fieldpy.core.helper_fns

filter_and_resample(...)

filter_and_resample(ndarray time, ndarray data, double filter_window, double new_sample_int, first_sample=None)

Filters and resamples a time signal with a running average in a given time intervall +/-filter_window/2. The filter has a PHASE SHIFT for a UN-equally sampled signal. Accepts masked arrays but not "structured arrays" or "recarrays".

Parameters:

time - the times of the timeseries
data - the data of the timeseries
filter_window - filter time interval
new_sample_int - the new sampling interval (in units of time)
first_sample - the time of the first sample in the resampled signal. If None (default) it is (approximatly) set to time[0]+filter_window

Returns: list of np.array

Returns [new_time, new_data]

Note: 1) ignores any masks in the time variable. 2) will not work if it finds no values within filter window

To Do: Correct above point 2) (as well in helper_fns.py)

>>> tim = np.linspace(-3,90,1000); data = tim**2
>>> filter_window = 30; new_sample_int = 30
>>> filter_and_resample(tim, data, filter_window, new_sample_int)
(array([ -0.,  30.,  60.,  90.]), array([   62.98183318,   975.90237234,  3678.22848073,  6826.19355542]))
>>> data = np.ma.MaskedArray(data)
>>> filter_and_resample(tim, data, filter_window, new_sample_int)
(array([ -0.,  30.,  60.,  90.]), array([   62.98183318,   975.90237234,  3678.22848073,  6826.19355542]))
>>> data[1:10] = np.ma.masked
>>> filter_and_resample(tim, data, filter_window, new_sample_int)
(array([ -0.,  30.,  60.,  90.]), array([   65.73049119,   975.90237234,  3678.22848073,  6826.19355542]))

get_ind_closest(...)

get_ind_closest(ndarray vec, double value, guess_ind=None)

Finds the index (ind) of vec such that vec[ind]-value is smallest. Only works reliably for monotone vectors. As start value it uses the value given in guess_ind.

This function is somewhat slower than get_ind_closest but can be called directly from Python. (When calling from cython use get_ind_closest_faster)

Parameters:

vec - the vector, usually a time
value - the value which is compared to vec
guess_ind - a guess for index around which the search (symmetric) will be started.

Returns:

The index

Note: The algorithm is fairly dum, something like bisection might work much faster.

>>> tim =np.linspace(-3,100,1000)
>>> get_ind_closest(tim, 49.2)
506
>>> get_ind_closest(tim, 49.2, 400)
506

test

Value:

{u'filter_and_resample (line 23)': u'''
    Filters and resamples a time signal with a running average in a
    given time intervall +/-filter_window/2.  The filter has a PHASE
    SHIFT for a UN-equally sampled signal.  Accepts masked arrays but
    not "structured arrays" or "recarrays".


    @param time: the times of the timeseries
...

Module helper_fns_cython

filter_and_resample(...)

get_ind_closest(...)

__test__

test