diff --git a/.trunk/trunk.yaml b/.trunk/trunk.yaml
index 53c261b5..776eb03d 100644
--- a/.trunk/trunk.yaml
+++ b/.trunk/trunk.yaml
@@ -41,30 +41,30 @@ lint:
       paths:
         - examples/Advanced_Sampling_Introduction.md
         - examples/Installing_a_PySAGES_Environment.md
-        - examples/openmm/Harmonic_Bias.md
         - examples/hoomd-blue/ann/Butane_ANN.md
-        - examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md
-        - examples/openmm/metad/Metadynamics-ADP.md
         - examples/hoomd-blue/cff/Butane_CFF.md
+        - examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md
         - examples/hoomd-blue/spectral_abf/Butane-SpectralABF.md
-        - examples/openmm/spectral_abf/ADP_SpectralABF.md
         - examples/hoomd-blue/funn/Butane_FUNN.md
         - examples/hoomd-blue/umbrella_integration/Umbrella_Integration.md
+        - examples/openmm/harmonic_bias/Harmonic_Bias.md
+        - examples/openmm/metad/Metadynamics-ADP.md
         - examples/openmm/metad/nacl/Metadynamics_NaCl.md
+        - examples/openmm/spectral_abf/ADP_SpectralABF.md
     - linters: [black]
       paths:
         - examples/Advanced_Sampling_Introduction.ipynb
         - examples/Installing_a_PySAGES_Environment.ipynb
-        - examples/openmm/Harmonic_Bias.ipynb
         - examples/hoomd-blue/ann/Butane_ANN.ipynb
         - examples/hoomd-blue/harmonic_bias/Harmonic_Bias.ipynb
-        - examples/openmm/metad/Metadynamics-ADP.ipynb
         - examples/hoomd-blue/cff/Butane_CFF.ipynb
         - examples/hoomd-blue/spectral_abf/Butane-SpectralABF.ipynb
-        - examples/openmm/spectral_abf/ADP_SpectralABF.ipynb
         - examples/hoomd-blue/funn/Butane_FUNN.ipynb
         - examples/hoomd-blue/umbrella_integration/Umbrella_Integration.ipynb
+        - examples/openmm/harmonic_bias/Harmonic_Bias.ipynb
+        - examples/openmm/metad/Metadynamics-ADP.ipynb
         - examples/openmm/metad/nacl/Metadynamics_NaCl.ipynb
+        - examples/openmm/spectral_abf/ADP_SpectralABF.ipynb
 
