{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Change-point study\n", "\n", "[Change point analysis](http://www.variation.com/cpa/tech/changepoint.html) or [change detection](https://en.wikipedia.org/wiki/Change_detection) deals with abrupt changes in statistical properties of time series. *bayesloop* includes two types of abrupt changes: an abrupt change in parameter values is modeled by the transition model `Changepoint`. In contrast to this change in value, the transition model itself may change at specific points in time, which we will refer to as *structural breaks*. These structural changes are implemented using the `SerialTransitionModel` class. The following two sections introduce the `ChangepointStudy` class and describe its usage to analyze both change-points and structural breaks in time series data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:15:25.427112Z", "iopub.status.busy": "2026-04-27T19:15:25.426670Z", "iopub.status.idle": "2026-04-27T19:15:26.382728Z", "shell.execute_reply": "2026-04-27T19:15:26.382222Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt # plotting\n", "plt.style.use('seaborn-v0_8-whitegrid') # plot styling\n", "\n", "import bayesloop as bl\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing abrupt changes of parameter values\n", "\n", "The `ChangepointStudy` class represents an extension of the `HyperStudy` introduced above and provides an easy-to-use interface to conduct change-point studies. By calling the `fit` method, this type of study first analyzes the defined transition model and detects all instances of the `Changepoint` class. Instead of directly using all combinations of predefined change-points provided by the user, it then computes a list of all valid combinations of change-point times and fits them (basically preventing the double-counting of change-point times due to reversed order). With Bayesian evidence as an objective fitness measure, this type of study can be used to answer the general question of when changes have happened. Furthermore, we may compute a distribution of change-point times to assess the (un-)certainty of these points in time.\n", "\n", "
\n", "**Note:** For simple change-point analyses with only one change-/break-point or multiple change-/break-points which do not \"overlap\", using the `HyperStudy` class is sufficient, too.\n", "
\n", "\n", "### Analysis of a single change-point\n", "In a first example, we assume a single change-point in our data set of coal mining disasters. Using the `ChangepointStudy`, we iterate over all possible time steps using the change-point model. After processing all time steps, the probability distribution of the change-point as well as an average model are computed. This study may also be carried out using MCMC methods, see e.g. the [PyMC tutorial](https://pymc-devs.github.io/pymc/tutorial.html) for comparison.\n", "\n", "The change-point study is set up as shown below. Note that we supply the value `'all'` to the change-point hyper-parameter `'t_change'`, indicating that all possible time steps are considered as a change-point. One could of course also provide a list of possible candidate time steps." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:15:26.384056Z", "iopub.status.busy": "2026-04-27T19:15:26.383921Z", "iopub.status.idle": "2026-04-27T19:15:26.774543Z", "shell.execute_reply": "2026-04-27T19:15:26.774060Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", " --> Hyper-study\n", " --> Change-point analysis\n", "+ Successfully imported example data.\n", "+ Observation model: Poisson. Parameter(s): ['accident_rate']\n", "+ Transition model: Change-point. Hyper-Parameter(s): ['change_point']\n", "+ Detected 1 change-point(s) in transition model: ['change_point']\n", "+ Set hyper-prior(s): ['uniform']\n", "+ Started new fit.\n", " + 109 analyses to run.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "17910cee3c82494691c56d4495979b96", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/109 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 4))\n", "\n", "S.plot('change_point', color='g', alpha=.8)\n", "\n", "plt.xlim([1875, 1905])\n", "plt.xlabel('year')\n", "\n", "\n", "x, p = S.get_hyper_parameter_distribution('change_point')\n", "p1 = np.sum(p[x < 1887])\n", "p2 = np.sum(p[x > 1893])\n", "\n", "# compute probability of change-point between 1887 and 1893\n", "print('Probability of change-point between 1887 and 1893: {}'.format(1.-p1-p2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this distribution, we may conclude that a change in safety conditions of coal mines in the UK happened during the seven-year interval from 1887 to 1893 with a probability of $\\approx 84\\%$.