def compute_GridData(xvals, yvals, f, ufunc=0, **keyw):
"""Evaluate a function of 2 variables and store the results in a GridData.
Computes a function 'f' of two variables on a rectangular grid
using 'tabulate_function', then store the results into a
'GridData' so that it can be plotted. After calculation the data
are written to a file; no copy is kept in memory. Note that this
is quite different than 'Func' (which tells gnuplot to evaluate
the function).
Arguments:
'xvals' -- a 1-d array with dimension 'numx'
'yvals' -- a 1-d array with dimension 'numy'
'f' -- the function to plot--a callable object for which
'f(x,y)' returns a number.
'ufunc=<bool>' -- evaluate 'f' as a ufunc?
Other keyword arguments are passed to the 'GridData' constructor.
'f' should be a callable object taking two arguments.
'f(x,y)' will be computed at all grid points obtained by
combining elements from 'xvals' and 'yvals'.
If called with 'ufunc=1', then 'f' should be a function that is
composed entirely of ufuncs, and it will be passed the 'xvals' and
'yvals' as rectangular matrices.
Thus if you have a function 'f' and two vectors 'xvals' and
'yvals' and a Gnuplot instance called 'g', you can plot the
function by typing 'g.splot(compute_GridData(f, xvals, yvals))'.
"""
xvals = utils.float_array(xvals)
yvals = utils.float_array(yvals)
# evaluate function:
data = tabulate_function(f, xvals, yvals, ufunc=ufunc)
return apply(Gnuplot.GridData, (data, xvals, yvals), keyw)
def compute_GridData(xvals, yvals, f, ufunc=0, **keyw):
"""Evaluate a function of 2 variables and store the results in a GridData.
Computes a function 'f' of two variables on a rectangular grid
using 'tabulate_function', then store the results into a
'GridData' so that it can be plotted. After calculation the data
are written to a file; no copy is kept in memory. Note that this
is quite different than 'Func' (which tells gnuplot to evaluate
the function).
Arguments:
'xvals' -- a 1-d array with dimension 'numx'
'yvals' -- a 1-d array with dimension 'numy'
'f' -- the function to plot--a callable object for which
'f(x,y)' returns a number.
'ufunc=<bool>' -- evaluate 'f' as a ufunc?
Other keyword arguments are passed to the 'GridData' constructor.
'f' should be a callable object taking two arguments.
'f(x,y)' will be computed at all grid points obtained by
combining elements from 'xvals' and 'yvals'.
If called with 'ufunc=1', then 'f' should be a function that is
composed entirely of ufuncs, and it will be passed the 'xvals' and
'yvals' as rectangular matrices.
Thus if you have a function 'f' and two vectors 'xvals' and
'yvals' and a Gnuplot instance called 'g', you can plot the
function by typing 'g.splot(compute_GridData(f, xvals, yvals))'.
"""
xvals = utils.float_array(xvals)
yvals = utils.float_array(yvals)
# evaluate function:
data = tabulate_function(f, xvals, yvals, ufunc=ufunc)
return apply(Gnuplot.GridData, (data, xvals, yvals), keyw)
def compute_Data(xvals, f, ufunc=0, **keyw):
"""Evaluate a function of 1 variable and store the results in a Data.
Computes a function f of one variable on a set of specified points
using 'tabulate_function', then store the results into a 'Data' so
that it can be plotted. After calculation, the data are written
to a file; no copy is kept in memory. Note that this is quite
different than 'Func' (which tells gnuplot to evaluate the
function).
Arguments:
'xvals' -- a 1-d array with dimension 'numx'
'f' -- the function to plot--a callable object for which
f(x) returns a number.
'ufunc=<bool>' -- evaluate 'f' as a ufunc?
Other keyword arguments are passed through to the Data
constructor.
'f' should be a callable object taking one argument. 'f(x)' will
be computed at all values in xvals.
If called with 'ufunc=1', then 'f' should be a function that is
composed entirely of ufuncs, and it will be passed the 'xvals' and
'yvals' as rectangular matrices.
Thus if you have a function 'f', a vector 'xvals', and a Gnuplot
instance called 'g', you can plot the function by typing
'g.splot(compute_Data(xvals, f))'.
"""
xvals = utils.float_array(xvals)
# evaluate function:
data = tabulate_function(f, xvals, ufunc=ufunc)
return apply(Gnuplot.Data, (xvals, data), keyw)
def compute_Data(xvals, f, ufunc=0, **keyw):
"""Evaluate a function of 1 variable and store the results in a Data.
