def IgorLoad(self,Source):
from igor import binarywave,igorpy
Waves={}
Variables={}
if Source[-3:] == 'ibw':
Waves[binarywave.load(Source)['wave']['wave_header']['bname']]=binarywave.load(Source)['wave']['wData']
Variables['SampleInterval']=1
elif Source[-3:] == 'pxp':
#tree=igorpy.load(Source).format()
#print tree
#print '#############'
b=igorpy.load(Source)
for i in b:
if isinstance(i, igorpy.Wave):
Waves[str(i.name)]=i.data
elif isinstance(i, igorpy.Variables):
Variables=i.uservar
elif Source[-3:] == 'txt':
b=numpy.loadtxt(Source)
Waves[Source.split("/")[-1].replace('.txt','').replace('.','_')]=b
Variables=None
elif Source[-3:] == 'csv':
b=numpy.loadtxt(Source)
Waves[Source.split("/")[-1].replace('.txt','').replace('.','_')]=b
Variables=None
elif Source[-3:] == 'wcp':
print "not supported yet, but you can import an igor file"
b,c=self.read_block(Source)
for i,j in enumerate(b):
Waves[str(j[0])+str(i)]=numpy.array(j[1])
for i in c:
Variables[i]=c[i]
try:
return Waves,Variables
except UnboundLocalError:
msgBox = QtGui.QMessageBox()
msgBox.setText(
"""
<b>Filtype not Supported</b>
<p>Only txt, ibw and pxp files are supported
""")
msgBox.exec_()
return None, None
def read_analogsignal(self, path=None, lazy=False):
assert not lazy, 'This IO does not support lazy mode'
if not HAVE_IGOR:
raise Exception("`igor` package not installed. "
"Try `pip install igor`")
if self.extension == 'ibw':
data = bw.load(str(self.filename))
version = data['version']
if version > 5:
raise IOError("Igor binary wave file format version {} "
"is not supported.".format(version))
elif self.extension == 'pxp':
assert type(path) is str, \
"A colon-separated Igor-style path must be provided."
if not self._filesystem:
_, self.filesystem = pxp.load(str(self.filename))
path = path.split(':')
location = self.filesystem['root']
for element in path:
if element != 'root':
location = location[element.encode('utf8')]
data = location.wave
return self._wave_to_analogsignal(data['wave'], [])
def loadAFM(path):
data = igorbinary.load(path)
note = data['wave']['note']
noteData = {}
for line in note.splitlines():
line = line.replace(b' \xb0C', b'')
line = line.replace(b'\xb0', b'')
line = str(line, encoding='ascii')
key, colon, value = line.partition(':')
value = value.strip()
try:
value = float(value)
except ValueError:
if ',' in value:
array = []
for number in value.split(','):
try:
array.append(float(number))
except ValueError:
pass
value = array
noteData[key] = value
data['wave']['note'] = noteData
return data
def _run(self, args):
wave = load(args.infile)
numpy.savetxt(args.outfile, wave["wave"]["wData"], fmt="%g", delimiter="\t")
self.plot_wave(args, wave)
if args.verbose > 0:
wave["wave"].pop("wData")
pprint.pprint(wave)
def _run(self, args):
self.wave = load(
args.infile
) #wave contains the data with col1 as ind and col2 as force
if self.wave:
self.wData = np.delete(self.wave['wave']['wData'], 2, 1)
return
def read_analogsignal(self, path=None, lazy=False):
assert not lazy, 'Do not support lazy'
if not HAVE_IGOR:
raise Exception(("`igor` package not installed. "
"Try `pip install igor`"))
if self.extension == 'ibw':
data = bw.load(self.filename)
version = data['version']
if version > 5:
raise IOError(("Igor binary wave file format version {0} "
"is not supported.".format(version)))
elif self.extension == 'pxp':
assert type(path) is str, \
"A colon-separated Igor-style path must be provided."
_, filesystem = pxp.load(self.filename)
path = path.split(':')
location = filesystem['root']
for element in path:
if element != 'root':
location = location[element.encode('utf8')]
data = location.wave
content = data['wave']
if "padding" in content:
assert content['padding'].size == 0, \
"Cannot handle non-empty padding"
signal = content['wData']
note = content['note']
header = content['wave_header']
name = str(header['bname'].decode('utf-8'))
units = "".join([x.decode() for x in header['dataUnits']])
try:
time_units = "".join([x.decode() for x in header['xUnits']])
assert len(time_units)
except:
time_units = "s"
try:
t_start = pq.Quantity(header['hsB'], time_units)
except KeyError:
t_start = pq.Quantity(header['sfB'][0], time_units)
try:
sampling_period = pq.Quantity(header['hsA'], time_units)
except:
sampling_period = pq.Quantity(header['sfA'][0], time_units)
if self.parse_notes:
try:
annotations = self.parse_notes(note)
except ValueError:
warn("Couldn't parse notes field.")
annotations = {'note': note}
else:
annotations = {'note': note}
signal = AnalogSignal(signal, units=units, copy=False, t_start=t_start,
sampling_period=sampling_period, name=name,
file_origin=self.filename, **annotations)
return signal
def read_analogsignal(self, path=None, lazy=False):
assert not lazy, 'Do not support lazy'
if not HAVE_IGOR:
raise Exception(("`igor` package not installed. "
"Try `pip install igor`"))
if self.extension == 'ibw':
data = bw.load(self.filename)
version = data['version']
if version > 5:
raise IOError(("Igor binary wave file format version {0} "
"is not supported.".format(version)))
elif self.extension == 'pxp':
assert type(path) is str, \
"A colon-separated Igor-style path must be provided."
_, filesystem = pxp.load(self.filename)
path = path.split(':')
location = filesystem['root']
for element in path:
if element != 'root':
location = location[element.encode('utf8')]
data = location.wave
content = data['wave']
if "padding" in content:
assert content['padding'].size == 0, \
"Cannot handle non-empty padding"
signal = content['wData']
note = content['note']
header = content['wave_header']
name = str(header['bname'].decode('utf-8'))
units = "".join([x.decode() for x in header['dataUnits']])
try:
time_units = "".join([x.decode() for x in header['xUnits']])
assert len(time_units)
except:
time_units = "s"
try:
t_start = pq.Quantity(header['hsB'], time_units)
except KeyError:
t_start = pq.Quantity(header['sfB'][0], time_units)
try:
sampling_period = pq.Quantity(header['hsA'], time_units)
except:
sampling_period = pq.Quantity(header['sfA'][0], time_units)
if self.parse_notes:
try:
annotations = self.parse_notes(note)
except ValueError:
warn("Couldn't parse notes field.")