\n", "\n", "*bayesloop* further weighs all fitted models by their probability from the change-point distribution and subsequently adds them up, resulting in an average model, which is stored in `S.posterior_sequence` and `S.posterior_mean_values`. Additionally, the log-evidence of the average model is set by the weighted sum of all log-evidence values. These results can be plotted as before, using `plot`:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:15:26.847198Z", "iopub.status.busy": "2026-04-27T19:15:26.847119Z", "iopub.status.idle": "2026-04-27T19:15:26.939647Z", "shell.execute_reply": "2026-04-27T19:15:26.939322Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 4))\n", "plt.bar(S.raw_timestamps, S.raw_data, align='center', facecolor='r', alpha=.5)\n", "S.plot('accident_rate')\n", "plt.xlim([1851, 1962])\n", "plt.xlabel('year');" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Exploring possible change-points\n", "The change-point study described above explicitly assumes the existence of a single change-point in the data set. Without any prior knowledge about the data, however, this assumption can rarely be made with certainty as the number of potential change-points is often unknown.\n", "\n", "In order to *explore* possible change-points without prior knowledge, *bayesloop* includes the transition model `RegimeSwitch`, which assigns a minimal probability (specified on a log10-scale by `log10p_min`, relative to the probability value of a flat distribution) to all parameter values on the parameter grid at every time step. This model allows for abrupt parameter changes only, and neglects gradually varying parameter dynamics. Note that no `ChangepointStudy` is needed for this kind of analysis, the *\"standard\"* `Study` class is sufficient.\n", "\n", "For the coal mining example, the results from the regime-switching model with a minimal probability of $10^{-7}$ resemble the average model of the change-point study:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:15:26.940930Z", "iopub.status.busy": "2026-04-27T19:15:26.940837Z", "iopub.status.idle": "2026-04-27T19:15:27.044468Z", "shell.execute_reply": "2026-04-27T19:15:27.044004Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", "+ Successfully imported example data.\n", "+ Observation model: Poisson. Parameter(s): ['accident_rate']\n", "+ Transition model: Regime-switching model. Hyper-Parameter(s): ['log10pMin']\n", "+ Started new fit:\n", " + Formatted data.\n", " + Set prior (function): jeffreys. Values have been re-normalized.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "aff97083020f4a6187feea845715ab7d", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/110 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "S = bl.Study()\n", "S.load_example_data()\n", "\n", "L = bl.om.Poisson('accident_rate', bl.oint(0, 6, 1000))\n", "T = bl.tm.RegimeSwitch('log10p_min', -7)\n", "\n", "S.set(L, T)\n", "S.fit()\n", "\n", "plt.figure(figsize=(8, 4))\n", "plt.bar(S.raw_timestamps, S.raw_data, align='center', facecolor='r', alpha=.5)\n", "S.plot('accident_rate')\n", "plt.xlim([1851, 1962])\n", "plt.xlabel('year');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analysis of multiple change-points\n", "Suppose the regime-switching process introduced above indicates two distinct change-points in a data set. In this case, the `ChangepointStudy` class can be used together with a `CombinedTransitionModel` to perform a comprehensive analysis assuming two change-points. The combined transition model here simply consists of two instances of the `ChangePoint` model. We use the example below to investigate possible change-points in the disaster rate after the significant decrease at the end of the 19th century, i.e. restricting the data set to the time after 1900. Note that the `ChangepointStudy` will only consider combinations of change-points that are in ascending temporal order, i.e. the second change-point must occur after the first one.\n", "\n", "After all fits are done, the resulting joint change-point distribution can be plotted using the `get_joint_hyper_parameter_distribution` (`get_joint_hyper_parameter_distribution`) method (similar to plotting the joint distribution of a hyper-study)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:15:27.045482Z", "iopub.status.busy": "2026-04-27T19:15:27.045398Z", "iopub.status.idle": "2026-04-27T19:15:31.725686Z", "shell.execute_reply": "2026-04-27T19:15:31.725151Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Created new study.\n", " --> Hyper-study\n", " --> Change-point analysis\n", "+ Successfully imported example data.\n", "+ Observation model: Poisson. Parameter(s): ['accident_rate']\n", "+ Transition model: Combined transition model. Hyper-Parameter(s): ['t_1', 't_2']\n", "+ Detected 2 change-point(s) in transition model: ['t_1', 't_2']\n", "+ Set hyper-prior(s): ['uniform', 'uniform']\n", "+ Started new fit.\n", " + 1770 analyses to run.