def neuropowermodel(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowermodel.html"
steps = get_neuropower_steps(template,sid)
context = {"steps":steps}
if not ParameterModel.objects.filter(SID=sid):
# We should not be able to get to this step
context["text"] = "No data found. Go to 'Input' and fill out the form."
return render(request,template,context)
else:
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
peakdata = PeakTableModel.objects.filter(SID=sid)[::-1][0]
peaks = peakdata.data
bum = BUM.bumOptim(peaks.pval.tolist(),starts=10) # :)
modelfit = neuropowermodels.modelfit(peaks.peak.tolist(),
bum['pi1'],
exc = float(parsdata.ExcZ),
starts=10,
method="RFT")
mixtureform = MixtureForm()
form = mixtureform.save(commit=False)
form.SID = sid
form.pi1 = bum['pi1']
form.a = bum['a']
form.mu = modelfit['mu']
form.sigma = modelfit['sigma']
form.save()
return render(request,template,context)
def neuropowercrosstab(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowercrosstab.html"
steps = get_neuropower_steps(template, sid)
context = {"steps": steps}
powerinputform = PowerForm(request.POST or None)
if not MixtureModel.objects.filter(SID=sid):
context[
"text"] = "Before doing any power calculations, the distribution of effects has to be estimated. Please go to 'Model Fit'to initiate and inspect the fit of the mixture model to the distribution."
if not ParameterModel.objects.filter(SID=sid):
context["text"] = "No data found. Go to 'Input' and fill out the form."
else:
powerdata = PowerTableModel.objects.filter(SID=sid)[::-1][0]
powertable = powerdata.data[['newsamplesize', 'RFT', 'BF', 'BH',
'UN']].round(decimals=2)
powertable.columns = [
'Sample Size', 'Random Field Theory', 'Bonferroni',
'Benjamini-Hochberg', 'Uncorrected'
]
context["power"] = powertable.to_html(index=False,
col_space='120px',
classes=["table table-striped"])
return render(request, template, context)
def neuropowerviewer(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowerviewer.html"
steps = get_neuropower_steps(template,sid)
viewer = ""
url = ""
text = ""
thr = ""
if not ParameterModel.objects.filter(SID=sid):
text = "Please first fill out the input."
else:
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
if parsdata.url == "":
text = "The viewer is only available for publicly available data (specify a url in the input)."
else:
url = parsdata.url
thr = parsdata.Exc
viewer = "<div class='papaya' data-params='params'></div>"
context = {"url":url,
"viewer":viewer,
"text":text,
"thr":thr,
"steps":steps}
return render(request,template,context)
def neuropowermodel(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowermodel.html"
steps = get_neuropower_steps(template, sid)
context = {"steps": steps}
if not ParameterModel.objects.filter(SID=sid):
# We should not be able to get to this step
context["text"] = "No data found. Go to 'Input' and fill out the form."
return render(request, template, context)
else:
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
peakdata = PeakTableModel.objects.filter(SID=sid)[::-1][0]
peaks = peakdata.data
bum = BUM.bumOptim(peaks.pval.tolist(), starts=10) # :)
modelfit = neuropowermodels.modelfit(peaks.peak.tolist(),
bum['pi1'],
exc=float(parsdata.ExcZ),
starts=10,
method="RFT")
mixtureform = MixtureForm()
form = mixtureform.save(commit=False)
form.SID = sid
form.pi1 = bum['pi1']
form.a = bum['a']
form.mu = modelfit['mu']
form.sigma = modelfit['sigma']
form.save()
return render(request, template, context)
def neuropowerviewer(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowerviewer.html"
steps = get_neuropower_steps(template, sid)
viewer = ""
url = ""
text = ""
thr = ""
if not ParameterModel.objects.filter(SID=sid):
text = "Please first fill out the input."
else:
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
if parsdata.url == "":
text = "The viewer is only available for publicly available data (specify a url in the input)."
