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In [1]:

    
#%matplotlib inline

# Import all the programs we want to use. If this gives an error, then you need to add these to your python path.

import matplotlib as mpl
import matplotlib.pyplot as plt

import numpy as np 
import pandas as pd
from pandas import DataFrame, Series  # for convenience

import pims
import trackpy as tp
#import holopy as hp
import os
#import av
import scipy
import scipy.optimize as sco
import seaborn as sns

%pylab inline 
# tell python how to display images

#%matplotlib notebook









    



Populating the interactive namespace from numpy and matplotlib



In [2]:

    
scaling =  0.08431 #um/pixel
mpp = scaling
fps = 11.935
moviename = 'tracer+janus_3%_H2O2_5(green)2016-06-14'



In [3]:

    
1/fps









    Out[3]:





0.0837871805613741



In [4]:

    
os.chdir('C:\\Users\\Viva\\Desktop\\EPJ folder\\analysis')



In [5]:

    
t1 = pd.read_pickle('filtered_data_tracer+janus_3%_H2O2_5(green)2016-06-14_pickled.pkl')



In [6]:

    
tm = pd.read_pickle('filtered_data_with_drift_subtracted_tracer+janus_3%_H2O2_5(green)2016-06-14_pickled.pkl')



In [7]:

    
fpscontrol = 11.959
t1control = pd.read_pickle('filtered_data_tracer+janus_no_H2O2_3(red)2016-06-14_pickled.pkl')



In [8]:

    
tmcontrolfilename = 'filtered_data_with_drift_subtracted_tracer+janus_no_H2O2_3(red)2016-06-14_pickled.pkl'
tmcontrol = pd.read_pickle(tmcontrolfilename)

controlmoviename = 'tracer+janus_no_H2O2_3(red)2016-06-14'



In [9]:

    
plt.axis('equal')
ax = tp.plot_traj(t1, mpp = scaling, legend=False)



In [10]:

    
plt.axis('equal')
ax = tp.plot_traj(tm, mpp = scaling, legend=False)



In [11]:

    
d = tp.compute_drift(t1, smoothing=15)

#plt.figure()
d.plot(grid=False)
plt.title('Translational drift in ' + moviename + '\n')
plt.ylabel('pixels')









    



C:\ProgramData\Anaconda2\lib\site-packages\trackpy\motion.py:270: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with 
	DataFrame.rolling(min_periods=0,window=15,center=False).mean()
  dx = pd.rolling_mean(dx, smoothing, min_periods=0)






    Out[11]:





Text(0,0.5,u'pixels')



In [12]:

    
d.head()



In [13]:

    
dmicrons= d*mpp
dmicrons.index = d.index /fps
dmicrons.index.name = 'time (s)'
dmicrons.head()



In [14]:

    
dmicrons.plot()
plt.title('Translational drift in ' + moviename + '\n')
plt.ylabel('Ensemble translational drift ($\mathrm{\mu}$m)')









    Out[14]:





Text(0,0.5,u'Ensemble translational drift ($\\mathrm{\\mu}$m)')



In [49]:

    
print(dmicrons.index[0] == 1/fps)
1/fps









    



True






    Out[49]:





0.0837871805613741



In [51]:

    
drift = dmicrons.diff()
velocitydf = (dmicrons.diff()/ (1/fps))
velocitydf.plot()
#xlabel('Time (s)')
ylabel("Ensemble mean velocity ($\mathrm{\mu}$m/s)")

pltdrift.plot()
ylabel("Ensemble mean drift ($\mathrm{\mu}$m)")
title("$\Delta{}t = 0.0838$ sec", loc='right').figure()
drift.plot()
ylabel("Ensemble mean drift ($\mathrm{\mu}$m)")
title("$\Delta{}t = 0.0838$ sec", loc='right')









    Out[51]:





Text(1,1,u'$\\Delta{}t = 0.0838$ sec')






    












    





<matplotlib.figure.Figure at 0xf86a4e0>



In [63]:

    
velocitydf['speed'] = sqrt(velocitydf.x**2 + velocitydf.y**2)



In [60]:

    
drift['distance']=sqrt(drift.x**2 + drift.y**2)


drift.distance.plot()
ylabel("Ensemble mean drift ($\mathrm{\mu}$m)")
plt.title("$\Delta{}t = 0.0838$ sec", loc='right')
ylabel("Ensemble mean drift ($\mathrm{\mu}$m)")
title("$\Delta{}t = 0.0838$ sec", loc='right')









    Out[60]:





Text(1,1,u'$\\Delta{}t = 0.0838$ sec')



In [59]:

    
drift.describe()









    Out[59]:







