Repository: PyEllips/pyElli (language: python)

Issue:
Change package structure to only use pyproject.toml
Problem: Currently, the package uses a structure of pyproject.toml, setup.cfg and setup.py.

Solution: Let pyproject.toml manage everything, i.e. remove setup.cfg and setup.py entirely. Also update packages to latest versions in pip-compile and pin setuptools.
Change package structure to only use pyproject.toml
Problem: Currently, the package uses a structure of pyproject.toml, setup.cfg and setup.py.

Solution: Let pyproject.toml manage everything, i.e. remove setup.cfg and setup.py entirely. Also update packages to latest versions in pip-compile and pin setuptools.
Change package structure to only use pyproject.toml
Problem: Currently, the package uses a structure of pyproject.toml, setup.cfg and setup.py.

Solution: Let pyproject.toml manage everything, i.e. remove setup.cfg and setup.py entirely. Also update packages to latest versions in pip-compile and pin setuptools.


Discussion / hints:

Fix patch:
diff --git a/.github/workflows/benchmark.yml b/.github/workflows/benchmark.yml
index c51a6451..479854b0 100644
--- a/.github/workflows/benchmark.yml
+++ b/.github/workflows/benchmark.yml
@@ -13,10 +13,10 @@ jobs:
     name: Run pytest-benchmark benchmark example
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
           submodules: recursive
-      - uses: actions/setup-python@v2
+      - uses: actions/setup-python@v4
         with:
           python-version: "3.10"
       - name: Install deps
diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml
index 06a5af42..32ff86fb 100644
--- a/.github/workflows/linting.yml
+++ b/.github/workflows/linting.yml
@@ -10,13 +10,13 @@ jobs:
   black-lint:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - uses: psf/black@stable
   sphinx-lint:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
-      - uses: actions/setup-python@v2
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
         with:
           python-version: "3.10"
       - name: Install deps
diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
index 3739eecb..d1e2f6f3 100644
--- a/.github/workflows/python-publish.yml
+++ b/.github/workflows/python-publish.yml
@@ -18,11 +18,11 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
       with:
         submodules: recursive
     - name: Set up Python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: '3.x'
     - name: Install dependencies
diff --git a/.github/workflows/requirements.yml b/.github/workflows/requirements.yml
index 82fe8854..ed219750 100644
--- a/.github/workflows/requirements.yml
+++ b/.github/workflows/requirements.yml
@@ -10,11 +10,11 @@ jobs:
   test_python:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
           submodules: recursive
       - name: Set up Python 3.10
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: "3.10"
       - name: Install requirements
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 00000000..6b5bcc73
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,1 @@
+recursive-include src/elli/database/refractiveindexinfo-database *.yml
\ No newline at end of file
diff --git a/examples/Ellipsometry uniaxial materials/Fujiwara-641.ipynb b/examples/Ellipsometry uniaxial materials/Fujiwara-641.ipynb
index 6bbfab28..f5f73d39 100644
--- a/examples/Ellipsometry uniaxial materials/Fujiwara-641.ipynb
+++ b/examples/Ellipsometry uniaxial materials/Fujiwara-641.ipynb
@@ -70,7 +70,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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HAa8bDRarQKEuZHrkdD7Z/gn2Jnue6/QcgxsMltmUECW45+etPHD0UX4ITOaoRx2W3Ly0wmXGJEEJwEhW+0+lMm/3KebtOkVcajbO9iYGNq/NbR2C6NjAq+r/QOfnwPYpsOZdyDwLLUZC35fAs36VHD4mJYYXNrzA7oTd9K/fnxc6v4CXo1eVHFuI6mRVZDzfTPmF91zeZmhQXR4Kf4jxrcdXeFxJUOIyhYWaLTGJ/LXrJAt2nyYtJ5+GPi7c1iGIm9oG4lurigvCZqcaCyk2fWkUp+30gLHqz8mz0g9dUFjAlIgpfLHzC2rZ1+KlLi/Rt17fSj+uENVFbn4hAz9Zy4dZL7Dc5xzTXBxYNnIZvs6+FR5bEpQoUVZuAQv3nmbm1li2xiRha6PoH+bPbR2C6NHYF1NV1gtMOQmr3jQK1Dq6G9en2t8Dpso/DXk46TDPrX+OA4kHuKXJLTzV4SmcbJ0q/bhCWLvv1h5h+eI/mOr4On0bNqZjUE8+6v2RWcaWBCVK7XB8OjO3xjJ3x0kSM3Kp5+XMnV3qc0v7INydqnDJetxeWPo8HF0D/i1g0HsQ3K3SD5tXkMcXu77gp30/0cC9Ae/1fI+mXk0r/bhCWKv41Gz6fLCaOc7vcNgljufc7Ph+wPd0rtPZLONLghJllptfyN8RcUzZGMPWmCSc7U2MbBfIuK7BNPKtoiaBWsOBeUaiSjkOLW6G/q+De0ClH/qf0//w3LrnSM5J5vF2jzMmdIwsoBA10hOzdnN6zwp+s32VsaEdSbJ3YN6N88xW41ISlKiQfSdT+GlDDPN3nyK3oJBeTXy5v2dDujbyrpo/2rmZxvWp9Z8YbT16PgVdHgbbyl0qn5SdxEsbXmL1idV0D+jOG93ewNvJu1KPKYQ12RmbxIivNrLa7yNy1AlGetnzZPsnGdd8nNmOYTUJSillArYBJ7XWQ5VSDYAZgDewHRirtc4taQxJUJaTkJbD9C2x/LLpGGfTc2gV6M6EXo0Y0Lx21VynSooxZlORC8A3FIZ9CvU6VeohtdbMjJrJB9s+wN3enQ96f0AbvzaVekwhrEFhoWbEVxuonbSdbwte4o02g/gj9RArblmBh6OH2Y5zpQRliR4EjwIHLnr+LvCx1joESALutUBMopR8aznwSN/GrH+mD2+OaEFKVh4Tpu2g/0drmLk1lpz8gsoNwDMYRk2D0TOMWn+Tr4cFEyErudIOqZRiVLNR/Dr4VxxsHbh7yd1M2T+F6nj2QYiymLPjBLtPpPC650IyavkzP/0oAxsMNGtyKkmVJiilVCAwBPih6LkCrgPmFG0yBbixKmMS5eNoZ2JMp/qsfKI3X9zeBid7E8/M3Uuv91YzdVNM5SeqpoPgoc3QeYJRkPbLTrD/T+OaVSVp5tWMmUNn0ieoDx9s+4CJqyeSmptaaccTwpJSs/N4b0kUd9Q+jt/ZzSwM609mfia3Nr21ymKo6hnUJ8DTQGHRc28gWWudX/T8BFDs1W+l1P1KqW1KqW0JCQmVHqgoHZONYmiruiz4v+5MuacjgZ5OvPjXfvq8v5ppm4+Rm1949UHKy8EVBr4N960AV1+YPQ5mjIG0M5V2yFr2tfio90c81f4p1hxfw6gFo4hMjKy04wlhKZ+vOMS5jByecf4L7erPrNzTNPVsSiufVlUWQ5UlKKXUUCBea729PPtrrb/TWrfXWrf39a34jWHCvJQySifNHt+FX+7piL+7I8//sY8+H6xmxpZY8goqMVEFtIX/rTZW9x1eDl91gr1zKm02pZTizuZ38tPAn8gpyOGORXcw/8j8SjmWEJZwOD6dnzbE8Eyzc9Q6tZE9bUcRlXyIW5veWqUrWatyBtUNGK6UisFYFHEd8CngoZT69w7MQOBkFcYkzEwpRc8mvvw+oSs/390BH1d7nv19L/0/WsPCPacr77qNyRa6PQLj14NXI5h7L8waazRPrCThfuHMHjabVr6teG79c7y/9X3yC/OvvqMQVkxroxGhk52Jewpmgas/s2xzcLZ1ZkjDIVUaS5UlKK31JK11oNY6GBgFrNRajwFWASOLNhsH/FVVMYnKo5Sid1M//nyoGz+Oa4+9rQ0P/baDEV9tZHP0uco7sG8TuPdv6PcqHFxqzKb2/V5ph/Ny9OLb/t8yutlofon4hQeXP0hKTkqlHU+IyrbiQDxrDybwTvs07GPXkdJ5PEuOLWdYo2FV3o3aEqv4/usZ4HGl1GGMa1I/WjgeYUZKKfqG+rP40Z68N7IVcSnZ3PbdP9z781YOnkmrnIPamKD7Y/DAOvCoD3Puhrn/g+zKSRx2NnY81+k5Xuv6GtvObGPUglEcSjpUKccSojLl5Bfw+sIIQvxcGXRuCrj686ebG7mFudzS5JYqj0du1BVVKjuvgMkbjvL16iNk5ORze6d6PNG/aeX1pirIh/UfGb2n3APgpu+hnnnKsxRnV/wuJq6eSEZeBm/3eFsKzopq5avVh3lvSRR/DYXWy29HD3iLYWcW4+ngydTBUyvtuNZ0H5SowRztTDzYO4S1T/Xhzi7BTN9ynN4frGbKxhjyK2MhhckWej0N9ywBFPw0CFa9bSSuShDuF86MITMI8Qhh4qqJ/LzvZ7lfSlQLcSnZfLHyMAPC/Gl95Btw9WdzUAuOpR6r0qXlF5MEJSzC08WeV4Y3Z/GjPWgR4MbL8/Yz5LP1bDxytnIOGNTRWEDR6jZY846RqBKPVsqh/F38mXz9ZPrX78+H2z/k9X9eJ68wr1KOJYS5vLskkvxCzevhKXB0LXR7jFlH/sLDwYMBwQMsEpMkKGFRTfxr8eu9nfjmjnZk5OZz+/ebeXDaduJSss1/MEc3GPEN3PwjJETBNz2Mm3srgaOtI+/3ep/7Wt7H7IOzeXjFw6TlVtI1NyEqaPuxRP7YeZL7ezTEf+cn4OpPQvNhrIpdxY0hN+JgquLecEUkQQmLU0oxsEVtlj/ei8f7N2HFgXj6fbSGKRtjKCishNNjLUfChPXg29S4uXfxM5BfYvnHcrFRNjza9lFe7foqW05v4c7Fd3Iq/ZTZjyNERRQUal6ZF0FtN0cebhR/fvb0+9FF5Ot8RjYZefVBKokkKGE1HO1MPNK3MX9P7Embeh68PG8/N321gf2nKmH1nUc9uHsxdH4QNn8DPw2EpGPmPw5wU+Ob+Lr/15zJOMPtC29n/7n9lXIcIcpj9rbj7D2ZwqTBzXDc8D64+lPQ9k7mHJpDlzpdqO9W32KxSYISVqe+twu/3NORT0eFczI5i+FfbOCNBRFk5Jh5YYOtvVEq6dapcPYQfNsTohab9xhFOtfpzNTBU3EwOXDPknvYdGpTpRxHiLJIycrjvaVRdAj2ZLhHjNEctNujrD2zlbiMOIstjviXJChhlZRS3BAewIrHe3Nr+yB+WH+UgZ+urZxFFGHD4YE1xqxq+ihY9jIUmr/YbSOPRkwdPJWAWgE8uOJBlhxdYvZjCFEWny4/RFJmLi8Pa45a8y64+EG7u5kZNRM/Jz96B/W2aHySoIRVc3e24+2bWjJ7fBdMSnH795t58c995p9NeTWEe5dBu7uMxojTboGsJPMeA/Bz9uPngT/TyqcVT699mmkHppn9GEKUxqEzaUzZFMPojvVokb/fmD11f4zj2efYcGoDI5uMxNbG9uoDVSJJUKJa6BDsxeJHe3JPtwb8uvlY5cym7ByNBohDPzEuFH/XB85EmPcYgJu9G9/2/5Y+QX14Z8s7fLbjM7lXSlQprTWvzo/Axd7EkwOaGjeyF82eZh2chUmZuLnJzZYOUxKUqD6c7E28NCyMWQ90wdbG5vxsKjPXzLOp9nfDXQshLxN+6AcR5i8P6WjryIe9P+Tmxjfz/d7veWXTKxRUwmlFIYrzd8QZ1h8+y+P9m+B1dtv52VO2jQ1/HP6D6+pdh5+zn6XDlAQlqp8OwV4seqQH93U3ZlNDP1vPnhPJ5j1IvU5w/xrwC4VZd8KK16DQvJUubG1sebnLy9zf6n5+P/Q7z657Vm7oFZUuO6+ANxZG0MTflTs6179k9vT3sb9JyUnhtqa3WTpMQBKUqKac7E28MDSM3+7rTHZeATd9tZEvVx02731TbnXg7kXQZiys+9Bo35GbYb7xMRaD/F+b/+OJdk+wJGYJj69+nJyCHLMeQ4iL/bAumuOJWbwyrDm2J/45P3vC3pmZUTMJdgumY+2Olg4TkAQlqrkujbxZ/GhPrm9Rm/eXRjH6+384kZRpvgPYOsDwz2HgOxC1yCiRlHrafOMXuavFXTzf6XlWH1/N/634P7Lys8x+DCFOJWfx5aojDGpRm64hPpfMng6cO8CehD3c1vS2Km1KWBJJUKLac3e244vRbfjwltbsP5nCoE/XMW+3GSs2KAWdJ8DoGXDuCHx/HZzeY77xi4xqNorXu73O5rjNTFg+gYw8887WhHh7cSSFWvPc4FA4tvGy2ZOjyZHhIcMtHeZ5kqDENUEpxc3tAln8aE8a+7nyyPSdTPp9L9l5Zlx40OR6oyq6UjB5YKXc1HtjyI282+Nddsfv5n9//0+aHwqz2XI0kfm7T/FAr0YEeTlfMntKy01j0dFFDG44GDd7N0uHep4kKHFNqeftzMwHujC+VyOmb4llxFcbOXrWjDOR2i3hfyuNzr3TR8OmL803dpGBDQbyUe+PiEyMlCQlzKKgUPPyvP3UdXdkQq9Gl82e5h2ZR1Z+lsUrR/yXJChxzbEz2fDsoGZMvqs9p1OyGPb5ehbsMeMpv1q14a5FEDoMlj4HS583+wq/PvX68Nl1n3E4+TD3L7tfkpSokBlbYzlwOpXnhoTiZG+6ZPaktWZW1Cxa+rSkuXdzS4d6CUlQ4pp1XTN/Fj7Sgyb+rjz8205e/HOf+U752TvDLVOg03jY9AX8/j+zV0TvHtCdT/p8wqGkQ4xfNp7U3FSzji9qhuTMXD5YGkWnBl4MaVkHjm26ZPa07cw2olOirW72BJKgxDUuwMOJmQ904f6eDZn6zzFu/XYTp5LNtELOxsZY3dfvVdg3B6aNhGzzJpGegT35uPfHRCZFMmHZBOkpJcrs42UHScnK45XhzY3VeWsuzJ4AZkTOwM3ejYHBAy0c6eUkQYlrnp3JhucGh/Ld2HZEJ2Qw/Iv1bI4+Z57BlTL+S/TGb+DYBvh5MKSdMc/YRXoF9eLDXh8ScS6C8cvHk56bbtbxxbUrMi6VXzfHMqZTfULruBmzp+jV0O1RsHcmITOBlbEruTHkRhxtHS0d7mUkQYkaY0Dz2vz5UDfcnOwY88Nmft5w1Hw18MJHw+iZcC4afuxvLEc3o+vqXccHvT5g/9n9sgRdlIrWmlfnRVDL0ZbH+zcxXlzzDrj4Qvt7AJh7aC75Ot8qT++BJChRw4T4ufLnQ93o3dSPV+ZH8MTs3ea7LtW4H9w136g2MXkgxO0zz7hF+tbvy3s932Pv2b08svIRqTghSrR4Xxybos/xRP8meLrYXzR7egzsnckvzGfOQcs3JSyJJChR47g52vHd2HY81q8xv+84yS3fbOJ0ipmuSwW0M+6VMtnBz0PgxHbzjFtkQPAAXu/2OlvitvDkmieldp8oVlZuAW8uPECz2rUY3bGe8eJ/Zk9rTqzhTOYZbmtmHXX3iiMJStRINjaKx/o14fs723P0bAY3fLHBfAVnfRob7eSdPOCX4RCz3jzjFhnWaBjPdXqO1cdX8+KGFynU5l3iLqq/b9ce4WRyFq8Mb46tyeay2RPArKhZ+Dv70yuwl0VjLYkkKFGj9Q/zZ+6ErtiZbLj1200s3mumOnue9eHuJeAeCL/eDAf/Ns+4RUY3G80jbR5hYfRC3tr8lvSTEuedTM7imzVHGNKqDp0behsvrn77ktnTsdRjbDy10SqaEpZEEpSo8ZrWrsWfD3UjtI4bE6bt4MtVh83zB9+tjnFDr29TmHE77P+j4mNe5L6W93F3c6M99+c7Pzfr2KL6envRAbTGqLcHF6pGXDR7mh01G1tly82NLd+UsCSSoIQAfGs5MP1/nRneui7vL43iydl7yMk3w+IJF28YN9+4NjXnHtg9s+JjFlFKMbHdxPNNDyfvm2y2sUX1tOVoIgv2nGZ8r0YEeDgZL65+27jvqWj2lJ2ffb4poa+zrwWjvTrrndsJUcUc7Ux8Oiqchr4ufLL8EMeTMvl+bHvcne0qOLA7jP0dfrsN/hxvvNbaPBemlVK82PlFMvIy+Hj7x3g5enFjyI1mGVtULwWFmlfn76eOuyPjezUyXozZAEfXwvVvnZ89LY1ZSmpuqtU0JSyJzKCEuIhSxuKJT0eFsys2mZHfbDRP5Ql7F7h9FtTvZiQpM86kTDYm3ur+Fp3rdOaVja+w/qR5F2WI6mH2tuPsP5XKpMFF9fbAmD25+p+fPQHMjJpJA/cGdKjdwUKRlp4kKCGKcUN4AD/f04G4lGxu+mojkXFmKGFk71xpScrOZMfHvT8mxCOEx1c/zv5z+802trB+qdl5vL80ivb1PRnWqo7xYsx6iFkH3SeCnXG6b/+5/ew9u9eqmhKWRBKUEFfQtZEPs8Z3QaO55ZtNbDpihvJI/yap4O7wxwNmTVKu9q581e8rPB08eXD5gxxPO262sYV1+3zFIRIzc3l5WPMLiWf1O+BaG9rddX67WVGzcLJ1YlijYZYJtIyqLEEppRyVUluUUruVUvuVUq8Wvd5AKbVZKXVYKTVTKWVfVTEJcTWhddz4/cFu+Ls5Mm7yFvO07bB3NsoiNehRlKRmVHzMIn7Ofnzd/2sKdAETlk8gKTvJbGML63QkIZ2fNsRwa7sgWga