Improved graphics
This commit is contained in:
Philipp Homan yoga ubuntu 17.10
parent
0a4af13260
commit
3be5f64c46
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src/3.png
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src/3.png
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@ -106,7 +106,7 @@
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\leavevmode
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\begin{columns}[t,onlytextwidth]
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\column{.87\textwidth}
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\begin{beamercolorbox}[wd=\columnwidth, leftskip=2.0cm]{headline}
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\begin{beamercolorbox}[wd=\columnwidth, leftskip=2.0cm, ht=11.55cm]{headline}
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% \vskip1.5cm
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% \centering
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\vskip7.6ex
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@ -1,6 +1,5 @@
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#+startup: beamer
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#+TITLE: A scientific poster entirely written in org-mode
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#+TITLE: using GNU emacs and the beamer library
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#+TITLE: Using org-mode to produce scientific posters
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* Preamble :ignore:
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** General comments :ignore:
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# ----------------------------------------------------------------------
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@ -138,15 +137,14 @@
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:BEAMER_env: fullframe
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:END:
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** Code :ignore:
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# Babel code can go here to populate the poster with dynamic output from
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# statistical calculations
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# Babel code can go here to populate the poster with dynamic output
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** Left column :BMCOL:
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** Left column :BMCOL:
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:PROPERTIES:
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:BEAMER_col: 0.45
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:BEAMER_opt: [t]
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:END:
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*** Background :B_block:
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*** Background :B_block:
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:PROPERTIES:
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:BEAMER_env: block
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:END:
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@ -168,17 +166,17 @@
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:END:
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#+NAME: code1
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#+BEGIN_SRC R :session :export both :results output graphics :file 3.png
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x <- rnorm(100, 0, 1)
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hist(x, col="gray")
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#+BEGIN_SRC R :file 3.png :session :exports both :results graphics
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set.seed(20180402)
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x1 <- rnorm(100, 0, 1)
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x2 <- rnorm(100, 0.5, 1)
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hist(x1, col="red")
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hist(x2, col="blue", add=TRUE)
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#+END_SRC
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#+RESULTS: code1
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[[file:3.png]]
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#+NAME: figcode1
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#+CAPTION: This is the output.
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#+RESULTS: code1
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[[file:3.png]]
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*** Inline code and tables :B_block:
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@ -188,46 +186,51 @@ hist(x, col="gray")
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- In addition to inline code, we can also produce tables
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- Tables are very powerful in org-mode, they even include spreadsheet
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capabilities
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- Some code to process the vector from above to make a table out of its
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summary could look like this, which would result in a little table
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(Table [[tabcode2]]) :
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- Some code to process the first vector from above to make a table out
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of its summary could look like this, which would result in a little
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table (Table [[tabcode2]]) :
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**** Block
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:PROPERTIES:
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:BEAMER_col: 0.48
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:BEAMER_col: 0.88
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:BEAMER_opt: [T]
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:END:
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#+NAME: code2
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#+BEGIN_SRC R :session :exports both :results value :colnames yes :cache yes
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m <- round(mean(x), 2)
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s <- round(sd(x), 2)
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data.frame(Mean=m, SD=s)
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library(broom)
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library(dplyr)
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t1 <- tidy(round(summary(x1), 2))
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t2 <- tidy(round(summary(x2), 2))
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# This will export as a table
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rbind(t1, t2) %>%
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mutate(name=c("x1", "x2"))
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#+END_SRC
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\vspace{2cm}
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#+CAPTION: A table.
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#+CAPTION: A table summarizing the two distributions.
