auto: Rebuild docs.

Author: github-actions

jupyter_execute/286616453aae485882994e55141d1ff25dacf518da3fc4d40e095f954fed7ddf.png

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f436067e",
+   "id": "506bae01",
    "metadata": {},
    "source": [
     "## Table of Contents\n",

jupyter_execute/2de0ee6cc57fd2d065e55c1efd3c967c990295dc5b529618eefe717a5ded952f.png

@@ -38,7 +38,10 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import tempfile\n",
+    "from pathlib import Path\n",
+    "import platform"
    ]
   },
   {
@@ -322,7 +325,9 @@
     ")\n",
     "\n",
     "# generate a temporary SQLite DB\n",
-    "abc_id = abc.new(\"sqlite:////tmp/mjp.db\", observations)"
+    "prefix = \"sqlite:///\" if platform.system() == \"Windows\" else \"sqlite:////\"\n",
+    "db_path = (Path(tempfile.gettempdir()).absolute() / \"mjp.db\").as_uri().replace(\"file:///\", prefix)\n",
+    "abc_id = abc.new(db_path, observations)"
    ]
   },
   {
@@ -474,7 +479,7 @@
     "\n",
     "# For BayesFlow devs: this ensures that the latest dev version can be found\n",
     "import sys\n",
-    "sys.path.append('../')\n",
+    "sys.path.append(\"../\")\n",
     "\n",
     "import bayesflow as bf"
    ]
@@ -508,11 +513,11 @@
    "source": [
     "def prior_helper():\n",
     "    \"\"\"The ABC prior returns a Parameter Object from pyabc which we convert to a dict.\"\"\"\n",
-    "    return dict(rate=prior.rvs()['rate'])\n",
+    "    return dict(rate=prior.rvs()[\"rate\"])\n",
     "\n",
     "def sim_helper(rate):\n",
     "    \"\"\"The simulator returns a dict, we extract the output at the test times.\"\"\"\n",
-    "    temp = sim({'rate': rate})\n",
+    "    temp = sim({\"rate\": rate})\n",
     "    xt_ind = np.searchsorted(temp[\"t\"], t_test_times) - 1\n",
     "    obs = temp[\"X\"][:, 1][xt_ind]\n",
     "    return dict(obs=obs)"
@@ -563,8 +568,8 @@
    ],
    "source": [
     "adapter = bf.approximators.ContinuousApproximator.build_adapter(\n",
-    "    inference_variables='rate',\n",
-    "    inference_conditions='obs',\n",
+    "    inference_variables=\"rate\",\n",
+    "    inference_conditions=\"obs\",\n",
     "    summary_variables=None\n",
     ")\n",
     "adapter"
@@ -824,7 +829,7 @@
     "obs = observations[\"X\"][:, 1][xt_ind]\n",
     "\n",
     "# Obtain 1000 posterior samples\n",
-    "samples = workflow.sample(conditions={'obs': [obs]}, num_samples=num_samples)"
+    "samples = workflow.sample(conditions={\"obs\": [obs]}, num_samples=num_samples)"
    ]
   },
   {
@@ -876,9 +881,9 @@
    "source": [
     "# abc gives us weighted samples, we resample them to get comparable samples\n",
     "df, w = abc_history.get_distribution()\n",
-    "abc_samples = weighted_statistics.resample(df['rate'].values, w, 1000)\n",
+    "abc_samples = weighted_statistics.resample(df[\"rate\"].values, w, 1000)\n",
     "\n",
-    "f = bf.diagnostics.plots.pairs_posterior({'rate': abc_samples}, targets=np.array([true_rate]))"
+    "f = bf.diagnostics.plots.pairs_posterior({\"rate\": abc_samples}, targets=np.array([true_rate]))"
    ]
   },
   {

jupyter_execute/66db14218d7a7079067b117801acccf0925b4e4d4b235ba252c18e7cd3c24eaa.png

@@ -12,35 +12,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "0383ba66",
+   "execution_count": null,
+   "id": "5fb5c0f856b6bcf4",
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
     "# Set to your favorite backend\n",
-    "os.environ[\"KERAS_BACKEND\"] = \"jax\""
+    "if \"KERAS_BACKEND\" not in os.environ:\n",
+    "    # set this to \"torch\", \"tensorflow\", or \"jax\"\n",
+    "    os.environ[\"KERAS_BACKEND\"] = \"tensorflow\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "684f2d7e19d40e09",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-04-11T19:54:02.700953Z",
-     "start_time": "2025-04-11T19:53:33.926075Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:2025-04-21 12:41:48,425:jax._src.xla_bridge:967: An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "id": "4a9355783f1314a",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import datetime\n",
     "\n",

jupyter_execute/8b88bdc1d257b583cabeb58531c50d8ee3a6b4ab54f788c23ad0393abe3a116c.png

@@ -24,7 +24,11 @@
    "source": [
     "import os\n",
     "# Set to your favorite backend\n",
-    "os.environ[\"KERAS_BACKEND\"] = \"jax\""
+    "if \"KERAS_BACKEND\" not in os.environ:\n",
+    "    # set this to \"torch\", \"tensorflow\", or \"jax\"\n",
+    "    os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
+    "else:\n",
+    "    print(f\"Using '{os.environ['KERAS_BACKEND']}' backend\")"
    ]
   },
   {