annotations = {'note': note}
else:
annotations = {'note': note}
signal = AnalogSignal(signal, units=units, copy=False, t_start=t_start,
sampling_period=sampling_period, name=name,
file_origin=self.filename, **annotations)
return signal
def _run(self, args):
wave = load(args.infile)
numpy.savetxt(args.outfile,
wave['wave']['wData'],
fmt='%g',
delimiter='\t')
self.plot_wave(args, wave)
if args.verbose > 0:
wave['wave'].pop('wData')
pprint.pprint(wave)
def load_data(file_path):
d = binarywave.load(file_path)
dat = d['wave']['wData']
if dat.shape[-1] not in [4, 6]:
print("invalid file")
return
labels = [
'Height', 'Amplitude 1', 'Amplitude 2', 'Phase 1', 'Phase 2',
'Resonance Frequency'
] if dat.shape[-1] == 6 else ['Height', 'Amplitude', 'Phase', 'z sensor']
return Panel(dat, minor_axis=labels).transpose(2, 0, 1).to_frame()
def load(cls, dirname, filename, IV, IF, time, features):
path = os.path.join(dirname, filename)
data = binarywave.load(path)['wave']['wData']
time = np.linspace(0, time, num=data.size, endpoint=False)
a, b, c, d, e, f = os.path.basename(filename)[:-4].split('_')
fileinfo = Fileinfo(a, b, int(c), int(d), int(e), f)
injection = _calculate_current(fileinfo, IV, IF)
return cls(filename, fileinfo, injection, time, data, features)
def load_data(file_path):
d = binarywave.load(file_path)
dat = d['wave']['wData']
if dat.shape[-1] not in [4, 6]:
print("invalid file")
return
labels = [
'Height', 'Amplitude 1', 'Amplitude 2',
'Phase 1', 'Phase 2', 'Resonance Frequency'
] if dat.shape[-1] == 6 else [
'Height', 'Amplitude', 'Phase', 'z sensor'
]
return Panel(dat, minor_axis=labels).transpose(2,0,1).to_frame()
def load_IgorWave__(path): # This reads Igor wave
r = io.IgorIO(filename=path)
data = bw.load(path)
timestamp = int(data["wave"]["wave_header"]["modDate"])
analogsignal_number = int(data["wave"]['wave_header']['nDim'][1])
sampling__ = float(r.read_analogsignal().sampling_rate)
sweep_position__ = int(r.filename.split('\\R')[1].split('_')[0])
if analogsignal_number > 1:
sweep_lag = np.array(str(data["wave"]['note']).split(
'%SweepStartTimes^>')[1].split('|')[0:analogsignal_number],
dtype=np.float64)
else:
sweep_lag = [0]
return r, sampling__, sweep_position__, timestamp, sweep_lag
def loadbinary(self, bpath):
thewave=ibw.load(bpath)
if thewave['wave']['wave_header']['nDim'][1:].sum() > 0:
print("Error: "+os.path.split(bpath)[1]+" > one dimension")
else:
h=thewave['wave']['wave_header']
self.wavename = h['bname']
self.xdelta = h['sfA'][0]
self.data = thewave['wave']['wData']
self.pcsrB = self.data.size
for string in h['dimUnits'][0]:
self.xUnits += string.decode()
for string in h['dataUnits']:
self.dataUnits += string.decode()
def load_from_ibw(self, filename) :
"""
Load scan data from an IGOR binary wave file. Luckily someone has
already written an interface for this (the python `igor` package).
"""
wave = binarywave.load(filename)['wave']
data = np.array([wave['wData']])
# The `header` contains some metadata
header = wave['wave_header']
nDim = header['nDim']
steps = header['sfA']
starts = header['sfB']
# Construct the x and y scales from start, stop and n
yscale = start_step_n(starts[0], steps[0], nDim[0])
xscale = start_step_n(starts[1], steps[1], nDim[1])
# Convert `note`, which is a bytestring of ASCII characters that
# contains some metadata, to a list of strings
note = wave['note']
note = note.decode('ASCII').split('\r')
# Now the extraction fun begins. Most lines are of the form
# `Some-kind-of-name=some-value`
metadata = dict()
for line in note :
# Split at '='. If it fails, we are not in a line that contains
# useful information
try :
name, val = line.split('=')
except ValueError :
continue
# Put the pair in a dictionary for later access
metadata.update({name: val})
# NOTE Unreliable hv
hv = metadata['Excitation Energy']
res = Namespace(
data = data,
xscale = xscale,
yscale = yscale,
zscale = None,
angles = xscale,
theta = 0,
phi = 0,
E_b = 0,
hv = hv)
return res
def readibw(fdname, fname):
# the code needs to be a founder outside individual expmt folder
# Get the current directory
_thisDir = os.getcwd()
# name of the folder & the file. e.g. fdname = "041917fly2cell1", # fname = "ch0_2.ibw"
# directory of the file
f_dir = os.path.join(_thisDir, fdname, 'waves', fname).replace("\\", "/")
#print(f_dir)
# load the wave
wave = load(f_dir)
v = wave['wave']['wData'] # voltage
af = wave['wave']['wave_header']['sfA'][
0] # 1/(acquisition frequency in s).
t = np.linspace(0, len(v) * af, len(v)) # generate time
return (v, t)
def load(cls, dirname, filename, IV, IF, endtime, features):
path = os.path.join(dirname, filename)
dat = binarywave.load(path)
data = dat['wave']['wData']
if dat['version'] == 2:
dt = dat['wave']['wave_header']['hsA']
elif dat['version'] == 5:
dt = dat['wave']['wave_header']['sfA'][0]
numpts = binarywave.load(path)['wave']['wave_header']['npnts']
tot_time = dt * numpts
#time = np.linspace(0, endtime, num=data.size, endpoint=False)
time = np.linspace(0, tot_time, num=numpts, endpoint=False)
#optionally shorten the data
#if endtime<tot_time:
# end_index=np.abs(time-endtime).argmin()
# data=data[0:end_index]
# time=time[0:end_index]
a, b, c, d, e, f = os.path.basename(filename)[:-4].split('_')
fileinfo = Fileinfo(a, b, int(c), int(d), int(e), f)
injection = _calculate_current(fileinfo, IV, IF)
return cls(filename, fileinfo, injection, time, data, features)
def load(cls, dirname, filename, IV, IF, params, IVseries, time):
path = os.path.join(dirname, filename)
wave = binarywave.load(path)['wave']
data = wave['wData']
dtfile = wave['wave_header']['hsA']
time = np.arange(len(data)) * dtfile
#what does basename do: it just returns the filename
#why is this fileinfo important - allows us to refer to aspects of the filename
a, b, c, d, e, f, g = os.path.basename(filename)[:-8].split('_')
fileinfo = Fileinfo(a + b + c, d, int(e), int(f), int(g))
injection = _calculate_current(fileinfo, IV, IF, IVseries)
return cls(filename, fileinfo, injection, time, data, params)
def read_analogsignal(self, lazy=False, cascade=True):
if not HAVE_IGOR:
raise Exception(
"igor package not installed. Try `pip install igor`")
data = bw.load(self.filename)
version = data['version']
if version > 3:
raise IOError(
"Igor binary wave file format version {0} is not supported.".
