def add_bibcodes(master):
"""
Add bibcodes to a master record.
@param master: master record of photometry
@type master: numpy record array
@return: master record of photometry with additional column
@rtype: numpy record array
"""
bibcodes = []
for source in master['source']:
bibcodes.append('None')
#-- check if it's a ViZieR catalog
if source.count('/')>=2:
#-- some catalogs have no ADS links but do have a bibcode, or the
# bibcode is manually put in the *cats_phot.cfg file
if 'bibcode' in vizier.cat_info.options(source):
bibcodes[-1] = vizier.cat_info.get(source,'bibcode')
#-- others may be retrieved immediately from the ViZieR site
else:
bibcodes[-1] = vizier.catalog2bibcode(source)
elif source=='GCPD':
bibcodes[-1] = '1997A&AS..124..349M'
elif source in gator.cat_info.sections():
if 'bibcode' in gator.cat_info.options(source):
bibcodes[-1] = gator.cat_info.get(source,'bibcode')
elif source in mast.cat_info.sections():
if 'bibcode' in mast.cat_info.options(source):
bibcodes[-1] = mast.cat_info.get(source,'bibcode')
if bibcodes[-1]=='None' or bibcodes[-1] is None:
logger.info('Bibcode of source {0:25s} not found'.format(source))
bibcodes = np.array(bibcodes,str)
bibcodes = np.rec.fromarrays([bibcodes],names=['bibcode'])
return numpy_ext.recarr_join(master,bibcodes)
def add_bibcodes(master):
"""
Add bibcodes to a master record.
@param master: master record of photometry
@type master: numpy record array
@return: master record of photometry with additional column
@rtype: numpy record array
"""
bibcodes = []
for source in master['source']:
bibcodes.append('None')
#-- check if it's a ViZieR catalog
if source.count('/')>=2:
#-- some catalogs have no ADS links but do have a bibcode, or the
# bibcode is manually put in the *cats_phot.cfg file
if 'bibcode' in vizier.cat_info.options(source):
bibcodes[-1] = vizier.cat_info.get(source,'bibcode')
#-- others may be retrieved immediately from the ViZieR site
else:
bibcodes[-1] = vizier.catalog2bibcode(source)
elif source=='GCPD':
bibcodes[-1] = '1997A&AS..124..349M'
elif source in gator.cat_info.sections():
if 'bibcode' in gator.cat_info.options(source):
bibcodes[-1] = gator.cat_info.get(source,'bibcode')
elif source in mast.cat_info.sections():
if 'bibcode' in mast.cat_info.options(source):
bibcodes[-1] = mast.cat_info.get(source,'bibcode')
if bibcodes[-1]=='None' or bibcodes[-1] is None:
logger.info('Bibcode of source {0:25s} not found'.format(source))
bibcodes = np.array(bibcodes,str)
bibcodes = np.rec.fromarrays([bibcodes],names=['bibcode'])
return numpy_ext.recarr_join(master,bibcodes)
def single_prewhitening(times,
signal,
freq,
optimize=0,
model='sine',
full_output=False,
correlation_correction=True):
"""
Fit a functions to a timeseries via a single iteration of prewhitening.
Use this function in combination with C{find_frequency} to do step-by-step
prewhitening.
This function will fit previously found frequencies C{freq} to the original signal
and optimize the found parameters if needed.
