From 7037893e4ab9011b881c4169cec5e9e681f02294 Mon Sep 17 00:00:00 2001 From: Mike Jarvis Date: Fri, 10 Feb 2023 03:19:40 -0500 Subject: [PATCH] build docs --- docs/_build/doctrees/changes.doctree | Bin 15158 -> 15586 bytes docs/_build/doctrees/environment.pickle | Bin 995858 -> 998004 bytes .../html/_modules/treecorr/binnedcorr2.html | 6 +++ .../html/_modules/treecorr/ggcorrelation.html | 38 ++++++++++---- .../html/_modules/treecorr/kgcorrelation.html | 20 +++++-- .../html/_modules/treecorr/kkcorrelation.html | 20 +++++-- .../html/_modules/treecorr/ngcorrelation.html | 49 ++++++++++++------ .../html/_modules/treecorr/nkcorrelation.html | 47 +++++++++++------ .../html/_modules/treecorr/nncorrelation.html | 7 ++- docs/_build/html/changes.html | 2 + docs/_build/html/searchindex.js | 2 +- 11 files changed, 136 insertions(+), 55 deletions(-) diff --git a/docs/_build/doctrees/changes.doctree b/docs/_build/doctrees/changes.doctree index 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zMZ2C$;0rgXd@LGfbuDu7=27P+TqB&KHJNitr?;1N9gGa&dM^N`FO$R4&~;1YENPrk|l zFY5v!L>Z-)mPE3Q{X5z6tV_;;3I6Os9?u)ETP3&f3VKg&oSe_XFJU!88O+`KwWe{|D zCi?Z=w>uFJ#@D6C;a?wFoq&ha<&MV!Tn<~GVl43RIx91lUXW%)I`2stPVW^t?Ci@$ Y0s7GKt3ZVkCXaXV#hF_?-X-w=0px4lumAu6 diff --git a/docs/_build/html/_modules/treecorr/binnedcorr2.html b/docs/_build/html/_modules/treecorr/binnedcorr2.html index 01b82577..523d1b90 100644 --- a/docs/_build/html/_modules/treecorr/binnedcorr2.html +++ b/docs/_build/html/_modules/treecorr/binnedcorr2.html @@ -644,6 +644,12 @@

Source code for treecorr.binnedcorr2

     @property
     def _d2(self): return self._ro._d2
 
+    @property
+    def cov(self):
+        if self._cov is None:
+            self._cov = self.estimate_cov(self.var_method)
+        return self._cov
+
     def __getstate__(self):
         d = self.__dict__.copy()
         d.pop('_corr',None)
diff --git a/docs/_build/html/_modules/treecorr/ggcorrelation.html b/docs/_build/html/_modules/treecorr/ggcorrelation.html
index e0bb114f..3570e803 100644
--- a/docs/_build/html/_modules/treecorr/ggcorrelation.html
+++ b/docs/_build/html/_modules/treecorr/ggcorrelation.html
@@ -191,12 +191,14 @@ 
Source code for treecorr.ggcorrelation
         self.xim = np.zeros_like(self.rnom, dtype=float)
         self.xip_im = np.zeros_like(self.rnom, dtype=float)
         self.xim_im = np.zeros_like(self.rnom, dtype=float)
-        self.varxip = np.zeros_like(self.rnom, dtype=float)
-        self.varxim = np.zeros_like(self.rnom, dtype=float)
         self.meanr = np.zeros_like(self.rnom, dtype=float)
         self.meanlogr = np.zeros_like(self.rnom, dtype=float)
         self.weight = np.zeros_like(self.rnom, dtype=float)
         self.npairs = np.zeros_like(self.rnom, dtype=float)
+        self._varxip = None
+        self._varxim = None
+        self._cov = None
+        self._var_num = 0
         self.logger.debug('Finished building GGCorr')

 
     @property
@@ -439,10 +441,23 @@ 
Source code for treecorr.ggcorrelation
             varg2 (float):  The shear variance per component for the second field.
         """
         self._finalize()
-        self._var_num = 2. * varg1 * varg2
-        self.cov = self.estimate_cov(self.var_method)
-        self.varxip.ravel()[:] = self.cov.diagonal()[:self._nbins]
-        self.varxim.ravel()[:] = self.cov.diagonal()[self._nbins:]

