The Kitchin Research Group

A new and improved Emacs gnuplot DSL

Fri, 05 May 2017 10:26:00 EDT

A significant limitation of the previous DSL I wrote is that all the plotting commands have to go in one macro. It would be nice to accumulate them in different forms, and when you want to run them all. A classic way to do that in Emacs lisp is to make a global variable, e.g. *gnuplot-cmds* and append commands to it. Then when you want to, run the commands.

A more modern approach is to use a closure to encapsulate the commands. Here is a "let over lambda" that defines a few functions that encapsulate an enclosed variable gnuplot-commands. We define one function to add commands to the list of commands, one to clear the commands, one to generate the gnuplot script as a string, and one to run the program. The enclosed variable gnuplot-commands is basically only accessible by these functions. It is encapsulated, similar to if we defined a class in Python then made an instance of it with an attribute that was accessible only be instance methods. On one hand, this "protects" the variable, and keeps it out of the global namespace. On the other hand, we lose the documentation that would have come with a defvar, and we have to define a function to access the contents of that variable.

(let ((gnuplot-commands '("set terminal qt")))

  (defun gnuplot-add-cmd (s)
    "Append the command S to gnuplot-cmds."
    (setq gnuplot-commands (append gnuplot-commands (list s))))

  (defun gnuplot-clear ()
    (setq gnuplot-commands '("set terminal qt")))

  (defun gnuplot-script ()
    (s-join "\n" gnuplot-commands)))

gnuplot-script

To run the commands, we define this function. It does not need to be in the closure because it only accesses the commands through functions we defined in the closure.

(defun gnuplot-show ()
    (let* ((temporary-file-directory ".")
           (cmdfile (make-temp-file "gnuplot-cmds-" nil ".gpl"))
           (shellcmd (format "gnuplot --persist -c \"%s\"" cmdfile))
           (cmds (gnuplot-script)))
      (with-temp-file cmdfile
        (insert cmds))
      (shell-command shellcmd)
      (delete-file cmdfile)
      cmds))

gnuplot-show

Last time I noted I had a new idea for the DSL syntax that would give us more flexibility to inject variables and code into the DSL. The idea is to use keywords, symbols that start with :, to indicate they should be replaced by the value of the non-keyword symbol in the environment, and for any form that starts with : to evaluate that form. So, (: - 5 4) would get replaced by 1. Here is the new macro for that.

(defmacro kargs (&rest args)
  "Convert symbols to strings, quote strings, and (expr) to what they evaluate to."
  `(s-join " " (list ,@(cl-mapcan
                        (lambda (s)
                          (list
                           (cond
                            ((keywordp s)
                             (format "%s"
                                     (symbol-value (intern (substring (symbol-name s) 1)))))
                            ((symbolp s)
                             (symbol-name s))
                            ((stringp s)
                             (format "\"%s\"" s))
                            ((and (listp s) (eq : (car s)))
                             `(with-output-to-string
                                (princ ,(cdr s))))
                            (t
                             (format "%s" s)))))
                        args))))

kargs

Now, our gnuplot macro is simpler, since all it does is add commands to the list. If the form is a string, we add it as is, if the form starts with (: stuff) we evaluate the cdr of the form, and otherwise, we pass the form contents to the kargs macro for processing.

(defmacro gnuplot (&rest forms)
  `(loop for s in (list ,@(mapcar (lambda (x)
                                    (cond
                                     ((stringp x)
                                      x)
                                     ((and (listp x) (eq : (car x)))
                                      `,(cdr x))
                                     (t
                                      `(kargs ,@x))))
                                  forms))
         do (gnuplot-add-cmd s)))

gnuplot

What did that gain us? First, we can break up a script so we can talk about it, maybe do some calculations, etc… Let's look at the example at http://gnuplot.sourceforge.net/demo/linkedaxes.html.

We can start with the basic settings.

(gnuplot-clear)

(gnuplot
 (set terminal png)
 (set output "linkedaxes.png")
 (set encoding utf8)
 (set key outside Left)
 (set bmargin 5)
 (set tmargin 6)
 (set style data lines)
 (set tics in)
 (set ticslevel 0.5)
 (set xlabel  "X-ray energy in eV")

 (set format y  \'%5.1fe\')
 (set title " Anomalous scattering factors ")
 (set xrange  [9000:14400])
 (set offset 0\,0\,1.0\,0)
 (set xtics nomirror)
 (set link x via 12398./x inverse 12398./x)

 (set x2label  "X-ray wavelength in Å")
 (set x2tics 0.1  format "%.1f Å" nomirror))

We need to download some data files. We can do that, and add another line to the gnuplot script. The escaping on the quotes and commas is especially tedious in this one ;) but, we don't need those pesky line-continuations here.

(shell-command "wget http://skuld.bmsc.washington.edu/scatter/data/Br.dat")
(shell-command "wget http://skuld.bmsc.washington.edu/scatter/data/Ta.dat")


(gnuplot
 (plot "Br.dat" volatile using 1:3 title \'Br f\"\'  lt 1 lw 3\, \'\' volatile using 1:2 title "Br f'"  lt 1 lw 1\,
       "Ta.dat" volatile using 1:3 title \'Ta f\"\' lt 2 lw 3\, \'\' volatile using 1:2 title \"Ta f\'\"  lt 2 lw 1))

(gnuplot-script)

set terminal qt
set terminal png
set output "linkedaxes.png"
set encoding utf8
set key outside Left
set bmargin 5
set tmargin 6
set style data lines
set tics in
set ticslevel 0.5
set xlabel "X-ray energy in eV"
set format y '%5.1fe'
set title " Anomalous scattering factors "
set xrange [9000:14400]
set offset 0,0,1.0,0
set xtics nomirror
set link x via 12398./x inverse 12398./x
set x2label "X-ray wavelength in Å"
set x2tics 0.1 format "%.1f Å" nomirror
plot "Br.dat" volatile using 1:3 title 'Br f"' lt 1 lw 3, '' volatile using 1:2 title "Br f'" lt 1 lw 1, "Ta.dat" volatile using 1:3 title 'Ta f"' lt 2 lw 3, '' volatile using 1:2 title "Ta f'" lt 2 lw 1

Finally, we can set the output to png, and run our program.

(gnuplot-show)

Looks good.

What about the fancy keyword formatting? Here is an example of that in action. :term gets replaced by the term variable, :png gets replaced by the filename, and :x is replaced by 4.

(gnuplot-clear)
(let ((x 4)
      (term "png")
      (png "\"polar.png\""))
  (gnuplot
   (set terminal :term)
   (set output :png)
   (set polar)
   (set dummy t)
   (plot sin\( :x *t\) \,cos\( :x *t\))
   (set offset 0\,0\,0\,0)))

(gnuplot-show)

set terminal qt
set terminal png
set output "polar.png"
set polar
set dummy t
plot sin( 4 *t) ,cos( 4 *t)
set offset 0,0,0,0

There are a few nuances I didn't expect. First, you have to escape the parentheses in this case because otherwise it looks like a form that will be ignored. Second, you have to quote the string to get quotes into the gnuplot script. Third, there has to be a space before and after the keywords for emacs to parse it correctly and do the substitution.

