from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import scipy.optimize as op
from six.moves import range


def four_param_logistic(p):
    """4p logistic function maker.

    Returns a function that accepts x and returns y for
    the 4-parameter logistic defined by p.

    The 4p logistic is defined by:
    y = A + (K - A) / (1 + exp(-B*(x-M)))

    Args:
        p: an iterable of length 4
            A, K, B, M = p

    Returns:
        A function that accepts a numpy array as an argument
        for x values and returns the y values for the defined 4pl curve.
    """
    A, K, B, M = p

    def f(x):
        return A + (K - A) / (1 + np.exp(-B * (x - M)))

    return f


def normalized_four_param_logistic(p):
    """removes the scaling of A and K"""
    A, K, B, M = p

    def f(x):
        return 1.0 / (1.0 + np.exp(-B * (x - M)))

    return f


def ln_like(p, x, y):
    """log likelihood for fitting the four parameter logistic.

    Args:
        p: an iterable of length 4
            A, K, B, M = p
        x: a numpy array of length n
        y: a numpy array of length n
            must be of dtype double or float so multiplication works

    Returns:
        The log-likelihood that the samples y are drawn from a distribution
        where the 4pl(x; p) is the probability of getting y=1
    """
    p_4pl = four_param_logistic(p)
    probs = p_4pl(x)
    return np.sum(y * np.log(probs) + (1 - y) * np.log(1 - probs))


def dln_like(p, x, y):
    """gradient of the log likelihood for fitting the four parameter logistic.

    Args:
        p: an iterable of length 4
            A, K, B, M = p
        x: a numpy array of length n
        y: a numpy array of length n
            must be of dtype double or float so multiplication works

    Returns:
        The gradient of the log-likelihood that the samples y are drawn from
        a distribution where the 4pl(x; p) is the probability of getting y=1
    """
    A, K, B, M = p

    def f(x):
        return A + (K - A) / (1 + np.exp(-B * (x - M)))

    def df(x):
        temp1 = np.exp(-B * (x - M))
        dK = 1.0 / (1.0 + temp1)
        dA = 1.0 - dK
        temp2 = temp1 / (1.0 + temp1) ** 2
        dB = (K - A) * (x - M) * temp2
        dM = -(K - A) * B * temp2
        return np.vstack((dA, dK, dB, dM))

    p_4pl = f(x)
    d_p_4pl = df(x)
    return np.sum(y * d_p_4pl / (p_4pl) - (1 - y) * d_p_4pl / (1 - p_4pl), 1)


def nll(*args):
    """negative log-likelihood for fitting the 4 param logistic."""
    return -ln_like(*args)


def ndll(*args):
    """negative grad of the log-likelihood for fitting the 4 param logistic."""
    return -dln_like(*args)


def est_pstart(x, y):
    """basic estimation of a good place to start log likelihood maximization.

    Args:
        x: a numpy array of length n
            assumes a finite number of unique x values
        y: a numpy array of length n
            must be of dtype double or float so multiplication works

    Returns:
        p_start: an iterable of length 4 that should be a reasonable spot to
            start the optimization
            A, K, B, M = p_start
    """
    p_start = [0.01, 0.99, 0.2, 0]
    x_vals = np.unique(x)
    p_start[3] = np.mean(x_vals)
    y_est = np.array([np.mean(y[x == i]) for i in x_vals])
    midpoint_est = np.mean(np.where((y_est[0:-1] < 0.5) & (y_est[1:] >= 0.5)))
    if np.isnan(midpoint_est):
        return p_start
    p_start[3] = midpoint_est
    return p_start


def fit_4pl(x, y, p_start=None, verbose=False, epsilon=1e-16):
    """Fits a 4 parameter logistic function to the data.