@return: parameters(, model)
@rtype: rec array(, ndarray)
"""
# do the fit including all frequencies
all_params = getattr(fit, model)(times, signal, freq)
# if there's a need to optimize, optimize the last n parameters
if optimize > 0:
# todo: ask about this, why the previous residuals??
residuals_for_optimization = residuals # signal - getattr(evaluate, model)(times, all_params)
if optimize <= len(freq):
model_fixed_params = getattr(evaluate,
model)(times, all_params[:-optimize])
residuals_for_optimization -= model_fixed_params
uparams, e_uparams, gain = fit.optimize(times,
residuals_for_optimization,
all_params[-optimize:], model)
# only accept the optimization if we gained prediction power
if gain > 0:
all_params[-optimize:] = uparams
logger.info('Accepted optimization (gained %g%%)' % gain)
# compute the model and the errors
model_func = getattr(evaluate, model)(times, all_params)
e_allparams = getattr(fit, 'e_' + model)(
times,
signal,
all_params,
correlation_correction=correlation_correction)
all_params = numpy_ext.recarr_join(all_params, e_allparams)
if full_output:
return all_params, model_func
else:
return all_params
def iterative_prewhitening(times,
signal,
maxiter=1000,
optimize=0,
method='scargle',
model='sine',
full_output=False,
stopcrit=None,
correlation_correction=True,
prewhiteningorder_snr=False,
prewhiteningorder_snr_window=1.,
**kwargs):
"""
Fit one or more functions to a timeseries via iterative prewhitening.
This function will use C{find_frequency} to fit the function parameters to
the original signal (including any previously found parameters),
optimize the parameters if needed, and remove it from the data to search
for a new frequency in the residuals.
It is always the original signal that is used to fit all parameters again;
B{only the (optimized) frequency is remembered from step to step} (Vanicek's
method).
You best set C{maxiter} to some sensable value, to hard-limit the number of
frequencies that will be searched for. You can additionally use a C{stopcrit}
and stop looking for frequencies once it is reached. C{stopcrit} should be
a tuple; the first argument is the function to call, the other arguments
are passed to the function, after the mandatory arguments C{times,signal,
modelfunc,allparams,pergram}. See L{stopcrit_scargle_snr} for an example of
such a function.
By default, the function looks for the highest (or deepest in the case of the pdm
method) peak, but instead it is possible to go for the peak having the highest
SNR before prewhitening by setting C{prewhiteningorder_snr} to True. In this case,
the noise spectrum is calculated using a convolution with a
C{prewhiteningorder_snr_window} wide box. Usage of this is strongly encouraged,
especially combined with L{stopcrit_scargle_snr} as C{stopcrit}.
@return: parameters, model(, model function)
@rtype: rec array(, ndarray)
"""
residuals = signal.copy()
frequencies = []
stop_criteria = []
while maxiter:
#-- compute the next frequency from the residuals
params, pergram, this_fit = find_frequency(
times,
residuals,
method=method,
full_output=True,
correlation_correction=correlation_correction,
prewhiteningorder_snr=prewhiteningorder_snr,
prewhiteningorder_snr_window=prewhiteningorder_snr_window,
**kwargs)
#-- do the fit including all frequencies
frequencies.append(params['freq'][-1])
allparams = getattr(fit, model)(times, signal, frequencies)
#-- if there's a need to optimize, optimize the last n parameters
if optimize > 0:
residuals_for_optimization = residuals
if optimize <= len(params):
model_fixed_params = getattr(evaluate,
model)(times,
allparams[:-optimize])
residuals_for_optimization -= model_fixed_params
uparams, e_uparams, gain = fit.optimize(
times, residuals_for_optimization, allparams[-optimize:],
model)
#-- only accept the optimization if we gained prediction power
if gain > 0:
allparams[-optimize:] = uparams
logger.info('Accepted optimization (gained %g%%)' % gain)
#-- compute the residuals to use in the next prewhitening step
modelfunc = getattr(evaluate, model)(times, allparams)
residuals = signal - modelfunc
#-- exhaust the counter
maxiter -= 1
#-- check stop criterion
if stopcrit is not None:
func = stopcrit[0]
args = stopcrit[1:]
condition, value = func(times, signal, modelfunc, allparams,
pergram, *args)
logger.info('Stop criterion (%s): %.3g' % (func.__name__, value))
stop_criteria.append(value)
if condition:
logger.info('Stop criterion reached')
break
#-- calculate the errors
e_allparams = getattr(fit, 'e_' + model)(
times,
signal,
allparams,
correlation_correction=correlation_correction)
allparams = numpy_ext.recarr_join(allparams, e_allparams)
if stopcrit is not None:
allparams = numpy_ext.recarr_join(
allparams, np.rec.fromarrays([stop_criteria], names=['stopcrit']))
if full_output:
return allparams, modelfunc
else:
return allparams
def find_frequency(times,
signal,
method='scargle',
model='sine',
full_output=False,
optimize=0,
max_loops=20,
scale_region=0.1,
scale_df=0.20,
model_kwargs=None,
correlation_correction=True,
prewhiteningorder_snr=False,
prewhiteningorder_snr_window=1.,
**kwargs):
"""
Find one frequency, automatically going to maximum precision and return
parameters & error estimates.
