diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index 506756ab..eb75021a 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -1,10 +1,9 @@
-
 name: Publish Documentation
 
 on:
   push:
     branches:
-      - '**'
+      - "**"
 
 jobs:
   deploy:
@@ -27,15 +26,18 @@ jobs:
         shell: bash -l {0}
         run: |
           mamba list
+      - name: Execute notebooks
+        shell: bash -l {0}
+        run: |
+          bash scripts/execute_notebooks.bash
       - name: Build Docs
         shell: bash -l {0}
         run: |
           mkdocs build
           zip -r pytao-examples.zip docs/examples/
-          mv pytao-examples.zip ./site/assets/.
-          
+          mv pytao-examples.zip ./site/assets/
       - name: Deploy to gh-pages
         uses: peaceiris/actions-gh-pages@v3
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
-          publish_dir: ./site/
\ No newline at end of file
+          publish_dir: ./site/
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index c605cdb7..205d735c 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -12,6 +12,14 @@ jobs:
         with:
           fetch-depth: 1
 
+      - uses: conda-incubator/setup-miniconda@v3
+        with:
+          python-version: 3.12
+          mamba-version: "*"
+          channels: conda-forge
+          activate-environment: pytao-dev
+          environment-file: dev-environment.yml
+
       - name: Install pre-commit
         run: python -m pip install pre-commit
 
diff --git a/.gitignore b/.gitignore
index af673605..a6faf61b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -116,3 +116,10 @@ ENV/
 *.swp
 
 pytao/_version.py
+pytao/tests/artifacts
+docs/examples/alpha*.html
+docs/examples/beta*.html
+docs/examples/beta*.png
+docs/examples/fodo10.bmad 
+docs/examples/markers.bmad 
+docs/examples/wake.dat
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 558872fe..1e5b67e1 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -31,7 +31,7 @@ repos:
         args: ["--maxkb=100000"]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.0
+    rev: v0.6.1
     hooks:
       - id: ruff
         args: ["--config", "pyproject.toml"]
@@ -41,3 +41,13 @@ repos:
         args: ["--config", "pyproject.toml"]
         types_or: [python]
         exclude: "^(pytao/_version.py)$"
+
+  - repo: local
+    hooks:
+      - id: jupyter-notebook-clear-output
+        name: jupyter-notebook-jupyter-clear
+        files: \.ipynb$
+        stages: [commit]
+        language: system
+        entry: jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace
+        # exclude: "^.*().ipynb$"
diff --git a/dev-environment.yml b/dev-environment.yml
index ffc43860..8637d37c 100644
--- a/dev-environment.yml
+++ b/dev-environment.yml
@@ -6,7 +6,7 @@ dependencies:
   - python >=3.9
   - openPMD-beamphysics
   - numpydoc
-  - bmad >=20240628.2
+  - bmad >=20240809
   - bokeh
   - jupyterlab>3
   - ipywidgets
@@ -14,6 +14,7 @@ dependencies:
   - numpy
   - h5py
   - pexpect
+  - pydantic >=2
   # Developer
   - pygments
   - pytest
diff --git a/docs/api/plot-bokeh.md b/docs/api/plot-bokeh.md
new file mode 100644
index 00000000..0d9e5310
--- /dev/null
+++ b/docs/api/plot-bokeh.md
@@ -0,0 +1 @@
+::: pytao.plotting.bokeh
diff --git a/docs/api/plot-mpl.md b/docs/api/plot-mpl.md
new file mode 100644
index 00000000..40edfb67
--- /dev/null
+++ b/docs/api/plot-mpl.md
@@ -0,0 +1 @@
+::: pytao.plotting.mpl
diff --git a/docs/api/plot.md b/docs/api/plot.md
new file mode 100644
index 00000000..ad41de49
--- /dev/null
+++ b/docs/api/plot.md
@@ -0,0 +1,5 @@
+::: pytao.plotting.plot
+::: pytao.plotting.curves
+::: pytao.plotting.fields
+::: pytao.plotting.pgplot
+::: pytao.plotting.settings
diff --git a/docs/api/subprocesstao.md b/docs/api/subprocesstao.md
new file mode 100644
index 00000000..d9401d19
--- /dev/null
+++ b/docs/api/subprocesstao.md
@@ -0,0 +1 @@
+::: pytao.SubprocessTao
diff --git a/docs/api/pytao.md b/docs/api/tao.md
similarity index 100%
rename from docs/api/pytao.md
rename to docs/api/tao.md
diff --git a/docs/examples/advanced.ipynb b/docs/examples/advanced.ipynb
index 66cfd863..be9a1c29 100644
--- a/docs/examples/advanced.ipynb
+++ b/docs/examples/advanced.ipynb
@@ -9,8 +9,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:01.866218Z",
+     "iopub.status.busy": "2024-08-08T19:06:01.865795Z",
+     "iopub.status.idle": "2024-08-08T19:06:01.877393Z",
+     "shell.execute_reply": "2024-08-08T19:06:01.876159Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Useful for debugging\n",
@@ -23,8 +30,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:01.881604Z",
+     "iopub.status.busy": "2024-08-08T19:06:01.881288Z",
+     "iopub.status.idle": "2024-08-08T19:06:02.110503Z",
+     "shell.execute_reply": "2024-08-08T19:06:02.110201Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -33,8 +47,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:02.112113Z",
+     "iopub.status.busy": "2024-08-08T19:06:02.111966Z",
+     "iopub.status.idle": "2024-08-08T19:06:02.304780Z",
+     "shell.execute_reply": "2024-08-08T19:06:02.304518Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pytao import Tao, TaoModel, util, run_tao\n",
@@ -51,8 +72,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:02.306217Z",
+     "iopub.status.busy": "2024-08-08T19:06:02.306142Z",
+     "iopub.status.idle": "2024-08-08T19:06:02.308059Z",
+     "shell.execute_reply": "2024-08-08T19:06:02.307805Z"
+    }
+   },
    "outputs": [],
    "source": [
     "INPUT_FILE = os.path.expandvars(\n",
@@ -70,20 +98,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pytao.tao_ctypes.core.TaoModel at 0x10b369820>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:02.309351Z",
+     "iopub.status.busy": "2024-08-08T19:06:02.309276Z",
+     "iopub.status.idle": "2024-08-08T19:06:02.392332Z",
+     "shell.execute_reply": "2024-08-08T19:06:02.392121Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "M = run_tao(input_file=INPUT_FILE, ploton=False)\n",
     "M"
@@ -98,22 +122,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'lat::orbit.x[FF.PIP02A]': '  0.00000000000000E+00',\n",
-       " 'beam::norm_emit.x[end]': '  9.99937630588196E-07',\n",
-       " 'beam_archive': '/Users/klauer/Repos/pytao/docs/examples/bmad_beam_d2fca186ca9b848bb3924bf20dc69440.h5'}"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:02.410517Z",
+     "iopub.status.busy": "2024-08-08T19:06:02.410413Z",
+     "iopub.status.idle": "2024-08-08T19:06:02.756794Z",
+     "shell.execute_reply": "2024-08-08T19:06:02.756537Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "res = evaluate_tao(\n",
     "    settings={\"space_charge_com:ds_track_step\": 0.001},\n",
@@ -129,23 +147,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['/data/00001/particles/',\n",
-       " '/data/00002/particles/',\n",
-       " '/data/00003/particles/',\n",
-       " '/data/00004/particles/']"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:02.758073Z",
+     "iopub.status.busy": "2024-08-08T19:06:02.757991Z",
+     "iopub.status.idle": "2024-08-08T19:06:04.783014Z",
+     "shell.execute_reply": "2024-08-08T19:06:04.782750Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "from pmd_beamphysics import ParticleGroup, particle_paths\n",
     "from h5py import File\n",
@@ -165,45 +176,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['csr_wake', 'data', 'expressions', 'input', 'settings']"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:04.784643Z",
+     "iopub.status.busy": "2024-08-08T19:06:04.784501Z",
+     "iopub.status.idle": "2024-08-08T19:06:04.786687Z",
+     "shell.execute_reply": "2024-08-08T19:06:04.786451Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "list(h5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 434,
-       "width": 589
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:04.787968Z",
+     "iopub.status.busy": "2024-08-08T19:06:04.787876Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.165975Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.165725Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "P = ParticleGroup(h5[ppaths[-1]])\n",
     "P.plot(\"delta_t\", \"delta_pz\", bins=200)"
@@ -211,8 +209,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.167383Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.167284Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.168963Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.168735Z"
+    }
+   },
    "outputs": [],
    "source": [
     "os.remove(afile)"
@@ -227,20 +232,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'  0.00000000000000E+00'"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.170221Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.170151Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.172146Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.171943Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "M.evaluate(\"lat::orbit.x[end]\")"
    ]
@@ -254,8 +255,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.173414Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.173332Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.174877Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.174681Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pytao.misc.csr import read_csr_wake_data_h5, process_csr_wake_data\n",
@@ -271,8 +279,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.176065Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.175991Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.180138Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.179895Z"
+    }
+   },
    "outputs": [],
    "source": [
     "cdat = read_csr_wake_data_h5(h5)"
@@ -287,20 +302,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(134, 40, 5)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.181356Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.181284Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.183200Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.182976Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "dat = cdat[\"3:FF.BEN01\"][\"data\"]\n",
     "dat.shape"
@@ -315,69 +326,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.   , 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008,\n",
-       "       0.009, 0.01 , 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017,\n",
-       "       0.018, 0.019, 0.02 , 0.021, 0.022, 0.023, 0.024, 0.025, 0.026,\n",
-       "       0.027, 0.028, 0.029, 0.03 , 0.031, 0.032, 0.033, 0.034, 0.035,\n",
-       "       0.036, 0.037, 0.038, 0.039, 0.04 , 0.041, 0.042, 0.043, 0.044,\n",
-       "       0.045, 0.046, 0.047, 0.048, 0.049, 0.05 , 0.051, 0.052, 0.053,\n",
-       "       0.054, 0.055, 0.056, 0.057, 0.058, 0.059, 0.06 , 0.06 , 0.061,\n",
-       "       0.062, 0.063, 0.064, 0.065, 0.066, 0.067, 0.068, 0.069, 0.07 ,\n",
-       "       0.071, 0.072, 0.073, 0.074, 0.075, 0.076, 0.077, 0.078, 0.079,\n",
-       "       0.08 , 0.081, 0.082, 0.083, 0.084, 0.085, 0.086, 0.087, 0.088,\n",
-       "       0.089, 0.09 , 0.091, 0.092, 0.093, 0.094, 0.095, 0.096, 0.097,\n",
-       "       0.098, 0.099, 0.1  , 0.101, 0.102, 0.103, 0.104, 0.105, 0.106,\n",
-       "       0.107, 0.108, 0.109, 0.11 , 0.111, 0.112, 0.113, 0.114, 0.115,\n",
-       "       0.116, 0.117, 0.118, 0.119, 0.12 , 0.121, 0.122, 0.123, 0.124,\n",
-       "       0.125, 0.126, 0.127, 0.128, 0.129, 0.13 , 0.131, 0.132, 0.133,\n",
-       "       0.134, 0.135, 0.136, 0.137, 0.138, 0.139, 0.14 , 0.141, 0.142,\n",
-       "       0.143, 0.144, 0.145, 0.146, 0.147, 0.148, 0.149, 0.15 , 0.151,\n",
-       "       0.152, 0.153, 0.154, 0.155, 0.156, 0.157, 0.158, 0.159, 0.16 ,\n",
-       "       0.161, 0.162, 0.163, 0.164, 0.165, 0.166, 0.167, 0.168, 0.169,\n",
-       "       0.17 , 0.171, 0.172, 0.173, 0.174, 0.175, 0.176, 0.177, 0.178,\n",
-       "       0.179, 0.18 , 0.181, 0.182, 0.183, 0.184, 0.185, 0.186, 0.187,\n",
-       "       0.188, 0.189, 0.19 , 0.191, 0.192, 0.193, 0.193, 0.194, 0.195,\n",
-       "       0.196, 0.197, 0.198, 0.199, 0.2  , 0.201, 0.202, 0.203, 0.204,\n",
-       "       0.205, 0.206, 0.207, 0.208, 0.209, 0.21 , 0.211, 0.212, 0.213,\n",
-       "       0.214, 0.215, 0.216, 0.217, 0.218, 0.219, 0.22 , 0.221, 0.222,\n",
-       "       0.223, 0.224, 0.225, 0.226, 0.227, 0.228, 0.229, 0.23 , 0.231,\n",
-       "       0.232, 0.233, 0.234, 0.235, 0.236, 0.237, 0.238, 0.239, 0.24 ,\n",
-       "       0.241, 0.242, 0.243, 0.244, 0.245, 0.246, 0.247, 0.248, 0.249,\n",
-       "       0.25 , 0.251, 0.252, 0.253, 0.254, 0.255, 0.256, 0.257, 0.258,\n",
-       "       0.259, 0.26 , 0.261, 0.262, 0.263, 0.263, 0.264, 0.265, 0.266,\n",
-       "       0.267, 0.268, 0.269, 0.27 , 0.271, 0.272, 0.273, 0.274, 0.275,\n",
-       "       0.276, 0.277, 0.278, 0.279, 0.28 , 0.281, 0.282, 0.283, 0.284,\n",
-       "       0.285, 0.286, 0.287, 0.288, 0.289, 0.29 , 0.291, 0.292, 0.293,\n",
-       "       0.294, 0.295, 0.296, 0.297, 0.298, 0.299, 0.3  , 0.301, 0.302,\n",
-       "       0.303, 0.304, 0.305, 0.306, 0.307, 0.308, 0.309, 0.31 , 0.311,\n",
-       "       0.312, 0.313, 0.314, 0.315, 0.316, 0.317, 0.318, 0.319, 0.32 ,\n",
-       "       0.321, 0.322, 0.323, 0.324, 0.325, 0.326, 0.327, 0.328, 0.329,\n",
-       "       0.33 , 0.331, 0.332, 0.333, 0.334, 0.335, 0.336, 0.337, 0.338,\n",
-       "       0.339, 0.34 , 0.341, 0.342, 0.343, 0.344, 0.345, 0.346, 0.347,\n",
-       "       0.348, 0.349, 0.35 , 0.351, 0.352, 0.353, 0.354, 0.355, 0.356,\n",
-       "       0.357, 0.358, 0.359, 0.36 , 0.361, 0.362, 0.363, 0.364, 0.365,\n",
-       "       0.366, 0.367, 0.368, 0.369, 0.37 , 0.371, 0.372, 0.373, 0.374,\n",
-       "       0.375, 0.376, 0.377, 0.378, 0.379, 0.38 , 0.381, 0.382, 0.383,\n",
-       "       0.384, 0.385, 0.385, 0.386, 0.387, 0.388, 0.389, 0.39 , 0.391,\n",
-       "       0.392, 0.393, 0.394, 0.395, 0.396, 0.397, 0.398, 0.399, 0.4  ,\n",
-       "       0.401, 0.402, 0.403, 0.404, 0.405, 0.406, 0.407, 0.408, 0.409,\n",
-       "       0.41 , 0.411, 0.412, 0.413, 0.414, 0.415, 0.416, 0.417, 0.418,\n",
-       "       0.419, 0.42 , 0.421, 0.422, 0.423, 0.424, 0.425, 0.426, 0.427,\n",
-       "       0.428, 0.429, 0.43 , 0.431, 0.432, 0.433, 0.434, 0.435, 0.436,\n",
-       "       0.437, 0.438, 0.439, 0.44 , 0.441, 0.442, 0.443, 0.444, 0.445])"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.184476Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.184406Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.187529Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.187327Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "pdat = process_csr_wake_data(cdat)\n",
     "\n",
@@ -386,8 +344,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.188698Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.188631Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.192814Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.192622Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pytao.misc.csr_plot import plot_csr_wake, plot_csr_stats\n",
@@ -396,24 +361,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "89abfde2c5c04b69aa4c0f7146aae84e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(IntSlider(value=0, description='step', max=449), Output()), _dom_classes=('widget-intera…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.193993Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.193912Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.315866Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.315637Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "from ipywidgets import interact\n",
     "\n",
@@ -435,25 +392,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 413,
-       "width": 547
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.318168Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.318098Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.373924Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.373705Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "plt.plot(pdat[\"s_position\"], marker=\".\");"
    ]
@@ -467,32 +415,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(450, 4)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 434,
-       "width": 693
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.375195Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.375120Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.485644Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.485403Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "plot_csr_stats(pdat)"
    ]
@@ -506,8 +438,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:05.486978Z",
+     "iopub.status.busy": "2024-08-08T19:06:05.486887Z",
+     "iopub.status.idle": "2024-08-08T19:06:05.493727Z",
+     "shell.execute_reply": "2024-08-08T19:06:05.493530Z"
+    },
     "tags": []
    },
    "outputs": [],
@@ -538,6 +476,279 @@
    "interpreter": {
     "hash": "8acfe5d4ac94dcea04347ba5d21ed6ccc77e9ec6b4167c9c2482da2d61a71842"
    }
+  },
+  "widgets": {
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+       "_model_module_version": "2.0.0",
+       "_model_name": "SliderStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "description_width": "",
+       "handle_color": null
+      }
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+     "1f02c357cbaa4507868f3e1d22c2cfca": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
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+       "_view_module": "@jupyter-widgets/base",
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+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
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+     "8820854e7d6a452cada83f1831f70e0a": {
+      "model_module": "@jupyter-widgets/output",
+      "model_module_version": "1.0.0",
+      "model_name": "OutputModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/output",
+       "_model_module_version": "1.0.0",
+       "_model_name": "OutputModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/output",
+       "_view_module_version": "1.0.0",
+       "_view_name": "OutputView",
+       "layout": "IPY_MODEL_e8e1800085ef4f89821ef5c9dfbe1805",
+       "msg_id": "",
+       "outputs": [
+        {
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+     },
+     "c2f4f64ea7944702bdfaad81cbbf122b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "VBoxModel",
+      "state": {
+       "_dom_classes": [
+        "widget-interact"
+       ],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "VBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "VBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_c04025ea966c4f608dcb50bbc76219f3",
+        "IPY_MODEL_8820854e7d6a452cada83f1831f70e0a"
+       ],
+       "layout": "IPY_MODEL_1f02c357cbaa4507868f3e1d22c2cfca",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "e8e1800085ef4f89821ef5c9dfbe1805": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "2.0.0",
+      "model_name": "LayoutModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "2.0.0",
+       "_model_name": "LayoutModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border_bottom": null,
+       "border_left": null,
+       "border_right": null,
+       "border_top": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
+      }
+     }
+    },
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/basic.ipynb b/docs/examples/basic.ipynb
index 61fd2f7f..005936fa 100644
--- a/docs/examples/basic.ipynb
+++ b/docs/examples/basic.ipynb
@@ -13,8 +13,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.306868Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.306459Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.728111Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.727785Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pytao import Tao"
@@ -22,8 +29,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.729768Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.729658Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.889848Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.889524Z"
+    }
+   },
    "outputs": [],
    "source": [
     "tao = Tao(\"-init $ACC_ROOT_DIR/bmad-doc/tao_examples/cesr/tao.init -noplot\")"
@@ -40,35 +54,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['# Values shown are for the Downstream End of each Element:',\n",
-       " '# Index  name          key                       s       l    beta   phi_a    eta   orbit    beta   phi_b    eta   orbit  Track',\n",
-       " '#                                                                a   [2pi]      x  x [mm]       b   [2pi]      y  y [mm]  State',\n",
-       " '      1  IP_L0         Marker                0.000   0.000    0.95   0.000  -0.00  -0.017    0.02   0.000   0.00   0.001  Alive',\n",
-       " '      2  CLEO_SOL#3    Solenoid              0.622   0.622    1.34   0.093  -0.02   1.470   21.81   0.244   0.00   0.041  Alive',\n",
-       " '      3  DET_00W       Marker                0.622   0.000    1.34   0.093  -0.02   1.470   21.81   0.244   0.00   0.041  Alive',\n",
-       " '      4  CLEO_SOL#4    Solenoid              0.638   0.016    1.36   0.094  -0.02   1.507   22.92   0.244   0.00   0.043  Alive',\n",
-       " '      5  Q00W\\\\CLEO_SOL Sol_Quad              1.755   1.117    7.73   0.160  -0.09   5.505   88.01   0.247  -0.01   0.486  Alive',\n",
-       " '      6  Q00W#1        Quadrupole            2.163   0.408   15.96   0.166  -0.13   8.151   76.38   0.248  -0.01   0.717  Alive',\n",
-       " '      7  D003          Drift                 2.493   0.331   27.02   0.169  -0.17  10.705   60.25   0.249  -0.02   0.931  Alive',\n",
-       " '      8  DET_01W       Marker                2.493   0.000   27.02   0.169  -0.17  10.705   60.25   0.249  -0.02   0.931  Alive',\n",
-       " '      9  D004          Drift                 2.924   0.431   45.79   0.171  -0.22  14.030   42.12   0.250  -0.02   1.209  Alive',\n",
-       " '     10  Q01W          Quadrupole            3.874   0.950   66.94   0.173  -0.26  16.851   28.95   0.255  -0.02   1.213  Alive',\n",
-       " '# Index  name          key                       s       l    beta   phi_a    eta   orbit    beta   phi_b    eta   orbit  Track',\n",
-       " '#                                                                a   [2pi]      x  x [mm]       b   [2pi]      y  y [mm]  State',\n",
-       " '# Values shown are for the Downstream End of each Element:']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.891427Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.891343Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.895528Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.895313Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.cmd(\"show lat 1:10\")"
    ]
@@ -82,20 +77,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[], []]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.914076Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.913953Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.923494Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.923258Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.cmds([\"set lattice model=design\", \"set ele Q00W x_offset = 1e-6\"])"
    ]
@@ -116,90 +107,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-------------------------\n",
-      "Tao> sho lat 1:10\n",
-      "# Values shown are for the Downstream End of each Element:\n",
-      "# Index  name          key                       s       l    beta   phi_a    eta   orbit    beta   phi_b    eta   orbit  Track\n",
-      "#                                                                a   [2pi]      x  x [mm]       b   [2pi]      y  y [mm]  State\n",
-      "      1  IP_L0         Marker                0.000   0.000    0.95   0.000  -0.00  -0.018    0.02   0.000   0.00   0.001  Alive\n",
-      "      2  CLEO_SOL#3    Solenoid              0.622   0.622    1.34   0.093  -0.02   1.469   21.81   0.244   0.00   0.042  Alive\n",
-      "      3  DET_00W       Marker                0.622   0.000    1.34   0.093  -0.02   1.469   21.81   0.244   0.00   0.042  Alive\n",
-      "      4  CLEO_SOL#4    Solenoid              0.638   0.016    1.36   0.094  -0.02   1.507   22.92   0.244   0.00   0.044  Alive\n",
-      "      5  Q00W\\CLEO_SOL Sol_Quad              1.755   1.117    7.73   0.160  -0.09   5.505   88.01   0.247  -0.01   0.487  Alive\n",
-      "      6  Q00W#1        Quadrupole            2.163   0.408   15.96   0.166  -0.13   8.151   76.38   0.248  -0.01   0.719  Alive\n",
-      "      7  D003          Drift                 2.493   0.331   27.02   0.169  -0.17  10.705   60.25   0.249  -0.02   0.932  Alive\n",
-      "      8  DET_01W       Marker                2.493   0.000   27.02   0.169  -0.17  10.705   60.25   0.249  -0.02   0.932  Alive\n",
-      "      9  D004          Drift                 2.924   0.431   45.79   0.171  -0.22  14.030   42.12   0.250  -0.02   1.210  Alive\n",
-      "     10  Q01W          Quadrupole            3.874   0.950   66.94   0.173  -0.26  16.851   28.95   0.255  -0.02   1.213  Alive\n",
-      "# Index  name          key                       s       l    beta   phi_a    eta   orbit    beta   phi_b    eta   orbit  Track\n",
-      "#                                                                a   [2pi]      x  x [mm]       b   [2pi]      y  y [mm]  State\n",
-      "# Values shown are for the Downstream End of each Element:\n",
-      "-------------------------\n",
-      "Tao> sho ele 4\n",
-      "Element # 4\n",
-      "Element Name: CLEO_SOL#4\n",
-      "Key: Solenoid\n",
-      "S_start, S:      0.622301,      0.637956\n",
-      "Ref_time_start, Ref_time:  2.075773E-09,  2.127992E-09\n",
-      "\n",
-      "Attribute values [Only non-zero values shown]:\n",
-      "    1  L                           =  1.5655000E-02 m        31  L_SOFT_EDGE                 =  0.0000000E+00 m\n",
-      "    3  R_SOLENOID                  =  0.0000000E+00 m\n",
-      "    5  KS                          = -8.5023386E-02 1/m      49  BS_FIELD                    =  1.5000000E+00 T\n",
-      "   10  FRINGE_TYPE                 =  None (1)               11  FRINGE_AT                   =  No_End (4)\n",
-      "   13  SPIN_FRINGE_ON              =  T (1)\n",
-      "   17  STATIC_LINEAR_MAP           =  F (0)\n",
-      "   47  PTC_CANONICAL_COORDS        =  T (1)\n",
-      "   53  P0C                         =  5.2890000E+09 eV           BETA                        =  1.0000000E+00\n",
-      "   54  E_TOT                       =  5.2890000E+09 eV           GAMMA                       =  1.0350315E+04\n",
-      "   64  REF_TIME_START              =  2.0757727E-09 sec      50  DELTA_REF_TIME              =  5.2219459E-11 sec\n",
-      "   65  INTEGRATOR_ORDER            = 0\n",
-      "   67  DS_STEP                     =  2.0000000E-01 m        66  NUM_STEPS                   = 1\n",
-      "   68  CSR_DS_STEP                 =  0.0000000E+00 m\n",
-      "\n",
-      "       TRACKING_METHOD              =  Bmad_Standard             APERTURE_AT                =  Exit_End\n",
-      "       MAT6_CALC_METHOD             =  Auto                      APERTURE_TYPE              =  Rectangular\n",
-      "       SPIN_TRACKING_METHOD         =  Tracking                  OFFSET_MOVES_APERTURE      =  F\n",
-      "       PTC_INTEGRATION_TYPE         =  Matrix_Kick               SYMPLECTIFY                =  F\n",
-      "       CSR_METHOD                   =  Off                       FIELD_MASTER               =  F\n",
-      "       SPACE_CHARGE_METHOD          =  Off                       LONGITUDINAL ORIENTATION   =       1\n",
-      "       FIELD_CALC                   =  Refer_to_Lords.           REF_SPECIES                =  Electron\n",
-      "\n",
-      "Slave_status: Super_Slave\n",
-      "Associated Super_Lord(s):\n",
-      "   Index   Name                             Type\n",
-      "     872   CLEO_SOL                         Solenoid\n",
-      "\n",
-      "Lord_status:  Not_a_Lord\n",
-      "\n",
-      "Twiss at end of element:\n",
-      "                          A              B            Cbar                        C_mat\n",
-      "  Beta (m)         1.36491299    22.91993604  |  -0.11412810   0.00652709     -0.08500246   0.03650720\n",
-      "  Alpha           -0.65684268   -35.88090683  |  -0.16215602   0.00350746     -0.09239853   0.03194148\n",
-      "  Gamma (1/m)      1.04874253    56.21479364  |   Gamma_c =   0.99967089       Mode_Flip = F\n",
-      "  Phi (rad)        0.59356742     1.53299611            X              Y              Z\n",
-      "  Eta (m)         -0.02444701     0.00048962    -0.02453414     0.00007942     0.00000000\n",
-      "  Etap            -0.03262118    -0.00146685    -0.03270254    -0.00198038     1.00000000\n",
-      "  dEta/ds         -0.03501127    -0.00147071    -0.03509154    -0.00204811     1.00000000\n",
-      "  Sigma            0.00052596     0.00002030     0.00000000     0.00000000\n",
-      "\n",
-      "Tracking: Electron,   State: Alive\n",
-      "         Position[mm] Momentum[mrad]        Spin   |\n",
-      "  X:       1.50654732     2.38873152               | t_particle [sec]:        2.12933094E-09  E_tot: 5.28896E+09\n",
-      "  Y:       0.04370624     0.06771334               | t_part-t_ref [sec]:      1.33877127E-12  PC:    5.28896E+09\n",
-      "  Z:      -0.40135353    -0.00717210               | (t_ref-t_part)*Vel [m]: -4.01353529E-04  Beta:  0.999999995\n",
-      "-------------------------\n",
-      "Tao> \n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.925018Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.924925Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.931787Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.931549Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "%%tao\n",
     "sho lat 1:10\n",
@@ -219,72 +136,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-------------------------\n",
-      "Tao> help python\n",
-      "The \"python\" command is like the \"show\" command in that the \"python\" command prints\n",
-      "information to the terminal. The difference is that the output from the \"show\" command is meant\n",
-      "for viewing by the user while the output of the \"python\" command is meant for easy\n",
-      "parsing. Format:\n",
-      "  python {-append <file_name>} {-noprint} <subcommand> <arguments>\n",
-      "  python {-write <file_name>} {-noprint} <subcommand> <arguments>\n",
-      "\n",
-      "The \"python\" command has \"-append\" and \"-write\" optional arguments which can be used to\n",
-      "write the results to a file. The \"python -append\" command will appended to the output file. The\n",
-      "\"python -write\" command will first erase the contents of the output file. Example:\n",
-      "  python -write d2.dat data_d2    ! Write to file \"d2.dat\"\n",
-      "\n",
-      "The \"-noprint\" option suppresses printing and is useful when writing large amounts of data to a\n",
-      "file.  The \"python\" command can be used to pass information to a parent process when Tao is run\n",
-      "as a subprocess.  The parent process may be any scripting program like Python, Perl, Tcl, etc.  In\n",
-      "particular, see the \"Python/GUI Interface\" chapter  for details on how to run\n",
-      "Tao as a Python subprocess.\n",
-      "\n",
-      "In terms of long term maintainability, the advantage of using the \"python\" command in the scripts\n",
-      "over the \"show\" command comes from the fact that the output syntax of \"show\" commands can (and\n",
-      "does) change.\n",
-      "\n",
-      "For further documentation on the python command and interfacing to python is in the \"Python/GUI\n",
-      "Interface chapter .\n",
-      "\n",
-      "Documentation on interfacing Python scripts to Tao's python command is given in the \"Tao Python\n",
-      "Command section .\n",
-      "\n",
-      "Note to programmers: For debugging, the \"show internal -python\" command will show the \"c_real\"\n",
-      "and \"c_integer\" arrays.\n",
-      "\n",
-      "List of possible \"<what_to_print>\" choices:\n",
-      "  beam, beam_init, branch1, bunch_comb, bunch_params, bunch1, bmad_com,\n",
-      "  building_wall_list, building_wall_graph, building_wall_point,\n",
-      "  building_wall_section, constraints, da_params, da_aperture, data,\n",
-      "  data_d2_create, data_d2_destroy, data_d_array, data_d1_array,\n",
-      "  data_d2, data_d2_array, data_set_design_value, data_parameter,\n",
-      "  datum_create, datum_has_ele, derivative, ele:ac_kicker, ele:cartesian_map,\n",
-      "  ele:chamber_wall, ele:control_var, ele:cylindrical_map, ele:elec_multipoles,\n",
-      "  ele:floor, ele:gen_grad_map, ele:grid_field, ele:gen_attribs, ele:head, ele:lord_slave,\n",
-      "  ele:mat6, ele:methods, ele:multipoles, ele:orbit, ele:param, ele:photon,\n",
-      "  ele:spin_taylor, ele:taylor, ele:twiss, ele:wake, ele:wall3d, em_field, enum,\n",
-      "  evaluate, floor_plan, floor_orbit, global, help, inum, lat_branch_list,\n",
-      "  lat_calc_done, lat_ele_list, lat_list, lat_param_units, matrix, merit, orbit_at_s,\n",
-      "  place_buffer, plot_curve, plot_graph, plot_histogram, plot_lat_layout, plot_line,\n",
-      "  plot_plot_manage, plot_graph_manage, plot_curve_manage, plot_list, plot_symbol,\n",
-      "  plot_transfer, plot1, ptc_com, ring_general, shape_list, shape_manage,\n",
-      "  shape_pattern_list, shape_pattern_manage, shape_pattern_point_manage, shape_set,\n",
-      "  show, species_to_int, species_to_str, spin_invariant, spin_polarization,\n",
-      "  spin_resonance, super_universe, twiss_at_s, universe, var_v1_create, var_v1_destroy,\n",
-      "  var_create, var_general, var_v1_array, var_v_array, var, wave\n",
-      "\n",
-      "-------------------------\n",
-      "Tao> \n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.933096Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.933015Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.935516Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.935307Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "%%tao\n",
     "help python"
@@ -299,39 +160,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['x;REAL;F; -1.77207910291870E-05',\n",
-       " 'px;REAL;F;  2.39054798135142E-03',\n",
-       " 'y;REAL;F;  9.77805901533289E-07',\n",
-       " 'py;REAL;F;  2.91412237463650E-06',\n",
-       " 'z;REAL;F; -3.99530687471794E-04',\n",
-       " 'pz;REAL;F; -7.17210139296654E-06',\n",
-       " 'spin;REAL_ARR;F;  0.00000000000000E+00;  0.00000000000000E+00;  0.00000000000000E+00',\n",
-       " 'field;REAL_ARR;F;  0.00000000000000E+00;  0.00000000000000E+00',\n",
-       " 'phase;REAL_ARR;F;  0.00000000000000E+00;  0.00000000000000E+00',\n",
-       " 's;REAL;F;  7.68426421416168E+02',\n",
-       " 't;REAL;F;  2.56319600136698E-06',\n",
-       " 'charge;REAL;F;  0.00000000000000E+00',\n",
-       " 'dt_ref;REAL;F;  0.00000000000000E+00',\n",
-       " 'p0c;REAL;F;  5.28899997531481E+09',\n",
-       " 'beta;REAL;F;  9.99999995332663E-01',\n",
-       " 'ix_ele;INT;F;868',\n",
-       " 'state;STR;F;Alive',\n",
-       " 'direction;INT;F;1',\n",
-       " 'species;SPECIES;F;Electron',\n",
-       " 'location;STR;F;Downstream_End']"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.936753Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.936670Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.938647Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.938461Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.cmd(\"python orbit_at_s end\")"
    ]
@@ -345,20 +183,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.939765Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.939693Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.941556Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.941368Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.cmd(\"python lat_list -array_out 1@0>>Q*|model orbit.floor.x\")"
    ]
@@ -372,47 +206,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.00000000e+00,  5.50519227e-03,  8.15061849e-03,  1.68506712e-02,\n",
-       "        1.30498232e-02, -1.28475438e-01, -6.17368437e-01, -1.63573126e+00,\n",
-       "       -3.15361609e+00, -4.96008216e+00, -8.44394576e+00, -1.25353213e+01,\n",
-       "       -1.53643077e+01, -1.93160719e+01, -2.35334256e+01, -2.86596035e+01,\n",
-       "       -3.40012341e+01, -4.11157702e+01, -4.73379418e+01, -5.39309791e+01,\n",
-       "       -6.08761235e+01, -6.66395259e+01, -7.38887343e+01, -8.14004767e+01,\n",
-       "       -8.91380421e+01, -9.70602503e+01, -1.07067453e+02, -1.15219118e+02,\n",
-       "       -1.23415239e+02, -1.31835984e+02, -1.39984608e+02, -1.48267474e+02,\n",
-       "       -1.57243533e+02, -1.65204340e+02, -1.72728163e+02, -1.80184446e+02,\n",
-       "       -1.85357654e+02, -1.92035945e+02, -2.00803297e+02, -2.06870811e+02,\n",
-       "       -2.12665465e+02, -2.18176442e+02, -2.23048894e+02, -2.27424214e+02,\n",
-       "       -2.31268351e+02, -2.34552350e+02, -2.35722776e+02, -2.38140768e+02,\n",
-       "       -2.39786174e+02, -2.41460795e+02, -2.42244506e+02, -2.42601932e+02,\n",
-       "       -2.42642705e+02, -2.42650645e+02, -2.42653709e+02, -2.42654860e+02,\n",
-       "       -2.42659283e+02, -2.42665715e+02, -2.42598206e+02, -2.42184674e+02,\n",
-       "       -2.41366882e+02, -2.39606403e+02, -2.37970183e+02, -2.35481963e+02,\n",
-       "       -2.34317756e+02, -2.30993039e+02, -2.27125231e+02, -2.22727551e+02,\n",
-       "       -2.17816004e+02, -2.12280141e+02, -2.06437555e+02, -2.00332917e+02,\n",
-       "       -1.91531576e+02, -1.84820355e+02, -1.79653411e+02, -1.72176809e+02,\n",
-       "       -1.64644374e+02, -1.56674250e+02, -1.47685973e+02, -1.39396225e+02,\n",
-       "       -1.31238484e+02, -1.22815238e+02, -1.14621366e+02, -1.06478606e+02,\n",
-       "       -9.64783788e+01, -8.85696847e+01, -8.08418091e+01, -7.33402427e+01,\n",
-       "       -6.61118703e+01, -6.03400294e+01, -5.34245487e+01, -4.68515948e+01,\n",
-       "       -4.06754335e+01, -3.36162850e+01, -2.82979320e+01, -2.32077253e+01,\n",
-       "       -1.88775830e+01, -1.50782620e+01, -1.22578107e+01, -8.16225874e+00,\n",
-       "       -4.68317228e+00, -2.92445682e+00, -1.48689125e+00, -5.48022495e-01,\n",
-       "       -1.20827634e-01, -1.39453226e-02, -1.41528499e-02,  1.91718055e-06,\n",
-       "       -5.55948643e-03, -1.54020138e-03])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.942723Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.942642Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.944988Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.944771Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.cmd_real(\"python lat_list -array_out 1@0>>Q*|model orbit.floor.x\")"
    ]
@@ -430,39 +233,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'x': 0.00310869012747717,\n",
-       " 'px': 0.00344600568175254,\n",
-       " 'y': 0.000183189785854024,\n",
-       " 'py': 0.000248941211794017,\n",
-       " 'z': -0.00040368167168426,\n",
-       " 'pz': -7.17210139174442e-06,\n",
-       " 'spin': array([0., 0., 0.]),\n",
-       " 'field': array([0., 0.]),\n",
-       " 'phase': array([0., 0.]),\n",
-       " 's': 1.2,\n",
-       " 't': 4.00411569818175e-09,\n",
-       " 'charge': 0.0,\n",
-       " 'dt_ref': 0.0,\n",
-       " 'p0c': 5288999975.31481,\n",
-       " 'beta': 0.999999995332663,\n",
-       " 'ix_ele': 5,\n",
-       " 'state': 'Alive',\n",
-       " 'direction': 1,\n",
-       " 'species': 'Electron',\n",
-       " 'location': 'Inside'}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.946237Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.946166Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.948463Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.948254Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.orbit_at_s(s_offset=1.2)"
    ]
@@ -476,22 +256,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 2.81123075e-03, -1.06250116e-03,  1.37663906e-04,  3.08061464e-04,\n",
-       "       -3.66558772e-04, -3.42869819e-04, -9.92517182e-06,  1.28279238e-03,\n",
-       "        2.66250141e-03,  2.68364369e-03])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.949682Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.949591Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.952015Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.951785Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.evaluate(\"data::cbar.11[1:10]|model\")"
    ]
@@ -507,27 +281,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "python lat_list -array_out -track_only @>>*|model ele.s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([0.      , 0.      , 0.622301, 0.622301, 0.637956])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.953272Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.953199Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.955639Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.955423Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "s = tao.lat_list(\"*\", \"ele.s\", verbose=True)\n",
     "s[0:5]"
@@ -542,20 +305,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(dtype('<i4'), dtype('<i4'))"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.956903Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.956815Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.959232Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.959013Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "state = tao.lat_list(\"*\", \"orbit.state\")\n",
     "ix = tao.lat_list(\"*\", \"ele.ix_ele\")\n",
@@ -571,20 +330,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['BEGINNING', 'IP_L0', 'CLEO_SOL#3', 'DET_00W', 'CLEO_SOL#4']"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.960535Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.960446Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.963246Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.963024Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "names = tao.lat_list(\"*\", \"ele.name\")\n",
     "names[0:5]"
@@ -592,8 +347,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.964470Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.964382Z",
+     "iopub.status.idle": "2024-08-08T19:06:07.966783Z",
+     "shell.execute_reply": "2024-08-08T19:06:07.966573Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%config InlineBackend.figure_format = 'retina'\n",
@@ -610,25 +372,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 413,
-       "width": 586
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:07.968006Z",
+     "iopub.status.busy": "2024-08-08T19:06:07.967921Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.054049Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.053799Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "plt.plot(tao.lat_list(\"*\", \"ele.s\"), tao.lat_list(\"*\", \"orbit.vec.1\"), marker=\".\");"
    ]
@@ -642,25 +395,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 415,
-       "width": 552
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.055650Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.055537Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.120610Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.120332Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "plt.plot(tao.lat_list(\"*\", \"ele.s\", flags=\"-array_out -track_only\"));"
    ]
@@ -674,25 +418,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 415,
-       "width": 552
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.122125Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.122014Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.192030Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.191776Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "plt.plot(tao.lat_list(\"*\", \"ele.s\", flags=\"-array_out -index_order\"));"
    ]
@@ -708,134 +443,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "beam\n",
-      "beam_init\n",
-      "bmad_com\n",
-      "branch1\n",
-      "building_wall_graph\n",
-      "building_wall_list\n",
-      "building_wall_point\n",
-      "building_wall_section\n",
-      "bunch1\n",
-      "bunch_comb\n",
-      "bunch_data\n",
-      "bunch_params\n",
-      "cmd\n",
-      "cmd_integer\n",
-      "cmd_real\n",
-      "cmds\n",
-      "constraints\n",
-      "da_aperture\n",
-      "da_params\n",
-      "data\n",
-      "data_d1_array\n",
-      "data_d2\n",
-      "data_d2_array\n",
-      "data_d2_create\n",
-      "data_d2_destroy\n",
-      "data_d_array\n",
-      "data_parameter\n",
-      "data_set_design_value\n",
-      "datum_create\n",
-      "datum_has_ele\n",
-      "derivative\n",
-      "ele_ac_kicker\n",
-      "ele_cartesian_map\n",
-      "ele_chamber_wall\n",
-      "ele_control_var\n",
-      "ele_cylindrical_map\n",
-      "ele_elec_multipoles\n",
-      "ele_floor\n",
-      "ele_gen_attribs\n",
-      "ele_gen_grad_map\n",
-      "ele_grid_field\n",
-      "ele_head\n",
-      "ele_lord_slave\n",
-      "ele_mat6\n",
-      "ele_methods\n",
-      "ele_multipoles\n",
-      "ele_orbit\n",
-      "ele_param\n",
-      "ele_photon\n",
-      "ele_spin_taylor\n",
-      "ele_taylor\n",
-      "ele_twiss\n",
-      "ele_wake\n",
-      "ele_wall3d\n",
-      "em_field\n",
-      "enum\n",
-      "evaluate\n",
-      "floor_orbit\n",
-      "floor_plan\n",
-      "get_output\n",
-      "global_opti_de\n",
-      "global_optimization\n",
-      "help\n",
-      "init\n",
-      "inum\n",
-      "lat_branch_list\n",
-      "lat_calc_done\n",
-      "lat_ele_list\n",
-      "lat_list\n",
-      "lat_param_units\n",
-      "matrix\n",
-      "merit\n",
-      "orbit_at_s\n",
-      "place_buffer\n",
-      "plot1\n",
-      "plot_curve\n",
-      "plot_curve_manage\n",
-      "plot_graph\n",
-      "plot_graph_manage\n",
-      "plot_histogram\n",
-      "plot_lat_layout\n",
-      "plot_line\n",
-      "plot_list\n",
-      "plot_symbol\n",
-      "plot_template_manage\n",
-      "plot_transfer\n",
-      "ptc_com\n",
-      "register_cell_magic\n",
-      "reset_output\n",
-      "ring_general\n",
-      "shape_list\n",
-      "shape_manage\n",
-      "shape_pattern_list\n",
-      "shape_pattern_manage\n",
-      "shape_pattern_point_manage\n",
-      "shape_set\n",
-      "show\n",
-      "space_charge_com\n",
-      "species_to_int\n",
-      "species_to_str\n",
-      "spin_invariant\n",
-      "spin_polarization\n",
-      "spin_resonance\n",
-      "super_universe\n",
-      "tao_global\n",
-      "taylor_map\n",
-      "twiss_at_s\n",
-      "universe\n",
-      "var\n",
-      "var_create\n",
-      "var_general\n",
-      "var_v1_array\n",
-      "var_v1_create\n",
-      "var_v1_destroy\n",
-      "var_v_array\n",
-      "wave\n"
-     ]
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.193460Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.193347Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.195469Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.195247Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "from pytao import interface_commands\n",
     "\n",
@@ -853,20 +470,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "116"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.196744Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.196655Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.198538Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.198315Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "len(all_cmds)"
    ]
@@ -880,57 +493,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\u001b[0;31mSignature:\u001b[0m\n",
-       "\u001b[0mtao\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_d2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0md2_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mix_uni\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mas_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mraises\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mDocstring:\u001b[0m\n",
-       "Output information on a d2_datum.\n",
-       "\n",
-       "Parameters\n",
-       "----------\n",
-       "d2_name\n",
-       "ix_uni : optional\n",
-       "\n",
-       "Returns\n",
-       "-------\n",
-       "string_list\n",
-       "\n",
-       "Notes\n",
-       "-----\n",
-       "Command syntax:\n",
-       "  python data_d2 {ix_uni}@{d2_name}\n",
-       "\n",
-       "Where:\n",
-       "  {ix_uni} is a universe index. Defaults to s%global%default_universe.\n",
-       "  {d2_name} is the name of the d2_data structure.\n",
-       "\n",
-       "Examples\n",
-       "--------\n",
-       "Example: 1\n",
-       " init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching\n",
-       " args:\n",
-       "   ix_uni: 1\n",
-       "   d2_name: twiss\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/Repos/pytao/pytao/interface_commands.py\n",
-       "\u001b[0;31mType:\u001b[0m      method"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.199716Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.199647Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.216906Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.216665Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.data_d2?"
    ]
@@ -948,8 +520,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.218095Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.218019Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.246792Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.246575Z"
+    }
+   },
    "outputs": [],
    "source": [
     "tao2 = Tao(\n",
@@ -966,28 +545,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['BEGINNING',\n",
-       " 'MAR.CSR',\n",
-       " 'FF.PIP00B',\n",
-       " 'FF.BEN01',\n",
-       " 'FF.PIP01',\n",
-       " 'FF.BEN02',\n",
-       " 'FF.PIP02A',\n",
-       " 'MAR.END',\n",
-       " 'END']"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.248065Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.247982Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.250028Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.249838Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.lat_list(\"*\", \"ele.name\")"
    ]
@@ -1011,20 +578,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['twiss_beta_x', 'twiss_alpha_x', 'twiss_gamma_x', 'twiss_phi_x', 'twiss_eta_x', 'twiss_etap_x', 'twiss_sigma_x', 'twiss_sigma_p_x', 'twiss_emit_x', 'twiss_norm_emit_x', 'twiss_beta_y', 'twiss_alpha_y', 'twiss_gamma_y', 'twiss_phi_y', 'twiss_eta_y', 'twiss_etap_y', 'twiss_sigma_y', 'twiss_sigma_p_y', 'twiss_emit_y', 'twiss_norm_emit_y', 'twiss_beta_z', 'twiss_alpha_z', 'twiss_gamma_z', 'twiss_phi_z', 'twiss_eta_z', 'twiss_etap_z', 'twiss_sigma_z', 'twiss_sigma_p_z', 'twiss_emit_z', 'twiss_norm_emit_z', 'twiss_beta_a', 'twiss_alpha_a', 'twiss_gamma_a', 'twiss_phi_a', 'twiss_eta_a', 'twiss_etap_a', 'twiss_sigma_a', 'twiss_sigma_p_a', 'twiss_emit_a', 'twiss_norm_emit_a', 'twiss_beta_b', 'twiss_alpha_b', 'twiss_gamma_b', 'twiss_phi_b', 'twiss_eta_b', 'twiss_etap_b', 'twiss_sigma_b', 'twiss_sigma_p_b', 'twiss_emit_b', 'twiss_norm_emit_b', 'twiss_beta_c', 'twiss_alpha_c', 'twiss_gamma_c', 'twiss_phi_c', 'twiss_eta_c', 'twiss_etap_c', 'twiss_sigma_c', 'twiss_sigma_p_c', 'twiss_emit_c', 'twiss_norm_emit_c', 'sigma_11', 'sigma_12', 'sigma_13', 'sigma_14', 'sigma_15', 'sigma_16', 'sigma_21', 'sigma_22', 'sigma_23', 'sigma_24', 'sigma_25', 'sigma_26', 'sigma_31', 'sigma_32', 'sigma_33', 'sigma_34', 'sigma_35', 'sigma_36', 'sigma_41', 'sigma_42', 'sigma_43', 'sigma_44', 'sigma_45', 'sigma_46', 'sigma_51', 'sigma_52', 'sigma_53', 'sigma_54', 'sigma_55', 'sigma_56', 'sigma_61', 'sigma_62', 'sigma_63', 'sigma_64', 'sigma_65', 'sigma_66', 'rel_min_1', 'rel_max_1', 'centroid_vec_1', 'rel_min_2', 'rel_max_2', 'centroid_vec_2', 'rel_min_3', 'rel_max_3', 'centroid_vec_3', 'rel_min_4', 'rel_max_4', 'centroid_vec_4', 'rel_min_5', 'rel_max_5', 'centroid_vec_5', 'rel_min_6', 'rel_max_6', 'centroid_vec_6', 'centroid_t', 'centroid_p0c', 'centroid_beta', 'ix_ele', 'direction', 'species', 'location', 's', 't', 'sigma_t', 'charge_live', 'n_particle_tot', 'n_particle_live', 'n_particle_lost_in_ele', 'beam_saved'])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.251226Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.251142Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.253159Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.252993Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "stats = tao.bunch_params(\"end\")\n",
     "stats.keys()"
@@ -1039,25 +602,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 413,
-       "width": 585
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.254297Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.254234Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.325683Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.325464Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "x = tao.bunch1(\"end\", coordinate=\"x\")\n",
     "px = tao.bunch1(\"end\", coordinate=\"px\")\n",
@@ -1073,20 +627,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('<i4')"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.327038Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.326944Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.329151Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.328945Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "state = tao.bunch1(\"end\", coordinate=\"state\")\n",
     "state.dtype"
@@ -1109,8 +659,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.330350Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.330278Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.556401Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.556039Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pmd_beamphysics import ParticleGroup"
@@ -1125,20 +682,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['x', 'px', 'y', 'py', 't', 'pz', 'status', 'weight', 'z', 'species'])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.558048Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.557935Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.560673Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.560448Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "data = tao.bunch_data(\"end\")\n",
     "data.keys()"
@@ -1146,25 +699,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 437,
-       "width": 576
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.561903Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.561808Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.768513Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.768262Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "P = ParticleGroup(data=data)\n",
     "\n",
@@ -1180,45 +724,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[INFO] tao_write_cmd:', '    Written: test.h5']"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.769838Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.769762Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.774124Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.773937Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.cmd(\"write beam -at end test.h5\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 437,
-       "width": 576
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.775311Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.775227Z",
+     "iopub.status.idle": "2024-08-08T19:06:08.939935Z",
+     "shell.execute_reply": "2024-08-08T19:06:08.939684Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "P2 = ParticleGroup(\"test.h5\")\n",
     "P2.plot(\"x\", \"px\")"
@@ -1226,8 +757,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:08.941343Z",
+     "iopub.status.busy": "2024-08-08T19:06:08.941234Z",
+     "iopub.status.idle": "2024-08-08T19:06:09.088745Z",
+     "shell.execute_reply": "2024-08-08T19:06:09.088167Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Cleanup\n",
@@ -1245,41 +783,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:09.091339Z",
+     "iopub.status.busy": "2024-08-08T19:06:09.091135Z",
+     "iopub.status.idle": "2024-08-08T19:06:09.095273Z",
+     "shell.execute_reply": "2024-08-08T19:06:09.094902Z"
+    },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.   , 0.   , 0.06 , 0.193, 0.263, 0.385, 0.445, 0.445, 0.445])"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tao.lat_list(\"*\", \"ele.s\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Exception handled: Command: python var foobar  causes error: ERROR detected: [ERROR | 2024-JUN-27 10:35:34] tao_python_cmd:\n",
-      "    \"python var foobar\": Not a valid variable name\n",
-      "INVALID\n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:09.097130Z",
+     "iopub.status.busy": "2024-08-08T19:06:09.096993Z",
+     "iopub.status.idle": "2024-08-08T19:06:09.099609Z",
+     "shell.execute_reply": "2024-08-08T19:06:09.099267Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "try:\n",
     "    tao.var(\"foobar\")\n",
@@ -1296,21 +826,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['[ERROR | 2024-JUN-27 10:35:34] tao_command:',\n",
-       " '    UNRECOGNIZED COMMAND: invalid_command']"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:09.101413Z",
+     "iopub.status.busy": "2024-08-08T19:06:09.101302Z",
+     "iopub.status.idle": "2024-08-08T19:06:09.104055Z",
+     "shell.execute_reply": "2024-08-08T19:06:09.103792Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.cmd(\"invalid_command\", raises=False)"
    ]
@@ -1328,8 +853,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:09.105707Z",
+     "iopub.status.busy": "2024-08-08T19:06:09.105590Z",
+     "iopub.status.idle": "2024-08-08T19:06:09.107600Z",
+     "shell.execute_reply": "2024-08-08T19:06:09.107326Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import logging\n",
@@ -1340,17 +872,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DEBUG:pytao.tao_ctypes.core:Tao> sho ele 2\n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:09.109172Z",
+     "iopub.status.busy": "2024-08-08T19:06:09.109030Z",
+     "iopub.status.idle": "2024-08-08T19:06:09.112306Z",
+     "shell.execute_reply": "2024-08-08T19:06:09.111962Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.cmd(\"sho ele 2\");"
    ]
@@ -1364,17 +895,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "rm: csr_wake.dat: No such file or directory\n"
-     ]
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:09.113840Z",
+     "iopub.status.busy": "2024-08-08T19:06:09.113736Z",
+     "iopub.status.idle": "2024-08-08T19:06:09.234680Z",
+     "shell.execute_reply": "2024-08-08T19:06:09.234086Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "!rm csr_wake.dat"
    ]
diff --git a/docs/examples/bunch.ipynb b/docs/examples/bunch.ipynb
index cd92d7b7..5d1b2403 100644
--- a/docs/examples/bunch.ipynb
+++ b/docs/examples/bunch.ipynb
@@ -9,8 +9,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.051652Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.051238Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.474468Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.474143Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pytao import Tao"
@@ -18,8 +25,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.476162Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.476030Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.477719Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.477498Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -35,8 +49,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.479020Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.478945Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.553162Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.552948Z"
+    }
+   },
    "outputs": [],
    "source": [
     "tao = Tao(\n",
@@ -55,150 +76,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'twiss_beta_x': 0.301344571266128,\n",
-       " 'twiss_alpha_x': -2.15210497361385,\n",
-       " 'twiss_gamma_x': 18.6880944753441,\n",
-       " 'twiss_phi_x': 0.0,\n",
-       " 'twiss_eta_x': -0.0481939319240897,\n",
-       " 'twiss_etap_x': -0.454973392190619,\n",
-       " 'twiss_sigma_x': 6.05472920750718e-05,\n",
-       " 'twiss_sigma_p_x': 0.000476810212736378,\n",
-       " 'twiss_emit_x': 1.21653911408495e-08,\n",
-       " 'twiss_norm_emit_x': 9.99817439089453e-07,\n",
-       " 'twiss_beta_y': 0.40783204160816,\n",
-       " 'twiss_alpha_y': 1.94408133190082,\n",
-       " 'twiss_gamma_y': 11.719168033485,\n",
-       " 'twiss_phi_y': 0.0,\n",
-       " 'twiss_eta_y': -0.0457317680186297,\n",
-       " 'twiss_etap_y': 0.0263984769531296,\n",
-       " 'twiss_sigma_y': 7.04370887189116e-05,\n",
-       " 'twiss_sigma_p_y': 0.000377580116845047,\n",
-       " 'twiss_emit_y': 1.21652615808021e-08,\n",
-       " 'twiss_norm_emit_y': 9.99806791146173e-07,\n",
-       " 'twiss_beta_z': 95.8242541991128,\n",
-       " 'twiss_alpha_z': -1.24059179279425,\n",
-       " 'twiss_gamma_z': 0.0264971328769493,\n",
-       " 'twiss_phi_z': 0.0,\n",
-       " 'twiss_eta_z': 0.0,\n",
-       " 'twiss_etap_z': 0.0,\n",
-       " 'twiss_sigma_z': 0.000899458451089799,\n",
-       " 'twiss_sigma_p_z': 1.49569425173884e-05,\n",
-       " 'twiss_emit_z': 8.44280513319509e-09,\n",
-       " 'twiss_norm_emit_z': 6.93875248996962e-07,\n",
-       " 'twiss_beta_a': 0.248530085331005,\n",
-       " 'twiss_alpha_a': -1.77499320489309,\n",
-       " 'twiss_gamma_a': 15.4136110252092,\n",
-       " 'twiss_phi_a': 0.0,\n",
-       " 'twiss_eta_a': 0.00124844704286057,\n",
-       " 'twiss_etap_a': 0.0111101590168622,\n",
-       " 'twiss_sigma_a': 0.0,\n",
-       " 'twiss_sigma_p_a': 0.0,\n",
-       " 'twiss_emit_a': 1.21664545105205e-08,\n",
-       " 'twiss_norm_emit_a': 9.99904832542642e-07,\n",
-       " 'twiss_beta_b': 0.335544868515057,\n",
-       " 'twiss_alpha_b': 1.59897669921574,\n",
-       " 'twiss_gamma_b': 9.63845045654778,\n",
-       " 'twiss_phi_b': 0.0,\n",
-       " 'twiss_eta_b': 0.00454529158526128,\n",
-       " 'twiss_etap_b': -0.0218933442291171,\n",
-       " 'twiss_sigma_b': 0.0,\n",
-       " 'twiss_sigma_p_b': 0.0,\n",
-       " 'twiss_emit_b': 1.21741461356351e-08,\n",
-       " 'twiss_norm_emit_b': 1.00053697176739e-06,\n",
-       " 'twiss_beta_c': 95.7148956246458,\n",
-       " 'twiss_alpha_c': -1.2389734965013,\n",
-       " 'twiss_gamma_c': 0.0264430499032274,\n",
-       " 'twiss_phi_c': 0.0,\n",
-       " 'twiss_eta_c': 1.2389734965013,\n",
-       " 'twiss_etap_c': 0.0264430499032274,\n",
-       " 'twiss_sigma_c': 0.0,\n",
-       " 'twiss_sigma_p_c': 0.0,\n",
-       " 'twiss_emit_c': 8.43349923229653e-09,\n",
-       " 'twiss_norm_emit_c': 6.93110439884201e-07,\n",
-       " 'sigma_11': 3.66649417909144e-09,\n",
-       " 'sigma_12': 2.61861040625002e-08,\n",
-       " 'sigma_13': -1.48398138752216e-13,\n",
-       " 'sigma_14': -4.74391717589424e-14,\n",
-       " 'sigma_15': -4.67916776621502e-10,\n",
-       " 'sigma_16': -1.07814707503323e-11,\n",
-       " 'sigma_21': 2.61861040625002e-08,\n",
-       " 'sigma_22': 2.27394287142704e-07,\n",
-       " 'sigma_23': -1.40343913297772e-12,\n",
-       " 'sigma_24': 4.92122677207163e-14,\n",
-       " 'sigma_25': -4.44903635737789e-09,\n",
-       " 'sigma_26': -1.01782156471668e-10,\n",
-       " 'sigma_31': -1.48398138752216e-13,\n",
-       " 'sigma_32': -1.40343913297772e-12,\n",
-       " 'sigma_33': 4.96185133335392e-09,\n",
-       " 'sigma_34': -2.36505280107631e-08,\n",
-       " 'sigma_35': 1.51286568415934e-13,\n",
-       " 'sigma_36': -1.02306597442692e-11,\n",
-       " 'sigma_41': -4.74391717589424e-14,\n",
-       " 'sigma_42': 4.92122677207163e-14,\n",
-       " 'sigma_43': -2.36505280107631e-08,\n",
-       " 'sigma_44': 1.42566900535742e-07,\n",
-       " 'sigma_45': -3.27450023921361e-13,\n",
-       " 'sigma_46': 5.90560669695479e-12,\n",
-       " 'sigma_51': -4.67916776621502e-10,\n",
-       " 'sigma_52': -4.44903635737789e-09,\n",
-       " 'sigma_53': 1.51286568415934e-13,\n",
-       " 'sigma_54': -3.27450023921361e-13,\n",
-       " 'sigma_55': 8.09025505236861e-07,\n",
-       " 'sigma_56': 1.0474074756403e-08,\n",
-       " 'sigma_61': -1.07814707503323e-11,\n",
-       " 'sigma_62': -1.01782156471668e-10,\n",
-       " 'sigma_63': -1.02306597442692e-11,\n",
-       " 'sigma_64': 5.90560669695479e-12,\n",
-       " 'sigma_65': 1.0474074756403e-08,\n",
-       " 'sigma_66': 2.2371012946846e-10,\n",
-       " 'rel_min_1': -0.000190703788737589,\n",
-       " 'rel_max_1': 0.000175099474147119,\n",
-       " 'centroid_vec_1': -1.0077897456586e-07,\n",
-       " 'rel_min_2': -0.00145920753512278,\n",
-       " 'rel_max_2': 0.00133407063334133,\n",
-       " 'centroid_vec_2': -9.21157518145313e-07,\n",
-       " 'rel_min_3': -0.000205532005074851,\n",
-       " 'rel_max_3': 0.000204275481451832,\n",
-       " 'centroid_vec_3': 5.35418306356941e-13,\n",
-       " 'rel_min_4': -0.00119689779899842,\n",
-       " 'rel_max_4': 0.00125449039844888,\n",
-       " 'centroid_vec_4': -2.53681610104627e-11,\n",
-       " 'rel_min_5': -0.00261715445721329,\n",
-       " 'rel_max_5': 0.00278367096446691,\n",
-       " 'centroid_vec_5': -7.53655670445237e-08,\n",
-       " 'rel_min_6': -1.6798314003975e-05,\n",
-       " 'rel_max_6': 2.8011707294875e-05,\n",
-       " 'centroid_vec_6': -5.77005927461274e-06,\n",
-       " 'centroid_t': 1.48447035006252e-09,\n",
-       " 'centroid_p0c': 41996891.3143949,\n",
-       " 'centroid_beta': 0.999925982822,\n",
-       " 'ix_ele': 8,\n",
-       " 'direction': 1,\n",
-       " 'species': 'Electron',\n",
-       " 'location': 'Downstream_End',\n",
-       " 's': 0.444999999999986,\n",
-       " 't': 1.48447035006252e-09,\n",
-       " 'sigma_t': 3.00049253444132e-12,\n",
-       " 'charge_live': 7.70000000000011e-11,\n",
-       " 'n_particle_tot': 1000,\n",
-       " 'n_particle_live': 1000,\n",
-       " 'n_particle_lost_in_ele': 0,\n",
-       " 'beam_saved': True}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.554560Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.554465Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.558252Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.558045Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "stats = tao.bunch_params(\"end\")\n",
     "stats"
@@ -213,20 +100,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.576223Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.576125Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.578083Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.577858Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "stats[\"beam_saved\"]"
    ]
@@ -246,42 +129,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.69327762e-07, -4.58088151e-06,  4.46517206e-06, -1.72815751e-06,\n",
-       "        9.38761727e-06,  5.93297413e-05, -6.70659004e-05,  4.15474873e-05,\n",
-       "       -7.45202256e-05,  5.06597769e-05])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.579304Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.579214Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.581499Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.581254Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.bunch1(\"end\", \"x\")[0:10]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8], dtype=int32)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.582778Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.582691Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.584759Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.584527Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.bunch1(\"end\", \"ix_ele\")[0:10]"
    ]
@@ -297,8 +170,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.586019Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.585934Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.587358Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.587151Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt"
@@ -306,8 +186,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.588513Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.588439Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.593745Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.593515Z"
+    }
+   },
    "outputs": [],
    "source": [
     "%config InlineBackend.figure_format = 'retina' # Nicer plotting\n",
@@ -316,8 +203,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.594975Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.594905Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.596850Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.596622Z"
+    }
+   },
    "outputs": [],
    "source": [
     "xdat = tao.bunch1(\"end\", \"x\")\n",
@@ -331,25 +225,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OOii9e/fORRddlBdffDE//vGPc9lll21QvU9+8pMb2jIAAABQIO4pVAETJkyou4fPkCFDVgiElvnWt76VPn36JEl+8pOfZPHixdVsEQAAACg4oVAF3H333XXLZ5555irHtGjRIl/84heTJLNnz3YjaAAAAKCqhEIV8MgjjyRJ2rdvn7322mu14wYMGFC3/Oijj1a8LwAAAIBlhEIVMHHixCRJr169UlOz+ts27bTTTitts74OOeSQbLnllmndunW6du2aAw88MD/84Q8ze/bsDZoXAAAAaJ7caLqBLVy4MLNmzUqSdO/efY1jO3XqlPbt22fevHl54403NqjuqFGj6pZnzpyZsWPHZuzYsbnqqqsyYsSIHH300es177Rp09a4fsaMGes1LwAAANC4hEIN7IMPPqhbXpfHzC8LhebOnbte9XbZZZccc8wx6d+/f7p165bFixfnpZdeym9/+9s8+OCDee+993L88cfn3nvvzeGHH17v+Xv06LFefQEAAAAbN6FQA1u4cGHdcuvWrdc6vk2bNkmSBQsW1LvW+eefnyuuuGKlz++999754he/mGHDhuWcc87JkiVLMnTo0EyePDnt2rWrdx0AAACg+REKNbC2bdvWLS9atGit4z/88MMkWa+wZvPNN1/j+rPPPjtPPPFEbrrppkyfPj133XVXTj/99HrVWNtlbTNmzEj//v3rNScAUBxjFw+peI0BrYZXdP5/7V/5y+V/81ivitf4VLeZFa8xfXqXitfYre8LFa6wc4Xnr46Hp2xW8RrXT2v6f05W4z2qGprL66D6mv538UamQ4cOdcvrcknYvHnzkqzbpWbr4+yzz85NN92UJBk7dmy9Q6G13RcJAAAAaJo8fayBtW3bNp07d06y9ps0z549uy4UqtS9e3be+f/+p+PNN9+sSA0AAACg6REKVUCfPn2SJJMnT05tbe1qx7344osrbdPQyuVyReYFAAAAmjahUAXsv//+ST66NOzJJ59c7bixY8fWLe+3334V6eWFF/7vmutu3bpVpAYAAADQ9AiFKuCYY46pW7755ptXOWbp0qX59a9/neSjG0YPHDiwIr0MGzasbnnAgAEVqQEAAAA0PUKhCujfv38OOOCAJMnw4cMzbty4lcb8+7//eyZOnJgk+cY3vpFWrVqtsH7EiBEplUoplUqrfOz8c889l8mTJ6+xj2HDhmX48I+exvGxj30sxx577Pq8HAAAAKAZ8vSxCrn22muz3377ZcGCBfnsZz+bSy65JAMHDsyCBQsycuTI3HDDDUmSHXbYId/61rfqPf+TTz6ZoUOHZuDAgTn88MOzyy67ZMstt0xtbW1efPHF3HLLLXnooYeSJC1btsywYcPSvn37Bn2NAAAAQNMlFKqQPfbYI7/73e/y+c9/PnPmzMkll1yy0pgddtgh999//wqPsa+PJUuWZNSoURk1atRqx2y55ZYZPnx4jjrqqPWqAQAAADRPQqEKOvLII/Pss8/m2muvzf33359p06aldevW6dWrV0488cScd9552WSTTdZr7kGDBtVdmvb000/nf//3f/POO++kXC5niy22yG677ZbDDjssgwcPTseOHRv4lQEAAABNnVCowrbZZpv8+Mc/zo9//ON6bTd48OAMHjx4teu7du2aL33pS/nSl760gR0CAAAAReRG0wAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABRQTWM3AABA4xhaGlnxGpNq5lW8Ru/a9hWd/zeP9aro/EnyzRPGVbzGFv80q+I1urw5s+I1Zk7vUtH5p0zvWNH5q6Ua33vVMHbxkMZuAZo1ZwoBAAAAFJBQCAAAAKCAhEIAAAAABSQUAgAAACggoRAAAABAAQmFAAAAAApIKAQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggGoauwEAgIY2oNXwis4/dvGQis5fLdu3Lle8xsHdaite45iv/bKi878/tUtF50+Slyb0qXiNmdMr/zr6H/+Xitf448X7VLxGpb2yqFT5IlX4S6+5vBdCkTlTCAAAAKCAhEIAAAAABSQUAgAAACggoRAAAABAAQmFAAAAAApIKAQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggGoauwEAgKZmQKvhFa/Ru7Z9xWts37riJXLM1+6sfJFmYMd+EyteY/QfP13xGuPvrHyNT/V9paLz/+axXhWdP0km1cyreI2xi4dUvEY13gsrrRpfJ9iYOVMIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAhEIAAAAABVTT2A0AADS03rXtKzr/pJp5FZ2/ORl/56crXmPHfhMrXqM56NZtZsVrXDZ+64rXqPT3dzWMXTyksVtoEM3ldUCROVMIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABRQTTWLLVmyJH//+98zbdq0zJw5M++8807atWuXLl26pEuXLtlll12y1VZbVbMlAAAAgEKqeCg0adKk/O53v8uYMWPy+OOPZ8GCBWscv/322+eAAw7I5z73uRxxxBFp3bp1pVsEAAAAKJyKhUJ33nlnrr322vz1r39NkpTL5XXabvLkyXnllVcyYsSIbLbZZhkyZEi+9rWv5eMf/3ilWgUAAAAonAYPhf7whz/k0ksvzcSJE+uCoLZt22b33XdP//79s9dee6Vr167ZYost0qlTpyxYsCDvvvtuZs+enZdffjkTJkzI+PHj88Ybb+S9997Lj3/84/znf/5nvvSlL+Xyyy93eRkAAABAA2jQUOiggw7K2LFjUy6X07Zt2wwaNCinn356Pve5z9X7MrDJkyfn1ltvzW233ZaXXnopw4YNy6233ppbbrklRxxxREO2DQAAAFA4Dfr0sTFjxmTLLbfMlVdembfeeit33HFHjj322PW6L1CvXr1y2WWXZeLEifnLX/6Sz3zmM5kzZ06eeuqphmwZAAAAoJAa9Eyhq666Kl/96lezySabNOS02X///fPAAw9kwoQJmTVrVoPODQAAAFBEDRoKXXjhhQ053Ur69etX0fkBAAAAiqJBLx8DAAAAoGmo2CPpAaBIBrQa3tgtNIixi4dUvEZVvlYV/g3nX/vPqGyBJF26zax4jZnTu1S8RjVs9vHKfq3G3/npis6fJP2P/0vFa2RCn8rXaAZuKp/S2C0AVI0zhQAAAAAKqEH/H+1LX/pSQ06XJCmVShk+vHn87ysAAADAxqJBQ6ERI0akVCo12HzlclkoBAAAAFABDRoKffzjH19jKDR//vzMnPl/13y3bt06W2yxRcrlcmbPnp1FixYl+ejsoM6dOzf4o+0BAAAA+EiDhkJTpkxZ7bqnnnoqJ5xwQmbPnp0vf/nL+dKXvpTdd989LVu2TJIsWbIkzzzzTIYPH54bb7wx7du3zx133JE999yzIVsEAAAAIFV6+tiMGTMyaNCgvP/++3nggQcycODAlca0bNkye+65Z/bcc8+cdNJJOeywwzJo0KA8/fTT2XrrravRJgAAAEBhVOXpY9dcc03efvvtnH/++asMhP7RgAEDcv755+ftt9/Oj370oyp0CAAAAFAsVQmF7rvvvpRKpXzuc59b522Wjb3//vsr1RYAAABAYVUlFJo2bVqSpG3btuu8zbKxy7YFAAAAoOFUJRRq3759kmTChAnrvM348eOTxBPIAAAAACqgKqFQ3759Uy6X8/3vf3+FR9Kvzttvv50f/OAHKZVK6devXxU6BAAAACiWqoRC5513XpJk+vTp2XvvvXP33Xdn6dKlK41bunRp/vjHP2bffffNm2++mST5+te/Xo0WAQAAAAqlKo+kHzRoUL7+9a/npz/9aV5//fUcf/zx6dSpU/bYY4907do1pVIp//u//5u///3veffdd1Mul5N8FAgddthh1WgRAAAAoFCqEgolyU9+8pP06NEjl19+eebPn5933303//3f/73CmGVhULt27fK9730v3/zmN6vVHgAAAEChVC0USpJvfetb+cIXvpBf/epXGTVqVJ577rnMnj07SdKpU6fssssu+cxnPpMzzjgjXbt2rWZrAAAAAIVS1VAoSbp27ZoLL7wwF154YbVLAwAAAPD/q8qNpgEAAADYuAiFAAAAAAqo6pePAUBzNHbxkIrXGNBqeLOoUQ3/2n9GRefv0m1mRedPkpnTu1S8xu6jf1nxGq1/1qviNSptx34TK17jJxefWfEaPbvNqXiN3rXtK15jUs28is5fjffBavzMAFgXVT1TaNGiRbn55ptz9NFHp2fPntl0003TsmXLNX7U1MitAAAAABpa1RKXl19+Occcc0xeeumlukfPAwAAANA4qhIKzZs3L4cffnhee+21tGjRIkcffXS6dOmSG2+8MaVSKd/5zncye/bsPPHEE3n88cdTKpWy77775pBDDqlGewAAAACFU5VQ6Prrr89rr72Wli1b5oEHHshBBx2U559/PjfeeGOS5Lvf/W7d2L///e/5/Oc/n8cffzynnHJKzjvvvGq0CAAAAFAoVbmn0L333ptSqZSTTjopBx100BrH7r777hk9enS6du2ab37zm3nyySer0SIAAABAoVQlFHrhhReSJMcee+wq1//jPYa6dOmSb37zm6mtrc3PfvazivcHAAAAUDRVCYXee++9JMk222xT97k2bdrULc+dO3elbfbbb78kydixYyvbHAAAAEABVSUU2mSTTZIkpVKp7nObb7553fLUqVNX2mbZ2LfeequyzQEAAAAUUFVCoW233TZJMn369LrPde7cOVtssUWS5K9//etK2yy7l1Dr1q2r0CEAAABAsVQlFOrbt2+S5Iknnljh8wcffHDK5XJ+9KMf5Z133qn7/JQpU3LVVVelVCpl9913r0aLAAAAAIVSlVDokEMOSblczj333LPC57/+9a8nSV599dXssMMOOfHEE/O5z30uu+22W6ZNm5YkOeuss6rRYsVMnTo1F1xwQfr06ZP27dtniy22SP/+/XPNNddk/vz5DVZn5MiROfTQQ7P11lunbdu26dmzZ77whS/k8ccfb7AaAAAAQPNRlVDoiCOOyKc//el06NAhr7zySt3n99tvv1x22WUpl8uZPXt27rrrrvz5z3/OBx98kCQ588wzc9ppp1WjxYq4//77s+uuu+bf//3f8+KLL2b+/PmZPXt2JkyYkAsvvDB77rlnXn311Q2qsXDhwhx55JE59dRT8+CDD+att97Khx9+mNdffz233HJL9ttvv3zve99roFcEAAAANBdVu9H0mDFj8sgjj2T77bdfYd0VV1yRhx56KCeffHI+8YlPZMcdd8yRRx6Z3//+97npppuq0V5FPPPMMznppJPy/vvvZ9NNN82VV16Zxx57LA8//HC+/OUvJ0leeumlfO5zn1vl09fW1ZAhQ3LfffclSQYOHJi7774748ePz/Dhw7P99ttn6dKlueyyy5r01xIAAABoeDWN3UDy0b2FDj744MZuo0Gdf/75mT9/fmpqavLggw9m3333rVt30EEHpXfv3rnooovy4osv5sc//nEuu+yyetcYO3Zsbr311iTJkUcemT/84Q9p2bJlkqRfv3456qijstdee2Xq1Km56KKLcsIJJ6zw1DcAAACguKpyptCvf/3r/PrXv87f/va3apRrdBMmTMiYMWOSfHQmz/KB0DLf+ta30qdPnyTJT37ykyxevLjeda6++uokScuWLXPdddfVBULLdO7cOVdddVWSZPbs2Rk+fHi9awAAAADNU1VCocGDB+fMM8/M66+/Xo1yje7uu++uWz7zzDNXOaZFixb54he/mOSjwGZZiLSu5s6dm4cffjjJRzfy7t69+yrHHXfccenYsWOS5K677qpXDQAAAKD5qkootNlmmyVJevfuXY1yje6RRx5JkrRv3z577bXXascNGDCgbvnRRx+tV43x48fnww8/XGmef9S6devss88+dduszxlJAAAAQPNTlXsKbbvttnnmmWcye/bsapRrdBMnTkyS9OrVKzU1q/8S77TTTittU98a/zjP6uo8+OCDqa2tzaRJk7Lzzjuvc51p06atcf2MGTPWeS4ANszYxUMqXuO2bYdVvMbDUzareI3p07tUvEalHXDBHype4+2LVr7EvaEtqHiFZLOPz6zo/O++2bmi8yfJK4tKFa/x52lV+NW/Zl7FS1TjvRCgKKoSCh177LH5+9//nnvvvTcHHXRQNUo2moULF2bWrFlJstpLupbp1KlT2rdvn3nz5uWNN96oV53lx6+tTo8ePVbYrj6h0PLbAgAAAM1HVS4f+8Y3vpFtttkmv/jFL/Lf//3f1SjZaD744IO65U033XSt49u3b58k9X4sfX3qLKuxPnUAAACA5qkqZwp17NgxDz30UE444YQceuihOfPMM3Paaadl1113TadOnVIqVf502WpZuHBh3XLr1q3XOr5NmzZJkgUL6ndydX3qLKuxPnXWdgbTjBkz0r9//3rNCQAAADS+qoRCyz8qvVwuZ/jw4ev8ePRSqZTa2tpKtdbg2rZtW7e8aNGitY5fdrPodu3aVazOshrrU2dtl6YBAAAATVNVQqFyubzGfzcnHTp0qFtel0u15s376GZ863Kp2frWWVZjfeoAAAAAzVNVQqHLL7+8GmU2Cm3btk3nzp0za9astT65a/bs2XWBTX1v6Lz8GTzTpk1L3759Vzt2+UvA3DgaAAAASIRCFdGnT5888sgjmTx5cmpra1f7WPoXX3xxhW3qY/kniC0/z5rq1NTUpFevXvWqAwAAADRPVXn6WNHsv//+ST66bOvJJ59c7bixY8fWLe+33371qtGvX7+6G0wvP88/WrRoUR5//PGVtgEAAACKTShUAcccc0zd8s0337zKMUuXLs2vf/3rJMnmm2+egQMH1qtGhw4dcvDBBydJRo0atdpL1e66667MmTMnSXLsscfWqwYAAADQfAmFKqB///454IADkiTDhw/PuHHjVhrz7//+75k4cWKS5Bvf+EZatWq1wvoRI0akVCqlVCrliiuuWGWdCy64IElSW1ubr371q1myZMkK62fNmpVvf/vbST4KnoYOHbpBrwsAAABoPqpyT6HlPfPMM3nkkUfy6quv5oMPPlgpyPhHpVJpnR9fvzG59tprs99++2XBggX57Gc/m0suuSQDBw7MggULMnLkyNxwww1Jkh122CHf+ta31qvGQQcdlFNOOSUjR47MPffck0MOOSTnn39+unXrlueeey5XXnllpk6dmiT54Q9/mE6dOjXY6wMAAACatqqFQhMnTsyQIUPyt7/9bZ23KZfLTTYU2mOPPfK73/0un//85zNnzpxccsklK43ZYYcdcv/996/wePn6+uUvf5k5c+bkT3/6U0aPHp3Ro0evsL5Fixa59NJLc/bZZ693DQAAAKD5qUoo9Oqrr2b//ffPe++9l3K5nOSje+JsvvnmadGi+V7BduSRR+bZZ5/Ntddem/vvvz/Tpk1L69at06tXr5x44ok577zzsskmm2xQjXbt2uX+++/PrbfemhEjRuSZZ57Je++9l6222ioHHHBAzjvvvOy7774N9IoAAACA5qIqodBll12W2bNnp0WLFrngggty7rnnpmfPntUo3ei22Wab/PjHP86Pf/zjem03ePDgDB48eJ3Hn3baaTnttNPq2R0AAABQVFUJhUaNGpVSqZTzzz8/V111VTVKAgAAALAGVbl2a9kj0Y8//vhqlAMAAABgLaoSCvXo0SNJUlNT9YedAQAAALAKVQmFDj300CTJ+PHjq1EOAAAAgLUolZc9DqyCXn/99ey2227ZfPPN89RTT2WLLbaodEmqZNq0aXVngr3xxhvp3r17I3cE0HwNaDW84jV617aveI1vnjCu4jWeeWLnis5/zNfurOj8SfL+1C4Vr7HZx2dWvEY1Xse7b3au6Pzn3r1rRedPkl8c82zFa1TjdVTD2MVDGrsFgEZRib+/q3Km0DbbbJO77rors2fPzqc+9amMGjWqGmUBAAAAWI2q3eTnoIMOylNPPZVPfepTOfTQQ9OpU6f06tUrm2yyyRq3K5VKefjhh6vUJQAAAEAxVC0Ueuyxx/KFL3whs2bNSrlczrvvvrvGewyVSqWUy+WUSqVqtQgAAABQGFUJhV544YV89rOfzYIFC1Iul9O2bdv07t07m2++eVq0qMoVbAAAAAAspyqh0He/+93Mnz8/bdq0yY9//OOceeaZadu2bTVKAwAAALAKVQmF/vrXv6ZUKuWSSy7JueeeW42SAAAAAKxBVa7dmj17dpLksMMOq0Y5AAAAANaiKqFQ9+7dkyRLliypRjkAAAAA1qIqodCRRx6ZJPnLX/5SjXIAAAAArEVVQqELL7wwXbt2zY9+9KNMmTKlGiUBAAAAWIOqhEJbbbVVHnjggXTs2DF77713brzxxrz33nvVKA0AAADAKlTl6WPbbbddkmT+/PmZOXNmzjnnnJx77rnp3LlzNtlkkzVuWyqV8sorr1SjTQAAAIDCqEoo9I+XjJXL5ZTL5bz99ttr3bZUKlWoKwAAAIDiqkoodMYZZ1SjDAAAAADrqCqh0M0331yNMgAAAACso6rcaBoAAACAjYtQCAAAAKCAqnL5GACsyYBWwyteo3dt+4rXqLgq/NQ+uOf7Fa+xxT/NqniN3fJCxWuwbkb/8dON3UKTsPPvr618kSq8145dPKTiNQBoOA16ptDvf//7hpxuJdOmTctjjz1W0RoAAAAARdCgodDJJ5+cXXbZpcHDoalTp+bcc89Nr169MmrUqAadGwAAAKCIGjQU6t27d55//vmccsop6dmzZy655JI8//zz6zXXvHnzcsstt+Twww9Pr169csMNN2TJkiXp1atXQ7YMAAAAUEgNeneC559/Pj/96U/zwx/+MFOnTs1VV12Vq666Kr17984+++yTfv36ZY899kjXrl3TqVOndOrUKQsWLMi7776b2bNn5+WXX86ECRMyfvz4jB8/PgsXLky5XE6SHH744bnqqqvyyU9+siFbBgAAACikBg2Fampq8s1vfjNnn312rrvuuvz85z/P1KlT8/LLL2fSpEn5zW9+s07zLAuCWrZsmaOPPjoXXnhh9t5774ZsFQAAAKDQKvJI+vbt2+fCCy/Mq6++mv/6r//KmWeemW222SblcnmtH23bts2AAQNy9dVX5/XXX88dd9whEAIAAABoYBV9uG2LFi1y6KGH5tBDD02SvPnmm3nssccybdq0zJw5M++++27atm2bLl26pEuXLtlll13St2/ftGrVqpJtAQAAABReRUOhf/RP//RPOfHEE6tZEgAAAIBVqMjlYwAAAABs3IRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAhEIAAAAABVTT2A2szdKlS9OihewKoDkbu3hIY7ewwW7bdljFa0yaVvkf2926zax4jc0+3jxqVNroP3664jV26/tCxWtMmd6x4jVeWVSqbIGaeZWdP8mAVsMrXqM5vNcC0LCqkrZceumlWbJkSb23e+WVV7L//vtXoCMAAACAYqtKKHTllVdmv/32yyuvvLLO2wwfPjx77LFH/va3v1WwMwAAAIBiqtp1WRMmTMjuu++e4cPXfGrsO++8k+OOOy5nnXVW5s6dm7Zt21apQwAAAIDiqEoo9B//8R9p06ZN5s2bl7POOivHHXdc3nnnnZXGPfDAA9lll13yxz/+MeVyObvttlsmTJhQjRYBAAAACqUqodA3vvGNPPHEE9l1111TLpfzxz/+MbvuumseeuihJMmHH36Yr33taxk0aFDeeuutlEqlXHDBBRk/fnx23nnnarQIAAAAUChVu3xs5513zoQJE/Ktb30rSTJjxowcdthhOfvss7PnnnvmuuuuS7lcTo8ePTJq1KhcffXVadWqVbXaAwAAACiUqj7rvVWrVvnRj36UUaNGpUePHimXy7npppsyceLEJMmpp56aZ599NgceeGA12wIAAAAonKqGQsv07t0722yzTUqlUpKkVCqlY8eOOeuss7LZZps1RksAAAAAhVL1UOj222/Pbrvtlr/+9a8pl8vp3bt3yuVy5syZk8985jO5+OKLU1tbW+22AAAAAAqlaqHQ3Llzc8YZZ+TUU0/N7NmzU1NTkx/+8IeZOHFi7rvvvnTt2jVLlizJ1Vdfnb333jsvvvhitVoDAAAAKJyqhEKPPfZYdtttt9xyyy0pl8vZcccdM27cuFx00UUplUoZNGhQnnvuuRxxxBEpl8v5+9//nr322is///nPq9EeAAAAQOFUJRT69Kc/nSlTpqRcLuess87KU089lT333HOFMZ07d84999yT6667Lu3atcuCBQvy9a9/PYMGDapGiwAAAACFUpVQaOnSpdlyyy3zxz/+Mddff33atWu32rHnnHNOnnrqqey1114pl8t54IEHqtEiAAAAQKFUJRQ69NBD89xzz+XII49cp/E77LBDxo0bl4svvrjuCWUAAAAANJyaahT5r//6r3pvU1NTkyuvvNLlYwAAAAAVUPVH0tfXfvvt19gtAAAAADQ7VTlTaHVqa2sze/bsJEmnTp1SU9Oo7QAAAAAURtVTmOeffz7XX399Ro0alUmTJqVcLidJSqVSevfunc985jM5++yz88lPfrLarQGwCkNLIyteY1LNvIrX6F3bvqLzH9yzotMnSc7pXlvxGv2P/0vFa7w2rk/FazQH3brNrHiNP96zT8VrXPzhqRWvwboZ0Gp4xWuMXTyk4jUAaDhVu3xs6dKlOf/887P77rvnuuuuy0svvZSlS5emXC6nXC5n6dKleemll3Lddddljz32yD//8z9n6dKl1WoPAAAAoFCqdqbQKaeckjvvvLPuzKBPfOIT6d+/f7baaquUy+W8/fbbmTBhQv7nf/4nS5YsyU9/+tNMnz49v/vd76rVIgAAAEBhVCUUuvXWW3PHHXekVCplt912yw033JB+/fqtcuwTTzyRs88+O08//XTuuOOOjBw5Mqeccko12gQAAAAojKpcPnbjjTcmSXbYYYc8+uijqw2EkqRv3775y1/+kh133DHlcjnDhg2rRosAAAAAhVKVUOjZZ59NqVTKt7/97bRvv/YbfbZv3z7f/va3kyTPPPNMpdsDAAAAKJyqhEKLFi1Kkuy6667rvM2ysYsXL65ITwAAAABFVpVQaJtttkmSvP/+++u8zZw5c1bYFgAAAICGU5VQ6Pjjj0+5XM6dd965ztssuzH1scceW8HOAAAAAIqpKqHQN7/5zWy33XYZNmxYbr/99rWOv+OOOzJs2LBsu+22ueCCC6rQIQAAAECxVCUU2myzzTJq1KjsueeeOfXUU3PMMcfk7rvvzptvvpnFixentrY2b775Zu6+++4ce+yxOfnkk7Pnnnvm4YcfzmabbVaNFgEAAAAKpaYaRVq2bFm3XC6Xc++99+bee+9d7fhyuZwnnngi22233WrHlEql1NbWNmifAAAAAEVRlVCoXC6v8d/rsg0AAAAADacqodDll19ejTIAAAAArCOhEAAAAEABVeVG0wAAAABsXIRCAAAAAAUkFAIAAAAoIKEQAAAAQAFV5UbTABujAa2GV3T+sYuHVHT+armpfErFa1R6X1TD9dMq/yP1F8c8W/EaP7n4zIrXOPPc+ype4903O1d0/mee2Lmi8yfJw1M2q3iNaqjG93dzeb+tNF8nAP6RM4UAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAhEIAAAAABSQUAgAAACggoRAAAABAAQmFAAAAAAqoprEbAGgsYxcPaewWmoShpZEVr3FY600qXuOVCs//i2OerXCF6ujZbU7Fa4z+46crXmPK9I4Vnb8aX6eDe75f8RoPT9ms4jWq8V47oNXwis7v5wUAzZUzhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAhEIAAAAABSQUAgAAACggoRAAAABAAQmFAAAAAApIKAQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQhU0f/78/OhHP0r//v2zxRZbZNNNN02fPn1ywQUXZOrUqRs8/5gxY1Iqldbp44orrtjwFwQAAAA0GzWN3UBz9corr+Rzn/tcXnrppRU+/+KLL+bFF1/MTTfdlFtvvTWDBg1qpA4BAACAIhMKVcDcuXNzxBFH1AVCX/7yl3PKKaekXbt2GT16dH7wgx/k/fffz4knnphx48Zl11133eCav/zlL9OvX7/Vru/atesG1wAAAACaD6FQBVxzzTV58cUXkyRXX311Lrzwwrp1++67bwYOHJhPf/rTmT9/fs4///z893//9wbX3HbbbfPJT35yg+cBAAAAisE9hRrY4sWLc+211yZJ+vTpk29961srjdl3330zZMiQJMno0aPz5JNPVrVHAAAAAGcKNbAxY8bkvffeS5KcccYZadFi1bnb4MGDM2zYsCTJXXfdlb322qtaLQJVMqDV8MZuoUH0TvuK1+jZbU7la1S8QuXNnN6l4jV26/tCxWtU43VU2vXTKv8r1NjFQype49SKVwAANmbOFGpgjzzySN3ygAEDVjuub9++ad/+oz+0Hn300Yr3BQAAALA8Zwo1sIkTJ9Yt77TTTqsdV1NTk+233z7PPvvsCtusr0suuSRTp07N22+/nfbt26dnz5458MADc+6552aHHXZY73mnTZu2xvUzZsxY77kBAACAxiMUamBvvPFGkqR9+/bZfPPN1zi2R48eefbZZzNz5sx8+OGHadOmzXrXHTduXN3ye++9l7///e/5+9//np/+9Ke59NJLc/nll6dUKtV73h49eqx3TwAAAMDGSyjUwD744IMkyaabbrrWscsuH0s+eoz9+oRCW2+9dY477rjsv//+2W677VJTU5OpU6fm3nvvzW9+85ssXrw43/3ud7No0aJ8//vfr/f8AAAAQPMkFGpgCxcuTJK0bt16rWOXD4EWLFhQ71r9+vXL66+/nlatWq3w+T333DPHHHNMzj777Hz2s5/N+++/nx/+8Ic56aSTsvvuu9erxrIzn1ZnxowZ6d+/f31bBwAAABpZYW80XVtbm1KptMEfI0aMWGHetm3bJkkWLVq01h4+/PDDuuV27drV+zW0b99+pUBoef3798/Pf/7zJEm5XK5bro/u3buv8WPrrbeu95wAAABA4ytsKFQpHTp0SPLR5WBrM2/evLrldbncbH2cfPLJ2WyzzZIkY8eOrUgNAAAAoOkp7OVjNTU1DfLUr388U6Z79+7529/+lnnz5uW9995b482ml12a1aVLlw26yfSa1NTUZIcddsiECRPy5ptvVqQGAAAA0PQUNhRK1vzI+PW18847584770ySvPjii9lnn31WOa62tjavvPJKkqRPnz4N3sfyyuVyRecHAAAAmh6XjzWw/fffv255TZdrPfHEE3WXj+23334V66e2tjYvv/xykqRbt24VqwMAAAA0LUKhBnbggQfW3cPnV7/61WrP0ln+BtXHHntsxfoZOXJk5syZkyQZMGBAxeoAAAAATYtQqIG1bt06X//615MkEydOzDXXXLPSmHHjxmX48OFJPgpq+vXrt9KYKVOm1D3h7MADD1xp/ezZszNmzJg19jJ+/Ph87WtfS5KUSqWcc8459Xw1AAAAQHNV6HsKVcqFF16Y3/3ud3n55Zdz0UUXZfLkyTnllFPSrl27jB49Ot///vdTW1ubdu3a5Sc/+cl61Xj//fczcODA7LrrrjnmmGOy1157Zeutt07Lli0zderU3HvvvfnNb36TxYsXJ0kuuOCC9O3btwFfJQAAANCUCYUqoEOHDrn//vszaNCgTJo0KTfccENuuOGGFcZ07Ngxv/3tb7P77rtvUK1nn302zz777GrXt2zZMpdeemkuu+yyDaoDAAAANC9CoQrp1atXnn766fz85z/P73//+0yePDmLFi1Kjx49MmjQoHzjG9/INttss97zd+vWLb///e8zbty4jB8/Pm+++WZmzZqVhQsXZrPNNsuOO+6YAw88MEOHDk3Pnj0b7oUBAAAAzYJQqILat2+fiy66KBdddFG9t+3Zs+caHyXfunXrnHDCCTnhhBM2pEUAAACgoNxoGgAAAKCAnCkE1NuAVsMrXmPs4iEVr9EcVOPrNLQ0suI1unWbWfEav3msV0XnnzJ9n4rOnyQ9u82peI3Hnti+4jVeWVSqeI2byqdUdP5TKzr7R7zXrrvm8joAoNqcKQQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACqmnsBoCmZ+ziIY3dQoP4QZvbKjp/Nb5OQ0sjK15j+9blite4bPzWFa9xWBVeR3Nw8YenVrzGgFbDK16j0t8bN5VPqej8SfN5rwUANl7OFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAhEIAAAAABSQUAgAAACggoRAAAABAAQmFAAAAAApIKAQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKKCaxm4AYFUGtBre2C1ssFdKIyte46byKRWv8YM2t1W8xmEtNql4jUp7ZVGp8kWmd6x4iWp8741dPKTiNQAAWDtnCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAahq7AYBVGbt4SMVrDGg1vOI1Ku0HbW6reI0/L51f8RrV0Lu2fUXnn1Qzr6LzJ8mkpRUvUZXvvWqo9Pd3c/k6AQDF5kwhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACqmnsBqBIBrQaXvEaYxcPqXiNaryO3rXtK16j0u+Ak2rmVbZAkixq+l+npDr7+6byKRWvUWnV+N5rLqrxXggA0NQ5UwgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAhEIAAAAABSQUAgAAACggoRAAAABAAQmFAAAAAApIKAQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFVNPYDUCRjF08pLFbaBDVeB0DWg2veI3ete0rOv+kmnkVnb9aNZrL/v5Bm9sqOv/FH55a0fmT5vMeAgDAxsGZQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQqALmzp2bv/zlL7nmmmty0kknZdttt02pVEqpVErPnj0rUnPcuHH5whe+kJ49e6Zt27bZeuutc9hhh2XkyJEVqQcAAAA0bTWN3UBzdOSRR2bMmDFVq/ev//qv+e53v5ulS5fWfe6tt97KW2+9lQceeCC33nprbr/99rRt27ZqPQEAAAAbN6FQBZTL5brlTp06pW/fvhk3blzmzp3b4LVuuummXH755UmS7bffPpdcckl22WWXTJ8+Pddee21Gjx6de++9N0OHDs0tt9zS4PUppgGthjd2Cw1iUs28xm5hg41dPKTiNaqxv6vxOgAAgBUJhSrgtNNOy1lnnZX+/funV69eSZKePXs2eCj03nvv5cILL0ySfPzjH8/jjz+ezp07160/4ogjcuyxx+bee+/Nb3/725x11ln59Kc/3aA9AAAAAE2TewpVwFlnnZXTTjutLhCqlBtvvDHvvfdekuSqq65aIRBKkpYtW+a6665Ly5YtkyQ/+tGPKtoPAAAA0HQIhZqwu+++O0nSsWPHHHfccasc071793zmM59Jkjz00EMVuYQNAAAAaHqEQk3UokWLMn78+CTJvvvum9atW6927IABA5IkH374YSZMmFCV/gAAAICNm1CoiZo0aVJqa2uTJDvttNMaxy6/fuLEiRXtCwAAAGga3Gi6iXrjjTfqlrt3777GsT169Fjlduti2rRpa1w/Y8aMes0HAAAAbByEQk3UBx98ULe86aabrnFs+/bt65bre0+h5QMlAAAAoPlw+VgTtXDhwrrlNd1PKEnatGlTt7xgwYKK9QQAAAA0HYU9U6i2tjatWrXa4HluvvnmDB48eMMbqqe2bdvWLS9atGiNYz/88MO65Xbt2tWrztouN5sxY0b69+9frzkBAACAxlfYUKip69ChQ93y2i4JmzdvXt3y2i41+0dru18RAAAA0DQVNhSqqalpkCdxbb311g3QTf0tH9as7WbQy5/t4x5BAAAAQFLgUChZ+6PcN2Y77LBDWrZsmSVLluTFF19c49jl1/fp06fSrQEAAABNgBtNN1GtW7euu5fPuHHj1nhfobFjxyb56IbTffv2rUp/AAAAwMZNKNSEHXPMMUmSOXPm5K677lrlmGnTpmXUqFFJkoMPPniFexEBAAAAxSUU2khNmTIlpVIppVIpBx544CrHDB06NJtttlmS5F/+5V/yzjvvrLB+yZIl+cpXvpIlS5YkSS644IKK9gwAAAA0HYW+p1ClTJ48OY8++ugKn1v2hLC5c+dmxIgRK6w77LDD8rGPfazedbbYYotcddVVOeecc/L6669n7733zv/7f/8vu+yyS6ZPn56f/OQnGT16dJLk1FNPzcCBA9fvBQEAAADNjlCoAh599NGceeaZq1z3zjvvrLRu9OjR6xUKJcnZZ5+d6dOn53vf+15eeeWVfOlLX1ppzKBBg/LLX/5yveYHAAAAmieXjzUD3/3ud/Poo4/mtNNOS48ePdK6det07do1hxxySG699dbcf//9adu2bWO3CQAAAGxESuVyudzYTdB0TZs2LT169EiSvPHGG+nevXsjd7RxG9BqeMVrjF08pOI1motK74/msi+ay3FbjddRac3lmAIAoP4q8fe3M4UAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAhEIAAAAABSQUAgAAACggoRAAAABAAQmFAAAAAAqoprEbAGiuBrQa3tgtNIixi4c0dgsNorm8DgAAaCjOFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAhEIAAAAABSQUAgAAACggoRAAAABAAQmFAAAAAApIKAQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEAB1TR2A0DTM6DV8IrXGLt4SLOoAQAAsLFyphAAAABAAQmFAAAAAApIKAQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAHVNHYDNB8n9LwtbUpbVGz+sYuHVGxu6se+AAAAaPqcKQQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEA1jd0AzccdU05N9+7dG7sNAAAAYB04UwgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAhEIAAAAABSQUAgAAACggoRAAAABAAQmFAAAAAApIKAQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFVNPYDUCRjF08pOI1hpZGVrzGTeVTKl4DAACAynKmEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCAhEIAAAAABVTT2A3QtNXW1tYtz5gxoxE7YZl5eafiNaZNm1bxGgAAAPyf5f/mXv5v8Q0hFGKDzJw5s265f//+jdgJ1TSyx3mN3QIAAEBhzZw5Mz179tzgeVw+BgAAAFBApXK5XG7sJmi6Fi5cmOeeey5J0qVLl9TUOPmsqZgxY0bd2V3jx4/P1ltv3cgdsbFwbLAmjg9Wx7HBmjg+WBPHB6vj2FhRbW1t3dU6u+yyS9q2bbvBc/oLng3Stm3b9OvXr7HbYANtvfXW6d69e2O3wUbIscGaOD5YHccGa+L4YE0cH6yOY+MjDXHJ2PJcPgYAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABlcrlcrmxmwAAAACgupwpBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCJqZuXPn5i9/+UuuueaanHTSSdl2221TKpVSKpXSs2fPdZpjypQpddus7WPw4MHrNOfIkSNz6KGHZuutt07btm3Ts2fPfOELX8jjjz++/i+WemmIY2N5zz//fM4555z06tUr7dq1S5cuXfLpT386w4YNS21t7TrP49hoOg488MB1fm9YFw11DNH4pk6dmgsuuCB9+vRJ+/bts8UWW6R///655pprMn/+/MZujwa2ru8DBx544Frn+vOf/5zjjjsu3bt3T5s2bdK9e/ccd9xx+fOf/1z5F0K9vf3227nvvvty2WWX5fDDD0/nzp3r/Tvh8hpi/8+fPz8/+tGP0r9//2yxxRbZdNNN06dPn1xwwQWZOnVqvXti/TXE8TFixIh1fo8ZMWLEWudzfKyDMtCsHHjggeUkq/zYZptt1mmO1157bbVz/OPHGWecsca5FixYUD7iiCNWu32LFi3K//qv/7rhL5y1aohjY5mbbrqp3KZNm9XOt88++5RnzZq1xjkcG03PgAED1vm9YW0a4hhi43DfffeVN9tss9Xuyx133LH8yiuvNHabNKB1fR8YMGDAaudYunRp+ayzzlrj9meddVZ56dKl1XthrNWG/E64vIba/5MnTy7vuOOOq51js802K99///0b+KpZVw1xfNx8883r/B5z8803r3Eux8e6qQnQrJTL5brlTp06pW/fvhk3blzmzp27XvP927/9W44++ujVru/UqdMatx8yZEjuu+++JMnAgQPzjW98I926dctzzz2X73//+3nllVdy2WWXZeutt87QoUPXq0fWTUMdGw888EDOOuusLF26NFtttVX+3//7f9l7773z7rvv5sYbb8xdd92Vxx9/PMcdd1xGjx6dFi1WfVKqY6Pp6tu3b26++eb13r6hjiEa3zPPPJOTTjop8+fPz6abbpqLL744AwcOzIIFCzJy5MjceOONeemll/K5z30uEyZMyKabbtrYLdOAzj333HzlK19Z7fr27duvdt13vvOd3HDDDUmSPfbYIxdddFG23377vPLKK7n66qvz9NNP54YbbkiXLl3yb//2bw3eOxuuR48e6dOnTx588MF6b9sQ+3/u3Lk54ogj8tJLLyVJvvzlL+eUU05Ju3btMnr06PzgBz/I+++/nxNPPDHjxo3Lrrvuuv4vlnrbkONjmQceeCDdunVb7fru3buvdp3jox4aO5UCGtawYcPKv/3tb8uTJk2q+9w222yz3mcKrS2BX5MxY8bUzXPkkUeWa2trV1g/c+bM8sc//vFyknKnTp3Ks2fPXu9arF1DHBuLFy8u9+rVq5yk3LFjx/LkyZNXGvOVr3ylbr//6le/WuU8jo2madmZQmv63/+1aahjiI3DsjMQa2pqyo899thK66+++uq6ffnd7363ETqkEpbt08svv3y9tp80aVK5pqamnKTct2/f8vz581dYP2/evHLfvn3rjq1VvU/QOC677LLyvffeW37rrbfK5fKKvzOu65kgDbX/L7/88rraV1999UrrH3vssbo6AwcOrN8LZb00xPGx/JlCr7322nr34vhYd0IhKIDGCoUGDRpUTlJu2bJl+Y033ljlmNtuu62u1jXXXLPetVg/9T02br/99rr99YMf/GCVY+bNm1fu1KlTOUn5k5/85CrHODaapoYIhRrqGKLxjR8/vm5fnn322ascs2TJknKfPn3qAt5FixZVuUsqYUNDoeWD33Hjxq1yzLhx4+rGnHfeeRvQLZW0Pn/0N8T+X7RoUXnzzTcvJyn36dOnvGTJklXOc/bZZ9fN88QTT6zz66JhNFYo5PioH+djAxUxd+7cPPzww0mSQw45ZLWndx533HHp2LFjkuSuu+6qWn+sn7vvvrtueXU3DNxkk01y0kknJUn+53/+J5MmTVphvWOj2BriGGLjsPy+PPPMM1c5pkWLFvniF7+YJJk9e3bGjBlThc7YmJXL5fzxj39Mkuy0007ZZ599Vjlun332yY477pjko2OtvNwl0DRdDbX/x4wZk/feey9JcsYZZ6z2MuPlf874XaI4HB/1IxQCKmL8+PH58MMPkyQDBgxY7bjWrVvX/UIwfvz4LF68uCr9sX4eeeSRJMmOO+6Yj33sY6sdt/w+f/TRR1dY59gotoY4htg4LNuX7du3z1577bXacfYly3vttdfy5ptvJlnzz4Dl10+bNi1TpkypdGtUQUPt/2XvP2ubp2/fvnX3tvL+UxyOj/oRCgFr9J//+Z/Zdttt06ZNm2y22Wb5xCc+kXPOOSdPPfXUGrebOHFi3fJOO+20xrHL1tfW1jojYCM2d+7cTJs2Lcm679NkxWPhH//t2GiaXnzxxfTr1y8dOnRI27Zt07179xx99NH59a9/vcbwrqGOITYOy/ZLr169UlOz+meX2JfN1+9///vsuOOOadeuXTp06JDevXvnjDPOyOjRo1e7zfr8DPjH7Wi6Gmr/r+s8NTU12X777Vc5Bxu/wYMHZ6uttkrr1q3TuXPn7LPPPvnOd75TFyyujuOjfoRCwBo99dRTmTJlShYtWpQ5c+bkhRdeyLBhw7LXXnvlnHPOqTvj4x+98cYbdctrejJA8tHTCVa1HRuXadOm1Z2+vSH71LHR9P3v//5vnnjiicydOzcffvhh3nzzzdxzzz0544wzsvvuu6/2F6uGOoZofAsXLsysWbOSrH1fdurUqe5/Yu3L5uWFF17Iyy+/nIULF2bu3LmZPHlyfv3rX+eggw7Ksccem/fff3+lbfwMKLaG2v/L/t2+fftsvvnm6zTPzJkzV/t7KxunsWPH5u23387ixYvzzjvv5G9/+1uuvPLK9OrVK8OGDVvtdo6P+vFIemCVNt988xx77LE58MAD07t377Rt2zYzZszIgw8+mOHDh2fu3LkZNmxYPvjgg/z2t79dafsPPvigbnltjyBe/pG19X08OtXTUPvUsdF0tWjRIgcffHAGDRqU3XbbLVtuuWU++OCDPPXUUxk2bFgmTpyYF154IQMHDsz48ePz8Y9/fIXt7fvmoz77Mvlof86bN8++bCY22WSTHHXUUTn44IOz0047ZdNNN83MmTMzduzYXH/99XnnnXdy99135+ijj85DDz2UVq1a1W3rfaDYGvp3iXV9/1l+njZt2qxTrzSe7bbbLscdd1z23XffutDm1VdfzZ133pk77rgjCxcuzDnnnJNSqZSzzjprpe0dH/UjFAJW0q1bt7z55pvZZJNNVvj8HnvskUGDBuWrX/1qPvOZz2Tq1Km59dZbc/LJJ+eoo45aYezChQvrllu3br3Gesu/+S5YsKABXgGV0FD71LHRdN11112r/B+3Aw44IF/5ylfy5S9/Ob/61a/yv//7vzn//PNXummjfd981GdfJv+3P+3L5uHNN99c5XvBIYcckq997Ws5/PDD8/TTT2fs2LH5xS9+ka9//et1Y7wPFFtD/y5Rn/efVc3DxufYY4/NGWeckVKptMLn+/Xrl5NPPjn33XdfjjvuuCxevDj//M//nKOOOmqlexQ6PurH5WPQCGpra1MqlTb4Y8SIERXpr3Xr1isFQsvr3bv3CmcH/ed//udKY9q2bVu3vGjRojXWW/5UzXbt2tWn1WZnYz42GmqfOjYqq5LH0JpOwW7VqlVuuummumv3//CHP6x0zb9933zUZ18m/7c/7cvmYU3vBVtttVXuuOOOuj/G/vF3BO8DxdbQv0vU5/1nVfOw8dlss81WCoSWd8QRR+Tyyy9PksyfPz/Dhw9faYzjo36EQsB62X///fOJT3wiyUd361+6dOkK6zt06FC3vLZTvufNm1e3vC6nedI4GmqfOjaar5qamgwZMqTu32PHjl1hvX3ffNRnXyb/tz/ty2LYbrvtcsghhyRJJk+enOnTp9et8z5QbA39u0R93n9WNQ9N05e//OW64Ogff9dIHB/15fIxaAQ1NTUNcof7rbfeugG6WX8777xznn/++SxcuDDvvPNOunTpUrdu+ZsHTps2LX379l3tPMvfPHD5mwoW0cZ8bPzjPl2TNe1Tx0ZlNfYxtPPOO9ct/+OZQg11DNH42rZtm86dO2fWrFlr3ZezZ8+u+6XbviyOnXfeOffff3+Sj94LunXrlsT7QNE15O8Sf/vb3zJv3ry89957azx7bdk8Xbp0KeT9Ypqjrl27pnPnzpk5c+Yqn0Tm+KgfoRA0krU9hrMpWPYUoVVZ/g/DF198cY3zLFtfU1OTXr16NUxzTdjGemxsuumm6dGjR95444113qdJ0qdPnxXWOTYqrzGPoTW9LzTUMcTGoU+fPnnkkUcyefLk1NbWrvax9PZlMa3uvWB9fgYkjp3moqH2/84775w777yzbtw+++yzyjlqa2vzyiuvrHIOmra1/R3i+Fh3Lh8D1tsLL7yQ5KMbtG255ZYrrOvXr1/d/QRWdVrnMosWLcrjjz++0jZsnPbff/8kyUsvvZS33nprteOW3+f77bffCuscG83bsveFJHVnBiyvIY4hNg7L9uW8efPy5JNPrnacfVlMq3sv2Hbbbev+vaafAUnyl7/8JUnyT//0T+nZs2fDN0nVNdT+X/b+s7Z5nnjiibozFb3/NB9vv/123nnnnSRr/l0jcXysC6EQsF4effTRul/49t9//7RoseLbSYcOHXLwwQcnSUaNGrXaU4TvuuuuzJkzJ8lHTxtg43bMMcfULa/uZtbz58/P7bffnuSj/6nZYYcdVljv2Gi+amtr88tf/rLu35/+9KdXGtMQxxAbh+X35c0337zKMUuXLs2vf/3rJB/dnHjgwIHVaI1G9uqrr+ahhx5K8tH9hf7pn/6pbl2pVMrRRx+d5KP/wV8W/v+jxx9/vO5MkaOPPnqNN56l6Wio/X/ggQdms802S5L86le/Wu1ZI8v/nPG7RPNxww031O3zAQMGrLTe8VFPZaDZ22abbcpJyttss806jf/DH/5QXrp06WrXT5o0qfzxj3+8nKScpHznnXeuctzDDz9cN+aoo44q19bWrrB+5syZdfNsvvnm5XfffXedXxMNo77HxqJFi8rbb799OUm5Y8eO5cmTJ6805itf+Urdfr/55ptXOY9jo+n57//+7/Ls2bNXu37RokXlM844o26/Hnnkkasd1xDHEBuHAw44oJykXFNTU37sscdWWn/11VfX7cvLL7+8+g3S4O65557y4sWLV7v+rbfeKu+xxx51+/3f//3fVxrz0ksvlWtqaspJyn379i3Pnz9/hfXz588v9+3bt+7Yevnllxv8ddAwXnvttbp9fcYZZ6zTNg21/y+99NK62ldfffVK6x977LG6OgMGDKjvS6MB1Pf4eO2118pPPfXUGsfce++95datW5eTlNu2bVueNm3aKsc5PtZdqVxew8V4QJMzefLkPProoyt87oILLsg777yTLbfcMtdcc80K6w477LB87GMfW+FzpVIpvXr1ynHHHZf+/fune/fuadOmTaZPn54HH3wwN910U92plieddFJ+97vfrbafU089NSNHjkySDBw4MOeff366deuW5557LldeeWXddbzXX399zj777A1+/axeQxwbSfKnP/0pRx55ZJYuXZqtttoq3/nOd9K/f//Mnj07N954Y9013Pvvv3/GjBmTli1brrIfx0bTMnjw4Nx555056qijcuCBB2bHHXdMx44dM3fu3Dz55JMZNmxY3Q2uu3btmscffzzbbrvtKudqqGOIxvf0009nv/32y4IFC7LpppvmkksuycCBA7NgwYKMHDkyN9xwQ5Jkhx12yBNPPLHCk4domnr27JnFixfn+OOPz7777puePXumXbt2mTVrVsaMGZPrr7++7rKO/fffP6NGjVrlzVsvvvji/PCHP0yS7LHHHvn2t7+d7bffPq+88kquuuqqPP3003Xjvv/971fvBbJGjz76aCZPnlz371mzZuXCCy9M8tHlN0OHDl1h/ODBg1c5T0Ps/w8++CB9+/bNyy+/nCQ566yzcsopp6Rdu3YZPXp0vv/972fu3Llp165dHnvssey+++4b8tJZBxt6fIwZMyYDBw7MvvvumyOPPDK77757unbtmnK5nFdffTV33HFH7rjjjrozf372s5/lq1/96ip7cXzUQ+NmUkBDu/nmm+tS8XX5GD169EpzrOu25557bnnhwoVr7Gf+/PnlQYMGrXaOFi1a+N/jKmmIY2OZG264oe5/aVb10b9///LMmTPX2I9jo2lZ/iygNX3ssssu5eeff36t8zXEMcTG4Z577il37Nhxtftyhx12KE+aNKmx26SBLDvDdG0fxx9//BrPLlyyZEn5S1/60hrnGDJkSHnJkiXVe3Gs1br+LFj2sToNtf8nTZpU7t2792rn6NixY/nee+9t6C8Dq7Ghx8fo0aPXabtNNtmkPGzYsLX24/hYN54+Bqzknnvuybhx4/K3v/0tr7/+embNmpV58+alY8eO2W677XLAAQfkS1/6Uj75yU+uda527drl/vvvz6233poRI0bkmWeeyXvvvZetttoqBxxwQM4777zsu+++VXhVNKQvf/nL2XffffPTn/40Dz/8cKZPn5727dunT58+Of300zN06NDVPoloGcdG0/Ltb387u+++e8aNG5cXXnghM2fOzLvvvps2bdpkq622St++fXPCCSfk2GOPXaczexriGGLjcOSRR+bZZ5/Ntddem/vvvz/Tpk1L69at06tXr5x44ok577zzsskmmzR2mzSQX/3qVxk7dmzGjRuXV199NbNmzcqcOXPqni74qU99KmecccZa379btGiR4cOH5/jjj88NN9yQCRMmZNasWencuXP69euXs88+O4cffniVXhXV1lD7v1evXnn66afz85//PL///e8zefLkLFq0KD169MigQYPyjW98I9tss00VXhENYa+99sott9yScePG5YknnsiMGTMya9as1NbWplOnTvnEJz6Rgw8+OEOHDk3Xrl3XOp/jY924fAwAAACggDx9DAAAAKCAhEIAAAAABSQUAgAAACggoRAAAABAAQmFAAAAAApIKAQAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAoIn47ne/m1KplMMPP7yxW6maadOmpU2bNmndunVefvnlxm4HAJoVoRAAQBMwbdq0XHXVVUmSyy+/vJG7qZ7u3bvnzDPPzOLFi/Otb32rsdsBgGZFKAQA0AR873vfy4IFC3LooYdmn332aex2quriiy9Oq1atct9992XcuHGN3Q4ANBtCIQCAjdybb76Zm2++OUkKebbMNttsk+OPPz5J8m//9m+N3A0ANB9CIQCAjdx1112XxYsXZ+utt87BBx/c2O00itNOOy1J8l//9V/uLQQADUQoBACwEVu6dGlGjBiRJDn11FPTokUxf3077LDDsuWWW6ZcLtedNQUAbJhi/lYBALABbrvttpRKpZRKpZx99tmrHTd16tRsvvnmKZVK2WGHHTJv3rx613r00Uczffr0JKm7hGpVxowZU9fTmDFjUi6XM3z48Oy///7Zcsst07Fjx/Tv3z+/+c1vVthu0aJFuf7667PPPvtkiy22SIcOHbLffvvl9ttvX22tKVOm1NVaFljddddd+exnP5uuXbumffv22W233fKf//mfWbx4cd125XI5t956aw488MB07do1m2yySfbcc89cf/31KZfLa/w6tGrVKkceeWSS5He/+90axwIA60YoBABQT6eeempOP/30JMkNN9yQP/7xjyuNWbp0ab74xS/m/fffT01NTX7729+mffv29a41evToJB+FInvuuec6bbN48eIcffTRGTp0aP7617/m3XffzQcffJAJEybki1/8Yr7xjW8kSWbPnp1DDjkk5557bv72t79l9uzZmTt3bh577LGcfPLJ+f73v79O9b7yla/k+OOPz0MPPZSZM2dm/vz5efbZZ/P1r389p5xySpYsWZIPP/wwJ510Uk4//fSMHTs2M2fOzIIFC/L000/n3HPPXWO4tsyyG2y/9tprmTp16jr1BgCsnlAIAGA9XHfddenZs2eSZOjQoXnrrbdWWP+jH/0oY8eOTZJcccUV6dev33rVeeSRR5Iku+yyS9q2bbtO21x66aW59957c/rpp+f+++/Pk08+mdtuuy077rhjkuSnP/1pRo0alcGDB+exxx7LueeemwcffDBPPvlkhg8fnm7duiVJLrvssjz//PNrrHX99dfnF7/4RQYNGpS77rorTz75ZO6+++7svffeST46g+jmm2/OhRdemDvuuCOnnXZa7rvvvjz55JMZOXJkdtpppyTJjTfemD//+c9rrNW/f/+Vvi4AwPorldd2ri4AAKv06KOP5sADD8ySJUty2GGH5U9/+lNKpVKefvrp7LPPPlm0aFH233//jBkzJi1btqz3/OVyOR06dMi8efMyZMiQ3HTTTasdO2bMmAwcOLDu3z/5yU/qzgha5q233sqOO+6YOXPmpEuXLpk1a1buuuuuHHPMMSuMe/bZZ7PHHntk6dKl+frXv55rr712hfVTpkzJtttuW/fv888/P//xH/+xwpj58+fnE5/4RKZMmZLOnTvnnXfeyX/8x3+ssqcddtghH3zwQY466qhVnnW1TG1tbTbZZJMsXrw4X/nKV/Lzn/98tWMBgLVzphAAwHraf//9c/HFFydJ/vznP+dnP/tZFixYkNNPPz2LFi1Kx44d85vf/Ga9AqHko8u7lt2HqGvXruu83d57771S+JIkH/vYx3LssccmSWbOnJmTTz55pUAoSXbdddfsv//+SdZ+Rk6PHj1y9dVXr/T5TTbZJGeccUaSZNasWevU09pq1dTUZIsttkiSTJs2bY1jAYC1EwoBAGyAyy+/vO6yposuuiinnXZaJk6cmCT5+c9/XneJ2fqYOXNm3XKnTp3WebtTTjlltet23XXXuuWTTz55teN22223JMmrr766xlrHHXdcWrVq1SC1Zs+enffee2+N9ZaFQst/bQCA9SMUAgDYAMvfRHrhwoW5++67k3wUzHz+85/foLnffffduuX6hEI77LDDatdtvvnm9Rr3wQcfVK3WutRb9nV455131jgOAFg7oRAAwAbq1atX/uVf/qXu3507d84vfvGLDZ53+RtLL1iwYJ2322STTVa7rkWLFvUat3Tp0qrVSpIlS5assd6yr0O7du3WOA4AWDuhEADABpo7d25uvvnmun+/8847eeqppzZ43i5dutQtL3/WUJEt+zos/7UBANaPUAgAYAN97Wtfq7v3TocOHVIul3PGGWdk9uzZGzTv8sHHhs7VXCz7OgiFAGDDCYUAADbAnXfemREjRiRJBg8enNtvvz3JR0/HOuecczZo7jZt2qR3795JkpdffnmD5moO3n777cyZMydJsssuuzRyNwDQ9AmFAADW0/Tp03PWWWclSbbbbrv89Kc/zWGHHZbzzjsvSXL77bfnN7/5zQbVOOCAA5IkEyZM2LBmm4Hx48fXLS/7ugAA608oBACwHsrlcgYPHpx33303LVu2zC233JIOHTokSa6++ursvPPOSZLzzjsvr7/++nrXWRZ+zJo1K6+99tqGN96ELQuF2rRpk379+jVyNwDQ9AmFAADWw09+8pM89NBDSZJLLrkk++67b926du3a5ZZbbknr1q0zZ86cfOELX1jrU7xW57DDDkvLli2TJA8//PCGN96ELXv9n/nMZ9KmTZtG7gYAmj6hEABAPT333HO5+OKLkyT9+/fPZZddttKYPfbYI//6r/+aJHnkkUdy1VVXrVetj33sY/nMZz6TJLn11lvXs+Om7/XXX8+4ceOSJJ///OcbuRsAaB6EQgAA9fDhhx/m9NNPz4cffpj27dvnlltuSU1NzSrHXnjhhRkwYECS5PLLL8+TTz65XjW/+tWvJknGjh2bN998c/0ab+JuvfXWlMvlbLXVVjnuuOMaux0AaBZK5XK53NhNAACwekuXLs0nP/nJTJw4Md/73vfyne98p7FbqqqlS5emT58+efnll3PllVfmkksuaeyWAKBZEAoBADQBt99+e04++eRsueWWmTJlSjbddNPGbqlqbrvttpx22mnZcsst89prr9Xd0BsA2DAuHwMAaAJOPPHE7LPPPnnnnXfys5/9rLHbqZpyuZwrr7wySXLFFVcIhACgAa36AngAADYqpVIpN954Y+64445CnSU0Y8aMnHDCCTnttNNy7rnnNnY7ANCsuHwMAAAAoIBcPgYAAABQQEIhAAAAgAISCgEAAAAUkFAIAAAAoICEQgAAAAAFJBQCAAAAKCChEAAAAEABCYUAAAAACkgoBAAAAFBAQiEAAACAAhIKAQAAABSQUAgAAACggIRCAAAAAAUkFAIAAAAoIKEQAAAAQAEJhQAAAAAKSCgEAAAAUEBCIQAAAIACEgoBAAAAFJBQCAAAAKCA/j9bQKEWw4RPvQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 432,