else:
url = parsdata.url
thr = parsdata.Exc
viewer = "<div class='papaya' data-params='params'></div>"
context = {
"url": url,
"viewer": viewer,
"text": text,
"thr": thr,
"steps": steps
}
return render(request, template, context)
def neuropowertable(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowertable.html"
steps = get_neuropower_steps(template,sid)
context = {"steps":steps}
if not ParameterModel.objects.filter(SID=sid):
# Should not be able to reach this condition
context["text"] = "No data found. Go to 'Input' and fill out the form."
return render(request,template,context)
else:
sid = request.session.session_key #why are we getting session id again?
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
SPM = nib.load(parsdata.location).get_data()
if parsdata.ZorT == 'T':
SPM = -norm.ppf(t.cdf(-SPM,df=float(parsdata.DoF)))
cluster.cluster(SPM,parsdata.ExcZ,parsdata.peaktable)
peaks = pd.read_csv(parsdata.peaktable,sep="\t")
if len(peaks) < 30:
context["text"] = "There are too few peaks for a good estimation. Either the ROI is too small or the screening threshold is too high."
else:
pvalues = np.exp(-float(parsdata.ExcZ)*(np.array(peaks.peak)-float(parsdata.ExcZ)))
pvalues = [max(10**(-6),p) for p in pvalues]
peaks['pval'] = pvalues
peakform = PeakTableForm()
form = peakform.save(commit=False)
form.SID = sid
form.data = peaks
form.save()
context["peaks"] = peaks.to_html(classes=["table table-striped"])
return render(request,template,context)
def neuropowersamplesize(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowersamplesize.html"
steps = get_neuropower_steps(template,sid)
context = {"steps":steps}
powerinputform = PowerForm(request.POST or None)
if not ParameterModel.objects.filter(SID=sid):
texttop = "No data found. Go to 'Input' and fill out the form."
elif not MixtureModel.objects.filter(SID=sid):
texttop = "Before doing any power calculations, the distribution of effects has to be estimated. Please go to 'Model Fit'to initiate and inspect the fit of the mixture model to the distribution."
else:
texttop = "Hover over the lines to see detailed power predictions"
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
peakdata = PeakTableModel.objects.filter(SID=sid)[::-1][0]
mixdata = MixtureModel.objects.filter(SID=sid)[::-1][0]
peaks = peakdata.data
thresholds = neuropowermodels.threshold(peaks.peak,peaks.pval,FWHM=8,nvox=float(parsdata.nvox),alpha=0.05,exc=float(parsdata.ExcZ))
effect_cohen = float(mixdata.mu)/np.sqrt(float(parsdata.Subj))
power_predicted = []
newsubs = range(parsdata.Subj,301)
for s in newsubs:
projected_effect = float(effect_cohen)*np.sqrt(float(s))
powerpred = {k:1-neuropowermodels.altCDF(v,projected_effect,float(mixdata.sigma),exc=float(parsdata.ExcZ),method="RFT") for k,v in thresholds.items()}
power_predicted.append(powerpred)
powertable = pd.DataFrame(power_predicted)
powertable['newsamplesize']=newsubs
powertableform = PowerTableForm()
savepowertableform = powertableform.save(commit=False)
savepowertableform.SID = sid
savepowertableform.data = powertable
savepowertableform.save()
powerinputform = PowerForm(request.POST or None)
plothtml = plotPower(sid)['code']
if request.method == "POST":
if powerinputform.is_valid():
savepowerinputform = powerinputform.save(commit=False)
savepowerinputform.SID = sid
savepowerinputform.save()
powerinputdata = PowerModel.objects.filter(SID=sid)[::-1][0]
pow = float(powerinputdata.reqPow)
ss = powerinputdata.reqSS
plotpower = plotPower(sid,powerinputdata.MCP,pow,ss)
plothtml = plotpower['code']
context["textbottom"] = plotpower['text']
context["texttop"] = texttop
context["plothtml"] = plothtml
context["powerinputform"] = powerinputform
return render(request,template,context)
def neuropowerstart(request):
'''step 1: start'''
# Get the template/step status
sid = get_session_id(request)
print(sid)
template = "neuropower/neuropowerstart.html"
steps = get_neuropower_steps(template, sid)
context = {"steps": steps}
return render(request, template, context)
def neuropowerviewer(request):
# Get the template
sid = get_session_id(request)
template = "neuropower/neuropowerviewer.html"
context = {}
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
if parsdata.url == "":
context[
"text"] = "The viewer is only available for publicly available data (specify a url in the input)."
