From f37d4c59683d5875280036717de12837472b6786 Mon Sep 17 00:00:00 2001
From: Eve Kovacs <kovacs@anl.gov>
Date: Wed, 11 Sep 2019 13:44:54 -0500
Subject: [PATCH 01/10] Add jackknife errors to angular correlation function;
 add some cosmetic options

---
 descqa/CorrelationsTwoPoint.py               | 187 ++++++++++++++++---
 descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml |  79 ++++++++
 2 files changed, 244 insertions(+), 22 deletions(-)
 create mode 100644 descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml

diff --git a/descqa/CorrelationsTwoPoint.py b/descqa/CorrelationsTwoPoint.py
index 101d7440..d7c077be 100644
--- a/descqa/CorrelationsTwoPoint.py
+++ b/descqa/CorrelationsTwoPoint.py
@@ -2,8 +2,11 @@
 import os
 from collections import defaultdict
 import numpy as np
+import re
 import scipy.special as scsp
 import treecorr
+import healpy as hp
+from sklearn.cluster import k_means
 from GCR import GCRQuery
 
 from .base import BaseValidationTest, TestResult
@@ -58,9 +61,10 @@ def __init__(self, **kwargs):
 
         self.fig_xlabel = kwargs['fig_xlabel']
         self.fig_ylabel = kwargs['fig_ylabel']
-        self.fig_ylim = kwargs['fig_ylim']
+        self.fig_ylim = kwargs.get('fig_ylim', None)
         self.fig_subplots_nrows, self.fig_subplots_ncols = kwargs.get('fig_subplots', (1, 1))
         self.fig_subplot_groups = kwargs.get('fig_subplot_groups', [None])
+        self.fig_xlim = kwargs.get('fig_xlim', None)
 
         self.treecorr_config = {
             'min_sep': kwargs['min_sep'],
@@ -70,6 +74,25 @@ def __init__(self, **kwargs):
         self.random_nside = kwargs.get('random_nside', 1024)
         self.random_mult = kwargs.get('random_mult', 3)
 
+        # jackknife errors
+        self.jackknife = kwargs.get('jackknife', False)
+        if self.jackknife:
+            self.N_jack = kwargs.get('N_jack', 30)
+            jackknife_quantities = kwargs.get('jackknife_quantities',
+                                              {'ra':['ra', 'ra_true'], 'dec':['dec', 'dec_true']})
+            if 'ra' not in self.requested_columns or 'dec' not in self.requested_columns:
+                self.requested_columns.update(jackknife_quantities)
+            self.use_diagonal_only = kwargs.get('use_diagonal_only', True)
+
+        self.r_validation_min = kwargs.get('r_validation_min', 1)
+        self.r_validation_max = kwargs.get('r_validation_max', 10)
+
+        self.truncate_cat_name = kwargs.get('truncate_cat_name', False)
+        self.title_in_legend = kwargs.get('title_in_legend', False)
+        self.font_size = kwargs.get('font_size', 16)
+        self.legend_size = kwargs.get('legend_size', 10)
+        self.survey_label = kwargs.get('survey_label', '')
+        self.no_title = kwargs.get('no_title', False)
 
     @staticmethod
     def load_catalog_data(catalog_instance, requested_columns, test_samples):
@@ -196,27 +219,65 @@ def plot_data_comparison(self, corr_data, catalog_name, output_dir):
                 ax.loglog(self.validation_data[:, 0],
                         self.validation_data[:, sample_data['data_col']],
                         c=color,
-                        label=sample_label)
+                        label=' '.join([self.survey_label, sample_label]))
                 if 'data_err_col' in sample_data:
                     y1 = (self.validation_data[:, sample_data['data_col']] +
                         self.validation_data[:, sample_data['data_err_col']])
                     y2 = (self.validation_data[:, sample_data['data_col']] -
                         self.validation_data[:, sample_data['data_err_col']])
-                    y2[y2 <= 0] = self.fig_ylim[0]*0.9
-                    ax.fill_between(self.validation_data[:, 0], y1, y2, lw=0, color=color, alpha=0.2)
-                ax.errorbar(sample_corr[0], sample_corr[1], sample_corr[2], marker='o', ls='', c=color)
+                    if self.fig_xlim is not None:
+                        y2[y2 <= 0] = self.fig_ylim[0]*0.9
+                    ax.fill_between(self.validation_data[:, 0], y1, y2, lw=0, color=color, alpha=0.25)
+                ax.errorbar(sample_corr[0], sample_corr[1], sample_corr[2],
+                            label=' '.join([catalog_name, sample_label]),
+                            marker='o', ls='', c=color)
 
-            ax.legend(loc='best')
-            ax.set_xlabel(self.fig_xlabel)
-            ax.set_ylim(*self.fig_ylim)
-            ax.set_ylabel(self.fig_ylabel)
-            ax.set_title('{} vs. {}'.format(catalog_name, self.data_label), fontsize='medium')
+            self.decorate_plot(ax, catalog_name)
 
         fig.tight_layout()
         fig.savefig(os.path.join(output_dir, '{:s}.png'.format(self.test_name)), bbox_inches='tight')
         plt.close(fig)
 
 
+    def get_legend_title(self, test_samples, exclude='mstellar'):
+        legend_title = ''
+        filter_ids  = list(set([k for v in test_samples.values() for k in v.keys() if exclude not in k]))
+        for filter_id in filter_ids:
+            legend_title = self.get_legend_subtitle(test_samples, filter_id=filter_id, legend_title=legend_title)
+            
+        return legend_title
+
+
+    @staticmethod
+    def get_legend_subtitle(test_samples, filter_id='z', legend_title=''):
+        legend_title = legend_title  if len(legend_title) == 0 else '{}; '.format(legend_title)
+        min_values = [test_samples[k][filter_id].get('min', None) for k in test_samples if test_samples[k].get(filter_id, None) is not None]
+        max_values = [test_samples[k][filter_id].get('max', None) for k in test_samples if test_samples[k].get(filter_id, None) is not None]
+        min_title = ''
+        if len(min_values) > 0 and any([k != None for k in min_values]):
+            min_title = '{} < {}'.format(min([k for k in min_values if k is not None]), filter_id)
+        max_title = ''
+        if len(max_values) > 0 and any([k != None for k in max_values]):
+            max_values = [k for k in max_values if k is not None]
+            max_title = '${} < {}$'.format(filter_id, max(max_values)) if len(min_title) == 0 else '${} < {}$'.format(min_title, max(max_values))
+
+        return legend_title + max_title
+
+
+    def decorate_plot(self, ax, catalog_name):
+        title = '{} vs. {}'.format(catalog_name, self.data_label)
+        lgnd_title = self.get_legend_title(self.test_samples) if self.title_in_legend else None
+        ax.legend(loc='lower left', fontsize=self.legend_size, title=lgnd_title)
+        ax.set_xlabel(self.fig_xlabel, size=self.font_size)
+        if self.fig_ylim is not None:
+            ax.set_ylim(*self.fig_ylim)
+        if self.fig_xlim is not None:
+            ax.set_xlim(*self.fig_xlim)
+        ax.set_ylabel(self.fig_ylabel, size=self.font_size)
+        if not self.no_title:
+            ax.set_title(title, fontsize='medium')
+
+
     @staticmethod
     def score_and_test(corr_data): # pylint: disable=unused-argument
         """ Given the resultant correlations, compute the test score and return
@@ -237,6 +298,70 @@ def score_and_test(corr_data): # pylint: disable=unused-argument
         return TestResult(inspect_only=True)
 
