# -*- coding: utf-8 -*- """ MetaArray.py - Class encapsulating ndarray with meta data Copyright 2010 Luke Campagnola Distributed under MIT/X11 license. See license.txt for more infomation. MetaArray is an array class based on numpy.ndarray that allows storage of per-axis meta data such as axis values, names, units, column names, etc. It also enables several new methods for slicing and indexing the array based on this meta data. More info at http://www.scipy.org/Cookbook/MetaArray """ import numpy as np import types, copy, threading, os, re import pickle from functools import reduce #import traceback ## By default, the library will use HDF5 when writing files. ## This can be overridden by setting USE_HDF5 = False USE_HDF5 = True try: import h5py HAVE_HDF5 = True except: USE_HDF5 = False HAVE_HDF5 = False def axis(name=None, cols=None, values=None, units=None): """Convenience function for generating axis descriptions when defining MetaArrays""" ax = {} cNameOrder = ['name', 'units', 'title'] if name is not None: ax['name'] = name if values is not None: ax['values'] = values if units is not None: ax['units'] = units if cols is not None: ax['cols'] = [] for c in cols: if type(c) != list and type(c) != tuple: c = [c] col = {} for i in range(0,len(c)): col[cNameOrder[i]] = c[i] ax['cols'].append(col) return ax class sliceGenerator(object): """Just a compact way to generate tuples of slice objects.""" def __getitem__(self, arg): return arg def __getslice__(self, arg): return arg SLICER = sliceGenerator() class MetaArray(object): """N-dimensional array with meta data such as axis titles, units, and column names. May be initialized with a file name, a tuple representing the dimensions of the array, or any arguments that could be passed on to numpy.array() The info argument sets the metadata for the entire array. It is composed of a list of axis descriptions where each axis may have a name, title, units, and a list of column descriptions. An additional dict at the end of the axis list may specify parameters that apply to values in the entire array. For example: A 2D array of altitude values for a topographical map might look like info=[ {'name': 'lat', 'title': 'Lattitude'}, {'name': 'lon', 'title': 'Longitude'}, {'title': 'Altitude', 'units': 'm'} ] In this case, every value in the array represents the altitude in feet at the lat, lon position represented by the array index. All of the following return the value at lat=10, lon=5: array[10, 5] array['lon':5, 'lat':10] array['lat':10][5] Now suppose we want to combine this data with another array of equal dimensions that represents the average rainfall for each location. We could easily store these as two separate arrays or combine them into a 3D array with this description: info=[ {'name': 'vals', 'cols': [ {'name': 'altitude', 'units': 'm'}, {'name': 'rainfall', 'units': 'cm/year'} ]}, {'name': 'lat', 'title': 'Lattitude'}, {'name': 'lon', 'title': 'Longitude'} ] We can now access the altitude values with array[0] or array['altitude'], and the rainfall values with array[1] or array['rainfall']. All of the following return the rainfall value at lat=10, lon=5: array[1, 10, 5] array['lon':5, 'lat':10, 'val': 'rainfall'] array['rainfall', 'lon':5, 'lat':10] Notice that in the second example, there is no need for an extra (4th) axis description since the actual values are described (name and units) in the column info for the first axis. """ version = '2' ## Types allowed as axis or column names nameTypes = [basestring, tuple] @staticmethod def isNameType(var): return any([isinstance(var, t) for t in MetaArray.nameTypes]) ## methods to wrap from embedded ndarray / HDF5 wrapMethods = set(['__eq__', '__ne__', '__le__', '__lt__', '__ge__', '__gt__']) 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: self._data = None self.readFile(file, **kwargs) if self._data is None: raise Exception("File read failed: %s" % file) else: self._info = info if (hasattr(data, 'implements') and data.implements('MetaArray')): self._info = data._info self._data = data.asarray() elif isinstance(data, tuple): ## create empty array with specified shape self._data = np.empty(data, dtype=dtype) else: self._data = np.array(data, dtype=dtype, copy=copy) ## run sanity checks on info structure self.checkInfo() def checkInfo(self): info = self._info if info is None: if self._data is None: return else: self._info = [{} for i in range(self.ndim)] return else: try: info = list(info) except: raise Exception("Info must be a list of axis specifications") if len(info) < self.ndim+1: info.extend([{}]*(self.ndim+1-len(info))) elif len(info) > self.ndim+1: raise Exception("Info parameter must be list of length ndim+1 or less.") for i in range(len(info)): if not isinstance(info[i], dict): if info[i] is None: info[i] = {} else: raise Exception("Axis specification must be Dict or None") if i < self.ndim and 'values' in info[i]: if type(info[i]['values']) is list: info[i]['values'] = np.array(info[i]['values']) elif type(info[i]['values']) is not np.ndarray: raise Exception("Axis values must be specified as list or ndarray") if info[i]['values'].ndim != 1 or info[i]['values'].shape[0] != self.shape[i]: 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],)))) if i < self.ndim and 'cols' in info[i]: if not isinstance(info[i]['cols'], list): info[i]['cols'] = list(info[i]['cols']) if len(info[i]['cols']) != self.shape[i]: 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])) def implements(self, name=None): ## Rather than isinstance(obj, MetaArray) use object.implements('MetaArray') if name is None: 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 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 for k in newInfo: if k == 'cols': if 'cols' not in extraInfo: extraInfo['cols'] = [] extraInfo['cols'].append(newInfo[k]) if 'units' in newInfo[k]: extraInfo['units'] = newInfo[k]['units'] if 'name' in newInfo[k]: colName = newInfo[k]['name'] elif k == 'name': name = newInfo[k] else: if k not in extraInfo: extraInfo[k] = newInfo[k] extraInfo[k] = newInfo[k] if 'name' not in extraInfo: if name is None: if colName is not None: extraInfo['name'] = colName else: if colName is not None: extraInfo['name'] = str(name) + ': ' + str(colName) 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 def ndim(self): return len(self.shape) ## hdf5 objects do not have ndim property. @property def shape(self): return self._data.shape @property def dtype(self): return self._data.dtype def __len__(self): return len(self._data) def __getslice__(self, *args): return self.__getitem__(slice(*args)) def __setitem__(self, ind, val): nInd = self._interpretIndexes(ind) try: self._data[nInd] = val except: print(self, nInd, val) raise def __getattr__(self, attr): if attr in self.wrapMethods: 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) def __mul__(self, b): return self._binop('__mul__', b) def __div__(self, b): return self._binop('__div__', b) def __truediv__(self, b): return self._binop('__truediv__', b) def _binop(self, op, b): if isinstance(b, MetaArray): b = b.asarray() a = self.asarray() c = getattr(a, op)(b) if c.shape != a.shape: raise Exception("Binary operators with MetaArray must return an array of the same shape (this shape is %s, result shape was %s)" % (a.shape, c.shape)) return MetaArray(c, info=self.infoCopy()) def asarray(self): if isinstance(self._data, np.ndarray): return self._data else: return np.array(self._data) def __array__(self): ## supports np.array(metaarray_instance) return self.asarray() 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""" ax = self._interpretAxis(axis) if 'values' in self._info[ax]: return self._info[ax]['values'] else: raise Exception('Array axis %s (%d) has no associated values.' % (str(axis), ax)) def xvals(self, axis): """Synonym for axisValues()""" return self.axisValues(axis) def axisHasValues(self, axis): ax = self._interpretAxis(axis) return 'values' in self._info[ax] def axisHasColumns(self, axis): ax = self._interpretAxis(axis) return 'cols' in self._info[ax] def axisUnits(self, axis): """Return the units for axis""" ax = self._info[self._interpretAxis(axis)] if 'units' in ax: return ax['units'] def hasColumn(self, axis, col): ax = self._info[self._interpretAxis(axis)] if 'cols' in ax: for c in ax['cols']: if c['name'] == col: return True return False def listColumns(self, axis=None): """Return a list of column names for axis. If axis is not specified, then return a dict of {axisName: (column names), ...