else:
context["url"] = parsdata.url
context["thr"] = parsdata.Exc
context["viewer"] = "<div class='papaya' data-params='params'></div>"
context["steps"] = get_neuropower_steps(template, sid)
return render(request, template, context)
def neuropowermodel(request):
# Get the template
sid = get_session_id(request)
template = "neuropower/neuropowermodel.html"
context = {}
# Load model data
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
peakdata = PeakTableModel.objects.filter(SID=sid)[::-1][0]
if not peakdata.err == "":
context["text"] = peakdata.err
return render(request, template, context)
peaks = peakdata.data
# Estimate pi1
bum = BUM.bumOptim(peaks.pval.tolist(), starts=10) # :)
# Estimate mixture model
modelfit = neuropowermodels.modelfit(peaks.peak.tolist(),
bum['pi1'],
exc=float(parsdata.ExcZ),
starts=10,
method="RFT")
# Save estimates to form
mixtureform = MixtureForm()
form = mixtureform.save(commit=False)
form.SID = sid
form.pi1 = bum['pi1']
form.a = bum['a']
form.mu = modelfit['mu']
form.sigma = modelfit['sigma']
form.save()
# Get step status
context["steps"] = get_neuropower_steps(template, sid)
return render(request, template, context)
def neuropowertable(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowertable.html"
context = {}
# Initiate peak table
peakform = PeakTableForm()
form = peakform.save(commit=False)
form.SID = sid
# Load model data
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
# Compute peaks
SPM = nib.load(parsdata.location).get_data()
MASK = nib.load(parsdata.masklocation).get_data()
if parsdata.ZorT == 'T':
SPM = -norm.ppf(t.cdf(-SPM, df=float(parsdata.DoF)))
peaks = cluster.cluster(SPM, float(parsdata.ExcZ), MASK)
if len(peaks) < 30:
context[
"text"] = "There are too few peaks for a good estimation. Either the ROI is too small or the screening threshold is too high."
form.err = context["text"]
else:
pvalues = np.exp(-float(parsdata.ExcZ) *
(np.array(peaks.peak) - float(parsdata.ExcZ)))
pvalues = [max(10**(-6), p) for p in pvalues]
peaks['pval'] = pvalues
form.data = peaks
context["peaks"] = peaks.to_html(classes=["table table-striped"])
form.save()
# Get step status
context["steps"] = get_neuropower_steps(template, sid)
return render(request, template, context)
def neuropowertable(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowertable.html"
steps = get_neuropower_steps(template, sid)
context = {"steps": steps}
if not ParameterModel.objects.filter(SID=sid):
# Should not be able to reach this condition
context["text"] = "No data found. Go to 'Input' and fill out the form."
return render(request, template, context)
else:
sid = request.session.session_key #why are we getting session id again?
