MSAMetafile

class msaexp.msa.MSAMetafile(filename)[source]

Bases: object

Helper for parsing MSAMETFL metadata files

Parameters
filenamestr

Filename of an _msa.fits metadata file or a FITS file with a keyword MSAMETFL in the primary header, e.g., a _rate.fits file.

Examples

### Make a plot with slitlets

import numpy as np
import matplotlib.pyplot as plt
from msaexp import msa

uri = 'https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/'
meta = msa.MSAMetafile(uri+'jw02756001001_01_msa.fits')

fig, axes = plt.subplots(1, 3, figsize=(9, 2.6),
                         sharex=True, sharey=True)
cosd = np.cos(np.median(meta.src_table['dec'])/180*np.pi)

# Show offset slitlets from three dithered exposures
for i in [0,1,2]:
    ax = axes[i]
    ax.scatter(meta.src_table['ra'], meta.src_table['dec'],
               marker='.', color='k', alpha=0.5)
    slits = meta.regions_from_metafile(dither_point_index=i+1,
                                       as_string=False,
                                       with_bars=True)
    for s in slits:
        if s.meta['is_source']:
            if s.meta['source_id'] in [110003, 410044, 410045]:
                ax.text(s.meta['ra'] - 0.8/3600, s.meta['dec'],
                        s.meta['source_id'],
                        fontsize=7, ha='left', va='center')
            fc = '0.5'
        else:
            fc = 'pink'

        for patch in s.get_patch(fc=fc, ec='None', alpha=0.8,
                                 zorder=100):
            ax.add_patch(patch)

    ax.set_aspect(1./cosd)
    ax.set_xlim(3.5936537138517317, 3.588363444812261)
    ax.set_ylim(-30.39750646306242, -30.394291511397544)

    ax.grid()
    ax.set_title(f'Dither point #{i+1}')

x0 = np.mean(ax.get_xlim())
ax.set_xticks(np.array([-5, 0, 5])/3600./cosd + x0)
ax.set_xticklabels(['+5"', 'R.A.', '-5"'])

y0 = np.mean(ax.get_ylim())
ax.set_yticks(np.array([-5, 0, 5])/3600. + y0)
axes[0].set_yticklabels(['-5"', 'Dec.', '+5"'])
axes[1].scatter(x0, y0, marker='x', c='b')
axes[1].text(0.5, 0.45, f'({x0:.6f}, {y0:.6f})',
             ha='left', va='top',
             transform=axes[1].transAxes, fontsize=6,
             color='b')

fig.tight_layout(pad=0.5)

(Source code, png, hires.png, pdf)

../_images/msaexp-msa-MSAMetafile-1.png
Attributes
filenamestr

Input filename

metafilestr

Filename of the MSAMETFL, either filename itself or derived from it

shutter_tableTable

Table of shutter metadata

src_tableTable

Table of source information

mastTable, None

Result of query_mast_exposures

Attributes Summary

key_pairs

List of unique msa_metadata_id, dither_point_index pairs from the shutter_table

mast_key_pairs

List of unique msametid, exposure pairs from the mast metadata table

metadata_id_list

Returns

metadata_id_unique

Returns

query_product_level

Methods Summary

all_regions_from_metafile_siaf(**kwargs)

Run regions_from_metafile_siaf for all exposures

fit_mast_pointing_offset([iterations, ...])

Fit for offsets to the pointing attitude derived from the MAST metadata

get_exposure_info([msa_metadata_id, ...])

Get MAST keywords for a particular exposure

get_siaf_aperture([msa_metadata_id, ...])

Generate a pysiaf aperture object based on pointing information

get_siaf_transforms([prefix, check_rms])

Read shutter (i,j) > (v2,v3) transformations from the files at https://github.com/spacetelescope/pysiaf/tree/master/pysiaf/source_data/NIRSpec/delivery/test_data/apertures_testData

get_transforms([dither_point_index, ...])

Fit for Polynomial2D transforms between slit (row, col) and (ra, dec).

make_summary_table([msa_metadata_id, ...])

Make a summary table for all sources in the mask

merge_source_ids([sort_count, verbose])

Merge cases where a particular slitlet_id has multiple source_ids

plot_slitlet([source_id, ...])

