PypeIt QA

As part of the standard reduction, PypeIt generates a series of fixed-format Quality Assurance (QA) figures. This document describes the typical outputs, in the typical order that they appear.

This page is still a work in progress.

The basic arrangement is that individual PNG files are created and then a set of HTML files are generated to organize viewing of the PNGs.

HTML

When the code completes (or crashes out), an HTML file is generated in the QA/ folder, one per setup that has been reduced (typically one). An example filename is MF_A.html. These HTML files are out of date, so you’re better off opening the PNG files in the PNGs directory directly.

Calibration QA

The first QA PNG files generated are related to calibration processing. There is a unique one generated for each setup and detector and (possibly) calibration set.

Generally, the title describes the type of QA plotted.

Echelle Order Prediction

When reducing echelle observations and inserting missing orders, a QA plot is produced to assess the success of the predicted locations. The example below is for Keck/HIRES.

Example QA plot showing the measured order spatial widths (blue) and gaps (green) in pixels. The widths should be nearly constant as a function of position, whereas the gaps should change monotonically with spatial pixel.

In the figure above, measured values that are included in the polynomial fit are shown as filled points. The colored lines show the best fit polynomial model used for the predicted order locations. The fit allows for an iterative rejection of points; measured widths and gaps that are rejected during the fit are shown as orange and purple crosses, respectively. The measurements that are rejected during the fit are not necessarily removed as invalid traces, but the code allows you to identify outlier traces that will be removed. None of the traces in the example image above are identified as outliers; if they exist, they will be plotted as orange and purple triangles for widths and gaps, respectively. Missing orders that will be added are included as open squares; gaps are green, widths are blue. To deal with overlap, “bracketing” orders are added for the overlap calculation but are removed in the final set of traces; the title of the plot indicates if bracketing orders are included and the vertical dashed lines shows the edges of the detector/mosaic.

Wavelength Fit QA

PypeIt produces plots like the one below showing the result of the wavelength calibration.

An example QA plot for Keck/DEIMOS wavelength calibration. The extracted arc spectrum is shown to the left with arc lines used for the wavelength solution marked in green. The upper-right plot shows the best-fit calibration between pixel number and wavelength, and the bottom-right plot shows the residuals as a function of pixel number.

See WaveCalib for more discussion of this QA.

Wavelength Tilts QA

PypeIt produces plots like the one below showing the result of tracing the tilts in the wavelength as a function of spatial position within the slits.

An example QA plot for a single slit in a Keck/MOSFIRE tilt QA plot. Each horizontal line of black dots is an OH line. Red points were rejected in the 2D fitting. Provided most were not rejected, the fit should be good.

See Tilts for more discussion of this QA.

Exposure QA

For each processed, science exposure there are a series of PNGs generated, per detector and (sometimes) per slit.

Flexure QA

If a flexure correction was performed (default), the fit to the correlation lags per object is shown and the adopted shift is listed. Here is an example:

There is then a plot showing several sky lines for the analysis of a single object (brightest) from the data compared against an archived sky spectrum. These should coincide well in wavelength. Here is an example: