Executed: Mon Mar 27 11:36:27 2017
Duration: 8 seconds.
This notebook is executed through 8-spots paper analysis. For a direct execution, uncomment the cell below.
In [1]:
ph_sel_name = "None"
In [2]:
data_id = "12d"
In [3]:
# data_id = "7d"
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from fretbursts import *
In [5]:
init_notebook()
from IPython.display import display
Data folder:
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data_dir = './data/singlespot/'
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import os
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 [8]:
from glob import glob
file_list = sorted(f for f in glob(data_dir + '*.hdf5') if '_BKG' not in f)
## Selection for POLIMI 2012-11-26 datatset
labels = ['17d', '27d', '7d', '12d', '22d']
files_dict = {lab: fname for lab, fname in zip(labels, file_list)}
files_dict
Out[8]:
In [9]:
data_id
Out[9]:
Initial loading of the data:
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d = loader.photon_hdf5(filename=files_dict[data_id])
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d.ph_times_t, d.det_t
Out[11]:
We need to define some parameters: donor and acceptor ch, excitation period and donor and acceptor excitiations:
In [12]:
d.add(det_donor_accept=(0, 1), alex_period=4000, D_ON=(2850, 580), A_ON=(900, 2580), offset=0)
We should check if everithing is OK with an alternation histogram:
In [13]:
plot_alternation_hist(d)
If the plot looks good we can apply the parameters with:
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loader.alex_apply_period(d)
All the measurement data is in the d variable. We can print it:
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d
Out[15]:
Or check the measurements duration:
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d.time_max
Out[16]:
Compute the background using automatic threshold:
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d.calc_bg(bg.exp_fit, time_s=60, tail_min_us='auto', F_bg=1.7)
In [18]:
dplot(d, timetrace_bg)
Out[18]:
In [19]:
d.rate_m, d.rate_dd, d.rate_ad, d.rate_aa
Out[19]:
In [20]:
d_orig = d
d = bext.burst_search_and_gate(d, m=10, F=7)
In [21]:
assert d.dir_ex == 0
assert d.leakage == 0
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print(d.ph_sel)
dplot(d, hist_fret);
In [23]:
# if data_id in ['7d', '27d']:
# ds = d.select_bursts(select_bursts.size, th1=20)
# else:
# ds = d.select_bursts(select_bursts.size, th1=30)
In [24]:
ds = d.select_bursts(select_bursts.size, add_naa=False, th1=30)
In [25]:
n_bursts_all = ds.num_bursts[0]
In [26]:
def select_and_plot_ES(fret_sel, do_sel):
ds_fret= ds.select_bursts(select_bursts.ES, **fret_sel)
ds_do = ds.select_bursts(select_bursts.ES, **do_sel)
bpl.plot_ES_selection(ax, **fret_sel)
bpl.plot_ES_selection(ax, **do_sel)
return ds_fret, ds_do
In [27]:
ax = dplot(ds, hist2d_alex, S_max_norm=2, scatter_alpha=0.1)
if data_id == '7d':
fret_sel = dict(E1=0.60, E2=1.2, S1=0.2, S2=0.9, rect=False)
do_sel = dict(E1=-0.2, E2=0.5, S1=0.8, S2=2, rect=True)
ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)
elif data_id == '12d':
fret_sel = dict(E1=0.30,E2=1.2,S1=0.131,S2=0.9, rect=False)
do_sel = dict(E1=-0.4, E2=0.4, S1=0.8, S2=2, rect=False)
ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)
elif data_id == '17d':
fret_sel = dict(E1=0.01, E2=0.98, S1=0.14, S2=0.88, rect=False)
do_sel = dict(E1=-0.4, E2=0.4, S1=0.80, S2=2, rect=False)
ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)
elif data_id == '22d':
fret_sel = dict(E1=-0.16, E2=0.6, S1=0.2, S2=0.80, rect=False)
do_sel = dict(E1=-0.2, E2=0.4, S1=0.85, S2=2, rect=True)
ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)
elif data_id == '27d':
fret_sel = dict(E1=-0.1, E2=0.5, S1=0.2, S2=0.82, rect=False)
do_sel = dict(E1=-0.2, E2=0.4, S1=0.88, S2=2, rect=True)
ds_fret, ds_do = select_and_plot_ES(fret_sel, do_sel)
In [28]:
bandwidth = 0.03
n_bursts_fret = ds_fret.num_bursts[0]
n_bursts_fret
Out[28]:
In [29]:
dplot(ds_fret, hist2d_alex, scatter_alpha=0.1);
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nt_th1 = 50
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dplot(ds_fret, hist_size, which='all', add_naa=False)
xlim(-0, 250)
plt.axvline(nt_th1)
Out[31]:
In [32]:
Th_nt = np.arange(35, 120)
nt_th = np.zeros(Th_nt.size)
for i, th in enumerate(Th_nt):
ds_nt = ds_fret.select_bursts(select_bursts.size, th1=th)
nt_th[i] = (ds_nt.nd[0] + ds_nt.na[0]).mean() - th
In [33]:
plt.figure()
plot(Th_nt, nt_th)
plt.axvline(nt_th1)
Out[33]:
