Merge branch 'develop' into add_type_checking_to_gauge_groups

Author: nkoskelo

.github/workflows/reuseable-main.yml

@@ -81,12 +81,12 @@ jobs:
       if: ${{ inputs.run-unit-tests == 'true' }}
       run: |
         python -Ic "import pygsti; print(pygsti.__version__); print(pygsti.__path__)"
-        python -m pytest -n auto --dist loadscope --cov=pygsti test/unit
+        python -m pytest -n auto --dist loadscope --cov=pygsti --durations=25 test/unit
     - name: Run test_packages
       if: ${{ inputs.run-extra-tests == 'true' }}
       run: |
         python -Ic "import pygsti; print(pygsti.__version__); print(pygsti.__path__)"
-        python -m pytest -v -n auto --dist loadscope --ignore=test/test_packages/mpi --ignore=test/test_packages/notebooks test/test_packages
+        python -m pytest -v -n auto --dist loadscope --ignore=test/test_packages/mpi --ignore=test/test_packages/notebooks --durations=25 test/test_packages
     - name: Run notebook regression
       if: ${{ inputs.run-notebook-tests == 'true' }}
       run: |
@@ -95,4 +95,4 @@ jobs:
         gcc -o ./jupyter_notebooks/Tutorials/algorithms/advanced/chp ./jupyter_notebooks/Tutorials/algorithms/advanced/chp.c
 
         python -Ic "import pygsti; print(pygsti.__version__); print(pygsti.__path__)"
-        python -m pytest -n auto --nbval-lax --dist loadscope --nbval-current-env jupyter_notebooks
+        python -m pytest -n auto --nbval-lax --dist loadscope --nbval-current-env --durations=25 jupyter_notebooks

pygsti/__init__.py

@@ -7,6 +7,9 @@
 # http://www.apache.org/licenses/LICENSE-2.0 or in the LICENSE file in the root pyGSTi directory.
 #***************************************************************************************************
 """ A Python implementation of LinearOperator Set Tomography """
+from .pgtypes import (
+    SpaceT
+)
 
 from . import baseobjs
 from . import algorithms as alg
@@ -19,6 +22,7 @@
 from . import protocols
 from . import report as rpt
 from . import serialization
+from . import enums
 
 # Import the most important/useful routines of each module/sub-package
 # into the package namespace
@@ -30,6 +34,7 @@
 from pygsti.tools.gatetools import *  # *_qubit_gate fns
 from .drivers import *
 from .tools import *
+
 # NUMPY BUG FIX (imported from tools)
 from pygsti.baseobjs._compatibility import _numpy14einsumfix
 

pygsti/algorithms/gaugeopt.py

@@ -517,8 +517,8 @@ def _jacobian_fn(gauge_group_el):
                 # d(op_term) = S_inv * (-dS * S_inv * G * S + G * dS) = S_inv * (-dS * G' + G * dS)
                 #   Note: (S_inv * G * S) is G' (transformed G)
                 wt = item_weights.get(lbl, opWeight)
-                left = -1 * _np.dot(dS, mdl_post.operations[lbl].to_dense(on_space='minimal'))  # shape (n,d1,d2)
-                right = _np.swapaxes(_np.dot(G.to_dense(on_space='minimal'), dS), 0, 1)  # shape (d1,n,d2) -> (n,d1,d2)
+                left = -1 * _np.dot(dS, mdl_post.operations[lbl].to_dense('minimal'))  # shape (n,d1,d2)
+                right = _np.swapaxes(_np.dot(G.to_dense('minimal'), dS), 0, 1)  # shape (d1,n,d2) -> (n,d1,d2)
                 result = _np.swapaxes(_np.dot(S_inv, left + right), 1, 2)  # shape (d1, d2, n)
                 result = result.reshape((d**2, n))  # must copy b/c non-contiguous
                 my_jacMx[start:start + d**2] = wt * result
@@ -530,8 +530,8 @@ def _jacobian_fn(gauge_group_el):
                 wt = item_weights.get(ilbl, opWeight)
                 for lbl, G in Inst.items():
                     # same calculation as for operation terms
-                    left = -1 * _np.dot(dS, mdl_post.instruments[ilbl][lbl].to_dense(on_space='minimal'))  # (n,d1,d2)
-                    right = _np.swapaxes(_np.dot(G.to_dense(on_space='minimal'), dS), 0, 1)  # (d1,n,d2) -> (n,d1,d2)
+                    left = -1 * _np.dot(dS, mdl_post.instruments[ilbl][lbl].to_dense('minimal'))  # (n,d1,d2)
+                    right = _np.swapaxes(_np.dot(G.to_dense('minimal'), dS), 0, 1)  # (d1,n,d2) -> (n,d1,d2)
                     result = _np.swapaxes(_np.dot(S_inv, left + right), 1, 2)  # shape (d1, d2, n)
                     result = result.reshape((d**2, n))  # must copy b/c non-contiguous
                     my_jacMx[start:start + d**2] = wt * result
@@ -544,7 +544,7 @@ def _jacobian_fn(gauge_group_el):
                 #   Note: (S_inv * rho) is transformed rho
                 wt = item_weights.get(lbl, spamWeight)
                 Sinv_dS = _np.dot(S_inv, dS)  # shape (d1,n,d2)
-                result = -1 * _np.dot(Sinv_dS, rho.to_dense(on_space='minimal'))  # shape (d,n)
+                result = -1 * _np.dot(Sinv_dS, rho.to_dense('minimal'))  # shape (d,n)
                 my_jacMx[start:start + d] = wt * result
                 start += d
 
