Typos and deprecation tutorial fixes

jupyter_notebooks/Tutorials/00-Protocols.ipynb

@@ -106,7 +106,7 @@
    "metadata": {},
    "source": [
     "## Randomized benchmarking\n",
-    "Randomized benchmarking (RB) can be used to estimate the average per-Clifford error rate by fitting a simple curve to the data from randomized circuits of different depths.  To create the experiment design, the user specifies a `QubitProcessorSpec` object that describes the quantum processor (see the [ProcessorSpec tutorial](objects/ProcessorSpec.pynb), the depths (in number of Clifford gates) to use, and the number of circuits at each depth.  The results from running the protocol are then used to create a plot of the RB decay curve along with the data.  For more information, see the [RB Overview tutorial](algorithms/RB-Overview.ipynb)."
+    "Randomized benchmarking (RB) can be used to estimate the average per-Clifford error rate by fitting a simple curve to the data from randomized circuits of different depths.  To create the experiment design, the user specifies a `QubitProcessorSpec` object that describes the quantum processor (see the [ProcessorSpec tutorial](objects/ProcessorSpec.pynb)), the depths (in number of Clifford gates) to use, and the number of circuits at each depth.  The results from running the protocol are then used to create a plot of the RB decay curve along with the data.  For more information, see the [RB Overview tutorial](algorithms/RB-Overview.ipynb)."
    ]
   },
   {
@@ -234,7 +234,7 @@
     "exp_design = smq1Q_Xpi2_rpe.create_rpe_experiment_design(max_max_length=64)\n",
     "\n",
     "# write an empty data object (creates a template to fill in)\n",
-    "pygsti.io.write_empty_protocol_data(exp_design, 'tutorial_files/test_rpe_dir', clobber_ok=True)\n",
+    "pygsti.io.write_empty_protocol_data('tutorial_files/test_rpe_dir', exp_design, clobber_ok=True)\n",
     "\n",
     "# fill in the template with simulated data (you would run the experiment and use actual data)\n",
     "pygsti.io.fill_in_empty_dataset_with_fake_data(\n",
@@ -332,7 +332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

jupyter_notebooks/Tutorials/01-Essential-Objects.ipynb

@@ -343,7 +343,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Another thing to note is that `DataSet` objects are \"sparse\" in that 0-counts are not typically stored:"
+    "Another thing to note is that `DataSet` objects can be made \"sparse\" by dropping 0-counts:"
    ]
   },
   {
@@ -352,9 +352,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "ds_sparse = ds_fake.drop_zero_counts()\n",
+    "\n",
     "c = Circuit([('Gxpi2',0)], line_labels=(0,1))\n",
     "print(\"No 01 or 11 outcomes here: \",ds_fake[c])\n",
-    "for outlbl, cnt in ds_fake[c].counts.items():\n",
+    "for outlbl, cnt in ds_sparse[c].counts.items():\n",
     "    print(\"Item: \",outlbl, cnt) # Note: this loop never loops over 01 or 11!"
    ]
   },
@@ -411,7 +413,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

jupyter_notebooks/Tutorials/02-Using-Essential-Objects.ipynb

@@ -210,7 +210,7 @@
    "metadata": {},
    "source": [
     "## Randomized Benchmarking (RB)\n",
-    "PyGSTi is able to perform two types of Randomized Benchmarking (RB).  First, there is the [standard Clifford-circuit-based RB](http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.106.180504) protocol first defined by Magesan et al. Second, there is [\"Direct RB\"](https://arxiv.org/abs/1807.07975), which is particularly suited to multi-qubit benchmarking.  More more details on using these protocols (e.g. how to generate a set of RB sequences) see the separate [RB overview tutorial](algorithms/RB-Overview.ipynb) and related tutorials."
+    "PyGSTi is able to perform two types of Randomized Benchmarking (RB).  First, there is the [standard Clifford-circuit-based RB](http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.106.180504) protocol first defined by Magesan et al. Second, there is [\"Direct RB\"](https://arxiv.org/abs/1807.07975), which is particularly suited to multi-qubit benchmarking.  More details on using these protocols (e.g. how to generate a set of RB sequences) see the separate [RB overview tutorial](algorithms/RB-Overview.ipynb) and related tutorials."
    ]
   },
   {