  
    
      
      x
      y
      distance
    
  
  
    
      count
      1521.000000
      1521.000000
      1521.000000
    
    
      mean
      -0.008725
      0.000917
      0.054457
    
    
      std
      0.077369
      0.057259
      0.079838
    
    
      min
      -0.469474
      -0.284691
      0.000170
    
    
      25%
      -0.014459
      -0.017953
      0.015796
    
    
      50%
      0.002949
      -0.002240
      0.028795
    
    
      75%
      0.019397
      0.014553
      0.050920
    
    
      max
      0.201745
      0.335312
      0.485223



In [18]:

    
velocitydf.describe()









    Out[18]:







  
    
      
      x
      y
      speed
    
  
  
    
      count
      1521.000000
      1521.000000
      1521.000000
    
    
      mean
      -0.104131
      0.010947
      0.649944
    
    
      std
      0.923401
      0.683382
      0.952862
    
    
      min
      -5.603169
      -3.397785
      0.002034
    
    
      25%
      -0.172565
      -0.214264
      0.188527
    
    
      50%
      0.035197
      -0.026740
      0.343663
    
    
      75%
      0.231503
      0.173689
      0.607726
    
    
      max
      2.407828
      4.001952
      5.791142



In [19]:

    
velocitydf.speed.hist(bins=50)
xlabel("Lateral translational drift speed ($\mathrm{\mu}$m/s)")
ylabel("Occurrences")









    Out[19]:





Text(0,0.5,u'Occurrences')



In [20]:

    
tm.head()









    Out[20]:







  
    
      
      x
      y
      relative_to_frame
      x_raw
      y_raw
      mass
      size
      ecc
      signal
      raw_mass
      ep
      frame
      particle
    
    
      frame
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      334.271179
      212.315119
      0
      332.555782
      212.905931
      2400.095318
      4.244409
      0.022435
      26.408594
      9782.0
      0.113745
      1
      12.0
    
    
      1
      673.982923
      253.024125
      0
      672.318464
      253.187598
      3871.088289
      4.323736
      0.037196
      41.842188
      11701.0
      0.067698
      1
      16.0
    
    
      1
      461.220389
      329.668033
      0
      459.652504
      330.099069
      5590.047668
      4.382587
      0.005051
      56.246875
      16907.0
      0.032264
      1
      21.0
    
    
      1
      108.901070
      344.408378
      0
      107.352006
      345.282568
      2439.536724
      4.300599
      0.051282
      27.780469
      8240.0
      0.250852
      1
      24.0
    
    
      1
      67.245335
      500.488073
      0
      65.892629
      501.414536
      2054.725786
      4.132527
      0.037877
      26.408594
      8049.0
      0.294879
      1
      43.0



In [ ]:



In [21]:

    
tracks = t1['particle'].astype(int).unique()

print size(tracks)
if size(tracks) < 400:
    print tracks



In [22]:

    
traj4 = tm[tm['particle']==tracks[4]]



In [23]:

    
#plot(traj4.x*mpp, traj4.y*mpp)
#plt.axis('equal')
fig1 = plt.figure()
plt.axis('equal')
ax1 = tp.plot_traj(traj4, mpp = scaling, legend=False)
ax1.set_xlabel('x ($\mathrm{\mu}$m)')
plt.figure()
plot(traj4.frame/fps, traj4.x*mpp)
#plot(traj4.frame/fps, traj4.y*mpp)
xlabel("Time (s)")
ylabel('x position (s)')









    












    Out[23]:





Text(0,0.5,u'x position (s)')



In [24]:

    
plt.figure()
xlabel("Time (s)")
ylabel('y position (microns)')
title('Trajectory of drift-subtracted particles')# in\n' + moviename)
count = 0
for i in tracks:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    plot(tm[tm['particle']==i].frame/fps, tm[tm['particle']==i].y*mpp)
    count = count+1
print(count)

plt.figure()
xlabel("Time (s)")
ylabel('y position (microns)')
count = 0
for i in tracks[::4]:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    plot(tm[tm['particle']==i].frame/fps, tm[tm['particle']==i].y*mpp)
    count = count+1
print(count)
title('Trajectory of some of drift-subtracted particles')# in\n' + moviename)

plt.figure()
xlabel("Time (s)")
ylabel('y position (microns)')
count = 0
for i in tracks[::4]:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    plot(t1[t1['particle']==i].frame/fps, t1[t1['particle']==i].y*mpp)
    count = count+1
print(count)
title('Raw Trajectory of some of particles')# in\n' + moviename)









    



401
101
101






    Out[24]:





Text(0.5,1,u'Raw Trajectory of some of particles')



