def combine_grids(mode, inpath1, inpath2, outpath):
from VolumeData import fileformats
grids = []
failed_paths = []
for path in (inpath1, inpath2):
try:
glist = fileformats.open_file(path)
grids.append(glist)
except fileformats.Unknown_File_Type:
failed_paths.append(path)
if failed_paths:
warning = fileformats.suffix_warning(failed_paths)
sys.stderr.write(warning)
sys.exit(1)
combined = map(lambda g1, g2: Combined_Grid(mode, g1, g2), grids[0],
grids[1])
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(combined, outpath)
# -----------------------------------------------------------------------------
#
def apply_function(array_func, inpath, outpath):
from VolumeData import fileformats
try:
grids = fileformats.open_file(inpath)
except fileformats.Unknown_File_Type, e:
sys.stderr.write(str(e))
sys.exit(1)
fvalues = [Mapped_Grid(g, array_func) for g in grids]
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(fvalues, outpath)
# -----------------------------------------------------------------------------
#
class Mapped_Grid(Grid_Data):
def __init__(self, grid_data, array_func):
self.array_func = array_func
Grid_Data.__init__(self,
grid_data.size,
grid_data.value_type,
grid_data.origin,
grid_data.step,
name=grid_data.name,
default_color=grid_data.rgba)
# -----------------------------------------------------------------------------
#
def gaussian_convolve(inpath, linewidth, outpath):
from VolumeData import fileformats
try:
glist = fileformats.open_file(inpath)
except fileformats.Unknown_File_Type, e:
sys.stderr.write(str(e))
sys.exit(1)
gaussian_data = [Gaussian_Convolution(g, linewidth) for g in glist]
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(gaussian_data, outpath)
# -----------------------------------------------------------------------------
# Convolve a grid with a Gaussian. Linewidth is specified in xyz units
# (not ijk indices).
#
class Gaussian_Convolution(Grid_Data):
def __init__(self, grid_data, linewidth):
self.grid_data = grid_data
self.linewidth = linewidth
Grid_Data.__init__(self,
grid_data.size,
origin=grid_data.origin,
# -----------------------------------------------------------------------------
#
if len(sys.argv) != 9:
syntax()
inpath = sys.argv[1]
try:
imin = int(sys.argv[2])
imax = int(sys.argv[3])
jmin = int(sys.argv[4])
jmax = int(sys.argv[5])
kmin = int(sys.argv[6])
kmax = int(sys.argv[7])
except:
syntax()
outpath = sys.argv[8]
from VolumeData import fileformats
try:
glist = fileformats.open_file(inpath)
except fileformats.Unknown_File_Type, e:
sys.stderr.write(str(e))
sys.exit(1)
from VolumeData import Grid_Subregion
r = [Grid_Subregion(g, (imin, jmin, kmin), (imax, jmax, kmax)) for g in glist]
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(r, outpath)
# -----------------------------------------------------------------------------
#
def count_neighbors(inpath, outpath):
from VolumeData import fileformats
try:
glist = fileformats.open_file(inpath)
except fileformats.Unknown_File_Type, e:
sys.stderr.write(str(e))
sys.exit(1)
counts = [Lower_Neighbor_Counts(g) for g in glist]
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(counts, outpath)
# -----------------------------------------------------------------------------
#
class Lower_Neighbor_Counts(Grid_Data):
def __init__(self, grid_data):
from numpy import int8
size = map(lambda s: s-2, grid_data.size)
xyz_origin = map(lambda o, s: o + s,
grid_data.origin, grid_data.step)
Grid_Data.__init__(self, size, int8, xyz_origin, grid_data.step,
default_color = grid_data.rgba)
# ---------------------------------------------------------------------------
# -----------------------------------------------------------------------------
#
def gaussian_convolve(inpath, linewidth, outpath):
from VolumeData import fileformats
try:
glist = fileformats.open_file(inpath)
except fileformats.Unknown_File_Type, e:
sys.stderr.write(str(e))
sys.exit(1)
gaussian_data = [Gaussian_Convolution(g, linewidth) for g in glist]
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(gaussian_data, outpath)