 
+    def get_jackknife_randoms(self, N_jack, catalog_data, ra='ra', dec='dec'):
+        """
+        Computes the jackknife regions and random catalogs for each region 
+        Parameters
+        ----------
+        N_jack : number of regions
+        catalog_data : input catalog
+
+        Returns
+        -------
+        jack_labels: array of regions in catalog data
+        randoms: dict of randoms labeled by region
+        """
+        catalogs = {}
+        #cluster
+        nn = np.stack((catalog_data[ra], catalog_data[dec]), axis=1)
+        _, jack_labels, _ = k_means(n_clusters=N_jack, random_state=0, X=nn)
+
+        randoms = {}
+        for nj in range(N_jack):
+            catalog_data_jk = dict(zip(catalog_data.keys(), [v[(jack_labels != nj)] for v in catalog_data.values()]))
+            rand_cat, rr = self.generate_processed_randoms(catalog_data_jk) #get randoms for this footprint
+            #print('nj = {}, area = {:.2f}'.format(nj, self.check_footprint(catalog_data_jk))) #check footprint
+            randoms[str(nj)] = {'ran': rand_cat, 'rr':rr}
+
+        return jack_labels, randoms
+
+    def get_jackknife_errors(self, N_jack, catalog_data, sample_conditions, r, xi, jack_labels, randoms,
+                             diagonal_errors=True):
+        
+        #run treecorr for jackknife regions
+        Nrbins = len(r)
+        Njack_array = np.zeros((N_jack, Nrbins), dtype=np.float)
+        print(sample_conditions)
+        for nj in range(N_jack):
+            catalog_data_jk = dict(zip(catalog_data.keys(), [v[(jack_labels != nj)] for v in catalog_data.values()]))
+            tmp_catalog_data = self.create_test_sample(catalog_data_jk, sample_conditions) #apply sample cut
+            # run treecorr
+            xi_rad, Njack_array[nj], _ = self.run_treecorr(catalog_data=tmp_catalog_data,
+                                                   treecorr_rand_cat=randoms[str(nj)]['ran'], 
+                                                   rr=randoms[str(nj)]['rr'], 
+                                                   output_file_name=None)
+            print(nj, Njack_array[nj])
+
+        covariance = np.zeros((Nrbins, Nrbins))
+        for i in range(Nrbins):
+            if diagonal_errors:
+                for njack in Njack_array:
+                    covariance[i][i] += (N_jack - 1.)/N_jack * (xi[i] - njack[i]) ** 2
+            else:
+                for j in range(Nrbins):
+                    for njack in Njack_array:
+                        covariance[i][j] += (N_jack - 1.)/N_jack * (xi[i] - njack[i]) * (xi[j] - njack[j])
+            print('bin/cov', i, covariance[i][i])
+
+        return covariance
+
+    def check_footprint(self, catalog_data):
+        pix_footprint = get_healpixel_footprint(catalog_data['ra'], catalog_data['dec'], self.random_nside)
+        area_footprint = 4.*np.pi*(180./np.pi)**2*len(pix_footprint)/hp.nside2npix(self.random_nside)
+
+        return area_footprint
+
+
 class CorrelationsAngularTwoPoint(CorrelationUtilities):
     """
     Validation test for an angular 2pt correlation function.
@@ -252,11 +377,16 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
         catalog_data = self.load_catalog_data(catalog_instance=catalog_instance,
                                               requested_columns=self.requested_columns,
                                               test_samples=self.test_samples)
-
         if not catalog_data:
             return TestResult(skipped=True, summary='Missing requested quantities')
 
-        rand_cat, rr = self.generate_processed_randoms(catalog_data)
+        if self.truncate_cat_name:
+            catalog_name = re.split('_', catalog_name)[0]
+
+        rand_cat, rr = self.generate_processed_randoms(catalog_data) #assumes ra and dec exist
+
+        if self.jackknife:                     #evaluate randoms for jackknife footprints
+            jack_labels, randoms = self.get_jackknife_randoms(self.N_jack, catalog_data)
 
         correlation_data = dict()
         for sample_name, sample_conditions in self.test_samples.items():
@@ -278,6 +408,14 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
                 rr=rr,
                 output_file_name=output_treecorr_filepath)
 
+            print('xi_rad', len(xi_rad))
+            #jackknife errors
+            if self.jackknife:
+                covariance = self.get_jackknife_errors(self.N_jack, catalog_data, sample_conditions,
+                                                       xi_rad, xi, jack_labels, randoms,
+                                                       diagonal_errors=self.use_diagonal_only)
+                xi_sig = np.sqrt(np.diag(covariance)) 
+
             correlation_data[sample_name] = (xi_rad, xi, xi_sig)
 
         self.plot_data_comparison(corr_data=correlation_data,
@@ -348,7 +486,8 @@ def run_treecorr(self, catalog_data, treecorr_rand_cat, rr, output_file_name):
         dr.process(treecorr_rand_cat, cat)
         rd.process(cat, treecorr_rand_cat)
 
-        dd.write(output_file_name, rr, dr, rd)
+        if output_file_name is not None:
+            dd.write(output_file_name, rr, dr, rd)
 
         xi, var_xi = dd.calculateXi(rr, dr, rd)
         xi_rad = np.exp(dd.meanlogr)
@@ -377,6 +516,10 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
         if not catalog_data:
             return TestResult(skipped=True, summary='Missing requested quantities')
 
