More typos, part 3

pyproject.toml

@@ -120,4 +120,4 @@ skip = '.git*,*.css,pyproject.toml,data,swc,*.swc,obj,*.obj,*.pyi,*.c'
 check-hidden = true
 # embedded images into jupyter notebooks, acronyms and names starting with capital letter
 ignore-regex = '(^\s*"image/\S+": ".*|\b([A-Z][a-zA-Z]+|scl/fo/|ser: Series|ot\.lp|networkx\.algorithms\.mis)\b)'
-ignore-words-list = 'coo,ot'
+ignore-words-list = 'coo,ot,thi'

src/cajal/ugw/lib/github.com/diku-dk/containers/array.fut

@@ -11,7 +11,7 @@ import "../sorts/merge_sort"
 
 module engine = xorshift128plus
 type rng = xorshift128plus.rng
-                            
+
 local def estimate_distinct' [n] 't
                              (r: rng)
                              (hash: i64 -> t -> i64)
@@ -26,7 +26,7 @@ local def estimate_distinct' [n] 't
   -- Multiply by two for a better estimate in cases of collisions.
   -- Since roughly half of the keys are expected to result in a collision.
   in (new_rng, 2 * count)
-     
+
 local def create_sample [n] 't
                         (arr: [n]t)
                         (rng: rng)
@@ -52,7 +52,7 @@ local def estimate_distinct [n] 't
                             (arr: [n]t):
                             (rng, i64) =
   let (new_rng, count) =
-    loop (rng, acc) = (rng, 0) for _i < times do 
+    loop (rng, acc) = (rng, 0) for _i < times do
     let (rng, sample) = create_sample arr rng sample_size
     let (rng, c) = estimate_distinct' rng hash sample
     in (rng, acc + c / times)
@@ -61,7 +61,7 @@ local def estimate_distinct [n] 't
   in (new_rng, estimate)
 
 -- | `reduce_by_key`@term, but paramatized. Here an initial `seed`@term
--- for a random number generater needed. And a `sample_fraction`@term
+-- for a random number generator needed. And a `sample_fraction`@term
 -- will be needed to determine the amount that will be sampled in each
 -- iteration. This is used to estimate the number of distinct
 -- elements. A `sample_fraction`@term of 2 will sample half of the pairs in that iteration
@@ -76,7 +76,7 @@ def reduce_by_key_param [n] 'k 'v
                         (eq: k -> k -> bool)
                         (ne: v)
                         (op: v -> v -> v)
-                        (arr: [n](k, v)): [](k, v) = 
+                        (arr: [n](k, v)): [](k, v) =
   let r = engine.rng_from_seed [seed]
   let (reduction, _, _) =
     -- Expected number of iterations is O(log n).
@@ -117,7 +117,7 @@ def dedup_param [n] 't
                 (times: i64)
                 (hash: i64 -> t -> i64)
                 (eq: t -> t -> bool)
-                (arr: [n]t): []t = 
+                (arr: [n]t): []t =
   let r = engine.rng_from_seed [seed]
   let (uniques, _, _) =
     loop (uniques, elems, old_rng) = ([], arr, r) while length elems != 0 do
@@ -158,7 +158,7 @@ def hash_i64 (a: i64) (x: i64): i64 =
 -- some array it can instead correspond to a key. Here an array of `n`@term
 -- key `k`@term and value `v`@term pairs are given as an array. And
 -- every value with the same key will be reduced with an associative
--- and commutative operator `op`@term, futhermore an neutral element `ne`@term
+-- and commutative operator `op`@term, furthermore an neutral element `ne`@term
 -- must be given. To perform this reduction a definition of key
 -- equality must be given `eq`@term, this is done as to figure out which
 -- elements will be reduced. And a good hash function `hash`@term must be
@@ -189,14 +189,14 @@ def dedup [n] 't
           (eq: t -> t -> bool)
           (arr: [n]t): []t =
   dedup_param 1 1024 5 hash eq arr
-    
+
 local def hash_i32 a x = hash_i64 a (i64.i32 x)
 
 local entry replicate_i32 (n: i64) (m: i32): [n]i32 =
-  replicate n m 
+  replicate n m
 
 local entry replicate_i64 (n: i64) (m: i64): [n]i64 =
-  replicate n m 
+  replicate n m
 
 -- ==
 -- entry: bench_dedup
@@ -220,7 +220,7 @@ local entry bench_dedup [n] (arr: [n]i32) =
 local entry bench_dedup_mod [n] (arr: [n]i32) =
   dedup hash_i32 (==) (map (% 40000) arr)
 
-  
+
 -- ==
 -- entry: bench_count_occourences_i32
 -- compiled random input { [100000]i32 }
@@ -244,7 +244,7 @@ local entry bench_count_occourences_i32 [n] (arr: [n]i32) =
 local entry bench_count_occourences_mod_i32 [n] (arr: [n]i32) =
   reduce_by_key hash_i32 (==) 0i32 (+) <| map (\a -> (a % 40000, 1)) arr
   |> map (.0)
-  
+
 
