def test_fit_FSLModel_on_invivo_sim():
FIDs, hdr, trueconcs = syntheticFromBasisFile(basis_path,
noisecovariance=[[1E-3]],
broadening=(9.0, 9.0),
concentrations={'Mac': 2.0})
basis, names, header = mrs_io.read_basis(basis_path)
mrs = MRS(FID=FIDs,
header=hdr,
basis=basis,
basis_hdr=header[0],
names=names)
mrs.processForFitting()
metab_groups = [0] * mrs.numBasis
Fitargs = {
'ppmlim': [0.2, 4.2],
'method': 'MH',
'baseline_order': -1,
'metab_groups': metab_groups,
'MHSamples': 100
}
res = fit_FSLModel(mrs, **Fitargs)
fittedRelconcs = res.getConc(scaling='internal', metab=mrs.names)
answers = np.asarray(trueconcs)
answers /= (answers[names.index('Cr')] + trueconcs[names.index('PCr')])
assert np.allclose(fittedRelconcs, answers, atol=5E-2)
def data():
noiseCov = 0.01
amplitude = np.asarray([0.5, 0.5, 1.0]) * 10
chemshift = np.asarray([3.0, 3.05, 2.0]) - 4.65
lw = [10, 10, 10]
phases = [0, 0, 0]
g = [0, 0, 0]
basisNames = ['Cr', 'PCr', 'NAA']
basisFIDs = []
for idx, _ in enumerate(amplitude):
tmp, basisHdr = syntheticFID(noisecovariance=[[0.0]],
chemicalshift=[chemshift[idx]],
amplitude=[1.0],
linewidth=[lw[idx] / 5],
phase=[phases[idx]],
g=[g[idx]])
basisFIDs.append(tmp[0])
basisFIDs = np.asarray(basisFIDs)
synFID, synHdr = syntheticFID(noisecovariance=[[noiseCov]],
chemicalshift=chemshift,
amplitude=amplitude,
linewidth=lw,
phase=phases,
g=g)
synMRS = MRS(FID=synFID[0],
header=synHdr,
basis=basisFIDs,
basis_hdr=basisHdr,
names=basisNames)
return synMRS, amplitude
def shiftToRef(FID, target, bw, cf, ppmlim=(2.8, 3.2), shift=True):
#Find maximum of absolute spectrum in ppm limit
padFID = pad(FID, FID.size * 3)
MRSargs = {'FID': padFID, 'bw': bw, 'cf': cf}
mrs = MRS(**MRSargs)
spec = extract_spectrum(mrs, padFID, ppmlim=ppmlim, shift=shift)
if shift:
extractedAxis = mrs.getAxes(ppmlim=ppmlim)
else:
extractedAxis = mrs.getAxes(ppmlim=ppmlim, axis='ppm')
maxIndex = np.argmax(np.abs(spec))
shiftAmount = extractedAxis[maxIndex] - target
shiftAmountHz = shiftAmount * mrs.centralFrequency / 1E6
return freqshift(FID, 1 / bw, -shiftAmountHz), shiftAmount
def phase_freq_align_diff(FIDlist0,
FIDlist1,
bandwidth,
centralFrequency,
diffType='add',
ppmlim=None,
shift=True,
target=None):
""" Align subspectra from difference methods.
Only spectra in FIDlist0 are shifted.
Parameters:
-----------
FIDlist0 : list - shifted
FIDlist1 : list - fixed
bandwidth : float (unit=Hz)
centralFrequency : float (unit=Hz)
diffType : string - add or subtract
ppmlim : tuple
shift : apply H20 shift to ppm limit
ref : reference data to align to
Returns:
--------
two lists of FID aligned to each other, phase and shift applied to first list.