+        self._var_num = 2. * varg1 * varg2
+ + @property + def varxip(self): + if self._varxip is None: + self._varxip = np.zeros_like(self.rnom, dtype=float) + if self._var_num != 0: + self._varxip.ravel()[:] = self.cov.diagonal()[:self._nbins] + return self._varxip + + @property + def varxim(self): + if self._varxim is None: + self._varxim = np.zeros_like(self.rnom, dtype=float) + if self._var_num != 0: + self._varxim.ravel()[:] = self.cov.diagonal()[self._nbins:] + return self._varxim def _clear(self): """Clear the data vectors @@ -455,6 +470,9 @@

Source code for treecorr.ggcorrelation

         self.meanlogr.ravel()[:] = 0
         self.weight.ravel()[:] = 0
         self.npairs.ravel()[:] = 0
+        self._varxip = None
+        self._varxim = None
+        self._cov = None
 
 
[docs]    def __iadd__(self, other):
         """Add a second `GGCorrelation`'s data to this one.
@@ -656,11 +674,11 @@ 
Source code for treecorr.ggcorrelation
         self.xim_im = data['xim_im'].reshape(s)
         # Read old output files without error.
         if 'sigma_xi' in data.dtype.names:  # pragma: no cover
-            self.varxip = data['sigma_xi'].reshape(s)**2
-            self.varxim = data['sigma_xi'].reshape(s)**2
+            self._varxip = data['sigma_xi'].reshape(s)**2
+            self._varxim = data['sigma_xi'].reshape(s)**2
         else:
-            self.varxip = data['sigma_xip'].reshape(s)**2
-            self.varxim = data['sigma_xim'].reshape(s)**2
+            self._varxip = data['sigma_xip'].reshape(s)**2
+            self._varxim = data['sigma_xim'].reshape(s)**2
         self.weight = data['weight'].reshape(s)
         self.npairs = data['npairs'].reshape(s)
         self.coords = params['coords'].strip()
diff --git a/docs/_build/html/_modules/treecorr/kgcorrelation.html b/docs/_build/html/_modules/treecorr/kgcorrelation.html
index 1235be4c..5c81585e 100644
--- a/docs/_build/html/_modules/treecorr/kgcorrelation.html
+++ b/docs/_build/html/_modules/treecorr/kgcorrelation.html
@@ -189,11 +189,13 @@ 
Source code for treecorr.kgcorrelation
         self._ro._d2 = 3  # GData
         self.xi = np.zeros_like(self.rnom, dtype=float)
         self.xi_im = np.zeros_like(self.rnom, dtype=float)
-        self.varxi = np.zeros_like(self.rnom, dtype=float)
         self.meanr = np.zeros_like(self.rnom, dtype=float)
         self.meanlogr = np.zeros_like(self.rnom, dtype=float)
         self.weight = np.zeros_like(self.rnom, dtype=float)
         self.npairs = np.zeros_like(self.rnom, dtype=float)
+        self._varxi = None
+        self._cov = None
+        self._var_num = 0
         self.logger.debug('Finished building KGCorr')

 
     @property
@@ -377,9 +379,15 @@ 
Source code for treecorr.kgcorrelation
             varg (float):   The shear variance per component for the second field.
         """
         self._finalize()
-        self._var_num = vark * varg
-        self.cov = self.estimate_cov(self.var_method)
-        self.varxi.ravel()[:] = self.cov.diagonal()

+        self._var_num = vark * varg

+
+    @property
+    def varxi(self):
+        if self._varxi is None:
+            self._varxi = np.zeros_like(self.rnom, dtype=float)
+            if self._var_num != 0:
+                self._varxi.ravel()[:] = self.cov.diagonal()
+        return self._varxi
 
     def _clear(self):
         """Clear the data vectors
@@ -390,6 +398,8 @@ 
Source code for treecorr.kgcorrelation
         self.meanlogr.ravel()[:] = 0
         self.weight.ravel()[:] = 0
         self.npairs.ravel()[:] = 0
+        self._varxi = None
+        self._cov = None
 