 merge:
   required_statuses:
diff --git a/examples/Advanced_Sampling_Introduction.ipynb b/examples/Advanced_Sampling_Introduction.ipynb
index 26b805f8..b9a488d3 100644
--- a/examples/Advanced_Sampling_Introduction.ipynb
+++ b/examples/Advanced_Sampling_Introduction.ipynb
@@ -9,13 +9,11 @@
     "\n",
     "# Introduction to Advanced Sampling\n",
     "\n",
-    "Ludwig Schneider and Juan de Pablo\n",
+    "Ludwig Schneider, Pablo Zubieta, and Juan de Pablo\n",
     "\n",
     "Pritzker School of Molecular Engineering\n",
     "\n",
-    "The University of Chicago\n",
-    "\n",
-    "Berlin, July 28th, 2022\n"
+    "The University of Chicago\n"
    ]
   },
   {
@@ -27,8 +25,8 @@
     "\n",
     "# Setting up the environment\n",
     "\n",
-    "First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin.\n",
-    "We copy it from Google Drive and install PySAGES for it.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance.\n"
    ]
   },
   {
@@ -41,9 +39,12 @@
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
@@ -54,7 +55,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "KRPmkpd9n_NG",
-    "outputId": "34bb6ffa-98ad-42dd-acef-30d11fc66459"
+    "outputId": "0e3ce982-ab90-4d70-aad6-18768a9e047c"
    },
    "outputs": [
     {
@@ -97,46 +98,39 @@
     "ver = sys.version_info\n",
     "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "os.environ[\"XLA_FLAGS\"] = \"--xla_gpu_strict_conv_algorithm_picker=false\"\n",
     "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "we_mTkFioS6R"
+    "id": "Wy-75Pt7Bqs1"
    },
    "source": [
-    "\n",
-    "## PySAGES\n",
-    "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
+    "We'll also need some additional python dependencies"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "vK0RZtbroQWe"
+    "id": "LpBucu3V81xm"
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "pip install -q --upgrade pip\n",
-    "# Installs the wheel compatible with CUDA\n",
-    "pip install -q --upgrade \"jax[cuda]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null"
+    "!pip install -qq \"numpy<2\" gsd > /dev/null"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "wAtjM-IroYX8"
+    "id": "we_mTkFioS6R"
    },
    "source": [
     "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
+    "## PySAGES\n",
+    "\n",
+    "The next step is to install PySAGES. First, we need to install JAX. Fortunately, Colab already ships with JAX pre-installed (to learn how to install it you can look at the [JAX documentation](https://jax.readthedocs.io) for more details). To install PySAGES, we retrieve the latest version from GitHub and add its dependecies via `pip`.\n"
    ]
   },
   {
@@ -147,12 +141,7 @@
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
    ]
   },
   {
@@ -169,21 +158,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "ppTzMmyyobHB",
-    "outputId": "9ba2e260-1585-4bd7-8fee-4f0404dd1449"
+    "id": "ppTzMmyyobHB"
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "mkdir: cannot create directory ‘/content/advanced_sampling’: File exists\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
@@ -324,15 +301,22 @@
    "source": [
     "import numpy as np\n",
     "\n",
-    "def potential(x, rmin=0, rmax=100, amplitude=1., roughness=4, periodicity=1):\n",
-    "  energy = x**2\n",
-    "  energy += (1-np.exp(-x**2))*roughness*np.cos(periodicity*x*np.pi)\n",
-    "  energy *= amplitude\n",
-    "  force = 2*x\n",
-    "  force -= np.pi*periodicity*roughness*(1-np.exp(-x**2))*np.sin(periodicity*x*np.pi)\n",
-    "  force += 2*roughness*np.exp(-x**2)*x*np.cos(periodicity*x*np.pi)\n",
-    "  force *= -amplitude\n",
-    "  return energy, force"
+    "def energy_and_forces(x, amplitude=1., roughness=5, periodicity=1):\n",
+    "    omega = np.pi * periodicity\n",
+    "    energy = x**2\n",
+    "    energy += (1 - np.exp(-x**2)) * roughness * np.cos(omega * x)\n",
+    "    energy *= amplitude\n",
+    "    forces = 2 * x\n",
+    "    forces -= omega * roughness * (1 - np.exp(-x**2)) * np.sin(omega * x)\n",
+    "    forces += 2 * roughness * np.exp(-x**2) * x * np.cos(omega * x)\n",
+    "    forces *= -amplitude\n",
+    "    return energy, forces\n",
+    "\n",
+    "def energy(x, **kwargs):\n",
+    "    return energy_and_forces(x, **kwargs)[0]\n",
+    "\n",
+    "def forces(x, **kwargs):\n",
+    "    return energy_and_forces(x, **kwargs)[1]"
    ]
   },
   {
@@ -352,41 +336,40 @@
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 283
+     "height": 472
     },
     "id": "7N11Y8GOSY1_",
-    "outputId": "38faa096-7a15-42fc-b1c8-80795a0dade9"
+    "outputId": "91823ec0-17cc-469b-ddd6-d58185410328"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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7pWems9pvNXXs6rwWcxNP6XTVk6IohsBloDKwDLgLxAkhntbEDgHKvuLc4cBwgPLly2s/WD1SqVQYGRXcn2bOnDnZHtOlSxe6dNH/3ryS9CbbfWc3YUlhzGo867XpTYCOJ7OFEJlCiLqAA9AAqJ6Lc38RQrgLIdzt7Oy0FmNeBQUFUb16dYYMGULVqlXp378/R44coUmTJlSpUuVZifCYmBi6deuGi4sLjRo1wsfHB4BZs2YxcOBAmjRpwsCBA4mMjOSDDz6gfv361K9fnzNnzrxwzXXr1tGtWzfatWuHo6MjS5cuZeHChdSrV49GjRoRExMDwJAhQ9ixQ9Npc3R0ZObMmbi6uuLs7MyNGzeetTV69Ohnx48cOZJGjRpRsWJFTpw4gYeHBzVq1GDIkCHPrj9y5Ejc3d2pVasWM2fO1NrvVpLeBBnqDDx9PXGxdeGdMu/oO5xc0ct9FEKIOEVRjgONgeKKohg96VU4AA/z237YN9+QFlCwZcZNalSn1NSpWR5z584dfvvtN9asWUP9+vXZsmULp0+fxsvLi2+++Ybdu3czc+ZM6tWrx+7duzl27BiDBg3i6tWrAPj7+3P69GnMzMzo168f48ePp2nTpgQHB9OhQwcCAgJeuKafnx/e3t6kpqZSuXJl5s+fj7e3N+PHj2fDhg2MGzfuhXNsbW25cuUKy5cvZ8GCBaxevfqFY2JjYzl79ixeXl506dKFM2fOsHr1aurXr8/Vq1epW7cu8+bNw9ramszMTNq0aYOPjw8uLoVjj19JKmz2B+7nUdIjpjac+lr1JkC3q57sFEUp/uRnM6AdEAAcB3o+OWwwsEdXMRU0JycnnJ2dMTAwoFatWrRp0wZFUXB2diYoKAjQFPcbOHAgAK1btyY6OpqEhARAM/xjZmYGwJEjRxg9ejR169alS5cuJCQkPNvo6N9atWqFpaUldnZ2WFlZ8f777wM8d83/6tGjBwBubm6vPOb9999/Fru9vf1z7+vpOdu3b8fV1ZV69epx/fp1/P398/Jrk6Q3XqY6k9W+q6lWoprWS3Vogy57FKWB9U/mKQyA7UKIfYqi+AO/KooyF/AGPPN7oey++WuLick/deQNDAyePTYwMMjR1qT/LhGuVqs5d+4cpqamBX7Np8cYGhpme8y/2/x3u/fu3WPBggVcvHiREiVKMGTIEFJTU7N9j5L0NjocfJighCAWtFjw2vUmQLernnyEEPWEEC5CiNpCiDlPng8UQjQQQlQWQvQSQqTpKiZ9aNasGZs3bwY0JTpsbW0pVqzYC8e1b9+eJUuWPHv8dHiqsEhISKBo0aJYWVkRHh7OgQMH9B2SJBVKQghW+6zGsZgjbcsX3gqxWZG1nnRs1qxZeHh44OLigrm5OevXr3/pcYsXL2bUqFG4uLigUqlo3rw5K1eu1HG0r1anTh3q1atH9erVKVeu3LOd+yRJet7JkJPcjL3J3CZzC+1+E9mRZcYlrZJ/F+ltJoRgwIEBRKdEs7f7XowNjPUWiywzLkmSVAhdDLuIT6QPQ2sN1WuSyC+ZKCRJkrRkte9qbExt6Fbl9S44IROFJEmSFlyPvs7Z0LMMrDkQE0OT7E8oxN6oRPE6zre8yeTfQ3qbefp6YmlsSZ9qffQdSr69MYnC1NSU6Oho+eFUSAghiI6OzvY+EEl6EwXFB3Hk/hH6VO+DRRELfYeTb2/M8lgHBwdCQkKIjIzUdyjSE6ampjg4OOg7DEnSubXX11LEsAj9a/TXdygF4o1JFMbGxjg5Oek7DEmS3nJhSWF43fXigyofYGtmq+9wCsQbM/QkSZJUGGz034gQgiG1hug7lAIjE4UkSVIBiU+LZ8etHXR06oiD5Zsz7CoThSRJUgH59cavJKuSGVprqL5DKVAyUUiSJBWAFFUKW25soVnZZlSzrqbvcAqUTBSSJEkFYPed3cSkxuBR20PfoRQ4mSgkSZLySaVWsf76elzsXHCzd9N3OAVOJgpJkqR8+jPoTx4mPmRY7WGv5cZE2ZGJQpIkKR+EEKz1W0tFq4q0LNdS3+FohUwUkiRJ+XDm0Rluxt5kSK0hGChv5kfqm/muJEmSdMTT1xN7c3veq/ievkPRGpkoJEmS8uha5DUuhV9iUM1BGBu+vhsTZUcmCkmSpDxa47uGYkWK0bNqT32HolUyUUiSJOVBYFwgxx4co2/1vpgbm+s7HK2SiUKSJCkP1l5fi6mhKf1q9NN3KFonE4UkSVIuhSWFsS9wH92rdMfa1Frf4WidTBSSJEm5tMF/A0IIBtUcpO9QdEImCkmSpFyIS41jx60ddHLq9EaVEs+KTBSSJEm5sPXGVlJUKW9k8b9XkYlCkiQph5Izktl8YzMtHVpSpUQVfYejMzJRSJIk5dDO2zuJT4tnmPMwfYeiUzJRSJIk5UBGZgbrr6/Hzd6NuiXr6jscnZKJQpIkKQf2Be4jPDmcj5w/0ncouaNWw/Xf89WEUQGFIkmS9MbKVGfi6edJdevqNCnTRN/h5IxaDQF74MR8iAzIV1PZ9igURbHOwb/i+YpCkiSpEDt8/zD3E+7zsfPHr8fGRKHXYHVr+G0IiEz4wDNfzeWkRxEKPASy+u0YAuXzFYkkSVIhpBZqfvH9BScrJ9pWaKvvcLKWlggnvoVzy8HcFrqtBJfeYGCYr2Zzkij8hRD1sjpAURTvfEUhSZJUSP314C9ux97mm6bfFO6NiR5cgB3DID4Y3IZC25lgVqJAms5JojiWg2Ma5zcQSZKkwkYIwSrfVZS1KEsnp076Dufl1Go4uxSOzoZiZWHoQahQsB/JOUkUrbI7QAiRWgCxSJIkFSpnQ8/iG+XLV42+wsigEK79SY6B3SPh1kGo0QW6LAGzgp8y1lk/SlGUcoqiHFcUxV9RlOuKoox98ry1oiiHFUW5/eS/BdNXkiRJyqdVPqsoaVaSbpW76TuUF8UEgmc7uHMUOn0PvTdoJUlAzhJFHUVR7imK4qUoyjeKovRVFMVZUZTcplcVMEEIURNoBIxSFKUmMBk4KoSoAhx98liSJEmvLoVd4lL4JYbUHkIRwyL6Dud5Dy7C6raQHA2D90LD4aDF1Vg5SRQ+QBNgKRANtAfWAtGKovjl9EJCiFAhxJUnPz8GAoCyQFdg/ZPD1gOFMHVLkvS2WXltJTamNvSq2kvfoTzP3wvWvwcmxWDYkRzNRwR438jXJXPUKxBCPAIeAYeePqdoFhNXzstFFUVxBOoB5wF7IUTok5fCAPtXnDMcGA5QvrxciStJkvZcCb/C+bDzfOH+BaZGpvoO5x/XtsHuT6CsG/T9FYraZnn41bsRXJy/mPqn9+TrsjnpUSx92ZNC43ZuL6goigWwExgnhEj4b5uAeMX1fhFCuAsh3O3s7HJ7WUmSpBxbcW0F1qbW9K7WW9+h/MN7E/w+Aio0gUF7skwSt8MfM/3rTUT26UnTkzuJrdMgX5fONlEIIZ67pU9RlBqKorR78rNZbi6mKIoxmiSxWQix68nT4YqilH7yemkgIjdtSpIkFSTvCG/OhZ7Do7YHZka5+ojTnktrYc8oqNQK+m2HIkVfelhUYhrTfvdlxqQV9N76HTamhtguX0mrrdq/M/u/lgD7FEX5FFApihIghJiR3UlPhqo8gQAhxMJ/veQFDAa+e/Lf/PWRJEmS8mHFVU1votDMTVzZCPvGQZUOmpVNxi8OhanVgq0Xg/lu/w3qB15k+uWtFKlRk4qeqzAsnv+VUHlJFP5CiJ8URakqhPhUUZSXDk29RBNgIOCrKMrVJ89NRZMgtiuKMgy4DxSivp4kSW+TqxFXORt6lgluEzA3Ntd3OJqJ671joFJr6LMRjExeOCQwMpHJu3y5cC+GUYk+vHdpC+b16+OwfDmGFi/veeRWXhJF4yfJobKiKM5kXQPqGSHE6SyObZOHOCRJkgrUUu+lhWduIvAE7BwGZd2hz6YXkoRaLVhz5h7/+/MmJkYGrCgfj+PiDVi0aEHZRT9hYFpwk/C5ThRCiPqKojgAbkAvoEKBRSNJkqQn50PPcz7sPBPrT9R/b+LhZdjaD2yqQP8X5yQiHqcyYfs1Tt2Oom0Ne+a4mJIwbComdVwou2QxBkUK9r6PHCcKRVHmCiGmAwghQoAQRVH2CSEyCzQiSZIkHRNCsNh7Mfbm9vrvTcTehy19NKuaBu56obDf8RsRfPHbNRLTVMztVpsPqxUjqHcfDIqa47C44JME5K6ER1lFUfo+faAoih1wpMAjkiRJ0rGTISfxifRhRJ0RmBi+OA+gMylxsKU3ZKZD/x1gWerZS2q1YOHhWwxddxE7SxP2fdaU/vUdePTFl2SEheGwaDHG9i+9DS3fcjP0NAL4U1GUu2judVgLTNJKVJIkSTqiFmqWeC+hnGU5/dZ0ysyA3wZD9B0Y+DvYVX32UnxKBuO3XeXYjQh6uJblm+7OmBobErl0GUlnzlBqzmzMXbPcDSJfsk0UiqJsAK4A3sAoYAuauk3dhBB3tBaZJEmSDhy6f4ibsTf5puk3GBsY6ycIIWD/F5oJ7K7Lwan5s5fuRCTy0fqLhMSm8HXXWgxoVAFFUUj19ydq5UqKvfceJXprd7gsJz2KdUAdYCjgAjgCF4EBiqL4CSF2aC06SZIkLcpQZ7DMexmVrCrxrtO7+gvk4mq4vA6afg71+j97+u87UXyy6TJFjAz4dXgj3B2tARDp6TyaPAXDEsUpNX2a1sPLNlEIIY7xr82LnlSNrYEmeTQEZKKQJOm1tPPWToISgljSegmG+dwuNM+CzsDByVC1I7T+6tnT2y89YOouX5xsi7JmSH3KWf+zEityxQrSbt3CYcXyArmhLjt5WR6rAnyf/NtU4BFJkiTpQGJ6IiuurcDd3p0WDi30E0R8CGwfBCWcoMcvYGCAEIIfD99i8bE7NK1sy7L+rliZ/TMkluLrR/Qvq7Dq3h3LVtnuK1cgsl31pCjKlYI4RpIkqTBZ47eGmNQYvnD/AkWLezm8UkYKbBsAqjT4cAuYWpGpFkzb7cfiY3fo7e7A2qH1n0sSIiOD0GnTMLK1xX6K7rbuyUmPooaiKD5ZvK4AVgUUjyRJktaFJ4Wz0X8jnZw6Ucu2ln6CODARHnnDh1vBrippqkzG/XqVA35hjGxZiYkdqr2QwGI2b9YMOS1bimGxYjoLNSeJonoOjpE33UnSS4QlheEX5ceDxw8IfhxMWFIYBooBRooRxobG2JnZUbVEVaqUqEKl4pUKT7XSN9yyq8vIFJmMdR2rnwCuboUrG6DZBKj+LklpKoZvvMSZO9FM71yDj5pVfOGUjIgIopYspWjzZli0bq3TcHMymX1fF4FI0pviZsxNjgYf5cSDEwTEBDx7vrhJcUoXLY2iKGSoM8jIzOCvpL9IzUwFwNjAmIalG9KmfBtalmuJrVnWm9JIeXMz5ia77+xmUM1BlLUoq/sAwq/DvvHg2AxaTiUhNQOPtRe5EhzLD73q8IGbw0tPi/h+ASI9nVLTpul8qEzR7BX0enF3dxeXLl3SdxiS9IwQgvNh51ntu5rzoedRUKhbsi4tHFrQsHRDyhcrT7EiLw4VZKozeZj4kNuxt7kScYWjwUd5mPgQBYWW5VoyqOYg3Ozd9DOG/gYSQjDk4BDuxd9jb/e9WJnoeNQ87TH80grSEmDEKeIMSzB4zQWuP0pgcd96vOtc+qWnJV+8yP2Bg7AZ+Qklx+atF6QoymUhhHuezs0uUSiKUksIcT1PkWmJTBRSYXIx7CILLy3EL9oPWzNbBtUcRNfKXbE2tc51W0IIbsXe4mDQQXbc2kFcWhw1rGvg4exBhwodZMLIpz8C/2DyqcnMajyLD6p+oNuLCwE7P4Lru2CQFzElGzJg9XnuRCSyvL8rbWu+vPyGUKm4170H6qQkKv6xDwOzvA1P5idR5GSOYiPg+uRCHwkhVv/rwuZCiOS8XFiSXnfxafH8ePlHdt7eSZmiZZjReAZdKnXJV60gRVGoZl2NatbVGO4ynH2B+9jov5Ev//qSDbYb+ML9C1ztXQvwXbw9kjOSWXhpIbVsatG9SnfdB+C9Efx2QOuviCnZkH6rznEvKolVg91pUfXV2zvH/rqNtNu3NVVh85gk8isnRQH//RXm0/+8dqoAY5Gk1/iA6CMAACAASURBVMbR4KN03d2V3Xd2M7TWUHZ3202vqr0KtKCcmZEZvar24vcuv/N1k68JTwpn8MHBjD8+nrCksAK7ztviZ5+fiUiJYGrDqRgouamHWgAibsD+iVCxJbGuo+m/+jz3opLwHFw/yySRGR9P1JIlmDdqhGXbtrqL9z9y8tv699jUf/u9Ov5tS5J+Zaoz+enyT4w7Po6S5iXZ0nkLn7t/rtXVSoYGhnSr3I293fcyuu5ozjw6Q7c93dh+cztqodbadd8kQfFBbPDfQNdKXXGxc9HtxTNSYMdQMLEgvuNS+nte5G5kIqsGudO0StYLFqKWryAzIQH7SRP1OuyYkw/6UoqiDFEUpR4vJorXbyZckvIoPi2eUUdH4ennSc+qPdn07iZq2tTU2fXNjc0ZUWcEO7vspLZtbb4+9zUfHfqIB48f6CyG15EQgnnn52FqaMo4t3G6D+DPqRDhT9K7yxiw7T53niSJ5ln0JADSg4KI2bIFqw96YFqjho6CfbmcJIpZaHaz+wlwUBTFX1GUnYqizAPk+j3prRCcEMyH+z7kfNh5ZjSewczGMyliWPAbxOREOctyrGq3itnvzOZG9A167e3FH4F/6CWW18HuO7s5F3qO8W7jdb/k2N8LLq0hveFnDPzLghthCfw8wC3L4aanwhcswMDYOM+rnApSTu6j+OXfj59sg+qMppLsSS3FJUmFxp3YO3x8+GNUahVrO6ylbsm6+g4JRVHoUaUHjUs3ZvKpyUw+NZm/H/3NtIbT9L+NZyESlRLFgksLcC3pSs+qPXV78fiH4PUZ6tL1GBrcnmsh8SzrV49W1Utme2rS+QskHjmK3bhxGNlln1S0LddzDEKIECHEASHEfCHEAG0EJUmFhX+0P0P/HIqCUmiSxL+VtiiNZwdPRtYZyb7AffTZ14fbsbf1HVah8e35b0lRpTDrnVm6ncBWZ8LvIxCZGUxiDH8HPWZh7zp0rP3y+yT+TWRmEv7ddxiVKY31kME6CDZ7cjJakl7hWuQ1hv05DDMjM9Z1XEflEpX1HdJLGRkY8WndT/Fs70liRiL99/fnwL0D+g5L744FH+PQ/UN8UucTnKycdHvxM4sg6BQbin/Kb/dM+K6HM13r5uwu8Pg9XqQFBFDy8wkYmJpqOdCckYlCkl7iTuwdPj3yKSVMS7C+43rKFyuv75Cy5V7Kne3vbae6dXUmnpzI/AvzyVBn6DssvYhLjWPeuXlUKVGFobWH6vbiDy8jjs/Du1grZj6oy4z3atKnfs7+96NOTibyxx8xreNCsc563EjpP2SikKT/CE0MZcSREZgYmvBLu18obZH9cEFhYWduh2cHT/rX6M+mgE2MODyC2NRYfYelU0IIZp+dTUxaDHObzNXt9qZpiYidHxNvaMPgiL6Mb1sNj6Y5781Er1mLKjIS+0mTC9Vd+DJRSNK/xKbGMvzwcFIyUljRdgUOli8v0FaYGRsYM7nBZL5p+g3XIq7R94++3Iy5qe+wdGbX7V0cCT7CmHpjdLp8GdAshY0JZETicPo0c2ZMm5wPV2aEhxPt6Yllp46Yu9bTYpC5l+sd7iTpTZWqSmX00dGEJoXyc7ufqWZdTd8h5cv7ld7HsZgj446PY+CBgcxrOo92FdrpO6wcUaekkOLtTfKlSyRfvET6gwcYWpfAyNYWo5IlsWjRAstWrVCMnv8IC4oPYv7F+TQs1ZDBtXQ8ERywD66sZ6XqfZzc2zP13Rq56hVE/rQIVCpKTpigxSDzRvYoJIl/hit8onyY32w+bvZu+g6pQDjbOfPre79SpUQVPj/xOSuvraQwV4wWmZnEbtvOndZtCPYYRtTKn1GnpFC0YUOM7UqSGR1D4uEjPPxsDHdatSZi0SIywsMByMjMYNKpSRQxLMK8pvN0u8opIZS0XaPwVTsSUOMz5nV3zlWSSPH1JX73bkoMGkgRh8LXi5U9CkkCNvhvYF/gPkbVHUWbCm30HU6BsjO3Y02HNcz+ezbLri7jbtxd5jSZU+g2SUq+coWwuXNJ8w/A3N0dm+EfY+bqiqGFxXPHCZWKxJMnid22jeiVPxOzfgP2X37BCsdA/KP9+bHlj9gXfXklVq1Qq4ncNAyL9GQ2l/2BBX3qY2iQ8yQh1GrC587D0MYG25EjtRho3slEIb31zjw8w8LLC2lXoR0jXEboOxytMDE0YV7TeVQuUZmfLv9E8ONgFrVaRKmipfQdGgDRnp5EfL8Ao1KlKLvwByw7dXrlN3LFyAjL1q2xbN2a9OBgwmbNImz2HCpXUBgxtidtK+i2eN6dPxZSOeIMvxQbxYyh3ShilLueTLyXFynXrlH6229fSIqFhdy4SHqrPUh4QJ8/+lC6aGk2dtr4VtzVfOLBCSafmoypoSk/tfpJrzcRCrWaiO8XELN2LcXe7UTpuXMxMM/d38An4hrrvxvIwKNqTA1NKLtgAZatW2kp4uddv3qWyr+/z9Uidak+fj9W5rkr65KZmMjdTp0wLlMGx61bUQy0N1yWn/0o5ByF9NZKz0xnwl8TUFBY1GrRW5EkAFqWa8nmdzdjbmyOx58e/H77d73EITIyCJ0yhZi1aynRvz9lFizIdZKITI5k/InPud6kDOV278CkYkVCRo8mZtNmLUX9jxshkRjtHkGSgTmVPlqX6yQBT6rDRkZRavp0rSaJ/Cq8kUmSli24tICAmAC+bvL1a7kMNj8qFa/E1s5bcbN3Y8bfM5h3bh4Zmbq7OU+o1TyaNIn4PV7YjR2D/fRpuf6gTExP5LNjn/E44zGLWi3Cxqk6FTasx6JlS8LnziX8228RmZlaif9eVBIXPT+nGvdRv7cEW/vc/+8nLTCQmA0bsPqgB2bOzlqIsuDIRCG9lQ7fP8zWG1sZUGMArcu31nc4emFlYsWKtisYVHMQv978FY8/PYhIjtDJtaOWLSdh/wHsJnyO7ciRub65LFWVymfHPuNmzE2+b/79s6XMBubmOCxZTIlBA4lZv4GHX3yByCjYBPgwLoUff17FQOFFfK1B2Lp1zXUbQq0mdMYMDMzNKTl+fIHGpw0yUUhvnZDHIcw8M5PaNrX53O1zfYejV0YGRnxZ/0u+b/49N2Nv0ntvby6FaXf+L+HAAaKWLcOqWzdsPvoo1+dnqDOY8NcELodfZl7TebQo1+K51xVDQ0pNnUrJL7/g8YGDPJzwBSI9vUBij3ycxqhVh5mW/hOpxStj1XV+ntqJ27aNlEuXsZ80CSPbwr9bg0wU0ltFpVYx6dQkAL5v8T3Ghjos71CIdXTqyJZ3t2BZxJJhh4ax4toKMtUFP2yT4uvLo8lTMHN1pdSc2bnuSajUKqadmsbJkJNMbzSddyu+uh6SzbBh2E+ZzONDhwgZ/3m+k0V8cgaDPM8zKnExdoaJmPZZA0VyP6+VERpKxPcLKPpOY6x66GHv7jyQiUJ6q3j6euIT6cP0RtPfunmJ7FQuUZmtnbfSyakTy68uZ9ihYQW6N7cqNpaQUaMxsrHBYcliDIrkbvI3OSOZscfHciDoAOPdxtO7Wu9sz7EePBj76dNJPHqUkDFjUecxWSSmqRiy7gJuUV60Uy5i0HYmlK6T63aEEITNmo0QglJz5hSqek5ZkYlCemtcj7rOymsr6eTUKctvom8ziyIWfNfsO75p+g3+0f703NuTPwL/yPfd3EIIwmbMRBUbi8PSJRjZ2OTq/OiUaIb9OYzTD0/zVaOv8KjtkeNzrQf0p9TMGSSeOMHDPPQsUtIz8Vh3kcchAcwy2QQVW0GjUblq46mEfX+Q+NdflBw/rlDegf0qOksUiqKsURQlQlEUv389Z60oymFFUW4/+W8JXcUjvV1SValMOT0FazNrpjWcpu9wCr33K73Pb+//RgXLCkw+NZnPjn1GeFJ4ntuL372Hx4cPU3LsGExr5q5QX2B8IAMPDORO3B0WtVqUo57Ef5Xo2xf7rzQ9i4cTcj7BnZqRyfCNl7gWFM4Ou9UYFTGDbisgD0tZ0x88IGzOHMzq1KFE//65Pl+fdNmjWAd0/M9zk4GjQogqwNEnjyWpwP105Sfuxd9jbpO5WJlY6Tuc10KFYhXY0GkDX7p/yfnQ83Tb042tN7bmeo+L9JAQwufOxdzdHeuhOd8bQgjBthvb6LO3D4npiXh28KRluZa5fBf/sO7fXzNncfgwD7+ciFCpso5bpWb0liucuh3F3uqHKR4fAN2WQ7Hcl51Xp6URMnYsKAplfliAYmiY17ehFzor4SGEOKkoiuN/nu4KtHzy83rgBDBJVzFJb4cLoRfYHLCZ/jX607hMY+1dKDMDHl2F4LMQew/iQzT7JqfGg2Kg+RZqWASKlYUSjpp/pV2gXEMoUlR7ceWDoYEhg2oNolW5Vsw+N5tvzn/DloAtfO72OS3Ltcx2jF1kZvJo0mTNB+T873L8ARmVEsWMMzM49fAUTco04esmX2Nnnv+9o60HD0Zkqon43/94qFZTdsH3KC+ZK0lXqRm15QpHAiJY8040Va9sgoafQLVOebpu+DffkuYfgMPy5a/VkNNT+q71ZC+ECH3ycxjwykpeiqIMB4YDlC9f+HcbkwqH5IxkZvw9g/KW5RnrOlYLF4iB67sgYC88uAAZyZrnzazBykGTDMyKg1Br/qlSIe4B+O+BlBjNsQZGUMYVKraA2j2hZPWCjzOfyhUrx6p2q/gr5C9+uPQDY46PwbWkKx61PWjm0OyVlVpjNmwk5fJlysz/DuOy2W8FmpSRxCb/Tay/vp50dTqTG0ymX/V+BTrpa+MxFAwUIr6bT8iYdMou+gkDE5Nnrz/tSRz2D+f7Dna0vjAaSjlDuzl5ul783r3EbduGzUfDdFZapKDptNbTkx7FPiFE7SeP44QQxf/1eqwQItt5ClnrScqpb89/y9YbW1nbcW3BlQ4XAgKPw6W1cOsgZKaDbTWo1AoqvAPl3wGLHHz7TYmDh5ch6DTcPwMhl0Bkaj6UnHtD3f5QNHeTvrqQoc5gx60dePp6Ep4cjmMxR/rX6E8np07PDetlPHzI3ffep2jDhjisWJ7lh31cahx77u7B09eT2LRYWpVrxTjXcVQsXlFr7yN261bCZs+h6Dvv4LBsKQZmZmRkapLEn9fDmfN+dQbdHqv5G404CbZVcn2NlKtXuT/UA9NaNamwbt0L+2foUn5qPek7UdwEWgohQhVFKQ2cEEJku1uMTBRSTlwOv8yQg0PoV70fUxpOyX+DQsDtQ/DXfM2Hh7ktuPSGOh9CKRfI77fexAjw2wW+2zXtG5lBvf7Q6FOwqZT/+AtYhjqDw0GH2ei/Eb9oPwwVQ9zs3WhdvjX17etjMuUHUi5eotK+vRiXKfPcuUIIwpLCOBd6jj/v/8n5R+dRCRWNSjfis3qf4WLnopP3ELfrd0KnT8fM2Rm7RYsYd+gBh/3DmfV+TYakb9X8rbsug3oDct12iq8fwUOHYmhtTYVNGzEuWVIL7yDnXudE8T0QLYT4TlGUyYC1EGJidu3IRCFlJ0WVQk+vnmSKTHZ12ZX/gn/B5+HgJHjkDcXLQ7MJUKcfGOW+EFyORATA2aXgs10z91GrO7SeXigThhAC/2h/jgYf5VjwMe7G36VRgJrPd6vZ19mO4HfrYGRg9KxH8SjxEffi75Gs0gzTlbUoSwfHDnR07EgNmxo6jz/hz0OaulPG5kx1G8zgwe0ZVDIQNvaAOn2h+4pct5nq78/9IUMxtLSkwsYNLyRKfXgtEoWiKFvRTFzbAuHATGA3sB0oD9wHegshYrJrSyYKKTs/XPqBddfX4dnekwalG+S9oeQYODITrmzQTEK3nKz58NDVHd2Pw+D8Sjj/s2aIy20otJiUs6EtPQl6eJ3HPYeSbGXC1gl1eZDyCLVQI4RAjRp7c3sqFa9ERauK1LKpRU2bmnq98SwxTcX073fQfecibDNTKDdzIsXufgVF7eDjo7leaJDi68eDjz9GMTOjwsaNFHHIfm5GF16LRFGQZKKQsnI9+jr9/uhHjyo9mNl4Zt4b8t0B+7/UrFpq/Cm0mAwmetpY5nE4/PUdXF4PxmbQaho0GA6G+l6P8qLQ2bOJ27Ydx+3bMatdS9/hZCk2KZ2h6y7i+zCeRe3LU3vlXFKuXsOyQjr2P27EuOY7OW5LqFREr1pF5LLlGNnZUWH9OooUooU3cj8KSXpCpVYx++/Z2JjaMN4tj1U50x7D75/AzmGaoZ5PTkH7ufpLEgCW9vDejzDqPJRvDH9OgVUtNRPghUiKrx9xv26jxID+hT5JPIpLodfPZ/EPTWBZP1fea1mb8gOdsK2dQOIjMwIHfEb0unU5KvuRFniP+/0HELloMcXat6fi77sKVZLIL9mjkN4oa/3WsvDyQha21GxtmmsPr2gSRGwQNJ8Izb/M97f21IxM4pIziE/J4HGq5mY1AwMFQ0XBwtQIWwsTipka5Xz4RQgI8IIDkzRDUw2GQ9uZer8XQ6jVBH3Yl4xHj6h0YD+GlpZ6jScrt8MfM2jNBRJTVawa7E6jijbgtxN2eID7MNJdxhE2dy5JJ09hYGmJZetWWHboiLmbKxgaoRgoZCYk8PjQYRIOHCDF2xuDYsUoNXMGVp076/vtvVR+ehSFr98qSXn0IOEBy64uo3W51rQtn4d9k703w75xULQkDPlDs9Q1F9JVavy8bxF0+jwhj1XcVpvhl2rMfWGK+hX3GTxlbKhQ0tKUinZFqWRnQeWSFrg4WFGjdDGMDf9zrqJAza5QqTUc/Rou/KxZjdVtBVTQ4g2F2YjbuZNUHx/K/G9+oU4S5wOjGb7xMkWMDNg2ojE1yxSD8OuwZ7Tm5seO31HEqAjlfv6ZpL//JmHfHzw+epT4PV4vbc+kalXsxo3FqkcPva9s0hbZo5DeCEIIhh8ejl+UH7u77sa+6Cvv3XxRpgoOz4Bzy8CpBfRaB+bWOTo1MDKRs3v/wshrB6WCb1IyOfaFY9ItixPdqhPpnbphVlZT/kEtBGq14HGqiqjENKIS0wmNTyEwMom7kYkkp2tKfJsaG+BStjj1nUrQvIodrhVKvJg4gk7D7k8hLhgaj4I2M8DI5L9haFVmXBx3O3aiSOVKVNi4sdBWRd15OYTJu3woZ23OuiENKG9jDimx8EsryEiBEX+BZakXzhPp6SSdO0daYCCoBQg1ipERRZs0waRyZT28k9yTk9nSW2/v3b1MPT2V6Q2n06d6n5yfmBoPvw2Fu0ehwQjo8E22Q02h8Sn8dimEM2f8aHHyN1qFeJNkWpSYqi4UdXOjYqvGFDcrgioyAlV4OIknT5F44gQYGGDZti0lv/wiyzIOarXgYVwK10LiuHI/jivBsfg+jCdTLbAwMeKdSjZ0qFWKtjXssTJ/svoqLVGT7C55gr0z9PQEu2xvSSowobNmEffbDpx27cS0mu6um1NqteCHwzdZdvwuTSrbsLyfm+Z3l6mCrR9C4AlNL7J8Q32HqjUyUUhvtbjUOLrs7kK5YuXY2GnjK8tJvOBxOGz6ACIDoPMP4DbklYeq1YK/bkWy+XwwxwLC+ODWcQbePIyhgYLpgEE4jf4Eg6KvniNIDwkh7tdfid36KwD206Zh1b1bjr95J6Rm8PedaE7ejuT4jQhC41MxMlB4p7It77uUppNzaSxMjODmQdjzKaQnQ8dvNe9Jy9/uU/yuE9SrFyUGDqDU1KlavVZeJKRm8MX2axzyD6dvg/LM6Vrrn17ZgclwfgW89xO457xg4etIJgrprfbVma/Yd3cf297fRtUSVXN2UkwgbOwOiZHQZwNUfvmchipTzT6fUJafuMOt8ETszY2Yc8eL8heOYdmuHfZTJufqZqqMhw95NHkKyRcvYtmuLaXmzMGoRO6q6wshuBYSzwHfUPb7hfIgJgVTYwPa1yxFD9eyNCuVieGekZoyIzW7QpclYKqdirmFfQL7RlgCn2y8TEhsClPfrcHQJo7/JOeLq+GPCZq9JTp+o99AdUAmCumtdTHsIh5/ejCs9jDGuY3L2UmhPpqehFoF/XeAw4s1oDLVgl1XQlh87DYPYlKoam/B6AalcfGcT8rff2M7ahS2o0flaSxeZGYSs24dET8twrh0acp7rqZIuXK5bgc0SeNKcBy/e4ewzyeUuOQMyliZ0sutLEOVvRT/+1soXg56roWyrnm6RlZit28nbMZMyvxvPlZduhR4+/nxu3cIU3b5UszUmGX9Xanv+K95pztHYHNvzReEvlvB4PUq+50XMlFIb6X0zHQ+8PqADHUGv3f9HTMjs+xPenhZ05MwKQYDdoHd8z0QITRDTN8duMGNsMe4OFgxulVlWpU24cGwYaTdukXp2bMo3rNnvuNP9vbmwScjUYoYU3716nyP7aepMjkaEMGvFx9w6nYkAB+Vj2DC4/mYpEahtJ8LDUcU2FCUKjaWwI6dMKlShfIbNxSaCez4lAxm7PFjz9VHNHCyZmm/epS0NP3ngPDrsKYjWJWDYX+CSeHqBWmLTBTSW2nF1RUsv7aclW1X0qRsk+xPeHARNvUAsxIwZJ+mZtO/BEYmMtPrOqduR1He2pyJHavR2bk0Ij2dYI9hpPr44LBsKRbNmxfYe0i7fZvgjz5GnZxMuRXLMXfP0/+PXxASm8z2iw/49eID0h9HscR8Fc3Ul0it8h6mHywvkKGo0Bkzidu5E6ffd2FaNYdDflp29m40E7ZfJfxxGmPbVOHTlpUw+vcqsdgg8OygSZbDDmt6W28JmSikt05gfCA9vXrStkJb/tf8f9mfEHxeM9xU1FaTJKz+WXWUmpHJ8uN3WPlXICbGBoxrW5WBjSpQxMgAoVbzcMIEHh84SNkfF1KsU942rslKxqNHBA/7iIzQUMqvXlVgyQIgI1PN0YBwNp29T42g9Uwy+pXYIqUIa/8ztd2b5bkXkOLjQ1CfD7EePBj7yfrfaywhNYMFf95k47n7ONoU5cc+dalbrvjzByVGwpr2mvpdHgehpO4LEOqTTBTSW0UIgcefHtyMvYlXNy9szWyzPuHBRdjYDSzsNUmi2D+Tz3/fjWLKLl/uRyfTtW4ZpnWu8dwwRfj33xPjuYaSX36JzTAPbb0lVNHR3B84CFV4OOXXrcPMuXaBXyMwMpG/juzl3ZtTKS4e87PZMKyafUJ3NweKmea8yKHIyOBer95kRkdT8cB+DC30V9pECMFen1C+3udPdGIagxo7MrFjNcyL/GeJc2oCrOsMUbdhsBeUy0ehyNeUvDNbeqvsubuHS+GXmNl4ZvZJIvTak56EnWad/JP9jpPTVcw/cIP1Z+/jaGPO5o8a0qTy823Fbt9OjOcaSvTrh7WHdpdOGtnYUH6NJ/f7D+DBRx9RfuOGAh/OqWhnQcW+fUmJbU3MFg/GRK5k78GrtD44glYulfiwQXlcyxfPtpcRvXYdaTdu4LB0iV6TxLUHccw/eIO/70bj4mDFmsH1cXZ4yZBa2mPY3Asi/KHvr29lksgv2aOQXiuxqbF02d0FJysn1nVcl/U9ExE3YN27mg2APA48m5O4GBTDhO3XCI5JZmgTRyZ2qI5ZkedXvaQGBBDU50PMGzSg3M8rc7zXc36lBwdzv/8ABALHzZu1V1hOrYYzPyGOzSXauDQjUkdzOb08VUpa0Kd+ObrULfP8BPDT+IKCCOzSFYuWLXFYvEg7sWXjbmQiC/68yQG/MGyKFmFs2yr0b1gBQ4OXJLjUBNjcU1M8saenZl+Pt5QcepLeGtNOT2N/4H5+e/83KpfIonRC9F1Y+67m56H7waYSqkw1i4/dYemx2ziUMOd/PV00xeD+IzMxiaAPPkCdkoLT7t8xss5ZOY+CknbnDvf7D8CguBWOW7ZgZKPF7VDv/w07hiGSo/CuNp45Ec25GhKPgQJNq9jRo15Z2ta0x8LECCEEwYOHkBoQQMU/9um0rpEQgotBsXieDuSQfzjmxoZ81KwiHzevqLnR8GVS4zW9yUfe0HON5p6St5gcepLeCn8/+huvu1587Pxx1kkiIVQzJ5GZDkMPgE0lHsQkM/ZXb64Ex/GBqwOzu9Z66QeMEIKwWbNIf/CACuvX6TxJAJhUrozDyhUED/XgwScjqbBubZZ3fedLhXfgk9Moe0bh6j+f3VWuEvj+9+y8kcJu70eM23aVIkYGNK9iR98ob8pcuECpr+foLEnEJadzwC+MrReC8QmJp7i5MSNbVMKjqRO2FlnUs0qKgi29NUOPvdZBjfd1Eu+bSvYopNdCiiqFHnt6YGhgyM4uOzExfMWHRHKMZtIyLhgG74WyrhzwDWXiDh8A5navTde6r95xLG7HDkKnf4Xd2DHYjhypjbeSY4+PHSNk9GcUbdqEcsuWoRhrcVc9IeDCL3Boumb5cJelqCu343JwLPt9Q7l47joz93xLYPEy/Np3Cs2rlqRZFVtql7XC1Lhgh+XCE1I5cyeK/b5h/HUrgoxMQeWSFgx5x5EPXB1eGCZ8QfRdzXBTwiPNjYbV3y3Q+F5XcuhJeuMtvLyQtX5rWdNhDfVL1X/5QelJsKEbhF6F/jtIL9+Mbw8EsPZMEHXKFWdp33qUs3713tnpQUEEduuOuWs9yq1apbN5iaw8vfPZqnt3Sn8zT/s3tYX5wq4REHFdUyeq/TyEoSlBAwaScus2Jyf+yJ8xBlx7EIdaaMqj1yxjRb1yxaleypJKJS2oZGeBddGc7SWekJrB7fBEboU/xv9RAmcDo7kTkQiAfTETutQpQ9e6ZalVpljO3vuDi7C1jybx9dsmJ67/RQ49SW+0gOgANlzfQI8qPV6dJDIzYPsgeHgJeq3noXUDRv18lqsP4hjaxJEpnWpQxOjVE98iM5NHk6egmJhQ+tvvCkWSACjRuzeqiEiili7F0LoE9l9+qd0LlnKG4cfh2Fz4ewkEniAqri2pV69S9ocFfNK5GZ+gGRK6cC+GK8FxeAfHsu3iA1IyMp81Y2ZsiHXRIthYFMHKzBgDRXl2Q/jjVBUxSelEJ6aRaMk78QAAHMxJREFUkKp6do55EUPqO1rTy82BJpVtqVG62MsnqF/FZzt4jdGUCR+wU7M7oVQgZKKQCjWVWsXss7MpblKcz90+f/lBarVm05k7R+D9xZwybsyYxadQZQpW9Helk3PpbK8TvWYNKVevUub77zG2L1ybz9iO+pT/t3fn4VFV5wPHv2e2ZLKThayQsAjITqyA2FZErYgCxQVFQWxBqxaL2tatKrXWgopLVVbFpWwq/goF627dKoqisoU9CSE7mSyTfdbz+2MGypKEhIRkIu/neeZhJnPuzDsn3Hlz7z3nPZ6yMsqWvYwpOpqYGTNO7xuaguAXj0Lfy6hZeCu2f/2byHMSibzwf4siRYVY+MWABH4xwLd2w+HS6Jkl1ew/VE2Rvd6XDGqc2OtcaK3R+P7QDw82MSApguhQC/ERwfSND6dPfDgpXawYWpIYDnPVwXv3wXev+paJnbwcwuLapi8EIIlCBLjlO5eTUZrBkxc8SWRQI2UnPpoD217HO/pPLKo8n6fe+obeXcNYPPUcesadfJx//Z692J57nvBLLyXiisBbxlIpRfyDf8JTUcGhJ+djjOpC1FVXnvb3dUecTcHX0VjiakjosQ0WDIdLHoXB14Lh2KMzg0HRLTqEbtEhjO7bjom2NBPWTPedMjv/ThjzUKuXrhUnkh4VAeuA/cCRpU0vTb204UZfLYCNz+FMn8GsnAv5YNceJgxJYt5Vg06cndsA7XRScN99R9Y7DpTCdsdTBgNJ8+biqayk8KGHUBYLkeOvOG3v562vJ+/23+IpLydl1SoMsRo2zIZ1t/qWXr10bocuu4rH5Ts19tnjYAqGKW9A37EdF8+PnCSKM5zX6cSZlYW7xIa7pASP3Y45viuWHj2wpKVhsDajIuvpiEt7mbNxDhajhQdHPtjwF/j2t+D9B6judTmT9l5OVlkJD1/R/9g1B07CtmQpjl27SFnwQocMhW0JZbGQ8tzfyb3tdgruuQftchF1ZdtPINMeDwV//CN1W7aQ/MwzWAcO8D0x40PYvgY+fgReGesbcvrzeyBxcJvH0KS8zb5rEYcyoN8VMO7JY8qyiLYnieIM5K2ro/qLL6j64EOqP/0Ub3V1o22DBw4k6ppriLj8coxhp2ksfwPe2PMG3x/6nkfPf5S4kAbON2d+AmtvpSxuOBfvn4IyeVk5c0SDE+gaU79rF7YlS4icOIHwiy5qw+hPH0NICN0WLyJv1h0UPvAA2uWiy7WT2+z1tdYUz51H1YcfEX//fUSMPepIzmCAIdf6EsTG5+GrF2DXBugzFn72B+jWyECDtlK4zXcEsfttCE+C61ZBv8A7VfhjJMNjzyDemhrKVqyk9OWX8drtGKOiCLtoDGHnn48pIQFTbCzGyEhcRUU4s7NxZGZS9f4HOPbuRYWEEDl+PHF3zMIUe5L6Sq1UUF3ApH9NYljXYSy6eNGJRweFW9GvjMNmSuCisntJS0li8dRzSIpq/tGPdjrJnnwt7lIbvTZswBgVdfKNAojX4SD/d7Op/uwz4u68k5jf3NLq02Zaa2yLFmF77nlfVdj772t6g7oK+OZF+Hoh1JVB0jAYNhUGXg3WNupPrxdy/gublvgSRFAkjLwNzvstBEe0zXucIWQehWiSdjopX70a29IX8ZSWEjZ6NNHTbyTk3HNRpqYPKrXW1G/dSvmaNVSu34AKDqbr7+8mavJklKGZa1O3gFd7ufmDm9lh28HaiWtJCjvulEJZNt5lv6C8HsbVPMxP0wfz2KSBLZ70VfLCAmwvvEDKwgWEjxnThp+g/Wink4IH/kTl228TdtFFJM2be8pLkXqdTooenoN93Toixo8n6fF5zf/9Oqphyyr4/h9QvN13zeCsX/hWj+t90TEl3ZvF44biHbBzHWxbA5V5vvUzRv7Wt/BSWyWhM4wkCtGouowMCu9/AMfevYScN5Kus2djHTr0lF7LkZVN0SOPULtpE8FDBpM0dy5BPXu2abwrdq7g8W8f5y+j/sKks447/159COfSi6mrLGOycw43jL+EaSNTW/yXdP3u3WRffQ0Rl11G8pPNWMsigGmtKV++guInnsCcnETKc8+1eKU8t81G3qw7qNuyhdg7ZhF7++2ndnSitW+y4w8rYPc7UFXg+3lMb+jaH+L6QVxfCIn2FWo0W8Hrgepi382e67v+kP8dOKtBGX2JZvC10HccWBqfLClOThKFOIHX6cS2cCGlL76EKSaGhD//mfAxF7b6dbXWVK5fT/G8x9EOB0lPPE74xRe3QcSQVZHF5LcnMzJxJM+Pef7YL6v6SuyLL8VcnsntpjncdsN1jGjB9YjDvE4nByZfi9tmo+eG9Zi6dGmT2Dta7XffkX/nXbjLy4maNInY227FnNT0BV6v04l97TpsCxfisdtJmjeXiLFtNHJIayjZDfs/9hUeLNkN5dmgvY1vowwQPxC6jfDdel4AYYE1p6Uzk0QhjuE8cIC8u+7GsWsXkZMmEX/fvRgjW7/05dFchYXk/W429du3E3v7bcTOmtWqU1Eur4tp70wjvzqftRPXHrPOhNtRR96CK0ixf8/fIudw84zfkBh5aqOxDj39DKVLl5KycGGbJM5A4i4txbZwEeVvvokCoq65hrAxYwge0P9IQtRuN67CQqo//5zSF1/CXVRE8JDBJDz8MNYBA05zgA4oy/JVdXXVgqvelxzC432LSoXGgfE01rM6w0miEEfY//1vih56GGU2kzh37mn9MvQ6HBQ98hfs//wnYRdeSPLTT53ycNrD618/PfppLkm95MjPbZW1ZC6azIi6L1jT7UEmTL+bINOpldeo/f4HcqZOJfLKSST99a+n9BqdgSs/H9vixVT8cy14fGU1zCkpKKMRZ34+uH1lM6zp6cTefjuh548K2Pkjou1IohB4HQ6K586l4vU3sA4bRvLTT2FOPHnpitbSWlO+chXFjz2Gddgwui1ehDGiZaNRviv+jl+//2vG9RjH3J/NPfLzzdml5C+/mYnej9nS/48MnfzgKcfpra0la9IkcHvo8a91HboyW3vxVFZSv3Mn9Tt2ULcjA7TGkpqKJbU7QX36EDxwoCSIM4gUBTzDufLzfaeBMjKImTmDuNmzT29J6qMopYieegOm2Bjy/3gPOVOn0e2lF5u9XoHdYefez+8lJSyFB0f6EoHWmmVfZMGHDzLT+DElw37H0ImnniQADs2fj+tgLt1fe/WMSBIAxogIQkeOJHTkyI4ORXRybT++UbSr6i/+S/aVV+HMySFlwQt0/cMf2i1JHC1i7Fi6L1mMMy+PnOtvwJmbe9JttNY89OVDlNaX8sQFTxBqDqWsxsnM1zZT8f5cZhrfwXHOzcRN+EurYqv6+GPKV60mevp0QodL2WkhWkoSRSelPR5Knn+B3FtuwRQfT4+31nT47OLQUaNIfe1VvFVV5EydhiM7u8n2q3ev5pPcT7gr/S4GxAxgY6aNy/7+Ob0yX+MP5jXoIdcRdPkT0IrTI87cXAruu5/ggQOJu/uuU34dIc5kkig6IXdJCQd/PQPbggVEThhP2uursaSldXRYAFgHDaL7P15Du1zkTLsRx759DbbbYdvB/M3z+XnKz7m2zw08+f5ubnhpE1N5jweMy6H/RNSEBSdUKW0Jr9NJ/p2+5JD87DMYLM1bTEcIcSxJFJ1M9ZdfkvXLSdRt3UriY4+ROG8ehpDAmogU3Lcvqcv/gVKKnBunU79z5zHP2+pszP5kNnHWOG7sfS+/XLiRBZ9k8lTat9zhfNFX6O2qZa0uF33o8Seoz8ggad5cLCktnB0shDhCLmb71bvrya/OJ786n+LaYopriimpK6G8vpxKZyWVzkrq3fV4tReP9g05DDGFEGYOI9QcSlxIHElhSSSHJZMWkUafLn0IMbfdF7i3pobi+fOpWP06lt69SH31FYLOOqvNXr+tBfXqReqK5eT86lfkTL+JbksWE5Kejsvj4vef/p5KRyVXdH2MqUt3EGm18O6ofZz9/TO+GbhXv9Lq8fT2DRsoX7mS6Jtu6vBTckJ0dgExPFYpNRb4O2AEXtJaz2uq/akOj3V4HORV5ZFTmeO7VWSRU5HJweo8DjnKj40JiMZEpDYQrhXhXo1VKxQKAwpQ1BqN1BgM1BigFBc2by2He9OgDPSM7MnA2IGMSBzBqKRRRAefWhnrmq+/pvBPD+IqKCB6+nTiZv+uw8p/t5SroICDv56Bq6iIlOef5+/mz3h9z+tE1/yKnIN9GTcogfnJXxDy6RxfFdLJ//CtsNYKNV99xcFbfkPI0KF0f3lZh1zcFyLQdOp5FEopI7AXuATIA74Fpmitdza2TUOJQmtNtauaksp8iiuyyC/NoqAkmyJ7LqXVNsrq7ZRrB0qD0uAxQJjykOB101W5ScZNN5ebZLebaBeYPcHU6lDqCMKBGYc240VhRGNQXiy4CaOOSFVDJDUEKRdOoMhkItNi5htLFNusoey3eKk1uAFFamgfLug2msn9xpMamXrSvnEePEjJs89S+c67WFJTSZz7N0LS01vR2x3DXVrKwZk3U7dvL8+M13wRdwFd6q/iz+P7c6ntFdRnj0P/X8KVL4KpddcR6nfuJGfajZiTk0ldsbzFczqE+LHq7IniPODPWutL/Y/vB9Baz21sm7MirPr5c3oAYPBqDG4wesDsAYsLgtxgcTe2dcM8JhOukDBc4VG4YruiuyaiEhJQ3VMx9OiJJTkZs9mMUmDwj8Jxebw43F6cLg+OKhue8oMoey7miizCK/fStXYf8a5c9lmMfGG18pE1kj3+AwGrJ40+YRdwadrlnJeWQs/YsCPrBbtLS7EtWUL56tdRJhMxv7qJmJtv7jRHEcdzebzM27CcgS88Tp88zbZx13PFY/cQ/tkj8PUCGDoVJjwHhlObcX2YMy+PA9dNQZnNpL2+GnN8fBt9AiE6v84+4S4ZOHrQfR4w4vhGSqlbgFsAeocH4zUrtFJ4DAptNIDRCCYTBkswxqAwzMHhBIfGYQ6PxRgZQ1B4FCHWIEKCzQRZTOD2oF1OtNOFt6YGT0UFHrsdd6kNd0Ehro0ZeGtq/vf+QUEE9epF0Nn9CD67P8H9+xPcr+9RF5LjgLNP/HSuOs7O/Zbu+77guuz/UpP7HR+EWng71M3WugNsz1iBa+NQDNU/Y4LHzMV7vyRh61cor4eoq64idtYszPGdszCa16v5YGcxj330HuWRz/He5DQWf5fG0HdWUZX/AaF9tmEYdatvWc1Wlix37N9P7m9uRbtcpL72qiQJIdpQIBxRXA2M1VrP9D+eBozQWs9qbJv2KOGhtcZrt+PIzsaZlYVj334ce/dSv2sXnvLyw8Fj6dGD4P79Cerbh6BevQk6qzfm5OTGC+Q5qiH7M9j7Hjt3vMt/SzT1xUEMOKBJKoNqs4WPug3n/V6jiOnfl/N6xjCyZwznpHbBamndX9ztxePVvLujkBf+s589ZVmE91hMjDWSNRNWEuOF0jsvp+S/lVh7xpP80qqTVjk9meovvyT/zrtQQUF0W7QI66CBbfRJhPjxOONOPXVkrSetNe7iYl8NnZ27/P/uxF1UdKSNMpsxde165KYsFl/iMBjwVlfhKizCVViIp7TU195ioDzezb/7G3hvoJHk4C70sl5N1qF0tudV4fFqLEYDw7pHcX7vWEb1imFwShQWU2CNbq6sd7Huh3xe3XiArJIa0uLr8MQvQhlcrBi3gu5OB6yaDPZ8KpPuoGDBOhQQ9/u76TJlSourz2qtqXjjDYoe/StBvXrRbfGiVicdIX6sOnuiMOG7mH0RkI/vYvb1WuuMxrYJxKKAnspKHPszcWTux5WTg6v4EO7iYtw2G9rlAq8X7fViCA3BnJCIOTEBc0o3Qs79CdaBA1HKgzNjLW9vWcIrrmIOWMz0VsHMOGsK1qgpbMquYGOmjYyCSrQGq9nIuT2iOa9nDMN7RDMoObJDEofXq/nuYDlvbc5j/dYC6lweBqdEcuXwIJbn3IfD42DpJUvpX7gb1t/hW/1symroNhxnXh5FD8+hZuNGrOnpJDz8EMH9+jXrfeu27+DQ/PnUbtpE6AU/J/mpp9t1TW8hOptOnSgAlFLjgGfxDY99WWv9WFPtAzFRtCWPbS/vfzmXRbZNHDAZ6eNR3J46jjHn/wm7N4ivs8r4KtPGxsxS9h2qBiDYbGBYty6kp0YxrFsXhnaPIjasdcNMG1PrdLP5QDkf7Czi/YxiSqocWM1GJg5N4voR3QkJtTHzg5loNEvHLKTvt6/BN0sgZThc8ypEJh95La019nX/onjePLx2O9ahQ4maPJmIy8aecPHeW1dHfUYG5atWUfnOuxi7dCH2ttvocv2Uky7pKsSZrtMnipb6sSeKwzyOat7d+DcWH/g3OQYvA51u7oj/Keedfz8qxrcEqa3aweYDZWzKLuPbA2XsKvSdqgJIjAymX0I4fRMi6JsQRvfoEFK6hBAXFnRkhNXJVNW7yCqpIbOkml2FlXxzoJyMfDtur8ZqNjK6bxxjByYwpl9XwoPNbC3Zyh0f34HJYOKln9xPz48eg4IffOsdX/JIoxPp3OXl2Nf9i4o338SZnY2yWDAlJGDqGocpJhZXbi71e/aAx4OyWom+aToxM2acMZVghWgtSRQ/cm6Piw2bn2PRnlUUaifp9fXMCu3HuefdBT0vPKZoXp3Tw44CO1sOVpBRYGd3URWZJdW4PP/7PVtMBmJDLURYzURazYQGmTj8Ch6tqaxzUV7rorzWSUWt65jthqZEcW6PLpybFs2IHjHHXGBfu28tj379KF1DurIk6lxSNy4GSxiMfxb6T2zWZ9VaU7d5M1WffOo7dXfoEO6SEkyJCViHDME6eAgh6cMwRkW1rlOFOMNIojhDOD1O/m/7y7y4fRkl3npG1NUzyxvB0PSZMOQ6sDa8/rPL4yWntIbc8jryymrJK6+jtMaJvc6FvdZFret/k04UikirmagQM9GhFhIig+kdF0avrr4jErPxxOsgLq+LpzY/xcpdKxkR1Y/5hflEFWXAgCvhsicgLO609YkQonkkUZxh6t31vLlrFcu2LaHMXcuIunpuqarn3N7jUefc6FuYvp1WLsusyGTOxjlsLdnKNEMMd2f+gCk8EcY9CWePb5cYhBAnJ4niDFXrqmXN3jW8uu1FbE47Qx0ubqqoYHRQPMYh18OgqyGm12l5b5fHxbIdy1i6dQkhWvNAySHGuYzw0zthxG1gCayKtkKc6SRRnOEcHgdr963lle3LKKgtIkkbua7cxpVVNUTG9vOV7e43DhKGtHoGtMfr4cM9b7F460IyHWVcVl3DvdUeYoZNg5/eDSGnVvhQCHF6SaIQALi9bj7N/ZSVu1ayuXgzJgz8TAcxriSP0bW1BAdFQPfzIPV8SBwMsX0gPLHp01RaQ2UBdYVbeG/fWl62fcsBg5c0p4u7vRFceM6tMGgyBMnoIyECmSQKcYI9ZXtYn7med7PfpaSuBKvBTLohnOHVdoaX5tPH6cQCvlFJUakQHAFB4WAOAXc92lFJscPOpvpi/mOBjdZg6g0GztYmbo7/KWOGzsSYMLjdroUIIVpHEoVolMfrYXPxZj7M+ZBvi74ly54F+EY3xZtC6aYsxHo84HGB143b6ybPqMhRXmqV7/9GvCmMMV1/wsV9fsm53cegJDkI0el09uqx4jQyGoyMSBzBiERfQV5bnY3NRZvJtmeTW5VLblUuGfVlR778FYrksGTSI9NIi0hjUNwg+kf3l+QgxBlMEsUZJtYay9geYzs6DCFEJxJY5UeFEEIEHEkUQgghmiSJQgghRJMkUQghhGiSJAohhBBNkkQhhBCiSZIohBBCNEkShRBCiCZ1yhIeSqkqYE9Hx3ESsYCto4NoBomzbUmcbUvibDt9tdbhp7JhZ52ZvedUa5a0F6XU5kCPESTOtiZxti2Js+0opU65QJ6cehJCCNEkSRRCCCGa1FkTxdKODqAZOkOMIHG2NYmzbUmcbeeUY+yUF7OFEEK0n856RCGEEKKdSKIQQgjRpIBNFEqpsUqpPUqp/Uqp+xp4Pkgp9Yb/+U1KqbT2j7JZcd6klCpRSm3x32Z2UJwvK6UOKaV2NPK8Uko95/8c25RS6QEY42illP2ovny4vWP0x9FNKfWJUmqnUipDKTW7gTaB0J/NibPD+1QpFayU+kYptdUf5yMNtOnQ/b2ZMQbEvu6PxaiU+kEp9XYDz7W8L7XWAXcDjEAm0BOwAFuB/se1uR1Y7L9/HfBGgMZ5E/BCAPTpz4F0YEcjz48D3gUUMBLYFIAxjgbeDoC+TATS/ffDgb0N/N4DoT+bE2eH96m/j8L8983AJmDkcW06dH9vZowBsa/7Y7kbWNXQ7/ZU+jJQjyiGA/u11llaayfwOjDxuDYTgdf8998CLlLtv7Bzc+IMCFrrz4GyJppMBP6hfb4GopRSie0TnU8zYgwIWutCrfX3/vtVwC4g+bhmgdCfzYmzw/n7qNr/0Oy/HT/KpkP392bGGBCUUinA5cBLjTRpcV8GaqJIBnKPepzHif/Bj7TRWrsBOxDTLtE1EINfQ3ECXOU//fCWUqpb+4TWYs39LB3tPP/h/7tKqQEdHYz/sH0Yvr8wjxZQ/dlEnBAAfeo/VbIFOAR8qLVutD87an9vRowQGPv6s8A9gLeR51vcl4GaKH5MNgBpWuvBwIf8L5OLlvseSNVaDwGeB9Z1ZDBKqTDg/4A7tdaVHRlLU04SZ0D0qdbao7UeCqQAw5VSAzsijqY0I8YO39eVUlcAh7TW37Xl6wZqosgHjs7GKf6fNdhGKWUCIoHSdomugRj8TohTa12qtXb4H74EnNNOsbVUc/q8Q2mtKw8f/mut3wHMSqnYjohFKWXG9+W7Umv9zwaaBER/nizOQOpTfwwVwCfA2OOeCoT9HWg8xgDZ188HJiilDuA7FT5GKbXiuDYt7stATRTfAmcppXoopSz4LrisP67NemC6//7VwH+0/+pMOzppnMedl56A7zxxIFoP3OgfrTMSsGutCzs6qKMppRIOn0tVSg3H9/+33b8s/DEsA3ZprZ9upFmH92dz4gyEPlVKxSmlovz3rcAlwO7jmnXo/t6cGANhX9da36+1TtFap+H7PvqP1nrqcc1a3JcBWT1Wa+1WSs0C3sc3suhlrXWGUuovwGat9Xp8O8BypdR+fBdArwvQOH+nlJoAuP1x3tTecQIopVbjG+ESq5TKA+bguyCH1nox8A6+kTr7gVrgVwEY49XAbUopN1AHXNcBfxyA76+2acB2/zlrgAeA7kfF2uH92cw4A6FPE4HXlFJGfInqTa312wG2vzcnxoDY1xvS2r6UEh5CCCGaFKinnoQQQgQISRRCCCGaJIlCCCFEkyRRCCGEaJIkCiGEEE2SRCGEEKJJkiiEaCWlVJpSqu6ouQqNtbP6y087O3L2sxAtJYlCiFPgn3h1tEx/HaBGaa3r/G0KTl9kQrQ9SRRCNJNSao1SaolS6mvg/pO0HaKU+lz5Fg3yKqW0f3asEJ1OQJbwECJADcJXumFkU42UUsHAG8CNWutvlFKPAsH4SpII0enIEYUQzeD/8o8GmnNUcDHwvdb6G//jbUB0B9WlEqLVJFEI0TwD8C1n6m5G24HA9qMep+Nb90GITklOPQnRPIPwHRk0RykwBkAp1Qe4Ehh1muIS4rSTRCFE8wwCvjlpK5/V+BaP2QHYgCla6w5ZZEeItiCJQohm0Fr/vgVtq4HxpzEcIdqVXKMQovU8QGRzJ9zhW4ypsYXvhQg4snCREEKIJskRhRBCiCZJohBCCNEkSRRCCCGaJIlCCCFEkyRRCCGEaJIkCiGEEE2SRCGEEKJJkiiEEEI06f8B30ZAFclkk5YAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "fig, ax = plt.subplots()\n",
     "ax.set_xlabel(r\"$r$ $[\\sigma]$\")\n",
     "ax.set_ylabel(r\"$E$ $[k_B T]$\")\n",
     "ax.set_xlim((0,4))\n",
     "\n",
     "x = np.linspace(0,4,100)\n",
-    "ax.plot(x, potential(x)[0], label=\"reference\")\n",
-    "ax.plot(x, potential(x, roughness=6)[0], label=\"rougher\")\n",
-    "ax.plot(x, potential(x, amplitude=2)[0], label=\"steeper\")\n",
-    "ax.plot(x, potential(x, periodicity=2)[0], label=\"more minima\")\n",
+    "ax.plot(x, energy(x), label=\"reference\")\n",
+    "ax.plot(x, energy(x, roughness=9), label=\"rougher\")\n",
+    "ax.plot(x, energy(x, amplitude=2), label=\"steeper\")\n",
+    "ax.plot(x, energy(x, periodicity=2), label=\"more minima\")\n",
     "\n",
-    "# Uncommet to inspect the forces\n",
-    "# ax.plot(x, potential(x)[1], label=\"analytic force\")\n",
-    "# ax.plot(x[:-1], -np.diff(potential(x)[0])/(x[1]-x[0]), label=\"numeric force\")\n",
+    "# Uncomment to inspect the forces\n",
+    "# ax.plot(x, forces(x), label=\"analytic force\")\n",
+    "# ax.plot(x[:-1], -np.diff(energy(x)) / (x[1] - x[0]), label=\"numeric force\")\n",
     "\n",
     "ax.legend(loc=\"best\")\n",
     "fig.show()"
@@ -417,57 +400,51 @@
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 422
+     "height": 472
     },
     "id": "LH8Pw8MT8naI",
-    "outputId": "3286b4ec-1a73-4a40-d07a-399ec53c537e"
+    "outputId": "1100c590-e06f-4bd1-9eb5-27e2b8dccf6e"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: RuntimeWarning: divide by zero encountered in log\n",
-      "  \n",
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: RuntimeWarning: divide by zero encountered in log\n",
-      "  \n",
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: RuntimeWarning: divide by zero encountered in log\n",
-      "  \n",
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: RuntimeWarning: divide by zero encountered in log\n",
-      "  \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "fig, ax = plt.subplots()\n",
     "ax.set_xlabel(r\"$r$ $[\\sigma]$\")\n",
     "ax.set_ylabel(r\"$A$ $[k_B T]$\")\n",
     "ax.set_xlim((0,4))\n",
     "\n",
-    "def correct_free_energy(x, energy):\n",
-    "  corrected_free_energy = energy - np.log(2*np.pi*x**2)\n",
-    "  corrected_free_energy -= corrected_free_energy[1]\n",
-    "  return corrected_free_energy\n",
+    "def free_energy(energy, kT: float = 1):\n",
+    "    \"\"\"\n",
+    "    Modifies function energy, such that it returns the free energy\n",
+    "    with the appropriate logarithmic correction.\n",
+    "    \"\"\"\n",
+    "    beta = 1 / kT\n",
+    "    tau = 2 * np.pi\n",
     "\n",
+    "    def log_corrected_energy(x):\n",
+    "        corrected_energy = beta * energy(x) - np.log(tau * x**2)\n",
+    "        corrected_energy -= corrected_energy[0]\n",
+    "        return corrected_energy\n",
     "\n",
-    "x = np.linspace(0,4,100)\n",
-    "ax.plot(x, correct_free_energy(x, potential(x)[0]), label=\"reference\")\n",
-    "ax.plot(x, correct_free_energy(x,potential(x, roughness=6)[0]), label=\"rougher\")\n",
-    "ax.plot(x, correct_free_energy(x, potential(x, amplitude=2)[0]), label=\"steeper\")\n",
-    "ax.plot(x, correct_free_energy(x, potential(x, periodicity=2)[0]), label=\"more minima\")\n",
+    "    return log_corrected_energy\n",
+    "\n",
+    "x = np.linspace(0.01, 4, 100)\n",
+    "ax.plot(x, free_energy(energy)(x), label=\"reference\")\n",
+    "ax.plot(x, free_energy(lambda x: energy(x, roughness=9))(x), label=\"rougher\")\n",
+    "ax.plot(x, free_energy(lambda x: energy(x, amplitude=2))(x), label=\"steeper\")\n",
+    "ax.plot(x, free_energy(lambda x: energy(x, periodicity=2))(x), label=\"more minima\")\n",
     "\n",
     "ax.legend(loc=\"best\")\n",
     "fig.show()"
@@ -494,52 +471,54 @@
    "outputs": [],
    "source": [
     "import hoomd\n",
-    "import hoomd.md\n",
+    "import gsd.hoomd\n",
     "\n",
-    "kBT=1\n",
+    "kT = 1\n",
+    "dt = 1e-3\n",
+    "fes_params = dict(amplitude=1, roughness=5, periodicity=1)\n",
     "\n",
-    "def generate_context(**kwargs):\n",
+    "def generate_context(kT=kT, dt=dt, fes_params=fes_params, **kwargs):\n",
     "    \"\"\"\n",
     "    Generates a simulation context, we pass this function to the attribute\n",
     "    `run` of our sampling method.\n",
     "    \"\"\"\n",
-    "    fes_coeffs = kwargs.get(\"fes_coeffs\", {\"amplitude\": 1., \"roughness\": 4, \"periodicity\": 1})\n",
-    "    hoomd.context.initialize(\"\")\n",
+    "    sim = hoomd.Simulation(device=hoomd.device.auto_select(), seed=42)\n",
     "\n",
-    "    ### System Definition\n",
-    "    snapshot = hoomd.data.make_snapshot(\n",
-    "        N = 2,\n",
-    "        box = hoomd.data.boxdim(Lx = 50, Ly = 50, Lz = 50),\n",
-    "        particle_types = ['P', 'G'],\n",
-    "        bond_types = [\"bond\"],\n",
-    "    )\n",
+    "    # System Definition\n",
+    "    snapshot = gsd.hoomd.Frame()\n",
     "\n",
-    "    snapshot.particles.typeid[0] = 0\n",
-    "    snapshot.particles.typeid[1] = 1\n",
+    "    snapshot.configuration.box = [50, 50, 50, 0, 0, 0]\n",
     "\n",
-    "    # Refernce particle at an extension and a ghost particle at origin\n",
-    "    positions = np.array([[3.0,  0,  0], [0, 0,  0]])\n",
+    "    snapshot.particles.N = 2\n",
+    "    snapshot.particles.types = ['P', 'G']\n",
+    "    snapshot.particles.typeid = [0, 1]\n",
+    "    snapshot.particles.position = [[3.0,  0,  0], [0, 0, 0]]\n",
+    "    snapshot.bonds.N = 1\n",
+    "    snapshot.bonds.types = [\"bond\"]\n",
+    "    snapshot.bonds.typeid = [0]\n",
+    "    snapshot.bonds.group = [[0, 1]]\n",
     "\n",
-    "    snapshot.particles.position[:] = positions[:]\n",
+    "    sim.create_state_from_snapshot(snapshot)\n",
+    "    sim.run(0)\n",
     "\n",
-    "    snapshot.bonds.resize(1)\n",
-    "    snapshot.bonds.typeid[0] = 0\n",
+    "    integrator = hoomd.md.Integrator(dt=dt)\n",
     "\n",
-    "    snapshot.bonds.group[:] = [[0, 1]]\n",
+    "    # Interaction Potential\n",
+    "    r_min, r_max = 0, 10\n",
+    "    n_points = 512\n",
+    "    fes_points = np.linspace(r_min, r_max, n_points)\n",
+    "    energy, forces = energy_and_forces(fes_points, **fes_params)\n",
+    "    fes = hoomd.md.bond.Table(n_points)\n",
+    "    fes.params[\"bond\"] = dict(r_min=r_min, r_max=r_max, U=energy, F=forces)\n",
+    "    integrator.forces.append(fes)\n",
     "\n",
-    "    hoomd.init.read_snapshot(snapshot)\n",
+    "    mobile_particles = hoomd.filter.Type(\"P\")\n",
+    "    langevin = hoomd.md.methods.Langevin(filter=mobile_particles, kT=kT)\n",
+    "    integrator.methods.append(langevin)\n",
     "\n",
-    "    # Connect custom bond to create energy landscape\n",
-    "    fes = hoomd.md.bond.table(width=500)\n",
-    "    fes.bond_coeff.set(\"bond\", func=potential, rmin=0, rmax=10, coeff=fes_coeffs)\n",
+    "    sim.operations.integrator = integrator\n",
     "\n",
-    "    dt=1e-3\n",
-    "    hoomd.md.integrate.mode_standard(dt = dt)\n",
-    "    # We do not integrate the ghost particle\n",
-    "    integrator = hoomd.md.integrate.nvt(group = hoomd.group.all(), kT = kBT, tau = 100*dt)\n",
-    "    integrator.randomize_velocities(seed = 42)\n",
-    "\n",
-    "    return hoomd.context.current"
+    "    return sim"
    ]
   },
   {
@@ -579,7 +558,7 @@
     "import pysages\n",
     "\n",
     "# Distance from our particle to origin (particle 1)\n",
-    "cvs = [Distance(([0], [1]))]"
+    "cvs = [Distance([0, 1])]"
    ]
   },
   {
@@ -593,7 +572,7 @@
     "\n",
     "Next, we are interested in an unbiased simulation.\n",
     "\n",
-    "PySAGES offers a special method for unbiased simulations, that can still record the collective variable.\n"
+    "PySAGES offers a special method for unbiased simulations, that can record a collective variable.\n"
    ]
   },
   {
@@ -605,6 +584,7 @@
    "outputs": [],
    "source": [
     "from pysages.methods import Unbiased\n",
+    "\n",
     "method = Unbiased(cvs)"
    ]
   },
@@ -627,6 +607,7 @@
    "outputs": [],
    "source": [
     "from pysages.methods.utils import HistogramLogger\n",
+    "\n",
     "hist = HistogramLogger(period=100)"
    ]
   },
@@ -645,27 +626,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "K951m4BbpUar",
-    "outputId": "2051295a-ad51-4b43-e4a7-5daf893b2c87"
+    "id": "K951m4BbpUar"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 82600 / 100000 | TPS 8259.83 | ETA 00:00:02\n",
-      "Time 00:00:12 | Step 100000 / 100000 | TPS 8558.28 | ETA 00:00:00\n",
-      "Average TPS: 8308\n",
-      "---------\n",
-      "** run complete **\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "result = pysages.run(method, generate_context, int(1e5), callback=hist)"
    ]
@@ -680,64 +643,63 @@
     "Let's see how the particle moved in this potential landscape.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "olRutIrvc9un"
+   },
+   "outputs": [],
+   "source": [
+    "def plot_cv_trajectory(result, x_range=(0, 4)):\n",
+    "    histogram_log = result.callbacks[0]\n",
+    "    cv_log = np.asarray(histogram_log.data)\n",
+    "    time = np.linspace(0, 0.1 * len(cv_log), len(cv_log))\n",
+    "\n",
+    "    x = np.linspace(x_range[0] + 0.01, x_range[1], 200)\n",
+    "    landscape = free_energy(energy)(x)\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "\n",
+    "    ax.set_xlabel(r\"$t$ $[\\tau]$\")\n",
+    "    ax.set_ylabel(r\"$\\xi$ $[\\sigma]$\")\n",
+    "    ax.set_ylim(x_range)\n",
+    "    ax.plot(time, cv_log, label=\"cv trajectory\")\n",
+    "    ax.legend(loc=\"center right\")\n",
+    "\n",
+    "    ax2 = ax.twiny()\n",
+    "    ax2.set_xlabel(r\"$A(\\xi)$ $[k_BT]$\")\n",
+    "    ax2.plot(landscape, x, label=\"energy landscape\", color=\"orange\")\n",
+    "    ax2.legend(loc=\"upper left\")\n",
+    "\n",
+    "    fig.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 383
+     "height": 514
     },
-    "id": "X69d1R7OpW4P",
-    "outputId": "63ba3b7b-b4fe-4e52-9e0c-1d07e446e30d"
+    "id": "XIadmcZhHPTJ",
+    "outputId": "5d925d2e-c8b3-41b5-aa8c-36fc4ac64f2f"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in log\n",
-      "  from ipykernel import kernelapp as app\n",
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log\n",
-      "  import sys\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "def plot_one_result(result):\n",
-    "  histogram_log = result.callbacks[0]\n",
-    "  cv_log = np.asarray(histogram_log.data)\n",
-    "  time = np.linspace(0, len(cv_log)*0.1, len(cv_log))\n",
-    "  fig, ax = plt.subplots()\n",
-    "  ax.set_xlabel(r\"$t$ $[\\tau]$\")\n",
-    "  ax.set_ylim((0, 4))\n",
-    "  ax.set_ylabel(r\"$\\xi$ $[\\sigma]$\")\n",
-    "\n",
-    "  ax.plot(time, cv_log, label=\"cv trajectory\")\n",
-    "\n",
-    "  ax2 = ax.twiny()\n",
-    "  ax2.set_xlabel(r\"$A(\\xi)$ $[k_BT]$\")\n",
-    "  x = np.linspace(0, 4, 200)\n",
-    "  corrected_free_energy = potential(x)[0]- np.log(2*np.pi*x**2)\n",
-    "  corrected_free_energy -= corrected_free_energy[1]\n",
-    "  ax2.plot(correct_free_energy(x, potential(x)[0]), x, label=\"energy landscape\", color=\"orange\")\n",
-    "\n",
-    "  ax.legend(loc=\"center right\")\n",
-    "  ax2.legend(loc=\"upper left\")\n",
-    "  fig.show()\n",
-    "plot_one_result(result)"
+    "plot_cv_trajectory(result)"
    ]
   },
   {
@@ -748,7 +710,7 @@
    "source": [
     "\n",
     "We see, that the system never leaves the local minimum around $\\xi=3$.\n",
-    "Since the phase space is not fully explored the prediction of the free energy is not complete. Here the system is not even equilibrated.\n",
+    "Since the phase space is not fully explored we would be unable to predict the free energy. Actually, the system is not even equilibrated.\n",
     "\n",
     "The sampling is not ergodic!\n",
     "This is common for normal MD (although not as easy to spot usually).\n"
@@ -783,7 +745,7 @@
     "\n",
     "$$\\Rightarrow H^w(\\{(r,p)\\} = k_BT \\ln(w(\\xi(\\{(r,p)\\})))$$\n",
     "\n",
-    "Here is where [PySAGES](https://github.com/SSAGESLabs/PySAGES) comes into play! PySAGES allows you to easily (python code) introduce a biasing Hamiltonian into a given MD backend (like [HOOMD-blue](http://glotzerlab.engin.umich.edu/hoomd-blue/), [OpenMM](https://openmm.org), or [ASE](https://wiki.fysik.dtu.dk/ase/)).\n",
+    "Here is where [PySAGES](https://github.com/SSAGESLabs/PySAGES) comes into play! PySAGES allows you to easily introduce a biasing Hamiltonian into a given MD backend (like [HOOMD-blue](http://glotzerlab.engin.umich.edu/hoomd-blue/), [OpenMM](https://openmm.org), or [ASE](https://wiki.fysik.dtu.dk/ase/)).\n",
     "So it is not necessary to modify the MD backend and via [JAX](https://jax.readthedocs.io/en/latest/index.html) we offer automatic differentiation, so forces are calculated automatically.\n",
     "\n",
     "## Harmonic Biasing\n",
@@ -792,45 +754,26 @@
     "\n",
     "$$H^b(r) = \\frac{k}{2} (c-r)^2$$\n",
     "\n",
-    "PySAGES offers a pre-implemented method class, that we are utilizing.\n",
+    "PySAGES offers a pre-defined class that implements this, which we will take advantage of.\n",
     "\n",
-    "In our example toy system, we choose $c=2\\sigma$ as a maximum of our external potential.\n",
-    "\n",
-    "We don't know a priori what a good spring constant is. Let's start with $k=1 \\frac{k_BT}{\\sigma^2}$.\n"
+    "In our example toy system, we choose $c=2\\sigma$ as a maximum of our external potential."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Wt4LVNYe0Q_4",
-    "outputId": "334a584b-bf7e-433c-b773-0e283707f6c8"
+    "id": "Wt4LVNYe0Q_4"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 27509 / 100000 | TPS 2750.84 | ETA 00:00:26\n",
-      "Time 00:00:20 | Step 56384 / 100000 | TPS 2887.46 | ETA 00:00:15\n",
-      "Time 00:00:30 | Step 87366 / 100000 | TPS 3098.18 | ETA 00:00:04\n",
-      "Time 00:00:34 | Step 100000 / 100000 | TPS 3068.28 | ETA 00:00:00\n",
-      "Average TPS: 2930.86\n",
-      "---------\n",
-      "** run complete **\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pysages.methods import HarmonicBias\n",
-    "method = HarmonicBias(cvs, kspring=1, center=2)\n",
-    "hist = HistogramLogger(period=100)\n",
-    "result = pysages.run(method, generate_context, int(1e5), callback=hist)"
+    "\n",
+    "def apply_harmonic_bias(kspring, center=2, cvs=cvs, timesteps=int(1e5), log_period=100):\n",
+    "    method = HarmonicBias(cvs, kspring=kspring, center=center)\n",
+    "    hist = HistogramLogger(period=log_period)\n",
+    "    result = pysages.run(method, generate_context, timesteps, callback=hist)\n",
+    "    return result"
    ]
   },
   {
@@ -839,8 +782,7 @@
     "id": "_PtTExcaOyFt"
    },
    "source": [
-    "\n",
-    "Ok, we analyze the trajectory as before to see how the energy landscape is explored now.\n"
+    "We don't know a priori what a good spring constant is. Let's start with $k = 10 \\frac{k_BT}{\\sigma^2}$, and let's analyze the trajectory as before to see how the energy landscape is explored."
    ]
   },
   {
@@ -849,37 +791,27 @@
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 383
+     "height": 514
     },
-    "id": "238HVay7O3TA",
-    "outputId": "1d2fe838-12ca-4596-c58a-4d226fa02dfd"
+    "id": "CkR5zrA0hWrN",
+    "outputId": "3b742357-8642-4328-b75f-d75307410a0c"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in log\n",
-      "  from ipykernel import kernelapp as app\n",
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log\n",
-      "  import sys\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "plot_one_result(result)"
+    "kspring = 10\n",
+    "result = apply_harmonic_bias(kspring)\n",
+    "plot_cv_trajectory(result)"
    ]
   },
   {
@@ -889,9 +821,9 @@
    },
    "source": [
     "\n",
-    "We observe that the free-energy barrier at $c=2\\sigma$ is already much better explored, but the biasing force is only strong enough to pull the particle across the barrier once.\n",
+    "We observe that the free-energy barrier around $c=2\\sigma$ is better explored now, but the biasing force is only strong enough to pull the particle across the barrier a couple of times.\n",
     "\n",
-    "Let's try $k=100\\frac{k_BT}{\\sigma^2}$.\n"
+    "Let's try $k = 100 \\frac{k_BT}{\\sigma^2} = 10^2 \\frac{k_BT}{\\sigma^2}$.\n"
    ]
   },
   {
@@ -900,55 +832,27 @@
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 540
+     "height": 514
     },
-    "id": "JvHWb-sSO6Qe",
-    "outputId": "cf24fa42-46c3-4112-c0a5-526d8b0fa499"
+    "id": "VZPrQoN0TlXe",
+    "outputId": "29d866c2-fd39-4113-dd9f-671ba6fe9b65"
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 28488 / 100000 | TPS 2848.79 | ETA 00:00:25\n",
-      "Time 00:00:20 | Step 59509 / 100000 | TPS 3102.1 | ETA 00:00:13\n",
-      "Time 00:00:30 | Step 90743 / 100000 | TPS 3123.32 | ETA 00:00:02\n",
-      "Time 00:00:32 | Step 100000 / 100000 | TPS 3153.14 | ETA 00:00:00\n",
-      "Average TPS: 3036.03\n",
-      "---------\n",
-      "** run complete **\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in log\n",
-      "  from ipykernel import kernelapp as app\n",
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log\n",
-      "  import sys\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "method = HarmonicBias(cvs, kspring=100, center=2)\n",
-    "hist = HistogramLogger(period=100)\n",
-    "result = pysages.run(method, generate_context, int(1e5), callback=hist)\n",
-    "plot_one_result(result)"
+    "kspring = 100\n",
+    "result = apply_harmonic_bias(kspring)\n",
+    "plot_cv_trajectory(result)"
    ]
   },
   {
@@ -958,9 +862,9 @@
    },
    "source": [
     "\n",
-    "Ok, now the system mostly oscillates around the maximum with two minima, but these two minima are not close to the actual minima of the free-energy landscape.\n",
+    "Ok, now the system mostly oscillates around the local maximum, but is no longer able to come close to the actual minima of the free-energy landscape.\n",
     "\n",
-    "The spring constant is so strong, that restricts the exploration of the phase space too much. Let's try the middle ground instead $k=10\\frac{k_BT}{\\sigma^2}$.\n"
+    "The spring constant is so strong, that restricts the exploration of the phase space too much. Let's try the middle ground instead $k = 30 \\frac{k_BT}{\\sigma^2} \\approx 10^{1.5} \\frac{k_BT}{\\sigma^2}$."
    ]
   },
   {
@@ -969,56 +873,27 @@
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 540
+     "height": 514
     },
-    "id": "OLwF9M6qTWv5",
-    "outputId": "4f894a37-5076-48ed-dcf9-dcb7f5c4b331"
+    "id": "oOtsXxhrTtxv",
+    "outputId": "94c7a792-ca9e-43e6-b2c8-647885d289f2"
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 26900 / 100000 | TPS 2689.57 | ETA 00:00:27\n",
-      "Time 00:00:20 | Step 57040 / 100000 | TPS 3013.98 | ETA 00:00:14\n",
-      "Time 00:00:30 | Step 87662 / 100000 | TPS 3062.19 | ETA 00:00:04\n",
-      "Time 00:00:33 | Step 100000 / 100000 | TPS 3131.84 | ETA 00:00:00\n",
-      "Average TPS: 2945.78\n",
-      "---------\n",
-      "** run complete **\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in log\n",
-      "  from ipykernel import kernelapp as app\n",
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log\n",
-      "  import sys\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "kspring=10\n",
-    "method = HarmonicBias(cvs, kspring=kspring, center=2)\n",
-    "hist = HistogramLogger(period=100)\n",
-    "result = pysages.run(method, generate_context, int(1e5), callback=hist)\n",
-    "plot_one_result(result)"
+    "kspring = 30\n",
+    "result = apply_harmonic_bias(kspring)\n",
+    "plot_cv_trajectory(result)"
    ]
   },
   {
@@ -1030,57 +905,67 @@
     "\n",
     "This looks much better!\n",
     "\n",
-    "We observe multiple transitions between the minima at $c=1\\sigma$ and $c=3\\sigma$ (rare events), so the phase space is better explored. We also see that the lower minimum is frequented more than the upper one as expected.\n",
+    "We observe multiple transitions between the minima at $c \\approx 1\\sigma$ and $c \\approx 3\\sigma$ (which initially where rare events), so the phase space is better explored. We also see that the lower minimum is frequented more than the upper one as expected.\n",
     "\n",
     "We now analyze the histograms of this trajectory to determine the free-energy landscape $A(\\xi)$ from the biased simulation.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "w4VYI8eBzwng"
+   },
+   "outputs": [],
+   "source": [
+    "from scipy import integrate\n",
+    "\n",
+    "def plot_cv_histogram(result, x_range=(0, 4), bins=30):\n",
+    "    histogram_log = result.callbacks[0]\n",
+    "    hist, edges = histogram_log.get_histograms(bins=bins, range=[x_range])\n",
+    "    x_hist = edges[0][:-1] + np.diff(edges[0]) / 2\n",
+    "\n",
+    "    weight = np.exp(-kT * kspring / 2 * (x_hist - 2)**2)\n",
+    "    unbiased_distribution = hist / weight\n",
+    "    unbiased_distribution /= integrate.simpson(unbiased_distribution, x=x_hist)\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "\n",
+    "    ax.set_xlabel(r\"$\\xi$ $[\\sigma]$\")\n",
+    "    ax.set_ylabel(r\"p(\\xi)\")\n",
+    "    ax.set_xlim(x_range)\n",
+    "    ax.plot(x_hist, hist, label=r\"biased $p(\\xi)$\")\n",
+    "    ax.plot(x_hist, unbiased_distribution, label=r\"unbiased $p_{eq}(\\xi)$\")\n",
+    "    ax.legend(loc=\"best\")\n",
+    "\n",
+    "    fig.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 283
+     "height": 473
     },
-    "id": "uT8IyjLqR4cE",
-    "outputId": "17253273-ae96-43ca-d10a-1d35ccb196c3"
+    "id": "ilgBwmj2JIFc",
+    "outputId": "e79eb32c-7522-4021-a049-773ba9de124b"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "from scipy import integrate\n",
-    "def plot_one_histogram(result):\n",
-    "  histogram_log = result.callbacks[0]\n",
-    "\n",
-    "  hist, edges = histogram_log.get_histograms(bins=30, range=[(0,4)])\n",
-    "  fig, ax = plt.subplots()\n",
-    "  ax.set_xlabel(r\"$\\xi$ $[\\sigma]$\")\n",
-    "  ax.set_ylabel(r\"p(\\xi)\")\n",
-    "  ax.set_xlim((0, 4))\n",
-    "\n",
-    "  x = edges[0][:-1] + np.diff(edges[0])/2\n",
-    "  ax.plot(x, hist, label=r\"biased $p(\\xi)$\")\n",
-    "  weight = np.exp(-kBT*kspring/2*(x-2)**2)\n",
-    "  unbiased_distribution = hist/weight\n",
-    "  unbiased_distribution /= integrate.simpson(unbiased_distribution, x)\n",
-    "  ax.plot(x, unbiased_distribution, label=r\"unbiased $p_{eq}(\\xi)$\")\n",
-    "\n",
-    "  ax.legend(loc=\"best\")\n",
-    "  fig.show()\n",
-    "plot_one_histogram(result)"
+    "plot_cv_histogram(result)"
    ]
   },
   {
@@ -1090,12 +975,47 @@
    },
    "source": [
     "\n",
-    "We can see, that the unbiased distribution puts the minima in the wrong place, but correcting it with the weight gives us the correct minima positions.\n",
-    "However, we can't be sure that this is the correct profile yet.\n",
+    "We can't be sure that this is the correct profile yet.\n",
     "\n",
     "So let's compare to the expected free-energy profile.\n",
     "\n",
-    "$$A(\\xi) = -k_BT \\ln(p_{eq}(\\xi) + C$$\n"
+    "$$A(\\xi) = -k_BT \\ln\\left( p_{eq}(\\xi) \\right) + C$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "r4l3B7-QLt4H"
+   },
+   "outputs": [],
+   "source": [
+    "def plot_free_energy(result, x_range=(0, 4), bins=30):\n",
+    "    x = np.linspace(x_range[0] + 0.01, x_range[1], 200)\n",
+    "    corrected_free_energy = free_energy(energy)(x)\n",
+    "\n",
+    "    histogram_log = result.callbacks[0]\n",
+    "    hist, edges = histogram_log.get_histograms(bins=bins, range=[x_range])\n",
+    "    x_hist = edges[0][:-1] + np.diff(edges[0]) / 2\n",
+    "\n",
+    "    weight = np.exp(-kT * kspring / 2 * (x_hist - 2)**2)\n",
+    "    unbiased_distribution = hist / weight\n",
+    "    unbiased_distribution /= integrate.simpson(unbiased_distribution, x=x_hist)\n",
+    "\n",
+    "    mask = unbiased_distribution != 0\n",
+    "    estimated_profile = -kT * np.log(unbiased_distribution[mask])\n",
+    "    constant_C = -np.min(estimated_profile) + np.min(corrected_free_energy)\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "    ax.set_xlabel(r\"$\\xi$ $[\\sigma]$\")\n",
+    "    ax.set_ylabel(r\"A(\\xi)\")\n",
+    "    ax.set_xlim(x_range)\n",
+    "\n",
+    "    ax.plot(x, corrected_free_energy, label=r\"true $A(\\xi)$\")\n",
+    "    ax.plot(x_hist[mask], estimated_profile + constant_C, label=r\"estimated $A(\\xi)$\")\n",
+    "\n",
+    "    ax.legend(loc=\"best\")\n",
+    "    fig.show()"
    ]
   },
   {
@@ -1104,84 +1024,58 @@
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 318
+     "height": 478
     },
-    "id": "22xGRaXk8jyG",
-    "outputId": "d70e5c6e-8c34-449f-92d9-30f864187672"
+    "id": "kzWTZ93vNDQI",
+    "outputId": "f5c5bef7-71c0-4d62-f87e-14a40e10288d"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: RuntimeWarning: divide by zero encountered in log\n",
-      "  app.launch_new_instance()\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "def plot_one_free_energy(result):\n",
-    "  histogram_log = result.callbacks[0]\n",
-    "\n",
-    "  hist, edges = histogram_log.get_histograms(bins=30, range=[(0,4)])\n",
-    "  fig, ax = plt.subplots()\n",
-    "  ax.set_xlabel(r\"$\\xi$ $[\\sigma]$\")\n",
-    "  ax.set_ylabel(r\"A(\\xi)\")\n",
-    "  ax.set_xlim((0, 4))\n",
-    "\n",
-    "  x = edges[0][:-1] + np.diff(edges[0])/2\n",
-    "\n",
-    "  weight = np.exp(-kBT*kspring/2*(x-2)**2)\n",
-    "  unbiased_distribution = hist/weight\n",
-    "  unbiased_distribution /= integrate.simpson(unbiased_distribution, x)\n",
-    "\n",
-    "  estimated_profile = -kBT * np.log(unbiased_distribution)\n",
-    "  constant_C = -np.min(estimated_profile) + np.min(potential(x)[0])\n",
-    "  ax.plot(x, estimated_profile + constant_C, label=r\"estimated $A(\\xi)$\")\n",
-    "  ax.plot(x, correct_free_energy(x, potential(x)[0]), label=r\"true $A(\\xi)$\")\n",
-    "\n",
-    "  ax.legend(loc=\"best\")\n",
-    "  fig.show()\n",
-    "plot_one_free_energy(result)"
+    "plot_free_energy(result)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "e2YtfQlQ8jO9"
+    "id": "Dr3si4QLVppr"
    },
    "source": [
-    "\n",
     "That estimation is not bad.\n",
     "We get the approximate right shape in the middle and that could be further improved by running the sampling trajectory longer. Or try a different spring constant. [Try it out!]\n",
     "\n",
     "But there are still some issues because we still cannot sample the entire space:\n",
     "\n",
-    "- the up trend on the right is uncovered\n",
-    "- the energy barrier is underestimated\n",
-    "- the first minimum is under-sampled\n",
-    "\n",
-    "Can we bias simulations in these regions too, to improve sampling coverage?\n",
+    "- the right and left barriers are uncovered\n",
+    "- the height and maximum of the sampled barrier are slightly off\n",
+    "- the highest local minimum is under-sampled\n",
     "\n",
+    "Can we bias simulations in these regions too, to improve sampling coverage?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "e2YtfQlQ8jO9"
+   },
+   "source": [
     "## Umbrella Sampling\n",
     "\n",
     "We want to find the free-energy profile along a given path in the space for collective variables. Usually, this path can be multidimensional.\n",
     "\n",
     "Example dihedral angles of Alanine Dipeptide. [PySAGES Alanine Dipentide examples](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/Harmonic_Bias.ipynb)\n",
     "\n",
-    "<img src=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4715807/bin/nihms-706794-f0001.jpg width=500>\n",
+    "<img src=https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1dd/4715807/e8dece12c024/nihms-706794-f0001.jpg width=500>\n",
     "\n",
     "Wu, Xiongwu, Bernard R. Brooks, and Eric Vanden‐Eijnden. Journal of computational chemistry 37.6 (2016): 595-601.\n",
     "\n",
@@ -1254,51 +1148,45 @@
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 322
+     "height": 479
     },
     "id": "34u5P_jKpcqZ",
-    "outputId": "d193536c-1ef4-4eaa-abcc-e4d0af9e9501"
+    "outputId": "165c2cce-f1ad-4006-92d1-c9acfb7f4ff6"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log\n",
-      "  import sys\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "centers = np.linspace(0, 4, 10)\n",
     "kspring = 100\n",
+    "\n",
+    "x = np.linspace(0.01, 4, 200)\n",
+    "landscape = free_energy(energy)(x)\n",
+    "\n",
     "fig, ax = plt.subplots()\n",
+    "\n",
     "ax.set_xlabel(r\"$\\xi$\")\n",
     "ax.set_ylabel(r\"$H_i(\\xi)$\")\n",
-    "ax.set_ylim((-10, 20))\n",
-    "ax.set_xlim((0,4))\n",
+    "ax.set_ylim((-12, 20))\n",
+    "ax.set_xlim((0, 4))\n",
     "\n",
-    "x = np.linspace(0, 4, 200)\n",
-    "ax.plot(x, correct_free_energy(x, potential(x)[0]), label=\"potential\")\n",
-    "for point in centers:\n",
-    "  label = None\n",
-    "  if point == 0:\n",
-    "    label = \"biasing potential\"\n",
-    "  ax.plot(x, kspring/2*(x-point)**2, label=label)\n",
+    "for x_c in centers:\n",
+    "    label = \"biasing potential\" if x_c == 4 else None\n",
+    "    ax.plot(x, kspring / 2 * (x - x_c)**2, label=label)\n",
+    "\n",
+    "ax.plot(x, landscape, label=\"potential\")\n",
     "ax.legend(loc=\"best\")\n",
-    "fig.show()\n"
+    "\n",
+    "fig.show()"
    ]
   },
   {
@@ -1318,143 +1206,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "CtaNDUQ0SrTZ",
-    "outputId": "3255e692-bfbd-4f97-cc21-8659aa2b5b37"
+    "id": "CtaNDUQ0SrTZ"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 28132 / 200000 | TPS 2813.16 | ETA 00:01:01\n",
-      "Time 00:00:20 | Step 58761 / 200000 | TPS 3062.85 | ETA 00:00:46\n",
-      "Time 00:00:30 | Step 89375 / 200000 | TPS 3061.39 | ETA 00:00:36\n",
-      "Time 00:00:40 | Step 120376 / 200000 | TPS 3100.04 | ETA 00:00:25\n",
-      "Time 00:00:50 | Step 151652 / 200000 | TPS 3127.55 | ETA 00:00:15\n",
-      "Time 00:01:00 | Step 182467 / 200000 | TPS 3081.44 | ETA 00:00:05\n",
-      "Time 00:01:05 | Step 200000 / 200000 | TPS 3070.13 | ETA 00:00:00\n",
-      "Average TPS: 3043.52\n",
-      "---------\n",
-      "** run complete **\n",
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 28234 / 200000 | TPS 2823.39 | ETA 00:01:00\n",
-      "Time 00:00:20 | Step 58115 / 200000 | TPS 2988.03 | ETA 00:00:47\n",
-      "Time 00:00:30 | Step 89123 / 200000 | TPS 3100.77 | ETA 00:00:35\n",
-      "Time 00:00:40 | Step 117749 / 200000 | TPS 2862.6 | ETA 00:00:28\n",
-      "Time 00:00:50 | Step 148613 / 200000 | TPS 3086.33 | ETA 00:00:16\n",
-      "Time 00:01:00 | Step 179100 / 200000 | TPS 3048.38 | ETA 00:00:06\n",
-      "Time 00:01:06 | Step 200000 / 200000 | TPS 3099.48 | ETA 00:00:00\n",
-      "Average TPS: 2996.25\n",
-      "---------\n",
-      "** run complete **\n",
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 28276 / 200000 | TPS 2827.58 | ETA 00:01:00\n",
-      "Time 00:00:20 | Step 59199 / 200000 | TPS 3092.28 | ETA 00:00:45\n",
-      "Time 00:00:30 | Step 89700 / 200000 | TPS 3049.92 | ETA 00:00:36\n",
-      "Time 00:00:40 | Step 120968 / 200000 | TPS 3126.75 | ETA 00:00:25\n",
-      "Time 00:00:50 | Step 151866 / 200000 | TPS 3089.76 | ETA 00:00:15\n",
-      "Time 00:01:00 | Step 183087 / 200000 | TPS 3122.07 | ETA 00:00:05\n",
-      "Time 00:01:05 | Step 200000 / 200000 | TPS 3093.81 | ETA 00:00:00\n",
-      "Average TPS: 3054.82\n",
-      "---------\n",
-      "** run complete **\n",
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 27682 / 200000 | TPS 2768.13 | ETA 00:01:02\n",
-      "Time 00:00:20 | Step 58414 / 200000 | TPS 3073.14 | ETA 00:00:46\n",
-      "Time 00:00:30 | Step 86989 / 200000 | TPS 2857.48 | ETA 00:00:39\n",
-      "Time 00:00:40 | Step 118357 / 200000 | TPS 3136.71 | ETA 00:00:26\n",
-      "Time 00:00:50 | Step 149871 / 200000 | TPS 3151.4 | ETA 00:00:15\n",
-      "Time 00:01:00 | Step 180902 / 200000 | TPS 3103.06 | ETA 00:00:06\n",
-      "Time 00:01:06 | Step 200000 / 200000 | TPS 3075.39 | ETA 00:00:00\n",
-      "Average TPS: 3020.56\n",
-      "---------\n",
-      "** run complete **\n",
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 28683 / 200000 | TPS 2868.29 | ETA 00:00:59\n",
-      "Time 00:00:20 | Step 59800 / 200000 | TPS 3111.65 | ETA 00:00:45\n",
-      "Time 00:00:30 | Step 91058 / 200000 | TPS 3125.76 | ETA 00:00:34\n",
-      "Time 00:00:40 | Step 122400 / 200000 | TPS 3133.72 | ETA 00:00:24\n",
-      "Time 00:00:50 | Step 152741 / 200000 | TPS 3033.94 | ETA 00:00:15\n",
-      "Time 00:01:00 | Step 169287 / 200000 | TPS 1654.59 | ETA 00:00:18\n",
-      "Time 00:01:10 | Step 196562 / 200000 | TPS 2727.42 | ETA 00:00:01\n",
-      "Time 00:01:11 | Step 200000 / 200000 | TPS 3006.38 | ETA 00:00:00\n",
-      "Average TPS: 2811.03\n",
-      "---------\n",
-      "** run complete **\n",
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 26110 / 200000 | TPS 2610.95 | ETA 00:01:06\n",
-      "Time 00:00:20 | Step 56828 / 200000 | TPS 3071.79 | ETA 00:00:46\n",
-      "Time 00:00:30 | Step 87518 / 200000 | TPS 3068.97 | ETA 00:00:36\n",
-      "Time 00:00:40 | Step 118567 / 200000 | TPS 3104.88 | ETA 00:00:26\n",
-      "Time 00:00:50 | Step 149062 / 200000 | TPS 3049.48 | ETA 00:00:16\n",
-      "Time 00:01:00 | Step 179769 / 200000 | TPS 3070.68 | ETA 00:00:06\n",
-      "Time 00:01:06 | Step 200000 / 200000 | TPS 3112.19 | ETA 00:00:00\n",
-      "Average TPS: 3007.36\n",
-      "---------\n",
-      "** run complete **\n",
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 28029 / 200000 | TPS 2802.88 | ETA 00:01:01\n",
-      "Time 00:00:20 | Step 59001 / 200000 | TPS 3097.09 | ETA 00:00:45\n",
-      "Time 00:00:30 | Step 89382 / 200000 | TPS 3038.06 | ETA 00:00:36\n",
-      "Time 00:00:40 | Step 118224 / 200000 | TPS 2884.12 | ETA 00:00:28\n",
-      "Time 00:00:50 | Step 148998 / 200000 | TPS 3077.32 | ETA 00:00:16\n",
-      "Time 00:01:00 | Step 179803 / 200000 | TPS 3080.5 | ETA 00:00:06\n",
-      "Time 00:01:07 | Step 200000 / 200000 | TPS 2787.24 | ETA 00:00:00\n",
-      "Average TPS: 2973.8\n",
-      "---------\n",
-      "** run complete **\n",
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 28563 / 200000 | TPS 2856.23 | ETA 00:01:00\n",
-      "Time 00:00:20 | Step 59327 / 200000 | TPS 3076.36 | ETA 00:00:45\n",
-      "Time 00:00:30 | Step 90056 / 200000 | TPS 3072.82 | ETA 00:00:35\n",
-      "Time 00:00:40 | Step 120952 / 200000 | TPS 3089.55 | ETA 00:00:25\n",
-      "Time 00:00:50 | Step 151620 / 200000 | TPS 3066.77 | ETA 00:00:15\n",
-      "Time 00:01:00 | Step 182728 / 200000 | TPS 3110.79 | ETA 00:00:05\n",
-      "Time 00:01:05 | Step 200000 / 200000 | TPS 3109.32 | ETA 00:00:00\n",
-      "Average TPS: 3050.76\n",
-      "---------\n",
-      "** run complete **\n",
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 28428 / 200000 | TPS 2842.76 | ETA 00:01:00\n",
-      "Time 00:00:20 | Step 58873 / 200000 | TPS 3044.43 | ETA 00:00:46\n",
-      "Time 00:00:30 | Step 89737 / 200000 | TPS 3086.32 | ETA 00:00:35\n",
-      "Time 00:00:40 | Step 120800 / 200000 | TPS 3106.21 | ETA 00:00:25\n",
-      "Time 00:00:50 | Step 151841 / 200000 | TPS 3104.03 | ETA 00:00:15\n",
-      "Time 00:01:00 | Step 180555 / 200000 | TPS 2871.39 | ETA 00:00:06\n",
-      "Time 00:01:06 | Step 200000 / 200000 | TPS 3108.42 | ETA 00:00:00\n",
-      "Average TPS: 3018.48\n",
-      "---------\n",
-      "** run complete **\n",
-      "notice(2): Group \"all\" created containing 2 particles\n",
-      "** starting run **\n",
-      "Time 00:00:10 | Step 28626 / 200000 | TPS 2862.53 | ETA 00:00:59\n",
-      "Time 00:00:20 | Step 59647 / 200000 | TPS 3102.06 | ETA 00:00:45\n",
-      "Time 00:00:30 | Step 90919 / 200000 | TPS 3127.12 | ETA 00:00:34\n",
-      "Time 00:00:40 | Step 122096 / 200000 | TPS 3117.64 | ETA 00:00:24\n",
-      "Time 00:00:50 | Step 152657 / 200000 | TPS 3056.04 | ETA 00:00:15\n",
-      "Time 00:01:00 | Step 183663 / 200000 | TPS 3100.56 | ETA 00:00:05\n",
-      "Time 00:01:05 | Step 200000 / 200000 | TPS 3053.82 | ETA 00:00:00\n",
-      "Average TPS: 3060.33\n",
-      "---------\n",
-      "** run complete **\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pysages.methods import UmbrellaIntegration\n",
+    "\n",
     "method = UmbrellaIntegration(cvs, kspring, centers, 100)\n",
     "result = pysages.run(method, generate_context, int(1e5))"
    ]
@@ -1471,42 +1228,59 @@
     "Let's see what the histograms look like.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xdUuQ9z5XCEH"
+   },
+   "outputs": [],
+   "source": [
+    "def plot_multi_histogram(result, x_range=(0, 4), bins=30):\n",
+    "    xs = []\n",
+    "    histograms = []\n",
+    "\n",
+    "    for histogram_log in result.callbacks:\n",
+    "        hist, edges = histogram_log.get_histograms(bins=bins, range=[x_range])\n",
+    "        xs.append(edges[0][:-1] + np.diff(edges[0]) / 2)\n",
+    "        histograms.append(hist)\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "\n",
+    "    ax.set_xlabel(r\"$\\xi$ $[\\sigma]$\")\n",
+    "    ax.set_ylabel(r\"p(\\xi)\")\n",
+    "    ax.set_xlim(x_range)\n",
+    "\n",
+    "    for x, hist in zip(xs, histograms):\n",
+    "        ax.plot(x, hist, label=r\"biased $p(\\xi)$\")\n",
+    "\n",
+    "    fig.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 283
+     "height": 473
     },
-    "id": "xdUuQ9z5XCEH",
-    "outputId": "453c6048-9992-4b30-e91d-e0c81c146b83"
+    "id": "eJswUAgwBgBN",
+    "outputId": "861641a1-bae5-4844-c3a7-a646c498a503"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "def plot_multi_histogram(result):\n",
-    "  fig, ax = plt.subplots()\n",
-    "  ax.set_xlabel(r\"$\\xi$ $[\\sigma]$\")\n",
-    "  ax.set_ylabel(r\"p(\\xi)\")\n",
-    "  ax.set_xlim((0, 4))\n",
-    "  for histogram_log in result.callbacks:\n",
-    "    hist, edges = histogram_log.get_histograms(bins=30, range=[(0,4)])\n",
-    "    x = edges[0][:-1] + np.diff(edges[0])/2\n",
-    "    ax.plot(x, hist, label=r\"biased $p(\\xi)$\")\n",
-    "  fig.show()\n",
     "plot_multi_histogram(result)"
    ]
   },
@@ -1562,55 +1336,59 @@
     "Let's see how PySAGES analyzes it for us and produces the free-energy result.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RpoHwWULX2Nj"
+   },
+   "outputs": [],
+   "source": [
+    "def plot_umbrella_free_energy(pre_result, x_range=(0, 4)):\n",
+    "    x = np.linspace(x_range[0] + 0.01, x_range[1], 50)\n",
+    "    landscape = free_energy(energy)(x)\n",
+    "\n",
+    "    result = pysages.analyze(pre_result)\n",
+    "    centers = np.asarray(result[\"centers\"])[:, 0]\n",
+    "    estimate = np.asarray(result[\"free_energy\"])\n",
+    "    estimate = estimate - np.min(estimate) + np.min(landscape)\n",
+    "\n",
+    "    fig, ax = plt.subplots()\n",
+    "\n",
+    "    ax.set_xlabel(r\"$\\xi$ $[\\sigma]$\")\n",
+    "    ax.set_ylabel(r\"A(\\xi)\")\n",
+    "    ax.set_xlim(x_range)\n",
+    "    ax.plot(x, landscape, label=r\"true $A(\\xi)$\")\n",
+    "    ax.plot(centers, estimate, label=r\"estimated $A(\\xi)$\")\n",
+    "    ax.legend(loc=\"best\")\n",
+    "\n",
+    "    fig.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 318
+     "height": 478
     },
-    "id": "RpoHwWULX2Nj",
-    "outputId": "4292617c-3b4a-4278-b484-b40eea8a0fea"
+    "id": "GcWEtXv1Cktp",
+    "outputId": "7793047d-55a2-47a2-d06d-765901f28a6a"
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log\n",
-      "  import sys\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "def plot_umbrella_free_energy(result):\n",
-    "  processed_result = pysages.analyze(result)\n",
-    "\n",
-    "  fig, ax = plt.subplots()\n",
-    "  ax.set_xlabel(r\"$\\xi$ $[\\sigma]$\")\n",
-    "  ax.set_ylabel(r\"A(\\xi)\")\n",
-    "  ax.set_xlim((0, 4))\n",
-    "\n",
-    "  centers = np.asarray(processed_result[\"centers\"])[:,0]\n",
-    "  free_energy = np.asarray(processed_result[\"free_energy\"])\n",
-    "  ax.plot(centers, free_energy, label=r\"estimated $A(\\xi)$\")\n",
-    "  x = np.linspace(0, 4, 50)\n",
-    "  ax.plot(x, correct_free_energy(x, potential(x)[0]), label=r\"true $A(\\xi)$\")\n",
-    "  ax.legend(loc=\"best\")\n",
-    "  fig.show()\n",
     "plot_umbrella_free_energy(result)"
    ]
   },
@@ -1620,17 +1398,15 @@
     "id": "nswuCmWdhvQb"
    },
    "source": [
-    "\n",
-    "This appears to be much better.\n",
-    "Even with the crude approximations, we were doing we can estimate the shape of the potential.\n",
+    "Even with the crude finite-differences approximations we are doing we can estimate the shape of the potential.\n",
     "\n",
     "Just the second minimum is underestimated, which could be fixed with more sampling and more sampling points in that vicinity. [Try it out!]\n",
     "\n",
     "Difficulties:\n",
     "\n",
     "1.  choose a good spring constant\n",
-    "    > - too large and the histograms don't overlap\n",
-    "    > - too small and you can sample barriers\n",
+    "    - if it is too large, the histograms won't overlap\n",
+    "    - if it too small, you won't be able to sample some barriers\n",
     "2.  choose a good number of replicas\n",
     "\n",
     "Can we do better than this?\n",
@@ -1638,6 +1414,7 @@
     "\n",
     "- [Meta-dynamics](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/hoomd-blue/Umbrella_Integration.ipynb): approximate one weight function with a sum of Gaussians\n",
     "- [ANN](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/hoomd-blue/Butane_ANN.ipynb): approximate biasing force with artificial neuronal networks (ANN)\n",
+    "- And many more sampling methods implemented in PySAGES\n",
     "\n",
     "## GPU computing\n",
     "\n",
@@ -1740,7 +1517,7 @@
     "For a given point $r_i$ we can define the probability that a simulation started from this point ends in minima $B$ first before it moves through $B$.\n",
     "$$\\text{commitor probability B: } p_B(r_i)$$\n",
     "\n",
-    "This is a probability since we can have multiple realizations of $r_i$ in momentum space (Maxwell-Boltzmann distribution). Each of these realizations has its path and we simulate them and measure if they arrive in $A$ or $B$ first.\n"
+    "This is a probability since we can have multiple realizations of $r_i$ in momentum space (Maxwell-Boltzmann distribution). Each of these realizations has its path and we simulate them and measure if they arrive in $A$ or $B$ first."
    ]
   },
   {
@@ -1749,7 +1526,6 @@
     "id": "GFT1lolLm13Y"
    },
    "source": [
-    "\n",
     "![commitor.png](data:image/png;base64,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)\n"
    ]
   },
@@ -1770,22 +1546,20 @@
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
-     "height": 281
+     "height": 469
     },
     "id": "9N5aSjdbmlZ0",
-    "outputId": "b69bdb1f-06bc-459e-c593-9dc8b60f5706"
+    "outputId": "0eced722-a3dc-465f-a9ce-b830881cf60a"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1798,9 +1572,9 @@
     "x = np.linspace(0, 1, 50)\n",
     "p_good = np.tan(2*(x-.5))\n",
     "p_good -= np.min(p_good)\n",
-    "p_good /= integrate.simpson(p_good, x)\n",
+    "p_good /= integrate.simpson(p_good, x=x)\n",
     "p_bad = x*0+1\n",
-    "p_bad /= integrate.simpson(p_bad, x)\n",
+    "p_bad /= integrate.simpson(p_bad, x=x)\n",
     "\n",
     "ax.plot(x, p_good, label=\"good path\")\n",
     "ax.plot(x, p_bad, label=\"bad path\")\n",
@@ -1843,8 +1617,8 @@
  ],
  "metadata": {
   "colab": {
-   "collapsed_sections": [],
-   "name": "Copy of Advanced sampling Introduction.ipynb",
+   "include_colab_link": true,
+   "name": "Advanced Sampling Introduction.ipynb",
    "provenance": []
   },
   "gpuClass": "standard",
diff --git a/examples/Advanced_Sampling_Introduction.md b/examples/Advanced_Sampling_Introduction.md
index 73ec65b0..a9ea1798 100644
--- a/examples/Advanced_Sampling_Introduction.md
+++ b/examples/Advanced_Sampling_Introduction.md
@@ -17,33 +17,34 @@ jupyter:
 