}.""" if axis is None: ret = {} for i in range(self.ndim): if 'cols' in self._info[i]: cols = [c['name'] for c in self._info[i]['cols']] else: cols = [] ret[self.axisName(i)] = cols return ret else: axis = self._interpretAxis(axis) return [c['name'] for c in self._info[axis]['cols']] def columnName(self, axis, col): ax = self._info[self._interpretAxis(axis)] return ax['cols'][col]['name'] def axisName(self, n): return self._info[n].get('name', n) def columnUnits(self, axis, column): """Return the units for column in axis""" ax = self._info[self._interpretAxis(axis)] if 'cols' in ax: for c in ax['cols']: if c['name'] == column: return c['units'] raise Exception("Axis %s has no column named %s" % (str(axis), str(column))) else: raise Exception("Axis %s has no column definitions" % str(axis)) def rowsort(self, axis, key=0): """Return this object with all records sorted along axis using key as the index to the values to compare. Does not yet modify meta info.""" ## make sure _info is copied locally before modifying it! keyList = self[key] order = keyList.argsort() if type(axis) == int: ind = [slice(None)]*axis ind.append(order) elif isinstance(axis, basestring): ind = (slice(axis, order),) return self[tuple(ind)] def append(self, val, axis): """Return this object with val appended along axis. Does not yet combine meta info.""" ## make sure _info is copied locally before modifying it! s = list(self.shape) axis = self._interpretAxis(axis) s[axis] += 1 n = MetaArray(tuple(s), info=self._info, dtype=self.dtype) ind = [slice(None)]*self.ndim ind[axis] = slice(None,-1) n[tuple(ind)] = self ind[axis] = -1 n[tuple(ind)] = val return n def extend(self, val, axis): """Return the concatenation along axis of this object and val. Does not yet combine meta info.""" ## make sure _info is copied locally before modifying it! axis = self._interpretAxis(axis) return MetaArray(np.concatenate(self, val, axis), info=self._info) def infoCopy(self, axis=None): """Return a deep copy of the axis meta info for this object""" if axis is None: return copy.deepcopy(self._info) else: return copy.deepcopy(self._info[self._interpretAxis(axis)]) def copy(self): return MetaArray(self._data.copy(), info=self.infoCopy()) def _interpretIndexes(self, ind): #print "interpret", ind if not isinstance(ind, tuple): ## a list of slices should be interpreted as a tuple of slices. if isinstance(ind, list) and len(ind) > 0 and isinstance(ind[0], slice): ind = tuple(ind) ## everything else can just be converted to a length-1 tuple else: ind = (ind,) nInd = [slice(None)]*self.ndim 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) def _interpretAxis(self, axis): if isinstance(axis, basestring) or isinstance(axis, tuple): return self._getAxis(axis) else: return axis def _interpretIndex(self, ind, pos, numOk): #print "Interpreting index", ind, pos, numOk ## should probably check for int first to speed things up.. if type(ind) is int: if not numOk: raise Exception("string and integer indexes may not follow named indexes") #print " normal numerical index" return (pos, ind, False) if MetaArray.isNameType(ind): if not numOk: raise Exception("string and integer indexes may not follow named indexes") #print " String index, column is ", self._getIndex(pos, ind) return (pos, self._getIndex(pos, ind), False) elif type(ind) is slice: #print " Slice index" if MetaArray.isNameType(ind.start) or MetaArray.isNameType(ind.stop): ## Not an actual slice! #print " ..not a real slice" axis = self._interpretAxis(ind.start) #print " axis is", axis ## x[Axis:Column] if MetaArray.isNameType(ind.stop): #print " column name, column is ", self._getIndex(axis, ind.stop) index = self._getIndex(axis, ind.stop) ## x[Axis:min:max] elif (isinstance(ind.stop, float) or isinstance(ind.step, float)) and ('values' in self._info[axis]): #print " axis value range" if ind.stop is None: mask = self.xvals(axis) < ind.step elif ind.step is None: mask = self.xvals(axis) >= ind.stop else: mask = (self.xvals(axis) >= ind.stop) * (self.xvals(axis) < ind.step) ##print "mask:", mask index = mask ## x[Axis:columnIndex] elif isinstance(ind.stop, int) or isinstance(ind.step, int): #print " normal slice after named axis" if ind.step is None: index = ind.stop else: index = slice(ind.stop, ind.step) ## x[Axis: [list]] elif type(ind.stop) is list: #print " list of indexes from named axis" index = [] for i in ind.stop: if type(i) is int: index.append(i) elif MetaArray.isNameType(i): index.append(self._getIndex(axis, i)) else: ## unrecognized type, try just passing on to array index = ind.stop break else: #print " other type.. forward on to array for handling", type(ind.stop) index = ind.stop #print "Axis %s (%s) : %s" % (ind.start, str(axis), str(type(index))) #if type(index) is np.ndarray: #print " ", index.shape return (axis, index, True) else: #print " Looks like a real slice, passing on to array" return (pos, ind, False) elif type(ind) is list: #print " List index., interpreting each element individually" indList = [self._interpretIndex(i, pos, numOk)[1] for i in ind] return (pos, indList, False) else: if not numOk: raise Exception("string and integer indexes may not follow named indexes") #print " normal numerical index" return (pos, ind, False) def _getAxis(self, name): for i in range(0, len(self._info)): axis = self._info[i] if 'name' in axis and axis['name'] == name: return i raise Exception("No axis named %s.\n info=%s" % (name, self._info)) def _getIndex(self, axis, name): ax = self._info[axis] if ax is not None and 'cols' in ax: for i in range(0, len(ax['cols'])): if 'name' in ax['cols'][i] and ax['cols'][i]['name'] == name: return i raise Exception("Axis %d has no column named %s.\n info=%s" % (axis, name, self._info)) def _axisCopy(self, i): return copy.deepcopy(self._info[i]) def _axisSlice(self, i, cols): #print "axisSlice", i, cols if 'cols' in self._info[i] or 'values' in self._info[i]: ax = self._axisCopy(i) if 'cols' in ax: #print " slicing columns..", array(ax['cols']), cols sl = np.array(ax['cols'])[cols] if isinstance(sl, np.ndarray): sl = list(sl) ax['cols'] = sl #print " result:", ax['cols'] if 'values' in ax: ax['values'] = np.array(ax['values'])[cols] else: ax = self._info[i] #print " ", ax return ax def prettyInfo(self): s = '' titles = [] maxl = 0 for i in range(len(self._info)-1): ax = self._info[i] axs = '' if 'name' in ax: axs += '"%s"' % str(ax['name']) else: axs += "%d" % i if 'units' in ax: axs += " (%s)" % str(ax['units']) titles.append(axs) if len(axs) > maxl: maxl = len(axs) for i in range(min(self.ndim, len(self._info)-1)): ax = self._info[i] axs = titles[i] axs += '%s[%d] :' % (' ' * (maxl + 2 - len(axs)), self.shape[i]) if 'values' in ax: v0 = ax['values'][0] v1 = ax['values'][-1] axs += " values: [%g ... %g] (step %g)" % (v0, v1, (v1-v0)/(self.shape[i]-1)) if 'cols' in ax: axs += " columns: " colstrs = [] for c in range(len(ax['cols'])): col = ax['cols'][c] cs = str(col.get('name', c)) if 'units' in col: cs += " (%s)" % col['units'] colstrs.append(cs) axs += '[' + ', '.join(colstrs) + ']' s += axs + "\n" s += str(self._info[-1]) return s def __repr__(self): return "%s\n-----------------------------------------------\n%s" % (self.view(np.ndarray).