Let's look at one last example that uses the (: form). We reproduce a figure from http://gnuplot.sourceforge.net/demo/transparent_solids.html here.

(gnuplot-clear)
(gnuplot
 (set terminal pngcairo  background "#ffffff" enhanced font "arial,9" fontscale 1.0 size 512\, 384 )
 (set output "transparent-solids.png")
 ;; construct the title
 (set title (: format "\"%s\"" (concat "Interlocking Tori - PM3D surface" "with depth sorting and transparency")))

 ;; use lisp code to create a gnuplot command
 (: concat "unset" " " "border")

 (unset key)
 (set object 1 rect from screen 0\, 0\, 0 to screen 1\, 1\, 0 behind)
 (set object 1 rect fc  rgb \"gray\"  fillstyle solid 1.0  border -1)
 (set view 64\, 345\, 1.24375\, 0.995902)
 (set isosamples 50\, 20)
 (unset xtics)
 (unset ytics)
 (unset ztics)
 (set dummy u\,v)
 (set parametric)
 (set urange [ -pi : pi ])
 (set vrange [ -pi : pi ])

 (set style fill  transparent solid 0.30 border)
 (set pm3d depthorder)
 (set palette rgbformulae 8\, 9\, 7)
 (set pm3d interpolate 1\,1 flush begin noftriangles border lt black linewidth 0.500 dashtype solid corners2color mean)
 (set colorbox vertical origin screen 0.9\, 0.2\, 0 size screen 0.05\, 0.6\, 0 front  noinvert bdefault)

 (splot (: concat "cos(u)+.5*cos(u)*cos(v),sin(u)+.5*sin(u)*cos(v),.5*sin(v) with pm3d,"
           "1+cos(u)+.5*cos(u)*cos(v),.5*sin(v),sin(u)+.5*sin(u)*cos(v) with pm3d")))
(gnuplot-show)

set terminal qt
set terminal pngcairo background "#ffffff" enhanced font "arial,9" fontscale 1.0 size 512, 384
set output "transparent-solids.png"
set title "Interlocking Tori - PM3D surfacewith depth sorting and transparency"
unset border
unset key
set object 1 rect from screen 0, 0, 0 to screen 1, 1, 0 behind
set object 1 rect fc rgb "gray" fillstyle solid 1.0 border -1
set view 64, 345, 1.24375, 0.995902
set isosamples 50, 20
unset xtics
unset ytics
unset ztics
set dummy u,v
set parametric
set urange [-pi : pi]
set vrange [-pi : pi]
set style fill transparent solid 0.3 border
set pm3d depthorder
set palette rgbformulae 8, 9, 7
set pm3d interpolate 1,1 flush begin noftriangles border lt black linewidth 0.5 dashtype solid corners2color mean
set colorbox vertical origin screen 0.9, 0.2, 0 size screen 0.05, 0.6, 0 front noinvert bdefault
splot cos(u)+.5*cos(u)*cos(v),sin(u)+.5*sin(u)*cos(v),.5*sin(v) with pm3d,1+cos(u)+.5*cos(u)*cos(v),.5*sin(v),sin(u)+.5*sin(u)*cos(v) with pm3d

Overall this seems like an improvement to the DSL. I didn't invent the idea of reusing keywords this way out of the blue. In On Lisp, Paul graham uses "special" variable names in Chapter 18, where he shows how to use gensyms for special purposes, and also variables with special names like ?x. Even Emacs is using a variation of this idea. Check out this new let-alist macro:

(let-alist '((x . 5))
  (+ 1 .x))

There is a special variable inside the body that is a dot-name. The macro expands to provide a value for that symbol. I wonder if I should have tried to use an approach like this instead. Maybe another day. After I read and study the four defuns and single defmacro that make this possible!

You can see here what happens:

(macroexpand '(let-alist '((x . 5))
  (+ 1 .x)))

(let
    ((alist
      '((x . 5))))
  (let ((\.x (cdr (assq 'x alist))))
    (+ 1 \.x)))

The macro builds up an internal alist for the dot-names.

org-mode source

Org-mode version = 9.1.6

An emacs-lisp dsl for gnuplot

Thu, 04 May 2017 19:33:55 EDT

1. Embedding Python or gnuplot
2. An alternative approach using a DSL
3. Summary

Plotting is a pretty general feature we need in scientific work. In this post we examine a way we could get at least minimal plotting into Emacs-lisp with as lispy a syntax as reasonable.

1 Embedding Python or gnuplot

With org-mode we can fluidly integrate many languages in one document. That is not the goal here, where I want to integrate plotting into a program. You certainly could go this route to embed python programs in your lisp programs for plotting.

(defun python (code)
  (let* ((temporary-file-directory ".")
        (tmpfile (make-temp-file "py-" nil ".py")))
    (with-temp-file tmpfile
      (insert code))
    (shell-command-to-string (format "python %s" tmpfile))))

Here is that function in action.

(python "import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 1)
y = np.exp(x)
plt.plot(x, y, label='data')
plt.title('A Title')
plt.xlim([0, 1])
plt.ylim([1, 2.75])
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.savefig('figpy.png')")

And the corresponding figure:

This is irritating for a few reasons. One is it is annoying to write python programs in string form; you don't get much editor support for indentation or syntax highlighting, and you have to be careful with quotes. It is not that easy to switch that snippet to Python mode either. You are pretty limited in writing programs that expand and modify the code too. Basically you have to do that all by string manipulation.

Along these lines, you could imagine a gnuplot function. It ends up not being much better.

(defun gnuplot (cmds)
  (let* ((temporary-file-directory ".")
         (cmdfile (make-temp-file "gnuplot-cmds-" nil ".gpl"))
         (shellcmd (format "gnuplot --persist -c \"%s\"" cmdfile)))
    (with-temp-file cmdfile
      (insert cmds))
    (shell-command shellcmd)
    (delete-file cmdfile)))

You use this the same way.

(gnuplot "set title \"Simple Plots\" font \",20\"
set key left box
set samples 50
set style data points
set terminal png
set output \"gnuplot.png\"

plot [-10:10] sin(x),atan(x),cos(atan(x))")

It has the same limitations as our string-based Python solution. The benefit of them is the native command structure for Python or gnuplot is used, so anything they can do you can too.

2 An alternative approach using a DSL

As an alternative, we consider here a domain specific language (DSL) that maps onto gnuplot. Suppose we could do this instead.

(gnuplot
 (set terminal png)
 (set output "test.png")
 (set title "Simple Plots" font ",20")
 (set key left box)
 (set samples 50)
 (set style data points)

 (plot [-10:10] sin\(x\) \,atan\(x\) \,cos\(atan\(x\)\)))

Here is the figure from that code. The most annoying part of this is in the plot function we have to escape all the parentheses and commas, but otherwise it looks pretty lispy. The output of that program is the gnuplot commands that were generated for making the plot.

This retains a lot of benefits of programming in lisp. gnuplot has to be a macro though because we do not want to evaluate the s-expressions inside as lisp. For starters they just look lispy, I don't actually use them as lisp at all. Instead we transform them to the gnuplot code.

