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 locate_particles(self, frames, diameter, minmass, filename='data.h5',
progress_listener=lambda *args: None):
self.log.info('Starting particle tracking to file: ' + filename)
with trackpy.PandasHDFStore(filename, t_column='frame', mode='w') as s:
for i, image in enumerate(frames):
features = self.locate_on_frame(image, diameter, minmass)
features['frame'] = i
s.put(features)
progress_listener(i)
self.log.info('Particles located')
return self
def compute_drift(path, smoothing=0, pos_columns=None):
"""Return the ensemble drift, xy(t).
Parameters
----------
path : string p
path to the HDF5 file which contains DataFrames(['x','y','particle'])
smoothing : integer
Smooth the drift using a forward-looking rolling mean over
this many frames.
Returns
-------
drift : DataFrame([x, y], index=frame)
"""
if pos_columns is None:
pos_columns = ['x', 'y']
# Drift calculation
print('Drift calc')
with tp.PandasHDFStore(path) as traj:
Nframe = traj.max_frame
# initialize drift DataFrame
dx = pd.DataFrame(data=np.zeros((Nframe + 1, 2)), columns=['x', 'y'])
for f, frameB in enumerate(traj): # loop frame
print('Frame:', f)
if f > 0:
delta = frameB.set_index('particle')[
pos_columns] - frameA.set_index('particle')[pos_columns]
dx.iloc[f].x = np.nanmean(delta.x.values)
dx.iloc[f].y = np.nanmean(delta.y.values) # compute drift
#remember the current frame
frameA = frameB
if smoothing > 0:
dx = pd.rolling_mean(dx, smoothing, min_periods=0)
x = np.cumsum(dx)
return x
def batch_locate_particles(self, frames, diameter, minmass, filename='data.h5',
progress_listener=lambda *args: None):
self.log.info('Starting particle tracking to file: ' + filename)
self.log.info('Diameter: ' + str(diameter) + "; Minmass: " + str(minmass))
def batches_gen(iterator, size):
batch = []
i = 0
for item in iterator:
batch.append(item)
i += 1
if i == size:
yield batch
batch = []
i = 0
with trackpy.PandasHDFStore(filename, t_column='frame') as s:
for batch in batches_gen(frames, 5):
trackpy.batch(batch, diameter, minmass=minmass, invert=True,
output=s, engine='numba')
self.log.info('Particles located')
return self
def __init__(self, filename='data.h5', particle_id=None):
self.log = logging.getLogger("Analytics")
self.plt_lock = threading.Lock()
with tp.PandasHDFStore(filename) as store:
trajectories = pd.concat(iter(store))
self.chart_data = ChartData(trajectories, particle_id)
def trajectory_length(filename, particle_id):
with tp.PandasHDFStore(filename) as store:
df = pd.concat(iter(store))
track = df.loc[df['particle'] == particle_id]
return len(track.index)
lg = 1
grid = RegularGrid([110,200], [30,30], [10,10])
Mtot = np.zeros(grid.shape+(3,))
Ntot = np.zeros(grid.shape, np.int64)
Ctot = np.zeros(grid.shape+(2,2))
Ttot = np.zeros_like(Mtot)
Na_tot = np.zeros_like(Ntot)
Nd_tot = np.zeros_like(Ntot)
Nc_tot = np.zeros_like(Ntot)
for f in f0:
# get two frames
with tp.PandasHDFStore(path_traj.format(act)) as s:
frame0 = s.get(f)
frame1 = s.get(f + lg)
# work only on particles that exist in both frames
## gather in one DataFrame the two times so that in a single row we have the information about the particle at the two time steps
joined = frame0.join(frame1.set_index('particle'), on ="particle", how='inner', lsuffix='0', rsuffix='1')
# extract only the coordinates at each time step. Thanks to the previous step, we have ensured that the rows are sorted consistently
pos0 = joined[['x0', 'y0']].to_numpy()
pos1 = joined[['x1', 'y1']].to_numpy()
# Voronoi neighbors
## You may want to refine this with a distance criterion or use another definition of the bonds
edges0 = voro_edges(pos0)
edges1 = voro_edges(pos1)
# ## Optionally, label the axes.
# ax.set(xlabel='size', ylabel='count');
#
# fig, ax = plt.subplots()
# ax.hist(f['ecc'], bins=20)
# ## Optionally, label the axes.
# ax.set(xlabel='ecc', ylabel='count');
#
# ## Subpixel accuracy
# plt.figure()
# tp.subpx_bias(f);
# =============================================================================
# Collect all detected positions GB
with tp.PandasHDFStore(savepath_position.format(act)) as s:
tp.batch(frames[fmin[ind]:fmax[ind]], size,
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:
plt.legend(frameon=False)
plt.text(901, 30, "Average x displacement: %.4f px/fm" % (x_velocity))
plt.text(901, 30.5, "Average y displacement: %.4f px/fm" % (y_velocity))
plt.axes([0.2, 0.2, .3, .3])
squared_displacements = []
a = coeff[0]
for i in range(len(x)):
disp = (1 / (1 + a**2)) * ((y[i] - y_approx[i])**2) / len(x)
squared_displacements.append(disp)
plt.title('MSD')
plt.hist(squared_displacements, 100)
plt.show()
with tp.PandasHDFStore('data.h5') as store:
trajectories = pd.concat(iter(store))
#filtered = tp.filter_stubs(trajectories)
filtered = trajectories
drift = tp.compute_drift(filtered)
im = tp.imsd(filtered, 1, 1)
plt.plot(im.index, im, 'k-', alpha=0.1)
plt.xscale('log')
plt.yscale('log')
plt.title("Mean squared displacement for each particle")
plt.show()
disp_x = []
disp_y = []
for i in range(1, len(drift.x.values)):
