def get_trajectories_singleAxis_for_DF(DF):
from random import randint
sns.set_palette(sns.color_palette("Paired"))
fig = plt.figure(figsize=(5, 5),frameon=False)
ax=fig.add_subplot(1, 1, 1)
ax.spines['left'].set_position('zero')
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_color('none')
#ax.spines['left'].set_smart_bounds(True)
#ax.spines['bottom'].set_smart_bounds(True)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.set_ylim([-300,300])
ax.set_xlim([-300,300])
ticklab = ax.xaxis.get_ticklabels()[0]
ax.xaxis.set_label_coords(300, -40,transform=ticklab.get_transform())
ax.set_xlabel('x($\mu$m)',fontsize=14)
ticklab = ax.yaxis.get_ticklabels()[0]
ax.yaxis.set_label_coords(90, 280,transform=ticklab.get_transform())
ax.set_ylabel('y($\mu$m)',rotation=0,fontsize=14)
GetXYS=ct.get_xys(DF)
xys_s=ct.get_xys_s(GetXYS['xys'],GetXYS['Nmin'])
for n in list(range(12)):
m=randint(0,len(xys_s))-1
xys_s_x_n=xys_s[m]['X']-(xys_s[m]['X'][xys_s[m]['X'].index[0]])
xys_s_y_n=xys_s[m]['Y']-(xys_s[m]['Y'][xys_s[m]['X'].index[0]])
xys_s_x_n=[x*(100/(60.)) for x in xys_s_x_n]
xys_s_y_n=[x*(100/(60.)) for x in xys_s_y_n]
ax.plot(xys_s_x_n,xys_s_y_n)
return fig
def get_Velocities_for_Dataframe_at_lag(DF):
dt=DF['tframe'][0]/60
tlag_n=int(tlag/dt)
GetXYS=ct.get_xys(DataFrames[i][j]) #This returns the XY trajectories for the specific DataFrame
xys_s_temp=ct.get_xys_s(GetXYS['xys'],GetXYS['Nmin'])
GetVel_lag=ct.get_velocity_lag(xys_s_temp,tlag_n)
return GetVel_lag
def get_MSD_for_DataFrame_raw(DF):
GetXYS=ct.get_xys(DF)
xys_s_temp=ct.get_xys_s(GetXYS['xys'],GetXYS['Nmin'])
i=0
for j in list(range(len(xys_s_temp))):
if np.isnan(xys_s_temp[i]['X']).any():
xys_s_temp.pop(i)
i=i-1
i=i+1
msd=ct.get_msd(xys_s_temp)['Raw']#See get_msd function
return msd
def get_trajectories_for_DF(DF):
GetXYS=ct.get_xys(DF)
xys_s=ct.get_xys_s(GetXYS['xys'],GetXYS['Nmin'])
plt.figure(figsize=(5, 5),frameon=False)
for m in list(range(9)):
plt.plot()
plt.subplot(3,3,m+1)
xys_s_x_n=xys_s[m]['X']-min(xys_s[m]['X'])
xys_s_y_n=xys_s[m]['Y']-min(xys_s[m]['Y'])
plt.plot(xys_s_x_n,xys_s_y_n)
plt.axis('off')
axes = plt.gca()
axes.set_ylim([0,125])
axes.set_xlim([0,125])
def get_Path_length_for_DataFrame(DF):
GetXYS=ct.get_xys(DF)
CumulativeDistance=ct.get_invdist_Cummulative(ct.get_xys_s(GetXYS['xys'],GetXYS['Nmin']))
return CumulativeDistance