In the following code, I will develop the gnuplot macro. It has some sticky and tricky points, and it is not obvious it will support all the features of gnuplot, but I learned a lot doing it that I will share here.

Starting with a simple form inside the macro, I wanted to convert (set output "test.png") to "set output \"test.png\"". For this DSL, I want to treat every symbol in the form as if it should be turned into a string, anything that is a string should be quoted, and anything that is in parentheses (i.e. it passes listp) should be evaluated and converted to a string. Then all those strings should be joined by spaces. Here is a macro that does that (adapted from a solution at https://emacs.stackexchange.com/questions/32558/eval-some-arguments-in-a-macro/32570?noredirect=1#comment50186_32570).

There are a couple of corner cases that are handled here. If the arg is a string, we quote it. If the arg is not a symbol or string, then it is evaluated and converted to a string. Importantly, this is done in the run environment though, so we can inject variables into the gnuplot code.

(defmacro gargs (&rest args)
  "Convert symbols to strings, quote strings, and (expr) to what they evaluate to."
  `(s-join " " (list ,@(cl-mapcan
                        (lambda (s)
                          (list
                           (cond
                            ((symbolp s)
                             (symbol-name s))
                            ((stringp s)
                             (format "\"%s\"" s))
                            (t
                             `(with-output-to-string
                                (princ ,s))))))
                        args))))

Here are a few examples of how it works. The loop is just to get a vertical table in org-mode for the blog post.

(loop for s in
      (list (gargs set key title "before fit" size \, (+ 5 5))
            (gargs set title "red")
            (gargs set yrange [0:*])
            (gargs "5")
            (let ((x 6)) (gargs (identity x)))
            (gargs 'x)
            (gargs '(x))
            (gargs set label 1 "plot for [n=2:10] sin(x*n)/n" at graph .95\, graph .92 right))
      collect
      (list s))

A limitation of this is that we either have quote things like parentheses, commas, semi-colons and sometimes square brackets:

(gargs plot for [n=2:10] sin\(x*n\)/n notitle lw \(13-n\)/2)

Or we have to use the string form instead; we can always fall back to that.

(gargs "plot for [n=2:10] sin(x*n)/n notitle lw (13-n)/2")

The macro above will do the grunt work on each form in the gnuplot macro. Finally, for the gnuplot macro, I want to take all the forms, convert them to gnuplot commands, write them to a temporary file, and then run gnuplot on the file, and finally delete the temp file. I assume we start with a gui terminal so graphs pop up unless you change it in your macro body. Here is that macro. It returns the generated code so it easy to see if you got the right program.

(defmacro gnuplot (&rest forms)
  (let* ((temporary-file-directory ".")
         (cmdfile (make-temp-file "gnuplot-cmds-" nil ".gpl"))
         (shellcmd (format "gnuplot --persist -c \"%s\"" cmdfile))
         (cmd-string))
    `(let ((cmd-string (s-join "\n" (list ,@(mapcar (lambda (x)
                                                      (if (stringp x)
                                                          x
                                                        `(gargs ,@x)))
                                                    forms)))))
       (with-temp-file ,cmdfile
         (insert "set terminal qt\n")
         (insert cmd-string)
         (setq cmd-string (buffer-string)))
       (shell-command ,shellcmd)
       (delete-file ,cmdfile)
       cmd-string)))

Here is a figure adapted from http://gnuplot.sourceforge.net/demo/iterate.html. I use the string form for the last line to avoid escaping all the special characters.

(gnuplot
 (set terminal png)
 (set output "iteration.png")
 (set title "Iteration within plot command")
 (set xrange [0:3])
 (set label 1 "plot for [n=2:10] sin(x*n)/n" at graph .95\, graph .92 right)
 "plot for [n=2:10] sin(x*n)/n notitle lw (13-n)/2")

Here is the resulting figure.

That is overall pretty sweet. There is a little dissonance between the strings, escaped comma, etc.., and it is not terribly ideal for integrating with regular lisp code inside the macro yet. That seems to be a feature of my choice to use (expr) as the syntax to evaluate a form. It means you have to do some gymnastics to get some s-expressions into the graphs. For example below I use a couple of variables to inject values. To get a string I have to use format to add the extra quotes, and to get the number I have to use the identity function. I also used escaped characters in the last line to see the difference.

(let ((ts "Iteration and substitution")
      (x0 0)
      (xf 3)
      (g1 0.95)
      (g2 0.92))
  (gnuplot
   (set terminal png)
   (set output "iteration-2.png")
   (set title (format "\"%s\"" ts))
   ;; Note the escaped square brackets
   (set xrange \[ (identity x0) : (identity xf) \])
   (set label 1 "plot for [n=2:10] sin(x*n)/n" at graph (identity g1) \, graph (identity g2) right)
   ;; note here I escaped the parentheses!
   (plot for [n=2:10] sin\(x*n\)/n notitle lw \(13-n\)/2)))

3 Summary

For the simple plots here, my DSL worked ok. There is a tradeoff in the syntax I chose that has some consequences. We cannot use the values of symbols in this DSL without resorting to hackery like (identity sym). We also cannot use the infix notation for sin(x) without quoting it as "sin(x)" or escaping the parentheses, e.g. sin\(x\), likewise square brackets which lisp will read as a vector. Commas have to be escaped, which is probably an emacs-lisp issue. To address that would require a reader macro which emacs-lisp does not have great support for. I am calling this experiment done for now. I have another syntax idea to try out another day.

Here is a preview of what it might look like. It is basically the same but I reuse keywords to indicate that :x0 should be replaced by whatever x0 evaluates to, and (: - 1 0.05) should be evaluated. The special character escaping is still there of course, since that is a limitation of the emacs lisp reader I think. I might try using x0? and (? - 1 0.05) instead. That might be less confusing. I like that the keywords are syntax highlighted for free though, and you can't use them for anything else.

(let ((ts "Iteration and substitution")
      (x0 0)
      (xf 3)
      (g2 0.92))
  (gnuplot
   (set terminal png)
   (set output "iteration-2.png")
   (set title :ts)
   ;; Note the escaped square brackets
   (set xrange \[ :x0 : :xf \])
   (set label 1 "plot for [n=2:10] sin(x*n)/n" at graph (: - 1 0.05) \, graph :g2 right)
   ;; note here I escaped the parentheses!
   (plot for [n=2:10] sin(x*n)/n notitle lw (13-n)/2)))

This has the benefit of a little cleaner injection of variables and selective execution of parenthetical expressions, we will just ignore any that don't pass (= (car expr) :). That May not work for sin((: + 1 1) x) though, unless I escape the outer parentheses too.

org-mode source

Org-mode version = 9.0.5

Calling remote code-blocks in org-mode

Tue, 09 Feb 2016 20:40:59 EST

1. References

We often write code in supporting information files that generates figures or tables in a scientific manuscript. Today, we explore how to call those code blocks remotely but get the output in the file we call it from. We will write code in si.org that generates an interactive figure that is presented in this file. We will use data published in hallenbeck-2015-compar-co2. You can find the data we used in the SI for that paper, or more conveniently here .