+        test = self.get_legend_title(self.test_samples)
+
+        if self.truncate_cat_name:
+            catalog_name = re.split('_', catalog_name)[0]
         rand_ra, rand_dec = generate_uniform_random_ra_dec_footprint(
             catalog_data['ra'].size*self.random_mult,
             get_healpixel_footprint(catalog_data['ra'], catalog_data['dec'], self.random_nside),
@@ -572,19 +715,19 @@ def plot_data_comparison(self, corr_data, catalog_name, output_dir):
             ax.loglog(sample_corr[0],
                       p_law,
                       c=color,
-                      label=sample_label)
+                      label=' '.join([self.survey_label, sample_label]))
+                            
             ax.fill_between(sample_corr[0],
                             p_law - p_law_err,
                             p_law + p_law_err,
                             lw=0, color=color, alpha=0.2)
-            ax.errorbar(sample_corr[0], sample_corr[1], sample_corr[2], marker='o', ls='', c=color)
+            ax.errorbar(sample_corr[0], sample_corr[1], sample_corr[2], marker='o', ls='', c=color,
+                        label=' '.join([catalog_name, sample_label]))
 
-        ax.legend(loc='best')
-        ax.set_xlabel(self.fig_xlabel)
-        ax.set_ylim(*self.fig_ylim)
-        ax.set_ylabel(self.fig_ylabel)
-        ax.set_title('{} vs. {}'.format(catalog_name, self.data_label), fontsize='medium')
+            ax.fill_between([self.r_validation_min, self.r_validation_max], [0, 0], [10**4, 10**4],
+                            alpha=0.15, color='grey') #validation region
 
+        self.decorate_plot(ax, catalog_name)
         fig.tight_layout()
         fig.savefig(os.path.join(output_dir, '{:s}.png'.format(self.test_name)), bbox_inches='tight')
         plt.close(fig)
@@ -597,8 +740,8 @@ def score_and_test(self, corr_data):
         chi_per_nu = 0
         total_sample = 0
         rbins = list(corr_data.values()).pop()[0]
-        r_idx_min = np.searchsorted(rbins, 1.)
-        r_idx_max = np.searchsorted(rbins, 10., side='right')
+        r_idx_min = np.searchsorted(rbins, self.r_validation_min)
+        r_idx_max = np.searchsorted(rbins, self.r_validation_max, side='right')
         for sample_name in self.test_samples:
             sample_corr = corr_data[sample_name]
             sample_data = self.test_data[sample_name]
diff --git a/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml b/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
new file mode 100644
index 00000000..4d73300d
--- /dev/null
+++ b/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
@@ -0,0 +1,79 @@
+subclass_name: CorrelationsTwoPoint.CorrelationsAngularTwoPoint
+
+# Catalog columns to attempt to load. The simplified names (e.g. ra, dec) are
+# the names that we be used to cut on in test_samples. Make sure these match.
+requested_columns:
+  ra:
+    - ra
+    - ra_true
+  dec:
+    - dec
+    - dec_true
+  mag:
+    - mag_r_sdss
+    - mag_r_des
+    - mag_r_lsst
+    - mag_true_r_sdss
+    - mag_true_r_des
+    - mag_true_r_lsst
+
+# Definition of samples and cuts to apply.  The names of these columns must
+# match the simple column name definitions above.
+test_samples:
+  r_17_18:
+    mag: {min: 17, max: 18}
+  r_18_19:
+    mag: {min: 18, max: 19}
+  r_19_20:
+    mag: {min: 19, max: 20}
+  r_20_21:
+    mag: {min: 20, max: 21}
+#  r_17_21:
+#    mag: {min: 17, max: 21}
+
+# Output file naming format for output of the resultant correlation values.
+output_filename_template: 'w_theta_r_{}.dat'
+
+# Name of file and columns to load and compare against the test samples.
+data_filename: 'tpcf/Wang_2013_MNRAS_stt450_Table2.txt'
+data_label: 'Y.Wang+2013'
+# Specify the columns to load from the data for comparison. The names here
+# should match the sample names from test_samples.
+test_data:
+  r_17_18: {data_col: 3, data_err_col: 4}
+  r_18_19: {data_col: 5, data_err_col: 6}
+  r_19_20: {data_col: 7, data_err_col: 8}
+  r_20_21: {data_col: 9, data_err_col: 10}
+#  r_17_21: {data_col: 1, data_err_col: 2}
+
+#Jackknife Parameters
+jackknife: true
+N_jack: 20
+jackknife_quantities:
+  ra:
+    - ra
+    - ra_true
+  dec:
+    - dec
+    - dec_true
+use_diagonal_only: true
+
+# Plotting configuration.
+fig_xlabel: '$\theta\quad[{\rm deg}]}$'
+fig_ylabel: '$w(\theta)$'
+fig_ylim: [0.0001, 5.0]
+fig_xlim: [0.008, 2]
+test_sample_labels:
+  r_17_18: '$17 < r < 18$'
+  r_18_19: '$18 < r < 19$'
+  r_19_20: '$19 < r < 20$'
+  r_20_21: '$20 < r < 21$'
+  r_17_21: '$17 < r < 21$'
+
+#Treecorr parameters
+min_sep: 0.01
+max_sep: 1.3
+bin_size: 0.5
+
+description: |
+  Compare angular correlation functions of catalog and Wang et al (2013) SDSS r-band observations

From f403ab9ca0d58acbcf3dea2b4e0644edca2d4244 Mon Sep 17 00:00:00 2001
From: Eve Kovacs <kovacs@anl.gov>
Date: Thu, 12 Sep 2019 15:53:34 -0500
Subject: [PATCH 02/10] Fix travis issues

---
 descqa/CorrelationsTwoPoint.py | 171 +++++++++++++++++++--------------
 1 file changed, 97 insertions(+), 74 deletions(-)

diff --git a/descqa/CorrelationsTwoPoint.py b/descqa/CorrelationsTwoPoint.py
index d7c077be..d717a8e0 100644
--- a/descqa/CorrelationsTwoPoint.py
+++ b/descqa/CorrelationsTwoPoint.py
@@ -1,8 +1,8 @@
 from __future__ import print_function, division, unicode_literals, absolute_import
 import os
 from collections import defaultdict
-import numpy as np
 import re
+import numpy as np
 import scipy.special as scsp
 import treecorr
 import healpy as hp
@@ -12,8 +12,8 @@
 from .base import BaseValidationTest, TestResult
 from .plotting import plt
 from .utils import (generate_uniform_random_ra_dec_footprint,
-                   get_healpixel_footprint,
-                   generate_uniform_random_dist)
+                    get_healpixel_footprint,
+                    generate_uniform_random_dist)
 
