"""mrecords Defines the equivalent of recarrays for maskedarray. Masked arrays already support named fields, but masking works only by records. By comparison, mrecarrays support masking individual fields. :author: Pierre Gerard-Marchant """ #TODO: We should make sure that no field is called '_mask','mask','_fieldmask', #TODO: ...or whatever restricted keywords. #TODO: An idea would be to no bother in the first place, and then rename the #TODO: invalid fields with a trailing underscore... #TODO: Maybe we could just overload the parser function ? __author__ = "Pierre GF Gerard-Marchant" import sys import types import numpy as np from numpy import bool_, complex_, float_, int_, str_, object_, dtype, \ chararray, ndarray, recarray, record, array as narray import numpy.core.numerictypes as ntypes from numpy.core.records import find_duplicate, format_parser from numpy.core.records import fromarrays as recfromarrays, \ fromrecords as recfromrecords _byteorderconv = np.core.records._byteorderconv _typestr = ntypes._typestr import numpy.ma as ma from numpy.ma import MAError, MaskedArray, masked, nomask, masked_array,\ make_mask, mask_or, getdata, getmask, getmaskarray, filled, \ default_fill_value, masked_print_option _check_fill_value = ma.core._check_fill_value import warnings __all__ = ['MaskedRecords','mrecarray', 'fromarrays','fromrecords','fromtextfile','addfield', ] reserved_fields = ['_data','_mask','_fieldmask', 'dtype'] def _getformats(data): "Returns the formats of each array of arraylist as a comma-separated string." if hasattr(data,'dtype'): return ",".join([desc[1] for desc in data.dtype.descr]) formats = '' for obj in data: obj = np.asarray(obj) # if not isinstance(obj, ndarray): ## if not isinstance(obj, ndarray): # raise ValueError, "item in the array list must be an ndarray." formats += _typestr[obj.dtype.type] if issubclass(obj.dtype.type, ntypes.flexible): formats += `obj.itemsize` formats += ',' return formats[:-1] def _checknames(descr, names=None): """Checks that the field names of the descriptor ``descr`` are not some reserved keywords. If this is the case, a default 'f%i' is substituted. If the argument `names` is not None, updates the field names to valid names. """ ndescr = len(descr) default_names = ['f%i' % i for i in range(ndescr)] if names is None: new_names = default_names else: if isinstance(names, (tuple, list)): new_names = names elif isinstance(names, str): new_names = names.split(',') else: raise NameError, "illegal input names %s" % `names` nnames = len(new_names) if nnames < ndescr: new_names += default_names[nnames:] ndescr = [] for (n, d, t) in zip(new_names, default_names, descr.descr): if n in reserved_fields: if t[0] in reserved_fields: ndescr.append((d,t[1])) else: ndescr.append(t) else: ndescr.append((n,t[1])) return numeric.dtype(ndescr) def _get_fieldmask(self): mdescr = [(n,'|b1') for n in self.dtype.names] fdmask = np.empty(self.shape, dtype=mdescr) fdmask.flat = tuple([False]*len(mdescr)) return fdmask class MaskedRecords(MaskedArray, object): """ *IVariables*: _data : {recarray} Underlying data, as a record array. _mask : {boolean array} Mask of the records. A record is masked when all its fields are masked. _fieldmask : {boolean recarray} Record array of booleans, setting the mask of each individual field of each record. _fill_value : {record} Filling values for each field. """ _defaultfieldmask = nomask _defaulthardmask = False #............................................ def __new__(cls, shape, dtype=None, buf=None, offset=0, strides=None, formats=None, names=None, titles=None, byteorder=None, aligned=False, mask=nomask, hard_mask=False, fill_value=None, keep_mask=True, copy=False, **options): # self = recarray.