In [25]:

    
plt.figure()
xlabel("Time (s)")
ylabel('x position (microns)')
title('Trajectory of drift-subtracted particles')# in\n' + moviename)
for i in tracks:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    plot(tm[tm['particle']==i].frame/fps, tm[tm['particle']==i].x*mpp)

plt.figure()
xlabel("Time (s)")
ylabel('x position (microns)')
title('Trajectory of some of drift-subtracted particles')# in\n' + moviename)
for i in tracks[::4]:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    plot(tm[tm['particle']==i].frame/fps, tm[tm['particle']==i].x*mpp)
    
plt.figure()
xlabel("Time (s)")
ylabel('x position (microns)')
title('Raw Trajectory of particles')# in\n' + moviename)
for i in tracks:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    plot(t1[t1['particle']==i].frame/fps, t1[t1['particle']==i].x*mpp)
    
plt.figure()
xlabel("Time (s)")
ylabel('x position (microns)')
title('Raw Trajectory of some of particles')# in\n' + moviename)
for i in tracks[::4]:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    plot(t1[t1['particle']==i].frame/fps, t1[t1['particle']==i].x*mpp)



In [26]:

    
i=12
velx12 = fps*(tm[tm['particle']==i].x*mpp).diff()

#print(tm[tm['particle']==i].head())
#velx12.head()



In [27]:

    
plt.figure()
xlabel("Time (frame)")
ylabel('x velocity (microns/s)')
title('Velocity of some of drift-subtracted particles')# in\n' + moviename)
for i in tracks[::4]:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    tm[tm['particle']==i].x*mpp
    plot(tm[tm['particle']==i].frame, fps*(tm[tm['particle']==i].x*mpp).diff())
    try:
        tmvel.append(fps*(tm[tm['particle']==i].x*mpp), ignore_index=False)
    except NameError:
        tmvel = fps*(tm[tm['particle']==i].x*mpp)



In [28]:

    
try:
    del(tmxvel)
except NameError:
    print('No problem')

plt.figure()
xlabel("Time (s)")
ylabel('x velocity (microns/s)')
title('Velocity of some of drift-subtracted particles')# in\n' + moviename)
for i in tracks:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    #tm[tm['particle']==i].x*mpp
    plot(tm[tm['particle']==i].frame/fps, fps*(tm[tm['particle']==i].x*mpp).diff())
    try:
        tmxvel=tmxvel.append(fps*(tm[tm['particle']==i].x*mpp).diff(), ignore_index=False)
    except NameError:
        tmxvel = fps*(tm[tm['particle']==i].x*mpp).diff()









    



No problem



In [29]:

    
tmxvel.describe()









    Out[29]:





count    44803.000000
mean         0.012246
std          2.984327
min        -17.804466
25%         -1.812909
50%         -0.021510
75%          1.811331
max         43.445884
Name: x, dtype: float64



In [30]:

    
tmxvel.hist(bins=200)
xlabel("x-velocity of drift-subtracted particles ($\mathrm{\mu}$m/s)")
ylabel('Occurrences')









    Out[30]:





Text(0,0.5,u'Occurrences')



In [31]:

    
try:
    del(tmyvel)
except NameError:
    print('No problem')

plt.figure()
xlabel("Time (s)")
ylabel('y velocity (microns/s)')
title('Velocity of some of drift-subtracted particles')# in\n' + moviename)
for i in tracks:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    #tm[tm['particle']==i].y*mpp
    plot(tm[tm['particle']==i].frame/fps, fps*(tm[tm['particle']==i].y*mpp).diff())
    try:
        tmyvel=tmyvel.append(fps*(tm[tm['particle']==i].y*mpp).diff(), ignore_index=False)
    except NameError:
        tmyvel = fps*(tm[tm['particle']==i].y*mpp).diff()









    



No problem



In [32]:

    
tmyvel.hist(bins=200)
xlabel("y-velocity of drift subtracted particles ($\mathrm{\mu}$m/s)")
ylabel('Occurrences')

tmyvel.describe()









    Out[32]:





count    44803.000000
mean        -0.001810
std          2.975322
min        -26.916616
25%         -1.824484
50%          0.011979
75%          1.824878
max         46.751098
Name: y, dtype: float64



In [42]:

    
try:
    del(tmspeed)
except NameError:
    print('No problem')

plt.figure()
xlabel("Time (s)")
ylabel('speed (microns/s)')
title('Speed of drift-subtracted particles')# in\n' + moviename)
for i in tracks:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    #tm[tm['particle']==i].y*mpp
    plot(tm[tm['particle']==i].frame/fps, 
         fps*sqrt(((tm[tm['particle']==i].x*mpp).diff())**2+
                  ((tm[tm['particle']==i].y*mpp).diff())**2))
    try:
        tmspeed=tmspeed.append(
            fps*sqrt(((tm[tm['particle']==i].x*mpp).diff())**2
                     +((tm[tm['particle']==i].y*mpp).diff())**2),
            ignore_index=False)
        tmdisplacement=tmdisplacement.append(
            sqrt(((tm[tm['particle']==i].x*mpp).diff())**2
                     +((tm[tm['particle']==i].y*mpp).diff())**2),
            ignore_index=False)
    except NameError:
        tmspeed = fps*sqrt(((tm[tm['particle']==i].x*mpp).diff())**2
                     +((tm[tm['particle']==i].y*mpp).diff())**2)
        tmdisplacement=sqrt(((tm[tm['particle']==i].x*mpp).diff())**2
                            +((tm[tm['particle']==i].y*mpp).diff())**2)



