from __future__ import division, print_function, absolute_import
from itertools import groupby
from warnings import warn
import numpy as np
from scipy.sparse import find, coo_matrix


EPS = np.finfo(float).eps


def validate_first_step(first_step, t0, t_bound):
    """Assert that first_step is valid and return it."""
    if first_step <= 0:
        raise ValueError("`first_step` must be positive.")
    if first_step > np.abs(t_bound - t0):
        raise ValueError("`first_step` exceeds bounds.")
    return first_step


def validate_max_step(max_step):
    """Assert that max_Step is valid and return it."""
    if max_step <= 0:
        raise ValueError("`max_step` must be positive.")
    return max_step


def warn_extraneous(extraneous):
    """Display a warning for extraneous keyword arguments.

    The initializer of each solver class is expected to collect keyword
    arguments that it doesn't understand and warn about them. This function
    prints a warning for each key in the supplied dictionary.

    Parameters
    ----------
    extraneous : dict
        Extraneous keyword arguments
    """
    if extraneous:
        warn("The following arguments have no effect for a chosen solver: {}."
             .format(", ".join("`{}`".format(x) for x in extraneous)))


def validate_tol(rtol, atol, n):
    """Validate tolerance values."""
    if rtol < 100 * EPS:
        warn("`rtol` is too low, setting to {}".format(100 * EPS))
        rtol = 100 * EPS

    atol = np.asarray(atol)
    if atol.ndim > 0 and atol.shape != (n,):
        raise ValueError("`atol` has wrong shape.")

    if np.any(atol < 0):
        raise ValueError("`atol` must be positive.")

    return rtol, atol


def norm(x):
    """Compute RMS norm."""
    return np.linalg.norm(x) / x.size ** 0.5


def select_initial_step(fun, t0, y0, f0, direction, order, rtol, atol):
    """Empirically select a good initial step.

    The algorithm is described in [1]_.

    Parameters
    ----------
    fun : callable
        Right-hand side of the system.
    t0 : float
        Initial value of the independent variable.
    y0 : ndarray, shape (n,)
        Initial value of the dependent variable.
    f0 : ndarray, shape (n,)
        Initial value of the derivative, i. e. ``fun(t0, y0)``.
    direction : float
        Integration direction.
    order : float
        Error estimator order. It means that the error controlled by the
        algorithm is proportional to ``step_size ** (order + 1)`.
    rtol : float
        Desired relative tolerance.
    atol : float
        Desired absolute tolerance.

    Returns
    -------
    h_abs : float
        Absolute value of the suggested initial step.

    References
    ----------
    .. [1] E. Hairer, S. P. Norsett G. Wanner, "Solving Ordinary Differential
           Equations I: Nonstiff Problems", Sec. II.4.
    """
    if y0.size == 0:
        return np.inf

    scale = atol + np.abs(y0) * rtol
    d0 = norm(y0 / scale)
    d1 = norm(f0 / scale)
    if d0 < 1e-5 or d1 < 1e-5:
        h0 = 1e-6
    else:
        h0 = 0.01 * d0 / d1

    y1 = y0 + h0 * direction * f0
    f1 = fun(t0 + h0 * direction, y1)
    d2 = norm((f1 - f0) / scale) / h0

    if d1 <= 1e-15 and d2 <= 1e-15:
        h1 = max(1e-6, h0 * 1e-3)
    else:
        h1 = (0.01 / max(d1, d2)) ** (1 / (order + 1))

    return min(100 * h0, h1)


class OdeSolution(object):
    """Continuous ODE solution.

    It is organized as a collection of `DenseOutput` objects which represent
    local interpolants. It provides an algorithm to select a right interpolant
    for each given point.

    The interpolants cover the range between `t_min` and `t_max` (see
    Attributes below). Evaluation outside this interval is not forbidden, but
    the accuracy is not guaranteed.

    When evaluating at a breakpoint (one of the values in `ts`) a segment with
    the lower index is selected.

