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Hippounit integration #3

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6db648f
Use HippoUnit for fitness calculation
blazma Sep 1, 2022
6795724
Fix bugs in Hippounit integration
blazma Oct 7, 2022
783f52e
Do not pre-load HU model
blazma Oct 7, 2022
0e630b6
Allow for multiple HU tests, integration of DepolBlockTest and bAPTest
blazma Nov 15, 2022
f8b86c4
Integrate PSPAttenuationTest
blazma Nov 21, 2022
98a5299
Integrate ObliqueIntegrationTest, add test scores to output file
blazma Dec 20, 2022
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74 changes: 50 additions & 24 deletions optimizer/Core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,6 +112,8 @@ def htmlTable(self,header_list,data):
def htmlPciture(self,inp):
return "<p align=\"center\"><img style=\"border:none;\" src=\""+inp+"\" ></p>"

def htmlPdf(self, inp):
return "<p align=\"center\"><embed src = \""+inp+"#toolbar=0&navpanes=0&scrollbar=0\" width = \"800px\" height = \"630px\" /></p>"

def Print(self):
print([self.option_handler.GetFileOption(),
Expand All @@ -136,11 +138,13 @@ def FirstStep(self,args):
self.option_handler.SetFileOptions(args.get("file"))
self.option_handler.SetInputOptions(args.get("input"))

self.data_handler.Read([self.option_handler.input_dir],self.option_handler.input_size,self.option_handler.input_scale,self.option_handler.input_length,self.option_handler.input_freq,self.option_handler.type[-1])


if self.option_handler.type[-1]=='features':
self.option_handler.input_size= len(self.data_handler.features_data['stim_amp'])
stim_type = self.option_handler.type[-1]
if stim_type == "hippounit":
self.option_handler.SetSimParam(["hippounit", []])
else:
self.data_handler.Read([self.option_handler.input_dir],self.option_handler.input_size,self.option_handler.input_scale,self.option_handler.input_length,self.option_handler.input_freq,stim_type)
if stim_type == "features":
self.option_handler.input_size= len(self.data_handler.features_data['stim_amp'])

def LoadModel(self,args):
"""
Expand All @@ -156,6 +160,8 @@ def LoadModel(self,args):
if self.option_handler.GetSimParam()[0]=="Neuron":
self.option_handler.SetModelOptions(args.get("model"))
self.model_handler=modelHandlerNeuron(self.option_handler.model_path,self.option_handler.model_spec_dir,self.option_handler.base_dir)
elif self.option_handler.GetSimParam()[0] == "hippounit":
return
else:
self.model_handler=externalHandler(self.option_handler.GetSimParam()[1])
self.model_handler.SetNParams(self.option_handler)
Expand Down Expand Up @@ -347,7 +353,7 @@ def ThirdStep(self,args):
#tmp.append(args.get("starting_points"))
self.option_handler.SetOptimizerOptions(tmp)

if self.option_handler.type[-1]!='features':
if self.option_handler.type[-1]!='features' and self.option_handler.type[-1]!='hippounit':
if self.option_handler.run_controll_dt<self.data_handler.data.step:
print("re-sampling because integration step is smaller then data step")
print((self.option_handler.run_controll_dt,self.data_handler.data.step))
Expand Down Expand Up @@ -376,7 +382,9 @@ def ThirdStep(self,args):

exec("self.optimizer="+self.option_handler.algorithm_name+"(self.data_handler,self.option_handler)")

if self.option_handler.type[-1]!= 'features':
if self.option_handler.type[-1] == 'hippounit':
self.option_handler.feat_str = "" # TODO
elif self.option_handler.type[-1]!= 'features':
self.option_handler.feat_str=", ".join([self.ffun_mapper[x.__name__] for x in self.option_handler.feats])
else:
self.option_handler.feat_str=", ".join(self.option_handler.feats)
Expand Down Expand Up @@ -435,22 +443,29 @@ def FourthStep(self,args={}):
A report of the results is generated in the form of a html document.
:param args: currently not in use
"""
if self.option_handler.type[-1] == 'hippounit':
self.optimizer.fit_obj.is_figures_saved = True

self.best_fit=self.optimizer.fit_obj.single_objective_fitness([self.optimizer.fit_obj.normalize(self.optimal_params)],delete_model=False)
self.final_result=[]
self.error_comps=[]
if self.option_handler.type[-1]!= 'features':
k_range=self.data_handler.number_of_traces()
if self.option_handler.type[-1] == 'hippounit':
k_range = 1
self.error_comps.append(self.optimizer.fit_obj.getTestErrorComponents())
else:
k_range=len(self.data_handler.features_data["stim_amp"])

