bugfixes
MetaArray update - no longer subclass of ndarray
This commit is contained in:
Luke Campagnola
parent
194f90aa4d
commit
4d1a5ded1b
@ -16,11 +16,12 @@ class GraphicsWidget(GraphicsItem, QtGui.QGraphicsWidget):
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GraphicsItem.__init__(self)
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GraphicsScene.registerObject(self) ## workaround for pyqt bug in graphicsscene.items()
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def itemChange(self, change, value):
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ret = QtGui.QGraphicsWidget.itemChange(self, change, value)
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if change in [self.ItemParentHasChanged, self.ItemSceneHasChanged]:
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self._updateView()
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return ret
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## Removed because this causes segmentation faults. Don't know why.
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# def itemChange(self, change, value):
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# ret = QtGui.QGraphicsWidget.itemChange(self, change, value) ## segv occurs here
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# if change in [self.ItemParentHasChanged, self.ItemSceneHasChanged]:
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# self._updateView()
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# return ret
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#def getMenu(self):
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#pass
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@ -22,7 +22,7 @@ class GridItem(UIGraphicsItem):
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def viewRangeChanged(self):
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GraphicsObject.viewRangeChanged(self)
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UIGraphicsItem.viewRangeChanged(self)
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self.picture = None
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#UIGraphicsItem.viewRangeChanged(self)
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#self.update()
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@ -1,9 +1,4 @@
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try:
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import metaarray
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HAVE_METAARRAY = True
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except:
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HAVE_METAARRAY = False
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import pyqtgraph.metaarray as metaarray
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from pyqtgraph.Qt import QtCore
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from .GraphicsObject import GraphicsObject
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from .PlotCurveItem import PlotCurveItem
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@ -252,6 +247,7 @@ class PlotDataItem(GraphicsObject):
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if len(args) == 1:
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data = args[0]
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dt = dataType(data)
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print "plot:", dt, type(data)
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if dt == 'empty':
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pass
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elif dt == 'listOfValues':
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@ -493,9 +489,10 @@ def dataType(obj):
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return 'empty'
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if isSequence(obj):
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first = obj[0]
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if isinstance(obj, np.ndarray):
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if HAVE_METAARRAY and isinstance(obj, metaarray.MetaArray):
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return 'MetaArray'
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if isinstance(obj, metaarray.MetaArray):
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return 'MetaArray'
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elif isinstance(obj, np.ndarray):
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if obj.ndim == 1:
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if obj.dtype.names is None:
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return 'listOfValues'
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@ -514,7 +511,7 @@ def dataType(obj):
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def isSequence(obj):
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return isinstance(obj, list) or isinstance(obj, np.ndarray)
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return isinstance(obj, list) or isinstance(obj, np.ndarray) or isinstance(obj, metaarray.MetaArray)
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@ -29,6 +29,13 @@ import pyqtgraph.debug as debug
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from pyqtgraph.SignalProxy import SignalProxy
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try:
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import pyqtgraph.metaarray as metaarray
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HAVE_METAARRAY = True
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except:
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HAVE_METAARRAY = False
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class PlotROI(ROI):
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def __init__(self, size):
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ROI.__init__(self, pos=[0,0], size=size, scaleSnap=True, translateSnap=True)
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@ -188,6 +195,9 @@ class ImageView(QtGui.QWidget):
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"""
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prof = debug.Profiler('ImageView.setImage', disabled=True)
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if HAVE_METAARRAY and isinstance(img, metaarray.MetaArray):
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img = img.asarray()
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if not isinstance(img, np.ndarray):
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raise Exception("Image must be specified as ndarray.")
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self.image = img
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@ -57,7 +57,7 @@ class sliceGenerator:
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SLICER = sliceGenerator()
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class MetaArray(np.ndarray):
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class MetaArray(object):
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"""N-dimensional array with meta data such as axis titles, units, and column names.
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May be initialized with a file name, a tuple representing the dimensions of the array,
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@ -109,94 +109,91 @@ class MetaArray(np.ndarray):
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@staticmethod
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def isNameType(var):
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return any([isinstance(var, t) for t in MetaArray.nameTypes])
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## methods to wrap from embedded ndarray / HDF5
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wrapMethods = set(['__eq__', '__ne__', '__le__', '__lt__', '__ge__', '__gt__'])
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def __new__(subtype, data=None, file=None, info=None, dtype=None, copy=False, **kwargs):
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if data is not None:
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if type(data) is tuple:
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subarr = np.empty(data, dtype=dtype)
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else:
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subarr = np.array(data, dtype=dtype, copy=copy)
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subarr = subarr.view(subtype)
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#### Sanity checks on info
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if info is not None:
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try:
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info = list(info)
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except:
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raise Exception("Info must be a list of axis specifications")
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if len(info) < subarr.ndim+1:
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info.extend([{}]*(subarr.ndim+1-len(info)))
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elif len(info) > subarr.ndim+1:
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raise Exception("Info parameter must be list of length ndim+1 or less.")
