def image_data(Z,
X=[0, 1.0],
Y=[0, 1.0],
aspect=1.0,
zmin=None,
zmax=None,
clear=1,
title='',
clabel='z',
autoformat=True,
colormap="Last Used",
shell_history=1,
**kwargs):
"""
Generates an image or 3d plot
X 1-d array of x-values
Y 1-d array of y-values
Z 2-d array of z-values
X and Y can be something like [0,2] or an array of X-values
kwargs are sent to pylab.imshow()
"""
global _colormap
_pylab.ioff()
fig = _pylab.gcf()
if clear:
fig.clear()
_pylab.axes()
# generate the 3d axes
X = _n.array(X)
Y = _n.array(Y)
Z = _n.array(Z)
# assume X and Y are the bin centers and figure out the bin widths
x_width = abs(float(X[-1] - X[0]) / (len(Z[0]) - 1))
y_width = abs(float(Y[-1] - Y[0]) / (len(Z) - 1))
# reverse the Z's
Z = Z[-1::-1]
# get rid of the label and title kwargs
xlabel = ''
ylabel = ''
title = ''
if kwargs.has_key('xlabel'): xlabel = kwargs.pop('xlabel')
if kwargs.has_key('ylabel'): ylabel = kwargs.pop('ylabel')
if kwargs.has_key('title'): title = kwargs.pop('title')
_pylab.imshow(Z,
extent=[
X[0] - x_width / 2.0, X[-1] + x_width / 2.0,
Y[0] - y_width / 2.0, Y[-1] + y_width / 2.0
],
**kwargs)
cb = _pylab.colorbar()
_pt.image_set_clim(zmin, zmax)
_pt.image_set_aspect(aspect)
cb.set_label(clabel)
a = _pylab.gca()
a.set_xlabel(xlabel)
a.set_ylabel(ylabel)
#_pt.close_sliders()
#_pt.image_sliders()
# title
history = _fun.get_shell_history()
for n in range(0, min(shell_history, len(history))):
title = title + "\n" + history[n].split('\n')[0].strip()
title = title + '\nPlot created ' + _time.asctime()
a.set_title(title.strip())
if autoformat: _pt.image_format_figure(fig)
_pylab.ion()
_pylab.show()
#_pt.raise_figure_window()
#_pt.raise_pyshell()
_pylab.draw()
# add the color sliders
if colormap:
if _colormap: _colormap.close()
_colormap = _pt.image_colormap(colormap, image=a.images[0])
def image_data(Z, X=[0,1.0], Y=[0,1.0], aspect=1.0, zmin=None, zmax=None, clear=1, clabel='z', autoformat=True, colormap="Last Used", shell_history=1, **kwargs):
"""
Generates an image or 3d plot
X 1-d array of x-values
Y 1-d array of y-values
Z 2-d array of z-values
X and Y can be something like [0,2] or an array of X-values
kwargs are sent to pylab.imshow()
"""
global _colormap
_pylab.ioff()
fig = _pylab.gcf()
if clear:
fig.clear()
_pylab.axes()
# generate the 3d axes
X = _n.array(X)
Y = _n.array(Y)
Z = _n.array(Z)
# assume X and Y are the bin centers and figure out the bin widths
x_width = abs(float(X[-1] - X[0])/(len(Z[0])-1))
y_width = abs(float(Y[-1] - Y[0])/(len(Z)-1))
# reverse the Z's
Z = Z[-1::-1]
# get rid of the label and title kwargs
xlabel=''
ylabel=''
title =''
if kwargs.has_key('xlabel'): xlabel = kwargs.pop('xlabel')
if kwargs.has_key('ylabel'): ylabel = kwargs.pop('ylabel')
if kwargs.has_key('title'): title = kwargs.pop('title')
_pylab.imshow(Z, extent=[X[0]-x_width/2.0, X[-1]+x_width/2.0,
Y[0]-y_width/2.0, Y[-1]+y_width/2.0], **kwargs)
cb = _pylab.colorbar()
_pt.image_set_clim(zmin,zmax)
_pt.image_set_aspect(aspect)
cb.set_label(clabel)
a = _pylab.gca()
a.set_xlabel(xlabel)
a.set_ylabel(ylabel)
#_pt.close_sliders()
#_pt.image_sliders()
# title
history = _fun.get_shell_history()
for n in range(0, min(shell_history, len(history))):
title = title + "\n" + history[n].split('\n')[0].strip()
title = title + '\nPlot created ' + _time.asctime()
a.set_title(title.strip())
if autoformat: _pt.image_format_figure(fig)
_pylab.ion()
_pylab.show()
#_pt.raise_figure_window()
#_pt.raise_pyshell()
_pylab.draw()
# add the color sliders
if colormap:
if _colormap: _colormap.close()
_colormap = _pt.image_colormap(colormap, image=a.images[0])
def xy_data(xdata, ydata, eydata=None, exdata=None, label=None, xlabel='', ylabel='', \
title='', shell_history=1, xshift=0, yshift=0, xshift_every=1, yshift_every=1, \
coarsen=0, style=None, clear=True, axes=None, xscale='linear', yscale='linear', grid=False, \
legend='best', legend_max=20, autoformat=True, tall=False, draw=True, **kwargs):
"""
Plots specified data.
