Repository: AUTODIAL/AutoEIS (language: python)

Issue:
Migrate from flat to src layout



Discussion / hints:

Fix patch:
diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml
new file mode 100644
index 00000000..60d915ed
--- /dev/null
+++ b/.github/workflows/pypi.yml
@@ -0,0 +1,56 @@
+name: Deploy to PyPI
+
+on:
+  workflow_dispatch:
+    inputs:
+        version:
+            description: 'Version (major/minor/patch)'
+            required: true
+            default: 'patch'
+
+jobs:
+  deploy:
+    name: Publish 🐍 📦 to PyPI
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        ref: main
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -e .[build]
+
+    - name: Ensure no mismatch between recent tag and new version
+      run: |
+        export TAG=$(git describe --tags --abbrev=0)
+        export VERSION=$(hatch version)
+        if [ "$TAG" != "$VERSION" ]; then
+          echo "Tag $TAG does not match version $VERSION"
+          exit 1
+        fi
+
+    - name: Bump up version
+      run: |
+        hatch version ${{ github.event.inputs.version }}
+        echo "VERSION=v$(hatch version)" >> $GITHUB_ENV
+
+    - name: Commit files
+      uses: stefanzweifel/git-auto-commit-action@v5
+      with:
+        commit_message: Bump version to ${{ env.VERSION }}
+        commit_author: GitHub Actions <actions@github.com>
+
+    - name: Build and publish to PyPI 📦
+      run: |
+        export HATCH_INDEX_USER="__token__"
+        export HATCH_INDEX_AUTH="${{ secrets.HATCH_INDEX_AUTH }}"
+        hatch build
+        hatch publish
diff --git a/README.md b/README.md
index c523ff9f..a04b9681 100644
--- a/README.md
+++ b/README.md
@@ -38,8 +38,14 @@ import autoeis as ae
 Z, freq = ae.io.load_test_dataset()
 
 # Perform automated EIS analysis
-circuits = ae.perform_full_analysis(Z, freq, iters=100, parallel=True)
+circuits = ae.perform_full_analysis(freq, Z, iters=100, parallel=True)
 print(circuits)
+
+# Print summary of the results
+mcmcs, status = circuits["MCMC (chain)"], circuits["MCMC (status)"]
+for mcmc, stat, circuit in zip(mcmcs, status, circuits.circuitstring):
+    if stat == 0:
+        ae.visualization.print_summary_statistics(mcmc, circuit)
 ```
 
 - `Z`: Electrochemical impedance measurements (complex array)
diff --git a/doc/basic_usage.md b/doc/basic_usage.md
index 139e0a50..2d4b88e3 100644
--- a/doc/basic_usage.md
+++ b/doc/basic_usage.md
@@ -8,9 +8,15 @@ import autoeis as ae
 Z, freq = ae.io.load_test_dataset()
 ae.visualization.plot_impedance_combo(Z, freq)
 
-# Perform the full analysis
-circuits = ae.perform_full_analysis(Z, freq, iters=24, parallel=True)
+# Perform automated EIS analysis
+circuits = ae.perform_full_analysis(freq, Z, iters=24, parallel=True)
 print(circuits)
+
+# Print summary of the results
+mcmcs, status = circuits["MCMC (chain)"], circuits["MCMC (status)"]
+for mcmc, stat, circuit in zip(mcmcs, status, circuits.circuitstring):
+    if stat == 0:
+        ae.visualization.print_summary_statistics(mcmc, circuit)
 ```
 
 :::{seealso}
diff --git a/examples/autoeis_demo.ipynb b/examples/autoeis_demo.ipynb
index 50af5725..a3448ab3 100644
--- a/examples/autoeis_demo.ipynb
+++ b/examples/autoeis_demo.ipynb
@@ -21,16 +21,23 @@
     "AutoEIS relies on `EquivalentCircuits.jl` package to perform the EIS analysis. The package is not written in Python, so we need to install it first. AutoEIS ships with `julia_helpers` module that helps to install and manage Julia dependencies with minimal user interaction. For convenience, installing Julia and the required packages is done automatically when you import `autoeis` for the first time. If you have Julia installed already (discoverable in system PATH), it'll get detected and used, otherwise, it'll be installed automatically."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::{tip}\n",
+    "If this is the first time you're importing AutoEIS, executing the next cell will take a while, outputting a lot of logs. Re-run the cell to get rid of the logs.\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
     "import ipywidgets as widgets\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -44,7 +51,7 @@
     "ae.visualization.set_plot_style()\n",
     "\n",
     "# Set this to True if you're running the notebook locally\n",
-    "interactive = False"
+    "interactive = True"
    ]
   },
   {
@@ -60,21 +67,25 @@
    "source": [
     "Once the environment is set up, we can load the EIS data. You can use [`pyimpspec`](https://vyrjana.github.io/pyimpspec/guide_data.html) to load EIS data from a variety of popular formats. Eventually, AutoEIS requires two arrays: `Z` and `freq`. `Z` is a complex impedance array, and `freq` is a frequency array. Both arrays must be 1D and have the same length. The impedance array must be in Ohms, and the frequency array must be in Hz.\n",
     "\n",
-    "Here, we use `numpy` to load the test data from a txt file, which contains the frequency array in the first column and the impedance array in the second and third columns (Re and -Im parts). We then convert the impedance array to complex numbers, and it should be ready to use."
+    "For convenience, we provide a function `load_test_dataset()` in `autoeis.io` to load a test dataset. The function returns a tuple of `Z` and `freq`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Load impedance data (skip header row); Columns 1 -> 3: frequency, Re(Z), Im(Z)\n",
-    "ASSETS = ae.io.get_assets_path()\n",
-    "fpath = os.path.join(ASSETS, \"test_data.txt\")\n",
-    "freq, Zreal, Zimag = np.loadtxt(fpath, skiprows=1, unpack=True, usecols=(0, 1, 2))\n",
-    "# Convert to complex impedance (the file contains -Im(Z) hence the minus sign)\n",
-    "Z = Zreal - 1j*Zimag"
+    "Z, freq = ae.io.load_test_dataset()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::{tip}\n",
+    "If your EIS data is stored as text, you can easily load them using `numpy.loadtxt`. See NumPy's documentation for more details.\n",
+    ":::"
    ]
   },
   {
@@ -86,12 +97,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x300 with 3 Axes>"
       ]
@@ -118,7 +129,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## EIS analysis 🪄"
+    "## EIS analysis 🔍"
    ]
   },
   {
@@ -131,12 +142,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 500x350 with 1 Axes>"
       ]
@@ -167,33 +178,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "af8bd6b94fae40e78dd6c5e3cc712d99",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Circuit Evolution:   0%|          | 0/24 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[ Info: Starting circuit evolution on 6 workers\n",
-      "[ Info: Starting circuit evolution on 6 workers\n",
-      "[ Info: Starting circuit evolution on 6 workers\n",
-      "[ Info: Starting circuit evolution on 6 workers\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -222,163 +209,101 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>[C1-R2,R3-P4-[P5,R6]]</td>\n",
-       "      <td>{'C1': 1.2518230659909423e-06, 'R2': 158.85747...</td>\n",
+       "      <td>[P1-R2,R3]</td>\n",
+       "      <td>{'P1w': 1.986535009696659e-06, 'P1n': 0.937307...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>[R1-C2,P3-R4]</td>\n",
-       "      <td>{'R1': 146.56715696639486, 'C2': 1.47824291323...</td>\n",
+       "      <td>[P1,R2]-R3</td>\n",
+       "      <td>{'P1w': 1.986654419964975e-06, 'P1n': 0.937307...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>C1-[R2,P3]-R4</td>\n",
-       "      <td>{'C1': 6.429085835426775e-05, 'R2': 4086839.40...</td>\n",
+       "      <td>P1-[L2,R3]-[P4,R5]</td>\n",
+       "      <td>{'P1w': 0.007186637598734201, 'P1n': 5.5757649...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>[R1,P2-R3-L4-[R5,L6]]</td>\n",
-       "      <td>{'R1': 4629990.101374098, 'P2w': 1.98653501071...</td>\n",
+       "      <td>[R1,L2]-[P3,R4]</td>\n",
+       "      <td>{'R1': 575681380.7164713, 'L2': 2.552295283120...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>[P1-R2,R3]</td>\n",
-       "      <td>{'P1w': 2.0968493965977216e-08, 'P1n': 0.99999...</td>\n",
+       "      <td>[R1,[R2,R3]-P4]</td>\n",
+       "      <td>{'R1': 3340406.185539958, 'R2': 891728744.7719...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>[P1,R2]-R3</td>\n",
-       "      <td>{'P1w': 1.986654419718411e-06, 'P1n': 0.937307...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>[R1-P2,R3]</td>\n",
-       "      <td>{'R1': 139.15131244656453, 'P2w': 1.9865350108...</td>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>[P1-R2,R3]</td>\n",
-       "      <td>{'P1w': 2.3702129309162755e-08, 'P1n': 0.99999...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>[P1-R2,R3]</td>\n",
-       "      <td>{'P1w': 1.633555512329923e-08, 'P1n': 0.999999...</td>\n",
+       "      <th>113</th>\n",
+       "      <td>[P1-[L2,P3],[[R4-C5,L6-P7],R8]]</td>\n",
+       "      <td>{'P1w': 2.2534535379254496e-06, 'P1n': 0.87445...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>R1-[R2,[R3,P4]]</td>\n",
-       "      <td>{'R1': 139.14713047789797, 'R2': 4654954.87137...</td>\n",
+       "      <th>114</th>\n",
+       "      <td>R1-[P2,[R3,P4]]</td>\n",
+       "      <td>{'R1': 142.06322312672248, 'P2w': 1.7374203222...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>[P1,R2]-[L3,R4]</td>\n",
-       "      <td>{'P1w': 2.302924485371552e-06, 'P1n': 0.869686...</td>\n",
