def read_geo_file(s, geo_filename):
from scipy.io.numpyio import fread
print 'Reading the line-array from the %s.line.geo...' % geo_filename
fd = open(geo_filename + '.line.geo', 'rb')
data_line_array = fread(fd, 3*(s.Nx+1)*(s.Ny+1)*(s.Nz+1), 'f')
data_line_array = data_line_array.reshape(3, s.Nx+1, s.Ny+1, s.Nz+1)
s.line_x = data_line_array[0]
s.line_y = data_line_array[1]
s.line_z = data_line_array[2]
fd.close()
print 'Reading the area-array from the %s.area.geo...' % geo_filename
fd = open(geo_filename + '.area.geo', 'rb')
data_area_array = fread(fd, 3*(s.Nx+2)*(s.Ny+2)*(s.Nz+2), 'f')
data_area_array = data_area_array.reshape(3, s.Nx+2, s.Ny+2, s.Nz+2)
s.area_x = data_area_array[0]
s.area_y = data_area_array[1]
s.area_z = data_area_array[2]
fd.close()
s.arr = {'x':(s.line_x,s.area_x), 'y':(s.line_y,s.area_y), 'z':(s.line_z,s.area_z)}
s.geo_dirname = '%s.gview' % geo_filename
import os
try:
os.mkdir(s.geo_dirname)
except:
print 'Remove the exist dir \'%s\'' %s.geo_dirname
cmd = 'rm -rf %s' %s.geo_dirname
os.system(cmd)
os.mkdir(s.geo_dirname)
print 'Create the png dir \'%s\'' %s.geo_dirname
def __init__(self,dir):
"""Constructor
@param dir -- path of MNIST dataset
"""
fd = open(dir+'/train-labels.idx1-ubyte')
fread(fd,8,'c')
self.data_labels = np.fromfile(file=fd, dtype=np.uint8).reshape((60000,1))
fd.close()
fd = open(dir+'/train-images.idx3-ubyte')
fread(fd,16,'c')
self.data = np.fromfile(file=fd, dtype=np.uint8).reshape((60000,784))
fd.close()
fd = open(dir+'/t10k-images.idx3-ubyte')
fread(fd,16,'c')
self.test = np.fromfile(file=fd, dtype=np.uint8).reshape((10000,784))
fd.close()
fd = open(dir+'/t10k-labels.idx1-ubyte')
fread(fd,8,'c')
self.test_labels = np.fromfile(file=fd, dtype=np.uint8).reshape((10000,1))
fd.close()
def __init__(self, dir):
"""Constructor
@param dir -- path of MNIST dataset
"""
fd = open(dir + '/train-labels.idx1-ubyte')
fread(fd, 8, 'c')
self.data_labels = np.fromfile(file=fd, dtype=np.uint8).reshape(
(60000, 1))
fd.close()
fd = open(dir + '/train-images.idx3-ubyte')
fread(fd, 16, 'c')
self.data = np.fromfile(file=fd, dtype=np.uint8).reshape((60000, 784))
fd.close()
fd = open(dir + '/t10k-images.idx3-ubyte')
fread(fd, 16, 'c')
self.test = np.fromfile(file=fd, dtype=np.uint8).reshape((10000, 784))
fd.close()
fd = open(dir + '/t10k-labels.idx1-ubyte')
fread(fd, 8, 'c')
self.test_labels = np.fromfile(file=fd, dtype=np.uint8).reshape(
(10000, 1))
fd.close()
def read_scbin_2d(scbin_path, Nx, Ny): fd = open(scbin_path, 'rb') array = fread(fd, Nx*Ny, 'f') array = array.reshape(Nx, Ny) fd.close() return array
def read_geo_file(s, geo_filename):
from scipy.io.numpyio import fread
print 'Reading the line-array from the %s.line.geo...' % geo_filename
fd = open(geo_filename + '.line.geo', 'rb')
data_line_array = fread(fd, 3 * (s.Nx + 1) * (s.Ny + 1) * (s.Nz + 1),
'f')
data_line_array = data_line_array.reshape(3, s.Nx + 1, s.Ny + 1,
s.Nz + 1)
s.line_x = data_line_array[0]
s.line_y = data_line_array[1]
s.line_z = data_line_array[2]
fd.close()
print 'Reading the area-array from the %s.area.geo...' % geo_filename
fd = open(geo_filename + '.area.geo', 'rb')
data_area_array = fread(fd, 3 * (s.Nx + 2) * (s.Ny + 2) * (s.Nz + 2),
'f')
data_area_array = data_area_array.reshape(3, s.Nx + 2, s.Ny + 2,