    Parameters
    ----------
    ts : array_like, shape (n_segments + 1,)
        Time instants between which local interpolants are defined. Must
        be strictly increasing or decreasing (zero segment with two points is
        also allowed).
    interpolants : list of DenseOutput with n_segments elements
        Local interpolants. An i-th interpolant is assumed to be defined
        between ``ts[i]`` and ``ts[i + 1]``.

    Attributes
    ----------
    t_min, t_max : float
        Time range of the interpolation.
    """
    def __init__(self, ts, interpolants):
        ts = np.asarray(ts)
        d = np.diff(ts)
        # The first case covers integration on zero segment.
        if not ((ts.size == 2 and ts[0] == ts[-1])
                or np.all(d > 0) or np.all(d < 0)):
            raise ValueError("`ts` must be strictly increasing or decreasing.")

        self.n_segments = len(interpolants)
        if ts.shape != (self.n_segments + 1,):
            raise ValueError("Numbers of time stamps and interpolants "
                             "don't match.")

        self.ts = ts
        self.interpolants = interpolants
        if ts[-1] >= ts[0]:
            self.t_min = ts[0]
            self.t_max = ts[-1]
            self.ascending = True
            self.ts_sorted = ts
        else:
            self.t_min = ts[-1]
            self.t_max = ts[0]
            self.ascending = False
            self.ts_sorted = ts[::-1]

    def _call_single(self, t):
        # Here we preserve a certain symmetry that when t is in self.ts,
        # then we prioritize a segment with a lower index.
        if self.ascending:
            ind = np.searchsorted(self.ts_sorted, t, side='left')
        else:
            ind = np.searchsorted(self.ts_sorted, t, side='right')

        segment = min(max(ind - 1, 0), self.n_segments - 1)
        if not self.ascending:
            segment = self.n_segments - 1 - segment

        return self.interpolants[segment](t)

    def __call__(self, t):
        """Evaluate the solution.

        Parameters
        ----------
        t : float or array_like with shape (n_points,)
            Points to evaluate at.

        Returns
        -------
        y : ndarray, shape (n_states,) or (n_states, n_points)
            Computed values. Shape depends on whether `t` is a scalar or a
            1-d array.
        """
        t = np.asarray(t)

        if t.ndim == 0:
            return self._call_single(t)

        order = np.argsort(t)
        reverse = np.empty_like(order)
        reverse[order] = np.arange(order.shape[0])
        t_sorted = t[order]

        # See comment in self._call_single.
        if self.ascending:
            segments = np.searchsorted(self.ts_sorted, t_sorted, side='left')
        else:
            segments = np.searchsorted(self.ts_sorted, t_sorted, side='right')
        segments -= 1
        segments[segments < 0] = 0
        segments[segments > self.n_segments - 1] = self.n_segments - 1
        if not self.ascending:
            segments = self.n_segments - 1 - segments

        ys = []
        group_start = 0
        for segment, group in groupby(segments):
            group_end = group_start + len(list(group))
            y = self.interpolants[segment](t_sorted[group_start:group_end])
            ys.append(y)
            group_start = group_end

        ys = np.hstack(ys)
        ys = ys[:, reverse]

        return ys


NUM_JAC_DIFF_REJECT = EPS ** 0.875
NUM_JAC_DIFF_SMALL = EPS ** 0.75
NUM_JAC_DIFF_BIG = EPS ** 0.25
NUM_JAC_MIN_FACTOR = 1e3 * EPS
NUM_JAC_FACTOR_INCREASE = 10
NUM_JAC_FACTOR_DECREASE = 0.1


def num_jac(fun, t, y, f, threshold, factor, sparsity=None):
    """Finite differences Jacobian approximation tailored for ODE solvers.

    This function computes finite difference approximation to the Jacobian
    matrix of `fun` with respect to `y` using forward differences.
    The Jacobian matrix has shape (n, n) and its element (i, j) is equal to
    ``d f_i / d y_j``.

