#-----------------------------------------------------------------------
# imports
#-----------------------------------------------------------------------
from hep.draw import postscript
from hep.draw import xwindow
import hep.hist.function
import hep.hist.plot
import sys
#-----------------------------------------------------------------------
# test
#-----------------------------------------------------------------------
fn = hep.hist.function.Function1D("sin(x) / x")
plot = hep.hist.plot.Plot(1, x_axis_range=(-12, 12), overflows=False)
plot.append(fn)
postscript.EPSFile("plotfn2.eps", (0.20, 0.13)).render(plot)
window = xwindow.FigureWindow(plot)
if len(sys.argv) > 1:
raw_input("hit enter to end> ")
def iplot(*data_args, **kw_args):
"""Plot with the current style.
Sets 'ifig' to the produced 'Plot' object.
'*data_args' -- The data to plot. Pass '1' or '2' to make an empty
one- or two-dimensional plot.
'normalize' (keyword argument) -- Normalize histograms to this
value.
'statistics' (keyword argument) -- If provided, display statistics.
The value is a list of statistic names.
'**kw_args' -- Additional style attributes with which to override
the current style. The style is applied to the new plot object."""
global ifig
global istyle
global iwin
if len(data_args) == 0:
# Nothing to do.
return
# If there is a single numerical positional argument, treat it as
# the number of dimensions for an empty plot.
if len(data_args) == 1 and isinstance(data_args[0], int):
dimensions = data_args[0]
if dimensions not in (1, 2):
raise ValueError, \
"%d-dimensional plots not supported" % dimensions
series_data = []
# Otherwise, interpret positional arguments as data items to plot.
else:
# Perform some convenience conversions of data objects.
series_data = [ _convertDataForPlot(d, **kw_args)
for d in data_args ]
dimensions = series_data[0].dimensions
# Make sure all have the same number of dimensions.
for data in series_data[1 :]:
if data.dimensions != dimensions:
raise ValueError, "data have different dimensionality"
# Make sure we have a window to draw to.
__setupWindow()
# Construct a style, starting with the default style and adding any
# unused keyword arguments.
statistics = hep.popkey(kw_args, "statistics", None)
style = dict(istyle)
style.update(kw_args)
# Make the new plot.
plot = hep.hist.plot.Plot(dimensions, **style)
# Add in the data.
for data in series_data:
plot.append(data)
# Show the plot.
ishow(plot)
if statistics is not None:
ifig.append(hep.hist.plot.Statistics(statistics, *series_data))
66, 58, 59, 44, 53, 39, 38, 27, 18, 13, 7, 5, 3,
2, 0, 0, 0, 0]):
histogram.setBinContent(b, v - 30)
histogram.setBinContent("underflow", -15)
layout = GridLayout(3, 3, border=0.01)
x_ranges = ((-1, 2), (-2, 0), (0, 1))
y_ranges = ((-30, 70), (-80, -5), (10, 50))
for x in range(3):
for y in range(3):
plot = hep.hist.plot.Plot(
1,
overflows=False,
x_axis_range=x_ranges[x],
y_axis_range=y_ranges[y])
plot.append(histogram,
bins="skyline",
errors=True,
fill_color=Gray(0.7),
line_color=None)
layout[x, y] = plot
window = hep.draw.xwindow.FigureWindow(layout, (0.3, 0.2))
if len(sys.argv) > 1:
raw_input("hit enter to end: ")
hep.draw.postscript.EPSFile("plot1d3.eps", window.size).render(layout)
#-----------------------------------------------------------------------
# tests
#-----------------------------------------------------------------------
histogram = hep.hist.Histogram(
(10, (0.0, 1.0), "X"), (20, (-1.0, 1.0), "Y"),
bin_type=float)
for i in range(10000):
# Generate a flat distribution.
x = random()
y = random() * 2 - 1
w = random() * 1.5 - x
histogram.accumulate((x, y), w)
layout = GridLayout(2, 1)
plot = hep.hist.plot.Plot(2, overflows=False)
plot.append(histogram)
layout[0, 0] = plot
plot = hep.hist.plot.Plot(2, overflows=False, z_range=(-10, 20))
plot.append(histogram)
layout[1, 0] = plot
hep.draw.postscript.EPSFile("plot6.eps", (0.23, 0.1)).render(layout)
window = hep.draw.xwindow.FigureWindow(layout, (0.23, 0.1))
if len(sys.argv) > 1:
raw_input("hit enter to end: ")
float, name="$m_{ES}$", units="GeV/$c^2$")
y_axis = hep.hist.UnevenlyBinnedAxis(
(-1, -0.5, 0, 0.1, 0.2, 0.3, 0.4, 0.6, 0.8, 1.0),
float, name=r"$\Delta E$", units="GeV")
histogram = hep.hist.Histogram(x_axis, y_axis, title="Test Histogram")
for i in range(10000):
# Generate a flat distribution.
m = random() * 5
e = random() * 2 - 1
histogram << (m, e)
layout = GridLayout(2, 2)
plot = hep.hist.plot.Plot(2, overflows=False)
plot.append(histogram, normalize_bin_size=None)
layout[0, 0] = plot
plot = hep.hist.plot.Plot(2, overflows=False)
plot.append(histogram)
layout[1, 0] = plot
plot = hep.hist.plot.Plot(2, overflows=False)
plot.append(histogram, bins="box", normalize_bin_size=None)
layout[0, 1] = plot
plot = hep.hist.plot.Plot(2, bins="box", overflows=False)
plot.append(histogram)
layout[1, 1] = plot
window = hep.draw.xwindow.FigureWindow(layout, (0.2, 0.2))
if len(sys.argv) > 1:
