Typo fix by Remi (#187)

jorgensd · RemDelaporteMathurin · web-flow · commit a679ec4a3d04 · 2024-04-30T14:08:23.000+02:00
* fixed typo

* Add back typo fix that disappeared in merge

---------

Co-authored-by: Rémi Delaporte-Mathurin &lt;40028739+RemDelaporteMathurin@users.noreply.github.com&gt;
diff --git a/chapter1/fundamentals_code.py b/chapter1/fundamentals_code.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter1/membrane_code.py b/chapter1/membrane_code.py
@@ -6,7 +6,7 @@
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 #       format_name: light
 #       format_version: '1.5'
-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter1/nitsche.ipynb b/chapter1/nitsche.ipynb
diff --git a/chapter1/nitsche.py b/chapter1/nitsche.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter2/diffusion_code.py b/chapter2/diffusion_code.py
@@ -6,7 +6,7 @@
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 #       format_version: '1.5'
-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter2/heat_code.py b/chapter2/heat_code.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter2/hyperelasticity.py b/chapter2/hyperelasticity.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter2/linearelasticity_code.py b/chapter2/linearelasticity_code.py
@@ -6,7 +6,7 @@
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+#       jupytext_version: 1.15.2
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diff --git a/chapter2/nonlinpoisson_code.py b/chapter2/nonlinpoisson_code.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter2/ns_code1.py b/chapter2/ns_code1.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter2/ns_code2.py b/chapter2/ns_code2.py
@@ -6,7 +6,7 @@
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 #       format_name: light
 #       format_version: '1.5'
-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter3/component_bc.py b/chapter3/component_bc.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter3/em.py b/chapter3/em.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter3/multiple_dirichlet.py b/chapter3/multiple_dirichlet.py
@@ -6,7 +6,7 @@
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diff --git a/chapter3/neumann_dirichlet_code.py b/chapter3/neumann_dirichlet_code.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter3/robin_neumann_dirichlet.py b/chapter3/robin_neumann_dirichlet.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter3/subdomains.py b/chapter3/subdomains.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter4/compiler_parameters.py b/chapter4/compiler_parameters.py
@@ -6,7 +6,7 @@
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diff --git a/chapter4/convergence.py b/chapter4/convergence.py
@@ -6,7 +6,7 @@
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-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
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diff --git a/chapter4/newton-solver.py b/chapter4/newton-solver.py
@@ -6,6 +6,7 @@
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 #       format_name: light
 #       format_version: '1.5'
+#       jupytext_version: 1.15.2
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diff --git a/chapter4/solvers.ipynb b/chapter4/solvers.ipynb
@@ -208,7 +208,7 @@
     "For large problems, we instead need to use an iterative method which are faster and require less memory.\n",
     "## Choosing a linear solver and preconditioner\n",
     "As the Poisson equation results in a symmetric, positive definite system matrix, the optimal Krylov solver is the conjugate gradient (Lagrange) method. The default preconditioner is the incomplete LU factorization (ILU), which is a popular and robous overall preconditioner. We can change the preconditioner by setting `\"pc_type\"` to some of the other preconditioners in petsc, which you can find in at [PETSc KSP solvers](https://petsc.org/release/manual/ksp/#tab-kspdefaults) and [PETSc preconditioners](https://petsc.org/release/manual/ksp/#tab-pcdefaults).\n",
-    "You can set any opition in `PETSc` through the `petsc_options` input, such as the absolute tolerance (`\"ksp_atol\"`), relative tolerance (`\"ksp_rtol\"`) and maximum number of iterations (`\"ksp_max_it\"`)."
+    "You can set any option in `PETSc` through the `petsc_options` input, such as the absolute tolerance (`\"ksp_atol\"`), relative tolerance (`\"ksp_rtol\"`) and maximum number of iterations (`\"ksp_max_it\"`)."
    ]
   },
   {
@@ -383,7 +383,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
diff --git a/chapter4/solvers.py b/chapter4/solvers.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: light
 #       format_version: '1.5'
-#       jupytext_version: 1.16.1
+#       jupytext_version: 1.15.2
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -90,7 +90,7 @@ def u_ex(mod):
 # For large problems, we instead need to use an iterative method which are faster and require less memory.
 # ## Choosing a linear solver and preconditioner
 # As the Poisson equation results in a symmetric, positive definite system matrix, the optimal Krylov solver is the conjugate gradient (Lagrange) method. The default preconditioner is the incomplete LU factorization (ILU), which is a popular and robous overall preconditioner. We can change the preconditioner by setting `"pc_type"` to some of the other preconditioners in petsc, which you can find in at [PETSc KSP solvers](https://petsc.org/release/manual/ksp/#tab-kspdefaults) and [PETSc preconditioners](https://petsc.org/release/manual/ksp/#tab-pcdefaults).
-# You can set any opition in `PETSc` through the `petsc_options` input, such as the absolute tolerance (`"ksp_atol"`), relative tolerance (`"ksp_rtol"`) and maximum number of iterations (`"ksp_max_it"`).
+# You can set any option in `PETSc` through the `petsc_options` input, such as the absolute tolerance (`"ksp_atol"`), relative tolerance (`"ksp_rtol"`) and maximum number of iterations (`"ksp_max_it"`).
 
 cg_problem = LinearProblem(a, L, bcs=bcs,
                            petsc_options={"ksp_type": "cg", "ksp_rtol": 1e-6, "ksp_atol": 1e-10, "ksp_max_it": 1000})

Original file line number	Diff line number	Diff line change
`@@ -208,7 +208,7 @@`
`208`	`208`	`"For large problems, we instead need to use an iterative method which are faster and require less memory.\n",`
`209`	`209`	`"## Choosing a linear solver and preconditioner\n",`
`210`	`210`	"As the Poisson equation results in a symmetric, positive definite system matrix, the optimal Krylov solver is the conjugate gradient (Lagrange) method. The default preconditioner is the incomplete LU factorization (ILU), which is a popular and robous overall preconditioner. We can change the preconditioner by setting `\"pc_type\"` to some of the other preconditioners in petsc, which you can find in at [PETSc KSP solvers](https://petsc.org/release/manual/ksp/#tab-kspdefaults) and [PETSc preconditioners](https://petsc.org/release/manual/ksp/#tab-pcdefaults).\n",
`211`		- "You can set any opition in `PETSc` through the `petsc_options` input, such as the absolute tolerance (`\"ksp_atol\"`), relative tolerance (`\"ksp_rtol\"`) and maximum number of iterations (`\"ksp_max_it\"`)."
	`211`	+ "You can set any option in `PETSc` through the `petsc_options` input, such as the absolute tolerance (`\"ksp_atol\"`), relative tolerance (`\"ksp_rtol\"`) and maximum number of iterations (`\"ksp_max_it\"`)."
`212`	`212`	`]`
`213`	`213`	`},`
`214`	`214`	`{`
`@@ -383,7 +383,7 @@`
`383`	`383`	`"name": "python",`
`384`	`384`	`"nbconvert_exporter": "python",`
`385`	`385`	`"pygments_lexer": "ipython3",`
`386`		`- "version": "3.10.6"`
	`386`	`+ "version": "3.10.12"`
`387`	`387`	`}`
`388`	`388`	`},`
`389`	`389`	`"nbformat": 4,`