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 execute_script(self, script, g={}):
"""
Runs a script, returning the result.
Scripts are of the form:
"3.0 + x/y - self[0] where x=3.0*c('my_column')+h('setting'); y=c(1)"
"self" refers to the data object, giving access to everything, enabling
complete control over the universe. c() and h() give quick reference
to self.c() and self.h() to get columns and header lines
Additionally, these scripts can see all of the numpy functions like sin,
cos, sqrt, etc.
Finally, if you would like access to additional globals, set
self.extra_globals to the appropriate globals dictionary or add globals
using insert_global(). Setting g=globals() will automatically insert
your globals into this databox instance.
There are a few shorthand scripts available as well. You can simply type
a column name such as "my_column" or a column number like 2. However, I
only added this functionality as a shortcut, and something like
"2.0*a where a=F" will not work unless F is defined somehow. I figure
since you're already writing a complicated script, you don't want to
accidentally shortcut your way into using a column instead of a constant!
Use "2.0*a where a=c('F')" instead.
NOTE: You shouldn't try to use variables like 'c=...' or 'h=...' because
they are already column and header functions!
"""
if self.debug: print "Generating column '"+str(name)+"' = "+str(script)+"..."
# add any extra user-supplied global variables for the eventual eval() call.
self.extra_globals.update(g)
g = {} # clear out the existing dictionary
# If the script is not a list of scripts, return the script value.
# This is the termination of a recursive call.
if not _fun.is_iterable(script):
if script == None: return None
# get the expression and variables
[expression, v] = self._parse_script(script)
# if there was a problem parsing the script
if v == None:
print "ERROR: Could not parse '"+script+"'"
return None
# otherwise, evaluate the script using python's eval command
return eval(expression, v)
# Otherwise, this is a list of scripts. Make the recursive call.
output = []
for s in script: output.append(self.execute_script(s))
return output
def _match_data_sets(x,y):
"""
Makes sure everything is the same shape. "Intelligently".
"""
# 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]]
if _fun.is_iterable(y[0]):
# make an array of arrays to match
x = []
for n in range(len(y)):
x.append(list(range(len(y[n]))))
else: x = list(range(len(y)))
if y is None or len(y) == 0:
# If y is none, x can be either [1,2] or [[1,2],[1,2]]
if _fun.is_iterable(x[0]):
# make an array of arrays to match
y = []
for n in range(len(x)):
y.append(list(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 _match_data_sets(x, y):
"""
Makes sure everything is the same shape. "Intelligently".
"""
# Handle the None for x or y
if x is None:
# If x is none, y can be either [1,2] or [[1,2],[1,2]]
if _fun.is_iterable(y[0]):
# make an array of arrays to match
x = []
for n in range(len(y)):
x.append(list(range(len(y[n]))))
else:
x = list(range(len(y)))
if y is None:
# If x is none, y can be either [1,2] or [[1,2],[1,2]]
if _fun.is_iterable(x[0]):
# make an array of arrays to match
y = []
for n in range(len(x)):
y.append(list(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) and _fun.elements_are_numbers(y):
x = [x]
y = [y]
# 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 _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 databoxes(ds,
xscript=0,
yscript=1,
eyscript=None,
exscript=None,
plotter=xy_data,
**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
**kwargs are sent to plotter()
"""
if not _fun.is_iterable(ds): ds = [ds]
if not kwargs.has_key('xlabel'): kwargs['xlabel'] = str(xscript)
if not kwargs.has_key('ylabel'): 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] == None and yscript[n] == None:
print "Two None scripts? But why?"
return
if xscript[n] == None:
if type(yscript[n]) == str:
xscript[n] = 'range(len(' + yscript[n] + '))'
else:
xscript[n] = 'range(len(c(' + str(yscript[n]) + ')))'
if yscript[n] == 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 = []
for d in ds:
xdata = d(xscript)
for n in range(len(xdata)):
xdatas.append(xdata[n])
if len(xdata) > 1:
labels.append(str(n) + ": " + _os.path.split(d.path)[-1])
else:
labels.append(_os.path.split(d.path)[-1])
for y in d(yscript):
ydatas.append(y)
for x in d(exscript):
exdatas.append(x)
for y in d(eyscript):
eydatas.append(y)
if kwargs.has_key("label"): labels = kwargs.pop("label")
plotter(xdatas, ydatas, eydatas, exdatas, label=labels, **kwargs)
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 databoxes(ds, xscript=0, yscript=1, eyscript=None, exscript=None, plotter=xy_data, **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
**kwargs are sent to plotter()
"""
if not _fun.is_iterable(ds): ds = [ds]
if not kwargs.has_key('xlabel'): kwargs['xlabel'] = str(xscript)
if not kwargs.has_key('ylabel'): 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 = []
for d in ds:
xdata = d(xscript)
for n in range(len(xdata)):
xdatas.append(xdata[n])
if len(xdata)>1: labels.append(str(n)+": "+_os.path.split(d.path)[-1])
else: labels.append(_os.path.split(d.path)[-1])
for y in d(yscript): ydatas.append(y)
for x in d(exscript): exdatas.append(x)
for y in d(eyscript): eydatas.append(y)
if kwargs.has_key("label"): labels = kwargs.pop("label")
plotter(xdatas, ydatas, eydatas, exdatas, label=labels, **kwargs)
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 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])
if len(xdata) > 1:
labels.append(str(n) + ": " + _os.path.split(d.path)[-1])
else:
labels.append(_os.path.split(d.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)
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])
if len(xdata)>1: labels.append(str(n)+": "+_os.path.split(d.path)[-1])
else: labels.append(_os.path.split(d.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)
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 _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 execute_script(self, script, g={}):
"""
Runs a script, returning the result.
Scripts are of the form:
"3.0 + x/y - self[0] where x=3.0*c('my_column')+h('setting'); y=c(1)"
"self" refers to the data object, giving access to everything, enabling
complete control over the universe. c() and h() give quick reference
to self.c() and self.h() to get columns and header lines
Additionally, these scripts can see all of the numpy functions like sin,
cos, sqrt, etc.
Finally, if you would like access to additional globals, set
self.extra_globals to the appropriate globals dictionary or add globals
using insert_global(). Setting g=globals() will automatically insert
your globals into this databox instance.
There are a few shorthand scripts available as well. You can simply type
a column name such as "my_column" or a column number like 2. However, I
only added this functionality as a shortcut, and something like
"2.0*a where a=F" will not work unless F is defined somehow. I figure
since you're already writing a complicated script, you don't want to
accidentally shortcut your way into using a column instead of a constant!
Use "2.0*a where a=c('F')" instead.
NOTE: You shouldn't try to use variables like 'c=...' or 'h=...' because
they are already column and header functions!
"""
if self.debug:
print "Generating column '" + str(name) + "' = " + str(
script) + "..."
# add any extra user-supplied global variables for the eventual eval() call.
self.extra_globals.update(g)
g = {} # clear out the existing dictionary
# If the script is not a list of scripts, return the script value.
# This is the termination of a recursive call.
if not _fun.is_iterable(script):
if script == None: return None
# get the expression and variables
[expression, v] = self._parse_script(script)
# if there was a problem parsing the script
if v == None:
print "ERROR: Could not parse '" + script + "'"
return None
# otherwise, evaluate the script using python's eval command
return eval(expression, v)
# Otherwise, this is a list of scripts. Make the recursive call.
output = []
for s in script:
output.append(self.execute_script(s))
return output
