def read_hgrid(self, fid):
"Read horizontal grid info from SELFE binary output filefile"
#read dimensions
self.np = io.fread(fid, 1, 'i')
self.ne = io.fread(fid, 1, 'i')
#read grid and bathymetry
pos = fid.tell()
hgridtmp = io.fread(fid, 4 * self.np, 'f')
self.x, self.y, self.dp, tmp1 = hgridtmp.reshape(self.np,
4).transpose()
#read bottom index
fid.seek(pos)
hgridtmp = io.fread(fid, 4 * self.np, 'i')
tmp1, tmp2, tmp3, self.bot_idx = hgridtmp.reshape(self.np,
4).transpose()
#read element connectivity list
self.elem = io.fread(fid, 4 * self.ne, 'i')
self.elem = self.elem.reshape(self.ne, 4)[:, 1:4]
#create kdtree
self.kdtree = KDTree(zip(self.x, self.y))
def subReadWrite(a, size, fn, fread, fwrite, what='Some'):
import os
print " %s testing .." % what,
# Speed tests
@timed
def innerSpeedTestWrite():
fwrite(fid, size, a)
@timed
def innerSpeedTestRead():
return fread(fid, size, 'd')
with open(fn, 'w+b') as fid:
tmp, dtW = innerSpeedTestWrite()
with open(fn, 'rb') as fid:
a1, dtR = innerSpeedTestRead()
os.remove(fn)
# Test more options
b = a[:10] # only use a few values for these tests
with open(fn, 'w+b') as fid:
fwrite(fid, b.size, b, 'd', 1)
fid.seek(0)
# all data types: 'bBcS1hHfiIlu4dFD'
b2 = fread(fid, b.size, 'd', 'f', 1) # convert to floats
fid.seek(0)
b3 = fread(fid, b.size, 'd', 'i', 1) # convert to int32
fid.seek(0)
b4 = fread(fid, b.size, 'd', 'b', 1) # convert to int8
fid.seek(0)
b5 = fread(fid, b.size, 'd', 'h', 1) # convert to int16
os.remove(fn)
# Finalize stuff
if all(a == a1) and all(abs(b-b2) < 0.01) and \
all(abs(b-b3) < 1) and all(abs(b-b4) < 1) and all(abs(b-b5) < 1):
print ".. passed"
else:
print ".. failed"
print " a=%s\na1=%s\nb2=%s\nb3=%s\nb4=%s\nb5=%s" % (a, a1, b2, b3, b4,
b5)
return dtW + dtR
def read_hgrid(self,fid):
"Read horizontal grid info from SELFE binary output filefile"
#read dimensions
self.np = io.fread(fid,1,'i')
self.ne = io.fread(fid,1,'i')
#read grid and bathymetry
pos=fid.tell()
hgridtmp = io.fread(fid,4*self.np,'f')
self.x, self.y, self.dp, tmp1 = hgridtmp.reshape(self.np,4).transpose()
#read bottom index
fid.seek(pos)
hgridtmp = io.fread(fid,4*self.np,'i')
tmp1, tmp2, tmp3, self.bot_idx = hgridtmp.reshape(self.np,4).transpose()
#read element connectivity list
self.elem = io.fread(fid,4*self.ne,'i')
self.elem = self.elem.reshape(self.ne,4)[:,1:4]
def read_hgrid(self, fid):
"""Read horizontal grid info from SELFE binary output file."""
# Read dimensions.
self.np = io.fread(fid, 1, 'i')
self.ne = io.fread(fid, 1, 'i')
# Read grid and bathymetry.
pos = fid.tell()
hgridtmp = io.fread(fid, 4*self.np, 'f')
self.x, self.y, self.dp, tmp1 = hgridtmp.reshape(self.np, 4).T
# Read bottom index.
fid.seek(pos)
hgridtmp = io.fread(fid, 4*self.np, 'i')
tmp1, tmp2, tmp3, self.bot_idx = hgridtmp.reshape(self.np, 4).T
# Read element connectivity list.
self.elem = io.fread(fid, 4*self.ne, 'i')
self.elem = self.elem.reshape(self.ne, 4)[:, 1:4]
