def test_linear_squared():
fig, ax = plt.subplots(1, 1)
linear(method1, method2, square=True, ax=ax)
return fig
18.09,
19.13,
19.54,
]
CI = 0.95
x_label = "$M_1$"
y_label = "$M_2$"
# Make subplots
fig, axs = plt.subplots(1, 2, figsize=(10, 5))
# Legacy method
linear(
method1,
method2,
CI=CI,
ax=axs[0],
square=True,
x_label=x_label,
y_label=y_label,
title="Linear: Legacy",
)
# Regressor method - preferred approach
Linear(method1, method2, CI=CI).plot(ax=axs[1],
square=True,
x_label=x_label,
y_label=y_label,
title="Linear: Regressor")
plt.show()
def test_linear_basic_title():
fig, ax = plt.subplots(1, 1)
linear(method1, method2, title='Test', ax=ax)
return fig
def test_linear_with_ci():
fig, ax = plt.subplots(1, 1)
linear(method1, method2, line_CI=True, ax=ax)
return fig
def test_linear_basic():
fig, ax = plt.subplots(1, 1)
linear(method1, method2, ax=ax)
return fig
from methcomp import linear
import matplotlib.pyplot as plt
method1 = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
]
method2 = [
1.03, 2.05, 2.79, 3.67, 5.00, 5.82, 7.16, 7.69, 8.53, 10.38, 11.11, 12.17,
13.47, 13.83, 15.15, 16.12, 16.94, 18.09, 19.13, 19.54
]
linear(method1, method2, CI=.95)
plt.show()
def test_linear_squared():
fig, ax = plt.subplots(1, 1)
with pytest.deprecated_call():
linear(method1, method2, square=True, ax=ax)
return fig
def test_linear_with_ci():
fig, ax = plt.subplots(1, 1)
with pytest.deprecated_call():
linear(method1, method2, line_CI=True, ax=ax)
return fig
def test_linear_basic_title():
fig, ax = plt.subplots(1, 1)
with pytest.deprecated_call():
linear(method1, method2, title="Test", ax=ax)
return fig