wavy.filtermod
Module Contents
Functions
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Governing function of filtermod |
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Mask out parts covering land |
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discards all values closer to shoreline than threshold |
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extract from each geometry the name of the country |
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Get the global coastline |
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Compute distance to shore. |
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Applies a smoother to the data |
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Calculate weights for a low pass Lanczos filter |
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Attributes
- wavy.filtermod.ROAR
- wavy.filtermod.variable_info
- wavy.filtermod.filter_main(vardict_in, varalias='Hs', **kwargs)
Governing function of filtermod
- Tasks:
check if prior/post transforms are needed
check if cleaning is needed
check if filter is needed
- check if land mask is needed
if so apply cleaning/filters to subsets i.e. each chunk will be fed into filter_data and consolidated when finished with all chunks
- Args:
vardict
- Returns:
vardict
- wavy.filtermod.vardict_unique(vardict)
- wavy.filtermod.filter_slider(vardict, varalias, **kwargs)
- wavy.filtermod.rm_nan_from_vardict(varalias, vardict)
- wavy.filtermod.start_stop(a, trigger_val)
- wavy.filtermod.apply_land_mask(vardict, **kwargs)
Mask out parts covering land
- Args:
vardicy (dict)
- Returns:
vardict, sea_mask
- wavy.filtermod.apply_distance_to_coast_mask(vardict, **kwargs)
discards all values closer to shoreline than threshold between statement can be:
inclusive (“both”), exclusive (“neither”), left/right
- wavy.filtermod.extract_geom_meta(country)
extract from each geometry the name of the country and the geom_point data. The output will be a list of tuples and the country name as the last element.
- wavy.filtermod.get_coastline_shape_file(pathtofile=None, **kwargs)
Get the global coastline
- wavy.filtermod.distance_to_shore(lon, lat, coastline)
Compute distance to shore.
- Args:
lon, lat -> pd.dataFrame coastline -> shp
- Returns:
numpy array of distances and country names
- wavy.filtermod.apply_limits(varalias, vardict)
- wavy.filtermod.square_data(varalias, vardict)
- wavy.filtermod.root_data(varalias, vardict)
- wavy.filtermod.apply_priorOp(varalias, vardict, method=None)
- wavy.filtermod.apply_postOp(varalias, vardict, method=None)
- wavy.filtermod.apply_cleaner(varalias, vardict, method='linearGAM', **kwargs)
- wavy.filtermod.apply_smoother(varalias, vardict, output_dates=None, method=None, date_incr=None, **kwargs)
Applies a smoother to the data **kwargs includes method specific input for chosen method Methods are:
blockMean GP GAM Lanczos …
- Caution: for some smoothers much more of time series has
to be included.
- wavy.filtermod.smoothing(varalias, vardict, output_grid, output_dates, method='linearGAM', date_incr=None, **kwargs)
- wavy.filtermod.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.filtermod.smoother_lanczos(y, window, cutoff)
- wavy.filtermod.smoother_blockMean(dt, y, output_dates, date_incr, mode='l')
- wavy.filtermod.smoother_blockCircMean(dt, y, output_dates, date_incr, mode='l')
- wavy.filtermod.smoother_linearGAM(x, y, X, **kwargs)
- wavy.filtermod.smoother_expectileGAM(x, y, X, **kwargs)
- wavy.filtermod.smoother_GP(x, y, X, **kwargs)
- wavy.filtermod.smoother_NIGP(x, y, X, **kwargs)
- wavy.filtermod.cleaner_expectileGAM(x, y, **kwargs)
- wavy.filtermod.cleaner_linearGAM(x, y, **kwargs)
- wavy.filtermod.cleaner_GP(x, y, **kwargs)