category data and plot selection update|

system/annotations.py

@@ -1,6 +1,9 @@
 
 from sets import Set as set
 set.update = set.union_update
+import dataset
+import scipy
+
 
 class AnnotationsException(Exception):
     pass
@@ -63,3 +66,28 @@ class Annotations:
         """
         return self.dimensions.has_key(dim)
 
+    def to_dataset(self,dim):
+        """ Returns a dataset representation of annotations.
+        """
+        if self.has_dimension(dim):
+            num_dim1 = len(set(self.dimensions[dim])) #number of unique genes
+            all_genes = set(self.dimensions[dim])
+            all_categories = set()
+            for cat in self.dimensions[dim].values():
+                all_categories.update(cat)
+            num_dim1 = len(all_genes) #number of unique genes
+            num_dim2 = len(all_categories) #number of unique categories
+            gene_list=[]
+            cat_list=[]
+        matrix = scipy.zeros((num_dim1,num_dim2),'bwu')
+        for i,gene in enumerate(all_genes):
+            gene_list.append(gene)
+            for j,cat in enumerate(all_categories):
+                cat_list.append(cat)
+                matrix[i,j] = 1
+        def_list = [['genes',gene_list],['go',cat_list]]
+
+        return dataset.Dataset(matrix,def_list)
+        
+        
+    

system/dataset.py

@@ -1,5 +1,5 @@
 import logger
-from scipy import array,take,asarray,shape
+from scipy import array,take,asarray,shape,nonzero
 import project
 from itertools import izip 
 
@@ -10,19 +10,14 @@ class Dataset:
     A Dataset is an n-way array with defined string identifiers across
     all dimensions.
     """
-    def __init__(self,input_array,def_list,parents=None):
+    def __init__(self,input_array,def_list):
         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):
@@ -37,11 +32,6 @@ class Dataset:
             self._ids_set = self._ids_set.union(set(ids))
             self._dim_num[dim_name] = axis
             self._dim_names.append(dim_name)
-            #if dim_name in project.c_p.dim_names:
-            #    if ids:
-            #        # check that identifers are same as before
-            #        raise NotImplementedError
-            #    else:
                     
         for df,d in izip(def_list,self.dims): #check that data and labels match 
             df=df[1]
@@ -75,9 +65,49 @@ class Dataset:
         n_dim = self.dims[axis]
         return [str(axis) + '_' + str(i) for i in range(n_dim)]
 
-    def index_to_id(self,dim_name,index):
+    def extract_id_from_index(self,dim_name,index):
+        """Returns a set of ids from array/list of indexes."""
         dim_ids = self.ids[dim_name]
-        return [id for id,ind in dim_ids.items() if ind in index]
+        return set([id for id,ind in dim_ids.items() if ind in index])
+
+    def extract_index_from_id(self,dim_name,id):
+        """Returns an array of indexes from a set/list of identifiers
+        (or a single id)"""
+        dim_ids = self.ids[dim_name]
+        return array([ind for name,ind in dim_ids.items() if name in id])
+        
+    
+class CategoryDataset(Dataset):
+    def __init__(self,array,def_list):
+        Dataset.__init__(self,array,def_list)
+
+    def get_elements_by_category(self,dim,category):
+        """Returns all elements along input dim belonging to category.
+        Assumes a two-dim category data only!
+        """
+        if type(category)!=list:
+            raise ValueError, "category must be list"
+        gene_ids = []
+        axis_dim = self._dim_num[dim]
+        cat_index = self.extract_index_from_id(category)
+        for ind in cat_index:
+            if axis_dim==0:
+                gene_indx = nonzero(self._data[:,ind])
+            elif axis_dim==1:
+                gene_indx = nonzero(self._data[ind,:])
+            else:
+                ValueError, "Only support for 2-dim data"
+            gene_ids.append(self.extract_id_from_index(dim,gene_index))
+        return gene_ids
+                
+        
+        
+                
+            
+        
+        
+
+    
 
 class Selection:
     """Handles selected identifiers along each dimension of a dataset"""

system/plots.py

@@ -237,9 +237,9 @@ class ScatterPlot (Plot):
         self.ax = ax = fig.add_subplot(111)
         self.x_dataset = project.c_p.datasets[0]
         x = self.x_dataset._data
-        self.a = a =  x[:,0]
+        self.xaxis_data = xaxis_data =  x[:,0]
-        self.b = b = x[:,1]
+        self.yaxis_data = yaxis_data = x[:,1]
-        ax.plot(a,b,'og')
+        ax.plot(xaxis_data,yaxis_data,'og')
         self.canvas = FigureCanvas(fig)
         self.add(self.canvas)
         rectprops = dict(facecolor='blue', edgecolor = 'black',
@@ -256,14 +256,24 @@ class ScatterPlot (Plot):
         logger.log('debug', "(%3.2f, %3.2f) --> (%3.2f, %3.2f)"%(x1,y1,x2,y2))
         logger.log('debug',"The button you used were:%s, %s "%(event1.button, event2.button))
         # get all points within x1, y1, x2, y2
-        ydata = self.b
+        ydata = self.yaxis_data
-        xdata = self.a
+        xdata = self.xaxis_data
-        index =scipy.nonzero((xdata<x2) & (xdata>x1) & (ydata<y1) & (ydata>y2))
+        if x1>x2:
+            if y1<y2:
+                index =scipy.nonzero((xdata<x1) & (xdata>x2) & (ydata>y1) & (ydata<y2))
+            else:
+                index =scipy.nonzero((xdata<x1) & (xdata>x2) & (ydata<y1) & (ydata>y2))
+        else:
+            if y1<y2:
+                index =scipy.nonzero((xdata>x2) & (xdata<x1) & (ydata>y1) & (ydata<y2))
+            else:
+                index =scipy.nonzero((xdata>x2) & (xdata<x1) & (ydata<y1) & (ydata>y2))
+        
         if len(index)==0:
             logger.log('debug','No points selected!')
         else:
             logger.log('debug','Selected:\n%s'%index)
-            ids = self.x_dataset.index_to_id('samples',index)
+            ids = self.x_dataset.extract_id_from_index('samples',index)
             logger.log('debug','Selected identifiers:\n%s'%ids)
             xdata_new = scipy.take(xdata,index)
             ydata_new = scipy.take(ydata,index)