def BatchWorker(*params):
f, names, shape, bsParams, crop, peakLimits, parguess, bounds, index, folder = params
dt = DATA()
result = pd.Series(name = os.path.basename(f), index = index, dtype = object)
try:
dt.loadData(f)
dt.crop(crop.min, crop.max)
dt.fitBaseline(bsParams, peakLimits, abs = True)
text = ''
for deg in np.arange(0, dt.bsDegree+1):
if dt.bsCoef[deg]>=0 and deg!=0:
text += '+'
text += '{:.4E}*x^{}'.format(dt.bsCoef[deg], dt.bsDegree-deg)
result.loc['baseline'] = text
except Exception as e:
print("Error {}".format(e))
try:
fit = FIT(shape, names)
fit.deconvolute(dt, parguess, bounds, batch=True)
for i, pars in enumerate(zip(names, shape)):
result.loc[pars[0]+'_'+'Position'] = fit.pars[int(np.sum(fit.args[:i])+2)]
result.loc[pars[0]+'_'+'Amplitude'] = fit.pars[int(np.sum(fit.args[:i]))]
result.loc[pars[0]+'_'+'FWHM'] = fit.fwhm[i]
result.loc[pars[0]+'_'+'Area'] = fit.area[i]
if pars[1] == 'V':
result.loc[pars[0]+'_'+'L/G'] = fit.pars[int(np.sum(fit.args[:i])+3)]
elif pars[1] == 'B':
result.loc[pars[0]+'_'+'1/q'] = fit.pars[int(np.sum(fit.args[:i])+3)]
# result.iloc[1::4] = fit.pars[2::3]
# result.iloc[2::4] = fit.pars[::3]
# result.iloc[3::4] = fit.fwhm
# result.iloc[4::4] = fit.area
except Exception as e:
print('Could not deconvolute the {} file'.format(f))
result.iloc[1:len(index)] = -1*np.ones(len(index)-1)
print(e)
path = folder+ '/' +os.path.basename(f)+'.png'
fig = plt.figure(figsize=(12,8))
fit.plot(figure = fig, path = path)
return result
class RD(QMainWindow, gui.Ui_MainWindow):
header = 50*'*'+'\n\n'+10*' '+'DECONVOLUTION OF RAMAN SPECTRA'
header += 10*''+'\n\n'+50*'*'
def __init__(self):
super(RD, self).__init__()
self.setupUi(self)
#variables
self.data = DATA()
self.fit = 0
self.changed = False
self.peaksLimits = Limits(900, 1850)
self.spike = 0 #scatter plot for spikes
self.limit_low, self.limit_high = 0, 0 #exclude region
self.baseline = 0
self.path =os.path.dirname(os.path.realpath(__file__))
self.actionQuit.triggered.connect(self.close)
self.actionAbout.triggered.connect(self.about)
self.actionGuess.triggered.connect(lambda: [self.dockGuess.setVisible(self.actionGuess.isChecked()),
self.dockGuess.raise_()])
self.actionOutput.triggered.connect(lambda: [self.dockOut.setVisible(self.actionOutput.isChecked()),
self.dockOut.raise_()])
self.dockGuess.visibilityChanged.connect(self.actionGuess.setChecked)
self.dockOut.visibilityChanged.connect(self.actionOutput.setChecked)
self.actionToolbar.triggered.connect(self.toolBar.toggleViewAction().trigger)
self.actionNew.triggered.connect(self.New)
self.actionSave.triggered.connect(self.Save)
self.actionCrop.triggered.connect(self.Crop)
self.actionRemove_Baseline.triggered.connect(self.Baseline)
self.actionSpike_Removal.triggered.connect(self.removeSpikes)
self.actionSmoothing.triggered.connect(self.Smoothing)
self.actionDeconvolute.triggered.connect(self.Deconvolution)
self.actionDeconvolute_MCMC.triggered.connect(self.DeconvMCMC)
self.actionBatch_deconvolution.triggered.connect(self.BatchDeconv)
self.actionLoadGuess.triggered.connect(self.LoadGuess)
self.actionLoad_defaults.triggered.connect(lambda: self.LoadGuess(fname = self.path+'/config/initialData.csv'))
self.actionExportGuess.triggered.connect(self.ExportGuess)
self.tabifyDockWidget(self.dockGuess, self.dockOut)
self.tableWidget.horizontalHeader().setSectionResizeMode(QHeaderView.ResizeMode.ResizeToContents)
self.tableWidget.itemChanged.connect(self.itemCheck)
self.textOut.setReadOnly(True)
self.textOut.setText(self.header)
self.readConfig()
self.dockGuess.raise_()
self.spikeDialog = SpikeDialog(self.threshold)