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#+NAME: tabcode2
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#+RESULTS[31e41e0f8cc2db2fb601af81fe4f5e218ea48f57]: code2
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|-------+------|
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| Mean | SD |
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|-------+------|
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| -0.07 | 0.97 |
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|-------+------|
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#+RESULTS[9d0ec7348265a5cb6de39440ff06a8dbb8e5ecf1]: code2
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|---------+-------+--------+------+------+---------+------|
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| minimum | q1 | median | mean | q3 | maximum | name |
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|---------+-------+--------+------+------+---------+------|
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| -2.29 | -0.49 | 0.11 | 0.14 | 0.8 | 2.47 | x1 |
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| -2.17 | -0.45 | 0.07 | 0.13 | 0.85 | 2.23 | x2 |
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|---------+-------+--------+------+------+---------+------|
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** Right column :BMCOL:
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** Right column :BMCOL:
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:PROPERTIES:
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:BEAMER_col: 0.45
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:BEAMER_opt: [t]
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:END:
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*** Graphics :B_block:
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*** Graphics :B_block:
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:PROPERTIES:
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:BEAMER_env: block
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:END:
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- Of course we can also include graphics
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- Here, we use shell scripting to grab an image with curl from the
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- We can use shell scripting to grab an image with curl from the
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internet (Fig. [[figcode3]]):
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**** Block
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@ -239,6 +242,7 @@ data.frame(Mean=m, SD=s)
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\footnotesize
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#+NAME: code3
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#+BEGIN_SRC bash :exports both :file emacs.png
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# Download emacs icon from gnu.org
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curl -0 https://www.gnu.org/software/emacs/images/emacs.png
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#+END_SRC
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\normalsize
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@ -251,12 +255,14 @@ curl -0 https://www.gnu.org/software/emacs/images/emacs.png
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#+RESULTS: code3
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[[file:emacs.png]]
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*** Math :B_block:
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*** Math :B_block:
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:PROPERTIES:
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:BEAMER_env: block
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:END:
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- We can easily include math:
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- We can easily include math
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- For example, let's describe how to compute the distance between the
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two simulated distributions $x1$ and $x2$ from before:
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**** Block
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:PROPERTIES:
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@ -284,7 +290,7 @@ derived as follows:
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\DKLPQ{P}{Q}{,} = \sumin \Big(\Xoi{P} \log \frakPQ{P}{Q} + \Xoi{Q} \log \frakPQ{Q}{P} \Big).
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\end{align}
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*** Columns :B_block:
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*** Columns :B_block:
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:PROPERTIES:
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:BEAMER_env: block
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:END:
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@ -295,11 +301,19 @@ derived as follows:
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:BEAMER_opt: [T]
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:END:
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\captionsetup{justification=justified,width=.8\linewidth}
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#+NAME: figge
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#+CAPTION: *This is the left figure of a two-column block*
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#+ATTR_LATEX: :width 0.9\textwidth :options page=3
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[[file:org-mode-poster-4.png]]
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#+NAME: codeleft
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#+BEGIN_SRC R :file 4l.png :session org_org :exports results :results graphics
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d1 <- density(x1)
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plot(d1, col="red", lwd=3)
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#+END_SRC
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\captionsetup{justification=justified,width=.85\linewidth}
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#+NAME: figcodeleft
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#+CAPTION: This is the left figure of a two-column block, showing
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#+CAPTION: the density of $x1$.
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#+RESULTS: codeleft
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[[file:4l.png]]
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**** Right
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:PROPERTIES:
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@ -307,16 +321,22 @@ derived as follows:
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:BEAMER_opt: [T]
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:END:
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\captionsetup{justification=justified,width=.8\linewidth}
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#+NAME: figclus
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#+CAPTION: *This is the right figure.*
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#+ATTR_LATEX: :width 0.9\textwidth :options page=9
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[[file:org-mode-poster-4.png]]
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#+NAME: coderight
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#+BEGIN_SRC R :file 4r.png :session org_org :exports results :results graphics
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d2 <- density(x2)
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plot(d2, col="blue", lwd=3)
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#+END_SRC
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*** Conclusions :B_block:
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\captionsetup{justification=justified,width=.85\linewidth}
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#+NAME: figcoderight
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#+CAPTION: This is the right figure. It shows the density of $x2$.
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#+RESULTS: coderight
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[[file:4r.png]]
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*** Conclusions :B_block:
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:PROPERTIES:
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:BEAMER_env: block
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:END:
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- This little example is meant to show how incredibly versatile
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org-mode is
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- One can now produce scientific posters with a simple text editor
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- Scientific posters can be produced with a simple text editor
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Binary file not shown.