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2e17aab45fc6454c937b0cb327fff252", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1770 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "S = bl.ChangepointStudy()\n", "S.load_example_data()\n", "\n", "mask = S.raw_timestamps > 1900 # select timestamps greater than the year 1900\n", "S.raw_timestamps = S.raw_timestamps[mask]\n", "S.raw_data = S.raw_data[mask]\n", "\n", "L = bl.om.Poisson('accident_rate', bl.oint(0, 6, 1000))\n", "T = bl.tm.CombinedTransitionModel(bl.tm.ChangePoint('t_1', 'all'),\n", " bl.tm.ChangePoint('t_2', 'all'))\n", "\n", "S.set(L, T)\n", "S.fit()\n", "\n", "S.get_joint_hyper_parameter_distribution(['t_1', 't_2'], plot=True, color=[0.1, 0.8, 0.1])\n", "plt.xlim([1899, 1962])\n", "plt.ylim([1899, 1962])\n", "\n", "# set proper view-point\n", "ax = plt.gca()\n", "ax.view_init(elev=35, azim=150)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of focusing on the exact time steps of the change-points, some applications may call for the analysis of the time interval between two change-points. The `ChangepointStudy` class provides a method called `get_duration_distribution` which computes the probabilities of different time intervals between two change-points and optionally plots them in a bar graph. Based on the example above, the resulting *\"duration distribution\"* is shown below:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:15:31.726876Z", "iopub.status.busy": "2026-04-27T19:15:31.726774Z", "iopub.status.idle": "2026-04-27T19:15:31.782791Z", "shell.execute_reply": "2026-04-27T19:15:31.782366Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 4))\n", "S.get_duration_distribution(['t_1', 't_2'], plot=True, color='g', alpha=.8)\n", "plt.xlim([0, 62]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we plot the averaged parameter evolution of the two-change-point model. From the duration distribution as well as from the temporal evolution of the inferred disaster rate we may conclude that there is indeed a time period with a slightly increased disaster rate, which begins in $\\approx$ 1930 and ends in $\\approx$ 1945. The duration distribution underlines this finding with high probability values for durations up to 20 years." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:15:31.784012Z", "iopub.status.busy": "2026-04-27T19:15:31.783935Z", "iopub.status.idle": "2026-04-27T19:15:31.862459Z", "shell.execute_reply": "2026-04-27T19:15:31.861993Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 4))\n", "plt.bar(S.raw_timestamps, S.raw_data, align='center', facecolor='r', alpha=.5)\n", "S.plot('accident_rate')\n", "plt.xlim([1901, 1961])\n", "plt.xlabel('year');" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Analyzing structural breaks in time series models\n", "In contrast to a pure change in parameter value, the whole type of parameter dynamics may change at a given point in time. We will use the term *structural break* to describe such events. We have already investigated so-called serial transition models that describe parameter dynamics which change from time to time. While these serial transition models are a recurring topic in this tutorial (see [here](modelselection.ipynb#Serial-transition-model), [here](hyperparameteroptimization.ipynb) and [here](hyperstudy.ipynb)), the times at which the structural breaks happen have - up to this point - always been user-defined and fixed. This restriction can be lifted by the `ChangepointStudy` class. If a `SerialTransitionModel` is defined within the change-point study, all structural breaks will be treated as variables and the `fit` method will iterate over all possible combinations, just as with *\"normal\"* change-points.\n", "\n", "In this section, our goal is to build a model to determine how long it took for the disaster rate to decrease from $\\approx$ 3 disasters per year to only $\\approx$ 1 per year. This kind of study may generally be applied to assess the effectiveness of policies like safety regulations. Here, we devise a simple serial transition model that consists of three phases to describe the change in the annual number of coal mining disasters: in the first and the last phase, we assume a constant disaster rate, while the intermediate phase is modeled by a linear decrease of the disaster rate. By providing multiple values for the slope of the intermediate phase, we can combine the advantages of both hyper- and change-point study in order to consider the uncertainty of the change-points *and* the uncertainty of the slope of the intermediate phase. We restrict the data set to the years from 1870 to 1910, and assume an improvement of the disaster rate by 0 to 2.0 disasters per year.\n", "\n", "_**Note:** The following analysis consists of 23,400 individual model fits. It may take several minutes to complete. Some candidate break-point and slope combinations imply parameter values outside the grid; bayesloop assigns zero evidence to those invalid fits and prints only the first few boundary warnings._" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:15:31.863529Z", "iopub.status.busy": "2026-04-27T19:15:31.863454Z", "iopub.status.idle": "2026-04-27T19:16:46.347650Z", "shell.execute_reply": "2026-04-27T19:16:46.347255Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+ Successfully imported example data.\n", "+ Transition model: Serial transition model. Hyper-Parameter(s): ['slope', 't_1', 't_2']\n", "+ Detected 2 break-point(s) in transition model: ['t_1', 't_2']\n", "+ Set hyper-prior(s): ['uniform', 'uniform', 'uniform']\n", "+ Started new fit.\n", " + 23400 analyses to run.