Tutorial¶

In [1]:

# import modules
import lifefit as lf
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os

# seaborn settings
sns.set_style('white')
sns.set_context("notebook")
sns.set_theme(font='Arial')

# plot settings
def set_ticksStyle(x_size=4, y_size=4, x_dir='in', y_dir='in'):
    sns.set_style('ticks', {'xtick.major.size': x_size, 'ytick.major.size': y_size, 'xtick.direction': x_dir, 'ytick.direction': y_dir})

# import modules import lifefit as lf import numpy as np import matplotlib.pyplot as plt import seaborn as sns import os # seaborn settings sns.set_style('white') sns.set_context("notebook") sns.set_theme(font='Arial') # plot settings def set_ticksStyle(x_size=4, y_size=4, x_dir='in', y_dir='in'): sns.set_style('ticks', {'xtick.major.size': x_size, 'ytick.major.size': y_size, 'xtick.direction': x_dir, 'ytick.direction': y_dir})

Lifetime¶

First, define the path to the data

In [2]:

atto550_dna_path = lf.DATA_DIR.joinpath("lifetime", "Atto550_DNA.txt")
irf_path = lf.DATA_DIR.joinpath("IRF", "irf.txt")

atto550_dna_path = lf.DATA_DIR.joinpath("lifetime", "Atto550_DNA.txt") irf_path = lf.DATA_DIR.joinpath("IRF", "irf.txt")

Next, we read in our datafile for the fluorescence decay and the instrument response function (IRF). Instead of using the lf.read_decay() function we can define a custom import function that outputs a two-column array containing numbered channels and intensity counts.

In [3]:

atto550_dna, timestep_ns = lf.tcspc.read_decay(atto550_dna_path)
irf, _ = lf.tcspc.read_decay(irf_path)

atto550_dna, timestep_ns = lf.tcspc.read_decay(atto550_dna_path) irf, _ = lf.tcspc.read_decay(irf_path)

Note: If your intensity counts are along a time axis, i.e. a file with two columns time and intensity, you can use the fileformat="time_intensity" argument of the lf.read_decay() function.

In [4]:

atto550_dna, timestep_ns = lf.tcspc.read_decay(lf.DATA_DIR.joinpath("lifetime", "Atto550_DNA_time_intensity.txt"), fileformat="time_intensity")
irf, _ = lf.tcspc.read_decay(lf.DATA_DIR.joinpath("IRF", "IRF_time_intensity.txt"), fileformat="time_intensity")

atto550_dna, timestep_ns = lf.tcspc.read_decay(lf.DATA_DIR.joinpath("lifetime", "Atto550_DNA_time_intensity.txt"), fileformat="time_intensity") irf, _ = lf.tcspc.read_decay(lf.DATA_DIR.joinpath("IRF", "IRF_time_intensity.txt"), fileformat="time_intensity")

Next we instantiate a Lifetime object by providing the data arrays of the fluorescence decay and the IRF along with the timestep between two channels

In [5]:

atto550_dna_life = lf.tcspc.Lifetime(atto550_dna, timestep_ns, irf)

atto550_dna_life = lf.tcspc.Lifetime(atto550_dna, timestep_ns, irf)

Fit the fluorecence decay by iterative reconvolution with the IRF

In [6]:

atto550_dna_life.reconvolution_fit([1,5])

atto550_dna_life.reconvolution_fit([1,5])

=======================================
Reconvolution fit with experimental IRF
tau0: 1.01 ± 0.01 ns (29%)
tau1: 3.89 ± 0.01 ns (71%)
mean tau: 3.61 ± 0.01 ns

irf shift: 0.11 ns
offset: 1 counts
=======================================

Plot the IRF, the fluorescence decay and the fit

In [7]:

with sns.axes_style('ticks'):
    set_ticksStyle()
    f, ax = plt.subplots(nrows=1, ncols=1, figsize=(2.5,2.25), sharex=False, sharey=True, squeeze=False)
    
    ax[0,0].semilogy(atto550_dna_life.fluor[:,0], atto550_dna_life.fluor[:,2], color=[0.45, 0.57, 0.44])
    ax[0,0].semilogy(atto550_dna_life.irf[:,0], atto550_dna_life.irf[:,2], color=[0.7,0.7,0.7])
    ax[0,0].semilogy(atto550_dna_life.fluor[:,0], atto550_dna_life.fit_y, color='k')
    