format(version))
content = data['wave']
if "padding" in content:
assert content[
'padding'].size == 0, "Cannot handle non-empty padding"
if lazy:
# not really lazy, since the `igor` module loads the data anyway
signal = np.array((), dtype=content['wData'].dtype)
else:
signal = content['wData']
note = content['note']
header = content['wave_header']
name = header['bname']
assert header['botFullScale'] == 0
assert header['topFullScale'] == 0
units = "".join(header['dataUnits'])
time_units = "".join(header['xUnits']) or "s"
t_start = pq.Quantity(header['hsB'], time_units)
sampling_period = pq.Quantity(header['hsA'], time_units)
if self.parse_notes:
try:
annotations = self.parse_notes(note)
except ValueError:
warn("Couldn't parse notes field.")
annotations = {'note': note}
else:
annotations = {'note': note}
signal = AnalogSignal(signal,
units=units,
copy=False,
t_start=t_start,
sampling_period=sampling_period,
name=name,
file_origin=self.filename,
**annotations)
if lazy:
signal.lazy_shape = content['wData'].shape
return signal
def load_and_prep( ibw_location ):
'''Loads an ibw file
Given the location of an ibw file, this function loads the file and cleans
the data as specified in 'cleanup_ibw_labels()' and 'cleanup_note().' The
returned ibw is stored as a dictionary.'''
#Code from the igor package loads the .ibw file
loadedibw = igoribw.load(ibw_location)
#the labels are rearranged to be more amenable to our analysis
cleanup_ibw_labels( loadedibw )
#The note, which stores all of the metadata, is rearranged to be more searchable
cleanup_note( loadedibw )
return loadedibw
def exper_spikes(wavedir, expername, TBS_ending, axes, fig, stim_times):
FileDir = wavedir + expername + TBS_ending
TBSfiles = glob.glob(FileDir)
print expername, "NUM FILES:", len(TBSfiles)
if (len(TBSfiles) == 0):
print "No files in:", FileDir
return 0, 0
else:
TBSfiles = sorted(TBSfiles, key=sortorder)
numspikes = np.zeros(len(TBSfiles))
peaktime = []
#read in data file
for i, filename in enumerate(TBSfiles):
#read in data
data = binarywave.load(filename)
trace = data['wave']['wData']
dt = data['wave']['wave_header']['hsA']
stim_index = [int(round(st / dt)) for st in stim_times]
npnts = data['wave']['wave_header']['npnts']
endtime = len(trace) * dt
tracetime = np.arange(
0, endtime,
dt) #array of points between 0 and endtime stepping by dt
#
#find peaks and peaktime using modification of Zbyszek's find_spikes
peaks, thresholds = find_spikes([tracetime, trace])
peaktime.append(tracetime[peaks])
numspikes[i] = len(peaks)
#plot trace and peaks to verify correct identification
axes[(i / num_windows)].plot(tracetime, trace)
for peak in peaks:
if peak in stim_index:
axes[(i / num_windows)].plot(
tracetime[peak],
trace[peak],
'^',
label=filename.split('/')[-1].split('_')[5:7])
print filename.split(
'/')[-1], "peaks:", peaks, "check:", tracetime[peak]
axes[(i / num_windows)].legend(fontsize='xx-small')
else:
axes[(i / num_windows)].plot(tracetime[peak], trace[peak],
'*r')
fig.canvas.draw()
pyplot.show()
return peaktime, numspikes
def load_data(file_path):
d = binarywave.load(file_path)
dat = d["wave"]["wData"]
if dat.shape[-1] not in [4, 6]:
print ("invalid file")
return
labels = (
[
"Height",
"Amplitude 1",
"Amplitude 2",
"Phase 1",
"Phase 2",
"Resonance Frequency",
]
if dat.shape[-1] == 6
else ["Height", "Amplitude", "Phase", "z sensor"]
)
return Panel(dat, minor_axis=labels).transpose(2, 0, 1).to_frame()
def load_specs_ibw(filename):
dat = igor.load(filename)
mat = dat['wave']['wData']
wavenote = load_wave_note(dat)
ke = float(wavenote['Kinetic Energy'])
pe = float(wavenote['Pass Energy'])
e_delta = (0.000060241 - 0.00000030146 * pe) * pe * (1920 / mat.shape[0])
e_offset = ke - e_delta * mat.shape[0] * (937 / 1920)
a_offset = -16.430
a_delta = 0.0255102 * (1200 / mat.shape[1])
energy = e_offset + e_delta * np.arange(mat.shape[0])
angle = a_offset + a_delta * np.arange(mat.shape[1])
return xr.DataArray(mat,
coords={
'energy': energy,
'slit': angle
},
dims=('energy', 'slit'),
attrs=wavenote)
def load_wave(file_name):
'''
trace, sr, stim = load_wave(file_name)
Load trace from an igor data file, as well as sampleing rate and stimulation
amplitude.
parameters:
file_name (string) - directory of the igor data file
return:
trace (array_like) - data trace in the file, return as numpy array
sr (float) - sampling rate
stim (array_like)- stimulation step properties in an array, including start time,
duration and amplitude
'''
try:
sr, stim_amp, stim_dur, stim_start = 10000, 0, 0, 0
data = binarywave.load(file_name)
trace = data['wave']['wData']
# Search for sampling rate
searched = re.search(r'XDelta\(s\):(.*?);',
data['wave']['note'].decode())
if (searched != None):
sr = 1 / float(searched.group(1))
# Search for stimulation amplitude
searched = re.search(r';Stim Amp.:(.+?);',
data['wave']['note'].decode())
if (searched != None):
stim_amp = float(searched.group(1))
# Search for stimulation duration
searched = re.search(r';Width:(.+?);', data['wave']['note'].decode())
if (searched != None):
stim_dur = float(searched.group(1))
# Search for stimulation strat
searched = re.search(r';StepStart\(s\):(.+?);',
data['wave']['note'].decode())
if (searched != None):
stim_start = float(searched.group(1))
return (trace, sr, [stim_start, stim_dur, stim_amp])
except IOError:
print('Igor wave file (' + file_name + ') reading error')
raise IOError
def load_ibw(path, flatten=True):
"""
Given a path to an Igor Binary Wave, return the image file as a 3 dimensional
numpy array.
Input:
path: string file path to .ibw file
flatten (optional): boolean input to flatten topography data.
Output:
data: 3 dimensional numpy array containing .ibw data.
"""
data = load(path)['wave']['wData']
# Flatten the topography data by extracting any linear response.
if flatten == True:
flat_topo = data.copy()
flat_topo[:, :, 0] = detrend(flat_topo[:, :, 0])
data = flat_topo
data = np.rot90(data)
return data
def read_from_handle(f):
"""Reads Igor's (Wavemetric) binary wave format, .ibw or .bwav, files.
