import numpy as np
from dlsmicro.backend import analysis_tools
from dlsmicro.backend import io
from dlsmicro.backend import utils
import matplotlib.pyplot as plt
import pandas as pd
[docs]def analyze_time_points(file_path, T, r, ergodic, n_points, n_positions,
Laplace=False, df_save_path=None, df_file_name=None,
save_as_txt=True, save_as_df=True,
plot_corr=False, plot_msd=False, plot_G=False):
""" Analyze files exported from Zetasizer software for time-
dependent measurements and plot data per time point.
Parameters
----------
file_path : str
Path to exported csv file
T : float
Temperature of the experiment in Kelvin
r : float
Radius of particle in experiment in nanometers
ergodic : boolean
Ergodicity in experiment
n_points : int
Corresponds to the number of measurements taken over
duration of experiment
n_positions : int
Corresponds to the number of measurements taken
over different positions at end of experiment
Laplace : boolean, `optional`
If `True`, use direct Laplace transform to find
shear modulus. This is useful because it can
smooth the data to noise.
df_save_path : str, `optional`
Path to Dataframe to be saved containing results
from DLS microrheology analysis.
If `None`, path will be set to file_path.
df_file_name : str, `optional`
Name of Dataframe to be saved containing results
from DLS microrheology analysis.
If None, `condition_data.pkl` is default name
save_as_text : boolean, `optional`
If `True`, save each element of Dataframe separately
as a text file
save_as_df : boolean, `optional`
If `True`, save the Dataframe
plot_corr : boolean, `optional`
If `True`, show plot of the correlation function of
each replicate
plot_msd : boolean, `optional`
If `True`, show plot of the mean-squared displacement
of each replicate
plot_G : boolean, `optional`
If `True`, show plot of the shear modulus of each replicate
"""
if df_save_path == None:
files = file_path.split('/')
file_name = files[-1]
df_save_path = file_path[:-(len(file_name))]
if df_file_name == None:
df_file_name = 'time_course.pkl'
# Row numbers for scattering intensity sweep (int_rcds)
# Row numbers for correlation time points (time_points)
int_rcds = range(n_points + 1, n_points + n_positions)
time_points = range(1, n_points, 1)
# if cuvette == 'disposable':
# int_rcds = range(13, 33)
# time_points = range(1, 12, 1)
# elif cuvette == 'quartz':
# int_rcds = range(19, 51)
# time_points = range(1, 18, 1)
# else:
# raise Exception('cuvette type entered is not valid')
# Define scattering vector parameters about solvent
n = 1.333 # index of refraction of water (default)
theta = 173.*np.pi/180.
lam = 633.
# Create pandas dataframe to organize time point data
df = pd.DataFrame(columns=['time', 'MSD', 'alpha', 'omega', 'G1', 'G2',
'time_point', 'scattering','epos'])
for tp in time_points:
save_suffix = 'time_point_%s.txt' % tp
####################################################
# Analyze data
# Don't edit this unless you know what you're doing
####################################################
# Read the data and truncate over the desired time windows
data_dict = io.read_zetasizer_csv_to_dict(file_path, tp, intensities_rows=int_rcds)
[t, g, I, Ie, point_pos, epos] = list(data_dict.values())
# Figure out where data is no longer trustworthy (correlation function goes to zero)
tinds = [3, len(g)]
tinds[1] = np.argmax(g<0.05)
if tinds[1] == 0:
tinds[1] = len(g) - 1
g = g[tinds[0]:tinds[1]]
t = t[tinds[0]:tinds[1]]
# Get a DLS microrheology object that contains all raw data
# and analyzed results.
q = analysis_tools.calc_q(n, theta, lam)
dlsmicro_df = analysis_tools.full_dlsur_analysis(t, g, ergodic, r, T, q,
I, Ie)
# Store the scattering vs. position data
scattering = np.zeros(len(dlsmicro_df['t']))
positions = np.zeros_like(scattering)
scattering[0:len(Ie)] = Ie
positions[0:len(epos)] = epos
# Laplace transformed modulus
if Laplace:
[omega_L, G1_L, G2_L] = analysis_tools.shear_modulus_laplace_transform(t,
dlsmicro_df['msd_smooth'], r, T)
dlsmicro_df['G1'], dlsmicro_df['G2'] = utils.laplace_merge(dlsmicro_df['t'],
dlsmicro_df['G1'],
dlsmicro_df['G2'],
G1_L,G2_L)
# Construct pandas dataframe for this replicate and append it to the
# master dataframe
dlsmicro_df['time_point'] = [tp]*len(dlsmicro_df['t'])
dlsmicro_df['scattering'] = scattering
dlsmicro_df['epos'] = positions
df = pd.concat((df,dlsmicro_df), axis=0, sort=True)
###############################################
# Plot the analyzed data
###############################################
if plot_corr:
plt.plot(t, g, '-r')
plt.xscale('log')
plt.xlabel('$\\mathregular{time\ (\\mu s)}$')
plt.ylabel('g')
plt.show()
if plot_msd:
plt.plot(dlsmicro_df['t'], dlsmicro_df['msd_smooth'], '-r')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('$\\mathregular{time\ (\\mu s)}$')
plt.ylabel('MSD')
plt.show()
if plot_G:
plt.plot(dlsmicro_df['omega'], dlsmicro_df['G1'], '-r', lw=2.0)
plt.plot(dlsmicro_df['omega'], dlsmicro_df['G2'], '--r', lw=2.0)
plt.ylabel('$\\mathregular{G^*\ (Pa)}$')
plt.xlabel('$\\mathregular{\\omega\ (s^{-1})}$')
plt.legend(['$\\mathregular{G^{\\prime}}$',
'$\\mathregular{G^{\\prime \\prime}}$'], frameon=False,)
plt.xscale('log')
plt.yscale('log')
plt.show()
##############################################
# Save analysis results
##############################################
if save_as_txt:
for i in dlsmicro_df.columns:
np.savetxt('%s/%s' % (df_save_path,i), dlsmicro_df[i].values)
#################################################
# Save the pandas dataframe
#################################################
if save_as_df:
save_path = df_save_path + '/' + df_file_name
df.to_pickle(save_path)