def testWeights(name, values):
flistwidget, fnames = values['flist']
weightfile = values['weightfile'][1]
if not weightfile:
Error('You must first generate weights or select a file from which ' + \
'to load the weights.')
return
errors = []
label, value = values['test-args'][1]['matrixshape']
matrixshape = parsematlab.parse(value.lower().replace('x', ' '))
if isinstance(matrixshape, str):
errors.append(label + '\n ' + value.replace('\n', '\n '))
if np.isscalar(matrixshape):
matrixshape = [matrixshape]
label, value = values['test-args'][1]['repetitions']
repetitions = parsematlab.parse(value)
if isinstance(repetitions, str):
errors.append(label + '\n ' + value.replace('\n', '\n '))
if len(errors) > 0:
Error('\n\n'.join(errors))
return
classifier = loaddata.load_weights(weightfile)
if isinstance(classifier, str):
Error(classifier)
return
removeanomalies = values['generation-args'][1]['removeanomalies'][1]
data = []
type = []
samplingrate = None
try:
for fname in fnames:
result = loaddata.load_data(fname, [0, classifier.shape[0]],
None, True, removeanomalies = removeanomalies)
if isinstance(result, str):
Error(result)
return
if samplingrate == None:
samplingrate = result[2]
if samplingrate != result[2]:
Error('Not all data files have the same sampling rate.')
return
data.append(result[0])
type.append(result[1])
if len(data) == 0 or len(type) == 0:
Error('You must select some data upon which to test the weights.')
return
try:
data = np.concatenate(data)
except ValueError:
Error('Not all data files have the same number of channels.')
return
type = np.concatenate(type)
result = testweights.test_weights(data, type, classifier,
matrixshape, repetitions)
if isinstance(result, str):
Error(result)
return
score, correctness = result
message = '\n'.join(fnames)
message += '\n\n%s\n\nExpected accuracy for a %s matrix:\n\n' % \
(
weightfile,
'x'.join(str(i) for i in matrixshape)
)
for i in range(len(repetitions)):
if repetitions[i] != 1:
message += '%i repetitions: %0.1f%%\n' % \
(repetitions[i], correctness[i] * 100)
else:
message += '1 repetition: %0.1f%%\n' % (correctness[i] * 100)
message += '\nTarget STDEV: %f\nNontarget STDEV: %f\n' % score
Info(message)
except MemoryError:
Error('Could not fit all the selected data in memory.\n' + \
'Try loading fewer data files.')
return
def diagnosticPlot(name, values):
args = values['generation-args'][1]
errors = []
for key in ['responsewindow']:
label, value = args[key]
value = parsematlab.parse(value)
if isinstance(value, str):
errors.append(label + '\n ' + value.replace('\n', '\n '))
args[key] = value
if len(errors) > 0:
Error('\n\n'.join(errors))
return
response_window = args['responsewindow']
fnames = values['flist'][1]
removeanomalies = args['removeanomalies'][1]
weightfile = values['weightfile'][1]
data = []
type = []
samplingrate = None
try:
for fname in fnames:
result = loaddata.load_data(fname, response_window, None,
removeanomalies = removeanomalies)
if isinstance(result, str):
Error(result)
return
if samplingrate == None:
samplingrate = result[2]
if samplingrate != result[2]:
Error('Not all data files have the same sampling rate.')
return
data.append(result[0])
type.append(result[1])
if len(data) == 0 or len(type) == 0:
Error('You must select some data to plot.')
return
try:
data = np.concatenate(data)
except ValueError:
Error('Not all data files have the same number of channels.')
