def plotImageFigure(self, row):
"""
derived from self.plotLcrHist()
"""
print('myDualTable.plotImageFigure() row:', row)
# params from selected row
dfRow = self.masterDf.iloc[row]
region = dfRow['region']
tifPath, abfPath = self._getPathFromRow(row)
dr = dualAnalysis.dualRecord(tifPath, abfPath)
# need vm analysis to get peak seconds
dr._loadAnalyzeAbf_from_df(self.masterDf, row)
fig, axImg, axLcr = dr.plotFigure9(region=region)
# fig9 sup/inf
if region == 'superior':
print('!!!!********* myDualTable.plotSpikeClip() setting superior xlim() !!!!!!!!!!!')
axImg.set_xlim(7, 8)
axLcr.set_ylim(0, 12)
elif region == 'inferior':
print('!!!!********* myDualTable.plotSpikeClip() setting superior xlim() !!!!!!!!!!!')
axImg.set_xlim(5.1, 6.1)
axLcr.set_ylim(0, 12)
if fig is not None:
fig.show()
def plotSpikeClip(self, row, forFig9=False):
print('myDualTable.plotSpikeClip() row:', row)
tifPath, abfPath = self._getPathFromRow(row)
dr = dualAnalysis.dualRecord(tifPath, abfPath)
# masterDf is backend model loaded from .csv and displayed in table
fig = dr.plotSpikeClip_df(self.masterDf, row, forFig9=forFig9, myParent=self)
if fig is not None:
fig.show()
def myRightClick_LCR(self):
selectedRow = self.getSelectedRow()
print('myRightClick_LCR selectedRow:', selectedRow)
tifFile = self.model.df['tifPath'].loc[selectedRow]
abfFile = self.model.df['abfPath'].loc[selectedRow]
print(' plotting tifFile:', tifFile)
df = self.model.df
dr = dualAnalysis.dualRecord(tifFile, abfFile)
dr.new_plotSparkMaster()
plt.show()
def myPlotPhase(self, rowNumber):
tifFile = self.model.df['tifPath'].loc[rowNumber]
abfFile = self.model.df['abfPath'].loc[rowNumber]
print(' plotting tifFile:', tifFile)
df = self.model.df
dr = dualAnalysis.dualRecord(tifFile, abfFile)
fig = dr.plotSpikeClip_df(df, rowNumber, myParent=self)
#dr = dualAnalysis.dualRecord(tifFile, abfFile)
#fig = dr.plotSpikeClip(doPhasePlot=True, fileNumber=rowNumber, myParent=self)
fig.show()
return fig
def myRightClickDetection(self):
rowNumber = self.getSelectedRow()
tifFile = self.model.df['tifPath'].loc[rowNumber]
abfFile = self.model.df['abfPath'].loc[rowNumber]
print(' myRightClickDetection plotting tifFile:', tifFile)
df = self.model.df
dr = dualAnalysis.dualRecord(tifFile, abfFile)
figTif, figAbf = dr.plotSpikeDetection_df(df, rowNumber, myParent=self)
figTif.show()
figAbf.show()
return figTif # todo: fix this
def plotDualDetection(self, row):
print('myDualTable.plotDualDetection() row:', row)
# params from selected row
dfRow = self.masterDf.iloc[row]
region = dfRow['region']
tifPath, abfPath = self._getPathFromRow(row)
dr = dualAnalysis.dualRecord(tifPath, abfPath)
figTif, figAbf = dr.plotSpikeDetection_df(self.masterDf, row, myParent=self)
figTif.show()
figAbf.show()
def plotLcrHist(self, row): # params from selected row dfRow = self.masterDf.iloc[row] region = dfRow['region'] tifPath, abfPath = self._getPathFromRow(row) dr = dualAnalysis.dualRecord(tifPath, abfPath) # need vm analysis to get peak seconds dr._loadAnalyzeAbf_from_df(self.masterDf, row) fig = dr.new_plotSparkMaster(region=region) if fig is not None: fig.show()
def plotDualRecording(self, row):
print('myDualTable.plotDualRecording() row:', row)
# params from selected row
dfRow = self.masterDf.iloc[row]