s.Nz + 2)
s.area_x = data_area_array[0]
s.area_y = data_area_array[1]
s.area_z = data_area_array[2]
fd.close()
s.arr = {
'x': (s.line_x, s.area_x),
'y': (s.line_y, s.area_y),
'z': (s.line_z, s.area_z)
}
s.geo_dirname = '%s.gview' % geo_filename
import os
try:
os.mkdir(s.geo_dirname)
except:
print 'Remove the exist dir \'%s\'' % s.geo_dirname
cmd = 'rm -rf %s' % s.geo_dirname
os.system(cmd)
os.mkdir(s.geo_dirname)
print 'Create the png dir \'%s\'' % s.geo_dirname
def bin2lut(descfname, **kwargs):
"""Load a raw binary file as lut directly.
Parameters:
descfname: a description txt file in the format below:
# Comment lines starts with #
binary_file : /path/to/binary_file
dtype : datatype of the lut, a character in 'cb1silfdFD'
dimname1 : 1 2 3 4 5
dimname2 : 10.0, 20.0, 30.0, 40.0
...
# dims can either be seperated with ',' or ' '
kwargs:
mem_type:
a character in 'cb1silfdFD'
byteswap:
0 : no swap
1 : swap
"""
from scipy.io.numpyio import fread
descf_folder_path = os.path.dirname(descfname)
descf_basename = os.path.basename(descfname)
descf = open(descfname)
dims = []
shp = []
binary_file = '%s.bin' % descf_basename
dtype = 'f'
for l in descf:
l = l.strip()
if len(l) == 0 or l.startswith('#'):
continue
name, value = l.split(':', 1)
name = name.strip()
value = value.strip()
if name == 'binary_file':
binary_file = value
elif name == 'dtype':
dtype = value
else:
if ',' in value:
sep = ','
else:
sep = ' '
arrayvalue = np.fromstring(value, sep=sep, dtype='f4')
dims.append((name, arrayvalue))
shp.append(arrayvalue.shape[0])
total_elements_num = np.multiply.reduce(shp)
shp = tuple(shp)
binary_file_abspath = os.path.join(descf_folder_path, binary_file)
f = open(binary_file_abspath, 'rb')
lut_arr = fread(f, total_elements_num, dtype).reshape(shp)
f.close()
lut = lookup_table(lut_arr, dims)
return lut
def read_structure_files():
print 'Reading the structure files...'
structures = []
s_number = 0
flist = glob.glob('./structures/*')
for file in flist:
if '_info' in file:
print file
structures.append( __import__(file) )
structure = structures[-1]
Nx, Ny, Nz = structure.discrete_effective_region_sides
filename = './structures/%.3d_%s_ispts_x.scbin' \
% (s_number, structure.__name__)
print filename
array_file = open(filename, 'rb')
ISPTs_array_x = fread(filename, 2*Ny*Nz, 'f').reshape(2,Ny,Nz)
array_file.close()
filename = './structures/%.3d_%s_ispts_y.scbin' \
% (s_number, structure.__name__)
print filename
array_file = open(filename, 'rb')
ISPTs_array_y = fread(filename, 2*Nx*Nz, 'f').reshape(2,Nx,Nz)
array_file.close()
filename = './structures/%.3d_%s_ispts_z.scbin' \
% (s_number, structure.__name__)
print filename
array_file = open(filename, 'rb')
ISPTs_array_z = fread(filename, 2*Nx*Ny, 'f').reshape(2,Nx,Ny)
array_file.close()
structure.intersection_points_arrays = [ \
ISPTs_array_x, ISPTs_array_y, ISPTs_array_z]
s_number += 1
return structures
def test_basic(self):
# Generate some data
a = 255*rand(20)
# Open a file
fname = tempfile.mktemp('.dat')
fid = open(fname,"wb")
# Write the data as shorts
numpyio.fwrite(fid,20,a,N.Int16)
fid.close()
# Reopen the file and read in data
fid = open(fname,"rb")
if verbose >= 3:
print "\nDon't worry about a warning regarding the number of bytes read."