__new__(cls, shape, dtype=dtype, buf=buf, offset=offset, strides=strides, formats=formats, byteorder=byteorder, aligned=aligned,) # self = self.view(cls) # mdtype = [(k,'|b1') for (k,_) in self.dtype.descr] if mask is nomask or not np.size(mask): if not keep_mask: self._fieldmask = tuple([False]*len(mdtype)) else: mask = np.array(mask, copy=copy) if mask.shape != self.shape: (nd, nm) = (self.size, mask.size) if nm == 1: mask = np.resize(mask, self.shape) elif nm == nd: mask = np.reshape(mask, self.shape) else: msg = "Mask and data not compatible: data size is %i, "+\ "mask size is %i." raise MAError(msg % (nd, nm)) copy = True if not keep_mask: self.__setmask__(mask) self._sharedmask = True else: if mask.dtype == mdtype: _fieldmask = mask else: _fieldmask = np.array([tuple([m]*len(mdtype)) for m in mask], dtype=mdtype) self._fieldmask = _fieldmask return self #...................................................... def __array_finalize__(self,obj): # Make sure we have a _fieldmask by default .. _fieldmask = getattr(obj, '_fieldmask', None) if _fieldmask is None: mdescr = [(n,'|b1') for (n,_) in self.dtype.descr] _mask = getattr(obj, '_mask', nomask) if _mask is nomask: _fieldmask = np.empty(self.shape, dtype=mdescr).view(recarray) _fieldmask.flat = tuple([False]*len(mdescr)) else: _fieldmask = narray([tuple([m]*len(mdescr)) for m in _mask], dtype=mdescr).view(recarray) # Update some of the attributes if obj is not None: _baseclass = getattr(obj,'_baseclass',type(obj)) else: _baseclass = recarray attrdict = dict(_fieldmask=_fieldmask, _hardmask=getattr(obj,'_hardmask',False), _fill_value=getattr(obj,'_fill_value',None), _sharedmask=getattr(obj,'_sharedmask',False), _baseclass=_baseclass) self.__dict__.update(attrdict) # Finalize as a regular maskedarray ..... # Update special attributes ... self._basedict = getattr(obj, '_basedict', getattr(obj,'__dict__',{})) self.__dict__.update(self._basedict) return #...................................................... def _getdata(self): "Returns the data as a recarray." return ndarray.view(self,recarray) _data = property(fget=_getdata) #...................................................... def __setmask__(self, mask): "Sets the mask and update the fieldmask." names = self.dtype.names fmask = self.__dict__['_fieldmask'] # if isinstance(mask,ndarray) and mask.dtype.names == names: for n in names: fmask[n] = mask[n].astype(bool) # self.__dict__['_fieldmask'] = fmask.view(recarray) return newmask = make_mask(mask, copy=False) if names is not None: if self._hardmask: for n in names: fmask[n].__ior__(newmask) else: for n in names: fmask[n].flat = newmask return _setmask = __setmask__ # def _getmask(self): """Return the mask of the mrecord. A record is masked when all the fields are masked. """ if self.size > 1: return self._fieldmask.view((bool_, len(self.dtype))).all(1) else: return self._fieldmask.view((bool_, len(self.dtype))).all() mask = _mask = property(fget=_getmask, fset=_setmask) #...................................................... def get_fill_value(self): """Return the filling value. """ if self._fill_value is None: ddtype = self.dtype fillval = _check_fill_value(None, ddtype) self._fill_value = np.array(tuple(fillval), dtype=ddtype) return self._fill_value def set_fill_value(self, value=None): """Set the filling value to value. If value is None, use a default based on the data type. """ ddtype = self.dtype fillval = _check_fill_value(value, ddtype) self._fill_value = np.array(tuple(fillval), dtype=ddtype) fill_value = property(fget=get_fill_value, fset=set_fill_value, doc="Filling value.") #...................................................... def __len__(self): "Returns the length" # We have more than one record if self.ndim: return len(self._data) # We have only one record: return the nb of fields return len(self.dtype) #...................................................... def __getattribute__(self, attr): "Returns the given attribute." try: # Returns a generic attribute return object.__getattribute__(self,attr) except AttributeError: # OK, so attr must be a field name pass # Get the list of fields ...... _names = self.dtype.names if attr in _names: _data = self._data _mask = self._fieldmask # obj = masked_array(_data.__getattribute__(attr), copy=False, # mask=_mask.