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measurement_id = 0
windows = (60, 180)
This notebook executes the realtime-kinetics analysis.
The first cell of this notebook selects which measurement is analyzed. Measurements can be processed one-by-one, by manually running this notebook, or in batch by using the notebook: "1-spot bubble-bubble kinetics - Run-All".
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import time
from pathlib import Path
import pandas as pd
from scipy.stats import linregress
from scipy import optimize
from IPython.display import display
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from fretbursts import *
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sns = init_notebook(fs=14)
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import lmfit; lmfit.__version__
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import phconvert; phconvert.__version__
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path = Path('./data/')
pattern = 'singlespot*.hdf5'
filenames = list(str(f) for f in path.glob(pattern))
filenames
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basenames = list(f.stem for f in path.glob(pattern))
basenames
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start_times = [600, 900, 900,
600, 600, 600, 600, 600, 600,
600, 600, 600] # time of NTP injection and start of kinetics
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filename = filenames[measurement_id]
start_time = start_times[measurement_id]
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filename
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import os
assert os.path.exists(filename)
Load and process the data:
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d = loader.photon_hdf5(filename)
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plot_alternation_hist(d)
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loader.alex_apply_period(d)
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d.time_max
Compute background and burst search:
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d.calc_bg(bg.exp_fit, time_s=10, tail_min_us='auto', F_bg=1.7)
Let's take a look at the photon waiting times histograms and at the fitted background rates:
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dplot(d, hist_bg);
Using dplot exactly in the same way as for the single-spot data has now generated 8 subplots, one for each channel.
Let's plot a timetrace for the background to see is there are significat variations during the measurement:
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dplot(d, timetrace_bg);
xlim(start_time - 150, start_time + 150)
We can look at the timetrace of the photon stream (binning):
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#dplot(d, timetrace)
#xlim(2, 3); ylim(-100, 100);
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#%%timeit -n1 -r1
ddc = bext.burst_search_and_gate(d)
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ds1 = ddc.select_bursts(select_bursts.size, th1=25)
ds = ds1.select_bursts(select_bursts.naa, th1=25)
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bpl.alex_jointplot(ds)
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ds0 = ds.select_bursts(select_bursts.time, time_s1=0, time_s2=start_time-10)
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dplot(ds0, hist_fret, pdf=False);
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weights = 'size'
bext.bursts_fitter(ds0, weights=weights)
ds0.E_fitter.fit_histogram(mfit.factory_two_gaussians(p1_center=0.5, p2_center=0.9), verbose=False)
dplot(ds0, hist_fret, show_model=True, weights=weights);
ds0.E_fitter.params
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weights = None
bext.bursts_fitter(ds0, weights=weights)
ds0.E_fitter.fit_histogram(mfit.factory_two_gaussians(p1_center=0.5, p2_center=0.9), verbose=False)
dplot(ds0, hist_fret, show_model=True, weights=weights);
ds0.E_fitter.params