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
SPM = nib.load(parsdata.location).get_data()
if parsdata.ZorT == 'T':
SPM = -norm.ppf(t.cdf(-SPM, df=float(parsdata.DoF)))
cluster.cluster(SPM, parsdata.ExcZ, parsdata.peaktable)
peaks = pd.read_csv(parsdata.peaktable, sep="\t")
if len(peaks) < 30:
context[
"text"] = "There are too few peaks for a good estimation. Either the ROI is too small or the screening threshold is too high."
else:
pvalues = np.exp(-float(parsdata.ExcZ) *
(np.array(peaks.peak) - float(parsdata.ExcZ)))
pvalues = [max(10**(-6), p) for p in pvalues]
peaks['pval'] = pvalues
peakform = PeakTableForm()
form = peakform.save(commit=False)
form.SID = sid
form.data = peaks
form.save()
context["peaks"] = peaks.to_html(classes=["table table-striped"])
return render(request, template, context)
def neuropowercrosstab(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowercrosstab.html"
context = {}
# Load model data
peakdata = PeakTableModel.objects.filter(SID=sid)[::-1][0]
if not peakdata.err == "":
context["text"] = peakdata.err
return render(request, template, context)
powerdata = PowerTableModel.objects.filter(SID=sid)[::-1][0]
# Restyle power table for export
names = powerdata.data.columns.tolist()[:-1]
names.insert(0, 'newsamplesize')
powertable = powerdata.data[names].round(decimals=2)
repldict = {
'BF': 'Bonferroni',
'BH': 'Benjamini-Hochberg',
'UN': 'Uncorrected',
'RFT': 'Random Field Theory',
'newsamplesize': 'Samplesize'
}
for word, initial in repldict.items():
names = [i.replace(word, initial) for i in names]
powertable.columns = names
context["power"] = powertable.to_html(index=False,
col_space='120px',
classes=["table table-striped"])
# Get step status
context["steps"] = get_neuropower_steps(template, sid)
return render(request, template, context)
def neuropowercrosstab(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowercrosstab.html"
steps = get_neuropower_steps(template,sid)
context = {"steps":steps}
powerinputform = PowerForm(request.POST or None)
if not MixtureModel.objects.filter(SID=sid):
context["text"] = "Before doing any power calculations, the distribution of effects has to be estimated. Please go to 'Model Fit'to initiate and inspect the fit of the mixture model to the distribution."
if not ParameterModel.objects.filter(SID=sid):
context["text"] = "No data found. Go to 'Input' and fill out the form."
else:
powerdata = PowerTableModel.objects.filter(SID=sid)[::-1][0]
powertable = powerdata.data[['newsamplesize','RFT','BF','BH','UN']].round(decimals=2)
powertable.columns=['Sample Size','Random Field Theory','Bonferroni','Benjamini-Hochberg','Uncorrected']
context["power"] = powertable.to_html(index=False,col_space='120px',classes=["table table-striped"])
return render(request,template,context)
def neuropowersamplesize(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowersamplesize.html"
steps = get_neuropower_steps(template, sid)
context = {"steps": steps}
powerinputform = PowerForm(request.POST or None)
if not ParameterModel.objects.filter(SID=sid):
texttop = "No data found. Go to 'Input' and fill out the form."
elif not MixtureModel.objects.filter(SID=sid):
texttop = "Before doing any power calculations, the distribution of effects has to be estimated. Please go to 'Model Fit'to initiate and inspect the fit of the mixture model to the distribution."