    Args:
        x: a numpy array of length n
            assumes a finite number of unique x values
        y: a numpy array of length n
            must be of dtype double or float so multiplication works
    optional:
        p_start: an iterable of length 4 that would be a reasonable spot to
            start the optimization. If None, tries to estimate it.
            A, K, B, M = p_start
            default=None
        verbose: boolean flag that allows printing of more error messages.
        epsilon: limits A and K between (epsilon, 1 - epsilon) for stability

    Returns:
        p_result: an iterable of length 4 that defines the model that
        is maximally likely
            A, K, B, M = p_result
    """
    try:
        if not p_start:
            p_start = est_pstart(x, y)
    except TypeError:
        pass
    for i in range(3):
        if verbose and i > 0:
            print("retry", i)
        result = op.minimize(
            nll,
            p_start,
            args=(x, y),
            jac=ndll,
            bounds=(
                (epsilon, 1 - epsilon),
                (epsilon, 1 - epsilon),
                (None, None),
                (None, None),
            ),
        )
        if result.success:
            return result.x
        else:
            if verbose:
                print(p_start, "failure", result)
            p_start = result.x
    return False

from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import scipy.optimize as op
from six.moves import range


def four_param_logistic(p):
    """4p logistic function maker.

    Returns a function that accepts x and returns y for
    the 4-parameter logistic defined by p.

    The 4p logistic is defined by:
    y = A + (K - A) / (1 + exp(-B*(x-M)))

    Args:
        p: an iterable of length 4
            A, K, B, M = p

    Returns:
        A function that accepts a numpy array as an argument
        for x values and returns the y values for the defined 4pl curve.
    """
    A, K, B, M = p

    def f(x):
        return A + (K - A) / (1 + np.exp(-B * (x - M)))

    return f


def normalized_four_param_logistic(p):
    """removes the scaling of A and K"""
    A, K, B, M = p

    def f(x):
        return 1.0 / (1.0 + np.exp(-B * (x - M)))

    return f


def ln_like(p, x, y):
    """log likelihood for fitting the four parameter logistic.

    Args:
        p: an iterable of length 4
            A, K, B, M = p
        x: a numpy array of length n
        y: a numpy array of length n
            must be of dtype double or float so multiplication works

    Returns:
        The log-likelihood that the samples y are drawn from a distribution
        where the 4pl(x; p) is the probability of getting y=1
    """
    p_4pl = four_param_logistic(p)
    probs = p_4pl(x)
    return np.sum(y * np.log(probs) + (1 - y) * np.log(1 - probs))


def dln_like(p, x, y):
    """gradient of the log likelihood for fitting the four parameter logistic.

    Args:
        p: an iterable of length 4
            A, K, B, M = p
        x: a numpy array of length n
        y: a numpy array of length n
            must be of dtype double or float so multiplication works

    Returns:
        The gradient of the log-likelihood that the samples y are drawn from
        a distribution where the 4pl(x; p) is the probability of getting y=1
    """
    A, K, B, M = p

    def f(x):
        return A + (K - A) / (1 + np.exp(-B * (x - M)))

    def df(x):
        temp1 = np.exp(-B * (x - M))
        dK = 1.0 / (1.0 + temp1)
        dA = 1.0 - dK
        temp2 = temp1 / (1.0 + temp1) ** 2
        dB = (K - A) * (x - M) * temp2
        dM = -(K - A) * B * temp2
        return np.vstack((dA, dK, dB, dM))

    p_4pl = f(x)
    d_p_4pl = df(x)
    return np.sum(y * d_p_4pl / (p_4pl) - (1 - y) * d_p_4pl / (1 - p_4pl), 1)


def nll(*args):
    """negative log-likelihood for fitting the 4 param logistic."""
    return -ln_like(*args)


def ndll(*args):
    """negative grad of the log-likelihood for fitting the 4 param logistic."""
    return -dln_like(*args)


def est_pstart(x, y):
    """basic estimation of a good place to start log likelihood maximization.