# Create kdtree.
self.kdtree = KDTree(list(zip(self.x, self.y)))
def testAutoNum():
import os
print "Testing auto num feature of numpyIO ..",
a = np.array((1, 2, 3), 'd')
fn = 'temp2.bin'
with open(fn, 'w+b') as fid:
fwrite(fid, -1, a)
fid.seek(0)
a1 = fread(fid, -1, 'd')
os.remove(fn)
passed = all(a == a1)
assert passed
if passed:
print "passed"
else:
print "failed"
print " a=%s\na1=%s" % (a, a1)
def innerSpeedTestRead():
return fread(fid, size, 'd')
def read_header(self,fid):
"Read header information from SELFE binary output file"
#read misc header info
self.data_format = fid.read(48)
self.version = fid.read(48)
self.start_time = fid.read(48)
self.var_type = fid.read(48)
self.var_dimension = fid.read(48)
self.nsteps = io.fread(fid,1,'i')
self.dt = io.fread(fid,1,'f')
self.skip = io.fread(fid,1,'i')
self.flag_sv = io.fread(fid,1,'i')
self.flag_dm = io.fread(fid,1,'i')
#@todo check when zDes needs to be read
#self.zDes = io.fread(fid,1,'f')
#read vert grid info
self.nlevels = io.fread(fid,1,'i')
self.kz = io.fread(fid,1,'i')
self.h0 = io.fread(fid,1,'f')
self.hs = io.fread(fid,1,'f')
self.hc = io.fread(fid,1,'f')
self.theta_b = io.fread(fid,1,'f')
self.theta = io.fread(fid,1,'f')
self.zlevels = io.fread(fid,self.kz,'f')
self.slevels = io.fread(fid,self.nlevels-self.kz,'f')
def read_time_series(self,fname,nodes=None,levels=None,xy=N.array([]),nfiles=3,sfile=1,datadir=None):
"""
Main function to extract a spatial and temporal slice of entire 3D Time series.
Returns [t,t_iter,eta,dp,data] where
t : time in seconds from simulation start
t_iter : iteration number from simulation start
eta : Surface water elevation time series
dp : Bathymetry (depth of sea bed from MSL)
data[t,nodes,levels,vars] : extracted data slice (ie Salinity, Temp, Velocity etc)
Options:
nodes : list of nodes to extract (default is all nodes)
level : list of levels to extract (default is all levels)
xy : array of x,y coordinates to extract (default is none)
sfile : serial number of starting file (default is one)
nfiles : number of files in data sequence (default is one)
NOTE : node index starts at zero so add one to match up with node numbers in
SELFE hgrid.gr3 file
"""
#initialize vars
t = N.array([])
t_iter = N.array([])
eta = []
data = []
if nfiles is None:
nfiles = self.nfiles
if datadir is None:
datadir = self.datadir
#convert xy points to list of nodes,
#find parent elements & calculate interpolation weights
if xy.size!=0:
if xy.shape[1]!=2:
sys.exit('xy array shape wrong')
nodes=N.array([],dtype='int32')
arco=N.array([])
for xy00 in xy:
parent, tmparco, node3 = self.find_parent_element(xy00[0],xy00[1])
nodes = N.append(nodes,node3-1)
arco = N.append(arco,tmparco)
#set default for nodes to be all nodes
#node index starts at zero
elif nodes is None:
nodes = N.arange(self.np)
#set default for level to be all levels
if levels is None:
levels = N.arange(self.nlevels)
#check whether 2D or 3D variable is being read
if self.flag_dm==2:
nlevs = 1
levels = N.array([0])
else:
nlevs = self.nlevels
#read time series slice
for files in N.arange(sfile, sfile + nfiles):
try:
fname1 = datadir + '/' + str(files) + '_' + fname
fid = open(fname1,'rb')
fid.seek(self.data_start_pos)
for i in N.arange(self.nsteps):
t = N.append(t, io.fread(fid, 1, 'f'))
t_iter = N.append(t_iter, io.fread(fid, 1, 'i'))
eta.append(io.fread(fid, self.np, 'f'))
tmpdata = io.fread(fid, self.flag_sv*self.grid_size, 'f')
tmpdata = tmpdata.reshape(self.np, nlevs, self.flag_sv)
#only keep requested slice of tmpdata
#i.e. tmpdata[nodes,levels,var]
tmpdata = tmpdata[nodes,:,:]
tmpdata = tmpdata[:,levels,:]
data.append(tmpdata)
except:
continue
# import pdb; pdb.set_trace()
eta = N.column_stack(eta[:]).T
eta = eta[:,nodes]
data = N.array(data)
dp = self.dp[nodes]
#convert nodal values back to xy point values if needed
if xy.size!=0:
#try:#not sure about this. need to look at it ion more detail put in to remove shape error
tmpdata = N.zeros((data.shape[0],data.shape[1]//3,data.shape[2],data.shape[3]))/0.