 
[docs]    def __iadd__(self, other):
         """Add a second `KGCorrelation`'s data to this one.
@@ -566,7 +576,7 @@ 
Source code for treecorr.kgcorrelation
             self.meanlogr = data['meanlogr'].reshape(s)
         self.xi = data['kgamT'].reshape(s)
         self.xi_im = data['kgamX'].reshape(s)
-        self.varxi = data['sigma'].reshape(s)**2
+        self._varxi = data['sigma'].reshape(s)**2
         self.weight = data['weight'].reshape(s)
         self.npairs = data['npairs'].reshape(s)
         self.coords = params['coords'].strip()
diff --git a/docs/_build/html/_modules/treecorr/kkcorrelation.html b/docs/_build/html/_modules/treecorr/kkcorrelation.html
index 5d1795dd..f3e4ce5c 100644
--- a/docs/_build/html/_modules/treecorr/kkcorrelation.html
+++ b/docs/_build/html/_modules/treecorr/kkcorrelation.html
@@ -188,11 +188,13 @@ 
Source code for treecorr.kkcorrelation
         self._ro._d1 = 2  # KData
         self._ro._d2 = 2  # KData
         self.xi = np.zeros_like(self.rnom, dtype=float)
-        self.varxi = np.zeros_like(self.rnom, dtype=float)
         self.meanr = np.zeros_like(self.rnom, dtype=float)
         self.meanlogr = np.zeros_like(self.rnom, dtype=float)
         self.weight = np.zeros_like(self.rnom, dtype=float)
         self.npairs = np.zeros_like(self.rnom, dtype=float)
+        self._varxi = None
+        self._cov = None
+        self._var_num = 0
         self.logger.debug('Finished building KKCorr')

 
     @property
@@ -410,9 +412,15 @@ 
Source code for treecorr.kkcorrelation
             vark2 (float):  The kappa variance for the second field.
         """
         self._finalize()
-        self._var_num = vark1 * vark2
-        self.cov = self.estimate_cov(self.var_method)
-        self.varxi.ravel()[:] = self.cov.diagonal()

+        self._var_num = vark1 * vark2

+
+    @property
+    def varxi(self):
+        if self._varxi is None:
+            self._varxi = np.zeros_like(self.rnom, dtype=float)
+            if self._var_num != 0:
+                self._varxi.ravel()[:] = self.cov.diagonal()
+        return self._varxi
 
     def _clear(self):
         """Clear the data vectors
@@ -422,6 +430,8 @@ 
Source code for treecorr.kkcorrelation
         self.meanlogr.ravel()[:] = 0
         self.weight.ravel()[:] = 0
         self.npairs.ravel()[:] = 0
+        self._varxi = None
+        self._cov = None
 
 
[docs]    def __iadd__(self, other):
         """Add a second `KKCorrelation`'s data to this one.
@@ -603,7 +613,7 @@ 
Source code for treecorr.kkcorrelation
             self.meanr = data['meanr'].reshape(s)
             self.meanlogr = data['meanlogr'].reshape(s)
         self.xi = data['xi'].reshape(s)
-        self.varxi = data['sigma_xi'].reshape(s)**2
+        self._varxi = data['sigma_xi'].reshape(s)**2
         self.weight = data['weight'].reshape(s)
         self.npairs = data['npairs'].reshape(s)
         self.coords = params['coords'].strip()
diff --git a/docs/_build/html/_modules/treecorr/ngcorrelation.html b/docs/_build/html/_modules/treecorr/ngcorrelation.html
index 57dfb0d1..60387bcf 100644
--- a/docs/_build/html/_modules/treecorr/ngcorrelation.html
+++ b/docs/_build/html/_modules/treecorr/ngcorrelation.html
@@ -187,15 +187,17 @@ 
Source code for treecorr.ngcorrelation
         self._ro._d2 = 3  # GData
         self.xi = np.zeros_like(self.rnom, dtype=float)
         self.xi_im = np.zeros_like(self.rnom, dtype=float)
-        self.varxi = np.zeros_like(self.rnom, dtype=float)
         self.meanr = np.zeros_like(self.rnom, dtype=float)
         self.meanlogr = np.zeros_like(self.rnom, dtype=float)
         self.weight = np.zeros_like(self.rnom, dtype=float)
         self.npairs = np.zeros_like(self.rnom, dtype=float)
         self.raw_xi = self.xi
         self.raw_xi_im = self.xi_im
-        self.raw_varxi = self.varxi
         self._rg = None
+        self._raw_varxi = None
+        self._varxi = None
+        self._cov = None
+        self._var_num = 0
         self.logger.debug('Finished building NGCorr')

