From: Jean-Philippe ARGAUD Date: Sat, 30 Jun 2018 04:02:09 +0000 (+0200) Subject: Adding user helper function ImportFromFile X-Git-Tag: V9_2_0b_ok_ADAO~29 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=92d2f5fb70111b71877e43dcf6be0d00ad496f6f;p=modules%2Fadao.git Adding user helper function ImportFromFile --- diff --git a/src/daComposant/daCore/Interfaces.py b/src/daComposant/daCore/Interfaces.py index 589e718..19a42d5 100644 --- a/src/daComposant/daCore/Interfaces.py +++ b/src/daComposant/daCore/Interfaces.py @@ -533,6 +533,274 @@ class ImportFromScript(object): "Renvoie le script complet" return self.__filestring +# ============================================================================== +class ImportFromFile(object): + """ + Obtention de variables disrétisées en 1D, définies par une ou des variables + nommées, et sous la forme d'une série de points éventuellement indexés. La + lecture d'un fichier au format spécifié (ou intuité) permet de charger ces + fonctions depuis : + - des fichiers textes en colonnes de type TXT, CSV, TSV... + - des fichiers de données binaires NPY, NPZ... + La lecture du fichier complet ne se fait que si nécessaire, pour assurer la + performance tout en disposant de l'interprétation du contenu. Les fichiers + textes doivent présenter en première ligne (hors commentaire ou ligne vide) + les noms des variables de colonnes. Les commentaires commencent par un "#". + """ + __slots__ = ( + "_filename", "_varsline", "_format", "_delimiter", "_skiprows", + "__colnames", "__colindex", "__filestring", "__header") + def __enter__(self): return self + def __exit__(self, exc_type, exc_val, exc_tb): return False + # + def __init__(self, Filename=None, ColNames=None, ColIndex=None, Format="Guess"): + """ + Verifie l'existence et les informations de définition du fichier. Les + noms de colonnes ou de variables sont ignorées si le format ne permet + pas de les indiquer. + Arguments : + - Filename : nom du fichier + - ColNames : noms de la ou des colonnes/variables à lire + - ColIndex : nom unique de la colonne/variable servant d'index + - Format : format du fichier et/ou des données inclues + """ + if Filename is None: + raise ValueError("The name of the file, containing the variables to be read, has to be specified.") + if not os.path.isfile(Filename): + raise ValueError("The file, containing the variables to be read, doesn't seem to exist. Please check the file. The given file name is:\n \"%s\""%str(Filename)) + self._filename = os.path.abspath(Filename) + # + self.__header, self._varsline, self._skiprows = self.__getentete(self._filename) + # + self._delimiter = None + self.__filestring = "".join(self.__header) + if Format.upper() == "GUESS": + if self._filename.split(".")[-1].lower() == "npy": + self._format = "NPY" + elif self._filename.split(".")[-1].lower() == "npz": + self._format = "NPZ" + elif self.__filestring.count(",") > 1 and self._filename.split(".")[-1].lower() == "csv": + self._format = "CSV" + self._delimiter = "," + elif self.__filestring.count(";") > 1 and self._filename.split(".")[-1].lower() == "csv": + self._format = "CSV" + self._delimiter = ";" + elif self.__filestring.count("\t") > 1 and self._filename.split(".")[-1].lower() == "tsv": + self._format = "TSV" + self._delimiter = "\t" + elif self.__filestring.count(" ") > 1 and self._filename.split(".")[-1].lower() == "txt": + self._format = "TXT" + else: + raise ValueError("Can not guess the file format, please specify the good one") + elif Format.upper() == "CSV" and self._delimiter is None: + if self.__filestring.count(",") > 1 and self._filename.split(".")[-1].lower() == "csv": + self._format = "CSV" + self._delimiter = "," + elif self.__filestring.count(";") > 1 and self._filename.split(".")[-1].lower() == "csv": + self._format = "CSV" + self._delimiter = ";" + elif Format.upper() == "TSV" and self._delimiter is None: + self._format = "TSV" + self._delimiter = "\t" + else: + self._format = str(Format).upper() + # + if ColNames is not None: self.