6Gy8eXXfZ7Ck1N5VF0YsY3MC6mhKWpCpnUDnAdVrr1kA4MFAp1Rl4F/hYax0CJAH3VmFMQlxVgIcTc8Z3oXWQO/83fSc/bzha8UHPJ6me8OcEsy5Bb+jekM+v+5y4jDgeXvEwWflmqpIhrNKbCw/gaGfiyeubGi9oXXTtqTa0G3d+u3mH55FdkG21dfeKU2UJShv+LcNsV/TQwHXAnKLXpwA3VlVMQpSWh7M9U+/txIAwf16ZH8HnKw5V/F4pe2cYPR2COsHc++DgUvMEC7Txa8O7Pd5l79m9PL32aQoKzVRvUFiV1VHxrIyM55G+IfjWcjBejFlnVNbv8fj52ZPWmplRM2nl04ow7zALRlw2VXoNSillUkrtAuKBZcARIFlrnV+0yQkg4Ar73q+U2qaU2paQkFAl8QpxMUc7E1/e3pab2gbw4bKDvLXogBmSlAvcPtNoJT9zLESvMU+wGMVln+n4DKuPr+aTHZ+YbVxhHfIKCnl9QQQNfFy4q2sD40WtYdXbUKsOtL0we9oSt4WY1BirrrtXnCpNUFrrAq11OBAIdASalWHf77TW7bXW7X19rfvmMnHtsjXZ8MHI1ozrUp/v1x3luT/2UlBYwSTl6A53/A7ejWD6aDi+xTzBAmNCxzCq6Sh+3v8zcw/ONdu4wvKmbjrGkYQMXhgSir1t0Z/yo2shdiN0fxzsLvR3mhk1E3cHd64Pvt5C0ZaPRVbxaa2TgVVAF8BDKfXvDcOBwElLxCREadnYKF4Z3pyH+4QwfctxHp2xk9z8ChZsdfaCsX9CLX/4dSSc3m2WWAGe6fgM3ep2441/3mDz6c1mG1dYzrn0HD5efpCeTXy5rpmf8eK/155q1YW2d57fNj4znpWxKxkRMgIHk4OFIi6fqlzF56uU8ij62QnoDxzASFQjizYbB/xVVTEJUV5KKZ68vimTBjVjwZ7TPDB1W8X7StXyhzvngaMbTB0B8ZFmidXWxpb3e71Pfbf6TFw9kZiUGLOMKyzno2UHycwt4KWhoReWlUevhthNRdeeLsye5h6aS4Eu4JYmt1gm2AqoyhlUHWCVUmoPsBVYprVeADwDPK6UOgx4Az9WYUxCVMgDvRrx1oiWrD6YwP9+2UZWbgWTlEcQ3PkX2NjCrzdBygmzxFnLvhZf9P0COxs7HlrxEMnZyWYZV1S9iFOpTN8Sy51d6hPiV8t4UWvjvie3gEtmT3mFecyJmkPXul2p51bPQhGXX1Wu4tujtW6jtW6ltW6htX6t6PVorXVHrXWI1voWrbW0CRXVyu2d6vH+yNasP3yWe6dsrXiS8m4Ed8yFnDSYehNkJpolzsBagXza51NOZ5xm4uqJ5BVIs8PqRmvNawv24+5kx2N9m1x4I3oVHP/HmD3ZXjiNtyp2FfFZ8YxuNtoC0VacVJIQwgxGtgvkw1tasyn6HPf8vJXM3Pyr71SS2i2NJehJMUaR2dxMs8QZ7hfOa91eY9uZbby15S2zjCmqzpJ9cfwTncjjA5peKGJ8fvYUCG3GXrL99MjpBLgG0COghwWirThJUEKYyU1tA/n41nA2Hz3H3T9tJSOngkkquDvc/AOc2Aqz7wIzzXiGNhzKvS3uZc7BOcyKmmWWMUXly84r4M1FRW3cOwRdeOPISji++bLZ06GkQ2w7s41bm96KycZkgYgrThKUEGZ0Y5sAPr4tnK0xidz901bSK5qkwobDkA/h0FKY/6jxX8tm8H9t/o9uAd14e8vb7IzfaZYxReX6YV00J5KyeGlomNHGHS6s3HMLhDZ3XLL9jMgZOJgcuCnkJgtEax7lTlBKKW9zBiLEteKG8AA+HdWG7bFJ3P3Tloqf7utwL/SeBLumwYpXzRKjycbEuz3epa5LXSaumsiZjDNmGVdUjriUbL5cdYSBzWvTNcTnwhtHVhgz7J5PXDJ7SstNY370fAYGD8TD0aPqAzaTciUopdRc4A2l1DSlVB8zxyREtTesdV0+G9WG7ceSuG+KGZag93rG6Omz/mPY8r1ZYnR3cOfTPp+SlZ/FxNUTySmQ9UnW6t0lkRRofaGNO1yoGuEeBOGXzp7mHZlHVn4Wo0Or5+KIf5V3BhWptZ6gtR7DhXuYhBAXGdKqDh/eaiycGP/r9oq1kFcKBn8ATQfD4qchaolZYgzxDOGt7m+x9+xeXt/0esVLNwmz2xGbxB87T/K/Hg2o5+184Y3Dy+HkNujxBNheaAJRqAuZETmDVj6taO7d3AIRm095E9RApdQTSql+gHmWFwlxDRrRJtC4TyoqgUem7ySvoAIVJ2xMxqKJ2q1gzj1wapdZYuxbvy8PtHqAv478xfTI6WYZU5hHYaHm1fkR+NVy4MHeIRfe+Pfak3s9CB9zyT7/nP6HmNQYRjUbVcXRml+5ExRGFYhuQIBSaor5QhLi2jK6Yz1eHhbG0v1neGLW7orV7vu3uKyzl7H83Ew38j4Y/iC9A3vz3tb32HFmh1nGFBX3x86T7D6ezLODmuHiYHvhjUPL4OR26PnkJbMnMBZHeDl6Vbu6e8UpV4LSWicAm7XWr2qtb9daj7vqTkLUYHd3a8AzA5sxb/cpnp27h8KKJKlatY2uvHmZMO1WyK54O3obZcObPd6krmtdnlrzFOeyzNA9WFRIek4+7y6JJDzIgxvDL2ry8O/syaMehN9+yT6n0k+x5sQabm58M/am6t/7VRZJCFFFJvRuxKN9GzN7+wlenb+/Ytd7/MPg1ilwNsps90i52bvxUe+PSM5J5pl1z0gPKQv7atVh4tNyeHlYGDY26sIbh/6GUzug51Ngsrtkn3/va6uOdfeKI4skhKhCj/VrzH3dGzBl0zE+W3G4YoM1ug6GfmwsNV70pFnukWrm1YznOz/P5tOb+Xr31xUeT5RP7LlMflh3lJvaBtCmnueFN87PnupD60tX6OUU5DD30Fz6BPWhjmudKo64cthefZNiDVRKJQK7kUUSQpSaUornBoeSmJnLx8sP4uVqz9jO9cs/YNs7ITHaWH7uGwqdx1c4xhEhI9hxZgff7vmW1r6t6RFYPcvkVGdvLorA1qR4ZuB/WuYdXAqndsLwLy6bPS2NWUpyTvI1sTjiX1edQSml7ix63KSUCi96WRZJCFFONjaKd29uRd9mfrz01z4W7DlVsQGvewmaDoGlk4yyNxWklOL5zs/TxLMJk9ZP4nT66QqPKUpvw+GzLN1/hof6hODvdqFtxvnZk2cwtL48Cc2InEED9wZ0qt2p6oKtZKU5xden6HEbMKWoZXt9rfUiWSQhRPnYmWz4ckxb2tf3ZOLMXaw9mFD+wWxs4KZvwbeZcT3qbAVPHQJOtk581Psj8gvzeXLNk1L5vIrkFxTy2vwIgrycuLd7g0vfjFoMp3cVe+1p39l97D27l1FNR13oD3UNuGqC0lrfXfS4TWvdGnge+FMp5VX54Qlx7XK0M/HDuA408nVl/K/b2RmbVP7BHGoZ1c9tbGH6KMhKrnB89d3q83q319lzdg8fbv+wwuOJq5u+JZaoM2k8PzgUR7uLCryenz01gFaXz56mR07H2daZ4Y2GV2G0la80p/gev/gBNAWSgfeKngshysndyY5f7umIj6sDd/+8lcPx6eUfzDMYbv0Fko7C3HvBDKvw+tfvzx2hdzDtwDRWxa6q8HjiypIzc/lw2UG6NPTm+ua1L30zahHE7SmaPV26dCApO4klR5cwrNEwXO1dqzDiyleaU3y1innYAM7AtfVpCGEBfm6OTL23I7Y2irt/3kJCWgVq4gV3N0oiHV4Oy14yS3wT200k1CuUFze+SFxGnFnGFJf7ZPkhUrPyeGlY2KWn6f6dPXk1hFa3Xbbf74d+J7cwt9o2JSxJaU7xvXrxA9gIuAOP/tsVVwhRMfW9XfhxXAcS0nK4d0oFGx62vxs63g+bvoCd0yocm73Jnvd6vkduQS6T1k2S+6MqwcEzaUz95xi3d6pHaB23S9+MXAhxe6Hn05fNngoKC5gVNYuOtTvSyKNRFUZcNUpzim9y0WO6Umon8BEwsqiahBDCTFoHefDF6LbsO5nCw7/tJL8idfuufxsa9IQFE41lyRUU7B7MC51fYNuZbXy/1zzV1IVBa83rCyJwsTfxeP+ml75ZWGh0y/VqBC0vv/l27Ym1nMo4dU3OnqB0p/jWFD3mAvcCrbTWmyo1KiFqqH5h/rx6QwtWRsbz8rwKVJsw2cLIn8DFF2aOhYyKly4a3mg4QxsO5evdX0u9PjNacSCedYfOMrF/E7xc/lOeKGohnNkLvS6fPYGxOMLf2Z/eQb2rJtgqVppTfFOKHnO01ju01OMXolKN7Vyf8b0aMW1zLF+vOVL+gVx84LapkB4Pc+8xy6KJFzq/QKBrIM+se4aUnJQKj1fT5eQX8MbCCEL8XLnjvzdsXzx7anF5wZ6jKUfZdHoTtza9FVub8tZcsG7S8l0IK/T09U0Z3rou7y2J4q9dJ8s/UEBbo2V89GpY+XqF43Kxc+G9Xu9xNussL298WfpHVdDPG2KIOZfJi0PDsDP9589x5AI4s89oVlnM7Glm1ExsbWy5qXH1bel+NZKghLBCNjaK929pReeGXjw1ew/bYhLLP1jbsdDubqMcUsRfFY6tuXdzHmv7GCtiVzD74OwKj1dTxadl8/nKw/QL9aNXE99L3/x39uQdAi0vnz1l5mXy1+G/uD74enycfC57/1ohCUoIK+Vga+KbO9oR4OnEA1O3czyxAmUvB70LAe3hzwchIarCsY0NG0vXul35YNsHHEs9VuHxaqIPlkaRk1/A80PCLn/zwDyI32/MnmxMl729IHoB6XnpjGp67dTdK44kKCGsmIezPT+Ma09eQSH3TdlGek45l5/bOhg38do6wowxFe4hZaNseK3ra9jZ2PHcuufIL6zAsvgaaM+JZGZvP8E93RrQwMfl0jcLC2HNu+DdGFrcfNm+WmumR04n1CuU1r6tqyhiy5AEJYSVa+Tryldj2nE4IZ1Hpu8sf0de9wC45WdIPALzH61wew5/F39e7PIie87ukaXnZaC10cbd28Weh68LuXyDA39BfMQVZ0/bzmzjcPJhRjcbfU3V3SuOJCghqoHujX14ZXhzVkbG887iA+UfqEEPuO4F2P87bP2hwnENDB7IkIZD+Hb3t+xN2Fvh8WqCebtPsf1YEk9f34xajpcWfTWuPb0LPk2hRfGLH6YdmIaHgweDGgyqgmgtSxKUENXE2M71uatrMN+vO8rMrbHlH6jbRAjpD0ufM8tNvM91eg5fZ18mrZ9EZp60hytJZm4+7yyOpGWAOyPbBV6+QcQfkHDAuO+pmNnTyfSTrDq+ipFNRuJo63j5/tcYSVBCVCMvDAmlR2Mfnv9jH/9El/PmWxsbGPGtcRPvrHEVrnzuZu/Gm93e5FjqMT7a/lGFxrrWfbMmmtMp2Ze3cQfjPrXV7xiNJ5uPKHb/mZEzUShua3p5Tb5rkSQoIaoRW5MNX9zelvrezoz/dTvHzmWUbyAXb6PSROpJ+OuhCl+P6linI3eG3cnMqJmsPbG2QmNdq04kZfLtmiMMa12X9sHFdCvaOwfOHoTezxY7e8rKz2Luobn0rdeX2i61L9//GiQJSohqxt3Jjh/HdQCo2Mq+ep2g3yvGDaH/fF3huB5p+wghHiG8tOElkrOTKzzetebtxZEoBc8Oanb5mwX5sOYd8G8JocX3dFoYvZDU3FTGhI6p5EithyQoIaqhYB8Xvry9LUcS0nlq9u7yV3To8rDRLn7Zi3B8a4VicjA58HaPt0nJSeHtLW9XaKxrzZajiSzcc5rxvRoR4OF0+QZ7ZkJiNPSZZJyC/Q+tNdMOTCPUK5Q2fm2qIGLrUGUJSikVpJRapZSKUErtV0o9WvS6l1JqmVLqUNG/nlUVkxDVWbcQHyYNCmXxvrjy1+xTCm78Etzqwpy7IasCXX2BZl7N+F+r/7Ho6CJWxK6o0FjXioJCzavz91PX3ZEHehbTEqMgz7jvqU44NB1c7Bhb47ZyOPkwt4fefs0vLb9YVc6g8oEntNZhQGfgIaVUGPAssEJr3RhYUfRcCFEK9/VowPDWdXl/aRSro+LLN4iTJ4z8GdJOw/zHKnw96n8t/0dTz6a8vul1OdUHzN52nP2nUpk0OBQn+8uvLbFrGiQfgz7PG//BUIxpB6bh6eBZI5aWX6zKEpTW+rTWekfRz2nAASAAuAGYUrTZFODGqopJiOpOKcW7N7eiWW03Hpm+s/yLJgLbGX8gI/6EHb9UKCY7kx1vdH+DlJwU3tn6ToXGqu5Ss/N4f2kUHYI9GdqqzuUb5OfA2g+MMlSN+xc7xom0E6w+sZqRTUbiYHKo5Iiti0WuQSmlgoE2wGbAX2t9uuitOMD/Cvvcr5TappTalpAgvRKF+JeTvYnvxrbDxkZx/y/bySjvooluj0GDXrDk2QrX6/v3VN/C6IWsjF1ZobGqs89XHCIxM5eXhzUv/tTcjl8g5Thcd+XZ08woY2n5rU1vreRorU+VJyillCtG88PHtNaXFAQr6jVV7PkFrfV3Wuv2Wuv2vr6+xW0iRI0V5OXM56PbcCg+jafn7infool/74+yc4I590JedoVi+vdU32ubXquRvaOOJKTz04YYbmsfRIsA98s3yMuGdR9CvS7QsE+xY2TmZTL30Fz61e9XY5aWX6xKE5RSyg4jOU3TWv9e9PIZpVSdovfrAOU8kS5EzdajsS9PD2zGwj2n+W5tdPkGcasDN35tdHFd/kqF4rn4VF9NXNX35sIDONmZeGJA0+I32P6zcd2vz3NXnD0tiF5AWm5ajVpafrGqXMWngB+BA1rri283nweMK/p5HFDxhjVC1FAP9GzIkJZ1eHdJJOsPnS3fIE2uh07jYfPXcHBpheKpqaf6VkXFszIynkf6Nsa3VjHXjXIzjdlTcA9o0LPYMbTW/HbgN0K9Qgn3Da/cgK1UVc6gugFjgeuUUruKHoOBd4D+SqlDQL+i50KIclBK8d7IVoT4ufLojJ3EpZTzNF2/V42bRv+cAGlxFYrp31N9b/zzBqm5FWvzUR3kFRTy+oIIGvi4MK5rcPEbbfsRMuKN2dMVbI7bzJGUI4wJHVOjlpZfrCpX8a3XWiutdSutdXjRY5HW+pzWuq/WurHWup/WugKtQ4UQLg62fDWmHdl5BTz82w7yCgrLPoidI4ycDHlZRpKqwNJzO5Mdr3Z9lXPZ5/hk+yflHqe6+GXTMaITMnhxaCj2tsX8ic1Jh/WfGNed6ne94jjTDkzDy9GLgQ0GVl6wVk4qSQhxDQrxc+Xtm1ux7VgS7y8t54o83yYw4A04srLCrTma+zTnjtA7mH1wNtvPbK/QWNbsXHoOnyw/SK8mvvRp6lf8Rlu+g8yzxrL+Kziedpw1x9fUyKXlF5MEJcQ1anjruoztXJ/v1kbz9/5ynqZrf4/RmuPvFyHhYIXieSj8IQJcA3hl4yvkFORUaCxr9eGyg2TlFvDi0NDiT8tlp8LGz6DxAAjqcMVxZkTOwKRM3Nqk5i0tv5gkKCGuYS8MDaVVoDtPzN5N7Lly9GpSCm74wlh6/sf9RlmecnK2c+alzi8RkxrD93uuvQ68EadSmbEllrFd6hPiV6v4jTZ/a5STKuHaU2ZeJn8c+oP+9fvj71LsbaE1hiQoIa5hDrYmvry9LQp48LftZOcVlH2QWrVh2CdGc8O171conq4BXRnWcBg/7vuRQ0mHKjSWNTHauO/H3cmOx/o2KX6jrGTY9LlRnLfulQu+zj8yn7S8NG4Pvb1ygq1GJEEJcY0L8nLmo1vD2XcyldcXRJRvkLAboPVooyxPBaueP9XhKWrZ1eKVTa9QUFiOhGmFFu+LY/PRRJ4Y0BR3Z7viN/rnK8hOMSqWX4HWmt8if6O5d3Na+7aupGirD0lQQtQA/cL8eaBXQ6ZtjuWvXSfLN8igd42q53/cD7nlrPkHeDp68nTHp9mTsIeZUTPLPY61yM4r4M2FB2hWuxajO9YrfqPMRNj0lZHoa7e84libTm0iOiW6xlUtvxJJUELUEE8NaErHYC8m/b6Xw/HpZR/A0R1GfAOJR+HvFyoUy5AGQ+hWtxuf7viUuIyK3Wdlad+vjeZkchYvDQvD9N827v/a+DnkpkOvkps1/HLgF3ycfBgUXLOqll+JJCghaghbkw2f394GRzsTD/+2o3zXo4K7Q9eHYdtkOPh3uWNRSvFilxcp1IW8s6X63pt/OiWLr1YfYVCL2nRt5FP8RhlnjcURLW4C/7ArjhWdHM2GkxsY1XQUdqYrnCasYSRBCVGD+Ls58uEtrYmMS+PtRQfKN8h1L4Jfc/jrIcg4V+5YAlwDeKD1A6yIXcHaE2vLPY4lvbs4kgKteW5w6JU32vAJ5GdddfY09cBUHEwO3NL0FvMGWY1JghKihunTzI97uzdgyqZj5bs/ytYBbvrOWC696MkKxTIubByN3Bvx1ua3yMrPqtBYVW37sUT+3HWK+3s0JMjLufiN0s7Alh+g5a3Gjc9XkJSdxPwj8xnacChejl6VFHH1IwlKiBro6YFNaRHgxtNz93A6pRyJoXYL6PUM7P8dIspf39nOZMfznZ/nZPrJanVvVGGh5tX5Efi7OTChdzFt3P+14RMoyIVeT5c43uyDs8kpyOGO0DvMG2g1JwlKiBrIwdbEZ6PakJtfyKMzdlFQWI5ae90fgzqtYeETFTrV16F2B4Y1HMZP+3/iaMrRco9TlebuOMGeEyk8O6gZLg62xW+Uegq2/gjho8H7ykksryCPGZEz6Fq3KyGeIZUUcfUkCUqIGqqhryuv39CCLUcT+XxlOW6aNdnBDV8ZN6AufqpCsTzR/gmcbJ148583y9dssQqlZefx7pIo2tTz4IbWAVfecN1HoAugZ8mfzZKYJSRkJTA2bKyZI63+JEEJUYPd3C6QEW0C+GzFITZHl2MWVLuFcfpq31yImFfuOLydvHms7WNsjtvMoqOLyj1OVfhy1RHOpufw8rDm2FxpWXnycdgxBdqMBc/gK46ltWZqxFQaujekW91ulRNwNSYJSoga7vUbW1DPy5nHZu4iKSO37AN0nwi1W8HCx40bUsvp5sY308K7Be9vfZ+03LRyj1OZYs5mMHn9UW5uG0h4kMeVN1z3gfFvjydKHG/7me0cSDzAHWF3yI25xZAEJUQN5+pgy+ej23I2PYen5+4p+yk2k53RJj4rCRaXvBigxGFsTLzY5UWScpL4fOfn5R6nMr256AB2JsUzA6/Qxh2MG5l3/gptx4FHUInjTY2YioeDB8MaDjNzpNcGSVBCCFoGuvPMwGYsizjDjK3Hyz5A7RbQ82nYOxsOLCh3HGHeYYxqOoqZUTM5cK6c92lVknWHElgWcYaHrgvBz83xyhuufgds7KBnyUvwj6ceZ9XxVdzS5BYcbUsYrwaTBCWEAOCebg3oFuLNa/MjOHq2HLX2ejxu1JlbMLFCp/oeavMQ7vbuvLPlHatZMJFfUMhr8yOo5+XMPd0aXHnD+EjYMxM6/s+oAl+CaZHTMNmYGNVslJmjvXZIghJCAGBjo/jgltbY29rw2MxdZW8Vf35VXyIsfqbccbjZu/Fo20fZEb/DahZMTNscy6H4dJ4fEoqjnenKG65+C+xdjetyJUjLTeOPQ38wMHggfs5X6LwrJEEJIS6o4+7EmyNasPt4Ml+sPFyOAVoZy6r3zoKoJeWOY0TjETT3bs5H2z4iM68cjRbNKCkjl4+WHaRbiDcDwkpoIHh6t3HTcpeHwLnkahC/H/qdzPxMWVp+FZKghBCXGNqqLje1CeCLVYfZfiyp7AN0fxx8Q41VfTnlW41no2yY1GkS8VnxfLfnu3KNYS4fLz9IWnYeLw1tXvJKu5VvgKMHdHmwxPHyC/P57cBvtPNvR5j3lYvHCklQQohivHJDc2q7OfL4rF2k5+SXbWdbexj+uVFJYcVr5Y6htW9rhjcazi8Rv3As9Vi5x6mIyLhUfv3nGHd0rk/T2ldo4w4QuxkO/W1U13B0L3HMlbErOZVxSmZPpSAJSghxGTdHOz6+LZzYxExen1+OLrxBHYyFAlu+h+Nbyh3HxHYTsTfZ897W98o9RnlprXltfgS1HO2Y2O/KhV7RGla+Di5+0PH+q447NWIqga6B9A7sbb5gr1GSoIQQxerYwIvxvRoxc9txlpan6nnfl4wOvPMegfxy3AAM+Dj5MKH1BNaeWFvlLTn+jjjDxiPneLx/Ezxd7K+8YfRqiFlnLCu3dylxzL0Je9mVsIs7wu7AZFPCYgsBSIISQpRgYr8mNK/rxrNz9xCfml22nR1qwZCPIOGAUdW7nG5vdjvBbsG8u+VdcgvKl+jK6t827k38XRnT6Qpt3KFo9vQGuAVCu7uuOu7UiKm42rlyY8iNZov1WiYJSghxRfa2Nnw6KpzM3ILyVZloOhCa3wRr34eEg+WKwc5kx6SOk4hNi2VqxNRyjVFWkzccJTYxk5eGNsfWVMKfyYNL4OQ2ox6hrUOJY8ZlxPH3sb+5qfFNuNiVPNMSBklQQogShfjVYtKgZqyOSihflYlB74KdM8x/BArLeG9Vka4BXekd1Jvv937P2ayz5RqjtM6kZvPFysP0D/One+MrtHEH43dZ+QZ4NYTw26867rQD0wAYEzrGXKFe8yRBCSGu6s4uwXRp6M0bCyI4nljG+5Jc/WDAGxC7CXb8XO4Ynmj3BDn5OXyx84tyj1Ea7y2JIr9A83xJbdwBIv6AM/ug93PGTcolSMtNY/bB2QwIHkBd17pmjPbaJglKCHFVNjaK90a2QinFU3N2U1jWBodt7oAGPWHZy8by83IIdg9mVLNR/HH4D6ISo8o1xtXsOp7M3B0nuKd7A4J9SjgNV5APq94CvzBocfNVx517cC4ZeRnc1fwu8wVbA0iCEkKUSpCXMy8MCeWf6ER+2RRTtp2VgqGfGO3PF5W/ueH41uOpZV+L97e9b/Y6fYWFmlfm7ce3lgMPX3eVzrZ7ZsC5w9DnebAp+c9oXkEeUw9MpVPtTnJjbhlJghJClNptHYLo3dSXd5ZElr2grHcj6PUMRC6AqMXlOr67gzsTWk9g8+nNZl92/tfuk+w6nszT1zfF9Upt3MFYMr/6XajbBpoNueq4S2KWEJ8Zz7jm48wYbc1QpQlKKTVZKRWvlNp30WteSqllSqlDRf96VmVMQojSU0rx7s2tsDfZ8MSsXRSU9VRfl4fBtxksehpyy1ExHbi16a0EuwXzwbYPyCvMK9cY/5WRk887iyNpFejOzW0DS954xxRIiYXrXjBmhiXQWvPz/p9p5N6I7gHdzRJrTVLVM6ifgYH/ee1ZYIXWujGwoui5EMJK+bs58toNLdgRm8wP66LLtrOtvXFvVEosrClfdQg7GzuebP8kMakxzIqaVa4x/uvr1Uc4k3qVNu4AuZmw9gOo1xUa9b3quJtOb+Jg0kHGNR8nHXPLoUoTlNZ6LfDfRjE3AFOKfp4C3FiVMQkhyu6G8LoMbF6bD/8+yMEzZSwIG9wNwsfApi8gvnxNCXsG9qRznc58tesrUnJSyjXGv44nZvLdumhuDK9Lu/pXOYGz9QdIj4O+L1519gQwZf8UfJ18GdLw6qcCxeWs4RqUv9b6dNHPcUCx9eyVUvcrpbYppbYlJCRUXXRCiMsopXhjRAtcHW15YtbusveO6v+aUWliweNGNYZyHP+pDk+RnpfON7u/KfP+F3tr0QFMSvHMoGYlb5idCus/NmZO9bteddyoxCg2ntrI7aG3Y28qoVSSuCJrSFDnaWNZTrHfVq31d1rr9lrr9r6+vlUcmRDiv3xcHXhrRAv2nkzh69VHyraziw/0exViN8Ku38p1/CaeTbip8U3MiJxBTEpMucbYeOQsi/fF8WDvRtRxdyp543++NpoxXvd8qcaesn8KTrZO3NLklnLFJqwjQZ1RStUBKPo33sLxCCFKaWCLOgxvXZfPVx4iKq6Mp/rajIWgTrDsxXK3iH8o/CHsTfZ8uuPTMu+bX1DIK/P2E+jpxP96Nix548xE45Rks6EQ0O6qY8dlxLH46GJubnwz7g4lt98QV2YNCWoe8O/6y3HAXxaMRQhRRq8Mb46box1Pz9lNfllO9dnYGAsmspJh+SvlOraPkw93tbiL5bHL2RW/q0z7/vrPMQ6eSeeFIWElt3EH49ReTppx31Mp/HbgNzSaO8LuKFNM4lJVvcx8OrAJaKqUOqGUuhd4B+ivlDoE9Ct6LoSoJrxc7Hn1hubsPpHCj+uPlm3n2i2g8wRj6Xbs5nIdf1zYOLwdvfl4+8elvnn3XHoOHy07SPcQH65vXkIbdzAqX2z5DlqPAv+r32ibnptulDWqP4AA14BSxSOKV9Wr+EZrretore201oFa6x+11ue01n211o211v201uWb6wshLGZIyzoMCPPno2UHiU5IL9vOvSeBW4DRIr6gjN17AWc7Zx4Mf5Ad8TtYfXx1qfb54O8oMnMLeGV42NWXf695FwoLjDhLYe6huaTnpTOuhdyYW1HWcIpPCFHNKaV448YWONja8MzcPWWr1efgalQ8P7MPNn9druOPaDyCYLdgPtnxCfmFJSe5vSdSmLH1OOO6BhPiV0Ibd4Czh2HHVOhwL3jWv2oceYV5TI2YSsfaHWnu3bwsv4IohiQoIYRZ+Lk58uLQMLbGJDH1n2Nl27nZUGh8Pax+B1JPX337/7CzseOxto8RnRLNn4f/vOJ2hYWal+ftw9vFnkf7Nb76wKveAFtH6PFkqeJYGrOUM5lnpKyRmUiCEkKYzch2gfRs4su7SyLL1pZDKRj0DhTkwbKXynXs6+pdR7hvOF/t+orMvOKP/eeuk+yITebpgc1wcyy5RQandsL+P6DLQ+B69VtbtNZM2T9FyhqZkSQoIYTZKKV4+6aWKGDS73vLVnHcqyF0exT2zoKYDeU69uPtHychK4FfD/x62ftp2Xm8vTiS1kEejLxavT2AFa+Bkxd0fbhUx990ahORiZGMaz4OGyV/Ws1BPkUhhFkFeDjx7OBQ1h8+y6xtZezA230iuAfB4qfLtWCijV8b+gT1YfK+ySRmX7re6ouVh0lIy+HV4VeptwcQvQaOrIQeT4Bj6e5j+nHfj/g5+0lZIzOSBCWEMLsxHevRqYEXbyw8QFxKdul3tHeG698yFkxsm1yuYz/W9jGy8rP4bs935187kpDO5A1HuaVdIOFBHiUPoDWseNVYWdjhvlIdc0/CHrbEbeHOsDulrJEZSYISQpidjY3RliOvoJDn/yjjqb7QYdCwD6x8A9LLXnezoUdDRoSMYGbUTI6nHkdrzWvzI3C0NfH0wKvU2wOjX9XJ7caycjvHUh1z8r7JuNm7MbLJyDLHK65MEpQQolIE+7jw5ICmrIiMZ97uMrR5VwoGvQd5GbDilXId+8HwB7GzseOznZ+x4kA8aw4m8Gi/xvjWcih5x8ICWPE6+DSB1qNLdazo5GhWxK5gdLPRuNiV0CZelJkkKCFEpbm7WwPCgzx4bX4ESRm5pd/Rtwl0fhB2/gontpX5uH7OfowNG8uSmCW8tGQJIX6ujOsafPUdd8+As1Fw3YtgKqGr7kV+2v8TjiZHbg+9vcxxipJJghJCVBqTjbGqLyUrjzcXlbH3U6+nwbU2LHzCmNmU0V3N78LBxpUkh3m8PCwMO9NV/tzlZcPqt6FuW+M0YynEZcSxIHoBNzW+CS9HrzLHKEomCUoIUalC67hxf8+GzNl+go2Hz5Z+R4daMOANOL0Ldk4t83HTs2zJjO+JretBnN1ir77DtsmQchz6vVyqZoQAv0T8gtZabsytJJKghBCV7pG+jQn2dua5P/aSnVeG2VDLkUZ79eWvlrklx1uLIilI7oqngzef7fis5IUa2amw7gNo2Nt4lEJydjJzDs5hcIPB1HWtW6bYROlIghJCVDpHOxNvjWhJzLlMPl95qPQ7KgWD34fsFFj1Zql32xx9jvm7T/FAz1AmhD/AjvgdbDy18co7bPoSMs9B39JXsZgeNZ2s/CzuaXFPqfcRZSMJSghRJbqG+DCyXSDfronmwOnU0u9YuwW0v9s4BXcm4qqb5xcU8vK8/dR1d2RCr0aMbDySANcAPtt5hVlUxlmjGWHYDaVqRgiQmZfJbwd+o3dQb0I8Q0r/u4gykQQlhKgyzw8Oxd3Jjkm/76WgLBXP+zwPDm6wdJJxI20Jpm+JJTIujeeHhOFkb8LOZMf41uOJOBfBitgVl++w7kPIyzJW7pXSH4f/IDknmXtb3Fv630GUmSQoIUSV8XSx56VhYew6nsyvZal47uxl3DgbvRqiFl9xs6SMXD74+yBdGnozuGXt868PbTiUBu4N+Hzn5xRcvCIwORa2/gBtxoBPKaqbY7TU+Hn/z7Tzb0e4X3jpfwdRZpKghBBVanjruvRs4st7SyI5lZxV+h073As+TeHv5yE/p9hNPvg7ivScfF7+TyNCWxtbHgp/iOiUaBYdXXRhh5VvAgp6PVPqMBYfXUxcRpzMnqqAJCghRJVSSvHmjS0o1PDSX/tLXwbJZGfU6UuMhs3fXvb23hMp/LYllrGd69Ostttl7/ev359Qr1C+3PUleQV5cHoP7JlptJx3L0V1c6BQFzJ572SaeDaRlhpVQBKUEKLKBXk583j/Jiw/cIYl++JKv2PjftB4AKx5D9Ljz79cWKh58S+jEeHE/k2K3dVG2fBwm4c5mX6SPw7/ActfBicPo4J6Ka05voYjKUe4t8W9V28VLypMEpQQwiLu7hZM87puvDxvPylZeaXf8fq3ID8LVr5+/qXZ24+z63gykwYZizCupEdAD9r4teHbHZ+RHb0Kej5tJKlS0Frz474fCXANYEDwgNLHK8pNEpQQwiJsTTa8e3Mrzqbn8N6SyNLv6NMYOj4AO6bC6d0kZ+by7pIoOgR7clPbgBJ3VUrxSPj/EZ+bwky/IOO6ViltjdvK7oTd3N38bmxtSlenT1SMJCghhMW0CHDnrq4N+G1LLDtjk0q/Y6+njZV9SybxwdJIkjNzeXV4i1Kddmsff4SumVn8UMuJDF36pojf7fkOXydfbmx8Y+njFBUiCUoIYVGPD2iCfy1Hnv9jH/kFhaXbycnDuDfq2AYSt83hzi7BhNW9fGHEZfKyYeUbPGzyJ7kgi+mR00t1uF3xu9gct9koQGu6SssOYTaSoIQQFuXqYMvLw8KIOJ3KlE2lvzeqsM2dxJiCecHuNyb2DirdTlu+g5TjtOz3Fj0CevDz/p9Jz02/6m7f7vkWTwdPaUhYxSRBCSEsbmCL2vRp6stHf0dxOqV090bN3nmaSVljqEsC7rsuX3Z+mcxEoyBsSH9o2IsHwx8kJSeF3yJ/K3G3/ef2s/7keu5sfifOds6lik2YhyQoIYTFKaV47YYW5Bca7dmv5t+FEfn1uqObDYV1H0Pq6ZJ3Wv+RUbW8/6sAtPBpQa/AXkzZP6XEWdT3e76nln0tRjUdVabfSVScJCghhFUI8nLmkb6NWbwvjlWR8SVu+8HfURcWRgx4AwrzYMVrV94h6Zhxc2/47eDf/PzLE8InkJqbyrQD04rd7VDSIVbEruCO0DtwtXct1+8lyk8SlBDCavyvR0NC/Fx5ad4+snKL7xu190QK0zbHXlgY4dUAOo2H3dPh9O7iB171Jigb6PPcJS83925O78DeTImYQlpu2mW7fb/ne5xtnRkTOqbCv5soO0lQQgirYW9rw5s3tuB4YhZfrLq8b9QVK0b0eAKcPGHp85dXOz+9u8SSRhPCJ5CWm8avB3695PWjKUdZErOEUc1G4e7gbpbfT5SNJCghhFXp1NCbke0C+W5tNIfOXDqruWLFCCcPY3YUs+7yaufLXjaSV7fHij1emHcYfYL6MHX/VFJzL/Sp+nHvjziYHLgz7E4z/WairCRBCSGszqRBzXC2t+X5P/edLyZ71YoR7e4C78aw7EUoKCqddHgFlKKk0YPhD5KWl8avEcYs6mT6SRZEL2Bkk5F4O3mb+bcTpWU1CUopNVApFaWUOqyUetbS8QghLMfb1YFJg5qx5Wgic3ecBP6zMKK4ihEmOxjwBpw7bHTfLSw0Zk8e9a9a0qiZVzP61uvL1IippOSkMHnvZGyUDXc1v6sSfjtRWlZRUEopZQK+BPoDJ4CtSql5WuurrzcVQlyTbm0fxOztJ3hr0QG8XeyZtjmWcVerGNHkemjQE1a/AwW5cGYv3Pwj2F69+sOE1hNYEbuCtza/xbJjyxgRMgJ/F38z/kairKxlBtUROKy1jtZa5wIzgBssHJMQwoJsbBRvjmhBenY+d/+8tcRWGucpBQPehKwk+PsFqNcVmt9UquM19WrKmNAxLDq6iEJdyD0t7zHDbyEqwipmUEAAcPyi5yeAThaKRQhhJZrVdmPBI935fOVhRrSpW2IrjfPqtIK2d8Le2XDDF2BT+v8Of7L9kyTnJBPoGkiAa8mV0UXlU6XuZlmZQSg1Ehiotb6v6PlYoJPW+uGLtrkfuB+gXr167Y4dK33NLiFEDVNYCFmJ4OJj6UhEKSiltmut2//3dWs5xXcSuLjaY2DRa+dprb/TWrfXWrf39fWt0uCEENWMjY0kp2uAtSSorUBjpVQDpZQ9MAqYZ+GYhBBCWJBVXIPSWucrpR4GlgImYLLWer+FwxJCCGFBVpGgALTWi4BFlo5DCCGEdbCWU3xCCCHEJSRBCSGEsEqSoIQQQlglSVBCCCGskiQoIYQQVskqKkmUlVIqAahoKQkf4KwZwqkp5PMqG/m8ykY+r7K51j6v+lrryyowVMsEZQ5KqW3FldYQxZPPq2zk8yob+bzKpqZ8XnKKTwghhFWSBCWEEMIq1eQE9Z2lA6hm5PMqG/m8ykY+r7KpEZ9Xjb0GJYQQwrrV5BmUEEIIKyYJSgghhFWqkQlKKTVQKRWllDqslHrW0vFYG6VUkFJqlVIqQim1Xyn1aNHrXkqpZUqpQ0X/elo6VmuhlDIppXYqpRYUPW+glNpc9B2bWdTnTBRRSnkopeYopSKVUgeUUl3k+3VlSqmJRf9f3KeUmq6UcqwJ37Eal6CUUibgS2AQEAaMVkqFWTYqq5MPPKG1DgM6Aw8VfUbPAiu01o2BFUXPheFR4MBFz98FPtZahwBJwL0Wicp6fQos0Vo3A1pjfHby/SqGUioAeARor7VugdEzbxQ14DtW4xIU0BE4rLWO1lrnAjOAGywck1XRWp/WWu8o+jkN449HAMbnNKVosynAjRYJ0MoopQKBIcAPRc8VcB0wp2gT+awuopRyB3oCPwJorXO11snI96sktoCTUsoWcAZOUwO+YzUxQQUAxy96fqLoNVEMpVQw0AbYDPhrrU8XvRUH+FsqLivzCfA0UFj03BtI1lrnFz2X79ilGgAJwE9Fp0V/UEq5IN+vYmmtTwIfALEYiSkF2E4N+I7VxAQlSkkp5QrMBR7TWqde/J427k+o8fcoKKWGAvFa6+2WjqUasQXaAl9rrdsAGfzndJ58vy4ouhZ3A0Zirwu4AAMtGlQVqYkJ6iQQdNHzwKLXxEWUUnYYyWma1vr3opfPKKXqFL1fB4i3VHxWpBswXCkVg3G6+DqM6yseRadjQL5j/3UCOKG13lz0fA5GwpLvV/H6AUe11gla6zzgd4zv3TX/HauJCWor0LhoBYw9xsXGeRaOyaoUXUP5ETigtf7oorfmAeOKfh4H/FXVsVkbrfUkrXWg1joY47u0Ums9BlgFjCzaTD6ri2it44DjSqmmRS/1BSKQ79eVxAKdlVLORf/f/Pfzuua/YzWykoRSajDGdQMTMFlr/aZlI7IuSqnuwDpgLxeuqzyHcR1qFlAPo93JrVrrRIsEaYWUUr2BJ7XWQ5VSDTFmVF7ATuAOrXWOBcOzKkqpcIxFJfZANHA3xn8wy/erGEqpV4HbMFbY7gTuw7jmdE1/x2pkghJCCGH9auIpPiGEENWAJCghhBBWSRKUEEIIqyQJSgghhFWSBCWEEMIqSYISQghhlSRBCSGEsEqSoISwIkV9pT4t6v2zt+iGXyFqJElQQliXSUC01ro58BnwoIXjEcJibK++iRCiKhS1nBihtW5X9NJRjD5TQtRIkqCEsB79gCCl1K6i517AcsuFI4RlySk+IaxHOPCS1jpcax0O/A3sAlBKvW65sISwDElQQlgPTyAToKjPzwBgvlKqNmBnycCEsARJUEJYj4NA56KfJwILtdZHMWZWuywUkxAWIwlKCOsxHWirlDoMtAIeL3o9HElQogaSRRJCWAmtdRIXZlAXa4wxuxKiRpGGhUIIIaySnOITQghhlSRBCSGEsEqSoIQQQlglSVBCCCGskiQoIYQQVkkSlBBCCKskCUoIIYRVkgQlhBDCKv0/dHvb+pPRWRwAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -125,7 +125,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -184,9 +184,9 @@
    "notebook_metadata_filter": "-all"
   },
   "kernelspec": {
-   "display_name": "Python (elli)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "myenv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -198,7 +198,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.9"
+   "version": "3.11.1"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/examples/Interfaces/Interferences.ipynb b/examples/Interfaces/Interferences.ipynb
index 08d841c4..6e5d7a21 100644
--- a/examples/Interfaces/Interferences.ipynb
+++ b/examples/Interfaces/Interferences.ipynb
@@ -84,7 +84,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -127,9 +127,9 @@
    "notebook_metadata_filter": "-all"
   },
   "kernelspec": {
-   "display_name": "Python (elli)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "myenv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -141,7 +141,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.9"
+   "version": "3.11.1"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/examples/Total internal reflection/TIR.ipynb b/examples/Total internal reflection/TIR.ipynb
index 800549cb..06d9c2ce 100644
--- a/examples/Total internal reflection/TIR.ipynb
+++ b/examples/Total internal reflection/TIR.ipynb
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "b127e651",
    "metadata": {
     "lines_to_next_cell": 2,