Args:
f(file): file handle
Returns:
A tuple of (headerType instance, numpy vector) where `headerType
instance` contains a meta info about the wave and `numpy vector`
contains wave data. `numpy vector` is writeable.
"""
data = binarywave.load(f)
version = data['version']
assert version in (2, 3, 5), "Fileversion is '" + \
str(version) + "', not supported"
content = data['wave']
wdata = np.copy(content['wData'])
header = IgorHeader(version, content)
return header, wdata
def read_analogsignal(self, lazy=False, cascade=True):
if not HAVE_IGOR:
raise Exception("igor package not installed. Try `pip install igor`")
data = bw.load(self.filename)
version = data['version']
if version > 3:
raise IOError("Igor binary wave file format version {0} is not supported.".format(version))
content = data['wave']
if "padding" in content:
assert content['padding'].size == 0, "Cannot handle non-empty padding"
if lazy:
# not really lazy, since the `igor` module loads the data anyway
signal = np.array((), dtype=content['wData'].dtype)
else:
signal = content['wData']
note = content['note']
header = content['wave_header']
name = header['bname']
assert header['botFullScale'] == 0
assert header['topFullScale'] == 0
units = "".join(header['dataUnits'])
time_units = "".join(header['xUnits']) or "s"
t_start = pq.Quantity(header['hsB'], time_units)
sampling_period = pq.Quantity(header['hsA'], time_units)
if self.parse_notes:
try:
annotations = self.parse_notes(note)
except ValueError:
warn("Couldn't parse notes field.")
annotations = {'note': note}
else:
annotations = {'note': note}
signal = AnalogSignal(signal, units=units, copy=False, t_start=t_start,
sampling_period=sampling_period, name=name,
file_origin=self.filename, **annotations)
if lazy:
signal.lazy_shape = content['wData'].shape
return signal
# -*- coding: utf-8 -*-
"""
This code is written to generate the Chlorophyll colormap.
Therefore, instead of importing it, it should just be run.
I'm looking into a more proper way to do this.
Created on Mon Aug 13 2018
@author: joe
"""
import igor.binarywave as igoribw
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
chl = igoribw.load('Chlorophyll.ibw')
colordata = np.transpose(chl['wave']['wData'])
chlor = {}
L = len(np.transpose(colordata))
chlor['red'] = [(i / (L - 1), colordata[0][i] / 65533, colordata[0][i] / 65533)
for i, x in enumerate(colordata[0])]
chlor['green'] = [(i / (L - 1), colordata[1][i] / 65533,
colordata[1][i] / 65533)
for i, x in enumerate(colordata[0])]
chlor['blue'] = [(i / (L - 1), colordata[2][i] / 65533,
colordata[2][i] / 65533) for i, x in enumerate(colordata[0])]
chlorophyll = LinearSegmentedColormap('Chlorophyll', chlor)
plt.register_cmap(cmap=chlorophyll)
tracestring = []
#-0.5e9 converts to nA and leak due to 5 mV (not 10 mV) Vm change
RampLeak = zeros(numtraces)
fig = pyplot.figure(figsize=(6, 6))
fig.canvas.set_window_title('Experiment ' + ExperName)
axes = fig.add_subplot(111)
#loop through each trace from each series
for fileindex, eachfile in enumerate(filenames):
parts = eachfile.split('_')
tracestring.append(
ExperName + '_' + parts[-3] + "_" + parts[-2] + " "
) #extra spaces forces column_stack to use more significant figures when converting floats to string. There should be a better way
data = binarywave.load(eachfile) #read in data
trace = data['wave']['wData']
LeakVsTime = zeros(len(trace))
#pp(data) # optional - look at data array to see what else is there
dtfile = data['wave']['wave_header'][
'hsA'] #verify that the dt we have is correct
if (dt != dtfile):
print "Error, dt of file is different than expected"
#calculate various measures
Ihold[fileindex] = mean(trace[basestartpnt:baseEndpnt])
IMin[fileindex] = min(trace[PeakStartpnt:PeakEndpnt])
mintimePt = trace[PeakStartpnt:PeakEndpnt].argmin() + PeakStartpnt
mintime = mintimePt * dt
maxtimePt = trace[PeakStartpnt:maxEndpt].argmax() + PeakStartpnt
maxtime = maxtimePt * dt
IMax[fileindex] = mean(trace[maxtimePt - 10:maxtimePt + 10])
def translate(self, file_path, verbose=False, parm_encoding='utf-8'):
"""
Translates the provided file to .h5
Parameters
----------
file_path : String / unicode
Absolute path of the .ibw file
verbose : Boolean (Optional)
Whether or not to show print statements for debugging
parm_encoding : str, optional
Codec to be used to decode the bytestrings into Python strings if needed.
Default 'utf-8'
Returns
-------
h5_path : String / unicode
Absolute path of the .h5 file
"""
file_path = path.abspath(file_path)
# Prepare the .h5 file:
folder_path, base_name = path.split(file_path)
base_name = base_name[:-4]
h5_path = path.join(folder_path, base_name + '.h5')
if path.exists(h5_path):
remove(h5_path)
h5_file = h5py.File(h5_path, 'w')
# Load the ibw file first
ibw_obj = bw.load(file_path)
ibw_wave = ibw_obj.get('wave')
parm_dict = self._read_parms(ibw_wave, parm_encoding)
chan_labels, chan_units = self._get_chan_labels(ibw_wave, parm_encoding)
if verbose:
print('Channels and units found:')
print(chan_labels)
print(chan_units)
# Get the data to figure out if this is an image or a force curve
images = ibw_wave.get('wData')
if images.shape[2] != len(chan_labels):
chan_labels = chan_labels[1:] # for layer 0 null set errors in older AR software
if images.ndim == 3: # Image stack
if verbose:
print('Found image stack of size {}'.format(images.shape))
type_suffix = 'Image'
num_rows = parm_dict['ScanLines']
num_cols = parm_dict['ScanPoints']
images = images.transpose(2, 1, 0) # now ordered as [chan, Y, X] image
images = np.reshape(images, (images.shape[0], -1, 1)) # 3D [chan, Y*X points,1]
pos_desc = [Dimension('X', 'm', np.linspace(0, parm_dict['FastScanSize'], num_cols)),
Dimension('Y', 'm', np.linspace(0, parm_dict['SlowScanSize'], num_rows))]
spec_desc = Dimension('arb', 'a.u.', [1])
else: # single force curve
if verbose:
print('Found force curve of size {}'.format(images.shape))
type_suffix = 'ForceCurve'
images = np.atleast_3d(images) # now [Z, chan, 1]