 # Introduction to Advanced Sampling
 
-Ludwig Schneider and Juan de Pablo
+Ludwig Schneider, Pablo Zubieta, and Juan de Pablo
 
 Pritzker School of Molecular Engineering
 
 The University of Chicago
 
-Berlin, July 28th, 2022
-
 <!-- #endregion -->
 
 <!-- #region id="T-Qkg9C9n7Cc" -->
 
 # Setting up the environment
 
-First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin.
-We copy it from Google Drive and install PySAGES for it.
+First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 
 <!-- #endregion -->
 
 ```bash id="3eTbKklCnyd_"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
+
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
-```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="34bb6ffa-98ad-42dd-acef-30d11fc66459"
+```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="0e3ce982-ab90-4d70-aad6-18768a9e047c"
 %env PYSAGES_ENV=/env/pysages
 ```
 
@@ -60,38 +61,27 @@ import sys
 ver = sys.version_info
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
 
-os.environ["XLA_FLAGS"] = "--xla_gpu_strict_conv_algorithm_picker=false"
 os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
-<!-- #region id="we_mTkFioS6R" -->
-
-## PySAGES
-
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
+<!-- #region id="Wy-75Pt7Bqs1" -->
+We'll also need some additional python dependencies
 <!-- #endregion -->
 
-```bash id="vK0RZtbroQWe"
-
-pip install -q --upgrade pip
-# Installs the wheel compatible with CUDA
-pip install -q --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null
+```python id="LpBucu3V81xm"
+!pip install -qq "numpy<2" gsd > /dev/null
 ```
 
-<!-- #region id="wAtjM-IroYX8" -->
+<!-- #region id="we_mTkFioS6R" -->
+
+## PySAGES
 
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
+The next step is to install PySAGES. First, we need to install JAX. Fortunately, Colab already ships with JAX pre-installed (to learn how to install it you can look at the [JAX documentation](https://jax.readthedocs.io) for more details). To install PySAGES, we retrieve the latest version from GitHub and add its dependecies via `pip`.
 
 <!-- #endregion -->
 
-```bash id="B-HB9CzioV5j"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
 <!-- #region id="KBFVcG1FoeMq" -->
@@ -100,7 +90,7 @@ pip install -q . &> /dev/null
 
 <!-- #endregion -->
 
-```bash colab={"base_uri": "https://localhost:8080/"} id="ppTzMmyyobHB" outputId="9ba2e260-1585-4bd7-8fee-4f0404dd1449"
+```bash id="ppTzMmyyobHB"
 
 mkdir /content/advanced_sampling
 cd /content/advanced_sampling
@@ -209,15 +199,22 @@ $$P(r) = Ar^2 + A(1-e^{-r^2})\cos(r p \pi)$$
 ```python id="lD3EKXNDRJiL"
 import numpy as np
 
-def potential(x, rmin=0, rmax=100, amplitude=1., roughness=4, periodicity=1):
-  energy = x**2
-  energy += (1-np.exp(-x**2))*roughness*np.cos(periodicity*x*np.pi)
-  energy *= amplitude
-  force = 2*x
-  force -= np.pi*periodicity*roughness*(1-np.exp(-x**2))*np.sin(periodicity*x*np.pi)
-  force += 2*roughness*np.exp(-x**2)*x*np.cos(periodicity*x*np.pi)
-  force *= -amplitude
-  return energy, force
+def energy_and_forces(x, amplitude=1., roughness=5, periodicity=1):
+    omega = np.pi * periodicity
+    energy = x**2
+    energy += (1 - np.exp(-x**2)) * roughness * np.cos(omega * x)
+    energy *= amplitude
+    forces = 2 * x
+    forces -= omega * roughness * (1 - np.exp(-x**2)) * np.sin(omega * x)
+    forces += 2 * roughness * np.exp(-x**2) * x * np.cos(omega * x)
+    forces *= -amplitude
+    return energy, forces
+
+def energy(x, **kwargs):
+    return energy_and_forces(x, **kwargs)[0]
+
+def forces(x, **kwargs):
+    return energy_and_forces(x, **kwargs)[1]
 ```
 
 <!-- #region id="Mx-KolPTSAJR" -->
@@ -226,22 +223,23 @@ def potential(x, rmin=0, rmax=100, amplitude=1., roughness=4, periodicity=1):
 - symmetric around the origin
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 283} id="7N11Y8GOSY1_" outputId="38faa096-7a15-42fc-b1c8-80795a0dade9"
+```python colab={"base_uri": "https://localhost:8080/", "height": 472} id="7N11Y8GOSY1_" outputId="91823ec0-17cc-469b-ddd6-d58185410328"
 import matplotlib.pyplot as plt
+
 fig, ax = plt.subplots()
 ax.set_xlabel(r"$r$ $[\sigma]$")
 ax.set_ylabel(r"$E$ $[k_B T]$")
 ax.set_xlim((0,4))
 
 x = np.linspace(0,4,100)
-ax.plot(x, potential(x)[0], label="reference")
-ax.plot(x, potential(x, roughness=6)[0], label="rougher")
-ax.plot(x, potential(x, amplitude=2)[0], label="steeper")
-ax.plot(x, potential(x, periodicity=2)[0], label="more minima")
+ax.plot(x, energy(x), label="reference")
+ax.plot(x, energy(x, roughness=9), label="rougher")
+ax.plot(x, energy(x, amplitude=2), label="steeper")
+ax.plot(x, energy(x, periodicity=2), label="more minima")
 
-# Uncommet to inspect the forces
-# ax.plot(x, potential(x)[1], label="analytic force")
-# ax.plot(x[:-1], -np.diff(potential(x)[0])/(x[1]-x[0]), label="numeric force")
+# Uncomment to inspect the forces
+# ax.plot(x, forces(x), label="analytic force")
+# ax.plot(x[:-1], -np.diff(energy(x)) / (x[1] - x[0]), label="numeric force")
 
 ax.legend(loc="best")
 fig.show()
@@ -262,24 +260,34 @@ Hence we can obtain the free energy with a logarithmic correction.
 
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 422} id="LH8Pw8MT8naI" outputId="3286b4ec-1a73-4a40-d07a-399ec53c537e"
+```python colab={"base_uri": "https://localhost:8080/", "height": 472} id="LH8Pw8MT8naI" outputId="1100c590-e06f-4bd1-9eb5-27e2b8dccf6e"
 import matplotlib.pyplot as plt
+
 fig, ax = plt.subplots()
 ax.set_xlabel(r"$r$ $[\sigma]$")
 ax.set_ylabel(r"$A$ $[k_B T]$")
 ax.set_xlim((0,4))
 
-def correct_free_energy(x, energy):
-  corrected_free_energy = energy - np.log(2*np.pi*x**2)
-  corrected_free_energy -= corrected_free_energy[1]
-  return corrected_free_energy
+def free_energy(energy, kT: float = 1):
+    """
+    Modifies function energy, such that it returns the free energy
+    with the appropriate logarithmic correction.
+    """
+    beta = 1 / kT
+    tau = 2 * np.pi
 
+    def log_corrected_energy(x):
+        corrected_energy = beta * energy(x) - np.log(tau * x**2)
+        corrected_energy -= corrected_energy[0]
+        return corrected_energy
 
-x = np.linspace(0,4,100)
-ax.plot(x, correct_free_energy(x, potential(x)[0]), label="reference")
-ax.plot(x, correct_free_energy(x,potential(x, roughness=6)[0]), label="rougher")
-ax.plot(x, correct_free_energy(x, potential(x, amplitude=2)[0]), label="steeper")
-ax.plot(x, correct_free_energy(x, potential(x, periodicity=2)[0]), label="more minima")
+    return log_corrected_energy
+
+x = np.linspace(0.01, 4, 100)
+ax.plot(x, free_energy(energy)(x), label="reference")
+ax.plot(x, free_energy(lambda x: energy(x, roughness=9))(x), label="rougher")
+ax.plot(x, free_energy(lambda x: energy(x, amplitude=2))(x), label="steeper")
+ax.plot(x, free_energy(lambda x: energy(x, periodicity=2))(x), label="more minima")
 
 ax.legend(loc="best")
 fig.show()
@@ -294,52 +302,54 @@ HOOMD-blue
 
 ```python id="BBvC7Spoog82"
 import hoomd
-import hoomd.md
+import gsd.hoomd
 
-kBT=1
+kT = 1
+dt = 1e-3
+fes_params = dict(amplitude=1, roughness=5, periodicity=1)
 
-def generate_context(**kwargs):
+def generate_context(kT=kT, dt=dt, fes_params=fes_params, **kwargs):
     """
     Generates a simulation context, we pass this function to the attribute
     `run` of our sampling method.
     """
-    fes_coeffs = kwargs.get("fes_coeffs", {"amplitude": 1., "roughness": 4, "periodicity": 1})
-    hoomd.context.initialize("")
+    sim = hoomd.Simulation(device=hoomd.device.auto_select(), seed=42)
 
-    ### System Definition
-    snapshot = hoomd.data.make_snapshot(
-        N = 2,
-        box = hoomd.data.boxdim(Lx = 50, Ly = 50, Lz = 50),
-        particle_types = ['P', 'G'],
-        bond_types = ["bond"],
-    )
+    # System Definition
+    snapshot = gsd.hoomd.Frame()
 
-    snapshot.particles.typeid[0] = 0
-    snapshot.particles.typeid[1] = 1
+    snapshot.configuration.box = [50, 50, 50, 0, 0, 0]
 
-    # Refernce particle at an extension and a ghost particle at origin
-    positions = np.array([[3.0,  0,  0], [0, 0,  0]])
+    snapshot.particles.N = 2
+    snapshot.particles.types = ['P', 'G']
+    snapshot.particles.typeid = [0, 1]
+    snapshot.particles.position = [[3.0,  0,  0], [0, 0, 0]]
+    snapshot.bonds.N = 1
+    snapshot.bonds.types = ["bond"]
+    snapshot.bonds.typeid = [0]
+    snapshot.bonds.group = [[0, 1]]
 
-    snapshot.particles.position[:] = positions[:]
+    sim.create_state_from_snapshot(snapshot)
+    sim.run(0)
 
-    snapshot.bonds.resize(1)
-    snapshot.bonds.typeid[0] = 0
+    integrator = hoomd.md.Integrator(dt=dt)
 
-    snapshot.bonds.group[:] = [[0, 1]]
+    # Interaction Potential
+    r_min, r_max = 0, 10
+    n_points = 512
+    fes_points = np.linspace(r_min, r_max, n_points)
+    energy, forces = energy_and_forces(fes_points, **fes_params)
+    fes = hoomd.md.bond.Table(n_points)
+    fes.params["bond"] = dict(r_min=r_min, r_max=r_max, U=energy, F=forces)
+    integrator.forces.append(fes)
 
-    hoomd.init.read_snapshot(snapshot)
+    mobile_particles = hoomd.filter.Type("P")
+    langevin = hoomd.md.methods.Langevin(filter=mobile_particles, kT=kT)
+    integrator.methods.append(langevin)
 
-    # Connect custom bond to create energy landscape
-    fes = hoomd.md.bond.table(width=500)
-    fes.bond_coeff.set("bond", func=potential, rmin=0, rmax=10, coeff=fes_coeffs)
+    sim.operations.integrator = integrator
 
-    dt=1e-3
-    hoomd.md.integrate.mode_standard(dt = dt)
-    # We do not integrate the ghost particle
-    integrator = hoomd.md.integrate.nvt(group = hoomd.group.all(), kT = kBT, tau = 100*dt)
-    integrator.randomize_velocities(seed = 42)
-
-    return hoomd.context.current
+    return sim
 ```
 
 <!-- #region id="j3XPDwd0jR1q" -->
@@ -364,7 +374,7 @@ from pysages.colvars import Distance
 import pysages
 
 # Distance from our particle to origin (particle 1)
-cvs = [Distance(([0], [1]))]
+cvs = [Distance([0, 1])]
 ```
 
 <!-- #region id="LknkRvo1o4av" -->
@@ -373,12 +383,13 @@ cvs = [Distance(([0], [1]))]
 
 Next, we are interested in an unbiased simulation.
 
-PySAGES offers a special method for unbiased simulations, that can still record the collective variable.
+PySAGES offers a special method for unbiased simulations, that can record a collective variable.
 
 <!-- #endregion -->
 
 ```python id="B1Z8FWz0o7u_"
 from pysages.methods import Unbiased
+
 method = Unbiased(cvs)
 ```
 
@@ -390,6 +401,7 @@ We also want to track the collective variable over time and as a histogram, so w
 
 ```python id="EKkWWb7PrSzI"
 from pysages.methods.utils import HistogramLogger
+
 hist = HistogramLogger(period=100)
 ```
 
@@ -400,7 +412,7 @@ To investigate the unbiased trajectory and statistics.
 
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/"} id="K951m4BbpUar" outputId="2051295a-ad51-4b43-e4a7-5daf893b2c87"
+```python id="K951m4BbpUar"
 result = pysages.run(method, generate_context, int(1e5), callback=hist)
 ```
 
@@ -410,35 +422,39 @@ Let's see how the particle moved in this potential landscape.
 
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 383} id="X69d1R7OpW4P" outputId="63ba3b7b-b4fe-4e52-9e0c-1d07e446e30d"
-def plot_one_result(result):
-  histogram_log = result.callbacks[0]
-  cv_log = np.asarray(histogram_log.data)
-  time = np.linspace(0, len(cv_log)*0.1, len(cv_log))
-  fig, ax = plt.subplots()
-  ax.set_xlabel(r"$t$ $[\tau]$")
-  ax.set_ylim((0, 4))
-  ax.set_ylabel(r"$\xi$ $[\sigma]$")
+```python id="olRutIrvc9un"
+def plot_cv_trajectory(result, x_range=(0, 4)):
+    histogram_log = result.callbacks[0]
+    cv_log = np.asarray(histogram_log.data)
+    time = np.linspace(0, 0.1 * len(cv_log), len(cv_log))
+
+    x = np.linspace(x_range[0] + 0.01, x_range[1], 200)
+    landscape = free_energy(energy)(x)
+
+    fig, ax = plt.subplots()
 
-  ax.plot(time, cv_log, label="cv trajectory")
+    ax.set_xlabel(r"$t$ $[\tau]$")
+    ax.set_ylabel(r"$\xi$ $[\sigma]$")
+    ax.set_ylim(x_range)
+    ax.plot(time, cv_log, label="cv trajectory")
+    ax.legend(loc="center right")
 
-  ax2 = ax.twiny()
-  ax2.set_xlabel(r"$A(\xi)$ $[k_BT]$")
-  x = np.linspace(0, 4, 200)
-  corrected_free_energy = potential(x)[0]- np.log(2*np.pi*x**2)
-  corrected_free_energy -= corrected_free_energy[1]
-  ax2.plot(correct_free_energy(x, potential(x)[0]), x, label="energy landscape", color="orange")
+    ax2 = ax.twiny()
+    ax2.set_xlabel(r"$A(\xi)$ $[k_BT]$")
+    ax2.plot(landscape, x, label="energy landscape", color="orange")
+    ax2.legend(loc="upper left")
 
-  ax.legend(loc="center right")
-  ax2.legend(loc="upper left")
-  fig.show()
-plot_one_result(result)
+    fig.show()
+```
+
+```python colab={"base_uri": "https://localhost:8080/", "height": 514} id="XIadmcZhHPTJ" outputId="5d925d2e-c8b3-41b5-aa8c-36fc4ac64f2f"
+plot_cv_trajectory(result)
 ```
 
 <!-- #region id="Kf_CMdih90Cd" -->
 
 We see, that the system never leaves the local minimum around $\xi=3$.
-Since the phase space is not fully explored the prediction of the free energy is not complete. Here the system is not even equilibrated.
+Since the phase space is not fully explored we would be unable to predict the free energy. Actually, the system is not even equilibrated.
 
 The sampling is not ergodic!
 This is common for normal MD (although not as easy to spot usually).
@@ -469,7 +485,7 @@ $$p(\{(r,p)\}) \propto e^{-\beta H^0(\{(p,r)\})} \frac{1}{w(\xi(\{(r,p)\}))} = e
 
 $$\Rightarrow H^w(\{(r,p)\} = k_BT \ln(w(\xi(\{(r,p)\})))$$
 
-Here is where [PySAGES](https://github.com/SSAGESLabs/PySAGES) comes into play! PySAGES allows you to easily (python code) introduce a biasing Hamiltonian into a given MD backend (like [HOOMD-blue](http://glotzerlab.engin.umich.edu/hoomd-blue/), [OpenMM](https://openmm.org), or [ASE](https://wiki.fysik.dtu.dk/ase/)).
+Here is where [PySAGES](https://github.com/SSAGESLabs/PySAGES) comes into play! PySAGES allows you to easily introduce a biasing Hamiltonian into a given MD backend (like [HOOMD-blue](http://glotzerlab.engin.umich.edu/hoomd-blue/), [OpenMM](https://openmm.org), or [ASE](https://wiki.fysik.dtu.dk/ase/)).
 So it is not necessary to modify the MD backend and via [JAX](https://jax.readthedocs.io/en/latest/index.html) we offer automatic differentiation, so forces are calculated automatically.
 
 ## Harmonic Biasing
@@ -478,152 +494,160 @@ We can start biasing by using a simple harmonic biasing, where we bias the syste
 
 $$H^b(r) = \frac{k}{2} (c-r)^2$$
 
-PySAGES offers a pre-implemented method class, that we are utilizing.
+PySAGES offers a pre-defined class that implements this, which we will take advantage of.
 
 In our example toy system, we choose $c=2\sigma$ as a maximum of our external potential.
-
-We don't know a priori what a good spring constant is. Let's start with $k=1 \frac{k_BT}{\sigma^2}$.
-
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/"} id="Wt4LVNYe0Q_4" outputId="334a584b-bf7e-433c-b773-0e283707f6c8"
+```python id="Wt4LVNYe0Q_4"
 from pysages.methods import HarmonicBias
-method = HarmonicBias(cvs, kspring=1, center=2)
-hist = HistogramLogger(period=100)
-result = pysages.run(method, generate_context, int(1e5), callback=hist)
+
+def apply_harmonic_bias(kspring, center=2, cvs=cvs, timesteps=int(1e5), log_period=100):
+    method = HarmonicBias(cvs, kspring=kspring, center=center)
+    hist = HistogramLogger(period=log_period)
+    result = pysages.run(method, generate_context, timesteps, callback=hist)
+    return result
 ```
 
 <!-- #region id="_PtTExcaOyFt" -->
-
-Ok, we analyze the trajectory as before to see how the energy landscape is explored now.
-
+We don't know a priori what a good spring constant is. Let's start with $k = 10 \frac{k_BT}{\sigma^2}$, and let's analyze the trajectory as before to see how the energy landscape is explored.
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 383} id="238HVay7O3TA" outputId="1d2fe838-12ca-4596-c58a-4d226fa02dfd"
-plot_one_result(result)
+```python colab={"base_uri": "https://localhost:8080/", "height": 514} id="CkR5zrA0hWrN" outputId="3b742357-8642-4328-b75f-d75307410a0c"
+kspring = 10
+result = apply_harmonic_bias(kspring)
+plot_cv_trajectory(result)
 ```
 
 <!-- #region id="Wy1I3QdCPOqm" -->
 
-We observe that the free-energy barrier at $c=2\sigma$ is already much better explored, but the biasing force is only strong enough to pull the particle across the barrier once.
+We observe that the free-energy barrier around $c=2\sigma$ is better explored now, but the biasing force is only strong enough to pull the particle across the barrier a couple of times.
 
-Let's try $k=100\frac{k_BT}{\sigma^2}$.
+Let's try $k = 100 \frac{k_BT}{\sigma^2} = 10^2 \frac{k_BT}{\sigma^2}$.
 
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 540} id="JvHWb-sSO6Qe" outputId="cf24fa42-46c3-4112-c0a5-526d8b0fa499"
-method = HarmonicBias(cvs, kspring=100, center=2)
-hist = HistogramLogger(period=100)
-result = pysages.run(method, generate_context, int(1e5), callback=hist)
-plot_one_result(result)
+```python colab={"base_uri": "https://localhost:8080/", "height": 514} id="VZPrQoN0TlXe" outputId="29d866c2-fd39-4113-dd9f-671ba6fe9b65"
+kspring = 100
+result = apply_harmonic_bias(kspring)
+plot_cv_trajectory(result)
 ```
 
 <!-- #region id="jUxAakh1SnS5" -->
 
-Ok, now the system mostly oscillates around the maximum with two minima, but these two minima are not close to the actual minima of the free-energy landscape.
-
-The spring constant is so strong, that restricts the exploration of the phase space too much. Let's try the middle ground instead $k=10\frac{k_BT}{\sigma^2}$.
+Ok, now the system mostly oscillates around the local maximum, but is no longer able to come close to the actual minima of the free-energy landscape.
 
+The spring constant is so strong, that restricts the exploration of the phase space too much. Let's try the middle ground instead $k = 30 \frac{k_BT}{\sigma^2} \approx 10^{1.5} \frac{k_BT}{\sigma^2}$.
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 540} id="OLwF9M6qTWv5" outputId="4f894a37-5076-48ed-dcf9-dcb7f5c4b331"
-kspring=10
-method = HarmonicBias(cvs, kspring=kspring, center=2)
-hist = HistogramLogger(period=100)
-result = pysages.run(method, generate_context, int(1e5), callback=hist)
-plot_one_result(result)
+```python colab={"base_uri": "https://localhost:8080/", "height": 514} id="oOtsXxhrTtxv" outputId="94c7a792-ca9e-43e6-b2c8-647885d289f2"
+kspring = 30
+result = apply_harmonic_bias(kspring)
+plot_cv_trajectory(result)
 ```
 
 <!-- #region id="XuMle2u3R1MS" -->
 
 This looks much better!
 
-We observe multiple transitions between the minima at $c=1\sigma$ and $c=3\sigma$ (rare events), so the phase space is better explored. We also see that the lower minimum is frequented more than the upper one as expected.
+We observe multiple transitions between the minima at $c \approx 1\sigma$ and $c \approx 3\sigma$ (which initially where rare events), so the phase space is better explored. We also see that the lower minimum is frequented more than the upper one as expected.
 
 We now analyze the histograms of this trajectory to determine the free-energy landscape $A(\xi)$ from the biased simulation.
 
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 283} id="uT8IyjLqR4cE" outputId="17253273-ae96-43ca-d10a-1d35ccb196c3"
+```python id="w4VYI8eBzwng"
 from scipy import integrate
-def plot_one_histogram(result):
-  histogram_log = result.callbacks[0]
 
-  hist, edges = histogram_log.get_histograms(bins=30, range=[(0,4)])
-  fig, ax = plt.subplots()
-  ax.set_xlabel(r"$\xi$ $[\sigma]$")
-  ax.set_ylabel(r"p(\xi)")
-  ax.set_xlim((0, 4))
+def plot_cv_histogram(result, x_range=(0, 4), bins=30):
+    histogram_log = result.callbacks[0]
+    hist, edges = histogram_log.get_histograms(bins=bins, range=[x_range])
+    x_hist = edges[0][:-1] + np.diff(edges[0]) / 2
+
+    weight = np.exp(-kT * kspring / 2 * (x_hist - 2)**2)
+    unbiased_distribution = hist / weight
+    unbiased_distribution /= integrate.simpson(unbiased_distribution, x=x_hist)
+
+    fig, ax = plt.subplots()
 
-  x = edges[0][:-1] + np.diff(edges[0])/2
-  ax.plot(x, hist, label=r"biased $p(\xi)$")
-  weight = np.exp(-kBT*kspring/2*(x-2)**2)
-  unbiased_distribution = hist/weight
-  unbiased_distribution /= integrate.simpson(unbiased_distribution, x)
-  ax.plot(x, unbiased_distribution, label=r"unbiased $p_{eq}(\xi)$")
+    ax.set_xlabel(r"$\xi$ $[\sigma]$")
+    ax.set_ylabel(r"p(\xi)")
+    ax.set_xlim(x_range)
+    ax.plot(x_hist, hist, label=r"biased $p(\xi)$")
+    ax.plot(x_hist, unbiased_distribution, label=r"unbiased $p_{eq}(\xi)$")
+    ax.legend(loc="best")
 
-  ax.legend(loc="best")
-  fig.show()
-plot_one_histogram(result)
+    fig.show()
+```
+
+```python colab={"base_uri": "https://localhost:8080/", "height": 473} id="ilgBwmj2JIFc" outputId="e79eb32c-7522-4021-a049-773ba9de124b"
+plot_cv_histogram(result)
 ```
 
 <!-- #region id="-ki0CfQv8EGU" -->
 
-We can see, that the unbiased distribution puts the minima in the wrong place, but correcting it with the weight gives us the correct minima positions.
-However, we can't be sure that this is the correct profile yet.
+We can't be sure that this is the correct profile yet.
 
 So let's compare to the expected free-energy profile.
 
-$$A(\xi) = -k_BT \ln(p_{eq}(\xi) + C$$
+$$A(\xi) = -k_BT \ln\left( p_{eq}(\xi) \right) + C$$
 
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 318} id="22xGRaXk8jyG" outputId="d70e5c6e-8c34-449f-92d9-30f864187672"
-def plot_one_free_energy(result):
-  histogram_log = result.callbacks[0]
+```python id="r4l3B7-QLt4H"
+def plot_free_energy(result, x_range=(0, 4), bins=30):
+    x = np.linspace(x_range[0] + 0.01, x_range[1], 200)
+    corrected_free_energy = free_energy(energy)(x)
 
-  hist, edges = histogram_log.get_histograms(bins=30, range=[(0,4)])
-  fig, ax = plt.subplots()
-  ax.set_xlabel(r"$\xi$ $[\sigma]$")
-  ax.set_ylabel(r"A(\xi)")
-  ax.set_xlim((0, 4))
+    histogram_log = result.callbacks[0]
+    hist, edges = histogram_log.get_histograms(bins=bins, range=[x_range])
+    x_hist = edges[0][:-1] + np.diff(edges[0]) / 2
 
-  x = edges[0][:-1] + np.diff(edges[0])/2
+    weight = np.exp(-kT * kspring / 2 * (x_hist - 2)**2)
+    unbiased_distribution = hist / weight
+    unbiased_distribution /= integrate.simpson(unbiased_distribution, x=x_hist)
 
-  weight = np.exp(-kBT*kspring/2*(x-2)**2)
-  unbiased_distribution = hist/weight
-  unbiased_distribution /= integrate.simpson(unbiased_distribution, x)
+    mask = unbiased_distribution != 0
+    estimated_profile = -kT * np.log(unbiased_distribution[mask])
+    constant_C = -np.min(estimated_profile) + np.min(corrected_free_energy)
 
-  estimated_profile = -kBT * np.log(unbiased_distribution)
-  constant_C = -np.min(estimated_profile) + np.min(potential(x)[0])
-  ax.plot(x, estimated_profile + constant_C, label=r"estimated $A(\xi)$")
-  ax.plot(x, correct_free_energy(x, potential(x)[0]), label=r"true $A(\xi)$")
+    fig, ax = plt.subplots()
+    ax.set_xlabel(r"$\xi$ $[\sigma]$")
+    ax.set_ylabel(r"A(\xi)")
+    ax.set_xlim(x_range)
 
-  ax.legend(loc="best")
-  fig.show()
-plot_one_free_energy(result)
+    ax.plot(x, corrected_free_energy, label=r"true $A(\xi)$")
+    ax.plot(x_hist[mask], estimated_profile + constant_C, label=r"estimated $A(\xi)$")
+
+    ax.legend(loc="best")
+    fig.show()
 ```
 
-<!-- #region id="e2YtfQlQ8jO9" -->
+```python colab={"base_uri": "https://localhost:8080/", "height": 478} id="kzWTZ93vNDQI" outputId="f5c5bef7-71c0-4d62-f87e-14a40e10288d"
+plot_free_energy(result)
+```
 
+<!-- #region id="Dr3si4QLVppr" -->
 That estimation is not bad.
 We get the approximate right shape in the middle and that could be further improved by running the sampling trajectory longer. Or try a different spring constant. [Try it out!]
 
 But there are still some issues because we still cannot sample the entire space:
 
-- the up trend on the right is uncovered
-- the energy barrier is underestimated
-- the first minimum is under-sampled
+- the right and left barriers are uncovered
+- the height and maximum of the sampled barrier are slightly off
+- the highest local minimum is under-sampled
 
 Can we bias simulations in these regions too, to improve sampling coverage?
+<!-- #endregion -->
 
+<!-- #region id="e2YtfQlQ8jO9" -->
 ## Umbrella Sampling
 
 We want to find the free-energy profile along a given path in the space for collective variables. Usually, this path can be multidimensional.
 
 Example dihedral angles of Alanine Dipeptide. [PySAGES Alanine Dipentide examples](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/Harmonic_Bias.ipynb)
 
-<img src=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4715807/bin/nihms-706794-f0001.jpg width=500>
+<img src=https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1dd/4715807/e8dece12c024/nihms-706794-f0001.jpg width=500>
 
 Wu, Xiongwu, Bernard R. Brooks, and Eric Vanden‐Eijnden. Journal of computational chemistry 37.6 (2016): 595-601.
 
@@ -686,25 +710,28 @@ Ideal $e^{-\beta H^b_{i}}(\xi) \propto p_{eq}(\xi)$ and differentiable.
 - combine different points into one analysis
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 322} id="34u5P_jKpcqZ" outputId="d193536c-1ef4-4eaa-abcc-e4d0af9e9501"
+```python colab={"base_uri": "https://localhost:8080/", "height": 479} id="34u5P_jKpcqZ" outputId="165c2cce-f1ad-4006-92d1-c9acfb7f4ff6"
 centers = np.linspace(0, 4, 10)
 kspring = 100
+
+x = np.linspace(0.01, 4, 200)
+landscape = free_energy(energy)(x)
+
 fig, ax = plt.subplots()
+
 ax.set_xlabel(r"$\xi$")
 ax.set_ylabel(r"$H_i(\xi)$")
-ax.set_ylim((-10, 20))
-ax.set_xlim((0,4))
+ax.set_ylim((-12, 20))
+ax.set_xlim((0, 4))
+
+for x_c in centers:
+    label = "biasing potential" if x_c == 4 else None
+    ax.plot(x, kspring / 2 * (x - x_c)**2, label=label)
 
-x = np.linspace(0, 4, 200)
-ax.plot(x, correct_free_energy(x, potential(x)[0]), label="potential")
-for point in centers:
-  label = None
-  if point == 0:
-    label = "biasing potential"
-  ax.plot(x, kspring/2*(x-point)**2, label=label)
+ax.plot(x, landscape, label="potential")
 ax.legend(loc="best")
-fig.show()
 
+fig.show()
 ```
 
 <!-- #region id="Wdb0TKstSz-j" -->
@@ -716,8 +743,9 @@ $$\int \text{d} \xi p_i^b(\xi) p_{i+1}^b(\xi) \gg 0$$
 
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/"} id="CtaNDUQ0SrTZ" outputId="3255e692-bfbd-4f97-cc21-8659aa2b5b37"
+```python id="CtaNDUQ0SrTZ"
 from pysages.methods import UmbrellaIntegration
+
 method = UmbrellaIntegration(cvs, kspring, centers, 100)
 result = pysages.run(method, generate_context, int(1e5))
 ```
@@ -730,17 +758,29 @@ Let's see what the histograms look like.
 
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 283} id="xdUuQ9z5XCEH" outputId="453c6048-9992-4b30-e91d-e0c81c146b83"
-def plot_multi_histogram(result):
-  fig, ax = plt.subplots()
-  ax.set_xlabel(r"$\xi$ $[\sigma]$")
-  ax.set_ylabel(r"p(\xi)")
-  ax.set_xlim((0, 4))
-  for histogram_log in result.callbacks:
-    hist, edges = histogram_log.get_histograms(bins=30, range=[(0,4)])
-    x = edges[0][:-1] + np.diff(edges[0])/2
-    ax.plot(x, hist, label=r"biased $p(\xi)$")
-  fig.show()
+```python id="xdUuQ9z5XCEH"
+def plot_multi_histogram(result, x_range=(0, 4), bins=30):
+    xs = []
+    histograms = []
+
+    for histogram_log in result.callbacks:
+        hist, edges = histogram_log.get_histograms(bins=bins, range=[x_range])
+        xs.append(edges[0][:-1] + np.diff(edges[0]) / 2)
+        histograms.append(hist)
+
+    fig, ax = plt.subplots()
+
+    ax.set_xlabel(r"$\xi$ $[\sigma]$")
+    ax.set_ylabel(r"p(\xi)")
+    ax.set_xlim(x_range)
+
+    for x, hist in zip(xs, histograms):
+        ax.plot(x, hist, label=r"biased $p(\xi)$")
+
+    fig.show()
+```
+
+```python colab={"base_uri": "https://localhost:8080/", "height": 473} id="eJswUAgwBgBN" outputId="861641a1-bae5-4844-c3a7-a646c498a503"
 plot_multi_histogram(result)
 ```
 
@@ -792,37 +832,42 @@ Let's see how PySAGES analyzes it for us and produces the free-energy result.
 
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 318} id="RpoHwWULX2Nj" outputId="4292617c-3b4a-4278-b484-b40eea8a0fea"
-def plot_umbrella_free_energy(result):
-  processed_result = pysages.analyze(result)
+```python id="RpoHwWULX2Nj"
+def plot_umbrella_free_energy(pre_result, x_range=(0, 4)):
+    x = np.linspace(x_range[0] + 0.01, x_range[1], 50)
+    landscape = free_energy(energy)(x)
+
+    result = pysages.analyze(pre_result)
+    centers = np.asarray(result["centers"])[:, 0]
+    estimate = np.asarray(result["free_energy"])
+    estimate = estimate - np.min(estimate) + np.min(landscape)
+
+    fig, ax = plt.subplots()
+
+    ax.set_xlabel(r"$\xi$ $[\sigma]$")
+    ax.set_ylabel(r"A(\xi)")
+    ax.set_xlim(x_range)
+    ax.plot(x, landscape, label=r"true $A(\xi)$")
+    ax.plot(centers, estimate, label=r"estimated $A(\xi)$")
+    ax.legend(loc="best")
 
-  fig, ax = plt.subplots()
-  ax.set_xlabel(r"$\xi$ $[\sigma]$")
-  ax.set_ylabel(r"A(\xi)")
-  ax.set_xlim((0, 4))
+    fig.show()
+```
 
-  centers = np.asarray(processed_result["centers"])[:,0]
-  free_energy = np.asarray(processed_result["free_energy"])
-  ax.plot(centers, free_energy, label=r"estimated $A(\xi)$")
-  x = np.linspace(0, 4, 50)
-  ax.plot(x, correct_free_energy(x, potential(x)[0]), label=r"true $A(\xi)$")
-  ax.legend(loc="best")
-  fig.show()
+```python colab={"base_uri": "https://localhost:8080/", "height": 478} id="GcWEtXv1Cktp" outputId="7793047d-55a2-47a2-d06d-765901f28a6a"
 plot_umbrella_free_energy(result)
 ```
 
 <!-- #region id="nswuCmWdhvQb" -->
-
-This appears to be much better.
-Even with the crude approximations, we were doing we can estimate the shape of the potential.
+Even with the crude finite-differences approximations we are doing we can estimate the shape of the potential.
 
 Just the second minimum is underestimated, which could be fixed with more sampling and more sampling points in that vicinity. [Try it out!]
 
 Difficulties:
 
 1.  choose a good spring constant
-    > - too large and the histograms don't overlap
-    > - too small and you can sample barriers
+    - if it is too large, the histograms won't overlap
+    - if it too small, you won't be able to sample some barriers
 2.  choose a good number of replicas
 
 Can we do better than this?
@@ -830,6 +875,7 @@ Yes, of course:
 
 - [Meta-dynamics](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/hoomd-blue/Umbrella_Integration.ipynb): approximate one weight function with a sum of Gaussians
 - [ANN](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/hoomd-blue/Butane_ANN.ipynb): approximate biasing force with artificial neuronal networks (ANN)
+- And many more sampling methods implemented in PySAGES
 
 ## GPU computing
 
@@ -929,11 +975,9 @@ For a given point $r_i$ we can define the probability that a simulation started
 $$\text{commitor probability B: } p_B(r_i)$$
 
 This is a probability since we can have multiple realizations of $r_i$ in momentum space (Maxwell-Boltzmann distribution). Each of these realizations has its path and we simulate them and measure if they arrive in $A$ or $B$ first.
-
 <!-- #endregion -->
 
 <!-- #region id="GFT1lolLm13Y" -->
-
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)
 
 <!-- #endregion -->
@@ -945,7 +989,7 @@ And the probability decreases if we move towards $A$ ($t < 1/2$) the probability
 
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/", "height": 281} id="9N5aSjdbmlZ0" outputId="b69bdb1f-06bc-459e-c593-9dc8b60f5706"
+```python colab={"base_uri": "https://localhost:8080/", "height": 469} id="9N5aSjdbmlZ0" outputId="0eced722-a3dc-465f-a9ce-b830881cf60a"
 fig, ax =plt.subplots()
 ax.set_xlabel("$t$")
 ax.set_xlim((0,1))
@@ -954,9 +998,9 @@ ax.set_ylabel("$ p_B(t)$")
 x = np.linspace(0, 1, 50)
 p_good = np.tan(2*(x-.5))
 p_good -= np.min(p_good)
-p_good /= integrate.simpson(p_good, x)
+p_good /= integrate.simpson(p_good, x=x)
 p_bad = x*0+1
-p_bad /= integrate.simpson(p_bad, x)
+p_bad /= integrate.simpson(p_bad, x=x)
 
 ax.plot(x, p_good, label="good path")
 ax.plot(x, p_bad, label="bad path")
diff --git a/examples/README.md b/examples/README.md
index d61dfaf4..4e524ebb 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -45,7 +45,7 @@ Examples for Methods using OpenMM can be found in the subfolder [openmm](openmm)
 
 ### OpenMM notebooks
 
-- Harmonic bias for the dihedral angle of Alanine Dipeptide: [![Harmonic Bias](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/Harmonic_Bias.ipynb)
+- Harmonic bias for the dihedral angle of Alanine Dipeptide: [![Harmonic Bias](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/harmonic_bias/Harmonic_Bias.ipynb)
 - Metadynamics sampling with Alanine Dipeptide: [![Metadynamics](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/metad/Metadynamics-ADP.ipynb)
 - Metadynamics sampling with NaCL [![MetadynamicsNaCl](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/metad/nacl/Metadynamics_NaCl.ipynb)
 - Spectral ABF sampling with Alanine Dipeptide: [![SpectralABF](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SSAGESLabs/PySAGES/blob/main/examples/openmm/spectral_abf/ADP_SpectralABF.ipynb)
diff --git a/examples/hoomd-blue/ann/Butane_ANN.ipynb b/examples/hoomd-blue/ann/Butane_ANN.ipynb
index 7e776d32..4ca6c997 100644
--- a/examples/hoomd-blue/ann/Butane_ANN.ipynb
+++ b/examples/hoomd-blue/ann/Butane_ANN.ipynb
@@ -6,11 +6,10 @@
     "id": "T-Qkg9C9n7Cc"
    },
    "source": [
-    "\n",
     "# Setting up the environment\n",
     "\n",
-    "First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin.\n",
-    "We copy it from Google Drive and install PySAGES for it.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance."
    ]
   },
   {
@@ -23,9 +22,12 @@
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
@@ -79,46 +81,38 @@
     "ver = sys.version_info\n",
     "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "os.environ[\"XLA_FLAGS\"] = \"--xla_gpu_strict_conv_algorithm_picker=false\"\n",
     "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "we_mTkFioS6R"
+    "id": "Wy-75Pt7Bqs1"
    },
    "source": [
-    "\n",
-    "## PySAGES\n",
-    "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
+    "We'll also need some additional python dependencies"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "id": "vK0RZtbroQWe"
+    "id": "LpBucu3V81xm"
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "pip install -q --upgrade pip\n",
-    "# Installs the wheel compatible with CUDA.\n",
-    "pip install -q --upgrade \"jax[cuda]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null"
+    "!pip install -qq \"numpy<2\" gsd > /dev/null"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "wAtjM-IroYX8"
+    "id": "we_mTkFioS6R"
    },
    "source": [
+    "## PySAGES\n",
     "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
+    "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`."
    ]
   },
   {
@@ -129,12 +123,16 @@
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KBFVcG1FoeMq"
+   },
+   "source": [
+    "# ANN-biased simulations"
    ]
   },
   {
@@ -151,26 +149,15 @@
     "cd /content/ann"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KBFVcG1FoeMq"
-   },
-   "source": [
-    "\n",
-    "# ANN-biased simulations\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "0W2ukJuuojAl"
    },
    "source": [
-    "\n",
     "ANN gradually learns the free energy from a probability density estimate based on the frequency of visits to the grid on collective variable space.\n",
     "\n",
-    "For this Colab, we are using butane as the example molecule.\n"
+    "For this Colab, we are using butane as the example molecule."
    ]
   },
   {
@@ -182,36 +169,31 @@
    "outputs": [],
    "source": [
     "import hoomd\n",
-    "import hoomd.md\n",
+    "import gsd.hoomd\n",
+    "import numpy as np\n",
     "\n",
-    "import numpy\n",
     "\n",
-    "\n",
-    "pi = numpy.pi\n",
+    "pi = np.pi\n",
     "kT = 0.596161\n",
     "dt = 0.02045\n",
-    "mode = \"--mode=gpu\"\n",
     "\n",
     "\n",
-    "def generate_context(kT = kT, dt = dt, mode = mode):\n",
+    "def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), seed = 42):\n",
     "    \"\"\"\n",
-    "    Generates a simulation context, we pass this function to the attribute\n",
-    "    `run` of our sampling method.\n",
+    "    Generates a simulation context to which will attatch our sampling method.\n",
     "    \"\"\"\n",
-    "    hoomd.context.initialize(mode)\n",
-    "\n",
-    "    ### System Definition\n",
-    "    snapshot = hoomd.data.make_snapshot(\n",
-    "        N = 14,\n",
-    "        box = hoomd.data.boxdim(Lx = 41, Ly = 41, Lz = 41),\n",
-    "        particle_types = ['C', 'H'],\n",
-    "        bond_types = [\"CC\", \"CH\"],\n",
-    "        angle_types = [\"CCC\", \"CCH\", \"HCH\"],\n",
-    "        dihedral_types = [\"CCCC\", \"HCCC\", \"HCCH\"],\n",
-    "        pair_types = [\"CCCC\", \"HCCC\", \"HCCH\"],\n",
-    "        dtype = \"double\"\n",
-    "    )\n",
+    "    simulation = hoomd.Simulation(device=device, seed=seed)\n",
+    "\n",
+    "    snapshot = gsd.hoomd.Frame()\n",
     "\n",
+    "    snapshot.configuration.box = [41, 41, 41, 0, 0, 0]\n",
+    "\n",
+    "    snapshot.particles.N = N = 14\n",
+    "    snapshot.particles.types = [\"C\", \"H\"]\n",
+    "    snapshot.particles.typeid = np.zeros(N, dtype=int)\n",
+    "    snapshot.particles.position = np.zeros((N, 3))\n",
+    "    snapshot.particles.mass = np.zeros(N, dtype=float)\n",
+    "    snapshot.particles.charge = np.zeros(N, dtype=float)\n",
     "    snapshot.particles.typeid[0] = 0\n",
     "    snapshot.particles.typeid[1:4] = 1\n",
     "    snapshot.particles.typeid[4] = 0\n",
@@ -221,52 +203,60 @@
     "    snapshot.particles.typeid[10] = 0\n",
     "    snapshot.particles.typeid[11:14] = 1\n",
     "\n",
-    "    positions = numpy.array([\n",
-    "        [-2.990196,  0.097881,  0.000091],\n",
-    "        [-2.634894, -0.911406,  0.001002],\n",
-    "        [-2.632173,  0.601251, -0.873601],\n",
-    "        [-4.060195,  0.099327, -0.000736],\n",
-    "        [-2.476854,  0.823942,  1.257436],\n",
-    "        [-2.832157,  1.833228,  1.256526],\n",
-    "        [-2.834877,  0.320572,  2.131128],\n",
-    "        [-0.936856,  0.821861,  1.258628],\n",
-    "        [-0.578833,  1.325231,  0.384935],\n",
-    "        [-0.581553, -0.187426,  1.259538],\n",
-    "        [-0.423514,  1.547922,  2.515972],\n",
-    "        [-0.781537,  1.044552,  3.389664],\n",
-    "        [ 0.646485,  1.546476,  2.516800],\n",
-    "        [-0.778816,  2.557208,  2.515062]\n",
-    "    ])\n",
-    "\n",
-    "    reference_box_low_coords = numpy.array([-22.206855, -19.677099, -19.241968])\n",
-    "    box_low_coords = numpy.array([\n",
-    "        -snapshot.box.Lx / 2,\n",
-    "        -snapshot.box.Ly / 2,\n",
-    "        -snapshot.box.Lz / 2\n",
-    "    ])\n",
-    "    positions += (box_low_coords - reference_box_low_coords)\n",
+    "    positions = np.array(\n",
+    "        [\n",
+    "            [-2.990196, 0.097881, 0.000091],\n",
+    "            [-2.634894, -0.911406, 0.001002],\n",
+    "            [-2.632173, 0.601251, -0.873601],\n",
+    "            [-4.060195, 0.099327, -0.000736],\n",
+    "            [-2.476854, 0.823942, 1.257436],\n",
+    "            [-2.832157, 1.833228, 1.256526],\n",
+    "            [-2.834877, 0.320572, 2.131128],\n",
+    "            [-0.936856, 0.821861, 1.258628],\n",
+    "            [-0.578833, 1.325231, 0.384935],\n",
+    "            [-0.581553, -0.187426, 1.259538],\n",
+    "            [-0.423514, 1.547922, 2.515972],\n",
+    "            [-0.781537, 1.044552, 3.389664],\n",
+    "            [0.646485, 1.546476, 2.516800],\n",
+    "            [-0.778816, 2.557208, 2.515062],\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    reference_box_low_coords = np.array([-22.206855, -19.677099, -19.241968])\n",
+    "    box_low_coords = np.array([-41.0 / 2, -41.0 / 2, -41.0 / 2])\n",
+    "    positions += box_low_coords - reference_box_low_coords\n",
     "\n",
     "    snapshot.particles.position[:] = positions[:]\n",
     "\n",
     "    mC = 12.00\n",
     "    mH = 1.008\n",
+    "\n",
+    "    # fmt: off\n",
     "    snapshot.particles.mass[:] = [\n",
-    "        mC, mH, mH, mH,\n",
+    "        mC, mH, mH, mH,  # grouped by carbon atoms\n",
     "        mC, mH, mH,\n",
     "        mC, mH, mH,\n",
-    "        mC, mH, mH, mH\n",
+    "        mC, mH, mH, mH,\n",
     "    ]\n",
     "\n",
-    "    reference_charges = numpy.array([\n",
-    "        -0.180000, 0.060000, 0.060000, 0.060000,\n",
-    "        -0.120000, 0.060000, 0.060000,\n",
-    "        -0.120000, 0.060000, 0.060000,\n",
-    "        -0.180000, 0.060000, 0.060000, 0.060000]\n",
+    "    reference_charges = np.array(\n",
+    "        [\n",
+    "            -0.180000, 0.060000, 0.060000, 0.060000,  # grouped by carbon atoms\n",
+    "            -0.120000, 0.060000, 0.060000,\n",
+    "            -0.120000, 0.060000, 0.060000,\n",
+    "            -0.180000, 0.060000, 0.060000, 0.060000,\n",
+    "        ]\n",
     "    )\n",
+    "    # fmt: on\n",
+    "\n",
     "    charge_conversion = 18.22262\n",
     "    snapshot.particles.charge[:] = charge_conversion * reference_charges[:]\n",
     "\n",
-    "    snapshot.bonds.resize(13)\n",
+    "    snapshot.particles.validate()\n",
+    "\n",
+    "    snapshot.bonds.N = 13\n",
+    "    snapshot.bonds.types = [\"CC\", \"CH\"]\n",
+    "    snapshot.bonds.typeid = np.zeros(13, dtype=int)\n",
     "    snapshot.bonds.typeid[0:3] = 1\n",
     "    snapshot.bonds.typeid[3] = 0\n",
     "    snapshot.bonds.typeid[4:6] = 1\n",
@@ -275,14 +265,19 @@
     "    snapshot.bonds.typeid[9] = 0\n",
     "    snapshot.bonds.typeid[10:13] = 1\n",
     "\n",
+    "    snapshot.bonds.group = np.zeros((13, 2), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.bonds.group[:] = [\n",
-    "        [0, 2], [0, 1], [0, 3], [0, 4],\n",
+    "        [0, 2], [0, 1], [0, 3], [0, 4],  # grouped by carbon atoms\n",
     "        [4, 5], [4, 6], [4, 7],\n",
     "        [7, 8], [7, 9], [7, 10],\n",
-    "        [10, 11], [10, 12], [10, 13]\n",
+    "        [10, 11], [10, 12], [10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.angles.resize(24)\n",
+    "    snapshot.angles.N = 24\n",
+    "    snapshot.angles.types = [\"CCC\", \"CCH\", \"HCH\"]\n",
+    "    snapshot.angles.typeid = np.zeros(24, dtype=int)\n",
     "    snapshot.angles.typeid[0:2] = 2\n",
     "    snapshot.angles.typeid[2] = 1\n",
     "    snapshot.angles.typeid[3] = 2\n",
@@ -295,18 +290,26 @@
     "    snapshot.angles.typeid[16:21] = 1\n",
     "    snapshot.angles.typeid[21:24] = 2\n",
     "\n",
+    "    snapshot.angles.group = np.zeros((24, 3), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.angles.group[:] = [\n",
-    "        [1, 0, 2], [2, 0, 3], [2, 0, 4],\n",
+    "        [1, 0, 2], [2, 0, 3], [2, 0, 4],  # grouped by carbon atoms\n",
     "        [1, 0, 3], [1, 0, 4], [3, 0, 4],\n",
+    "        # ---\n",
     "        [0, 4, 5], [0, 4, 6], [0, 4, 7],\n",
     "        [5, 4, 6], [5, 4, 7], [6, 4, 7],\n",
+    "        # ---\n",
     "        [4, 7, 8], [4, 7, 9], [4, 7, 10],\n",
     "        [8, 7, 9], [8, 7, 10], [9, 7, 10],\n",
+    "        # ---\n",
     "        [7, 10, 11], [7, 10, 12], [7, 10, 13],\n",
-    "        [11, 10, 12], [11, 10, 13], [12, 10, 13]\n",
+    "        [11, 10, 12], [11, 10, 13], [12, 10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.dihedrals.resize(27)\n",
+    "    snapshot.dihedrals.N = 27\n",
+    "    snapshot.dihedrals.types = [\"CCCC\", \"HCCC\", \"HCCH\"]\n",
+    "    snapshot.dihedrals.typeid = np.zeros(27, dtype=int)\n",
     "    snapshot.dihedrals.typeid[0:2] = 2\n",
     "    snapshot.dihedrals.typeid[2] = 1\n",
     "    snapshot.dihedrals.typeid[3:5] = 2\n",
@@ -320,81 +323,109 @@
     "    snapshot.dihedrals.typeid[17:21] = 1\n",
     "    snapshot.dihedrals.typeid[21:27] = 2\n",
     "\n",
+    "    snapshot.dihedrals.group = np.zeros((27, 4), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.dihedrals.group[:] = [\n",
-    "        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],\n",
+    "        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],  # grouped by pairs of central atoms\n",
     "        [1, 0, 4, 5], [1, 0, 4, 6], [1, 0, 4, 7],\n",
     "        [3, 0, 4, 5], [3, 0, 4, 6], [3, 0, 4, 7],\n",
+    "        # ---\n",
     "        [0, 4, 7, 8], [0, 4, 7, 9], [0, 4, 7, 10],\n",
     "        [5, 4, 7, 8], [5, 4, 7, 9], [5, 4, 7, 10],\n",
     "        [6, 4, 7, 8], [6, 4, 7, 9], [6, 4, 7, 10],\n",
+    "        # ---\n",
     "        [4, 7, 10, 11], [4, 7, 10, 12], [4, 7, 10, 13],\n",
     "        [8, 7, 10, 11], [8, 7, 10, 12], [8, 7, 10, 13],\n",
-    "        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13]\n",
+    "        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.pairs.resize(27)\n",
+    "    snapshot.pairs.N = 27\n",
+    "    snapshot.pairs.types = [\"CCCC\", \"HCCC\", \"HCCH\"]\n",
+    "    snapshot.pairs.typeid = np.zeros(27, dtype=int)\n",
     "    snapshot.pairs.typeid[0:1] = 0\n",
     "    snapshot.pairs.typeid[1:11] = 1\n",
     "    snapshot.pairs.typeid[11:27] = 2\n",
+    "    snapshot.pairs.group = np.zeros((27, 2), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.pairs.group[:] = [\n",
     "        # CCCC\n",
     "        [0, 10],\n",
     "        # HCCC\n",
-    "        [0, 8], [0, 9], [5, 10], [6, 10],\n",
+    "        [0, 8],\n",
+    "        [0, 9],\n",
+    "        [5, 10], [6, 10],\n",
     "        [1, 7], [2, 7], [3, 7],\n",
     "        [11, 4], [12, 4], [13, 4],\n",
     "        # HCCH\n",
-    "        [1, 5], [1, 6], [2, 5], [2, 6], [3, 5], [3, 6],\n",
-    "        [5, 8], [6, 8], [5, 9], [6, 9],\n",
-    "        [8, 11], [8, 12], [8, 13], [9, 11], [9, 12], [9, 13]\n",
+    "        [1, 5], [1, 6],\n",
+    "        [2, 5], [2, 6],\n",
+    "        [3, 5], [3, 6],\n",
+    "        [5, 8], [6, 8],\n",
+    "        [5, 9], [6, 9],\n",
+    "        [8, 11], [8, 12], [8, 13],\n",
+    "        [9, 11], [9, 12], [9, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    hoomd.init.read_snapshot(snapshot)\n",
-    "\n",
-    "    ### Set interactions\n",
-    "    nl_ex = hoomd.md.nlist.cell()\n",
-    "    nl_ex.reset_exclusions(exclusions = [\"1-2\", \"1-3\", \"1-4\"])\n",
-    "\n",
-    "    lj = hoomd.md.pair.lj(r_cut = 12.0, nlist = nl_ex)\n",
-    "    lj.pair_coeff.set('C', 'C', epsilon = 0.07, sigma = 3.55)\n",
-    "    lj.pair_coeff.set('H', 'H', epsilon = 0.03, sigma = 2.42)\n",
-    "    lj.pair_coeff.set('C', 'H', epsilon = numpy.sqrt(0.07*0.03), sigma = numpy.sqrt(3.55*2.42))\n",
-    "\n",
-    "    coulomb = hoomd.md.charge.pppm(hoomd.group.charged(), nlist = nl_ex)\n",
-    "    coulomb.set_params(Nx = 64, Ny = 64, Nz = 64, order = 6, rcut = 12.0)\n",
-    "\n",
-    "    harmonic = hoomd.md.bond.harmonic()\n",
-    "    harmonic.bond_coeff.set(\"CC\", k = 2*268.0, r0 = 1.529)\n",
-    "    harmonic.bond_coeff.set(\"CH\", k = 2*340.0, r0 = 1.09)\n",
+    "    simulation.create_state_from_snapshot(snapshot, domain_decomposition=(None, None, None))\n",
+    "    simulation.run(0)\n",
     "\n",
-    "    angle = hoomd.md.angle.harmonic()\n",
-    "    angle.angle_coeff.set(\"CCC\", k = 2*58.35, t0 = 112.7 * pi / 180)\n",
-    "    angle.angle_coeff.set(\"CCH\", k = 2*37.5, t0 = 110.7 * pi / 180)\n",
-    "    angle.angle_coeff.set(\"HCH\", k = 2*33.0, t0 = 107.8 * pi / 180)\n",
+    "    exclusions = [\"bond\", \"1-3\", \"1-4\"]\n",
+    "    nl = hoomd.md.nlist.Cell(buffer=0.4, exclusions=exclusions)\n",
+    "    lj = hoomd.md.pair.LJ(nlist=nl, default_r_cut=12.0)\n",
+    "    lj.params[(\"C\", \"C\")] = dict(epsilon=0.07, sigma=3.55)\n",
+    "    lj.params[(\"H\", \"H\")] = dict(epsilon=0.03, sigma=2.42)\n",
+    "    lj.params[(\"C\", \"H\")] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))\n",
     "\n",
-    "\n",
-    "    dihedral = hoomd.md.dihedral.opls()\n",
-    "    dihedral.dihedral_coeff.set(\"CCCC\", k1 = 1.3, k2 = -0.05, k3 = 0.2, k4 = 0.0)\n",
-    "    dihedral.dihedral_coeff.set(\"HCCC\", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)\n",
-    "    dihedral.dihedral_coeff.set(\"HCCH\", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)\n",
-    "\n",
-    "    lj_special_pairs = hoomd.md.special_pair.lj()\n",
-    "    lj_special_pairs.pair_coeff.set(\"CCCC\", epsilon = 0.07, sigma = 3.55, r_cut = 12.0)\n",
-    "    lj_special_pairs.pair_coeff.set(\"HCCH\", epsilon = 0.03, sigma = 2.42, r_cut = 12.0)\n",
-    "    lj_special_pairs.pair_coeff.set(\"HCCC\",\n",
-    "        epsilon = numpy.sqrt(0.07 * 0.03), sigma = numpy.sqrt(3.55 * 2.42), r_cut = 12.0\n",
+    "    coulomb = hoomd.md.long_range.pppm.make_pppm_coulomb_forces(\n",
+    "        nlist=nl, resolution=[64, 64, 64], order=6, r_cut=12.0\n",
     "    )\n",
     "\n",
-    "    coulomb_special_pairs = hoomd.md.special_pair.coulomb()\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"CCCC\", alpha = 0.5, r_cut = 12.0)\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"HCCC\", alpha = 0.5, r_cut = 12.0)\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"HCCH\", alpha = 0.5, r_cut = 12.0)\n",
-    "\n",
-    "    hoomd.md.integrate.mode_standard(dt = dt)\n",
-    "    integrator = hoomd.md.integrate.nvt(group = hoomd.group.all(), kT = kT, tau = 100*dt)\n",
-    "    integrator.randomize_velocities(seed = 42)\n",
-    "\n",
-    "    return hoomd.context.current"
+    "    harmonic = hoomd.md.bond.Harmonic()\n",
+    "    harmonic.params[\"CC\"] = dict(k=2 * 268.0, r0=1.529)\n",
+    "    harmonic.params[\"CH\"] = dict(k=2 * 340.0, r0=1.09)\n",
+    "\n",
+    "    angle = hoomd.md.angle.Harmonic()\n",
+    "    angle.params[\"CCC\"] = dict(k=2 * 58.35, t0=112.7 * pi / 180)\n",
+    "    angle.params[\"CCH\"] = dict(k=2 * 37.5, t0=110.7 * pi / 180)\n",
+    "    angle.params[\"HCH\"] = dict(k=2 * 33.0, t0=107.8 * pi / 180)\n",
+    "\n",
+    "    dihedral = hoomd.md.dihedral.OPLS()\n",
+    "    dihedral.params[\"CCCC\"] = dict(k1=1.3, k2=-0.05, k3=0.2, k4=0.0)\n",
+    "    dihedral.params[\"HCCC\"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)\n",
+    "    dihedral.params[\"HCCH\"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)\n",
+    "\n",
+    "    lj_special_pairs = hoomd.md.special_pair.LJ()\n",
+    "    lj_special_pairs.params[\"CCCC\"] = dict(epsilon=0.07, sigma=3.55)\n",
+    "    lj_special_pairs.params[\"HCCH\"] = dict(epsilon=0.03, sigma=2.42)\n",
+    "    lj_special_pairs.params[\"HCCC\"] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))\n",
+    "    lj_special_pairs.r_cut[\"CCCC\"] = 12.0\n",
+    "    lj_special_pairs.r_cut[\"HCCC\"] = 12.0\n",
+    "    lj_special_pairs.r_cut[\"HCCH\"] = 12.0\n",
+    "    coulomb_special_pairs = hoomd.md.special_pair.Coulomb()\n",
+    "    coulomb_special_pairs.params[\"CCCC\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.params[\"HCCC\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.params[\"HCCH\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.r_cut[\"HCCH\"] = 12.0\n",
+    "    coulomb_special_pairs.r_cut[\"CCCC\"] = 12.0\n",
+    "    coulomb_special_pairs.r_cut[\"HCCC\"] = 12.0\n",
+    "\n",
+    "    nvt = hoomd.md.methods.Langevin(filter=hoomd.filter.All(), kT=kT)\n",
+    "\n",
+    "    integrator = hoomd.md.Integrator(dt=dt)\n",
+    "    integrator.forces.append(lj)\n",
+    "    integrator.forces.append(coulomb[0])\n",
+    "    integrator.forces.append(coulomb[1])\n",
+    "    integrator.forces.append(harmonic)\n",
+    "    integrator.forces.append(angle)\n",
+    "    integrator.forces.append(dihedral)\n",
+    "    integrator.forces.append(lj_special_pairs)\n",
+    "    integrator.forces.append(coulomb_special_pairs)\n",
+    "    integrator.methods.append(nvt)\n",
+    "    simulation.operations.integrator = integrator\n",
+    "\n",
+    "    return simulation"
    ]
   },
   {
@@ -648,7 +679,7 @@
     }
    ],
    "source": [
-    "run_result = pysages.run(method, generate_context, int(5e5))"
+    "run_result = pysages.run(method, generate_simulation, int(5e5))"
    ]
   },
   {
@@ -726,9 +757,9 @@
     "\n",
     "ax.set_xlabel(r\"Dihedral Angle, $\\xi$\")\n",
     "ax.set_ylabel(r\"$A(\\xi)$\")\n",
-    "\n",
     "ax.plot(mesh, A)\n",
-    "plt.gca()"
+    "\n",
+    "fig.show()"
    ]
   },
   {
diff --git a/examples/hoomd-blue/ann/Butane_ANN.md b/examples/hoomd-blue/ann/Butane_ANN.md
index 38914349..1a25055f 100644
--- a/examples/hoomd-blue/ann/Butane_ANN.md
+++ b/examples/hoomd-blue/ann/Butane_ANN.md
@@ -14,19 +14,20 @@ jupyter:
 ---
 
