def read2DFMR2magFields(filepattern, path_field_after,
path_field_before, path_frequency,
path_real_signal, path_imag_signal,
field_points=None, freq_points=None,
tdms_file=None):
"""
Read a VNA- or Lock-In measurement with a field-before and a field-after channel.
As an input this routine requires a field_before- and a field_after-channel and
the groups respectively. For the frequency-channel and the signal-channels it
assumes that these are in the same group.
Parameters
----------
filepattern : string
filename (relative) of tdms file. may include wilcards (see glob)
path_* : tuple of strings
path i.e. [group, channel] to the corresponding data
nFieldPoints : integer, optional
number of expected points in x direction, gets calculated if not specified
nFrequencyPoints : integer, optional
number of expected points in y direction, gets calculated if not specified
Returns
-------
x : dim. (nXPoints x 1) numpy array
averaged field values from field_before and field_after
y : dim. (nYPoints x 1) numpy array
frequency values
complexSignal : (nXPoints x nYPoints) complex numpy array
signal as matrix
tdms_file : (TdmsFile)
loaded tdms file object
Usage example (matplotlib):
--------------
>>> x, y, aSignal, _ = read2DFMR2magFields(fname, signal_group, group_field_before,
group_field_after, field_before_channel, field_after_channel,
freq_channel, signal_channel,
imag_channel, tdms_file=tdms_file)
"""
if tdms_file is None:
files = glob(filepattern)
if len(files) > 1:
l.warn("Provided file pattern %s did return multiple results."%filepattern)
if len(files) < 1:
l.warn("Provided file pattern %s did not match any files."%filepattern)
l.info("Reading file %s"%files[0])
tdms_file = TdmsFile(files[0])
x = average_channels(tdms_file, path_field_before, path_field_after, True)
y = tdms_file.channel_data(*path_frequency)
signal = tdms_file.channel_data(*path_real_signal) + 1j*tdms_file.channel_data(*path_imag_signal)
return list(reshape(x, y, signal, field_points, freq_points)) + [tdms_file]
def f_open_tdms_2(filename):
if filename == 'Input':
filename = filedialog.askopenfilename()
tdms_file = TdmsFile(filename)
group_names = tdms_file.groups()
# print('groups')
# print(group_names)
channel_object = tdms_file.group_channels(group_names[0])
# print('channel')
# print(channel_object)
channel_name = channel_object[0].channel
# print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
# print(channel_name)
# print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
data = tdms_file.channel_data(group_names[0], channel_name)
# channel_object = tdms_file('Thiele_Versuche', 'AE Signal')
# group_name = file.groups()
# print(group_name[0])
# channel_name = file.group_channels(group_name[0])
# print(channel_name[0])
# canal = file.object(group_name[0], 'AE Signal')
# group_name = file.groups()
# group_name = group_name[0]
# data = file.object('Thiele_Versuche', 'AE Signal')
# data = file.channel_data(group_name[0], 'AE Signal')
return data
def loadTDMSImages(self, file):
tdms_file = TdmsFile(file)
p = tdms_file.object().properties
self.dimx = int(p['dimx'])
self.dimy = int(p['dimy'])
self.binning = int(p['binning'])
self.frames = int(p['dimz'])
self.exposure = float(p['exposure'].replace(',', '.'))
images = tdms_file.channel_data('Image', 'Image')
return images.reshape(self.frames, self.dimx, self.dimy)
def cut_log_spectra(fileinpaths, times, fileoutpaths_list, **kwargs):
for i, fileinpath in enumerate(fileinpaths):
fileoutpaths = fileoutpaths_list[i]
tdmsfile = TF(fileinpath)
for j, t in enumerate(times):
fileoutpath = fileoutpaths[j]
direc = os.path.split(fileoutpath)[0]
if not os.path.exists(direc):
os.makedirs(direc)
root_object = RootObject(properties={})
try:
with TdmsWriter(fileoutpath, mode='w') as tdms_writer:
timedata = [
dt64(y)
for y in tdmsfile.channel_data('Global', 'Time')
]
idx1, idx2 = _get_indextime(timedata, t[0], t[1])
if idx1 == idx2:
pass
else:
for group in tdmsfile.groups():
group_object = GroupObject(group, properties={})
if group == "Global":
for channel in tdmsfile.group_channels(group):
if channel.channel == 'Wavelength':
channel_object = ChannelObject(
channel.group, channel.channel,
channel.data)
else:
channel_object = ChannelObject(
channel.group, channel.channel,
channel.data[idx1:idx2])
tdms_writer.write_segment([
root_object, group_object,
channel_object
])
else:
for channel_object in tdmsfile.group_channels(
group)[idx1:idx2]:
tdms_writer.write_segment([
root_object, group_object,
channel_object
])
except ValueError as error:
print(error)
print('removing the file at: \n', fileoutpath)
os.remove(fileoutpath)
def f_open_tdms_old(filename, channel):
if filename == 'Input':
filename = filedialog.askopenfilename()
file = TdmsFile(filename)
group_name = file.groups()
group_name = group_name[0]
# print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
# print(group_name)
# print(file)
# print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
data = file.channel_data(group_name, channel)
return data
def tdms_to_json(directory, target_directory, name):
'''
Parses the TDMS file name located in the directory to json file named like the tdms file
'''
print("Parsing: " + directory + name + ".tdms")
# load in data, take the TDMS data type as example
tdms_file = TdmsFile(directory + name + ".tdms")
groups = tdms_file.groups()
df_list = []
#df_test = pd.DataFrame()
df_list.append(pd.DataFrame())
for group in groups:
print(group)
for channel in tdms_file.group_channels(group):
try:
chan_name = str(channel).split("'/'")[1].replace("'>", "")
data = tdms_file.channel_data(group, chan_name)
chan_name = chan_name.replace("(", " ").replace(
")", " ").replace(" ", " ").replace(" ", "_")
df_test = pd.DataFrame()
#print(chan_name)
# print(data)
#time.sleep(0.5)
df_test[chan_name] = chan_name #data
df_list[-1][chan_name] = chan_name #data
#df_test.to_json(target_directory + chan_name + ".json", orient='split')
# print(chan_name)
# print(data)
#df_test.to_json(target_directory + chan_name + ".json", orient='split')
#df_test.to_json(target_directory + chan_name + '_' + name + ".json", orient='split')
with open(
target_directory + chan_name + '_' + name +
".json", 'w') as fp:
json.dump({chan_name: data.tolist()}, fp)
except:
print("An Error Occured at: X !")
