-
diff --git a/doc/pub/week34/html/._week34-bs035.html b/doc/pub/week34/html/._week34-bs035.html index 68965d064..190e60cef 100644 --- a/doc/pub/week34/html/._week34-bs035.html +++ b/doc/pub/week34/html/._week34-bs035.html @@ -451,12 +451,9 @@ $$ The Numpy function np.cov calculates the covariance elements using the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have the exact mean values. The following simple function uses the np.vstack function which takes each vector of dimension \( 1\times n \) and produces a \( 3\times n \) matrix \( \boldsymbol{W} \) $$ -\boldsymbol{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\ - x_1 & y_1 & z_1 \\ - x_2 & y_2 & z_2 \\ - \dots & \dots & \dots \\ - x_{n-2} & y_{n-2} & z_{n-2} \\ - x_{n-1} & y_{n-1} & z_{n-1} +\boldsymbol{W} = \begin{bmatrix} x_0 & x_1 & x_2 & \dots & x_{n-2} & x_{n-1} \\ + y_0 & y_1 & y_2 & \dots & y_{n-2} & y_{n-1} \\ + z_0 & z_1 & z_2 & \dots & z_{n-2} & z_{n-1} \\ \end{bmatrix}, $$ diff --git a/doc/pub/week34/html/week34-bs.html b/doc/pub/week34/html/week34-bs.html index 1c2ca5bc1..05510b462 100644 --- a/doc/pub/week34/html/week34-bs.html +++ b/doc/pub/week34/html/week34-bs.html @@ -384,7 +384,7 @@ MathJax.Hub.Config({
-
diff --git a/doc/pub/week34/html/week34-reveal.html b/doc/pub/week34/html/week34-reveal.html index aed011496..7e2e57557 100644 --- a/doc/pub/week34/html/week34-reveal.html +++ b/doc/pub/week34/html/week34-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({
-
@@ -1257,12 +1257,9 @@ The Numpy function np.cov calculates the covariance elements using the fa The following simple function uses the np.vstack function which takes each vector of dimension \( 1\times n \) and produces a \( 3\times n \) matrix \( \boldsymbol{W} \)
$$
-\boldsymbol{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\
- x_1 & y_1 & z_1 \\
- x_2 & y_2 & z_2 \\
- \dots & \dots & \dots \\
- x_{n-2} & y_{n-2} & z_{n-2} \\
- x_{n-1} & y_{n-1} & z_{n-1}
+\boldsymbol{W} = \begin{bmatrix} x_0 & x_1 & x_2 & \dots & x_{n-2} & x_{n-1} \\
+ y_0 & y_1 & y_2 & \dots & y_{n-2} & y_{n-1} \\
+ z_0 & z_1 & z_2 & \dots & z_{n-2} & z_{n-1} \\
\end{bmatrix},
$$
diff --git a/doc/pub/week34/html/week34-solarized.html b/doc/pub/week34/html/week34-solarized.html
index ac99ec414..97ee03361 100644
--- a/doc/pub/week34/html/week34-solarized.html
+++ b/doc/pub/week34/html/week34-solarized.html
@@ -300,7 +300,7 @@ MathJax.Hub.Config({
-
@@ -1362,12 +1362,9 @@ $$
The Numpy function np.cov calculates the covariance elements using the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have the exact mean values.
The following simple function uses the np.vstack function which takes each vector of dimension \( 1\times n \) and produces a \( 3\times n \) matrix \( \boldsymbol{W} \)
$$
-\boldsymbol{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\
- x_1 & y_1 & z_1 \\
- x_2 & y_2 & z_2 \\
- \dots & \dots & \dots \\
- x_{n-2} & y_{n-2} & z_{n-2} \\
- x_{n-1} & y_{n-1} & z_{n-1}
+\boldsymbol{W} = \begin{bmatrix} x_0 & x_1 & x_2 & \dots & x_{n-2} & x_{n-1} \\
+ y_0 & y_1 & y_2 & \dots & y_{n-2} & y_{n-1} \\
+ z_0 & z_1 & z_2 & \dots & z_{n-2} & z_{n-1} \\
\end{bmatrix},
$$
diff --git a/doc/pub/week34/html/week34.html b/doc/pub/week34/html/week34.html
index fe4613252..5e2dee325 100644
--- a/doc/pub/week34/html/week34.html
+++ b/doc/pub/week34/html/week34.html
@@ -305,7 +305,7 @@ MathJax.Hub.Config({
-
@@ -1367,12 +1367,9 @@ $$
The Numpy function np.cov calculates the covariance elements using the factor \( 1/(n-1) \) instead of \( 1/n \) since it assumes we do not have the exact mean values.
