removed shape in constructor, added all_dims to dataset base class, updated get_identifiers and get_indices
Denne linjen og de som er under vil bli ignorert-- M dataset.py
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@ -1,5 +1,6 @@
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from scipy import atleast_2d,asarray,ArrayType
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from scipy import atleast_2d,asarray,ArrayType,shape
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from scipy import sort as array_sort
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from itertools import izip
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class Dataset:
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"""The Dataset base class.
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@ -32,45 +33,26 @@ class Dataset:
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data = Dataset(rand(10,20)) (generates dims and ids (no links))
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"""
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def __init__(self,array=None,identifiers=None,shape=None,all_dims=[],**kwds):
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self._name = kwds.get("name","Unnamed data")
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def __init__(self,array,identifiers=None,name='Unnamed dataset'):
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self._dims = [] #existing dimensions in this dataset
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self._map = {} # internal mapping for dataset: identifier <--> index
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self.has_array = False
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self.shape = None
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if array==None:
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if shape == None:
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raise ValueError, "Must define shape if array is None"
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else:
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self.shape = shape
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if identifiers!=None:
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self._set_identifiers(identifiers,all_dims)
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else:
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ids = self._create_identifiers(shape,all_dims)
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self._set_identifiers(ids,all_dims)
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elif isinstance(array,ArrayType):
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self._name = name
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if isinstance(array,ArrayType):
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array = atleast_2d(asarray(array))
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self.shape = array.shape
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if shape != None:
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if self.shape!=shape:
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raise ValueError, "Differing in array and provided. %s != %s" % (self.shape, shape)
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if identifiers!=None:
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self._set_identifiers(identifiers,all_dims)
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self._set_identifiers(identifiers,self._all_dims)
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else:
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ids = self._create_identifiers(self.shape,all_dims)
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self._set_identifiers(ids,all_dims)
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ids = self._create_identifiers(self.shape,self._all_dims)
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self._set_identifiers(ids,self._all_dims)
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self._array = array
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self.has_array = True
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else:
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raise ValueError, "array input must be of ArrayType or None"
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self._all_dims = all_dims
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raise ValueError, "Array input must be of ArrayType"
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def __str__(self):
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return self._name + ":" + self._dims.__str__()
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return self._name + ":\n" + "Dim names: " + self._dims.__str__()
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def __iter__(self):
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"""Returns an iterator over dimensions of dataset."""
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@ -100,11 +82,10 @@ class Dataset:
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dim_suggestion = dim_names[axis]
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else:
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dim_suggestion = 'dim'
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while dim_suggestion in all_dims:
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dim_suggestion = self._suggest_dim_name(dim_suggestion,all_dims)
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identifier_creation = [str(axis) + "_" + i for i in map(str,range(n))]
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ids.append((dim_suggestion,identifier_creation))
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all_dims.append(dim_suggestion)
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all_dims.add(dim_suggestion)
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return ids
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def _set_identifiers(self,identifiers,all_dims):
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@ -113,24 +94,22 @@ class Dataset:
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pos_map={}
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if dim not in self._dims:
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self._dims.append(dim)
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all_dims.append(dim)
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all_dims.add(dim)
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else:
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raise ValueError, "Dimension names must be unique"
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raise ValueError, "Dimension names must be unique whitin dataset"
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for pos,id in enumerate(ids):
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pos_map[id] = pos
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self._map[dim] = pos_map
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shape_chk = [len(i) for j,i in identifiers]
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if shape_chk != list(self.shape):
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raise ValueError, "Shape input: %s and array: %s mismatch" %(shape_chk,self.shape)
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def _suggest_dim_name(self,dim_name,all_dims):
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"""Suggests a unique name for dim and returns it"""
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c = 0
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while dim_name in all_dims:
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dim_name = dim_name + "_" + str(c)
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new_name = dim_name
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while new_name in all_dims:
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new_name = dim_name + "_" + str(c)
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c+=1
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return dim_name
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return new_name
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def asarray(self):
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"""Returns the numeric array (data) of dataset"""
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@ -144,15 +123,9 @@ class Dataset:
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A one-dim array is transformed to a two-dim array (row-vector)
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"""
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if self.has_array:
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raise ValueError, "Dataset has array"
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else:
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if (len(self._map)!=len(array.shape)):
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raise ValueError, "range(array_dims) and range(dim_names) mismatch"
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if self.shape!=array.shape:
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raise ValueError, "Input array must be of similar dimensions as dataset"
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self._array = atleast_2d(asarray(array))
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self.has_array = True
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def get_name(self):
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"""Returns dataset name"""
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@ -162,34 +135,50 @@ class Dataset:
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"""Returns all dimensions in project"""
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return self._all_dims
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def get_dim_names(self):
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"""Returns dim names"""
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return [dim for dim in self._dims]
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def get_dim_name(self,axis=None):
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"""Returns dim name for an axis, if no axis is provided it returns a list of dims"""
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if type(axis)==int:
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return self._dims[axis]
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else:
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return [dim for dim in self]
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def get_identifiers(self, dim, indices=None):
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"""Returns identifiers along dim, sorted by position (index).