+       "      <th>115</th>\n",
+       "      <td>[R1,P2]-R3</td>\n",
+       "      <td>{'R1': 4629850.951700082, 'P2w': 1.98665442152...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>116</th>\n",
        "      <td>[P1,R2]-R3</td>\n",
-       "      <td>{'P1w': 1.9866544198483474e-06, 'P1n': 0.93730...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>[R1,P2-R3]</td>\n",
-       "      <td>{'R1': 4629990.101402926, 'P2w': 1.98653500943...</td>\n",
+       "      <td>{'P1w': 1.986654419725023e-06, 'P1n': 0.937307...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>117</th>\n",
        "      <td>R1-[P2,R3]</td>\n",
-       "      <td>{'R1': 139.14713045260206, 'P2w': 1.9866544187...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>[R1-P2,P3]</td>\n",
-       "      <td>{'R1': 4847243.661018436, 'P2w': 3.61491996853...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>R1-[P2,R3]</td>\n",
-       "      <td>{'R1': 139.14713057315635, 'P2w': 1.9866544205...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>R1-[P2-L3,R4]</td>\n",
-       "      <td>{'R1': 139.14713044066318, 'P2w': 1.9866544191...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>[P1-R2,R3]</td>\n",
-       "      <td>{'P1w': 1.9865350111025016e-06, 'P1n': 0.93730...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>R1-[P2,R3]</td>\n",
-       "      <td>{'R1': 139.14713059613737, 'P2w': 1.9866544211...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>R1-[R2,P3]</td>\n",
-       "      <td>{'R1': 139.14713072472173, 'R2': 4629850.95198...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>P1-[P2,R3]-[R4,L5]</td>\n",
-       "      <td>{'P1w': 0.0071866376038625815, 'P1n': 2.344409...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>[L1-P2-R3,P4-[R5,L6]]</td>\n",
-       "      <td>{'L1': 4.999999995043704, 'P2w': 3.61491672511...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>P1-[P2-R3,L4-R5-L6]</td>\n",
-       "      <td>{'P1w': 3.0600477978470834e-05, 'P1n': 0.86566...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>[R1,P2]-R3</td>\n",
-       "      <td>{'R1': 4629850.946599953, 'P2w': 1.98665441944...</td>\n",
+       "      <td>{'R1': 139.14713051260458, 'P2w': 1.9866544182...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>118 rows × 2 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "            circuitstring                                         Parameters\n",
-       "0   [C1-R2,R3-P4-[P5,R6]]  {'C1': 1.2518230659909423e-06, 'R2': 158.85747...\n",
-       "1           [R1-C2,P3-R4]  {'R1': 146.56715696639486, 'C2': 1.47824291323...\n",
-       "2           C1-[R2,P3]-R4  {'C1': 6.429085835426775e-05, 'R2': 4086839.40...\n",
-       "3   [R1,P2-R3-L4-[R5,L6]]  {'R1': 4629990.101374098, 'P2w': 1.98653501071...\n",
-       "4              [P1-R2,R3]  {'P1w': 2.0968493965977216e-08, 'P1n': 0.99999...\n",
-       "5              [P1,R2]-R3  {'P1w': 1.986654419718411e-06, 'P1n': 0.937307...\n",
-       "6              [R1-P2,R3]  {'R1': 139.15131244656453, 'P2w': 1.9865350108...\n",
-       "7              [P1-R2,R3]  {'P1w': 2.3702129309162755e-08, 'P1n': 0.99999...\n",
-       "8              [P1-R2,R3]  {'P1w': 1.633555512329923e-08, 'P1n': 0.999999...\n",
-       "9         R1-[R2,[R3,P4]]  {'R1': 139.14713047789797, 'R2': 4654954.87137...\n",
-       "10        [P1,R2]-[L3,R4]  {'P1w': 2.302924485371552e-06, 'P1n': 0.869686...\n",
-       "11             [P1,R2]-R3  {'P1w': 1.9866544198483474e-06, 'P1n': 0.93730...\n",
-       "12             [R1,P2-R3]  {'R1': 4629990.101402926, 'P2w': 1.98653500943...\n",
-       "13             R1-[P2,R3]  {'R1': 139.14713045260206, 'P2w': 1.9866544187...\n",
-       "14             [R1-P2,P3]  {'R1': 4847243.661018436, 'P2w': 3.61491996853...\n",
-       "15             R1-[P2,R3]  {'R1': 139.14713057315635, 'P2w': 1.9866544205...\n",
-       "16          R1-[P2-L3,R4]  {'R1': 139.14713044066318, 'P2w': 1.9866544191...\n",
-       "17             [P1-R2,R3]  {'P1w': 1.9865350111025016e-06, 'P1n': 0.93730...\n",
-       "18             R1-[P2,R3]  {'R1': 139.14713059613737, 'P2w': 1.9866544211...\n",
-       "19             R1-[R2,P3]  {'R1': 139.14713072472173, 'R2': 4629850.95198...\n",
-       "20     P1-[P2,R3]-[R4,L5]  {'P1w': 0.0071866376038625815, 'P1n': 2.344409...\n",
-       "21  [L1-P2-R3,P4-[R5,L6]]  {'L1': 4.999999995043704, 'P2w': 3.61491672511...\n",
-       "22    P1-[P2-R3,L4-R5-L6]  {'P1w': 3.0600477978470834e-05, 'P1n': 0.86566...\n",
-       "23             [R1,P2]-R3  {'R1': 4629850.946599953, 'P2w': 1.98665441944..."
+       "                       circuitstring  \\\n",
+       "0                         [P1-R2,R3]   \n",
+       "1                         [P1,R2]-R3   \n",
+       "2                 P1-[L2,R3]-[P4,R5]   \n",
+       "3                    [R1,L2]-[P3,R4]   \n",
+       "4                    [R1,[R2,R3]-P4]   \n",
+       "..                               ...   \n",
+       "113  [P1-[L2,P3],[[R4-C5,L6-P7],R8]]   \n",
+       "114                  R1-[P2,[R3,P4]]   \n",
+       "115                       [R1,P2]-R3   \n",
+       "116                       [P1,R2]-R3   \n",
+       "117                       R1-[P2,R3]   \n",
+       "\n",
+       "                                            Parameters  \n",
+       "0    {'P1w': 1.986535009696659e-06, 'P1n': 0.937307...  \n",
+       "1    {'P1w': 1.986654419964975e-06, 'P1n': 0.937307...  \n",
+       "2    {'P1w': 0.007186637598734201, 'P1n': 5.5757649...  \n",
+       "3    {'R1': 575681380.7164713, 'L2': 2.552295283120...  \n",
+       "4    {'R1': 3340406.185539958, 'R2': 891728744.7719...  \n",
+       "..                                                 ...  \n",
+       "113  {'P1w': 2.2534535379254496e-06, 'P1n': 0.87445...  \n",
+       "114  {'R1': 142.06322312672248, 'P2w': 1.7374203222...  \n",
+       "115  {'R1': 4629850.951700082, 'P2w': 1.98665442152...  \n",
+       "116  {'P1w': 1.986654419725023e-06, 'P1n': 0.937307...  \n",
+       "117  {'R1': 139.14713051260458, 'P2w': 1.9866544182...  \n",
+       "\n",
+       "[118 rows x 2 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "use_pregenerated_circuits = False\n",
+    "use_pregenerated_circuits = True\n",
     "\n",
     "if use_pregenerated_circuits:\n",
     "    circuits_unfiltered = ae.io.load_test_circuits()\n",
@@ -412,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -444,30 +369,119 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>[P1,R2]-R3</td>\n",
-       "      <td>{'P1w': 1.986654419718411e-06, 'P1n': 0.937307...</td>\n",
+       "      <td>{'P1w': 1.986654419964975e-06, 'P1n': 0.937307...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>R1-[R2,[R3,P4]]</td>\n",
-       "      <td>{'R1': 139.14713047789797, 'R2': 4654954.87137...</td>\n",
+       "      <td>R1-P2-[P3,R4]</td>\n",
+       "      <td>{'R1': 130.21245496329337, 'P2w': 2.7265188132...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>R1-[P2-L3,R4]</td>\n",
-       "      <td>{'R1': 139.14713044066318, 'P2w': 1.9866544191...</td>\n",
+       "      <td>P1-R2-[P3,R4]-[L5,R6]</td>\n",
+       "      <td>{'P1w': 3.060047763952672e-05, 'P1n': 0.865667...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>R1-[R2-L3,P4]</td>\n",
+       "      <td>{'R1': 139.14713070316256, 'R2': 4629850.95146...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>R1-[R2-[R3,P4],[L5,R6]-P7]</td>\n",
+       "      <td>{'R1': 141.23906880866465, 'R2': 2757765.10158...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>P1-[P2,R3]-L4-R5</td>\n",
+       "      <td>{'P1w': 3.060047791227673e-05, 'P1n': 0.865667...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[P1,P2]-[P3,R4]-R5</td>\n",
+       "      <td>{'P1w': 1.4706292096441107e-06, 'P1n': 0.34376...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>R1-[R2,P3-R4]</td>\n",
+       "      <td>{'R1': 35.07220572671824, 'R2': 4629955.027337...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>P1-R2-[P3,R4]-R5</td>\n",
+       "      <td>{'P1w': 2.7265188153219155e-06, 'P1n': 0.86814...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>L1-[P2,R3]-[R4,L5]-P6-R7</td>\n",
+       "      <td>{'L1': 7.336357952082608e-20, 'P2w': 2.0355768...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>[P1,P2-R3]-R4</td>\n",
+       "      <td>{'P1w': 1.8172983928967454e-06, 'P1n': 0.95491...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>R1-[P2-R3,[L4,R5]-P6]</td>\n",
+       "      <td>{'R1': 141.24023286281513, 'P2w': 2.2755039323...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>R1-[R2,P3-L4]</td>\n",
+       "      <td>{'R1': 139.14713051318796, 'R2': 4629850.95049...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>[P1,P2-[R3,L4]]-R5</td>\n",
+       "      <td>{'P1w': 9.503614832162238, 'P1n': 1.0, 'P2w': ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>R1-[P2,[R3,P4]]</td>\n",
+       "      <td>{'R1': 142.06322312672248, 'P2w': 1.7374203222...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     circuitstring                                         Parameters\n",
-       "0       [P1,R2]-R3  {'P1w': 1.986654419718411e-06, 'P1n': 0.937307...\n",
-       "1  R1-[R2,[R3,P4]]  {'R1': 139.14713047789797, 'R2': 4654954.87137...\n",
-       "2    R1-[P2-L3,R4]  {'R1': 139.14713044066318, 'P2w': 1.9866544191..."
