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sigpyproc.core module

sigpyproc.core module

sigpyproc.core.stats

sigpyproc.core.stats.running_median(array, window)

Calculate the running median of an array.

Parameters

array (numpy.ndarray) – The array to calculate the running median of.

Returns

The running median of the array.

Return type

numpy.ndarray

sigpyproc.core.stats.running_mean(array, window)

Calculate the running mean of an array.

Parameters

array (numpy.ndarray) – The array to calculate the running mean of.

Returns

The running mean of the array.

Return type

numpy.ndarray

class sigpyproc.core.stats.ChannelStats(nchans, nsamps)

Bases: object

property mbag

The central moments of the data.

Type

MomentsBag

property nchans

Get the number of channels.

Type

int

property nsamps

Get the number of samples.

Type

int

property maxima

Get the maximum value of each channel.

Type

numpy.ndarray

property minima

Get the minimum value of each channel.

Type

numpy.ndarray

property mean

Get the mean of each channel.

Type

numpy.ndarray

property var

Get the variance of each channel.

Type

numpy.ndarray

property std

Get the standard deviation of each channel.

Type

numpy.ndarray

property skew

Get the skewness of each channel.

Type

numpy.ndarray

property kurtosis

Get the kurtosis of each channel.

Type

numpy.ndarray

push_data(array, gulp_size, start_index, mode='basic')

sigpyproc.core.kernels

sigpyproc.core.kernels.unpack1_8(array)
sigpyproc.core.kernels.unpack2_8(array)
sigpyproc.core.kernels.unpack4_8(array)
sigpyproc.core.kernels.pack2_8(array)
sigpyproc.core.kernels.pack4_8(array)
sigpyproc.core.kernels.np_apply_along_axis(func1d, axis, arr)
sigpyproc.core.kernels.np_mean(array, axis)
sigpyproc.core.kernels.downcast(intype, result)
sigpyproc.core.kernels.downsample_1d(array, factor)
sigpyproc.core.kernels.downsample_2d(array, tfactor, ffactor, nchans, nsamps)
sigpyproc.core.kernels.extract_tim(inarray, outarray, nchans, nsamps, index)
sigpyproc.core.kernels.extract_bpass(inarray, outarray, nchans, nsamps)
sigpyproc.core.kernels.mask_channels(array, mask, nchans, nsamps)
sigpyproc.core.kernels.dedisperse(inarray, outarray, delays, maxdelay, nchans, nsamps, index)
sigpyproc.core.kernels.invert_freq(array, nchans, nsamps)
sigpyproc.core.kernels.subband(inarray, outarray, delays, chan_to_sub, maxdelay, nchans, nsubs, nsamps)
sigpyproc.core.kernels.fold(inarray, fold_ar, count_ar, delays, maxdelay, tsamp, period, accel, total_nsamps, nsamps, nchans, nbins, nints, nsubs, index)
sigpyproc.core.kernels.resample_tim(array, accel, tsamp)
sigpyproc.core.kernels.remove_zerodm(inarray, outarray, bpass, chanwts, nchans, nsamps)
sigpyproc.core.kernels.form_spec(fft_ar, interpolated=False)
sigpyproc.core.kernels.remove_rednoise(fftbuffer, startwidth, endwidth, endfreq, tsamp)
sigpyproc.core.kernels.sum_harms(spec_arr, sum_arr, harm_arr, fact_arr, nharms, nsamps, nfold)
sigpyproc.core.kernels.compute_online_moments_basic(array, bag, nsamps, startflag)
sigpyproc.core.kernels.compute_online_moments(array, bag, nsamps, startflag)

Computing central moments in one pass through the data.

sigpyproc.core.kernels.add_online_moments(bag_a, bag_b, bag_c)

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