def baseline(browser):
""" Subtract a baseline from the currently plotted traces.
Baseline is the average of all datapoints between the
current position of the data cursors.
Options:
1) keep original traces intact and create processed copies
"""
plotWidget = browser.ui.dataPlotsWidget
toolsWidget = browser.ui.toolStackedWidget
c1, c2 = aux.get_cursors(plotWidget)
# Check selected options
for option in toolsWidget.baselineToolOptions:
if option.isChecked():
if option.text()=='Keep original data':
aux.make_data_copy(browser, plotWidget)
# Get the data now, in case we are working on a copy
plotWidget.clear()
data = aux.get_data(browser)
# Get dt list
dt = aux.get_attr(plotWidget.plotDataItems, 'dt')[0]
# Make average between cursors and subract for each trace
for item in plotWidget.plotDataItems:
bsl = np.mean(item.data[c1/dt:c2/dt])
item.data = item.data - bsl
# Re-plot data
pgplot.replot(browser, plotWidget)
pgplot.replot_cursors(browser, plotWidget)
def custom_func(browser):
""" Temporary function for user defined analysis.
All options are harcoded and results are saved in a
"Analysis_results" folder in Root
Additional items can be created by appending to the
'results' list in the forms ['name', data_to_be_stored, data_attributes]
"""
# Get data and widgets
plotWidget = browser.ui.dataPlotsWidget
toolsWidget = browser.ui.toolStackedWidget
# Get dt
dt = aux.get_attr(plotWidget.plotDataItems, 'dt')[0]
# Analyse data
results = []
for item in plotWidget.plotDataItems:
# Copy attributes and add some new ones
attrs = item.attrs
# USER DEFINED TRANSFORMATION
USER_TRANSFORMED_DATA = item.data
# Append to storage list
results.append(['NAME', USER_TRANSFORMED_DATA, attrs])
# Plot modified trace if needed
x = np.arange(0, len(USER_TRANSFORMED_DATA)*dt, dt)
plotWidget.plot(x, USER_TRANSFORMED_DATA, pen=pg.mkPen('#F2EF44', width=1))
# Store results
aux.save_results(browser, 'Analysis_results', results)
def baseline(browser):
""" Subtract a baseline from the currently plotted traces.
Baseline is the average of all datapoints between the
current position of the data cursors.
Options:
1) keep original traces intact and create processed copies
"""
plotWidget = browser.ui.dataPlotsWidget
toolsWidget = browser.ui.toolStackedWidget
c1, c2 = aux.get_cursors(plotWidget)
# Check selected options
for option in toolsWidget.baselineToolOptions:
if option.isChecked():
if option.text() == 'Keep original data':
aux.make_data_copy(browser, plotWidget)
# Get the data now, in case we are working on a copy
plotWidget.clear()
data = aux.get_data(browser)
# Get dt list
dt = aux.get_attr(plotWidget.plotDataItems, 'dt')[0]
# Make average between cursors and subract for each trace
for item in plotWidget.plotDataItems:
bsl = np.mean(item.data[c1 / dt:c2 / dt])
item.data = item.data - bsl
# Re-plot data
pgplot.replot(browser, plotWidget)
pgplot.replot_cursors(browser, plotWidget)
def measure_cursor_stats(browser):
""" Measure selected properties or statistics in the region defined
by the data cursors.
