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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Executed
Step1:  Load software and filenames definitions
Step2:  Data folder
Step3:  List of data files
Step4:  Data load
Initial loading of the data
Step5:  Laser alternation selection
At this point we have only the timestamps and the detector numbers
Step6:  We need to define some parameters
Step7:  We should check if everithing is OK with an alternation histogram
Step8:  If the plot looks good we can apply the parameters with
Step9:  Measurements infos
All the measurement data is in the d variable. We can print it
Step10:  Or check the measurements duration
Step11:  Compute background
Compute the background using automatic threshold
Step12:  Burst search and selection
Step14:  Donor Leakage fit
Half-Sample Mode
Fit peak usng the mode computed with the half-sample algorithm (Bickel 2005).
Step15:  Gaussian Fit
Fit the histogram with a gaussian
Step16:  KDE maximum
Step17:  Leakage summary
Step18:  Burst size distribution
Step19:  Fret fit
Max position of the Kernel Density Estimation (KDE)
Step20:  Weighted mean of $E$ of each burst
Step21:  Gaussian fit (no weights)
Step22:  Gaussian fit (using burst size as weights)
Step23:  Stoichiometry fit
Max position of the Kernel Density Estimation (KDE)
Step24:  The Maximum likelihood fit for a Gaussian population is the mean
Step25:  Computing the weighted mean and weighted standard deviation we get
Step26:  Save data to file
Step27:  The following string contains the list of variables to be saved. When saving, the order of the variables is preserved.
Step28:  This is just a trick to format the different variables
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Python Code:
ph_sel_name = "Dex"
data_id = "17d"
# ph_sel_name = "all-ph"
# data_id = "7d"
Explanation: Executed: Mon Mar 27 11:35:55 2017
Duration: 9 seconds.
usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
from fretbursts import *
init_notebook()
from IPython.display import display
Explanation: Load software and filenames definitions
End of explanation
data_dir = './data/singlespot/'
import os
data_dir = os.path.abspath(data_dir) + '/'
assert os.path.exists(data_dir), "Path '%s' does not exist." % data_dir
Explanation: Data folder:
End of explanation
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
ph_sel_map = {'all-ph': Ph_sel('all'), 'Dex': Ph_sel(Dex='DAem'), 
              'DexDem': Ph_sel(Dex='Dem')}
ph_sel = ph_sel_map[ph_sel_name]
data_id, ph_sel_name
Explanation: List of data files:
End of explanation
d = loader.photon_hdf5(filename=files_dict[data_id])
Explanation: Data load
Initial loading of the data:
End of explanation
d.ph_times_t, d.det_t
Explanation: Laser alternation selection
At this point we have only the timestamps and the detector numbers:
End of explanation
d.add(det_donor_accept=(0, 1), alex_period=4000, D_ON=(2850, 580), A_ON=(900, 2580), offset=0)
Explanation: We need to define some parameters: donor and acceptor ch, excitation period and donor and acceptor excitiations:
End of explanation
plot_alternation_hist(d)
Explanation: We should check if everithing is OK with an alternation histogram:
End of explanation
loader.alex_apply_period(d)
Explanation: If the plot looks good we can apply the parameters with:
End of explanation
d
Explanation: Measurements infos
All the measurement data is in the d variable. We can print it:
End of explanation
d.time_max
Explanation: Or check the measurements duration:
End of explanation
d.calc_bg(bg.exp_fit, time_s=60, tail_min_us='auto', F_bg=1.7)
dplot(d, timetrace_bg)
d.rate_m, d.rate_dd, d.rate_ad, d.rate_aa
Explanation: Compute background
Compute the background using automatic threshold:
End of explanation
bs_kws = dict(L=10, m=10, F=7, ph_sel=ph_sel)
d.burst_search(**bs_kws)
th1 = 30
ds = d.select_bursts(select_bursts.size, th1=30)
bursts = (bext.burst_data(ds, include_bg=True, include_ph_index=True)
          .round({'E': 6, 'S': 6, 'bg_d': 3, 'bg_a': 3, 'bg_aa': 3, 'nd': 3, 'na': 3, 'naa': 3, 'nda': 3, 'nt': 3, 'width_ms': 4}))
bursts.head()
burst_fname = ('results/bursts_usALEX_{sample}_{ph_sel}_F{F:.1f}_m{m}_size{th}.csv'
               .format(sample=data_id, th=th1, **bs_kws))
burst_fname
bursts.to_csv(burst_fname)
assert d.dir_ex == 0
assert d.leakage == 0
print(d.ph_sel)
dplot(d, hist_fret);
# 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)
ds = d.select_bursts(select_bursts.size, add_naa=False, th1=30)
n_bursts_all = ds.num_bursts[0]
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
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)    
n_bursts_do = ds_do.num_bursts[0]
n_bursts_fret = ds_fret.num_bursts[0]
n_bursts_do, n_bursts_fret
d_only_frac = 1.*n_bursts_do/(n_bursts_do + n_bursts_fret)
print ('D-only fraction:', d_only_frac)
dplot(ds_fret, hist2d_alex, scatter_alpha=0.1);
dplot(ds_do, hist2d_alex, S_max_norm=2, scatter=False);
Explanation: Burst search and selection
End of explanation
def hsm_mode(s):
    
    Half-sample mode (HSM) estimator of `s`.
    `s` is a sample from a continuous distribution with a single peak.
    