-       "width": 578
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.598048Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.597978Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.658295Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.658041Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# hist2d\n",
     "\n",
@@ -374,25 +259,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 432,
-       "width": 578
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.659592Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.659518Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.748004Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.747752Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "import matplotlib.colors as colors\n",
     "\n",
@@ -415,20 +291,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, 8.469999999999999e-13)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.749344Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.749267Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.751372Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.751138Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "np.min(image), np.max(image)"
    ]
@@ -442,329 +314,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "'use strict';\n",
-       "(function(root) {\n",
-       "  function now() {\n",
-       "    return new Date();\n",
-       "  }\n",
-       "\n",
-       "  const force = true;\n",
-       "\n",
-       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
-       "    root._bokeh_onload_callbacks = [];\n",
-       "    root._bokeh_is_loading = undefined;\n",
-       "  }\n",
-       "\n",
-       "const JS_MIME_TYPE = 'application/javascript';\n",
-       "  const HTML_MIME_TYPE = 'text/html';\n",
-       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
-       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
-       "\n",
-       "  /**\n",
-       "   * Render data to the DOM node\n",
-       "   */\n",
-       "  function render(props, node) {\n",
-       "    const script = document.createElement(\"script\");\n",
-       "    node.appendChild(script);\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when an output is cleared or removed\n",
-       "   */\n",
-       "  function handleClearOutput(event, handle) {\n",
-       "    function drop(id) {\n",
-       "      const view = Bokeh.index.get_by_id(id)\n",
-       "      if (view != null) {\n",
-       "        view.model.document.clear()\n",
-       "        Bokeh.index.delete(view)\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    const cell = handle.cell;\n",
-       "\n",
-       "    const id = cell.output_area._bokeh_element_id;\n",
-       "    const server_id = cell.output_area._bokeh_server_id;\n",
-       "\n",
-       "    // Clean up Bokeh references\n",
-       "    if (id != null) {\n",
-       "      drop(id)\n",
-       "    }\n",
-       "\n",
-       "    if (server_id !== undefined) {\n",
-       "      // Clean up Bokeh references\n",
-       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
-       "      cell.notebook.kernel.execute(cmd_clean, {\n",
-       "        iopub: {\n",
-       "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim()\n",
-       "            drop(id)\n",
-       "          }\n",
-       "        }\n",
-       "      });\n",
-       "      // Destroy server and session\n",
-       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
-       "      cell.notebook.kernel.execute(cmd_destroy);\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when a new output is added\n",
-       "   */\n",
-       "  function handleAddOutput(event, handle) {\n",
-       "    const output_area = handle.output_area;\n",
-       "    const output = handle.output;\n",
-       "\n",
-       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
-       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
-       "      return\n",
-       "    }\n",
-       "\n",
-       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
-       "\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
-       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
-       "      // store reference to embed id on output_area\n",
-       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
-       "    }\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
-       "      const bk_div = document.createElement(\"div\");\n",
-       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
-       "      const script_attrs = bk_div.children[0].attributes;\n",
-       "      for (let i = 0; i < script_attrs.length; i++) {\n",
-       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
-       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
-       "      }\n",
-       "      // store reference to server id on output_area\n",
-       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function register_renderer(events, OutputArea) {\n",
-       "\n",
-       "    function append_mime(data, metadata, element) {\n",
-       "      // create a DOM node to render to\n",
-       "      const toinsert = this.create_output_subarea(\n",
-       "        metadata,\n",
-       "        CLASS_NAME,\n",
-       "        EXEC_MIME_TYPE\n",
-       "      );\n",
-       "      this.keyboard_manager.register_events(toinsert);\n",
-       "      // Render to node\n",
-       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
-       "      render(props, toinsert[toinsert.length - 1]);\n",
-       "      element.append(toinsert);\n",
-       "      return toinsert\n",
-       "    }\n",
-       "\n",
-       "    /* Handle when an output is cleared or removed */\n",
-       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
-       "    events.on('delete.Cell', handleClearOutput);\n",
-       "\n",
-       "    /* Handle when a new output is added */\n",
-       "    events.on('output_added.OutputArea', handleAddOutput);\n",
-       "\n",
-       "    /**\n",
-       "     * Register the mime type and append_mime function with output_area\n",
-       "     */\n",
-       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
-       "      /* Is output safe? */\n",
-       "      safe: true,\n",
-       "      /* Index of renderer in `output_area.display_order` */\n",
-       "      index: 0\n",
-       "    });\n",
-       "  }\n",
-       "\n",
-       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
-       "  if (root.Jupyter !== undefined) {\n",
-       "    const events = require('base/js/events');\n",
-       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
-       "\n",
-       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
-       "      register_renderer(events, OutputArea);\n",
-       "    }\n",
-       "  }\n",
-       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
-       "    root._bokeh_timeout = Date.now() + 5000;\n",
-       "    root._bokeh_failed_load = false;\n",
-       "  }\n",
-       "\n",
-       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
-       "     \"<div style='background-color: #fdd'>\\n\"+\n",
-       "     \"<p>\\n\"+\n",
-       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
-       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
-       "     \"</p>\\n\"+\n",
-       "     \"<ul>\\n\"+\n",
-       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
-       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
-       "     \"</ul>\\n\"+\n",
-       "     \"<code>\\n\"+\n",
-       "     \"from bokeh.resources import INLINE\\n\"+\n",
-       "     \"output_notebook(resources=INLINE)\\n\"+\n",
-       "     \"</code>\\n\"+\n",
-       "     \"</div>\"}};\n",
-       "\n",
-       "  function display_loaded(error = null) {\n",
-       "    const el = document.getElementById(null);\n",
-       "    if (el != null) {\n",
-       "      const html = (() => {\n",
-       "        if (typeof root.Bokeh === \"undefined\") {\n",
-       "          if (error == null) {\n",
-       "            return \"BokehJS is loading ...\";\n",
-       "          } else {\n",
-       "            return \"BokehJS failed to load.\";\n",
-       "          }\n",
-       "        } else {\n",
-       "          const prefix = `BokehJS ${root.Bokeh.version}`;\n",
-       "          if (error == null) {\n",
-       "            return `${prefix} successfully loaded.`;\n",
-       "          } else {\n",
-       "            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n",
-       "          }\n",
-       "        }\n",
-       "      })();\n",
-       "      el.innerHTML = html;\n",
-       "\n",
-       "      if (error != null) {\n",
-       "        const wrapper = document.createElement(\"div\");\n",
-       "        wrapper.style.overflow = \"auto\";\n",
-       "        wrapper.style.height = \"5em\";\n",
-       "        wrapper.style.resize = \"vertical\";\n",
-       "        const content = document.createElement(\"div\");\n",
-       "        content.style.fontFamily = \"monospace\";\n",
-       "        content.style.whiteSpace = \"pre-wrap\";\n",
-       "        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n",
-       "        content.textContent = error.stack ?? error.toString();\n",
-       "        wrapper.append(content);\n",
-       "        el.append(wrapper);\n",
-       "      }\n",
-       "    } else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(() => display_loaded(error), 100);\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function run_callbacks() {\n",
-       "    try {\n",
-       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
-       "        if (callback != null)\n",
-       "          callback();\n",
-       "      });\n",
-       "    } finally {\n",
-       "      delete root._bokeh_onload_callbacks\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
-       "  }\n",
-       "\n",
-       "  function load_libs(css_urls, js_urls, callback) {\n",
-       "    if (css_urls == null) css_urls = [];\n",
-       "    if (js_urls == null) js_urls = [];\n",
-       "\n",
-       "    root._bokeh_onload_callbacks.push(callback);\n",
-       "    if (root._bokeh_is_loading > 0) {\n",
-       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
-       "      return null;\n",
-       "    }\n",
-       "    if (js_urls == null || js_urls.length === 0) {\n",
-       "      run_callbacks();\n",
-       "      return null;\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
-       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
-       "\n",
-       "    function on_load() {\n",
-       "      root._bokeh_is_loading--;\n",
-       "      if (root._bokeh_is_loading === 0) {\n",
-       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
-       "        run_callbacks()\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    function on_error(url) {\n",
-       "      console.error(\"failed to load \" + url);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < css_urls.length; i++) {\n",
-       "      const url = css_urls[i];\n",
-       "      const element = document.createElement(\"link\");\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.rel = \"stylesheet\";\n",
-       "      element.type = \"text/css\";\n",
-       "      element.href = url;\n",
-       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
-       "      document.body.appendChild(element);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < js_urls.length; i++) {\n",
-       "      const url = js_urls[i];\n",
-       "      const element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "  };\n",
-       "\n",
-       "  function inject_raw_css(css) {\n",
-       "    const element = document.createElement(\"style\");\n",
-       "    element.appendChild(document.createTextNode(css));\n",
-       "    document.body.appendChild(element);\n",
-       "  }\n",
-       "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.4.2.min.js\"];\n",
-       "  const css_urls = [];\n",
-       "\n",
-       "  const inline_js = [    function(Bokeh) {\n",
-       "      Bokeh.set_log_level(\"info\");\n",
-       "    },\n",
-       "function(Bokeh) {\n",
-       "    }\n",
-       "  ];\n",
-       "\n",
-       "  function run_inline_js() {\n",
-       "    if (root.Bokeh !== undefined || force === true) {\n",
-       "      try {\n",
-       "            for (let i = 0; i < inline_js.length; i++) {\n",
-       "      inline_js[i].call(root, root.Bokeh);\n",
-       "    }\n",
-       "\n",
-       "      } catch (error) {throw error;\n",
-       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(run_inline_js, 100);\n",
-       "    } else if (!root._bokeh_failed_load) {\n",
-       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
-       "      root._bokeh_failed_load = true;\n",
-       "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
-       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  if (root._bokeh_is_loading === 0) {\n",
-       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
-       "    run_inline_js();\n",
-       "  } else {\n",
-       "    load_libs(css_urls, js_urls, function() {\n",
-       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
-       "      run_inline_js();\n",
-       "    });\n",
-       "  }\n",
-       "}(window));"
-      ],
-      "application/vnd.bokehjs_load.v0+json": "'use strict';\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded(error = null) {\n    const el = document.getElementById(null);\n    if (el != null) {\n      const html = (() => {\n        if (typeof root.Bokeh === \"undefined\") {\n          if (error == null) {\n            return \"BokehJS is loading ...\";\n          } else {\n            return \"BokehJS failed to load.\";\n          }\n        } else {\n          const prefix = `BokehJS ${root.Bokeh.version}`;\n          if (error == null) {\n            return `${prefix} successfully loaded.`;\n          } else {\n            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n          }\n        }\n      })();\n      el.innerHTML = html;\n\n      if (error != null) {\n        const wrapper = document.createElement(\"div\");\n        wrapper.style.overflow = \"auto\";\n        wrapper.style.height = \"5em\";\n        wrapper.style.resize = \"vertical\";\n        const content = document.createElement(\"div\");\n        content.style.fontFamily = \"monospace\";\n        content.style.whiteSpace = \"pre-wrap\";\n        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n        content.textContent = error.stack ?? error.toString();\n        wrapper.append(content);\n        el.append(wrapper);\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(() => display_loaded(error), 100);\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.4.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n      try {\n            for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n\n      } catch (error) {throw error;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.752645Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.752569Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.925850Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.925603Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "from bokeh.plotting import figure, show, output_notebook\n",
     "from bokeh import palettes, colors\n",
@@ -779,8 +338,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.927211Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.927091Z",
+     "iopub.status.idle": "2024-08-08T19:06:11.929129Z",
+     "shell.execute_reply": "2024-08-08T19:06:11.928901Z"
+    }
+   },
    "outputs": [],
    "source": [
     "H, xedges, yedges = np.histogram2d(xdata, ydata, weights=chargedat, bins=40)\n",
@@ -790,57 +356,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "  <div id=\"aedaba27-5f09-4c39-a73d-bc46b3882e1e\" data-root-id=\"p1004\" style=\"display: contents;\"></div>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "(function(root) {\n",
-       "  function embed_document(root) {\n",
-       "  const docs_json = {\"6e13a9b3-03ef-409b-8578-af83576a8b06\":{\"version\":\"3.4.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1004\",\"attributes\":{\"width\":500,\"height\":500,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1014\",\"attributes\":{\"start\":-0.19080456771215437,\"end\":0.1749986951725533}},\"y_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1015\",\"attributes\":{\"start\":-1.4601286926409278,\"end\":1.3331494758231812}},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1016\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1017\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1007\",\"attributes\":{\"text\":\"Bunch at 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TickFormatter\",\"id\":\"p1025\"},\"axis_label\":\"px (mrad)\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1026\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1018\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1019\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1020\"},\"axis_label\":\"x (mm)\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1021\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1022\",\"attributes\":{\"axis\":{\"id\":\"p1018\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1027\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1023\"}}}]}}]}};\n",
-       "  const render_items = [{\"docid\":\"6e13a9b3-03ef-409b-8578-af83576a8b06\",\"roots\":{\"p1004\":\"aedaba27-5f09-4c39-a73d-bc46b3882e1e\"},\"root_ids\":[\"p1004\"]}];\n",
-       "  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
-       "  }\n",
-       "  if (root.Bokeh !== undefined) {\n",
-       "    embed_document(root);\n",
-       "  } else {\n",
-       "    let attempts = 0;\n",
-       "    const timer = setInterval(function(root) {\n",
-       "      if (root.Bokeh !== undefined) {\n",
-       "        clearInterval(timer);\n",
-       "        embed_document(root);\n",
-       "      } else {\n",
-       "        attempts++;\n",
-       "        if (attempts > 100) {\n",
-       "          clearInterval(timer);\n",
-       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
-       "        }\n",
-       "      }\n",
-       "    }, 10, root)\n",
-       "  }\n",
-       "})(window);"
-      ],
-      "application/vnd.bokehjs_exec.v0+json": ""
-     },
-     "metadata": {
-      "application/vnd.bokehjs_exec.v0+json": {
-       "id": "p1004"
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:11.930266Z",
+     "iopub.status.busy": "2024-08-08T19:06:11.930181Z",
+     "iopub.status.idle": "2024-08-08T19:06:12.104030Z",
+     "shell.execute_reply": "2024-08-08T19:06:12.103788Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "ds = ColumnDataSource(data=dict(image=[H.transpose()]))\n",
     "p = figure(\n",
@@ -875,20 +400,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['x', 'px', 'y', 'py', 't', 'pz', 'status', 'weight', 'z', 'species'])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:12.105482Z",
+     "iopub.status.busy": "2024-08-08T19:06:12.105358Z",
+     "iopub.status.idle": "2024-08-08T19:06:12.107955Z",
+     "shell.execute_reply": "2024-08-08T19:06:12.107737Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "data = tao.bunch_data(\"end\")\n",
     "data.keys()"
@@ -896,20 +417,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<ParticleGroup with 1000 particles at 0x1444fdf70>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:12.109142Z",
+     "iopub.status.busy": "2024-08-08T19:06:12.109057Z",
+     "iopub.status.idle": "2024-08-08T19:06:12.324472Z",
+     "shell.execute_reply": "2024-08-08T19:06:12.324228Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "from pmd_beamphysics import ParticleGroup\n",
     "\n",
@@ -919,58 +436,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 437,
-       "width": 576
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:12.325810Z",
+     "iopub.status.busy": "2024-08-08T19:06:12.325685Z",
+     "iopub.status.idle": "2024-08-08T19:06:12.534200Z",
+     "shell.execute_reply": "2024-08-08T19:06:12.533958Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "P.plot(\"x\", \"px\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'alpha_x': -2.1521049736138544,\n",
-       " 'beta_x': 0.3013428324900916,\n",
-       " 'gamma_x': 18.688202307379118,\n",
-       " 'emit_x': 1.2177638975257695e-08,\n",
-       " 'eta_x': -0.04819365384510438,\n",
-       " 'etap_x': -0.4549733922149835,\n",
-       " 'norm_emit_x': 1.0008240321607808e-06,\n",
-       " 'alpha_y': 1.9440813319008459,\n",
-       " 'beta_y': 0.40782968839310874,\n",
-       " 'gamma_y': 11.719235654169523,\n",
-       " 'emit_y': 1.2177509284772322e-08,\n",
-       " 'eta_y': -0.04573150414457775,\n",
-       " 'etap_y': 0.026398476953048353,\n",
-       " 'norm_emit_y': 1.0008133734974086e-06}"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:12.535522Z",
+     "iopub.status.busy": "2024-08-08T19:06:12.535425Z",
+     "iopub.status.idle": "2024-08-08T19:06:12.538098Z",
+     "shell.execute_reply": "2024-08-08T19:06:12.537876Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "P.twiss(\"xy\")"
    ]
@@ -984,31 +475,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 3.70284435e-22,  1.52112706e-14,  3.04225407e-14,  4.56338110e-14,\n",
-       "        6.08450813e-14,  7.60563516e-14,  9.12676217e-14, -1.16282940e-09,\n",
-       "       -1.14870454e-09, -1.12526206e-09, -1.09261268e-09, -1.05084624e-09,\n",
-       "       -9.99970453e-10, -9.39813419e-10, -8.69809925e-10, -7.88320099e-10,\n",
-       "       -6.91745919e-10, -5.74407015e-10, -4.27876634e-10,  1.42339073e-09,\n",
-       "        1.63091681e-09,  1.83850625e-09,  2.04615922e-09,  2.25387404e-09,\n",
-       "        2.46164780e-09,  2.66947691e-09,  2.87735726e-09,  9.86158250e-09,\n",
-       "        9.32199246e-09,  8.20091852e-09,  6.43516375e-09,  3.96715881e-09,\n",
-       "        7.44494068e-10, -3.28054979e-09, -8.15630332e-09, -1.39318757e-08,\n",
-       "       -2.06607116e-08, -2.83988809e-08, -4.55143879e-08, -5.47252817e-08,\n",
-       "       -6.39361217e-08, -7.31469081e-08, -8.23576435e-08, -9.15683309e-08,\n",
-       "       -1.00778975e-07])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:12.539365Z",
+     "iopub.status.busy": "2024-08-08T19:06:12.539273Z",
+     "iopub.status.idle": "2024-08-08T19:06:12.541453Z",
+     "shell.execute_reply": "2024-08-08T19:06:12.541219Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.bunch_comb(\"x\")"
    ]
@@ -1022,25 +498,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 432,
-       "width": 605
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:12.542665Z",
+     "iopub.status.busy": "2024-08-08T19:06:12.542579Z",
+     "iopub.status.idle": "2024-08-08T19:06:12.630563Z",
+     "shell.execute_reply": "2024-08-08T19:06:12.630335Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "s = tao.bunch_comb(\"s\")\n",
     "mean_x = tao.bunch_comb(\"x\")\n",
@@ -1067,25 +534,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 432,
-       "width": 571
-      }
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:12.631848Z",
+     "iopub.status.busy": "2024-08-08T19:06:12.631773Z",
+     "iopub.status.idle": "2024-08-08T19:06:12.748443Z",
+     "shell.execute_reply": "2024-08-08T19:06:12.748182Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "plt.plot(tao.bunch_comb(\"s\"), 1000 * tao.bunch_comb(\"x.beta\"), label=\"beam beta_x\")\n",
     "plt.plot(tao.bunch_comb(\"s\"), 1000 * tao.bunch_comb(\"y.beta\"), label=\"beam beta_y\")\n",
diff --git a/docs/examples/fodo.ipynb b/docs/examples/fodo.ipynb
index ad35539c..52cd93f4 100644
--- a/docs/examples/fodo.ipynb
+++ b/docs/examples/fodo.ipynb
@@ -16,9 +16,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "e9cad9ba-e9a5-40dd-9b92-73186a2b8ae4",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:14.546688Z",
+     "iopub.status.busy": "2024-08-08T19:06:14.546280Z",
+     "iopub.status.idle": "2024-08-08T19:06:14.967243Z",
+     "shell.execute_reply": "2024-08-08T19:06:14.966907Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pytao import Tao\n",
@@ -31,9 +38,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "792aef00-8f6b-4e11-9737-11addb070c10",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:14.968840Z",
+     "iopub.status.busy": "2024-08-08T19:06:14.968728Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.032632Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.032360Z"
+    }
+   },
    "outputs": [],
    "source": [
     "tao = Tao(\n",
@@ -43,9 +57,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "da32a879-df05-4826-9b7a-516d8629be77",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.034044Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.033966Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.035926Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.035715Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def add_info(d):\n",
@@ -61,35 +82,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "f5a7188a-68c9-4b94-af92-a55643f45f9b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-------------------------\n",
-      "Tao> sho lat\n",
-      "# Values shown are for the Downstream End of each Element:\n",
-      "# Index  name      key                       s       l    beta   phi_a    eta   orbit    beta   phi_b    eta   orbit  Track\n",
-      "#                                                            a   [2pi]      x  x [mm]       b   [2pi]      y  y [mm]  State\n",
-      "      0  BEGINNING Beginning_Ele         0.000     ---    0.67   0.000   0.00   0.000    3.22   0.000   0.00   0.000  Alive\n",
-      "      1  P1        Pipe                  0.900   0.900    3.22   0.105   0.00   0.000    0.67   0.105   0.00   0.000  Alive\n",
-      "      2  Q1        Quadrupole            1.000   0.100    3.22   0.110   0.00   0.000    0.67   0.129   0.00   0.000  Alive\n",
-      "      3  P1        Pipe                  1.900   0.900    0.67   0.215   0.00   0.000    3.22   0.235   0.00   0.000  Alive\n",
-      "      4  Q2        Quadrupole            2.000   0.100    0.67   0.239   0.00   0.000    3.22   0.239   0.00   0.000  Alive\n",
-      "      5  END       Marker                2.000   0.000    0.67   0.239   0.00   0.000    3.22   0.239   0.00   0.000  Alive\n",
-      "Lord Elements:\n",
-      "      6  O_L       Overlay               1.900     ---    0.67   0.215   0.00     ---    3.22   0.235   0.00     ---  Not_Set\n",
-      "# Index  name      key                       s       l    beta   phi_a    eta   orbit    beta   phi_b    eta   orbit  Track\n",
-      "#                                                            a   [2pi]      x  x [mm]       b   [2pi]      y  y [mm]  State\n",
-      "# Values shown are for the Downstream End of each Element:\n",
-      "-------------------------\n",
-      "Tao> \n"
-     ]
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.037084Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.037016Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.039645Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.039427Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "%%tao\n",
     "sho lat"
@@ -105,25 +108,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "85c21987-a7b5-42d5-a5f5-7a65dce11a31",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'good': True,\n",
-       " 'mean_beta_a': 1.9442223177869156,\n",
-       " 'mean_beta_b': 1.9442223177869151,\n",
-       " 'phi_a': 1.50388821541239,\n",
-       " 'phi_b': 1.5038882154124}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.040927Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.040856Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.045221Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.044995Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "def set_kx(k1):\n",
     "    cmds = [f\"set ele q1 k1 = {k1}\", f\"set ele q2 k1 = {-k1}\"]\n",
@@ -147,9 +142,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "c602c82b-fac0-4882-8dd9-e0583a3fa20c",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.046436Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.046351Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.064727Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.064526Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Scan k1\n",
@@ -167,22 +169,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "2357ea31-2055-491c-9b33-566b06a95984",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "good\n",
-      "mean_beta_a\n",
-      "mean_beta_b\n",
-      "phi_a\n",
-      "phi_b\n"
-     ]
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.065872Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.065781Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.067479Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.067278Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# Reshape data\n",
     "DAT = {}\n",
@@ -199,107 +196,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "d2b18bee-f598-45dd-bb23-4a0f0d484a9a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['good', 'mean_beta_a', 'mean_beta_b', 'phi_a', 'phi_b'])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.068559Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.068473Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.070212Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.070009Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "DAT.keys()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "652a3472-d726-44be-9305-5441dbd71e4f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 435,
-       "width": 567
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 435,
-       "width": 576
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 435,
-       "width": 576
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 435,
-       "width": 567
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 435,
-       "width": 567
-      }
-     },
-     "output_type": "display_data"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.071288Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.071210Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.470813Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.470535Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "for key in KEYS:\n",
     "    plt.plot(qvec1, DAT[key])\n",
@@ -310,25 +234,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "0e7b5796-2ab8-4ac2-ad85-4e09d6d27c20",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-------------------------\n",
-      "Tao> sho dat\n",
-      "\n",
-      "  Name                                 Using for Optimization\n",
-      "  fodo.betas[1:2]                                Using: 1:2\n",
-      "  fodo.stability[1:1]                            Using: 1\n",
-      "-------------------------\n",
-      "Tao> \n"
-     ]
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.472236Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.472132Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.474026Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.473809Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "%%tao\n",
     "sho dat"
@@ -346,25 +262,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "e27c52bd-9cf7-42e8-ab74-dece4569cdb5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'good': True,\n",
-       " 'mean_beta_a': 1.9442223177869156,\n",
-       " 'mean_beta_b': 1.9442223177869151,\n",
-       " 'phi_a': 1.50388821541239,\n",
-       " 'phi_b': 1.5038882154124}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.475296Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.475206Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.479544Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.479337Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "def set_k(k1, k2):\n",
     "    cmds = [f\"set ele q1 k1 = {k1}\", f\"set ele q2 k1 = {-k2}\"]\n",
@@ -388,34 +296,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "0847d120-cf80-42d8-8ee8-1afa03e8fb52",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'good': True,\n",
-       " 'mean_beta_a': 20.7230562019829,\n",
-       " 'mean_beta_b': 20.7230562019829,\n",
-       " 'phi_a': 0.0966467384116868,\n",
-       " 'phi_b': 0.0966467384116869}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.480709Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.480624Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.483526Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.483326Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "set_k(1, 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "be51cc3a-f022-4824-ac0a-499845f1ec0a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.484714Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.484640Z",
+     "iopub.status.idle": "2024-08-08T19:06:15.486483Z",
+     "shell.execute_reply": "2024-08-08T19:06:15.486309Z"
+    }
+   },
    "outputs": [],
    "source": [
     "n1 = 50\n",
@@ -430,21 +337,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "3bfca083-1314-42f1-8399-3d1e26ab95fa",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:15.487592Z",
+     "iopub.status.busy": "2024-08-08T19:06:15.487509Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.071921Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.071660Z"
+    },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 4.28 s, sys: 931 ms, total: 5.21 s\n",
-      "Wall time: 5.44 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "# Make data\n",
@@ -463,22 +367,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "01622876-ab36-4cff-82e2-2dff1dbb1a29",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "good\n",
-      "mean_beta_a\n",
-      "mean_beta_b\n",
-      "phi_a\n",
-      "phi_b\n"
-     ]
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.073209Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.073114Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.076602Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.076391Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# Reshape data\n",
     "DAT = {}\n",
@@ -504,9 +403,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "66105948-267e-4a21-a3f0-375b13adce33",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.077780Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.077694Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.079335Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.079129Z"
+    }
+   },
    "outputs": [],
    "source": [
     "NICE = {}\n",
@@ -522,41 +428,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "e9689fc2-c433-468d-99aa-594ae07038a2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 440,
-       "width": 531
-      }
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 440,
-       "width": 532
-      }
-     },
-     "output_type": "display_data"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.080494Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.080404Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.280925Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.280673Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# fig, ax = plt.subplots(figsize=(10,8))\n",
     "\n",
@@ -589,37 +471,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "bd151056-66eb-4f4a-ad07-39b3106bc445",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'mode_flip': False,\n",
-       " 'beta_a': 19.8980601747812,\n",
-       " 'alpha_a': 20.8824960367296,\n",
-       " 'gamma_a': 21.9658919957425,\n",
-       " 'phi_a': 0.688888454799629,\n",
-       " 'eta_a': 0.0,\n",
-       " 'etap_a': 0.0,\n",
-       " 'beta_b': 8.56179989648832,\n",
-       " 'alpha_b': -8.68869255013931,\n",
-       " 'gamma_b': 8.93426372440924,\n",
-       " 'phi_b': 0.066970264649724,\n",
-       " 'eta_b': 0.0,\n",
-       " 'etap_b': 0.0,\n",
-       " 'eta_x': 0.0,\n",
-       " 'etap_x': 0.0,\n",
-       " 'eta_y': 0.0,\n",
-       " 'etap_y': 0.0}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.282262Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.282184Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.287644Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.287383Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "def optimize(beta_a, beta_b):\n",
     "    cmds = f\"\"\"\n",
@@ -652,21 +514,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "bdf6e2cb-1e55-4141-b37f-b21d3b0bdba6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.78867804672306e-24"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.288860Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.288789Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.290716Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.290506Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# Check merit\n",
     "tao.merit()"
@@ -674,21 +532,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "4ec11177-a124-4ede-afd5-6279a1688dae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10.0000000000002, 19.9999999999994)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.291931Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.291860Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.294168Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.293954Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# Check that the optimization worked\n",
     "average_beta_a = tao.data(\"fodo\", \"betas\", dat_index=1)[\"model_value\"]\n",
@@ -698,21 +552,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "30c1e68c-5868-4b67-b8f4-c90b4fc8c36a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(20.6297896339797, -10.5500557883924)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.295366Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.295294Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.297728Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.297522Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# These are the K\n",
     "kq1 = tao.ele_gen_attribs(\"Q1\")[\"K1\"]\n",
@@ -732,21 +582,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "e2b1c62c-86df-47fb-b9cb-34fb206a5693",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.298945Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.298874Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.301339Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.301118Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.cmd(\n",
     "    \"alias setbetas veto var *;veto dat *;use datafodo.stability;use dat fodo.betas[1,2];set dat fodo.betas[1]|meas=[[1]];set dat fodo.betas[2]|meas=[[2]];use var quad;run;show var -bmad -good\"\n",
@@ -760,21 +606,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "cf9ea816-a8ee-4269-99e3-a0226983e3b6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{}"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.302511Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.302437Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.304355Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.304154Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "T = tao.ele_twiss(\"Q1\")\n",
     "T"
@@ -792,9 +634,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "7eb23d45-05d2-46e6-9929-9596cfc7e00a",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.305599Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.305529Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.309063Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.308846Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pytao.misc.markers import make_markers"
@@ -802,47 +651,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "bf23526d-8a29-4b88-ac0c-a3acfac6de82",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\u001b[0;31mSignature:\u001b[0m \u001b[0mmake_markers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mDocstring:\u001b[0m\n",
-       "Makes markers relative to ref ele.\n",
-       "\n",
-       "If filename is given, the lines will be written to ta file. \n",
-       "\u001b[0;31mFile:\u001b[0m      ~/Code/GitHub/pytao/pytao/misc/markers.py\n",
-       "\u001b[0;31mType:\u001b[0m      function"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.310232Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.310161Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.327674Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.327450Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "?make_markers"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "07e31b2e-14a2-4d14-9629-28033c0712b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.0"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.328887Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.328817Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.331580Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.331391Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "smax = 20.0  # m\n",
     "\n",
@@ -857,21 +693,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "3944eae5-6328-4b67-bad9-5cecca431350",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "181"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.332667Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.332590Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.334974Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.334793Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# Make a lattice and write to a local file\n",
     "\n",
@@ -895,9 +727,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "24532821-6d8d-4ace-84e7-21748ee98af0",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.336071Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.336004Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.384939Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.384686Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Run with this lattice\n",
@@ -908,45 +747,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "a2f0a836-03f8-4fc9-99fb-0a3f31ced8ba",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'-init $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/tao.init -lat /Users/chrisonian/Code/GitHub/pytao/docs/examples/fodo10.bmad -noplot'"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.386182Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.386115Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.388027Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.387817Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "f\"-init $ACC_ROOT_DIR/bmad-doc/tao_examples/fodo/tao.init -lat {latfile} -noplot\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "b29e148b-053a-4666-90d7-be7b47ee4b86",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['',\n",
-       " 'Tao: set global track_type = beam',\n",
-       " '',\n",
-       " 'Tao: set global track_type = single']"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.389116Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.389046Z",
+     "iopub.status.idle": "2024-08-08T19:06:17.900303Z",
+     "shell.execute_reply": "2024-08-08T19:06:17.900025Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# Toggle the beam on and off\n",
     "tao.cmd(\"set beam_init n_particle = 1000\")\n",
@@ -965,9 +793,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "8047a9d2-d92d-4dcc-af75-3d98f9bd53a9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:17.901703Z",
+     "iopub.status.busy": "2024-08-08T19:06:17.901608Z",
+     "iopub.status.idle": "2024-08-08T19:06:18.484495Z",
+     "shell.execute_reply": "2024-08-08T19:06:18.484154Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import h5py\n",
@@ -990,26 +825,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "067d3f0a-7a7b-4c97-bb24-05d75222df4d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 685,
-       "width": 1028
-      }
-     },
-     "output_type": "display_data"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:18.486238Z",
+     "iopub.status.busy": "2024-08-08T19:06:18.486115Z",
+     "iopub.status.idle": "2024-08-08T19:06:19.565424Z",
+     "shell.execute_reply": "2024-08-08T19:06:19.565145Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "skip = 1  # make larger for faster plotting\n",
     "fig, axes = plt.subplots(2, figsize=(12, 8))\n",
@@ -1047,36 +873,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "78e1f010-5bef-47a9-af3c-7d488df46717",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x156de98e0>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 371,
-       "width": 1006
-      }
-     },
-     "output_type": "display_data"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:19.568406Z",
+     "iopub.status.busy": "2024-08-08T19:06:19.568305Z",
+     "iopub.status.idle": "2024-08-08T19:06:19.681082Z",
+     "shell.execute_reply": "2024-08-08T19:06:19.680829Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "k1 = \"sigma_x\"\n",
     "k2 = \"sigma_y\"\n",
@@ -1103,9 +910,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "3bf14254-fca3-4a2d-82b6-48f65774398b",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:19.682432Z",
+     "iopub.status.busy": "2024-08-08T19:06:19.682343Z",
+     "iopub.status.idle": "2024-08-08T19:06:20.069134Z",
+     "shell.execute_reply": "2024-08-08T19:06:20.067167Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Cleanup\n",
@@ -1131,7 +945,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.0"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/examples/plot-bokeh-vars.ipynb b/docs/examples/plot-bokeh-vars.ipynb
new file mode 100644
index 00000000..50d5df49
--- /dev/null
+++ b/docs/examples/plot-bokeh-vars.ipynb
@@ -0,0 +1,77 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyTao Bokeh \"Single mode\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytao import Tao"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init_file = \"$ACC_ROOT_DIR/bmad-doc/tao_examples/cbeta_cell/tao.init\"\n",
+    "\n",
+    "tao = Tao(init_file=init_file, plot=\"bokeh\")\n",
+    "\n",
+    "_ = tao.update_plot_shapes(\"quadrupole\", type_label=\"name\", layout=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot(\"beta\", vars=True, width=800, height=200, layout_height=75)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.var(\"q[1]\")[\"model_value\"]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "610c699f0cd8c4f129acd9140687fff6866bed0eb8e82f249fc8848b827b628c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/examples/plot-bokeh.ipynb b/docs/examples/plot-bokeh.ipynb
new file mode 100644
index 00000000..168e6ac8
--- /dev/null
+++ b/docs/examples/plot-bokeh.ipynb
@@ -0,0 +1,358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyTao plotting with Bokeh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PyTao supports plotting directly from the notebook, without a separate (X11) plot window.\n",
+    "\n",
+    "PyTao provides two backends:\n",
+    "* Bokeh (with interactive plotting support)\n",
+    "* Matplotlib \n",
+    "\n",
+    "When plotting is enabled, PyTao will automatically select the best available backend.\n",
+    "\n",
+    "\n",
+    "The plotting backend may be specified explicitly, as we will do in this notebook in order to show off\n",
+    "this backend's functionality."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Tao setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytao import SubprocessTao, Tao"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init_file = \"$ACC_ROOT_DIR/bmad-doc/tao_examples/optics_matching/tao.init\"\n",
+    "\n",
+    "tao = Tao(init_file=init_file, plot=\"bokeh\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shape setup\n",
+    "\n",
+    "Let's update Tao's plotting shapes first, so that we see label names in the layout and floor plan.\n",
+    "Customizing this in the Bmad init files is an alternative to doing this in PyTao."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = tao.update_plot_shapes(\"quadrupole\", type_label=\"name\", layout=True, floor=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The floor plan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot(\"floor_plan\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.bokeh.layout_template = \"lat_layout\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Single data plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot(\"dispersion\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot fields"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.cmd(\"set var quad[1]|model = -5\")\n",
+    "tao.plot_field(\"Q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stacked plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot([\"alpha\", \"beta\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot([\"alpha\", \"beta\"], include_layout=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gridded plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot([\"beta\", \"dispersion\", \"orbit\"], grid=(2, 2), width=350, height=100, layout_height=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving plots\n",
+    "\n",
+    "The parameter `save` makes it convenient to simultaneously display and save the plot to a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot(\"beta\", save=\"beta\", include_layout=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Change defaults"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pytao.plotting.bokeh\n",
+    "\n",
+    "pytao.plotting.bokeh.set_defaults(\n",
+    "    width=500,\n",
+    "    height=200,\n",
+    "    layout_height=25,\n",
+    "    palette=\"Magma256\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot(\"beta\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing plot data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graphs, app = tao.last_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graphs[0].curves[0].line.xs[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced plotting settings\n",
+    "\n",
+    "`TaoGraphSettings` may be used to customize per-graph settings in single or gridded plots.\n",
+    "\n",
+    "Since each graph has its own settings, we specify a list of `TaoGraphSettings`.\n",
+    "`TaoGraphSettings` includes customization of titles, legends, scales, per-axis settings (with `TaoAxisSettings`), and so on.  \n",
+    "\n",
+    "For example, to change the title of a plot, one could use:\n",
+    "`TaoGraphSettings(title=\"something\")` - or equivalently a custom Tao command can be sent with `TaoGraphSettings(commands=[\"set graph {graph} title = something\"])`.\n",
+    "\n",
+    "See `TaoGraphSettings` documentation for further information on what may be customized. Not all settings will be supported by PyTao's plotting backends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytao.plotting import TaoGraphSettings, TaoAxisSettings\n",
+    "\n",
+    "# Let's use SubprocessTao to make an independent Tao instance in a subprocess.\n",
+    "# Now we can use `tao` from above (`optics_matching`) and `erl` here simultaneously.\n",
+    "erl = pytao.SubprocessTao(init_file=\"$ACC_ROOT_DIR/bmad-doc/tao_examples/erl/tao.init\", plot=\"bokeh\")\n",
+    "\n",
+    "erl.plot(\n",
+    "    \"alpha\",\n",
+    "    settings=TaoGraphSettings(\n",
+    "        title=\"My Custom Alpha Plot\",\n",
+    "        component=\"model\",\n",
+    "        draw_grid=False,\n",
+    "        x=TaoAxisSettings(\n",
+    "            label=\"Position - s [m]\",\n",
+    "        ),\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced curve settings\n",
+    "\n",
+    "`TaoCurveSettings` may be used to customize per-curve settings in simple or gridded plots.\n",
+    "\n",
+    "The below example has 4 plots in a 2x2 grid.\n",
+    "\n",
+    "Since each plot has a set of curves, we must specify a dictionary for each plot.\n",
+    "\n",
+    "That dictionary contains a mapping of `curve_index` (starting with 1) to a `TaoCurveSettings` instance.\n",
+    "\n",
+    "See `TaoCurveSettings` for further information on what may be customized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytao.plotting.curves import TaoCurveSettings\n",
+    "\n",
+    "erl.plot(\n",
+    "    [\"zphase\", \"zphase\", \"zphase\", \"zphase2\"],\n",
+    "    grid=(2, 2),\n",
+    "    curves=[\n",
+    "        {1: TaoCurveSettings(ele_ref_name=r\"linac.beg\\1\")},\n",
+    "        {1: TaoCurveSettings(ele_ref_name=r\"linac.end\\1\")},\n",
+    "        {1: TaoCurveSettings(ele_ref_name=r\"linac.beg\\2\")},\n",
+    "        {1: TaoCurveSettings(ele_ref_name=r\"linac.end\\2\")},\n",
+    "    ],\n",
+    "    share_x=False,\n",
+    "    include_layout=False,\n",
+    "    width=350, # per plot\n",
+    "    vars=True\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "610c699f0cd8c4f129acd9140687fff6866bed0eb8e82f249fc8848b827b628c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/examples/plot-matplotlib.ipynb b/docs/examples/plot-matplotlib.ipynb
new file mode 100644
index 00000000..b260fce7
--- /dev/null
+++ b/docs/examples/plot-matplotlib.ipynb
@@ -0,0 +1,377 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyTao plotting with Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PyTao supports plotting directly from the notebook, without a separate (X11) plot window.\n",
+    "\n",
+    "PyTao provides two backends:\n",
+    "* Bokeh (with interactive plotting support)\n",
+    "* Matplotlib \n",
+    "\n",
+    "When plotting is enabled, PyTao will automatically select the best available backend.\n",
+    "\n",
+    "\n",
+    "The plotting backend may be specified explicitly, as we will do in this notebook in order to show off\n",
+    "this backend's functionality."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Tao setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%config InlineBackend.figure_format = \"retina\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pytao\n",
+    "from pytao import Tao\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init_file = \"$ACC_ROOT_DIR/bmad-doc/tao_examples/optics_matching/tao.init\"\n",
+    "\n",
+    "tao = Tao(init_file=init_file, plot=\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shape setup\n",
+    "\n",
+    "Let's update Tao's plotting shapes first, so that we see label names in the layout and floor plan.\n",
+    "Customizing this in the Bmad init files is an alternative to doing this in PyTao."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.update_plot_shapes(\"quadrupole\", type_label=\"name\", layout=True, floor=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The floor plan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot(\"floor_plan\", ylim=(-2, 2), figsize=(6, 4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Single data plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot(\"dispersion\", include_layout=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot fields"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.cmd(\"set var quad[1]|model = -5\")\n",
+    "tao.plot_field(\"Q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stacked plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot([\"alpha\", \"beta\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot([\"alpha\", \"beta\"], include_layout=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gridded plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot([\"beta\", \"dispersion\", \"orbit\"], grid=(2, 2), figsize=(8, 8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving plots\n",
+    "\n",
+    "The parameter `save` makes it convenient to simultaneously display and save the plot to a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot(\"beta\", save=\"beta.png\", figsize=(3, 3), include_layout=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customized plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot([\"beta\", \"dispersion\", \"orbit\"], grid=(2, 2))\n",
+    "\n",
+    "# Access the figure by using `plt.gcf()` (\"get current figure\")\n",
+    "fig = plt.gcf()\n",
+    "fig.suptitle(\"Customized plot title\")\n",
+    "\n",
+    "# Access individual Axes objects by indexing `fig.axes`:\n",
+    "fig.axes[0].set_title(\"Beta [model]\")\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Change defaults"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pytao.plotting.mpl.set_defaults(layout_height=0.25, figsize=(4, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tao.plot(\"beta\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing plot data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graphs, beta_fig, beta_ax = tao.last_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graphs[0].curves[0].line.xs[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced plotting settings\n",
+    "\n",
+    "`TaoGraphSettings` may be used to customize per-graph settings in single or gridded plots.\n",
+    "\n",
+    "Since each graph has its own settings, we specify a list of `TaoGraphSettings`.\n",
+    "`TaoGraphSettings` includes customization of titles, legends, scales, per-axis settings (with `TaoAxisSettings`), and so on.  \n",
+    "\n",
+    "For example, to change the title of a plot, one could use:\n",
+    "`TaoGraphSettings(title=\"something\")` - or equivalently a custom Tao command can be sent with `TaoGraphSettings(commands=[\"set graph {graph} title = something\"])`.\n",
+    "\n",
+    "See `TaoGraphSettings` documentation for further information on what may be customized. Not all settings will be supported by PyTao's plotting backends."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytao.plotting import TaoGraphSettings, TaoAxisSettings\n",
+    "\n",
+    "# Let's use SubprocessTao to make an independent Tao instance in a subprocess.\n",
+    "# Now we can use `tao` from above (`optics_matching`) and `erl` here simultaneously.\n",
+    "erl = pytao.SubprocessTao(init_file=\"$ACC_ROOT_DIR/bmad-doc/tao_examples/erl/tao.init\", plot=\"mpl\")\n",
+    "\n",
+    "erl.plot(\n",
+    "    \"alpha\",\n",
+    "    settings=TaoGraphSettings(\n",
+    "        title=\"My Custom Alpha Plot\",\n",
+    "        component=\"model\",\n",
+    "        draw_grid=False,\n",
+    "        x=TaoAxisSettings(\n",
+    "            label=\"Position - s [m]\",\n",
+    "        ),\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced curve settings\n",
+    "\n",
+    "`TaoCurveSettings` may be used to customize per-curve settings in simple or gridded plots.\n",
+    "\n",
+    "The below example has 4 plots in a 2x2 grid.\n",
+    "\n",
+    "Since each plot has a set of curves, we must specify a dictionary for each plot.\n",
+    "\n",
+    "That dictionary contains a mapping of `curve_index` (starting with 1) to a `TaoCurveSettings` instance.\n",
+    "\n",
+    "See `TaoCurveSettings` for further information on what may be customized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytao.plotting import TaoCurveSettings\n",
+    "\n",
+    "erl.plot(\n",
+    "    [\"zphase\", \"zphase\", \"zphase\", \"zphase2\"],\n",
+    "    grid=(2, 2),\n",
+    "    curves=[\n",
+    "        {1: TaoCurveSettings(ele_ref_name=r\"linac.beg\\1\")},\n",
+    "        {1: TaoCurveSettings(ele_ref_name=r\"linac.end\\1\")},\n",
+    "        {1: TaoCurveSettings(ele_ref_name=r\"linac.beg\\2\")},\n",
+    "        {1: TaoCurveSettings(ele_ref_name=r\"linac.end\\2\")},\n",
+    "    ],\n",
+    "    share_x=False,\n",
+    "    include_layout=False,\n",
+    "    figsize=(6, 6),\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "610c699f0cd8c4f129acd9140687fff6866bed0eb8e82f249fc8848b827b628c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/examples/special_parsers.ipynb b/docs/examples/special_parsers.ipynb
index 18669e84..30f822b1 100644
--- a/docs/examples/special_parsers.ipynb
+++ b/docs/examples/special_parsers.ipynb
@@ -12,9 +12,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "fefc9efd-b53a-4ef8-b64e-91a0401b6959",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:36.849906Z",
+     "iopub.status.busy": "2024-08-08T19:06:36.849466Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.282558Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.282192Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pytao import Tao"
@@ -22,12 +29,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "b91bb8e2-d3dd-4b8d-b5b1-75c14b5d4fa9",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.284215Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.284104Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.447527Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.447225Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "tao = Tao(\"-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init -noplot\")"
+    "tao = Tao(\"-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init -noplot\")"
    ]
   },
   {
@@ -40,34 +54,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "6f549bd6-a8d3-472d-8a17-74ffb4a7b3ea",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'ix_d1': 7,\n",
-       " 'data_type': 'orbit.x',\n",
-       " 'merit_type': 'target',\n",
-       " 'ele_ref_name': '',\n",
-       " 'ele_start_name': '',\n",
-       " 'ele_name': 'DET_07W',\n",
-       " 'meas_value': 0.0,\n",
-       " 'model_value': -0.00909995345026739,\n",
-       " 'design_value': -0.00909995345026739,\n",
-       " 'useit_opt': False,\n",
-       " 'useit_plot': False,\n",
-       " 'good_user': True,\n",
-       " 'weight': 1000000.0,\n",
-       " 'exists': True}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.449254Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.449165Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.452789Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.452548Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.data_d_array(\"orbit\", \"x\")[7]"
    ]
@@ -82,9 +79,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "ed6c0fe6-0836-47b1-b6e2-ace2433b247e",
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.454091Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.454013Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.465135Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.464857Z"
+    }
+   },
    "outputs": [],
    "source": [
     "tao.cmd(\"veto var *;veto dat *;\")\n",
@@ -95,24 +99,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "d8b4ede2-8437-4050-a2c0-7fdf65046cd9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1: array([[-0.01758468, -0.03303896,  0.00216133],\n",
-       "        [-0.01711307, -0.03578883,  0.00283783],\n",
-       "        [ 0.00189157, -0.00956715,  0.002403  ],\n",
-       "        [-0.00893899,  0.00192382,  0.00267012]])}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.466571Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.466481Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.486412Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.486152Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "result = tao.derivative()\n",
     "result"
@@ -128,21 +125,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "f8123cb7-7707-401d-ab12-d1167ec420a0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4, 3)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.487859Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.487770Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.489842Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.489607Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "result[1].shape"
    ]
@@ -157,21 +150,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "b55cec3d-1b71-4b58-8b6b-b2f4d66137be",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'HKICK': 0.0}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.491075Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.490998Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.492988Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.492745Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.ele_control_var(\"H01W\")"
    ]
@@ -186,21 +175,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "c7760026-de43-44d7-987b-22f3b7cdb13a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['BEGINNING', 'IP_L0', 'CLEO_SOL#3', 'DET_00W', 'CLEO_SOL#4']"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.494276Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.494199Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.497352Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.497119Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "result = tao.lat_ele_list()\n",
     "\n",
@@ -217,34 +202,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "c71d455c-cb84-4fbc-9d91-c2cfcd046108",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'mat6': array([[-9.85453321e-01, -1.78459151e-01, -2.95064264e-02,\n",
-       "         -3.72328912e-05, -1.28463592e-03,  1.69890832e-03],\n",
-       "        [ 1.94136498e-01, -9.79571649e-01, -7.19934541e-03,\n",
-       "          8.91976963e-04, -5.89572298e-03, -3.81443689e-02],\n",
-       "        [-8.56326169e-04,  6.02559410e-05, -8.76189778e-01,\n",
-       "         -8.41353610e-03,  1.67854914e-05,  2.64910033e-03],\n",
-       "        [ 1.16108943e-02,  2.96794811e-02,  2.66272918e+01,\n",
-       "         -8.85649653e-01,  2.27202188e-05, -4.04349279e-02],\n",
-       "        [-8.86152164e-02, -6.13453961e-03,  3.48586759e-02,\n",
-       "         -2.68733720e-03,  9.47817657e-01, -8.79514378e+00],\n",
-       "        [ 5.30821801e-03, -2.93203403e-04,  1.07833932e-04,\n",
-       "         -1.59653540e-05,  1.15997844e-02,  9.47355161e-01]]),\n",
-       " 'vec0': array([ 3.92874185e-04,  4.73093997e-03,  1.86151033e-06, -9.57458176e-05,\n",
-       "        -7.06702236e-05,  5.03940903e-06])}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.498628Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.498544Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.500839Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.500620Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.matrix(\"beginning\", \"end\")"
    ]
@@ -259,21 +227,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "140f2f74-7056-476e-ada1-39b47aaabd2d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "411.826621947889"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.502177Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.502084Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.505644Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.505425Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.merit()"
    ]
@@ -288,36 +252,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "33ca7c73-45c3-4d42-9dbc-a22c73eeb94d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'region': 'top',\n",
-       "  'ix': 1,\n",
-       "  'plot_name': '',\n",
-       "  'visible': False,\n",
-       "  'x1': 0.0,\n",
-       "  'x2': 1.0,\n",
-       "  'y1': 0.48,\n",
-       "  'y2': 0.95},\n",
-       " {'region': 'bottom',\n",
-       "  'ix': 2,\n",
-       "  'plot_name': '',\n",
-       "  'visible': False,\n",
-       "  'x1': 0.0,\n",
-       "  'x2': 1.0,\n",
-       "  'y1': 0.0,\n",
-       "  'y2': 0.48}]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.506935Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.506838Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.510305Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.510068Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "result = tao.plot_list(\"r\")\n",
     "\n",
@@ -326,21 +271,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "cdebf45e-f4ed-40c3-8c99-db95bcce8e0b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.511553Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.511470Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.513696Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.513448Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# 't' gives a mapping of template plot to index\n",
     "result = tao.plot_list(\"t\")\n",
@@ -358,27 +299,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "f395652e-ddba-402a-a95e-02e02d35a31b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.        ,  0.99835693,  0.05730134],\n",
-       "       [ 0.        ,  0.99835693,  0.05730134],\n",
-       "       [-0.05286846,  0.99704202,  0.05578657],\n",
-       "       ...,\n",
-       "       [-0.24326432,  0.96860132,  0.05132215],\n",
-       "       [-0.29421324,  0.95443762,  0.04987387],\n",
-       "       [-0.29421324,  0.95443762,  0.04987387]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.515031Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.514952Z",
+     "iopub.status.idle": "2024-08-08T19:06:37.528848Z",
+     "shell.execute_reply": "2024-08-08T19:06:37.528599Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "tao.spin_invariant(\"l0\")"
    ]
@@ -393,190 +324,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "278f52c1-1055-44de-9714-4810e04e7bb1",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:37.530214Z",
+     "iopub.status.busy": "2024-08-08T19:06:37.530134Z",
+     "iopub.status.idle": "2024-08-08T19:06:40.896726Z",
+     "shell.execute_reply": "2024-08-08T19:06:40.896436Z"
+    },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1: {(0, 0, 0, 0, 0, 0): -1.66366332399e-05,\n",
-       "  (1, 0, 0, 0, 0, 0): -0.985196712163254,\n",
-       "  (0, 1, 0, 0, 0, 0): -0.179943541449763,\n",
-       "  (0, 0, 1, 0, 0, 0): -0.0296993930597934,\n",
-       "  (2, 0, 0, 0, 0, 0): 0.446014935799591,\n",
-       "  (1, 1, 0, 0, 0, 0): -1.0082815874369,\n",
-       "  (0, 2, 0, 0, 0, 0): 1.02617393824841,\n",
-       "  (1, 0, 1, 0, 0, 0): -0.826515800051477,\n",
-       "  (0, 1, 1, 0, 0, 0): 0.667382863899269,\n",
-       "  (0, 0, 2, 0, 0, 0): 44.7241374179086,\n",
-       "  (0, 0, 0, 1, 0, 0): -3.8970786607117e-05,\n",
-       "  (1, 0, 0, 1, 0, 0): -0.00123739509633878,\n",
-       "  (0, 1, 0, 1, 0, 0): -0.0371117192737988,\n",
-       "  (0, 0, 1, 1, 0, 0): -4.03450589571566,\n",
-       "  (0, 0, 0, 0, 1, 0): -0.00131163342274997,\n",
-       "  (1, 0, 0, 0, 1, 0): -0.14952266472463,\n",
-       "  (0, 1, 0, 0, 1, 0): 0.0299330163946578,\n",
-       "  (0, 0, 1, 0, 1, 0): 0.105807427956391,\n",
-       "  (0, 0, 0, 0, 0, 1): 0.00175860485756863,\n",
-       "  (1, 0, 0, 0, 0, 1): 2.90302998820428,\n",
-       "  (0, 1, 0, 0, 0, 1): 3.49576360623984,\n",
-       "  (0, 0, 1, 0, 0, 1): 7.97083175659058,\n",
-       "  (0, 0, 0, 2, 0, 0): -0.0184811091743142,\n",
-       "  (0, 0, 0, 1, 1, 0): -0.00392766890504798,\n",
-       "  (0, 0, 0, 0, 2, 0): 0.00074282261016428,\n",
-       "  (0, 0, 0, 1, 0, 1): 0.0279423475598951,\n",
-       "  (0, 0, 0, 0, 1, 1): -0.01340355160728,\n",
-       "  (0, 0, 0, 0, 0, 2): -2.38972031608605},\n",
-       " 2: {(0, 0, 0, 0, 0, 0): 0.00239048642749099,\n",
-       "  (1, 0, 0, 0, 0, 0): 0.195138002156719,\n",
-       "  (0, 1, 0, 0, 0, 0): -0.979350347537081,\n",
-       "  (0, 0, 1, 0, 0, 0): -0.00732021294143655,\n",
-       "  (2, 0, 0, 0, 0, 0): 1.3249225261134,\n",
-       "  (1, 1, 0, 0, 0, 0): -0.168996332588702,\n",
-       "  (0, 2, 0, 0, 0, 0): 0.295739189067929,\n",
-       "  (1, 0, 1, 0, 0, 0): -0.962169276481757,\n",
-       "  (0, 1, 1, 0, 0, 0): 0.697969162655591,\n",
-       "  (0, 0, 2, 0, 0, 0): 100.484273040881,\n",
-       "  (0, 0, 0, 1, 0, 0): 0.000900228227216367,\n",
-       "  (1, 0, 0, 1, 0, 0): 0.0452519623233577,\n",
-       "  (0, 1, 0, 1, 0, 0): 0.00972379532280073,\n",
-       "  (0, 0, 1, 1, 0, 0): -2.89154417505891,\n",
-       "  (0, 0, 0, 0, 1, 0): -0.00589560314244696,\n",
-       "  (1, 0, 0, 0, 1, 0): -0.0608681441596298,\n",
-       "  (0, 1, 0, 0, 1, 0): 0.131509258514247,\n",
-       "  (0, 0, 1, 0, 1, 0): 0.0812501763521962,\n",
-       "  (0, 0, 0, 0, 0, 1): -0.0381872457210249,\n",
-       "  (1, 0, 0, 0, 0, 1): -9.55209206188755,\n",
-       "  (0, 1, 0, 0, 0, 1): -5.33586099635078,\n",
-       "  (0, 0, 1, 0, 0, 1): 4.52923341679699,\n",
-       "  (0, 0, 0, 2, 0, 0): 0.0629079369870873,\n",
-       "  (0, 0, 0, 1, 1, 0): -0.00149863569373015,\n",
-       "  (0, 0, 0, 0, 2, 0): 0.000587985962294018,\n",
-       "  (0, 0, 0, 1, 0, 1): -0.31853579207818,\n",
-       "  (0, 0, 0, 0, 1, 1): 0.0412939308727921,\n",
-       "  (0, 0, 0, 0, 0, 2): 0.76601614933276},\n",
-       " 3: {(0, 0, 0, 0, 0, 0): 1.05157632159555e-06,\n",
-       "  (1, 0, 0, 0, 0, 0): -0.000864193360286813,\n",
-       "  (0, 1, 0, 0, 0, 0): 6.23497458058423e-05,\n",
-       "  (0, 0, 1, 0, 0, 0): -0.8735664544964,\n",
-       "  (2, 0, 0, 0, 0, 0): -0.0217550050697433,\n",
-       "  (1, 1, 0, 0, 0, 0): 0.000630827921344786,\n",
-       "  (0, 2, 0, 0, 0, 0): -0.0181604455258593,\n",
-       "  (1, 0, 1, 0, 0, 0): 4.98227511703817,\n",
-       "  (0, 1, 1, 0, 0, 0): -3.43330383649897,\n",
-       "  (0, 0, 2, 0, 0, 0): -0.305561098724387,\n",
-       "  (0, 0, 0, 1, 0, 0): -0.00856834872278235,\n",
-       "  (1, 0, 0, 1, 0, 0): -0.132656652144751,\n",
-       "  (0, 1, 0, 1, 0, 0): -0.0220531121048376,\n",
-       "  (0, 0, 1, 1, 0, 0): 0.00641085055272046,\n",
-       "  (0, 0, 0, 0, 1, 0): 1.68093509057174e-05,\n",
-       "  (1, 0, 0, 0, 1, 0): 0.00244665691322182,\n",
-       "  (0, 1, 0, 0, 1, 0): -0.00396412713502785,\n",
-       "  (0, 0, 1, 0, 1, 0): -0.299055160891415,\n",
-       "  (0, 0, 0, 0, 0, 1): 0.00264467550336364,\n",
-       "  (1, 0, 0, 0, 0, 1): 0.319510712558262,\n",
-       "  (0, 1, 0, 0, 0, 1): 0.014171146893256,\n",
-       "  (0, 0, 1, 0, 0, 1): -8.26626633959935,\n",
-       "  (0, 0, 0, 2, 0, 0): -0.000318155048161447,\n",
-       "  (0, 0, 0, 1, 1, 0): 0.000633772505878565,\n",
-       "  (0, 0, 0, 0, 2, 0): -2.10025873360676e-05,\n",
-       "  (0, 0, 0, 1, 0, 1): 0.0846818379113344,\n",
-       "  (0, 0, 0, 0, 1, 1): -0.00127341179496129,\n",
-       "  (0, 0, 0, 0, 0, 2): 0.0218742193846649},\n",
-       " 4: {(0, 0, 0, 0, 0, 0): 1.82087247551517e-06,\n",
-       "  (1, 0, 0, 0, 0, 0): 0.0117108773410044,\n",
-       "  (0, 1, 0, 0, 0, 0): 0.0298731605771444,\n",
-       "  (0, 0, 1, 0, 0, 0): 26.6637945908307,\n",
-       "  (2, 0, 0, 0, 0, 0): 0.0868240483566442,\n",
-       "  (1, 1, 0, 0, 0, 0): 0.86103323300697,\n",
-       "  (0, 2, 0, 0, 0, 0): 0.190824817094323,\n",
-       "  (1, 0, 1, 0, 0, 0): 92.6293350991725,\n",
-       "  (0, 1, 1, 0, 0, 0): 46.0396587460772,\n",
-       "  (0, 0, 2, 0, 0, 0): 0.449570908257767,\n",
-       "  (0, 0, 0, 1, 0, 0): -0.883232864347968,\n",
-       "  (1, 0, 0, 1, 0, 0): -0.0824329649632892,\n",
-       "  (0, 1, 0, 1, 0, 0): 4.59607349264319,\n",
-       "  (0, 0, 1, 1, 0, 0): 0.608976435371927,\n",
-       "  (0, 0, 0, 0, 1, 0): 2.46914545805481e-05,\n",
-       "  (1, 0, 0, 0, 1, 0): 0.0364417603959309,\n",
-       "  (0, 1, 0, 0, 1, 0): 0.00478845202559958,\n",
-       "  (0, 0, 1, 0, 1, 0): -0.706485566536203,\n",
-       "  (0, 0, 0, 0, 0, 1): -0.040481745636089,\n",
-       "  (1, 0, 0, 0, 0, 1): -2.75083077446046,\n",
-       "  (0, 1, 0, 0, 0, 1): -8.49871721620549,\n",
-       "  (0, 0, 1, 0, 0, 1): -131.350468854564,\n",
-       "  (0, 0, 0, 2, 0, 0): 0.00467518361753533,\n",
-       "  (0, 0, 0, 1, 1, 0): 0.276221869154924,\n",
-       "  (0, 0, 0, 0, 2, 0): -1.88223240798573e-05,\n",
-       "  (0, 0, 0, 1, 0, 1): 4.50404232769246,\n",
-       "  (0, 0, 0, 0, 1, 1): 0.00433746050352927,\n",
-       "  (0, 0, 0, 0, 0, 2): 0.120062779434416},\n",
-       " 5: {(0, 0, 0, 0, 0, 0): -0.000398882588537139,\n",
-       "  (1, 0, 0, 0, 0, 0): -0.0886757519916239,\n",
-       "  (0, 1, 0, 0, 0, 0): -0.00609686912285954,\n",
-       "  (0, 0, 1, 0, 0, 0): 0.0349098882028274,\n",
-       "  (2, 0, 0, 0, 0, 0): -4.47044214172974,\n",
-       "  (1, 1, 0, 0, 0, 0): -3.31988559556483,\n",
-       "  (0, 2, 0, 0, 0, 0): -1.97937737937025,\n",
-       "  (1, 0, 1, 0, 0, 0): 6.72117517990414,\n",
-       "  (0, 1, 1, 0, 0, 0): -7.6648695472956,\n",
-       "  (0, 0, 2, 0, 0, 0): -193.642470636415,\n",
-       "  (0, 0, 0, 1, 0, 0): -0.00268389910185181,\n",
-       "  (1, 0, 0, 1, 0, 0): -0.311156736963806,\n",
-       "  (0, 1, 0, 1, 0, 0): -0.03869533588054,\n",
-       "  (0, 0, 1, 1, 0, 0): 7.80657913492895,\n",
-       "  (0, 0, 0, 0, 1, 0): 0.9478074307323,\n",
-       "  (1, 0, 0, 0, 1, 0): 0.0358466617148942,\n",
-       "  (0, 1, 0, 0, 1, 0): 0.010165655302366,\n",
-       "  (0, 0, 1, 0, 1, 0): -0.0420291778081111,\n",
-       "  (0, 0, 0, 0, 0, 1): -8.79504644245375,\n",
-       "  (1, 0, 0, 0, 0, 1): 1.19143615751156,\n",
-       "  (0, 1, 0, 0, 0, 1): 5.25211714015594,\n",
-       "  (0, 0, 1, 0, 0, 1): 1.1222846771318,\n",
-       "  (0, 0, 0, 2, 0, 0): -0.0114705854836205,\n",
-       "  (0, 0, 0, 1, 1, 0): 0.00185575794581298,\n",
-       "  (0, 0, 0, 0, 2, 0): -0.000314319937158193,\n",
-       "  (0, 0, 0, 1, 0, 1): -0.00772739903188279,\n",
-       "  (0, 0, 0, 0, 1, 1): 0.0127413092157923,\n",
-       "  (0, 0, 0, 0, 0, 2): 0.364396954728399},\n",
-       " 6: {(0, 0, 0, 0, 0, 0): -7.15739820527216e-06,\n",
-       "  (1, 0, 0, 0, 0, 0): 0.00531272403470964,\n",
-       "  (0, 1, 0, 0, 0, 0): -0.000311134298030367,\n",
-       "  (0, 0, 1, 0, 0, 0): 0.000106214695876511,\n",
-       "  (2, 0, 0, 0, 0, 0): -0.00928547217254939,\n",
-       "  (1, 1, 0, 0, 0, 0): -0.1769104192046,\n",
-       "  (0, 2, 0, 0, 0, 0): -0.0273916759401037,\n",
-       "  (1, 0, 1, 0, 0, 0): -0.0170628885813259,\n",
-       "  (0, 1, 1, 0, 0, 0): 0.00533000221930043,\n",
-       "  (0, 0, 2, 0, 0, 0): 2.02217623395134,\n",
-       "  (0, 0, 0, 1, 0, 0): -1.59179292331625e-05,\n",
-       "  (1, 0, 0, 1, 0, 0): -0.00164361093394946,\n",
-       "  (0, 1, 0, 1, 0, 0): -0.00408631872975518,\n",
-       "  (0, 0, 1, 1, 0, 0): -0.155773826018652,\n",
-       "  (0, 0, 0, 0, 1, 0): 0.0115997890354962,\n",
-       "  (1, 0, 0, 0, 1, 0): -0.000236576535579418,\n",
-       "  (0, 1, 0, 0, 1, 0): 0.00143001544142555,\n",
-       "  (0, 0, 1, 0, 1, 0): 0.000135808759997953,\n",
-       "  (0, 0, 0, 0, 0, 1): 0.947365020933153,\n",
-       "  (1, 0, 0, 0, 0, 1): -0.0566185345310877,\n",
-       "  (0, 1, 0, 0, 0, 1): 0.013436309919337,\n",
-       "  (0, 0, 1, 0, 0, 1): 0.00297868830544677,\n",
-       "  (0, 0, 0, 2, 0, 0): -0.00088980065810877,\n",
-       "  (0, 0, 0, 1, 1, 0): -1.10290390902046e-07,\n",
-       "  (0, 0, 0, 0, 2, 0): 4.34150580706664e-06,\n",
-       "  (0, 0, 0, 1, 0, 1): -0.00200400188548278,\n",
-       "  (0, 0, 0, 0, 1, 1): 0.000905968623531625,\n",
-       "  (0, 0, 0, 0, 0, 2): 0.00962216934673814}}"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tt = tao.taylor_map(\"beginning\", \"end\", order=2)\n",
     "tt"
@@ -584,23 +343,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "8d9c95ab-02ce-4982-880c-e723665d35d1",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:40.898166Z",
+     "iopub.status.busy": "2024-08-08T19:06:40.898086Z",
+     "iopub.status.idle": "2024-08-08T19:06:40.900521Z",
+     "shell.execute_reply": "2024-08-08T19:06:40.900300Z"
+    },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-0.985453321293537, -0.985196712163254)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Compare some terms with the matrix calc:\n",
     "tao.matrix(\"beginning\", \"end\")[\"mat6\"][0, 0], tt[1][(1, 0, 0, 0, 0, 0)]"
@@ -608,23 +362,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "d55272fc-1ff1-4106-9361-5d4f44edd158",
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:40.901706Z",
+     "iopub.status.busy": "2024-08-08T19:06:40.901617Z",
+     "iopub.status.idle": "2024-08-08T19:06:40.903781Z",
+     "shell.execute_reply": "2024-08-08T19:06:40.903552Z"
+    },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.194136497704459, 0.195138002156719)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tao.matrix(\"beginning\", \"end\")[\"mat6\"][1, 0], tt[2][(1, 0, 0, 0, 0, 0)]"
    ]
@@ -639,36 +388,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "fe424bda-eb97-4e17-a5f9-6c7a1cb21634",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'ix_v1': 0,\n",
-       "  'var_attrib_name': 'Q00W[K1]',\n",
-       "  'meas_value': 0.0,\n",
-       "  'model_value': -0.841784836453016,\n",
-       "  'design_value': -0.841784836453016,\n",
-       "  'useit_opt': False,\n",
-       "  'good_user': False,\n",
-       "  'weight': 100000.0},\n",
-       " {'ix_v1': 3,\n",
-       "  'var_attrib_name': 'Q03W[K1]',\n",
-       "  'meas_value': 0.0,\n",
-       "  'model_value': -0.128947,\n",
-       "  'design_value': -0.128947,\n",
-       "  'useit_opt': True,\n",
-       "  'good_user': True,\n",
-       "  'weight': 100000.0}]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-08-08T19:06:40.905017Z",
+     "iopub.status.busy": "2024-08-08T19:06:40.904920Z",
+     "iopub.status.idle": "2024-08-08T19:06:40.907396Z",
+     "shell.execute_reply": "2024-08-08T19:06:40.907185Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "result = tao.var_v_array(\"quad_k1\")\n",
     "result[0:2]"
diff --git a/docs/installation.md b/docs/installation.md
index 2b77063e..37d99b2b 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -1,26 +1,23 @@
-Installation
-============
+# Installation
 
-Note! The **Bmad Distribution** (which includes *Tao*) must be installed
+Note! The **Bmad Distribution** (which includes _Tao_) must be installed
 before installing PyTao. Additionally, the Bmad Distribution must be
 compiled with the `ACC_ENABLE_SHARED="Y"` flag set in the
 `bmad_dist/util/dist_prefs` file.
