def plot_1D_so(som, pix_id, **kwargs):
"""
This function allows one to plot the data in a C{SOM.SO}. This happens by
passing the C{SOM.SOM} and a pixel ID. The pixel ID is searched in the
C{SOM.SOM} and the returned C{SOM.SO} is plotted. The C{SOM.SOM} is passed
since it contains the metadata information. NOTE: This function only
creates the plot. Viewing the actual plot requires invoking
I{pylab.show()}.
@param som: The object containing the data to plot.
@type som: C{SOM.SOM}
@param pix_id: The pixel ID for the C{SOM.SO} to plot.
@type pix_id: various
"""
# Retrieve the SO
so = som.getSO(pix_id)
# Get the data type
dataset_type = som.getDataSetType()
# Get data arrays
if dataset_type == "histogram":
x = so.axis[0].val.toNumPy(True)
else:
x = so.axis[0].val.toNumPy()
y = so.y.toNumPy()
var_y = so.var_y.toNumPy()
try:
var_x = so.axis[0].var.toNumPy()
except AttributeError:
var_x = None
# Set plot attributes
try:
xlabel = kwargs["xlabel"]
del kwargs["xlabel"]
except KeyError:
xlabel = som.getAxisLabel(0) + " [" + som.getAxisUnits(0) + "]"
try:
ylabel = kwargs["ylabel"]
del kwargs["ylabel"]
except KeyError:
ylabel = som.getYLabel() + " [" + som.getYUnits() + "]"
try:
title = kwargs["title"]
del kwargs["title"]
except KeyError :
title = str(pix_id)
# Make 1D plot
drplot.plot_1D_arr(x, y, var_y, var_x, xlabel=xlabel, ylabel=ylabel,
title=title, **kwargs)
def plot_1D_slice(som, axis, xslice, yslice, **kwargs):
"""
This function plots a 1D slice from a 2D spectrum. The function requires
the axis to project onto and a set of slice ranges. NOTE: This
function only creates the plot. Viewing the actual plot requires invoking
I{pylab.show()}.
@param som: The object containing the data to plot.
@type som: C{SOM.SOM}
@param axis: The particular axis to clean. This is either I{x} or I{y}.
@type axis: C{string}
@param xslice: A set of axis values that determine the x-axis values to
get the slice over in the format of (min, max). Passing
I{None} for either min or max gets the first or last bin
respectively.
@type xslice: C{tuple} of two numbers
@param yslice: A set of axis values that determine the y-axis values to
get the slice over in the format of (min, max). Passing
I{None} for either min or max gets the first or last bin
respectively.
@type yslice: C{tuple} of two numbers
@param kwargs: A list of keyword arguments that the function accepts. The
function also takes keywords for L{drplot.plot_1D_arr}.
@keyword index: A flag that tells the function that the slice ranges are
indicies and not axis values. The default is I{False}.
@type index: C{boolean}
@raise RuntimeError: The axis value is not recognized
"""
# Lookup all the keywords
try:
index = kwargs["index"]
except KeyError:
index = False
try:
xlabel = kwargs["xlabel"]
del kwargs["xlabel"]
except KeyError:
xlabel = None
[(x, y, z, var_z)] = som.toXY(withYvar=True)
# Get dimensions of data
Nx = x.size
Ny = y.size
# z values are filtered since plotting has trouble with NaNs. The
# I{nan_to_num} function zeros NaNs and sets (-)Inf to the largest
# (negative) positive value.
z = numpy.reshape(numpy.nan_to_num(z), (Nx, Ny))
var_z = numpy.reshape(numpy.nan_to_num(var_z), (Nx, Ny))
# Find the x and y slice ranges in terms of array indicies
if index:
sx = __get_slice(xslice, Nx)
sy = __get_slice(yslice, Ny)
else:
x_min = __find_index(x, xslice[0])
x_max = __find_index(x, xslice[1])
y_min = __find_index(y, yslice[0])
y_max = __find_index(y, yslice[1])
sx = __get_slice((x_min, x_max), Nx)
sy = __get_slice((y_min, y_max), Ny)
# Setup axis specific values
if axis == "y":
naxis = 0
if xlabel is None:
xlabel = som.getAxisLabel(1) + " [" + som.getAxisUnits(1) + "]"
xp = y[sy]
elif axis == "x":
naxis = 1
if xlabel is None:
xlabel = som.getAxisLabel(0) + " [" + som.getAxisUnits(0) + "]"
xp = x[sx]
else:
raise RuntimeError("Only understand x or y for axis and not: %s" \
% axis)
yp = z[sx, sy].sum(axis=naxis)
var_yp = var_z[sx, sy].sum(axis=naxis)
drplot.plot_1D_arr(xp, yp, var_yp, xlabel=xlabel, **kwargs)
def plot_1D_slice(som, axis, xslice, yslice, **kwargs):
"""
This function plots a 1D slice from a 2D spectrum. The function requires
the axis to project onto and a set of slice ranges. NOTE: This
function only creates the plot. Viewing the actual plot requires invoking
I{pylab.show()}.
