Executed: Mon Mar 27 22:24:18 2017
Duration: 12 seconds.
In [1]:
data_id = '17d'
In [2]:
ph_sel_name = "None"
In [3]:
data_id = "12d"
In [4]:
from fretbursts import *
sns = init_notebook()
In [5]:
import os
import pandas as pd
from IPython.display import display, Math
In [6]:
import lmfit
print('lmfit version:', lmfit.__version__)
In [7]:
figure_size = (5, 4)
default_figure = lambda: plt.subplots(figsize=figure_size)
save_figures = True
def savefig(filename, **kwargs):
if not save_figures:
return
import os
dir_ = 'figures/'
kwargs_ = dict(dpi=300, bbox_inches='tight')
#frameon=True, facecolor='white', transparent=False)
kwargs_.update(kwargs)
plt.savefig(dir_ + filename, **kwargs_)
print('Saved: %s' % (dir_ + filename))
In [8]:
PLOT_DIR = './figure/'
In [9]:
import matplotlib as mpl
from cycler import cycler
bmap = sns.color_palette("Set1", 9)
colors = np.array(bmap)[(1,0,2,3,4,8,6,7), :]
mpl.rcParams['axes.prop_cycle'] = cycler('color', colors)
colors_labels = ['blue', 'red', 'green', 'violet', 'orange', 'gray', 'brown', 'pink', ]
for c, cl in zip(colors, colors_labels):
locals()[cl] = tuple(c) # assign variables with color names
sns.palplot(colors)
Data folder:
In [10]:
data_dir = './data/multispot/'
Check that the folder exists:
In [11]:
data_dir = os.path.abspath(data_dir) + '/'
assert os.path.exists(data_dir), "Path '%s' does not exist." % data_dir
List of data files in data_dir:
In [12]:
from glob import glob
file_list = sorted(glob(data_dir + '*_?.hdf5'))
In [13]:
labels = ['12d', '7d', '17d', '22d', '27d', 'DO']
files_dict = {lab: fname for lab, fname in zip(sorted(labels), file_list)}
files_dict
Out[13]:
Load the multispot leakage coefficient from disk (computed in Multi-spot 5-Samples analyis - Leakage coefficient fit):
In [14]:
_fname = 'results/Multi-spot - leakage coefficient KDE wmean DexDem.csv'
leakageM = np.loadtxt(_fname, ndmin=1)
print('Leakage coefficient:', leakageM)
Load the multispot direct excitation coefficient ($d_{dirT}$) from disk (computed in usALEX - Corrections - Direct excitation physical parameter):
In [15]:
_fname = 'results/usALEX - direct excitation coefficient dir_ex_t beta.csv'
dir_ex_tM = np.loadtxt(_fname, ndmin=1)
print('Direct excitation coefficient (dir_ex_t):', dir_ex_tM)
Load the multispot gamma ($\gamma_M$) coefficient (computed in Multi-spot Gamma Fitting):
In [16]:
_fname = 'results/Multi-spot - gamma factor.csv'
gammaM = np.loadtxt(_fname, ndmin=1)
print('Multispot gamma coefficient:', gammaM)
Load the usALEX leakage coefficient from disk (computed in usALEX - Corrections - Leakage fit):
In [17]:
_fname = 'results/usALEX - leakage coefficient DexDem.csv'
leakageA = np.loadtxt(_fname)
print('usALEX Leakage coefficient:', leakageA)
Load the usALEX gamma coefficient (computed in usALEX - Corrections - Gamma factor fit):
In [18]:
_fname = 'results/usALEX - gamma factor - all-ph.csv'
gammaA = np.loadtxt(_fname)
print('usALEX Gamma-factor:', gammaA)
Load the usALEX beta coefficient (computed in usALEX - Corrections - Gamma factor fit):
In [19]:
_fname = 'results/usALEX - beta factor - all-ph.csv'
betaA = np.loadtxt(_fname)
print('usALEX Gamma-factor:', betaA)
Load the usALEX direct-excitation coefficient ($d_{exAA}$) (computed in usALEX - Corrections - Direct excitation fit):
In [20]:
_fname = 'results/usALEX - direct excitation coefficient dir_ex_aa.csv'
dir_ex_aa = np.loadtxt(_fname)
print('Direct excitation coefficient (dir_ex_aa):', dir_ex_aa)
Compute usALEX direct-excitation coefficient ($d_{exT}$) (see usALEX - Corrections - Direct excitation physical parameter):
In [21]:
dir_ex_tA = betaA * dir_ex_aa
dir_ex_tA
Out[21]:
Analysis parameters:
In [22]:
donor_ref = False # False -> gamma correction is: g*nd + na
# True -> gamma correction is: nd + na/g
hist_weights = 'size'
## Background fit parameters
bg_kwargs_auto = dict(fun=bg.exp_fit,
time_s = 30,
tail_min_us = 'auto',
F_bg=1.7,
)
## Burst search
F=6
dither = False
size_th = 30 # Burst size threshold (selection on corrected burst sizes)
## FRET fit parameters
bandwidth = 0.03 # KDE bandwidth
E_range = {'7d': (0.7, 1.0), '12d': (0.4, 0.8), '17d': (0.2, 0.4),
'22d': (0.0, 0.1), '27d': (0.0, 0.1), 'DO': (0.0, 0.1)}
E_axis_kde = np.arange(-0.2, 1.2, 0.0002)
In [23]:
def print_fit_report(E_pr, gamma=1, leakage=0, dir_ex_t=0, math=True):
"""Print fit and standard deviation for both corrected and uncorrected E
Returns d.E_fit.