 <!-- #region id="T-Qkg9C9n7Cc" -->
-
 # Setting up the environment
 
-First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin.
-We copy it from Google Drive and install PySAGES for it.
-
+First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 <!-- #endregion -->
 
 ```bash id="3eTbKklCnyd_"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
+
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="b757f2aa-38cc-4726-c4ab-5197810b9d77"
@@ -46,92 +47,70 @@ import sys
 ver = sys.version_info
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
 
-os.environ["XLA_FLAGS"] = "--xla_gpu_strict_conv_algorithm_picker=false"
 os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
-<!-- #region id="we_mTkFioS6R" -->
-
-## PySAGES
-
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
+<!-- #region id="Wy-75Pt7Bqs1" -->
+We'll also need some additional python dependencies
 <!-- #endregion -->
 
-```bash id="vK0RZtbroQWe"
-
-pip install -q --upgrade pip
-# Installs the wheel compatible with CUDA.
-pip install -q --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null
+```python id="LpBucu3V81xm"
+!pip install -qq "numpy<2" gsd > /dev/null
 ```
 
-<!-- #region id="wAtjM-IroYX8" -->
-
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
+<!-- #region id="we_mTkFioS6R" -->
+## PySAGES
 
+The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`.
 <!-- #endregion -->
 
-```bash id="B-HB9CzioV5j"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
+<!-- #region id="KBFVcG1FoeMq" -->
+# ANN-biased simulations
+<!-- #endregion -->
+
 ```bash id="ppTzMmyyobHB"
 
 mkdir /content/ann
 cd /content/ann
 ```
 
-<!-- #region id="KBFVcG1FoeMq" -->
-
-# ANN-biased simulations
-
-<!-- #endregion -->
-
 <!-- #region id="0W2ukJuuojAl" -->
-
 ANN gradually learns the free energy from a probability density estimate based on the frequency of visits to the grid on collective variable space.
 
 For this Colab, we are using butane as the example molecule.
-
 <!-- #endregion -->
 
 ```python id="BBvC7Spoog82"
 import hoomd
-import hoomd.md
-
-import numpy
+import gsd.hoomd
+import numpy as np
 
 
-pi = numpy.pi
+pi = np.pi
 kT = 0.596161
 dt = 0.02045
-mode = "--mode=gpu"
 
 
-def generate_context(kT = kT, dt = dt, mode = mode):
+def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), seed = 42):
     """
-    Generates a simulation context, we pass this function to the attribute
-    `run` of our sampling method.
+    Generates a simulation context to which will attatch our sampling method.
     """
-    hoomd.context.initialize(mode)
-
-    ### System Definition
-    snapshot = hoomd.data.make_snapshot(
-        N = 14,
-        box = hoomd.data.boxdim(Lx = 41, Ly = 41, Lz = 41),
-        particle_types = ['C', 'H'],
-        bond_types = ["CC", "CH"],
-        angle_types = ["CCC", "CCH", "HCH"],
-        dihedral_types = ["CCCC", "HCCC", "HCCH"],
-        pair_types = ["CCCC", "HCCC", "HCCH"],
-        dtype = "double"
-    )
+    simulation = hoomd.Simulation(device=device, seed=seed)
+
+    snapshot = gsd.hoomd.Frame()
+
+    snapshot.configuration.box = [41, 41, 41, 0, 0, 0]
 
+    snapshot.particles.N = N = 14
+    snapshot.particles.types = ["C", "H"]
+    snapshot.particles.typeid = np.zeros(N, dtype=int)
+    snapshot.particles.position = np.zeros((N, 3))
+    snapshot.particles.mass = np.zeros(N, dtype=float)
+    snapshot.particles.charge = np.zeros(N, dtype=float)
     snapshot.particles.typeid[0] = 0
     snapshot.particles.typeid[1:4] = 1
     snapshot.particles.typeid[4] = 0
@@ -141,52 +120,60 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.particles.typeid[10] = 0
     snapshot.particles.typeid[11:14] = 1
 
-    positions = numpy.array([
-        [-2.990196,  0.097881,  0.000091],
-        [-2.634894, -0.911406,  0.001002],
-        [-2.632173,  0.601251, -0.873601],
-        [-4.060195,  0.099327, -0.000736],
-        [-2.476854,  0.823942,  1.257436],
-        [-2.832157,  1.833228,  1.256526],
-        [-2.834877,  0.320572,  2.131128],
-        [-0.936856,  0.821861,  1.258628],
-        [-0.578833,  1.325231,  0.384935],
-        [-0.581553, -0.187426,  1.259538],
-        [-0.423514,  1.547922,  2.515972],
-        [-0.781537,  1.044552,  3.389664],
-        [ 0.646485,  1.546476,  2.516800],
-        [-0.778816,  2.557208,  2.515062]
-    ])
-
-    reference_box_low_coords = numpy.array([-22.206855, -19.677099, -19.241968])
-    box_low_coords = numpy.array([
-        -snapshot.box.Lx / 2,
-        -snapshot.box.Ly / 2,
-        -snapshot.box.Lz / 2
-    ])
-    positions += (box_low_coords - reference_box_low_coords)
+    positions = np.array(
+        [
+            [-2.990196, 0.097881, 0.000091],
+            [-2.634894, -0.911406, 0.001002],
+            [-2.632173, 0.601251, -0.873601],
+            [-4.060195, 0.099327, -0.000736],
+            [-2.476854, 0.823942, 1.257436],
+            [-2.832157, 1.833228, 1.256526],
+            [-2.834877, 0.320572, 2.131128],
+            [-0.936856, 0.821861, 1.258628],
+            [-0.578833, 1.325231, 0.384935],
+            [-0.581553, -0.187426, 1.259538],
+            [-0.423514, 1.547922, 2.515972],
+            [-0.781537, 1.044552, 3.389664],
+            [0.646485, 1.546476, 2.516800],
+            [-0.778816, 2.557208, 2.515062],
+        ]
+    )
+
+    reference_box_low_coords = np.array([-22.206855, -19.677099, -19.241968])
+    box_low_coords = np.array([-41.0 / 2, -41.0 / 2, -41.0 / 2])
+    positions += box_low_coords - reference_box_low_coords
 
     snapshot.particles.position[:] = positions[:]
 
     mC = 12.00
     mH = 1.008
+
+    # fmt: off
     snapshot.particles.mass[:] = [
-        mC, mH, mH, mH,
+        mC, mH, mH, mH,  # grouped by carbon atoms
         mC, mH, mH,
         mC, mH, mH,
-        mC, mH, mH, mH
+        mC, mH, mH, mH,
     ]
 
-    reference_charges = numpy.array([
-        -0.180000, 0.060000, 0.060000, 0.060000,
-        -0.120000, 0.060000, 0.060000,
-        -0.120000, 0.060000, 0.060000,
-        -0.180000, 0.060000, 0.060000, 0.060000]
+    reference_charges = np.array(
+        [
+            -0.180000, 0.060000, 0.060000, 0.060000,  # grouped by carbon atoms
+            -0.120000, 0.060000, 0.060000,
+            -0.120000, 0.060000, 0.060000,
+            -0.180000, 0.060000, 0.060000, 0.060000,
+        ]
     )
+    # fmt: on
+
     charge_conversion = 18.22262
     snapshot.particles.charge[:] = charge_conversion * reference_charges[:]
 
-    snapshot.bonds.resize(13)
+    snapshot.particles.validate()
+
+    snapshot.bonds.N = 13
+    snapshot.bonds.types = ["CC", "CH"]
+    snapshot.bonds.typeid = np.zeros(13, dtype=int)
     snapshot.bonds.typeid[0:3] = 1
     snapshot.bonds.typeid[3] = 0
     snapshot.bonds.typeid[4:6] = 1
@@ -195,14 +182,19 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.bonds.typeid[9] = 0
     snapshot.bonds.typeid[10:13] = 1
 
+    snapshot.bonds.group = np.zeros((13, 2), dtype=int)
+    # fmt: off
     snapshot.bonds.group[:] = [
-        [0, 2], [0, 1], [0, 3], [0, 4],
+        [0, 2], [0, 1], [0, 3], [0, 4],  # grouped by carbon atoms
         [4, 5], [4, 6], [4, 7],
         [7, 8], [7, 9], [7, 10],
-        [10, 11], [10, 12], [10, 13]
+        [10, 11], [10, 12], [10, 13],
     ]
+    # fmt: on
 
-    snapshot.angles.resize(24)
+    snapshot.angles.N = 24
+    snapshot.angles.types = ["CCC", "CCH", "HCH"]
+    snapshot.angles.typeid = np.zeros(24, dtype=int)
     snapshot.angles.typeid[0:2] = 2
     snapshot.angles.typeid[2] = 1
     snapshot.angles.typeid[3] = 2
@@ -215,18 +207,26 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.angles.typeid[16:21] = 1
     snapshot.angles.typeid[21:24] = 2
 
+    snapshot.angles.group = np.zeros((24, 3), dtype=int)
+    # fmt: off
     snapshot.angles.group[:] = [
-        [1, 0, 2], [2, 0, 3], [2, 0, 4],
+        [1, 0, 2], [2, 0, 3], [2, 0, 4],  # grouped by carbon atoms
         [1, 0, 3], [1, 0, 4], [3, 0, 4],
+        # ---
         [0, 4, 5], [0, 4, 6], [0, 4, 7],
         [5, 4, 6], [5, 4, 7], [6, 4, 7],
+        # ---
         [4, 7, 8], [4, 7, 9], [4, 7, 10],
         [8, 7, 9], [8, 7, 10], [9, 7, 10],
+        # ---
         [7, 10, 11], [7, 10, 12], [7, 10, 13],
-        [11, 10, 12], [11, 10, 13], [12, 10, 13]
+        [11, 10, 12], [11, 10, 13], [12, 10, 13],
     ]
+    # fmt: on
 
-    snapshot.dihedrals.resize(27)
+    snapshot.dihedrals.N = 27
+    snapshot.dihedrals.types = ["CCCC", "HCCC", "HCCH"]
+    snapshot.dihedrals.typeid = np.zeros(27, dtype=int)
     snapshot.dihedrals.typeid[0:2] = 2
     snapshot.dihedrals.typeid[2] = 1
     snapshot.dihedrals.typeid[3:5] = 2
@@ -240,81 +240,109 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.dihedrals.typeid[17:21] = 1
     snapshot.dihedrals.typeid[21:27] = 2
 
+    snapshot.dihedrals.group = np.zeros((27, 4), dtype=int)
+    # fmt: off
     snapshot.dihedrals.group[:] = [
-        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],
+        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],  # grouped by pairs of central atoms
         [1, 0, 4, 5], [1, 0, 4, 6], [1, 0, 4, 7],
         [3, 0, 4, 5], [3, 0, 4, 6], [3, 0, 4, 7],
+        # ---
         [0, 4, 7, 8], [0, 4, 7, 9], [0, 4, 7, 10],
         [5, 4, 7, 8], [5, 4, 7, 9], [5, 4, 7, 10],
         [6, 4, 7, 8], [6, 4, 7, 9], [6, 4, 7, 10],
+        # ---
         [4, 7, 10, 11], [4, 7, 10, 12], [4, 7, 10, 13],
         [8, 7, 10, 11], [8, 7, 10, 12], [8, 7, 10, 13],
-        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13]
+        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13],
     ]
+    # fmt: on
 
-    snapshot.pairs.resize(27)
+    snapshot.pairs.N = 27
+    snapshot.pairs.types = ["CCCC", "HCCC", "HCCH"]
+    snapshot.pairs.typeid = np.zeros(27, dtype=int)
     snapshot.pairs.typeid[0:1] = 0
     snapshot.pairs.typeid[1:11] = 1
     snapshot.pairs.typeid[11:27] = 2
+    snapshot.pairs.group = np.zeros((27, 2), dtype=int)
+    # fmt: off
     snapshot.pairs.group[:] = [
         # CCCC
         [0, 10],
         # HCCC
-        [0, 8], [0, 9], [5, 10], [6, 10],
+        [0, 8],
+        [0, 9],
+        [5, 10], [6, 10],
         [1, 7], [2, 7], [3, 7],
         [11, 4], [12, 4], [13, 4],
         # HCCH
-        [1, 5], [1, 6], [2, 5], [2, 6], [3, 5], [3, 6],
-        [5, 8], [6, 8], [5, 9], [6, 9],
-        [8, 11], [8, 12], [8, 13], [9, 11], [9, 12], [9, 13]
+        [1, 5], [1, 6],
+        [2, 5], [2, 6],
+        [3, 5], [3, 6],
+        [5, 8], [6, 8],
+        [5, 9], [6, 9],
+        [8, 11], [8, 12], [8, 13],
+        [9, 11], [9, 12], [9, 13],
     ]
+    # fmt: on
 
-    hoomd.init.read_snapshot(snapshot)
-
-    ### Set interactions
-    nl_ex = hoomd.md.nlist.cell()
-    nl_ex.reset_exclusions(exclusions = ["1-2", "1-3", "1-4"])
-
-    lj = hoomd.md.pair.lj(r_cut = 12.0, nlist = nl_ex)
-    lj.pair_coeff.set('C', 'C', epsilon = 0.07, sigma = 3.55)
-    lj.pair_coeff.set('H', 'H', epsilon = 0.03, sigma = 2.42)
-    lj.pair_coeff.set('C', 'H', epsilon = numpy.sqrt(0.07*0.03), sigma = numpy.sqrt(3.55*2.42))
-
-    coulomb = hoomd.md.charge.pppm(hoomd.group.charged(), nlist = nl_ex)
-    coulomb.set_params(Nx = 64, Ny = 64, Nz = 64, order = 6, rcut = 12.0)
-
-    harmonic = hoomd.md.bond.harmonic()
-    harmonic.bond_coeff.set("CC", k = 2*268.0, r0 = 1.529)
-    harmonic.bond_coeff.set("CH", k = 2*340.0, r0 = 1.09)
-
-    angle = hoomd.md.angle.harmonic()
-    angle.angle_coeff.set("CCC", k = 2*58.35, t0 = 112.7 * pi / 180)
-    angle.angle_coeff.set("CCH", k = 2*37.5, t0 = 110.7 * pi / 180)
-    angle.angle_coeff.set("HCH", k = 2*33.0, t0 = 107.8 * pi / 180)
+    simulation.create_state_from_snapshot(snapshot, domain_decomposition=(None, None, None))
+    simulation.run(0)
 
+    exclusions = ["bond", "1-3", "1-4"]
+    nl = hoomd.md.nlist.Cell(buffer=0.4, exclusions=exclusions)
+    lj = hoomd.md.pair.LJ(nlist=nl, default_r_cut=12.0)
+    lj.params[("C", "C")] = dict(epsilon=0.07, sigma=3.55)
+    lj.params[("H", "H")] = dict(epsilon=0.03, sigma=2.42)
+    lj.params[("C", "H")] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))
 
-    dihedral = hoomd.md.dihedral.opls()
-    dihedral.dihedral_coeff.set("CCCC", k1 = 1.3, k2 = -0.05, k3 = 0.2, k4 = 0.0)
-    dihedral.dihedral_coeff.set("HCCC", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)
-    dihedral.dihedral_coeff.set("HCCH", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)
-
-    lj_special_pairs = hoomd.md.special_pair.lj()
-    lj_special_pairs.pair_coeff.set("CCCC", epsilon = 0.07, sigma = 3.55, r_cut = 12.0)
-    lj_special_pairs.pair_coeff.set("HCCH", epsilon = 0.03, sigma = 2.42, r_cut = 12.0)
-    lj_special_pairs.pair_coeff.set("HCCC",
-        epsilon = numpy.sqrt(0.07 * 0.03), sigma = numpy.sqrt(3.55 * 2.42), r_cut = 12.0
+    coulomb = hoomd.md.long_range.pppm.make_pppm_coulomb_forces(
+        nlist=nl, resolution=[64, 64, 64], order=6, r_cut=12.0
     )
 
-    coulomb_special_pairs = hoomd.md.special_pair.coulomb()
-    coulomb_special_pairs.pair_coeff.set("CCCC", alpha = 0.5, r_cut = 12.0)
-    coulomb_special_pairs.pair_coeff.set("HCCC", alpha = 0.5, r_cut = 12.0)
-    coulomb_special_pairs.pair_coeff.set("HCCH", alpha = 0.5, r_cut = 12.0)
-
-    hoomd.md.integrate.mode_standard(dt = dt)
-    integrator = hoomd.md.integrate.nvt(group = hoomd.group.all(), kT = kT, tau = 100*dt)
-    integrator.randomize_velocities(seed = 42)
-
-    return hoomd.context.current
+    harmonic = hoomd.md.bond.Harmonic()
+    harmonic.params["CC"] = dict(k=2 * 268.0, r0=1.529)
+    harmonic.params["CH"] = dict(k=2 * 340.0, r0=1.09)
+
+    angle = hoomd.md.angle.Harmonic()
+    angle.params["CCC"] = dict(k=2 * 58.35, t0=112.7 * pi / 180)
+    angle.params["CCH"] = dict(k=2 * 37.5, t0=110.7 * pi / 180)
+    angle.params["HCH"] = dict(k=2 * 33.0, t0=107.8 * pi / 180)
+
+    dihedral = hoomd.md.dihedral.OPLS()
+    dihedral.params["CCCC"] = dict(k1=1.3, k2=-0.05, k3=0.2, k4=0.0)
+    dihedral.params["HCCC"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)
+    dihedral.params["HCCH"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)
+
+    lj_special_pairs = hoomd.md.special_pair.LJ()
+    lj_special_pairs.params["CCCC"] = dict(epsilon=0.07, sigma=3.55)
+    lj_special_pairs.params["HCCH"] = dict(epsilon=0.03, sigma=2.42)
+    lj_special_pairs.params["HCCC"] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))
+    lj_special_pairs.r_cut["CCCC"] = 12.0
+    lj_special_pairs.r_cut["HCCC"] = 12.0
+    lj_special_pairs.r_cut["HCCH"] = 12.0
+    coulomb_special_pairs = hoomd.md.special_pair.Coulomb()
+    coulomb_special_pairs.params["CCCC"] = dict(alpha=0.5)
+    coulomb_special_pairs.params["HCCC"] = dict(alpha=0.5)
+    coulomb_special_pairs.params["HCCH"] = dict(alpha=0.5)
+    coulomb_special_pairs.r_cut["HCCH"] = 12.0
+    coulomb_special_pairs.r_cut["CCCC"] = 12.0
+    coulomb_special_pairs.r_cut["HCCC"] = 12.0
+
+    nvt = hoomd.md.methods.Langevin(filter=hoomd.filter.All(), kT=kT)
+
+    integrator = hoomd.md.Integrator(dt=dt)
+    integrator.forces.append(lj)
+    integrator.forces.append(coulomb[0])
+    integrator.forces.append(coulomb[1])
+    integrator.forces.append(harmonic)
+    integrator.forces.append(angle)
+    integrator.forces.append(dihedral)
+    integrator.forces.append(lj_special_pairs)
+    integrator.forces.append(coulomb_special_pairs)
+    integrator.methods.append(nvt)
+    simulation.operations.integrator = integrator
+
+    return simulation
 ```
 
 <!-- #region id="3UrzENm_oo6U" -->
@@ -362,7 +390,7 @@ Make sure to run with GPU support, otherwise, it can take a very long time.
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="K951m4BbpUar" outputId="f01ca7e3-69f4-4218-9eb5-cdc022f877b8"
-run_result = pysages.run(method, generate_context, int(5e5))
+run_result = pysages.run(method, generate_simulation, int(5e5))
 ```
 
 <!-- #region id="PXBKUfK0p9T2" -->
@@ -387,9 +415,9 @@ fig, ax = plt.subplots()
 
 ax.set_xlabel(r"Dihedral Angle, $\xi$")
 ax.set_ylabel(r"$A(\xi)$")
-
 ax.plot(mesh, A)
-plt.gca()
+
+fig.show()
 ```
 
 <!-- #region id="Kf_CMdih90Cd" -->
diff --git a/examples/hoomd-blue/cff/Butane_CFF.ipynb b/examples/hoomd-blue/cff/Butane_CFF.ipynb
index be10b98a..049fe1aa 100644
--- a/examples/hoomd-blue/cff/Butane_CFF.ipynb
+++ b/examples/hoomd-blue/cff/Butane_CFF.ipynb
@@ -6,11 +6,10 @@
     "id": "T-Qkg9C9n7Cc"
    },
    "source": [
-    "\n",
     "# Setting up the environment\n",
     "\n",
-    "First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin.\n",
-    "We copy it from Google Drive and install PySAGES for it.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance."
    ]
   },
   {
@@ -23,9 +22,12 @@
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
@@ -79,46 +81,38 @@
     "ver = sys.version_info\n",
     "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "os.environ[\"XLA_FLAGS\"] = \"--xla_gpu_strict_conv_algorithm_picker=false\"\n",
     "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "we_mTkFioS6R"
+    "id": "Wy-75Pt7Bqs1"
    },
    "source": [
-    "\n",
-    "## PySAGES\n",
-    "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
+    "We'll also need some additional python dependencies"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "id": "vK0RZtbroQWe"
+    "id": "LpBucu3V81xm"
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "pip install -q --upgrade pip\n",
-    "# Installs the wheel compatible with CUDA.\n",
-    "pip install -q --upgrade \"jax[cuda]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null"
+    "!pip install -qq \"numpy<2\" gsd > /dev/null"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "wAtjM-IroYX8"
+    "id": "we_mTkFioS6R"
    },
    "source": [
+    "## PySAGES\n",
     "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
+    "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`."
    ]
   },
   {
@@ -129,12 +123,16 @@
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KBFVcG1FoeMq"
+   },
+   "source": [
+    "# CFF-biased simulations"
    ]
   },
   {
@@ -151,26 +149,15 @@
     "cd /content/cff"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KBFVcG1FoeMq"
-   },
-   "source": [
-    "\n",
-    "# CFF-biased simulations\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "0W2ukJuuojAl"
    },
    "source": [
-    "\n",
     "CFF gradually learns both the free energy and its gradient from a discrete estimate of the generalized mean forces (based on the same algorithm as the ABF method), and frequency of visits to sites in phase space. It employs a couple of neural networks to provide a continuous approximation to the free energy.\n",
     "\n",
-    "For this Colab, we are using butane as the example molecule.\n"
+    "For this Colab, we are using butane as the example molecule."
    ]
   },
   {
@@ -182,36 +169,31 @@
    "outputs": [],
    "source": [
     "import hoomd\n",
-    "import hoomd.md\n",
-    "\n",
-    "import numpy\n",
+    "import gsd.hoomd\n",
+    "import numpy as np\n",
     "\n",
     "\n",
-    "pi = numpy.pi\n",
+    "pi = np.pi\n",
     "kT = 0.596161\n",
     "dt = 0.02045\n",
-    "mode = \"--mode=gpu\"\n",
     "\n",
     "\n",
-    "def generate_context(kT = kT, dt = dt, mode = mode):\n",
+    "def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), seed = 42):\n",
     "    \"\"\"\n",
-    "    Generates a simulation context, we pass this function to the attribute\n",
-    "    `run` of our sampling method.\n",
+    "    Generates a simulation context to which will attatch our sampling method.\n",
     "    \"\"\"\n",
-    "    hoomd.context.initialize(mode)\n",
-    "\n",
-    "    ### System Definition\n",
-    "    snapshot = hoomd.data.make_snapshot(\n",
-    "        N = 14,\n",
-    "        box = hoomd.data.boxdim(Lx = 41, Ly = 41, Lz = 41),\n",
-    "        particle_types = ['C', 'H'],\n",
-    "        bond_types = [\"CC\", \"CH\"],\n",
-    "        angle_types = [\"CCC\", \"CCH\", \"HCH\"],\n",
-    "        dihedral_types = [\"CCCC\", \"HCCC\", \"HCCH\"],\n",
-    "        pair_types = [\"CCCC\", \"HCCC\", \"HCCH\"],\n",
-    "        dtype = \"double\"\n",
-    "    )\n",
+    "    simulation = hoomd.Simulation(device=device, seed=seed)\n",
+    "\n",
+    "    snapshot = gsd.hoomd.Frame()\n",
     "\n",
+    "    snapshot.configuration.box = [41, 41, 41, 0, 0, 0]\n",
+    "\n",
+    "    snapshot.particles.N = N = 14\n",
+    "    snapshot.particles.types = [\"C\", \"H\"]\n",
+    "    snapshot.particles.typeid = np.zeros(N, dtype=int)\n",
+    "    snapshot.particles.position = np.zeros((N, 3))\n",
+    "    snapshot.particles.mass = np.zeros(N, dtype=float)\n",
+    "    snapshot.particles.charge = np.zeros(N, dtype=float)\n",
     "    snapshot.particles.typeid[0] = 0\n",
     "    snapshot.particles.typeid[1:4] = 1\n",
     "    snapshot.particles.typeid[4] = 0\n",
@@ -221,52 +203,60 @@
     "    snapshot.particles.typeid[10] = 0\n",
     "    snapshot.particles.typeid[11:14] = 1\n",
     "\n",
-    "    positions = numpy.array([\n",
-    "        [-2.990196,  0.097881,  0.000091],\n",
-    "        [-2.634894, -0.911406,  0.001002],\n",
-    "        [-2.632173,  0.601251, -0.873601],\n",
-    "        [-4.060195,  0.099327, -0.000736],\n",
-    "        [-2.476854,  0.823942,  1.257436],\n",
-    "        [-2.832157,  1.833228,  1.256526],\n",
-    "        [-2.834877,  0.320572,  2.131128],\n",
-    "        [-0.936856,  0.821861,  1.258628],\n",
-    "        [-0.578833,  1.325231,  0.384935],\n",
-    "        [-0.581553, -0.187426,  1.259538],\n",
-    "        [-0.423514,  1.547922,  2.515972],\n",
-    "        [-0.781537,  1.044552,  3.389664],\n",
-    "        [ 0.646485,  1.546476,  2.516800],\n",
-    "        [-0.778816,  2.557208,  2.515062]\n",
-    "    ])\n",
-    "\n",
-    "    reference_box_low_coords = numpy.array([-22.206855, -19.677099, -19.241968])\n",
-    "    box_low_coords = numpy.array([\n",
-    "        -snapshot.box.Lx / 2,\n",
-    "        -snapshot.box.Ly / 2,\n",
-    "        -snapshot.box.Lz / 2\n",
-    "    ])\n",
-    "    positions += (box_low_coords - reference_box_low_coords)\n",
+    "    positions = np.array(\n",
+    "        [\n",
+    "            [-2.990196, 0.097881, 0.000091],\n",
+    "            [-2.634894, -0.911406, 0.001002],\n",
+    "            [-2.632173, 0.601251, -0.873601],\n",
+    "            [-4.060195, 0.099327, -0.000736],\n",
+    "            [-2.476854, 0.823942, 1.257436],\n",
+    "            [-2.832157, 1.833228, 1.256526],\n",
+    "            [-2.834877, 0.320572, 2.131128],\n",
+    "            [-0.936856, 0.821861, 1.258628],\n",
+    "            [-0.578833, 1.325231, 0.384935],\n",
+    "            [-0.581553, -0.187426, 1.259538],\n",
+    "            [-0.423514, 1.547922, 2.515972],\n",
+    "            [-0.781537, 1.044552, 3.389664],\n",
+    "            [0.646485, 1.546476, 2.516800],\n",
+    "            [-0.778816, 2.557208, 2.515062],\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    reference_box_low_coords = np.array([-22.206855, -19.677099, -19.241968])\n",
+    "    box_low_coords = np.array([-41.0 / 2, -41.0 / 2, -41.0 / 2])\n",
+    "    positions += box_low_coords - reference_box_low_coords\n",
     "\n",
     "    snapshot.particles.position[:] = positions[:]\n",
     "\n",
     "    mC = 12.00\n",
     "    mH = 1.008\n",
+    "\n",
+    "    # fmt: off\n",
     "    snapshot.particles.mass[:] = [\n",
-    "        mC, mH, mH, mH,\n",
+    "        mC, mH, mH, mH,  # grouped by carbon atoms\n",
     "        mC, mH, mH,\n",
     "        mC, mH, mH,\n",
-    "        mC, mH, mH, mH\n",
+    "        mC, mH, mH, mH,\n",
     "    ]\n",
     "\n",
-    "    reference_charges = numpy.array([\n",
-    "        -0.180000, 0.060000, 0.060000, 0.060000,\n",
-    "        -0.120000, 0.060000, 0.060000,\n",
-    "        -0.120000, 0.060000, 0.060000,\n",
-    "        -0.180000, 0.060000, 0.060000, 0.060000]\n",
+    "    reference_charges = np.array(\n",
+    "        [\n",
+    "            -0.180000, 0.060000, 0.060000, 0.060000,  # grouped by carbon atoms\n",
+    "            -0.120000, 0.060000, 0.060000,\n",
+    "            -0.120000, 0.060000, 0.060000,\n",
+    "            -0.180000, 0.060000, 0.060000, 0.060000,\n",
+    "        ]\n",
     "    )\n",
+    "    # fmt: on\n",
+    "\n",
     "    charge_conversion = 18.22262\n",
     "    snapshot.particles.charge[:] = charge_conversion * reference_charges[:]\n",
     "\n",
-    "    snapshot.bonds.resize(13)\n",
+    "    snapshot.particles.validate()\n",
+    "\n",
+    "    snapshot.bonds.N = 13\n",
+    "    snapshot.bonds.types = [\"CC\", \"CH\"]\n",
+    "    snapshot.bonds.typeid = np.zeros(13, dtype=int)\n",
     "    snapshot.bonds.typeid[0:3] = 1\n",
     "    snapshot.bonds.typeid[3] = 0\n",
     "    snapshot.bonds.typeid[4:6] = 1\n",
@@ -275,14 +265,19 @@
     "    snapshot.bonds.typeid[9] = 0\n",
     "    snapshot.bonds.typeid[10:13] = 1\n",
     "\n",
+    "    snapshot.bonds.group = np.zeros((13, 2), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.bonds.group[:] = [\n",
-    "        [0, 2], [0, 1], [0, 3], [0, 4],\n",
+    "        [0, 2], [0, 1], [0, 3], [0, 4],  # grouped by carbon atoms\n",
     "        [4, 5], [4, 6], [4, 7],\n",
     "        [7, 8], [7, 9], [7, 10],\n",
-    "        [10, 11], [10, 12], [10, 13]\n",
+    "        [10, 11], [10, 12], [10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.angles.resize(24)\n",
+    "    snapshot.angles.N = 24\n",
+    "    snapshot.angles.types = [\"CCC\", \"CCH\", \"HCH\"]\n",
+    "    snapshot.angles.typeid = np.zeros(24, dtype=int)\n",
     "    snapshot.angles.typeid[0:2] = 2\n",
     "    snapshot.angles.typeid[2] = 1\n",
     "    snapshot.angles.typeid[3] = 2\n",
@@ -295,18 +290,26 @@
     "    snapshot.angles.typeid[16:21] = 1\n",
     "    snapshot.angles.typeid[21:24] = 2\n",
     "\n",
+    "    snapshot.angles.group = np.zeros((24, 3), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.angles.group[:] = [\n",
-    "        [1, 0, 2], [2, 0, 3], [2, 0, 4],\n",
+    "        [1, 0, 2], [2, 0, 3], [2, 0, 4],  # grouped by carbon atoms\n",
     "        [1, 0, 3], [1, 0, 4], [3, 0, 4],\n",
+    "        # ---\n",
     "        [0, 4, 5], [0, 4, 6], [0, 4, 7],\n",
     "        [5, 4, 6], [5, 4, 7], [6, 4, 7],\n",
+    "        # ---\n",
     "        [4, 7, 8], [4, 7, 9], [4, 7, 10],\n",
     "        [8, 7, 9], [8, 7, 10], [9, 7, 10],\n",
+    "        # ---\n",
     "        [7, 10, 11], [7, 10, 12], [7, 10, 13],\n",
-    "        [11, 10, 12], [11, 10, 13], [12, 10, 13]\n",
+    "        [11, 10, 12], [11, 10, 13], [12, 10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.dihedrals.resize(27)\n",
+    "    snapshot.dihedrals.N = 27\n",
+    "    snapshot.dihedrals.types = [\"CCCC\", \"HCCC\", \"HCCH\"]\n",
+    "    snapshot.dihedrals.typeid = np.zeros(27, dtype=int)\n",
     "    snapshot.dihedrals.typeid[0:2] = 2\n",
     "    snapshot.dihedrals.typeid[2] = 1\n",
     "    snapshot.dihedrals.typeid[3:5] = 2\n",
@@ -320,81 +323,109 @@
     "    snapshot.dihedrals.typeid[17:21] = 1\n",
     "    snapshot.dihedrals.typeid[21:27] = 2\n",
     "\n",
+    "    snapshot.dihedrals.group = np.zeros((27, 4), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.dihedrals.group[:] = [\n",
-    "        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],\n",
+    "        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],  # grouped by pairs of central atoms\n",
     "        [1, 0, 4, 5], [1, 0, 4, 6], [1, 0, 4, 7],\n",
     "        [3, 0, 4, 5], [3, 0, 4, 6], [3, 0, 4, 7],\n",
+    "        # ---\n",
     "        [0, 4, 7, 8], [0, 4, 7, 9], [0, 4, 7, 10],\n",
     "        [5, 4, 7, 8], [5, 4, 7, 9], [5, 4, 7, 10],\n",
     "        [6, 4, 7, 8], [6, 4, 7, 9], [6, 4, 7, 10],\n",
+    "        # ---\n",
     "        [4, 7, 10, 11], [4, 7, 10, 12], [4, 7, 10, 13],\n",
     "        [8, 7, 10, 11], [8, 7, 10, 12], [8, 7, 10, 13],\n",
-    "        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13]\n",
+    "        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.pairs.resize(27)\n",
+    "    snapshot.pairs.N = 27\n",
+    "    snapshot.pairs.types = [\"CCCC\", \"HCCC\", \"HCCH\"]\n",
+    "    snapshot.pairs.typeid = np.zeros(27, dtype=int)\n",
     "    snapshot.pairs.typeid[0:1] = 0\n",
     "    snapshot.pairs.typeid[1:11] = 1\n",
     "    snapshot.pairs.typeid[11:27] = 2\n",
+    "    snapshot.pairs.group = np.zeros((27, 2), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.pairs.group[:] = [\n",
     "        # CCCC\n",
     "        [0, 10],\n",
     "        # HCCC\n",
-    "        [0, 8], [0, 9], [5, 10], [6, 10],\n",
+    "        [0, 8],\n",
+    "        [0, 9],\n",
+    "        [5, 10], [6, 10],\n",
     "        [1, 7], [2, 7], [3, 7],\n",
     "        [11, 4], [12, 4], [13, 4],\n",
     "        # HCCH\n",
-    "        [1, 5], [1, 6], [2, 5], [2, 6], [3, 5], [3, 6],\n",
-    "        [5, 8], [6, 8], [5, 9], [6, 9],\n",
-    "        [8, 11], [8, 12], [8, 13], [9, 11], [9, 12], [9, 13]\n",
+    "        [1, 5], [1, 6],\n",
+    "        [2, 5], [2, 6],\n",
+    "        [3, 5], [3, 6],\n",
+    "        [5, 8], [6, 8],\n",
+    "        [5, 9], [6, 9],\n",
+    "        [8, 11], [8, 12], [8, 13],\n",
+    "        [9, 11], [9, 12], [9, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    hoomd.init.read_snapshot(snapshot)\n",
-    "\n",
-    "    ### Set interactions\n",
-    "    nl_ex = hoomd.md.nlist.cell()\n",
-    "    nl_ex.reset_exclusions(exclusions = [\"1-2\", \"1-3\", \"1-4\"])\n",
-    "\n",
-    "    lj = hoomd.md.pair.lj(r_cut = 12.0, nlist = nl_ex)\n",
-    "    lj.pair_coeff.set('C', 'C', epsilon = 0.07, sigma = 3.55)\n",
-    "    lj.pair_coeff.set('H', 'H', epsilon = 0.03, sigma = 2.42)\n",
-    "    lj.pair_coeff.set('C', 'H', epsilon = numpy.sqrt(0.07*0.03), sigma = numpy.sqrt(3.55*2.42))\n",
+    "    simulation.create_state_from_snapshot(snapshot, domain_decomposition=(None, None, None))\n",
+    "    simulation.run(0)\n",
     "\n",
-    "    coulomb = hoomd.md.charge.pppm(hoomd.group.charged(), nlist = nl_ex)\n",
-    "    coulomb.set_params(Nx = 64, Ny = 64, Nz = 64, order = 6, rcut = 12.0)\n",
+    "    exclusions = [\"bond\", \"1-3\", \"1-4\"]\n",
+    "    nl = hoomd.md.nlist.Cell(buffer=0.4, exclusions=exclusions)\n",
+    "    lj = hoomd.md.pair.LJ(nlist=nl, default_r_cut=12.0)\n",
+    "    lj.params[(\"C\", \"C\")] = dict(epsilon=0.07, sigma=3.55)\n",
+    "    lj.params[(\"H\", \"H\")] = dict(epsilon=0.03, sigma=2.42)\n",
+    "    lj.params[(\"C\", \"H\")] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))\n",
     "\n",
-    "    harmonic = hoomd.md.bond.harmonic()\n",
-    "    harmonic.bond_coeff.set(\"CC\", k = 2*268.0, r0 = 1.529)\n",
-    "    harmonic.bond_coeff.set(\"CH\", k = 2*340.0, r0 = 1.09)\n",
-    "\n",
-    "    angle = hoomd.md.angle.harmonic()\n",
-    "    angle.angle_coeff.set(\"CCC\", k = 2*58.35, t0 = 112.7 * pi / 180)\n",
-    "    angle.angle_coeff.set(\"CCH\", k = 2*37.5, t0 = 110.7 * pi / 180)\n",
-    "    angle.angle_coeff.set(\"HCH\", k = 2*33.0, t0 = 107.8 * pi / 180)\n",
-    "\n",
-    "\n",
-    "    dihedral = hoomd.md.dihedral.opls()\n",
-    "    dihedral.dihedral_coeff.set(\"CCCC\", k1 = 1.3, k2 = -0.05, k3 = 0.2, k4 = 0.0)\n",
-    "    dihedral.dihedral_coeff.set(\"HCCC\", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)\n",
-    "    dihedral.dihedral_coeff.set(\"HCCH\", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)\n",
-    "\n",
-    "    lj_special_pairs = hoomd.md.special_pair.lj()\n",
-    "    lj_special_pairs.pair_coeff.set(\"CCCC\", epsilon = 0.07, sigma = 3.55, r_cut = 12.0)\n",
-    "    lj_special_pairs.pair_coeff.set(\"HCCH\", epsilon = 0.03, sigma = 2.42, r_cut = 12.0)\n",
-    "    lj_special_pairs.pair_coeff.set(\"HCCC\",\n",
-    "        epsilon = numpy.sqrt(0.07 * 0.03), sigma = numpy.sqrt(3.55 * 2.42), r_cut = 12.0\n",
+    "    coulomb = hoomd.md.long_range.pppm.make_pppm_coulomb_forces(\n",
+    "        nlist=nl, resolution=[64, 64, 64], order=6, r_cut=12.0\n",
     "    )\n",
     "\n",
-    "    coulomb_special_pairs = hoomd.md.special_pair.coulomb()\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"CCCC\", alpha = 0.5, r_cut = 12.0)\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"HCCC\", alpha = 0.5, r_cut = 12.0)\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"HCCH\", alpha = 0.5, r_cut = 12.0)\n",
-    "\n",
-    "    hoomd.md.integrate.mode_standard(dt = dt)\n",
-    "    integrator = hoomd.md.integrate.nvt(group = hoomd.group.all(), kT = kT, tau = 100*dt)\n",
-    "    integrator.randomize_velocities(seed = 42)\n",
-    "\n",
-    "    return hoomd.context.current"
+    "    harmonic = hoomd.md.bond.Harmonic()\n",
+    "    harmonic.params[\"CC\"] = dict(k=2 * 268.0, r0=1.529)\n",
+    "    harmonic.params[\"CH\"] = dict(k=2 * 340.0, r0=1.09)\n",
+    "\n",
+    "    angle = hoomd.md.angle.Harmonic()\n",
+    "    angle.params[\"CCC\"] = dict(k=2 * 58.35, t0=112.7 * pi / 180)\n",
+    "    angle.params[\"CCH\"] = dict(k=2 * 37.5, t0=110.7 * pi / 180)\n",
+    "    angle.params[\"HCH\"] = dict(k=2 * 33.0, t0=107.8 * pi / 180)\n",
+    "\n",
+    "    dihedral = hoomd.md.dihedral.OPLS()\n",
+    "    dihedral.params[\"CCCC\"] = dict(k1=1.3, k2=-0.05, k3=0.2, k4=0.0)\n",
+    "    dihedral.params[\"HCCC\"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)\n",
+    "    dihedral.params[\"HCCH\"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)\n",
+    "\n",
+    "    lj_special_pairs = hoomd.md.special_pair.LJ()\n",
+    "    lj_special_pairs.params[\"CCCC\"] = dict(epsilon=0.07, sigma=3.55)\n",
+    "    lj_special_pairs.params[\"HCCH\"] = dict(epsilon=0.03, sigma=2.42)\n",
+    "    lj_special_pairs.params[\"HCCC\"] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))\n",
+    "    lj_special_pairs.r_cut[\"CCCC\"] = 12.0\n",
+    "    lj_special_pairs.r_cut[\"HCCC\"] = 12.0\n",
+    "    lj_special_pairs.r_cut[\"HCCH\"] = 12.0\n",
+    "    coulomb_special_pairs = hoomd.md.special_pair.Coulomb()\n",
+    "    coulomb_special_pairs.params[\"CCCC\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.params[\"HCCC\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.params[\"HCCH\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.r_cut[\"HCCH\"] = 12.0\n",
+    "    coulomb_special_pairs.r_cut[\"CCCC\"] = 12.0\n",
+    "    coulomb_special_pairs.r_cut[\"HCCC\"] = 12.0\n",
+    "\n",
+    "    nvt = hoomd.md.methods.Langevin(filter=hoomd.filter.All(), kT=kT)\n",
+    "\n",
+    "    integrator = hoomd.md.Integrator(dt=dt)\n",
+    "    integrator.forces.append(lj)\n",
+    "    integrator.forces.append(coulomb[0])\n",
+    "    integrator.forces.append(coulomb[1])\n",
+    "    integrator.forces.append(harmonic)\n",
+    "    integrator.forces.append(angle)\n",
+    "    integrator.forces.append(dihedral)\n",
+    "    integrator.forces.append(lj_special_pairs)\n",
+    "    integrator.forces.append(coulomb_special_pairs)\n",
+    "    integrator.methods.append(nvt)\n",
+    "    simulation.operations.integrator = integrator\n",
+    "\n",
+    "    return simulation"
    ]
   },
   {
@@ -597,7 +628,7 @@
     }
    ],
    "source": [
-    "raw_result = pysages.run(method, generate_context, int(5e5))"
+    "raw_result = pysages.run(method, generate_simulation, int(5e5))"
    ]
   },
   {
@@ -697,19 +728,10 @@
     "\n",
     "ax.set_xlabel(r\"Dihedral Angle, $\\xi$\")\n",
     "ax.set_ylabel(r\"$A(\\xi)$\")\n",
-    "\n",
     "ax.plot(mesh, A)\n",
-    "plt.gca()"
+    "\n",
+    "fig.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "z0ye1g-1sH2g"
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/hoomd-blue/cff/Butane_CFF.md b/examples/hoomd-blue/cff/Butane_CFF.md
index 35aea699..5016bee3 100644
--- a/examples/hoomd-blue/cff/Butane_CFF.md
+++ b/examples/hoomd-blue/cff/Butane_CFF.md
@@ -14,19 +14,20 @@ jupyter:
 ---
 
 <!-- #region id="T-Qkg9C9n7Cc" -->
-
 # Setting up the environment
 
-First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin.
-We copy it from Google Drive and install PySAGES for it.
-
+First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 <!-- #endregion -->
 
 ```bash id="3eTbKklCnyd_"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
+
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="acc3a92c-182f-415b-d8dc-b5af076b3d01"
@@ -46,92 +47,70 @@ import sys
 ver = sys.version_info
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
 
-os.environ["XLA_FLAGS"] = "--xla_gpu_strict_conv_algorithm_picker=false"
 os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
-<!-- #region id="we_mTkFioS6R" -->
-
-## PySAGES
-
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
+<!-- #region id="Wy-75Pt7Bqs1" -->
+We'll also need some additional python dependencies
 <!-- #endregion -->
 
-```bash id="vK0RZtbroQWe"
-
-pip install -q --upgrade pip
-# Installs the wheel compatible with CUDA.
-pip install -q --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null
+```python id="LpBucu3V81xm"
+!pip install -qq "numpy<2" gsd > /dev/null
 ```
 
-<!-- #region id="wAtjM-IroYX8" -->
-
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
+<!-- #region id="we_mTkFioS6R" -->
+## PySAGES
 
+The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`.
 <!-- #endregion -->
 
-```bash id="B-HB9CzioV5j"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
+<!-- #region id="KBFVcG1FoeMq" -->
+# CFF-biased simulations
+<!-- #endregion -->
+
 ```bash id="ppTzMmyyobHB"
 
 mkdir /content/cff
 cd /content/cff
 ```
 
-<!-- #region id="KBFVcG1FoeMq" -->
-
-# CFF-biased simulations
-
-<!-- #endregion -->
-
 <!-- #region id="0W2ukJuuojAl" -->
-
 CFF gradually learns both the free energy and its gradient from a discrete estimate of the generalized mean forces (based on the same algorithm as the ABF method), and frequency of visits to sites in phase space. It employs a couple of neural networks to provide a continuous approximation to the free energy.
 