region = dfRow['region']
tifPath, abfPath = self._getPathFromRow(row)
print('doAction1()')
print(' tifPath:', tifPath)
print(' abfPath:', abfPath)
dr = dualAnalysis.dualRecord(tifPath, abfPath)
# works
fig = dr.myPlot(fileNumber=row, region=region, myParent=self)
# works
# masterDf is backend model loaded from .csv and displayed in table
#fig = dr.plotSpikeClip_df(self.masterDf, row, myParent=self)
if fig is not None:
fig.show()
def myPlotRow(self, rowNumber):
tifFile = self.model.df['tifPath'].loc[rowNumber]
abfFile = self.model.df['abfPath'].loc[rowNumber]
region = self.model.df['region'].loc[rowNumber]
print(' plotting tifFile:', tifFile)
dr = dualAnalysis.dualRecord(tifFile, abfFile)
'''
print('matplotlib.rcsetup.interactive_bk:', matplotlib.rcsetup.interactive_bk)
print('matplotlib.get_backend()', matplotlib.get_backend())
matplotlib.use('Qt4Agg',force=True)
from matplotlib import pyplot as plt
print('matplotlib.get_backend()', matplotlib.get_backend())
'''
fig = dr.myPlot(fileNumber=rowNumber, region=region, myParent=self)
fig.show()
'''
matplotlib.use('TkAgg',force=True)
from matplotlib import pyplot as plt
'''
return fig
def __init__(self, row, df, doVmDetect=True):
"""
doVmDetect: False to not detect spikes in Vm,
just step through trace in 500 ms chunks
"""
self.row = row
self.df = df
self.doVmDetect = doVmDetect
# used with (not doVmDetect)
self.endOfCurrentWindow = None
self.noDetectWindowWidth_sec = 1
# ms to bin lcr to make hist CRITICAL PARAMETER
self.tifBins_ms = 50 #10
self.vmBinSeconds = 0.01 # to get avg vm for (lcr, preMv, postMv)
self.region = self.df['region'].loc[row]
self.cellNumber = self.df['cell number'].loc[row]
self.trial = self.df['trial'].loc[row]
self.spikeNumber = None # zoom to spike with (left, right)
self.lcrNum = None # to step through existing lcr
# and then set corresponding Vm (pre lcr)
self.fig = None
self.axRight2 = None
self.tPos_offset = None #has x-axis of lcr(s) shifted wrt delay to start of imaging
self.lcrAmp = None
self.lcrBins = None
self.lastKey = None
self.selectedLcr = None
# df to hold results
self.colList = [
'idx', 'spikeNum', 'spikeSec', 'lcrSec', 'lcrSum', 'lcrNum',
'lcrVm', 'preVmSec', 'preVmInt', 'preVmMean', 'postVmSec',
'postVmInt', 'postVmMean', 'lcrPreDepol', 'lcrPostDepol', 'region',
'cell number', 'trial', 'tifBins_ms', 'vmBinSeconds', 'tifFile',
'tifPath'
]
self.printCols = [
'idx',
'spikeNum',
'spikeSec',
'lcrSec',
'lcrSum',
'lcrVm',
'preVmSec',
'preVmMean',
'postVmSec',
'postVmMean',
'lcrPreDepol',
'lcrPostDepol',
'region',
]
self.dfAnalysis = pd.DataFrame(columns=self.colList)
tifPath, abfPath = dualAnalysis.getPathFromRow(df, row)
print('lcrPicker()')
print(' tifPath:', tifPath)
print(' abfPath:', abfPath)
# load dualAnalysis from df(row)
self.dr = dualAnalysis.dualRecord(tifPath, abfPath)
# need vm analysis to get peak seconds
if doVmDetect:
self.dr._loadAnalyzeAbf_from_df(self.df, self.row)
# analyze spike
# plot
self.fig, self.axs, self.tPos_offset, self.lcrAmp, self.lcrBins, \
self.axsLcrAnalysisLeft, \
self.axsLcrAnalysisRight = self.plot()
self.tifPath = self.dr.abfTif.tifHeader['tif']
self.tifFile = os.path.split(self.dr.abfTif.tifHeader['tif'])[1]
self.fig.show()