#except:
# tmpdata = N.zeros((data.shape[0],data.shape[1]//3,data.shape[2]))/0.
tmpeta = N.zeros((eta.shape[0],eta.shape[1]//3))/0.
tmpdp = N.zeros(dp.shape[0]//3)/0.
for i in range(xy.shape[0]):
n1 = i*3
n2 = n1+1
n3 = n2+1
tmpdata[:,i,:,:] = data[:,n1,:,:]*arco[n1] + data[:,n2,:,:]*arco[n2] + data[:,n3,:,:]*arco[n3]
tmpeta[:,i] = eta[:,n1]*arco[n1] + eta[:,n2]*arco[n2] + eta[:,n3]*arco[n3]
tmpdp[i] = dp[n1]*arco[n1] + dp[n2]*arco[n2] + dp[n3]*arco[n3]
data = tmpdata
eta = tmpeta
dp = tmpdp
return [t,t_iter,eta,dp,data]
def readArray(fileName, type='float'):
"""Read in a matrix of floats or doubles from a data file.
If type = 'float', then use single precision (32 bit) float. This
is the default.
If type = 'double', then use double precision (64 bit) float.
"""
inFile = open(fileName, 'rb')
dimPacked = inFile.read(4)
dimensionsTuple = unpack('<i', dimPacked)
dimensions = dimensionsTuple[0]
if (dimensions == 1):
dim1Packed = inFile.read(4)
dim1Tuple = unpack('<i', dim1Packed)
dim1 = dim1Tuple[0]
num = dim1
if type == 'float':
flatArray = numpyIO.fread(inFile, num, 'f')
elif type == 'double':
flatArray = numpyIO.fread(inFile, num, 'd')
else:
raise 'Error: Invalid type.'
shape = (dim1)
arrayData = flatArray.reshape(shape)
elif (dimensions == 2):
dim1Packed = inFile.read(4)
dim1Tuple = unpack('<i', dim1Packed)
dim1 = dim1Tuple[0]
dim2Packed = inFile.read(4)
dim2Tuple = unpack('<i', dim2Packed)
dim2 = dim2Tuple[0]
num = dim1 * dim2
if type == 'float':
flatArray = numpyIO.fread(inFile, num, 'f')
elif type == 'double':
flatArray = numpyIO.fread(inFile, num, 'd')
else:
raise 'Error: Invalid type.'
shape = (dim1, dim2)
arrayData = flatArray.reshape(shape)
elif (dimensions == 3):
dim1Packed = inFile.read(4)
dim1Tuple = unpack('<i', dim1Packed)
dim1 = dim1Tuple[0]
dim2Packed = inFile.read(4)
dim2Tuple = unpack('<i', dim2Packed)
dim2 = dim2Tuple[0]
dim3Packed = inFile.read(4)
dim3Tuple = unpack('<i', dim3Packed)
dim3 = dim3Tuple[0]
num = dim1 * dim2 * dim3
if type == 'float':
flatArray = numpyIO.fread(inFile, num, 'f')
elif type == 'double':
flatArray = numpyIO.fread(inFile, num, 'd')
else:
raise 'Error: Invalid type.'
shape = (dim1, dim2, dim3)
arrayData = flatArray.reshape(shape)
elif (dimensions == 4):
dim1Packed = inFile.read(4)
dim1Tuple = unpack('<i', dim1Packed)
dim1 = dim1Tuple[0]
dim2Packed = inFile.read(4)
dim2Tuple = unpack('<i', dim2Packed)
dim2 = dim2Tuple[0]
dim3Packed = inFile.read(4)
dim3Tuple = unpack('<i', dim3Packed)
dim3 = dim3Tuple[0]
dim4Packed = inFile.read(4)
dim4Tuple = unpack('<i', dim4Packed)
dim4 = dim4Tuple[0]
num = dim1 * dim2 * dim3 * dim4
if type == 'float':
flatArray = numpyIO.fread(inFile, num, 'f')
elif type == 'double':
flatArray = numpyIO.fread(inFile, num, 'd')
else:
raise 'Error: Invalid type.'
