def transform_file(f, model=None, test=False, tri=False):
print(f, 'starting')
tic = time.time()
s = cranium.brain()
df = cranium.read_psi(f)
if 'ac' not in df.columns:
# if tri==True:
# s.df_align = df[['x','y','z']]
# pt1x = np.max(s.df_align.x)
# pt1z = s.df_align[s.df_align.x == pt1x].z.values[0]
# pt2x = np.min(s.df_align.x)
# pt2z = s.df_align[s.df_align.x == pt2x].z.values[0]
#
# s.mm = cranium.math_model(np.polyfit([pt1x,0,pt2x],[pt1z,0,pt2z],2))
#
# if test==True:
# return(s,[pt1x,pt2x],[pt1z,pt2z])
# if tri==False:
s.df_align = df[['x', 'y', 'z']]
snum = re.findall(r'\d+', f.split('.')[0])[0]
s.mm = cranium.math_model(model.loc[int(snum)].values)
num = int(snum)
# return(s)
# if test==False:
s.transform_coordinates()
cranium.write_data(f, s.df_align)
print(f, 'complete', time.time() - tic)
def transform_file(f,model=None):
print(f,'starting')
tic = time.time()
s = ds.brain()
df = ds.read_psi(f)
# Transform coordinates if cylindrical column not present
if 'ac' not in df.columns:
# Add psi data to brain object
s.df_align = df[['x','y','z']]
#snum = re.findall(r'\d+',f.split('.')[0])[0]
snum = f.split('_')[1]
num = int(snum)
# Extract math model and add to brain object
try:
mm_val = model.loc[num].values
s.mm = ds.math_model(mm_val)
except:
s.mm = s.fit_model(s.df_align,deg,fit_dim)
# Try to transform and report errors
try:
s.transform_coordinates()
except:
print(f,'failed on transform coordinates',time.time()-tic)
traceback.print_exc()
# Try to save data and report errors
try:
ds.write_data(f,s.df_align)
except:
print(f,'failed on write data',time.time()-tic)
traceback.print_exc()
print(f,'complete',time.time()-tic)
else:
# Report if file already appears transformed
print(f,'already transformed')
def preprocess(fpath,p,stop=None,pca=None,mm=None,vertex=None):
'''
Generate brain object and process until stop is triggered
:param str stop: 'df_thresh' 'median' 'df_align'
:return: Cranium brain object
'''
b = cranium.brain()
b.read_data(fpath)
b.preprocess_data(p['gthresh'],p['scale'],p['microns'])
if stop == 'df_thresh':
return(b)
if pca == None:
b.calculate_pca_median(b.raw_data,p['mthresh'],p['radius'],p['microns'])
pca = b.pcamed
if stop == 'median':
return(b)
b.pca_transform_3d(b.df_thresh,pca,p['comp_order'],p['fit_dim'],deg=p['deg'],mm=mm,vertex=vertex)
if (stop == 'df_align') | (stop == None):
return(b)