 
-For instructions on how to install the *Bmad Distribution*, please refer
-to the instructions available at the *Bmad* website.
+For instructions on how to install the _Bmad Distribution_, please refer
+to the instructions available at the _Bmad_ website.
 
 Since PyTao is a python package, it can be installed in a couple of
 different ways:
 
-Using setuptools
-----------------
+## Using setuptools
 
 ```bash
 python setup.py install
 ```
 
-Using pip
----------
+## Using pip
 
 ```bash
 # From PyPI distribution
@@ -30,8 +27,7 @@ pip install pytao
 pip install .
 ```
 
-Using conda
------------
+## Using conda
 
 ```bash
 conda install -c conda-forge pytao
diff --git a/docs/usage.md b/docs/usage.md
new file mode 100644
index 00000000..8429f2cb
--- /dev/null
+++ b/docs/usage.md
@@ -0,0 +1,133 @@
+# Usage
+
+## PyTao with JupyterLab
+
+PyTao has advanced JupyterLab integration for plotting and is generally the recommended method for using PyTao.
+
+Start up JupyterLab as you would normally:
+
+```bash
+jupyter lab
+
+```
+
+And then use PyTao, enabling your preferred plotting backend:
+
+```python
+from pytao import Tao
+
+# Best available (Bokeh first, Matplotlib second)
+tao = Tao(init_file="$ACC_ROOT_DIR/bmad-doc/tao_examples/cbeta_cell/tao.init", plot=True)
+# Matplotlib:
+tao = Tao(init_file="$ACC_ROOT_DIR/bmad-doc/tao_examples/cbeta_cell/tao.init", plot="mpl")
+# Bokeh
+tao = Tao(init_file="$ACC_ROOT_DIR/bmad-doc/tao_examples/cbeta_cell/tao.init", plot="bokeh")
+
+```
+
+If you wish to use Tao's internal plotting mechanism, leave the `plot` keyword argument off or specify `plot="tao"`:
+
+```python
+from pytao import Tao
+
+# Use Tao's internal plotting mechanism:
+tao = Tao(init_file="$ACC_ROOT_DIR/bmad-doc/tao_examples/cbeta_cell/tao.init")
+
+```
+
+To disable plotting entirely, use:
+
+```python
+from pytao import Tao
+
+# Use Tao's internal plotting mechanism:
+tao = Tao(init_file="$ACC_ROOT_DIR/bmad-doc/tao_examples/cbeta_cell/tao.init", noplot=True)
+
+```
+
+The `Tao` object supports all Tao initialization arguments as Python keyword arguments.
+That is, any of the following may be specified to `Tao`:
+
+```python
+
+
+Tao(
+  beam_file="file_name",                # File containing the tao_beam_init namelist.
+  beam_init_position_file="file_name",  # File containing initial particle positions.
+  building_wall_file="file_name",       # Define the building tunnel wall
+  command="command_string",             # Commands to run after startup file commands
+  data_file="file_name",                # Define data for plotting and optimization
+  debug=True,                           # Debug mode for Wizards
+  disable_smooth_line_calc=True,        # Disable the smooth line calc used in plotting
+  external_plotting=True,               # Tells Tao that plotting is done externally to Tao.
+  geometry="<width>x<height>",          # Plot window geometry (pixels)
+  hook_init_file="file_name",           # Init file for hook routines (Default = tao_hook.init)
+  init_file="file_name",                # Tao init file
+  lattice_file="file_name",             # Bmad lattice file
+  log_startup=True,                     # Write startup debugging info
+  no_stopping=True,                     # For debugging: Prevents Tao from exiting on errors
+  noinit=True,                          # Do not use Tao init file.
+  noplot=True,                          # Do not open a plotting window
+  nostartup=True,                       # Do not open a startup command file
+  no_rad_int=True,                      # Do not do any radiation integrals calculations.
+  plot_file="file_name",                # Plotting initialization file
+  prompt_color="color",                 # Set color of prompt string. Default is blue.
+  reverse=True,                         # Reverse lattice element order?
+  rf_on=True,                           # Use "--rf_on" to turn off RF (default is now RF on)
+  quiet=True,                           # Suppress terminal output when running a command file?
+  slice_lattice="ele_list",             # Discards elements from lattice that are not in the list
+  start_branch_at="ele_name",           # Start lattice branch at element.
+  startup_file="file_name",             # Commands to run after parsing Tao init file
+  symbol_import=True,                   # Import symbols defined in lattice files(s)?
+  var_file="file_name",                 # Define variables for plotting and optimization
+)
+```
+
+## PyTao on the command-line
+
+PyTao has a simple IPython entrypoint, giving you quick access to Tao in Python.
+
+The following will start IPython with a `Tao` instance available as `tao`:
+
+```bash
+pytao -init_file "$ACC_ROOT_DIR/bmad-doc/tao_examples/cbeta_cell/tao.init"
+```
+
+To use PyTao's Matplotlib backend, do the following:
+
+```bash
+PYTAO_PLOT=mpl pytao -init_file "$ACC_ROOT_DIR/bmad-doc/tao_examples/cbeta_cell/tao.init"
+```
+
+```python
+In [1]: tao.plot("beta")
+
+In [2]: plt.show()
+```
+
+## PyTao (deprecated/experimental) GUI
+
+Start the experimental (and mostly unsupported/deprecated) GUI by using the following:
+
+```bash
+pytao-gui -init_file "$ACC_ROOT_DIR/bmad-doc/tao_examples/cbeta_cell/tao.init"
+```
+
+## Notes about Bokeh on JupyterHub
+
+If you are using PyTao with Bokeh on your computer and are running JupyterLab
+locally, you can safely ignore this section.
+
+If you are accessing a JupyterHub deployment on an HPC cluster through a
+non-`localhost` website, there are some additional setup steps required.
+
+Please read [this document](https://docs.bokeh.org/en/latest/docs/user_guide/output/jupyter.html) from Bokeh for full details.
+
+At minimum, you will need to:
+
+- Configure `JUPYTER_BOKEH_EXTERNAL_URL` to point to your server. For example, users may specify the following in their `~/.bashrc`: `export JUPYTER_BOKEH_EXTERNAL_URL="https//example.com"` (replacing `example.com` with the URL you use to access JupyterHub)
+
+Additionally, you may be required to:
+
+- In the JupyterHub environment, `conda install jupyter-server-proxy`
+- Restart JupyterHub
diff --git a/environment.yml b/environment.yml
index 3b5e8a9d..ce00a2ea 100644
--- a/environment.yml
+++ b/environment.yml
@@ -7,11 +7,12 @@ dependencies:
   - ipykernel
   - ipywidgets
   - jupyterlab
-  - bmad >=20240628.2
+  - bmad >=20240809
   - openPMD-beamphysics
   - numpy
   - h5py
   - pexpect
+  - pydantic >=2
   - pip
   - pip:
       # Install pytao from here.
diff --git a/generate_interface_commands.py b/generate_interface_commands.py
index 1c4b0969..7d74e3c3 100644
--- a/generate_interface_commands.py
+++ b/generate_interface_commands.py
@@ -8,18 +8,29 @@
 import keyword
 import os
 import shutil
+import sys
 
 CMDS_OUTPUT = "./pytao/interface_commands.py"
 TEST_OUTPUT = "./pytao/tests/test_interface_commands.py"
 
-# ## Read the JSON File
-f_name = f'{os.getenv("ACC_ROOT_DIR")}/tao/doc/python-interface-commands.json'
-print(f"Reading JSON from: {f_name}")
-
-with open(f_name, "r") as f:
-    cmds_from_tao = json.load(f)
+tao_docs = os.path.join(os.getenv("ACC_ROOT_DIR", "../bmad"), "tao", "doc")
 
-with open("interface.tpl.py", "r") as f:
+# ## Read the JSON File
+for command_name in ("pipe", "python"):
+    f_name = os.path.join(tao_docs, f"{command_name}-interface-commands.json")
+    if os.path.exists(f_name):
+        print(f"Reading JSON from: {f_name}")
+
+        with open(f_name, "r") as f:
+            cmds_from_tao = json.load(f)
+        break
+else:
+    print(
+        f"Unable to find an interface commands JSON file in path: {tao_docs})", file=sys.stderr
+    )
+    exit(1)
+
+with open("pytao/interface.tpl.py", "r") as f:
     interface_tpl_py = f.read()
 
 
@@ -256,7 +267,7 @@ def get_tests(examples):
         args.append("verbose=True")
         test_code = f"""
 with ensure_successful_parsing(caplog):
-    with new_tao(tao_cls, '{test_meta['init']}') as tao:
+    with new_tao(tao_cls, '{test_meta['init']}', external_plotting=False) as tao:
         tao.{clean_method}({', '.join(args)})
         """
         method_template = f"""
diff --git a/interface.tpl.py b/interface.tpl.py
deleted file mode 100644
index 994f70f7..00000000
--- a/interface.tpl.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import logging
-import numpy as np
-
-from .tao_ctypes.core import TaoCore
-from .tao_ctypes.util import parse_tao_python_data
-from .util.parameters import tao_parameter_dict
-from .util import parsers as _pytao_parsers
-
-
-logger = logging.getLogger(__name__)
-
-
-class Tao(TaoCore):
-    def __execute(
-        self,
-        cmd: str,
-        as_dict: bool = True,
-        raises: bool = True,
-        method_name=None,
-        cmd_type: str = "string_list",
-    ):
-        """
-
-        A wrapper to handle commonly used options when running a command through tao.
-
-        Parameters
-        ----------
-        cmd : str
-            The command to run
-        as_dict : bool, optional
-            Return string data as a dict? by default True
-        raises : bool, optional
-            Raise exception on tao errors? by default True
-        method_name : str/None, optional
-            Name of the caller. Required for custom parsers for commands, by
-            default None
-        cmd_type : str, optional
-            The type of data returned by tao in its common memory, by default
-            "string_list"
-
-        Returns
-        -------
-        Any
-        Result from running tao. The type of data depends on configuration, but is generally a list of strings, a dict, or a
-        numpy array.
-        """
-        func_for_type = {
-            "string_list": self.cmd,
-            "real_array": self.cmd_real,
-            "integer_array": self.cmd_integer,
-        }
-        func = func_for_type.get(cmd_type, self.cmd)
-        raw_output = func(cmd, raises=raises)
-        special_parser = getattr(_pytao_parsers, f"parse_{method_name}", None)
-        try:
-            if special_parser and callable(special_parser):
-                return special_parser(raw_output, cmd=cmd)
-            if "string" not in cmd_type:
-                return raw_output
-            if as_dict:
-                return parse_tao_python_data(raw_output)
-            return tao_parameter_dict(raw_output)
-        except Exception:
-            if raises:
-                raise
-            logger.exception(
-                "Failed to parse string data with custom parser. Returning raw value."
-            )
-            return raw_output
-
-    def bunch_data(self, ele_id, *, which="model", ix_bunch=1, verbose=False):
-        """
-        Returns bunch data in openPMD-beamphysics format/notation.
-
-        Notes
-        -----
-        Note that Tao's 'write beam' will also write a proper h5 file in this format.
-
-        Expected usage:
-            data = bunch_data(tao, 'end')
-            from pmd_beamphysics import ParticleGroup
-            P = ParicleGroup(data=data)
-
-
-        Returns
-        -------
-        data : dict
-            dict of arrays, with keys 'x', 'px', 'y', 'py', 't', 'pz',
-            'status', 'weight', 'z', 'species'
-
-
-        Examples
-        --------
-        Example: 1
-        init: $ACC_ROOT_DIR/tao/examples/csr_beam_tracking/tao.init
-        args:
-        ele_id: end
-        which: model
-        ix_bunch: 1
-
-        """
-
-        # Get species
-        stats = self.bunch_params(ele_id, which=which, verbose=verbose)
-        species = stats["species"]
-
-        dat = {}
-        for coordinate in ["x", "px", "y", "py", "t", "pz", "p0c", "charge", "state"]:
-            dat[coordinate] = self.bunch1(
-                ele_id,
-                coordinate=coordinate,
-                which=which,
-                ix_bunch=ix_bunch,
-                verbose=verbose,
-            )
-
-        # Remove normalizations
-        p0c = dat.pop("p0c")
-
-        dat["status"] = dat.pop("state")
-        dat["weight"] = dat.pop("charge")
-
-        # px from Bmad is px/p0c
-        # pz from Bmad is delta = p/p0c -1.
-        # pz = sqrt( (delta+1)**2 -px**2 -py**2)*p0c
-        dat["pz"] = np.sqrt((dat["pz"] + 1) ** 2 - dat["px"] ** 2 - dat["py"] ** 2) * p0c
-        dat["px"] = dat["px"] * p0c
-        dat["py"] = dat["py"] * p0c
-
-        # z = 0 by definition
-        dat["z"] = np.full(len(dat["x"]), 0)
-
-        dat["species"] = species.lower()
-
-        return dat
diff --git a/mkdocs.yml b/mkdocs.yml
index f2dbfeae..7dd65d9a 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -7,8 +7,13 @@ nav:
 
   - Installation: installation.md
 
+  - Usage: usage.md
+
   - Examples:
       - examples/basic.ipynb
+      - examples/plot-matplotlib.ipynb
+      - examples/plot-bokeh.ipynb
+      - examples/plot-bokeh-vars.ipynb
       - examples/bunch.ipynb
       - examples/special_parsers.ipynb
       - examples/fodo.ipynb
@@ -16,8 +21,13 @@ nav:
 
   - Dev:
       - development.md
+
   - API:
-      - PyTao: api/pytao.md
+      - Tao: api/tao.md
+      - SubprocessTao: api/subprocesstao.md
+      - Plotting: api/plot.md
+      - Matplotlib: api/plot-mpl.md
+      - Bokeh: api/plot-bokeh.md
 
 theme:
   icon:
@@ -62,18 +72,39 @@ plugins:
 
   - mkdocs-jupyter:
       include_source: True
-      # no_input: True
 
   - mkdocstrings:
       default_handler: python
       handlers:
         python:
-          selection:
-            docstring_style: "numpy"
-            inherited_members: false
+          options:
             filters:
               - "!^_" # exclude all members starting with _
               - "^__init__$" # but always include __init__ modules and methods
+            docstring_style: numpy
+            docstring_options:
+              ignore_init_summary: false
+            heading_level: 3
+            show_root_heading: true
+            show_root_toc_entry: true
+            show_root_full_path: true
+            show_root_members_full_path: false
+            show_object_full_path: true
+            show_category_heading: true
+            show_if_no_docstring: false
+            show_signature: true
+            signature_crossrefs: true
+            show_signature_annotations: false
+            separate_signature: true
+            line_length: 100
+            merge_init_into_class: true
+            show_source: true
+            show_bases: true
+            show_submodules: false
+            group_by_category: true
+            unwrap_annotated: true
+            import:
+              - https://docs.python.org/3/objects.inv
           rendering:
             show_source: true
             show_root_heading: true
diff --git a/pytao/__init__.py b/pytao/__init__.py
index 80e31a81..31a5ea63 100644
--- a/pytao/__init__.py
+++ b/pytao/__init__.py
@@ -6,8 +6,8 @@
 in the util package.
 """
 
-from .interface_commands import Tao
-from .subproc import SubprocessTao
+from .interface_commands import Tao, TaoStartup
+from .subproc import AnyTao, SubprocessTao
 from .tao_ctypes import TaoModel, run_tao
 from .tao_ctypes.evaluate import evaluate_tao
 from .tao_interface import tao_interface
@@ -19,11 +19,13 @@
     __version__ = "0.0.0+unknown"
 
 __all__ = [
-    "tao_io",
-    "TaoModel",
+    "AnyTao",
+    "SubprocessTao",
     "Tao",
-    "run_tao",
+    "TaoModel",
+    "TaoStartup",
     "evaluate_tao",
+    "run_tao",
     "tao_interface",
-    "SubprocessTao",
+    "tao_io",
 ]
diff --git a/pytao/__main__.py b/pytao/__main__.py
index 05a18f0a..3aeba6b0 100644
--- a/pytao/__main__.py
+++ b/pytao/__main__.py
@@ -1,3 +1,4 @@
+import os
 import sys
 
 
@@ -19,8 +20,15 @@ def main():
     print("-" * len(startup_message))
     print()
 
-    tao = Tao(init=init_args)
-    return IPython.start_ipython(user_ns={"tao": tao}, argv=[])
+    plot = os.environ.get("PYTAO_PLOT", "tao").lower()
+    tao = Tao(init=init_args, plot=plot)
+    user_ns = {"tao": tao}
+    if plot == "mpl":
+        import matplotlib.pyplot as plt
+
+        user_ns["plt"] = plt
+
+    return IPython.start_ipython(user_ns=user_ns, argv=[])
 
 
 if __name__ == "__main__":
diff --git a/pytao/gui/main.py b/pytao/gui/main.py
index 7f3ab7a1..941bd6ef 100644
--- a/pytao/gui/main.py
+++ b/pytao/gui/main.py
@@ -1,14 +1,11 @@
 # Check for required modules:
 import os
 import sys
-
-from .module_check import module_check
-
 import tkinter as tk
 from tkinter import filedialog, font, messagebox
 
 from ..util.parameters import str_to_tao_param, tao_startup_param_dict
-
+from .module_check import module_check
 from .tao_beam_windows import tao_beam_init_window
 from .tao_console import tao_console
 from .tao_data_windows import tao_d2_data_window, tao_new_data_window
diff --git a/pytao/gui/tao_beam_windows.py b/pytao/gui/tao_beam_windows.py
index 9af591ea..1ed186c9 100644
--- a/pytao/gui/tao_beam_windows.py
+++ b/pytao/gui/tao_beam_windows.py
@@ -5,7 +5,6 @@
 import tkinter as tk
 
 from ..util.parameters import str_to_tao_param
-
 from .tao_base_windows import tao_list_window, tao_parameter_window
 from .tao_set import tao_set
 from .tao_widget import tk_tao_parameter
diff --git a/pytao/gui/tao_data_windows.py b/pytao/gui/tao_data_windows.py
index df3dca06..4a07c924 100644
--- a/pytao/gui/tao_data_windows.py
+++ b/pytao/gui/tao_data_windows.py
@@ -6,7 +6,6 @@
 from tkinter import messagebox, ttk
 
 from ..util.parameters import str_to_tao_param
-
 from .tao_base_windows import (
     Tao_Toplevel,
     lw_table_window,
diff --git a/pytao/gui/tao_interface.py b/pytao/gui/tao_interface.py
index 2a94966f..b56a7dbf 100644
--- a/pytao/gui/tao_interface.py
+++ b/pytao/gui/tao_interface.py
@@ -6,7 +6,8 @@
 import time
 import tkinter as tk
 
-from ..tao_interface import filter_output, new_stdout, tao_interface as _tao_interface
+from ..tao_interface import filter_output, new_stdout
+from ..tao_interface import tao_interface as _tao_interface
 
 
 class tao_interface(_tao_interface):
diff --git a/pytao/gui/tao_lat_windows.py b/pytao/gui/tao_lat_windows.py
index a3837426..63bd9012 100644
--- a/pytao/gui/tao_lat_windows.py
+++ b/pytao/gui/tao_lat_windows.py
@@ -7,7 +7,6 @@
 
 from ..util.lattice_element import lat_element
 from ..util.parameters import str_to_tao_param
-
 from .tao_base_windows import (
     Tao_Toplevel,
     tao_branch_widgets,
diff --git a/pytao/gui/tao_misc_windows.py b/pytao/gui/tao_misc_windows.py
index 7bc3e3bd..ed11c57b 100644
--- a/pytao/gui/tao_misc_windows.py
+++ b/pytao/gui/tao_misc_windows.py
@@ -5,7 +5,6 @@
 import tkinter as tk
 
 from ..util.parameters import str_to_tao_param
-
 from .tao_base_windows import tao_list_window, tao_parameter_window
 from .tao_set import tao_set
 
diff --git a/pytao/gui/tao_mpl_toolbar.py b/pytao/gui/tao_mpl_toolbar.py
index c4757487..67db422e 100644
--- a/pytao/gui/tao_mpl_toolbar.py
+++ b/pytao/gui/tao_mpl_toolbar.py
@@ -51,9 +51,13 @@ def __init__(self, canvas_, parent_, width_, GUI_DIR_):
         self.gRangeList = []  # list of original x min, x max, y min, and y max for each graph
         try:
             for i in self.templateGraphList:
-                gInfo = self.parent.pipe.cmd_in(
-                    "python plot_graph " + i, no_warn=True
-                ).splitlines()
+                try:
+                    gInfo = self.parent.pipe.cmd_in(
+                        "python plot_graph " + i, no_warn=True
+                    ).splitlines()
+                except RuntimeError:
+                    continue
+
                 gRange = []
                 gInfoDict = {}
                 for i in range(len(gInfo)):
diff --git a/pytao/gui/tao_plot_windows.py b/pytao/gui/tao_plot_windows.py
index 8fa97164..ad0a9f6c 100644
--- a/pytao/gui/tao_plot_windows.py
+++ b/pytao/gui/tao_plot_windows.py
@@ -5,15 +5,11 @@
 import tkinter as tk
 from tkinter import messagebox, ttk
 
-import matplotlib
-
-matplotlib.use("TkAgg")
 from matplotlib.backend_bases import key_press_handler
 from matplotlib.backends._backend_tk import FigureManagerTk
 from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
 
 from ..util.parameters import str_to_tao_param, tao_parameter_dict
-
 from .tao_base_windows import (
     Tao_Popup,
     Tao_Toplevel,
diff --git a/pytao/gui/tao_var_windows.py b/pytao/gui/tao_var_windows.py
index 8b7b1581..9bcf34c7 100644
--- a/pytao/gui/tao_var_windows.py
+++ b/pytao/gui/tao_var_windows.py
@@ -6,7 +6,6 @@
 from tkinter import messagebox, ttk
 
 from ..util.parameters import str_to_tao_param
-
 from .tao_base_windows import (
     Tao_Toplevel,
     lw_table_window,
diff --git a/pytao/gui/tao_widget.py b/pytao/gui/tao_widget.py
index 3a633f1e..caf35788 100644
--- a/pytao/gui/tao_widget.py
+++ b/pytao/gui/tao_widget.py
@@ -8,7 +8,6 @@
 from tkinter import filedialog, ttk
 
 from ..util.parameters import str_to_tao_param, tao_parameter
-
 from .data_type_list import data_type_list
 
 # from .tao_data_windows import tao_new_data_window, tao_d1_data_window
diff --git a/pytao/gui/taoplot.py b/pytao/gui/taoplot.py
index bc23f54a..5387cee4 100644
--- a/pytao/gui/taoplot.py
+++ b/pytao/gui/taoplot.py
@@ -1,3 +1,5 @@
+import pprint
+
 import matplotlib as mp
 import matplotlib.patches as patches
 import matplotlib.pyplot as plt
@@ -797,10 +799,20 @@ def plot(self, width):
             branch = layInfoDict["-1^ix_branch"].value
 
             # List of strings containing information about each element
-            eleInfo = pipe.cmd_in(
-                "python plot_lat_layout " + str(universe) + "@" + str(branch),
-                no_warn=True,
-            ).splitlines()
+            try:
+                eleInfo = pipe.cmd_in(
+                    "python plot_lat_layout " + str(universe) + "@" + str(branch),
+                    no_warn=True,
+                ).splitlines()
+            except RuntimeError as ex:
+                print(f"Failed to plot layout: {ex}")
+                print("The layout information dictionary is as follows:")
+                pprint.pprint(layInfoDict)
+                print("Trying branch 0...")
+                eleInfo = pipe.cmd_in(
+                    f"python plot_lat_layout {universe}@0",
+                    no_warn=True,
+                ).splitlines()
 
             # All dict keys and entries are strings which match a lattice layout element index (eg: '1') string to the corresponding information
             eleIndexList = []
@@ -813,17 +825,18 @@ def plot(self, width):
             eleColorDict = {}
             eleNameDict = {}
             for i in range(len(eleInfo)):
-                eleIndexList.append(int(eleInfo[i].split(";")[0]))
-                eleStartDict[eleInfo[i].split(";")[0]] = float(eleInfo[i].split(";")[1])
-                eleEndDict[eleInfo[i].split(";")[0]] = float(eleInfo[i].split(";")[2])
-                eleLwDict[eleInfo[i].split(";")[0]] = float(eleInfo[i].split(";")[3])
-                eleShapeDict[eleInfo[i].split(";")[0]] = eleInfo[i].split(";")[4].lower()
-                eleY1Dict[eleInfo[i].split(";")[0]] = float(eleInfo[i].split(";")[5])
-                eleY2Dict[eleInfo[i].split(";")[0]] = float(eleInfo[i].split(";")[6])
-                eleColorDict[eleInfo[i].split(";")[0]] = mpl_color(
-                    eleInfo[i].split(";")[7].lower()
-                )
-                eleNameDict[eleInfo[i].split(";")[0]] = eleInfo[i].split(";")[8]
+                # NOTE: unhandled branch is element 0
+                split_ = eleInfo[i].split(";")
+                index = split_[1]
+                eleIndexList.append(int(split_[1]))
+                eleStartDict[index] = float(split_[2])
+                eleEndDict[index] = float(split_[3])
+                eleLwDict[index] = float(split_[4])
+                eleShapeDict[index] = split_[5].lower()
+                eleY1Dict[index] = float(split_[6])
+                eleY2Dict[index] = float(split_[7])
+                eleColorDict[index] = mpl_color(split_[8].lower())
+                eleNameDict[index] = split_[9]
 
             # Plotting line segments one-by-one can be slow if there are thousands of lattice elements.
             # So keep a list of line segments and plot all at once at the end.
diff --git a/pytao/interface.tpl.py b/pytao/interface.tpl.py
new file mode 100644
index 00000000..eb7d919a
--- /dev/null
+++ b/pytao/interface.tpl.py
@@ -0,0 +1,950 @@
+from __future__ import annotations
+
+import contextlib
+import datetime
+import logging
+import pathlib
+import typing
+from dataclasses import asdict
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+from pydantic import ConfigDict, dataclasses
+from typing_extensions import Literal, override
+
+from .plotting import MatplotlibGraphManager
+from .plotting.types import ShapeListInfo
+from .plotting.util import select_graph_manager_class
+from .tao_ctypes.core import TaoCore
+from .tao_ctypes.util import parse_tao_python_data
+from .util import parsers as _pytao_parsers
+from .util.command import make_tao_init
+from .util.parameters import tao_parameter_dict
+
+if typing.TYPE_CHECKING:
+    from .plotting.bokeh import BokehGraphManager, NotebookGraphManager  # noqa: F401
+    from .subproc import SubprocessTao
+
+    AnyTao = Union["Tao", SubprocessTao]
+
+
+logger = logging.getLogger(__name__)
+AnyPath = Union[pathlib.Path, str]
+
+
+@dataclasses.dataclass(config=ConfigDict(extra="forbid", validate_assignment=True))
+class TaoStartup:
+    """
+    All Tao startup settings.
+
+    Attributes
+    ----------
+    init : str, optional
+        Initialization string for Tao.  Same as the tao command-line, including
+        "-init" and such.  Shell variables in `init` strings will be expanded
+        by Tao.  For example, an `init` string containing `$HOME` would be
+        replaced by your home directory.
+    so_lib : str, optional
+        Path to the Tao shared library.  Auto-detected if not specified.
+    plot : str, bool, optional
+        Use pytao's plotting mechanism with matplotlib or bokeh, if available.
+        If `True`, pytao will pick an appropriate plotting backend.
+        If `False` or "tao", Tao plotting will be used. (Default)
+        If "mpl", the pytao matplotlib plotting backend will be selected.
+        If "bokeh", the pytao Bokeh plotting backend will be selected.
+    metadata : dict[str, Any], optional
+        User-specified metadata about this startup.  Not passed to Tao.
+    beam_file : str or pathlib.Path, default=None
+        File containing the tao_beam_init namelist.
+    beam_init_position_file : pathlib.Path or str, default=None
+        File containing initial particle positions.
+    building_wall_file : str or pathlib.Path, default=None
+        Define the building tunnel wall
+    command : str, optional
+        Commands to run after startup file commands
+    data_file : str or pathlib.Path, default=None
+        Define data for plotting and optimization
+    debug : bool, default=False
+        Debug mode for Wizards
+    disable_smooth_line_calc : bool, default=False
+        Disable the smooth line calc used in plotting
+    external_plotting : bool, default=False
+        Tells Tao that plotting is done externally to Tao.
+    geometry : "wxh" or (width, height) tuple, optional
+        Plot window geometry (pixels)
+    hook_init_file :  pathlib.Path or str, default=None
+        Init file for hook routines (Default = tao_hook.init)
+    init_file : str or pathlib.Path, default=None
+        Tao init file
+    lattice_file : str or pathlib.Path, default=None
+        Bmad lattice file
+    log_startup : bool, default=False
+        Write startup debugging info
+    no_stopping : bool, default=False
+        For debugging : Prevents Tao from exiting on errors
+    noinit : bool, default=False
+        Do not use Tao init file.
+    noplot : bool, default=False
+        Do not open a plotting window
+    nostartup : bool, default=False
+        Do not open a startup command file
+    no_rad_int : bool, default=False
+        Do not do any radiation integrals calculations.
+    plot_file : str or pathlib.Path, default=None
+        Plotting initialization file
+    prompt_color : str, optional
+        Set color of prompt string. Default is blue.
+    reverse : bool, default=False
+        Reverse lattice element order?
+    rf_on : bool, default=False
+        Use "--rf_on" to turn off RF (default is now RF on)
+    quiet : bool, default=False
+        Suppress terminal output when running a command file?
+    slice_lattice : str, optional
+        Discards elements from lattice that are not in the list
+    start_branch_at : str, optional
+        Start lattice branch at element.
+    startup_file : str or pathlib.Path, default=None
+        Commands to run after parsing Tao init file
+    symbol_import : bool, default=False
+        Import symbols defined in lattice files(s)?
+    var_file : str or pathlib.Path, default=None
+        Define variables for plotting and optimization
+    """
+
+    # General case 'init' string:
+    init: str = dataclasses.Field(default="", kw_only=False)
+
+    # Tao ctypes-specific - shared library location.
+    so_lib: str = dataclasses.Field(default="", kw_only=False)
+
+    # pytao specific
+    metadata: Dict[str, Any] = dataclasses.Field(default_factory=dict)
+    plot: Union[str, bool] = "tao"
+
+    # All remaining flags:
+    beam_file: Optional[AnyPath] = None
+    beam_init_position_file: Optional[AnyPath] = None
+    building_wall_file: Optional[AnyPath] = None
+    command: str = ""
+    data_file: Optional[AnyPath] = None
+    debug: bool = False
+    disable_smooth_line_calc: bool = False
+    external_plotting: bool = False
+    geometry: Union[str, Tuple[int, int]] = ""
+    hook_init_file: Optional[AnyPath] = None
+    init_file: Optional[AnyPath] = None
+    lattice_file: Optional[AnyPath] = None
+    log_startup: bool = False
+    no_stopping: bool = False
+    noinit: bool = False
+    noplot: bool = False
+    nostartup: bool = False
+    no_rad_int: bool = False
+    plot_file: Optional[AnyPath] = None
+    prompt_color: str = ""
+    reverse: bool = False
+    rf_on: bool = False
+    quiet: bool = False
+    slice_lattice: str = ""
+    start_branch_at: str = ""
+    startup_file: Optional[AnyPath] = None
+    symbol_import: bool = False
+    var_file: Optional[AnyPath] = None
+
+    @property
+    def tao_class_params(self) -> Dict[str, Any]:
+        """Parameters used to initialize Tao or make a new Tao instance."""
+        # TODO: handle abbreviated/shortened keys from the user
+        init_parts = self.init.split()
+        params = {
+            key: value
+            for key, value in asdict(self).items()
+            if value != getattr(type(self), key, None) and f"-{key}" not in init_parts
+        }
+        params["init"] = self.init
+        params.pop("metadata")
+
+        geometry = params.get("geometry", "")
+        if not isinstance(geometry, str):
+            width, height = geometry
+            params["geometry"] = f"{width}x{height}"
+        return params
+
+    @property
+    def can_initialize(self) -> bool:
+        """
+        Can Tao be initialized with these settings?
+
+        Tao requires one or more of the following to be initialized:
+
+        * `-init_file` to specify the initialization file.
+        * `-lattice_file` to specify the lattice file.
+
+        These are commonly shortened to `-init` or `-lat`.  Tao accepts
+        shortened flags if they are not ambiguous.
+        """
+        tao_init_parts = self.tao_init.split()
+        return any(part.startswith(flag) for part in tao_init_parts for flag in {"-i", "-la"})
+
+    @property
+    def tao_init(self) -> str:
+        """Tao.init() command string."""
+        params = self.tao_class_params
+        # For tao.init(), we throw away Tao class-specific things:
+        params.pop("so_lib", None)
+        params.pop("plot", None)
+        return make_tao_init(**params)
+
+    def run(self, use_subprocess: bool = False) -> AnyTao:
+        """Create a new Tao instance and run it using these settings."""
+        params = self.tao_class_params
+        if use_subprocess:
+            from .subproc import SubprocessTao
+
+            return SubprocessTao(**params)
+        return Tao(**params)
+
+    @contextlib.contextmanager
+    def run_context(self, use_subprocess: bool = False):
+        """
+        Create a new Tao instance and run it using these settings in a context manager.
+
+        Yields
+        ------
+        Tao
+            Tao instance.
+        """
+        tao = self.run(use_subprocess=use_subprocess)
+
+        try:
+            yield tao
+        finally:
+            from .subproc import SubprocessTao
+
+            if isinstance(tao, SubprocessTao):
+                tao.close_subprocess()
+
+
+class Tao(TaoCore):
+    """
+    Communicate with Tao using ctypes.
+
+    Parameters
+    ----------
+    init : str, optional
+        Initialization string for Tao.  Same as the tao command-line, including
+        "-init" and such.  Shell variables in `init` strings will be expanded
+        by Tao.  For example, an `init` string containing `$HOME` would be
+        replaced by your home directory.
+    so_lib : str, optional
+        Path to the Tao shared library.  Auto-detected if not specified.
+    plot : str, bool, optional
+        Use pytao's plotting mechanism with matplotlib or bokeh, if available.
+        If `True`, pytao will pick an appropriate plotting backend.
+        If `False` or "tao", Tao plotting will be used. (Default)
+        If "mpl", the pytao matplotlib plotting backend will be selected.
+        If "bokeh", the pytao Bokeh plotting backend will be selected.
+
+    beam_file : str or pathlib.Path, default=None
+        File containing the tao_beam_init namelist.
+    beam_init_position_file : pathlib.Path or str, default=None
+        File containing initial particle positions.
+    building_wall_file : str or pathlib.Path, default=None
+        Define the building tunnel wall
+    command : str, optional
+        Commands to run after startup file commands
+    data_file : str or pathlib.Path, default=None
+        Define data for plotting and optimization
+    debug : bool, default=False
+        Debug mode for Wizards
+    disable_smooth_line_calc : bool, default=False
+        Disable the smooth line calc used in plotting
+    external_plotting : bool, default=False
+        Tells Tao that plotting is done externally to Tao.
+    geometry : "wxh" or (width, height) tuple, optional
+        Plot window geometry (pixels)
+    hook_init_file :  pathlib.Path or str, default=None
+        Init file for hook routines (Default = tao_hook.init)
+    init_file : str or pathlib.Path, default=None
+        Tao init file
+    lattice_file : str or pathlib.Path, default=None
+        Bmad lattice file
+    log_startup : bool, default=False
+        Write startup debugging info
+    no_stopping : bool, default=False
+        For debugging : Prevents Tao from exiting on errors
+    noinit : bool, default=False
+        Do not use Tao init file.
+    noplot : bool, default=False
+        Do not open a plotting window
+    nostartup : bool, default=False
+        Do not open a startup command file
+    no_rad_int : bool, default=False
+        Do not do any radiation integrals calculations.
+    plot_file : str or pathlib.Path, default=None
+        Plotting initialization file
+    prompt_color : str, optional
+        Set color of prompt string. Default is blue.
+    reverse : bool, default=False
+        Reverse lattice element order?
+    rf_on : bool, default=False
+        Use "--rf_on" to turn off RF (default is now RF on)
+    quiet : bool, default=False
+        Suppress terminal output when running a command file?
+    slice_lattice : str, optional
+        Discards elements from lattice that are not in the list
+    start_branch_at : str, optional
+        Start lattice branch at element.
+    startup_file : str or pathlib.Path, default=None
+        Commands to run after parsing Tao init file
+    symbol_import : bool, default=False
+        Import symbols defined in lattice files(s)?
+    var_file : str or pathlib.Path, default=None
+        Define variables for plotting and optimization
+    """
+
+    plot_backend_name: Optional[str]
+    _graph_managers: dict
+    _min_tao_version = datetime.datetime(2024, 8, 4)
+
+    def __init__(
+        self,
+        init: str = "",
+        so_lib: str = "",
+        *,
+        plot: Union[str, bool] = "tao",
+        beam_file: Optional[AnyPath] = None,
+        beam_init_position_file: Optional[AnyPath] = None,
+        building_wall_file: Optional[AnyPath] = None,
+        command: str = "",
+        data_file: Optional[AnyPath] = None,
+        debug: bool = False,
+        disable_smooth_line_calc: bool = False,
+        external_plotting: bool = False,
+        geometry: Union[str, Tuple[int, int]] = "",
+        hook_init_file: Optional[AnyPath] = None,
+        init_file: Optional[AnyPath] = None,
+        lattice_file: Optional[AnyPath] = None,
+        log_startup: bool = False,
+        no_stopping: bool = False,
+        noinit: bool = False,
+        noplot: bool = False,
+        nostartup: bool = False,
+        no_rad_int: bool = False,
+        plot_file: Optional[AnyPath] = None,
+        prompt_color: str = "",
+        reverse: bool = False,
+        rf_on: bool = False,
+        quiet: bool = False,
+        slice_lattice: str = "",
+        start_branch_at: str = "",
+        startup_file: Optional[AnyPath] = None,
+        symbol_import: bool = False,
+        var_file: Optional[AnyPath] = None,
+    ):
+        self.plot_backend_name = None
+        self._graph_managers = {}
+        self._tao_version_checked = False
+        super().__init__(init="", so_lib=so_lib)
+        self.init(
+            cmd=init,
+            plot=plot,
+            beam_file=beam_file,
+            beam_init_position_file=beam_init_position_file,
+            building_wall_file=building_wall_file,
+            command=command,
+            data_file=data_file,
+            debug=debug,
+            disable_smooth_line_calc=disable_smooth_line_calc,
+            external_plotting=external_plotting,
+            geometry=geometry,
+            hook_init_file=hook_init_file,
+            init_file=init_file,
+            lattice_file=lattice_file,
+            log_startup=log_startup,
+            no_stopping=no_stopping,
+            noinit=noinit,
+            noplot=noplot,
+            nostartup=nostartup,
+            no_rad_int=no_rad_int,
+            plot_file=plot_file,
+            prompt_color=prompt_color,
+            reverse=reverse,
+            rf_on=rf_on,
+            quiet=quiet,
+            slice_lattice=slice_lattice,
+            start_branch_at=start_branch_at,
+            startup_file=startup_file,
+            symbol_import=symbol_import,
+            var_file=var_file,
+        )
+
+    @override
+    def init(
+        self,
+        cmd: str = "",
+        *,
+        plot: Union[str, bool] = "tao",
+        beam_file: Optional[AnyPath] = None,
+        beam_init_position_file: Optional[AnyPath] = None,
+        building_wall_file: Optional[AnyPath] = None,
+        command: str = "",
+        data_file: Optional[AnyPath] = None,
+        debug: bool = False,
+        disable_smooth_line_calc: bool = False,
+        external_plotting: bool = False,
+        geometry: Union[str, Tuple[int, int]] = "",
+        hook_init_file: Optional[AnyPath] = None,
+        init_file: Optional[AnyPath] = None,
+        lattice_file: Optional[AnyPath] = None,
+        log_startup: bool = False,
+        no_stopping: bool = False,
+        noinit: bool = False,
+        noplot: bool = False,
+        nostartup: bool = False,
+        no_rad_int: bool = False,
+        plot_file: Optional[AnyPath] = None,
+        prompt_color: str = "",
+        reverse: bool = False,
+        rf_on: bool = False,
+        quiet: bool = False,
+        slice_lattice: str = "",
+        start_branch_at: str = "",
+        startup_file: Optional[AnyPath] = None,
+        symbol_import: bool = False,
+        var_file: Optional[AnyPath] = None,
+    ) -> List[str]:
+        """(Re-)Initialize Tao with the given command."""
+        if plot in {"mpl", "bokeh"}:
+            self.plot_backend_name = plot
+        else:
+            self.plot_backend_name = None
+
+        use_pytao_plotting = plot in {"mpl", "bokeh", True}
+
+        self.init_settings = TaoStartup(
+            init=cmd,
+            plot=plot,
+            beam_file=beam_file,
+            beam_init_position_file=beam_init_position_file,
+            building_wall_file=building_wall_file,
+            command=command,
+            data_file=data_file,
+            debug=debug,
+            disable_smooth_line_calc=disable_smooth_line_calc,
+            external_plotting=use_pytao_plotting or external_plotting,
+            geometry=geometry,
+            hook_init_file=hook_init_file,
+            init_file=init_file,
+            lattice_file=lattice_file,
+            log_startup=log_startup,
+            no_stopping=no_stopping,
+            noinit=noinit,
+            noplot=use_pytao_plotting or noplot,
+            nostartup=nostartup,
+            no_rad_int=no_rad_int,
+            plot_file=plot_file,
+            prompt_color=prompt_color,
+            reverse=reverse,
+            rf_on=rf_on,
+            quiet=quiet,
+            slice_lattice=slice_lattice,
+            start_branch_at=start_branch_at,
+            startup_file=startup_file,
+            symbol_import=symbol_import,
+            var_file=var_file,
+        )
+
+        if not self.init_settings.can_initialize:
+            return []
+
+        res = self._init(self.init_settings)
+        if not self._tao_version_checked:
+            self._tao_version_checked = True
+            self._check_tao_version()
+        return res
+
+    def _check_tao_version(self):
+        version = self.version()
+        if version is None:
+            # Don't continue to warn about failing to parse the version
+            return
+
+        if version.date() < self._min_tao_version.date():
+            logger.warning(
+                f"Installed Tao version is lower than pytao's recommended and tested version. "
+                f"\n   You have Tao version: {version.date()}"
+                f"\n   Recommended version:  {self._min_tao_version.date()}"
+                f"\nSome features may not work as expected.  Please upgrade bmad."
+            )
+
+    def _init(self, startup: TaoStartup):
+        for manager in self._graph_managers.values():
+            try:
+                manager.tao_init_hook()
+            except Exception:
+                logger.exception(
+                    "Tao plot manager re-initialization failure (%s)", type(manager)
+                )
+        return super().init(startup.tao_init)
+
+    def __execute(
+        self,
+        cmd: str,
+        as_dict: bool = True,
+        raises: bool = True,
+        method_name=None,
+        cmd_type: str = "string_list",
+    ):
+        """
+
+        A wrapper to handle commonly used options when running a command through tao.
+
+        Parameters
+        ----------
+        cmd : str
+            The command to run
+        as_dict : bool, optional
+            Return string data as a dict? by default True
+        raises : bool, optional
+            Raise exception on tao errors? by default True
+        method_name : str/None, optional
+            Name of the caller. Required for custom parsers for commands, by
+            default None
+        cmd_type : str, optional
+            The type of data returned by tao in its common memory, by default
+            "string_list"
+
+        Returns
+        -------
+        Any
+        Result from running tao. The type of data depends on configuration, but is generally a list of strings, a dict, or a
+        numpy array.
+        """
+        func_for_type = {
+            "string_list": self.cmd,
+            "real_array": self.cmd_real,
+            "integer_array": self.cmd_integer,
+        }
+        func = func_for_type.get(cmd_type, self.cmd)
+        raw_output = func(cmd, raises=raises)
+        special_parser = getattr(_pytao_parsers, f"parse_{method_name}", None)
+        try:
+            if special_parser and callable(special_parser):
+                return special_parser(raw_output, cmd=cmd)
+            if "string" not in cmd_type:
+                return raw_output
+            if as_dict:
+                return parse_tao_python_data(raw_output)
+            return tao_parameter_dict(raw_output)
+        except Exception:
+            if raises:
+                raise
+            logger.exception(
+                "Failed to parse string data with custom parser. Returning raw value."
+            )
+            return raw_output
+
+    def bunch_data(self, ele_id, *, which="model", ix_bunch=1, verbose=False):
+        """
+        Returns bunch data in openPMD-beamphysics format/notation.
+
+        Notes
+        -----
+        Note that Tao's 'write beam' will also write a proper h5 file in this format.
+
+        Expected usage:
+            data = bunch_data(tao, 'end')
+            from pmd_beamphysics import ParticleGroup
+            P = ParicleGroup(data=data)
+
+
+        Returns
+        -------
+        data : dict
+            dict of arrays, with keys 'x', 'px', 'y', 'py', 't', 'pz',
+            'status', 'weight', 'z', 'species'
+
+
+        Examples
+        --------
+        Example: 1
+        init: $ACC_ROOT_DIR/tao/examples/csr_beam_tracking/tao.init
+        args:
+        ele_id: end
+        which: model
+        ix_bunch: 1
+
+        """
+
+        # Get species
+        stats = self.bunch_params(ele_id, which=which, verbose=verbose)
+        species = stats["species"]
+
+        dat = {}
+        for coordinate in ["x", "px", "y", "py", "t", "pz", "p0c", "charge", "state"]:
+            dat[coordinate] = self.bunch1(
+                ele_id,
+                coordinate=coordinate,
+                which=which,
+                ix_bunch=ix_bunch,
+                verbose=verbose,
+            )
+
+        # Remove normalizations
+        p0c = dat.pop("p0c")
+
+        dat["status"] = dat.pop("state")
+        dat["weight"] = dat.pop("charge")
+
+        # px from Bmad is px/p0c
+        # pz from Bmad is delta = p/p0c -1.
+        # pz = sqrt( (delta+1)**2 -px**2 -py**2)*p0c
+        dat["pz"] = np.sqrt((dat["pz"] + 1) ** 2 - dat["px"] ** 2 - dat["py"] ** 2) * p0c
+        dat["px"] = dat["px"] * p0c
+        dat["py"] = dat["py"] * p0c
+
+        # z = 0 by definition
+        dat["z"] = np.full(len(dat["x"]), 0)
+
+        dat["species"] = species.lower()
+
+        return dat
+
+    def version(self) -> Optional[datetime.datetime]:
+        """Get the date-coded version."""
+        cmd = "show version"
+        return _pytao_parsers.parse_show_version(self.cmd(cmd), cmd=cmd)
+
+    def plot_page(self):
+        """Get plot page parameters."""
+        cmd = "show plot_page"
+        return _pytao_parsers.parse_show_plot_page(self.cmd(cmd), cmd=cmd)
+
+    def _get_graph_manager_by_key(self, key: str):
+        graph_manager = self._graph_managers.get(key, None)
+
+        if graph_manager is None:
+            if key == "bokeh":
+                from .plotting.bokeh import select_graph_manager_class
+
+                cls = select_graph_manager_class()
+            elif key == "mpl":
+                from .plotting.mpl import MatplotlibGraphManager as cls
+
+            else:
+                raise NotImplementedError(key)
+
+            graph_manager = cls(self)
+            self._graph_managers[key] = graph_manager
+        return graph_manager
+
+    @property
+    def matplotlib(self) -> MatplotlibGraphManager:
+        """Get the Matplotlib graph manager."""
+        return typing.cast(MatplotlibGraphManager, self._get_graph_manager_by_key("mpl"))
+
+    @property
+    def bokeh(self) -> BokehGraphManager:
+        """Get the Bokeh graph manager."""
+        from .plotting.bokeh import BokehGraphManager
+
+        return typing.cast(BokehGraphManager, self._get_graph_manager_by_key("bokeh"))
+
+    @property
+    def plot_manager(
+        self,
+    ) -> Union[BokehGraphManager, NotebookGraphManager, MatplotlibGraphManager]:
+        """
+        The currently-configured plot graph manager.
+
+        This can be configured at initialization time by specifying
+        `plot="mpl"`, for example.
+        This may also be reconfigured by changing the attribute
+        `plot_backend_name`.
+        """
+        return self._get_graph_manager_by_key(self.plot_backend_name or "mpl")
+
+    def _get_user_specified_backend(self, backend: Optional[str]):
+        if backend is None:
+            backend = self.plot_backend_name or select_graph_manager_class()._key_
+
+        if not self.init_settings.external_plotting:
+            raise RuntimeError(
+                "Tao was not configured for external plotting, which pytao requires. "
+                "Please re-initialize Tao and set `plot=True` (or specify a backend). "
+                "For example: tao.init(..., plot=True)"
+            )
+
+        if backend not in {"mpl", "bokeh"}:
+            raise ValueError(f"Unsupported backend: {backend}")
+
+        return self._get_graph_manager_by_key(backend)
+
+    def update_plot_shapes(
+        self,
+        ele_name: Optional[str] = None,
+        *,
+        layout: bool = False,
+        floor: bool = False,
+        shape_index: Optional[int] = None,
+        shape: Optional[str] = None,
+        color: Optional[str] = None,
+        shape_size: Optional[float] = None,
+        type_label: Optional[Literal["s", "name", "none"]] = None,
+        shape_draw: Optional[bool] = None,
+        multi_shape: Optional[bool] = None,
+        line_width: Optional[int] = None,
+    ) -> List[ShapeListInfo]:
+        """
+        Update shape plotting settings for layouts/floor plans.
+
+        * Must set either (or both of) `layout` / `floor` to `True`.
+        * Only the specified parameters will be updated for each shape. That is,
+          if you only specify `color` then the color of every matching shape
+          will be updated and the other settings (such as `line_width`) will
+          remain the same.
+
+        Parameters
+        ----------
+        ele_name : str, optional
+            Update the shape only for this element name.
+            If `ele_name` and `shape_index` are unspecified, these settings
+            apply to all shapes.
+        shape_index : int, optional
+            The numerical index of the shape to change.
+            If `ele_name` and `shape_index` are unspecified, these settings
+            apply to all shapes.
+        layout : bool, default=False
+            Apply the settings to lattice layout shapes.
+        floor : bool, default=False
+            Apply the settings to floor plan shapes.
+        shape : str, optional
+            The shape to use. Choose from one of the following:
+            * "box"
+            * "xbox"
+            * "bow_tie"
+            * "rbow_tie"
+            * "circle"
+            * "diamond"
+            * "x",
+            * "r_triangle"
+            * "l_triangle"
+            * "u_triangle"
+            * "d_triangle"
+        color : str, optional
+            Color for the shape. Choose from one of the following:
+            * "Not_Set"
+            * "White"
+            * "Black"
+            * "Red"
+            * "Green"
+            * "Blue"
+            * "Cyan"
+            * "Magenta"
+            * "Yellow"
+            * "Orange"
+            * "Yellow_Green"
+            * "Light_Green"
+            * "Navy_Blue"
+            * "Purple"
+            * "Reddish_Purple"
+            * "Dark_Grey"
+            * "Light_Grey"
+            * "Transparent"
+        shape_size : float, optional
+            Shape size.
+        type_label : "s", "name", or "none", optional
+            Show this label for each shape. `None` indicates no shape.
+        shape_draw : bool, optional
+            Draw the shape.
+        multi_shape : bool, optional
+            If it can be part of a multi-shape.
+        line_width : int, optional
+            Width of lines used to draw the shape.
+
+        Returns
+        -------
+        list of ShapeListInfo
+        """
+
+        who_list = []
+        if layout:
+            who_list.append("lat_layout")
+        if floor:
+            who_list.append("floor_plan")
+        if not who_list:
+            raise ValueError("Must specify either `layout` or `floor` plots")
+
+        res = []
+        for who in who_list:
+            shape_list_info = typing.cast(List[ShapeListInfo], self.shape_list(who))
+            res.extend(shape_list_info)
+            for info in shape_list_info:
+                should_set = any(
+                    (
+                        (ele_name is None and shape_index is None),
+                        (ele_name == info["ele_name"]),
+                        (ele_name and info["ele_name"].startswith(ele_name)),
+                        (shape_index == info["shape_index"]),
+                    )
+                )
+                if not should_set:
+                    continue
+
+                if type_label is not None:
+                    info["type_label"] = type_label
+                if shape is not None:
+                    info["shape"] = shape
+                if color is not None:
+                    info["color"] = color
+                if shape_size is not None:
+                    info["shape_size"] = shape_size
+                if shape_draw is not None:
+                    info["shape_draw"] = shape_draw
+                if multi_shape is not None:
+                    info["multi_shape"] = multi_shape
+                if line_width is not None:
+                    info["line_width"] = line_width
+
+                self.shape_set(who=who, **info)
+
+        return res
+
+    def plot(
+        self,
+        template: Optional[Union[str, List[str]]] = None,
+        *,
+        region_name: Optional[str] = None,
+        include_layout: bool = True,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        layout_height: Optional[int] = None,
+        share_x: Optional[bool] = None,
+        backend: Optional[str] = None,
+        grid: Optional[Tuple[int, int]] = None,
+        **kwargs,
+    ) -> None:
+        """
+        Make a plot with the provided backend.
+
+        Plot a graph, or all placed graphs.
+
+        To plot a specific graph, specify `template` (optionally `region_name`).
+        The default is to plot all placed graphs.
+
+        For full details on available parameters, see the specific backend's
+        graph manager. For example:
+
+        In [1]: tao.bokeh.plot?
+        In [2]: tao.matplotlib.plot?
+
+        Parameters
+        ----------
+        template : str or list[str]
+            Graph template name or names.
+        region_name : str, optional
+            Graph region name.  Chosen automatically if not specified.
+        include_layout : bool, optional
+            Include a layout plot at the bottom, if not already placed and if
+            appropriate (i.e., another plot uses longitudinal coordinates on
+            the x-axis).
+        width : int, optional
+            Width of each plot.
+        height : int, optional
+            Height of each plot.
+        layout_height : int, optional
+            Height of the layout plot.
+        share_x : bool or None, default=None
+            Share x-axes where sensible (`None`) or force sharing x-axes (True)
+            for all plots.
+        save : pathlib.Path or str, optional
+            Save the plot to the given filename.
+        xlim : (float, float), optional
+            X axis limits.
+        ylim : (float, float), optional
+            Y axis limits.
+        grid : (nrows, ncols), optional
+            If multiple graph names are specified, the plots will be placed
+            in a grid according to this parameter.  The default is to have
+            stacked plots if this parameter is unspecified.
+        backend : {"bokeh", "mpl"}, optional
+            The backend to use.  Auto-detects Jupyter and availability of bokeh
+            to select a backend.
+
+        Returns
+        -------
+        None
+            To gain access to the resulting plot objects, use the backend's
+            `plot` method directly.
+        """
+        manager = self._get_user_specified_backend(backend)
+
+        if width is not None:
+            kwargs["width"] = width
+        if height is not None:
+            kwargs["height"] = height
+        if layout_height is not None:
+            kwargs["layout_height"] = layout_height
+        if share_x is not None:
+            kwargs["share_x"] = share_x
+
+        if not template:
+            self.last_plot = manager.plot_all(
+                include_layout=include_layout,
+                **kwargs,
+            )
+        elif not isinstance(template, str):
+            templates = list(template)
+            grid = grid or (len(templates), 1)
+            self.last_plot = manager.plot_grid(
+                templates=templates,
+                grid=grid,
+                include_layout=include_layout,
+                **kwargs,
+            )
+        else:
+            self.last_plot = manager.plot(
+                region_name=region_name,
+                template=template,
+                include_layout=include_layout,
+                **kwargs,
+            )
+
+    def plot_field(
+        self,
+        ele_id: str,
+        *,
+        colormap: Optional[str] = None,
+        radius: float = 0.015,
+        num_points: int = 100,
+        backend: Optional[str] = None,
+        **kwargs,
+    ):
+        """
+        Plot field information for a given element.
+
+        Parameters
+        ----------
+        ele_id : str
+            Element ID.
+        colormap : str, optional
+            Colormap for the plot.
+            Matplotlib defaults to "PRGn_r", and bokeh defaults to "Magma256".
+        radius : float, default=0.015
+            Radius.
+        num_points : int, default=100
+            Number of data points.
+        backend : {"bokeh", "mpl"}, optional
+            The backend to use.  Auto-detects Jupyter and availability of bokeh
+            to select a backend.
+        """
+        manager = self._get_user_specified_backend(backend)
+        self.last_plot = manager.plot_field(
+            ele_id,
+            colormap=colormap,
+            radius=radius,
+            num_points=num_points,
+            **kwargs,
+        )
diff --git a/pytao/interface_commands.py b/pytao/interface_commands.py
index 0da117fd..31343260 100644
--- a/pytao/interface_commands.py
+++ b/pytao/interface_commands.py
@@ -2,22 +2,500 @@
 # AUTOGENERATED FILE - DO NOT MODIFY
 # This file was generated by the script `generate_interface_commands.py`.
 # Any modifications may be overwritten.
-# Generated on: 2024-06-27 16:03:01
+# Generated on: 2024-08-16 10:08:54
 # ==============================================================================
 
+from __future__ import annotations
+
+import contextlib
+import datetime
 import logging
+import pathlib
+import typing
+from dataclasses import asdict
+from typing import Any, Dict, List, Optional, Tuple, Union
+
 import numpy as np
+from pydantic import ConfigDict, dataclasses
+from typing_extensions import Literal, override
 
+from .plotting import MatplotlibGraphManager
+from .plotting.types import ShapeListInfo
+from .plotting.util import select_graph_manager_class
 from .tao_ctypes.core import TaoCore
 from .tao_ctypes.util import parse_tao_python_data
-from .util.parameters import tao_parameter_dict
 from .util import parsers as _pytao_parsers
+from .util.command import make_tao_init
+from .util.parameters import tao_parameter_dict
+
+if typing.TYPE_CHECKING:
+    from .plotting.bokeh import BokehGraphManager, NotebookGraphManager  # noqa: F401
+    from .subproc import SubprocessTao
+
+    AnyTao = Union["Tao", SubprocessTao]
 
 
 logger = logging.getLogger(__name__)
+AnyPath = Union[pathlib.Path, str]
+
+
+@dataclasses.dataclass(config=ConfigDict(extra="forbid", validate_assignment=True))
+class TaoStartup:
+    """
+    All Tao startup settings.
+
+    Attributes
+    ----------
+    init : str, optional
+        Initialization string for Tao.  Same as the tao command-line, including
+        "-init" and such.  Shell variables in `init` strings will be expanded
+        by Tao.  For example, an `init` string containing `$HOME` would be
+        replaced by your home directory.
+    so_lib : str, optional
+        Path to the Tao shared library.  Auto-detected if not specified.
+    plot : str, bool, optional
+        Use pytao's plotting mechanism with matplotlib or bokeh, if available.
+        If `True`, pytao will pick an appropriate plotting backend.
+        If `False` or "tao", Tao plotting will be used. (Default)
+        If "mpl", the pytao matplotlib plotting backend will be selected.
+        If "bokeh", the pytao Bokeh plotting backend will be selected.
+    metadata : dict[str, Any], optional
+        User-specified metadata about this startup.  Not passed to Tao.
+    beam_file : str or pathlib.Path, default=None
+        File containing the tao_beam_init namelist.
+    beam_init_position_file : pathlib.Path or str, default=None
+        File containing initial particle positions.
+    building_wall_file : str or pathlib.Path, default=None
+        Define the building tunnel wall
+    command : str, optional
+        Commands to run after startup file commands
+    data_file : str or pathlib.Path, default=None
+        Define data for plotting and optimization
+    debug : bool, default=False
+        Debug mode for Wizards
+    disable_smooth_line_calc : bool, default=False
+        Disable the smooth line calc used in plotting
+    external_plotting : bool, default=False
+        Tells Tao that plotting is done externally to Tao.
+    geometry : "wxh" or (width, height) tuple, optional
+        Plot window geometry (pixels)
+    hook_init_file :  pathlib.Path or str, default=None
+        Init file for hook routines (Default = tao_hook.init)
+    init_file : str or pathlib.Path, default=None
+        Tao init file
+    lattice_file : str or pathlib.Path, default=None
+        Bmad lattice file
+    log_startup : bool, default=False
+        Write startup debugging info
+    no_stopping : bool, default=False
+        For debugging : Prevents Tao from exiting on errors
+    noinit : bool, default=False
+        Do not use Tao init file.
+    noplot : bool, default=False
+        Do not open a plotting window
+    nostartup : bool, default=False
+        Do not open a startup command file
+    no_rad_int : bool, default=False
+        Do not do any radiation integrals calculations.
+    plot_file : str or pathlib.Path, default=None
+        Plotting initialization file
+    prompt_color : str, optional
+        Set color of prompt string. Default is blue.
+    reverse : bool, default=False
+        Reverse lattice element order?
+    rf_on : bool, default=False
+        Use "--rf_on" to turn off RF (default is now RF on)
+    quiet : bool, default=False
+        Suppress terminal output when running a command file?
+    slice_lattice : str, optional
+        Discards elements from lattice that are not in the list
+    start_branch_at : str, optional
+        Start lattice branch at element.
+    startup_file : str or pathlib.Path, default=None
+        Commands to run after parsing Tao init file
+    symbol_import : bool, default=False
+        Import symbols defined in lattice files(s)?
+    var_file : str or pathlib.Path, default=None
+        Define variables for plotting and optimization
+    """
+
+    # General case 'init' string:
+    init: str = dataclasses.Field(default="", kw_only=False)
+
+    # Tao ctypes-specific - shared library location.
+    so_lib: str = dataclasses.Field(default="", kw_only=False)
+
+    # pytao specific
+    metadata: Dict[str, Any] = dataclasses.Field(default_factory=dict)
+    plot: Union[str, bool] = "tao"
+
+    # All remaining flags:
+    beam_file: Optional[AnyPath] = None
+    beam_init_position_file: Optional[AnyPath] = None
+    building_wall_file: Optional[AnyPath] = None
+    command: str = ""
+    data_file: Optional[AnyPath] = None
+    debug: bool = False
+    disable_smooth_line_calc: bool = False
+    external_plotting: bool = False
+    geometry: Union[str, Tuple[int, int]] = ""
+    hook_init_file: Optional[AnyPath] = None
+    init_file: Optional[AnyPath] = None
+    lattice_file: Optional[AnyPath] = None
+    log_startup: bool = False
+    no_stopping: bool = False
+    noinit: bool = False
+    noplot: bool = False
+    nostartup: bool = False
+    no_rad_int: bool = False
+    plot_file: Optional[AnyPath] = None
+    prompt_color: str = ""
+    reverse: bool = False
+    rf_on: bool = False
+    quiet: bool = False
+    slice_lattice: str = ""
+    start_branch_at: str = ""
+    startup_file: Optional[AnyPath] = None
+    symbol_import: bool = False
+    var_file: Optional[AnyPath] = None
+
+    @property
+    def tao_class_params(self) -> Dict[str, Any]:
+        """Parameters used to initialize Tao or make a new Tao instance."""
+        # TODO: handle abbreviated/shortened keys from the user
+        init_parts = self.init.split()
+        params = {
+            key: value
+            for key, value in asdict(self).items()
+            if value != getattr(type(self), key, None) and f"-{key}" not in init_parts
+        }
+        params["init"] = self.init
+        params.pop("metadata")
+
+        geometry = params.get("geometry", "")
+        if not isinstance(geometry, str):
+            width, height = geometry
+            params["geometry"] = f"{width}x{height}"
+        return params
+
+    @property
+    def can_initialize(self) -> bool:
+        """
+        Can Tao be initialized with these settings?
+
+        Tao requires one or more of the following to be initialized:
+
+        * `-init_file` to specify the initialization file.
+        * `-lattice_file` to specify the lattice file.
+
+        These are commonly shortened to `-init` or `-lat`.  Tao accepts
+        shortened flags if they are not ambiguous.
+        """
+        tao_init_parts = self.tao_init.split()
+        return any(part.startswith(flag) for part in tao_init_parts for flag in {"-i", "-la"})
+
+    @property
+    def tao_init(self) -> str:
+        """Tao.init() command string."""
+        params = self.tao_class_params
+        # For tao.init(), we throw away Tao class-specific things:
+        params.pop("so_lib", None)
+        params.pop("plot", None)
+        return make_tao_init(**params)
+
+    def run(self, use_subprocess: bool = False) -> AnyTao:
+        """Create a new Tao instance and run it using these settings."""
+        params = self.tao_class_params
+        if use_subprocess:
+            from .subproc import SubprocessTao
+
+            return SubprocessTao(**params)
+        return Tao(**params)
+
+    @contextlib.contextmanager
+    def run_context(self, use_subprocess: bool = False):
+        """
+        Create a new Tao instance and run it using these settings in a context manager.
+
+        Yields
+        ------
+        Tao
+            Tao instance.
+        """
+        tao = self.run(use_subprocess=use_subprocess)
+
+        try:
+            yield tao
+        finally:
+            from .subproc import SubprocessTao
+
+            if isinstance(tao, SubprocessTao):
+                tao.close_subprocess()
 
 
 class Tao(TaoCore):
+    """
+    Communicate with Tao using ctypes.
+
+    Parameters
+    ----------
+    init : str, optional
+        Initialization string for Tao.  Same as the tao command-line, including
+        "-init" and such.  Shell variables in `init` strings will be expanded
+        by Tao.  For example, an `init` string containing `$HOME` would be
+        replaced by your home directory.
+    so_lib : str, optional
+        Path to the Tao shared library.  Auto-detected if not specified.
+    plot : str, bool, optional
+        Use pytao's plotting mechanism with matplotlib or bokeh, if available.
+        If `True`, pytao will pick an appropriate plotting backend.
+        If `False` or "tao", Tao plotting will be used. (Default)
+        If "mpl", the pytao matplotlib plotting backend will be selected.
+        If "bokeh", the pytao Bokeh plotting backend will be selected.
+
+    beam_file : str or pathlib.Path, default=None
+        File containing the tao_beam_init namelist.
+    beam_init_position_file : pathlib.Path or str, default=None
+        File containing initial particle positions.
+    building_wall_file : str or pathlib.Path, default=None
+        Define the building tunnel wall
+    command : str, optional
+        Commands to run after startup file commands
+    data_file : str or pathlib.Path, default=None
+        Define data for plotting and optimization
+    debug : bool, default=False
+        Debug mode for Wizards
+    disable_smooth_line_calc : bool, default=False
+        Disable the smooth line calc used in plotting
+    external_plotting : bool, default=False
+        Tells Tao that plotting is done externally to Tao.
+    geometry : "wxh" or (width, height) tuple, optional
+        Plot window geometry (pixels)
+    hook_init_file :  pathlib.Path or str, default=None
+        Init file for hook routines (Default = tao_hook.init)
+    init_file : str or pathlib.Path, default=None
+        Tao init file
+    lattice_file : str or pathlib.Path, default=None
+        Bmad lattice file
+    log_startup : bool, default=False
+        Write startup debugging info
+    no_stopping : bool, default=False
+        For debugging : Prevents Tao from exiting on errors
+    noinit : bool, default=False
+        Do not use Tao init file.
+    noplot : bool, default=False
+        Do not open a plotting window
+    nostartup : bool, default=False
+        Do not open a startup command file
+    no_rad_int : bool, default=False
+        Do not do any radiation integrals calculations.
+    plot_file : str or pathlib.Path, default=None
+        Plotting initialization file
+    prompt_color : str, optional
+        Set color of prompt string. Default is blue.
+    reverse : bool, default=False
+        Reverse lattice element order?
+    rf_on : bool, default=False
+        Use "--rf_on" to turn off RF (default is now RF on)
+    quiet : bool, default=False
+        Suppress terminal output when running a command file?
+    slice_lattice : str, optional
+        Discards elements from lattice that are not in the list
+    start_branch_at : str, optional
+        Start lattice branch at element.
+    startup_file : str or pathlib.Path, default=None
+        Commands to run after parsing Tao init file
+    symbol_import : bool, default=False
+        Import symbols defined in lattice files(s)?
+    var_file : str or pathlib.Path, default=None
+        Define variables for plotting and optimization
+    """
+
+    plot_backend_name: Optional[str]
+    _graph_managers: dict
+    _min_tao_version = datetime.datetime(2024, 8, 4)
+
+    def __init__(
+        self,
+        init: str = "",
+        so_lib: str = "",
+        *,
+        plot: Union[str, bool] = "tao",
+        beam_file: Optional[AnyPath] = None,
+        beam_init_position_file: Optional[AnyPath] = None,
+        building_wall_file: Optional[AnyPath] = None,
+        command: str = "",
+        data_file: Optional[AnyPath] = None,
+        debug: bool = False,
+        disable_smooth_line_calc: bool = False,
+        external_plotting: bool = False,
+        geometry: Union[str, Tuple[int, int]] = "",
+        hook_init_file: Optional[AnyPath] = None,
+        init_file: Optional[AnyPath] = None,
+        lattice_file: Optional[AnyPath] = None,
+        log_startup: bool = False,
+        no_stopping: bool = False,
+        noinit: bool = False,
+        noplot: bool = False,
+        nostartup: bool = False,
+        no_rad_int: bool = False,
+        plot_file: Optional[AnyPath] = None,
+        prompt_color: str = "",
+        reverse: bool = False,
+        rf_on: bool = False,
+        quiet: bool = False,
+        slice_lattice: str = "",
+        start_branch_at: str = "",
+        startup_file: Optional[AnyPath] = None,
+        symbol_import: bool = False,
+        var_file: Optional[AnyPath] = None,
+    ):
+        self.plot_backend_name = None
+        self._graph_managers = {}
+        self._tao_version_checked = False
+        super().__init__(init="", so_lib=so_lib)
+        self.init(
+            cmd=init,
+            plot=plot,
+            beam_file=beam_file,
+            beam_init_position_file=beam_init_position_file,
+            building_wall_file=building_wall_file,
+            command=command,
+            data_file=data_file,
+            debug=debug,
+            disable_smooth_line_calc=disable_smooth_line_calc,
+            external_plotting=external_plotting,
+            geometry=geometry,
+            hook_init_file=hook_init_file,
+            init_file=init_file,
+            lattice_file=lattice_file,
+            log_startup=log_startup,
+            no_stopping=no_stopping,
+            noinit=noinit,
+            noplot=noplot,
+            nostartup=nostartup,
+            no_rad_int=no_rad_int,
+            plot_file=plot_file,
+            prompt_color=prompt_color,
+            reverse=reverse,
+            rf_on=rf_on,
+            quiet=quiet,
+            slice_lattice=slice_lattice,
+            start_branch_at=start_branch_at,
+            startup_file=startup_file,
+            symbol_import=symbol_import,
+            var_file=var_file,
+        )
+
+    @override
+    def init(
+        self,
+        cmd: str = "",
+        *,
+        plot: Union[str, bool] = "tao",
+        beam_file: Optional[AnyPath] = None,
+        beam_init_position_file: Optional[AnyPath] = None,
+        building_wall_file: Optional[AnyPath] = None,
+        command: str = "",
+        data_file: Optional[AnyPath] = None,
+        debug: bool = False,
+        disable_smooth_line_calc: bool = False,
+        external_plotting: bool = False,
+        geometry: Union[str, Tuple[int, int]] = "",
+        hook_init_file: Optional[AnyPath] = None,
+        init_file: Optional[AnyPath] = None,
+        lattice_file: Optional[AnyPath] = None,
+        log_startup: bool = False,
+        no_stopping: bool = False,
+        noinit: bool = False,
+        noplot: bool = False,
+        nostartup: bool = False,
+        no_rad_int: bool = False,
+        plot_file: Optional[AnyPath] = None,
+        prompt_color: str = "",
+        reverse: bool = False,
+        rf_on: bool = False,
+        quiet: bool = False,
+        slice_lattice: str = "",
+        start_branch_at: str = "",
+        startup_file: Optional[AnyPath] = None,
+        symbol_import: bool = False,
+        var_file: Optional[AnyPath] = None,
+    ) -> List[str]:
+        """(Re-)Initialize Tao with the given command."""
+        if plot in {"mpl", "bokeh"}:
+            self.plot_backend_name = plot
+        else:
+            self.plot_backend_name = None
+
+        use_pytao_plotting = plot in {"mpl", "bokeh", True}
+
+        self.init_settings = TaoStartup(
+            init=cmd,
+            plot=plot,
+            beam_file=beam_file,
+            beam_init_position_file=beam_init_position_file,
+            building_wall_file=building_wall_file,
+            command=command,
+            data_file=data_file,
+            debug=debug,
+            disable_smooth_line_calc=disable_smooth_line_calc,
+            external_plotting=use_pytao_plotting or external_plotting,
+            geometry=geometry,
+            hook_init_file=hook_init_file,
+            init_file=init_file,
+            lattice_file=lattice_file,
+            log_startup=log_startup,
+            no_stopping=no_stopping,
+            noinit=noinit,
+            noplot=use_pytao_plotting or noplot,
+            nostartup=nostartup,
+            no_rad_int=no_rad_int,
+            plot_file=plot_file,
+            prompt_color=prompt_color,
+            reverse=reverse,
+            rf_on=rf_on,
+            quiet=quiet,
+            slice_lattice=slice_lattice,
+            start_branch_at=start_branch_at,
+            startup_file=startup_file,
+            symbol_import=symbol_import,
+            var_file=var_file,
+        )
+
+        if not self.init_settings.can_initialize:
+            return []
+
+        res = self._init(self.init_settings)
+        if not self._tao_version_checked:
+            self._tao_version_checked = True
+            self._check_tao_version()
+        return res
+
+    def _check_tao_version(self):
+        version = self.version()
+        if version is None:
+            # Don't continue to warn about failing to parse the version
+            return
+
+        if version.date() < self._min_tao_version.date():
+            logger.warning(
+                f"Installed Tao version is lower than pytao's recommended and tested version. "
+                f"\n   You have Tao version: {version.date()}"
+                f"\n   Recommended version:  {self._min_tao_version.date()}"
+                f"\nSome features may not work as expected.  Please upgrade bmad."
+            )
+
+    def _init(self, startup: TaoStartup):
+        for manager in self._graph_managers.values():
+            try:
+                manager.tao_init_hook()
+            except Exception:
+                logger.exception(
+                    "Tao plot manager re-initialization failure (%s)", type(manager)
+                )
+        return super().init(startup.tao_init)
+
     def __execute(
         self,
         cmd: str,
@@ -141,6 +619,343 @@ def bunch_data(self, ele_id, *, which="model", ix_bunch=1, verbose=False):
 
         return dat
 
+    def version(self) -> Optional[datetime.datetime]:
+        """Get the date-coded version."""
+        cmd = "show version"
+        return _pytao_parsers.parse_show_version(self.cmd(cmd), cmd=cmd)
+
+    def plot_page(self):
+        """Get plot page parameters."""
+        cmd = "show plot_page"
+        return _pytao_parsers.parse_show_plot_page(self.cmd(cmd), cmd=cmd)
+
+    def _get_graph_manager_by_key(self, key: str):
+        graph_manager = self._graph_managers.get(key, None)
+
+        if graph_manager is None:
+            if key == "bokeh":
+                from .plotting.bokeh import select_graph_manager_class
+
+                cls = select_graph_manager_class()
+            elif key == "mpl":
+                from .plotting.mpl import MatplotlibGraphManager as cls
+
+            else:
+                raise NotImplementedError(key)
+
+            graph_manager = cls(self)
+            self._graph_managers[key] = graph_manager
+        return graph_manager
+
+    @property
+    def matplotlib(self) -> MatplotlibGraphManager:
+        """Get the Matplotlib graph manager."""
+        return typing.cast(MatplotlibGraphManager, self._get_graph_manager_by_key("mpl"))
+
+    @property
+    def bokeh(self) -> BokehGraphManager:
+        """Get the Bokeh graph manager."""
+        from .plotting.bokeh import BokehGraphManager
+
+        return typing.cast(BokehGraphManager, self._get_graph_manager_by_key("bokeh"))
+
+    @property
+    def plot_manager(
+        self,
+    ) -> Union[BokehGraphManager, NotebookGraphManager, MatplotlibGraphManager]:
+        """
+        The currently-configured plot graph manager.
+
+        This can be configured at initialization time by specifying
+        `plot="mpl"`, for example.
+        This may also be reconfigured by changing the attribute
+        `plot_backend_name`.