 __all__ = ['CorrelationsAngularTwoPoint', 'CorrelationsProjectedTwoPoint',
            'DEEP2StellarMassTwoPoint']
@@ -217,14 +217,14 @@ def plot_data_comparison(self, corr_data, catalog_name, output_dir):
                 sample_label = self.test_sample_labels.get(sample_name)
 
                 ax.loglog(self.validation_data[:, 0],
-                        self.validation_data[:, sample_data['data_col']],
-                        c=color,
-                        label=' '.join([self.survey_label, sample_label]))
+                          self.validation_data[:, sample_data['data_col']],
+                          c=color,
+                          label=' '.join([self.survey_label, sample_label]))
                 if 'data_err_col' in sample_data:
                     y1 = (self.validation_data[:, sample_data['data_col']] +
-                        self.validation_data[:, sample_data['data_err_col']])
+                          self.validation_data[:, sample_data['data_err_col']])
                     y2 = (self.validation_data[:, sample_data['data_col']] -
-                        self.validation_data[:, sample_data['data_err_col']])
+                          self.validation_data[:, sample_data['data_err_col']])
                     if self.fig_xlim is not None:
                         y2[y2 <= 0] = self.fig_ylim[0]*0.9
                     ax.fill_between(self.validation_data[:, 0], y1, y2, lw=0, color=color, alpha=0.25)
@@ -240,21 +240,25 @@ def plot_data_comparison(self, corr_data, catalog_name, output_dir):
 
 
     def get_legend_title(self, test_samples, exclude='mstellar'):
+        """
+        """
         legend_title = ''
-        filter_ids  = list(set([k for v in test_samples.values() for k in v.keys() if exclude not in k]))
+        filter_ids = list(set([k for v in test_samples.values() for k in v.keys() if exclude not in k]))
         for filter_id in filter_ids:
             legend_title = self.get_legend_subtitle(test_samples, filter_id=filter_id, legend_title=legend_title)
-            
+
         return legend_title
 
 
     @staticmethod
     def get_legend_subtitle(test_samples, filter_id='z', legend_title=''):
+        """
+        """
         legend_title = legend_title  if len(legend_title) == 0 else '{}; '.format(legend_title)
         min_values = [test_samples[k][filter_id].get('min', None) for k in test_samples if test_samples[k].get(filter_id, None) is not None]
         max_values = [test_samples[k][filter_id].get('max', None) for k in test_samples if test_samples[k].get(filter_id, None) is not None]
         min_title = ''
-        if len(min_values) > 0 and any([k != None for k in min_values]):
+        if len(min_values) > 0 and any([k is not None for k in min_values]):
             min_title = '{} < {}'.format(min([k for k in min_values if k is not None]), filter_id)
         max_title = ''
         if len(max_values) > 0 and any([k != None for k in max_values]):
@@ -265,6 +269,9 @@ def get_legend_subtitle(test_samples, filter_id='z', legend_title=''):
 
 
     def decorate_plot(self, ax, catalog_name):
+        """
+        Decorates plot with axes labels, title, etc.
+        """
         title = '{} vs. {}'.format(catalog_name, self.data_label)
         lgnd_title = self.get_legend_title(self.test_samples) if self.title_in_legend else None
         ax.legend(loc='lower left', fontsize=self.legend_size, title=lgnd_title)
@@ -311,7 +318,6 @@ def get_jackknife_randoms(self, N_jack, catalog_data, ra='ra', dec='dec'):
         jack_labels: array of regions in catalog data
         randoms: dict of randoms labeled by region
         """
-        catalogs = {}
         #cluster
         nn = np.stack((catalog_data[ra], catalog_data[dec]), axis=1)
         _, jack_labels, _ = k_means(n_clusters=N_jack, random_state=0, X=nn)
@@ -327,19 +333,34 @@ def get_jackknife_randoms(self, N_jack, catalog_data, ra='ra', dec='dec'):
 
     def get_jackknife_errors(self, N_jack, catalog_data, sample_conditions, r, xi, jack_labels, randoms,
                              diagonal_errors=True):
-        
+        """
+        Computes jacknife errors 
+        Parameters
+        ----------
+        N_jack : number of regions
+        catalog_data : input catalog
+        sample_conditions : sample selections
+        r : r data for full region
+        xi : correlation data for full region
+        jack_labels: array of regions in catalog data
+        randoms: dict of randoms labeled by region
+         Returns
+        --------
+        covariance : covariance matrix
+        """
         #run treecorr for jackknife regions
         Nrbins = len(r)
         Njack_array = np.zeros((N_jack, Nrbins), dtype=np.float)
         print(sample_conditions)
         for nj in range(N_jack):
-            catalog_data_jk = dict(zip(catalog_data.keys(), [v[(jack_labels != nj)] for v in catalog_data.values()]))
+            catalog_data_jk = dict(zip(catalog_data.keys(), 
+                                       [v[(jack_labels != nj)] for v in catalog_data.values()]))
             tmp_catalog_data = self.create_test_sample(catalog_data_jk, sample_conditions) #apply sample cut
             # run treecorr
             xi_rad, Njack_array[nj], _ = self.run_treecorr(catalog_data=tmp_catalog_data,
-                                                   treecorr_rand_cat=randoms[str(nj)]['ran'], 
-                                                   rr=randoms[str(nj)]['rr'], 
-                                                   output_file_name=None)
+                                                           treecorr_rand_cat=randoms[str(nj)]['ran'], 
+                                                           rr=randoms[str(nj)]['rr'], 
+                                                           output_file_name=None)
             print(nj, Njack_array[nj])
 
         covariance = np.zeros((Nrbins, Nrbins))
@@ -356,7 +377,10 @@ def get_jackknife_errors(self, N_jack, catalog_data, sample_conditions, r, xi, j
         return covariance
 
     def check_footprint(self, catalog_data):
-        pix_footprint = get_healpixel_footprint(catalog_data['ra'], catalog_data['dec'], self.random_nside)
+        """
+        """
+        pix_footprint = get_healpixel_footprint(catalog_data['ra'], 
+                                                catalog_data['dec'], self.random_nside)
         area_footprint = 4.*np.pi*(180./np.pi)**2*len(pix_footprint)/hp.nside2npix(self.random_nside)
 
         return area_footprint
@@ -372,59 +396,6 @@ def __init__(self, **kwargs):
         self.treecorr_config['sep_units'] = 'deg'
 