+       "                 circuitstring  \\\n",
+       "0                   [P1,R2]-R3   \n",
+       "1                R1-P2-[P3,R4]   \n",
+       "2        P1-R2-[P3,R4]-[L5,R6]   \n",
+       "3                R1-[R2-L3,P4]   \n",
+       "4   R1-[R2-[R3,P4],[L5,R6]-P7]   \n",
+       "5             P1-[P2,R3]-L4-R5   \n",
+       "6           [P1,P2]-[P3,R4]-R5   \n",
+       "7                R1-[R2,P3-R4]   \n",
+       "8             P1-R2-[P3,R4]-R5   \n",
+       "9     L1-[P2,R3]-[R4,L5]-P6-R7   \n",
+       "10               [P1,P2-R3]-R4   \n",
+       "11       R1-[P2-R3,[L4,R5]-P6]   \n",
+       "12               R1-[R2,P3-L4]   \n",
+       "13          [P1,P2-[R3,L4]]-R5   \n",
+       "14             R1-[P2,[R3,P4]]   \n",
+       "\n",
+       "                                           Parameters  \n",
+       "0   {'P1w': 1.986654419964975e-06, 'P1n': 0.937307...  \n",
+       "1   {'R1': 130.21245496329337, 'P2w': 2.7265188132...  \n",
+       "2   {'P1w': 3.060047763952672e-05, 'P1n': 0.865667...  \n",
+       "3   {'R1': 139.14713070316256, 'R2': 4629850.95146...  \n",
+       "4   {'R1': 141.23906880866465, 'R2': 2757765.10158...  \n",
+       "5   {'P1w': 3.060047791227673e-05, 'P1n': 0.865667...  \n",
+       "6   {'P1w': 1.4706292096441107e-06, 'P1n': 0.34376...  \n",
+       "7   {'R1': 35.07220572671824, 'R2': 4629955.027337...  \n",
+       "8   {'P1w': 2.7265188153219155e-06, 'P1n': 0.86814...  \n",
+       "9   {'L1': 7.336357952082608e-20, 'P2w': 2.0355768...  \n",
+       "10  {'P1w': 1.8172983928967454e-06, 'P1n': 0.95491...  \n",
+       "11  {'R1': 141.24023286281513, 'P2w': 2.2755039323...  \n",
+       "12  {'R1': 139.14713051318796, 'R2': 4629850.95049...  \n",
+       "13  {'P1w': 9.503614832162238, 'P1n': 1.0, 'P2w': ...  \n",
+       "14  {'R1': 142.06322312672248, 'P2w': 1.7374203222...  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -493,38 +507,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " "
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aadc9d9ef12943ff9c35fca9541f9555",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mcmc_results = ae.core.perform_bayesian_inference(circuits, freq, Z)\n",
     "mcmcs, status = zip(*mcmc_results)"
@@ -543,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -553,12 +538,12 @@
        "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
        "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
-       "│        P1n │ 6.27e-01 │ 1.34e-03 │ 6.27e-01 │ 6.25e-01 │ 6.29e-01 │\n",
-       "│        P1w │ 1.44e-06 │ 4.37e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
-       "│         R2 │ 7.39e+06 │ 2.59e+04 │ 7.39e+06 │ 7.35e+06 │ 7.44e+06 │\n",
-       "│<span style=\"background-color: #808000\">         R3 </span>│<span style=\"background-color: #808000\"> 2.43e+01 </span>│<span style=\"background-color: #808000\"> 3.05e+01 </span>│<span style=\"background-color: #808000\"> 1.35e+01 </span>│<span style=\"background-color: #808000\"> 7.35e-01 </span>│<span style=\"background-color: #808000\"> 8.58e+01 </span>│\n",
-       "│ sigma_imag │ 1.11e+04 │ 7.17e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
-       "│ sigma_real │ 1.11e+04 │ 7.04e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "│        P1n │ 6.27e-01 │ 1.36e-03 │ 6.27e-01 │ 6.25e-01 │ 6.29e-01 │\n",
+       "│        P1w │ 1.44e-06 │ 4.48e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
+       "│         R2 │ 7.39e+06 │ 2.52e+04 │ 7.40e+06 │ 7.35e+06 │ 7.44e+06 │\n",
+       "│<span style=\"background-color: #808000\">         R3 </span>│<span style=\"background-color: #808000\"> 2.24e+01 </span>│<span style=\"background-color: #808000\"> 2.57e+01 </span>│<span style=\"background-color: #808000\"> 1.32e+01 </span>│<span style=\"background-color: #808000\"> 8.63e-01 </span>│<span style=\"background-color: #808000\"> 7.32e+01 </span>│\n",
+       "│ sigma_imag │ 1.11e+04 │ 7.09e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "│ sigma_real │ 1.11e+04 │ 6.91e+01 │ 1.11e+04 │ 1.10e+04 │ 1.13e+04 │\n",
        "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
        "</pre>\n"
       ],
@@ -567,12 +552,478 @@
        "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
-       "│        P1n │ 6.27e-01 │ 1.34e-03 │ 6.27e-01 │ 6.25e-01 │ 6.29e-01 │\n",
-       "│        P1w │ 1.44e-06 │ 4.37e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
-       "│         R2 │ 7.39e+06 │ 2.59e+04 │ 7.39e+06 │ 7.35e+06 │ 7.44e+06 │\n",
-       "│\u001b[43m \u001b[0m\u001b[43m        R3\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.43e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.05e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.35e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.35e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.58e+01\u001b[0m\u001b[43m \u001b[0m│\n",
-       "│ sigma_imag │ 1.11e+04 │ 7.17e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
-       "│ sigma_real │ 1.11e+04 │ 7.04e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "│        P1n │ 6.27e-01 │ 1.36e-03 │ 6.27e-01 │ 6.25e-01 │ 6.29e-01 │\n",
+       "│        P1w │ 1.44e-06 │ 4.48e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
+       "│         R2 │ 7.39e+06 │ 2.52e+04 │ 7.40e+06 │ 7.35e+06 │ 7.44e+06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R3\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.24e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.57e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.32e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.63e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.32e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 1.11e+04 │ 7.09e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "│ sigma_real │ 1.11e+04 │ 6.91e+01 │ 1.11e+04 │ 1.10e+04 │ 1.13e+04 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                  R1-P2-[P3,R4], 0/1000 divergences                  </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│        P2n │ 3.51e-01 │ 6.26e-04 │ 3.51e-01 │ 3.50e-01 │ 3.52e-01 │\n",
+       "│        P2w │ 1.00e-06 │ 2.95e-09 │ 1.00e-06 │ 1.00e-06 │ 1.01e-06 │\n",
+       "│        P3n │ 4.85e-01 │ 2.92e-01 │ 4.66e-01 │ 5.28e-02 │ 9.47e-01 │\n",
+       "│<span style=\"background-color: #808000\">        P3w </span>│<span style=\"background-color: #808000\"> 1.02e+10 </span>│<span style=\"background-color: #808000\"> 5.18e+10 </span>│<span style=\"background-color: #808000\"> 9.77e+08 </span>│<span style=\"background-color: #808000\"> 2.06e+07 </span>│<span style=\"background-color: #808000\"> 3.58e+10 </span>│\n",
+       "│<span style=\"background-color: #808000\">         R1 </span>│<span style=\"background-color: #808000\"> 2.22e+01 </span>│<span style=\"background-color: #808000\"> 2.50e+01 </span>│<span style=\"background-color: #808000\"> 1.37e+01 </span>│<span style=\"background-color: #808000\"> 1.00e+00 </span>│<span style=\"background-color: #808000\"> 7.62e+01 </span>│\n",
+       "│<span style=\"background-color: #808000\">         R4 </span>│<span style=\"background-color: #808000\"> 1.46e+09 </span>│<span style=\"background-color: #808000\"> 9.22e+09 </span>│<span style=\"background-color: #808000\"> 1.18e+08 </span>│<span style=\"background-color: #808000\"> 3.18e+06 </span>│<span style=\"background-color: #808000\"> 4.75e+09 </span>│\n",
+       "│ sigma_imag │ 1.58e+04 │ 7.85e+01 │ 1.58e+04 │ 1.57e+04 │ 1.59e+04 │\n",
+       "│ sigma_real │ 1.95e+04 │ 8.56e+01 │ 1.95e+04 │ 1.94e+04 │ 1.97e+04 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m                  R1-P2-[P3,R4], 0/1000 divergences                  \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│        P2n │ 3.51e-01 │ 6.26e-04 │ 3.51e-01 │ 3.50e-01 │ 3.52e-01 │\n",
+       "│        P2w │ 1.00e-06 │ 2.95e-09 │ 1.00e-06 │ 1.00e-06 │ 1.01e-06 │\n",
+       "│        P3n │ 4.85e-01 │ 2.92e-01 │ 4.66e-01 │ 5.28e-02 │ 9.47e-01 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m       P3w\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.02e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.18e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m9.77e+08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.06e+07\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.58e+10\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R1\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.22e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.50e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.37e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.00e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.62e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R4\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.46e+09\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m9.22e+09\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.18e+08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.18e+06\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.75e+09\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 1.58e+04 │ 7.85e+01 │ 1.58e+04 │ 1.57e+04 │ 1.59e+04 │\n",
+       "│ sigma_real │ 1.95e+04 │ 8.56e+01 │ 1.95e+04 │ 1.94e+04 │ 1.97e+04 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">              P1-R2-[P3,R4]-[L5,R6], 0/1000 divergences              </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│<span style=\"background-color: #808000\">         L5 </span>│<span style=\"background-color: #808000\"> 5.10e-19 </span>│<span style=\"background-color: #808000\"> 1.84e-18 </span>│<span style=\"background-color: #808000\"> 4.21e-20 </span>│<span style=\"background-color: #808000\"> 9.91e-22 </span>│<span style=\"background-color: #808000\"> 2.17e-18 </span>│\n",
+       "│        P1n │ 5.55e-01 │ 1.75e-03 │ 5.55e-01 │ 5.52e-01 │ 5.58e-01 │\n",
+       "│        P1w │ 5.93e-06 │ 7.13e-08 │ 5.93e-06 │ 5.81e-06 │ 6.05e-06 │\n",
+       "│        P3n │ 8.94e-01 │ 1.38e-03 │ 8.94e-01 │ 8.92e-01 │ 8.97e-01 │\n",
+       "│        P3w │ 2.41e-06 │ 1.26e-08 │ 2.41e-06 │ 2.39e-06 │ 2.43e-06 │\n",
+       "│<span style=\"background-color: #808000\">         R2 </span>│<span style=\"background-color: #808000\"> 1.49e+01 </span>│<span style=\"background-color: #808000\"> 1.59e+01 </span>│<span style=\"background-color: #808000\"> 9.70e+00 </span>│<span style=\"background-color: #808000\"> 8.92e-01 </span>│<span style=\"background-color: #808000\"> 5.04e+01 </span>│\n",