+        """
+        return self._get_graph_manager_by_key(self.plot_backend_name or "mpl")
+
+    def _get_user_specified_backend(self, backend: Optional[str]):
+        if backend is None:
+            backend = self.plot_backend_name or select_graph_manager_class()._key_
+
+        if not self.init_settings.external_plotting:
+            raise RuntimeError(
+                "Tao was not configured for external plotting, which pytao requires. "
+                "Please re-initialize Tao and set `plot=True` (or specify a backend). "
+                "For example: tao.init(..., plot=True)"
+            )
+
+        if backend not in {"mpl", "bokeh"}:
+            raise ValueError(f"Unsupported backend: {backend}")
+
+        return self._get_graph_manager_by_key(backend)
+
+    def update_plot_shapes(
+        self,
+        ele_name: Optional[str] = None,
+        *,
+        layout: bool = False,
+        floor: bool = False,
+        shape_index: Optional[int] = None,
+        shape: Optional[str] = None,
+        color: Optional[str] = None,
+        shape_size: Optional[float] = None,
+        type_label: Optional[Literal["s", "name", "none"]] = None,
+        shape_draw: Optional[bool] = None,
+        multi_shape: Optional[bool] = None,
+        line_width: Optional[int] = None,
+    ) -> List[ShapeListInfo]:
+        """
+        Update shape plotting settings for layouts/floor plans.
+
+        * Must set either (or both of) `layout` / `floor` to `True`.
+        * Only the specified parameters will be updated for each shape. That is,
+          if you only specify `color` then the color of every matching shape
+          will be updated and the other settings (such as `line_width`) will
+          remain the same.
+
+        Parameters
+        ----------
+        ele_name : str, optional
+            Update the shape only for this element name.
+            If `ele_name` and `shape_index` are unspecified, these settings
+            apply to all shapes.
+        shape_index : int, optional
+            The numerical index of the shape to change.
+            If `ele_name` and `shape_index` are unspecified, these settings
+            apply to all shapes.
+        layout : bool, default=False
+            Apply the settings to lattice layout shapes.
+        floor : bool, default=False
+            Apply the settings to floor plan shapes.
+        shape : str, optional
+            The shape to use. Choose from one of the following:
+            * "box"
+            * "xbox"
+            * "bow_tie"
+            * "rbow_tie"
+            * "circle"
+            * "diamond"
+            * "x",
+            * "r_triangle"
+            * "l_triangle"
+            * "u_triangle"
+            * "d_triangle"
+        color : str, optional
+            Color for the shape. Choose from one of the following:
+            * "Not_Set"
+            * "White"
+            * "Black"
+            * "Red"
+            * "Green"
+            * "Blue"
+            * "Cyan"
+            * "Magenta"
+            * "Yellow"
+            * "Orange"
+            * "Yellow_Green"
+            * "Light_Green"
+            * "Navy_Blue"
+            * "Purple"
+            * "Reddish_Purple"
+            * "Dark_Grey"
+            * "Light_Grey"
+            * "Transparent"
+        shape_size : float, optional
+            Shape size.
+        type_label : "s", "name", or "none", optional
+            Show this label for each shape. `None` indicates no shape.
+        shape_draw : bool, optional
+            Draw the shape.
+        multi_shape : bool, optional
+            If it can be part of a multi-shape.
+        line_width : int, optional
+            Width of lines used to draw the shape.
+
+        Returns
+        -------
+        list of ShapeListInfo
+        """
+
+        who_list = []
+        if layout:
+            who_list.append("lat_layout")
+        if floor:
+            who_list.append("floor_plan")
+        if not who_list:
+            raise ValueError("Must specify either `layout` or `floor` plots")
+
+        res = []
+        for who in who_list:
+            shape_list_info = typing.cast(List[ShapeListInfo], self.shape_list(who))
+            res.extend(shape_list_info)
+            for info in shape_list_info:
+                should_set = any(
+                    (
+                        (ele_name is None and shape_index is None),
+                        (ele_name == info["ele_name"]),
+                        (ele_name and info["ele_name"].startswith(ele_name)),
+                        (shape_index == info["shape_index"]),
+                    )
+                )
+                if not should_set:
+                    continue
+
+                if type_label is not None:
+                    info["type_label"] = type_label
+                if shape is not None:
+                    info["shape"] = shape
+                if color is not None:
+                    info["color"] = color
+                if shape_size is not None:
+                    info["shape_size"] = shape_size
+                if shape_draw is not None:
+                    info["shape_draw"] = shape_draw
+                if multi_shape is not None:
+                    info["multi_shape"] = multi_shape
+                if line_width is not None:
+                    info["line_width"] = line_width
+
+                self.shape_set(who=who, **info)
+
+        return res
+
+    def plot(
+        self,
+        template: Optional[Union[str, List[str]]] = None,
+        *,
+        region_name: Optional[str] = None,
+        include_layout: bool = True,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        layout_height: Optional[int] = None,
+        share_x: Optional[bool] = None,
+        backend: Optional[str] = None,
+        grid: Optional[Tuple[int, int]] = None,
+        **kwargs,
+    ) -> None:
+        """
+        Make a plot with the provided backend.
+
+        Plot a graph, or all placed graphs.
+
+        To plot a specific graph, specify `template` (optionally `region_name`).
+        The default is to plot all placed graphs.
+
+        For full details on available parameters, see the specific backend's
+        graph manager. For example:
+
+        In [1]: tao.bokeh.plot?
+        In [2]: tao.matplotlib.plot?
+
+        Parameters
+        ----------
+        template : str or list[str]
+            Graph template name or names.
+        region_name : str, optional
+            Graph region name.  Chosen automatically if not specified.
+        include_layout : bool, optional
+            Include a layout plot at the bottom, if not already placed and if
+            appropriate (i.e., another plot uses longitudinal coordinates on
+            the x-axis).
+        width : int, optional
+            Width of each plot.
+        height : int, optional
+            Height of each plot.
+        layout_height : int, optional
+            Height of the layout plot.
+        share_x : bool or None, default=None
+            Share x-axes where sensible (`None`) or force sharing x-axes (True)
+            for all plots.
+        save : pathlib.Path or str, optional
+            Save the plot to the given filename.
+        xlim : (float, float), optional
+            X axis limits.
+        ylim : (float, float), optional
+            Y axis limits.
+        grid : (nrows, ncols), optional
+            If multiple graph names are specified, the plots will be placed
+            in a grid according to this parameter.  The default is to have
+            stacked plots if this parameter is unspecified.
+        backend : {"bokeh", "mpl"}, optional
+            The backend to use.  Auto-detects Jupyter and availability of bokeh
+            to select a backend.
+
+        Returns
+        -------
+        None
+            To gain access to the resulting plot objects, use the backend's
+            `plot` method directly.
+        """
+        manager = self._get_user_specified_backend(backend)
+
+        if width is not None:
+            kwargs["width"] = width
+        if height is not None:
+            kwargs["height"] = height
+        if layout_height is not None:
+            kwargs["layout_height"] = layout_height
+        if share_x is not None:
+            kwargs["share_x"] = share_x
+
+        if not template:
+            self.last_plot = manager.plot_all(
+                include_layout=include_layout,
+                **kwargs,
+            )
+        elif not isinstance(template, str):
+            templates = list(template)
+            grid = grid or (len(templates), 1)
+            self.last_plot = manager.plot_grid(
+                templates=templates,
+                grid=grid,
+                include_layout=include_layout,
+                **kwargs,
+            )
+        else:
+            self.last_plot = manager.plot(
+                region_name=region_name,
+                template=template,
+                include_layout=include_layout,
+                **kwargs,
+            )
+
+    def plot_field(
+        self,
+        ele_id: str,
+        *,
+        colormap: Optional[str] = None,
+        radius: float = 0.015,
+        num_points: int = 100,
+        backend: Optional[str] = None,
+        **kwargs,
+    ):
+        """
+        Plot field information for a given element.
+
+        Parameters
+        ----------
+        ele_id : str
+            Element ID.
+        colormap : str, optional
+            Colormap for the plot.
+            Matplotlib defaults to "PRGn_r", and bokeh defaults to "Magma256".
+        radius : float, default=0.015
+            Radius.
+        num_points : int, default=100
+            Number of data points.
+        backend : {"bokeh", "mpl"}, optional
+            The backend to use.  Auto-detects Jupyter and availability of bokeh
+            to select a backend.
+        """
+        manager = self._get_user_specified_backend(backend)
+        self.last_plot = manager.plot_field(
+            ele_id,
+            colormap=colormap,
+            radius=radius,
+            num_points=num_points,
+            **kwargs,
+        )
+
     def beam(self, ix_branch, *, ix_uni="", verbose=False, as_dict=True, raises=True):
         """
 
@@ -158,7 +973,7 @@ def beam(self, ix_branch, *, ix_uni="", verbose=False, as_dict=True, raises=True
         Notes
         -----
         Command syntax:
-          python beam {ix_uni}@{ix_branch}
+          pipe beam {ix_uni}@{ix_branch}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -169,13 +984,13 @@ def beam(self, ix_branch, *, ix_uni="", verbose=False, as_dict=True, raises=True
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init
          args:
            ix_uni: 1
            ix_branch: 0
 
         """
-        cmd = f"python beam {ix_uni}@{ix_branch}"
+        cmd = f"pipe beam {ix_uni}@{ix_branch}"
         if verbose:
             print(cmd)
         return self.__execute(cmd, as_dict, raises, method_name="beam", cmd_type="string_list")
@@ -197,7 +1012,7 @@ def beam_init(self, ix_branch, *, ix_uni="", verbose=False, as_dict=True, raises
         Notes
         -----
         Command syntax:
-          python beam_init {ix_uni}@{ix_branch}
+          pipe beam_init {ix_uni}@{ix_branch}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -208,13 +1023,13 @@ def beam_init(self, ix_branch, *, ix_uni="", verbose=False, as_dict=True, raises
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init
          args:
            ix_uni: 1
            ix_branch: 0
 
         """
-        cmd = f"python beam_init {ix_uni}@{ix_branch}"
+        cmd = f"pipe beam_init {ix_uni}@{ix_branch}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -233,16 +1048,16 @@ def bmad_com(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python bmad_com
+          pipe bmad_com
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python bmad_com"
+        cmd = "pipe bmad_com"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -266,7 +1081,7 @@ def branch1(self, ix_uni, ix_branch, *, verbose=False, as_dict=True, raises=True
         Notes
         -----
         Command syntax:
-          python branch1 {ix_uni}@{ix_branch}
+          pipe branch1 {ix_uni}@{ix_branch}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -275,13 +1090,13 @@ def branch1(self, ix_uni, ix_branch, *, verbose=False, as_dict=True, raises=True
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_uni: 1
            ix_branch: 0
 
         """
-        cmd = f"python branch1 {ix_uni}@{ix_branch}"
+        cmd = f"pipe branch1 {ix_uni}@{ix_branch}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -323,7 +1138,7 @@ def bunch_comb(
         Notes
         -----
         Command syntax:
-          python bunch_comb {flags} {who} {ix_uni}@{ix_branch} {ix_bunch}
+          pipe bunch_comb {flags} {who} {ix_uni}@{ix_branch} {ix_bunch}
 
         Where:
           {flags} are optional switches:
@@ -342,17 +1157,17 @@ def bunch_comb(
           Note: If ix_uni or ix_branch is present, "@" must be present.
 
         Example:
-          python bunch_comb py 2@1 1
+          pipe bunch_comb py 2@1 1
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init
          args:
            who: x.beta
 
         """
-        cmd = f"python bunch_comb {flags} {who} {ix_uni}@{ix_branch} {ix_bunch}"
+        cmd = f"pipe bunch_comb {flags} {who} {ix_uni}@{ix_branch} {ix_bunch}"
         if verbose:
             print(cmd)
         if "-array_out" not in flags:
@@ -381,25 +1196,25 @@ def bunch_params(self, ele_id, *, which="model", verbose=False, as_dict=True, ra
         Notes
         -----
         Command syntax:
-          python bunch_params {ele_id}|{which}
+          pipe bunch_params {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python bunch_params end|model  ! parameters at model lattice element named "end".
+          pipe bunch_params end|model  ! parameters at model lattice element named "end".
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init
          args:
            ele_id: end
            which: model
 
         """
-        cmd = f"python bunch_params {ele_id}|{which}"
+        cmd = f"pipe bunch_params {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -438,7 +1253,7 @@ def bunch1(
         Notes
         -----
         Command syntax:
-          python bunch1 {ele_id}|{which} {ix_bunch} {coordinate}
+          pipe bunch1 {ele_id}|{which} {ix_bunch} {coordinate}
 
         Where:
           {ele_id} is an element name or index.
@@ -453,7 +1268,7 @@ def bunch1(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init
          args:
            ele_id: end
            coordinate: x
@@ -461,7 +1276,7 @@ def bunch1(
            ix_bunch: 1
 
         """
-        cmd = f"python bunch1 {ele_id}|{which} {ix_bunch} {coordinate}"
+        cmd = f"pipe bunch1 {ele_id}|{which} {ix_bunch} {coordinate}"
         if verbose:
             print(cmd)
         if coordinate in ["x", "px", "y", "py", "z", "pz", "s", "t", "charge", "p0c"]:
@@ -489,7 +1304,7 @@ def building_wall_list(self, *, ix_section="", verbose=False, as_dict=True, rais
         Notes
         -----
         Command syntax:
-          python building_wall_list {ix_section}
+          pipe building_wall_list {ix_section}
 
         Where:
           {ix_section} is a building wall section index.
@@ -500,25 +1315,21 @@ def building_wall_list(self, *, ix_section="", verbose=False, as_dict=True, rais
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall
          args:
            ix_section:
 
         Example: 2
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall
          args:
            ix_section: 1
 
         """
-        cmd = f"python building_wall_list {ix_section}"
+        cmd = f"pipe building_wall_list {ix_section}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd,
-            as_dict,
-            raises,
-            method_name="building_wall_list",
-            cmd_type="string_list",
+            cmd, as_dict, raises, method_name="building_wall_list", cmd_type="string_list"
         )
 
     def building_wall_graph(self, graph, *, verbose=False, as_dict=True, raises=True):
@@ -537,7 +1348,7 @@ def building_wall_graph(self, graph, *, verbose=False, as_dict=True, raises=True
         Notes
         -----
         Command syntax:
-          python building_wall_graph {graph}
+          pipe building_wall_graph {graph}
 
         Where:
           {graph} is a plot region graph name.
@@ -547,20 +1358,16 @@ def building_wall_graph(self, graph, *, verbose=False, as_dict=True, raises=True
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall
          args:
            graph: floor_plan.g
 
         """
-        cmd = f"python building_wall_graph {graph}"
+        cmd = f"pipe building_wall_graph {graph}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd,
-            as_dict,
-            raises,
-            method_name="building_wall_graph",
-            cmd_type="string_list",
+            cmd, as_dict, raises, method_name="building_wall_graph", cmd_type="string_list"
         )
 
     def building_wall_point(
@@ -598,7 +1405,7 @@ def building_wall_point(
         Notes
         -----
         Command syntax:
-          python building_wall_point {ix_section}^^{ix_point}^^{z}^^{x}^^{radius}^^{z_center}^^{x_center}
+          pipe building_wall_point {ix_section}^^{ix_point}^^{z}^^{x}^^{radius}^^{z_center}^^{x_center}
 
         Where:
           {ix_section}    -- Section index.
@@ -610,7 +1417,7 @@ def building_wall_point(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall
          args:
            ix_section: 1
            ix_point: 1
@@ -621,7 +1428,7 @@ def building_wall_point(
            x_center: 0
 
         """
-        cmd = f"python building_wall_point {ix_section}^^{ix_point}^^{z}^^{x}^^{radius}^^{z_center}^^{x_center}"
+        cmd = f"pipe building_wall_point {ix_section}^^{ix_point}^^{z}^^{x}^^{radius}^^{z_center}^^{x_center}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -629,14 +1436,7 @@ def building_wall_point(
         )
 
     def building_wall_section(
-        self,
-        ix_section,
-        sec_name,
-        sec_constraint,
-        *,
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, ix_section, sec_name, sec_constraint, *, verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -655,7 +1455,7 @@ def building_wall_section(
         Notes
         -----
         Command syntax:
-          python building_wall_section {ix_section}^^{sec_name}^^{sec_constraint}
+          pipe building_wall_section {ix_section}^^{sec_name}^^{sec_constraint}
 
         Where:
           {ix_section}      -- Section index. Sections with higher indexes will be
@@ -670,14 +1470,14 @@ def building_wall_section(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_section: 1
            sec_name: test
            sec_constraint: none
 
         """
-        cmd = f"python building_wall_section {ix_section}^^{sec_name}^^{sec_constraint}"
+        cmd = f"pipe building_wall_section {ix_section}^^{sec_name}^^{sec_constraint}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -701,7 +1501,7 @@ def constraints(self, who, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python constraints {who}
+          pipe constraints {who}
 
         Where:
           {who} is one of: "data" or "var"
@@ -733,17 +1533,17 @@ def constraints(self, who, *, verbose=False, as_dict=True, raises=True):
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            who: data
 
         Example: 2
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            who:var
 
         """
-        cmd = f"python constraints {who}"
+        cmd = f"pipe constraints {who}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -766,13 +1566,13 @@ def da_aperture(self, *, ix_uni="", verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python da_aperture {ix_uni}
+          pipe da_aperture {ix_uni}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
 
         """
-        cmd = f"python da_aperture {ix_uni}"
+        cmd = f"pipe da_aperture {ix_uni}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -795,13 +1595,13 @@ def da_params(self, *, ix_uni="", verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python da_params {ix_uni}
+          pipe da_params {ix_uni}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
 
         """
-        cmd = f"python da_params {ix_uni}"
+        cmd = f"pipe da_params {ix_uni}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -837,7 +1637,7 @@ def data(
         Notes
         -----
         Command syntax:
-          python data {ix_uni}@{d2_name}.{d1_name}[{dat_index}]
+          pipe data {ix_uni}@{d2_name}.{d1_name}[{dat_index}]
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -845,15 +1645,15 @@ def data(
           {d1_datum} is the name of the d1_data structure the datum is in.
           {dat_index} is the index of the datum.
 
-        Use the "python data-d1" command to get detailed info on a specific d1 array.
+        Use the "pipe data-d1" command to get detailed info on a specific d1 array.
 
         Example:
-          python data 1@orbit.x[10]
+          pipe data 1@orbit.x[10]
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            ix_uni:
            d2_name: twiss
@@ -861,7 +1661,7 @@ def data(
            dat_index: 1
 
         Example: 2
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            ix_uni: 1
            d2_name: twiss
@@ -869,7 +1669,7 @@ def data(
            dat_index: 1
 
         """
-        cmd = f"python data {ix_uni}@{d2_name}.{d1_name}[{dat_index}]"
+        cmd = f"pipe data {ix_uni}@{d2_name}.{d1_name}[{dat_index}]"
         if verbose:
             print(cmd)
         return self.__execute(cmd, as_dict, raises, method_name="data", cmd_type="string_list")
@@ -900,7 +1700,7 @@ def data_d_array(
         Notes
         -----
         Command syntax:
-          python data_d_array {ix_uni}@{d2_name}.{d1_name}
+          pipe data_d_array {ix_uni}@{d2_name}.{d1_name}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -908,19 +1708,19 @@ def data_d_array(
           {d1_name} is the name of the d1_data structure containing the array of datums.
 
         Example:
-          python data_d_array 1@orbit.x
+          pipe data_d_array 1@orbit.x
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            ix_uni: 1
            d2_name: twiss
            d1_name: end
 
         """
-        cmd = f"python data_d_array {ix_uni}@{d2_name}.{d1_name}"
+        cmd = f"pipe data_d_array {ix_uni}@{d2_name}.{d1_name}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -944,7 +1744,7 @@ def data_d1_array(self, d2_datum, *, ix_uni="", verbose=False, as_dict=True, rai
         Notes
         -----
         Command syntax:
-          python data_d1_array {d2_datum}
+          pipe data_d1_array {d2_datum}
 
         {d2_datum} should be of the form
           {ix_uni}@{d2_datum_name}
@@ -952,13 +1752,13 @@ def data_d1_array(self, d2_datum, *, ix_uni="", verbose=False, as_dict=True, rai
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            ix_uni: 1
            d2_datum: twiss
 
         """
-        cmd = f"python data_d1_array {d2_datum}"
+        cmd = f"pipe data_d1_array {d2_datum}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -982,7 +1782,7 @@ def data_d2(self, d2_name, *, ix_uni="", verbose=False, as_dict=True, raises=Tru
         Notes
         -----
         Command syntax:
-          python data_d2 {ix_uni}@{d2_name}
+          pipe data_d2 {ix_uni}@{d2_name}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -991,13 +1791,13 @@ def data_d2(self, d2_name, *, ix_uni="", verbose=False, as_dict=True, raises=Tru
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            ix_uni: 1
            d2_name: twiss
 
         """
-        cmd = f"python data_d2 {ix_uni}@{d2_name}"
+        cmd = f"pipe data_d2 {ix_uni}@{d2_name}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1020,23 +1820,23 @@ def data_d2_array(self, ix_uni, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python data_d2_array {ix_uni}
+          pipe data_d2_array {ix_uni}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
 
         Example:
-          python data_d2_array 1
+          pipe data_d2_array 1
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_uni : 1
 
         """
-        cmd = f"python data_d2_array {ix_uni}"
+        cmd = f"pipe data_d2_array {ix_uni}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1072,7 +1872,7 @@ def data_d2_create(
         Notes
         -----
         Command syntax:
-          python data_d2_create {ix_uni}@{d2_name}^^{n_d1_data}^^{d_data_arrays_name_min_max}
+          pipe data_d2_create {ix_uni}@{d2_name}^^{n_d1_data}^^{d_data_arrays_name_min_max}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -1083,7 +1883,7 @@ def data_d2_create(
           where {name} is the data array name and {lower_bound} and {upper_bound} are the bounds of the array.
 
         Example:
-          python data_d2_create 2@orbit^^2^^x^^0^^45^^y^^1^^47
+          pipe data_d2_create 2@orbit^^2^^x^^0^^45^^y^^1^^47
         This example creates a d2 data structure called "orbit" with
         two d1 structures called "x" and "y".
 
@@ -1100,7 +1900,7 @@ def data_d2_create(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            ix_uni: 1
            d2_name: orbit
@@ -1108,7 +1908,7 @@ def data_d2_create(
            d_data_arrays_name_min_max: x^^0^^45^^y^^1^^47
 
         """
-        cmd = f"python data_d2_create {ix_uni}@{d2_name}^^{n_d1_data}^^{d_data_arrays_name_min_max}"
+        cmd = f"pipe data_d2_create {ix_uni}@{d2_name}^^{n_d1_data}^^{d_data_arrays_name_min_max}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1132,25 +1932,25 @@ def data_d2_destroy(self, d2_name, *, ix_uni="", verbose=False, as_dict=True, ra
         Notes
         -----
         Command syntax:
-          python data_d2_destroy {ix_uni}@{d2_name}
+          pipe data_d2_destroy {ix_uni}@{d2_name}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
           {d2_name} is the name of the d2_data structure to destroy.
 
         Example:
-          python data_d2_destroy 2@orbit
+          pipe data_d2_destroy 2@orbit
         This destroys the orbit d2_data structure in universe 2.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            d2_name: orbit
 
         """
-        cmd = f"python data_d2_destroy {ix_uni}@{d2_name}"
+        cmd = f"pipe data_d2_destroy {ix_uni}@{d2_name}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1176,22 +1976,22 @@ def data_parameter(
         Notes
         -----
         Command syntax:
-          python data_parameter {data_array} {parameter}
+          pipe data_parameter {data_array} {parameter}
 
         {parameter} may be any tao_data_struct parameter.
         Example:
-          python data_parameter orbit.x model_value
+          pipe data_parameter orbit.x model_value
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            data_array: twiss.end
            parameter: model_value
 
         """
-        cmd = f"python data_parameter {data_array} {parameter}"
+        cmd = f"pipe data_parameter {data_array} {parameter}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1210,21 +2010,21 @@ def data_set_design_value(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python data_set_design_value
+          pipe data_set_design_value
 
         Example:
-          python data_set_design_value
+          pipe data_set_design_value
 
         Note: Use the "data_d2_create" and "datum_create" first to create datums.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
 
         """
-        cmd = "python data_set_design_value"
+        cmd = "pipe data_set_design_value"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1298,7 +2098,7 @@ def datum_create(
         Notes
         -----
         Command syntax:
-          python datum_create {datum_name}^^{data_type}^^{ele_ref_name}^^{ele_start_name}^^
+          pipe datum_create {datum_name}^^{data_type}^^{ele_ref_name}^^{ele_start_name}^^
                               {ele_name}^^{merit_type}^^{meas}^^{good_meas}^^{ref}^^
                               {good_ref}^^{weight}^^{good_user}^^{data_source}^^
                               {eval_point}^^{s_offset}^^{ix_bunch}^^{invalid_value}^^
@@ -1314,7 +2114,7 @@ def datum_create(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            datum_name: twiss.end[6]
            data_type: beta.y
@@ -1335,7 +2135,7 @@ def datum_create(
            invalid_value: 0
 
         """
-        cmd = f"python datum_create {datum_name}^^{data_type}^^{ele_ref_name}^^{ele_start_name}^^{ele_name}^^{merit_type}^^{meas}^^{good_meas}^^{ref}^^{good_ref}^^{weight}^^{good_user}^^{data_source}^^{eval_point}^^{s_offset}^^{ix_bunch}^^{invalid_value}^^{spin_axis_n0_1}^^{spin_axis_n0_2}^^{spin_axis_n0_3}^^{spin_axis_l_1}^^{spin_axis_l_2}^^{spin_axis_l_3}"
+        cmd = f"pipe datum_create {datum_name}^^{data_type}^^{ele_ref_name}^^{ele_start_name}^^{ele_name}^^{merit_type}^^{meas}^^{good_meas}^^{ref}^^{good_ref}^^{weight}^^{good_user}^^{data_source}^^{eval_point}^^{s_offset}^^{ix_bunch}^^{invalid_value}^^{spin_axis_n0_1}^^{spin_axis_n0_2}^^{spin_axis_n0_3}^^{spin_axis_l_1}^^{spin_axis_l_2}^^{spin_axis_l_3}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1359,17 +2159,17 @@ def datum_has_ele(self, datum_type, *, verbose=False, as_dict=True, raises=True)
         Notes
         -----
         Command syntax:
-          python datum_has_ele {datum_type}
+          pipe datum_has_ele {datum_type}
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            datum_type: twiss.end
 
         """
-        cmd = f"python datum_has_ele {datum_type}"
+        cmd = f"pipe datum_has_ele {datum_type}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1391,7 +2191,7 @@ def derivative(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python derivative
+          pipe derivative
 
         Note: To save time, this command will not recalculate derivatives.
         Use the "derivative" command beforehand to recalcuate if needed.
@@ -1399,11 +2199,11 @@ def derivative(self, *, verbose=False, as_dict=True, raises=True):
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
 
         """
-        cmd = "python derivative"
+        cmd = "pipe derivative"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1429,26 +2229,26 @@ def ele_ac_kicker(
         Notes
         -----
         Command syntax:
-          python ele:ac_kicker {ele_id}|{which}
+          pipe ele:ac_kicker {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:ac_kicker 3@1>>7|model
+          pipe ele:ac_kicker 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
 
         """
-        cmd = f"python ele:ac_kicker {ele_id}|{which}"
+        cmd = f"pipe ele:ac_kicker {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1456,15 +2256,7 @@ def ele_ac_kicker(
         )
 
     def ele_cartesian_map(
-        self,
-        ele_id,
-        index,
-        who,
-        *,
-        which="model",
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, ele_id, index, who, *, which="model", verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -1484,7 +2276,7 @@ def ele_cartesian_map(
         Notes
         -----
         Command syntax:
-          python ele:cartesian_map {ele_id}|{which} {index} {who}
+          pipe ele:cartesian_map {ele_id}|{which} {index} {who}
 
         Where:
           {ele_id} is an element name or index
@@ -1493,13 +2285,13 @@ def ele_cartesian_map(
           {who} is one of: "base", or "terms"
 
         Example:
-          python ele:cartesian_map 3@1>>7|model 2 base
+          pipe ele:cartesian_map 3@1>>7|model 2 base
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_em_field
          args:
           ele_id: 1@0>>1
           which: model
@@ -1507,27 +2299,15 @@ def ele_cartesian_map(
           who: base
 
         """
-        cmd = f"python ele:cartesian_map {ele_id}|{which} {index} {who}"
+        cmd = f"pipe ele:cartesian_map {ele_id}|{which} {index} {who}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd,
-            as_dict,
-            raises,
-            method_name="ele_cartesian_map",
-            cmd_type="string_list",
+            cmd, as_dict, raises, method_name="ele_cartesian_map", cmd_type="string_list"
         )
 
     def ele_chamber_wall(
-        self,
-        ele_id,
-        index,
-        who,
-        *,
-        which="model",
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, ele_id, index, who, *, which="model", verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -1547,7 +2327,7 @@ def ele_chamber_wall(
         Notes
         -----
         Command syntax:
-          python ele:chamber_wall {ele_id}|{which} {index} {who}
+          pipe ele:chamber_wall {ele_id}|{which} {index} {who}
 
         Where:
           {ele_id} is an element name or index.
@@ -1560,7 +2340,7 @@ def ele_chamber_wall(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall3d
          args:
           ele_id: 1@0>>1
           which: model
@@ -1568,7 +2348,7 @@ def ele_chamber_wall(
           who: x
 
         """
-        cmd = f"python ele:chamber_wall {ele_id}|{which} {index} {who}"
+        cmd = f"pipe ele:chamber_wall {ele_id}|{which} {index} {who}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1595,26 +2375,26 @@ def ele_control_var(
         Notes
         -----
         Command syntax:
-          python ele:control_var {ele_id}|{which}
+          pipe ele:control_var {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:control_var 3@1>>7|model
+          pipe ele:control_var 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>873
           which: model
 
         """
-        cmd = f"python ele:control_var {ele_id}|{which}"
+        cmd = f"pipe ele:control_var {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1622,15 +2402,7 @@ def ele_control_var(
         )
 
     def ele_cylindrical_map(
-        self,
-        ele_id,
-        index,
-        who,
-        *,
-        which="model",
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, ele_id, index, who, *, which="model", verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -1650,7 +2422,7 @@ def ele_cylindrical_map(
         Notes
         -----
         Command syntax:
-          python ele:cylindrical_map {ele_id}|{which} {index} {who}
+          pipe ele:cylindrical_map {ele_id}|{which} {index} {who}
 
         Where
           {ele_id} is an element name or index.
@@ -1659,13 +2431,13 @@ def ele_cylindrical_map(
           {who} is one of: "base", or "terms"
 
         Example:
-          python ele:cylindrical_map 3@1>>7|model 2 base
+          pipe ele:cylindrical_map 3@1>>7|model 2 base
         This gives map #2 of element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_em_field
          args:
           ele_id: 1@0>>5
           which: model
@@ -1673,15 +2445,11 @@ def ele_cylindrical_map(
           who: base
 
         """
-        cmd = f"python ele:cylindrical_map {ele_id}|{which} {index} {who}"
+        cmd = f"pipe ele:cylindrical_map {ele_id}|{which} {index} {who}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd,
-            as_dict,
-            raises,
-            method_name="ele_cylindrical_map",
-            cmd_type="string_list",
+            cmd, as_dict, raises, method_name="ele_cylindrical_map", cmd_type="string_list"
         )
 
     def ele_elec_multipoles(
@@ -1703,45 +2471,34 @@ def ele_elec_multipoles(
         Notes
         -----
         Command syntax:
-          python ele:elec_multipoles {ele_id}|{which}
+          pipe ele:elec_multipoles {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:elec_multipoles 3@1>>7|model
+          pipe ele:elec_multipoles 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
 
         """
-        cmd = f"python ele:elec_multipoles {ele_id}|{which}"
+        cmd = f"pipe ele:elec_multipoles {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd,
-            as_dict,
-            raises,
-            method_name="ele_elec_multipoles",
-            cmd_type="string_list",
+            cmd, as_dict, raises, method_name="ele_elec_multipoles", cmd_type="string_list"
         )
 
     def ele_floor(
-        self,
-        ele_id,
-        *,
-        which="model",
-        where="end",
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, ele_id, *, which="model", where="end", verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -1763,7 +2520,7 @@ def ele_floor(
         Notes
         -----
         Command syntax:
-          python ele:floor {ele_id}|{which} {where}
+          pipe ele:floor {ele_id}|{which} {where}
 
         Where:
           {ele_id} is an element name or index.
@@ -1775,27 +2532,27 @@ def ele_floor(
         Note: {where} ignored for photonic elements crystal, mirror, and multilayer_mirror.
 
         Example:
-          python ele:floor 3@1>>7|model
+          pipe ele:floor 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
           where:
 
         Example: 2
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
           where: center
 
         """
-        cmd = f"python ele:floor {ele_id}|{which} {where}"
+        cmd = f"pipe ele:floor {ele_id}|{which} {where}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1821,26 +2578,26 @@ def ele_gen_attribs(
         Notes
         -----
         Command syntax:
-          python ele:gen_attribs {ele_id}|{which}
+          pipe ele:gen_attribs {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:gen_attribs 3@1>>7|model
+          pipe ele:gen_attribs 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
 
         """
-        cmd = f"python ele:gen_attribs {ele_id}|{which}"
+        cmd = f"pipe ele:gen_attribs {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1848,15 +2605,7 @@ def ele_gen_attribs(
         )
 
     def ele_gen_grad_map(
-        self,
-        ele_id,
-        index,
-        who,
-        *,
-        which="model",
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, ele_id, index, who, *, which="model", verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -1878,7 +2627,7 @@ def ele_gen_grad_map(
         Notes
         -----
         Command syntax:
-          python ele:gen_grad_map {ele_id}|{which} {index} {who}
+          pipe ele:gen_grad_map {ele_id}|{which} {index} {who}
 
         Where:
           {ele_id} is an element name or index.
@@ -1887,13 +2636,13 @@ def ele_gen_grad_map(
           {who} is one of: "base", or "derivs".
 
         Example:
-          python ele:gen_grad_map 3@1>>7|model 2 base
+          pipe ele:gen_grad_map 3@1>>7|model 2 base
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_em_field
          args:
           ele_id: 1@0>>9
           which: model
@@ -1901,7 +2650,7 @@ def ele_gen_grad_map(
           who: derivs
 
         """
-        cmd = f"python ele:gen_grad_map {ele_id}|{which} {index} {who}"
+        cmd = f"pipe ele:gen_grad_map {ele_id}|{which} {index} {who}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1909,15 +2658,7 @@ def ele_gen_grad_map(
         )
 
     def ele_grid_field(
-        self,
-        ele_id,
-        index,
-        who,
-        *,
-        which="model",
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, ele_id, index, who, *, which="model", verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -1937,7 +2678,7 @@ def ele_grid_field(
         Notes
         -----
         Command syntax:
-          python ele:grid_field {ele_id}|{which} {index} {who}
+          pipe ele:grid_field {ele_id}|{which} {index} {who}
 
         Where:
           {ele_id} is an element name or index.
@@ -1946,13 +2687,13 @@ def ele_grid_field(
           {who} is one of: "base", or "points"
 
         Example:
-          python ele:grid_field 3@1>>7|model 2 base
+          pipe ele:grid_field 3@1>>7|model 2 base
         This gives grid #2 of element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_grid
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_grid
          args:
           ele_id: 1@0>>1
           which: model
@@ -1960,7 +2701,7 @@ def ele_grid_field(
           who: base
 
         """
-        cmd = f"python ele:grid_field {ele_id}|{which} {index} {who}"
+        cmd = f"pipe ele:grid_field {ele_id}|{which} {index} {who}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -1984,26 +2725,26 @@ def ele_head(self, ele_id, *, which="model", verbose=False, as_dict=True, raises
         Notes
         -----
         Command syntax:
-          python ele:head {ele_id}|{which}
+          pipe ele:head {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:head 3@1>>7|model
+          pipe ele:head 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
 
         """
-        cmd = f"python ele:head {ele_id}|{which}"
+        cmd = f"pipe ele:head {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2029,14 +2770,14 @@ def ele_lord_slave(
         Notes
         -----
         Command syntax:
-          python ele:lord_slave {ele_id}|{which}
+          pipe ele:lord_slave {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:lord_slave 3@1>>7|model
+          pipe ele:lord_slave 3@1>>7|model
         This gives lord and slave info on element number 7 in branch 1 of universe 3.
         Note: The lord/slave info is independent of the setting of {which}.
 
@@ -2048,13 +2789,13 @@ def ele_lord_slave(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
 
         """
-        cmd = f"python ele:lord_slave {ele_id}|{which}"
+        cmd = f"pipe ele:lord_slave {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2062,14 +2803,7 @@ def ele_lord_slave(
         )
 
     def ele_mat6(
-        self,
-        ele_id,
-        *,
-        which="model",
-        who="mat6",
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, ele_id, *, which="model", who="mat6", verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -2088,7 +2822,7 @@ def ele_mat6(
         Notes
         -----
         Command syntax:
-          python ele:mat6 {ele_id}|{which} {who}
+          pipe ele:mat6 {ele_id}|{which} {who}
 
         Where:
           {ele_id} is an element name or index.
@@ -2096,20 +2830,20 @@ def ele_mat6(
           {who} is one of: "mat6", "vec0", or "err"
 
         Example:
-          python ele:mat6 3@1>>7|model mat6
+          pipe ele:mat6 3@1>>7|model mat6
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
           who: mat6
 
         """
-        cmd = f"python ele:mat6 {ele_id}|{which} {who}"
+        cmd = f"pipe ele:mat6 {ele_id}|{which} {who}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2133,26 +2867,26 @@ def ele_methods(self, ele_id, *, which="model", verbose=False, as_dict=True, rai
         Notes
         -----
         Command syntax:
-          python ele:methods {ele_id}|{which}
+          pipe ele:methods {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:methods 3@1>>7|model
+          pipe ele:methods 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
 
         """
-        cmd = f"python ele:methods {ele_id}|{which}"
+        cmd = f"pipe ele:methods {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2178,26 +2912,26 @@ def ele_multipoles(
         Notes
         -----
         Command syntax:
-          python ele:multipoles {ele_id}|{which}
+          pipe ele:multipoles {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:multipoles 3@1>>7|model
+          pipe ele:multipoles 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
 
         """
-        cmd = f"python ele:multipoles {ele_id}|{which}"
+        cmd = f"pipe ele:multipoles {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2221,26 +2955,26 @@ def ele_orbit(self, ele_id, *, which="model", verbose=False, as_dict=True, raise
         Notes
         -----
         Command syntax:
-          python ele:orbit {ele_id}|{which}
+          pipe ele:orbit {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:orbit 3@1>>7|model
+          pipe ele:orbit 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
 
         """
-        cmd = f"python ele:orbit {ele_id}|{which}"
+        cmd = f"pipe ele:orbit {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2267,34 +3001,34 @@ def ele_param(
         Notes
         -----
         Command syntax:
-          python ele:param {ele_id}|{which} {who}
+          pipe ele:param {ele_id}|{which} {who}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
-          {who} values are the same as {who} values for "python lat_list".
+          {who} values are the same as {who} values for "pipe lat_list".
                 Note: Here {who} must be a single parameter and not a list.
 
         Example:
-          python ele:param 3@1>>7|model e_tot
+          pipe ele:param 3@1>>7|model e_tot
         This gives E_tot of element number 7 in branch 1 of universe 3.
 
         Note: On output the {variable} component will always be "F" (since this
         command cannot tell if a parameter is allowed to vary).
 
-        Also see: "python lat_list".
+        Also see: "pipe lat_list".
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_photon
          args:
           ele_id: 1@0>>1
           which: model
           who: orbit.vec.1
 
         """
-        cmd = f"python ele:param {ele_id}|{which} {who}"
+        cmd = f"pipe ele:param {ele_id}|{which} {who}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2321,7 +3055,7 @@ def ele_photon(
         Notes
         -----
         Command syntax:
-          python ele:photon {ele_id}|{which} {who}
+          pipe ele:photon {ele_id}|{which} {who}
 
         Where:
           {ele_id} is an element name or index.
@@ -2329,20 +3063,20 @@ def ele_photon(
           {who} is one of: "base", "material", or "curvature"
 
         Example:
-          python ele:photon 3@1>>7|model base
+          pipe ele:photon 3@1>>7|model base
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_photon
          args:
           ele_id: 1@0>>1
           which: model
           who: base
 
         """
-        cmd = f"python ele:photon {ele_id}|{which} {who}"
+        cmd = f"pipe ele:photon {ele_id}|{which} {who}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2368,26 +3102,26 @@ def ele_spin_taylor(
         Notes
         -----
         Command syntax:
-          python ele:spin_taylor {ele_id}|{which}
+          pipe ele:spin_taylor {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:spin_taylor 3@1>>7|model
+          pipe ele:spin_taylor 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_spin
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_spin
          args:
           ele_id: 1@0>>2
           which: model
 
         """
-        cmd = f"python ele:spin_taylor {ele_id}|{which}"
+        cmd = f"pipe ele:spin_taylor {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2411,26 +3145,26 @@ def ele_taylor(self, ele_id, *, which="model", verbose=False, as_dict=True, rais
         Notes
         -----
         Command syntax:
-          python ele:taylor {ele_id}|{which}
+          pipe ele:taylor {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:taylor 3@1>>7|model
+          pipe ele:taylor 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_taylor
          args:
           ele_id: 1@0>>34
           which: model
 
         """
-        cmd = f"python ele:taylor {ele_id}|{which}"
+        cmd = f"pipe ele:taylor {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2454,26 +3188,26 @@ def ele_twiss(self, ele_id, *, which="model", verbose=False, as_dict=True, raise
         Notes
         -----
         Command syntax:
-          python ele:twiss {ele_id}|{which}
+          pipe ele:twiss {ele_id}|{which}
 
         Where:
           {ele_id} is an element name or index.
           {which} is one of: "model", "base" or "design"
 
         Example:
-          python ele:twiss 3@1>>7|model
+          pipe ele:twiss 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
           ele_id: 1@0>>1
           which: model
 
         """
-        cmd = f"python ele:twiss {ele_id}|{which}"
+        cmd = f"pipe ele:twiss {ele_id}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2500,7 +3234,7 @@ def ele_wake(
         Notes
         -----
         Command syntax:
-          python ele:wake {ele_id}|{which} {who}
+          pipe ele:wake {ele_id}|{which} {who}
 
         Where:
           {ele_id} is an element name or index.
@@ -2511,20 +3245,20 @@ def ele_wake(
               "lr_mode_table"  "base"
 
         Example:
-          python ele:wake 3@1>>7|model
+          pipe ele:wake 3@1>>7|model
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wake
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wake
          args:
           ele_id: 1@0>>1
           which: model
           who: sr_long
 
         """
-        cmd = f"python ele:wake {ele_id}|{which} {who}"
+        cmd = f"pipe ele:wake {ele_id}|{which} {who}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2532,15 +3266,7 @@ def ele_wake(
         )
 
     def ele_wall3d(
-        self,
-        ele_id,
-        index,
-        who,
-        *,
-        which="model",
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, ele_id, index, who, *, which="model", verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -2560,7 +3286,7 @@ def ele_wall3d(
         Notes
         -----
         Command syntax:
-          python ele:wall3d {ele_id}|{which} {index} {who}
+          pipe ele:wall3d {ele_id}|{which} {index} {who}
 
         Where:
           {ele_id} is an element name or index.
@@ -2568,13 +3294,13 @@ def ele_wall3d(
           {index} is the index number in the ele%wall3d(:) array (size obtained from "ele:head").
           {who} is one of: "base", or "table".
         Example:
-          python ele:wall3d 3@1>>7|model 2 base
+          pipe ele:wall3d 3@1>>7|model 2 base
         This gives element number 7 in branch 1 of universe 3.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall3d
          args:
           ele_id: 1@0>>1
           which: model
@@ -2582,7 +3308,7 @@ def ele_wall3d(
           who: table
 
         """
-        cmd = f"python ele:wall3d {ele_id}|{which} {index} {who}"
+        cmd = f"pipe ele:wall3d {ele_id}|{which} {index} {who}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2590,13 +3316,7 @@ def ele_wall3d(
         )
 
     def evaluate(
-        self,
-        expression,
-        *,
-        flags="-array_out",
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, expression, *, flags="-array_out", verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -2618,7 +3338,7 @@ def evaluate(
         Notes
         -----
         Command syntax:
-          python evaluate {flags} {expression}
+          pipe evaluate {flags} {expression}
 
         Where:
           Optional {flags} are:
@@ -2626,17 +3346,17 @@ def evaluate(
           {expression} is expression to be evaluated.
 
         Example:
-          python evaluate 3+data::cbar.11[1:10]|model
+          pipe evaluate 3+data::cbar.11[1:10]|model
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            expression: data::cbar.11[1:10]|model
 
         """
-        cmd = f"python evaluate {flags} {expression}"
+        cmd = f"pipe evaluate {flags} {expression}"
         if verbose:
             print(cmd)
         if "-array_out" not in flags:
@@ -2681,7 +3401,7 @@ def em_field(
         Notes
         -----
         Command syntax:
-          python em_field {ele_id}|{which} {x} {y} {z} {t_or_z}
+          pipe em_field {ele_id}|{which} {x} {y} {z} {t_or_z}
 
         Where:
           {which} is one of: "model", "base" or "design"
@@ -2692,7 +3412,7 @@ def em_field(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ele_id: 1@0>>22
            which: model
@@ -2702,7 +3422,7 @@ def em_field(
            t_or_z: 0
 
         """
-        cmd = f"python em_field {ele_id}|{which} {x} {y} {z} {t_or_z}"
+        cmd = f"pipe em_field {ele_id}|{which} {x} {y} {z} {t_or_z}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2725,20 +3445,20 @@ def enum(self, enum_name, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python enum {enum_name}
+          pipe enum {enum_name}
 
         Example:
-          python enum tracking_method
+          pipe enum tracking_method
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            enum_name: tracking_method
 
         """
-        cmd = f"python enum {enum_name}"
+        cmd = f"pipe enum {enum_name}"
         if verbose:
             print(cmd)
         return self.__execute(cmd, as_dict, raises, method_name="enum", cmd_type="string_list")
@@ -2759,17 +3479,17 @@ def floor_plan(self, graph, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python floor_plan {graph}
+          pipe floor_plan {graph}
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            graph: r13.g
 
         """
-        cmd = f"python floor_plan {graph}"
+        cmd = f"pipe floor_plan {graph}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2792,17 +3512,17 @@ def floor_orbit(self, graph, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python floor_orbit {graph}
+          pipe floor_orbit {graph}
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_floor_orbit
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_floor_orbit
          args:
            graph: r33.g
 
         """
-        cmd = f"python floor_orbit {graph}"
+        cmd = f"pipe floor_orbit {graph}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2821,7 +3541,7 @@ def tao_global(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python global
+          pipe global
 
         Output syntax is parameter list form. See documentation at the beginning of this file.
 
@@ -2835,11 +3555,11 @@ def tao_global(self, *, verbose=False, as_dict=True, raises=True):
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python global"
+        cmd = "pipe global"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2859,26 +3579,22 @@ def global_optimization(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python global:optimization
+          pipe global:optimization
 
         Output syntax is parameter list form. See documentation at the beginning of this file.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python global:optimization"
+        cmd = "pipe global:optimization"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd,
-            as_dict,
-            raises,
-            method_name="global_optimization",
-            cmd_type="string_list",
+            cmd, as_dict, raises, method_name="global_optimization", cmd_type="string_list"
         )
 
     def global_opti_de(self, *, verbose=False, as_dict=True, raises=True):
@@ -2893,18 +3609,18 @@ def global_opti_de(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python global:opti_de
+          pipe global:opti_de
 
         Output syntax is parameter list form. See documentation at the beginning of this file.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python global:opti_de"
+        cmd = "pipe global:opti_de"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -2923,16 +3639,16 @@ def help(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python help
+          pipe help
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python help"
+        cmd = "pipe help"
         if verbose:
             print(cmd)
         return self.__execute(cmd, as_dict, raises, method_name="help", cmd_type="string_list")
@@ -2954,17 +3670,17 @@ def inum(self, who, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python inum {who}
+          pipe inum {who}
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            who: ix_universe
 
         """
-        cmd = f"python inum {who}"
+        cmd = f"pipe inum {who}"
         if verbose:
             print(cmd)
         return self.__execute(cmd, as_dict, raises, method_name="inum", cmd_type="string_list")
@@ -2973,7 +3689,7 @@ def lat_calc_done(self, branch_name, *, verbose=False, as_dict=True, raises=True
         """
 
         Output if a lattice recalculation has been proformed since the last
-          time "python lat_calc_done" was called.
+          time "pipe lat_calc_done" was called.
 
         Parameters
         ----------
@@ -2986,17 +3702,17 @@ def lat_calc_done(self, branch_name, *, verbose=False, as_dict=True, raises=True
         Notes
         -----
         Command syntax:
-          python lat_calc_done
+          pipe lat_calc_done
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            branch_name: 1@0
 
         """
-        cmd = "python lat_calc_done"
+        cmd = "pipe lat_calc_done"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3019,7 +3735,7 @@ def lat_ele_list(self, *, branch_name="", verbose=False, as_dict=True, raises=Tr
         Notes
         -----
         Command syntax:
-          python lat_ele_list {branch_name}
+          pipe lat_ele_list {branch_name}
 
         {branch_name} should have the form:
           {ix_uni}@{ix_branch}
@@ -3027,12 +3743,12 @@ def lat_ele_list(self, *, branch_name="", verbose=False, as_dict=True, raises=Tr
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            branch_name: 1@0
 
         """
-        cmd = f"python lat_ele_list {branch_name}"
+        cmd = f"pipe lat_ele_list {branch_name}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3055,7 +3771,7 @@ def lat_branch_list(self, *, ix_uni="", verbose=False, as_dict=True, raises=True
         Notes
         -----
         Command syntax:
-          python lat_branch_list {ix_uni}
+          pipe lat_branch_list {ix_uni}
 
         Output syntax:
           branch_index;branch_name;n_ele_track;n_ele_max
@@ -3063,12 +3779,12 @@ def lat_branch_list(self, *, ix_uni="", verbose=False, as_dict=True, raises=True
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_uni: 1
 
         """
-        cmd = f"python lat_branch_list {ix_uni}"
+        cmd = f"pipe lat_branch_list {ix_uni}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3113,7 +3829,7 @@ def lat_list(
         Notes
         -----
         Command syntax:
-          python lat_list {flags} {ix_uni}@{ix_branch}>>{elements}|{which} {who}
+          pipe lat_list {flags} {ix_uni}@{ix_branch}>>{elements}|{which} {who}
 
         Where:
          Optional {flags} are:
@@ -3151,15 +3867,15 @@ def lat_list(
             Use "*" to match to all elements.
 
         Examples:
-          python lat_list -track 3@0>>Q*|base ele.s,orbit.vec.2
-          python lat_list 3@0>>Q*|base real:ele.s
+          pipe lat_list -track 3@0>>Q*|base ele.s,orbit.vec.2
+          pipe lat_list 3@0>>Q*|base real:ele.s
 
-        Also see: "python ele:param"
+        Also see: "pipe ele:param"
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_uni: 1
            ix_branch: 0
@@ -3168,7 +3884,7 @@ def lat_list(
            who: orbit.floor.x
 
         Example: 2
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_uni: 1
            ix_branch: 0
@@ -3177,7 +3893,7 @@ def lat_list(
            who: ele.ix_ele
 
         """
-        cmd = f"python lat_list {flags} {ix_uni}@{ix_branch}>>{elements}|{which} {who}"
+        cmd = f"pipe lat_list {flags} {ix_uni}@{ix_branch}>>{elements}|{which} {who}"
         if verbose:
             print(cmd)
         if ("-array_out" not in flags) or (who in ["ele.name", "ele.key"]):
@@ -3209,17 +3925,17 @@ def lat_param_units(self, param_name, *, verbose=False, as_dict=True, raises=Tru
         Notes
         -----
         Command syntax:
-          python lat_param_units {param_name}
+          pipe lat_param_units {param_name}
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            param_name: L
 
         """
-        cmd = f"python lat_param_units {param_name}"
+        cmd = f"pipe lat_param_units {param_name}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3245,7 +3961,7 @@ def matrix(self, ele1_id, ele2_id, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python matrix {ele1_id} {ele2_id}
+          pipe matrix {ele1_id} {ele2_id}
 
         Where:
           {ele1_id} is the start element.
@@ -3254,18 +3970,18 @@ def matrix(self, ele1_id, ele2_id, *, verbose=False, as_dict=True, raises=True):
         Note: {ele2_id} should just be an element name or index without universe, branch, or model/base/design specification.
 
         Example:
-          python matrix 2@1>>q01w|design q02w
+          pipe matrix 2@1>>q01w|design q02w
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ele1_id: 1@0>>q01w|design
            ele2_id: q02w
 
         """
-        cmd = f"python matrix {ele1_id} {ele2_id}"
+        cmd = f"pipe matrix {ele1_id} {ele2_id}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3285,16 +4001,16 @@ def merit(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python merit
+          pipe merit
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python merit"
+        cmd = "pipe merit"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3330,7 +4046,7 @@ def orbit_at_s(
         Notes
         -----
         Command syntax:
-          python orbit_at_s {ix_uni}@{ele}->{s_offset}|{which}
+          pipe orbit_at_s {ix_uni}@{ele}->{s_offset}|{which}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -3340,12 +4056,12 @@ def orbit_at_s(
           {which} is one of: "model", "base" or "design".
 
         Example:
-          python orbit_at_s Q10->0.4|model   ! Orbit at 0.4 meters from Q10 element exit end in model lattice.
+          pipe orbit_at_s Q10->0.4|model   ! Orbit at 0.4 meters from Q10 element exit end in model lattice.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_uni: 1
            ele: 10
@@ -3353,7 +4069,7 @@ def orbit_at_s(
            which: model
 
         """
-        cmd = f"python orbit_at_s {ix_uni}@{ele}->{s_offset}|{which}"
+        cmd = f"pipe orbit_at_s {ix_uni}@{ele}->{s_offset}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3369,21 +4085,21 @@ def place_buffer(self, *, verbose=False, as_dict=True, raises=True):
 
         Returns
         -------
-        None
+        list of dict
 
         Notes
         -----
         Command syntax:
-          python place_buffer
+          pipe place_buffer
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python place_buffer"
+        cmd = "pipe place_buffer"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3406,179 +4122,179 @@ def plot_curve(self, curve_name, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python plot_curve {curve_name}
+          pipe plot_curve {curve_name}
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            curve_name: r13.g.a
 
         """
-        cmd = f"python plot_curve {curve_name}"
+        cmd = f"pipe plot_curve {curve_name}"
         if verbose:
             print(cmd)
         return self.__execute(
             cmd, as_dict, raises, method_name="plot_curve", cmd_type="string_list"
         )
 
-    def plot_lat_layout(
-        self, ix_uni: 1, ix_branch: 0, *, verbose=False, as_dict=True, raises=True
-    ):
+    def plot_graph(self, graph_name, *, verbose=False, as_dict=True, raises=True):
         """
 
-        Output plot Lat_layout info
+        Output graph info.
 
         Parameters
         ----------
-        ix_uni: 1
-        ix_branch: 0
+        graph_name
 
         Returns
         -------
-        list of dict
+        dict
 
         Notes
         -----
         Command syntax:
-          python plot_lat_layout {ix_uni}@{ix_branch}
+          pipe plot_graph {graph_name}
 
-        Note: The returned list of element positions is not ordered in increasing
-              longitudinal position.
+        {graph_name} is in the form:
+          {p_name}.{g_name}
+        where
+          {p_name} is the plot region name if from a region or the plot name if a template plot.
+          This name is obtained from the pipe plot_list command.
+          {g_name} is the graph name obtained from the pipe plot1 command.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
-           ix_uni: 1
-           ix_branch: 0
+           graph_name: beta.g
 
         """
-        cmd = f"python plot_lat_layout {ix_uni}@{ix_branch}"
+        cmd = f"pipe plot_graph {graph_name}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd, as_dict, raises, method_name="plot_lat_layout", cmd_type="string_list"
+            cmd, as_dict, raises, method_name="plot_graph", cmd_type="string_list"
         )
 
-    def plot_list(self, r_or_g, *, verbose=False, as_dict=True, raises=True):
+    def plot_histogram(self, curve_name, *, verbose=False, as_dict=True, raises=True):
         """
 
-        Output list of plot templates or plot regions.
+        Output plot histogram info.
 
         Parameters
         ----------
-        r_or_g
+        curve_name
 
         Returns
         -------
-        if r_or_g == 't'
-            dict with template_name:index
-        if r_or_g == 'r'
-            list of dicts with keys:
-                region
-                ix
-                plot_name
-                visible
-                x1, x2, y1, y1
+        string_list
 
         Notes
         -----
         Command syntax:
-          python plot_list {r_or_g}
-
-        where "{r/g}" is:
-          "r"      ! list regions of the form ix;region_name;plot_name;visible;x1;x2;y1;y2
-          "t"      ! list template plots of the form ix;name
+          pipe plot_histogram {curve_name}
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
-           r_or_g: r
+           curve_name: r33.g.x
 
         """
-        cmd = f"python plot_list {r_or_g}"
+        cmd = f"pipe plot_histogram {curve_name}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd, as_dict, raises, method_name="plot_list", cmd_type="string_list"
+            cmd, as_dict, raises, method_name="plot_histogram", cmd_type="string_list"
         )
 
-    def plot_graph(self, graph_name, *, verbose=False, as_dict=True, raises=True):
+    def plot_lat_layout(
+        self, ix_uni: 1, ix_branch: 0, *, verbose=False, as_dict=True, raises=True
+    ):
         """
 
-        Output graph info.
+        Output plot Lat_layout info
 
         Parameters
         ----------
-        graph_name
+        ix_uni: 1
+        ix_branch: 0
 
         Returns
         -------
-        dict
+        list of dict
 
         Notes
         -----
         Command syntax:
-          python plot_graph {graph_name}
+          pipe plot_lat_layout {ix_uni}@{ix_branch}
 
-        {graph_name} is in the form:
-          {p_name}.{g_name}
-        where
-          {p_name} is the plot region name if from a region or the plot name if a template plot.
-          This name is obtained from the python plot_list command.
-          {g_name} is the graph name obtained from the python plot1 command.
+        Note: The returned list of element positions is not ordered in increasing
+              longitudinal position.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
-           graph_name: beta.g
+           ix_uni: 1
+           ix_branch: 0
 
         """
-        cmd = f"python plot_graph {graph_name}"
+        cmd = f"pipe plot_lat_layout {ix_uni}@{ix_branch}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd, as_dict, raises, method_name="plot_graph", cmd_type="string_list"
+            cmd, as_dict, raises, method_name="plot_lat_layout", cmd_type="string_list"
         )
 
-    def plot_histogram(self, curve_name, *, verbose=False, as_dict=True, raises=True):
+    def plot_list(self, r_or_g, *, verbose=False, as_dict=True, raises=True):
         """
 
-        Output plot histogram info.
+        Output list of plot templates or plot regions.
 
         Parameters
         ----------
-        curve_name
+        r_or_g
 
         Returns
         -------
-        string_list
+        if r_or_g == 't'
+            dict with template_name:index
+        if r_or_g == 'r'
+            list of dicts with keys:
+                region
+                ix
+                plot_name
+                visible
+                x1, x2, y1, y1
 
         Notes
         -----
         Command syntax:
-          python plot_histogram {curve_name}
+          pipe plot_list {r_or_g}
+
+        where "{r/g}" is:
+          "r"      ! list regions of the form ix;region_name;plot_name;visible;x1;x2;y1;y2
+          "t"      ! list template plots of the form ix;name
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
-           curve_name: r33.g.x
+           r_or_g: r
 
         """
-        cmd = f"python plot_histogram {curve_name}"
+        cmd = f"pipe plot_list {r_or_g}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd, as_dict, raises, method_name="plot_histogram", cmd_type="string_list"
+            cmd, as_dict, raises, method_name="plot_list", cmd_type="string_list"
         )
 
     def plot_template_manage(
@@ -3610,7 +4326,7 @@ def plot_template_manage(
         Notes
         -----
         Command syntax:
-          python plot_template_manage {template_location}^^{template_name}^^
+          pipe plot_template_manage {template_location}^^{template_name}^^
                                  {n_graph}^^{graph_names}
 
         Where:
@@ -3624,7 +4340,7 @@ def plot_template_manage(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            template_location: @T1
            template_name: beta
@@ -3632,7 +4348,7 @@ def plot_template_manage(
            graph_names: g1^^g2
 
         """
-        cmd = f"python plot_template_manage {template_location}^^{template_name}^^{n_graph}^^{graph_names}"
+        cmd = f"pipe plot_template_manage {template_location}^^{template_name}^^{n_graph}^^{graph_names}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3640,14 +4356,7 @@ def plot_template_manage(
         )
 
     def plot_curve_manage(
-        self,
-        graph_name,
-        curve_index,
-        curve_name,
-        *,
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, graph_name, curve_index, curve_name, *, verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -3666,7 +4375,7 @@ def plot_curve_manage(
         Notes
         -----
         Command syntax:
-          python plot_curve_manage {graph_name}^^{curve_index}^^{curve_name}
+          pipe plot_curve_manage {graph_name}^^{curve_index}^^{curve_name}
 
         If {curve_index} corresponds to an existing curve then this curve is deleted.
         In this case the {curve_name} is ignored and does not have to be present.
@@ -3676,14 +4385,14 @@ def plot_curve_manage(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            graph_name: beta.g
            curve_index: 1
            curve_name: r13.g.a
 
         """
-        cmd = f"python plot_curve_manage {graph_name}^^{curve_index}^^{curve_name}"
+        cmd = f"pipe plot_curve_manage {graph_name}^^{curve_index}^^{curve_name}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3691,14 +4400,7 @@ def plot_curve_manage(
         )
 
     def plot_graph_manage(
-        self,
-        plot_name,
-        graph_index,
-        graph_name,
-        *,
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, plot_name, graph_index, graph_name, *, verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -3717,7 +4419,7 @@ def plot_graph_manage(
         Notes
         -----
         Command syntax:
-          python plot_graph_manage {plot_name}^^{graph_index}^^{graph_name}
+          pipe plot_graph_manage {plot_name}^^{graph_index}^^{graph_name}
 
         If {graph_index} corresponds to an existing graph then this graph is deleted.
         In this case the {graph_name} is ignored and does not have to be present.
@@ -3727,14 +4429,14 @@ def plot_graph_manage(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            plot_name: beta
            graph_index: 1
            graph_name: beta.g
 
         """
-        cmd = f"python plot_graph_manage {plot_name}^^{graph_index}^^{graph_name}"
+        cmd = f"pipe plot_graph_manage {plot_name}^^{graph_index}^^{graph_name}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3773,21 +4475,21 @@ def plot_line(
         Notes
         -----
         Command syntax:
-          python plot_line {region_name}.{graph_name}.{curve_name} {x_or_y}
+          pipe plot_line {region_name}.{graph_name}.{curve_name} {x_or_y}
 
         Optional {x-or-y} may be set to "x" or "y" to get the smooth line points x or y
         component put into the real array buffer.
         Note: The plot must come from a region, and not a template, since no template plots
               have associated line data.
         Examples:
-          python plot_line r13.g.a   ! String array output.
-          python plot_line r13.g.a x ! x-component of line points loaded into the real array buffer.
-          python plot_line r13.g.a y ! y-component of line points loaded into the real array buffer.
+          pipe plot_line r13.g.a   ! String array output.
+          pipe plot_line r13.g.a x ! x-component of line points loaded into the real array buffer.
+          pipe plot_line r13.g.a y ! y-component of line points loaded into the real array buffer.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line -external_plotting
          args:
            region_name: beta
            graph_name: g
@@ -3795,7 +4497,7 @@ def plot_line(
            x_or_y:
 
         Example: 2
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line -external_plotting
          args:
            region_name: beta
            graph_name: g
@@ -3803,7 +4505,7 @@ def plot_line(
            x_or_y: y
 
         """
-        cmd = f"python plot_line {region_name}.{graph_name}.{curve_name} {x_or_y}"
+        cmd = f"pipe plot_line {region_name}.{graph_name}.{curve_name} {x_or_y}"
         if verbose:
             print(cmd)
         if x_or_y == "":
@@ -3847,23 +4549,23 @@ def plot_symbol(
         Notes
         -----
         Command syntax:
-          python plot_symbol {region_name}.{graph_name}.{curve_name} {x_or_y}
+          pipe plot_symbol {region_name}.{graph_name}.{curve_name} {x_or_y}
 
         Optional {x_or_y} may be set to "x" or "y" to get the symbol x or y
         positions put into the real array buffer.
         Note: The plot must come from a region, and not a template,
               since no template plots have associated symbol data.
         Examples:
-          python plot_symbol r13.g.a       ! String array output.
-          python plot_symbol r13.g.a x     ! x-component of the symbol positions
+          pipe plot_symbol r13.g.a       ! String array output.
+          pipe plot_symbol r13.g.a x     ! x-component of the symbol positions
                                              loaded into the real array buffer.
-          python plot_symbol r13.g.a y     ! y-component of the symbol positions
+          pipe plot_symbol r13.g.a y     ! y-component of the symbol positions
                                              loaded into the real array buffer.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line -external_plotting
          args:
            region_name: r13
            graph_name: g
@@ -3871,7 +4573,7 @@ def plot_symbol(
            x_or_y:
 
         Example: 2
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line -external_plotting
          args:
            region_name: r13
            graph_name: g
@@ -3879,7 +4581,7 @@ def plot_symbol(
            x_or_y: y
 
         """
-        cmd = f"python plot_symbol {region_name}.{graph_name}.{curve_name} {x_or_y}"
+        cmd = f"pipe plot_symbol {region_name}.{graph_name}.{curve_name} {x_or_y}"
         if verbose:
             print(cmd)
         if x_or_y == "":
@@ -3908,7 +4610,7 @@ def plot_transfer(self, from_plot, to_plot, *, verbose=False, as_dict=True, rais
         Notes
         -----
         Command syntax:
-          python plot_transfer {from_plot} {to_plot}
+          pipe plot_transfer {from_plot} {to_plot}
 
         To avoid confusion, use "@Tnnn" and "@Rnnn" syntax for {from_plot}.
         If {to_plot} is not present and {from_plot} is a template plot, the "to plots"
@@ -3918,13 +4620,13 @@ def plot_transfer(self, from_plot, to_plot, *, verbose=False, as_dict=True, rais
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            from_plot: r13
            to_plot: r23
 
         """
-        cmd = f"python plot_transfer {from_plot} {to_plot}"
+        cmd = f"pipe plot_transfer {from_plot} {to_plot}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3947,7 +4649,7 @@ def plot1(self, name, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python plot1 {name}
+          pipe plot1 {name}
 
         {name} should be the region name if the plot is associated with a region.
         Output syntax is parameter list form. See documentation at the beginning of this file.
@@ -3955,12 +4657,12 @@ def plot1(self, name, *, verbose=False, as_dict=True, raises=True):
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            name: beta
 
         """
-        cmd = f"python plot1 {name}"
+        cmd = f"pipe plot1 {name}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -3979,16 +4681,16 @@ def ptc_com(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python ptc_com
+          pipe ptc_com
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python ptc_com"
+        cmd = "pipe ptc_com"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4022,26 +4724,26 @@ def ring_general(
         Notes
         -----
         Command syntax:
-          python ring_general {ix_uni}@{ix_branch}|{which}
+          pipe ring_general {ix_uni}@{ix_branch}|{which}
 
         where {which} is one of:
           model
           base
           design
         Example:
-          python ring_general 1@0|model
+          pipe ring_general 1@0|model
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
             ix_uni: 1
             ix_branch: 0
             which: model
 
         """
-        cmd = f"python ring_general {ix_uni}@{ix_branch}|{which}"
+        cmd = f"pipe ring_general {ix_uni}@{ix_branch}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4064,7 +4766,7 @@ def shape_list(self, who, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python shape_list {who}
+          pipe shape_list {who}
 
         {who} is one of:
           lat_layout
@@ -4073,12 +4775,12 @@ def shape_list(self, who, *, verbose=False, as_dict=True, raises=True):
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            who: floor_plan
 
         """
-        cmd = f"python shape_list {who}"
+        cmd = f"pipe shape_list {who}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4105,7 +4807,7 @@ def shape_manage(
         Notes
         -----
         Command syntax:
-          python shape_manage {who} {index} {add_or_delete}
+          pipe shape_manage {who} {index} {add_or_delete}
 
         {who} is one of:
           lat_layout
@@ -4117,21 +4819,21 @@ def shape_manage(
                      Shapes with higher index get moved down one to fill the gap.
 
         Example:
-          python shape_manage floor_plan 2 add
-        Note: After adding a shape use "python shape_set" to set shape parameters.
+          pipe shape_manage floor_plan 2 add
+        Note: After adding a shape use "pipe shape_set" to set shape parameters.
         This is important since an added shape is in a ill-defined state.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            who: floor_plan
            index: 1
            add_or_delete: add
 
         """
-        cmd = f"python shape_manage {who} {index} {add_or_delete}"
+        cmd = f"pipe shape_manage {who} {index} {add_or_delete}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4154,7 +4856,7 @@ def shape_pattern_list(self, *, ix_pattern="", verbose=False, as_dict=True, rais
         Notes
         -----
         Command syntax:
-          python shape_pattern_list {ix_pattern}
+          pipe shape_pattern_list {ix_pattern}
 
         If optional {ix_pattern} index is omitted then list all the patterns.
         If {ix_pattern} is present, list points of given pattern.
@@ -4162,31 +4864,20 @@ def shape_pattern_list(self, *, ix_pattern="", verbose=False, as_dict=True, rais
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_shape
          args:
            ix_pattern:
 
         """
-        cmd = f"python shape_pattern_list {ix_pattern}"
+        cmd = f"pipe shape_pattern_list {ix_pattern}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd,
-            as_dict,
-            raises,
-            method_name="shape_pattern_list",
-            cmd_type="string_list",
+            cmd, as_dict, raises, method_name="shape_pattern_list", cmd_type="string_list"
         )
 
     def shape_pattern_manage(
-        self,
-        ix_pattern,
-        pat_name,
-        pat_line_width,
-        *,
-        verbose=False,
-        as_dict=True,
-        raises=True,
+        self, ix_pattern, pat_name, pat_line_width, *, verbose=False, as_dict=True, raises=True
     ):
         """
 
@@ -4205,7 +4896,7 @@ def shape_pattern_manage(
         Notes
         -----
         Command syntax:
-          python shape_pattern_manage {ix_pattern}^^{pat_name}^^{pat_line_width}
+          pipe shape_pattern_manage {ix_pattern}^^{pat_name}^^{pat_line_width}
 
         Where:
           {ix_pattern}      -- Pattern index. Patterns with higher indexes will be moved up
@@ -4217,14 +4908,14 @@ def shape_pattern_manage(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_shape
          args:
            ix_pattern : 1
            pat_name : new_pat
            pat_line_width : 1
 
         """
-        cmd = f"python shape_pattern_manage {ix_pattern}^^{pat_name}^^{pat_line_width}"
+        cmd = f"pipe shape_pattern_manage {ix_pattern}^^{pat_name}^^{pat_line_width}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4252,7 +4943,7 @@ def shape_pattern_point_manage(
         Notes
         -----
         Command syntax:
-          python shape_pattern_point_manage {ix_pattern}^^{ix_point}^^{s}^^{x}
+          pipe shape_pattern_point_manage {ix_pattern}^^{ix_point}^^{s}^^{x}
 
         Where:
           {ix_pattern}      -- Pattern index.
@@ -4263,7 +4954,7 @@ def shape_pattern_point_manage(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_shape
          args:
            ix_pattern: 1
            ix_point: 1
@@ -4271,15 +4962,11 @@ def shape_pattern_point_manage(
            x: 0
 
         """
-        cmd = f"python shape_pattern_point_manage {ix_pattern}^^{ix_point}^^{s}^^{x}"
+        cmd = f"pipe shape_pattern_point_manage {ix_pattern}^^{ix_point}^^{s}^^{x}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd,
-            as_dict,
-            raises,
-            method_name="shape_pattern_point_manage",
-            cmd_type="None",
+            cmd, as_dict, raises, method_name="shape_pattern_point_manage", cmd_type="None"
         )
 
     def shape_set(
@@ -4323,7 +5010,7 @@ def shape_set(
         Notes
         -----
         Command syntax:
-          python shape_set {who}^^{shape_index}^^{ele_name}^^{shape}^^{color}^^
+          pipe shape_set {who}^^{shape_index}^^{ele_name}^^{shape}^^{color}^^
                            {shape_size}^^{type_label}^^{shape_draw}^^
                            {multi_shape}^^{line_width}
 
@@ -4334,7 +5021,7 @@ def shape_set(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            who: floor_plan
            shape_index: 1
@@ -4348,7 +5035,7 @@ def shape_set(
            line_width:
 
         """
-        cmd = f"python shape_set {who}^^{shape_index}^^{ele_name}^^{shape}^^{color}^^{shape_size}^^{type_label}^^{shape_draw}^^{multi_shape}^^{line_width}"
+        cmd = f"pipe shape_set {who}^^{shape_index}^^{ele_name}^^{shape}^^{color}^^{shape_size}^^{type_label}^^{shape_draw}^^{multi_shape}^^{line_width}"
         if verbose:
             print(cmd)
         return self.__execute(cmd, as_dict, raises, method_name="shape_set", cmd_type="None")
@@ -4371,21 +5058,21 @@ def show(self, line, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python show {line}
+          pipe show {line}
 
         {line} is the string to pass through to the show command.
         Example:
-          python show lattice -python
+          pipe show lattice -pipe
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
-           line: -python
+           line: -pipe
 
         """
-        cmd = f"python show {line}"
+        cmd = f"pipe show {line}"
         if verbose:
             print(cmd)
         return self.__execute(cmd, as_dict, raises, method_name="show", cmd_type="string_list")
@@ -4402,18 +5089,18 @@ def space_charge_com(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python space_charge_com
+          pipe space_charge_com
 
         Output syntax is parameter list form. See documentation at the beginning of this file.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python space_charge_com"
+        cmd = "pipe space_charge_com"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4436,20 +5123,20 @@ def species_to_int(self, species_str, *, verbose=False, as_dict=True, raises=Tru
         Notes
         -----
         Command syntax:
-          python species_to_int {species_str}
+          pipe species_to_int {species_str}
 
         Example:
-          python species_to_int CO2++
+          pipe species_to_int CO2++
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            species_str: electron
 
         """
-        cmd = f"python species_to_int {species_str}"
+        cmd = f"pipe species_to_int {species_str}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4472,20 +5159,20 @@ def species_to_str(self, species_int, *, verbose=False, as_dict=True, raises=Tru
         Notes
         -----
         Command syntax:
-          python species_to_str {species_int}
+          pipe species_to_str {species_int}
 
         Example:
-          python species_to_str -1     ! Returns 'Electron'
+          pipe species_to_str -1     ! Returns 'Electron'
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            species_int: -1
 
         """
-        cmd = f"python species_to_str {species_int}"
+        cmd = f"pipe species_to_str {species_int}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4530,7 +5217,7 @@ def spin_invariant(
         Notes
         -----
         Command syntax:
-          python spin_invariant {flags} {who} {ix_uni}@{ix_branch}|{which}
+          pipe spin_invariant {flags} {who} {ix_uni}@{ix_branch}|{which}
 
         Where:
           {flags} are optional switches:
@@ -4544,14 +5231,14 @@ def spin_invariant(
             design
 
         Example:
-          python spin_invariant 1@0|model
+          pipe spin_invariant 1@0|model
 
         Note: This command is under development. If you want to use please contact David Sagan.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            who: l0
            ix_uni: 1
@@ -4559,24 +5246,16 @@ def spin_invariant(
            which: model
 
         """
-        cmd = f"python spin_invariant {flags} {who} {ix_uni}@{ix_branch}|{which}"
+        cmd = f"pipe spin_invariant {flags} {who} {ix_uni}@{ix_branch}|{which}"
         if verbose:
             print(cmd)
         if "-array_out" not in flags:
             return self.__execute(
-                cmd,
-                as_dict,
-                raises,
-                method_name="spin_invariant",
-                cmd_type="string_list",
+                cmd, as_dict, raises, method_name="spin_invariant", cmd_type="string_list"
             )
         if "-array_out" in flags:
             return self.__execute(
-                cmd,
-                as_dict,
-                raises,
-                method_name="spin_invariant",
-                cmd_type="real_array",
+                cmd, as_dict, raises, method_name="spin_invariant", cmd_type="real_array"
             )
 
     def spin_polarization(
@@ -4606,7 +5285,7 @@ def spin_polarization(
         Notes
         -----
         Command syntax:
-          python spin_polarization {ix_uni}@{ix_branch}|{which}
+          pipe spin_polarization {ix_uni}@{ix_branch}|{which}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -4617,29 +5296,25 @@ def spin_polarization(
             design
 
         Example:
-          python spin_polarization 1@0|model
+          pipe spin_polarization 1@0|model
 
         Note: This command is under development. If you want to use please contact David Sagan.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_uni: 1
            ix_branch: 0
            which: model
 
         """
-        cmd = f"python spin_polarization {ix_uni}@{ix_branch}|{which}"
+        cmd = f"pipe spin_polarization {ix_uni}@{ix_branch}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
-            cmd,
-            as_dict,
-            raises,
-            method_name="spin_polarization",
-            cmd_type="string_list",
+            cmd, as_dict, raises, method_name="spin_polarization", cmd_type="string_list"
         )
 
     def spin_resonance(
@@ -4672,7 +5347,7 @@ def spin_resonance(
         Notes
         -----
         Command syntax:
-          python spin_resonance {ix_uni}@{ix_branch}|{which} {ref_ele}
+          pipe spin_resonance {ix_uni}@{ix_branch}|{which} {ref_ele}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -4687,14 +5362,14 @@ def spin_resonance(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_uni: 1
            ix_branch: 0
            which: model
 
         """
-        cmd = f"python spin_resonance {ix_uni}@{ix_branch}|{which} {ref_ele}"
+        cmd = f"pipe spin_resonance {ix_uni}@{ix_branch}|{which} {ref_ele}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4713,16 +5388,16 @@ def super_universe(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python super_universe
+          pipe super_universe
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python super_universe"
+        cmd = "pipe super_universe"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4751,7 +5426,7 @@ def taylor_map(
         Notes
         -----
         Command syntax:
-          python taylor_map {ele1_id} {ele2_id} {order}
+          pipe taylor_map {ele1_id} {ele2_id} {order}
 
         Where:
           {ele1_id} is the start element.
@@ -4761,19 +5436,19 @@ def taylor_map(
         If {ele2_id} = {ele1_id}, the 1-turn transfer map is computed.
         Note: {ele2_id} should just be an element name or index without universe, branch, or model/base/design specification.
         Example:
-          python taylor_map 2@1>>q01w|design q02w  2
+          pipe taylor_map 2@1>>q01w|design q02w  2
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ele1_id: 1@0>>q01w|design
            ele2_id: q02w
            order: 1
 
         """
-        cmd = f"python taylor_map {ele1_id} {ele2_id} {order}"
+        cmd = f"pipe taylor_map {ele1_id} {ele2_id} {order}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4809,7 +5484,7 @@ def twiss_at_s(
         Notes
         -----
         Command syntax:
-          python twiss_at_s {ix_uni}@{ele}->{s_offset}|{which}
+          pipe twiss_at_s {ix_uni}@{ele}->{s_offset}|{which}
 
         Where:
           {ix_uni} is a universe index. Defaults to s%global%default_universe.
@@ -4821,7 +5496,7 @@ def twiss_at_s(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_uni: 1
            ele: 10
@@ -4829,7 +5504,7 @@ def twiss_at_s(
            which: model
 
         """
-        cmd = f"python twiss_at_s {ix_uni}@{ele}->{s_offset}|{which}"
+        cmd = f"pipe twiss_at_s {ix_uni}@{ele}->{s_offset}|{which}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4852,19 +5527,19 @@ def universe(self, ix_uni, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python universe {ix_uni}
+          pipe universe {ix_uni}
 
-        Use "python global" to get the number of universes.
+        Use "pipe global" to get the number of universes.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            ix_uni: 1
 
         """
-        cmd = f"python universe {ix_uni}"
+        cmd = f"pipe universe {ix_uni}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -4890,18 +5565,20 @@ def var(self, var, *, slaves="", verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python var {var} slaves
+          pipe var {var} {slaves}
+
+        Note: use "pipe var_general" to get a list of variables.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            var: quad[1]
            slaves:
 
         Example: 2
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            var: quad[1]
            slaves: slaves
@@ -4957,18 +5634,18 @@ def var_create(
         Notes
         -----
         Command syntax:
-          python var_create {var_name}^^{ele_name}^^{attribute}^^{universes}^^
+          pipe var_create {var_name}^^{ele_name}^^{attribute}^^{universes}^^
                             {weight}^^{step}^^{low_lim}^^{high_lim}^^{merit_type}^^
                             {good_user}^^{key_bound}^^{key_delta}
 
         {var_name} is something like "kick[5]".
         Before using var_create, setup the appropriate v1_var array using
-        the "python var_v1_create" command.
+        the "pipe var_v1_create" command.
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching
          args:
            var_name: quad[1]
            ele_name: Q1
@@ -4984,7 +5661,7 @@ def var_create(
            key_delta: 0.01
 
         """
-        cmd = f"python var_create {var_name}^^{ele_name}^^{attribute}^^{universes}^^{weight}^^{step}^^{low_lim}^^{high_lim}^^{merit_type}^^{good_user}^^{key_bound}^^{key_delta}"
+        cmd = f"pipe var_create {var_name}^^{ele_name}^^{attribute}^^{universes}^^{weight}^^{step}^^{low_lim}^^{high_lim}^^{merit_type}^^{good_user}^^{key_bound}^^{key_delta}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -5003,7 +5680,7 @@ def var_general(self, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python var_general
+          pipe var_general
 
         Output syntax:
           {v1_var name};{v1_var%v lower bound};{v1_var%v upper bound}
@@ -5011,11 +5688,11 @@ def var_general(self, *, verbose=False, as_dict=True, raises=True):
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
 
         """
-        cmd = "python var_general"
+        cmd = "pipe var_general"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -5038,20 +5715,20 @@ def var_v_array(self, v1_var, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python var_v_array {v1_var}
+          pipe var_v_array {v1_var}
 
         Example:
-          python var_v_array quad_k1
+          pipe var_v_array quad_k1
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            v1_var: quad_k1
 
         """
-        cmd = f"python var_v_array {v1_var}"
+        cmd = f"pipe var_v_array {v1_var}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -5074,17 +5751,17 @@ def var_v1_array(self, v1_var, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python var_v1_array {v1_var}
+          pipe var_v1_array {v1_var}
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            v1_var: quad_k1
 
         """
-        cmd = f"python var_v1_array {v1_var}"
+        cmd = f"pipe var_v1_array {v1_var}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -5111,11 +5788,11 @@ def var_v1_create(
         Notes
         -----
         Command syntax:
-          python var_v1_create {v1_name} {n_var_min} {n_var_max}
+          pipe var_v1_create {v1_name} {n_var_min} {n_var_max}
 
         {n_var_min} and {n_var_max} are the lower and upper bounds of the var
         Example:
-          python var_v1_create quad_k1 0 45
+          pipe var_v1_create quad_k1 0 45
         This example creates a v1 var structure called "quad_k1" with an associated
         variable array that has the range [0, 45].
 
@@ -5134,14 +5811,14 @@ def var_v1_create(
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            v1_name: quad_k1
            n_var_min: 0
            n_var_max: 45
 
         """
-        cmd = f"python var_v1_create {v1_name} {n_var_min} {n_var_max}"
+        cmd = f"pipe var_v1_create {v1_name} {n_var_min} {n_var_max}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -5164,17 +5841,17 @@ def var_v1_destroy(self, v1_datum, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python var_v1_destroy {v1_datum}
+          pipe var_v1_destroy {v1_datum}
 
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            v1_datum: quad_k1
 
         """
-        cmd = f"python var_v1_destroy {v1_datum}"
+        cmd = f"pipe var_v1_destroy {v1_datum}"
         if verbose:
             print(cmd)
         return self.__execute(
@@ -5197,7 +5874,7 @@ def wave(self, who, *, verbose=False, as_dict=True, raises=True):
         Notes
         -----
         Command syntax:
-          python wave {who}
+          pipe wave {who}
 
         Where {who} is one of:
           params
@@ -5208,12 +5885,12 @@ def wave(self, who, *, verbose=False, as_dict=True, raises=True):
         Examples
         --------
         Example: 1
-         init: -init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init
+         init: -init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init
          args:
            who: params
 
         """
-        cmd = f"python wave {who}"
+        cmd = f"pipe wave {who}"
         if verbose:
             print(cmd)
         return self.__execute(cmd, as_dict, raises, method_name="wave", cmd_type="string_list")
diff --git a/pytao/plotting/__init__.py b/pytao/plotting/__init__.py
new file mode 100644
index 00000000..91f4a15e
--- /dev/null
+++ b/pytao/plotting/__init__.py
@@ -0,0 +1,23 @@
+from .curves import TaoCurveSettings
+from .mpl import MatplotlibGraphManager
+from .plot import GraphManager
+from .settings import (
+    QuickPlotPoint,
+    QuickPlotRectangle,
+    TaoAxisSettings,
+    TaoFloorPlanSettings,
+    TaoGraphSettings,
+)
+from .util import select_graph_manager_class
+
+__all__ = [
+    "GraphManager",
+    "MatplotlibGraphManager",
+    "QuickPlotPoint",
+    "QuickPlotRectangle",
+    "TaoAxisSettings",
+    "TaoCurveSettings",
+    "TaoFloorPlanSettings",
+    "TaoGraphSettings",
+    "select_graph_manager_class",
+]
diff --git a/pytao/plotting/bokeh.py b/pytao/plotting/bokeh.py
new file mode 100644
index 00000000..e064d7e5
--- /dev/null
+++ b/pytao/plotting/bokeh.py
@@ -0,0 +1,2121 @@
+from __future__ import annotations
+
+import functools
+import logging
+import math
+import os
+import pathlib
+import time
+import typing
+from abc import ABC, abstractmethod
+from typing import (
+    ClassVar,
+    Dict,
+    Generic,
+    List,
+    NamedTuple,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
+
+import bokeh.colors.named
+import bokeh.embed
+import bokeh.events
+import bokeh.io
+import bokeh.layouts
+import bokeh.models
+import bokeh.plotting
+
+# TODO remove mpl dep - only used in a single spot
+import matplotlib
+import matplotlib.patches
+import numpy as np
+from bokeh.core.enums import SizingModeType
+from bokeh.document.callbacks import EventCallback
+from bokeh.models.sources import ColumnDataSource
+from bokeh.plotting import figure
+from pydantic.dataclasses import dataclass
+from typing_extensions import NotRequired, TypedDict
+
+from ..interface_commands import AnyPath
+from . import floor_plan_shapes, pgplot, util
+from .curves import CurveIndexToCurve, PlotCurveLine, PlotCurveSymbols, TaoCurveSettings
+from .fields import ElementField
+from .layout_shapes import LayoutShape
+from .patches import (
+    PlotPatch,
+    PlotPatchArc,
+    PlotPatchCircle,
+    PlotPatchEllipse,
+    PlotPatchPolygon,
+    PlotPatchRectangle,
+    PlotPatchSbend,
+)
+from .plot import (
+    AnyGraph,
+    BasicGraph,
+    FloorPlanElement,
+    FloorPlanGraph,
+    GraphBase,
+    GraphManager,
+    LatticeLayoutElement,
+    LatticeLayoutGraph,
+    PlotAnnotation,
+    PlotCurve,
+    UnsupportedGraphError,
+)
+from .settings import TaoGraphSettings
+from .types import FloatVariableInfo
+from .util import Limit, OptionalLimit, fix_grid_limits
+
+if typing.TYPE_CHECKING:
+    from .. import Tao
+
+
+logger = logging.getLogger(__name__)
+
+
+def bokeh_color(color):
+    color = color.lower().replace("_", "")
+    return getattr(bokeh.colors.named, color, "black")
+
+
+class CurveData(TypedDict):
+    line: NotRequired[ColumnDataSource]
+    symbol: NotRequired[ColumnDataSource]
+
+
+class _Defaults:
+    """
+    Defaults used for Bokeh plots internally.
+
+    To change these values, use `set_defaults`.
+    """
+
+    width: int = 400
+    height: int = 400
+    stacked_height: int = 200
+    layout_height: int = 100
+    show_bokeh_logo: bool = False
+    palette: str = "Magma256"
+    tools: str = "pan,wheel_zoom,box_zoom,reset,hover,crosshair"
+    grid_toolbar_location: str = "right"
+    lattice_layout_tools: str = "pan,wheel_zoom,box_zoom,reset,hover,crosshair"
+    floor_plan_tools: str = "pan,wheel_zoom,box_zoom,reset,hover,crosshair"
+    layout_font_size: str = "0.75em"
+    floor_plan_font_size: str = "0.75em"
+    limit_scale_factor: float = 1.01
+
+    @classmethod
+    def get_size_for_class(
+        cls,
+        typ: Type[AnyBokehGraph],
+        user_width: Optional[int] = None,
+        user_height: Optional[int] = None,
+    ) -> Tuple[int, int]:
+        default = {
+            BokehBasicGraph: (cls.width, cls.height),
+            BokehLatticeLayoutGraph: (cls.width, cls.layout_height),
+            BokehFloorPlanGraph: (cls.width, cls.height),
+        }[typ]
+        return (user_width or default[0], user_height or default[1])
+
+
+def set_defaults(
+    width: Optional[int] = None,
+    height: Optional[int] = None,
+    stacked_height: Optional[int] = None,
+    layout_height: Optional[int] = None,
+    palette: Optional[str] = None,
+    show_bokeh_logo: Optional[bool] = None,
+    tools: Optional[str] = None,
+    grid_toolbar_location: Optional[str] = None,
+    lattice_layout_tools: Optional[str] = None,
+    floor_plan_tools: Optional[str] = None,
+    layout_font_size: Optional[str] = None,
+    floor_plan_font_size: Optional[str] = None,
+    limit_scale_factor: Optional[float] = None,
+):
+    """Change defaults used for Bokeh plots."""