The following simple function uses the np.vstack function which takes each vector of dimension \( 1\times n \) and produces a \( 3\times n \) matrix \( \boldsymbol{W} \)
$$
-\boldsymbol{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\
- x_1 & y_1 & z_1 \\
- x_2 & y_2 & z_2 \\
- \dots & \dots & \dots \\
- x_{n-2} & y_{n-2} & z_{n-2} \\
- x_{n-1} & y_{n-1} & z_{n-1}
+\boldsymbol{W} = \begin{bmatrix} x_0 & x_1 & x_2 & \dots & x_{n-2} & x_{n-1} \\
+ y_0 & y_1 & y_2 & \dots & y_{n-2} & y_{n-1} \\
+ z_0 & z_1 & z_2 & \dots & z_{n-2} & z_{n-1} \\
\end{bmatrix},
$$
diff --git a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz
index ff964c767..8d89d0ef9 100644
Binary files a/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz and b/doc/pub/week34/ipynb/ipynb-week34-src.tar.gz differ
diff --git a/doc/pub/week34/ipynb/week34.ipynb b/doc/pub/week34/ipynb/week34.ipynb
index 23ae33855..348dec82e 100644
--- a/doc/pub/week34/ipynb/week34.ipynb
+++ b/doc/pub/week34/ipynb/week34.ipynb
@@ -10,7 +10,7 @@
" \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
- "Date: **Sep 2, 2021**\n",
+ "Date: **Sep 4, 2021**\n",
"\n",
"Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -1175,12 +1175,9 @@
"metadata": {},
"source": [
"$$\n",
- "\\boldsymbol{W} = \\begin{bmatrix} x_0 & y_0 & z_0 \\\\\n",
- " x_1 & y_1 & z_1 \\\\\n",
- " x_2 & y_2 & z_2 \\\\\n",
- " \\dots & \\dots & \\dots \\\\\n",
- " x_{n-2} & y_{n-2} & z_{n-2} \\\\\n",
- " x_{n-1} & y_{n-1} & z_{n-1}\n",
+ "\\boldsymbol{W} = \\begin{bmatrix} x_0 & x_1 & x_2 & \\dots & x_{n-2} & x_{n-1} \\\\\n",
+ " y_0 & y_1 & y_2 & \\dots & y_{n-2} & y_{n-1} \\\\\n",
+ "\t\t\t z_0 & z_1 & z_2 & \\dots & z_{n-2} & z_{n-1} \\\\\n",
" \\end{bmatrix},\n",
"$$"
]
diff --git a/doc/src/week34/week34.do.txt b/doc/src/week34/week34.do.txt
index af2347939..868cb0788 100644
--- a/doc/src/week34/week34.do.txt
+++ b/doc/src/week34/week34.do.txt
@@ -854,12 +854,9 @@ The Numpy function _np.cov_ calculates the covariance elements using the factor
The following simple function uses the _np.vstack_ function which takes each vector of dimension $1\times n$ and produces a $3\times n$ matrix $\bm{W}$
!bt
\[
-\bm{W} = \begin{bmatrix} x_0 & y_0 & z_0 \\
- x_1 & y_1 & z_1 \\
- x_2 & y_2 & z_2 \\
- \dots & \dots & \dots \\
- x_{n-2} & y_{n-2} & z_{n-2} \\
- x_{n-1} & y_{n-1} & z_{n-1}
+\bm{W} = \begin{bmatrix} x_0 & x_1 & x_2 & \dots & x_{n-2} & x_{n-1} \\
+ y_0 & y_1 & y_2 & \dots & y_{n-2} & y_{n-1} \\
+ z_0 & z_1 & z_2 & \dots & z_{n-2} & z_{n-1} \\
\end{bmatrix},
\]
!et