__repr__(), self.prettyInfo()) 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) else: info = self.infoCopy() axis = self._interpretAxis(axis) info.pop(axis) return MetaArray(fn(axis, *args, **kargs), info=info) def mean(self, axis=None, *args, **kargs): return self.axisCollapsingFn('mean', axis, *args, **kargs) def min(self, axis=None, *args, **kargs): return self.axisCollapsingFn('min', axis, *args, **kargs) def max(self, axis=None, *args, **kargs): return self.axisCollapsingFn('max', axis, *args, **kargs) def transpose(self, *args): if len(args) == 1 and hasattr(args[0], '__iter__'): order = args[0] else: order = args order = [self._interpretAxis(ax) for ax in order] infoOrder = order + list(range(len(order), len(self._info))) info = [self._info[i] for i in infoOrder] order = order + list(range(len(order), self.ndim)) try: 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* Different arguments are allowed depending on the type of file. For HDF5 files: *writable* (bool) if True, then any modifications to data in the array will be stored to disk. *readAllData* (bool) if True, then all data in the array is immediately read from disk and the file is closed (this is the default for files < 500MB). Otherwise, the file will be left open and data will be read only as requested (this is the default for files >= 500MB). """ ## 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): """Read meta array from the top of a file. Read lines until a blank line is reached. This function should ideally work for ALL versions of MetaArray. """ meta = '' ## Read meta information until the first blank line while True: line = fd.readline().strip() if line == '': break meta += line ret = eval(meta) #print ret return ret 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']: if 'values_len' in ax: ax['values'] = np.fromstring(fd.read(ax['values_len']), dtype=ax['values_type']) frameSize *= ax['values_len'] del ax['values_len'] del ax['values_type'] ## the remaining data is the actual array if mmap: subarr = np.memmap(fd, dtype=meta['type'], mode='r', shape=meta['shape']) else: subarr = np.fromstring(fd.read(), dtype=meta['type']) subarr.shape = meta['shape'] self._info = meta['info'] self._data = subarr def _readData2(self, fd, meta, mmap=False, subset=None): ## 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 'values_len' in ax: 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'] = np.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: #if rewriteDynamic is not None: #raise Exception("") if meta['type'] == 'object': if mmap: raise Exception('memmap not supported for arrays with dtype=object') subarr = pickle.loads(fd.read()) else: if mmap: 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: 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 = reduce(lambda a,b: a*b, 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 = np.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 if subset is not None: dSlice = subset[dynAxis] if dSlice.start is None: dStart = 0 else: dStart = max(0, dSlice.start - n) if dSlice.stop is None: dStop = data.shape[dynAxis] else: dStop = min(data.shape[dynAxis], dSlice.stop - n) newSubset = list(subset[:]) newSubset[dynAxis] = slice(dStart, dStop) if dStop > dStart: #print n, data.shape, " => ", newSubset, data[tuple(newSubset)].shape frames.append(data[tuple(newSubset)].copy()) else: #data = data[subset].copy() ## what's this for?? 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'] = np.array(xVals, dtype=ax['values_type']) del ax['values_len'] del ax['values_type'] #subarr = subarr.view(subtype) #subarr._info = meta['info'] self._info = meta['info'] self._data = subarr #raise Exception() ## stress-testing #return subarr def _readHDF5(self, fileName, readAllData=None, writable=False, **kargs): if 'close' in kargs and readAllData is None: ## for backward compatibility readAllData = kargs['close'] if readAllData is True and writable is True: raise Exception("Incompatible arguments: readAllData=True and writable=True") if not HAVE_HDF5: try: assert writable==False assert readAllData != False self._readHDF5Remote(fileName) return except: raise Exception("The file '%s' is HDF5-formatted, but the HDF5 library (h5py) was not found." % fileName) ## by default, readAllData=True for files < 500MB if readAllData is None: size = os.stat(fileName).st_size readAllData = (size < 500e6) if writable is True: mode = 'r+' else: mode = 