In [46]:

    
tmspeed.hist(bins=300)
xlabel("Speed of drift-subtracted particles ($\mathrm{\mu}$m/s)")
ylabel('Occurrences')
xlim(xmin=0,xmax=20)

tmspeed.describe()

plt.figure()
tmdisplacement.hist(bins=300)
xlim(xmin=0,xmax=1.5)









    Out[46]:





(0, 1.5)



In [40]:

    
try:
    del(t1speed)
except NameError:
    print('No problem')
    
try:
    del(t1displacement)
except NameError:
    print('No problem')

plt.figure()
xlabel("Time (s)")
ylabel('speed (microns/s)')
title('Raw Speed of particles')# in\n' + moviename)
for i in tracks:
    if (tm[tm['particle']==i].x*mpp)[1]>80:
        continue
    #tm[tm['particle']==i].y*mpp
    plot(t1[t1['particle']==i].frame/fps, 
         fps*sqrt(((t1[t1['particle']==i].x*mpp).diff())**2+
                  ((t1[t1['particle']==i].y*mpp).diff())**2))
    try:
        t1speed=t1speed.append(
            fps*sqrt(((t1[t1['particle']==i].x*mpp).diff())**2
                     +((t1[t1['particle']==i].y*mpp).diff())**2),
            ignore_index=False)
        t1displacement = t1displacement.append(
            sqrt(((t1[t1['particle']==i].x*mpp).diff())**2
                     +((t1[t1['particle']==i].y*mpp).diff())**2),
            ignore_index=False)
    except NameError:
        t1speed = fps*sqrt(((t1[t1['particle']==i].x*mpp).diff())**2
                     +((t1[t1['particle']==i].y*mpp).diff())**2)
        t1displacement = sqrt(((t1[t1['particle']==i].x*mpp).diff())**2
                              +((t1[t1['particle']==i].y*mpp).diff())**2)



In [36]:

    
np.arange(0,15,.5)









    Out[36]:





array([  0. ,   0.5,   1. ,   1.5,   2. ,   2.5,   3. ,   3.5,   4. ,
         4.5,   5. ,   5.5,   6. ,   6.5,   7. ,   7.5,   8. ,   8.5,
         9. ,   9.5,  10. ,  10.5,  11. ,  11.5,  12. ,  12.5,  13. ,
        13.5,  14. ,  14.5])



In [37]:

    
sns.set(style='ticks')
sns.set_context('notebook')

plt.rcParams.update({'xtick.major.size': 3 ,
                     'ytick.major.size': 3,
                     'axes.linewidth' : .5,
                     'xtick.minor.size': 1.5 ,
                     'ytick.minor.size': 1.5})

plt.subplot(3,1,1)
velocitydf.speed.hist(bins=np.arange(0,15,.1))
xlabel("Lateral translational drift speed ($\mathrm{\mu}$m/s)")
ylabel("Occurrences")
xlim(xmin=0,xmax=15)

ax=plt.gca()
ax.grid(False)

plt.subplot(3,1,3)
tmspeed.hist(bins=np.arange(0,50,.1))
xlabel("Lateral speed of drift-subtracted Janus particles ($\mathrm{\mu}$m/s)")
ylabel('Occurrences')
xlim(xmin=0,xmax=15)

ax=plt.gca()
ax.grid(False)

print("Drift subtracted:")
print(tmspeed.describe())

plt.subplot(3,1,2)
t1speed.hist(bins=np.arange(0,15,.1))
xlabel("Raw lateral speed of Janus particles ($\mathrm{\mu}$m/s)")
ylabel('Occurrences')
xlim(xmin=0,xmax=15)

ax=plt.gca()
ax.grid(False)

#xlabel("Lateral speed of particles ($\mathrm{\mu}$m/s)")

fig = plt.gcf()
thisheight=5#/1.618
thiswidth=5
fig.set_size_inches(thiswidth,thisheight)

plt.tight_layout()

print("Raw:")
print(t1speed.describe())









    



Drift subtracted:
count    44803.000000
mean         3.554177
std          2.264165
min          0.000000
25%          1.994944
50%          3.166472
75%          4.623917
max         63.821705
dtype: float64
Raw:
count    44817.000000
mean         3.745106
std          2.369916
min          0.004920
25%          2.079135
50%          3.331673
75%          4.883984
max         18.109529
dtype: float64



In [38]:

    
sns.set(style='ticks')
sns.set_context('paper')

