# Path: pwkit/numutil.py
# def _broadcastize_spec_to_scalar_filter(s):
#     def fixup(self, newobj):
#     def __call__(self, *args, **kwargs):
#     def __init__(self, n_arr, ret_spec=0, force_float=True):
#     def __call__(self, subfunc):
# def fits_recarray_to_data_frame(recarray, drop_nonscalar_ok=True):
#     def normalize():
# def data_frame_to_astropy_table(dataframe):
# def page_data_frame(df, pager_argv=['less'], **kwargs):
# def slice_around_gaps(values, maxgap):
# def slice_evenly_with_gaps(values, target_len, maxgap):
# def reduce_data_frame(df, chunk_slicers,
#                       avg_cols=(),
#                       uavg_cols=(),
#                       minmax_cols=(),
#                       nchunk_colname='nchunk',
#                       uncert_prefix='u',
#                       min_points_per_chunk=3):
# def reduce_data_frame_evenly_with_gaps(df, valcol, target_len, maxgap, **kwargs):
# def usmooth(window, uncerts, *data, **kwargs):
# def dfsmooth(window, df, ucol, k=None):
# def smooth_data_frame_with_gaps(
#         window, df, uncert_col,
#         time_col, max_gap,
#         min_points_per_chunk = 3,
#         k = None,
# ):
# def parallel_newton(func, x0, fprime=None, par_args=(), simple_args=(), tol=1.48e-8,
#                     maxiter=50, parallel=True, **kwargs):
#     def gen_var_args():
#     def helper(i, _, var_args):
# def parallel_quad(func, a, b, par_args=(), simple_args=(), parallel=True, **kwargs):
#     def gen_var_args():
#     def helper(i, _, var_args):
# def rms(x):
# def weighted_mean(values, uncerts, **kwargs):
# def weighted_mean_df(df, **kwargs):
# def weighted_variance(x, weights):
# def unit_tophat_ee(x):
# def unit_tophat_ei(x):
# def unit_tophat_ie(x):
# def unit_tophat_ii(x):
# def make_tophat_ee(lower, upper):
#     def range_tophat_ee(x):
# def make_tophat_ei(lower, upper):
#     def range_tophat_ei(x):
# def make_tophat_ie(lower, upper):
#     def range_tophat_ie(x):
# def make_tophat_ii(lower, upper):
#     def range_tophat_ii(x):
# def make_step_lcont(transition):
#     def step_lcont(x):
# def make_step_rcont(transition):
#     def step_rcont(x):
# class _Broadcaster(method_decorator):
# class _BroadcasterDecorator(object):


import numpy as np
    import omega as om
from .numutil import broadcastize
    from scipy.special import erfc
    from numpy import arctan2, sqrt
    from numpy import pi, sqrt
    from numpy.random import multivariate_normal as mvn
    from numpy import cos, sin
    from numpy import arctan2, sqrt

    """
    _bivcheck (sx_a, sy_a, cxy_a)
    _bivcheck (sx_b, sy_b, cxy_b)

    sx_c = np.sqrt (sx_a**2 + sx_b**2)
    sy_c = np.sqrt (sy_a**2 + sy_b**2)
    cxy_c = cxy_a + cxy_b

    return _bivcheck (sx_c, sy_c, cxy_c)


def bivplot (sx, sy, cxy, **kwargs):
    _bivcheck (sx, sy, cxy)
    return ellplot (*bivell (sx, sy, cxy), **kwargs)


# Ellipse form: major, minor, pa

def _ellcheck (mjr, mnr, pa):
    if mjr <= 0:
        raise ValueError ('mjr must be positive (%.10e)' % mjr)
    if mnr <= 0:
        raise ValueError ('mnr must be positive (%.10e)' % mnr)
    if mnr > mjr:
        raise ValueError ('mnr must be less than mjr (mnr=%.10e, '
                          'mjr=%.10e)' % (mnr, mjr))
    return mjr, mnr, pa