This routine makes the frequency grid finer until it is finer than the
estimated error on the frequency. After that, it will compute (harmonic)
parameters and estimate errors.
There is a possibility to escape this optimization by setting scale_df=0 or
freqregscale=0.
You can include a nonlinear least square update of the parameters, by
setting the keyword C{optimize=1} (optimization outside loop) or
C{optimize=2} (optimization after each iteration).
The method with which to find the frequency can be set with the keyword
C{method}, the model used to fit and optimize should be set with C{model}.
Extra keywords for the model functions should go in C{model_kwargs}. If
C{method} is a tuple, the first method will be used for the first frequency
search only. This could be useful to take advantage of such methods as
fasper which do not allow for iterative zoom-ins. By default, the function looks
for the highest (or deepest in the case of the pdm method) peak, but instead it is
possible to go for the peak having the highest SNR before prewhitening by setting
C{prewhiteningorder_snr} to True. In this case, the noise spectrum is calculated
using a convolution with a C{prewhiteningorder_snr_window} wide box.
Possible extra keywords: see definition of the used periodogram function.
B{Warning}: the timeseries must be B{sorted in time} and B{cannot contain
the same timepoint twice}. Otherwise, a 'ValueError, concatenation problem'
can occur.
Example keywords:
- 'correlation_correction', default=True
- 'freqregscale', default=0.5: factor for zooming in on frequency
- 'dfscale', default = 0.25: factor for optimizing frequency resolution
Example usage: We generate a sine signal
>>> times = np.linspace(0,100,1000)
>>> signal = np.sin(2*np.pi*2.5*times) + np.random.normal(size=len(times))
Compute the frequency
>>> parameters, pgram, model = find_frequency(times,signal,full_output=True)
Make a plot:
>>> p = pl.figure()
>>> p = pl.axes([0.1,0.3,0.85,0.65])
>>> p = pl.plot(pgram[0],pgram[1],'k-')
>>> p = pl.xlim(2.2,2.8)
>>> p = pl.ylabel('Amplitude')
>>> p = pl.axes([0.1,0.1,0.85,0.2])
>>> p = pl.plot(pgram[0][:-1],np.diff(pgram[0]),'k-')
>>> p = pl.xlim(2.2,2.8)
>>> p,q = pl.xlabel('Frequency (c/d)'),pl.ylabel('Frequency resolution $\Delta F$')
]]include figure]]timeseries_freqanalyse_06.png]
@rtype: record array(, 2x1Darray, 1Darray)
@return: parameters and errors(, periodogram, model function)
"""
if model_kwargs is None:
model_kwargs = dict()
#-- initial values
e_f = 0
freq_diff = np.inf
prev_freq = -np.inf
counter = 0
f_max = np.inf
f_min = 0. #-np.inf
#-- calculate periodogram until frequency precision is
# under 1/10th of correlation corrected version of frequency error
method_kwargs = kwargs.copy() # don't modify the dictionary the user gave
while freq_diff > e_f / 10.:
#-- possibly, we might want to use different periodograms for the first
# calculation than for the zoom in, since some periodograms are faster
# than others but do not have the ability to 'zoom in' (e.g. the FFT)
if freq_diff == np.inf and not isinstance(method, str):
method_ = method[1]
method = method[0] # override method to be a string the next time
#-- calculate periodogram
freqs, ampls = getattr(pergrams, method)(times, signal,
**method_kwargs)
f0, fn, df = freqs[0], freqs[-1], freqs[1] - freqs[0]
#-- now use the second method for the zoom-ins from now on
if freq_diff == np.inf and not isinstance(method, str):
method = method_
#-- extract the frequency: this part should be generalized, but for now,
# it will do:
if method in ['pdm']:
frequency = freqs[np.argmin(ampls)]
#-- instead of going for the highest peak, let's get the most significant one
