def _match_error_to_data_set(x, ex):
"""
Inflates ex to match the dimensionality of x, "intelligently".
x is assumed to be a 2D array.
"""
# Simplest case, ex is None or a number
if not _fun.is_iterable(ex):
# Just make a matched list of Nones
if ex is None: ex = [ex] * len(x)
# Make arrays of numbers
if _fun.is_a_number(ex):
value = ex # temporary storage
ex = []
for n in range(len(x)):
ex.append([value] * len(x[n]))
# Otherwise, ex is iterable
# Default behavior: If the elements are all numbers and the length matches
# that of the first x-array, assume this is meant to match all the x
# data sets
if _fun.elements_are_numbers(ex) and len(ex) == len(x[0]):
ex = [ex] * len(x)
# The user may specify a list of some iterable and some not. Assume
# in this case that at least the lists are the same length
for n in range(len(x)):
# do nothing to the None's
# Inflate single numbers to match
if _fun.is_a_number(ex[n]): ex[n] = [ex[n]] * len(x[n])
return ex
def _match_error_to_data_set(x, ex):
"""
Inflates ex to match the dimensionality of x, "intelligently".
x is assumed to be a 2D array.
"""
# Simplest case, ex is None or a number
if not _fun.is_iterable(ex):
# Just make a matched list of Nones
if ex is None: ex = [ex]*len(x)
# Make arrays of numbers
if _fun.is_a_number(ex):
value = ex # temporary storage
ex = []
for n in range(len(x)):
ex.append([value]*len(x[n]))
# At this point, ex is iterable
# Default behavior: If the elements are all numbers and the length matches
# that of the first x-array, assume this is meant to match all the x
# data sets
if _fun.elements_are_numbers(ex) and len(ex) == len(x[0]): ex = [ex]*len(x)
# Make sure it's a list (for appending)
if not type(ex) == list: ex = list(ex)
# The user may specify a list of some iterable and some not.
# The list length may not match
while len(ex) < len(x): ex.append(ex[-1])
# Now they are the same length
for n in range(len(x)):
# do nothing to the None's
# Inflate single numbers to match
if _fun.is_a_number(ex[n]): ex[n] = [ex[n]]*len(x[n])
return ex
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 _match_data_sets(x,y):
"""
Makes sure everything is the same shape. "Intelligently".
"""
# If x is a value, use this for the step size.
dx = 1.0
if _fun.is_a_number(x):
dx = x
x = None
# Handle the None for x or y
if x is None or len(x) == 0:
# If x is None, y can be either [1,2] or [[1,2],[1,2]] or []
if len(y) == 0: x = []
elif _fun.is_iterable(y[0]):
# make an array of arrays to match
x = []
for n in range(len(y)):
x.append(list(dx*_n.array(range(len(y[n])))))
else: x = list(range(len(y)))
# If y is a value, use this for the step size.
dy = 1.0
if _fun.is_a_number(y):
dy = y
y = None
if y is None or len(y) == 0:
# If y is none, x can be either [1,2] or [[1,2],[1,2]] or []
if len(x) == 0: y = []
elif _fun.is_iterable(x[0]):
# make an array of arrays to match
y = []
for n in range(len(x)):
y.append(list(dy*_n.array(range(len(x[n])))))
else: y = list(range(len(x)))
# At this point they should be matched, but may still be 1D
# Default behavior: if all elements are numbers in both, assume they match
if _fun.elements_are_numbers(x): x=[x]
if _fun.elements_are_numbers(y): y=[y]
# Make sure they are both lists (so append works!)
if not type(x) == list: x = list(x)
if not type(y) == list: y = list(y)
# Make sure they're the same length
while len(x) > len(y): y.append(y[-1])
while len(y) > len(x): x.append(x[-1])
# Second default behavior: shared array [1,2,3], [[1,2,1],[1,2,1]] or vis versa
if _fun.elements_are_numbers(x) and not _fun.elements_are_numbers(y): x = [x]*len(y)
if _fun.elements_are_numbers(y) and not _fun.elements_are_numbers(x): y = [y]*len(x)
# Clean up any remaining Nones
for n in range(len(x)):
if x[n] is None: x[n] = list(range(len(y[n])))
if y[n] is None: y[n] = list(range(len(x[n])))
return x, y
def databoxes(ds,
xscript=0,
yscript=1,
eyscript=None,
exscript=None,
g=None,
plotter=xy_data,
transpose=False,
**kwargs):
"""
Plots the listed databox objects with the specified scripts.