"""
# Process target
if target is not None:
tgt_FID = target
else:
diffFIDList = []
for fid0, fid1 in zip(FIDlist0, FIDlist1):
if diffType.lower() == 'add':
diffFIDList.append(add(fid1, fid0))
elif diffType.lower() == 'sub':
diffFIDList.append(subtract(fid1, fid0))
else:
raise ValueError('diffType must be add or sub.')
tgt_FID = get_target_FID(diffFIDList, target='nearest_to_mean')
# Pass to phase_freq_align
mrs = MRS(FID=FIDlist0[0], cf=centralFrequency, bw=bandwidth)
phiOut, epsOut = [], []
alignedFIDs0 = []
for fid0, fid1 in zip(FIDlist0, FIDlist1):
# breakpoint()
out = align_FID_diff(mrs,
fid0,
fid1,
tgt_FID,
diffType=diffType,
ppmlim=ppmlim,
shift=shift)
alignedFIDs0.append(out[0])
phiOut.append(out[1])
epsOut.append(out[2])
return alignedFIDs0, FIDlist1, phiOut, epsOut
def mrs_by_index(self,index):
mrs_out = MRS(FID=self.data[index[0],index[1],index[2],:],
header=self.header,
basis=self.basis,
names=self.names,
basis_hdr=self.basis_hdr,
H2O=self.H2O[index[0],index[1],index[2],:])
self._process_mrs(mrs_out)
return mrs_out
def identifyUnlikeFIDs(FIDList,bandwidth,centralFrequency,sdlimit = 1.96,iterations=2,ppmlim=None,shift=True):
""" Identify FIDs in a list that are unlike the others
Args:
FIDList (list of ndarray): Time domain data
bandwidth (float) : Bandwidth in Hz
centralFrequency (float) : Central frequency in Hz
sdlimit (float,optional) : Exclusion limit (number of stnadard deviations). Default = 3.
iterations (int,optional): Number of iterations to use.
ppmlim (tuple,optional) : Limit to this ppm range
shift (bool,optional) : Apply H20 shft
Returns:
goodFIDS (list of ndarray): FIDs that passed the criteria
badFIDS (list of ndarray): FIDs that failed the likeness critera
rmIndicies (list of int): Indicies of those FIDs that have been removed
metric (list of floats): Likeness metric of each FID
"""
# Calculate the FID to compare to
target = get_target_FID(FIDList,target='median')
if ppmlim is not None:
MRSargs = {'FID':target,'bw':bandwidth,'cf':centralFrequency}
mrs = MRS(**MRSargs)
target = extract_spectrum(mrs,target,ppmlim=ppmlim,shift=shift)
compareList = [extract_spectrum(mrs,f,ppmlim=ppmlim,shift=shift) for f in FIDList]
else:
compareList = [FIDToSpec(f) for f in FIDList]
target = FIDToSpec(target)
# Do the comparison
for idx in range(iterations):
metric = []
for data in compareList:
metric.append(np.linalg.norm(data-target))
metric = np.asarray(metric)
metric_avg = np.mean(metric)
metric_std = np.std(metric)
goodFIDs,badFIDs,rmIndicies,keepIndicies = [],[],[],[]
for iDx,(data,m) in enumerate(zip(FIDList,metric)):
if m > ((sdlimit*metric_std)+metric_avg) or m < (-(sdlimit*metric_std)+metric_avg):
badFIDs.append(data)
rmIndicies.append(iDx)
else:
goodFIDs.append(data)
keepIndicies.append(iDx)
target = get_target_FID(goodFIDs,target='median')
if ppmlim is not None:
target = extract_spectrum(mrs,target,ppmlim=ppmlim,shift=shift)
else:
target = FIDToSpec(target)
return goodFIDs,badFIDs,keepIndicies,rmIndicies,metric.tolist()
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 test_align_diff():
shift0 = np.random.randn(10) * 0.15
phs = np.random.randn(10) * 0.1 * np.pi
shiftedFIDs0 = []
shiftedFIDs1 = []
for s0, p in zip(shift0, phs):
testFIDs, testHdrs = syn.syntheticFID(amplitude=[1, 1],
chemicalshift=[-2 + s0, 3 + s0],
phase=[p + np.pi, p],
damping=[100, 100],
points=2048,
noisecovariance=[[1E-6]])
shiftedFIDs0.append(testFIDs[0])
testFIDs, testHdrs = syn.syntheticFID(amplitude=[1, 1],
chemicalshift=[-2, 3],
phase=[0, 0],
damping=[100, 100],
points=2048,
noisecovariance=[[1E-6]])
shiftedFIDs1.append(testFIDs[0])
testFIDs0, _ = syn.syntheticFID(amplitude=[2, 1],
chemicalshift=[-2, 3],
phase=[np.pi, 0],
damping=[100, 100],
