def test_xprob_yprob(self):
z2, _y = stats.probplot(self.y, fit=False)
p2 = stats.norm.cdf(z2) * 100
scales = {'fitlogs': None, 'fitprobs': 'both'}
x, y = self.probs, p2,
x_, y_, res = viz._fit_line(x, y, **scales)
nptest.assert_array_almost_equal(y_, self.known_y_probprob)
nt.assert_true(isinstance(res, np.ndarray))
def test_custom_xhat(self):
x, y = self.zscores, self.data
x_, y_, res = viz._fit_line(x, y, xhat=self.custom_xhat)
nptest.assert_array_almost_equal(y_, self.known_custom_yhat)
def test_bad_fitprobs(self):
x, y = self.zscores, self.data
x_, y_, res = viz._fit_line(x, y, fitprobs='junk')
def test_xprob_ylog(self):
scales = {'fitlogs': 'y', 'fitprobs': 'x'}
x, y = self.probs, self.data
x_, y_, res = viz._fit_line(x, y, **scales)
nptest.assert_array_almost_equal(y_, self.known_y_problog)
nt.assert_true(isinstance(res, np.ndarray))
def test_xlog_yprob(self):
scales = {'fitlogs': 'x', 'fitprobs': 'y'}
x, y = self.data, self.probs
x_, y_, res = viz._fit_line(x, y, **scales)
nptest.assert_array_almost_equal(y_, self.known_y_logprob)
nt.assert_true(isinstance(res, np.ndarray))
def test_xlog_ylog(self):
scales = {'fitlogs': 'both', 'fitprobs': None}
x, y = self.data, self.y
x_, y_, res = viz._fit_line(x, y, **scales)
nptest.assert_array_almost_equal(y_, self.known_y_loglog)
nt.assert_true(isinstance(res, np.ndarray))
def test_xlinear_ylinear(self):
scales = {'fitlogs': None, 'fitprobs': None}
x, y = self.zscores, self.data
x_, y_, res = viz._fit_line(x, y, **scales)
nptest.assert_array_almost_equal(y_, self.known_y_linlin)
nt.assert_true(isinstance(res, np.ndarray))