xdata, ydata Arrays (or arrays of arrays) of data to plot
eydata, exdata Arrays of x and y errorbar values
label string or array of strings for the line labels
xlabel='' label for the x-axis
ylabel='' label for the y-axis
title='' title for the axes; set to None to have nothing.
shell_history=1 how many commands from the pyshell history to include
with the title
xshift=0, yshift=0 progressive shifts on the data, to make waterfall plots
xshift_every=1 perform the progressive shift every 1 or n'th line.
yshift_every=1 perform the progressive shift every 1 or n'th line.
style style cycle object.
clear=True if no axes are specified, clear the figure, otherwise
clear just the axes.
axes=None which axes to use, or "gca" for the current axes
xscale,yscale 'linear' by default. Set either to 'log' for log axes
grid=False Should we draw a grid on the axes?
legend='best' where to place the legend (see pylab.legend())
Set this to None to ignore the legend.
legend_max=20 number of legend entries before it's truncated with '...'
autoformat=True Should we format the figure for printing?
False Should the format be tall?
draw=True whether or not to draw the plot after plotting
**kwargs are sent to pylab.errorbar()
"""
_pylab.ioff()
# make sure everything is at least iterable.
if not _fun.is_iterable(xdata): xdata = [xdata]
if not _fun.is_iterable(exdata): exdata = [exdata]
if not _fun.is_iterable(ydata): ydata = [ydata]
if not _fun.is_iterable(eydata): eydata = [eydata]
# make sure at least xdata and ydata are 2-D
if _fun.is_a_number(xdata[0]): xdata = [xdata]
if _fun.is_a_number(ydata[0]): ydata = [ydata]
# make sure the number of data sets agrees
N = max(len(xdata), len(ydata))
for n in range(N - len(xdata)):
xdata.append(xdata[0])
for n in range(N - len(ydata)):
ydata.append(ydata[0])
for n in range(N - len(exdata)):
exdata.append(exdata[0])
for n in range(N - len(eydata)):
eydata.append(eydata[0])
# loop over each x and y data set, making sure None's are all converted
# to counting arrays
for n in range(N):
# clean up the [None]'s
if _fun.is_iterable(xdata[n]) and xdata[n][0] == None: xdata[n] = None
if _fun.is_iterable(ydata[n]) and ydata[n][0] == None: ydata[n] = None
if xdata[n] == None and ydata[n] == None:
print "ERROR: " + str(n) + "'th data set is (None, None)."
return
if xdata[n] == None: xdata[n] = _n.arange(len(ydata[n]))
if ydata[n] == None: ydata[n] = _n.arange(len(xdata[n]))
# check that the labels is a list of strings of the same length
if not _fun.is_iterable(label): label = [label] * N
while len(label) < len(ydata):
label.append(label[0])
# concatenate if necessary
if len(label) > legend_max:
label[legend_max - 2] = '...'