continue
def cut_powermeter(fileinpaths, times, fileoutpaths_list, **kwargs):
"""Cut up a power meter tdms file based on input times."""
localtz = tzlocal.get_localzone()
for i in range(len(fileinpaths)):
fileinpath = fileinpaths[i]
fileoutpaths = fileoutpaths_list[i]
tdmsfile = TF(fileinpath)
for j in range(len(times)):
time1 = times[j][0].astype('O')
time1 = time1.replace(tzinfo=pytz.utc) #convert to datetime
time2 = times[j][1].astype('O')
time2 = time2.replace(tzinfo=pytz.utc)
fileoutpath = fileoutpaths[j]
direc = os.path.split(fileoutpath)[0]
if not os.path.exists(direc):
os.makedirs(direc)
root_object = RootObject(properties={ #TODO root properties
})
try:
with TdmsWriter(fileoutpath, mode='w') as tdms_writer:
for group in tdmsfile.groups():
timedata = tdmsfile.channel_data(group, 'Time_LV')
for channel in tdmsfile.group_channels(group):
if type(channel.data_type.size) == type(None):
break #skips over non numeric channels
channel_object = _cut_channel(channel,
time1,
time2,
timedata=timedata)
tdms_writer.write_segment(
[root_object, channel_object])
timechannel = tdmsfile.object(group, 'Time_LV')
timechannel_cut = _cut_datetime_channel(
timechannel, time1, time2)
tdms_writer.write_segment(
[root_object, timechannel_cut])
except ValueError as error:
print(error)
print('removing the file at: \n', fileoutpath)
os.remove(fileoutpath)
root_object_2 = rack_2.object()
# Get groups (rack names) # automatize in the future
group_names_1 = rack_1.groups()
group_names_2 = rack_2.groups()
print('List of groups of the rack_1 TDMS:', group_names_1[0])
print('List of groups of the rack_2 TDMS:', group_names_2[0])
# Get channel names of the group
if len(group_names_1) == 1:
channel_names_1 = rack_1.group_channels(group_names_1[0])
if len(group_names_2) == 1:
channel_names_2 = rack_2.group_channels(group_names_2[0])
# Get the data from channels
PT101 = rack_1.channel_data(group_names_1[0], "PT101")
PT102 = rack_1.channel_data(group_names_1[0], "PT102")
PT103 = rack_2.channel_data(group_names_2[0], "PT103")
PT104 = rack_2.channel_data(group_names_2[0], "PT104")
PT551 = rack_1.channel_data(group_names_1[0], "PT551")
PT552 = rack_1.channel_data(group_names_1[0], "PT552")
PT553 = rack_1.channel_data(group_names_1[0], "PT553")
PT421 = rack_1.channel_data(group_names_1[0], "PT421")
MT401S = rack_1.channel_data(group_names_1[0], "MT401S")
MT401T = rack_1.channel_data(group_names_1[0], "MT401T")
MT401J = rack_1.channel_data(group_names_1[0], "MT401J")
MV101F = rack_1.channel_data(group_names_1[0], "MV101F")
TTCEMEDIA = rack_1.channel_data(group_names_1[0], "TTCEMEDIA")
# Synchronize relative times from both racks
Time_1 = rack_1.channel_data(group_names_1[0], "System Time")
for i in range(nbSubjects): #looping through each subject
subjectNo = str(i + 1).zfill(2)
dir = 'V:\\S%s\\BBGraphData_postStim' % subjectNo #navigate to subject-specific folder
filepath = list_files(dir, exact_file)
tdms_file = TdmsFile(filepath) #opens the data from the tdms file
data = deque(
) #another deque, this time for storing subject-specific data
for parcel in range(nbParcels): #looping through each parcel
real_values = [
] #empty list that will contain the real values of a parcel
for sample in range(
nbTimeSamples): #going through each time sample
#the real value for that time point is appended to the list
real_values.append(
tdms_file.channel_data(
str(sample), 'Vertex Data (CSGreim)__real')[parcel])
data.append(
real_values
) #the real values for the parcel are appended to the deque object
a.append(data) #the real values for the subject are appended
#side note: its important that the the order in which the values are appended to the deque object
#is correct as that will determine the dimensions of the numpy array that's created. In this
#case we first appended from each time point, then each parcel, then each subject.