+    if width is not None:
+        _Defaults.width = width
+    if height is not None:
+        _Defaults.height = height
+    if stacked_height is not None:
+        _Defaults.stacked_height = stacked_height
+    if layout_height is not None:
+        _Defaults.layout_height = layout_height
+    if palette is not None:
+        _Defaults.palette = palette
+    if show_bokeh_logo is not None:
+        _Defaults.show_bokeh_logo = show_bokeh_logo
+    if tools is not None:
+        _Defaults.tools = tools
+    if grid_toolbar_location is not None:
+        _Defaults.grid_toolbar_location = grid_toolbar_location
+    if lattice_layout_tools is not None:
+        _Defaults.lattice_layout_tools = lattice_layout_tools
+    if floor_plan_tools is not None:
+        _Defaults.floor_plan_tools = floor_plan_tools
+    if layout_font_size is not None:
+        _Defaults.layout_font_size = layout_font_size
+    if floor_plan_font_size is not None:
+        _Defaults.floor_plan_font_size = floor_plan_font_size
+    if limit_scale_factor is not None:
+        _Defaults.limit_scale_factor = limit_scale_factor
+
+
+def _get_curve_data(curve: PlotCurve) -> CurveData:
+    data: CurveData = {}
+    if curve.line is not None:
+        data["line"] = ColumnDataSource(
+            data={
+                "x": curve.line.xs,
+                "y": curve.line.ys,
+            }
+        )
+    if curve.symbol is not None:
+        data["symbol"] = ColumnDataSource(
+            data={
+                "x": curve.symbol.xs,
+                "y": curve.symbol.ys,
+            }
+        )
+    return data
+
+
+def _get_graph_data(graph) -> List[CurveData]:
+    return [_get_curve_data(curve) for curve in graph.curves]
+
+
+def share_x_axes(figs: List[figure]):
+    if not figs:
+        return
+    fig0, *others = figs
+    for other in others:
+        other.x_range = fig0.x_range
+
+
+class BGraphAndFigure(NamedTuple):
+    bgraph: AnyBokehGraph
+    fig: figure
+
+
+T_Tool = TypeVar("T_Tool", bound=bokeh.models.Tool)
+
+
+def get_tool_from_figure(fig: figure, tool_cls: Type[T_Tool]) -> Optional[T_Tool]:
+    tools = [tool for tool in fig.tools if isinstance(tool, tool_cls)]
+    return tools[0] if tools else None
+
+
+def link_crosshairs(figs: List[figure]):
+    first, *rest = figs
+    crosshair = get_tool_from_figure(first, bokeh.models.CrosshairTool)
+    if crosshair is None:
+        return
+
+    if crosshair.overlay == "auto":
+        crosshair.overlay = (
+            bokeh.models.Span(dimension="width", line_dash="dotted", line_width=1),
+            bokeh.models.Span(dimension="height", line_dash="dotted", line_width=1),
+        )
+
+    for fig in rest:
+        other_crosshair = get_tool_from_figure(fig, bokeh.models.CrosshairTool)
+        if other_crosshair:
+            other_crosshair.overlay = crosshair.overlay
+
+
+def share_common_x_axes(
+    pairs: List[BGraphAndFigure],
+    crosshairs: bool = True,
+) -> List[List[BGraphAndFigure]]:
+    res: List[List[BGraphAndFigure]] = []
+
+    s_plots = []
+    for pair in pairs:
+        if pair.bgraph.graph.is_s_plot:
+            s_plots.append(pair)
+
+    if s_plots:
+        res.append(s_plots)
+
+    by_xlabel: Dict[str, List[BGraphAndFigure]] = {}
+    for pair in pairs:
+        if pair in s_plots:
+            continue
+        by_xlabel.setdefault(pair.bgraph.graph.xlabel, []).append(pair)
+
+    for sharing_set in by_xlabel.values():
+        if len(sharing_set) > 1:
+            res.append(sharing_set)
+
+    for sharing_set in res:
+        figs = [pair.fig for pair in sharing_set]
+        share_x_axes(figs)
+        if crosshairs:
+            link_crosshairs(figs)
+
+    return res
+
+
+def _plot_curve_symbols(
+    fig: figure,
+    symbol: PlotCurveSymbols,
+    name: str,
+    source: Optional[ColumnDataSource] = None,
+    legend_label: Optional[str] = None,
+):
+    marker = pgplot.bokeh_symbols.get(symbol.marker, "dot")
+    if not marker:
+        return
+
+    if source is None:
+        source = ColumnDataSource(data={})
+
+    source.data.update(
+        {
+            "x": symbol.xs,
+            "y": symbol.ys,
+        }
+    )
+
+    if legend_label is not None:
+        # Can't pass legend_label unless it's set to non-None
+        kw = {"legend_label": legend_label}
+    else:
+        kw = {}
+    return fig.scatter(
+        "x",
+        "y",
+        source=source,
+        fill_color=bokeh_color(symbol.color),
+        marker=marker,
+        size=symbol.markersize * 4 if marker == "dot" else symbol.markersize,
+        name=name,
+        **kw,
+    )
+
+
+def _plot_curve_line(
+    fig: figure,
+    line: PlotCurveLine,
+    name: Optional[str] = None,
+    source: Optional[ColumnDataSource] = None,
+):
+    if source is None:
+        source = ColumnDataSource(data={})
+
+    source.data.update({"x": line.xs, "y": line.ys})
+    if name is not None:
+        # Can't pass legend_label unless it's set to non-None
+        kw = {"legend_label": name}
+    else:
+        kw = {}
+    return fig.line(
+        "x",
+        "y",
+        line_width=line.linewidth,
+        source=source,
+        color=bokeh_color(line.color),
+        name=name,
+        **kw,
+    )
+
+
+def _plot_curve(fig: figure, curve: PlotCurve, source: CurveData) -> None:
+    name = pgplot.mathjax_string(curve.info["name"])
+    if "line" in source and curve.line is not None:
+        _plot_curve_line(fig=fig, line=curve.line, name=name, source=source["line"])
+
+    if "symbol" in source and curve.symbol is not None:
+        legend = None if "line" in source else name
+        _plot_curve_symbols(
+            fig=fig,
+            symbol=curve.symbol,
+            source=source["symbol"],
+            name=name,
+            legend_label=legend,
+        )
+
+
+def _plot_patch_arc(
+    fig: figure,
+    patch: PlotPatchArc,
+    source: Optional[ColumnDataSource] = None,
+    linewidth: Optional[float] = None,
+):
+    if source is None:
+        source = ColumnDataSource(data={})
+    if not np.isclose(patch.width, patch.height):
+        logger.warning(
+            "Arcs only support circular arcs for now (w=%f h=%f)",
+            patch.width,
+            patch.height,
+        )
+
+    source.data.update(
+        {
+            "x": [patch.xy[0]],
+            "y": [patch.xy[1]],
+            "radius": [patch.width / 2],
+            "start_angle": [math.radians(patch.theta1)],
+            "end_angle": [math.radians(patch.theta2)],
+            # NOTE: debugging with aspect ratios...
+            # "x": [0],
+            # "y": [0],
+            # "radius": [1],  # patch.width / 2],
+            # "start_angle": [0],
+            # "end_angle": [math.radians(345)],
+        }
+    )
+    return fig.arc(
+        x="x",
+        y="y",
+        radius="radius",
+        start_angle="start_angle",
+        end_angle="end_angle",
+        line_width=linewidth if linewidth is not None else patch.linewidth,
+        source=source,
+    )
+
+
+def _plot_sbend_patch(fig: figure, patch: PlotPatchSbend):
+    ((s1x0, s1y0), (s1cx0, s1cy0), (s1x1, s1y1)) = patch.spline1
+    ((s2x0, s2y0), (s2cx0, s2cy0), (s2x1, s2y1)) = patch.spline2
+    fig.bezier(x0=s1x0, y0=s1y0, cx0=s1cx0, cy0=s1cy0, x1=s1x1, y1=s1y1)
+    fig.line(x=[s1x1, s2x0], y=[s1y1, s2y0])
+
+    fig.bezier(x0=s2x0, y0=s2y0, cx0=s2cx0, cy0=s2cy0, x1=s2x1, y1=s2y1)
+    fig.line(x=[s2x1, s1x0], y=[s2y1, s1y0])
+
+
+def _draw_layout_elems(
+    fig: figure,
+    elems: List[LatticeLayoutElement],
+    skip_labels: bool = True,
+):
+    line_data = {
+        "xs": [],
+        "ys": [],
+        "name": [],
+        "s_start": [],
+        "s_end": [],
+        "line_width": [],
+        "color": [],
+    }
+    rectangles: List[Tuple[LatticeLayoutElement, LayoutShape, PlotPatchRectangle]] = []
+
+    _draw_annotations(
+        fig,
+        {elem.name: elem.annotations for elem in elems},
+        font_size=_Defaults.layout_font_size,
+        skip_labels=skip_labels,
+    )
+
+    for elem in elems:
+        color = bokeh_color(elem.color)
+        shape = elem.shape
+        if not shape:
+            continue
+
+        lines = shape.to_lines()
+        line_data["xs"].extend([line.xs for line in lines])
+        line_data["ys"].extend([line.ys for line in lines])
+        line_data["name"].extend([elem.name] * len(lines))
+        line_data["s_start"].extend([elem.info["ele_s_start"]] * len(lines))
+        line_data["s_end"].extend([elem.info["ele_s_end"]] * len(lines))
+        line_data["line_width"].extend([shape.line_width] * len(lines))
+        line_data["color"].extend([color] * len(lines))
+
+        if isinstance(shape, LayoutShape):
+            for patch in shape.to_patches():
+                if isinstance(patch, PlotPatchRectangle):
+                    rectangles.append((elem, shape, patch))
+                else:
+                    _plot_patch(fig, patch, line_width=shape.line_width)
+
+    if rectangles:
+        source = ColumnDataSource(
+            data={
+                "xs": [[[_patch_rect_to_points(patch)[0]]] for _, _, patch in rectangles],
+                "ys": [[[_patch_rect_to_points(patch)[1]]] for _, _, patch in rectangles],
+                "name": [shape.name for _, shape, _ in rectangles],
+                "color": [bokeh_color(shape.color) for _, shape, _ in rectangles],
+                "line_width": [shape.line_width for _, shape, _ in rectangles],
+                "s_start": [elem.info["ele_s_start"] for elem, _, _ in rectangles],
+                "s_end": [elem.info["ele_s_end"] for elem, _, _ in rectangles],
+            }
+        )
+        fig.multi_polygons(
+            xs="xs",
+            ys="ys",
+            color="color",
+            line_width="line_width",
+            source=source,
+            fill_alpha=0.0,
+        )
+
+    if line_data:
+        fig.multi_line(
+            xs="xs",
+            ys="ys",
+            color="color",
+            line_width="line_width",
+            source=ColumnDataSource(data=line_data),
+        )
+
+
+def _draw_annotations(
+    fig: figure,
+    name_to_annotations: Dict[str, List[PlotAnnotation]],
+    *,
+    font_size: str,
+    skip_labels: bool = False,
+):
+    data = {
+        "x": [],
+        "y": [],
+        "name": [],
+        "text": [],
+        "color": [],
+        "baseline": [],
+        "align": [],
+        "rotation": [],
+        "font_size": [],
+    }
+
+    for name, annotations_ in name_to_annotations.items():
+        for annotation in annotations_:
+            if annotation.text == name and skip_labels:
+                # We skip labels here as they work better as X tick labels
+                continue
+
+            baseline = annotation.verticalalignment
+            if baseline == "center":
+                baseline = "middle"
+            data["x"].append(annotation.x)
+            data["y"].append(annotation.y)
+            data["text"].append(pgplot.mathjax_string(annotation.text))
+            data["name"].append(name)
+            data["rotation"].append(math.radians(annotation.rotation))
+            data["align"].append(annotation.horizontalalignment)
+            data["baseline"].append(baseline)
+            data["color"].append(bokeh_color(annotation.color))
+            data["font_size"].append(font_size)
+
+    return fig.text(
+        "x",
+        "y",
+        angle="rotation",
+        text_align="align",
+        text_baseline="baseline",
+        text_font_size="font_size",
+        color="color",
+        source=ColumnDataSource(data=data),
+    )
+
+
+def _draw_floor_plan_shapes(
+    fig: figure,
+    elems: List[FloorPlanElement],
+):
+    polygon_data = {
+        "xs": [],
+        "ys": [],
+        "name": [],
+        "line_width": [],
+        "color": [],
+    }
+    line_data = {
+        "xs": [],
+        "ys": [],
+        "name": [],
+        "line_width": [],
+        "color": [],
+    }
+    for elem in elems:
+        shape = elem.shape
+        if not shape:
+            continue
+
+        if isinstance(
+            shape,
+            (
+                floor_plan_shapes.Box,
+                floor_plan_shapes.XBox,
+                floor_plan_shapes.BowTie,
+                floor_plan_shapes.Diamond,
+            ),
+        ):
+            vx, vy = shape.vertices
+            polygon_data["xs"].append([[vx]])
+            polygon_data["ys"].append([[vy]])
+            polygon_data["name"].append(shape.name)
+            polygon_data["line_width"].append(shape.line_width)
+            polygon_data["color"].append(bokeh_color(shape.color))
+        else:
+            for patch in shape.to_patches():
+                assert not isinstance(patch, (PlotPatchRectangle, PlotPatchPolygon))
+                _plot_patch(fig, patch, line_width=shape.line_width)
+
+            lines = shape.to_lines()
+            if lines:
+                line_data["xs"].extend([line.xs for line in lines])
+                line_data["ys"].extend([line.ys for line in lines])
+                line_data["name"].extend([shape.name] * len(lines))
+                line_data["line_width"].extend([line.linewidth for line in lines])
+                line_data["color"].extend([bokeh_color(line.color) for line in lines])
+
+    if line_data["xs"]:
+        fig.multi_line(
+            xs="xs",
+            ys="ys",
+            color="color",
+            line_width="line_width",
+            source=ColumnDataSource(data=line_data),
+        )
+
+    if polygon_data["xs"]:
+        fig.multi_polygons(
+            xs="xs",
+            ys="ys",
+            color="color",
+            line_width="line_width",
+            source=ColumnDataSource(data=polygon_data),
+            fill_alpha=0.0,
+        )
+
+
+def _patch_rect_to_points(patch: PlotPatchRectangle) -> Tuple[List[float], List[float]]:
+    mpl_patch = matplotlib.patches.Rectangle(
+        xy=patch.xy,
+        width=patch.width,
+        height=patch.height,
+        angle=patch.angle,
+        rotation_point=patch.rotation_point,
+        **patch._patch_args,
+    )
+
+    points = mpl_patch.get_corners()
+    return (
+        points[:, 0].tolist() + [points[0, 0]],
+        points[:, 1].tolist() + [points[0, 1]],
+    )
+
+
+def _draw_limit_border(
+    fig: figure,
+    xlim: Tuple[float, float],
+    ylim: Tuple[float, float],
+    alpha: float = 1.0,
+):
+    width = xlim[1] - xlim[0]
+    height = ylim[1] - ylim[0]
+    rect = PlotPatchRectangle(xy=(xlim[0], ylim[0]), width=width, height=height, alpha=alpha)
+    px, py = _patch_rect_to_points(rect)
+
+    return fig.line(px, py, alpha=alpha)
+
+
+def _plot_patch(
+    fig: figure,
+    patch: PlotPatch,
+    line_width: Optional[float] = None,
+    source: Optional[ColumnDataSource] = None,
+):
+    if source is None:
+        source = ColumnDataSource()
+
+    line_width = line_width if line_width is not None else patch.linewidth
+    if isinstance(patch, PlotPatchRectangle):
+        cx, cy = patch.center
+        source.data["x"] = [cx]
+        source.data["y"] = [cy]
+        source.data["width"] = [patch.width]
+        source.data["height"] = [patch.height]
+        return fig.rect(
+            x="x",
+            y="y",
+            width="width",
+            height="height",
+            angle=math.radians(patch.angle),
+            fill_alpha=0.0,
+            line_alpha=1.0,
+            line_color=bokeh_color(patch.color),
+            line_width=line_width,
+            source=source,
+        )
+    elif isinstance(patch, PlotPatchPolygon):
+        source.data["xs"] = [p[0] for p in patch.vertices + patch.vertices[:1]]
+        source.data["ys"] = [p[1] for p in patch.vertices + patch.vertices[:1]]
+        return fig.line(
+            x="xs",
+            y="ys",
+            line_width=line_width,
+            color=bokeh_color(patch.color),
+            source=source,
+        )
+    if isinstance(patch, PlotPatchArc):
+        return _plot_patch_arc(fig, patch, source=source, linewidth=line_width)
+
+    if isinstance(patch, PlotPatchCircle):
+        source.data["x"], source.data["y"] = [patch.xy[0]], [patch.xy[1]]
+        source.data["radii"] = [patch.radius]
+        return fig.circle(
+            x="x",
+            y="y",
+            radius="radii",
+            line_width=line_width,
+            fill_alpha=int(patch.fill),
+            source=source,
+        )
+    if isinstance(patch, PlotPatchEllipse):
+        source.data["x"], source.data["y"] = [patch.xy[0]], [patch.xy[1]]
+        source.data["width"] = [patch.width]
+        source.data["height"] = [patch.height]
+        source.data["angle"] = [math.radians(patch.angle)]
+        return fig.ellipse(
+            x="x",
+            y="y",
+            width="width",
+            height="height",
+            angle="angle",
+            line_width=line_width,
+            fill_alpha=int(patch.fill),
+            source=source,
+        )
+    if isinstance(patch, PlotPatchSbend):
+        return _plot_sbend_patch(fig, patch)
+    raise NotImplementedError(f"{type(patch).__name__}")
+
+
+def _fields_to_data_source(fields: List[ElementField], x_scale: float = 1.0):
+    return ColumnDataSource(
+        data={
+            "ele_id": [field.ele_id for field in fields],
+            "by": [np.asarray(field.by).T for field in fields],
+            "x": [np.min(field.s) for field in fields],
+            "dw": [np.max(field.s) - np.min(field.s) for field in fields],
+            "dh": [15.0 for _ in fields],
+        }
+    )
+
+
+TGraph = TypeVar("TGraph", bound=GraphBase)
+
+
+class BokehGraphBase(ABC, Generic[TGraph]):
+    manager: GraphManager
+    graph: TGraph
+    sizing_mode: SizingModeType
+    width: Optional[int]
+    height: Optional[int]
+    aspect_ratio: Optional[float]
+    x_range: Optional[bokeh.models.Range]
+    y_range: Optional[bokeh.models.Range]
+
+    def __init__(
+        self,
+        manager: GraphManager,
+        graph: TGraph,
+        sizing_mode: SizingModeType,
+        aspect_ratio: Optional[float] = None,  # w/h
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        x_range: Optional[bokeh.models.Range] = None,
+        y_range: Optional[bokeh.models.Range] = None,
+        limit_scale_factor: Optional[float] = None,
+    ) -> None:
+        self.graph = graph
+        self.manager = manager
+        self.sizing_mode = sizing_mode
+        self.width = width
+        self.height = height
+        self.aspect_ratio = aspect_ratio
+
+        limit_scale_factor = limit_scale_factor or _Defaults.limit_scale_factor
+        self.x_range = x_range or bokeh.models.Range1d(
+            *util.apply_factor_to_limits(*graph.xlim, limit_scale_factor)
+        )
+        self.y_range = y_range or bokeh.models.Range1d(
+            *util.apply_factor_to_limits(*graph.ylim, limit_scale_factor)
+        )
+
+    def create_widgets(self, fig: figure) -> List[bokeh.models.UIElement]:
+        return []
+
+    @abstractmethod
+    def create_figure(
+        self,
+        *,
+        tools: str = "pan,wheel_zoom,box_zoom,save,reset,crosshair",
+        toolbar_location: str = "above",
+    ) -> figure:
+        raise NotImplementedError()
+
+
+class BokehLatticeLayoutGraph(BokehGraphBase[LatticeLayoutGraph]):
+    graph_type: ClassVar[str] = "lat_layout"
+    graph: LatticeLayoutGraph
+
+    def __init__(
+        self,
+        manager: GraphManager,
+        graph: LatticeLayoutGraph,
+        sizing_mode: SizingModeType = "inherit",
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        aspect_ratio: Optional[float] = None,  # w/h
+    ) -> None:
+        super().__init__(
+            manager=manager,
+            graph=graph,
+            sizing_mode=sizing_mode,
+            aspect_ratio=aspect_ratio,
+            width=width,
+            height=height,
+        )
+
+    def update_plot(
+        self,
+        fig: figure,
+        *,
+        widgets: Optional[List[bokeh.models.Widget]] = None,
+        tao: Optional[Tao] = None,
+    ) -> None:
+        if tao is None:
+            return
+
+    def create_figure(
+        self,
+        *,
+        tools: Optional[str] = None,
+        toolbar_location: str = "above",
+    ) -> figure:
+        if tools is None:
+            tools = _Defaults.lattice_layout_tools
+
+        add_named_hover_tool = isinstance(tools, str) and "hover" in tools.split(",")
+        if add_named_hover_tool:
+            tools = ",".join(tool for tool in tools.split(",") if tool != "hover")
+
+        graph = self.graph
+        fig = figure(
+            title=pgplot.mathjax_string(graph.title),
+            x_axis_label=pgplot.mathjax_string(graph.xlabel),
+            # y_axis_label=pgplot.mathjax_string(graph.ylabel),
+            toolbar_location=toolbar_location,
+            tools=tools,
+            aspect_ratio=self.aspect_ratio,
+            sizing_mode=self.sizing_mode,
+        )
+
+        box_zoom = get_tool_from_figure(fig, bokeh.models.BoxZoomTool)
+        if box_zoom is not None:
+            box_zoom.match_aspect = False
+
+        fig.xaxis.ticker = bokeh.models.FixedTicker(
+            ticks=[elem.info["ele_s_start"] for elem in graph.elements],
+            minor_ticks=[elem.info["ele_s_end"] for elem in graph.elements],
+        )
+        fig.xaxis.major_label_overrides = {
+            elem.info["ele_s_start"]: elem.info["label_name"] for elem in graph.elements
+        }
+        fig.xaxis.major_label_orientation = math.pi / 4
+        fig.yaxis.ticker = []
+        fig.yaxis.visible = False
+
+        _draw_layout_elems(fig, self.graph.elements, skip_labels=True)
+
+        if add_named_hover_tool:
+            hover = bokeh.models.HoverTool(
+                renderers=get_hoverable_renderers(fig),
+                tooltips=[
+                    ("name", "@name"),
+                    ("s start [m]", "@s_start"),
+                    ("s end [m]", "@s_end"),
+                ],
+                mode="vline",
+            )
+
+            fig.add_tools(hover)
+
+        fig.renderers.append(
+            bokeh.models.Span(location=0, dimension="width", line_color="black", line_width=1)
+        )
+
+        if self.x_range is not None:
+            fig.x_range = self.x_range
+        if self.y_range is not None:
+            fig.y_range = self.y_range
+        return fig
+
+
+class BokehBasicGraph(BokehGraphBase[BasicGraph]):
+    graph_type: ClassVar[str] = "basic"
+    graph: BasicGraph
+    curve_data: List[CurveData]
+    num_points: int
+    view_x_range: Tuple[float, float]
+
+    def __init__(
+        self,
+        manager: GraphManager,
+        graph: BasicGraph,
+        sizing_mode: SizingModeType = "inherit",
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        aspect_ratio: Optional[float] = None,  # w/h
+        variables: Optional[List[Variable]] = None,
+    ) -> None:
+        super().__init__(
+            manager=manager,
+            graph=graph,
+            sizing_mode=sizing_mode,
+            width=width,
+            height=height,
+            aspect_ratio=aspect_ratio,
+        )
+        self.curve_data = _get_graph_data(graph)
+        self.num_points = graph.get_num_points()
+        self.view_x_range = graph.get_x_range()
+        self.variables = variables
+
+    @property
+    def tao(self) -> Tao:
+        return self.manager.tao
+
+    def _disable_widgets(self, widgets: List[bokeh.models.Widget]) -> None:
+        for widget in widgets:
+            if hasattr(widget, "disabled"):
+                widget.disabled = True
+            if hasattr(widget, "title"):
+                widget.title = "(plot type changed; disabled)"
+
+    def update_plot(
+        self,
+        fig: figure,
+        *,
+        widgets: Optional[List[bokeh.models.Widget]] = None,
+    ) -> None:
+        try:
+            self.tao.cmd("set global lattice_calc_on = F")
+            self.tao.cmd(f"set plot {self.graph.region_name} n_curve_pts = {self.num_points}")
+            self.tao.cmd(
+                f"x_scale {self.graph.region_name} {self.view_x_range[0]} {self.view_x_range[1]}"
+            )
+        finally:
+            self.tao.cmd("set global lattice_calc_on = T")
+
+        logger.debug(f"x={self.view_x_range} points={self.num_points}")
+
+        try:
+            updated = self.graph.update(self.manager)
+            if updated is None:
+                raise ValueError("update() returned None")
+        except Exception:
+            logger.exception("Failed to update graph")
+            self._disable_widgets(widgets or [])
+            return
+
+        # In case the user mistakenly reuses the same plot region, ensure
+        # that at least our axis labels are consistent.
+        fig.title.text = pgplot.mathjax_string(updated.title)
+        fig.xaxis.axis_label = pgplot.mathjax_string(updated.xlabel)
+        fig.yaxis.axis_label = pgplot.mathjax_string(updated.ylabel)
+
+        for orig_data, new_data in zip(self.curve_data, _get_graph_data(updated)):
+            line = new_data.get("line", None)
+            if line is not None:
+                assert "line" in orig_data
+                orig_data["line"].data = dict(line.data)
+
+            symbol = new_data.get("symbol", None)
+            if symbol is not None:
+                assert "symbol" in orig_data
+                orig_data["symbol"].data = dict(symbol.data)
+
+    def create_figure(
+        self,
+        *,
+        tools: Optional[str] = None,
+        toolbar_location: str = "above",
+        sizing_mode: SizingModeType = "inherit",
+    ) -> figure:
+        graph = self.graph
+
+        if tools is None:
+            tools = _Defaults.tools
+
+        fig = figure(
+            title=pgplot.mathjax_string(graph.title),
+            x_axis_label=pgplot.mathjax_string(graph.xlabel),
+            y_axis_label=pgplot.mathjax_string(graph.ylabel),
+            toolbar_location=toolbar_location,
+            tools=tools,
+            sizing_mode=sizing_mode,
+            width=self.width,
+            height=self.height,
+        )
+        if self.x_range is not None:
+            fig.x_range = self.x_range
+        if self.y_range is not None:
+            fig.y_range = self.y_range
+        for curve, source in zip(graph.curves, self.curve_data):
+            _plot_curve(fig, curve, source)
+        return fig
+
+
+def get_hoverable_renderers(fig: figure) -> List[bokeh.models.GlyphRenderer]:
+    return [rend for rend in list(fig.renderers) if any(rend.data_source.data.get("name", []))]
+
+
+class BokehFloorPlanGraph(BokehGraphBase[FloorPlanGraph]):
+    graph_type: ClassVar[str] = "floor_plan"
+    graph: FloorPlanGraph
+
+    def __init__(
+        self,
+        manager: GraphManager,
+        graph: FloorPlanGraph,
+        sizing_mode: SizingModeType = "inherit",
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+    ) -> None:
+        super().__init__(
+            manager=manager,
+            graph=graph,
+            sizing_mode=sizing_mode,
+            width=width,
+            height=height,
+        )
+
+    @property
+    def tao(self) -> Tao:
+        return self.manager.tao
+
+    def create_figure(
+        self,
+        *,
+        tools: Optional[str] = None,
+        toolbar_location: str = "above",
+        sizing_mode: SizingModeType = "inherit",
+    ) -> figure:
+        if tools is None:
+            tools = _Defaults.floor_plan_tools
+
+        add_named_hover_tool = isinstance(tools, str) and "hover" in tools.split(",")
+        if add_named_hover_tool:
+            tools = ",".join(tool for tool in tools.split(",") if tool != "hover")
+
+        graph = self.graph
+        fig = figure(
+            title=pgplot.mathjax_string(graph.title),
+            x_axis_label=pgplot.mathjax_string(graph.xlabel),
+            y_axis_label=pgplot.mathjax_string(graph.ylabel),
+            toolbar_location=toolbar_location,
+            tools=tools,
+            sizing_mode=sizing_mode,
+            width=self.width,
+            height=self.height,
+            # This is vitally important for glyphs to render properly.
+            # Compare how a circle centered at (0, 0) with a radius 1
+            # looks with/without this setting
+            match_aspect=True,
+        )
+        # TODO: specifying limits for floor plans can cause malformed glyphs.
+        # Setting x_range/y_range apparently does away with `match_aspect`.
+        # if self.x_range is not None:
+        #     fig.x_range = self.x_range
+        # if self.y_range is not None:
+        #     fig.y_range = self.y_range
+
+        box_zoom = get_tool_from_figure(fig, bokeh.models.BoxZoomTool)
+        if box_zoom is not None:
+            box_zoom.match_aspect = True
+
+        _draw_floor_plan_shapes(fig, self.graph.elements)
+
+        if add_named_hover_tool:
+            hover = bokeh.models.HoverTool(
+                renderers=get_hoverable_renderers(fig),
+                tooltips=[
+                    ("name", "@name"),
+                    # ("Position [m]", "(@x, @y)"),
+                ],
+            )
+
+            fig.add_tools(hover)
+
+        for line in self.graph.building_walls.lines:
+            _plot_curve_line(fig, line)
+        for patch in self.graph.building_walls.patches:
+            _plot_patch(fig, patch)
+        orbits = self.graph.floor_orbits
+        if orbits is not None:
+            _plot_curve_symbols(fig, orbits.curve, name="floor_orbits")
+
+        _draw_annotations(
+            fig,
+            {elem.name: elem.annotations for elem in self.graph.elements},
+            font_size=_Defaults.floor_plan_font_size,
+            skip_labels=False,
+        )
+        _draw_limit_border(fig, graph.xlim, graph.ylim, alpha=0.1)
+        return fig
+
+    def create_widgets(self, fig: figure) -> List[bokeh.models.UIElement]:
+        controls = []
+        try:
+            (orbits,) = fig.select("floor_orbits")
+        except ValueError:
+            pass
+        else:
+            orbits.visible = False
+            show_orbits_toggle = bokeh.models.Toggle(label="Show orbits", active=False)
+
+            def orbits_toggled(_attr, _old, show):
+                orbits.visible = show
+
+            show_orbits_toggle.on_change("active", orbits_toggled)
+            controls.append(show_orbits_toggle)
+
+        if controls:
+            return [bokeh.layouts.row(controls)]
+        return []
+
+
+AnyBokehGraph = Union[BokehBasicGraph, BokehLatticeLayoutGraph, BokehFloorPlanGraph]
+
+
+UIGridLayoutList = List[Optional[bokeh.models.UIElement]]
+
+
+class BokehAppState:
+    pairs: List[BGraphAndFigure]
+    layout_pairs: List[BGraphAndFigure]
+    grid: List[UIGridLayoutList]
+
+    def __init__(
+        self,
+        pairs: List[BGraphAndFigure],
+        layout_pairs: List[BGraphAndFigure],
+        grid: List[UIGridLayoutList],
+    ) -> None:
+        self.pairs = pairs
+        self.layout_pairs = layout_pairs
+        self.grid = grid
+
+    def to_gridplot(
+        self,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        **kwargs,
+    ) -> bokeh.models.GridPlot:
+        if not _Defaults.show_bokeh_logo:
+            for pair in self.pairs + self.layout_pairs:
+                pair.fig.toolbar.logo = None
+
+        gridplot = bokeh.layouts.gridplot(
+            children=self.grid,
+            width=width,
+            height=height,
+            **kwargs,
+        )
+        gridplot.toolbar.tools.append(bokeh.models.SaveTool())
+        return gridplot
+
+    @property
+    def figures(self) -> List[figure]:
+        return [pair.fig for pair in [*self.pairs, *self.layout_pairs]]
+
+    def to_html(
+        self,
+        title: Optional[str] = None,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+    ) -> str:
+        layout = self.to_gridplot(width=width, height=height)
+        return bokeh.embed.file_html(models=layout, title=title)
+
+    def save(
+        self,
+        filename: AnyPath = "",
+        *,
+        title: Optional[str] = None,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+    ) -> Optional[pathlib.Path]:
+        title = title or self.pairs[0].bgraph.graph.title or f"plot-{time.time()}"
+        if not filename:
+            filename = f"{title}.html"
+        if not pathlib.Path(filename).suffix:
+            filename = f"{filename}.html"
+        source = self.to_html(title=title, width=width, height=height)
+        with open(filename, "wt") as fp:
+            fp.write(source)
+        return pathlib.Path(filename)
+
+
+class BokehAppCreator:
+    """
+    A composite Bokeh application creator made up of 1 or more graphs.
+
+    This can be used to:
+    * Generate a static HTML page without Python widgets
+    * Generate a Notebook (or standalone) application with Python widgets
+
+    Interactive widgets will use the `Tao` object to adjust variables during
+    callbacks resulting from user interaction.
+    """
+
+    manager: Union[BokehGraphManager, NotebookGraphManager]
+    graphs: List[AnyGraph]
+    bgraphs: List[AnyBokehGraph]
+    share_x: Optional[bool]
+    variables: List[Variable]
+    grid: Tuple[int, int]
+    width: Optional[int]
+    height: Optional[int]
+    include_layout: bool
+    layout_height: Optional[int]
+    xlim: List[OptionalLimit]
+    ylim: List[OptionalLimit]
+    figures: List[figure]
+    graph_sizing_mode: Optional[SizingModeType]
+
+    def __init__(
+        self,
+        manager: Union[BokehGraphManager, NotebookGraphManager],
+        graphs: List[AnyGraph],
+        share_x: Optional[bool] = None,
+        include_variables: bool = False,
+        grid: Optional[Tuple[int, int]] = None,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        include_layout: bool = False,
+        graph_sizing_mode: Optional[SizingModeType] = None,
+        layout_height: Optional[int] = None,
+        xlim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+        ylim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+    ) -> None:
+        if not len(graphs):
+            raise ValueError("BokehAppCreator requires 1 or more graph")
+
+        if any(isinstance(graph, LatticeLayoutGraph) for graph in graphs):
+            include_layout = False
+        elif not any(graph.is_s_plot for graph in graphs):
+            include_layout = False
+
+        if not grid:
+            grid = (len(graphs), 1)
+
+        if include_layout:
+            grid = (grid[0] + 1, grid[1])
+
+        if include_variables:
+            variables = Variable.from_tao_all(manager.tao)
+        else:
+            variables = []
+
+        self.manager = manager
+        self.graphs = graphs
+        self.share_x = share_x
+        self.variables = variables
+        self.grid = grid
+        self.width = width
+        self.height = height
+        self.graph_sizing_mode = graph_sizing_mode
+        self.include_layout = include_layout
+        self.layout_height = layout_height
+        self.xlim = fix_grid_limits(xlim, num_graphs=len(graphs))
+        self.ylim = fix_grid_limits(ylim, num_graphs=len(graphs))
+
+    def create_state(self) -> BokehAppState:
+        """Create an independent application state based on the graph data."""
+        pairs, layout_pairs = self._create_figures()
+        grid = self._grid_figures(pairs, layout_pairs)
+        return BokehAppState(
+            pairs=pairs,
+            layout_pairs=layout_pairs,
+            grid=grid,
+        )
+
+    def save(
+        self,
+        filename: AnyPath = "",
+        *,
+        title: Optional[str] = None,
+    ) -> Optional[pathlib.Path]:
+        state = self.create_state()
+        state.save(filename=filename, title=title, width=self.width, height=self.height)
+
+    def _create_figures(self) -> Tuple[List[BGraphAndFigure], List[BGraphAndFigure]]:
+        bgraphs = [self.manager.to_bokeh_graph(graph) for graph in self.graphs]
+        figures = [
+            bgraph.create_figure(
+                tools=_Defaults.tools,
+                toolbar_location=_Defaults.grid_toolbar_location,
+            )
+            for bgraph in bgraphs
+        ]
+        pairs = [
+            BGraphAndFigure(bgraph=bgraph, fig=fig) for bgraph, fig in zip(bgraphs, figures)
+        ]
+
+        if not self.include_layout:
+            layout_pairs = []
+        else:
+            lattice_layout = self.manager.to_bokeh_graph(self.manager.lattice_layout_graph)
+            layout_pairs = [
+                BGraphAndFigure(
+                    fig=lattice_layout.create_figure(
+                        tools=_Defaults.lattice_layout_tools,
+                        toolbar_location=_Defaults.grid_toolbar_location,
+                    ),
+                    bgraph=lattice_layout,
+                )
+                for _ in range(self.ncols)
+            ]
+
+        if len(figures) > 1 or layout_pairs:
+            if self.share_x is None:
+                share_common_x_axes(pairs + layout_pairs)
+            elif self.share_x:
+                all_figs = figures + [pair.fig for pair in layout_pairs]
+                share_x_axes(all_figs)
+                link_crosshairs(all_figs)
+
+        return pairs, layout_pairs
+
+    def _grid_figures(
+        self,
+        pairs: List[BGraphAndFigure],
+        layout_pairs: List[BGraphAndFigure],
+    ) -> List[UIGridLayoutList]:
+        nrows, ncols = self.grid
+        rows = [[] for _ in range(nrows)]
+        rows_cols = [(row, col) for row in range(nrows) for col in range(ncols)]
+
+        for pair, xl, yl, (row, _col) in zip(pairs, self.xlim, self.ylim, rows_cols):
+            fig = pair.fig
+
+            if not isinstance(pair.bgraph, BokehFloorPlanGraph):
+                if xl is not None:
+                    fig.x_range = bokeh.models.Range1d(*xl)
+                if yl is not None:
+                    fig.y_range = bokeh.models.Range1d(*yl)
+
+            if self.graph_sizing_mode is not None:
+                fig.sizing_mode = self.graph_sizing_mode
+
+            rows[row].append(fig)
+
+        for pair in pairs + layout_pairs:
+            is_layout = isinstance(pair.bgraph, BokehLatticeLayoutGraph)
+            width, height = _Defaults.get_size_for_class(
+                type(pair.bgraph),
+                user_width=self.width,
+                user_height=self.layout_height if is_layout else self.height,
+            )
+
+            pair.fig.frame_width = width
+            pair.fig.frame_height = height
+            logger.debug("fig %s width=%s height=%s", pair.fig, width, height)
+
+        rows.append([pair.fig for pair in layout_pairs])
+
+        for pair in layout_pairs:
+            if pair.fig is not None:
+                # pair.fig.min_border_bottom = 80
+                pass
+
+        for row in rows:
+            for fig in row:
+                # NOTE: this value is somewhat arbitrary; it helps align the X axes
+                # between consecutive plots
+                if fig is not None:
+                    fig.min_border_left = 80
+
+        return [row for row in rows if row]
+
+    @property
+    def nrows(self) -> int:
+        return self.grid[0]
+
+    @property
+    def ncols(self) -> int:
+        return self.grid[1]
+
+    def _add_update_button(self, state: BokehAppState):
+        update_button = bokeh.models.Button(label="Update")
+
+        def update_plot():
+            for pair in state.pairs:
+                bgraph = pair.bgraph
+                if not isinstance(bgraph, BokehBasicGraph):
+                    continue
+
+                try:
+                    bgraph.update_plot(pair.fig, widgets=[update_button])
+                except Exception:
+                    logger.exception("Failed to update number of points")
+
+        update_button.on_click(update_plot)
+        return update_button
+
+    def _add_num_points_slider(self, state: BokehAppState):
+        num_points_slider = bokeh.models.Slider(
+            title="Data Points",
+            start=10,
+            end=10_000,
+            step=1_000,
+            value=401,
+        )
+
+        def num_points_changed(_attr, _old, num_points: int):
+            if num_points < 1:
+                logger.error("Internal error: unexpected number of points")
+                return
+
+            for pair in state.pairs:
+                bgraph = pair.bgraph
+                if not isinstance(bgraph, BokehBasicGraph):
+                    continue
+
+                bgraph.num_points = num_points
+                try:
+                    bgraph.update_plot(pair.fig, widgets=[num_points_slider])
+                except Exception:
+                    logger.exception("Failed to update number of points")
+
+        num_points_slider.on_change("value", num_points_changed)
+        return num_points_slider
+
+    def _monitor_range_updates(self, state: BokehAppState):
+        def ranges_update(
+            bgraph: BokehBasicGraph, fig: figure, event: bokeh.events.RangesUpdate
+        ) -> None:
+            x0, x1 = event.x0, event.x1
+            if x0 is None or x1 is None or x1 < x0:
+                logger.error(f"Internal error: unexpected range: {x0} {x1}")
+                return
+
+            new_xrange = bgraph.graph.clamp_x_range(x0, x1)
+            if new_xrange != bgraph.view_x_range:
+                bgraph.view_x_range = new_xrange
+
+            try:
+                bgraph.update_plot(fig, widgets=[])
+            except Exception:
+                logger.exception("Failed to update number ranges")
+
+        callbacks = []
+        for pair in state.pairs:
+            if not isinstance(pair.bgraph, BokehBasicGraph):
+                continue
+
+            cb = cast(EventCallback, functools.partial(ranges_update, pair.bgraph, pair.fig))
+            pair.fig.on_event(bokeh.events.RangesUpdate, cb)
+            callbacks.append(cb)
+
+        return callbacks
+
+    def create_app_ui(self):
+        # Ensure we get a new set of data sources and figures for each app
+        state = self.create_state()
+
+        if not state.pairs:
+            return
+
+        widget_models: List[bokeh.layouts.UIElement] = []
+        if self.variables:
+            status_label = bokeh.models.PreText()
+            spinners = [
+                var.create_spinner(
+                    tao=self.manager.tao,
+                    status_label=status_label,
+                    pairs=state.pairs,
+                )
+                for var in self.variables
+            ]
+            widget_models.insert(0, bokeh.layouts.row([status_label]))
+            per_row = 6
+            while spinners:
+                row = bokeh.layouts.row(spinners[-per_row:])
+                spinners = spinners[:-per_row]
+                widget_models.insert(0, row)
+
+        if any(isinstance(pair.bgraph, BokehBasicGraph) for pair in state.pairs):
+            update_button = self._add_update_button(state)
+            num_points_slider = self._add_num_points_slider(state)
+            widget_models.insert(0, bokeh.layouts.row([update_button, num_points_slider]))
+
+            self._monitor_range_updates(state)
+
+        for pair in state.pairs:
+            if isinstance(pair.bgraph, BokehFloorPlanGraph):
+                widget_models.extend(pair.bgraph.create_widgets(pair.fig))
+                break
+
+        gridplot = state.to_gridplot(
+            width=self.width,
+            height=self.height,
+            merge_tools=False,
+            toolbar_options={} if _Defaults.show_bokeh_logo else {"logo": None},
+        )
+
+        all_elems: List[bokeh.models.UIElement] = [*widget_models, gridplot]
+        return bokeh.layouts.column(all_elems)
+
+    def create_full_app(self):
+        if os.environ.get("PYTAO_BOKEH_NBCONVERT", "").lower() in {"1", "y"}:
+            # Do not show full Bokeh server-backed applications when converting
+            # Jupyter notebooks to HTML as they are not supported (and will
+            # show up blank).  This is a way around it by only showing the
+            # graphs without Python-backed widgets - similar to how static HTML
+            # pages are saved.
+            state = self.create_state()
+            return state.to_gridplot()
+
+        def bokeh_app(doc):
+            doc.add_root(self.create_app_ui())
+
+        return bokeh_app
+
+
+@dataclass
+class Variable:
+    name: str
+    value: float
+    step: float
+    info: FloatVariableInfo
+    parameter: str = "model"
+
+    def update_info(self, tao: Tao) -> FloatVariableInfo:
+        self.info = cast(FloatVariableInfo, tao.var(self.name))
+        return self.info
+
+    def set_value(self, tao: Tao, value: float):
+        self.value = value
+        tao.cmd(f"set var {self.name}|{self.parameter} = {self.value}")
+
+    def create_spinner(
+        self,
+        tao: Tao,
+        status_label: bokeh.models.PreText,
+        pairs: List[BGraphAndFigure],
+    ) -> bokeh.models.Spinner:
+        spinner = bokeh.models.Spinner(
+            title=self.name,
+            value=self.value,
+            step=self.step,
+            low=self.info["low_lim"],
+            high=self.info["high_lim"],
+        )
+        spinner.on_change(
+            "value",
+            functools.partial(self.ui_update, tao=tao, status_label=status_label, pairs=pairs),
+        )
+        return spinner
+
+    @classmethod
+    def from_tao(cls, tao: Tao, name: str, *, parameter: str = "model") -> Variable:
+        info = cast(FloatVariableInfo, tao.var(name))
+        return Variable(
+            name=name,
+            info=info,
+            step=info["key_delta"] or 0.01,
+            value=info[f"{parameter}_value"],
+            parameter=parameter,
+        )
+
+    @classmethod
+    def from_tao_all(cls, tao: Tao, *, parameter: str = "model") -> List[Variable]:
+        return [
+            cls.from_tao(
+                tao=tao,
+                name=f'{var_info["name"]}[{idx}]',
+                parameter=parameter,
+            )
+            for var_info in tao.var_general()
+            for idx in range(var_info["lbound"], var_info["ubound"] + 1)
+        ]
+
+    def ui_update(
+        self,
+        attr: str,
+        old: float,
+        new: float,
+        *,
+        tao: Tao,
+        status_label: bokeh.models.PreText,
+        pairs: List[BGraphAndFigure],
+    ):
+        try:
+            self.set_value(tao, new)
+        except RuntimeError as ex:
+            status_label.text = _clean_tao_exception_for_user(
+                str(ex),
+                command="tao_set_invalid",
+            )
+        else:
+            status_label.text = ""
+
+        for pair in pairs:
+            if isinstance(pair.bgraph, (BokehBasicGraph, BokehLatticeLayoutGraph)):
+                pair.bgraph.update_plot(pair.fig)
+
+
+def _clean_tao_exception_for_user(text: str, command: str) -> str:
+    def clean_line(line: str) -> str:
+        # "[ERROR | 2024-JUL-22 09:20:20] tao_set_invalid:"
+        if line.startswith("[") and line.endswith(f"{command}:"):
+            return line.split(f"{command}:", 1)[1]
+        return line
+
+    text = text.replace("ERROR detected: ", "\n")
+    lines = [clean_line(line.rstrip()) for line in text.splitlines()]
+    return "\n".join(line for line in lines if line.strip())
+
+
+class BokehGraphManager(GraphManager):
+    """Bokeh backend graph manager - for non-Jupyter contexts."""
+
+    _key_: ClassVar[str] = "bokeh"
+
+    def to_bokeh_graph(self, graph: AnyGraph) -> AnyBokehGraph:
+        """
+        Create a Bokeh graph instance from the backend-agnostic AnyGraph version.
+
+        For example, `BasicGraph` becomes `BokehBasicGraph`.
+
+        Parameters
+        ----------
+        graph : AnyGraph
+
+        Returns
+        -------
+        AnyBokehGraph
+        """
+        if isinstance(graph, BasicGraph):
+            return BokehBasicGraph(self, graph)
+        elif isinstance(graph, LatticeLayoutGraph):
+            return BokehLatticeLayoutGraph(self, graph)
+        elif isinstance(graph, FloorPlanGraph):
+            return BokehFloorPlanGraph(self, graph)
+        raise NotImplementedError(type(graph).__name__)
+
+    def plot_grid(
+        self,
+        templates: List[str],
+        grid: Tuple[int, int],
+        *,
+        include_layout: bool = False,
+        share_x: Optional[bool] = None,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        figsize: Optional[Tuple[int, int]] = None,
+        layout_height: Optional[int] = None,
+        xlim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+        ylim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+        curves: Optional[List[CurveIndexToCurve]] = None,
+        settings: Optional[List[TaoGraphSettings]] = None,
+        save: Union[bool, str, pathlib.Path, None] = None,
+    ):
+        """
+        Plot graphs on a grid with Bokeh.
+
+        Parameters
+        ----------
+        templates : list of str
+            Graph template names.
+        grid : (nrows, ncols), optional
+            Grid the provided graphs into this many rows and columns.
+        include_layout : bool, default=False
+            Include a layout plot at the bottom of each column.
+        share_x : bool or None, default=None
+            Share x-axes where sensible (`None`) or force sharing x-axes (True)
+            for all plots.
+        figsize : (int, int), optional
+            Figure size. Alternative to specifying `width` and `height`
+            separately.  This takes precedence over `width` and `height`.
+        width : int, optional
+            Width of the whole plot.
+        height : int, optional
+            Height of the whole plot.
+        layout_height : int, optional
+            Height of the layout plot.
+        xlim : list of (float, float), optional
+            X axis limits for each graph.
+        ylim : list of (float, float), optional
+            Y axis limits for each graph.
+        curves : list of Dict[int, TaoCurveSettings], optional
+            One dictionary per graph, with each dictionary mapping the curve
+            index to curve settings. These settings will be applied to the
+            placed graphs prior to plotting.
+        settings : list of TaoGraphSettings, optional
+            Graph customization settings, per graph.
+        save : pathlib.Path or str, optional
+            Save the plot to the given filename.
+
+        Returns
+        -------
+        list of graphs
+
+        BokehAppCreator
+        """
+        graphs = self.prepare_grid_by_names(
+            template_names=templates,
+            curves=curves,
+            settings=settings,
+            xlim=xlim,
+            ylim=ylim,
+        )
+
+        if figsize is not None:
+            width, height = figsize
+
+        app = BokehAppCreator(
+            manager=self,
+            graphs=graphs,
+            share_x=share_x,
+            include_variables=False,
+            grid=grid,
+            width=width or _Defaults.width,
+            height=height or _Defaults.stacked_height,
+            layout_height=layout_height or _Defaults.layout_height,
+            include_layout=include_layout,
+            xlim=xlim,
+            ylim=ylim,
+        )
+
+        if save:
+            if save is True:
+                save = ""
+            filename = app.save(save)
+            logger.info(f"Saving plot to {filename!r}")
+        return graphs, app
+
+    def plot(
+        self,
+        template: str,
+        *,
+        region_name: Optional[str] = None,
+        include_layout: bool = True,
+        sizing_mode: Optional[SizingModeType] = None,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        layout_height: Optional[int] = None,
+        share_x: Optional[bool] = None,
+        xlim: Optional[Tuple[float, float]] = None,
+        ylim: Optional[Tuple[float, float]] = None,
+        save: Union[bool, str, pathlib.Path, None] = None,
+        curves: Optional[Dict[int, TaoCurveSettings]] = None,
+        settings: Optional[TaoGraphSettings] = None,
+    ) -> Tuple[List[AnyGraph], BokehAppCreator]:
+        """
+        Plot a graph with Bokeh.
+
+        Parameters
+        ----------
+        template : str
+            Graph template name.
+        region_name : str, optional
+            Graph region name.
+        include_layout : bool
+            Include a layout plot at the bottom, if not already placed and if
+            appropriate (i.e., another plot uses longitudinal coordinates on
+            the x-axis).
+        sizing_mode : Optional[SizingModeType]
+            Set the sizing mode for all graphs.  Default is configured on a
+            per-graph basis, typically "inherit".
+        width : int, optional
+            Width of each plot.
+        height : int, optional
+            Height of each plot.
+        layout_height : int, optional
+            Height of the layout plot.
+        share_x : bool or None, default=None
+            Share x-axes where sensible (`None`) or force sharing x-axes (True)
+            for all plots.
+        xlim : (float, float), optional
+            X axis limits.
+        ylim : (float, float), optional
+            Y axis limits.
+        curves : Dict[int, TaoCurveSettings], optional
+            Dictionary of curve index to curve settings. These settings will be
+            applied to the placed graph prior to plotting.
+        settings : TaoGraphSettings, optional
+            Graph customization settings.
+        save : str or bool, optional
+            Save the plot to a static HTML file with the given name.
+            If `True`, saves to a filename based on the plot title.
+
+        Returns
+        -------
+        list of graphs
+
+        BokehAppCreator
+        """
+        graphs = self.prepare_graphs_by_name(
+            template_name=template,
+            region_name=region_name,
+            curves=curves,
+            settings=settings,
+            xlim=xlim,
+            ylim=ylim,
+        )
+
+        if not graphs:
+            raise UnsupportedGraphError(f"No supported plots from this template: {template}")
+
+        app = BokehAppCreator(
+            manager=self,
+            graphs=graphs,
+            share_x=share_x,
+            include_variables=False,
+            grid=None,
+            width=width or _Defaults.width,
+            height=height or _Defaults.height,
+            layout_height=layout_height or _Defaults.layout_height,
+            include_layout=include_layout,
+            graph_sizing_mode=sizing_mode,
+            xlim=[xlim],
+            ylim=[ylim],
+        )
+
+        if save:
+            if save is True:
+                save = ""
+            filename = app.save(save)
+            logger.info(f"Saving plot to {filename!r}")
+
+        return graphs, app
+
+    def plot_field(
+        self,
+        ele_id: str,
+        *,
+        colormap: Optional[str] = None,
+        radius: float = 0.015,
+        num_points: int = 100,
+        x_scale: float = 1.0,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        save: Union[bool, str, pathlib.Path, None] = None,
+    ):
+        """
+        Plot field information for a given element.
+
+        Parameters
+        ----------
+        ele_id : str
+            Element ID.
+        colormap : str, optional
+            Colormap for the plot.
+            Matplotlib defaults to "PRGn_r", and bokeh defaults to "".
+        radius : float, default=0.015
+            Radius.
+        num_points : int, default=100
+            Number of data points.
+        width : int, optional
+        height : int, optional
+        save : pathlib.Path or str, optional
+            Save the plot to the given filename.
+
+        Returns
+        -------
+        ElementField
+
+        figure
+        """
+        field = ElementField.from_tao(self.tao, ele_id, num_points=num_points, radius=radius)
+        fig = figure(title=f"Field of {ele_id}")
+
+        palette = colormap or _Defaults.palette
+
+        source = _fields_to_data_source([field], x_scale=x_scale)
+        cmap = bokeh.models.LinearColorMapper(
+            palette=palette or _Defaults.palette,
+            low=np.min(source.data["by"]),
+            high=np.max(source.data["by"]),
+        )
+
+        fig.image(
+            image="by",
+            x="x",
+            y=-1,
+            dw="dw",
+            dh="dh",
+            color_mapper=cmap,
+            source=source,
+            name="field_images",
+        )
+        color_bar = bokeh.models.ColorBar(color_mapper=cmap, location=(0, 0))
+        fig.add_layout(color_bar, "right")
+
+        fig.frame_width = width or _Defaults.width
+        fig.frame_height = height or _Defaults.height
+
+        if save:
+            if save is True:
+                save = f"{ele_id}_field.html"
+            if not pathlib.Path(save).suffix:
+                save = f"{save}.html"
+            filename = bokeh.io.save(fig, filename=save)
+            logger.info(f"Saving plot to {filename!r}")
+
+        return field, fig
+
+
+class NotebookGraphManager(BokehGraphManager):
+    """Jupyter notebook Bokeh backend graph manager."""
+
+    def plot_grid(
+        self,
+        templates: List[str],
+        grid: Tuple[int, int],
+        *,
+        curves: Optional[List[CurveIndexToCurve]] = None,
+        settings: Optional[List[TaoGraphSettings]] = None,
+        include_layout: bool = False,
+        share_x: Optional[bool] = None,
+        vars: bool = False,
+        figsize: Optional[Tuple[int, int]] = None,
+        layout_height: Optional[int] = None,
+        xlim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+        ylim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        save: Union[bool, str, pathlib.Path, None] = None,
+    ):
+        """
+        Plot graphs on a grid with Bokeh.
+
+        Parameters
+        ----------
+        templates : list of str
+            Graph template names.
+        grid : (nrows, ncols), optional
+            Grid the provided graphs into this many rows and columns.
+        include_layout : bool, default=False
+            Include a layout plot at the bottom of each column.
+        share_x : bool or None, default=None
+            Share x-axes where sensible (`None`) or force sharing x-axes (True)
+            for all plots.
+        vars : bool, default=False
+            Show Tao variables as adjustable widgets, like "single mode".
+        figsize : (int, int), optional
+            Figure size. Alternative to specifying `width` and `height`
+            separately.  This takes precedence over `width` and `height`.
+        width : int, optional
+            Width of the whole plot.
+        height : int, optional
+            Height of the whole plot.
+        layout_height : int, optional
+            Height of the layout plot.
+        xlim : list of (float, float), optional
+            X axis limits for each graph.
+        ylim : list of (float, float), optional
+            Y axis limits for each graph.
+        curves : list of Dict[int, TaoCurveSettings], optional
+            One dictionary per graph, with each dictionary mapping the curve
+            index to curve settings. These settings will be applied to the
+            placed graphs prior to plotting.
+        settings : list of TaoGraphSettings, optional
+            Graph customization settings, per graph.
+        save : pathlib.Path or str, optional
+            Save the plot to the given filename.
+
+        Returns
+        -------
+        list of graphs
+
+        BokehAppCreator
+        """
+        graphs, app = super().plot_grid(
+            templates=templates,
+            grid=grid,
+            curves=curves,
+            settings=settings,
+            include_layout=include_layout,
+            share_x=share_x,
+            figsize=figsize,
+            width=width,
+            height=height,
+            xlim=xlim,
+            ylim=ylim,
+            layout_height=layout_height,
+            save=save,
+        )
+        if vars:
+            app.variables = Variable.from_tao_all(self.tao)
+        bokeh.plotting.show(app.create_full_app())
+        return graphs, app
+
+    def plot(
+        self,
+        template: str,
+        *,
+        region_name: Optional[str] = None,
+        include_layout: bool = True,
+        sizing_mode: Optional[SizingModeType] = None,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        layout_height: Optional[int] = None,
+        share_x: Optional[bool] = None,
+        vars: bool = False,
+        xlim: Optional[Limit] = None,
+        ylim: Optional[Limit] = None,
+        notebook_handle: bool = False,
+        save: Union[bool, str, pathlib.Path, None] = None,
+        curves: Optional[Dict[int, TaoCurveSettings]] = None,
+        settings: Optional[TaoGraphSettings] = None,
+    ) -> Tuple[List[AnyGraph], BokehAppCreator]:
+        """
+        Plot a graph with Bokeh.
+
+        Parameters
+        ----------
+        template : str
+            Graph template name.
+        region_name : str, optional
+            Graph region name.
+        include_layout : bool
+            Include a layout plot at the bottom, if not already placed and if
+            appropriate (i.e., another plot uses longitudinal coordinates on
+            the x-axis).
+        sizing_mode : Optional[SizingModeType]
+            Set the sizing mode for all graphs.  Default is configured on a
+            per-graph basis, typically "inherit".
+        width : int, optional
+            Width of each plot.
+        height : int, optional
+            Height of each plot.
+        layout_height : int, optional
+            Height of the layout plot.
+        share_x : bool or None, default=None
+            Share x-axes where sensible (`None`) or force sharing x-axes (True)
+            for all plots.
+        vars : bool, default=False
+            Show Tao variables as adjustable widgets, like "single mode".
+        xlim : (float, float), optional
+            X axis limits.
+        ylim : (float, float), optional
+            Y axis limits.
+        curves : Dict[int, TaoCurveSettings], optional
+            Dictionary of curve index to curve settings. These settings will be
+            applied to the placed graph prior to plotting.
+        settings : TaoGraphSettings, optional
+            Graph customization settings.
+        save : str or bool, optional
+            Save the plot to a static HTML file with the given name.
+            If `True`, saves to a filename based on the plot title.
+
+        Returns
+        -------
+        BokehAppCreator
+        """
+        graphs, app = super().plot(
+            region_name=region_name,
+            template=template,
+            include_layout=include_layout,
+            sizing_mode=sizing_mode,
+            width=width,
+            height=height,
+            layout_height=layout_height,
+            xlim=xlim,
+            ylim=ylim,
+            curves=curves,
+            settings=settings,
+            share_x=share_x,
+            save=save,
+        )
+
+        if vars:
+            app.variables = Variable.from_tao_all(self.tao)
+
+        bokeh.plotting.show(
+            app.create_full_app(),
+            notebook_handle=notebook_handle,
+        )
+        return graphs, app
+
+    def plot_field(
+        self,
+        ele_id: str,
+        *,
+        colormap: Optional[str] = None,
+        radius: float = 0.015,
+        num_points: int = 100,
+        width: Optional[int] = None,
+        height: Optional[int] = None,
+        x_scale: float = 1.0,
+        save: Union[bool, str, pathlib.Path, None] = None,
+    ):
+        """
+        Plot field information for a given element.
+
+        Parameters
+        ----------
+        ele_id : str
+            Element ID.
+        colormap : str, optional
+            Colormap for the plot.
+            Matplotlib defaults to "PRGn_r", and bokeh defaults to "".
+        radius : float, default=0.015
+            Radius.
+        num_points : int, default=100
+            Number of data points.
+        width : int, optional
+        height : int, optional
+        save : pathlib.Path or str, optional
+            Save the plot to the given filename.
+        """
+        field, fig = super().plot_field(
+            ele_id,
+            colormap=colormap,
+            radius=radius,
+            num_points=num_points,
+            width=width,
+            height=height,
+            save=save,
+            x_scale=x_scale,
+        )
+        bokeh.plotting.show(fig, notebook_handle=True)
+
+        return field, fig
+
+
+@functools.cache
+def select_graph_manager_class():
+    if util.is_jupyter():
+        initialize_jupyter()
+        return NotebookGraphManager
+    return BokehGraphManager
+
+
+def initialize_jupyter():
+    # Is this public bokeh API? An attempt at forward-compatibility
+    try:
+        from bokeh.io.state import curstate
+    except ImportError:
+        pass
+    else:
+        state = curstate()
+        if getattr(state, "notebook", False):
+            # Jupyter already initialized
+            logger.debug("Bokeh output_notebook already called; not re-initializing")
+            return
+
+    from bokeh.plotting import output_notebook
+
+    output_notebook()
diff --git a/pytao/plotting/curves.py b/pytao/plotting/curves.py
new file mode 100644
index 00000000..e15e9429
--- /dev/null
+++ b/pytao/plotting/curves.py
@@ -0,0 +1,158 @@
+from __future__ import annotations
+
+from typing import (
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Union,
+)
+
+import pydantic
+import pydantic.dataclasses as dataclasses
+
+_dcls_config = pydantic.ConfigDict()
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotCurveLine:
+    xs: List[float]
+    ys: List[float]
+    color: str = "black"
+    linestyle: str = "solid"
+    linewidth: float = 1.0
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotCurveSymbols:
+    xs: List[float]
+    ys: List[float]
+    color: str
+    markerfacecolor: str
+    markersize: float
+    marker: str
+    markeredgewidth: float
+    linewidth: float = 0
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotHistogram:
+    xs: List[float]
+    bins: Union[int, Sequence[float], str, None]
+    weights: List[float]
+    histtype: Literal["bar", "barstacked", "step", "stepfilled"]
+    color: str
+
+
+class TaoCurveSettings(pydantic.BaseModel, extra="forbid", validate_assignment=True):
+    """
+    TaoCurveSettings are per-curve settings for Tao's `set curve` command.
+
+    All parameters are `None` by default and will only be applied if
+    user-specified.
+
+    Attributes
+    ----------
+    ele_ref_name : str
+        Name or index or the reference element.  An empty string means no
+        reference element.
+    ix_ele_ref : int
+        Same as setting `ele_ref_name`. -1 = No reference element.
+    component : str
+        Who to plot. Eg: 'meas - design'
+        A "data" graph is used to draw lattice parameters such as orbits, or
+        Tao data, or variable values such as quadrupole strengths. The
+        data values will depend upon where the data comes from. This is
+        determined, in part, by the setting of the component parameter in the
+        tao_template_graph namelist. The component may be one of:
+
+            "model", for model values. This is the default.
+            "design", for design values.
+            "base", for base values.
+            "meas", for data values.
+            "ref", for reference data values.
+            "beam_chamber_wall", for beam chamber wall data.
+
+        Additionally, component may be set to plot a linear combination of the
+        above. For example:
+            "model - design"
+        This will plot the difference between the model and design values.
+    ix_branch: int
+        Branch index.
+    ix_bunch: int
+        Bunch index.
+    ix_universe: int
+        Universe index.
+    symbol_every: int
+        Symbol skip number.
+    y_axis_scale_factor: int
+        Scaling of y axis
+    draw_line : bool
+        Draw a line through the data points?
+    draw_symbols : bool
+        Draw a symbol at the data points?
+    draw_symbol_index : bool
+        Draw the symbol index number curve%ix_symb?
+    """
+
+    ele_ref_name: Optional[str] = pydantic.Field(
+        default=None,
+        max_length=40,
+        description="Reference element.",
+    )
+    ix_ele_ref: Optional[int] = pydantic.Field(
+        default=None,
+        description="Index in lattice of reference element.",
+    )
+    component: Optional[str] = pydantic.Field(
+        default=None,
+        max_length=60,
+        description="Who to plot. Eg: 'meas - design'",
+    )
+    ix_branch: Optional[int] = pydantic.Field(
+        default=None,
+    )
+    ix_bunch: Optional[int] = pydantic.Field(
+        default=None,
+        description="Bunch to plot.",
+    )
+    ix_universe: Optional[int] = pydantic.Field(
+        default=None,
+        description="Universe where data is. -1 => use s%global%default_universe",
+    )
+    symbol_every: Optional[int] = pydantic.Field(
+        default=None,
+        description="Symbol every how many points.",
+    )
+    y_axis_scale_factor: Optional[float] = pydantic.Field(
+        default=None,
+        description="y-axis conversion from internal to plotting units.",
+    )
+    draw_line: Optional[bool] = pydantic.Field(
+        default=None,
+        description="Draw a line through the data points?",
+    )
+    draw_symbols: Optional[bool] = pydantic.Field(
+        default=None,
+        description="Draw a symbol at the data points?",
+    )
+    draw_symbol_index: Optional[bool] = pydantic.Field(
+        default=None,
+        description="Draw the symbol index number curve%ix_symb?",
+    )
+
+    def get_commands(
+        self,
+        region_name: str,
+        graph_name: str,
+        curve_index: int,
+    ) -> List[str]:
+        return [
+            f"set curve {region_name}.{graph_name}.c{curve_index} {key} = {value}"
+            for key, value in self.model_dump().items()
+            if value is not None
+        ]
+
+
+CurveIndexToCurve = Dict[int, TaoCurveSettings]
diff --git a/pytao/plotting/fields.py b/pytao/plotting/fields.py
new file mode 100644
index 00000000..331f255a
--- /dev/null
+++ b/pytao/plotting/fields.py
@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+import typing
+
+import numpy as np
+from pydantic import dataclasses
+
+if typing.TYPE_CHECKING:
+    from .. import Tao
+
+
+def get_field_grid(
+    tao: Tao,
+    ele_id: str,
+    radius: float = 0.015,
+    num_points: int = 100,
+) -> typing.Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    ele_head = tao.ele_head(ele_id)
+    s0 = ele_head["s_start"]
+    s1 = ele_head["s"]
+    s_length = s1 - s0
+
+    S, X = np.meshgrid(
+        np.linspace(0, s_length, num_points),
+        np.linspace(-radius, radius, num_points),
+        indexing="ij",
+    )
+
+    @np.vectorize
+    def get_By(s: float, x: float) -> float:
+        # x, y, s, t in the element frame
+        em_field = tao.em_field(ele_id=ele_id, x=x, y=0, z=s, t_or_z=0)
+        return em_field["B2"]
+
+    By = get_By(S, X)
+    return S + s0, X, By
+
+
+@dataclasses.dataclass
+class ElementField:
+    ele_id: str
+    s: typing.List[typing.List[float]]
+    x: typing.List[typing.List[float]]
+    by: typing.List[typing.List[float]]
+
+    @classmethod
+    def from_tao(
+        cls,
+        tao: Tao,
+        ele_id: str,
+        num_points: int = 100,
+        radius: float = 0.015,
+    ):
+        s, x, by = get_field_grid(tao, ele_id, radius=radius, num_points=num_points)
+        return cls(ele_id=ele_id, s=s.tolist(), x=x.tolist(), by=by.tolist())
diff --git a/pytao/plotting/floor_plan_shapes.py b/pytao/plotting/floor_plan_shapes.py
new file mode 100644
index 00000000..8b2a37fa
--- /dev/null
+++ b/pytao/plotting/floor_plan_shapes.py
@@ -0,0 +1,472 @@
+from __future__ import annotations
+
+import math
+from functools import cached_property
+from typing import List, Optional, Union
+
+import numpy as np
+import pydantic.dataclasses as dataclasses
+from pydantic import ConfigDict
+
+from . import util
+from .curves import PlotCurveLine
+from .patches import (
+    PlotPatch,
+    PlotPatchArc,
+    PlotPatchCircle,
+    PlotPatchRectangle,
+    PlotPatchSbend,
+)
+
+_dcls_config = ConfigDict()
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class Shape:
+    x1: float
+    x2: float
+    y1: float
+    y2: float
+    off1: float
+    off2: float
+    angle_start: float
+    angle_end: float = 0.0
+
+    rel_angle_start: float = 0.0  # Only for sbend
+    rel_angle_end: float = 0.0  # Only for sbend
+    line_width: float = 1.0
+    color: str = "black"
+    name: str = ""
+
+    @property
+    def corner_vertices(self):
+        px0 = self.x1 + self.off2 * np.sin(self.angle_start)  # x1 + off2 * sin
+        py0 = self.y1 - self.off2 * np.cos(self.angle_start)  # y1 - off2 * cos
+
+        px1 = self.x1 - self.off1 * np.sin(self.angle_start)  # x1 - off1 * sin
+        py1 = self.y1 + self.off1 * np.cos(self.angle_start)  # y1 + off1 * cos
+
+        px2 = self.x2 - self.off1 * np.sin(self.angle_start)  # x2 - off1 * sin
+        py2 = self.y2 + self.off1 * np.cos(self.angle_start)  # y2 + off1 * cos
+
+        px3 = self.x2 + self.off2 * np.sin(self.angle_start)  # x2 + off2 * sin
+        py3 = self.y2 - self.off2 * np.cos(self.angle_start)  # y2 - off2 * cos
+        return [
+            [px0, px1, px2, px3],
+            [py0, py1, py2, py3],
+        ]
+
+    @property
+    def vertices(self):
+        return []
+
+    def to_lines(self) -> List[PlotCurveLine]:
+        vertices = self.vertices
+        if not vertices:
+            return []
+        vx, vy = self.vertices
+        return [PlotCurveLine(vx, vy, linewidth=self.line_width, color=self.color)]
+
+    def to_patches(self) -> List[PlotPatch]:
+        return []
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LineSegment(Shape):
+    @property
+    def vertices(self):
+        return [[self.x1, self.x2], [self.y1, self.y2]]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class Circle(Shape):
+    def to_patches(self) -> List[PlotPatch]:
+        circle = PlotPatchCircle(
+            xy=(self.x1 + (self.x2 - self.x1) / 2, self.y1 + (self.y2 - self.y1) / 2),
+            radius=self.off1,
+            linewidth=self.line_width,
+            color=self.color,
+            fill=False,
+        )
+        return [circle]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class KickerLine(LineSegment):
+    @property
+    def vertices(self):
+        return [[self.x1, self.x2], [self.y1, self.y2]]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class DriftLine(LineSegment):
+    pass
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class BowTie(Shape):
+    @property
+    def vertices(self):
+        l1x = [
+            self.x1 + self.off2 * np.sin(self.angle_start),
+            self.x2 - self.off1 * np.sin(self.angle_start),
+        ]
+        l1y = [
+            self.y1 - self.off2 * np.cos(self.angle_start),
+            self.y2 + self.off1 * np.cos(self.angle_start),
+        ]
+        l2x = [
+            self.x1 - self.off1 * np.sin(self.angle_start),
+            self.x2 + self.off2 * np.sin(self.angle_start),
+        ]
+        l2y = [
+            self.y1 + self.off1 * np.cos(self.angle_start),
+            self.y2 - self.off2 * np.cos(self.angle_start),
+        ]
+        return [
+            [l1x[0], l1x[1], l2x[0], l2x[1], l1x[0]],
+            [l1y[0], l1y[1], l2y[0], l2y[1], l1y[0]],
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class Box(Shape):
+    def to_patches(self) -> List[PlotPatch]:
+        patch = PlotPatchRectangle(
+            xy=(
+                self.x1 + self.off2 * np.sin(self.angle_start),
+                self.y1 - self.off2 * np.cos(self.angle_start),
+            ),
+            width=np.sqrt((self.x2 - self.x1) ** 2 + (self.y2 - self.y1) ** 2),
+            height=self.off1 + self.off2,
+            linewidth=self.line_width,
+            color=self.color,
+            fill=False,
+            angle=math.degrees(self.angle_start),
+        )
+        return [patch]
+
+    @property
+    def vertices(self):
+        px, py = self.corner_vertices
+        return [px + px[:1], py + py[:1]]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class XBox(Shape):
+    @property
+    def vertices(self):
+        ((px0, px1, px2, px3), (py0, py1, py2, py3)) = self.corner_vertices
+        return [
+            [px0, px1, px2, px3, px0, px2, px3, px1],
+            [py0, py1, py2, py3, py0, py2, py3, py1],
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LetterX(Shape):
+    def to_lines(self):
+        px, py = self.corner_vertices
+        return [
+            PlotCurveLine(
+                [px[0], px[2]],
+                [py[0], py[2]],
+                linewidth=self.line_width,
+                color=self.color,
+            ),
+            PlotCurveLine(
+                [px[1], px[3]],
+                [py[1], py[3]],
+                linewidth=self.line_width,
+                color=self.color,
+            ),
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class Diamond(Shape):
+    @property
+    def vertices(self):
+        l1x1 = self.x1 + (self.x2 - self.x1) / 2 - self.off1 * np.sin(self.angle_start)
+        l1y1 = self.y1 + (self.y2 - self.y1) / 2 + self.off1 * np.cos(self.angle_start)
+        l2x1 = self.x1 + (self.x2 - self.x1) / 2 + self.off2 * np.sin(self.angle_start)
+        l2y1 = self.y1 + (self.y2 - self.y1) / 2 - self.off2 * np.cos(self.angle_start)
+
+        return [
+            [self.x1, l1x1, self.x2, l2x1, self.x1],
+            [self.y1, l1y1, self.y2, l2y1, self.y1],
+        ]
+
+
+def _sbend_intersection_to_patch(
+    intersection: util.Intersection,
+    x1: float,
+    x2: float,
+    y1: float,
+    y2: float,
+    off1: float,
+    off2: float,
+    angle_start: float,
+    angle_end: float,
+    rel_angle_start: float,
+    rel_angle_end: float,
+):
+    ix, iy = intersection
+
+    sin_start = np.sin(angle_start - rel_angle_start)
+    cos_start = np.cos(angle_start - rel_angle_start)
+    sin_end = np.sin(angle_end + rel_angle_end)
+    cos_end = np.cos(angle_end + rel_angle_end)
+
+    # corners of sbend
+    c1 = (x1 - off1 * sin_start, y1 + off1 * cos_start)
+    c2 = (x2 - off1 * sin_end, y2 + off1 * cos_end)
+    c3 = (x1 + off2 * sin_start, y1 - off2 * cos_start)
+    c4 = (x2 + off2 * sin_end, y2 - off2 * cos_end)
+
+    # radii of sbend arc edges
+    outer_radius = np.sqrt(
+        (x1 - off1 * sin_start - ix) ** 2 + (y1 + off1 * cos_start - iy) ** 2
+    )
+    inner_radius = np.sqrt(
+        (x1 + off2 * sin_start - ix) ** 2 + (y1 - off2 * cos_start - iy) ** 2
+    )
+    if angle_start <= angle_end:
+        outer_radius *= -1
+        inner_radius *= -1
+
+    # midpoints of top and bottom arcs in an sbend
+    mid_angle = (angle_start + angle_end) / 2
+
+    top = (
+        ix - outer_radius * np.sin(mid_angle),
+        iy + outer_radius * np.cos(mid_angle),
+    )
+    bottom = (
+        ix - inner_radius * np.sin(mid_angle),
+        iy + inner_radius * np.cos(mid_angle),
+    )
+
+    # corresponding control points for a quadratic Bezier curve that
+    # passes through the corners and arc midpoint
+    top_cp = (
+        2 * (top[0]) - 0.5 * (c1[0]) - 0.5 * (c2[0]),
+        2 * (top[1]) - 0.5 * (c1[1]) - 0.5 * (c2[1]),
+    )
+    bottom_cp = (
+        2 * (bottom[0]) - 0.5 * (c3[0]) - 0.5 * (c4[0]),
+        2 * (bottom[1]) - 0.5 * (c3[1]) - 0.5 * (c4[1]),
+    )
+
+    return PlotPatchSbend(
+        spline1=(c1, top_cp, c2),
+        spline2=(c4, bottom_cp, c3),
+        facecolor="green",
+        alpha=0.5,
+    )
+
+
+def _create_sbend_patches(
+    intersection: util.Intersection,
+    x1: float,
+    x2: float,
+    y1: float,
+    y2: float,
+    off1: float,
+    off2: float,
+    line_width: float,
+    color: str,
+    angle_start: float,
+    angle_end: float,
+    rel_angle_start: float,
+    rel_angle_end: float,
+) -> List[PlotPatch]:
+    ix, iy = intersection
+
+    a0 = angle_start - rel_angle_start
+    a1 = angle_end + rel_angle_end
+
+    # draw sbend edges if bend angle is 0
+    angle1 = 360 + math.degrees(
+        np.arctan2(
+            y1 + off1 * np.cos(a0) - iy,
+            x1 - off1 * np.sin(a0) - ix,
+        )
+    )
+    angle2 = 360 + math.degrees(
+        np.arctan2(y2 + off1 * np.cos(a1) - iy, x2 - off1 * np.sin(a1) - ix)
+    )
+    # angles of further curve endpoints relative to center of circle
+    angle3 = 360 + math.degrees(
+        np.arctan2(
+            y1 - off2 * np.cos(a0) - iy,
+            x1 + off2 * np.sin(a0) - ix,
+        )
+    )
+    angle4 = 360 + math.degrees(
+        np.arctan2(
+            y2 - off2 * np.cos(a1) - iy,
+            x2 + off2 * np.sin(a1) - ix,
+        )
+    )
+    # angles of closer curve endpoints relative to center of circle
+
+    if abs(angle1 - angle2) < 180:
+        a1 = min(angle1, angle2)
+        a2 = max(angle1, angle2)
+    else:
+        a1 = max(angle1, angle2)
+        a2 = min(angle1, angle2)
+
+    if abs(angle3 - angle4) < 180:
+        a3 = min(angle3, angle4)
+        a4 = max(angle3, angle4)
+    else:
+        a3 = max(angle3, angle4)
+        a4 = min(angle3, angle4)
+    # determines correct start and end angles for arcs
+
+    rel_sin = np.sin(a0)
+    rel_cos = np.cos(a0)
+    width1 = 2.0 * np.sqrt((x1 - off1 * rel_sin - ix) ** 2 + (y1 + off1 * rel_cos - iy) ** 2)
+    width2 = 2.0 * np.sqrt((x1 + off2 * rel_sin - ix) ** 2 + (y1 - off2 * rel_cos - iy) ** 2)
+    patches: List[PlotPatch] = [
+        PlotPatchArc(
+            xy=(ix, iy),
+            width=width1,
+            height=width1,
+            theta1=a1,
+            theta2=a2,
+            linewidth=line_width,
+            color=color,
+        ),
+        PlotPatchArc(
+            xy=(ix, iy),
+            width=width2,
+            height=width2,
+            theta1=a3,
+            theta2=a4,
+            linewidth=line_width,
+            color=color,
+        ),
+    ]
+    patch = _sbend_intersection_to_patch(
+        intersection=intersection,
+        x1=x1,
+        x2=x2,
+        y1=y1,
+        y2=y2,
+        off1=off1,
+        off2=off2,
+        angle_start=angle_start,
+        angle_end=angle_end,
+        rel_angle_start=rel_angle_start,
+        rel_angle_end=rel_angle_end,
+    )
+    patches.append(patch)
+    return patches
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class SBend(Shape):
+    @property
+    def box_lines(self):
+        a0 = self.angle_start - self.rel_angle_start
+        a1 = self.angle_end + self.rel_angle_end
+        return [
+            PlotCurveLine(
+                [self.x1 - self.off1 * np.sin(a0), self.x1 + self.off2 * np.sin(a0)],
+                [self.y1 + self.off1 * np.cos(a0), self.y1 - self.off2 * np.cos(a0)],
+                linewidth=self.line_width,
+                color=self.color,
+            ),
+            PlotCurveLine(
+                [self.x2 - self.off1 * np.sin(a1), self.x2 + self.off2 * np.sin(a1)],
+                [self.y2 + self.off1 * np.cos(a1), self.y2 - self.off2 * np.cos(a1)],
+                linewidth=self.line_width,
+                color=self.color,
+            ),
+        ]
+
+    @cached_property
+    def intersection(self) -> Optional[util.Intersection]:
+        line1 = util.line(
+            (
+                self.x1 - self.off1 * np.sin(self.angle_start),
+                self.y1 + self.off1 * np.cos(self.angle_start),
+            ),
+            (
+                self.x1 + self.off2 * np.sin(self.angle_start),
+                self.y1 - self.off2 * np.cos(self.angle_start),
+            ),
+        )
+        line2 = util.line(
+            (
+                self.x2 - self.off1 * np.sin(self.angle_end),
+                self.y2 + self.off1 * np.cos(self.angle_end),
+            ),
+            (
+                self.x2 + self.off2 * np.sin(self.angle_end),
+                self.y2 - self.off2 * np.cos(self.angle_end + self.rel_angle_end),
+            ),
+        )
+        try:
+            return util.intersect(line1, line2)
+        except util.NoIntersectionError:
+            return None
+
+    def to_lines(self) -> List[PlotCurveLine]:
+        """Lines to draw when there's no intersection."""
