Flux and telluric correction.

.dockerignore

@@ -1,2 +1,5 @@
 .tox
 banzai_floyds.egg-info
+build
+tmp
+test_data

.github/workflows/unit_tests.yml

@@ -26,14 +26,9 @@ jobs:
         include:
           - name: Code style checks
             os: ubuntu-latest
-            python: '3.8'
+            python: '3.9'
             toxenv: codestyle
 
-          - name: Python 3.7 with minimal dependencies
-            os: ubuntu-latest
-            python: '3.7'
-            toxenv: py37-test
-
           - name: Python 3.9 with minimal dependencies
             os: ubuntu-latest
             python: '3.9'
@@ -44,24 +39,24 @@ jobs:
             python: '3.10'
             toxenv: py310-test
 
-          - name: Python 3.8 with all optional dependencies and coverage checking
+          - name: Python 3.9 with all optional dependencies and coverage checking
             os: ubuntu-latest
-            python: '3.8'
-            toxenv: py38-test-alldeps-cov
+            python: '3.9'
+            toxenv: py39-test-alldeps-cov
 
-          - name: OS X - Python 3.8 with all optional dependencies
+          - name: OS X - Python 3.9 with all optional dependencies
             os: macos-latest
-            python: '3.8'
-            toxenv: py38-test-alldeps
+            python: '3.9'
+            toxenv: py39-test-alldeps
 
-          - name: Windows - Python 3.8 with all optional dependencies
+          - name: Windows - Python 3.9 with all optional dependencies
             os: windows-latest
-            python: '3.8'
-            toxenv: py38-test-alldeps
+            python: '3.9'
+            toxenv: py39-test-alldeps
 
           - name: Test building of Sphinx docs
             os: ubuntu-latest
-            python: '3.8'
+            python: '3.9'
             toxenv: build_docs
 
     steps:

Dockerfile

@@ -1,4 +1,4 @@
-FROM docker.lco.global/banzai:1.10.1
+FROM docker.lco.global/banzai:1.11.0
 
 USER root
 

banzai_floyds/data/orders/coj-order-1.reg

@@ -0,0 +1,5 @@
+# Region file format: DS9 version 4.1
+global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1
+physical
+polygon(662.5,491.16667,717.5,477,783.33333,463.66667,875.83333,448.66667,1007.5,435.33333,1126.6667,430.33333,1197.5,430.33333,1285,433.66667,1384.1667,438.66667,1478.75,445.86111,1561,454.16667,1631,461.16667,1687,468.16667,1778.0556,481.97222,1868.3333,496.55556,1947,510.16667,2048,511.16667,2048,432.16667,1899,405.16667,1823,393.16667,1772,385.16667,1738.4722,379.88889,1735.6944,381.27778,1734.3056,379.88889,1701.6667,376.41667,1698.1944,374.33333,1657.9167,370.86111,1614.8611,366,1579.4444,361.83333,1519.7222,355.58333,1464.8611,350.72222,1422.2222,347.22222,1400.6944,346.52778,1372.9167,344.44444,1333.3333,340.97222,1290.9722,339.58333,1250.6944,338.88889,1206.25,338.88889,1145.1389,339.58333,1105.5556,340.27778,1039.5833,343.75,939.58333,350.69444,886.80556,357.63889,856.94444,362.5,827.77778,366.66667,791.66667,373.61111,739.58333,384.02778,693.05556,394.44444,668.75,400.69444,643.05556,407.63889,552.77778,434.02778,470.11111,468.08333,399.97222,511.83333,584,511.16667,603.33333,507)
+polygon(1.4352,189.16,107,171,212,154,318,142,442,137,735,154,875,166,1020,182,1338,228,1745,301,1749,206,1444,150,1335,133,1124,102,991,84,920,77,684,55,524,50,412,49,306,54,165,66,75,83,1,103)

banzai_floyds/data/orders/coj-order-2.reg

@@ -0,0 +1,4 @@
+# Region file format: DS9 version 4.1
+global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1
+physical
+polygon(662.5,491.16667,717.5,477,783.33333,463.66667,875.83333,448.66667,1007.5,435.33333,1126.6667,430.33333,1197.5,430.33333,1285,433.66667,1384.1667,438.66667,1478.75,445.86111,1561,454.16667,1631,461.16667,1687,468.16667,1778.0556,481.97222,1868.3333,496.55556,1947,510.16667,2048,511.16667,2048,432.16667,1899,405.16667,1823,393.16667,1772,385.16667,1738.4722,379.88889,1735.6944,381.27778,1734.3056,379.88889,1701.6667,376.41667,1698.1944,374.33333,1657.9167,370.86111,1614.8611,366,1579.4444,361.83333,1519.7222,355.58333,1464.8611,350.72222,1422.2222,347.22222,1400.6944,346.52778,1372.9167,344.44444,1333.3333,340.97222,1290.9722,339.58333,1250.6944,338.88889,1206.25,338.88889,1145.1389,339.58333,1105.5556,340.27778,1039.5833,343.75,939.58333,350.69444,886.80556,357.63889,856.94444,362.5,827.77778,366.66667,791.66667,373.61111,739.58333,384.02778,693.05556,394.44444,668.75,400.69444,643.05556,407.63889,552.77778,434.02778,470.11111,468.08333,399.97222,511.83333,584,511.16667,603.33333,507)

banzai_floyds/data/standards/README

@@ -0,0 +1,15 @@
+Relevant links:
+https://www.eso.org/sci/observing/tools/standards/spectra/stanlis.html
+https://ftp.eso.org/pub/usg/standards/ctiostan/
+https://ftp.eso.org/pub/stecf/standards/okestan/
+
+TODO: More involved detail is needed about how this data is actually accessed and used. Specifically WHAT standards are needed.
+
+Convert flux units
+TODO: What are the final desired Units
+
+Save table as fits file in banzai_floyds/data
+TODO: We should Possibly write a small bit of utility code that handles this and results in the correct final format for the fits file
+
+Add RA/DEC to fits header in decimal degrees
+TODO: Include this step in the utility code above.