+       "│         R4 │ 3.21e+06 │ 1.21e+04 │ 3.21e+06 │ 3.19e+06 │ 3.23e+06 │\n",
+       "│<span style=\"background-color: #808000\">         R6 </span>│<span style=\"background-color: #808000\"> 7.93e+09 </span>│<span style=\"background-color: #808000\"> 3.11e+10 </span>│<span style=\"background-color: #808000\"> 8.42e+08 </span>│<span style=\"background-color: #808000\"> 2.14e+07 </span>│<span style=\"background-color: #808000\"> 2.74e+10 </span>│\n",
+       "│ sigma_imag │ 3.50e+03 │ 3.71e+01 │ 3.50e+03 │ 3.44e+03 │ 3.56e+03 │\n",
+       "│ sigma_real │ 5.27e+03 │ 4.42e+01 │ 5.27e+03 │ 5.20e+03 │ 5.34e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m              P1-R2-[P3,R4]-[L5,R6], 0/1000 divergences              \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        L5\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.10e-19\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.84e-18\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.21e-20\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m9.91e-22\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.17e-18\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│        P1n │ 5.55e-01 │ 1.75e-03 │ 5.55e-01 │ 5.52e-01 │ 5.58e-01 │\n",
+       "│        P1w │ 5.93e-06 │ 7.13e-08 │ 5.93e-06 │ 5.81e-06 │ 6.05e-06 │\n",
+       "│        P3n │ 8.94e-01 │ 1.38e-03 │ 8.94e-01 │ 8.92e-01 │ 8.97e-01 │\n",
+       "│        P3w │ 2.41e-06 │ 1.26e-08 │ 2.41e-06 │ 2.39e-06 │ 2.43e-06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R2\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.49e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.59e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m9.70e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.92e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.04e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│         R4 │ 3.21e+06 │ 1.21e+04 │ 3.21e+06 │ 3.19e+06 │ 3.23e+06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R6\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.93e+09\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.11e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.42e+08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.14e+07\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.74e+10\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 3.50e+03 │ 3.71e+01 │ 3.50e+03 │ 3.44e+03 │ 3.56e+03 │\n",
+       "│ sigma_real │ 5.27e+03 │ 4.42e+01 │ 5.27e+03 │ 5.20e+03 │ 5.34e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                  R1-[R2-L3,P4], 0/1000 divergences                  </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│<span style=\"background-color: #808000\">         L3 </span>│<span style=\"background-color: #808000\"> 6.92e-08 </span>│<span style=\"background-color: #808000\"> 7.43e-07 </span>│<span style=\"background-color: #808000\"> 4.40e-09 </span>│<span style=\"background-color: #808000\"> 8.29e-11 </span>│<span style=\"background-color: #808000\"> 1.72e-07 </span>│\n",
+       "│        P4n │ 6.27e-01 │ 1.40e-03 │ 6.27e-01 │ 6.25e-01 │ 6.30e-01 │\n",
+       "│        P4w │ 1.44e-06 │ 4.66e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
+       "│<span style=\"background-color: #808000\">         R1 </span>│<span style=\"background-color: #808000\"> 2.18e+01 </span>│<span style=\"background-color: #808000\"> 2.77e+01 </span>│<span style=\"background-color: #808000\"> 1.19e+01 </span>│<span style=\"background-color: #808000\"> 8.10e-01 </span>│<span style=\"background-color: #808000\"> 7.77e+01 </span>│\n",
+       "│         R2 │ 7.39e+06 │ 2.65e+04 │ 7.39e+06 │ 7.35e+06 │ 7.44e+06 │\n",
+       "│ sigma_imag │ 1.11e+04 │ 7.06e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "│ sigma_real │ 1.11e+04 │ 7.37e+01 │ 1.11e+04 │ 1.10e+04 │ 1.13e+04 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m                  R1-[R2-L3,P4], 0/1000 divergences                  \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        L3\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m6.92e-08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.43e-07\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.40e-09\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.29e-11\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.72e-07\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│        P4n │ 6.27e-01 │ 1.40e-03 │ 6.27e-01 │ 6.25e-01 │ 6.30e-01 │\n",
+       "│        P4w │ 1.44e-06 │ 4.66e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R1\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.18e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.77e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.19e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.10e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.77e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│         R2 │ 7.39e+06 │ 2.65e+04 │ 7.39e+06 │ 7.35e+06 │ 7.44e+06 │\n",
+       "│ sigma_imag │ 1.11e+04 │ 7.06e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "│ sigma_real │ 1.11e+04 │ 7.37e+01 │ 1.11e+04 │ 1.10e+04 │ 1.13e+04 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">           R1-[R2-[R3,P4],[L5,R6]-P7], 0/1000 divergences            </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│<span style=\"background-color: #808000\">         L5 </span>│<span style=\"background-color: #808000\"> 1.82e-15 </span>│<span style=\"background-color: #808000\"> 1.04e-14 </span>│<span style=\"background-color: #808000\"> 1.22e-16 </span>│<span style=\"background-color: #808000\"> 2.06e-18 </span>│<span style=\"background-color: #808000\"> 5.58e-15 </span>│\n",
+       "│        P4n │ 6.08e-01 │ 2.98e-03 │ 6.08e-01 │ 6.03e-01 │ 6.13e-01 │\n",
+       "│        P4w │ 6.49e-06 │ 9.99e-08 │ 6.49e-06 │ 6.33e-06 │ 6.65e-06 │\n",
+       "│        P7n │ 9.28e-01 │ 6.61e-04 │ 9.28e-01 │ 9.27e-01 │ 9.29e-01 │\n",
+       "│        P7w │ 1.82e-06 │ 1.23e-09 │ 1.82e-06 │ 1.82e-06 │ 1.83e-06 │\n",
+       "│<span style=\"background-color: #808000\">         R1 </span>│<span style=\"background-color: #808000\"> 3.94e+01 </span>│<span style=\"background-color: #808000\"> 4.76e+01 </span>│<span style=\"background-color: #808000\"> 2.28e+01 </span>│<span style=\"background-color: #808000\"> 1.45e+00 </span>│<span style=\"background-color: #808000\"> 1.40e+02 </span>│\n",
+       "│         R2 │ 3.53e+06 │ 9.73e+03 │ 3.53e+06 │ 3.51e+06 │ 3.54e+06 │\n",
+       "│         R3 │ 1.22e+07 │ 1.86e+05 │ 1.22e+07 │ 1.18e+07 │ 1.25e+07 │\n",
+       "│<span style=\"background-color: #808000\">         R6 </span>│<span style=\"background-color: #808000\"> 1.58e+10 </span>│<span style=\"background-color: #808000\"> 1.84e+11 </span>│<span style=\"background-color: #808000\"> 8.90e+08 </span>│<span style=\"background-color: #808000\"> 2.12e+07 </span>│<span style=\"background-color: #808000\"> 3.91e+10 </span>│\n",
+       "│ sigma_imag │ 1.67e+03 │ 3.06e+01 │ 1.67e+03 │ 1.62e+03 │ 1.72e+03 │\n",
+       "│ sigma_real │ 1.52e+03 │ 2.86e+01 │ 1.52e+03 │ 1.48e+03 │ 1.57e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m           R1-[R2-[R3,P4],[L5,R6]-P7], 0/1000 divergences            \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        L5\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.82e-15\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.04e-14\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.22e-16\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.06e-18\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.58e-15\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│        P4n │ 6.08e-01 │ 2.98e-03 │ 6.08e-01 │ 6.03e-01 │ 6.13e-01 │\n",
+       "│        P4w │ 6.49e-06 │ 9.99e-08 │ 6.49e-06 │ 6.33e-06 │ 6.65e-06 │\n",
+       "│        P7n │ 9.28e-01 │ 6.61e-04 │ 9.28e-01 │ 9.27e-01 │ 9.29e-01 │\n",
+       "│        P7w │ 1.82e-06 │ 1.23e-09 │ 1.82e-06 │ 1.82e-06 │ 1.83e-06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R1\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.94e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.76e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.28e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.45e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.40e+02\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│         R2 │ 3.53e+06 │ 9.73e+03 │ 3.53e+06 │ 3.51e+06 │ 3.54e+06 │\n",
+       "│         R3 │ 1.22e+07 │ 1.86e+05 │ 1.22e+07 │ 1.18e+07 │ 1.25e+07 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R6\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.58e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.84e+11\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.90e+08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.12e+07\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.91e+10\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 1.67e+03 │ 3.06e+01 │ 1.67e+03 │ 1.62e+03 │ 1.72e+03 │\n",
+       "│ sigma_real │ 1.52e+03 │ 2.86e+01 │ 1.52e+03 │ 1.48e+03 │ 1.57e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                P1-[P2,R3]-L4-R5, 0/1000 divergences                 </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│<span style=\"background-color: #808000\">         L4 </span>│<span style=\"background-color: #808000\"> 1.03e-19 </span>│<span style=\"background-color: #808000\"> 7.65e-19 </span>│<span style=\"background-color: #808000\"> 7.26e-21 </span>│<span style=\"background-color: #808000\"> 1.34e-22 </span>│<span style=\"background-color: #808000\"> 3.58e-19 </span>│\n",
+       "│        P1n │ 5.55e-01 │ 1.66e-03 │ 5.55e-01 │ 5.53e-01 │ 5.58e-01 │\n",
+       "│        P1w │ 5.93e-06 │ 6.77e-08 │ 5.93e-06 │ 5.82e-06 │ 6.04e-06 │\n",
+       "│        P2n │ 8.94e-01 │ 1.34e-03 │ 8.94e-01 │ 8.92e-01 │ 8.96e-01 │\n",
+       "│        P2w │ 2.41e-06 │ 1.20e-08 │ 2.41e-06 │ 2.39e-06 │ 2.43e-06 │\n",
+       "│         R3 │ 3.21e+06 │ 1.17e+04 │ 3.21e+06 │ 3.19e+06 │ 3.23e+06 │\n",
+       "│<span style=\"background-color: #808000\">         R5 </span>│<span style=\"background-color: #808000\"> 1.48e+01 </span>│<span style=\"background-color: #808000\"> 1.68e+01 </span>│<span style=\"background-color: #808000\"> 8.92e+00 </span>│<span style=\"background-color: #808000\"> 5.13e-01 </span>│<span style=\"background-color: #808000\"> 4.85e+01 </span>│\n",
+       "│ sigma_imag │ 3.50e+03 │ 3.88e+01 │ 3.50e+03 │ 3.44e+03 │ 3.56e+03 │\n",