 
-    def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
-
-        catalog_data = self.load_catalog_data(catalog_instance=catalog_instance,
-                                              requested_columns=self.requested_columns,
-                                              test_samples=self.test_samples)
-        if not catalog_data:
-            return TestResult(skipped=True, summary='Missing requested quantities')
-
-        if self.truncate_cat_name:
-            catalog_name = re.split('_', catalog_name)[0]
-
-        rand_cat, rr = self.generate_processed_randoms(catalog_data) #assumes ra and dec exist
-
-        if self.jackknife:                     #evaluate randoms for jackknife footprints
-            jack_labels, randoms = self.get_jackknife_randoms(self.N_jack, catalog_data)
-
-        correlation_data = dict()
-        for sample_name, sample_conditions in self.test_samples.items():
-            tmp_catalog_data = self.create_test_sample(
-                catalog_data, sample_conditions)
-
-            with open(os.path.join(output_dir, 'galaxy_count.dat'), 'a') as f:
-                f.write('{} {}\n'.format(sample_name, len(tmp_catalog_data['ra'])))
-
-            if not len(tmp_catalog_data['ra']):
-                continue
-
-            output_treecorr_filepath = os.path.join(
-                output_dir, self.output_filename_template.format(sample_name))
-
-            xi_rad, xi, xi_sig = self.run_treecorr(
-                catalog_data=tmp_catalog_data,
-                treecorr_rand_cat=rand_cat,
-                rr=rr,
-                output_file_name=output_treecorr_filepath)
-
-            print('xi_rad', len(xi_rad))
-            #jackknife errors
-            if self.jackknife:
-                covariance = self.get_jackknife_errors(self.N_jack, catalog_data, sample_conditions,
-                                                       xi_rad, xi, jack_labels, randoms,
-                                                       diagonal_errors=self.use_diagonal_only)
-                xi_sig = np.sqrt(np.diag(covariance)) 
-
-            correlation_data[sample_name] = (xi_rad, xi, xi_sig)
-
-        self.plot_data_comparison(corr_data=correlation_data,
-                                  catalog_name=catalog_name,
-                                  output_dir=output_dir)
-
-        return self.score_and_test(correlation_data)
-
-
     def generate_processed_randoms(self, catalog_data):
         """ Create and process random data for the 2pt correlation function.
 
@@ -496,6 +467,60 @@ def run_treecorr(self, catalog_data, treecorr_rand_cat, rr, output_file_name):
         return xi_rad, xi, xi_sig
 
 
+    def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
+
+        catalog_data = self.load_catalog_data(catalog_instance=catalog_instance,
+                                              requested_columns=self.requested_columns,
+                                              test_samples=self.test_samples)
+        if not catalog_data:
+            return TestResult(skipped=True, summary='Missing requested quantities')
+
+        if self.truncate_cat_name:
+            catalog_name = re.split('_', catalog_name)[0]
+
+        rand_cat, rr = self.generate_processed_randoms(catalog_data) #assumes ra and dec exist
+
+        if self.jackknife:                     #evaluate randoms for jackknife footprints
+            jack_labels, randoms = self.get_jackknife_randoms(self.N_jack, catalog_data)
+
+        correlation_data = dict()
+        for sample_name, sample_conditions in self.test_samples.items():
+            tmp_catalog_data = self.create_test_sample(
+                catalog_data, sample_conditions)
+
+            with open(os.path.join(output_dir, 'galaxy_count.dat'), 'a') as f:
+                f.write('{} {}\n'.format(sample_name, len(tmp_catalog_data['ra'])))
+
+            if not len(tmp_catalog_data['ra']):
+                continue
+
+            output_treecorr_filepath = os.path.join(
+                output_dir, self.output_filename_template.format(sample_name))
+
+            xi_rad, xi, xi_sig = self.run_treecorr(
+                catalog_data=tmp_catalog_data,
+                treecorr_rand_cat=rand_cat,
+                rr=rr,
+                output_file_name=output_treecorr_filepath)
+
+            print('xi_rad', len(xi_rad))
+            #jackknife errors
+            if self.jackknife:
+                covariance = self.get_jackknife_errors(self.N_jack, catalog_data, sample_conditions,
+                                                       xi_rad, xi, jack_labels, randoms,
+                                                       diagonal_errors=self.use_diagonal_only)
+                xi_sig = np.sqrt(np.diag(covariance))
+
+            correlation_data[sample_name] = (xi_rad, xi, xi_sig)
+
+        self.plot_data_comparison(corr_data=correlation_data,
+                                  catalog_name=catalog_name,
+                                  output_dir=output_dir)
+
+        return self.score_and_test(correlation_data)
+
+
+
 class CorrelationsProjectedTwoPoint(CorrelationUtilities):
     """
     Validation test for an radial 2pt correlation function.
@@ -516,8 +541,6 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
         if not catalog_data:
             return TestResult(skipped=True, summary='Missing requested quantities')
 
-        test = self.get_legend_title(self.test_samples)
-
         if self.truncate_cat_name:
             catalog_name = re.split('_', catalog_name)[0]
         rand_ra, rand_dec = generate_uniform_random_ra_dec_footprint(
@@ -716,7 +739,7 @@ def plot_data_comparison(self, corr_data, catalog_name, output_dir):
                       p_law,
                       c=color,
                       label=' '.join([self.survey_label, sample_label]))
-                            
+
             ax.fill_between(sample_corr[0],
                             p_law - p_law_err,
                             p_law + p_law_err,

From 2e1dcc127cf7e65cf27d529f56d76c61c127a614 Mon Sep 17 00:00:00 2001
From: Eve Kovacs <kovacs@anl.gov>
Date: Thu, 12 Sep 2019 18:03:14 -0500
Subject: [PATCH 03/10] Fix travis issues

