update h5py deps in metaarray
- update h5py usage to support latest version - bugfix in __getitem__ for fancy indexing - code cleanup
This commit is contained in:
Luke Campagnola
parent
268d25c125
commit
5e971b646f
@ -21,6 +21,13 @@ from ..python2_3 import basestring
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USE_HDF5 = True
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try:
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import h5py
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# Older h5py versions tucked Group and Dataset deeper inside the library:
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if not hasattr(h5py, 'Group'):
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import h5py.highlevel
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h5py.Group = h5py.highlevel.Group
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h5py.Dataset = h5py.highlevel.Dataset
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HAVE_HDF5 = True
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except:
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USE_HDF5 = False
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@ -124,7 +131,6 @@ class MetaArray(object):
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def __init__(self, data=None, info=None, dtype=None, file=None, copy=False, **kwargs):
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object.__init__(self)
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#self._infoOwned = False
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self._isHDF = False
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if file is not None:
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@ -191,57 +197,21 @@ class MetaArray(object):
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else:
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return name == 'MetaArray'
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#def __array_finalize__(self,obj):
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### array_finalize is called every time a MetaArray is created
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### (whereas __new__ is not necessarily called every time)
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### obj is the object from which this array was generated (for example, when slicing or view()ing)
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## We use the getattr method to set a default if 'obj' doesn't have the 'info' attribute
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##print "Create new MA from object", str(type(obj))
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##import traceback
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##traceback.print_stack()
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##print "finalize", type(self), type(obj)
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#if not hasattr(self, '_info'):
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##if isinstance(obj, MetaArray):
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##print " copy info:", obj._info
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#self._info = getattr(obj, '_info', [{}]*(obj.ndim+1))
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#self._infoOwned = False ## Do not make changes to _info until it is copied at least once
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##print " self info:", self._info
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## We could have checked first whether self._info was already defined:
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##if not hasattr(self, 'info'):
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## self._info = getattr(obj, 'info', {})
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def __getitem__(self, ind):
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#print "getitem:", ind
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## should catch scalar requests as early as possible to speed things up (?)
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nInd = self._interpretIndexes(ind)
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#a = np.ndarray.__getitem__(self, nInd)
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a = self._data[nInd]
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if len(nInd) == self.ndim:
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if np.all([not isinstance(ind, slice) for ind in nInd]): ## no slices; we have requested a single value from the array
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if np.all([not isinstance(ind, (slice, np.ndarray)) for ind in nInd]): ## no slices; we have requested a single value from the array
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return a
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#if type(a) != type(self._data) and not isinstance(a, np.ndarray): ## indexing returned single value
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#return a
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## indexing returned a sub-array; generate new info array to go with it
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#print " new MA:", type(a), a.shape
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info = []
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extraInfo = self._info[-1].copy()
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for i in range(0, len(nInd)): ## iterate over all axes
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#print " axis", i
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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
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#print " slice axis", i, nInd[i]
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#a._info[i] = self._axisSlice(i, nInd[i])
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#print " info:", a._info[i]
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info.append(self._axisSlice(i, nInd[i]))
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else: ## If the axis is indexed, then move the information from that single index to the last info dictionary
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#print "indexed:", i, nInd[i], type(nInd[i])
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newInfo = self._axisSlice(i, nInd[i])
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name = None
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colName = None
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@ -270,16 +240,8 @@ class MetaArray(object):
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else:
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extraInfo['name'] = name
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#print "Lost info:", newInfo
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#a._info[i] = None
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#if 'name' in newInfo:
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#a._info[-1][newInfo['name']] = newInfo
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info.append(extraInfo)
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#self._infoOwned = False
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#while None in a._info:
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#a._info.remove(None)
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return MetaArray(a, info=info)
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@property
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@ -313,22 +275,15 @@ class MetaArray(object):
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return getattr(self._data, attr)
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else:
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raise AttributeError(attr)
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#return lambda *args, **kwargs: MetaArray(getattr(a.view(ndarray), attr)(*args, **kwargs)
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def __eq__(self, b):
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return self._binop('__eq__', b)
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def __ne__(self, b):
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return self._binop('__ne__', b)
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#if isinstance(b, MetaArray):
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#b = b.asarray()
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#return self.asarray() != b
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def __sub__(self, b):
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return self._binop('__sub__', b)
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#if isinstance(b, MetaArray):
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#b = b.asarray()
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#return MetaArray(self.asarray() - b, info=self.infoCopy())
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def __add__(self, b):
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return self._binop('__add__', b)
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@ -498,14 +453,7 @@ class MetaArray(object):
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numOk = True ## Named indices not started yet; numbered sill ok
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for i in range(0,len(ind)):
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(axis, index, isNamed) = self._interpretIndex(ind[i], i, numOk)
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#try:
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nInd[axis] = index
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#except:
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#print "ndim:", self.ndim
