def _lasertiming(filepaths):
"""generates a dataframe of intensities and timestamps from a list of spe filepaths"""
#folderpath = os.path.split(filepaths[0])
#filenames = [f for f in os.listdir(folderpath) if os.path.isfile(os.path.join(folderpath,f))]
#filenames = [os.path.split(filepath) for filepath in filepaths if os.path.splitext(filepaths)[1] == '.spe']
spe_files = sl.load_from_files(filepaths)
if type(spe_files) != type(list()):
spe_files = [spe_files]
gatedelays = _get_gatedelays(spe_files[0])
intensities = pd.DataFrame(index=gatedelays, columns=range(len(filepaths)))
timestamps = pd.DataFrame(index=gatedelays, columns=range(len(filepaths)))
i = 0
for spe_file in spe_files:
frames = spe_file.data
intensity = list(map(lambda x: x[0].max(), frames))
try:
timestamps.iloc[:, i] = pd.Series(_get_starttimes(spe_file),
index=timestamps.index)
intensities.iloc[:, i] = pd.Series(intensity,
index=intensities.index)
i = i + 1
except ValueError: #comes up if there is an incomplete file.
print(
'A SPE file did not have correct number of data points. Note that first file must have correct number of data points'
)
intensities = intensities.truncate(after=i, axis='columns')
timestamps = timestamps.truncate(after=i, axis='columns')
return intensities, timestamps, gatedelays
def SPEtoTDMS_seq(spefilepath,meastype):
"""convert a sequential SPE file (image or spectral) into a Tdms file. """
if isinstance(spefilepath,bytes): #The labview addin passes a bytes instead of string.
spefilepath = spefilepath.decode("utf-8")
folder = os.path.splitext(os.path.dirname(spefilepath))[0]
base = os.path.splitext(os.path.basename(spefilepath))[0]
tdmsfilepath = os.path.join(folder,base + ".tdms")
spe_file = sl.load_from_files([spefilepath])
frames = spe_file.data
gatedelays = _get_gatedelays(spe_file)
root_object = RootObject(properties={})
common_group_object = GroupObject("Common", properties={})
with TdmsWriter(tdmsfilepath) as tdms_writer:
channel_object = ChannelObject("Common", "Gate Delays" ,gatedelays, properties={})
tdms_writer.write_segment([root_object,common_group_object,channel_object])
if(meastype == 0):
wavelength = spe_file.wavelength
with TdmsWriter(tdmsfilepath, mode = 'a') as tdms_writer:
channel_object = ChannelObject("Common", "Wavelength" ,wavelength, properties={})
tdms_writer.write_segment([root_object,common_group_object,channel_object])
write_spectra(tdmsfilepath, root_object, frames,wavelength )
if(meastype == 1):
write_image(tdmsfilepath, root_object, frames )
def open_file_callback(self):
path_to_file = os.path.join(self.directory_input.value,
self.file_view.value[0])
if not os.path.isfile(path_to_file):
return
self.spe_file = spe_loader.load_from_files([path_to_file])
self.full_sensor_data = self.spe_file.data[0][0]
self.full_sensor_image_label.text = ''
self.full_sensor_image.title.text = 'Source Data'
self.update_full_sensor_image()
def _open_callback(self, event):
# calls general observation load function
root = tk.Tk()
root.withdraw()
filename = filedialog.askopenfilename()
if filename is not '':
self.spe_file = spe_loader.load_from_files([filename])
self.full_sensor_data = self.spe_file.data[0][0]
self._image_full_sensor_data()
else:
return
def spe2df_spect(spefilepath, gatingtype='rep'):
#convert a sequential spectral SPE file to a pandas dataframe with one axis wl and other axis gate delay.
spe_file = sl.load_from_files([spefilepath])
frames = spe_file.data
gatedelays = _get_gatedelays(spe_file)
wavelength = spe_file.wavelength
datamatrix = np.zeros((len(wavelength), len(gatedelays)))
for i, frame in enumerate(frames):
datamatrix[:, i] = frame[0]
if gatingtype == 'rep':
spectraldf = pd.DataFrame(datamatrix, index=wavelength)
elif gatingtype == 'seq':
spectraldf = pd.DataFrame(datamatrix,
index=wavelength,
columns=gatedelays)
return spectraldf