+       "│ sigma_real │ 5.27e+03 │ 4.54e+01 │ 5.27e+03 │ 5.20e+03 │ 5.35e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m                P1-[P2,R3]-L4-R5, 0/1000 divergences                 \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        L4\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.03e-19\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.65e-19\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.26e-21\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.34e-22\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.58e-19\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│        P1n │ 5.55e-01 │ 1.66e-03 │ 5.55e-01 │ 5.53e-01 │ 5.58e-01 │\n",
+       "│        P1w │ 5.93e-06 │ 6.77e-08 │ 5.93e-06 │ 5.82e-06 │ 6.04e-06 │\n",
+       "│        P2n │ 8.94e-01 │ 1.34e-03 │ 8.94e-01 │ 8.92e-01 │ 8.96e-01 │\n",
+       "│        P2w │ 2.41e-06 │ 1.20e-08 │ 2.41e-06 │ 2.39e-06 │ 2.43e-06 │\n",
+       "│         R3 │ 3.21e+06 │ 1.17e+04 │ 3.21e+06 │ 3.19e+06 │ 3.23e+06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R5\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.48e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.68e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.92e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.13e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.85e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 3.50e+03 │ 3.88e+01 │ 3.50e+03 │ 3.44e+03 │ 3.56e+03 │\n",
+       "│ sigma_real │ 5.27e+03 │ 4.54e+01 │ 5.27e+03 │ 5.20e+03 │ 5.35e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">               [P1,P2]-[P3,R4]-R5, 0/1000 divergences                </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│        P1n │ 3.58e-01 │ 2.89e-03 │ 3.58e-01 │ 3.54e-01 │ 3.63e-01 │\n",
+       "│        P1w │ 1.67e-06 │ 3.56e-08 │ 1.67e-06 │ 1.61e-06 │ 1.73e-06 │\n",
+       "│        P2n │ 9.71e-01 │ 8.06e-03 │ 9.70e-01 │ 9.57e-01 │ 9.84e-01 │\n",
+       "│        P2w │ 1.04e-05 │ 6.14e-07 │ 1.04e-05 │ 9.41e-06 │ 1.14e-05 │\n",
+       "│        P3n │ 9.14e-01 │ 1.29e-03 │ 9.14e-01 │ 9.12e-01 │ 9.17e-01 │\n",
+       "│        P3w │ 2.18e-06 │ 2.44e-08 │ 2.18e-06 │ 2.14e-06 │ 2.22e-06 │\n",
+       "│         R4 │ 2.91e+06 │ 3.47e+04 │ 2.91e+06 │ 2.85e+06 │ 2.97e+06 │\n",
+       "│<span style=\"background-color: #808000\">         R5 </span>│<span style=\"background-color: #808000\"> 3.18e+01 </span>│<span style=\"background-color: #808000\"> 3.93e+01 </span>│<span style=\"background-color: #808000\"> 1.57e+01 </span>│<span style=\"background-color: #808000\"> 1.17e+00 </span>│<span style=\"background-color: #808000\"> 1.19e+02 </span>│\n",
+       "│ sigma_imag │ 1.76e+03 │ 3.21e+01 │ 1.76e+03 │ 1.71e+03 │ 1.81e+03 │\n",
+       "│ sigma_real │ 1.77e+03 │ 3.15e+01 │ 1.77e+03 │ 1.72e+03 │ 1.82e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m               [P1,P2]-[P3,R4]-R5, 0/1000 divergences                \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│        P1n │ 3.58e-01 │ 2.89e-03 │ 3.58e-01 │ 3.54e-01 │ 3.63e-01 │\n",
+       "│        P1w │ 1.67e-06 │ 3.56e-08 │ 1.67e-06 │ 1.61e-06 │ 1.73e-06 │\n",
+       "│        P2n │ 9.71e-01 │ 8.06e-03 │ 9.70e-01 │ 9.57e-01 │ 9.84e-01 │\n",
+       "│        P2w │ 1.04e-05 │ 6.14e-07 │ 1.04e-05 │ 9.41e-06 │ 1.14e-05 │\n",
+       "│        P3n │ 9.14e-01 │ 1.29e-03 │ 9.14e-01 │ 9.12e-01 │ 9.17e-01 │\n",
+       "│        P3w │ 2.18e-06 │ 2.44e-08 │ 2.18e-06 │ 2.14e-06 │ 2.22e-06 │\n",
+       "│         R4 │ 2.91e+06 │ 3.47e+04 │ 2.91e+06 │ 2.85e+06 │ 2.97e+06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R5\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.18e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.93e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.57e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.17e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.19e+02\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 1.76e+03 │ 3.21e+01 │ 1.76e+03 │ 1.71e+03 │ 1.81e+03 │\n",
+       "│ sigma_real │ 1.77e+03 │ 3.15e+01 │ 1.77e+03 │ 1.72e+03 │ 1.82e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                  R1-[R2,P3-R4], 0/1000 divergences                  </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│        P3n │ 6.27e-01 │ 1.39e-03 │ 6.27e-01 │ 6.25e-01 │ 6.29e-01 │\n",
+       "│        P3w │ 1.44e-06 │ 4.60e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
+       "│<span style=\"background-color: #808000\">         R1 </span>│<span style=\"background-color: #808000\"> 1.52e+01 </span>│<span style=\"background-color: #808000\"> 2.23e+01 </span>│<span style=\"background-color: #808000\"> 6.87e+00 </span>│<span style=\"background-color: #808000\"> 3.76e-01 </span>│<span style=\"background-color: #808000\"> 6.04e+01 </span>│\n",
+       "│         R2 │ 7.39e+06 │ 2.62e+04 │ 7.39e+06 │ 7.35e+06 │ 7.44e+06 │\n",
+       "│<span style=\"background-color: #808000\">         R4 </span>│<span style=\"background-color: #808000\"> 2.18e+01 </span>│<span style=\"background-color: #808000\"> 2.69e+01 </span>│<span style=\"background-color: #808000\"> 1.23e+01 </span>│<span style=\"background-color: #808000\"> 5.98e-01 </span>│<span style=\"background-color: #808000\"> 7.38e+01 </span>│\n",
+       "│ sigma_imag │ 1.11e+04 │ 6.90e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "│ sigma_real │ 1.11e+04 │ 6.89e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m                  R1-[R2,P3-R4], 0/1000 divergences                  \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│        P3n │ 6.27e-01 │ 1.39e-03 │ 6.27e-01 │ 6.25e-01 │ 6.29e-01 │\n",
+       "│        P3w │ 1.44e-06 │ 4.60e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R1\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.52e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.23e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m6.87e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.76e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m6.04e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│         R2 │ 7.39e+06 │ 2.62e+04 │ 7.39e+06 │ 7.35e+06 │ 7.44e+06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R4\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.18e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.69e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.23e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.98e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.38e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 1.11e+04 │ 6.90e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "│ sigma_real │ 1.11e+04 │ 6.89e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                P1-R2-[P3,R4]-R5, 0/1000 divergences                 </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│        P1n │ 3.51e-01 │ 5.71e-04 │ 3.51e-01 │ 3.50e-01 │ 3.52e-01 │\n",
+       "│        P1w │ 1.00e-06 │ 2.59e-09 │ 1.00e-06 │ 1.00e-06 │ 1.01e-06 │\n",
+       "│        P3n │ 5.15e-01 │ 2.91e-01 │ 5.19e-01 │ 5.93e-02 │ 9.59e-01 │\n",
+       "│<span style=\"background-color: #808000\">        P3w </span>│<span style=\"background-color: #808000\"> 4.61e+09 </span>│<span style=\"background-color: #808000\"> 3.14e+10 </span>│<span style=\"background-color: #808000\"> 4.39e+08 </span>│<span style=\"background-color: #808000\"> 1.09e+07 </span>│<span style=\"background-color: #808000\"> 1.59e+10 </span>│\n",
+       "│<span style=\"background-color: #808000\">         R2 </span>│<span style=\"background-color: #808000\"> 1.94e+01 </span>│<span style=\"background-color: #808000\"> 2.56e+01 </span>│<span style=\"background-color: #808000\"> 1.05e+01 </span>│<span style=\"background-color: #808000\"> 5.79e-01 </span>│<span style=\"background-color: #808000\"> 7.06e+01 </span>│\n",
+       "│<span style=\"background-color: #808000\">         R4 </span>│<span style=\"background-color: #808000\"> 8.16e+09 </span>│<span style=\"background-color: #808000\"> 6.22e+10 </span>│<span style=\"background-color: #808000\"> 4.11e+08 </span>│<span style=\"background-color: #808000\"> 8.41e+06 </span>│<span style=\"background-color: #808000\"> 2.61e+10 </span>│\n",
+       "│<span style=\"background-color: #808000\">         R5 </span>│<span style=\"background-color: #808000\"> 1.86e+01 </span>│<span style=\"background-color: #808000\"> 2.54e+01 </span>│<span style=\"background-color: #808000\"> 8.69e+00 </span>│<span style=\"background-color: #808000\"> 5.79e-01 </span>│<span style=\"background-color: #808000\"> 7.01e+01 </span>│\n",
+       "│ sigma_imag │ 1.58e+04 │ 7.53e+01 │ 1.58e+04 │ 1.57e+04 │ 1.59e+04 │\n",
+       "│ sigma_real │ 1.95e+04 │ 8.20e+01 │ 1.95e+04 │ 1.94e+04 │ 1.97e+04 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m                P1-R2-[P3,R4]-R5, 0/1000 divergences                 \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│        P1n │ 3.51e-01 │ 5.71e-04 │ 3.51e-01 │ 3.50e-01 │ 3.52e-01 │\n",
+       "│        P1w │ 1.00e-06 │ 2.59e-09 │ 1.00e-06 │ 1.00e-06 │ 1.01e-06 │\n",
+       "│        P3n │ 5.15e-01 │ 2.91e-01 │ 5.19e-01 │ 5.93e-02 │ 9.59e-01 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m       P3w\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.61e+09\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.14e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.39e+08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.09e+07\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.59e+10\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R2\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.94e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.56e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.05e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.79e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.06e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R4\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.16e+09\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m6.22e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.11e+08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.41e+06\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.61e+10\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R5\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.86e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.54e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.69e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.79e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.01e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 1.58e+04 │ 7.53e+01 │ 1.58e+04 │ 1.57e+04 │ 1.59e+04 │\n",