    Reference:
        Bickel, Fruehwirth (2005). arXiv:math/0505419
    
    s = memoryview(np.sort(s))
    i1 = 0
    i2 = len(s)
    while i2 - i1 > 3:
        n = (i2 - i1) // 2
        w = [s[n-1+i+i1] - s[i+i1] for i in range(n)]
        i1 = w.index(min(w)) + i1
        i2 = i1 + n
    if i2 - i1 == 3:
        if s[i1+1] - s[i1] < s[i2] - s[i1 + 1]:
            i2 -= 1
        elif s[i1+1] - s[i1] > s[i2] - s[i1 + 1]:
            i1 += 1
        else:
            i1 = i2 = i1 + 1
    return 0.5*(s[i1] + s[i2])
E_pr_do_hsm = hsm_mode(ds_do.E[0])
print ("%s: E_peak(HSM) = %.2f%%" % (ds.ph_sel, E_pr_do_hsm*100))
Explanation: Donor Leakage fit
Half-Sample Mode
Fit peak usng the mode computed with the half-sample algorithm (Bickel 2005).
End of explanation
E_fitter = bext.bursts_fitter(ds_do, weights=None)
E_fitter.histogram(bins=np.arange(-0.2, 1, 0.03))
E_fitter.fit_histogram(model=mfit.factory_gaussian())
E_fitter.params
res = E_fitter.fit_res[0]
res.params.pretty_print()
E_pr_do_gauss = res.best_values['center']
E_pr_do_gauss
Explanation: Gaussian Fit
Fit the histogram with a gaussian:
End of explanation
bandwidth = 0.03
E_range_do = (-0.1, 0.15)
E_ax = np.r_[-0.2:0.401:0.0002]
E_fitter.calc_kde(bandwidth=bandwidth)
E_fitter.find_kde_max(E_ax, xmin=E_range_do[0], xmax=E_range_do[1])
E_pr_do_kde = E_fitter.kde_max_pos[0]
E_pr_do_kde
Explanation: KDE maximum
End of explanation
mfit.plot_mfit(ds_do.E_fitter, plot_kde=True, plot_model=False)
plt.axvline(E_pr_do_hsm, color='m', label='HSM')
plt.axvline(E_pr_do_gauss, color='k', label='Gauss')
plt.axvline(E_pr_do_kde, color='r', label='KDE')
plt.xlim(0, 0.3)
plt.legend()
print('Gauss: %.2f%%\n  KDE: %.2f%%\n  HSM: %.2f%%' % 
      (E_pr_do_gauss*100, E_pr_do_kde*100, E_pr_do_hsm*100))
Explanation: Leakage summary
End of explanation
nt_th1 = 50
dplot(ds_fret, hist_size, which='all', add_naa=False)
xlim(-0, 250)
plt.axvline(nt_th1)
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
plt.figure()
plot(Th_nt, nt_th)
plt.axvline(nt_th1)
nt_mean = nt_th[np.where(Th_nt == nt_th1)][0]
nt_mean
Explanation: Burst size distribution
End of explanation
E_pr_fret_kde = bext.fit_bursts_kde_peak(ds_fret, bandwidth=bandwidth, weights='size')
E_fitter = ds_fret.E_fitter
E_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])
E_fitter.fit_histogram(mfit.factory_gaussian(center=0.5))
E_fitter.fit_res[0].params.pretty_print()
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)
Explanation: Fret fit
Max position of the Kernel Density Estimation (KDE):
End of explanation
ds_fret.fit_E_m(weights='size')
Explanation: Weighted mean of $E$ of each burst:
End of explanation
ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.03], weights=None)
Explanation: Gaussian fit (no weights):
End of explanation
ds_fret.fit_E_generic(fit_fun=bl.gaussian_fit_hist, bins=np.r_[-0.1:1.1:0.005], weights='size')
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
Explanation: Gaussian fit (using burst size as weights):
End of explanation
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
S_fitter.histogram(bins=np.r_[-0.1:1.1:0.03])
S_fitter.fit_histogram(mfit.factory_gaussian(), center=0.5)
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)
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
Explanation: Stoichiometry fit
Max position of the Kernel Density Estimation (KDE):
End of explanation
S = ds_fret.S[0]
S_ml_fit = (S.mean(), S.std())
S_ml_fit
Explanation: The Maximum likelihood fit for a Gaussian population is the mean:
End of explanation
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
Explanation: Computing the weighted mean and weighted standard deviation we get:
End of explanation
sample = data_id
Explanation: Save data to file
End of explanation
variables = ('sample n_bursts_all n_bursts_do 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 '
             'E_pr_do_kde E_pr_do_hsm E_pr_do_gauss nt_mean\n')
Explanation: The following string contains the list of variables to be saved. When saving, the order of the variables is preserved.
End of explanation
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)
# NOTE: The file name should be the notebook name but with .csv extension
with open('results/usALEX-5samples-PR-raw-%s.csv' % ph_sel_name, 'a') as f:
    f.seek(0, 2)
    if f.tell() == 0:
        f.write(variables_csv)
    f.write(data_str)
Explanation: This is just a trick to format the different variables:
End of explanation
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