Repository: meyer-nils/fiberoripy (language: python)

Issue:
Dev update reorientation
Update calling signature of fiber orientation ODEs and add specific wrapper function for ODE integration using `scipy` solvers.
Orientation examples are updated, except `orthofitclosures_perfectshear.ipynb`.

Would make sense to split `orthotropic_fitted_closures` into individual functions or wrappers  (`orf_closure`, `orw_closure`, ...) to enable a consistent calling signature of the closure functions, namely `closure(a)`.


Discussion / hints:

Fix patch:
diff --git a/.flake8 b/.flake8
index e78fe15..e186350 100644
--- a/.flake8
+++ b/.flake8
@@ -1,3 +1,3 @@
 [flake8]
 max-line-length = 88
-extend-ignore = E203, E741
\ No newline at end of file
+extend-ignore = E203, E741
diff --git a/.github/workflows/publish-to-pypi.yml b/.github/workflows/publish-to-pypi.yml
index 79ad0a3..238ab4e 100644
--- a/.github/workflows/publish-to-pypi.yml
+++ b/.github/workflows/publish-to-pypi.yml
@@ -5,21 +5,21 @@ on: push
 jobs:
   build-n-publish:
     name: Build and publish Python 🐍 distributions 📦 to PyPI
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@master
-    - name: Set up Python 3.9
+    - name: Set up Python 3.11
       uses: actions/setup-python@v1
       with:
-        python-version: 3.9
+        python-version: 3.11
 
     - name: Install setuptools and wheel
       run: >-
-        python -m pip install --user --upgrade setuptools wheel
+        python -m pip install --user --upgrade setuptools wheel build
 
     - name: Build a binary wheel and a source tarball
       run: >-
-        python setup.py sdist bdist_wheel
+        python -m build --sdist --wheel
 
     - name: Publish distribution 📦 to PyPI
       if: startsWith(github.ref, 'refs/tags')
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c84d574..150702c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,17 +2,16 @@
 repos:
 # Pre-commit itself
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
     -   id: trailing-whitespace
     -   id: end-of-file-fixer
     -   id: check-yaml
     -   id: check-added-large-files
-    -   id: fix-encoding-pragma
     -   id: check-toml
 # Black code style formatter
 -   repo: https://github.com/psf/black
-    rev: 24.4.2
+    rev: 24.10.0
     hooks:
       - id: black
 # isort import sorting
@@ -21,3 +20,8 @@ repos:
     hooks:
       - id: isort
         additional_dependencies: ["toml"]
+# nbstripout to remove outputs from Jupyter notebooks
+-   repo: https://github.com/kynan/nbstripout
+    rev: 0.8.1
+    hooks:
+        - id: nbstripout
diff --git a/README.md b/README.md
index de5b16c..ffaab5e 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,6 @@
 [![LICENSE](https://black.readthedocs.io/en/stable/_static/license.svg)](https://raw.github.com/nilsmeyerkit/fiberoripy/master/LICENSE)
 [![Documentation Status](https://readthedocs.org/projects/fiberoripy/badge/?version=latest)](https://fiberoripy.readthedocs.io/en/latest/?badge=latest)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/fiberoripy)
 [![PyPI version](https://badge.fury.io/py/fiberoripy.svg)](https://badge.fury.io/py/fiberoripy)
 [![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![DOI](https://zenodo.org/badge/282262907.svg)](https://zenodo.org/badge/latestdoi/282262907)
@@ -17,7 +18,6 @@ should reproduce Figure 2 in *Favaloro, A.J., Tucker III, C.L., Composites Part
 
   ![example_image](https://raw.github.com/nilsmeyerkit/fiberoripy/master/docs/images/example.png)
 
-
 ## Orientation models
 Following models have been implemented:
 
@@ -60,6 +60,11 @@ Following models have been implemented:
  'An anisotropic rotary diffusion model for fiber orientation in short- and long-fiber thermoplastics',\
  Journal of Non-Newtonian Fluid Mechanics 156, 165-176, 2009.\
  (https://doi.org/10.1016/j.jnnfm.2008.08.002)
+ * __Mori-Tanaka__:\
+ T. Karl, T. Böhlke,\
+ 'Generalized Micromechanical Formulation of Fiber Orientation Tensor Evolution Equations',\
+ International Journal of Mechanical Sciences, 2023.\
+ (https://doi.org/10.1016/j.ijmecsci.2023.108771)
 
 ## Closures
 * __Linear__, __Quadratic__, __Hybrid__:\
diff --git a/docs/conf.py b/docs/conf.py
index aa9b7e1..9326f12 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -26,8 +26,8 @@
 # -- Project information -----------------------------------------------------
 
 project = "fiberoripy"
-copyright = "2020, Nils Meyer"
-author = "Nils Meyer"
+copyright = "2024, Nils Meyer, Constantin Krauß, Louis Schreyer, Julian Bauer"
+author = "Nils Meyer, Constantin Krauß, Louis Schreyer, Julian Bauer"
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/examples/closures/plot_closure_comparison.ipynb b/examples/closures/plot_closure_comparison.ipynb
index a3eede0..aecc6d2 100644
--- a/examples/closures/plot_closure_comparison.ipynb
+++ b/examples/closures/plot_closure_comparison.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,40 +51,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "02a698bd392140d687f66c31d8eb5605",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "\n",
-       "            <div style=\"display: inline-block;\">\n",
-       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
-       "                    Figure\n",
-       "                </div>\n",
-       "                <img 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' width=1200.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Second order tensor plot\n",
     "fig = plt.figure(figsize=(12, 8))\n",
     "\n",
     "ax = fig.add_subplot(221, projection=Axes3D.name)\n",
-    "plotargs = {\"rstride\":4, \"cstride\":4}\n",
+    "plotargs = {\"rstride\": 4, \"cstride\": 4}\n",
     "plot_orbit2(ax, plotargs, a)\n",
     "\n",
     "ax = fig.add_subplot(222)\n",
@@ -101,40 +76,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "571257a4beda498680a206ecaeaa0d7c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "\n",
-       "            <div style=\"display: inline-block;\">\n",
-       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
-       "                    Figure\n",
-       "                </div>\n",
-       "                <img 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' width=1200.0/>\n",
-       "            </div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Fourth order tensor plot\n",
     "fig = plt.figure(figsize=(12, 8))\n",
     "\n",
     "ax = fig.add_subplot(221, projection=Axes3D.name)\n",
-    "plotargs = {\"alpha\":0.1, \"rstride\":4, \"cstride\":4}\n",
+    "plotargs = {\"alpha\": 0.1, \"rstride\": 4, \"cstride\": 4}\n",
     "plot_orbit4(ax, plotargs, A, A_hybrid, A_lin, A_quad, A_ibof)\n",
     "\n",
     "ax = fig.add_subplot(222)\n",
@@ -154,7 +104,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.2 64-bit ('shims')",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -168,12 +118,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "27a5fe6e35c892a943dc8ba0add9e0e72bbd78f1ea55c7a21f1efdd89ab3b17d"
-   }
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/examples/closures/plot_extrema.ipynb b/examples/closures/plot_extrema.ipynb
index 8fdc844..1f128fc 100644
--- a/examples/closures/plot_extrema.ipynb
+++ b/examples/closures/plot_extrema.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,65 +18,40 @@
     "import numpy as np\n",
     "from mpl_toolkits.mplot3d import Axes3D\n",
     "\n",
-    "from fiberoripy.closures import (\n",
-    "    IBOF_closure,\n",
-    "    compute_closure,\n",
-    "    hybrid_closure,\n",
-    "    linear_closure,\n",
-    "    quadratic_closure,\n",
-    ")\n",
-    "from fiberoripy.tensorplot import (\n",
-    "    plot_orbit2,\n",
-    "    plot_orbit4,\n",
-    "    plot_projection2,\n",
-    "    plot_projection4,\n",
-    ")"
+    "from fiberoripy.closures import get_random_tensor_pair\n",
+    "\n",
+    "from fiberoripy.tensorplot import plot_orbit2, plot_orbit4\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# define second order fiber orientation tensor\n",
     "a_iso = 1.0 / 3.0 * np.eye(3)\n",
-    "a_uni = np.array([[0.9,0,0],[0,0.05, 0],[0,0,0.05]])\n",
-    "a_pln = np.array([[0.4,0,0],[0,0.4, 0],[0,0,0.1]])\n",
+    "a_uni = np.array([[0.9, 0, 0], [0, 0.05, 0], [0, 0, 0.05]])\n",
+    "a_pln = np.array([[0.4, 0, 0], [0, 0.4, 0], [0, 0, 0.1]])\n",
     "\n",
     "# create a fourth order fiber orientation tensor\n",
-    "A_iso = compute_closure(a_iso, \"RANDOM\", N=1000)\n",
-    "A_uni = compute_closure(a_uni, \"RANDOM\", N=1000)\n",
-    "A_pln = compute_closure(a_pln, \"RANDOM\", N=1000)"
+    "_, A_iso = get_random_tensor_pair(seed=a_iso, N=5000)\n",
+    "_, A_uni = get_random_tensor_pair(seed=a_uni, N=5000)\n",
+    "_, A_pln = get_random_tensor_pair(seed=a_pln, N=5000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e710d3dd22534b28937261bf1db80c28",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Second order tensor plot (isotropic)\n",
     "fig = plt.figure(figsize=(12, 8))\n",
     "\n",
     "ax = fig.add_subplot(111, projection=Axes3D.name)\n",
     "ax._axis3don = False\n",
-    "plotargs = {\"color\":[0,0.588,0.510], \"rstride\":1, \"cstride\":1}\n",
+    "plotargs = {\"color\": [0, 0.588, 0.510], \"rstride\": 1, \"cstride\": 1}\n",
     "plot_orbit2(ax, plotargs, a_iso)\n",
     "\n",
     "plt.show()"
@@ -84,31 +59,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c259da2fd6e64d34b59acf3d98a17feb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Second order tensor plot (unidirectional)\n",
     "fig = plt.figure(figsize=(12, 8))\n",
     "\n",
     "ax = fig.add_subplot(111, projection=Axes3D.name)\n",
     "ax._axis3don = False\n",
-    "plotargs = {\"color\":[0,0.588,0.510], \"rstride\":1, \"cstride\":1}\n",
+    "plotargs = {\"color\": [0, 0.588, 0.510], \"rstride\": 1, \"cstride\": 1}\n",
     "plot_orbit2(ax, plotargs, a_uni)\n",
     "\n",
     "plt.show()"
@@ -116,31 +76,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "63d737f971c54dfc9f5d77ccd1cca695",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Second order tensor plot (planar)\n",
     "fig = plt.figure(figsize=(12, 8))\n",
     "\n",
     "ax = fig.add_subplot(111, projection=Axes3D.name)\n",
     "ax._axis3don = False\n",
-    "plotargs = {\"color\":[0,0.588,0.510], \"rstride\":4, \"cstride\":4}\n",
+    "plotargs = {\"color\": [0, 0.588, 0.510], \"rstride\": 4, \"cstride\": 4}\n",
     "plot_orbit2(ax, plotargs, a_pln)\n",
     "\n",
     "plt.show()"
@@ -148,46 +93,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9fca6f4e29b545feaab4909884f70fae",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Fourth order tensor plot\n",
     "fig = plt.figure(figsize=(12, 8))\n",
     "\n",
     "ax = fig.add_subplot(111, projection=Axes3D.name)\n",
-    "plotargs = {\"color\":[0,0.588,0.510], \"rstride\":1, \"cstride\":1}\n",
+    "plotargs = {\"color\": [0, 0.588, 0.510], \"rstride\": 1, \"cstride\": 1}\n",
     "plot_orbit4(ax, plotargs, A_iso)\n",
     "\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -201,7 +124,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/examples/fitting/fit_shear_parameters.py b/examples/fitting/fit_shear_parameters.py
index b253cfc..f4ca2b6 100644
--- a/examples/fitting/fit_shear_parameters.py
+++ b/examples/fitting/fit_shear_parameters.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """Fit parameters of orientation models to micro-simulation data.
 
 This result should match Figure 7 in:
@@ -8,19 +7,18 @@
 Journal of Composites Science 4 (2020)
 DOI: 10.3390/jcs4020077
 """
+
 import os
 
 import matplotlib.pyplot as plt
 import numpy as np
-import tikzplotlib
-from matplotlib import cm
 
 from fiberoripy.aspect_ratios import get_zhang_aspect_ratio
 from fiberoripy.constants import COMPS
 from fiberoripy.fit import fit_optimal_params
 from fiberoripy.orientation import rsc_ode
 
-dark2 = cm.get_cmap("Dark2", 2)
+dark2 = plt.get_cmap("Dark2", 2)
 
 ar = get_zhang_aspect_ratio(4.431135)
 xi = (ar**2 - 1) / (ar**2 + 1)
@@ -36,9 +34,7 @@ def L(t):
 data_list = []
 volfrac = "30"
 full_path = os.path.realpath(__file__)
-rootdir = os.path.join(
-    os.path.dirname(full_path), "data", "volfrac%s" % volfrac
-)
+rootdir = os.path.join(os.path.dirname(full_path), "data", "volfrac%s" % volfrac)
 print(rootdir)
 file_name = os.path.join(rootdir, "README.md")
 pic_name = os.path.join(rootdir, "volfrac%s.png" % volfrac)
@@ -62,9 +58,10 @@ def L(t):
 with open(file_name, "w") as f:
     f.write("# Fitting Results #\n")
 
+
 p0 = [0.001, 1.0]
 p_opt, N_rsc, msg = fit_optimal_params(
-    t, mean, rsc_ode, xi, L, p0, ([0.0, 0.0], [0.1, 1.0])
+    t, mean, rsc_ode, xi, L, ["Ci", "kappa"], p0, ([0.0, 0.0], [0.1, 1.0])
 )
 with open(file_name, "a") as f:
     f.write("## RSC Model ##\n")
@@ -81,6 +78,7 @@ def L(t):
     i = COMPS[c]
     plt.subplot(int("14" + str(j + 1)))
     plt.plot(G * t, mean[:, i], color=dark2(0), linewidth=2)
+    plt.plot(G * t, N_rsc[:, i], color=dark2(1), linewidth=2)
     plt.fill_between(
         G * t,
         mean[:, i] + std[:, i],
@@ -92,13 +90,10 @@ def L(t):
     plt.title("$%s_{%s}$" % (c[0], c[1:]))
     plt.ylim([-(i % 2), 1])
     plt.xlim([0, 150])
-    plt.plot(G * t, N_rsc[:, i], color=dark2(1), linewidth=2)
 
     if j == 2:
         plt.legend(legend_list)
 