+        if self.intersection is not None:
+            return []
+
+        a0 = self.angle_start - self.rel_angle_start
+        a1 = self.angle_end + self.rel_angle_end
+        return [
+            PlotCurveLine(
+                [self.x1 - self.off1 * np.sin(a0), self.x2 - self.off1 * np.sin(a1)],
+                [self.y1 + self.off1 * np.cos(a0), self.y2 + self.off1 * np.cos(a1)],
+                linewidth=self.line_width,
+                color=self.color,
+            ),
+            PlotCurveLine(
+                [self.x1 + self.off2 * np.sin(a0), self.x2 + self.off2 * np.sin(a1)],
+                [self.y1 - self.off2 * np.cos(a0), self.y2 - self.off2 * np.cos(a1)],
+                linewidth=self.line_width,
+                color=self.color,
+            ),
+        ]
+
+    def to_patches(self) -> List[PlotPatch]:
+        if self.intersection is None:
+            return []
+
+        return _create_sbend_patches(
+            intersection=self.intersection,
+            x1=self.x1,
+            x2=self.x2,
+            y1=self.y1,
+            y2=self.y2,
+            off1=self.off1,
+            off2=self.off2,
+            line_width=self.line_width,
+            color=self.color,
+            angle_start=self.angle_start,
+            angle_end=self.angle_end,
+            rel_angle_start=self.rel_angle_start,
+            rel_angle_end=self.rel_angle_end,
+        )
+
+
+AnyFloorPlanShape = Union[
+    BowTie,
+    Box,
+    Circle,
+    Diamond,
+    DriftLine,
+    LineSegment,
+    KickerLine,
+    LetterX,
+    SBend,
+    XBox,
+]
diff --git a/pytao/plotting/hershey_fonts.py b/pytao/plotting/hershey_fonts.py
new file mode 100644
index 00000000..5aad9418
--- /dev/null
+++ b/pytao/plotting/hershey_fonts.py
@@ -0,0 +1,1115 @@
+def from_csv(fn: str):
+    mapping = {}
+    with open(fn) as fp:
+        for line in fp.read().splitlines():
+            unicode_hex, char_index, *_ = line.split(",")
+            mapping[int(char_index)] = chr(int(unicode_hex, 16))
+
+    return mapping
+
+
+if __name__ == "__main__":
+    # Ref: https://git.code.sf.net/p/plplot/plplot
+    # file: fonts/plhershey-unicode.csv
+    print("string_to_unicode = [")
+    for idx, ch in from_csv("plhershey-unicode.csv").items():
+        print(rf'    (r"\\({idx:04})", "\u{ord(ch):04x}"),')
+    print("]")
+
+string_to_unicode = [
+    (r"\\(0001)", "\u0041"),
+    (r"\\(0002)", "\u0042"),
+    (r"\\(0003)", "\u0043"),
+    (r"\\(0004)", "\u0044"),
+    (r"\\(0005)", "\u0045"),
+    (r"\\(0006)", "\u0046"),
+    (r"\\(0007)", "\u0047"),
+    (r"\\(0008)", "\u0048"),
+    (r"\\(0009)", "\u0049"),
+    (r"\\(0010)", "\u004a"),
+    (r"\\(0011)", "\u004b"),
+    (r"\\(0012)", "\u004c"),
+    (r"\\(0013)", "\u004d"),
+    (r"\\(0014)", "\u004e"),
+    (r"\\(0015)", "\u004f"),
+    (r"\\(0016)", "\u0050"),
+    (r"\\(0017)", "\u0051"),
+    (r"\\(0018)", "\u0052"),
+    (r"\\(0019)", "\u0053"),
+    (r"\\(0020)", "\u0054"),
+    (r"\\(0021)", "\u0055"),
+    (r"\\(0022)", "\u0056"),
+    (r"\\(0023)", "\u0057"),
+    (r"\\(0024)", "\u0058"),
+    (r"\\(0025)", "\u0059"),
+    (r"\\(0026)", "\u005a"),
+    (r"\\(0027)", "\u0391"),
+    (r"\\(0028)", "\u0392"),
+    (r"\\(0029)", "\u0393"),
+    (r"\\(0030)", "\u0394"),
+    (r"\\(0031)", "\u0395"),
+    (r"\\(0032)", "\u0396"),
+    (r"\\(0033)", "\u0397"),
+    (r"\\(0034)", "\u0398"),
+    (r"\\(0035)", "\u0399"),
+    (r"\\(0036)", "\u039a"),
+    (r"\\(0037)", "\u039b"),
+    (r"\\(0038)", "\u039c"),
+    (r"\\(0039)", "\u039d"),
+    (r"\\(0040)", "\u039e"),
+    (r"\\(0041)", "\u039f"),
+    (r"\\(0042)", "\u03a0"),
+    (r"\\(0043)", "\u03a1"),
+    (r"\\(0044)", "\u03a3"),
+    (r"\\(0045)", "\u03a4"),
+    (r"\\(0046)", "\u03d2"),
+    (r"\\(0047)", "\u03a6"),
+    (r"\\(0048)", "\u03a7"),
+    (r"\\(0049)", "\u03a8"),
+    (r"\\(0050)", "\u03a9"),
+    (r"\\(0051)", "\u0061"),
+    (r"\\(0052)", "\u0062"),
+    (r"\\(0053)", "\u0063"),
+    (r"\\(0054)", "\u0064"),
+    (r"\\(0055)", "\u0065"),
+    (r"\\(0056)", "\u0066"),
+    (r"\\(0057)", "\u0067"),
+    (r"\\(0058)", "\u0068"),
+    (r"\\(0059)", "\u0069"),
+    (r"\\(0060)", "\u006a"),
+    (r"\\(0061)", "\u006b"),
+    (r"\\(0062)", "\u006c"),
+    (r"\\(0063)", "\u006d"),
+    (r"\\(0064)", "\u006e"),
+    (r"\\(0065)", "\u006f"),
+    (r"\\(0066)", "\u0070"),
+    (r"\\(0067)", "\u0071"),
+    (r"\\(0068)", "\u0072"),
+    (r"\\(0069)", "\u0073"),
+    (r"\\(0070)", "\u0074"),
+    (r"\\(0071)", "\u0075"),
+    (r"\\(0072)", "\u0076"),
+    (r"\\(0073)", "\u0077"),
+    (r"\\(0074)", "\u0078"),
+    (r"\\(0075)", "\u0079"),
+    (r"\\(0076)", "\u007a"),
+    (r"\\(0077)", "\u03b1"),
+    (r"\\(0078)", "\u03b2"),
+    (r"\\(0079)", "\u03b3"),
+    (r"\\(0080)", "\u03b4"),
+    (r"\\(0081)", "\u03b6"),
+    (r"\\(0082)", "\u03b7"),
+    (r"\\(0083)", "\u03b9"),
+    (r"\\(0084)", "\u03ba"),
+    (r"\\(0085)", "\u03bb"),
+    (r"\\(0086)", "\u03bc"),
+    (r"\\(0087)", "\u03bd"),
+    (r"\\(0088)", "\u03be"),
+    (r"\\(0089)", "\u03bf"),
+    (r"\\(0090)", "\u03c0"),
+    (r"\\(0091)", "\u03c1"),
+    (r"\\(0092)", "\u03c3"),
+    (r"\\(0093)", "\u03c4"),
+    (r"\\(0094)", "\u03c5"),
+    (r"\\(0095)", "\u03c7"),
+    (r"\\(0096)", "\u03c8"),
+    (r"\\(0097)", "\u03c9"),
+    (r"\\(0098)", "\u03f5"),
+    (r"\\(0099)", "\u03b8"),
+    (r"\\(0100)", "\u03d5"),
+    (r"\\(0102)", "\u0030"),
+    (r"\\(0103)", "\u0031"),
+    (r"\\(0104)", "\u0032"),
+    (r"\\(0105)", "\u0033"),
+    (r"\\(0106)", "\u0034"),
+    (r"\\(0107)", "\u0035"),
+    (r"\\(0108)", "\u0036"),
+    (r"\\(0109)", "\u0037"),
+    (r"\\(0110)", "\u0038"),
+    (r"\\(0111)", "\u0039"),
+    (r"\\(0112)", "\u002e"),
+    (r"\\(0113)", "\u002c"),
+    (r"\\(0114)", "\u003a"),
+    (r"\\(0115)", "\u003b"),
+    (r"\\(0116)", "\u0021"),
+    (r"\\(0117)", "\u003f"),
+    (r"\\(0118)", "\u0027"),
+    (r"\\(0119)", "\u0022"),
+    (r"\\(0120)", "\u0024"),
+    (r"\\(0121)", "\u002f"),
+    (r"\\(0122)", "\u0028"),
+    (r"\\(0123)", "\u0029"),
+    (r"\\(0124)", "\u007c"),
+    (r"\\(0125)", "\u2212"),
+    (r"\\(0126)", "\u002b"),
+    (r"\\(0127)", "\u003d"),
+    (r"\\(0128)", "\u2217"),
+    (r"\\(0129)", "\u0023"),
+    (r"\\(0130)", "\u0026"),
+    (r"\\(0132)", "\u005f"),
+    (r"\\(0133)", "\u005c"),
+    (r"\\(0134)", "\u005e"),
+    (r"\\(0135)", "\u25cb"),
+    (r"\\(0136)", "\u25a1"),
+    (r"\\(0137)", "\u25b3"),
+    (r"\\(0138)", "\u2662"),
+    (r"\\(0139)", "\u2729"),
+    (r"\\(0140)", "\u002b"),
+    (r"\\(0141)", "\u00d7"),
+    (r"\\(0142)", "\u2217"),
+    (r"\\(0143)", "\u2022"),
+    (r"\\(0144)", "\u25a0"),
+    (r"\\(0145)", "\u22c6"),
+    (r"\\(0146)", "\u0000"),
+    (r"\\(0147)", "\u2721"),
+    (r"\\(0148)", "\u22c5"),
+    (r"\\(0157)", "\u005b"),
+    (r"\\(0158)", "\u005d"),
+    (r"\\(0159)", "\u007b"),
+    (r"\\(0160)", "\u007d"),
+    (r"\\(0161)", "\u003c"),
+    (r"\\(0162)", "\u003e"),
+    (r"\\(0163)", "\u007e"),
+    (r"\\(0164)", "\u0060"),
+    (r"\\(0165)", "\u2192"),
+    (r"\\(0166)", "\u2191"),
+    (r"\\(0167)", "\u2190"),
+    (r"\\(0168)", "\u2193"),
+    (r"\\(0169)", "\u0025"),
+    (r"\\(0170)", "\u0040"),
+    (r"\\(0171)", "\u2299"),
+    (r"\\(0172)", "\u2295"),
+    (r"\\(0200)", "\u0030"),
+    (r"\\(0201)", "\u0031"),
+    (r"\\(0202)", "\u0032"),
+    (r"\\(0203)", "\u0033"),
+    (r"\\(0204)", "\u0034"),
+    (r"\\(0205)", "\u0035"),
+    (r"\\(0206)", "\u0036"),
+    (r"\\(0207)", "\u0037"),
+    (r"\\(0208)", "\u0038"),
+    (r"\\(0209)", "\u0039"),
+    (r"\\(0210)", "\u002e"),
+    (r"\\(0211)", "\u002c"),
+    (r"\\(0212)", "\u003a"),
+    (r"\\(0213)", "\u003b"),
+    (r"\\(0214)", "\u0021"),
+    (r"\\(0215)", "\u003f"),
+    (r"\\(0216)", "\u0027"),
+    (r"\\(0217)", "\u0022"),
+    (r"\\(0218)", "\u00b0"),
+    (r"\\(0219)", "\u0024"),
+    (r"\\(0220)", "\u002f"),
+    (r"\\(0221)", "\u0028"),
+    (r"\\(0222)", "\u0029"),
+    (r"\\(0223)", "\u007c"),
+    (r"\\(0224)", "\u2212"),
+    (r"\\(0225)", "\u002b"),
+    (r"\\(0226)", "\u003d"),
+    (r"\\(0227)", "\u00d7"),
+    (r"\\(0228)", "\u2217"),
+    (r"\\(0229)", "\u22c5"),
+    (r"\\(0230)", "\u2018"),
+    (r"\\(0231)", "\u2019"),
+    (r"\\(0232)", "\u2192"),
+    (r"\\(0233)", "\u0023"),
+    (r"\\(0234)", "\u0026"),
+    (r"\\(0235)", "\u0000"),
+    (r"\\(0236)", "\u0040"),
+    (r"\\(0238)", "\u2025"),
+    (r"\\(0239)", "\u2025"),
+    (r"\\(0240)", "\u0000"),
+    (r"\\(0242)", "\u0000"),
+    (r"\\(0248)", "\u2248"),
+    (r"\\(0250)", "\u2245"),
+    (r"\\(0252)", "\u21cc"),
+    (r"\\(0261)", "\u00bd"),
+    (r"\\(0262)", "\u2153"),
+    (r"\\(0263)", "\u2159"),
+    (r"\\(0264)", "\u215b"),
+    (r"\\(0265)", "\u2154"),
+    (r"\\(0266)", "\u215c"),
+    (r"\\(0267)", "\u215d"),
+    (r"\\(0268)", "\u215e"),
+    (r"\\(0269)", "\u215a"),
+    (r"\\(0270)", "\u00bc"),
+    (r"\\(0271)", "\u00be"),
+    (r"\\(0272)", "\u00a3"),
+    (r"\\(0273)", "\u00ae"),
+    (r"\\(0274)", "\u00a9"),
+    (r"\\(0275)", "\u2262"),
+    (r"\\(0276)", "\u22ef"),
+    (r"\\(0278)", "\u2194"),
+    (r"\\(0279)", "\u2195"),
+    (r"\\(0280)", "\u2284"),
+    (r"\\(0281)", "\u2285"),
+    (r"\\(0282)", "\u220b"),
+    (r"\\(0284)", "\u2980"),
+    (r"\\(0501)", "\u0041"),
+    (r"\\(0502)", "\u0042"),
+    (r"\\(0503)", "\u0043"),
+    (r"\\(0504)", "\u0044"),
+    (r"\\(0505)", "\u0045"),
+    (r"\\(0506)", "\u0046"),
+    (r"\\(0507)", "\u0047"),
+    (r"\\(0508)", "\u0048"),
+    (r"\\(0509)", "\u0049"),
+    (r"\\(0510)", "\u004a"),
+    (r"\\(0511)", "\u004b"),
+    (r"\\(0512)", "\u004c"),
+    (r"\\(0513)", "\u004d"),
+    (r"\\(0514)", "\u004e"),
+    (r"\\(0515)", "\u004f"),
+    (r"\\(0516)", "\u0050"),
+    (r"\\(0517)", "\u0051"),
+    (r"\\(0518)", "\u0052"),
+    (r"\\(0519)", "\u0053"),
+    (r"\\(0520)", "\u0054"),
+    (r"\\(0521)", "\u0055"),
+    (r"\\(0522)", "\u0056"),
+    (r"\\(0523)", "\u0057"),
+    (r"\\(0524)", "\u0058"),
+    (r"\\(0525)", "\u0059"),
+    (r"\\(0526)", "\u005a"),
+    (r"\\(0527)", "\u0391"),
+    (r"\\(0528)", "\u0392"),
+    (r"\\(0529)", "\u0393"),
+    (r"\\(0530)", "\u0394"),
+    (r"\\(0531)", "\u0395"),
+    (r"\\(0532)", "\u0396"),
+    (r"\\(0533)", "\u0397"),
+    (r"\\(0534)", "\u0398"),
+    (r"\\(0535)", "\u0399"),
+    (r"\\(0536)", "\u039a"),
+    (r"\\(0537)", "\u039b"),
+    (r"\\(0538)", "\u039c"),
+    (r"\\(0539)", "\u039d"),
+    (r"\\(0540)", "\u039e"),
+    (r"\\(0541)", "\u039f"),
+    (r"\\(0542)", "\u03a0"),
+    (r"\\(0543)", "\u03a1"),
+    (r"\\(0544)", "\u03a3"),
+    (r"\\(0545)", "\u03a4"),
+    (r"\\(0546)", "\u03d2"),
+    (r"\\(0547)", "\u03a6"),
+    (r"\\(0548)", "\u03a7"),
+    (r"\\(0549)", "\u03a8"),
+    (r"\\(0550)", "\u03a9"),
+    (r"\\(0551)", "\u0041"),
+    (r"\\(0552)", "\u0042"),
+    (r"\\(0553)", "\u0043"),
+    (r"\\(0554)", "\u0044"),
+    (r"\\(0555)", "\u0045"),
+    (r"\\(0556)", "\u0046"),
+    (r"\\(0557)", "\u0047"),
+    (r"\\(0558)", "\u0048"),
+    (r"\\(0559)", "\u0049"),
+    (r"\\(0560)", "\u004a"),
+    (r"\\(0561)", "\u004b"),
+    (r"\\(0562)", "\u004c"),
+    (r"\\(0563)", "\u004d"),
+    (r"\\(0564)", "\u004e"),
+    (r"\\(0565)", "\u004f"),
+    (r"\\(0566)", "\u0050"),
+    (r"\\(0567)", "\u0051"),
+    (r"\\(0568)", "\u0052"),
+    (r"\\(0569)", "\u0053"),
+    (r"\\(0570)", "\u0054"),
+    (r"\\(0571)", "\u0055"),
+    (r"\\(0572)", "\u0056"),
+    (r"\\(0573)", "\u0057"),
+    (r"\\(0574)", "\u0058"),
+    (r"\\(0575)", "\u0059"),
+    (r"\\(0576)", "\u005a"),
+    (r"\\(0583)", "\u2207"),
+    (r"\\(0601)", "\u0061"),
+    (r"\\(0602)", "\u0062"),
+    (r"\\(0603)", "\u0063"),
+    (r"\\(0604)", "\u0064"),
+    (r"\\(0605)", "\u0065"),
+    (r"\\(0606)", "\u0066"),
+    (r"\\(0607)", "\u0067"),
+    (r"\\(0608)", "\u0068"),
+    (r"\\(0609)", "\u0069"),
+    (r"\\(0610)", "\u006a"),
+    (r"\\(0611)", "\u006b"),
+    (r"\\(0612)", "\u006c"),
+    (r"\\(0613)", "\u006d"),
+    (r"\\(0614)", "\u006e"),
+    (r"\\(0615)", "\u006f"),
+    (r"\\(0616)", "\u0070"),
+    (r"\\(0617)", "\u0071"),
+    (r"\\(0618)", "\u0072"),
+    (r"\\(0619)", "\u0073"),
+    (r"\\(0620)", "\u0074"),
+    (r"\\(0621)", "\u0075"),
+    (r"\\(0622)", "\u0076"),
+    (r"\\(0623)", "\u0077"),
+    (r"\\(0624)", "\u0078"),
+    (r"\\(0625)", "\u0079"),
+    (r"\\(0626)", "\u007a"),
+    (r"\\(0627)", "\u03b1"),
+    (r"\\(0628)", "\u03b2"),
+    (r"\\(0629)", "\u03b3"),
+    (r"\\(0630)", "\u03b4"),
+    (r"\\(0631)", "\u03b5"),
+    (r"\\(0632)", "\u03b6"),
+    (r"\\(0633)", "\u03b7"),
+    (r"\\(0634)", "\u03d1"),
+    (r"\\(0635)", "\u03b9"),
+    (r"\\(0636)", "\u03ba"),
+    (r"\\(0637)", "\u03bb"),
+    (r"\\(0638)", "\u03bc"),
+    (r"\\(0639)", "\u03bd"),
+    (r"\\(0640)", "\u03be"),
+    (r"\\(0641)", "\u03bf"),
+    (r"\\(0642)", "\u03c0"),
+    (r"\\(0643)", "\u03c1"),
+    (r"\\(0644)", "\u03c3"),
+    (r"\\(0645)", "\u03c4"),
+    (r"\\(0646)", "\u03c5"),
+    (r"\\(0647)", "\u03c6"),
+    (r"\\(0648)", "\u03c7"),
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+    (r"\\(2366)", "\u0000"),
+    (r"\\(2367)", "\u22c5"),
+    (r"\\(2368)", "\u0000"),
+    (r"\\(2369)", "\u0000"),
+    (r"\\(2370)", "\u1d15d"),
+    (r"\\(2371)", "\u1d157"),
+    (r"\\(2372)", "\u1d158"),
+    (r"\\(2373)", "\u266f"),
+    (r"\\(2374)", "\u266e"),
+    (r"\\(2375)", "\u266d"),
+    (r"\\(2376)", "\u1d13b"),
+    (r"\\(2377)", "\u1d13c"),
+    (r"\\(2378)", "\u1d13d"),
+    (r"\\(2379)", "\u1d13e"),
+    (r"\\(2380)", "\u1d11e"),
+    (r"\\(2381)", "\u1d122"),
+    (r"\\(2382)", "\u1d121"),
+    (r"\\(2401)", "\u220f"),
+    (r"\\(2402)", "\u2211"),
+    (r"\\(2403)", "\u0028"),
+    (r"\\(2404)", "\u0029"),
+    (r"\\(2405)", "\u005b"),
+    (r"\\(2406)", "\u005d"),
+    (r"\\(2407)", "\u007b"),
+    (r"\\(2408)", "\u007d"),
+    (r"\\(2409)", "\u23b0"),
+    (r"\\(2410)", "\u23b1"),
+    (r"\\(2411)", "\u221a"),
+    (r"\\(2412)", "\u222b"),
+    (r"\\(2501)", "\u0041"),
+    (r"\\(2502)", "\u0042"),
+    (r"\\(2503)", "\u0043"),
+    (r"\\(2504)", "\u0044"),
+    (r"\\(2505)", "\u0045"),
+    (r"\\(2506)", "\u0046"),
+    (r"\\(2507)", "\u0047"),
+    (r"\\(2508)", "\u0048"),
+    (r"\\(2509)", "\u0049"),
+    (r"\\(2510)", "\u004a"),
+    (r"\\(2511)", "\u004b"),
+    (r"\\(2512)", "\u004c"),
+    (r"\\(2513)", "\u004d"),
+    (r"\\(2514)", "\u004e"),
+    (r"\\(2515)", "\u004f"),
+    (r"\\(2516)", "\u0050"),
+    (r"\\(2517)", "\u0051"),
+    (r"\\(2518)", "\u0052"),
+    (r"\\(2519)", "\u0053"),
+    (r"\\(2520)", "\u0054"),
+    (r"\\(2521)", "\u0055"),
+    (r"\\(2522)", "\u0056"),
+    (r"\\(2523)", "\u0057"),
+    (r"\\(2524)", "\u0058"),
+    (r"\\(2525)", "\u0059"),
+    (r"\\(2526)", "\u005a"),
+    (r"\\(2551)", "\u0041"),
+    (r"\\(2552)", "\u0042"),
+    (r"\\(2553)", "\u0043"),
+    (r"\\(2554)", "\u0044"),
+    (r"\\(2555)", "\u0045"),
+    (r"\\(2556)", "\u0046"),
+    (r"\\(2557)", "\u0047"),
+    (r"\\(2558)", "\u0048"),
+    (r"\\(2559)", "\u0049"),
+    (r"\\(2560)", "\u004a"),
+    (r"\\(2561)", "\u004b"),
+    (r"\\(2562)", "\u004c"),
+    (r"\\(2563)", "\u004d"),
+    (r"\\(2564)", "\u004e"),
+    (r"\\(2565)", "\u004f"),
+    (r"\\(2566)", "\u0050"),
+    (r"\\(2567)", "\u0051"),
+    (r"\\(2568)", "\u0052"),
+    (r"\\(2569)", "\u0053"),
+    (r"\\(2570)", "\u0054"),
+    (r"\\(2571)", "\u0055"),
+    (r"\\(2572)", "\u0056"),
+    (r"\\(2573)", "\u0057"),
+    (r"\\(2574)", "\u0058"),
+    (r"\\(2575)", "\u0059"),
+    (r"\\(2576)", "\u005a"),
+    (r"\\(2601)", "\u0061"),
+    (r"\\(2602)", "\u0062"),
+    (r"\\(2603)", "\u0063"),
+    (r"\\(2604)", "\u0064"),
+    (r"\\(2605)", "\u0065"),
+    (r"\\(2606)", "\u0066"),
+    (r"\\(2607)", "\u0067"),
+    (r"\\(2608)", "\u0068"),
+    (r"\\(2609)", "\u0069"),
+    (r"\\(2610)", "\u006a"),
+    (r"\\(2611)", "\u006b"),
+    (r"\\(2612)", "\u006c"),
+    (r"\\(2613)", "\u006d"),
+    (r"\\(2614)", "\u006e"),
+    (r"\\(2615)", "\u006f"),
+    (r"\\(2616)", "\u0070"),
+    (r"\\(2617)", "\u0071"),
+    (r"\\(2618)", "\u0072"),
+    (r"\\(2619)", "\u0073"),
+    (r"\\(2620)", "\u0074"),
+    (r"\\(2621)", "\u0075"),
+    (r"\\(2622)", "\u0076"),
+    (r"\\(2623)", "\u0077"),
+    (r"\\(2624)", "\u0078"),
+    (r"\\(2625)", "\u0079"),
+    (r"\\(2626)", "\u007a"),
+    (r"\\(2628)", "\u0000"),
+    (r"\\(2651)", "\u0061"),
+    (r"\\(2652)", "\u0062"),
+    (r"\\(2653)", "\u0063"),
+    (r"\\(2654)", "\u0064"),
+    (r"\\(2655)", "\u0065"),
+    (r"\\(2656)", "\u0066"),
+    (r"\\(2657)", "\u0067"),
+    (r"\\(2658)", "\u0068"),
+    (r"\\(2659)", "\u0069"),
+    (r"\\(2660)", "\u006a"),
+    (r"\\(2661)", "\u006b"),
+    (r"\\(2662)", "\u006c"),
+    (r"\\(2663)", "\u006d"),
+    (r"\\(2664)", "\u006e"),
+    (r"\\(2665)", "\u006f"),
+    (r"\\(2666)", "\u0070"),
+    (r"\\(2667)", "\u0071"),
+    (r"\\(2668)", "\u0072"),
+    (r"\\(2669)", "\u0073"),
+    (r"\\(2670)", "\u0074"),
+    (r"\\(2671)", "\u0075"),
+    (r"\\(2672)", "\u0076"),
+    (r"\\(2673)", "\u0077"),
+    (r"\\(2674)", "\u0078"),
+    (r"\\(2675)", "\u0079"),
+    (r"\\(2676)", "\u007a"),
+    (r"\\(2700)", "\u0030"),
+    (r"\\(2701)", "\u0031"),
+    (r"\\(2702)", "\u0032"),
+    (r"\\(2703)", "\u0033"),
+    (r"\\(2704)", "\u0034"),
+    (r"\\(2705)", "\u0035"),
+    (r"\\(2706)", "\u0036"),
+    (r"\\(2707)", "\u0037"),
+    (r"\\(2708)", "\u0038"),
+    (r"\\(2709)", "\u0039"),
+    (r"\\(2710)", "\u002e"),
+    (r"\\(2711)", "\u002c"),
+    (r"\\(2712)", "\u003a"),
+    (r"\\(2713)", "\u003b"),
+    (r"\\(2714)", "\u0021"),
+    (r"\\(2715)", "\u003f"),
+    (r"\\(2716)", "\u2018"),
+    (r"\\(2717)", "\u2019"),
+    (r"\\(2718)", "\u0026"),
+    (r"\\(2719)", "\u0024"),
+    (r"\\(2720)", "\u002f"),
+    (r"\\(2721)", "\u0028"),
+    (r"\\(2722)", "\u0029"),
+    (r"\\(2723)", "\u2217"),
+    (r"\\(2724)", "\u2212"),
+    (r"\\(2725)", "\u002b"),
+    (r"\\(2726)", "\u003d"),
+    (r"\\(2727)", "\u0027"),
+    (r"\\(2728)", "\u0022"),
+    (r"\\(2729)", "\u00b0"),
+    (r"\\(2750)", "\u0030"),
+    (r"\\(2751)", "\u0031"),
+    (r"\\(2752)", "\u0032"),
+    (r"\\(2753)", "\u0033"),
+    (r"\\(2754)", "\u0034"),
+    (r"\\(2755)", "\u0035"),
+    (r"\\(2756)", "\u0036"),
+    (r"\\(2757)", "\u0037"),
+    (r"\\(2758)", "\u0038"),
+    (r"\\(2759)", "\u0039"),
+    (r"\\(2760)", "\u002e"),
+    (r"\\(2761)", "\u002c"),
+    (r"\\(2762)", "\u003a"),
+    (r"\\(2763)", "\u003b"),
+    (r"\\(2764)", "\u0021"),
+    (r"\\(2765)", "\u003f"),
+    (r"\\(2766)", "\u0000"),
+    (r"\\(2767)", "\u0000"),
+    (r"\\(2768)", "\u0026"),
+    (r"\\(2769)", "\u0024"),
+    (r"\\(2770)", "\u002f"),
+    (r"\\(2771)", "\u0028"),
+    (r"\\(2772)", "\u0029"),
+    (r"\\(2773)", "\u2217"),
+    (r"\\(2774)", "\u2212"),
+    (r"\\(2775)", "\u002b"),
+    (r"\\(2776)", "\u003d"),
+    (r"\\(2777)", "\u0027"),
+    (r"\\(2778)", "\u0022"),
+    (r"\\(2779)", "\u00b0"),
+    (r"\\(2801)", "\u0410"),
+    (r"\\(2802)", "\u0411"),
+    (r"\\(2803)", "\u0412"),
+    (r"\\(2804)", "\u0413"),
+    (r"\\(2805)", "\u0414"),
+    (r"\\(2806)", "\u0415"),
+    (r"\\(2807)", "\u0416"),
+    (r"\\(2808)", "\u0417"),
+    (r"\\(2809)", "\u0418"),
+    (r"\\(2810)", "\u0419"),
+    (r"\\(2811)", "\u041a"),
+    (r"\\(2812)", "\u041b"),
+    (r"\\(2813)", "\u041c"),
+    (r"\\(2814)", "\u041d"),
+    (r"\\(2815)", "\u041e"),
+    (r"\\(2816)", "\u041f"),
+    (r"\\(2817)", "\u0420"),
+    (r"\\(2818)", "\u0421"),
+    (r"\\(2819)", "\u0422"),
+    (r"\\(2820)", "\u0423"),
+    (r"\\(2821)", "\u0424"),
+    (r"\\(2822)", "\u0425"),
+    (r"\\(2823)", "\u0426"),
+    (r"\\(2824)", "\u0427"),
+    (r"\\(2825)", "\u0428"),
+    (r"\\(2826)", "\u0429"),
+    (r"\\(2827)", "\u042a"),
+    (r"\\(2828)", "\u042b"),
+    (r"\\(2829)", "\u042c"),
+    (r"\\(2830)", "\u042d"),
+    (r"\\(2831)", "\u042e"),
+    (r"\\(2832)", "\u042f"),
+    (r"\\(2901)", "\u0430"),
+    (r"\\(2902)", "\u0431"),
+    (r"\\(2903)", "\u0432"),
+    (r"\\(2904)", "\u0433"),
+    (r"\\(2905)", "\u0434"),
+    (r"\\(2906)", "\u0435"),
+    (r"\\(2907)", "\u0436"),
+    (r"\\(2908)", "\u0437"),
+    (r"\\(2909)", "\u0438"),
+    (r"\\(2910)", "\u0439"),
+    (r"\\(2911)", "\u043a"),
+    (r"\\(2912)", "\u043b"),
+    (r"\\(2913)", "\u043c"),
+    (r"\\(2914)", "\u043d"),
+    (r"\\(2915)", "\u043e"),
+    (r"\\(2916)", "\u043f"),
+    (r"\\(2917)", "\u0440"),
+    (r"\\(2918)", "\u0441"),
+    (r"\\(2919)", "\u0442"),
+    (r"\\(2920)", "\u0443"),
+    (r"\\(2921)", "\u0444"),
+    (r"\\(2922)", "\u0445"),
+    (r"\\(2923)", "\u0446"),
+    (r"\\(2924)", "\u0447"),
+    (r"\\(2925)", "\u0448"),
+    (r"\\(2926)", "\u0449"),
+    (r"\\(2927)", "\u044a"),
+    (r"\\(2928)", "\u044b"),
+    (r"\\(2929)", "\u044c"),
+    (r"\\(2930)", "\u044d"),
+    (r"\\(2931)", "\u044e"),
+    (r"\\(2932)", "\u044f"),
+]
diff --git a/pytao/plotting/layout_shapes.py b/pytao/plotting/layout_shapes.py
new file mode 100644
index 00000000..2329bc16
--- /dev/null
+++ b/pytao/plotting/layout_shapes.py
@@ -0,0 +1,349 @@
+from __future__ import annotations
+
+from abc import ABC
+from typing import List, Tuple, Union
+
+import pydantic.dataclasses as dataclasses
+from pydantic import ConfigDict
+from typing_extensions import Literal
+
+from .curves import PlotCurveLine
+from .patches import (
+    PlotPatch,
+    PlotPatchEllipse,
+    PlotPatchPolygon,
+    PlotPatchRectangle,
+)
+
+_dcls_config = ConfigDict()
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutShape:
+    s1: float
+    s2: float
+    y1: float
+    y2: float
+    name: str = ""
+    color: str = "black"
+    line_width: float = 1.0
+    fill: bool = False
+
+    @property
+    def corner_vertices(self):
+        return [
+            [self.s1, self.s1, self.s2, self.s2],
+            [self.y1, self.y2, self.y2, self.y1],
+        ]
+
+    @property
+    def dimensions(self):
+        return (
+            self.s2 - self.s1,
+            self.y2 - self.y1,
+        )
+
+    @property
+    def center(self) -> Tuple[float, float]:
+        return (
+            (self.s1 + self.s2) / 2,
+            (self.y1 + self.y2) / 2,
+        )
+
+    @property
+    def lines(self):
+        return []
+
+    def to_lines(self) -> List[PlotCurveLine]:
+        lines = self.lines
+        if not lines:
+            return []
+        return [
+            PlotCurveLine(
+                [x for x, _ in line],
+                [y for _, y in line],
+                linewidth=self.line_width,
+                color=self.color,
+            )
+            for line in self.lines
+        ]
+
+    def to_patches(self) -> List[PlotPatch]:
+        return []
+
+    @property
+    def patch_kwargs(self):
+        return {
+            "linewidth": self.line_width,
+            "color": self.color,
+            "fill": self.fill,
+        }
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutBox(LayoutShape):
+    def to_patches(self) -> List[PlotPatch]:
+        width, height = self.dimensions
+        return [
+            PlotPatchRectangle(
+                xy=(self.s1, self.y1),
+                width=width,
+                height=height,
+                **self.patch_kwargs,
+            )
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutXBox(LayoutShape):
+    @property
+    def lines(self):
+        return [
+            [(self.s1, self.y1), (self.s2, self.y2)],
+            [(self.s1, self.y2), (self.s2, self.y1)],
+        ]
+
+    def to_patches(self) -> List[PlotPatch]:
+        width, height = self.dimensions
+        return [
+            PlotPatchRectangle(
+                xy=(self.s1, self.y1),
+                width=width,
+                height=height,
+                **self.patch_kwargs,
+            )
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutLetterX(LayoutShape):
+    @property
+    def lines(self):
+        return [
+            [(self.s1, self.y1), (self.s2, self.y2)],
+            [(self.s1, self.y2), (self.s2, self.y1)],
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutBowTie(LayoutShape):
+    @property
+    def lines(self):
+        return [
+            [
+                (self.s1, self.y1),
+                (self.s2, self.y2),
+                (self.s2, self.y1),
+                (self.s1, self.y2),
+                (self.s1, self.y1),
+            ]
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutRBowTie(LayoutShape):
+    @property
+    def lines(self):
+        return [
+            [
+                (self.s1, self.y1),
+                (self.s2, self.y2),
+                (self.s1, self.y2),
+                (self.s2, self.y1),
+                (self.s1, self.y1),
+            ]
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutDiamond(LayoutShape):
+    @property
+    def lines(self):
+        s_mid, _ = self.center
+        return [
+            [
+                (self.s1, 0),
+                (s_mid, self.y1),
+                (self.s2, 0),
+                (s_mid, self.y2),
+                (self.s1, 0),
+            ]
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutCircle(LayoutShape):
+    def to_patches(self) -> List[PlotPatch]:
+        s_mid, _ = self.center
+        width, height = self.dimensions
+        return [
+            PlotPatchEllipse(
+                xy=(s_mid, 0),
+                width=width,
+                height=height,
+                **self.patch_kwargs,
+            )
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutTriangle(LayoutShape):
+    orientation: Literal["u", "d", "l", "r"] = "u"
+
+    @property
+    def vertices(self):
+        s_mid, y_mid = self.center
+        if self.orientation == "u":
+            return [(self.s1, self.y2), (self.s2, self.y2), (s_mid, self.y1)]
+        if self.orientation == "d":
+            return [(self.s1, self.y1), (self.s2, self.y1), (s_mid, self.y2)]
+        if self.orientation == "l":
+            return [(self.s1, y_mid), (self.s2, self.y2), (self.s2, self.y1)]
+        if self.orientation == "r":
+            return [(self.s1, self.y1), (self.s1, self.y2), (self.s2, y_mid)]
+        raise ValueError(f"Unsupported orientation: {self.orientation}")
+
+    def to_patches(self) -> List[PlotPatch]:
+        return [PlotPatchPolygon(vertices=self.vertices, **self.patch_kwargs)]
+
+
+shape_to_class = {
+    "box": LayoutBox,
+    "xbox": LayoutXBox,
+    "x": LayoutLetterX,
+    "bowtie": LayoutBowTie,
+    "diamond": LayoutDiamond,
+    "circle": LayoutCircle,
+    "utriangle": LayoutTriangle,
+    "dtriangle": LayoutTriangle,
+    "ltriangle": LayoutTriangle,
+    "rtriangle": LayoutTriangle,
+}
+
+AnyNormalLayoutShape = Union[
+    LayoutBox,
+    LayoutXBox,
+    LayoutLetterX,
+    LayoutBowTie,
+    LayoutDiamond,
+    LayoutCircle,
+    LayoutTriangle,
+]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutWrappedShape(ABC):
+    s1: float
+    s2: float
+    y1: float
+    y2: float
+    s_min: float
+    s_max: float
+    name: str = ""
+    color: str = "black"
+    line_width: float = 1.0
+
+    @property
+    def lines(self):
+        return []
+
+    def to_lines(self) -> List[PlotCurveLine]:
+        lines = self.lines
+        if not lines:
+            return []
+        return [
+            PlotCurveLine(lx, ly, linewidth=self.line_width, color=self.color)
+            for lx, ly in self.lines
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutWrappedBox(LayoutWrappedShape):
+    @property
+    def lines(self):
+        return [
+            ([self.s1, self.s_max], [self.y1, self.y1]),
+            ([self.s1, self.s_max], [self.y2, self.y2]),
+            ([self.s_min, self.s2], [self.y1, self.y1]),
+            ([self.s_min, self.s2], [self.y2, self.y2]),
+            ([self.s1, self.s1], [self.y1, self.y2]),
+            ([self.s2, self.s2], [self.y1, self.y2]),
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutWrappedXBox(LayoutWrappedShape):
+    @property
+    def lines(self):
+        return [
+            ([self.s1, self.s_max], [self.y1, self.y1]),
+            ([self.s1, self.s_max], [self.y2, self.y2]),
+            ([self.s1, self.s_max], [self.y1, 0]),
+            ([self.s1, self.s_max], [self.y2, 0]),
+            ([self.s_min, self.s2], [self.y1, self.y1]),
+            ([self.s_min, self.s2], [self.y2, self.y2]),
+            ([self.s_min, self.s2], [0, self.y1]),
+            ([self.s_min, self.s2], [0, self.y2]),
+            ([self.s1, self.s1], [self.y1, self.y2]),
+            ([self.s2, self.s2], [self.y1, self.y2]),
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutWrappedLetterX(LayoutWrappedShape):
+    @property
+    def lines(self):
+        return [
+            ([self.s1, self.s_max], [self.y1, 0]),
+            ([self.s1, self.s_max], [self.y2, 0]),
+            ([self.s_min, self.s2], [0, self.y1]),
+            ([self.s_min, self.s2], [0, self.y2]),
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutWrappedBowTie(LayoutWrappedShape):
+    @property
+    def lines(self):
+        return [
+            ([self.s1, self.s_max], [self.y1, self.y1]),
+            ([self.s1, self.s_max], [self.y2, self.y2]),
+            ([self.s1, self.s_max], [self.y1, 0]),
+            ([self.s1, self.s_max], [self.y2, 0]),
+            ([self.s_min, self.s2], [self.y1, self.y1]),
+            ([self.s_min, self.s2], [self.y2, self.y2]),
+            ([self.s_min, self.s2], [0, self.y1]),
+            ([self.s_min, self.s2], [0, self.y2]),
+        ]
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LayoutWrappedDiamond(LayoutWrappedShape):
+    @property
+    def lines(self):
+        return [
+            ([self.s1, self.s_max], [0, self.y1]),
+            ([self.s1, self.s_max], [0, self.y2]),
+            ([self.s_min, self.s2], [self.y1, 0]),
+            ([self.s_min, self.s2], [self.y2, 0]),
+        ]
+
+
+wrapped_shape_to_class = {
+    "box": LayoutWrappedBox,
+    "xbox": LayoutWrappedXBox,
+    "x": LayoutWrappedLetterX,
+    "bowtie": LayoutWrappedBowTie,
+    "diamond": LayoutWrappedDiamond,
+}
+
+AnyWrappedLayoutShape = Union[
+    LayoutWrappedBox,
+    LayoutWrappedXBox,
+    LayoutWrappedLetterX,
+    LayoutWrappedBowTie,
+    LayoutWrappedDiamond,
+]
+
+
+AnyLayoutShape = Union[AnyNormalLayoutShape, AnyWrappedLayoutShape]
diff --git a/pytao/plotting/mpl.py b/pytao/plotting/mpl.py
new file mode 100644
index 00000000..ce4c32e6
--- /dev/null
+++ b/pytao/plotting/mpl.py
@@ -0,0 +1,774 @@
+from __future__ import annotations
+
+import logging
+import pathlib
+import time
+from typing import ClassVar, Dict, List, Literal, Optional, Sequence, Tuple, Union
+
+import matplotlib.axes
+import matplotlib.axis
+import matplotlib.cm
+import matplotlib.collections
+import matplotlib.patches
+import matplotlib.path
+import matplotlib.pyplot as plt
+import matplotlib.text
+import matplotlib.ticker
+import numpy as np
+
+from . import floor_plan_shapes, layout_shapes, pgplot
+from .curves import PlotCurveLine, PlotCurveSymbols, PlotHistogram, TaoCurveSettings
+from .fields import ElementField
+from .patches import (
+    PlotPatch,
+    PlotPatchArc,
+    PlotPatchCircle,
+    PlotPatchEllipse,
+    PlotPatchPolygon,
+    PlotPatchRectangle,
+    PlotPatchSbend,
+)
+from .plot import (
+    AnyGraph,
+    BasicGraph,
+    FloorPlanGraph,
+    GraphManager,
+    LatticeLayoutGraph,
+    PlotAnnotation,
+    PlotCurve,
+    UnsupportedGraphError,
+)
+from .settings import TaoGraphSettings
+from .types import Limit, OptionalLimit, Point
+from .util import fix_grid_limits
+
+logger = logging.getLogger(__name__)
+
+
+class _Defaults:
+    layout_height: float = 0.5
+    colormap: str = "PRGn_r"
+
+
+def set_defaults(
+    layout_height: Optional[float] = None,
+    colormap: Optional[str] = None,
+    figsize: Optional[Tuple[float, float]] = None,
+    width: Optional[int] = None,
+    height: Optional[int] = None,
+    dpi: Optional[int] = None,
+):
+    if layout_height is not None:
+        _Defaults.layout_height = layout_height
+    if colormap is not None:
+        _Defaults.colormap = colormap
+    if figsize is not None:
+        matplotlib.rcParams["figure.figsize"] = figsize
+    if width and height:
+        matplotlib.rcParams["figure.figsize"] = (width, height)
+    if dpi is not None:
+        matplotlib.rcParams["figure.dpi"] = dpi
+
+
+def setup_matplotlib_ticks(
+    graph: AnyGraph,
+    ax: matplotlib.axes.Axes,
+    user_xlim: Optional[Limit],
+    user_ylim: Optional[Limit],
+) -> None:
+    if user_xlim is None:
+        _setup_matplotlib_xticks(graph, ax)
+    else:
+        ax.set_xlim(user_xlim)
+
+    if user_ylim is None:
+        _setup_matplotlib_yticks(graph, ax)
+    else:
+        ax.set_ylim(user_ylim)
+
+
+def _fix_limits(lim: Point, pad_factor: float = 0.0) -> Point:
+    low, high = lim
+    if np.isclose(low, 0.0) and np.isclose(high, 0.0):
+        # TODO: matplotlib can sometimes get in a bad spot trying to plot empty data
+        # with very small limits
+        return (-0.001, 0.001)
+    return (low - abs(low * pad_factor), high + abs(high * pad_factor))
+
+
+def _setup_matplotlib_xticks(graph: AnyGraph, ax: matplotlib.axes.Axes):
+    """Configure ticks on the provided matplotlib x-axis."""
+    ax.set_xlim(_fix_limits(graph.xlim))
+
+    xlim = ax.get_xlim()
+    if graph.info["x_minor_div"] > 0:
+        ax.xaxis.set_minor_locator(
+            matplotlib.ticker.AutoMinorLocator(graph.info["x_minor_div"])
+        )
+        ax.tick_params(axis="x", which="minor", length=4, color="black")
+
+    if graph.info["x_major_div_nominal"] > 2:
+        ticks = np.linspace(*xlim, graph.info["x_major_div_nominal"])
+        ax.set_xticks(ticks)
+
+
+def _setup_matplotlib_yticks(graph: AnyGraph, ax: matplotlib.axes.Axes):
+    """Configure ticks on the provided matplotlib y-axis."""
+    ax.set_ylim(_fix_limits(graph.ylim))
+    ylim = ax.get_ylim()
+    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+    ax.tick_params(axis="y", which="minor", length=4, color="black")
+    if graph.info["y_major_div_nominal"] > 2:
+        ax.set_yticks(np.linspace(*ylim, graph.info["y_major_div_nominal"]))
+
+
+def setup_matplotlib_axis(graph: AnyGraph, ax: matplotlib.axes.Axes):
+    """Configure limits, title, and basic info for the given axes."""
+    if not graph.show_axes:
+        ax.set_axis_off()
+
+    ax.set_title(pgplot.mpl_string(graph.title))
+    ax.set_xlabel(pgplot.mpl_string(graph.xlabel))
+    ax.set_ylabel(pgplot.mpl_string(graph.ylabel))
+    ax.set_axisbelow(True)
+
+    if graph.draw_grid:
+        ax.grid(graph.draw_grid, which="major", axis="both")
+
+
+def get_figsize(
+    figsize: Optional[Tuple[float, float]] = None,
+    width: Optional[float] = None,
+    height: Optional[float] = None,
+):
+    if figsize is not None:
+        return figsize
+
+    if width or height:
+        return (
+            width or plt.rcParams["figure.figsize"][0],
+            height or plt.rcParams["figure.figsize"][1],
+        )
+    return None
+
+
+def plot_annotation(annotation: PlotAnnotation, ax: matplotlib.axes.Axes):
+    return ax.annotate(
+        xy=(annotation.x, annotation.y),
+        text=pgplot.mpl_string(annotation.text),
+        horizontalalignment=annotation.horizontalalignment,
+        verticalalignment=annotation.verticalalignment,
+        clip_on=annotation.clip_on,
+        color=pgplot.mpl_color(annotation.color),
+        rotation=annotation.rotation,
+        rotation_mode=annotation.rotation_mode,
+        fontsize=8,
+    )
+
+
+def plot_curve_line(
+    curve: PlotCurveLine,
+    ax: matplotlib.axes.Axes,
+    label: Optional[str] = None,
+):
+    return ax.plot(
+        curve.xs,
+        curve.ys,
+        color=pgplot.mpl_color(curve.color or "black"),
+        linestyle=curve.linestyle,
+        linewidth=curve.linewidth,
+        label=label,
+    )
+
+
+def plot_curve_symbols(
+    curve: PlotCurveSymbols,
+    ax: matplotlib.axes.Axes,
+    label: Optional[str] = None,
+):
+    return ax.plot(
+        curve.xs,
+        curve.ys,
+        color=pgplot.mpl_color(curve.color),
+        markerfacecolor=curve.markerfacecolor,
+        markersize=curve.markersize,
+        marker=pgplot.symbols.get(curve.marker, "."),
+        markeredgewidth=curve.markeredgewidth,
+        linewidth=curve.linewidth,
+        label=label,
+    )
+
+
+def plot_histogram(
+    hist: PlotHistogram,
+    ax: matplotlib.axes.Axes,
+):
+    return ax.hist(
+        hist.xs,
+        bins=hist.bins,
+        weights=hist.weights,
+        histtype=hist.histtype,
+        color=pgplot.mpl_color(hist.color),
+    )
+
+
+def plot_curve(curve: PlotCurve, ax: matplotlib.axes.Axes):
+    res = []
+    if curve.line is not None:
+        res.append(
+            plot_curve_line(
+                curve.line,
+                ax,
+                label=pgplot.mpl_string(curve.legend_label),
+            )
+        )
+    if curve.symbol is not None:
+        res.append(
+            plot_curve_symbols(
+                curve.symbol,
+                ax,
+                label=pgplot.mpl_string(curve.legend_label) if curve.line is None else None,
+            )
+        )
+    if curve.histogram is not None:
+        res.append(plot_histogram(curve.histogram, ax))
+    for patch in curve.patches or []:
+        res.append(plot_patch(patch, ax))
+    return res
+
+
+def patch_to_mpl(patch: PlotPatch):
+    if isinstance(patch, PlotPatchRectangle):
+        return matplotlib.patches.Rectangle(
+            xy=patch.xy,
+            width=patch.width,
+            height=patch.height,
+            angle=patch.angle,
+            rotation_point=patch.rotation_point,
+            **patch._patch_args,
+        )
+    if isinstance(patch, PlotPatchArc):
+        return matplotlib.patches.Arc(
+            xy=patch.xy,
+            width=patch.width,
+            height=patch.height,
+            angle=patch.angle,
+            theta1=patch.theta1,
+            theta2=patch.theta2,
+            **patch._patch_args,
+        )
+    if isinstance(patch, PlotPatchCircle):
+        return matplotlib.patches.Circle(
+            xy=patch.xy,
+            radius=patch.radius,
+            **patch._patch_args,
+        )
+    if isinstance(patch, PlotPatchPolygon):
+        return matplotlib.patches.Polygon(
+            xy=patch.vertices,
+            **patch._patch_args,
+        )
+
+    if isinstance(patch, PlotPatchEllipse):
+        return matplotlib.patches.Ellipse(
+            xy=patch.xy,
+            width=patch.width,
+            height=patch.height,
+            angle=patch.angle,
+            **patch._patch_args,
+        )
+    if isinstance(patch, PlotPatchSbend):
+        codes = [
+            matplotlib.path.Path.MOVETO,
+            matplotlib.path.Path.CURVE3,
+            matplotlib.path.Path.CURVE3,
+            matplotlib.path.Path.LINETO,
+            matplotlib.path.Path.CURVE3,
+            matplotlib.path.Path.CURVE3,
+            matplotlib.path.Path.CLOSEPOLY,
+        ]
+        vertices = [
+            patch.spline1[0],
+            patch.spline1[1],
+            patch.spline1[2],
+            patch.spline2[0],
+            patch.spline2[1],
+            patch.spline2[2],
+            patch.spline1[0],
+        ]
+        return matplotlib.patches.PathPatch(
+            matplotlib.path.Path(vertices, codes),
+            facecolor="green",
+            alpha=0.5,
+        )
+
+    raise NotImplementedError(f"Unsupported patch type: {type(patch).__name__}")
+
+
+def plot_patch(patch: PlotPatch, ax: matplotlib.axes.Axes):
+    mpl = patch_to_mpl(patch)
+    ax.add_patch(mpl)
+    return mpl
+
+
+def plot_layout_shape(shape: layout_shapes.AnyLayoutShape, ax: matplotlib.axes.Axes):
+    if isinstance(shape, layout_shapes.LayoutWrappedShape):
+        ax.add_collection(
+            matplotlib.collections.LineCollection(
+                [[(x, y) for x, y in zip(line[0], line[1])] for line in shape.lines],
+                colors=pgplot.mpl_color(shape.color),
+                linewidths=shape.line_width,
+            )
+        )
+    else:
+        lines = shape.lines
+        if lines:
+            ax.add_collection(
+                matplotlib.collections.LineCollection(
+                    lines,
+                    colors=pgplot.mpl_color(shape.color),
+                    linewidths=shape.line_width,
+                )
+            )
+        for patch in shape.to_patches():
+            plot_patch(patch, ax)
+
+
+def plot_floor_plan_shape(shape: floor_plan_shapes.Shape, ax: matplotlib.axes.Axes):
+    for line in shape.to_lines():
+        plot_curve_line(line, ax)
+    if not isinstance(shape, floor_plan_shapes.Box):
+        for patch in shape.to_patches():
+            plot_patch(patch, ax)
+
+
+def plot(graph: AnyGraph, ax: Optional[matplotlib.axes.Axes] = None) -> matplotlib.axes.Axes:
+    if ax is None:
+        _, ax = plt.subplots()
+
+    assert ax is not None
+
+    if isinstance(graph, BasicGraph):
+        for curve in graph.curves:
+            assert not curve.info["use_y2"], "TODO: y2 support"
+            plot_curve(curve, ax)
+
+        if graph.draw_legend and any(curve.legend_label for curve in graph.curves):
+            ax.legend()
+
+    elif isinstance(graph, LatticeLayoutGraph):
+        ax.axhline(y=0, color="Black", linewidth=1)
+
+        for elem in graph.elements:
+            if elem.shape is not None:
+                plot_layout_shape(elem.shape, ax)
+            # ax.add_collection(
+            #     matplotlib.collections.LineCollection(
+            #         elem.lines,
+            #         colors=pgplot.mpl_color(elem.color),
+            #         linewidths=elem.width,
+            #     )
+            # )
+            # for patch in elem.patches:
+            #     plot_patch(patch, ax)
+            for annotation in elem.annotations:
+                plot_annotation(annotation, ax)
+
+        # Invisible line to give the lat layout enough vertical space.
+        # Without this, the tops and bottoms of shapes could be cut off
+        # ax.plot([0, 0], [-1.7 * self.y_max, 1.3 * self.y_max], alpha=0)
+        ax.yaxis.set_visible(False)
+
+        # ax.set_xticks([elem.info["ele_s_start"] for elem in self.elements])
+        # ax.set_xticklabels([elem.info["label_name"] for elem in self.elements], rotation=90)
+        ax.grid(visible=False)
+    elif isinstance(graph, FloorPlanGraph):
+        for elem in graph.elements:
+            if elem.shape is not None:
+                plot_floor_plan_shape(elem.shape, ax)
+            for annotation in elem.annotations:
+                plot_annotation(annotation, ax)
+
+        for line in graph.building_walls.lines:
+            plot_curve_line(line, ax)
+        for patch in graph.building_walls.patches:
+            plot_patch(patch, ax)
+        if graph.floor_orbits is not None:
+            plot_curve_symbols(graph.floor_orbits.curve, ax)
+    else:
+        raise NotImplementedError(f"Unsupported graph for matplotlib: {type(graph)}")
+
+    setup_matplotlib_axis(graph, ax)
+    return ax
+
+
+class MatplotlibGraphManager(GraphManager):
+    """Matplotlib backend graph manager."""
+
+    _key_: ClassVar[str] = "mpl"
+
+    def plot_grid(
+        self,
+        templates: List[str],
+        grid: Tuple[int, int],
+        *,
+        include_layout: bool = False,
+        figsize: Optional[Tuple[float, float]] = None,
+        tight_layout: bool = True,
+        share_x: Union[bool, Literal["row", "col", "all"]] = "col",
+        layout_height: Optional[float] = None,
+        width: Optional[float] = None,
+        height: Optional[float] = None,
+        xlim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+        ylim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+        curves: Optional[List[Dict[int, TaoCurveSettings]]] = None,
+        settings: Optional[List[TaoGraphSettings]] = None,
+        save: Union[bool, str, pathlib.Path, None] = None,
+        axes: Optional[List[List[matplotlib.axes.Axes]]] = None,
+    ):
+        """
+        Plot graphs on a grid with Matplotlib.
+
+        Parameters
+        ----------
+        templates : list of str
+            Graph template names.
+        grid : (nrows, ncols), optional
+            Grid the provided graphs into this many rows and columns.
+        include_layout : bool, default=False
+            Include a layout plot at the bottom of each column.
+        tight_layout : bool, default=True
+            Apply a tight layout with matplotlib.
+        figsize : (float, float), optional
+            Figure size. Alternative to specifying `width` and `height`
+            separately.  This takes precedence over `width` and `height`.
+            Defaults to Matplotlib's `rcParams["figure.figsize"]``.
+        width : float, optional
+            Width of the whole plot.
+        height : float, optional
+            Height of the whole plot.
+        layout_height : int, optional
+            Normalized height of the layout plot - assuming regular plots are
+            of height 1.  Default is 0.5 which is configurable with `set_defaults`.
+        share_x : bool, "row", "col", "all", default="col"
+            Share all x-axes (`True` or "all"), share x-axes in rows ("row") or
+            in columns ("col").
+        xlim : list of (float, float), optional
+            X axis limits for each graph.
+        ylim : list of (float, float), optional
+            Y axis limits for each graph.
+        curves : list of Dict[int, TaoCurveSettings], optional
+            One dictionary per graph, with each dictionary mapping the curve
+            index to curve settings. These settings will be applied to the
+            placed graphs prior to plotting.
+        settings : list of TaoGraphSettings, optional
+            Graph customization settings, per graph.
+        save : pathlib.Path or str, optional
+            Save the plot to the given filename.
+
+        Returns
+        -------
+        list of graphs
+            List of plotted graphs.
+        matplotlib.Figure
+            To gain access to the resulting plot objects, use the backend's
+            `plot` method directly.
+        List[List[matplotlib.axes.Axes]]
+            Gridded axes, accessible with `grid[row][col]`.
+        """
+
+        graphs = self.prepare_grid_by_names(
+            template_names=templates,
+            curves=curves,
+            settings=settings,
+            xlim=xlim,
+            ylim=ylim,
+        )
+        nrows, ncols = grid
+        height_ratios = None
+
+        figsize = get_figsize(figsize, width, height)
+
+        if include_layout:
+            layout_height = layout_height or _Defaults.layout_height
+            empty_graph_count = nrows * ncols - len(templates)
+            if empty_graph_count < ncols:
+                # Add a row for the layout
+                nrows += 1
+            height_ratios = [1] * (nrows - 1) + [layout_height]
+
+        if axes is not None:
+            tight_layout = False
+            fig = None
+        else:
+            fig, gs = plt.subplots(
+                nrows=nrows,
+                ncols=ncols,
+                sharex=share_x,
+                figsize=figsize,
+                squeeze=False,
+                height_ratios=height_ratios,
+            )
+            axes = [list(gs[row, :]) for row in range(nrows)]
+            for row in axes:
+                for ax in row:
+                    ax.set_axis_off()
+
+        all_xlim = fix_grid_limits(xlim, num_graphs=len(graphs))
+        all_ylim = fix_grid_limits(ylim, num_graphs=len(graphs))
+
+        rows_cols = [(row, col) for row in range(nrows) for col in range(ncols)]
+
+        for graph, xl, yl, (row, col) in zip(graphs, all_xlim, all_ylim, rows_cols):
+            ax = axes[row][col]
+            try:
+                plot(graph, ax)
+            except UnsupportedGraphError:
+                continue
+
+            ax.set_axis_on()
+            setup_matplotlib_ticks(graph, ax, user_xlim=xl, user_ylim=yl)
+
+        if include_layout:
+            layout_graph = self.lattice_layout_graph
+            for col in range(ncols):
+                ax = axes[-1][col]
+                plot(layout_graph, ax)
+                ax.set_axis_on()
+
+                xl = None
+                if share_x in {"all", "col", True} and nrows > 1:
+                    try:
+                        xl = axes[0][col].get_xlim()
+                    except IndexError:
+                        pass
+
+                setup_matplotlib_ticks(layout_graph, ax, user_xlim=xl, user_ylim=None)
+
+        if tight_layout and fig is not None:
+            fig.tight_layout()
+
+        if save and fig is not None:
+            title = graphs[0].title or f"plot-{time.time()}"
+            if save is True:
+                save = f"{title}.png"
+            logger.info(f"Saving plot to {save!r}")
+            fig.savefig(save)
+
+        return graphs, fig, axes
+
+    def plot(
+        self,
+        template: str,
+        *,
+        region_name: Optional[str] = None,
+        include_layout: bool = True,
+        tight_layout: bool = True,
+        width: Optional[float] = None,
+        height: Optional[float] = None,
+        layout_height: Optional[float] = None,
+        figsize: Optional[Tuple[float, float]] = None,
+        share_x: bool = True,
+        xlim: Optional[Limit] = None,
+        ylim: Optional[Limit] = None,
+        save: Union[bool, str, pathlib.Path, None] = None,
+        settings: Optional[TaoGraphSettings] = None,
+        curves: Optional[Dict[int, TaoCurveSettings]] = None,
+        axes: Optional[List[matplotlib.axes.Axes]] = None,
+    ):
+        """
+        Plot a graph with Matplotlib.
+
+        Parameters
+        ----------
+        template : str
+            Graph template name.
+        region_name : str, optional
+            Graph region name.
+        include_layout : bool, optional
+            Include a layout plot at the bottom, if not already placed and if
+            appropriate (i.e., another plot uses longitudinal coordinates on
+            the x-axis).
+        tight_layout : bool, default=True
+            Apply a tight layout with matplotlib.
+        figsize : (float, float), optional
+            Figure size. Alternative to specifying `width` and `height`
+            separately.  This takes precedence over `width` and `height`.
+            Defaults to Matplotlib's `rcParams["figure.figsize"]``.
+        width : float, optional
+            Width of the whole plot.
+        height : float, optional
+            Height of the whole plot.
+        layout_height : float, optional
+            Normalized height of the layout plot - assuming regular plots are
+            of height 1.  Default is 0.5 which is configurable with `set_defaults`.
+        share_x : bool, default=True
+            Share x-axes for all plots.
+        xlim : (float, float), optional
+            X axis limits.
+        ylim : (float, float), optional
+            Y axis limits.
+        save : pathlib.Path or str, optional
+            Save the plot to the given filename.
+        curves : Dict[int, TaoCurveSettings], optional
+            Dictionary of curve index to curve settings. These settings will be
+            applied to the placed graph prior to plotting.
+        settings : TaoGraphSettings, optional
+            Graph customization settings.
+
+        Returns
+        -------
+        list of graphs
+            List of plotted graphs.
+        matplotlib.Figure
+            To gain access to the resulting plot objects, use the backend's
+            `plot` method directly.
+        List[matplotlib.axes.Axes]
+        """
+        graphs = self.prepare_graphs_by_name(
+            template_name=template,
+            region_name=region_name,
+            curves=curves,
+            settings=settings,
+            xlim=xlim,
+            ylim=ylim,
+        )
+        if not graphs:
+            raise UnsupportedGraphError(f"No supported plots from this template: {template}")
+
+        figsize = get_figsize(figsize, width, height)
+
+        if (
+            include_layout
+            and not any(isinstance(graph, LatticeLayoutGraph) for graph in graphs)
+            and any(graph.is_s_plot for graph in graphs)
+        ):
+            layout_graph = self.lattice_layout_graph
+            graphs.append(layout_graph)
+        else:
+            include_layout = False
+
+        if axes is not None:
+            if len(axes) != len(graphs):
+                raise ValueError(
+                    f"Not enough axes provided. Expected {len(graphs)}, got {len(axes)}"
+                )
+            fig = axes[0].figure
+        else:
+            if include_layout:
+                layout_height = layout_height or _Defaults.layout_height
+                fig, gs = plt.subplots(
+                    nrows=len(graphs),
+                    ncols=1,
+                    sharex=share_x,
+                    height_ratios=[1] * (len(graphs) - 1) + [layout_height],
+                    figsize=figsize,
+                    squeeze=False,
+                )
+            else:
+                fig, gs = plt.subplots(
+                    nrows=len(graphs),
+                    ncols=1,
+                    sharex=share_x,
+                    figsize=figsize,
+                    squeeze=False,
+                )
+            axes = list(gs[:, 0])
+            assert axes is not None
+
+        if include_layout:
+            layout_graph = self.lattice_layout_graph
+
+        for ax, graph in zip(axes, graphs):
+            try:
+                plot(graph, ax)
+            except UnsupportedGraphError:
+                continue
+
+            if isinstance(graph, LatticeLayoutGraph) and len(graphs) > 1:
+                # Do not set ylimits if the user specifically requested a layout graph
+                yl = None
+            else:
+                yl = ylim
+
+            setup_matplotlib_ticks(graph, ax, user_xlim=xlim, user_ylim=yl)
+
+        if fig is not None:
+            if tight_layout:
+                fig.tight_layout()
+
+            if save:
+                title = graphs[0].title or f"plot-{time.time()}"
+                if save is True:
+                    save = f"{title}.png"
+                logger.info(f"Saving plot to {save!r}")
+                fig.savefig(save)
+
+        return graphs, fig, axes
+
+    def plot_field(
+        self,
+        ele_id: str,
+        *,
+        colormap: Optional[str] = None,
+        radius: float = 0.015,
+        num_points: int = 100,
+        figsize: Optional[Tuple[float, float]] = None,
+        width: int = 4,
+        height: int = 4,
+        x_scale: float = 1e3,
+        ax: Optional[matplotlib.axes.Axes] = None,
+        save: Union[bool, str, pathlib.Path, None] = None,
+    ):
+        """
+        Plot field information for a given element.
+
+        Parameters
+        ----------
+        ele_id : str
+            Element ID.
+        colormap : str, optional
+            Colormap for the plot.
+            Matplotlib defaults to "PRGn_r", and bokeh defaults to "".
+        radius : float, default=0.015
+            Radius.
+        num_points : int, default=100
+            Number of data points.
+        ax : matplotlib.axes.Axes, optional
+            The axes to place the plot in.
+        save : pathlib.Path or str, optional
+            Save the plot to the given filename.
+        """
+        user_specified_axis = ax is not None
+
+        figsize = get_figsize(figsize, width, height)
+
+        if ax is None:
+            _, ax = plt.subplots(figsize=figsize)
+        assert ax is not None
+
+        colormap = colormap or _Defaults.colormap
+
+        field = ElementField.from_tao(self.tao, ele_id, num_points=num_points, radius=radius)
+        mesh = ax.pcolormesh(
+            np.asarray(field.s),
+            np.asarray(field.x) * x_scale,
+            np.asarray(field.by),
+            # vmin=min_field,
+            # vmax=max_field,
+            cmap=colormap,
+        )
+        fig = ax.figure
+        if fig is not None:
+            if not user_specified_axis:
+                fig.colorbar(mesh)
+
+            if save:
+                if save is True:
+                    save = f"{ele_id}_field.png"
+                if not pathlib.Path(save).suffix:
+                    save = f"{save}.png"
+                logger.info(f"Saving plot to {save!r}")
+                fig.savefig(save)
+
+        return field, fig, ax
diff --git a/pytao/plotting/patches.py b/pytao/plotting/patches.py
new file mode 100644
index 00000000..3f2862d0
--- /dev/null
+++ b/pytao/plotting/patches.py
@@ -0,0 +1,172 @@
+from __future__ import annotations
+
+import math
+from typing import (
+    List,
+    Literal,
+    Optional,
+    Tuple,
+    Union,
+)
+
+import numpy as np
+import pydantic
+import pydantic.dataclasses as dataclasses
+from pydantic.fields import Field
+
+from . import pgplot, util
+from .types import Point
+
+_dcls_config = pydantic.ConfigDict()
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotPatchBase:
+    edgecolor: Optional[str] = None
+    facecolor: Optional[str] = None
+    color: Optional[str] = None
+    linewidth: Optional[float] = None
+    linestyle: Optional[str] = None
+    antialiased: Optional[bool] = None
+    hatch: Optional[str] = None
+    fill: bool = True
+    capstyle: Optional[str] = None
+    joinstyle: Optional[str] = None
+    alpha: float = 1.0
+
+    @property
+    def _patch_args(self):
+        return {
+            "edgecolor": self.edgecolor,
+            "facecolor": self.facecolor,
+            "color": pgplot.mpl_color(self.color or "black"),
+            "linewidth": self.linewidth,
+            "linestyle": self.linestyle,
+            "antialiased": self.antialiased,
+            "hatch": self.hatch,
+            "fill": self.fill,
+            "capstyle": self.capstyle,
+            "joinstyle": self.joinstyle,
+            "alpha": self.alpha,
+        }
+
+
+_point_field = Field(default_factory=lambda: (0.0, 0.0))
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotPatchRectangle(PlotPatchBase):
+    xy: Point = _point_field
+    width: float = 0.0
+    height: float = 0.0
+    angle: float = 0.0
+    rotation_point: Union[Literal["xy", "center"], Point] = "xy"
+
+    @property
+    def center(self) -> Point:
+        return (
+            self.xy[0] + self.width / 2,
+            self.xy[1] + self.height / 2,
+        )
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotPatchArc(PlotPatchBase):
+    xy: Point = _point_field
+    width: float = 0.0
+    height: float = 0.0
+    angle: float = 0.0
+    theta1: float = 0.0
+    theta2: float = 360.0
+    fill: bool = False  # override
+
+    @classmethod
+    def from_building_wall(
+        cls,
+        mx: float,
+        my: float,
+        kx: float,
+        ky: float,
+        k_radii: float,
+        color: str,
+        linewidth: float,
+    ):
+        (c0x, c0y), (c1x, c1y) = util.circle_intersection(mx, my, kx, ky, abs(k_radii))
+        # radius and endpoints specify 2 possible circle centers for arcs
+        mpx = (mx + kx) / 2
+        mpy = (my + ky) / 2
+        if (
+            np.arctan2((my - mpy), (mx - mpx))
+            < np.arctan2(c0y, c0x)
+            < np.arctan2((my - mpy), (mx - mpx))
+            and k_radii > 0
+        ):
+            center = (c1x, c1y)
+        elif (
+            np.arctan2((my - mpy), (mx - mpx))
+            < np.arctan2(c0y, c0x)
+            < np.arctan2((my - mpy), (mx - mpx))
+            and k_radii < 0
+        ):
+            center = (c0x, c0y)
+        elif k_radii > 0:
+            center = (c0x, c0y)
+        else:
+            center = (c1x, c1y)
+
+        m_angle = 360 + math.degrees(np.arctan2((my - center[1]), (mx - center[0])))
+        k_angle = 360 + math.degrees(np.arctan2((ky - center[1]), (kx - center[0])))
+        if k_angle > m_angle:
+            t1 = m_angle
+            t2 = k_angle
+        else:
+            t1 = k_angle
+            t2 = m_angle
+
+        if abs(k_angle - m_angle) > 180:
+            t1, t2 = t2, t1
+
+        return cls(
+            xy=center,
+            width=k_radii * 2,
+            height=k_radii * 2,
+            theta1=t1,
+            theta2=t2,
+            color=color,
+            linewidth=linewidth,
+        )
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotPatchCircle(PlotPatchBase):
+    xy: Point = _point_field
+    radius: float = 0.0
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotPatchPolygon(PlotPatchBase):
+    vertices: List[Point] = Field(default_factory=list)
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotPatchEllipse(PlotPatchBase):
+    xy: Point = _point_field
+    width: float = 0.0
+    height: float = 0.0
+    angle: float = 0.0
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotPatchSbend(PlotPatchBase):
+    spline1: Tuple[Point, Point, Point] = Field(default_factory=tuple)
+    spline2: Tuple[Point, Point, Point] = Field(default_factory=tuple)
+
+
+PlotPatch = Union[
+    PlotPatchRectangle,
+    PlotPatchArc,
+    PlotPatchCircle,
+    PlotPatchEllipse,
+    PlotPatchPolygon,
+    PlotPatchSbend,
+]
diff --git a/pytao/plotting/pgplot.py b/pytao/plotting/pgplot.py
new file mode 100644
index 00000000..8b4beff7
--- /dev/null
+++ b/pytao/plotting/pgplot.py
@@ -0,0 +1,255 @@
+import logging
+import re
+
+from . import hershey_fonts
+
+logger = logging.getLogger(__name__)
+
+
+def mpl_color(pgplot_color: str) -> str:
+    """Pgplot color to matplotlib color."""
+    return {
+        "yellow_green": "greenyellow",
+        "light_green": "limegreen",
+        "navy_blue": "navy",
+        "reddish_purple": "mediumvioletred",
+        "dark_grey": "gray",
+        "light_grey": "lightgray",
+        "transparent": "none",
+    }.get(pgplot_color.lower(), pgplot_color)
+
+
+_pgplot_to_mpl_chars = [
+    (" ", r"\ "),
+    ("%", r"\%"),
+    (r"\\ga", r"\alpha "),
+    (r"\\gb", r"\beta "),
+    (r"\\gg", r"\gamma "),
+    (r"\\gd", r"\delta "),
+    (r"\\ge", r"\epsilon "),
+    (r"\\gz", r"\zeta "),
+    (r"\\gy", r"\eta "),
+    (r"\\gh", r"\theta "),
+    (r"\\gi", r"\iota "),
+    (r"\\gk", r"\kappa "),
+    (r"\\gl", r"\lambda "),
+    (r"\\gm", r"\mu "),
+    (r"\\gn", r"\nu "),
+    (r"\\gc", r"\xi "),
+    (r"\\go", r"\omicron "),
+    (r"\\gp", r"\pi "),
+    (r"\\gr", r"\rho "),
+    (r"\\gs", r"\sigma "),
+    (r"\\gt", r"\tau "),
+    (r"\\gu", r"\upsilon "),
+    (r"\\gf", r"\phi "),
+    (r"\\gx", r"\chi "),
+    (r"\\gq", r"\psi "),
+    (r"\\gw", r"\omega "),
+    (r"\\gA", "A"),
+    (r"\\gB", "B"),
+    (r"\\gG", r"\Gamma "),
+    (r"\\gD", r"\Delta "),
+    (r"\\gE", "E"),
+    (r"\\gZ", "Z"),
+    (r"\\gY", "H"),
+    (r"\\gH", r"\Theta "),
+    (r"\\gI", "I"),
+    (r"\\gK", r"\Kappa "),
+    (r"\\gL", r"\Lambda "),
+    (r"\\gM", "M"),
+    (r"\\gN", "N"),
+    (r"\\gC", r"\Xi "),
+    (r"\\gO", "O"),
+    (r"\\gP", r"\Pi "),
+    (r"\\gR", "P"),
+    (r"\\gS", r"\Sigma "),
+    (r"\\gT", "T"),
+    (r"\\gU", r"\Upsilon "),
+    (r"\\gF", r"\Phi "),
+    (r"\\gX", "X"),
+    (r"\\gQ", r"\Psi "),
+    (r"\\gW", r"\Omega "),
+    (r"\\fn", ""),
+    *hershey_fonts.string_to_unicode,
+]
+
+
+def mpl_string(value: str) -> str:
+    """Takes string with pgplot characters and returns string with characters replaced with matplotlib equivalent."""
+    backslash = "\\"
+    if backslash not in value:
+        return value
+
+    value = value.replace(backslash, r"\\")
+
+    result = f"${value}$"
+    while r"\\d" in result and r"\\u" in result:
+        d_pos = result.find(r"\\d")
+        u_pos = result.find(r"\\u")
+        if d_pos < u_pos:
+            sx = result[d_pos : u_pos + 3]
+            result = result.replace(sx, "_" + sx[3:-3])
+        else:
+            sx = result[u_pos : d_pos + 3]
+            result = result.replace(sx, "^" + sx[3:-3])
+
+    # TODO this is far from performant, but this is unlikely to be called
+    # frequently so I'm leaving it for now
+    for from_, to in _pgplot_to_mpl_chars:
+        result = result.replace(from_, to)
+
+    # Replace any instances of \1 with non-LaTeX equivalents, as these can be
+    # used in component names.
+    result = re.sub(r"\\\\(\d+)", r"\\backslash\1", result)
+
+    if r"\\" in result:
+        logger.debug(f"Unknown pgplot character in string: {result}")
+
+    return result
+
+
+def mathjax_string(value: str) -> str:
+    """
+    Takes string with pgplot characters and returns string with characters replaced with MathJax equivalent.
+    """
+    res = mpl_string(value)
+    if res.startswith("$") and res.endswith("$"):
+        # MathJAX strings are $$ ... $$ instead of just $ ... $
+        return f"${res}$"
+    return res
+
+
+styles = {
+    "solid": "solid",
+    "dashed": "dashed",
+    "dash_dot": "dashdot",
+    "dotted": "dotted",
+    "dash_dot3": "dashdot",  # currently the same as dashdot
+    "1": "solid",
+    "2": "dashed",
+    "3": "dashdot",
+    "4": "dotted",
+    "5": "dashdot",
+}
+
+fills = {
+    "solid_fill": "full",
+    "no_fill": "none",
+    "hatched": "full",
+    "cross_hatched": "full",
+    "1": "full",
+    "2": "none",
+    "3": "full",
+    "4": "full",
+}
+
+# Dictionary with pgplot symbol strings as keys and corresponding matplotlib symbol strings as values
+symbols = {
+    "do_not_draw": "",
+    "square": "s",  # no fill
+    "dot": ".",
+    "plus": "+",
+    "times": (6, 2, 0),
+    "circle": "$\\circ$",
+    "x": "x",
+    "x_symbol": "x",
+    "triangle": "^",  # no fill
+    "circle_plus": "$\\oplus$",
+    "circle_dot": "$\\odot$",
+    "square_concave": (4, 1, 45),
+    "diamond": "d",  # no fill
+    "star5": "*",  # no fill
+    "triangle_filled": "^",  # fill
+    "red_cross": "P",
+    "star_of_david": (6, 1, 0),
+    "square_filled": "s",  # fill
+    "circle_filled": "o",  # fill
+    "star5_filled": "*",  # fill
+    "0": "s",  # no fill
+    "1": ".",
+    "2": "+",
+    "3": (6, 2, 0),
+    "4": "$\\circ$",
+    "5": "x",
+    "6": "s",
+    "7": "^",  # no fill
+    "8": "$\\oplus$",
+    "9": "$\\odot$",
+    "10": (4, 1, 45),
+    "11": "d",  # no fill
+    "12": "*",  # no fill
+    "13": "^",  # fill
+    "14": "P",
+    "15": (6, 1, 0),
+    "16": "s",  # fill
+    "17": "o",  # fill
+    "18": "*",  # fill
+    "-1": ",",
+    "-2": ",",
+    "-3": (3, 0, 0),
+    "-4": (4, 0, 0),
+    "-5": (5, 0, 0),
+    "-6": (6, 0, 0),
+    "-7": (7, 0, 0),
+    "-8": (8, 0, 0),
+    "-9": (9, 0, 0),
+    "-10": (10, 0, 0),
+    "-11": (11, 0, 0),
+    "-12": (12, 0, 0),
+}
+
+# TODO: these are not very good mappings
+bokeh_symbols = {
+    "do_not_draw": None,
+    "square": "square",
+    "dot": "dot",
+    "plus": "plus",
+    "times": "asterisk",
+    "circle": "circle",
+    "x": "x",
+    "x_symbol": "x",
+    "triangle": "triangle",
+    "circle_plus": "circle_cross",
+    "circle_dot": "circle_dot",
+    "square_concave": "square_pin",
+    "diamond": "diamond",
+    "star5": "star",
+    "triangle_filled": "triangle_dot",
+    "red_cross": "cross",
+    "star_of_david": "star_dot",
+    "square_filled": "square_dot",
+    "circle_filled": "circle_dot",
+    "star5_filled": "star_dot",
+    "0": "dot",
+    "1": "dot",
+    "2": "dot",
+    "3": "dot",
+    "4": "dot",
+    "5": "dot",
+    "6": "dot",
+    "7": "dot",
+    "8": "dot",
+    "9": "dot",
+    "10": "dot",
+    "11": "dot",
+    "12": "dot",
+    "13": "dot",
+    "14": "dot",
+    "15": "dot",
+    "16": "dot",
+    "17": "dot",
+    "18": "dot",
+    "-1": "dot",
+    "-2": "dot",
+    "-3": "dot",
+    "-4": "dot",
+    "-5": "dot",
+    "-6": "dot",
+    "-7": "dot",
+    "-8": "dot",
+    "-9": "dot",
+    "-10": "dot",
+    "-11": "dot",
+    "-12": "dot",
+}
diff --git a/pytao/plotting/plot.py b/pytao/plotting/plot.py
new file mode 100644
index 00000000..b25ebfae
--- /dev/null
+++ b/pytao/plotting/plot.py
@@ -0,0 +1,1760 @@
+from __future__ import annotations
+
+import logging
+import math
+import typing
+from abc import ABC, abstractmethod
+from typing import (
+    Any,
+    ClassVar,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
+
+import numpy as np
+import pydantic.dataclasses as dataclasses
+from pydantic import ConfigDict
+from pydantic.fields import Field
+from typing_extensions import Literal
+
+from . import floor_plan_shapes, layout_shapes, pgplot
+from .curves import (
+    CurveIndexToCurve,
+    PlotCurveLine,
+    PlotCurveSymbols,
+    PlotHistogram,
+    TaoCurveSettings,
+)
+from .patches import (
+    PlotPatch,
+    PlotPatchArc,
+    PlotPatchRectangle,
+)
+from .settings import TaoGraphSettings
+from .types import (
+    BuildingWallGraphInfo,
+    BuildingWallInfo,
+    FloorOrbitInfo,
+    FloorPlanElementInfo,
+    Limit,
+    PlotCurveInfo,
+    PlotGraphInfo,
+    PlotHistogramInfo,
+    PlotLatLayoutInfo,
+    PlotPage,
+    PlotRegionInfo,
+    Point,
+    WaveParams,
+    OptionalLimit,
+)
+from .util import fix_grid_limits
+
+
+if typing.TYPE_CHECKING:
+    from .. import Tao
+
+logger = logging.getLogger(__name__)
+
+
+class GraphInvalidError(Exception):
+    pass
+
+
+class NoLayoutError(Exception):
+    pass
+
+
+class NoCurveDataError(Exception):
+    pass
+
+
+class UnsupportedGraphError(NotImplementedError):
+    pass
+
+
+class AllPlotRegionsInUseError(Exception):
+    pass
+
+
+T = TypeVar("T")
+
+
+def _clean_pytao_output(dct: dict, typ: Type[T]) -> T:
+    return {key: dct.get(key, None) for key in typ.__required_keys__}
+
+
+def _should_use_symbol_color(symbol_type: str, fill_pattern: str) -> bool:
+    if (
+        symbol_type in ("dot", "1")
+        or symbol_type.endswith("filled")
+        or symbol_type.startswith("-")
+    ):
+        return True
+
+    if pgplot.fills[fill_pattern] == "full":
+        return True
+
+    return False
+
+
+# We don't want a single new key from bmad commands to break our implementation,
+# so in gneeral we should allow 'extra' keys:
+_dcls_config = ConfigDict()
+
+# However, for testing and development, this should be toggled on:
+# _dcls_config = ConfigDict(extra="forbid")
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotAnnotation:
+    x: float
+    y: float
+    text: str
+    horizontalalignment: str = "left"
+    verticalalignment: str = "baseline"
+    clip_on: bool = False
+    color: str = "black"
+    rotation: float = 0
+    rotation_mode: str = "default"
+
+
+_point_field = Field(default_factory=lambda: (0.0, 0.0))
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class GraphBase:
+    info: PlotGraphInfo
+    region_info: PlotRegionInfo
+    # The region name where the graph is placed.
+    region_name: str
+    # The name of the placed graph.
+    graph_name: str
+    # The template used to create this graph.
+    template_name: Optional[str] = None
+    # The index of this graph after placing a template
+    template_graph_index: Optional[int] = None
+    # The Tao-specified x- and y-limits.
+    xlim: Point = _point_field
+    ylim: Point = _point_field
+    xlabel: str = ""
+    ylabel: str = ""
+    title: str = ""
+    show_axes: bool = True
+    draw_grid: bool = True
+    draw_legend: bool = True
+
+    def update(
+        self,
+        manager: GraphManager,
+        *,
+        error_on_new_type: bool = True,
+        raise_if_invalid: bool = False,
+    ):
+        """
+        Ask Tao to update the plot region. Returns a new Graph instance.
+
+        Raises
+        ------
+        GraphInvalidError
+            If `raise_if_invalid` is set and Tao reports the graph data as
+            invalid.
+        ValueError
+            If `error_on_new_type` is set and the graph type changes after the
+            update.
+        RuntimeError
+            If the same graph is no longer found after the update.
+        """
+        try:
+            graphs = manager.prepare_graphs_by_name(
+                region_name=self.region_name,
+                template_name=self.template_name or self.graph_name,
+                ignore_invalid=False,
+                place=False,
+            )
+        except GraphInvalidError:
+            if raise_if_invalid:
+                raise
+            return self
+
+        if self.template_graph_index is not None:
+            return graphs[self.template_graph_index]
+
+        for graph in graphs:
+            if graph.graph_name == self.graph_name:
+                if error_on_new_type and not isinstance(graph, type(self)):
+                    raise ValueError(
+                        f"Graph type changed from {type(self).__name__} to {type(graph).__name__}"
+                    )
+                return graph
+        raise RuntimeError("Plot not found after update?")
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class PlotCurve:
+    info: PlotCurveInfo
+    line: Optional[PlotCurveLine]
+    symbol: Optional[PlotCurveSymbols]
+    histogram: Optional[PlotHistogram] = None
+    patches: Optional[List[PlotPatch]] = None
+
+    @property
+    def legend_label(self) -> str:
+        legend_text = self.info["legend_text"]
+        if legend_text:
+            return legend_text
+
+        data_type = self.info["data_type"]
+        return data_type if data_type == "physical_aperture" else ""
+
+    @classmethod
+    def from_tao(
+        cls,
+        tao: Tao,
+        region_name: str,
+        graph_name: str,
+        curve_name: str,
+        *,
+        graph_type: Optional[str] = None,
+    ) -> PlotCurve:
+        full_name = f"{region_name}.{graph_name}.{curve_name}"
+        curve_info = cast(PlotCurveInfo, tao.plot_curve(full_name))
+        try:
+            points = [
+                (line["x"], line["y"])
+                for line in tao.plot_line(region_name, graph_name, curve_name) or []
+            ]
+        except RuntimeError:
+            points = []
+
+        try:
+            symbol_points = [
+                (sym["x_symb"], sym["y_symb"])
+                for sym in tao.plot_symbol(region_name, graph_name, curve_name, x_or_y="")
+                or []
+            ]
+        except RuntimeError:
+            symbol_points = []
+
+        if graph_type is None:
+            graph_info = get_plot_graph_info(tao, region_name, graph_name)
+            graph_type = graph_info["graph^type"]
+
+        if graph_type == "histogram":
+            histogram_info = cast(PlotHistogramInfo, tao.plot_histogram(full_name))
+        else:
+            histogram_info = None
+
+        wave_params = cast(WaveParams, tao.wave("params"))
+        return cls.from_info(
+            graph_type=graph_type,
+            curve_info=curve_info,
+            points=points,
+            symbol_points=symbol_points,
+            histogram_info=histogram_info,
+            wave_params=wave_params,
+        )
+
+    @classmethod
+    def from_info(
+        cls,
+        graph_type: str,
+        curve_info: PlotCurveInfo,
+        points: List[Point],
+        symbol_points: List[Point],
+        histogram_info: Optional[PlotHistogramInfo] = None,
+        wave_params: Optional[WaveParams] = None,
+    ) -> PlotCurve:
+        line_color = pgplot.mpl_color(curve_info["line"]["color"])
+        # TODO: line^pattern typo?
+        line_style = pgplot.styles[curve_info["line"]["line^pattern"].lower()]
+        if curve_info["draw_line"]:
+            line_width = curve_info["line"]["width"]
+        else:
+            line_width = 0.0
+        symbol_color = pgplot.mpl_color(curve_info["symbol"]["color"])
+
+        # TODO: symbol^type typo?
+        symbol_info = curve_info["symbol"]
+        symbol_type = symbol_info["symbol^type"]
+        if _should_use_symbol_color(
+            symbol_type=symbol_type,
+            fill_pattern=symbol_info["fill_pattern"],
+        ):
+            marker_color = symbol_info["color"]
+        else:
+            marker_color = "none"
+
+        if curve_info["draw_symbols"] and pgplot.symbols[symbol_type]:
+            marker_size = curve_info["symbol"]["height"]
+        else:
+            marker_size = 0
+
+        symbol_line_width = curve_info["symbol"]["line_width"]
+
+        xpoints = [p[0] for p in points]
+        ypoints = [p[1] for p in points]
+        symbol_xs = [p[0] for p in symbol_points]
+        symbol_ys = [p[1] for p in symbol_points]
+        if ypoints and symbol_ys:
+            y_max = max(
+                0.5 * max(max(ypoints), max(symbol_ys)),
+                2 * max(max(ypoints), max(symbol_ys)),
+            )
+            y_min = min(
+                0.5 * min(min(ypoints), min(symbol_ys)),
+                2 * min(min(ypoints), min(symbol_ys)),
+            )
+        elif symbol_ys:
+            y_max = max(symbol_ys)
+            y_min = min(symbol_ys)
+        elif ypoints:
+            y_max = max(ypoints)
+            y_min = min(ypoints)
+        else:
+            raise NoCurveDataError("No points found, make sure data is properly initialized")
+
+        if xpoints:
+            curve_line = PlotCurveLine(
+                xs=xpoints,
+                ys=ypoints,
+                color=line_color,
+                linestyle=line_style,
+                linewidth=line_width / 2,
+            )
+        else:
+            curve_line = None
+
+        if symbol_xs:
+            curve_symbols = PlotCurveSymbols(
+                xs=symbol_xs,
+                ys=symbol_ys,
+                color=symbol_color,
+                linewidth=0,
+                markerfacecolor=marker_color,
+                markersize=marker_size / 2,
+                marker=symbol_type,
+                markeredgewidth=symbol_line_width / 2,
+            )
+        else:
+            curve_symbols = None
+
+        if graph_type in {"data", "dynamic_aperture", "phase_space"}:
+            return cls(
+                info=curve_info,
+                line=curve_line,
+                symbol=curve_symbols,
+            )
+
+        if graph_type in {"wave.0", "wave.a", "wave.b"}:
+            # Wave region boundaries
+            # wave analysis rectangles
+            if wave_params is None:
+                raise ValueError(f"wave_params required for graph type: {graph_type}")
+            if symbol_color in {"blue", "navy", "cyan", "green", "purple"}:
+                wave_color = "orange"
+            else:
+                wave_color = "blue"
+
+            patches = []
+            if graph_type in {"wave.0", "wave.a"}:
+                a1, a2 = wave_params["ix_a1"], wave_params["ix_a2"]
+                patches.append(
+                    PlotPatchRectangle(
+                        xy=(a1, y_min),
+                        width=a2 - a1,
+                        height=y_max - y_min,
+                        fill=False,
+                        color=wave_color,
+                    )
+                )
+
+            if graph_type in {"wave.0", "wave.b"}:
+                b1, b2 = wave_params["ix_b1"], wave_params["ix_b2"]
+                patches.append(
+                    PlotPatchRectangle(
+                        xy=(b1, y_min),
+                        width=b2 - b1,
+                        height=y_max - y_min,
+                        fill=False,
+                        color=wave_color,
+                    )
+                )
+
+            return cls(
+                info=curve_info,
+                line=curve_line,
+                symbol=curve_symbols,
+                patches=patches,
+            )
+
+        if graph_type == "histogram":
+            assert histogram_info is not None
+            return cls(
+                info=curve_info,
+                line=None,
+                symbol=None,
+                histogram=PlotHistogram(
+                    xs=xpoints,
+                    bins=int(histogram_info["number"]),
+                    weights=ypoints,
+                    histtype="step",
+                    color=symbol_color,
+                ),
+            )
+
+        raise NotImplementedError(f"graph_type: {graph_type}")
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class BasicGraph(GraphBase):
+    graph_type: ClassVar[str] = "basic"
+    curves: List[PlotCurve] = Field(default_factory=list)
+
+    def clamp_x_range(self, x0: Optional[float], x1: Optional[float]) -> Tuple[float, float]:
+        if x0 is None:
+            x0 = self.get_x_range()[0]
+        if x1 is None:
+            x1 = self.get_x_range()[1]
+
+        if self.is_s_plot:
+            # Don't go to negative 's' values
+            x0 = max((0.0, x0))
+        return (x0, x1)
+
+    @property
+    def is_s_plot(self) -> bool:
+        if self.region_info["x_axis_type"] == "s":
+            return True
+        return self.info["x_label"].lower().replace(" ", "") in {"s[m]", "s(m)"}
+
+    def get_x_range(self) -> Tuple[float, float]:
+        return (self.info["x_min"], self.info["x_max"])
+
+    def get_num_points(self) -> int:
+        for curve in self.curves:
+            if curve.line is not None:
+                return len(curve.line.xs)
+        return 401  # per the docs, this is the default for n_curve_points
+
+    @classmethod
+    def from_tao(
+        cls,
+        tao: Tao,
+        region_name: str,
+        graph_name: str,
+        *,
+        info: Optional[PlotGraphInfo] = None,
+        template_name: Optional[str] = None,
+        template_graph_index: Optional[int] = None,
+    ) -> BasicGraph:
+        if info is None:
+            info = get_plot_graph_info(tao, region_name, graph_name)
+        else:
+            # We'll mutate it to remove curve names below to conform to PlotGraphInfo
+            info = info.copy()
+
+        region_info = _clean_pytao_output(tao.plot1(region_name), PlotRegionInfo)
+
+        graph_type = info["graph^type"]
+        if graph_type in {"lat_layout", "floor_plan", "key_table"}:
+            raise ValueError(f"Incorrect graph type: {graph_type} for {cls.__name__}")
+
+        if info["why_invalid"]:
+            raise GraphInvalidError(f"Graph not valid: {info['why_invalid']}")
+
+        curve_keys = [f"curve[{idx}]" for idx in range(1, info["num_curves"] + 1)]
+        all_curve_names: List[str] = [info.pop(key) for key in curve_keys]
+
+        curves = []
+        for curve_name in all_curve_names:
+            try:
+                curve = PlotCurve.from_tao(tao, region_name, graph_name, curve_name)
+            except NoCurveDataError:
+                logger.warning(f"No curve data for {region_name}.{graph_name}.{curve_name}")
+            else:
+                curves.append(curve)
+
+        return cls(
+            info=info,
+            region_info=region_info,
+            region_name=region_name,
+            graph_name=graph_name,
+            template_name=template_name,
+            template_graph_index=template_graph_index,
+            curves=curves,
+            show_axes=info["draw_axes"],
+            title="{title} {title_suffix}".format(**info),
+            xlabel=info["x_label"],
+            ylabel=info["y_label"],
+            draw_grid=info["draw_grid"],
+            xlim=(info["x_min"], info["x_max"]),
+            ylim=(info["y_min"], info["y_max"]),
+            draw_legend=info["draw_curve_legend"],
+        )
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LatticeLayoutElement:
+    info: PlotLatLayoutInfo
+    shape: Optional[layout_shapes.AnyLayoutShape]
+    annotations: List[PlotAnnotation]
+    color: str
+    width: float
+
+    @property
+    def name(self) -> str:
+        return self.info["label_name"]
+
+    @classmethod
+    def regular_shape(
+        cls,
+        s1: float,
+        s2: float,
+        y1: float,
+        y2: float,
+        width: float,
+        color: str,
+        shape: str,
+        name: str,
+        y2_floor: float,
+    ) -> Tuple[Optional[layout_shapes.AnyLayoutShape], List[PlotAnnotation]]:
+        assert s2 > s1
+        if name:
+            annotations = [
+                PlotAnnotation(
+                    x=(s1 + s2) / 2,
+                    y=1.1 * y2_floor,
+                    text=name,
+                    horizontalalignment="center",
+                    verticalalignment="top",
+                    clip_on=False,
+                    color=color,
+                    rotation=90,
+                )
+            ]
+        else:
+            annotations = []
+
+        try:
+            shape_cls = layout_shapes.shape_to_class[shape]
+        except KeyError:
+            logger.debug(f"Unsupported layout shape type: {shape}")
+            shape_instance = None
+        else:
+            shape_instance = shape_cls(
+                s1=s1,
+                s2=s2,
+                y1=y1,
+                y2=y2,
+                line_width=width,
+                color=color,
+                name=name,
+                fill=False,
+            )
+        if isinstance(shape_instance, layout_shapes.LayoutTriangle):
+            orientation = cast(Literal["u", "d", "l", "r"], shape[0])
+            shape_instance.orientation = orientation
+
+        return shape_instance, annotations
+
+    @classmethod
+    def wrapped_shape(
+        cls,
+        s1: float,
+        s2: float,
+        y1: float,
+        y2: float,
+        color: str,
+        shape: str,
+        name: str,
+        x_min: float,
+        x_max: float,
+        y2_floor: float,
+    ) -> Tuple[Optional[layout_shapes.AnyWrappedLayoutShape], List[PlotAnnotation]]:
+        """
+        Element is wrapped around the lattice ends, and s1 >= s2.
