def test_ccipcanode_v1():
line_x = numx.zeros((1000, 2), "d")
line_y = numx.zeros((1000, 2), "d")
line_x[:, 0] = numx.linspace(-1, 1, num=1000, endpoint=1)
line_y[:, 1] = numx.linspace(-0.2, 0.2, num=1000, endpoint=1)
mat = numx.concatenate((line_x, line_y))
utils.rotate(mat, uniform() * 2 * numx.pi)
mat += uniform(2)
mat -= mat.mean(axis=0)
pca = CCIPCANode()
for i in range(5):
pca.train(mat)
bpca = PCANode()
bpca.train(mat)
bpca.stop_training()
v = pca.get_projmatrix()
bv = bpca.get_projmatrix()
dcosines = numx.zeros(v.shape[1])
for dim in range(v.shape[1]):
dcosines[dim] = numx.fabs(numx.dot(v[:, dim], bv[:, dim].T)) / (
numx.linalg.norm(v[:, dim]) * numx.linalg.norm(bv[:, dim]))
assert_almost_equal(numx.ones(v.shape[1]), dcosines)
def test_mcanode_v1():
line_x = numx.zeros((1000, 2), "d")
line_y = numx.zeros((1000, 2), "d")
line_x[:, 0] = numx.linspace(-1, 1, num=1000, endpoint=1)
line_y[:, 1] = numx.linspace(-0.2, 0.2, num=1000, endpoint=1)
mat = numx.concatenate((line_x, line_y))
utils.rotate(mat, uniform() * 2 * numx.pi)
mat += uniform(2)
mat -= mat.mean(axis=0)
mca = MCANode()
for i in xrange(5):
mca.train(mat)
bpca = PCANode()
bpca.train(mat)
bpca.stop_training()
v = mca.get_projmatrix()
bv = bpca.get_projmatrix()[:, ::-1]
dcosines = numx.zeros(v.shape[1])
for dim in xrange(v.shape[1]):
dcosines[dim] = numx.fabs(numx.dot(v[:, dim], bv[:, dim].T)) / (
numx.linalg.norm(v[:, dim]) * numx.linalg.norm(bv[:, dim]))
assert_almost_equal(numx.ones(v.shape[1]), dcosines)
def pca(multidim_data, output_dim):
"""Principal Component Analysis"""
pcanode = PCANode(input_dim=multidim_data.shape[1],
output_dim=output_dim,
dtype=np.float32,
svd=True,
reduce=True,
var_rel=1E-15,
var_abs=1E-15)
pcanode.train(data)
return np.dot(multidim_data, pcanode.get_projmatrix())
class spikesorter(graphics.diagnosticGUI):
def __init__(self, title):
#global plotter
plotter = graphics.Plotter()
graphics.diagnosticGUI.__init__(self, plotter)
#Recover data:
#data = importPointset('shortsimdata1.dat',t=0,sep=',')
data = importPointset('simdata1_100000.dat', t=0, sep=',')
vs = data['vararray'][0]
vs = self.bandpass_filter(vs, 300, 3000, 32000)
ts = data['t']
self.N = len(vs)
self.traj = numeric_to_traj([vs],
'test_traj', ['x'],
ts,
discrete=False)
#Threshold used in martinez et al.
self.mthresh = 4 * (np.median(np.abs(vs)) / 0.6475)
self.selected_pcs = []
self.fovea_setup()
#x = self.traj.sample(tlo= 0, thi = self.N)['x']
#r = x > 10
#above_thresh = np.where(r == True)[0]
#spike = []
#peaks = []
#crosses = []
#last_i = above_thresh[0] - 1
#for i in above_thresh:
#if i - 1 != last_i:
#crosses.append(i)
##Return x value of the highest y value.
#peaks.append(np.where(x == max(list(x[spike])))[0][0])
#spike = []
#spike.append(i)
#last_i = i
if tutorial_on:
print("STEP 1:")
print("Create a horizontal line of interest by pressing 'l'.")
print(
"Once created, this line can be forced to extent by pressing 'm'."
)
print(
"Enter 'ssort.selected_object.update(name = 'thresh')' to identify the line as a threshold for spike detection"
)
print(
"Once the line is renamed to 'thresh', the arrow keys can be used to move it up and down."
