def analysis_pdf(datafile, Nmax=1000000):
pdf_filename = os.path.join(summary_pdf_folder(datafile), 'rois.pdf')
data = MultimodalData(datafile)
data.correctedFluo, data.F0 = data.build_dFoF(
return_corrected_F_and_F0=True)
with PdfPages(pdf_filename) as pdf:
print('* plotting ROI analysis as "rois.pdf" [...]')
print(' - plotting imaging FOV')
fig, AX = plt.subplots(1, 5, figsize=(11.4, 2.5))
plt.subplots_adjust(left=0.03, right=0.97)
data.show_CaImaging_FOV(key='meanImg', NL=1, cmap='viridis', ax=AX[0])
data.show_CaImaging_FOV(key='meanImg', NL=2, cmap='viridis', ax=AX[1])
data.show_CaImaging_FOV(key='meanImgE', NL=2, cmap='viridis', ax=AX[2])
data.show_CaImaging_FOV(key='max_proj', NL=2, cmap='viridis', ax=AX[3])
try:
data.show_CaImaging_FOV(key='meanImg_chan2',
NL=2,
cmap='viridis',
ax=AX[4])
except KeyError:
AX[4].annotate('no red channel', (0.5, 0.5),
xycoords='axes fraction',
fontsize=9,
va='center',
ha='center')
AX[4].axis('off')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
for i in np.arange(data.nROIs)[:Nmax]:
print(' - plotting analysis of ROI #%i' % (i + 1))
try:
fig = raw_fluo_fig(data, roiIndex=i)
pdf.savefig(fig) # saves the current figure into a pdf page
plt.close(fig)
fig = analysis_fig(data, roiIndex=i)
pdf.savefig(fig) # saves the current figure into a pdf page
plt.close(fig)
except BaseException as be:
print(be)
print(' /!\ Pb with ROI #%i /!\ ' % (i + 1))
print('[ok] roi analysis saved as: "%s" ' % pdf_filename)
class FiguresWindow(NewWindow):
def __init__(
self,
parent=None,
dt_sampling=10, # ms
fig_name=os.path.join(os.path.expanduser('~'), 'Desktop',
'fig.svg'),
title='Figures'):
super(FiguresWindow, self).__init__(parent=parent.app,
title=title,
i=-10,
size=(800, 800))
self.modalities = []
for key1, key2 in zip([
'Photodiode-Signal', 'Electrophysiological-Signal',
'Running-Speed', 'Pupil', 'CaImaging-TimeSeries',
'CaImaging-TimeSeries', 'Photodiode-Signal'
], [
'Photodiode', 'Electrophy', 'Locomotion', 'Pupil',
'CaImagingSum', 'CaImaging', 'VisualStim'
]):
if key1 in parent.nwbfile.acquisition:
self.modalities.append(key2)
self.parent = parent
self.get_varied_parameters()
self.fig_name = fig_name
self.data = MultimodalData(self.parent.datafile)
self.EPISODES = None
self.xlim, self.ylim = [-10, 10], [-10, 10]
mainLayout, Layouts = QtWidgets.QVBoxLayout(self.cwidget), []
# -- protocol
Layouts.append(QtWidgets.QHBoxLayout())
# Layouts[-1].addWidget(QtWidgets.QLabel('-- Protocol: ', self))
self.pbox = QtWidgets.QComboBox(self)
self.pbox.setFixedWidth(400)
self.pbox.addItem(' [PROTOCOL] ')
self.pbox.addItems(self.parent.protocols)
Layouts[-1].addWidget(self.pbox)
# -- quantity and subquantity
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel('-- Quantity: ', self))
self.qbox = QtWidgets.QComboBox(self)
self.qbox.addItem('')
if 'ophys' in self.parent.nwbfile.processing:
self.qbox.addItem('CaImaging')
for key in parent.nwbfile.acquisition:
if len(parent.nwbfile.acquisition[key].data.shape) == 1:
self.qbox.addItem(key) # only for scalar variables
self.qbox.setFixedWidth(400)
Layouts[-1].addWidget(self.qbox)
Layouts[-1].addWidget(
QtWidgets.QLabel(' -- Sub-quantity: ', self))
self.sqbox = QtWidgets.QLineEdit(self)
self.sqbox.setText(35 * ' ' + 'e.g. "dF/F", "Neuropil", "pLFP", ...')
