swarbhanu

import h5py
import numpy

# use the core driver of h5Py to write an hdf file
file = h5py.File('myfile_check.hdf5', mode = 'w', driver='core')
#file = h5py.File('myfile_check2.hdf5', mode = 'w', driver='log')
grp = file.create_group('myGroup')
dataset = grp.create_dataset('dataset', (10,10), '=f8', maxshape=(None,None))
file['dataset'] = numpy.ones((10,10))
file.flush()

swarbhanu

from dolfin import *
import numpy

mesh = UnitCube(4, 4, 4)

# Creating two mesh functions, empty to start with....
a = FacetFunction("double", mesh)
b = FacetFunction("double", mesh)
result = FacetFunction("double", mesh)

swarbhanu

from dolfin import *
import numpy

mesh = UnitSquare(8, 8)

# Creating two mesh functions, empty to start with....
a = CellFunction("double", mesh)
b = CellFunction("double", mesh)
result = CellFunction("double", mesh)

swarbhanu

from dolfin import *
import numpy

mesh = UnitSquare(8,8)

mesh = refine(mesh)

# editor = MeshEditor()
# editor.open(mesh,2,2)
# editor.add_cell(0,0,1,2)

swarbhanu

from dolfin import *
from numpy import *

#initialize an empty mesh
mesh1 = Mesh()

#initialize an empty mesh editor
editor1 = MeshEditor()

#################################################################################

swarbhanu

##################################################################################
#						                                 #																	
# This program creates a VLArray using PyTables. One dimensional numpy arrays of #
# varying length are appended as rows			                         #					#						                                 #	
#						                                 #																	 																				 				
###################################################################################

import tables as tb
import numpy as np

swarbhanu

#################################################################################
#                                                                               #
# This program measures the time needed to read an array from an hdf file and   #
# save it in a numpy array using a slow asarray() method and a fast method with #
# the dtype explicitly provided before construction. The fast method is to be   #
# preferred for converting arrays read from hdf files using PyTables to Numpy   #
# arrays.		                                              		#			
#			                                                     	#		                                                                                  
# This file is meant to be run as in the following example:	            	#                                                                               
#			                                                     	#

swarbhanu

from tables import *

# Describe a water class
class Water(IsDescription):
    waterbody_name        = StringCol(16, pos=1)   # 16-character String
    lati        = Int32Col(pos=2)        # integer
    longi       = Int32Col(pos=3)        # integer
    airpressure    = Float32Col(pos=4)    # float  (single-precision)
    temperature = Float64Col(pos=5)      # double (double-precision)

swarbhanu

import numpy as np
import h5py as hp

filename='h5py1.h5'

#open a file
f=hp.File(filename,driver='sec2',mode='w')

#create a dataset
mydset=f.create_dataset("mydset",(10,10),'=f8',maxshape=(None,None))

swarbhanu

import tables as tb
import numpy as np

#initialize
filename='earrayEx.h5'
atom=tb.StringAtom(itemsize=8)
shape=(0,)

#open hdf5 file and create an earray
fileh=tb.openFile(filename,'w')