Packaging cleanup and tesswcs migration

MANIFEST.in

@@ -1,6 +1,4 @@
 include README.md LICENSE
 include tessreduce/tess_straps.csv
-include tessreduce/calspec_mags.npy
 include tessreduce/Tonry_splines.txt
 include tessreduce/SMspline.txt
-include tessreduce/sector_mjd.csv

README.md

@@ -2,7 +2,7 @@
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![DOI](https://img.shields.io/badge/DOI-10.3847%2F1538--3881%2Fac2c2e-blue.svg)](https://doi.org/10.3847/1538-3881/ac2c2e)
 
-![plot](./figs/header.png)
+![plot](https://raw.githubusercontent.com/CheerfulUser/TESSreduce/main/figs/header.png)
 
 With this package that builds on lightkurve, you can reduce TESS data while preserving transient signals. You can supply a TPF or give coordinates and sector to construct a TPF with TESScut. The background subtraction accounts for the smooth background and
 detector straps. Alongisde background subtraction TESSreduce also aligns images, performs difference imaging, and can even detect transient events! 
@@ -29,7 +29,7 @@ obs = tr.sn_lookup('sn2018fub')
 ```python
 tess = tr.tessreduce(obs_list=obs)
 ```
-![plot](./figs/fub.png)
+![plot](https://raw.githubusercontent.com/CheerfulUser/TESSreduce/main/figs/fub.png)
 