 
     @property
@@ -259,11 +261,9 @@ 
Source code for treecorr.ngcorrelation
         if self.xi is self.raw_xi:
             ret.raw_xi = ret.xi
             ret.raw_xi_im = ret.xi_im
-            ret.raw_varxi = ret.varxi
         else:
             ret.raw_xi = self.raw_xi.copy()
             ret.raw_xi_im = self.raw_xi_im.copy()
-            ret.raw_varxi = self.raw_varxi.copy()
         if self._rg is not None:
             ret._rg = self._rg.copy()
         return ret

@@ -389,26 +389,38 @@ 
Source code for treecorr.ngcorrelation
         """
         self._finalize()
         self._var_num = varg
-        self.cov = self.estimate_cov(self.var_method)
-        self.raw_varxi.ravel()[:] = self.cov.diagonal()
 
         self.xi = self.raw_xi
-        self.xi_im = self.raw_xi_im
-        self.varxi = self.raw_varxi

+        self.xi_im = self.raw_xi_im

+
+    @property
+    def raw_varxi(self):
+        if self._raw_varxi is None:
+            self._raw_varxi = np.zeros_like(self.rnom, dtype=float)
+            if self._var_num != 0:
+                self._raw_varxi.ravel()[:] = self.cov.diagonal()
+        return self._raw_varxi
+
+    @property
+    def varxi(self):
+        if self._varxi is None:
+            self._varxi = self.raw_varxi
+        return self._varxi
 
     def _clear(self):
         """Clear the data vectors
         """
         self.raw_xi.ravel()[:] = 0
         self.raw_xi_im.ravel()[:] = 0
-        self.raw_varxi.ravel()[:] = 0
         self.meanr.ravel()[:] = 0
         self.meanlogr.ravel()[:] = 0
         self.weight.ravel()[:] = 0
         self.npairs.ravel()[:] = 0
         self.xi = self.raw_xi
         self.xi_im = self.raw_xi_im
-        self.varxi = self.raw_varxi
+        self._raw_varxi = None
+        self._varxi = None
+        self._cov = None
 
 
[docs]    def __iadd__(self, other):
         """Add a second `NGCorrelation`'s data to this one.
@@ -448,7 +460,9 @@ 
Source code for treecorr.ngcorrelation
         np.sum([c.npairs for c in others], axis=0, out=self.npairs)
         self.xi = self.raw_xi
         self.xi_im = self.raw_xi_im
-        self.varxi = self.raw_varxi
+        self._raw_varxi = None
+        self._varxi = None
+        self._cov = None
 
 
[docs]    @depr_pos_kwargs
     def process(self, cat1, cat2, *, metric=None, num_threads=None, comm=None, low_mem=False,
@@ -541,14 +555,15 @@ 
Source code for treecorr.ngcorrelation
                     new_cij.weight.ravel()[:] = 0
                     self.results[ij] = new_cij
 
-                self.cov = self.estimate_cov(self.var_method)
-                self.varxi.ravel()[:] = self.cov.diagonal()
+                self._cov = self.estimate_cov(self.var_method)
+                self._varxi = np.zeros_like(self.rnom, dtype=float)
+                self._varxi.ravel()[:] = self.cov.diagonal()
             else:
-                self.varxi = self.raw_varxi + rg.varxi
+                self._varxi = self.raw_varxi + rg.varxi
         else:
             self.xi = self.raw_xi
             self.xi_im = self.raw_xi_im
-            self.varxi = self.raw_varxi
+            self._varxi = self.raw_varxi
 