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def gauss2(**params0):
peak1 = lmfit.models.GaussianModel(prefix='p1_')
peak2 = lmfit.models.GaussianModel(prefix='p2_')
model = peak1 + peak2
model.set_param_hint('p1_center', **{'value': 0.6, 'min': 0.3, 'max': 0.8, **params0.get('p1_center', {})})
model.set_param_hint('p2_center', **{'value': 0.9, 'min': 0.8, 'max': 1.0, **params0.get('p2_center', {})})
for sigma in ['p%d_sigma' % i for i in (1, 2)]:
model.set_param_hint(sigma, **{'value': 0.02, 'min': 0.01, **params0.get(sigma, {})})
for ampl in ['p%d_amplitude' % i for i in (1, 2)]:
model.set_param_hint(ampl, **{'value': 0.5, 'min': 0.01, **params0.get(ampl, {})})
model.name = '3 gauss peaks'
return model
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#%matplotlib notebook
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#fig, ax = plt.subplots(figsize=(12, 8))
#dplot(dm0, scatter_fret_size, ax=ax)
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bext.bursts_fitter(ds0, weights=None)
ds0.E_fitter.fit_histogram(gauss2(), verbose=False)
mfit.plot_mfit(ds0.E_fitter)
params_2gauss = ds0.E_fitter.params
plt.xlabel('E')
plt.ylabel('PDF')
plt.title('')
params_2gauss
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ds_final = ds.select_bursts(select_bursts.time, time_s1=start_time+300, time_s2=ds.time_max + 1)
ds_final.num_bursts
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bext.bursts_fitter(ds_final, weights=None)
model = gauss2()
model.set_param_hint('p2_center', value=params_2gauss.p2_center[0], vary=False)
ds_final.E_fitter.fit_histogram(model, verbose=False)
fig, ax = plt.subplots(figsize=(12, 6))
mfit.plot_mfit(ds_final.E_fitter, ax=ax)
params_2gauss1 = ds_final.E_fitter.params
params_2gauss1
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#del params_2gauss0
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is_runoff = 'runoff' in filename.lower()
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if 'params_2gauss0' not in locals():
params_2gauss0 = params_2gauss.copy()
if is_runoff:
params_2gauss0.p2_center = params_2gauss1.p2_center
else:
params_2gauss0.p1_center = params_2gauss1.p1_center
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params_2gauss0.p1_amplitude + params_2gauss0.p2_amplitude
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'params_2gauss0' in locals()
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from scipy import optimize
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params_fixed = dict(
mu1=float(params_2gauss0.p1_center),
mu2=float(params_2gauss0.p2_center),
sig1=float(params_2gauss0.p1_sigma),
sig2=float(params_2gauss0.p2_sigma),
)
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def em_weights_2gauss(x, a2, mu1, mu2, sig1, sig2):
"""Responsibility function for a 2-Gaussian model.
Return 2 arrays of size = x.size: the responsibility of
each Gaussian population.
"""
a1 = 1 - a2
assert np.abs(a1 + a2 - 1) < 1e-3
f1 = a1 * gauss_pdf(x, mu1, sig1)
f2 = a2 * gauss_pdf(x, mu2, sig2)
γ1 = f1 / (f1 + f2)
γ2 = f2 / (f1 + f2)
return γ1, γ2
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def em_fit_2gauss(x, a2_0, params_fixed, print_every=10, max_iter=100, rtol=1e-3):
a2_new = a2_0
rel_change = 1
i = 0
while rel_change > rtol and i < max_iter:
# E-step
γ1, γ2 = em_weights_2gauss(x, a2_new, **params_fixed)
assert np.allclose(γ1.sum() + γ2.sum(), x.size)
# M-step
a2_old = a2_new
a2_new = γ2.sum()/γ2.size
# Convergence
rel_change = np.abs((a2_old - a2_new)/a2_new)
i += 1
if (i % print_every) == 0:
print(i, a2_new, rel_change)
return a2_new, i
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from matplotlib.pylab import normpdf as gauss_pdf
# Model PDF to be maximized
def model_pdf(x, a2, mu1, mu2, sig1, sig2):
a1 = 1 - a2
#assert np.abs(a1 + a2 + a3 - 1) < 1e-3
return (a1 * gauss_pdf(x, mu1, sig1) +
a2 * gauss_pdf(x, mu2, sig2))
def func2min_lmfit(params, x):
a2 = params['a2'].value
mu1 = params['mu1'].value
mu2 = params['mu2'].value
sig1 = params['sig1'].value