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for i in range(len(info)):
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if not isinstance(info[i], dict):
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if info[i] is None:
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info[i] = {}
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else:
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raise Exception("Axis specification must be Dict or None")
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if i < subarr.ndim and 'values' in info[i]:
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if type(info[i]['values']) is list:
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info[i]['values'] = np.array(info[i]['values'])
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elif type(info[i]['values']) is not np.ndarray:
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raise Exception("Axis values must be specified as list or ndarray")
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if info[i]['values'].ndim != 1 or info[i]['values'].shape[0] != subarr.shape[i]:
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raise Exception("Values array for axis %d has incorrect shape. (given %s, but should be %s)" % (i, str(info[i]['values'].shape), str((subarr.shape[i],))))
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if i < subarr.ndim and 'cols' in info[i]:
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if not isinstance(info[i]['cols'], list):
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info[i]['cols'] = list(info[i]['cols'])
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if len(info[i]['cols']) != subarr.shape[i]:
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raise Exception('Length of column list for axis %d does not match data. (given %d, but should be %d)' % (i, len(info[i]['cols']), subarr.shape[i]))
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subarr._info = info
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elif hasattr(data, '_info'):
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subarr._info = data._info
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elif file is not None:
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## decide which read function to use
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fd = open(file, 'rb')
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magic = fd.read(8)
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if magic == '\x89HDF\r\n\x1a\n':
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fd.close()
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return MetaArray._readHDF5(file, subtype, **kwargs)
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else:
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fd.seek(0)
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meta = MetaArray._readMeta(fd)
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if 'version' in meta:
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ver = meta['version']
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else:
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ver = 1
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rFuncName = '_readData%s' % str(ver)
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if not hasattr(MetaArray, rFuncName):
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raise Exception("This MetaArray library does not support array version '%s'" % ver)
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rFunc = getattr(MetaArray, rFuncName)
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subarr = rFunc(fd, meta, subtype, **kwargs)
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return subarr
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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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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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## obj is the object from which this array was generated (for example, when slicing or view()ing)
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if file is not None:
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self._data = None
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self.readFile(file, **kwargs)
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if self._data is None:
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raise Exception("File read failed: %s" % file)
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else:
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self._info = info
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if isinstance(data, MetaArray):
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self._info = data._info
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self._data = data.asarray()
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elif isinstance(data, tuple): ## create empty array with specified shape
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self._data = np.empty(data, dtype=dtype)
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else:
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self._data = np.array(data, dtype=dtype, copy=copy)
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## run sanity checks on info structure
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self.checkInfo()
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def checkInfo(self):
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info = self._info
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if info is None:
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if self._data is None:
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return
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else:
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self._info = [{} for i in range(self.ndim)]
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return
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else:
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try:
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info = list(info)
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except:
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raise Exception("Info must be a list of axis specifications")
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if len(info) < self.ndim+1:
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info.extend([{}]*(self.ndim+1-len(info)))
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elif len(info) > self.ndim+1:
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raise Exception("Info parameter must be list of length ndim+1 or less.")
