José Miguel González Aguilera

Rust en español fácil

Sun, 30 Oct 2022 00:00:00 +0000

I started to study Rust in 2021. I has new concepts to me like Onwership, borrowed types and traits (something like aspect oriented programming). As usual, I serached for a book which I could translate as part of my learning. Now I finished after more than one year translating in holidays. You can find it here.

I hope this translation will facilitate the learning of this compiled language to Spanish-speaking people. I appreciate your feedback if you find mistakes I can correct.

Happy Christmas and Happy New 2017

Fri, 23 Dec 2016 00:00:00 +0000

Pues eso, otro año que termina y otro año que nos felicitamos, y, aunque sea unos minutos, nos acordamos que lo bueno y lo malo; de los que estamos y los que perdimos; de lo que deseamos y lo que luchamos por nuestros sueños.

Y así, aprovechando que la tecnología nos lo pone fácil, nos felicitamos y alegramos de estar aquí y deseamos que el futuro sea todo lo mejor posible para nuestros familiares, amistades y compañeros.

Utilidad de traducción de textos a tengwar

Thu, 08 Dec 2016 00:00:00 +0000

Link to the utility
Source code

Translator to the writing system, invented by J.R.R. Tolkien, Tengwar. I have developed a grammar in javascript to implement “spanish mode of 2006” to translate texts. This mode, as I know, didn’t have a complete implementation in Internet. Mine has these functions:

Selects tehtar or tengwar (full) vowels.
Selects optional rules of spanish mode.
Shows the results using two diferents fonts (one of them is based in the famous one of the One Ring).
Shows the results with commands of the LaTeX package tengwarscript, to facilitate it’s inclusion in LaTeX documents without learning the meaning of those commands.

Writing Tengwar with LaTeX in Spanish

Sun, 13 Nov 2016 00:00:00 +0000

I have written an article for the LaTeX wikibook in Spanish with the different alternatives to use the Tengwar writing system of J.R.R. Tolkien with LaTeX. A tribute to the author of the Lord of the Rings, book that I have read again thanks to the new limited edition of 5,000 copies of Minotauro. By the way, my copy is number 287 😊.

The Tengwar writing system is the most developed that Tolkien designed, but not the only one. This is what many people call “Elvish”, but it is not. Actually, in the world of Tolkien there are several Elvish languages, and can be written in alphabetic writing and in “invented” writing systems such as Tengwar.

Tengwar, besides being the most developed, is the most famous for being used in ring inscription.

If you are interested, you can learn more in the links to the PDF version, source code and wikibook:

Dive into Python 3

Fri, 19 Aug 2016 00:00:00 +0000

Finally, I completed my translation of this book!

I started in 2009, after I completed the translation of Snake Wrangling for Kids. Mark Pilgrim published online an new version of his famous book, now about Python 3, so, after talking to him, I started the translation.

Circunstances of life, personal and work related, I stopped the translation at chapter fourteen. The two final chapters and four appendices were missing… And all the effort in revision and format edition.

Last may, I took LaTeX code of the translation, changed it from mercurial to git, and I migrated it to XeLaTeX.

And now! At the end of holiday, I completed translation and edition. And, as a complement, I generated a HTML version.

PDF: http://github.com/jmgaguilera/inmersionenpython3/releases
HTML: http://www.jmgaguilera.com/inmersionenpython3html
Código LaTeX: http://github.com/jmgaguilera/inmersionenpython3

I hope this translantion will facilitate the learning of this elegant, dinamic, object oriented language to Spanish-speaking people. I appreciate your feedback if you find mistakes I can correct.

Graphics in LaTeX with the package TikZ

Sun, 15 May 2016 00:00:00 +0000

To make a graphic using TikZ can be a crazy endeavour in our days. We have a lot of graphics tools that ease visual editing (WYSIWYG). But TikZ is adictive for it’s precision and semantic power. Yes, I am, again, talking about semantic: it’s so nice to define a tree just writing who is child of who; it’s so easy to define a graph as a link between nodes and edges; so simple to draw a function with precision, just by writing the function…

…And more: the organization and pause that it is necesary to design a graphic with TikZ, help to ensure that it has sense and consistency, not just visual impact.