sig2 = params['sig2'].value
return -np.sqrt(np.log(model_pdf(x, a2, mu1, mu2, sig1, sig2)))
def func2min_scipy(params_fit, params_fixed, x):
a2 = params_fit
mu1 = params_fixed['mu1']
mu2 = params_fixed['mu2']
sig1 = params_fixed['sig1']
sig2 = params_fixed['sig2']
return -np.log(model_pdf(x, a2, mu1, mu2, sig1, sig2)).sum()
# create a set of Parameters
params = lmfit.Parameters()
params.add('a2', value=0.5, min=0)
for k, v in params_fixed.items():
params.add(k, value=v, vary=False)
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x = ds0.E_
#x
#result = lmfit.minimize(func2min_lmfit, params, args=(x,), method='nelder')
#lmfit.report_fit(result.params)
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#optimize.brute(func2min_scipy, ranges=((0.01, 0.99), (0.01, 0.99)), Ns=101, args=(params, x))
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res_em = em_fit_2gauss(x, 0.5, params_fixed)
res_em
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res = optimize.minimize(func2min_scipy, x0=[0.5], args=(params_fixed, x), method='Nelder-Mead')
res
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res = optimize.minimize(func2min_scipy, x0=[0.5], args=(params_fixed, x), bounds=((0,1),), method='SLSQP')
res
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res = optimize.minimize(func2min_scipy, x0=[0.5], args=(params_fixed, x), bounds=((0,1),), method='TNC')
res
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bins = np.arange(-0.1, 1.1, 0.025)
plt.hist(x, bins, histtype='step', lw=2, normed=True);
xx = np.arange(-0.1, 1.1, 0.005)
#plt.plot(xx, model_pdf(xx, params))
plt.plot(xx, model_pdf(xx, a2=res_em[0], **params_fixed))
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def _kinetics_fit_em(dx, a2_0, params_fixed, **kwargs):
kwargs = {'max_iter': 100, 'print_every': 101, **kwargs}
a2, i = em_fit_2gauss(dx.E_, a2_0, params_fixed, **kwargs)
return a2, i < kwargs['max_iter']
def _kinetics_fit_ll(dx, a2_0, params_fixed, **kwargs):
kwargs = {'method':'Nelder-Mead', **kwargs}
res = optimize.minimize(func2min_scipy, x0=[a2_0], args=(params_fixed, dx.E_),
**kwargs)
return res.x[0], res.success
def _kinetics_fit_hist(dx, a2_0, params_fixed):
E_fitter = bext.bursts_fitter(dx)
model = mfit.factory_two_gaussians()
model.set_param_hint('p1_center', value=params_fixed['mu1'], vary=False)
model.set_param_hint('p2_center', value=params_fixed['mu2'], vary=False)
model.set_param_hint('p1_sigma', value=params_fixed['sig1'], vary=False)
model.set_param_hint('p2_sigma', value=params_fixed['sig2'], vary=False)
E_fitter.fit_histogram(model, verbose=False)
return (float(E_fitter.params.p2_amplitude),
dx.E_fitter.fit_res[0].success)
def kinetics_fit(ds_slices, params_fixed, a2_0=0.5, method='em', **method_kws):
fit_func = {
'em': _kinetics_fit_em,
'll': _kinetics_fit_ll,
'hist': _kinetics_fit_hist}
fit_list = []
for dx in ds_slices:
a2, success = fit_func[method](dx, a2_0, params_fixed, **method_kws)
df_i = pd.DataFrame(data=dict(p2_amplitude=a2,
p1_center=params_fixed['mu1'], p2_center=params_fixed['mu2'],
p1_sigma=params_fixed['sig1'], p2_sigma=params_fixed['sig2'],
tstart=dx.slice_tstart, tstop=dx.slice_tstop,
tmean=0.5*(dx.slice_tstart + dx.slice_tstop)),
index=[0.5*(dx.slice_tstart + dx.slice_tstop)])
if not success:
print('* ', end='', flush=True)
continue
fit_list.append(df_i)
print(flush=True)
return pd.concat(fit_list)
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start_time/60
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def print_slices(moving_window_params):
msg = ' - Slicing measurement:'
for name in ('start', 'stop', 'step', 'window'):
msg += ' %s = %.1fs' % (name, moving_window_params[name])
print(msg, flush=True)
num_slices = len(bext.moving_window_startstop(**moving_window_params))
print(' Number of slices %d' % num_slices, flush=True)
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t1 = time.time()
time.ctime()
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ds.calc_max_rate(m=10)