for k in range(k_range):
self.error_comps.append(self.optimizer.fit_obj.getErrorComponents(k, self.optimizer.fit_obj.model_trace[k]))
trace_handler=open("result_trace"+str(k)+".txt","w+")
for l in self.optimizer.fit_obj.model_trace[k]:
trace_handler.write(str(l))
trace_handler.write("\n")
trace_handler.close()
self.final_result.append(self.optimizer.fit_obj.model_trace[k])
if self.option_handler.type[-1]!= 'features':
k_range=self.data_handler.number_of_traces()
else:
k_range=len(self.data_handler.features_data["stim_amp"])

for k in range(k_range):
self.error_comps.append(self.optimizer.fit_obj.getErrorComponents(k, self.optimizer.fit_obj.model_trace[k]))
trace_handler=open("result_trace"+str(k)+".txt","w+")
for l in self.optimizer.fit_obj.model_trace[k]:
trace_handler.write(str(l))
trace_handler.write("\n")
trace_handler.close()
self.final_result.append(self.optimizer.fit_obj.model_trace[k])

if isinstance(self.optimizer.fit_obj.model, externalHandler):
self.optimizer.fit_obj.model.record[0]=[]
Expand All @@ -469,7 +484,15 @@ def FourthStep(self,args={}):
tmp_str+=self.htmlTable(["Parameter Name","Minimum","Maximum","Optimum"], tmp_list)+"\n"
tmp_str+="<center><p>"+self.htmlStrBold("Fitness: ")
tmp_str+=self.htmlStrBold(str(self.best_fit))+"</p></center>\n"
tmp_str+=self.htmlPciture("result_trace.png")+"\n"
if self.option_handler.type[-1] == "hippounit":
hippounit_settings = self.optimizer.fit_obj.model.settings
model_name = hippounit_settings["model"]["name"]
test_name = 'somaticfeat'
dataset_name = hippounit_settings["model"]["dataset"]
pdf_path = "output/figs/{}_{}/{}/traces.pdf".format(test_name, dataset_name, model_name)
tmp_str+=self.htmlPdf(pdf_path)+"\n"
else:
tmp_str+=self.htmlPciture("result_trace.png")+"\n"
for k in list(self.option_handler.GetOptimizerOptions().keys()):
tmp_str+="<p><b>"+k+" =</b> "+str(self.option_handler.GetOptimizerOptions()[k])+"</p>\n"
tmp_str+="<p><b>feats =</b> "+self.option_handler.feat_str +"</p>\n"
Expand All @@ -483,7 +506,7 @@ def FourthStep(self,args={}):
tmp_list=[]
for t in self.error_comps:
for c in t:
if self.option_handler.type[-1]!='features':
if self.option_handler.type[-1]!='features' and self.option_handler.type[-1]!= 'hippounit':
#tmp_str.append( "*".join([str(c[0]),c[1].__name__]))
tmp_list.append([self.ffun_mapper[c[1].__name__],
str(c[2]),
Expand All @@ -507,7 +530,7 @@ def FourthStep(self,args={}):
tmp[1]+=c[t_idx][2]
tmp[2]=c[t_idx][0]
tmp[3]+=c[t_idx][2]*c[t_idx][0]
if self.option_handler.type[-1]!='features':
if self.option_handler.type[-1]!='features' and self.option_handler.type[-1]!= 'hippounit':
tmp[0]=self.ffun_mapper[c[t_idx][1].__name__]
else:
tmp[0]=(c[t_idx][1])
Expand All @@ -525,8 +548,11 @@ def FourthStep(self,args={}):
target_dict = {"data_type":self.option_handler.type[-1],"file_name":self.option_handler.input_dir.split('/')[-1],"number_of_traces":k_range,"stim_delay":self.option_handler.stim_del,
"stim_duration":self.option_handler.stim_dur}
json_var = {"model":self.name,"optimization":opt_dict,"parameters":param_dict,"error_function":error_dict, "algorithm":alg_dict,"target_data":target_dict}

if self.option_handler.type[-1]=='features':

