From 99a1c88f6470bd2585fb61085198f4ea1107c3bb Mon Sep 17 00:00:00 2001
From: Steve Schmerler <git@elcorto.com>
Date: Sun, 18 May 2025 10:44:50 +0200
Subject: [PATCH] Cleanup jupytext markers

---
 BLcourse2.3/02_two_dim.py     | 10 +---------
 BLcourse2.3/03_one_dim_SVI.py |  2 --
 2 files changed, 1 insertion(+), 11 deletions(-)

diff --git a/BLcourse2.3/02_two_dim.py b/BLcourse2.3/02_two_dim.py
index f808f86..0ff95c6 100644
--- a/BLcourse2.3/02_two_dim.py
+++ b/BLcourse2.3/02_two_dim.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: light
 #       format_version: '1.5'
-#       jupytext_version: 1.16.2
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -29,11 +29,9 @@
 # $\DeclareMathOperator{\diag}{diag}$
 # $\DeclareMathOperator{\cov}{cov}$
 
-# +
 # ##%matplotlib notebook
 # %matplotlib widget
 # ##%matplotlib inline
-# -
 
 # +
 from collections import defaultdict
@@ -137,10 +135,8 @@ X_pred = data_pred.X
 
 # Keep the settings below and explore the notebook till the end first.
 
-# +
 use_noise = False
 use_gap = False
-# -
 
 # # Exercise 2
 
@@ -160,7 +156,6 @@ use_gap = False
 ##use_gap = True
 # -
 
-# +
 if use_noise:
     # noisy train data
     noise_std = 0.2
@@ -172,7 +167,6 @@ else:
     # noise-free train data
     noise_std = 0
     y_train = data_train.z
-# -
 
 # +
 # Cut out part of the train data to create out-of-distribution predictions.
@@ -484,8 +478,6 @@ ax.set_zlim((contour_z, zlim[1] + abs(contour_z)))
 ax.contourf(data_pred.XG, data_pred.YG, y_std, zdir="z", offset=contour_z)
 # -
 
-# +
 # When running as script
 if not is_interactive():
     plt.show()
-# -
diff --git a/BLcourse2.3/03_one_dim_SVI.py b/BLcourse2.3/03_one_dim_SVI.py
index 00e5d40..7eb6171 100644
--- a/BLcourse2.3/03_one_dim_SVI.py
+++ b/BLcourse2.3/03_one_dim_SVI.py
@@ -175,13 +175,11 @@ pprint(extract_model_params(model))
 print("likelihood params:")
 pprint(extract_model_params(likelihood))
 
-# +
 # Set new start hyper params
 model.mean_module.constant = 3.0
 model.covar_module.base_kernel.lengthscale = 1.0
 model.covar_module.outputscale = 1.0
 likelihood.noise_covar.noise = 0.3
-# -
 
 # # Fit GP to data: optimize hyper params
 #
-- 
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