So, we make a named code block in the si.org file called "figure-1". Then we call it like this:

#+call: si.org:figure-1() :wrap html

That executes the code block in the other file, and wraps the output in an HTML block in this file! I do not like my code blocks to execute when I export because they are usually expensive calculations, so I have to manually run the line with C-c C-c, but you can override that behavior with a local setting of org-export-babel-evaluate. So, without further delay, here is the result. Now we have a nice, neat blog post file, with code in an si.org file!

1 References

Bibliography

[hallenbeck-2015-compar-co2] "Alexander Hallenbeck, Adefemi Egbebi, Kevin Resnik, , David Hopkinson, Shelley Anna & John Kitchin", Comparative Microfluidic Screening of Amino Acid Salt Solutions for Post-Combustion \ceCO2 Capture, "International Journal of Greenhouse Gas Control ", 43, 189 - 197 (2015). link. doi.

org-mode source

Org-mode version = 8.2.10

Interactive figures in blog posts with mpld3

Mon, 08 Feb 2016 07:33:23 EST

Continuing the exploration of interactive figures, today we consider the Python plotting library mpld3 . We will again use our own published data. We wrote this great paper on core level shifts (CLS) in Cu-Pd alloys boes-2015-core-cu. I want an interactive figure that shows the name of the calculation on each point as a tooltip. This data is all stored in the supporting information file, and you can see how we use it here. This figure shows how the core level shift of a Cu atom changes depending on the number of nearest neighbor Cu atoms. Just hover your mouse over a point to see the name and CLS for that point.

1 Data and code

You can check out our preprint at https://github.com/KitchinHUB/kitchingroup-51 . We are going to adapt the code to make Figure 6a in the manuscript interactive. The code needed a somewhat surprising amount of adaptation. Apparently the ase database interface has changed a lot since we write that paper, so the code here looks a bit different than what we published. The biggest difference is due to name-mangling so each key that started with a number now starts with _, and and periods are replaced by _ also. The rest of the script is nearly unchanged. At the end is the very small bit of mpld3 code that generates the figure for html. We will add tooltips onto datapoints to indicate what the name associated with each data point is. Here is the code.

import matplotlib.pyplot as plt
from ase.db import connect

# loads the ASE database and select certain keywords
db = connect('~/Desktop/cappa/kitchingroup-51/supporting-information/data.json')

keys = ['bcc', 'GS', '_54atom', 'ensam']

CLS, IMP, labels = [], [], []
for k in db.select(keys + ['_1cl']):
    name = k.keywords[-2]

    Cu0 = db.select('bcc,GS,_72atom,_0cl,_1_00Cu').next().energy
    Cu1 = db.select('bcc,GS,_72atom,_1cl,_1_00Cu').next().energy
    x0 = db.select(','.join(keys + [name, '_0cl'])).next().energy
    x1 = k.energy

    cls0 = x0 - Cu0
    cls1 = x1 - Cu1

    IMP.append(int(name[1]))
    CLS.append(cls1 - cls0)
    labels += ['{0} ({1}, {2})'.format(name, int(name[1]), cls1 - cls0)]

Cu0 = db.select(','.join(['bcc', 'GS', '_72atom',
                          '_0cl', '_1_00Cu'])).next().energy
Cu1 = db.select(','.join(['bcc', 'GS', '_72atom',
                          '_1cl', '_1_00Cu'])).next().energy

x0 = db.select(','.join(['bcc', 'GS', '_54atom',
                         '_0cl', '_1'])).next().energy
x1 = db.select(','.join(['bcc', 'GS', '_54atom',
                         '_1cl', '_1'])).next().energy

cls0 = x0 - Cu0
cls1 = x1 - Cu1

IMP.append(1)
CLS.append(cls1 - cls0)
labels += ['(1, {0})'.format(cls1 - cls0)]

Cu0 = db.select(','.join(['bcc', 'GS', '_72atom',
                          '_0cl', '_1_00Cu'])).next().energy
Cu1 = db.select(','.join(['bcc', 'GS', '_72atom',
                          '_1cl', '_1_00Cu'])).next().energy

x0 = db.select(','.join(['bcc', 'GS', '_54atom',
                         '_0cl', '_0'])).next().energy
x1 = db.select(','.join(['bcc', 'GS', '_54atom',
                         '_1cl', '_0'])).next().energy

cls0 = x0 - Cu0
cls1 = x1 - Cu1

IMP.append(0)
CLS.append(cls1 - cls0)
labels += ['(0, {0})'.format(cls1 - cls0)]

fig = plt.figure()

p = plt.scatter(IMP, CLS, c='g', marker='o', s=25)
ax1 = plt.gca()
ax1.set_ylim(-1.15, -0.6)
ax1.set_xlim(-0.1, 5.1)

ax1.set_xlabel('# Cu Nearest neighbors')
ax1.set_ylabel('Cu 2p(3/2) Core Level Shift (eV)')

ax1.set_title('Hover over a point to see the calculation name')

# Now the mpld3 stuff.
import mpld3
from mpld3 import plugins

tooltip = plugins.PointHTMLTooltip(p, labels, voffset=0, hoffset=10)
plugins.connect(fig, tooltip)

print mpld3.fig_to_html(fig)

I like this workflow pretty well. It seems less functional than plotly and Bokeh (e.g. it does not look like it you can export the data from the html here), but it is well integrated with Matplotlib, with my blogging style, and does not require a server, oran account. The code outputs html that is self-contained in the body of the html. The smooth integration with Matplotlib means I could have static images in org-mode, and dynamic images in HTML potentially. Overall, this is a nice tool for making interactive plots in blog posts.

2 References

Bibliography

[boes-2015-core-cu] Jacob Boes, Peter Kondratyuk, Chunrong Yin, James, Miller, Andrew Gellman & John Kitchin, Core Level Shifts in Cu-Pd Alloys As a Function of Bulk Composition and Structure, Surface Science, 640, 127-132 (2015). link. doi.

org-mode source

Org-mode version = 8.2.10

Interactive Bokeh plots in HTML

Sun, 07 Feb 2016 10:53:45 EST

1. The data and code
2. References

In our last post we examined the use of plotly to generate interactive plots in HTML. Today we expand the idea, and use Bokeh . One potential issue with plotly is the need for an account and API-key, some limitations on how many times a graph can be viewed per day (although I should aspire to have my graphs viewed 1000+ times a day!), and who knows what happens to the graphs if plotly ever goes out of business. While the static images we usually use have limited utility, at least they stick around.

So, today we look at Bokeh which allows you to embed some json data in your HTML, which is made interactive by your browser with more javascript magic. We get straight to the image here so you can see what this is all about. Briefly, this data shows trends (or lack of) in the adsorption energies of some atoms on the atop and fcc sites of several transition metals as a function of adsorbate coverage xu-2014-probin-cover. The code to do this is found here.

Using Bokeh does not integrate real smoothly with my blog workflow, which only generates the body of HTML posts. Bokeh needs some javascript injected into the header to work. To get around that, I show the plot in a frame here. You can see a full HTML version here: bokeh-plot.html .