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f10e214c7abd463a87cf84dac846fe02", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/23400 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "S.load_example_data()\n", "\n", "mask = (S.raw_timestamps >= 1870)*(S.raw_timestamps <= 1910) # restrict analysis to 1870-1910\n", "S.raw_timestamps = S.raw_timestamps[mask]\n", "S.raw_data = S.raw_data[mask]\n", "\n", "T = bl.tm.SerialTransitionModel(bl.tm.Static(),\n", " bl.tm.BreakPoint('t_1', 'all'),\n", " bl.tm.Deterministic(lambda t, slope=np.linspace(-2.0, 0.0, 30): t*slope, \n", " target='accident_rate'),\n", " bl.tm.BreakPoint('t_2', 'all'),\n", " bl.tm.Static()\n", " )\n", "\n", "S.set(T)\n", "S.fit()\n", "\n", "S.get_joint_hyper_parameter_distribution(['t_1', 't_2'], plot=True, color=[0.1, 0.8, 0.1])\n", "plt.xlim([1869, 1911])\n", "plt.ylim([1869, 1911])\n", "\n", "# set proper view-point\n", "ax = plt.gca()\n", "ax.view_init(elev=35, azim=125)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apart from this rather non-intuitive joint distribution of the two structural break times, we can also plot the marginal distribution of the inferred slope of the intermediate phase:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:16:46.348876Z", "iopub.status.busy": "2026-04-27T19:16:46.348776Z", "iopub.status.idle": "2026-04-27T19:16:46.391822Z", "shell.execute_reply": "2026-04-27T19:16:46.391368Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,4))\n", "S.plot('slope', color='g', alpha=.8)\n", "plt.xlim([-2.1, 0.1]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot above indicates that the decrease of the disaster rate is indeed a gradual process, as high probabilities are assigned to rather small slopes with an absolute value < 0.5. However, there is still a significant probability of slopes with an absolute value that is larger than 0.5:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:16:46.392947Z", "iopub.status.busy": "2026-04-27T19:16:46.392872Z", "iopub.status.idle": "2026-04-27T19:16:46.394922Z", "shell.execute_reply": "2026-04-27T19:16:46.394478Z" } }, "outputs": [], "source": [ "x, p = S.get_hyper_parameter_distribution('slope')\n", "np.sum(p[np.abs(x) > 0.5]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More intuitive than the slope is the duration between the two structural breaks. This period of time directly measures the time it takes for the disaster rate to decrease from three to one disaster per year. The plot below shows the distribution for this period of time:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:16:46.395978Z", "iopub.status.busy": "2026-04-27T19:16:46.395901Z", "iopub.status.idle": "2026-04-27T19:16:46.527472Z", "shell.execute_reply": "2026-04-27T19:16:46.527023Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,4))\n", "d, p = S.get_duration_distribution(['t_1', 't_2'], plot=True, color='g', alpha=.8)\n", "plt.xlim([-0.5, 40.5]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this plot, we see that the period between the two structural breaks cannot be inferred with great accuracy. The accuracy may be improved by extending the simple serial model used in this example or by incorporating more data points (only 40 data points have been used here). We may still conclude that this analysis indicates an intermediate phase of improvement that is shorter than 15 years, with a probability of $\\approx 70\\%$:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:16:46.528533Z", "iopub.status.busy": "2026-04-27T19:16:46.528456Z", "iopub.status.idle": "2026-04-27T19:16:46.530700Z", "shell.execute_reply": "2026-04-27T19:16:46.530349Z" } }, "outputs": [ { "data": { "text/plain": [ "np.float64(0.7152847684469197)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(p[np.abs(d) < 15])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results from the break-point analysis are further illustrated by the temporal evolution of the inferred disaster rate. Below, the average model of the complete analysis is used to display the inferred changes in the disaster rate:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-04-27T19:16:46.531698Z", "iopub.status.busy": "2026-04-27T19:16:46.531621Z", "iopub.status.idle": "2026-04-27T19:16:46.609425Z", "shell.execute_reply": "2026-04-27T19:16:46.608966Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 4))\n", "plt.bar(S.raw_timestamps, S.raw_data, align='center', facecolor='r', alpha=.5)\n", "S.plot('accident_rate')\n", "plt.xlim([1870, 1910])\n", "plt.xlabel('year');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we want to stress the difference between change-points and break-points again: If the `BreakPoint` transition model is used within the `SerialTransitionModel` class, different transition models will be specified before and after the break-point, but the parameter values will not be *reset* at the break-point. In contrast, the `ChangePoint` transition model can also be used in a `SerialTransitionModel` (not shown in this tutorial). In addition to the different parameter dynamics before and after the change-point, this case allows for an abrupt change of the parameter values. Finally, if the `ChangePoint` model is used *without* a `SerialTransitionModel`, the parameter dynamics will stay the same before and after the change-point, but an abrupt change of the parameter values is expected." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0f543ed2111243e5b9682f82e69c0a4f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": 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