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#print "axis:", axis
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#print "index spec:", ind[i]
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#print "index num:", index
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#raise
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if isNamed:
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numOk = False
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return tuple(nInd)
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@ -684,9 +632,7 @@ class MetaArray(object):
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def __str__(self):
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return self.__repr__()
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def axisCollapsingFn(self, fn, axis=None, *args, **kargs):
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#arr = self.view(np.ndarray)
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fn = getattr(self._data, fn)
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if axis is None:
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return fn(axis, *args, **kargs)
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@ -823,8 +769,6 @@ class MetaArray(object):
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## No axes are dynamic, just read the entire array in at once
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if dynAxis is None:
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#if rewriteDynamic is not None:
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#raise Exception("")
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if meta['type'] == 'object':
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if mmap:
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raise Exception('memmap not supported for arrays with dtype=object')
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@ -834,9 +778,7 @@ class MetaArray(object):
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subarr = np.memmap(fd, dtype=meta['type'], mode='r', shape=meta['shape'])
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else:
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subarr = np.fromstring(fd.read(), dtype=meta['type'])
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#subarr = subarr.view(subtype)
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subarr.shape = meta['shape']
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#subarr._info = meta['info']
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## One axis is dynamic, read in a frame at a time
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else:
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if mmap:
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@ -993,9 +935,9 @@ class MetaArray(object):
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data[k] = val
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for k in root:
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obj = root[k]
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if isinstance(obj, h5py.highlevel.Group):
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if isinstance(obj, h5py.Group):
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val = MetaArray.readHDF5Meta(obj)
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elif isinstance(obj, h5py.highlevel.Dataset):
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elif isinstance(obj, h5py.Dataset):
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if mmap:
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val = MetaArray.mapHDF5Array(obj)
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else:
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@ -1258,89 +1200,6 @@ class MetaArray(object):
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return ret
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#class H5MetaList():
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#def rewriteContiguous(fileName, newName):
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#"""Rewrite a dynamic array file as contiguous"""
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#def _readData2(fd, meta, subtype, mmap):
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### read in axis values
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#dynAxis = None
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#frameSize = 1
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### read in axis values for any axis that specifies a length
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#for i in range(len(meta['info'])):
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#ax = meta['info'][i]
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#if ax.has_key('values_len'):
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#if ax['values_len'] == 'dynamic':
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#if dynAxis is not None:
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#raise Exception("MetaArray has more than one dynamic axis! (this is not allowed)")
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#dynAxis = i
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#else:
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#ax['values'] = fromstring(fd.read(ax['values_len']), dtype=ax['values_type'])
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#frameSize *= ax['values_len']
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#del ax['values_len']
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#del ax['values_type']
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### No axes are dynamic, just read the entire array in at once
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#if dynAxis is None:
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#raise Exception('Array has no dynamic axes.')
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### One axis is dynamic, read in a frame at a time
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#else:
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#if mmap:
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#raise Exception('memmap not supported for non-contiguous arrays. Use rewriteContiguous() to convert.')
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#ax = meta['info'][dynAxis]
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#xVals = []
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#frames = []
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#frameShape = list(meta['shape'])
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#frameShape[dynAxis] = 1
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#frameSize = np.prod(frameShape)
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#n = 0
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#while True:
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### Extract one non-blank line
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#while True:
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#line = fd.readline()
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#if line != '\n':
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#break
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#if line == '':
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#break
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### evaluate line
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#inf = eval(line)
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### read data block
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##print "read %d bytes as %s" % (inf['len'], meta['type'])
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#if meta['type'] == 'object':
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#data = pickle.loads(fd.read(inf['len']))
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#else:
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#data = fromstring(fd.read(inf['len']), dtype=meta['type'])
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#if data.size != frameSize * inf['numFrames']:
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##print data.size, frameSize, inf['numFrames']
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#raise Exception("Wrong frame size in MetaArray file! (frame %d)" % n)
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### read in data block
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#shape = list(frameShape)
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#shape[dynAxis] = inf['numFrames']
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#data.shape = shape
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#frames.append(data)
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#n += inf['numFrames']
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#if 'xVals' in inf:
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#xVals.extend(inf['xVals'])
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#subarr = np.concatenate(frames, axis=dynAxis)
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#if len(xVals)> 0:
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#ax['values'] = array(xVals, dtype=ax['values_type'])
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#del ax['values_len']
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#del ax['values_type']
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#subarr = subarr.view(subtype)
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#subarr._info = meta['info']
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#return subarr
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if __name__ == '__main__':
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## Create an array with every option possible
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