Computes a function f of one variable on a set of specified points
using 'tabulate_function', then store the results into a 'Data' so
that it can be plotted. After calculation, the data are written
to a file; no copy is kept in memory. Note that this is quite
different than 'Func' (which tells gnuplot to evaluate the
function).
Arguments:
'xvals' -- a 1-d array with dimension 'numx'
'f' -- the function to plot--a callable object for which
f(x) returns a number.
'ufunc=<bool>' -- evaluate 'f' as a ufunc?
Other keyword arguments are passed through to the Data
constructor.
'f' should be a callable object taking one argument. 'f(x)' will
be computed at all values in xvals.
If called with 'ufunc=1', then 'f' should be a function that is
composed entirely of ufuncs, and it will be passed the 'xvals' and
'yvals' as rectangular matrices.
Thus if you have a function 'f', a vector 'xvals', and a Gnuplot
instance called 'g', you can plot the function by typing
'g.splot(compute_Data(xvals, f))'.
"""
xvals = utils.float_array(xvals)
# evaluate function:
data = tabulate_function(f, xvals, ufunc=ufunc)
return apply(Gnuplot.Data, (xvals, data), keyw)
def GridData(
data, xvals=None, yvals=None, inline=_unset, filename=None, **keyw
):
"""Return a _FileItem representing a function of two variables.
'GridData' represents a function that has been tabulated on a
rectangular grid. The data are written to a file; no copy is kept
in memory.
Arguments:
'data' -- the data to plot: a 2-d array with dimensions
(numx,numy).
'xvals' -- a 1-d array with dimension 'numx'
'yvals' -- a 1-d array with dimension 'numy'
'binary=<bool>' -- send data to gnuplot in binary format?
'inline=<bool>' -- send data to gnuplot "inline"?
'filename=<string>' -- save data to a permanent file.
Note the unusual argument order! The data are specified *before*
the x and y values. (This inconsistency was probably a mistake;
after all, the default xvals and yvals are not very useful.)
'data' must be a data array holding the values of a function
f(x,y) tabulated on a grid of points, such that 'data[i,j] ==
f(xvals[i], yvals[j])'. If 'xvals' and/or 'yvals' are omitted,
integers (starting with 0) are used for that coordinate. The data
are written to a temporary file; no copy of the data is kept in
memory.
If 'binary=0' then the data are written to a datafile as 'x y
f(x,y)' triplets (y changes most rapidly) that can be used by
gnuplot's 'splot' command. Blank lines are included each time the
value of x changes so that gnuplot knows to plot a surface through
the data.
If 'binary=1' then the data are written to a file in a binary
format that 'splot' can understand. Binary format is faster and
usually saves disk space but is not human-readable. If your
version of gnuplot doesn't support binary format (it is a
recently-added feature), this behavior can be disabled by setting
the configuration variable
'gp.GnuplotOpts.recognizes_binary_splot=0' in the appropriate
gp*.py file.
Thus if you have three arrays in the above format and a Gnuplot
instance called g, you can plot your data by typing
'g.splot(Gnuplot.GridData(data,xvals,yvals))'.
"""
# Try to interpret data as an array:
data = utils.float_array(data)
try:
(numx, numy) = data.shape
except ValueError:
raise Errors.DataError('data array must be two-dimensional')
if xvals is None:
xvals = numpy.arange(numx)
else:
xvals = utils.float_array(xvals)
if xvals.shape != (numx,):
raise Errors.DataError(
'The size of xvals must be the same as the size of '
'the first dimension of the data array')
if yvals is None:
yvals = numpy.arange(numy)
else:
yvals = utils.float_array(yvals)
if yvals.shape != (numy,):
raise Errors.DataError(
'The size of yvals must be the same as the size of '
'the second dimension of the data array')
# Binary defaults to true if recognizes_binary_plot is set;
# otherwise it is forced to false.
binary = keyw.get('binary', 1) and gp.GnuplotOpts.recognizes_binary_splot
keyw['binary'] = binary
if inline is _unset:
inline = (
(not binary) and (not filename)
and gp.GnuplotOpts.prefer_inline_data
)
elif inline and filename:
raise Errors.OptionError(
'cannot pass data both inline and via a file'
)