images = images.transpose((1, 2, 0)) # [chan ,1, Z] force curve
# The data generated above varies linearly. Override.
# For now, we'll shove the Z sensor data into the spectroscopic values.
# Find the channel that corresponds to either Z sensor or Raw:
try:
chan_ind = chan_labels.index('ZSnsr')
spec_data = np.atleast_2d(VALUES_DTYPE(images[chan_ind]))
except ValueError:
try:
chan_ind = chan_labels.index('Raw')
spec_data = np.atleast_2d(VALUES_DTYPE(images[chan_ind]))
except ValueError:
# We don't expect to come here. If we do, spectroscopic values remains as is
spec_data = np.arange(images.shape[2])
pos_desc = Dimension('X', 'm', [1])
spec_desc = Dimension('Z', 'm', spec_data)
# Create measurement group
meas_grp = create_indexed_group(h5_file, 'Measurement')
# Write file and measurement level parameters
global_parms = generate_dummy_main_parms()
global_parms['data_type'] = 'IgorIBW_' + type_suffix
global_parms['translator'] = 'IgorIBW'
write_simple_attrs(h5_file, global_parms)
write_simple_attrs(meas_grp, parm_dict)
# Create Position and spectroscopic datasets
h5_pos_inds, h5_pos_vals = write_ind_val_dsets(meas_grp, pos_desc, is_spectral=False)
h5_spec_inds, h5_spec_vals = write_ind_val_dsets(meas_grp, spec_desc, is_spectral=True)
# Prepare the list of raw_data datasets
for chan_data, chan_name, chan_unit in zip(images, chan_labels, chan_units):
chan_grp = create_indexed_group(meas_grp, 'Channel')
write_main_dataset(chan_grp, np.atleast_2d(chan_data), 'Raw_Data',
chan_name, chan_unit,
None, None,
h5_pos_inds=h5_pos_inds, h5_pos_vals=h5_pos_vals,
h5_spec_inds=h5_spec_inds, h5_spec_vals=h5_spec_vals,
dtype=np.float32)
if verbose:
print('Finished preparing raw datasets')
h5_file.close()
return h5_path
def ibwToNx(self, inputfile, entryId=None, rotation=None):
filecontent = igorbw.load(inputfile)
filename = os.path.basename(inputfile).split(".")[0]
header = {}
entryname = None
wave = []
axes = []
print "---- Start: ibw->nexus ----"
print "Parsing: " + filename
# Loop through and find header data
entryname = str(copy(filecontent["wave"]["wave_header"]["bname"]))
wave = np.asarray(copy(filecontent["wave"]["wData"]))
note = str(copy(filecontent["wave"]["note"]))
noteList = [x.split("=") for x in note.split("\r")]
noteList = [x for x in noteList if len(x) == 2]
noteDict = dict(noteList)
# Check if file is loaded correctly
if entryname and len(wave.shape) == 2:
print "File seems valid"
else:
print "File not valid"
return -1
axesSteps = filecontent["wave"]["wave_header"]["sfA"]
axesInit = filecontent["wave"]["wave_header"]["sfB"]
axesDim = filecontent["wave"]["wave_header"]["nDim"]
axesUnits = filecontent["wave"]["wave_header"]["dimUnits"]
axes = [
np.linspace(axesInit[0], axesDim[0] * axesSteps[0] + axesInit[0], axesDim[0]),
np.linspace(axesInit[1], axesDim[1] * axesSteps[1] + axesInit[1], axesDim[1]),
]
units = ["".join(axesUnits[0]), "".join(axesUnits[1])]
print "Create nexus format"
# Nexus format
if entryId == None:
entry = nxtemplate.arpes("entry1")
else:
entry = nxtemplate.arpes(entryId)
entry.title = entryname
# Meta data
if "Ep" in noteDict:
entry.instrument.analyser.pass_energy = noteDict["Ep"]
entry.instrument.analyser.pass_energy.units = "eV"
if "LensMode" in noteDict:
entry.instrument.analyser.lens_mode = noteDict["LensMode"].strip("\x00")
if "Ek" in noteDict:
entry.instrument.analyser.kinetic_energy = noteDict["Ek"]
entry.instrument.analyser.kinetic_energy.units = "eV"
entry.instrument.manipulator.rangle = rotation
entry.instrument.manipulator.rangle.units = "deg"
if int(wave.shape[0]) == len(axes[0]) and int(wave.shape[1]) == len(axes[1]):
wave = wave.transpose()
# Data
data = nx.NXfield(wave, name="data")
energies = nx.NXfield(axes[0], units=units[0], name="energies")
angles = nx.NXfield(axes[1], units=units[1], name="angles")
entry.analyser = nx.NXdata(data, (angles, energies))
print "Done with " + entryname
self.nxEntry = entry
print "---- End: ibw->nexus ------"
return self
def translate(self, file_path, verbose=False, parm_encoding='utf-8', ftype='FF',
subfolder='Measurement_000', h5_path='', channel_label_name=True):
"""
Translates the provided file to .h5
Adapted heavily from pycroscopy IBW file, modified to work with Ginger format
:param file_path: Absolute path of the .ibw file
:type file_path: String / unicode
:param verbose: Whether or not to show print statements for debugging
:type verbose: boolean, optional
:param parm_encoding: Codec to be used to decode the bytestrings into Python strings if needed.