---
 descqa/CorrelationsTwoPoint.py | 25 ++++++++++++++++---------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/descqa/CorrelationsTwoPoint.py b/descqa/CorrelationsTwoPoint.py
index d717a8e0..e8ee0ffc 100644
--- a/descqa/CorrelationsTwoPoint.py
+++ b/descqa/CorrelationsTwoPoint.py
@@ -261,7 +261,7 @@ def get_legend_subtitle(test_samples, filter_id='z', legend_title=''):
         if len(min_values) > 0 and any([k is not None for k in min_values]):
             min_title = '{} < {}'.format(min([k for k in min_values if k is not None]), filter_id)
         max_title = ''
-        if len(max_values) > 0 and any([k != None for k in max_values]):
+        if len(max_values) > 0 and any([k is not None for k in max_values]):
             max_values = [k for k in max_values if k is not None]
             max_title = '${} < {}$'.format(filter_id, max(max_values)) if len(min_title) == 0 else '${} < {}$'.format(min_title, max(max_values))
 
@@ -305,13 +305,15 @@ def score_and_test(corr_data): # pylint: disable=unused-argument
         return TestResult(inspect_only=True)
 
 
-    def get_jackknife_randoms(self, N_jack, catalog_data, ra='ra', dec='dec'):
+    @staticmethod
+    def get_jackknife_randoms(N_jack, catalog_data, generate_randoms, ra='ra', dec='dec'):
         """
         Computes the jackknife regions and random catalogs for each region 
         Parameters
         ----------
         N_jack : number of regions
         catalog_data : input catalog
+        generate_randoms: function to generate randoms (eg self.generate_processed_randoms)
 
         Returns
         -------
@@ -325,14 +327,15 @@ def get_jackknife_randoms(self, N_jack, catalog_data, ra='ra', dec='dec'):
         randoms = {}
         for nj in range(N_jack):
             catalog_data_jk = dict(zip(catalog_data.keys(), [v[(jack_labels != nj)] for v in catalog_data.values()]))
-            rand_cat, rr = self.generate_processed_randoms(catalog_data_jk) #get randoms for this footprint
+            rand_cat, rr = generate_randoms(catalog_data_jk) #get randoms for this footprint
             #print('nj = {}, area = {:.2f}'.format(nj, self.check_footprint(catalog_data_jk))) #check footprint
             randoms[str(nj)] = {'ran': rand_cat, 'rr':rr}
 
         return jack_labels, randoms
 
-    def get_jackknife_errors(self, N_jack, catalog_data, sample_conditions, r, xi, jack_labels, randoms,
-                             diagonal_errors=True):
+    @staticmethod
+    def get_jackknife_errors(N_jack, catalog_data, sample_conditions, r, xi, jack_labels, randoms,
+                             run_treecorr, diagonal_errors=True):
         """
         Computes jacknife errors 
         Parameters
@@ -344,6 +347,8 @@ def get_jackknife_errors(self, N_jack, catalog_data, sample_conditions, r, xi, j
         xi : correlation data for full region
         jack_labels: array of regions in catalog data
         randoms: dict of randoms labeled by region
+        run_treecorr: method to run treecorr 
+
          Returns
         --------
         covariance : covariance matrix
@@ -357,9 +362,9 @@ def get_jackknife_errors(self, N_jack, catalog_data, sample_conditions, r, xi, j
                                        [v[(jack_labels != nj)] for v in catalog_data.values()]))
             tmp_catalog_data = self.create_test_sample(catalog_data_jk, sample_conditions) #apply sample cut
             # run treecorr
-            xi_rad, Njack_array[nj], _ = self.run_treecorr(catalog_data=tmp_catalog_data,
+            _, Njack_array[nj], _ = run_treecorr(catalog_data=tmp_catalog_data,
                                                            treecorr_rand_cat=randoms[str(nj)]['ran'], 
-                                                           rr=randoms[str(nj)]['rr'], 
+                                                           rr=randoms[str(nj)]['rr'],
                                                            output_file_name=None)
             print(nj, Njack_array[nj])
 
@@ -481,7 +486,8 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
         rand_cat, rr = self.generate_processed_randoms(catalog_data) #assumes ra and dec exist
 
         if self.jackknife:                     #evaluate randoms for jackknife footprints
-            jack_labels, randoms = self.get_jackknife_randoms(self.N_jack, catalog_data)
+            jack_labels, randoms = self.get_jackknife_randoms(self.N_jack, catalog_data,
+                                                              self.generate_processed_randoms)
 
         correlation_data = dict()
         for sample_name, sample_conditions in self.test_samples.items():
@@ -503,11 +509,11 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
                 rr=rr,
                 output_file_name=output_treecorr_filepath)
 
-            print('xi_rad', len(xi_rad))
             #jackknife errors
             if self.jackknife:
                 covariance = self.get_jackknife_errors(self.N_jack, catalog_data, sample_conditions,
                                                        xi_rad, xi, jack_labels, randoms,
+                                                       self.run_treecorr,
                                                        diagonal_errors=self.use_diagonal_only)
                 xi_sig = np.sqrt(np.diag(covariance))
 
@@ -543,6 +549,7 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
 
         if self.truncate_cat_name:
             catalog_name = re.split('_', catalog_name)[0]
+
         rand_ra, rand_dec = generate_uniform_random_ra_dec_footprint(
             catalog_data['ra'].size*self.random_mult,
             get_healpixel_footprint(catalog_data['ra'], catalog_data['dec'], self.random_nside),

From 0cd7ef46476c5d538fa93edbdf96a87b690d4ebd Mon Sep 17 00:00:00 2001
From: evevkovacs <kovacs@anl.gov>
Date: Mon, 16 Sep 2019 11:01:40 -0700
Subject: [PATCH 04/10] Removed some extra print statements