# xvals, yvals, and data are now all filled with arrays of data.
if binary:
if inline:
raise Errors.OptionError('binary inline data not supported')
# write file in binary format
# It seems that the gnuplot documentation for binary mode
# disagrees with its actual behavior (as of v. 3.7). The
# documentation has the roles of x and y exchanged. We ignore
# the documentation and go with the code.
mout = numpy.zeros((numy + 1, numx + 1), numpy.float32)
mout[0,0] = numx
mout[0,1:] = xvals.astype(numpy.float32)
mout[1:,0] = yvals.astype(numpy.float32)
try:
# try copying without the additional copy implied by astype():
mout[1:,1:] = numpy.transpose(data)
except:
# if that didn't work then downcasting from double
# must be necessary:
mout[1:,1:] = numpy.transpose(data.astype(numpy.float32))
content = mout.tostring()
if (not filename) and gp.GnuplotOpts.prefer_fifo_data:
return _FIFOFileItem(content, **keyw)
else:
return _NewFileItem(content, filename=filename, **keyw)
else:
# output data to file as "x y f(x)" triplets. This
# requires numy copies of each x value and numx copies of
# each y value. First reformat the data:
set = numpy.transpose(
numpy.array(
(numpy.transpose(numpy.resize(xvals, (numy, numx))),
numpy.resize(yvals, (numx, numy)),
data)), (1,2,0))
# Now output the data with the usual routine. This will
# produce data properly formatted in blocks separated by blank
# lines so that gnuplot can connect the points into a grid.
f = StringIO()
utils.write_array(f, set)
content = f.getvalue()
if inline:
return _InlineFileItem(content, **keyw)
elif filename:
return _NewFileItem(content, filename=filename, **keyw)
elif gp.GnuplotOpts.prefer_fifo_data:
return _FIFOFileItem(content, **keyw)
else:
return _NewFileItem(content, **keyw)
def Data(*data, **keyw):
"""Create and return a _FileItem representing the data from *data.
Create a '_FileItem' object (which is a type of 'PlotItem') out of
one or more Float Python numpy arrays (or objects that can be
converted to a float numpy array). If the routine is passed a
single with multiple dimensions, then the last index ranges over
the values comprising a single data point (e.g., [<x>, <y>,
<sigma>]) and the rest of the indices select the data point. If
passed a single array with 1 dimension, then each point is
considered to have only one value (i.e., by default the values
will be plotted against their indices). If the routine is passed
more than one array, they must have identical shapes, and then
each data point is composed of one point from each array. E.g.,
'Data(x,x**2)' is a 'PlotItem' that represents x squared as a
function of x. For the output format, see the comments for
'write_array()'.
How the data are written to gnuplot depends on the 'inline'
argument and preference settings for the platform in use.
Keyword arguments:
'cols=<tuple>' -- write only the specified columns from each
data point to the file. Since cols is used by python, the
columns should be numbered in the python style (starting
from 0), not the gnuplot style (starting from 1).
'inline=<bool>' -- transmit the data to gnuplot 'inline'
rather than through a temporary file. The default is the
value of gp.GnuplotOpts.prefer_inline_data.
'filename=<string>' -- save data to a permanent file.
The keyword arguments recognized by '_FileItem' can also be used
here.
"""
if len(data) == 1:
# data was passed as a single structure
data = utils.float_array(data[0])
# As a special case, if passed a single 1-D array, then it is
# treated as one value per point (by default, plotted against
# its index):
if len(data.shape) == 1:
data = data[:,numpy.newaxis]
else:
# data was passed column by column (for example,
# Data(x,y)); pack it into one big array (this will test
# that sizes are all the same):
data = utils.float_array(data)
dims = len(data.shape)
# transpose so that the last index selects x vs. y:
data = numpy.transpose(data, (dims-1,) + tuple(range(dims-1)))
if 'cols' in keyw:
cols = keyw['cols']
del keyw['cols']
if isinstance(cols, types.IntType):
cols = (cols,)
data = numpy.take(data, cols, -1)
if 'filename' in keyw:
filename = keyw['filename'] or None
del keyw['filename']
else:
filename = None
if 'inline' in keyw:
inline = keyw['inline']
del keyw['inline']
if inline and filename:
raise Errors.OptionError(
'cannot pass data both inline and via a file'
)
else:
inline = (not filename) and gp.GnuplotOpts.prefer_inline_data
# Output the content into a string:
f = StringIO()
utils.write_array(f, data)
content = f.getvalue()
if inline:
return _InlineFileItem(content, **keyw)
elif filename:
return _NewFileItem(content, filename=filename, **keyw)
elif gp.GnuplotOpts.prefer_fifo_data:
return _FIFOFileItem(content, **keyw)
else:
return _NewFileItem(content, **keyw)
def Data(*set, **keyw):
"""Create and return a _FileItem representing the data from *set.