__colnames = tuple(ColNames) + else: self.__colnames = None + # + if ColIndex is not None: self.__colindex = str(ColIndex) + else: self.__colindex = None + + def __getentete(self, __filename, __nblines = 3): + "Lit l'entête du fichier pour trouver la définition des variables" + __header, __varsline, __skiprows = [], "", 1 + if __filename.split(".")[-1].lower() in ("npy", "npz"): + pass + else: + with open(__filename,'r') as fid: + __line = fid.readline().strip() + while "#" in __line or len(__line) < 1: + __header.append(__line) + __skiprows += 1 + __line = fid.readline().strip() + __varsline = __line # Première ligne non commentée non vide + for i in range(max(0,__nblines)): + __header.append(fid.readline()) + return (__header, __varsline, __skiprows) + + def __getindices(self, __colnames, __colindex, __delimiter=None ): + "Indices de colonnes correspondants à l'index et aux variables" + if __delimiter is None: + __varserie = self._varsline.strip('#').strip().split() + else: + __varserie = self._varsline.strip('#').strip().split(str(__delimiter)) + # + if __colnames is not None: + __usecols = [] + __colnames = tuple(__colnames) + for v in __colnames: + for i, n in enumerate(__varserie): + if v == n: __usecols.append(i) + __usecols = tuple(__usecols) + if len(__usecols) == 0: __usecols = None + else: + __usecols = None + # + if __colindex is not None: + __useindex = None + __colindex = str(__colindex) + for i, n in enumerate(__varserie): + if __colindex == n: __useindex = i + else: + __useindex = None + # + return (__usecols, __useindex) + + def getvalue(self, ColNames=None, ColIndex=None ): + "Renvoie la ou les variables demandees par la liste de leurs noms" + # Uniquement si mise à jour + if ColNames is not None: self.__colnames = tuple(ColNames) + if ColIndex is not None: self.__colindex = str(ColIndex) + # + __index = None + if self._format == "NPY": + __columns = numpy.load(self._filename) + elif self._format == "NPZ": + __columns = None + with numpy.load(self._filename) as __allcolumns: + if self.__colnames is None: + self.__colnames = __allcolumns.files + for nom in self.__colnames: + if nom in __allcolumns.files: + if __columns is not None: + # Attention : toutes les variables doivent avoir la même taille + __columns = numpy.vstack((__columns, numpy.reshape(__allcolumns[nom], (1,-1)))) + else: + # Première colonne + __columns = numpy.reshape(__allcolumns[nom], (1,-1)) + if self.__colindex is not None and self.__colindex in __allcolumns.files: + __index = numpy.reshape(__allcolumns[self.__colindex], (1,-1)) + elif self._format == "TXT": + __usecols, __useindex = self.__getindices(self.__colnames, self.__colindex) + __columns = numpy.loadtxt(self._filename, usecols = __usecols, skiprows=self._skiprows) + if __useindex is not None: + __index = numpy.loadtxt(self._filename, usecols = __useindex, skiprows=self._skiprows) + # + elif self._format == "CSV": + __usecols, __useindex = self.__getindices(self.__colnames, self.__colindex, self._delimiter) + __columns = numpy.loadtxt(self._filename, usecols = __usecols, delimiter = self._delimiter, skiprows=self._skiprows) + if __useindex is not None: + __index = numpy.loadtxt(self._filename, usecols = __useindex, delimiter = self._delimiter, skiprows=self._skiprows) + # + elif self._format == "TSV": + __usecols, __useindex = self.__getindices(self.__colnames, self.__colindex, self._delimiter) + __columns = numpy.loadtxt(self._filename, usecols = __usecols, delimiter = self._delimiter, skiprows=self._skiprows) + if __useindex is not None: + __index = numpy.loadtxt(self._filename, usecols = __useindex, delimiter = self._delimiter, skiprows=self._skiprows) + else: + raise ValueError("Unkown file format %s"%self._format) + # + return (self.__colnames, __columns, self.