for n in range(legend_max - 1, len(label) - 1):
label[n] = "_nolegend_"
# clear the figure?
if clear and not axes: _pylab.gcf().clear() # axes cleared later
# setup axes
if axes == "gca" or axes == None: axes = _pylab.gca()
# if we're clearing the axes
if clear: axes.clear()
# set the current axes
_pylab.axes(axes)
# now loop over the list of data in xdata and ydata
for n in range(0, len(xdata)):
# get the label
if label: l = str(label[n])
else: l = str(n)
# calculate the x an y progressive shifts
dx = xshift * (n / xshift_every)
dy = yshift * (n / yshift_every)
# if we're supposed to coarsen the data, do so.
x = _fun.coarsen_array(xdata[n], coarsen)
y = _fun.coarsen_array(ydata[n], coarsen)
ey = _fun.coarsen_array(eydata[n], coarsen, 'quadrature')
ex = _fun.coarsen_array(exdata[n], coarsen, 'quadrature')
# update the style
if not style == None: kwargs.update(style.next())
axes.errorbar(x + dx, y + dy, label=l, yerr=ey, xerr=ex, **kwargs)
_pylab.xscale(xscale)
_pylab.yscale(yscale)
if legend: axes.legend(loc=legend)
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
# for some arguments there should be no title.
if title in [None, False, 0]:
axes.set_title('')
# add the commands to the title
else:
title = str(title)
history = _fun.get_shell_history()
for n in range(0, min(shell_history, len(history))):
title = title + "\n" + history[n].split('\n')[0].strip()
title = title + '\nPlot created ' + _time.asctime()
axes.set_title(title)
if grid: _pylab.grid(True)
if autoformat:
_pt.format_figure(draw=False)
_pt.auto_zoom(axes=axes, draw=False)
# update the canvas
if draw:
_pylab.ion()
_pylab.draw()
_pylab.show()
return axes
def xy_data(xdata, ydata, eydata=None, exdata=None, label=None, xlabel='', ylabel='', \
title='', shell_history=1, xshift=0, yshift=0, xshift_every=1, yshift_every=1, \
coarsen=0, style=None, clear=True, axes=None, xscale='linear', yscale='linear', grid=False, \
legend='best', legend_max=20, autoformat=True, tall=False, draw=True, **kwargs):
"""
Plots specified data.
xdata, ydata Arrays (or arrays of arrays) of data to plot
eydata, exdata Arrays of x and y errorbar values
label string or array of strings for the line labels
xlabel='' label for the x-axis
ylabel='' label for the y-axis
title='' title for the axes; set to None to have nothing.
shell_history=1 how many commands from the pyshell history to include
with the title
xshift=0, yshift=0 progressive shifts on the data, to make waterfall plots
xshift_every=1 perform the progressive shift every 1 or n'th line.
yshift_every=1 perform the progressive shift every 1 or n'th line.
style style cycle object.
clear=True if no axes are specified, clear the figure, otherwise
clear just the axes.
axes=None which axes to use, or "gca" for the current axes
xscale,yscale 'linear' by default. Set either to 'log' for log axes
grid=False Should we draw a grid on the axes?
legend='best' where to place the legend (see pylab.legend())
Set this to None to ignore the legend.
legend_max=20 number of legend entries before it's truncated with '...'
autoformat=True Should we format the figure for printing?
False Should the format be tall?
draw=True whether or not to draw the plot after plotting
**kwargs are sent to pylab.errorbar()
"""
_pylab.ioff()
# make sure everything is at least iterable.
if not _fun.is_iterable(xdata): xdata = [xdata]
if not _fun.is_iterable(exdata): exdata = [exdata]
if not _fun.is_iterable(ydata): ydata = [ydata]
if not _fun.is_iterable(eydata): eydata = [eydata]
# make sure at least xdata and ydata are 2-D
if _fun.is_a_number(xdata[0]): xdata = [xdata]
if _fun.is_a_number(ydata[0]): ydata = [ydata]
# make sure the number of data sets agrees
N = max(len(xdata),len(ydata))
for n in range(N-len( xdata)): xdata.append( xdata[0])
for n in range(N-len( ydata)): ydata.append( ydata[0])
for n in range(N-len(exdata)): exdata.append(exdata[0])
for n in range(N-len(eydata)): eydata.append(eydata[0])
# loop over each x and y data set, making sure None's are all converted
# to counting arrays
for n in range(N):
# clean up the [None]'s
if _fun.is_iterable(xdata[n]) and xdata[n][0] is None: xdata[n] = None
if _fun.is_iterable(ydata[n]) and ydata[n][0] is None: ydata[n] = None
if xdata[n] is None and ydata[n] is None:
print "ERROR: "+str(n)+"'th data set is (None, None)."