self.sqbox.setMinimumWidth(400)
Layouts[-1].addWidget(self.sqbox)
# -- roi
if 'ophys' in self.parent.nwbfile.processing:
Layouts[-1].addWidget(QtWidgets.QLabel(' -- ROI: ', self))
self.roiPick = QtGui.QLineEdit()
self.roiPick.setText(
' [select ROI] e.g.: "1", "10-20", "3, 45, 7", ... ')
self.roiPick.setMinimumWidth(400)
self.roiPick.returnPressed.connect(self.select_ROI)
Layouts[-1].addWidget(self.roiPick)
### PLOT RAW DATA
Layouts.append(QtWidgets.QHBoxLayout())
self.rawPlotBtn = QtWidgets.QPushButton(' plot raw data ', self)
self.rawPlotBtn.setFixedWidth(200)
self.rawPlotBtn.clicked.connect(self.plot_raw_data)
Layouts[-1].addWidget(self.rawPlotBtn)
for mod in self.modalities:
Layouts[-1].addWidget(QtWidgets.QLabel(' ' + mod + ':', self))
setattr(self, mod + 'Plot', QtWidgets.QDoubleSpinBox(self))
getattr(self, mod + 'Plot').setValue(1)
getattr(self, mod + 'Plot').setSuffix(' (%-fig)')
Layouts[-1].addWidget(getattr(self, mod + 'Plot'))
### PLOT FIELD OF VIEW
if 'ophys' in self.parent.nwbfile.processing:
Layouts.append(QtWidgets.QHBoxLayout())
self.fovPlotBtn = QtWidgets.QPushButton(
'plot imaging field of view ', self)
self.fovPlotBtn.setFixedWidth(250)
self.fovPlotBtn.clicked.connect(self.plot_FOV)
Layouts[-1].addWidget(self.fovPlotBtn)
Layouts[-1].addWidget(
QtWidgets.QLabel(10 * ' ' + 'FOV type:', self)) # SEPARATOR
self.fovType = QtWidgets.QComboBox(self)
self.fovType.addItems(['meanImg', 'meanImgE', 'max_proj'])
Layouts[-1].addWidget(self.fovType)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ' + 'NL:',
self)) # SEPARATOR
self.fovNL = QtWidgets.QSpinBox(self)
self.fovNL.setValue(1)
Layouts[-1].addWidget(self.fovNL)
Layouts[-1].addWidget(
QtWidgets.QLabel(10 * ' ' + 'colormap:', self)) # SEPARATOR
self.fovMap = QtWidgets.QComboBox(self)
self.fovMap.addItems(
['viridis', 'grey', 'white_to_green', 'black_to_green'])
Layouts[-1].addWidget(self.fovMap)
Layouts[-1].addWidget(QtWidgets.QLabel(' label:',
self)) # SEPARATOR
self.fovLabel = QtGui.QLineEdit()
Layouts[-1].addWidget(self.fovLabel)
### PLOT CORRELATIONS
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel(50 * '<->', self)) # SEPARATOR
Layouts[-1].addWidget(QtWidgets.QLabel(' -* CORRELATIONS *-', self))
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel(50 * '<->', self)) # SEPARATOR
# varied keys
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(
QtWidgets.QLabel(' -* STIMULI PARAMETERS *-', self))
for key in self.varied_parameters:
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel('--- %s ' % key, self))
setattr(self, '%s_plot' % key, QtWidgets.QComboBox(self))
getattr(self, '%s_plot' % key).addItems([
'merged', 'single-value', 'column-panels', 'row-panels',
'color-coded', 'x-axis', 'N/A'
])
Layouts[-1].addWidget(getattr(self, '%s_plot' % key))
setattr(self, '%s_values' % key, QtWidgets.QComboBox(self))
getattr(
self, '%s_values' %
key).addItems(['full', 'custom'] +
[str(s) for s in self.varied_parameters[key]])
Layouts[-1].addWidget(getattr(self, '%s_values' % key))
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
Layouts[-1].addWidget(QtWidgets.QLabel(' custom values : ', self))
setattr(self, '%s_customvalues' % key, QtWidgets.QLineEdit(self))
getattr(self, '%s_customvalues' % key).setMaximumWidth(150)
Layouts[-1].addWidget(getattr(self, '%s_customvalues' % key))
Layouts[-1].addWidget(QtWidgets.QLabel(30 * ' ', self))
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel(50 * '<->', self)) # SEPARATOR
# figure props type
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(
QtWidgets.QLabel(' -* RESPONSES *- ', self))
self.responseType = QtWidgets.QComboBox(self)
self.responseType.addItems(
['stim-evoked-traces', 'mean-stim-evoked', 'integral-stim-evoked'])
Layouts[-1].addWidget(self.responseType)
Layouts[-1].addWidget(QtWidgets.QLabel(' color: ', self))
self.color = QtGui.QLineEdit()
Layouts[-1].addWidget(self.color)
Layouts[-1].addWidget(QtWidgets.QLabel(' label: ', self))
self.label = QtGui.QLineEdit()
Layouts[-1].addWidget(self.label)
Layouts[-1].addWidget(QtWidgets.QLabel(' n-label: ', self))