)
self.tutorial = 'step2'
else:
self.tutorial = None
def fovea_setup(self):
#Setup code
DOI = [(0, self.N), (-30, 30)]
self.plotter.clean() # in case rerun in same session
self.plotter.add_fig('master',
title='spikesort',
xlabel='time',
ylabel='mV',
domain=DOI)
#Setup all layers
self.plotter.add_layer('spikes')
self.plotter.add_layer('thresh_crosses')
self.plotter.add_layer('detected')
self.plotter.add_layer('pcs')
self.plotter.add_layer('scores')
self.setup(
{
'11': {
'name': 'Waveform',
'scale': DOI,
'layers': ['spikes', 'thresh_crosses'],
'callbacks': '*',
'axes_vars': ['x', 'y']
},
'12': {
'name': 'Detected Spikes',
'scale': [(0, default_sw), (-80, 80)],
'layers': ['detected'],
#'callbacks':'*',
'axes_vars': ['x', 'y']
},
'21': {
'name': 'Principal Components',
'scale': [(0, default_sw), (-0.5, 0.5)],
'layers': ['pcs'],
#'callbacks':'*',
'axes_vars': ['x', 'y']
},
'22': {
'name': 'Projected Spikes',
#'scale': [(-100, 100), (-100, 100)],
'scale': [(-300, 300), (-300, 300)],
'layers': ['scores'],
'callbacks': '*',
'axes_vars': ['firstPC', 'secondPC']
}
},
size=(8, 8),
with_times=False,
basic_widgets=True)
#self.plotter.set_text('load_perc', Loading: %d\%'%n, 'loading')
#Bad code carried over from fovea_game:
fig_struct, figure = self.plotter._resolve_fig(None)
#self.ax = fig_struct.arrange['11']['axes_obj']
coorddict = {'x': {'x': 't', 'layer': 'spikes', 'style': 'b-'}}
self.add_data_points(self.traj.sample(), coorddict=coorddict)
evKeyOn = self.fig.canvas.mpl_connect('key_press_event',
self.ssort_key_on)
self.plotter.auto_scale_domain(subplot='11', xcushion=0)
self.plotter.show()
def bandpass_filter(self, data, lowcut, highcut, fs, order=5):
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = butter(order, [low, high], btype='band')
y = lfilter(b, a, data)
return y
def user_pick_func(self, ev):
if self.selected_object.layer == 'detected' or self.selected_object.layer == 'scores':
if hasattr(self, 'last_name'):
self.plotter.set_data_2(
self.last_name,
layer='scores',
markersize=6,
zorder=1,
style=self.default_colors[self.last_name])
self.plotter.set_data_2(
self.last_name,
layer='detected',
linewidth=1,
zorder=1,
style=self.default_colors[self.last_name])
self.plotter.set_data_2(self.selected_object.name,
layer='scores',
markersize=12,
zorder=10,
style='y*')
self.plotter.set_data_2(self.selected_object.name,
layer='detected',
linewidth=2.5,
zorder=10,
style='y-')
self.last_name = self.selected_object.name
elif self.selected_object.layer == 'pcs':
self.proj_PCs.insert(0, self.selected_object.name)
self.proj_PCs = self.proj_PCs[0:2]
for name in fig_struct['layers']['pcs']['data'].keys():
if name not in self.proj_PCs:
self.plotter.set_data_2(name,
layer='pcs',
style=fig_struct['layers']['pcs']
['data'][name]['style'][0] + '--')
for pc in self.proj_PCs:
self.plotter.set_data_2(
pc,
layer='pcs',
style=fig_struct['layers']['pcs']['data'][pc]['style'][0] +
'-')
self.proj_vec1 = fig_struct['layers']['pcs']['handles'][
self.proj_PCs[0]].get_ydata()
self.proj_vec2 = fig_struct['layers']['pcs']['handles'][
self.proj_PCs[1]].get_ydata()
fig_struct.arrange['22']['axes_vars'] = list(
reversed(self.proj_PCs))
self.project_to_PC()
self.plotter.show()
def user_update_func(self):
if self.selected_object.name is 'thresh':
if self.selected_object.m != 0:
print("Make 'thresh' a horizontal threshold by pressing 'm'.")
return
try:
self.search_width = self.context_objects['ref_box'].dx
self.context_objects['ref_box'].remove()
except KeyError:
self.search_width = default_sw
if self.tutorial == 'step2':
print("STEP 2: ")
print(
"When thresh is in place, press 'd' to capture each spike crossing the threshold in a bounding box."