return
if xdata[n] is None: xdata[n] = _n.arange(len(ydata[n]))
if ydata[n] is None: ydata[n] = _n.arange(len(xdata[n]))
# check that the labels is a list of strings of the same length
if not _fun.is_iterable(label): label = [label]*N
while len(label) < len(ydata): label.append(label[0])
# concatenate if necessary
if len(label) > legend_max:
label[legend_max-2] = '...'
for n in range(legend_max-1,len(label)-1): label[n] = "_nolegend_"
# clear the figure?
if clear and not axes: _pylab.gcf().clear() # axes cleared later
# setup axes
if axes=="gca" or axes is None: axes = _pylab.gca()
# if we're clearing the axes
if clear: axes.clear()
# set the current axes
_pylab.axes(axes)
# now loop over the list of data in xdata and ydata
for n in range(0,len(xdata)):
# get the label
if label: l = str(label[n])
else: l = str(n)
# calculate the x an y progressive shifts
dx = xshift*(n/xshift_every)
dy = yshift*(n/yshift_every)
# if we're supposed to coarsen the data, do so.
x = _fun.coarsen_array(xdata[n], coarsen)
y = _fun.coarsen_array(ydata[n], coarsen)
ey = _fun.coarsen_array(eydata[n], coarsen, 'quadrature')
ex = _fun.coarsen_array(exdata[n], coarsen, 'quadrature')
# update the style
if not style is None: kwargs.update(style.next())
axes.errorbar(x+dx, y+dy, label=l, yerr=ey, xerr=ex, **kwargs)
_pylab.xscale(xscale)
_pylab.yscale(yscale)
if legend: axes.legend(loc=legend)
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
# for some arguments there should be no title.
if title in [None, False, 0]:
axes.set_title('')
# add the commands to the title
else:
title = str(title)
history = _fun.get_shell_history()
for n in range(0, min(shell_history, len(history))):
title = title + "\n" + history[n].split('\n')[0].strip()
title = title + '\nPlot created ' + _time.asctime()
axes.set_title(title)
if grid: _pylab.grid(True)
if autoformat:
_pt.format_figure(draw=False)
_pt.auto_zoom(axes=axes, draw=False)
# update the canvas
if draw:
_pylab.ion()
_pylab.draw()
_pylab.show()
return axes
def xy_data(xdata, ydata, eydata=None, exdata=None, label=None, xlabel='', ylabel='', \
title='', shell_history=0, xshift=0, yshift=0, xshift_every=1, yshift_every=1, \
coarsen=0, style=None, clear=True, axes=None, xscale='linear', yscale='linear', grid=False, \
legend='best', legend_max=20, autoformat=True, autoformat_window=True, tall=False, draw=True, **kwargs):
"""
Plots specified data.
Parameters
----------
xdata, ydata
Arrays (or arrays of arrays) of data to plot
eydata=None, exdata=None
Arrays of x and y errorbar values
label=None
String or array of strings for the line labels
xlabel=''
Label for the x-axis
ylabel=''
Label for the y-axis
title=''
Title for the axes; set to None to have nothing.
shell_history=0
How many commands from the pyshell history to include with the title
xshift=0, yshift=0
Progressive shifts on the data, to make waterfall plots
xshift_every=1
Perform the progressive shift every 1 or n'th line.
yshift_every=1
perform the progressive shift every 1 or n'th line.
style=None
style cycle object.
clear=True
If no axes are specified (see below), clear the figure, otherwise clear just the axes.
axes=None
Which matplotlib axes to use, or "gca" for the current axes
xscale='linear', yscale='linear'
'linear' or 'log' x and y axis scales.
grid=False
Should we draw a grid on the axes?
legend='best'
Where to place the legend (see pylab.legend() for options)
Set this to None to ignore the legend.
legend_max=20
Number of legend entries before it's truncated with '...'
autoformat=True
Should we format the figure for printing?
autoformat_window=True
Should we resize and reposition the window when autoformatting?
tall=False
Should the format be tall?
draw=True
Whether or not to draw the plot after plotting.