 -- Contains code from the segmented library.
 -- https://github.com/diku-dk/segmented
@@ -293,7 +293,7 @@ local entry bench_count_occourences_i64 [n] (arr: [n]i64) =
 local entry bench_count_occourences_mod_i64 [n] (arr: [n]i64) =
   reduce_by_key hash_i64 (==) 0i64 (+) <| map (\a -> (a % 40000, 1)) arr
   |> map (.0)
-  
+
 -- Contains code from the segmented library.
 -- https://github.com/diku-dk/segmented
 -- ==
@@ -317,4 +317,4 @@ local entry bench_count_occourences_radix_sort_and_segscan_i64 [n] (arr: [n]i64)
   let index i f = if f then i-1 else -1
   in scatter scratch (map2 index segment_end_offsets segment_ends) as
      |> map (.0)
-  
+

src/cajal/ugw/lib/github.com/diku-dk/containers/bitset.fut

@@ -3,7 +3,7 @@
 -- A bitset data structure is an array of bits where a bit
 -- can be set or not set. If the bit is set then it is a
 -- member of the set otherwise it is not. The indexes of
--- these bits can then be related to the indexes of 
+-- these bits can then be related to the indexes of
 -- another array.
 --
 -- `nbs`@term is assumed to be constant in the time
@@ -122,7 +122,7 @@ module type bitset = {
   val to_array [n] : bitset[(n - 1) / nbs + 1] -> []i64
 }
 
--- | Creates a bitset module depending on a intergral type.
+-- | Creates a bitset module depending on a integral type.
 module mk_bitset (I: integral) : bitset = {
   def nbs = i64.i32 I.num_bits
 
@@ -131,10 +131,10 @@ module mk_bitset (I: integral) : bitset = {
   type bitset [n] = [n]t
 
   def zero : t = I.u64 0
-  
+
   def empty (n : i64) : bitset[(n - 1) / nbs + 1] =
     replicate ((n - 1) / nbs + 1) zero
-  
+
   def find_bitset_index (i : i64) (n : i64) : (i64, i32) =
     if i < 0 || n <= i
     then (-1, -1)
@@ -158,7 +158,7 @@ module mk_bitset (I: integral) : bitset = {
 
   def is_empty [n] (s : bitset[(n - 1) / nbs + 1]) : bool =
     all (I.==zero) s
-  
+
   def delete [n] (i : i64) (s : bitset[(n - 1) / nbs + 1]) : bitset[(n - 1) / nbs + 1] =
     let index = find_bitset_index i n
     in if index.0 < 0 || index.1 < 0
@@ -173,7 +173,7 @@ module mk_bitset (I: integral) : bitset = {
 
   def union [n] (a : bitset[(n - 1) / nbs + 1]) (b : bitset[(n - 1) / nbs + 1]) : bitset[(n - 1) / nbs + 1] =
     map2 (I.|) a b
-  
+
   def intersection [n] (a : bitset[(n - 1) / nbs + 1]) (b : bitset[(n - 1) / nbs + 1]) : bitset[(n - 1) / nbs + 1] =
     map2 (I.&) a b
 
@@ -184,11 +184,11 @@ module mk_bitset (I: integral) : bitset = {
     in if l == 0
        then s
        else copy s with [l - 1] = s[l - 1] I.& to_keep
-  
+
   def complement [n] (s : bitset[(n - 1) / nbs + 1]) : bitset[(n - 1) / nbs + 1] =
     map I.not s
     |> set_leading_bits_zero
-  
+
   def size [n] (s : bitset[(n - 1) / nbs + 1]) : i64 =
     map (i64.i32 <-< I.popc) s
     |> i64.sum
@@ -199,10 +199,10 @@ module mk_bitset (I: integral) : bitset = {
 
   def is_subset [n] (a : bitset[(n - 1) / nbs + 1]) (b : bitset[(n - 1) / nbs + 1]) : bool =
     (a `union` b) == b
-  
+
   def difference [n] (a : bitset[(n - 1) / nbs + 1]) (b : bitset[(n - 1) / nbs + 1]) : bitset[(n - 1) / nbs + 1] =
     a `intersection` complement b
-  
+
   def set_capacity [m] (n : i64) (s : bitset[(m - 1) / nbs + 1]) : bitset[(n - 1) / nbs + 1] =
     let s' = empty n
     let len = length s
@@ -221,7 +221,7 @@ module mk_bitset (I: integral) : bitset = {
     let empty' = empty n
     in map (singleton n) arr
        |> reduce_comm union empty'
-  
+
   def to_array [n] (s : bitset[(n - 1) / nbs + 1]) : []i64 =
     map2 (\i v ->
       let m = i * i64.i32 I.num_bits

src/cajal/ugw/lib/github.com/diku-dk/containers/opt.fut

@@ -4,7 +4,7 @@
 
 -- | The `opt`@term type
 --
--- A `opt`@term type is a sum type. It is either `#some` containg a
+-- A `opt`@term type is a sum type. It is either `#some` containing a
 -- value `a`@term or `#none`.
 type opt 'a = #some a | #none
 

src/cajal/ugw/lib/github.com/diku-dk/cpprandom/random.fut

@@ -41,7 +41,7 @@
 --
 -- Since both `dist` and `norm_dist` have been initialised with the
 -- same underlying `rng_engine`@mtype (`minstd_rand`@term), we can
--- re-use the same RNG state.  This is often convenient when a program
+-- reuse the same RNG state.  This is often convenient when a program
 -- needs to generate random numbers from several different
 -- distributions, as we still only have to manage a single RNG state.
 --

src/cajal/ugw/lib/github.com/diku-dk/segmented/segmented.fut

@@ -57,7 +57,7 @@ def segmented_iota [n] (flags:[n]bool) : [n]i64 =
   in map (\x -> x-1) iotas
 
 -- | Replicated and segmented iota generated together
--- in a slighly more efficient way.
+-- in a slightly more efficient way.
 -- each segment in the segmented iota corresponds to a segment
 -- in the replicated iota. As an example repl_segm_iota [2,3,1]
 -- returns the arrays [0,0,1,1,1,2] and [0,1,0,1,2,0].