)
print(
"Each detected spike will be placed in the top right subplot."
)
self.tutorial = 'step3'
cutoff = self.selected_object.y1
traj_samp = self.traj.sample()['x']
r = traj_samp > cutoff
above_thresh = np.where(r == True)[0]
spike = []
spikes = []
crosses = []
last_i = above_thresh[0] - 1
for i in above_thresh:
if i - 1 != last_i:
crosses.append(i)
#Return x value of the highest y value.
spikes.append(spike)
spike = []
spike.append(i)
last_i = i
self.traj_samp = traj_samp
self.crosses = crosses
self.spikes = spikes
self.plotter.add_data([self.crosses, [cutoff] * len(self.crosses)],
layer='thresh_crosses',
style='r*',
name='crossovers',
force=True)
self.show()
def compute_bbox(self):
fig_struct, figs = self.plotter._resolve_fig(None)
#Clear existing bounding boxes
rem_names = []
for con_name, con_obj in self.context_objects.items():
if isinstance(con_obj, box_GUI) and con_name is not 'ref_box':
rem_names.append(con_name)
for name in rem_names:
self.context_objects[name].remove(draw=False)
fig_struct['layers']['detected']['data'] = {}
self.plotter.show(rebuild=True)
#Create new bounding boxes
c = 0
for spike in self.spikes:
peak = np.where(
self.traj_samp == max(list(self.traj_samp[spike])))[0][0]
tlo = peak - 20
thi = tlo + self.search_width
valley = min(self.traj.sample()['x'][tlo:thi])
box_GUI(self,
pp.Point2D(tlo,
self.traj.sample()['x'][peak]),
pp.Point2D(thi, valley),
name='spike_box' + str(c),
select=False)
spike_seg = self.traj_samp[tlo:thi]
try:
X = np.row_stack((X, spike_seg))
except NameError:
X = spike_seg
c += 1
return X
def project_to_PC(self):
Y = np.dot(self.X, np.column_stack((self.proj_vec1, self.proj_vec2)))
#If moving to a smaller number of spikes, just forcing out data by reassigning names won't work. Must clear.
self.clear_data('scores')
self.show()
self.default_colors = {}
#Add spikes as individual lines, so they can be referenced individually.
c = 0
for spike in Y:
name = 'spike' + str(c)
self.default_colors[name] = 'k'
self.add_data_points([spike[0], spike[1]],
layer='scores',
style=self.default_colors[name] + '*',
name=name)
c += 1
self.plotter.auto_scale_domain(subplot='22')
self.show(rebuild=True)
def ssort_key_on(self, ev):
self._key = k = ev.key # keep record of last keypress
fig_struct, fig = self.plotter._resolve_fig(None)
class_keys = ['1', '2', '3', '0']
if k in class_keys:
if isinstance(self.selected_object, box_GUI):
for dname, dstruct in fig_struct['layers']['scores'][
'data'].items():
if self.selected_object.x1 < dstruct['data'][0] < self.selected_object.x2 and \
self.selected_object.y1 < dstruct['data'][1] < self.selected_object.y2:
if k == '1':
self.default_colors[dname] = 'r'
self.plotter.set_data_2(dname,
layer='detected',
style='r-')
self.plotter.set_data_2(dname,
layer='scores',
style='r*')
if k == '2':
self.default_colors[dname] = 'g'
self.plotter.set_data_2(dname,
layer='detected',
style='g-')
self.plotter.set_data_2(dname,
layer='scores',
style='g*')
if k == '3':
self.default_colors[dname] = 'b'
self.plotter.set_data_2(dname,
layer='detected',
style='b-')
self.plotter.set_data_2(dname,
layer='scores',
style='b*')
if k == '0':
self.default_colors[dname] = 'k'
self.plotter.set_data_2(dname,
layer='detected',
style='k-')
self.plotter.set_data_2(dname,
layer='scores',
style='k*')
self.plotter.show()
if k == 'd':
try:
self.crosses
except AttributeError:
print(
"Can't detect spikes until threshold crossings have been found."