Options:
1) create new entries in Working Data tree with the results
"""
plotWidget = browser.ui.dataPlotsWidget
toolsWidget = browser.ui.toolStackedWidget
data = aux.get_data(browser)
c1, c2 = aux.get_cursors(plotWidget)
dataIndex = plotWidget.plotDataIndex
saveData = False
# Get dt list
dtList = aux.get_attr(plotWidget.plotDataItems, 'dt')
# Go through data and check selected values to measure
# Can probably do this in a more efficient way
results = []
for option in toolsWidget.measureToolOptions:
if option.isChecked():
if option.text() == 'Store result':
saveData = True
if option.text() == 'Minimum':
dataMin = []
for t in range(len(data)):
dt = dtList[t]
y = np.min(data[t][c1 / dt:c2 / dt])
#x = np.where(data[t][c1:c2]==y)
x = np.argmin(data[t][c1 / dt:c2 / dt])
dataMin.append(y)
aux.plot_point(plotWidget, c1 / dt, x, y, dt)
results.append(['Minimum', dataMin])
if option.text() == 'Maximum':
dataMax = []
for t in range(len(data)):
dt = dtList[t]
y = np.max(data[t][c1 / dt:c2 / dt])
#x = np.where(data[t][c1:c2]==y)
x = np.argmax(data[t][c1 / dt:c2 / dt])
dataMax.append(y)
aux.plot_point(plotWidget, c1 / dt, x, y, dt)
results.append(['Maximum', dataMax])
if option.text() == 'Mean':
dataMean = []
for t in range(len(data)):
dt = dtList[t]
y = np.mean(data[t][c1 / dt:c2 / dt])
dataMean.append(y)
plotWidget.plot([c1, c2], [y, y],
pen=pg.mkPen('#CF1C04', width=1))
results.append(['Mean', dataMean])
# Store results
if saveData: aux.save_results(browser, 'Measurements', results)
def measure_cursor_stats(browser):
""" Measure selected properties or statistics in the region defined
by the data cursors.
Options:
1) create new entries in Working Data tree with the results
"""
plotWidget = browser.ui.dataPlotsWidget
toolsWidget = browser.ui.toolStackedWidget
data = aux.get_data(browser)
c1, c2 = aux.get_cursors(plotWidget)
dataIndex = plotWidget.plotDataIndex
saveData = False
# Get dt list
dtList = aux.get_attr(plotWidget.plotDataItems, 'dt')
# Go through data and check selected values to measure
# Can probably do this in a more efficient way
results = []
for option in toolsWidget.measureToolOptions:
if option.isChecked():
if option.text()=='Store result':
saveData = True
if option.text()=='Minimum':
dataMin = []
for t in range(len(data)):
dt = dtList[t]
y = np.min(data[t][c1/dt:c2/dt])
#x = np.where(data[t][c1:c2]==y)
x = np.argmin(data[t][c1/dt:c2/dt])
dataMin.append(y)
aux.plot_point(plotWidget, c1/dt, x, y, dt)
results.append(['Minimum', dataMin])
if option.text()=='Maximum':
dataMax = []
for t in range(len(data)):
dt = dtList[t]
y = np.max(data[t][c1/dt:c2/dt])
#x = np.where(data[t][c1:c2]==y)
x = np.argmax(data[t][c1/dt:c2/dt])
dataMax.append(y)
aux.plot_point(plotWidget, c1/dt, x, y, dt)
results.append(['Maximum', dataMax])
if option.text()=='Mean':
dataMean = []
for t in range(len(data)):
dt = dtList[t]
y = np.mean(data[t][c1/dt:c2/dt])
dataMean.append(y)
plotWidget.plot([c1,c2], [y,y], pen=pg.mkPen('#CF1C04', width=1))
results.append(['Mean', dataMean])
# Store results
if saveData: aux.save_results(browser, 'Measurements', results)
def average_traces(browser):
""" Calculate average trace from currently plotted traces.
Options:
1) create new entry in Working Data tree with the result
2) plot average with orginal traces
"""
plotWidget = browser.ui.dataPlotsWidget
toolsWidget = browser.ui.toolStackedWidget
plotWidget.clear()
data = aux.get_data(browser)
# Get dt list (at this stage they will all be the same
# because otherwise get_data would have thrown an error
dt = aux.get_attr(plotWidget.plotDataItems, 'dt')[0]
# Calculate average
avgData = np.mean(data, 0)
# Check selected options
for option in toolsWidget.avgToolOptions:
if option.isChecked():
if option.text() == 'Store result':
results = []
# Get attributes from plotted items
item = plotWidget.plotDataItems[0]
attrs = item.attrs
# Store data
results.append(['avg_trace', avgData, attrs])
aux.save_results(browser,
item.parent().text(0) + '_average', results)
if option.text() == 'Show traces':
pgplot.replot(browser, plotWidget)
# Plot average
x = np.arange(0, len(avgData) * dt, dt)
plotWidget.plot(x, avgData, pen=pg.mkPen('#CF1C04', width=2))
if browser.ui.actionShowCursors.isChecked():
pgplot.replot_cursors(browser, plotWidget)
def average_traces(browser):
""" Calculate average trace from currently plotted traces.