# Path: pwkit/numutil.py
# def _broadcastize_spec_to_scalar_filter(s):
#     def fixup(self, newobj):
#     def __call__(self, *args, **kwargs):
#     def __init__(self, n_arr, ret_spec=0, force_float=True):
#     def __call__(self, subfunc):
# def fits_recarray_to_data_frame(recarray, drop_nonscalar_ok=True):
#     def normalize():
# def data_frame_to_astropy_table(dataframe):
# def page_data_frame(df, pager_argv=['less'], **kwargs):
# def slice_around_gaps(values, maxgap):
# def slice_evenly_with_gaps(values, target_len, maxgap):
# def reduce_data_frame(df, chunk_slicers,
#                       avg_cols=(),
#                       uavg_cols=(),
#                       minmax_cols=(),
#                       nchunk_colname='nchunk',
#                       uncert_prefix='u',
#                       min_points_per_chunk=3):
# def reduce_data_frame_evenly_with_gaps(df, valcol, target_len, maxgap, **kwargs):
# def usmooth(window, uncerts, *data, **kwargs):
# def dfsmooth(window, df, ucol, k=None):
# def smooth_data_frame_with_gaps(
#         window, df, uncert_col,
#         time_col, max_gap,
#         min_points_per_chunk = 3,
#         k = None,
# ):
# def parallel_newton(func, x0, fprime=None, par_args=(), simple_args=(), tol=1.48e-8,
#                     maxiter=50, parallel=True, **kwargs):
#     def gen_var_args():
#     def helper(i, _, var_args):
# def parallel_quad(func, a, b, par_args=(), simple_args=(), parallel=True, **kwargs):
#     def gen_var_args():
#     def helper(i, _, var_args):
# def rms(x):
# def weighted_mean(values, uncerts, **kwargs):
# def weighted_mean_df(df, **kwargs):
# def weighted_variance(x, weights):
# def unit_tophat_ee(x):
# def unit_tophat_ei(x):
# def unit_tophat_ie(x):
# def unit_tophat_ii(x):
# def make_tophat_ee(lower, upper):
#     def range_tophat_ee(x):
# def make_tophat_ei(lower, upper):
#     def range_tophat_ei(x):
# def make_tophat_ie(lower, upper):
#     def range_tophat_ie(x):
# def make_tophat_ii(lower, upper):
#     def range_tophat_ii(x):
# def make_step_lcont(transition):
#     def step_lcont(x):
# def make_step_rcont(transition):
#     def step_rcont(x):
# class _Broadcaster(method_decorator):
# class _BroadcasterDecorator(object):


# Path: pwkit/ellipses.py
import numpy as np
    import omega as om
from .numutil import broadcastize
    from scipy.special import erfc
    from numpy import arctan2, sqrt
    from numpy import pi, sqrt
    from numpy.random import multivariate_normal as mvn
    from numpy import cos, sin
    from numpy import arctan2, sqrt


    """
    _bivcheck (sx_a, sy_a, cxy_a)
    _bivcheck (sx_b, sy_b, cxy_b)

    sx_c = np.sqrt (sx_a**2 + sx_b**2)
    sy_c = np.sqrt (sy_a**2 + sy_b**2)
    cxy_c = cxy_a + cxy_b

    return _bivcheck (sx_c, sy_c, cxy_c)


def bivplot (sx, sy, cxy, **kwargs):
    _bivcheck (sx, sy, cxy)
    return ellplot (*bivell (sx, sy, cxy), **kwargs)


# Ellipse form: major, minor, pa

def _ellcheck (mjr, mnr, pa):
    if mjr <= 0:
        raise ValueError ('mjr must be positive (%.10e)' % mjr)
    if mnr <= 0:
        raise ValueError ('mnr must be positive (%.10e)' % mnr)
    if mnr > mjr:
        raise ValueError ('mnr must be less than mjr (mnr=%.10e, '
                          'mjr=%.10e)' % (mnr, mjr))
    return mjr, mnr, pa



@broadcastize (3, ret_spec=(0, 0, 0))