swarbhanu

#################################################################################################
#												#
# This program takes a numpy array and feeds it into a hyperslab in a CArray. The numpy array	#
# is then changed to a different shape and fed into a hyperslab in a VLArray. The end result	#
# is that one has two differently shaped hyperslabs of the same data.				#
# 										                #
#################################################################################################

import sys
import tables as tb

swarbhanu

import sys
import numpy
from numpy import *
from tables import *

filename = 'arrayEx.h5'
fileh=openFile(filename,mode="w")
root=fileh.root

swarbhanu

import sys
import numpy as np
import tables as tb

filename='carrayEx1.h5'

fileh=tb.openFile(filename, 'w')
carray = fileh.createCArray(fileh.root, 'carray', tb.UInt32Atom(), shape=(200,300))

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')

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

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

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

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

from dolfin import *
from numpy import *

#initialize an empty mesh
mesh1 = Mesh()

#initialize an empty mesh editor
editor1 = MeshEditor()

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

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)