__getattribute__(attr)) # Use a view in order to avoid the copy of the mask in MaskedArray.__new__ obj = narray(_data.__getattribute__(attr), copy=False).view(MaskedArray) obj._mask = _mask.__getattribute__(attr) if not obj.ndim and obj._mask: return masked return obj raise AttributeError,"No attribute '%s' !" % attr def __setattr__(self, attr, val): "Sets the attribute attr to the value val." # newattr = attr not in self.__dict__ try: # Is attr a generic attribute ? ret = object.__setattr__(self, attr, val) except: # Not a generic attribute: exit if it's not a valid field fielddict = self.dtype.names or {} if attr not in fielddict: exctype, value = sys.exc_info()[:2] raise exctype, value else: if attr in ['_mask','fieldmask']: self.__setmask__(val) return # Get the list of names ...... _names = self.dtype.names if _names is None: _names = [] else: _names = list(_names) # Check the attribute self_dict = self.__dict__ if attr not in _names+list(self_dict): return ret if attr not in self_dict: # We just added this one try: # or this setattr worked on an internal # attribute. object.__delattr__(self, attr) except: return ret # Case #1.: Basic field ............ base_fmask = self._fieldmask _names = self.dtype.names or [] if attr in _names: if val is masked: fval = self.fill_value[attr] mval = True else: fval = filled(val) mval = getmaskarray(val) if self._hardmask: mval = mask_or(mval, base_fmask.__getattr__(attr)) self._data.__setattr__(attr, fval) base_fmask.__setattr__(attr, mval) return #............................................ def __getitem__(self, indx): """Returns all the fields sharing the same fieldname base. The fieldname base is either `_data` or `_mask`.""" _localdict = self.__dict__ _fieldmask = _localdict['_fieldmask'] _data = self._data # We want a field ........ if isinstance(indx, basestring): obj = _data[indx].view(MaskedArray) obj._set_mask(_fieldmask[indx]) # Force to nomask if the mask is empty if not obj._mask.any(): obj._mask = nomask # Force to masked if the mask is True if not obj.ndim and obj._mask: return masked return obj # We want some elements .. # First, the data ........ obj = narray(_data[indx], copy=False).view(mrecarray) obj._fieldmask = narray(_fieldmask[indx], copy=False).view(recarray) return obj #.... def __setitem__(self, indx, value): "Sets the given record to value." MaskedArray.__setitem__(self, indx, value) if isinstance(indx, basestring): self._fieldmask[indx] = ma.getmaskarray(value) #............................................ def __setslice__(self, i, j, value): "Sets the slice described by [i,j] to `value`." _localdict = self.__dict__ d = self._data m = _localdict['_fieldmask'] names = self.dtype.names if value is masked: for n in names: m[i:j][n] = True elif not self._hardmask: fval = filled(value) mval = getmaskarray(value) for n in names: d[n][i:j] = fval m[n][i:j] = mval else: mindx = getmaskarray(self)[i:j] dval = np.asarray(value) valmask = getmask(value) if valmask is nomask: for n in names: mval = mask_or(m[n][i:j], valmask) d[n][i:j][~mval] = value elif valmask.size > 1: for n in names: mval = mask_or(m[n][i:j], valmask) d[n][i:j][~mval] = dval[~mval] m[n][i:j] = mask_or(m[n][i:j], mval) self._fieldmask = m #...................................................... def __str__(self): "Calculates the string representation." if self.size > 1: mstr = ["(%s)" % ",".join([str(i) for i in s]) for s in zip(*[getattr(self,f) for f in self.dtype.names])] return "[%s]" % ", ".join(mstr) else: mstr = ["%s" % ",".join([str(i) for i in s]) for s in zip([getattr(self,f) for f in self.dtype.names])] return "(%s)" % ", ".join(mstr) # def __repr__(self): "Calculates the repr representation." _names = self.dtype.names fmt = "%%%is : %%s" % (max([len(n) for n in _names])+4,) reprstr = [fmt % (f,getattr(self,f)) for f in self.dtype.names] reprstr.insert(0,'masked_records(') reprstr.extend([fmt % (' fill_value', self.fill_value), ' )']) return str("\n".join(reprstr)) #...................................................... def view(self, obj): """Returns a view of the mrecarray.""" try: if issubclass(obj, ndarray): return ndarray.view(self, obj) except TypeError: pass dtype = np.dtype(obj) if dtype.fields is None: return self.