    A special feature of this function is the ability to correct the step
    size from iteration to iteration. The main idea is to keep the finite
    difference significantly separated from its round-off error which
    approximately equals ``EPS * np.abs(f)``. It reduces a possibility of a
    huge error and assures that the estimated derivative are reasonably close
    to the true values (i.e. the finite difference approximation is at least
    qualitatively reflects the structure of the true Jacobian).

    Parameters
    ----------
    fun : callable
        Right-hand side of the system implemented in a vectorized fashion.
    t : float
        Current time.
    y : ndarray, shape (n,)
        Current state.
    f : ndarray, shape (n,)
        Value of the right hand side at (t, y).
    threshold : float
        Threshold for `y` value used for computing the step size as
        ``factor * np.maximum(np.abs(y), threshold)``. Typically the value of
        absolute tolerance (atol) for a solver should be passed as `threshold`.
    factor : ndarray with shape (n,) or None
        Factor to use for computing the step size. Pass None for the very
        evaluation, then use the value returned from this function.
    sparsity : tuple (structure, groups) or None
        Sparsity structure of the Jacobian, `structure` must be csc_matrix.

    Returns
    -------
    J : ndarray or csc_matrix, shape (n, n)
        Jacobian matrix.
    factor : ndarray, shape (n,)
        Suggested `factor` for the next evaluation.
    """
    y = np.asarray(y)
    n = y.shape[0]
    if n == 0:
        return np.empty((0, 0)), factor

    if factor is None:
        factor = np.full(n, EPS ** 0.5)
    else:
        factor = factor.copy()

    # Direct the step as ODE dictates, hoping that such a step won't lead to
    # a problematic region. For complex ODEs it makes sense to use the real
    # part of f as we use steps along real axis.
    f_sign = 2 * (np.real(f) >= 0).astype(float) - 1
    y_scale = f_sign * np.maximum(threshold, np.abs(y))
    h = (y + factor * y_scale) - y

    # Make sure that the step is not 0 to start with. Not likely it will be
    # executed often.
    for i in np.nonzero(h == 0)[0]:
        while h[i] == 0:
            factor[i] *= 10
            h[i] = (y[i] + factor[i] * y_scale[i]) - y[i]

    if sparsity is None:
        return _dense_num_jac(fun, t, y, f, h, factor, y_scale)
    else:
        structure, groups = sparsity
        return _sparse_num_jac(fun, t, y, f, h, factor, y_scale,
                               structure, groups)


def _dense_num_jac(fun, t, y, f, h, factor, y_scale):
    n = y.shape[0]
    h_vecs = np.diag(h)
    f_new = fun(t, y[:, None] + h_vecs)
    diff = f_new - f[:, None]
    max_ind = np.argmax(np.abs(diff), axis=0)
    r = np.arange(n)
    max_diff = np.abs(diff[max_ind, r])
    scale = np.maximum(np.abs(f[max_ind]), np.abs(f_new[max_ind, r]))

    diff_too_small = max_diff < NUM_JAC_DIFF_REJECT * scale
    if np.any(diff_too_small):
        ind, = np.nonzero(diff_too_small)
        new_factor = NUM_JAC_FACTOR_INCREASE * factor[ind]
        h_new = (y[ind] + new_factor * y_scale[ind]) - y[ind]
        h_vecs[ind, ind] = h_new
        f_new = fun(t, y[:, None] + h_vecs[:, ind])
        diff_new = f_new - f[:, None]
        max_ind = np.argmax(np.abs(diff_new), axis=0)
        r = np.arange(ind.shape[0])
        max_diff_new = np.abs(diff_new[max_ind, r])
        scale_new = np.maximum(np.abs(f[max_ind]), np.abs(f_new[max_ind, r]))