 plt.tight_layout()
-# save tikz figure (width means individual subplot width!)
-tikzplotlib.save(pic_name.replace(".png", ".tex"), axis_width=r"\textwidth/4")
 plt.savefig(pic_name)
 plt.show()
diff --git a/examples/orientation/ardrsc_perfectrotation.ipynb b/examples/orientation/ardrsc_perfectrotation.ipynb
index 64d3e11..798d898 100644
--- a/examples/orientation/ardrsc_perfectrotation.ipynb
+++ b/examples/orientation/ardrsc_perfectrotation.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Testing re-orientation in perfectly rotating flow.\n",
     "\n",
-    "The result should be a sine shape in components A_11 and A_22"
+    "The result should be a sine shape in components $A_{11}$ and $A_{22}$."
    ]
   },
   {
@@ -17,13 +17,15 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from scipy.integrate import odeint\n",
-    "from fiberoripy.orientation import ard_rsc_ode"
+    "from scipy.integrate import solve_ivp\n",
+    "\n",
+    "from fiberoripy.closures import IBOF_closure\n",
+    "from fiberoripy.orientation import ard_rsc_ode, integrate_ori_ode"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +33,8 @@
     "xi = 1.0\n",
     "\n",
     "# time steps\n",
-    "t = np.linspace(0, np.pi, 500)\n",
+    "t0, tf = 0, np.pi\n",
+    "t = np.linspace(t0, tf, 500)\n",
     "\n",
     "# initial fiber orientation state\n",
     "A0 = np.zeros((3, 3))\n",
@@ -41,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,43 +56,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# compute solution by integrating the ODE\n",
-    "A = odeint(\n",
-    "    ard_rsc_ode,\n",
+    "kwargs = {\n",
+    "    \"xi\": xi,\n",
+    "    \"b1\": 1.924e-4,\n",
+    "    \"kappa\": 1.0 / 30.0,\n",
+    "    \"b2\": 5.839e-3,\n",
+    "    \"b3\": 0.04,\n",
+    "    \"b4\": 1.168e-5,\n",
+    "    \"b5\": 0.0,\n",
+    "}\n",
+    "sol = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t0, tf),\n",
     "    A0.ravel(),\n",
-    "    t,\n",
-    "    args=(xi, L, 1.924e-4, 1.0 / 30.0, 5.839e-3, 0.04, 1.168e-5, 0.0),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, ard_rsc_ode, kwargs),\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plot components\n",
-    "plt.plot(t, A[:, 0], \"-\", label=\"A11\")\n",
-    "plt.plot(t, A[:, 4], \"--\", label=\"A22\")\n",
-    "plt.plot(t, A[:, 8], \"-.\", label=\"A33\")\n",
-    "plt.plot(t, A[:, 2], \":\", label=\"A31\")\n",
+    "plt.plot(sol.t, sol.y[0], \"-\", label=\"A11\")\n",
+    "plt.plot(sol.t, sol.y[4], \"--\", label=\"A22\")\n",
+    "plt.plot(sol.t, sol.y[8], \"-.\", label=\"A33\")\n",
+    "plt.plot(sol.t, sol.y[2], \":\", label=\"A31\")\n",
     "\n",
     "# adjust some plot settings.\n",
     "plt.xlabel(\"Time $t$ in s\")\n",
@@ -103,7 +103,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -117,7 +117,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/examples/orientation/ardrsc_perfectshear.ipynb b/examples/orientation/ardrsc_perfectshear.ipynb
index 7251f6e..1a60ed9 100644
--- a/examples/orientation/ardrsc_perfectshear.ipynb
+++ b/examples/orientation/ardrsc_perfectshear.ipynb
@@ -15,19 +15,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from scipy.integrate import odeint\n",
-    "from fiberoripy.orientation import ard_rsc_ode"
+    "from scipy.integrate import solve_ivp\n",
+    "\n",
+    "from fiberoripy.closures import IBOF_closure\n",
+    "from fiberoripy.orientation import ard_rsc_ode, integrate_ori_ode"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +37,8 @@
     "xi = 1.0\n",
     "\n",
     "# time steps\n",
-    "t = np.linspace(0, 4000, 500)\n",
+    "t0, tf = 0, 4000\n",
+    "t = np.linspace(t0, tf, 500)\n",
     "\n",
     "# initial fiber orientation state\n",
     "A0 = 1.0 / 3.0 * np.eye(3)"
@@ -43,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,43 +58,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# compute solution by integrating the ODE\n",
-    "A = odeint(\n",
-    "    ard_rsc_ode,\n",
+    "kwargs = {\n",
+    "    \"xi\": xi,\n",
+    "    \"b1\": 1.924e-4,\n",
+    "    \"kappa\": 1.0 / 30.0,\n",
+    "    \"b2\": 5.839e-3,\n",
+    "    \"b3\": 0.04,\n",
+    "    \"b4\": 1.168e-5,\n",
+    "    \"b5\": 0.0,\n",
+    "}\n",
+    "\n",
+    "sol = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t0, tf),\n",
     "    A0.ravel(),\n",
-    "    t,\n",
-    "    args=(xi, L, 1.924e-4, 1.0 / 30.0, 5.839e-3, 0.04, 1.168e-5, 0.0),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, ard_rsc_ode, kwargs),\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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kfU4pH31/IFDHZoERm5YxPC2OIb1iGZwaw+BesQxIicZhC53rdkKI8HPcJKiCylpe+mo3M0/sw5kje3d3OCHBYbP6r00lNJpfVedhZ4GTHYcq+WLNFqptdlbsKub97/ICdZSC9IQoBveKYXCqGSljkD+BpSdGyW+3hBDH7LhJUM99vhOXx8fdM0Z2dyghLyYygnGZiYzLTKS3czdZWacA4KzzsK+oij1FVewtrGJPkZO9RVW8910ezjpPYH271UK/pCgykqLITI6mf3I0mUnRZCZHkZkUTWK0Lax6JwkhOsdxkaCKnHW8syaXH5+cycDUmO4Op8eKjYwI/PYqmNaaIqeLPYUmYe0triK3pIac0mo2bsynrNp92Hbqk1e/xCj6Jjjok+Cgb4KZ7h0fKWMVCiGOjwQ1d9V+XF4fv5g2sLtDCUtKKXrFRdIrLpJTBqcctryy1k2OP2HllFSTW1pDTkk12cVVrNxd3Ojsq15qrJ0+CQ76xDcksLR4M0p8r7hIesVGkhwjI8YLEc7CPkG5vT7eXJXNacNSGdq760eKEGa0jFHpNkalxze7vLLWzaGKWvLLzeNg4LmG3NJq1mSXHHYWBuY6WFK0ndRYO6mxkaTGRlJbVscWdpEaa5JYYrSNxGg7SdE24h02GYxXiB4k7BPU0h2FHKqo4/FLxnR3KKIFcQ4bcQ5bq18galxeDlXUUuSso8hZR6HTRVGlf9r/vD6njEPlHhZlb292GxYFCVEmYSVG20jyPydGmQSWGGMnIcpGXGQEcY4I4hw2Yh1mOtYeIclNiC4W9gnqk40HiXNEcMbwtu8kKUJXlN3KwNSYNq8hLlmyhMmnTqeo0kVRVR1l1S7Kqt2UVruDps3zoYpath+spKzaRZXL22YMsYHEFeGftgXKMfYIou1WogLPVqLtVmLsEYHpwHKbWR4Zhr/DE6IjhXWCcnl8fLblIOeOSgvLH+WK5kXbI+ifEkH/lOh2r1Pn8VJe7aai1k1lrSfwcNY1LlfWunHWmemyahc5pdVU1nqoqvNQ3Y4kF8yiwG6BuK8/J8pm7sxsj7AQGWGSV2T9tK1h2t7C/MgIC5E2C3arhUibFZtFEWG1YLMqbFYLEVZFhMUsj7AqIqwKm8WCLcJChMXUkZ8GiFAT1glqxe4iKmo9nH9i3+4ORYS4yAgrveOt9I5v/7hiTWmtqXX7qHaZZGUeHmrqp91eaoKW1bi87Nizj5S03lS7vLg8Puo8Puo8XurcPiprPf55Xv98H3VuM+3xdfwIH0qZgYobkptJcIFkZrVQU11DwsblWCwKqzJjRFqUqWNRCqtFYVXKv9xf9j/McoKmG+pbLQ3r1D/Xb9OiwKIUSpkOOQqT3C0WM638yyyBZQ11t+e5KV2Xi8X/swazvfr6AGb7Kmg/NLutoHlN9qsCx08FTfufUYFpWllW/5xb6WPHoUr/8uBlqsm6R77P+p92BNdXLWyXFpY13UaNp3NHmgnrBLVoyyFiIyM4bXhq25WFOEZKKaL8zXuH92Vs3pIl+WRljT3ifXm8PlxeX0NScwcnMi9ur8bj1bh9Ptz+hOb2+vzzfbh9/ufAPI3HZ7bpCarTeF0fhwqqSYq149VmsGKvT+P1J2avT+PT/nlNpr1a4/MRNG2evV7/c1D9Ds+9G7/v4A12sq+XdncE7TYqxcLMczpv+2GdoFbtKWbyoGT5TY0IOxFWCxFWC9H2rt3vkiVLyMqa3Kn70NokKY/Ph88HPq3R+J+1Wa61Kfs0aOrnB9X1mXnfrPqGkyefEtgmmGdf0Dbq19VNlungbdG4bnBMJmizvomfwLMOek3+akF1mtQHNm3ezOhRoxttq+k2CNpGu/cZ2Edz2215GUHbaNhHw7yS3N1N//s6VNgmqMLKOnYXVgXupySE6BmUqm8+PPYvlnuiLQzqQT/Ojy7eTtbYnnNJYsmS7E7dftj2HFi9twSAUwYld3MkQgghjkYYJ6hiou3Ww4blEUII0TOEbYJatbeEiQOS5CZ8QgjRQ4Xlp3dVnYdtByuZNECa94QQoqcKywS13f87gpbGfhNCCBH62pWglFIzlFLblVK7lFL3tFDnx0qpLUqpzUqpf3VsmEdm+0GToEb2kcFhhRCip2qzm7lSygq8CJwL5ALfKqUWaK23BNUZBtwLTNNalyqluvWWtdvyK4ixW+mXGNWdYQghhDgG7TmDmgzs0lrv0Vq7gHnArCZ1bgBe1FqXAmitCzo2zCOz7WAlw/vEyejTQgjRg6mmv04+rIJSlwMztNbX+8uzgVO01rcF1fkA2AFMA6zAw1rrT5vZ1o3AjQBpaWkT582bd8wvwOl0EhsbGyhrrbnty2pOTovg2hMjj3n7Ha1pvKFO4u1cEm/nkng7V0fFe+aZZ67VWk86bIEZNqTlB3A58HJQeTbwQpM6HwP/BmzAICAHSGxtuxMnTtQdYfHixY3KB8tr9IC7P9avfr23Q7bf0ZrGG+ok3s4l8XYuibdzdVS8wBrdTJ5oTxNfHhA8XlCGf16wXGCB1tqttd6LOZsa1u702YG2+TtIjJAOEkII0aO1J0F9CwxTSg1SStmBK4EFTep8AGQBKKVSgeHAno4Ls/12+ruYj0iTBCWEED1ZmwlKa+0BbgMWAluB+VrrzUqpR5VSF/urLQSKlVJbgMXAb7XWxZ0VdGuyi6uJd0SQFNPFwzwLIYToUO0azVxr/QnwSZN5DwZNa+BO/6NbZZdUMyCl54xeLIQQonlhN5LE/uKqI7rVtxBCiNAUVgnK4/WRW1rDgGRJUEII0dOFVYLKL6/F49P0lwQlhBA9XlglqANlNQD0S5IhjoQQoqcLrwRVbhJUuozBJ4QQPV54JaiyWgDSEyRBCSFETxdmCaqGxGgbUXZrd4cihBDiGIVVgsovr5WzJyGECBNhlaAOlNWQnujo7jCEEEJ0gLBKUAWVdfSOlwQlhBDhIGwSlNvro6TKRe+40LsHlBBCiCMXNgmqyFkHQC9JUEIIERbCJkEVVJgE1TtOmviEECIchE2CKqysT1ByBiWEEOEgbBJUQaU08QkhRDgJowRlRpFIjZUEJYQQ4SBsElRJlYuEKBv2iLB5SUIIcVwLm0/z0mo3SdG27g5DCCFEBwmfBFXlIjHa3t1hCCGE6CDhk6CqXSTHSIISQohwETYJqqzaTaI08QkhRNgImwRVWu0iSZr4hBAibIRFgqp1e6l2eaWJTwghwkhYJKiyajeANPEJIUQYCYsEVVrtApAmPiGECCNhlaDkDEoIIcJHeCSoKtPEJ9eghBAifIRHgpImPiGECDthkaDKpIlPCCHCTlgkqNJqNzF2K5ER1u4ORQghRAcJjwQl4/AJIUTYCY8EVe0iKUaa94QQIpyESYJySwcJIYQIM2GRoMpkHD4hhAg7YZGgSqpccrNCIYQIMz0+QXl9mopaj3SSEEKIMNPjE1SVxzzLKBJCCBFe2pWglFIzlFLblVK7lFL3tFLvMqWUVkpN6rgQW+d0aUB+pCuEEOGmzQSllLICLwIzgVHAT5RSo5qpFwfcDqzq6CBb43SbBCWdJIQQIry05wxqMrBLa71Ha+0C5gGzmqn3GPAHoLYD42tT/RmUNPEJIUR4iWhHnX5ATlA5FzgluIJSagKQqbX+j1Lqty1tSCl1I3AjQFpaGkuWLDnigJsqrqwFFNs2rKVoZ+hfUnM6nR3yuruKxNu5JN7OJfF2rs6Otz0JqlVKKQvwJ+Datupqrf8G/A1g0qRJOisr61h3zyd7FwFuZp51OjGRx/xyOt2SJUvoiNfdVSTeziXxdi6Jt3N1drztOeXIAzKDyhn+efXigBOBJUqpfcAUYEFXdZRwusButRBtl4FihRAinLQnQX0LDFNKDVJK2YErgQX1C7XW5VrrVK31QK31QOAb4GKt9ZpOibgJp1uTFGNDKdUVuxNCCNFF2kxQWmsPcBuwENgKzNdab1ZKPaqUurizA2xLpUtLDz4hhAhD7bpoo7X+BPikybwHW6ibdexhtV9FnSa9d2RX7lIIIUQXCP1ub22ocGlSpIu5EEKEnfBIULFyBiWEEOGmRyeoapeHOi+kSoISQoiw06MTVLHTBUBKrDTxCSFEuOnRCarQWQdALzmDEkKIsNOjE5ScQQkhRPgK/bGBWrGn0AlARlJ0N0ciRJjz+cDnxuKthdpyM8+RYJ4rDoC7Bnxe8HnA5wZbDKQONcv3rwJ3FXg9oH3mEZsGGRPN8i0LwFPXsEz7IHkQDDjVLP/2H2bb2gfa/9xnDAzOMttc/ufGy7TPrDv0HKyeGvj0XjOvfhtoGHEBDDsHqorh84fMPI3/WcPYH8OQM6EsB754tGF+/fPJ18HA6VC4A758LGg55nn6HZA5GQ6sh8VPNCyrr3f2g5A+DvZ9DV89GVg+rrQU9ibABc9A7xNgxyJY9sfG+wa47O+QPBg2vQ8r/tKw7Xo/mQdxfeC7N+DbvzfeP8A1H0NUInzzEqx7oyHuejd9BVYbLH0aNr7XsJ7WZv4tX7f/vXMMenSC+m5/Kb2jlYxkLsKHz2s+7N01EJMKSkHZfijPA09NwzKfF066wqyz8zM4uAE8LvD6H1YbnPOwWb7iBdi/Erxu8NaZetHJcOVbZvm/b4Z9y/3Jxf9IGQbXf2aWv3wu5K4G4HSAZUDG5Iblb/wQCrc2fh1DzoLZ/zbT710H5TmNl59wEVzxppn+6NdQU9p4+UlXNSSo/95tkl6wyTeaBKW9sPjxhvnKYh4oGHoOSntg3ZvmONYvUxZIHW4SlKcGdn1u6ivV8DzoNLM9T63/tTdZXh+vpwaKdhy+3FVllntd4CxoWAZm2ue/06r2mv+PwHLdUA/AYoEIR+Nto0D5h3aLcEB0StDrr9+Hv3HMHgNxfesXNtSpXx6VCEkDaVFMb0gd1njblq67916PTVBaa9ZnFzMhrqK7QxHHI6/H/JFbLFBdAiV7oK7SfDC5nGZ67I/NWcauL8w3XZfTPOqTzOz3zbaWPQPLnzMfdl5Xwz7uOwQ2B6x8EVa91Hj/ytKQoLZ8YD6EwXx4WO0Qk9KQoCryTHxWG1gjISISbEGtDr1PABRYI8DifwQ+1IDxP4Oh54DFyu59+xkybETj5Wc/AHVOsFgb1o/t3bD8R6+aD2RlNXWUpeHsC+C6zwHdOIHYYxuW37nVP9//wWqxmtcI5vmB4qDljYc889ji4N4myTFYQgbcta3l5anD4PbvW17e9yT4ZSu3wMucbM5GWjLodLju9EBxfdPBV4eeYx4tGXm+ebTkxB+aR0tOutI8WjLxGvPoJj02QR2qqKVX/DNM8e6CklNMk4AQR0Jr/7fhMijYYp5ryxqeT7rSNKPsXQqL/88/v9wsc1fDDYuh3wTYugA+uv3w7Q88zXwQl2XD7i8hMtZ8o7XFmDMYn8/USzsRxv0EbFEQEWWSki264VvuxJ/D8PP8y6LMt+aIyIb4z3/GPKx2kzCbmvH71o/DtGZiDxb0AZXjW8KQqVmNl4+8oPX1M9oYN7q+KbAlsb1aXqb8iVWEpR77P9snIYrojGi+LLRx7aL7G5orxPHLXUtkbQHkrYWqItOMkzwISvfBV0+ZedVF/udimPUijL4EDqyDNy45fHv9JpkEpfxnBilDTJOIIxEi4xvOEoacBVfNN9/6I2PNsz22oell0i/MoyXDzzOPlvQeaR4tsTlaPSxC9FQ9NkEBTBlwOm9U7KF6+3+ILt5tPkBE+PH5oCIXKg9BZT5UHjTPA6eZ5o+yHJhzGtSUMhXMePoAM56EKbeYay+7F5tmr5hekDzEXN+pb3vvexLM/sCffBIaElD9N/OB0+Daj1uOL7G/eQghOlSPTlBjUsfgUZpseyQnrHkFznuiu0MSR+vgRpNoynP8nQJyYMA0OOUmc2H/2TGN6yuraeYaeo45UznxMojrw7a8MkZOmA7RqebsB8x1hLu2Hr7PetHJpseWECKk9OgElRadBkDhwKmcsGUBnPtY823wovuVZkPxLnOxvmQPlOyFXsPh3EfN8tcuaugZFeEwF6/T/EnJFgWX/NUknbg+5gJ9dErD/7U92nTLBQ4uWcLIEVld+9qEEJ2iRyeo3tHmGkDBqAvhh1dIcupOWkN5LhRuh5LdDb3GfuDvAjzvKji0yUzbYsy1ofruqwCXv2Ka1xL6N3SvDjbuqq55HUKIkNGjE1RKVAoKRaF2Q2RcQ68m0Xl8PijfbxJRea75wSLA2z+DbUHXaeyxkHFyQ/m8J0wX6JQh5keaTf+fhpzV+bELIXqUHp2gbBYbsZZYDlUfgkOb4b0b4NK/move4thVlzT8uvy71+Hbl80v5z01/grKnNnYoszz0LMhdQSkDDU93IKT0OCsro5eCNHD9egEBZAQkUBhTSHE9zPNSqv/DrNe6O6wep6qIsheYTor1D8qcnGcMscst0SYa0CTpkOvEdBrpLmGZIsyy9v6LYwQQhyhHp+gMu2ZJDuSTRfh8T+Fta9B1r2Q0K+7QwtNWptecge+M78XGvdTM5LAvuXwzjWmd1zqcDPMTJ8xeKv8v7EZd5VcBxJCdKken6CuSrmKrGlZpjDtdlj7Kqx4Hmb+oTvDCh0+n+k8Up4LH99pklJ1kVlmjYS+40yCGnyGGRmh9wkNZ0WAe8mSbglbCCF6fIJqJLG/GZ5m7asw7Q6I79vWGuHF54Oi7aapbv83kPut+X3Q2Q+YH5+W55gRC/pNgH4TofdoiPCPaRaVBP2SujV8IYQI1uMT1Prq9Ty/4Hle+cErJDoS4Yy7of+ppqdYuPO4wHnQJGat4fmTTPMdmNefORn6nGjKkbFw68rui1UIIY5Qj09QDuUgIzYDd/1w/In9zbUoMB/gEWF0K466SshZbc6O9q+E3DWm2/YtX5sec5NvMmdCA6ZC0iDpci+E6NF6fIIaGTWSm7NuPnzBtk/g07vh55/23A4TzkLIWwMjZpryR7fDpvfMKNd9xsDEa804cfVOva1bwhRCiM7Q4xNUi1KGmrtlzr8arllgbnMQ6ioPmtsyZK8wZ0jFu8z8OzaaM8Mpt5ped5mTzQ+ThRAijPX4sYGqfdVkvZ3FvG3zGi/oNRx+OMd0p/7XFQ13uAwVPq/5rdGqv5lx6cB09f7gFtj6kUmw5zwC133WcHO4jEnmx7CSnIQQx4EefwblUA5K60opqC44fOEJF8Glc+D9G+GV8+Dn/+3eD/fqEgbsmwdvPAc534Kr0syPeN6MTTf0HLhlpfkRrIwrKIQ4zvX4BGVRFpIikyipLWm+wtgfmy7W+5Y1JKf63wZ1Fq1Nl+7cNZCzytwxdcJssFgZkD3f/NZo7I+h/xToPxUSM816UYnmIYQQoucnKIDkqGSKa4tbrjD8B+YBkP+9uS518g0w5nJz+4Zj4fOZW4BHJ5vy/Kth7zKo8SfMiCiYfIOZdiTw9bS3OO2cmce2TyGEOA6ERYJKcaS0fAbVlNdt7qq66D747AFzW+/+p8DpvzW3e6gpA+1rGE3BXQM+T8Ptvbd/akZjKN4JRTuheLe53nXTUrPckWDGpUsfB+njoc9Yc9uJ+t1HRCGEEKJtYZGgkh3J5Bbmtq9yxiS4/nMzKvfGd2DvUlj3Jpz9kFn+2QNm5O5gUUlw9z4z/f1c2LoAEgeYMesGZ0Ha6Ia6F//lWF+OEEIIwihBtdrE15xew+Gs+4D7wOsBq/9QjPmxuWZU3+vPFmUe9feauug5+OHfw+sHwEIIEYLCIkGlRKVQ46mh2l1NtC36yDdgDToMg04zj5ZIJwYhhOgSYdGXOcWRAkBpXWk3RyKEEKKjhEWCGpI4hPMHnY8lPF6OEEIIwqSJb2yvsYztNba7wxBCCNGBwuqUw6d93R2CEEKIDtKuBKWUmqGU2q6U2qWUuqeZ5XcqpbYopTYopb5QSg3o+FBb5tM+ps+bzgvrXujK3QohhOhEbSYopZQVeBGYCYwCfqKUGtWk2jpgktZ6LPAu8FRHB9oai7JwxYgrGN97fFfuVgghRCdqzzWoycAurfUeAKXUPGAWsKW+gtZ6cVD9b4CfdWSQ7fGr8b/q6l0KIYToREpr3XoFpS4HZmitr/eXZwOnaK2bvTueUuoF4KDW+vFmlt0I3AiQlpY2cd68eU2rHDGn00lsbCxu7abKW0ViROIxb7Mz1cfbU0i8nUvi7VwSb+fqqHjPPPPMtVrrSYct0Fq3+gAuB14OKs8GXmih7s8wZ1CRbW134sSJuiMsXrxYa6314ysf11P/NbVDttmZ6uPtKSTeziXxdi6Jt3N1VLzAGt1MnmhPJ4k8IDOonOGf14hS6hzgPuBirXXdkWTPjtA3ti+Vrkqq3CF2Y0IhhBBHpT0J6ltgmFJqkFLKDlwJLAiuoJQaD8zBJKdm7hzY+dJj0wE44DzQHbsXQgjRwdpMUFprD3AbsBDYCszXWm9WSj2qlLrYX+1pIBZ4Rym1Xim1oIXNdZp+Mf0ASVBCCBEu2jWShNb6E+CTJvMeDJo+p4PjOmL1Z1B5zsNaH4UQQvRAYTOSRLIjGYfVIWdQQggRJsImQSmlSI9NJ9fZzhsXCiGECGlhk6AA+sf3J7siu7vDEEII0QHCKkENih9EdkU2Xp+3u0MRQghxjMLidhv1zh5wNumx6Xi0ByvW7g5HCCHEMQirBHVSr5M4qddJ3R2GEEKIDhBWTXwAe8v3sqd8T3eHIYQQ4hiFXYK66bObmPP9nO4OQwghxDEKqyY+gEenPUrvqN7dHYYQQohjFHYJakrfKd0dghBCiA4QNk187oMHcS5bTpW7ig92fcDe8r3dHZIQQohjEBYJylNYyN7LLqfq66+p89bxwNcP8OX+L7s7LCGEEMcgLJr4rKmp9H/576AUDkcyg2IHsL5gfXeHJYQQ4hiExRmUUgrHCSfgGDmS2i1buO/5Ag5uWI3H5+nu0IQQQhylnp+gfD4KX3yRms2bAYjo0wd7r97U1VWxqWhTNwcnhBDiaPX4BGUpr6DohRep3eRPUMnJDH7rLbLTrXyV+1U3RyeEEOJo9fhrUL6kREasXwc+X2BeQmQC03qfgvuf86iNnYlj+PBujFAIIcTR6PFnUACWyEgsUVGN5l3Q+0xOW17Gzvdf66aohBBCHIsen6Ai135H8csvHzb/zDGzeOCmGD44w9ENUQkhhDhWPT9Bbd5M2TvvHjY/2hbNhNHnUOOpwZ2XR+UXX3RDdEIIIY5Wj78GVXH1bMaffnqzyx479TFsVht5d95F1erVxEydiiU6uosjFEIIcTR6fIICUJbmTwRtVhsAnt9cT2rxT7FER6O1Bq1bXEcIIURo6PGf0rHvvd9q812+M59LPr+K111LASh7ez4511+P1+nsqhCFEEIchR6doLTWOL79lppNLf8gt29sXx6Z9gjXjL4GAGWzYYmJCTT1aa27JFYhhBBHpkcnKKUURU/+nt63395qvYuHXEySI4k6bx2+C7Lo9/zzKIsFb2Uley66iMolS7omYCGEEO3WoxPUkdBac/uXt3PL57fgdJvmPW9pKREpqUT06gWAOy8P59dfo73e7gxVCCEEPTxB1e3ZQ9wbb+Lat6/NukoprjrhKnaU7uAXC39BUU0R9v79GfDaq0SNHg1A2XvvkXPDjXgrKgBwZWfjPlTQmS9BCCFEC3p0gvIUFBK5cSO+6up21T8943T+ctZfyK7I5oqPrmDNwTWNlqfcdBP9X/kHEUlJABQ+9zz7fvzjwPKqb76hZv36DotfCCFEy3p0goqZcgpFT/0Bx6hR7V5ner/pvDHzDaJsUfx84c95aMVDlNSWAGbIpJgpDbeMT7n5Jvo89GCgXPD0Hyl49rlA+eAT/0fJaw1DKdXu2IGnpORYXpIQQgi/Hp2gjtaI5BG8feHbXDv6WhbsWsAF71/As2ufpaimqFE9x/DhxJ11VqCc+dJf6fPgA4Gye/9+3AfyA+Wc62+g4Ok/Bsr7b7iRkjffCpRL571NRHZ2oOzKzsbrrOrQ1yaEEOGiRyeoik8+If7114+qq3iMLYa7Jt3Fexe/x9T0qbyy6RWW5y0HoNpdjcvrOmydiF69iBw8OFDOnPMSaffeEyj3feJxkq76CeDvvm5R5gFoj4eDjzxC5IYNgfLu82ZQ8ro5A/PV1bHzjCxK58835aoqcn99O85lywPlwr+8ELjvla+6moqFi3DnmwTpc7mo27UrkPC014uvqko6fAgheqwenaBceXnY9uxFKXXU2xicOJg/Zf2Jjy79iAsGXQDA29vf5oy3z6DCZTpLVLgq8Glfa5sBIPa004gaMwYwnTL6z5lD8lVXmYVWK8NWfE11/RmZ1qT/4cmGMzSPh5jTpmPrmw6YhFW3Zzfe8nIAvGVlFL34InXbtpvqBQXk3X471WvMdTR3djZ7LryIquXLAKjbvp3tEyfh9Hehr9m4ie2nTKFq5cpAec+sS6jxJ8zaLVvIufkW6nbvNuWtWzlw7/9iKTJnlbXbt3PoD0/hLjCdRup27qRozt/wlJaa8p69lL49P/ADaFdODhULF+GrrTXx5edT9c0qtNttygUF1GzYEEignuJi6nbuRPtvm+ItL8eVmxf48uGrqgrsC0xC9tXUBMryezYhwk+PTlCpN9xA8cMPdci2BsQPCAyNNL73eH426mfE2+MBuG/5fUyfO50bFt3Ac989xyd7PmFbyTZqPbXt3r5SioikJHRMjCnbbCTMmoVj5EgALDExpD/+OLGnTQfMjReHfPwxCReapGnr14+RmzeRMOtiszw9nUEffkjsaaeZcloa/f70DFEnnWTKvXrR+7e/IXLYMACsiQkkXHQREb17m/3b7dgyMlAOM9q7r7YO96FDaI8HAE9JCVXffIOqqwPAtX8/pfPm4fP3cKzdupXCP/85UK5Z9x0HH3oInz+hVi1fTt7tt+OrrASg8rPP2H/ttfiqzBlexUcfse/HV6D9Caxs/nz2XHQx+BNWyWuvsfuccwJfPopeeoldZ2QFjmfhM39i5/TTAuVDjz3GjqmnBsoHH32UXeedFyjnP/wwey79YUP5gQfZ95OrgsoPsP8XvwiUD/zvfeTc+suG8j33knfnXQ3lu+/mwH33NSoffPSxQDnvd7/j0B+eaij/9ncUPPdco3LRX//aqFz8j1caynfeScnrbwTKubffQem8eQ3lX/2KsncbBknOufWXlH/4IQDa5yPnllsp/89/TNnlIueXt1GxcBEAvpoacm67jcovvwTAW1lJ7q9+jXOpGW3FU1pK7u13ULVihSkXFZF7x/9QtXo1AO5Dh8i7806qv1tnynl55N31G2o2bgTMeyXvN7+ldssWAOr27iXvd7+jdscOU961iwN330Pdnj0A1G7fwYF77sW1f78pb9nCgf+9D3deHmC+TB24/37chw6Z8vr15D/wAB7/l6fqtWtN2f8Fpmr1avIfeACv/71XtXIl+Q88EOhM5Vy2nPwHHsTnMq0kzq++Iv+BBwPv/covviD/wYbPlYpFizj46KMN5f/+l4NP/F+gXP7Rxxx68g8N5Q8/pOCZZwLlsvfep+DZZwPl0nfeofD5vzSU573d6L1Q8tZbFP3t7w3l11+n+B//CJSLX/lno2vfxS+/3OhSQtGcv1E6d26gXPj//l+gZQag8C8vUPbe+4FywbPPUr5gQUP5mWco//g/gfKhp5+m4tNPG8q/f5KKzz6jK/ToBNVZxvUexy/HNXw4XTj4QmYOmkl5XTmvbnqVu5fdzY8++hGT35rMjPdmcMOiG3hx/YuB+puLN3Ow6mCHx6WsVlSEGT7RYrfjGDEca2IiANb4eOLPPx9b376ASVAp112HvX9/AOyZmfS5/z4ihwwBwDFiOJkvvhC4mWP0hPEM/vf7OEaMACB22jSGLf4Sb79+AMSfey4j131H5NChpnzhhYz4fj22zExTPv98hn61hIi0NFOeOZNBH36A1d8jMu688+j/+mtY/Ak67gc/IHPOS4EEGfeDH9Dv2T+D1WrK55xD3/9r+BCIPess0v733obymVn0+vWvAuWY008n+Rc/D5SjJ08mYdashvKECcSdfXag7Bg7hphp0wLlyJEjiRo3vqE8dCiOE04IlO0DB2IfNChQjkhPx9anb6BsTUnFmpzcUI6NC7xWAGW3Bf7vAPD5Gp31+aqr0e6GZmVvVRW+uoYvQN6KcnzVDWeMnpLSRr1XPUVFeKsarme6Dx3EV9/ci0kiPqf5wNZeH+6c3MCXB7xeXPv24a3wlz0e6nbvCpS1203dzp2B+trlonbrNrwV5suIr7aW2k2bAj/P8NXUULNxQ2B9n7OKmnXr8fnPrr0VlVSvWRPYnre8jOrVqwMJxVNSStXKlYHX4ykqpGrZcnxV5vW6CwpwfrU0cAbtPngQ51dL0f6E4z5woFHZlZtryv4E5MrZb1oW/F+GXNnZpuz//3Dt2xdoeQBw7dmDc0nD3bnrdu6kyp/MAep27MDpb7kAqN26LdA0DybhVn29oqG8cRNV33wTKNesX0/VyqDyuvVUr1oVKFev/Y7q1d82lNesCbScAFStWk312qDyihWNehtXfb2Cmu+/D5SdS5cGWk4AnEu+ombDxkC5cvFiav2XEgAqF31G7ZatgXLFokXUbd1GV1Dd1TQyadIkvWbNmrYrtqLgz8+SnX+Ak596qu3KHaTOW0d2RTZ7y/eyp3wPe8v2kleVR/+4/vz+tN8DMOO9GYzvPT5QvuD9C4iKiCLZkYy73M3IASOJs8cRa4sl1h5LrC2WgQkDGZ5kkkVRTRHx9njsVnuXva6WLFmyhKysrO4Oo90k3s4l8Xau4zVepdRarfWkpvN79Gjm7rw8LEHXJbpCpDWS4UnDA8mkOU9MfwJHhDkz8Pq8TOoziZKaEkrqSjjgOsDWXVupcjfuvfej4T/iwakP4vV5OXP+mdxy0i3cOu5WCqsLuWzBZTgiHOZhdRAVERWYrp9/dv+zOT3jdJwuJ29tfYuszCxGJI+guKaYr3K/wmaxmYfV1jBtsWG32rFZbKTHppMQmYDL66K0tpQkRxJ2qx2v9lLlrsKqrOZhsWJRcuIthOh8PTpB9fvj0+wMwXH0JqZNDExbLVYeOfWRQLn+G4fX56XKU4XT5cTpdhJjM81BGs39p9zP6FQzuoXNYuMHA39AjaeGOm8dtZ5aaj21ON1OimqKqPPWUeOpCSTMsroyXlj/An1i+jAieQT7K/fz0Iq2r9M9Nu0xLhl6CVuKtzD7v7OZc84cTu13KhuqN3DHv+44rH6EisCiLFgtJnH9+cw/M6XvFJbmLuWxbx5jzrlzGJwwmI/3fMw/Nv4DqzKJrf5ZKYVCBaYfm/YYmXGZfLH/C97e9jZ/yvoTsfZYPtz1IZ9lf4ZSCguWQH2LsmDBAgosysIDUx4gxhbDuqp1LF25lAenmt+vfbjrQzYWbTTrBe1PYa5tKRRWi5X/mfg/AHy691PynHlcN+Y6AP6989/sr9wfqF8veBsJkQnMHjUbgI92f4TH5+HSYZcCMH/7fEprSxvvM6hTz97yvTj3OLlw8IUAfLDrAxIjE8nKzALg7W1vU+eta3b9+nL/+P5M7zc9EO+A+AFMSJuAx+fh/Z0N1xpaMixpGON7j8fr8/L+rvcZkzqGkckjcbqcfLrv00Z1t1dup2hH459jnJh6IiOTR1LlruLTvZ8yMW0iAxMGUlRTxNLcpbRlYtpEBsQPoKimiGW5yzg1/VTSYtLIc+axOn91m+sH1//24LecmXkmCZEJ7CnbwyrnKsp3lbe6flZmVqD+puJNzBg4A7vVzpbiLewq23VY/abvhfMGnofdamdr8Vb2lO/hgsHmuvH6gvXkVOYcvn7Q/79VWZk5aGag/obqDWSRBcCq/FWH/fSlqeiIaM7sfyYAq/NX49VepqZPBWBZ7rLAsG4tSYxMbFQ/xhbDhLQJAHye/Tlun7vFdRUKB517x/J2JSil1AzgOcAKvKy1frLJ8kjgdWAiUAxcobXe17GhhherxUq8PT7QEaNehCWCK0ZeESgnOhK5f8r97d5uv9h+rJu9LlAenTKazy7/DJfXhdvnNg+vG5fPFZh2+9yMTDadNTLiMnho6kMMTjTd6fvZ+3HXxLvwam/Dw+fFp314tAefz4dXe0mLNteekh3JnNLnFGIiTMKNt8czMH6gqat9+LS59uLTPnz4QIMPX+CP3u1zU+1puLbidDspqC5AoxuvT9B2tIkBoNBTyLoDDa9/U9EmFu1bhA9TD02grsY0b9ut9kCCWnFgBesL1wcS1MLshaw6sKpRfY0OXD/SaPrF9gskqAW7F1DrqQ0kqH9t/Re7y3e3+n+2f9v+QIJ6ddOrDE4cHEhQz697PtCbtCXnDjg3kKD+uOaPXDj4QiakTcCrvTz2zWOtrgtw1cirGN97PB7t4dGVj3L7hNsZmTySsroyHln5yOErrGxcrK9fWlvKwysf5vFpjzMwYSA5lTnt+nL0+LTHGRA/gJzKHB5c8SBzzp1DWkwam4s28+CKB9tcP7j+A18/wPsXv09CZAIr81fyZvGb8HXr6wfXf3L1k5yRcQZ2q52F+xbyyqZXWl8ZM0KN3Wrn032f8uaWNwMJ6p0d77Bg94JW17Vb7IEE9c6Od1hespxf82sAXt74Mt/kf9Pa6vSL7RdIUH/f+HdqPbWBhPOntX9qNsEGG9drXKP6gxIGBRLUQyseavW9Z1VWnu3/bKvbP1ZtXoNSSlmBHcC5QC7wLfATrfWWoDq3AmO11jcrpa4ELtVaX9HsBv2O9RqU1pqcm24ib9gwpv72t0e9na52vLYxd5Xujrf+5wj1zaAen8ckNv+fmfb/A/MeXrp0KaeffnqgSbjaXY1SiqiIKACcLmcgGdfTWjdKljaLjTh7HADFNcVEWiOJtceitW7zGziAI8JBnD0OrTWFNYXE2GKIscXg8XkoriluVHflypVMnTq10bxYe2ygfv3102hbNC6vKzBKS2uC6xfXFJPkSMIR4aDaXU1ZXVmb6yc7kgP1S+tK6R3VG5vVRqWrkoVfLWRK0OgwzUmLTgvUL6srIz0mHavFSlltGZXuysaVm/m4TI819UtrS3G6nGTGm45DRTVFVLurm6x++AYGxA8I1F+yfAmXn3s5AAerDrbZU9hqsZIZZ/aX78xHo0mPNT9VyanMafUMCMBhdTSqb7fYSYsxXzb3le8zXyJbsX/d/m6/BjUZ2KW13uPf0DxgFrAlqM4s4GH/9LvAC0oppTuxB4Z2ufCWlaNch/+gVoju0vT6XISl9T8xu8UeSE4A0bboRstj7bFHtP+UqJTAtFKKXtG92r2uUore0b0D5QhLRODDql5iROJh84Lr94npEyjbrfZG5bbYrXb6xjb0jIy2RR92PFrTtH6cPY5UWyoZcRntWj/OHhdI9GBaLxIdie3ef5IjiSRHUqCcGpUKUe1endSoVFJtqYHykRw7oNGxAwKJq72a1h+YMLDNdfaz/4j2caTak6D6AcENqbnAKS3V0Vp7lFLlQArQ6OubUupG4EZ/0amU2n40QTfyzvxU7r+/7a+JoSOVJsclxEm8nUvi7VwSb+fqqHgHNDezSztJaK3/BvytI7eplFrT3KlhqJJ4O5fE27kk3s4l8TbWnv7CeUDwuV+Gf16zdZRSEUACprOEEEIIcVTak6C+BYYppQYppezAlUDTrikLgGv805cDX3bm9SchhBDhr80mPv81pduAhZhu5q9orTcrpR4F1mitFwD/AN5QSu0CSjBJrKt0aJNhF5B4O5fE27kk3s4l8QbptqGOhBBCiNbImDVCCCFCkiQoIYQQIanHJiil1Ayl1Hal1C6l1D1tr9E1lFL7lFIblVLrlVJr/POSlVKfKaV2+p+T/POVUup5/2vYoJSa0AXxvaKUKlBKbQqad8TxKaWu8dffqZS6prl9dWK8Dyul8vzHeL1S6vygZff6492ulDovaH6XvF+UUplKqcVKqS1Kqc1Kqdv980PyGLcSb0geY6WUQym1Win1vT/eR/zzBymlVvn3/ba/QxdKqUh/eZd/+cC2XkcXxfuqUmpv0PEd55/f7X9z/n1ZlVLrlFIf+8vdc3y11j3ugemssRsYDNiB74FR3R2XP7Z9QGqTeU8B9/in7wH+4J8+H/gvoIApwKouiO90YAKw6WjjA5KBPf7nJP90UhfG+zDwm2bqjvK/FyKBQf73iLUr3y9AX2CCfzoOM0zYqFA9xq3EG5LH2H+cYv3TNmCV/7jNB670z38JuMU/fSvwkn/6SuDt1l5HF8b7KnB5M/W7/W/Ov787gX8BH/vL3XJ8e+oZVGD4Ja21C6gffilUzQLqb4H5GnBJ0PzXtfENkKiU6tvM+h1Ga70U09PyWOI7D/hMa12itS4FPgNmdGG8LZkFzNNa12mt9wK7MO+VLnu/aK3ztdbf+acrga2YkVZC8hi3Em9LuvUY+49T/RDdNv9DA2dhhlmDw49v/XF/FzhbKaVaeR1dFW9Luv1vTimVAVwAvOwvK7rp+PbUBNXc8Eut/VF1JQ0sUkqtVWZoJ4A0rXW+f/ogUD+YWai8jiONLxTivs3fBPJKfXNZK3F1S7z+5o7xmG/NIX+Mm8QLIXqM/c1P64ECzAf1bqBMa+1pZt+NhmED6odh67Z4tdb1x/cJ//H9szJ3hGgUb5O4uvL98CzwOwiMFJtCNx3fnpqgQtl0rfUEYCbwS6XU6cELtTn/Ddm+/aEen99fgSHAOCAfeKZbo2mGUioWeA+4Q2vd6J4FoXiMm4k3ZI+x1tqrtR6HGdVmMjCyeyNqXdN4lVInAvdi4j4Z02x3d/dF2EApdSFQoLVe292xQM9NUO0ZfqlbaK3z/M8FwL8xf0CH6pvu/M8F/uqh8jqONL5ujVtrfcj/R+8D/k5D00FIxKuUsmE+7N/SWtffMTBkj3Fz8Yb6MfbHWAYsBqZimsLqBx4I3ndLw7B1Z7wz/E2rWmtdB/yT0Dm+04CLlVL7MM20Z2HuBdg9x/doLqB19wMzAsYezMW3+guyo0MgrhggLmh6Baad+GkaXyB/yj99AY0viK7uojgH0rjTwRHFh/nGtxdzsTbJP53chfH2DZr+H0xbN8BoGl+Y3YO5eN9l7xf/sXodeLbJ/JA8xq3EG5LHGOgFJPqno4BlwIXAOzS+iH+rf/qXNL6IP7+119GF8fYNOv7PAk+GwvuhSexZNHSS6Jbj22kvrrMfmN4uOzDtz/d1dzz+mAb7/1O+BzbXx4Vpk/0C2Al8Xv/G8r8JX/S/ho3ApC6IcS6mycaNaRe+7mjiA36BufC5C/h5F8f7hj+eDZhxIIM/TO/zx7sdmNnV7xdgOqb5bgOw3v84P1SPcSvxhuQxBsYC6/xxbQIeDPrbW+0/Vu8Akf75Dn95l3/54LZeRxfF+6X/+G4C3qShp1+3/80F7S+LhgTVLcdXhjoSQggRknrqNSghhBBhThKUEEKIkCQJSgghREiSBCWEECIkSYISQggRkiRBCSGECEmSoIQQQoQkSVBCNKGUSgm6T8/BJvdFsiulVnTSfjOUUle0sKxT9ilEKJMf6grRCqXUw4BTa/3HLtjXNZh7KIXEwKFCdDc5gxLiCCmlnEqpgUqpbf47o+5QSr2llDpHKfW1/46nk4Pq/8x/V9X1Sqk5SilrM9ucDvwJuNxfb3AL+9yqlPq7/+6si5RSUc1sK0Yp9R//XVw3tXRWJkSokwQlxNEbirkNxUj/4yrM2Ha/Af4XQCl1AnAFME2bWy54gZ823ZDWejnwLTBLaz1Oa72nhX0OA17UWo8GyoDLmqkzAzigtT5Ja30i8OnRvkAhulNE21WEEC3Yq7XeCKCU2gx8obXWSqmNmBHYAc4GJgLfmhuNEkXDrTaaGgFsa8c+1/un1wbtJ9hG4Bml1B8wg30ua9erESLESIIS4ujVBU37gso+Gv62FPCa1vre1jaklEoFynXDXUvbs08vJuE1orXeoZSagBld/HGl1Bda60fb2K4QIUea+IToXF9griv1BlBKJSulBjRTbyBwoCN2qJRKB6q11m9i7kM1oSO2K0RXkzMoITqR1nqLUup+YJFSyoK5r9UvgewmVbcBqUqpTcCNWutj6VY+BnhaKeXz7++WY9iWEN1GupkLIYQISdLEJ4QQIiRJghJCCBGSJEEJIYQISZKghBBChCRJUEIIIUKSJCghhBAhSRKUEEKIkPT/AYTwlWmzlORdAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plot components\n",
-    "plt.plot(t, A[:, 0], \"-\", label=\"A11\")\n",
-    "plt.plot(t, A[:, 4], \"--\", label=\"A22\")\n",
-    "plt.plot(t, A[:, 8], \"-.\", label=\"A33\")\n",
-    "plt.plot(t, A[:, 2], \":\", label=\"A31\")\n",
+    "plt.plot(sol.t, sol.y[0], \"-\", label=\"A11\")\n",
+    "plt.plot(sol.t, sol.y[4], \"--\", label=\"A22\")\n",
+    "plt.plot(sol.t, sol.y[8], \"-.\", label=\"A33\")\n",
+    "plt.plot(sol.t, sol.y[2], \":\", label=\"A31\")\n",
     "\n",
     "# adjust some plot settings.\n",
     "plt.xlabel(\"Time $t$ in s\")\n",
@@ -105,7 +106,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -119,7 +120,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/examples/orientation/comparison_perfectshear.ipynb b/examples/orientation/comparison_perfectshear.ipynb
index e92f902..56bdb4e 100644
--- a/examples/orientation/comparison_perfectshear.ipynb
+++ b/examples/orientation/comparison_perfectshear.ipynb
@@ -14,17 +14,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from scipy.integrate import odeint\n",
+    "from scipy.integrate import solve_ivp\n",
+    "\n",
+    "from fiberoripy.closures import IBOF_closure\n",
     "from fiberoripy.orientation import (\n",
     "    ard_rsc_ode,\n",
     "    folgar_tucker_ode,\n",
     "    iard_ode,\n",
+    "    integrate_ori_ode,\n",
     "    mrd_ode,\n",
     "    pard_ode,\n",
     ")"
@@ -32,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +43,8 @@
     "xi = 1.0\n",
     "\n",
     "# time steps\n",
-    "t = np.linspace(0, 80, 100)\n",
+    "t0, tf = 0, 80\n",
+    "t = np.linspace(t0, tf, 500)\n",
     "\n",
     "# initial fiber orientation state\n",
     "A0 = 1.0 / 3.0 * np.eye(3)"
@@ -48,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,45 +64,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# compute solutions by integrating the ODEs\n",
-    "A_ft = odeint(folgar_tucker_ode, A0.ravel(), t, args=(xi, L, 0.0311),)\n",
-    "A_iard = odeint(iard_ode, A0.ravel(), t, args=(xi, L, 0.0562, 0.9977),)\n",
-    "A_pard = odeint(pard_ode, A0.ravel(), t, args=(xi, L, 0.0169, 0.9868),)\n",
-    "A_mrd = odeint(mrd_ode, A0.ravel(), t, args=(xi, L, 0.0198, 1.0, 0.4796, 0.0120),)\n",
-    "A_wpt = odeint(ard_rsc_ode, A0.ravel(), t, args=(xi, L, 0.0504 * (1.0 - 0.995), 1.0, 0.0, 0.0504 * 0.995, 0.0, 0.0),)\n",
-    "A_pt = odeint(ard_rsc_ode, A0.ravel(), t, args=(xi, L, 1.924e-4, 1.0, 5.839e-3, 0.04, 1.168e-5, 0.0),)"
+    "kwargs_ft = {\"xi\": xi, \"Ci\": 0.0311}\n",
+    "sol_ft = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t0, tf),\n",
+    "    A0.ravel(),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, folgar_tucker_ode, kwargs_ft),\n",
+    ")\n",
+    "\n",
+    "kwargs_iard = {\"xi\": xi, \"Ci\": 0.0562, \"Cm\": 0.9977}\n",
+    "sol_iard = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t0, tf),\n",
+    "    A0.ravel(),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, iard_ode, kwargs_iard),\n",
+    ")\n",
+    "\n",
+    "kwargs_pard = {\"xi\": xi, \"Ci\": 0.0169, \"Omega\": 0.9868}\n",
+    "sol_pard = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t0, tf),\n",
+    "    A0.ravel(),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, pard_ode, kwargs_pard),\n",
+    ")\n",
+    "\n",
+    "kwargs_mrd = {\"xi\": xi, \"Ci\": 0.0198, \"D1\": 1.0, \"D2\": 0.4796, \"D3\": 0.0120}\n",
+    "sol_mrd = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t0, tf),\n",
+    "    A0.ravel(),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, mrd_ode, kwargs_mrd),\n",
+    ")\n",
+    "\n",
+    "kwargs_wpt = {\n",
+    "    \"xi\": xi,\n",
+    "    \"b1\": 0.0504 * (1.0 - 0.995),\n",
+    "    \"kappa\": 1.0,\n",
+    "    \"b2\": 0.0,\n",
+    "    \"b3\": 0.0504 * 0.995,\n",
+    "    \"b4\": 0.0,\n",
+    "    \"b5\": 0.0,\n",
+    "}\n",
+    "sol_wpt = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t0, tf),\n",
+    "    A0.ravel(),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, ard_rsc_ode, kwargs_wpt),\n",
+    ")\n",
+    "\n",
+    "kwargs_pt = {\n",
+    "    \"xi\": xi,\n",
+    "    \"b1\": 1.924e-4,\n",
+    "    \"kappa\": 1.0,\n",
+    "    \"b2\": 5.839e-3,\n",
+    "    \"b3\": 0.04,\n",
+    "    \"b4\": 1.168e-5,\n",
+    "    \"b5\": 0.0,\n",
+    "}\n",
+    "sol_pt = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t0, tf),\n",
+    "    A0.ravel(),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, ard_rsc_ode, kwargs_pt),\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plot components\n",
-    "plt.plot(t, A_ft[:, 0], \"k-\", label=\"FT\")\n",
-    "plt.plot(t, A_iard[:, 0], \"b-.\", label=\"iARD\")\n",
-    "plt.plot(t, A_pard[:, 0], \"b--\", label=\"pARD\")\n",
-    "plt.plot(t, A_wpt[:, 0], \"b:\", label=\"WPT\")\n",
-    "plt.plot(t, A_mrd[:, 0], \"r:\", label=\"MRD\")\n",
-    "plt.plot(t, A_pt[:, 0], \"r--\", label=\"PT\")\n",
+    "plt.plot(sol_ft.t, sol_ft.y[0], \"k-\", label=\"FT\")\n",
+    "plt.plot(sol_iard.t, sol_iard.y[0], \"b-.\", label=\"iARD\")\n",
+    "plt.plot(sol_pard.t, sol_pard.y[0], \"b--\", label=\"pARD\")\n",
+    "plt.plot(sol_wpt.t, sol_wpt.y[0], \"b:\", label=\"WPT\")\n",
+    "plt.plot(sol_mrd.t, sol_mrd.y[0], \"r:\", label=\"MRD\")\n",
+    "plt.plot(sol_pt.t, sol_pt.y[0], \"r--\", label=\"PT\")\n",
     "\n",
     "# adjust some plot settings.\n",
     "plt.xlabel(\"Time $t$ in s\")\n",
@@ -108,11 +161,18 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -126,7 +186,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/examples/orientation/iard_perfectshear.ipynb b/examples/orientation/iard_perfectshear.ipynb
index 3595cb8..451da77 100644
--- a/examples/orientation/iard_perfectshear.ipynb
+++ b/examples/orientation/iard_perfectshear.ipynb
@@ -15,19 +15,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from scipy.integrate import odeint\n",
-    "from fiberoripy.orientation import ard_rsc_ode, iardrpr_ode"
+    "from scipy.integrate import solve_ivp\n",
+    "\n",
+    "from fiberoripy.closures import IBOF_closure\n",
+    "from fiberoripy.orientation import ard_rsc_ode, iardrpr_ode, integrate_ori_ode"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +37,8 @@
     "xi = 1.0\n",
     "\n",
     "# time steps\n",
-    "t = np.linspace(0, 2000, 100)\n",
+    "t0, tf = 0, 2000\n",
+    "t = np.linspace(t0, tf, 100)\n",
     "\n",
     "# initial fiber orientation state\n",
     "A0 = 1.0 / 3.0 * np.eye(3)"
@@ -43,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -55,43 +58,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# compute solutions by integrating the ODEs\n",
-    "Aref = odeint(ard_rsc_ode, A0.ravel(), t, args=(xi, L, 3.842e-4, 1.0 / 30.0, -1.786e-3, 5.25e-2, 1.168e-5, -5.0e-4),)\n",
-    "A = odeint(iardrpr_ode, A0.ravel(), t, args=(xi, L, 0.025, 1.0, 0.967),)"
+    "kwargs_ref = {\n",
+    "    \"xi\": xi,\n",
+    "    \"b1\": 3.842e-4,\n",
+    "    \"kappa\": 1.0 / 30.0,\n",
+    "    \"b2\": -1.786e-3,\n",
+    "    \"b3\": 5.25e-2,\n",
+    "    \"b4\": 1.168e-5,\n",
+    "    \"b5\": -5.0e-4,\n",
+    "}\n",
+    "sol_ref = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t0, tf),\n",
+    "    A0.ravel(),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, ard_rsc_ode, kwargs_ref),\n",
+    ")\n",
+    "\n",
+    "kwargs = {\"xi\": xi, \"Ci\": 0.025, \"Cm\": 1.0, \"alpha\": 0.967}\n",
+    "sol = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t0, tf),\n",
+    "    A0.ravel(),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, iardrpr_ode, kwargs),\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plot components\n",
-    "plt.plot(t, Aref[:, 0], \"k-\", label=\"A11 (ARD-RSC)\")\n",
-    "plt.plot(t, Aref[:, 4], \"r-\", label=\"A22 (ARD-RSC)\")\n",
-    "plt.plot(t, Aref[:, 8], \"b-\", label=\"A33 (ARD-RSC)\")\n",
-    "plt.plot(t, Aref[:, 2], \"g-\", label=\"A31 (ARD-RSC)\")\n",
-    "plt.plot(t, A[:, 0], \"k.\", label=\"A11 (iARD-RPR)\")\n",
-    "plt.plot(t, A[:, 4], \"r.\", label=\"A22 (iARD-RPR)\")\n",
-    "plt.plot(t, A[:, 8], \"b.\", label=\"A33 (iARD-RPR)\")\n",
-    "plt.plot(t, A[:, 2], \"g.\", label=\"A31 (iARD-RPR)\")\n",
+    "plt.plot(sol_ref.t, sol_ref.y[0], \"k-\", label=\"A11 (ARD-RSC)\")\n",
+    "plt.plot(sol_ref.t, sol_ref.y[4], \"r-\", label=\"A22 (ARD-RSC)\")\n",
+    "plt.plot(sol_ref.t, sol_ref.y[8], \"b-\", label=\"A33 (ARD-RSC)\")\n",
+    "plt.plot(sol_ref.t, sol_ref.y[2], \"g-\", label=\"A31 (ARD-RSC)\")\n",
+    "plt.plot(sol.t, sol.y[0], \"k.\", label=\"A11 (iARD-RPR)\")\n",
+    "plt.plot(sol.t, sol.y[4], \"r.\", label=\"A22 (iARD-RPR)\")\n",
+    "plt.plot(sol.t, sol.y[8], \"b.\", label=\"A33 (iARD-RPR)\")\n",
+    "plt.plot(sol.t, sol.y[2], \"g.\", label=\"A31 (iARD-RPR)\")\n",
     "\n",
     "# adjust some plot settings.\n",
     "plt.xlabel(\"Time $t$ in s\")\n",
@@ -101,18 +114,11 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -126,7 +132,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/examples/orientation/orthofitclosures_perfectshear.ipynb b/examples/orientation/orthofitclosures_perfectshear.ipynb
index 428f5aa..2914c9f 100644
--- a/examples/orientation/orthofitclosures_perfectshear.ipynb
+++ b/examples/orientation/orthofitclosures_perfectshear.ipynb
@@ -14,19 +14,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from scipy.integrate import odeint\n",
-    "from fiberoripy.orientation import folgar_tucker_ode"
+    "from scipy.integrate import solve_ivp\n",
+    "from fiberoripy.orientation import folgar_tucker_ode, integrate_ori_ode\n",
+    "from fiberoripy.closures import (\n",
+    "    hybrid_closure,\n",
+    "    orthotropic_fitted_closures,\n",
+    "    IBOF_closure,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,90 +62,97 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# compute solutions by integrating the ODEs\n",
-    "a_ibof = odeint(\n",
-    "    folgar_tucker_ode,\n",
+    "kwargs_ft = {\"xi\": xi, \"Ci\": C_I}\n",
+    "sol_ibof = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t.min(), t.max()),\n",
+    "    A0.ravel(),\n",
+    "    t_eval=t,\n",
+    "    args=(L, IBOF_closure, folgar_tucker_ode, kwargs_ft),\n",
+    ")\n",