a = np.array(
a, dtype=float
) #we convert it to a numpy array. dtype is float(64) for greater precision
final = condition[0] + '_collapsed' #should match directory
np.save(final, a) #the array is saved in the current working directory
def getPoiData(self, data_directory, poi_series_number, pmt=1):
poi_name = 'points' + ('0000' + str(poi_series_number))[-4:]
full_file_path = os.path.join(data_directory, 'points', poi_name,
poi_name + '_pmt' + str(pmt) + '.tdms')
if os.path.exists(full_file_path):
tdms_file = TdmsFile(full_file_path)
time_points = tdms_file.channel_data(
'PMT' + str(pmt), 'POI time') / 1e3 # msec -> sec
poi_data_matrix = np.ndarray(
shape=(len(tdms_file.group_channels('PMT' + str(pmt))[1:]),
len(time_points)))
poi_data_matrix[:] = np.nan
for poi_ind in range(
len(tdms_file.group_channels('PMT' + str(pmt))[1:])
): # first object is time points. Subsequent for POIs
poi_data_matrix[poi_ind, :] = tdms_file.channel_data(
'PMT' + str(pmt), 'POI ' + str(poi_ind) + ' ')
# get poi locations in raw coordinates:
poi_x = [
int(v)
for v in tdms_file.channel_data('parameter', 'parameter')[21:]
]
poi_y = [
int(v)
for v in tdms_file.channel_data('parameter', 'value')[21:]
]
poi_xy = np.array(list(zip(poi_x, poi_y)))
# get Snap image and poi locations in snap coordinates
metadata = self.getMetaData(data_directory, poi_series_number,
'poi')
snap_name = metadata['Image']['name'].replace('"', '')
snap_ct = 0
while (
('points' in snap_name) and
(snap_ct < 1000)): # used snap image from a previous POI scan
snap_ct += 1
alt_dict = self.getMetaData(data_directory, int(snap_name[6:]),
'poi')
temp_image = alt_dict.get('Image')
if temp_image is not None:
snap_name = temp_image['name'].replace('"', '')
snap_image, snap_settings, poi_locations = self.getSnapImage(
data_directory, snap_name, poi_xy, pmt=1)
results_dict = {
'time_points': time_points,
'poi_data_matrix': poi_data_matrix,
'poi_xy': poi_xy,
'snap_image': snap_image,
'snap_settings': snap_settings,
'poi_locations': poi_locations
}
else:
results_dict = {}
return results_dict
def read2DSweep(filepattern, group, xChan, yChan, signalChan,
xGroup=None, yGroup=None,
nXPoints=None, nYPoints=None,
tdms_file=None):
"""
Read 2D data from TDMS file, guesses number of points if possible. The re-
turned data can be directly plotted with imshow() of guiqwt or matplotlib.
The function assumes that x and y sweep values are the same for every scan.
Therefore only vectors for x and y are returned, while the returned signal
is an [len(x) x len(y)] array.
Parameters
----------
filepattern : string
filename (relative) of tdms file. may include wilcards (see glob)
group : string
datagroup name in tdms file
xChan : string
x channel name in tdms file
xGroup : string, optional
if set, the xdata is read from this group (prepend "Read." if neccessary)
yChan : string
y channel name in tdms file
yGroup : string, optional
if set, y data is read from this group (prepend "Read." if neccessary)
signalChan : string
signal channel name. signal has to come in len(x) blocks of len(y) entries
nXPoints : integer, optional
number of expected points in x direction, gets calculated if not specified
nYPoints : integer, optional
number of expected points in y direction, gets calculated if not specified
tdms_file : TdmsFile, optional
If set, file with filepattern is ignored and data from tdms_file is used
Returns
-------
vX : dim. (nXPoints x 1) numpy array
x values
vY : dim. (nYPoints x 1) numpy array
y values
aSignal : (nXPoints x nYPoints) numpy array
signal as matrix
tdms_file : TdmsFile
TdmsFile object of read tdms.
"""
# Load data from TDMS file
if not tdms_file:
files = glob(filepattern)
if len(files) > 1:
l.warn("Provided file pattern %s did return multiple results."%filepattern)
if len(files) < 1:
l.warn("Provided file pattern %s did not match any files."%filepattern)
l.info("Reading file %s"%files[0])
tdms_file = TdmsFile(files[0])
x = tdms_file.channel_data(xGroup or group, xChan)
y = tdms_file.channel_data(yGroup or group, yChan)
signal = tdms_file.channel_data(group, signalChan)
return list(reshape(x, y, signal, nXPoints, nYPoints)) + [tdms_file]