if prewhiteningorder_snr:
if counter == 0: #we calculate a noise spectrum with a convolution in a 1 d-1 window
windowlength = float(prewhiteningorder_snr_window) / (
freqs[1] - freqs[0])
window = np.ones(int(windowlength)) / float(windowlength)
ampls_ = np.concatenate(
(ampls[::-1], ampls, ampls[::-1])
) #we mirror the amplitude spectrum on both ends so the convolution will be better near the edges
noises_ = np.convolve(ampls_, window, 'same')
noises = np.split(
noises_, 3
)[1] #and we recut the resulted convolution to match the original frequency range
freqs_old = np.copy(freqs)
noises_old = np.copy(noises)
else:
noises = np.interp(
freqs, freqs_old, noises_old
) #we use the original noise spectrum in this narrower windows too, which should save some time, and avoid the problem of having a wider window for the SNR calculation than the width of the zoom-in window
frequency = freqs[np.argmax(ampls / noises)]
else:
frequency = freqs[np.argmax(ampls)]
if full_output and counter == 0:
freqs_, ampls_ = freqs, ampls
#-- estimate parameters and calculate a fit, errors and residuals
params = getattr(fit, model)(times, signal, frequency, **model_kwargs)
if hasattr(fit, 'e_' + model):
errors = getattr(fit, 'e_' + model)(
times,
signal,
params,
correlation_correction=correlation_correction)
e_f = errors['e_freq'][-1]
#-- possibly there are not errors defined for this fitting functions
else:
errors = None
#-- optimize inside loop if necessary and if we gained prediction
# value:
if optimize == 2:
params_, errors_, gain = fit.optimize(times, signal, params, model)
if gain > 0:
params = params_
logger.info('Accepted optimization (gained %g%%)' % gain)
#-- improve precision
freq_diff = abs(frequency - prev_freq)
prev_freq = frequency
freq_region = fn - f0
f0 = max(f_min, frequency - freq_region * scale_region / 2.)
fn = min(f_max, frequency + freq_region * scale_region / 2.)
df *= scale_df
method_kwargs['f0'] = f0
method_kwargs['fn'] = fn
method_kwargs['df'] = df
#-- possibilities to escape iterative zoom in
#print '---> {counter}/{max_loops}: freq={frequency} ({f0}-->{fn}/{df}), e_f={e_f}, freq_diff={freq_diff}'.format(**locals()),max(ampls)
if scale_region == 0 or scale_df == 0:
break
if counter >= max_loops:
logger.error(
"Frequency precision not reached in %d steps, breaking loop" %
(max_loops))
break
if (fn - f0) / df < 5:
logger.error(
"Frequency precision not reached with stepsize %e , breaking loop"
% (df / scale_df))
break
counter += 1
#-- optimize parameters outside of loop if necessary:
if optimize == 1:
params_, errors_, gain = fit.optimize(times, signal, params, model)
if gain > 0:
params = params_
logger.info('Accepted optimization (gained %g%%)' % gain)
#-- add the errors to the parameter array if possible
if errors is not None:
params = numpy_ext.recarr_join(params, errors)
# logger.info("%s model parameters via %s periodogram:\n"%(model,method)+pl.mlab.rec2txt(params,precision=8))
# params.tofile('log_params', sep=' ', format='%s')
# logger.info("%s model parameters via %s periodogram:\n"%(model, method) + np.fromfile('log_params'))
logger.info("%s model parameters via %s periodogram:\n" % (model, method) +
str(params))
#-- when full output is required, return parameters, periodogram and fitting
# function
if full_output:
mymodel = getattr(evaluate, model)(times, params)
return params, (freqs_, ampls_), mymodel
else:
return params
def iterative_prewhitening(times,signal,maxiter=1000,optimize=0,method='scargle',
model='sine',full_output=False,stopcrit=None,correlation_correction=True,
prewhiteningorder_snr=False,prewhiteningorder_snr_window=1.,**kwargs):
"""
Fit one or more functions to a timeseries via iterative prewhitening.