@@ -554,7 +554,7 @@ def _jacobian_fn(gauge_group_el):
                 for lbl, E in povm.items():
                     # d(ET_term) = E.T * dS
                     wt = item_weights.get(povmlbl + "_" + lbl, spamWeight)
-                    result = _np.dot(E.to_dense(on_space='minimal')[None, :], dS).T  # shape (1,n,d2).T => (d2,n,1)
+                    result = _np.dot(E.to_dense('minimal')[None, :], dS).T  # shape (1,n,d2).T => (d2,n,1)
                     my_jacMx[start:start + d] = wt * result.squeeze(2)  # (d2,n)
                     start += d
 
@@ -851,7 +851,7 @@ def _spam_penalty_jac_fill(spam_penalty_vec_grad_to_fill, mdl_pre, mdl_post,
 
         #get sgn(denMx) == d(|denMx|_Tr)/d(denMx) in std basis
         # dmDim = denMx.shape[0]
-        denMx = _tools.vec_to_stdmx(prepvec.to_dense(on_space='minimal')[:, None], op_basis)
+        denMx = _tools.vec_to_stdmx(prepvec.to_dense('minimal')[:, None], op_basis)
         assert(_np.linalg.norm(denMx - denMx.T.conjugate()) < 1e-4), \
             "denMx should be Hermitian!"
 
@@ -865,7 +865,7 @@ def _spam_penalty_jac_fill(spam_penalty_vec_grad_to_fill, mdl_pre, mdl_post,
         # get d(prepvec')/dp = d(S_inv * prepvec)/dp in op_basis [shape == (n,dim)]
         #                    = (-S_inv*dS*S_inv) * prepvec = -S_inv*dS * prepvec'
         Sinv_dS = _np.dot(S_inv, dS)  # shape (d1,n,d2)
-        dVdp = -1 * _np.dot(Sinv_dS, prepvec.to_dense(on_space='minimal')[:, None]).squeeze(2)  # shape (d,n,1) => (d,n)
+        dVdp = -1 * _np.dot(Sinv_dS, prepvec.to_dense('minimal')[:, None]).squeeze(2)  # shape (d,n,1) => (d,n)
         assert(dVdp.shape == (d, n))
 
         # denMx = sum( spamvec[i] * Bmx[i] )
@@ -890,7 +890,7 @@ def _spam_penalty_jac_fill(spam_penalty_vec_grad_to_fill, mdl_pre, mdl_post,
         for lbl, effectvec in povm.items():
 
             #get sgn(EMx) == d(|EMx|_Tr)/d(EMx) in std basis
-            EMx = _tools.vec_to_stdmx(effectvec.to_dense(on_space='minimal')[:, None], op_basis)
+            EMx = _tools.vec_to_stdmx(effectvec.to_dense('minimal')[:, None], op_basis)
             # dmDim = EMx.shape[0]
             assert(_np.linalg.norm(EMx - EMx.T.conjugate()) < 1e-4), \
                 "denMx should be Hermitian!"
@@ -905,7 +905,7 @@ def _spam_penalty_jac_fill(spam_penalty_vec_grad_to_fill, mdl_pre, mdl_post,
             # get d(effectvec')/dp = [d(effectvec.T * S)/dp].T in op_basis [shape == (n,dim)]
             #                      = [effectvec.T * dS].T
             #  OR = dS.T * effectvec
-            pre_effectvec = mdl_pre.povms[povmlbl][lbl].to_dense(on_space='minimal')[:, None]
+            pre_effectvec = mdl_pre.povms[povmlbl][lbl].to_dense('minimal')[:, None]
             dVdp = _np.dot(pre_effectvec.T, dS).squeeze(0).T
             # shape = (1,d) * (n, d1,d2) = (1,n,d2) => (n,d2) => (d2,n)
             assert(dVdp.shape == (d, n))