Make a plot showing a slitlet

query_mast_exposures([force])

Query MAST database for exposures for this MSA file

regions_from_metafile([as_string, with_bars])

Get slit footprints in sky coords

regions_from_metafile_siaf([as_string, ...])

MSA shutter regions using pointing info and SIAF shutter transformations

write([prefix, overwrite, verbose])

Write a new metadata file

Attributes Documentation

key_pairs

List of unique msa_metadata_id, dither_point_index pairs from the shutter_table

Returns
keyslist

List of key pairs

mast_key_pairs

List of unique msametid, exposure pairs from the mast metadata table

Returns
keyslist

List of key pairs

metadata_id_list
Returns
idslist

list of metadata_id from shutter_table

metadata_id_unique
Returns
ungrizli.utils.Unique

Unique of metadata_id from shutter_table

query_product_level = ['2b']

Methods Documentation

all_regions_from_metafile_siaf(**kwargs)[source]

Run regions_from_metafile_siaf for all exposures

Parameters
kwargsdict

Passed to regions_from_metafile_siaf

Returns
outputlist, str

Depending on as_string input keyword

fit_mast_pointing_offset(iterations=3, with_ransac=True, verbose=True, apply=True, init_ref_columns=False, **kwargs)[source]

Fit for offsets to the pointing attitude derived from the MAST metadata

Parameters
iterationsint

Number of fitting iterations

with_ransacbool

Refine transformation with skimage.measure inliers

verbosebool

Print messages

applybool

Add updated pointing parameters to ref columns in mast metadata

init_ref_columnsbool

Initialize using “ref” columns in the MAST table

Returns
resTable

Table summarizing fit results

get_exposure_info(msa_metadata_id=1, dither_point_index=1, **kwargs)[source]

Get MAST keywords for a particular exposure

Parameters
msa_metadata_id, dither_point_indexint

Exposure definition

Returns
rowRow

Row of the mast info table for a particular exposure

get_siaf_aperture(msa_metadata_id=1, dither_point_index=1, pa_offset=-0.1124, ra_ref=None, dec_ref=None, roll_ref=None, use_ref_columns=True, apername='NRS_FULL_MSA', **kwargs)[source]

Generate a pysiaf aperture object based on pointing information

Parameters
msa_metadata_id, dither_point_indexint

Exposure definition

pa_offsetfloat

Empirical offset added to gs_v3_pa from the MAST query to match ROLL_REF in the science headers

ra_ref, dec_ref, roll_refNone, float

Specify a reference parameters aperture attitude, e.g., taken from the ROLL_REF science header keywords

use_ref_columnsbool

Use “ref” columns in mast metadata table if found, e.g., generated from fit_mast_pointing_offset

Returns
ra, dec, rollfloat

The V2/V3 reference ra, dec, roll used for the aperture attitude

appysiaf.aperture.NirspecAperture

Aperture object with attitude set based on database pointing keywords and with various coordinate transformation methods

get_siaf_transforms(prefix='https://github.com/spacetelescope/pysiaf/raw/master/pysiaf/source_data/NIRSpec/delivery/test_data/apertures_testData/', check_rms=True)[source]

Read shutter (i,j) > (v2,v3) transformations from the files at https://github.com/spacetelescope/pysiaf/tree/master/pysiaf/source_data/NIRSpec/delivery/test_data/apertures_testData

Parameters
prefixstr, optional

URL prefix for the files containing the transformations.

check_rmsbool, optional

If True, calculate and print the root mean square (RMS) of the transformations.

Returns
transformsdict

A dictionary containing the (i,j) > (v2,v3) transformations for each quadrant of the MSA.

get_transforms(dither_point_index=None, msa_metadata_id=None, fit_degree=2, verbose=False, **kwargs)[source]

Fit for Polynomial2D transforms between slit (row, col) and (ra, dec).

