add basic JS support by using custom Tensor replacement

.github/workflows/ci.yml

@@ -61,6 +61,7 @@ jobs:
         run: |
           cd datamancer
           nimble -y test
+          nimble -y testJs
 
       - name: Build docs
         if: >

README.org

@@ -1,17 +1,18 @@
-
 * Datamancer
 [[https://github.com/SciNim/datamancer/workflows/datamancer%20CI/badge.svg]]
 [[https://matrix.to/#/#nim-science:envs.net][https://img.shields.io/static/v1?message=join%20chat&color=blue&label=nim-science&logo=matrix&logoColor=gold&style=flat-square&.svg]]
 [[https://discord.gg/f5hA9UK3dY][https://img.shields.io/discord/371759389889003530?color=blue&label=nim-science&logo=discord&logoColor=gold&style=flat-square&.svg]]
 
+~Datamancer~ is a DataFrame library for Nim, which is heavily inspired
+by [[https://dplyr.tidyverse.org/][dplyr]].
+
 ** Comparison to other dataframe libraries
 
 Check out the following gist for a comparison of this library with
 dplyr (R) and pandas (Python):
 
 https://gist.github.com/Vindaar/6908c038707c7d8293049edb3d204f84
 
-
 ** Documentation
 
 The documentation is found at:
@@ -34,6 +35,19 @@ nimble install datamancer
 #+END_SRC
 away.
 
+** Backend targets
+
+The library supports both Nim's C/C++ backends as well as the
+JavaScript target. The latter was added in ~v0.4.2~ and is still
+experimental. Certain features are currently not supported (reading
+files from disk, reading CSVs from a URL).
+
+Note also that on older versions than current devel (as of
+<2024-02-19 Mon 14:47>) including the current stable, in certain
+applications the formula macro ~f{}~ (see below) does not resolve
+types in the way it should on the JS backend. You might need to give
+explicit type hints in more cases than usual (also see below).
+
 ** Features and formulas
 
 The data frame provides the "5 verbs" of [[https://dplyr.tidyverse.org/][dplyr]] and more. Main implemented functions:

datamancer.nimble

@@ -10,7 +10,7 @@ srcDir        = "src"
 # Dependencies
 
 requires "nim >= 1.2.0"
-requires "https://github.com/Vindaar/seqmath >= 0.1.11"
+requires "https://github.com/Vindaar/seqmath >= 0.2.1"
 requires "arraymancer >= 0.7.28"
 
 task test, "Run standard tests":
@@ -21,6 +21,9 @@ task test, "Run standard tests":
   exec "nim c -r tests/testsFormula.nim"
   exec "nim c -r tests/testParse.nim"
 
+task testJs, "Run test for JS":
+  exec "nim js -r tests/testdf_js_simple.nim"
+
 import os, strutils, strformat
 const
   pkgName = "datamancer"

src/datamancer.nim

@@ -1,4 +1,8 @@
 ## .. include:: ./docs/datamancer.rst
 
-import datamancer / [dataframe, io]
-export dataframe, io
+when not defined(js):
+  import datamancer / [dataframe, io]
+  export dataframe, io
+else:
+  import datamancer / [dataframe, io]
+  export dataframe, io

src/datamancer/column.nim

@@ -1,4 +1,8 @@
-import arraymancer/tensor
+when not defined(js):
+  import arraymancer/tensor
+else:
+  import seq_tensor
+
 import std / [sugar, strformat, tables, macros, strutils]
 import value
 
@@ -346,14 +350,15 @@ proc toColumn*[C: ColumnLike; T](_: typedesc[C], t: Tensor[T]): C =
       doAssert false, "This should not happen!"
       #{.error: "Cannot store " & $T & " in a regular `Column`.".}
 
-proc toColumn*[T: not SupportedTypes](t: openArray[T] | Tensor[T]): auto =
-  ## Tries to convert the given input data to a matching generic `Column*`
-  ## type. Errors at CT if there is no matching `Column*` defined so far.
-  when typeof(t) is Tensor:
-    let x = t
-  else:
-    let x = t.toTensor()
-  result = colType(T).toColumn(t)
+when not defined(js) or (NimMajor, NimMinor, NimPatch) >= (2, 1, 0):
+  proc toColumn*[T: not SupportedTypes](t: openArray[T] | Tensor[T]): auto =
+    ## Tries to convert the given input data to a matching generic `Column*`
+    ## type. Errors at CT if there is no matching `Column*` defined so far.
+    when typeof(t) is Tensor:
+      let x = t
+    else:
+      let x = t.toTensor()
+    result = colType(T).toColumn(t)
 
 proc toColumn*[C: ColumnLike; T](_: typedesc[C], t: openArray[T]): C =
   if t.len > 0:

src/datamancer/dataframe.nim

@@ -1,8 +1,12 @@
 import std / [macros, tables, strutils, options, sets, hashes, math,
               sequtils, stats, strformat, algorithm, typetraits]
 
-import arraymancer/tensor
-export tensor
+when not defined(js):
+  import arraymancer/tensor
+  export tensor
+else:
+  import seq_tensor
+  export seq_tensor
 
 import value
 export value
@@ -784,7 +788,7 @@
     doAssert df.len == 3
     doAssert df["x", int] == [1, 3, 5].toTensor
     doAssert df["y", int] == [2, 5, 10].toTensor
   {.warning: "Using `add` to add rows to a DF individually is very slow. Be " &
     "sure to only add very few rows using this proc!".}
   if args.tupleLen != df.ncols and df.ncols != 0:
     raise newException(ValueError, "Input tuple `args` length must be equal " &
@@ -1983,7 +1987,7 @@
       for k in getKeys(result):
         if result[k].kind != colConstant: # if constant nothing to short
           withNativeTensor(result[k], t):
-            result.asgn(k, toColumn(t[_ ..< result.len]))
+            result.asgn(k, toColumn(t[0 ..< result.len]))
         result[k].len = result.len
 
 proc innerJoin*[C: ColumnLike](dfs: varargs[DataTable[C]], by: string): DataTable[C] =

src/datamancer/formulaExp.nim

@@ -709,8 +709,11 @@ proc convertLoop(p: Preface, dtype, fctColResType, loop: NimNode,
                  fnKind: FormulaKind,
                  generateLoop: bool): NimNode =
   let memCopyable = ["float", "int", "bool"]
-  let isMemCopyable = dtype.strVal in memCopyable and
-    p.args.allIt(it.colType.strVal in memCopyable)
+  when defined(js):
+    let isMemcopyable = false
+  else:
+    let isMemCopyable = dtype.strVal in memCopyable and
+      p.args.allIt(it.colType.strVal in memCopyable)
   proc genForLoop(p: Preface, loop: NimNode, fkKind: FormulaKind): NimNode =
     var mpreface = p
     let loopIndexed = fixupTensorIndices(loop, mpreface,