working on elgs

py/desitarget/mock/mockmaker.py

@@ -785,65 +785,65 @@ def KDTree_query(self, matrix, return_dist=False, south=True, subtype=''):
         else:
             return indx.astype('i4')
 
-    def sample_gmm_nospectra(self, meta, rand=None, target=None):
-        """Sample from one of the Gaussian mixture models generated by
-        desitarget/doc/nb/gmm-quicksurvey.ipynb.
-
-        Note: Any changes to the quantities fitted in that notebook need to be
-        updated here!
-        
-        """
-        if rand is None:
-            rand = np.random.RandomState()
-
-        nsample = len(meta)
-
-        # For contaminants, sample from the appropriate Gaussian mixture model. 
-        if type(self.GMM_nospectra) == dict:
-            data = self.GMM_nospectra[target].sample(nsample, random_state=rand)
-        else:
-            target = meta['OBJTYPE'][0].upper()
-            data = self.GMM_nospectra.sample(nsample, random_state=rand)
-
-        alltags = dict()
-        alltags['ELG'] = ('r', 'g - r', 'r - z', 'z - W1', 'W1 - W2', 'oii')
-        alltags['LRG'] = ('z', 'g - r', 'r - z', 'z - W1', 'W1 - W2')
-        alltags['BGS'] = ('r', 'g - r', 'r - z', 'z - W1', 'W1 - W2')
-        alltags['QSO'] = ('g', 'g - r', 'r - z', 'z - W1', 'W1 - W2')
-        alltags['LYA'] = ('g', 'g - r', 'r - z', 'z - W1', 'W1 - W2')
-
-        tags = alltags[target]
-        sample = np.empty( nsample, dtype=np.dtype( [(tt, 'f4') for tt in tags] ) )
-        for ii, tt in enumerate(tags):
-            sample[tt] = data[:, ii]
-
-        if target == 'ELG' or target == 'BGS':
-            rmag = sample['r']
-            zmag = rmag - sample['r - z']
-            gmag = sample['g - r'] + rmag
-            normmag = rmag
-        elif target == 'LRG':
-            zmag = sample['z']
-            rmag = sample['r - z'] + zmag
-            gmag = sample['g - r'] + rmag
-            normmag = zmag
-        elif target == 'QSO' or target == 'LYA':
-            gmag = sample['g']
-            rmag = gmag - sample['g - r']
-            zmag = rmag - sample['r - z']
-            normmag = gmag
-
-        W1mag = zmag - sample['z - W1'] 
-        W2mag = W1mag - sample['W1 - W2'] 
-
-        meta['MAG'][:] = normmag
-        meta['FLUX_G'][:] = 1e9 * 10**(-0.4 * gmag)
-        meta['FLUX_R'][:] = 1e9 * 10**(-0.4 * rmag)
-        meta['FLUX_Z'][:] = 1e9 * 10**(-0.4 * zmag)
-        meta['FLUX_W1'][:] = 1e9 * 10**(-0.4 * W1mag)
-        meta['FLUX_W2'][:] = 1e9 * 10**(-0.4 * W2mag)
-
-        return meta
+    #def sample_gmm_nospectra(self, meta, rand=None, target=None):
+    #    """Sample from one of the Gaussian mixture models generated by
+    #    desitarget/doc/nb/gmm-quicksurvey.ipynb.
+    #
+    #    Note: Any changes to the quantities fitted in that notebook need to be
+    #    updated here!
+    #    
+    #    """
+    #    if rand is None:
+    #        rand = np.random.RandomState()
+    #
+    #    nsample = len(meta)
+    #
+    #    # For contaminants, sample from the appropriate Gaussian mixture model. 
+    #    if type(self.GMM_nospectra) == dict:
+    #        data = self.GMM_nospectra[target].sample(nsample, random_state=rand)
+    #    else:
+    #        target = meta['OBJTYPE'][0].upper()
+    #        data = self.GMM_nospectra.sample(nsample, random_state=rand)
+    #
+    #    alltags = dict()
+    #    alltags['ELG'] = ('r', 'g - r', 'r - z', 'z - W1', 'W1 - W2', 'oii')
+    #    alltags['LRG'] = ('z', 'g - r', 'r - z', 'z - W1', 'W1 - W2')
+    #    alltags['BGS'] = ('r', 'g - r', 'r - z', 'z - W1', 'W1 - W2')
+    #    alltags['QSO'] = ('g', 'g - r', 'r - z', 'z - W1', 'W1 - W2')
+    #    alltags['LYA'] = ('g', 'g - r', 'r - z', 'z - W1', 'W1 - W2')
+    #
+    #    tags = alltags[target]
+    #    sample = np.empty( nsample, dtype=np.dtype( [(tt, 'f4') for tt in tags] ) )
+    #    for ii, tt in enumerate(tags):
+    #        sample[tt] = data[:, ii]
+    #
+    #    if target == 'ELG' or target == 'BGS':
+    #        rmag = sample['r']
+    #        zmag = rmag - sample['r - z']
+    #        gmag = sample['g - r'] + rmag
+    #        normmag = rmag
+    #    elif target == 'LRG':
+    #        zmag = sample['z']
+    #        rmag = sample['r - z'] + zmag
+    #        gmag = sample['g - r'] + rmag
+    #        normmag = zmag
+    #    elif target == 'QSO' or target == 'LYA':
+    #        gmag = sample['g']
+    #        rmag = gmag - sample['g - r']
+    #        zmag = rmag - sample['r - z']
+    #        normmag = gmag
+    #
+    #    W1mag = zmag - sample['z - W1'] 
+    #    W2mag = W1mag - sample['W1 - W2'] 
+    #    
+    #    meta['MAG'][:] = normmag
+    #    meta['FLUX_G'][:] = 1e9 * 10**(-0.4 * gmag)
+    #    meta['FLUX_R'][:] = 1e9 * 10**(-0.4 * rmag)
+    #    meta['FLUX_Z'][:] = 1e9 * 10**(-0.4 * zmag)
+    #    meta['FLUX_W1'][:] = 1e9 * 10**(-0.4 * W1mag)
+    #    meta['FLUX_W2'][:] = 1e9 * 10**(-0.4 * W2mag)
+    #
+    #    return meta
 
     def _nospectra_photometry(self, meta, rand, data, indx, target_name, contaminants=False):
         """Populate the photometry in meta in no-spectra mode."""
@@ -3433,6 +3433,8 @@ class LRGMaker(SelectTargets):
     """
     wave, KDTree_north, KDTree_south, template_maker = None, None, None, None
     GMM_LRG, GMM_nospectra = None, None
+
+    here remove gmm_nospectra
 
     def __init__(self, seed=None, nside_chunk=128, **kwargs):
         from desisim.templates import LRG
@@ -3581,6 +3583,10 @@ def make_spectra(self, data=None, indx=None, seed=None, no_spectra=False):
         if no_spectra:
             flux = []
             meta, objmeta = empty_metatable(nmodel=nobj, objtype=self.objtype)
+            self._nospectra_photometry(meta, rand, data, indx, data['TARGET_NAME'])
+
+            here!
+
             meta['SEED'][:] = rand.randint(2**31, size=nobj)
             meta['REDSHIFT'][:] = data['Z'][indx]
             self.sample_gmm_nospectra(meta, rand=rand) # noiseless photometry from pre-computed GMMs