Removed some more dead code

gargantext.cabal

@@ -377,12 +377,7 @@ library
       Gargantext.Core.Text.Metrics.FrequentItemSet
       Gargantext.Core.Text.Metrics.SpeGen.IncExc
       Gargantext.Core.Text.Metrics.Utils
-      Gargantext.Core.Text.Samples.DE
       Gargantext.Core.Text.Samples.EN
-      Gargantext.Core.Text.Samples.ES
-      Gargantext.Core.Text.Samples.FR
-      Gargantext.Core.Text.Samples.PL
-      Gargantext.Core.Text.Samples.ZH
       Gargantext.Core.Text.Terms.Mono.Token.En
       Gargantext.Core.Text.Terms.Multi.Group
       Gargantext.Core.Text.Terms.Multi.PosTagging

src/Gargantext/API/Ngrams/Types.hs

@@ -195,10 +195,6 @@ mkNgramsElement :: NgramsTerm
 mkNgramsElement ngrams list' rp children =
   NgramsElement ngrams (size (unNgramsTerm ngrams)) list' mempty (_rp_root <$> rp) (_rp_parent <$> rp) children
 
-newNgramsElement :: Maybe ListType -> NgramsTerm -> NgramsElement
-newNgramsElement mayList ngrams =
-  mkNgramsElement ngrams (fromMaybe MapTerm mayList) Nothing mempty
-
 instance ToSchema NgramsElement where
   declareNamedSchema = genericDeclareNamedSchema (unPrefixSwagger "_ne_")
 
@@ -467,9 +463,6 @@ newtype NgramsTablePatch = NgramsTablePatch (PatchMap NgramsTerm NgramsPatch)
   deriving stock (Eq, Show, Generic)
   deriving newtype (ToJSON, FromJSON, Semigroup, Monoid, Validity, Transformable)
 
-mkNgramsTablePatch :: Map NgramsTerm NgramsPatch -> NgramsTablePatch
-mkNgramsTablePatch = NgramsTablePatch . PM.fromMap
-
 instance FromField NgramsTablePatch
   where
     fromField = fromJSONField

src/Gargantext/Core/Text/Learn.hs

@@ -30,14 +30,7 @@ import Data.String (String)
 import Data.Text (pack, unpack, toLower)
 import Data.Tuple.Extra (both)
 import GHC.Generics
-import Gargantext.Core (Lang(..), allLangs)
 import Gargantext.Core.Text.Metrics.Count (occurrencesWith)
-import Gargantext.Core.Text.Samples.DE qualified as DE
-import Gargantext.Core.Text.Samples.EN qualified as EN
-import Gargantext.Core.Text.Samples.ES qualified as ES
-import Gargantext.Core.Text.Samples.FR qualified as FR
-import Gargantext.Core.Text.Samples.PL qualified as PL
-import Gargantext.Core.Text.Samples.ZH qualified as ZH
 import Gargantext.Core.Text.Terms.Mono (words)
 import Gargantext.Database.GargDB
 import Gargantext.Prelude hiding (Word, toList, toLower, words)
@@ -73,20 +66,6 @@ instance (Serialise a, Ord a) => ReadFile (Events a) where
   readFile' filepath = deserialise <$> BSL.readFile filepath
 
 ------------------------------------------------------------------------
-detectStopDefault :: Text -> Maybe Bool
-detectStopDefault = undefined
-
-detectBool :: [(Bool, Text)] -> Text -> Maybe Bool
-detectBool events = detectDefault False events
-
-detectDefault :: Ord a => a -> [(a, Text)] -> Text -> Maybe a
-detectDefault = detectDefaultWith identity
-
-detectDefaultWith :: Ord a => (b -> Text) -> a -> [(a, b)] -> b -> Maybe a
-detectDefaultWith f d events = detectDefaultWithPriors f ps
-  where
-    ps = priorEventsWith f d events
-
 detectDefaultWithPriors :: Ord b => (a -> Text) -> Events b -> a -> Maybe b
 detectDefaultWithPriors f priors = detectCat 99 priors . f
 
@@ -95,28 +74,6 @@ priorEventsWith f d e = toEvents d [0..2] 10 es
   where
     es = map (\(a,b) -> CatWord a (unpack $ toLower $ f b)) e
 