 For this Colab, we are using butane as the example molecule.
-
 <!-- #endregion -->
 
 ```python id="BBvC7Spoog82"
 import hoomd
-import hoomd.md
-
-import numpy
+import gsd.hoomd
+import numpy as np
 
 
-pi = numpy.pi
+pi = np.pi
 kT = 0.596161
 dt = 0.02045
-mode = "--mode=gpu"
 
 
-def generate_context(kT = kT, dt = dt, mode = mode):
+def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), seed = 42):
     """
-    Generates a simulation context, we pass this function to the attribute
-    `run` of our sampling method.
+    Generates a simulation context to which will attatch our sampling method.
     """
-    hoomd.context.initialize(mode)
-
-    ### System Definition
-    snapshot = hoomd.data.make_snapshot(
-        N = 14,
-        box = hoomd.data.boxdim(Lx = 41, Ly = 41, Lz = 41),
-        particle_types = ['C', 'H'],
-        bond_types = ["CC", "CH"],
-        angle_types = ["CCC", "CCH", "HCH"],
-        dihedral_types = ["CCCC", "HCCC", "HCCH"],
-        pair_types = ["CCCC", "HCCC", "HCCH"],
-        dtype = "double"
-    )
+    simulation = hoomd.Simulation(device=device, seed=seed)
 
+    snapshot = gsd.hoomd.Frame()
+
+    snapshot.configuration.box = [41, 41, 41, 0, 0, 0]
+
+    snapshot.particles.N = N = 14
+    snapshot.particles.types = ["C", "H"]
+    snapshot.particles.typeid = np.zeros(N, dtype=int)
+    snapshot.particles.position = np.zeros((N, 3))
+    snapshot.particles.mass = np.zeros(N, dtype=float)
+    snapshot.particles.charge = np.zeros(N, dtype=float)
     snapshot.particles.typeid[0] = 0
     snapshot.particles.typeid[1:4] = 1
     snapshot.particles.typeid[4] = 0
@@ -141,52 +120,60 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.particles.typeid[10] = 0
     snapshot.particles.typeid[11:14] = 1
 
-    positions = numpy.array([
-        [-2.990196,  0.097881,  0.000091],
-        [-2.634894, -0.911406,  0.001002],
-        [-2.632173,  0.601251, -0.873601],
-        [-4.060195,  0.099327, -0.000736],
-        [-2.476854,  0.823942,  1.257436],
-        [-2.832157,  1.833228,  1.256526],
-        [-2.834877,  0.320572,  2.131128],
-        [-0.936856,  0.821861,  1.258628],
-        [-0.578833,  1.325231,  0.384935],
-        [-0.581553, -0.187426,  1.259538],
-        [-0.423514,  1.547922,  2.515972],
-        [-0.781537,  1.044552,  3.389664],
-        [ 0.646485,  1.546476,  2.516800],
-        [-0.778816,  2.557208,  2.515062]
-    ])
-
-    reference_box_low_coords = numpy.array([-22.206855, -19.677099, -19.241968])
-    box_low_coords = numpy.array([
-        -snapshot.box.Lx / 2,
-        -snapshot.box.Ly / 2,
-        -snapshot.box.Lz / 2
-    ])
-    positions += (box_low_coords - reference_box_low_coords)
+    positions = np.array(
+        [
+            [-2.990196, 0.097881, 0.000091],
+            [-2.634894, -0.911406, 0.001002],
+            [-2.632173, 0.601251, -0.873601],
+            [-4.060195, 0.099327, -0.000736],
+            [-2.476854, 0.823942, 1.257436],
+            [-2.832157, 1.833228, 1.256526],
+            [-2.834877, 0.320572, 2.131128],
+            [-0.936856, 0.821861, 1.258628],
+            [-0.578833, 1.325231, 0.384935],
+            [-0.581553, -0.187426, 1.259538],
+            [-0.423514, 1.547922, 2.515972],
+            [-0.781537, 1.044552, 3.389664],
+            [0.646485, 1.546476, 2.516800],
+            [-0.778816, 2.557208, 2.515062],
+        ]
+    )
+
+    reference_box_low_coords = np.array([-22.206855, -19.677099, -19.241968])
+    box_low_coords = np.array([-41.0 / 2, -41.0 / 2, -41.0 / 2])
+    positions += box_low_coords - reference_box_low_coords
 
     snapshot.particles.position[:] = positions[:]
 
     mC = 12.00
     mH = 1.008
+
+    # fmt: off
     snapshot.particles.mass[:] = [
-        mC, mH, mH, mH,
+        mC, mH, mH, mH,  # grouped by carbon atoms
         mC, mH, mH,
         mC, mH, mH,
-        mC, mH, mH, mH
+        mC, mH, mH, mH,
     ]
 
-    reference_charges = numpy.array([
-        -0.180000, 0.060000, 0.060000, 0.060000,
-        -0.120000, 0.060000, 0.060000,
-        -0.120000, 0.060000, 0.060000,
-        -0.180000, 0.060000, 0.060000, 0.060000]
+    reference_charges = np.array(
+        [
+            -0.180000, 0.060000, 0.060000, 0.060000,  # grouped by carbon atoms
+            -0.120000, 0.060000, 0.060000,
+            -0.120000, 0.060000, 0.060000,
+            -0.180000, 0.060000, 0.060000, 0.060000,
+        ]
     )
+    # fmt: on
+
     charge_conversion = 18.22262
     snapshot.particles.charge[:] = charge_conversion * reference_charges[:]
 
-    snapshot.bonds.resize(13)
+    snapshot.particles.validate()
+
+    snapshot.bonds.N = 13
+    snapshot.bonds.types = ["CC", "CH"]
+    snapshot.bonds.typeid = np.zeros(13, dtype=int)
     snapshot.bonds.typeid[0:3] = 1
     snapshot.bonds.typeid[3] = 0
     snapshot.bonds.typeid[4:6] = 1
@@ -195,14 +182,19 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.bonds.typeid[9] = 0
     snapshot.bonds.typeid[10:13] = 1
 
+    snapshot.bonds.group = np.zeros((13, 2), dtype=int)
+    # fmt: off
     snapshot.bonds.group[:] = [
-        [0, 2], [0, 1], [0, 3], [0, 4],
+        [0, 2], [0, 1], [0, 3], [0, 4],  # grouped by carbon atoms
         [4, 5], [4, 6], [4, 7],
         [7, 8], [7, 9], [7, 10],
-        [10, 11], [10, 12], [10, 13]
+        [10, 11], [10, 12], [10, 13],
     ]
+    # fmt: on
 
-    snapshot.angles.resize(24)
+    snapshot.angles.N = 24
+    snapshot.angles.types = ["CCC", "CCH", "HCH"]
+    snapshot.angles.typeid = np.zeros(24, dtype=int)
     snapshot.angles.typeid[0:2] = 2
     snapshot.angles.typeid[2] = 1
     snapshot.angles.typeid[3] = 2
@@ -215,18 +207,26 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.angles.typeid[16:21] = 1
     snapshot.angles.typeid[21:24] = 2
 
+    snapshot.angles.group = np.zeros((24, 3), dtype=int)
+    # fmt: off
     snapshot.angles.group[:] = [
-        [1, 0, 2], [2, 0, 3], [2, 0, 4],
+        [1, 0, 2], [2, 0, 3], [2, 0, 4],  # grouped by carbon atoms
         [1, 0, 3], [1, 0, 4], [3, 0, 4],
+        # ---
         [0, 4, 5], [0, 4, 6], [0, 4, 7],
         [5, 4, 6], [5, 4, 7], [6, 4, 7],
+        # ---
         [4, 7, 8], [4, 7, 9], [4, 7, 10],
         [8, 7, 9], [8, 7, 10], [9, 7, 10],
+        # ---
         [7, 10, 11], [7, 10, 12], [7, 10, 13],
-        [11, 10, 12], [11, 10, 13], [12, 10, 13]
+        [11, 10, 12], [11, 10, 13], [12, 10, 13],
     ]
+    # fmt: on
 
-    snapshot.dihedrals.resize(27)
+    snapshot.dihedrals.N = 27
+    snapshot.dihedrals.types = ["CCCC", "HCCC", "HCCH"]
+    snapshot.dihedrals.typeid = np.zeros(27, dtype=int)
     snapshot.dihedrals.typeid[0:2] = 2
     snapshot.dihedrals.typeid[2] = 1
     snapshot.dihedrals.typeid[3:5] = 2
@@ -240,81 +240,109 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.dihedrals.typeid[17:21] = 1
     snapshot.dihedrals.typeid[21:27] = 2
 
+    snapshot.dihedrals.group = np.zeros((27, 4), dtype=int)
+    # fmt: off
     snapshot.dihedrals.group[:] = [
-        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],
+        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],  # grouped by pairs of central atoms
         [1, 0, 4, 5], [1, 0, 4, 6], [1, 0, 4, 7],
         [3, 0, 4, 5], [3, 0, 4, 6], [3, 0, 4, 7],
+        # ---
         [0, 4, 7, 8], [0, 4, 7, 9], [0, 4, 7, 10],
         [5, 4, 7, 8], [5, 4, 7, 9], [5, 4, 7, 10],
         [6, 4, 7, 8], [6, 4, 7, 9], [6, 4, 7, 10],
+        # ---
         [4, 7, 10, 11], [4, 7, 10, 12], [4, 7, 10, 13],
         [8, 7, 10, 11], [8, 7, 10, 12], [8, 7, 10, 13],
-        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13]
+        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13],
     ]
+    # fmt: on
 
-    snapshot.pairs.resize(27)
+    snapshot.pairs.N = 27
+    snapshot.pairs.types = ["CCCC", "HCCC", "HCCH"]
+    snapshot.pairs.typeid = np.zeros(27, dtype=int)
     snapshot.pairs.typeid[0:1] = 0
     snapshot.pairs.typeid[1:11] = 1
     snapshot.pairs.typeid[11:27] = 2
+    snapshot.pairs.group = np.zeros((27, 2), dtype=int)
+    # fmt: off
     snapshot.pairs.group[:] = [
         # CCCC
         [0, 10],
         # HCCC
-        [0, 8], [0, 9], [5, 10], [6, 10],
+        [0, 8],
+        [0, 9],
+        [5, 10], [6, 10],
         [1, 7], [2, 7], [3, 7],
         [11, 4], [12, 4], [13, 4],
         # HCCH
-        [1, 5], [1, 6], [2, 5], [2, 6], [3, 5], [3, 6],
-        [5, 8], [6, 8], [5, 9], [6, 9],
-        [8, 11], [8, 12], [8, 13], [9, 11], [9, 12], [9, 13]
+        [1, 5], [1, 6],
+        [2, 5], [2, 6],
+        [3, 5], [3, 6],
+        [5, 8], [6, 8],
+        [5, 9], [6, 9],
+        [8, 11], [8, 12], [8, 13],
+        [9, 11], [9, 12], [9, 13],
     ]
+    # fmt: on
 
-    hoomd.init.read_snapshot(snapshot)
+    simulation.create_state_from_snapshot(snapshot, domain_decomposition=(None, None, None))
+    simulation.run(0)
 
-    ### Set interactions
-    nl_ex = hoomd.md.nlist.cell()
-    nl_ex.reset_exclusions(exclusions = ["1-2", "1-3", "1-4"])
+    exclusions = ["bond", "1-3", "1-4"]
+    nl = hoomd.md.nlist.Cell(buffer=0.4, exclusions=exclusions)
+    lj = hoomd.md.pair.LJ(nlist=nl, default_r_cut=12.0)
+    lj.params[("C", "C")] = dict(epsilon=0.07, sigma=3.55)
+    lj.params[("H", "H")] = dict(epsilon=0.03, sigma=2.42)
+    lj.params[("C", "H")] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))
 
-    lj = hoomd.md.pair.lj(r_cut = 12.0, nlist = nl_ex)
-    lj.pair_coeff.set('C', 'C', epsilon = 0.07, sigma = 3.55)
-    lj.pair_coeff.set('H', 'H', epsilon = 0.03, sigma = 2.42)
-    lj.pair_coeff.set('C', 'H', epsilon = numpy.sqrt(0.07*0.03), sigma = numpy.sqrt(3.55*2.42))
-
-    coulomb = hoomd.md.charge.pppm(hoomd.group.charged(), nlist = nl_ex)
-    coulomb.set_params(Nx = 64, Ny = 64, Nz = 64, order = 6, rcut = 12.0)
-
-    harmonic = hoomd.md.bond.harmonic()
-    harmonic.bond_coeff.set("CC", k = 2*268.0, r0 = 1.529)
-    harmonic.bond_coeff.set("CH", k = 2*340.0, r0 = 1.09)
-
-    angle = hoomd.md.angle.harmonic()
-    angle.angle_coeff.set("CCC", k = 2*58.35, t0 = 112.7 * pi / 180)
-    angle.angle_coeff.set("CCH", k = 2*37.5, t0 = 110.7 * pi / 180)
-    angle.angle_coeff.set("HCH", k = 2*33.0, t0 = 107.8 * pi / 180)
-
-
-    dihedral = hoomd.md.dihedral.opls()
-    dihedral.dihedral_coeff.set("CCCC", k1 = 1.3, k2 = -0.05, k3 = 0.2, k4 = 0.0)
-    dihedral.dihedral_coeff.set("HCCC", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)
-    dihedral.dihedral_coeff.set("HCCH", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)
-
-    lj_special_pairs = hoomd.md.special_pair.lj()
-    lj_special_pairs.pair_coeff.set("CCCC", epsilon = 0.07, sigma = 3.55, r_cut = 12.0)
-    lj_special_pairs.pair_coeff.set("HCCH", epsilon = 0.03, sigma = 2.42, r_cut = 12.0)
-    lj_special_pairs.pair_coeff.set("HCCC",
-        epsilon = numpy.sqrt(0.07 * 0.03), sigma = numpy.sqrt(3.55 * 2.42), r_cut = 12.0
+    coulomb = hoomd.md.long_range.pppm.make_pppm_coulomb_forces(
+        nlist=nl, resolution=[64, 64, 64], order=6, r_cut=12.0
     )
 
-    coulomb_special_pairs = hoomd.md.special_pair.coulomb()
-    coulomb_special_pairs.pair_coeff.set("CCCC", alpha = 0.5, r_cut = 12.0)
-    coulomb_special_pairs.pair_coeff.set("HCCC", alpha = 0.5, r_cut = 12.0)
-    coulomb_special_pairs.pair_coeff.set("HCCH", alpha = 0.5, r_cut = 12.0)
-
-    hoomd.md.integrate.mode_standard(dt = dt)
-    integrator = hoomd.md.integrate.nvt(group = hoomd.group.all(), kT = kT, tau = 100*dt)
-    integrator.randomize_velocities(seed = 42)
-
-    return hoomd.context.current
+    harmonic = hoomd.md.bond.Harmonic()
+    harmonic.params["CC"] = dict(k=2 * 268.0, r0=1.529)
+    harmonic.params["CH"] = dict(k=2 * 340.0, r0=1.09)
+
+    angle = hoomd.md.angle.Harmonic()
+    angle.params["CCC"] = dict(k=2 * 58.35, t0=112.7 * pi / 180)
+    angle.params["CCH"] = dict(k=2 * 37.5, t0=110.7 * pi / 180)
+    angle.params["HCH"] = dict(k=2 * 33.0, t0=107.8 * pi / 180)
+
+    dihedral = hoomd.md.dihedral.OPLS()
+    dihedral.params["CCCC"] = dict(k1=1.3, k2=-0.05, k3=0.2, k4=0.0)
+    dihedral.params["HCCC"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)
+    dihedral.params["HCCH"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)
+
+    lj_special_pairs = hoomd.md.special_pair.LJ()
+    lj_special_pairs.params["CCCC"] = dict(epsilon=0.07, sigma=3.55)
+    lj_special_pairs.params["HCCH"] = dict(epsilon=0.03, sigma=2.42)
+    lj_special_pairs.params["HCCC"] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))
+    lj_special_pairs.r_cut["CCCC"] = 12.0
+    lj_special_pairs.r_cut["HCCC"] = 12.0
+    lj_special_pairs.r_cut["HCCH"] = 12.0
+    coulomb_special_pairs = hoomd.md.special_pair.Coulomb()
+    coulomb_special_pairs.params["CCCC"] = dict(alpha=0.5)
+    coulomb_special_pairs.params["HCCC"] = dict(alpha=0.5)
+    coulomb_special_pairs.params["HCCH"] = dict(alpha=0.5)
+    coulomb_special_pairs.r_cut["HCCH"] = 12.0
+    coulomb_special_pairs.r_cut["CCCC"] = 12.0
+    coulomb_special_pairs.r_cut["HCCC"] = 12.0
+
+    nvt = hoomd.md.methods.Langevin(filter=hoomd.filter.All(), kT=kT)
+
+    integrator = hoomd.md.Integrator(dt=dt)
+    integrator.forces.append(lj)
+    integrator.forces.append(coulomb[0])
+    integrator.forces.append(coulomb[1])
+    integrator.forces.append(harmonic)
+    integrator.forces.append(angle)
+    integrator.forces.append(dihedral)
+    integrator.forces.append(lj_special_pairs)
+    integrator.forces.append(coulomb_special_pairs)
+    integrator.methods.append(nvt)
+    simulation.operations.integrator = integrator
+
+    return simulation
 ```
 
 <!-- #region id="3UrzENm_oo6U" -->
@@ -361,7 +389,7 @@ Make sure to run with GPU support, otherwise, it can take a very long time.
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="K951m4BbpUar" outputId="8005b8a9-2967-4eb9-f9db-e0dc0d523835"
-raw_result = pysages.run(method, generate_context, int(5e5))
+raw_result = pysages.run(method, generate_simulation, int(5e5))
 ```
 
 <!-- #region id="26zdu6yAht5Y" -->
@@ -397,11 +425,7 @@ fig, ax = plt.subplots()
 
 ax.set_xlabel(r"Dihedral Angle, $\xi$")
 ax.set_ylabel(r"$A(\xi)$")
-
 ax.plot(mesh, A)
-plt.gca()
-```
-
-```python id="z0ye1g-1sH2g"
 
+fig.show()
 ```
diff --git a/examples/hoomd-blue/funn/Butane_FUNN.ipynb b/examples/hoomd-blue/funn/Butane_FUNN.ipynb
index cc32ed70..ccbeb77c 100644
--- a/examples/hoomd-blue/funn/Butane_FUNN.ipynb
+++ b/examples/hoomd-blue/funn/Butane_FUNN.ipynb
@@ -6,11 +6,10 @@
     "id": "T-Qkg9C9n7Cc"
    },
    "source": [
-    "\n",
     "# Setting up the environment\n",
     "\n",
-    "First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin.\n",
-    "We copy it from Google Drive and install PySAGES for it.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance."
    ]
   },
   {
@@ -23,9 +22,12 @@
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
@@ -36,7 +38,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "KRPmkpd9n_NG",
-    "outputId": "2c72bbc3-0731-4d62-98e1-d48f8254adcb"
+    "outputId": "acc3a92c-182f-415b-d8dc-b5af076b3d01"
    },
    "outputs": [
     {
@@ -79,28 +81,23 @@
     "ver = sys.version_info\n",
     "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "os.environ[\"XLA_FLAGS\"] = \"--xla_gpu_strict_conv_algorithm_picker=false\"\n",
     "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "we_mTkFioS6R"
+    "id": "Wy-75Pt7Bqs1"
    },
    "source": [
-    "\n",
-    "## PySAGES\n",
-    "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
+    "We'll also need some additional python dependencies"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "id": "vK0RZtbroQWe"
+    "id": "LpBucu3V81xm"
    },
    "outputs": [
     {
@@ -112,21 +109,18 @@
     }
    ],
    "source": [
-    "%%bash\n",
-    "\n",
-    "pip install -q --upgrade pip\n",
-    "# Installs the wheel compatible with CUDA.\n",
-    "pip install -q --upgrade \"jax[cuda]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null"
+    "!pip install -qq \"numpy<2\" gsd > /dev/null"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "wAtjM-IroYX8"
+    "id": "we_mTkFioS6R"
    },
    "source": [
+    "## PySAGES\n",
     "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
+    "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`."
    ]
   },
   {
@@ -137,12 +131,16 @@
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KBFVcG1FoeMq"
+   },
+   "source": [
+    "# FUNN-biased simulations"
    ]
   },
   {
@@ -167,26 +165,15 @@
     "cd /content/funn"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KBFVcG1FoeMq"
-   },
-   "source": [
-    "\n",
-    "# FUNN-biased simulations\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "0W2ukJuuojAl"
    },
    "source": [
-    "\n",
     "FUNN gradually learns the free energy gradient from a discrete estimate based on the same algorithm as the ABF method, but employs a neural network to provide a continuous approximation to it.\n",
     "\n",
-    "For this Colab, we are using butane as the example molecule.\n"
+    "For this Colab, we are using butane as the example molecule."
    ]
   },
   {
@@ -198,36 +185,31 @@
    "outputs": [],
    "source": [
     "import hoomd\n",
-    "import hoomd.md\n",
+    "import gsd.hoomd\n",
+    "import numpy as np\n",
     "\n",
-    "import numpy\n",
     "\n",
-    "\n",
-    "pi = numpy.pi\n",
+    "pi = np.pi\n",
     "kT = 0.596161\n",
     "dt = 0.02045\n",
-    "mode = \"--mode=gpu\"\n",
     "\n",
     "\n",
-    "def generate_context(kT = kT, dt = dt, mode = mode):\n",
+    "def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), seed = 42):\n",
     "    \"\"\"\n",
-    "    Generates a simulation context, we pass this function to the attribute\n",
-    "    `run` of our sampling method.\n",
+    "    Generates a simulation context to which will attatch our sampling method.\n",
     "    \"\"\"\n",
-    "    hoomd.context.initialize(mode)\n",
-    "\n",
-    "    ### System Definition\n",
-    "    snapshot = hoomd.data.make_snapshot(\n",
-    "        N = 14,\n",
-    "        box = hoomd.data.boxdim(Lx = 41, Ly = 41, Lz = 41),\n",
-    "        particle_types = ['C', 'H'],\n",
-    "        bond_types = [\"CC\", \"CH\"],\n",
-    "        angle_types = [\"CCC\", \"CCH\", \"HCH\"],\n",
-    "        dihedral_types = [\"CCCC\", \"HCCC\", \"HCCH\"],\n",
-    "        pair_types = [\"CCCC\", \"HCCC\", \"HCCH\"],\n",
-    "        dtype = \"double\"\n",
-    "    )\n",
+    "    simulation = hoomd.Simulation(device=device, seed=seed)\n",
+    "\n",
+    "    snapshot = gsd.hoomd.Frame()\n",
     "\n",
+    "    snapshot.configuration.box = [41, 41, 41, 0, 0, 0]\n",
+    "\n",
+    "    snapshot.particles.N = N = 14\n",
+    "    snapshot.particles.types = [\"C\", \"H\"]\n",
+    "    snapshot.particles.typeid = np.zeros(N, dtype=int)\n",
+    "    snapshot.particles.position = np.zeros((N, 3))\n",
+    "    snapshot.particles.mass = np.zeros(N, dtype=float)\n",
+    "    snapshot.particles.charge = np.zeros(N, dtype=float)\n",
     "    snapshot.particles.typeid[0] = 0\n",
     "    snapshot.particles.typeid[1:4] = 1\n",
     "    snapshot.particles.typeid[4] = 0\n",
@@ -237,52 +219,60 @@
     "    snapshot.particles.typeid[10] = 0\n",
     "    snapshot.particles.typeid[11:14] = 1\n",
     "\n",
-    "    positions = numpy.array([\n",
-    "        [-2.990196,  0.097881,  0.000091],\n",
-    "        [-2.634894, -0.911406,  0.001002],\n",
-    "        [-2.632173,  0.601251, -0.873601],\n",
-    "        [-4.060195,  0.099327, -0.000736],\n",
-    "        [-2.476854,  0.823942,  1.257436],\n",
-    "        [-2.832157,  1.833228,  1.256526],\n",
-    "        [-2.834877,  0.320572,  2.131128],\n",
-    "        [-0.936856,  0.821861,  1.258628],\n",
-    "        [-0.578833,  1.325231,  0.384935],\n",
-    "        [-0.581553, -0.187426,  1.259538],\n",
-    "        [-0.423514,  1.547922,  2.515972],\n",
-    "        [-0.781537,  1.044552,  3.389664],\n",
-    "        [ 0.646485,  1.546476,  2.516800],\n",
-    "        [-0.778816,  2.557208,  2.515062]\n",
-    "    ])\n",
-    "\n",
-    "    reference_box_low_coords = numpy.array([-22.206855, -19.677099, -19.241968])\n",
-    "    box_low_coords = numpy.array([\n",
-    "        -snapshot.box.Lx / 2,\n",
-    "        -snapshot.box.Ly / 2,\n",
-    "        -snapshot.box.Lz / 2\n",
-    "    ])\n",
-    "    positions += (box_low_coords - reference_box_low_coords)\n",
+    "    positions = np.array(\n",
+    "        [\n",
+    "            [-2.990196, 0.097881, 0.000091],\n",
+    "            [-2.634894, -0.911406, 0.001002],\n",
+    "            [-2.632173, 0.601251, -0.873601],\n",
+    "            [-4.060195, 0.099327, -0.000736],\n",
+    "            [-2.476854, 0.823942, 1.257436],\n",
+    "            [-2.832157, 1.833228, 1.256526],\n",
+    "            [-2.834877, 0.320572, 2.131128],\n",
+    "            [-0.936856, 0.821861, 1.258628],\n",
+    "            [-0.578833, 1.325231, 0.384935],\n",
+    "            [-0.581553, -0.187426, 1.259538],\n",
+    "            [-0.423514, 1.547922, 2.515972],\n",
+    "            [-0.781537, 1.044552, 3.389664],\n",
+    "            [0.646485, 1.546476, 2.516800],\n",
+    "            [-0.778816, 2.557208, 2.515062],\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    reference_box_low_coords = np.array([-22.206855, -19.677099, -19.241968])\n",
+    "    box_low_coords = np.array([-41.0 / 2, -41.0 / 2, -41.0 / 2])\n",
+    "    positions += box_low_coords - reference_box_low_coords\n",
     "\n",
     "    snapshot.particles.position[:] = positions[:]\n",
     "\n",
     "    mC = 12.00\n",
     "    mH = 1.008\n",
+    "\n",
+    "    # fmt: off\n",
     "    snapshot.particles.mass[:] = [\n",
-    "        mC, mH, mH, mH,\n",
+    "        mC, mH, mH, mH,  # grouped by carbon atoms\n",
     "        mC, mH, mH,\n",
     "        mC, mH, mH,\n",
-    "        mC, mH, mH, mH\n",
+    "        mC, mH, mH, mH,\n",
     "    ]\n",
     "\n",
-    "    reference_charges = numpy.array([\n",
-    "        -0.180000, 0.060000, 0.060000, 0.060000,\n",
-    "        -0.120000, 0.060000, 0.060000,\n",
-    "        -0.120000, 0.060000, 0.060000,\n",
-    "        -0.180000, 0.060000, 0.060000, 0.060000]\n",
+    "    reference_charges = np.array(\n",
+    "        [\n",
+    "            -0.180000, 0.060000, 0.060000, 0.060000,  # grouped by carbon atoms\n",
+    "            -0.120000, 0.060000, 0.060000,\n",
+    "            -0.120000, 0.060000, 0.060000,\n",
+    "            -0.180000, 0.060000, 0.060000, 0.060000,\n",
+    "        ]\n",
     "    )\n",
+    "    # fmt: on\n",
+    "\n",
     "    charge_conversion = 18.22262\n",
     "    snapshot.particles.charge[:] = charge_conversion * reference_charges[:]\n",
     "\n",
-    "    snapshot.bonds.resize(13)\n",
+    "    snapshot.particles.validate()\n",
+    "\n",
+    "    snapshot.bonds.N = 13\n",
+    "    snapshot.bonds.types = [\"CC\", \"CH\"]\n",
+    "    snapshot.bonds.typeid = np.zeros(13, dtype=int)\n",
     "    snapshot.bonds.typeid[0:3] = 1\n",
     "    snapshot.bonds.typeid[3] = 0\n",
     "    snapshot.bonds.typeid[4:6] = 1\n",
@@ -291,14 +281,19 @@
     "    snapshot.bonds.typeid[9] = 0\n",
     "    snapshot.bonds.typeid[10:13] = 1\n",
     "\n",
+    "    snapshot.bonds.group = np.zeros((13, 2), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.bonds.group[:] = [\n",
-    "        [0, 2], [0, 1], [0, 3], [0, 4],\n",
+    "        [0, 2], [0, 1], [0, 3], [0, 4],  # grouped by carbon atoms\n",
     "        [4, 5], [4, 6], [4, 7],\n",
     "        [7, 8], [7, 9], [7, 10],\n",
-    "        [10, 11], [10, 12], [10, 13]\n",
+    "        [10, 11], [10, 12], [10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.angles.resize(24)\n",
+    "    snapshot.angles.N = 24\n",
+    "    snapshot.angles.types = [\"CCC\", \"CCH\", \"HCH\"]\n",
+    "    snapshot.angles.typeid = np.zeros(24, dtype=int)\n",
     "    snapshot.angles.typeid[0:2] = 2\n",
     "    snapshot.angles.typeid[2] = 1\n",
     "    snapshot.angles.typeid[3] = 2\n",
@@ -311,18 +306,26 @@
     "    snapshot.angles.typeid[16:21] = 1\n",
     "    snapshot.angles.typeid[21:24] = 2\n",
     "\n",
+    "    snapshot.angles.group = np.zeros((24, 3), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.angles.group[:] = [\n",
-    "        [1, 0, 2], [2, 0, 3], [2, 0, 4],\n",
+    "        [1, 0, 2], [2, 0, 3], [2, 0, 4],  # grouped by carbon atoms\n",
     "        [1, 0, 3], [1, 0, 4], [3, 0, 4],\n",
+    "        # ---\n",
     "        [0, 4, 5], [0, 4, 6], [0, 4, 7],\n",
     "        [5, 4, 6], [5, 4, 7], [6, 4, 7],\n",
+    "        # ---\n",
     "        [4, 7, 8], [4, 7, 9], [4, 7, 10],\n",
     "        [8, 7, 9], [8, 7, 10], [9, 7, 10],\n",
+    "        # ---\n",
     "        [7, 10, 11], [7, 10, 12], [7, 10, 13],\n",
-    "        [11, 10, 12], [11, 10, 13], [12, 10, 13]\n",
+    "        [11, 10, 12], [11, 10, 13], [12, 10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.dihedrals.resize(27)\n",
+    "    snapshot.dihedrals.N = 27\n",
+    "    snapshot.dihedrals.types = [\"CCCC\", \"HCCC\", \"HCCH\"]\n",
+    "    snapshot.dihedrals.typeid = np.zeros(27, dtype=int)\n",
     "    snapshot.dihedrals.typeid[0:2] = 2\n",
     "    snapshot.dihedrals.typeid[2] = 1\n",
     "    snapshot.dihedrals.typeid[3:5] = 2\n",
@@ -336,81 +339,109 @@
     "    snapshot.dihedrals.typeid[17:21] = 1\n",
     "    snapshot.dihedrals.typeid[21:27] = 2\n",
     "\n",
+    "    snapshot.dihedrals.group = np.zeros((27, 4), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.dihedrals.group[:] = [\n",
-    "        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],\n",
+    "        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],  # grouped by pairs of central atoms\n",
     "        [1, 0, 4, 5], [1, 0, 4, 6], [1, 0, 4, 7],\n",
     "        [3, 0, 4, 5], [3, 0, 4, 6], [3, 0, 4, 7],\n",
+    "        # ---\n",
     "        [0, 4, 7, 8], [0, 4, 7, 9], [0, 4, 7, 10],\n",
     "        [5, 4, 7, 8], [5, 4, 7, 9], [5, 4, 7, 10],\n",
     "        [6, 4, 7, 8], [6, 4, 7, 9], [6, 4, 7, 10],\n",
+    "        # ---\n",
     "        [4, 7, 10, 11], [4, 7, 10, 12], [4, 7, 10, 13],\n",
     "        [8, 7, 10, 11], [8, 7, 10, 12], [8, 7, 10, 13],\n",
-    "        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13]\n",
+    "        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.pairs.resize(27)\n",
+    "    snapshot.pairs.N = 27\n",
+    "    snapshot.pairs.types = [\"CCCC\", \"HCCC\", \"HCCH\"]\n",
+    "    snapshot.pairs.typeid = np.zeros(27, dtype=int)\n",
     "    snapshot.pairs.typeid[0:1] = 0\n",
     "    snapshot.pairs.typeid[1:11] = 1\n",
     "    snapshot.pairs.typeid[11:27] = 2\n",
+    "    snapshot.pairs.group = np.zeros((27, 2), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.pairs.group[:] = [\n",
     "        # CCCC\n",
     "        [0, 10],\n",
     "        # HCCC\n",
-    "        [0, 8], [0, 9], [5, 10], [6, 10],\n",
+    "        [0, 8],\n",
+    "        [0, 9],\n",
+    "        [5, 10], [6, 10],\n",
     "        [1, 7], [2, 7], [3, 7],\n",
     "        [11, 4], [12, 4], [13, 4],\n",
     "        # HCCH\n",
-    "        [1, 5], [1, 6], [2, 5], [2, 6], [3, 5], [3, 6],\n",
-    "        [5, 8], [6, 8], [5, 9], [6, 9],\n",
-    "        [8, 11], [8, 12], [8, 13], [9, 11], [9, 12], [9, 13]\n",
+    "        [1, 5], [1, 6],\n",
+    "        [2, 5], [2, 6],\n",
+    "        [3, 5], [3, 6],\n",
+    "        [5, 8], [6, 8],\n",
+    "        [5, 9], [6, 9],\n",
+    "        [8, 11], [8, 12], [8, 13],\n",
+    "        [9, 11], [9, 12], [9, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    hoomd.init.read_snapshot(snapshot)\n",
-    "\n",
-    "    ### Set interactions\n",
-    "    nl_ex = hoomd.md.nlist.cell()\n",
-    "    nl_ex.reset_exclusions(exclusions = [\"1-2\", \"1-3\", \"1-4\"])\n",
-    "\n",
-    "    lj = hoomd.md.pair.lj(r_cut = 12.0, nlist = nl_ex)\n",
-    "    lj.pair_coeff.set('C', 'C', epsilon = 0.07, sigma = 3.55)\n",
-    "    lj.pair_coeff.set('H', 'H', epsilon = 0.03, sigma = 2.42)\n",
-    "    lj.pair_coeff.set('C', 'H', epsilon = numpy.sqrt(0.07*0.03), sigma = numpy.sqrt(3.55*2.42))\n",
-    "\n",
-    "    coulomb = hoomd.md.charge.pppm(hoomd.group.charged(), nlist = nl_ex)\n",
-    "    coulomb.set_params(Nx = 64, Ny = 64, Nz = 64, order = 6, rcut = 12.0)\n",
-    "\n",
-    "    harmonic = hoomd.md.bond.harmonic()\n",
-    "    harmonic.bond_coeff.set(\"CC\", k = 2*268.0, r0 = 1.529)\n",
-    "    harmonic.bond_coeff.set(\"CH\", k = 2*340.0, r0 = 1.09)\n",
-    "\n",
-    "    angle = hoomd.md.angle.harmonic()\n",
-    "    angle.angle_coeff.set(\"CCC\", k = 2*58.35, t0 = 112.7 * pi / 180)\n",
-    "    angle.angle_coeff.set(\"CCH\", k = 2*37.5, t0 = 110.7 * pi / 180)\n",
-    "    angle.angle_coeff.set(\"HCH\", k = 2*33.0, t0 = 107.8 * pi / 180)\n",
+    "    simulation.create_state_from_snapshot(snapshot, domain_decomposition=(None, None, None))\n",
+    "    simulation.run(0)\n",
     "\n",
+    "    exclusions = [\"bond\", \"1-3\", \"1-4\"]\n",
+    "    nl = hoomd.md.nlist.Cell(buffer=0.4, exclusions=exclusions)\n",
+    "    lj = hoomd.md.pair.LJ(nlist=nl, default_r_cut=12.0)\n",
+    "    lj.params[(\"C\", \"C\")] = dict(epsilon=0.07, sigma=3.55)\n",
+    "    lj.params[(\"H\", \"H\")] = dict(epsilon=0.03, sigma=2.42)\n",
+    "    lj.params[(\"C\", \"H\")] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))\n",
     "\n",
-    "    dihedral = hoomd.md.dihedral.opls()\n",
-    "    dihedral.dihedral_coeff.set(\"CCCC\", k1 = 1.3, k2 = -0.05, k3 = 0.2, k4 = 0.0)\n",
-    "    dihedral.dihedral_coeff.set(\"HCCC\", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)\n",
-    "    dihedral.dihedral_coeff.set(\"HCCH\", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)\n",
-    "\n",
-    "    lj_special_pairs = hoomd.md.special_pair.lj()\n",
-    "    lj_special_pairs.pair_coeff.set(\"CCCC\", epsilon = 0.07, sigma = 3.55, r_cut = 12.0)\n",
-    "    lj_special_pairs.pair_coeff.set(\"HCCH\", epsilon = 0.03, sigma = 2.42, r_cut = 12.0)\n",
-    "    lj_special_pairs.pair_coeff.set(\"HCCC\",\n",
-    "        epsilon = numpy.sqrt(0.07 * 0.03), sigma = numpy.sqrt(3.55 * 2.42), r_cut = 12.0\n",
+    "    coulomb = hoomd.md.long_range.pppm.make_pppm_coulomb_forces(\n",
+    "        nlist=nl, resolution=[64, 64, 64], order=6, r_cut=12.0\n",
     "    )\n",
     "\n",
-    "    coulomb_special_pairs = hoomd.md.special_pair.coulomb()\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"CCCC\", alpha = 0.5, r_cut = 12.0)\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"HCCC\", alpha = 0.5, r_cut = 12.0)\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"HCCH\", alpha = 0.5, r_cut = 12.0)\n",
-    "\n",
-    "    hoomd.md.integrate.mode_standard(dt = dt)\n",
-    "    integrator = hoomd.md.integrate.nvt(group = hoomd.group.all(), kT = kT, tau = 100*dt)\n",
-    "    integrator.randomize_velocities(seed = 42)\n",
-    "\n",
-    "    return hoomd.context.current"
+    "    harmonic = hoomd.md.bond.Harmonic()\n",
+    "    harmonic.params[\"CC\"] = dict(k=2 * 268.0, r0=1.529)\n",
+    "    harmonic.params[\"CH\"] = dict(k=2 * 340.0, r0=1.09)\n",
+    "\n",
+    "    angle = hoomd.md.angle.Harmonic()\n",
+    "    angle.params[\"CCC\"] = dict(k=2 * 58.35, t0=112.7 * pi / 180)\n",
+    "    angle.params[\"CCH\"] = dict(k=2 * 37.5, t0=110.7 * pi / 180)\n",
+    "    angle.params[\"HCH\"] = dict(k=2 * 33.0, t0=107.8 * pi / 180)\n",
+    "\n",
+    "    dihedral = hoomd.md.dihedral.OPLS()\n",
+    "    dihedral.params[\"CCCC\"] = dict(k1=1.3, k2=-0.05, k3=0.2, k4=0.0)\n",
+    "    dihedral.params[\"HCCC\"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)\n",
+    "    dihedral.params[\"HCCH\"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)\n",
+    "\n",
+    "    lj_special_pairs = hoomd.md.special_pair.LJ()\n",
+    "    lj_special_pairs.params[\"CCCC\"] = dict(epsilon=0.07, sigma=3.55)\n",
+    "    lj_special_pairs.params[\"HCCH\"] = dict(epsilon=0.03, sigma=2.42)\n",
+    "    lj_special_pairs.params[\"HCCC\"] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))\n",
+    "    lj_special_pairs.r_cut[\"CCCC\"] = 12.0\n",
+    "    lj_special_pairs.r_cut[\"HCCC\"] = 12.0\n",
+    "    lj_special_pairs.r_cut[\"HCCH\"] = 12.0\n",
+    "    coulomb_special_pairs = hoomd.md.special_pair.Coulomb()\n",
+    "    coulomb_special_pairs.params[\"CCCC\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.params[\"HCCC\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.params[\"HCCH\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.r_cut[\"HCCH\"] = 12.0\n",
+    "    coulomb_special_pairs.r_cut[\"CCCC\"] = 12.0\n",
+    "    coulomb_special_pairs.r_cut[\"HCCC\"] = 12.0\n",
+    "\n",
+    "    nvt = hoomd.md.methods.Langevin(filter=hoomd.filter.All(), kT=kT)\n",
+    "\n",
+    "    integrator = hoomd.md.Integrator(dt=dt)\n",
+    "    integrator.forces.append(lj)\n",
+    "    integrator.forces.append(coulomb[0])\n",
+    "    integrator.forces.append(coulomb[1])\n",
+    "    integrator.forces.append(harmonic)\n",
+    "    integrator.forces.append(angle)\n",
+    "    integrator.forces.append(dihedral)\n",
+    "    integrator.forces.append(lj_special_pairs)\n",
+    "    integrator.forces.append(coulomb_special_pairs)\n",
+    "    integrator.methods.append(nvt)\n",
+    "    simulation.operations.integrator = integrator\n",
+    "\n",
+    "    return simulation"
    ]
   },
   {
@@ -419,8 +450,7 @@
     "id": "3UrzENm_oo6U"
    },
    "source": [
-    "\n",
-    "Next, we load PySAGES and the relevant classes and methods for our problem\n"
+    "Next, we load PySAGES and the relevant classes and methods for our problem"
    ]
   },
   {
@@ -621,7 +651,7 @@
     }
    ],
    "source": [
-    "run_result = pysages.run(method, generate_context, int(5e5))"
+    "run_result = pysages.run(method, generate_simulation, int(5e5))"
    ]
   },
   {
@@ -700,9 +730,9 @@
     "\n",
     "ax.set_xlabel(r\"Dihedral Angle, $\\xi$\")\n",
     "ax.set_ylabel(r\"$\\nabla A(\\xi)$\")\n",
-    "\n",
     "ax.plot(mesh, A)\n",
-    "plt.gca()"
+    "\n",
+    "fig.show()"
    ]
   },
   {
diff --git a/examples/hoomd-blue/funn/Butane_FUNN.md b/examples/hoomd-blue/funn/Butane_FUNN.md
index dd301eee..03a5caad 100644
--- a/examples/hoomd-blue/funn/Butane_FUNN.md
+++ b/examples/hoomd-blue/funn/Butane_FUNN.md
@@ -14,22 +14,23 @@ jupyter:
 ---
 
 <!-- #region id="T-Qkg9C9n7Cc" -->
-
 # Setting up the environment
 
-First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin.
-We copy it from Google Drive and install PySAGES for it.
-
+First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 <!-- #endregion -->
 
 ```bash id="3eTbKklCnyd_"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
+
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
-```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="2c72bbc3-0731-4d62-98e1-d48f8254adcb"
+```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="acc3a92c-182f-415b-d8dc-b5af076b3d01"
 %env PYSAGES_ENV=/env/pysages
 ```
 
@@ -46,92 +47,70 @@ import sys
 ver = sys.version_info
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
 
-os.environ["XLA_FLAGS"] = "--xla_gpu_strict_conv_algorithm_picker=false"
 os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
-<!-- #region id="we_mTkFioS6R" -->
-
-## PySAGES
-
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
+<!-- #region id="Wy-75Pt7Bqs1" -->
+We'll also need some additional python dependencies
 <!-- #endregion -->
 
-```bash id="vK0RZtbroQWe"
-
-pip install -q --upgrade pip
-# Installs the wheel compatible with CUDA.
-pip install -q --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null
+```python id="LpBucu3V81xm"
+!pip install -qq "numpy<2" gsd > /dev/null
 ```
 
-<!-- #region id="wAtjM-IroYX8" -->
-
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
+<!-- #region id="we_mTkFioS6R" -->
+## PySAGES
 
+The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`.
 <!-- #endregion -->
 
-```bash id="B-HB9CzioV5j"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
+<!-- #region id="KBFVcG1FoeMq" -->
+# FUNN-biased simulations
+<!-- #endregion -->
+
 ```bash id="ppTzMmyyobHB"
 
 mkdir /content/funn
 cd /content/funn
 ```
 
-<!-- #region id="KBFVcG1FoeMq" -->
-
-# FUNN-biased simulations
-
-<!-- #endregion -->
-
 <!-- #region id="0W2ukJuuojAl" -->
-
 FUNN gradually learns the free energy gradient from a discrete estimate based on the same algorithm as the ABF method, but employs a neural network to provide a continuous approximation to it.
 
 For this Colab, we are using butane as the example molecule.
-
 <!-- #endregion -->
 
 ```python id="BBvC7Spoog82"
 import hoomd
-import hoomd.md
-
-import numpy
+import gsd.hoomd
+import numpy as np
 
 
-pi = numpy.pi
+pi = np.pi
 kT = 0.596161
 dt = 0.02045
-mode = "--mode=gpu"
 
 
-def generate_context(kT = kT, dt = dt, mode = mode):
+def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), seed = 42):
     """
-    Generates a simulation context, we pass this function to the attribute
-    `run` of our sampling method.
+    Generates a simulation context to which will attatch our sampling method.
     """
-    hoomd.context.initialize(mode)
-
-    ### System Definition
-    snapshot = hoomd.data.make_snapshot(
-        N = 14,
-        box = hoomd.data.boxdim(Lx = 41, Ly = 41, Lz = 41),
-        particle_types = ['C', 'H'],
-        bond_types = ["CC", "CH"],
-        angle_types = ["CCC", "CCH", "HCH"],
-        dihedral_types = ["CCCC", "HCCC", "HCCH"],
-        pair_types = ["CCCC", "HCCC", "HCCH"],
-        dtype = "double"
-    )
+    simulation = hoomd.Simulation(device=device, seed=seed)
 
+    snapshot = gsd.hoomd.Frame()
+
+    snapshot.configuration.box = [41, 41, 41, 0, 0, 0]
+
+    snapshot.particles.N = N = 14
+    snapshot.particles.types = ["C", "H"]
+    snapshot.particles.typeid = np.zeros(N, dtype=int)
+    snapshot.particles.position = np.zeros((N, 3))
+    snapshot.particles.mass = np.zeros(N, dtype=float)
+    snapshot.particles.charge = np.zeros(N, dtype=float)
     snapshot.particles.typeid[0] = 0
     snapshot.particles.typeid[1:4] = 1
     snapshot.particles.typeid[4] = 0
@@ -141,52 +120,60 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.particles.typeid[10] = 0
     snapshot.particles.typeid[11:14] = 1
 
-    positions = numpy.array([
-        [-2.990196,  0.097881,  0.000091],
-        [-2.634894, -0.911406,  0.001002],
-        [-2.632173,  0.601251, -0.873601],
-        [-4.060195,  0.099327, -0.000736],
-        [-2.476854,  0.823942,  1.257436],
-        [-2.832157,  1.833228,  1.256526],
-        [-2.834877,  0.320572,  2.131128],
-        [-0.936856,  0.821861,  1.258628],
-        [-0.578833,  1.325231,  0.384935],
-        [-0.581553, -0.187426,  1.259538],
-        [-0.423514,  1.547922,  2.515972],
-        [-0.781537,  1.044552,  3.389664],
-        [ 0.646485,  1.546476,  2.516800],
-        [-0.778816,  2.557208,  2.515062]
-    ])
-
-    reference_box_low_coords = numpy.array([-22.206855, -19.677099, -19.241968])
-    box_low_coords = numpy.array([
-        -snapshot.box.Lx / 2,
-        -snapshot.box.Ly / 2,
-        -snapshot.box.Lz / 2
-    ])
-    positions += (box_low_coords - reference_box_low_coords)
+    positions = np.array(
+        [
+            [-2.990196, 0.097881, 0.000091],
+            [-2.634894, -0.911406, 0.001002],
+            [-2.632173, 0.601251, -0.873601],
+            [-4.060195, 0.099327, -0.000736],
+            [-2.476854, 0.823942, 1.257436],
+            [-2.832157, 1.833228, 1.256526],
+            [-2.834877, 0.320572, 2.131128],
+            [-0.936856, 0.821861, 1.258628],
+            [-0.578833, 1.325231, 0.384935],
+            [-0.581553, -0.187426, 1.259538],
+            [-0.423514, 1.547922, 2.515972],
+            [-0.781537, 1.044552, 3.389664],
+            [0.646485, 1.546476, 2.516800],
+            [-0.778816, 2.557208, 2.515062],
+        ]
+    )
+
+    reference_box_low_coords = np.array([-22.206855, -19.677099, -19.241968])
+    box_low_coords = np.array([-41.0 / 2, -41.0 / 2, -41.0 / 2])
+    positions += box_low_coords - reference_box_low_coords
 
     snapshot.particles.position[:] = positions[:]
 
     mC = 12.00
     mH = 1.008
+
+    # fmt: off
     snapshot.particles.mass[:] = [
-        mC, mH, mH, mH,
+        mC, mH, mH, mH,  # grouped by carbon atoms
         mC, mH, mH,
         mC, mH, mH,
-        mC, mH, mH, mH
+        mC, mH, mH, mH,
     ]
 
-    reference_charges = numpy.array([
-        -0.180000, 0.060000, 0.060000, 0.060000,
-        -0.120000, 0.060000, 0.060000,
-        -0.120000, 0.060000, 0.060000,
-        -0.180000, 0.060000, 0.060000, 0.060000]
+    reference_charges = np.array(
+        [
+            -0.180000, 0.060000, 0.060000, 0.060000,  # grouped by carbon atoms
+            -0.120000, 0.060000, 0.060000,
+            -0.120000, 0.060000, 0.060000,
+            -0.180000, 0.060000, 0.060000, 0.060000,
+        ]
     )
+    # fmt: on
+
     charge_conversion = 18.22262
     snapshot.particles.charge[:] = charge_conversion * reference_charges[:]
 
-    snapshot.bonds.resize(13)
+    snapshot.particles.validate()
+
+    snapshot.bonds.N = 13
+    snapshot.bonds.types = ["CC", "CH"]
+    snapshot.bonds.typeid = np.zeros(13, dtype=int)
     snapshot.bonds.typeid[0:3] = 1
     snapshot.bonds.typeid[3] = 0
     snapshot.bonds.typeid[4:6] = 1
@@ -195,14 +182,19 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.bonds.typeid[9] = 0
     snapshot.bonds.typeid[10:13] = 1
 
+    snapshot.bonds.group = np.zeros((13, 2), dtype=int)
+    # fmt: off
     snapshot.bonds.group[:] = [
-        [0, 2], [0, 1], [0, 3], [0, 4],
+        [0, 2], [0, 1], [0, 3], [0, 4],  # grouped by carbon atoms
         [4, 5], [4, 6], [4, 7],
         [7, 8], [7, 9], [7, 10],
-        [10, 11], [10, 12], [10, 13]
+        [10, 11], [10, 12], [10, 13],
     ]
+    # fmt: on
 
-    snapshot.angles.resize(24)
+    snapshot.angles.N = 24
+    snapshot.angles.types = ["CCC", "CCH", "HCH"]
+    snapshot.angles.typeid = np.zeros(24, dtype=int)
     snapshot.angles.typeid[0:2] = 2
     snapshot.angles.typeid[2] = 1
     snapshot.angles.typeid[3] = 2
@@ -215,18 +207,26 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.angles.typeid[16:21] = 1
     snapshot.angles.typeid[21:24] = 2
 
+    snapshot.angles.group = np.zeros((24, 3), dtype=int)
+    # fmt: off
     snapshot.angles.group[:] = [
-        [1, 0, 2], [2, 0, 3], [2, 0, 4],
+        [1, 0, 2], [2, 0, 3], [2, 0, 4],  # grouped by carbon atoms
         [1, 0, 3], [1, 0, 4], [3, 0, 4],
+        # ---
         [0, 4, 5], [0, 4, 6], [0, 4, 7],
         [5, 4, 6], [5, 4, 7], [6, 4, 7],
+        # ---
         [4, 7, 8], [4, 7, 9], [4, 7, 10],
         [8, 7, 9], [8, 7, 10], [9, 7, 10],
+        # ---
         [7, 10, 11], [7, 10, 12], [7, 10, 13],
-        [11, 10, 12], [11, 10, 13], [12, 10, 13]
+        [11, 10, 12], [11, 10, 13], [12, 10, 13],
     ]
+    # fmt: on
 
-    snapshot.dihedrals.resize(27)
+    snapshot.dihedrals.N = 27
+    snapshot.dihedrals.types = ["CCCC", "HCCC", "HCCH"]
+    snapshot.dihedrals.typeid = np.zeros(27, dtype=int)
     snapshot.dihedrals.typeid[0:2] = 2
     snapshot.dihedrals.typeid[2] = 1
     snapshot.dihedrals.typeid[3:5] = 2
@@ -240,87 +240,113 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.dihedrals.typeid[17:21] = 1
     snapshot.dihedrals.typeid[21:27] = 2
 
+    snapshot.dihedrals.group = np.zeros((27, 4), dtype=int)
+    # fmt: off
     snapshot.dihedrals.group[:] = [
-        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],
+        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],  # grouped by pairs of central atoms
         [1, 0, 4, 5], [1, 0, 4, 6], [1, 0, 4, 7],
         [3, 0, 4, 5], [3, 0, 4, 6], [3, 0, 4, 7],
+        # ---
         [0, 4, 7, 8], [0, 4, 7, 9], [0, 4, 7, 10],
         [5, 4, 7, 8], [5, 4, 7, 9], [5, 4, 7, 10],
         [6, 4, 7, 8], [6, 4, 7, 9], [6, 4, 7, 10],
+        # ---
         [4, 7, 10, 11], [4, 7, 10, 12], [4, 7, 10, 13],
         [8, 7, 10, 11], [8, 7, 10, 12], [8, 7, 10, 13],
-        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13]
+        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13],
     ]
+    # fmt: on
 
-    snapshot.pairs.resize(27)
+    snapshot.pairs.N = 27
+    snapshot.pairs.types = ["CCCC", "HCCC", "HCCH"]
+    snapshot.pairs.typeid = np.zeros(27, dtype=int)
     snapshot.pairs.typeid[0:1] = 0
     snapshot.pairs.typeid[1:11] = 1
     snapshot.pairs.typeid[11:27] = 2
+    snapshot.pairs.group = np.zeros((27, 2), dtype=int)
+    # fmt: off
     snapshot.pairs.group[:] = [
         # CCCC
         [0, 10],
         # HCCC
-        [0, 8], [0, 9], [5, 10], [6, 10],
+        [0, 8],
+        [0, 9],
+        [5, 10], [6, 10],
         [1, 7], [2, 7], [3, 7],
         [11, 4], [12, 4], [13, 4],
         # HCCH
-        [1, 5], [1, 6], [2, 5], [2, 6], [3, 5], [3, 6],
-        [5, 8], [6, 8], [5, 9], [6, 9],
-        [8, 11], [8, 12], [8, 13], [9, 11], [9, 12], [9, 13]
+        [1, 5], [1, 6],
+        [2, 5], [2, 6],
+        [3, 5], [3, 6],
+        [5, 8], [6, 8],
+        [5, 9], [6, 9],
+        [8, 11], [8, 12], [8, 13],
+        [9, 11], [9, 12], [9, 13],
     ]
+    # fmt: on
 
-    hoomd.init.read_snapshot(snapshot)
+    simulation.create_state_from_snapshot(snapshot, domain_decomposition=(None, None, None))
+    simulation.run(0)
 
-    ### Set interactions
-    nl_ex = hoomd.md.nlist.cell()
-    nl_ex.reset_exclusions(exclusions = ["1-2", "1-3", "1-4"])
+    exclusions = ["bond", "1-3", "1-4"]
+    nl = hoomd.md.nlist.Cell(buffer=0.4, exclusions=exclusions)
+    lj = hoomd.md.pair.LJ(nlist=nl, default_r_cut=12.0)
+    lj.params[("C", "C")] = dict(epsilon=0.07, sigma=3.55)
+    lj.params[("H", "H")] = dict(epsilon=0.03, sigma=2.42)
+    lj.params[("C", "H")] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))
 
-    lj = hoomd.md.pair.lj(r_cut = 12.0, nlist = nl_ex)
-    lj.pair_coeff.set('C', 'C', epsilon = 0.07, sigma = 3.55)
-    lj.pair_coeff.set('H', 'H', epsilon = 0.03, sigma = 2.42)
-    lj.pair_coeff.set('C', 'H', epsilon = numpy.sqrt(0.07*0.03), sigma = numpy.sqrt(3.55*2.42))
-
-    coulomb = hoomd.md.charge.pppm(hoomd.group.charged(), nlist = nl_ex)
-    coulomb.set_params(Nx = 64, Ny = 64, Nz = 64, order = 6, rcut = 12.0)
-
-    harmonic = hoomd.md.bond.harmonic()
-    harmonic.bond_coeff.set("CC", k = 2*268.0, r0 = 1.529)
-    harmonic.bond_coeff.set("CH", k = 2*340.0, r0 = 1.09)
-
-    angle = hoomd.md.angle.harmonic()
-    angle.angle_coeff.set("CCC", k = 2*58.35, t0 = 112.7 * pi / 180)
-    angle.angle_coeff.set("CCH", k = 2*37.5, t0 = 110.7 * pi / 180)
-    angle.angle_coeff.set("HCH", k = 2*33.0, t0 = 107.8 * pi / 180)
-
-
-    dihedral = hoomd.md.dihedral.opls()
-    dihedral.dihedral_coeff.set("CCCC", k1 = 1.3, k2 = -0.05, k3 = 0.2, k4 = 0.0)
-    dihedral.dihedral_coeff.set("HCCC", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)
-    dihedral.dihedral_coeff.set("HCCH", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)
-
-    lj_special_pairs = hoomd.md.special_pair.lj()
-    lj_special_pairs.pair_coeff.set("CCCC", epsilon = 0.07, sigma = 3.55, r_cut = 12.0)
-    lj_special_pairs.pair_coeff.set("HCCH", epsilon = 0.03, sigma = 2.42, r_cut = 12.0)
-    lj_special_pairs.pair_coeff.set("HCCC",
-        epsilon = numpy.sqrt(0.07 * 0.03), sigma = numpy.sqrt(3.55 * 2.42), r_cut = 12.0
+    coulomb = hoomd.md.long_range.pppm.make_pppm_coulomb_forces(
+        nlist=nl, resolution=[64, 64, 64], order=6, r_cut=12.0
     )
 