         return self.xi, self.xi_im, self.varxi

 
@@ -665,7 +680,7 @@ 
Source code for treecorr.ngcorrelation
             self.meanlogr = data['meanlogr'].reshape(s)
         self.xi = data['gamT'].reshape(s)
         self.xi_im = data['gamX'].reshape(s)
-        self.varxi = data['sigma'].reshape(s)**2
+        self._varxi = data['sigma'].reshape(s)**2
         self.weight = data['weight'].reshape(s)
         self.npairs = data['npairs'].reshape(s)
         self.coords = params['coords'].strip()
@@ -674,7 +689,7 @@ 
Source code for treecorr.ngcorrelation
         self._ro.bin_type = params['bin_type'].strip()
         self.raw_xi = self.xi
         self.raw_xi_im = self.xi_im
-        self.raw_varxi = self.varxi
+        self._raw_varxi = self._varxi
         self.npatch1 = params.get('npatch1', 1)
         self.npatch2 = params.get('npatch2', 1)
 
diff --git a/docs/_build/html/_modules/treecorr/nkcorrelation.html b/docs/_build/html/_modules/treecorr/nkcorrelation.html
index 48f31a9a..99636710 100644
--- a/docs/_build/html/_modules/treecorr/nkcorrelation.html
+++ b/docs/_build/html/_modules/treecorr/nkcorrelation.html
@@ -191,14 +191,15 @@ 
Source code for treecorr.nkcorrelation
         self._ro._d1 = 1  # NData
         self._ro._d2 = 2  # KData
         self.xi = np.zeros_like(self.rnom, dtype=float)
-        self.varxi = np.zeros_like(self.rnom, dtype=float)
         self.meanr = np.zeros_like(self.rnom, dtype=float)
         self.meanlogr = np.zeros_like(self.rnom, dtype=float)
         self.weight = np.zeros_like(self.rnom, dtype=float)
         self.npairs = np.zeros_like(self.rnom, dtype=float)
         self.raw_xi = self.xi
-        self.raw_varxi = self.varxi
         self._rk = None
+        self._raw_varxi = None
+        self._cov = None
+        self._var_num = 0
         self.logger.debug('Finished building NKCorr')

 
     @property
@@ -260,10 +261,8 @@ 
Source code for treecorr.nkcorrelation
         ret._corr = None # We'll want to make a new one of these if we need it.
         if self.xi is self.raw_xi:
             ret.raw_xi = ret.xi
-            ret.raw_varxi = ret.varxi
         else:
             ret.raw_xi = self.raw_xi.copy()
-            ret.raw_varxi = self.raw_varxi.copy()
         if self._rk is not None:
             ret._rk = self._rk.copy()
         return ret

@@ -387,21 +386,34 @@ 
Source code for treecorr.nkcorrelation
             vark:    The kappa variance for the second field.
         """
         self._finalize()
-        self._var_num = vark
-        self.cov = self.estimate_cov(self.var_method)
-        self.raw_varxi.ravel()[:] = self.cov.diagonal()

+        self._var_num = vark

+
+    @property
+    def raw_varxi(self):
+        if self._raw_varxi is None:
+            self._raw_varxi = np.zeros_like(self.rnom, dtype=float)
+            if self._var_num != 0:
+                self._raw_varxi.ravel()[:] = self.cov.diagonal()
+        return self._raw_varxi
+
+    @property
+    def varxi(self):
+        if self._varxi is None:
+            self._varxi = self.raw_varxi
+        return self._varxi
 
     def _clear(self):
         """Clear the data vectors
         """
         self.raw_xi.ravel()[:] = 0
-        self.raw_varxi.ravel()[:] = 0
         self.meanr.ravel()[:] = 0
         self.meanlogr.ravel()[:] = 0
         self.weight.ravel()[:] = 0
         self.npairs.ravel()[:] = 0
         self.xi = self.raw_xi
-        self.varxi = self.raw_varxi
+        self._raw_varxi = None
+        self._varxi = None
+        self._cov = None
 
 
[docs]    def __iadd__(self, other):
         """Add a second `NKCorrelation`'s data to this one.
@@ -438,7 +450,9 @@ 
Source code for treecorr.nkcorrelation
         np.sum([c.weight for c in others], axis=0, out=self.weight)
         np.sum([c.npairs for c in others], axis=0, out=self.npairs)
         self.xi = self.raw_xi
-        self.varxi = self.raw_varxi
+        self._raw_varxi = None
+        self._varxi = None
+        self._cov = None
 
 
[docs]    @depr_pos_kwargs
     def process(self, cat1, cat2, *, metric=None, num_threads=None, comm=None, low_mem=False,
@@ -529,13 +543,14 @@ 
Source code for treecorr.nkcorrelation
                     new_cij.weight.ravel()[:] = 0
                     self.results[ij] = new_cij
 