        update = max_diff[ind] * scale_new < max_diff_new * scale[ind]
        if np.any(update):
            update, = np.nonzero(update)
            update_ind = ind[update]
            factor[update_ind] = new_factor[update]
            h[update_ind] = h_new[update]
            diff[:, update_ind] = diff_new[:, update]
            scale[update_ind] = scale_new[update]
            max_diff[update_ind] = max_diff_new[update]

    diff /= h

    factor[max_diff < NUM_JAC_DIFF_SMALL * scale] *= NUM_JAC_FACTOR_INCREASE
    factor[max_diff > NUM_JAC_DIFF_BIG * scale] *= NUM_JAC_FACTOR_DECREASE
    factor = np.maximum(factor, NUM_JAC_MIN_FACTOR)

    return diff, factor


def _sparse_num_jac(fun, t, y, f, h, factor, y_scale, structure, groups):
    n = y.shape[0]
    n_groups = np.max(groups) + 1
    h_vecs = np.empty((n_groups, n))
    for group in range(n_groups):
        e = np.equal(group, groups)
        h_vecs[group] = h * e
    h_vecs = h_vecs.T

    f_new = fun(t, y[:, None] + h_vecs)
    df = f_new - f[:, None]

    i, j, _ = find(structure)
    diff = coo_matrix((df[i, groups[j]], (i, j)), shape=(n, n)).tocsc()
    max_ind = np.array(abs(diff).argmax(axis=0)).ravel()
    r = np.arange(n)
    max_diff = np.asarray(np.abs(diff[max_ind, r])).ravel()
    scale = np.maximum(np.abs(f[max_ind]),
                       np.abs(f_new[max_ind, groups[r]]))

    diff_too_small = max_diff < NUM_JAC_DIFF_REJECT * scale
    if np.any(diff_too_small):
        ind, = np.nonzero(diff_too_small)
        new_factor = NUM_JAC_FACTOR_INCREASE * factor[ind]
        h_new = (y[ind] + new_factor * y_scale[ind]) - y[ind]
        h_new_all = np.zeros(n)
        h_new_all[ind] = h_new

        groups_unique = np.unique(groups[ind])
        groups_map = np.empty(n_groups, dtype=int)
        h_vecs = np.empty((groups_unique.shape[0], n))
        for k, group in enumerate(groups_unique):
            e = np.equal(group, groups)
            h_vecs[k] = h_new_all * e
            groups_map[group] = k
        h_vecs = h_vecs.T

        f_new = fun(t, y[:, None] + h_vecs)
        df = f_new - f[:, None]
        i, j, _ = find(structure[:, ind])
        diff_new = coo_matrix((df[i, groups_map[groups[ind[j]]]],
                               (i, j)), shape=(n, ind.shape[0])).tocsc()

        max_ind_new = np.array(abs(diff_new).argmax(axis=0)).ravel()
        r = np.arange(ind.shape[0])
        max_diff_new = np.asarray(np.abs(diff_new[max_ind_new, r])).ravel()
        scale_new = np.maximum(
            np.abs(f[max_ind_new]),
            np.abs(f_new[max_ind_new, groups_map[groups[ind]]]))

        update = max_diff[ind] * scale_new < max_diff_new * scale[ind]
        if np.any(update):
            update, = np.nonzero(update)
            update_ind = ind[update]
            factor[update_ind] = new_factor[update]
            h[update_ind] = h_new[update]
            diff[:, update_ind] = diff_new[:, update]
            scale[update_ind] = scale_new[update]
            max_diff[update_ind] = max_diff_new[update]

    diff.data /= np.repeat(h, np.diff(diff.indptr))

    factor[max_diff < NUM_JAC_DIFF_SMALL * scale] *= NUM_JAC_FACTOR_INCREASE
    factor[max_diff > NUM_JAC_DIFF_BIG * scale] *= NUM_JAC_FACTOR_DECREASE
    factor = np.maximum(factor, NUM_JAC_MIN_FACTOR)

    return diff, factor