern
# is represented as spiketrain_id * winlen + bin_id
# - The ids of the windows in which the pattern occurred in discretized
# time (clipping)
output_dict = {'itemset': itemset, 'windows_ids': window_ids}
# Bins relative to the sliding window in which the spikes of patt fall
itemset = np.array(itemset)
bin_ids_unsort = itemset % winlen
order_bin_ids = np.argsort(bin_ids_unsort)
bin_ids = bin_ids_unsort[order_bin_ids]
# id of the neurons forming the pattern
output_dict['neurons'] = list(itemset[order_bin_ids] // winlen)
# Lags (in bin_sizes units) of the pattern
output_dict['lags'] = bin_ids[1:] * bin_size
# Times (in bin_size units) in which the pattern occurs
output_dict['times'] = sorted(window_ids) * bin_size + t_start
# pattern dictionary appended to the output
if spectrum == '#':
# Signature (size, n occ) of the pattern
signature = (len(itemset), len(window_ids))
else: # spectrum == '3d#':
# Signature (size, n occ, duration) of the pattern
# duration is position of the last bin
signature = (len(itemset), len(window_ids), bin_ids[-1])
output_dict['signature'] = signature
# If None is given in input to the pval spectrum the pvalue
# is set to -1 (pvalue spectrum not available)
if pv_spec is None:
output_dict['pvalue'] = -1
else:
# p-value assigned to the pattern from the pvalue spectrum
output_dict['pvalue'] = pvalue_dict[signature]
output.append(output_dict)
return output
```