+    "sol_ft = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t.min(), t.max()),\n",
     "    A0.ravel(),\n",
-    "    t,\n",
-    "    args=(xi, L, C_I, \"HYBRID\"),\n",
+    "    t_eval=t,\n",
+    "    args=(L, hybrid_closure, folgar_tucker_ode, kwargs_ft),\n",
     ")\n",
-    "a_orf = odeint(\n",
-    "    folgar_tucker_ode,\n",
+    "sol_orf = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t.min(), t.max()),\n",
     "    A0.ravel(),\n",
-    "    t,\n",
-    "    args=(xi, L, C_I, \"ORF\"),\n",
+    "    t_eval=t,\n",
+    "    args=(\n",
+    "        L,\n",
+    "        lambda a: orthotropic_fitted_closures(a, \"ORF\"),\n",
+    "        folgar_tucker_ode,\n",
+    "        kwargs_ft,\n",
+    "    ),\n",
     ")\n",
-    "a_orw = odeint(\n",
-    "    folgar_tucker_ode,\n",
+    "sol_orw = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t.min(), t.max()),\n",
     "    A0.ravel(),\n",
-    "    t,\n",
-    "    args=(xi, L, C_I, \"ORW\"),\n",
+    "    t_eval=t,\n",
+    "    args=(\n",
+    "        L,\n",
+    "        lambda a: orthotropic_fitted_closures(a, \"ORW\"),\n",
+    "        folgar_tucker_ode,\n",
+    "        kwargs_ft,\n",
+    "    ),\n",
     ")\n",
-    "a_orw3 = odeint(\n",
-    "    folgar_tucker_ode,\n",
+    "sol_orw3 = solve_ivp(\n",
+    "    integrate_ori_ode,\n",
+    "    (t.min(), t.max()),\n",
     "    A0.ravel(),\n",
-    "    t,\n",
-    "    args=(xi, L, C_I, \"ORW3\"),\n",
+    "    t_eval=t,\n",
+    "    args=(\n",
+    "        L,\n",
+    "        lambda a: orthotropic_fitted_closures(a, \"ORW3\"),\n",
+    "        folgar_tucker_ode,\n",
+    "        kwargs_ft,\n",
+    "    ),\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# plot components\n",
-    "plt.plot(t, a_ibof[:, 0], linestyle=\"-\", label=\"$a_{11}$ Hybrid\", color=\"b\")\n",
-    "plt.plot(t, a_orf[:, 0], linestyle=\"-\", label=\"$a_{11}$ ORF\", color=\"r\")\n",
-    "plt.plot(t, a_orw[:, 0], linestyle=\"-\", label=\"$a_{11}$ ORW\", color=\"g\")\n",
-    "plt.plot(t, a_orw3[:, 0], linestyle=\"-.\", label=\"$a_{11}$ ORW3\", color=\"k\")\n",
+    "plt.plot(t, sol_ibof.y[0], linestyle=\"-\", label=\"$a_{11}$ Hybrid\", color=\"b\")\n",
+    "plt.plot(t, sol_orf.y[0], linestyle=\"-\", label=\"$a_{11}$ ORF\", color=\"r\")\n",
+    "plt.plot(t, sol_orw.y[0], linestyle=\"-\", label=\"$a_{11}$ ORW\", color=\"g\")\n",
+    "plt.plot(t, sol_orw3.y[0], linestyle=\"-.\", label=\"$a_{11}$ ORW3\", color=\"k\")\n",
     "\n",
-    "plt.plot(t, a_ibof[:, 1], linestyle=\"--\", label=\"$a_{12}$ Hybrid\", color=\"b\")\n",
-    "plt.plot(t, a_orf[:, 1], linestyle=\"--\", label=\"$a_{12}$ ORF\", color=\"r\")\n",
-    "plt.plot(t, a_orw[:, 1], linestyle=\"--\", label=\"$a_{12}$ ORW\", color=\"g\")\n",
-    "plt.plot(t, a_orw3[:, 1], linestyle=\"-.\", label=\"$a_{12}$ ORW3\", color=\"k\")\n",
+    "plt.plot(t, sol_ibof.y[1], linestyle=\"--\", label=\"$a_{12}$ Hybrid\", color=\"b\")\n",
+    "plt.plot(t, sol_orf.y[1], linestyle=\"--\", label=\"$a_{12}$ ORF\", color=\"r\")\n",
+    "plt.plot(t, sol_orw.y[1], linestyle=\"--\", label=\"$a_{12}$ ORW\", color=\"g\")\n",
+    "plt.plot(t, sol_orw3.y[1], linestyle=\"-.\", label=\"$a_{12}$ ORW3\", color=\"k\")\n",
     "\n",
     "# adjust some plot settings.\n",
-    "plt.ylim=([-0.2, 1])\n",
-    "plt.grid(b=True, which=\"major\", linestyle=\"-\")\n",
+    "plt.ylim = [-0.2, 1]\n",
+    "plt.grid(which=\"major\", linestyle=\"-\")\n",
     "plt.minorticks_on()\n",
-    "plt.grid(b=True, which=\"minor\", linestyle=\"--\", alpha=0.2)\n",
+    "plt.grid(which=\"minor\", linestyle=\"--\", alpha=0.2)\n",
     "plt.xlabel(\"Time $t$ in $s$\")\n",
     "plt.ylabel(\"$a_{11}, a_{12}$\")\n",
     "plt.legend(loc=\"center right\")\n",
-    "plt.title(r\"Simple shear flow, $\\xi = 1$, $C_1 = 0,01$\")\n",
+    "plt.title(r\"Simple shear flow, $\\xi = 1$, $C_I = 0.01$\")\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -154,7 +166,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/examples/orientation/rsc_combiload.ipynb b/examples/orientation/rsc_combiload.ipynb
index 0581994..be1a3c1 100644
--- a/examples/orientation/rsc_combiload.ipynb
+++ b/examples/orientation/rsc_combiload.ipynb
@@ -14,19 +14,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from scipy.integrate import odeint\n",
-    "from fiberoripy.orientation import rsc_ode"
+    "from scipy.integrate import solve_ivp\n",
+    "\n",
+    "from fiberoripy.closures import IBOF_closure\n",
+    "from fiberoripy.orientation import rsc_ode, integrate_ori_ode"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +36,8 @@
     "xi = 1.0\n",
     "\n",
     "# time steps\n",
-    "t = np.linspace(0, 400, 500)\n",
+    "t0, tf = 0, 400\n",
+    "t = np.linspace(t0, tf, 500)\n",
     "\n",
     "# initial fiber orientation state\n",
     "A0 = 1.0 / 3.0 * np.eye(3)"
@@ -42,22 +45,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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jfRhjrJ+UVyL761vxeYSy/KN/qHus6O0W9bhHFcKtEG52zy3OOZSUDzdDuA2iIYi0OQ9UIonpWF2srA0i4fZ0NCGd3DbWZ7QLAYiGOTsSgtdcYclWxOMe3ni6aOSFfe42K77kDQaDVFdXU1ZW1qvINLW5q5ei4ID/aExVqa6uJhjs21udfiPcBqFGaGuCUBO0NbrnJre8sUPZxG0boGFJgm1zkkAkCkiLk460pm+83oBzeHztaa/fPdy0x00H8sFb2l7viZ29zi+hx+ceXnZV7eaE8RMSyr3u4dpIYj6x3NOhn3hdYj/idZ64erwJItDL0clWOghHZ7uuf0+2VVZyQh//yLNCYMrLy6mqqopHTIzR0tLS6Zez2v3S1VscpDoLnr0Eg0HKy8uP7UUjYWiphdZaaK2HljporUtKJ+brO9e3NRzxv9hjPAGoLnR+ef354A+CLxdyCiB/KPhywBd0z7lJ+aBrH+zCzk17A+CLiUiycPi6/UXqK9sqKzlhED68TgdZITB+v58JEyZ0Kq+srGTmzJnx/NrdtVz9xBt8+8IpLJg3+VgOsX+IRqH5EDTXuOdD0FTTQ5l7tNb13rfHD8EiyCmCnEIIFkPJOCedU+iIgj8fAnngz4NAQUI6v/0cS/vzeP211wblWySje7JCYFLloVe3UBj0cd0nxvf3UI6ecCs5LQdg19+h8SA07ofGA9BwwDk37nfKG/ZD00HngVyXCOSWQG4p5A6B/GEwdArkDXHKgiVJAhJLu3l/lt62GQOKQSMwG/fW8+IHe7lxwWSKc/39PZzOqDqrh7rd7rEL6vc453jZHmitZR7A20nt/fnObUTBcCg5AcbMckQjf1i7aOQOaReVYAlYsDejnxk0AvN/X91MQY6Pfz5z/LG/eDTqrC46iEVMQHa3l4dbkhoKFI6EotFQNgkmnAMFw9lYdYiTPnaWIx4FrogE8o/9vAyjjwwKgdmyv54/r9nD1+efSEleIL2dh1ocoYiJRf0eZ6VRv7tdTOr3dH7g6fFD0SgoGgOjZ8LUS6BwtCMmRWOcuoIRzgPIJPZUVnLS1PnpnYdh9AODQmB++vImcv0erv/4MOeXP9TkfkTUywdK4RbnYWhTTcL5ENTvdY/dTj4Zf167SIw/yxWN0R0FJK/MblGM456sFhiJhtiy9Ocs3vAbfhrYTvDB7jc+poQv13l+UTAcSsfBCXMdESkc3X4uHOm8UcmCV+CG0d9krcC8/+rTTH3j+4yI7iPoG4V3+hUw9ET39Wle+wdKXX545Oa9fvcB6RDn7M/t72kZxqAiawWmfs8WcsN+7s29na9d/y/4h5u3fsMYaGStwJy1+FaWVc7l3vMX2G5pwxigZO9TSK8Pr9dn4mIYA5jsFRjDMAY8JjCGYWSMlARGRBaKyEYR2SIit3ZR/20RWSciq0VkmYiMS6iLiMgq91iSzsEbhjGwSVdcpPeA2araJCJfA34MfN6ta1bVGekdtmEY2UAqK5h4XCRVbQNicZHiqOpyVW1ys28Dx9jBiWEYA5F0xkWKcT3wYkI+KCIrcPz13q2qzyU36C1sSXdkc3iPnrB5ZRc2rx5QN2RBdwewCCcedSx/DU7s6a5sr8ZZweQklI1xzxOB7cCJPV1v1qxZmirLly9P2TabsHllFzYvVZwAAJ1+n1O5RdoFjE3Il9Me5D6OiFwAfA+4TFXjjlRVdZd73oYTxWlmclvDMAYnqQhMPC6SiARw4iJ1eBskIjOBh3HEZX9CeamI5LjpocCZpBbT2jCMQUC64iLdCxQAv3O/rN2hqpcBJwMPi0gUR8zu1o5vnwzDGMSkKy5Sl/ElVfVN4LS+DNAwjOzFvuQ1DCNjmMAYhpExTGAMw8gYWesPpie21W7j/f3vU9taS0GggOF5w5lcMpmR+SPNvYNhHEMGlcDUtNTwgzd/wPKdy7usL/QXMql0ElNKpzCldAqTSyczuWQyBYGCYzxSwzg+GDQCU9dWx/VLr2dn/U6+OfObfHLcJynLLaMx1Miexj1sPrSZTYc2sfnQZl7Y9gLPhJ6Jtx2dP5pxReMYXTCaMQVjGF0wmtEFoykLllEaLKXAX2ArH8M4CgaNwNzzzj18WPshP7/g58wbPS9eXhgoZGT+SGYOb/+AWFXZ27iXzYcd0dl0aBNV9VUs37mcmpaaTn37PD5Kc0opCZZQ6C8k159Lni+PfH8+eb488vx55PnyCHgD+D1+/F4/fo+fgCfQZdrv8eMRDx7x4BUvXo8Xr3jj+cPhwxxsPhjPJ9ollhnGQGdQCMzqA6tZsnUJ/3Lav3QQl+4QEUYVjGJUwSjOKT+nQ11zuJk9DXvY3bibmpYaDrUcco7WQ9S01NAUaqK2pZbd4d00hZpoCjfRFGoiopH0Tuq3vZskik/sLCKICB48ThrBIx3THvEguHaJ6VgbNx0TsR7b9NA+eRzV1dUsqVwStxecVaEgOP+X+N9PYl3i6rFT24R0qm276ifWNl6XZNthfEl1Ow7tYM17a7q+htu2q34Tx9Uh38Nquavxdll3lH0m2ra0tDCf+d22S4VBITC/eP8XlOSU8KXTvtTnvnJ9uUwsmcjEkokpt1FVQtEQbZG2DudYOhwNd8iHoiEiGiGqUSIaIRJtT0c1yvoN65k0eVIHm+5sk8+q6pxxNptFccoUjdfH0h3suku77eP99NJncv+AkyZKQ6iBhsMN8fbxPz/iG2M7pWN/vvG65Hql+7qEv5/Y/xLzyW071CW3TRqDm4nPVdZIp7bZzrmF5/a5j6wXmJ31O3l91+t8/fSvk+fP65cxiAgBb4CANz1hayt3VTJ/EIaOraysZP78+f09jLTT3bwSBSdZ5NqNkrPaZTrWRyp1ncaRJJjd1SXn337j7W77TJWsF5jntzyPIFw++fL+HophdKDDLVgWviMIePr+D2ZWPylUVf687c/MHTWXkfkj+3s4hmEkkdUCsye0h6qGKi4Y1+VeS8Mw+pmsFpg1zc6T+/lj5/fvQAzD6JJ0hS3JEZFn3Pq/icj4hLrb3PKNIvKpNI6d1U2rOW3oaQzPG57Obg3DSBO9CkxC2JKLgGnAlSIyLcnseuCQqk4C7gfucdtOw/GAdwqwEPgvt78+s79pPzvadnDe2PPS0Z1hGBkgLWFL3PwTbvpZYIE4X+t8BnhaVVtV9UNgi9tfn6ncWQlgAmMYA5h0hS2J27guNmuBMrf87aS2Y5IvkBi2BGgQkY0pjR6GTmbywRRts4mhgM0re7B5wbiuCgfEdzCq+kvgl0faTkRWqOrsDAypX7F5ZRc2r+5JV9iSuI2I+IBioDrFtoZhDFLSErbEzV/nphcBr7rBmJYAi923TBOAycA76Rm6YRgDnXSFLXkUeEpEtgA1OCKEa/dbnFhIYeAG1bRuOz7i26osweaVXdi8ukF62iRlGIbRF7L6S17DMAY2JjCGYWSMrBWY3rYvDGRE5DER2S8iHySUDRGRl0Vks3sudctFRB5057laRD7WfyPvHhEZKyLLRWSdiKwVkZvc8myfV1BE3hGR99153eGWT3C3xWxxt8kE3PJut80MRETEKyLvicif3Hxa55WVApPi9oWBzOM4WycSuRVYpqqTgWVuHpw5TnaPLwM/P0ZjPFLCwHdUdRowF7jB/TvJ9nm1Auer6unADGChiMzF2Q5zv7s95hDOdhnoZtvMAOYmYH1CPr3zirlBzKYDmAcsTcjfBtzW3+M6wjmMBz5IyG8ERrnpUcBGN/0wcGVXdgP5AJ4HLhxM8wLygL/jfMl+EPC55fGfR5y3rfPctM+1k/4eezfzKccR/fOBP+G4xUrrvLJyBUPX2xc6bUHIMkao6h43vRcY4aazbq7u8nkm8DcGwbzc24hVwH7gZWArcFhVw65J4tg7bJsBYttmBiL/CfwrEHXzZaR5XtkqMIMadf6ZyMrvB0SkAPgf4GZVrUusy9Z5qWpEVWfg/Is/B5javyPqOyJyKbBfVVdm8jrZKjCDcQvCPhEZBeCe97vlWTNXEfHjiMtvVPX3bnHWzyuGqh4GluPcOpS422Kg49i72zYz0DgTuExEtuN4SDgfeIA0zytbBSaV7QvZRuJ2i+twnmHEyq9137rMBWoTbjkGDK57jkeB9ar604SqbJ/XMBEpcdO5OM+V1uMIzSLXLHleXW2bGVCo6m2qWq6q43F+f15V1atI97z6+0FTHx5QXQxswrkf/l5/j+cIx14B7AFCOPe51+Pczy4DNgOvAENcW8F5Y7YVWAPM7u/xdzOns3Buf1YDq9zj4kEwr+nAe+68PgC+75ZPxNlXtwX4HZDjlgfd/Ba3fmJ/zyGFOc4H/pSJedlWAcMwMka23iIZhpEFmMAYhpExTGAMw8gYJjCGYWQMExjDMDKGCYxhGBnDBMYwjIxhAnOcIyJlIrLKPfaKyK6EfEBE3szQdctF5PPd1GXkmsaxxz60M+KIyA+ABlX9yTG41nXANFW9JdPXMvoPW8EYPSIiDSIyXkQ2iMjjIrJJRH4jIheIyF9dT3VzEuyvdj3ArRKRh6WLWOQichbwU2CRazexm2uuF5H/dj3JveTuBUruK19E/ux6nPugu1WR0T+YwBipMgm4D8dVwVTgCzj7j/4X8G8AInIy8HngTHXcG0SAq5I7UtU3cDasfkZVZ6jqtm6uORn4maqeAhwGrujCZiGwW1VPV9VTgb8c7QSN9DMgQscaWcGHqroGQETW4rjBVBFZg+OdD2ABMAt419lcTS7t7hmSOQnYkMI1V7nplQnXSWQNcJ+I3IOzYe/1lGZjHBNMYIxUaU1IRxPyUdp/jgR4QlVv66kjERmK454h3JNd0jUjOILVAVXd5DoMvxj4DxFZpqp39tKvcYywWyQjnSzDea4yHOIRBcZ1YTce2J2OC4rIaKBJVX8N3AsMyOgExyu2gjHShqquE5F/B14SEQ+Ov5sbgI+STDcAQ8UJ2/JlVe3La+nTgHtFJOpe72t96MtIM/aa2jCMjGG3SIZhZAwTGMMwMoYJjGEYGcMExjCMjGECYxhGxjCBMQwjY5jAGIaRMUxgDMPIGCYwhmFkDBMYwzAyhgmMYRgZwwTGMIyM0avAiMhjIrLf3fnaVb2IyIMiskVEVru+OWJ117kuFTe7PlgNwziOSGUF8ziOW8LuuAjHteFk4MvAz8HxBQLcDpwBzAFuF5HSvgzWMIzsoleBUdXXgJoeTD4DPKkObwMlIjIK+BTwsqrWqOoh4GV6FirDMAYZ6XA4NQbYmZCvcsu6K++EiHwZZ/VDbm7urLFjx6Z04Wg0iscz+B4j2byyC5sXbNq06aCqDksuHxAe7VT1l8AvAWbPnq0rVqxIqV1lZSXz58/P4Mj6B5tXdmHzAhFJ9loIpOct0i4gcclR7pZ1V24YxnFCOgRmCXCt+zZpLo63+D3AUuCTIlLqPtz9pFtmGMZxQq+3SCJSAczHcdJchfNmyA+gqr8AXsAJGbEFaAK+6NbViMgPcQJsAdypqj09LDYMY5DRq8Co6pW91CuO5/iu6h4DHju6obUTCoWoqqqipaWlQ3lxcTHr16/va/f9SjAYpLy8HL/f399DMYy0MyAe8vZGVVUVhYWFjB8/HjdiIAD19fUUFhb248j6hqpSXV1NVVUVEyZM6O/hGEbayYp3ay0tLZSVlXUQl8GAiFBWVtZpZWYYg4WsEBhg0IlLjME6L8OALBIYwzCyDxOYI+S5555DRNiwYUO8bOHChZSUlHDppZd2sH3ooYeYNGkSIsLBgweP9VANo98xgTlCKioqOOuss6ioqIiXffe73+Wpp57qZHvmmWfyyiuvMG5cV/HfDWPwkxVvkRK5449rWbe7DoBIJILX6+1zn9NGF3H7p0/p1a6hoYE33niD5cuX8+lPf5o77rgDgAULFlBZWdnJfubMmX0em2FkM7aCOQKef/55Fi5cyJQpUygrK2PlypX9PSTDGNBk3QomcaVxrL+Dqaio4KabbgJg8eLFVFRUMGvWrGN2fcPINrJOYPqLmpoaXn31VdasWYOIEIlEEBHuvfdee9VsGN1gt0gp8uyzz3LNNdfw0UcfsX37dnbu3MmECRN4/fXX+3tohjFgMYFJkYqKCi6//PIOZVdccQUVFRWcffbZfPazn2XZsmWUl5ezdKmzafzBBx+kvLycqqoqpk+fzpe+9KX+GLph9Bt2i5Qiy5cv71R244039tjmxhtv7NXGMAYztoIxDCNjpCQwIrJQRDa6oUlu7aL+fhFZ5R6bRORwQl0koW5JGsduGMYAJxWHU17gZ8CFOI673xWRJaq6Lmajqt9KsP8mkPiFWbOqzkjbiA3DyBpSWcHMAbao6jZVbQOexglV0h1XAhU91BuGcZyQykPersKPnNGVoYiMAyYAryYUB0VkBRAG7lbV57poFw9bMmLEiE6f3RcXF1NfX9/pepFIpMvybKOlpaXDnBsaGrrcepDt2Lyyi3TMK91vkRYDz6pqJKFsnKruEpGJwKsiskZVtyY2Sg5bkhwqYf369V1+sZvtHu1iBIPBDvuWLAxGdmHz6p5UbpGOJPzIYpJuj1R1l3veBlTS8flM1pHsrmHVqlXMmzePU045henTp/PMM8/Eba+66ipOOukkTj31VP75n/+ZUCjUX8M2jH4hFYF5F5gsIhNEJIAjIp3eBonIVKAUeCuhrFREctz0UOBMYF1y22wi2V1DXl4eTz75JGvXruUvf/kLN998M4cPHwYcgdmwYQNr1qyhubmZRx55pB9HbhjHnlSiCoRF5Bs4MY28wGOqulZE7gRWqGpMbBYDT7tRBmKcDDwsIlEcMbs78e3TUfHirbB3DQC5kTB403CXN/I0uOjuXs26ctcwZcqUeP3o0aMZPnw4Bw4coKSkhIsvvjheN2fOHKqqqvo+VsPIIlL67VTVF3DiHyWWfT8p/4Mu2r0JnNaH8Q0ounLXkLib+p133qGtrY0TTzyxQ7tQKMRTTz3FAw88cKyHbBj9SvZtFUhYaTQPIHcNe/bs4ZprruGJJ57oFDD861//Oueccw5nn332MRurYQwEsk9g+ome3DXU19dzySWXcNdddzF37twO7e644w4OHDjAww8/3E8jN4z+w/YipUhP7houv/xyrr32WhYtWtShzSOPPMLSpUupqKjotKoxjOMB+6lPke7cNVx33XW89tprPP7448yYMYMZM2awatUqAL761a+yb98+5s2bx4wZM7jzzjv7YeSG0X/YLVKKdOeuoSd3DOFwOJNDMowBj61gDMPIGCYwhmFkDBMYwzAyhgmMYRgZwwTGMIyMYQJjGEbGMIE5QpLdNQAsXLiQkpISLr300g62119/PaeffjrTp09n0aJFNDQ0HOvhGka/YgLjoqqEIlFaQhGa2yI0tYVpCUVoC0cIR6JE3U3iye4aAL773e/y1FNPderz/vvv5/3332f16tWccMIJPPTQQ8dsPoYxEMi6D+3ueeceNtQ4q4dIJILX6z2qflQhHI0SVTghfxJXTbmRjp4mOtPa3ETla6/z/z33Al+++nPc8L9uI8fr4RNnn8ubb3SO8FhUVOReS2lubrYQs8ZxR0oCIyILgQdw/ME8oqp3J9X/E3Av7Z7uHlLVR9y664B/d8v/Q1WfSMO4j5qoKm3hKJGoIyYigtcjDC0I4Pd68HkEEUFc26g650hU+e0Lv2f++RdywvgTKSou5dXX32ba9BkAbK9upKElzJb9DeT4PAR8HnJ8Hr75tX/hpb/8hWnTpnHffff138SN1IhGIRqCSBtEQu7R5pYllbt2Q6r/DusbXLtwu41G3P7CbjoM0QhotD0dr+ujXexIzqsm5WNHpJf6KBOHngd9dJmZlrAlLs+o6jeS2g4BbgdmAwqsdNseOtoB3zLnlnj6SHzyRqJRdh9u4VBTG16PMCQ/QElugKDfk/LKYumS33PTTTcxeUQhX7z2Kt5Z9kc+c8FZtIWjbM0P4PMKItDQGibUFAXgu3c9wLfvjHDP92/h//73E3zhmus6CFDAd3QrsEGPqvOL2tbYfoRi6SYnHW6FcAuEWpxzuBXCze3l4VYIJeWT68MtHYWjgzvp1JgOsOYo5ige8PhAvM7Z40lIu2fxJKS7sRMveP2Orbh18bQHRJLyCYfH2219fUPJUUyqI6msYOJhSwBEJBa2JBXPdJ8CXlbVGrfty8BCjnFYk5ZQhO3VjYTCUYYV5jCsMAffEe5u7s5dw30/+Qm5AR+leQGCfi8nDisAIBpVWiNR2sIRWsNRFn32c/zXgz/lss9dxSFXfGIcrG3h9l+8xbiyPMYPzadpX5jhu+sYPzSPvECW3cWG26C1Dlpq288tddBaR/nOv8Pyt9zyOmhr6Fo8YvnoUezl8uaALwj+IPjcdPzIgfxhSeU54A04v6Aef3va66Y9vo5lXdisXLWaWR+f27FNh6Mb4Rjgt8wH0hApIZ1hS64QkXOATcC3VHVnN23HJDfMZNiStoiypzGKACPzPQQ9YZobj/wH99e//jWLFy/u4JXuoosuYunSpZx55pk0NTURDofj41FVtm3bxoknnkhQldde+hOnTp1CeYEQVQ+hKISjEIootaIcrj3Mxj2HqG11bt1+tsp5plOSI4zIE4bmeijLFcqC4p49DMkVcryZ+SGVaARfuB5/qB5/qC7hXOeWt5f5wo3xwxtt67bPSQBbIezNJezLI+LNdY8cIt5cop5CIvlBIkVBIt7OR9STmM8h6gkkHH6iHvdf8b4ScY9eiQItNHhGU7mxpu/XHWAMpLAlfwQqVLVVRL4CPAGcn2rjTIUtaQtH2HmgEa/Hw8Rh+eT04XbkD3/4A7fcckuH633uc5/j+eef56677mLDhg00NDRw8skn8+ijj3LhhRdyww03UFdXh6py+umn8/Of/7zL8TYczOWlWy5y0q1h/mfp/6Ns/MlsP9jIhweb+Ki6kQ8PN/PWnhaiSc+hy/IDjC7JZXRJkDEleYwuCTK8KMjwwhxGuOf8HPevORqBpmqo3wsN+6FhX9LhljUehJbD3f9h+IKQVwa5Q6BoBOSWQrAYcoqcc4d0UTz9xoo1nLXgInweb/a9XegBC1vSPan8PfcatkRVqxOyjwA/Tmg7P6lt5ZEO8miIqrKjpglVZeKwgj6JC3TvrqEn/vrXvx7xdQpyfIwr8jJ/+uhOdaFIlH11Lew61Mzu2mZ2HWpm1+EWdh9u5sP9dWzetIEh4X2MlhpGSTWj5SCjpYYxnmpGeGop1Vo8RDv1G/EXoAUj8BSOwDPiVOc2Iq8M8oa0n3Nj6TII5B3xvADC/g+dWwTjuCEVgYmHLcERjMXAFxINRGSUqu5xs5cB6930UuBHIlLq5j8J3NbnUafAvroWmtoijBuSR9A/CH6ooxH89bsoP/Qh5Yc+hJoP4fAOqK2Cul3QsAe8Uec9n0ubv4j6wHCqvaPYwCnsjRSxI1TIh80FVIWLOEAxB7WY5pYg1AN7ID/gpTQ/QFl+gNL8AEPyAwzJCzCkIMCQPKUkr5aiYBNFuX6Kgn6Kc/0UBH14PQP7eYLRP6QrbMmNInIZTnjYGuCf3LY1IvJDHJECuDP2wDeTtIQiHKxvozQvQHFeINOXSx+REBzaTtnBv8Fbax0RSRSTaELgNo8fisuhZCxMONdJF4+BovJ4OpBTSBlQBkxJuIyq0tAaZn99KzWNbd0e1Q1tbN7XQE1jG82hnh9KFOb4KMr1Uxj0UZzrjwtQUa6PwqCf/ICXXTtCHHqviryAj4IcH3kBL/nu2cn7CPjs28/BRFrClqjqbXSzMlHVx4DH+jDGWD8pvU5WVXYdbsbjgVHFwb5eNjNEI/HXpBpqhsYD8NB1ULMNoiEnzssHOM8uSsfDyFPh5E/DkAlQOsE5F4056tsNEaEw6Kcw6OfEYam1aW6LUNPURm1TiLqWEHXNIepawtQ1h6htjpWF43VVh5qpa66jrjlEfWv7Q/Un173f43X8XiE/x0d+wBGevICXHL+XoN9Lrt9D0O8l6PMSdNNOncctay+PlcXqc3we/N72I+D14PcJAa8Hr/vtk5F+suJZWzAYpLq6mrKysl5/EBpawzS2hhldkovP28//GmrU/U6j2flWI9Ts5N2ViKpS3RghWPchDJ0MUy+GoSexckcdsxYscp59DJAf/NyAlzGBXMaU5B5x22hUaQ5FeLnyNWbMOoOG1jBNbREaW8M0toVpao24ZWEaY+WtkXi+JRShtqmNfaEoLWEn3xJytnW0hjs/UzpSRGgXHa84aV8s7whRrD7gCpXPI/i8gtfj4eCBFv504H18Huejzdjh5D3xcp9H8MTL3bO3vd4rsT7b23o97segInhE8IiT9whxYfQIbp3g8SSkk2w94nyn1ZOtY+eUh5PfKBwFWSEw5eXlVFVVceDAgQ7lLS0tBIMdVykH6luJRBVfXQ4HjuUvZzTift2Z+AVoCOf7QgBJ+JbCF/+mIphXTPnZXwD/dfGu6g9XQn7ZsRt7hvF4nFVJSY6H8UPz09q3qtIajnYSHScfocVNhyJR5wg73yeFwtF4WVvE2YfWllgW1oR0lDY33dQWIRQJxb8Gj6jS0BhlR1M1kagSjiqRaNQ9a/wcScMv67HmghN8XJDyu+CuyQqB8fv9TJgwoVN5ZWUlM2fOjOff2HyQa3//N374D6dy3rRxmRlMJAzVW5zwtfvWwN4PYN8HzuvdGIWjYMSpzq3NiFOd0LRDTkxPmFujAyLi3hL134P8VF7nqrvtJByNtgtPRBOEKEo0mlTvnqOq8fbRaPv2lcStLKpKJEpHW3eLi8bt3XO0Y7vubCMHt/f5z2bQ/MSrKg8s28TIoiCfm12enk6bamDfWkdA9n3giMn+9RBpdeo9fhg2FU48v6Og5A9Nz/WNQYNzmwPeLHpNX1m5s3ejXhg0AvPW1mre3X6IOy475ci/eWlrct7S7F/rCEpsVVKX8LlP3lBHQOb8i7MiGXEqDJ0Cvix6S2UYx5hBITCqyn++spnhhTl8/uNjOxu0NrjfjOx0zoc/gsOx9A5oOthu6/HB0JNg3JnuiuQUGHEaFAwfMA9cDSNbGBQC8/rmg6zevof/PMdD8P0nnWckhz9yBWQnNCd9euPNcb4fKTkBRk13zsUnwPCp7qokp38mYhiDjKwXmPDOlXie/T6rgu8QfMfdaOfLdUSjZCyMmeWmXREpOcH5FN5iRRtGxslagdlfXcPItb/AV/kip2keu0/8LBPnXOrc0hSPNQExjAFA1grM9opvM+fgizwavogt077Jj678hD0jMYwBRtYKzNTP/Qe/fPZUCmd/jh/OKrdPvQ1jAJK1AlM0vJwp02Yzf84J/T0UwzC6wR5UGIaRMUxgDMPIGCkJjIgsFJGNIrJFRG7tov7bIrJORFaLyDIRGZdQFxGRVe6xJJ2DNwxjYJOusCXvAbNVtUlEvobjMvPzbl2zqs5I77ANw8gGUlnBxMOWqGobEAtbEkdVl6tqk5t9G8f3rmEYxznpDFsS43rgxYR8UERW4LjTvFtVn0tu0FvYku5IR1iFgYjNK7uwefWAuj4hujuARTjhYmP5a3BCw3ZlezXOCiYnoWyMe54IbAdO7Ol6s2bN0lRZvnx5yrbZhM0ru7B5qeL45+70+5zKLVKvYUsAROQC4HvAZaramiBgu9zzNpyQJTOT2xqGMThJRWDiYUtEJIATtqTD2yARmQk8jCMu+xPKS0Ukx00PBc4ktZCzhmEMAtIVtuReoAD4nfvJ/g5VvQw4GXhYRKI4Yna3dnz7ZBjGICZdYUsu6Kbdm+BE4TAM4/jDvuQ1DCNjmMAYhpExTGAMw8gYWeuuoSdUleqWaqqbq8nz5TEkdwj5/vQG/DIMo3cGlcCoKs9teY5H1jzCjvodHepG5o/kxOITmVQyiUmlk5hUMomJxRPJ8+f102gNY/AzaARGVfk/7/wfKjZUMH3odK6ceiXD84bTEmlhX+M+ttZuZevhrby74V3aoo5zcEEYUzAmLjhjC8cyKn8UowtGMyp/FAGvxTwyjL4waATmfzb/DxUbKrh22rV8Z/Z38EjXj5ci0Qg763ey9fBWNh/ezJbDW9hyaAuvV71ORCMdbMuCZQzJHcKQnCGUBEsozSllSHAIRTlF5PnyyPfnk+/PJ8+f1yGf480hx5uTVVH8DCMTDAqBqWmp4ScrfsIZI8/oUVzACd05vng844vHs2Dcgnh5KBpiX+M+9jTuYXfDbnY37mZf4z5qWmo43HqYjTUbqWmpoa6tLuVx+cSH3+snx5tDwBMg4A04aW+gw+H3+PGJD5/HOQ4cPEDlm5XxvFe88XTMzuvxdmiTaBer84gHr3jdsKXebvMe8XQ4urX1eBHcvMetR/B63D5w2pt/ZCPGoBCYJ9c+SVOoiX+b+289iktP+D1+ygvLKS/s2dNEKBqisa2RxnAjjaFGmkJNNIbcdNhJt0ZaaY20EoqEaI200hZpoy3aRlukrWN5tI261jpC0RDhaJhwNExEIzS0NLCtahsRjcTrItEIYQ0T1ehRze9Y4hEPEvuftJ81qnh/44hSzAbp3j757KFdvOJtkuxif/+J9h3sYjZCx3EkEbtOrC45n5g+dPgQv37p1+22SW06tJOO+Z5su2vT07i6EvdubZP6T7YtbShlPvM79XckZL3ANIebeWbjM3xy/CeZWDwx49fze/yUBEsooSRj16isrGT+/Pld1kU1SiTqCE9EIx2EKVGMIuocqkpEI04795yc7i6f3LZDPhpB6ZiPEo2nARRnR22UKCjs2LmDMeVj4uWJ55hwxuydDbrOfLuyj7dR4mkloU039uo06NBvjFj7+Fm1wzwSbRPbhDVMa7i1yz7cTLf9JtsmXyO5TU/j7KqPXm27uHas7DRP3z/Cz3qBeeWjV2gINfD5kz7fu/EgwCMePF4Pfq+/v4dyxFQ2VDL/4/P7exhpp6d/ELKZdPi4yfoP7Z7f8jxjC8cye8Ts/h6KYRhJZLXANEYaWbFvBQvHL7QHi4YxAMlqgVnbvJaIRjhv7Hn9PRTDMLogXWFLckTkGbf+byIyPqHuNrd8o4h8Ko1jZ03zGoblDuOUoaeks1vDMNJErwKTELbkImAacKWITEsyux44pKqTgPuBe9y203A84J0CLAT+y+2vz7RF2ljfvJ5zx5571K+mDcPILGkJW+Lmn3DTzwILxHko8hngaVVtVdUPgS1uf33mnb3v0KqtdntkGAOYdIUtidu4LjZrgTK3/O2ktmOSL5AYtgRoEJGNKY0ehp7LuQdTtM0mhgI2r+zB5gXjuiocEN/BqOovgV8eaTsRWaGqg+79tM0ru7B5dU+6wpbEbUTEBxQD1Sm2NQxjkJKWsCVu/jo3vQh41Q3GtARY7L5lmgBMBt5Jz9ANwxjopCtsyaPAUyKyBajBESFcu9/ixEIKAzeoJvlE6BtHfFuVJdi8sgubVzdIVxu4DMMw0oF9QGIYRsYwgTEMI2NkrcD0tn1hICMij4nIfhH5IKFsiIi8LCKb3XOpWy4i8qA7z9Ui8rH+G3n3iMhYEVkuIutEZK2I3OSWZ/u8giLyjoi8787rDrd8grstZou7TSbglne7bWYgIiJeEXlPRP7k5tM6r6wUmBS3LwxkHsfZOpHIrcAyVZ0MLHPz4Mxxsnt8Gfj5MRrjkRIGvqOq04C5wA3u30m2z6sVOF9VTwdmAAtFZC7Odpj73e0xh3C2y0A322YGMDcB6xPy6Z1XzFNXNh3APGBpQv424Lb+HtcRzmE88EFCfiMwyk2PAja66YeBK7uyG8gH8Dxw4WCaF5AH/B3nS/aDgM8tj/884rxtneemfa6d9PfYu5lPOY7onw/8Ccd5ZlrnlZUrGLrevtBpC0KWMUJV97jpvcAIN511c3WXzzOBvzEI5uXeRqwC9gMvA1uBw6oadk0Sx95h2wwQ2zYzEPlP4F+BmKPnMtI8r2wVmEGNOv9MZOX3AyJSAPwPcLOqdgjBkK3zUtWIqs7A+Rd/DjC1f0fUd0TkUmC/qq7M5HWyVWAG4xaEfSIyCsA973fLs2auIuLHEZffqOrv3eKsn1cMVT0MLMe5dShxt8VAx7F3t21moHEmcJmIbMfxkHA+8ABpnle2Ckwq2xeyjcTtFtfhPMOIlV/rvnWZC9Qm3HIMGFz3HI8C61X1pwlV2T6vYSJS4qZzcZ4rrccRmkWuWfK8uto2M6BQ1dtUtVxVx+P8/ryqqleR7nn194OmPjyguhjYhHM//L3+Hs8Rjr0C2AOEcO5zr8e5n10GbAZeAYa4toLzxmwrsAaY3d/j72ZOZ+Hc/qwGVrnHxYNgXtOB99x5fQB83y2fiLOvbgvwOyDHLQ+6+S1u/cT+nkMKc5wP/CkT87KtAoZhZIxsvUUyDCMLMIExDCNjmMAYhpExTGAMw8gYJjCGYWQMExjDMDKGCYxhGBnDBOY4R0TKRGSVe+wVkV0J+YCIvJmh65aLyOe7qcvINY1jj31oZ8QRkR8ADar6k2NwreuAaap6S6avZfQftoIxekREGkRkvIhsEJHHRWSTiPxGRC4Qkb+6nurmJNhf7XqAWyUiD0sXschF5Czgp8Ai125iN9dcLyL/7XqSe8ndC5TcV76I/Nn1OPdBd6sio38wgTFSZRJwH46rgqnAF3D2H/0v4N8ARORk4PPAmeq4N4gAVyV3pKpv4GxY/YyqzlDVbd1cczLwM1U9BTgMXNGFzUJgt6qerqqnAn852gka6WdAhI41soIPVXUNgIisxXGDqSKyBsc7H8ACYBbwrrO5mlza3TMkcxKwIYVrrnLTKxOuk8ga4D4RuQdnw97rKc3GOCaYwBip0pqQjibko7T/HAnwhKre1lNHIjIUxz1DuCe7pGtGcASrA6q6yXUYfjHwHyKyTFXv7KVf4xhht0hGOlmG81xlOMQjCozrwm48sDsdFxSR0UCTqv4auBcYkNEJjldsBWOkDVVdJyL/DrwkIh4cfzc3AB8lmW4AhooTtuXLqtqX19KnAfeKSNS93tf60JeRZuw1tWEYGcNukQzDyBgmMIZhZAwTGMMwMoYJjGEYGcMExjCMjGECYxhGxjCBMQwjY5jAGIaRMUxgDMPIGCYwhmFkDBMYwzAyhgmMYRgZo1eBEZHHRGS/u/O1q3oRkQdFZIuIrHZ9c8TqrnNdKm52fbAahnEckcoK5nEct4TdcRGOa8PJwJeBn4PjCwS4HTgDmAPcLiKlfRmsYRjZRa8Co6qvATU9mHwGeFId3gZKRGQU8CngZVWtUdVDwMv0LFSGYQwy0uFwagywMyFf5ZZ1V94JEfkyzuqH3NzcWWPHjk3pwtFoFI9n8D1GsnllFzYv2LRp00FVHZZcPiA82qnqL4FfAsyePVtXrFiRUrvKykrmz5+fwZH1Dzav7MLmBSKS7LUQSM9bpF1A4pKj3C3rrtwwjOOEdAjMEuBa923SXBxv8XuApcAnRaTUfbj7SbfMMIzjhF5vkUSkApiP46S5CufNkB9AVX8BvIATMmIL0AR80a2rEZEf4gTYArhTVXt6WGwYxiCjV4FR1St7qVccz/Fd1T0GPHZ0QzOM7CQUClFVVUVLS0t/D6VPFBcXs379+g5lwWCQ8vJy/H5/Sn0MiIe8hjGYqKqqorCwkPHjx+NGuMxK6uvrKSwsjOdVlerqaqqqqpgwYUJKfQy+d2uG0c+0tLRQVlaW1eLSFSJCWVnZEa3MTGAMIwMMNnGJcaTzMoExDCNjmMAYxiDlueeeQ0TYsGFDvGzhwoWUlJRw6aWXdrB96KGHmDRpEiLCwYMH0zYGExjDGKRUVFRw1llnUVFRES/77ne/y1NPPdXJ9swzz+SVV15h3LhxaR2DCYxhDEIaGhp44403ePTRR3n66afj5QsWLOjwZijGzJkzGT9+fNrHYa+pDSOD3PHHtazbXZfWPqeNLuL2T5/So83zzz/PwoULmTJlCmVlZaxcuZJZs2aldRypYCsYwxiEVFRUsHjxYgAWL17c4TbpWGIrGMPIIL2tNDJBTU0Nr776KmvWrEFEiEQiiAj33nvvMX99bisYwxhkPPvss1xzzTV89NFHbN++nZ07dzJhwgRef/31Yz4WExjDGGRUVFRw+eWXdyi74oorqKio4Oyzz+azn/0sy5Yto7y8nKVLHQcHDz74IOXl5VRVVTF9+nS+9KUvpWUsdotkGIOM5cuXdyq78cYbe2xz4403drKpr6/v81hsBWMYRsZISWBEZKGIbHRDk9zaRf39IrLKPTaJyOGEukhC3ZI0jt0wjAFOKg6nvMDPgAtxHHe/KyJLVHVdzEZVv5Vg/01gZkIXzao6I20jNgwja0hlBTMH2KKq21S1DXgaJ1RJd1wJ9M9Ld8MwBhSpPOTtKvzIGV0Zisg4YALwakJxUERWAGHgblV9rot28bAlI0aMoLKyMpWx09DQkLJtNmHzyi6S51VcXJyWB6T9TSQS6XIeLS0tKf89pvst0mLgWVWNJJSNU9VdIjIReFVE1qjq1sRGyWFLUg2VYOEisovjZV7r16/vvN9HFVBQcP/TQz6xrEMnXVQl22kXTbvoS7tol1TfEI1QUFjSqWkwGGTmzJmdyrsiFYE5kvAji0nyz6uqu9zzNhGpxHk+s7VzU8PogWgEwi0QboVQs5t2j1BL9/lIG0TCEA256RBEw0lpty6WjobcsoR0NOyMQSPuOeoc0QifaG2Gv3mdOlU47zHY3eoOvCuhODY895flXH79d1j///6HqZMmsOqDjXztth9R19CI1+vhe9+8ns9/5lMAXPWN77Hi/XX4/T7mzDiFh+/5HoG8oUBZn8aQisC8C0wWkQk4wrIY+EKykYhMBUqBtxLKSoEmVW0VkaHAmcCP+zRiI3uIRqClFloOQ/NhSmtWwQfV0FoPrQ3Q1ght9c65y3zsaHJ+0fuKxw9ef/s5nvaBN9A5HchzywLg8TqHJJ49IB4O7tnH6PKxTpl4IFAA+cMg/lm+gLjnrvKJdnGkY7ZTfaxIuq2veOGHnPWJeVQs/Rt3zFlA3qgoTz7xBJMnncju3XuYdea5fOryL1BSWspV1/0zv/7UJwH4wrXX88iSN7nm2msIHNEfcGdSiSoQFpFv4MQ08gKPqepaEbkTWKGqsVfPi4Gn3SgDMU4GHhaRKM4D5bsT3z4ZWUZbIzQegIYD0LgfGvZD40En3XgAmg+5x2FHVFrqSPwX/HSA1Ul9+nIhkA85Bc4vZqAAgiVQXN6e9+c6hy/oHP5ge7rHfA54c1wh8SX9MqaPTZWVjE689Vu/Hoq7jJJ8zGhoaOCNt/7G8uXL+fSnP80dd93DlNPad1OPnljE8OEjOFDfSsnIQi7+zKJ43Zx5n6Bq7wHU01d5SfEZjKq+gBP/KLHs+0n5H3TR7k3gtD6MzzgWqDrCULcLandBXZV73tV+btgPocau2weLnX+xc4dAwQgYehLklkJuiSMWuSWQW8p76z9k5ifOg5wiR1D8+c6KYTDz4q2wd016+xx5Glx0d48mvblreOedd2hra+PEE0/s0C4UCvHUU0/xwAMPpGWog/xv14gTjUBtFRz6EGq2uYebPvRRZ/Hw+KBwtPMv8ZiPQcFIyB8KBcMhfzgUDHNEJX+Ys1JIgdo9lTD85PTPzehERUUFN910E9DuriEmMHv27OGaa67hiSee6BTc/utf/zrnnHMOZ599dlrehJnADDaiEUc49q+D/evhwHrnXLPNeZAZw5sDQybAkIkwcb5zS1I0pv1cMNx51mD0jV5WGpmgJ3cN9fX1XHLJJdx1113MnTu3Q7s77riDAwcO8PDDD6dtLCYw2UyoBfZ9ALvfg11/h31r4MAmiLS225SOh+HTYPInoexER1CGTHRWJx7bijYYiblrSBSKc889l9dff5077riDa6+9lkWLFnVo88gjj7B06VKWLVvWaVXTF0xgsgVVOLgZdrwFu//uCMr+dc7rU4C8oTDqdJhwriMow0+GYSc5D1CN44qKigpuueWWDmVXXHEF1113HVVVVVRXV/P4448D8PjjjzNjxgy++tWvMm7cOObNmwfAP/7jP/Ktb30ruesjxgRmoBKNwL618NGb8NFfnXOTG04iWAyjZ8InbnTOo2c6tzaDNNiXcWR0566hJ5cN4XC4U5k9gxls1FbBlmWc8kEFvHUttNY65SUnwOQLYdwn4IRPOLc6JiZGFmAC059EwrDjTdj8Emx+xXkgCxTmlMG0y5zbnXHznNWJYWQhJjDHmnArfPgarHseNr4ATdXOl6LjPgEzr4JJF/D22r3MP++8/h6pYfQZE5hjQTQC2yph9TOw8UVorYNAIZy0EE6+DCYt6Pgwdt2+fhuqYaQTE5hMsm8tvF8Bq38HDXudh7MnX+bc/kycn/IHaoaRrZjApJvWelj9W1j5K+cTcY/P+Qbl9MUwZaGJinFcYV9apYt96+DP34H7ToY/f9spu+he+M5GuLICpn3GxMU4pjz33HOICBs2bIiXLVy4kJKSEi699NIOttdffz2nn34606dPZ9GiRTQ0NKRlDCYwfSEagXVL4LGL4Ofz4O9PwcmXwpeWwVdehzO+7OzfMYx+oKKigrPOOqtD2Njvfve7PPXUU51s77//ft5//31Wr17NCSecwEMPPZSWMZjAHA2hZljxGDz0cfjtNc5u4wt/CN/ZAJf/Aspn23cqRr/S0NDAG2+8waOPPsrTTz8dL1+wYEFnb3tAUVERAKpKc3Nz2kLMpvQMRkQWAg/g+IN5RFXvTqr/J+Be2j3dPaSqj7h11wH/7pb/h6o+kYZx9w9NNfDuo/DOw47/k9Ez4bOPOw9ubWOg0QX3vHMPG2o29G54BEwdMpVb5tzSo01v7hq64otf/CIvvPAC06ZN47777iMSifRonwq9rmASwpZcBEwDrhSRaV2YPqOqM9wjJi5DgNtxnITPAW53vdxlFy218Opd8J+nwfL/gFEz4Lo/wb8sh1MuN3ExBhwVFRUsXrwYaHfX0Bu/+tWv2L17NyeffDLPPPNMWsaRygomHrYEQERiYUtS8Uz3KeBlVa1x274MLCRbwpq0NcLfHoa/PuB4aJv2D3Duv8KIU/p7ZEaW0NtKIxP05K6ht1sfr9fL4sWL+fGPf9xpx/XRkMozmK7ClnTlD/AKEVktIs+KSMxJeKptBxahFnj75/DA6bDsDhh7BnzlNfjcEyYuxoAn5q7ho48+Yvv27ezcuZMJEybw+uuvd2mvqmzZsiWeXrJkCVOnTk3LWNL1HcwfgQrXufdXgCeA81NtPFDiIkk0zMi9rzLuo2cIth7kUMlpfDjzO9QVnwwbD8HG9F2rJ46X+EGDhYEWF+nXv/41N998c4cxXHLJJTz55JPceuutbNq0icbGRsaMGcNDDz3E+eefz9VXX019fT2qyqmnnsr999+flrhIqGqPBzAPWJqQvw24rQd7L1Drpq8EHk6oexi4sqfrzZo1S1Nl+fLlKdv2SCSi+v5vVR+YoXp7keovz1fdmqa+j4K0zWuAcbzMa926df0zkDRTV1fXZXlX88MJANDp9zktYUtEZJSq7nGzlwHr3fRS4EcJD3Y/6QrUwEAVNvwZlt/lOG8acSpc+bTzxa29ZjaMPpOusCU3ishlOOFha4B/ctvWiMgPcUQK4E51H/geS1SV93Ye5sU1e3i/qpYDdS2c1rqSr0efZmp0MwdzTmDrrJ8w/pyrGFGcd6yHZxiDlrSELVHV2+hmZaKqjwGP9WGMfWLt7lq+//xaVn50iIDXw6JhO7kr+hSTw6up9o3gwbybebj24zT+VeCvyzlpRCEXThvBp08fzUkjO3+QZBipoKpp+1htIKGdQs72zKDe7PjbFTv5t9+voSTPz8/mC5/a/9/4ti1zYvdc/BPKPnYtN/pyuCGqbNhbx+ubD/L/Nh7gvyq38NDyLZw0opDLZozmio+VM7I42N/TMbKEYDBIdXU1ZWVlg0pkVJXq6mqCwdR/FwatwDzx5nZuX7KWz45v5kdFz+F/+49OMLAL74SP/4sTFtTF6xFOGV3MKaOL+eq5J3KgvpUXP9jDklW7uXfpRu57aSPnnTScz398LOdNHY7fazssjO4pLy+nqqqKAwcO9PdQ+kRLS0snMQkGg5SXp+5hcVAKzMvr9vGzP77B48Ne5Nx9LyI1uTD/Npj7dQgW9dp+WGEO184bz7XzxrP9YCO/XbGT362sYtmG/QwrzGHRrHI+P3ss44eax36jM36/nwkTJvT3MPpMZWUlM2fO7FMfg05gag7Xsu133+O1nCXkNEaQj3/J+fr2KHc1jx+az78unMq3L5zC8o0HeObdHfzytW38vHIrcycO4XOzx7Lw1JHkBQbdH6Vh9JnB9Vux7f8RqfgaX9Fd1J14KcGL73Q88KcBn9fDhdNGcOG0Eeyra+HZlVX8dsVOvv3b9/n+82u55LRRLJpdzuxxpYPqvtsw+sLgEJhoxPmW5fX7aIyOoPK0n/HZz16dscuNKApyw3mT+Pr8E3l3+yF+t2Inf1y9m2dW7GR8WR6LZpVz+cfKGVOSm7ExGEY2kP0CE2qB3/0TbHqRJZ4F/KLwq/zh8gXH5NIiwpwJQ5gzYQg/uOwUXvxgL8+u3MlPXtrEfS9v4uPjhnDJ9FFcdOpIhhfZWyjj+COrBUaiIcfh0+aX+HP5t7lp6yyeve7j5PiOvfuE/Bwfi2aVs2hWOTtrmvjDe7v48+o93L5kLT/441rmjB/CpdNH8alTRzK80MTGOD7IaoGZuPkR2PMSW864i2+8NoHr5o1n1rj+dzczdkgeNy6YzI0LJrN5Xz1/XrOHP63ew/9+fi3/+/m1nF5ezPlTR3D+1OGcMroIj8ee2RiDk6wVmLeevZ95e/7CzmlfYfHKqUwY6uNfF57U38PqxOQRhdw8opCbL5jCxr31vLxuL8s27Oc/l23i/lc2Mbwwh/NOGs7ZU4Yyd2IZQwvMMbgxeMhagSkoGc6LkTnc8PezGVYEv7xm1oB/VXzSyEJOGlnIN86fTHVDK5UbD/Dqhv28sGYPz6xw3OacNKKQsTmttAzdy9yJQyjJC/TzqA3j6BnYv5E9cNoFV7GqeRQ/GjGJBSePYFhhdv3LX1aQwxWzyrliVjmhSJQ1u2p5a2s1b2+r5rWt9bzy65WIwJThhcw8oYQZY0uYeUIpk4YX4LVbKiNLyFqBARhb6GH+nBP6exh9xu/18LETSvnYCaXccN4kXn51OcUTTuetrdX8fcchXvxgL0+/66xwCnJ8TC8v5rQxxZw8qoiTRxUxcVi+bV8wBiRZLTCDFb+n/fU3OJvMPjzYyKqdh3lvx2FW7TzMr/66nbZIFICA18PkEQWcPKqIqSMLOXF4AROH5lNemmerHaNfSVfYkm8DX8LxB3MA+GdV/citiwBrXNMdqnpZmsZ+3CAiTBxWwMRhBfzjx5yNZqFIlG0HGlm/p471e+pYt6eOyo0HeHZlVbxdwOthXFkeE4flM3FYAROG5nPCkDzKS3MZWRTEZ6seI8P0KjAJYUsuxHHa/a6ILFHVxKgC7wGzVbVJRL4G/Bj4vFvXrKoz0jtsw+/1xB8a/8PMdj/q1Q2tbDvYyLYDDWw70MjWA41s3t/AsvX7CUfbfXl4PcLIoiDlpbmMKc2lvDSP8pJchhXlMLwwh+GFQcryA/YK3egTaQlboqrLE+zfBjL3nb7RI2UFOZQV5PDx8UM6lIcjUXYeaqbqUBNVh5rZdaiZXYed/Ntbq9lbt4toki8hr0cYWhBgeGGQ4YU5DCt0xKc0P0BpXoCSPD+leW46309hjs/2YRkdSEVgugo9ckYP9tcDLybkgyKyAuf26W5Vfe5IB2n0HZ/Xw4Sh+UzoxsVEKBJlb20L++tb2F/Xyv76Vg7Utzr5+lb21LbwflUt1Y2tdOfUzOcRSvL8lOQFKM3zUxj0U5DjoyDoozDHx4E9bXzo/5CCHB+FQR8FOX4Kgj4Kcnzk53jJ9XsJ+r3k+DwmVIOEtD7kFZGrgdnAuQnF41R1l4hMBF4VkTWqujWp3YAIWzJQ6O955QLjgHE5QA4Q93ThI6peGkPQ0KY0hNyjTWkIQWNIaWiL0hBqpqGuiQM10BxW94BQFH6/ufd4fQL4vZDjgYBXnLRXCHgg4HXKArE6D/g8jrh5PeAT8Huc1ZfPzcfqfR7wivMQ3echbu/1CB5x6jydDulU7hVnjDER7O+/r0yRjnmlIjC7gLEJ+XLaY1DHEZELgO8B56pqa6xcVXe5520iUgnMBDoIjKr+EvglwOzZs3X+/PkpDb6yspJUbbOJwTqvV15dzqwzzqShNUx9S5iG1jANrSHqW8I0tkZoCUVoDkVodc/NoQgtoahzbovlIzSHohwORWhuitAWiRIKR2mLOOkjdBnbJ7wewSsCCAFfq5OPHeKcxRWp+Bk6lUHHvMc18rhC5pQ5GadM8Hicc9f9x/KODe41JZZ3F4dxW3c+kiScQ8Mhvnfp/D79GaUrbMlMnJhHC1V1f0J5KdCkTkC2ocCZOA+AjeMQn0ec5zf5mfk6WVWJRJVQRGmLRGkLRwlF2o/WcJRQRJ18OBq3iUSVcFSJqhKOKBG3n05HQnk4qkTd8/aPdjB6TLnTPholEoVINOo8VHf+T1QVVffsjjUaBUWJqhNBR1Xj9VGNxSxzbaJOW6c8ikbo0jaadAanvbrjiDntVvc/mvBn59S3t5k1pO9qna6wJfcCBcDvXPWLvY4+GXhYRKI4YWrvTnr7ZBhpQ0TweQWfF3I5djvqKyv3Mn/+tGN2vWNFOm770hW25IJu2r0JnNaXARqGkb3Yl1aGYWQMExjDMDKGCYxhGBlj0G52rG2tZXfDbvwePyXBEsqCgyvKnmFkA4NOYP666688+N6DrKvu+LKq0F/IhJIJTCmdwkmlJzF1yFSmlE4hz2/B7g0jUwwqgfnN+t9w9zt3M65oHDd97CbGFY0johGqm6v5sPZDttVu46XtL/HspmcB56OjE4pO4KTSkzhpiCM6J5WexPC84bbaMYw0MGgE5q3db3H3O3ez4IQF3HPOPeR4u/Zwp6rsa9rHhpoNbKjZwKZDm1hfs56XPnopblOSU8K4onGMLhhNeUE5YwrGMKZwDKPzRzM0d6itegwjRQaFwLRGWrnjrTsYXzSeu8++u1txAedjrJH5IxmZP5L5Y+fHyxvaGth8eDMbajawsWYjVfVVrD6wmpe2v0REIx36yPfnMzR3aPwYljuMstwyyoJlFOcUU5JTQklOCUU5RRTnFOP3+DM1dcMY0AwKgXl+y/PsatjFwxc8TNB3dDGHCgIFzBw+k5nDOwb7DkfD7G/az66GXexu2M3B5oPx40DzATbUbOCN5jdoDDV237e/gOKcYucIOAJUGCikIFBAgb+AfH8+hYFC8v35FPgL2NG6gx11O5x8oKBHwTSMgUzWC0xUozyx9glOLTuVeaPnpb1/n8fH6ILRjC4Y3aNdU6iJmpYaattqqW2p5XDrYWrb3HNrLbWt7emqhioa2hqoD9UTjoa77O/eP9wbT/s9fgr8BeT58wh6gwR9QXJ9ue1nb3s+VhYrj5UFPAECXvfwBPB7/R3OAW8Av8dPwBvA58n6HwtjgJD1P0kr961kR/0OfnTWj/r1wWyeP488fx7llB9Ru7ZIGw2hBhraGmgINdAYauTtv7/N+JPGx/OxuuZwM83hZlrCLTSHmznccpi9kb2dypW+bVLziMcRm5gAuaLk8/jwerz4xIdXvHg9XrzidcrdvE988fLk/L7qfbz1t7c6lHvEg1e87s5gwSMePOJxdgwnpeP1eLq3Feloj6fLdOx68d3GHXYZx3Ygd6xP/PlKtNnaspWifUUd7ON23fSRfL3E/hL7T7WPDtfsorDLsqTfl2Sb+kh9586PkKwXmCVbl5Dvz2fBCccmHnW6CXgDDPEOYUiw3QNdY14j80+cf1T9qSpt0ba42MSOtkgboWiIUCREW7SNtkgbbdE2QpEQoWgonm+LtLXbxsrdukg0QkQjRKIRwhqO58PRMK3a2p5Pqou1aW5tZvW21Z366KsgDgj+0t8DSD/nFp7Lp/l0n/rIaoGJaIRlO5ax4IQF9mbHRUTI8eaQ482hOKe4v4fTge783DiuApSoRl3XAk46lo+nVYmSkO6pvpt+4mVE464LgLjIxcYSSyfWdap3r/H+++8z/fTpcd8HyfU99pHUn5voto+ubJL/LDuVdWWXVNZVu5otNZ3KjpSsFphtrduob6vnvLHn9fdQjD6QeFuTjbRuauUToz/R38NIO5U7K/vcR3b+jbqsaVqD3+MflH+5hjEYSElgRGShiGwUkS0icmsX9Tki8oxb/zcRGZ9Qd5tbvlFEPpWugasqa5rXcMaoM+z2yDAGKL0KTEJcpIuAacCVIpLsvut64JCqTgLuB+5x207DcbF5CrAQ+C+3vz7zYe2HHAwftNsjwxjApLKCicdFUtU2IBYXKZHPAE+46WeBBeK8A/sM8LSqtqrqh8AWt78+s3ynE4rpnPJz0tGdYRgZIF1xkeI2rg/fWqDMLX87qe2YpLYdwpYADSKyMaXRw9BR/zTqYIq22cRQwOaVPdi8nEg3nRgQb5ESw5YcCSKyQlVnZ2BI/YrNK7uweXVPKrdIqcRFituIiA8oBqpTbGsYxiAlFYGJx0USkQDOQ9slSTZLgOvc9CLgVXW+3FkCLHbfMk0AJgPvpGfohmEMdNIVF+lR4CkR2QLU4IgQrt1vgXU4salvUE3yfdA3jvi2KkuweWUXNq9ukK4+ETYMw0gHWf0lr2EYAxsTGMMwMkbWCkxv2xcGMiLymIjsF5EPEsqGiMjLIrLZPZe65SIiD7rzXC0iH+u/kXePiIwVkeUisk5E1orITW55ts8rKCLviMj77rzucMsnuNtitrjbZAJuebfbZgYiIuIVkfdE5E9uPq3zykqBSXH7wkDmcZytE4ncCixT1cnAMjcPzhwnu8eXgZ8fozEeKWHgO6o6DZgL3OD+nWT7vFqB81X1dGAGsFBE5uJsh7nf3R5zCGe7DHSzbWYAcxOwPiGf3nmpatYdwDxgaUL+NuC2/h7XEc5hPPBBQn4jMMpNjwI2uumHgSu7shvIB/A8cOFgmheQB/wd50v2g4DPLY//POK8bZ3npn2unfT32LuZTzmO6J8P/AnHP15a55WVKxi63r7QaQtCljFCVfe46b3ACDeddXN1l88zgb8xCObl3kasAvYDLwNbgcOqGnOonDj2DttmgNi2mYHIfwL/CkTdfBlpnle2CsygRp1/JrLy+wERKQD+B7hZVesS67J1XqoaUdUZOP/izwGm9u+I+o6IXArsV9WVmbxOtgrMYNyCsE9ERgG45/1uedbMVUT8OOLyG1X9vVuc9fOKoaqHgeU4tw4l7rYY6Dj27rbNDDTOBC4Tke04HhLOBx4gzfPKVoFJZftCtpG43eI6nGcYsfJr3bcuc4HahFuOAYPrnuNRYL2q/jShKtvnNUxEStx0Ls5zpfU4QrPINUueV1fbZgYUqnqbqpar6nic359XVfUq0j2v/n7Q1IcHVBcDm3Duh7/X3+M5wrFXAHuAEM597vU497PLgM3AK8AQ11Zw3phtBdYAs/t7/N3M6Syc25/VwCr3uHgQzGs68J47rw+A77vlE3H21W0BfgfkuOVBN7/FrZ/Y33NIYY7zgT9lYl62VcAwjIyRrbdIhmFkASYwhmFkDBMYwzAyhgmMYRgZwwTGMIyMYQJjGEbGMIExDCNjmMAc54hImYisco+9IrIrIR8QkTczdN1yEfl8N3UZuaZx7LEP7Yw4IvIDoEFVf3IMrnUdME1Vb8n0tYz+w1YwRo+ISIOIjBeRDSLyuIhsEpHfiMgFIvJX11PdnAT7q10PcKtE5GHpIha5iJwF/BRY5NpN7Oaa60Xkv11Pci+5e4GS+8oXkT+7Huc+6G5VZPQPJjBGqkwC7sNxVTAV+ALO/qP/BfwbgIicDHweOFMd9wYR4KrkjlT1DZwNq59R1Rmquq2ba04GfqaqpwCHgSu6sFkI7FbV01X1VOAvRztBI/0MiNCxRlbwoaquARCRtThuMFVE1uB45wNYAMwC3nU2V5NLu3uGZE4CNqRwzVVuemXCdRJZA9wnIvfgbNh7PaXZGMcEExgjVVoT0tGEfJT2nyMBnlDV23rqSESG4rhnCPdkl3TNCI5gdUBVN7kOwy8G/kNElqnqnb30axwj7BbJSCfLcJ6rDId4RIFxXdiNB3an44IiMhpoUtVfA/cCAzI6wfGKrWCMtKGq60Tk34GXRMSD4+/mBuCjJNMNwFBxwrZ8WVX78lr6NOBeEYm61/taH/oy0oy9pjYMI2PYLZJhGBnDBMYwjIxhAmMYRsYwgTEMI2OYwBiGkTFMYAzDyBgmMIZhZIz/H9wX2/ZWPZZUAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 288x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axes = plt.subplots(3, 1, figsize=(4, 6))\n",
     "\n",
@@ -65,16 +55,20 @@
     "\n",
     "    def L(t):\n",
     "        \"\"\"Velocity gradient.\"\"\"\n",
-    "        return np.array(\n",
-    "            [[-ratio, 0.0, 1.0], [0.0, ratio, 0.0], [0.0, 0.0, 0.0]]\n",
-    "        )\n",
+    "        return np.array([[-ratio, 0.0, 1.0], [0.0, ratio, 0.0], [0.0, 0.0, 0.0]])\n",
     "\n",
     "    # computed solution\n",
-    "    A = odeint(rsc_ode, A0.ravel(), t, args=(xi, L, 0.01, 0.1))\n",
+    "    sol = solve_ivp(\n",
+    "        integrate_ori_ode,\n",
+    "        (t0, tf),\n",
+    "        A0.ravel(),\n",
+    "        t_eval=t,\n",
+    "        args=(L, IBOF_closure, rsc_ode, {\"xi\": 1.0, \"Ci\": 0.01, \"kappa\": 0.1}),\n",
+    "    )\n",
     "\n",
-    "    ax.plot(t, A[:, 0], label=\"A11\")\n",
-    "    ax.plot(t, A[:, 4], label=\"A22\")\n",
-    "    ax.plot(t, A[:, 2], label=\"A13\")\n",
+    "    ax.plot(sol.t, sol.y[0], label=\"A11\")\n",
+    "    ax.plot(sol.t, sol.y[4], label=\"A22\")\n",
+    "    ax.plot(sol.t, sol.y[2], label=\"A13\")\n",
     "\n",
     "    ax.set_xlabel(\"Time $t$ in s\")\n",
     "    ax.set_ylim([0, 1])\n",
@@ -83,18 +77,11 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -108,7 +95,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/fiberoripy/__main__.py b/fiberoripy/__main__.py
index f3bc335..6f6d1a0 100644
--- a/fiberoripy/__main__.py
+++ b/fiberoripy/__main__.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-"""Explanation and hint for examples."""
 import os
 
 import fiberoripy
@@ -9,13 +7,21 @@ def main(args=None):
     """Show usage information."""
     path = os.path.dirname(os.path.dirname(fiberoripy.__file__))
 
-    print("Copyright (c) 2020 Nils Meyer")
     print(
+        "  ______ _ _                ____       _ _____       \n"
+        " |  ____(_) |              / __ \     (_)  __ \      \n"
+        " | |__   _| |__   ___ _ __| |  | |_ __ _| |__) |   _ \n"
+        " |  __| | | '_ \ / _ \ '__| |  | | '__| |  ___/ | | |\n"
+        " | |    | | |_) |  __/ |  | |__| | |  | | |   | |_| |\n"
+        " |_|    |_|_.__/ \___|_|   \____/|_|  |_|_|    \__, |\n"
+        "                                                __/ |\n"
+        "                                               |___/ \n"
+        "Copyright (c) 2024 "
+        "Nils Meyer, Constantin Krauß, Louis Schreyer, Julian Bauer\n\n"
         "Fiberoripy is a python package that provides fiber orientation models"
-        " and closure models."
+        " and closures for fourth order orientation tensos.\n\n"
+        f"Check out the examples at: {os.path.join(path, 'examples')}"
     )
-    print("Check out the examples at:")
-    print(os.path.join(path, "examples"))
 
