Merge branch 'adaptive-localization-notebook' of https://github.com/tommyod/iterative_ensemble_smoother into adaptive-localization-notebook

Tommy Odland · Tommy Odland · commit 7cae0a0ce135 · 2023-12-08T08:33:06.000+01:00
diff --git a/docs/source/Adaptive.py b/docs/source/Adaptive.py
@@ -113,7 +113,7 @@ def g(X):
 # %% [markdown]
 # ## Solve the maximum likelihood problem
 #
-# We can solve $Ax = b$, where $b$ is the observations,
+# We can solve $Ax = b$, where $b$ are the observations,
 # for the maximum likelihood estimate.
 #
 # Notice that unlike using a Ridge model,
@@ -134,7 +134,7 @@ def g(X):
 # %% [markdown]
 # ## Solve using ESMDA
 #
-# We crease an `ESMDA` instance and solve the Guass-linear problem.
+# We create an `ESMDA` instance and solve the Guass-linear problem.
 
 # %%
 smoother = ESMDA(
@@ -177,7 +177,7 @@ def g(X):
 # %% [markdown]
 # ## Solve using AdaptiveESMDA
 #
-# We crease an `AdaptiveESMDA` instance and solve the Guass-linear problem.
+# We create an `AdaptiveESMDA` instance and solve the Guass-linear problem.
 
 # %%
 adaptive_smoother = AdaptiveESMDA(

Original file line number	Diff line number	Diff line change
`@@ -113,7 +113,7 @@ def g(X):`
`113`	`113`	`# %% [markdown]`
`114`	`114`	`# ## Solve the maximum likelihood problem`
`115`	`115`	`#`
`116`		`-# We can solve $Ax = b$, where $b$ is the observations,`
	`116`	`+# We can solve $Ax = b$, where $b$ are the observations,`
`117`	`117`	`# for the maximum likelihood estimate.`
`118`	`118`	`#`
`119`	`119`	`# Notice that unlike using a Ridge model,`
`@@ -134,7 +134,7 @@ def g(X):`
`134`	`134`	`# %% [markdown]`
`135`	`135`	`# ## Solve using ESMDA`
`136`	`136`	`#`
`137`		-# We crease an `ESMDA` instance and solve the Guass-linear problem.
	`137`	+# We create an `ESMDA` instance and solve the Guass-linear problem.
`138`	`138`
`139`	`139`	`# %%`
`140`	`140`	`smoother = ESMDA(`
`@@ -177,7 +177,7 @@ def g(X):`
`177`	`177`	`# %% [markdown]`
`178`	`178`	`# ## Solve using AdaptiveESMDA`
`179`	`179`	`#`
`180`		-# We crease an `AdaptiveESMDA` instance and solve the Guass-linear problem.
	`180`	+# We create an `AdaptiveESMDA` instance and solve the Guass-linear problem.
`181`	`181`
`182`	`182`	`# %%`
`183`	`183`	`adaptive_smoother = AdaptiveESMDA(`