def test_axes_plot_masked_nan():
x = numpy.linspace(0, 2 * numpy.pi, 51)
y = numpy.ma.column_stack((
1 + 0.5 * numpy.sin(x),
1 + 0.5 * numpy.cos(x),
1 + 0.2 * numpy.sin(2 * x),
))
y[8:18, 0] = numpy.nan
y[33:43, 1] = numpy.ma.masked
canvas, axes, mark = toyplot.plot(x, y, marker="o")
assert_canvas_matches(canvas, "axes-plot-masked-nan")
def test_axes_plot_masked_nan():
x = numpy.linspace(0, 2 * numpy.pi, 51)
y = numpy.ma.column_stack((
1 + 0.5 * numpy.sin(x),
1 + 0.5 * numpy.cos(x),
1 + 0.2 * numpy.sin(2 * x),
))
y[8:18, 0] = numpy.nan
y[33:43, 1] = numpy.ma.masked
canvas, axes, mark = toyplot.plot(x, y, marker="o")
assert_canvas_matches(canvas, "axes-plot-masked-nan")
def genome_graph(self):
"""
Graphs rolling average of likelihoods along the linear genome, identifies
regions that deviate significantly from null expectations
TO DO: change color of outliers
"""
self.likes['rollingav'] = self.likes[0].rolling(
50, win_type='triang').mean()
a, b, c = toyplot.plot(
self.likes['rollingav'],
width=500,
height=500,
color='blue',
)
b.hlines(self.high_lik, style={
"stroke": "red",
"stroke-width": 2
})
def step_impl(context):
canvas, axes, mark = toyplot.plot(numpy.linspace(0, 1) ** 2, style={"stroke-dasharray":"5,5"})
testing.assert_canvas_equal(canvas, "style-stroke-dasharray")
def step_impl(context):
canvas, axes, mark = toyplot.plot(numpy.linspace(0, 1)**2,
style={"stroke-dasharray": "5,5"})
testing.assert_canvas_equal(canvas, "style-stroke-dasharray")