 
 if __name__ == "__main__":
diff --git a/fiberoripy/aspect_ratios.py b/fiberoripy/aspect_ratios.py
index 7f155ca..12249fc 100644
--- a/fiberoripy/aspect_ratios.py
+++ b/fiberoripy/aspect_ratios.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-"""Compute equivalent aspect ratios."""
 import numpy as np
 
 
diff --git a/fiberoripy/closures.py b/fiberoripy/closures.py
index 9cbd30a..d1e3c68 100644
--- a/fiberoripy/closures.py
+++ b/fiberoripy/closures.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-"""Collection of closure approximations."""
 from itertools import permutations
 
 import numpy as np
@@ -400,10 +398,7 @@ def IBOF_closure(a):
         / 5.0
         * (
             -1.0 / 7.0
-            + 1.0
-            / 5.0
-            * beta3
-            * (1.0 / 7.0 + 4.0 / 7.0 * II + 8.0 / 3.0 * III)
+            + 1.0 / 5.0 * beta3 * (1.0 / 7.0 + 4.0 / 7.0 * II + 8.0 / 3.0 * III)
             - beta4 * (1.0 / 5.0 - 8.0 / 15.0 * II - 14.0 / 15.0 * III)
             - beta6
             * (
@@ -424,12 +419,7 @@ def IBOF_closure(a):
             - 1.0 / 5.0 * beta3 * (1.0 + 4.0 * II)
             + 7.0 / 5.0 * beta4 * (1.0 / 6.0 - II)
             - beta6
-            * (
-                -1.0 / 5.0
-                + 2.0 / 3.0 * III
-                + 4.0 / 5.0 * II
-                - 8.0 / 5.0 * II**2
-            )
+            * (-1.0 / 5.0 + 2.0 / 3.0 * III + 4.0 / 5.0 * II - 8.0 / 5.0 * II**2)
         )
     )
 
@@ -447,16 +437,10 @@ def IBOF_closure(a):
         symm(np.einsum("..., ij,kl->...ijkl", beta1, delta, delta))
         + symm(np.einsum("..., ij, ...kl-> ...ijkl", beta2, delta, a))
         + symm(np.einsum("..., ...ij, ...kl -> ...ijkl", beta3, a, a))
+        + symm(np.einsum("..., ij, ...km, ...ml -> ...ijkl", beta4, delta, a, a))
+        + symm(np.einsum("..., ...ij, ...km, ...ml -> ...ijkl", beta5, a, a, a))
         + symm(
-            np.einsum("..., ij, ...km, ...ml -> ...ijkl", beta4, delta, a, a)
-        )
-        + symm(
-            np.einsum("..., ...ij, ...km, ...ml -> ...ijkl", beta5, a, a, a)
-        )
-        + symm(
-            np.einsum(
-                "..., ...im, ...mj, ...kn, ...nl -> ...ijkl", beta6, a, a, a, a
-            )
+            np.einsum("..., ...im, ...mj, ...kn, ...nl -> ...ijkl", beta6, a, a, a, a)
         )
     )
 
@@ -473,9 +457,7 @@ def symm(A):
     S = np.stack(
         [
             np.transpose(A, axes=(*range(A.ndim - 4), *p))
-            for p in permutations(
-                [A.ndim - 4, A.ndim - 3, A.ndim - 2, A.ndim - 1]
-            )
+            for p in permutations([A.ndim - 4, A.ndim - 3, A.ndim - 2, A.ndim - 1])
         ]
     )
     return S.sum(axis=0) / 24
@@ -678,9 +660,7 @@ def orthotropic_fitted_closures(a, closure="ORF"):
     A[..., 1, 0, 1, 0] = A_sol[..., 5]
     A[..., 1, 0, 0, 1] = A_sol[..., 5]
     A[..., 0, 1, 1, 0] = A_sol[..., 5]
-    A = np.einsum(
-        "...im, ...jn, ...ko, ...lp, ...mnop -> ...ijkl", R, R, R, R, A
-    )
+    A = np.einsum("...im, ...jn, ...ko, ...lp, ...mnop -> ...ijkl", R, R, R, R, A)
 
     return A
 
@@ -758,9 +738,7 @@ def symmetric_implicit_closure(a, eps_newton=1.0e-12, n_iter_newton=25):
 
     while err > eps_newton and it < n_iter_newton:
 
-        f = (d + 4.0) * s - np.sum(
-            np.sqrt(1.5 * evs + s[..., None] ** 2.0), axis=-1
-        )
+        f = (d + 4.0) * s - np.sum(np.sqrt(1.5 * evs + s[..., None] ** 2.0), axis=-1)
         f_prime = 4.0 + np.sum(
             1.0 - s[..., None] / np.sqrt(1.5 * evs + s[..., None] ** 2.0),
             axis=-1,
@@ -807,8 +785,8 @@ def implicit_hybrid_closure(a, eps_newton=1.0e-12, n_iter_newton=25):
     References
     ----------
     .. [1] Karl, Tobias, Matti Schneider, and Thomas Böhlke,
-       'On fully symmetric implicit closure approximations for fiber orientation tensors',
-       Journal of Non-Newtonian Fluid Mechanics 318 : 105049,
+       'On fully symmetric implicit closure approximations for fiber orientation
+       tensors', Journal of Non-Newtonian Fluid Mechanics 318 : 105049,
        https://doi.org/10.1016/j.jnnfm.2023.105049
     Notes
     ----------
@@ -832,9 +810,7 @@ def implicit_hybrid_closure(a, eps_newton=1.0e-12, n_iter_newton=25):
 
             # l1 and l2 correspond to a, b in the original work
             l1 = np.nan_to_num(0.5 * (3.0 + (4.0 * s - 3.0) * k) / k)
-            l2 = np.nan_to_num(
-                (3.0 * (1.0 - k) * (4.0 * s - 1.0)) / (14.0 * k)
-            )
+            l2 = np.nan_to_num((3.0 * (1.0 - k) * (4.0 * s - 1.0)) / (14.0 * k))
 
             l1_prime = 4.0 * np.ones(l1.shape)
             l2_prime = np.nan_to_num((6.0 * (1.0 - k)) / (7.0 * k))
@@ -886,17 +862,13 @@ def implicit_hybrid_closure(a, eps_newton=1.0e-12, n_iter_newton=25):
         l2 = np.nan_to_num((3.0 * (1.0 - k) * (4.0 * s - 1.0)) / (14.0 * k))
 
         mus = -0.5 * l1[..., None] + np.sqrt(
-            1.5 * evs / k[..., None]
-            + 0.25 * l1[..., None] ** 2
-            - l2[..., None]
+            1.5 * evs / k[..., None] + 0.25 * l1[..., None] ** 2 - l2[..., None]
         )
 
     b = np.einsum("...ij, ...j, ...kj -> ...ik", r, mus, r)
 
     w1 = 1.0 - k
-    sym_ii = (
-        -3.0 / 35.0 * symm(np.einsum("ij, kl -> ijkl", np.eye(3), np.eye(3)))
-    )
+    sym_ii = -3.0 / 35.0 * symm(np.einsum("ij, kl -> ijkl", np.eye(3), np.eye(3)))
     sym_ib = 6.0 / 7.0 * symm(np.einsum("ij, ...kl -> ...ijkl", np.eye(3), b))
     sym_bb = symm(np.einsum("...ij, ...kl -> ...ijkl", b, b))
 
diff --git a/fiberoripy/constants.py b/fiberoripy/constants.py
index 027f169..fc4e636 100644
--- a/fiberoripy/constants.py
+++ b/fiberoripy/constants.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-"""Constants for this package."""
 COMPS = {
     "A11": 0,
     "A12": 1,
diff --git a/fiberoripy/fit.py b/fiberoripy/fit.py
index ad740b5..85bdf15 100644
--- a/fiberoripy/fit.py
+++ b/fiberoripy/fit.py
@@ -1,20 +1,30 @@
-# -*- coding: utf-8 -*-
-"""Fit parameters to model."""
 import numpy as np
-from scipy.integrate import odeint
+from scipy.integrate import solve_ivp
 from scipy.optimize import least_squares
 
+from fiberoripy.closures import IBOF_closure
+from fiberoripy.orientation import integrate_ori_ode
 
-def compute_error(params, t, reference, ode, xi, L):
+
+def compute_error(values, keys, t, reference, ode, xi, L):
     """Compute the error between dataset mean and solution."""
+    kwargs = dict(zip(keys, values))
+    kwargs["xi"] = xi
+
     A0 = reference[0, :]
     mask = [0, 1, 2, 4, 5, 8]
-    sol = odeint(ode, A0, t, args=(xi, L) + tuple(params))
-    diff = np.abs(sol[:, mask] - reference[:, mask])
+    sol = solve_ivp(
+        integrate_ori_ode,
+        (t.min(), t.max()),
+        A0,
+        t_eval=t,
+        args=(L, IBOF_closure, ode, kwargs),
+    )
+    diff = np.abs(sol.y[mask, :].T - reference[:, mask])
     return np.linalg.norm(diff, axis=1)
 
 
-def fit_optimal_params(t, reference, ode, xi, L, params, bounds):
+def fit_optimal_params(t, reference, ode, xi, L, keys, values, bounds):
     """Apply least-squares optimization to find optimal parameters.
 
     Parameters
@@ -29,8 +39,10 @@ def fit_optimal_params(t, reference, ode, xi, L, params, bounds):
         shape_factor
     L : function hanlde
         Velocity gradient as function of time.
-    params : list of float
-        parameters passed for optimization.
+    keys : list of str
+        parameters names passed for optimization.
+    values: list of floats
+        Initial guess for the parameters to be optimized.
     bounds : tuple of list of floats
         Upper and lower bounds of the parameters for optimization.
 
@@ -40,14 +52,24 @@ def fit_optimal_params(t, reference, ode, xi, L, params, bounds):
         Optimal parameter set, resulting fiber orientation evolution, optimizer message.
 
     """
+
     opt = least_squares(
         compute_error,
-        params,
+        values,
         bounds=bounds,
         verbose=2,
-        args=[t, reference, ode, xi, L],
+        args=[keys, t, reference, ode, xi, L],
     )
 
+    kwargs = dict(zip(keys, opt.x))
+    kwargs["xi"] = xi
+
     A0 = reference[0, :]
-    A = odeint(ode, A0, t, args=(xi, L) + tuple(opt.x))
-    return [opt.x, A, opt.message]
+    sol = solve_ivp(
+        integrate_ori_ode,
+        (t.min(), t.max()),
+        A0.ravel(),
+        t_eval=t,
+        args=(L, IBOF_closure, ode, kwargs),
+    )
+    return [opt.x, sol.y.T, opt.message]
diff --git a/fiberoripy/orientation.py b/fiberoripy/orientation.py
index f64af3d..bf85927 100644
--- a/fiberoripy/orientation.py
+++ b/fiberoripy/orientation.py
@@ -1,29 +1,25 @@
-# -*- coding: utf-8 -*-
-"""Orientation models."""
 import numpy as np
 
-from .closures import compute_closure
 
-
-def jeffery_ode(a, t, xi, L, closure="IBOF", **kwargs):
+def jeffery_ode(a, A, D, W, xi, **kwargs):
     """ODE describing Jeffery's model.
 
     Parameters
     ----------
-    a : 9x1 numpy array
-        Flattened fiber orientation tensor.
-    t : float
-        Time of evaluation.
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
     xi : float
         Shape factor computed from aspect ratio.
-    L : function handle
-        Function to compute velocity gradient at time t.
-    closure: str
-        Name of closure to be used.
 
     Returns
     -------
-    9x1 numpy array
+    3x3 numpy array
         Orientation tensor rate.
 
     References
@@ -34,12 +30,7 @@ def jeffery_ode(a, t, xi, L, closure="IBOF", **kwargs):
        https://doi.org/10.1098/rspa.1922.0078
 
     """
-    a = np.reshape(a, (3, 3))
-    A = compute_closure(a, closure)
-    D = 0.5 * (L(t) + np.transpose(L(t)))
-    W = 0.5 * (L(t) - np.transpose(L(t)))
-
-    dadt = (
+    return (
         np.einsum("ik,kj->ij", W, a)
         - np.einsum("ik,kj->ij", a, W)
         + xi
@@ -49,31 +40,29 @@ def jeffery_ode(a, t, xi, L, closure="IBOF", **kwargs):
             - 2 * np.einsum("ijkl,kl->ij", A, D)
         )
     )
-    return dadt.ravel()
 
 
-def folgar_tucker_ode(a, t, xi, L, Ci=0.0, closure="IBOF", **kwargs):
+def folgar_tucker_ode(a, A, D, W, xi, Ci=0.0, **kwargs):
     """ODE describing the Folgar-Tucker model.
 
     Parameters
     ----------
-    a : 9x1 numpy array
-        Flattened fiber orientation tensor.
-    t : float
-        Time of evaluation.
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
     xi : float
         Shape factor computed from aspect ratio.
-    L : function handle
-        Function to compute velocity gradient at time t.
     Ci : float
         Fiber interaction constant (typically 0 < Ci < 0.1).
-    closure: str
-        Name of closure to be used.
-
 
     Returns
     -------
-    9x1 numpy array
+    3x3 numpy array
         Orientation tensor rate.
 
     References
@@ -84,10 +73,6 @@ def folgar_tucker_ode(a, t, xi, L, Ci=0.0, closure="IBOF", **kwargs):
        https://doi.org/10.1177%2F073168448400300201
 
     """
-    a = np.reshape(a, (3, 3))
-    A = compute_closure(a, closure)
-    D = 0.5 * (L(t) + np.transpose(L(t)))
-    W = 0.5 * (L(t) - np.transpose(L(t)))
     G = np.sqrt(2.0 * np.einsum("ij,ij", D, D))
     delta = np.eye(3)
 
@@ -102,32 +87,32 @@ def folgar_tucker_ode(a, t, xi, L, Ci=0.0, closure="IBOF", **kwargs):
         )
         + 2 * Ci * G * (delta - 3 * a)
     )
-    return dadt.ravel()
+    return dadt
 
 
-def maier_saupe_ode(a, t, xi, L, Ci=0.0, U0=0.0, closure="IBOF", **kwargs):
+def maier_saupe_ode(a, A, D, W, xi, Ci=0.0, U0=0.0, **kwargs):
     """ODE using Folgar-Tucker constant and Maier-Saupe potential.
 
     Parameters
     ----------
-    a : 9x1 numpy array
-        Flattened fiber orientation tensor.
-    t : float
-        Time of evaluation.
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
     xi : float
         Shape factor computed from aspect ratio.
-    L : function handle
-        Function to compute velocity gradient at time t.
     Ci : float
         Fiber interaction constant (typically 0 < Ci < 0.1).
     U0 : float
         Maier-Saupe Potential (in 3D stable for y U0 < 8 Ci).
-    closure: str
-        Name of closure to be used.
 
     Returns
     -------
-    9x1 numpy array
+    3x3 numpy array
         Orientation tensor rate.
 
     References
@@ -138,10 +123,6 @@ def maier_saupe_ode(a, t, xi, L, Ci=0.0, U0=0.0, closure="IBOF", **kwargs):
        https://doi.org/10.1016/j.jnnfm.2010.04.001
 
     """
-    a = np.reshape(a, (3, 3))
-    A = compute_closure(a, closure)
-    D = 0.5 * (L(t) + np.transpose(L(t)))
-    W = 0.5 * (L(t) - np.transpose(L(t)))
     G = np.sqrt(2.0 * np.einsum("ij,ij", D, D))
     delta = np.eye(3)
 
@@ -158,36 +139,35 @@ def maier_saupe_ode(a, t, xi, L, Ci=0.0, U0=0.0, closure="IBOF", **kwargs):
         * G
         * (
             Ci * (delta - 3 * a)
-            + U0
-            * (np.einsum("ik,kj->ij", a, a) - np.einsum("ijkl,kl->ij", A, a))
+            + U0 * (np.einsum("ik,kj->ij", a, a) - np.einsum("ijkl,kl->ij", A, a))
         )
     )
-    return dadt.ravel()
+    return dadt
 
 
-def iard_ode(a, t, xi, L, Ci=0.0, Cm=0.0, closure="IBOF", **kwargs):
+def iard_ode(a, A, D, W, xi, Ci=0.0, Cm=0.0, **kwargs):
     """ODE describing iARD model.
 
