# Copyright (c) 2017,2019 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Contains calculation of various derived indices."""
import numpy as np

from .basic import wind_speed
from .thermo import mixing_ratio, saturation_vapor_pressure
from .tools import _remove_nans, get_layer
from .. import constants as mpconsts
from ..package_tools import Exporter
from ..units import check_units, concatenate, units
from ..xarray import preprocess_and_wrap

exporter = Exporter(globals())


@exporter.export
@preprocess_and_wrap()
@check_units('[pressure]', '[temperature]', bottom='[pressure]', top='[pressure]')
def precipitable_water(pressure, dewpoint, *, bottom=None, top=None):
    r"""Calculate precipitable water through the depth of a sounding.

    Formula used is:

    .. math::  -\frac{1}{\rho_l g} \int\limits_{p_\text{bottom}}^{p_\text{top}} r dp

    from [Salby1996]_, p. 28

    Parameters
    ----------
    pressure : `pint.Quantity`
        Atmospheric pressure profile

    dewpoint : `pint.Quantity`
        Atmospheric dewpoint profile

    bottom: `pint.Quantity`, optional
        Bottom of the layer, specified in pressure. Defaults to None (highest pressure).

    top: `pint.Quantity`, optional
        Top of the layer, specified in pressure. Defaults to None (lowest pressure).

    Returns
    -------
    `pint.Quantity`
        Precipitable water in the layer

    Examples
    --------
    >>> pressure = np.array([1000, 950, 900]) * units.hPa
    >>> dewpoint = np.array([20, 15, 10]) * units.degC
    >>> pw = precipitable_water(pressure, dewpoint)

    Notes
    -----
    Only functions on 1D profiles (not higher-dimension vertical cross sections or grids).

    .. versionchanged:: 1.0
       Signature changed from ``(dewpt, pressure, bottom=None, top=None)``

    """
    # Sort pressure and dewpoint to be in decreasing pressure order (increasing height)
    sort_inds = np.argsort(pressure)[::-1]
    pressure = pressure[sort_inds]
    dewpoint = dewpoint[sort_inds]

    pressure, dewpoint = _remove_nans(pressure, dewpoint)

    min_pressure = np.nanmin(pressure)
    max_pressure = np.nanmax(pressure)

    if top is None:
        top = min_pressure
    elif not min_pressure <= top <= max_pressure:
        raise ValueError(f'The pressure and dewpoint profile ranges from {max_pressure} to '
                         f'{min_pressure}, after removing missing values. {top} is outside '
                         'this range.')

    if bottom is None:
        bottom = max_pressure
    elif not min_pressure <= bottom <= max_pressure:
        raise ValueError(f'The pressure and dewpoint profile ranges from {max_pressure} to '
                         f'{min_pressure}, after removing missing values. {bottom} is outside '
                         'this range.')

    pres_layer, dewpoint_layer = get_layer(pressure, dewpoint, bottom=bottom,
                                           depth=bottom - top)

    w = mixing_ratio(saturation_vapor_pressure(dewpoint_layer), pres_layer)

    # Since pressure is in decreasing order, pw will be the opposite sign of that expected.
    pw = -np.trapz(w, pres_layer) / (mpconsts.g * mpconsts.rho_l)
    return pw.to('millimeters')


@exporter.export
@preprocess_and_wrap()
@check_units('[pressure]')
def mean_pressure_weighted(pressure, *args, height=None, bottom=None, depth=None):
    r"""Calculate pressure-weighted mean of an arbitrary variable through a layer.

    Layer bottom and depth specified in height or pressure.

    .. math::  MPW = \frac{\int_{p_s}^{p_b} A p dp}{\int_{p_s}^{p_b} p dp}

    where:

    * :math:`MPW` is the pressure-weighted mean of a variable.
    * :math:`p_b` is the bottom pressure level.
    * :math:`p_s` is the top pressure level.
    * :math:`A` is the variable whose pressure-weighted mean is being calculated.