else:
texttop = "Hover over the lines to see detailed power predictions"
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
peakdata = PeakTableModel.objects.filter(SID=sid)[::-1][0]
mixdata = MixtureModel.objects.filter(SID=sid)[::-1][0]
peaks = peakdata.data
thresholds = neuropowermodels.threshold(peaks.peak,
peaks.pval,
FWHM=8,
nvox=float(parsdata.nvox),
alpha=0.05,
exc=float(parsdata.ExcZ))
effect_cohen = float(mixdata.mu) / np.sqrt(float(parsdata.Subj))
power_predicted = []
newsubs = range(parsdata.Subj, 301)
for s in newsubs:
projected_effect = float(effect_cohen) * np.sqrt(float(s))
powerpred = {
k: 1 - neuropowermodels.altCDF(v,
projected_effect,
float(mixdata.sigma),
exc=float(parsdata.ExcZ),
method="RFT")
for k, v in thresholds.items()
}
power_predicted.append(powerpred)
powertable = pd.DataFrame(power_predicted)
powertable['newsamplesize'] = newsubs
powertableform = PowerTableForm()
savepowertableform = powertableform.save(commit=False)
savepowertableform.SID = sid
savepowertableform.data = powertable
savepowertableform.save()
powerinputform = PowerForm(request.POST or None)
plothtml = plotPower(sid)['code']
if request.method == "POST":
if powerinputform.is_valid():
savepowerinputform = powerinputform.save(commit=False)
savepowerinputform.SID = sid
savepowerinputform.save()
powerinputdata = PowerModel.objects.filter(SID=sid)[::-1][0]
pow = float(powerinputdata.reqPow)
ss = powerinputdata.reqSS
plotpower = plotPower(sid, powerinputdata.MCP, pow, ss)
plothtml = plotpower['code']
context["textbottom"] = plotpower['text']
context["texttop"] = texttop
context["plothtml"] = plothtml
context["powerinputform"] = powerinputform
return render(request, template, context)
def neuropowerinput(request, neurovault_id=None, end_session=False):
'''step 2: input'''
# Create the session id for the user
sid = get_session_id(request)
# Get the template/step status
template = "neuropower/neuropowerinput.html"
steps = get_neuropower_steps(template, sid)
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="")
neurovault_id = request.GET.get('neurovault', '')
context = {"steps": steps}
# If the user has ended their session, give message
if end_session == True:
context["message"] = "Session has been successfully reset."
if neurovault_id:
neurovault_image = get_url(
"http://neurovault.org/api/images/%s/?format=json" %
(neurovault_id))
collection_id = str(neurovault_image['collection_id'])
neurovault_collection = get_url(
"http://neurovault.org/api/collections/%s/?format=json" %
(collection_id))
parsform = ParameterForm(
request.POST or None,
request.FILES or None,
default_url="",
err="",
initial={
"url": neurovault_image["file"],
"ZorT":
"T" if neurovault_image["map_type"] == "T map" else "Z",
"Subj": neurovault_collection["number_of_subjects"]
})
context["parsform"] = parsform
# fields = ['url','spmfile','maskfile','ZorT','Exc','Subj','Samples',
# 'alpha','Smoothx','Smoothy','Smoothz','Voxx','Voxy','Voxz']
return render(request, template, context)
if not request.method == "POST" or not parsform.is_valid():
context["parsform"] = parsform
return render(request, template, context)
else:
form = parsform.save(commit=False)
form.SID = sid
mapID = "%s_%s" % (str(sid), str(uuid.uuid4()))
form.mapID = mapID
peaktable = os.path.join(settings.MEDIA_ROOT, "peaktables",
"peaks_%s.csv" % (mapID))
form.peaktable = peaktable
form.save()
# handle data: copy to temporary location
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
mapID = "%s_%s" % (str(sid), str(uuid.uuid4()))
form.mapID = mapID
if not parsdata.url == "":
url = parsform.cleaned_data['url']