+       "│ sigma_real │ 1.95e+04 │ 8.20e+01 │ 1.95e+04 │ 1.94e+04 │ 1.97e+04 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">            L1-[P2,R3]-[R4,L5]-P6-R7, 0/1000 divergences             </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│<span style=\"background-color: #808000\">         L1 </span>│<span style=\"background-color: #808000\"> 9.68e-19 </span>│<span style=\"background-color: #808000\"> 5.43e-18 </span>│<span style=\"background-color: #808000\"> 9.50e-20 </span>│<span style=\"background-color: #808000\"> 1.98e-21 </span>│<span style=\"background-color: #808000\"> 3.00e-18 </span>│\n",
+       "│<span style=\"background-color: #808000\">         L5 </span>│<span style=\"background-color: #808000\"> 4.22e-19 </span>│<span style=\"background-color: #808000\"> 2.01e-18 </span>│<span style=\"background-color: #808000\"> 3.84e-20 </span>│<span style=\"background-color: #808000\"> 8.65e-22 </span>│<span style=\"background-color: #808000\"> 1.69e-18 </span>│\n",
+       "│        P2n │ 8.94e-01 │ 1.35e-03 │ 8.94e-01 │ 8.92e-01 │ 8.96e-01 │\n",
+       "│        P2w │ 2.41e-06 │ 1.19e-08 │ 2.41e-06 │ 2.39e-06 │ 2.43e-06 │\n",
+       "│        P6n │ 5.55e-01 │ 1.64e-03 │ 5.55e-01 │ 5.52e-01 │ 5.58e-01 │\n",
+       "│        P6w │ 5.93e-06 │ 6.69e-08 │ 5.93e-06 │ 5.82e-06 │ 6.04e-06 │\n",
+       "│         R3 │ 3.21e+06 │ 1.15e+04 │ 3.21e+06 │ 3.19e+06 │ 3.23e+06 │\n",
+       "│<span style=\"background-color: #808000\">         R4 </span>│<span style=\"background-color: #808000\"> 1.21e+10 </span>│<span style=\"background-color: #808000\"> 7.67e+10 </span>│<span style=\"background-color: #808000\"> 8.35e+08 </span>│<span style=\"background-color: #808000\"> 1.99e+07 </span>│<span style=\"background-color: #808000\"> 4.09e+10 </span>│\n",
+       "│<span style=\"background-color: #808000\">         R7 </span>│<span style=\"background-color: #808000\"> 1.63e+01 </span>│<span style=\"background-color: #808000\"> 1.90e+01 </span>│<span style=\"background-color: #808000\"> 9.01e+00 </span>│<span style=\"background-color: #808000\"> 6.20e-01 </span>│<span style=\"background-color: #808000\"> 5.10e+01 </span>│\n",
+       "│ sigma_imag │ 3.50e+03 │ 3.67e+01 │ 3.50e+03 │ 3.44e+03 │ 3.57e+03 │\n",
+       "│ sigma_real │ 5.27e+03 │ 4.40e+01 │ 5.27e+03 │ 5.20e+03 │ 5.34e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m            L1-[P2,R3]-[R4,L5]-P6-R7, 0/1000 divergences             \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        L1\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m9.68e-19\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.43e-18\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m9.50e-20\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.98e-21\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.00e-18\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        L5\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.22e-19\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.01e-18\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.84e-20\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.65e-22\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.69e-18\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│        P2n │ 8.94e-01 │ 1.35e-03 │ 8.94e-01 │ 8.92e-01 │ 8.96e-01 │\n",
+       "│        P2w │ 2.41e-06 │ 1.19e-08 │ 2.41e-06 │ 2.39e-06 │ 2.43e-06 │\n",
+       "│        P6n │ 5.55e-01 │ 1.64e-03 │ 5.55e-01 │ 5.52e-01 │ 5.58e-01 │\n",
+       "│        P6w │ 5.93e-06 │ 6.69e-08 │ 5.93e-06 │ 5.82e-06 │ 6.04e-06 │\n",
+       "│         R3 │ 3.21e+06 │ 1.15e+04 │ 3.21e+06 │ 3.19e+06 │ 3.23e+06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R4\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.21e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.67e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.35e+08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.99e+07\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.09e+10\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R7\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.63e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.90e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m9.01e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m6.20e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.10e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 3.50e+03 │ 3.67e+01 │ 3.50e+03 │ 3.44e+03 │ 3.57e+03 │\n",
+       "│ sigma_real │ 5.27e+03 │ 4.40e+01 │ 5.27e+03 │ 5.20e+03 │ 5.34e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                  [P1,P2-R3]-R4, 0/1000 divergences                  </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│        P1n │ 9.55e-01 │ 7.79e-04 │ 9.55e-01 │ 9.54e-01 │ 9.57e-01 │\n",
+       "│        P1w │ 1.81e-06 │ 1.63e-09 │ 1.81e-06 │ 1.81e-06 │ 1.81e-06 │\n",
+       "│        P2n │ 4.18e-01 │ 1.07e-03 │ 4.18e-01 │ 4.17e-01 │ 4.20e-01 │\n",
+       "│        P2w │ 2.54e-06 │ 1.90e-08 │ 2.54e-06 │ 2.51e-06 │ 2.57e-06 │\n",
+       "│         R3 │ 2.86e+06 │ 9.65e+03 │ 2.86e+06 │ 2.84e+06 │ 2.87e+06 │\n",
+       "│         R4 │ 1.53e+03 │ 3.03e+02 │ 1.54e+03 │ 1.01e+03 │ 2.04e+03 │\n",
+       "│ sigma_imag │ 2.08e+03 │ 3.15e+01 │ 2.08e+03 │ 2.03e+03 │ 2.13e+03 │\n",
+       "│ sigma_real │ 1.83e+03 │ 2.85e+01 │ 1.83e+03 │ 1.79e+03 │ 1.88e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m                  [P1,P2-R3]-R4, 0/1000 divergences                  \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│        P1n │ 9.55e-01 │ 7.79e-04 │ 9.55e-01 │ 9.54e-01 │ 9.57e-01 │\n",
+       "│        P1w │ 1.81e-06 │ 1.63e-09 │ 1.81e-06 │ 1.81e-06 │ 1.81e-06 │\n",
+       "│        P2n │ 4.18e-01 │ 1.07e-03 │ 4.18e-01 │ 4.17e-01 │ 4.20e-01 │\n",
+       "│        P2w │ 2.54e-06 │ 1.90e-08 │ 2.54e-06 │ 2.51e-06 │ 2.57e-06 │\n",
+       "│         R3 │ 2.86e+06 │ 9.65e+03 │ 2.86e+06 │ 2.84e+06 │ 2.87e+06 │\n",
+       "│         R4 │ 1.53e+03 │ 3.03e+02 │ 1.54e+03 │ 1.01e+03 │ 2.04e+03 │\n",
+       "│ sigma_imag │ 2.08e+03 │ 3.15e+01 │ 2.08e+03 │ 2.03e+03 │ 2.13e+03 │\n",
+       "│ sigma_real │ 1.83e+03 │ 2.85e+01 │ 1.83e+03 │ 1.79e+03 │ 1.88e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">              R1-[P2-R3,[L4,R5]-P6], 0/1000 divergences              </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│<span style=\"background-color: #808000\">         L4 </span>│<span style=\"background-color: #808000\"> 9.73e-20 </span>│<span style=\"background-color: #808000\"> 3.59e-19 </span>│<span style=\"background-color: #808000\"> 9.21e-21 </span>│<span style=\"background-color: #808000\"> 1.81e-22 </span>│<span style=\"background-color: #808000\"> 4.07e-19 </span>│\n",
+       "│        P2n │ 4.19e-01 │ 1.15e-03 │ 4.19e-01 │ 4.17e-01 │ 4.20e-01 │\n",
+       "│        P2w │ 2.54e-06 │ 2.02e-08 │ 2.54e-06 │ 2.50e-06 │ 2.57e-06 │\n",
+       "│        P6n │ 9.55e-01 │ 7.76e-04 │ 9.55e-01 │ 9.54e-01 │ 9.57e-01 │\n",
+       "│        P6w │ 1.81e-06 │ 1.62e-09 │ 1.81e-06 │ 1.81e-06 │ 1.81e-06 │\n",
+       "│         R1 │ 1.56e+03 │ 3.10e+02 │ 1.56e+03 │ 1.06e+03 │ 2.07e+03 │\n",
+       "│         R3 │ 2.86e+06 │ 1.01e+04 │ 2.86e+06 │ 2.84e+06 │ 2.87e+06 │\n",
+       "│<span style=\"background-color: #808000\">         R5 </span>│<span style=\"background-color: #808000\"> 1.02e+10 </span>│<span style=\"background-color: #808000\"> 7.25e+10 </span>│<span style=\"background-color: #808000\"> 6.71e+08 </span>│<span style=\"background-color: #808000\"> 1.44e+07 </span>│<span style=\"background-color: #808000\"> 3.39e+10 </span>│\n",
+       "│ sigma_imag │ 2.08e+03 │ 2.91e+01 │ 2.08e+03 │ 2.03e+03 │ 2.13e+03 │\n",
+       "│ sigma_real │ 1.84e+03 │ 2.97e+01 │ 1.84e+03 │ 1.78e+03 │ 1.88e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m              R1-[P2-R3,[L4,R5]-P6], 0/1000 divergences              \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        L4\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m9.73e-20\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.59e-19\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m9.21e-21\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.81e-22\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.07e-19\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│        P2n │ 4.19e-01 │ 1.15e-03 │ 4.19e-01 │ 4.17e-01 │ 4.20e-01 │\n",
+       "│        P2w │ 2.54e-06 │ 2.02e-08 │ 2.54e-06 │ 2.50e-06 │ 2.57e-06 │\n",
+       "│        P6n │ 9.55e-01 │ 7.76e-04 │ 9.55e-01 │ 9.54e-01 │ 9.57e-01 │\n",
+       "│        P6w │ 1.81e-06 │ 1.62e-09 │ 1.81e-06 │ 1.81e-06 │ 1.81e-06 │\n",
+       "│         R1 │ 1.56e+03 │ 3.10e+02 │ 1.56e+03 │ 1.06e+03 │ 2.07e+03 │\n",
+       "│         R3 │ 2.86e+06 │ 1.01e+04 │ 2.86e+06 │ 2.84e+06 │ 2.87e+06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R5\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.02e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.25e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m6.71e+08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.44e+07\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.39e+10\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 2.08e+03 │ 2.91e+01 │ 2.08e+03 │ 2.03e+03 │ 2.13e+03 │\n",
+       "│ sigma_real │ 1.84e+03 │ 2.97e+01 │ 1.84e+03 │ 1.78e+03 │ 1.88e+03 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                  R1-[R2,P3-L4], 0/1000 divergences                  </span>\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│<span style=\"background-color: #808000\">         L4 </span>│<span style=\"background-color: #808000\"> 3.66e-20 </span>│<span style=\"background-color: #808000\"> 1.13e-19 </span>│<span style=\"background-color: #808000\"> 4.35e-21 </span>│<span style=\"background-color: #808000\"> 8.20e-23 </span>│<span style=\"background-color: #808000\"> 1.85e-19 </span>│\n",
+       "│        P3n │ 6.27e-01 │ 1.45e-03 │ 6.27e-01 │ 6.25e-01 │ 6.30e-01 │\n",
+       "│        P3w │ 1.44e-06 │ 4.75e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
+       "│<span style=\"background-color: #808000\">         R1 </span>│<span style=\"background-color: #808000\"> 2.25e+01 </span>│<span style=\"background-color: #808000\"> 2.55e+01 </span>│<span style=\"background-color: #808000\"> 1.33e+01 </span>│<span style=\"background-color: #808000\"> 1.31e+00 </span>│<span style=\"background-color: #808000\"> 7.48e+01 </span>│\n",