+
+        `$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wake` shows off
+        this functionality.
+        """
+        assert s1 >= s2
+        s_min = max((x_min, s1 + (s1 + s2) / 2.0))
+        s_max = min((x_max, s1 - (s1 + s2) / 2.0))
+
+        try:
+            shape_cls = layout_shapes.wrapped_shape_to_class[shape]
+        except KeyError:
+            logger.debug(f"Unsupported wrappedlayout shape type: {shape}")
+            shape_instance = None
+        else:
+            shape_instance = shape_cls(
+                s1=s1,
+                s2=s2,
+                y1=y1,
+                y2=y2,
+                color=color,
+                s_min=s_min,
+                s_max=s_max,
+                name=name,
+                fill=False,
+            )
+
+        annotations = [
+            PlotAnnotation(
+                x=s_max,
+                y=1.1 * y2_floor,
+                text=name,
+                horizontalalignment="right",
+                verticalalignment="top",
+                clip_on=True,
+                color=color,
+            ),
+            PlotAnnotation(
+                x=s_min,
+                y=1.1 * y2_floor,
+                text=name,
+                horizontalalignment="left",
+                verticalalignment="top",
+                clip_on=True,
+                color=color,
+            ),
+        ]
+
+        return shape_instance, annotations
+
+    @classmethod
+    def from_info(
+        cls,
+        graph_info: PlotGraphInfo,
+        info: PlotLatLayoutInfo,
+        y2_floor: float,
+        plot_page: PlotPage,
+    ):
+        s1 = info["ele_s_start"]
+        s2 = info["ele_s_end"]
+        y1 = info["y1"] * plot_page["lat_layout_shape_scale"]
+        y2 = -info["y2"] * plot_page["lat_layout_shape_scale"]  # Note negative sign.
+        width = info["line_width"]
+        color = info["color"]
+        shape = info["shape"]
+        name = info["label_name"]
+
+        if ":" in shape:
+            _shape_prefix, shape = shape.split(":", 1)
+        else:
+            _shape_prefix, shape = "", shape
+
+        # Normal case where element is not wrapped around ends of lattice.
+        if s2 > s1:
+            shape, annotations = cls.regular_shape(
+                s1=s1,
+                s2=s2,
+                y1=y1,
+                y2=y2,
+                width=width,
+                color=color,
+                shape=shape,
+                name=name,
+                y2_floor=y2_floor,
+            )
+
+        else:
+            shape, annotations = cls.wrapped_shape(
+                s1=s1,
+                s2=s2,
+                y1=y1,
+                y2=y2,
+                color=color,
+                shape=shape,
+                name=name,
+                x_min=graph_info["x_min"],
+                x_max=graph_info["x_max"],
+                y2_floor=y2_floor,
+            )
+
+        return cls(
+            info=info,
+            shape=shape,
+            color=color,
+            width=width,
+            annotations=annotations,
+        )
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class LatticeLayoutGraph(GraphBase):
+    graph_type: ClassVar[str] = "lat_layout"
+    elements: List[LatticeLayoutElement] = Field(default_factory=list)
+    border_xlim: Point = _point_field
+    universe: int = 0
+    branch: int = 0
+    y2_floor: float = 0
+
+    @property
+    def is_s_plot(self) -> bool:
+        return True
+
+    @property
+    def y_min(self) -> float:
+        ele_y1s = [elem.info["y1"] for elem in self.elements]
+        ele_y2s = [elem.info["y2"] for elem in self.elements]
+        return min(ele_y1s + ele_y2s)
+
+    @property
+    def y_max(self) -> float:
+        ele_y1s = [elem.info["y1"] for elem in self.elements]
+        ele_y2s = [elem.info["y2"] for elem in self.elements]
+        return max(ele_y1s + ele_y2s)
+
+    @classmethod
+    def from_tao(
+        cls,
+        tao: Tao,
+        region_name: str = "lat_layout",
+        graph_name: str = "g",
+        *,
+        branch: Optional[int] = None,
+        info: Optional[PlotGraphInfo] = None,
+        plot_page: Optional[PlotPage] = None,
+        template_name: Optional[str] = None,
+        template_graph_index: Optional[int] = None,
+    ) -> LatticeLayoutGraph:
+        if info is None:
+            try:
+                info = get_plot_graph_info(tao, region_name, graph_name)
+            except RuntimeError:
+                raise NoLayoutError(f"No layout named {region_name}.{graph_name}") from None
+
+        if plot_page is None:
+            plot_page = cast(PlotPage, tao.plot_page())
+
+        region_info = _clean_pytao_output(tao.plot1(region_name), PlotRegionInfo)
+
+        graph_type = info["graph^type"]
+        if graph_type != "lat_layout":
+            raise ValueError(f"Incorrect graph type: {graph_type} for {cls.__name__}")
+
+        universe = 1 if info["ix_universe"] == -1 else info["ix_universe"]
+        branch = info["-1^ix_branch"]
+        try:
+            all_elem_info = tao.plot_lat_layout(ix_uni=universe, ix_branch=branch)
+        except RuntimeError as ex:
+            if branch != -1:
+                raise
+
+            logger.debug(
+                f"Lat layout failed for universe={universe} branch={branch}; trying branch 0"
+            )
+            try:
+                all_elem_info = tao.plot_lat_layout(ix_uni=universe, ix_branch=0)
+            except RuntimeError:
+                logger.error(f"Failed to plot layout: {ex}")
+                raise
+
+        all_elem_info = cast(List[PlotLatLayoutInfo], all_elem_info)
+
+        ele_y2s = [elem["y2"] for elem in all_elem_info]
+        y2_floor = -max(ele_y2s)  # Note negative sign
+        elements = [
+            LatticeLayoutElement.from_info(
+                graph_info=info,
+                info=elem,
+                y2_floor=y2_floor,
+                plot_page=plot_page,
+            )
+            for elem in all_elem_info
+        ]
+
+        return cls(
+            info=info,
+            region_info=region_info,
+            region_name=region_name,
+            graph_name=graph_name,
+            template_name=template_name,
+            template_graph_index=template_graph_index,
+            xlim=(info["x_min"], info["x_max"]),
+            ylim=(info["y_min"], info["y_max"]),
+            border_xlim=(1.1 * info["x_min"], 1.1 * info["x_max"]),
+            universe=universe,
+            branch=branch,
+            y2_floor=y2_floor,
+            elements=elements,
+        )
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class FloorPlanElement:
+    branch_index: int
+    index: int
+    info: FloorPlanElementInfo
+    annotations: List[PlotAnnotation]
+    shape: Optional[floor_plan_shapes.AnyFloorPlanShape]
+
+    @property
+    def name(self) -> str:
+        return self.info["label_name"]
+
+    @classmethod
+    def from_info(
+        cls,
+        info: FloorPlanElementInfo,
+        graph_info: PlotGraphInfo,
+        plot_page: PlotPage,
+    ):
+        is_absolute = graph_info["floor_plan_size_is_absolute"]
+        floor_plan_shape_scale = plot_page["floor_plan_shape_scale"]
+        if is_absolute or floor_plan_shape_scale == 1.0:
+            # plot_page floor_plan_size_is_absolute=False means coordinates are
+            # in units of points (72 DPI and as such 72 points per "inch" of
+            # screen real estate).
+            # This is not always set manually or correctly.
+            # Let's assume (for now) that the default shape scale of 1.0 is
+            # incorrect and that DPI units are to be assumed...
+            scale = 1 / 72.0
+        else:
+            scale = 1.0
+
+        # Handle some renaming and reduce dictionary key usage
+        return cls._from_info(
+            info,
+            branch_index=info["branch_index"],
+            index=info["index"],
+            ele_key=info["ele_key"],
+            x1=info["end1_r1"],
+            y1=info["end1_r2"],
+            angle_start=info["end1_theta"],
+            x2=info["end2_r1"],
+            y2=info["end2_r2"],
+            angle_end=info["end2_theta"],
+            line_width=info["line_width"],
+            shape=info["shape"],
+            off1=info["y1"] * scale,
+            off2=info["y2"] * scale,
+            color=info["color"],
+            label_name=info["label_name"],
+            # ele_l=info["ele_l"],
+            # ele_angle=info["ele_angle"],
+            rel_angle_start=info.get("ele_e1", 0.0),
+            rel_angle_end=info.get("ele_e", 0.0),
+        )
+
+    @classmethod
+    def _from_info(
+        cls,
+        info: FloorPlanElementInfo,
+        *,
+        branch_index: int,
+        index: int,
+        ele_key: str,
+        x1: float,
+        y1: float,
+        angle_start: float,
+        x2: float,
+        y2: float,
+        angle_end: float,
+        line_width: float,
+        shape: str,
+        off1: float,
+        off2: float,
+        color: str,
+        label_name: str,
+        # Only for sbend:
+        rel_angle_start: float = 0.0,
+        rel_angle_end: float = 0.0,
+    ) -> FloorPlanElement:
+        ele_key = ele_key.lower()
+
+        annotations: List[PlotAnnotation] = []
+
+        if ":" in shape:
+            _shape_prefix, shape = shape.split(":", 1)
+        else:
+            _shape_prefix, shape = "", shape
+
+        shape_cls = {
+            "drift": floor_plan_shapes.DriftLine,
+            "kicker": floor_plan_shapes.KickerLine,
+            "box": floor_plan_shapes.Box,
+            "xbox": floor_plan_shapes.XBox,
+            "x": floor_plan_shapes.LetterX,
+            "bow_tie": floor_plan_shapes.BowTie,
+            "diamond": floor_plan_shapes.Diamond,
+            "circle": floor_plan_shapes.Circle,
+        }.get(shape, None)
+
+        if ele_key == "sbend" and shape == "box":
+            # An SBend box is a potentially curvy shape
+            shape_cls = floor_plan_shapes.SBend
+        elif off1 == 0 and off2 == 0:
+            # Zero width/height -> just a line segment
+            shape_cls = floor_plan_shapes.LineSegment
+
+        if not color and shape_cls not in {
+            floor_plan_shapes.DriftLine,
+            floor_plan_shapes.KickerLine,
+        }:
+            # Don't draw colorless shapes, with a couple exceptions
+            shape_cls = None
+
+        if shape and not shape_cls:
+            raise ValueError(f"Unhandled shape: {shape}")
+
+        if shape_cls is None:
+            shape_instance = None
+        else:
+            shape_instance = shape_cls(
+                x1=x1,
+                x2=x2,
+                y1=y1,
+                y2=y2,
+                off1=off1,
+                off2=off2,
+                line_width=line_width,
+                color=color,
+                angle_start=angle_start,
+                angle_end=angle_end,
+                rel_angle_start=rel_angle_start,
+                rel_angle_end=rel_angle_end,
+                name=label_name,
+            )
+
+        if label_name and color:
+            annotation_angle = math.degrees((angle_end + angle_start) / 2)
+            if np.sin(((angle_end + angle_start) / 2)) > 0:
+                annotation_angle += -90
+                align = "right"
+            else:
+                annotation_angle += 90
+                align = "left"
+
+            annotations.append(
+                PlotAnnotation(
+                    x=x1 + (x2 - x1) / 2 - 1.3 * off1 * np.sin(angle_start),
+                    y=y1 + (y2 - y1) / 2 + 1.3 * off1 * np.cos(angle_start),
+                    text=label_name,
+                    horizontalalignment=align,
+                    verticalalignment="center",
+                    color="black",
+                    rotation=annotation_angle,
+                    clip_on=True,
+                    rotation_mode="anchor",
+                )
+            )
+
+        return cls(
+            branch_index=branch_index,
+            index=index,
+            info=info,
+            annotations=annotations,
+            shape=shape_instance,
+        )
+
+
+def sort_building_wall_graph_info(
+    info: List[BuildingWallGraphInfo],
+) -> Dict[int, Dict[int, BuildingWallGraphInfo]]:
+    res = {}
+    for item in info:
+        index = item["index"]
+        point = item["point"]
+        res.setdefault(index, {})[point] = item
+    return res
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class BuildingWalls:
+    building_wall_graph: List[BuildingWallGraphInfo] = Field(default_factory=list)
+    lines: List[PlotCurveLine] = Field(default_factory=list)
+    patches: List[PlotPatch] = Field(default_factory=list)
+
+    @classmethod
+    def from_info(
+        cls,
+        building_wall_graph: List[BuildingWallGraphInfo],
+        wall_list: List[BuildingWallInfo],
+    ) -> BuildingWalls:
+        walls = sort_building_wall_graph_info(building_wall_graph)
+        wall_info_by_index = {info["index"]: info for info in wall_list}
+        lines = []
+        patches = []
+        for wall_idx, wall in walls.items():
+            wall_points = list(reversed(wall.values()))
+            wall_info = wall_info_by_index[wall_idx]
+            color = wall_info["color"]
+            line_width = wall_info["line_width"]
+            for point, next_point in zip(wall_points, wall_points[1:]):
+                radius = point["radius"]
+                p0x, p0y = point["offset_x"], point["offset_y"]
+                p1x, p1y = next_point["offset_x"], next_point["offset_y"]
+                if np.isclose(radius, 0.0):
+                    lines.append(
+                        PlotCurveLine(
+                            xs=[p0x, p1x],
+                            ys=[p0y, p1y],
+                            color=color,
+                            linewidth=line_width,
+                        )
+                    )
+
+                else:
+                    patches.append(
+                        PlotPatchArc.from_building_wall(
+                            mx=p1x,
+                            my=p1y,
+                            kx=p0x,
+                            ky=p0y,
+                            k_radii=radius,
+                            color=color,
+                            linewidth=line_width,
+                        )
+                    )
+
+        return cls(building_wall_graph=building_wall_graph, lines=lines, patches=patches)
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class FloorOrbits:
+    info: List[FloorOrbitInfo]
+    curve: PlotCurveSymbols
+
+    @classmethod
+    def from_tao(
+        cls,
+        tao: Tao,
+        region_name: str,
+        graph_name: str,
+        color: str,
+    ) -> FloorOrbits:
+        floor_orbit_info = cast(
+            List[FloorOrbitInfo],
+            tao.floor_orbit(f"{region_name}.{graph_name}"),
+        )
+
+        xs = []
+        ys = []
+        for info in floor_orbit_info:
+            if info["ele_key"] == "x":
+                xs.extend(info["orbits"])
+            elif info["ele_key"] == "y":
+                ys.extend(info["orbits"])
+
+        return cls(
+            info=floor_orbit_info,
+            curve=PlotCurveSymbols(
+                xs=xs,
+                ys=ys,
+                color=color,
+                markerfacecolor=color,
+                markersize=1,
+                marker="circle_filled",
+                markeredgewidth=1,
+            ),
+        )
+
+
+@dataclasses.dataclass(config=_dcls_config)
+class FloorPlanGraph(GraphBase):
+    graph_type: ClassVar[str] = "floor_plan"
+    building_walls: BuildingWalls = Field(default_factory=BuildingWalls)
+    floor_orbits: Optional[FloorOrbits] = None
+    elements: List[FloorPlanElement] = Field(default_factory=list)
+
+    @property
+    def is_s_plot(self) -> bool:
+        return False
+
+    @classmethod
+    def from_tao(
+        cls,
+        tao: Tao,
+        region_name: str,
+        graph_name: str,
+        *,
+        info: Optional[PlotGraphInfo] = None,
+        plot_page: Optional[PlotPage] = None,
+        template_name: Optional[str] = None,
+        template_graph_index: Optional[int] = None,
+    ) -> FloorPlanGraph:
+        full_name = f"{region_name}.{graph_name}"
+        if info is None:
+            info = get_plot_graph_info(tao, region_name, graph_name)
+        if plot_page is None:
+            plot_page = cast(PlotPage, tao.plot_page())
+        region_info = _clean_pytao_output(tao.plot1(region_name), PlotRegionInfo)
+
+        graph_type = info["graph^type"]
+        if graph_type != "floor_plan":
+            raise ValueError(f"Incorrect graph type: {graph_type} for {cls.__name__}")
+
+        elem_infos = cast(
+            List[FloorPlanElementInfo],
+            tao.floor_plan(full_name),
+        )
+        elements = [
+            FloorPlanElement.from_info(
+                info=fpe_info,
+                graph_info=info,
+                plot_page=plot_page,
+            )
+            for fpe_info in elem_infos
+        ]
+        building_walls = BuildingWalls.from_info(
+            building_wall_graph=cast(
+                List[BuildingWallGraphInfo],
+                tao.building_wall_graph(full_name),
+            ),
+            wall_list=cast(List[BuildingWallInfo], tao.building_wall_list()),
+        )
+        floor_orbits = None
+        if float(info["floor_plan_orbit_scale"]) != 0:
+            floor_orbits = FloorOrbits.from_tao(
+                tao,
+                region_name=region_name,
+                graph_name=graph_name,
+                color=info["floor_plan_orbit_color"].lower(),
+            )
+
+        return cls(
+            info=info,
+            region_info=region_info,
+            region_name=region_name,
+            graph_name=graph_name,
+            template_name=template_name,
+            template_graph_index=template_graph_index,
+            elements=elements,
+            building_walls=building_walls,
+            floor_orbits=floor_orbits,
+            title="{title} {title_suffix}".format(**info),
+            xlabel=info["x_label"],
+            ylabel=info["y_label"],
+            draw_grid=info["draw_grid"],
+            xlim=(info["x_min"], info["x_max"]),
+            ylim=(info["y_min"], info["y_max"]),
+            draw_legend=info["draw_curve_legend"],
+        )
+
+
+def get_plots_in_region(tao: Tao, region_name: str):
+    plot1_info = tao.plot1(region_name)
+
+    if "num_graphs" not in plot1_info:
+        raise RuntimeError("Plotting disabled?")
+
+    return [plot1_info[f"graph[{idx}]"] for idx in range(1, plot1_info["num_graphs"] + 1)]
+
+
+def make_graph(
+    tao: Tao,
+    region_name: str,
+    graph_name: str,
+    template_name: Optional[str] = None,
+    template_graph_index: Optional[int] = None,
+) -> AnyGraph:
+    graph_info = get_plot_graph_info(tao, region_name, graph_name)
+    graph_type = graph_info["graph^type"]
+
+    logger.debug(f"Creating graph {region_name}.{graph_name} ({graph_type})")
+
+    if graph_type == "floor_plan":
+        cls = FloorPlanGraph
+    elif graph_type == "lat_layout":
+        cls = LatticeLayoutGraph
+    elif graph_type == "key_table":
+        raise UnsupportedGraphError(graph_type)
+    else:
+        cls = BasicGraph
+
+    return cls.from_tao(
+        tao=tao,
+        region_name=region_name,
+        graph_name=graph_name,
+        info=graph_info,
+        template_name=template_name,
+        template_graph_index=template_graph_index,
+    )
+
+
+def get_plot_graph_info(tao: Tao, region_name: str, graph_name: str) -> PlotGraphInfo:
+    return cast(PlotGraphInfo, tao.plot_graph(f"{region_name}.{graph_name}"))
+
+
+def find_unused_plot_region(tao: Tao, skip: Set[str]) -> str:
+    for info in tao.plot_list("r"):
+        region_name = info["region"]
+        if region_name not in skip and not info["plot_name"]:
+            return region_name
+
+    raise AllPlotRegionsInUseError("No more available plot regions.")
+
+
+AnyGraph = Union[BasicGraph, LatticeLayoutGraph, FloorPlanGraph]
+
+
+class GraphManager(ABC):
+    """
+    Graph backend manager base class.
+    """
+
+    _key_: ClassVar[str] = "GraphManager"
+
+    tao: Tao
+    regions: Dict[str, List[AnyGraph]]
+    _to_place: Dict[str, str]
+    layout_template: str = "lat_layout"
+    floor_plan_template: str = "floor_plan"
+
+    def __init__(self, tao: Tao) -> None:
+        self.tao = tao
+        self.regions = {}
+        self._to_place = {}
+
+    def tao_init_hook(self) -> None:
+        """Tao has reinitialized; clear our state."""
+        self.regions.clear()
+        self._to_place.clear()
+
+    def _update_place_buffer(self):
+        for item in self.tao.place_buffer():
+            region = item["region"]
+            graph = item["graph"]
+            if graph == "none":
+                if region == "*":
+                    self._to_place.clear()
+                else:
+                    self._to_place.pop(region, None)
+            else:
+                self._to_place[region] = graph
+
+    @property
+    def to_place(self) -> Dict[str, str]:
+        """Graphs to place - region name to graph name."""
+        self._update_place_buffer()
+        return self._to_place
+
+    @property
+    def lattice_layout_graph(self) -> LatticeLayoutGraph:
+        """The lattice layout graph.  Placed if not already available."""
+        for region in self.regions.values():
+            for graph in region:
+                if isinstance(graph, LatticeLayoutGraph):
+                    return graph
+
+        (graph,) = self.place(self.layout_template)
+        assert isinstance(graph, LatticeLayoutGraph)
+        return graph
+
+    @property
+    def floor_plan_graph(self) -> FloorPlanGraph:
+        """The floor plan graph. Placed if not already available."""
+        for region in self.regions.values():
+            for graph in region:
+                if isinstance(graph, FloorPlanGraph):
+                    return graph
+        (graph,) = self.place(self.floor_plan_template)
+        assert isinstance(graph, FloorPlanGraph)
+        return graph
+
+    def get_region_to_place_template(self, template_name: str) -> str:
+        """Get a region for placing the graph."""
+        for region_name, to_place in self.to_place.items():
+            if to_place == template_name:
+                logger.debug("Graph %s found in region %s", template_name, region_name)
+                return region_name
+
+        try:
+            region_name = find_unused_plot_region(self.tao, set(self.to_place))
+        except AllPlotRegionsInUseError:
+            region_name = list(self.regions)[0]
+            plots_in_region = list(graph.template_name for graph in self.regions[region_name])
+            if plots_in_region:
+                logger.warning(
+                    f"All plot regions are in use; reusing plot region {region_name} which has graphs: {plots_in_region}"
+                )
+        else:
+            logger.debug("New region for graph %s: %s", template_name, region_name)
+        return region_name
+
+    def place_all(
+        self,
+        *,
+        ignore_invalid: bool = True,
+        ignore_unsupported: bool = True,
+    ) -> Dict[str, List[AnyGraph]]:
+        """
+        Place all graphs in the place buffer.
+
+        Side effect: clears `to_place`.
+
+        Parameters
+        ----------
+        ignore_invalid : bool
+            Ignore graphs marked as invalid by bmad.
+        ignore_unsupported : bool
+            Ignore unsupported graph types (e.g., key tables).
+
+        Returns
+        -------
+        Dict[str, List[AnyGraph]]
+            Region to list of graphs.
+        """
+        to_place = list(self.to_place.items())
+        self.to_place.clear()
+
+        logger.debug("Placing all plots: %s", to_place)
+        result = {}
+        for region_name, template_name in to_place:
+            try:
+                result[region_name] = self.place(
+                    template_name=template_name,
+                    region_name=region_name,
+                    ignore_invalid=ignore_invalid,
+                )
+            except UnsupportedGraphError:
+                if not ignore_unsupported:
+                    raise
+
+        return result
+
+    def update_region(
+        self,
+        region_name: str,
+        template_name: str,
+        ignore_invalid: bool = True,
+        ignore_unsupported: bool = True,
+    ) -> List[AnyGraph]:
+        """
+        Query information about already-placed graphs in a given region.
+
+        Parameters
+        ----------
+        region_name : str, optional
+            The region name where the graph was placed.
+        template_name : str
+            The template name the user placed.
+        ignore_invalid : bool
+            Ignore graphs marked as invalid by bmad.
+        ignore_unsupported : bool
+            Ignore unsupported graph types (e.g., key tables).
+
+        Returns
+        -------
+        list of graphs
+            The type of each graph is backend-dependent.
+        """
+        self._clear_region(region_name)
+
+        result = []
+        plot_names = get_plots_in_region(self.tao, region_name)
+        for idx, plot_name in enumerate(plot_names):
+            try:
+                result.append(
+                    self.make_graph(
+                        region_name=region_name,
+                        graph_name=plot_name,
+                        template_name=template_name,
+                        template_graph_index=idx,
+                    )
+                )
+            except UnsupportedGraphError as ex:
+                if ignore_unsupported:
+                    logger.debug(f"Unsupported graph in region {region_name}: {ex}")
+                    continue
+                raise
+            except GraphInvalidError as ex:
+                if ignore_invalid:
+                    logger.warning(f"Invalid graph in region {region_name}: {ex}")
+                    continue
+                raise
+
+        self.regions[region_name] = result
+        logger.debug(
+            "Updating region: %s template: %s generated %d plots",
+            region_name,
+            template_name,
+            len(result),
+        )
+        return result
+
+    def _place(
+        self,
+        template_name: str,
+        region_name: Optional[str] = None,
+    ) -> str:
+        if region_name is None:
+            region_name = self.get_region_to_place_template(template_name)
+            logger.debug(f"Picked {region_name} for template {template_name}")
+
+        self.to_place.pop(region_name, None)
+
+        logger.debug(f"Placing {template_name} in {region_name}")
+        self.tao.cmd(f"place -no_buffer {region_name} {template_name}")
+        return region_name
+
+    def place(
+        self,
+        template_name: str,
+        *,
+        region_name: Optional[str] = None,
+        ignore_invalid: bool = True,
+    ) -> List[AnyGraph]:
+        """
+        Place `template_name` in `region_name`.
+
+        Parameters
+        ----------
+        template_name : str
+            The graph template name.
+        region_name : str, optional
+            The region name to place it.  Determined automatically if unspecified.
+        ignore_invalid : bool
+            Ignore graphs marked as invalid by bmad.
+
+        Returns
+        -------
+        list of graphs
+            The type of each graph is backend-dependent.
+        """
+        region_name = self._place(template_name, region_name)
+        return self.update_region(
+            region_name=region_name,
+            template_name=template_name,
+            ignore_invalid=ignore_invalid,
+        )
+
+    def _clear_region(self, region_name: str):
+        if region_name == "*":
+            self.regions.clear()
+            logger.debug("Clearing all regions")
+            return
+
+        logger.debug("Clearing region %s", region_name)
+        if region_name in self.regions:
+            self.regions[region_name].clear()
+
+    def clear(self, region_name: str = "*"):
+        """
+        Clear a single region or all regions.
+
+        Parameters
+        ----------
+        region_name : str, optional
+            Defaults to '*', which is all regions.
+        """
+        try:
+            self.tao.cmd(f"place -no_buffer {region_name} none")
+        except RuntimeError as ex:
+            logger.warning(f"Region clear failed: {ex}")
+
+        self._clear_region(region_name)
+
+    def prepare_grid_by_names(
+        self,
+        template_names: List[str],
+        curves: Optional[List[CurveIndexToCurve]] = None,
+        settings: Optional[List[TaoGraphSettings]] = None,
+        xlim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+        ylim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+    ):
+        """
+        Prepare multiple graphs for a grid plot.
+
+        Applies per-graph curve settings and also region/graph settings.
+
+        Parameters
+        ----------
+        template_names : list of str
+            Graph names.
+        curves : list of Dict[int, TaoCurveSettings], optional
+            One dictionary per graph, with each dictionary mapping the curve
+            index to curve settings. These settings will be applied to the
+            placed graphs prior to plotting.
+        settings : list of TaoGraphSettings, optional
+            Graph customization settings.
+        xlim : list of (float, float), optional
+            X axis limits for each graph.
+        ylim : list of (float, float), optional
+            Y axis limits for each graph.
+
+        Returns
+        -------
+        list of graphs
+        """
+        num_graphs = len(template_names)
+        if not curves:
+            curves = [{}] * num_graphs
+        elif len(curves) < num_graphs:
+            assert len(curves)
+            curves = list(curves) + [{}] * (num_graphs - len(curves))
+
+        if not settings:
+            settings = [TaoGraphSettings()] * num_graphs
+        elif len(settings) < num_graphs:
+            settings = list(settings) + [TaoGraphSettings()] * (num_graphs - len(settings))
+
+        xlim = fix_grid_limits(xlim, num_graphs=num_graphs)
+        ylim = fix_grid_limits(ylim, num_graphs=num_graphs)
+        for setting, xl, yl in zip(settings, xlim, ylim):
+            setting.xlim = xl
+            setting.ylim = yl
+
+        graphs = sum(
+            (
+                self.prepare_graphs_by_name(
+                    template_name=template_name,
+                    curves=graph_curves,
+                    settings=graph_settings,
+                )
+                for template_name, graph_curves, graph_settings in zip(
+                    template_names,
+                    curves,
+                    settings,
+                )
+            ),
+            [],
+        )
+
+        if not graphs:
+            raise UnsupportedGraphError(
+                f"No supported plots from these templates: {template_names}"
+            )
+        return graphs
+
+    def prepare_graphs_by_name(
+        self,
+        template_name: str,
+        *,
+        region_name: Optional[str] = None,
+        settings: Optional[TaoGraphSettings] = None,
+        curves: Optional[Dict[int, TaoCurveSettings]] = None,
+        ignore_unsupported: bool = True,
+        ignore_invalid: bool = True,
+        place: bool = True,
+        xlim: Optional[Limit] = None,
+        ylim: Optional[Limit] = None,
+    ) -> List[AnyGraph]:
+        """
+        Prepare a graph for plotting.
+
+        Parameters
+        ----------
+        template_name : str
+            The graph template name.
+        region_name : str, optional
+            The region name to place it.  Determined automatically if unspecified.
+        settings : TaoGraphSettings, optional
+            Graph customization settings.
+        curves : Dict[int, TaoCurveSettings], optional
+            Curve settings, keyed by curve number.
+        ignore_unsupported : bool
+            Ignore unsupported graph types (e.g., key tables).
+        ignore_invalid : bool
+            Ignore graphs marked as invalid by bmad.
+        place : bool, default=True
+            Tell Tao to place the template first.
+        xlim : (float, float), optional
+            X axis limits.
+        ylim : (float, float), optional
+            Y axis limits.
+
+        Returns
+        -------
+        list of graphs
+            The type of each graph is backend-dependent.
+        """
+        if place:
+            region_name = self._place(template_name=template_name, region_name=region_name)
+        elif not region_name:
+            region_name = self.get_region_to_place_template(template_name)
+
+        if settings is None:
+            settings = TaoGraphSettings()
+        if xlim is not None:
+            settings.xlim = xlim
+        if ylim is not None:
+            settings.ylim = ylim
+
+        self.configure_graph(region_name, settings)
+
+        if curves is not None:
+            self.configure_curves(region_name, curves)
+
+        return self.update_region(
+            region_name=region_name,
+            template_name=template_name,
+            ignore_unsupported=ignore_unsupported,
+            ignore_invalid=ignore_invalid,
+        )
+
+    def configure_curves(
+        self,
+        region_name: str,
+        settings: Dict[int, TaoCurveSettings],
+        *,
+        graph_name: Optional[str] = None,
+    ):
+        """
+        Configure curves in a region.
+
+        Parameters
+        ----------
+        region_name : str
+            Already-placed region name.
+        settings : Dict[int, TaoCurveSettings]
+            Per-curve settings, keyed by integer curve index (starting at 1).
+        graph_name : str, optional
+            The graph name, if available.  If unspecified, settings will be
+            applied to all plots in the region.
+        """
+        if not graph_name:
+            for plot_name in get_plots_in_region(self.tao, region_name):
+                self.configure_curves(region_name, settings=settings, graph_name=plot_name)
+            return
+
+        for curve_idx, curve in settings.items():
+            for command in curve.get_commands(
+                region_name,
+                graph_name,
+                curve_index=curve_idx,
+            ):
+                self.tao.cmd(command)
+
+    def configure_graph(
+        self,
+        region_name: str,
+        settings: TaoGraphSettings,
+        *,
+        graph_name: Optional[str] = None,
+    ):
+        """
+        Configure graph settings for a region.
+
+        Parameters
+        ----------
+        region_name : str
+            Already-placed region name.
+        settings : TaoGraphSettings
+            Graph customization settings.
+        graph_name : str, optional
+            The graph name, if available.  If unspecified, settings will be
+            applied to all plots in the region.
+        """
+        if not graph_name:
+            for plot_name in get_plots_in_region(self.tao, region_name):
+                self.configure_graph(region_name, settings=settings, graph_name=plot_name)
+            return
+
+        graph_info = get_plot_graph_info(self.tao, region_name, graph_name)
+        graph_type = graph_info["graph^type"]
+        for command in settings.get_commands(
+            region_name,
+            graph_name,
+            graph_type=graph_type,
+        ):
+            self.tao.cmd(command)
+
+    def plot_all(
+        self,
+        grid: Optional[Tuple[int, int]] = None,
+        include_layout: bool = False,
+        **kwargs,
+    ):
+        """
+        Plot all "placed" graphs.
+
+        Parameters
+        ----------
+        grid : Tuple[int, int], optional
+            Grid plots into this shape - (rows, cols).
+        include_layout : bool, default=False
+            Include a layout plot.
+        **kwargs
+            Keyword arguments are passed to `.plot_grid()`.
+        """
+        template_names = list(self.to_place.values())
+        if not grid:
+            grid = (len(template_names), 1)
+        return self.plot_grid(
+            template_names,
+            grid=grid,
+            include_layout=include_layout,
+            **kwargs,
+        )
+
+    def make_graph(
+        self,
+        region_name: str,
+        graph_name: str,
+        template_name: Optional[str] = None,
+        template_graph_index: Optional[int] = None,
+    ) -> AnyGraph:
+        """
+        Create a graph instance from an already-placed graph.
+
+        Parameters
+        ----------
+        region_name : str
+            The region name of the graph.
+        graph_name : str
+            The placed graph name (tao_template_graph graph%name).
+        template_name : str, optional
+            The graph template name.
+        template_graph_index : str, optional
+            The zero-based graph index of those placed for `template_name`.
+
+        Returns
+        -------
+        AnyGraph
+        """
+        return make_graph(
+            self.tao,
+            region_name=region_name,
+            graph_name=graph_name,
+            template_name=template_name,
+            template_graph_index=template_graph_index,
+        )
+
+    @abstractmethod
+    def plot(
+        self,
+        template: str,
+        *,
+        region_name: Optional[str] = None,
+        include_layout: bool = True,
+        settings: Optional[TaoGraphSettings] = None,
+        xlim: Optional[Limit] = None,
+        ylim: Optional[Limit] = None,
+    ) -> Any:
+        pass
+
+    @abstractmethod
+    def plot_grid(
+        self,
+        templates: List[str],
+        grid: Tuple[int, int],
+        *,
+        include_layout: bool = False,
+        curves: Optional[List[Dict[int, TaoCurveSettings]]] = None,
+        settings: Optional[List[TaoGraphSettings]] = None,
+        xlim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+        ylim: Union[OptionalLimit, Sequence[OptionalLimit]] = None,
+    ) -> Any:
+        pass
+
+    @abstractmethod
+    def plot_field(
+        self,
+        ele_id: str,
+        *,
+        colormap: Optional[str] = None,
+        radius: float = 0.015,
+        num_points: int = 100,
+    ) -> Any:
+        pass
diff --git a/pytao/plotting/settings.py b/pytao/plotting/settings.py
new file mode 100644
index 00000000..52032d50
--- /dev/null
+++ b/pytao/plotting/settings.py
@@ -0,0 +1,670 @@
+from dataclasses import asdict
+from typing import Dict, List, Optional, Tuple, Union
+
+import pydantic
+from typing_extensions import Literal
+
+from .types import Limit
+
+tao_colors = frozenset(
+    {
+        "Not_Set",
+        "White",
+        "Black",
+        "Red",
+        "Green",
+        "Blue",
+        "Cyan",
+        "Magenta",
+        "Yellow",
+        "Orange",
+        "Yellow_Green",
+        "Light_Green",
+        "Navy_Blue",
+        "Purple",
+        "Reddish_Purple",
+        "Dark_Grey",
+        "Light_Grey",
+        "Transparent",
+    }
+)
+
+
+@pydantic.dataclasses.dataclass
+class QuickPlotPoint:
+    """
+    Tao QuickPlot Point.
+
+    Attributes
+    ----------
+    x : float
+    y : float
+    units : str, optional
+    """
+
+    x: float
+    y: float
+    units: Optional[str] = pydantic.Field(max_length=16, default=None)
+
+    def get_commands(
+        self,
+        region_name: str,
+        graph_name: str,
+        parent_name: str,
+    ) -> List[str]:
+        """
+        Get command strings to apply these settings with Tao.
+
+        Parameters
+        ----------
+        region_name : str
+            Region name.
+        graph_name : str
+            Graph name.
+        parent_name : str
+            Parent item name.
+
+        Returns
+        -------
+        list of str
+            Commands to send to Tao to apply these settings.
+        """
+        return [
+            f"set graph {region_name}.{graph_name} {parent_name}%{key} = {value}"
+            for key, value in asdict(self).items()
+            if value is not None
+        ]
+
+
+QuickPlotPointTuple = Tuple[float, float, str]
+
+
+@pydantic.dataclasses.dataclass
+class QuickPlotRectangle:
+    """
+    Tao QuickPlot Rectangle.
+
+    Attributes
+    ----------
+    x1 : float
+    x2 : float
+    y1 : float
+    y2 : float
+    units : str, optional
+    """
+
+    x1: float
+    x2: float
+    y1: float
+    y2: float
+    units: Optional[str] = pydantic.Field(default=None, max_length=16)
+
+    def get_commands(
+        self,
+        region_name: str,
+        graph_name: str,
+        parent_name: str,
+    ) -> List[str]:
+        """
+        Get command strings to apply these settings with Tao.
+
+        Parameters
+        ----------
+        region_name : str
+            Region name.
+        graph_name : str
+            Graph name.
+        parent_name : str
+            Parent item name.
+
+        Returns
+        -------
+        list of str
+            Commands to send to Tao to apply these settings.
+        """
+        return [
+            f"set graph {region_name}.{graph_name} {parent_name}%{key} = {value}"
+            for key, value in asdict(self).items()
+            if value is not None
+        ]
+
+
+QuickPlotRectangleTuple = Tuple[float, float, float, float, str]
+
+
+class TaoAxisSettings(pydantic.BaseModel, extra="forbid", validate_assignment=True):
+    """
+    Tao per-axis (x, x2, y, or y2) settings in a graph.
+
+    Intended for use with:
+
+    `tao.plot(..., settings=TaoGraphSettings(y=TaoAxisSettings(...)))`.
+
+    All attributes may be `None`. A value of `None` indicates that the setting
+    should not be configured in Tao.
+
+    Not all parameters are useful for PyTao plotting.  This class intends to
+    support Tao's internal plotting mechanism as well for users who prefer
+    to use it instead.
+
+    Attributes
+    ----------
+    bounds : One of {"zero_at_end", "zero_symmetric", "general", "exact"}
+        Axis bounds.
+    min : float
+        Left or bottom axis number range.
+    max : float
+        Right or top axis number range.
+    number_offset : float
+        Offset from the axis line in inches.
+    label_offset : float
+        Offset from numbers in inches.
+    major_tick_len : float
+        Major tick length in inches.
+    minor_tick_len : float
+        Minor tick length in inches.
+    label_color : str
+        Color of the label string.
+    major_div : int
+        Number of major divisions.
+    major_div_nominal : int
+        Major divisions nominal value.
+    minor_div : int
+        Number of minor divisions, where 0 is automatic.
+    minor_div_max : int
+        Maximum minor divisions, if `minor_div` is set to automatic (0).
+    places : int
+        Number of digits to print.
+    tick_side : -1, 0, or 1
+        * 1: draw ticks to the inside
+        * 0: draw ticks both inside and outside
+        * -1: draw ticks to the outside
+    number_side : -1 or 1
+        * 1: draw numbers to the inside
+        * -1: draw numbers to the outside
+    label : str
+        Axis label string.
+    type : "log" or "linear"
+        Axis type.
+    draw_label : bool
+        Draw the label string.
+    draw_numbers : bool
+        Draw the numbers.
+    """
+
+    bounds: Optional[Literal["zero_at_end", "zero_symmetric", "general", "exact"]] = None
+    min: Optional[float] = None
+    max: Optional[float] = None
+    number_offset: Optional[float] = None
+    label_offset: Optional[float] = None
+    major_tick_len: Optional[float] = None
+    minor_tick_len: Optional[float] = None
+    label_color: Optional[str] = pydantic.Field(max_length=16, default=None)
+    major_div: Optional[int] = None
+    major_div_nominal: Optional[int] = None
+    minor_div: Optional[int] = None
+    minor_div_max: Optional[int] = None
+    places: Optional[int] = None
+    tick_side: Optional[Literal[-1, 0, 1]] = None
+    number_side: Optional[Literal[-1, 1]] = None
+    label: Optional[str] = pydantic.Field(max_length=80, default=None)
+    type: Optional[Literal["log", "linear"]] = None
+    draw_label: Optional[bool] = None
+    draw_numbers: Optional[bool] = None
+
+    scale: Optional[Tuple[float, float]] = None
+    scale_gang: Optional[bool] = None
+
+    def get_commands(
+        self,
+        region_name: str,
+        axis_name: str,
+    ) -> List[str]:
+        """
+        Get command strings to apply these settings with Tao.
+
+        Parameters
+        ----------
+        region_name : str
+            Region name.
+        axis_name : str
+            Axis name.
+
+        Returns
+        -------
+        list of str
+            Commands to send to Tao to apply these settings.
+        """
+        items = {key: value for key, value in self.model_dump().items() if value is not None}
+        scale = items.pop("scale", None)
+        scale_gang = items.pop("scale_gang", None)
+
+        commands = []
+        if scale is not None:
+            scale_low, scale_high = scale
+            scale_cmd = {
+                "x": "x_scale",
+                "x2": "x_scale",
+                "y": "scale -y",
+                "y2": "scale -y2",
+            }[axis_name]
+            if scale_gang:
+                scale_cmd = f"{scale_cmd} -gang"
+            elif scale_gang is False:  # note: may be None
+                scale_cmd = f"{scale_cmd} -nogang"
+            commands.append(f"{scale_cmd} {region_name} {scale_low} {scale_high}")
+
+        return commands + [
+            f"set graph {region_name} {axis_name}%{key} = {value}"
+            for key, value in items.items()
+        ]
+
+
+class TaoFloorPlanSettings(pydantic.BaseModel, extra="forbid", validate_assignment=True):
+    """
+    Tao graph settings specifically for floor plans.
+
+    Intended for use with:
+
+    `tao.plot(..., settings=TaoGraphSettings(floor_plan=TaoFloorPlanSettings(...)))`.
+
+    All attributes may be `None`. A value of `None` indicates that the setting
+    should not be configured in Tao.
+
+    Not all parameters are useful for PyTao plotting.  This class intends to
+    support Tao's internal plotting mechanism as well for users who prefer
+    to use it instead.
+
+    Attributes
+    ----------
+    correct_distortion : bool
+        Correct distortion. By default, the horizontal or vertical margins of
+        the graph will be increased so that the horizontal scale (meters per
+        plotting inch) is equal to the vertical scale. If `correct_distortion`
+        is set to False, this scaling will not be done.
+    size_is_absolute : bool
+        Shape sizes scaled to absolute dimensions.
+        The size_is_absolute logical is combined with the <size> setting for a
+        shape to determine the size transverse to the center line curve of the
+        drawn shape. If size_is_absolute is False (the default), <size> is
+        taken to be the size of the shape in points (72 points is approximately
+        1 inch). If size_is_absolute is True, <size> is taken to be the size in
+        meters. That is, if size_is_absolute is False, zooming in or out will
+        not affect the size of an element shape while if size_is_absolute is
+        True, the size of an element will scale when zooming.
+    draw_only_first_pass : bool
+        Suppresses drawing of multipass_slave lattice elements that are
+        associated with the second and higher passes. Setting to True is only
+        useful in some extreme circumstances where the plotting of additional
+        passes leads to large pdf/ps file sizes.
+    flip_label_side : bool
+        Draw element label on other side of element.
+    rotation : float
+        Rotation of floor plan plot: 1.0 -> 360 deg.
+        An overall rotation of the floor plan can be controlled by setting rotation
+        parameter. A setting of 1.0 corresponds to 360◦. Positive values correspond
+        to counter-clockwise rotations. Alternatively, the global coordinates at
+        the start of the lattice can be defined in the lattice file and this can
+        rotate the floor plan. Unless there is an offset specified in the lattice
+        file, a lattice will start at (x, y) = (0, 0). Assuming that the machine
+        lies in the horizontal plane with no negative bends, the reference orbit
+        will start out pointing in the negative x direction and will circle
+        clockwise in the (x, y) plane.
+    orbit_scale : float
+        Scale for the orbit.  If 0 (the default), no orbit will be drawn.
+    orbit_color : str
+        Orbit color.
+    orbit_lattice : One of {"model", "design", "base"}
+        Orbit lattice.
+    orbit_pattern : str
+        Orbit pattern.
+    orbit_width : int
+        Orbit width.
+    view : One of {"xy", "xz", "yx", "yz", "zx", "zy"}
+    """
+
+    correct_distortion: Optional[bool] = None
+    size_is_absolute: Optional[bool] = None
+    draw_only_first_pass: Optional[bool] = None
+    flip_label_side: Optional[bool] = None
+    rotation: Optional[float] = None
+    orbit_scale: Optional[float] = None
+    orbit_color: Optional[str] = None
+    orbit_lattice: Optional[Literal["model", "design", "base"]] = None
+    orbit_pattern: Optional[str] = None
+    orbit_width: Optional[int] = None
+    view: Optional[Literal["xy", "xz", "yx", "yz", "zx", "zy"]] = None
+
+    def get_commands(
+        self,
+        region_name: str,
+        graph_name: str,
+    ) -> List[str]:
+        """
+        Get command strings to apply these settings with Tao.
+
+        Parameters
+        ----------
+        region_name : str
+            Region name.
+        graph_name : str
+            Graph name.
+
+        Returns
+        -------
+        list of str
+            Commands to send to Tao to apply these settings.
+        """
+        return [
+            f"set graph {region_name} floor_plan%{key} = {value}"
+            for key, value in self.model_dump().items()
+            if value is not None
+        ]
+
+
+class TaoGraphSettings(pydantic.BaseModel, extra="forbid", validate_assignment=True):
+    """
+    Per-graph settings for Tao.
+
+    Intended for use with `tao.plot(..., settings=TaoGraphSettings())`.
+
+    All attributes may be `None`. A value of `None` indicates that the setting
+    should not be configured in Tao.
+
+    Not all parameters are useful for PyTao plotting.  This class intends to
+    support Tao's internal plotting mechanism as well for users who prefer
+    to use it instead.
+
+    Attributes
+    ----------
+    text_legend : Dict[int, str]
+        Dictionary of text legend index to legend string.
+        The text legend is a legend that can be setup by either the user or by
+        Tao itself. Tao uses the text legend in conjunction with phase space
+        plotting or histogram displays. The text legend is distinct from the
+        curve legend.
+    allow_wrap_around : bool
+        If `plot%x_axis_type` is set to "s", and if the plotted data is from a
+        lattice branch with a closed geometry, and if `graph%x%min` is negative,
+        then the `graph%allow_wrap_around` parameter sets if the curves contained
+        in the graph are “wrapped around” the beginning of the lattice so that
+        the curves are not cut off at s = 0. The Tao default is True.
+    box : Dict[int, int]
+        The `graph%box parameter` sets the layout of the box which the graph is
+        placed. The Tao default is 1,1,1,1 which scales a graph to cover the
+        entire region the plot is placed in.
+    commands : List[str]
+        Custom commands to be sent to Tao for this graph.
+        Each string will be formatted with the following keys:
+        * `settings` - the `TaoGraphSettings` object itself
+        * `region` - the region name (e.g., `r12`)
+        * `graph_name` - the graph name (e.g., `g`)
+        * `graph_type` - the graph type (e.g., `lat_layout`)
+        * `graph` - the full graph name (e.g., `r12.g`)
+    component : str
+        Who to plot - applied to all curves. For example: `'meas - design'`
+        A "data" graph is used to draw lattice parameters such as orbits, or
+        Tao data, or variable values such as quadrupole strengths. The
+        data values will depend upon where the data comes from. This is
+        determined, in part, by the setting of the component parameter in the
+        tao_template_graph namelist. The component may be one of:
+
+            "model", for model values. This is the default.
+            "design", for design values.
+            "base", for base values.
+            "meas", for data values.
+            "ref", for reference data values.
+            "beam_chamber_wall", for beam chamber wall data.
+
+        Additionally, component may be set to plot a linear combination of the
+        above. For example:
+            "model - design"
+        This will plot the difference between the model and design values.
+    clip : bool
+        Clip curves at the boundary.
+    curve_legend_origin : tuple[float, float, str] or QuickPlotPoint
+        The curve legend displays which curves are associated with which of the
+        plotted lines and symbols. This defines where the upper left hand
+        corner of the legend is.
+    draw_axes : bool
+        Draw the graph axes.
+    draw_title : bool
+        Draw the graph title.
+    draw_curve_legend : bool
+        Draw the curve legend.
+    draw_grid : bool
+        Draw the graph grid.
+    draw_only_good_user_data_or_vars : bool
+        When plotting Tao data or variables, if
+        `draw_only_good_user_data_or_vars` is set to True (the default), symbol
+        points of curves in the graph associated with data or variables whose
+        `good_user` parameter is set to False will be ignored. That is, data and
+        variables that will not be used in an optimization will be ignored. If
+        `draw_only_good_user_data_or_vars` is set to False, data or variables
+        that have a valid value will be plotted.
+    floor_plan : TaoFloorPlanSettings
+        Settings only for floor plan graphs.
+    ix_universe : int
+        The default universe for curves of the graph.
+    ix_branch : int
+        The default branch for curves of the graph.
+    margin : tuple[float, float, float, float, str] or QuickPlotRectangle
+        Margin between the graph and the box: (x1, x2, y1, y2, units)
+    scale_margin : Union[QuickPlotRectangle, QuickPlotRectangleTuple]
+        (x1, x2, y1, y2, units)
+        Used to set the minimum space between what is being drawn and the edges
+        of the graph when a `scale`, `x_scale`, or an `xy_scale` command is
+        issued. Normally this is zero but is useful for floor plan drawings.
+    symbol_size_scale : float
+        Symbol size scale.
+    text_legend_origin : tuple[float, float, float, float, str] or QuickPlotRectangle
+        (x1, x2, y1, y2, units)
+        Text legend origin.
+    title : str
+        The `title` component is the string printed just above the graph
+        box. The full string will also include information about what is being
+        plotted and the horizontal axis type. To fully suppress the title leave
+        it blank. Note: A graph also has a `title_suffix` which Tao uses to
+        hold the string which is printed to the right of the `graph%title`.
+        This string contains information like what curve%component is being
+        plotted. The `graph%title_suffix` cannot be set by the user.
+    type : str
+        The type of graph. Tao knows about the following types:
+
+        * `"data"`
+
+        Lattice parameters, data and/or variable plots (default)
+
+        With `type` set to `"data"`, data such as orbits and/or variable
+        values such as quadrupole strengths are plotted. Here “data” can be
+        data from a defined data structure or computed directly from the
+        lattice, beam tracking, etc. A "data" graph type will contain a number
+        of curves and multiple data and variable curves can be drawn in one
+        graph.
+
+        * `"floor_plan"`
+
+        With `type` set to `"floor_plan"`, the two dimensional layout of the
+        machine is drawn.
+
+        * `"dynamic_aperture"`
+
+        Dynamic aperture plot.
+
+        * `"histogram"`
+
+        With `type` set to `"histogram"`, such things such as beam
+        densities can be his- togrammed.
+
+        * `"lat_layout"`
+
+        With `type` set to `"lat_layout"`, the elements of the
+        lattice are symbolical drawn in a one dimensional line as a function of
+        the longitudinal distance along the machine centerline.
+
+        * `"phase_space"`
+
+        With `type` set to `"phase_space"`, phase space plots are
+        produced.
+
+        * `"key_table"` - unsupported by PyTao plotting.
+
+        With `type` set to `"key_table"`, the key bindings for use in single
+        mode are displayed. Note: The "key_table" graph type does not have any
+        associated curves.
+
+    x : TaoAxisSettings
+        Primary x-axis settings.
+    x2 : TaoAxisSettings
+        Secondary x-axis settings.
+    y : TaoAxisSettings
+        Primary y-axis settings.
+    y2 : TaoAxisSettings
+        Secondary y-axis settings.
+    y2_mirrors_y : bool
+        Keep y2 min/max the same as y-axis.
+    x_axis_scale_factor : float
+        Sets the horizontal x-axis scale factor. For a given datum value, the
+        plotted value will be: `x(plotted) = scale_factor * x(datum)`
+        The default value is 1.
+        For example, a %x_axis_scale_factor of 1000 will draw a 1.0 mm phase
+        space z value at the 1.0 mark on the horizontal scale. That is, the
+        horizontal scale will be in millimeters. Also see
+        `curve(N)%y_axis_scale_factor`.
+    """
+
+    text_legend: Dict[int, str] = pydantic.Field(default_factory=dict)
+    allow_wrap_around: Optional[bool] = None
+    box: Dict[int, int] = pydantic.Field(
+        default_factory=dict,
+        description="Defines which box the plot is put in.",
+    )
+    commands: Optional[List[str]] = None
+    component: Optional[str] = None
+    clip: Optional[bool] = None
+    curve_legend_origin: Optional[Union[QuickPlotPoint, QuickPlotPointTuple]] = None
+    draw_axes: Optional[bool] = None
+    draw_title: Optional[bool] = None
+    draw_curve_legend: Optional[bool] = None
+    draw_grid: Optional[bool] = None
+    draw_only_good_user_data_or_vars: Optional[bool] = None
+    floor_plan: Optional[TaoFloorPlanSettings] = None
+    ix_universe: Optional[int] = None
+    ix_branch: Optional[int] = None
+    margin: Optional[Union[QuickPlotRectangle, QuickPlotRectangleTuple]] = None
+    name: Optional[str] = None
+    scale_margin: Optional[Union[QuickPlotRectangle, QuickPlotRectangleTuple]] = None
+    symbol_size_scale: Optional[float] = None
+    text_legend_origin: Optional[Union[QuickPlotRectangle, QuickPlotRectangleTuple]] = None
+    title: Optional[str] = None
+    type: Optional[str] = None
+    x: Optional[TaoAxisSettings] = None
+    x2: Optional[TaoAxisSettings] = None
+    y: Optional[TaoAxisSettings] = None
+    y2: Optional[TaoAxisSettings] = None
+    y2_mirrors_y: Optional[bool] = None
+    x_axis_scale_factor: Optional[float] = None
+
+    # 'set plot':
+    n_curve_points: Optional[int] = None
+
+    def get_commands(
+        self,
+        region_name: str,
+        graph_name: str,
+        graph_type: str,
+    ) -> List[str]:
+        """
+        Get command strings to apply these settings with Tao.
+
+        Parameters
+        ----------
+        region_name : str
+            Region name.
+        graph_name : str
+            Graph name.
+        graph_type : str
+            Graph type.  Required to determine which commands to send - that is,
+            `TaoFloorPlanSettings` will be skipped for non-floor plan graphs.
+
+        Returns
+        -------
+        list of str
+            Commands to send to Tao to apply these settings.
+        """
+        result = []
+        for key in self.model_dump().keys():
+            value = getattr(self, key)
+            if value is None:
+                continue
+
+            if key == "commands":
+                result.extend(
+                    [
+                        cmd.format(
+                            settings=self,
+                            region=region_name,
+                            graph_name=graph_name,
+                            graph_type=graph_type,
+                            graph=f"{region_name}.{graph_name}",
+                        )
+                        for cmd in value
+                        if cmd
+                    ]
+                )
+                continue
+            if key in ("curve_legend_origin",) and isinstance(value, tuple):
+                value = QuickPlotPoint(*value)
+            elif key in ("scale_margin", "margin", "text_legend_origin") and isinstance(
+                value, tuple
+            ):
+                value = QuickPlotRectangle(*value)
+
+            if isinstance(value, QuickPlotPoint):
+                result.extend(value.get_commands(region_name, graph_name, key))
+            elif isinstance(value, TaoFloorPlanSettings):
+                result.extend(value.get_commands(region_name, graph_name))
+            elif isinstance(value, QuickPlotRectangle):
+                result.extend(value.get_commands(region_name, graph_name, key))
+            elif isinstance(value, TaoAxisSettings):
+                result.extend(value.get_commands(region_name, key))
+            elif key == "n_curve_points":
+                result.append(f"set plot {region_name} n_curve_pts = {value}")
+            elif key == "text_legend":
+                for legend_index, legend_value in value.items():
+                    result.append(
+                        f"set graph {region_name} text_legend({legend_index}) = {legend_value}"
+                    )
+            elif key == "box":
+                for box_index, box_value in value.items():
+                    result.append(f"set graph {region_name} box({box_index}) = {box_value}")
+            elif isinstance(value, TaoFloorPlanSettings):
+                if graph_type == "floor_plan":
+                    result.extend(value.get_commands(region_name, graph_name))
+            else:
+                result.append(f"set graph {region_name}.{graph_name} {key} = {value}")
+        return result
+
+    @property
+    def xlim(self) -> Optional[Limit]:
+        if self.x is None:
+            return None
+        return self.x.scale
+
+    @xlim.setter
+    def xlim(self, xlim: Optional[Limit]):
+        if self.x is None:
+            self.x = TaoAxisSettings()
+        self.x.scale = xlim
+
+    @property
+    def ylim(self) -> Optional[Limit]:
+        if self.y is None:
+            return None
+        return self.y.scale
+
+    @ylim.setter
+    def ylim(self, ylim: Optional[Limit]):
+        if self.y is None:
+            self.y = TaoAxisSettings()
+        self.y.scale = ylim
diff --git a/pytao/plotting/types.py b/pytao/plotting/types.py
new file mode 100644
index 00000000..62f0210d
--- /dev/null
+++ b/pytao/plotting/types.py
@@ -0,0 +1,370 @@
+from typing import List, Tuple, Optional
+
+from typing_extensions import NotRequired, TypedDict
+
+FloorOrbitInfo = TypedDict(
+    "FloorOrbitInfo",
+    {
+        "branch_index": int,
+        "index": int,
+        "ele_key": str,
+        "axis": str,
+        "orbits": List[float],
+    },
+)
+BuildingWallGraphInfo = TypedDict(
+    "BuildingWallGraphInfo",
+    {
+        "index": int,
+        "point": int,
+        "offset_x": float,
+        "offset_y": float,
+        "radius": float,
+    },
+)
+
+BuildingWallInfo = TypedDict(
+    "BuildingWallInfo",
+    {
+        "index": int,
+        "name": str,
+        "constraint": str,
+        "shape": str,
+        "color": str,
+        "line_width": float,
+    },
+)
+BuildingWallGlobalInfo = TypedDict(
+    "BuildingWallGlobalInfo",
+    {
+        "index": int,
+        "z": float,
+        "x": float,
+        "radius": float,
+        "z_center": float,
+        "x_center": float,
+    },
+)
+
+WaveParams = TypedDict(
+    "WaveParams",
+    {
+        "ix_a1": float,
+        "ix_a2": float,
+        "ix_b1": float,
+        "ix_b2": float,
+    },
+)
+
+PlotCurveLineInfo = TypedDict(
+    "PlotCurveLineInfo",
+    {
+        "color": str,
+        "line^pattern": str,
+        "width": int,
+    },
+)
+
+PlotCurveSymbolInfo = TypedDict(
+    "PlotCurveSymbolInfo",
+    {
+        "color": str,
+        "fill_pattern": str,
+        "height": float,
+        "line_width": int,
+        "symbol^type": str,
+    },
+)
+PlotCurveInfo = TypedDict(
+    "PlotCurveInfo",
+    {
+        "-1^ix_branch": int,
+        "-1^ix_bunch": int,
+        "component": str,
+        "data_source": str,
+        "data_type": str,
+        "data_type_x": str,
+        "draw_error_bars": bool,
+        "draw_line": bool,
+        "draw_symbol_index": bool,
+        "draw_symbols": bool,
+        "ele_ref_name": str,
+        "ix_ele_ref": int,
+        "ix_ele_ref_track": int,
+        "ix_universe": int,
+        "legend_text": str,
+        "line": PlotCurveLineInfo,
+        "message_text": str,
+        "name": str,
+        "smooth_line_calc": bool,
+        "symbol": PlotCurveSymbolInfo,
+        "symbol_every": int,
+        "symbol_line_width": int,
+        "use_y2": bool,
+        "valid": bool,
+        "why_invalid": str,
+        "y_axis_scale_factor": float,
+        "z_color_autoscale": bool,
+        "z_color_data_type": str,
+        "z_color_is_on": bool,
+        "z_color_max": float,
+        "z_color_min": float,
+    },
+)
+
+
+PlotRegionInfo = TypedDict(
+    "PlotRegionInfo",
+    {
+        "num_graphs": int,
+        # "graph[1]": str,
+        "name": str,
+        "description": str,
+        "x_axis_type": str,
+        "autoscale_x": bool,
+        "autoscale_y": bool,
+        "autoscale_gang_x": bool,
+        "autoscale_gang_y": bool,
+        "n_curve_pts": int,
+    },
+)
+
+PlotGraphInfo = TypedDict(
+    "PlotGraphInfo",
+    {
+        "-1^ix_branch": int,
+        "clip": bool,
+        "draw_axes": bool,
+        "draw_curve_legend": bool,
+        "draw_grid": bool,
+        "draw_only_good_user_data_or_vars": bool,
+        "floor_plan_correct_distortion": bool,
+        "floor_plan_draw_building_wall": bool,
+        "floor_plan_draw_only_first_pass": bool,
+        "floor_plan_flip_label_side": bool,
+        "floor_plan_orbit_color": str,
+        "floor_plan_orbit_lattice": str,
+        "floor_plan_orbit_pattern": str,
+        "floor_plan_orbit_scale": float,
+        "floor_plan_orbit_width": int,
+        "floor_plan_rotation": float,
+        "floor_plan_size_is_absolute": bool,
+        "floor_plan_view": str,
+        "graph^type": str,
+        "is_valid": bool,
+        "ix_universe": int,
+        "limited": bool,
+        "name": str,
+        "num_curves": int,
+        "symbol_size_scale": float,
+        "title": str,
+        "title_suffix": str,
+        "why_invalid": str,
+        "x_axis^type": str,
+        "x_axis_scale_factor": float,
+        "x_bounds": str,
+        "x_draw_label": bool,
+        "x_draw_numbers": bool,
+        "x_label": str,
+        "x_label_color": str,
+        "x_label_offset": float,
+        "x_major_div_nominal": int,
+        "x_major_tick_len": float,
+        "x_max": float,
+        "x_min": float,
+        "x_minor_div": int,
+        "x_minor_div_max": int,
+        "x_minor_tick_len": float,
+        "x_number_offset": float,
+        "x_number_side": int,
+        "x_tick_side": int,
+        "y2_axis^type": str,
+        "y2_bounds": str,
+        "y2_draw_label": bool,
+        "y2_draw_numbers": bool,
+        "y2_label": str,
+        "y2_label_color": str,
+        "y2_label_offset": float,
+        "y2_major_div_nominal": int,
+        "y2_major_tick_len": float,
+        "y2_max": float,
+        "y2_min": float,
+        "y2_minor_div": int,
+        "y2_minor_div_max": int,
+        "y2_minor_tick_len": float,
+        "y2_mirrors_y": bool,
+        "y2_number_offset": float,
+        "y2_number_side": int,
+        "y2_tick_side": int,
+        "y_axis^type": str,
+        "y_bounds": str,
+        "y_draw_label": bool,
+        "y_draw_numbers": bool,
+        "y_label": str,
+        "y_label_color": str,
+        "y_label_offset": float,
+        "y_major_div_nominal": int,
+        "y_major_tick_len": float,
+        "y_max": float,
+        "y_min": float,
+        "y_minor_div": int,
+        "y_minor_div_max": int,
+        "y_minor_tick_len": float,
+        "y_number_offset": float,
+        "y_number_side": int,
+        "y_tick_side": int,
+        # **{f"curve[{n}]": NotRequired[str] for n in range(1, 100)},
+    },
+)
+
+
+PlotHistogramInfo = TypedDict(
+    "PlotHistogramInfo",
+    {
+        "center": float,
+        "density_normalized": bool,
+        "maximum": float,
+        "minimum": float,
+        "number": float,
+        "weight_by_charge": bool,
+        "width": float,
+    },
+)
+
+PlotLatLayoutInfo = TypedDict(
+    "PlotLatLayoutInfo",
+    {
+        "ix_branch": int,
+        "ix_ele": int,
+        "ele_s_start": float,
+        "ele_s_end": float,
+        "line_width": float,
+        "shape": str,
+        "y1": float,
+        "y2": float,
+        "color": str,
+        "label_name": str,
+    },
+)
+
+FloorPlanElementInfo = TypedDict(
+    "FloorPlanElementInfo",
+    {
+        "branch_index": int,
+        "color": str,
+        "ele_key": str,
+        "end1_r1": float,
+        "end1_r2": float,
+        "end1_theta": float,
+        "end2_r1": float,
+        "end2_r2": float,
+        "end2_theta": float,
+        "index": int,
+        "label_name": str,
+        "line_width": float,
+        "shape": str,
+        "y1": float,
+        "y2": float,
+        # Only for sbend
+        "ele_l": NotRequired[float],
+        "ele_angle": NotRequired[float],
+        "ele_e1": NotRequired[float],
+        "ele_e": NotRequired[float],
+    },
+)
+
+PlotPage = TypedDict(
+    "PlotPage",
+    {
+        "title_string": str,
+        "title__x": float,
+        "title_y": float,
+        "title_units": str,
+        "title_justify": str,
+        "subtitle_string": str,
+        "subtitle__x": float,
+        "subtitle_y": float,
+        "subtitle_units": str,
+        "subtitle_justify": str,
+        "size": list,
+        "n_curve_pts": int,
+        "border": list,
+        "text_height": float,
+        "main_title_text_scale": float,
+        "graph_title_text_scale": float,
+        "axis_number_text_scale": float,
+        "axis_label_text_scale": float,
+        "key_table_text_scale": float,
+        "legend_text_scale": float,
+        "floor_plan_shape_scale": float,
+        "floor_plan_text_scale": float,
+        "lat_layout_shape_scale": float,
+        "lat_layout_text_scale": float,
+        "delete_overlapping_plots": str,
+        "draw_graph_title_suffix": str,
+        "curve_legend_line_len": float,
+        "curve_legend_text_offset": float,
+    },
+)
+
+
+FloatVariableInfo = TypedDict(
+    "FloatVariableInfo",
+    {
+        "model_value": float,
+        "base_value": float,
+        "ele_name": str,
+        "attrib_name": str,
+        "ix_v1": int,
+        "ix_var": int,
+        "ix_dvar": int,
+        "ix_attrib": int,
+        "ix_key_table": int,
+        "design_value": float,
+        "scratch_value": float,
+        "old_value": float,
+        "meas_value": float,
+        "ref_value": float,
+        "correction_value": float,
+        "high_lim": float,
+        "low_lim": float,
+        "step": float,
+        "weight": float,
+        "delta_merit": float,
+        "merit": float,
+        "dmerit_dvar": float,
+        "key_val0": float,
+        "key_delta": float,
+        "s": float,
+        "var^merit_type": str,
+        "exists": bool,
+        "good_var": bool,
+        "good_user": bool,
+        "good_opt": bool,
+        "good_plot": bool,
+        "useit_opt": bool,
+        "useit_plot": bool,
+        "key_bound": bool,
+    },
+)
+
+
+Point = Tuple[float, float]
+Limit = Tuple[float, float]
+OptionalLimit = Optional[Limit]
+
+
+ShapeListInfo = TypedDict(
+    "ShapeListInfo",
+    {
+        "shape_index": int,
+        "ele_name": str,
+        "shape": str,
+        "color": str,
+        "shape_size": float,
+        "type_label": str,
+        "shape_draw": bool,
+        "multi_shape": bool,
+        "line_width": int,
+    },
+)
diff --git a/pytao/plotting/util.py b/pytao/plotting/util.py
new file mode 100644
index 00000000..43ddf98f
--- /dev/null
+++ b/pytao/plotting/util.py
@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+import functools
+import logging
+import sys
+from typing import List, Optional, Sequence, Tuple, Union
+
+from .types import OptionalLimit, Limit
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+class NoIntersectionError(Exception):
+    pass
+
+
+def circle_intersection(
+    x1: float,
+    y1: float,
+    x2: float,
+    y2: float,
+    r: float,
+) -> Tuple[Tuple[float, float], Tuple[float, float]]:
+    """Get 2 intersection points of overlapping circles with equal radii."""
+    dx = x2 - x1
+    dy = y2 - y1
+    d = np.sqrt(dx**2 + dy**2)
+    a = d / 2
+    h = np.sqrt(r**2 - a**2)
+    xm = x1 + dx / 2
+    ym = y1 + dy / 2
+    xs1 = xm + h * dy / d
+    xs2 = xm - h * dy / d
+    ys1 = ym - h * dx / d
+    ys2 = ym + h * dx / d
+    return (xs1, ys1), (xs2, ys2)
+
+
+Line = Tuple[float, float, float]
+Intersection = Tuple[float, float]
+
+
+def line(p1: Tuple[float, float], p2: Tuple[float, float]) -> Line:
+    """returns lines based on given points to be used with intersect"""
+    p1x, p1y = p1
+    p2x, p2y = p2
+    return p1y - p2y, p2x - p1x, -(p1x * p2y - p2x * p1y)
+
+
+def intersect(L1: Line, L2: Line) -> Intersection:
+    """Intersection point of 2 lines from the line function, or false if the lines don't intersect"""
+    D = L1[0] * L2[1] - L1[1] * L2[0]
+    Dx = L1[2] * L2[1] - L1[1] * L2[2]
+    Dy = L1[0] * L2[2] - L1[2] * L2[0]
+
+    if D == 0:
+        raise NoIntersectionError()
+
+    x = Dx / D
+    y = Dy / D
+    return x, y
+
+
+def apply_factor_to_limits(low: float, high: float, factor: float) -> Tuple[float, float]:
+    center = (high + low) / 2
+    span = factor * (high - low)
+    return center - span / 2.0, center + span / 2.0
+
+
+@functools.cache
+def is_jupyter() -> bool:
+    """
+    Determine if we're in a Jupyter notebook session.
+
+    This works by way of interacting with IPython display and seeing what
+    choice it makes regarding reprs.
+
+    Returns
+    -------
+    bool
+    """
+    if "IPython" not in sys.modules or "IPython.display" not in sys.modules:
+        return False
+
+    from IPython.display import display
+
+    class ReprCheck:
+        def _repr_html_(self) -> str:
+            self.mode = "jupyter"
+            logger.info("Detected Jupyter. Using the notebook graph backend.")
+            return "<!-- Detected Jupyter. -->"
+
+        def __repr__(self) -> str:
+            self.mode = "console"
+            return ""
+
+    check = ReprCheck()
+    display(check)
+    return check.mode == "jupyter"
+
+
+@functools.cache
+def select_graph_manager_class():
+    from .mpl import MatplotlibGraphManager
+
+    if not is_jupyter():
+        return MatplotlibGraphManager
+
+    from .bokeh import select_graph_manager_class as select_bokeh_class
+
+    return select_bokeh_class()
+
+
+def fix_grid_limits(
+    limits: Union[OptionalLimit, Sequence[OptionalLimit]],
+    num_graphs: int,
+) -> List[Optional[Limit]]:
+    if not limits:
+        return [None] * num_graphs
+
+    if all(isinstance(v, (float, int)) for v in limits):
+        res = [limits]
+    else:
+        res = list(limits or [None])
+
+    if len(res) >= num_graphs:
+        return res[:num_graphs]
+
+    return res + [res[-1]] * (num_graphs - len(res))
diff --git a/pytao/subproc.py b/pytao/subproc.py
index 545256ee..17fa7c28 100644
--- a/pytao/subproc.py
+++ b/pytao/subproc.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import ctypes
 import logging
 import os
@@ -6,13 +8,16 @@
 import sys
 import threading
 import typing
+from typing import Union
 
 import numpy as np
 
-from .interface_commands import Tao
+from .interface_commands import Tao, TaoStartup
 
 logger = logging.getLogger(__name__)
 
+AnyTao = Union[Tao, "SubprocessTao"]
+
 
 class TaoDisconnectedError(Exception):
     """The Tao subprocess quit, crashed, or otherwise disconnected."""
@@ -268,9 +273,14 @@ def __init__(self, *args, **kwargs):
     def close_subprocess(self):
         self._pipe.close()
 
-    def init(self, cmd):
-        """Initialize Tao with the given `cmd`."""
-        return self._pipe.send_receive("init", cmd, raises=True)
+    def __del__(self) -> None:
+        try:
+            self.close_subprocess()
+        except Exception:
+            pass
+
+    def _init(self, startup: TaoStartup):
+        return self._pipe.send_receive("init", startup.tao_init, raises=True)
 
     def cmd(self, cmd, raises=True):
         """Runs a command, and returns the output."""
diff --git a/pytao/subproc_main.py b/pytao/subproc_main.py
index e08ea6e8..ccce7775 100644
--- a/pytao/subproc_main.py
+++ b/pytao/subproc_main.py
@@ -58,4 +58,11 @@ def _tao_subprocess(output_fd: int) -> None:
             file=sys.stderr,
         )
         exit(1)
-    _tao_subprocess(output_fd)
+
+    try:
+        _tao_subprocess(output_fd)
+    except OSError:
+        exit(1)
+    except KeyboardInterrupt:
+        logger.debug("Caught KeyboardInterrupt; exiting.")
+        exit(0)
diff --git a/pytao/tao_ctypes/core.py b/pytao/tao_ctypes/core.py
index 27c3b936..d8e14d37 100644
--- a/pytao/tao_ctypes/core.py
+++ b/pytao/tao_ctypes/core.py
@@ -4,13 +4,14 @@
 import shutil
 import tempfile
 import textwrap
+from typing import List
 
 import numpy as np
 
-from pytao import tao_ctypes
-from pytao.tao_ctypes.tools import full_path
-from pytao.tao_ctypes.util import error_in_lines
+from .. import tao_ctypes
 from ..util.parameters import tao_parameter_dict
+from .tools import full_path
+from .util import error_in_lines
 
 logger = logging.getLogger(__name__)
 
@@ -34,8 +35,6 @@ class TaoCore:
     tao.init("command line args here...")
     """
 
-    # ---------------------------------------------
-
     def __init__(self, init="", so_lib=""):
         # TL/DR; Leave this import out of the global scope.
         #
@@ -106,7 +105,7 @@ def reset_output(self):
     # ---------------------------------------------
     # Init Tao
 
-    def init(self, cmd):
+    def init(self, cmd: str) -> List[str]:
         if not tao_ctypes.initialized:
             logger.debug(f"Initializing Tao with: {cmd}")
             err = self.so_lib.tao_c_init_tao(cmd.encode("utf-8"))
@@ -115,9 +114,9 @@ def init(self, cmd):
                 raise ValueError(f"Unable to init Tao with: {cmd!r}. Tao output: {message}")
             tao_ctypes.initialized = True
             return self.get_output()
-        else:
-            # Reinit
-            return self.cmd(f"reinit tao -clear {cmd}", raises=True)
+
+        # Reinit
+        return self.cmd(f"reinit tao -clear {cmd}", raises=True)
 
     # ---------------------------------------------
     # Send a command to Tao and return the output
diff --git a/pytao/tests/conftest.py b/pytao/tests/conftest.py
index 1bc0e5b5..62f53b8c 100644
--- a/pytao/tests/conftest.py
+++ b/pytao/tests/conftest.py
@@ -1,10 +1,27 @@
 import contextlib
 import logging
 import os
+import pathlib
+from typing import Generator, Type, TypeVar
 
+import matplotlib
 import pytest
+from typing_extensions import Literal
 
-from .. import SubprocessTao, Tao
+from .. import SubprocessTao, Tao, TaoStartup
+
+matplotlib.use("Agg")
+
+test_root = pathlib.Path(__file__).parent.resolve()
+packaged_examples_root = test_root / "input_files"
+test_artifacts = test_root / "artifacts"
+
+regression_test_root = pathlib.Path("$ACC_ROOT_DIR/regression_tests/pipe_test/")
+example_root = pathlib.Path("$ACC_ROOT_DIR/bmad-doc/tao_examples")
+init_files = list(pathlib.Path(os.path.expandvars(regression_test_root)).glob("tao.init*"))
+example_init_files = list(
+    path for path in pathlib.Path(os.path.expandvars(example_root)).glob("*/tao.init")
+)
 
 
 @pytest.fixture
@@ -35,6 +52,79 @@ def ensure_successful_parsing(caplog):
             pytest.fail(error.message)
 
 
+def get_packaged_example(name: str) -> TaoStartup:
+    """PyTao packaged bmad input data."""
+    init_file = packaged_examples_root / name / "tao.init"
+    startup = TaoStartup(
+        init_file=init_file,
+        # nostartup=nostartup,
+        metadata={"name": name},
+    )
+    print(f"Packaged example {name}: {startup.tao_init}")
+    return startup
+
+
+def get_example(name: str) -> TaoStartup:
+    """Bmad-doc example startup data."""
+    init_file = example_root / name / "tao.init"
+    if name == "multi_turn_orbit":
+        pytest.skip(
+            "Multi-turn orbit example fails with: CANNOT SCALE GRAPH multi_turn.x SINCE NO DATA IS WITHIN THE GRAPH X-AXIS RANGE. "
+        )
+    if name == "custom_tao_with_measured_data":
+        # Looks to require some additional compilation and such
+        pytest.skip(
+            "'custom tao with measured data' example fails with PARSER ERROR DETECTED FOR UNIVERSE: 1"
+        )
+    if name == "x_axis_param_plot":
+        pytest.skip("'x_axis_param_plot' example fails saying no data is in range")
+
+    nostartup = name in {
+        # "multi_turn_orbit",
+        "custom_tao_with_measured_data",
+        "x_axis_param_plot",
+    }
+    startup = TaoStartup(
+        init_file=init_file,
+        nostartup=nostartup,
+        metadata={"name": name},
+    )
+    print(f"Example {name}: {startup.tao_init}")
+    return startup
+
+
+def get_regression_test(name: str) -> TaoStartup:
+    """Bmad-doc 'pipe' interface command regression test files."""
+    init_file = regression_test_root / name
+    nostartup = init_file.name == "tao.init_floor_orbit"
+    return TaoStartup(
+        init_file=init_file,
+        nostartup=nostartup,
+        metadata={"name": init_file.name},
+    )
+
+
+@pytest.fixture(params=init_files, ids=[f"regression_tests-{fn.name}" for fn in init_files])
+def init_filename(
+    request: pytest.FixtureRequest,
+) -> pathlib.Path:
+    return request.param
+
+
+@pytest.fixture(params=init_files, ids=[f"regression_tests-{fn.name}" for fn in init_files])
+def tao_regression_test(
+    request: pytest.FixtureRequest,
+) -> TaoStartup:
+    return get_regression_test(request.param.name)
+
+
+@pytest.fixture(params=example_init_files, ids=[fn.parts[-2] for fn in example_init_files])
+def tao_example(
+    request: pytest.FixtureRequest,
+) -> TaoStartup:
+    return get_example(request.param.parts[-2])
+
+
 @pytest.fixture(
     params=[Tao, SubprocessTao],
     ids=["Tao", "SubprocessTao"],
@@ -43,10 +133,41 @@ def tao_cls(request: pytest.FixtureRequest):
     return request.param
 
 
+T = TypeVar("T", bound=Tao)
+
+
 @contextlib.contextmanager
-def new_tao(tao_cls, init):
-    tao = tao_cls(os.path.expandvars(f"{init} -noplot"))
+def new_tao(
+    tao_cls: Type[T],
+    init: str = "",
+    plot: bool = False,
+    external_plotting: bool = True,
+    **kwargs,
+) -> Generator[T, None, None]:
+    # init = os.path.expandvars(init)
+    if external_plotting:
+        init = " ".join((init, "-external_plotting"))
+    if not plot:
+        init = " ".join((init, "-noplot"))
+    tao = tao_cls(init, **kwargs)
     yield tao
     if hasattr(tao, "close_subprocess"):
         print("Closing tao subprocess")
         tao.close_subprocess()
+
+
+BackendName = Literal["mpl", "bokeh"]
+
+
+@pytest.fixture(params=["bokeh", "mpl"])
+def plot_backend(
+    request: pytest.FixtureRequest,
+) -> BackendName:
+    return request.param
+
+
+@pytest.fixture(params=[False, True], ids=["Tao", "SubprocessTao"])
+def use_subprocess(
+    request: pytest.FixtureRequest,
+) -> bool:
+    return request.param
diff --git a/pytao/tests/input_files/__init__.py b/pytao/tests/input_files/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/pytao/tests/input_files/optics_matching_tweaked/__init__.py b/pytao/tests/input_files/optics_matching_tweaked/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/pytao/tests/input_files/optics_matching_tweaked/tao.init b/pytao/tests/input_files/optics_matching_tweaked/tao.init
new file mode 100755
index 00000000..f6730479
--- /dev/null
+++ b/pytao/tests/input_files/optics_matching_tweaked/tao.init
@@ -0,0 +1,103 @@
+!------------------------------------------------------------------------
+
+&tao_start
+  plot_file = 'tao_plot.init' 
+  startup_file = '$ACC_ROOT_DIR/bmad-doc/tao_examples/optics_matching/tao.startup'
+/
+!Beam Initialization
+!--------------------------------------------------------
+&tao_design_lattice
+  n_universes =1
+ ! unique_name_suffix="*::_?"
+  design_lattice(1)%file = "$ACC_ROOT_DIR/bmad-doc/tao_examples/optics_matching/lat.bmad"
+/
+
+!------------------------------------------------------------------------
+&tao_params
+  !global%plot_on = True
+  global%track_type = 'single'
+  global%beam_timer_on = T
+  global%random_engine = 'pseudo'
+  global%de_lm_step_ratio = 1500
+  global%optimizer = 'lmdif'
+  global%n_opti_cycles = 100
+
+  global%prompt_color = 'BLUE'
+  !---Bmad---
+  bmad_com%radiation_damping_on = F
+  bmad_com%radiation_fluctuations_on = F
+  /
+
+
+&tao_beam_init
+ix_universe = 1
+saved_at =  "MARKER::*"
+beam_init%n_particle = 0
+beam_init%random_engine = 'quasi' ! or 'pseudo'
+beam_init%bunch_charge = 100.0e-12  
+beam_init%a_norm_emit = 1.0e-6  ! normalized emit = emit * gamma
+beam_init%b_norm_emit = 1.0e-6  ! normalized emit = emit * gamma
+beam_init%n_bunch = 1     
+beam_init%sig_pz = 1e-3
+beam_init%sig_z = 0.00059958  ! 2 ps * cLight
+!---Ellipse
+beam_init%distribution_type = 'ellipse', 'ran_gauss', 'grid'
+beam_init%ellipse(1)%part_per_ellipse = 50
+beam_init%ellipse(1)%n_ellipse = 3
+beam_init%ellipse(1)%sigma_cutoff = 6
+
+beam_init%grid(3)%n_x = 1
+beam_init%grid(3)%n_px = 3
+beam_init%grid(3)%x_min = 0 
+beam_init%grid(3)%x_max = 0
+beam_init%grid(3)%px_min = -0.01 
+beam_init%grid(3)%px_max =  0.01
+/
+
+
+
+
+!------------------------Data--------------------------------------------
+!------------------------------------------------------------------------
+
+&tao_d2_data
+  d2_data%name = 'twiss'
+  n_d1_data = 2
+/
+
+&tao_d1_data
+  ix_d1_data = 1
+  d1_data%name = 'end'
+  datum( 1) =  'beta.a'     '' '' 'END'   'target'  12.5   1e1
+  datum( 2) =  'alpha.a'    '' '' 'END'   'target' -1.0    1e2
+  datum( 3) =  'beta.b'     '' '' 'END'   'target'  12.5   1e1
+  datum( 4) =  'alpha.b'    '' '' 'END'   'target' -1.0    1e2
+  datum( 5) =  'eta.x'      '' '' 'END'   'target'  0.0    1e1
+  datum( 6) =  'etap.x'     '' '' 'END'   'target'  0.0    1e2
+/
+
+&tao_d1_data
+  ix_d1_data = 2
+  d1_data%name = 'max'
+  datum( 1) =  'beta.a'    '' 'Q1' 'END'   'max'      100   1e1
+  datum( 2) =  'eta.x'     '' 'Q1' 'END'   'abs_max'  1     1e2
+/ 
+
+!------------------------Variables---------------------------------------
+!------------------------------------------------------------------------
+
+&tao_var
+  v1_var%name = 'quad'
+  default_step = 1e-4
+  default_universe = '1'
+  default_attribute = 'k1'
+  !default_low_lim = -50
+  !default_high_lim = 50
+  default_key_delta = 1e-2
+  ix_min_var = 1
+  search_for_lat_eles = 'Quad::*'
+  ! or:
+  !var(1:)%ele_name = 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6'
+  default_key_bound = T
+/
+
diff --git a/pytao/tests/input_files/optics_matching_tweaked/tao_plot.init b/pytao/tests/input_files/optics_matching_tweaked/tao_plot.init
new file mode 100755
index 00000000..c9ebf834
--- /dev/null
+++ b/pytao/tests/input_files/optics_matching_tweaked/tao_plot.init
@@ -0,0 +1,206 @@
+This initialization file defines how plotting is done.
+
+The following namelist block defines how the plot window (also called
+the plot page) is broken up.
+
+&tao_plot_page
+  plot_page%size = 800, 600
+  plot_page%text_height = 12.0 
+  plot_page%border = 0, 0, 0, 0, '%PAGE'
+  plot_page%n_curve_pts = 900
+/
+
+
+!------------------ layout ------
+&tao_template_plot
+  plot%name = 'layout'
+  default_graph%x%label = ' '
+  plot%n_graph = 1
+  plot%x_axis_type = 's'
+/
+
+&tao_template_graph
+  graph_index = 1
+  graph%name = 'u1'
+  graph%type = 'lat_layout'
+  graph%box = 1, 1, 1, 1
+  graph%x%draw_numbers = False
+  graph%ix_universe = -1 !Syntax Changed from 0
+  graph%margin =  0.15, 0.05, 0.12, 0.12, '%BOX'
+  graph%y%max =  20
+  graph%y%min = -20
+  graph%y%major_div = 4
+/
+
+&lat_layout_drawing
+  ele_shape(1)  = "Quadrupole::*"      "asym_var_box"  "Blue"     1     'none'
+  ele_shape(2)  = "SBend::*"           "Box"           "Red"      1     'none'
+  ele_shape(3)  = "lcavity::*"         "XBox"          "Green"    0.5   'none'
+  ele_shape(4)  = "wiggler::*"         "XBox"          "Orange"   0.5   'none'
+  ele_shape(5)  = "Sextupole::*"       "asym_var_box"  "magenta"  0.1   'none'
+  ele_shape(6)  = "ECOLLIMATOR::*"     "Xbox"          "Black"   20     'none'
+  ele_shape(7)  = "hkicker::*"         "XBox"          "Red"      1     'none'
+  ele_shape(8)  = "vkicker::*"         "bow_tie"       "Red"      1     'none'
+  ele_shape(9)  = "INSTRUMENT::*BPM*"  "Diamond"       "Black"    1     'none'
+  ele_shape(10) = "kicker::*"          "Box"           "Red"      5     'none'
+  ele_shape(11) = "PIPE::*"            "Box"           "Black"    0.01  'none'
+  ele_shape(12) = "INSTRUMENT::*"      "Xbox"          "Black"    1     'none'
+  ele_shape(13) = "SOLENOID::*"        "Xbox"          "Blue"     1     'none'
+  ele_shape(14) = "rfcavity::*"        "XBox"          "Red"    100     'none'
+  ele_shape(15) = "E_GUN::*"           "XBox"          "Black"   20     'none'
+  ele_shape(16) = "EM_FIELD::*"        "Box"           "Blue"    20     'none'
+/       
+
+&floor_plan_drawing
+  ele_shape(1)  = "Quadrupole::*"      "Box"      "Blue"    15    'none'
+  ele_shape(2)  = "SBend::*"           "Box"      "Red"     15    'none'
+  ele_shape(3)  = "lcavity::*"         "XBox"     "Green"   20    'none'
+  ele_shape(4)  = "wiggler::*"         "XBox"     "Orange"  10    'none'
+  ele_shape(5)  = "Sextupole::*"       "Box"      "orange"   4    'none'
+  ele_shape(6)  = "ECOLLIMATOR::*"     "Xbox"     "Black"   10    'none'
+  ele_shape(7)  = "hkicker::*"         "XBox"     "Red"      5    'none'
+  ele_shape(8)  = "vkicker::*"         "bow_tie"  "Red"      5    'none'
+  ele_shape(9)  = "INSTRUMENT::*BPM*"  "Diamond"  "Black"    5    'none'
+  ele_shape(10) = "kicker::*"          "Box"      "Red"      5    'none'
+  ele_shape(11) = "PIPE::*"            "Box"      "Black"    2.5  'none'
+  ele_shape(12) = "INSTRUMENT::*"      "Xbox"     "Black"    5    'none'
+  ele_shape(13) = "SOLENOID::*"        "Xbox"     "Blue"     5    'none'
+  ele_shape(14) = "rfcavity::*"        "XBox"     "Red"     10    'none'
+  ele_shape(15) = "E_GUN::*"           "XBox"     "Black"   20    'none'
+  ele_shape(16) = "EM_FIELD::*"        "Box"      "Blue"    20    'none'
+/  
+                                            
+! Colors: 
+!"BLACK" 
+!"RED" 
+!"ORANGE" 
+!"MAGENTA" 
+!"YELLOW" 
+!"GREEN" 
+!"CYAN" 
+!"BLUE" 
+!"PURPLE" 
+
+
+
+!The Quick Plot line patterns (curve(1)%line%pattern= ) are: 
+!1 -- solid$ Solid 
+!2 -- dashed$ Dashed 
+!3 -- dash_dot$ Dash--dot 
+!4 -- dotted$ Dotted 
+!5 -- dash_dot3$ Dash--dot--dot--dot 
+!The color patterns in Quick Plot are: 
+!0 -- White$ (actually the background color) 
+!1 -- Black$ (actually the foreground color) 
+!2 -- Red$ 
+!3 -- Green$ 
+!4 -- Blue$ 
+!5 -- Cyan$ 
+!6 -- Magenta$ 
+!7 -- Yellow$ 
+!8 -- Orange$ 
+!9 -- Yellow_Green$ 
+!10 -- Light_Green$ 
+!11 -- Navy_Blue$ 
+!12 -- Purple$ 
+!13 -- Reddish_Purple$ 
+!14 -- Dark_Grey$ 
+!15 -- Light_Grey$
+
+
+! Our additional templates for testing:
+
+! * alpha
+
+&tao_template_plot
+  plot%name = 'alpha1'
+  plot%x%min =  0
+  plot%x%max = 10
+  !plot%x%major_div =10
+  !plot%x%label = 's (m)'
+  plot%x_axis_type = 's'
+  !plot%x%label_offset = 1.2
+  plot%n_graph = 1
+/
+
+&tao_template_graph
+  graph%name = 'a'
+  graph%x%draw_numbers = .false.