location = utils.create_temporary_copy(url,
mapID,
mask=False,
url=True)
elif not parsdata.spmfile == "":
spmfile = os.path.join(settings.MEDIA_ROOT, str(parsdata.spmfile))
location = utils.create_temporary_copy(spmfile,
mapID,
mask=False,
url=False)
form.location = location
form.save()
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
SPM = nib.load(parsdata.location)
# check if the IQR is realistic (= check whether these are Z- or T-values)
IQR = np.subtract(*np.percentile(SPM.get_data(), [75, 25]))
if IQR > 20:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="median")
context["parsform"] = parsform
return render(request, template, context)
# save other parameters
form.DoF = parsdata.Subj - 1 if parsdata.Samples == 1 else parsdata.Subj - 2
form.ExcZ = float(parsdata.Exc) if float(
parsdata.Exc) > 1 else -norm.ppf(float(parsdata.Exc))
# handle mask
if parsdata.maskfile == "":
mask = masking.compute_background_mask(SPM,
border_size=2,
opening=True)
nvox = np.sum(mask.get_data())
form.nvox = nvox
else:
maskfile = os.path.join(settings.MEDIA_ROOT,
str(parsdata.maskfile))
masklocation = utils.create_temporary_copy(maskfile,
mapID,
mask=True,
url=False)
mask = nib.load(masklocation).get_data()
# return error when dimensions are different
if SPM.get_data().shape != mask.shape:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="dim")
context["parsform"] = parsform
return render(request, template, context)
else:
SPM_masked = np.multiply(SPM.get_data(), mask)
SPM_nib = nib.Nifti1Image(SPM_masked, np.eye(4))
nib.save(SPM_nib, parsdata.location)
form.nvox = np.sum(mask)
form.save()
if parsdata.spmfile == "":
return HttpResponseRedirect('/neuropowerviewer/')
else:
return HttpResponseRedirect('/neuropowertable/')
def neuropowerinput(request,neurovault_id=None,end_session=False):
'''step 2: input'''
# Create the session id for the user
sid = get_session_id(request)
# Get the template/step status
template = "neuropower/neuropowerinput.html"
steps = get_neuropower_steps(template,sid)
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="")
neurovault_id = request.GET.get('neurovault','')
context = {"steps":steps}
# If the user has ended their session, give message
if end_session == True:
context["message"] = "Session has been successfully reset."
if neurovault_id:
neurovault_image = get_url("http://neurovault.org/api/images/%s/?format=json" %(neurovault_id))
collection_id = str(neurovault_image['collection_id'])
neurovault_collection = get_url("http://neurovault.org/api/collections/%s/?format=json" %(collection_id))
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url = "",
err = "",
initial = {"url":neurovault_image["file"],
"ZorT":"T" if neurovault_image["map_type"] =="T map" else "Z",
"Subj":neurovault_collection["number_of_subjects"]})
context["parsform"] = parsform
# fields = ['url','spmfile','maskfile','ZorT','Exc','Subj','Samples',
# 'alpha','Smoothx','Smoothy','Smoothz','Voxx','Voxy','Voxz']
return render(request,template,context)
if not request.method=="POST" or not parsform.is_valid():
context["parsform"] = parsform
return render(request,template,context)
else:
form = parsform.save(commit=False)
form.SID = sid
mapID = "%s_%s" %(str(sid),str(uuid.uuid4()))
form.mapID = mapID
peaktable = os.path.join(settings.MEDIA_ROOT,"peaktables","peaks_%s.csv" %(mapID))
form.peaktable = peaktable
form.save()
# handle data: copy to temporary location
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
mapID = "%s_%s" %(str(sid),str(uuid.uuid4()))
form.mapID = mapID
if not parsdata.url == "":
url = parsform.cleaned_data['url']