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@ -1,4 +1,4 @@
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% Created 2018-04-02 Mon 18:38
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% Created 2018-04-02 Mon 19:58
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% Intended LaTeX compiler: pdflatex
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\documentclass[final]{beamer}
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\usetheme{ph}
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@ -33,11 +33,11 @@ Philipp Homan$^{1}$,
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\normalsize{Hempstead, NY}
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}
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\usetheme{default}
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\date{2018-04-02 18:38}
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\title{A scientific poster entirely written in org-mode using GNU emacs and the beamer library}
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\date{2018-04-02 19:58}
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\title{Using org-mode to produce scientific posters}
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\begin{document}
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\begin{frame}[fragile,label={sec:org727f6f5}]{}
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\begin{frame}[fragile,label={sec:org10a116a}]{}
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\begin{columns}
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\begin{column}[t]{0.45\columnwidth}
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\begin{block}{Background}
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@ -51,21 +51,23 @@ org-mode syntax
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code, graphs and numbers from inline code in languages such as R,
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python, Matlab and even shell scripting
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\item Inline code would look like this, which will produce a graph
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(Fig. \ref{fig:orgaca79ae}):
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(Fig. \ref{fig:org8cce396}):
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\end{itemize}
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\begin{columns}
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\begin{column}[T]{0.48\columnwidth}
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\begin{minted}[linenos=true]{r}
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x <- rnorm(100, 0, 1)
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hist(x, col="gray")
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set.seed(20180402)
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x1 <- rnorm(100, 0, 1)
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x2 <- rnorm(100, 0.5, 1)
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hist(x1, col="red")
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hist(x2, col="blue", add=TRUE)
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\end{minted}
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\begin{figure}[htbp]
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\centering
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\includegraphics[width=.9\linewidth]{3.png}
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\caption{\label{fig:orgaca79ae}
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\caption{\label{fig:org8cce396}
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This is the output.}
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\end{figure}
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\end{column}
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@ -77,30 +79,38 @@ This is the output.}
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\item In addition to inline code, we can also produce tables
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\item Tables are very powerful in org-mode, they even include spreadsheet
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capabilities
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\item Some code to process the vector from above to make a table out of its
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summary could look like this, which would result in a little table
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(Table \ref{tab:org6921627}) :
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\item Some code to process the first vector from above to make a table out
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of its summary could look like this, which would result in a little
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table (Table \ref{tab:orgfdd29a9}) :
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\end{itemize}
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\begin{columns}
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\begin{column}[T]{0.48\columnwidth}
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\begin{column}[T]{0.88\columnwidth}
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\begin{minted}[linenos=true]{r}
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m <- round(mean(x), 2)
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s <- round(sd(x), 2)
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data.frame(Mean=m, SD=s)
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library(broom)
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library(dplyr)