self.spikeDialog.slider.sliderReleased.connect(self.showSpikes)
self.smoothDialog = SmoothDialog()
self.smoothDialog.slider.sliderReleased.connect(self.previewSmoothed)
self.figure = plt.figure()
self.figure.set_tight_layout(True)
self.subplot = self.figure.add_subplot(111) #add a subfigure
#add widget
self.canvas = FigureCanvas(self.figure)
self.canvas.setFocusPolicy(Qt.ClickFocus )
self.canvas.setFocus()
self.canvas.mpl_connect('button_press_event', self.onclick)
self.toolbar = NavigationToolbar(self.canvas, self)
self.gridLayout.addWidget(self.toolbar)
self.gridLayout.addWidget(self.canvas)
actionOpen = QAction(QIcon("graphics/open.png"),"Open",self)
actionSave = QAction(QIcon("graphics/save.png"),"Save",self)
actionCrop = QAction(QIcon("graphics/crop.png"),"Crop",self)
actionSpike = QAction(QIcon("graphics/spike.svg"),"Spike removal",self)
actionSmooth = QAction(QIcon("graphics/smooth.svg"),"Smoothing",self)
actionBaseline = QAction(QIcon("graphics/baseline.png"),"Remove baseline",self)
actionDeconv = QAction(QIcon("graphics/deconv.svg"),"Deconvolution", self)
actionMCMC = QAction(QIcon("graphics/mcmc.png"),"Deconvolution\nwith MCMC", self)
actionBatch = QAction(QIcon("graphics/batch.png"),"Batch deconvolution", self)
self.toolBar.addAction(actionOpen)
self.toolBar.addAction(actionSave)
self.toolBar.addSeparator()
self.toolBar.addAction(actionCrop)
self.toolBar.addAction(actionBaseline)
self.toolBar.addAction(actionSpike)
self.toolBar.addAction(actionSmooth)
self.toolBar.addSeparator()
self.toolBar.addAction(actionDeconv)
self.toolBar.addAction(actionMCMC)
self.toolBar.addSeparator()
self.toolBar.addAction(actionBatch)
self.toolBar.addSeparator()
self.toolBar.toggleViewAction().setChecked(self.actionToolbar.isChecked())
actionOpen.triggered.connect(self.New)
actionCrop.triggered.connect(self.Crop)
actionBaseline.triggered.connect(self.Baseline)
actionSpike.triggered.connect(self.removeSpikes)
actionSave.triggered.connect(self.Save)
actionSmooth.triggered.connect(self.Smoothing)
actionDeconv.triggered.connect(self.Deconvolution)
actionMCMC.triggered.connect(self.DeconvMCMC)
actionBatch.triggered.connect(self.BatchDeconv)
self.startUp()
self.tableWidget.setContextMenuPolicy(Qt.CustomContextMenu)
self.tableWidget.customContextMenuRequested.connect(self.tableItemRightClicked)
def startUp(self):
#generate dummy data to plot at start-up
X = np.arange(800, 2000, 2)
Y1 = FIT.lorents(X, 1800, 1358, 110)
Y2 = FIT.lorents(X, 1700, 1590, 50)
self.subplot.plot(X, (Y1+Y2)+20*np.random.randn(len(X)), 'o')
self.subplot.plot(X, Y1+Y2, 'r--', label = 'Sample data')
self.subplot.legend()
self.subplot.plot(X, Y1)
self.subplot.plot(X, Y2)
self.subplot.set_xlim(np.min(X), np.max(X))
self.plotAdjust()
def removeSpikes(self):
#detect and remove spikes in the data (current data)
if not np.shape(self.data.X):
self.errorBox('There is no data', 'No data...')
return
self.Plot(self.data.X, self.data.current, 'Experimental data')
self.showSpikes()
result = self.spikeDialog.exec_()
if not result:
self.data.spikes = []
if self.spike != 0:
self.spike.remove()
self.spike = 0
self.subplot.legend()
self.canvas.draw()
return
self.data.removeSpikes()
self.statusbar.showMessage("%d datapoints removed" %len(self.data.spikes), 2000)
self.Plot(self.data.X, self.data.current, "Experimental data", clear=True)
if not self.changed:
self.changed = True
self.setWindowTitle(self.windowTitle()+'*')
def Smoothing(self):
#smooth the current data according to the user defined parameters
if not np.shape(self.data.X):
self.errorBox('There is no data', 'No data...')