See matplotlib's errorbar() function for additional optional keyword arguments.
"""
_pylab.ioff()
# Make sure the dimensionality of the data sets matches
xdata, ydata = _match_data_sets(xdata, ydata)
exdata = _match_error_to_data_set(xdata, exdata)
eydata = _match_error_to_data_set(ydata, eydata)
# check that the labels is a list of strings of the same length
if not _fun.is_iterable(label): label = [label] * len(xdata)
while len(label) < len(ydata):
label.append(label[0])
# concatenate if necessary
if len(label) > legend_max:
label[legend_max - 2] = '...'
for n in range(legend_max - 1, len(label) - 1):
label[n] = "_nolegend_"
# clear the figure?
if clear and not axes: _pylab.gcf().clear() # axes cleared later
# setup axes
if axes == "gca" or axes is None: axes = _pylab.gca()
# if we're clearing the axes
if clear: axes.clear()
# set the current axes
_pylab.axes(axes)
# now loop over the list of data in xdata and ydata
for n in range(0, len(xdata)):
# get the label
l = str(n) + ": " + str(label[n])
# calculate the x an y progressive shifts
dx = xshift * (n / xshift_every)
dy = yshift * (n / yshift_every)
# if we're supposed to coarsen the data, do so.
x = _fun.coarsen_array(xdata[n], coarsen)
y = _fun.coarsen_array(ydata[n], coarsen)
ey = _fun.coarsen_array(eydata[n], coarsen, 'quadrature')
ex = _fun.coarsen_array(exdata[n], coarsen, 'quadrature')
# update the style
if not style is None: kwargs.update(next(style))
axes.errorbar(x + dx, y + dy, label=l, yerr=ey, xerr=ex, **kwargs)
_pylab.xscale(xscale)
_pylab.yscale(yscale)
if legend: axes.legend(loc=legend)
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
# for some arguments there should be no title.
if title in [None, False, 0]:
axes.set_title('')
# add the commands to the title
else:
title = str(title)
history = _fun.get_shell_history()
for n in range(0, min(shell_history, len(history))):
title = title + "\n" + history[n].split('\n')[0].strip()
title = title + '\nPlot created ' + _time.asctime()
axes.set_title(title)
if grid: _pylab.grid(True)
if autoformat:
_pt.format_figure(draw=False, modify_geometry=autoformat_window)
_pt.auto_zoom(axes=axes, draw=False)
# update the canvas
if draw:
_pylab.ion()
_pylab.draw()
_pylab.show()
return axes
def xy_data(xdata, ydata, eydata=None, exdata=None, label=None, xlabel='', ylabel='', \
title='', shell_history=0, xshift=0, yshift=0, xshift_every=1, yshift_every=1, \
coarsen=0, style=None, clear=True, axes=None, xscale='linear', yscale='linear', grid=False, \
legend='best', legend_max=20, autoformat=True, autoformat_window=True, tall=False, draw=True, **kwargs):
"""
Plots specified data.
Parameters
----------
xdata, ydata
Arrays (or arrays of arrays) of data to plot
eydata=None, exdata=None
Arrays of x and y errorbar values
label=None
String or array of strings for the line labels
xlabel=''
Label for the x-axis
ylabel=''
Label for the y-axis
title=''
Title for the axes; set to None to have nothing.
shell_history=0
How many commands from the pyshell history to include with the title
xshift=0, yshift=0
Progressive shifts on the data, to make waterfall plots
xshift_every=1
Perform the progressive shift every 1 or n'th line.
yshift_every=1
perform the progressive shift every 1 or n'th line.
style=None
style cycle object.
clear=True
If no axes are specified (see below), clear the figure, otherwise clear just the axes.
axes=None
Which matplotlib axes to use, or "gca" for the current axes
xscale='linear', yscale='linear'
'linear' or 'log' x and y axis scales.
grid=False
Should we draw a grid on the axes?
legend='best'
Where to place the legend (see pylab.legend() for options)
Set this to None to ignore the legend.
legend_max=20
Number of legend entries before it's truncated with '...'
autoformat=True
Should we format the figure for printing?
autoformat_window=True
Should we resize and reposition the window when autoformatting?
tall=False
Should the format be tall?
draw=True
Whether or not to draw the plot after plotting.