# -----------------------------------------------------------------------------
# Convolve a grid with a Gaussian. Linewidth is specified in xyz units
# (not ijk indices).
#
class Gaussian_Convolution(Grid_Data):
def __init__(self, grid_data, linewidth):
self.grid_data = grid_data
self.linewidth = linewidth
Grid_Data.__init__(self, grid_data.size,
origin = grid_data.origin, step = grid_data.step)
del args[i:i+4]
return values
# -----------------------------------------------------------------------------
#
def write_netcdf(inpath, outpath, xyz_origin, xyz_step):
from VolumeData import fileformats
try:
glist = fileformats.open_file(inpath)
except fileformats.Unknown_File_Type, e:
sys.stderr.write(str(e))
sys.exit(1)
if xyz_origin:
for g in glist:
g.origin = xyz_origin
if xyz_step:
for g in glist:
g.step = xyz_step
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(glist, outpath)
# -----------------------------------------------------------------------------
#
inpath, outpath, xyz_origin, xyz_step = parse_arguments(sys.argv)
write_netcdf(inpath, outpath, xyz_origin, xyz_step)
return values
# -----------------------------------------------------------------------------
#
def write_netcdf(inpath, outpath, xyz_origin, xyz_step):
from VolumeData import fileformats
try:
glist = fileformats.open_file(inpath)
except fileformats.Unknown_File_Type, e:
sys.stderr.write(str(e))
sys.exit(1)
if xyz_origin:
for g in glist:
g.origin = xyz_origin
if xyz_step:
for g in glist:
g.step = xyz_step
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(glist, outpath)
# -----------------------------------------------------------------------------
#
inpath, outpath, xyz_origin, xyz_step = parse_arguments(sys.argv)
write_netcdf(inpath, outpath, xyz_origin, xyz_step)
# -----------------------------------------------------------------------------
#
def downsize(mode, cell_size, inpath, outpath):
from VolumeData import fileformats
try:
glist = fileformats.open_file(inpath)
except fileformats.Unknown_File_Type, e:
sys.stderr.write(str(e))
sys.exit(1)
reduced = [Reduced_Grid(g, mode, cell_size) for g in glist]
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(reduced, outpath)
# -----------------------------------------------------------------------------
# Average over cells to produce reduced size grid object.
#
# If the grid data sizes are not multiples of the cell size then the
# final data values along the dimension are not included in the reduced
# data (ie ragged blocks are not averaged).
#
class Reduced_Grid(Grid_Data):
def __init__(self, grid_data, mode, cell_size):
self.grid_data = grid_data
self.mode = mode
self.cell_size = cell_size
# -----------------------------------------------------------------------------
#
def downsize(mode, cell_size, inpath, outpath):
from VolumeData import fileformats
try:
glist = fileformats.open_file(inpath)
except fileformats.Unknown_File_Type, e:
sys.stderr.write(str(e))
sys.exit(1)
reduced = [Reduced_Grid(g, mode, cell_size) for g in glist]
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(reduced, outpath)
# -----------------------------------------------------------------------------
# Average over cells to produce reduced size grid object.
#
# If the grid data sizes are not multiples of the cell size then the
# final data values along the dimension are not included in the reduced
# data (ie ragged blocks are not averaged).
#
class Reduced_Grid(Grid_Data):
def __init__(self, grid_data, mode, cell_size):
self.grid_data = grid_data
self.mode = mode
self.cell_size = cell_size
# -----------------------------------------------------------------------------
#
def apply_function(array_func, inpath, outpath):
from VolumeData import fileformats
try:
grids = fileformats.open_file(inpath)
except fileformats.Unknown_File_Type, e:
sys.stderr.write(str(e))
sys.exit(1)
fvalues = [Mapped_Grid(g, array_func) for g in grids]
from VolumeData.netcdf import write_grid_as_netcdf
write_grid_as_netcdf(fvalues, outpath)
# -----------------------------------------------------------------------------
#
class Mapped_Grid(Grid_Data):
def __init__(self, grid_data, array_func):
self.array_func = array_func
Grid_Data.__init__(self, grid_data.size, grid_data.value_type,
grid_data.origin, grid_data.step,
name = grid_data.name, default_color = grid_data.rgba)
# ---------------------------------------------------------------------------
#
def read_matrix(self, ijk_origin, ijk_size, ijk_step, progress):