"""
E_corr = fretmath.correct_E_gamma_leak_dir(E_pr, gamma=gamma, leakage=leakage, dir_ex_t=dir_ex_t)
E_pr_mean = E_pr.mean()*100
E_pr_delta = (E_pr.max() - E_pr.min())*100
E_corr_mean = E_corr.mean()*100
E_corr_delta = (E_corr.max() - E_corr.min())*100
if math:
display(Math(r'\text{Pre}\;\gamma\quad\langle{E}_{fit}\rangle = %.1f\%% \qquad'
'\Delta E_{fit} = %.2f \%%' % \
(E_pr_mean, E_pr_delta)))
display(Math(r'\text{Post}\;\gamma\quad\langle{E}_{fit}\rangle = %.1f\%% \qquad'
'\Delta E_{fit} = %.2f \%%' % \
(E_corr_mean, E_corr_delta)))
else:
print('Pre-gamma E (delta, mean): %.2f %.2f' % (E_pr_mean, E_pr_delta))
print('Post-gamma E (delta, mean): %.2f %.2f' % (E_corr_mean, E_corr_delta))
In [24]:
d = loader.photon_hdf5(files_dict[data_id])
d.calc_bg(**bg_kwargs_auto)
d.burst_search(m=10, F=F, dither=dither)
In [25]:
d.time_max
Out[25]:
In [26]:
ds = Sel(d, select_bursts.size, th1=30, gamma=gammaM, donor_ref=donor_ref)
In [27]:
ds.num_bursts
Out[27]:
In [28]:
# fitter = bext.bursts_fitter(ds)
# fitter.histogram(bins=np.r_[-0.2 : 1.2 : bandwidth])
# fitter.model = mfit.factory_two_gaussians(add_bridge=False, p2_center=0.4)
# fitter.fit_histogram()
# display(fitter.params['p2_center'])
# print_fit_report(fitter.params['p2_center'], gamma=gammaM, leakage=leakageM, dir_ex_t=dir_ex_tM)
In [29]:
dplot(ds, hist_fret);
#show_model=True, show_fit_stats=True, fit_from='p2_center', show_fit_value=True);
In [30]:
d_all = ds.collapse()
In [31]:
d_all_chunk = Sel(d_all, select_bursts.time, time_s2=600/8)
In [32]:
dplot(d_all_chunk, hist_fret)
Out[32]:
In [33]:
Eraw = d_all_chunk.E[0]
In [34]:
E = fretmath.correct_E_gamma_leak_dir(Eraw, gamma=gammaM, leakage=leakageM, dir_ex_t=dir_ex_tM)
In [35]:
sns.set_style('whitegrid')
%config InlineBackend.figure_format='retina' # for hi-dpi displays
In [36]:
plt.hist(E, bins=np.arange(-0.2, 1.2, 0.025) + 0.5*0.025);
In [37]:
bursts_usalex = pd.read_csv('results/bursts_usALEX_{sample}_{ph_sel}_F{F:.1f}_m{m}_size{th}.csv'
.format(sample=data_id, ph_sel='Dex', m=10, th=30, F=7), index_col=0)
bursts_usalex
Out[37]:
In [38]:
Eraw_alex = bursts_usalex.E
In [39]:
E_alex = fretmath.correct_E_gamma_leak_dir(Eraw_alex, gamma=gammaA, leakage=leakageA, dir_ex_t=dir_ex_tA)
In [40]:
kws = dict(bins=np.arange(-0.2, 1.2, 0.025) + 0.5*0.025, histtype='step', lw=1.8)
plt.hist(E, label='Multispot', **kws)
plt.hist(E_alex, label='μs-ALEX', **kws)
plt.legend(loc=2)
plt.title('Sample %s: Multispot vs μs-ALEX comparison' % data_id)
plt.xlabel('FRET Efficiency')
plt.ylabel('# Bursts');
savefig('Multispot vs usALEX FRET hist comp sample %s' % data_id)
In [41]:
kws = dict(bins=np.arange(-0.2, 1.2, 0.025) + 0.5*0.025, histtype='step', lw=1.8, normed=True)
plt.hist(E, label='Multispot', **kws)
plt.hist(E_alex, label='μs-ALEX', **kws)
plt.legend(loc=2)
plt.title('Sample %s: Multispot vs μs-ALEX comparison' % data_id)
plt.xlabel('FRET Efficiency')
plt.ylabel('Probabiltity');
savefig('Multispot vs usALEX FRET hist comp sample %s normed' % data_id)
In [42]: