def plot_fit1d(res, fit_xvals=None,fit_num_points=100,ax=None, ret=None, **kw):
'''
function to plot a fitresult as returned by analysis.lib.fitting.fit.fit1d().
'''
# know keywords:
print_info = kw.pop('print_info', True)
info_xy = kw.pop('info_xy', 'auto')
plot_data = kw.pop('plot_data', True)
if res == None:
return False
if ax == None:
fig = plt.figure()
ax = fig.add_subplot(111)
if plot_data:
if 'yerr' in res.keys():
ax.errorbar(res['x'],res['y'],fmt='o',yerr=res['yerr'])
else:
ax.plot(res['x'], res['y'], 'o')
if fit_xvals == None:
fit_xvals=np.linspace(res['x'][0],res['x'][-1],fit_num_points)
ax.plot(fit_xvals, res['fitfunc'](fit_xvals), '-', lw=2)
if print_info:
params_str = res['fitfunc_str'] + '\n' + fit.str_fit_params(res)
if info_xy == 'auto':
info_x = ax.get_xlim()[0] + (ax.get_xlim()[-1]-ax.get_xlim()[0])*0.02
info_y = ax.get_ylim()[0] + (ax.get_ylim()[-1]-ax.get_ylim()[0])*0.02
else:
info_x = info_xy[0]
info_y = info_xy[1]
ax.text(info_x, info_y, params_str, size='x-small',
color='k', ha='left', va='bottom',
bbox=dict(facecolor='white', alpha=0.5))
if ret == None:
return
if ret == 'ax':
return ax
return
def fit_tail(fp, **kw):
(h0, b0), (h1, b1) = get_photon_hist(fp, **kw)
_x0 = (b0[:-1] + b0[1:]) / 2.
_x1 = (b1[:-1] + b1[1:]) / 2.
binwidth = _x0[1] - _x0[0]
tail0 = (_x0 >= settings.CH0_START) & (
_x0 <= settings.CH0_START + settings.FIT_TAIL_LENGTH)
tail1 = (_x1 >= settings.CH1_START) & (
_x1 <= settings.CH1_START + settings.FIT_TAIL_LENGTH)
x0, y0 = _x0[tail0], h0[tail0]
x1, y1 = _x1[tail1], h1[tail1]
fitres0 = _fit_tail(y0, x0)
fitres1 = _fit_tail(y1, x1)
starts = stats.get_sequence_starts(fp) * settings.SEQREPS
tcpsh0 = fitres0['params_dict']['A'] * fitres0['params_dict'][
'T'] / binwidth / starts
tcpsh1 = fitres1['params_dict']['A'] * fitres1['params_dict'][
'T'] / binwidth / starts
_x0 = np.linspace(x0[0], x0[-1], 201)
ret0 = {
'params': fitres0['params_dict'],
'errors': fitres0['error_dict'],
'data_x': x0,
'data_y': y0,
'fit_x': _x0,
'fit_y': fitres0['fitfunc'](_x0 - _x0[0]),
'info': fitres0['fitfunc_str'] + '\n' + fit.str_fit_params(fitres0),
'tcpsh': tcpsh0,
}
_x1 = np.linspace(x1[0], x1[-1], 201)
ret1 = {
'params': fitres1['params_dict'],
'errors': fitres1['error_dict'],
'data_x': x1,
'data_y': y1,
'fit_x': _x1,
'fit_y': fitres1['fitfunc'](_x1 - _x1[0]),
'info': fitres1['fitfunc_str'] + '\n' + fit.str_fit_params(fitres1),
'tcpsh': tcpsh1,
}
keys0 = []
params0 = np.empty((0, 2))
for k in ret0['params']:
keys0.append(k)
params0 = np.vstack(
(params0, np.array([ret0['params'][k], ret0['errors'][k]])))
files.set_analysis_data(fp,
'ch0_fitparams',
params0,
subgroup='tail',
info=ret0['info'],
columns=('fitvalue', 'error'),
rows=keys0)
files.set_analysis_data(fp,
'ch0_fit_data',
np.vstack(
(ret0['data_x'], ret0['data_y'])).transpose(),
subgroup='tail',
columns=('time bin center (ns)', 'counts per bin'))
files.set_analysis_data(fp,
'ch0_fit_curve',
np.vstack(
(ret0['fit_x'], ret0['fit_y'])).transpose(),
subgroup='tail',
columns=('time (ns)', 'counts per bin'))
files.set_analysis_data(fp,
'ch0_tcpsh',
ret0['tcpsh'],
subgroup='tail',
starts=starts)
keys1 = []
params1 = np.empty((0, 2))
for k in ret1['params']:
keys1.append(k)
params1 = np.vstack(
(params1, np.array([ret1['params'][k], ret1['errors'][k]])))
files.set_analysis_data(fp,
'ch1_fitparams',
params1,
subgroup='tail',
info=ret1['info'],
columns=('fitvalue', 'error'),
rows=keys1)
files.set_analysis_data(fp,
'ch1_fit_data',
np.vstack(
(ret1['data_x'], ret1['data_y'])).transpose(),
subgroup='tail',
columns=('time bin center (ns)', 'counts per bin'))
files.set_analysis_data(fp,
'ch1_fit_curve',
np.vstack(
(ret1['fit_x'], ret1['fit_y'])).transpose(),
subgroup='tail',
columns=('time (ns)', 'counts per bin'))
files.set_analysis_data(fp,
'ch1_tcpsh',
ret1['tcpsh'],
subgroup='tail',
starts=starts)
return ret0, ret1
def fit_tail(fp, **kw):
(h0, b0), (h1, b1) = get_photon_hist(fp, **kw)
_x0 = (b0[:-1]+b0[1:])/2.
