def test_plot_scatter1():
plt.close()
plt.clf()
x = isopy.random(20, seed=46)
y = x * 3 + isopy.random(20, seed=47)
isopy.tb.plot_scatter(plt, x, y)
return plt
def test_plot_hstack1():
plt.close()
plt.clf()
array1 = isopy.random(100, -0.5, seed=46)
array2 = isopy.random(100, 0.5, seed=47)
isopy.tb.plot_hstack(plt, array1)
isopy.tb.plot_hstack(plt, array2, cval=np.mean, pmval=isopy.sd2)
return plt
def test_plot_vcompare2():
plt.close()
plt.clf()
array1 = isopy.random(100, -0.5, seed=46)
array2 = isopy.random(100, 0.5, seed=47)
isopy.tb.plot_vstack(plt, array1, cval=np.mean, pmval=isopy.sd2)
isopy.tb.plot_vstack(plt, array2, cval=np.mean, pmval=isopy.sd2)
isopy.tb.plot_vcompare(plt, pmval=isopy.sd, sigfig=3)
return plt
def test_plot_hcompare1():
plt.close()
plt.clf()
array1 = isopy.random(100, 0.9, seed=46)
array2 = isopy.random(100, 1.1, seed=47)
isopy.tb.plot_hstack(plt, array1, cval=np.mean, pmval=isopy.sd2)
isopy.tb.plot_hstack(plt, array2, cval=np.mean, pmval=isopy.sd2)
isopy.tb.plot_hcompare(plt)
return plt
def test_plot_regression2():
plt.close()
plt.clf()
x = isopy.random(20, seed=46)
y = x * 3 + isopy.random(20, seed=47)
regression = lambda x: 2 * x + x**2 # Any callable that takes x and return y is a valid
isopy.tb.plot_scatter(plt, x, y, color='red')
isopy.tb.plot_regression(plt, regression, color='red', xlim=(-1, 1))
return plt
def test_plot_regression1():
plt.close()
plt.clf()
x = isopy.random(20, seed=46)
y = x * 3 + isopy.random(20, seed=47)
regression = isopy.tb.regression_linear(x, y)
isopy.tb.plot_scatter(plt, x, y)
isopy.tb.plot_regression(plt, regression)
return plt
def test_plot_vcompare4():
plt.close()
plt.clf()
keys = isopy.keylist('pd105', 'ru101', 'cd111')
array1 = isopy.random(100, 0.9, keys, seed=46)
array2 = isopy.random(100, 1.1, keys, seed=47)
isopy.tb.plot_vstack(plt, array1)
isopy.tb.plot_vstack(plt, array2, cval=np.mean, pmval=isopy.sd2)
isopy.tb.plot_vcompare(plt.gcf(), combine_ticklabels=True)
return plt
def test_plot_hstack2():
plt.close()
plt.clf()
keys = isopy.keylist('pd105', 'ru101', 'cd111')
array1 = isopy.random(100, -0.5, keys, seed=46)
array2 = isopy.random(100, 0.5, keys, seed=47)
isopy.tb.create_subplots(plt, keys.sorted(), (-1, 1))
isopy.tb.plot_hstack(plt, array1)
isopy.tb.plot_hstack(plt, array2, cval=np.mean, pmval=isopy.sd2)
return plt
def test_plot_regression3():
plt.close()
plt.clf()
x = isopy.random(20, seed=46)
y = x * 3 + isopy.random(20, seed=47)
xerr = 0.2
yerr = isopy.random(20, seed=48)
regression = isopy.tb.regression_york1(x, y, xerr, yerr)
isopy.tb.plot_scatter(plt, x, y, xerr, yerr)
isopy.tb.plot_regression(plt, regression)
return plt
def test_plot_regression4():
plt.close()
plt.clf()
x = isopy.random(20, seed=46)
y = x * 3 + isopy.random(20, seed=47)
xerr = 0.2
yerr = isopy.random(20, seed=48)
regression = isopy.tb.regression_york1(x, y, xerr, yerr)
isopy.tb.plot_scatter(plt, x, y, xerr, yerr, color='red')
isopy.tb.plot_regression(plt,
regression,
color='red',
line='dashed',
edgeline=False)
return plt
def test_plot_vcompare3():
plt.close()
plt.clf()
pmunits = ['diff', 'abs', '%', 'ppt', 'ppm', 'ppb']
subplots = isopy.tb.create_subplots(plt, pmunits, (1, -1), figure_width=8)
array1 = isopy.random(100, 0.9, seed=46)
array2 = isopy.random(100, 1.1, seed=47)
for unit, axes in subplots.items():
isopy.tb.plot_vstack(axes, array1, cval=np.mean, pmval=isopy.sd2)
isopy.tb.plot_vstack(axes, array2, cval=np.mean, pmval=isopy.sd2)
isopy.tb.plot_vcompare(axes,
pmval=isopy.sd,
pmunit=unit,
combine_ticklabels=True)
axes.set_xlabel(f'pmunit="{unit}"')
return plt
def test_plot_scatter2():
plt.close()
plt.clf()
x = isopy.random(20, seed=46)
y = x * 3 + isopy.random(20, seed=47)
xerr = 0.2
yerr = isopy.random(20, seed=48)
isopy.tb.plot_scatter(plt,
x,
y,
xerr,
yerr,
regression='york1',
color='red',
marker='s')
return plt
def test_plot_hstack3():
plt.close()
plt.clf()
keys = isopy.keylist('pd105', 'ru101', 'cd111')
array = isopy.random(100, -0.5, keys, seed=46)
mean = np.mean(array)
sd = isopy.sd(array)
outliers = isopy.tb.find_outliers(array, mean, sd)
isopy.tb.create_subplots(plt, keys.sorted(), (-1, 1))
isopy.tb.plot_hstack(plt,
array,
cval=mean,
pmval=sd,
outliers=outliers,
color=('red', 'pink'))
return plt
def test_create_legend1():
plt.close()
plt.clf()
data = isopy.random(100, keys='ru pd cd'.split(), seed=46)
axes = isopy.tb.create_subplots(plt, [['left', 'right']], figure_width=8)
isopy.tb.plot_scatter(axes['left'],
data['pd'],
data['ru'],
label='ru/pd',
color='red')
isopy.tb.plot_scatter(axes['right'],
data['pd'],
data['cd'],
label='cd/pd',
color='blue')
isopy.tb.create_legend(axes['right'], axes['left'])
return plt
def test_create_legend2():
plt.close()
plt.clf()
data = isopy.random(100, keys='ru pd cd'.split(), seed=46)
axes = isopy.tb.create_subplots(plt, [['left', 'right', 'legend']],
figure_width=9,
gridspec_width_ratios=[4, 4, 1])
isopy.tb.plot_scatter(axes['left'],
data['pd'],
data['ru'],
label='ru/pd',
color='red')
isopy.tb.plot_scatter(axes['right'],
data['pd'],
data['cd'],
label='cd/pd',
color='blue')
isopy.tb.create_legend(axes['legend'], axes, hide_axis=True)
return plt