This function will use C{find_frequency} to fit the function parameters to
the original signal (including any previously found parameters),
optimize the parameters if needed, and remove it from the data to search
for a new frequency in the residuals.
It is always the original signal that is used to fit all parameters again;
B{only the (optimized) frequency is remembered from step to step} (Vanicek's
method).
You best set C{maxiter} to some sensable value, to hard-limit the number of
frequencies that will be searched for. You can additionally use a C{stopcrit}
and stop looking for frequencies once it is reached. C{stopcrit} should be
a tuple; the first argument is the function to call, the other arguments
are passed to the function, after the mandatory arguments C{times,signal,
modelfunc,allparams,pergram}. See L{stopcrit_scargle_snr} for an example of
such a function.
By default, the function looks for the highest (or deepest in the case of the pdm
method) peak, but instead it is possible to go for the peak having the highest
SNR before prewhitening by setting C{prewhiteningorder_snr} to True. In this case,
the noise spectrum is calculated using a convolution with a
C{prewhiteningorder_snr_window} wide box. Usage of this is strongly encouraged,
especially combined with L{stopcrit_scargle_snr} as C{stopcrit}.
@return: parameters, model(, model function)
@rtype: rec array(, ndarray)
"""
residuals = signal.copy()
frequencies = []
stop_criteria = []
while maxiter:
#-- compute the next frequency from the residuals
params,pergram,this_fit = find_frequency(times,residuals,method=method,
full_output=True,correlation_correction=correlation_correction,
prewhiteningorder_snr=prewhiteningorder_snr,
prewhiteningorder_snr_window=prewhiteningorder_snr_window,**kwargs)
#-- do the fit including all frequencies
frequencies.append(params['freq'][-1])
allparams = getattr(fit,model)(times,signal,frequencies)
#-- if there's a need to optimize, optimize the last n parameters
if optimize>0:
residuals_for_optimization = residuals
if optimize<=len(params):
model_fixed_params = getattr(evaluate,model)(times,allparams[:-optimize])
residuals_for_optimization -= model_fixed_params
uparams,e_uparams, gain = fit.optimize(times,residuals_for_optimization,allparams[-optimize:],model)
#-- only accept the optimization if we gained prediction power
if gain>0:
allparams[-optimize:] = uparams
logger.info('Accepted optimization (gained %g%%)'%gain)
#-- compute the residuals to use in the next prewhitening step
modelfunc = getattr(evaluate,model)(times,allparams)
residuals = signal - modelfunc
#-- exhaust the counter
maxiter -= 1
#-- check stop criterion
if stopcrit is not None:
func = stopcrit[0]
args = stopcrit[1:]
condition,value = func(times,signal,modelfunc,allparams,pergram,*args)
logger.info('Stop criterion (%s): %.3g'%(func.__name__,value))
stop_criteria.append(value)
if condition:
logger.info('Stop criterion reached')
break
#-- calculate the errors
e_allparams = getattr(fit,'e_'+model)(times,signal,allparams,correlation_correction=correlation_correction)
allparams = numpy_ext.recarr_join(allparams,e_allparams)
if stopcrit is not None:
allparams = numpy_ext.recarr_join(allparams,np.rec.fromarrays([stop_criteria],names=['stopcrit']))
if full_output:
return allparams,modelfunc
else:
return allparams
def find_frequency(times,signal,method='scargle',model='sine',full_output=False,
optimize=0,max_loops=20, scale_region=0.1, scale_df=0.20, model_kwargs=None,
correlation_correction=True,prewhiteningorder_snr=False,
prewhiteningorder_snr_window=1.,**kwargs):
"""
Find one frequency, automatically going to maximum precision and return
parameters & error estimates.