@@ -98,7 +98,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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VxwD/kIugnCs2lujuVPYmIxdd2SSEb5rZg2b2FwAz20A4QqlzUad4BzGvIbiIy+bsPq6XdScMViDOFTMlYt6H4CJvwDuVJf0d8FlghqTlaZvqgD/kKjDnikYygVJxOqyKygpPCC66Mhm64i6CYSq+A1yZtr7NzLbmJCrnikk4W1qMKirLvA/BRVcmg9u1Aq0E8x47V3rC2dL8KiMXdRkPbhdeZnoaMC19PzO7dvDDcq6IhLOlBfMheEJw0ZXNaKe/IqgpLAE6cxOOc0UonC0tZt5k5KItm4TQYGbH5ywS54pVWEPooNprCC7Ssjm7/yhpTs4ica5YhX0Iftmpi7psaghHARdKWk3QZCTAzGxuTiJzrlh01xDMp9B00ZZNQvCb0FxpSngNwZWGbM7utcD7gfPNbA1gwL45icq5YhJPuw/BE4KLsGzO7h8CR/D2/QhtwE2DHpFzxaY7IXiTkYu4bJqMDjezQyS9CGBm2yT5fAgu+hJv35hW5TUEF2HZnN1xSeUETUVIGg+kchKVc8Uk7FSOUeVTaLpIy+bsvgF4CNhH0reA54Bv5yQq54rJLpedepORi66Mm4zM7E5JSwjmQBCwwMxezVlkzhWLeDtJVZKizDuVXaRl04eAmb0GvJajWJwrTokYibJqAE8ILtIymQ/hOTM7SlIbYf9B9yaCG9NG5iw654pBvIN4WQ1lgnIfy8hF2IA/d8zsqPC5zsxGpj3qMk0Gko6X9LqkVZKu7KPMmZJWSloh6a7s/gzncijeQbzMh7520ZfxGS7pZ5JGpS2PlnRbBvuVE9yvcAIwCzhH0qweZWYCXwWONLPZwBcyjcu5nEt00CW/5NRFXzZn+Fwza+leMLNtwMEZ7HcYsMrMVptZF3APcGqPMpcAN4XHxMw2ZRGXc7nVXUPwkU5dxGVzhpdJGt29IGkMmXVKTwbWpS03huvS7Q/sL+kPkv4kqddhtiVdKmmxpMWbN2/OInTn9kI8RpeqqfD+Axdx2Vxl9H3geUn3h8tnAN8axDhmAscADcDvJc1Jr5EAmNktwC0A8+fPN5zLh3g7nT59pisB2dyHcHt4H8Kx4apPmNnKDHZtAqakLTeE69I1Ai+YWRx4Q9KfCRLEokzjcy5nEjG6NJwqbzJyEZftfQgrgBVZvsciYKak6QSJ4Gzgkz3K/JJg0LyfShpH0IS0Osv3cS434u3EmOR3KbvIG/Anj6Tnwuc2SdvTHm2Stg+0v5klgMuBx4BXgfvMbIWkayWdEhZ7DGiWtBJ4GvgHM2ve0z/KuUEVj9FJJRVlXkNw0ZZJDeGN8Pmfzew/9+RNzOxR4NEe665Ke23Al8KHc8Ul0UGswq8yctGXyRl+iKRJBNNnjpY0Jv2R6wCdK7h4BzGqqPImIxdxmdQQfgw8CcwAlhAMWdHNwvXORVMqCckuOny2NFcCMhm64gYzOxC4zcxmmNn0tIcnAxdt4WxpHeZzIbjoy6RT+Y7wpY9y6kpPOFtau3mTkYu+TH7yvCfsQ7jI+xBcyQlnS+uwSm8ycpGXSR/Cj3i7D2Fpj23eh+CiLZwtrT3lfQgu+rLtQ5jufQiupIQ1hJ1WSYU3GbmIy+Ynz+cknSvpnwEkTZV0WI7icq44dPchpCp9+GsXedmc4TcBR/D2sBNt4TrnomtHMBL7llSdNxm5yMvmDD/czD4HxOCt+RCqchKVc8WiNRi5fU1ynCcEF3nZnOHxcPYzA5A0HkjlJCrnikXLWqgaQXOq1ge3c5GXTUK4AXgI2FfSt4DngG/nJCrnikXLOhg1lXgSryG4yMtmPoQ7w/kQPhSuWmBmr+YmLOeKRMtarH4KybXmCcFFXrZneDXBWEbC+w9cKWhdS3JkML9TZYU3GbloyzghSPo8cCcwHtgH+LmkK3IVmHMFF2uFWCvJkQ0AVPp8CC7ispkx7WKCK412Akj6HvA8cGMuAnOu4FqCK4zidWFC8E5lF3HZ/OQRkExbTrLrUNjORUvLWgC6hk8G8AlyXORlU0P4KfCCpIfC5QXATwY9IueKRXgPQmxEA7DNO5Vd5GVzldH1kp4BjgpXXWhmL+YkKueKQctaqKilqyoY1NebjFzUZVNDwMyWsvuIp85FU8taGDWFeMoAvw/BRV82Vxn9TNKotOXRkm7LSVTOFYOWtVA/ha5kcEO+JwQXddmc4XPNrKV7IRzL6OBBj8i5YtHafZdyUEPw0U5d1GVzhpdJGt29EM6WllWTk3NDRtdOaG+GUVNIhDUEnw/BRV02X+jfB56XdH+4fAbwrcEPybkiEN6DwKj9vMnIlYxsrjK6XdJi4IPhqk+Y2crchOVcgYX3IDBqKvEO71R2pSHbq4xWAp4EXPS1hgmhfgqJHUENwfsQXNT5Ge5cb1rWQnkVjNiXuPchuBLhCcG53rSsg/oGKCujK+lNRq40ZNxkJKkaOA2Ylr6fmV07+GE5V2Ata2HUVADiCW8ycqUhmzP8V8CpQALYmfZwLnpa10F9MA9CIhVeZeTzIbiIy6ZTucHMjs9ZJM4Vi3gMdrwJo/YDeKvJqMLnQ3ARl80Z/kdJc/bkTSQdL+l1SaskXdlPudMkmaT5e/I+zg2K1sbgeVRQQ/AmI1cqsqkhHAVcIOkNoJNgLgQzs7n97SSpHLgJOA5oBBZJerjnPQyS6oDPAy9kEZNzg69lTfDc3YeQ9CYjVxqySQgn7OF7HAasMrPVAJLuIeiL6Hk/w78A3wP+YQ/fx7nB0fL2PQgAiZQ3GbnSkPEZbmZrgFHAyeFjVLhuIJOBdWnLjeG6t0g6BJhiZr/p70CSLpW0WNLizZs3Zxq6c9lpXQdlFVA3EYCuRPfQFV5DcNGWzfDXnwfuBPYJHz+XdMXeBiCpDLge+PJAZc3sFjObb2bzx48fv7dv7Vzvtv0NRk6C8qACHU+mqCwXkicEF23ZNBldDBxuZjsBJH0PeB64cYD9moApacsN4bpudcBBwDPhf7gJwMOSTjGzxVnE59zgWP8iTHi7ayxICN5c5KIvm7NcQDJtORmuG8giYKak6ZKqgLOBh7s3mlmrmY0zs2lmNg34E+DJwBXGzmbYuhoaDn1rVTxpVJR57cBFXzY1hJ8CL0h6KFxeAPxkoJ3MLCHpcuAxoBy4zcxWSLoWWGxmD/d/BOfyqHFR8DzlsLdWxZMpqiq8huCiL5vhr6+X9DvgyHDVhWb2Yob7Pgo82mPdVX2UPSbTmJwbdI2Lgg7lifPeWuVNRq5UZDv89RJgSY5ica7wGhfCvgdB1bC3VsWT5gnBlYQBz3JJz4XPbZK2pz3aJG3PfYjO5UkqCU1Ld+k/gKCG4ENfu1IwYA3BzI4Kn+tyH45zBbTpVejasUv/AYR9CF5DcCUgm/sQZvWy7pjBDMa5gmpcGDw37DqUljcZuVKRzVl+n6R/UqBW0o3Ad3IVmHN517gYho2D0dN3Wd19Y5pzUZdNQjic4AazPxLcW7Cet684cm7oW7cw6D/ocUdy0IfgNQQXfdmc5XGgA6gFaoA3zCyVk6icy7f2rdD8F5hy6G6b4knzPgRXErI5yxcRJIRDgfcD50i6PydROZdvTeHV1A29JQRvMnKlIauxjNKGk9gAnCrpvBzE5Fz+rVsIKoNJh+y2yTuVXanI5ixfIulcSVcBSJoKvJ6bsJzLs8ZFsM9sqB6x2ya/U9mVimzO8h8CRwDnhMttBDOhOTe0pVJBk1Ev/QfgTUaudGTTZHS4mR0i6UUAM9sWjl7q3NDWtBg6t8PUI3rdHE94DcGVhqyuMgrnRzYASeMBv8rIDX0rfgnlVbD/R3vdHE8ZlT7aqSsB2ZzlNwAPAftI+hbwHPDtnETlXL6kUrDyV/COD0JNfa9F4skUlT4fgisB2Qx/faekJcCHCCbGWWBmr+YsMufyoWkJbG+ED36jzyLeZORKRbbDX78GvJajWJzLv5W/hLJKeNcJfRaJJ73JyJWGAROCpDbCfoOemwAzs5GDHpVz+WD2dnNR7ag+ihjxlNcQXGnI5Cz/Vfilf5WZjUx71HkycENa0xJoXQezF/RZJJkyzPA+BFcSMkkIB0uaBFwoabSkMemPXAfoXM6seChsLjqxzyLxZFA59iYjVwoy6UP4MfAkMINg+sz0n0oWrnduaDGDlQ/DO47ts7kIoCsZXFntTUauFAx4lpvZDWZ2IHCbmc0ws+lpD08GbmhqWgqta2HWgn6LJcKEUOV3KrsSkPHPHjP7u1wG4lxevXR30Fx0QN/NRfB2k5HPh+BKQTZTaKrn4HaSDhtoP+eKTvtWWHYnzD0Takf3WzTuTUauhPjgdq70LLoV4u3wvisGLPp2H4I3Gbno88HtXGmJd8ALP4aZH4V9DhyweCJsMvIZ01wp8MHtXGlZdhe0b4Ej/z6j4t1NRt6H4EqBD27nSkcqCc//F0x+D+x3ZEa7eJORKyV7OrgdwOnA3JxE5VwuvPYIbF0NZ/wMlNkXfFei+7JTryG46BvwLJc0UtJXJf0XMJWgc7kM+DVwZo7jc25wmMFz/wmjp8OBJ2e828bWGAD7jKzOUWDOFY9Magh3ANuA54HPAF/j7eGvl+UuNOcG0Uv3wPqlcOpNUFae8W5rt7YD0DB6WK4ic65oZJIQZpjZHABJtwIbgKlmFsv0TSQdD/wAKAduNbPv9tj+JYJkkwA2AxeZ2ZpMj+9cvzpa4Il/hoZD4d2fzGrXNc3tTBhZQ01l5knEuaEqk4bRePcLM0sCjVkmg3KC+xVOAGYB50ia1aPYi8B8M5sLPAD8W6bHd25AT38L2pvhpO9DWXZ9Aeu2tjN1jNcOXGnI5H/HuyVtDx9twNzu15K2Z7D/YcAqM1ttZl3APcCp6QXM7Gkzaw8X/wQ0ZPNHONen9cuCG9EO/QxMfHfWu6/ZupOpYz0huNIwYJORme1tXXkysC5tuRE4vJ/yFwO/7W2DpEuBSwGmTp26l2G5yEul4NGvwLCxcOzXs949Fk/y5vZOryG4klFU19JJOheYD1zX23Yzu8XM5pvZ/PHjx+c3ODf0LPwxNC6C4/6l3yGu+7Iu7FDez2sIrkRkNafyHmoCpqQtN4TrdiHpw8DXgQ+YWWce4nJRtm4hPP6NYPKbd5+9R4fovsJoitcQXInIRw1hETBT0vRw7KOzgYfTC0g6mGAinlPMbFMeYnJRtnML3H8BjJwMC27O+Ca0ntY0hzUETwiuROQ8IZhZArgceAx4FbjPzFZIulbSKWGx64ARwP2Slkl6uI/DOde/VBIevCRICmfevkdNRd3Wbm1neFU5Y4b7GI6uNOSjyQgzexR4tMe6q9JefzgfcbgS8Mx34K9Pwck/gEnz9upQa7e2M3XscLSHNQznhpqi6lR2bq/86Wb4/XUw71w45Py9Ptzare1MHVM7CIE5NzR4QnDRsPQO+N8r4YCPBbWDvfxVn0oZa7e2s9/Y4YMUoHPFzxOCG/pe+QU8fAW844Nw+m1QvvctoZvaOulKpPwKI1dSPCG4oW3ZXfDgpTD1CDjrTqgYnFFJ1zTvBPwKI1daPCG4ockMnv42/PLvgsluPnkvVA3el3f3PQh+l7IrJXm5ysi5QZXoCpqIlt8D8z4FH/tPqBjcS0PXbm2nTDB5tHcqu9LhCcENLVvfgF98BpoWw7HfgKO/stcdyL1Zu7WdSaNqqfSZ0lwJ8YTgho6XH4BffwFUBmf8D8z+eM7eKrjCyJuLXGnxnz+u+LVvhYcug19cDPvOgsuezWkyAFjb7PMguNLjNQRXvFIpeOkueOKqYNazo/8RPvBPg3JZaX92dCZo3tnF1DF+D4IrLZ4QXHFa/yL89kpY9yeY8l742PWw7+y8vPXaZr/CyJUmTwiuuGx6DZ7+V3j118HENqfeFMyDnOXUl3tjrc+D4EqUJwRXHDa+DH+4AV55ACqHwzFfhfd+FmpG5j2UtVuDm9L8LmVXajwhuMJJpeCvT8Ifb4Q3fhckgiMuh6O+CMPGFCystVvbGTWskvrayoLF4FwheEJw+bd9PSy7MxiQrmUN1E2ED18D77lgr+YvGCwvrN7KARPqCh2Gc3nnCcHlR6wVXvtNMBDdX58CS8H0o+FDV8GBpwz6ncZ76vWNbfxl0w7OOyI/HdjOFRNPCC53dmyCPz8Gr/8WVj0ByS6onwpHfgEOPhfGvqPQEe7mkeXrKROccNDEQofiXN55QnCDJxmHpiWw+hn4yxPBayyY23j+xXDQadAwPydDTQwGM+OR5Rt474yxjK8bnFFTnRtKPCG4PZfohKalsPb54LHmj9C1AxBMOhiO/RrsfzxMmFO0SSDdivXbeWPLTi55/4xCh+JcQXhCcJlJJaH5r8ENY01LgsfGlyHZGWwfOxPmngkzjoFp7y/oVUJ76pHlGygvE8cfNKHQoThXEJ4Q3K7Mgrb/za/C5tdh00rY+Aq8uQISHUGZyuHBBPaHhxPTTDkcho8raNh7K2guWs+R7xzHmOHF0cHtXL55QihFZsGAcdvegG1/C4aUbl4VPv4SXBHUrWZU0OQz/6LgeeK7Yfy7oKy8UNHnxEuNrTRu6+DzH5pZ6FCcKxhPCFFjFnyh73gT2jYE1/xvbwqeW9ZB67rgOb5z1/3qJsG4d8JBp8O4/WGfA2D8ATBi3yHR/r+3HnlpPVXlZXxktjcXudLlCaHYJRPBF3zHtrTHVti5Bdqbg8fOzUEzz85NwXMitvtxakdD/RQY+06YcSyMmgqjp8GY6TBqv0GdfnKoSaaM37y8gaP3H+d3J7uS5glhMJhBKhFcdZPoDDpa4x3BF3M8FrS9xzsg3g5d7cGv867w0bkDutqC58426NwePMe2Q6wlvGqnD2UVwQBww8cHj7HvCJ7rJkLdhODX/chJwaPSp4Lsy63PrmZDa4xvnjyr0KE4V1AllxCWPPoTql+6AwBhCAOgLHxdRuqtR3nac7klKSdBuSV2fZ2KU27xt46TtaoRUDU8eK6uCwZzGz4eauqD9vua+uBROzq4cqd2dPh6bLC+BJpzcmnl+u38++Ovc/zsCXzUm4tciSu5hGCJOBWpzre+vrtTQtzKMAi//qtIIpKUkbByEpSRMBG3CuKU02VlxFNlxKyCTqugi3K6rJIuKuikii4qiFkVMaropJIOqumwamJU0UEVO62WdqqJqYraVCW1qQqGWznDrIIRVs6IVAXDUxXUpSqpS1UwIllBXbKCkYlK6roqGNlVSX1XGfW1MeprKxlWVY48MWQtFk/ypfuWUV9bxbc/Mcc/Q1fySi4hzD/lMjjlskE7npnRlUwRTxpdiRSdiSSd8RRdyRSd8RSxRJJYPEksnqIjniTWlaQjnqS9K0lHV4L2riQ7u5Ls7EzQ3pVgR2eCLTu6WNPcTltngh2xBB3xZL8xVJaL+toqRg2rZPSwSkYNq2L0sEpGD69izLAqxgyvYuyIKsYOr2ZcXTVjh1dRUxmtq4T2xPVP/JnXNrbx0wsO9UtNnaMEE8Jgk0R1RTnVFUCORjuIJ1PsiCXYHovTFkuwvSNOa/ho6X5uj9PS3kVLe5x1W9tZ3tjFtp1xupKpXo85orqC8XXVjBtRxfi6asaPqGafkTWMr6tm35E17Duymgkja6ivrYzkL+f/W/km//3saj51+FSOPWCfQofjXFHwhDAEVJaXMXp4FaOz/BVrZuzsSrJ1RxfNOztpDp+37Ohiy47geXNbjNc2tvFs2xbaYondjlFdUcaE+hr2HVnDxPoaJtTXMKm+lgn1NUweVcukUbWMHjZ0kkYyZfzg//7MjU+v4oAJI/n6SQcWOiTnioYnhAiTxIjqCkZUVzA1g+kgY/Ekm7Z38mZbjE3bO9m4Pcab22NsbA0eS9du483Wzt1qHTWVZUyqD5LD5FG1TB5dS8Po4HXDmGHsW1dNRXn+psDsy6a2GJ+/exnPr27m9Pc0cO2psxlW5f8FnOuWl/8Nko4HfgCUA7ea2Xd7bK8GbgfeAzQDZ5nZ3/IRm3tbTWU5U8cO6zd5pFJG884uNrbGaGrpYENrB+tbOmhq6aCpJcaTr21iy47OXfYpLxMT62vCJDHsrYTRECaPCfU1VFfkrk/jlaZW7lm0ll+9uJ54KsV1p8/ljPlTcvZ+zg1VOU8IksqBm4DjgEZgkaSHzWxlWrGLgW1m9k5JZwPfA87KdWwue2VlCvoc6qqZ01Dfa5lYPPlWkmjc1kHjtnaatgWv/7BqC2+2xbAeV+mOr6tmUn0NE8PmqH1H1jChvppxI6rDzvAq6msrB0wcsXiSNc3trNzQysr123l+dTOvNG2nuqKMk+ZM5O+OeQcz9/XZ0JzrTT5qCIcBq8xsNYCke4BTgfSEcCpwdfj6AeC/JMms59eGGwpqKsuZMX4EM8aP6HV7VyLFxtYYjS3tNG7rYENLjPUtHaxv7WDV5h38YdUW2jp3788AqCovo66mgtqqcsrLRLkEgh2xBK0dcToTbzdnVVWUMXvSSK45ZTYL5k2mfpjfhexcf/KRECYD69KWG4HD+ypjZglJrcBYYEt6IUmXApcCTJ06NVfxuhyrqigbsGlqZ2eCN7fH2LKji+YdnWzZ0cn2tCutYl1JkmYkU4YBI6oqqB9WSX1tJZNG1TB7Uj0zxg0vir4L54aKIdWjZma3ALcAzJ8/32sPETa8uiKsZRQ6EudKRz5+PjUB6T14DeG6XstIqgDqCTqXnXPO5Uk+EsIiYKak6ZKqgLOBh3uUeRg4P3x9OvCU9x8451x+5bzJKOwTuBx4jOCy09vMbIWka4HFZvYw8BPgDkmrgK0EScM551we5aUPwcweBR7tse6qtNcx4Ix8xOKcc653fgmGc845wBOCc865kCcE55xzgCcE55xzIQ3VqzslbQbW7OHu4+hxF7Tzz6QH/zx25Z/H7obqZ7KfmfV6y+eQTQh7Q9JiM5tf6DiKiX8mu/LPY1f+eewuip+JNxk555wDPCE455wLlWpCuKXQARQh/0x25Z/Hrvzz2F3kPpOS7ENwzjm3u1KtITjnnOvBE4JzzjmgBBOCpOMlvS5plaQrCx1PvkmaIulpSSslrZD0+XD9GElPSPpL+Dy60LHmk6RySS9KeiRcni7phfA8uTccur1kSBol6QFJr0l6VdIRpXyOSPpi+P/lFUl3S6qJ4jlSUglBUjlwE3ACMAs4R9KswkaVdwngy2Y2C3gv8LnwM7gSeNLMZgJPhsul5PPAq2nL3wP+w8zeCWwDLi5IVIXzA+B/zewA4N0En01JniOSJgN/D8w3s4MIhvE/mwieIyWVEIDDgFVmttrMuoB7gFMLHFNemdkGM1savm4j+I8+meBz+FlY7GfAgoIEWACSGoCTgFvDZQEfBB4Ii5Ta51EPHE0wTwlm1mVmLZTwOUIwVUBtOKPjMGADETxHSi0hTAbWpS03hutKkqRpwMHAC8C+ZrYh3LQR2LdQcRXAfwL/CKTC5bFAi5klwuVSO0+mA5uBn4bNaLdKGk6JniNm1gT8O7CWIBG0AkuI4DlSagnBhSSNAH4BfMHMtqdvC6cvLYnrkSV9DNhkZksKHUsRqQAOAW42s4OBnfRoHiqxc2Q0Qe1oOjAJGA4cX9CgcqTUEkITMCVtuSFcV1IkVRIkgzvN7MFw9ZuSJobbJwKbChVfnh0JnCLpbwRNiB8kaD8fFTYPQOmdJ41Ao5m9EC4/QJAgSvUc+TDwhpltNrM48CDBeRO5c6TUEsIiYGZ4dUAVQcfQwwWOKa/C9vGfAK+a2fVpmx4Gzg9fnw/8Kt+xFYKZfdXMGsxsGsH58JSZfQp4Gjg9LFYynweAmW0E1kl6V7jqQ8BKSvQcIWgqeq+kYeH/n+7PI3LnSMndqSzpRII243LgNjP7VmEjyi9JRwHPAi/zdpv51wj6Ee4DphIMK36mmW0tSJAFIukY4Ctm9jFJMwhqDGOAF4FzzayzgOHllaR5BJ3sVcBq4EKCH5AleY5IugY4i+AqvReBzxD0GUTqHCm5hOCcc653pdZk5Jxzrg+eEJxzzgGeEJxzzoU8ITjnnAM8ITjnnAt5QnAFJ2mBJJN0wF4e5wJJ/zVIMd0tabmkL/ZYf5mkT+/hMW/tbTDFwYx7D2I6WNJPwtdlkm6X9EdJs8N1cyT9TyFic/nnCcEVg3OA58LngpM0ATjUzOaa2X+kbzOzH5nZ7XtyXDP7jJmtHJQgB8/XgBvC1x8huB/l48CXAczsZaBB0tTChOfyyROCK6hwTKWjCIYOPjtt/TGSnkkbk//O8C5RJJ0Yrlsi6YbuOQx6HHe8pF9IWhQ+juylTI2kn0p6ORzE7dhw0+PAZEnLJL2/xz5XS/pK+PoZSd+TtFDSn7vLhnMr/Hs4dv5ySVeklZ8fvr4w3GchwTAI/cYdvu9t4TFWS/r7tH0+Hb7PS5LuyOLvrwPmmtlL4apygpsVU4DSiv46/d/GRVfFwEWcy6lTCcbd/7OkZknvSRto7mBgNrAe+ANwpKTFwI+Bo83sDUl393HcHxCMVf9c+Ov2MeDAHmU+RzBO25ywuepxSfsDpwCPmNm8DOKvMLPDwjvgv0kw7s2lwDRgnpklJI1J3yEcB+ga4D0EI2c+TXCn60BxHwAcC9QBr0u6Gdgf+AbwPjPbkvZemfz984FX0pYfA34OfDr8G7otJhjc7t8y+DzcEOYJwRXaOQRfXhAMA3AOwdDCAAvNrBFA0jKCL9kdwGozeyMscze7fnl1+zAwK6xUAIyUNMLMdqSVOQq4EcDMXpO0huALdpfRXwfQPTjgkjC+7vf+UffQyL0M73A48IyZbQ7/tnvD9+0z7vD1b8KhETolbSIYfvqDwP1mtqXHe2Xy908kGOaacN8EvdcENhGM8ukizhOCK5jw1+wHgTmSjKDJwiT9Q1gkfVyYJNmdr2XAe80sNijB9q07xmzj60uvcYdf7Nl8Hpn8/R1ATQYx1YRlXcR5H4IrpNOBO8xsPzObZmZTgDeA9/ezz+vADAWT+0Aw4FhvHgeu6F4IB2vr6VngU+H2/QkGbXs9mz+gD08A/0/h0Mg9m4wIOm4/IGmsgqHIz8gy7nRPAWdIGtvjvTI5zqvAOwf6YwhqL68MWMoNeZ4QXCGdAzzUY90v6OdqIzPrAD4L/K+kJUAbQTt8T38PzA87W1cCl/VS5odAmaSXgXuBCwZptMpbCYZMXi7pJeCTPf6GDcDVwPMEfSPpczlnEnf6sVYA3wJ+F75X95DmAx7HzF4D6sPO5f4cC/xmgDIuAny0UzfkdLeFh1cd3QT8pefloS4zCu6zaDOzW/vYXg38DjgqbbpIF1FeQ3BD0SVhJ/MKoJ7gqiO3Z25m176JnqYCV3oyKA1eQ3DOOQd4DcE551zIE4JzzjnAE4JzzrmQJwTnnHOAJwTnnHOh/w8PrlD/Ve39aQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -139,9 +139,9 @@