__colindex, __index) + + def getstring(self): + "Renvoie le fichier complet" + with open(self._filename,'r') as fid: + return fid.read() + +# ============================================================================== +class ImportScalarLinesFromFile(ImportFromFile): + """ + Importation de fichier contenant des variables scalaires nommées. Le + fichier comporte soit 2, soit 4 colonnes, obligatoirement nommées "Name", + "Value", "Minimum", "Maximum" si les noms sont précisés. Sur chaque ligne + est indiqué le nom, la valeur, et éventuelement deux bornes min et max (ou + None si nécessaire pour une borne). + + Seule la méthode "getvalue" est changée. + """ + def __enter__(self): return self + def __exit__(self, exc_type, exc_val, exc_tb): return False + # + def __init__(self, Filename=None, ColNames=None, ColIndex=None, Format="Guess"): + ImportFromFile.__init__(self, Filename, ColNames, ColIndex, Format) + if self._format not in ["TXT", "CSV", "TSV"]: + raise ValueError("Unkown file format \"%s\""%self._format) + # + def getvalue(self, VarNames = None, HeaderNames=()): + "Renvoie la ou les variables demandees par la liste de leurs noms" + if VarNames is not None: __varnames = tuple(VarNames) + else: __varnames = None + # + if "Name" in self._varsline and "Value" in self._varsline and "Minimum" in self._varsline and "Maximum" in self._varsline: + __ftype = "NamValMinMax" + __dtypes = {'names' : ('Name', 'Value', 'Minimum', 'Maximum'), + 'formats': ('S128', 'g', 'g', 'g')} + __usecols = (0, 1, 2, 3) + def __replaceNoneN( s ): + if s.strip() == b'None': return numpy.NINF + else: return s + def __replaceNoneP( s ): + if s.strip() == b'None': return numpy.PINF + else: return s + __converters = {2: __replaceNoneN, 3: __replaceNoneP} + elif "Name" in self._varsline and "Value" in self._varsline and ("Minimum" not in self._varsline or "Maximum" not in self._varsline): + __ftype = "NamVal" + __dtypes = {'names' : ('Name', 'Value'), + 'formats': ('S128', 'g')} + __converters = None + __usecols = (0, 1) + elif len(HeaderNames)>0 and numpy.all([kw in self._varsline for kw in HeaderNames]): + __ftype = "NamLotOfVals" + __dtypes = {'names' : HeaderNames, + 'formats': tuple(['S128',]+['g']*(len(HeaderNames)-1))} + __usecols = tuple(range(len(HeaderNames))) + def __replaceNone( s ): + if s.strip() == b'None': return numpy.NAN + else: return s + __converters = dict() + for i in range(1,len(HeaderNames)): + __converters[i] = __replaceNone + else: + raise ValueError("Can not find names of columns for initial values. Wrong first line is:\n \"%s\""%__firstline) + # + if self._format == "TXT": + __content = numpy.loadtxt(self._filename, dtype = __dtypes, usecols = __usecols, skiprows = self._skiprows, converters = __converters) + elif self._format in ["CSV", "TSV"]: + __content = numpy.loadtxt(self._filename, dtype = __dtypes, usecols = __usecols, skiprows = self._skiprows, converters = __converters, delimiter = self._delimiter) + else: + raise ValueError("Unkown file format \"%s\""%self._format) + # + __names, __background, __bounds = [], [], [] + for sub in __content: + if len(__usecols) == 4: + na, va, mi, ma = sub + if numpy.isneginf(mi): mi = None # Réattribue les variables None + elif numpy.isnan(mi): mi = None # Réattribue les variables None + if numpy.isposinf(ma): ma = None # Réattribue les variables None + elif numpy.isnan(ma): ma = None # Réattribue les variables None + elif len(__usecols) == 2 and __ftype == "NamVal": + na, va = sub + mi, ma = None, None + else: + nsub = list(sub) + na = sub[0] + for i, v in enumerate(nsub[1:]): + if numpy.isnan(v): nsub[i+1] = None + va = nsub[1:] + mi, ma = None, None + na = na.decode() + if (__varnames is None or na in __varnames) and (na not in __names): + # Ne stocke que la premiere occurence d'une variable + __names.append(na) + __background.append(va) + __bounds.append((mi,ma)) + # + __names = tuple(__names) + __background = numpy.array(__background) + __bounds = tuple(__bounds) + # + return (__names, __background, __bounds) + # ============================================================================== if __name__ == "__main__": print('\n AUTODIAGNOSTIC \n')