This is somewhat similar to the plotly concept. The data is embedded in the html in this case, which is different. For very large plots, I actually had some trouble exporting the blog post (it was taking a long time to export and I killed it)! I suspect that is a limitation of the org-mode exporter though, because I could save the html files from Python and view them fine. I also noted that having all the javascript in the org-file make font-lock work very slow. It would be better to generate that only on export.

Note to make this work, we need these lines in our HTML header:

#+HTML_HEAD: <link rel="stylesheet" href="http://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css" type="text/css" />
#+HTML_HEAD: <script type="text/javascript" src="http://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js"></script>

Since we do not host those locally, if they ever disappear, our plots will not show ;(

1 The data and code

We will get the data from our paper on coverage dependent adsorption energies xu-2014-probin-cover. There are some data rich figures there that would benefit from some interactivity. You can get the data here: http://pubs.acs.org/doi/suppl/10.1021/jp508805h . Extract out the supporting-information.org and energies.json file to follow here. We will make Figure 2a in the SI document here, and make it interactive with hover tooltips.

import json

from collections import OrderedDict
from bokeh import mpl
from bokeh.plotting import *
from bokeh.models import HoverTool
from bokeh.embed import components

with open('/users/jkitchin/Desktop/energies.json', 'r') as f:
    data = json.load(f)


# color for metal
# letter symbol for adsorbate
colors = {'Cu':'Orange',
          'Ag':'Silver',
          'Au':'Yellow',
          'Pd':'Green',
          'Pt':'Red',
          'Rh':'Blue',
          'Ir':'Purple'}

all_ads = ['O', 'S']

TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover,previewsave"
p = figure(title="Correlation between atop and fcc sites", tools=TOOLS)

for metal in ['Rh', 'Pd', 'Cu', 'Ag']:
    for adsorbate in all_ads:
        E1, E2 = [], []
        for coverage in '0.25', '0.5', '0.75', '1.0':
            if (isinstance(data[metal][adsorbate]['ontop'][coverage], float) and
                isinstance(data[metal][adsorbate]['fcc'][coverage], float)):
                E1.append(data[metal][adsorbate]['ontop'][coverage])
                E2.append(data[metal][adsorbate]['fcc'][coverage])
        labels = ['{0}-{1} {2} ML'.format(metal, adsorbate, x)
                  for x in ['0.25', '0.5', '0.75', '1.0']]
        p.line('x', 'y', color=colors[metal],
               source=ColumnDataSource(data={'x': E1,
                                             'y': E2,
                                             'label': labels}))
        p.circle('x', 'y', color=colors[metal],
               source=ColumnDataSource(data={'x': E1,
                                             'y': E2,
                                             'label': labels}))


hover =p.select({'type': HoverTool})
hover.tooltips = OrderedDict([("(atop,fcc)", "(@x, @y)"),
                              ("label", "@label")])

p.xaxis.axis_label = 'Adsorption energy on the atop site'
p.yaxis.axis_label = 'Adsorption energy on the fcc site'

script, div = components(p)
script = '\n'.join(['#+HTML_HEAD_EXTRA: ' + line for line in script.split('\n')])

print '''{script}

#+BEGIN_HTML
<a name="figure"></a>
{div}
#+END_HTML
'''.format(script=script, div=div)

2 References

Bibliography

[xu-2014-probin-cover] Zhongnan Xu & John Kitchin, Probing the Coverage Dependence of Site and Adsorbate Configurational Correlations on (111) Surfaces of Late Transition Metals, J. Phys. Chem. C, 118(44), 25597-25602 (2014). link. doi.

org-mode source

Org-mode version = 8.2.10

Interactive plots in HTML with Plotly

Sat, 06 Feb 2016 12:44:53 EST

Most of the plots in this blog are static. Today, I look at making them interactive. I will use https://plot.ly for this. I want to use some data from a paper we published on the relative stabilities of oxide polymorphs mehta-2015-ident-poten. We will make an interactive figure showing the relative stabilities of the RuO₂ polymorphs. When you hover on a point, it will show you which polymorph the point refers to. Let's see the figure first here. If you think its interesting read on to see how we made it!

We get our data source here: http://pubs.acs.org/doi/suppl/10.1021/am4059149/suppl_file/am4059149_si_001.pdf .

Now, we extract the data files:

pdftk ~/Desktop/am4059149_si_001.pdf  unpack_files

That extracts a json file called supporting-information.json. We use it as suggested in the SI pdf to plot the equations of state for RuO₂ for several polymorphs.

# coding=utf-8

import plotly.plotly as py
import plotly.graph_objs as go
import plotly.tools as tls
import numpy as np

import json
import matplotlib.pyplot as plt
from ase.utils.eos import EquationOfState
with open('supporting-information.json', 'rb') as f:
    d = json.loads(f.read())

BO2 = 'RuO2'
xc = 'PBE'

layout = go.Layout(title='Energy vs. Volume for RuO<sub>2</sub> polymorphs',
                   xaxis=dict(title='Volume (Å<sup>3</sup>)'),
                   yaxis=dict(title='Energy (eV)'))

traces = []

for polymorph in ['rutile','anatase','brookite','columbite','pyrite','fluorite']:

    # number of atoms in the unit cell - used to normalize
    natoms= len(d[BO2][polymorph][xc]['EOS']['calculations']
                [0]['atoms']['symbols'])
    volumes = [entry['data']['volume']*3./natoms for entry in
               d[BO2][polymorph][xc]['EOS']['calculations']]
    energies =  [entry['data']['total_energy']*3./natoms for entry in
                 d[BO2][polymorph][xc]['EOS']['calculations']]

    trace = go.Scatter(x=np.array(volumes),
                       y=np.array(energies),
                       mode='lines+markers',
                       name=polymorph,
                       text=polymorph)

    traces += [trace]

fig = go.Figure(data=traces, layout=layout)
plot_url = py.plot(fig, filename='ruo2-2')

print tls.get_embed(plot_url)

Pretty nice, now we should have an interactive plot in our browser with the data points labeled with tags, zooming, etc… That is nice for the blog. It isn't so nice for daily work, as there is no visual version of the plot in my org-file. Of course, I can visit the url to see the plot in my browser, it is just different from what I am used to. For everyone else, this is probably better. It looks like you can actually get the data from the web page, including some minimal analysis like regression, and save your view to an image! That could be pretty nice for some data sets.

1 Using Plotly yourself

First, go to https://plot.ly and sign up for an account. You will want to register your API key like this, which will save it in a file for your convenience. Then you can do things like I did above too.

import plotly.tools as tls
tls.set_credentials_file(username='jkitchin', api_key='xxxxxxx')

2 References

Bibliography

[mehta-2015-ident-poten] Prateek Mehta, Paul Salvador & John Kitchin, Identifying Potential \ceBO2 Oxide Polymorphs for Epitaxial Growth Candidates, ACS Appl. Mater. Interfaces, 6(5), 3630-3639 (2015). link. doi.

org-mode source

Org-mode version = 8.2.10

Hatched symbols in matplotlib

Sat, 26 Oct 2013 14:35:19 EDT

I learned something new about matplotlib today: How to make hatched (patterned) symbols in a plot. Well, sort of. The scatter plot in matplotlib has a hatch keyword argument that specifies a pattern on the marker. Below, is an example that runs through a handful of hatch patterns, on randomly selected symbols.