+       "│         R2 │ 7.39e+06 │ 2.75e+04 │ 7.39e+06 │ 7.35e+06 │ 7.44e+06 │\n",
+       "│ sigma_imag │ 1.11e+04 │ 7.30e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "│ sigma_real │ 1.11e+04 │ 7.45e+01 │ 1.11e+04 │ 1.10e+04 │ 1.13e+04 │\n",
+       "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[3m                  R1-[R2,P3-L4], 0/1000 divergences                  \u001b[0m\n",
+       "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        L4\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.66e-20\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.13e-19\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.35e-21\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.20e-23\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.85e-19\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│        P3n │ 6.27e-01 │ 1.45e-03 │ 6.27e-01 │ 6.25e-01 │ 6.30e-01 │\n",
+       "│        P3w │ 1.44e-06 │ 4.75e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R1\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.25e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.55e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.33e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.31e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.48e+01\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│         R2 │ 7.39e+06 │ 2.75e+04 │ 7.39e+06 │ 7.35e+06 │ 7.44e+06 │\n",
+       "│ sigma_imag │ 1.11e+04 │ 7.30e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
+       "│ sigma_real │ 1.11e+04 │ 7.45e+01 │ 1.11e+04 │ 1.10e+04 │ 1.13e+04 │\n",
        "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
       ]
      },
@@ -582,32 +1033,36 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                R1-[R2,[R3,P4]], 18/1000 divergences                 </span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">               [P1,P2-[R3,L4]]-R5, 3/1000 divergences                </span>\n",
        "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
        "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
-       "│        P4n │ 6.27e-01 │ 1.39e-03 │ 6.27e-01 │ 6.25e-01 │ 6.29e-01 │\n",
-       "│        P4w │ 1.44e-06 │ 4.57e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
-       "│<span style=\"background-color: #808000\">         R1 </span>│<span style=\"background-color: #808000\"> 3.16e+01 </span>│<span style=\"background-color: #808000\"> 4.20e+01 </span>│<span style=\"background-color: #808000\"> 1.58e+01 </span>│<span style=\"background-color: #808000\"> 8.14e-01 </span>│<span style=\"background-color: #808000\"> 1.40e+02 </span>│\n",
-       "│         R2 │ 7.69e+06 │ 3.79e+05 │ 7.51e+06 │ 7.38e+06 │ 8.61e+06 │\n",
-       "│<span style=\"background-color: #808000\">         R3 </span>│<span style=\"background-color: #808000\"> 1.45e+10 </span>│<span style=\"background-color: #808000\"> 1.37e+11 </span>│<span style=\"background-color: #808000\"> 5.16e+08 </span>│<span style=\"background-color: #808000\"> 5.11e+07 </span>│<span style=\"background-color: #808000\"> 2.62e+10 </span>│\n",
-       "│ sigma_imag │ 1.11e+04 │ 6.91e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
-       "│ sigma_real │ 1.11e+04 │ 6.82e+01 │ 1.11e+04 │ 1.10e+04 │ 1.13e+04 │\n",
+       "│<span style=\"background-color: #808000\">         L4 </span>│<span style=\"background-color: #808000\"> 4.71e+01 </span>│<span style=\"background-color: #808000\"> 2.28e+02 </span>│<span style=\"background-color: #808000\"> 4.93e+00 </span>│<span style=\"background-color: #808000\"> 1.13e-01 </span>│<span style=\"background-color: #808000\"> 1.53e+02 </span>│\n",
+       "│        P1n │ 4.87e-01 │ 2.90e-01 │ 4.73e-01 │ 5.41e-02 │ 9.50e-01 │\n",
+       "│<span style=\"background-color: #808000\">        P1w </span>│<span style=\"background-color: #808000\"> 1.08e+02 </span>│<span style=\"background-color: #808000\"> 3.98e+02 </span>│<span style=\"background-color: #808000\"> 1.17e+01 </span>│<span style=\"background-color: #808000\"> 2.51e-01 </span>│<span style=\"background-color: #808000\"> 4.77e+02 </span>│\n",
+       "│        P2n │ 4.88e-01 │ 2.88e-01 │ 4.82e-01 │ 4.96e-02 │ 9.45e-01 │\n",
+       "│<span style=\"background-color: #808000\">        P2w </span>│<span style=\"background-color: #808000\"> 1.00e+10 </span>│<span style=\"background-color: #808000\"> 4.53e+10 </span>│<span style=\"background-color: #808000\"> 8.60e+08 </span>│<span style=\"background-color: #808000\"> 3.18e+07 </span>│<span style=\"background-color: #808000\"> 3.59e+10 </span>│\n",
+       "│<span style=\"background-color: #808000\">         R3 </span>│<span style=\"background-color: #808000\"> 1.11e+10 </span>│<span style=\"background-color: #808000\"> 4.45e+10 </span>│<span style=\"background-color: #808000\"> 1.09e+09 </span>│<span style=\"background-color: #808000\"> 2.31e+07 </span>│<span style=\"background-color: #808000\"> 4.51e+10 </span>│\n",
+       "│         R5 │ 7.31e+05 │ 6.71e+03 │ 7.31e+05 │ 7.20e+05 │ 7.42e+05 │\n",
+       "│ sigma_imag │ 3.68e+04 │ 1.22e+02 │ 3.68e+04 │ 3.66e+04 │ 3.70e+04 │\n",
+       "│ sigma_real │ 4.95e+04 │ 1.36e+02 │ 4.95e+04 │ 4.93e+04 │ 4.97e+04 │\n",
        "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[3m                R1-[R2,[R3,P4]], 18/1000 divergences                 \u001b[0m\n",
+       "\u001b[3m               [P1,P2-[R3,L4]]-R5, 3/1000 divergences                \u001b[0m\n",
        "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
-       "│        P4n │ 6.27e-01 │ 1.39e-03 │ 6.27e-01 │ 6.25e-01 │ 6.29e-01 │\n",
-       "│        P4w │ 1.44e-06 │ 4.57e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
-       "│\u001b[43m \u001b[0m\u001b[43m        R1\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.16e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.20e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.58e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.14e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.40e+02\u001b[0m\u001b[43m \u001b[0m│\n",
-       "│         R2 │ 7.69e+06 │ 3.79e+05 │ 7.51e+06 │ 7.38e+06 │ 8.61e+06 │\n",
-       "│\u001b[43m \u001b[0m\u001b[43m        R3\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.45e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.37e+11\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.16e+08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.11e+07\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.62e+10\u001b[0m\u001b[43m \u001b[0m│\n",
-       "│ sigma_imag │ 1.11e+04 │ 6.91e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
-       "│ sigma_real │ 1.11e+04 │ 6.82e+01 │ 1.11e+04 │ 1.10e+04 │ 1.13e+04 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        L4\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.71e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.28e+02\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.93e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.13e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.53e+02\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│        P1n │ 4.87e-01 │ 2.90e-01 │ 4.73e-01 │ 5.41e-02 │ 9.50e-01 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m       P1w\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.08e+02\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.98e+02\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.17e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.51e-01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.77e+02\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│        P2n │ 4.88e-01 │ 2.88e-01 │ 4.82e-01 │ 4.96e-02 │ 9.45e-01 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m       P2w\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.00e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.53e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.60e+08\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.18e+07\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m3.59e+10\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R3\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.11e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.45e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.09e+09\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.31e+07\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.51e+10\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│         R5 │ 7.31e+05 │ 6.71e+03 │ 7.31e+05 │ 7.20e+05 │ 7.42e+05 │\n",
+       "│ sigma_imag │ 3.68e+04 │ 1.22e+02 │ 3.68e+04 │ 3.66e+04 │ 3.70e+04 │\n",
+       "│ sigma_real │ 4.95e+04 │ 1.36e+02 │ 4.95e+04 │ 4.93e+04 │ 4.97e+04 │\n",
        "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
       ]
      },
@@ -617,32 +1072,34 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                  R1-[P2-L3,R4], 0/1000 divergences                  </span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                 R1-[P2,[R3,P4]], 0/1000 divergences                 </span>\n",
        "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\">  Parameter </span>┃<span style=\"font-weight: bold\">     Mean </span>┃<span style=\"font-weight: bold\">      Std </span>┃<span style=\"font-weight: bold\">   Median </span>┃<span style=\"font-weight: bold\">     5.0% </span>┃<span style=\"font-weight: bold\">    95.0% </span>┃\n",
        "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
-       "│<span style=\"background-color: #808000\">         L3 </span>│<span style=\"background-color: #808000\"> 5.89e-20 </span>│<span style=\"background-color: #808000\"> 2.79e-19 </span>│<span style=\"background-color: #808000\"> 4.50e-21 </span>│<span style=\"background-color: #808000\"> 6.35e-23 </span>│<span style=\"background-color: #808000\"> 2.02e-19 </span>│\n",
-       "│        P2n │ 6.27e-01 │ 1.40e-03 │ 6.27e-01 │ 6.25e-01 │ 6.29e-01 │\n",
-       "│        P2w │ 1.44e-06 │ 4.50e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
-       "│<span style=\"background-color: #808000\">         R1 </span>│<span style=\"background-color: #808000\"> 2.36e+01 </span>│<span style=\"background-color: #808000\"> 2.96e+01 </span>│<span style=\"background-color: #808000\"> 1.35e+01 </span>│<span style=\"background-color: #808000\"> 1.18e+00 </span>│<span style=\"background-color: #808000\"> 7.83e+01 </span>│\n",
