#import logger from scipy import array,take,asarray,shape import project #from sets import Set as set from itertools import izip class Dataset: """Dataset base class. A Dataset is an n-way array with defined string identifiers across all dimensions. """ def __init__(self,input_array,def_list,parents=None): self._data = asarray(input_array) self.dims = shape(self._data) self.parents = parents self.def_list = def_list self._ids_set = set() self.ids={} self.children=[] self._dim_num = {} self._dim_names = [] if parents!=None: for parent in self.parents: parent.children.append(self) if len(def_list)!=len(self.dims): raise ValueError,"array dims and identifyer mismatch" for axis,(dim_name,ids) in enumerate(def_list): enum_ids = {} if dim_name not in project.c_p.dim_names: dim_name = project.c_p.suggest_dim_name(dim_name) if not ids: ids = self._create_identifiers(axis) for num,name in enumerate(ids): enum_ids[name] = num self.ids[dim_name] = enum_ids self._ids_set = self._ids_set.union(set(ids)) self._dim_num[dim_name] = axis self._dim_names.append(dim_name) for df,d in izip(def_list,self.dims): df=df[1] if len(df)!=d and df: raise ValueError,"dim size and identifyer mismatch" def names(self,axis=0): """Returns identifier names of a dimension. NB: not in any order! """ if type(axis)==int: dim_name = self._dim_names[axis] elif type(axis)==str: dim_name = axis return self.ids[dim_name].keys() def extract_data(self,ids,dim_name): """Extracts data along a dimension by identifiers""" new_def_list = self.def_list[:] ids_index = [self.ids[dim_name][id_name] for id_name in ids] dim_number = self._dim_num[dim_name] try: out_data = take(self._data,ids_index,axis=dim_number) except: raise ValueError new_def_list[dim_number][1] = ids extracted_data = Dataset(out_data,def_list=new_def_list,parents=self.parents) return extracted_data def _create_identifiers(self,axis): """Creates identifiers along an axis""" n_dim = self.dims[axis] return [str(axis) + '_' + str(i) for i in range(n_dim)] class Selection: """Handles selected identifiers along each dimension of a dataset""" def __init__(self): self.current_selection={}