This routine makes the frequency grid finer until it is finer than the
estimated error on the frequency. After that, it will compute (harmonic)
parameters and estimate errors.
There is a possibility to escape this optimization by setting scale_df=0 or
freqregscale=0.
You can include a nonlinear least square update of the parameters, by
setting the keyword C{optimize=1} (optimization outside loop) or
C{optimize=2} (optimization after each iteration).
The method with which to find the frequency can be set with the keyword
C{method}, the model used to fit and optimize should be set with C{model}.
Extra keywords for the model functions should go in C{model_kwargs}. If
C{method} is a tuple, the first method will be used for the first frequency
search only. This could be useful to take advantage of such methods as
fasper which do not allow for iterative zoom-ins. By default, the function looks
for the highest (or deepest in the case of the pdm method) peak, but instead it is
possible to go for the peak having the highest SNR before prewhitening by setting
C{prewhiteningorder_snr} to True. In this case, the noise spectrum is calculated
using a convolution with a C{prewhiteningorder_snr_window} wide box.
Possible extra keywords: see definition of the used periodogram function.
B{Warning}: the timeseries must be B{sorted in time} and B{cannot contain
the same timepoint twice}. Otherwise, a 'ValueError, concatenation problem'
can occur.
Example keywords:
- 'correlation_correction', default=True
- 'freqregscale', default=0.5: factor for zooming in on frequency
- 'dfscale', default = 0.25: factor for optimizing frequency resolution
Example usage: We generate a sine signal
>>> times = np.linspace(0,100,1000)
>>> signal = np.sin(2*np.pi*2.5*times) + np.random.normal(size=len(times))
Compute the frequency
>>> parameters, pgram, model = find_frequency(times,signal,full_output=True)
Make a plot:
>>> p = pl.figure()
>>> p = pl.axes([0.1,0.3,0.85,0.65])
>>> p = pl.plot(pgram[0],pgram[1],'k-')
>>> p = pl.xlim(2.2,2.8)
>>> p = pl.ylabel('Amplitude')
>>> p = pl.axes([0.1,0.1,0.85,0.2])
>>> p = pl.plot(pgram[0][:-1],np.diff(pgram[0]),'k-')
>>> p = pl.xlim(2.2,2.8)
>>> p,q = pl.xlabel('Frequency (c/d)'),pl.ylabel('Frequency resolution $\Delta F$')
]]include figure]]timeseries_freqanalyse_06.png]
@rtype: record array(, 2x1Darray, 1Darray)
@return: parameters and errors(, periodogram, model function)
"""
if model_kwargs is None:
model_kwargs = dict()
#-- initial values
e_f = 0
freq_diff = np.inf
prev_freq = -np.inf
counter = 0
f_max = np.inf
f_min = 0.#-np.inf
#-- calculate periodogram until frequency precision is
# under 1/10th of correlation corrected version of frequency error
method_kwargs = kwargs.copy() # don't modify the dictionary the user gave
while freq_diff>e_f/10.:
#-- possibly, we might want to use different periodograms for the first
# calculation than for the zoom in, since some periodograms are faster
# than others but do not have the ability to 'zoom in' (e.g. the FFT)
if freq_diff==np.inf and not isinstance(method,str):
method_ = method[1]
method = method[0] # override method to be a string the next time
#-- calculate periodogram
freqs,ampls = getattr(pergrams,method)(times,signal,**method_kwargs)
f0,fn,df = freqs[0],freqs[-1],freqs[1]-freqs[0]
#-- now use the second method for the zoom-ins from now on