__array__().view(dtype) return ndarray.view(self, obj) #...................................................... def filled(self, fill_value=None): """Returns an array of the same class as the _data part, where masked values are filled with fill_value. If fill_value is None, self.fill_value is used instead. Subclassing is preserved. """ _localdict = self.__dict__ d = self._data fm = _localdict['_fieldmask'] if not np.asarray(fm, dtype=bool_).any(): return d # if fill_value is None: value = _check_fill_value(_localdict['_fill_value'],self.dtype) else: value = fill_value if np.size(value) == 1: value = [value,] * len(self.dtype) # if self is masked: result = np.asanyarray(value) else: result = d.copy() for (n, v) in zip(d.dtype.names, value): np.putmask(np.asarray(result[n]), np.asarray(fm[n]), v) return result #...................................................... def harden_mask(self): "Forces the mask to hard" self._hardmask = True def soften_mask(self): "Forces the mask to soft" self._hardmask = False #...................................................... def copy(self): """Returns a copy of the masked record.""" _localdict = self.__dict__ copied = self._data.copy().view(type(self)) copied._fieldmask = self._fieldmask.copy() return copied #...................................................... def tolist(self, fill_value=None): """Copy the data portion of the array to a hierarchical python list and returns that list. Data items are converted to the nearest compatible Python type. Masked values are converted to fill_value. If fill_value is None, the corresponding entries in the output list will be ``None``. """ if fill_value is not None: return self.filled(fill_value).tolist() result = narray(self.filled().tolist(), dtype=object) mask = narray(self._fieldmask.tolist()) result[mask] = None return result.tolist() #-------------------------------------------- # Pickling def __getstate__(self): """Return the internal state of the masked array, for pickling purposes. """ state = (1, self.shape, self.dtype, self.flags.fnc, self._data.tostring(), self._fieldmask.tostring(), self._fill_value, ) return state # def __setstate__(self, state): """Restore the internal state of the masked array, for pickling purposes. ``state`` is typically the output of the ``__getstate__`` output, and is a 5-tuple: - class name - a tuple giving the shape of the data - a typecode for the data - a binary string for the data - a binary string for the mask. """ (ver, shp, typ, isf, raw, msk, flv) = state ndarray.__setstate__(self, (shp, typ, isf, raw)) mdtype = dtype([(k,bool_) for (k,_) in self.dtype.descr]) self.__dict__['_fieldmask'].__setstate__((shp, mdtype, isf, msk)) self.fill_value = flv # def __reduce__(self): """Return a 3-tuple for pickling a MaskedArray. """ return (_mrreconstruct, (self.__class__, self._baseclass, (0,), 'b', ), self.__getstate__()) def _mrreconstruct(subtype, baseclass, baseshape, basetype,): """Internal function that builds a new MaskedArray from the information stored in a pickle. """ _data = ndarray.__new__(baseclass, baseshape, basetype).view(subtype) # _data._mask = ndarray.__new__(ndarray, baseshape, 'b1') # return _data _mask = ndarray.