update h5py deps in metaarray

- update h5py usage to support latest version
- bugfix in __getitem__ for fancy indexing
- code cleanup

pyqtgraph/metaarray/MetaArray.py

@@ -21,6 +21,13 @@ from ..python2_3 import basestring
 USE_HDF5 = True
 try:
     import h5py
+
+    # Older h5py versions tucked Group and Dataset deeper inside the library:
+    if not hasattr(h5py, 'Group'):
+        import h5py.highlevel
+        h5py.Group = h5py.highlevel.Group
+        h5py.Dataset = h5py.highlevel.Dataset
+        
     HAVE_HDF5 = True
 except:
     USE_HDF5 = False
@@ -124,7 +131,6 @@ class MetaArray(object):
   
     def __init__(self, data=None, info=None, dtype=None, file=None, copy=False, **kwargs):
         object.__init__(self)
-        #self._infoOwned = False
         self._isHDF = False
         
         if file is not None:
@@ -190,58 +196,22 @@ class MetaArray(object):
             return ['MetaArray']
         else:
             return name == 'MetaArray'
-    
-    #def __array_finalize__(self,obj):
-        ### array_finalize is called every time a MetaArray is created 
-        ### (whereas __new__ is not necessarily called every time)
-        
-        ### obj is the object from which this array was generated (for example, when slicing or view()ing)
-        
-        ## We use the getattr method to set a default if 'obj' doesn't have the 'info' attribute
-        ##print "Create new MA from object", str(type(obj))
-        ##import traceback
-        ##traceback.print_stack()
-        ##print "finalize", type(self), type(obj)
-        #if not hasattr(self, '_info'):
-            ##if isinstance(obj, MetaArray):
-                ##print "  copy info:", obj._info
-            #self._info = getattr(obj, '_info', [{}]*(obj.ndim+1))
-            #self._infoOwned = False  ## Do not make changes to _info until it is copied at least once
-        ##print "  self info:", self._info
-      
-        ## We could have checked first whether self._info was already defined:
-        ##if not hasattr(self, 'info'):
-        ##    self._info = getattr(obj, 'info', {})
-    
   
     def __getitem__(self, ind):
-        #print "getitem:", ind
-        
-        ## should catch scalar requests as early as possible to speed things up (?)
-        
         nInd = self._interpretIndexes(ind)
-        
+
-        #a = np.ndarray.__getitem__(self, nInd)
         a = self._data[nInd]
         if len(nInd) == self.ndim:
-            if np.all([not isinstance(ind, slice) for ind in nInd]):  ## no slices; we have requested a single value from the array
+            if np.all([not isinstance(ind, (slice, np.ndarray)) for ind in nInd]):  ## no slices; we have requested a single value from the array
                 return a
-        #if type(a) != type(self._data) and not isinstance(a, np.ndarray):  ## indexing returned single value
-            #return a
         
         ## indexing returned a sub-array; generate new info array to go with it
-        #print "   new MA:", type(a), a.shape
         info = []
         extraInfo = self._info[-1].copy()
         for i in range(0, len(nInd)):   ## iterate over all axes
-            #print "   axis", i
             if type(nInd[i]) in [slice, list] or isinstance(nInd[i], np.ndarray):  ## If the axis is sliced, keep the info but chop if necessary
-                #print "      slice axis", i, nInd[i]
-                #a._info[i] = self._axisSlice(i, nInd[i])
-                #print "         info:", a._info[i]
                 info.append(self._axisSlice(i, nInd[i]))
             else: ## If the axis is indexed, then move the information from that single index to the last info dictionary
-                #print "indexed:", i, nInd[i], type(nInd[i])
                 newInfo = self._axisSlice(i, nInd[i])
                 name = None
                 colName = None
@@ -270,16 +240,8 @@ class MetaArray(object):
                         else:
                             extraInfo['name'] = name
                         
-                        
-                #print "Lost info:", newInfo
-                #a._info[i] = None
-                #if 'name' in newInfo:
-                    #a._info[-1][newInfo['name']] = newInfo
         info.append(extraInfo)
         
-        #self._infoOwned = False
-        #while None in a._info:
-            #a._info.remove(None)
         return MetaArray(a, info=info)
   
     @property
@@ -313,22 +275,15 @@ class MetaArray(object):
             return getattr(self._data, attr)
         else:
             raise AttributeError(attr)
-            #return lambda *args, **kwargs: MetaArray(getattr(a.view(ndarray), attr)(*args, **kwargs)
         
     def __eq__(self, b):
         return self._binop('__eq__', b)
         
     def __ne__(self, b):
         return self._binop('__ne__', b)
-        #if isinstance(b, MetaArray):
-            #b = b.asarray()
-        #return self.asarray() != b
         
     def __sub__(self, b):
         return self._binop('__sub__', b)
-        #if isinstance(b, MetaArray):
-            #b = b.asarray()
-        #return MetaArray(self.asarray() - b, info=self.infoCopy())
 
     def __add__(self, b):
         return self._binop('__add__', b)
@@ -498,14 +453,7 @@ class MetaArray(object):
         numOk = True  ## Named indices not started yet; numbered sill ok
         for i in range(0,len(ind)):
             (axis, index, isNamed) = self._interpretIndex(ind[i], i, numOk)
-            #try:
             nInd[axis] = index
-            #except:
-                #print "ndim:", self.ndim
-                #print "axis:", axis
-                #print "index spec:", ind[i]
-                #print "index num:", index
-                #raise
             if isNamed:
                 numOk = False
         return tuple(nInd)
@@ -684,9 +632,7 @@ class MetaArray(object):
     def __str__(self):
         return self.__repr__()
 