These are the links to PDF version, LaTeX source and spanish LaTex wikibook entry:

I finish with a remark: User manual of TikZ has more than… One thousand pages!!. My article just scratches it surface!

Composing documents about Chess using LaTeX

Sat, 07 May 2016 00:00:00 +0000

It’s very complex to edit a document about chess games in any word processor, but LaTeX makes it easy. So, I decided to write an article about this and show you how to do it.

These are the links to PDF version, LaTeX source and spanish LaTex wikibook entry:

David Bowie Tribute (and III)

Sat, 26 Mar 2016 00:00:00 +0000

In the previous article I clusterized the songs and did a tree anlysis. Now, I will try a practical model to dynamically look for the nearest songs to a given one.

Los datos

Again, There are 254 songs in 25 albums.

In this case, to calculate the distance between songs, I will use the Term frequency - inverse document frequency (tf-idf) for each word in songs, instead of the absolute frequency of occurrence of each word.

The advantage of this algorithm is that it gives more weight to words that are very common in some documents, but doesn’t appears in a many documents in the corpus. That is, it makes some words more significant than others. With this system, the word cloud change a bit:

Nearest song to a given one

With the matrix of terms and using tf-idf frequencies we can easily determine which songs are the closest to a given one, based, as in the previous article, in the Euclidean distance between songs.

This method gives better results than the previous classification tree, in last article, which It was based on the absolute frequencies.

Let’s search the nearest songs to Lazarus:

song	distance
Lazarus	0.0000000
Cygnet Committee	0.6709544
Candidate	0.7180088
You Feel So Lonely You Could Die	0.7256923
We Are The Dead	0.7280794
Shining Star (Makin' My Love)	0.7353172
Somebody Up There Likes Me	0.7379833
Something In The Air	0.7549199
Ashes To Ashes	0.7621797
Five Years	0.7644335

Now, let’s see the nearest songs to Space Oditty:

song	distance
Space Oddity	0.0000000
Ashes To Ashes	0.8647541
Cygnet Committee	0.9391786
Candidate	0.9655204
You Feel So Lonely You Could Die	0.9688882
We Are The Dead	0.9700174
Shining Star (Makin' My Love)	0.9855299
If You Can See Me	0.9894812
Somebody Up There Likes Me	0.9911007
Five Years	1.0024585

The algorithm finds that the nearest song is Ashes to Ashes_. That is, the song in which appears also Major Tom. This is a significant improvement over the system based on the absolute frequencies in the previous article (You can look for this song there and observe the difference).

Let’s try one more song _ “Heroes” _:

song	distance
“Heroes”	0.0000000
Cygnet Committee	0.7270792
You Feel So Lonely You Could Die	0.7499512
Candidate	0.7565930
We Are The Dead	0.7641921
Shining Star (Makin' My Love)	0.7683600
Somebody Up There Likes Me	0.7725134
If You Can See Me	0.7843719
When I Live My Dream	0.7858126
Something In The Air	0.8051492

Network of songs

With the distances between songs, we can visualize the relationships between them through a network.

With the distances between all of them, the network would be unmanageable. We include just the most important relationships to have an idea of what is the result (in any case 254 songs, create a very complex network).

You can do infinite zoom in this network if you open this image in its own tab following this link: network of songs. Also you can download the image and open it with a svg viewer.

In the center of the image we find the songs who have more relationships. The other songs can be found around these.

References

The R document (rmarkdown) to reproduce this analysis can be found following this link

David Bowie Tribute (II)

Wed, 23 Mar 2016 00:00:00 +0000

David Bowie II (classifying his songs)

last article in my blog was dedicated to David Bowie.