if freq_diff==np.inf and not isinstance(method,str):
method = method_
#-- extract the frequency: this part should be generalized, but for now,
# it will do:
if method in ['pdm']:
frequency = freqs[np.argmin(ampls)]
#-- instead of going for the highest peak, let's get the most significant one
if prewhiteningorder_snr:
if counter == 0: #we calculate a noise spectrum with a convolution in a 1 d-1 window
windowlength = float(prewhiteningorder_snr_window)/(freqs[1]-freqs[0])
window = np.ones(int(windowlength))/float(windowlength)
ampls_ = np.concatenate((ampls[::-1],ampls,ampls[::-1])) #we mirror the amplitude spectrum on both ends so the convolution will be better near the edges
noises_ = np.convolve(ampls_, window, 'same')
noises = np.split(noises_,3)[1] #and we recut the resulted convolution to match the original frequency range
freqs_old = np.copy(freqs)
noises_old = np.copy(noises)
else:
noises = np.interp(freqs,freqs_old,noises_old) #we use the original noise spectrum in this narrower windows too, which should save some time, and avoid the problem of having a wider window for the SNR calculation than the width of the zoom-in window
frequency = freqs[np.argmax(ampls/noises)]
else:
frequency = freqs[np.argmax(ampls)]
if full_output and counter==0:
freqs_,ampls_ = freqs,ampls
#-- estimate parameters and calculate a fit, errors and residuals
params = getattr(fit,model)(times,signal,frequency,**model_kwargs)
if hasattr(fit,'e_'+model):
errors = getattr(fit,'e_'+model)(times,signal,params,correlation_correction=correlation_correction)
e_f = errors['e_freq'][-1]
#-- possibly there are not errors defined for this fitting functions
else:
errors = None
#-- optimize inside loop if necessary and if we gained prediction
# value:
if optimize==2:
params_,errors_,gain = fit.optimize(times,signal,params,model)
if gain>0:
params = params_
logger.info('Accepted optimization (gained %g%%)'%gain)
#-- improve precision
freq_diff = abs(frequency-prev_freq)
prev_freq = frequency
freq_region = fn-f0
f0 = max(f_min,frequency-freq_region*scale_region/2.)
fn = min(f_max,frequency+freq_region*scale_region/2.)
df *= scale_df
method_kwargs['f0'] = f0
method_kwargs['fn'] = fn
method_kwargs['df'] = df
#-- possibilities to escape iterative zoom in
#print '---> {counter}/{max_loops}: freq={frequency} ({f0}-->{fn}/{df}), e_f={e_f}, freq_diff={freq_diff}'.format(**locals()),max(ampls)
if scale_region==0 or scale_df==0:
break
if counter >= max_loops:
logger.error("Frequency precision not reached in %d steps, breaking loop"%(max_loops))
break
if (fn-f0)/df<5:
logger.error("Frequency precision not reached with stepsize %e , breaking loop"%(df/scale_df))
break
counter += 1
#-- optimize parameters outside of loop if necessary:
if optimize==1:
params_,errors_,gain = fit.optimize(times,signal,params,model)
if gain>0:
params = params_
logger.info('Accepted optimization (gained %g%%)'%gain)
#-- add the errors to the parameter array if possible
if errors is not None:
params = numpy_ext.recarr_join(params,errors)
logger.info("%s model parameters via %s periodogram:\n"%(model,method)+pl.mlab.rec2txt(params,precision=8))
#-- when full output is required, return parameters, periodogram and fitting
# function
if full_output:
mymodel = getattr(evaluate,model)(times,params)
return params,(freqs_,ampls_),mymodel
else:
return params