self.nlabel = QtGui.QLineEdit()
self.nlabel.setText('1')
Layouts[-1].addWidget(self.nlabel)
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(
QtWidgets.QLabel(' pre-stimulus window (s):', self))
self.preWindow = QtGui.QLineEdit()
self.preWindow.setText('[-1,0]')
Layouts[-1].addWidget(self.preWindow)
Layouts[-1].addWidget(
QtWidgets.QLabel(' post-stimulus window (s):', self))
self.postWindow = QtGui.QLineEdit()
self.postWindow.setText('[1,4]')
Layouts[-1].addWidget(self.postWindow)
Layouts.append(QtWidgets.QHBoxLayout())
self.baseline = QtGui.QCheckBox("baseline substraction")
Layouts[-1].addWidget(self.baseline)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.withStatTest = QtGui.QCheckBox("with stat. test")
Layouts[-1].addWidget(self.withStatTest)
Layouts.append(QtWidgets.QHBoxLayout())
self.compute = QtWidgets.QPushButton('compute episodes', self)
self.compute.setFixedWidth(200)
self.compute.clicked.connect(self.compute_episodes)
Layouts[-1].addWidget(self.compute)
self.plotBtn = QtWidgets.QPushButton(' -* PLOT *- ', self)
self.plotBtn.setFixedWidth(200)
self.plotBtn.clicked.connect(self.plot)
Layouts[-1].addWidget(self.plotBtn)
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel(50 * '<->', self)) # SEPARATOR
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(
QtWidgets.QLabel(' -* Figure Properties *- ', self))
self.fig_presets = QtWidgets.QComboBox(self)
self.fig_presets.setFixedWidth(400)
self.fig_presets.addItems(
['', 'raw-traces-preset', 'IO-curves-preset'])
Layouts[-1].addWidget(self.fig_presets)
Layouts[-1].addWidget(QtWidgets.QLabel('Panel size: ', self))
self.panelsize = QtGui.QLineEdit()
self.panelsize.setText('(1,1)')
Layouts[-1].addWidget(self.panelsize)
#
self.samplingBox = QtWidgets.QDoubleSpinBox(self)
self.samplingBox.setValue(dt_sampling)
self.samplingBox.setMaximum(500)
self.samplingBox.setMinimum(0.1)
self.samplingBox.setSuffix(' (ms) sampling')
Layouts[-1].addWidget(self.samplingBox)
# plot type
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel('Plot type: ', self))
self.plotbox = QtWidgets.QComboBox(self)
self.plotbox.addItems(['2d-plot', 'polar-plot', 'bar-plot'])
Layouts[-1].addWidget(self.plotbox)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.plotbox2 = QtWidgets.QComboBox(self)
self.plotbox2.addItems(['line', 'dot'])
Layouts[-1].addWidget(self.plotbox2)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.withSTDbox = QtGui.QCheckBox("with s.d.")
Layouts[-1].addWidget(self.withSTDbox)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.stim = QtGui.QCheckBox("with stim. ")
Layouts[-1].addWidget(self.stim)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.screen = QtGui.QCheckBox("with screen inset ")
Layouts[-1].addWidget(self.screen)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.annot = QtGui.QCheckBox("with annot. ")
Layouts[-1].addWidget(self.annot)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.axis = QtGui.QCheckBox("with axis")
Layouts[-1].addWidget(self.axis)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.grid = QtGui.QCheckBox("with grid")
Layouts[-1].addWidget(self.grid)
# X-scales
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel('X-SCALE --- ', self))
Layouts[-1].addWidget(QtWidgets.QLabel('x-min:', self))
self.xmin = QtGui.QLineEdit()
self.xmin.setText('')
Layouts[-1].addWidget(self.xmin)
Layouts[-1].addWidget(QtWidgets.QLabel('x-max:', self))
self.xmax = QtGui.QLineEdit()
self.xmax.setText('')
Layouts[-1].addWidget(self.xmax)
Layouts[-1].addWidget(QtWidgets.QLabel('x-bar:', self))
self.xbar = QtGui.QLineEdit()
self.xbar.setText('')
Layouts[-1].addWidget(self.xbar)
Layouts[-1].addWidget(QtWidgets.QLabel('x-barlabel:', self))
self.xbarlabel = QtGui.QLineEdit()
self.xbarlabel.setText('')
Layouts[-1].addWidget(self.xbarlabel)
# Y-scales
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel('Y-SCALE --- ', self))
Layouts[-1].addWidget(QtWidgets.QLabel('y-min:', self))
self.ymin = QtGui.QLineEdit()
self.ymin.setText('')
Layouts[-1].addWidget(self.ymin)
Layouts[-1].addWidget(QtWidgets.QLabel('y-max:', self))