I want to extend it searching relationships in his songs using as a features just his lyrics. Using ununstructured text analysis tools to find the distance between songs (I will use Euclidean distances).

Background

We have lyrics of 254 songs.

Like in last article, we have to convert lyrics to terms, discarding, if possible, the differences between plurals and verbs.

Términos

First of all, we create a word list to discard. Articles, pronoums, ...

i, me, my, myself, we, our, ours, ourselves, you, your, yours, yourself, yourselves, he, him, his, himself, she, her, hers, herself, it, its, itself, they, them, their, theirs, themselves, what, which, who, whom, this, that, these, those, am, is, are, was, were, be, been, being, have, has, had, having, do, does, did, doing, a, an, the, and, but, if, or, because, as, until, while, of, at, by, for, with, about, against, between, into, through, during, before, after, above, below, to, from, up, down, in, out, on, off, over, under, again, further, then, once, here, there, when, where, why, how, all, any, both, each, few, more, most, other, some, such, no, nor, not, only, own, same, so, than, too, very, s, t, can, will, just, don, should, now, d, ll, m, o, re, ve, y, ain, aren, couldn, didn, doesn, hadn, hasn, haven, isn, ma, mightn, mustn, needn, shan, shouldn, wasn, weren, won, wouldn, chorus, 'm, 's, 'll, n't, 'd, ca, wo, oh, yeah, x2.

Then we tokenize al lyrics, e.g., Blackstar lyrics:

in the villa of ormen in the villa of ormen stands a solitary candle ah-ah ah-ah in the centre of it all in the centre of it all your eyes on the day of execution on the day of execution only women kneel and smile ah-ah ah-ah at the centre of it all at the centre of it all your eyes your eyes ah-ah-ah ah-ah-ah in the villa of ormen in the villa of ormen stands a solitary candle ah-ah ah-ah in the centre of it all in the centre of it all your eyes ah-ah-ah something happened on the day he died spirit rose a metre and stepped aside somebody else took his place and bravely cried i’m a blackstar i’m a blackstar how many times does an angel fall how many people lie instead of talking tall he trod on sacred ground he cried loud into the crowd i’m a blackstar i’m a blackstar i’m not a gangster i can’t answer why i’m a blackstar just go with me i’m not a filmstar i’m-a take you home i’m a blackstar take your passport and shoes i’m not a popstar and your sedatives boo i’m a blackstar you’re a flash in the pan i’m not a marvel star i’m the great i am i’m a blackstar i’m a blackstar way up oh honey i’ve got game i see right so white so open-heart it’s pain i want eagles in my daydreams diamonds in my eyes i’m a blackstar i’m a blackstar something happened on the day he died spirit rose a metre and stepped aside somebody else took his place and bravely cried i’m a blackstar i’m a star star i’m a blackstar i can’t answer why i’m not a gangster but i can tell you how i’m not a flam star we were born upside-down i’m a star star born the wrong way ‘round i’m not a white star i’m a blackstar i’m not a gangster i’m a blackstar i’m a blackstar i’m not a pornstar i’m not a wandering star i’m a blackstar i’m a blackstar in the villa of ormen stands a solitary candle ah-ah ah-ah at the centre of it all your eyes on the day of execution only women kneel and smile ah-ah ah-ah at the centre of it all your eyes your eyes ah-ah-ah

Now we supress diferences between similar words, discarded words, etc. e.g. Blackstar:

villa ormen villa ormen stand solitari candl ah-ah ah-ah centr centr eye day execut day execut women kneel smile ah-ah ah-ah centr centr eye eye ah-ah-ah ah-ah-ah villa ormen villa ormen stand solitari candl ah-ah ah-ah centr centr eye ah-ah-ah someth happen day die spirit rose metr step asid somebodi els took place brave cri i'm blackstar i'm blackstar mani time angel fall mani peopl lie instead talk tall trod sacr ground cri loud crowd i'm blackstar i'm blackstar i'm gangster can't answer i'm blackstar go i'm filmstar i'm-a take home i'm blackstar take passport shoe i'm popstar sedat boo i'm blackstar you'r flash pan i'm marvel star i'm great i'm blackstar i'm blackstar way honey i'v got game see right white open-heart it pain want eagl daydream diamond eye i'm blackstar i'm blackstar someth happen day die spirit rose metr step asid somebodi els took place brave cri i'm blackstar i'm star star i'm blackstar can't answer i'm gangster tell i'm flam star born upside-down i'm star star born wrong way round i'm white star i'm blackstar i'm gangster i'm blackstar i'm blackstar i'm pornstar i'm wander star i'm blackstar i'm blackstar villa ormen stand solitari candl ah-ah ah-ah centr eye day execut women kneel smile ah-ah ah-ah centr eye eye ah-ah-ah

We do this with all lyrics. At the end we select the list of more frequent words. We will measure the distance between songs using de frequency of words. These are the 30 most frequent words:

freq        tok      word
---------  -----  -------
16.082115   love     love
13.795538   like     like
11.673021   look     look
11.623957    got      got
11.104668   know     know
11.050255   time     time
10.515605    day      day
10.002770    say     says
 9.683665    eye     eyes
 9.519472  could    could
 9.511280    one      one
 9.362057  never    never
 9.320950    let  letting
 9.214944   come     come
 9.207492   want     want
 9.036289    get      get
 8.794052    man      man
 8.770926   life     life
 8.730971    boy      boy
 8.549849  thing   things
 8.469426     go       go
 8.214135    see      see
 8.118021   feel     feel
 8.037416   live    lives
 7.983238  littl   little
 7.935172   girl    girls
 7.839930   take     take
 7.751650   noth  nothing
 7.165395   make     make
 7.109152   well     well
(...)

We saw this frequent words with the wordcloud of the last article.

Now we generate the distance matrix between songs from the matrix of frequencies of terms and documents. With this distance matrix clusterize songs using the ward method, to form a tree that allows you to see the potential clusters.

Como hay demasiadas canciones, cuesta distinguirlas en un árbol tan denso. There are too many songs, so it is difficult to guess the clusters in this dense tree. We can see the trunk of the tree:

We can assume that there are eight large groups. We'll show one by one to see if we find any relationship between the songs contained in them.

The most frequent words in group 0 are:
         freq   term    word
170  5.365048    say    says
224  4.782514   want    want
76   3.478711    got     got
18   2.581732    boy     boy
200  2.482339  thing  things

The first group consists of songs that include the words say, want, got, things and boys, among others.

The most frequent words in group 1 are:
         freq  term  word
122  4.380216   man   man
196  4.167142  take  take
18   4.055294   boy   boy
116  4.042343  long  long
67   4.020220   get   get

This group has 112 songs, it can be segregated more than this to analyze his songs. Nevertheless, you can detect that the two nearest songs are It's no game, parts 1 y 2.

The most frequent words in group 2 are:
         freq  term  word
119  7.463742  love  love
111  6.080673  like  like
32   5.357936  come  come
204  4.716788  time  time
8    4.685532  babi  baby

The songs of this group speak of love, time, etc.

The most frequent words in group 3 are:
         freq    term    word
113  3.624713   littl  little
68   1.478087    girl   girls
56   0.751800    feel    feel
7    0.749316    away    away
233  0.705218  wonder  wonder

The songs of this group speak of girls...

The most frequent words in group 4 are:
         freq  term     word
38   3.690027   day      day
144  3.295195   one      one
141  2.358883  noth  nothing
41   1.519879   die    dying
109  1.143182  life     life

The songs of this group speak of the day, life and death.

The most frequent words in group 5 are:
         freq  term   word
119  3.861197  love   love
68   0.995499  girl  girls
138  0.991495   new    new
70   0.553128    go     go
121  0.524290  make   make

The songs of this group are about girls and love.