Options:
1) create new entry in Working Data tree with the result
2) plot average with orginal traces
"""
plotWidget = browser.ui.dataPlotsWidget
toolsWidget = browser.ui.toolStackedWidget
plotWidget.clear()
data = aux.get_data(browser)
# Get dt list (at this stage they will all be the same
# because otherwise get_data would have thrown an error
dt = aux.get_attr(plotWidget.plotDataItems, 'dt')[0]
# Calculate average
avgData = np.mean(data,0)
# Check selected options
for option in toolsWidget.avgToolOptions:
if option.isChecked():
if option.text()=='Store result':
results = []
# Get attributes from plotted items
item = plotWidget.plotDataItems[0]
attrs = item.attrs
# Store data
results.append(['avg_trace', avgData, attrs])
aux.save_results(browser, item.parent().text(0)+'_average', results)
if option.text()=='Show traces':
pgplot.replot(browser, plotWidget)
# Plot average
x = np.arange(0, len(avgData)*dt, dt)
plotWidget.plot(x, avgData, pen=pg.mkPen('#CF1C04', width=2))
if browser.ui.actionShowCursors.isChecked(): pgplot.replot_cursors(browser, plotWidget)
def custom_func(browser):
""" Temporary function for user defined analysis.
All options are harcoded and results are saved in a
"Analysis_results" folder in Root
Additional items can be created by appending to the
'results' list in the forms ['name', data_to_be_stored, data_attributes]
"""
# Get data and widgets
plotWidget = browser.ui.dataPlotsWidget
toolsWidget = browser.ui.toolStackedWidget
# Get dt
dt = aux.get_attr(plotWidget.plotDataItems, 'dt')[0]
# Analyse data
results = []
for item in plotWidget.plotDataItems:
# Copy attributes and add some new ones
attrs = item.attrs
# USER DEFINED TRANSFORMATION
USER_TRANSFORMED_DATA = item.data
# Append to storage list
results.append(['NAME', USER_TRANSFORMED_DATA, attrs])
# Plot modified trace if needed
x = np.arange(0, len(USER_TRANSFORMED_DATA) * dt, dt)
plotWidget.plot(x,
USER_TRANSFORMED_DATA,
pen=pg.mkPen('#F2EF44', width=1))
# Store results
aux.save_results(browser, 'Analysis_results', results)
def event_detect(browser):
""" Temporary event detection function using amplitude
threshold only. Noise safety is for when coming down from
peak, go down an extra amount from threshold before starting
to search for the next event.
"""
# Read detection options
threshold = float(browser.ui.dataPlotsWidget.cursorThsPos)
noiseSafety = float(browser.ui.toolStackedWidget.eventNoiseSafety.text())
smoothFactor = float(browser.ui.toolStackedWidget.eventSmooth.text())
direction = str(browser.ui.toolStackedWidget.eventDirection.currentText())
c1, c2 = aux.get_cursors(browser.ui.dataPlotsWidget)
# Ensure that noise safety has the same sign as the threshold
noiseSafety = np.sign(threshold) * abs(noiseSafety)
# Get dt list and attrs for use in concatenated data
dtList = aux.get_attr(browser.ui.dataPlotsWidget.plotDataItems, 'dt')
dt = dtList[0]
item = browser.ui.dataPlotsWidget.plotDataItems[0]
attrs = item.attrs
# Get data currently plotted within the cursors and concatenate in a single sweep
data = aux.get_data(browser)
if browser.ui.dataPlotsWidget.cursor1Pos: data = data[:, c1 / dt:c2 / dt]
data = data.ravel()
# Smooth
original_data = data
if smoothFactor > 1:
data = smooth.smooth(data, window_len=smoothFactor, window='hanning')
# Run detection
if direction == 'negative':
comp = lambda a, b: a < b
elif direction == 'positive':
comp = lambda a, b: a > b
eventCounter, i = 0, 0
xOnsets, yOnsets = [], []
while i < len(data):
if comp(data[i], threshold):
xOnsets.append(i)
yOnsets.append(data[i])
eventCounter += 1
while i < len(data) and comp(data[i], (threshold - noiseSafety)):
i += 1 # skip values if index in bounds AND until the value is below/above threshold again