Create a '_FileItem' object (which is a type of 'PlotItem') out of
one or more Float Python Numeric arrays (or objects that can be
converted to a Float Numeric array). If the routine is passed a
single with multiple dimensions, then the last index ranges over
the values comprising a single data point (e.g., [<x>, <y>,
<sigma>]) and the rest of the indices select the data point. If
passed a single array with 1 dimension, then each point is
considered to have only one value (i.e., by default the values
will be plotted against their indices). If the routine is passed
more than one array, they must have identical shapes, and then
each data point is composed of one point from each array. E.g.,
'Data(x,x**2)' is a 'PlotItem' that represents x squared as a
function of x. For the output format, see the comments for
'write_array()'.
How the data are written to gnuplot depends on the 'inline'
argument and preference settings for the platform in use.
Keyword arguments:
'cols=<tuple>' -- write only the specified columns from each
data point to the file. Since cols is used by python, the
columns should be numbered in the python style (starting
from 0), not the gnuplot style (starting from 1).
'inline=<bool>' -- transmit the data to gnuplot 'inline'
rather than through a temporary file. The default is the
value of gp.GnuplotOpts.prefer_inline_data.
The keyword arguments recognized by '_FileItem' can also be used
here.
"""
if len(set) == 1:
# set was passed as a single structure
set = utils.float_array(set[0])
# As a special case, if passed a single 1-D array, then it is
# treated as one value per point (by default, plotted against
# its index):
if len(set.shape) == 1:
set = set[:,Numeric.NewAxis]
else:
# set was passed column by column (for example,
# Data(x,y)); pack it into one big array (this will test
# that sizes are all the same):
set = utils.float_array(set)
dims = len(set.shape)
# transpose so that the last index selects x vs. y:
set = Numeric.transpose(set, (dims-1,) + tuple(range(dims-1)))
if keyw.has_key('cols'):
cols = keyw['cols']
del keyw['cols']
if type(cols) is types.IntType:
cols = (cols,)
set = Numeric.take(set, cols, -1)
if keyw.has_key('inline'):
inline = keyw['inline']
del keyw['inline']
else:
inline = gp.GnuplotOpts.prefer_inline_data
# Output the content into a string:
f = StringIO()
utils.write_array(f, set)
content = f.getvalue()
if inline:
return apply(_InlineFileItem, (content,), keyw)
elif gp.GnuplotOpts.prefer_fifo_data:
return apply(_FIFOFileItem, (content,), keyw)
else:
return apply(_TempFileItem, (content,), keyw)
def GridData(data,
xvals=None,
yvals=None,
inline=_unset,
filename=None,
**keyw):
"""Return a _FileItem representing a function of two variables.
'GridData' represents a function that has been tabulated on a
rectangular grid. The data are written to a file; no copy is kept
in memory.
Arguments:
'data' -- the data to plot: a 2-d array with dimensions
(numx,numy).
'xvals' -- a 1-d array with dimension 'numx'
'yvals' -- a 1-d array with dimension 'numy'
'binary=<bool>' -- send data to gnuplot in binary format?
'inline=<bool>' -- send data to gnuplot "inline"?
'filename=<string>' -- save data to a permanent file.
Note the unusual argument order! The data are specified *before*
the x and y values. (This inconsistency was probably a mistake;
after all, the default xvals and yvals are not very useful.)
'data' must be a data array holding the values of a function
f(x,y) tabulated on a grid of points, such that 'data[i,j] ==
f(xvals[i], yvals[j])'. If 'xvals' and/or 'yvals' are omitted,
integers (starting with 0) are used for that coordinate. The data
are written to a temporary file; no copy of the data is kept in
memory.
If 'binary=0' then the data are written to a datafile as 'x y
f(x,y)' triplets (y changes most rapidly) that can be used by
gnuplot's 'splot' command. Blank lines are included each time the
value of x changes so that gnuplot knows to plot a surface through
the data.
If 'binary=1' then the data are written to a file in a binary
format that 'splot' can understand. Binary format is faster and
usually saves disk space but is not human-readable. If your
version of gnuplot doesn't support binary format (it is a
recently-added feature), this behavior can be disabled by setting
the configuration variable
'gp.GnuplotOpts.recognizes_binary_splot=0' in the appropriate
gp*.py file.
