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Merge branch 'main' into dev
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‎examples/tests/test_mcmc.py‎ renamed to ‎examples/sampling/test_mcmc.py‎

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‎examples/sampling/test_samp.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "2c795b88-378b-489d-85bb-3e1786930b4a",
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"metadata": {},
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"source": [
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"# Test sampling algorithms"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "eafc9aac-719c-434f-8816-7aae59f171db",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2025-05-08T16:21:49.363904Z",
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"iopub.status.busy": "2025-05-08T16:21:49.363456Z",
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"iopub.status.idle": "2025-05-08T16:21:50.671986Z",
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"shell.execute_reply": "2025-05-08T16:21:50.671739Z"
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}
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"\n",
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"import ment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b2fda8d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.style.use(\"../style.mplstyle\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e5922304-a9c9-47a7-b09f-7ae4f4845d2d",
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"metadata": {},
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"source": [
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"## Create distribution"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3b8a7941-17c2-446d-aa36-6a3cb7308de2",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2025-05-08T16:21:50.676745Z",
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"iopub.status.busy": "2025-05-08T16:21:50.676655Z",
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"iopub.status.idle": "2025-05-08T16:21:52.023604Z",
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"shell.execute_reply": "2025-05-08T16:21:52.023261Z"
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}
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},
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"outputs": [],
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"source": [
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"class RingDistribution:\n",
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" def __init__(self) -> None:\n",
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" self.ndim = 2\n",
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"\n",
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" def prob(self, x: np.ndarray) -> np.ndarray:\n",
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" x1 = x[:, 0]\n",
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" x2 = x[:, 1]\n",
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" log_prob = np.sin(np.pi * x1) - 2.0 * (x1**2 + x2**2 - 2.0) ** 2\n",
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" return np.exp(log_prob)\n",
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"\n",
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" def prob_grid(\n",
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" self, shape: tuple[int], limits: list[tuple[float, float]]\n",
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" ) -> tuple[np.ndarray, list[np.ndarray]]:\n",
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" edges = [\n",
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" np.linspace(limits[i][0], limits[i][1], shape[i] + 1)\n",
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" for i in range(self.ndim)\n",
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" ]\n",
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" coords = [0.5 * (e[:-1] + e[1:]) for e in edges]\n",
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" points = np.stack(\n",
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" [c.ravel() for c in np.meshgrid(*coords, indexing=\"ij\")], axis=-1\n",
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" )\n",
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" values = self.prob(points)\n",
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" values = values.reshape(shape)\n",
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" return values, coords"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fe88acb4-1a21-4565-81f2-e41de80586bc",
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"metadata": {},
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"outputs": [],
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"source": [
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"ndim = 2\n",
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"xmax = 3.0\n",
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"dist = RingDistribution()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e083ac94",
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"metadata": {},
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"outputs": [],
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"source": [
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"grid_limits = 2 * [(-xmax, xmax)]\n",
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"grid_shape = (128, 128)\n",
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"grid_values, grid_coords = dist.prob_grid(grid_shape, grid_limits)\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(2.5, 2.5))\n",
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"ax.pcolormesh(grid_coords[0], grid_coords[1], grid_values.T)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4a72fdfd",
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_samples(x_pred: np.ndarray) -> tuple:\n",
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" fig, axs = plt.subplots(ncols=2, figsize=(5.0, 2.75), sharex=True, sharey=True)\n",
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" hist, edges = np.histogramdd(x_pred, bins=80, range=grid_limits)\n",
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" axs[0].pcolormesh(edges[0], edges[1], hist.T)\n",
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" axs[1].pcolormesh(grid_coords[0], grid_coords[1], grid_values.T)\n",
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" axs[0].set_title(\"PRED\", fontsize=\"medium\")\n",
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" axs[1].set_title(\"TRUE\", fontsize=\"medium\")\n",
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" return fig, axs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f5ff2f9d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def evaluate_sampler(sampler, size: int = 100_000):\n",
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" x_pred = sampler(dist.prob, size=size)\n",
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" return plot_samples(x_pred)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "18c50faa",
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"metadata": {},
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"source": [
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"## Grid sampler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "09414cdc",
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"metadata": {},
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"outputs": [],
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"source": [
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"sampler = ment.GridSampler(grid_limits=grid_limits, grid_shape=grid_shape)\n",
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"\n",
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"evaluate_sampler(sampler);"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1cfb7494",
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"metadata": {},
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"source": [
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"## Metropolis-Hastings sampler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "37e61a8a",
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"metadata": {},
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"outputs": [],
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"source": [
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"chains = 4\n",
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"proposal_cov = np.identity(ndim) * 0.25\n",
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"start_loc = np.zeros(ndim)\n",
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"start_cov = np.identity(ndim) * 0.25\n",
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"start_point = np.random.multivariate_normal(start_loc, start_cov, size=chains)\n",
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"\n",
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"sampler = ment.samp.MetropolisHastingsSampler(\n",
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" ndim=ndim,\n",
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" proposal_cov=proposal_cov,\n",
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" start=start_point,\n",
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" chains=chains,\n",
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" burnin=0,\n",
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")\n",
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"evaluate_sampler(sampler);\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "00ab115a",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "ment",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎examples/style.mplstyle‎

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axes.linewidth: 1.25
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axes.titlesize: "medium"
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figure.constrained_layout.use: True
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xtick.minor.visible: True
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ytick.minor.visible: True
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savefig.dpi: 300
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savefig.format: "png"

‎examples/tests/test_samp.ipynb‎

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