pypeit.core.plot module

Convenience functions for plotting.

pypeit.core.plot.zsc_compute_sigma(flat, badpix)[source]

Compute the rms deviation from the mean of a flattened array. Ignore rejected pixels

Parameters:
Returns:

  • ngoodpixels (int) – Number of good pixels

  • mean (float) – Mean of the good pixels

  • sigma (float) – RMS of the good pixels

pypeit.core.plot.zsc_fit_line(samples, npix, krej, ngrow, maxiter)[source]

zscale fit line

Parameters:

  • samples (numpy.ndarray) – 1-d array of samples to analyze

  • npix (int) – Number of pixels in the samples array

  • krej (float) – Rejection factor

  • ngrow (int) – Number of pixels to grow around the rejected pixels

  • maxiter (int) – Maximum number of iterations to perform

Returns:

  • ngoodpix (int) – Number of good pixels

  • zstart (float) – zscale parameter

  • zslope (float) – zscale parameter

pypeit.core.plot.zsc_sample(image, maxpix, bpmask=None, zmask=None)[source]

Figure out which pixels to use for the zscale algorithm Returns the 1-d array samples

Don’t worry about the bad pixel mask or zmask for the moment Sample in a square grid, and return the first maxpix in the sample

Parameters:

image (numpy.ndarray) – Image to scale

Returns:

samples – 1-d array of samples

Return type:

numpy.ndarray

pypeit.core.plot.zscale(image, nsamples=1000, contrast=0.25, bpmask=None, zmask=None)[source]

Implement IRAF zscale algorithm nsamples=1000 and contrast=0.25 are the IRAF display task defaults bpmask and zmask not implemented yet image is a 2-d np array returns (z1, z2)

Parameters:

  • image (numpy.ndarray) – Image to scale

  • nsamples (int, optional) – Number of samples to use in the calculation. Passed to zsc_sample.

  • contrast (float, optional) – Desired contrast.

  • bpmask (numpy.ndarray, optional) – Pixel mask for bad pixels. Not implemented yet.

  • zmask (numpy.ndarray, optional) – Not implemented yet.

Returns:

  • z1 (float) – zscale parameter

  • z2 (float) – zscale parameter