Thus if you have three arrays in the above format and a Gnuplot
instance called g, you can plot your data by typing
'g.splot(Gnuplot.GridData(data,xvals,yvals))'.
"""
# Try to interpret data as an array:
data = utils.float_array(data)
try:
(numx, numy) = data.shape
except ValueError:
raise Errors.DataError('data array must be two-dimensional')
if xvals is None:
xvals = numpy.arange(numx)
else:
xvals = utils.float_array(xvals)
if xvals.shape != (numx, ):
raise Errors.DataError(
'The size of xvals must be the same as the size of '
'the first dimension of the data array')
if yvals is None:
yvals = numpy.arange(numy)
else:
yvals = utils.float_array(yvals)
if yvals.shape != (numy, ):
raise Errors.DataError(
'The size of yvals must be the same as the size of '
'the second dimension of the data array')
# Binary defaults to true if recognizes_binary_plot is set;
# otherwise it is forced to false.
binary = keyw.get('binary', 1) and gp.GnuplotOpts.recognizes_binary_splot
keyw['binary'] = binary
if inline is _unset:
inline = ((not binary) and (not filename)
and gp.GnuplotOpts.prefer_inline_data)
elif inline and filename:
raise Errors.OptionError('cannot pass data both inline and via a file')
# xvals, yvals, and data are now all filled with arrays of data.
if binary:
if inline:
raise Errors.OptionError('binary inline data not supported')
# write file in binary format
# It seems that the gnuplot documentation for binary mode
# disagrees with its actual behavior (as of v. 3.7). The
# documentation has the roles of x and y exchanged. We ignore
# the documentation and go with the code.
mout = numpy.zeros((numy + 1, numx + 1), numpy.float32)
mout[0, 0] = numx
mout[0, 1:] = xvals.astype(numpy.float32)
mout[1:, 0] = yvals.astype(numpy.float32)
try:
# try copying without the additional copy implied by astype():
mout[1:, 1:] = numpy.transpose(data)
except:
# if that didn't work then downcasting from double
# must be necessary:
mout[1:, 1:] = numpy.transpose(data.astype(numpy.float32))
content = mout.tostring()
if (not filename) and gp.GnuplotOpts.prefer_fifo_data:
return _FIFOFileItem(content, **keyw)
else:
return _NewFileItem(content, filename=filename, **keyw)
else:
# output data to file as "x y f(x)" triplets. This
# requires numy copies of each x value and numx copies of
# each y value. First reformat the data:
set = numpy.transpose(
numpy.array((numpy.transpose(numpy.resize(xvals, (numy, numx))),
numpy.resize(yvals, (numx, numy)), data)), (1, 2, 0))
# Now output the data with the usual routine. This will
# produce data properly formatted in blocks separated by blank
# lines so that gnuplot can connect the points into a grid.
f = StringIO()
utils.write_array(f, set)
content = f.getvalue()
if inline:
return _InlineFileItem(content, **keyw)
elif filename:
return _NewFileItem(content, filename=filename, **keyw)
elif gp.GnuplotOpts.prefer_fifo_data:
return _FIFOFileItem(content, **keyw)
else:
return _NewFileItem(content, **keyw)
def Data(*data, **keyw):
"""Create and return a _FileItem representing the data from *data.
Create a '_FileItem' object (which is a type of 'PlotItem') out of
one or more Float Python numpy arrays (or objects that can be
converted to a float numpy array). If the routine is passed a
single with multiple dimensions, then the last index ranges over
the values comprising a single data point (e.g., [<x>, <y>,
<sigma>]) and the rest of the indices select the data point. If
passed a single array with 1 dimension, then each point is
considered to have only one value (i.e., by default the values
will be plotted against their indices). If the routine is passed
more than one array, they must have identical shapes, and then
each data point is composed of one point from each array. E.g.,
'Data(x,x**2)' is a 'PlotItem' that represents x squared as a
function of x. For the output format, see the comments for
'write_array()'.
How the data are written to gnuplot depends on the 'inline'
argument and preference settings for the platform in use.
Keyword arguments:
'cols=<tuple>' -- write only the specified columns from each
data point to the file. Since cols is used by python, the
columns should be numbered in the python style (starting
from 0), not the gnuplot style (starting from 1).