b = numpyio.fread(fid,1000000,N.Int16,N.Int)
fid.close()
assert(N.product(a.astype(N.Int16) == b,axis=0))
os.remove(fname)
def test_basic(self):
# Generate some data
a = 255 * rand(20)
# Open a file
fname = tempfile.mktemp('.dat')
fid = open(fname, "wb")
# Write the data as shorts
numpyio.fwrite(fid, 20, a, N.Int16)
fid.close()
# Reopen the file and read in data
fid = open(fname, "rb")
if verbose >= 3:
print "\nDon't worry about a warning regarding the number of bytes read."
b = numpyio.fread(fid, 1000000, N.Int16, N.Int)
fid.close()
assert (N.product(a.astype(N.Int16) == b, axis=0))
os.remove(fname)
def petsc_binary_read(filename, spymatrix):
fd = open(filename,'rb');
datatype = 'i'
header = fread(fd, 2, datatype, datatype, 1)
if np.size(header) <= 0:
print 'File does not have that many items'
sys.exit(1)
if header[0] == 1211216: # read matrix
m = header[1]
header = fread(fd, 2, datatype, datatype, 1)
n = header[0]
nz = header[1]
nnz = fread(fd, m, datatype, datatype, 1)
j = fread(fd, nz, 'i', 'i', 1) + 1 # columns
s = fread(fd, nz, 'd', 'd', 1) # data
i = np.ones((nz)) # pointers
cnt = 0
for k in xrange(m):
next = cnt + nnz[k] - 1
i[cnt:next+1] = k * np.ones((nnz[k]))
cnt = next + 1
# adjust to python-zero based indexing
j = j - 1
A = sparse.coo_matrix( (s,(i,j)), (m,n) )
if spymatrix == 1:
plt.spy(A,marker='.')
plt.show()
fd.close()
return A
elif header[0] == 1211214: # read vector
m = header[1]
datatype = 'd'
v = fread(fd, m, datatype, datatype, 1)
fd.close()
return v
def fort_read(self,fmt,dtype=None):
lookup_dict = {'ieee-le':"<",'ieee-be':">",'native':''}
if dtype is None:
fmt = lookup_dict[self.format] + fmt
numbytes = struct.calcsize(fmt)
nn = struct.calcsize("i");
self.fid.read(nn)
data = struct.unpack(fmt,self.fid.read(numbytes))
self.fid.read(nn)
return data
else: # Ignore format string and read in next record as an array.
fmt = lookup_dict[self.format] + "i"
nn = struct.calcsize(fmt)
nbytes = struct.unpack(fmt,self.fid.read(nn))[0]
howmany, dtype = getsize_type(dtype)
ncount = nbytes / howmany
if ncount*howmany != nbytes:
self.rewind(4)
raise ValueError, "A mismatch between the type requested and the data stored."
