def test_find_intersections_invalid_direction():
"""Test exception if an invalid direction is given."""
x = np.linspace(5, 30, 17)
y1 = 3 * x**2
y2 = 100 * x - 650
with pytest.raises(ValueError):
find_intersections(x, y1, y2, direction='increaing')
def test_find_intersections_invalid_direction():
"""Test exception if an invalid direction is given."""
x = np.linspace(5, 30, 17)
y1 = 3 * x ** 2
y2 = 100 * x - 650
with pytest.raises(ValueError):
find_intersections(x, y1, y2, direction='increaing')
def test_find_intersections(direction, expected):
"""Test finding the intersection of two curves functionality."""
x = np.linspace(5, 30, 17)
y1 = 3 * x**2
y2 = 100 * x - 650
# Note: Truth is what we will get with this sampling, not the mathematical intersection
assert_array_almost_equal(expected, find_intersections(x, y1, y2, direction=direction), 2)
def test_find_intersections(direction, expected):
"""Test finding the intersection of two curves functionality."""
x = np.linspace(5, 30, 17)
y1 = 3 * x**2
y2 = 100 * x - 650
# Note: Truth is what we will get with this sampling, not the mathematical intersection
assert_array_almost_equal(expected, find_intersections(x, y1, y2, direction=direction), 2)
def test_find_intersections_no_intersections():
"""Test finding the intersection of two curves with no intersections."""
x = np.linspace(5, 30, 17)
y1 = 3 * x + 0
y2 = 5 * x + 5
# Note: Truth is what we will get with this sampling, not the mathematical intersection
truth = np.array([[], []])
assert_array_equal(truth, find_intersections(x, y1, y2))
def test_find_intersections_units():
"""Test handling of units when logarithmic interpolation is called."""
x = np.linspace(5, 30, 17) * units.hPa
y1 = 3 * x.m**2
y2 = 100 * x.m - 650
truth = np.array([24.43, 1794.54])
x_test, y_test = find_intersections(x, y1, y2, direction='increasing', log_x=True)
assert_array_almost_equal(truth, np.array([x_test.m, y_test.m]).flatten(), 2)
assert x_test.units == units.hPa
def test_find_intersections_no_intersections():
"""Test finding the intersection of two curves with no intersections."""
x = np.linspace(5, 30, 17)
y1 = 3 * x + 0
y2 = 5 * x + 5
# Note: Truth is what we will get with this sampling, not the mathematical intersection
truth = np.array([[],
[]])
assert_array_equal(truth, find_intersections(x, y1, y2))
def test_find_intersections_intersections_in_data_at_ends(direction, expected):
"""Test finding intersections when intersections are in the data.
Test data includes points of intersection, sequential points of intersection, intersection
at the ends of the data, and intersections in increasing/decreasing direction.
"""
x = np.arange(14)
y1 = np.array([0, 3, 2, 1, -1, 2, 2, 0, 1, 0, 0, -2, 2, 0])
y2 = np.zeros_like(y1)
assert_array_almost_equal(expected, find_intersections(x, y1, y2, direction=direction), 2)
def test_find_intersections_intersections_in_data_at_ends(direction, expected):
"""Test finding intersections when intersections are in the data.
Test data includes points of intersection, sequential points of intersection, intersection
at the ends of the data, and intersections in increasing/decreasing direction.
"""
x = np.arange(14)
y1 = np.array([0, 3, 2, 1, -1, 2, 2, 0, 1, 0, 0, -2, 2, 0])
y2 = np.zeros_like(y1)
assert_array_almost_equal(expected, find_intersections(x, y1, y2, direction=direction), 2)
def test_find_intersections():
"""Test finding the intersection of two curves functionality."""
x = np.linspace(5, 30, 17)
y1 = 3 * x**2
y2 = 100 * x - 650
# Truth is what we will get with this sampling,
# not the mathematical intersection
truth = np.array([[8.88, 24.44], [238.84, 1794.53]])
assert_array_almost_equal(truth, find_intersections(x, y1, y2), 2)
def test_find_intersections():
"""Test finding the intersection of two curves functionality."""
x = np.linspace(5, 30, 17)
y1 = 3 * x**2
y2 = 100 * x - 650
# Truth is what we will get with this sampling,
# not the mathematical intersection
truth = np.array([[8.88, 24.44],
[238.84, 1794.53]])
assert_array_almost_equal(truth, find_intersections(x, y1, y2), 2)
def _find_append_zero_crossings(x, y):
r"""
Find and interpolate zero crossings.
Estimate the zero crossings of an x,y series and add estimated crossings to series,
returning a sorted array with no duplicate values.
Parameters
----------
x : `pint.Quantity`
x values of data
y : `pint.Quantity`
y values of data
Returns
-------
x : `pint.Quantity`
x values of data
y : `pint.Quantity`
y values of data
"""
# Find and append crossings to the data
crossings = find_intersections(x[1:], y[1:],
np.zeros_like(y[1:]) * y.units)
print(crossings)
x = concatenate((x, crossings[0]))
y = concatenate((y, crossings[1]))
# Resort so that data are in order
sort_idx = np.argsort(x)
x = x[sort_idx]
y = y[sort_idx]
# Remove duplicate data points if there are any
keep_idx = np.ediff1d(x, to_end=[1]) > 0
x = x[keep_idx]
y = y[keep_idx]
return x, y