# 10pt font
font = {'size'   : 10}
plt.rc('font', **font)
plt.rcParams.update({'legend.handlelength': 0.5, 'legend.fontsize':10})
plt.rcParams.update({'axes.titlesize' : 10 })
plt.rcParams.update({'axes.linewidth' : .2, 
                     'xtick.major.size': 3 ,'ytick.major.size': 3,
                     'xtick.minor.size': 1.5 ,'ytick.minor.size': 1.5,
                     'xtick.labelsize':10, 'ytick.labelsize':10,
                     'axes.labelpad': 0})


plt.subplot(2,1,1)
velocitydf.speed.hist(bins=np.arange(0,15,.1))
xlabel("Lateral translational drift speed ($\mathrm{\mu}$m/s)")
ylabel("Occurrences")
xlim(xmin=0,xmax=15)
plt.title("C", loc='left')

ax=plt.gca()
ax.grid(False)

plt.subplot(2,1,2)
tmspeed.hist(bins=np.arange(0,50,.1), histtype='step', label="after drift-subtraction", linewidth=1)
#xlabel("Lateral speed of drift-subtracted Janus particles ($\mathrm{\mu}$m/s)")
#ylabel('Occurrences')
xlim(xmin=0,xmax=15)

ax=plt.gca()
ax.grid(False)

print("Drift subtracted:")
print(tmspeed.describe())

#plt.subplot(3,1,2)
t1speed.hist(bins=np.arange(0,15,.1), histtype='step', label="before drift-subtraction", linewidth=1)
xlabel("Lateral speed of Janus particles ($\mathrm{\mu}$m/s)")
ylabel('Occurrences')
xlim(xmin=0,xmax=15)
plt.legend()
plt.title("D", loc='left')

ax=plt.gca()
ax.grid(False)

#xlabel("Lateral speed of particles ($\mathrm{\mu}$m/s)")

fig = plt.gcf()
thisheight=5/1.618
thiswidth=5
fig.set_size_inches(thiswidth,thisheight)

sns.despine();

plt.tight_layout()

print("Raw:")
print(t1speed.describe())









    



Drift subtracted:
count    44803.000000
mean         3.554177
std          2.264165
min          0.000000
25%          1.994944
50%          3.166472
75%          4.623917
max         63.821705
dtype: float64
Raw:
count    44817.000000
mean         3.745106
std          2.369916
min          0.004920
25%          2.079135
50%          3.331673
75%          4.883984
max         18.109529
dtype: float64



In [64]:

    
sns.set(style='ticks')
sns.set_context('paper')

# 10pt font
font = {'size'   : 10}
plt.rc('font', **font)
plt.rcParams.update({'legend.handlelength': 0.5, 'legend.fontsize':10})
plt.rcParams.update({'axes.titlesize' : 10 })
plt.rcParams.update({'axes.linewidth' : .2, 
                     'xtick.major.size': 3 ,'ytick.major.size': 3,
                     'xtick.minor.size': 1.5 ,'ytick.minor.size': 1.5,
                     'xtick.labelsize':10, 'ytick.labelsize':10,
                     'axes.labelpad': 0})


plt.figure()
velocitydf.speed.hist(bins=np.arange(0,15,.1),histtype='step', label="ensemble drift speed", linewidth=1)
xlabel("Lateral translational drift speed ($\mathrm{\mu}$m/s)")
ylabel("Occurrences")
xlim(xmin=0,xmax=15)
plt.title("C", loc='left')

ax=plt.gca()
ax.grid(False)

#plt.subplot(3,1,2)
t1speed.hist(bins=np.arange(0,15,.1), histtype='step', label="before drift-subtraction", linewidth=1, color="#7a7a7aff")
xlabel("Lateral speed of Janus particles ($\mathrm{\mu}$m/s)")
ylabel('Occurrences')
xlim(xmin=0,xmax=15)

#plt.title("D", loc='left')
ax=plt.gca()
ax.grid(False)

#plt.subplot(2,1,2)
tmspeed.hist(bins=np.arange(0,50,.1), histtype='step', label="after drift-subtraction", linewidth=1, color="k")
#xlabel("Lateral speed of drift-subtracted Janus particles ($\mathrm{\mu}$m/s)")
#ylabel('Occurrences')
xlim(xmin=0,xmax=15)

ax=plt.gca()
ax.grid(False)

print("Drift subtracted:")
print(tmspeed.describe())

plt.legend()

#xlabel("Lateral speed of particles ($\mathrm{\mu}$m/s)")

fig = plt.gcf()
thisheight=1.8
thiswidth=4.3
fig.set_size_inches(thiswidth,thisheight)

sns.despine();

plt.tight_layout()

print("Raw:")
print(t1displacement.describe())

os.chdir('C:\\Users\\Viva\\Desktop\\EPJ folder\\paperfigures')
try:
    plt.savefig('particle_speed,' + moviename + '.pdf', 
                bbox_inches='tight', figsize=(thiswidth, thisheight))
    print('pdf saved')
    plt.savefig('particle_speed,' + moviename + '.png', 
                bbox_inches='tight', dpi=600, figsize=(thiswidth, thisheight))
    plt.savefig('particle_speed,' + moviename + '.svg', 
                bbox_inches='tight', figsize=(thiswidth, thisheight))
except IOError:
    print('Close the pdf file so I can overwrite it.')