return
self.Plot(self.data.X, self.data.current, "Experimental data")
self.previewSmoothed()
result = self.smoothDialog.exec_()
self.baseline.remove()
self.baseline = 0
if not result:
self.subplot.legend()
self.canvas.draw()
return
self.data.prev = np.column_stack((self.data.X, self.data.current))
self.data.current = self.data.smooth(self.data.current, 2*self.smoothDialog.slider.value()+1)
self.Plot(self.data.X, self.data.current, "Smoothed data")
self.plotAdjust()
if not self.changed:
#if the current data was changed add '*' to the title
self.changed = True
self.setWindowTitle(self.windowTitle()+'*')
def previewSmoothed(self):
#plot a preview of the smoothed data
y_ = self.data.smooth(self.data.current, 2*self.smoothDialog.slider.value()+1)
if self.baseline != 0:
self.baseline.remove()
self.baseline, = self.subplot.plot(self.data.X, y_, 'r--', label = 'Smoothed data')
self.subplot.legend()
self.canvas.draw()
return y_
def prepDataForDeconv(self, **kwargs):
#prepare the data for deconvolution
#the function first checks if the data is present and that the baseline was removed
#fitting parameters are then taken from the table and put in a list
batch = kwargs.get('batch', False)
if not np.shape(self.data.X) and not batch:
self.errorBox('There is no data', 'No data...')
return -1, -1, -1
if not np.shape(self.data.baseline) and not batch:
result = QMessageBox.question(self,
"Baseline...",
"The baseline is still present!\nWould you like to remove it first?",
QMessageBox.Yes| QMessageBox.No)
if result == QMessageBox.Yes:
res = self.Baseline()
if res == -1:
return -1, -1, -1
else:
return -1, -1, -1
rows = self.tableWidget.rowCount()
cols = self.tableWidget.columnCount()
params = pd.DataFrame(columns = ['lb', 'pos', 'pos_min', 'pos_max', 'int', 'width', 'shape'])
for row in range(rows):
if self.tableWidget.item(row, 0).checkState() & Qt.Checked:
error = [self.itemCheck(self.tableWidget.item(row, col), silent=True) for col in range(1, cols-1)]
if True in error:
self.errorBox("Bad parameters", "Bad values")
self.dockGuess.raise_()
return -1, -1, -1
d =[self.tableWidget.item(row, col).text() for col in range(1, cols-1)]
p_shape = self.tableWidget.cellWidget(row, cols-1).currentText().strip()[0]
params = params.append({'lb': d[0], 'pos' : d[1], 'pos_min' : d[2],
'pos_max' : d[3], 'int' : d[4], 'width' : d[5], 'shape' : p_shape}, ignore_index =True)
self.fit = FIT(params['shape'], params['lb'])
parguess = np.concatenate([[params['int'][i], params['width'][i], params['pos'][i]]
for i in range(len(params['lb'])) ]).astype(float)
lower = np.concatenate([[5, 5, params['pos_min'][i]] for i in range(len(params['lb'])) ]).astype(float)
upper = np.concatenate([[float('inf'), float('inf'), params['pos_max'][i]] for i in range(len(params['lb'])) ]).astype(float)
return parguess, lower, upper
def Deconvolution(self):
#deconvolute the data in a different thread
parguess, lower, upper = self.prepDataForDeconv()
if not np.shape(parguess):
return
bounds = [lower, upper]
progress = QProgressDialog("Deconvoluting...", "Cancel", 0, 100)
progress.setCancelButton(None)
progress.show()
progress.setValue(5)
self.error = False
self.deconvolutionThread = Deconvolute(self.fit.deconvolute, self.data, parguess, bounds, False)
progress.canceled.connect(self.deconvolutionThread.exit)
self.deconvolutionThread.finished.connect(lambda: [progress.setValue(100), self.plotDeconvResult()])
self.deconvolutionThread.error.connect(lambda: [progress.setValue(100), self.errorBox("Wrong parameters...")])
self.deconvolutionThread.start()
def DeconvMCMC(self):
parguess, lower, upper = self.prepDataForDeconv()
if not np.shape(parguess):
return
bounds = [lower, upper]
progress = QProgressDialog("Deconvoluting...", "Cancel", 0, 100)
progress.setCancelButton(None)
progress.show()
progress.setValue(0)
self.error = False
self.deconvolutionThread = Deconvolute(self.fit.decMCMC, self.data, parguess, bounds, 10000)
self.fit.pg.connect(progress.setValue)
progress.canceled.connect(self.deconvolutionThread.exit)
self.deconvolutionThread.finished.connect(lambda: [progress.setValue(100), self.plotDeconvResult()])
self.deconvolutionThread.error.connect(lambda: [progress.setValue(100), self.errorBox("Wrong parameters...")])