    Args:
        x: a numpy array of length n
            assumes a finite number of unique x values
        y: a numpy array of length n
            must be of dtype double or float so multiplication works

    Returns:
        p_start: an iterable of length 4 that should be a reasonable spot to
            start the optimization
            A, K, B, M = p_start
    """
    p_start = [0.01, 0.99, 0.2, 0]
    x_vals = np.unique(x)
    p_start[3] = np.mean(x_vals)
    y_est = np.array([np.mean(y[x == i]) for i in x_vals])
    midpoint_est = np.mean(np.where((y_est[0:-1] < 0.5) & (y_est[1:] >= 0.5)))
    if np.isnan(midpoint_est):
        return p_start
    p_start[3] = midpoint_est
    return p_start


def fit_4pl(x, y, p_start=None, verbose=False, epsilon=1e-16):
    """Fits a 4 parameter logistic function to the data.

    Args:
        x: a numpy array of length n
            assumes a finite number of unique x values
        y: a numpy array of length n
            must be of dtype double or float so multiplication works
    optional:
        p_start: an iterable of length 4 that would be a reasonable spot to
            start the optimization. If None, tries to estimate it.
            A, K, B, M = p_start
            default=None
        verbose: boolean flag that allows printing of more error messages.
        epsilon: limits A and K between (epsilon, 1 - epsilon) for stability

    Returns:
        p_result: an iterable of length 4 that defines the model that
        is maximally likely
            A, K, B, M = p_result
    """
    try:
        if not p_start:
            p_start = est_pstart(x, y)
    except TypeError:
        pass
    for i in range(3):
        if verbose and i > 0:
            print("retry", i)
        result = op.minimize(
            nll,
            p_start,
            args=(x, y),
            jac=ndll,
            bounds=(
                (epsilon, 1 - epsilon),
                (epsilon, 1 - epsilon),
                (None, None),
                (None, None),
            ),
        )
        if result.success:
            return result.x
        else:
            if verbose:
                print(p_start, "failure", result)
            p_start = result.x
    return False

4p logistic function maker.      Returns a function that accepts x and returns y for     the 4-parameter logistic defined by p.      The 4p logistic is defined by:     y = A + (K - A) / (1 + exp(-B*(x-M)))      Args:         p: an iterable of length 4             A, K, B, M = p      Returns:         A function that accepts a numpy array as an argument         for x values and returns the y values for the defined 4pl curve. removes the scaling of A and K log likelihood for fitting the four parameter logistic.      Args:         p: an iterable of length 4             A, K, B, M = p         x: a numpy array of length n         y: a numpy array of length n             must be of dtype double or float so multiplication works      Returns:         The log-likelihood that the samples y are drawn from a distribution         where the 4pl(x; p) is the probability of getting y=1 gradient of the log likelihood for fitting the four parameter logistic.      Args:         p: an iterable of length 4             A, K, B, M = p         x: a numpy array of length n         y: a numpy array of length n             must be of dtype double or float so multiplication works      Returns:         The gradient of the log-likelihood that the samples y are drawn from         a distribution where the 4pl(x; p) is the probability of getting y=1 negative log-likelihood for fitting the 4 param logistic. negative grad of the log-likelihood for fitting the 4 param logistic. basic estimation of a good place to start log likelihood maximization.      Args:         x: a numpy array of length n             assumes a finite number of unique x values         y: a numpy array of length n             must be of dtype double or float so multiplication works      Returns:         p_start: an iterable of length 4 that should be a reasonable spot to             start the optimization             A, K, B, M = p_start Fits a 4 parameter logistic function to the data.      Args:         x: a numpy array of length n             assumes a finite number of unique x values         y: a numpy array of length n             must be of dtype double or float so multiplication works     optional:         p_start: an iterable of length 4 that would be a reasonable spot to             start the optimization. If None, tries to estimate it.             A, K, B, M = p_start             default=None         verbose: boolean flag that allows printing of more error messages.         epsilon: limits A and K between (epsilon, 1 - epsilon) for stability      Returns:         p_result: an iterable of length 4 that defines the model that         is maximally likely             A, K, B, M = p_result