banzai_floyds/dbs.py

@@ -0,0 +1,62 @@
+from banzai.dbs import Base
+from sqlalchemy import Column, Integer, String, Float
+from banzai.dbs import get_session
+from astropy.coordinates import SkyCoord
+from astropy import units
+from banzai.utils.fits_utils import open_fits_file
+from astropy.table import Table
+import pkg_resources
+from glob import glob
+import os
+from astropy.io import fits
+
+
+def get_standard(ra, dec, db_address, offset_threshold=5):
+    """
+    Check if a position is in the table of flux standards
+
+    ra: float
+        RA in decimal degrees
+    dec: float
+        Declination in decimal degrees
+    db_address: str
+        Database address in SQLAlchemy format
+    offset_threshold: float
+        Match radius in arcseconds
+    """
+    found_standard = None
+    test_coordinate = SkyCoord(ra, dec, unit=(units.deg, units.deg))
+    with get_session(db_address) as db_session:
+        standards = db_session.query(FluxStandard).all()
+        for standard in standards:
+            standard_coordinate = SkyCoord(standard.ra, standard.dec, unit=(units.deg, units.deg))
+            if standard_coordinate.offset(test_coordinate) < (offset_threshold * units.arcsec):
+                found_standard = standard
+    if found_standard is not None:
+        found_standard = open_fits_file({'path': found_standard.filepath, 'frameid': found_standard.frame_id,
+                                         'filename': found_standard.filename})
+
+    return Table(found_standard)
+
+
+class FluxStandard(Base):
+    __tablename__ = 'fluxstandards'
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    filename = Column(String(100), unique=True)
+    filepath = Column(String(150))
+    frameid = Column(Integer, nullable=True)
+    ra = Column(Float)
+    dec = Column(Float)
+
+
+def ingest_standards(db_address):
+    standard_files = glob(pkg_resources.resource_filename('banzai_floyds.tests', 'data/standards/*.fits'))
+    for standard_file in standard_files:
+        standard_hdu = fits.open(standard_file)
+        standard_record = FluxStandard(filename=os.path.basename(standard_file),
+                                       filepath=os.path.dirname(standard_file),
+                                       ra=standard_hdu[0].header['RA'],
+                                       dec=standard_hdu[0].header['DEC'])
+        with get_session(db_address) as db_session:
+            db_session.add(standard_record)
+            db_session.commit()

banzai_floyds/extract.py

@@ -1,7 +1,7 @@
 from banzai.stages import Stage
 import numpy as np
 from astropy.table import Table, vstack
-from banzai_floyds.matched_filter import maximize_match_filter
+from banzai_floyds.matched_filter import optimize_match_filter
 from numpy.polynomial.legendre import Legendre
 from banzai_floyds.utils.fitting_utils import gauss, fwhm_to_sigma, Legendre2d
 
@@ -71,7 +71,7 @@ def fit_profile(data, profile_width=4):
     for data_to_fit in data.groups:
         # Pass a match filter (with correct s/n scaling) with a gaussian with a default width
         initial_guess = (data_to_fit['y_order'][np.argmax(data_to_fit['data'])], 0.05)
-        best_fit_center, _ = maximize_match_filter(initial_guess, data_to_fit['data'], data_to_fit['uncertainty'],
+        best_fit_center, _ = optimize_match_filter(initial_guess, data_to_fit['data'], data_to_fit['uncertainty'],
                                                    profile_gauss_fixed_width, data_to_fit['y_order'],
                                                    args=(fwhm_to_sigma(profile_width),))
         # If the peak pixel of the match filter is > 2 times the median (or something like that) keep the point
@@ -107,12 +107,14 @@ def fit_profile_width(data, profile_fits, poly_order=3, background_poly_order=2,
         if peak_snr < 2.0 * median_snr:
             continue
 
+        # TODO: Only fit the profile width where it is much larger than the background value,
+        # otherwise use a heuristic width
         # Pass a match filter (with correct s/n scaling) with a gaussian with a default width
         initial_coeffs = np.zeros(background_poly_order + 1)
         initial_coeffs[0] = np.median(data_to_fit['data']) / data_to_fit['data'][peak]
 
         initial_guess = fwhm_to_sigma(default_width), *initial_coeffs
-        best_fit_sigma, *_ = maximize_match_filter(initial_guess, data_to_fit['data'],
+        best_fit_sigma, *_ = optimize_match_filter(initial_guess, data_to_fit['data'],
                                                    data_to_fit['uncertainty'],
                                                    background_fixed_profile_center,
                                                    data_to_fit['y_order'],
@@ -140,7 +142,9 @@ def fit_background(data, profile_centers, profile_widths, x_poly_order=2, y_poly
         # Pass a match filter (with correct s/n scaling) with a gaussian with a default width
         initial_coeffs = np.zeros((x_poly_order + 1) + y_poly_order)
         initial_coeffs[0] = np.median(data_to_fit['data']) / data_to_fit['data'][peak]
-        best_fit_coeffs = maximize_match_filter(initial_coeffs, data_to_fit['data'],
+        # TODO: Fit the background with a totally fixed profile, and no need to iterate
+        # since our filter is linear
+        best_fit_coeffs = optimize_match_filter(initial_coeffs, data_to_fit['data'],
                                                 data_to_fit['uncertainty'],
                                                 background_fixed_profile,
                                                 (data_to_fit['wavelength'], data_to_fit['y_order']),