Default 'utf-8'
:type parm_encoding: str, optional
:param ftype: Delineates Ginger Lab imaging file type to be imported (not case-sensitive)
'FF' : FF-trEFM
'SKPM' : FM-SKPM
'ringdown' : Ringdown
'trEFM' : normal trEFM
:type ftype: str, optional
:param subfolder: Specifies folder under root (/) to save data in. Default is standard pycroscopy format
:type subfolder: str, optional
:param h5_path: Existing H5 file to append to
:type h5_path: str, optional
:param channel_label_name: If True, uses the Channel as the subfolder name (e.g. Height, Phase, Amplitude, Charging)
:type channel_label_name: bool, optional
:returns: Absolute path of the .h5 file
:rtype: String / unicode
"""
# Prepare the .h5 file:
if not any(h5_path):
folder_path, base_name = path.split(file_path)
base_name = base_name[:-4]
h5_path = path.join(folder_path, base_name + '.h5')
# hard-coded exception, rarely occurs but can be useful
if path.exists(h5_path):
h5_path = path.join(folder_path, base_name + '_00.h5')
h5_file = h5py.File(h5_path, 'w')
# If subfolder improperly formatted
if subfolder == '':
subfolder = '/'
# Load the ibw file first
ibw_obj = bw.load(file_path)
ibw_wave = ibw_obj.get('wave')
parm_dict = self._read_parms(ibw_wave, parm_encoding)
chan_labels, chan_units = self._get_chan_labels(ibw_wave, parm_encoding)
if verbose:
print('Channels and units found:')
print(chan_labels)
print(chan_units)
# Get the data to figure out if this is an image or a force curve
images = ibw_wave.get('wData')
if images.shape[2] != len(chan_labels):
chan_labels = chan_labels[1:] # for weird null set errors in older AR software
# Check if a Ginger Lab format ibw (has 'UserIn' in channel labels)
_is_gl_type = any(['UserIn0' in str(s) for s in chan_labels])
if _is_gl_type == True:
chan_labels = self._get_image_type(chan_labels, ftype)
if verbose:
print('Processing image type', ftype, 'with channels', chan_labels)
type_suffix = 'Image'
num_rows = ibw_wave['wave_header']['nDim'][1] # lines
num_cols = ibw_wave['wave_header']['nDim'][0] # points
num_imgs = ibw_wave['wave_header']['nDim'][2] # layers
unit_scale = self._get_unit_factor(''.join([str(s)[-2] for s in ibw_wave['wave_header']['dimUnits'][0][0:2]]))
data_scale = self._get_unit_factor(str(ibw_wave['wave_header']['dataUnits'][0])[-2])
parm_dict['FastScanSize'] = unit_scale * num_cols * ibw_wave['wave_header']['sfA'][0]
parm_dict['SlowScanSize'] = unit_scale * num_rows * ibw_wave['wave_header']['sfA'][1]
images = images.transpose(2, 0, 1) # now ordered as [chan, Y, X] image
images = np.reshape(images, (images.shape[0], -1, 1)) # 3D [chan, Y*X points,1]
pos_desc = [Dimension(name='X', units='m', values=np.linspace(0, parm_dict['FastScanSize'], num_cols)),
Dimension(name='Y', units='m', values=np.linspace(0, parm_dict['SlowScanSize'], num_rows))]
spec_desc = [Dimension(name='arb', units='a.u.', values=[1])]
# Create Position and spectroscopic datasets
h5_pos_inds, h5_pos_vals = write_ind_val_dsets(h5_file['/'], pos_desc, is_spectral=False)
h5_spec_inds, h5_spec_vals = write_ind_val_dsets(h5_file['/'], spec_desc, is_spectral=True)
# Prepare the list of raw_data datasets
for chan_data, chan_name, chan_unit in zip(images, chan_labels, chan_units):
chan_grp = create_indexed_group(h5_file['/'], chan_name)
write_main_dataset(chan_grp, np.atleast_2d(chan_data), 'Raw_Data',
chan_name, chan_unit,
pos_desc, spec_desc,
dtype=np.float32)
if verbose:
print('Finished writing all channels')
h5_file.close()
return h5_path
def transfer_function(h5_file,
tf_file='',
params_file='',
psd_freq=1e6,
offset=0.0016,
sample_freq=10e6,
plot=False):
'''
Reads in the transfer function .ibw, then creates two datasets within
a parent folder 'Transfer_Function'
This will destructively overwrite an existing Transfer Function in there
1) TF (transfer function)
2) Freq (frequency axis for computing Fourier Transforms)
:param h5_file:
:type h5_file:
:param tf_file: Transfer Function .ibw File
:type tf_file: ibw
:param params_file: The filepath in string format for the parameters file containing
Q, AMPINVOLS, etc.
:type params_file: string
:param psd_freq: The maximum range of the Power Spectral Density.
For Asylum Thermal Tunes, this is often 1 MHz on MFPs and 2 MHz on Cyphers
:type psd_freq: float
:param offset: To avoid divide-by-zero effects since we will divide by the transfer function
when generating GKPFM data
:type offset: float
:param sample_freq: The desired output sampling. This should match your data.
:type sample_freq: float
:param plot:
:type plot: bool, optional
:returns: the Transfer Function group
:rtype:
'''
if not any(tf_file):
tf_file = usid.io_utils.file_dialog(
caption='Select Transfer Function file ',
file_filter='IBW Files (*.ibw)')
data = bw.load(tf_file)
tf = data.get('wave').get('wData')
if 'Transfer_Function' in h5_file:
del h5_file['/Transfer_Function']
h5_file.create_group('Transfer_Function')
h5_file['Transfer_Function'].create_dataset('TF', data=tf)
freq = np.linspace(0, psd_freq, len(tf))
h5_file['Transfer_Function'].create_dataset('Freq', data=freq)
parms = params_list(params_file, psd_freq=psd_freq)
for k in parms:
h5_file['Transfer_Function'].attrs[k] = float(parms[k])
tfnorm = float(parms['Q']) * (tf - np.min(tf)) / (np.max(tf) - np.min(tf))
tfnorm += offset
h5_file['Transfer_Function'].create_dataset('TFnorm', data=tfnorm)
TFN_RS, FQ_RS = resample_tf(h5_file,
psd_freq=psd_freq,
sample_freq=sample_freq)
TFN_RS = float(parms['Q']) * (TFN_RS - np.min(TFN_RS)) / (np.max(TFN_RS) -
np.min(TFN_RS))
TFN_RS += offset
h5_file['Transfer_Function'].create_dataset('TFnorm_resampled',
data=TFN_RS)
h5_file['Transfer_Function'].create_dataset('Freq_resampled', data=FQ_RS)
if plot:
plt.figure()
plt.plot(freq, tfnorm, 'b')
plt.plot(FQ_RS, TFN_RS, 'r')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Amplitude (m)')
plt.yscale('log')
plt.title('Transfer Function')
return h5_file['Transfer_Function']
tracestring = []
fig = pyplot.figure(figsize=(6, 6))
fig.canvas.set_window_title('Experiment ' + args.experiment)
axes = fig.add_subplot(111)
#loop through each trace from each series
tempRMP = []
tempAccess = []
goodtraces = 0
badcount = 0
reallybad = 0
baselineVm = 0
for i, filename in enumerate(filenames):
data = binarywave.load(filename)
trace = data['wave']['wData']
Vm = np.mean(trace[basestartpnt:baseendpnt])
tempRMP.append(Vm)
access = (tempRMP[i] - np.mean(trace[hyperstartpnt:hyperendpnt])
) / Iaccess / 1e6 #converts access units to megaohms
tempAccess.append(access)
if (i == 9):
baselineVm = np.mean(tempRMP) #this is mean over traces
baselineAccess = np.mean(tempAccess)
if i >= 10:
if (abs(Vm - baselineVm) / abs(baselineVm)) > 0.2 or (
abs(access - baselineAccess) / abs(baselineAccess)
) > .4: #20% baseline change or 40% access change
def get_header_dict(filename):
return binarywave.load(filename)["wave"]["wave_header"]
def translate(self, file_path, verbose=False, parm_encoding='utf-8'):
"""
Translates the provided file to .h5
Parameters
----------
file_path : String / unicode
Absolute path of the .ibw file
verbose : Boolean (Optional)
Whether or not to show print statements for debugging
parm_encoding : str, optional
Codec to be used to decode the bytestrings into Python strings if needed.