    "notebook_metadata_filter": "-all"
   },
   "kernelspec": {
-   "display_name": "Python (elli)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "myenv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -153,7 +153,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.9"
+   "version": "3.11.1"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/pyproject.toml b/pyproject.toml
index 56640da0..6be18267 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,9 +1,61 @@
 [build-system]
-# AVOID CHANGING REQUIRES: IT WILL BE UPDATED BY PYSCAFFOLD!
-requires = ["setuptools>=46.1.0", "setuptools_scm[toml]>=5", "wheel"]
+requires = ["setuptools>=46.1.0", "setuptools-scm[toml]>=5"]
 build-backend = "setuptools.build_meta"
 
+[project]
+name = "pyElli"
+description = "Python implementation of Berremans 4x4 matrix method"
+dynamic = ["version"]
+authors = [
+    { name = "Marius Müller", email = "marius.mueller@physik.uni-giessen.de" },
+    { name = "Florian Dobener", email = "pyelli@schroedingerscat.org" }
+]
+requires-python = ">=3.7"
+license = { file = "LICENSE.txt" }
+readme = "README.md"
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10"
+]
+dependencies = [
+    "scipy",
+    "numpy>=1.20",
+    "pandas>=1.0.0",
+    "h5py",
+    "pyyaml",
+    "importlib-resources",
+    "rapidfuzz"
+]
+
+[project.optional-dependencies]
+fitting = [
+    "ipython",
+    "ipywidgets",
+    "plotly",
+    "matplotlib",
+    "lmfit"
+]
+testing = [
+    "pytest",
+    "pytest-cov",
+    "nbmake"
+]
+
+[project.urls]
+homepage = "https://github.com/PyEllips/pyElli/"
+repository = "https://github.com/PyEllips/pyElli/"
+documentation = "https://pyelli.readthedocs.io/en/latest/"
+changelog = "https://github.com/PyEllips/pyElli/blob/master/CHANGELOG.md"
+tracker = "https://github.com/PyEllips/pyElli/issues"
+download = "https://github.com/PyEllips/pyElli/releases"
+
 [tool.setuptools_scm]
-# See configuration details in https://github.com/pypa/setuptools_scm
 version_scheme = "python-simplified-semver"
 local_scheme = "node-and-date"
+
+[tool.setuptools.packages.find]
+where = ["src"]
+exclude = ["tests*"]
diff --git a/requirements/dev-requirements.txt b/requirements/dev-requirements.txt
index 38cf91d5..63f0b677 100644
--- a/requirements/dev-requirements.txt
+++ b/requirements/dev-requirements.txt
@@ -2,7 +2,7 @@
 # This file is autogenerated by pip-compile with python 3.9
 # To update, run:
 #
-#    pip-compile --extra=fitting --extra=testing --generate-hashes --output-file=requirements/dev-requirements.txt
+#    pip-compile --extra=fitting --extra=testing --generate-hashes --output-file=requirements/dev-requirements.txt pyproject.toml
 #
 appnope==0.1.3 \
     --hash=sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24 \
@@ -253,11 +253,15 @@ h5py==3.7.0 \
     --hash=sha256:f084bbe816907dfe59006756f8f2d16d352faff2d107f4ffeb1d8de126fc5dc7 \
     --hash=sha256:f514b24cacdd983e61f8d371edac8c1b780c279d0acb8485639e97339c866073 \
     --hash=sha256:f73307c876af49aa869ec5df1818e9bb0bdcfcf8a5ba773cc45a4fba5a286a5c
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 importlib-metadata==4.12.0 \
     --hash=sha256:637245b8bab2b6502fcbc752cc4b7a6f6243bb02b31c5c26156ad103d3d45670 \
     --hash=sha256:7401a975809ea1fdc658c3aa4f78cc2195a0e019c5cbc4c06122884e9ae80c23
     # via nbconvert
+importlib-resources==5.10.2 \
+    --hash=sha256:7d543798b0beca10b6a01ac7cafda9f822c54db9e8376a6bf57e0cbd74d486b6 \
+    --hash=sha256:e4a96c8cc0339647ff9a5e0550d9f276fc5a01ffa276012b58ec108cfd7b8484
+    # via pyElli (pyproject.toml)
 iniconfig==1.1.1 \
     --hash=sha256:011e24c64b7f47f6ebd835bb12a743f2fbe9a26d4cecaa7f53bc4f35ee9da8b3 \
     --hash=sha256:bc3af051d7d14b2ee5ef9969666def0cd1a000e121eaea580d4a313df4b37f32
@@ -275,7 +279,7 @@ ipython==8.5.0 \
     # via
     #   ipykernel
     #   ipywidgets
-    #   pyElli (setup.py)
+    #   pyElli (pyproject.toml)
 ipython-genutils==0.2.0 \
     --hash=sha256:72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8 \
     --hash=sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8
@@ -285,7 +289,7 @@ ipython-genutils==0.2.0 \
 ipywidgets==7.7.2 \
     --hash=sha256:3d47a7826cc6e2644d7cb90db26699451f8b42379cf63b761431b63d19984ca2 \
     --hash=sha256:449ab8e7872d0f388ee5c5b3666b9d6af5e5618a5749fd62652680be37dff2af
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 jedi==0.18.1 \
     --hash=sha256:637c9635fcf47945ceb91cd7f320234a7be540ded6f3e99a50cb6febdfd1ba8d \
     --hash=sha256:74137626a64a99c8eb6ae5832d99b3bdd7d29a3850fe2aa80a4126b2a7d949ab
@@ -395,7 +399,7 @@ kiwisolver==1.4.4 \
     # via matplotlib
 lmfit==1.0.3 \
     --hash=sha256:d067c3ea501f035af5d3c079e6e6e35dc3cc1ac7d439429a425b0aeb5a7858a2
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 lxml==4.9.1 \
     --hash=sha256:04da965dfebb5dac2619cb90fcf93efdb35b3c6994fea58a157a834f2f94b318 \
     --hash=sha256:0538747a9d7827ce3e16a8fdd201a99e661c7dee3c96c885d8ecba3c35d1032c \
@@ -548,7 +552,7 @@ matplotlib==3.5.3 \
     --hash=sha256:dacddf5bfcec60e3f26ec5c0ae3d0274853a258b6c3fc5ef2f06a8eb23e042be \
     --hash=sha256:f3840c280ebc87a48488a46f760ea1c0c0c83fcf7abbe2e6baf99d033fd35fd8 \
     --hash=sha256:f814504e459c68118bf2246a530ed953ebd18213dc20e3da524174d84ed010b2
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 matplotlib-inline==0.1.6 \
     --hash=sha256:f1f41aab5328aa5aaea9b16d083b128102f8712542f819fe7e6a420ff581b311 \
     --hash=sha256:f887e5f10ba98e8d2b150ddcf4702c1e5f8b3a20005eb0f74bfdbd360ee6f304
@@ -580,7 +584,7 @@ nbformat==5.4.0 \
 nbmake==1.3.4 \
     --hash=sha256:70464b131b25dc91bd80bc79806673bd9e694b0d353a42402b7359c10c890350 \
     --hash=sha256:88f263ecf3dadb80141c7653626cf056db3f731513c69674cf4e7b8bdb483e61
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 nest-asyncio==1.5.5 \
     --hash=sha256:b98e3ec1b246135e4642eceffa5a6c23a3ab12c82ff816a92c612d68205813b2 \
     --hash=sha256:e442291cd942698be619823a17a86a5759eabe1f8613084790de189fe9e16d65
@@ -627,7 +631,7 @@ numpy==1.23.2 \
     #   lmfit
     #   matplotlib
     #   pandas
-    #   pyElli (setup.py)
+    #   pyElli (pyproject.toml)
     #   scipy
 packaging==21.3 \
     --hash=sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb \
@@ -659,7 +663,7 @@ pandas==1.4.4 \
     --hash=sha256:d0022fe6a313df1c4869b5edc012d734c6519a6fffa3cf70930f32e6a1078e49 \
     --hash=sha256:e7cc960959be28d064faefc0cb2aef854d46b827c004ebea7e79b5497ed83e7d \
     --hash=sha256:ee6f1848148ed3204235967613b0a32be2d77f214e9623f554511047705c1e04
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 pandocfilters==1.5.0 \
     --hash=sha256:0b679503337d233b4339a817bfc8c50064e2eff681314376a47cb582305a7a38 \
     --hash=sha256:33aae3f25fd1a026079f5d27bdd52496f0e0803b3469282162bafdcbdf6ef14f
@@ -742,7 +746,7 @@ pillow==9.3.0 \
 plotly==5.10.0 \
     --hash=sha256:4d36d9859b7a153b273562deeed8c292587a472eb1fd57cd4158ec89d9defadb \
     --hash=sha256:989b13825cc974390aa0169479485d9257d37848a47bc63957251f8e1a7046ba
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 pluggy==1.0.0 \
     --hash=sha256:4224373bacce55f955a878bf9cfa763c1e360858e330072059e10bad68531159 \
     --hash=sha256:74134bbf457f031a36d68416e1509f34bd5ccc019f0bcc952c7b909d06b37bd3
@@ -886,12 +890,12 @@ pytest==7.1.3 \
     --hash=sha256:4f365fec2dff9c1162f834d9f18af1ba13062db0c708bf7b946f8a5c76180c39
     # via
     #   nbmake
-    #   pyElli (setup.py)
+    #   pyElli (pyproject.toml)
     #   pytest-cov
 pytest-cov==3.0.0 \
     --hash=sha256:578d5d15ac4a25e5f961c938b85a05b09fdaae9deef3bb6de9a6e766622ca7a6 \
     --hash=sha256:e7f0f5b1617d2210a2cabc266dfe2f4c75a8d32fb89eafb7ad9d06f6d076d470
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 python-dateutil==2.8.2 \
     --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \
     --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
@@ -903,6 +907,48 @@ pytz==2022.2.1 \
     --hash=sha256:220f481bdafa09c3955dfbdddb7b57780e9a94f5127e35456a48589b9e0c0197 \
     --hash=sha256:cea221417204f2d1a2aa03ddae3e867921971d0d76f14d87abb4414415bbdcf5
     # via pandas
+pyyaml==6.0 \
+    --hash=sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf \
+    --hash=sha256:0283c35a6a9fbf047493e3a0ce8d79ef5030852c51e9d911a27badfde0605293 \
+    --hash=sha256:055d937d65826939cb044fc8c9b08889e8c743fdc6a32b33e2390f66013e449b \
+    --hash=sha256:07751360502caac1c067a8132d150cf3d61339af5691fe9e87803040dbc5db57 \
+    --hash=sha256:0b4624f379dab24d3725ffde76559cff63d9ec94e1736b556dacdfebe5ab6d4b \
+    --hash=sha256:0ce82d761c532fe4ec3f87fc45688bdd3a4c1dc5e0b4a19814b9009a29baefd4 \
+    --hash=sha256:1e4747bc279b4f613a09eb64bba2ba602d8a6664c6ce6396a4d0cd413a50ce07 \
+    --hash=sha256:213c60cd50106436cc818accf5baa1aba61c0189ff610f64f4a3e8c6726218ba \
+    --hash=sha256:231710d57adfd809ef5d34183b8ed1eeae3f76459c18fb4a0b373ad56bedcdd9 \
+    --hash=sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287 \
+    --hash=sha256:2cd5df3de48857ed0544b34e2d40e9fac445930039f3cfe4bcc592a1f836d513 \
+    --hash=sha256:40527857252b61eacd1d9af500c3337ba8deb8fc298940291486c465c8b46ec0 \
+    --hash=sha256:432557aa2c09802be39460360ddffd48156e30721f5e8d917f01d31694216782 \
+    --hash=sha256:473f9edb243cb1935ab5a084eb238d842fb8f404ed2193a915d1784b5a6b5fc0 \
+    --hash=sha256:48c346915c114f5fdb3ead70312bd042a953a8ce5c7106d5bfb1a5254e47da92 \
+    --hash=sha256:50602afada6d6cbfad699b0c7bb50d5ccffa7e46a3d738092afddc1f9758427f \
+    --hash=sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2 \
+    --hash=sha256:77f396e6ef4c73fdc33a9157446466f1cff553d979bd00ecb64385760c6babdc \
+    --hash=sha256:81957921f441d50af23654aa6c5e5eaf9b06aba7f0a19c18a538dc7ef291c5a1 \
+    --hash=sha256:819b3830a1543db06c4d4b865e70ded25be52a2e0631ccd2f6a47a2822f2fd7c \
+    --hash=sha256:897b80890765f037df3403d22bab41627ca8811ae55e9a722fd0392850ec4d86 \
+    --hash=sha256:98c4d36e99714e55cfbaaee6dd5badbc9a1ec339ebfc3b1f52e293aee6bb71a4 \
+    --hash=sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c \
+    --hash=sha256:9fa600030013c4de8165339db93d182b9431076eb98eb40ee068700c9c813e34 \
