def test_line_broadening():
# Complex time series:
arr = np.random.rand(10,10,10,1024) * np.exp(1j * np.pi)
ts = nts.TimeSeries(data=arr, sampling_rate=5000.)
lbr = 1000.
new_ts = ut.line_broadening(ts, lbr)
npt.assert_equal(new_ts.shape, ts.shape)
def get_spectra(data,
filt_method=dict(lb=0.1, filt_order=256),
spect_method=dict(NFFT=1024, n_overlap=1023, BW=2),
phase_zero=None,
line_broadening=None,
zerofill=None):
"""
Derive the spectra from MRS data
Parameters
----------
data : nitime TimeSeries class instance or array
Time-series object with data of shape (echos, transients, time-points),
containing the FID data. If an array is provided, we will assume that a
sampling rate of 5000.0 Hz was used
filt_method : dict
Details for the filtering method. A FIR zero phase-delay method is used
with parameters set according to these parameters
spect_method : dict
Details for the spectral analysis. Per default, we use
line_broadening : float
Linewidth for apodization (in Hz).
zerofill : int
Number of bins to zero fill with.
Returns
-------
f :
the center frequency of the frequencies represented in the
spectra
spectrum_water, spectrum_water_suppressed:
The first spectrum is for the data with water not suppressed and
the second spectrum is for the water-suppressed data.
Notes
-----
This function performs the following operations:
1. Filtering.
2. Apodizing/windowing. Optionally, this is done with line-broadening (see
page 92 of Keeler2005_.
3. Spectral analysis.
.. [Keeler2005] Keeler, J (2005). Understanding NMR spectroscopy, second
edition. Wiley (West Sussex, UK).
"""
if not isinstance(data, nt.TimeSeries):
data = nt.TimeSeries(data, sampling_rate=5000.0)
if filt_method is not None:
filtered = nta.FilterAnalyzer(data, **filt_method).fir
else:
filtered = data
if line_broadening is not None:
lbr_time = line_broadening * np.pi # Conversion from Hz to
# time-constant, see Keeler page 94
else:
lbr_time = 0
apodized = ut.line_broadening(filtered, lbr_time)
if zerofill is not None:
new_apodized = np.concatenate(
[apodized.data,
np.zeros(apodized.shape[:-1] + (zerofill, ))], -1)
apodized = nt.TimeSeries(new_apodized,
sampling_rate=apodized.sampling_rate)
S = nta.SpectralAnalyzer(apodized,
method=dict(NFFT=spect_method['NFFT'],
n_overlap=spect_method['n_overlap']),
BW=spect_method['BW'])
f, c = S.spectrum_fourier
return f, c
def get_spectra(data, filt_method = dict(lb=0.1, filt_order=256),
spect_method=dict(NFFT=1024, n_overlap=1023, BW=2),
phase_zero=None, line_broadening=None, zerofill=None):
"""
Derive the spectra from MRS data
Parameters
----------
data : nitime TimeSeries class instance or array
Time-series object with data of shape (echos, transients, time-points),
containing the FID data. If an array is provided, we will assume that a
sampling rate of 5000.0 Hz was used
filt_method : dict
Details for the filtering method. A FIR zero phase-delay method is used
with parameters set according to these parameters
spect_method : dict
Details for the spectral analysis. Per default, we use
line_broadening : float
Linewidth for apodization (in Hz).
zerofill : int
Number of bins to zero fill with.
Returns
-------
f, spectrum_water, spectrum_water_suppressed :
f is the center frequency of the frequencies represented in the
spectra. The first spectrum is for the data with water not suppressed and
the s
Notes
-----
This function performs the following operations:
1. Filtering.
2. Apodizing/windowing. Optionally, this is done with line-broadening (see
page 92 of Keeler2005_.
3. Spectral analysis.
Notes
-----
.. [Keeler2005] Keeler, J (2005). Understanding NMR spectroscopy, second
edition. Wiley (West Sussex, UK).
"""
if not isinstance(data, nt.TimeSeries):
data = nt.TimeSeries(data, sampling_rate=5000.0)
if filt_method is not None:
filtered = nta.FilterAnalyzer(data, **filt_method).fir
else:
filtered = data
if line_broadening is not None:
lbr_time = line_broadening * np.pi # Conversion from Hz to
# time-constant, see Keeler page 94
else:
lbr_time = 0
apodized = ut.line_broadening(filtered, lbr_time)
if zerofill is not None:
new_apodized = np.concatenate([apodized.data,
np.zeros(apodized.shape[:-1] + (zerofill,))], -1)
apodized = nt.TimeSeries(new_apodized,
sampling_rate=apodized.sampling_rate)
S = nta.SpectralAnalyzer(apodized,
method=dict(NFFT=spect_method['NFFT'],
n_overlap=spect_method['n_overlap']),
BW=spect_method['BW'])
f, c = S.spectrum_fourier
return f, c