    Parameters
    ----------
    pressure : `pint.Quantity`
        Atmospheric pressure profile.

    args : `pint.Quantity`
        Parameters for which the weighted-continuous mean is to be calculated.

    height : `pint.Quantity`, optional
        Heights from sounding. Standard atmosphere heights assumed (if needed)
        if no heights are given.

    bottom: `pint.Quantity`, optional
        The bottom of the layer in either the provided height coordinate
        or in pressure. Don't provide in meters AGL unless the provided
        height coordinate is meters AGL. Default is the first observation,
        assumed to be the surface.

    depth: `pint.Quantity`, optional
        Depth of the layer in meters or hPa. Defaults to 100 hPa.

    Returns
    -------
    list of `pint.Quantity`
        list of layer mean value for each profile in args.

    See Also
    --------
    weighted_continuous_average

    Examples
    --------
    >>> from metpy.calc import mean_pressure_weighted
    >>> from metpy.units import units
    >>> p = [1000, 850, 700, 500] * units.hPa
    >>> T = [30, 15, 5, -5] * units.degC
    >>> mean_pressure_weighted(p, T)
    [<Quantity(298.54368, 'kelvin')>]

    Notes
    -----
    Only functions on 1D profiles (not higher-dimension vertical cross sections or grids).
    Since this function returns scalar values when given a profile, this will return Pint
    Quantities even when given xarray DataArray profiles.

    .. versionchanged:: 1.0
       Renamed ``heights`` parameter to ``height``

    """
    # Split pressure profile from other variables to average
    pres_prof, *others = get_layer(pressure, *args, height=height, bottom=bottom, depth=depth)

    # Taking the integral of the weights (pressure) to feed into the weighting
    # function. Said integral works out to this function:
    pres_int = 0.5 * (pres_prof[-1] ** 2 - pres_prof[0] ** 2)

    # Perform integration on the profile for each variable
    return [np.trapz(var_prof * pres_prof, x=pres_prof) / pres_int for var_prof in others]


@exporter.export
@preprocess_and_wrap()
@check_units('[pressure]')
def weighted_continuous_average(pressure, *args, height=None, bottom=None, depth=None):
    r"""Calculate weighted-continuous mean of an arbitrary variable through a layer.

    Layer top and bottom specified in height or pressure.

    Formula based on that from [Holton2004]_ pg. 76 and the NCL function ``wgt_vertical_n``

    .. math::  WCA = \frac{\int_{p_s}^{p_b} A dp}{\int_{p_s}^{p_b} dp}

    where:

    * :math:`WCA` is the weighted continuous average of a variable.
    * :math:`p_b` is the bottom pressure level.
    * :math:`p_s` is the top pressure level.
    * :math:`A` is the variable whose weighted continuous average is being calculated.

    Parameters
    ----------
    pressure : `pint.Quantity`
        Atmospheric pressure profile.

    args : `pint.Quantity`
        Parameters for which the weighted-continuous mean is to be calculated.

    height : `pint.Quantity`, optional
        Heights from sounding. Standard atmosphere heights assumed (if needed)
        if no heights are given.

    bottom: `pint.Quantity`, optional
        The bottom of the layer in either the provided height coordinate
        or in pressure. Don't provide in meters AGL unless the provided
        height coordinate is meters AGL. Default is the first observation,
        assumed to be the surface.

    depth: `pint.Quantity`, optional
        Depth of the layer in meters or hPa.

    Returns
    -------
    list of `pint.Quantity`
        list of layer mean value for each profile in args.

    Notes
    -----
    Only functions on 1D profiles (not higher-dimension vertical cross sections or grids).
    Since this function returns scalar values when given a profile, this will return Pint
    Quantities even when given xarray DataArray profiles.

    """
    # Split pressure profile from other variables to average
    pres_prof, *others = get_layer(
        pressure, *args, height=height, bottom=bottom, depth=depth
    )

    return [np.trapz(var_prof, x=pres_prof) / (pres_prof[-1] - pres_prof[0])
            for var_prof in others]


@exporter.export
@preprocess_and_wrap()
@check_units('[pressure]', '[speed]', '[speed]', '[length]')
def bunkers_storm_motion(pressure, u, v, height):
    r"""Calculate right-mover and left-mover supercell storm motions using the Bunkers method.

