def read_from_pickle(self, fname=None):
if fname==None:
fname = '%s_matrix.pool'%(self.name)
print 'Reading:', fname
if os.path.exists(fname):
fInp = open(fname, 'rb')
D = pickle.load( fInp )
fInp.close()
# reinit self
self.axisPoolObj = AxisPool()
self.matrixL = [] # list of Matrix objects
self.matrixD = {} # cross ref by name
self.descD = D.get('descD',{}) # if descriptive dictionary was saved, restore it
for AD in D['axisL']:
#print 'Adding',AD
self.add_axis( AD )
#print self.axisPoolObj
#print
#print self
for MD in D['matrixL']:
#print 'add Matrix',MD
self.add_matrix( **MD )
else:
print '...WARNING... could not find:',fname
def __init__(self, name='Pool Data', descD=None):
self.name = name
self.descD = descD # descriptive data in dictionary format
self.axisPoolObj = AxisPool()
self.matrixL = [] # list of Matrix objects
self.matrixD = {} # cross ref by name
def read_from_hdf5(self, fname=None):
if fname==None:
fname = '%s_mpool.h5'%(self.name)
print 'Reading:', fname
if os.path.exists(fname):
h5file = tables.openFile(fname, mode='r')
# reinit self
self.axisPoolObj = AxisPool()
self.matrixL = [] # list of Matrix objects
self.matrixD = {} # cross ref by name
self.descD = None
root = h5file.root
# First get the axes
axis_nameL = [_ for _ in root.axes_name_list]
print 'axis_nameL =',axis_nameL
for aname in axis_nameL:
val_arr = getattr( root.axes, aname ).read()
dname = 'desc_' + aname
desc = getattr( root.axes, dname ).read()
units, trans, alen = desc
A = Axis({'name':aname, 'valueL':val_arr, 'units':units, 'transform':trans})
self.add_axis( A )
# Then get the matrices
matrix_nameL = [_ for _ in root.matrix_name_list]
print 'matrix_nameL =',matrix_nameL
for mname in matrix_nameL:
m = getattr( root.matrices, mname ).read()
units = getattr( root.matrices, mname ).attrs.units
a_list = getattr( root.matrices, mname+'_axis_list' ).read()
self.add_matrix( name=mname, units=units, axisNameL=list(a_list), matValArr=m )
h5file.close()
return M
if __name__=="__main__":
epsAxis = Axis({'name':'eps', 'valueL':[10., 20., 30., 40.],
'units':'', 'transform':'log10'})
# Just a dict, not an Axis obj
pcAxis = {'name':'pc', 'valueL':[100.,200.,300], 'units':'psia', 'transform':'log10'}
mrAxis = Axis({'name':'mr', 'valueL':[1,2,3,4,5],
'units':'', 'transform':''})
axesDefL = [epsAxis, pcAxis, mrAxis]
AP = AxisPool( {'axesDefL':axesDefL} )
axisNameL = ['eps','pc','mr']
shape = [len(AP.axisD[name]) for name in axisNameL]
print 'shape =',shape
matValArr = np.zeros( shape )
n0,n1,n2 = axisNameL
for i0,v0 in enumerate(AP.axisD[n0]):
for i1,v1 in enumerate(AP.axisD[n1]):
for i2,v2 in enumerate(AP.axisD[n2]):
matValArr[i0,i1,i2] = v0+v1+v2
M = Matrix( {'name':'isp_ode', 'matValArr':matValArr, 'units':'',
'axisNameL':axisNameL, 'axisPoolObj':AP} )
#print M.axisL
class MatrixPool(object):
"""
A wrapper for numpy arrays providing named axes, interpolation, iteration, disk persistence and numerical calcs
A MatrixPool object is a Collection of Axis and Matrix objects.