Default 'utf-8'
Returns
-------
h5_path : String / unicode
Absolute path of the .h5 file
"""
# Load the ibw file first
ibw_obj = bw.load(file_path)
ibw_wave = ibw_obj.get('wave')
parm_dict = self._read_parms(ibw_wave, parm_encoding)
chan_labels, chan_units = self._get_chan_labels(
ibw_wave, parm_encoding)
if verbose:
print('Channels and units found:')
print(chan_labels)
print(chan_units)
# Get the data to figure out if this is an image or a force curve
images = ibw_wave.get('wData')
if images.ndim == 3: # Image stack
if verbose:
print('Found image stack of size {}'.format(images.shape))
type_suffix = 'Image'
num_rows = parm_dict['ScanLines']
num_cols = parm_dict['ScanPoints']
images = images.transpose(2, 0,
1) # now ordered as [chan, Y, X] image
images = np.reshape(
images, (images.shape[0], -1, 1)) # 3D [chan, Y*X points,1]
ds_pos_ind, ds_pos_val = build_ind_val_dsets(
[num_cols, num_rows],
is_spectral=False,
steps=[
1.0 * parm_dict['FastScanSize'] / num_cols,
1.0 * parm_dict['SlowScanSize'] / num_rows
],
labels=['X', 'Y'],
units=['m', 'm'],
verbose=verbose)
ds_spec_inds, ds_spec_vals = build_ind_val_dsets([1],
is_spectral=True,
steps=[1],
labels=['arb'],
units=['a.u.'],
verbose=verbose)
else: # single force curve
if verbose:
print('Found force curve of size {}'.format(images.shape))
type_suffix = 'ForceCurve'
images = np.atleast_3d(images) # now [Z, chan, 1]
images = images.transpose((1, 2, 0)) # [chan ,1, Z] force curve
ds_pos_ind, ds_pos_val = build_ind_val_dsets([1],
is_spectral=False,
steps=[25E-9],
labels=['X'],
units=['m'],
verbose=verbose)
ds_spec_inds, ds_spec_vals = build_ind_val_dsets([images.shape[2]],
is_spectral=True,
labels=['Z'],
units=['m'],
verbose=verbose)
# The data generated above varies linearly. Override.
# For now, we'll shove the Z sensor data into the spectroscopic values.
# Find the channel that corresponds to either Z sensor or Raw:
try:
chan_ind = chan_labels.index('ZSnsr')
ds_spec_vals.data = np.atleast_2d(np.float32(images[chan_ind]))
except ValueError:
try:
chan_ind = chan_labels.index('Raw')
ds_spec_vals.data = np.atleast_2d(
np.float32(images[chan_ind]))
except ValueError:
# We don't expect to come here. If we do, spectroscopic values remains as is
pass
# Prepare the list of raw_data datasets
chan_raw_dsets = list()
for chan_data, chan_name, chan_unit in zip(images, chan_labels,
chan_units):
ds_raw_data = MicroDataset('Raw_Data',
data=np.atleast_2d(chan_data),
dtype=np.float32,
compression='gzip')
ds_raw_data.attrs['quantity'] = chan_name
ds_raw_data.attrs['units'] = [chan_unit]
chan_raw_dsets.append(ds_raw_data)
if verbose:
print('Finished preparing raw datasets')
# Prepare the tree structure
# technically should change the date, etc.
spm_data = MicroDataGroup('')
global_parms = generate_dummy_main_parms()
global_parms['data_type'] = 'IgorIBW_' + type_suffix
global_parms['translator'] = 'IgorIBW'
spm_data.attrs = global_parms
meas_grp = MicroDataGroup('Measurement_000')
meas_grp.attrs = parm_dict
spm_data.addChildren([meas_grp])
if verbose:
print('Finished preparing tree trunk')
# Prepare the .h5 file:
folder_path, base_name = path.split(file_path)
base_name = base_name[:-4]
h5_path = path.join(folder_path, base_name + '.h5')
if path.exists(h5_path):
remove(h5_path)
# Write head of tree to file:
hdf = ioHDF5(h5_path)
# spm_data.showTree()
hdf.writeData(spm_data, print_log=verbose)
if verbose:
print('Finished writing tree trunk')
# Standard list of auxiliary datasets that get linked with the raw dataset:
aux_ds_names = [
'Position_Indices', 'Position_Values', 'Spectroscopic_Indices',
'Spectroscopic_Values'
]
# Create Channels, populate and then link:
for chan_index, raw_dset in enumerate(chan_raw_dsets):
chan_grp = MicroDataGroup(
'{:s}{:03d}'.format('Channel_', chan_index),
'/Measurement_000/')
chan_grp.attrs['name'] = raw_dset.attrs['quantity']
chan_grp.addChildren(
[ds_pos_ind, ds_pos_val, ds_spec_inds, ds_spec_vals, raw_dset])
h5_refs = hdf.writeData(chan_grp, print_log=verbose)
h5_raw = getH5DsetRefs(['Raw_Data'], h5_refs)[0]
linkRefs(h5_raw, getH5DsetRefs(aux_ds_names, h5_refs))
if verbose:
print('Finished writing all channels')
hdf.close()
return h5_path
def translate(self, file_path, verbose=False, append_path='',
grp_name='Measurement', parm_encoding='utf-8'):
"""
Translates the provided file to .h5
Parameters
----------
file_path : String / unicode
Absolute path of the .ibw file
verbose : Boolean (Optional)
Whether or not to show print statements for debugging
append_path : string (Optional)
h5_file to add these data to, must be a path to the h5_file on disk
grp_name : string (Optional)
Change from default "Measurement" name to something specific
parm_encoding : str, optional
Codec to be used to decode the bytestrings into Python strings if needed.
Default 'utf-8'
Returns
-------
h5_path : String / unicode
Absolute path of the .h5 file
"""
file_path = path.abspath(file_path)
# Prepare the .h5 file:
folder_path, base_name = path.split(file_path)
base_name = base_name[:-4]
if not append_path:
h5_path = path.join(folder_path, base_name + '.h5')
if path.exists(h5_path):
remove(h5_path)
h5_file = h5py.File(h5_path, 'w')
else:
h5_path = append_path
if not path.exists(append_path):
raise Exception('File does not exist. Check pathname.')