self.deconvolutionThread.start()
def BatchDeconv(self):
if self.changed:
result = QMessageBox.question(self,
"Unsaved file...",
"Do you want to save the data before exiting?",
QMessageBox.Yes| QMessageBox.No |QMessageBox.Cancel)
if result == QMessageBox.Yes:
self.Save()
elif result == QMessageBox.Cancel:
return
self.changed = False
path = self.initialDir
fnames, _filter = QFileDialog.getOpenFileNames(self, 'Open files', path,"Text files (*.txt *.dat);; Wire data files (*.wdf);; All files (*.*)")
if not fnames:
return
elif self.initialDir!=ntpath.dirname(fnames[0]):
self.initialDir=ntpath.dirname(fnames[0]) #update the initial directory for the Open/Save dialog
self.settings.setValue('directory', self.initialDir)
self.setWindowTitle( 'Raman Deconvolution - Batch Deconvolution')
if _filter == 'Wire data files (*.wdf)':
for i, fname in enumerate(fnames):
convert(fname)
fnames[i] = fname[:-3]+'txt'
fnames.sort()
dialog = BatchDialog(len(fnames), self.degree, self.peakLimits.min, self.peakLimits.max, 700, 2500, cpu_count())
result = dialog.exec_()
if not result:
return
params = dialog.getData()
parguess, lower, upper = self.prepDataForDeconv(batch=True)
if not np.shape(parguess):
return
bounds = [lower, upper]
progress = QProgressDialog("Deconvoluting", "Cancel", 0, 100)
progress.setCancelButton(None)
progress.show()
progress.setValue(0)
self.batch = BatchDeconvolute(fnames, parguess, bounds, self.fit, params)
self.textOut.clear()
self.subplot.clear()
self.batch.progress.connect(progress.setValue)
self.batch.finished.connect(lambda x: [progress.setValue(100), self.errorBox('Some files were not deconvoluted...') if x!='OK' else print('all done')])
self.batch.error.connect(lambda: [progress.setValue(100), self.errorBox("Wrong parameters...")])
self.batch.saved.connect(lambda x:[self.textOut.append('Results are saved at {}'.format(x)), self.dockOut.raise_()])
self.batch.start()
def plotDeconvResult(self):
if self.error or self.fit.error:
return
self.statusbar.showMessage("Thread finished succesfully", 1000)
self.Plot(self.data.X, self.data.current, "Experimental data")
labels = self.fit.names
[self.subplot.plot(self.data.X, self.fit.peaks[lb], label = lb) for lb in labels]
self.subplot.plot(self.data.X, self.fit.peaks['cumulative'], 'r--', label = 'cumulative')
self.plotAdjust()
self.textOut.append(self.fit.report)
self.dockOut.raise_()
def showSpikes(self):
self.data.detectSpikes(self.spikeDialog.slider.value())
if self.spike != 0:
self.spike.remove()
self.spike = 0
self.canvas.draw()
sp = self.data.spikes
if len(sp):
self.spike, = self.subplot.plot(self.data.X[sp], self.data.current[sp], 'ro', label = 'Spikes')
self.subplot.legend()
self.canvas.draw()
def plotAdjust(self):
self.subplot.set_xlabel(r'$\mathbf{Raman\ shift,\ cm^{-1}}$')
self.subplot.set_ylabel(r'$\mathbf{Intensty}$')
self.subplot.legend()
self.canvas.draw()
#right click on the plot
def onclick(self, event):
if np.shape(self.data.X):
if event.button == 3: #right click
self.listMenu= QMenu()
menu_item_0 = self.listMenu.addAction("Delete datapoint (spike)")
idx = np.abs(self.data.X - event.xdata).argmin()
self.statusbar.showMessage('Datapoint selected: X = %f, Y = %f' %(self.data.X[idx], self.data.current[idx]), 1000)
spike, = self.subplot.plot(self.data.X[idx], self.data.current[idx], 'rs', label = 'Selected datapoint')
self.subplot.legend()
self.canvas.draw()
cursor = QCursor()
menu_item_0.triggered.connect(lambda: self.deleteSpike(idx))
self.listMenu.move(cursor.pos() )
self.listMenu.show()
self.listMenu.aboutToHide.connect(lambda : (spike.remove(), self.canvas.draw()))
def deleteSpike(self, x):
self.statusbar.showMessage('Spike deleted: X = %f, Y = %f' %(self.data.X[x], self.data.current[x]), 1000)
self.data.spikes = x
self.data.removeSpikes()
self.Plot(self.data.X, self.data.current, "Experimental data")
if not self.changed:
self.changed = True
self.setWindowTitle(self.windowTitle()+'*')
def Baseline(self):
if not np.shape(self.data.X):
self.errorBox('There is no data', 'No data...')