-    coulomb_special_pairs = hoomd.md.special_pair.coulomb()
-    coulomb_special_pairs.pair_coeff.set("CCCC", alpha = 0.5, r_cut = 12.0)
-    coulomb_special_pairs.pair_coeff.set("HCCC", alpha = 0.5, r_cut = 12.0)
-    coulomb_special_pairs.pair_coeff.set("HCCH", alpha = 0.5, r_cut = 12.0)
-
-    hoomd.md.integrate.mode_standard(dt = dt)
-    integrator = hoomd.md.integrate.nvt(group = hoomd.group.all(), kT = kT, tau = 100*dt)
-    integrator.randomize_velocities(seed = 42)
-
-    return hoomd.context.current
+    harmonic = hoomd.md.bond.Harmonic()
+    harmonic.params["CC"] = dict(k=2 * 268.0, r0=1.529)
+    harmonic.params["CH"] = dict(k=2 * 340.0, r0=1.09)
+
+    angle = hoomd.md.angle.Harmonic()
+    angle.params["CCC"] = dict(k=2 * 58.35, t0=112.7 * pi / 180)
+    angle.params["CCH"] = dict(k=2 * 37.5, t0=110.7 * pi / 180)
+    angle.params["HCH"] = dict(k=2 * 33.0, t0=107.8 * pi / 180)
+
+    dihedral = hoomd.md.dihedral.OPLS()
+    dihedral.params["CCCC"] = dict(k1=1.3, k2=-0.05, k3=0.2, k4=0.0)
+    dihedral.params["HCCC"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)
+    dihedral.params["HCCH"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)
+
+    lj_special_pairs = hoomd.md.special_pair.LJ()
+    lj_special_pairs.params["CCCC"] = dict(epsilon=0.07, sigma=3.55)
+    lj_special_pairs.params["HCCH"] = dict(epsilon=0.03, sigma=2.42)
+    lj_special_pairs.params["HCCC"] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))
+    lj_special_pairs.r_cut["CCCC"] = 12.0
+    lj_special_pairs.r_cut["HCCC"] = 12.0
+    lj_special_pairs.r_cut["HCCH"] = 12.0
+    coulomb_special_pairs = hoomd.md.special_pair.Coulomb()
+    coulomb_special_pairs.params["CCCC"] = dict(alpha=0.5)
+    coulomb_special_pairs.params["HCCC"] = dict(alpha=0.5)
+    coulomb_special_pairs.params["HCCH"] = dict(alpha=0.5)
+    coulomb_special_pairs.r_cut["HCCH"] = 12.0
+    coulomb_special_pairs.r_cut["CCCC"] = 12.0
+    coulomb_special_pairs.r_cut["HCCC"] = 12.0
+
+    nvt = hoomd.md.methods.Langevin(filter=hoomd.filter.All(), kT=kT)
+
+    integrator = hoomd.md.Integrator(dt=dt)
+    integrator.forces.append(lj)
+    integrator.forces.append(coulomb[0])
+    integrator.forces.append(coulomb[1])
+    integrator.forces.append(harmonic)
+    integrator.forces.append(angle)
+    integrator.forces.append(dihedral)
+    integrator.forces.append(lj_special_pairs)
+    integrator.forces.append(coulomb_special_pairs)
+    integrator.methods.append(nvt)
+    simulation.operations.integrator = integrator
+
+    return simulation
 ```
 
 <!-- #region id="3UrzENm_oo6U" -->
-
 Next, we load PySAGES and the relevant classes and methods for our problem
-
 <!-- #endregion -->
 
 ```python id="fpMg-o8WomAA"
@@ -361,7 +387,7 @@ Make sure to run with GPU support, otherwise, it can take a very long time.
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="K951m4BbpUar" outputId="f3e79872-41da-479a-caec-5bca7a6792e5"
-run_result = pysages.run(method, generate_context, int(5e5))
+run_result = pysages.run(method, generate_simulation, int(5e5))
 ```
 
 <!-- #region id="PXBKUfK0p9T2" -->
@@ -385,8 +411,8 @@ fig, ax = plt.subplots()
 
 ax.set_xlabel(r"Dihedral Angle, $\xi$")
 ax.set_ylabel(r"$\nabla A(\xi)$")
-
 ax.plot(mesh, A)
-plt.gca()
+
+fig.show()
 ```
 
diff --git a/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.ipynb b/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.ipynb
index db2e9027..c191b26d 100644
--- a/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.ipynb
+++ b/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.ipynb
@@ -3,36 +3,32 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "p49wJ0IjLAVD"
+    "id": "T-Qkg9C9n7Cc"
    },
    "source": [
+    "# Setting up the environment\n",
     "\n",
-    "# Setup of the environment\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "WM_9PpDwKuoA"
-   },
-   "source": [
-    "\n",
-    "First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin. We copy it from Google Drive and install pysages for it. This may require you to have read permissions to the shared Google Drive. We also have a Google Colab that performs this installation for reference.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "f7cf6962",
    "metadata": {
-    "id": "nMThqa-DjVcb"
+    "id": "3eTbKklCnyd_"
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
@@ -42,8 +38,8 @@
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
-    "id": "25H3kl03wzJe",
-    "outputId": "c424222d-bf8f-4a4f-eaa1-517910c500a6"
+    "id": "KRPmkpd9n_NG",
+    "outputId": "b757f2aa-38cc-4726-c4ab-5197810b9d77"
    },
    "outputs": [
     {
@@ -62,22 +58,21 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "id": "CPkgxfj6w4te"
+    "id": "J7OY5K9VoBBh"
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
-    "mkdir -p $PYSAGES_ENV\n",
-    "unzip -qquo pysages-env.zip -d $PYSAGES_ENV\n",
-    "rm pysages-env.zip"
+    "mkdir -p $PYSAGES_ENV .\n",
+    "unzip -qquo pysages-env.zip -d $PYSAGES_ENV"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "id": "JMO5fiRTxAWB"
+    "id": "EMAWp8VloIk4"
    },
    "outputs": [],
    "source": [
@@ -85,57 +80,47 @@
     "import sys\n",
     "\n",
     "ver = sys.version_info\n",
+    "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")"
+    "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "lf2KeHt5_eFv"
+    "id": "Wy-75Pt7Bqs1"
    },
    "source": [
-    "\n",
-    "## PySAGES\n",
-    "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
+    "We'll also need some additional python dependencies"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "id": "R_gW2ERpi9tw"
+    "id": "LpBucu3V81xm"
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "pip install -q --upgrade pip &> /dev/null\n",
-    "# Installs the wheel compatible with CUDA.\n",
-    "pip install -q --upgrade \"jax[cuda]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null"
+    "!pip install -qq \"numpy<2\" gsd > /dev/null"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "mx0IRythaTyG"
+    "id": "we_mTkFioS6R"
    },
    "source": [
+    "## PySAGES\n",
     "\n",
-    "We test the jax installation and check the versions.\n"
+    "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Z4E914qBHbZS",
-    "outputId": "dbc3fade-b58d-49f7-8607-655b0e89e710"
+    "id": "B-HB9CzioV5j"
    },
    "outputs": [
     {
@@ -148,36 +133,7 @@
     }
    ],
    "source": [
-    "import jax\n",
-    "import jaxlib\n",
-    "print(jax.__version__)\n",
-    "print(jaxlib.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vtAmA51IAYxn"
-   },
-   "source": [
-    "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "id": "xYRGOcFJjEE6"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
    ]
   },
   {
@@ -186,8 +142,7 @@
     "id": "fRZDARPsDQHF"
    },
    "source": [
-    "\n",
-    "# Harmonic Bias simulation\n"
+    "# Harmonic Bias simulation"
    ]
   },
   {
@@ -196,12 +151,11 @@
     "id": "Uh2y2RXDDZub"
    },
    "source": [
-    "\n",
     "A harmonic bias simulation constraints a collective variable with a harmonic potential. This is useful for a variety of advanced sampling methods, in particular, umbrella sampling.\n",
     "\n",
     "For this Colab, we are generating a small system of soft DPD particles first. This system of soft particles allows fast reliable execution.\n",
     "For this, we use the [GSD](https://gsd.readthedocs.io/en/stable/) file format and its python frontend to generate the initial conditions.\n",
-    "Since all particles are soft, it is OK to start with random positions inside the simulation box. We also assign random velocities drawn from the Maxwell-Boltzmann distribution. The final configuration is written to disk and can be opened by HOOMD-blue for simulations.\n"
+    "Since all particles are soft, it is OK to start with random positions inside the simulation box. We also assign random velocities drawn from the Maxwell-Boltzmann distribution. The final configuration is written to disk and can be opened by HOOMD-blue for simulations."
    ]
   },
   {
@@ -212,12 +166,9 @@
    },
    "outputs": [],
    "source": [
-    "!pip install -q gsd &> /dev/null\n",
-    "\n",
-    "import sys\n",
-    "import numpy as np\n",
     "import gsd\n",
     "import gsd.hoomd\n",
+    "import numpy as np\n",
     "\n",
     "\n",
     "class System:\n",
@@ -239,7 +190,6 @@
     "    snapshot.configuration.box = [L, L, L, 0, 0, 0]\n",
     "\n",
     "    snapshot.particles.N = N = system.N\n",
-    "\n",
     "    snapshot.particles.types = [\"A\"]\n",
     "    snapshot.particles.position = np.zeros((N, 3))\n",
     "    snapshot.particles.velocity = np.random.standard_normal((N, 3))\n",
@@ -254,12 +204,14 @@
     "\n",
     "    return snapshot\n",
     "\n",
+    "\n",
     "system = System()\n",
     "snap = get_snap(system)\n",
     "snap = post_process_pos(snap)\n",
     "snap.particles.validate()\n",
+    "\n",
     "with gsd.hoomd.open(\"harmonic_start.gsd\", \"w\") as f:\n",
-    "   f.append(snap)\n"
+    "   f.append(snap)"
    ]
   },
   {
@@ -268,11 +220,14 @@
     "id": "n0Rd-hMnCD-B"
    },
    "source": [
-    "\n",
     "Next, we start running the system, we start with importing the required libraries.\n",
-    "Noteworthy are here the hoomd package with the MD and dlext module, and the pysages objects.\n",
-    "We are going to use a collective variable that constrains a particle position. In PySAGES the `Component` class from the `colvars` package can achieve this for us.\n",
-    "The `HarmonicBias` class is responsible for introducing the bias into the simulation run, while `HistogramLogger` collects the state of the collective variable during the run.\n"
+    "Noteworthy are here the hoomd and the pysages package.\n",
+    "\n",
+    "We are going to use a collective variable that constrains a particle position.\n",
+    "In PySAGES the `Component` class from the `colvars` package can achieve this for us.\n",
+    "\n",
+    "The `HarmonicBias` class is responsible for introducing the bias into the simulation run,\n",
+    "while `HistogramLogger` collects the state of the collective variable during the run."
    ]
   },
   {
@@ -295,11 +250,7 @@
     }
    ],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
     "import hoomd\n",
-    "import hoomd.md\n",
-    "import hoomd.dlext\n",
     "\n",
     "import pysages\n",
     "from pysages.colvars import Component\n",
@@ -312,13 +263,12 @@
     "id": "YibErIQhC0Lv"
    },
    "source": [
-    "\n",
     "The next step is to write a function that generates the simulation context.\n",
     "Inside this function is the HOOMD-blue specific code, that you would normally write to execute a HOOMD-blue simulation. Here it is packaged into a function, such that PySAGES can deploy the simulation context when needed.\n",
     "In this case, we use the GSD file read in the initial, and define the DPD forcefield with parameters.\n",
     "DPD is a special case in HOOMD-blue. The thermostat is part of the pair-potential and not part of the integrator. Hence, we specify NVE integration and all thermostat parameter for NVT in the potential. The function returns the simulation context for PySAGES to work with.\n",
     "\n",
-    "The second function is a helper function to generate the theoretically expected distribution of a harmonically biased simulation of an ideal gas in NVT. And helps to verify the results of the simulation.\n"
+    "The second function is a helper function to generate the theoretically expected distribution of a harmonically biased simulation of an ideal gas in NVT. And helps to verify the results of the simulation."
    ]
   },
   {
@@ -329,30 +279,38 @@
    },
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "Generates a simulation context, we pass this function to the attribute `run` of our sampling method.\n",
-    "\"\"\"\n",
-    "def generate_context(**kwargs):\n",
-    "    hoomd.context.initialize('')\n",
-    "    context = hoomd.context.SimulationContext()\n",
-    "    with context:\n",
-    "        hoomd.init.read_gsd(\"harmonic_start.gsd\")\n",
-    "        hoomd.md.integrate.nve(group=hoomd.group.all())\n",
-    "        hoomd.md.integrate.mode_standard(dt=0.01)\n",
-    "\n",
-    "        nl = hoomd.md.nlist.cell()\n",
-    "        dpd = hoomd.md.pair.dpd(r_cut=1, nlist=nl, seed=42, kT=1.0)\n",
-    "        dpd.pair_coeff.set(\n",
-    "            \"A\", \"A\", A=kwargs.get(\"A\", 5.0), gamma=kwargs.get(\"gamma\", 1.0)\n",
-    "        )\n",
-    "    return context\n",
+    "def generate_simulation(\n",
+    "    kT=1, dt=0.01, A=5, gamma=1, r_cut=1,\n",
+    "    device=hoomd.device.auto_select(), seed=42,\n",
+    "    **kwargs\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Generates a simulation context to which will attatch our sampling method.\n",
+    "    \"\"\"\n",
+    "    simulation = hoomd.Simulation(device=device, seed=seed)\n",
+    "    simulation.create_state_from_gsd(\"harmonic_start.gsd\")\n",
+    "    simulation.run(0)\n",
+    "\n",
+    "    nlist = hoomd.md.nlist.Cell(buffer=0.4)\n",
+    "    dpd = hoomd.md.pair.DPD(nlist=nlist, kT=kT, default_r_cut=r_cut)\n",
+    "    dpd.params[(\"A\", \"A\")] = dict(A=A, gamma=gamma)\n",
+    "\n",
+    "    nve = hoomd.md.methods.ConstantVolume(filter=hoomd.filter.All())\n",
+    "\n",
+    "    integrator = hoomd.md.Integrator(dt=dt)\n",
+    "    integrator.forces.append(dpd)\n",
+    "    integrator.methods.append(nve)\n",
+    "    simulation.operations.integrator = integrator\n",
+    "\n",
+    "    return simulation\n",
+    "\n",
     "\n",
     "def get_target_dist(center, k, lim, bins):\n",
     "    x = np.linspace(lim[0], lim[1], bins)\n",
     "    p = np.exp(-0.5 * k * (x - center)**2)\n",
     "    # norm numerically\n",
     "    p *= (lim[1] - lim[0]) / np.sum(p)\n",
-    "    return p\n"
+    "    return p"
    ]
   },
   {
@@ -361,12 +319,11 @@
     "id": "BgQ88M0sIfbp"
    },
    "source": [
-    "\n",
     "The next step is to define the collective variables (CVs) we are interested in.\n",
     "In this case, we are using the `Component` CV to describe the position in space. We choose particle `[0]` for this and log in 3 different CVS the Z- `2`, Y- `1`, and X- `0` position of the particle.\n",
     "The center describes where we are restraining the CVs to, which is also specified for each of the CVs described earlier.\n",
     "\n",
-    "Finally, we define the spring constant for the harmonic biasing potential and the `HarmonicBias` method itself.\n"
+    "Finally, we define the spring constant for the harmonic biasing potential and the `HarmonicBias` method itself."
    ]
   },
   {
@@ -377,15 +334,10 @@
    },
    "outputs": [],
    "source": [
-    "cvs = [Component([0], 2)]\n",
-    "cvs += [Component([0], 1)]\n",
-    "cvs += [Component([0], 0)]\n",
-    "\n",
-    "center_cv = [0.0]\n",
-    "center_cv += [1.0, -0.3]\n",
-    "\n",
+    "cvs = [Component([0], 2), Component([0], 1), Component([0], 0)]\n",
+    "cv_centers = [0.0, 1.0, -0.3]\n",
     "k = 15\n",
-    "method = HarmonicBias(cvs, k, center_cv)\n"
+    "method = HarmonicBias(cvs, k, cv_centers)"
    ]
   },
   {
@@ -394,11 +346,10 @@
     "id": "bGIDE56RLCcP"
    },
    "source": [
-    "\n",
     "Next, we define the `HistogramLogger` callback. The callback interacts with the simulation every timestep after the biasing. In this case, we use it to log the state of the collective variables every `100` time-steps.\n",
     "\n",
     "And we can finally run the simulations. This happens through the PySAGES method run and is transparent to the user which backend is running.\n",
-    "Here, the run is just a simple simulation for the number of steps specified with the biasing potential. Other advanced sampling methods can have more advanced run schemes.\n"
+    "Here, the run is just a simple simulation for the number of steps specified with the biasing potential. Other advanced sampling methods can have more advanced run schemes."
    ]
   },
   {
@@ -473,7 +424,7 @@
    ],
    "source": [
     "callback = HistogramLogger(100)\n",
-    "pysages.run(method, generate_context, int(1e4), callback, {\"A\": 7.0}, profile=True)"
+    "pysages.run(method, generate_simulation, int(1e4), callback, {\"A\": 7.0})"
    ]
   },
   {
@@ -482,11 +433,10 @@
     "id": "_vigR7XaMUD3"
    },
    "source": [
-    "\n",
     "After the simulation run, we collect the results for comparison with the analytic prediction for an ideal gas.\n",
     "First, we generate the analytic predictions for each of the CVs in a list `target_hist`.\n",
     "\n",
-    "After that, we are using the collected results from the callback to build the histograms from the simulations, and store the results in `hist_list`.\n"
+    "After that, we are using the collected results from the callback to build the histograms from the simulations, and store the results in `hist_list`."
    ]
   },
   {
@@ -500,10 +450,12 @@
     "Lmax = 5.0\n",
     "bins = 25\n",
     "target_hist = []\n",
+    "\n",
     "for i in range(len(center_cv)):\n",
     "    target_hist.append(\n",
     "        get_target_dist(center_cv[i], k, (-Lmax / 2, Lmax / 2), bins)\n",
     "    )\n",
+    "\n",
     "lims = [(-Lmax / 2, Lmax / 2) for i in range(3)]\n",
     "hist, edges = callback.get_histograms(bins=bins, range=lims)\n",
     "hist_list = [\n",
@@ -520,9 +472,8 @@
     "id": "2xwriftjNKgz"
    },
    "source": [
-    "\n",
     "Finally, we want to evaluate how the simulations turned out.\n",
-    "We use matplotlib to visualize the expected (dashed) and actual results of the simulations (solid).\n"
+    "We use matplotlib to visualize the expected (dashed) and actual results of the simulations (solid)."
    ]
   },
   {
@@ -561,6 +512,8 @@
     }
    ],
    "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "fig, ax = plt.subplots()\n",
     "\n",
     "ax.set_xlabel(r\"CV $\\xi_i$\")\n",
@@ -572,7 +525,9 @@
     "    (line,) = ax.plot(x, hist_list[i], label=\"i= {0}\".format(i))\n",
     "    ax.plot(x, target_hist[i], \"--\", color=line.get_color())\n",
     "\n",
-    "ax.legend(loc=\"best\")\n"
+    "ax.legend(loc=\"best\")\n",
+    "\n",
+    "fig.show()"
    ]
   },
   {
@@ -581,8 +536,7 @@
     "id": "IXryBllMNiKM"
    },
    "source": [
-    "\n",
-    "We can see, that the particle positions are indeed centered around the constraints we set up earlier. Also, we see the shape of the histograms is very similar to the expected analytical prediction. We expect this since a liquid of soft particles is not that much different from an ideal gas.\n"
+    "We can see, that the particle positions are indeed centered around the constraints we set up earlier. Also, we see the shape of the histograms is very similar to the expected analytical prediction. We expect this since a liquid of soft particles is not that much different from an ideal gas."
    ]
   }
  ],
diff --git a/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md b/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md
index 70231897..32542844 100644
--- a/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md
+++ b/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md
@@ -13,111 +13,77 @@ jupyter:
     name: python3
 ---
 
-<!-- #region id="p49wJ0IjLAVD" -->
-
-# Setup of the environment
+<!-- #region id="T-Qkg9C9n7Cc" -->
+# Setting up the environment
 
+First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 <!-- #endregion -->
 
-<!-- #region id="WM_9PpDwKuoA" -->
-
-First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin. We copy it from Google Drive and install pysages for it. This may require you to have read permissions to the shared Google Drive. We also have a Google Colab that performs this installation for reference.
-
-<!-- #endregion -->
+```bash id="3eTbKklCnyd_"
 
-```bash id="nMThqa-DjVcb"
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
-```python colab={"base_uri": "https://localhost:8080/"} id="25H3kl03wzJe" outputId="c424222d-bf8f-4a4f-eaa1-517910c500a6"
+```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="b757f2aa-38cc-4726-c4ab-5197810b9d77"
 %env PYSAGES_ENV=/env/pysages
 ```
 
-```bash id="CPkgxfj6w4te"
+```bash id="J7OY5K9VoBBh"
 
-mkdir -p $PYSAGES_ENV
+mkdir -p $PYSAGES_ENV .
 unzip -qquo pysages-env.zip -d $PYSAGES_ENV
-rm pysages-env.zip
 ```
 
-```python id="JMO5fiRTxAWB"
+```python id="EMAWp8VloIk4"
 import os
 import sys
 
 ver = sys.version_info
-
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
-```
 
-<!-- #region id="lf2KeHt5_eFv" -->
-
-## PySAGES
-
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
-<!-- #endregion -->
-
-```bash id="R_gW2ERpi9tw"
-
-pip install -q --upgrade pip &> /dev/null
-# Installs the wheel compatible with CUDA.
-pip install -q --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null
+os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
-<!-- #region id="mx0IRythaTyG" -->
-
-We test the jax installation and check the versions.
-
+<!-- #region id="Wy-75Pt7Bqs1" -->
+We'll also need some additional python dependencies
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/"} id="Z4E914qBHbZS" outputId="dbc3fade-b58d-49f7-8607-655b0e89e710"
-import jax
-import jaxlib
-print(jax.__version__)
-print(jaxlib.__version__)
+```python id="LpBucu3V81xm"
+!pip install -qq "numpy<2" gsd > /dev/null
 ```
 
-<!-- #region id="vtAmA51IAYxn" -->
-
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
+<!-- #region id="we_mTkFioS6R" -->
+## PySAGES
 
+The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`.
 <!-- #endregion -->
 
-```bash id="xYRGOcFJjEE6"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
 <!-- #region id="fRZDARPsDQHF" -->
-
 # Harmonic Bias simulation
-
 <!-- #endregion -->
 
 <!-- #region id="Uh2y2RXDDZub" -->
-
 A harmonic bias simulation constraints a collective variable with a harmonic potential. This is useful for a variety of advanced sampling methods, in particular, umbrella sampling.
 
 For this Colab, we are generating a small system of soft DPD particles first. This system of soft particles allows fast reliable execution.
 For this, we use the [GSD](https://gsd.readthedocs.io/en/stable/) file format and its python frontend to generate the initial conditions.
 Since all particles are soft, it is OK to start with random positions inside the simulation box. We also assign random velocities drawn from the Maxwell-Boltzmann distribution. The final configuration is written to disk and can be opened by HOOMD-blue for simulations.
-
 <!-- #endregion -->
 
 ```python id="aIP9vx8yDdr1"
-!pip install -q gsd &> /dev/null
-
-import sys
-import numpy as np
 import gsd
 import gsd.hoomd
+import numpy as np
 
 
 class System:
@@ -139,7 +105,6 @@ def get_snap(system):
     snapshot.configuration.box = [L, L, L, 0, 0, 0]
 
     snapshot.particles.N = N = system.N
-
     snapshot.particles.types = ["A"]
     snapshot.particles.position = np.zeros((N, 3))
     snapshot.particles.velocity = np.random.standard_normal((N, 3))
@@ -154,30 +119,29 @@ def get_snap(system):
 
     return snapshot
 
+
 system = System()
 snap = get_snap(system)
 snap = post_process_pos(snap)
 snap.particles.validate()
+
 with gsd.hoomd.open("harmonic_start.gsd", "w") as f:
    f.append(snap)
-
 ```
 
 <!-- #region id="n0Rd-hMnCD-B" -->
-
 Next, we start running the system, we start with importing the required libraries.
-Noteworthy are here the hoomd package with the MD and dlext module, and the pysages objects.
-We are going to use a collective variable that constrains a particle position. In PySAGES the `Component` class from the `colvars` package can achieve this for us.
-The `HarmonicBias` class is responsible for introducing the bias into the simulation run, while `HistogramLogger` collects the state of the collective variable during the run.
+Noteworthy are here the hoomd and the pysages package.
+
+We are going to use a collective variable that constrains a particle position.
+In PySAGES the `Component` class from the `colvars` package can achieve this for us.
 
+The `HarmonicBias` class is responsible for introducing the bias into the simulation run,
+while `HistogramLogger` collects the state of the collective variable during the run.
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="HkHOzXMzExps" outputId="27c1f5c0-43d4-4911-f1f8-069709242593"
-import numpy as np
-import matplotlib.pyplot as plt
 import hoomd
-import hoomd.md
-import hoomd.dlext
 
 import pysages
 from pysages.colvars import Component
@@ -185,34 +149,40 @@ from pysages.methods import HarmonicBias, HistogramLogger
 ```
 
 <!-- #region id="YibErIQhC0Lv" -->
-
 The next step is to write a function that generates the simulation context.
 Inside this function is the HOOMD-blue specific code, that you would normally write to execute a HOOMD-blue simulation. Here it is packaged into a function, such that PySAGES can deploy the simulation context when needed.
 In this case, we use the GSD file read in the initial, and define the DPD forcefield with parameters.
 DPD is a special case in HOOMD-blue. The thermostat is part of the pair-potential and not part of the integrator. Hence, we specify NVE integration and all thermostat parameter for NVT in the potential. The function returns the simulation context for PySAGES to work with.
 
 The second function is a helper function to generate the theoretically expected distribution of a harmonically biased simulation of an ideal gas in NVT. And helps to verify the results of the simulation.
-
 <!-- #endregion -->
 
 ```python id="67488aXwQXba"
-"""
-Generates a simulation context, we pass this function to the attribute `run` of our sampling method.
-"""
-def generate_context(**kwargs):
-    hoomd.context.initialize('')
-    context = hoomd.context.SimulationContext()
-    with context:
-        hoomd.init.read_gsd("harmonic_start.gsd")
-        hoomd.md.integrate.nve(group=hoomd.group.all())
-        hoomd.md.integrate.mode_standard(dt=0.01)
-
-        nl = hoomd.md.nlist.cell()
-        dpd = hoomd.md.pair.dpd(r_cut=1, nlist=nl, seed=42, kT=1.0)
-        dpd.pair_coeff.set(
-            "A", "A", A=kwargs.get("A", 5.0), gamma=kwargs.get("gamma", 1.0)
-        )
-    return context
+def generate_simulation(
+    kT=1, dt=0.01, A=5, gamma=1, r_cut=1,
+    device=hoomd.device.auto_select(), seed=42,
+    **kwargs
+):
+    """
+    Generates a simulation context to which will attatch our sampling method.
+    """
+    simulation = hoomd.Simulation(device=device, seed=seed)
+    simulation.create_state_from_gsd("harmonic_start.gsd")
+    simulation.run(0)
+
+    nlist = hoomd.md.nlist.Cell(buffer=0.4)
+    dpd = hoomd.md.pair.DPD(nlist=nlist, kT=kT, default_r_cut=r_cut)
+    dpd.params[("A", "A")] = dict(A=A, gamma=gamma)
+
+    nve = hoomd.md.methods.ConstantVolume(filter=hoomd.filter.All())
+
+    integrator = hoomd.md.Integrator(dt=dt)
+    integrator.forces.append(dpd)
+    integrator.methods.append(nve)
+    simulation.operations.integrator = integrator
+
+    return simulation
+
 
 def get_target_dist(center, k, lim, bins):
     x = np.linspace(lim[0], lim[1], bins)
@@ -220,63 +190,52 @@ def get_target_dist(center, k, lim, bins):
     # norm numerically
     p *= (lim[1] - lim[0]) / np.sum(p)
     return p
-
 ```
 
 <!-- #region id="BgQ88M0sIfbp" -->
-
 The next step is to define the collective variables (CVs) we are interested in.
 In this case, we are using the `Component` CV to describe the position in space. We choose particle `[0]` for this and log in 3 different CVS the Z- `2`, Y- `1`, and X- `0` position of the particle.
 The center describes where we are restraining the CVs to, which is also specified for each of the CVs described earlier.
 
 Finally, we define the spring constant for the harmonic biasing potential and the `HarmonicBias` method itself.
-
 <!-- #endregion -->
 
 ```python id="r911REinQdLF"
-cvs = [Component([0], 2)]
-cvs += [Component([0], 1)]
-cvs += [Component([0], 0)]
-
-center_cv = [0.0]
-center_cv += [1.0, -0.3]
-
+cvs = [Component([0], 2), Component([0], 1), Component([0], 0)]
+cv_centers = [0.0, 1.0, -0.3]
 k = 15
-method = HarmonicBias(cvs, k, center_cv)
-
+method = HarmonicBias(cvs, k, cv_centers)
 ```
 
 <!-- #region id="bGIDE56RLCcP" -->
-
 Next, we define the `HistogramLogger` callback. The callback interacts with the simulation every timestep after the biasing. In this case, we use it to log the state of the collective variables every `100` time-steps.
 
 And we can finally run the simulations. This happens through the PySAGES method run and is transparent to the user which backend is running.
 Here, the run is just a simple simulation for the number of steps specified with the biasing potential. Other advanced sampling methods can have more advanced run schemes.
-
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="aOXCppWkQnJI" outputId="a34ae4f3-92a9-47ce-cac6-7a02f1aa4a72"
 callback = HistogramLogger(100)
-pysages.run(method, generate_context, int(1e4), callback, {"A": 7.0}, profile=True)
+pysages.run(method, generate_simulation, int(1e4), callback, {"A": 7.0})
 ```
 
 <!-- #region id="_vigR7XaMUD3" -->
-
 After the simulation run, we collect the results for comparison with the analytic prediction for an ideal gas.
 First, we generate the analytic predictions for each of the CVs in a list `target_hist`.
 
 After that, we are using the collected results from the callback to build the histograms from the simulations, and store the results in `hist_list`.
-
 <!-- #endregion -->
 
 ```python id="jBiATDSaSqUw"
 Lmax = 5.0
 bins = 25
 target_hist = []
+
 for i in range(len(center_cv)):
     target_hist.append(
         get_target_dist(center_cv[i], k, (-Lmax / 2, Lmax / 2), bins)
     )
+
 lims = [(-Lmax / 2, Lmax / 2) for i in range(3)]
 hist, edges = callback.get_histograms(bins=bins, range=lims)
 hist_list = [
@@ -288,13 +247,13 @@ lim = (-Lmax / 2, Lmax / 2)
 ```
 
 <!-- #region id="2xwriftjNKgz" -->
-
 Finally, we want to evaluate how the simulations turned out.
 We use matplotlib to visualize the expected (dashed) and actual results of the simulations (solid).
-
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/", "height": 301} id="ZCkylgdvS3To" outputId="440269b2-ef60-4bce-b9fc-f34c823c8299"
+import matplotlib.pyplot as plt
+
 fig, ax = plt.subplots()
 
 ax.set_xlabel(r"CV $\xi_i$")
@@ -308,10 +267,9 @@ for i in range(len(hist_list)):
 
 ax.legend(loc="best")
 
+fig.show()
 ```
 
 <!-- #region id="IXryBllMNiKM" -->
-
 We can see, that the particle positions are indeed centered around the constraints we set up earlier. Also, we see the shape of the histograms is very similar to the expected analytical prediction. We expect this since a liquid of soft particles is not that much different from an ideal gas.
-
 <!-- #endregion -->
diff --git a/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.ipynb b/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.ipynb
index e346d8cd..b9c3ae86 100644
--- a/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.ipynb
+++ b/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.ipynb
@@ -6,11 +6,10 @@
     "id": "T-Qkg9C9n7Cc"
    },
    "source": [
-    "\n",
     "# Setting up the environment\n",
     "\n",
-    "First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin.\n",
-    "We copy it from Google Drive and install PySAGES for it.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance."
    ]
   },
   {
@@ -23,9 +22,12 @@
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
@@ -36,7 +38,7 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "KRPmkpd9n_NG",
-    "outputId": "acc3a92c-182f-415b-d8dc-b5af076b3d01"
+    "outputId": "b757f2aa-38cc-4726-c4ab-5197810b9d77"
    },
    "outputs": [
     {
@@ -79,46 +81,38 @@
     "ver = sys.version_info\n",
     "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "os.environ[\"XLA_FLAGS\"] = \"--xla_gpu_strict_conv_algorithm_picker=false\"\n",
     "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "we_mTkFioS6R"
+    "id": "Wy-75Pt7Bqs1"
    },
    "source": [
-    "\n",
-    "## PySAGES\n",
-    "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
+    "We'll also need some additional python dependencies"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "id": "vK0RZtbroQWe"
+    "id": "LpBucu3V81xm"
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "pip install -q --upgrade pip\n",
-    "# Installs the wheel compatible with CUDA.\n",
-    "pip install -q --upgrade \"jax[cuda]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null"
+    "!pip install -qq \"numpy<2\" gsd > /dev/null"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "wAtjM-IroYX8"
+    "id": "we_mTkFioS6R"
    },
    "source": [
+    "## PySAGES\n",
     "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
+    "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`."
    ]
   },
   {
@@ -129,36 +123,31 @@
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 8,
+   "cell_type": "markdown",
    "metadata": {
-    "id": "ppTzMmyyobHB"
+    "id": "KBFVcG1FoeMq"
    },
-   "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "mkdir /content/cff\n",
-    "cd /content/cff"
+    "# SpectralABF-biased simulations"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a230059f",
    "metadata": {
-    "id": "KBFVcG1FoeMq"
+    "id": "ppTzMmyyobHB"
    },
+   "outputs": [],
    "source": [
+    "%%bash\n",
     "\n",
-    "# SpectralABF-biased simulations\n"
+    "mkdir /content/spectral-abf\n",
+    "cd /content/spectral-abf"
    ]
   },
   {
@@ -167,10 +156,9 @@
     "id": "0W2ukJuuojAl"
    },
    "source": [
-    "\n",
     "SpectralABF gradually learns a better approximation to the coefficients of a basis functions expansion of the free energy of a system, from the generalized mean forces in a similar fashion to the ABF sampling method.\n",
     "\n",
-    "For this Colab, we are using butane as the example molecule.\n"
+    "For this Colab, we are using butane as the example molecule."
    ]
   },
   {
@@ -182,36 +170,31 @@
    "outputs": [],
    "source": [
     "import hoomd\n",
-    "import hoomd.md\n",
+    "import gsd.hoomd\n",
+    "import numpy as np\n",
     "\n",
-    "import numpy\n",
     "\n",
-    "\n",
-    "pi = numpy.pi\n",
+    "pi = np.pi\n",
     "kT = 0.596161\n",
     "dt = 0.02045\n",
-    "mode = \"--mode=gpu\"\n",
     "\n",
     "\n",
-    "def generate_context(kT = kT, dt = dt, mode = mode):\n",
+    "def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), seed = 42):\n",
     "    \"\"\"\n",
-    "    Generates a simulation context, we pass this function to the attribute\n",
-    "    `run` of our sampling method.\n",
+    "    Generates a simulation context to which will attatch our sampling method.\n",
     "    \"\"\"\n",
-    "    hoomd.context.initialize(mode)\n",
-    "\n",
-    "    ### System Definition\n",
-    "    snapshot = hoomd.data.make_snapshot(\n",
-    "        N = 14,\n",
-    "        box = hoomd.data.boxdim(Lx = 41, Ly = 41, Lz = 41),\n",
-    "        particle_types = ['C', 'H'],\n",
-    "        bond_types = [\"CC\", \"CH\"],\n",
-    "        angle_types = [\"CCC\", \"CCH\", \"HCH\"],\n",
-    "        dihedral_types = [\"CCCC\", \"HCCC\", \"HCCH\"],\n",
-    "        pair_types = [\"CCCC\", \"HCCC\", \"HCCH\"],\n",
-    "        dtype = \"double\"\n",
-    "    )\n",
+    "    simulation = hoomd.Simulation(device=device, seed=seed)\n",
+    "\n",
+    "    snapshot = gsd.hoomd.Frame()\n",
+    "\n",
+    "    snapshot.configuration.box = [41, 41, 41, 0, 0, 0]\n",
     "\n",
+    "    snapshot.particles.N = N = 14\n",
+    "    snapshot.particles.types = [\"C\", \"H\"]\n",
+    "    snapshot.particles.typeid = np.zeros(N, dtype=int)\n",
+    "    snapshot.particles.position = np.zeros((N, 3))\n",
+    "    snapshot.particles.mass = np.zeros(N, dtype=float)\n",
+    "    snapshot.particles.charge = np.zeros(N, dtype=float)\n",
     "    snapshot.particles.typeid[0] = 0\n",
     "    snapshot.particles.typeid[1:4] = 1\n",
     "    snapshot.particles.typeid[4] = 0\n",
@@ -221,52 +204,60 @@
     "    snapshot.particles.typeid[10] = 0\n",
     "    snapshot.particles.typeid[11:14] = 1\n",
     "\n",
-    "    positions = numpy.array([\n",
-    "        [-2.990196,  0.097881,  0.000091],\n",
-    "        [-2.634894, -0.911406,  0.001002],\n",
-    "        [-2.632173,  0.601251, -0.873601],\n",
-    "        [-4.060195,  0.099327, -0.000736],\n",
-    "        [-2.476854,  0.823942,  1.257436],\n",
-    "        [-2.832157,  1.833228,  1.256526],\n",
-    "        [-2.834877,  0.320572,  2.131128],\n",
-    "        [-0.936856,  0.821861,  1.258628],\n",
-    "        [-0.578833,  1.325231,  0.384935],\n",
-    "        [-0.581553, -0.187426,  1.259538],\n",
-    "        [-0.423514,  1.547922,  2.515972],\n",
-    "        [-0.781537,  1.044552,  3.389664],\n",
-    "        [ 0.646485,  1.546476,  2.516800],\n",
-    "        [-0.778816,  2.557208,  2.515062]\n",
-    "    ])\n",
-    "\n",
-    "    reference_box_low_coords = numpy.array([-22.206855, -19.677099, -19.241968])\n",
-    "    box_low_coords = numpy.array([\n",
-    "        -snapshot.box.Lx / 2,\n",
-    "        -snapshot.box.Ly / 2,\n",
-    "        -snapshot.box.Lz / 2\n",
-    "    ])\n",
-    "    positions += (box_low_coords - reference_box_low_coords)\n",
+    "    positions = np.array(\n",
+    "        [\n",
+    "            [-2.990196, 0.097881, 0.000091],\n",
+    "            [-2.634894, -0.911406, 0.001002],\n",
+    "            [-2.632173, 0.601251, -0.873601],\n",
+    "            [-4.060195, 0.099327, -0.000736],\n",
+    "            [-2.476854, 0.823942, 1.257436],\n",
+    "            [-2.832157, 1.833228, 1.256526],\n",
+    "            [-2.834877, 0.320572, 2.131128],\n",
+    "            [-0.936856, 0.821861, 1.258628],\n",
+    "            [-0.578833, 1.325231, 0.384935],\n",
+    "            [-0.581553, -0.187426, 1.259538],\n",
+    "            [-0.423514, 1.547922, 2.515972],\n",
+    "            [-0.781537, 1.044552, 3.389664],\n",
+    "            [0.646485, 1.546476, 2.516800],\n",
+    "            [-0.778816, 2.557208, 2.515062],\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    reference_box_low_coords = np.array([-22.206855, -19.677099, -19.241968])\n",
+    "    box_low_coords = np.array([-41.0 / 2, -41.0 / 2, -41.0 / 2])\n",
+    "    positions += box_low_coords - reference_box_low_coords\n",
     "\n",
     "    snapshot.particles.position[:] = positions[:]\n",
     "\n",
     "    mC = 12.00\n",
     "    mH = 1.008\n",
+    "\n",
+    "    # fmt: off\n",
     "    snapshot.particles.mass[:] = [\n",
-    "        mC, mH, mH, mH,\n",
+    "        mC, mH, mH, mH,  # grouped by carbon atoms\n",
     "        mC, mH, mH,\n",
     "        mC, mH, mH,\n",
-    "        mC, mH, mH, mH\n",
+    "        mC, mH, mH, mH,\n",
     "    ]\n",
     "\n",
-    "    reference_charges = numpy.array([\n",
-    "        -0.180000, 0.060000, 0.060000, 0.060000,\n",
-    "        -0.120000, 0.060000, 0.060000,\n",
-    "        -0.120000, 0.060000, 0.060000,\n",
-    "        -0.180000, 0.060000, 0.060000, 0.060000]\n",
+    "    reference_charges = np.array(\n",
+    "        [\n",
+    "            -0.180000, 0.060000, 0.060000, 0.060000,  # grouped by carbon atoms\n",
+    "            -0.120000, 0.060000, 0.060000,\n",
+    "            -0.120000, 0.060000, 0.060000,\n",
+    "            -0.180000, 0.060000, 0.060000, 0.060000,\n",
+    "        ]\n",
     "    )\n",
+    "    # fmt: on\n",
+    "\n",
     "    charge_conversion = 18.22262\n",
     "    snapshot.particles.charge[:] = charge_conversion * reference_charges[:]\n",
     "\n",
-    "    snapshot.bonds.resize(13)\n",
+    "    snapshot.particles.validate()\n",
+    "\n",
+    "    snapshot.bonds.N = 13\n",
+    "    snapshot.bonds.types = [\"CC\", \"CH\"]\n",
+    "    snapshot.bonds.typeid = np.zeros(13, dtype=int)\n",
     "    snapshot.bonds.typeid[0:3] = 1\n",
     "    snapshot.bonds.typeid[3] = 0\n",
     "    snapshot.bonds.typeid[4:6] = 1\n",
@@ -275,14 +266,19 @@
     "    snapshot.bonds.typeid[9] = 0\n",
     "    snapshot.bonds.typeid[10:13] = 1\n",
     "\n",
+    "    snapshot.bonds.group = np.zeros((13, 2), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.bonds.group[:] = [\n",
-    "        [0, 2], [0, 1], [0, 3], [0, 4],\n",
+    "        [0, 2], [0, 1], [0, 3], [0, 4],  # grouped by carbon atoms\n",
     "        [4, 5], [4, 6], [4, 7],\n",
     "        [7, 8], [7, 9], [7, 10],\n",
-    "        [10, 11], [10, 12], [10, 13]\n",
+    "        [10, 11], [10, 12], [10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.angles.resize(24)\n",
+    "    snapshot.angles.N = 24\n",
+    "    snapshot.angles.types = [\"CCC\", \"CCH\", \"HCH\"]\n",
+    "    snapshot.angles.typeid = np.zeros(24, dtype=int)\n",
     "    snapshot.angles.typeid[0:2] = 2\n",
     "    snapshot.angles.typeid[2] = 1\n",
     "    snapshot.angles.typeid[3] = 2\n",
@@ -295,18 +291,26 @@
     "    snapshot.angles.typeid[16:21] = 1\n",
     "    snapshot.angles.typeid[21:24] = 2\n",
     "\n",
+    "    snapshot.angles.group = np.zeros((24, 3), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.angles.group[:] = [\n",
-    "        [1, 0, 2], [2, 0, 3], [2, 0, 4],\n",
+    "        [1, 0, 2], [2, 0, 3], [2, 0, 4],  # grouped by carbon atoms\n",
     "        [1, 0, 3], [1, 0, 4], [3, 0, 4],\n",
+    "        # ---\n",
     "        [0, 4, 5], [0, 4, 6], [0, 4, 7],\n",
     "        [5, 4, 6], [5, 4, 7], [6, 4, 7],\n",
+    "        # ---\n",
     "        [4, 7, 8], [4, 7, 9], [4, 7, 10],\n",
     "        [8, 7, 9], [8, 7, 10], [9, 7, 10],\n",
+    "        # ---\n",
     "        [7, 10, 11], [7, 10, 12], [7, 10, 13],\n",
-    "        [11, 10, 12], [11, 10, 13], [12, 10, 13]\n",
+    "        [11, 10, 12], [11, 10, 13], [12, 10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.dihedrals.resize(27)\n",
+    "    snapshot.dihedrals.N = 27\n",
+    "    snapshot.dihedrals.types = [\"CCCC\", \"HCCC\", \"HCCH\"]\n",
+    "    snapshot.dihedrals.typeid = np.zeros(27, dtype=int)\n",
     "    snapshot.dihedrals.typeid[0:2] = 2\n",
     "    snapshot.dihedrals.typeid[2] = 1\n",
     "    snapshot.dihedrals.typeid[3:5] = 2\n",
@@ -320,81 +324,109 @@
     "    snapshot.dihedrals.typeid[17:21] = 1\n",
     "    snapshot.dihedrals.typeid[21:27] = 2\n",
     "\n",
+    "    snapshot.dihedrals.group = np.zeros((27, 4), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.dihedrals.group[:] = [\n",
-    "        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],\n",
+    "        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],  # grouped by pairs of central atoms\n",
     "        [1, 0, 4, 5], [1, 0, 4, 6], [1, 0, 4, 7],\n",
     "        [3, 0, 4, 5], [3, 0, 4, 6], [3, 0, 4, 7],\n",
+    "        # ---\n",
     "        [0, 4, 7, 8], [0, 4, 7, 9], [0, 4, 7, 10],\n",
     "        [5, 4, 7, 8], [5, 4, 7, 9], [5, 4, 7, 10],\n",
     "        [6, 4, 7, 8], [6, 4, 7, 9], [6, 4, 7, 10],\n",
+    "        # ---\n",
     "        [4, 7, 10, 11], [4, 7, 10, 12], [4, 7, 10, 13],\n",
     "        [8, 7, 10, 11], [8, 7, 10, 12], [8, 7, 10, 13],\n",
-    "        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13]\n",
+    "        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    snapshot.pairs.resize(27)\n",
+    "    snapshot.pairs.N = 27\n",
+    "    snapshot.pairs.types = [\"CCCC\", \"HCCC\", \"HCCH\"]\n",
+    "    snapshot.pairs.typeid = np.zeros(27, dtype=int)\n",
     "    snapshot.pairs.typeid[0:1] = 0\n",
     "    snapshot.pairs.typeid[1:11] = 1\n",
     "    snapshot.pairs.typeid[11:27] = 2\n",
+    "    snapshot.pairs.group = np.zeros((27, 2), dtype=int)\n",
+    "    # fmt: off\n",
     "    snapshot.pairs.group[:] = [\n",
     "        # CCCC\n",
     "        [0, 10],\n",
     "        # HCCC\n",
-    "        [0, 8], [0, 9], [5, 10], [6, 10],\n",
+    "        [0, 8],\n",
+    "        [0, 9],\n",
+    "        [5, 10], [6, 10],\n",
     "        [1, 7], [2, 7], [3, 7],\n",
     "        [11, 4], [12, 4], [13, 4],\n",
     "        # HCCH\n",
-    "        [1, 5], [1, 6], [2, 5], [2, 6], [3, 5], [3, 6],\n",
-    "        [5, 8], [6, 8], [5, 9], [6, 9],\n",
-    "        [8, 11], [8, 12], [8, 13], [9, 11], [9, 12], [9, 13]\n",
+    "        [1, 5], [1, 6],\n",
+    "        [2, 5], [2, 6],\n",
+    "        [3, 5], [3, 6],\n",
+    "        [5, 8], [6, 8],\n",
+    "        [5, 9], [6, 9],\n",
+    "        [8, 11], [8, 12], [8, 13],\n",
+    "        [9, 11], [9, 12], [9, 13],\n",
     "    ]\n",
+    "    # fmt: on\n",
     "\n",
-    "    hoomd.init.read_snapshot(snapshot)\n",
-    "\n",
-    "    ### Set interactions\n",
-    "    nl_ex = hoomd.md.nlist.cell()\n",
-    "    nl_ex.reset_exclusions(exclusions = [\"1-2\", \"1-3\", \"1-4\"])\n",
-    "\n",
-    "    lj = hoomd.md.pair.lj(r_cut = 12.0, nlist = nl_ex)\n",
-    "    lj.pair_coeff.set('C', 'C', epsilon = 0.07, sigma = 3.55)\n",
-    "    lj.pair_coeff.set('H', 'H', epsilon = 0.03, sigma = 2.42)\n",
-    "    lj.pair_coeff.set('C', 'H', epsilon = numpy.sqrt(0.07*0.03), sigma = numpy.sqrt(3.55*2.42))\n",
+    "    simulation.create_state_from_snapshot(snapshot, domain_decomposition=(None, None, None))\n",
+    "    simulation.run(0)\n",
     "\n",
-    "    coulomb = hoomd.md.charge.pppm(hoomd.group.charged(), nlist = nl_ex)\n",
-    "    coulomb.set_params(Nx = 64, Ny = 64, Nz = 64, order = 6, rcut = 12.0)\n",
+    "    exclusions = [\"bond\", \"1-3\", \"1-4\"]\n",
+    "    nl = hoomd.md.nlist.Cell(buffer=0.4, exclusions=exclusions)\n",
+    "    lj = hoomd.md.pair.LJ(nlist=nl, default_r_cut=12.0)\n",
+    "    lj.params[(\"C\", \"C\")] = dict(epsilon=0.07, sigma=3.55)\n",
+    "    lj.params[(\"H\", \"H\")] = dict(epsilon=0.03, sigma=2.42)\n",
+    "    lj.params[(\"C\", \"H\")] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))\n",
     "\n",
-    "    harmonic = hoomd.md.bond.harmonic()\n",
-    "    harmonic.bond_coeff.set(\"CC\", k = 2*268.0, r0 = 1.529)\n",
-    "    harmonic.bond_coeff.set(\"CH\", k = 2*340.0, r0 = 1.09)\n",
-    "\n",
-    "    angle = hoomd.md.angle.harmonic()\n",
-    "    angle.angle_coeff.set(\"CCC\", k = 2*58.35, t0 = 112.7 * pi / 180)\n",
-    "    angle.angle_coeff.set(\"CCH\", k = 2*37.5, t0 = 110.7 * pi / 180)\n",
-    "    angle.angle_coeff.set(\"HCH\", k = 2*33.0, t0 = 107.8 * pi / 180)\n",
-    "\n",
-    "\n",
-    "    dihedral = hoomd.md.dihedral.opls()\n",
-    "    dihedral.dihedral_coeff.set(\"CCCC\", k1 = 1.3, k2 = -0.05, k3 = 0.2, k4 = 0.0)\n",
-    "    dihedral.dihedral_coeff.set(\"HCCC\", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)\n",
-    "    dihedral.dihedral_coeff.set(\"HCCH\", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)\n",
-    "\n",
-    "    lj_special_pairs = hoomd.md.special_pair.lj()\n",
-    "    lj_special_pairs.pair_coeff.set(\"CCCC\", epsilon = 0.07, sigma = 3.55, r_cut = 12.0)\n",
-    "    lj_special_pairs.pair_coeff.set(\"HCCH\", epsilon = 0.03, sigma = 2.42, r_cut = 12.0)\n",
-    "    lj_special_pairs.pair_coeff.set(\"HCCC\",\n",
-    "        epsilon = numpy.sqrt(0.07 * 0.03), sigma = numpy.sqrt(3.55 * 2.42), r_cut = 12.0\n",
+    "    coulomb = hoomd.md.long_range.pppm.make_pppm_coulomb_forces(\n",
+    "        nlist=nl, resolution=[64, 64, 64], order=6, r_cut=12.0\n",
     "    )\n",
     "\n",
-    "    coulomb_special_pairs = hoomd.md.special_pair.coulomb()\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"CCCC\", alpha = 0.5, r_cut = 12.0)\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"HCCC\", alpha = 0.5, r_cut = 12.0)\n",
-    "    coulomb_special_pairs.pair_coeff.set(\"HCCH\", alpha = 0.5, r_cut = 12.0)\n",
-    "\n",
-    "    hoomd.md.integrate.mode_standard(dt = dt)\n",
-    "    integrator = hoomd.md.integrate.nvt(group = hoomd.group.all(), kT = kT, tau = 100*dt)\n",
-    "    integrator.randomize_velocities(seed = 42)\n",
-    "\n",
-    "    return hoomd.context.current"
+    "    harmonic = hoomd.md.bond.Harmonic()\n",
+    "    harmonic.params[\"CC\"] = dict(k=2 * 268.0, r0=1.529)\n",
+    "    harmonic.params[\"CH\"] = dict(k=2 * 340.0, r0=1.09)\n",
+    "\n",
+    "    angle = hoomd.md.angle.Harmonic()\n",
+    "    angle.params[\"CCC\"] = dict(k=2 * 58.35, t0=112.7 * pi / 180)\n",
+    "    angle.params[\"CCH\"] = dict(k=2 * 37.5, t0=110.7 * pi / 180)\n",
+    "    angle.params[\"HCH\"] = dict(k=2 * 33.0, t0=107.8 * pi / 180)\n",
+    "\n",
+    "    dihedral = hoomd.md.dihedral.OPLS()\n",
+    "    dihedral.params[\"CCCC\"] = dict(k1=1.3, k2=-0.05, k3=0.2, k4=0.0)\n",
+    "    dihedral.params[\"HCCC\"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)\n",
+    "    dihedral.params[\"HCCH\"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)\n",
+    "\n",
+    "    lj_special_pairs = hoomd.md.special_pair.LJ()\n",
+    "    lj_special_pairs.params[\"CCCC\"] = dict(epsilon=0.07, sigma=3.55)\n",
+    "    lj_special_pairs.params[\"HCCH\"] = dict(epsilon=0.03, sigma=2.42)\n",
+    "    lj_special_pairs.params[\"HCCC\"] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))\n",
+    "    lj_special_pairs.r_cut[\"CCCC\"] = 12.0\n",
+    "    lj_special_pairs.r_cut[\"HCCC\"] = 12.0\n",
+    "    lj_special_pairs.r_cut[\"HCCH\"] = 12.0\n",
+    "    coulomb_special_pairs = hoomd.md.special_pair.Coulomb()\n",
+    "    coulomb_special_pairs.params[\"CCCC\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.params[\"HCCC\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.params[\"HCCH\"] = dict(alpha=0.5)\n",
+    "    coulomb_special_pairs.r_cut[\"HCCH\"] = 12.0\n",
+    "    coulomb_special_pairs.r_cut[\"CCCC\"] = 12.0\n",
+    "    coulomb_special_pairs.r_cut[\"HCCC\"] = 12.0\n",
+    "\n",
+    "    nvt = hoomd.md.methods.Langevin(filter=hoomd.filter.All(), kT=kT)\n",
+    "\n",
+    "    integrator = hoomd.md.Integrator(dt=dt)\n",
+    "    integrator.forces.append(lj)\n",
+    "    integrator.forces.append(coulomb[0])\n",
+    "    integrator.forces.append(coulomb[1])\n",
+    "    integrator.forces.append(harmonic)\n",
+    "    integrator.forces.append(angle)\n",
+    "    integrator.forces.append(dihedral)\n",
+    "    integrator.forces.append(lj_special_pairs)\n",
+    "    integrator.forces.append(coulomb_special_pairs)\n",
+    "    integrator.methods.append(nvt)\n",
+    "    simulation.operations.integrator = integrator\n",
+    "\n",
+    "    return simulation"
    ]
   },
   {
@@ -403,8 +435,7 @@
     "id": "3UrzENm_oo6U"
    },
    "source": [
-    "\n",
-    "Next, we load PySAGES and the relevant classes and methods for our problem\n"
+    "Next, we load PySAGES and the relevant classes and methods for our problem"
    ]
   },
   {
@@ -428,10 +459,9 @@
     "id": "LknkRvo1o4av"
    },
    "source": [
-    "\n",
     "The next step is to define the collective variable (CV). In this case, we choose the central dihedral angle.\n",
     "\n",
-    "We define a grid, which will be used to indicate how we want to bin the forces that will be used to approximate the biasing potential and its gradient.\n"
+    "We define a grid, which will be used to indicate how we want to bin the forces that will be used to approximate the biasing potential and its gradient."
    ]
   },
   {
@@ -455,9 +485,8 @@
     "id": "Fz8BfU34pA_N"
    },
    "source": [
-    "\n",
     "We now simulate $5\\times10^5$ time steps.\n",
-    "Make sure to run with GPU support, otherwise, it can take a very long time.\n"
+    "Make sure to run with GPU support, otherwise, it can take a very long time."
    ]
   },
   {
@@ -579,7 +608,7 @@
     }
    ],
    "source": [
-    "run_result = pysages.run(method, generate_context, timesteps)"
+    "run_result = pysages.run(method, generate_simulation, timesteps)"
    ]
   },
   {
@@ -590,8 +619,7 @@
    "source": [
     "\n",
     "## Analysis\n",
-    "\n",
-    "PySAGES provides an `analyze` method that makes it easier to get the free energy of different simulation runs.\n"
+    "PySAGES provides an `analyze` method that makes it easier to get the free energy of different simulation runs."
    ]
   },
   {
@@ -611,8 +639,7 @@
     "id": "PXBKUfK0p9T2"
    },
    "source": [
-    "\n",
-    "Let's plot now the free energy!\n"
+    "Let's plot now the free energy!"
    ]
   },
   {
@@ -681,9 +708,9 @@
     "\n",
     "ax.set_xlabel(r\"Dihedral Angle, $\\xi$\")\n",
     "ax.set_ylabel(r\"$A(\\xi)$\")\n",
-    "\n",
     "ax.plot(mesh, A)\n",
-    "plt.gca()"
+    "\n",
+    "fig.show()"
    ]
   }
  ],
diff --git a/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.md b/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.md
index 78d15f3f..2f002781 100644
--- a/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.md
+++ b/examples/hoomd-blue/spectral_abf/Butane-SpectralABF.md
@@ -14,22 +14,23 @@ jupyter:
 ---
 
 <!-- #region id="T-Qkg9C9n7Cc" -->
-
 # Setting up the environment
 
-First, we are setting up our environment. We use an already compiled and packaged installation of HOOMD-blue and the DLExt plugin.
-We copy it from Google Drive and install PySAGES for it.
-
+First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 <!-- #endregion -->
 
 ```bash id="3eTbKklCnyd_"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
+
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
-```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="acc3a92c-182f-415b-d8dc-b5af076b3d01"
+```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="b757f2aa-38cc-4726-c4ab-5197810b9d77"
 %env PYSAGES_ENV=/env/pysages
 ```
 
@@ -46,92 +47,70 @@ import sys
 ver = sys.version_info
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
 
-os.environ["XLA_FLAGS"] = "--xla_gpu_strict_conv_algorithm_picker=false"
 os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
-<!-- #region id="we_mTkFioS6R" -->
-
-## PySAGES
-
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
+<!-- #region id="Wy-75Pt7Bqs1" -->
+We'll also need some additional python dependencies
 <!-- #endregion -->
 