points=2048,
noisecovariance=[[1E-6]])
testFIDs1, _ = syn.syntheticFID(amplitude=[2, 1],
chemicalshift=[-2, 3],
phase=[0, 0],
damping=[100, 100],
points=2048,
noisecovariance=[[1E-6]])
tgt = testFIDs0[0] + testFIDs1[0]
mrs = MRS(FID=tgt, header=testHdrs)
sub0_aa, sub1_aa, phi, eps = preproc.phase_freq_align_diff(
shiftedFIDs0,
shiftedFIDs1,
testHdrs['bandwidth'],
testHdrs['centralFrequency'],
target=tgt,
shift=False,
ppmlim=(-5, 5))
# align.phase_freq_align_diff_report(shiftedFIDs0,shiftedFIDs1,sub0_aa,sub1_aa,testHdrs,eps,phi,shift=False,ppmlim=(-5,5))
shiftInHz = shift0 * testHdrs['centralFrequency']
assert np.allclose(eps, shiftInHz, atol=1E-0)
assert np.allclose(phi, phs, atol=1E-0)
def mrs_from_average(self):
FID = misc.volume_to_list(self.data,self.mask)
H2O = misc.volume_to_list(self.H2O,self.mask)
FID = sum(FID)/len(FID)
H2O = sum(H2O)/len(H2O)
mrs_out = MRS(FID=FID,
header=self.header,
basis=self.basis,
names=self.names,
basis_hdr=self.basis_hdr,
H2O=H2O)
self._process_mrs(mrs_out)
return mrs_out
def data():
noiseCov = 0.001
amplitude = np.asarray([0.5, 0.5, 1.0]) * 10
chemshift = np.asarray([3.0, 3.05, 2.0]) - 4.65
lw = [10, 10, 10]
phases = [0, 0, 0]
g = [0, 0, 0]
basisNames = ['Cr', 'PCr', 'NAA']
begintime = 0.00005
basisFIDs = []
for idx, _ in enumerate(amplitude):
tmp, basisHdr = syntheticFID(noisecovariance=[[0.0]],
chemicalshift=[chemshift[idx] + 0.1],
amplitude=[1.0],
linewidth=[lw[idx] / 5],
phase=[phases[idx]],
g=[g[idx]],
begintime=0)
basisFIDs.append(tmp[0])
basisFIDs = np.asarray(basisFIDs)
synFID, synHdr = syntheticFID(noisecovariance=[[noiseCov]],
chemicalshift=chemshift,
amplitude=amplitude,
linewidth=lw,
phase=phases,
g=g,
begintime=begintime)
synMRS = MRS(FID=synFID[0],
header=synHdr,
basis=basisFIDs,
basis_hdr=basisHdr,
names=basisNames)
metab_groups = [0] * synMRS.numBasis
Fitargs = {
'ppmlim': [0.2, 4.2],
'method': 'MH',
'baseline_order': -1,
'metab_groups': metab_groups,
'MHSamples': 100,
'disable_mh_priors': True
}
res = fit_FSLModel(synMRS, **Fitargs)
return res, amplitude
def generateBasisFromRes(mrs, res, resparams):
""" Return mrs objects for each fitted basis spectrum"""
mrsFits = []
for metab in mrs.names:
pred = models.getFittedModel(res.model,
resparams,
res.base_poly,
res.metab_groups,
mrs,
basisSelect=metab,
noBaseline=True)
pred = SpecToFID(pred) # predict FID not Spec
mrsOut = MRS(pred, cf=mrs.centralFrequency, bw=mrs.bandwidth)
mrsFits.append(mrsOut)
return mrsFits
def test_quantifyWater():
basis, names, headerb = mrsio.read_basis(basisfile)
crIndex = names.index('Cr')
data, header = mrsio.read_FID(metabfile)
dataw, headerw = mrsio.read_FID(h2ofile)
mrs = MRS(FID=data,
header=header,
basis=basis[:, crIndex],
names=['Cr'],
basis_hdr=headerb[crIndex],
H2O=dataw)
mrs.check_FID(repair=True)
mrs.check_Basis(repair=True)
Fitargs = {
'ppmlim': [0.2, 5.2],
'method': 'MH',
'baseline_order': -1,
'metab_groups': [0]
}
res = fit_FSLModel(mrs, **Fitargs)
tissueFractions = {'GM': 0.6, 'WM': 0.4, 'CSF': 0.0}
TE = 0.03
T2dict = {
'H2O_GM': 0.110,
'H2O_WM': 0.080,
'H2O_CSF': 2.55,
'METAB': 0.160
}
res.calculateConcScaling(mrs,
referenceMetab=['Cr'],
waterRefFID=mrs.H2O,
tissueFractions=tissueFractions,
TE=TE,
T2=T2dict,
waterReferenceMetab='Cr',
wRefMetabProtons=5,
reflimits=(2, 5),
verbose=False)
print(res.getConc(scaling='raw'))
print(res.getConc(scaling='internal'))
print(res.getConc(scaling='molality'))
print(res.getConc(scaling='molarity'))
assert np.allclose(res.getConc(scaling='internal'), 1.0)
assert np.allclose(res.getConc(scaling='molarity'), 10.59, atol=1E-1)
def phaseCorrect(FID, bw, cf, ppmlim=(2.8, 3.2), shift=True, hlsvd=False):
""" Phase correction based on the phase of a maximum point.