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for i in range(len(info)):
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if not isinstance(info[i], dict):
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if info[i] is None:
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info[i] = {}
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else:
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raise Exception("Axis specification must be Dict or None")
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if i < self.ndim and 'values' in info[i]:
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if type(info[i]['values']) is list:
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info[i]['values'] = np.array(info[i]['values'])
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elif type(info[i]['values']) is not np.ndarray:
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raise Exception("Axis values must be specified as list or ndarray")
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if info[i]['values'].ndim != 1 or info[i]['values'].shape[0] != self.shape[i]:
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raise Exception("Values array for axis %d has incorrect shape. (given %s, but should be %s)" % (i, str(info[i]['values'].shape), str((self.shape[i],))))
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if i < self.ndim and 'cols' in info[i]:
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if not isinstance(info[i]['cols'], list):
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info[i]['cols'] = list(info[i]['cols'])
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if len(info[i]['cols']) != self.shape[i]:
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raise Exception('Length of column list for axis %d does not match data. (given %d, but should be %d)' % (i, len(info[i]['cols']), self.shape[i]))
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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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# 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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### 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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## 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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@ -206,80 +203,122 @@ class MetaArray(np.ndarray):
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nInd = self._interpretIndexes(ind)
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#print "Indexes:", nInd
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try:
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a = np.ndarray.__getitem__(self, nInd)
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except:
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#print nInd, self.shape
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raise
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if type(a) == type(self): ## generate new info array
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#print " new MA:", type(a), a.shape
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a._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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a._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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for k in newInfo:
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if k == 'cols':
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if 'cols' not in extraInfo:
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extraInfo['cols'] = []
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extraInfo['cols'].append(newInfo[k])
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if 'units' in newInfo[k]:
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extraInfo['units'] = newInfo[k]['units']
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if 'name' in newInfo[k]:
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colName = newInfo[k]['name']
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elif k == 'name':
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name = newInfo[k]
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else:
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if k not in extraInfo:
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extraInfo[k] = newInfo[k]
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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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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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for k in newInfo:
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if k == 'cols':
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if 'cols' not in extraInfo:
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extraInfo['cols'] = []
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extraInfo['cols'].append(newInfo[k])
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if 'units' in newInfo[k]:
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extraInfo['units'] = newInfo[k]['units']
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if 'name' in newInfo[k]:
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colName = newInfo[k]['name']
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elif k == 'name':
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name = newInfo[k]
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else:
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if k not in extraInfo:
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extraInfo[k] = newInfo[k]
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if 'name' not in extraInfo:
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if name is None:
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if colName is not None:
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extraInfo['name'] = colName
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extraInfo[k] = newInfo[k]
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if 'name' not in extraInfo:
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if name is None:
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if colName is not None:
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extraInfo['name'] = colName
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else:
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if colName is not None:
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extraInfo['name'] = str(name) + ': ' + str(colName)
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else:
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if colName is not None:
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extraInfo['name'] = str(name) + ': ' + str(colName)
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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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a._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 a
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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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def ndim(self):