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t1 <- tidy(round(summary(x1), 2))
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t2 <- tidy(round(summary(x2), 2))
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# This will export as a table
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rbind(t1, t2) %>%
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mutate(name=c("x1", "x2"))
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\end{minted}
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\vspace{2cm}
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\begin{table}[htbp]
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\centering
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\begin{tabular}{rr}
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Mean & SD\\
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\begin{tabular}{rrrrrrl}
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\hline
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minimum & q1 & median & mean & q3 & maximum & name\\
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\hline
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-2.29 & -0.49 & 0.11 & 0.14 & 0.8 & 2.47 & x1\\
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-2.17 & -0.45 & 0.07 & 0.13 & 0.85 & 2.23 & x2\\
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\hline
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0.07 & 0.98\\
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\end{tabular}
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\caption{\label{tab:org6921627}
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A table.}
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\caption{\label{tab:orgfdd29a9}
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A table summarizing the two distributions.}
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\end{table}
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\end{column}
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@ -111,15 +121,15 @@ A table.}
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\begin{column}[t]{0.45\columnwidth}
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\begin{block}{Graphics}
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\begin{itemize}
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\item Of course we can also include graphics
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\item Here, we use shell scripting to grab an image with curl from the
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internet (Fig. \ref{fig:orgf35b3ea}):
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\item We can use shell scripting to grab an image with curl from the
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internet (Fig. \ref{fig:orga373a15}):
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\end{itemize}
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\begin{columns}
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\begin{column}[T]{0.78\columnwidth}
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\footnotesize
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\begin{minted}[linenos=true]{bash}
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# Download emacs icon from gnu.org
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curl -0 https://www.gnu.org/software/emacs/images/emacs.png
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\end{minted}
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\normalsize
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@ -129,7 +139,7 @@ curl -0 https://www.gnu.org/software/emacs/images/emacs.png
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\begin{figure}[htbp]
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\centering
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\includegraphics[page=9,width=0.2\textwidth]{emacs.png}
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\caption{\label{fig:orgf35b3ea}
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\caption{\label{fig:orga373a15}
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This is the downloaded image.}
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\end{figure}
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\end{column}
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@ -138,7 +148,9 @@ This is the downloaded image.}
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\begin{block}{Math}
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\begin{itemize}
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\item We can easily include math:
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\item We can easily include math
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\item For example, let's describe how to compute the distance between the
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two simulated distributions \(x1\) and \(x2\) from before:
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\end{itemize}
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\begin{columns}
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@ -169,22 +181,22 @@ derived as follows:
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\begin{block}{Columns}
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\begin{columns}
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\begin{column}[T]{0.48\columnwidth}
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\captionsetup{justification=justified,width=.8\linewidth}
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\captionsetup{justification=justified,width=.85\linewidth}
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\begin{figure}[htbp]