In [34]:
nt_mean = nt_th[np.where(Th_nt == nt_th1)][0]
nt_mean
Out[34]:
Max position of the Kernel Density Estimation (KDE):
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E_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, bandwidth=bandwidth, weights='size')
E_fitter = ds_fret.E_fitter
In [36]:
E_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])
E_fitter.fit_histogram(mfit.factory_gaussian(center=0.5))
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E_fitter.fit_res[0].params.pretty_print()
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fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))
mfit.plot_mfit(E_fitter, ax=ax[0])
mfit.plot_mfit(E_fitter, plot_model=False, plot_kde=True, ax=ax[1])
print('%s\nKDE peak %.2f ' % (ds_fret.ph_sel, E_pr_fret_kde*100))
display(E_fitter.params*100)
In [39]:
# ds_fret.add(E_fitter = E_fitter)
# dplot(ds_fret, hist_fret_kde, weights='size', bins=np.r_[-0.2:1.2:bandwidth], bandwidth=bandwidth);
# plt.axvline(E_pr_fret_kde, ls='--', color='r')
# print(ds_fret.ph_sel, E_pr_fret_kde)
Weighted mean of $E$ of each burst:
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ds_fret.fit_E_m(weights='size')
Out[40]:
Gaussian fit (no weights):
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ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.03], weights=None)
Out[41]:
Gaussian fit (using burst size as weights):
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ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.005], weights='size')
Out[42]:
In [43]:
E_kde_w = E_fitter.kde_max_pos[0]
E_gauss_w = E_fitter.params.loc[0, 'center']
E_gauss_w_sig = E_fitter.params.loc[0, 'sigma']
E_gauss_w_err = float(E_gauss_w_sig/np.sqrt(ds_fret.num_bursts[0]))
E_gauss_w_fiterr = E_fitter.fit_res[0].params['center'].stderr
E_kde_w, E_gauss_w, E_gauss_w_sig, E_gauss_w_err, E_gauss_w_fiterr
Out[43]:
Max position of the Kernel Density Estimation (KDE):
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S_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, burst_data='S', bandwidth=0.03) #weights='size', add_naa=True)
S_fitter = ds_fret.S_fitter
In [45]:
S_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])
S_fitter.fit_histogram(mfit.factory_gaussian(), center=0.5)
In [46]:
fig, ax = plt.subplots(1, 2, figsize=(14, 4.5))
mfit.plot_mfit(S_fitter, ax=ax[0])
mfit.plot_mfit(S_fitter, plot_model=False, plot_kde=True, ax=ax[1])
print('%s\nKDE peak %.2f ' % (ds_fret.ph_sel, S_pr_fret_kde*100))
display(S_fitter.params*100)
In [47]:
S_kde = S_fitter.kde_max_pos[0]
S_gauss = S_fitter.params.loc[0, 'center']
S_gauss_sig = S_fitter.params.loc[0, 'sigma']
S_gauss_err = float(S_gauss_sig/np.sqrt(ds_fret.num_bursts[0]))
S_gauss_fiterr = S_fitter.fit_res[0].params['center'].stderr
S_kde, S_gauss, S_gauss_sig, S_gauss_err, S_gauss_fiterr
Out[47]:
The Maximum likelihood fit for a Gaussian population is the mean:
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S = ds_fret.S[0]
S_ml_fit = (S.mean(), S.std())
S_ml_fit
Out[48]:
Computing the weighted mean and weighted standard deviation we get:
In [49]:
weights = bl.fret_fit.get_weights(ds_fret.nd[0], ds_fret.na[0], weights='size', naa=ds_fret.naa[0], gamma=1.)
S_mean = np.dot(weights, S)/weights.sum()
S_std_dev = np.sqrt(
np.dot(weights, (S - S_mean)**2)/weights.sum())
S_wmean_fit = [S_mean, S_std_dev]
S_wmean_fit
Out[49]:
In [50]:
sample = data_id
The following string contains the list of variables to be saved. When saving, the order of the variables is preserved.
In [51]:
variables = ('sample n_bursts_all n_bursts_fret '
'E_kde_w E_gauss_w E_gauss_w_sig E_gauss_w_err E_gauss_w_fiterr '
'S_kde S_gauss S_gauss_sig S_gauss_err S_gauss_fiterr '
'nt_mean\n')
This is just a trick to format the different variables:
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variables_csv = variables.replace(' ', ',')
fmt_float = '{%s:.6f}'
fmt_int = '{%s:d}'
fmt_str = '{%s}'
fmt_dict = {**{'sample': fmt_str},
**{k: fmt_int for k in variables.split() if k.startswith('n_bursts')}}
var_dict = {name: eval(name) for name in variables.split()}
var_fmt = ', '.join([fmt_dict.get(name, fmt_float) % name for name in variables.split()]) + '\n'
data_str = var_fmt.format(**var_dict)
print(variables_csv)
print(data_str)
In [53]:
# NOTE: The file name should be the notebook name but with .csv extension
with open('results/usALEX-5samples-PR-raw-AND-gate.csv', 'a') as f:
f.seek(0, 2)
if f.tell() == 0:
f.write(variables_csv)
f.write(data_str)