Vindaar

type
  BitArray[T: static[int]] = array[T, '0'..'1']
  
proc `[]=`(b: var BitArray, ind: int, val: int) =
    b[ind] = if val == 1: '1' else: '0'

proc createBitarray(size: static[int]): BitArray[size] =
  for i in 0 ..< size:
    result[i] = '0'

Vindaar

import macros, times, strutils, tables

type
  Data* = object
    value*: int
    fvalue*: float
    tvalue*: Time

  Container* = object
    desc*: string

Vindaar

import macros

type
  Container = object
    field1: string
    field2: string
    fieldInt: int

macro setField(obj: untyped, name: string, val: untyped): untyped =
  result = newStmtList()

Vindaar

import ../src/arraymancer, random

# This is an early minimum viable example of handwritten digits recognition.
# It uses convolutional neural networks to achieve high accuracy.
#
# Data files (MNIST) can be downloaded here http://yann.lecun.com/exdb/mnist/
# and must be decompressed in "./build/" (or change the path "build/..." below)
#

Vindaar

import sequtils
proc `[]`*[T](a: openArray[T], inds: seq[int]): seq[T] {.inline.} =
  ## given two openArrays, return a sequence of all elements whose indices
  ## are given in 'inds'
  ## inputs:
  ##    a: seq[T] = the sequence from which we take values
  ##    inds: openArray[int] = the array which contains the indices for the
  ##         arrays, which we take from 'array'
  ## outputs:
  ##    seq[T] = a sequence of all elements s.t. array[ind] in numpy indexing

Vindaar

proc compilerError*[T](x: seq[T], y = @[]) =
  discard
  
proc linkerError*[T](x: seq[T], y: seq[T] = @[]) =
  discard

when isMainModule:
  let a = @[0, 1, 2]
  #compilerError(a)
  linkerError(a)

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macro setTabFields[T: (float | int), N: int](tab: Table[string, seq[seq[T]]],
                                             names: array[N, string],
                                             chip: int,
                                             obj: untyped): untyped =
  ## taking some table `tab` sets all fields of the table taken from an array `names` to the
  ## fields of the object `obj` of the same names as the names in the table
  result = newStmtList()
  let namesImpl = names.symbol.getImpl
  for name in namesImpl:
    let field = parseExpr(name.strVal)

Vindaar

# same as the Julia example, but in Python
# I hope I made no mistake (mistaking an attribute as a
# function or something like this while converting it
# from the Julia to Python. Didn't test it)

def draw_new_frame(fig, cb, image, frame, file)
    image.set_data(frame)

    # get the filename written prev.
    texts = fig.texts

Vindaar

function draw_new_frame(fig, cb, image, frame, file)
    image[:set_data](frame)

    # get the filename written prev.
    texts = fig[:texts]
    for i in range(1, length(texts))
        # and remove each
        texts[1][:remove]()
    end
    # write new filename

Vindaar

import pylab
# taken from some random webpage several years ago. Sorry author :(

def fancy_plotting():
    # set up some LaTeX plotting parameters
    # still need to change parameters
    # next line is for standard article document
    # fig_width_pt = 478.00812#246.0  # Get this from LaTeX using \showthe\columnwidth
    # next line is for thesis after Brock thesis guide
    fig_width_pt = 451.58598