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\centering
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\includegraphics[page=3,width=0.9\textwidth]{org-mode-poster-4.png}
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\caption{\label{fig:org623938b}
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\textbf{This is the left figure of a two-column block}}
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\includegraphics[width=.9\linewidth]{4l.png}
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\caption{\label{fig:orgf47550b}
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This is the left figure of a two-column block, showing the density of \(x1\).}
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\end{figure}
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\end{column}
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\begin{column}[T]{0.48\columnwidth}
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\captionsetup{justification=justified,width=.8\linewidth}
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\captionsetup{justification=justified,width=.85\linewidth}
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\begin{figure}[htbp]
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\centering
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\includegraphics[page=9,width=0.9\textwidth]{org-mode-poster-4.png}
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\caption{\label{fig:orgb76a1ef}
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\textbf{This is the right figure.}}
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\includegraphics[width=.9\linewidth]{4r.png}
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\caption{\label{fig:org96112b0}
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This is the right figure. It shows the density of \(x2\).}
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\end{figure}
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\end{column}
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\end{columns}
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@ -194,7 +206,7 @@ derived as follows:
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\begin{itemize}
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\item This little example is meant to show how incredibly versatile
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org-mode is
|
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\item One can now produce scientific posters with a simple text editor
|
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\item Scientific posters can be produced with a simple text editor
|
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\end{itemize}
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\end{block}
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\end{column}
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|
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@ -1,4 +1,4 @@
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% Created 2018-04-02 Mon 18:37
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% Created 2018-04-02 Mon 19:58
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% Intended LaTeX compiler: pdflatex
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\documentclass[final]{beamer}
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\usetheme{ph}
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@ -33,11 +33,11 @@ Philipp Homan$^{1}$,
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\normalsize{Hempstead, NY}
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}
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\usetheme{default}
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\date{2018-04-02 18:37}
|
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\title{A scientific poster entirely written in org-mode using GNU emacs and the beamer library}
|
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\date{2018-04-02 19:58}
|
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\title{Using org-mode to produce scientific posters}
|
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\begin{document}
|
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|
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\begin{frame}[fragile,label={sec:org4b06f5e}]{}
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\begin{frame}[fragile,label={sec:org5deb971}]{}
|
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\begin{columns}
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\begin{column}[t]{0.45\columnwidth}
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\begin{block}{Background}
|
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|
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@ -51,21 +51,23 @@ org-mode syntax
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code, graphs and numbers from inline code in languages such as R,
|
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python, Matlab and even shell scripting
|
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\item Inline code would look like this, which will produce a graph
|
||||
(Fig. \ref{fig:org042638b}):
|
||||
(Fig. \ref{fig:org71455a2}):
|
||||
\end{itemize}
|
||||
|
||||
\begin{columns}
|
||||
\begin{column}[T]{0.48\columnwidth}
|
||||
\begin{minted}[linenos=true]{r}
|
||||
x <- rnorm(100, 0, 1)
|
||||
hist(x, col="gray")
|
||||
set.seet(20180402)
|
||||
x1 <- rnorm(100, 0, 1)
|
||||
x2 <- rnorm(100, 0.5, 1)
|
||||
hist(x1, col="red")
|
||||
hist(x2, col="blue", add=TRUE)
|
||||
\end{minted}
|
||||
|
||||
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
