diff --git a/pyqtgraph/metaarray/MetaArray.py b/pyqtgraph/metaarray/MetaArray.py index 374c9acf..eba6f3d8 100644 --- a/pyqtgraph/metaarray/MetaArray.py +++ b/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__':