-```bash id="vK0RZtbroQWe"
-
-pip install -q --upgrade pip
-# Installs the wheel compatible with CUDA.
-pip install -q --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null
+```python id="LpBucu3V81xm"
+!pip install -qq "numpy<2" gsd > /dev/null
 ```
 
-<!-- #region id="wAtjM-IroYX8" -->
-
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
+<!-- #region id="we_mTkFioS6R" -->
+## PySAGES
 
+The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`.
 <!-- #endregion -->
 
-```bash id="B-HB9CzioV5j"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
-```
-
-```bash id="ppTzMmyyobHB"
-
-mkdir /content/cff
-cd /content/cff
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
 <!-- #region id="KBFVcG1FoeMq" -->
-
 # SpectralABF-biased simulations
-
 <!-- #endregion -->
 
-<!-- #region id="0W2ukJuuojAl" -->
+```bash id="ppTzMmyyobHB"
 
+mkdir /content/spectral-abf
+cd /content/spectral-abf
+```
+
+<!-- #region id="0W2ukJuuojAl" -->
 SpectralABF gradually learns a better approximation to the coefficients of a basis functions expansion of the free energy of a system, from the generalized mean forces in a similar fashion to the ABF sampling method.
 
 For this Colab, we are using butane as the example molecule.
-
 <!-- #endregion -->
 
 ```python id="BBvC7Spoog82"
 import hoomd
-import hoomd.md
+import gsd.hoomd
+import numpy as np
 
-import numpy
 
-
-pi = numpy.pi
+pi = np.pi
 kT = 0.596161
 dt = 0.02045
-mode = "--mode=gpu"
 
 
-def generate_context(kT = kT, dt = dt, mode = mode):
+def generate_simulation(kT = kT, dt = dt, device = hoomd.device.auto_select(), seed = 42):
     """
-    Generates a simulation context, we pass this function to the attribute
-    `run` of our sampling method.
+    Generates a simulation context to which will attatch our sampling method.
     """
-    hoomd.context.initialize(mode)
-
-    ### System Definition
-    snapshot = hoomd.data.make_snapshot(
-        N = 14,
-        box = hoomd.data.boxdim(Lx = 41, Ly = 41, Lz = 41),
-        particle_types = ['C', 'H'],
-        bond_types = ["CC", "CH"],
-        angle_types = ["CCC", "CCH", "HCH"],
-        dihedral_types = ["CCCC", "HCCC", "HCCH"],
-        pair_types = ["CCCC", "HCCC", "HCCH"],
-        dtype = "double"
-    )
+    simulation = hoomd.Simulation(device=device, seed=seed)
+
+    snapshot = gsd.hoomd.Frame()
 
+    snapshot.configuration.box = [41, 41, 41, 0, 0, 0]
+
+    snapshot.particles.N = N = 14
+    snapshot.particles.types = ["C", "H"]
+    snapshot.particles.typeid = np.zeros(N, dtype=int)
+    snapshot.particles.position = np.zeros((N, 3))
+    snapshot.particles.mass = np.zeros(N, dtype=float)
+    snapshot.particles.charge = np.zeros(N, dtype=float)
     snapshot.particles.typeid[0] = 0
     snapshot.particles.typeid[1:4] = 1
     snapshot.particles.typeid[4] = 0
@@ -141,52 +120,60 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.particles.typeid[10] = 0
     snapshot.particles.typeid[11:14] = 1
 
-    positions = numpy.array([
-        [-2.990196,  0.097881,  0.000091],
-        [-2.634894, -0.911406,  0.001002],
-        [-2.632173,  0.601251, -0.873601],
-        [-4.060195,  0.099327, -0.000736],
-        [-2.476854,  0.823942,  1.257436],
-        [-2.832157,  1.833228,  1.256526],
-        [-2.834877,  0.320572,  2.131128],
-        [-0.936856,  0.821861,  1.258628],
-        [-0.578833,  1.325231,  0.384935],
-        [-0.581553, -0.187426,  1.259538],
-        [-0.423514,  1.547922,  2.515972],
-        [-0.781537,  1.044552,  3.389664],
-        [ 0.646485,  1.546476,  2.516800],
-        [-0.778816,  2.557208,  2.515062]
-    ])
-
-    reference_box_low_coords = numpy.array([-22.206855, -19.677099, -19.241968])
-    box_low_coords = numpy.array([
-        -snapshot.box.Lx / 2,
-        -snapshot.box.Ly / 2,
-        -snapshot.box.Lz / 2
-    ])
-    positions += (box_low_coords - reference_box_low_coords)
+    positions = np.array(
+        [
+            [-2.990196, 0.097881, 0.000091],
+            [-2.634894, -0.911406, 0.001002],
+            [-2.632173, 0.601251, -0.873601],
+            [-4.060195, 0.099327, -0.000736],
+            [-2.476854, 0.823942, 1.257436],
+            [-2.832157, 1.833228, 1.256526],
+            [-2.834877, 0.320572, 2.131128],
+            [-0.936856, 0.821861, 1.258628],
+            [-0.578833, 1.325231, 0.384935],
+            [-0.581553, -0.187426, 1.259538],
+            [-0.423514, 1.547922, 2.515972],
+            [-0.781537, 1.044552, 3.389664],
+            [0.646485, 1.546476, 2.516800],
+            [-0.778816, 2.557208, 2.515062],
+        ]
+    )
+
+    reference_box_low_coords = np.array([-22.206855, -19.677099, -19.241968])
+    box_low_coords = np.array([-41.0 / 2, -41.0 / 2, -41.0 / 2])
+    positions += box_low_coords - reference_box_low_coords
 
     snapshot.particles.position[:] = positions[:]
 
     mC = 12.00
     mH = 1.008
+
+    # fmt: off
     snapshot.particles.mass[:] = [
-        mC, mH, mH, mH,
+        mC, mH, mH, mH,  # grouped by carbon atoms
         mC, mH, mH,
         mC, mH, mH,
-        mC, mH, mH, mH
+        mC, mH, mH, mH,
     ]
 
-    reference_charges = numpy.array([
-        -0.180000, 0.060000, 0.060000, 0.060000,
-        -0.120000, 0.060000, 0.060000,
-        -0.120000, 0.060000, 0.060000,
-        -0.180000, 0.060000, 0.060000, 0.060000]
+    reference_charges = np.array(
+        [
+            -0.180000, 0.060000, 0.060000, 0.060000,  # grouped by carbon atoms
+            -0.120000, 0.060000, 0.060000,
+            -0.120000, 0.060000, 0.060000,
+            -0.180000, 0.060000, 0.060000, 0.060000,
+        ]
     )
+    # fmt: on
+
     charge_conversion = 18.22262
     snapshot.particles.charge[:] = charge_conversion * reference_charges[:]
 
-    snapshot.bonds.resize(13)
+    snapshot.particles.validate()
+
+    snapshot.bonds.N = 13
+    snapshot.bonds.types = ["CC", "CH"]
+    snapshot.bonds.typeid = np.zeros(13, dtype=int)
     snapshot.bonds.typeid[0:3] = 1
     snapshot.bonds.typeid[3] = 0
     snapshot.bonds.typeid[4:6] = 1
@@ -195,14 +182,19 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.bonds.typeid[9] = 0
     snapshot.bonds.typeid[10:13] = 1
 
+    snapshot.bonds.group = np.zeros((13, 2), dtype=int)
+    # fmt: off
     snapshot.bonds.group[:] = [
-        [0, 2], [0, 1], [0, 3], [0, 4],
+        [0, 2], [0, 1], [0, 3], [0, 4],  # grouped by carbon atoms
         [4, 5], [4, 6], [4, 7],
         [7, 8], [7, 9], [7, 10],
-        [10, 11], [10, 12], [10, 13]
+        [10, 11], [10, 12], [10, 13],
     ]
+    # fmt: on
 
-    snapshot.angles.resize(24)
+    snapshot.angles.N = 24
+    snapshot.angles.types = ["CCC", "CCH", "HCH"]
+    snapshot.angles.typeid = np.zeros(24, dtype=int)
     snapshot.angles.typeid[0:2] = 2
     snapshot.angles.typeid[2] = 1
     snapshot.angles.typeid[3] = 2
@@ -215,18 +207,26 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.angles.typeid[16:21] = 1
     snapshot.angles.typeid[21:24] = 2
 
+    snapshot.angles.group = np.zeros((24, 3), dtype=int)
+    # fmt: off
     snapshot.angles.group[:] = [
-        [1, 0, 2], [2, 0, 3], [2, 0, 4],
+        [1, 0, 2], [2, 0, 3], [2, 0, 4],  # grouped by carbon atoms
         [1, 0, 3], [1, 0, 4], [3, 0, 4],
+        # ---
         [0, 4, 5], [0, 4, 6], [0, 4, 7],
         [5, 4, 6], [5, 4, 7], [6, 4, 7],
+        # ---
         [4, 7, 8], [4, 7, 9], [4, 7, 10],
         [8, 7, 9], [8, 7, 10], [9, 7, 10],
+        # ---
         [7, 10, 11], [7, 10, 12], [7, 10, 13],
-        [11, 10, 12], [11, 10, 13], [12, 10, 13]
+        [11, 10, 12], [11, 10, 13], [12, 10, 13],
     ]
+    # fmt: on
 
-    snapshot.dihedrals.resize(27)
+    snapshot.dihedrals.N = 27
+    snapshot.dihedrals.types = ["CCCC", "HCCC", "HCCH"]
+    snapshot.dihedrals.typeid = np.zeros(27, dtype=int)
     snapshot.dihedrals.typeid[0:2] = 2
     snapshot.dihedrals.typeid[2] = 1
     snapshot.dihedrals.typeid[3:5] = 2
@@ -240,87 +240,113 @@ def generate_context(kT = kT, dt = dt, mode = mode):
     snapshot.dihedrals.typeid[17:21] = 1
     snapshot.dihedrals.typeid[21:27] = 2
 
+    snapshot.dihedrals.group = np.zeros((27, 4), dtype=int)
+    # fmt: off
     snapshot.dihedrals.group[:] = [
-        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],
+        [2, 0, 4, 5], [2, 0, 4, 6], [2, 0, 4, 7],  # grouped by pairs of central atoms
         [1, 0, 4, 5], [1, 0, 4, 6], [1, 0, 4, 7],
         [3, 0, 4, 5], [3, 0, 4, 6], [3, 0, 4, 7],
+        # ---
         [0, 4, 7, 8], [0, 4, 7, 9], [0, 4, 7, 10],
         [5, 4, 7, 8], [5, 4, 7, 9], [5, 4, 7, 10],
         [6, 4, 7, 8], [6, 4, 7, 9], [6, 4, 7, 10],
+        # ---
         [4, 7, 10, 11], [4, 7, 10, 12], [4, 7, 10, 13],
         [8, 7, 10, 11], [8, 7, 10, 12], [8, 7, 10, 13],
-        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13]
+        [9, 7, 10, 11], [9, 7, 10, 12], [9, 7, 10, 13],
     ]
+    # fmt: on
 
-    snapshot.pairs.resize(27)
+    snapshot.pairs.N = 27
+    snapshot.pairs.types = ["CCCC", "HCCC", "HCCH"]
+    snapshot.pairs.typeid = np.zeros(27, dtype=int)
     snapshot.pairs.typeid[0:1] = 0
     snapshot.pairs.typeid[1:11] = 1
     snapshot.pairs.typeid[11:27] = 2
+    snapshot.pairs.group = np.zeros((27, 2), dtype=int)
+    # fmt: off
     snapshot.pairs.group[:] = [
         # CCCC
         [0, 10],
         # HCCC
-        [0, 8], [0, 9], [5, 10], [6, 10],
+        [0, 8],
+        [0, 9],
+        [5, 10], [6, 10],
         [1, 7], [2, 7], [3, 7],
         [11, 4], [12, 4], [13, 4],
         # HCCH
-        [1, 5], [1, 6], [2, 5], [2, 6], [3, 5], [3, 6],
-        [5, 8], [6, 8], [5, 9], [6, 9],
-        [8, 11], [8, 12], [8, 13], [9, 11], [9, 12], [9, 13]
+        [1, 5], [1, 6],
+        [2, 5], [2, 6],
+        [3, 5], [3, 6],
+        [5, 8], [6, 8],
+        [5, 9], [6, 9],
+        [8, 11], [8, 12], [8, 13],
+        [9, 11], [9, 12], [9, 13],
     ]
+    # fmt: on
 
-    hoomd.init.read_snapshot(snapshot)
-
-    ### Set interactions
-    nl_ex = hoomd.md.nlist.cell()
-    nl_ex.reset_exclusions(exclusions = ["1-2", "1-3", "1-4"])
-
-    lj = hoomd.md.pair.lj(r_cut = 12.0, nlist = nl_ex)
-    lj.pair_coeff.set('C', 'C', epsilon = 0.07, sigma = 3.55)
-    lj.pair_coeff.set('H', 'H', epsilon = 0.03, sigma = 2.42)
-    lj.pair_coeff.set('C', 'H', epsilon = numpy.sqrt(0.07*0.03), sigma = numpy.sqrt(3.55*2.42))
-
-    coulomb = hoomd.md.charge.pppm(hoomd.group.charged(), nlist = nl_ex)
-    coulomb.set_params(Nx = 64, Ny = 64, Nz = 64, order = 6, rcut = 12.0)
-
-    harmonic = hoomd.md.bond.harmonic()
-    harmonic.bond_coeff.set("CC", k = 2*268.0, r0 = 1.529)
-    harmonic.bond_coeff.set("CH", k = 2*340.0, r0 = 1.09)
-
-    angle = hoomd.md.angle.harmonic()
-    angle.angle_coeff.set("CCC", k = 2*58.35, t0 = 112.7 * pi / 180)
-    angle.angle_coeff.set("CCH", k = 2*37.5, t0 = 110.7 * pi / 180)
-    angle.angle_coeff.set("HCH", k = 2*33.0, t0 = 107.8 * pi / 180)
-
+    simulation.create_state_from_snapshot(snapshot, domain_decomposition=(None, None, None))
+    simulation.run(0)
 
-    dihedral = hoomd.md.dihedral.opls()
-    dihedral.dihedral_coeff.set("CCCC", k1 = 1.3, k2 = -0.05, k3 = 0.2, k4 = 0.0)
-    dihedral.dihedral_coeff.set("HCCC", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)
-    dihedral.dihedral_coeff.set("HCCH", k1 = 0.0, k2 = 0.0, k3 = 0.3, k4 = 0.0)
+    exclusions = ["bond", "1-3", "1-4"]
+    nl = hoomd.md.nlist.Cell(buffer=0.4, exclusions=exclusions)
+    lj = hoomd.md.pair.LJ(nlist=nl, default_r_cut=12.0)
+    lj.params[("C", "C")] = dict(epsilon=0.07, sigma=3.55)
+    lj.params[("H", "H")] = dict(epsilon=0.03, sigma=2.42)
+    lj.params[("C", "H")] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))
 
-    lj_special_pairs = hoomd.md.special_pair.lj()
-    lj_special_pairs.pair_coeff.set("CCCC", epsilon = 0.07, sigma = 3.55, r_cut = 12.0)
-    lj_special_pairs.pair_coeff.set("HCCH", epsilon = 0.03, sigma = 2.42, r_cut = 12.0)
-    lj_special_pairs.pair_coeff.set("HCCC",
-        epsilon = numpy.sqrt(0.07 * 0.03), sigma = numpy.sqrt(3.55 * 2.42), r_cut = 12.0
+    coulomb = hoomd.md.long_range.pppm.make_pppm_coulomb_forces(
+        nlist=nl, resolution=[64, 64, 64], order=6, r_cut=12.0
     )
 
-    coulomb_special_pairs = hoomd.md.special_pair.coulomb()
-    coulomb_special_pairs.pair_coeff.set("CCCC", alpha = 0.5, r_cut = 12.0)
-    coulomb_special_pairs.pair_coeff.set("HCCC", alpha = 0.5, r_cut = 12.0)
-    coulomb_special_pairs.pair_coeff.set("HCCH", alpha = 0.5, r_cut = 12.0)
-
-    hoomd.md.integrate.mode_standard(dt = dt)
-    integrator = hoomd.md.integrate.nvt(group = hoomd.group.all(), kT = kT, tau = 100*dt)
-    integrator.randomize_velocities(seed = 42)
-
-    return hoomd.context.current
+    harmonic = hoomd.md.bond.Harmonic()
+    harmonic.params["CC"] = dict(k=2 * 268.0, r0=1.529)
+    harmonic.params["CH"] = dict(k=2 * 340.0, r0=1.09)
+
+    angle = hoomd.md.angle.Harmonic()
+    angle.params["CCC"] = dict(k=2 * 58.35, t0=112.7 * pi / 180)
+    angle.params["CCH"] = dict(k=2 * 37.5, t0=110.7 * pi / 180)
+    angle.params["HCH"] = dict(k=2 * 33.0, t0=107.8 * pi / 180)
+
+    dihedral = hoomd.md.dihedral.OPLS()
+    dihedral.params["CCCC"] = dict(k1=1.3, k2=-0.05, k3=0.2, k4=0.0)
+    dihedral.params["HCCC"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)
+    dihedral.params["HCCH"] = dict(k1=0.0, k2=0.0, k3=0.3, k4=0.0)
+
+    lj_special_pairs = hoomd.md.special_pair.LJ()
+    lj_special_pairs.params["CCCC"] = dict(epsilon=0.07, sigma=3.55)
+    lj_special_pairs.params["HCCH"] = dict(epsilon=0.03, sigma=2.42)
+    lj_special_pairs.params["HCCC"] = dict(epsilon=np.sqrt(0.07 * 0.03), sigma=np.sqrt(3.55 * 2.42))
+    lj_special_pairs.r_cut["CCCC"] = 12.0
+    lj_special_pairs.r_cut["HCCC"] = 12.0
+    lj_special_pairs.r_cut["HCCH"] = 12.0
+    coulomb_special_pairs = hoomd.md.special_pair.Coulomb()
+    coulomb_special_pairs.params["CCCC"] = dict(alpha=0.5)
+    coulomb_special_pairs.params["HCCC"] = dict(alpha=0.5)
+    coulomb_special_pairs.params["HCCH"] = dict(alpha=0.5)
+    coulomb_special_pairs.r_cut["HCCH"] = 12.0
+    coulomb_special_pairs.r_cut["CCCC"] = 12.0
+    coulomb_special_pairs.r_cut["HCCC"] = 12.0
+
+    nvt = hoomd.md.methods.Langevin(filter=hoomd.filter.All(), kT=kT)
+
+    integrator = hoomd.md.Integrator(dt=dt)
+    integrator.forces.append(lj)
+    integrator.forces.append(coulomb[0])
+    integrator.forces.append(coulomb[1])
+    integrator.forces.append(harmonic)
+    integrator.forces.append(angle)
+    integrator.forces.append(dihedral)
+    integrator.forces.append(lj_special_pairs)
+    integrator.forces.append(coulomb_special_pairs)
+    integrator.methods.append(nvt)
+    simulation.operations.integrator = integrator
+
+    return simulation
 ```
 
 <!-- #region id="3UrzENm_oo6U" -->
-
 Next, we load PySAGES and the relevant classes and methods for our problem
-
 <!-- #endregion -->
 
 ```python id="fpMg-o8WomAA"
@@ -332,11 +358,9 @@ import pysages
 ```
 
 <!-- #region id="LknkRvo1o4av" -->
-
 The next step is to define the collective variable (CV). In this case, we choose the central dihedral angle.
 
 We define a grid, which will be used to indicate how we want to bin the forces that will be used to approximate the biasing potential and its gradient.
-
 <!-- #endregion -->
 
 ```python id="B1Z8FWz0o7u_"
@@ -348,22 +372,18 @@ method = SpectralABF(cvs, grid)
 ```
 
 <!-- #region id="Fz8BfU34pA_N" -->
-
 We now simulate $5\times10^5$ time steps.
 Make sure to run with GPU support, otherwise, it can take a very long time.
-
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="K951m4BbpUar" outputId="8005b8a9-2967-4eb9-f9db-e0dc0d523835"
-run_result = pysages.run(method, generate_context, timesteps)
+run_result = pysages.run(method, generate_simulation, timesteps)
 ```
 
 <!-- #region id="26zdu6yAht5Y" -->
 
 ## Analysis
-
 PySAGES provides an `analyze` method that makes it easier to get the free energy of different simulation runs.
-
 <!-- #endregion -->
 
 ```python id="2NWmahlfhoj8"
@@ -371,9 +391,7 @@ result = pysages.analyze(run_result)
 ```
 
 <!-- #region id="PXBKUfK0p9T2" -->
-
 Let's plot now the free energy!
-
 <!-- #endregion -->
 
 ```python id="X69d1R7OpW4P"
@@ -393,7 +411,7 @@ fig, ax = plt.subplots()
 
 ax.set_xlabel(r"Dihedral Angle, $\xi$")
 ax.set_ylabel(r"$A(\xi)$")
-
 ax.plot(mesh, A)
-plt.gca()
+
+fig.show()
 ```
diff --git a/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.ipynb b/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.ipynb
index 79f24f99..803602ea 100644
--- a/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.ipynb
+++ b/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.ipynb
@@ -3,36 +3,32 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "p49wJ0IjLAVD"
+    "id": "T-Qkg9C9n7Cc"
    },
    "source": [
+    "# Setting up the environment\n",
     "\n",
-    "# Setup of the environment\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pF-5oR_GMkuI"
-   },
-   "source": [
-    "\n",
-    "We download and install the environment of HOOMD-blue and OpenMM with their respective plugins.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "f730dbd3",
    "metadata": {
-    "id": "nMThqa-DjVcb"
+    "id": "3eTbKklCnyd_"
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
@@ -42,8 +38,8 @@
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
-    "id": "25H3kl03wzJe",
-    "outputId": "55526734-bcea-4d1a-f1ae-de0b017126b7"
+    "id": "KRPmkpd9n_NG",
+    "outputId": "b757f2aa-38cc-4726-c4ab-5197810b9d77"
    },
    "outputs": [
     {
@@ -62,22 +58,21 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "id": "V6MZXhOJMz7P"
+    "id": "J7OY5K9VoBBh"
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
-    "mkdir -p $PYSAGES_ENV\n",
-    "unzip -qquo pysages-env.zip -d $PYSAGES_ENV\n",
-    "rm pysages-env.zip"
+    "mkdir -p $PYSAGES_ENV .\n",
+    "unzip -qquo pysages-env.zip -d $PYSAGES_ENV"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
-    "id": "JMO5fiRTxAWB"
+    "id": "EMAWp8VloIk4"
    },
    "outputs": [],
    "source": [
@@ -85,57 +80,47 @@
     "import sys\n",
     "\n",
     "ver = sys.version_info\n",
+    "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")"
+    "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "lf2KeHt5_eFv"
+    "id": "Wy-75Pt7Bqs1"
    },
    "source": [
-    "\n",
-    "## PySAGES\n",
-    "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this collab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
+    "We'll also need some additional python dependencies"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "id": "RUX1RAT3NF9s"
+    "id": "LpBucu3V81xm"
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "pip install -q --upgrade pip &> /dev/null\n",
-    "# Installs the wheel compatible with CUDA.\n",
-    "pip install -q --upgrade \"jax[cuda]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null"
+    "!pip install -qq \"numpy<2\" gsd > /dev/null"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "mx0IRythaTyG"
+    "id": "we_mTkFioS6R"
    },
    "source": [
+    "## PySAGES\n",
     "\n",
-    "We test the jax installation and check the versions.\n"
+    "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Z4E914qBHbZS",
-    "outputId": "94391314-23b5-4726-f34a-fd927a0d4da1"
+    "id": "B-HB9CzioV5j"
    },
    "outputs": [
     {
@@ -148,36 +133,7 @@
     }
    ],
    "source": [
-    "import jax\n",
-    "import jaxlib\n",
-    "print(jax.__version__)\n",
-    "print(jaxlib.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vtAmA51IAYxn"
-   },
-   "source": [
-    "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "id": "rEsRX7GZNJ_R"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
    ]
   },
   {
@@ -186,13 +142,12 @@
     "id": "LjPjqVjSOzTL"
    },
    "source": [
-    "\n",
     "# Umbrella integration\n",
     "\n",
-    "In [this tutorial](https://github.com/SSAGESLabs/PySAGES/docs/notebooks/Harmonic_Bias_PySAGES_HOOMD.md), we demonstrated how PySAGES can be used to run a single simulation with a biasing potential.\n",
+    "In [this tutorial](https://github.com/SSAGESLabs/PySAGES/blob/main/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md), we demonstrated how PySAGES can be used to run a single simulation with a biasing potential.\n",
     "However, if we want to look into the free-energy landscape a single simulation is not enough. Instead, we have to perform a series of simulations along a path in the space of the collective variables (CVs). From the histograms of the biasing, we can deduce the differences in free energy. For a more detailed explanation look at the literature, for example [J. Kaestner 2009](https://doi.org/10.1063/1.3175798).\n",
     "\n",
-    "The first step here is also to generate a simulation snapshot that can be used as an initial condition.\n"
+    "The first step here is also to generate a simulation snapshot that can be used as an initial condition."
    ]
   },
   {
@@ -255,29 +210,30 @@
     }
    ],
    "source": [
-    "!pip install gsd &> /dev/null\n",
     "import gsd\n",
     "import gsd.hoomd\n",
     "import numpy as np\n",
     "\n",
+    "\n",
     "class System:\n",
     "    def __init__(self):\n",
     "        self.L = 5\n",
     "        self.N = 200\n",
     "\n",
+    "\n",
     "def post_process_pos(snapshot):\n",
     "    box_size = snapshot.configuration.box[:3]\n",
     "    snapshot.particles.image = np.rint(snapshot.particles.position / box_size)\n",
     "    snapshot.particles.position -= snapshot.particles.image * box_size\n",
     "    return snapshot\n",
     "\n",
+    "\n",
     "def get_snap(system):\n",
     "    L = system.L\n",
     "    snapshot = gsd.hoomd.Frame()\n",
     "    snapshot.configuration.box = [L, L, L, 0, 0, 0]\n",
     "\n",
     "    snapshot.particles.N = N = system.N\n",
-    "\n",
     "    snapshot.particles.types = [\"A\", \"B\"]\n",
     "    snapshot.particles.position = np.zeros((N, 3))\n",
     "    snapshot.particles.velocity = np.random.standard_normal((N, 3))\n",
@@ -298,12 +254,14 @@
     "\n",
     "    return snapshot\n",
     "\n",
+    "\n",
     "system = System()\n",
     "snap = get_snap(system)\n",
     "snap = post_process_pos(snap)\n",
     "snap.particles.validate()\n",
+    "\n",
     "with gsd.hoomd.open(\"start.gsd\", \"w\") as f:\n",
-    "    f.append(snap)\n"
+    "    f.append(snap)"
    ]
   },
   {
@@ -312,13 +270,12 @@
     "id": "AgFXHafmVUAi"
    },
    "source": [
-    "\n",
     "For this simulation, we are using the PySAGES method `UmbrellaIntegration` so we start with importing this.\n",
     "\n",
     "In the next step, we write a function that generates the simulation context. We need to make sure that the context can depend on the replica of the simulation along the path. PySAGES sets variable `replica_num` in the keyword arguments of the function.\n",
     "We also set some general parameters for all replicas.\n",
     "\n",
-    "In contrast to the single harmonic bias simulation, the simulation now contains an external potential `hoomd.external.periodic` which changes the expected density of particles. See hoomd-blue's [documentation](https://hoomd-blue.readthedocs.io/en/stable/module-md-external.html#hoomd.md.external.periodic) for details on the potential. For this example, the potential generates the free-energy landscape we are exploring.\n"
+    "In contrast to the single harmonic bias simulation, the simulation now contains an external potential `hoomd.md.external.field.Periodic` which changes the expected density of particles. See hoomd-blue's [documentation](https://hoomd-blue.readthedocs.io/en/stable/module-md-external-field.html) for details on the potential. For this example, the potential generates the free-energy landscape we are exploring."
    ]
   },
   {
@@ -342,8 +299,6 @@
    ],
    "source": [
     "import hoomd\n",
-    "import hoomd.md\n",
-    "import hoomd.dlext\n",
     "\n",
     "import pysages\n",
     "from pysages.colvars import Component\n",
@@ -358,31 +313,48 @@
    },
    "outputs": [],
    "source": [
-    "params = {\"A\": 0.5, \"w\": 0.2, \"p\": 2}\n",
+    "dpd_params = dict(\n",
+    "    AA = dict(A = 5, gamma = 1),\n",
+    "    AB = dict(A = 5, gamma = 1),\n",
+    "    BB = dict(A = 5, gamma = 1),\n",
+    ")\n",
+    "periodic_params = dict(\n",
+    "    A = dict(A = 0.5, i = 0, w = 0.2, p = 2),\n",
+    "    B = dict(A = 0.0, i = 0, w = 0.02, p = 1),\n",
+    ")\n",
     "\n",
-    "\"\"\"\n",
-    "Generates a simulation context, we pass this function to the attribute `run` of our sampling method.\n",
-    "\"\"\"\n",
-    "def generate_context(**kwargs):\n",
-    "    hoomd.context.initialize(\"\")\n",
-    "    context = hoomd.context.SimulationContext()\n",
-    "    with context:\n",
-    "        print(f\"Operating replica {kwargs.get('replica_num')}\")\n",
-    "        system = hoomd.init.read_gsd(\"start.gsd\")\n",
+    "def generate_simulation(\n",
+    "    kT=1, dt=0.01, r_cut=1, dpd_params=dpd_params, periodic_params=periodic_params,\n",
+    "    device=hoomd.device.auto_select(), seed=42,\n",
+    "    **kwargs\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Generates a simulation context to which will attatch our sampling method.\n",
+    "    \"\"\"\n",
+    "    print(f\"Operating replica {kwargs.get('replica_num')}\")\n",
     "\n",
-    "        hoomd.md.integrate.nve(group=hoomd.group.all())\n",
-    "        hoomd.md.integrate.mode_standard(dt=0.01)\n",
+    "    simulation = hoomd.Simulation(device=device, seed=seed)\n",
+    "    simulation.create_state_from_gsd(\"start.gsd\")\n",
+    "    simulation.run(0)\n",
     "\n",
-    "        nl = hoomd.md.nlist.cell()\n",
-    "        dpd = hoomd.md.pair.dpd(r_cut=1, nlist=nl, seed=42, kT=1.)\n",
-    "        dpd.pair_coeff.set(\"A\", \"A\", A=5., gamma=1.0)\n",
-    "        dpd.pair_coeff.set(\"A\", \"B\", A=5., gamma=1.0)\n",
-    "        dpd.pair_coeff.set(\"B\", \"B\", A=5., gamma=1.0)\n",
+    "    nlist = hoomd.md.nlist.Cell(buffer=0.4)\n",
+    "    dpd = hoomd.md.pair.DPD(nlist=nlist, kT=kT, default_r_cut=r_cut)\n",
+    "    dpd.params[(\"A\", \"A\")] = dpd_params[\"AA\"]\n",
+    "    dpd.params[(\"A\", \"B\")] = dpd_params[\"AB\"]\n",
+    "    dpd.params[(\"B\", \"B\")] = dpd_params[\"BB\"]\n",
     "\n",
-    "        periodic = hoomd.md.external.periodic()\n",
-    "        periodic.force_coeff.set('A', A=params[\"A\"], i=0, w=params[\"w\"], p=params[\"p\"])\n",
-    "        periodic.force_coeff.set('B', A=0.0, i=0, w=0.02, p=1)\n",
-    "    return context\n"
+    "    periodic = hoomd.md.external.field.Periodic()\n",
+    "    periodic.params[\"A\"] = periodic_params[\"A\"]\n",
+    "    periodic.params[\"B\"] = periodic_params[\"B\"]\n",
+    "\n",
+    "    nve = hoomd.md.methods.ConstantVolume(filter=hoomd.filter.All())\n",
+    "\n",
+    "    integrator = hoomd.md.Integrator(dt=dt)\n",
+    "    integrator.forces.append(dpd)\n",
+    "    integrator.methods.append(nve)\n",
+    "    simulation.operations.integrator = integrator\n",
+    "\n",
+    "    return simulation"
    ]
   },
   {
@@ -391,8 +363,7 @@
     "id": "YRPnU0CJY31J"
    },
    "source": [
-    "\n",
-    "With the ability to generate the simulation context, we start to set up the umbrella integration method - starting with the CV that describes the single A-particle along the varying axis of the external potential.\n"
+    "With the ability to generate the simulation context, we start to set up the umbrella integration method - starting with the CV that describes the single A-particle along the varying axis of the external potential."
    ]
   },
   {
@@ -403,7 +374,7 @@
    },
    "outputs": [],
    "source": [
-    "cvs = [Component([0], 0),]\n"
+    "cvs = [Component([0], 0),]"
    ]
   },
   {
@@ -412,8 +383,7 @@
     "id": "jhs3vpglaux4"
    },
    "source": [
-    "\n",
-    "Next, we define the path along the CV space. In this case, we start at position $-1.5$ and end the path at the position $1.5$. We are using linear interpolation with $25$ replicas.\n"
+    "Next, we define the path along the CV space. In this case, we start at position $-1.5$ and end the path at the position $1.5$. We are using linear interpolation with $25$ replicas."
    ]
   },
   {
@@ -433,12 +403,11 @@
     "id": "q37sUT-tbOMS"
    },
    "source": [
-    "\n",
     "The next parameters we need to define and run the method are the harmonic biasing spring constant,\n",
-    "(which we set to to $50$), the log frequency for the histogram ($50$), the number of steps we discard\n",
+    "(which we set to to $100$), the log frequency for the histogram ($50$), the number of steps we discard\n",
     "as equilibration before logging ($10^3$), and the number of time steps per replica ($10^4$).\n",
     "\n",
-    "Since this runs multiple simulations, we expect the next cell to execute for a while.\n"
+    "Since this runs multiple simulations, we expect the next cell to execute for a while."
    ]
   },
   {
@@ -1060,8 +1029,8 @@
     }
    ],
    "source": [
-    "method = UmbrellaIntegration(cvs, 50.0, centers, 50, int(1e3))\n",
-    "raw_result = pysages.run(method, generate_context, int(1e4))\n",
+    "method = UmbrellaIntegration(cvs, 100.0, centers, 50, int(1e3))\n",
+    "raw_result = pysages.run(method, generate_simulation, int(1e4))\n",
     "result = pysages.analyze(raw_result)"
    ]
   },
@@ -1071,8 +1040,7 @@
     "id": "_xFSKCpKb6XF"
    },
    "source": [
-    "\n",
-    "What is left after the run is evaluating the resulting histograms for each of the replicas. For a better visualization, we group the histogram into 4 separate plots. This also helps to demonstrate that the histograms overlap.\n"
+    "What is left after the run is evaluating the resulting histograms for each of the replicas. For a better visualization, we group the histogram into 4 separate plots. This also helps to demonstrate that the histograms overlap."
    ]
   },
   {
@@ -1102,11 +1070,14 @@
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "bins =50\n",
+    "\n",
+    "bins = 50\n",
+    "\n",
     "fig, ax = plt.subplots(2, 2)\n",
     "\n",
     "counter = 0\n",
-    "hist_per = len(result[\"centers\"])//4+1\n",
+    "hist_per = len(result[\"centers\"]) // 4 + 1\n",
+    "\n",
     "for x in range(2):\n",
     "    for y in range(2):\n",
     "        for i in range(hist_per):\n",
@@ -1119,6 +1090,7 @@
     "                ax[x, y].legend(loc=\"best\", fontsize=\"xx-small\")\n",
     "                ax[x, y].set_yscale(\"log\")\n",
     "        counter += hist_per\n",
+    "\n",
     "while counter < len(result[\"centers\"]):\n",
     "    center = np.asarray(result[\"centers\"][counter])\n",
     "    histo, edges = result[\"histograms\"][counter].get_histograms(bins=bins)\n",
@@ -1134,8 +1106,7 @@
     "id": "5YZYZPUqdG7S"
    },
    "source": [
-    "\n",
-    "And finally, as the last step, we can visualize the estimated free-energy path from the histograms and compare it with the analytical shape of the input external potential.\n"
+    "And finally, as the last step, we can visualize the estimated free-energy path from the histograms and compare it with the analytical shape of the input external potential."
    ]
   },
   {
@@ -1174,22 +1145,23 @@
     }
    ],
    "source": [
-    "def external_field(r, A, p, w):\n",
+    "def external_field(r, A, p, w, **kwargs):\n",
     "    return A * np.tanh(1 / (2 * np.pi * p * w) * np.cos(p * r))\n",
     "\n",
+    "x = np.linspace(-2, 2, 100)\n",
+    "data = external_field(x, **periodic_params[\"A\"])\n",
+    "\n",
+    "centers = np.asarray(result[\"centers\"])\n",
+    "free_energy = np.asarray(result[\"free_energy\"])\n",
+    "\n",
     "fig, ax = plt.subplots()\n",
     "\n",
     "ax.set_xlabel(\"CV\")\n",
     "ax.set_ylabel(\"Free energy $[\\epsilon]$\")\n",
-    "centers = np.asarray(result[\"centers\"])\n",
-    "free_energy = np.asarray(result[\"free_energy\"])\n",
-    "offset = np.min(free_energy)\n",
-    "ax.plot(centers, free_energy - offset, color=\"teal\")\n",
+    "ax.plot(x, data - np.min(data), label=\"test\")\n",
+    "ax.plot(centers, free_energy - np.min(free_energy), color=\"teal\")\n",
     "\n",
-    "x = np.linspace(-2, 2, 50)\n",
-    "data = external_field(x, **params)\n",
-    "offset = np.min(data)\n",
-    "ax.plot(x, data - offset, label=\"test\")\n"
+    "fig.show()"
    ]
   },
   {
@@ -1199,8 +1171,7 @@
     "id": "IXryBllMNiKM"
    },
    "source": [
-    "\n",
-    "We can see, that the particle positions are indeed centered around the constraint values we set up earlier. Also, we see the shape of the histograms is very similar to the expected analytical prediction. We expect this since a liquid of soft particles is not that much different from an ideal gas.\n"
+    "We can see, that the particle positions are indeed centered around the constraint values we set up earlier. Also, we see the shape of the histograms is very similar to the expected analytical prediction. We expect this since a liquid of soft particles is not that much different from an ideal gas."
    ]
   }
  ],
diff --git a/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.md b/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.md
index 8f7a25ca..e34ab57f 100644
--- a/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.md
+++ b/examples/hoomd-blue/umbrella_integration/Umbrella_Integration.md
@@ -13,123 +13,95 @@ jupyter:
     name: python3
 ---
 
-<!-- #region id="p49wJ0IjLAVD" -->
-
-# Setup of the environment
+<!-- #region id="T-Qkg9C9n7Cc" -->
+# Setting up the environment
 
+First, we set up our environment. We use an already compiled and packaged installation of HOOMD-blue and the hoomd-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 <!-- #endregion -->
 
-<!-- #region id="pF-5oR_GMkuI" -->
-
-We download and install the environment of HOOMD-blue and OpenMM with their respective plugins.
-
-<!-- #endregion -->
+```bash id="3eTbKklCnyd_"
 
-```bash id="nMThqa-DjVcb"
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
-```python colab={"base_uri": "https://localhost:8080/"} id="25H3kl03wzJe" outputId="55526734-bcea-4d1a-f1ae-de0b017126b7"
+```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="b757f2aa-38cc-4726-c4ab-5197810b9d77"
 %env PYSAGES_ENV=/env/pysages
 ```
 
-```bash id="V6MZXhOJMz7P"
+```bash id="J7OY5K9VoBBh"
 
-mkdir -p $PYSAGES_ENV
+mkdir -p $PYSAGES_ENV .
 unzip -qquo pysages-env.zip -d $PYSAGES_ENV
-rm pysages-env.zip
 ```
 
-```python id="JMO5fiRTxAWB"
+```python id="EMAWp8VloIk4"
 import os
 import sys
 
 ver = sys.version_info
-
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
-```
-
-<!-- #region id="lf2KeHt5_eFv" -->
 
-## PySAGES
-
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this collab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
-<!-- #endregion -->
-
-```bash id="RUX1RAT3NF9s"
-
-pip install -q --upgrade pip &> /dev/null
-# Installs the wheel compatible with CUDA.
-pip install -q --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null
+os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
-<!-- #region id="mx0IRythaTyG" -->
-
-We test the jax installation and check the versions.
-
+<!-- #region id="Wy-75Pt7Bqs1" -->
+We'll also need some additional python dependencies
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/"} id="Z4E914qBHbZS" outputId="94391314-23b5-4726-f34a-fd927a0d4da1"
-import jax
-import jaxlib
-print(jax.__version__)
-print(jaxlib.__version__)
+```python id="LpBucu3V81xm"
+!pip install -qq "numpy<2" gsd > /dev/null
 ```
 
-<!-- #region id="vtAmA51IAYxn" -->
-
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
+<!-- #region id="we_mTkFioS6R" -->
+## PySAGES
 
+The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`.
 <!-- #endregion -->
 
-```bash id="rEsRX7GZNJ_R"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
 <!-- #region id="LjPjqVjSOzTL" -->
-
 # Umbrella integration
 
-In [this tutorial](https://github.com/SSAGESLabs/PySAGES/docs/notebooks/Harmonic_Bias_PySAGES_HOOMD.md), we demonstrated how PySAGES can be used to run a single simulation with a biasing potential.
+In [this tutorial](https://github.com/SSAGESLabs/PySAGES/blob/main/examples/hoomd-blue/harmonic_bias/Harmonic_Bias.md), we demonstrated how PySAGES can be used to run a single simulation with a biasing potential.
 However, if we want to look into the free-energy landscape a single simulation is not enough. Instead, we have to perform a series of simulations along a path in the space of the collective variables (CVs). From the histograms of the biasing, we can deduce the differences in free energy. For a more detailed explanation look at the literature, for example [J. Kaestner 2009](https://doi.org/10.1063/1.3175798).
 
 The first step here is also to generate a simulation snapshot that can be used as an initial condition.
-
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="QOrufad1RaMF" outputId="02f68f5a-54cc-435c-e3df-78f4826dc374"
-!pip install gsd &> /dev/null
 import gsd
 import gsd.hoomd
 import numpy as np
 
+
 class System:
     def __init__(self):
         self.L = 5
         self.N = 200
 
+
 def post_process_pos(snapshot):
     box_size = snapshot.configuration.box[:3]
     snapshot.particles.image = np.rint(snapshot.particles.position / box_size)
     snapshot.particles.position -= snapshot.particles.image * box_size
     return snapshot
 
+
 def get_snap(system):
     L = system.L
     snapshot = gsd.hoomd.Frame()
     snapshot.configuration.box = [L, L, L, 0, 0, 0]
 
     snapshot.particles.N = N = system.N
-
     snapshot.particles.types = ["A", "B"]
     snapshot.particles.position = np.zeros((N, 3))
     snapshot.particles.velocity = np.random.standard_normal((N, 3))
@@ -150,30 +122,27 @@ def get_snap(system):
 
     return snapshot
 
+
 system = System()
 snap = get_snap(system)
 snap = post_process_pos(snap)
 snap.particles.validate()
+
 with gsd.hoomd.open("start.gsd", "w") as f:
     f.append(snap)
-
 ```
 
 <!-- #region id="AgFXHafmVUAi" -->
-
 For this simulation, we are using the PySAGES method `UmbrellaIntegration` so we start with importing this.
 
 In the next step, we write a function that generates the simulation context. We need to make sure that the context can depend on the replica of the simulation along the path. PySAGES sets variable `replica_num` in the keyword arguments of the function.
 We also set some general parameters for all replicas.
 
-In contrast to the single harmonic bias simulation, the simulation now contains an external potential `hoomd.external.periodic` which changes the expected density of particles. See hoomd-blue's [documentation](https://hoomd-blue.readthedocs.io/en/stable/module-md-external.html#hoomd.md.external.periodic) for details on the potential. For this example, the potential generates the free-energy landscape we are exploring.
-
+In contrast to the single harmonic bias simulation, the simulation now contains an external potential `hoomd.md.external.field.Periodic` which changes the expected density of particles. See hoomd-blue's [documentation](https://hoomd-blue.readthedocs.io/en/stable/module-md-external-field.html) for details on the potential. For this example, the potential generates the free-energy landscape we are exploring.
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="tG6JhN7SNpSj" outputId="979d5793-f459-4202-ac4e-74f2aaabc1f3"
 import hoomd
-import hoomd.md
-import hoomd.dlext
 
 import pysages
 from pysages.colvars import Component
@@ -181,49 +150,60 @@ from pysages.methods import UmbrellaIntegration
 ```
 
 ```python id="RsZhjfm2U5ps"
-params = {"A": 0.5, "w": 0.2, "p": 2}
-
-"""
-Generates a simulation context, we pass this function to the attribute `run` of our sampling method.
-"""
-def generate_context(**kwargs):
-    hoomd.context.initialize("")
-    context = hoomd.context.SimulationContext()
-    with context:
-        print(f"Operating replica {kwargs.get('replica_num')}")
-        system = hoomd.init.read_gsd("start.gsd")
-
-        hoomd.md.integrate.nve(group=hoomd.group.all())
-        hoomd.md.integrate.mode_standard(dt=0.01)
-
-        nl = hoomd.md.nlist.cell()
-        dpd = hoomd.md.pair.dpd(r_cut=1, nlist=nl, seed=42, kT=1.)
-        dpd.pair_coeff.set("A", "A", A=5., gamma=1.0)
-        dpd.pair_coeff.set("A", "B", A=5., gamma=1.0)
-        dpd.pair_coeff.set("B", "B", A=5., gamma=1.0)
-
-        periodic = hoomd.md.external.periodic()
-        periodic.force_coeff.set('A', A=params["A"], i=0, w=params["w"], p=params["p"])
-        periodic.force_coeff.set('B', A=0.0, i=0, w=0.02, p=1)
-    return context
-
+dpd_params = dict(
+    AA = dict(A = 5, gamma = 1),
+    AB = dict(A = 5, gamma = 1),
+    BB = dict(A = 5, gamma = 1),
+)
+periodic_params = dict(
+    A = dict(A = 0.5, i = 0, w = 0.2, p = 2),
+    B = dict(A = 0.0, i = 0, w = 0.02, p = 1),
+)
+
+def generate_simulation(
+    kT=1, dt=0.01, r_cut=1, dpd_params=dpd_params, periodic_params=periodic_params,
+    device=hoomd.device.auto_select(), seed=42,
+    **kwargs
+):
+    """
+    Generates a simulation context to which will attatch our sampling method.
+    """
+    print(f"Operating replica {kwargs.get('replica_num')}")
+
+    simulation = hoomd.Simulation(device=device, seed=seed)
+    simulation.create_state_from_gsd("start.gsd")
+    simulation.run(0)
+
+    nlist = hoomd.md.nlist.Cell(buffer=0.4)
+    dpd = hoomd.md.pair.DPD(nlist=nlist, kT=kT, default_r_cut=r_cut)
+    dpd.params[("A", "A")] = dpd_params["AA"]
+    dpd.params[("A", "B")] = dpd_params["AB"]
+    dpd.params[("B", "B")] = dpd_params["BB"]
+
+    periodic = hoomd.md.external.field.Periodic()
+    periodic.params["A"] = periodic_params["A"]
+    periodic.params["B"] = periodic_params["B"]
+
+    nve = hoomd.md.methods.ConstantVolume(filter=hoomd.filter.All())
+
+    integrator = hoomd.md.Integrator(dt=dt)
+    integrator.forces.append(dpd)
+    integrator.methods.append(nve)
+    simulation.operations.integrator = integrator
+
+    return simulation
 ```
 
 <!-- #region id="YRPnU0CJY31J" -->
-
 With the ability to generate the simulation context, we start to set up the umbrella integration method - starting with the CV that describes the single A-particle along the varying axis of the external potential.
-
 <!-- #endregion -->
 
 ```python id="_o7puY5Sao5h"
 cvs = [Component([0], 0),]
-
 ```
 
 <!-- #region id="jhs3vpglaux4" -->
-
 Next, we define the path along the CV space. In this case, we start at position $-1.5$ and end the path at the position $1.5$. We are using linear interpolation with $25$ replicas.
-
 <!-- #endregion -->
 
 ```python id="Uvkeedv4atn3"
@@ -231,34 +211,33 @@ centers = list(np.linspace(-1.5, 1.5, 25))
 ```
 
 <!-- #region id="q37sUT-tbOMS" -->
-
 The next parameters we need to define and run the method are the harmonic biasing spring constant,
-(which we set to to $50$), the log frequency for the histogram ($50$), the number of steps we discard
+(which we set to to $100$), the log frequency for the histogram ($50$), the number of steps we discard
 as equilibration before logging ($10^3$), and the number of time steps per replica ($10^4$).
 
 Since this runs multiple simulations, we expect the next cell to execute for a while.
-
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/"} id="wIrPB2N0bFIl" outputId="2f018685-a115-4c66-a21a-eef1d515bd02"
-method = UmbrellaIntegration(cvs, 50.0, centers, 50, int(1e3))
-raw_result = pysages.run(method, generate_context, int(1e4))
+method = UmbrellaIntegration(cvs, 100.0, centers, 50, int(1e3))
+raw_result = pysages.run(method, generate_simulation, int(1e4))
 result = pysages.analyze(raw_result)
 ```
 
 <!-- #region id="_xFSKCpKb6XF" -->
-
 What is left after the run is evaluating the resulting histograms for each of the replicas. For a better visualization, we group the histogram into 4 separate plots. This also helps to demonstrate that the histograms overlap.
-
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/", "height": 265} id="OOpagwvlb3_d" outputId="62b507a1-d404-4924-ec1b-00b9d3f39085"
 import matplotlib.pyplot as plt
-bins =50
+
+bins = 50
+
 fig, ax = plt.subplots(2, 2)
 
 counter = 0
-hist_per = len(result["centers"])//4+1
+hist_per = len(result["centers"]) // 4 + 1
+
 for x in range(2):
     for y in range(2):
         for i in range(hist_per):
@@ -271,6 +250,7 @@ for x in range(2):
                 ax[x, y].legend(loc="best", fontsize="xx-small")
                 ax[x, y].set_yscale("log")
         counter += hist_per
+
 while counter < len(result["centers"]):
     center = np.asarray(result["centers"][counter])
     histo, edges = result["histograms"][counter].get_histograms(bins=bins)
@@ -281,33 +261,29 @@ while counter < len(result["centers"]):
 ```
 
 <!-- #region id="5YZYZPUqdG7S" -->
-
 And finally, as the last step, we can visualize the estimated free-energy path from the histograms and compare it with the analytical shape of the input external potential.
-
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/", "height": 297} id="_UKh6FyLcN9y" outputId="cba839f6-78e8-43c3-f540-5567c5c4b00e"
-def external_field(r, A, p, w):
+def external_field(r, A, p, w, **kwargs):
     return A * np.tanh(1 / (2 * np.pi * p * w) * np.cos(p * r))
 
-fig, ax = plt.subplots()
+x = np.linspace(-2, 2, 100)
+data = external_field(x, **periodic_params["A"])
 
-ax.set_xlabel("CV")
-ax.set_ylabel("Free energy $[\epsilon]$")
 centers = np.asarray(result["centers"])
 free_energy = np.asarray(result["free_energy"])
-offset = np.min(free_energy)
-ax.plot(centers, free_energy - offset, color="teal")
 
-x = np.linspace(-2, 2, 50)
-data = external_field(x, **params)
-offset = np.min(data)
-ax.plot(x, data - offset, label="test")
+fig, ax = plt.subplots()
+
+ax.set_xlabel("CV")
+ax.set_ylabel("Free energy $[\epsilon]$")
+ax.plot(x, data - np.min(data), label="test")
+ax.plot(centers, free_energy - np.min(free_energy), color="teal")
 
+fig.show()
 ```
 
 <!-- #region id="IXryBllMNiKM" -->
-
 We can see, that the particle positions are indeed centered around the constraint values we set up earlier. Also, we see the shape of the histograms is very similar to the expected analytical prediction. We expect this since a liquid of soft particles is not that much different from an ideal gas.
-
 <!-- #endregion -->
diff --git a/examples/openmm/Harmonic_Bias.ipynb b/examples/openmm/harmonic_bias/Harmonic_Bias.ipynb
similarity index 89%
rename from examples/openmm/Harmonic_Bias.ipynb
rename to examples/openmm/harmonic_bias/Harmonic_Bias.ipynb
index 55524515..c646d936 100644
--- a/examples/openmm/Harmonic_Bias.ipynb
+++ b/examples/openmm/harmonic_bias/Harmonic_Bias.ipynb
@@ -6,36 +6,35 @@
     "id": "_UgEohXC8n0g"
    },
    "source": [
-    "\n",
     "# Setting up the environment\n",
     "\n",
-    "First, we are setting up our environment. We use an already compiled and packaged installation of OpenMM and the DLExt plugin. We copy it from Google Drive and install PySAGES for it. We also have a Google Colab that performs this installation for reference, but that requires permissions that we do not want on our Google Drive.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "id": "nMThqa-DjVcb"
+    "id": "3eTbKklCnyd_"
    },
    "outputs": [],
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "25H3kl03wzJe",
-    "outputId": "528d12be-8cc4-42d9-d460-692d46a0e662"
+    "id": "25H3kl03wzJe"
    },
    "outputs": [
     {
@@ -76,8 +75,9 @@
     "import sys\n",
     "\n",
     "ver = sys.version_info\n",
+    "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")"
+    "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
@@ -86,74 +86,21 @@
     "id": "lf2KeHt5_eFv"
    },
    "source": [
-    "\n",
     "## PySAGES\n",
     "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mx0IRythaTyG"
-   },
-   "source": [
-    "\n",
-    "We test the jax installation and check the versions.\n"
+    "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
+   "id": "0cde9d43",
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Z4E914qBHbZS",
-    "outputId": "56c47936-19c1-4de8-fbc7-1cace7282498"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.2.27\n",
-      "0.1.75\n"
-     ]
-    }
-   ],
-   "source": [
-    "import jax\n",
-    "import jaxlib\n",
-    "print(jax.__version__)\n",
-    "print(jaxlib.__version__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "vtAmA51IAYxn"
-   },
-   "source": [
-    "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "id": "xYRGOcFJjEE6"
+    "id": "B-HB9CzioV5j"
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
    ]
   },
   {
@@ -162,8 +109,7 @@
     "id": "h5xD1zfj-J2z"
    },
    "source": [
-    "\n",
-    "# Harmonic Bias simulations\n"
+    "# Harmonic Bias simulations"
    ]
   },
   {
@@ -186,10 +132,9 @@
     "id": "Uh2y2RXDDZub"
    },
    "source": [
-    "\n",
     "A harmonic bias simulation constraints a collective variable with a harmonic potential. This is useful for a variety of advanced sampling methods, in particular, umbrella sampling.\n",
     "\n",
-    "For this Colab, we are using alanine dipeptide as the example molecule, a system widely-used for benchmarking enhanced sampling methods. So first, we fetch the molecule from the examples of PySAGES.\n"
+    "For this Colab, we are using alanine dipeptide as the example molecule, a system widely-used for benchmarking enhanced sampling methods. So first, we fetch the molecule from the examples of PySAGES."
    ]
   },
   {
@@ -202,49 +147,8 @@
    "source": [
     "%%bash\n",
     "\n",
-    "cp /content/PySAGES/examples/inputs/alanine-dipeptide/adp-explicit.pdb ./"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "SqaG8YdK__FU"
-   },
-   "source": [
-    "\n",
-    "Next we load the PySAGES/OpenMM environment.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "P6kPLtGI_-66",
-    "outputId": "98e496cb-b78d-47bf-8b96-f2af942b10fc"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pysages.colvars import DihedralAngle\n",
-    "from pysages.methods import HarmonicBias, HistogramLogger\n",
-    "import numpy as np\n",
-    "from pysages.utils import try_import\n",
-    "\n",
-    "import pysages\n",
-    "\n",
-    "openmm = try_import(\"openmm\", \"simtk.openmm\")\n",
-    "unit = try_import(\"openmm.unit\", \"simtk.unit\")\n",
-    "app = try_import(\"openmm.app\", \"simtk.openmm.app\")"
+    "# Download pdb file with the initial configuration of our system\n",
+    "wget -q https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/alanine-dipeptide/adp-explicit.pdb"
    ]
   },
   {
@@ -253,8 +157,7 @@
     "id": "3TV4h_WEAdSm"
    },
    "source": [
-    "\n",
-    "Next, we write a function that can generate an execution context for OpenMM. This is everything you would normally write in an OpenMM script, just wrapped as a function that returns the context of the simulation.\n"
+    "Next, we write a function that can generate an execution context for OpenMM. This is everything you would normally write in an OpenMM script, just wrapped as a function that returns the context of the simulation."
    ]
   },
   {
@@ -265,13 +168,21 @@
    },
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "Generates a simulation context, we pass this function to the attribute `run` of our sampling method.\n",
-    "\"\"\"\n",
-    "def generate_simulation(**kwargs):\n",
-    "    pdb_filename = \"adp-explicit.pdb\"\n",
-    "    T = 298.15 * unit.kelvin\n",
-    "    dt = 2.0 * unit.femtoseconds\n",
+    "import numpy as np\n",
+    "import openmm\n",
+    "import openmm.app as app\n",
+    "import openmm.unit as unit\n",
+    "\n",
+    "\n",
+    "T = 298.15 * unit.kelvin\n",
+    "dt = 2.0 * unit.femtoseconds\n",
+    "pdb_file = \"adp-explicit.pdb\"\n",
+    "\n",
+    "\n",
+    "def generate_simulation(filename=pdb_file, T=T, dt=dt, **kwargs):\n",
+    "    \"\"\"\n",
+    "    Generates a simulation context to which will attatch our sampling method.\n",
+    "    \"\"\"\n",
     "    pdb = app.PDBFile(pdb_filename)\n",
     "\n",
     "    ff = app.ForceField(\"amber99sb.xml\", \"tip3p.xml\")\n",
@@ -299,21 +210,35 @@
     "id": "YtUoUMEdKtH8"
    },
    "source": [
-    "\n",
     "The next step is to define the collective variable (CV). In this case, we choose the two dihedral angles on the molecule as defined by the atom positions. We also choose an equilibrium value to constrain the dihedrals and the corresponding spring constant.\n",
-    "The `HarmonicBias` class is responsible for introducing the bias into the simulation run.\n"
+    "The `HarmonicBias` class is responsible for introducing the bias into the simulation run."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
+   "metadata": {
+    "id": "P6kPLtGI_-66"
+   },
+   "outputs": [],
+   "source": [
+    "import pysages\n",
+    "\n",
+    "from pysages.colvars import DihedralAngle\n",
+    "from pysages.methods import HarmonicBias, HistogramLogger"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "732e9f0e",
    "metadata": {
     "id": "zEH5jrRoKszT"
    },
    "outputs": [],
    "source": [
-    "cvs = (DihedralAngle((4, 6, 8, 14)), DihedralAngle((6, 8, 14, 16)))\n",
-    "center =[ -0.33*np.pi, -0.4*np.pi]\n",
+    "cvs = [DihedralAngle([4, 6, 8, 14]), DihedralAngle([6, 8, 14, 16])]\n",
+    "center = [-0.33*np.pi, -0.4*np.pi]\n",
     "k = 100\n",
     "method = HarmonicBias(cvs, k, center)"
    ]
@@ -324,9 +249,8 @@
     "id": "sqKuZo92K9n9"
    },
    "source": [
-    "\n",
     "We now define a Histogram callback to log the measured values of the CVs and run the simulation for $10^4$ time steps. The `HistogramLogger` collects the state of the collective variable during the run.\n",
-    "Make sure to run with GPU support. Using the CPU platform with OpenMM is possible and supported, but can take a very long time.\n"
+    "Make sure to run with GPU support. Using the CPU platform with OpenMM is possible and supported, but can take a very long time."
    ]
   },
   {
@@ -347,8 +271,7 @@
     "id": "z8V0iX70RF1m"
    },
    "source": [
-    "\n",
-    "Next, we want to plot the histogram as recorded from the simulations.\n"
+    "Next, we want to plot the histogram as recorded from the simulations."
    ]
   },
   {
@@ -403,17 +326,19 @@
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = np.linspace(lim[0], lim[1], hist_list[0].shape[0])\n",
+    "\n",
     "fig, ax = plt.subplots()\n",
     "\n",
+    "ax.legend(loc=\"best\")\n",
     "ax.set_xlabel(r\"CV $\\xi_i$\")\n",
     "ax.set_ylabel(r\"$p(\\xi_i)$\")\n",
     "\n",
-    "x = np.linspace(lim[0], lim[1], hist_list[0].shape[0])\n",
-    "\n",
     "for i in range(len(hist_list)):\n",
     "    (line,) = ax.plot(x, hist_list[i], label=\"i= {0}\".format(i))\n",
     "\n",
-    "ax.legend(loc=\"best\")"
+    "fig.show()"
    ]
   },
   {
@@ -422,8 +347,7 @@
     "id": "m9JjGXq_ha-6"
    },
    "source": [
-    "\n",
-    "We see how the dihedral angles are distributed. The histograms are not perfect in this example because we ran the simulation only for a few time steps and hence sampling is quite limited.\n"
+    "We see how the dihedral angles are distributed. The histograms are not perfect in this example because we ran the simulation only for a few time steps and hence sampling is quite limited."
    ]
   }
  ],
diff --git a/examples/openmm/Harmonic_Bias.md b/examples/openmm/harmonic_bias/Harmonic_Bias.md
similarity index 63%
rename from examples/openmm/Harmonic_Bias.md
rename to examples/openmm/harmonic_bias/Harmonic_Bias.md
index f89ee2de..05ada2fc 100644
--- a/examples/openmm/Harmonic_Bias.md
+++ b/examples/openmm/harmonic_bias/Harmonic_Bias.md
@@ -14,21 +14,23 @@ jupyter:
 ---
 
