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 save_zrf(k,df,outdir,expname):
'''
Save dataframe as psi file in outdir
:param str k: Sample number
:param pd.DataFrame df: Dataframe containing 'x','y','z'
:param str outdir: Filepath to destination directory for psi
:param str expname: Descriptive name for experiment to be used in filename
'''
cranium.write_data(os.path.join(outdir,'ZRF_'+k+'_'+expname+'.psi'),df)
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 process(num, P=None):
'''
Run through the processing steps for a single sample through saving psi files
:param int num: Index of the file that is currently being processed
:param :class:`paramClass` P: Object containing all variables from config file
'''
tic = time.time()
print(num, 'Starting sample')
try:
e = cranium.embryo(P.expname, num, P.outdir)
#Add channels and preprocess data
e.add_channel(os.path.join(P.c1_dir, P.c1_files[num]), P.c1_key)
e.chnls[P.c1_key].preprocess_data(P.genthresh, P.scale, P.microns)
for i in range(len(P.Lcdir)):
e.add_channel(os.path.join(P.Lcdir[i], P.Lcfiles[i][num]),
P.Lckey[i])
e.chnls[P.Lckey[i]].preprocess_data(P.genthresh, P.scale,
P.microns)
#Calculate PCA transformation for structural channel, c1
if P.twoD == True:
e.chnls[P.c1_key].calculate_pca_median_2d(
e.chnls[P.c1_key].raw_data, P.medthresh, P.radius, P.microns)
pca = e.chnls[P.c1_key].pcamed
e.chnls[P.c1_key].pca_transform_2d(e.chnls[P.c1_key].df_thresh,
pca,
P.comporder,
P.fitdim,
deg=P.deg)
#Transform additional channels
for i in range(len(P.Lcdir)):
e.chnls[P.Lckey[i]].pca_transform_2d(
e.chnls[P.Lckey[i]].df_thresh,
pca,
P.comporder,
P.fitdim,
deg=P.deg)
else:
e.chnls[P.c1_key].calculate_pca_median(e.chnls[P.c1_key].raw_data,
P.medthresh, P.radius,
P.microns)
pca = e.chnls[P.c1_key].pcamed
e.chnls[P.c1_key].pca_transform_3d(e.chnls[P.c1_key].df_thresh,
pca,
P.comporder,
P.fitdim,
deg=P.deg)
#Transform additional channels
for i in range(len(P.Lcdir)):
e.chnls[P.Lckey[i]].pca_transform_3d(
e.chnls[P.Lckey[i]].df_thresh,
pca,
P.comporder,
P.fitdim,
deg=P.deg)
# print(num,'Starting coordinate transformation')
# e.chnls[P.c1_key].transform_coordinates()
# for i in range(len(P.Lcdir)):
# e.chnls[P.Lckey[i]].transform_coordinates()
#e.save_psi()
columns = ['x', 'y', 'z']
for ch in e.chnls.keys():
cranium.write_data(
os.path.join(e.outdir,
e.name + '_' + str(e.number) + '_' + ch + '.psi'),
e.chnls[ch].df_align[columns])
toc = time.time()
print(num, 'Complete', toc - tic)
except:
toc = time.time()
print(num, 'Failed', toc - tic, exc_info())
def save_caax(k,df,outdir,expname):
cranium.write_data(os.path.join(outdir,'caax_'+k+'_'+expname+'.psi'),df)