clean_image

sofia_redux.toolkit.resampling.clean_image(image, error=None, mask=None, window=None, order=1, fix_order=True, robust=None, negthresh=None, mode=None, leaf_size=None, **kwargs)

Uses ResamplePolynomial to correct NaNs in image and/or supplied in mask.

Parameters:

imagearray_like of float

(M, N) array of data values. NaN values will automatically be considered ‘bad’ and replaced.

errorarray_like of float, optional

(M, N) array of error (1-sigma) values associated with the data array. error will be used to weight fits and also be propagated to the output error values.

maskarray_like of bool, optional

(M, N) array of bool where True indicates a valid data point that can be included the fitting and False indicates data points that should be excluded from the fit and replaced. Masked points will be reflected in the output counts array.

windowarray_like or float or int, optional

(2,) array or single float value specifying the maximum euclidean distance of a data sample from a resampling point such that it can be included in a local fit. window may be declared for each feature. For example, when fitting 2-dimensional (x, y) data, a window of 1.0 would create a circular fitting window around each resampling point, whereas a window of (1.0, 0.5) would create an eliptical fitting window with a semi-major axis of 1.0 in x and semi-minor axis of 0.5 in y. If not supplied, window is calculated based on an estimate of the median population density of the data for each feature.

orderarray_like or int, optional

(2,) array or single integer value specifying the polynomial fit order for each feature.

fix_orderbool, optional

In order for local polynomial fitting to occur, the default requirement is that nsamples >= (order + 1) ** 2, where nsamples is the number of data samples within window. If fix_order is True and this condition is not met, then local fitting will be aborted for that point and a value of cval will be returned instead. If fix_order is False, then order will be reduced to the maximum value where this condition can be met. NOTE: this is only available if order is symmetrical. i.e. it was passed in as a single integer to be applied across both x and y. Otherwise, it is unclear as to which dimension order should be reduced to meet the condition.

robustfloat, optional

Specifies an outlier rejection threshold for data. A data point is identified as an outlier if:

|x_i - x_med|/MAD > robust,

where x_med is the median, and MAD is the Median Absolute Deviation defined as:

1.482 * median(|x_i - x_med|).

negthreshfloat, optional

Specifies a negative value rejection threshold such that data < (-stddev(data) * negthresh) will be excluded from the fit.

modestr, optional

The type of check to perform on whether the sample distribution for each resampling point is adequate to derive a polynomial fit. Depending on order and fix_order, if the distribution does not meet the criteria for mode, either the fit will be aborted, returning a value of cval or the fit order will be reduced. Available modes are:

• ‘edges’: Require that there are order samples in both the negative and positive directions of each feature from the resampling point.

• ‘counts’: Require that there are (order + 1) ** 2 samples within the window of each resampling point.

• ‘extrapolate’: Attempt to fit regardless of the sample distribution.

Note that ‘edges’ is the most robust mode as it ensures that no singular values will be encountered during the least-squares fitting of polynomial coefficients.

leaf_sizeint, optional

Number of points at which to switch to brute-force during the ball tree query algorithm. See sklearn.neighbours.BallTree for further details.

Returns:

cleaned_image, [variance_out], [counts]n_tuple of numpy.ndarray (M, N)

See sofia_redux.toolkit.resampling.ResamplePolynomial