ds list of databoxes
xscript script for x data
yscript script for y data
eyscript script for y error
exscript script for x error
plotter function used to do the plotting
transpose applies databox.transpose() prior to plotting
g optional dictionary of globals for the supplied scripts
**kwargs are sent to plotter()
"""
if not _fun.is_iterable(ds): ds = [ds]
if 'xlabel' not in kwargs: kwargs['xlabel'] = str(xscript)
if 'ylabel' not in kwargs: kwargs['ylabel'] = str(yscript)
# First make sure everything is a list of scripts (or None's)
if not _fun.is_iterable(xscript): xscript = [xscript]
if not _fun.is_iterable(yscript): yscript = [yscript]
if not _fun.is_iterable(exscript): exscript = [exscript]
if not _fun.is_iterable(eyscript): eyscript = [eyscript]
# make sure exscript matches shape with xscript (and the same for y)
if len(exscript) < len(xscript):
for n in range(len(xscript) - 1):
exscript.append(exscript[0])
if len(eyscript) < len(yscript):
for n in range(len(yscript) - 1):
eyscript.append(eyscript[0])
# Make xscript and exscript match in shape with yscript and eyscript
if len(xscript) < len(yscript):
for n in range(len(yscript) - 1):
xscript.append(xscript[0])
exscript.append(exscript[0])
# check for the reverse possibility
if len(yscript) < len(xscript):
for n in range(len(xscript) - 1):
yscript.append(yscript[0])
eyscript.append(eyscript[0])
# now check for None's (counting scripts)
for n in range(len(xscript)):
if xscript[n] is None and yscript[n] is None:
print("Two None scripts? But... why?")
return
if xscript[n] is None:
if type(yscript[n]) == str:
xscript[n] = 'range(len(' + yscript[n] + '))'
else:
xscript[n] = 'range(len(c(' + str(yscript[n]) + ')))'
if yscript[n] is None:
if type(xscript[n]) == str:
yscript[n] = 'range(len(' + xscript[n] + '))'
else:
yscript[n] = 'range(len(c(' + str(xscript[n]) + ')))'
xdatas = []
ydatas = []
exdatas = []
eydatas = []
labels = []
# Loop over all the data boxes
for i in range(len(ds)):
# Reset the default globals
all_globals = dict(n=i, m=len(ds) - 1 - i)
# Update them with the user-specified globals
if not g == None: all_globals.update(g)
# For ease of coding
d = ds[i]
# Take the transpose if necessary
if transpose: d = d.transpose()
# Generate the x-data; returns a list of outputs, one for each xscript
xdata = d(xscript, all_globals)
# Loop over each xdata, appending to the master list, and generating a label
for n in range(len(xdata)):
xdatas.append(xdata[n])
# Normal data set is a 1d array
labels.append(_os.path.split(d.path)[-1])
# Special case: single-point per file
if _fun.is_a_number(xdata[0]) == 1 and len(ds) > 1:
labels = [
_os.path.split(ds[0].path)[-1] + ' - ' +
_os.path.split(ds[-1].path)[-1]
]
# Append the other data sets to their master lists
for y in d(yscript, all_globals):
ydatas.append(y)
for x in d(exscript, all_globals):
exdatas.append(x)
for y in d(eyscript, all_globals):
eydatas.append(y)
if "label" in kwargs: labels = kwargs.pop("label")
plotter(xdatas, ydatas, eydatas, exdatas, label=labels, **kwargs)