Category: Coding

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WordPress Script Debugging

Code I used for echoing out the WordPress enqueued scripts and styles into the JS console (here).

Tl;DR

So I recently noticed that when developing child themes some of my CSS was being overwritten by the parent theme. This makes sense because according to WordPress’s developer resources: “the child theme is loaded before the parent theme“. Although that may work in many cases, this wasn’t going to cut it for mine.

I wrote a custom function to enqueue first the parent theme THEN the child theme so that the CSS from the child theme would be read latest and overwrite some of the rules of the parent. Done! Great! Wow! … Not quite…

This lead to a problem of a duplicate child stylesheet being loaded, which in most circumstances wouldn’t cause too much of an issue but in this case it caused errors when performing minor updates. The WordPress generator was stuck enqueuing an older version of my stylesheet along with the update. Needless these two running in congruence caused several issues.

I used some code from Lakewood echo out the scripts and styles enqueued by WordPress so I could find the duplicate script handle and dequeue it. Simple enough! I slapped it in there but unfortunately my duplicate problem stylesheet persisted.

Not to worry. Doing some digging I found that with messing with the priority of the enqueue/dequeue I was able to successfully remove the duplicate. I had to dequeue the duplicate after it was enqueued but before my custom one had enqueued. Anyways thanks for reading.

Code

Function to echo out enqueued scripts and styles.

/**
 * Print enqueued style/script handles
 * https://lakewood.media/list-enqueued-scripts-handle/
 * Modified by Chris Fust for a more JS friendly
 * readabily thing.
 */
function lakewood_print_scripts_styles() {
    if( !is_admin() && is_user_logged_in() && current_user_can( 'manage_options' )) {

        // push scripts and styles to arrays
        // so that they appear in the JSON
        // in the order they would enqueue
        // with wp_enqueue_*()

        // local vars
        $scripts = [];
        $styles = [];

        // Print Scripts
        global $wp_scripts;
        foreach( $wp_scripts->queue as $handle ) :
           $scripts[] = array( $handle => $wp_scripts->registered[$handle]->src );
        endforeach;

        // Print Styles
        global $wp_styles;
        foreach( $wp_styles->queue as $handle ) :
           $styles[] = array( $handle => $wp_styles->registered[$handle]->src );
        endforeach;

        // echo out scripts and styles
        // as separate objects
        echo '<script>';
        echo 'console.log({ "scripts" : ' . json_encode( $scripts ) . '});';
        echo 'console.log({ "styles" : ' . json_encode( $styles ) . '});';
        echo '</script>';
    }
}
// This is primarily used for debugging so
// I'm going to skip adding this function to the queue
// add_action( 'wp_print_scripts', 'lakewood_print_scripts_styles', 101 );
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Insect Counter

Necrophilous Beetles

Hello world! For my research on scavenging guilds in the Australian Alps, I tallied nearly 30,000 insects. To do so, I made a quick web-based tool that I could quickly tally what I found. Its easy to use including features to copy the tally in a table format to paste into MS Excel.

I know this is a tool taylored specifically for my project, but I think its functionality can be applied for others out there.

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Permutation Pyramid

OK so here’s an update to the permutation pyramid I made.

I had to make a list of different permutations of models but it became hard to keep track of all the different variables and their combinations. Hence, the permutation pyramid. The goal is to make a list of vector combinations and permutations. I found this super useful when generating formulas for AICc/Model selection.

Enjoy!

The function has a few options:

  1. vec
    • The vector to draw the combinations/permutations from
  2. order.matters = FALSE
    • A simple boolean TRUE / FALSE to add in the permutations of each vector combination. This will generate permutations of the combinations and add them to the list. This may only be useful for certain models
  3. req
    • A vector of elements that should be required in the combinations.
  4. interact
    • A list of vector combinations that can be found in the original combinations

Function

pyramid <- function( vec, order.matters = FALSE, req, interact ){
  # pyramid of variable combinations
  # this doesn't include different
  # arrangements
  vrz <- lapply(
    1:length( vec ), 
    function( x ){ 
      combn( vec, x ) %>% as.data.frame()
    } 
  ) %>% purrr::flatten() %>% unname()
  
  # If there are interactions
  if( !missing( interact ) ){
    # possible interaction combos
    intx <- seq( from = 1, to = length( interact ), by = 1 ) %>% knp.perm.pyramid()
    
    # extra interactions
    vrz.int <- list()
    
    # look through vars
    for( v in vrz ){
      
      # make a list of interactions
      # and add them to the list
      vrz.int <- append( 
        vrz.int, 
        lapply( intx, function( ints ){
          # count number of matches to compare
          # and filter out unaltered var lists
          mt <- 0
          
          # for each interaction
          # check if its in the array
          # then add it if it is
          for ( int in ints ) {
            if( 
                length( intersect( v, interact[[ int ]] ) ) == length( interact[[ int ]] ) & 
                length( interact[[ int ]] ) > 1 
              ){
                v <- c( v, paste0( interact[[ int ]], collapse = ":" ) )
                mt <- mt + 1
            }
          }
          
          # if the number of matches equals
          # the number of interactions then
          # return the altered array
          if( mt == length(ints) ){
            return( v )
          }
          return( NULL )
          
        }) %>% plyr::compact()
      )
    }
    
    # append interactions
    vrz <- append( vrz, vrz.int )
  }
  
  # order matters so lets rearrange
  if( order.matters ){
    vrz <- lapply( vrz, function( x ){
      combinat::permn( x )
    }) %>% purrr::flatten()
  }
  
  # if there's any required variables in each combination
  if( !missing( req ) ){
    vrz <- lapply(vrz, function( x ){
      if( length( intersect( x, req ) ) == length( req ) ){
        return( x )
      }
      return( NULL )
    }) %>% plyr::compact()
  }
  
  # return list of character
  # permutations
  return( vrz )
}

Examples / Usage

Plain vanilla use

> a <- c( "A", "B", "C" )
> pyramid( a )
[[1]]
[1] "A"

[[2]]
[1] "B"

[[3]]
[1] "C"

[[4]]
[1] "A" "B"

[[5]]
[1] "A" "C"

[[6]]
[1] "B" "C"

[[7]]
[1] "A" "B" "C"

If the order of the elements matters

> a <- c( "A", "B", "C" )
> pyramid( a, order.matters = TRUE )
[[1]]
[1] "A"

[[2]]
[1] "B"

[[3]]
[1] "C"

[[4]]
[1] "A" "B"

[[5]]
[1] "B" "A"

[[6]]
[1] "A" "C"

[[7]]
[1] "C" "A"

[[8]]
[1] "B" "C"

[[9]]
[1] "C" "B"

[[10]]
[1] "A" "B" "C"

[[11]]
[1] "A" "C" "B"

[[12]]
[1] "C" "A" "B"

[[13]]
[1] "C" "B" "A"

[[14]]
[1] "B" "C" "A"

[[15]]
[1] "B" "A" "C"

Require variables

> a <- c( "A", "B", "C", "D", "E" )
> b <- c( "B", "D" )
> pyramid( a, req = b )
[[1]]
[1] "B" "D"

[[2]]
[1] "A" "B" "D"

[[3]]
[1] "B" "C" "D"

[[4]]
[1] "B" "D" "E"

[[5]]
[1] "A" "B" "C" "D"

[[6]]
[1] "A" "B" "D" "E"

[[7]]
[1] "B" "C" "D" "E"

[[8]]
[1] "A" "B" "C" "D" "E"

Include interactions

> a <- c( "A", "B", "C")
> b <- list( c( "A", "B" ), c( "A", "B", "C" ) )
> pyramid( a, interact = b )
[[1]]
[1] "A"

[[2]]
[1] "B"

[[3]]
[1] "C"

[[4]]
[1] "A" "B"

[[5]]
[1] "A" "C"

[[6]]
[1] "B" "C"

[[7]]
[1] "A" "B" "C"

[[8]]
[1] "A"   "B"   "A:B"

[[9]]
[1] "A"   "B"   "C"   "A:B"