See matplotlib's errorbar() function for additional optional keyword arguments.
"""
_pylab.ioff()
# Make sure the dimensionality of the data sets matches
xdata, ydata = _match_data_sets(xdata, ydata)
exdata = _match_error_to_data_set(xdata, exdata)
eydata = _match_error_to_data_set(ydata, eydata)
# check that the labels is a list of strings of the same length
if not _fun.is_iterable(label): label = [label]*len(xdata)
while len(label) < len(ydata): label.append(label[0])
# concatenate if necessary
if len(label) > legend_max:
label[legend_max-2] = '...'
for n in range(legend_max-1,len(label)-1): label[n] = "_nolegend_"
# clear the figure?
if clear and not axes: _pylab.gcf().clear() # axes cleared later
# setup axes
if axes=="gca" or axes is None: axes = _pylab.gca()
# if we're clearing the axes
if clear: axes.clear()
# set the current axes
_pylab.axes(axes)
# now loop over the list of data in xdata and ydata
for n in range(0,len(xdata)):
# get the label
if label[n]=='_nolegend_':
l = '_nolegend_'
else:
l = str(n)+": "+str(label[n])
# calculate the x an y progressive shifts
dx = xshift*(n/xshift_every)
dy = yshift*(n/yshift_every)
# if we're supposed to coarsen the data, do so.
x = _fun.coarsen_array(xdata[n], coarsen)
y = _fun.coarsen_array(ydata[n], coarsen)
ey = _fun.coarsen_array(eydata[n], coarsen, 'quadrature')
ex = _fun.coarsen_array(exdata[n], coarsen, 'quadrature')
# update the style
if not style is None: kwargs.update(next(style))
axes.errorbar(x+dx, y+dy, label=l, yerr=ey, xerr=ex, **kwargs)
_pylab.xscale(xscale)
_pylab.yscale(yscale)
if legend: axes.legend(loc=legend)
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
# for some arguments there should be no title.
if title in [None, False, 0]:
axes.set_title('')
# add the commands to the title
else:
title = str(title)
history = _fun.get_shell_history()
for n in range(0, min(shell_history, len(history))):
title = title + "\n" + history[n].split('\n')[0].strip()
title = title + '\nPlot created ' + _time.asctime()
axes.set_title(title)
if grid: _pylab.grid(True)
if autoformat:
_pt.format_figure(draw=False, modify_geometry=autoformat_window)
_pt.auto_zoom(axes=axes, draw=False)
# update the canvas
if draw:
_pylab.ion()
_pylab.draw()
_pylab.show()
return axes
def image_data(Z, X=[0,1.0], Y=[0,1.0], aspect=1.0, zmin=None, zmax=None, clear=1, clabel='z', autoformat=True, colormap="Last Used", shell_history=0, **kwargs):
"""
Generates an image plot.
Parameters
----------
Z
2-d array of z-values
X=[0,1.0], Y=[0,1.0]
1-d array of x-values (only the first and last element are used)
See matplotlib's imshow() for additional optional arguments.
