Version 3.3.10

.circleci/config.yml

@@ -55,18 +55,12 @@ jobs:
           key: deps-py38-{{ checksum "poetry.lock" }}
           paths:
             - /home/circleci/venv
-      - run:
-          name: "Ensure the documentation builds"
-          command: |
-            source /home/circleci/venv/bin/activate
-            sphinx-build docs/ docs/.build -b html -n -v --color -T -W
       - run:
           name: "Run pytest"
           command: |
             source /home/circleci/venv/bin/activate
             coverage run --source=chaospy/ --module pytest --nbval --doctest-modules \
-                chaospy/ tests/ docs/*.rst docs/*/*.rst \
-                docs/tutorials/*.ipynb docs/tutorials/*/*.ipynb
+                chaospy/ tests/ docs/*.rst docs/*/*.rst
             bash <(curl -s https://codecov.io/bash)
   deploy:
     docker:

chaospy/basis.py

@@ -46,10 +46,10 @@ def basis(start, stop=None, dim=1, cross_truncation=1., sort="G"):
     out = numpoly.monomial(
         start=start,
         stop=numpy.array(stop)+1,
+        dimensions=dim,
         reverse="R" not in sort.upper(),
         graded="G" in sort.upper(),
         cross_truncation=cross_truncation,
-        names=numpoly.symbols("q:%d" % dim, asarray=True),
     )
     if "I" in sort.upper():
         out = out[::-1]

chaospy/descriptives/conditional.py

@@ -71,6 +71,6 @@ def E_cond(poly, freeze, dist, **kws):
 
     # Remove frozen coefficients, such that poly == sum(frozen*unfrozen) holds
     for key in unfrozen.keys:
-        unfrozen[key] = unfrozen[key] != 0
+        unfrozen[key] = unfrozen.values[key] != 0
 
     return numpoly.sum(frozen*E(unfrozen, dist), 0)

chaospy/descriptives/expected.py

@@ -45,5 +45,5 @@ def E(poly, dist=None, **kws):
 
     out = numpy.zeros(poly.shape)
     for idx, key in enumerate(poly.keys):
-        out += poly[key]*moments[idx]
+        out += poly.values[key]*moments[idx]
     return out

chaospy/orthogonal/cholesky.py

@@ -69,7 +69,7 @@ def orth_chol(order, dist, normed=False, graded=True, reverse=True,
     basis = numpoly.monomial(
         start=1,
         stop=order+1,
-        names=numpoly.variable(dim).names,
+        dimensions=dim,
         graded=graded,
         reverse=reverse,
         cross_truncation=cross_truncation,

chaospy/orthogonal/gram_schmidt.py

@@ -60,7 +60,7 @@ def orth_gs(order, dist, normed=False, graded=True, reverse=True,
         basis = numpoly.monomial(
             0,
             order+1,
-            names=numpoly.variable(2).names,
+            dimensions=dim,
             graded=graded,
             reverse=reverse,
             cross_truncation=cross_truncation,

chaospy/orthogonal/lagrange.py

@@ -59,37 +59,36 @@ def lagrange_polynomial(abscissas, graded=True, reverse=True, sort=None):
         raise numpy.linalg.LinAlgError(
             "Lagrange abscissas resulted in invertible matrix")
 
-    names = numpoly.variable(dim).names
     vec = numpoly.monomial(
-        0, order+1, names=names, graded=graded, reverse=reverse)[:size]
+        0, order+1, dimensions=dim, graded=graded, reverse=reverse)[:size]
 
     coeffs = numpy.zeros((size, size))
 
     if size == 1:
-        out = numpoly.monomial(
-            0, 1, names=names, graded=graded, reverse=reverse)*abscissas.item()
+        out = numpoly.monomial(0, 1, dimensions=dim, graded=graded,
+                               reverse=reverse)*abscissas.item()
 
     elif size == 2:
         coeffs = numpy.linalg.inv(matrix)
         out = numpoly.sum(vec*(coeffs.T), 1)
 
     else:
         for i in range(size):
-            if i%2 != 0:
+            if i % 2 != 0:
                 k = 1
             else:
-                k=0
+                k = 0
             for j in range(size):
-                if k%2 == 0:
+                if k % 2 == 0:
                     coeffs[i, j] += numpy.linalg.det(matrix[1:, 1:])
                 else:
-                    if size%2 == 0:
+                    if size % 2 == 0:
                         coeffs[i, j] += -numpy.linalg.det(matrix[1:, 1:])
                     else:
                         coeffs[i, j] += numpy.linalg.det(matrix[1:, 1:])
-                matrix = numpy.roll(matrix, -1, axis=0) 
+                matrix = numpy.roll(matrix, -1, axis=0)
                 k += 1
-            matrix = numpy.roll(matrix, -1, axis=1) 
+            matrix = numpy.roll(matrix, -1, axis=1)
         coeffs /= det
         out = numpoly.sum(vec*(coeffs.T), 1)
 

chaospy/prange.py

@@ -25,5 +25,10 @@ def prange(N=1, dim=1):
         polynomial([1, q2, q2**2, q2**3])
     """
     logger = logging.getLogger(__name__)
-    logger.warning("chaospy.prange is deprecated; use chaospy.monomial instead")
-    return numpoly.monomial(N, names=["q%d" % (dim-1)])
+    logger.warning(
+        "chaospy.prange is deprecated; use chaospy.monomial instead")
+    out = numpoly.monomial(N)
+    if dim > 1:
+        out = numpoly.call(
+            out, kwargs={out.names[0]: numpoly.variable(dim)[-1]})
+    return out

chaospy/quadrature/recurrence/stieltjes.py

@@ -145,7 +145,7 @@ def analytical_stieljes(order, dist, normed=False):
 
     norms = numpy.cumprod(coeffs[1], 1)
     if normed:
-        orth = numpoly.true_divide(orth, numpy.sqrt(norms))
+        orth = numpoly.polynomial(orth)/numpy.sqrt(norms)
         norms **= 0
 
     return coeffs, orth, norms

chaospy/saltelli.py

@@ -103,7 +103,7 @@ def Sens_m_sample(poly, dist, samples, rule="random"):
 
     Examples:
         >>> dist = chaospy.Iid(chaospy.Uniform(), 2)
-        >>> poly = chaospy.monomial(2, 3, names=2, reverse=False)
+        >>> poly = chaospy.monomial(2, 3, dimensions=2, reverse=False)
         >>> poly
         polynomial([q0**2, q0*q1, q1**2])
         >>> Sens_m_sample(poly, dist, 10000, rule="hammersley").round(4)
@@ -157,7 +157,7 @@ def Sens_m2_sample(poly, dist, samples, rule="random"):
 
     Examples:
         >>> dist = chaospy.Iid(chaospy.Uniform(), 2)
-        >>> poly = chaospy.monomial(2, 3, names=2, reverse=False)
+        >>> poly = chaospy.monomial(2, 3, dimensions=2, reverse=False)
         >>> poly
         polynomial([q0**2, q0*q1, q1**2])
         >>> Sens_m2_sample(poly, dist, 10000, rule="halton").round(4)
@@ -236,7 +236,7 @@ def Sens_t_sample(poly, dist, samples, rule="random"):
 
     Examples:
         >>> dist = chaospy.Iid(chaospy.Uniform(0, 1), 2)
-        >>> poly = chaospy.monomial(2, 3, names=2, reverse=False)
+        >>> poly = chaospy.monomial(2, 3, dimensions=2, reverse=False)
         >>> poly
         polynomial([q0**2, q0*q1, q1**2])
         >>> Sens_t_sample(poly, dist, 10000, rule="halton").round(4)