def consinput(request):
# Get the template/step status
template = "design/cons.html"
context = {}
# Get the session ID and database entry
sid = get_session_id(request)
context["steps"] = get_design_steps(template, sid)
try:
desdata = DesignModel.objects.get(SID=sid)
except DesignModel.DoesNotExist:
return HttpResponseRedirect('../maininput/')
# Define form
# If page was result of POST or not valid: show form with db entries
# Else: go to next page
if desdata.nested == True:
a = np.array(['P0','P1','P2','P3','P4','P5','P6','P7','P8','P9'])
b = np.array(desdata.nest_structure)
Pmat = [a[b==(i+1)].tolist() for i in xrange(desdata.nest_classes)]
consform = DesignNestedConsForm(
request.POST or None, instance=desdata, stim=desdata.S, cons=desdata.Clen, structure=Pmat, classes=desdata.nest_structure)
else:
consform = DesignConsForm(
request.POST or None, instance=desdata, stim=desdata.S, cons=desdata.Clen)
if not request.method == "POST":
context["consform"] = consform
return render(request, template, context)
else:
form = consform.save(commit=False)
form.SID = sid
form.conpars = True
form.save()
# get data and change parameters
consform = DesignProbsForm(None, instance=desdata)
consform = consform.save(commit=False)
matrices = probs_and_cons(sid)
if matrices['empty'] == True:
context['message'] = "Please fill out all probabilities and contrasts"
context["consform"] = DesignConsForm(
request.POST or None, instance=desdata, stim=desdata.S, cons=desdata.Clen)
return render(request, "design/cons.html", context)
consform.P = matrices['P']
consform.C = matrices['C']
if desdata.HardProb == True:
consform.G = 200
consform.I = 100
consform.save()
return HttpResponseRedirect('../review/')
def GeneticAlgorithm(sid,ignore_result=False):
desdata = DesignModel.objects.get(SID=sid)
subject = "NeuroDesign: optimisation process started"
sender = "NeuroDesign"
sendermail = "*****@*****.**"
message = "Your design optimisation has now started. You can follow the progress here:"+" http://development.neuropowertools.org/design/runGA/?retrieve="+str(desdata.shareID)+". Thank you for using NeuroDesign."
recipient = str(desdata.email)
key = settings.MAILGUN_KEY
command = "curl -s --user '" + key + "' https://api.mailgun.net/v3/neuropowertools.org/messages -F from='" + sender + \
" <" + sendermail + ">' -F to=" + recipient + " -F subject="+subject+" -F text='" + message + "'"
os.system(command)
matrices = probs_and_cons(sid)
if desdata.ITImodel == 1:
model = "fixed"
ITImean = desdata.ITIfixed
ITImin = None
ITImax = None
elif desdata.ITImodel == 2:
model = "exponential"
ITImean = desdata.ITItruncmean
ITImin = desdata.ITItruncmin
ITImax = desdata.ITItruncmax
elif desdata.ITImodel == 3:
model = "uniform"
ITImin = desdata.ITIunifmin
ITImax = desdata.ITIunifmax
ITImean = (desdata.ITIunifmin+desdata.ITIunifmax)/2.
EXP = geneticalgorithm.experiment(
TR = desdata.TR,
n_trials = desdata.L,
P = matrices['P'],
C = matrices['C'],
duration = desdata.duration,
n_stimuli = desdata.S,
rho = desdata.rho,
resolution = desdata.resolution,
stim_duration = desdata.stim_duration,
restnum = desdata.RestNum,
restdur = desdata.RestDur,
ITImodel = model,
ITImin = ITImin,
ITImean = ITImean,
ITImax = ITImax,
maxrep = desdata.MaxRepeat,
hardprob = desdata.HardProb,
t_prestim = desdata.t_prestim,
t_poststim = desdata.t_poststim
)
seed = np.random.randint(10000)
POP = geneticalgorithm.population(
experiment = EXP,
confoundorder = desdata.ConfoundOrder,
G = desdata.G,
R = [0.4,0.4,0.2],
q = desdata.q,
weights = desdata.W,
I = desdata.I,
preruncycles = desdata.preruncycles,
cycles = desdata.cycles,
convergence=desdata.conv_crit,
folder=desdata.onsetsfolder,
Aoptimality = True if desdata.Aoptimality == 1 else False,
seed=seed
)
POP.print_cmd()
desdata = DesignModel.objects.get(SID=sid)
runform = DesignRunForm(None, instance=desdata)
form = runform.save(commit=False)
form.running = 1
form.seed = seed
form.cmd = POP.cmd
form.save()
POP.naturalselection()
POP.download()
# Select optimal design
desdata = DesignModel.objects.get(SID=sid)
runform = DesignRunForm(None, instance=desdata)
form = runform.save(commit=False)
form.convergence = POP.finished
form.zip_filename = POP.zip_filename
form.zipfile = POP.file
form.save()
subject = "NeuroDesign: optimisation process ended"
sender = "NeuroDesign"
sendermail = "*****@*****.**"
message = "Your design optimisation has now ended. You can download the results here:"+" http://development.neuropowertools.org/design/runGA/?retrieve="+str(desdata.SID)+". Thank you for using NeuroDesign."
recipient = str(desdata.email)
key = settings.MAILGUN_KEY
command = "curl -s --user '" + key + "' https://api.mailgun.net/v3/neuropowertools.org/messages -F from='" + sender + \
" <" + sendermail + ">' -F to=" + recipient + " -F subject="+subject+" -F text='" + message + "'"
os.system(command)
def review(request):
# Get the template/step status
template = "design/review.html"
context = {}
# Get the session ID and database entry
sid = get_session_id(request)
context["steps"] = get_design_steps(template, sid)
try:
desdata = DesignModel.objects.get(SID=sid)
except DesignModel.DoesNotExist:
return HttpResponseRedirect('../maininput/')
# Define form
revform = DesignReviewForm(request.POST or None, instance=desdata)
context["revform"] = revform
# Set summary variables in context
matrices = probs_and_cons(sid)
context["Phtml"] = matrices["Phtml"]
context["Chtml"] = matrices["Chtml"]
context["Whtml"] = weights_html(desdata.W)
context['desdata'] = desdata
context["message"] = ""
if desdata.HardProb == True:
context["message"] = context["message"] + \
"<br><p><b>Warning:</b> Because of the hard limit on the frequencies, we increased the size of the generation and the number of random designs per generation. This might slow down the optimisation. </p>"
if desdata.MaxRepeat < 10 and desdata.S == 2:
context["message"] = context["message"] + "<br><p><b>Warning:</b> With only 2 stimuli, many random designs have repetitions larger than " + \
str(desdata.MaxRepeat) + \
". We increased the number of random designs per generation, but this might slow down the optimisation. </p>"
if desdata.S>5 and desdata.L>200 and desdata.ITImax>3 and (desdata.Restnum<30 and desdata.Resdur>30) and desdata.C.shape[0]>5:
context['message'] = context['message']+"<br><p><b>Warning:</b>This is a long and complex design. Be aware that the optimisation will take a <b>long</b> time.</p>"
# Duration
if desdata.ITImodel == 1:
context['ITImodel'] = "fixed"
mean = desdata.ITIfixed
context['ITI'] = "The ITI's are equal to "+str(mean)+" seconds."
elif desdata.ITImodel == 2:
context['ITImodel'] = 'truncated exponential'
mean = desdata.ITItruncmean
context['ITI'] = "The ITI's are between "+str(desdata.ITItruncmin)+" and "+str(desdata.ITItruncmax)+" seconds and on average "+str(mean)+" seconds."
elif desdata.ITImodel == 3:
context['ITImodel'] = 'uniform'
mean = (desdata.ITIunifmin+desdata.ITIunifmax)/2.
context['ITI'] = "The ITI's are between "+str(desdata.ITIunifmin)+" and "+str(desdata.ITIunifmax)+" seconds and on average "+str(mean)+" seconds."