ds_high = ds.select_bursts(select_bursts.E, E1=0.85)
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step = 10
params = {}
for window in windows:
moving_window_params = dict(start=0, stop=ds.time_max, step=step, window=window)
print_slices(moving_window_params)
ds_slices = bext.moving_window_chunks(ds, time_zero=start_time, **moving_window_params)
for meth in ['em', 'll', 'hist']:
print(' >>> Fitting method %s ' % meth, end='', flush=True)
p = kinetics_fit(ds_slices, params_fixed, method=meth)
print(flush=True)
p['kinetics'] = p.p2_amplitude
p = p.round(dict(p1_center=3, p1_sigma=4, p2_amplitude=4, p2_center=3, p2_sigma=4, kinetics=4))
params[meth, window, step] = p
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print('Moving-window processing duration: %d seconds.' % (time.time() - t1))
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#moving_window_params = dict(start=0, stop=dsc.time_max, step=1, window=30)
moving_window_params
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ds_slices_high = bext.moving_window_chunks(ds_high, **moving_window_params)
df = bext.moving_window_dataframe(**moving_window_params) - start_time
df['size_mean'] = [di.nt_.mean() for di in ds_slices]
df['size_max'] = [di.nt_.max() for di in ds_slices]
df['num_bursts'] = [di.num_bursts[0] for di in ds_slices]
df['burst_width'] = [di.mburst_.width.mean()*di.clk_p*1e3 for di in ds_slices]
df['burst_width_high'] = [di.mburst_.width.mean()*di.clk_p*1e3 for di in ds_slices_high]
df['phrate_mean'] = [di.max_rate_.mean() for di in ds_slices]
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df = df.round(dict(tmean=1, tstart=1, tstop=1, size_mean=2, size_max=1,
burst_width=2, burst_width_high=2, phrate_mean=1))
df
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labels = ('num_bursts', 'burst_width', 'size_mean', 'phrate_mean',)
fig, axes = plt.subplots(len(labels), 1, figsize=(12, 3*len(labels)))
for ax, label in zip(axes, labels):
ax.plot('tstart', label, data=df)
ax.legend(loc='best')
#ax.set_ylim(0)
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# %%timeit -n1 -r1
# meth = 'em'
# print(' >>> Fitting method %s' % meth, flush=True)
# p = kinetics_fit(ds_slices, params_fixed, method=meth)
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# %%timeit -n1 -r1
# meth = 'hist'
# print(' >>> Fitting method %s' % meth, flush=True)
# p = kinetics_fit(ds_slices, params_fixed, method=meth)
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# %%timeit -n1 -r1
# meth = 'll'
# print(' >>> Fitting method %s' % meth, flush=True)
# p = kinetics_fit(ds_slices, params_fixed, method=meth)
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out_fname = 'results/%s_burst_data_vs_time__window%ds_step%ds.csv' % (
Path(filename).stem, moving_window_params['window'], moving_window_params['step'])
out_fname
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df.to_csv(out_fname)
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# np.abs((params['em', 30, 1] - params['ll', 30, 1]).p2_amplitude).max()
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methods = ('em', 'll', 'hist')
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for meth in methods:
plt.figure(figsize=(14, 3))
plt.plot(params['em', windows[0], step].index, params['em', windows[0], step].kinetics, 'h', color='gray', alpha=0.2)
plt.plot(params['em', windows[1], step].index, params['em', windows[1], step].kinetics, 'h', alpha=0.3)
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# (params['em', 5, 1].kinetics - params['ll', 5, 1].kinetics).plot()
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for window in windows:
for meth in methods:
out_fname = ('results/' + Path(filename).stem +
'_%sfit_ampl_only__window%ds_step%ds.csv' % (meth, window, step))
print('- Saving: ', out_fname)
params[meth, window, step].to_csv(out_fname)
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d
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