shape = (dim1, dim2, dim3, dim4)
arrayData = flatArray.reshape(shape)
elif (dimensions == 5):
dim1Packed = inFile.read(4)
dim1Tuple = unpack('<i', dim1Packed)
dim1 = dim1Tuple[0]
dim2Packed = inFile.read(4)
dim2Tuple = unpack('<i', dim2Packed)
dim2 = dim2Tuple[0]
dim3Packed = inFile.read(4)
dim3Tuple = unpack('<i', dim3Packed)
dim3 = dim3Tuple[0]
dim4Packed = inFile.read(4)
dim4Tuple = unpack('<i', dim4Packed)
dim4 = dim4Tuple[0]
dim5Packed = inFile.read(4)
dim5Tuple = unpack('<i', dim5Packed)
dim5 = dim5Tuple[0]
num = dim1 * dim2 * dim3 * dim4 * dim5
if type == 'float':
flatArray = numpyIO.fread(inFile, num, 'f')
elif type == 'double':
flatArray = numpyIO.fread(inFile, num, 'd')
else:
raise 'Error: Invalid type.'
shape = (dim1, dim2, dim3, dim4, dim5)
arrayData = flatArray.reshape(shape)
else:
raise "Error. Only 1 to 4-dimensional arrays (i.e., matrices) are supported!"
inFile.close
return arrayData
fig.subplots_adjust(top = 0.9,wspace = 0.12,hspace = 0.2,left = 0.05, right = 0.96)
#Xgr = range(0,xsize)
#Zgr = range(0,zsize)
Xgr,Zgr = np.mgrid[0:zsize,0:xsize]
#data = array.array('d')
#data.read(fileobj,65*xsize*zsize*3)
for i in range (1,Nt,1):
for j in range (0,N):
spsize = xsizes[j]*zsizes[j]
shape = (xsizes[j],zsizes[j])
fileobj[j].seek(Header*4 + 4*(i)*spsize)
data = fread(fileobj[j],spsize,'f')
data = data.reshape(shape).transpose()
pres[zstart[j]/zstep[j]:zend[j]/zstep[j]+1,xstart[j]/xstep[j]:xend[j]/xstep[j]+1] = data
# print pres[:,xstart[i]/xstep[i]:xend[i]/xstep[i]+1].shape
# print data.shape
# outfile = open("%s\\data.dat"%(outdir),mode='w')
# c=0
print amax(pres)
fig.clear()
ax = fig.add_subplot(111)
ax.clear()
cax = ax.imshow(pres[:,:], interpolation='nearest')
axis('off')
cb = colorbar(cax,orientation='horizontal',format ='%.3f',pad=0.01)
fig.subplots_adjust(top=0.9, wspace=0.12, hspace=0.2, left=0.05, right=0.96)
# Xgr = range(0,xsize)
# Zgr = range(0,zsize)
Xgr, Zgr = np.mgrid[0:zsize, 0:xsize]
# data = array.array('d')
# data.read(fileobj,65*xsize*zsize*3)
for i in range(1, Nt, 1):
for j in range(0, N):
spsize = xsizes[j] * zsizes[j]
shape = (xsizes[j], zsizes[j])
fileobj[j].seek(Header * 4 + 4 * (i) * spsize)
data = fread(fileobj[j], spsize, "f")
data = data.reshape(shape).transpose()
pres[zstart[j] / zstep[j] : zend[j] / zstep[j] + 1, xstart[j] / xstep[j] : xend[j] / xstep[j] + 1] = data
# print pres[:,xstart[i]/xstep[i]:xend[i]/xstep[i]+1].shape
# print data.shape
# outfile = open("%s\\data.dat"%(outdir),mode='w')
# c=0
print amax(pres)
fig.clear()
ax = fig.add_subplot(111)
ax.clear()
cax = ax.imshow(pres[:, :], interpolation="nearest")
axis("off")
cb = colorbar(cax, orientation="horizontal", format="%.3f", pad=0.01)
def readArray(fileName, type='float'):
"""Read in a matrix of floats or doubles from a data file.
If type = 'float', then use single precision (32 bit) float. This
is the default.