if desdata.L:
dur = mean*desdata.L+desdata.RestNum*desdata.RestDur
elif desdata.duration:
dur = desdata.duration
if dur > 1800:
context['message'] = context['message'] + "<p><b>Warning:</b> The run you request is longer dan 30 minutes. This optimisation will take <b>a long</b> time. You could set the resolution lower, or split the experiment in multiple shorter runs. Or you could grab a coffee and wait a few hours for the optimisation to complete.</p>"
# If page was result of POST: show summary
# Else: go to next page
if not request.method == "POST":
return render(request, template, context)
else:
form = revform.save(commit=False)
form.SID = sid
form.save()
return HttpResponseRedirect('../runGA/')
def GeneticAlgorithm(sid,ignore_result=False):
desdata = DesignModel.objects.filter(SID=sid).last()
matrices = probs_and_cons(sid)
if desdata.ITImodel == 1:
model = "fixed"
ITImean = desdata.ITIfixed
ITImin = None
ITImax = None
elif desdata.ITImodel == 2:
model = "exponential"
ITImean = desdata.ITItruncmean
ITImin = desdata.ITItruncmin
ITImax = desdata.ITItruncmax
elif desdata.ITImodel == 3:
model = "uniform"
ITImin = desdata.ITIunifmin
ITImax = desdata.ITIunifmax
ITImean = (desdata.ITIunifmin+desdata.ITIunifmax)/2.
if (desdata.MaxRepeat<4 and desdata.S<5) or desdata.S==2:
R = [0,1,0]
else:
R = [0.4,0.4,0.2]
print("R: "+str(R))
EXP = geneticalgorithm.experiment(
TR = desdata.TR,
n_trials = desdata.L,
P = matrices['P'],
C = matrices['C'],
duration = desdata.duration,
n_stimuli = desdata.S,
rho = desdata.rho,
resolution = desdata.resolution,
stim_duration = desdata.stim_duration,
restnum = desdata.RestNum,
restdur = desdata.RestDur,
ITImodel = model,
ITImin = ITImin,
ITImean = ITImean,
ITImax = ITImax,
confoundorder = desdata.ConfoundOrder,
maxrep = desdata.MaxRepeat,
hardprob = desdata.HardProb,
t_pre = desdata.t_prestim,
t_post = desdata.t_poststim
)
seed = np.random.randint(10000)
POP = geneticalgorithm.population(
experiment = EXP,
G = desdata.G,
R = R,
q = desdata.q,
weights = desdata.W,
I = desdata.I,
preruncycles = desdata.preruncycles,
cycles = desdata.cycles,
convergence=desdata.conv_crit,
folder=desdata.onsets_folder,
outdes=desdata.outdes,
Aoptimality = True if desdata.Aoptimality == 1 else False,
seed=seed
)
POP.print_cmd()
desdata = DesignModel.objects.filter(SID=sid).last()
runform = DesignRunForm(None, instance=desdata)
form = runform.save(commit=False)
form.taskstatus = 2
form.running = 1
form.seed = seed
form.cmd = POP.cmd
form.save()
local_naturalselection(POP,sid)
POP.download()
desdata = DesignModel.objects.filter(SID=sid).last()
runform = DesignRunForm(None, instance=desdata)
form = runform.save(commit=False)
form.finished = True
form.running = 0
form.taskstatus = 3
form.save()
# list all files (with full path) for report
infiles = [os.path.join(dp, f) for dp, dn, fn in os.walk(os.path.expanduser(desdata.onsets_folder)) for f in fn]
# strip away /var/tmp --> design_suffix/xxx
outfiles = [x[9:] for x in infiles]
for file in range(len(infiles)):
push_to_s3(infiles[file],"designs/"+outfiles[file])
# Select optimal design
desdata = DesignModel.objects.filter(SID=sid).last()
runform = DesignRunForm(None, instance=desdata)
form = runform.save(commit=False)
form.convergence = POP.finished
form.files = outfiles
form.save()
subject = "NeuroDesign: optimisation process ended"
sender = "NeuroDesign"
sendermail = "*****@*****.**"
message = "Your design optimisation has now ended. You can download the results here:"+" http://www.neuropowertools.org/design/runGA/?retrieve="+str(desdata.shareID)+". Thank you for using NeuroDesign."
recipient = str(desdata.email)
#recipient = '*****@*****.**'
key = settings.MAILGUN_KEY
command = "curl -s --user '" + key + "' https://api.mailgun.net/v3/neuropowertools.org/messages -F from='" + sender + \
" <" + sendermail + ">' -F to=" + recipient + " -F subject="+subject+" -F text='" + message + "'"
os.system(command)