-
-------------------------------------------------------------------------
-detectLangDefault :: Text -> Maybe Lang
-detectLangDefault = detectCat 99 eventLang
-  where
-    eventLang :: Events Lang
-    eventLang = toEvents FR [0..2] 10 [ langWord l | l <- allLangs ]
-
-    langWord :: Lang -> CatWord Lang
-    langWord l = CatWord l (textSample l)
-
-    textSample :: Lang -> String
-    textSample EN = EN.textSample
-    textSample FR = FR.textSample
-    textSample DE = DE.textSample
-    textSample ES = ES.textSample
-    textSample ZH = ZH.textSample
-    textSample PL = PL.textSample
-    textSample _  = panic "[G.C.T.L:detectLangDefault] not impl yet"
-    --textSample DE = DE.textSample
-    --textSample SP = SP.textSample
-    --textSample CH = CH.textSample
 ------------------------------------------------------------------------
 detectCat :: Ord a => Int -> Events a -> Text -> Maybe a
 detectCat n es = head . map fst . (detectCat' n es) . unpack

src/Gargantext/Core/Text/List/Formats/TSV.hs

@@ -79,11 +79,6 @@ tsvDecodeOptions :: DecodeOptions
 tsvDecodeOptions = (defaultDecodeOptions
                       {decDelimiter = fromIntegral $ ord tsvListFieldDelimiter}
                     )
-
-tsvEncodeOptions :: EncodeOptions
-tsvEncodeOptions = ( defaultEncodeOptions 
-                      {encDelimiter = fromIntegral $ ord tsvListFieldDelimiter}
-                    )
 ------------------------------------------------------------------------
 fromTsvListFile :: FilePath -> IO (Header, Vector TsvList)
 fromTsvListFile fp = do
@@ -92,7 +87,3 @@ fromTsvListFile fp = do
       Left e        -> panicTrace (pack e)
       Right tsvList -> pure tsvList
 ------------------------------------------------------------------------
-toTsvListFile :: FilePath -> (Header, Vector TsvList) -> IO ()
-toTsvListFile fp (h, vs) = BL.writeFile fp $
-                      encodeByNameWith tsvEncodeOptions h (V.toList vs)
-------------------------------------------------------------------------

src/Gargantext/Core/Text/List/Group/WithScores.hs

@@ -96,12 +96,6 @@ toGroupedTreeInstitutes' institutesTree m = case HashMap.lookup Nothing m of
   Nothing  -> mempty
   Just  m' -> toGroupedTreeInstitutes'' m m' institutesTree
 
-filterGroupedTree :: (GroupedTreeScores a -> Bool)
-                  -> HashMap Parent (GroupedTreeScores a)
-                  -> HashMap Parent (GroupedTreeScores a)
-filterGroupedTree f = HashMap.filter f
-
-
 toGroupedTree'' :: Eq a => HashMap (Maybe Parent) (HashMap NgramsTerm (GroupedTreeScores a))
                -> (HashMap NgramsTerm (GroupedTreeScores a))
                ->  HashMap Parent (GroupedTreeScores a)
@@ -153,4 +147,4 @@ toGroupedTreeInstitutes'' m notEmpty institutesTree
                                   $ HashMap.lookup (Just key) dict'
                                   )
           )
-          val
+          val
\ No newline at end of file

src/Gargantext/Core/Text/List/Social/Prelude.hs

@@ -21,7 +21,6 @@ module Gargantext.Core.Text.List.Social.Prelude
 import Control.Lens
 import Data.HashMap.Strict (HashMap)
 import Data.HashMap.Strict qualified as HashMap
-import Data.Map.Strict qualified as Map
 import Data.Map.Strict.Patch qualified as PatchMap
 import Data.Monoid
 import Gargantext.API.Ngrams.Types
@@ -79,21 +78,6 @@ instance Monoid FlowListScores where
 -- | Tools to inherit groupings
 ------------------------------------------------------------------------
 -- | Tools
-parentUnionsMerge :: (Ord a, Ord b, Num c, Hashable a, Hashable b)
-                   => [HashMap a (HashMap b c)]
-                   ->  HashMap a (HashMap b c)
-parentUnionsMerge = HashMap.unionsWith (HashMap.unionWith (+))
-
--- This Parent union is specific
--- [Private, Shared, Public]
--- means the following preferences:
--- Private > Shared > Public
--- if data have not been tagged privately, then use others tags
--- This unions behavior takes first key only and ignore others
-parentUnionsExcl :: (Ord a, Hashable a)
-                 => [HashMap a b]
-                 ->  HashMap a b
-parentUnionsExcl = HashMap.unions
 
