In [1]:
import string
import scipy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import sys
import glob
import cPickle
import skimage
sys.path.append(os.path.abspath("C:\Users\Scherer Lab E\Documents\GitHub\Python_Data_Analysis"))
import common_functions as cf
import pims
import passing_event_functions as pef
import trackpy_helper_functions as tphf
import optical_binding_of_driven_particles as obdp
Load the database
In [2]:
store = pd.HDFStore("K:\Pat's_Projects\ParticleTrajectoryData\passing_event_data_trackpy_refined_extra.h5", mode='r')
keys = store.index['key']
Create a giant dataframe that has all the data in it. Note that track id names are reduntact between different experiments.
In [3]:
df_list = []
for key in keys:
df = store.get(key)
df_list.append(df)
Distribution of Particle Deviation from the Mean Radius¶
When a passing event occurs look at how far each particle is from the mean radius of the trap. This is separated into two histograms, one for the particle with the smaller deviation from the mean radius and one for the particle with the larger deviation from the mean radius. When separating with these two conditions the characteristics of each histogram become apparent. Particles that have a smaller deviation from the average radius are distributed in a much sharper around 0 deviation. While particles a that are further from the average radius are distributed bimodally. This may illustrate the idea that during a passing event it is essential for one of the two particles to be in the trap for a passing to occur.
In [4]:
df_all = pd.concat(df_list)
df_pass = df_all[df_all['passing_event'] ==True]
output = plt.hist(df_pass.drop_duplicates(['frame', 'track id']).r, bins=100)
plt.ylabel('Counts')
plt.xlabel('r (pixels)')
plt.show()
df_pass['dist_avg_r'] = df_pass['r'] - df_pass['r_avg']
output = plt.hist(abs(df_pass.drop_duplicates(['frame', 'track id']).dist_avg_r), bins=100)
plt.ylabel('Counts')
plt.xlabel('abs(r_avg - r) (pixels)')
plt.show()
df_pass['dist_avg_r_other_part'] = df_pass['nn_r'] - df_pass['r_avg']
df_case_1 = df_pass[abs(df_pass.dist_avg_r) > abs(df_pass.dist_avg_r_other_part)]
df_case_2 = df_pass[abs(df_pass.dist_avg_r) < abs(df_pass.dist_avg_r_other_part)]
output_1 = plt.hist(df_case_1['dist_avg_r'], bins=100, label='particle with\n larger abs(r_avg - r)', range=[-30, 30], normed=True, alpha=0.5)
output_2 = plt.hist(df_case_2['dist_avg_r'], bins=100, label='particle with\n smaller abs(r_avg - r)', range=[-30, 30], normed=True, alpha=0.5)
plt.ylabel('Probabilty Density')
plt.xlabel('r_avg - r (pixels)')
plt.legend()
plt.show()
In [5]:
f = open("C:\Users\Scherer Lab E\Box Sync\Passing\Figures and Data\Data\pass_event_r_deviation_closer_vs_further.pkl", mode='w')
cPickle.dump([df_case_1['dist_avg_r'], df_case_2['dist_avg_r']], f)
f.close()
Plot which particle stayed on trap in successful passing event¶
In [5]:
part_behind_r_dev = []
part_ahead_r_dev = []
for df in df_list:
last_pass_success = df[df.last_pass_event==True]
last_pass_success = last_pass_success[last_pass_success.del_theta < 0]
part_behind_r_dev.append(last_pass_success.dist_avg_r_other_part)
part_ahead_r_dev.append(last_pass_success.dist_avg_r)
In [6]:
part_behind_r_dev_full = pd.concat(part_behind_r_dev)
part_ahead_r_dev_full = pd.concat(part_ahead_r_dev)
