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pyqtgraph/metaarray/MetaArray.py
Luke Campagnola a4963f93b7 added flowchart node for removing periodic noise from waveform 
metaarray updates:
  - better handling of HDF5 files
  - fixed some isinstance problems that appear during reloads
2012-06-18 13:45:47 -04:00

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# -*- 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:
"""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):
if isinstance(b, MetaArray):
b = b.asarray()
return self._data == b
def __ne__(self, b):
if isinstance(b, MetaArray):
b = b.asarray()
return self._data != b
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"""
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 not HAVE_HDF5:
raise Exception("The file '%s' is HDF5-formatted, but the HDF5 library (h5py) was not found." % fileName)
if readAllData is True and writable is True:
raise Exception("Incompatible arguments: readAllData=True and writable=True")
## 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()
@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)
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