 **OR**
 ```python
@@ -75,7 +75,7 @@ Several options are available for flux and are interchangeable, however, mag is
 ```python
 tess.plotter()
 ```
-![plot](./figs/fub_cal.png)
+![plot](https://raw.githubusercontent.com/CheerfulUser/TESSreduce/main/figs/fub_cal.png)
 
 
 # Extracting key variables 

setup.py

@@ -38,7 +38,7 @@
             'sep',
             'tqdm',
             'alerce',
-            'tess-point',
+            'tesswcs>=1.8',
             'tabulate',
             'TESS_PRF']
 
@@ -137,9 +137,11 @@ def run(self):
         'License :: OSI Approved :: MIT License',
         'Programming Language :: Python',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
+        'Programming Language :: Python :: 3.11',
+        'Programming Language :: Python :: 3.12',
         'Programming Language :: Python :: Implementation :: CPython',
-        'Programming Language :: Python :: Implementation :: PyPy'
     ],
     # $ setup.py publish support.
     cmdclass={

tessreduce/helpers.py

@@ -38,7 +38,7 @@
 from photutils.detection import StarFinder
 
 from PRF import TESS_PRF
-from tess_stars2px import tess_stars2px_function_entry as focal_plane
+import tesswcs
 from tabulate import tabulate
 
 package_directory = os.path.dirname(os.path.abspath(__file__)) + '/'
@@ -520,6 +520,62 @@ def grad_flux_rad(flux):
 	return rad
 
 
+def _tess_pointing_table():
+	"""
+	Sector start/end times (MJD), sourced from tesswcs.pointings so the table
+	stays current with tesswcs releases rather than a file pinned in this repo.
+
+	Returns
+	-------
+	sec_times : pd.DataFrame
+		Indexed by Sector, with mjd_start and mjd_end columns.
+	"""
+	pointings = tesswcs.pointings.to_pandas()[['Sector','Start','End']]
+	pointings = pointings.rename(columns={'Start':'mjd_start','End':'mjd_end'})
+	pointings['mjd_start'] = Time(pointings['mjd_start'].values,format='jd').mjd
+	pointings['mjd_end'] = Time(pointings['mjd_end'].values,format='jd').mjd
+	return pointings.set_index('Sector').sort_index()
+
+
+def _target_sectors(ra,dec):
+	"""
+	Find which TESS sectors, cameras and CCDs observe a given coordinate.
+	Replaces tess_stars2px_function_entry (tess-point) with tesswcs, which
+	covers both archived and predicted sector pointings.
+
+	Returns
+	-------
+	outSecs, outCam, outCcd, outColPix, outRowPix : np.array
+		Sector number, camera, CCD, and pixel column/row for each match,
+		sorted by sector.
+	"""
+	import logging
+	coord = SkyCoord(ra,dec,unit='deg')
+
+	level = tesswcs.log.level
+	tesswcs.log.setLevel(logging.ERROR)
+	secs, cams, ccds, cols, rows = [], [], [], [], []
+	try:
+		for sector in tesswcs.pointings['Sector']:
+			sector = int(sector)
+			for camera in range(1,5):
+				for ccd in range(1,5):
+					try:
+						wcs = tesswcs.WCS.from_sector(sector,camera,ccd)
+					except ValueError:
+						continue
+					if wcs.footprint_contains(coord):
+						col, row = wcs.world_to_pixel(coord)
+						secs += [sector]; cams += [camera]; ccds += [ccd]
+						cols += [float(col)]; rows += [float(row)]
+	finally:
+		tesswcs.log.setLevel(level)
+
+	order = np.argsort(secs)
+	return (np.array(secs)[order], np.array(cams)[order], np.array(ccds)[order],
+			np.array(cols)[order], np.array(rows)[order])
+
+
 def sn_lookup(name,time='disc',buffer=0,print_table=True, df = False):
 	"""
 	Check for overlapping TESS ovservations for a transient. Uses the Open SNe Catalog for 
@@ -589,16 +645,15 @@ def sn_lookup(name,time='disc',buffer=0,print_table=True, df = False):
 	ra = c.ra.deg
 	dec = c.dec.deg
 
-	outID, outEclipLong, outEclipLat, outSecs, outCam, outCcd, outColPix, \
-	outRowPix, scinfo = focal_plane(0, ra, dec)
-
-	sec_times = pd.read_csv(package_directory + 'sector_mjd.csv')
-	if len(outSecs) > 0:
-		ind = outSecs - 1 
+	outSecs, outCam, outCcd, outColPix, outRowPix = _target_sectors(ra, dec)
 
-		new_ind = [i for i in ind if i < len(sec_times)]
+	sec_times = _tess_pointing_table()
+	if len(outSecs) > 0:
+		keep = np.isin(outSecs, sec_times.index)
+		outSecs, outCam, outCcd, outColPix, outRowPix = \
+			outSecs[keep], outCam[keep], outCcd[keep], outColPix[keep], outRowPix[keep]
 
-		secs = sec_times.iloc[new_ind]
+		secs = sec_times.loc[outSecs].reset_index()
 		if type(time) == str:
 			if (time.lower() == 'disc') | (time.lower() == 'discovery'):
 				disc_start = secs['mjd_start'].values - disc_t.mjd
@@ -679,16 +734,15 @@ def spacetime_lookup(ra,dec,time=None,buffer=0,print_table=True, df = False, pri
 		ra = c.ra.deg
 		dec = c.dec.deg
 
-	outID, outEclipLong, outEclipLat, outSecs, outCam, outCcd, outColPix, \
-	outRowPix, scinfo = focal_plane(0, ra, dec)
-
-	sec_times = pd.read_csv(package_directory + 'sector_mjd.csv')
-	if len(outSecs) > 0:
-		ind = outSecs - 1 
+	outSecs, outCam, outCcd, outColPix, outRowPix = _target_sectors(ra, dec)
 
-		new_ind = [i for i in ind if i < len(sec_times)]
+	sec_times = _tess_pointing_table()
+	if len(outSecs) > 0:
+		keep = np.isin(outSecs, sec_times.index)
+		outSecs, outCam, outCcd, outColPix, outRowPix = \
+			outSecs[keep], outCam[keep], outCcd[keep], outColPix[keep], outRowPix[keep]
 
-		secs = sec_times.iloc[new_ind]
+		secs = sec_times.loc[outSecs].reset_index()
 		disc_start = secs['mjd_start'].values - time
 		disc_end = secs['mjd_end'].values - time
 

tests/test_helpers.py

@@ -22,6 +22,8 @@
     smooth_zp,
     Smooth_bkg,
     regional_stats_mask,
+    _tess_pointing_table,
+    _target_sectors,
 )
 
 
@@ -315,5 +317,43 @@ def test_outlier_masked(self):
         self.assertTrue(mask[15, 15])
 
 
+class TestTessPointingTable(unittest.TestCase):
+
+    def test_indexed_by_sector_with_expected_columns(self):
+        table = _tess_pointing_table()
+        self.assertEqual(table.index.name, 'Sector')
+        self.assertIn('mjd_start', table.columns)
+        self.assertIn('mjd_end', table.columns)
+
+    def test_end_after_start(self):
+        table = _tess_pointing_table()
+        self.assertTrue((table['mjd_end'] > table['mjd_start']).all())
+
+    def test_known_sector_one_start_time(self):
+        # Sector 1 start is 2018-07-25 19:00 UT, JD 2458324.5 -> MJD 58324.0
+        table = _tess_pointing_table()
+        self.assertAlmostEqual(table.loc[1, 'mjd_start'], 58324.0, places=3)
+
+
+class TestTargetSectors(unittest.TestCase):
+
+    def test_known_target_returns_expected_sectors(self):
+        # Reference target cross-checked against tess_stars2px_function_entry
+        # (tess-point) output: sectors 2, 29, 69, 96, 103, 104, 105, 106.
+        outSecs, outCam, outCcd, outColPix, outRowPix = _target_sectors(10.127, -50.687)
+        expected = {2, 29, 69, 96, 103, 104, 105, 106}
+        self.assertTrue(expected.issubset(set(outSecs.tolist())))
+
+    def test_arrays_aligned_and_sorted(self):
+        outSecs, outCam, outCcd, outColPix, outRowPix = _target_sectors(10.127, -50.687)
+        lengths = {len(outSecs), len(outCam), len(outCcd), len(outColPix), len(outRowPix)}
+        self.assertEqual(len(lengths), 1)
+        assert_array_equal(outSecs, np.sort(outSecs))
+
+    def test_pixel_coordinates_within_ccd_bounds(self):
+        outSecs, outCam, outCcd, outColPix, outRowPix = _target_sectors(10.127, -50.687)
+        self.assertTrue(np.all((outColPix >= 0) & (outColPix <= 2136)))
+        self.assertTrue(np.all((outRowPix >= 0) & (outRowPix <= 2078)))
+
 if __name__ == '__main__':
     unittest.main()