Another round of notebook updates

More notebook updates aimed at speeding up testing. Main changes here are to switch from XYI to XY in a few places where the gate set isn't particularly important, and to decrease the maximum circuit depth for GST experiment designs in some places.

jupyter_notebooks/Tutorials/00-Protocols.ipynb

@@ -67,10 +67,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pygsti.modelpacks import smq1Q_XYI\n",
+    "from pygsti.modelpacks import smq1Q_XY\n",
     "\n",
     "# get experiment design\n",
-    "exp_design = smq1Q_XYI.create_gst_experiment_design(max_max_length=32)  \n",
+    "exp_design = smq1Q_XY.create_gst_experiment_design(max_max_length=316)  \n",
     "\n",
     "# write an empty data object (creates a template to fill in)\n",
     "pygsti.io.write_empty_protocol_data('tutorial_files/test_gst_dir', exp_design, clobber_ok=True)\n",
@@ -230,7 +230,7 @@
    "outputs": [],
    "source": [
     "# An experiment design\n",
-    "from pygsti.modelpacks import smq1Q_Xpi2_rpe, smq1Q_XYI\n",
+    "from pygsti.modelpacks import smq1Q_Xpi2_rpe, smq1Q_XY\n",
     "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",
@@ -239,7 +239,7 @@
     "# 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",
     "    \"tutorial_files/test_rpe_dir/data/dataset.txt\",\n",
-    "    smq1Q_XYI.target_model().depolarize(op_noise=0.01, spam_noise=0.1),\n",
+    "    smq1Q_XY.target_model().depolarize(op_noise=0.01, spam_noise=0.1),\n",
     "    num_samples=1000, seed=1234)\n",
     "\n",
     "# read the data object back in, now with the experimental data\n",
@@ -273,12 +273,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pygsti.modelpacks import smq1Q_XYI\n",
-    "exp_design = smq1Q_XYI.create_gst_experiment_design(max_max_length=4)\n",
+    "from pygsti.modelpacks import smq1Q_XY\n",
+    "exp_design = smq1Q_XY.create_gst_experiment_design(max_max_length=4)\n",
     "pygsti.io.write_empty_protocol_data('tutorial_files/test_drift_dir', exp_design, clobber_ok=True)\n",
     "\n",
     "# Simulate time dependent data (right now, this just uses a time-independent model so this is uninteresting)                                                       \n",
-    "datagen_model = smq1Q_XYI.target_model().depolarize(op_noise=0.05, spam_noise=0.1)\n",
+    "datagen_model = smq1Q_XY.target_model().depolarize(op_noise=0.05, spam_noise=0.1)\n",
     "datagen_model.sim = \"map\" # only map-type can generate time-dep data\n",
     "                          # can also construct this as target_model(simulator=\"map\") above\n",
     "pygsti.io.fill_in_empty_dataset_with_fake_data('tutorial_files/test_drift_dir/data/dataset.txt',\n",
@@ -318,7 +318,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -332,9 +332,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

jupyter_notebooks/Tutorials/algorithms/GST-Driverfunctions.ipynb

@@ -37,7 +37,7 @@
    "metadata": {},
    "source": [
     "### Setup\n",
-    "First, we set our desired *target model* to be the standard $I$, $X(\\pi/2)$, $Y(\\pi/2)$ model that we've been using in many of these tutorials, and use the standard fiducial and germ sequences needed to generate the GST operation sequences (see the [standard module tutorial](../objects/advanced/StandardModules.ipynb)).  We also specify a list of maximum lengths.  We'll analyze the simulated data generated in the [DataSet tutorial](../objects/DataSet.ipynb), so you'll need to run that tutorial if you haven't already."
+    "First, we set our desired *target model* to be the standard $X(\\pi/2)$, $Y(\\pi/2)$ model that we've been using in many of these tutorials, and use the standard fiducial and germ sequences needed to generate the GST operation sequences (see the [standard module tutorial](../objects/advanced/StandardModules.ipynb)).  We also specify a list of maximum lengths.  We'll analyze the simulated data generated in the [DataSet tutorial](../objects/DataSet.ipynb), so you'll need to run that tutorial if you haven't already."
    ]
   },
   {
@@ -64,12 +64,12 @@
     }
    ],
    "source": [
-    "from pygsti.modelpacks import smq1Q_XYI\n",
-    "target_model = smq1Q_XYI.target_model()\n",
-    "prep_fiducials, meas_fiducials = smq1Q_XYI.prep_fiducials(), smq1Q_XYI.meas_fiducials()\n",
-    "germs = smq1Q_XYI.germs()\n",
+    "from pygsti.modelpacks import smq1Q_XY\n",
+    "target_model = smq1Q_XY.target_model()\n",
+    "prep_fiducials, meas_fiducials = smq1Q_XY.prep_fiducials(), smq1Q_XY.meas_fiducials()\n",
+    "germs = smq1Q_XY.germs()\n",
     "\n",
-    "maxLengths = [1,2,4,8,16,32]\n",
+    "maxLengths = [1,2,4,8,16]\n",
     "\n",
     "ds = pygsti.io.load_dataset(\"../tutorial_files/Example_Dataset.txt\", cache=True)"
    ]