'r' f = h5py.File(fileName, mode) ver = f.attrs['MetaArray'] 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 writable or not readAllData: ## read all data, convert to ndarray, close file self._data = f['data'] self._openFile = f else: self._data = f['data'][:] f.close() def _readHDF5Remote(self, fileName): ## Used to read HDF5 files via remote process. ## This is needed in the case that HDF5 is not importable due to the use of python-dbg. proc = getattr(MetaArray, '_hdf5Process', None) if proc == False: raise Exception('remote read failed') if proc == None: import pyqtgraph.multiprocess as mp #print "new process" proc = mp.Process(executable='/usr/bin/python') proc.setProxyOptions(deferGetattr=True) MetaArray._hdf5Process = proc MetaArray._h5py_metaarray = proc._import('pyqtgraph.metaarray') ma = MetaArray._h5py_metaarray.MetaArray(file=fileName) self._data = ma.asarray()._getValue() self._info = ma._info._getValue() #print MetaArray._hdf5Process #import inspect #print MetaArray, id(MetaArray), inspect.getmodule(MetaArray) @staticmethod def mapHDF5Array(data, writable=False): off = data.id.get_offset() if writable: mode = 'r+' else: mode = 'r' if off is None: raise Exception("This dataset uses chunked storage; it can not be memory-mapped. (store using mappable=True)") return np.memmap(filename=data.file.filename, offset=off, dtype=data.dtype, shape=data.shape, mode=mode) @staticmethod def readHDF5Meta(root, mmap=False): data = {} ## Pull list of values from attributes and child objects for k in root.attrs: val = root.attrs[k] if isinstance(val, basestring): ## strings need to be re-evaluated to their original types try: val = eval(val) except: raise Exception('Can not evaluate string: "%s"' % val) data[k] = val for k in root: obj = root[k] if isinstance(obj, h5py.highlevel.Group): val = MetaArray.readHDF5Meta(obj) elif isinstance(obj, h5py.highlevel.Dataset): if mmap: val = MetaArray.mapHDF5Array(obj) else: val = obj[:] else: raise Exception("Don't know what to do with type '%s'" % str(type(obj))) data[k] = val typ = root.attrs['_metaType_'] del data['_metaType_'] if typ == 'dict': return data elif typ == 'list' or typ == 'tuple': d2 = [None]*len(data) for k in data: d2[int(k)] = data[k] if typ == 'tuple': d2 = tuple(d2) return d2 else: raise Exception("Don't understand metaType '%s'" % typ) def write(self, fileName, **opts): """Write this object to a file. The object can be restored by calling MetaArray(file=fileName) opts: appendAxis: the name (or index) of the appendable axis. Allows the array to grow. compression: None, 'gzip' (good compression), 'lzf' (fast compression), etc. chunks: bool or tuple specifying chunk shape """ if USE_HDF5 and HAVE_HDF5: return self.writeHDF5(fileName, **opts) else: return self.writeMa(fileName, **opts) def writeMeta(self, fileName): """Used to re-write meta info to the given file. This feature is only available for HDF5 files.""" f = h5py.File(fileName, 'r+') if f.attrs['MetaArray'] != MetaArray.version: raise Exception("The file %s was created with a different version of MetaArray. Will not modify." % fileName) del f['info'] self.writeHDF5Meta(f, 'info', self._info) f.close() def writeHDF5(self, fileName, **opts): ## default options for writing datasets dsOpts = { 'compression': 'lzf', 'chunks': True, } ## if there is an appendable axis, then we can guess the desired chunk shape (optimized for appending) appAxis = opts.get('appendAxis', None) if appAxis is not None: appAxis = self._interpretAxis(appAxis) cs = [min(100000, x) for x in self.shape] cs[appAxis] = 1 dsOpts['chunks'] = tuple(cs) ## if there are columns, then we can guess a different chunk shape ## (read one column at a time) else: cs = [min(100000, x) for x in self.shape] for i in range(self.ndim): if 'cols' in self._info[i]: cs[i] = 1 dsOpts['chunks'] = tuple(cs) ## update options if they were passed in for k in dsOpts: if k in opts: dsOpts[k] = opts[k] ## If mappable is in options, it disables chunking/compression if opts.get('mappable', False): dsOpts = { 'chunks': None, 'compression': None } ## set maximum shape to allow expansion along appendAxis append = False if appAxis is not None: maxShape = list(self.shape) ax = self._interpretAxis(appAxis) maxShape[ax] = None if os.path.exists(fileName): append = True dsOpts['maxshape'] = tuple(maxShape) else: dsOpts['maxshape'] = None if append: f = h5py.File(fileName, 'r+') if f.attrs['MetaArray'] != MetaArray.version: raise Exception("The file %s was created with a different version of MetaArray. Will not modify." % fileName) ## resize data and write in new values data = f['data'] shape = list(data.shape) shape[ax] += self.shape[ax] data.resize(tuple(shape)) sl = [slice(None)] * len(data.shape) sl[ax] = slice(-self.shape[ax], None) data[tuple(sl)] = self.view(np.ndarray) ## add axis values if they are present. axInfo = f['info'][str(ax)] if 'values' in axInfo: v = axInfo['values'] v2 = self._info[ax]['values'] shape = list(v.shape) shape[0] += v2.shape[0] v.resize(shape) v[-v2.shape[0]:] = v2 f.close() else: f = h5py.File(fileName, 'w') f.attrs['MetaArray'] = MetaArray.version #print dsOpts f.create_dataset('data', data=self.view(np.ndarray), **dsOpts) ## dsOpts is used when storing meta data whenever an array is encountered ## however, 'chunks' will no longer be valid for these arrays if it specifies a chunk shape. ## 'maxshape' is right-out. if isinstance(dsOpts['chunks'], tuple): dsOpts['chunks'] = True if 'maxshape' in dsOpts: del dsOpts['maxshape'] self.writeHDF5Meta(f, 'info', self._info, **dsOpts) f.close() def writeHDF5Meta(self, root, name, data, **dsOpts): if isinstance(data, np.ndarray): dsOpts['maxshape'] = (None,) + data.shape[1:] root.create_dataset(name, data=data, **dsOpts) elif isinstance(data, list) or isinstance(data, tuple): gr = root.create_group(name) if isinstance(data, list): gr.attrs['_metaType_'] = 'list' else: gr.attrs['_metaType_'] = 'tuple' #n = int(np.log10(len(data))) + 1 for i in range(len(data)): self.writeHDF5Meta(gr, str(i), data[i], **dsOpts) elif isinstance(data, dict): gr = root.create_group(name) gr.attrs['_metaType_'] = 'dict' for k, v in data.items(): self.writeHDF5Meta(gr, k, v, **dsOpts) elif isinstance(data, int) or isinstance(data, float) or isinstance(data, np.integer) or isinstance(data, np.floating): root.attrs[name] = data else: try: ## strings, bools, None are stored as repr() strings root.attrs[name] = repr(data) except: print("Can not store meta data of type '%s' in HDF5. (key is '%s')" % (str(type(data)), str(name))) raise def writeMa(self, fileName, appendAxis=None, newFile=False): """Write an old-style .ma file""" meta = {'shape':self.shape, 'type':str(self.dtype), 'info':self.infoCopy(), 'version':MetaArray.version} axstrs = [] ## copy out axis values for dynamic axis if requested if appendAxis is not None: if MetaArray.isNameType(appendAxis): appendAxis = self._interpretAxis(appendAxis) ax = meta['info'][appendAxis] ax['values_len'] = 'dynamic' if 'values' in ax: ax['values_type'] = str(ax['values'].dtype) dynXVals = ax['values'] del ax['values'] else: dynXVals = None ## Generate axis data string, modify axis info so we know how to read it back in later for ax in meta['info']: if 'values' in ax: axstrs.append(ax['values'].tostring()) ax['values_len'] = len(axstrs[-1]) ax['values_type'] = str(ax['values'].dtype) del ax['values'] ## Decide whether to output the meta block for a new file if not newFile: ## If the file does not exist or its size is 0, then we must write the header newFile = (not os.path.exists(fileName)) or (os.stat(fileName).st_size == 0) ## write data to file if appendAxis is None or newFile: fd = open(fileName, 'wb') fd.write(str(meta) + '\n\n') for ax in axstrs: fd.write(ax) else: fd = open(fileName, 'ab') if self.dtype != object: dataStr = self.view(np.ndarray).tostring() else: dataStr = pickle.dumps(self.view(np.ndarray)) #print self.size, len(dataStr), self.dtype if appendAxis is not None: frameInfo = {'len':len(dataStr), 'numFrames':self.shape[appendAxis]} if dynXVals is not None: frameInfo['xVals'] = list(dynXVals) fd.write('\n'+str(frameInfo)+'\n') fd.write(dataStr) fd.close() def writeCsv(self, fileName=None): """Write 2D array to CSV file or return the string if no filename is given""" if self.ndim > 2: raise Exception("CSV Export is only for 2D arrays") if fileName is not None: file = open(fileName, 'w') ret = '' if 'cols' in self._info[0]: s = ','.join([x['name'] for x in self._info[0]['cols']]) + '\n' if fileName is not None: file.write(s) else: ret += s for row in range(0, self.shape[1]): s = ','.join(["%g" % x for x in self[:, row]]) + '\n' if fileName is not None: file.write(s) else: ret += s if fileName is not None: 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 = reduce(lambda a,b: a*b, 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__': ## Create an array with every option possible arr = np.zeros((2, 5, 3, 5), dtype=int) for i in range(arr.shape[0]): for j in range(arr.shape[1]): for k in range(arr.shape[2]): for l in range(arr.shape[3]): arr[i,j,k,l] = (i+1)*1000 + (j+1)*100 + (k+1)*10 + (l+1) info = [ axis('Axis1'), axis('Axis2', values=[1,2,3,4,5]), axis('Axis3', cols=[ ('Ax3Col1'), ('Ax3Col2', 'mV', 'Axis3 Column2'), (('Ax3','Col3'), 'A', 'Axis3 Column3')]), {'name': 'Axis4', 'values': np.array([1.1, 1.2, 1.3, 1.4, 1.5]), 'units': 's'}, {'extra': 'info'} ] ma = MetaArray(arr, info=info) print("==== Original Array =======") print(ma) print("\n\n") #### Tests follow: #### Index/slice tests: check that all values and meta info are correct after slice print("\n -- normal integer indexing\n") print("\n ma[1]") print(ma[1]) print("\n ma[1, 2:4]") print(ma[1, 2:4]) print("\n ma[1, 1:5:2]") print(ma[1, 1:5:2]) print("\n -- named axis indexing\n") print("\n ma['Axis2':3]") print(ma['Axis2':3]) print("\n ma['Axis2':3:5]") print(ma['Axis2':3:5]) print("\n ma[1, 'Axis2':3]") print(ma[1, 'Axis2':3]) print("\n ma[:, 'Axis2':3]") print(ma[:, 'Axis2':3]) print("\n ma['Axis2':3, 'Axis4':0:2]") print(ma['Axis2':3, 'Axis4':0:2]) print("\n -- column name indexing\n") print("\n ma['Axis3':'Ax3Col1']") print(ma['Axis3':'Ax3Col1']) print("\n ma['Axis3':('Ax3','Col3')]") print(ma['Axis3':('Ax3','Col3')]) print("\n ma[:, :, 'Ax3Col2']") print(ma[:, :, 'Ax3Col2']) print("\n ma[:, :, ('Ax3','Col3')]") print(ma[:, :, ('Ax3','Col3')]) print("\n -- axis value range indexing\n") print("\n ma['Axis2':1.5:4.5]") print(ma['Axis2':1.5:4.5]) print("\n ma['Axis4':1.15:1.45]") print(ma['Axis4':1.15:1.45]) print("\n ma['Axis4':1.15:1.25]") print(ma['Axis4':1.15:1.25]) print("\n -- list indexing\n") print("\n ma[:, [0,2,4]]") print(ma[:, [0,2,4]]) print("\n ma['Axis4':[0,2,4]]") print(ma['Axis4':[0,2,4]]) print("\n ma['Axis3':[0, ('Ax3','Col3')]]") print(ma['Axis3':[0, ('Ax3','Col3')]]) print("\n -- boolean indexing\n") print("\n ma[:, array([True, True, False, True, False])]") print(ma[:, np.array([True, True, False, True, False])]) print("\n ma['Axis4':array([True, False, False, False])]") print(ma['Axis4':np.array([True, False, False, False])]) #### Array operations # - Concatenate # - Append # - Extend # - Rowsort #### File I/O tests print("\n================ File I/O Tests ===================\n") import tempfile tf = tempfile.mktemp() tf = 'test.ma' # write whole array print("\n -- write/read test") ma.write(tf) ma2 = MetaArray(file=tf) #print ma2 print("\nArrays are equivalent:", (ma == ma2).all()) #print "Meta info is equivalent:", ma.infoCopy() == ma2.infoCopy() os.remove(tf) # CSV write # append mode print("\n================append test (%s)===============" % tf) ma['Axis2':0:2].write(tf, appendAxis='Axis2') for i in range(2,ma.shape[1]): ma['Axis2':[i]].write(tf, appendAxis='Axis2') ma2 = MetaArray(file=tf) #print ma2 print("\nArrays are equivalent:", (ma == ma2).all()) #print "Meta info is equivalent:", ma.infoCopy() == ma2.infoCopy() os.remove(tf) ## memmap test print("\n==========Memmap test============") ma.write(tf, mappable=True) ma2 = MetaArray(file=tf, mmap=True) print("\nArrays are equivalent:", (ma == ma2).all()) os.remove(tf)