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Changed how the selection list works. CategoryDatasets can be dragged down to

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the selection list, and will then be converted to Selections.
This commit is contained in:
Einar Ryeng
2006-09-08 18:25:03 +00:00
parent df7f98adec
commit e0ca48d4b3
5 changed files with 480 additions and 64 deletions
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44
system/dataset.py
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@@ -136,20 +136,23 @@ class Dataset:
return self._all_dims
def get_dim_name(self,axis=None):
"""Returns dim name for an axis, if no axis is provided it returns a list of dims"""
"""Returns dim name for an axis, if no axis is provided it
returns a list of dims"""
if type(axis)==int:
return self._dims[axis]
else:
return [dim for dim in self]
def get_identifiers(self, dim, indices=None,sorted=False):
"""Returns identifiers along dim, sorted by position (index) is optional.
"""Returns identifiers along dim, sorted by position (index)
is optional.
You can optionally provide a list/ndarray of indices to get only the
identifiers of a given position.
You can optionally provide a list/ndarray of indices to get
only the identifiers of a given position.
Identifiers are the unique names (strings) for a variable in a given dim.
Index (Indices) are the Identifiers position in a matrix in a given dim.
Identifiers are the unique names (strings) for a variable in a
given dim. Index (Indices) are the Identifiers position in a
matrix in a given dim.
"""
try:
if len(indices)==0:# if empty list or empty array
@@ -170,18 +173,22 @@ class Dataset:
def get_indices(self, dim, idents=None):
"""Returns indices for identifiers along dimension.
You can optionally provide a list of identifiers to retrieve a index subset.
You can optionally provide a list of identifiers to retrieve a
index subset.
Identifiers are the unique names (strings) for a variable in a given dim.
Index (Indices) are the Identifiers position in a matrix in a given dim.
If none of the input identifiers are found an empty index is returned
Identifiers are the unique names (strings) for a variable in a
given dim. Index (Indices) are the Identifiers position in a
matrix in a given dim. If none of the input identifiers are
found an empty index is returned
"""
if idents==None:
index = array_sort(self._map[dim].values())
else:
index = [self._map[dim][key] for key in idents if self._map[dim].has_key(key)]
index = [self._map[dim][key]
for key in idents if self._map[dim].has_key(key)]
return asarray(index)
class CategoryDataset(Dataset):
"""The category dataset class.
@@ -216,7 +223,8 @@ class CategoryDataset(Dataset):
"""
data={}
for name,ind in self._map[self.get_dim_name(0)].items():
data[name] = self.get_identifiers(self.get_dim_name(1),list(self._array[ind,:].nonzero()))
data[name] = self.get_identifiers(self.get_dim_name(1),
list(self._array[ind,:].nonzero()))
self._dictlists = data
self.has_dictlists = True
return data
@@ -226,9 +234,10 @@ class CategoryDataset(Dataset):
"""
ret_list = []
for cat_name,ind in self._map[self.get_dim_name(1)].items():
ids = self.get_identifiers(self.get_dim_name(0),self._array[:,ind].nonzero())
ids = self.get_identifiers(self.get_dim_name(0),
self._array[:,ind].nonzero()[0])
selection = Selection(cat_name)
selection.select(cat_name,ids)
selection.select(self.get_dim_name(0), ids)
ret_list.append(selection)
return ret_list
@@ -242,6 +251,7 @@ class GraphDataset(Dataset):
If the library NetworkX is installed, there is support for
representing the graph as a NetworkX.Graph, or NetworkX.XGraph structure.
"""
def __init__(self,array=None,identifiers=None,shape=None,all_dims=[],**kwds):
Dataset.__init__(self,array=array,identifiers=identifiers,name='A')
self.has_graph = False
@@ -256,9 +266,9 @@ class GraphDataset(Dataset):
return G
def _graph_from_adj_matrix(self,A,labels=None,nx_type='graph'):
"""Creates a networkx graph class from adjacency matrix and ordered labels.
nx_type = ['graph',['xgraph']]
labels = None, results in string-numbered labels
"""Creates a networkx graph class from adjacency matrix and
ordered labels. nx_type = ['graph',['xgraph']] labels = None,
results in string-numbered labels
"""
import networkx as nx