def track_len_hists(comp_lst,conn,ax=None,*args,**kwargs):
"""makes histograms of the track lengths for the tracking computations in comp_lst """
res = [_extract_track_lengths(c,conn) for c in comp_lst]
hist_res = [np.histogram(r[0],bins=np.max(r[0])) + (r[1],r[2]) for r in res]
tmps = [d[1] for d in res]
cm = plots.color_mapper(np.min(tmps),np.max(tmps))
if ax is None:
ax = plots.set_up_axis('n*dtime [s]','cumsum $n P(n)$','',func = matplotlib.axes.Axes.plot)
for hr in hist_res:
temp = hr[2]
ax.plot(np.cumsum((np.diff(hr[1]))+hr[1][0])*hr[3]/1000,np.cumsum(hr[0]*(hr[1][:-1]))/np.sum(hr[0]*(hr[1][:-1]))
,label='%(#)0.1f C'%{'#':temp},color=cm.get_color(temp),*args,**kwargs)
def plot_tracks(tracks):
"""
Takes in a
"""
print len(tracks)
# set up figure
(fig,ax) = plots.set_up_plot()
init_frame = np.min([t.start_frame for t in tracks])
ax.set_title(' frame: ' + str(init_frame)
+ ' dtime: ' + str(tracks.dtime) + 'ms')
def trk_len_hash(trk):
return len(trk)
def trk_disp_hash(trk):
return np.sqrt((np.sum(np.array(trk[-1]) - np.array(trk[0]))**2))
trk_hash = trk_len_hash
t_len = [trk_hash(trk) for trk in tracks]
cm = plots.color_mapper(min(t_len),max(t_len))
print (min(t_len),max(t_len))
# loop over tracks and plot
[ax.plot(np.array([t[0] for t in trk.positions])*tracks.sp_scale,
np.array([t[1] for t in trk.positions])*tracks.sp_scale,
'-',
color=cm.get_color(trk_hash(trk)))
for trk in tracks]
x,y,I = zip(*[trk.positions[0] for trk in tracks])
# plot the starting points
ax.plot(np.array(x)*tracks.sp_scale,
np.array(y)*tracks.sp_scale,'xk')
ax.set_aspect('equal')
plt.draw()
def plot_msd_old(comp_key, conn, fig=None):
if fig is None:
fig = plots.tac_figure("t[s]", "msd", "msd")
pass
(fin, dset) = conn.execute("select fout,dset_key from comps where comp_key = ?", (comp_key,)).fetchone()
(temp,) = conn.execute("select temp from dsets where key = ?", (dset,)).fetchone()
Fin = h5py.File(fin, "r")
g_name = _fd("mean_squared_disp", comp_key)
msd = Fin[g_name]
msd = Fin[g_name]["data"][:]
dt = Fin[g_name].attrs["dtime"]
print
print "the delta is ", dt, "for comp ", comp_key
t = (np.arange(len(msd)) + 1) * dt
cm = plots.color_mapper(27, 33)
fig.plot(t, msd, label=str(temp), color=cm.get_color(temp))
Fin.close()
del Fin
return fig
def plot_msd(comp_key, conn, fig=None):
if fig is None:
fig = plots.tac_figure("t[s]", "msd", "msd")
pass
(fin,) = conn.execute("select fout from msd where comp_key = ?", (comp_key,)).fetchone()
Fin = h5py.File(fin, "r")
g_name = _fd("mean_squared_disp", comp_key)
msd = Fin[g_name]["msd"][:]
dt = Fin[g_name].attrs["dtime"]
temp = Fin[g_name].attrs["temperature"]
mtl = Fin[g_name].attrs["min_track_length"]
print
print "the delta is ", dt, "for comp ", comp_key
t = (np.arange(len(msd)) + 1) * dt
cm = plots.color_mapper(27, 33)
fig.plot(t, msd, label="%(#).2fC, %(!)d" % {"!": mtl, "#": temp}, color=cm.get_color(temp))
Fin.close()
del Fin
return fig
