Errors when identifers dont match shape, + whitespace
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@ -36,28 +36,30 @@ 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,identifiers=None,name='Unnamed dataset'):
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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._name = name
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self._identifiers = identifiers
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self._type = 'n'
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try:
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if len(array.shape)==1:
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array = atleast_2d(asarray(array))
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except:
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print "Cant cast array as numpy-array"
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return
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# vectors are column vectors
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if array.shape[0]==1:
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array = array.T
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# vectors are column vectors
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if array.shape[0]==1:
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array = array.T
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self.shape = array.shape
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if identifiers!=None:
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self._set_identifiers(identifiers,self._all_dims)
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identifier_shape = [len(i[1]) for i in identifiers]
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if len(identifier_shape)!=len(self.shape):
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raise ValueError, "Identifier list length must equal array dims"
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for ni, na in zip(identifier_shape, self.shape):
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if ni!=na:
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raise ValueError, "identifier-array mismatch in %s: (idents: %s, array: %s)" %(self._name, ni, na)
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self._set_identifiers(identifiers, self._all_dims)
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else:
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self._identifiers = self._create_identifiers(self.shape,self._all_dims)
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self._set_identifiers(self._identifiers,self._all_dims)
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self._identifiers = self._create_identifiers(self.shape, self._all_dims)
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self._set_identifiers(self._identifiers, self._all_dims)
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self._array = array
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def __iter__(self):
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@ -94,17 +96,16 @@ class Dataset:
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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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def _set_identifiers(self, identifiers, all_dims):
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"""Creates internal mapping of identifiers structure."""
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for dim,ids in identifiers:
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for dim, ids in identifiers:
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pos_map = ReverseDict()
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if dim not in self._dims:
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self._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 whitin dataset"
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for pos,id in enumerate(ids):
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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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@ -121,11 +122,10 @@ class Dataset:
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"""Returns the numeric array (data) of dataset"""
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return self._array
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def add_array(self,array):
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def add_array(self, array):
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"""Adds array as an ArrayType object.
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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.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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@ -138,7 +138,7 @@ 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_name(self,axis=None):
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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
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returns a list of dims"""
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if type(axis)==int:
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@ -178,7 +178,6 @@ class Dataset:
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You can optionally provide a list of identifiers to retrieve a
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index subset.
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Identifiers are the unique names (strings) for a variable in a
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given dim. Index (Indices) are the Identifiers position in a
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matrix in a given dim. If none of the input identifiers are
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@ -218,8 +217,8 @@ class CategoryDataset(Dataset):
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.
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"""
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def __init__(self,array,identifiers=None,name='C'):
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Dataset.__init__(self,array,identifiers=identifiers,name=name)
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def __init__(self, array, identifiers=None, name='C'):
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Dataset.__init__(self, array, identifiers=identifiers, name=name)
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self.has_dictlists = False
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self._type = 'c'
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@ -229,7 +228,7 @@ class CategoryDataset(Dataset):
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ex: data['gene_id'] = ['map0030','map0010', ...]
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"""
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data={}
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for name,ind in self._map[self.get_dim_name(0)].items():
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for name, ind in self._map[self.get_dim_name(0)].items():
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data[name] = self.get_identifiers(self.get_dim_name(1),
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list(self._array[ind,:].nonzero()))
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self._dictlists = data
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@ -240,7 +239,7 @@ class CategoryDataset(Dataset):
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"""Returns data as a list of Selection objects.
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"""
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ret_list = []
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for cat_name,ind in self._map[self.get_dim_name(1)].items():
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for cat_name, ind in self._map[self.get_dim_name(1)].items():
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ids = self.get_identifiers(self.get_dim_name(0),
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self._array[:,ind].nonzero()[0])
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selection = Selection(cat_name)
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@ -254,26 +253,26 @@ class GraphDataset(Dataset):
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A dataset class for representing graphs using an (weighted)
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adjacency matrix
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(aka. restricted to square symmetric matrices)
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(restricted to square symmetric matrices)
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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,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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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._graph = None
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self._type = 'g'
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def asnetworkx(self,nx_type='graph'):
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def asnetworkx(self, nx_type='graph'):
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dim = self.get_dim_name()[0]
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ids = self.get_identifiers(dim,sorted=True)
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ids = self.get_identifiers(dim, sorted=True)
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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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G = self._graph_from_adj_matrix(adj_mat, labels=ids)
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self._graph = G
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return G
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def _graph_from_adj_matrix(self,A,labels=None):
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def _graph_from_adj_matrix(self, A, labels=None):
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"""Creates a networkx graph class from adjacency
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(possibly weighted) matrix and ordered labels.
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@ -286,7 +285,7 @@ class GraphDataset(Dataset):
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except:
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print "Failed in import of NetworkX"
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return
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m,n = A.shape# adjacency matrix must be of type that evals to true/false for neigbours
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m, n = A.shape# 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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@ -298,17 +297,18 @@ class GraphDataset(Dataset):
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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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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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if type(G)==nx.XGraph:
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G.add_edge(head,tail,nbr)
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G.add_edge(head, tail, nbr)
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else:
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G.add_edge(head,tail)
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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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Dataset._all_dims = set()
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class ReverseDict(dict):
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"""
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@ -336,30 +336,34 @@ def to_file(filepath,dataset,name=None):
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"""
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if not name:
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name = dataset._name
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data = shelve.open(filepath,flag='c',protocol=2)
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data = shelve.open(filepath, flag='c', protocol=2)
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if data: #we have an append
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names = data.keys()
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if name in names:
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print "Data with name: %s overwritten" %dataset._name
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sub_data = {'array':dataset._array,'idents':dataset._identifiers,'type':dataset._type}
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sub_data = {'array':dataset._array,
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'idents':dataset._identifiers,
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'type':dataset._type}
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data[name] = sub_data
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data.close()
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def from_file(filepath):
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"""Read dataset from file """
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data = shelve.open(filepath,flag='r')
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"""Read dataset(s) from file """
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data = shelve.open(filepath, flag='r')
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out_data = []
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for name in data.keys():
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sub_data = data[name]
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if sub_data['type']=='c':
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out_data.append(CategoryDataset(sub_data['array'],identifiers=sub_data['idents'],name=name))
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out_data.append(CategoryDataset(sub_data['array'], identifiers=sub_data['idents'], name=name))
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elif sub_data['type']=='g':
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out_data.append(GraphDataset(sub_data['array'],identifiers=sub_data['idents'],name=name))
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out_data.append(GraphDataset(sub_data['array'], identifiers=sub_data['idents'], name=name))
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else:
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out_data.append(Dataset(sub_data['array'],identifiers=sub_data['idents'],name=name))
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out_data.append(Dataset(sub_data['array'], identifiers=sub_data['idents'], name=name))
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return out_data
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class Selection(dict):
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"""Handles selected identifiers along each dimension of a dataset"""
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