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return len(self.shape) ## hdf5 objects do not have ndim property.
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@property
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def shape(self):
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return self._data.shape
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@property
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def dtype(self):
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return self._data.dtype
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def __len__(self):
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return len(self._data)
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def __getslice__(self, *args):
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return self.__getitem__(slice(*args))
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def __setitem__(self, ind, val):
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nInd = self._interpretIndexes(ind)
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try:
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return np.ndarray.__setitem__(self.view(np.ndarray), nInd, val)
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self._data[nInd] = val
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except:
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print(self, nInd, val)
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raise
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#def __getattr__(self, attr):
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#if attr in ['round']:
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def __getattr__(self, attr):
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if attr in self.wrapMethods:
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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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if isinstance(b, MetaArray):
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b = b.asarray()
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return self._data == b
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def __ne__(self, b):
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if isinstance(b, MetaArray):
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b = b.asarray()
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return self._data != b
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def asarray(self):
|
||||
if isinstance(self._data, np.ndarray):
|
||||
return self._data
|
||||
else:
|
||||
return np.array(self._data)
|
||||
|
||||
def view(self, typ):
|
||||
## deprecated; kept for backward compatibility
|
||||
if typ is np.ndarray:
|
||||
return self.asarray()
|
||||
else:
|
||||
raise Exception('invalid view type: %s' % str(typ))
|
||||
|
||||
def axisValues(self, axis):
|
||||
"""Return the list of values for an axis"""
|
||||
@ -591,8 +630,8 @@ class MetaArray(np.ndarray):
|
||||
|
||||
|
||||
def axisCollapsingFn(self, fn, axis=None, *args, **kargs):
|
||||
arr = self.view(np.ndarray)
|
||||
fn = getattr(arr, fn)
|
||||
#arr = self.view(np.ndarray)
|
||||
fn = getattr(self._data, fn)
|
||||
if axis is None:
|
||||
return fn(axis, *args, **kargs)
|
||||
else:
|
||||
@ -623,12 +662,37 @@ class MetaArray(np.ndarray):
|
||||
order = order + list(range(len(order), self.ndim))
|
||||
|
||||
try:
|
||||
return MetaArray(self.view(np.ndarray).transpose(order), info=info)
|
||||
if self._isHDF:
|
||||
return MetaArray(np.array(self._data).transpose(order), info=info)
|
||||
else:
|
||||
return MetaArray(self._data.transpose(order), info=info)
|
||||
except:
|
||||
print(order)
|
||||
raise
|
||||
|
||||
#### File I/O Routines
|
||||
def readFile(self, filename, **kwargs):
|
||||
"""Load the data and meta info stored in *filename*"""
|
||||
## decide which read function to use
|
||||
fd = open(filename, 'rb')
|
||||
magic = fd.read(8)
|
||||
if magic == '\x89HDF\r\n\x1a\n':
|
||||
fd.close()
|
||||
self._readHDF5(filename, **kwargs)
|
||||
self._isHDF = True
|
||||
else:
|
||||
fd.seek(0)
|
||||
meta = MetaArray._readMeta(fd)
|
||||
if 'version' in meta:
|
||||
ver = meta['version']
|
||||
else:
|
||||
ver = 1
|
||||
rFuncName = '_readData%s' % str(ver)
|
||||
if not hasattr(MetaArray, rFuncName):
|
||||
raise Exception("This MetaArray library does not support array version '%s'" % ver)
|
||||
rFunc = getattr(self, rFuncName)
|
||||
rFunc(fd, meta, **kwargs)
|
||||
self._isHDF = False
|
||||
|
||||
@staticmethod
|
||||
def _readMeta(fd):
|
||||
@ -646,10 +710,8 @@ class MetaArray(np.ndarray):
|
||||
#print ret
|
||||
return ret
|
||||
|
||||
@staticmethod
|
||||
def _readData1(fd, meta, subtype, mmap=False):
|
||||
"""Read array data from the file descriptor for MetaArray v1 files
|
||||
"""
|
||||
def _readData1(self, fd, meta, mmap=False):
|
||||
## Read array data from the file descriptor for MetaArray v1 files
|
||||
## read in axis values for any axis that specifies a length
|
||||
frameSize = 1
|
||||
for ax in meta['info']:
|
||||
@ -664,12 +726,10 @@ class MetaArray(np.ndarray):
|
||||
else:
|
||||
subarr = np.fromstring(fd.read(), dtype=meta['type'])
|
||||
subarr.shape = meta['shape']
|
||||
subarr = subarr.view(subtype)
|
||||
subarr._info = meta['info']
|
||||
return subarr
|
||||
self._info = meta['info']
|
||||
self._data = subarr
|
||||
|
||||
@staticmethod
|
||||
def _readData2(fd, meta, subtype, mmap=False, subset=None):
|
||||
def _readData2(self, fd, meta, mmap=False, subset=None):
|
||||
## read in axis values
|
||||
dynAxis = None
|
||||
frameSize = 1
|
||||
@ -768,13 +828,14 @@ class MetaArray(np.ndarray):
|
||||
ax['values'] = np.array(xVals, dtype=ax['values_type'])
|
||||
del ax['values_len']
|
||||
del ax['values_type']
|
||||
subarr = subarr.view(subtype)
|
||||
subarr._info = meta['info']
|
||||
#subarr = subarr.view(subtype)
|
||||
#subarr._info = meta['info']
|
||||
self._info = meta['info']
|
||||
self._data = subarr
|
||||
#raise Exception() ## stress-testing
|
||||
return subarr
|
||||
#return subarr
|
||||
|
||||
@staticmethod
|
||||
def _readHDF5(fileName, subtype, mmap=False, writable=False):
|
||||
def _readHDF5(self, fileName, close=False, writable=False):
|
||||
if not HAVE_HDF5:
|
||||
raise Exception("The file '%s' is HDF5-formatted, but the HDF5 library (h5py) was not found." % fileName)
|
||||
f = h5py.File(fileName, 'r')
|
||||
@ -782,16 +843,19 @@ class MetaArray(np.ndarray):
|
||||
if ver > MetaArray.version:
|
||||
print("Warning: This file was written with MetaArray version %s, but you are using version %s. (Will attempt to read anyway)" % (str(ver), str(MetaArray.version)))
|
||||
meta = MetaArray.readHDF5Meta(f['info'])
|
||||
self._info = meta
|
||||
|
||||
if mmap:
|
||||
arr = MetaArray.mapHDF5Array(f['data'], writable=writable)
|
||||
if close:
|
||||
self._data = f['data'][:]
|
||||
f.close()
|
||||
else:
|
||||
arr = f['data'][:]
|
||||
self._data = f['data']
|
||||
self._openFile = f
|
||||
#meta = H5MetaList(f['info'])
|
||||
subarr = arr.view(subtype)
|
||||
subarr._info = meta
|
||||
f.close()
|
||||
return subarr
|
||||
#subarr = arr.view(subtype)
|
||||
#subarr._info = meta
|
||||
#self._data = arr
|
||||
#return subarr
|
||||
|
||||
@staticmethod
|
||||
def mapHDF5Array(data, writable=False):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user