Parameters
dither_point_indexint, None

Dither index in shutter_table

msa_metadata_idint, None

Metadata id in shutter_table

fit_degreeint

Polynomial degree

verbosebool

Print status messages

Returns
dither_matchbool array

Boolean mask of shutter_table matching dither_point_index

meta_matchbool array

Boolean mask of shutter_table matching msa_metadata_id

coeffsdict

Polynomial2D transformations to sky coordinates in each of 4 MSA quadrants

>>> quadrant = 1
>>> pra, pdec = coeffs[quadrant]
>>> ra = pra(shutter_row, shutter_column)
>>> dec = pdec(shutter_row, shutter_column)
inv_coeffsdict

Inverse Polynomial2D transformations from sky to shutters:

>>> quadrant = 1
>>> prow, pcol = inv_coeffs[quadrant]
>>> shutter_row = prow(ra, dec)
>>> shutter_column = pcol(ra, dec)
make_summary_table(msa_metadata_id=None, image_path='slit_images', write_tables=True, **kwargs)[source]

Make a summary table for all sources in the mask

Parameters
msa_metadata_idint, None

Metadata id in shutter_table

image_pathstr

Path for slitlet thumbnail images with filename derived from self.metafile.

write_tablesbool

Write FITS and HTML versions of the summary table

kwargsdict

Arguments passed to plot_slitlet if image_path specified

Returns
tabTable

Summary table with slit information.

Examples

>>> from msaexp import msa
>>> uri = 'https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/'
>>> meta = msa.MSAMetafile(uri+'jw02756001001_01_msa.fits')
>>> res = meta.make_summary_table(msa_metadata_id=None,
                                  image_path=None,
                                  write_tables=False)
>>> print(res[-10:])
source_id    ra       dec     nexp Exp1 Exp2 Exp3
--------- -------- ---------- ---- ---- ---- ----
   320023 3.610363 -30.414991    3  -o-  o--  --o
   320029 3.557964 -30.426137    3  -o-  o--  --o
   320035 3.616975 -30.419344    3  -o-  o--  --o
   340975 3.576616 -30.401801    3  ---  ---  ---
   410005 3.604646 -30.392461    3  ---  ---  ---
   410044 3.592863 -30.396336    3  -o-  o--  --o
   410045 3.592619 -30.397096    3  -o-  o--  --o
   410067 3.571049 -30.388132    3  -o-  o--  --o
   500002 3.589697 -30.398156    2  --o  -o-
   500003 3.591399 -30.401982    3  -o-  o--  --o
merge_source_ids(sort_count=-1, verbose=True, **kwargs)[source]

Merge cases where a particular slitlet_id has multiple source_ids

Parameters
sort_countint

Direction in which to prefer among the multiple source_id values sorted by their count.

Returns
shutter_table attribute updated in place
plot_slitlet(source_id=110003, dither_point_index=1, msa_metadata_id=None, cutout_size=1.5, step=None, rgb_filters=None, rgb_scale=5, rgb_invert=False, figsize=(4, 4), ax=None, add_labels=True, set_axis_labels=True)[source]

Make a plot showing a slitlet

Parameters
source_idint

Source id, must be in src_table

dither_point_indexint

Dither to show

msa_metadata_idint

Optional specified msa_metadata_id in shutter_table

cutout_sizefloat

Cutout half-width, arcsec

stepint

Place to mark axis labels, defaults to floor(cutout_size)

rgb_filterslist, None

List of filters to use for an RGB cutout. Will be grayscale if just one item specified.

rgb_scalefloat

Scaling of the image thumbnail if rgb_filters specified

rgb_invertbool

Invert color map if rgb_filters specified

figsizetuple

Size if generating a new figure

axAxesSubplot, None

Plot axis

add_labelsbool

Add plot labels

set_axis_labelsbool

Set axis labels

Returns
figmatplotlib.figure.Figure

Figure object if generating a new figure, None otherwise

axAxesSubplot

Plot axes

Examples

# Simple figure

import matplotlib.pyplot as plt
from msaexp import msa

uri = 'https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/'
meta = msa.MSAMetafile(uri+'jw02756001001_01_msa.fits')

fig, ax = plt.subplots(1,1,figsize=(8,8))
_ = meta.plot_slitlet(source_id=110003, cutout_size=12.5,
                      rgb_filters=None, ax=ax)

fig.tight_layout(pad=1.0)
fig.show()

(Source code, png, hires.png, pdf)

../_images/msaexp-msa-MSAMetafile-2.png 
# With RGB cutout

import matplotlib.pyplot as plt
from msaexp import msa

uri = 'https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/'
meta = msa.MSAMetafile(uri+'jw02756001001_01_msa.fits')

fig, ax = plt.subplots(1,1,figsize=(4,4))

filters = ['f200w-clear','f150w-clear','f115w-clear']
_ = meta.plot_slitlet(source_id=110003, cutout_size=1.5,
                      rgb_filters=filters, ax=ax)

fig.tight_layout(pad=1.0)
fig.show()