self.ymax = QtGui.QLineEdit()
self.ymax.setText('')
Layouts[-1].addWidget(self.ymax)
Layouts[-1].addWidget(QtWidgets.QLabel('y-bar:', self))
self.ybar = QtGui.QLineEdit()
self.ybar.setText('')
Layouts[-1].addWidget(self.ybar)
Layouts[-1].addWidget(QtWidgets.QLabel('y-barlabel:', self))
self.ybarlabel = QtGui.QLineEdit()
self.ybarlabel.setText('')
Layouts[-1].addWidget(self.ybarlabel)
# curve
Layouts.append(QtWidgets.QHBoxLayout())
# self.Layout12.addWidget(self.baselineCB)
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel(50 * '<->', self)) # SEPARATOR
# BUTTONS
Layouts.append(QtWidgets.QHBoxLayout())
self.setBtn = QtWidgets.QPushButton('extract settings', self)
self.setBtn.setFixedWidth(200)
self.setBtn.clicked.connect(self.set_settings)
Layouts[-1].addWidget(self.setBtn)
self.rstBtn = QtWidgets.QPushButton('reset settings', self)
self.rstBtn.setFixedWidth(200)
self.rstBtn.clicked.connect(self.reset_settings)
Layouts[-1].addWidget(self.rstBtn)
Layouts.append(QtWidgets.QHBoxLayout())
self.newFig = QtWidgets.QPushButton('save as new figure', self)
self.newFig.setFixedWidth(200)
self.newFig.clicked.connect(self.new_fig)
Layouts[-1].addWidget(self.newFig)
self.append2Fig = QtWidgets.QPushButton('append to figure', self)
self.append2Fig.setFixedWidth(200)
self.append2Fig.clicked.connect(self.append)
Layouts[-1].addWidget(self.append2Fig)
self.exportBtn = QtWidgets.QPushButton('export to png', self)
self.exportBtn.setFixedWidth(200)
self.exportBtn.clicked.connect(self.export)
Layouts[-1].addWidget(self.exportBtn)
self.locBtn = QtWidgets.QComboBox(self)
self.locBtn.addItems(['Desktop', 'summary'])
self.locBtn.setFixedWidth(200)
Layouts[-1].addWidget(self.locBtn)
self.nameBtn = QtWidgets.QLineEdit(self)
self.nameBtn.setText('fig')
self.nameBtn.setFixedWidth(200)
Layouts[-1].addWidget(self.nameBtn)
self.dpi = QtWidgets.QSpinBox(self)
self.dpi.setValue(300)
self.dpi.setRange(50, 600)
self.dpi.setSuffix(' (dpi)')
self.dpi.setFixedWidth(80)
Layouts[-1].addWidget(self.dpi)
for l in Layouts:
mainLayout.addLayout(l)
def set_fig_name(self):
if self.locBtn.currentText() == 'Desktop':
self.fig_name = os.path.join(os.path.expanduser('~'), 'Desktop',
self.nameBtn.text() + '.svg')
elif self.locBtn.currentText() == 'summary':
summary_dir = os.path.join(
os.path.dirname(self.parent.datafile), 'summary',
os.path.basename(self.parent.datafile).replace('.nwb', ''))
pathlib.Path(summary_dir).mkdir(parents=True, exist_ok=True)
self.fig_name = os.path.join(summary_dir,
self.nameBtn.text() + '.svg')
def export(self):
export_drawing_as_png(self.fig_name,
dpi=self.dpi.value(),
background='white')
def append(self):
add_plot_to_svg(self.fig, self.fig_name)
def new_fig(self):
self.set_fig_name()
self.fig.savefig(self.fig_name, transparent=True)
def get_varied_parameters(self):
self.varied_parameters = {}
for key in self.parent.nwbfile.stimulus.keys():
if key not in [
'frame_run_type', 'index', 'protocol_id', 'time_duration',
'time_start', 'time_start_realigned', 'time_stop',
'time_stop_realigned', 'interstim', 'interstim-screen'
]:
unique = np.unique(self.parent.nwbfile.stimulus[key].data[:])
if len(unique) > 1:
self.varied_parameters[key] = unique
self.show()
def set_settings(self):
self.ymin.setText(str(self.ylim[0]))
self.ymax.setText(str(self.ylim[1]))
self.xmin.setText(str(self.xlim[0]))
self.xmax.setText(str(self.xlim[1]))
dx, dy = 0.1 * (self.xlim[1] - self.xlim[0]), 0.2 * (self.xlim[1] -
self.xlim[0])
self.xbar.setText('%.1f' % dx)
self.xbarlabel.setText('%.1f' % dx)
self.ybar.setText('%.1f' % dy)
self.ybarlabel.setText('%.1f' % dy)
def reset_settings(self):
for x in [
self.xmin, self.xmax, self.ymin, self.ymax, self.xbar,
self.ybar, self.xbarlabel, self.ybarlabel
]:
x.setText('')
def plot_row_column_of_quantity(self):
single_cond = self.build_single_conditions()
COL_CONDS = self.build_column_conditions()
ROW_CONDS = self.build_row_conditions()
COLOR_CONDS = self.build_color_conditions()
if (len(COLOR_CONDS) > 1) and (self.color.text() != ''):
COLORS = [
getattr(ge, self.color.text())((c % 10) / 10.)
for c in np.arange(len(COLOR_CONDS))
]
elif (len(COLOR_CONDS) > 1):
COLORS = [
ge.tab10((c % 10) / 10.) for c in np.arange(len(COLOR_CONDS))
]
elif self.color.text() != '':
COLORS = [getattr(ge, self.color.text())]
else:
COLORS = ['k']
if self.fig_presets.currentText() == 'raw-traces-preset':
fig, AX = ge.figure(axes=(len(COL_CONDS), len(ROW_CONDS)),
reshape_axes=False,
top=0.4,
bottom=0.4,
left=0.7,
right=0.7,
wspace=0.5,
hspace=0.5)
else:
fig, AX = ge.figure(axes=(len(COL_CONDS), len(ROW_CONDS)),
reshape_axes=False)
self.ylim = [np.inf, -np.inf]
for irow, row_cond in enumerate(ROW_CONDS):
for icol, col_cond in enumerate(COL_CONDS):
for icolor, color_cond in enumerate(COLOR_CONDS):
cond = np.array(
single_cond & col_cond & row_cond
& color_cond)[:self.EPISODES['resp'].shape[0]]
if self.EPISODES['resp'][cond, :].shape[0] > 0:
my = self.EPISODES['resp'][cond, :].mean(axis=0)
if self.withSTDbox.isChecked():
sy = self.EPISODES['resp'][cond, :].std(axis=0)
ge.plot(self.EPISODES['t'],
my,
sy=sy,
ax=AX[irow][icol],
color=COLORS[icolor],
lw=1)
self.ylim = [
min([self.ylim[0],
np.min(my - sy)]),
max([self.ylim[1],
np.max(my + sy)])
]
else:
AX[irow][icol].plot(self.EPISODES['t'],
my,
color=COLORS[icolor],
lw=1)
self.ylim = [
min([self.ylim[0], np.min(my)]),
max([self.ylim[1], np.max(my)])
]
if self.screen.isChecked() and not (self.parent.visual_stim
is not None):
print('initializing stim [...]')
self.parent.load_VisualStim()
if self.screen.isChecked():
inset = ge.inset(AX[irow][icol], [.8, .9, .3, .25])
self.parent.visual_stim.show_frame(\
self.EPISODES['index_from_start'][cond][0],
ax=inset, parent=self.parent, enhance=True, label=None)
if self.withStatTest.isChecked():
for irow, row_cond in enumerate(ROW_CONDS):
for icol, col_cond in enumerate(COL_CONDS):
for icolor, color_cond in enumerate(COLOR_CONDS):
cond = np.array(
single_cond & col_cond & row_cond
& color_cond)[:self.EPISODES['resp'].shape[0]]
test = stat_test_for_evoked_responses(
self.EPISODES,
cond,
interval_pre=[self.t0pre, self.t1pre],
interval_post=[self.t0post, self.t1post],
test='wilcoxon')
AX[irow][icol].plot([self.t0pre, self.t1pre],
self.ylim[0] * np.ones(2),
'k-',
lw=2)
AX[irow][icol].plot([self.t0post, self.t1post],
self.ylim[0] * np.ones(2),
'k-',
lw=2)
ps, size = pval_to_star(test)
AX[irow][icol].annotate(
ps,
((self.t1pre + self.t0post) / 2., self.ylim[0]),
va='top',
ha='center',
size=size,
xycoords='data')
if (self.ymin.text() != '') and (self.ymax.text() != ''):
self.ylim = [float(self.ymin.text()), float(self.ymax.text())]
if (self.xmin.text() != '') and (self.xmax.text() != ''):
self.xlim = [float(self.xmin.text()), float(self.xmax.text())]
else:
self.xlim = [self.EPISODES['t'][0], self.EPISODES['t'][-1]]
for irow, row_cond in enumerate(ROW_CONDS):
for icol, col_cond in enumerate(COL_CONDS):
ge.set_plot(AX[irow][icol],
spines=(['left', 'bottom']
if self.axis.isChecked() else []),
ylim=self.ylim,
xlim=self.xlim,
xlabel=(self.xbarlabel.text()
if self.axis.isChecked() else ''),
ylabel=(self.ybarlabel.text()
if self.axis.isChecked() else ''))
if self.stim.isChecked():
AX[irow][icol].fill_between(
[0, np.mean(self.EPISODES['time_duration'])],
self.ylim[0] * np.ones(2),
self.ylim[1] * np.ones(2),
color='grey',
alpha=.2,
lw=0)
if not self.axis.isChecked():
ge.draw_bar_scales(AX[0][0],
Xbar=(0. if self.xbar.text() == '' else float(
self.xbar.text())),
Xbar_label=self.xbarlabel.text(),
Ybar=(0. if self.ybar.text() == '' else float(
self.ybar.text())),
Ybar_label=self.ybarlabel.text(),
Xbar_fraction=0.1,
Xbar_label_format='%.1f',
Ybar_fraction=0.2,
Ybar_label_format='%.1f',
loc='top-left')
if self.label.text() != '':
ge.annotate(fig, ' '+self.label.text()+\
(1+int(self.nlabel.text()))*'\n', (0,0), color=COLORS[0],
ha='left', va='bottom')
if self.annot.isChecked():
S = ''
if hasattr(self, 'roiPick'):
S += 'roi #%s' % self.roiPick.text()
for i, key in enumerate(self.varied_parameters.keys()):
if 'single-value' in getattr(self,
'%s_plot' % key).currentText():
S += ', %s=%.2f' % (key, getattr(
self, '%s_values' % key).currentText())
ge.annotate(fig, S, (0, 0), color='k', ha='left', va='bottom')
return fig, AX
def plot_raw_data(self):
self.fig, ax = ge.figure(axes_extents=[[[6, 4]]],
bottom=0.1,
right=3.,
top=0.5)
Tbar = 1 # by default
if (self.xmin.text() == '') or (self.xmax.text() == ''):
tlim = [0, 20]
else:
tlim = [float(self.xmin.text()), float(self.xmax.text())]
if self.xbar.text() != '':
Tbar = float(self.xbar.text())
settings = {}
for mod in self.modalities:
if getattr(self, mod + 'Plot').value() > 0:
settings[mod] = dict(fig_fraction=getattr(self, mod +
'Plot').value())
for key in ['CaImaging', 'CaImagingSum']:
if (key in settings) and (self.sqbox.text() in [
'dF/F', 'Fluorescence', 'Neuropil', 'Deconvolved'
]):
settings[key]['subquantity'] = self.sqbox.text()
if 'CaImaging' in settings:
settings['CaImaging']['roiIndices'] = self.parent.roiIndices
self.data.plot(tlim, settings=settings, Tbar=Tbar, ax=ax)
ge.show()
def plot_FOV(self):
if self.fovMap.currentText() == 'black_to_green':
cmap = ge.get_linear_colormap('k', ge.green)
elif self.fovMap.currentText() == 'white_to_green':
cmap = ge.get_linear_colormap('w', 'darkgreen')
elif self.fovMap.currentText() == 'grey':
cmap = ge.get_linear_colormap('w', 'darkgrey')
else:
cmap = ge.viridis
self.fig, ax = ge.figure(figsize=(2., 4.),
bottom=0,
top=0,
right=0.,
left=0.)
