Merge fe2db94 into e9d5ffe

doc/changes.rst

@@ -5,6 +5,12 @@ desitarget Change Log
 0.53.1 (unreleased)
 -------------------
 
+* Some clean-up for the 1% Survey [`PR #691`_]. Includes:
+    * Don't allow ``BGS_FAINT`` targets to be observed in ``DARK``.
+    * Warn about primary targets that might be too bright.
+    * Have a single function for calculating UTC time stamps.
+    * Functionality to read ledgers strictly before a certain UTC time.
+    * Centralize and speed up routines to match arrays on ``TARGETID``.
 * Update ToO Ledger with TOOID and HI/LO priority options [`PR #690`_]
 * Add an ``sv3_cuts.py`` module and sv3 bitmask yaml file [`PR #689`_].
 * Don't pass the DR when constructing MTL filenames [`PR #688`_].
@@ -16,6 +22,7 @@ desitarget Change Log
 .. _`PR #688`: https://github.com/desihub/desitarget/pull/688
 .. _`PR #689`: https://github.com/desihub/desitarget/pull/689
 .. _`PR #690`: https://github.com/desihub/desitarget/pull/690
+.. _`PR #691`: https://github.com/desihub/desitarget/pull/691
 
 0.53.0 (2021-03-18)
 -------------------

py/desitarget/QA.py

@@ -6,7 +6,6 @@
 
 Module dealing with Quality Assurance tests for Target Selection
 """
-from __future__ import (absolute_import, division)
 from time import time
 import numpy as np
 import fitsio
@@ -22,8 +21,6 @@
 from glob import glob, iglob
 from scipy.optimize import leastsq
 from scipy.spatial import ConvexHull
-from astropy import units as u
-from astropy.coordinates import SkyCoord
 from desiutil import brick
 from desiutil.log import get_logger
 from desitarget.internal import sharedmem

py/desitarget/brightmask.py

@@ -19,9 +19,6 @@
 import numpy as np
 import numpy.lib.recfunctions as rfn
 
-from astropy.coordinates import SkyCoord
-from astropy import units as u
-
 from matplotlib.patches import Polygon
 from matplotlib.collections import PatchCollection
 
@@ -587,6 +584,8 @@ def is_in_bright_mask(targs, sourcemask, inonly=False):
     used_near_mask = np.zeros(len(sourcemask), dtype=bool)
 
     # ADM turn the mask and target coordinates into SkyCoord objects.
+    from astropy.coordinates import SkyCoord
+    from astropy import units as u
     ctargs = SkyCoord(targs["RA"]*u.degree, targs["DEC"]*u.degree)
     cmask = SkyCoord(sourcemask["RA"]*u.degree, sourcemask["DEC"]*u.degree)
 

py/desitarget/data/targetmask.yaml

@@ -54,16 +54,16 @@ desi_mask:
 
 #- Bright Galaxy Survey
 bgs_mask:
-    - [BGS_FAINT,           0, "BGS faint targets",                     {obsconditions: BRIGHT|GRAY|DARK}]
+    - [BGS_FAINT,           0, "BGS faint targets",                     {obsconditions: BRIGHT}]
     - [BGS_BRIGHT,          1, "BGS bright targets",                    {obsconditions: BRIGHT}]
     - [BGS_WISE,            2, "BGS wise targets",                      {obsconditions: BRIGHT}]
     - [BGS_FAINT_HIP,       3, "BGS faint targets at bright priorty",   {obsconditions: BRIGHT}]
 