 ------------------------------------------------------------------------
 -- | Takes key with max value if and only if value > 0
@@ -118,9 +102,3 @@ keyWithMaxValue m = do
 ------------------------------------------------------------------------
 unPatchMapToHashMap :: (Ord a, Hashable a) => PatchMap a b -> HashMap a b
 unPatchMapToHashMap = HashMap.fromList . PatchMap.toList
-
-unPatchMapToMap :: Ord a => PatchMap a b -> Map a b
-unPatchMapToMap = Map.fromList . PatchMap.toList
-
-unNgramsTablePatch :: NgramsTablePatch -> HashMap NgramsTerm NgramsPatch
-unNgramsTablePatch (NgramsTablePatch p) = unPatchMapToHashMap p

src/Gargantext/Core/Text/Samples/DE.hs

-{-|
-Module      : Gargantext.Core.Text.Samples.DE
-Description : Sample of German Text
-Copyright   : (c) CNRS, 2017 - present
-License     : AGPL + CECILL v3
-Maintainer  : team@gargantext.org
-Stability   : experimental
-Portability : POSIX
-
-Source: Wikipedia
-Page  : text mining
-
--}
-
-
-module Gargantext.Core.Text.Samples.DE where
-
-import Data.String (String)
-
-textSample :: String
-textSample = "Text Mining, seltener auch Textmining, Text Data Mining oder Textual Data Mining, ist ein Bündel von Algorithmus-basierten Analyseverfahren zur Entdeckung von Bedeutungsstrukturen aus un- oder schwachstrukturierten Textdaten. Mit statistischen und linguistischen Mitteln erschließt Text-Mining-Software aus Texten Strukturen, die die Benutzer in die Lage versetzen sollen, Kerninformationen der verarbeiteten Texte schnell zu erkennen. Im Optimalfall liefern Text-Mining-Systeme Informationen, von denen die Benutzer zuvor nicht wissen, ob und dass sie in den verarbeiteten Texten enthalten sind. Bei zielgerichteter Anwendung sind Werkzeuge des Text Mining außerdem in der Lage, Hypothesen zu generieren, diese zu überprüfen und schrittweise zu verfeinern."

src/Gargantext/Core/Text/Samples/ES.hs

-{-|
-Module      : Gargantext.Core.Text.Samples.ES
-Description : Sample of Spanish Text
-Copyright   : (c) CNRS, 2017 - present
-License     : AGPL + CECILL v3
-Maintainer  : team@gargantext.org
-Stability   : experimental
-Portability : POSIX
-
-Source: Wikipedia
-Page  : text mining
--}
-
-
-
-module Gargantext.Core.Text.Samples.ES where
-
-import Data.String (String)
-
-textSample :: String
-textSample = "La minería de textos se refiere al proceso de derivar información nueva de textos. A comienzos de los años ochenta surgieron los primeros esfuerzos de minería de textos que necesitaban una gran cantidad de esfuerzo humano, pero los avances tecnológicos han permitido que esta área progrese de manera rápida en la última década. La minería de textos es un área multidisciplinar basada en la recuperación de información, minería de datos, aprendizaje automático, estadísticas y la lingüística computacional. Como la mayor parte de la información (más de un 80%) se encuentra actualmente almacenada como texto, se cree que la minería de textos tiene un gran valor comercial."