location = utils.create_temporary_copy(url,mapID,mask=False,url=True)
elif not parsdata.spmfile == "":
spmfile = os.path.join(settings.MEDIA_ROOT,str(parsdata.spmfile))
location = utils.create_temporary_copy(spmfile,mapID,mask=False, url=False)
form.location = location
form.save()
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
SPM = nib.load(parsdata.location)
# check if the IQR is realistic (= check whether these are Z- or T-values)
IQR = np.subtract(*np.percentile(SPM.get_data(),[75,25]))
if IQR > 20:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url = "URL to nifti image",
err = "median")
context["parsform"] = parsform
return render(request,template,context)
# save other parameters
form.DoF = parsdata.Subj-1 if parsdata.Samples==1 else parsdata.Subj-2
form.ExcZ = float(parsdata.Exc) if float(parsdata.Exc)>1 else -norm.ppf(float(parsdata.Exc))
# handle mask
if parsdata.maskfile == "":
mask = masking.compute_background_mask(SPM,border_size=2, opening=True)
nvox = np.sum(mask.get_data())
form.nvox = nvox
else:
maskfile = os.path.join(settings.MEDIA_ROOT,str(parsdata.maskfile))
masklocation = utils.create_temporary_copy(maskfile,mapID,mask=True,url=False)
mask = nib.load(masklocation).get_data()
# return error when dimensions are different
if SPM.get_data().shape != mask.shape:
parsform = ParameterForm(request.POST or None,
request.FILES or None,
default_url="URL to nifti image",
err="dim")
context["parsform"] = parsform
return render(request,template,context)
else:
SPM_masked = np.multiply(SPM.get_data(),mask)
SPM_nib = nib.Nifti1Image(SPM_masked,np.eye(4))
nib.save(SPM_nib,parsdata.location)
form.nvox = np.sum(mask)
form.save()
if parsdata.spmfile == "":
return HttpResponseRedirect('/neuropowerviewer/')
else:
return HttpResponseRedirect('/neuropowertable/')
def plotModel(request):
plt.switch_backend('agg')
sid = get_session_id(request)
mixdata = MixtureModel.objects.filter(SID=sid)[::-1][0]
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
peakdata = PeakTableModel.objects.filter(SID=sid)[::-1][0]
peaks = peakdata.data
twocol = Paired_12.mpl_colors
xn = np.arange(-10, 30, 0.01)
nul = [1 - float(mixdata.pi1)] * npm.nulPDF(
xn, exc=float(parsdata.ExcZ), method="RFT")
alt = float(mixdata.pi1) * npm.altPDF(xn,
mu=float(mixdata.mu),
sigma=float(mixdata.sigma),
exc=float(parsdata.ExcZ),
method="RFT")
mix = npm.mixprobdens(xn,
pi1=float(mixdata.pi1),
mu=float(mixdata.mu),
sigma=float(mixdata.sigma),
exc=2,
method="RFT")
xn_p = np.arange(0, 1, 0.01)
alt_p = [1 - float(mixdata.pi1)] * scipy.stats.beta.pdf(
xn_p, float(mixdata.a), 1) + 1 - float(mixdata.pi1)
null_p = [1 - float(mixdata.pi1)] * len(xn_p)
mpl.rcParams['font.size'] = '11.0'
fig, axs = plt.subplots(1, 2, figsize=(14, 5))
fig.patch.set_facecolor('None')
fig.subplots_adjust(hspace=.5, wspace=0.3)
axs = axs.ravel()
axs[0].hist(peaks.pval,
lw=0,
normed=True,
facecolor=twocol[0],
bins=np.arange(0, 1.1, 0.1),
label="observed distribution")
axs[0].set_ylim([0, 3])
axs[0].plot(xn_p, null_p, color=twocol[3], lw=2, label="null distribution")
axs[0].plot(xn_p,
alt_p,
color=twocol[5],
lw=2,
label="alternative distribution")
axs[0].legend(loc="upper right", frameon=False)
axs[0].set_title("Distribution of " + str(len(peaks)) +
" peak p-values \n $\pi_1$ = " +
str(round(float(mixdata.pi1), 2)))
axs[0].set_xlabel("Peak p-values")
axs[0].set_ylabel("Density")
axs[1].hist(peaks.peak,
lw=0,
facecolor=twocol[0],
normed=True,
bins=np.arange(min(peaks.peak), 30, 0.3),
label="observed distribution")
axs[1].set_xlim([float(parsdata.ExcZ), np.max(peaks.peak)])
axs[1].set_ylim([0, 1])