-       "│         R4 │ 7.39e+06 │ 2.64e+04 │ 7.39e+06 │ 7.35e+06 │ 7.43e+06 │\n",
-       "│ sigma_imag │ 1.11e+04 │ 7.03e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
-       "│ sigma_real │ 1.11e+04 │ 7.55e+01 │ 1.11e+04 │ 1.10e+04 │ 1.13e+04 │\n",
+       "│        P2n │ 1.00e+00 │ 2.27e-05 │ 1.00e+00 │ 1.00e+00 │ 1.00e+00 │\n",
+       "│        P2w │ 1.55e-06 │ 2.91e-09 │ 1.55e-06 │ 1.54e-06 │ 1.55e-06 │\n",
+       "│        P4n │ 2.08e-01 │ 3.22e-04 │ 2.08e-01 │ 2.07e-01 │ 2.09e-01 │\n",
+       "│        P4w │ 4.98e-07 │ 6.73e-10 │ 4.98e-07 │ 4.97e-07 │ 4.99e-07 │\n",
+       "│<span style=\"background-color: #808000\">         R1 </span>│<span style=\"background-color: #808000\"> 4.22e+01 </span>│<span style=\"background-color: #808000\"> 5.56e+01 </span>│<span style=\"background-color: #808000\"> 2.17e+01 </span>│<span style=\"background-color: #808000\"> 1.00e+00 </span>│<span style=\"background-color: #808000\"> 1.63e+02 </span>│\n",
+       "│<span style=\"background-color: #808000\">         R3 </span>│<span style=\"background-color: #808000\"> 2.07e+11 </span>│<span style=\"background-color: #808000\"> 4.23e+11 </span>│<span style=\"background-color: #808000\"> 8.24e+10 </span>│<span style=\"background-color: #808000\"> 2.04e+10 </span>│<span style=\"background-color: #808000\"> 8.11e+11 </span>│\n",
+       "│ sigma_imag │ 5.06e+03 │ 4.91e+01 │ 5.06e+03 │ 4.98e+03 │ 5.14e+03 │\n",
+       "│ sigma_real │ 4.72e+03 │ 4.57e+01 │ 4.72e+03 │ 4.64e+03 │ 4.79e+03 │\n",
        "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[3m                  R1-[P2-L3,R4], 0/1000 divergences                  \u001b[0m\n",
+       "\u001b[3m                 R1-[P2,[R3,P4]], 0/1000 divergences                 \u001b[0m\n",
        "┏━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1m Parameter\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    Mean\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m     Std\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m  Median\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m    5.0%\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   95.0%\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩\n",
-       "│\u001b[43m \u001b[0m\u001b[43m        L3\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.89e-20\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.79e-19\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.50e-21\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m6.35e-23\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.02e-19\u001b[0m\u001b[43m \u001b[0m│\n",
-       "│        P2n │ 6.27e-01 │ 1.40e-03 │ 6.27e-01 │ 6.25e-01 │ 6.29e-01 │\n",
-       "│        P2w │ 1.44e-06 │ 4.50e-09 │ 1.44e-06 │ 1.44e-06 │ 1.45e-06 │\n",
-       "│\u001b[43m \u001b[0m\u001b[43m        R1\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.36e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.96e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.35e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.18e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m7.83e+01\u001b[0m\u001b[43m \u001b[0m│\n",
-       "│         R4 │ 7.39e+06 │ 2.64e+04 │ 7.39e+06 │ 7.35e+06 │ 7.43e+06 │\n",
-       "│ sigma_imag │ 1.11e+04 │ 7.03e+01 │ 1.11e+04 │ 1.10e+04 │ 1.12e+04 │\n",
-       "│ sigma_real │ 1.11e+04 │ 7.55e+01 │ 1.11e+04 │ 1.10e+04 │ 1.13e+04 │\n",
+       "│        P2n │ 1.00e+00 │ 2.27e-05 │ 1.00e+00 │ 1.00e+00 │ 1.00e+00 │\n",
+       "│        P2w │ 1.55e-06 │ 2.91e-09 │ 1.55e-06 │ 1.54e-06 │ 1.55e-06 │\n",
+       "│        P4n │ 2.08e-01 │ 3.22e-04 │ 2.08e-01 │ 2.07e-01 │ 2.09e-01 │\n",
+       "│        P4w │ 4.98e-07 │ 6.73e-10 │ 4.98e-07 │ 4.97e-07 │ 4.99e-07 │\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R1\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.22e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m5.56e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.17e+01\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.00e+00\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m1.63e+02\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│\u001b[43m \u001b[0m\u001b[43m        R3\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.07e+11\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m4.23e+11\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.24e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m2.04e+10\u001b[0m\u001b[43m \u001b[0m│\u001b[43m \u001b[0m\u001b[43m8.11e+11\u001b[0m\u001b[43m \u001b[0m│\n",
+       "│ sigma_imag │ 5.06e+03 │ 4.91e+01 │ 5.06e+03 │ 4.98e+03 │ 5.14e+03 │\n",
+       "│ sigma_real │ 4.72e+03 │ 4.57e+01 │ 4.72e+03 │ 4.64e+03 │ 4.79e+03 │\n",
        "└────────────┴──────────┴──────────┴──────────┴──────────┴──────────┘\n"
       ]
      },
diff --git a/pyproject.toml b/pyproject.toml
index dec7f28d..4877cb60 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,3 @@
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
-
 [project]
 name = "autoeis"
 dynamic = ["version"]
@@ -15,9 +11,7 @@ authors = [
     { name = "Robert Black", email = "robert.black@nrc-cnrc.gc.ca" },
     { name = "Jason Hattrick-Simpers", email = "jason.hattrick.simpers@utoronto.ca" },
 ]
-maintainers = [
-  { name = "Amin Sadeghi", email = "amin.sadeghi@live.com" }
-]
+maintainers = [{ name = "Amin Sadeghi", email = "amin.sadeghi@live.com" }]
 keywords = [
     "bayesian inference",
     "electrochemical impedance spectroscopy",
@@ -55,10 +49,8 @@ dependencies = [
     "multiprocess==0.70.15",
 ]
 [project.optional-dependencies]
-test = [
-  "pytest",
-  "nbmake",
-]
+build = ["hatch"]
+test = ["pytest", "nbmake"]
 doc = [
     "furo",
     "linkify-it-py",
@@ -69,20 +61,45 @@ doc = [
 ]
 [project.urls]
 "Homepage" = "https://github.com/AUTODIAL/AutoEIS"
+Repository = "https://github.com/AUTODIAL/AutoEIS"
 "Bug Tracker" = "https://github.com/AUTODIAL/AutoEIS/issues"
+Documentation = "https://autodial.github.io/AutoEIS"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.rye]
+managed = true
+dev-dependencies = [
+    "pytest>=8.1.1",
+    "hatch>=1.9.4",
+    "furo>=2024.1.29",
+    "linkify-it-py>=2.0.3",
+    "myst-parser>=2.0.0",
+    "sphinx>=7.2.6",
+    "sphinx-copybutton>=0.5.2",
+    "sphinx-autodoc2>=0.5.0",
+    "nbmake>=1.5.3",
+]
 
 [tool.hatch.version]
-path = "autoeis/version.py"
+path = "src/autoeis/version.py"
 
 [tool.hatch.build.targets.sdist]
-include = [
-    "/autoeis",
-]
+include = ["src/autoeis"]
 
 [tool.pytest.ini_options]
 minversion = "6.0"
 addopts = "-ra -v"
-testpaths = [
-    "tests",
-    "integration",
-]
+python_files = "*.py"
+python_classes = "*Test"
+python_functions = "test_*"
+testpaths = ["tests", "integration"]
+norecursedirs = [".git", ".github", ".ipynb_checkpoints", "build", "dist"]
+
+[tool.ruff]
+exclude = [".git", ".github", ".venv", "build"]
+line-length = 92
+[tool.ruff.lint.per-file-ignores]
+"__init__.py" = ["E402", "F401", "F403"]
diff --git a/autoeis/__init__.py b/src/autoeis/__init__.py
similarity index 100%
rename from autoeis/__init__.py
rename to src/autoeis/__init__.py
diff --git a/autoeis/__main__.py b/src/autoeis/__main__.py
similarity index 100%
rename from autoeis/__main__.py
rename to src/autoeis/__main__.py
diff --git a/autoeis/assets/circuits_filtered.csv b/src/autoeis/assets/circuits_filtered.csv
similarity index 100%
rename from autoeis/assets/circuits_filtered.csv
rename to src/autoeis/assets/circuits_filtered.csv
diff --git a/autoeis/assets/circuits_unfiltered.csv b/src/autoeis/assets/circuits_unfiltered.csv
similarity index 100%
rename from autoeis/assets/circuits_unfiltered.csv
rename to src/autoeis/assets/circuits_unfiltered.csv
diff --git a/autoeis/cli.py b/src/autoeis/cli.py
similarity index 100%
rename from autoeis/cli.py
rename to src/autoeis/cli.py
diff --git a/autoeis/cli_pyjulia.py b/src/autoeis/cli_pyjulia.py
similarity index 100%
rename from autoeis/cli_pyjulia.py
rename to src/autoeis/cli_pyjulia.py
diff --git a/autoeis/core.py b/src/autoeis/core.py
similarity index 96%
rename from autoeis/core.py
rename to src/autoeis/core.py
index 87cc54fc..6242c67a 100644
--- a/autoeis/core.py
+++ b/src/autoeis/core.py
@@ -821,11 +821,11 @@ def perform_full_analysis(
 
     # Perform Bayesian inference on the filtered ECMs
     kwargs_mcmc = {"num_warmup": num_warmup, "num_samples": num_samples}
-    mcmcs = perform_bayesian_inference(circuits, freq, Z, **kwargs_mcmc)
+    mcmc_results = perform_bayesian_inference(circuits, freq, Z, **kwargs_mcmc)
 
     # Add the results to the circuits dataframe as a new column
-    chains, status = zip(*mcmcs)
-    circuits["MCMC (chain)"] = chains
+    mcmcs, status = zip(*mcmc_results)
+    circuits["MCMC (chain)"] = mcmcs
     circuits["MCMC (status)"] = status
 
     return circuits
diff --git a/autoeis/io.py b/src/autoeis/io.py
similarity index 100%
rename from autoeis/io.py
rename to src/autoeis/io.py
diff --git a/autoeis/julia_helpers.py b/src/autoeis/julia_helpers.py
similarity index 100%
rename from autoeis/julia_helpers.py
rename to src/autoeis/julia_helpers.py
diff --git a/autoeis/julia_helpers_pyjulia.py b/src/autoeis/julia_helpers_pyjulia.py
similarity index 100%
rename from autoeis/julia_helpers_pyjulia.py
rename to src/autoeis/julia_helpers_pyjulia.py
diff --git a/autoeis/legacy.py b/src/autoeis/legacy.py
similarity index 100%
rename from autoeis/legacy.py
rename to src/autoeis/legacy.py
diff --git a/autoeis/metrics.py b/src/autoeis/metrics.py
similarity index 100%
rename from autoeis/metrics.py
rename to src/autoeis/metrics.py
diff --git a/autoeis/models.py b/src/autoeis/models.py
similarity index 100%
rename from autoeis/models.py
rename to src/autoeis/models.py
diff --git a/autoeis/parser.py b/src/autoeis/parser.py
similarity index 100%
rename from autoeis/parser.py
rename to src/autoeis/parser.py
diff --git a/autoeis/utils.py b/src/autoeis/utils.py
similarity index 100%
rename from autoeis/utils.py
rename to src/autoeis/utils.py
diff --git a/autoeis/version.py b/src/autoeis/version.py
similarity index 100%
rename from autoeis/version.py
rename to src/autoeis/version.py
diff --git a/autoeis/visualization.py b/src/autoeis/visualization.py
similarity index 100%
rename from autoeis/visualization.py
rename to src/autoeis/visualization.py


Test patch:
diff --git a/autoeis/assets/test_data.txt b/src/autoeis/assets/test_data.txt
similarity index 100%
rename from autoeis/assets/test_data.txt
rename to src/autoeis/assets/test_data.txt