def test_shiftToRef():
testFIDs, testHdrs = syn.syntheticFID(amplitude=[1, 0],
chemicalshift=[-2.1, 0],
phase=[0, 0],
points=1024,
noisecovariance=[[1E-3]])
shiftFID, _ = preproc.shiftToRef(testFIDs[0],
-2.0,
testHdrs['bandwidth'],
testHdrs['centralFrequency'],
ppmlim=(-2.2, -2.0),
shift=False)
mrs = MRS(FID=shiftFID, header=testHdrs)
maxindex = np.argmax(mrs.getSpectrum(shift=False))
position = mrs.getAxes(axis='ppm')[maxindex]
assert np.isclose(position, -2.0, atol=1E-1)
def shift_report(inFID,
outFID,
inHdr,
outHdr,
ppmlim=(0.2, 4.2),
html=None,
function='shift'):
"""
Generate report
"""
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from fsl_mrs.utils.preproc.reporting import plotStyles, plotAxesStyle
plotIn = MRS(FID=inFID, header=inHdr)
plotOut = MRS(FID=outFID, header=outHdr)
# Fetch line styles
lines, colors, _ = plotStyles()
# Make a new figure
fig = make_subplots(rows=1, cols=2, subplot_titles=['Spectra', 'FID'])
# Add lines to figure
trace1 = go.Scatter(x=plotIn.getAxes(ppmlim=ppmlim),
y=np.real(plotIn.getSpectrum(ppmlim=ppmlim)),
mode='lines',
name='Original',
line=lines['in'])
trace2 = go.Scatter(x=plotOut.getAxes(ppmlim=ppmlim),
y=np.real(plotOut.getSpectrum(ppmlim=ppmlim)),
mode='lines',
name='Shifted',
line=lines['out'])
fig.add_trace(trace1, row=1, col=1)
fig.add_trace(trace2, row=1, col=1)
# Add lines to figure
trace3 = go.Scatter(x=plotIn.getAxes(axis='time'),
y=np.real(plotIn.FID),
mode='lines',
name='Original',
line=lines['emph'])
trace4 = go.Scatter(x=plotOut.getAxes(axis='time'),
y=np.real(plotOut.FID),
mode='lines',
name='Shifted',
line=lines['diff'])
fig.add_trace(trace3, row=1, col=2)
fig.add_trace(trace4, row=1, col=2)
# Axes layout
plotAxesStyle(fig, ppmlim, title='Shift summary')
fig.layout.xaxis2.update(title_text='Time (s)')
fig.layout.yaxis2.update(zeroline=True,
zerolinewidth=1,
zerolinecolor='Gray',
showgrid=False,
showticklabels=False)
if html is not None:
from plotly.offline import plot
from fsl_mrs.utils.preproc.reporting import figgroup, singleReport
from datetime import datetime
import os.path as op
if op.isdir(html):
filename = 'report_' + datetime.now().strftime(
"%Y%m%d_%H%M%S%f")[:-3] + '.html'
htmlfile = op.join(html, filename)
elif op.isdir(op.dirname(html)) and op.splitext(html)[1] == '.html':
htmlfile = html
else:
raise ValueError('Report html path must be file or directory. ')
operation, function, description = reportStrings(function)
opName = operation
timestr = datetime.now().strftime("%H:%M:%S")
datestr = datetime.now().strftime("%d/%m/%Y")
headerinfo = f'Report for fsl_mrs.utils.preproc.shifting.{function}.\n'\
+ f'Generated at {timestr} on {datestr}.'
# Figures
div = plot(fig, output_type='div', include_plotlyjs='cdn')
figurelist = [
figgroup(fig=div, name='', foretext=f'{description}', afttext=f'')
]
singleReport(htmlfile, opName, headerinfo, figurelist)
return fig
else:
return fig
def test_calcQC():
# Syntetic data
synFID, synHdr = syntheticFID(noisecovariance=[[0.1]],
points=2 * 2048,
chemicalshift=[0],
amplitude=[6.0],
linewidth=[10])
synFIDNoise, synHdrNoise = syntheticFID(noisecovariance=[[0.1]],
points=2 * 2048,
chemicalshift=[0],
amplitude=[0],
linewidth=[10])
basisFID, basisHdr = syntheticFID(noisecovariance=[[0.0]],
points=2 * 2048,
chemicalshift=[0],
amplitude=[0.1],
linewidth=[2])
synMRS = MRS(FID=synFID[0], header=synHdr)
synMRSNoise = MRS(FID=synFIDNoise[0], header=synHdrNoise)
synMRSNoNoise = MRS(FID=synHdr['noiseless'], header=synHdr)
synMRS_basis = MRS(FID=synFID[0],
header=synHdr,
basis=basisFID[0],
basis_hdr=basisHdr,
names=['Peak1'])
truenoiseSD = np.sqrt(synHdrNoise['cov'][0, 0])
pureNoiseMeasured = np.std(synMRSNoise.getSpectrum())
realnoise = np.std(np.real(synMRSNoise.getSpectrum()))
imagNoise = np.std(np.imag(synMRSNoise.getSpectrum()))
print(
f'True cmplx noise = {truenoiseSD:0.3f}, pure noise measured = {pureNoiseMeasured:0.3f} (real/imag = {realnoise:0.3f}/{imagNoise:0.3f})'
)
# Calc SNR without apodisation from the no noise and pure noise spectra
truePeakHeight = np.max(np.real(synMRSNoNoise.getSpectrum()))
SNR_noApod = truePeakHeight / pureNoiseMeasured
print(
f'SNR no apod: {SNR_noApod:0.1f} ({truePeakHeight:0.2e}/{pureNoiseMeasured:0.2e})'
)
# Calc SNR with apodisation from the no noise and pure noise spectra
trueLW = synHdr['inputopts']['linewidth'][0]
trueApodSpec_Noise = specApodise(synMRSNoise, trueLW)
apodNoise = np.std(trueApodSpec_Noise)
trueApodSpec_noNoise = specApodise(synMRSNoNoise, trueLW)
peakHeigtApod = np.max(np.real(trueApodSpec_noNoise))
SNR = peakHeigtApod / apodNoise
print(f'SNR w. apod: {SNR:0.1f} ({peakHeigtApod:0.2e}/{apodNoise:0.2e})')
metab_groups = [0]
Fitargs = {
'ppmlim': [2.65, 6.65],
'method': 'Newton',
'baseline_order': -1,
'metab_groups': [0]
}
res = fit_FSLModel(synMRS_basis, **Fitargs)
fwhm_test, SNRObj = calcQC(synMRS_basis, res, ppmlim=[2.65, 6.65])
print(f'Measured FWHM: {fwhm_test.mean().to_numpy()[0]:0.1f}')
print(f'Measured spec SNR: {SNRObj.spectrum:0.1f}')
print(f'Measured peak SNR: {SNRObj.peaks.mean().to_numpy()[0]:0.1f}')
assert np.isclose(fwhm_test.mean().to_numpy(), trueLW, atol=1E0)
assert np.isclose(SNRObj.spectrum, SNR_noApod, atol=1E1)
assert np.isclose(SNRObj.peaks.mean().to_numpy(), SNR, atol=2E1)