+    --hash=sha256:a80a78046a72361de73f8f395f1f1e49f956c6be882eed58505a15f3e430962b \
+    --hash=sha256:afa17f5bc4d1b10afd4466fd3a44dc0e245382deca5b3c353d8b757f9e3ecb8d \
+    --hash=sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c \
+    --hash=sha256:b5b9eccad747aabaaffbc6064800670f0c297e52c12754eb1d976c57e4f74dcb \
+    --hash=sha256:bfaef573a63ba8923503d27530362590ff4f576c626d86a9fed95822a8255fd7 \
+    --hash=sha256:c5687b8d43cf58545ade1fe3e055f70eac7a5a1a0bf42824308d868289a95737 \
+    --hash=sha256:cba8c411ef271aa037d7357a2bc8f9ee8b58b9965831d9e51baf703280dc73d3 \
+    --hash=sha256:d15a181d1ecd0d4270dc32edb46f7cb7733c7c508857278d3d378d14d606db2d \
+    --hash=sha256:d4b0ba9512519522b118090257be113b9468d804b19d63c71dbcf4a48fa32358 \
+    --hash=sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53 \
+    --hash=sha256:d4eccecf9adf6fbcc6861a38015c2a64f38b9d94838ac1810a9023a0609e1b78 \
+    --hash=sha256:d67d839ede4ed1b28a4e8909735fc992a923cdb84e618544973d7dfc71540803 \
+    --hash=sha256:daf496c58a8c52083df09b80c860005194014c3698698d1a57cbcfa182142a3a \
+    --hash=sha256:dbad0e9d368bb989f4515da330b88a057617d16b6a8245084f1b05400f24609f \
+    --hash=sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174 \
+    --hash=sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5
+    # via pyElli (pyproject.toml)
 pyzmq==23.2.1 \
     --hash=sha256:022cf5ea7bcaa8a06a03c2706e0ae66904b6138b2155577cd34c64bc7cc637ab \
     --hash=sha256:044447ae4b2016a6b8697571fd633f799f860b19b76c4a2fd9b1140d52ee6745 \
@@ -982,6 +1028,97 @@ pyzmq==23.2.1 \
     #   ipykernel
     #   jupyter-client
     #   notebook
+rapidfuzz==2.13.7 \
+    --hash=sha256:020858dd89b60ce38811cd6e37875c4c3c8d7fcd8bc20a0ad2ed1f464b34dc4e \
+    --hash=sha256:042644133244bfa7b20de635d500eb9f46af7097f3d90b1724f94866f17cb55e \
+    --hash=sha256:08590905a95ccfa43f4df353dcc5d28c15d70664299c64abcad8721d89adce4f \
+    --hash=sha256:114810491efb25464016fd554fdf1e20d390309cecef62587494fc474d4b926f \
+    --hash=sha256:1333fb3d603d6b1040e365dca4892ba72c7e896df77a54eae27dc07db90906e3 \
+    --hash=sha256:16080c05a63d6042643ae9b6cfec1aefd3e61cef53d0abe0df3069b9d4b72077 \
+    --hash=sha256:16ffad751f43ab61001187b3fb4a9447ec2d1aedeff7c5bac86d3b95f9980cc3 \
+    --hash=sha256:1f50d1227e6e2a0e3ae1fb1c9a2e1c59577d3051af72c7cab2bcc430cb5e18da \
+    --hash=sha256:1fbad8fb28d98980f5bff33c7842efef0315d42f0cd59082108482a7e6b61410 \
+    --hash=sha256:23524635840500ce6f4d25005c9529a97621689c85d2f727c52eed1782839a6a \
+    --hash=sha256:24d3fea10680d085fd0a4d76e581bfb2b1074e66e78fd5964d4559e1fcd2a2d4 \
+    --hash=sha256:24eb6b843492bdc63c79ee4b2f104059b7a2201fef17f25177f585d3be03405a \
+    --hash=sha256:25b4cedf2aa19fb7212894ce5f5219010cce611b60350e9a0a4d492122e7b351 \
+    --hash=sha256:27be9c63215d302ede7d654142a2e21f0d34ea6acba512a4ae4cfd52bbaa5b59 \
+    --hash=sha256:2c836f0f2d33d4614c3fbaf9a1eb5407c0fe23f8876f47fd15b90f78daa64c34 \
+    --hash=sha256:3a9bd02e1679c0fd2ecf69b72d0652dbe2a9844eaf04a36ddf4adfbd70010e95 \
+    --hash=sha256:3d8b081988d0a49c486e4e845a547565fee7c6e7ad8be57ff29c3d7c14c6894c \
+    --hash=sha256:3dcffe1f3cbda0dc32133a2ae2255526561ca594f15f9644384549037b355245 \
+    --hash=sha256:3f11a7eff7bc6301cd6a5d43f309e22a815af07e1f08eeb2182892fca04c86cb \
+    --hash=sha256:42085d4b154a8232767de8296ac39c8af5bccee6b823b0507de35f51c9cbc2d7 \
+    --hash=sha256:424f82c35dbe4f83bdc3b490d7d696a1dc6423b3d911460f5493b7ffae999fd2 \
+    --hash=sha256:43fb8cb030f888c3f076d40d428ed5eb4331f5dd6cf1796cfa39c67bf0f0fc1e \
+    --hash=sha256:460853983ab88f873173e27cc601c5276d469388e6ad6e08c4fd57b2a86f1064 \
+    --hash=sha256:467c1505362823a5af12b10234cb1c4771ccf124c00e3fc9a43696512bd52293 \
+    --hash=sha256:46b9b8aa09998bc48dd800854e8d9b74bc534d7922c1d6e1bbf783e7fa6ac29c \
+    --hash=sha256:53dcae85956853b787c27c1cb06f18bb450e22cf57a4ad3444cf03b8ff31724a \
+    --hash=sha256:585206112c294e335d84de5d5f179c0f932837752d7420e3de21db7fdc476278 \
+    --hash=sha256:5ada0a14c67452358c1ee52ad14b80517a87b944897aaec3e875279371a9cb96 \
+    --hash=sha256:5e2b3d020219baa75f82a4e24b7c8adcb598c62f0e54e763c39361a9e5bad510 \
+    --hash=sha256:6120f2995f5154057454c5de99d86b4ef3b38397899b5da1265467e8980b2f60 \
+    --hash=sha256:68a89bb06d5a331511961f4d3fa7606f8e21237467ba9997cae6f67a1c2c2b9e \
+    --hash=sha256:7496e8779905b02abc0ab4ba2a848e802ab99a6e20756ffc967a0de4900bd3da \
+    --hash=sha256:759a3361711586a29bc753d3d1bdb862983bd9b9f37fbd7f6216c24f7c972554 \
+    --hash=sha256:75c45dcd595f8178412367e302fd022860ea025dc4a78b197b35428081ed33d5 \
+    --hash=sha256:7d005e058d86f2a968a8d28ca6f2052fab1f124a39035aa0523261d6baf21e1f \
+    --hash=sha256:7f7930adf84301797c3f09c94b9c5a9ed90a9e8b8ed19b41d2384937e0f9f5bd \
+    --hash=sha256:8109e0324d21993d5b2d111742bf5958f3516bf8c59f297c5d1cc25a2342eb66 \
+    --hash=sha256:81642a24798851b118f82884205fc1bd9ff70b655c04018c467824b6ecc1fabc \
+    --hash=sha256:8450d15f7765482e86ef9be2ad1a05683cd826f59ad236ef7b9fb606464a56aa \
+    --hash=sha256:875d51b3497439a72e2d76183e1cb5468f3f979ab2ddfc1d1f7dde3b1ecfb42f \
+    --hash=sha256:8b477b43ced896301665183a5e0faec0f5aea2373005648da8bdcb3c4b73f280 \
+    --hash=sha256:8d3e252d4127c79b4d7c2ae47271636cbaca905c8bb46d80c7930ab906cf4b5c \
+    --hash=sha256:916bc2e6cf492c77ad6deb7bcd088f0ce9c607aaeabc543edeb703e1fbc43e31 \
+    --hash=sha256:988f8f6abfba7ee79449f8b50687c174733b079521c3cc121d65ad2d38831846 \
+    --hash=sha256:99a84ab9ac9a823e7e93b4414f86344052a5f3e23b23aa365cda01393ad895bd \
+    --hash=sha256:9be02162af0376d64b840f2fc8ee3366794fc149f1e06d095a6a1d42447d97c5 \
+    --hash=sha256:a5585189b3d90d81ccd62d4f18530d5ac8972021f0aaaa1ffc6af387ff1dce75 \
+    --hash=sha256:ae33a72336059213996fe4baca4e0e4860913905c2efb7c991eab33b95a98a0a \
+    --hash=sha256:af4f7c3c904ca709493eb66ca9080b44190c38e9ecb3b48b96d38825d5672559 \
+    --hash=sha256:b20141fa6cee041917801de0bab503447196d372d4c7ee9a03721b0a8edf5337 \
+    --hash=sha256:b3210869161a864f3831635bb13d24f4708c0aa7208ef5baac1ac4d46e9b4208 \
+    --hash=sha256:b34e8c0e492949ecdd5da46a1cfc856a342e2f0389b379b1a45a3cdcd3176a6e \
+    --hash=sha256:b52ac2626945cd21a2487aeefed794c14ee31514c8ae69b7599170418211e6f6 \
+    --hash=sha256:b5dd713a1734574c2850c566ac4286594bacbc2d60b9170b795bee4b68656625 \
+    --hash=sha256:b5f705652360d520c2de52bee11100c92f59b3e3daca308ebb150cbc58aecdad \
+    --hash=sha256:b6389c50d8d214c9cd11a77f6d501529cb23279a9c9cafe519a3a4b503b5f72a \
+    --hash=sha256:b6bad92de071cbffa2acd4239c1779f66851b60ffbbda0e4f4e8a2e9b17e7eef \
+    --hash=sha256:b75dd0928ce8e216f88660ab3d5c5ffe990f4dd682fd1709dba29d5dafdde6de \
+    --hash=sha256:c2523f8180ebd9796c18d809e9a19075a1060b1a170fde3799e83db940c1b6d5 \
+    --hash=sha256:c31022d9970177f6affc6d5dd757ed22e44a10890212032fabab903fdee3bfe7 \
+    --hash=sha256:c36fd260084bb636b9400bb92016c6bd81fd80e59ed47f2466f85eda1fc9f782 \
+    --hash=sha256:c3741cb0bf9794783028e8b0cf23dab917fa5e37a6093b94c4c2f805f8e36b9f \
+    --hash=sha256:c3fbe449d869ea4d0909fc9d862007fb39a584fb0b73349a6aab336f0d90eaed \
+    --hash=sha256:c66546e30addb04a16cd864f10f5821272a1bfe6462ee5605613b4f1cb6f7b48 \
+    --hash=sha256:c71d9d512b76f05fa00282227c2ae884abb60e09f08b5ca3132b7e7431ac7f0d \
+    --hash=sha256:c8601a66fbfc0052bb7860d2eacd303fcde3c14e87fdde409eceff516d659e77 \
+    --hash=sha256:c88adbcb933f6b8612f6c593384bf824e562bb35fc8a0f55fac690ab5b3486e5 \
+    --hash=sha256:ca00fafd2756bc9649bf80f1cf72c647dce38635f0695d7ce804bc0f759aa756 \
+    --hash=sha256:ca8a23097c1f50e0fdb4de9e427537ca122a18df2eead06ed39c3a0bef6d9d3a \
+    --hash=sha256:cda1e2f66bb4ba7261a0f4c2d052d5d909798fca557cbff68f8a79a87d66a18f \
+    --hash=sha256:cdfc04f7647c29fb48da7a04082c34cdb16f878d3c6d098d62d5715c0ad3000c \
+    --hash=sha256:cf62dacb3f9234f3fddd74e178e6d25c68f2067fde765f1d95f87b1381248f58 \
+    --hash=sha256:d00df2e4a81ffa56a6b1ec4d2bc29afdcb7f565e0b8cd3092fece2290c4c7a79 \
+    --hash=sha256:d248a109699ce9992304e79c1f8735c82cc4c1386cd8e27027329c0549f248a2 \
+    --hash=sha256:d63def9bbc6b35aef4d76dc740301a4185867e8870cbb8719ec9de672212fca8 \
+    --hash=sha256:d82f20c0060ffdaadaf642b88ab0aa52365b56dffae812e188e5bdb998043588 \
+    --hash=sha256:dbcf5371ea704759fcce772c66a07647751d1f5dbdec7818331c9b31ae996c77 \
+    --hash=sha256:e8914dad106dacb0775718e54bf15e528055c4e92fb2677842996f2d52da5069 \
+    --hash=sha256:ebe303cd9839af69dd1f7942acaa80b1ba90bacef2e7ded9347fbed4f1654672 \
+    --hash=sha256:ec55a81ac2b0f41b8d6fb29aad16e55417036c7563bad5568686931aa4ff08f7 \
+    --hash=sha256:effe182767d102cb65dfbbf74192237dbd22d4191928d59415aa7d7c861d8c88 \
+    --hash=sha256:f42b82f268689f429def9ecfb86fa65ceea0eaf3fed408b570fe113311bf5ce7 \
+    --hash=sha256:f6fe570e20e293eb50491ae14ddeef71a6a7e5f59d7e791393ffa99b13f1f8c2 \
+    --hash=sha256:f799d1d6c33d81e983d3682571cc7d993ae7ff772c19b3aabb767039c33f6d1e \
+    --hash=sha256:f891b98f8bc6c9d521785816085e9657212621e93f223917fb8e32f318b2957e \
+    --hash=sha256:fa263135b892686e11d5b84f6a1892523123a00b7e5882eff4fbdabb38667347 \
+    --hash=sha256:fa4c598ed77f74ec973247ca776341200b0f93ec3883e34c222907ce72cb92a4 \
+    --hash=sha256:fe56659ccadbee97908132135de4b875543353351e0c92e736b7c57aee298b5a \
+    --hash=sha256:fe59a0c21a032024edb0c8e43f5dee5623fef0b65a1e3c1281836d9ce199af3b
+    # via pyElli (pyproject.toml)
 scipy==1.9.1 \
     --hash=sha256:0419485dbcd0ed78c0d5bf234c5dd63e86065b39b4d669e45810d42199d49521 \
     --hash=sha256:09412eb7fb60b8f00b328037fd814d25d261066ebc43a1e339cdce4f7502877e \
@@ -1008,7 +1145,7 @@ scipy==1.9.1 \
     --hash=sha256:f950a04b33e17b38ff561d5a0951caf3f5b47caa841edd772ffb7959f20a6af0
     # via
     #   lmfit
-    #   pyElli (setup.py)
+    #   pyElli (pyproject.toml)
 send2trash==1.8.0 \
     --hash=sha256:d2c24762fd3759860a0aff155e45871447ea58d2be6bdd39b5c8f966a0c99c2d \
     --hash=sha256:f20eaadfdb517eaca5ce077640cb261c7d2698385a6a0f072a4a5447fd49fa08
@@ -1102,8 +1239,6 @@ widgetsnbextension==3.6.1 \
 zipp==3.8.1 \
     --hash=sha256:05b45f1ee8f807d0cc928485ca40a07cb491cf092ff587c0df9cb1fd154848d2 \
     --hash=sha256:47c40d7fe183a6f21403a199b3e4192cca5774656965b0a4988ad2f8feb5f009
-    # via importlib-metadata
-
-# WARNING: The following packages were not pinned, but pip requires them to be
-# pinned when the requirements file includes hashes. Consider using the --allow-unsafe flag.