HLSVD is used to remove peaks outside the limits to flatten baseline first.
Args:
FID (ndarray): Time domain data
bw (float): bandwidth
cf (float): central frequency in Hz
ppmlim (tuple,optional) : Limit to this ppm range
shift (bool,optional) : Apply H20 shft
hlsvd (bool,optional) : Enable hlsvd step
Returns:
FID (ndarray): Phase corrected FID
"""
if hlsvd:
# Run HLSVD to remove peaks outside limits
try:
fid_hlsvd = hlsvd(FID,
1 / bw,
cf, (ppmlim[1] + 0.5, ppmlim[1] + 3.0),
limitUnits='ppm+shift')
fid_hlsvd = hlsvd(fid_hlsvd,
1 / bw,
cf, (ppmlim[0] - 3.0, ppmlim[0] - 0.5),
limitUnits='ppm+shift')
except:
fid_hlsvd = FID
print('HLSVD in phaseCorrect failed, proceeding to phasing.')
else:
fid_hlsvd = FID
# Find maximum of absolute spectrum in ppm limit
padFID = pad(fid_hlsvd, FID.size * 3)
MRSargs = {'FID': padFID, 'bw': bw, 'cf': cf}
mrs = MRS(**MRSargs)
spec = extract_spectrum(mrs, padFID, ppmlim=ppmlim, shift=shift)
maxIndex = np.argmax(np.abs(spec))
phaseAngle = -np.angle(spec[maxIndex])
return applyPhase(FID, phaseAngle), phaseAngle, int(np.round(maxIndex / 4))
def __iter__(self):
shape = self.data.shape
self._store_scalings = []
for idx in np.ndindex(shape[:3]):
if self.mask[idx]:
mrs_out = MRS(FID=self.data[idx],
header=self.header,
basis=self.basis,
names=self.names,
basis_hdr=self.basis_hdr,
H2O=self.H2O[idx])
self._process_mrs(mrs_out)
self._store_scalings.append(mrs_out.scaling)
if self.tissue_seg_loaded:
tissue_seg = {'CSF':self.csf[idx],'WM':self.wm[idx],'GM':self.gm[idx]}
else:
tissue_seg = None
yield mrs_out,idx,tissue_seg
def apodize_report(inFID, outFID, hdr, plotlim=(0.2, 6), html=None):
"""
Generate report
"""
# from matplotlib import pyplot as plt
from fsl_mrs.core import MRS
import plotly.graph_objects as go
from fsl_mrs.utils.preproc.reporting import plotStyles, plotAxesStyle
# Turn input FIDs into mrs objects
toMRSobj = lambda fid: MRS(FID=fid, header=hdr)
plotIn = toMRSobj(inFID)
plotOut = toMRSobj(outFID)
# Fetch line styles
lines, colors, _ = plotStyles()
# Make a new figure
fig = go.Figure()
# Add lines to figure
def addline(fig, mrs, lim, name, linestyle):
trace = go.Scatter(x=mrs.getAxes(ppmlim=lim),
y=np.real(mrs.getSpectrum(ppmlim=lim)),
mode='lines',
name=name,
line=linestyle)
return fig.add_trace(trace)
fig = addline(fig, plotIn, plotlim, 'Uncorrected', lines['in'])
fig = addline(fig, plotOut, plotlim, 'Corrected', lines['out'])
# Axes layout
plotAxesStyle(fig, plotlim, title='Apodization summary')
# Generate report
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. ')
opName = 'Apodization'
timestr = datetime.now().strftime("%H:%M:%S")
datestr = datetime.now().strftime("%d/%m/%Y")
headerinfo = 'Report for fsl_mrs.utils.preproc.filtering.apodize.\n'\
+ f'Generated at {timestr} on {datestr}.'
