def test_t_column(self):
f = self.features.copy()
cols = list(f.columns)
name = 'arbitrary name'
cols[cols.index('frame')] = name
f.columns = cols
# smoke tests
tp.link_df(f, 5, t_column=name, verify_integrity=True)
tp.link_df_iter(f, 5, t_column=name, verify_integrity=True)
def test_t_column(self):
f = self.features.copy()
cols = list(f.columns)
name = 'arbitrary name'
cols[cols.index('frame')] = name
f.columns = cols
# smoke tests
tp.link_df(f, 5, t_column=name, verify_integrity=True)
tp.link_df_iter(f, 5, t_column=name, verify_integrity=True)
def link_df_iter(self, *args, **kwargs):
kwargs.update(self.linker_opts)
args = list(args)
features = args.pop(0)
res = pd.concat(tp.link_df_iter(
(df for fr, df in features.groupby('frame')), *args, **kwargs))
return res.sort(['particle', 'frame']).reset_index(drop=True)
def link_df_iter(self, *args, **kwargs):
kwargs.update(self.linker_opts)
kwargs['diagnostics'] = self.do_diagnostics
args = list(args)
features = args.pop(0)
res = pd.concat(tp.link_df_iter(
(df for fr, df in features.groupby('frame')), *args, **kwargs))
return pandas_sort(res, ['particle', 'frame']).reset_index(drop=True)
def link_df_iter(self, *args, **kwargs):
kwargs.update(self.linker_opts)
kwargs['diagnostics'] = self.do_diagnostics
args = list(args)
features = args.pop(0)
res = pd.concat(tp.link_df_iter(
(df for fr, df in features.groupby('frame')), *args, **kwargs))
return pandas_sort(res, ['particle', 'frame']).reset_index(drop=True)
def link_trajectories(self, search_range, memory, filename='data.h5',
progress_listener=lambda *args: None):
self.log.info('Linking particles in file "' + filename + '"')
with trackpy.PandasHDFStore(filename) as store:
i = 0
for linked in trackpy.link_df_iter(store, search_range, memory=memory):
store.put(linked)
progress_listener(i)
i += 1
return self
def test_t_column(self):
f = self.features.copy()
cols = list(f.columns)
name = 'arbitrary name'
cols[cols.index('frame')] = name
f.columns = cols
# smoke tests
tp.link_df(f, 5, t_column=name, verify_integrity=True)
f_iter = (frame for fnum, frame in f.groupby('arbitrary name'))
list(tp.link_df_iter(f_iter, 5, t_column=name, verify_integrity=True))
def test_t_column(self):
f = self.features.copy()
cols = list(f.columns)
name = 'arbitrary name'
cols[cols.index('frame')] = name
f.columns = cols
# smoke tests
tp.link_df(f, 5, t_column=name, verify_integrity=True)
f_iter = (frame for fnum, frame in f.groupby('arbitrary name'))
list(tp.link_df_iter(f_iter, 5, t_column=name, verify_integrity=True))
def test_check_iter(self):
"""Check that link_df_iter() makes a useful error message if we
try to pass a single DataFrame."""
list(tp.link_df_iter(self.features.copy(), 5))
def test_check_iter(self):
"""Check that link_df_iter() makes a useful error message if we
try to pass a single DataFrame."""
list(tp.link_df_iter(self.features.copy(), 5))
##inputs must be of form 'filename', need single quotes
pretrack=input('pretrack file:') #load in pretrack file, gdf
trackfile=input('filename to save track file(.h5):') #name of file for track file to be saved as .h5
pref=input('prefix for position table and displacement matrix:') #prefix for position table and displacement matrix files
lifetime=input('lifetime=')
import softpy
import numpy as np
import pandas as pd
import trackpy as tp
pt=softpy.sftio.read_gdf(pretrack)
pt=pt.T
f=pd.DataFrame(pt,columns=['x','y','mass','size','ecc','frame']) #convert to data frame
(frame for fnum, frame in f.groupby('frame'))
tra=pd.concat(tp.link_df_iter((frame for fnum, frame in f.groupby('frame')),5,memory=10))
tra.to_hdf(trackfile,'table') #save track file
dis,tab=softpy.sftio.tra_df_2_disp(tra,lifetime,prefix=pref)
dismat=np.concatenate((dis[:,0,:],dis[:,1,:]),axis=1)
np.savetxt(pref+'displacements.txt',dismat) #save displacement matrix
np.savetxt(pref+'positiontable.txt',tab) #save average position table
minmass=mm ,
smoothing_size = smoothing_size,
separation = separation,
threshold = threshold,
percentile = percentile,
characterize = True,
output=s)
# =============================================================================
# with tp.PandasHDFStoreSingleNode(savepath_positionh5.format(act)) as s:
# with tp.PandasHDFStoreSingleNode(savepath_temp.format(act)) as temp:
# Nframe = s.max_frame
# for f in range(0,Nframe+1):
# frame = s.get(f)
# frame_filted = frame[((frame['size'] < 3) & (frame['mass'] <3000) & (frame['ecc'] < 0.3))] # filter 2
# frame_filted.drop(frame_filted.columns[[2,3,4,5,6,7]], axis = 1, inplace = True) # keep only x,y,frame,particle
# temp.put(frame_filted)
# =============================================================================
with tp.PandasHDFStore(savepath_position.format(act)) as pos:## Reconstruct trajectories
with tp.PandasHDFStore(savepath_traj.format(act)) as traj:
for linked in tp.link_df_iter(pos, maxdis, memory=mem):
traj.put(linked)
import softpy
import numpy as np
import pandas as pd
import trackpy as tp
ptbig=softpy.sftio.read_gdf(pretrackbig)
ptbig=ptbig.T
ptsmall=softpy.sftio.read_gdf(pretracksmall)
ptsmall=ptsmall.T
fbig=pd.DataFrame(ptbig,columns=['x','y','mass','size','ecc','frame']) #convert to data frame
fsmall=pd.DataFrame(ptsmall,columns=['x','y','mass','size','ecc','frame'])
(frame for fnum, frame in fbig.groupby('frame'))
trabig=pd.concat(tp.link_df_iter((frame for fnum, frame in fbig.groupby('frame')),5,memory=10))
(frame for fnum, frame in fsmall.groupby('frame'))
trasmall=pd.concat(tp.link_df_iter((frame for fnum, frame in fsmall.groupby('frame')),5,memory=10))
trabig.to_hdf(trackbig,'tablebig') #save track file
trasmall.to_hdf(tracksmall,'tablesmall')
disbig,tabbig=softpy.sftio.tra_df_2_disp(trabig,lifetime,prefix=prefbig)
dissmall,tabsmall=softpy.sftio.tra_df_2_disp(trasmall,lifetime,prefix=prefsmall)
dismatbig=np.concatenate((disbig[:,0,:],disbig[:,1,:]),axis=1)
dismatsmall=np.concatenate((dissmall[:,0,:],dissmall[:,1,:]),axis=1)
np.savetxt(prefbig+'_displacements.txt',dismatbig) #save displacement matrix
np.savetxt(prefbig+'_positiontable.txt',tabbig) #save average position table
np.savetxt(prefsmall+'_displacements.txt',dismatsmall) #save displacement matrix
