def _ax_utsStats(segments, sensor,
plotMean=True,
all=False,
ax=None,
lineprops={}, # c, ls, lw; doct or list of dicts
# e.g. [{'c':'b'},{'c':'r'},{'c':'g'},'m','c'],
test_segment=False, p=.05, softStats=False, #testWindowFreq='max',
sem=None, # 'sem' (float multiplier)
ylim=None,
plotLabel=False,
statsColorspace=None,
**kwargs):
"""
takes a list of segments and plots one sensor to ax.
Arguments
---------
sem: = None or float
plot standard error of the mean (e.g., ``sem=2`` plots the mean +/- 2
sem)
test_segment:
submit a test_segment to add to plot (efficient because usually
_ax_utsStats is called more than once for several segments
plottype:
``'mean'``: plots the mean for each stats segment
``'all'``: plots all data traces contained in the stats, colors mark the
segment NOT MAINTAINED
NOT IMPLEMENTED segments: submit mean segments
"""
# prepare arguments
if statsColorspace is None:
statsColorspace = _cs.get_sig_white()
if ax is None:
logging.debug(" _ax_utsStats got None ax")
ax = P.gca()
if ylim is None:
ylim = segments[0].properties.get('ylim', None)
# prepare args
segments = _basic_ops_.toTuple(segments)
seg = segments[0]
assert seg.ndim == 1
if ylim is None:
if all:
minV, maxV = -7.5, 7.5
else:
minV, maxV = -1.5, 1.5
else:
minV, maxV = -ylim, ylim
# t
# tmin = -seg.t0;
# xaxis_length = len(seg)
# tmax = ( float(xaxis_length)/seg.samplingrate ) - seg.t0
# t = np.r_[tmin : tmax : xaxis_length*1j]
# line properties
if type(lineprops) == dict:
lineprops = [deepcopy(lineprops) for i in range(len(segments))]
# (need separate instance because properties are is modified later)
#plot
handlesForLegend=[]
for s, lp in zip(segments, lineprops):
line_properties = {'alpha':.2,
'zorder':-1}
if ('color' not in lp) and s.color:
lp['color']=s.color
line_properties.update(lp)
mean = s.mean().subdata(out='data', sensors=[sensor])[:,0] #data[:,sensor]
t = s.t
logging.debug("_ax_utsStats plotting '%s'"%s.name)
if plotMean:
line = ax.plot(t, mean, label=s.name, **lp)[0] # label=label
line_properties.update({'color':line.get_color()})
if all:
#print "%s, %s"%(sensor, type(sensor))
single_line = ax.plot(t[...,None], s.data[:,sensor], **line_properties)[0]
if sem:
#print "%s, %s"%(sensor, type(sensor))
line_properties.update({'alpha':.2})
sem_array = sp.stats.sem(s.data[:,sensor], axis=-1) * sem
ax.fill_between(t, mean-sem_array, mean+sem_array, **line_properties)
handlesForLegend.append(line)
# p-bar
if test_segment:
imKwargs={'zorder': -100}
statsColorspace.toKwargs(imKwargs)
im = test_segment.P[:, [sensor]].T
# l = len(im)
# im = np.ma.array(np.vstack([np.ones(l), im, np.ones(l)]),
# mask=np.vstack([np.ones(l), im>p, np.ones(l)]))
if softStats:
pad = np.ones(im.shape)
im = np.vstack((pad,im,im,im,pad))
imKwargs['interpolation'] = 'bicubic'
else:
imKwargs['interpolation'] = 'nearest'
extent = (t[0], t[-1]) + ax.get_ylim()
ax.imshow(im, extent=extent, aspect='auto', **imKwargs)
else:
ax.set_xlim(t[0], t[-1])
if plotLabel:
ax.text(t[0]/2, maxV/2, plotLabel, horizontalalignment='center')
return handlesForLegend
def array(segments, sensors=None, plotAllSubjects=False, plotCbar=True, test='nonparametric', p=.05, **kwargs):
"""
NOT MAINTAINED
plots tv plots to a rectangular grid instead of topographic spacing
kwargs:
sensors: List of sensor IDs
test='nonparametric', None if len(segments) > 2
"""
P.clf()
# prepare args
segments = _basic_ops_.toTuple(segments)
kwargs['test']=test
if sensors==None:
sensors = range(len(segments[0].sensors))
else:
sensors = _basic_ops_.toTuple(sensors)
statsColorspace = _cs.get_sig(p)
# determine plotting grid
nPlots = len(sensors) * (1+plotAllSubjects) + plotCbar
nColumnsEst = np.sqrt( nPlots )
nColumns = int(nColumnsEst)
if nColumns != nColumnsEst:
nColumns += 1
if nColumns % (1+plotAllSubjects) != 0:
nColumns += 1
nRows = int( nPlots / nColumns ) + (1- ( nPlots % nColumns == 0 ))
logging.debug("plotting electrodes %s, fig shape (%s, %s)"%(str(sensors), nRows, nColumns))
# fig = P.gcf()
P.subplots_adjust(left=.05, bottom=.075, right=.99, top=.94, wspace=.25, hspace=.3)
#grid = AxesGrid( fig, 111,
# nrows_ncols = (nRows, nColumns),
# axes_pad = .01 )
# plot
kwargs['labelNames']=True
for i,sensor in enumerate(sensors):
kwargs['lastRow'] = ( i > (nRows-1) * nColumns)
if plotAllSubjects:
#axes = grid[ (i+1)*2 ] #
axes = P.subplot(nRows, nColumns, (i+1) * 2 -1 )
kwargs["plotType"]='mean'
_ax_utsStats(segments, sensor, statsColorspace=statsColorspace, **kwargs)
axes.set_title( segments[0].sensors[sensor].name )
#axes = grid[ (i+1)*2+1 ] #
axes = P.subplot(nRows, nColumns, (i+1) * (1+plotAllSubjects) )
kwargs["plotType"]='all'
_ax_utsStats(segments, sensor, statsColorspace=statsColorspace, **kwargs)
axes.set_title( ', '.join(( segments[0].sensors[sensor].name, "All Subjects")) )
else:
#axes = grid[i] #
axes = P.subplot(nRows, nColumns, (i+1) )
kwargs["plotType"]='mean'
_ax_utsStats(segments, sensor, statsColorspace=statsColorspace,
firstColumn=( i%nColumns==0 ),
**kwargs)
axes.set_title( segments[0].sensors[sensor].name )
kwargs['labelNames']=False
if test!=None and plotCbar:
#axes = grid[-1] #
axes = P.subplot(nRows, nColumns, nRows*nColumns)
pos = axes.get_position()
newPos = [pos.xmin, pos.ymin+pos.width*.3, pos.width, pos.width*.15]
axes.set_position(newPos)
statsColorspace.toAxes_(axes)