Curiously, hatch is not a kwarg of the scatter function, but of collections . Anyway, let us see how to get the hatched symbols.

import random
import numpy as np
import matplotlib.pyplot as plt

patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.', '/')
markers = 'os<^>p*'
for pattern in patterns:
    plt.scatter(np.random.uniform(size=(3,1)), np.random.uniform(size=(3,1)), s=1000,
                marker=random.choice(markers),
                facecolor='white',
                hatch=3*pattern, label=pattern)

plt.legend(scatterpoints=1, loc='best')
plt.savefig('images/hatched-symbols.png')

There are some other interesting things you can do with filled markers , hatched contours and with hatched bar graphs . Note this hatching is specific to plt.scatter. It does not work with plt.plot.

org-mode source

Customizing plots after the fact

Mon, 16 Sep 2013 16:27:27 EDT

Matlab post Sometimes it is desirable to make a plot that shows the data you want to present, and to customize the details, e.g. font size/type and line thicknesses afterwards. It can be tedious to try to add the customization code to the existing code that makes the plot. Today, we look at a way to do the customization after the plot is created.

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0,2)
y1 = x
y2 = x**2
y3 = x**3

plt.plot(x, y1, x, y2, x, y3)
xL = plt.xlabel('x')
yL = plt.ylabel('f(x)')
plt.title('plots of y = x^n')
plt.legend(['x', 'x^2', 'x^3'], loc='best')
plt.savefig('images/after-customization-1.png')

fig = plt.gcf()

plt.setp(fig, 'size_inches', (4, 6))
plt.savefig('images/after-customization-2.png')


# set lines to dashed
from matplotlib.lines import Line2D
for o in fig.findobj(Line2D):
    o.set_linestyle('--')

#set(allaxes,'FontName','Arial','FontWeight','Bold','LineWidth',2,'FontSize',14);

import matplotlib.text as text
for o in fig.findobj(text.Text):
    plt.setp(o, 'fontname','Arial', 'fontweight','bold', 'fontsize', 14)

plt.setp(xL, 'fontstyle', 'italic')
plt.setp(yL, 'fontstyle', 'italic')
plt.savefig('images/after-customization-3.png')
plt.show()

org-mode source

Plotting two datasets with very different scales

Fri, 13 Sep 2013 13:55:20 EDT

1. Make two plots!
2. Scaling the results
3. Double-y axis plot
4. Subplots

Matlab plot

Sometimes you will have two datasets you want to plot together, but the scales will be so different it is hard to seem them both in the same plot. Here we examine a few strategies to plotting this kind of data.

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2*np.pi)
y1 = np.sin(x);
y2 = 0.01 * np.cos(x);

plt.plot(x, y1, x, y2)
plt.legend(['y1', 'y2'])
plt.savefig('images/two-scales-1.png')
# in this plot y2 looks almost flat!

>>> >>> >>> >>> >>> >>> [<matplotlib.lines.Line2D object at 0x0000000006089128>, <matplotlib.lines.Line2D object at 0x000000000766B2E8>]
<matplotlib.legend.Legend object at 0x0000000007661668>

1 Make two plots!

this certainly solves the problem, but you have two full size plots, which can take up a lot of space in a presentation and report. Often your goal in plotting both data sets is to compare them, and it is easiest to compare plots when they are perfectly lined up. Doing that manually can be tedious.

plt.figure()
plt.plot(x,y1)
plt.legend(['y1'])
plt.savefig('images/two-scales-2.png')

plt.figure()
plt.plot(x,y2)
plt.legend(['y2'])
plt.savefig('images/two-scales-3.png')

<matplotlib.figure.Figure object at 0x0000000007D45438>
[<matplotlib.lines.Line2D object at 0x00000000081C61D0>]
<matplotlib.legend.Legend object at 0x0000000007FA1CF8>
>>> >>> <matplotlib.figure.Figure object at 0x00000000081C63C8>
[<matplotlib.lines.Line2D object at 0x00000000081C8F60>]
<matplotlib.legend.Legend object at 0x00000000081D7278>

2 Scaling the results

Sometimes you can scale one dataset so it has a similar magnitude as the other data set. Here we could multiply y2 by 100, and then it will be similar in size to y1. Of course, you need to indicate that y2 has been scaled in the graph somehow. Here we use the legend.

plt.figure()
plt.plot(x, y1, x, 100 * y2)
plt.legend(['y1', '100*y2'])
plt.savefig('images/two-scales-4.png')

<matplotlib.figure.Figure object at 0x0000000007FA7908>
[<matplotlib.lines.Line2D object at 0x000000000B0285C0>, <matplotlib.lines.Line2D object at 0x000000000B0287B8>]
<matplotlib.legend.Legend object at 0x000000000B028C88>

3 Double-y axis plot

Using two separate y-axes can solve your scaling problem. Note that each y-axis is color coded to the data. It can be difficult to read these graphs when printed in black and white

fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(x, y1)
ax1.set_ylabel('y1')

ax2 = ax1.twinx()
ax2.plot(x, y2, 'r-')
ax2.set_ylabel('y2', color='r')
for tl in ax2.get_yticklabels():
    tl.set_color('r')

plt.savefig('images/two-scales-5.png')

>>> [<matplotlib.lines.Line2D object at 0x000000000BA34208>]
<matplotlib.text.Text object at 0x000000000BA37C50>
>>> >>> [<matplotlib.lines.Line2D object at 0x000000000BA4DEF0>]
<matplotlib.text.Text object at 0x000000000BA594A8>

4 Subplots

An alternative approach to double y axes is to use subplots.

plt.figure()
f, axes = plt.subplots(2, 1)
axes[0].plot(x, y1)
axes[0].set_ylabel('y1')

axes[1].plot(x, y2)
axes[1].set_ylabel('y2')
plt.savefig('images/two-scales-6.png')

<matplotlib.figure.Figure object at 0x000000000BDC47B8>
>>> [<matplotlib.lines.Line2D object at 0x000000000BDE2F28>]
<matplotlib.text.Text object at 0x000000000BDD74E0>
>>> [<matplotlib.lines.Line2D object at 0x000000000D05E748>]
<matplotlib.text.Text object at 0x000000000BDEC438>

org-mode source

Org-mode version = 8.2.6

Picasso's short lived blue period with Python

Mon, 04 Mar 2013 16:07:55 EST

Matlab post

It is an unknown fact that Picasso had a brief blue plotting period with Matlab before moving on to his more famous paintings. It started from irritation with the default colors available in Matlab for plotting. After watching his friend van Gogh cut off his own ear out of frustration with the ugly default colors, Picasso had to do something different.

import numpy as np
import matplotlib.pyplot as plt

#this plots horizontal lines for each y value of m.
for m in np.linspace(1, 50, 100):
    plt.plot([0, 50], [m, m])

plt.savefig('images/blues-1.png')

Picasso copied the table available at http://en.wikipedia.org/wiki/List_of_colors and parsed it into a dictionary of hex codes for new colors. That allowed him to specify a list of beautiful blues for his graph. Picasso eventually gave up on python as an artform, and moved on to painting.

import numpy as np
import matplotlib.pyplot as plt

c = {}
with open('color.table') as f:
    for line in f:
        fields = line.split('\t')
        colorname = fields[0].lower()
        hexcode = fields[1]
        c[colorname] = hexcode

names = c.keys()
names = sorted(names)

print(names)

blues = [c['alice blue'],
         c['light blue'],
         c['baby blue'],
         c['light sky blue'],
         c['maya blue'],
         c['cornflower blue'],
         c['bleu de france'],
         c['azure'],
         c['blue sapphire'],
         c['cobalt'],
         c['blue'],
         c['egyptian blue'],
         c['duke blue']]

ax = plt.gca()
ax.set_color_cycle(blues)

#this plots horizontal lines for each y value of m.
for i, m in enumerate(np.linspace(1, 50, 100)):
    plt.plot([0, 50], [m, m])

plt.savefig('images/blues-2.png')