)
return
self.X = self.compute_bbox()
self.default_colors = {}
if len(self.X.shape) == 1:
self.default_colors['spike0'] = 'k'
self.add_data_points([list(range(0, len(self.X))), self.X],
layer='detected',
style=self.default_colors['spike0'] + '-',
name='spike0',
force=True)
else:
c = 0
for spike in self.X:
name = 'spike' + str(c)
self.default_colors[name] = 'k'
self.add_data_points([list(range(0, len(spike))), spike],
layer='detected',
style=self.default_colors[name] + '-',
name=name,
force=True)
c += 1
self.plotter.auto_scale_domain(xcushion=0, subplot='12')
self.show()
if self.tutorial == 'step3':
print("STEP 3: ")
print(
"You can now press 'p' to perform PCA on the detected spikes."
)
print(
"The bottom right subplot will display the first 3 principal components (in red, green, and yellow respectively.)"
)
print(
"The bottom left subplot will show the detected spikes projected onto the first two PCs"
)
self.tutorial = 'step4'
if k == 'p':
try:
X = self.X
except AttributeError:
print('Must detect spikes before performing PCA.')
return
print('doing PCA...')
self.p = PCANode(output_dim=0.99, reduce=True, svd=True)
self.p.train(X)
self.proj_vec1 = self.p.get_projmatrix()[:, 0]
self.proj_vec2 = self.p.get_projmatrix()[:, 1]
self.add_data_points(
[list(range(0, len(self.proj_vec1))), self.proj_vec1],
style='r-',
layer='pcs',
name='firstPC',
force=True)
self.add_data_points(
[list(range(0, len(self.proj_vec2))), self.proj_vec2],
style='g-',
layer='pcs',
name='secondPC',
force=True)
self.plotter.show()
self.proj_PCs = ['firstPC', 'secondPC']
try:
self.add_data_points([
list(range(0, len(self.p.get_projmatrix()))),
self.p.get_projmatrix()[:, 2]
],
style='y--',
layer='pcs',
name='thirdPC',
force=True)
except IndexError:
pass
self.add_legend(['r', 'g', 'y'], ['1st PC', '2nd PC', '3rd PC'],
'21')
self.plotter.auto_scale_domain(xcushion=0, subplot='21')
self.show()
self.project_to_PC()
if self.tutorial == 'step4':
print("STEP 4: ")
print("Use mouse clicks to explore the data.")
print(
"Clicking on detected spikes in the top-right will highlight the corresponding projection in the bottom right (and vice versa)."
)
print(
"You can also change the set of PCs onto which the data are projected by clicking the desired projection PCs in the bottom left"
)
print("NOTE ALSO: ")
print(
"Creating a bounding box in the upper-left plot and renaming it to 'ref_box', will change the search width of the detected spike."
)
print(
"e.g., if you want detected spikes to be 30 msec long, the box's .dx value must be 30."
)
print(
"After creating the box, it will be set to the current selected object. You can select the thresh line again by clicking on it."
)
class spikesorter(graphics.diagnosticGUI):
def __init__(self, title):
#global plotter
plotter = graphics.Plotter()
graphics.diagnosticGUI.__init__(self, plotter)
#Recover data:
#data = importPointset('shortsimdata1.dat',t=0,sep=',')
data = importPointset('simdata1_100000.dat',t=0,sep=',')
vs = data['vararray'][0]
vs = self.bandpass_filter(vs, 300, 3000, 32000)
ts = data['t']
self.N = len(vs)
self.traj = numeric_to_traj([vs], 'test_traj', ['x'], ts, discrete=False)
#Threshold used in martinez et al.
self.mthresh = 4*(np.median(np.abs(vs))/0.6475)
self.selected_pcs = []
self.fovea_setup()
#x = self.traj.sample(tlo= 0, thi = self.N)['x']
#r = x > 10
#above_thresh = np.where(r == True)[0]
#spike = []
#peaks = []
#crosses = []
#last_i = above_thresh[0] - 1
#for i in above_thresh:
#if i - 1 != last_i:
#crosses.append(i)
##Return x value of the highest y value.
