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diff --git a/...als/GMM_with_2_dimensions_and_4_components/GmmWith2DimensionsAnd4Components.mdx b/...als/GMM_with_2_dimensions_and_4_components/GmmWith2DimensionsAnd4Components.mdx
@@ -295,7 +295,7 @@ fig = draw_points_and_components(
 fig.show()
 ```
 
-<PlotlyFigure data={require('./assets/plot_data/314a9ca0-e1d0-4e52-925f-2b7856c8fffa.json')} />
+<PlotlyFigure data={require('./assets/plot_data/5569cf5d-1e3d-448b-a54c-b797704773f5.json')} />
 
 ## Inference
 
@@ -369,7 +369,7 @@ fig = draw_points_and_components(
 fig.show()
 ```
 
-<PlotlyFigure data={require('./assets/plot_data/600ba3b6-b365-4a48-a0fb-3764b7ba9f81.json')} />
+<PlotlyFigure data={require('./assets/plot_data/7905578b-b3ce-4a11-b6c4-adf91cbdbeae.json')} />
 
 After running `n_samples` MCMC steps, the effects of initialization should be
 diminished.
@@ -409,5 +409,5 @@ fig = draw_points_and_components(
 fig.show()
 ```
 
-<PlotlyFigure data={require('./assets/plot_data/8b40592d-9dac-4eb4-8563-d569258b098c.json')} />
+<PlotlyFigure data={require('./assets/plot_data/39c9d9ff-1835-4713-a7d2-f29df3b2c9aa.json')} />
 
diff --git a/...8b40592d-9dac-4eb4-8563-d569258b098c.json → ...39c9d9ff-1835-4713-a7d2-f29df3b2c9aa.json b/...8b40592d-9dac-4eb4-8563-d569258b098c.json → ...39c9d9ff-1835-4713-a7d2-f29df3b2c9aa.json
diff --git a/...314a9ca0-e1d0-4e52-925f-2b7856c8fffa.json → ...5569cf5d-1e3d-448b-a54c-b797704773f5.json b/...314a9ca0-e1d0-4e52-925f-2b7856c8fffa.json → ...5569cf5d-1e3d-448b-a54c-b797704773f5.json
diff --git a/...600ba3b6-b365-4a48-a0fb-3764b7ba9f81.json → ...7905578b-b3ce-4a11-b6c4-adf91cbdbeae.json b/...600ba3b6-b365-4a48-a0fb-3764b7ba9f81.json → ...7905578b-b3ce-4a11-b6c4-adf91cbdbeae.json
diff --git a/_src/overview/tutorials/Neals_funnel/NealsFunnel.mdx b/_src/overview/tutorials/Neals_funnel/NealsFunnel.mdx
@@ -412,16 +412,16 @@ look:
 bm.Diagnostics(single_site_nmc_samples).plot(display=True);
 ```
 
-<PlotlyFigure data={require('./assets/plot_data/c11ba2f0-8626-43f7-9f15-a82081b844aa.json')} />
+<PlotlyFigure data={require('./assets/plot_data/00d44702-36e0-45a0-a82c-01bf80b5ef02.json')} />
 
 
-<PlotlyFigure data={require('./assets/plot_data/e8d36802-2ce3-46f2-9282-e4fde102934e.json')} />
+<PlotlyFigure data={require('./assets/plot_data/b6dd745b-613d-47ea-9b28-d07355e66bc5.json')} />
 
 
-<PlotlyFigure data={require('./assets/plot_data/4128848d-0f96-4e22-a17f-70e2d3065fa5.json')} />
+<PlotlyFigure data={require('./assets/plot_data/dcd4d5c6-2eb0-45ed-9b87-c3ff8ccb29f6.json')} />
 
 
-<PlotlyFigure data={require('./assets/plot_data/01e0a2fa-66d4-4893-bc85-78e10a319f2b.json')} />
+<PlotlyFigure data={require('./assets/plot_data/bbd0dc87-8a1b-44e3-8b3f-4e8424ec3413.json')} />
 
 The diagnostics output shows two diagnostic plots for individual random variables: trace
 plots and autocorrelation plots.
@@ -565,16 +565,16 @@ And the unhealthy trace and autocorrelation plots:
 bm.Diagnostics(hmc_samples).plot(display=True);
 ```
 
-<PlotlyFigure data={require('./assets/plot_data/e4be886b-590e-44b8-9c5e-3728299970e8.json')} />
+<PlotlyFigure data={require('./assets/plot_data/6f778d0a-2e95-46a5-a15c-f675285bd7c5.json')} />
 
 
-<PlotlyFigure data={require('./assets/plot_data/565fe78a-e902-462c-80e5-b21d92f48c1a.json')} />
+<PlotlyFigure data={require('./assets/plot_data/d6465ca5-de87-4125-bd31-35038ecc77d9.json')} />
 
 
-<PlotlyFigure data={require('./assets/plot_data/a83930b3-46a9-4bb4-9f52-525dac2e9a11.json')} />
+<PlotlyFigure data={require('./assets/plot_data/0ac8f10b-3a28-4782-8225-310691a194b0.json')} />
 
 
-<PlotlyFigure data={require('./assets/plot_data/a3938a3e-dc1b-431c-85b0-96263fb5de12.json')} />
+<PlotlyFigure data={require('./assets/plot_data/09377f38-3ae1-440b-8ece-d2ff39c850b1.json')} />
 
 ## Appendix: Metropolis-Adjusted Langevin Algorithm
 

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