     #- BGS North vs. South selections
-    - [BGS_FAINT_NORTH,     8, "BGS faint cuts tuned for Bok/Mosaic",              {obsconditions: BRIGHT|GRAY|DARK}]
+    - [BGS_FAINT_NORTH,     8, "BGS faint cuts tuned for Bok/Mosaic",              {obsconditions: BRIGHT}]
     - [BGS_BRIGHT_NORTH,    9, "BGS bright cuts tuned for Bok/Mosaic",             {obsconditions: BRIGHT}]
     - [BGS_WISE_NORTH,      10, "BGS WISE cuts tuned for Bok/Mosaic",              {obsconditions: BRIGHT}]
-    - [BGS_FAINT_SOUTH,     16, "BGS faint cuts tuned for DECam",                  {obsconditions: BRIGHT|GRAY|DARK}]
+    - [BGS_FAINT_SOUTH,     16, "BGS faint cuts tuned for DECam",                  {obsconditions: BRIGHT}]
     - [BGS_BRIGHT_SOUTH,    17, "BGS bright cuts tuned for DECam",                 {obsconditions: BRIGHT}]
     - [BGS_WISE_SOUTH,      18, "BGS WISE cuts tuned for DECam",                   {obsconditions: BRIGHT}]
 

py/desitarget/geomask.py

@@ -4,9 +4,10 @@
 desitarget.geomask
 ==================
 
-Utility functions for geometry on the sky, masking, etc.
+Utility functions for geometry on the sky, masking, matching, etc.
 
 .. _`this post on Stack Overflow`: https://stackoverflow.com/questions/7392143/python-implementations-of-packing-algorithm
+.. _`by way of Stack Overflow`: https://stackoverflow.com/a/32654989
 """
 from __future__ import (absolute_import, division)
 #
@@ -32,6 +33,77 @@
 log = get_logger()
 
 
+def match_to(A, B):
+    """Return indexes where B matches A, holding A in place.
+
+    Parameters
+    ----------
+    A : :class:`~numpy.ndarray` or `list`
+        Array of integers to match TO.
+    B : :class:`~numpy.ndarray` or `list`
+        Array of integers matched to `A`
+
+    Returns
+    -------
+    :class:`~numpy.ndarray`
+        The integer indexes in A that B matches to.
+
+    Notes
+    -----
+    - Result should be such that for ii = match_to(A, B)
+      np.all(A[ii] == B) is True.
+    - We're looking up locations of B in A so B can be
+      a shorter array than A (provided the B->A matches are
+      unique) but A cannot be a shorter array than B.
+    """
+    # ADM grab the integer matchers.
+    Aii, Bii = match(A, B)
+
+    # ADM return, restoring the original order.
+    return Aii[np.argsort(Bii)]
+
+
+def match(A, B):
+    """Return matching indexes for two arrays on a unique key.
+
+    Parameters
+    ----------
+    A : :class:`~numpy.ndarray` or `list`
+        An array of integers.
+    B : :class:`~numpy.ndarray` or `list`
+        An array of integers.
+
+    Returns
+    -------
+    :class:`~numpy.ndarray`
+        The integer indexes in A to match to B.
+    :class:`~numpy.ndarray`
+        The integer indexes in B to match to A.
+
+    Notes
+    -----
+    - Result should be such that for Aii, Bii = match(A, B)
+      np.all(A[Aii] == B[Bii]) is True.
+    - Only works if there is a unique mapping from A->B, i.e
+      if A and B do NOT contain duplicates.
+    - h/t Anand Raichoor `by way of Stack Overflow`_.
+    """
+    # AR sorting A,B
+    tmpA = np.sort(A)
+    tmpB = np.sort(B)
+
+    # AR create mask equivalent to np.in1d(A,B) and np.in1d(B,A) for unique elements
+    maskA = (
+        np.searchsorted(tmpB, tmpA, "right") - np.searchsorted(tmpB, tmpA, "left")
+    ) == 1
+    maskB = (
+        np.searchsorted(tmpA, tmpB, "right") - np.searchsorted(tmpA, tmpB, "left")
+    ) == 1
+
+    # AR to get back to original indexes
+    return np.argsort(A)[maskA], np.argsort(B)[maskB]
+
+
 def get_imaging_maskbits(bitnamelist=None):
     """Return MASKBITS names and bits from the Legacy Surveys.