#peaks.append(np.where(x == max(list(x[spike])))[0][0])
#spike = []
#spike.append(i)
#last_i = i
if tutorial_on:
print("STEP 1:")
print("Create a horizontal line of interest by pressing 'l'.")
print("Once created, this line can be forced to extent by pressing 'm'.")
print("Enter 'ssort.selected_object.update(name = 'thresh')' to identify the line as a threshold for spike detection")
print("Once the line is renamed to 'thresh', the arrow keys can be used to move it up and down.")
self.tutorial = 'step2'
else:
self.tutorial = None
def fovea_setup(self):
#Setup code
DOI = [(0,self.N),(-30,30)]
self.plotter.clean() # in case rerun in same session
self.plotter.add_fig('master',
title='spikesort',
xlabel='time', ylabel='mV',
domain=DOI)
#Setup all layers
self.plotter.add_layer('spikes')
self.plotter.add_layer('thresh_crosses')
self.plotter.add_layer('detected')
self.plotter.add_layer('pcs')
self.plotter.add_layer('scores')
self.setup({'11':
{'name': 'Waveform',
'scale': DOI,
'layers':['spikes', 'thresh_crosses'],
'callbacks':'*',
'axes_vars': ['x', 'y']
},
'12':
{'name': 'Detected Spikes',
'scale': [(0, default_sw), (-80, 80)],
'layers':['detected'],
#'callbacks':'*',
'axes_vars': ['x', 'y']
},
'21':
{'name': 'Principal Components',
'scale': [(0, default_sw), (-0.5, 0.5)],
'layers':['pcs'],
#'callbacks':'*',
'axes_vars': ['x', 'y']
},
'22':
{'name': 'Projected Spikes',
#'scale': [(-100, 100), (-100, 100)],
'scale': [(-300, 300), (-300, 300)],
'layers':['scores'],
'callbacks':'*',
'axes_vars': ['firstPC', 'secondPC']
}
},
size=(8, 8), with_times=False, basic_widgets=True)
#self.plotter.set_text('load_perc', Loading: %d\%'%n, 'loading')
#Bad code carried over from fovea_game:
fig_struct, figure = self.plotter._resolve_fig(None)
#self.ax = fig_struct.arrange['11']['axes_obj']
coorddict = {'x':
{'x':'t', 'layer':'spikes', 'style':'b-'}
}
self.add_data_points(self.traj.sample(), coorddict = coorddict)
evKeyOn = self.fig.canvas.mpl_connect('key_press_event', self.ssort_key_on)
self.plotter.auto_scale_domain(subplot= '11', xcushion= 0)
self.plotter.show()
def bandpass_filter(self, data, lowcut, highcut, fs, order= 5):
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = butter(order, [low, high], btype= 'band')
y = lfilter(b, a, data)
return y
def user_pick_func(self, ev):
if self.selected_object.layer == 'detected' or self.selected_object.layer == 'scores':
if hasattr(self, 'last_name'):
self.plotter.set_data_2(self.last_name, layer='scores', markersize= 6, zorder= 1, style=self.default_colors[self.last_name])
self.plotter.set_data_2(self.last_name, layer='detected', linewidth= 1, zorder= 1, style=self.default_colors[self.last_name])
self.plotter.set_data_2(self.selected_object.name, layer='scores', markersize= 12, zorder= 10, style='y*')
self.plotter.set_data_2(self.selected_object.name, layer='detected', linewidth= 2.5, zorder= 10, style='y-')
self.last_name = self.selected_object.name
elif self.selected_object.layer == 'pcs':
self.proj_PCs.insert(0, self.selected_object.name)
self.proj_PCs = self.proj_PCs[0:2]
for name in fig_struct['layers']['pcs']['data'].keys():
if name not in self.proj_PCs:
self.plotter.set_data_2(name, layer='pcs', style= fig_struct['layers']['pcs']['data'][name]['style'][0]+'--')
for pc in self.proj_PCs:
self.plotter.set_data_2(pc, layer='pcs', style= fig_struct['layers']['pcs']['data'][pc]['style'][0]+'-')
self.proj_vec1 = fig_struct['layers']['pcs']['handles'][self.proj_PCs[0]].get_ydata()
self.proj_vec2 = fig_struct['layers']['pcs']['handles'][self.proj_PCs[1]].get_ydata()
fig_struct.arrange['22']['axes_vars'] = list(reversed(self.proj_PCs))
self.project_to_PC()
self.plotter.show()
def user_update_func(self):
if self.selected_object.name is 'thresh':
if self.selected_object.m != 0:
print("Make 'thresh' a horizontal threshold by pressing 'm'.")