h5_file = h5py.File(h5_path, 'r+')
# Load the ibw file first
ibw_obj = bw.load(file_path)
ibw_wave = ibw_obj.get('wave')
parm_dict = self._read_parms(ibw_wave, parm_encoding)
chan_labels, chan_units = self._get_chan_labels(ibw_wave, parm_encoding)
if verbose:
print('Channels and units found:')
print(chan_labels)
print(chan_units)
# Get the data to figure out if this is an image or a force curve
images = ibw_wave.get('wData')
if images.shape[-1] != len(chan_labels):
chan_labels = chan_labels[1:] # for layer 0 null set errors in older AR software
if images.ndim == 3: # Image stack
if verbose:
print('Found image stack of size {}'.format(images.shape))
type_suffix = 'Image'
num_rows = parm_dict['ScanLines']
num_cols = parm_dict['ScanPoints']
images = images.transpose(2, 1, 0) # now ordered as [chan, Y, X] image
images = np.reshape(images, (images.shape[0], -1, 1)) # 3D [chan, Y*X points,1]
pos_desc = [Dimension('X', 'm', np.linspace(0, parm_dict['FastScanSize'], num_cols)),
Dimension('Y', 'm', np.linspace(0, parm_dict['SlowScanSize'], num_rows))]
spec_desc = Dimension('arb', 'a.u.', [1])
else: # single force curve
if verbose:
print('Found force curve of size {}'.format(images.shape))
type_suffix = 'ForceCurve'
images = np.atleast_3d(images) # now [Z, chan, 1]
images = images.transpose((1, 2, 0)) # [chan ,1, Z] force curve
# The data generated above varies linearly. Override.
# For now, we'll shove the Z sensor data into the spectroscopic values.
# Find the channel that corresponds to either Z sensor or Raw:
try:
chan_ind = chan_labels.index('ZSnsr')
spec_data = VALUES_DTYPE(images[chan_ind]).squeeze()
except ValueError:
try:
chan_ind = chan_labels.index('Raw')
spec_data = VALUES_DTYPE(images[chan_ind]).squeeze()
except ValueError:
# We don't expect to come here. If we do, spectroscopic values remains as is
spec_data = np.arange(images.shape[2])
pos_desc = Dimension('X', 'm', [1])
spec_desc = Dimension('Z', 'm', spec_data)
# Create measurement group
meas_grp = create_indexed_group(h5_file, grp_name)
# Write file and measurement level parameters
global_parms = generate_dummy_main_parms()
global_parms['data_type'] = 'IgorIBW_' + type_suffix
global_parms['translator'] = 'IgorIBW'
write_simple_attrs(h5_file, global_parms)
write_simple_attrs(meas_grp, parm_dict)
# Create Position and spectroscopic datasets
h5_pos_inds, h5_pos_vals = write_ind_val_dsets(meas_grp, pos_desc, is_spectral=False)
h5_spec_inds, h5_spec_vals = write_ind_val_dsets(meas_grp, spec_desc, is_spectral=True)
# Prepare the list of raw_data datasets
for chan_data, chan_name, chan_unit in zip(images, chan_labels, chan_units):
if verbose:
print('channel', chan_name)
print('unit', chan_unit)
chan_grp = create_indexed_group(meas_grp, 'Channel')
write_main_dataset(chan_grp, np.atleast_2d(chan_data), 'Raw_Data',
chan_name, chan_unit,
None, None,
h5_pos_inds=h5_pos_inds, h5_pos_vals=h5_pos_vals,
h5_spec_inds=h5_spec_inds, h5_spec_vals=h5_spec_vals,
dtype=np.float32)
if verbose:
print('Finished preparing raw datasets')
h5_file.close()
return h5_path
def read(self, verbose=False, parm_encoding='utf-8'):
"""
Reads the file given in file_path into a sidpy dataset
Parameters
----------
verbose : Boolean (Optional)
Whether or not to show print statements for debugging
parm_encoding : str, optional
Codec to be used to decode the bytestrings into Python strings if
needed. Default 'utf-8'
Returns
-------
sidpy.Dataset : List of sidpy.Dataset objects.
Multi-channel inputs are separated into individual dataset objects
"""
file_path = self._input_file_path
# Load the ibw file first
ibw_obj = bw.load(file_path)
ibw_wave = ibw_obj.get('wave')
parm_dict = self._read_parms(ibw_wave, parm_encoding)
chan_labels, chan_units = self._get_chan_labels(ibw_wave, parm_encoding)
if verbose:
print('Channels and units found:')
print(chan_labels)
print(chan_units)
# Get the data to figure out if this is an image or a force curve
images = ibw_wave.get('wData')
datasets = [] #list of sidpy datasets
if images.shape[-1] != len(chan_labels):
chan_labels = chan_labels[1:] # for layer 0 null set errors in older AR software
if images.ndim == 3: # Image stack
if verbose:
print('Found image stack of size {}'.format(images.shape))
num_rows = parm_dict['ScanLines']
num_cols = parm_dict['ScanPoints']
for channel in range(images.shape[-1]):
#Convert it to sidpy dataset object
data_set = sid.Dataset.from_array(images[:,:,channel], name='Image')
data_set.data_type = 'Image'
#Add quantity and units
data_set.units = chan_units[channel]
data_set.quantity = chan_labels[channel]
#Add dimension info
data_set.set_dimension(0, sid.Dimension(np.linspace(0, parm_dict['FastScanSize'], num_cols),
name = 'x',
units=chan_units[channel], quantity = 'x',
dimension_type='spatial'))
data_set.set_dimension(1, sid.Dimension(np.linspace(0, parm_dict['SlowScanSize'], num_rows),
name = 'y',
units=chan_units[channel], quantity='y',
dimension_type='spatial'))
# append metadata
data_set.metadata = parm_dict
data_set.data_type = 'image'
#Finally, append it
datasets.append(data_set)
else: # single force curve
if verbose:
print('Found force curve of size {}'.format(images.shape))
images = np.atleast_3d(images) # now [Z, chan, 1]
# Find the channel that corresponds to either Z sensor or Raw:
try:
chan_ind = chan_labels.index('ZSnsr')
spec_data = images[:,chan_ind].squeeze()
except ValueError:
try:
chan_ind = chan_labels.index('Raw')
spec_data = images[:,chan_ind,0].squeeze()
except ValueError:
# We don't expect to come here. If we do, spectroscopic values remains as is
spec_data = np.arange(images.shape[2])
#Go through the channels
for channel in range(images.shape[-2]):
# The data generated above varies linearly. Override.
# For now, we'll shove the Z sensor data into the spectroscopic values.
#convert to sidpy dataset
data_set = sid.Dataset.from_array((images[:,channel,0]), name='Force Curve (from Igor)')
#Set units, quantity
data_set.units = chan_units[channel]
data_set.quantity = chan_labels[channel]
data_set.set_dimension(0, sid.Dimension('Z', spec_data,
units=chan_units[0], quantity=chan_labels[0],
dimension_type='spectral'))
#append metadata
data_set.data_type = 'line_plot'
data_set.metadata = parm_dict
#Add dataset to list
datasets.append(data_set)
# Return the dataset
return datasets
def run(args):
wave = load(args.infile)
numpy.savetxt(args.outfile, wave['wave']['wData'], fmt='%g', delimiter='\t')
if args.verbose > 0:
wave['wave'].pop('wData')
pprint.pprint(wave)