# Figures
div = plot(fig, output_type='div', include_plotlyjs='cdn')
figurelist = [
figgroup(fig=div,
name='',
foretext=f'Apodization of spectra.',
afttext=f'')
]
singleReport(htmlfile, opName, headerinfo, figurelist)
return fig
else:
return fig
def combine_FIDs_report(inFIDs,
outFID,
hdr,
ncha=2,
ppmlim=(0.0, 6.0),
method='not specified',
html=None):
""" Take list of FIDs that are passed to combine and output
If uncombined data it will display ncha channels (default 2).
"""
from fsl_mrs.core import MRS
import plotly.graph_objects as go
from fsl_mrs.utils.preproc.reporting import plotStyles, plotAxesStyle
from matplotlib.pyplot import cm
toMRSobj = lambda fid: MRS(FID=fid, header=hdr)
# Assemble data to plot
toPlotIn = []
colourVecIn = []
legendIn = []
if isinstance(inFIDs, list):
for idx, fid in enumerate(inFIDs):
if inFIDs[0].ndim > 1:
toPlotIn.extend([toMRSobj(f) for f in fid[:, :ncha].T])
colourVecIn.extend([idx / len(inFIDs)] * ncha)
legendIn.extend(
[f'FID #{idx}: CHA #{jdx}' for jdx in range(ncha)])
else:
toPlotIn.append(toMRSobj(fid))
colourVecIn.append(idx / len(inFIDs))
legendIn.append(f'FID #{idx}')
elif inFIDs.ndim > 1:
toPlotIn.extend([toMRSobj(f) for f in inFIDs[:, :ncha].T])
colourVecIn.extend([float(jdx) / ncha for jdx in range(ncha)])
legendIn.extend([f'FID #0: CHA #{jdx}' for jdx in range(ncha)])
toPlotOut = []
legendOut = []
if outFID.ndim > 1:
toPlotOut.extend([toMRSobj(f) for f in outFID[:, :ncha].T])
legendOut.extend([f'Combined: CHA #{jdx}' for jdx in range(ncha)])
else:
toPlotOut.append(toMRSobj(outFID))
legendOut.append('Combined')
def addline(fig, mrs, lim, name, linestyle):
trace = go.Scatter(x=mrs.getAxes(ppmlim=lim),
y=np.real(mrs.getSpectrum(ppmlim=lim)),
mode='lines',
name=name,
line=linestyle)
return fig.add_trace(trace)
lines, colors, _ = plotStyles()
colors = cm.Spectral(np.array(colourVecIn).ravel())
fig = go.Figure()
for idx, fid in enumerate(toPlotIn):
cval = np.round(255 * colors[idx, :])
linetmp = {'color': f'rgb({cval[0]},{cval[1]},{cval[2]})', 'width': 1}
fig = addline(fig, fid, ppmlim, legendIn[idx], linetmp)
for idx, fid in enumerate(toPlotOut):
fig = addline(fig, fid, ppmlim, legendOut[idx], lines['blk'])
plotAxesStyle(fig, ppmlim, 'Combined')
# Generate report
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. ')
opName = 'Combination'
timestr = datetime.now().strftime("%H:%M:%S")
datestr = datetime.now().strftime("%d/%m/%Y")
headerinfo = 'Report for fsl_mrs.utils.preproc.combine.combine_FIDs.\n'\
+ f'Generated at {timestr} on {datestr}.'
