Fixed testesti

setup.py

@@ -11,17 +11,24 @@ try:
 except AttributeError:
     numpy_include = numpy.get_numpy_include()
 
+print( sys.argv)
+
 if '--disable-openmp' in sys.argv:
     sys.argv.pop(sys.argv.index('--disable-openmp'))
     USE_OPENMP = False
 else:
     USE_OPENMP = True
 
+if '--icc' in sys.argv:
+    sys.argv.pop(sys.argv.index('--icc'))
+    USE_ICC = True
+else:
+    USE_ICC = False
+
 libs = ['m']
 
-use_icc = True if '--icc' in sys.argv else False
-extra = ['-std=c99']
-if use_icc:
+extra = ['-std=gnu99']
+if USE_ICC:
     if USE_OPENMP:
         libs += ['gomp', 'iomp5']
         extra += ['-openmp']

src/libcov.h

 #include <stdio.h>
 #include <stdlib.h>
+#include <string.h>
 #include <math.h>
 /* #include <chealpix.h> */
 

xqml/libcov.py

@@ -12,8 +12,8 @@ import healpy as hp
 import math
 from scipy import special
 
-from simulation import extrapolpixwin
-from xqml_utils import getstokes, progress_bar
+from .simulation import extrapolpixwin
+from .xqml_utils import getstokes, progress_bar
 
 import _libcov as clibcov
 

xqml/xqml.py

@@ -18,14 +18,14 @@ import random as rd
 
 from xqml_utils import getstokes, pd_inv
 
-from estimators import El
-from estimators import CovAB
-from estimators import CrossGisherMatrix
-from estimators import CrossWindowFunction
-from estimators import yQuadEstimator, ClQuadEstimator
-from estimators import biasQuadEstimator
-
-from libcov import compute_ds_dcb, S_bins_MC, compute_S, covth_bins_MC, compute_PlS, SignalCovMatrix
+from .estimators import El
+from .estimators import CovAB
+from .estimators import CrossGisherMatrix
+from .estimators import CrossWindowFunction
+from .estimators import yQuadEstimator, ClQuadEstimator
+from .estimators import biasQuadEstimator
+
+from .libcov import compute_ds_dcb, S_bins_MC, compute_S, covth_bins_MC, compute_PlS, SignalCovMatrix
 
 
 __all__ = ['xQML']

xqml/xqml_utils.py

@@ -368,3 +368,55 @@ def IsInvertible(F):
 #         P *= binsnorm
 
 #     return P, Q, ell, ellval
+
+
+
+
+
+
+
+class Sym(np.ndarray):
+
+    # wrapper class for numpy array for symmetric matrices. New attribute can pack matrix to optimize storage.
+    # Usage:
+    # If you have a symmetric matrix A as a shape (n,n) numpy ndarray, Sym(A).packed is a shape (n(n+1)/2,) numpy array 
+    # that is a packed version of A.  To convert it back, just wrap the flat list in Sym().  Note that Sym(Sym(A).packed)
+
+
+    def __new__(cls, input_array):
+        obj = np.asarray(input_array).view(cls)
+
+        if len(obj.shape) == 1:
+            l = obj.copy()
+            p = obj.copy()
+            m = int((np.sqrt(8 * len(obj) + 1) - 1) / 2)
+            sqrt_m = np.sqrt(m)
+
+            if np.isclose(sqrt_m, np.round(sqrt_m)):
+                A = np.zeros((m, m))
+                for i in range(m):
+                    A[i, i:] = l[:(m-i)]
+                    A[i:, i] = l[:(m-i)]
+                    l = l[(m-i):]
+                obj = np.asarray(A).view(cls)
+                obj.packed = p
+
+            else:
+                raise ValueError('One dimensional input length must be a triangular number.')
+
+        elif len(obj.shape) == 2:
+            if obj.shape[0] != obj.shape[1]:
+                raise ValueError('Two dimensional input must be a square matrix.')
+            packed_out = []
+            for i in range(obj.shape[0]):
+                packed_out.append(obj[i, i:])
+            obj.packed = np.concatenate(packed_out)
+
+        else:
+            raise ValueError('Input array must be 1 or 2 dimensional.')
+
+        return obj
+
+    def __array_finalize__(self, obj):
+        if obj is None: return
+        self.packed = getattr(obj, 'packed', None)