retval = numpyio.fread(self.fid, ncount, dtype, dtype, self.bs)
if len(retval) == 1:
retval = retval[0]
self.fid.read(nn)
return retval
def read_head_MODFLOW(infile,nlay,nrow,ncol,ss,nan_flag): ifp = open(infile,'rb') # holders for the data reading in kstp = [] kper = [] pertim = [] totim = [] heads = [] newTime = True i=-1 while 1: kstp_tmp = fread(ifp,1,'i') if not kstp_tmp: break else: kstp.append(kstp_tmp[0]) if newTime == True: heads.append(hds(nlay,nrow,ncol,kstp[-1])) newTime = False i +=1 kper.append(fread(ifp,1,'i')[0]) pertim.append(fread(ifp,1,'f')[0]) totim.append(fread(ifp,1,'f')[0]) junkus = fread(ifp,16,'c') pos = fread(ifp,3,'i') headin = fread(ifp,pos[0]*pos[1],'f').reshape(nrow,ncol) if nan_flag: headin[headin==999] = np.nan heads[i].heads[:,:,pos[2]-1]=headin if pos[2]==nlay: newTime = True # here we break out after only one pass if seeking only one # stress period (ss means steady-state). probably not really necessary.... if ss == True: break return kstp,kper,heads, pertim
import matplotlib.colors
import sys
from scipy.io.numpyio import fwrite, fread
filename=sys.argv[1]
#slide_number=int(sys.argv[2])
#print dir,slide_number
#rcParams['font.colors']='w'
fd=open(filename, 'rb')
datatype = 'i'
size = 2
shape=(2)
size=fread(fd, size, datatype)
nx=size[0]
ny=size[1]
data = zeros((nx,ny,3))
datatype='d'
size=nx*ny*3
shape=(3,ny,nx)
data=fread(fd, size, datatype)
data=data.reshape(shape)
print "Dimensions",nx,ny
nx1=0
nx2=800
ny1=0
import numpy as np
from scipy.io.numpyio import fwrite, fread
import ipdb as pdb
import os
import sys
sys.excepthook = __IPYTHON__.excepthook
path = "/home/local/datasets/MNIST"
with open(os.path.join(path, 't10k-images.idx3-ubyte')) as fd:
#with open(os.path.join(path,'train-images.idx3-ubyte')) as fd:
fread(fd, 16, 'c')
#data = np.fromfile(file=fd, dtype=np.uint8).reshape( (60000,784) )
data = np.fromfile(file=fd, dtype=np.uint8).reshape((10000, 784))
padded_data = []
for image in data:
padded_image = np.zeros((32, 32))
padded_image[2:30, 2:30] = image.reshape(28, 28)
padded_data.append(padded_image.flatten())
#np.save(os.path.join(path,'mnist_padded'),np.array(padded_data).T.astype(np.uint8))
np.save(os.path.join(path, 'mnist_padded_test'),
np.array(padded_data).T.astype(np.uint8))
def read_scbin_1d(scbin_path, Nx): fd = open(scbin_path, 'rb') array = fread(fd, Nx, 'f') fd.close() return array
# generate signal
#
sr = 48000.0
winlen = 2048
nfreqs = winlen//2+1
winstep = 64
nsamples = os.stat(sys.argv[1])[ST_SIZE] / 4
print nsamples, " samples"
if len(sys.argv) == 4:
nsamples = int(sys.argv[3])
fd = open(sys.argv[1], 'rb')
signal = fread(fd, nsamples, 'f')
print "signals is", type(signal), signal.shape
starts = range(0, (len(signal)-winlen), winstep)
results = n.zeros((len(starts), nfreqs))
# x_labels = n.array(starts)/sr
# y_labels = n.array([(sr*x)/winlen for x in range(winlen/2)])
# print x_labels
# print y_labels
index = 0
for start_off in starts:
if start_off % 1000 == 0:
# print "****", start_off, "****"
def main():
NBND=16;
NBND2=2*NBND
dirout=sys.argv[1]
NCELL=int(sys.argv[2])
NGPUX=int(sys.argv[3])