    



Drift subtracted:
count    44803.000000
mean         3.554177
std          2.264165
min          0.000000
25%          1.994944
50%          3.166472
75%          4.623917
max         63.821705
dtype: float64
Raw:
count    44817.000000
mean         3.745106
std          2.369916
min          0.004920
25%          2.079135
50%          3.331673
75%          4.883984
max         18.109529
dtype: float64
pdf saved



In [67]:

    
t1displacement.head()









    Out[67]:





frame
0         NaN
1    0.448386
2    0.080543
3    0.481516
4    0.520721
dtype: float64



In [76]:

    
# *** come back one I've replaced drift.speed
sns.set(style='ticks')
sns.set_context('paper')

# 10pt font
font = {'size'   : 10}
plt.rc('font', **font)
plt.rcParams.update({'legend.handlelength': 0.5, 'legend.fontsize':10})
plt.rcParams.update({'axes.titlesize' : 10 })
plt.rcParams.update({'axes.linewidth' : .2, 
                     'xtick.major.size': 2 ,'ytick.major.size': 2,
                     'xtick.minor.size': .5 ,'ytick.minor.size': .5,
                     'xtick.labelsize':10, 'ytick.labelsize':10,
                     'axes.labelpad': 0})

binsequence=np.arange(0,15,.01)

plt.figure()
drift.distance.hist(bins=binsequence,histtype='step', label="ensemble drift", linewidth=1)
xlabel("Translational drift ($\mathrm{\mu}$m)")
ylabel("Occurrences")
xlim(xmin=0,xmax=15)
plt.title("C", loc='left')
title("$\Delta{}t = 0.084$ sec", loc='right')

ax=plt.gca()
ax.grid(False)

#plt.subplot(3,1,2)
t1displacement.hist(bins=binsequence, histtype='step', label="before drift-subtraction", linewidth=1, color="#7a7a7aff")
#xlabel("Distance Janus particles move ($\mathrm{\mu}$m)")
xlabel("$\Delta r$ ($\mathrm{\mu}$m)")
ylabel('Occurrences')
#xlim(xmin=0,xmax=15)

#plt.title("D", loc='left')
ax=plt.gca()
ax.grid(False)

#plt.subplot(2,1,2)
tmdisplacement.hist(bins=binsequence, histtype='step', label="after drift-subtraction", linewidth=1, color="k")
#xlabel("Lateral speed of drift-subtracted Janus particles ($\mathrm{\mu}$m/s)")
#ylabel('Occurrences')
#xlim(xmin=0,xmax=15)

ax=plt.gca()
ax.grid(False)

print("Drift subtracted:")
print(tmspeed.describe())

xlim(0,1.5)
plt.legend()

#xlabel("Lateral speed of particles ($\mathrm{\mu}$m/s)")

fig = plt.gcf()
thisheight=1.8
thiswidth=4.3
fig.set_size_inches(thiswidth,thisheight)

sns.despine();

plt.tight_layout()

print("Raw:")
print(t1displacement.describe())

os.chdir('C:\\Users\\Viva\\Desktop\\EPJ folder\\paperfigures')
try:
    plt.savefig('particle_displacement,' + moviename + '.pdf', 
                bbox_inches='tight', figsize=(thiswidth, thisheight))
    print('pdf saved')
    plt.savefig('particle_displacement,' + moviename + '.png', 
                bbox_inches='tight', dpi=600, figsize=(thiswidth, thisheight))
    plt.savefig('particle_displacement,' + moviename + '.svg', 
                bbox_inches='tight', figsize=(thiswidth, thisheight))
except IOError:
    print('Close the pdf file so I can overwrite it.')









    



Drift subtracted:
count    44803.000000
mean         3.554177
std          2.264165
min          0.000000
25%          1.994944
50%          3.166472
75%          4.623917
max         63.821705
dtype: float64
Raw:
count    44817.000000
mean         0.313792
std          0.198569
min          0.000412
25%          0.174205
50%          0.279151
75%          0.409215
max          1.517346
dtype: float64
pdf saved



In [74]:

    
fps









    Out[74]:





11.935



In [75]:

    
1/fps









    Out[75]:





0.0837871805613741



In [40]:

    
plt.figure()

velocitydf.speed.hist(bins=np.arange(0,15,.1))
xlabel("Lateral translational drift speed ($\mathrm{\mu}$m/s)")
ylabel("Occurrences")
xlim(xmin=0,xmax=15)

ax=plt.gca()
ax.grid(False)

#plt.figure()
velocitydf['frame']=d.index
driftyvel = velocitydf[velocitydf.frame> 1100]
print('comparing full movie to drifty end of movie')
driftyvel.speed.hist(bins=np.arange(0,15,.1))
xlabel("Lateral translational drift speed ($\mathrm{\mu}$m/s)")
ylabel("Occurrences")
xlim(xmin=0,xmax=15)

ax=plt.gca()
ax.grid(False)









