"""
Lowess testing suite.

Expected outcomes are generated by R's lowess function given the same
arguments. The R script test_lowess_r_outputs.R can be used to
generate the expected outcomes.

The delta tests utilize Silverman's motorcycle collision data,
available in R's MASS package.
"""

import os

import numpy as np
from numpy.testing import (
    assert_,
    assert_allclose,
    assert_almost_equal,
    assert_equal,
)
import pandas as pd
import pytest

from statsmodels.nonparametric.smoothers_lowess import lowess

# Number of decimals to test equality with.
# The default is 7.
curdir = os.path.dirname(os.path.abspath(__file__))
rpath = os.path.join(curdir, "results")


class TestLowess:
    def test_import(self):
        # this does not work
        # from statsmodels.api.nonparametric import lowess as lowess1
        import statsmodels.api as sm

        lowess1 = sm.nonparametric.lowess
        assert_(lowess is lowess1)

    @pytest.mark.parametrize("use_pandas", [False, True])
    def test_flat(self, use_pandas):
        test_data = {
            "x": np.arange(20),
            "y": np.zeros(20),
            "out": np.zeros(20),
        }
        if use_pandas:
            test_data = {k: pd.Series(test_data[k]) for k in test_data}
        expected_lowess = np.array([test_data["x"], test_data["out"]]).T
        actual_lowess = lowess(test_data["y"], test_data["x"])
        assert_almost_equal(expected_lowess, actual_lowess, 7)

    def test_range(self):
        test_data = {
            "x": np.arange(20),
            "y": np.arange(20),
            "out": np.arange(20),
        }
        expected_lowess = np.array([test_data["x"], test_data["out"]]).T
        actual_lowess = lowess(test_data["y"], test_data["x"])
        assert_almost_equal(expected_lowess, actual_lowess, 7)

    @staticmethod
    def generate(name, fname, x="x", y="y", out="out", kwargs=None, decimal=7):
        kwargs = {} if kwargs is None else kwargs
        data = np.genfromtxt(os.path.join(rpath, fname), delimiter=",", names=True)
        assert_almost_equal.description = name
        if callable(kwargs):
            kwargs = kwargs(data)
        result = lowess(data[y], data[x], **kwargs)
        expect = np.array([data[x], data[out]]).T
        assert_almost_equal(result, expect, decimal)

    # TODO: Refactor as parametrized test once nose is permanently dropped
    def test_simple(self):
        self.generate("test_simple", "test_lowess_simple.csv")

    def test_iter_0(self):
        self.generate(
            "test_iter_0",
            "test_lowess_iter.csv",
            out="out_0",
            kwargs={"it": 0},
        )

    def test_iter_0_3(self):
        self.generate(
            "test_iter_0",
            "test_lowess_iter.csv",
            out="out_3",
            kwargs={"it": 3},
        )

    def test_frac_2_3(self):
        self.generate(
            "test_frac_2_3",
            "test_lowess_frac.csv",
            out="out_2_3",
            kwargs={"frac": 2.0 / 3},
        )

    def test_frac_1_5(self):
        self.generate(
            "test_frac_1_5",
            "test_lowess_frac.csv",
            out="out_1_5",
            kwargs={"frac": 1.0 / 5},
        )

    def test_delta_0(self):
        self.generate(
            "test_delta_0",
            "test_lowess_delta.csv",
            out="out_0",
            kwargs={"frac": 0.1},
        )

    def test_delta_rdef(self):
        self.generate(
            "test_delta_Rdef",
            "test_lowess_delta.csv",
            out="out_Rdef",
            kwargs=lambda data: {
                "frac": 0.1,
                "delta": 0.01 * np.ptp(data["x"]),
            },
        )

    def test_delta_1(self):
        self.generate(
            "test_delta_1",
            "test_lowess_delta.csv",
            out="out_1",
            kwargs={"frac": 0.1, "delta": 1 + 1e-10},
            decimal=10,
        )

    def test_options(self):
        rfile = os.path.join(rpath, "test_lowess_simple.csv")
        test_data = np.genfromtxt(open(rfile, "rb"), delimiter=",", names=True)
        y, x = test_data["y"], test_data["x"]
        expected_lowess = np.array([test_data["x"], test_data["out"]]).T

        # check skip sorting
        actual_lowess1 = lowess(y, x, is_sorted=True)
        assert_almost_equal(actual_lowess1, expected_lowess, decimal=13)

        # check skip sorting - DataFrame
        df = pd.DataFrame({"y": y, "x": x})
        actual_lowess1 = lowess(df["y"], df["x"], is_sorted=True)
        assert_almost_equal(actual_lowess1, expected_lowess, decimal=13)

        # check skip missing
        actual_lowess = lowess(y, x, is_sorted=True, missing="none")
        assert_almost_equal(actual_lowess, actual_lowess1, decimal=13)

        # check order/index, returns yfitted only
        actual_lowess = lowess(y[::-1], x[::-1], return_sorted=False)
        assert_almost_equal(actual_lowess, actual_lowess1[::-1, 1], decimal=13)

        # check returns yfitted only
        actual_lowess = lowess(
            y, x, return_sorted=False, missing="none", is_sorted=True
        )
        assert_almost_equal(actual_lowess, actual_lowess1[:, 1], decimal=13)

        # check integer input
        actual_lowess = lowess(np.round(y).astype(int), x, is_sorted=True)
        actual_lowess1 = lowess(np.round(y), x, is_sorted=True)
        assert_almost_equal(actual_lowess, actual_lowess1, decimal=13)
        assert_(actual_lowess.dtype is np.dtype(float))
        # this will also have duplicate x
        actual_lowess = lowess(y, np.round(x).astype(int), is_sorted=True)
        actual_lowess1 = lowess(y, np.round(x), is_sorted=True)
        assert_almost_equal(actual_lowess, actual_lowess1, decimal=13)
        assert_(actual_lowess.dtype is np.dtype(float))