['aero', 'aero blue', 'african violet', 'air force blue (raf)', 'air force blue (usaf)', 'air superiority blue', 'alabama crimson', 'alice blue', 'alizarin crimson', 'alloy orange', 'almond', 'amaranth', 'amazon', 'amber', 'american rose', 'amethyst', 'android green', 'anti-flash white', 'antique brass', 'antique bronze', 'antique fuchsia', 'antique ruby', 'antique white', 'ao (english)', 'apple green', 'apricot', 'aqua', 'aquamarine', 'army green', 'arsenic', 'arylide yellow', 'ash grey', 'asparagus', 'atomic tangerine', 'auburn', 'aureolin', 'aurometalsaurus', 'avocado', 'azure', 'azure mist/web', "b'dazzled blue", 'baby blue', 'baby blue eyes', 'baby pink', 'baby powder', 'baker-miller pink', 'ball blue', 'banana mania', 'banana yellow', 'barbie pink', 'barn red', 'battleship grey', 'bazaar', 'beau blue', 'beaver', 'beige', 'big dip o’ruby', 'bisque', 'bistre', 'bistre brown', 'bitter lemon', 'bitter lime', 'bittersweet', 'bittersweet shimmer', 'black', 'black bean', 'black leather jacket', 'black olive', 'blanched almond', 'blast-off bronze', 'bleu de france', 'blizzard blue', 'blond', 'blue', 'blue (crayola)', 'blue (munsell)', 'blue (ncs)', 'blue (pigment)', 'blue (ryb)', 'blue bell', 'blue sapphire', 'blue yonder', 'blue-gray', 'blue-green', 'blue-violet', 'blueberry', 'bluebonnet', 'blush', 'bole', 'bondi blue', 'bone', 'boston university red', 'bottle green', 'boysenberry', 'brandeis blue', 'brass', 'brick red', 'bright cerulean', 'bright green', 'bright lavender', 'bright maroon', 'bright pink', 'bright turquoise', 'bright ube', 'brilliant lavender', 'brilliant rose', 'brink pink', 'british racing green', 'bronze', 'bronze yellow', 'brown (traditional)', 'brown (web)', 'brown-nose', 'brunswick green', 'bubble gum', 'bubbles', 'buff', 'bulgarian rose', 'burgundy', 'burlywood', 'burnt orange', 'burnt sienna', 'burnt umber', 'byzantine', 'byzantium', 'cadet', 'cadet blue', 'cadet grey', 'cadmium green', 'cadmium orange', 'cadmium red', 'cadmium yellow', 'café au lait', 'café noir', 'cal poly green', 'cambridge blue', 'camel', 'cameo pink', 'camouflage green', 'canary yellow', 'candy apple red', 'candy pink', 'capri', 'caput mortuum', 'cardinal', 'caribbean green', 'carmine', 'carmine (m&p)', 'carmine pink', 'carmine red', 'carnation pink', 'carnelian', 'carolina blue', 'carrot orange', 'castleton green', 'catalina blue', 'catawba', 'cedar chest', 'ceil', 'celadon', 'celadon blue', 'celadon green', 'celeste (colour)', 'celestial blue', 'cerise', 'cerise pink', 'cerulean', 'cerulean blue', 'cerulean frost', 'cg blue', 'cg red', 'chamoisee', 'champagne', 'charcoal', 'charleston green', 'charm pink', 'chartreuse (traditional)', 'chartreuse (web)', 'cherry', 'cherry blossom pink', 'chestnut', 'china pink', 'china rose', 'chinese red', 'chinese violet', 'chocolate (traditional)', 'chocolate (web)', 'chrome yellow', 'cinereous', 'cinnabar', 'cinnamon', 'citrine', 'citron', 'claret', 'classic rose', 'cobalt', 'cocoa brown', 'coconut', 'coffee', 'columbia blue', 'congo pink', 'cool black', 'cool grey', 'copper', 'copper (crayola)', 'copper penny', 'copper red', 'copper rose', 'coquelicot', 'coral', 'coral pink', 'coral red', 'cordovan', 'corn', 'cornell red', 'cornflower blue', 'cornsilk', 'cosmic latte', 'cotton candy', 'cream', 'crimson', 'crimson glory', 'cyan', 'cyan (process)', 'cyber grape', 'cyber yellow', 'daffodil', 'dandelion', 'dark blue', 'dark blue-gray', 'dark brown', 'dark byzantium', 'dark candy apple red', 'dark cerulean', 'dark chestnut', 'dark coral', 'dark cyan', 'dark electric blue', 'dark goldenrod', 'dark gray', 'dark green', 'dark imperial blue', 'dark jungle green', 'dark khaki', 'dark lava', 'dark lavender', 'dark liver', 'dark liver (horses)', 'dark magenta', 'dark midnight blue', 'dark moss green', 'dark olive green', 'dark orange', 'dark orchid', 'dark pastel blue', 'dark pastel green', 'dark pastel purple', 'dark pastel red', 'dark pink', 'dark powder blue', 'dark raspberry', 'dark red', 'dark salmon', 'dark scarlet', 'dark sea green', 'dark sienna', 'dark sky blue', 'dark slate blue', 'dark slate gray', 'dark spring green', 'dark tan', 'dark tangerine', 'dark taupe', 'dark terra cotta', 'dark turquoise', 'dark vanilla', 'dark violet', 'dark yellow', 'dartmouth green', "davy's grey", 'debian red', 'deep carmine', 'deep carmine pink', 'deep carrot orange', 'deep cerise', 'deep champagne', 'deep chestnut', 'deep coffee', 'deep fuchsia', 'deep jungle green', 'deep lemon', 'deep lilac', 'deep magenta', 'deep mauve', 'deep moss green', 'deep peach', 'deep pink', 'deep ruby', 'deep saffron', 'deep sky blue', 'deep space sparkle', 'deep taupe', 'deep tuscan red', 'deer', 'denim', 'desert', 'desert sand', 'diamond', 'dim gray', 'dirt', 'dodger blue', 'dogwood rose', 'dollar bill', 'donkey brown', 'drab', 'duke blue', 'dust storm', 'earth yellow', 'ebony', 'ecru', 'eggplant', 'eggshell', 'egyptian blue', 'electric blue', 'electric crimson', 'electric cyan', 'electric green', 'electric indigo', 'electric lavender', 'electric lime', 'electric purple', 'electric ultramarine', 'electric violet', 'electric yellow', 'emerald', 'english green', 'english lavender', 'english red', 'english violet', 'eton blue', 'eucalyptus', 'fallow', 'falu red', 'fandango', 'fandango pink', 'fashion fuchsia', 'fawn', 'feldgrau', 'feldspar', 'fern green', 'ferrari red', 'field drab', 'fire engine red', 'firebrick', 'flame', 'flamingo pink', 'flattery', 'flavescent', 'flax', 'flirt', 'floral white', 'fluorescent orange', 'fluorescent pink', 'fluorescent yellow', 'folly', 'forest green (traditional)', 'forest green (web)', 'french beige', 'french bistre', 'french blue', 'french lilac', 'french lime', 'french mauve', 'french raspberry', 'french rose', 'french sky blue', 'french wine', 'fresh air', 'fuchsia', 'fuchsia (crayola)', 'fuchsia pink', 'fuchsia rose', 'fulvous', 'fuzzy wuzzy', 'gainsboro', 'gamboge', 'ghost white', 'giants orange', 'ginger', 'glaucous', 'glitter', 'go green', 'gold (metallic)', 'gold (web) (golden)', 'gold fusion', 'golden brown', 'golden poppy', 'golden yellow', 'goldenrod', 'granny smith apple', 'grape', 'gray', 'gray (html/css gray)', 'gray (x11 gray)', 'gray-asparagus', 'gray-blue', 'green (color wheel) (x11 green)', 'green (crayola)', 'green (html/css color)', 'green (munsell)', 'green (ncs)', 'green (pigment)', 'green (ryb)', 'green-yellow', 'grullo', 'guppie green', 'halayà úbe', 'han blue', 'han purple', 'hansa yellow', 'harlequin', 'harvard crimson', 'harvest gold', 'heart gold', 'heliotrope', 'hollywood cerise', 'honeydew', 'honolulu blue', "hooker's green", 'hot magenta', 'hot pink', 'hunter green', 'iceberg', 'icterine', 'illuminating emerald', 'imperial', 'imperial blue', 'imperial purple', 'imperial red', 'inchworm', 'india green', 'indian red', 'indian yellow', 'indigo', 'indigo (dye)', 'indigo (web)', 'international klein blue', 'international orange (aerospace)', 'international orange (engineering)', 'international orange (golden gate bridge)', 'iris', 'irresistible', 'isabelline', 'islamic green', 'italian sky blue', 'ivory', 'jade', 'japanese indigo', 'japanese violet', 'jasmine', 'jasper', 'jazzberry jam', 'jelly bean', 'jet', 'jonquil', 'june bud', 'jungle green', 'kelly green', 'kenyan copper', 'keppel', 'khaki (html/css) (khaki)', 'khaki (x11) (light khaki)', 'kobe', 'kobi', 'ku crimson', 'la salle green', 'languid lavender', 'lapis lazuli', 'laser lemon', 'laurel green', 'lava', 'lavender (floral)', 'lavender (web)', 'lavender blue', 'lavender blush', 'lavender gray', 'lavender indigo', 'lavender magenta', 'lavender mist', 'lavender pink', 'lavender purple', 'lavender rose', 'lawn green', 'lemon', 'lemon chiffon', 'lemon curry', 'lemon glacier', 'lemon lime', 'lemon meringue', 'lemon yellow', 'licorice', 'light apricot', 'light blue', 'light brown', 'light carmine pink', 'light coral', 'light cornflower blue', 'light crimson', 'light cyan', 'light fuchsia pink', 'light goldenrod yellow', 'light gray', 'light green', 'light khaki', 'light medium orchid', 'light moss green', 'light orchid', 'light pastel purple', 'light pink', 'light red ochre', 'light salmon', 'light salmon pink', 'light sea green', 'light sky blue', 'light slate gray', 'light steel blue', 'light taupe', 'light thulian pink', 'light yellow', 'lilac', 'lime (color wheel)', 'lime (web) (x11 green)', 'lime green', 'limerick', 'lincoln green', 'linen', 'lion', 'little boy blue', 'liver', 'liver (dogs)', 'liver (organ)', 'liver chestnut', 'lumber', 'lust', 'magenta', 'magenta (crayola)', 'magenta (dye)', 'magenta (pantone)', 'magenta (process)', 'magic mint', 'magnolia', 'mahogany', 'maize', 'majorelle blue', 'malachite', 'manatee', 'mango tango', 'mantis', 'mardi gras', 'maroon (crayola)', 'maroon (html/css)', 'maroon (x11)', 'mauve', 'mauve taupe', 'mauvelous', 'maya blue', 'meat brown', 'medium aquamarine', 'medium blue', 'medium candy apple red', 'medium carmine', 'medium champagne', 'medium electric blue', 'medium jungle green', 'medium lavender magenta', 'medium orchid', 'medium persian blue', 'medium purple', 'medium red-violet', 'medium ruby', 'medium sea green', 'medium sky blue', 'medium slate blue', 'medium spring bud', 'medium spring green', 'medium taupe', 'medium turquoise', 'medium tuscan red', 'medium vermilion', 'medium violet-red', 'mellow apricot', 'mellow yellow', 'melon', 'metallic seaweed', 'metallic sunburst', 'mexican pink', 'midnight blue', 'midnight green (eagle green)', 'midori', 'mikado yellow', 'mint', 'mint cream', 'mint green', 'misty rose', 'moccasin', 'mode beige', 'moonstone blue', 'mordant red 19', 'moss green', 'mountain meadow', 'mountbatten pink', 'msu green', 'mughal green', 'mulberry', 'mustard', 'myrtle green', 'sae/ece amber (color)']

org-mode source

Org-mode version = 9.1.6

The Kitchin Research Group

A new and improved Emacs gnuplot DSL

An emacs-lisp dsl for gnuplot

Table of Contents

1 Embedding Python or gnuplot

2 An alternative approach using a DSL

3 Summary

Calling remote code-blocks in org-mode

Table of Contents

1 References

Bibliography

Interactive figures in blog posts with mpld3

1 Data and code

2 References

Bibliography

Interactive Bokeh plots in HTML

Table of Contents

1 The data and code

2 References

Bibliography

Interactive plots in HTML with Plotly

1 Using Plotly yourself

2 References

Bibliography

Hatched symbols in matplotlib

Customizing plots after the fact

Plotting two datasets with very different scales

Table of Contents

1 Make two plots!

2 Scaling the results

3 Double-y axis plot

4 Subplots

Picasso's short lived blue period with Python