---
 descqa/CorrelationsTwoPoint.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/descqa/CorrelationsTwoPoint.py b/descqa/CorrelationsTwoPoint.py
index e8ee0ffc..ade58bc5 100644
--- a/descqa/CorrelationsTwoPoint.py
+++ b/descqa/CorrelationsTwoPoint.py
@@ -333,8 +333,7 @@ def get_jackknife_randoms(N_jack, catalog_data, generate_randoms, ra='ra', dec='
 
         return jack_labels, randoms
 
-    @staticmethod
-    def get_jackknife_errors(N_jack, catalog_data, sample_conditions, r, xi, jack_labels, randoms,
+    def get_jackknife_errors(self, N_jack, catalog_data, sample_conditions, r, xi, jack_labels, randoms,
                              run_treecorr, diagonal_errors=True):
         """
         Computes jacknife errors 
@@ -366,7 +365,6 @@ def get_jackknife_errors(N_jack, catalog_data, sample_conditions, r, xi, jack_la
                                                            treecorr_rand_cat=randoms[str(nj)]['ran'], 
                                                            rr=randoms[str(nj)]['rr'],
                                                            output_file_name=None)
-            print(nj, Njack_array[nj])
 
         covariance = np.zeros((Nrbins, Nrbins))
         for i in range(Nrbins):
@@ -377,7 +375,6 @@ def get_jackknife_errors(N_jack, catalog_data, sample_conditions, r, xi, jack_la
                 for j in range(Nrbins):
                     for njack in Njack_array:
                         covariance[i][j] += (N_jack - 1.)/N_jack * (xi[i] - njack[i]) * (xi[j] - njack[j])
-            print('bin/cov', i, covariance[i][i])
 
         return covariance
 

From 71ea8e46ab9c1b530aa949a0b9e454e3cfaf97ad Mon Sep 17 00:00:00 2001
From: evevkovacs <kovacs@anl.gov>
Date: Thu, 3 Oct 2019 09:03:01 -0700
Subject: [PATCH 05/10] Changes for adding object catalog to test

---
 descqa/CorrelationsTwoPoint.py               | 11 +++++++++--
 descqa/configs/tpcf_Wang2013_rSDSS.yaml      |  1 +
 descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml |  1 +
 3 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/descqa/CorrelationsTwoPoint.py b/descqa/CorrelationsTwoPoint.py
index ade58bc5..92aa2f1f 100644
--- a/descqa/CorrelationsTwoPoint.py
+++ b/descqa/CorrelationsTwoPoint.py
@@ -139,6 +139,10 @@ def load_catalog_data(catalog_instance, requested_columns, test_samples):
                     col_value_maxs[col_key].append(condition['max'])
 
         filters = [(np.isfinite, c) for c in colnames.values()]
+
+        if catalog_instance.has_quantity('extendedness'):
+            filters.append('extendedness == 1')
+
         for col_key, col_name in colnames.items():
             if col_key in col_value_mins and col_value_mins[col_key]:
                 filters.append('{} >= {}'.format(col_name, min(col_value_mins[col_key])))
@@ -481,7 +485,9 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
             catalog_name = re.split('_', catalog_name)[0]
 
         rand_cat, rr = self.generate_processed_randoms(catalog_data) #assumes ra and dec exist
-
+        with open(os.path.join(output_dir, 'galaxy_count.dat'), 'a') as f:
+            f.write('Random (= catalog) Area = {:.1f} sq. deg.\n'.format(self.check_footprint(catalog_data)))
+            
         if self.jackknife:                     #evaluate randoms for jackknife footprints
             jack_labels, randoms = self.get_jackknife_randoms(self.N_jack, catalog_data,
                                                               self.generate_processed_randoms)
@@ -492,7 +498,8 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
                 catalog_data, sample_conditions)
 
             with open(os.path.join(output_dir, 'galaxy_count.dat'), 'a') as f:
-                f.write('{} {}\n'.format(sample_name, len(tmp_catalog_data['ra'])))
+                f.write('{} {} {:.1f} sq. deg.\n'.format(sample_name, len(tmp_catalog_data['ra']),
+                                                          self.check_footprint(tmp_catalog_data))) #print area
 
             if not len(tmp_catalog_data['ra']):
                 continue
diff --git a/descqa/configs/tpcf_Wang2013_rSDSS.yaml b/descqa/configs/tpcf_Wang2013_rSDSS.yaml
index 6cbdbfe7..edefdad4 100644
--- a/descqa/configs/tpcf_Wang2013_rSDSS.yaml
+++ b/descqa/configs/tpcf_Wang2013_rSDSS.yaml
@@ -16,6 +16,7 @@ requested_columns:
     - mag_true_r_sdss
     - mag_true_r_des
     - mag_true_r_lsst
+    - mag_r_cModel
 
 # Definition of samples and cuts to apply.  The names of these columns must
 # match the simple column name definitions above.
diff --git a/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml b/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
index 4d73300d..95a5933b 100644
--- a/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
+++ b/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
@@ -16,6 +16,7 @@ requested_columns:
     - mag_true_r_sdss
     - mag_true_r_des
     - mag_true_r_lsst
+    - mag_r_CModel
 
 # Definition of samples and cuts to apply.  The names of these columns must
 # match the simple column name definitions above.

From 3ef91c8085baae0c23bf4adcd9bc041367a0dd26 Mon Sep 17 00:00:00 2001
From: evevkovacs <kovacs@anl.gov>
Date: Thu, 3 Oct 2019 11:42:39 -0700
Subject: [PATCH 06/10] Change to using cModel mag in test for object catalog

---
 descqa/apparent_mag_func_test.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/descqa/apparent_mag_func_test.py b/descqa/apparent_mag_func_test.py
index 101d1fb8..4cc802a8 100644
--- a/descqa/apparent_mag_func_test.py
+++ b/descqa/apparent_mag_func_test.py
@@ -61,7 +61,7 @@ def __init__(self, band='r', band_lim=(24.0, 27.5), fractional_tol=0.4, observat
                                'mag_true_{}_des',
                                'mag_{}_hsc',
                                'mag_true_{}_hsc',
-                               'mag_{}')
+                               'mag_{}_cModel')
         self.possible_mag_fields = [f.format(band) for f in possible_mag_fields]
 
         # attach some attributes to the test

From 7f1b545730f1b1916432472915b9f5fcdc1825ca Mon Sep 17 00:00:00 2001
From: evevkovacs <kovacs@anl.gov>
Date: Mon, 7 Oct 2019 12:18:16 -0700
Subject: [PATCH 07/10] improve error message

---
 descqa/CorrelationsTwoPoint.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/descqa/CorrelationsTwoPoint.py b/descqa/CorrelationsTwoPoint.py
index 92aa2f1f..e64c4971 100644
--- a/descqa/CorrelationsTwoPoint.py
+++ b/descqa/CorrelationsTwoPoint.py
@@ -479,7 +479,9 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
                                               requested_columns=self.requested_columns,
                                               test_samples=self.test_samples)
         if not catalog_data:
-            return TestResult(skipped=True, summary='Missing requested quantities')
+            cols = [i for c in self.requested_columns.values() for i in c]
+            return TestResult(skipped=True,
+                              summary='Missing requested quantities {}'.format(', '.join(cols)))
 
         if self.truncate_cat_name:
             catalog_name = re.split('_', catalog_name)[0]

From 1a6f4271a64c0a2d2ac5ddb64d04b14e4beff218 Mon Sep 17 00:00:00 2001
From: evevkovacs <kovacs@anl.gov>
Date: Wed, 9 Oct 2019 07:59:49 -0700
Subject: [PATCH 08/10] Fix typo in cModel

---
 descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml b/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
index 95a5933b..c4742fd4 100644
--- a/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
+++ b/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
@@ -16,7 +16,7 @@ requested_columns:
     - mag_true_r_sdss
     - mag_true_r_des
     - mag_true_r_lsst
-    - mag_r_CModel
+    - mag_r_cModel
 
 # Definition of samples and cuts to apply.  The names of these columns must
 # match the simple column name definitions above.