     Parameters
     ----------
-    a : 9x1 numpy array
-        Flattened fiber orientation tensor.
-    t : float
-        Time of evaluation.
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
     xi : float
         Shape factor computed from aspect ratio.
-    L : function handle
-        Function to compute velocity gradient at time t.
     Ci : float
-        Fiber interaction constant (typically 0 < Ci < 0.05).
+        Fiber interaction constant (typically 0 < Ci < 0.1).
     Cm : float
         Anisotropy factor (0 < Cm < 1).
-    closure: str
-        Name of closure to be used.
 
     Returns
     -------
-    9x1 numpy array
+    3x3 numpy array
         Orientation tensor rate.
 
     References
@@ -199,10 +179,6 @@ def iard_ode(a, t, xi, L, Ci=0.0, Cm=0.0, closure="IBOF", **kwargs):
        https://doi.org/10.1122/1.4939098
 
     """
-    a = np.reshape(a, (3, 3))
-    A = compute_closure(a, closure)
-    D = 0.5 * (L(t) + np.transpose(L(t)))
-    W = 0.5 * (L(t) - np.transpose(L(t)))
     G = np.sqrt(2.0 * np.einsum("ij,ij", D, D))
     delta = np.eye(3)
 
@@ -231,38 +207,36 @@ def iard_ode(a, t, xi, L, Ci=0.0, Cm=0.0, closure="IBOF", **kwargs):
     )
 
     dadt = dadt_HD + dadt_iard
-    return dadt.ravel()
+    return dadt
 
 
-def iardrpr_ode(
-    a, t, xi, L, Ci=0.0, Cm=0.0, alpha=0.0, beta=0.0, closure="IBOF", **kwargs
-):
+def iardrpr_ode(a, A, D, W, xi, Ci=0.0, Cm=0.0, alpha=0.0, beta=0.0, **kwargs):
     """ODE describing iARD-RPR model.
 
     Parameters
     ----------
-    a : 9x1 numpy array
-        Flattened fiber orientation tensor.
-    t : float
-        Time of evaluation.
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
     xi : float
         Shape factor computed from aspect ratio.
-    L : function handle
-        Function to compute velocity gradient at time t.
     Ci : float
-        Fiber interaction constant (typically 0 < Ci < 0.05).
+        Fiber interaction constant (typically 0 < Ci < 0.1).
     Cm : float
         Anisotropy factor (0 < Cm < 1).
     alpha : float
         Retardance rate (0 < alpha < 1).
     beta : float
-        Retardance tuning factor (0< beta < 1).
-    closure: str
-        Name of closure to be used.
+        Retardance tuning factor (0 < beta < 1).
 
     Returns
     -------
-    9x1 numpy array
+    3x3 numpy array
         Orientation tensor rate.
 
     References
@@ -274,10 +248,6 @@ def iardrpr_ode(
        https://doi.org/10.1122/1.4939098
 
     """
-    a = np.reshape(a, (3, 3))
-    A = compute_closure(a, closure)
-    D = 0.5 * (L(t) + np.transpose(L(t)))
-    W = 0.5 * (L(t) - np.transpose(L(t)))
     G = np.sqrt(2.0 * np.einsum("ij,ij", D, D))
     delta = np.eye(3)
 
@@ -328,38 +298,36 @@ def iardrpr_ode(
     dadt_rpr = -np.einsum("ik, kl, lj->ij", R, IOK, np.transpose(R))
 
     dadt = dadt_temp + dadt_rpr
-    return dadt.ravel()
+    return dadt
 
 
-def mrd_ode(
-    a, t, xi, L, Ci=0.0, D1=1.0, D2=0.8, D3=0.15, closure="IBOF", **kwargs
-):
+def mrd_ode(a, A, D, W, xi, Ci=0.0, D1=1.0, D2=0.8, D3=0.15, **kwargs):
     """ODE describing MRD model.
 
     Parameters
     ----------
-    a : 9x1 numpy array
-        Flattened fiber orientation tensor.
-    t : float
-        Time of evaluation.
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
     xi : float
         Shape factor computed from aspect ratio.
-    L : function handle
-        Function to compute velocity gradient at time t.
     Ci : float
-        Fiber interaction constant (typically 0 < Ci < 0.05).
+        Fiber interaction constant (typically 0 < Ci < 0.1).
     D1 : type
         Anisotropy factors (D1 > 0).
     D2 : type
         Anisotropy factors (D2 > 0).
     D3 : type
         Anisotropy factors (D3 > 0).
-    closure: str
-        Name of closure to be used.
 
     Returns
     -------
-    9x1 numpy array
+    3x3 numpy array
         Orientation tensor rate.
 
     References
@@ -370,10 +338,6 @@ def mrd_ode(
        ANTEC, Orlando, 2018.
 
     """
-    a = np.reshape(a, (3, 3))
-    A = compute_closure(a, closure)
-    D = 0.5 * (L(t) + np.transpose(L(t)))
-    W = 0.5 * (L(t) - np.transpose(L(t)))
     G = np.sqrt(2.0 * np.einsum("ij,ij", D, D))
 
     C_hat = np.array([[D1, 0.0, 0.0], [0.0, D2, 0.0], [0.0, 0.0, D3]])
@@ -405,32 +369,32 @@ def mrd_ode(
     )
 
     dadt = dadt_HD + dadt_mrd
-    return dadt.ravel()
+    return dadt
 
 
-def pard_ode(a, t, xi, L, Ci=0.0, Omega=0.0, closure="IBOF", **kwargs):
+def pard_ode(a, A, D, W, xi, Ci=0.0, Omega=0.0, **kwargs):
     """ODE describing pARD model.
 
     Parameters
     ----------
-    a : 9x1 numpy array
-        Flattened fiber orientation tensor.
-    t : float
-        Time of evaluation.
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
     xi : float
         Shape factor computed from aspect ratio.
-    L : function handle
-        Function to compute velocity gradient at time t.
     Ci : float
         Fiber interaction constant (typically 0 < Ci < 0.05).
     Omega : type
         Anisotropy factor (0.5 < Omega < 1).
-    closure: str
-        Name of closure to be used.
 
     Returns
     -------
-    9x1 numpy array
+    3x3 numpy array
         Orientation tensor rate.
 
     References
@@ -442,15 +406,9 @@ def pard_ode(a, t, xi, L, Ci=0.0, Omega=0.0, closure="IBOF", **kwargs):
        https://doi.org/10.1122/1.4998520
 
     """
-    a = np.reshape(a, (3, 3))
-    A = compute_closure(a, closure)
-    D = 0.5 * (L(t) + np.transpose(L(t)))
-    W = 0.5 * (L(t) - np.transpose(L(t)))
     G = np.sqrt(2.0 * np.einsum("ij,ij", D, D))
 
-    C_hat = np.array(
-        [[1.0, 0.0, 0.0], [0.0, Omega, 0.0], [0.0, 0.0, 1.0 - Omega]]
-    )
+    C_hat = np.array([[1.0, 0.0, 0.0], [0.0, Omega, 0.0], [0.0, 0.0, 1.0 - Omega]])
 
     # Spectral Decomposition
     eigenValues, eigenVectors = np.linalg.eig(a)
@@ -479,36 +437,34 @@ def pard_ode(a, t, xi, L, Ci=0.0, Omega=0.0, closure="IBOF", **kwargs):
     )
 
     dadt = dadt_HD + dadt_pard
-    return dadt.ravel()
+    return dadt
 
 
-def pardrpr_ode(
-    a, t, xi, L, Ci=0.0, Omega=0.0, alpha=0.0, closure="IBOF", **kwargs
-):
+def pardrpr_ode(a, A, D, W, xi, Ci=0.0, Omega=0.0, alpha=0.0, **kwargs):
     """ODE describing pARD-RPR model.
 
     Parameters
     ----------
-    a : 9x1 numpy array
-        Flattened fiber orientation tensor.
-    t : float
-        Time of evaluation.
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
     xi : float
         Shape factor computed from aspect ratio.
-    L : function handle
-        Function to compute velocity gradient at time t.
     Ci : float
         Fiber interaction constant (typically 0 < Ci < 0.05).
     Omega : type
         Anisotropy factor (0.5 < Omega < 1).
     alpha : float
         Retardance rate (0 < alpha < 1).
-    closure: str
-        Name of closure to be used.
 
     Returns
     -------
-    9x1 numpy array
+    3x3 numpy array
         Orientation tensor rate.
 
     References
@@ -520,15 +476,9 @@ def pardrpr_ode(
        https://doi.org/10.1122/1.4998520
 
     """
-    a = np.reshape(a, (3, 3))
-    A = compute_closure(a, closure)
-    D = 0.5 * (L(t) + np.transpose(L(t)))
-    W = 0.5 * (L(t) - np.transpose(L(t)))
     G = np.sqrt(2.0 * np.einsum("ij,ij", D, D))
 
-    C_hat = np.array(
-        [[1.0, 0.0, 0.0], [0.0, Omega, 0.0], [0.0, 0.0, 1.0 - Omega]]
-    )
+    C_hat = np.array([[1.0, 0.0, 0.0], [0.0, Omega, 0.0], [0.0, 0.0, 1.0 - Omega]])
 
     # Spectral Decomposition
     eigenValues, eigenVectors = np.linalg.eig(a)
@@ -574,32 +524,32 @@ def pardrpr_ode(
     dadt_rpr = -np.einsum("ik, kl, lj->ij", R, IOK, np.transpose(R))
 
     dadt = dadt_temp + dadt_rpr
-    return dadt.ravel()
+    return dadt
 
 
-def rsc_ode(a, t, xi, L, Ci=0.0, kappa=1.0, closure="IBOF", **kwargs):
+def rsc_ode(a, A, D, W, xi, Ci=0.0, kappa=1.0, **kwargs):
     """ODE describing RSC model.
 
     Parameters
     ----------
-    a : 9x1 numpy array
-        Flattened fiber orientation tensor.
-    t : float
-        Time of evaluation.
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
     xi : float
         Shape factor computed from aspect ratio.
-    L : function handle
-        Function to compute velocity gradient at time t.
     Ci : float
         Fiber interaction constant (typically 0 < Ci < 0.05).
     kappa : float
         Strain reduction factor (0 < kappa < 1).
-    closure: str
-        Name of closure to be used.
 
     Returns
     -------
-    9x1 numpy array
+    3x3 numpy array
         Orientation tensor rate.
 
     References
@@ -612,10 +562,6 @@ def rsc_ode(a, t, xi, L, Ci=0.0, kappa=1.0, closure="IBOF", **kwargs):
        https://doi.org/10.1122/1.2946437
 
     """
-    a = np.reshape(a, (3, 3))
-    A = compute_closure(a, closure)
-    D = 0.5 * (L(t) + np.transpose(L(t)))
-    W = 0.5 * (L(t) - np.transpose(L(t)))
     G = np.sqrt(2.0 * np.einsum("ij,ij", D, D))
     delta = np.eye(3)
 
@@ -644,35 +590,24 @@ def rsc_ode(a, t, xi, L, Ci=0.0, kappa=1.0, closure="IBOF", **kwargs):
         )
         + 2 * kappa * Ci * G * (delta - 3 * a)
     )
-    return dadt.ravel()
-
-
-def ard_rsc_ode(
-    a,
-    t,
-    xi,
-    L,
-    b1=0.0,
-    kappa=1.0,
-    b2=0,
-    b3=0,
-    b4=0,
-    b5=0,
-    closure="IBOF",
-    **kwargs
-):
+    return dadt
+
+
+def ard_rsc_ode(a, A, D, W, xi, b1=0.0, kappa=1.0, b2=0, b3=0, b4=0, b5=0, **kwargs):
     """ODE describing ARD-RSC model.
 
     Parameters
     ----------
-    a : 9x1 numpy array
-        Flattened fiber orientation tensor.
-    t : float
-        Time of evaluation.
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
     xi : float
         Shape factor computed from aspect ratio.
-    L : function handle
-        Function to compute velocity gradient at time t.
     b1 : float
         First parameter of rotary diffusion tensor (0 < b1 < 0.1).
     kappa : float
@@ -685,12 +620,10 @@ def ard_rsc_ode(
         Fourth parameter of rotary diffusion tensor.
     b5 : type
         Fith parameter of rotary diffusion tensor.
-    closure: str
-        Name of closure to be used.
 
     Returns
     -------
-    9x1 numpy array
+    3x3 numpy array
         Orientation tensor rate.
 
     References
@@ -702,10 +635,6 @@ def ard_rsc_ode(
        https://doi.org/10.1016/j.jnnfm.2008.08.002
 
     """
-    a = np.reshape(a, (3, 3))
-    A = compute_closure(a, closure)
-    D = 0.5 * (L(t) + np.transpose(L(t)))
-    W = 0.5 * (L(t) - np.transpose(L(t)))
     G = np.sqrt(2.0 * np.einsum("ij,ij", D, D))
     delta = np.eye(3)
 
@@ -750,4 +679,93 @@ def ard_rsc_ode(
             + 10 * np.einsum("ijkl,kl->ij", tensor4, C)
         )
     )
-    return dadt.ravel()
+    return dadt
+
+
+def mori_tanaka_ode(a, A, D, W, xi, c_f=0.0, **kwargs):
+    """ODE describing the modified Jeffery equation based on the Mori-Tanaka model.
+
+    Parameters
+    ----------
+    a : 3x3 numpy array
+        Second-order fiber orientation tensor.
+    A : 3x3x3x3 numpy array
+        Fourth-order fiber orientation tensor.
+    D : 3x3 numpy array
+        Symmetric part of velocity gradient tensor.
+    W : 3x3 numpy array
+        Skew-symmetric part of velocity gradient tensor.
+    xi : float
+        Shape factor computed from aspect ratio.
+    c_f : float
+        Fiber volume fraction.
+
+    Returns
+    -------
+    3x3 numpy array
+        Orientation tensor rate.
+
+    References
+    ----------
+    .. [1] T. Karl, T. Böhlke,
+       'Generalized Micromechanical Formulation of Fiber Orientation Tensor Evolution
+       Equations',
+       International Journal of Mechanical Sciences 2023.
+       https://doi.org/10.1016/j.ijmecsci.2023.108771
+    """
+    c_m_inv = 1.0 / (1.0 - c_f)
+    return (
+        np.einsum("ij, jk -> ik", W, a)
+        - np.einsum("ij, jk -> ik", a, W)
+        + xi
+        * c_m_inv
+        * (
+            np.einsum("ij, jk -> ik", D, a)
+            + np.einsum("ij, jk -> ik", a, D)
+            - 2.0 * np.einsum("ijkl, kl -> ij", A, D)
+        )
+        - xi
+        * c_f
+        * c_m_inv
+        * (
+            np.einsum("ij, jk, kl -> il", D, a, a)
+            + np.einsum("ij, jk, kl -> il", a, a, D)
+            - 2.0 * np.einsum("ij, jk, kl -> il", a, D, a)
+        )
+    )
+
+
+def integrate_ori_ode(t, a_flat, L, closure, ori_model, kwargs):
+    """Wrapper to solve fiber reorientation ODE using `scipy` solvers.
+
+    Parameters
+    ----------
+    t : float
+        Time of evaluation.
+    a_flat : 9x1 numpy array
+        Flattened second-order fiber orientation tensor.
+    L : function handle
+        Function `L(t)` to retrieve velocity gradient at time `t`.
+        Must return 3x3 ndarray.
+    closure: function handle
+        Function `closure(a)` to compute closure approximation.
+        Must return 3x3x3x3 ndarray.
+    ori_model: function handle
+        Function `ori_model(a, A, D, W, **kwargs)` computing the rate of the orientation
+        tensor.
+    kwargs : dict
+        Keyword arguments for function `ori_model`.
+
+    Returns
+    -------
+    9x1 numpy array
+        Orientation tensor rate.
+    """
+    a = a_flat.reshape((3, 3))
+    a = np.clip(a, -1.0, 1.0)
+    A = closure(a)
+
+    D = 0.5 * (L(t) + np.transpose(L(t)))
+    W = 0.5 * (L(t) - np.transpose(L(t)))
+
+    return ori_model(a, A, D, W, **kwargs).ravel()
diff --git a/fiberoripy/tensorplot.py b/fiberoripy/tensorplot.py
index fb66bc0..8143e22 100644
--- a/fiberoripy/tensorplot.py
+++ b/fiberoripy/tensorplot.py
@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-"""Tools to plot tensors."""
 import numpy as np
 
 
@@ -52,7 +50,7 @@ def plot_orbit2(ax, plotargs, *tensors):
         ax.set_ylabel("$y$")
         ax.set_zlabel("$z$")
         ax.set_title("Second Order Orientation Tensor")
-        # ax.set_aspect(1.0)
+        ax.set_aspect("equal")
 
 
 def plot_orbit4(ax, plotargs, *tensors):
@@ -77,7 +75,7 @@ def plot_orbit4(ax, plotargs, *tensors):
         ax.set_ylabel("$y$")
         ax.set_zlabel("$z$")
         ax.set_title("Fourth Order Orientation Tensor")
-        # ax.set_aspect(1.0)
+        ax.set_aspect("equal")
 
 
 def plot_projection2(ax, plane, *tensors):
diff --git a/pyproject.toml b/pyproject.toml
index b267b44..e283597 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,17 +1,45 @@
 [project]
 name = "fiberoripy"
-version = "1.1.4"
+version = "1.2.0"
 description = "Fiber orientation models and closures"
-authors = [{name="Nils Meyer", email = "nils.meyer@uni-a.de"}, {name="Constantin Krauß"}, {name="Louis Schreyer"}, {name="Julian Karl Bauer"}]
-license = {file = "LICENSE"}
+authors = [
+    { name = "Nils Meyer", email = "nils.meyer@uni-a.de" },
+    { name = "Constantin Krauß" },
+    { name = "Louis Schreyer" },
+    { name = "Julian Karl Bauer" },
+]
+license = { file = "LICENSE" }
 readme = "README.md"
+requires-python = ">=3.9"
+classifiers = [
+    'Programming Language :: Python',
+    'Programming Language :: Python :: 3',
+    'Programming Language :: Python :: 3.9',
+    'Programming Language :: Python :: 3.10',
+    'Programming Language :: Python :: 3.11',
+    'Programming Language :: Python :: 3.12',
+]
+dependencies = ["numpy", "matplotlib", "scipy", "pytest", "ipympl"]
+
+[project.urls]
+source = "https://github.com/meyer-nils/fiberoripy"
+
+[project.scripts]
+fiberoripy = "fiberoripy.__main__:main"
 
 [build-system]
 requires = ["setuptools"]
 build-backend = "setuptools.build_meta"
 
+[tool.setuptools]
+include-package-data = true
+packages = ["fiberoripy"]
+
+[tool.setuptools.package-data]
+torchfem = ["examples/*.*"]
+
 [tool.isort]
 profile = "black"
 
 [tool.black]
-line-length = 79
+line-length = 88
diff --git a/setup.py b/setup.py
deleted file mode 100644
index cf21985..0000000
--- a/setup.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from os import path, walk
-
-import setuptools
-
-this_directory = path.abspath(path.dirname(__file__))
-with open(path.join(this_directory, "README.md"), encoding="utf-8") as f:
-    long_description = f.read()
-
-
-def package_files(directory):
-    """List files of a directory that should be included by pip.
-
-    Parameters
-    ----------
-    directory : str
-        Included directory.
-
-    Returns
-    -------
-    list of str
-        Filenames to be included.
-
-    """
-    paths = []
-    for p, dir, filenames in walk(directory):
-        for filename in filenames:
-            paths.append(path.join("..", p, filename))
-    return paths
-
-
-setuptools.setup(
-    name="fiberoripy",
-    version="1.1.4",
-    author="Nils Meyer, Constantin Krauß, Louis Schreyer, Julian Karl Bauer",
-    author_email="nils.meyer@uni-a.de",
-    description="Fiber orientation models and closures",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    entry_points={
-        "console_scripts": ["fiberoripy = fiberoripy.__main__:main"]
-    },
-    packages=["fiberoripy"],
-    url="https://github.com/nilsmeyerkit/fiberoripy",
-    package_dir={"fiberoripy": "fiberoripy"},
-    package_data={"fiberoripy": package_files("examples")},
-    install_requires=["numpy", "matplotlib", "scipy", "pytest", "ipympl"],
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: MIT License",
-        "Operating System :: OS Independent",
-    ],
-)


Test patch:
diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index 1250456..35df121 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -13,7 +13,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
 
     steps:
     - uses: actions/checkout@v2
diff --git a/tests/test_closure.py b/tests/test_closure.py
index e5688a6..225a709 100644
--- a/tests/test_closure.py
+++ b/tests/test_closure.py
@@ -1,6 +1,3 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""Testing the closures."""
 import numpy as np
 import pytest
 
diff --git a/tests/test_orientation.py b/tests/test_orientation.py
index 325d82d..9b0dd29 100644
--- a/tests/test_orientation.py
+++ b/tests/test_orientation.py
@@ -1,16 +1,16 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""Testing the orientation models."""
 import numpy as np
 import pytest
 
+from fiberoripy.closures import IBOF_closure
 from fiberoripy.orientation import (
     ard_rsc_ode,
     folgar_tucker_ode,
     iard_ode,
     iardrpr_ode,
+    integrate_ori_ode,
     jeffery_ode,
     maier_saupe_ode,
+    mori_tanaka_ode,
     mrd_ode,
     pard_ode,
     pardrpr_ode,
@@ -30,6 +30,7 @@
         iardrpr_ode,
         pard_ode,
         pardrpr_ode,
+        mori_tanaka_ode,
     ],
 )
 def test_default_case(model):
@@ -43,6 +44,19 @@ def test_default_case(model):
     def L(t):
         return np.array([[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
 
-    a_ref = odeint(jeffery_ode, a0.ravel(), t, args=(1.0, L))
-    a_test = odeint(model, a0.ravel(), t, args=(1.0, L))
+    kwargs = {"xi": 1.0}
+    a_ref = odeint(
+        integrate_ori_ode,
+        a0.ravel(),
+        t,
+        args=(L, IBOF_closure, jeffery_ode, kwargs),
+        tfirst=True,
+    )
+    a_test = odeint(
+        integrate_ori_ode,
+        a0.ravel(),
+        t,
+        args=(L, IBOF_closure, model, kwargs),
+        tfirst=True,
+    )
     assert np.allclose(a_ref, a_test, atol=1e-12)