The most frequent words in group 6 are:
         freq  term     word
36   3.604493  danc    dance
107  1.408223   let  letting
100  0.982583  know     know
52   0.647080  face     face
197  0.479504  talk  talking

The songs of this group speak of dance.

The most frequent words in group 7 are:
         freq   term   word
117  3.509484   look   look
187  0.770862  sound  sound
70   0.555531     go     go
7    0.545412   away   away
105  0.494563   leav  leave

And the last group speak primarily about look for something.

Epilogue

We can do it better, we can group similar words, we can use bigrams, improve and implement tokenization and other algorithms like tf-idf. We can select weights in the words, e.g. rare words in general but very common in certain songs, to select the songs more similar to others in terms of subject. I just hope, have contributed a bit with this tribute to one of the great Pop musicians of our time.

References

This time I used Python to do the analysis. You can use the source in this notebook of Jupyter.

David Bowie Tribute

Sun, 24 Jan 2016 00:00:00 +0000

Now that everyone is talking nice things about David Bowie, I have to say that I did not like him very much when I was young. I rediscovered him a few years ago with album The Next Day I ended up buying the vinyl, at a time when I no longer buy Compact Disc, It is something I reserve for those albums that exceed my expectations when listening on streaming.

After listening to Lazarus few days before his death, the news did not surprise me and verses of the song took on its full meaning.

Look up here, I’m in heaven

I’ve got scars that can’t be seen

I’ve got drama, can’t be stolen

Everybody knows me now

Look up here, man, I’m in danger

I’ve got nothing left to lose

I’m so high it makes my brain whirl

Dropped my cell phone down below

This weekend, listening to the album Blackstar, I wanted to do my little tribute with this brief analysis of his lyrics.

The Data

I have developed a Python script that accesses AZLyrics, and, with it, I have compiled the lyrics of 254 songs and 25 albums:

album	year
David Bowie	1967
Space Oddity	1969
The Man Who…	1970
Hunky Dory	1971
The Rise And Fall…	1972
Pin Ups	1973
Aladdin Sane	1973
Diamond Dogs	1974
Young Americans	1975
Station To Station	1976
“Heroes”	1977
Low	1977
Lodger	1979
Scary Monsters	1980
Let's Dance	1983
Tonight	1984
Never Let Me Down	1987
Black Tie White Noise	1993
Outside	1995
Earthling	1997
Hours…	1999
Heathen	2002
Reality	2003
The Next Day	2013
Blackstar	2016

What about his songs

This is the wordcloud of his entire career:

Amd this of the album Space Oddity:

Do you see Major Tom to Ground Control calling?

Am I sitting in a tin can

Far above the world

Planet Earth is blue

And there’s nothing I can do

Space Oddity

And this of his latest album Blackstar:

Something happened on the day he died

Spirit rose a metre and stepped aside

Somebody else took his place, and bravely cried

(I’m a blackstar, I’m a blackstar)

Blackstar

Words through the years

I will now show some important words in their lyrics and their frequency over the years: god, love, time, star, live & die

God & Love

Check ignition and may God’s love be with you — Space Oddity

Time & Star

Time, he’s waiting in the wings

He speaks of senseless things

His script is you and me, boys

Time

There’s a starman waiting in the sky

He’s told us not to blow it

‘Cause he knows it’s all worthwhile

Starman

Die & Live

In the two last albums die appears more than before…

Here I am

Not quite dying

My body left to rot in a hollow tree

Its branches throwing shadows

On the gallows for me

And the next day

And the next

And another day

The next day

Epilogue

This chaos is killing me

Yeah bye, bye love

Bye, bye love

Eternal

Sweet, sweet love

Bye bye space boy

Bye, bye love

Moon dust will cover you

Hallo spaceboy

References

Document in R (rmarkdown) to reproduce this analysis can be found following this link