_x1 = (b1[:-1]+b1[1:])/2.
binwidth = _x0[1] - _x0[0]
tail0 = (_x0 >= settings.CH0_START) & (_x0 <= settings.CH0_START + settings.FIT_TAIL_LENGTH)
tail1 = (_x1 >= settings.CH1_START) & (_x1 <= settings.CH1_START + settings.FIT_TAIL_LENGTH)
x0, y0 = _x0[tail0], h0[tail0]
x1, y1 = _x1[tail1], h1[tail1]
fitres0 = _fit_tail(y0, x0)
fitres1 = _fit_tail(y1, x1)
starts = stats.get_sequence_starts(fp) * settings.SEQREPS
tcpsh0 = fitres0['params_dict']['A'] * fitres0['params_dict']['T'] / binwidth / starts
tcpsh1 = fitres1['params_dict']['A'] * fitres1['params_dict']['T'] / binwidth / starts
_x0 = np.linspace(x0[0], x0[-1], 201)
ret0 = { 'params' : fitres0['params_dict'],
'errors' : fitres0['error_dict'],
'data_x' : x0,
'data_y' : y0,
'fit_x' : _x0,
'fit_y' : fitres0['fitfunc'](_x0-_x0[0]),
'info' : fitres0['fitfunc_str'] + '\n' + fit.str_fit_params(fitres0),
'tcpsh' : tcpsh0,
}
_x1 = np.linspace(x1[0], x1[-1], 201)
ret1 = { 'params' : fitres1['params_dict'],
'errors' : fitres1['error_dict'],
'data_x' : x1,
'data_y' : y1,
'fit_x' : _x1,
'fit_y' : fitres1['fitfunc'](_x1-_x1[0]),
'info' : fitres1['fitfunc_str'] + '\n' + fit.str_fit_params(fitres1),
'tcpsh' : tcpsh1,
}
keys0 = []
params0 = np.empty((0,2))
for k in ret0['params']:
keys0.append(k)
params0 = np.vstack((params0, np.array([ret0['params'][k], ret0['errors'][k]])))
files.set_analysis_data(fp, 'ch0_fitparams', params0, subgroup='tail',
info=ret0['info'], columns=('fitvalue', 'error'),
rows=keys0)
files.set_analysis_data(fp, 'ch0_fit_data', np.vstack((ret0['data_x'], ret0['data_y'])).transpose(),
subgroup='tail', columns=('time bin center (ns)', 'counts per bin'))
files.set_analysis_data(fp, 'ch0_fit_curve', np.vstack((ret0['fit_x'], ret0['fit_y'])).transpose(),
subgroup='tail', columns=('time (ns)', 'counts per bin'))
files.set_analysis_data(fp, 'ch0_tcpsh', ret0['tcpsh'], subgroup='tail',
starts = starts)
keys1 = []
params1 = np.empty((0,2))
for k in ret1['params']:
keys1.append(k)
params1 = np.vstack((params1, np.array([ret1['params'][k], ret1['errors'][k]])))
files.set_analysis_data(fp, 'ch1_fitparams', params1, subgroup='tail',
info=ret1['info'], columns=('fitvalue', 'error'),
rows=keys1)
files.set_analysis_data(fp, 'ch1_fit_data', np.vstack((ret1['data_x'], ret1['data_y'])).transpose(),
subgroup='tail', columns=('time bin center (ns)', 'counts per bin'))
files.set_analysis_data(fp, 'ch1_fit_curve', np.vstack((ret1['fit_x'], ret1['fit_y'])).transpose(),
subgroup='tail', columns=('time (ns)', 'counts per bin'))
files.set_analysis_data(fp, 'ch1_tcpsh', ret1['tcpsh'], subgroup='tail',
starts = starts)
return ret0, ret1