-# setuptools
+    # via
+    #   importlib-metadata
+    #   importlib-resources
diff --git a/requirements/fitting-requirements.txt b/requirements/fitting-requirements.txt
index efb87838..52bc6fa3 100644
--- a/requirements/fitting-requirements.txt
+++ b/requirements/fitting-requirements.txt
@@ -2,7 +2,7 @@
 # This file is autogenerated by pip-compile with python 3.9
 # To update, run:
 #
-#    pip-compile --extra=fitting --generate-hashes --output-file=requirements/fitting-requirements.txt
+#    pip-compile --extra=fitting --generate-hashes --output-file=requirements/fitting-requirements.txt pyproject.toml
 #
 appnope==0.1.3 \
     --hash=sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24 \
@@ -199,11 +199,15 @@ h5py==3.7.0 \
     --hash=sha256:f084bbe816907dfe59006756f8f2d16d352faff2d107f4ffeb1d8de126fc5dc7 \
     --hash=sha256:f514b24cacdd983e61f8d371edac8c1b780c279d0acb8485639e97339c866073 \
     --hash=sha256:f73307c876af49aa869ec5df1818e9bb0bdcfcf8a5ba773cc45a4fba5a286a5c
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 importlib-metadata==4.12.0 \
     --hash=sha256:637245b8bab2b6502fcbc752cc4b7a6f6243bb02b31c5c26156ad103d3d45670 \
     --hash=sha256:7401a975809ea1fdc658c3aa4f78cc2195a0e019c5cbc4c06122884e9ae80c23
     # via nbconvert
+importlib-resources==5.10.2 \
+    --hash=sha256:7d543798b0beca10b6a01ac7cafda9f822c54db9e8376a6bf57e0cbd74d486b6 \
+    --hash=sha256:e4a96c8cc0339647ff9a5e0550d9f276fc5a01ffa276012b58ec108cfd7b8484
+    # via pyElli (pyproject.toml)
 ipykernel==6.15.2 \
     --hash=sha256:59183ef833b82c72211aace3fb48fd20eae8e2d0cae475f3d5c39d4a688e81ec \
     --hash=sha256:e7481083b438609c9c8a22d6362e8e1bc6ec94ba0741b666941e634f2d61bdf3
@@ -216,7 +220,7 @@ ipython==8.5.0 \
     # via
     #   ipykernel
     #   ipywidgets
-    #   pyElli (setup.py)
+    #   pyElli (pyproject.toml)
 ipython-genutils==0.2.0 \
     --hash=sha256:72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8 \
     --hash=sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8
@@ -226,7 +230,7 @@ ipython-genutils==0.2.0 \
 ipywidgets==7.7.2 \
     --hash=sha256:3d47a7826cc6e2644d7cb90db26699451f8b42379cf63b761431b63d19984ca2 \
     --hash=sha256:449ab8e7872d0f388ee5c5b3666b9d6af5e5618a5749fd62652680be37dff2af
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 jedi==0.18.1 \
     --hash=sha256:637c9635fcf47945ceb91cd7f320234a7be540ded6f3e99a50cb6febdfd1ba8d \
     --hash=sha256:74137626a64a99c8eb6ae5832d99b3bdd7d29a3850fe2aa80a4126b2a7d949ab
@@ -336,7 +340,7 @@ kiwisolver==1.4.4 \
     # via matplotlib
 lmfit==1.0.3 \
     --hash=sha256:d067c3ea501f035af5d3c079e6e6e35dc3cc1ac7d439429a425b0aeb5a7858a2
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 lxml==4.9.1 \
     --hash=sha256:04da965dfebb5dac2619cb90fcf93efdb35b3c6994fea58a157a834f2f94b318 \
     --hash=sha256:0538747a9d7827ce3e16a8fdd201a99e661c7dee3c96c885d8ecba3c35d1032c \
@@ -489,7 +493,7 @@ matplotlib==3.5.3 \
     --hash=sha256:dacddf5bfcec60e3f26ec5c0ae3d0274853a258b6c3fc5ef2f06a8eb23e042be \
     --hash=sha256:f3840c280ebc87a48488a46f760ea1c0c0c83fcf7abbe2e6baf99d033fd35fd8 \
     --hash=sha256:f814504e459c68118bf2246a530ed953ebd18213dc20e3da524174d84ed010b2
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 matplotlib-inline==0.1.6 \
     --hash=sha256:f1f41aab5328aa5aaea9b16d083b128102f8712542f819fe7e6a420ff581b311 \
     --hash=sha256:f887e5f10ba98e8d2b150ddcf4702c1e5f8b3a20005eb0f74bfdbd360ee6f304
@@ -561,7 +565,7 @@ numpy==1.23.2 \
     #   lmfit
     #   matplotlib
     #   pandas
-    #   pyElli (setup.py)
+    #   pyElli (pyproject.toml)
     #   scipy
 packaging==21.3 \
     --hash=sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb \
@@ -592,7 +596,7 @@ pandas==1.4.4 \
     --hash=sha256:d0022fe6a313df1c4869b5edc012d734c6519a6fffa3cf70930f32e6a1078e49 \
     --hash=sha256:e7cc960959be28d064faefc0cb2aef854d46b827c004ebea7e79b5497ed83e7d \
     --hash=sha256:ee6f1848148ed3204235967613b0a32be2d77f214e9623f554511047705c1e04
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 pandocfilters==1.5.0 \
     --hash=sha256:0b679503337d233b4339a817bfc8c50064e2eff681314376a47cb582305a7a38 \
     --hash=sha256:33aae3f25fd1a026079f5d27bdd52496f0e0803b3469282162bafdcbdf6ef14f
@@ -675,7 +679,7 @@ pillow==9.3.0 \
 plotly==5.10.0 \
     --hash=sha256:4d36d9859b7a153b273562deeed8c292587a472eb1fd57cd4158ec89d9defadb \
     --hash=sha256:989b13825cc974390aa0169479485d9257d37848a47bc63957251f8e1a7046ba
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 prometheus-client==0.14.1 \
     --hash=sha256:522fded625282822a89e2773452f42df14b5a8e84a86433e3f8a189c1d54dc01 \
     --hash=sha256:5459c427624961076277fdc6dc50540e2bacb98eebde99886e59ec55ed92093a
@@ -778,6 +782,48 @@ pytz==2022.2.1 \
     --hash=sha256:220f481bdafa09c3955dfbdddb7b57780e9a94f5127e35456a48589b9e0c0197 \
     --hash=sha256:cea221417204f2d1a2aa03ddae3e867921971d0d76f14d87abb4414415bbdcf5
     # via pandas
+pyyaml==6.0 \
+    --hash=sha256:01b45c0191e6d66c470b6cf1b9531a771a83c1c4208272ead47a3ae4f2f603bf \
+    --hash=sha256:0283c35a6a9fbf047493e3a0ce8d79ef5030852c51e9d911a27badfde0605293 \
+    --hash=sha256:055d937d65826939cb044fc8c9b08889e8c743fdc6a32b33e2390f66013e449b \
+    --hash=sha256:07751360502caac1c067a8132d150cf3d61339af5691fe9e87803040dbc5db57 \
+    --hash=sha256:0b4624f379dab24d3725ffde76559cff63d9ec94e1736b556dacdfebe5ab6d4b \
+    --hash=sha256:0ce82d761c532fe4ec3f87fc45688bdd3a4c1dc5e0b4a19814b9009a29baefd4 \
+    --hash=sha256:1e4747bc279b4f613a09eb64bba2ba602d8a6664c6ce6396a4d0cd413a50ce07 \
+    --hash=sha256:213c60cd50106436cc818accf5baa1aba61c0189ff610f64f4a3e8c6726218ba \
+    --hash=sha256:231710d57adfd809ef5d34183b8ed1eeae3f76459c18fb4a0b373ad56bedcdd9 \
+    --hash=sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287 \
+    --hash=sha256:2cd5df3de48857ed0544b34e2d40e9fac445930039f3cfe4bcc592a1f836d513 \
+    --hash=sha256:40527857252b61eacd1d9af500c3337ba8deb8fc298940291486c465c8b46ec0 \
+    --hash=sha256:432557aa2c09802be39460360ddffd48156e30721f5e8d917f01d31694216782 \
+    --hash=sha256:473f9edb243cb1935ab5a084eb238d842fb8f404ed2193a915d1784b5a6b5fc0 \
+    --hash=sha256:48c346915c114f5fdb3ead70312bd042a953a8ce5c7106d5bfb1a5254e47da92 \
+    --hash=sha256:50602afada6d6cbfad699b0c7bb50d5ccffa7e46a3d738092afddc1f9758427f \
+    --hash=sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2 \
+    --hash=sha256:77f396e6ef4c73fdc33a9157446466f1cff553d979bd00ecb64385760c6babdc \
+    --hash=sha256:81957921f441d50af23654aa6c5e5eaf9b06aba7f0a19c18a538dc7ef291c5a1 \
+    --hash=sha256:819b3830a1543db06c4d4b865e70ded25be52a2e0631ccd2f6a47a2822f2fd7c \
+    --hash=sha256:897b80890765f037df3403d22bab41627ca8811ae55e9a722fd0392850ec4d86 \
+    --hash=sha256:98c4d36e99714e55cfbaaee6dd5badbc9a1ec339ebfc3b1f52e293aee6bb71a4 \
+    --hash=sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c \
+    --hash=sha256:9fa600030013c4de8165339db93d182b9431076eb98eb40ee068700c9c813e34 \
+    --hash=sha256:a80a78046a72361de73f8f395f1f1e49f956c6be882eed58505a15f3e430962b \
+    --hash=sha256:afa17f5bc4d1b10afd4466fd3a44dc0e245382deca5b3c353d8b757f9e3ecb8d \
+    --hash=sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c \
+    --hash=sha256:b5b9eccad747aabaaffbc6064800670f0c297e52c12754eb1d976c57e4f74dcb \
+    --hash=sha256:bfaef573a63ba8923503d27530362590ff4f576c626d86a9fed95822a8255fd7 \
+    --hash=sha256:c5687b8d43cf58545ade1fe3e055f70eac7a5a1a0bf42824308d868289a95737 \
+    --hash=sha256:cba8c411ef271aa037d7357a2bc8f9ee8b58b9965831d9e51baf703280dc73d3 \
+    --hash=sha256:d15a181d1ecd0d4270dc32edb46f7cb7733c7c508857278d3d378d14d606db2d \
+    --hash=sha256:d4b0ba9512519522b118090257be113b9468d804b19d63c71dbcf4a48fa32358 \
+    --hash=sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53 \
+    --hash=sha256:d4eccecf9adf6fbcc6861a38015c2a64f38b9d94838ac1810a9023a0609e1b78 \
+    --hash=sha256:d67d839ede4ed1b28a4e8909735fc992a923cdb84e618544973d7dfc71540803 \
+    --hash=sha256:daf496c58a8c52083df09b80c860005194014c3698698d1a57cbcfa182142a3a \
+    --hash=sha256:dbad0e9d368bb989f4515da330b88a057617d16b6a8245084f1b05400f24609f \
+    --hash=sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174 \
+    --hash=sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5
+    # via pyElli (pyproject.toml)
 pyzmq==23.2.1 \
     --hash=sha256:022cf5ea7bcaa8a06a03c2706e0ae66904b6138b2155577cd34c64bc7cc637ab \
     --hash=sha256:044447ae4b2016a6b8697571fd633f799f860b19b76c4a2fd9b1140d52ee6745 \
@@ -857,6 +903,97 @@ pyzmq==23.2.1 \
     #   ipykernel
     #   jupyter-client
     #   notebook
+rapidfuzz==2.13.7 \
+    --hash=sha256:020858dd89b60ce38811cd6e37875c4c3c8d7fcd8bc20a0ad2ed1f464b34dc4e \
+    --hash=sha256:042644133244bfa7b20de635d500eb9f46af7097f3d90b1724f94866f17cb55e \
+    --hash=sha256:08590905a95ccfa43f4df353dcc5d28c15d70664299c64abcad8721d89adce4f \
+    --hash=sha256:114810491efb25464016fd554fdf1e20d390309cecef62587494fc474d4b926f \
+    --hash=sha256:1333fb3d603d6b1040e365dca4892ba72c7e896df77a54eae27dc07db90906e3 \
+    --hash=sha256:16080c05a63d6042643ae9b6cfec1aefd3e61cef53d0abe0df3069b9d4b72077 \
+    --hash=sha256:16ffad751f43ab61001187b3fb4a9447ec2d1aedeff7c5bac86d3b95f9980cc3 \
+    --hash=sha256:1f50d1227e6e2a0e3ae1fb1c9a2e1c59577d3051af72c7cab2bcc430cb5e18da \
+    --hash=sha256:1fbad8fb28d98980f5bff33c7842efef0315d42f0cd59082108482a7e6b61410 \
+    --hash=sha256:23524635840500ce6f4d25005c9529a97621689c85d2f727c52eed1782839a6a \
+    --hash=sha256:24d3fea10680d085fd0a4d76e581bfb2b1074e66e78fd5964d4559e1fcd2a2d4 \
+    --hash=sha256:24eb6b843492bdc63c79ee4b2f104059b7a2201fef17f25177f585d3be03405a \
+    --hash=sha256:25b4cedf2aa19fb7212894ce5f5219010cce611b60350e9a0a4d492122e7b351 \
+    --hash=sha256:27be9c63215d302ede7d654142a2e21f0d34ea6acba512a4ae4cfd52bbaa5b59 \
+    --hash=sha256:2c836f0f2d33d4614c3fbaf9a1eb5407c0fe23f8876f47fd15b90f78daa64c34 \
+    --hash=sha256:3a9bd02e1679c0fd2ecf69b72d0652dbe2a9844eaf04a36ddf4adfbd70010e95 \
+    --hash=sha256:3d8b081988d0a49c486e4e845a547565fee7c6e7ad8be57ff29c3d7c14c6894c \
+    --hash=sha256:3dcffe1f3cbda0dc32133a2ae2255526561ca594f15f9644384549037b355245 \
+    --hash=sha256:3f11a7eff7bc6301cd6a5d43f309e22a815af07e1f08eeb2182892fca04c86cb \
+    --hash=sha256:42085d4b154a8232767de8296ac39c8af5bccee6b823b0507de35f51c9cbc2d7 \
+    --hash=sha256:424f82c35dbe4f83bdc3b490d7d696a1dc6423b3d911460f5493b7ffae999fd2 \
+    --hash=sha256:43fb8cb030f888c3f076d40d428ed5eb4331f5dd6cf1796cfa39c67bf0f0fc1e \
+    --hash=sha256:460853983ab88f873173e27cc601c5276d469388e6ad6e08c4fd57b2a86f1064 \
+    --hash=sha256:467c1505362823a5af12b10234cb1c4771ccf124c00e3fc9a43696512bd52293 \
+    --hash=sha256:46b9b8aa09998bc48dd800854e8d9b74bc534d7922c1d6e1bbf783e7fa6ac29c \
+    --hash=sha256:53dcae85956853b787c27c1cb06f18bb450e22cf57a4ad3444cf03b8ff31724a \
+    --hash=sha256:585206112c294e335d84de5d5f179c0f932837752d7420e3de21db7fdc476278 \
+    --hash=sha256:5ada0a14c67452358c1ee52ad14b80517a87b944897aaec3e875279371a9cb96 \
+    --hash=sha256:5e2b3d020219baa75f82a4e24b7c8adcb598c62f0e54e763c39361a9e5bad510 \
+    --hash=sha256:6120f2995f5154057454c5de99d86b4ef3b38397899b5da1265467e8980b2f60 \
+    --hash=sha256:68a89bb06d5a331511961f4d3fa7606f8e21237467ba9997cae6f67a1c2c2b9e \
+    --hash=sha256:7496e8779905b02abc0ab4ba2a848e802ab99a6e20756ffc967a0de4900bd3da \
+    --hash=sha256:759a3361711586a29bc753d3d1bdb862983bd9b9f37fbd7f6216c24f7c972554 \
+    --hash=sha256:75c45dcd595f8178412367e302fd022860ea025dc4a78b197b35428081ed33d5 \
+    --hash=sha256:7d005e058d86f2a968a8d28ca6f2052fab1f124a39035aa0523261d6baf21e1f \
+    --hash=sha256:7f7930adf84301797c3f09c94b9c5a9ed90a9e8b8ed19b41d2384937e0f9f5bd \
+    --hash=sha256:8109e0324d21993d5b2d111742bf5958f3516bf8c59f297c5d1cc25a2342eb66 \
+    --hash=sha256:81642a24798851b118f82884205fc1bd9ff70b655c04018c467824b6ecc1fabc \
+    --hash=sha256:8450d15f7765482e86ef9be2ad1a05683cd826f59ad236ef7b9fb606464a56aa \
+    --hash=sha256:875d51b3497439a72e2d76183e1cb5468f3f979ab2ddfc1d1f7dde3b1ecfb42f \
+    --hash=sha256:8b477b43ced896301665183a5e0faec0f5aea2373005648da8bdcb3c4b73f280 \
+    --hash=sha256:8d3e252d4127c79b4d7c2ae47271636cbaca905c8bb46d80c7930ab906cf4b5c \
+    --hash=sha256:916bc2e6cf492c77ad6deb7bcd088f0ce9c607aaeabc543edeb703e1fbc43e31 \
+    --hash=sha256:988f8f6abfba7ee79449f8b50687c174733b079521c3cc121d65ad2d38831846 \
+    --hash=sha256:99a84ab9ac9a823e7e93b4414f86344052a5f3e23b23aa365cda01393ad895bd \
+    --hash=sha256:9be02162af0376d64b840f2fc8ee3366794fc149f1e06d095a6a1d42447d97c5 \
+    --hash=sha256:a5585189b3d90d81ccd62d4f18530d5ac8972021f0aaaa1ffc6af387ff1dce75 \
+    --hash=sha256:ae33a72336059213996fe4baca4e0e4860913905c2efb7c991eab33b95a98a0a \
+    --hash=sha256:af4f7c3c904ca709493eb66ca9080b44190c38e9ecb3b48b96d38825d5672559 \
+    --hash=sha256:b20141fa6cee041917801de0bab503447196d372d4c7ee9a03721b0a8edf5337 \
+    --hash=sha256:b3210869161a864f3831635bb13d24f4708c0aa7208ef5baac1ac4d46e9b4208 \
+    --hash=sha256:b34e8c0e492949ecdd5da46a1cfc856a342e2f0389b379b1a45a3cdcd3176a6e \
+    --hash=sha256:b52ac2626945cd21a2487aeefed794c14ee31514c8ae69b7599170418211e6f6 \
+    --hash=sha256:b5dd713a1734574c2850c566ac4286594bacbc2d60b9170b795bee4b68656625 \
+    --hash=sha256:b5f705652360d520c2de52bee11100c92f59b3e3daca308ebb150cbc58aecdad \
+    --hash=sha256:b6389c50d8d214c9cd11a77f6d501529cb23279a9c9cafe519a3a4b503b5f72a \
+    --hash=sha256:b6bad92de071cbffa2acd4239c1779f66851b60ffbbda0e4f4e8a2e9b17e7eef \
+    --hash=sha256:b75dd0928ce8e216f88660ab3d5c5ffe990f4dd682fd1709dba29d5dafdde6de \
+    --hash=sha256:c2523f8180ebd9796c18d809e9a19075a1060b1a170fde3799e83db940c1b6d5 \
+    --hash=sha256:c31022d9970177f6affc6d5dd757ed22e44a10890212032fabab903fdee3bfe7 \
+    --hash=sha256:c36fd260084bb636b9400bb92016c6bd81fd80e59ed47f2466f85eda1fc9f782 \
+    --hash=sha256:c3741cb0bf9794783028e8b0cf23dab917fa5e37a6093b94c4c2f805f8e36b9f \
+    --hash=sha256:c3fbe449d869ea4d0909fc9d862007fb39a584fb0b73349a6aab336f0d90eaed \
+    --hash=sha256:c66546e30addb04a16cd864f10f5821272a1bfe6462ee5605613b4f1cb6f7b48 \
+    --hash=sha256:c71d9d512b76f05fa00282227c2ae884abb60e09f08b5ca3132b7e7431ac7f0d \
+    --hash=sha256:c8601a66fbfc0052bb7860d2eacd303fcde3c14e87fdde409eceff516d659e77 \
+    --hash=sha256:c88adbcb933f6b8612f6c593384bf824e562bb35fc8a0f55fac690ab5b3486e5 \
+    --hash=sha256:ca00fafd2756bc9649bf80f1cf72c647dce38635f0695d7ce804bc0f759aa756 \
+    --hash=sha256:ca8a23097c1f50e0fdb4de9e427537ca122a18df2eead06ed39c3a0bef6d9d3a \