self.data.show_CaImaging_FOV(\
self.fovType.currentText(),
NL=float(self.fovNL.value()), cmap=cmap, ax=ax)
ge.annotate(ax,
self.fovLabel.text(), (.99, .99),
ha='right',
va='top',
color='white')
ge.show()
def plot_correl(self):
pass
def plot(self):
if self.responseType.currentText() == 'stim-evoked-traces':
self.fig, AX = self.plot_row_column_of_quantity()
else:
pass
ge.show()
def select_ROI(self):
""" see dataviz/gui.py // dataviz/guiparts.py """
roiIndices = self.select_ROI_from_pick(cls=self.parent)
if len(roiIndices) > 0:
self.parent.roiIndices = roiIndices
self.parent.roiPick.setText(self.roiPick.text())
self.statusBar.showMessage('ROIs set to %s ' % self.parent.roiIndices)
def compute_episodes(self):
if self.sqbox.text() in [
'dF/F', 'Fluorescence', 'Neuropil', 'Deconvolved'
]:
self.parent.CaImaging_key = self.sqbox.text()
self.t0pre = float(self.preWindow.text().replace('[', '').replace(
']', '').split(',')[0])
self.t1pre = float(self.preWindow.text().replace('[', '').replace(
']', '').split(',')[1])
self.t0post = float(self.postWindow.text().replace('[', '').replace(
']', '').split(',')[0])
self.t1post = float(self.postWindow.text().replace('[', '').replace(
']', '').split(',')[1])
if (self.qbox.currentIndex() > 0) and (self.pbox.currentIndex() > 0):
self.EPISODES = build_episodes(
self,
parent=self.parent,
protocol_id=self.pbox.currentIndex() - 1,
quantity=self.qbox.currentText(),
dt_sampling=self.samplingBox.value(), # ms
interpolation='linear',
baseline_substraction=self.baseline.isChecked(),
prestim_duration=(self.t1pre - self.t0pre),
verbose=True)
else:
print(' /!\ Pick a protocol and a quantity')
def build_conditions(self, X, K):
if len(K) > 0:
CONDS = []
XK = np.meshgrid(*X)
for i in range(len(
XK[0].flatten())): # looping over joint conditions
cond = np.ones(np.sum(self.Pcond), dtype=bool)
for k, xk in zip(K, XK):
cond = cond & (self.parent.nwbfile.stimulus[k].data[
self.Pcond] == xk.flatten()[i])
CONDS.append(cond)
return CONDS
else:
return [np.ones(np.sum(self.Pcond), dtype=bool)]
def build_single_conditions(self):
full_cond = np.ones(np.sum(self.Pcond), dtype=bool)
for i, key in enumerate(self.varied_parameters.keys()):
if 'single-value' in getattr(self, '%s_plot' % key).currentText():
cond = (np.array(
self.parent.nwbfile.stimulus[key].data[self.Pcond],
dtype=str) != getattr(self,
'%s_values' % key).currentText())
full_cond[cond] = False
return full_cond
def build_column_conditions(self):
X, K = [], []
for i, key in enumerate(self.varied_parameters.keys()):
if 'column' in getattr(self, '%s_plot' % key).currentText():
X.append(
np.sort(
np.unique(self.parent.nwbfile.stimulus[key].data[
self.Pcond])))
K.append(key)
return self.build_conditions(X, K)
def build_row_conditions(self):
X, K = [], []
for i, key in enumerate(self.varied_parameters.keys()):
if 'row' in getattr(self, '%s_plot' % key).currentText():
X.append(
np.sort(
np.unique(self.parent.nwbfile.stimulus[key].data[
self.Pcond])))
K.append(key)
return self.build_conditions(X, K)
def build_color_conditions(self):
X, K = [], []
for i, key in enumerate(self.varied_parameters.keys()):
if 'color' in getattr(self, '%s_plot' % key).currentText():
X.append(
np.sort(
np.unique(self.parent.nwbfile.stimulus[key].data[
self.Pcond])))
K.append(key)
return self.build_conditions(X, K)
def make_sumary_pdf(filename,
Nmax=1000000,
include=['exp', 'rois', 'raw', 'protocols'],
T_raw_data=60,
N_raw_data=5,
ROI_raw_data=20,
Tbar_raw_data=5):
data = MultimodalData(filename)
data.roiIndices = np.sort(
np.random.choice(np.arange(data.iscell.sum()),
size=min([data.iscell.sum(), ROI_raw_data]),
replace=False))
folder = filename.replace('.nwb', '')
if not os.path.isdir(folder):
os.mkdir(folder)
if 'exp' in include:
with PdfPages(os.path.join(folder, 'exp.pdf')) as pdf:
print('writing experimental metadata ')
fig = metadata_fig(data)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
print('plotting behavior analysis ')
fig = behavior_analysis_fig(data)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
if 'exp' in include:
with PdfPages(os.path.join(folder, 'rois.pdf')) as pdf:
print('plotting imaging FOV ')
fig, AX = plt.subplots(1, 4, figsize=(11.4, 2))
data.show_CaImaging_FOV(key='meanImg',
NL=1,
cmap='viridis',
ax=AX[0])
data.show_CaImaging_FOV(key='meanImg',
NL=2,
cmap='viridis',
ax=AX[1])
data.show_CaImaging_FOV(key='meanImgE',
NL=2,
cmap='viridis',
ax=AX[2])
data.show_CaImaging_FOV(key='max_proj',
NL=2,
cmap='viridis',
ax=AX[3])
pdf.savefig() # saves the current figure into a pdf page
plt.close()
if 'raw' in include:
with PdfPages(os.path.join(folder, 'raw.pdf')) as pdf:
print('plotting full data view [...]')