else:
i += 1
frequency = eventCounter / (len(data) * dt) * 1000 # in Hz
print eventCounter, 'events detected at', frequency, 'Hz'
# Store event onsets and peaks in h5 data tree
results = []
results.append(['trace', np.array(original_data), attrs])
results.append(['onsets', np.array(xOnsets)])
results.append(['peaks', np.array(yOnsets)])
results.append(['number', np.array([eventCounter])])
results.append(['frequency', np.array([frequency])])
listIndexes = aux.save_results(browser, 'Event_Detection', results)
# Store list indexes temporarily in stack widget list for further event analysis
browser.ui.toolStackedWidget.eventItemsIndex = listIndexes
# browser.ui.toolStackedWidget.eventData.append(np.array(original_data))
# browser.ui.toolStackedWidget.eventData.append(np.array(data))
# browser.ui.toolStackedWidget.eventData.append(np.array(xOnsets))
# browser.ui.toolStackedWidget.eventData.append(dt)
# Plot results
show_events(browser, data, np.array(xOnsets), np.array(yOnsets), dt)
def event_detect(browser):
""" Temporary event detection function using amplitude
threshold only. Noise safety is for when coming down from
peak, go down an extra amount from threshold before starting
to search for the next event.
"""
# Read detection options
threshold = float(browser.ui.dataPlotsWidget.cursorThsPos)
noiseSafety = float(browser.ui.toolStackedWidget.eventNoiseSafety.text())
smoothFactor = float(browser.ui.toolStackedWidget.eventSmooth.text())
direction = str(browser.ui.toolStackedWidget.eventDirection.currentText())
c1, c2 = aux.get_cursors(browser.ui.dataPlotsWidget)
# Ensure that noise safety has the same sign as the threshold
noiseSafety = np.sign(threshold) * abs(noiseSafety)
# Get dt list and attrs for use in concatenated data
dtList = aux.get_attr(browser.ui.dataPlotsWidget.plotDataItems, 'dt')
dt = dtList[0]
item = browser.ui.dataPlotsWidget.plotDataItems[0]
attrs = item.attrs
# Get data currently plotted within the cursors and concatenate in a single sweep
data = aux.get_data(browser)
if browser.ui.dataPlotsWidget.cursor1Pos: data = data[:,c1/dt:c2/dt]
data = data.ravel()
# Smooth
original_data = data
if smoothFactor > 1:
data = smooth.smooth(data, window_len=smoothFactor, window='hanning')
# Run detection
if direction=='negative':
comp = lambda a, b: a < b
elif direction=='positive':
comp = lambda a, b: a > b
eventCounter,i = 0,0
xOnsets, yOnsets = [], []
while i<len(data):
if comp(data[i],threshold):
xOnsets.append(i)
yOnsets.append(data[i])
eventCounter +=1
while i<len(data) and comp(data[i],(threshold-noiseSafety)):
i+=1 # skip values if index in bounds AND until the value is below/above threshold again
else:
i+=1
frequency = eventCounter/(len(data)*dt)*1000 # in Hz
print eventCounter, 'events detected at', frequency, 'Hz'
# Store event onsets and peaks in h5 data tree
results = []
results.append(['trace', np.array(original_data), attrs])
results.append(['onsets', np.array(xOnsets)])
results.append(['peaks', np.array(yOnsets)])
results.append(['number', np.array([eventCounter])])
results.append(['frequency', np.array([frequency])])
listIndexes = aux.save_results(browser, 'Event_Detection', results)
# Store list indexes temporarily in stack widget list for further event analysis
browser.ui.toolStackedWidget.eventItemsIndex = listIndexes
# browser.ui.toolStackedWidget.eventData.append(np.array(original_data))
# browser.ui.toolStackedWidget.eventData.append(np.array(data))
# browser.ui.toolStackedWidget.eventData.append(np.array(xOnsets))
# browser.ui.toolStackedWidget.eventData.append(dt)
# Plot results
show_events(browser, data, np.array(xOnsets), np.array(yOnsets), dt)