wavy.filter_module

Attributes

ROAR
variable_def

Classes

filter_class

Functions

start_stop(a, trigger_val)
apply_land_mask(longitudes, latitudes)	Mask out parts covering land
lanczos_weights(window, cutoff)	Calculate weights for a low pass Lanczos filter
smoother_GP(x, y, X, **kwargs)
smoother_linearGAM(x, y, X, **kwargs)
cleaner_GP(x, y, **kwargs)
cleaner_linearGAM(x, y, **kwargs)

Module Contents

wavy.filter_module.ROAR = None

wavy.filter_module.variable_def

class wavy.filter_module.filter_class

apply_limits(**kwargs)

filter_landMask(**kwargs)

filter_distance_to_coast(llim=0, ulim=100000000, **kwargs)

discards all values closer to shoreline than threshold

filter_lanczos(**kwargs)

filter_runmean(**kwargs)

filter_GP(**kwargs)

filter_linearGAM(**kwargs)

static cleaner_blockStd(y, **kwargs)

despike_blockStd(**kwargs)

static cleaner_blockQ(y, **kwargs)

despike_blockQ(**kwargs)

despike_GP(**kwargs)

despike_NIGP(**kwargs)

despike_linearGAM(**kwargs)

slider_chunks(**kwargs)

Purpose: chunk data to ease computational load

static time_gap_chunks(pdtime, **kwargs)

Purpose: chunk data according to sampling gaps to make

neighbour points match up and make filtering meaningful.

filter_footprint_radius(llim=None, ulim=None)

Filters all data according to given limits (llim, ulim) of footprint size

filter_footprint_land_interaction(**kwargs)

Checks if footprint interacts with land based on footprint size. Filters away the ones that do interact and returns a clean data set.

_generate_xtrack_footprints(**kwargs)

static _generate_xtrack_footprints_in_lonlat(P1: tuple, P2: tuple, n=None)

Input are tuples (lon, lat) for points P1, P2

static _generate_xtrack_footprints_in_cartesian()

static _lonlat_to_xy(lon, lat, utmzone)

static _xy_to_lonlat(x, y, utmzone)

static _distance(lon1, lat1, lon2, lat2)

wavy.filter_module.start_stop(a, trigger_val)

wavy.filter_module.apply_land_mask(longitudes: numpy.ndarray, latitudes: numpy.ndarray)

Mask out parts covering land

Args:

longitudes, latitudes

Returns:

vardict, sea_mask

wavy.filter_module.lanczos_weights(window, cutoff)

Calculate weights for a low pass Lanczos filter

args:

window: (integer) the length of the filter window cutoff: (float) the cutoff frequency in inverse time steps

returns: weights

example: https://scitools.org.uk/iris/docs/v1.2/examples/

graphics/SOI_filtering.html

wavy.filter_module.smoother_GP(x, y, X, **kwargs)

wavy.filter_module.smoother_linearGAM(x, y, X, **kwargs)

wavy.filter_module.cleaner_GP(x, y, **kwargs)

wavy.filter_module.cleaner_linearGAM(x, y, **kwargs)