 <!-- #region id="_UgEohXC8n0g" -->
-
 # Setting up the environment
 
-First, we are setting up our environment. We use an already compiled and packaged installation of OpenMM and the DLExt plugin. We copy it from Google Drive and install PySAGES for it. We also have a Google Colab that performs this installation for reference, but that requires permissions that we do not want on our Google Drive.
-
+First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 <!-- #endregion -->
 
-```bash id="nMThqa-DjVcb"
+```bash id="3eTbKklCnyd_"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
+
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
-```python colab={"base_uri": "https://localhost:8080/"} id="25H3kl03wzJe" outputId="528d12be-8cc4-42d9-d460-692d46a0e662"
+```python id="25H3kl03wzJe"
 %env PYSAGES_ENV=/env/pysages
 ```
 
@@ -43,50 +45,23 @@ import os
 import sys
 
 ver = sys.version_info
-
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
+
+os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
 <!-- #region id="lf2KeHt5_eFv" -->
-
 ## PySAGES
 
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
+The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`.
 <!-- #endregion -->
 
-<!-- #region id="mx0IRythaTyG" -->
-
-We test the jax installation and check the versions.
-
-<!-- #endregion -->
-
-```python colab={"base_uri": "https://localhost:8080/"} id="Z4E914qBHbZS" outputId="56c47936-19c1-4de8-fbc7-1cace7282498"
-import jax
-import jaxlib
-print(jax.__version__)
-print(jaxlib.__version__)
-```
-
-<!-- #region id="vtAmA51IAYxn" -->
-
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
-
-<!-- #endregion -->
-
-```bash id="xYRGOcFJjEE6"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
 <!-- #region id="h5xD1zfj-J2z" -->
-
 # Harmonic Bias simulations
-
 <!-- #endregion -->
 
 ```bash id="OIyRfOU9_cEJ"
@@ -96,51 +71,37 @@ cd /content/harmonic-bias
 ```
 
 <!-- #region id="Uh2y2RXDDZub" -->
-
 A harmonic bias simulation constraints a collective variable with a harmonic potential. This is useful for a variety of advanced sampling methods, in particular, umbrella sampling.
 
 For this Colab, we are using alanine dipeptide as the example molecule, a system widely-used for benchmarking enhanced sampling methods. So first, we fetch the molecule from the examples of PySAGES.
-
 <!-- #endregion -->
 
 ```bash id="5fxJMNyE-RdA"
 
-cp /content/PySAGES/examples/inputs/alanine-dipeptide/adp-explicit.pdb ./
+# Download pdb file with the initial configuration of our system
+wget -q https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/alanine-dipeptide/adp-explicit.pdb
 ```
 
-<!-- #region id="SqaG8YdK__FU" -->
-
-Next we load the PySAGES/OpenMM environment.
-
+<!-- #region id="3TV4h_WEAdSm" -->
+Next, we write a function that can generate an execution context for OpenMM. This is everything you would normally write in an OpenMM script, just wrapped as a function that returns the context of the simulation.
 <!-- #endregion -->
 
-```python colab={"base_uri": "https://localhost:8080/"} id="P6kPLtGI_-66" outputId="98e496cb-b78d-47bf-8b96-f2af942b10fc"
-from pysages.colvars import DihedralAngle
-from pysages.methods import HarmonicBias, HistogramLogger
+```python id="GAGw0s_cAcgP"
 import numpy as np
-from pysages.utils import try_import
+import openmm
+import openmm.app as app
+import openmm.unit as unit
 
-import pysages
-
-openmm = try_import("openmm", "simtk.openmm")
-unit = try_import("openmm.unit", "simtk.unit")
-app = try_import("openmm.app", "simtk.openmm.app")
-```
-
-<!-- #region id="3TV4h_WEAdSm" -->
 
-Next, we write a function that can generate an execution context for OpenMM. This is everything you would normally write in an OpenMM script, just wrapped as a function that returns the context of the simulation.
+T = 298.15 * unit.kelvin
+dt = 2.0 * unit.femtoseconds
+pdb_file = "adp-explicit.pdb"
 
-<!-- #endregion -->
 
-```python id="GAGw0s_cAcgP"
-"""
-Generates a simulation context, we pass this function to the attribute `run` of our sampling method.
-"""
-def generate_simulation(**kwargs):
-    pdb_filename = "adp-explicit.pdb"
-    T = 298.15 * unit.kelvin
-    dt = 2.0 * unit.femtoseconds
+def generate_simulation(filename=pdb_file, T=T, dt=dt, **kwargs):
+    """
+    Generates a simulation context to which will attatch our sampling method.
+    """
     pdb = app.PDBFile(pdb_filename)
 
     ff = app.ForceField("amber99sb.xml", "tip3p.xml")
@@ -163,24 +124,27 @@ def generate_simulation(**kwargs):
 ```
 
 <!-- #region id="YtUoUMEdKtH8" -->
-
 The next step is to define the collective variable (CV). In this case, we choose the two dihedral angles on the molecule as defined by the atom positions. We also choose an equilibrium value to constrain the dihedrals and the corresponding spring constant.
 The `HarmonicBias` class is responsible for introducing the bias into the simulation run.
-
 <!-- #endregion -->
 
+```python id="P6kPLtGI_-66"
+import pysages
+
+from pysages.colvars import DihedralAngle
+from pysages.methods import HarmonicBias, HistogramLogger
+```
+
 ```python id="zEH5jrRoKszT"
-cvs = (DihedralAngle((4, 6, 8, 14)), DihedralAngle((6, 8, 14, 16)))
-center =[ -0.33*np.pi, -0.4*np.pi]
+cvs = [DihedralAngle([4, 6, 8, 14]), DihedralAngle([6, 8, 14, 16])]
+center = [-0.33*np.pi, -0.4*np.pi]
 k = 100
 method = HarmonicBias(cvs, k, center)
 ```
 
 <!-- #region id="sqKuZo92K9n9" -->
-
 We now define a Histogram callback to log the measured values of the CVs and run the simulation for $10^4$ time steps. The `HistogramLogger` collects the state of the collective variable during the run.
 Make sure to run with GPU support. Using the CPU platform with OpenMM is possible and supported, but can take a very long time.
-
 <!-- #endregion -->
 
 ```python id="-XKSe3os_-Rg"
@@ -189,9 +153,7 @@ pysages.run(method, generate_simulation, int(1e4), callback)
 ```
 
 <!-- #region id="z8V0iX70RF1m" -->
-
 Next, we want to plot the histogram as recorded from the simulations.
-
 <!-- #endregion -->
 
 ```python id="Mvq9CWdg_qxl"
@@ -204,21 +166,21 @@ hist_list = [np.sum(hist, axis=(0)), np.sum(hist, axis=(1))]
 
 ```python colab={"base_uri": "https://localhost:8080/", "height": 301} id="mxZVBr2FR5FJ" outputId="2d0d189b-a1b8-400d-92cd-0fbbeaa783e8"
 import matplotlib.pyplot as plt
+
+x = np.linspace(lim[0], lim[1], hist_list[0].shape[0])
+
 fig, ax = plt.subplots()
 
+ax.legend(loc="best")
 ax.set_xlabel(r"CV $\xi_i$")
 ax.set_ylabel(r"$p(\xi_i)$")
 
-x = np.linspace(lim[0], lim[1], hist_list[0].shape[0])
-
 for i in range(len(hist_list)):
     (line,) = ax.plot(x, hist_list[i], label="i= {0}".format(i))
 
-ax.legend(loc="best")
+fig.show()
 ```
 
 <!-- #region id="m9JjGXq_ha-6" -->
-
 We see how the dihedral angles are distributed. The histograms are not perfect in this example because we ran the simulation only for a few time steps and hence sampling is quite limited.
-
 <!-- #endregion -->
diff --git a/examples/openmm/metad/Metadynamics-ADP.ipynb b/examples/openmm/metad/Metadynamics-ADP.ipynb
index 30553ef2..9779b12f 100644
--- a/examples/openmm/metad/Metadynamics-ADP.ipynb
+++ b/examples/openmm/metad/Metadynamics-ADP.ipynb
@@ -3,14 +3,13 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "T-Qkg9C9n7Cc"
+    "id": "_UgEohXC8n0g"
    },
    "source": [
-    "\n",
     "# Setting up the environment\n",
     "\n",
-    "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the DLExt plugin.\n",
-    "We copy it from Google Drive and install PySAGES for it.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance."
    ]
   },
   {
@@ -23,9 +22,12 @@
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
@@ -35,8 +37,8 @@
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
-    "id": "KRPmkpd9n_NG",
-    "outputId": "5e474d51-1c66-4d16-bab9-29747fd9d466"
+    "id": "25H3kl03wzJe",
+    "outputId": "528d12be-8cc4-42d9-d460-692d46a0e662"
    },
    "outputs": [
     {
@@ -55,7 +57,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "J7OY5K9VoBBh"
+    "id": "CPkgxfj6w4te"
    },
    "outputs": [],
    "source": [
@@ -69,7 +71,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "id": "EMAWp8VloIk4"
+    "id": "JMO5fiRTxAWB"
    },
    "outputs": [],
    "source": [
@@ -79,46 +81,18 @@
     "ver = sys.version_info\n",
     "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "os.environ[\"XLA_FLAGS\"] = \"--xla_gpu_strict_conv_algorithm_picker=false\"\n",
     "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "we_mTkFioS6R"
+    "id": "lf2KeHt5_eFv"
    },
    "source": [
-    "\n",
     "## PySAGES\n",
     "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "vK0RZtbroQWe"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install -q --upgrade pip\n",
-    "# Installs the wheel compatible with CUDA.\n",
-    "pip install -q --upgrade \"jax[cuda]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "wAtjM-IroYX8"
-   },
-   "source": [
-    "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
+    "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`."
    ]
   },
   {
@@ -129,12 +103,7 @@
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
    ]
   },
   {
@@ -143,8 +112,7 @@
     "id": "KBFVcG1FoeMq"
    },
    "source": [
-    "\n",
-    "# Metadynamics-biased simulations\n"
+    "# Metadynamics-biased simulations"
    ]
   },
   {
@@ -171,8 +139,7 @@
     "%%bash\n",
     "\n",
     "# Download pdb file with the initial configuration of our system\n",
-    "PDB_URL=\"https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/alanine-dipeptide/adp-vacuum.pdb\"\n",
-    "wget -q $PDB_URL"
+    "wget -q https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/alanine-dipeptide/adp-vacuum.pdb"
    ]
   },
   {
@@ -183,23 +150,19 @@
    },
    "outputs": [],
    "source": [
-    "import numpy\n",
-    "\n",
-    "from pysages.utils import try_import\n",
+    "import numpy as np\n",
+    "import openmm\n",
+    "import openmm.app as app\n",
+    "import openmm.unit as unit\n",
     "\n",
-    "openmm = try_import(\"openmm\", \"simtk.openmm\")\n",
-    "unit = try_import(\"openmm.unit\", \"simtk.unit\")\n",
-    "app = try_import(\"openmm.app\", \"simtk.openmm.app\")\n",
-    "\n",
-    "\n",
-    "pi = numpy.pi\n",
     "\n",
+    "pi = np.pi\n",
     "T = 298.15 * unit.kelvin\n",
     "dt = 2.0 * unit.femtoseconds\n",
     "adp_pdb = \"adp-vacuum.pdb\"\n",
     "\n",
     "\n",
-    "def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt):\n",
+    "def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt, **kwargs):\n",
     "    pdb = app.PDBFile(pdb_filename)\n",
     "\n",
     "    ff = app.ForceField(\"amber99sb.xml\")\n",
@@ -249,11 +212,11 @@
    },
    "outputs": [],
    "source": [
+    "import pysages\n",
+    "\n",
     "from pysages.grids import Grid\n",
     "from pysages.colvars import DihedralAngle\n",
-    "from pysages.methods import Metadynamics\n",
-    "\n",
-    "import pysages"
+    "from pysages.methods import Metadynamics"
    ]
   },
   {
@@ -262,7 +225,6 @@
     "id": "LknkRvo1o4av"
    },
    "source": [
-    "\n",
     "The next step is to define the collective variable (CV). In this case, we choose the so called $\\phi$ and $\\psi$ dihedral angles of alanine dipeptide.\n",
     "\n",
     "For this example we will use the well-tempered version without grids. But these options can be configured.\n",
@@ -271,7 +233,7 @@
     "\n",
     "We also define a grid, which can be used as optional parameter to accelerate Metadynamics by approximating the biasing potential and its gradient by the closest value at the centers of the grid cells.\n",
     "\n",
-    "_Note:_ when setting $\\Delta T$ we need to also provide a value for $k_B$ that matches the internal units used by the backend.\n"
+    "_Note:_ when setting $\\Delta T$ we need to also provide a value for $k_B$ that matches the internal units used by the backend."
    ]
   },
   {
@@ -310,10 +272,9 @@
     "id": "Fz8BfU34pA_N"
    },
    "source": [
-    "\n",
     "We now simulate the number of time steps set above.\n",
     "Make sure to run with GPU support, otherwise, it can take a very long time.\n",
-    "On the GPU this should run in around half an hour.\n"
+    "On the GPU this should run in around half an hour."
    ]
   },
   {
@@ -333,10 +294,9 @@
     "id": "PXBKUfK0p9T2"
    },
    "source": [
-    "\n",
     "## Analysis\n",
     "\n",
-    "Let's plot the negative of the sum of gaussians accumulated. This will get close to the free energy surface for long enough simulations (larger than what is practical to run on Colab, but we should get close enough for illustration purposes here).\n"
+    "Let's plot the negative of the sum of gaussians accumulated. This will get close to the free energy surface for long enough simulations (larger than what is practical to run on Colab, but we should get close enough for illustration purposes here)."
    ]
   },
   {
@@ -357,8 +317,7 @@
     "id": "6mrlIOfoszBJ"
    },
    "source": [
-    "\n",
-    "We are now going to gather the information for the heights, standard deviations and centers of the accumulated gaussians and build a function to evaluate their sum at any point of the collective variable space.\n"
+    "We are now going to gather the information for the heights, standard deviations and centers of the accumulated gaussians and build a function to evaluate their sum at any point of the collective variable space."
    ]
   },
   {
@@ -370,7 +329,7 @@
    "outputs": [],
    "source": [
     "fe_result = pysages.analyze(run_result)\n",
-    "metapotential = fe_result['metapotential']"
+    "metapotential = fe_result[\"metapotential\"]"
    ]
   },
   {
@@ -408,8 +367,7 @@
     "id": "Kf_CMdih90Cd"
    },
    "source": [
-    "\n",
-    "And plot it.\n"
+    "And plot it."
    ]
   },
   {
@@ -461,8 +419,7 @@
     "id": "a-LGmeZ_3_m-"
    },
    "source": [
-    "\n",
-    "Lastly, we plot the height of the gaussians as a function of time and observe that their height decreases at an exponential rate as expected for well-tempered metadynamics.\n"
+    "Lastly, we plot the height of the gaussians as a function of time and observe that their height decreases at an exponential rate as expected for well-tempered metadynamics."
    ]
   },
   {
@@ -491,24 +448,16 @@
     }
    ],
    "source": [
-    "_dt = dt #method.context[0].sampler.snapshot.dt\n",
-    "ts = _dt * 1e-3 * numpy.arange(0, fe_result['heights'].size) * run_result.method.stride\n",
+    "ts = dt * 1e-3 * np.arange(0, fe_result[\"heights\"].size) * run_result.method.stride\n",
     "\n",
     "fig, ax = plt.subplots(dpi=120)\n",
-    "ax.plot(ts, fe_result['heights'], \"o\", mfc=\"none\", ms=4)\n",
+    "\n",
     "ax.set_xlabel(\"time [ns]\")\n",
     "ax.set_ylabel(\"height [kJ/mol]\")\n",
-    "plt.show()"
+    "ax.plot(ts, fe_result[\"heights\"], \"o\", mfc=\"none\", ms=4)\n",
+    "\n",
+    "fig.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "R6rEuwWAZ8Qp"
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/openmm/metad/Metadynamics-ADP.md b/examples/openmm/metad/Metadynamics-ADP.md
index 6ba6a4e3..b30b119a 100644
--- a/examples/openmm/metad/Metadynamics-ADP.md
+++ b/examples/openmm/metad/Metadynamics-ADP.md
@@ -13,77 +13,55 @@ jupyter:
     name: python3
 ---
 
-<!-- #region id="T-Qkg9C9n7Cc" -->
-
+<!-- #region id="_UgEohXC8n0g" -->
 # Setting up the environment
 
-First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the DLExt plugin.
-We copy it from Google Drive and install PySAGES for it.
-
+First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 <!-- #endregion -->
 
 ```bash id="3eTbKklCnyd_"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
+
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
-```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="5e474d51-1c66-4d16-bab9-29747fd9d466"
+```python colab={"base_uri": "https://localhost:8080/"} id="25H3kl03wzJe" outputId="528d12be-8cc4-42d9-d460-692d46a0e662"
 %env PYSAGES_ENV=/env/pysages
 ```
 
-```bash id="J7OY5K9VoBBh"
+```bash id="CPkgxfj6w4te"
 
 mkdir -p $PYSAGES_ENV .
 unzip -qquo pysages-env.zip -d $PYSAGES_ENV
 ```
 
-```python id="EMAWp8VloIk4"
+```python id="JMO5fiRTxAWB"
 import os
 import sys
 
 ver = sys.version_info
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
 
-os.environ["XLA_FLAGS"] = "--xla_gpu_strict_conv_algorithm_picker=false"
 os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
-<!-- #region id="we_mTkFioS6R" -->
-
+<!-- #region id="lf2KeHt5_eFv" -->
 ## PySAGES
 
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
+The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`.
 <!-- #endregion -->
 
-```bash id="vK0RZtbroQWe"
-
-pip install -q --upgrade pip
-# Installs the wheel compatible with CUDA.
-pip install -q --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null
-```
-
-<!-- #region id="wAtjM-IroYX8" -->
-
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
-
-<!-- #endregion -->
-
-```bash id="B-HB9CzioV5j"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
 <!-- #region id="KBFVcG1FoeMq" -->
-
 # Metadynamics-biased simulations
-
 <!-- #endregion -->
 
 <!-- #region id="0W2ukJuuojAl" -->
@@ -98,28 +76,23 @@ For this Colab, we are using alanine peptide in vacuum as example system.
 ```bash id="fre3-LYso1hh"
 
 # Download pdb file with the initial configuration of our system
-PDB_URL="https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/alanine-dipeptide/adp-vacuum.pdb"
-wget -q $PDB_URL
+wget -q https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/alanine-dipeptide/adp-vacuum.pdb
 ```
 
 ```python id="BBvC7Spoog82"
-import numpy
+import numpy as np
+import openmm
+import openmm.app as app
+import openmm.unit as unit
 
-from pysages.utils import try_import
-
-openmm = try_import("openmm", "simtk.openmm")
-unit = try_import("openmm.unit", "simtk.unit")
-app = try_import("openmm.app", "simtk.openmm.app")
-
-
-pi = numpy.pi
 
+pi = np.pi
 T = 298.15 * unit.kelvin
 dt = 2.0 * unit.femtoseconds
 adp_pdb = "adp-vacuum.pdb"
 
 
-def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt):
+def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt, **kwargs):
     pdb = app.PDBFile(pdb_filename)
 
     ff = app.ForceField("amber99sb.xml")
@@ -158,15 +131,14 @@ Next, we load PySAGES and the relevant classes and methods for our problem
 <!-- #endregion -->
 
 ```python id="fpMg-o8WomAA"
+import pysages
+
 from pysages.grids import Grid
 from pysages.colvars import DihedralAngle
 from pysages.methods import Metadynamics
-
-import pysages
 ```
 
 <!-- #region id="LknkRvo1o4av" -->
-
 The next step is to define the collective variable (CV). In this case, we choose the so called $\phi$ and $\psi$ dihedral angles of alanine dipeptide.
 
 For this example we will use the well-tempered version without grids. But these options can be configured.
@@ -176,7 +148,6 @@ We set the initial height, standard deviation and deposition frequency `stride`
 We also define a grid, which can be used as optional parameter to accelerate Metadynamics by approximating the biasing potential and its gradient by the closest value at the centers of the grid cells.
 
 _Note:_ when setting $\Delta T$ we need to also provide a value for $k_B$ that matches the internal units used by the backend.
-
 <!-- #endregion -->
 
 ```python id="B1Z8FWz0o7u_"
@@ -203,11 +174,9 @@ method = Metadynamics(cvs, height, sigma, stride, ngauss, deltaT=deltaT, kB=kB,
 ```
 
 <!-- #region id="Fz8BfU34pA_N" -->
-
 We now simulate the number of time steps set above.
 Make sure to run with GPU support, otherwise, it can take a very long time.
 On the GPU this should run in around half an hour.
-
 <!-- #endregion -->
 
 ```python id="K951m4BbpUar"
@@ -215,11 +184,9 @@ run_result = pysages.run(method, generate_simulation, timesteps)
 ```
 
 <!-- #region id="PXBKUfK0p9T2" -->
-
 ## Analysis
 
 Let's plot the negative of the sum of gaussians accumulated. This will get close to the free energy surface for long enough simulations (larger than what is practical to run on Colab, but we should get close enough for illustration purposes here).
-
 <!-- #endregion -->
 
 ```python id="X69d1R7OpW4P"
@@ -228,14 +195,12 @@ from pysages.approxfun import compute_mesh
 ```
 
 <!-- #region id="6mrlIOfoszBJ" -->
-
 We are now going to gather the information for the heights, standard deviations and centers of the accumulated gaussians and build a function to evaluate their sum at any point of the collective variable space.
-
 <!-- #endregion -->
 
 ```python id="zJqvpbw8szxR"
 fe_result = pysages.analyze(run_result)
-metapotential = fe_result['metapotential']
+metapotential = fe_result["metapotential"]
 ```
 
 <!-- #region id="VfTQ5yeyxt8e" -->
@@ -257,9 +222,7 @@ A = A.reshape(plot_grid.shape)
 ```
 
 <!-- #region id="Kf_CMdih90Cd" -->
-
 And plot it.
-
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/", "height": 461} id="3s9LL9apBMVb" outputId="55abf4e5-fef0-4faa-bf01-9719cbe8aa2b"
@@ -281,22 +244,17 @@ plt.show()
 ```
 
 <!-- #region id="a-LGmeZ_3_m-" -->
-
 Lastly, we plot the height of the gaussians as a function of time and observe that their height decreases at an exponential rate as expected for well-tempered metadynamics.
-
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/", "height": 457} id="SI_fhUW9CGlP" outputId="5d32f99d-4911-44bb-9d89-69c3e6212cb7"
-_dt = dt #method.context[0].sampler.snapshot.dt
-ts = _dt * 1e-3 * numpy.arange(0, fe_result['heights'].size) * run_result.method.stride
+ts = dt * 1e-3 * np.arange(0, fe_result["heights"].size) * run_result.method.stride
 
 fig, ax = plt.subplots(dpi=120)
-ax.plot(ts, fe_result['heights'], "o", mfc="none", ms=4)
+
 ax.set_xlabel("time [ns]")
 ax.set_ylabel("height [kJ/mol]")
-plt.show()
-```
-
-```python id="R6rEuwWAZ8Qp"
+ax.plot(ts, fe_result["heights"], "o", mfc="none", ms=4)
 
+fig.show()
 ```
diff --git a/examples/openmm/metad/nacl/Metadynamics_NaCl.ipynb b/examples/openmm/metad/nacl/Metadynamics_NaCl.ipynb
index 642277fb..ee946588 100644
--- a/examples/openmm/metad/nacl/Metadynamics_NaCl.ipynb
+++ b/examples/openmm/metad/nacl/Metadynamics_NaCl.ipynb
@@ -3,14 +3,13 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "T-Qkg9C9n7Cc"
+    "id": "_UgEohXC8n0g"
    },
    "source": [
-    "\n",
     "# Setting up the environment\n",
     "\n",
-    "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the DLExt plugin.\n",
-    "We copy it from Google Drive and install PySAGES for it.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance."
    ]
   },
   {
@@ -23,20 +22,19 @@
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "KRPmkpd9n_NG",
-    "outputId": "d1929eaa-42df-4a7d-afed-6094eab16e72"
+    "id": "25H3kl03wzJe"
    },
    "outputs": [
     {
@@ -55,7 +53,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "id": "J7OY5K9VoBBh"
+    "id": "CPkgxfj6w4te"
    },
    "outputs": [],
    "source": [
@@ -69,7 +67,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "id": "EMAWp8VloIk4"
+    "id": "JMO5fiRTxAWB"
    },
    "outputs": [],
    "source": [
@@ -79,62 +77,29 @@
     "ver = sys.version_info\n",
     "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "os.environ[\"XLA_FLAGS\"] = \"--xla_gpu_strict_conv_algorithm_picker=false\"\n",
     "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "we_mTkFioS6R"
+    "id": "lf2KeHt5_eFv"
    },
    "source": [
-    "\n",
     "## PySAGES\n",
     "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
+    "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "id": "vK0RZtbroQWe"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install -q --upgrade pip\n",
-    "# Installs the wheel compatible with CUDA.\n",
-    "pip install -q --upgrade \"jax[cuda]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "wAtjM-IroYX8"
-   },
-   "source": [
-    "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "id": "B-HB9CzioV5j"
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
    ]
   },
   {
@@ -143,8 +108,7 @@
     "id": "KBFVcG1FoeMq"
    },
    "source": [
-    "\n",
-    "# Metadynamics-biased simulations\n"
+    "# Metadynamics-biased simulations"
    ]
   },
   {
@@ -153,11 +117,10 @@
     "id": "0W2ukJuuojAl"
    },
    "source": [
-    "\n",
     "Metadynamics gradually builds a biasing potential from a sum of gaussians that are deposited one at a time every certain number of (user defined) time steps.\n",
     "There are two flavors of the algorithm, _Standard Metadynamics_ in which the heights of the gaussians is time independent, and _Well-tempered Metadynamics_ for which the heights of the deposited gaussians decreases depending on how frequently are visited the explored regions of collective variable space.\n",
     "\n",
-    "For this Colab, we are estimating the free energy along the distance between Na and Cl in water as example system.\n"
+    "For this Colab, we are estimating the free energy along the distance between Na and Cl in water as example system."
    ]
   },
   {
@@ -171,11 +134,10 @@
     "%%bash\n",
     "\n",
     "# Download pdb file with the initial configuration of our system\n",
-    "cp PySAGES/examples/inputs/nacl/nacl-explicit.pdb .\n",
+    "wget -q https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/nacl/nacl-explicit.pdb\n",
     "\n",
     "# Download force field file\n",
-    "ff_URL=\"https://raw.githubusercontent.com/openmm/openmmforcefields/main/openmmforcefields/ffxml/amber/tip3p_standard.xml\"\n",
-    "wget -q $ff_URL"
+    "wget -q https://raw.githubusercontent.com/openmm/openmmforcefields/main/openmmforcefields/ffxml/amber/tip3p_standard.xml"
    ]
   },
   {
@@ -186,21 +148,17 @@
    },
    "outputs": [],
    "source": [
-    "import numpy\n",
-    "\n",
-    "from pysages.utils import try_import\n",
+    "import numpy as np\n",
+    "import openmm\n",
+    "import openmm.app as app\n",
+    "import openmm.unit as unit\n",
     "\n",
-    "openmm = try_import(\"openmm\", \"simtk.openmm\")\n",
-    "unit = try_import(\"openmm.unit\", \"simtk.unit\")\n",
-    "app = try_import(\"openmm.app\", \"simtk.openmm.app\")\n",
-    "\n",
-    "\n",
-    "pi = numpy.pi\n",
     "\n",
+    "pi = np.pi\n",
     "T = 298.15 * unit.kelvin\n",
     "dt = 2.0 * unit.femtoseconds\n",
     "nacl_pdb = \"nacl-explicit.pdb\"\n",
-    "nacl_ff = 'tip3p_standard.xml'\n",
+    "nacl_ff = \"tip3p_standard.xml\"\n",
     "\n",
     "\n",
     "def generate_simulation(pdb_filename=nacl_pdb, T=T, dt=dt):\n",
@@ -321,10 +279,9 @@
     "id": "Fz8BfU34pA_N"
    },
    "source": [
-    "\n",
     "We now simulate the number of time steps set above.\n",
     "Make sure to run with GPU support, otherwise, it can take a very long time.\n",
-    "On the GPU this should run in around half an hour.\n"
+    "On the GPU this should run in around half an hour."
    ]
   },
   {
@@ -344,10 +301,9 @@
     "id": "PXBKUfK0p9T2"
    },
    "source": [
-    "\n",
     "## Analysis\n",
     "\n",
-    "Let's plot the negative of the sum of gaussians accumulated. This will get close to the free energy surface for long enough simulations (larger than what is practical to run on Colab, but we should get close enough for illustration purposes here).\n"
+    "Let's plot the negative of the sum of gaussians accumulated. This will get close to the free energy surface for long enough simulations (larger than what is practical to run on Colab, but we should get close enough for illustration purposes here)."
    ]
   },
   {
@@ -368,8 +324,7 @@
     "id": "6mrlIOfoszBJ"
    },
    "source": [
-    "\n",
-    "We are now going to gather the information for the heights, standard deviations and centers of the accumulated gaussians and build a function to evaluate their sum at any point of the collective variable space.\n"
+    "We are now going to gather the information for the heights, standard deviations and centers of the accumulated gaussians and build a function to evaluate their sum at any point of the collective variable space."
    ]
   },
   {
@@ -390,8 +345,7 @@
     "id": "VfTQ5yeyxt8e"
    },
    "source": [
-    "\n",
-    "Next we use the biasing potential to estimate the free energy surface. For well-tempered metadynamics this is equal to the sum of accumulated gaussians scaled by the factor $-(T + \\Delta T)\\, / \\,\\Delta T$.\n"
+    "Next we use the biasing potential to estimate the free energy surface. For well-tempered metadynamics this is equal to the sum of accumulated gaussians scaled by the factor $-(T + \\Delta T)\\, / \\,\\Delta T$."
    ]
   },
   {
@@ -421,8 +375,7 @@
     "id": "Kf_CMdih90Cd"
    },
    "source": [
-    "\n",
-    "And plot it.\n"
+    "And plot it."
    ]
   },
   {
@@ -460,7 +413,7 @@
     "ax.set_ylim(0.1,10)\n",
     "\n",
     "#fig.savefig(\"nacl-fe.png\", dpi=fig.dpi)\n",
-    "plt.show()"
+    "fig.show()"
    ]
   },
   {
@@ -499,24 +452,16 @@
     }
    ],
    "source": [
-    "_dt = dt #method.context[0].sampler.snapshot.dt\n",
-    "ts = _dt * 1e-3 * numpy.arange(0, fe_result['heights'].size) * run_result.method.stride\n",
+    "ts = dt * 1e-3 * np.arange(0, fe_result['heights'].size) * run_result.method.stride\n",
     "\n",
     "fig, ax = plt.subplots(dpi=120)\n",
+    "\n",
     "ax.plot(ts, fe_result['heights'], \"o\", mfc=\"none\", ms=4)\n",
     "ax.set_xlabel(\"time [ns]\")\n",
     "ax.set_ylabel(\"height [kJ/mol]\")\n",
-    "plt.show()"
+    "\n",
+    "fig.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "R6rEuwWAZ8Qp"
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/openmm/metad/nacl/Metadynamics_NaCl.md b/examples/openmm/metad/nacl/Metadynamics_NaCl.md
index a7c990c1..06fc28e6 100644
--- a/examples/openmm/metad/nacl/Metadynamics_NaCl.md
+++ b/examples/openmm/metad/nacl/Metadynamics_NaCl.md
@@ -13,114 +13,85 @@ jupyter:
     name: python3
 ---
 
-<!-- #region id="T-Qkg9C9n7Cc" -->
-
+<!-- #region id="_UgEohXC8n0g" -->
 # Setting up the environment
 
-First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the DLExt plugin.
-We copy it from Google Drive and install PySAGES for it.
-
+First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 <!-- #endregion -->
 
 ```bash id="3eTbKklCnyd_"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
+
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
-```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="d1929eaa-42df-4a7d-afed-6094eab16e72"
+```python id="25H3kl03wzJe"
 %env PYSAGES_ENV=/env/pysages
 ```
 
-```bash id="J7OY5K9VoBBh"
+```bash id="CPkgxfj6w4te"
 
 mkdir -p $PYSAGES_ENV .
 unzip -qquo pysages-env.zip -d $PYSAGES_ENV
 ```
 
-```python id="EMAWp8VloIk4"
+```python id="JMO5fiRTxAWB"
 import os
 import sys
 
 ver = sys.version_info
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
 
-os.environ["XLA_FLAGS"] = "--xla_gpu_strict_conv_algorithm_picker=false"
 os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
-<!-- #region id="we_mTkFioS6R" -->
-
+<!-- #region id="lf2KeHt5_eFv" -->
 ## PySAGES
 
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
+The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`.
 <!-- #endregion -->
 
-```bash id="vK0RZtbroQWe"
-
-pip install -q --upgrade pip
-# Installs the wheel compatible with CUDA.
-pip install -q --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null
-```
-
-<!-- #region id="wAtjM-IroYX8" -->
-
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
-
-<!-- #endregion -->
-
-```bash id="B-HB9CzioV5j"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
 <!-- #region id="KBFVcG1FoeMq" -->
-
 # Metadynamics-biased simulations
-
 <!-- #endregion -->
 
 <!-- #region id="0W2ukJuuojAl" -->
-
 Metadynamics gradually builds a biasing potential from a sum of gaussians that are deposited one at a time every certain number of (user defined) time steps.
 There are two flavors of the algorithm, _Standard Metadynamics_ in which the heights of the gaussians is time independent, and _Well-tempered Metadynamics_ for which the heights of the deposited gaussians decreases depending on how frequently are visited the explored regions of collective variable space.
 
 For this Colab, we are estimating the free energy along the distance between Na and Cl in water as example system.
-
 <!-- #endregion -->
 
 ```bash id="fre3-LYso1hh"
 
 # Download pdb file with the initial configuration of our system
-cp PySAGES/examples/inputs/nacl/nacl-explicit.pdb .
+wget -q https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/nacl/nacl-explicit.pdb
 
 # Download force field file
-ff_URL="https://raw.githubusercontent.com/openmm/openmmforcefields/main/openmmforcefields/ffxml/amber/tip3p_standard.xml"
-wget -q $ff_URL
+wget -q https://raw.githubusercontent.com/openmm/openmmforcefields/main/openmmforcefields/ffxml/amber/tip3p_standard.xml
 ```
 
 ```python id="BBvC7Spoog82"
-import numpy
+import numpy as np
+import openmm
+import openmm.app as app
+import openmm.unit as unit
 
-from pysages.utils import try_import
-
-openmm = try_import("openmm", "simtk.openmm")
-unit = try_import("openmm.unit", "simtk.unit")
-app = try_import("openmm.app", "simtk.openmm.app")
-
-
-pi = numpy.pi
 
+pi = np.pi
 T = 298.15 * unit.kelvin
 dt = 2.0 * unit.femtoseconds
 nacl_pdb = "nacl-explicit.pdb"
-nacl_ff = 'tip3p_standard.xml'
+nacl_ff = "tip3p_standard.xml"
 
 
 def generate_simulation(pdb_filename=nacl_pdb, T=T, dt=dt):
@@ -214,11 +185,9 @@ method = Metadynamics(cvs, height, sigma, stride, ngauss, deltaT=deltaT, kB=kB,
 ```
 
 <!-- #region id="Fz8BfU34pA_N" -->
-
 We now simulate the number of time steps set above.
 Make sure to run with GPU support, otherwise, it can take a very long time.
 On the GPU this should run in around half an hour.
-
 <!-- #endregion -->
 
 ```python id="K951m4BbpUar"
@@ -226,11 +195,9 @@ run_result = pysages.run(method, generate_simulation, timesteps)
 ```
 
 <!-- #region id="PXBKUfK0p9T2" -->
-
 ## Analysis
 
 Let's plot the negative of the sum of gaussians accumulated. This will get close to the free energy surface for long enough simulations (larger than what is practical to run on Colab, but we should get close enough for illustration purposes here).
-
 <!-- #endregion -->
 
 ```python id="X69d1R7OpW4P"
@@ -239,9 +206,7 @@ from pysages.approxfun import compute_mesh
 ```
 
 <!-- #region id="6mrlIOfoszBJ" -->
-
 We are now going to gather the information for the heights, standard deviations and centers of the accumulated gaussians and build a function to evaluate their sum at any point of the collective variable space.
-
 <!-- #endregion -->
 
 ```python id="zJqvpbw8szxR"
@@ -250,9 +215,7 @@ metapotential = fe_result['metapotential']
 ```
 
 <!-- #region id="VfTQ5yeyxt8e" -->
-
 Next we use the biasing potential to estimate the free energy surface. For well-tempered metadynamics this is equal to the sum of accumulated gaussians scaled by the factor $-(T + \Delta T)\, / \,\Delta T$.
-
 <!-- #endregion -->
 
 ```python id="6W7Xf0ilqAcm"
@@ -270,9 +233,7 @@ A = A.reshape(plot_grid.shape)
 ```
 
 <!-- #region id="Kf_CMdih90Cd" -->
-
 And plot it.
-
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/", "height": 466} id="3s9LL9apBMVb" outputId="6384612f-d75a-4541-b988-c4660ab2e570"
@@ -285,7 +246,7 @@ ax.set_xlim(0.1,1.25)
 ax.set_ylim(0.1,10)
 
 #fig.savefig("nacl-fe.png", dpi=fig.dpi)
-plt.show()
+fig.show()
 ```
 
 <!-- #region id="a-LGmeZ_3_m-" -->
@@ -295,16 +256,13 @@ Lastly, we plot the height of the gaussians as a function of time and observe th
 <!-- #endregion -->
 
 ```python colab={"base_uri": "https://localhost:8080/", "height": 457} id="SI_fhUW9CGlP" outputId="82a23a39-2cd9-46ab-df80-fe6ac9a7cbf6"
-_dt = dt #method.context[0].sampler.snapshot.dt
-ts = _dt * 1e-3 * numpy.arange(0, fe_result['heights'].size) * run_result.method.stride
+ts = dt * 1e-3 * np.arange(0, fe_result['heights'].size) * run_result.method.stride
 
 fig, ax = plt.subplots(dpi=120)
+
 ax.plot(ts, fe_result['heights'], "o", mfc="none", ms=4)
 ax.set_xlabel("time [ns]")
 ax.set_ylabel("height [kJ/mol]")
-plt.show()
-```
-
-```python id="R6rEuwWAZ8Qp"
 
+fig.show()
 ```
diff --git a/examples/openmm/spectral_abf/ADP_SpectralABF.ipynb b/examples/openmm/spectral_abf/ADP_SpectralABF.ipynb
index 2fc5ecb4..c1c3e304 100644
--- a/examples/openmm/spectral_abf/ADP_SpectralABF.ipynb
+++ b/examples/openmm/spectral_abf/ADP_SpectralABF.ipynb
@@ -3,14 +3,13 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "T-Qkg9C9n7Cc"
+    "id": "_UgEohXC8n0g"
    },
    "source": [
-    "\n",
     "# Setting up the environment\n",
     "\n",
-    "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the DLExt plugin.\n",
-    "We copy it from Google Drive and install PySAGES for it.\n"
+    "First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.\n",
+    "We download it from Google Drive and make it visible to the running python process in this Colab instance."
    ]
   },
   {
@@ -23,20 +22,19 @@
    "source": [
     "%%bash\n",
     "\n",
-    "BASE_URL=\"https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download\"\n",
-    "wget -q --load-cookies /tmp/cookies.txt \"$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\" -O pysages-env.zip\n",
-    "rm -rf /tmp/cookies.txt"
+    "BASE_URL=\"https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7\"\n",
+    "COOKIES=\"/tmp/cookies.txt\"\n",
+    "CONFIRMATION=\"$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\\w+).*/\\1\\n/p')\"\n",
+    "\n",
+    "wget -q --load-cookies $COOKIES \"$BASE_URL&confirm=$CONFIRMATION\" -O pysages-env.zip\n",
+    "rm -rf $COOKIES"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "KRPmkpd9n_NG",
-    "outputId": "e577ce0d-0a62-4f48-fb05-fde3a39c2ccc"
+    "id": "25H3kl03wzJe"
    },
    "outputs": [
     {
@@ -55,7 +53,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "id": "J7OY5K9VoBBh"
+    "id": "CPkgxfj6w4te"
    },
    "outputs": [],
    "source": [
@@ -69,7 +67,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "id": "EMAWp8VloIk4"
+    "id": "JMO5fiRTxAWB"
    },
    "outputs": [],
    "source": [
@@ -79,62 +77,29 @@
     "ver = sys.version_info\n",
     "sys.path.append(os.environ[\"PYSAGES_ENV\"] + \"/lib/python\" + str(ver.major) + \".\" + str(ver.minor) + \"/site-packages/\")\n",
     "\n",
-    "os.environ[\"XLA_FLAGS\"] = \"--xla_gpu_strict_conv_algorithm_picker=false\"\n",
     "os.environ[\"LD_LIBRARY_PATH\"] = \"/usr/lib/x86_64-linux-gnu:\" + os.environ[\"LD_LIBRARY_PATH\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "we_mTkFioS6R"
+    "id": "lf2KeHt5_eFv"
    },
    "source": [
-    "\n",
     "## PySAGES\n",
     "\n",
-    "The next step is to install PySAGES.\n",
-    "First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.\n"
+    "The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "id": "vK0RZtbroQWe"
-   },
-   "outputs": [],
-   "source": [
-    "%%bash\n",
-    "\n",
-    "pip install -q --upgrade pip\n",
-    "# Installs the wheel compatible with CUDA.\n",
-    "pip install -q --upgrade \"jax[cuda]\" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "wAtjM-IroYX8"
-   },
-   "source": [
-    "\n",
-    "Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
    "metadata": {
     "id": "B-HB9CzioV5j"
    },
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "\n",
-    "rm -rf PySAGES\n",
-    "git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null\n",
-    "cd PySAGES\n",
-    "pip install -q . &> /dev/null"
+    "!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null"
    ]
   },
   {
@@ -143,8 +108,7 @@
     "id": "KBFVcG1FoeMq"
    },
    "source": [
-    "\n",
-    "# SpectralABF-biased simulations\n"
+    "# SpectralABF-biased simulations"
    ]
   },
   {
@@ -153,10 +117,9 @@
     "id": "0W2ukJuuojAl"
    },
    "source": [
-    "\n",
     "SpectralABF gradually learns a better approximation to the coefficients of a basis functions expansion of the free energy of a system, from the generalized mean forces in a similar fashion to the ABF sampling method.\n",
     "\n",
-    "For this Colab, we are using alanine peptide in vacuum as example system.\n"
+    "For this Colab, we are using alanine peptide in vacuum as example system."
    ]
   },
   {
@@ -170,8 +133,7 @@
     "%%bash\n",
     "\n",
     "# Download pdb file with the initial configuration of our system\n",
-    "PDB_URL=\"https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/alanine-dipeptide/adp-vacuum.pdb\"\n",
-    "wget -q $PDB_URL"
+    "wget -q https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/alanine-dipeptide/adp-vacuum.pdb"
    ]
   },
   {
@@ -182,23 +144,19 @@
    },
    "outputs": [],
    "source": [
-    "import numpy\n",
-    "\n",
-    "from pysages.utils import try_import\n",
-    "\n",
-    "openmm = try_import(\"openmm\", \"simtk.openmm\")\n",
-    "unit = try_import(\"openmm.unit\", \"simtk.unit\")\n",
-    "app = try_import(\"openmm.app\", \"simtk.openmm.app\")\n",
-    "\n",
+    "import numpy as np\n",
+    "import openmm\n",
+    "import openmm.app as app\n",
+    "import openmm.unit as unit\n",
     "\n",
-    "pi = numpy.pi\n",
     "\n",
+    "pi = np.pi\n",
     "T = 298.15 * unit.kelvin\n",
     "dt = 2.0 * unit.femtoseconds\n",
     "adp_pdb = \"adp-vacuum.pdb\"\n",
     "\n",
     "\n",
-    "def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt):\n",
+    "def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt, **kwargs):\n",
     "    pdb = app.PDBFile(pdb_filename)\n",
     "\n",
     "    ff = app.ForceField(\"amber99sb.xml\")\n",
@@ -236,8 +194,7 @@
     "id": "3UrzENm_oo6U"
    },
    "source": [
-    "\n",
-    "Next, we load PySAGES and the relevant classes and methods for our problem\n"
+    "Next, we load PySAGES and the relevant classes and methods for our problem"
    ]
   },
   {
@@ -261,10 +218,9 @@
     "id": "LknkRvo1o4av"
    },
    "source": [
-    "\n",
     "The next step is to define the collective variable (CV). In this case, we choose the so called $\\phi$ and $\\psi$ dihedral angles of alanine dipeptide.\n",
     "\n",
-    "We define a grid, which will be used to indicate how we want to bin the forces that will be used to approximate the biasing potential and its gradient.\n"
+    "We define a grid, which will be used to indicate how we want to bin the forces that will be used to approximate the biasing potential and its gradient."
    ]
   },
   {
@@ -288,9 +244,8 @@
     "id": "Fz8BfU34pA_N"
    },
    "source": [
-    "\n",
     "We now simulate the number of time steps set above.\n",
-    "Make sure to run with GPU support, otherwise, it can take a very long time.\n"
+    "Make sure to run with GPU support, otherwise, it can take a very long time."
    ]
   },
   {
@@ -310,10 +265,9 @@
     "id": "PXBKUfK0p9T2"
    },
    "source": [
-    "\n",
     "## Analysis\n",
     "\n",
-    "Let's plot the free energy!\n"
+    "Let's plot the free energy!"
    ]
   },
   {
@@ -324,8 +278,7 @@
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from pysages.approxfun import compute_mesh"
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -385,17 +338,8 @@
     "cbar = plt.colorbar(im)\n",
     "cbar.ax.set_ylabel(r\"$A~[kJ/mol]$\", rotation=270, labelpad=20)\n",
     "\n",
-    "plt.show()"
+    "fig.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "R6rEuwWAZ8Qp"
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/openmm/spectral_abf/ADP_SpectralABF.md b/examples/openmm/spectral_abf/ADP_SpectralABF.md
index f0bd66a1..adeb2fd8 100644
--- a/examples/openmm/spectral_abf/ADP_SpectralABF.md
+++ b/examples/openmm/spectral_abf/ADP_SpectralABF.md
@@ -13,112 +13,83 @@ jupyter:
     name: python3
 ---
 
-<!-- #region id="T-Qkg9C9n7Cc" -->
-
+<!-- #region id="_UgEohXC8n0g" -->
 # Setting up the environment
 
-First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the DLExt plugin.
-We copy it from Google Drive and install PySAGES for it.
-
+First, we set up our environment. We use an already compiled and packaged installation of OpenMM and the openmm-dlext plugin.
+We download it from Google Drive and make it visible to the running python process in this Colab instance.
 <!-- #endregion -->
 
 ```bash id="3eTbKklCnyd_"
 
-BASE_URL="https://drive.google.com/u/0/uc?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7&export=download"
-wget -q --load-cookies /tmp/cookies.txt "$BASE_URL&confirm=$(wget -q --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')" -O pysages-env.zip
-rm -rf /tmp/cookies.txt
+BASE_URL="https://drive.usercontent.google.com/download?id=1hsKkKtdxZTVfHKgqVF6qV2e-4SShmhr7"
+COOKIES="/tmp/cookies.txt"
+CONFIRMATION="$(wget -q --save-cookies $COOKIES --keep-session-cookies --no-check-certificate $BASE_URL -O- | sed -rn 's/.*confirm=(\w+).*/\1\n/p')"
+
+wget -q --load-cookies $COOKIES "$BASE_URL&confirm=$CONFIRMATION" -O pysages-env.zip
+rm -rf $COOKIES
 ```
 
-```python colab={"base_uri": "https://localhost:8080/"} id="KRPmkpd9n_NG" outputId="e577ce0d-0a62-4f48-fb05-fde3a39c2ccc"
+```python id="25H3kl03wzJe"
 %env PYSAGES_ENV=/env/pysages
 ```
 
-```bash id="J7OY5K9VoBBh"
+```bash id="CPkgxfj6w4te"
 
 mkdir -p $PYSAGES_ENV .
 unzip -qquo pysages-env.zip -d $PYSAGES_ENV
 ```
 
-```python id="EMAWp8VloIk4"
+```python id="JMO5fiRTxAWB"
 import os
 import sys
 
 ver = sys.version_info
 sys.path.append(os.environ["PYSAGES_ENV"] + "/lib/python" + str(ver.major) + "." + str(ver.minor) + "/site-packages/")
 
-os.environ["XLA_FLAGS"] = "--xla_gpu_strict_conv_algorithm_picker=false"
 os.environ["LD_LIBRARY_PATH"] = "/usr/lib/x86_64-linux-gnu:" + os.environ["LD_LIBRARY_PATH"]
 ```
 
-<!-- #region id="we_mTkFioS6R" -->
-
+<!-- #region id="lf2KeHt5_eFv" -->
 ## PySAGES
 
-The next step is to install PySAGES.
-First, we install the jaxlib version that matches the CUDA installation of this Colab setup. See the JAX documentation [here](https://github.com/google/jax) for more details.
-
-<!-- #endregion -->
-
-```bash id="vK0RZtbroQWe"
-
-pip install -q --upgrade pip
-# Installs the wheel compatible with CUDA.
-pip install -q --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html &> /dev/null
-```
-
-<!-- #region id="wAtjM-IroYX8" -->
-
-Now we can finally install PySAGES. We clone the newest version from [here](https://github.com/SSAGESLabs/PySAGES) and build the remaining pure python dependencies and PySAGES itself.
-
+The next step is to install PySAGES. We retrieve the latest version from GitHub and add its dependecies via `pip`.
 <!-- #endregion -->
 
-```bash id="B-HB9CzioV5j"
-
-rm -rf PySAGES
-git clone https://github.com/SSAGESLabs/PySAGES.git &> /dev/null
-cd PySAGES
-pip install -q . &> /dev/null
+```python id="B-HB9CzioV5j"
+!pip install -qq git+https://github.com/SSAGESLabs/PySAGES.git > /dev/null
 ```
 
 <!-- #region id="KBFVcG1FoeMq" -->
-
 # SpectralABF-biased simulations
-
 <!-- #endregion -->
 
 <!-- #region id="0W2ukJuuojAl" -->
-
 SpectralABF gradually learns a better approximation to the coefficients of a basis functions expansion of the free energy of a system, from the generalized mean forces in a similar fashion to the ABF sampling method.
 
 For this Colab, we are using alanine peptide in vacuum as example system.
-
 <!-- #endregion -->
 
 ```bash id="fre3-LYso1hh"
 
 # Download pdb file with the initial configuration of our system
-PDB_URL="https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/alanine-dipeptide/adp-vacuum.pdb"
-wget -q $PDB_URL
+wget -q https://raw.githubusercontent.com/SSAGESLabs/PySAGES/main/examples/inputs/alanine-dipeptide/adp-vacuum.pdb
 ```
 
 ```python id="BBvC7Spoog82"
-import numpy
-
-from pysages.utils import try_import
-
-openmm = try_import("openmm", "simtk.openmm")
-unit = try_import("openmm.unit", "simtk.unit")
-app = try_import("openmm.app", "simtk.openmm.app")
-
+import numpy as np
+import openmm
+import openmm.app as app
+import openmm.unit as unit
 
-pi = numpy.pi
 
+pi = np.pi
 T = 298.15 * unit.kelvin
 dt = 2.0 * unit.femtoseconds
 adp_pdb = "adp-vacuum.pdb"
 
 
-def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt):
+def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt, **kwargs):
     pdb = app.PDBFile(pdb_filename)
 
     ff = app.ForceField("amber99sb.xml")
@@ -151,9 +122,7 @@ def generate_simulation(pdb_filename=adp_pdb, T=T, dt=dt):
 ```
 
 <!-- #region id="3UrzENm_oo6U" -->
-
 Next, we load PySAGES and the relevant classes and methods for our problem
-
 <!-- #endregion -->
 
 ```python id="fpMg-o8WomAA"
@@ -165,11 +134,9 @@ import pysages
 ```
 
 <!-- #region id="LknkRvo1o4av" -->
-
 The next step is to define the collective variable (CV). In this case, we choose the so called $\phi$ and $\psi$ dihedral angles of alanine dipeptide.
 
 We define a grid, which will be used to indicate how we want to bin the forces that will be used to approximate the biasing potential and its gradient.
-
 <!-- #endregion -->
 
 ```python id="B1Z8FWz0o7u_"
@@ -181,10 +148,8 @@ method = SpectralABF(cvs, grid)
 ```
 
 <!-- #region id="Fz8BfU34pA_N" -->
-
 We now simulate the number of time steps set above.
 Make sure to run with GPU support, otherwise, it can take a very long time.
-
 <!-- #endregion -->
 
 ```python id="K951m4BbpUar"
@@ -192,16 +157,13 @@ run_result = pysages.run(method, generate_simulation, timesteps)
 ```
 
 <!-- #region id="PXBKUfK0p9T2" -->
-
 ## Analysis
 
 Let's plot the free energy!
-
 <!-- #endregion -->
 
 ```python id="X69d1R7OpW4P"
 import matplotlib.pyplot as plt
-from pysages.approxfun import compute_mesh
 ```
 
 ```python id="zJqvpbw8szxR"
@@ -229,9 +191,5 @@ ax.set_ylabel(r"$\psi$")
 cbar = plt.colorbar(im)
 cbar.ax.set_ylabel(r"$A~[kJ/mol]$", rotation=270, labelpad=20)
 
-plt.show()
-```
-
-```python id="R6rEuwWAZ8Qp"
-
+fig.show()
 ```