# Figures
div = plot(fig, output_type='div', include_plotlyjs='cdn')
figurelist = [
figgroup(fig=div,
name='',
foretext=f'Combination of spectra. Method = {method}',
afttext=f'')
]
singleReport(htmlfile, opName, headerinfo, figurelist)
return fig
else:
return fig
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 phaseCorrect_report(inFID,
outFID,
hdr,
position,
ppmlim=(2.8, 3.2),
html=None):
"""
Generate report for phaseCorrect
"""
# from matplotlib import pyplot as plt
from fsl_mrs.core import MRS
import plotly.graph_objects as go
from fsl_mrs.utils.preproc.reporting import plotStyles, plotAxesStyle
# Turn input FIDs into mrs objects
toMRSobj = lambda fid: MRS(FID=fid, header=hdr)
plotIn = toMRSobj(inFID)
plotOut = toMRSobj(outFID)
widelimit = (0, 6)
# Fetch line styles
lines, colors, _ = plotStyles()
# Make a new figure
fig = go.Figure()
# Add lines to figure
def addline(fig, mrs, lim, name, linestyle):
trace = go.Scatter(x=mrs.getAxes(ppmlim=lim),
y=np.real(mrs.getSpectrum(ppmlim=lim)),
mode='lines',
name=name,
line=linestyle)
return fig.add_trace(trace)
fig = addline(fig, plotIn, widelimit, 'Unphased', lines['in'])
fig = addline(fig, plotIn, ppmlim, 'Search region', lines['emph'])
if position is None:
# re-estimate here.
position = np.argmax(np.abs(plotIn.getSpectrum(ppmlim=ppmlim)))
axis = [plotIn.getAxes(ppmlim=ppmlim)[position]]
y_data = [np.real(plotIn.getSpectrum(ppmlim=ppmlim))[position]]
trace = go.Scatter(x=axis,
y=y_data,
mode='markers',
name='max point',
marker=dict(color=colors['emph'], symbol='x', size=8))
fig.add_trace(trace)
fig = addline(fig, plotOut, widelimit, 'Phased', lines['out'])
# Axes layout
plotAxesStyle(fig, widelimit, title='Phase correction summary')
# Axes
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. ')
opName = 'Phase correction'
timestr = datetime.now().strftime("%H:%M:%S")
datestr = datetime.now().strftime("%d/%m/%Y")
headerinfo = 'Report for fsl_mrs.utils.preproc.phasing.phaseCorrect.\n'\
+ f'Generated at {timestr} on {datestr}.'
# Figures
div = plot(fig, output_type='div', include_plotlyjs='cdn')
figurelist = [
figgroup(
fig=div,
name='',
foretext=
f'Phase correction of spectra based on maximum in the range {ppmlim[0]} to {ppmlim[1]} ppm.',
afttext=f'')
]
singleReport(htmlfile, opName, headerinfo, figurelist)
return fig
else:
return fig
def toMRSobj(fid):
return MRS(FID=fid, header=hdr)
def identifyUnlikeFIDs_report(goodFIDs,badFIDs,hdr,keepIndicies,rmIndicies,metric,ppmlim=(0.2,4.2),sdlimit = 1.96,html=None):
from fsl_mrs.utils.preproc.combine import combine_FIDs
import plotly.graph_objects as go
from fsl_mrs.utils.preproc.reporting import plotStyles,plotAxesStyle
metricGd = np.array(metric)[keepIndicies]
metricBd = np.array(metric)[rmIndicies]
metric_avg = np.mean(metric)
metric_std = np.std(metric)
metricGd_SD = np.abs(metricGd-metric_avg)/metric_std
metricBd_SD = np.abs(metricBd-metric_avg)/metric_std
gdIndex = np.argsort(metricGd_SD)
bdIndex = np.argsort(metricBd_SD)
plotGood,plotBad = [],[]
gdLegend,bdLegend = [],[]
toMRSobj = lambda fid : MRS(FID=fid,header=hdr)
for idx in gdIndex:
fid = goodFIDs[idx]
plotGood.append(toMRSobj(fid))
gdLegend.append(f'Kept (SD={metricGd_SD[idx]:0.2f})')
for idx in bdIndex:
fid = badFIDs[idx]
plotBad.append(toMRSobj(fid))
bdLegend.append(f'Removed (SD={metricBd_SD[idx]:0.2f})')
target = get_target_FID(goodFIDs,target='median')
tgtmrs = toMRSobj(target)
# Fetch line styles
lines,colors,_ = plotStyles()
# Make a new figure
fig = go.Figure()
# Add lines to figure
def addline(fig,mrs,lim,name,linestyle):
trace = go.Scatter(x=mrs.getAxes(ppmlim=lim),
y=np.real(mrs.getSpectrum(ppmlim=lim)),
mode='lines',
name=name,
line=linestyle)