@param som: The object containing the data to plot.
@type som: C{SOM.SOM}
@param axis: The particular axis to clean. This is either I{x} or I{y}.
@type axis: C{string}
@param xslice: A set of axis values that determine the x-axis values to
get the slice over in the format of (min, max). Passing
I{None} for either min or max gets the first or last bin
respectively.
@type xslice: C{tuple} of two numbers
@param yslice: A set of axis values that determine the y-axis values to
get the slice over in the format of (min, max). Passing
I{None} for either min or max gets the first or last bin
respectively.
@type yslice: C{tuple} of two numbers
@param kwargs: A list of keyword arguments that the function accepts. The
function also takes keywords for L{drplot.plot_1D_arr}.
@keyword index: A flag that tells the function that the slice ranges are
indicies and not axis values. The default is I{False}.
@type index: C{boolean}
@raise RuntimeError: The axis value is not recognized
"""
# Lookup all the keywords
try:
index = kwargs["index"]
except KeyError:
index = False
try:
xlabel = kwargs["xlabel"]
del kwargs["xlabel"]
except KeyError:
xlabel = None
[(x, y, z, var_z)] = som.toXY(withYvar=True)
# Get dimensions of data
Nx = x.size
Ny = y.size
# z values are filtered since plotting has trouble with NaNs. The
# I{nan_to_num} function zeros NaNs and sets (-)Inf to the largest
# (negative) positive value.
z = numpy.reshape(numpy.nan_to_num(z), (Nx, Ny))
var_z = numpy.reshape(numpy.nan_to_num(var_z), (Nx, Ny))
# Find the x and y slice ranges in terms of array indicies
if index:
sx = __get_slice(xslice, Nx)
sy = __get_slice(yslice, Ny)
else:
x_min = __find_index(x, xslice[0])
x_max = __find_index(x, xslice[1])
y_min = __find_index(y, yslice[0])
y_max = __find_index(y, yslice[1])
sx = __get_slice((x_min, x_max), Nx)
sy = __get_slice((y_min, y_max), Ny)
# Setup axis specific values
if axis == "y":
naxis = 0
if xlabel is None:
xlabel = som.getAxisLabel(1) + " [" + som.getAxisUnits(1) + "]"
xp = y[sy]
elif axis == "x":
naxis = 1
if xlabel is None:
xlabel = som.getAxisLabel(0) + " [" + som.getAxisUnits(0) + "]"
xp = x[sx]
else:
raise RuntimeError("Only understand x or y for axis and not: %s" \
% axis)
yp = z[sx, sy].sum(axis=naxis)
var_yp = var_z[sx, sy].sum(axis=naxis)
drplot.plot_1D_arr(xp, yp, var_yp, xlabel=xlabel, **kwargs)
def plot_1D_so(som, pix_id, **kwargs):
"""
This function allows one to plot the data in a C{SOM.SO}. This happens by
passing the C{SOM.SOM} and a pixel ID. The pixel ID is searched in the
C{SOM.SOM} and the returned C{SOM.SO} is plotted. The C{SOM.SOM} is passed
since it contains the metadata information. NOTE: This function only
creates the plot. Viewing the actual plot requires invoking
I{pylab.show()}.
@param som: The object containing the data to plot.
@type som: C{SOM.SOM}
@param pix_id: The pixel ID for the C{SOM.SO} to plot.
@type pix_id: various
"""
# Retrieve the SO
so = som.getSO(pix_id)
# Get the data type
dataset_type = som.getDataSetType()
# Get data arrays
if dataset_type == "histogram":
x = so.axis[0].val.toNumPy(True)
else:
x = so.axis[0].val.toNumPy()
y = so.y.toNumPy()
var_y = so.var_y.toNumPy()
try:
var_x = so.axis[0].var.toNumPy()
except AttributeError:
var_x = None
# Set plot attributes
try:
xlabel = kwargs["xlabel"]
del kwargs["xlabel"]
except KeyError:
xlabel = som.getAxisLabel(0) + " [" + som.getAxisUnits(0) + "]"
try:
ylabel = kwargs["ylabel"]
del kwargs["ylabel"]
except KeyError:
ylabel = som.getYLabel() + " [" + som.getYUnits() + "]"
try:
title = kwargs["title"]
del kwargs["title"]
except KeyError:
title = str(pix_id)
# Make 1D plot
drplot.plot_1D_arr(x,
y,
var_y,
var_x,
xlabel=xlabel,
ylabel=ylabel,
title=title,
**kwargs)