Like AxisPool, it is used to define a collection of Matrix objects
*** Structured to easily pickle via a dictionary of named values for properties. ***
"""
def __init__(self, name='Pool Data', descD=None):
self.name = name
self.descD = descD # descriptive data in dictionary format
self.axisPoolObj = AxisPool()
self.matrixL = [] # list of Matrix objects
self.matrixD = {} # cross ref by name
def add_axis(self, axOrD):
self.axisPoolObj.add_axis( axOrD )
def rename_matrix(self, old_name, new_name ):
M = self.matrixD[old_name]
M.name = new_name
del self.matrixD[old_name]
self.matrixD[new_name] = M
def add_matrix(self, name='matrixName', units='', axisNameL=None,
matValArr=None):
D={'name':name, 'units':units, 'matValArr':matValArr,
'axisNameL':axisNameL, 'axisPoolObj':self.axisPoolObj}
self.matrixL.append( Matrix(D) )
self.matrixD[name] = self.matrixL[-1]
return self.matrixL[-1]
def get_matrix_by_name(self, matName):
return self.matrixD.get(matName,None)
def get_axis_by_name(self, axName):
return self.axisPoolObj[axName]
def __str__(self):
sL = ['MatrixPool: %s'%self.name ]
for M in self.matrixL:
sL.append( ' Matrix:%s, shape=%s, units=%s, %%full=%i'%(M.name, M.shape(), M.units, M.iPercentFull()) )
return '\n'.join( sL )
def __len__(self):
return len( self.matrixL )
def __iter__(self):
for value in self.matrixL:
yield value
def __getitem__(self, i): # retrieve as: A[i]
return self.matrixL[i]
def save_to_hdf5(self, fname=None):
if fname==None:
fname = '%s_mpool.h5'%(self.name)
h5file = tables.openFile(fname, mode='w')
h5file.createArray("/", 'axes_name_list', [A.name for A in self.axisPoolObj], "String array")
h5file.createArray("/", 'matrix_name_list', [M.name for M in self.matrixL], "String array")
axes_group = h5file.createGroup("/", 'axes', 'All the Axes used in Matrix Pool')
mat_group = h5file.createGroup("/", 'matrices', 'All the Matrices used in Matrix Pool')
''' {'name':self.name, 'valueL':self.valueL, 'units':self.units,
'transform':self.transform, 'roundDigits':self.roundDigits}'''
for A in self.axisPoolObj:
d = h5file.createArray(axes_group, A.name, A.valueArr)
h5file.createArray(axes_group, 'transform_%s'%A.name, A.transArr)
h5file.createArray(axes_group, 'desc_%s'%A.name,
[str(A.units), str(A.transform), str(A.roundDigits)], "String array")
#d.transform_desc = A.transform
#d.roundDigits_desc = A.roundDigits
#d.units_desc = A.units
# Just use last axis "A" for atom creation
atom = tables.Atom.from_dtype(A.valueArr.dtype)
filters = tables.Filters(complib='blosc', complevel=5)
for M in self.matrixL:
ds = h5file.createCArray(mat_group, M.name, atom, M.matValArr.shape, filters=filters)
ds.attrs.units = M.units
ds[:] = M.matValArr
h5file.createArray(mat_group, M.name+'_axis_list', [aname for aname in M.axisNameL], "String array")
h5file.close()
def read_from_hdf5(self, fname=None):
if fname==None:
fname = '%s_mpool.h5'%(self.name)
print 'Reading:', fname
if os.path.exists(fname):
h5file = tables.openFile(fname, mode='r')
# reinit self
self.axisPoolObj = AxisPool()
self.matrixL = [] # list of Matrix objects
self.matrixD = {} # cross ref by name
self.descD = None
root = h5file.root
# First get the axes
axis_nameL = [_ for _ in root.axes_name_list]
print 'axis_nameL =',axis_nameL
for aname in axis_nameL:
val_arr = getattr( root.axes, aname ).read()
dname = 'desc_' + aname
desc = getattr( root.axes, dname ).read()
units, trans, alen = desc
A = Axis({'name':aname, 'valueL':val_arr, 'units':units, 'transform':trans})
self.add_axis( A )
# Then get the matrices
matrix_nameL = [_ for _ in root.matrix_name_list]
print 'matrix_nameL =',matrix_nameL
for mname in matrix_nameL:
m = getattr( root.matrices, mname ).read()
units = getattr( root.matrices, mname ).attrs.units
a_list = getattr( root.matrices, mname+'_axis_list' ).read()
self.add_matrix( name=mname, units=units, axisNameL=list(a_list), matValArr=m )
h5file.close()
def save_to_pickle(self, fname=None):
if fname==None:
fname = '%s_matrix.pool'%(self.name)
D = {}
D['axisL'] = [A.get_pickleable_dict() for A in self.axisPoolObj]
D['matrixL'] = [M.get_pickleable_dict() for M in self.matrixL]
D['descD'] = self.descD
fOut = open(fname, 'wb')
pickle.dump( D, fOut )
fOut.close()
def read_from_pickle(self, fname=None):
if fname==None:
fname = '%s_matrix.pool'%(self.name)
print 'Reading:', fname
if os.path.exists(fname):
fInp = open(fname, 'rb')
D = pickle.load( fInp )
fInp.close()
# reinit self
self.axisPoolObj = AxisPool()
self.matrixL = [] # list of Matrix objects
self.matrixD = {} # cross ref by name
self.descD = D.get('descD',{}) # if descriptive dictionary was saved, restore it
for AD in D['axisL']:
#print 'Adding',AD
self.add_axis( AD )
#print self.axisPoolObj
#print
#print self
for MD in D['matrixL']:
#print 'add Matrix',MD
self.add_matrix( **MD )
else:
print '...WARNING... could not find:',fname