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def get_identifiers(self, dim, indices=None,sorted=True):
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"""Returns identifiers along dim, sorted by position (index) is optional.
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You can optionally provide a list of indices to get only the
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identifiers of a given position.
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Identifiers are the unique names (strings) for a variable in a given dim.
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Index (Indices) are the Identifiers position in a matrix in a given dim.
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"""
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if sorted==True:
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items = self._map[dim].items()
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backitems=[ [v[1],v[0]] for v in items]
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backitems = [ [v[1],v[0]] for v in items]
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backitems.sort()
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sorted_ids=[ backitems[i][1] for i in range(0,len(backitems))]
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ids = [ backitems[i][1] for i in range(0,len(backitems))]
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# we use id as scipy-arrays return a new array on boolean
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# operations
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if id(indices) != id(None):
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return [sorted_ids[index] for index in indices]
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else:
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return sorted_ids
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ids = self._map[dim].keys()
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def get_indices(self, dim, idents):
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"""Get indices for identifiers along dimension."""
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reverse = {}
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for key, value in self._map[dim].items():
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reverse[value] = key
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return [self._map[dim][key] for key in idents]
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if indices != None:
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ids = [self._map[index] for index in indices]
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return ids
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def get_indices(self, dim, idents=None):
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"""Returns indices for identifiers along dimension.
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You can optionally provide a list of identifiers to retrieve a index subset.
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Identifiers are the unique names (strings) for a variable in a given dim.
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Index (Indices) are the Identifiers position in a matrix in a given dim."""
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if idents==None:
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index = array_sort(self._map[dim].values())
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else:
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index = [self.map[dim][key] for key in idents]
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return asarray(index)
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class CategoryDataset(Dataset):
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"""The category dataset class.
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@ -236,10 +225,47 @@ class GraphDataset(Dataset):
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If the library NetworkX is installed, there is support for
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representing the graph as a NetworkX.Graph, or NetworkX.XGraph structure.
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"""
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def __init__(self):
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Dataset.__init(self)
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def __init__(self,array=None,identifiers=None,shape=None,all_dims=[],**kwds):
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Dataset.__init__(self,array=array,identifiers=identifiers,name='A')
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self.has_graph = False
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def asnetworkx(self,nx_type='graph'):
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dim = self.get_dim_names()[0]
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ids = self.get_identifiers(dim)
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adj_mat = self.asarray()
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G = self._graph_from_adj_matrix(adj_mat,labels=ids)
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self.has_graph = True
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return G
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def _graph_from_adj_matrix(self,A,labels=None,nx_type='graph'):
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"""Creates a networkx graph class from adjacency matrix and ordered labels.
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nx_type = ['graph',['xgraph']]
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labels = None, results in string-numbered labels
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"""
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import networkx as nx
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m,n = shape(A)# adjacency matrix must be of type that evals to true/false for neigbours
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if m!=n:
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raise IOError, "Adjacency matrix must be square"
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if nx_type=='graph':
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G = nx.Graph()
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elif nx_type=='x_graph':
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G = nx.XGraph()
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else:
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raise IOError, "Unknown graph type: %s" %nx_type
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if labels==None: # if labels not provided mark vertices with numbers
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labels = [str(i) for i in range(m)]
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for nbrs,head in izip(A,labels):
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for i,nbr in enumerate(nbrs):
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if nbr:
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tail = labels[i]
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G.add_edge(head,tail)
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return G
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Dataset._all_dims=set()
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class Selection:
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"""Handles selected identifiers along each dimension of a dataset"""
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def __init__(self):
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