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changes in data plot
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@dokato
dokato committed Aug 18, 2015
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13 changes: 11 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,9 @@ ConnectiviPy
=========================
Python connectivity module.
It is a part of [GSOC 2015](http://www.google-melange.com/gsoc/project/details/google/gsoc2015/dokato/5649050225344512) project.
Project blog is [here](http://dokato.github.io/connpy-blog/)!
Project blog is [here](http://dokato.github.io/connpy-blog/).

Documentation on [ReadTheDocs](http://connectivipy.readthedocs.org/).

## Content

Expand All @@ -12,8 +14,15 @@ Project blog is [here](http://dokato.github.io/connpy-blog/)!

##Changelog

### 0.34
* short-time statistics
* documentation in sphinx ready on readthedocs
* visualization improved
* conversion to trans3d
* more examples

### 0.31
* connectivity methods: gDTF, gPDC, Coherency, PSI, GC
* connectivity methods: gDTF, gPDC, Coherency, PSI, GCI
* new mvar estimation criterion: FPE
* statistics for multitrial (bootstrap) and normal case (surrogate data)
* fitting mvar for multitrial
Expand Down
80 changes: 50 additions & 30 deletions connectivipy/conn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,6 @@
from abc import ABCMeta, abstractmethod
from mvar.comp import ldl
from mvarmodel import Mvar
import pdb
import scipy.stats as st

########################################################################
Expand All @@ -31,10 +30,10 @@ def spectrum(acoef, vcoef, fs=1, resolution=100):
"""
Generating data point from matrix *A* with MVAR coefficients.
Args:
*Acf* : numpy.array
*acoef* : numpy.array
array of shape (k, k, p) where *k* is number of channels and
*p* is a model order.
*Vcf* : numpy.array
*vcoef* : numpy.array
prediction error matrix (k, k)
*fs* = 1 : int
sampling rate
Expand Down Expand Up @@ -76,11 +75,11 @@ def spectrum_inst(acoef, vcoef, fs=1, resolution=100):
Generating data point from matrix *A* with MVAR coefficients taking
into account zero-lag effects.
Args:
*Acf* : numpy.array
*acoef* : numpy.array
array of shape (k, k, p+1) where *k* is number of channels and
*p* is a model order. Acf[0] - is (k, k) matrix for zero lag,
Acf[1] for one data point lag and so on.
*Vcf* : numpy.array
*p* is a model order. acoef[0] - is (k, k) matrix for zero lag,
acoef[1] for one data point lag and so on.
*vcoef* : numpy.array
prediction error matrix (k, k)
*fs* = 1 : int
sampling rate
Expand Down Expand Up @@ -198,7 +197,10 @@ def short_time_significance(self, data, Nrep=10, alpha=0.05,\
if not no:
no = int(N/10)
slices = xrange(0, N, int(nfft-no))
signi_st = np.zeros((len(slices, k, k)))
if self.two_sided:
signi_st = np.zeros((len(slices),2,k,k))
else:
signi_st = np.zeros((len(slices),k,k))
for e,i in enumerate(slices):
if i+nfft>=N:
if trls:
Expand All @@ -218,16 +220,31 @@ def significance(self, data, Nrep=10, alpha=0.05, **params):
return signific

def levels(self, signi, alpha, k):
"""
Levels of significance
Args:
*signi* : numpy.array
bootstraped values of each channel
*alpha* : float
type I error rate (significance level)
*k* : int
number of channels
Returns:
*ficance* : numpy.array
maximal value throughout frequency of score at percentile
at level 1-*alpha*
"""
if self.two_sided:
ficance = np.zeros((2, k, k))
else:
ficance = np.zeros((k, k))
for i in range(k):
for j in range(k):
if self.two_sided:
ficance[i][j] = np.max(st.scoreatpercentile(signi[:,:,i,j], alpha*100, axis=1))
ficance[0][i][j] = np.max(st.scoreatpercentile(signi[:,:,i,j], alpha*100, axis=1))
ficance[1][i][j] = np.max(st.scoreatpercentile(signi[:,:,i,j], (1-alpha)*100, axis=1))
else:
ficance[i][j] = np.max(st.scoreatpercentile(signi[:,:,i,j], alpha*100, axis=1))
ficance[i][j] = np.max(st.scoreatpercentile(signi[:,:,i,j], (1-alpha)*100, axis=1))
return ficance

def __calc_multitrial(self, data, **params):
Expand Down Expand Up @@ -284,6 +301,7 @@ class ConnectAR(Connect):
__metaclass__ = ABCMeta

def __init__(self):
super(ConnectAR, self).__init__()
self.values_range = [0, 1]