src/Gargantext/Core/Text/Samples/FR.hs

-{-|
-Module      : Gargantext.Core.Text.Samples.FR
-Description : Sample of French Text
-Copyright   : (c) CNRS, 2017 - present
-License     : AGPL + CECILL v3
-Maintainer  : team@gargantext.org
-Stability   : experimental
-Portability : POSIX
-
-Source: Wikipedia
-Page  : text mining
-
--}
-
-
-module Gargantext.Core.Text.Samples.FR where
-
-import Gargantext.Prelude ((<>))
-import Data.String (String)
-
-textSample :: String
-textSample = "La fouille de textes ou « l'extraction de connaissances » dans les textes est une spécialisation de la fouille de données et fait partie du domaine de l'intelligence artificielle. Cette technique est souvent désignée sous l'anglicisme text mining. Elle désigne un ensemble de traitements informatiques consistant à extraire des connaissances selon un critère de nouveauté ou de similarité dans des textes produits par des humains pour des humains. Dans la pratique, cela revient à mettre en algorithme un modèle simplifié des théories linguistiques dans des systèmes informatiques d'apprentissage et de statistiques. Les disciplines impliquées sont donc la linguistique calculatoire, l'ingénierie des langues, l'apprentissage artificiel, les statistiques et l'informatique." <> "Je pense donc je suis."
-
-

src/Gargantext/Core/Text/Samples/PL.hs

-{-|
-Module      : Gargantext.Core.Text.Samples.PL
-Description : Sample of Polish Text
-Copyright   : (c) CNRS, 2017 - present
-License     : AGPL + CECILL v3
-Maintainer  : team@gargantext.org
-Stability   : experimental
-Portability : POSIX
-
-Source: Wikipedia
-Page  : text mining
-
--}
-
-
-
-module Gargantext.Core.Text.Samples.PL where
-
-import Data.String (String)
-
-textSample :: String
-textSample = "Text mining (eksploracja tekstu) – ogólna nazwa metod eksploracji danych służących do wydobywania danych z tekstu i ich późniejszej obróbki. Metody text mining stosowane są np. do statystycznego przetwarzania: artykułów prasowych, wiadomości poczty elektronicznej, otwartych odpowiedzi na pytania ankietowe, opisów dolegliwości, podawanych przez pacjentów, komentarzy do sesji giełdowych i zdarzeń dotyczące spółek, życiorysów zawodowych i listów motywacyjnych, tekstów reklamacji konsumenckich. Text mining może polegać na znalezieniu kluczowych fraz, zdań, które zostają następnie zakodowane pod postacią zmiennych numerycznych. Później stosuje się metody statystyki i eksploracji danych w celu odkrycia zależności pomiędzy zmiennymi. Ze względu na to, że powstające zmienne są zwykle nominalne, szczególnie użyteczna jest analiza koszykowa."

src/Gargantext/Core/Text/Samples/ZH.hs

-{-|
-Module      : Gargantext.Core.Text.Samples.ZH
-Description : Sample of Chinese Text
-Copyright   : (c) CNRS, 2017 - present
-License     : AGPL + CECILL v3
-Maintainer  : team@gargantext.org
-Stability   : experimental
-Portability : POSIX
-
-Source: Wikipedia
-Page  : text mining
-
--}
-
-
-
-module Gargantext.Core.Text.Samples.ZH where
-
-import Data.String (String)
-
-textSample :: String
-textSample = "文本挖掘有时也被称为文字探勘、文本数据挖掘等，大致相当于文字分析，一般指文本处理过程中产生高质量的信息。高质量的信息通常通过分类和预测来产生，如模式识别。文本挖掘通常涉及输入文本的处理过程（通常进行分析，同时加上一些衍生语言特征以及消除杂音，随后插入到数据库中） ，产生结构化数据，并最终评价和解释输出。'高品质'的文本挖掘通常是指某种组合的相关性，新颖性和趣味性。典型的文本挖掘方法包括文本分类，文本聚类，概念/实体挖掘，生产精确分类，观点分析，文档摘要和实体关系模型（即，学习已命名实体之间的关系） 。 文本分析包括了信息检索、词典分析来研究词语的频数分布、模式识别、标签 注释、信息抽取，数据挖掘技术包括链接和关联分析、可视化和预测分析。本质上，首要的任务是，通过自然语言处理和分析方法，将文本转化为数据进行分析"

weeder.toml

@@ -10,7 +10,8 @@ roots = [ '^Main\.main$'
         # Definitions whose name (or the name of the module they are in) suggests
         # there is a good reason for them not to be included: "test", "example"
         
-        # Name begins with "test" or "trace":
+        # Name begins with "example", test" or "trace":
+        , '.*\.example[^\.]*$'
         , '.*\.test[^\.]*$'
         , '.*\.trace[^\.]*$'
         # Module is named "Example":