+    --hash=sha256:cda1e2f66bb4ba7261a0f4c2d052d5d909798fca557cbff68f8a79a87d66a18f \
+    --hash=sha256:cdfc04f7647c29fb48da7a04082c34cdb16f878d3c6d098d62d5715c0ad3000c \
+    --hash=sha256:cf62dacb3f9234f3fddd74e178e6d25c68f2067fde765f1d95f87b1381248f58 \
+    --hash=sha256:d00df2e4a81ffa56a6b1ec4d2bc29afdcb7f565e0b8cd3092fece2290c4c7a79 \
+    --hash=sha256:d248a109699ce9992304e79c1f8735c82cc4c1386cd8e27027329c0549f248a2 \
+    --hash=sha256:d63def9bbc6b35aef4d76dc740301a4185867e8870cbb8719ec9de672212fca8 \
+    --hash=sha256:d82f20c0060ffdaadaf642b88ab0aa52365b56dffae812e188e5bdb998043588 \
+    --hash=sha256:dbcf5371ea704759fcce772c66a07647751d1f5dbdec7818331c9b31ae996c77 \
+    --hash=sha256:e8914dad106dacb0775718e54bf15e528055c4e92fb2677842996f2d52da5069 \
+    --hash=sha256:ebe303cd9839af69dd1f7942acaa80b1ba90bacef2e7ded9347fbed4f1654672 \
+    --hash=sha256:ec55a81ac2b0f41b8d6fb29aad16e55417036c7563bad5568686931aa4ff08f7 \
+    --hash=sha256:effe182767d102cb65dfbbf74192237dbd22d4191928d59415aa7d7c861d8c88 \
+    --hash=sha256:f42b82f268689f429def9ecfb86fa65ceea0eaf3fed408b570fe113311bf5ce7 \
+    --hash=sha256:f6fe570e20e293eb50491ae14ddeef71a6a7e5f59d7e791393ffa99b13f1f8c2 \
+    --hash=sha256:f799d1d6c33d81e983d3682571cc7d993ae7ff772c19b3aabb767039c33f6d1e \
+    --hash=sha256:f891b98f8bc6c9d521785816085e9657212621e93f223917fb8e32f318b2957e \
+    --hash=sha256:fa263135b892686e11d5b84f6a1892523123a00b7e5882eff4fbdabb38667347 \
+    --hash=sha256:fa4c598ed77f74ec973247ca776341200b0f93ec3883e34c222907ce72cb92a4 \
+    --hash=sha256:fe56659ccadbee97908132135de4b875543353351e0c92e736b7c57aee298b5a \
+    --hash=sha256:fe59a0c21a032024edb0c8e43f5dee5623fef0b65a1e3c1281836d9ce199af3b
+    # via pyElli (pyproject.toml)
 scipy==1.9.1 \
     --hash=sha256:0419485dbcd0ed78c0d5bf234c5dd63e86065b39b4d669e45810d42199d49521 \
     --hash=sha256:09412eb7fb60b8f00b328037fd814d25d261066ebc43a1e339cdce4f7502877e \
@@ -883,7 +1020,7 @@ scipy==1.9.1 \
     --hash=sha256:f950a04b33e17b38ff561d5a0951caf3f5b47caa841edd772ffb7959f20a6af0
     # via
     #   lmfit
-    #   pyElli (setup.py)
+    #   pyElli (pyproject.toml)
 send2trash==1.8.0 \
     --hash=sha256:d2c24762fd3759860a0aff155e45871447ea58d2be6bdd39b5c8f966a0c99c2d \
     --hash=sha256:f20eaadfdb517eaca5ce077640cb261c7d2698385a6a0f072a4a5447fd49fa08
@@ -967,4 +1104,6 @@ widgetsnbextension==3.6.1 \
 zipp==3.8.1 \
     --hash=sha256:05b45f1ee8f807d0cc928485ca40a07cb491cf092ff587c0df9cb1fd154848d2 \
     --hash=sha256:47c40d7fe183a6f21403a199b3e4192cca5774656965b0a4988ad2f8feb5f009
-    # via importlib-metadata
+    # via
+    #   importlib-metadata
+    #   importlib-resources
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ffa552de..299ee43a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,8 +1,8 @@
 #
-# This file is autogenerated by pip-compile with python 3.10
+# This file is autogenerated by pip-compile with python 3.9
 # To update, run:
 #
-#    pip-compile --generate-hashes --output-file=requirements/requirements.txt
+#    pip-compile --generate-hashes --output-file=requirements/requirements.txt pyproject.toml
 #
 h5py==3.7.0 \
     --hash=sha256:03d64fb86bb86b978928bad923b64419a23e836499ec6363e305ad28afd9d287 \
@@ -25,11 +25,11 @@ h5py==3.7.0 \
     --hash=sha256:f084bbe816907dfe59006756f8f2d16d352faff2d107f4ffeb1d8de126fc5dc7 \
     --hash=sha256:f514b24cacdd983e61f8d371edac8c1b780c279d0acb8485639e97339c866073 \
     --hash=sha256:f73307c876af49aa869ec5df1818e9bb0bdcfcf8a5ba773cc45a4fba5a286a5c
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 importlib-resources==5.10.0 \
     --hash=sha256:c01b1b94210d9849f286b86bb51bcea7cd56dde0600d8db721d7b81330711668 \
     --hash=sha256:ee17ec648f85480d523596ce49eae8ead87d5631ae1551f913c0100b5edd3437
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 numpy==1.23.2 \
     --hash=sha256:17e5226674f6ea79e14e3b91bfbc153fdf3ac13f5cc54ee7bc8fdbe820a32da0 \
     --hash=sha256:2bd879d3ca4b6f39b7770829f73278b7c5e248c91d538aab1e506c628353e47f \
@@ -62,7 +62,7 @@ numpy==1.23.2 \
     # via
     #   h5py
     #   pandas
-    #   pyElli (setup.py)
+    #   pyElli (pyproject.toml)
     #   scipy
 pandas==1.4.4 \
     --hash=sha256:050aada67a5ec6699a7879e769825b510018a95fb9ac462bb1867483d0974a97 \
@@ -86,7 +86,7 @@ pandas==1.4.4 \
     --hash=sha256:d0022fe6a313df1c4869b5edc012d734c6519a6fffa3cf70930f32e6a1078e49 \
     --hash=sha256:e7cc960959be28d064faefc0cb2aef854d46b827c004ebea7e79b5497ed83e7d \
     --hash=sha256:ee6f1848148ed3204235967613b0a32be2d77f214e9623f554511047705c1e04
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 python-dateutil==2.8.2 \
     --hash=sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86 \
     --hash=sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
@@ -136,7 +136,7 @@ pyyaml==6.0 \
     --hash=sha256:dbad0e9d368bb989f4515da330b88a057617d16b6a8245084f1b05400f24609f \
     --hash=sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174 \
     --hash=sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 rapidfuzz==2.11.1 \
     --hash=sha256:036f904bcac16d726273eee7ec0636978af31d151f30c95b611240e22592ab79 \
     --hash=sha256:0429a7a51d1372afaca969ee3170f9975f2fe6e187b485aeef55d3e8d7d934e0 \
@@ -240,7 +240,7 @@ rapidfuzz==2.11.1 \
     --hash=sha256:f72d33b0d76a658d8b692b3e42c45539939bac26ff5b71b516cb20fa6d8ff7f6 \
     --hash=sha256:f9226824c132d38f2337d2c76e3009acc036f0b05f20e95e82f8195400e1e366 \
     --hash=sha256:faba219b270b78e9494cfe3d955d7b45c10799c18ee47ec24b1ada93978d491b
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 scipy==1.9.1 \
     --hash=sha256:0419485dbcd0ed78c0d5bf234c5dd63e86065b39b4d669e45810d42199d49521 \
     --hash=sha256:09412eb7fb60b8f00b328037fd814d25d261066ebc43a1e339cdce4f7502877e \
@@ -265,8 +265,12 @@ scipy==1.9.1 \
     --hash=sha256:eb954f5aca4d26f468bbebcdc5448348eb287f7bea536c6306f62ea062f63d9a \
     --hash=sha256:f7c39f7dbb57cce00c108d06d731f3b0e2a4d3a95c66d96bce697684876ce4d4 \
     --hash=sha256:f950a04b33e17b38ff561d5a0951caf3f5b47caa841edd772ffb7959f20a6af0
-    # via pyElli (setup.py)
+    # via pyElli (pyproject.toml)
 six==1.16.0 \
     --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \
     --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
     # via python-dateutil
+zipp==3.11.0 \
+    --hash=sha256:83a28fcb75844b5c0cdaf5aa4003c2d728c77e05f5aeabe8e95e56727005fbaa \
+    --hash=sha256:a7a22e05929290a67401440b39690ae6563279bced5f314609d9d03798f56766
+    # via importlib-resources
diff --git a/generate_ipynb_from_gallery.sh b/scripts/generate_ipynb_from_gallery.sh
similarity index 100%
rename from generate_ipynb_from_gallery.sh
rename to scripts/generate_ipynb_from_gallery.sh
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 0fd50a36..00000000
--- a/setup.cfg
+++ /dev/null
@@ -1,141 +0,0 @@
-# This file is used to configure your project.
-# Read more about the various options under:
-# https://setuptools.pypa.io/en/latest/userguide/declarative_config.html
-# https://setuptools.pypa.io/en/latest/references/keywords.html
-
-[metadata]
-name = pyElli
-description = Python implementation of Berremans 4x4 matrix method
-author = dobener
-author_email = florian.dobener@physik.uni-giessen.de
-license = GPL-3.0-or-later
-license_files = LICENSE.txt
-long_description = file: README.md
-long_description_content_type = text/markdown; charset=UTF-8
-url = https://github.com/PyEllips/pyElli/
-# Add here related links, for example:
-project_urls =
-    Documentation = https://pyelli.readthedocs.io/en/latest/
-    Source = https://github.com/PyEllips/pyElli/
-    Download = https://github.com/PyEllips/pyElli/releases
-    Changelog = https://github.com/PyEllips/pyElli/blob/master/CHANGELOG.md
-    Tracker = https://github.com/PyEllips/pyElli/issues
-#    Conda-Forge = https://anaconda.org/conda-forge/pyscaffold
-#    Twitter = https://twitter.com/PyScaffold
-
-# Change if running only on Windows, Mac or Linux (comma-separated)
-platforms = any
-
-# Add here all kinds of additional classifiers as defined under
-# https://pypi.org/classifiers/
-classifiers = 
-    Development Status :: 4 - Beta
-    Programming Language :: Python :: 3.7
-    Programming Language :: Python :: 3.8
-    Programming Language :: Python :: 3.9
-    Programming Language :: Python :: 3.10
-
-
-[options]
-zip_safe = False
-packages = find_namespace:
-package_dir =
-    =src
-
-# Require a min/specific Python version (comma-separated conditions)
-# python_requires = >=3.8
-
-# Add here dependencies of your project (line-separated), e.g. requests>=2.2,<3.0.
-# Version specifiers like >=2.2,<3.0 avoid problems due to API changes in
-# new major versions. This works if the required packages follow Semantic Versioning.
-# For more information, check out https://semver.org/.
-install_requires =
-    importlib-metadata; python_version<"3.8"
-    scipy
-    numpy>=1.20
-    pandas>=1.0.0
-    h5py
-    pyyaml
-    importlib-resources
-    rapidfuzz
-
-[options.package_data]
-* = *.yml
-
-[options.packages.find]
-where = src
-exclude =
-    tests
-
-[options.extras_require]
-# Add here additional requirements for extra features, to install with:
-# `pip install pyElli[PDF]` like:
-# PDF = ReportLab; RXP
-fitting = 
-    ipython
-    ipywidgets<8
-    plotly
-    matplotlib
-    lmfit
-
-# Add here test requirements (semicolon/line-separated)
-testing =
-    setuptools
-    pytest
-    pytest-cov
-    nbmake
-
-[options.entry_points]
-# Add here console scripts like:
-# console_scripts =
-#     script_name = elli.module:function
-# For example:
-# console_scripts =
-#     fibonacci = elli.skeleton:run
-# And any other entry points, for example:
-# pyscaffold.cli =
-#     awesome = pyscaffoldext.awesome.extension:AwesomeExtension
-
-[tool:pytest]
-# Specify command line options as you would do when invoking pytest directly.
-# e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml
-# in order to write a coverage file that can be read by Jenkins.
-# CAUTION: --cov flags may prohibit setting breakpoints while debugging.
-#          Comment those flags to avoid this pytest issue.
-addopts =
-    --cov elli --cov-report term-missing
-    --verbose
-norecursedirs =
-    dist
-    build
-    .tox
-testpaths = tests
-# Use pytest markers to select/deselect specific tests
-# markers =
-#     slow: mark tests as slow (deselect with '-m "not slow"')
-#     system: mark end-to-end system tests
-
-[devpi:upload]
-# Options for the devpi: PyPI server and packaging tool
-# VCS export must be deactivated since we are using setuptools-scm
-no_vcs = 1
-formats = bdist_wheel
-
-[flake8]
-# Some sane defaults for the code style checker flake8
-max_line_length = 88
-extend_ignore = E203, W503
-# ^  Black-compatible
-#    E203 and W503 have edge cases handled by black
-exclude =
-    .tox
-    build
-    dist
-    .eggs
-    docs/conf.py
-
-[pyscaffold]
-# PyScaffold's parameters when the project was created.
-# This will be used when updating. Do not change!
-version = 4.1.1
-package = elli
diff --git a/setup.py b/setup.py
deleted file mode 100644
index de720933..00000000
--- a/setup.py
+++ /dev/null
@@ -1,21 +0,0 @@
-"""
-    Setup file for pyElli.
-    Use setup.cfg to configure your project.
-
-    This file was generated with PyScaffold 4.1.1.
-    PyScaffold helps you to put up the scaffold of your new Python project.
-    Learn more under: https://pyscaffold.org/
-"""
-from setuptools import setup
-
-if __name__ == "__main__":
-    try:
-        setup()
-    except:  # noqa
-        print(
-            "\n\nAn error occurred while building the project, "
-            "please ensure you have the most updated version of setuptools, "
-            "setuptools_scm and wheel with:\n"
-            "   pip install -U setuptools setuptools_scm wheel\n\n"
-        )
-        raise
diff --git a/src/elli/__init__.py b/src/elli/__init__.py
index 1e29ad59..80f417f8 100644
--- a/src/elli/__init__.py
+++ b/src/elli/__init__.py
@@ -14,17 +14,3 @@
 from .solver4x4 import *
 from .structure import *
 from .utils import *
-
-if sys.version_info.major >= 3 and sys.version_info.minor > 7:
-    from importlib.metadata import PackageNotFoundError, version
-else:
-    from importlib_metadata import PackageNotFoundError, version
-
-try:
-    # Change here if project is renamed and does not equal the package name
-    dist_name = __name__
-    __version__ = version(dist_name)
-except PackageNotFoundError:  # pragma: no cover
-    __version__ = "unknown"
-finally:
-    del version, PackageNotFoundError
diff --git a/tox.ini b/tox.ini
deleted file mode 100644
index 970c2c34..00000000
--- a/tox.ini
+++ /dev/null
@@ -1,47 +0,0 @@
-[tox]
-envlist = py3{11,10,9,8,7}
-skip_missing_interpreters = true
-
-
-[testenv]
-envtmpdir = {toxworkdir}/tmp/{envname}
-deps =
-    -r requirements/dev-requirements.txt
-commands = pytest -v --tb=short --basetemp={envtmpdir} {posargs:tests}
-
-
-[testenv:style]
-deps = pre-commit
-skip_install = true
-commands = pre-commit run --all-files --show-diff-on-failure
-
-
-[testenv:requirements]
-# Be aware that this always uses the system python regardless of env
-deps = pip-tools
-setenv =
-  PYTHONPATH=.
-skip_install = true
-commands = 
-    pip-compile --output-file=requirements/requirements.txt --generate-hashes
-    pip-compile --extra=fitting --extra=testing --output-file=requirements/dev-requirements.txt --generate-hashes
-    pip-compile --extra=fitting --output-file=requirements/fitting-requirements.txt --generate-hashes
-
-
-[testenv:{docs,doctests,linkcheck}]
-description =
-    docs: Invoke sphinx-build to build the docs
-    doctests: Invoke sphinx-build to run doctests
-    linkcheck: Check for broken links in the documentation
-setenv =
-    DOCSDIR = {toxinidir}/docs
-    BUILDDIR = {toxinidir}/docs/_build
-    docs: BUILD = html
-    doctests: BUILD = doctest
-    linkcheck: BUILD = linkcheck
-deps =
-    -r {toxinidir}/docs/requirements.txt
-    # ^  requirements.txt shared with Read The Docs
-commands =
-    sphinx-build --color -b {env:BUILD} -d "{env:BUILDDIR}/doctrees" "{env:DOCSDIR}" "{env:BUILDDIR}/{env:BUILD}" {posargs}
-


Test patch:
diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index c9e21548..36ecd895 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -17,11 +17,11 @@ jobs:
         python_version: ["3.7", "3.8", "3.9", "3.10"]
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
           submodules: recursive
       - name: Set up Python ${{ matrix.python_version }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python_version }}
       - name: Install dependencies