"""
global _colormap
# Set interpolation to something more relevant for every day science
if not 'interpolation' in kwargs.keys(): kwargs['interpolation'] = 'nearest'
_pylab.ioff()
fig = _pylab.gcf()
if clear:
fig.clear()
_pylab.axes()
# generate the 3d axes
X = _n.array(X)
Y = _n.array(Y)
Z = _n.array(Z)
# assume X and Y are the bin centers and figure out the bin widths
x_width = abs(float(X[-1] - X[0])/(len(Z[0])-1))
y_width = abs(float(Y[-1] - Y[0])/(len(Z)-1))
# reverse the Z's
# Transpose and reverse
Z = Z.transpose()
Z = Z[-1::-1]
# get rid of the label and title kwargs
xlabel=''
ylabel=''
title =''
if 'xlabel' in kwargs: xlabel = kwargs.pop('xlabel')
if 'ylabel' in kwargs: ylabel = kwargs.pop('ylabel')
if 'title' in kwargs: title = kwargs.pop('title')
_pylab.imshow(Z, extent=[X[0]-x_width/2.0, X[-1]+x_width/2.0,
Y[0]-y_width/2.0, Y[-1]+y_width/2.0], **kwargs)
cb = _pylab.colorbar()
_pt.image_set_clim(zmin,zmax)
_pt.image_set_aspect(aspect)
cb.set_label(clabel)
a = _pylab.gca()
a.set_xlabel(xlabel)
a.set_ylabel(ylabel)
#_pt.close_sliders()
#_pt.image_sliders()
# title
history = _fun.get_shell_history()
for n in range(0, min(shell_history, len(history))):
title = title + "\n" + history[n].split('\n')[0].strip()
title = title + '\nPlot created ' + _time.asctime()
a.set_title(title.strip())
if autoformat: _pt.image_format_figure(fig)
_pylab.ion()
_pylab.show()
#_pt.raise_figure_window()
#_pt.raise_pyshell()
_pylab.draw()
# add the color sliders
if colormap:
if _colormap: _colormap.close()
_colormap = _pt.image_colormap(colormap, image=a.images[0])
def image_data(Z, X=[0,1.0], Y=[0,1.0], aspect=1.0, zmin=None, zmax=None, clear=1, clabel='z', autoformat=True, colormap="Last Used", shell_history=0, **kwargs):
"""
Generates an image plot.
Parameters
----------
Z
2-d array of z-values
X=[0,1.0], Y=[0,1.0]
1-d array of x-values (only the first and last element are used)
See matplotlib's imshow() for additional optional arguments.
"""
global _colormap
# Set interpolation to something more relevant for every day science
if not 'interpolation' in kwargs.keys(): kwargs['interpolation'] = 'nearest'
_pylab.ioff()
fig = _pylab.gcf()
if clear:
fig.clear()
_pylab.axes()
# generate the 3d axes
X = _n.array(X)
Y = _n.array(Y)
Z = _n.array(Z)
# assume X and Y are the bin centers and figure out the bin widths
x_width = abs(float(X[-1] - X[0])/(len(Z[0])-1))
y_width = abs(float(Y[-1] - Y[0])/(len(Z)-1))
# reverse the Z's
# Transpose and reverse
Z = Z.transpose()
Z = Z[-1::-1]
# get rid of the label and title kwargs
xlabel=''
ylabel=''
title =''
if 'xlabel' in kwargs: xlabel = kwargs.pop('xlabel')
if 'ylabel' in kwargs: ylabel = kwargs.pop('ylabel')
if 'title' in kwargs: title = kwargs.pop('title')
_pylab.imshow(Z, extent=[X[0]-x_width/2.0, X[-1]+x_width/2.0,
Y[0]-y_width/2.0, Y[-1]+y_width/2.0], **kwargs)
cb = _pylab.colorbar()
_pt.image_set_clim(zmin,zmax)
_pt.image_set_aspect(aspect)
cb.set_label(clabel)
a = _pylab.gca()
a.set_xlabel(xlabel)
a.set_ylabel(ylabel)
#_pt.close_sliders()
#_pt.image_sliders()
# title
history = _fun.get_shell_history()
for n in range(0, min(shell_history, len(history))):
title = title + "\n" + history[n].split('\n')[0].strip()
title = title + '\nPlot created ' + _time.asctime()
a.set_title(title.strip())
if autoformat: _pt.image_format_figure(fig)
_pylab.ion()
_pylab.show()
#_pt.raise_figure_window()
#_pt.raise_pyshell()
_pylab.draw()
# add the color sliders
if colormap:
if _colormap: _colormap.close()
_colormap = _pt.image_colormap(colormap, image=a.images[0])