+  graph%x%draw_label = .false.
+  graph_index = 1
+  graph%box = 1, 1, 1, 1
+ ! graph%title = 'Lattice \gb functions'
+ ! graph%margin =  0.15, 0.06, 0.12, 0.12, '%BOX'
+  graph%margin =  0.15, 0.06, 0.06, 0.0, '%BOX'
+  graph%y%label = '\ga\dx\u, \ga\dy\u (m)'
+  graph%y%label_offset=.1
+  graph%y%max =  20
+  graph%y%min = -20
+  graph%y%major_div = 4
+  !!not needed: graph%n_curve = 2
+  curve(1)%smooth_line_calc = T
+  curve(1)%data_source = 'lattice'
+  curve(1)%data_type   = 'alpha.a'
+  curve(1)%y_axis_scale_factor = 1
+  curve(1)%line%color=2
+  curve(1)%line%width=2
+  curve(1)%draw_symbols=.false.
+  curve(1)%legend_text = '\ga\dx\u'
+  curve(2)%smooth_line_calc = T
+  curve(2)%data_source = 'lattice'
+  curve(2)%data_type   = 'alpha.b'
+  curve(2)%y_axis_scale_factor = 1
+  curve(2)%draw_symbols= F
+  curve(2)%line%color = 3
+  curve(2)%line%width=2
+  curve(2)%legend_text = '\ga\dy\u'
+/
+
+
+! betadispersion
+&tao_template_plot
+  plot%name = 'betadispersion'
+  plot%x%min =  0
+  plot%x%max = 10
+  !plot%x%major_div =10
+  !plot%x%label = 's (m)'
+  plot%x_axis_type = 's'
+  plot%n_graph = 1
+/
+
+&tao_template_graph
+  graph%name = 'a'
+  graph%x%draw_numbers = .false.
+  graph%x%draw_label = .false.
+  graph_index = 1
+  graph%box = 1, 1, 1, 1
+  graph%title = 'Lattice \gb functions'
+ ! graph%margin =  0.15, 0.06, 0.12, 0.12, '%BOX'
+  
+  graph%margin =  0.15, 0.06, 0.06, 0.12, '%BOX'
+    !----y1
+  graph%y%label = '\gb\dx\u (m), \gb\dy\u (m)'
+  graph%y%max =  20
+  graph%y%min = -20
+  graph%y%major_div = 4
+   !-----y2
+  graph%y2%label='yafaf'
+  graph%y2%max =  20
+  graph%y2%min = -20
+  graph%y2%major_div = 4        
+  graph%y2%label_color=2
+  
+  !!not needed: graph%n_curve = 2
+  curve(1)%data_source = 'lattice'
+  curve(1)%data_type   = 'beta.a'
+  curve(1)%y_axis_scale_factor = 1
+  curve(1)%line%color=2
+  curve(1)%line%width=2
+  curve(1)%draw_symbols=.false.
+  curve(2)%data_source = 'lattice'
+  curve(2)%data_type   = 'eta.a'
+  curve(2)%y_axis_scale_factor = 1
+  curve(2)%draw_symbols=.false.
+  curve(2)%line%color = 3
+  curve(2)%line%width=2
+/
diff --git a/pytao/tests/test_floor_plan_shape.py b/pytao/tests/test_floor_plan_shape.py
new file mode 100644
index 00000000..44172036
--- /dev/null
+++ b/pytao/tests/test_floor_plan_shape.py
@@ -0,0 +1,198 @@
+import logging
+import math
+import re
+from typing import Union
+
+import bokeh.io
+import bokeh.layouts
+import bokeh.plotting
+import bokeh.resources
+import matplotlib.axes
+import matplotlib.pyplot as plt
+import pytest
+
+from bokeh.plotting import figure
+
+from .. import SubprocessTao, Tao
+from ..plotting.bokeh import _draw_floor_plan_shapes
+from ..plotting.floor_plan_shapes import (
+    AnyFloorPlanShape,
+    BowTie,
+    Box,
+    Circle,
+    Diamond,
+    LetterX,
+    SBend,
+    XBox,
+)
+from ..plotting.plot import FloorPlanElement
+from ..plotting.mpl import plot_floor_plan_shape as mpl_plot_floor_plan_shape
+from .conftest import test_artifacts
+
+logger = logging.getLogger(__name__)
+
+
+AnyTao = Union[Tao, SubprocessTao]
+
+
+def draw_floor_plan_shape(
+    fig: figure,
+    shape: AnyFloorPlanShape,
+):
+    elem = FloorPlanElement(
+        branch_index=0,
+        index=0,
+        info={
+            "branch_index": 0,
+            "color": "",
+            "ele_key": "",
+            "end1_r1": 0.0,
+            "end1_r2": 0.0,
+            "end1_theta": 0.0,
+            "end2_r1": 0.0,
+            "end2_r2": 0.0,
+            "end2_theta": 0.0,
+            "index": 0,
+            "label_name": "",
+            "line_width": 0.0,
+            "shape": "",
+            "y1": 0.0,
+            "y2": 0.0,
+        },
+        annotations=[],
+        shape=shape,
+    )
+    _draw_floor_plan_shapes(fig, elems=[elem])
+
+
+@pytest.fixture(autouse=True, scope="function")
+def _plot_show_to_savefig(
+    request: pytest.FixtureRequest,
+    monkeypatch: pytest.MonkeyPatch,
+    # plot_backend: BackendName,
+):
+    index = 0
+
+    def savefig():
+        nonlocal index
+        test_artifacts.mkdir(exist_ok=True)
+        for fignum in plt.get_fignums():
+            plt.figure(fignum)
+            name = re.sub(r"[/\\]", "_", request.node.name)
+            filename = test_artifacts / f"{name}_{index}.png"
+            print(f"Saving figure (_plot_show_to_savefig fixture) to {filename}")
+            plt.savefig(filename)
+            index += 1
+        plt.close("all")
+
+    monkeypatch.setattr(plt, "show", savefig)
+    yield
+    plt.show()
+
+
+def make_shapes(width: float, height: float, angle_low: int, angle_high: int):
+    for angle in range(angle_low, angle_high, 5):
+        x = angle
+        for shape in [
+            Box(
+                x1=x,
+                y1=0.0,
+                x2=x + width,
+                y2=0,
+                off1=width,
+                off2=height,
+                angle_start=math.radians(angle),
+            ),
+            XBox(
+                x1=x,
+                y1=height * 3,
+                x2=x + width,
+                y2=height * 3,
+                off1=width,
+                off2=height,
+                angle_start=math.radians(angle),
+            ),
+            LetterX(
+                x1=x,
+                y1=height * 6,
+                x2=x + width,
+                y2=height * 6,
+                off1=width,
+                off2=height,
+                angle_start=math.radians(angle),
+            ),
+            Diamond(
+                x1=x,
+                y1=height * 9,
+                x2=x + width,
+                y2=height * 9,
+                off1=width,
+                off2=width,  # height,
+                angle_start=math.radians(angle),
+            ),
+            SBend(
+                x1=x,
+                y1=height * 12,
+                x2=x + width,
+                y2=height * 12,
+                off1=width,
+                off2=height,  # height,
+                angle_start=math.radians(angle % 90),
+                angle_end=math.radians((angle + 1) % 90),
+                rel_angle_start=0,
+                rel_angle_end=0,
+            ),
+            Circle(
+                x1=x,
+                y1=height * 15,
+                x2=x + width,
+                y2=height * 15,
+                off1=width,
+                off2=height,
+                angle_start=math.radians(angle),
+            ),
+            BowTie(
+                x1=x,
+                y1=height * 18,
+                x2=x + width,
+                y2=height * 18,
+                off1=width,
+                off2=height,
+                angle_start=math.radians(angle),
+            ),
+        ]:
+            yield shape
+
+
+def test_floor_plan_shapes_mpl():
+    fig = plt.figure(figsize=(12, 12))
+    ax = fig.subplots()
+    assert isinstance(ax, matplotlib.axes.Axes)
+    for shape in make_shapes(width=1, height=2, angle_low=0, angle_high=90):
+        mpl_plot_floor_plan_shape(shape, ax)
+
+    plt.ylim(-5, 85)
+
+    fig = plt.figure(figsize=(12, 12))
+    ax = fig.subplots()
+    assert isinstance(ax, matplotlib.axes.Axes)
+    for shape in make_shapes(width=1, height=2, angle_low=90, angle_high=180):
+        mpl_plot_floor_plan_shape(shape, ax)
+
+    plt.ylim(-5, 85)
+
+    plt.show()
+
+
+def test_floor_plan_shapes_bokeh(request: pytest.FixtureRequest):
+    bokeh.io.output_file(test_artifacts / f"{request.node.name}.html")
+
+    fig1 = bokeh.plotting.figure(match_aspect=True)
+    for shape in make_shapes(width=1, height=2, angle_low=0, angle_high=90):
+        draw_floor_plan_shape(fig1, shape)
+
+    fig2 = bokeh.plotting.figure(match_aspect=True)
+    for shape in make_shapes(width=1, height=2, angle_low=90, angle_high=180):
+        draw_floor_plan_shape(fig2, shape)
+
+    bokeh.io.save(bokeh.layouts.column([fig1, fig2]), resources=bokeh.resources.INLINE)
diff --git a/pytao/tests/test_interface_commands.py b/pytao/tests/test_interface_commands.py
index 2bf5a913..0575bfb9 100644
--- a/pytao/tests/test_interface_commands.py
+++ b/pytao/tests/test_interface_commands.py
@@ -2,7 +2,7 @@
 # AUTOGENERATED FILE - DO NOT MODIFY
 # This file was generated by the script `generate_interface_commands.py`.
 # Any modifications may be overwritten.
-# Generated on: 2024-06-27 11:16:56
+# Generated on: 2024-08-16 10:08:54
 # ==============================================================================
 
 from .conftest import ensure_successful_parsing, new_tao
@@ -12,7 +12,8 @@ def test_beam_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.beam(ix_uni="1", ix_branch="0", verbose=True)
 
@@ -21,7 +22,8 @@ def test_beam_init_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.beam_init(ix_uni="1", ix_branch="0", verbose=True)
 
@@ -29,7 +31,9 @@ def test_beam_init_1(caplog, tao_cls):
 def test_bmad_com_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.bmad_com(verbose=True)
 
@@ -37,7 +41,9 @@ def test_bmad_com_1(caplog, tao_cls):
 def test_branch1_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.branch1(ix_uni="1", ix_branch="0", verbose=True)
 
@@ -46,7 +52,8 @@ def test_bunch_comb_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.bunch_comb(who="x.beta", verbose=True)
 
@@ -55,7 +62,8 @@ def test_bunch_params_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.bunch_params(ele_id="end", which="model", verbose=True)
 
@@ -64,7 +72,8 @@ def test_bunch1_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.bunch1(ele_id="end", coordinate="x", which="model", ix_bunch="1", verbose=True)
 
@@ -72,7 +81,9 @@ def test_bunch1_1(caplog, tao_cls):
 def test_building_wall_list_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall",
+            external_plotting=False,
         ) as tao:
             tao.building_wall_list(ix_section="", verbose=True)
 
@@ -80,7 +91,9 @@ def test_building_wall_list_1(caplog, tao_cls):
 def test_building_wall_list_2(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall",
+            external_plotting=False,
         ) as tao:
             tao.building_wall_list(ix_section="1", verbose=True)
 
@@ -88,7 +101,9 @@ def test_building_wall_list_2(caplog, tao_cls):
 def test_building_wall_graph_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall",
+            external_plotting=False,
         ) as tao:
             tao.building_wall_graph(graph="floor_plan.g", verbose=True)
 
@@ -96,7 +111,9 @@ def test_building_wall_graph_1(caplog, tao_cls):
 def test_building_wall_point_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall",
+            external_plotting=False,
         ) as tao:
             tao.building_wall_point(
                 ix_section="1",
@@ -113,7 +130,9 @@ def test_building_wall_point_1(caplog, tao_cls):
 def test_building_wall_section_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.building_wall_section(
                 ix_section="1", sec_name="test", sec_constraint="none", verbose=True
@@ -124,7 +143,8 @@ def test_constraints_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.constraints(who="data", verbose=True)
 
@@ -132,7 +152,9 @@ def test_constraints_1(caplog, tao_cls):
 def test_constraints_2(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.constraints(who="var", verbose=True)
 
@@ -141,7 +163,8 @@ def test_data_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.data(ix_uni="", d2_name="twiss", d1_name="end", dat_index="1", verbose=True)
 
@@ -150,7 +173,8 @@ def test_data_2(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.data(ix_uni="1", d2_name="twiss", d1_name="end", dat_index="1", verbose=True)
 
@@ -159,7 +183,8 @@ def test_data_d_array_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.data_d_array(ix_uni="1", d2_name="twiss", d1_name="end", verbose=True)
 
@@ -168,7 +193,8 @@ def test_data_d1_array_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.data_d1_array(ix_uni="1", d2_datum="twiss", verbose=True)
 
@@ -177,7 +203,8 @@ def test_data_d2_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.data_d2(ix_uni="1", d2_name="twiss", verbose=True)
 
@@ -185,7 +212,9 @@ def test_data_d2_1(caplog, tao_cls):
 def test_data_d2_array_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.data_d2_array(ix_uni="1", verbose=True)
 
@@ -194,7 +223,8 @@ def test_data_d2_create_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.data_d2_create(
                 ix_uni="1",
@@ -208,7 +238,9 @@ def test_data_d2_create_1(caplog, tao_cls):
 def test_data_d2_destroy_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.data_d2_destroy(d2_name="orbit", verbose=True)
 
@@ -217,7 +249,8 @@ def test_data_parameter_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.data_parameter(data_array="twiss.end", parameter="model_value", verbose=True)
 
@@ -226,7 +259,8 @@ def test_data_set_design_value_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.data_set_design_value(verbose=True)
 
@@ -235,7 +269,8 @@ def test_datum_create_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.datum_create(
                 datum_name="twiss.end[6]",
@@ -263,7 +298,8 @@ def test_datum_has_ele_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.datum_has_ele(datum_type="twiss.end", verbose=True)
 
@@ -272,7 +308,8 @@ def test_derivative_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.derivative(verbose=True)
 
@@ -280,7 +317,9 @@ def test_derivative_1(caplog, tao_cls):
 def test_ele_ac_kicker_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_ac_kicker(ele_id="1@0>>1", which="model", verbose=True)
 
@@ -289,7 +328,8 @@ def test_ele_cartesian_map_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_em_field",
+            external_plotting=False,
         ) as tao:
             tao.ele_cartesian_map(
                 ele_id="1@0>>1", which="model", index="1", who="base", verbose=True
@@ -299,7 +339,9 @@ def test_ele_cartesian_map_1(caplog, tao_cls):
 def test_ele_chamber_wall_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall3d",
+            external_plotting=False,
         ) as tao:
             tao.ele_chamber_wall(
                 ele_id="1@0>>1", which="model", index="1", who="x", verbose=True
@@ -309,7 +351,9 @@ def test_ele_chamber_wall_1(caplog, tao_cls):
 def test_ele_control_var_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_control_var(ele_id="1@0>>873", which="model", verbose=True)
 
@@ -318,7 +362,8 @@ def test_ele_cylindrical_map_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_em_field",
+            external_plotting=False,
         ) as tao:
             tao.ele_cylindrical_map(
                 ele_id="1@0>>5", which="model", index="1", who="base", verbose=True
@@ -328,7 +373,9 @@ def test_ele_cylindrical_map_1(caplog, tao_cls):
 def test_ele_elec_multipoles_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_elec_multipoles(ele_id="1@0>>1", which="model", verbose=True)
 
@@ -336,7 +383,9 @@ def test_ele_elec_multipoles_1(caplog, tao_cls):
 def test_ele_floor_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_floor(ele_id="1@0>>1", which="model", where="", verbose=True)
 
@@ -344,7 +393,9 @@ def test_ele_floor_1(caplog, tao_cls):
 def test_ele_floor_2(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_floor(ele_id="1@0>>1", which="model", where="center", verbose=True)
 
@@ -352,7 +403,9 @@ def test_ele_floor_2(caplog, tao_cls):
 def test_ele_gen_attribs_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_gen_attribs(ele_id="1@0>>1", which="model", verbose=True)
 
@@ -361,7 +414,8 @@ def test_ele_gen_grad_map_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_em_field",
+            external_plotting=False,
         ) as tao:
             tao.ele_gen_grad_map(
                 ele_id="1@0>>9", which="model", index="1", who="derivs", verbose=True
@@ -371,7 +425,9 @@ def test_ele_gen_grad_map_1(caplog, tao_cls):
 def test_ele_grid_field_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_grid"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_grid",
+            external_plotting=False,
         ) as tao:
             tao.ele_grid_field(
                 ele_id="1@0>>1", which="model", index="1", who="base", verbose=True
@@ -381,7 +437,9 @@ def test_ele_grid_field_1(caplog, tao_cls):
 def test_ele_head_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_head(ele_id="1@0>>1", which="model", verbose=True)
 
@@ -389,7 +447,9 @@ def test_ele_head_1(caplog, tao_cls):
 def test_ele_lord_slave_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_lord_slave(ele_id="1@0>>1", which="model", verbose=True)
 
@@ -397,7 +457,9 @@ def test_ele_lord_slave_1(caplog, tao_cls):
 def test_ele_mat6_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_mat6(ele_id="1@0>>1", which="model", who="mat6", verbose=True)
 
@@ -405,7 +467,9 @@ def test_ele_mat6_1(caplog, tao_cls):
 def test_ele_methods_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_methods(ele_id="1@0>>1", which="model", verbose=True)
 
@@ -413,7 +477,9 @@ def test_ele_methods_1(caplog, tao_cls):
 def test_ele_multipoles_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_multipoles(ele_id="1@0>>1", which="model", verbose=True)
 
@@ -421,7 +487,9 @@ def test_ele_multipoles_1(caplog, tao_cls):
 def test_ele_orbit_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_orbit(ele_id="1@0>>1", which="model", verbose=True)
 
@@ -429,7 +497,9 @@ def test_ele_orbit_1(caplog, tao_cls):
 def test_ele_param_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_photon",
+            external_plotting=False,
         ) as tao:
             tao.ele_param(ele_id="1@0>>1", which="model", who="orbit.vec.1", verbose=True)
 
@@ -437,7 +507,9 @@ def test_ele_param_1(caplog, tao_cls):
 def test_ele_photon_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_photon"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_photon",
+            external_plotting=False,
         ) as tao:
             tao.ele_photon(ele_id="1@0>>1", which="model", who="base", verbose=True)
 
@@ -445,7 +517,9 @@ def test_ele_photon_1(caplog, tao_cls):
 def test_ele_spin_taylor_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_spin"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_spin",
+            external_plotting=False,
         ) as tao:
             tao.ele_spin_taylor(ele_id="1@0>>2", which="model", verbose=True)
 
@@ -453,7 +527,9 @@ def test_ele_spin_taylor_1(caplog, tao_cls):
 def test_ele_taylor_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_taylor",
+            external_plotting=False,
         ) as tao:
             tao.ele_taylor(ele_id="1@0>>34", which="model", verbose=True)
 
@@ -461,7 +537,9 @@ def test_ele_taylor_1(caplog, tao_cls):
 def test_ele_twiss_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ele_twiss(ele_id="1@0>>1", which="model", verbose=True)
 
@@ -469,7 +547,9 @@ def test_ele_twiss_1(caplog, tao_cls):
 def test_ele_wake_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wake"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wake",
+            external_plotting=False,
         ) as tao:
             tao.ele_wake(ele_id="1@0>>1", which="model", who="sr_long", verbose=True)
 
@@ -477,7 +557,9 @@ def test_ele_wake_1(caplog, tao_cls):
 def test_ele_wall3d_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall3d",
+            external_plotting=False,
         ) as tao:
             tao.ele_wall3d(
                 ele_id="1@0>>1", which="model", index="1", who="table", verbose=True
@@ -487,7 +569,9 @@ def test_ele_wall3d_1(caplog, tao_cls):
 def test_evaluate_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.evaluate(expression="data::cbar.11[1:10]|model", verbose=True)
 
@@ -495,23 +579,21 @@ def test_evaluate_1(caplog, tao_cls):
 def test_em_field_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.em_field(
-                ele_id="1@0>>22",
-                which="model",
-                x="0",
-                y="0",
-                z="0",
-                t_or_z="0",
-                verbose=True,
+                ele_id="1@0>>22", which="model", x="0", y="0", z="0", t_or_z="0", verbose=True
             )
 
 
 def test_enum_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.enum(enum_name="tracking_method", verbose=True)
 
@@ -520,7 +602,8 @@ def test_floor_plan_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.floor_plan(graph="r13.g", verbose=True)
 
@@ -529,7 +612,8 @@ def test_floor_orbit_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_floor_orbit",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_floor_orbit",
+            external_plotting=False,
         ) as tao:
             tao.floor_orbit(graph="r33.g", verbose=True)
 
@@ -537,7 +621,9 @@ def test_floor_orbit_1(caplog, tao_cls):
 def test_tao_global_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.tao_global(verbose=True)
 
@@ -545,7 +631,9 @@ def test_tao_global_1(caplog, tao_cls):
 def test_global_optimization_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.global_optimization(verbose=True)
 
@@ -553,7 +641,9 @@ def test_global_optimization_1(caplog, tao_cls):
 def test_global_opti_de_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.global_opti_de(verbose=True)
 
@@ -561,7 +651,9 @@ def test_global_opti_de_1(caplog, tao_cls):
 def test_help_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.help(verbose=True)
 
@@ -569,7 +661,9 @@ def test_help_1(caplog, tao_cls):
 def test_inum_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.inum(who="ix_universe", verbose=True)
 
@@ -577,7 +671,9 @@ def test_inum_1(caplog, tao_cls):
 def test_lat_calc_done_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.lat_calc_done(branch_name="1@0", verbose=True)
 
@@ -585,7 +681,9 @@ def test_lat_calc_done_1(caplog, tao_cls):
 def test_lat_ele_list_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.lat_ele_list(branch_name="1@0", verbose=True)
 
@@ -593,7 +691,9 @@ def test_lat_ele_list_1(caplog, tao_cls):
 def test_lat_branch_list_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.lat_branch_list(ix_uni="1", verbose=True)
 
@@ -601,7 +701,9 @@ def test_lat_branch_list_1(caplog, tao_cls):
 def test_lat_list_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.lat_list(
                 ix_uni="1",
@@ -616,7 +718,9 @@ def test_lat_list_1(caplog, tao_cls):
 def test_lat_list_2(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.lat_list(
                 ix_uni="1",
@@ -631,7 +735,9 @@ def test_lat_list_2(caplog, tao_cls):
 def test_lat_param_units_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.lat_param_units(param_name="L", verbose=True)
 
@@ -639,7 +745,9 @@ def test_lat_param_units_1(caplog, tao_cls):
 def test_matrix_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.matrix(ele1_id="1@0>>q01w|design", ele2_id="q02w", verbose=True)
 
@@ -647,7 +755,9 @@ def test_matrix_1(caplog, tao_cls):
 def test_merit_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.merit(verbose=True)
 
@@ -655,7 +765,9 @@ def test_merit_1(caplog, tao_cls):
 def test_orbit_at_s_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.orbit_at_s(ix_uni="1", ele="10", s_offset="0.7", which="model", verbose=True)
 
@@ -663,7 +775,9 @@ def test_orbit_at_s_1(caplog, tao_cls):
 def test_place_buffer_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.place_buffer(verbose=True)
 
@@ -672,50 +786,58 @@ def test_plot_curve_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.plot_curve(curve_name="r13.g.a", verbose=True)
 
 
-def test_plot_lat_layout_1(caplog, tao_cls):
+def test_plot_graph_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
-            tao.plot_lat_layout(ix_uni="1", ix_branch="0", verbose=True)
+            tao.plot_graph(graph_name="beta.g", verbose=True)
 
 
-def test_plot_list_1(caplog, tao_cls):
+def test_plot_histogram_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
-            tao.plot_list(r_or_g="r", verbose=True)
+            tao.plot_histogram(curve_name="r33.g.x", verbose=True)
 
 
-def test_plot_graph_1(caplog, tao_cls):
+def test_plot_lat_layout_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
-            tao.plot_graph(graph_name="beta.g", verbose=True)
+            tao.plot_lat_layout(ix_uni="1", ix_branch="0", verbose=True)
 
 
-def test_plot_histogram_1(caplog, tao_cls):
+def test_plot_list_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
-            tao.plot_histogram(curve_name="r33.g.x", verbose=True)
+            tao.plot_list(r_or_g="r", verbose=True)
 
 
 def test_plot_template_manage_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.plot_template_manage(
                 template_location="@T1",
@@ -730,7 +852,8 @@ def test_plot_curve_manage_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.plot_curve_manage(
                 graph_name="beta.g", curve_index="1", curve_name="r13.g.a", verbose=True
@@ -741,7 +864,8 @@ def test_plot_graph_manage_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.plot_graph_manage(
                 plot_name="beta", graph_index="1", graph_name="beta.g", verbose=True
@@ -752,14 +876,11 @@ def test_plot_line_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line -external_plotting",
+            external_plotting=False,
         ) as tao:
             tao.plot_line(
-                region_name="beta",
-                graph_name="g",
-                curve_name="a",
-                x_or_y="",
-                verbose=True,
+                region_name="beta", graph_name="g", curve_name="a", x_or_y="", verbose=True
             )
 
 
@@ -767,14 +888,11 @@ def test_plot_line_2(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line -external_plotting",
+            external_plotting=False,
         ) as tao:
             tao.plot_line(
-                region_name="beta",
-                graph_name="g",
-                curve_name="a",
-                x_or_y="y",
-                verbose=True,
+                region_name="beta", graph_name="g", curve_name="a", x_or_y="y", verbose=True
             )
 
 
@@ -782,14 +900,11 @@ def test_plot_symbol_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line -external_plotting",
+            external_plotting=False,
         ) as tao:
             tao.plot_symbol(
-                region_name="r13",
-                graph_name="g",
-                curve_name="a",
-                x_or_y="",
-                verbose=True,
+                region_name="r13", graph_name="g", curve_name="a", x_or_y="", verbose=True
             )
 
 
@@ -797,14 +912,11 @@ def test_plot_symbol_2(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line -external_plotting",
+            external_plotting=False,
         ) as tao:
             tao.plot_symbol(
-                region_name="r13",
-                graph_name="g",
-                curve_name="a",
-                x_or_y="y",
-                verbose=True,
+                region_name="r13", graph_name="g", curve_name="a", x_or_y="y", verbose=True
             )
 
 
@@ -812,7 +924,8 @@ def test_plot_transfer_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.plot_transfer(from_plot="r13", to_plot="r23", verbose=True)
 
@@ -821,7 +934,8 @@ def test_plot1_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.plot1(name="beta", verbose=True)
 
@@ -829,7 +943,9 @@ def test_plot1_1(caplog, tao_cls):
 def test_ptc_com_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ptc_com(verbose=True)
 
@@ -837,7 +953,9 @@ def test_ptc_com_1(caplog, tao_cls):
 def test_ring_general_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.ring_general(ix_uni="1", ix_branch="0", which="model", verbose=True)
 
@@ -845,7 +963,9 @@ def test_ring_general_1(caplog, tao_cls):
 def test_shape_list_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.shape_list(who="floor_plan", verbose=True)
 
@@ -853,7 +973,9 @@ def test_shape_list_1(caplog, tao_cls):
 def test_shape_manage_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.shape_manage(who="floor_plan", index="1", add_or_delete="add", verbose=True)
 
@@ -861,7 +983,9 @@ def test_shape_manage_1(caplog, tao_cls):
 def test_shape_pattern_list_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_shape",
+            external_plotting=False,
         ) as tao:
             tao.shape_pattern_list(ix_pattern="", verbose=True)
 
@@ -869,7 +993,9 @@ def test_shape_pattern_list_1(caplog, tao_cls):
 def test_shape_pattern_manage_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_shape",
+            external_plotting=False,
         ) as tao:
             tao.shape_pattern_manage(
                 ix_pattern="1", pat_name="new_pat", pat_line_width="1", verbose=True
@@ -879,7 +1005,9 @@ def test_shape_pattern_manage_1(caplog, tao_cls):
 def test_shape_pattern_point_manage_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_shape",
+            external_plotting=False,
         ) as tao:
             tao.shape_pattern_point_manage(
                 ix_pattern="1", ix_point="1", s="0", x="0", verbose=True
@@ -889,7 +1017,9 @@ def test_shape_pattern_point_manage_1(caplog, tao_cls):
 def test_shape_set_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.shape_set(
                 who="floor_plan",
@@ -909,15 +1039,19 @@ def test_shape_set_1(caplog, tao_cls):
 def test_show_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
-            tao.show(line="-python", verbose=True)
+            tao.show(line="-pipe", verbose=True)
 
 
 def test_space_charge_com_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.space_charge_com(verbose=True)
 
@@ -925,7 +1059,9 @@ def test_space_charge_com_1(caplog, tao_cls):
 def test_species_to_int_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.species_to_int(species_str="electron", verbose=True)
 
@@ -933,7 +1069,9 @@ def test_species_to_int_1(caplog, tao_cls):
 def test_species_to_str_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.species_to_str(species_int="-1", verbose=True)
 
@@ -941,7 +1079,9 @@ def test_species_to_str_1(caplog, tao_cls):
 def test_spin_invariant_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.spin_invariant(
                 who="l0", ix_uni="1", ix_branch="0", which="model", verbose=True
@@ -951,7 +1091,9 @@ def test_spin_invariant_1(caplog, tao_cls):
 def test_spin_polarization_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.spin_polarization(ix_uni="1", ix_branch="0", which="model", verbose=True)
 
@@ -959,7 +1101,9 @@ def test_spin_polarization_1(caplog, tao_cls):
 def test_spin_resonance_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.spin_resonance(ix_uni="1", ix_branch="0", which="model", verbose=True)
 
@@ -967,7 +1111,9 @@ def test_spin_resonance_1(caplog, tao_cls):
 def test_super_universe_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.super_universe(verbose=True)
 
@@ -975,7 +1121,9 @@ def test_super_universe_1(caplog, tao_cls):
 def test_taylor_map_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.taylor_map(ele1_id="1@0>>q01w|design", ele2_id="q02w", order="1", verbose=True)
 
@@ -983,7 +1131,9 @@ def test_taylor_map_1(caplog, tao_cls):
 def test_twiss_at_s_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.twiss_at_s(ix_uni="1", ele="10", s_offset="0.7", which="model", verbose=True)
 
@@ -991,7 +1141,9 @@ def test_twiss_at_s_1(caplog, tao_cls):
 def test_universe_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.universe(ix_uni="1", verbose=True)
 
@@ -1000,7 +1152,8 @@ def test_var_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.var(var="quad[1]", slaves="", verbose=True)
 
@@ -1009,7 +1162,8 @@ def test_var_2(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.var(var="quad[1]", slaves="slaves", verbose=True)
 
@@ -1018,7 +1172,8 @@ def test_var_create_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
             tao_cls,
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+            external_plotting=False,
         ) as tao:
             tao.var_create(
                 var_name="quad[1]",
@@ -1040,7 +1195,9 @@ def test_var_create_1(caplog, tao_cls):
 def test_var_general_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.var_general(verbose=True)
 
@@ -1048,7 +1205,9 @@ def test_var_general_1(caplog, tao_cls):
 def test_var_v_array_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.var_v_array(v1_var="quad_k1", verbose=True)
 
@@ -1056,7 +1215,9 @@ def test_var_v_array_1(caplog, tao_cls):
 def test_var_v1_array_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.var_v1_array(v1_var="quad_k1", verbose=True)
 
@@ -1064,7 +1225,9 @@ def test_var_v1_array_1(caplog, tao_cls):
 def test_var_v1_create_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.var_v1_create(v1_name="quad_k1", n_var_min="0", n_var_max="45", verbose=True)
 
@@ -1072,7 +1235,9 @@ def test_var_v1_create_1(caplog, tao_cls):
 def test_var_v1_destroy_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.var_v1_destroy(v1_datum="quad_k1", verbose=True)
 
@@ -1080,6 +1245,8 @@ def test_var_v1_destroy_1(caplog, tao_cls):
 def test_wave_1(caplog, tao_cls):
     with ensure_successful_parsing(caplog):
         with new_tao(
-            tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+            tao_cls,
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init",
+            external_plotting=False,
         ) as tao:
             tao.wave(who="params", verbose=True)
diff --git a/pytao/tests/test_layout_plot_shape.py b/pytao/tests/test_layout_plot_shape.py
new file mode 100644
index 00000000..7a31c9fd
--- /dev/null
+++ b/pytao/tests/test_layout_plot_shape.py
@@ -0,0 +1,192 @@
+import logging
+import re
+from typing import List, Type, Union
+
+import bokeh.io
+import bokeh.layouts
+import bokeh.models
+import bokeh.plotting
+import matplotlib.axes
+import matplotlib.pyplot as plt
+import pytest
+
+from .. import SubprocessTao, Tao
+from ..plotting import layout_shapes
+from ..plotting.bokeh import _draw_layout_elems as bokeh_draw_layout_elems
+from ..plotting.bokeh import get_tool_from_figure
+from ..plotting.mpl import plot_layout_shape as mpl_plot_layout_shape
+from ..plotting.plot import LatticeLayoutElement
+from .conftest import test_artifacts
+
+logger = logging.getLogger(__name__)
+
+
+AnyTao = Union[Tao, SubprocessTao]
+
+
+@pytest.fixture(autouse=True, scope="function")
+def _plot_show_to_savefig(
+    request: pytest.FixtureRequest,
+    monkeypatch: pytest.MonkeyPatch,
+    # plot_backend: BackendName,
+):
+    index = 0
+
+    def savefig():
+        nonlocal index
+        test_artifacts.mkdir(exist_ok=True)
+        for fignum in plt.get_fignums():
+            plt.figure(fignum)
+            name = re.sub(r"[/\\]", "_", request.node.name)
+            filename = test_artifacts / f"{name}_{index}.png"
+            print(f"Saving figure (_plot_show_to_savefig fixture) to {filename}")
+            plt.savefig(filename)
+            index += 1
+        plt.close("all")
+
+    monkeypatch.setattr(plt, "show", savefig)
+    yield
+    plt.show()
+
+
+def make_shapes(
+    width: float,
+    height: float,
+    kwarg_list: List[dict],
+):
+    separation = width * 2.5
+    s = 0
+    for kwargs in kwarg_list:
+        for name, cls in layout_shapes.shape_to_class.items():
+            s += separation
+            yield cls(
+                s1=s,
+                s2=s + width,
+                y1=0.0,
+                y2=height,
+                name=name,
+                **kwargs,
+            )
+        s += separation
+
+
+def test_plot_layout_shapes_mpl():
+    fig = plt.figure(figsize=(12, 12))
+    ax = fig.subplots()
+    assert isinstance(ax, matplotlib.axes.Axes)
+    shape = None
+    for shape in make_shapes(
+        width=1,
+        height=2,
+        kwarg_list=[
+            {"color": "black"},
+            {"color": "blue", "line_width": 1.0},
+            {"color": "green", "line_width": 2.0},
+        ],
+    ):
+        mpl_plot_layout_shape(shape, ax)
+
+    assert shape is not None
+    plt.xlim(-5, shape.s2 + 5)
+    plt.ylim(-5, 5)
+
+    plt.show()
+
+
+def bokeh_draw_layout_shape(fig: bokeh.plotting.figure, shape: layout_shapes.AnyLayoutShape):
+    bokeh_draw_layout_elems(
+        fig=fig,
+        skip_labels=False,
+        elems=[
+            LatticeLayoutElement(
+                info={
+                    "ix_branch": 0,
+                    "ix_ele": 0,
+                    "ele_s_start": 0.0,
+                    "ele_s_end": 0.0,
+                    "line_width": 0.0,
+                    "shape": "",
+                    "y1": 0.0,
+                    "y2": 0.0,
+                    "color": "",
+                    "label_name": "",
+                },
+                shape=shape,
+                annotations=[],
+                color=shape.color,
+                width=shape.line_width,
+            ),
+        ],
+    )
+
+
+def test_plot_layout_shapes_bokeh():
+    bokeh.io.output_file(test_artifacts / "test_plot_layout_shapes_bokeh.html")
+
+    fig1 = bokeh.plotting.figure(match_aspect=True)
+    box_zoom = get_tool_from_figure(fig1, bokeh.models.BoxZoomTool)
+    if box_zoom is not None:
+        box_zoom.match_aspect = True
+    for shape in make_shapes(
+        width=1,
+        height=2,
+        kwarg_list=[
+            {"color": "black"},
+            {"color": "blue", "line_width": 1.0},
+            {"color": "green", "line_width": 2.0},
+        ],
+    ):
+        bokeh_draw_layout_shape(fig1, shape)
+
+    bokeh.io.save(bokeh.layouts.column([fig1]))
+
+
+shape_classes = pytest.mark.parametrize(
+    ("shape_cls",),
+    [
+        pytest.param(cls, id=shape)
+        for shape, cls in layout_shapes.wrapped_shape_to_class.items()
+    ],
+)
+
+
+@shape_classes
+def test_plot_layout_wrapped_shapes_mpl(shape_cls: Type[layout_shapes.AnyWrappedLayoutShape]):
+    fig = plt.figure(figsize=(3, 3))
+    ax = fig.subplots()
+    assert isinstance(ax, matplotlib.axes.Axes)
+
+    shape = shape_cls(
+        s1=20,  # s1 > s2 is required
+        s2=10,
+        y1=0,
+        y2=1,
+        s_min=0,
+        s_max=30,
+    )
+    mpl_plot_layout_shape(shape, ax)
+
+    plt.xlim(-1, 31)
+    plt.ylim(-5, 10)
+
+    plt.show()
+
+
+@shape_classes
+def test_plot_layout_wrapped_shapes_bokeh(
+    shape_cls: Type[layout_shapes.AnyWrappedLayoutShape],
+):
+    bokeh.io.output_file(test_artifacts / f"test_plot_shapes_bokeh_{shape_cls.__name__}.html")
+
+    fig1 = bokeh.plotting.figure(match_aspect=True)
+    shape = shape_cls(
+        s1=20,  # s1 > s2 is required
+        s2=10,
+        y1=0,
+        y2=1,
+        s_min=0,
+        s_max=30,
+    )
+    bokeh_draw_layout_shape(fig1, shape)
+
+    bokeh.io.save(bokeh.layouts.column([fig1]))
diff --git a/pytao/tests/test_parsers.py b/pytao/tests/test_parsers.py
index 6128091a..c66270f1 100644
--- a/pytao/tests/test_parsers.py
+++ b/pytao/tests/test_parsers.py
@@ -1,12 +1,16 @@
+from datetime import datetime
+from typing import Type
+
 import numpy as np
 import pytest
 
+from .. import AnyTao
 from .test_interface_commands import new_tao
 
 
-def test_building_wall_list_1(tao_cls):
+def test_building_wall_list_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall"
     ) as tao:
         assert set(tao.building_wall_list(ix_section="")[0].keys()) == {
             "index",
@@ -18,9 +22,9 @@ def test_building_wall_list_1(tao_cls):
         }
 
 
-def test_building_wall_list_2(tao_cls):
+def test_building_wall_list_2(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall"
     ) as tao:
         assert set(tao.building_wall_list(ix_section="1")[0].keys()) == {
             "index",
@@ -32,11 +36,13 @@ def test_building_wall_list_2(tao_cls):
         }
 
 
-def test_building_wall_graph_1(tao_cls):
+def test_building_wall_graph_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall"
+        tao_cls,
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall",
     ) as tao:
-        assert set(tao.building_wall_graph(graph="floor_plan.g")[0].keys()) == {
+        tao.cmd("place -no_buffer r11 floor_plan")
+        assert set(tao.building_wall_graph(graph="r11.g")[0].keys()) == {
             "index",
             "point",
             "offset_x",
@@ -45,32 +51,32 @@ def test_building_wall_graph_1(tao_cls):
         }
 
 
-def test_constraints_1(tao_cls):
+def test_constraints_1(tao_cls: Type[AnyTao]):
     with new_tao(
         tao_cls,
-        "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
     ) as tao:
         tao.constraints(who="data")
 
 
-def test_constraints_2(tao_cls):
+def test_constraints_2(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         tao.constraints(who="var")
 
 
-def test_data_d2_array_1(tao_cls):
+def test_data_d2_array_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         assert "orbit" in tao.data_d2_array(ix_uni="1")
 
 
-def test_data_parameter_1(tao_cls):
+def test_data_parameter_1(tao_cls: Type[AnyTao]):
     with new_tao(
         tao_cls,
-        "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
     ) as tao:
         assert (
             tao.data_parameter(data_array="twiss.end", parameter="model_value")[0]["index"]
@@ -78,10 +84,10 @@ def test_data_parameter_1(tao_cls):
         )
 
 
-def test_datum_has_ele_1(tao_cls):
+def test_datum_has_ele_1(tao_cls: Type[AnyTao]):
     with new_tao(
         tao_cls,
-        "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
     ) as tao:
         assert tao.datum_has_ele(datum_type="twiss.end") in {
             "no",
@@ -91,9 +97,9 @@ def test_datum_has_ele_1(tao_cls):
         }
 
 
-def test_ele_chamber_wall_1(tao_cls):
+def test_ele_chamber_wall_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall3d"
     ) as tao:
         assert set(
             tao.ele_chamber_wall(ele_id="1@0>>1", which="model", index="1", who="x")[0].keys()
@@ -105,16 +111,16 @@ def test_ele_chamber_wall_1(tao_cls):
         }
 
 
-def test_ele_elec_multipoles_1(tao_cls):
+def test_ele_elec_multipoles_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         assert "data" in tao.ele_elec_multipoles(ele_id="1@0>>1", which="model")
 
 
-def test_ele_gen_grad_map_1(tao_cls):
+def test_ele_gen_grad_map_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_em_field"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_em_field"
     ) as tao:
         assert set(
             tao.ele_gen_grad_map(ele_id="1@0>>9", which="model", index="1", who="derivs")[
@@ -123,9 +129,9 @@ def test_ele_gen_grad_map_1(tao_cls):
         ) == {"i", "j", "k", "dz", "deriv"}
 
 
-def test_ele_lord_slave_1(tao_cls):
+def test_ele_lord_slave_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         assert set(tao.ele_lord_slave(ele_id="1@0>>1", which="model")[0].keys()) == {
             "type",
@@ -136,9 +142,9 @@ def test_ele_lord_slave_1(tao_cls):
         }
 
 
-def test_ele_multipoles_1(tao_cls):
+def test_ele_multipoles_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         res = tao.ele_multipoles(ele_id="1@0>>1", which="model")
 
@@ -147,9 +153,9 @@ def test_ele_multipoles_1(tao_cls):
         assert "KnL" in res or "An" in res["data"][0]
 
 
-def test_ele_taylor_1(tao_cls):
+def test_ele_taylor_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_taylor"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_taylor"
     ) as tao:
         res = tao.ele_taylor(ele_id="1@0>>34", which="model")
     assert isinstance(res, dict)
@@ -157,9 +163,9 @@ def test_ele_taylor_1(tao_cls):
     assert res["data"][0]["index"] == 1
 
 
-def test_ele_spin_taylor_1(tao_cls):
+def test_ele_spin_taylor_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_spin"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_spin"
     ) as tao:
         res = tao.ele_spin_taylor(ele_id="1@0>>2", which="model")
     assert set(res[0].keys()) == {
@@ -175,44 +181,49 @@ def test_ele_spin_taylor_1(tao_cls):
     }
 
 
-def test_ele_wall3d_1(tao_cls):
+def test_ele_wall3d_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_wall3d"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_wall3d"
     ) as tao:
         res = tao.ele_wall3d(ele_id="1@0>>1", which="model", index="1", who="table")
     assert "data" in res[0]
     assert res[0]["section"] == 1
 
 
-def test_em_field_1(tao_cls):
+def test_em_field_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         res = tao.em_field(ele_id="1@0>>22", which="model", x="0", y="0", z="0", t_or_z="0")
     assert "B1" in res
 
 
-def test_enum_1(tao_cls):
+def test_enum_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         res = tao.enum(enum_name="tracking_method")
     assert set(res[0].keys()) == {"number", "name"}
 
 
-def test_floor_plan_1(tao_cls):
+def test_floor_plan_1(tao_cls: Type[AnyTao]):
     with new_tao(
         tao_cls,
-        "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
     ) as tao:
+        tao.cmd("place -no_buffer r13.g floor_plan")
         res = tao.floor_plan(graph="r13.g")
     assert "branch_index" in res[0]
 
 
-def test_floor_orbit_1(tao_cls):
+def test_floor_orbit_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_floor_orbit"
+        tao_cls,
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_floor_orbit",
+        nostartup=True,
     ) as tao:
+        tao.cmd("place -no_buffer r33 orbit")
+        tao.cmd("set graph r33 floor_plan%orbit_scale = 1")
         res = tao.floor_orbit(graph="r33.g")
     assert isinstance(res, list)
     assert isinstance(res[0], dict)
@@ -220,16 +231,16 @@ def test_floor_orbit_1(tao_cls):
     assert "orbits" in res[0]
 
 
-def test_help_1(tao_cls):
+def test_help_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         print(tao.help())
 
 
-def test_inum_1(tao_cls):
+def test_inum_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         res = tao.inum(who="ix_universe")
     assert isinstance(res, list)
@@ -237,47 +248,47 @@ def test_inum_1(tao_cls):
         assert isinstance(res[0], int)
 
 
-def test_lat_calc_done_1(tao_cls):
+def test_lat_calc_done_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         assert tao.lat_calc_done(branch_name="1@0") in {True, False}
 
 
-def test_lat_branch_list_1(tao_cls):
+def test_lat_branch_list_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         tao.lat_branch_list(ix_uni="1")
 
 
-def test_lat_param_units_1(tao_cls):
+def test_lat_param_units_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         assert isinstance(tao.lat_param_units(param_name="L"), str)
 
 
-def test_plot_lat_layout_1(tao_cls):
+def test_plot_lat_layout_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
-        assert "index" in tao.plot_lat_layout(ix_uni="1", ix_branch="0")[0]
+        assert "ix_ele" in tao.plot_lat_layout(ix_uni="1", ix_branch="0")[0]
 
 
-def test_plot_line_1(tao_cls):
+def test_plot_line_1(tao_cls: Type[AnyTao]):
     with new_tao(
         tao_cls,
-        "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting",
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line",
     ) as tao:
         res = tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="")
     assert "x" in res[0]
 
 
-def test_plot_line_2(tao_cls):
+def test_plot_line_2(tao_cls: Type[AnyTao]):
     with new_tao(
         tao_cls,
-        "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting",
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line",
     ) as tao:
         res = tao.plot_line(region_name="beta", graph_name="g", curve_name="a", x_or_y="y")
         assert isinstance(
@@ -288,25 +299,25 @@ def test_plot_line_2(tao_cls):
         assert "index" in res[0]
 
 
-def test_plot_symbol_1(tao_cls):
+def test_plot_symbol_1(tao_cls: Type[AnyTao]):
     with new_tao(
         tao_cls,
-        "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_plot_line -external_plotting",
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_plot_line",
     ) as tao:
         res = tao.plot_symbol(region_name="r13", graph_name="g", curve_name="a", x_or_y="")
     assert "index" in res[0]
 
 
-def test_shape_list_1(tao_cls):
+def test_shape_list_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
-        assert "index" in tao.shape_list(who="floor_plan")[0]
+        assert "shape_index" in tao.shape_list(who="floor_plan")[0]
 
 
-def test_shape_pattern_list_1(tao_cls):
+def test_shape_pattern_list_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_shape"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_shape"
     ) as tao:
         res = tao.shape_pattern_list(ix_pattern="")
     assert set(res[0].keys()) == {
@@ -315,29 +326,29 @@ def test_shape_pattern_list_1(tao_cls):
     }
 
 
-def test_show_1(tao_cls):
+def test_show_1(tao_cls: Type[AnyTao]):
     pytest.skip("TODO")
-    tao = new_tao("-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init")
+    tao = new_tao(init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init")
     tao.show(line="-python")
 
 
-def test_species_to_int_1(tao_cls):
+def test_species_to_int_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         assert isinstance(tao.species_to_int(species_str="electron"), int)
 
 
-def test_species_to_str_1(tao_cls):
+def test_species_to_str_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         assert isinstance(tao.species_to_str(species_int="-1"), str)
 
 
-def test_spin_invariant_1(tao_cls):
+def test_spin_invariant_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         assert isinstance(
             tao.spin_invariant(who="l0", ix_uni="1", ix_branch="0", which="model"),
@@ -353,27 +364,27 @@ def test_spin_invariant_1(tao_cls):
         assert "index" in res[0]
 
 
-def test_spin_polarization_1(tao_cls):
+def test_spin_polarization_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         res = tao.spin_polarization(ix_uni="1", ix_branch="0", which="model")
     assert isinstance(res, dict)
     assert "anom_moment_times_gamma" in res
 
 
-def test_spin_resonance_1(tao_cls):
+def test_spin_resonance_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         res = tao.spin_resonance(ix_uni="1", ix_branch="0", which="model")
     assert isinstance(res, dict)
     assert "spin_tune" in res
 
 
-def test_super_universe_1(tao_cls):
+def test_super_universe_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         res = tao.super_universe()
     assert isinstance(res, dict)
@@ -382,38 +393,38 @@ def test_super_universe_1(tao_cls):
     assert "n_var_used" in res
 
 
-def test_var_1(tao_cls):
+def test_var_1(tao_cls: Type[AnyTao]):
     with new_tao(
         tao_cls,
-        "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
     ) as tao:
         res = tao.var(var="quad[1]", slaves="")
     assert isinstance(res, dict)
     assert "weight" in res
 
 
-def test_var_2(tao_cls):
+def test_var_2(tao_cls: Type[AnyTao]):
     with new_tao(
         tao_cls,
-        "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
     ) as tao:
         res = tao.var(var="quad[1]", slaves="slaves")
     assert isinstance(res[0], dict)
     assert "index" in res[0]
 
 
-def test_var_general_1(tao_cls):
+def test_var_general_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         res = tao.var_general()
     assert isinstance(res[0], dict)
     assert "name" in res[0]
 
 
-def test_var_v1_array_1(tao_cls):
+def test_var_v1_array_1(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/regression_tests/python_test/cesr/tao.init"
+        tao_cls, init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/cesr/tao.init"
     ) as tao:
         res = tao.var_v1_array(v1_var="quad_k1")
     assert "ix_v1_var" in res
@@ -421,19 +432,28 @@ def test_var_v1_array_1(tao_cls):
     assert "name" in res["data"][0]
 
 
-def test_lat_list_from_chris(tao_cls):
+def test_lat_list_from_chris(tao_cls: Type[AnyTao]):
     with new_tao(
-        tao_cls, "-init $ACC_ROOT_DIR/bmad-doc/tao_examples/cesr/tao.init -noplot"
+        tao_cls, init_file="$ACC_ROOT_DIR/bmad-doc/tao_examples/cesr/tao.init"
     ) as tao:
         names = tao.lat_list("*", "ele.name")
     assert isinstance(names[0], str)
 
 
-def test_plot_graph_1(tao_cls):
+def test_plot_graph_1(tao_cls: Type[AnyTao]):
     with new_tao(
         tao_cls,
-        "-init $ACC_ROOT_DIR/regression_tests/python_test/tao.init_optics_matching",
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
     ) as tao:
         res = tao.plot_graph(graph_name="beta.g")
     assert isinstance(res, dict)
     assert "name" in res
+
+
+def test_parse_version(tao_cls: Type[AnyTao]):
+    with new_tao(
+        tao_cls,
+        init_file="$ACC_ROOT_DIR/regression_tests/pipe_test/tao.init_optics_matching",
+    ) as tao:
+        res = tao.version()
+    assert isinstance(res, datetime)
diff --git a/pytao/tests/test_plot.py b/pytao/tests/test_plot.py
new file mode 100644
index 00000000..45e5bb6b
--- /dev/null
+++ b/pytao/tests/test_plot.py
@@ -0,0 +1,228 @@
+import logging
+import re
+from typing import Union
+
+import matplotlib.pyplot as plt
+import pytest
+
+from .. import SubprocessTao, Tao, TaoStartup
+from ..plotting.curves import TaoCurveSettings
+from ..plotting import mpl
+from .conftest import (
+    BackendName,
+    get_example,
+    get_packaged_example,
+    get_regression_test,
+    test_artifacts,
+)
+
+logger = logging.getLogger(__name__)
+
+
+AnyTao = Union[Tao, SubprocessTao]
+
+
+@pytest.fixture(autouse=True, scope="function")
+def _plot_show_to_savefig(
+    request: pytest.FixtureRequest,
+    monkeypatch: pytest.MonkeyPatch,
+    plot_backend: BackendName,
+):
+    index = 0
+
+    def savefig():
+        nonlocal index
+        test_artifacts.mkdir(exist_ok=True)
+        for fignum in plt.get_fignums():
+            plt.figure(fignum)
+            name = re.sub(r"[/\\]", "_", request.node.name)
+            filename = test_artifacts / f"{name}_{index}.png"
+            print(f"Saving figure (_plot_show_to_savefig fixture) to {filename}")
+            plt.savefig(filename)
+            index += 1
+        plt.close("all")
+
+    monkeypatch.setattr(plt, "show", savefig)
+    yield
+    if plot_backend == "mpl":
+        plt.show()
+
+
+def test_plot_floor_plan(use_subprocess: bool, plot_backend: BackendName):
+    startup = get_regression_test("tao.init_wall")
+    startup.plot = plot_backend
+    with startup.run_context(use_subprocess=use_subprocess) as tao:
+        tao.plot("floor_plan")
+
+
+def test_plot_all_interface(plot_backend: BackendName):
+    startup = get_regression_test("tao.init_floor_orbit")
+    startup.plot = plot_backend
+    with startup.run_context(use_subprocess=False) as tao:
+        tao.plot()
+
+
+@pytest.mark.parametrize(
+    ("include_layout",),
+    [
+        pytest.param(True, id="include_layout"),
+        pytest.param(False, id="no_layout"),
+    ],
+)
+def test_plot_single_interface(plot_backend: BackendName, include_layout: bool):
+    startup = get_regression_test("tao.init_floor_orbit")
+    startup.plot = plot_backend
+    with startup.run_context(use_subprocess=False) as tao:
+        tao.plot("alpha", include_layout=include_layout)
+
+
+def test_plot_grid_interface(plot_backend: BackendName):
+    startup = get_regression_test("tao.init_floor_orbit")
+    startup.plot = plot_backend
+    with startup.run_context(use_subprocess=False) as tao:
+        tao.plot(["alpha", "beta"])
+
+
+def test_plot_floor_layout(use_subprocess: bool, plot_backend: BackendName):
+    startup = get_regression_test("tao.init_floor_orbit")
+    startup.plot = plot_backend
+    startup.nostartup = True
+    with startup.run_context(use_subprocess=use_subprocess) as tao:
+        tao.plot("alpha")
+        tao.plot("beta")
+        tao.plot("lat_layout")
+
+        tao.cmd("set plot_page%floor_plan_shape_scale = 0.01")
+        tao.plot("floor_plan", region_name="r33")
+        tao.cmd("set graph r33 floor_plan%orbit_scale = 1")
+        tao.plot("floor_plan", region_name="r33", ylim=(-0.3, 0.1))
+
+
+def test_plot_data(use_subprocess: bool, plot_backend: BackendName):
+    startup = get_example("cesr")
+    startup.plot = plot_backend
+    with startup.run_context(use_subprocess=use_subprocess) as tao:
+        tao.plot_manager.plot_all()
+        tao.plot("floor_plan")
+        tao.plot("lat_layout")
+
+
+def test_plot_all_requested_regression_tests(
+    tao_regression_test: TaoStartup,
+    plot_backend: BackendName,
+    use_subprocess: bool,
+):
+    tao_regression_test.plot = plot_backend
+    with tao_regression_test.run_context(use_subprocess=use_subprocess) as tao:
+        tao.plot_manager.plot_all()
+
+
+def test_plot_all_requested_examples_mpl(tao_example: TaoStartup):
+    tao_example.plot = "mpl"
+    example_name = tao_example.metadata["name"]
+    with tao_example.run_context(use_subprocess=True) as tao:
+        if example_name == "erl":
+            tao.cmd("place r11 zphase")
+        tao.plot_manager.plot_all()
+
+
+def test_plot_manager(
+    tao_regression_test: TaoStartup,
+    plot_backend: BackendName,
+    use_subprocess: bool,
+):
+    tao_regression_test.plot = plot_backend
+    with tao_regression_test.run_context(use_subprocess=use_subprocess) as tao:
+        manager = tao.plot_manager
+        manager.plot_all()
+
+        for region in list(manager.regions):
+            manager.clear(region)
+        assert not any(region for region in manager.regions.values())
+        manager.clear()
+        assert not manager.regions
+
+
+def test_plot_curve(plot_backend: BackendName):
+    example = get_example("erl")
+    example.plot = plot_backend
+    with example.run_context(use_subprocess=True) as tao:
+        manager = tao.plot_manager
+        manager.plot(
+            "zphase",
+            curves={
+                1: TaoCurveSettings(
+                    ele_ref_name=r"linac.beg\1",
+                    draw_line=True,
+                    draw_symbols=True,
+                    draw_symbol_index=True,
+                ),
+            },
+            save=test_artifacts / f"test_plot_curve-{plot_backend}",
+        )
+
+
+def test_plot_grid(plot_backend: BackendName):
+    example = get_example("erl")
+    example.plot = plot_backend
+    with example.run_context(use_subprocess=True) as tao:
+        manager = tao.plot_manager
+        manager.plot_grid(
+            templates=["zphase", "zphase", "zphase", "zphase2"],
+            grid=(2, 2),
+            curves=[
+                {1: TaoCurveSettings(ele_ref_name=r"linac.beg\1")},
+                {1: TaoCurveSettings(ele_ref_name=r"linac.end\1")},
+                {1: TaoCurveSettings(ele_ref_name=r"linac.beg\2")},
+                {1: TaoCurveSettings(ele_ref_name=r"linac.end\2")},
+            ],
+            share_x=False,
+            save=test_artifacts / f"test_plot_grid-{plot_backend}",
+        )
+
+
+def test_plot_grid_with_layout(plot_backend: BackendName):
+    example = get_example("erl")
+    example.plot = plot_backend
+    with example.run_context(use_subprocess=True) as tao:
+        manager = tao.plot_manager
+        manager.plot_grid(
+            templates=["zphase", "zphase", "zphase", "zphase2"],
+            grid=(3, 2),
+            include_layout=True,
+            curves=[
+                {1: TaoCurveSettings(ele_ref_name=r"linac.beg\1")},
+                {1: TaoCurveSettings(ele_ref_name=r"linac.end\1")},
+                {1: TaoCurveSettings(ele_ref_name=r"linac.beg\2")},
+                {1: TaoCurveSettings(ele_ref_name=r"linac.end\2")},
+            ],
+            # figsize=(10, 10),
+            share_x=False,
+            save=test_artifacts / f"test_plot_grid_with_layout-{plot_backend}",
+        )
+
+
+def test_plot_update(plot_backend: BackendName):
+    example = get_packaged_example("optics_matching_tweaked")
+    example.plot = plot_backend
+    with example.run_context(use_subprocess=True) as tao:
+        manager = tao.plot_manager
+        (graph,), *_ = manager.plot("alpha1", include_layout=False)
+        updated = graph.update(manager)
+        assert graph == updated
+
+
+default_options = sorted(
+    set(
+        attr
+        for attr in dir(mpl._Defaults)
+        if not attr.startswith("_") and attr not in {"get_size_for_class"}
+    )
+)
+
+
+@pytest.mark.parametrize(("attr",), [pytest.param(attr) for attr in default_options])
+def test_mpl_set_defaults(attr: str):
+    value = getattr(mpl._Defaults, attr)
+    mpl.set_defaults(**{attr: value})
+    assert getattr(mpl._Defaults, attr) == value
diff --git a/pytao/tests/test_plot_bokeh.py b/pytao/tests/test_plot_bokeh.py
new file mode 100644
index 00000000..db95fc35
--- /dev/null
+++ b/pytao/tests/test_plot_bokeh.py
@@ -0,0 +1,294 @@
+import contextlib
+import logging
+import re
+import unittest.mock
+
+import bokeh.events
+import bokeh.models
+import bokeh.plotting
+import pytest
+from bokeh.plotting import output_file
+
+from .. import TaoStartup
+from ..plotting.bokeh import (
+    _Defaults,
+    initialize_jupyter,
+    select_graph_manager_class,
+    set_defaults,
+    BokehAppCreator,
+    BokehAppState,
+    NotebookGraphManager,
+    Variable,
+)
+from ..plotting.plot import FloorPlanGraph
+from ..plotting.settings import TaoFloorPlanSettings, TaoGraphSettings
+from ..subproc import AnyTao
+from .conftest import get_example, test_artifacts
+
+logger = logging.getLogger(__name__)
+
+
+def annotate_and_save(state: BokehAppState, test_name: str, filename_base: str):
+    assert len(state.pairs)
+    for pair in state.pairs:
+        fig = pair.fig
+        graph = pair.bgraph.graph
+        fig.title.text = (
+            f"{fig.title.text} ({graph.region_name}.{graph.graph_name} of {test_name})"
+        )
+
+    fn = test_artifacts / f"{filename_base}.html"
+    state.save(fn)
+    return fn
+
+
+def test_bokeh_manager(
+    request: pytest.FixtureRequest,
+    tao_regression_test: TaoStartup,
+):
+    name = re.sub(r"[/\\]", "_", request.node.name)
+    filename_base = f"bokeh_{name}"
+    tao_regression_test.plot = "bokeh"
+    with tao_regression_test.run_context(use_subprocess=True) as tao:
+        manager = tao.bokeh
+
+        output_file(test_artifacts / f"{filename_base}.html")
+
+        _, app = manager.plot_all()
+
+        annotate_and_save(app.create_state(), request.node.name, filename_base)
+
+        for region in list(manager.regions):
+            manager.clear(region)
+        assert not any(region for region in manager.regions.values())
+        manager.clear()
+        assert not manager.regions
+
+
+def test_bokeh_examples(
+    request: pytest.FixtureRequest,
+    tao_example: TaoStartup,
+):
+    example_name = tao_example.metadata["name"]
+    name = re.sub(r"[/\\]", "_", request.node.name)
+    filename_base = f"bokeh_{name}"
+
+    tao_example.plot = "bokeh"
+
+    with tao_example.run_context(use_subprocess=True) as tao:
+        manager = tao.bokeh
+
+        if example_name == "erl":
+            tao.cmd("place r11 zphase")
+
+        _, app = manager.plot_all()
+        annotate_and_save(app.create_state(), request.node.name, filename_base)
+
+
+def test_bokeh_floor_plan(request: pytest.FixtureRequest):
+    tao_example = get_example("optics_matching")
+    name = re.sub(r"[/\\]", "_", request.node.name)
+    filename_base = f"bokeh_{name}"
+
+    tao_example.plot = "bokeh"
+
+    with tao_example.run_context(use_subprocess=True) as tao:
+        tao.update_plot_shapes("quadrupole", type_label="name", layout=True, floor=True)
+        _, app = tao.bokeh.plot("floor_plan")
+        annotate_and_save(app.create_state(), request.node.name, filename_base)
+
+
+@contextlib.contextmanager
+def optics_matching(request: pytest.FixtureRequest):
+    tao_example = get_example("optics_matching")
+    name = re.sub(r"[/\\]", "_", request.node.name)
+    tao_example.plot = "bokeh"
+
+    with tao_example.run_context(use_subprocess=True) as tao:
+        yield tao, name
+
+
+def get_ui_from_app(app: BokehAppCreator):
+    class Doc:
+        def add_root(self, ui):
+            self.ui = ui
+
+    doc = Doc()
+    app.create_full_app()(doc)
+    return doc.ui
+
+
+def test_bokeh_smoke_create_full_app(request: pytest.FixtureRequest):
+    with optics_matching(request) as (tao, _):
+        _, app = tao.bokeh.plot_grid(["alpha", "beta"], grid=(2, 1), include_layout=True)
+
+        print(get_ui_from_app(app))
+
+
+def test_bokeh_update_button(request: pytest.FixtureRequest, caplog: pytest.LogCaptureFixture):
+    with caplog.at_level(logging.ERROR):
+        with optics_matching(request) as (tao, _):
+            (_alpha, _beta), app = tao.bokeh.plot_grid(
+                ["alpha", "beta"], grid=(2, 1), include_layout=True
+            )
+
+            state = app.create_state()
+            button: bokeh.models.Button = app._add_update_button(state)
+
+            caplog.clear()
+            # NOTE: this is internal bokeh API and may break at some point
+            # I think that's OK in the context of a test suite
+            button._trigger_event(bokeh.events.ButtonClick(button))
+
+    assert not caplog.messages
+
+
+def test_bokeh_num_points(request: pytest.FixtureRequest, caplog: pytest.LogCaptureFixture):
+    with caplog.at_level(logging.ERROR):
+        with optics_matching(request) as (tao, _):
+            (_alpha, _beta), app = tao.bokeh.plot_grid(
+                ["alpha", "beta"], grid=(2, 1), include_layout=True
+            )
+
+            state = app.create_state()
+            slider: bokeh.models.Slider = app._add_num_points_slider(state)
+
+            caplog.clear()
+            slider.trigger("value", 0, 10)
+            slider.trigger("value", 0, 100)
+            assert not caplog.messages
+
+            caplog.clear()
+            slider.trigger("value", 0, -1)
+            assert len(caplog.messages)
+
+
+def test_bokeh_range_updates(request: pytest.FixtureRequest, caplog: pytest.LogCaptureFixture):
+    with caplog.at_level(logging.ERROR):
+        with optics_matching(request) as (tao, _):
+            (_alpha, _beta), app = tao.bokeh.plot_grid(
+                ["alpha", "beta"], grid=(2, 1), include_layout=True
+            )
+
+            state = app.create_state()
+
+            cbs = app._monitor_range_updates(state)
+
+            caplog.clear()
+            for cb in cbs:
+                cb(bokeh.events.RangesUpdate(model=None, x0=0, x1=10))
+            assert not caplog.messages
+
+            caplog.clear()
+            for cb in cbs:
+                cb(bokeh.events.RangesUpdate(model=None, x0=10, x1=0))
+            assert len(caplog.messages)
+
+            caplog.clear()
+            for cb in cbs:
+                cb(bokeh.events.RangesUpdate(model=None, x0=10, x1=None))
+            assert len(caplog.messages)
+
+
+def get_notebook_graph_manager(tao: AnyTao, monkeypatch: pytest.MonkeyPatch):
+    gm = NotebookGraphManager(tao)
+
+    def show(*args, **kwargs):
+        print("bokeh plotting show:", args, kwargs)
+
+    monkeypatch.setattr(bokeh.plotting, "show", show)
+    return gm
+
+
+@pytest.mark.parametrize(
+    ("grid",),
+    [
+        pytest.param(True, id="grid"),
+        pytest.param(False, id="normal"),
+    ],
+)
+def test_bokeh_notebook_plot_vars(
+    request: pytest.FixtureRequest,
+    caplog: pytest.LogCaptureFixture,
+    monkeypatch: pytest.MonkeyPatch,
+    grid: bool,
+):
+    with caplog.at_level(logging.ERROR):
+        with optics_matching(request) as (tao, _):
+            gm = get_notebook_graph_manager(tao, monkeypatch)
+            if grid:
+                _, app = gm.plot_grid(["alpha", "beta"], grid=(2, 1), vars=True)
+            else:
+                _, app = gm.plot("alpha", vars=True)
+
+            state = app.create_state()
+
+            status_label = bokeh.models.PreText()
+
+            def try_value(var: Variable, value: float) -> None:
+                var.ui_update(
+                    "",
+                    0.0,
+                    value,
+                    tao=tao,
+                    status_label=status_label,
+                    pairs=state.pairs,
+                )
+
+            def set_value_raise(*args, **kwargs):
+                raise RuntimeError("raised")
+
+            for var in app.variables:
+                status_label.text = ""
+                try_value(var, value=var.value)
+                assert not str(status_label.text)
+
+            for var in app.variables:
+                status_label.text = ""
+                monkeypatch.setattr(var, "set_value", set_value_raise)
+                try_value(var, value=var.value)
+                assert "raised" in str(status_label.text)
+
+
+def test_bokeh_floor_orbits(
+    request: pytest.FixtureRequest,
+    monkeypatch: pytest.MonkeyPatch,
+):
+    with optics_matching(request) as (tao, _):
+        gm = get_notebook_graph_manager(tao, monkeypatch)
+        (floor_plan,), app = gm.plot(
+            "floor_plan",
+            settings=TaoGraphSettings(floor_plan=TaoFloorPlanSettings(orbit_scale=1.0)),
+        )
+
+        assert isinstance(floor_plan, FloorPlanGraph)
+
+        ui = get_ui_from_app(app)
+        assert ui.children[0].children[0].label == "Show orbits"
+
+
+default_options = sorted(
+    set(
+        attr
+        for attr in dir(_Defaults)
+        if not attr.startswith("_") and attr not in {"get_size_for_class"}
+    )
+)
+
+
+@pytest.mark.parametrize(("attr",), [pytest.param(attr) for attr in default_options])
+def test_bokeh_set_defaults(attr: str):
+    value = getattr(_Defaults, attr)
+    set_defaults(**{attr: value})
+    assert getattr(_Defaults, attr) == value
+
+
+def test_smoke_select_graph_manager_class():
+    select_graph_manager_class()
+
+
+def test_smoke_init_jupyter(monkeypatch: pytest.MonkeyPatch):
+    output_notebook = unittest.mock.Mock()
+    monkeypatch.setattr(bokeh.plotting, "output_notebook", output_notebook)
+    initialize_jupyter()
+    assert output_notebook.called
diff --git a/pytao/tests/test_plot_field.py b/pytao/tests/test_plot_field.py
new file mode 100644
index 00000000..ce684faf
--- /dev/null
+++ b/pytao/tests/test_plot_field.py
@@ -0,0 +1,11 @@
+from .conftest import BackendName, get_example, test_artifacts
+
+
+def test_plot_field(plot_backend: BackendName):
+    example = get_example("cbeta_cell")
+    example.plot = plot_backend
+    with example.run_context(use_subprocess=True) as tao:
+        tao.plot_field(
+            "FF.QUA01#1",
+            save=test_artifacts / f"test_plot_field-{plot_backend}",
+        )
diff --git a/pytao/tests/test_plot_settings.py b/pytao/tests/test_plot_settings.py
new file mode 100644
index 00000000..742deab4
--- /dev/null
+++ b/pytao/tests/test_plot_settings.py
@@ -0,0 +1,157 @@
+from typing import List, Optional
+
+import pytest
+from pytest import FixtureRequest
+
+from ..plotting import (
+    TaoAxisSettings,
+    TaoCurveSettings,
+    TaoFloorPlanSettings,
+    TaoGraphSettings,
+)
+from ..plotting.types import Limit
+
+from .conftest import BackendName, get_example, test_artifacts
+
+
+def test_curve_settings_empty():
+    assert TaoCurveSettings().get_commands("a", "b", 0) == []
+
+
+def test_graph_settings_empty():
+    assert TaoGraphSettings().get_commands("a", "b", graph_type="lat_layout") == []
+
+
+@pytest.mark.parametrize(
+    ("settings", "expected_commands"),
+    [
+        pytest.param(
+            TaoGraphSettings(text_legend={1: "test"}),
+            ["set graph a text_legend(1) = test"],
+        ),
+        pytest.param(
+            TaoGraphSettings(box={1: 2}),
+            ["set graph a box(1) = 2"],
+        ),
+        pytest.param(
+            TaoGraphSettings(component="abc"),
+            ["set graph a.b component = abc"],
+        ),
+        pytest.param(
+            TaoGraphSettings(curve_legend_origin=(1, 1, "abc")),
+            [
+                "set graph a.b curve_legend_origin%x = 1.0",
+                "set graph a.b curve_legend_origin%y = 1.0",
+                "set graph a.b curve_legend_origin%units = abc",
+            ],
+        ),
+        pytest.param(
+            TaoGraphSettings(margin=(1, 2, 3, 4, "abc")),
+            [
+                "set graph a.b margin%x1 = 1.0",
+                "set graph a.b margin%x2 = 2.0",
+                "set graph a.b margin%y1 = 3.0",
+                "set graph a.b margin%y2 = 4.0",
+                "set graph a.b margin%units = abc",
+            ],
+        ),
+        pytest.param(
+            TaoGraphSettings(x=TaoAxisSettings(bounds="zero_at_end", label="text")),
+            [
+                "set graph a x%bounds = zero_at_end",
+                "set graph a x%label = text",
+            ],
+        ),
+        pytest.param(
+            TaoGraphSettings(floor_plan=TaoFloorPlanSettings(view="xz")),
+            [
+                "set graph a floor_plan%view = xz",
+            ],
+        ),
+    ],
+)
+def test_graph_settings(settings: TaoGraphSettings, expected_commands: List[str]):
+    assert settings.get_commands("a", "b", graph_type="lat_layout") == expected_commands
+
+
+@pytest.mark.parametrize(
+    ("xlim", "ylim", "expected_commands"),
+    [
+        pytest.param(None, None, [], id="no-lims"),
+        pytest.param(
+            (1.0, 2.0),
+            None,
+            ["x_scale a 1.0 2.0"],
+            id="xlim",
+        ),
+        pytest.param(
+            None,
+            (1.0, 2.0),
+            ["scale -y a 1.0 2.0"],
+            id="ylim",
+        ),
+        pytest.param(
+            (1.0, 2.0),
+            (1.0, 2.0),
+            ["x_scale a 1.0 2.0", "scale -y a 1.0 2.0"],
+            id="both",
+        ),
+    ],
+)
+def test_graph_settings_xlim_ylim(
+    xlim: Optional[Limit],
+    ylim: Optional[Limit],
+    expected_commands: List[str],
+):
+    settings = TaoGraphSettings()
+    settings.xlim = xlim
+    settings.ylim = ylim
+    assert settings.get_commands("a", "b", graph_type="lat_layout") == expected_commands
+
+
+def test_plot_settings_grid(plot_backend: BackendName, request: FixtureRequest):
+    example = get_example("erl")
+    example.plot = plot_backend
+    with example.run_context(use_subprocess=True) as tao:
+        manager = tao.plot_manager
+        graphs, *_ = manager.plot_grid(
+            templates=["zphase", "zphase"],
+            grid=(3, 2),
+            include_layout=True,
+            curves=[
+                {1: TaoCurveSettings(ele_ref_name=r"linac.beg\1")},
+                {1: TaoCurveSettings(ele_ref_name=r"linac.end\1")},
+            ],
+            settings=[
+                TaoGraphSettings(commands=["set graph {graph} title = Test Plot 1"]),
+                TaoGraphSettings(title="Test Plot 2"),
+            ],
+            share_x=False,
+            save=test_artifacts / request.node.name,
+        )
+        graph1, graph2, *_ = graphs
+        assert graph1.title.startswith("Test Plot 1")
+        assert graph2.title.startswith("Test Plot 2")
+
+
+def test_plot_settings(plot_backend: BackendName, request: FixtureRequest):
+    example = get_example("erl")
+    example.plot = plot_backend
+    with example.run_context(use_subprocess=True) as tao:
+        manager = tao.plot_manager
+        graphs, *_ = manager.plot(
+            "zphase",
+            include_layout=True,
+            curves={1: TaoCurveSettings(ele_ref_name=r"linac.beg\1")},
+            settings=TaoGraphSettings(
+                title="Test Plot 1",
+                y=TaoAxisSettings(
+                    label="Y axis label",
+                ),
+            ),
+            share_x=False,
+            save=test_artifacts / request.node.name,
+        )
+        graph1, *_ = graphs
+        assert graph1.title.startswith("Test Plot 1")
+        assert graph1.ylabel == "Y axis label"
diff --git a/pytao/tests/test_startup.py b/pytao/tests/test_startup.py
new file mode 100644
index 00000000..efa787c9
--- /dev/null
+++ b/pytao/tests/test_startup.py
@@ -0,0 +1,54 @@
+import pytest
+
+from .. import TaoStartup
+
+
+def test_examples_can_init(tao_example: TaoStartup) -> None:
+    assert tao_example.can_initialize
+
+
+def test_regression_tests_can_init(tao_regression_test: TaoStartup) -> None:
+    assert tao_regression_test.can_initialize
+
+
+@pytest.mark.parametrize(
+    ("startup"),
+    [
+        pytest.param(TaoStartup("-i foo")),
+        pytest.param(TaoStartup("-init foo")),
+        pytest.param(TaoStartup("-init_file foo")),
+        pytest.param(TaoStartup("-la foo")),
+        pytest.param(TaoStartup("-lat foo")),
+        pytest.param(TaoStartup("-lattice_file foo")),
+    ],
+)
+def test_can_init(startup: TaoStartup) -> None:
+    assert startup.can_initialize
+
+
+@pytest.mark.parametrize(
+    ("startup"),
+    [
+        pytest.param(TaoStartup()),
+        pytest.param(TaoStartup(external_plotting=True)),
+        pytest.param(TaoStartup(var_file="no")),
+    ],
+)
+def test_no_init(startup: TaoStartup) -> None:
+    assert not startup.can_initialize
+
+
+def test_plotting() -> None:
+    assert (
+        TaoStartup(plot="mpl", external_plotting=True, noplot=True).tao_init
+        == "-external_plotting -noplot"
+    )
+
+
+def test_init_override() -> None:
+    assert TaoStartup("-init_file foo", init_file="test").tao_init == "-init_file foo"
+
+
+def test_geometry() -> None:
+    assert TaoStartup(geometry="3x3").tao_init == "-geometry 3x3"
+    assert TaoStartup(geometry=(32, 23)).tao_init == "-geometry 32x23"
diff --git a/pytao/tests/test_subproc.py b/pytao/tests/test_subproc.py
index c9ab98a9..21f01534 100644
--- a/pytao/tests/test_subproc.py
+++ b/pytao/tests/test_subproc.py
@@ -11,11 +11,11 @@
 def test_crash_and_recovery() -> None:
     init = os.path.expandvars(
         os.path.expandvars(
-            "-init $ACC_ROOT_DIR/regression_tests/python_test/csr_beam_tracking/tao.init -noplot"
+            "-init $ACC_ROOT_DIR/regression_tests/pipe_test/csr_beam_tracking/tao.init -noplot"
         )
     )
     tao = SubprocessTao(init=init)
-    # tao.init("-init regression_tests/python_test/tao.init_plot_line -external_plotting")
+    # tao.init("-init regression_tests/pipe_test/tao.init_plot_line -external_plotting")
     bunch1 = tao.bunch1(ele_id="end", coordinate="x", which="model", ix_bunch="1")
     print("bunch1=", bunch1)
 
diff --git a/pytao/tests/test_update_plot_shapes.py b/pytao/tests/test_update_plot_shapes.py
new file mode 100644
index 00000000..00f7140f
--- /dev/null
+++ b/pytao/tests/test_update_plot_shapes.py
@@ -0,0 +1,67 @@
+import logging
+from typing import Union
+
+from .. import SubprocessTao, Tao
+from .conftest import get_example
+
+logger = logging.getLogger(__name__)
+
+
+AnyTao = Union[Tao, SubprocessTao]
+
+
+def test_update_plot_shapes(use_subprocess: bool):
+    example = get_example("optics_matching")
+    tao = example.run(use_subprocess=use_subprocess)
+
+    orig_shapes = set(shape["shape"] for shape in tao.shape_list("lat_layout"))
+    tao.update_plot_shapes(layout=True, shape="xbox")
+    new_shapes = set(shape["shape"] for shape in tao.shape_list("lat_layout"))
+
+    assert set(new_shapes) != set(orig_shapes)
+    assert set(new_shapes) == {"xbox"}
+
+
+def test_update_plot_shape_by_id():
+    example = get_example("optics_matching")
+    tao = example.run(use_subprocess=False)
+
+    (orig_shape,) = [
+        shape for shape in tao.shape_list("lat_layout") if shape["shape_index"] == 1
+    ]
+
+    expected_shape = "xbox" if orig_shape["shape"] == "box" else "box"
+    tao.update_plot_shapes(layout=True, shape_index=1, shape=expected_shape)
+    (new_shape,) = [
+        shape for shape in tao.shape_list("lat_layout") if shape["shape_index"] == 1
+    ]
+    assert new_shape["shape"] == expected_shape
+
+    # Minus the shape, they should be identical
+    orig_shape.pop("shape")
+    new_shape.pop("shape")
+
+    assert orig_shape == new_shape
+
+
+def test_update_plot_shape_by_name():
+    example = get_example("optics_matching")
+    tao = example.run(use_subprocess=False)
+
+    ele_name = "quadrupole::*"
+    (orig_shape,) = [
+        shape for shape in tao.shape_list("lat_layout") if shape["ele_name"] == ele_name
+    ]
+
+    expected_shape = "xbox" if orig_shape["shape"] == "box" else "box"
+    tao.update_plot_shapes(layout=True, ele_name=ele_name, shape=expected_shape)
+    (new_shape,) = [
+        shape for shape in tao.shape_list("lat_layout") if shape["ele_name"] == ele_name
+    ]
+    assert new_shape["shape"] == expected_shape
+
+    # Minus the shape, they should be identical
+    orig_shape.pop("shape")
+    new_shape.pop("shape")
+
+    assert orig_shape == new_shape
diff --git a/pytao/util/__init__.py b/pytao/util/__init__.py
index 2d818485..c39e7aa1 100644
--- a/pytao/util/__init__.py
+++ b/pytao/util/__init__.py
@@ -10,7 +10,6 @@
     tao_parameter_dict,
 )
 
-
 __all__ = [
     "lat_element",
     "InvalidParamError",
diff --git a/pytao/util/command.py b/pytao/util/command.py
new file mode 100644
index 00000000..3306f351
--- /dev/null
+++ b/pytao/util/command.py
@@ -0,0 +1,27 @@
+import shlex
+
+
+def make_tao_init(init: str, **kwargs) -> str:
+    """
+    Make Tao init string based on optional flags/command-line arguments.
+
+    Parameters
+    ----------
+    init : str
+        The user-specified init string.
+    **kwargs :
+        Command-line switches without the leading `-`,
+        mapped to their respective values.
+        Only added as an argument if not empty and not False.
+    """
+    result = shlex.split(init)
+    for name, value in kwargs.items():
+        switch = f"-{name}"
+        if switch in result:
+            continue
+        if not value:
+            continue
+        result.append(switch)
+        if value not in {True, False}:
+            result.append(str(value))
+    return shlex.join(result)
diff --git a/pytao/util/parsers.py b/pytao/util/parsers.py
index 79ea48ef..e9940be6 100644
--- a/pytao/util/parsers.py
+++ b/pytao/util/parsers.py
@@ -1,3 +1,5 @@
+import ast
+import datetime
 import logging
 from typing import Dict, List, Optional
 
@@ -1090,9 +1092,10 @@ def parse_plot_lat_layout(lines, cmd=""):
     return _parse_by_keys_to_types(
         lines,
         {
-            "index": int,
+            "ix_branch": int,
+            "ix_ele": int,
             "ele_s_start": float,
-            "ele_s": float,
+            "ele_s_end": float,
             "line_width": float,
             "shape": str,
             "y1": float,
@@ -1172,6 +1175,8 @@ def parse_shape_list(lines, cmd=""):
     """
     Parse shape_list results.
 
+    Keys match those on `shape_set` for convenience.
+
     Returns
     -------
     list of dict
@@ -1179,14 +1184,14 @@ def parse_shape_list(lines, cmd=""):
     return _parse_by_keys_to_types(
         lines,
         {
-            "index": int,
-            "ele_id": str,
+            "shape_index": int,
+            "ele_name": str,
             "shape": str,
             "color": str,
-            "size": float,
-            "label": str,
-            "draw": bool,
-            "multi": bool,
+            "shape_size": float,
+            "type_label": str,
+            "shape_draw": bool,
+            "multi_shape": bool,
             "line_width": int,
         },
     )
@@ -1275,10 +1280,14 @@ def parse_spin_resonance(lines, cmd=""):
     -------
     dict
     """
-    lines = [
-        line for line in lines if "[INFO]" not in line and "note: setting" not in line.lower()
-    ]
-    return parse_tao_python_data(lines)
+    # Filter lines as INFO/notes may appear in output
+    return parse_tao_python_data(
+        [
+            line
+            for line in lines
+            if "[INFO]" not in line and "note: setting" not in line.lower()
+        ]
+    )
 
 
 def parse_super_universe(lines, cmd=""):
@@ -1381,3 +1390,82 @@ def parse_lat_list(lines, cmd=""):
     list of str
     """
     return lines
+
+
+def parse_place_buffer(lines, cmd=""):
+    """
+    Parse place_buffer results.
+
+    Returns
+    -------
+    list of dict
+    """
+    return _parse_by_keys_to_types(
+        lines,
+        {
+            "region": str,
+            "graph": str,
+        },
+    )
+
+
+def parse_show_plot_page(lines, cmd=""):
+    """
+    Parse 'show plot_page' output.
+
+    Returns
+    -------
+    list of dict
+    """
+
+    def literal_eval(value: str):
+        try:
+            return ast.literal_eval(value)
+        except (ValueError, SyntaxError):
+            return value
+
+    result = {}
+    for line in lines:
+        line = line.strip()
+        if not line or "=" not in line:
+            continue
+
+        variable, value = line.split("=", 1)
+        variable = variable.strip().lstrip("%")
+        value = value.rsplit("!")[0].strip()
+        if value.startswith('"') or not value:
+            value = value.strip('"')
+        elif value in {"TF"}:
+            value = {"T": True, "F": False}[value]
+        else:
+            value = [literal_eval(part) for part in value.split()]
+
+            if "," in variable and "%" in variable:
+                prefix = variable[: variable.index("%")]
+                suffixes = [
+                    suffix.strip() for suffix in variable[variable.index("%") :].split(",")
+                ]
+                result.update(
+                    {f"{prefix}%{suffix}": val for suffix, val in zip(suffixes, value)}
+                )
+                continue
+            if len(value) == 1:
+                (value,) = value
+
+        result[variable] = value
+    return {key.replace("%", "_"): value for key, value in result.items()}
+
+
+def parse_show_version(lines, cmd=""):
+    """
+    Parse 'show version' output.
+
+    Returns
+    -------
+    datetime.datetime or None
+    """
+    try:
+        return datetime.datetime.strptime("".join(lines).strip(), "Date: %Y/%m/%d %H:%M:%S")
+    except ValueError:
+        logger.warning("Failed to parse version output: %s", lines)
+        return None
diff --git a/scripts/execute_notebooks.bash b/scripts/execute_notebooks.bash
new file mode 100755
index 00000000..de276ac5
--- /dev/null
+++ b/scripts/execute_notebooks.bash
@@ -0,0 +1,37 @@
+#!/bin/bash
+set -e
+
+# Important! Do not show full Bokeh server-backed applications in this
+# converted output as they are not supported.
+export PYTAO_BOKEH_NBCONVERT=1
+
+REPO_ROOT=$(git rev-parse --show-toplevel)
+PATTERN=${1-:"*"}
+cd "$REPO_ROOT/docs/examples" || exit 1
+
+NOTEBOOKS=$(git ls-files "${PATTERN}.ipynb")
+
+SKIP_PATTERNS=()
+
+# Silence Jupyterlab warning
+export PYDEVD_DISABLE_FILE_VALIDATION=1
+
+for file in $NOTEBOOKS; do
+  should_skip=false
+  for SKIP in "${SKIP_PATTERNS[@]}"; do
+    if [[ "$file" == *"$SKIP"* ]]; then
+      should_skip=true
+      break
+    fi
+  done
+
+  if [ "$should_skip" = true ]; then
+    echo "* Skipping: $(basename "$file") as it matches a skip_pattern"
+  else
+    pushd "$(dirname "$file")" >/dev/null || exit
+    echo "* Processing: $(basename "$file") in $PWD"
+    jupyter nbconvert --to notebook --execute "$(basename "$file")" --inplace
+    popd >/dev/null || exit
+  fi
+  echo
+done