    ax[0,0].set_ylabel('counts')
    ax[0,0].set_xlabel('time (ns)')
    ax[0,0].set_xlim((20,80))
    ax[0,0].set_ylim(bottom=1)

with sns.axes_style('ticks'): set_ticksStyle() f, ax = plt.subplots(nrows=1, ncols=1, figsize=(2.5,2.25), sharex=False, sharey=True, squeeze=False) ax[0,0].semilogy(atto550_dna_life.fluor[:,0], atto550_dna_life.fluor[:,2], color=[0.45, 0.57, 0.44]) ax[0,0].semilogy(atto550_dna_life.irf[:,0], atto550_dna_life.irf[:,2], color=[0.7,0.7,0.7]) ax[0,0].semilogy(atto550_dna_life.fluor[:,0], atto550_dna_life.fit_y, color='k') ax[0,0].set_ylabel('counts') ax[0,0].set_xlabel('time (ns)') ax[0,0].set_xlim((20,80)) ax[0,0].set_ylim(bottom=1)

Anisotropy¶

Read the four different fluorescence decays and generate a lifetime object from each channel

In [8]:

atto550_dna_path = {}
atto550_dna = {}
atto550_dna_life = {}
for c in ['VV','VH','HV','HH']:
    atto550_dna_path[c] = lf.DATA_DIR.joinpath("anisotropy", f"{c}.txt")
    atto550_dna[c], fluor_nsperchan = lf.tcspc.read_decay(atto550_dna_path[c])
    atto550_dna_life[c] = lf.tcspc.Lifetime(atto550_dna[c], fluor_nsperchan, irf)

atto550_dna_path = {} atto550_dna = {} atto550_dna_life = {} for c in ['VV','VH','HV','HH']: atto550_dna_path[c] = lf.DATA_DIR.joinpath("anisotropy", f"{c}.txt") atto550_dna[c], fluor_nsperchan = lf.tcspc.read_decay(atto550_dna_path[c]) atto550_dna_life[c] = lf.tcspc.Lifetime(atto550_dna[c], fluor_nsperchan, irf)

Compute an anisotropy object from the lifetime objects and fit a two-rotator model to the anisotropy decay

In [9]:

atto550_dna_aniso = lf.tcspc.Anisotropy(atto550_dna_life['VV'], atto550_dna_life['VH'], atto550_dna_life['HV'],atto550_dna_life['HH'])
atto550_dna_aniso.rotation_fit(p0=[0.4, 1, 10,1], model='two_rotations')

atto550_dna_aniso = lf.tcspc.Anisotropy(atto550_dna_life['VV'], atto550_dna_life['VH'], atto550_dna_life['HV'],atto550_dna_life['HH']) atto550_dna_aniso.rotation_fit(p0=[0.4, 1, 10,1], model='two_rotations')

====================
Anisotropy fit
model: two_rotations
r0: 0.19 ± 0.01
b: 0.00 ± 0.02
tau_r1: 8.54 ± 0.48 ns
tau_r2: 1.63 ± 0.16 ns
====================

Plot the anisotropy decay with the fit

In [10]:

with sns.axes_style('ticks'):
    set_ticksStyle()
    f, ax = plt.subplots(nrows=1, ncols=1, figsize=(2.5,2.25), sharex=False, sharey=True, squeeze=False)
    
    ax[0,0].plot(atto550_dna_aniso.time, atto550_dna_aniso.r, color=[0.45, 0.57, 0.44])
    ax[0,0].plot(atto550_dna_aniso.time, atto550_dna_aniso.fit_r, color='k')
    ax[0,0].set_ylim((-0.05,0.4))
    ax[0,0].set_xlabel('time (ns)')
    ax[0,0].set_ylabel('anisotropy')

with sns.axes_style('ticks'): set_ticksStyle() f, ax = plt.subplots(nrows=1, ncols=1, figsize=(2.5,2.25), sharex=False, sharey=True, squeeze=False) ax[0,0].plot(atto550_dna_aniso.time, atto550_dna_aniso.r, color=[0.45, 0.57, 0.44]) ax[0,0].plot(atto550_dna_aniso.time, atto550_dna_aniso.fit_r, color='k') ax[0,0].set_ylim((-0.05,0.4)) ax[0,0].set_xlabel('time (ns)') ax[0,0].set_ylabel('anisotropy')