If type = 'double', then use double precision (64 bit) float.
"""
inFile = open(fileName, 'rb')
dimPacked = inFile.read(4)
dimensionsTuple = unpack('<i', dimPacked)
dimensions = dimensionsTuple[0]
if (dimensions == 1):
dim1Packed = inFile.read(4)
dim1Tuple = unpack('<i', dim1Packed)
dim1 = dim1Tuple[0]
num = dim1
if type == 'float':
flatArray = numpyIO.fread( inFile, num, 'f')
elif type == 'double':
flatArray = numpyIO.fread( inFile, num, 'd')
else:
raise 'Error: Invalid type.'
shape = (dim1)
arrayData = flatArray.reshape(shape)
elif (dimensions == 2):
dim1Packed = inFile.read(4)
dim1Tuple = unpack('<i', dim1Packed)
dim1 = dim1Tuple[0]
dim2Packed = inFile.read(4)
dim2Tuple = unpack('<i', dim2Packed)
dim2 = dim2Tuple[0]
num = dim1*dim2
if type == 'float':
flatArray = numpyIO.fread( inFile, num, 'f')
elif type == 'double':
flatArray = numpyIO.fread( inFile, num, 'd')
else:
raise 'Error: Invalid type.'
shape = (dim1, dim2)
arrayData = flatArray.reshape(shape)
elif (dimensions == 3):
dim1Packed = inFile.read(4)
dim1Tuple = unpack('<i', dim1Packed)
dim1 = dim1Tuple[0]
dim2Packed = inFile.read(4)
dim2Tuple = unpack('<i', dim2Packed)
dim2 = dim2Tuple[0]
dim3Packed = inFile.read(4)
dim3Tuple = unpack('<i', dim3Packed)
dim3 = dim3Tuple[0]
num = dim1*dim2*dim3
if type == 'float':
flatArray = numpyIO.fread( inFile, num, 'f')
elif type == 'double':
flatArray = numpyIO.fread( inFile, num, 'd')
else:
raise 'Error: Invalid type.'
shape = (dim1, dim2, dim3)
arrayData = flatArray.reshape(shape)
elif (dimensions == 4):
dim1Packed = inFile.read(4)
dim1Tuple = unpack('<i', dim1Packed)
dim1 = dim1Tuple[0]
dim2Packed = inFile.read(4)
dim2Tuple = unpack('<i', dim2Packed)
dim2 = dim2Tuple[0]
dim3Packed = inFile.read(4)
dim3Tuple = unpack('<i', dim3Packed)
dim3 = dim3Tuple[0]
dim4Packed = inFile.read(4)
dim4Tuple = unpack('<i', dim4Packed)
dim4 = dim4Tuple[0]
num = dim1*dim2*dim3*dim4
if type == 'float':
flatArray = numpyIO.fread( inFile, num, 'f')
elif type == 'double':
flatArray = numpyIO.fread( inFile, num, 'd')
else:
raise 'Error: Invalid type.'
shape = (dim1, dim2, dim3, dim4)
arrayData = flatArray.reshape(shape)
elif (dimensions == 5):
dim1Packed = inFile.read(4)
dim1Tuple = unpack('<i', dim1Packed)
dim1 = dim1Tuple[0]
dim2Packed = inFile.read(4)
dim2Tuple = unpack('<i', dim2Packed)
dim2 = dim2Tuple[0]
dim3Packed = inFile.read(4)
dim3Tuple = unpack('<i', dim3Packed)
dim3 = dim3Tuple[0]
dim4Packed = inFile.read(4)
dim4Tuple = unpack('<i', dim4Packed)
dim4 = dim4Tuple[0]
dim5Packed = inFile.read(4)
dim5Tuple = unpack('<i', dim5Packed)
dim5 = dim5Tuple[0]
num = dim1*dim2*dim3*dim4*dim5
if type == 'float':
flatArray = numpyIO.fread( inFile, num, 'f')
elif type == 'double':
flatArray = numpyIO.fread( inFile, num, 'd')
else:
raise 'Error: Invalid type.'
shape = (dim1, dim2, dim3, dim4, dim5)
arrayData = flatArray.reshape(shape)
else:
raise "Error. Only 1 to 4-dimensional arrays (i.e., matrices) are supported!"
inFile.close
return arrayData