[[10]]
[1] "A"     "B"     "C"     "A:B:C"

[[11]]
[1] "A"     "B"     "C"     "A:B"   "A:B:C"
Posted on

Permutation Pyramid

So I had to make a combination of different values based on elements in a vector. With a few tweaks and what not I made a little function to find all the combinations and permutations of a given vector. Enjoy

The function:

# creat a list of combinations and
# permutations of elements from
# a single vector. This requires
# a few libraries:
# library( dplyr )
# library( purrr ) 
# library( combinat ) 

# "order.matters" is means that 
# for every combination of elements
# find every order they can be
# arranged.

permutation.pyramid <- function( v, order.matters = TRUE ){

  # get the unique combinations of elements
  # and flatten them into a one dimensional list
  out <- 1:length(v) %>% 
    lapply(function( x ){ 
      combn( v, x ) %>% as.data.frame()
    }) %>% 
    purrr::flatten() %>% 
    unname()
  
  # if order.matters then find all the 
  # arrangements of each combination
  if( order.matters ){
    out <- out %>% 
      lapply(function( x ){
        combinat::permn( x )
      }) %>% 
      purrr::flatten()
  }
  
  # return list of permutations
  return( out )
}

Use and examples.

Find all combinations and permutations of a given vector

> test <- c("A","B","C")
> permutation.pyramid( test )
[[1]]
[1] "A"

[[2]]
[1] "B"

[[3]]
[1] "C"

[[4]]
[1] "A" "B"

[[5]]
[1] "B" "A"

[[6]]
[1] "A" "C"

[[7]]
[1] "C" "A"

[[8]]
[1] "B" "C"

[[9]]
[1] "C" "B"

[[10]]
[1] "A" "B" "C"

[[11]]
[1] "A" "C" "B"

[[12]]
[1] "C" "A" "B"

[[13]]
[1] "C" "B" "A"

[[14]]
[1] "B" "C" "A"

[[15]]
[1] "B" "A" "C"

Find all combinations of vector

> test <- c("A","B","C")
> permutation.pyramid( test, order.matters = FALSE )
[[1]]
[1] "A"

[[2]]
[1] "B"

[[3]]
[1] "C"

[[4]]
[1] "A" "B"

[[5]]
[1] "A" "C"

[[6]]
[1] "B" "C"

[[7]]
[1] "A" "B" "C"

Anyways I hope that’ll be useful for someone!

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Confidence Intervals

A small little function written in R to get the 95% confidence intervals and some quick stats of a vector. I found this useful in error reporting in some stats. I got the concept from this tutorial.

This does require the dplyr library

Function

library( dplyr )

ci <- function( x ){
  cnf <- dplyr::tibble(
    mean = mean( x, na.rm = TRUE),
    st.dev = sd( x, na.rm = TRUE),
    n = length( x ),
    error = qnorm( 0.975 ) * st.dev / sqrt( n ),
    ci05 = mean - error,
    ci95 = mean + error
  )
  
  cat( cnf$mean, "(", cnf$ci05, "-", cnf$ci95, ")\n" )
  return( cnf )
}

Use

> x <- sample(10)
> ci(x)
5.5 ( 3.623477 - 7.376523 )
# A tibble: 1 x 6
   mean st.dev     n error  ci05  ci95
  <dbl>  <dbl> <int> <dbl> <dbl> <dbl>
1   5.5   3.03    10  1.88  3.62  7.38
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Quick update to Grade Calculator

Just a quick update to make the Grade Calculator more user friendly.

  1. Added a very simple notification system for the overall grade. It’ll now tell you if you pass a certain grade threshold for the overall mark. Default is 50% for all the Aussie grading system
  2. Added a very simple settings UI to adjust the passing grade threshold and turn notifications on/off

Hope y’all can enjoy that and find it useful.

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Grade Calculator

As a coding exercise and my first dip into Angular JS I thought I’d make a grade calculator to help me estimate grades. I hope it helps anyone interested in estimating your grades needed to pass a course.

The link to the calculator can be found here.