'Trackinghandle_ID183_a20_40nM_Pm2Cy3B_20uW_1_MMStack_Pos0.ome_locs_render_picked.hdf5'
])
file_names.extend([
'Trackinghandle_ID183_a20_40nM_Pm2Cy3B_20uW_1_MMStack_Pos0.ome_locs_render_picked_tprops.hdf5'
])
############################################ Set parameters
CycleTime = 0.2 #Aquisition cycle time [s]
#%%
############################################ Load _picked and _tprops
paths = [
os.path.join(dir_names[i], file_name)
for i, file_name in enumerate(file_names)
]
locs_init, info = addon_io.load_locs(paths[0]) # Load _picked
props, info = addon_io.load_locs(paths[1]) # Load _tprops
#%%
############################################ Query _picked for groups in _tprops
groups = props.group.values
locs = locs_init.query('group in @groups')
#%%
############################################ Select one group
g = 102
picked = locs[locs.group == g]
### Trace
trace = improps.get_trace(picked, info[0]['Frames'])
NewFirstFrame_list = [i * 1500 for i in range(1, 2)]
NewNoFrames_list = [4500] * 1
#%%
failed_path = []
for i in range(len(file_names)):
### Create full path
path = os.path.join(dir_names[i], file_names[i])
for j in range(len(NewFirstFrame_list)):
NewFirstFrame = NewFirstFrame_list[j]
NewNoFrames = NewNoFrames_list[j]
try:
### Load
locs, info = io.load_locs(path)
### Modify
istrue = locs.frame >= NewFirstFrame
istrue = istrue & (locs.frame < (NewFirstFrame + NewNoFrames))
locs_filter = locs[istrue]
locs_filter.frame = locs_filter.frame - NewFirstFrame # Set first frame to zero
### Save
info_filter = info.copy()
info_filter[0]['Frames'] = NewNoFrames
extension = '_f%i-%i' % (NewFirstFrame,
NewFirstFrame + NewNoFrames)
io.save_locs(path.replace('.hdf5', extension + '.hdf5'),
locs_filter,
info_filter + [{
# file_names.extend(['slb_id169_R1-54#_R1s1-8_40nM_exp200_p250uW_T21_1_MMStack_Pos0.ome_locs_picked0503.hdf5'])
############################################ Set maximum length of sub-trajectories
subN = 50
#%%
############################################ Main loop
failed_path = []
for i in range(0, len(file_names)):
### Create path
path = os.path.join(dir_names[i], file_names[i])
### Run main function
try:
### Load
locs, info = addon_io.load_locs(path)
### Split
locs_split = special.split_trajectories(locs, subN)
### Save
path = os.path.splitext(path)[0]
info_split = info.copy() + [{'subN': subN}]
addon_io.save_locs(path + '_split%i.hdf5' % subN,
locs_split,
info_split,
mode='picasso_compatible')
except Exception:
traceback.print_exc()
failed_path.extend([path])
file_names.extend([
'slb_id169_R1-54#_R1s1-8_40nM_exp200_p114uW_T21_1_MMStack_Pos0.ome_locs_picked0503_split50.hdf5'
])
file_names.extend([
'slb_id169_R1-54#_R1s1-8_40nM_exp200_p114uW_T21_1_MMStack_Pos0.ome_locs_picked0503_tmobprops.hdf5'
])
file_names.extend([
'slb_id169_R1-54#_R1s1-8_40nM_exp200_p114uW_T21_1_MMStack_Pos0.ome_locs_picked0503_split50_tmobprops.hdf5'
])
### Load datasets
path = [
os.path.join(dir_names[i], file_names[i]) for i in range(len(file_names))
]
locs, inf = addon_io.load_locs(path[0]) # Full_picked
locs_sub, info_sub = addon_io.load_locs(path[1]) # Sub _picked
props, info_props = addon_io.load_locs(path[2]) # Full _props
props_sub, info_props_sub = addon_io.load_locs(path[3]) # Sub _props
#%%
############################################################## Prepare
X = locs.copy()
Xsub = locs_sub.copy()
Y = props.copy()
Ysub = props_sub.copy()
### Unit conversion
Y.a_iter = Y.a_iter * a_iter_convert
Ysub.a_iter = Ysub.a_iter * a_iter_convert
[r'C:\Users\flori\Documents\data\SPT\mobile\th\L21_exp200_p038uW'])
file_names = []
file_names.extend([
r'slb_id169_R1-54#_R1s1-8_40nM_exp200_p038uW_T21_1_MMStack_Pos0.ome_locs_picked0503_tmobprops.hdf5'
])
############################################################## Read in data
#### Create list of paths
path = [
os.path.join(dir_names[i], file_names[i])
for i in range(0, len(file_names))
]
labels = [i for i in range(0, len(path))]
#### Read in locs
locs_props = pd.concat([io.load_locs(p)[0] for p in path],
keys=labels,
names=['rep'])
infos = [io.load_locs(p)[1] for p in path]
# CycleTime=float(savename.split('_')[3][3:])*1e-3
CycleTime = 0.2
px = 0.13 # px size in microns
############################################################## Inspect dataset
rep = 0
### No. of tracks per frame and fit
n_tracks = analyze.tracks_per_frame(locs_props.loc[(rep, slice(None)), :],
infos[rep])
popt, frames, n_tracks_fit = analyze.fit_tracks_per_frame(n_tracks)
dir_names.extend([
r'C:\Data\p06.SP-tracking\20-01-17_fix_slb_L_T21\id140_L_exp200_p114uW_T21_1'
] * 2)
file_names = []
file_names.extend([
'id140_L_exp200_p114uW_T21_1_MMStack_Pos2.ome_locs_render_picked_tprops.hdf5'
])
file_names.extend(
['id140_L_exp200_p114uW_T21_1_MMStack_Pos2.ome_locs_render_picked.hdf5'])
############################################ Load data
path = [os.path.join(dir_names[i], f)
for i, f in enumerate(file_names)] # Path
props, info_props = addon_io.load_locs(path[0]) # Load _props
locs, info = addon_io.load_locs(path[1]) # Load _picked
#%%
X = locs.copy()
Y = props.copy()
##############################################################################
''' Define criteria for ensemble selection based on _props.
Rewrite this section as needed ...
'''
### Only get center FOV
istrue = np.sqrt((Y.x - 350)**2 + (Y.y - 350)**2) <= 200
### Number of localizations threshold
istrue = istrue & (Y.n_locs >= 100) & (Y.n_locs <= 200)
savedir='~'
savename='dye_sample_buffer_exp200_p114uW'
############################################################## Define data
dir_names=[]
dir_names.extend([r'/fs/pool/pool-schwille-paint/Data/p06.SP-tracking/20-03-05_pseries_th_B21/id169_B_exp200_p114uW_T21_1/20-03-05_JS'])
file_names=[]
file_names.extend([r'id169_B_exp200_p114uW_T21_1_MMStack_Pos0.ome_locs_render_picked_tprops.hdf5'])
############################################################## Read in data
#### Create list of paths
path=[os.path.join(dir_names[i],file_names[i]) for i in range(0,len(file_names))]
labels=[i for i in range(0,len(path))]
#### Read in locs
locs_props=pd.concat([io.load_locs(p)[0] for p in path],keys=labels,names=['rep'])
X=locs_props.copy()
############################################################## Result prep
Y=analyze.get_result(X,savename)
#### Save
if save_results:
Y.to_hdf(os.path.join(savedir,savename+'_stats.h5'),key='result')
CycleTime=float(savename.split('_')[3][3:])*1e-3
#%%
############################################################## Plotting
##############################################################
''' 1st event length distribution