import math
import itertools

try:
    from scipy import stats
    from numpy import array, linalg
    from scipy.optimize import leastsq
    from numpy.polynomial.chebyshev import chebfit, chebval
    no_numpy = False
except ImportError:
    no_numpy = True


def med_dev(vals):
    med = sum(vals) / len(vals)
    dev = ((sum(abs(med - i) ** 2.0 for i in vals) / len(vals)) ** 0.5)
    return med, dev


def round_3_digit(val):
    return round_deviation((val, val / 10.0))[0]


def round_deviation(med_dev):
    med, dev = med_dev

    if dev < 1E-7:
        return med_dev

    dev_div = 10.0 ** (math.floor(math.log10(dev)) - 1)
    dev = int(dev / dev_div) * dev_div
    med = int(med / dev_div) * dev_div
    return [type(med_dev[0])(med),
            type(med_dev[1])(dev)]


def groupby_globally(data, key_func):
    grouped = {}
    grouped_iter = itertools.groupby(data, key_func)

    for (bs, cache_tp, act, conc), curr_data_it in grouped_iter:
        key = (bs, cache_tp, act, conc)
        grouped.setdefault(key, []).extend(curr_data_it)

    return grouped


def approximate_curve(x, y, xnew, curved_coef):
    """returns ynew - y values of some curve approximation"""
    if no_numpy:
        return None

    return chebval(xnew, chebfit(x, y, curved_coef))


def approximate_line(x, y, xnew, relative_dist=False):
    """ x, y - test data, xnew - dots, where we want find approximation
        if not relative_dist distance = y - newy
        returns ynew - y values of linear approximation"""

    if no_numpy:
        return None

    # convert to numpy.array (don't work without it)
    ox = array(x)
    oy = array(y)

    # set approximation function
    def func_line(tpl, x):
        return tpl[0] * x + tpl[1]

    def error_func_rel(tpl, x, y):
        return 1.0 - y / func_line(tpl, x)

    def error_func_abs(tpl, x, y):
        return y - func_line(tpl, x)

    # choose distance mode
    error_func = error_func_rel if relative_dist else error_func_abs

    tpl_initial = tuple(linalg.solve([[ox[0], 1.0], [ox[1], 1.0]],
                                     oy[:2]))

    # find line
    tpl_final, success = leastsq(error_func,
                                 tpl_initial[:],
                                 args=(ox, oy))

    # if error
    if success not in range(1, 5):
        raise ValueError("No line for this dots")

    # return new dots
    return func_line(tpl_final, array(xnew))


def difference(y, ynew):
    """returns average and maximum relative and
       absolute differences between y and ynew
       result may contain None values for y = 0
       return value - tuple:
       [(abs dif, rel dif) * len(y)],
       (abs average, abs max),
       (rel average, rel max)"""

    abs_dlist = []
    rel_dlist = []

    for y1, y2 in zip(y, ynew):
        # absolute
        abs_dlist.append(y1 - y2)

        if y1 > 1E-6:
            rel_dlist.append(abs(abs_dlist[-1] / y1))
        else:
            raise ZeroDivisionError("{0!r} is too small".format(y1))

    da_avg = sum(abs_dlist) / len(abs_dlist)
    dr_avg = sum(rel_dlist) / len(rel_dlist)

    return (zip(abs_dlist, rel_dlist),
            (da_avg, max(abs_dlist)), (dr_avg, max(rel_dlist))
            )


def calculate_distribution_properties(data):
    """chi, etc"""


def minimal_measurement_amount(data, max_diff, req_probability):
    """
    should returns amount of measurements to get results (avg and deviation)
    with error less, that max_diff in at least req_probability% cases
    """


class StatProps(object):
    def __init__(self):
        self.average = None
        self.mediana = None
        self.perc_95 = None
        self.perc_5 = None
        self.deviation = None
        self.confidence = None
        self.min = None
        self.max = None

    def rounded_average_conf(self):
        return round_deviation((self.average, self.confidence))


def data_property(data, confidence=0.95):
    res = StatProps()
    if len(data) == 0:
        return res

    data = sorted(data)
    res.average, res.deviation = med_dev(data)
    res.max = data[-1]
    res.min = data[0]

    ln = len(data)
    if ln % 2 == 0:
        res.mediana = (data[ln / 2] + data[ln / 2 - 1]) / 2
    else:
        res.mediana = data[ln / 2]

    res.perc_95 = data[int((ln - 1) * 0.95)]
    res.perc_5 = data[int((ln - 1) * 0.05)]

    if not no_numpy and ln >= 3:
        res.confidence = stats.sem(data) * \
                         stats.t.ppf((1 + confidence) / 2, ln - 1)
    else:
        res.confidence = res.deviation

    return res