return
type = np.concatenate(type)
if weightfile:
weights = loaddata.load_weights(weightfile)
if isinstance(weights, str):
Error(weights)
return
classifier = np.zeros(data.shape[1:])
classifier[:weights.shape[0], :weights.shape[1]] = weights
classifier_max = max(abs(classifier.max()), abs(classifier.min()))
else:
classifier = None
if isinstance(classifier, str):
Error(classifier)
return
num_plots = 3 if classifier == None else 4
signed_r = np.zeros(data.shape[1:])
for row in range(signed_r.shape[0]):
for col in range(signed_r.shape[1]):
signed_r[row, col] = stats.linregress(
data[:, row, col], type
)[2]
signed_r_max = max(abs(signed_r.max()), abs(signed_r.min()))
x = np.arange(data.shape[1]) * 1000 / samplingrate
target = data[type.nonzero()[0]].mean(axis = 0)
nontarget = data[(~type).nonzero()[0]].mean(axis = 0)
vmin, vmax = ylim = [min(target.min(), nontarget.min()),
max(target.max(), nontarget.max())]
fig = pylab.figure()
fig.subplots_adjust(bottom = 0.06, top = 0.93, hspace = 0.45)
master_ax = ax = pylab.subplot(num_plots, 1, 1)
pylab.title('Target', fontsize = 'medium')
pylab.imshow(target.transpose(), interpolation = 'nearest',
cmap = 'PRGn', aspect = 'auto', vmin = vmin, vmax = vmax,
origin = 'lower', extent = (
0,
data.shape[1] * 1000 / samplingrate,
-0.5,
data.shape[2] - 0.5
)
)
pylab.xticks(fontsize = 'small')
pylab.yticks(range(data.shape[2]),
[str(i) for i in range(1, data.shape[2] + 1)],
fontsize = 'small')
pylab.axes(pylab.colorbar().ax)
pylab.yticks(fontsize = 'small')
ax = pylab.subplot(num_plots, 1, 2, sharex = master_ax,
sharey = master_ax)
pylab.title('Non-Target', fontsize = 'medium')
pylab.imshow(nontarget.transpose(), interpolation = 'nearest',
cmap = 'PRGn', aspect = 'auto', vmin = vmin, vmax = vmax,
origin = 'lower', extent = (
0,
data.shape[1] * 1000 / samplingrate,
-0.5,
data.shape[2] - 0.5
)
)
pylab.xticks(fontsize = 'small')
pylab.yticks(range(data.shape[2]),
[str(i) for i in range(1, data.shape[2] + 1)],
fontsize = 'small')
pylab.axes(pylab.colorbar().ax)
pylab.yticks(fontsize = 'small')
ax = pylab.subplot(num_plots, 1, 3, sharex = master_ax,
sharey = master_ax)
pylab.title('Correlation Coefficient', fontsize = 'medium')
pylab.imshow(signed_r.transpose(), interpolation = 'nearest',
cmap = 'PRGn', aspect = 'auto', vmin = -signed_r_max,
vmax = signed_r_max, origin = 'lower', extent = (
0,
data.shape[1] * 1000 / samplingrate,
-0.5,
data.shape[2] - 0.5
)
)
pylab.xticks(fontsize = 'small')
pylab.yticks(range(data.shape[2]),
[str(i) for i in range(1, data.shape[2] + 1)],
fontsize = 'small')
pylab.axes(pylab.colorbar().ax)
pylab.yticks(fontsize = 'small')
if classifier == None:
return
ax = pylab.subplot(num_plots, 1, 4, sharex = master_ax,
sharey = master_ax)
pylab.title('Classifier Weights', fontsize = 'medium')
pylab.imshow(classifier.transpose(), interpolation = 'nearest',
cmap = 'PRGn', aspect = 'auto', vmin = -classifier_max,
vmax = classifier_max, origin = 'lower', extent = (
0,
data.shape[1] * 1000 / samplingrate,
-0.5,
data.shape[2] - 0.5
)
)
pylab.xticks(fontsize = 'small')
pylab.yticks(range(data.shape[2]),
[str(i) for i in range(1, data.shape[2] + 1)],
fontsize = 'small')
pylab.axes(pylab.colorbar().ax)
pylab.yticks(fontsize = 'small')
except MemoryError:
Error('Could not fit all the selected data in memory.\n' + \
'Try loading fewer data files.')
return