    



comparing full movie to drifty end of movie



In [ ]:



In [41]:

    
velocitydf.head()



In [42]:

    
velocitydf.tail()



In [43]:

    
t1.head()









    Out[43]:







  
    
      
      x
      y
      mass
      size
      ecc
      signal
      raw_mass
      ep
      frame
      particle
    
    
      frame
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      0
      335.207851
      217.515802
      2129.606663
      4.263106
      0.015743
      24.735661
      9744.0
      0.121135
      0
      12.0
    
    
      1
      332.555782
      212.905931
      2400.095318
      4.244409
      0.022435
      26.408594
      9782.0
      0.113745
      1
      12.0
    
    
      2
      331.783522
      213.468283
      1931.747908
      4.380896
      0.028253
      19.208907
      9288.0
      0.140322
      2
      12.0
    
    
      3
      326.338060
      211.746241
      2027.142279
      4.355480
      0.054004
      22.236171
      9256.0
      0.145731
      3
      12.0
    
    
      4
      320.591582
      209.482554
      2178.229462
      4.276222
      0.004696
      22.954505
      9362.0
      0.137708
      4
      12.0



In [44]:

    
t1_drifty = t1[t1['frame'] > 1100]
tm_drifty = tm[tm['frame'] > 1100]



In [45]:

    
d_drifty = tp.compute_drift(t1_drifty, smoothing=15)

#plt.figure()
d_drifty.plot(grid=False)
plt.title('Translational drift in ' + moviename + '\n')
plt.ylabel('pixels')









    Out[45]:





Text(0,0.5,u'pixels')



In [46]:

    
d_tmdrifty = tp.compute_drift(tm_drifty, smoothing=15)

#plt.figure()
d_tmdrifty.plot(grid=False)
plt.title('Translational drift in ' + moviename + '\n' + 'Should be none, and that\'s true')









    Out[46]:





Text(0.5,1,u"Translational drift in tracer+janus_3%_H2O2_5(green)2016-06-14\nShould be none, and that's true")



In [47]:

    
fig1 = plt.figure()
plt.axis('equal')
ax1 = tp.plot_traj(t1_drifty, mpp = scaling, legend=False)
ax1.set_xlabel('x ($\mu$m)')









    












    Out[47]:





Text(0.5,16.2,u'x ($\\mu$m)')



In [48]:

    
fig2 = plt.figure()
plt.axis('equal')
ax2 = tp.plot_traj(tm_drifty, mpp = scaling, legend=False)



In [49]:

    
size(tm_drifty['particle'].astype(int).unique())









    Out[49]:





88



In [50]:

    
import seaborn as sns
sns.set(style='ticks')
sns.set_context('paper')
sns.set_palette(sns.husl_palette(22,l=.55))

# Big font
font = {'size'   : 10}
plt.rc('font', **font)
plt.rcParams.update({'legend.handlelength': 0})
plt.rcParams.update({'axes.titlesize' : 10 })
plt.rcParams.update({'axes.linewidth' : .2, 
                     'xtick.major.size': 2 ,'ytick.major.size': 2, 
                     'xtick.labelsize':10, 'ytick.labelsize':10})


fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, sharex=True)

#plt.subplot(1,2, 2)
#ax2 = plt.gca()
plt.axis('equal')
tp.plot_traj(tm_drifty, mpp = scaling, legend=False, ax=ax2)
#tick_params( which='both', pad=2)

#plt.subplot(1,2, 1)
#fig = plt.gcf()
#ax1 = plt.gca()
#plt.figsize=(8,10)

tp.plot_traj(t1_drifty, mpp = scaling, legend=False, ax=ax1)
#tick_params( which='both', pad=2)
plt.axis('equal')


ax1.set_title('A', loc='left')
ax2.set_title('B', loc='left')

plt.rcParams.update({'axes.titlesize' : 10 })
ax1.set_title('before drift-subtraction', loc='right')
ax2.set_title('after drift-subtraction', loc='right')

ax1.set_ylabel('$y$ ($\mathrm{\mu}$m)')
ax1.set_xlabel('$x$ ($\mathrm{\mu}$m)')
ax2.set_xlabel('$x$ ($\mathrm{\mu}$m)')
ax2.set_ylabel('')

plt.xticks(np.arange(0,120, step=20)) # order I call this matters
plt.yticks(np.arange(0,81, step=20))

#fig.subplots_adjust(hspace=0)

plt.xlim(xmax=80)
#plt.ylim()

print('Figure 3 in EPJ paper')

thisheight=2.2
thiswidth=4.3
fig.set_figheight(thisheight)
fig.set_figwidth(thiswidth)

ax1.tick_params(width=.2)
ax2.tick_params(width=.2)


plt.tight_layout()


os.chdir('C:\\Users\\Viva\\Desktop\\EPJ folder\\paperfigures')
try:
    plt.savefig('t1_and_tm_drifty,' + moviename + '.pdf', 
                bbox_inches='tight', figsize=(thiswidth, thisheight))
    plt.savefig('t1_and_tm_drifty,' + moviename + '.svg', 
                bbox_inches='tight', figsize=(thiswidth, thisheight))
    print('pdf saved')
    plt.savefig('t1_and_tm_drifty,' + moviename + '.png', 
                bbox_inches='tight', dpi=600, figsize=(thiswidth, thisheight))
except IOError:
    print('Close the pdf file so I can overwrite it.')