axs[1].plot(xn, nul, color=twocol[3], lw=2, label="null distribution")
axs[1].plot(xn,
alt,
color=twocol[5],
lw=2,
label="alternative distribution")
axs[1].plot(xn, mix, color=twocol[1], lw=2, label="total distribution")
peak_heights_string = str(
round(float(mixdata.mu) / np.sqrt(parsdata.Subj), 2))
axs[1].set_title("Distribution of peak heights \n $\delta_1$ = %s" %
(peak_heights_string))
axs[1].set_xlabel("Peak heights (z-values)")
axs[1].set_ylabel("Density")
axs[1].legend(loc="upper right", frameon=False)
canvas = FigureCanvas(fig)
response = HttpResponse(content_type='image/png')
canvas.print_png(response)
return response
def neuropowersamplesize(request):
# Get the template/step status
sid = get_session_id(request)
template = "neuropower/neuropowersamplesize.html"
context = {}
# Load model data
context[
'texttop'] = "Hover over the lines to see detailed power predictions"
parsdata = ParameterModel.objects.filter(SID=sid)[::-1][0]
peakdata = PeakTableModel.objects.filter(SID=sid)[::-1][0]
if not peakdata.err == "":
context["text"] = peakdata.err
return render(request, template, context)
mixdata = MixtureModel.objects.filter(SID=sid)[::-1][0]
peaks = peakdata.data
# Estimate smoothness
if parsdata.SmoothEst == 1:
#Manual
FWHM = np.array([parsdata.Smoothx, parsdata.Smoothy, parsdata.Smoothz])
voxsize = np.array([parsdata.Voxx, parsdata.Voxy, parsdata.Voxz])
elif parsdata.SmoothEst == 2:
# Estimate from data
cmd_smooth = "smoothest -V -z " + parsdata.location + " -m " + parsdata.masklocation
tmp = os.popen(cmd_smooth).read()
FWHM = np.array(
[float(x[8:15]) for x in tmp.split("\n")[16].split(",")])
voxsize = np.array([1, 1, 1])
# Compute thresholds and standardised effect size
thresholds = neuropowermodels.threshold(peaks.peak,
peaks.pval,
FWHM=FWHM,
voxsize=voxsize,
nvox=float(parsdata.nvox),
alpha=float(parsdata.alpha),
exc=float(parsdata.ExcZ))
effect_cohen = float(mixdata.mu) / np.sqrt(float(parsdata.Subj))
# Compute predicted power
power_predicted = []
newsubs = range(parsdata.Subj, parsdata.Subj + 600)
for s in newsubs:
projected_effect = float(effect_cohen) * np.sqrt(float(s))
powerpred = {
k: 1 - neuropowermodels.altCDF([v],
projected_effect,
float(mixdata.sigma),
exc=float(parsdata.ExcZ),
method="RFT")[0]
for k, v in thresholds.items() if not v == 'nan'
}
power_predicted.append(powerpred)
# Check if there are thresholds (mainly BH) missing
missing = [k for k, v in thresholds.items() if v == 'nan']
if len(missing) > 0:
context[
'MCPwarning'] = "There is not enough power to estimate a threshold for " + " and ".join(
missing) + "."
# Save power calculation to table and model
powertable = pd.DataFrame(power_predicted)
powertable['newsamplesize'] = newsubs
powertableform = PowerTableForm()
savepowertableform = powertableform.save(commit=False)
savepowertableform.SID = sid
savepowertableform.data = powertable
savepowertableform.save()
powerinputform = PowerForm(request.POST or None)
context['plothtml'] = plotPower(sid)['code']
# Adjust plot with specific power or sample size question
if request.method == "POST":
if powerinputform.is_valid():
savepowerinputform = powerinputform.save(commit=False)
savepowerinputform.SID = sid
savepowerinputform.save()
powerinputdata = PowerModel.objects.filter(SID=sid)[::-1][0]
pow = float(powerinputdata.reqPow)
ss = powerinputdata.reqSS
plotpower = plotPower(sid, powerinputdata.MCP, pow, ss)
context['plothtml'] = plotpower['code']
context["textbottom"] = plotpower['text']
context["powerinputform"] = powerinputform
# Get step status
context["steps"] = get_neuropower_steps(template, sid)
return render(request, template, context)