(Source code, png, hires.png, pdf)

../_images/msaexp-msa-MSAMetafile-3.png 
# With grayscale cutout

import matplotlib.pyplot as plt
from msaexp import msa

uri = 'https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/'
meta = msa.MSAMetafile(uri+'jw02756001001_01_msa.fits')

fig, ax = plt.subplots(1,1,figsize=(4,4))

filters = ['f160w']
_ = meta.plot_slitlet(source_id=110003, cutout_size=1.5,
                      rgb_filters=filters, ax=ax, rgb_invert=True)

fig.tight_layout(pad=1.0)
fig.show()

(Source code, png, hires.png, pdf)

../_images/msaexp-msa-MSAMetafile-4.png
query_mast_exposures(force=False, **kwargs)[source]

Query MAST database for exposures for this MSA file

Parameters
forcebool

Running the query with this method stores the result in the mast attribute. If the attribute is None or if force==True then run/redo the query.

Returns
mastTable

Query results from mastquery.jwst.query_jwst.

regions_from_metafile(as_string=False, with_bars=True, **kwargs)[source]

Get slit footprints in sky coords

Parameters
as_stringbool

Return regions as DS9 region strings

with_barsbool

Account for bar vignetting

kwargsdict

Keyword arguments passed to msaexp.msa.MSAMetafile.get_transforms

Returns
String or a list of grizli.utils.SRegion objects, depending on
as_string

Examples

>>> from msaexp import msa
>>> uri = 'https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/'
>>> meta = msa.MSAMetafile(uri+'jw02756001001_01_msa.fits')
>>> regs = meta.regions_from_metafile(as_string=True, with_bars=True)
>>> print(regs)
# msametfl = https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw02756001001_01_msa.fits
# dither_point_index = 1
# msa_metadata_id = 1
# Q1 N=13  rms= 0.7, 0.4 mas
# Q2 N=29  rms= 1.3, 1.3 mas
# Q3 N=11  rms= 1.3, 0.3 mas
# Q4 N=27  rms= 1.2, 0.7 mas
icrs
polygon(3.623046,-30.427251,3.622983,-30.427262,3.622951,-30.427136,3.623014,-30.427125) # color=lightblue
circle(3.6229814, -30.4270337, 0.2") # color=cyan text={160159}
polygon(3.623009,-30.427106,3.622946,-30.427117,3.622915,-30.426991,3.622978,-30.426980) # color=cyan
polygon(3.622973,-30.426960,3.622910,-30.426971,3.622878,-30.426845,3.622941,-30.426834) # color=lightblue
polygon(3.613902,-30.392060,3.613840,-30.392071,3.613809,-30.391948,3.613871,-30.391936) # color=lightblue
circle(3.6137989, -30.3918859, 0.2") # color=cyan text={160321}
polygon(3.613867,-30.391918,3.613804,-30.391929,3.613774,-30.391805,3.613836,-30.391794) # color=cyan
polygon(3.613831,-30.391775,3.613769,-30.391786,3.613738,-30.391663,3.613800,-30.391652) # color=lightblue
polygon(3.610960,-30.384123,3.610897,-30.384134,3.610867,-30.384011,3.610929,-30.384000) # color=lightblue
...
regions_from_metafile_siaf(as_string=True, with_bars=True, msa_metadata_id=1, dither_point_index=1, shutter_table=None, meta_keys=['program', 'pi_name', 'proptarg', 'filename', 'filter', 'grating', 'expstart', 'effexptm', 'nod_type', 'final_0x0_EOSA'], source_color='cyan', other_color='lightblue', verbose=False, **kwargs)[source]

MSA shutter regions using pointing info and SIAF shutter transformations

Parameters
as_stringbool

Return regions as DS9 region strings

with_barsbool

Account for bar vignetting

msa_metadata_id, dither_point_indexint

Exposure definition

meta_keyslist of str, optional

List of metadata keys to include in the output

verbosebool, optional

Print verbose messages

Returns
String or a list of grizli.utils.SRegion objects, depending on
as_string
write(prefix='edit_', overwrite=True, verbose=True)[source]

Write a new metadata file