return -1
if np.shape(self.data.baseline):
self.data.setData(self.data.X, self.data.Y.copy())
self.Plot(self.data.X, self.data.current, 'Experimental data')
if self.peakLimits.min <= self.data.X[0]:
self.peakLimits.min = round(self.data.X[0] + 0.25*(self.data.X[-1]-self.data.X[0]))
if self.peakLimits.max >= self.data.X[-1]:
self.peakLimits.max = round(self.data.X[-1] - 0.25*(self.data.X[-1]-self.data.X[0]))
dialog = BaselineDialog(self.degree, self.peakLimits.min, self.peakLimits.max)
dialog.btPreview.clicked.connect(lambda: self.previewBaseline(dialog))
self.previewBaseline(dialog)
result = dialog.exec_()
self.limit_low.remove()
self.limit_high.remove()
self.baseline.remove()
self.limit_low, self.limit_high = 0, 0
self.baseline = 0
if not result:
self.subplot.legend()
self.canvas.draw()
return -1
params = dialog.getData()
try:
_min = int(params[2])
except ValueError:
self.statusbar.showMessage("Wrong value...setting to default", 3000)
try:
_max = int(params[3])
except ValueError:
self.statusbar.showMessage("Wrong value...setting to default", 3000)
self.peakLimits = Limits(_min, _max)
self.data.fitBaseline(params[1], self.peakLimits, abs = True)
self.Plot(self.data.X, self.data.current, "Baseline corrected data")
self.plotAdjust()
self.dockOut.setVisible(True)
self.textOut.clear()
self.textOut.append(' BASELINE FIT ')
self.textOut.append('*************************************************')
self.textOut.append('Polynomial fit -- degree: {}'.format(self.data.bsDegree))
self.textOut.append('Fitting equation:')
text = ''
for deg in np.arange(0, self.data.bsDegree+1):
if self.data.bsCoef[deg]>=0 and deg!=0:
text += '+'
text += '{:.4E}*x^{}'.format(self.data.bsCoef[deg], self.data.bsDegree-deg)
self.textOut.append(text + '\n')
self.dockOut.raise_()
if not self.changed:
self.changed = True
self.setWindowTitle(self.windowTitle()+'*')
def previewBaseline(self, dialog):
dt = DATA()
dt.setData(self.data.X, self.data.current)
try:
_min = int(dialog.lineEdit_min.text())
_max = int(dialog.lineEdit_max.text())
except ValueError:
self.statusbar.showMessage("Wrong value...setting to default", 3000)
_min = self.peakLimits.min
_max = self.peakLimits.max
if self.baseline != 0:
self.limit_low.remove()
self.limit_high.remove()
self.baseline.remove()
self.limit_low, = self.subplot.plot([_min, _min], [np.min(self.data.current), np.max(self.data.current)], color = 'red', label = 'Exclude region')
self.limit_high, = self.subplot.plot([_max, _max], [np.min(self.data.current), np.max(self.data.current)], color = 'red')
peakLimits = Limits(_min, _max)
dt.fitBaseline(dialog.spinBox.value(), peakLimits, abs = True)
self.baseline, = self.subplot.plot(dt.X, dt.baseline, 'r--', label = "Baseline")
self.subplot.legend()
self.canvas.draw()
del dt
def LoadGuess(self, **kwargs):
if 'fname' in kwargs:
fname = kwargs['fname']
else:
path = self.initialDir
fname, _filter = QFileDialog.getOpenFileName(self, 'Open file', path,"Comma separated values (*.csv);; All files (*.*)")
if not fname:
return
try:
parameters = pd.read_csv(fname)
self.tableWidget.setRowCount(0)
cols = ['labels', 'freq', 'freq_min', 'freq_max' ,'intens', 'width']
shape = ['Lorentzian', 'Gaussian', 'Voigt', 'BWF']
for i, l in enumerate(parameters['labels']):
self.tableWidget.insertRow(i)
self.tableWidget.setItem(i, 0, QTableWidgetItem(''))
self.tableWidget.item(i, 0).setFlags(Qt.ItemIsUserCheckable | Qt.ItemIsEnabled)
self.tableWidget.item(i, 0).setCheckState(Qt.Checked)
for j, col in enumerate(cols):
self.tableWidget.setItem(i, j+1, QTableWidgetItem(str(parameters[col][i])))
combo = QComboBox()
[combo.addItem(' '+s) for s in shape]
if parameters['shape'][i] in [s[0] for s in shape]:
combo.setCurrentIndex([s[0] for s in shape].index(parameters['shape'][i]))
self.tableWidget.setCellWidget(i, j+2, combo)
self.statusbar.showMessage("Initial parameters were loaded", 2000)
except FileNotFoundError:
self.errorBox('File not found', 'Parameters were not loaded')
except KeyError:
self.errorBox('Wrong file format', "Parameters were not loaded")
except Exception as e:
self.errorBox('Error\n{}'.format(e), 'Parameters were not loaded')
error = []
cols = self.tableWidget.columnCount()
for row in range(self.tableWidget.rowCount()):
error = np.append(error, [self.itemCheck(self.tableWidget.item(row, col), silent=True) for col in range(1, cols-1)])
# if True in error:
# self.errorBox("Bad parameters", "Bad values")
# return
def ExportGuess(self):
path = self.initialDir
fname, _filter = QFileDialog.getSaveFileName(self, 'Save file', path,"Initial guess (*.csv)")
if not fname:
return
cols = ['labels', 'freq', 'freq_min', 'freq_max' ,'intens', 'width']
rows = self.tableWidget.rowCount()
guess = pd.DataFrame()
try:
for i, c in enumerate(cols):
guess[c] = [self.tableWidget.item(row, i+1).text() for row in range(rows)]
guess['shape'] = [self.tableWidget.cellWidget(row, 7).currentText().strip()[0] for row in range(rows)]
guess.to_csv(fname, index=None)
self.statusbar.showMessage("Initial guess file saved succesfully", 2000)
except Exception as e:
self.errorBox('Error\n{}'.format(e), 'Parameters were not loaded')
def readConfig(self):
self.settings = QSettings("Raman Deconvolution")
self.initialDir = self.settings.value('directory', '/home/cat/')
self.restoreGeometry(self.settings.value('MainWindow/geometry', self.saveGeometry()))
self.restoreState(self.settings.value('MainWindow/state', self.saveState()))
self.actionToolbar.setChecked(strtobool(self.settings.value('MainWindow/toolbar', 'true')))
# self.actionGuess.setChecked(strtobool(self.settings.value('MainWindow/dockGuess', 'true')))
# self.actionOutput.setChecked(strtobool(self.settings.value('MainWindow/dockOut', 'true')))
# self.dockGuess.setVisible(self.actionGuess.isChecked())
# self.dockOut.setVisible(self.actionOutput.isChecked())
#read the configuration file
self.config = configparser.ConfigParser()
if len(self.config.read(self.path+'/config/config.ini')):
self.degree = int(self.config['DEFAULT']['degree'])
self.threshold = float(self.config['DEFAULT']['threshold'])
# font_size = int(config['DEFAULT']['font_size'])
self.peakLimits = Limits(int(self.config['PEAK']['low']), int(self.config['PEAK']['high']))
#load fitting parameters
self.LoadGuess(fname = self.path + '/config/initialData.csv')
def New(self):
if self.changed:
result = QMessageBox.question(self,
"Unsaved file...",
"Do you want to save the data before exiting?",
QMessageBox.Yes| QMessageBox.No |QMessageBox.Cancel)
if result == QMessageBox.Yes:
self.Save()
elif result == QMessageBox.Cancel:
return
path = self.initialDir
fname, _filter = QFileDialog.getOpenFileName(self, 'Open file', path,"Text files (*.txt *.dat);; Wire data files (*.wdf);; All files (*.*)")
if not fname:
return
elif self.initialDir!=ntpath.dirname(fname):
self.initialDir=ntpath.dirname(fname) #update the initial directory for the Open/Save dialog
self.settings.setValue('directory', self.initialDir)
if fname[-3:] == 'wdf':
convert(fname)
fname = fname[:-3]+'txt'
try:
# tmp = np.loadtxt(fname)
# self.data.setData(tmp[:,0], tmp[:,1])
self.data.loadData(fname)
# self.statusbar.showMessage("Data loaded", 2000)
self.Plot(self.data.X, self.data.Y, 'Experimental data')
self.setWindowTitle( 'Raman Deconvolution - ' + ntpath.basename(fname))
self.changed = False
self.textOut.clear()
self.textOut.setText(self.header)
except Exception as e:
self.errorBox('Could not load the file\n{}'.format(e), 'I/O error')
self.statusbar.showMessage("Error loading the file", 2000)
def Save(self):
if not np.shape(self.data.X):
self.statusbar.showMessage("No data...", 2000)
return
path = self.initialDir
fname, _filter = QFileDialog.getSaveFileName(self, 'Save file', path,"Text files (*.txt);; Comma separated values (*.csv)")
delimiter = '\t'
if not fname:
return
if _filter == "Text files (*.txt)" and fname[-3:]!='txt':
fname += '.txt'
if _filter == "Comma separated values (*.csv)" and fname[-3:]!='csv':
fname += '.csv'
delimiter = ','
data, comments = self.data.getData()
if hasattr(self.fit, 'report'):
comments += self.fit.report
data = pd.concat([data, self.fit.peaks], ignore_index=True, axis = 1)
f = open(fname, 'w')
f.close()
with open(fname, 'a') as f:
[f.write(s) for s in comments]
data.to_csv(f, index = None, sep = delimiter)
self.statusbar.showMessage('File {} saved'.format(fname), 3000)
if self.changed:
self.changed =False
self.setWindowTitle(self.windowTitle()[:-1])
def Plot(self, X, Y, label, clear = True, limits=False):
if clear:
self.subplot.clear()
if label == 'Baseline':
line, = self.subplot.plot(X, Y, 'r--', label = label)
else:
line, = self.subplot.plot(X, Y, label = label)
self.subplot.set_xlim(np.min(X), np.max(X))
if limits:
self.subplot.set_ylim(0.9*np.min(Y), 1.1*np.max(Y))
self.plotAdjust()
return line
def errorBox(self, message, title="Error"):
self.error = True
self.statusbar.showMessage(message, 5000)
msg = QMessageBox()
msg.setIcon(QMessageBox.Warning)
msg.setText(message)
msg.setWindowTitle(title)
msg.setStandardButtons(QMessageBox.Ok)
msg.exec_()
def Crop(self):
if not np.shape(self.data.X):
self.errorBox('There is no data', 'No data...')
return
_min = np.min(self.data.X)
_max = np.max(self.data.X)
dialog = CropDialog(_min, _max)
result = dialog.exec_()
params = dialog.getData()
try:
_min = int(params[0])
except ValueError:
self.statusbar.showMessage("Wrong value...setting to default", 3000)
try:
_max = int(params[1])
except ValueError:
self.statusbar.showMessage("Wrong value...setting to default", 3000)
self.data.crop(_min, _max)
self.statusbar.showMessage("Data cropped", 3000)
self.Plot(self.data.X, self.data.current, "Experimental data")
if self.peakLimits.min < self.data.X[0]:
self.peakLimits.min = self.data.X[0]
if self.peakLimits.max > self.data.X[-1]:
self.peakLimits.max = self.data.X[-1]
if not self.changed:
self.changed = True
self.setWindowTitle(self.windowTitle()+'*')
def tableItemRightClicked(self, QPos):
self.listMenu= QMenu()
menu_item_2 = self.listMenu.addAction("Preview band")
menu_item_3 = self.listMenu.addAction("Preview selected bands")
self.listMenu.addSeparator()
if self.tableWidget.item(self.tableWidget.currentRow(), 0).checkState() & Qt.Checked:
menu_item_0 = self.listMenu.addAction("Don't use for deconvolution")
else:
menu_item_0 = self.listMenu.addAction("Use for deconvolution")
menu_item_1 = self.listMenu.addAction("Use all")
self.listMenu.addSeparator()
menu_item_4 = self.listMenu.addAction("Add row")
menu_item_5 = self.listMenu.addAction("Remove row")
menu_item_0.triggered.connect(lambda: self.tableRowUse(self.tableWidget.currentRow()))
menu_item_1.triggered.connect( self.tableUseAll)
menu_item_2.triggered.connect( lambda: self.previewBand(self.tableWidget.currentRow()))
menu_item_3.triggered.connect( self.previewAll)
menu_item_4.triggered.connect(self.addRow)
menu_item_5.triggered.connect( lambda: self.tableWidget.removeRow(self.tableWidget.currentRow()))
parentPosition = self.tableWidget.mapToGlobal(QPoint(0, 0))
self.listMenu.move(parentPosition + QPos)
self.listMenu.show()
def addRow(self):
rows = self.tableWidget.rowCount()
self.tableWidget.insertRow(rows)
self.tableWidget.setItem(rows, 0, QTableWidgetItem(''))
self.tableWidget.item(rows, 0).setFlags(Qt.ItemIsUserCheckable | Qt.ItemIsEnabled)
self.tableWidget.item(rows, 0).setCheckState(Qt.Checked)
[self.tableWidget.setItem(rows, j, QTableWidgetItem(str('--'))) for j in range(1, 7)]
combo = QComboBox()
[combo.addItem(' '+s) for s in ['Lorentzian', 'Gaussian', 'Voigt', 'BWF']]
self.tableWidget.setCellWidget(rows, 7, combo)
def itemCheck(self, item, **kwargs):
col = item.column()
value = item.text()
error = False
bg = self.tableWidget.item(0,0).background()
if col in range(2, 7):
try:
float(value)
except ValueError:
error = True
bg = QBrush(Qt.red)
if 'silent' not in kwargs:
self.statusbar.showMessage('Not a valid number', 2000)
elif col == 7:
if value not in ['Gaussian', 'Lorentzian', 'Voigt', 'BWF']:
error = True
bg = QBrush(Qt.red)
if 'silent' not in kwargs:
self.statusbar.showMessage('Unknown shape', 2000)
item.setBackground(bg)
item.setSelected(False)
return error
def previewBand(self, row):
if not np.shape(self.data.X):
self.errorBox('There is no data', 'No data...')