return
try:
self.search_width = self.context_objects['ref_box'].dx
self.context_objects['ref_box'].remove()
except KeyError:
self.search_width = default_sw
if self.tutorial == 'step2':
print("STEP 2: ")
print("When thresh is in place, press 'd' to capture each spike crossing the threshold in a bounding box.")
print("Each detected spike will be placed in the top right subplot.")
self.tutorial = 'step3'
cutoff = self.selected_object.y1
traj_samp = self.traj.sample()['x']
r = traj_samp > cutoff
above_thresh = np.where(r == True)[0]
spike = []
spikes = []
crosses = []
last_i = above_thresh[0] - 1
for i in above_thresh:
if i - 1 != last_i:
crosses.append(i)
#Return x value of the highest y value.
spikes.append(spike)
spike = []
spike.append(i)
last_i = i
self.traj_samp = traj_samp
self.crosses = crosses
self.spikes = spikes
self.plotter.add_data([self.crosses, [cutoff]*len(self.crosses)], layer='thresh_crosses', style='r*', name='crossovers', force= True)
self.show()
def compute_bbox(self):
fig_struct, figs = self.plotter._resolve_fig(None)
#Clear existing bounding boxes
rem_names = []
for con_name, con_obj in self.context_objects.items():
if isinstance(con_obj, box_GUI) and con_name is not 'ref_box':
rem_names.append(con_name)
for name in rem_names:
self.context_objects[name].remove(draw= False)
fig_struct['layers']['detected']['data'] = {}
self.plotter.show(rebuild= True)
#Create new bounding boxes
c = 0
for spike in self.spikes:
peak = np.where(self.traj_samp == max(list(self.traj_samp[spike])))[0][0]
tlo = peak - 20
thi = tlo + self.search_width
valley = min(self.traj.sample()['x'][tlo:thi])
box_GUI(self, pp.Point2D(tlo, self.traj.sample()['x'][peak]),
pp.Point2D(thi, valley),name= 'spike_box'+str(c), select= False)
spike_seg = self.traj_samp[tlo:thi]
try:
X = np.row_stack((X, spike_seg))
except NameError:
X = spike_seg
c += 1
return X
def project_to_PC(self):
Y = np.dot(self.X, np.column_stack((self.proj_vec1, self.proj_vec2)))
#If moving to a smaller number of spikes, just forcing out data by reassigning names won't work. Must clear.
self.clear_data('scores')
self.show()
self.default_colors = {}
#Add spikes as individual lines, so they can be referenced individually.
c = 0
for spike in Y:
name = 'spike'+str(c)
self.default_colors[name] = 'k'
self.add_data_points([spike[0], spike[1]], layer='scores', style=self.default_colors[name]+'*', name= name)
c += 1
self.plotter.auto_scale_domain(subplot = '22')
self.show(rebuild = True)
def ssort_key_on(self, ev):
self._key = k = ev.key # keep record of last keypress
fig_struct, fig = self.plotter._resolve_fig(None)
class_keys = ['1','2','3','0']
if k in class_keys:
if isinstance(self.selected_object, box_GUI):
for dname, dstruct in fig_struct['layers']['scores']['data'].items():
if self.selected_object.x1 < dstruct['data'][0] < self.selected_object.x2 and \
self.selected_object.y1 < dstruct['data'][1] < self.selected_object.y2:
if k == '1':
self.default_colors[dname] = 'r'
self.plotter.set_data_2(dname, layer='detected', style= 'r-')
self.plotter.set_data_2(dname, layer='scores', style= 'r*')
if k == '2':
self.default_colors[dname] = 'g'
self.plotter.set_data_2(dname, layer='detected', style= 'g-')
self.plotter.set_data_2(dname, layer='scores', style= 'g*')
if k == '3':
self.default_colors[dname] = 'b'
self.plotter.set_data_2(dname, layer='detected', style= 'b-')
self.plotter.set_data_2(dname, layer='scores', style= 'b*')
if k == '0':
self.default_colors[dname] = 'k'
self.plotter.set_data_2(dname, layer='detected', style= 'k-')
self.plotter.set_data_2(dname, layer='scores', style= 'k*')
self.plotter.show()
if k== 'd':
try:
self.crosses
except AttributeError:
print("Can't detect spikes until threshold crossings have been found.")
