def ajax_overplot(request, model_id):
flux_data = oplot_process(file=None, model_id=model_id)
data = {
"flux_data": flux_data,
}
return HttpResponse(simplejson.dumps(data),
content_type="application/json")
def ajax_overplot(request, model_id):
print("ajax_overplot")
flux_data = oplot_process(file=None, model_id=model_id)
data = {
"flux_data": flux_data,
}
return HttpResponse(simplejson.dumps(data), content_type="application/json")
def fitter(request):
import random
if request.method == "POST":
uploaded_file = request.FILES.get("file")
search_option = request.POST.get("fitType")
flux_data = oplot_process(file=None, model_id=39)
matched_models = []
data = {
"flux_data": flux_data,
}
for x in range(10):
rand = int(random.random() * 40 + 1)
metatype = Spectra.objects.filter(model_id=rand).distinct(
"model_id")[0].metatype[:4].title() + Spectra.objects.filter(
model_id=rand).distinct("model_id")[0].metatype[
5:-1].title() + Spectra.objects.filter(
model_id=rand).distinct(
"model_id")[0].metatype[-1:].upper()
meta_data = eval(metatype).objects.filter(model_id=rand)[0]
matched_models.append(meta_data)
# fit(uploaded_file,search_option)
# going to need an array of 10 models, so I can get model_ids in the template
return render_to_response("fitter_results.html", {
"data": flux_data,
"matched_models": matched_models
},
context_instance=RequestContext(
request, {"home_url": HOME_URL}))
return render_to_response("fitter_form.html",
context_instance=RequestContext(
request, {"home_url": HOME_URL}))
def fitter(request):
import random
if request.method == "POST":
uploaded_file = request.FILES.get("file")
search_option = request.POST.get("fitType")
flux_data = oplot_process(file=None, model_id=39)
matched_models = []
data = {
"flux_data": flux_data,
}
for x in range(10):
rand = int(random.random() * 40 + 1)
metatype = Spectra.objects.filter(model_id = rand).distinct("model_id")[0].metatype[:4].title() + Spectra.objects.filter(model_id = rand).distinct("model_id")[0].metatype[5:-1].title() + Spectra.objects.filter(model_id = rand).distinct("model_id")[0].metatype[-1:].upper()
meta_data = eval(metatype).objects.filter(model_id = rand)[0]
matched_models.append(meta_data)
# fit(uploaded_file,search_option)
# going to need an array of 10 models, so I can get model_ids in the template
return render_to_response("fitter_results.html", {"data":flux_data, "matched_models":matched_models}, context_instance=RequestContext(request))
return render_to_response("fitter_form.html", context_instance=RequestContext(request))