NGPUY=int(sys.argv[4])
NGPUZ=int(sys.argv[5])
ISNAP=int(sys.argv[6])
NGPUTOT=NGPUX*NGPUY*NGPUZ
NX=NCELL/NGPUX
NY=NCELL/NGPUY
NZ=NCELL/NGPUZ
x=np.zeros([NCELL,NCELL,NCELL])
t=np.zeros([NCELL,NCELL,NCELL])
for k in range(NGPUZ):
for j in range(NGPUY):
for i in range(NGPUX):
idx=i+j*NGPUX+k*NGPUX*NGPUY
fname=dirout+'/snap.%05d.p%05d'%(ISNAP,idx)
print 'reading '+fname
fbuf=open(fname,mode="rb")
nc=fread(fbuf,1,'i')
tt=fread(fbuf,1,'f')
xtemp=fread(fbuf,(NX+NBND2)*(NY+NBND2)*(NZ+NBND2),'f')
ttemp=fread(fbuf,(NX+NBND2)*(NY+NBND2)*(NZ+NBND2),'f')
fbuf.close()
xtemp=xtemp.reshape((NZ+NBND2,NY+NBND2,NX+NBND2))
ttemp=ttemp.reshape((NZ+NBND2,NY+NBND2,NX+NBND2))
x[k*NZ:(k+1)*NZ,j*NY:(j+1)*NY,i*NX:(i+1)*NX]=xtemp[NBND:NBND+NZ,NBND:NBND+NY,NBND:NBND+NX]
t[k*NZ:(k+1)*NZ,j*NY:(j+1)*NY,i*NX:(i+1)*NX]=ttemp[NBND:NBND+NZ,NBND:NBND+NY,NBND:NBND+NX]
xx=np.linspace(-6.6,6.6,NCELL)
plt.title('time='+str(tt))
plt.subplot(221)
plt.imshow(1.-x[NCELL/2,:,:])
plt.subplot(222)
plt.plot(xx,1.-x[NCELL/2,NCELL/2,:])
plt.xlim(-6.6,6.6)
plt.yscale('log')
plt.subplot(223)
plt.imshow(t[NCELL/2,:,:])
plt.subplot(224)
plt.plot(xx,t[NCELL/2,NCELL/2,:])
plt.yscale('log')
plt.xlim(-6.6,6.6)
plt.ylim(5000.,3e4)
plt.show()
# know the datatype of your array, its size and its shape.
#
# <codecell>
>>> from scipy.io.numpyio import fwrite, fread
>>> data = zeros((3,3))
>>>#write: fd = open('myfile.dat', 'wb')
>>> fwrite(fd, data.size, data)
>>> fd.close()
>>>#read:
>>> fd = open('myfile.dat', 'rb')
>>> datatype = 'i'
>>> size = 9
>>> shape = (3,3)
>>> read_data = fread(fd, size, datatype)
>>> read_data = data.reshape(shape)
# <markdowncell>
# Or, you can simply use and . Following the previous example:
#
# <codecell>
>>> data.tofile('myfile.dat')
>>> fd = open('myfile.dat', 'rb')
>>> read_data = numpy.fromfile(file=fd, dtype=numpy.uint8).reshape(shape)
# <markdowncell>
#!/usr/bin/python -Wignore
from scipy.io.numpyio import fread, fwrite
from pylab import *
import sys
# load data
for slice in ( sys.argv[1:] ) :
print slice
input = open( slice, "r" )
(nx,nz) = fread( input, 2, 'i' )
density = fread( input, nx*nz, 'f' ).reshape(nz,nx)
input.close()
figure( figsize=(float(nz)/72,float(nx)/72), dpi=72 )
a = axes([0,0,1,1])
a.axis('off')
a.imshow(density,cmap=cm.jet,origin="lower",interpolation="Nearest")
a.set_autoscale_on(False)
savefig(slice.replace("dat","png"),dpi=72)
clf()
from pylab import *
from numpy import ma
import matplotlib.pyplot as plt
import numpy as np
import array
import scipy
#d:\Temp\POY\2\SP\node_0
rootdir ="f:/data/Bank/Constant Speed 001/RESULTS_MPI4-50ms/IM"
outdir = rootdir
maindir = rootdir + "/IMAGE.npz"
N = 1 #number of nodes
from scipy.io.numpyio import fwrite, fread
#/////////////////////////
Xsize = 13201
Zsize = 1921
vmodel = fread (open('f:/data/BP2004/modelq.bin','rb'),Xsize*Zsize,'f').reshape ((13201,1921)).transpose()
partmod = vmodel[::1,0:2600:1]
partmod = partmod - scipy.ndimage.filters.gaussian_filter(partmod,2)
partmod = np.abs (np.clip (partmod*0.001,-1.,1.))
modrgba = np.zeros ((partmod.shape[0],partmod.shape[1],4),dtype = 'f')
modrgba[:,:,0] = partmod
modrgba[:,:,3] = 1.