        # Test specifying xvals explicitly
        perm_idx = np.arange(len(x) // 2)
        np.random.shuffle(perm_idx)
        actual_lowess2 = lowess(y, x, xvals=x[perm_idx], return_sorted=False)
        assert_almost_equal(actual_lowess[perm_idx, 1], actual_lowess2, decimal=13)

        # check with nans,  this changes the arrays
        y[[5, 6]] = np.nan
        x[3] = np.nan
        mask_valid = np.isfinite(x) & np.isfinite(y)
        # actual_lowess1[[3, 5, 6], 1] = np.nan
        actual_lowess = lowess(y, x, is_sorted=True)
        actual_lowess1 = lowess(y[mask_valid], x[mask_valid], is_sorted=True)
        assert_almost_equal(actual_lowess, actual_lowess1, decimal=13)
        with pytest.raises(ValueError):
            lowess(y, x, missing="raise")

        perm_idx = np.arange(len(x))
        np.random.shuffle(perm_idx)
        yperm = y[perm_idx]
        xperm = x[perm_idx]
        actual_lowess2 = lowess(yperm, xperm, is_sorted=False)
        assert_almost_equal(actual_lowess, actual_lowess2, decimal=13)

        actual_lowess3 = lowess(yperm, xperm, is_sorted=False, return_sorted=False)
        mask_valid = np.isfinite(xperm) & np.isfinite(yperm)
        assert_equal(np.isnan(actual_lowess3), ~mask_valid)
        # get valid sorted smoothed y from actual_lowess3
        sort_idx = np.argsort(xperm)
        yhat = actual_lowess3[sort_idx]
        yhat = yhat[np.isfinite(yhat)]
        assert_almost_equal(yhat, actual_lowess2[:, 1], decimal=13)

        # Test specifying xvals explicitly, now with nans
        perm_idx = np.arange(actual_lowess.shape[0])
        actual_lowess4 = lowess(
            y, x, xvals=actual_lowess[perm_idx, 0], return_sorted=False
        )
        assert_almost_equal(actual_lowess[perm_idx, 1], actual_lowess4, decimal=13)

    def test_duplicate_xs(self):
        # see 2449
        # Generate cases with many duplicate x values
        x = [0] + [1] * 100 + [2] * 100 + [3]
        y = x + np.random.normal(size=len(x)) * 1e-8
        result = lowess(y, x, frac=50 / len(x), it=1)
        # fit values should be approximately averages of values at
        # a particular fit, which in this case are just equal to x
        assert_almost_equal(result[1:-1, 1], x[1:-1], decimal=7)

    def test_spike(self):
        # see 7700
        # Create a curve that is easy to fit at first but gets
        # harder further along.
        # This used to give an outlier bad fit at position 961
        x = np.linspace(0, 10, 1001)
        y = np.cos(x**2 / 5)
        result = lowess(y, x, frac=11 / len(x), it=1)
        assert_(np.all(result[:, 1] > np.min(y) - 0.1))
        assert_(np.all(result[:, 1] < np.max(y) + 0.1))

    def test_exog_predict(self):
        rfile = os.path.join(rpath, "test_lowess_simple.csv")
        test_data = np.genfromtxt(open(rfile, "rb"), delimiter=",", names=True)
        y, x = test_data["y"], test_data["x"]
        target = lowess(y, x, is_sorted=True)

        # Test specifying exog_predict explicitly
        perm_idx = np.arange(len(x) // 2)
        np.random.shuffle(perm_idx)
        actual_lowess = lowess(y, x, xvals=x[perm_idx], missing="none")
        assert_almost_equal(target[perm_idx, 1], actual_lowess, decimal=13)

        target_it0 = lowess(y, x, return_sorted=False, it=0)
        actual_lowess2 = lowess(y, x, xvals=x[perm_idx], it=0)
        assert_almost_equal(target_it0[perm_idx], actual_lowess2, decimal=13)

        # Check nans in exog_predict
        with pytest.raises(ValueError):
            lowess(y, x, xvals=np.array([np.nan, 5, 3]), missing="raise")

        # With is_sorted=True
        actual_lowess3 = lowess(y, x, xvals=x, is_sorted=True)
        assert_equal(actual_lowess3, target[:, 1])

        # check with nans,  this changes the arrays
        y[[5, 6]] = np.nan
        x[3] = np.nan
        target = lowess(y, x, is_sorted=True)

        # Test specifying exog_predict explicitly, now with nans
        perm_idx = np.arange(target.shape[0])
        actual_lowess1 = lowess(y, x, xvals=target[perm_idx, 0])
        assert_almost_equal(target[perm_idx, 1], actual_lowess1, decimal=13)

        # nans and missing='drop'
        actual_lowess2 = lowess(y, x, xvals=x, missing="drop")
        all_finite = np.isfinite(x) & np.isfinite(y)
        assert_equal(actual_lowess2[all_finite], target[:, 1])

        # Dimensional check
        with pytest.raises(ValueError):
            lowess(y, x, xvals=np.array([[5], [10]]))


def test_returns_inputs():
    # see 1960
    y = [0] * 10 + [1] * 10
    x = np.arange(20)
    result = lowess(y, x, frac=0.4)
    assert_almost_equal(result, np.column_stack((x, y)))


def test_xvals_dtype(reset_randomstate):
    y = [0] * 10 + [1] * 10
    x = np.arange(20)
    # Previously raised ValueError: Buffer dtype mismatch
    results_xvals = lowess(y, x, frac=0.4, xvals=x[:5])
    assert_allclose(results_xvals, np.zeros(5), atol=1e-12)
