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IterativeMethod.py
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IterativeMethod.py
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
"""
Created on Mon Jun 24 16:04:07 2019
@author: Hannah Choi
"""
"""
This code is called in main run files to refine hierarchy scores via iterations.
"""
# In[]: Cortico-cortical iterations
def iterativeCC(dfi,dfV,n_iter):
df=dfi.sort_values('areas')
hrc0=df["hrc"]
hr0=df["hr"]
areas=np.asarray(df.areas)
n_area=np.shape(dfi)[0]
hrc_iter_vals=np.zeros([n_area,n_iter])
hr_iter_vals=np.zeros([n_area,n_iter])
hrc_iter_vals[:,0]=hrc0
hr_iter_vals[:,0]=hr0
hrc_iter=pd.DataFrame(hrc_iter_vals)
hrc_iter['areas'] = areas
cols = hrc_iter.columns.tolist()
cols = cols[-1:] + cols[:-1]
hrc_iter = hrc_iter[cols]
hr_iter=pd.DataFrame(hr_iter_vals)
hr_iter['areas'] = areas
cols = hr_iter.columns.tolist()
cols = cols[-1:] + cols[:-1]
hr_iter = hr_iter[cols]
for i_iter in range(1,n_iter+1):
print i_iter
for i_area in range(0,n_area):
current_target=np.array(dfV[dfV.source == areas[i_area]].target)
current_source=np.array(dfV[dfV.target == areas[i_area]].source)
hrs_ffbnc=np.array(dfV[dfV.source == areas[i_area]].ffb_nc)
hrt_ffbnc=np.array(dfV[dfV.target == areas[i_area]].ffb_nc)
hrs_ffbc=np.array(dfV[dfV.source == areas[i_area]].ffb_c)
hrt_ffbc=np.array(dfV[dfV.target == areas[i_area]].ffb_c)
cfs=np.array(dfV[dfV.source == areas[i_area]].conf)
cft=np.array(dfV[dfV.target == areas[i_area]].conf)
# i_area as source; compute influences from its targets
hrc_asSource=np.zeros([np.size(current_target),1])
hr_asSource=np.zeros([np.size(current_target),1])
for j_area in range(0,np.size(current_target)):
if any(hrc_iter.areas==current_target[j_area])==True:
hc_current_target = hrc_iter[hrc_iter.areas==current_target[j_area]][i_iter-1]
h_current_target = hr_iter[hr_iter.areas==current_target[j_area]][i_iter-1]
hrc_asSource[j_area]=cfs[j_area]*(-hc_current_target+hrs_ffbc[j_area])
hr_asSource[j_area]=(-h_current_target+hrs_ffbnc[j_area])
else:
hrc_asSource[j_area]=0
hr_asSource[j_area]=0
# i_area as target; compute influences from its sources
hrc_asTarget=np.zeros([np.size(current_source),1])
hr_asTarget=np.zeros([np.size(current_source),1])
for k_area in range(0,np.size(current_source)):
if any(hrc_iter.areas==current_source[k_area])==True:
hc_current_source = hrc_iter[hrc_iter.areas==current_source[k_area]][i_iter-1]
h_current_source = hr_iter[hr_iter.areas==current_source[k_area]][i_iter-1]
hrc_asTarget[k_area]=cft[k_area]*(hc_current_source+hrt_ffbc[k_area])
hr_asTarget[k_area]=(h_current_source+hrt_ffbnc[k_area])
else:
hrc_asTarget[k_area]=0
hr_asTarget[k_area]=0
hrc_iter.loc[hrc_iter.areas==areas[i_area],i_iter]=0.5*(-np.mean(hrc_asSource)+np.mean(hrc_asTarget))
hr_iter.loc[hr_iter.areas==areas[i_area],i_iter]=0.5*(-np.mean(hr_asSource)+np.mean(hr_asTarget))
hrc_iter[:][i_iter]=hrc_iter[:][i_iter]-np.mean(hrc_iter[:][i_iter])
hr_iter[:][i_iter]=hr_iter[:][i_iter]-np.mean(hr_iter[:][i_iter])
return hr_iter, hrc_iter
# In[]: Thalamo-cortical iterations
def iterativeTC(dfiC, dfVC, dfiT, dfVT, n_iter):
dfi = dfiT.append(dfiC)
dfV = dfVT.append(dfVC)
df=dfi.sort_values('areas')
hrc0=df["h"]
areas=np.asarray(df.areas)
n_area=len(areas) #np.shape(dfi)[0]
hr_iter_vals=np.zeros([n_area,n_iter])