fig, ax = plt.subplots(1, figsize=(11.4, 5))
fig.subplots_adjust(top=0.8, bottom=0.05)
data.plot(data.tlim,
settings=raw_data_plot_settings(data,
subsampling_factor=1000),
ax=ax,
Tbar=Tbar_raw_data)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
# # plot raw data sample
# for t0 in np.linspace(T_raw_data, data.tlim[1], N_raw_data):
# TLIM = [np.max([10,t0-T_raw_data]),t0]
# print('plotting raw data sample at times ', TLIM)
# fig, ax = plt.subplots(1, figsize=(11.4, 5))
# fig.subplots_adjust(top=0.8, bottom=0.05)
# data.plot(TLIM, settings=raw_data_plot_settings(data),
# ax=ax, Tbar=Tbar_raw_data)
# # inset with time sample
# axT = plt.axes([0.6, 0.9, 0.3, 0.05])
# axT.axis('off')
# axT.plot(data.tlim, [0,0], 'k-', lw=2)
# axT.plot(TLIM, [0,0], '-', color=plt.cm.tab10(3), lw=5)
# axT.annotate('0 ', (0,0), xycoords='data', ha='right', fontsize=9)
# axT.annotate(' %.1fmin' % (data.tlim[1]/60.), (data.tlim[1],0), xycoords='data', fontsize=9)
# pdf.savefig() # saves the current figure into a pdf page
# plt.close()
print('looping over protocols for analysis [...]')
if 'protocols' in include:
# looping over protocols
for p, protocol in enumerate(data.protocols):
print('plotting protocol "%s" [...]' % protocol)
with PdfPages(os.path.join(folder, '%s.pdf' % protocol)) as pdf:
# finding protocol type
protocol_type = (data.metadata['Protocol-%i-Stimulus' %
(p + 1)] if
(len(data.protocols) > 1) else
data.metadata['Stimulus'])
print(protocol_type)
# then protocol-dependent analysis
if protocol_type == 'full-field-grating':
from analysis.orientation_direction_selectivity import orientation_selectivity_analysis
Nresp, SIs = 0, []
for i in range(data.iscell.sum())[:Nmax]:
fig, SI, responsive = orientation_selectivity_analysis(
data, roiIndex=i, verbose=False)
pdf.savefig(
) # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(SI)
# summary figure for this protocol
fig, AX = summary_fig(Nresp, data.iscell.sum(),
np.array(SIs))
pdf.savefig() # saves the current figure into a pdf page
plt.close()
elif protocol_type == 'drifting-full-field-grating':
from analysis.orientation_direction_selectivity import direction_selectivity_analysis
Nresp, SIs = 0, []
for i in range(data.iscell.sum())[:Nmax]:
fig, SI, responsive = direction_selectivity_analysis(
data, roiIndex=i, verbose=False)
pdf.savefig(
) # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(SI)
fig, AX = summary_fig(Nresp,
data.iscell.sum(),
np.array(SIs),
label='Direction Select. Index')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
elif protocol_type in [
'center-grating', 'drifting-center-grating'
]:
from surround_suppression import orientation_size_selectivity_analysis
Nresp, SIs = 0, []
for i in range(data.iscell.sum())[:Nmax]:
fig, responsive = orientation_size_selectivity_analysis(
data, roiIndex=i, verbose=False)
pdf.savefig(
) # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(0) # TO BE FILLED
fig, AX = summary_fig(Nresp,
data.iscell.sum(),
np.array(SIs),
label='none')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
elif 'noise' in protocol_type:
from receptive_field_mapping import RF_analysis
Nresp, SIs = 0, []
for i in range(data.iscell.sum())[:Nmax]:
fig, SI, responsive = RF_analysis(data,
roiIndex=i,
verbose=False)
pdf.savefig(
) # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(0) # TO BE FILLED
fig, AX = summary_fig(Nresp,
data.iscell.sum(),
np.array(SIs),
label='none')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
elif 'spatial-location' in protocol_type:
from surround_suppression import orientation_size_selectivity_analysis
Nresp, SIs = 0, []
for i in range(data.iscell.sum())[:Nmax]:
fig, responsive = orientation_size_selectivity_analysis(
data, roiIndex=i, verbose=False)
pdf.savefig(
) # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(0) # TO BE FILLED
fig, AX = summary_fig(Nresp,
data.iscell.sum(),
np.array(SIs),
label='none')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
print('[ok] pdfs succesfully saved in "%s" !' % folder)