From 707855ea2164283274675d87f57059e352994025 Mon Sep 17 00:00:00 2001
From: evevkovacs <kovacs@anl.gov>
Date: Wed, 9 Oct 2019 15:54:28 -0700
Subject: [PATCH 09/10] Minor update to order of possible_mag_fields so object
 add-on runs correctly

---
 descqa/apparent_mag_func_test.py             | 5 +++--
 descqa/configs/tpcf_Wang2013_rSDSS.yaml      | 2 +-
 descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml | 2 +-
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/descqa/apparent_mag_func_test.py b/descqa/apparent_mag_func_test.py
index 4cc802a8..195ae36e 100644
--- a/descqa/apparent_mag_func_test.py
+++ b/descqa/apparent_mag_func_test.py
@@ -53,7 +53,8 @@ def __init__(self, band='r', band_lim=(24.0, 27.5), fractional_tol=0.4, observat
         self.min_mag = kwargs.get('min_mag', 19.)
 
         # catalog quantities needed
-        possible_mag_fields = ('mag_{}_lsst',
+        possible_mag_fields = ('mag_{}_cModel',
+                               'mag_{}_lsst',
                                'mag_true_{}_lsst',
                                'mag_{}_sdss',
                                'mag_true_{}_sdss',
@@ -61,7 +62,7 @@ def __init__(self, band='r', band_lim=(24.0, 27.5), fractional_tol=0.4, observat
                                'mag_true_{}_des',
                                'mag_{}_hsc',
                                'mag_true_{}_hsc',
-                               'mag_{}_cModel')
+                              )
         self.possible_mag_fields = [f.format(band) for f in possible_mag_fields]
 
         # attach some attributes to the test
diff --git a/descqa/configs/tpcf_Wang2013_rSDSS.yaml b/descqa/configs/tpcf_Wang2013_rSDSS.yaml
index edefdad4..a8a44f99 100644
--- a/descqa/configs/tpcf_Wang2013_rSDSS.yaml
+++ b/descqa/configs/tpcf_Wang2013_rSDSS.yaml
@@ -12,11 +12,11 @@ requested_columns:
   mag:
     - mag_r_sdss
     - mag_r_des
+    - mag_r_cModel
     - mag_r_lsst
     - mag_true_r_sdss
     - mag_true_r_des
     - mag_true_r_lsst
-    - mag_r_cModel
 
 # Definition of samples and cuts to apply.  The names of these columns must
 # match the simple column name definitions above.
diff --git a/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml b/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
index c4742fd4..0c57b103 100644
--- a/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
+++ b/descqa/configs/tpcf_Wang2013_rSDSS_jack.yaml
@@ -12,11 +12,11 @@ requested_columns:
   mag:
     - mag_r_sdss
     - mag_r_des
+    - mag_r_cModel
     - mag_r_lsst
     - mag_true_r_sdss
     - mag_true_r_des
     - mag_true_r_lsst
-    - mag_r_cModel
 
 # Definition of samples and cuts to apply.  The names of these columns must
 # match the simple column name definitions above.

From 340e556f52bdfefdf9449eeed92f4474374d4981 Mon Sep 17 00:00:00 2001
From: evevkovacs <kovacs@anl.gov>
Date: Thu, 17 Oct 2019 15:33:37 -0700
Subject: [PATCH 10/10] addressed review comments by prlarsen

---
 descqa/CorrelationsTwoPoint.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/descqa/CorrelationsTwoPoint.py b/descqa/CorrelationsTwoPoint.py
index e64c4971..ac8b7778 100644
--- a/descqa/CorrelationsTwoPoint.py
+++ b/descqa/CorrelationsTwoPoint.py
@@ -332,7 +332,6 @@ def get_jackknife_randoms(N_jack, catalog_data, generate_randoms, ra='ra', dec='
         for nj in range(N_jack):
             catalog_data_jk = dict(zip(catalog_data.keys(), [v[(jack_labels != nj)] for v in catalog_data.values()]))
             rand_cat, rr = generate_randoms(catalog_data_jk) #get randoms for this footprint
-            #print('nj = {}, area = {:.2f}'.format(nj, self.check_footprint(catalog_data_jk))) #check footprint
             randoms[str(nj)] = {'ran': rand_cat, 'rr':rr}
 
         return jack_labels, randoms
@@ -488,8 +487,10 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
 
         rand_cat, rr = self.generate_processed_randoms(catalog_data) #assumes ra and dec exist
         with open(os.path.join(output_dir, 'galaxy_count.dat'), 'a') as f:
-            f.write('Random (= catalog) Area = {:.1f} sq. deg.\n'.format(self.check_footprint(catalog_data)))
-            
+            f.write('Total (= catalog) Area = {:.1f} sq. deg.\n'.format(self.check_footprint(catalog_data)))
+            f.write('NOTE: 1) assuming catalog is of equal depth over the full area\n')
+            f.write('      2) assuming sample contains enough galaxies to measure area\n')
+
         if self.jackknife:                     #evaluate randoms for jackknife footprints
             jack_labels, randoms = self.get_jackknife_randoms(self.N_jack, catalog_data,
                                                               self.generate_processed_randoms)
@@ -499,10 +500,6 @@ def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
             tmp_catalog_data = self.create_test_sample(
                 catalog_data, sample_conditions)
 
-            with open(os.path.join(output_dir, 'galaxy_count.dat'), 'a') as f:
-                f.write('{} {} {:.1f} sq. deg.\n'.format(sample_name, len(tmp_catalog_data['ra']),
-                                                          self.check_footprint(tmp_catalog_data))) #print area
-
             if not len(tmp_catalog_data['ra']):
                 continue