def short_time(self, data, nfft=None, no=None, mvarmethod='yw',\
Expand Down Expand Up @@ -425,10 +443,10 @@ def dtf_fun(Acoef, Vcoef, fs, resolution, generalized=False):
"""
Directed Transfer Function estimation from MVAR parameters.
Args:
*Acf* : numpy.array
*Acoef* : numpy.array
array of shape (k, k, p) where *k* is number of channels and
*p* is a model order.
*Vcf* : numpy.array
*Vcoef* : numpy.array
prediction error matrix (k, k)
*fs* = 1 : int
sampling rate
Expand Down Expand Up @@ -459,10 +477,10 @@ def pdc_fun(Acoef, Vcoef, fs, resolution, generalized=False):
"""
Partial Directed Coherence estimation from MVAR parameters.
Args:
*Acf* : numpy.array
*Acoef* : numpy.array
array of shape (k, k, p) where *k* is number of channels and
*p* is a model order.
*Vcf* : numpy.array
*Vcoef* : numpy.array
prediction error matrix (k, k)
*fs* = 1 : int
sampling rate
Expand Down Expand Up @@ -496,10 +514,10 @@ def calculate(self, Acoef=None, Vcoef=None, fs=None, resolution=None):
"""
Partial Coherence estimation from MVAR parameters.
Args:
*Acf* : numpy.array
*Acoef* : numpy.array
array of shape (k, k, p) where *k* is number of channels and
*p* is a model order.
*Vcf* : numpy.array
*Vcoef* : numpy.array
prediction error matrix (k, k)
*fs* = 1 : int
sampling rate
Expand Down Expand Up @@ -573,10 +591,10 @@ def calculate(self, Acoef=None, Vcoef=None, fs=None, resolution=100):
full-frequency Directed Transfer Function estimation from MVAR
parameters.
Args:
*Acf* : numpy.array
*Acoef* : numpy.array
array of shape (k, k, p) where *k* is number of channels and
*p* is a model order.
*Vcf* : numpy.array
*Vcoef* : numpy.array
prediction error matrix (k, k)
*fs* = 1 : int
sampling rate
Expand Down Expand Up @@ -614,10 +632,10 @@ def calculate(self, Acoef=None, Vcoef=None, fs=None, resolution=100):
parameters. dDTF is a DTF multiplied in each frequency by
Patrial Coherence.
Args:
*Acf* : numpy.array
*Acoef* : numpy.array
array of shape (k, k, p) where *k* is number of channels and
*p* is a model order.
*Vcf* : numpy.array
*Vcoef* : numpy.array
prediction error matrix (k, k)
*fs* = 1 : int
sampling rate
Expand Down Expand Up @@ -661,10 +679,10 @@ def calculate(self, Acoef=None, Vcoef=None, fs=None, resolution=100):
instantaneous Partial Directed Coherence from MVAR
parameters.
Args:
*Acf* : numpy.array
*Acoef* : numpy.array
array of shape (k, k, p+1) where *k* is number of channels and
*p* is a model order. It's zero lag case.
*Vcf* : numpy.array
*Vcoef* : numpy.array
prediction error matrix (k, k)
*fs* = 1 : int
sampling rate
Expand Down Expand Up @@ -699,10 +717,10 @@ def calculate(self, Acoef=None, Vcoef=None, fs=None, resolution=100):
instantaneous Partial Directed Coherence from MVAR
parameters.
Args:
*Acf* : numpy.array
*Acoef* : numpy.array
array of shape (k, k, p+1) where *k* is number of channels and
*p* is a model order. It's zero lag case.
*Vcf* : numpy.array
*Vcoef* : numpy.array
prediction error matrix (k, k)
*fs* = 1 : int
sampling rate
Expand Down Expand Up @@ -796,6 +814,7 @@ class PSI(Connect):
:func:`Connect.calculate` method.
"""
def __init__(self):
super(PSI, self).__init__()
self.two_sided = True

def calculate(self, data, band_width=4, psinfft=None, psino=0, window=np.hanning):
Expand All @@ -820,10 +839,10 @@ def calculate(self, data, band_width=4, psinfft=None, psino=0, window=np.hanning
.. [1] Nolte G. et all, Comparison of Granger Causality and
Phase Slope Index. 267–276 (2009).
"""
assert psinfft>psino, "overlap must be smaller than window"
k, N = data.shape
if not psinfft:
psinfft = int(N/4)
assert psinfft>psino, "overlap must be smaller than window"
coh = Coherency()
cohval = coh.calculate(data, cnfft=psinfft, cno=psino, window=window, im=True)
fq_bands = np.arange(0, int(psinfft/2)+1, band_width)
Expand All @@ -839,18 +858,19 @@ class GCI(Connect):
:func:`Connect.calculate` method.
"""
def __init__(self):
super(GCI, self).__init__()
self.two_sided = True

def calculate(self, data, method='yw', order=None):
def calculate(self, data, gcimethod='yw', gciorder=None):
"""
Granger Causality Index calculation from MVAR model.
Args:
*data* : numpy.array
array of shape (k, N) where *k* is number of channels and
*N* is number of data points.
*method* = 'yw' : int
*gcimethod* = 'yw' : int
MVAR parameters estimation model
*order* = None : int
*gciorder* = None : int
model order, if None appropiate value is chosen basic
on default criterion
Returns:
Expand All @@ -861,11 +881,11 @@ def calculate(self, data, method='yw', order=None):
Phase Slope Index. 267–276 (2009).
"""
k, N = data.shape
arfull, vrfull = Mvar().fit(data, order, method)
arfull, vrfull = Mvar().fit(data, gciorder, gcimethod)
gcval = np.zeros((k, k))
for i in xrange(k):
arix = [j for j in xrange(k) if i!=j]
ar_i, vr_i = Mvar().fit(data[arix,:], order, method)
ar_i, vr_i = Mvar().fit(data[arix,:], gciorder, gcimethod)
for e,c in enumerate(arix):
gcval[c,i] = np.log(vrfull[i,i]/vr_i[e,e])
return np.tile(gcval,(2,1,1))
Expand Down

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