-
     def axisCollapsingFn(self, fn, axis=None, *args, **kargs):
-        #arr = self.view(np.ndarray)
         fn = getattr(self._data, fn)
         if axis is None:
             return fn(axis, *args, **kargs)
@@ -823,8 +769,6 @@ class MetaArray(object):
 
         ## No axes are dynamic, just read the entire array in at once
         if dynAxis is None:
-            #if rewriteDynamic is not None:
-                #raise Exception("")
             if meta['type'] == 'object':
                 if mmap:
                     raise Exception('memmap not supported for arrays with dtype=object')
@@ -834,9 +778,7 @@ class MetaArray(object):
                     subarr = np.memmap(fd, dtype=meta['type'], mode='r', shape=meta['shape'])
                 else:
                     subarr = np.fromstring(fd.read(), dtype=meta['type'])
-            #subarr = subarr.view(subtype)
             subarr.shape = meta['shape']
-            #subarr._info = meta['info']
         ## One axis is dynamic, read in a frame at a time
         else:
             if mmap:
@@ -993,9 +935,9 @@ class MetaArray(object):
             data[k] = val
         for k in root:
             obj = root[k]
-            if isinstance(obj, h5py.highlevel.Group):
+            if isinstance(obj, h5py.Group):
                 val = MetaArray.readHDF5Meta(obj)
-            elif isinstance(obj, h5py.highlevel.Dataset):
+            elif isinstance(obj, h5py.Dataset):
                 if mmap:
                     val = MetaArray.mapHDF5Array(obj)
                 else:
@@ -1256,89 +1198,6 @@ class MetaArray(object):
             file.close()
         else:
             return ret
-        
-
-
-#class H5MetaList():
-    
-
-#def rewriteContiguous(fileName, newName):
-    #"""Rewrite a dynamic array file as contiguous"""
-    #def _readData2(fd, meta, subtype, mmap):
-        ### read in axis values
-        #dynAxis = None
-        #frameSize = 1
-        ### read in axis values for any axis that specifies a length
-        #for i in range(len(meta['info'])):
-            #ax = meta['info'][i]
-            #if ax.has_key('values_len'):
-                #if ax['values_len'] == 'dynamic':
-                    #if dynAxis is not None:
-                        #raise Exception("MetaArray has more than one dynamic axis! (this is not allowed)")
-                    #dynAxis = i
-                #else:
-                    #ax['values'] = fromstring(fd.read(ax['values_len']), dtype=ax['values_type'])
-                    #frameSize *= ax['values_len']
-                    #del ax['values_len']
-                    #del ax['values_type']
-                    
-        ### No axes are dynamic, just read the entire array in at once
-        #if dynAxis is None:
-            #raise Exception('Array has no dynamic axes.')
-        ### One axis is dynamic, read in a frame at a time
-        #else:
-            #if mmap:
-                #raise Exception('memmap not supported for non-contiguous arrays. Use rewriteContiguous() to convert.')
-            #ax = meta['info'][dynAxis]
-            #xVals = []
-            #frames = []
-            #frameShape = list(meta['shape'])
-            #frameShape[dynAxis] = 1
-            #frameSize = np.prod(frameShape)
-            #n = 0
-            #while True:
-                ### Extract one non-blank line
-                #while True:
-                    #line = fd.readline()
-                    #if line != '\n':
-                        #break
-                #if line == '':
-                    #break
-                    
-                ### evaluate line
-                #inf = eval(line)
-                
-                ### read data block
-                ##print "read %d bytes as %s" % (inf['len'], meta['type'])
-                #if meta['type'] == 'object':
-                    #data = pickle.loads(fd.read(inf['len']))
-                #else:
-                    #data = fromstring(fd.read(inf['len']), dtype=meta['type'])
-                
-                #if data.size != frameSize * inf['numFrames']:
-                    ##print data.size, frameSize, inf['numFrames']
-                    #raise Exception("Wrong frame size in MetaArray file! (frame %d)" % n)
-                    
-                ### read in data block
-                #shape = list(frameShape)
-                #shape[dynAxis] = inf['numFrames']
-                #data.shape = shape
-                #frames.append(data)
-                
-                #n += inf['numFrames']
-                #if 'xVals' in inf:
-                    #xVals.extend(inf['xVals'])
-            #subarr = np.concatenate(frames, axis=dynAxis)
-            #if len(xVals)> 0:
-                #ax['values'] = array(xVals, dtype=ax['values_type'])
-            #del ax['values_len']
-            #del ax['values_type']
-        #subarr = subarr.view(subtype)
-        #subarr._info = meta['info']
-        #return subarr
-    
-
-
   
   
 if __name__ == '__main__':