csv-types

CSV Types (csv-types-js)

CSV Types (csv-types-js) is a JavaScript library to parse CSV strings (comma separated values and text files with fields delimited by a character) and produce a JavaScript AST (abstract syntax tree) with the data. It also supports types specs: multiple headers-values (tables) per csv string.

Online tools:

This library is commonly used with FlexTable to facilitate the data manipulation produced by CSV Types (the data structure is consumed by FlexTable).

Table of Contents

1. Description
2. Installation
3. Examples
4. Options
5. Test & Coverage
6. Group4Layers use case (or why CSV Types)
7. New features
8. Author
9. ChangeLog
10. License

Description

CSV Types (csv-types-js) is a JavaScript library to parse CSV strings (comma separated values and text files with fields delimited by a character) and produce a JavaScript AST (abstract syntax tree) with the data. It also supports types specs: multiple headers-values (tables) per csv string.

It parses four types of CSV formats, being the first two common between different applications and parsers, but have disadvantages over the last two that we are using in Group4Layers.

Format 1: Values.

event,2017-01-03,sport,running,minutes,35
event,2017-02-05,sport,press bench,kg,85-100-104-106-106
event,2017-02-05,sport,press bench,repetitions,12-10-10-8-7
event,2017-02-07,sport,pull-up,repetitions,12-12-10-10-10

Format 2: Header and values.

date,activity,action,units,value
2017-01-03,sport,running,minutes,35
2017-02-05,sport,press bench,kg,85-100-104-106-106
2017-02-05,sport,press bench,repetitions,12-10-10-8-7
2017-02-07,sport,pull-up,repetitions,12-12-10-10-10

Format 3: Allow comments, header is commented and values. Now you can provide more info, avoid confusing values with comments and headers (using a highlighter editor/viewer like Emacs) easing the interpretation and modification.

#date,activity,action,units,value
2017-01-03,sport,running,minutes,35
# I slept just 5 hours
2017-02-05,sport,press bench,kg,85-100-104-106-106
2017-02-05,sport,press bench,repetitions,12-10-10-8-7
# I had some pain in my right shoulder
# the gym was closed 2017-02-06, so, I work-out the next day
2017-02-07,sport,pull-up,repetitions,12-12-10-10-10

Format 4: Types specs: comments, multiple commented headers and multiple type of values. Now you can provide CSV files that are self-contained, with multiple type of values in the same file. It is flexible and you can alter the number of columns in the future, improving the expressivity of the data. It has the advantages of the previous format but with the maximum flexibility (multiple tables in the same file).

#type-sport,date,activity,action,units,value
#type-sleep,date,hours
type-sport,2017-01-03,sport,running,minutes,35
type-sleep,2017-02-05,5
type-sport,2017-02-05,sport,press bench,kg,85-100-104-106-106
type-sport,2017-02-05,sport,press bench,repetitions,12-10-10-8-7
#type-body-condition,part,severity,description
type-body-condition,right shoulder,high,concentrated pain in the back part of my right shoulder
# the gym was closed 2017-02-06, so, I work-out the next day
type-sport,2017-02-07,sport,pull-up,repetitions,12-12-10-10-10

A real-world example of this format (Types specs) can be seen in section Group4Layers use case (or why CSV Types).

This library is commonly used with FlexTable to facilitate the data manipulation produced by CSV Types (the data structure is consumed by FlexTable).

Installation

npm i csv-types -S

Or from the repo:

npm i "http://github.com/Group4Layers/csv-types-js.git"

It has been tested with node >= 6, but it is widely used in Firefox and Chrome with building tools like webpack.

Examples

The best way to learn something is to see how it behaves.

The configuration is set in the constructor new CSV() and with the method lCSV.configure() when the object is built. Every consequent call to parse will use the last options configured (it is overwritten with every configure call).

const CSV = require('csv-types').CSV;
let lCSV = new CSV(yourOptions); // configure if you need to change defaults
// ...
lCSV.configure(yourNewOptions); // reconfigure if you want

You can just apply the defaults by doing configure(null) or configure({}) (the same for the constructor).

See the available options for configure in Options.

CSV with types

#type-a,col1,col2
type-a,1,2
type-a,2,3
#type-b,2
type-b,2

const CSV = require('csv-types').CSV;
let results = new CSV({types:true})
    .parse(`#type-a,col1,col2
type-a,1,2
type-a,2,3
#type-b,2
type-b,2`);

results:

{ "a": { headers: ["col1", "col2"],
         hlength: 2,
         values: [["1", "2"], ["2", "3"]], vlength: 2 },
  "b": { headers: ["2"], hlength: 1, values: [["2"]], vlength: 1 } }

Normal CSV (no types)

#type-a,col1,col2
type-a,1,2
type-a,2,3

const CSV = require('csv-types').CSV;
let lCSV = new CSV();
let results = lCSV.parse(`#type-a,col1,col2
type-a,1,2
type-a,2,3`);

results:

{ "a": { headers: ["type-a", "col1", "col2"],
         hlength: 3,
         values: [["type-a", "1", "2"], ["type-a", "2", "3"]], vlength: 2 } }

Normal csv (no types) and no header definition

#type-a,col1,col2
type-a,1,2
type-a,2,3,4,5

const CSV = require('csv-types').CSV;
let lCSV = new CSV({ headers: false });
let results = lCSV.parse(`#type-a,col1,col2
type-a,1,2
type-a,2,3,4,5`);

results:

{ "a": { headers: [],
         hlength: 0,
         values: [["type-a", "1", "2"], ["type-a", "2", "3", "4", "5"]],
         vlength: 2 } }

Using number casting

case,first,second
type-a,1.01,2
type-a,2,-3

const CSV = require('csv-types').CSV;
 
let lCSV = new CSV();
lCSV.configure({ cast: true, firstLineHeader: true });
let results = lCSV.parse(`case,first,second
type-a,1.01,2
type-a,2,-3`);

results:

{ headers: ["case", "first", "second"],
  hlength: 3,
  values: [["type-a", 1.01, 2], ["type-a", 2, -3]], vlength: 2 } }

Using casting with types

#type-a,1,2
type-a,1,2
type-a,2,3

const CSV = require('csv-types').CSV;
 
function castFn(value, isHeader, type, column){
  let ret = value;
  if (isHeader){
    ret = "the" + value;
  }else{
    if (/^[\d.]+$/.test(value)){
      ret = Number(value);
    }
  }
  return ret;
}
 
let results = new CSV({types:true, cast: castFn}).parse(`#type-a,1,2
type-a,1,2
type-a,2,3`);

results:

{ "a": { headers: ["the1", "the2"],
         hlength: 2,
         values: [[1, 2], [2, 3]], vlength: 2 } }

Using casting without types

#type-a,1,2,3
type-a,1,2,tres
# comment
type-a,1,2,tres

const CSV = require('csv-types').CSV;
 
function castFn(value, isHeader, type, column, row){
  let ret = value;
  if (/^[\d.]+$/.test(value)){
    ret = Number(value);
  }else if (type == ''){
    ret = `r${row}c${column}`;
  }
  return ret;
}
 
let lCSV = new CSV({ headers: false, cast: castFn });
let results = lCSV.parse(`#type-a,1,2,3
type-a,1,2,tres
# comment
type-a,1,2,tres`);

results:

{ headers: [],
  hlength: 0,
  values: [['r0c0', 1, 2, "r0c3"], ['r1c0', 1, 2, "r1c3"]], vlength: 2 } }

Using row function to alter based on post-processing

⮒
#type-a,1,2
type-a,1,2
type-a,3,5
#type-b,1,2,3
type-b,1,2,tres
⮒

const CSV = require('csv-types').CSV;
 
function rowFn(array, type, definition, row){
  if (type === 'b'){
    return false;
  }else{
    let idx = definition.headers.indexOf("2");
    if (array[0] == "1" && array[idx] == "2"){
      array[0] = "2";
      array[idx] = -1;
    }
  }
}
 
let lCSV = new CSV({row: rowFn});
lCSV.configure({row: rowFn, types:true}); // options are overwritten
let results = lCSV.parse(`
#type-a,1,2
type-a,1,2
type-a,3,5
#type-b,1,2,3
type-b,1,2,tres
`);

results:

{ a: { headers: [ '1', '2' ],
       hlength: 2,
       values: [ ["2", -1], ["3", "5"] ], vlength: 2 },
  b: { headers: [ '1', '2', '3' ], hlength: 3,
       values: [], vlength: 0 } }

Using row function to alter based on post-processing with no types

⮒
#type-a,b,c,d
type-a,1,2,3
type-a,4,0,-1
⮒

function rowFn(array, type, definition, row){
  let sum = 0;
  let i = 0;
  for (let col of array){
    if (i > 0){
      sum += col;
      array[i] = Number(col);
    }
    i++;
  }
  if (sum > 3){
    return false;
  }
}
let lCSV = new (require('csv-types')).CSV({ types: false, headers: false, row: rowFn });
let results = lCSV.parse(`
#type-a,b,c,d
type-a,1,2,3
type-a,4,0,-1
`);

results:

{ headers: [], values: [["type-a", 4, 0, -1]], vlength: 1 },

Capturing error

#type-a,1,2,3
type-a,1,2

const CSV = require('csv-types').CSV;
let lCSV = new CSV();
lCSV.configure({ fail: function(m){ popup.error(m); return m; } });
let results = CSV.parse(`
#type-a,1,2,3
type-a,1,2
`);

In this case the lCSV.parse method would trigger popup.error(m) instead of console.log(m).

results:

"invalid row length 2 (header length 3) in line 3 col 11:\ntype-a,1,2\n"

Custom delimiter, escape and comment chars

%field;num;str
% comment
`escaped; as you see`;243;string
`escaped`; as you see;243

const CSV = require('csv-types').CSV;
let lCSV = new CSV({ delimiter: ';', escape: "`", comment: '%' });
let results = lCSV.parse(`
%field;num;str
% comment
\`escaped; as you see\`;243;string
\`escaped\`; as you see;100
`);

results:

{ headers: [ 'field', 'num', 'str' ],
  hlength: 3,
  values:
   [ [ 'escaped; as you see', '243', 'string' ],
     [ 'escaped', 'as you see', '100' ] ],
  vlength: 2 }

Options

By default:

const opts = {
  fail: function(m){
    console.log(m);
    return {
      fail: m,
    };
  },
  trim: true,
  trimEscaped: false,
  types: false,
  headers: true,
  firstLineHeader: false,
  delimiter: ',',
  escape: '"',
  comment: '#',
  cast: false,
  row: false,
};

option	type	description
fail	func	function to fail (error is capturable)
trim	bool	trim space in value (headers are always trimmed)
trimEscaped	bool	trim space in those escaped values (eg. " a " to "a")
types	bool	use types (allows multiple definitions per string)
headers	bool	you can omit headers when used with no types (flexible values)
firstLineHeader	bool	headers are in the first not empty line (and not commented)
delimiter	char	column character delimiter
escape	char	column escape character
comment	char	comment char (omits the line)
cast	bool/func	cast function for every value (by default false: no casting)
row	bool/func	row function for every row values

If the cast function receives true it casts values that match the regexp /^[-+]?[\d.]+$/ to numbers. Those that do not match are not casted, so, they are considered strings.

The option firstLineHeader only works if headers is true.

The option headers only works if types is false (because types needs headers always).

The cast function receives this parameters:

• value (any): the value (after the trimming, if applicable)
• isHeader (bool): true if it is a header or not
• type (string): type of the row (receives an empty string '' if types are not used)
• column (int): the column index starting from 0 (the first)
• row (int): the row index starting from 0 (the first).

And the value returned is inserted as the column value.

function cast(value, isHeader, type, column, row){
  // the return value is used for this column
}

The row function is not called for the headers and it receives this parameters:

• value (any[]): array of values
• type (string): type of the row (receives '' if no types)
• definition (definition{}): the global object with definitions (headers) and values so far
• row (int): the row index starting from 0 (the first)

And if false is returned, the row is not inserted in values.

function row(value, type, definition, row){
  // if false is returned, the row is omitted
}

The definition{} object is:

{
  headers: any[],       // list of values
  hlength: int,         // headers length
  values: [any[], ...], // list of lists
  vlength: int          // values length (rows)
}

Options for formats

Depending on the CSV format different options are needed for the CSV constructor or the method configure.

Format 1: Values

event,2017-01-03,sport,running,minutes,35
event,2017-02-05,sport,press bench,kg,85-100-104-106-106

lCSV.configure({ headers: false });

Format 2: Header and values.

date,activity,action,units,value
2017-01-03,sport,running,minutes,35

new CSV{ firstLineHeader: true });

Format 3: Allow comments, header is commented and values.

#date,activity,action,units,value
2017-01-03,sport,running,minutes,35
# I slept just 5 hours
2017-02-05,sport,press bench,kg,85-100-104-106-106

Default options (new CSV()).

Format 4: Types specs: comments, multiple commented headers and multiple type of values.

#type-sport,date,activity,action,units,value
#type-sleep,date,hours
type-sport,2017-01-03,sport,running,minutes,35
type-sleep,2017-02-05,5
type-sport,2017-02-05,sport,press bench,kg,85-100-104-106-106
type-sport,2017-02-05,sport,press bench,repetitions,12-10-10-8-7
#type-body-condition,part,severity,description
type-body-condition,right shoulder,high,concentrated pain in the back part of my right shoulder
# the gym was closed 2017-02-06, so, I work-out the next day
type-sport,2017-02-07,sport,pull-up,repetitions,12-12-10-10-10

lCSV.configure({ types: true });

Group4Layers use case (or why CSV Types)

We develop the CSV types specification to allow self-contained CSV files for some applications we are developing. The advantage of CSV over other formats is that our clients (and ourselves) can modify the files without JavaScript knowledge (JSON or JavaScript objects) and with a simple text editor.

One of the applications is highly used in different areas of the company, involving benchmarking, analysis and comparisons. We have many systems/apps to be tested, and some of them create charts with data of different nature. After days of executions we ended with thousands of files, often, connected between them. With the application of CSV Types we ended writing CSV files self-contained (different format types in the same file), reducing drastically the amount of them and having a whole execution in the same file.

#type-bench,bench_ts,name,compilation_opts,use_c1,use_c2,use_c3,max_cs,devices,scheduler_num,scheduler,c1_power,c2_power,c2_power,num_packages,hguided_params,min_pkg_c1,min_pkg_c2,min_pkg_c3,k,program_args,total_time,total_ws,num_packages_launched,lws,gws,joules_cs,joules_cgs,rest
type-bench,1498616602,"binomial","-O2",1,0,0,0,"c1",1,"static",1.000000,1.270000,1.000000,80,2409901,40,99,1,2,"40960000 255",235.331238,163840000,1,256,2621440000,45147.984375,50370.000000,
#type-event,bench_ts,event_type,event_id,device,status,package_size,time_offset,index,value,event_info,rest
# ...
type-event,1498617107,"CB_KERNEL_END",159,"C1","NULL",736,66.736061,163833200,0.000000,"",
type-event,1498617107,"CB_KERNEL_END",160,"C2","NULL",1584,66.736267,163831616,0.000000,"",
type-event,1498617107,"CB_KERNEL_END",161,"C1","NULL",656,66.737885,163833936,0.000000,"",
type-event,1498617107,"CB_KERNEL_END",162,"C3","NULL",1584,66.739273,163834592,0.000000,"",
type-event,1498617107,"CB_KERNEL_END",163,"C1","NULL",640,66.739395,163836176,0.000000,"",
type-event,1498617107,"CB_KERNEL_END",164,"C3","NULL",640,66.740936,163838400,0.000000,"",
type-event,1498617107,"CB_KERNEL_END",165,"C2","NULL",1584,66.741219,163836816,0.000000,"",
type-event,1498617107,"CB_KERNEL_END",166,"C1","NULL",640,66.742310,163839040,0.000000,"",
#type-energy,bench_ts,id,time_offset,watts_cs,joules_total_cs,watts_cgs,joules_total_cgs,rest
# ...
type-energy,1498616602,1,0.001046,0.000000,0.000000,107.000000,42.800000,
type-energy,1498616602,2,0.215948,-0.000000,11.641693,109.000000,86.400000,
type-energy,1498616602,3,0.415947,61.951837,24.031937,109.000000,130.000000,
type-energy,1498616602,4,0.615965,56.104850,35.253860,109.000000,173.600000,
type-energy,1498616602,5,0.815941,56.532052,46.558914,107.000000,216.400000,
type-energy,1498616602,6,1.016038,57.687933,58.102097,107.000000,259.200000,
type-energy,1498616602,7,1.215941,52.626995,68.622391,107.000000,302.000000,

Test & Coverage

npm test
npm run coverage

Tests covered:

  CSV Types Parser
    ✓ single type
    ✓ multiple types
    ✓ escaped by double quotes
    ✓ trim
    ✓ trim escaped
    ✓ not trim
    ✓ not trim and trim escaped
    ✓ open escape double quotes fail
    ✓ open escape single quotes fail (custom escape char)
    ✓ no header definition works (no types, no headers)
    ✓ discard comments
    ✓ discard comments (custom comment char)
    ✓ use custom delimiter char (; with no headers)
    ✓ use custom delimiter char (; with one col)
    ✓ use custom delimiter, escape and comment chars
    ✓ no header definition fails
    ✓ no header definition fails (no types)
    ✓ diff header definition fails
    ✓ invalid header definition fails
    ✓ all comments are ok
    ✓ rows can start with optional spaces
    ✓ headers are trimmed
    ✓ headers can have no values
    ✓ last row value is parsed when EOF
    ✓ last header is parsed when EOF
    ✓ repeated type header definition fails
    ✓ single (no type)
    ✓ invalid row length fails (no type)
    ✓ different row lengths without headers (no type)
    ✓ types overwrite the config headers false
    ✓ cast with types
    ✓ cast with types (using number caster)
    ✓ cast with no types
    ✓ row postprocessing with types
    ✓ row postprocessing with no types
    ✓ valid type row has to start with type-
    ✓ overwrite options with defaults
    ✓ default is with headers but not types
    ✓ firstLineHeader is true
    ✓ firstLineHeader only works when headers is true
    ✓ firstLineHeader is true (with headers)
    ✓ wrong options are discarded
 
 
  42 passing (18ms)

New features

You can request new features for this library by opening new issues. If we find it useful (or there are at least 2 users interested in a proposal) we can implement it. Also, we accept pull requests with bugfixes or new features.

Author

nozalr [e-mail] (Group4Layers®).

ChangeLog

GitHub/Gitlab readers (repo, no docs): CHANGELOG.md.

License

CSV Types (csv-types-js) source code is released under the MIT License.

GitHub/Gitlab readers (repo, no docs): LICENSE.md.