def get_optimum_time_range(input_data, new_time_range):
""" This grabs a few more (or a few less, if enough not available)
points so that the FFT is more efficient. For FFTW, it is more
efficient to zero pad to a nice number above even if it is a long
way away. This is always true for Fourier filtering, in which
case you never see the zeros. For general applications, the zeros
might be confusing if you forget they have been put there.
"""
from pyfusion.utils.primefactors import fft_time_estimate
nt_args = searchsorted(input_data.timebase, new_time_range)
# try for 20 more points
extension = ((new_time_range[1]-new_time_range[0])
*float(20)/(nt_args[1]-nt_args[0]))
(dum,trial_upper) = searchsorted(input_data.timebase,
[new_time_range[0],
new_time_range[1]+extension])
# if not consider using less than the original request
trial_lower = trial_upper - 20
times = []
for num in range(trial_lower, trial_upper):
times.append(fft_time_estimate(num - nt_args[0]))
newupper = trial_lower+np.argmin(times)
if newupper != nt_args[1]:
pyfusion.utils.warn('Interval fft optimized from {old} to {n} points'
.format(n=newupper-nt_args[0],
old=nt_args[1]-nt_args[0]))
best_upper_time = input_data.timebase[newupper]
new_time_range[1] = (best_upper_time
- 0.5*np.average(np.diff(input_data.timebase)))
if pyfusion.VERBOSE>0:
print('returning new time range={n}'.format(n=new_time_range))
return(new_time_range)
def get_optimum_time_range(input_data, new_time_range):
""" This grabs a few more (or a few less, if enough not available)
points so that the FFT is more efficient. For FFTW, it is more
efficient to zero pad to a nice number above even if it is a long
way away. This is always true for Fourier filtering, in which
case you never see the zeros. For general applications, the zeros
might be confusing if you forget they have been put there.
"""
from pyfusion.utils.primefactors import fft_time_estimate
nt_args = searchsorted(input_data.timebase, new_time_range)
# try for 20 more points
extension = ((new_time_range[1] - new_time_range[0]) * float(20) /
(nt_args[1] - nt_args[0]))
(dum, trial_upper) = searchsorted(
input_data.timebase,
[new_time_range[0], new_time_range[1] + extension])
# if not consider using less than the original request
trial_lower = trial_upper - 20
times = []
for num in range(trial_lower, trial_upper):
times.append(fft_time_estimate(num - nt_args[0]))
newupper = trial_lower + np.argmin(times)
if newupper != nt_args[1]:
pyfusion.utils.warn(
'Interval fft optimized from {old} to {n} points'.format(
n=newupper - nt_args[0], old=nt_args[1] - nt_args[0]))
best_upper_time = input_data.timebase[newupper]
new_time_range[1] = (best_upper_time -
0.5 * np.average(np.diff(input_data.timebase)))
if pyfusion.VERBOSE > 0:
print('returning new time range={n}'.format(n=new_time_range))
return (new_time_range)
def get_optimum_time_range(input_data, new_time_range, try_more=0.0002):
"""This grabs a few more (or a few less, if enough not available in
input_data) points than requested so that the FFT is more
efficient. try_more is the number of samples to increase/decrease
as a fraction of the input data.
Note: For FFTW, it is more efficient to zero pad to a nice
number above, even if it is a long way away. This is always true
for Fourier filtering, in which case you never see the zeros. For
general applications, the zeros might be confusing if you forget
they have been put there.
"""
from pyfusion.utils.primefactors import fft_time_estimate
# obtain indices of the beginning and end times - not sure of precision effects.
nt_args = searchsorted(input_data.timebase, new_time_range)
if nt_args[0] == nt_args[1]:
raise LookupError(
'No samples in {dr} in the requested new time range {tr}'.format(
tr=str(new_time_range),
dr=str([input_data.timebase.min(),
input_data.timebase.max()])))
# try for try_more more points (as a fraction of the timebase)
# extension is in seconds
if abs(try_more) == 1:
try_more = 0.0002 * try_more
# avoid error on small samples
try_more = max(10, int(len(input_data.timebase) * try_more))
extension = ((new_time_range[1] - new_time_range[0]) * float(try_more) /
(nt_args[1] - nt_args[0]))
if pyfusion.VERBOSE > 0:
print('try_more = {tm}, nt_args = {nt}, extension = {ex}'.format(
tm=try_more, ex=extension, nt=nt_args))
(dum, trial_upper) = searchsorted(
input_data.timebase,
[new_time_range[0], new_time_range[1] + extension])
# if not consider using less than the original request
trial_lower = trial_upper - try_more
times = []
if pyfusion.VERBOSE > 0:
print('get_optimum_time_range {l} to {u}'.format(l=trial_lower,
u=trial_upper))
for num in range(trial_lower, trial_upper, np.sign(try_more)):
times.append(fft_time_estimate(num - nt_args[0]))
newupper = trial_lower + np.argmin(times)
if newupper != nt_args[1]:
pyfusion.utils.warn(
'Interval fft optimized from {old} to {n} points'.format(
n=newupper - nt_args[0], old=nt_args[1] - nt_args[0]))
best_upper_time = input_data.timebase[newupper]
new_time_range[1] = (best_upper_time -
0.5 * np.nanmean(np.diff(input_data.timebase)))
debug_(pyfusion.DEBUG, 5, key='reduce_time')
if pyfusion.VERBOSE > 0:
print('returning new time range={n}'.format(n=new_time_range))
return (new_time_range)