__new__(ndarray, baseshape, 'b1') return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,) mrecarray = MaskedRecords #####--------------------------------------------------------------------------- #---- --- Constructors --- #####--------------------------------------------------------------------------- def fromarrays(arraylist, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, fill_value=None): """Creates a mrecarray from a (flat) list of masked arrays. Parameters ---------- arraylist : sequence A list of (masked) arrays. Each element of the sequence is first converted to a masked array if needed. If a 2D array is passed as argument, it is processed line by line dtype : numeric.dtype Data type descriptor. shape : integer Number of records. If None, shape is defined from the shape of the first array in the list. formats : sequence Sequence of formats for each individual field. If None, the formats will be autodetected by inspecting the fields and selecting the highest dtype possible. names : sequence Sequence of the names of each field. titles : sequence (Description to write) aligned : boolean (Description to write, not used anyway) byteorder: boolean (Description to write, not used anyway) fill_value : sequence Sequence of data to be used as filling values. Notes ----- Lists of tuples should be preferred over lists of lists for faster processing. """ datalist = [getdata(x) for x in arraylist] masklist = [getmaskarray(x) for x in arraylist] _array = recfromarrays(datalist, dtype=dtype, shape=shape, formats=formats, names=names, titles=titles, aligned=aligned, byteorder=byteorder).view(mrecarray) _array._fieldmask[:] = zip(*masklist) if fill_value is not None: _array.fill_value = fill_value return _array #.............................................................................. def fromrecords(reclist, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, fill_value=None, mask=nomask): """Creates a MaskedRecords from a list of records. Parameters ---------- arraylist : sequence A list of (masked) arrays. Each element of the sequence is first converted to a masked array if needed. If a 2D array is passed as argument, it is processed line by line dtype : numeric.dtype Data type descriptor. shape : integer Number of records. If None, ``shape`` is defined from the shape of the first array in the list. formats : sequence Sequence of formats for each individual field. If None, the formats will be autodetected by inspecting the fields and selecting the highest dtype possible. names : sequence Sequence of the names of each field. titles : sequence (Description to write) aligned : boolean (Description to write, not used anyway) byteorder: boolean (Description to write, not used anyway) fill_value : sequence Sequence of data to be used as filling values. mask : sequence or boolean. External mask to apply on the data. *Notes*: Lists of tuples should be preferred over lists of lists for faster processing. """ # Grab the initial _fieldmask, if needed: _fieldmask = getattr(reclist, '_fieldmask', None) # Get the list of records..... nfields = len(reclist[0]) if isinstance(reclist, ndarray): # Make sure we don't have some hidden mask if isinstance(reclist,MaskedArray): reclist = reclist.filled().view(ndarray) # Grab the initial dtype, just in case if dtype is None: dtype = reclist.dtype reclist = reclist.tolist() mrec = recfromrecords(reclist, dtype=dtype, shape=shape, formats=formats, names=names, titles=titles, aligned=aligned, byteorder=byteorder).view(mrecarray) # Set the fill_value if needed if fill_value is not None: mrec.fill_value = fill_value # Now, let's deal w/ the mask if mask is not nomask: mask = np.array(mask, copy=False) maskrecordlength = len(mask.dtype) if maskrecordlength: mrec._fieldmask.flat = mask elif len(mask.shape) == 2: mrec._fieldmask.flat = [tuple(m) for m in mask] else: mrec._mask = mask if _fieldmask is not None: mrec._fieldmask[:] = _fieldmask return mrec def _guessvartypes(arr): """Tries to guess the dtypes of the str_ ndarray `arr`, by testing element-wise conversion. Returns a list of dtypes. The array is first converted to ndarray. If the array is 2D, the test is performed on the first line. An exception is raised if the file is 3D or more. """ vartypes = [] arr = np.asarray(arr) if len(arr.shape) == 2 : arr = arr[0] elif len(arr.shape) > 2: raise ValueError, "The array should be 2D at most!" # Start the conversion loop ....... for f in arr: try: val = int(f) except ValueError: try: val = float(f) except ValueError: try: val = complex(f) except ValueError: vartypes.append(arr.dtype) else: vartypes.append(complex) else: vartypes.append(float) else: vartypes.append(int) return vartypes def openfile(fname): "Opens the file handle of file `fname`" # A file handle ................... if hasattr(fname, 'readline'): return fname # Try to open the file and guess its type try: f = open(fname) except IOError: raise IOError, "No such file: '%s'" % fname if f.readline()[:2] != "\\x": f.seek(0,0) return f raise NotImplementedError, "Wow, binary file" def fromtextfile(fname, delimitor=None, commentchar='#', missingchar='', varnames=None, vartypes=None): """Creates a mrecarray from data stored in the file `filename`. *Parameters* : filename : {file name/handle} Handle of an opened file. delimitor : {string} Alphanumeric character used to separate columns in the file. If None, any (group of) white spacestring(s) will be used. commentchar : {string} Alphanumeric character used to mark the start of a comment. missingchar` : {string} String indicating missing data, and used to create the masks. varnames : {sequence} Sequence of the variable names. If None, a list will be created from the first non empty line of the file. vartypes : {sequence} Sequence of the variables dtypes. If None, it will be estimated from the first non-commented line. Ultra simple: the varnames are in the header, one line""" # Try to open the file ...................... f = openfile(fname) # Get the first non-empty line as the varnames while True: line = f.readline() firstline = line[:line.find(commentchar)].strip() _varnames = firstline.split(delimitor) if len(_varnames) > 1: break if varnames is None: varnames = _varnames # Get the data .............................. _variables = masked_array([line.strip().split(delimitor) for line in f if line[0] != commentchar and len(line) > 1]) (_, nfields) = _variables.shape # Try to guess the dtype .................... if vartypes is None: vartypes = _guessvartypes(_variables[0]) else: vartypes = [np.dtype(v) for v in vartypes] if len(vartypes) != nfields: msg = "Attempting to %i dtypes for %i fields!" msg += " Reverting to default." warnings.warn(msg % (len(vartypes), nfields)) vartypes = _guessvartypes(_variables[0]) # Construct the descriptor .................. mdescr = [(n,f) for (n,f) in zip(varnames, vartypes)] # Get the data and the mask ................. # We just need a list of masked_arrays. It's easier to create it like that: _mask = (_variables.T == missingchar) _datalist = [masked_array(a,mask=m,dtype=t) for (a,m,t) in zip(_variables.T, _mask, vartypes)] return fromarrays(_datalist, dtype=mdescr) #.................................................................... def addfield(mrecord, newfield, newfieldname=None): """Adds a new field to the masked record array, using `newfield` as data and `newfieldname` as name. If `newfieldname` is None, the new field name is set to 'fi', where `i` is the number of existing fields. """ _data = mrecord._data _mask = mrecord._fieldmask if newfieldname is None or newfieldname in reserved_fields: newfieldname = 'f%i' % len(_data.dtype) newfield = ma.array(newfield) # Get the new data ............ # Create a new empty recarray newdtype = np.dtype(_data.dtype.descr + [(newfieldname, newfield.dtype)]) newdata = recarray(_data.shape, newdtype) # Add the exisintg field [newdata.setfield(_data.getfield(*f),*f) for f in _data.dtype.fields.values()] # Add the new field newdata.setfield(newfield._data, *newdata.dtype.fields[newfieldname]) newdata = newdata.view(MaskedRecords) # Get the new mask ............. # Create a new empty recarray newmdtype = np.dtype([(n,bool_) for n in newdtype.names]) newmask = recarray(_data.shape, newmdtype) # Add the old masks [newmask.setfield(_mask.getfield(*f),*f) for f in _mask.dtype.fields.values()] # Add the mask of the new field newmask.setfield(getmaskarray(newfield), *newmask.dtype.fields[newfieldname]) newdata._fieldmask = newmask return newdata ###############################################################################