return
self.X = self.compute_bbox()
self.default_colors = {}
if len(self.X.shape) == 1:
self.default_colors['spike0'] = 'k'
self.add_data_points([list(range(0, len(self.X))), self.X], layer= 'detected', style= self.default_colors['spike0']+'-', name= 'spike0', force= True)
else:
c= 0
for spike in self.X:
name = 'spike'+str(c)
self.default_colors[name] = 'k'
self.add_data_points([list(range(0, len(spike))), spike], layer= 'detected', style= self.default_colors[name]+'-', name= name, force= True)
c += 1
self.plotter.auto_scale_domain(xcushion = 0, subplot = '12')
self.show()
if self.tutorial == 'step3':
print("STEP 3: ")
print("You can now press 'p' to perform PCA on the detected spikes.")
print("The bottom right subplot will display the first 3 principal components (in red, green, and yellow respectively.)")
print("The bottom left subplot will show the detected spikes projected onto the first two PCs")
self.tutorial = 'step4'
if k == 'p':
try:
X = self.X
except AttributeError:
print('Must detect spikes before performing PCA.')
return
print('doing PCA...')
self.p = PCANode(output_dim=0.99, reduce= True, svd= True)
self.p.train(X)
self.proj_vec1 = self.p.get_projmatrix()[:, 0]
self.proj_vec2 = self.p.get_projmatrix()[:, 1]
self.add_data_points([list(range(0, len(self.proj_vec1))) , self.proj_vec1], style= 'r-', layer= 'pcs', name= 'firstPC', force= True)
self.add_data_points([list(range(0, len(self.proj_vec2))) , self.proj_vec2], style= 'g-', layer= 'pcs', name= 'secondPC', force= True)
self.plotter.show()
self.proj_PCs = ['firstPC', 'secondPC']
try:
self.add_data_points([list(range(0, len(self.p.get_projmatrix()))) ,self.p.get_projmatrix()[:,2]],
style= 'y--', layer= 'pcs', name= 'thirdPC', force= True)
except IndexError:
pass
self.add_legend(['r', 'g', 'y'], ['1st PC', '2nd PC', '3rd PC'], '21')
self.plotter.auto_scale_domain(xcushion = 0, subplot = '21')
self.show()
self.project_to_PC()
if self.tutorial == 'step4':
print("STEP 4: ")
print("Use mouse clicks to explore the data.")
print("Clicking on detected spikes in the top-right will highlight the corresponding projection in the bottom right (and vice versa).")
print("You can also change the set of PCs onto which the data are projected by clicking the desired projection PCs in the bottom left")
print("NOTE ALSO: ")
print("Creating a bounding box in the upper-left plot and renaming it to 'ref_box', will change the search width of the detected spike.")
print("e.g., if you want detected spikes to be 30 msec long, the box's .dx value must be 30.")
print("After creating the box, it will be set to the current selected object. You can select the thresh line again by clicking on it.")
def pca(multidim_data, output_dim):
"""Principal Component Analysis"""
pcanode = PCANode(input_dim=multidim_data.shape[1], output_dim=output_dim, dtype=np.float32,
svd=True, reduce=True, var_rel=1E-15, var_abs=1E-15)
pcanode.train(data)
return np.dot(multidim_data, pcanode.get_projmatrix())