jupyter_notebooks/Tutorials/objects/advanced/CustomOperator.ipynb

@@ -44,8 +44,8 @@
     "        \n",
     "        theta = (np.pi/2 + self.over_rotation)/2\n",
     "        a = 1.0-self.depol_amt\n",
-    "        b = a*2*np.cos(theta)*np.sin(theta)\n",
-    "        c = a*(np.sin(theta)**2 - np.cos(theta)**2)\n",
+    "        b = a*np.sin(2*theta)\n",
+    "        c = a*np.cos(2*theta)\n",
     "        \n",
     "        # ._ptr is a member of DenseOperator and is a numpy array that is \n",
     "        # the dense Pauli transfer matrix of this operator\n",
@@ -65,7 +65,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll add a `MyXPi2Operator` instance as the `\"Gx\"` gate in pyGSTi's standard {Idle, $X(\\pi/2)$, $Y(\\pi/2)$} model (see the [standard modules tutorial](StandardModules.ipynb) for more information on standard models)."
+    "We'll add a `MyXPi2Operator` instance as the `(\"Gxpi2\",0)` gate in pyGSTi's {Idle, $X(\\pi/2)$, $Y(\\pi/2)$} modelpack (see the [modelpacks tutorial](ModelPacks.ipynb) for more information on modelpacks)."
    ]
   },
   {

pygsti/data/datacomparator.py

@@ -647,9 +647,9 @@ def run(self, significance=0.05, per_circuit_correction='Hochberg',
             if self.inconsistent_datasets_detected:
                 print("The data are INCONSISTENT at {0:.2f}% significance.".format(self.significance * 100))
                 print("  - Details:")
-                print("    - The aggregate log-_likelihood ratio test is "
+                print("    - The aggregate log-likelihood ratio test is "
                       "significant at {0:.2f} standard deviations.".format(self._aggregate_nsigma))
-                print("    - The aggregate log-_likelihood ratio test "
+                print("    - The aggregate log-likelihood ratio test "
                       "standard deviations signficance threshold is {0:.2f}".format(self._aggregate_nsigma_threshold))
                 print(
                     "    - The number of sequences with data that is "

pygsti/protocols/gst.py

@@ -532,7 +532,7 @@ def retrieve_model(self, edesign, gaugeopt_target, dataset, comm):
         if comm is None or comm.Get_rank() == 0:
             #Advanced Options can specify further manipulation of starting model
             if self.contract_start_to_cptp:
-                mdl_start = _alg.contract(mdl_start, "CPTP")
+                mdl_start = _alg.contract(mdl_start, "CPTPLND")
                 raise ValueError(
                     "'contractStartToCPTP' has been removed b/c it can change the parameterization of a model")
             if self.depolarize_start > 0:
@@ -1534,13 +1534,13 @@ class StandardGST(_proto.Protocol):
         parameterizations/constraints to apply to the estimated model.
         The default value is usually fine.  Allowed values are:
 
-        - "full" : full (completely unconstrained)
-        - "TP"   : TP-constrained
-        - "CPTP" : Lindbladian CPTP-constrained
-        - "H+S"  : Only Hamiltonian + Stochastic errors allowed (CPTP)
-        - "S"    : Only Stochastic errors allowed (CPTP)
-        - "Target" : use the target (ideal) gates as the estimate
-        - <model> : any key in the `models_to_test` argument
+        - "full"    : full (completely unconstrained)
+        - "TP"      : TP-constrained
+        - "CPTPLND" : Lindbladian CPTP-constrained
+        - "H+S"     : Only Hamiltonian + Stochastic errors allowed (CPTP)
+        - "S"       : Only Stochastic errors allowed (CPTP)
+        - "Target"  : use the target (ideal) gates as the estimate
+        - <model>   : any key in the `models_to_test` argument
 
     gaugeopt_suite : GSTGaugeOptSuite, optional
         Specifies which gauge optimizations to perform on each estimate.  Can also
@@ -1581,7 +1581,7 @@ class StandardGST(_proto.Protocol):
         be used.
     """
 
-    def __init__(self, modes="full TP,CPTP,Target", gaugeopt_suite='stdgaugeopt', target_model=None,
+    def __init__(self, modes="full TP,CPTPLND,Target", gaugeopt_suite='stdgaugeopt', target_model=None,
                  models_to_test=None, objfn_builders=None, optimizer=None, badfit_options=None, verbosity=2, name=None):
 
         super().__init__(name)