if self.option_handler.type[-1]=='hippounit':
with open('metadata.json', 'w+') as outfile:
json.dump(json_var, outfile, indent=4)
elif self.option_handler.type[-1]=='features':
with open(self.option_handler.input_dir, 'r') as outfile:
input_features=json.load(outfile)
amp_dict={amp_vals:[] for amp_vals in input_features["stimuli"]["amplitudes"]}
Expand Down
75 changes: 38 additions & 37 deletions optimizer/cmd_line.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ def main(fname, param=None):
"""
try:
with open(fname, "r") as f:
json_data = json.load(f)
json_data = json.load(f)
except IOError as ioe:
print(ioe)
sys.exit("File not found!\n")
Expand All @@ -46,45 +46,46 @@ def main(fname, param=None):
core.ThirdStep(kwargs)
core.FourthStep()
print("resulting parameters: ", core.optimal_params)
fig = figure(1, figsize=(7, 6))
fig = figure(figsize=(7, 6))
axes = fig.add_subplot(111)
exp_data = []
model_data = []
if core.option_handler.type[-1] != 'features':
for n in range(core.data_handler.number_of_traces()):
exp_data.extend(core.data_handler.data.GetTrace(n))
model_data.extend(core.final_result[n])
no_traces = core.data_handler.number_of_traces()
else:
for n in range(len(core.data_handler.features_data["stim_amp"])):
model_data.extend(core.final_result[n])
no_traces = len(core.data_handler.features_data["stim_amp"])
if core.option_handler.type[-1] != 'features':
t = int(ceil(core.option_handler.input_length))
else:
t = int(ceil(core.option_handler.run_controll_tstop))
step = core.option_handler.run_controll_dt
axes.set_xticks([n for n in range(0, int((t * no_traces) / (step)), int((t * no_traces) / (step) / 5.0)) ])
axes.set_xticklabels([str(n) for n in range(0, int(t * no_traces), int((t * no_traces) / 5))])
if core.option_handler.type[-1] != 'hippounit':
if core.option_handler.type[-1] != 'features':
for n in range(core.data_handler.number_of_traces()):
exp_data.extend(core.data_handler.data.GetTrace(n))
model_data.extend(core.final_result[n])
no_traces = core.data_handler.number_of_traces()
else:
for n in range(len(core.data_handler.features_data["stim_amp"])):
model_data.extend(core.final_result[n])
no_traces = len(core.data_handler.features_data["stim_amp"])
if core.option_handler.type[-1] != 'features' and core.option_handler.type[-1] != 'hippounit':
t = int(ceil(core.option_handler.input_length))
else:
t = int(ceil(core.option_handler.run_controll_tstop))
step = core.option_handler.run_controll_dt
axes.set_xticks([n for n in range(0, int((t * no_traces) / (step)), int((t * no_traces) / (step) / 5.0)) ])
axes.set_xticklabels([str(n) for n in range(0, int(t * no_traces), int((t * no_traces) / 5))])

axes.set_xlabel("time [ms]")
if core.option_handler.type[-1]!= 'features':
_type = core.data_handler.data.type
else:
_type = "Voltage" if core.option_handler.run_controll_record =="v" else "Current" if core.option_handler.run_controll_record == "c" else ""
axes.set_ylabel(_type + " [" + core.option_handler.input_scale + "]")
if core.option_handler.type[-1]!= 'features':
axes.plot(list(range(0, len(exp_data))), exp_data)
axes.plot(list(range(0, len(model_data))), model_data, 'r')
axes.legend(["target", "model"])
else:
axes.plot(list(range(0, len(model_data))), model_data, 'r')
axes.legend(["model"])
fig.savefig("result_trace.png", dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1)
fig.savefig("result_trace.eps", dpi=None, facecolor='w', edgecolor='w')
fig.savefig("result_trace.svg", dpi=None, facecolor='w', edgecolor='w')

axes.set_xlabel("time [ms]")
if core.option_handler.type[-1]!= 'features' and core.option_handler.type[-1] != 'hippounit':
_type = core.data_handler.data.type
else:
_type = "Voltage" if core.option_handler.run_controll_record =="v" else "Current" if core.option_handler.run_controll_record == "c" else ""
axes.set_ylabel(_type + " [" + core.option_handler.input_scale + "]")
if core.option_handler.type[-1]!= 'features' and core.option_handler.type[-1] != 'hippounit':
axes.plot(list(range(0, len(exp_data))), exp_data)
axes.plot(list(range(0, len(model_data))), model_data, 'r')
axes.legend(["target", "model"])
else:
axes.plot(list(range(0, len(model_data))), model_data, 'r')
axes.legend(["model"])
fig.savefig("result_trace.png", dpi=None, facecolor='w', edgecolor='w',
orientation='portrait', papertype=None, format=None,
transparent=False, bbox_inches=None, pad_inches=0.1)
fig.savefig("result_trace.eps", dpi=None, facecolor='w', edgecolor='w')
fig.savefig("result_trace.svg", dpi=None, facecolor='w', edgecolor='w')


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