def test_random_beta():
numpy.random.seed(0)
x1 = activity.random('beta', alpha=2, beta=3, shape=(5, 2))
numpy.random.seed(0)
x2 = numpy.random.beta(2, 3, size=(5, 2))
assert numpy.all((x1 - x2) < 0.0001)
def test_random_normal():
numpy.random.seed(0)
x1 = activity.random('normal', mu=0, sigma=1, shape=(3, 4))
numpy.random.seed(0)
x2 = numpy.random.normal(0, 1, size=(3, 4))
assert numpy.all((x1 - x2) < 0.0001)
def test_random_normal():
numpy.random.seed(0)
x1 = activity.random('normal', mu=0, sigma=1, shape=(3, 4))
numpy.random.seed(0)
x2 = numpy.random.normal(0, 1, size=(3, 4))
assert numpy.all((x1 - x2) < 0.0001)
def test_random_beta():
numpy.random.seed(0)
x1 = activity.random('beta', alpha=2, beta=3, shape=(5, 2))
numpy.random.seed(0)
x2 = numpy.random.beta(2, 3, size=(5, 2))
assert numpy.all((x1 - x2) < 0.0001)
def test_beta(self):
numpy.random.seed(0)
x1 = activity.random('beta', alpha=2, beta=3, shape=(5, 2))
numpy.random.seed(0)
x2 = numpy.random.beta(2, 3, size=(5, 2))
nptest.assert_array_almost_equal(x1, x2)
def test_normal(self):
numpy.random.seed(0)
x1 = activity.random('normal', mu=0, sigma=1, shape=(3, 4))
numpy.random.seed(0)
x2 = numpy.random.normal(0, 1, size=(3, 4))
nptest.assert_array_almost_equal(x1, x2)
def test_plot_fit():
# second
fig2, ax2 = pyplot.subplots()
norm_dist = dist.normal(μ=5.4, σ=2.5)
data = activity.random('normal', μ=5.4, σ=2.5, shape=37)
ax2 = activity.plot(norm_dist, ax=ax2, line_opts=dict(label='Theoretical PDF'))
ax2 = activity.plot('normal', data=data, ax=ax2, line_opts=dict(label='Fit PDF'))
ax2.legend()
def test_distplot_lognormal():
# fourth
fig4, ax4 = pyplot.subplots()
data = activity.random('lognormal', μ=0.75, σ=1.2, shape=125)
logdata = numpy.log10(data)
bins = numpy.logspace(logdata.min(), logdata.max(), num=30)
distplot_opts = dict(rug=True, kde=False, norm_hist=True, bins=bins)
line_opts = dict(color='firebrick', lw=3.5, label='Fit PDF')
ax4 = activity.plot('lognormal', data=data, distplot=True,
xscale='log', pad=0.01, ax=ax4,
line_opts=line_opts,
distplot_opts=distplot_opts)
def test_plot_pdf_fit():
# second
fig2, ax2 = pyplot.subplots()
norm_dist = dist.normal(μ=5.4, σ=2.5)
data = activity.random('normal', μ=5.4, σ=2.5, shape=37)
ax2 = activity.plot(norm_dist,
ax=ax2,
line_opts=dict(label='Theoretical PDF'))
ax2 = activity.plot('normal',
data=data,
ax=ax2,
line_opts=dict(label='Fit PDF'))
ax2.legend()
return fig2
def test_plot_distplot():
# third
fig3, ax3 = pyplot.subplots()
data = activity.random('normal', μ=5.4, σ=2.5, shape=37)
ax3 = activity.plot('normal', data=data, distplot=True, ax=ax3)
ax3.legend(loc='upper left')