    



Figure 3 in EPJ paper
pdf saved



In [51]:

    
tp.plot_traj(t1_drifty, mpp = scaling, legend=False)









    












    Out[51]:





<matplotlib.axes._subplots.AxesSubplot at 0x186757f0>



In [52]:

    
d_driftycontrol = tp.compute_drift(t1control, smoothing=15)

#plt.figure()
d_driftycontrol.plot(grid=False)
plt.title('Translational drift in ' + controlmoviename + '\n')









    Out[52]:





Text(0.5,1,u'Translational drift in tracer+janus_no_H2O2_3(red)2016-06-14\n')



In [53]:

    
t1control_drifty = t1control[t1control['frame'] > 900]
tmcontrol_drifty = tmcontrol[tmcontrol['frame'] > 900]
t1control_drifty = t1control_drifty[t1control_drifty['frame'] < 1100]
tmcontrol_drifty = tmcontrol_drifty[tmcontrol_drifty['frame'] < 1100]



In [54]:

    
plt.axis('equal')
ax = tp.plot_traj(t1control_drifty, mpp = scaling, legend=False)



In [55]:

    
plt.axis('equal')
ax = tp.plot_traj(tmcontrol_drifty, mpp = scaling, legend=False)

This shows that the control drifts too, which makes sense since the oil can evaporate.

	x	y
frame
1	-1.338733	0.463211
2	-2.283690	0.870435
3	-3.212503	0.981969
4	-4.226901	0.651283
5	-5.206964	0.156092

	x	y
time (s)
0.083787	-0.112869	0.039053
0.167574	-0.192538	0.073386
0.251362	-0.270846	0.082790
0.335149	-0.356370	0.054910
0.418936	-0.438999	0.013160

	x	y	speed	frame
time (s)
0.083787	NaN	NaN	NaN	1
0.167574	-0.950854	0.409764	1.035389	2
0.251362	-0.934609	0.112230	0.941323	3
0.335149	-1.020727	-0.332750	1.073595	4
0.418936	-0.986179	-0.498281	1.104913	5

	x	y	speed	frame
time (s)
127.188940	0.296081	-0.068589	0.303921	1518
127.272727	0.289215	-0.182263	0.341855	1519
127.356514	0.416275	-0.265348	0.493654	1520
127.440302	0.285373	-0.324068	0.431808	1521
127.524089	0.180815	-0.302582	0.352491	1522

	x	y	distance
count	1521.000000	1521.000000	1521.000000
mean	-0.008725	0.000917	0.054457
std	0.077369	0.057259	0.079838
min	-0.469474	-0.284691	0.000170
25%	-0.014459	-0.017953	0.015796
50%	0.002949	-0.002240	0.028795
75%	0.019397	0.014553	0.050920
max	0.201745	0.335312	0.485223

	x	y	relative_to_frame	x_raw	y_raw	mass	size	ecc	signal	raw_mass	ep	frame	particle
frame
1	334.271179	212.315119	0	332.555782	212.905931	2400.095318	4.244409	0.022435	26.408594	9782.0	0.113745	1	12.0
1	673.982923	253.024125	0	672.318464	253.187598	3871.088289	4.323736	0.037196	41.842188	11701.0	0.067698	1	16.0
1	461.220389	329.668033	0	459.652504	330.099069	5590.047668	4.382587	0.005051	56.246875	16907.0	0.032264	1	21.0
1	108.901070	344.408378	0	107.352006	345.282568	2439.536724	4.300599	0.051282	27.780469	8240.0	0.250852	1	24.0
1	67.245335	500.488073	0	65.892629	501.414536	2054.725786	4.132527	0.037877	26.408594	8049.0	0.294879	1	43.0

	x	y	mass	size	ecc	signal	raw_mass	ep	frame	particle
frame
0	335.207851	217.515802	2129.606663	4.263106	0.015743	24.735661	9744.0	0.121135	0	12.0
1	332.555782	212.905931	2400.095318	4.244409	0.022435	26.408594	9782.0	0.113745	1	12.0
2	331.783522	213.468283	1931.747908	4.380896	0.028253	19.208907	9288.0	0.140322	2	12.0
3	326.338060	211.746241	2027.142279	4.355480	0.054004	22.236171	9256.0	0.145731	3	12.0
4	320.591582	209.482554	2178.229462	4.276222	0.004696	22.954505	9362.0	0.137708	4	12.0