-                self.cov = self.estimate_cov(self.var_method)
-                self.varxi.ravel()[:] = self.cov.diagonal()
+                self._cov = self.estimate_cov(self.var_method)
+                self._varxi = np.zeros_like(self.rnom, dtype=float)
+                self._varxi.ravel()[:] = self.cov.diagonal()
             else:
-                self.varxi = self.raw_varxi + rk.varxi
+                self._varxi = self.raw_varxi + rk.varxi
         else:
             self.xi = self.raw_xi
-            self.varxi = self.raw_varxi
+            self._varxi = self.raw_varxi
 
         return self.xi, self.varxi

 
@@ -649,7 +664,7 @@ 
Source code for treecorr.nkcorrelation
             self.meanr = data['meanr'].reshape(s)
             self.meanlogr = data['meanlogr'].reshape(s)
         self.xi = data['kappa'].reshape(s)
-        self.varxi = data['sigma'].reshape(s)**2
+        self._varxi = data['sigma'].reshape(s)**2
         self.weight = data['weight'].reshape(s)
         self.npairs = data['npairs'].reshape(s)
         self.coords = params['coords'].strip()
@@ -657,7 +672,7 @@ 
Source code for treecorr.nkcorrelation
         self._ro.sep_units = params['sep_units'].strip()
         self._ro.bin_type = params['bin_type'].strip()
         self.raw_xi = self.xi
-        self.raw_varxi = self.varxi
+        self._raw_varxi = self._varxi
         self.npatch1 = params.get('npatch1', 1)
         self.npatch2 = params.get('npatch2', 1)

 

diff --git a/docs/_build/html/_modules/treecorr/nncorrelation.html b/docs/_build/html/_modules/treecorr/nncorrelation.html
index 997c16e2..47fdf866 100644
--- a/docs/_build/html/_modules/treecorr/nncorrelation.html
+++ b/docs/_build/html/_modules/treecorr/nncorrelation.html
@@ -192,6 +192,8 @@ 
Source code for treecorr.nncorrelation
         self._write_rr = None
         self._write_dr = None
         self._write_rd = None
+        self._cov = None
+        self._var_num = 0
         self.logger.debug('Finished building NNCorr')

 
     @property
@@ -256,6 +258,8 @@ 
Source code for treecorr.nncorrelation
             ret._dr = self._dr.copy()
         if self._rr is not None:
             ret._rr = self._rr.copy()
+        if self._cov is not None:
+            ret._cov = self._cov.copy()
         return ret

 
     @lazy_property
@@ -280,6 +284,7 @@ 
Source code for treecorr.nncorrelation
         ret._corr = None
         ret._rr = ret._dr = ret._rd = None
         ret._write_rr = ret._write_dr = ret._write_rd = None
+        ret._cov = None
         # This override is really the main advantage of using this:
         setattr(ret, '_nonzero', False)
         return ret
@@ -726,7 +731,7 @@ 
Source code for treecorr.nncorrelation
                 self.__dict__.pop('_ok',None)  # If it was already made, it will need to be redone.
 
         # Now that it's all set up, calculate the covariance and set varxi to the diagonal.
-        self.cov = self.estimate_cov(self.var_method)
+        self._cov = self.estimate_cov(self.var_method)
         self.varxi = self.cov.diagonal()
         return self.xi, self.varxi

 
diff --git a/docs/_build/html/changes.html b/docs/_build/html/changes.html
index 19c6b09b..6652cdc5 100644
--- a/docs/_build/html/changes.html
+++ b/docs/_build/html/changes.html
@@ -152,6 +152,8 @@ 
Changes from version 4.3.2 to 4.3.3
 
  • Fixed a bug in how TwoD and Linear bin_types handle bin_slop.
    • 
 
  • Raise an error if user tries to use TwoD bin_type with non-flat coords.
    • 
+
  • Delay calculation of covariance matrix until requested.  (Especially important for TwoD binning
+with many bins.)
    • 
 
 
    
 
diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js
index d66cda1b..1eba150a 100644
--- a/docs/_build/html/searchindex.js
+++ b/docs/_build/html/searchindex.js
@@ -1 +1 @@
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