\includegraphics[width=.9\linewidth]{3.png}
|
||||
\caption{\label{fig:org042638b}
|
||||
\caption{\label{fig:org71455a2}
|
||||
This is the output.}
|
||||
\end{figure}
|
||||
\end{column}
|
||||
|
|
@ -77,30 +79,38 @@ This is the output.}
|
|||
\item In addition to inline code, we can also produce tables
|
||||
\item Tables are very powerful in org-mode, they even include spreadsheet
|
||||
capabilities
|
||||
\item Some code to process the vector from above to make a table out of its
|
||||
summary could look like this, which would result in a little table
|
||||
(Table \ref{tab:orge51644d}) :
|
||||
\item Some code to process the first vector from above to make a table out
|
||||
of its summary could look like this, which would result in a little
|
||||
table (Table \ref{tab:orgf4d0fa2}) :
|
||||
\end{itemize}
|
||||
|
||||
\begin{columns}
|
||||
\begin{column}[T]{0.48\columnwidth}
|
||||
\begin{column}[T]{0.88\columnwidth}
|
||||
\begin{minted}[linenos=true]{r}
|
||||
m <- round(mean(x), 2)
|
||||
s <- round(sd(x), 2)
|
||||
data.frame(Mean=m, SD=s)
|
||||
library(broom)
|
||||
library(dplyr)
|
||||
t1 <- tidy(round(summary(x1), 2))
|
||||
t2 <- tidy(round(summary(x2), 2))
|
||||
|
||||
# This will export as a table
|
||||
rbind(t1, t2) %>%
|
||||
mutate(name=c("x1", "x2"))
|
||||
\end{minted}
|
||||
|
||||
\vspace{2cm}
|
||||
|
||||
\begin{table}[htbp]
|
||||
\centering
|
||||
\begin{tabular}{rr}
|
||||
Mean & SD\\
|
||||
\begin{tabular}{rrrrrrl}
|
||||
\hline
|
||||
minimum & q1 & median & mean & q3 & maximum & name\\
|
||||
\hline
|
||||
-2.29 & -0.49 & 0.11 & 0.14 & 0.8 & 2.47 & x1\\
|
||||
-2.17 & -0.45 & 0.07 & 0.13 & 0.85 & 2.23 & x2\\
|
||||
\hline
|
||||
0.23 & 1.07\\
|
||||
\end{tabular}
|
||||
\caption{\label{tab:orge51644d}
|
||||
A table.}
|
||||
\caption{\label{tab:orgf4d0fa2}
|
||||
A table summarizing the two distributions.}
|
||||
|
||||
\end{table}
|
||||
\end{column}
|
||||
|
|
@ -111,15 +121,15 @@ A table.}
|
|||
\begin{column}[t]{0.45\columnwidth}
|
||||
\begin{block}{Graphics}
|
||||
\begin{itemize}
|
||||
\item Of course we can also include graphics
|
||||
\item Here, we use shell scripting to grab an image with curl from the
|
||||
internet (Fig. \ref{fig:orga669d9a}):
|
||||
\item We can use shell scripting to grab an image with curl from the
|
||||
internet (Fig. \ref{fig:org5863a78}):
|
||||
\end{itemize}
|
||||
|
||||
\begin{columns}
|
||||
\begin{column}[T]{0.78\columnwidth}
|
||||
\footnotesize
|
||||
\begin{minted}[linenos=true]{bash}
|
||||
# Download emacs icon from gnu.org
|
||||
curl -0 https://www.gnu.org/software/emacs/images/emacs.png
|
||||
\end{minted}
|
||||
\normalsize
|
||||
|
|
@ -129,7 +139,7 @@ curl -0 https://www.gnu.org/software/emacs/images/emacs.png
|
|||
\begin{figure}[htbp]
|
||||
\centering
|
||||
\includegraphics[page=9,width=0.2\textwidth]{emacs.png}
|
||||
\caption{\label{fig:orga669d9a}
|
||||
\caption{\label{fig:org5863a78}
|
||||
This is the downloaded image.}
|
||||
\end{figure}
|
||||
\end{column}
|
||||
|
|
@ -138,7 +148,9 @@ This is the downloaded image.}
|
|||
|
||||
\begin{block}{Math}
|
||||
\begin{itemize}
|
||||
\item We can easily include math:
|
||||
\item We can easily include math
|
||||
\item For example, let's describe how to compute the distance between the
|
||||
two simulated distributions \(x1\) and \(x2\) from before:
|
||||
\end{itemize}
|
||||
|
||||
\begin{columns}
|
||||
|
|
@ -157,6 +169,11 @@ with \(P\) and \(Q\) being two probability distribution functions and \(n\)
|
|||
the number of sample points. Since \(\DKLPQ{P}{Q}{\|}\) is not equal to
|
||||
\(\DKLPQ{Q}{P}{\|}\), a symmetric variation of the KL divergence can be
|
||||
derived as follows:
|
||||
|
||||
\begin{align}
|
||||
\label{eq:KL2}
|
||||
\DKLPQ{P}{Q}{,} = \sumin \Big(\Xoi{P} \log \frakPQ{P}{Q} + \Xoi{Q} \log \frakPQ{Q}{P} \Big).
|
||||
\end{align}
|
||||
\end{column}
|
||||
\end{columns}
|
||||
\end{block}
|
||||
|
|
@ -164,22 +181,22 @@ derived as follows:
|
|||
\begin{block}{Columns}
|
||||
\begin{columns}
|
||||
\begin{column}[T]{0.48\columnwidth}
|
||||
\captionsetup{justification=justified,width=.8\linewidth}
|
||||
\captionsetup{justification=justified,width=.85\linewidth}
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
\includegraphics[page=3,width=0.9\textwidth]{org-mode-poster-4.png}
|
||||
\caption{\label{fig:org88ad0c6}
|
||||
\textbf{This is the left figure of a two-column block}}
|
||||
\includegraphics[width=.9\linewidth]{4l.png}
|
||||
\caption{\label{fig:orga83a496}
|
||||
This is the left figure of a two-column block, showing the density of \(x1\).}
|
||||
\end{figure}
|
||||
\end{column}
|
||||
|
||||
\begin{column}[T]{0.48\columnwidth}
|
||||
\captionsetup{justification=justified,width=.8\linewidth}
|
||||
\captionsetup{justification=justified,width=.85\linewidth}
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
\includegraphics[page=9,width=0.9\textwidth]{org-mode-poster-4.png}
|
||||
\caption{\label{fig:org9e1f136}
|
||||
\textbf{This is the right figure.}}
|
||||
\includegraphics[width=.9\linewidth]{4r.png}
|
||||
\caption{\label{fig:org63d9981}
|
||||
This is the right figure. It shows the density of \(x2\).}
|
||||
\end{figure}
|
||||
\end{column}
|
||||
\end{columns}
|
||||
|
|
@ -189,7 +206,7 @@ derived as follows:
|
|||
\begin{itemize}
|
||||
\item This little example is meant to show how incredibly versatile
|
||||
org-mode is
|
||||
\item One can now produce scientific posters with a simple text editor
|
||||
\item Scientific posters can be produced with a simple text editor
|
||||
\end{itemize}
|
||||
\end{block}
|
||||
\end{column}
|
||||
|
|
|
|||
Loading…
Reference in New Issue