Image = np.load("%s"%maindir, mmap_mode='r')['arr_0.npy']
#SNorm = np.load("%s/s_norm.npz"%rootdir, mmap_mode='r')['arr_0.npy']
#sigma =3
#blurred = scipy.ndimage.filters.gaussian_filter(Image,sigma)
#
#filtered = Image - blurred
import numpy as np
from scipy.io.numpyio import fwrite, fread
import ipdb as pdb
import os
import sys
sys.excepthook = __IPYTHON__.excepthook
path="/home/local/datasets/MNIST"
with open(os.path.join(path,'t10k-images.idx3-ubyte')) as fd:
#with open(os.path.join(path,'train-images.idx3-ubyte')) as fd:
fread(fd,16,'c')
#data = np.fromfile(file=fd, dtype=np.uint8).reshape( (60000,784) )
data = np.fromfile(file=fd, dtype=np.uint8).reshape( (10000,784) )
padded_data=[]
for image in data:
padded_image=np.zeros((32,32))
padded_image[2:30,2:30]=image.reshape(28,28)
padded_data.append(padded_image.flatten())
#np.save(os.path.join(path,'mnist_padded'),np.array(padded_data).T.astype(np.uint8))
np.save(os.path.join(path,'mnist_padded_test'),np.array(padded_data).T.astype(np.uint8))
def read_igb_slice (filename, is_gzipped=False):
"""
Reads IGB slice. If gzipped it uncompress and output a binary file
that is used to read the data
"""
if is_gzipped:
igbFile = gunzipFile(filename)
else:
igbFile = filename
hd = read_igb_header(igbFile, is_gzipped=False)
filestats = os.stat(igbFile)
filesize = filestats[stat.ST_SIZE]
if hd.systeme == 'big_endian' : byteswap=1
elif hd.systeme == 'little_endian': byteswap=0
# setup time slices to be read
t_slices = xrange(1,hd.t+1)
if size(t_slices) == 0:
t_slices = 1
print " WARNING: Trying to read at least one time slice!"
# how many time slices we are going to read ?
n_slices = len(t_slices)
# expected data size
data_in_file = hd.x * hd.y * hd.z * hd.t
## FORCE MY CASE in case of MEMORY ERROR ##
#n_slices = 1001
# data type
if hd.type == 'float':
dbytes = 4
dtype = 'f'
data = zeros( (n_slices, hd.x, hd.y, hd.z), dtype=float32 )
elif hd.type == 'double':
dbytes = 8
dtype = 'd'
data = zeros( (n_slices, hd.x, hd.y, hd.z), dtype=float64 )
# open data
fh = open(igbFile,'rb')
# size of one time slice
slice_size = hd.x * hd.y * hd.z
# read data till end of file
actual_timesteps = 0
for i in xrange(n_slices):
# compute position of time slice i
pos = (t_slices[i] - 1) * slice_size * dbytes + 1024
fh.seek(pos)
slice_buf = fread(fh, slice_size, dtype, dtype, byteswap)
count = size(slice_buf)
if count == slice_size:
data[i,:,:,:] = slice_buf.reshape(hd.x, hd.y, hd.z)
actual_timesteps = actual_timesteps + 1
#else:
# print " read_igb_slices: Incomplete time step %d of %d " % (i,n_slices)
hd.t = actual_timesteps
fh.close()
# if gzipped, remove temporary uncompressed file
if is_gzipped:
os.remove(igbFile)
return data, hd
import numpy as n
from pylab import *
#
# generate signal
#
sr = 48000.0
datfile = 'signal.dat'
nsamples = os.stat(datfile)[ST_SIZE] / 4
print "Reading %d samples from %s" % (nsamples, datfile)
fd = open(datfile, 'rb')
datatype = 'f'
shape = (nsamples,)