'inline=<bool>' -- transmit the data to gnuplot 'inline'
rather than through a temporary file. The default is the
value of gp.GnuplotOpts.prefer_inline_data.
'filename=<string>' -- save data to a permanent file.
The keyword arguments recognized by '_FileItem' can also be used
here.
"""
if len(data) == 1:
# data was passed as a single structure
data = utils.float_array(data[0])
# As a special case, if passed a single 1-D array, then it is
# treated as one value per point (by default, plotted against
# its index):
if len(data.shape) == 1:
data = data[:, numpy.newaxis]
else:
# data was passed column by column (for example,
# Data(x,y)); pack it into one big array (this will test
# that sizes are all the same):
data = utils.float_array(data)
dims = len(data.shape)
# transpose so that the last index selects x vs. y:
data = numpy.transpose(data, (dims - 1, ) + tuple(range(dims - 1)))
if 'cols' in keyw:
cols = keyw['cols']
del keyw['cols']
if isinstance(cols, types.IntType):
cols = (cols, )
data = numpy.take(data, cols, -1)
if 'filename' in keyw:
filename = keyw['filename'] or None
del keyw['filename']
else:
filename = None
if 'inline' in keyw:
inline = keyw['inline']
del keyw['inline']
if inline and filename:
raise Errors.OptionError(
'cannot pass data both inline and via a file')
else:
inline = (not filename) and gp.GnuplotOpts.prefer_inline_data
# Output the content into a string:
f = StringIO()
utils.write_array(f, data)
content = f.getvalue()
if inline:
return _InlineFileItem(content, **keyw)
elif filename:
return _NewFileItem(content, filename=filename, **keyw)
elif gp.GnuplotOpts.prefer_fifo_data:
return _FIFOFileItem(content, **keyw)
else:
return _NewFileItem(content, **keyw)
def Data(*set, **keyw):
"""Create and return a _FileItem representing the data from *set.
Create a '_FileItem' object (which is a type of 'PlotItem') out of
one or more Float Python Numeric arrays (or objects that can be
converted to a Float Numeric array). If the routine is passed a
single with multiple dimensions, then the last index ranges over
the values comprising a single data point (e.g., [<x>, <y>,
<sigma>]) and the rest of the indices select the data point. If
passed a single array with 1 dimension, then each point is
considered to have only one value (i.e., by default the values
will be plotted against their indices). If the routine is passed
more than one array, they must have identical shapes, and then
each data point is composed of one point from each array. E.g.,
'Data(x,x**2)' is a 'PlotItem' that represents x squared as a
function of x. For the output format, see the comments for
'write_array()'.
How the data are written to gnuplot depends on the 'inline'
argument and preference settings for the platform in use.
Keyword arguments:
'cols=<tuple>' -- write only the specified columns from each
data point to the file. Since cols is used by python, the
columns should be numbered in the python style (starting
from 0), not the gnuplot style (starting from 1).
'inline=<bool>' -- transmit the data to gnuplot 'inline'
rather than through a temporary file. The default is the
value of gp.GnuplotOpts.prefer_inline_data.
The keyword arguments recognized by '_FileItem' can also be used
here.
"""
if len(set) == 1:
# set was passed as a single structure
set = utils.float_array(set[0])
# As a special case, if passed a single 1-D array, then it is
# treated as one value per point (by default, plotted against
# its index):
if len(set.shape) == 1:
set = set[:,Numeric.NewAxis]
else:
# set was passed column by column (for example,
# Data(x,y)); pack it into one big array (this will test
# that sizes are all the same):
set = utils.float_array(set)
dims = len(set.shape)
# transpose so that the last index selects x vs. y:
set = Numeric.transpose(set, (dims-1,) + tuple(range(dims-1)))
if keyw.has_key('cols'):
cols = keyw['cols']
del keyw['cols']
if type(cols) is types.IntType:
cols = (cols,)
set = Numeric.take(set, cols, -1)
if keyw.has_key('inline'):
inline = keyw['inline']
del keyw['inline']
else:
inline = gp.GnuplotOpts.prefer_inline_data
# Output the content into a string:
f = StringIO()
utils.write_array(f, set)
content = f.getvalue()
if inline:
return apply(_InlineFileItem, (content,), keyw)
elif gp.GnuplotOpts.prefer_fifo_data:
return apply(_FIFOFileItem, (content,), keyw)
else:
return apply(_TempFileItem, (content,), keyw)