return fig.add_trace(trace)
for fid,leg in zip(plotGood,gdLegend):
fig = addline(fig,fid,ppmlim,leg,lines['out'])
for fid,leg in zip(plotBad,bdLegend):
fig = addline(fig,fid,ppmlim,leg,lines['emph'])
fig = addline(fig,tgtmrs,ppmlim,'Target',lines['blk'])
plotAxesStyle(fig,ppmlim,title = 'Bad average removal summary')
# Generate report
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. ')
opName = 'BadAverageRemoval'
timestr = datetime.now().strftime("%H:%M:%S")
datestr = datetime.now().strftime("%d/%m/%Y")
headerinfo = 'Report for fsl_mrs.utils.preproc.unlike.identifyUnlikeFIDs.\n'\
+ f'Generated at {timestr} on {datestr}.'
# Figures
div = plot(fig, output_type='div',include_plotlyjs='cdn')
figurelist = [figgroup(fig = div,
name= '',
foretext= f'Identification of FIDs unlike others. SD limit = {sdlimit:0.2f}',
afttext= f'')]
singleReport(htmlfile,opName,headerinfo,figurelist)
return fig
else:
return fig
def phase_freq_align(FIDlist,
bandwidth,
centralFrequency,
ppmlim=None,
niter=2,
apodize=10,
verbose=False,
shift=True,
target=None):
"""
Algorithm:
Average spectra
Loop over all spectra and find best phase/frequency shifts
Iterate
TODO:
test the code
add dedrifting?
Parameters:
-----------
FIDlist : list
bandwidth : float (unit=Hz)
centralFrequency : float (unit=Hz)
ppmlim : tuple
niter : int
apodize : float (unit=Hz)
verbose : bool
shift : apply H20 shift to ppm limit
ref : reference data to align to
Returns:
--------
list of FID aligned to each other
"""
all_FIDs = FIDlist.copy()
phiOut, epsOut = np.zeros(len(FIDlist)), np.zeros(len(FIDlist))
for iter in range(niter):
if verbose:
print(' ---- iteration {} ----\n'.format(iter))
if target is None:
target = get_target_FID(all_FIDs, target='nearest_to_mean')
MRSargs = {'FID': target, 'bw': bandwidth, 'cf': centralFrequency}
mrs = MRS(**MRSargs)
if apodize > 0:
target = apod(target, mrs.dwellTime, [apodize])
for idx, FID in enumerate(all_FIDs):
if verbose:
print(f'... aligning FID number {idx}\r')
if apodize > 0:
FID_apod = apod(FID.copy(), mrs.dwellTime, [apodize])
else:
FID_apod = FID
phi, eps = align_FID(mrs,
FID_apod,
target,
ppmlim=ppmlim,
shift=shift)
all_FIDs[idx] = np.exp(-1j * phi) * shift_FID(mrs, FID, eps)
phiOut[idx] += phi
epsOut[idx] += eps
if verbose:
print('\n')
return all_FIDs, phiOut, epsOut
def phase_freq_align_diff_report(inFIDs0,
inFIDs1,
outFIDs0,
outFIDs1,
hdr,
eps,
phi,
ppmlim=None,
diffType='add',
shift=True,
html=None):
from fsl_mrs.utils.preproc.combine import combine_FIDs
import plotly.graph_objects as go
from fsl_mrs.utils.preproc.reporting import plotStyles, plotAxesStyle
from plotly.subplots import make_subplots
# Fetch line styles
lines, _, _ = plotStyles()
# Make a new figure
fig = make_subplots(rows=1, cols=2, subplot_titles=['Phase', 'Shift'])
trace1 = go.Scatter(x=np.arange(1,
len(phi) + 1),
y=np.array(phi) * (180.0 / np.pi),
mode='lines',
name='Phase',
line=lines['out'])
fig.add_trace(trace1, row=1, col=1)
fig.layout.xaxis.update(title_text='Transient #')
fig.layout.yaxis.update(title_text=r'$\phi$ (degrees)')
trace2 = go.Scatter(x=np.arange(1,
len(eps) + 1),
y=eps,
mode='lines',
name='Shift',
line=lines['diff'])
fig.add_trace(trace2, row=1, col=2)
fig.layout.yaxis2.update(title_text='Shift (Hz)')
fig.layout.xaxis2.update(title_text='Transient #')
diffFIDListIn = []
diffFIDListOut = []
for fid0i, fid1i, fid0o, fid1o in zip(inFIDs0, inFIDs1, outFIDs0,
outFIDs1):
if diffType.lower() == 'add':
diffFIDListIn.append(add(fid1i, fid0i))
diffFIDListOut.append(add(fid1o, fid0o))
elif diffType.lower() == 'sub':
diffFIDListIn.append(subtract(fid1i, fid0i))
diffFIDListOut.append(subtract(fid1o, fid0o))
else:
raise ValueError('diffType must be add or sub.')