hr_iter_vals[:,0]=hrc0
hr_iter=pd.DataFrame(hr_iter_vals)
hr_iter['areas'] = areas
cols = hr_iter.columns.tolist()
cols = cols[-1:] + cols[:-1]
hr_iter = hr_iter[cols]
hr_iter.head()
for i_iter in range(1,n_iter+1):
print i_iter
for i_area in range(0,n_area):
current_target=np.array(dfV[dfV.source == areas[i_area]].target)
current_source=np.array(dfV[dfV.target == areas[i_area]].source)
hrs_ffbc=np.array(dfV[dfV.source == areas[i_area]].ffb)
hrt_ffbc=np.array(dfV[dfV.target == areas[i_area]].ffb)
# i_area as source; compute influences from its targets
if len(current_target)>0:
hr_asSource=np.zeros([np.size(current_target),1])
for j_area in range(0,np.size(current_target)):
if any(hr_iter.areas==current_target[j_area])==True:
h_current_target = hr_iter[hr_iter.areas==current_target[j_area]][i_iter-1]
hr_asSource[j_area]=(-h_current_target+hrs_ffbc[j_area])
else:
hr_asSource[j_area]=0
else:
hr_asSource = 0
# i_area as target; compute influences from its sources
if len(current_source)>0:
hr_asTarget=np.zeros([np.size(current_source),1])
for k_area in range(0,np.size(current_source)):
if any(hr_iter.areas==current_source[k_area])==True:
h_current_source = hr_iter[hr_iter.areas==current_source[k_area]][i_iter-1]
hr_asTarget[k_area]=(h_current_source+hrt_ffbc[k_area])
else:
hr_asTarget[k_area]=0
else:
hr_asTarget = 0
hr_iter.loc[hr_iter.areas==areas[i_area],i_iter]=0.5*(-np.mean(hr_asSource)+np.mean(hr_asTarget))
hr_iter[:][i_iter]=hr_iter[:][i_iter]-np.mean(hr_iter[:][i_iter])
return hr_iter
# In[]: Thalamo-cortical & cortico-thalamic iterations
def iterativeTCCT(dfiC, dfVC, dfiT, dfVT, n_iter):
dfi = dfiT.append(dfiC)
dfV = dfVT.append(dfVC)
df=dfi.sort_values('areas')
hrc0=df["h"]
areas=np.asarray(df.areas)
n_area=len(areas) #np.shape(dfi)[0]
hr_iter_vals=np.zeros([n_area,n_iter])
hr_iter_vals[:,0]=hrc0
hr_iter=pd.DataFrame(hr_iter_vals)
hr_iter['areas'] = areas
cols = hr_iter.columns.tolist()
cols = cols[-1:] + cols[:-1]
hr_iter = hr_iter[cols]
hr_iter.head()
for i_iter in range(1,n_iter+1):
print i_iter
for i_area in range(0,n_area):
current_target=np.array(dfV[dfV.source == areas[i_area]].target)
current_source=np.array(dfV[dfV.target == areas[i_area]].source)
hrs_ffbc=np.array(dfV[dfV.source == areas[i_area]].ffb)
hrt_ffbc=np.array(dfV[dfV.target == areas[i_area]].ffb)
# i_area as source; compute influences from its targets
if len(current_target)>0:
hr_asSource=np.zeros([np.size(current_target),1])
for j_area in range(0,np.size(current_target)):
if any(hr_iter.areas==current_target[j_area])==True:
h_current_target = hr_iter[hr_iter.areas==current_target[j_area]][i_iter-1]
hr_asSource[j_area]=(-h_current_target+hrs_ffbc[j_area])
else:
hr_asSource[j_area]=0
else:
hr_asSource = 0
# i_area as target; compute influences from its sources
if len(current_source)>0:
hr_asTarget=np.zeros([np.size(current_source),1])
for k_area in range(0,np.size(current_source)):
if any(hr_iter.areas==current_source[k_area])==True:
h_current_source = hr_iter[hr_iter.areas==current_source[k_area]][i_iter-1]
hr_asTarget[k_area]=(h_current_source+hrt_ffbc[k_area])
else:
hr_asTarget[k_area]=0
else:
hr_asTarget = 0
hr_iter.loc[hr_iter.areas==areas[i_area],i_iter]=0.5*(-np.mean(hr_asSource)+np.mean(hr_asTarget))
hr_iter[:][i_iter]=hr_iter[:][i_iter]-np.mean(hr_iter[:][i_iter])
return hr_iter