return
cols = self.tableWidget.columnCount()
params =[self.tableWidget.item(row, col).text() for col in range(1, cols-1)]
shape = self.tableWidget.cellWidget(row, cols-1).currentText().strip()[0]
label = params[0]
params = np.asarray([params[4], params[5], params[1]]).astype(float)
if shape == 'V':
params = np.append(params, 0.5)
elif shape == 'B':
params = np.append(params, 0.02)
self.Plot(self.data.X, self.data.current, "Experimental data")
self.subplot.plot(self.data.X, FIT.Peak(FIT, self.data.X, *params, shape = shape), label = label)
self.subplot.legend()
self.canvas.draw()
def previewAll(self):
if not np.shape(self.data.X):
self.errorBox('There is no data', 'No data...')
return
self.subplot.clear()
self.subplot.plot(self.data.X, self.data.current, label = 'Experimental data')
cols = self.tableWidget.columnCount()
rows = self.tableWidget.rowCount()
cumulative = np.zeros(len(self.data.X))
intensity = [float(self.tableWidget.item(row, 5).text()) for row in range(rows) if self.tableWidget.item(row, 0).checkState()&Qt.Checked]
norm = max(self.data.current)/max(intensity)
for row in range(rows):
if self.tableWidget.item(row, 0).checkState() & Qt.Checked:
error = [self.itemCheck(self.tableWidget.item(row, col), silent=True) for col in range(1, cols-1)]
if True in error:
self.errorBox("Bad parameters", "Bad values")
return
params =[float(self.tableWidget.item(row, col).text()) for col in [5, 6, 2]]
params[0] *= norm
label = self.tableWidget.item(row, 1).text()
shape = self.tableWidget.cellWidget(row, 7).currentText().strip()[0]
if shape == 'V':
params = np.append(params, 0.5)
elif shape == 'B':
params = np.append(params, 0.02)
p = FIT.Peak(FIT, self.data.X, *params, shape = shape)
cumulative += p
self.subplot.plot(self.data.X, p, label = label)
self.subplot.plot(self.data.X, cumulative, 'r--', label = "Cumulative")
self.plotAdjust()
def tableUseAll(self):
[self.tableWidget.item(row, 0).setCheckState(Qt.Checked) for row in range(self.tableWidget.rowCount())]
def tableRowUse(self, row):
cols = self.tableWidget.columnCount()
if self.tableWidget.item(row, 0).checkState() & Qt.Checked:
self.tableWidget.item(row, 0).setCheckState(Qt.Unchecked)
else:
self.tableWidget.item(row, 0).setCheckState(Qt.Checked)
def about(self):
msg = QMessageBox()
msg.setIcon(QMessageBox.Information)
msg.setText("Version v1.01α \nMade by CAT \nLille, 2020")
msg.setWindowTitle("About")
msg.setStandardButtons(QMessageBox.Ok)
msg.exec_()
def closeEvent(self, event):
self.settings.setValue('MainWindow/geometry', self.saveGeometry())
self.settings.setValue('MainWindow/state', self.saveState())
self.settings.setValue('MainWindow/toolbar', self.actionToolbar.isChecked())
# self.settings.setValue('MainWindow/dockGuess', self.actionGuess.isChecked())
# self.settings.setValue('MainWindow/dockOut', self.actionOutput.isChecked())
if self.changed:
result = QMessageBox.question(self,
"Unsaved file...",
"Do you want to save the data before exiting?",
QMessageBox.Yes| QMessageBox.No |QMessageBox.Cancel)
if result == QMessageBox.Yes:
self.Save()
event.ignore()
elif result == QMessageBox.Cancel:
event.ignore()
else:
event.accept()