blob = fread(fd, nsamples, datatype)
signal = blob.reshape(shape)
#
# autocorr routine
#
def autocorrelate(a):
winlen = int(sr/70)
freqs = n.zeros((len(a)-winlen,))
for outer in range(0, len(a)-(winlen*2)):
corr_coeff = n.zeros((winlen,))
for inner in range(winlen):
def fread(self,count,mtype):
howmany,mtype = getsize_type(mtype)
retval = numpyio.fread(self.fid, count, mtype, mtype, self.bs)
if len(retval) == 1:
retval = retval[0]
return retval
#!/usr/bin/python import numpy as n from scipy.io.numpyio import fread import os, sys from stat import * from pylab import * # # generate signal # sr = 48000.0 nsamples = os.stat(sys.argv[1])[ST_SIZE] / 4 fd = open(sys.argv[1], 'rb') blob = fread(fd, nsamples, 'f') shape = (nsamples,) signal = blob.reshape(shape) plot(signal) show()
# Check whether the file is binary or ascii
# The chenking is implemented by counting the number of
# lines is ascii mode. If it is equal to size, the file
# is an ascii file.
count=0
for line in open(fname+'.dat','r'):
if (line.strip() != ''): count += 1
if (count == (size*veclen)):
# Ascii file
read_data=np.loadtxt(fname+'.dat')
else:
# Binary file
fd=open(fname+'.dat','rb')
read_data = fread(fd, size, 'd')
fd.close()
f_out=open(fname+'.vtk','w')
f_out.write('# vtk DataFile Version 2.0\n')
f_out.write(fname+'\n')
f_out.write('ASCII\n')
f_out.write('DATASET RECTILINEAR_GRID\n')
if(ndim==3):
f_out.write('DIMENSIONS %6d %6d %6d\n'%(dim1-16,dim2-16,dim3-25))
else:
f_out.write('DIMENSIONS %6d %6d\n'%(dim1-16,dim2-16))
f_out.write('X_COORDINATES %8d float\n'%(dim1-16))
for i in range(8,dim1-8):
f_out.write('%18.10f\n'%(coord_1[i]))
f_out.write('Y_COORDINATES %8d float\n'%(dim2-16))
#!/usr/bin/env python
from pylab import *
from scipy.io.numpyio import fread
Nx, Ny = 400, 350
fd = open('./dat/050-A_binary.dat', 'rb')
A = fread( fd, Nx*Ny, 'f' )
A = A.reshape( Nx, Ny )
fd.close()
fd = open('./dat/050-dA_binary.dat', 'rb')
dA = fread( fd, Nx*Ny, 'f' )
dA = dA.reshape( Nx, Ny )
fd.close()
figure( figsize=(15,5) )
subplot(1,2,1)
imshow( A.T, cmap=cm.hot, origin='lower', interpolation='bilinear' )
title('2D Gaussian')
xlabel('x-axis')
ylabel('y-axis')
colorbar()
subplot(1,2,2)
imshow( dA.T, cmap=cm.hot, origin='lower', interpolation='bilinear' )
title('differential')
xlabel('x-axis')
ylabel('y-axis')
colorbar()
#!/usr/bin/python
import sys
import numpy as n
from scipy.io.numpyio import fread
import os, sys
from stat import *
from pylab import *
import matplotlib.ticker as mt
import matplotlib.mlab as mlab
print "Opening %s" % sys.argv[1]
fd = open(sys.argv[1], 'rb')
title = sys.argv[2]
shape = fread(fd, 2, 'f')
shape = shape.astype(n.int32)
print "shape=", shape, " going to read %d bytes", shape[0] * shape[1]
blob = fread(fd, shape[0] * shape[1], 'f')
print "Blob size is %d" % blob.size
Z = blob.reshape(shape)
imshow(Z.T) #, None)# , extent = (x_labels[0], x_labels[-1],y_labels[0], y_labels[-1]))
gca().axis('tight')
gca().set_title(title)
show()