plt.hist(part_behind_r_dev_full, bins=50,range=[-20,20], alpha=0.5)
plt.hist(part_ahead_r_dev_full, bins=50,range=[-20,20], alpha=0.5)
Out[6]:
In [7]:
f = open("C:\Users\Scherer Lab E\Box Sync\Passing\Figures and Data\Data\pass_event_r_deviation.pkl", mode='w')
cPickle.dump([part_behind_r_dev_full, part_ahead_r_dev_full], f)
f.close()
In [ ]:
test = df_list[-1]
iter_df = test[test.last_pass_event==True].drop(['x pos', 'y pos', 'theta_nn_id', 'theta_nn_dist', 'theta_nn_num', 'dist_avg_r', 'dist_avg_r_other_part'], axis=1)
print iter_df
for key,frame in iter_df.frame.iteritems():
selection = (test.frame < frame) & (test.pass_success == True)
print test[selection].drop(['x pos', 'y pos', 'theta_nn_id', 'theta_nn_dist', 'theta_nn_num', 'dist_avg_r', 'dist_avg_r_other_part'], axis=1)
In [5]:
def plot_rel_r_theta_particle_in_front_start_end(df, min_r):
df_events = df[(df.pass_event_start == True) & (df.del_theta < 0) & (df.del_r < min_r)]
for idx, series in df_events.iterrows():
lower_frame = series.frame
upper_frame = series.pass_frame_end
df_plot = df.query('@lower_frame <= frame <= @upper_frame')
df_plot = df_plot[df_plot['track id'] == series['track id']]
df_plot = df_plot[df_plot.theta_nn_id == series.theta_nn_id]
plt.plot(df_plot.del_theta, df_plot.del_r, lw=0.4)
In [6]:
def find_if_pass_was_successful(df):
"""Label the passing events where a pass was successful. Requires
a DataFrame where the pass_event_start and pass_event_end and
frame_start and frame_end are available.
"""
df_copy = df.copy()
df_copy['pass_success'] = np.nan
df_start = df[df.pass_event_start==True]
grouped = df_start.groupby(['frame', 'pair_id'])
for frame, pair_id in grouped.groups.keys():
single_part_start = df[(df.frame==frame) & (df.pair_id==pair_id) & (df.del_theta < 0)]
if len(single_part_start)==0 or single_part_start.pass_frame_end.values[0] == np.nan:
continue
frame_end = single_part_start.pass_frame_end.values[0]
track_id = single_part_start['track id'].values[0]
single_part_end = df[(df.frame==frame_end) & (df.pair_id==pair_id) & (df['track id'] == track_id)]
selection_crit = (df_copy.frame==frame) & (df_copy.pair_id==pair_id)
if len(single_part_end) == 0:
continue
if np.sign(single_part_start.del_theta.values[0]) != np.sign(single_part_end.del_theta.values[0]):
df_copy.loc[selection_crit, 'pass_success'] = True
else:
df_copy.loc[selection_crit, 'pass_success'] = False
return df_copy
In [95]:
def last_passing_event(df):
"""Find the last time that del_theta sign switches for only successful
passing events.
"""
df_copy = df.copy()
df_copy['last_pass_event'] = False
df_success = df_copy[(df_copy['pass_success']==True)]
grouped = df_success.groupby(['frame', 'pair_id'])
for frame, pair_id in grouped.groups.keys():
pass_df = df_copy[(df_copy['frame']==frame) & (df_copy['pair_id']==pair_id)]
pass_frame_end = pass_df['pass_frame_end'].iloc[0]
passing_frames = df_copy.query('@frame <= frame <= @pass_frame_end')
passing_frames = passing_frames[passing_frames.pair_id==pair_id]
passing_frames = passing_frames[passing_frames.passing_event==True]
passing_frames = passing_frames.sort_values('frame')
last_pass_frame = passing_frames['frame'].iloc[-1]
selection_crit = (df_copy.frame==last_pass_frame) & (df_copy.pair_id==pair_id)
df_copy.loc[selection_crit, 'last_pass_event'] = True
return df_copy
In [101]:
store.close()