meanIn = combine_FIDs(diffFIDListIn, 'mean')
meanOut = combine_FIDs(diffFIDListOut, 'mean')
toMRSobj = lambda fid: MRS(FID=fid, header=hdr)
meanIn = toMRSobj(meanIn)
meanOut = toMRSobj(meanOut)
if shift:
axis = 'ppmshift'
else:
axis = 'ppm'
toPlotIn, toPlotOut = [], []
for fid in diffFIDListIn:
toPlotIn.append(toMRSobj(fid))
for fid in diffFIDListOut:
toPlotOut.append(toMRSobj(fid))
def addline(fig, mrs, lim, name, linestyle):
trace = go.Scatter(x=mrs.getAxes(ppmlim=lim, axis=axis),
y=np.real(mrs.getSpectrum(ppmlim=lim, shift=shift)),
mode='lines',
name=name,
line=linestyle)
return fig.add_trace(trace)
fig2 = go.Figure()
for idx, fid in enumerate(toPlotIn):
cval = np.round(255 * idx / len(toPlotIn))
linetmp = {'color': f'rgb(0,{cval},{cval})', 'width': 1}
fig2 = addline(fig2, fid, ppmlim, f'#{idx}', linetmp)
fig2 = addline(fig2, meanIn, ppmlim, f'Mean - Unligned', lines['blk'])
plotAxesStyle(fig2, ppmlim, 'Unaligned')
fig3 = go.Figure()
for idx, fid in enumerate(toPlotOut):
cval = np.round(255 * idx / len(toPlotIn))
linetmp = {'color': f'rgb(0,{cval},{cval})', 'width': 1}
fig3 = addline(fig3, fid, ppmlim, f'#{idx}', linetmp)
fig3 = addline(fig3, meanIn, ppmlim, f'Mean - Unligned', lines['out'])
fig3 = addline(fig3, meanOut, ppmlim, f'Mean - Aligned', lines['blk'])
plotAxesStyle(fig3, ppmlim, 'Aligned')
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. ')
opName = 'AlignDiff'
timestr = datetime.now().strftime("%H:%M:%S")
datestr = datetime.now().strftime("%d/%m/%Y")
headerinfo = 'Report for fsl_mrs.utils.align.phase_freq_align_diff.\n'\
+ f'Generated at {timestr} on {datestr}.'
# Figures
div = plot(fig, output_type='div', include_plotlyjs='cdn')
figurelist = [
figgroup(fig=div,
name='',
foretext=f'Alignment parameters.',
afttext=f'')
]
div2 = plot(fig2, output_type='div', include_plotlyjs='cdn')
figurelist.append(
figgroup(fig=div2,
name='',
foretext=f'Transients before alignment.',
afttext=f''))
div3 = plot(fig3, output_type='div', include_plotlyjs='cdn')
figurelist.append(
figgroup(fig=div3,
name='',
foretext=f'Transients after alignment.',
afttext=f''))
singleReport(htmlfile, opName, headerinfo, figurelist)
return fig, fig2, fig3
else:
return fig, fig2, fig3
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)