    This is a physically based, shear-relative, and Galilean invariant method for predicting
    supercell motion. Full atmospheric profiles of wind components, as well as pressure and
    heights, need to be provided so that calculation can properly calculate the required
    surface to 6 km mean flow.

    The calculation in summary is (from [Bunkers2000]_):

    * surface to 6 km non-pressure-weighted mean wind
    * a deviation from the sfc to 6 km mean wind of 7.5 m s−1
    * a 5.5 to 6 km mean wind for the head of the vertical wind shear vector
    * a surface to 0.5 km mean wind for the tail of the vertical wind shear vector

    Parameters
    ----------
    pressure : `pint.Quantity`
        Pressure from full profile

    u : `pint.Quantity`
        Full profile of the U-component of the wind

    v : `pint.Quantity`
        Full profile of the V-component of the wind

    height : `pint.Quantity`
        Full profile of height

    Returns
    -------
    right_mover: (`pint.Quantity`, `pint.Quantity`)
        Scalar U- and V- components of Bunkers right-mover storm motion

    left_mover: (`pint.Quantity`, `pint.Quantity`)
        Scalar U- and V- components of Bunkers left-mover storm motion

    wind_mean: (`pint.Quantity`, `pint.Quantity`)
        Scalar U- and V- components of surface to 6 km mean flow

    Examples
    --------
    >>> from metpy.calc import bunkers_storm_motion, wind_components
    >>> from metpy.units import units
    >>> p = [1000, 925, 850, 700, 500, 400] * units.hPa
    >>> h = [250, 700, 1500, 3100, 5720, 7120] * units.meters
    >>> wdir = [165, 180, 190, 210, 220, 250] * units.degree
    >>> sped = [5, 15, 20, 30, 50, 60] * units.knots
    >>> u, v = wind_components(sped, wdir)
    >>> bunkers_storm_motion(p, u, v, h)
    (<Quantity([22.09618172 12.43406736], 'knot')>,
    <Quantity([ 6.02861839 36.76517865], 'knot')>,
    <Quantity([14.06240005 24.599623  ], 'knot')>)

    Notes
    -----
    Only functions on 1D profiles (not higher-dimension vertical cross sections or grids).
    Since this function returns scalar values when given a profile, this will return Pint
    Quantities even when given xarray DataArray profiles.

    .. versionchanged:: 1.0
       Renamed ``heights`` parameter to ``height``

    """
    # remove nans from input data
    pressure, u, v, height = _remove_nans(pressure, u, v, height)

    # mean wind from sfc-6km
    wind_mean = weighted_continuous_average(pressure, u, v, height=height,
                                            depth=units.Quantity(6000, 'meter'))

    wind_mean = units.Quantity.from_list(wind_mean)

    # mean wind from sfc-500m
    wind_500m = weighted_continuous_average(pressure, u, v, height=height,
                                            depth=units.Quantity(500, 'meter'))

    wind_500m = units.Quantity.from_list(wind_500m)

    # mean wind from 5.5-6km
    wind_5500m = weighted_continuous_average(
        pressure, u, v, height=height,
        depth=units.Quantity(500, 'meter'),
        bottom=height[0] + units.Quantity(5500, 'meter'))

    wind_5500m = units.Quantity.from_list(wind_5500m)

    # Calculate the shear vector from sfc-500m to 5.5-6km
    shear = wind_5500m - wind_500m

    # Take the cross product of the wind shear and k, and divide by the vector magnitude and
    # multiply by the deviation empirically calculated in Bunkers (2000) (7.5 m/s)
    shear_cross = concatenate([shear[1], -shear[0]])
    shear_mag = np.hypot(*shear)
    rdev = shear_cross * (units.Quantity(7.5, 'm/s').to(u.units) / shear_mag)

    # Add the deviations to the layer average wind to get the RM motion
    right_mover = wind_mean + rdev

    # Subtract the deviations to get the LM motion
    left_mover = wind_mean - rdev

    return right_mover, left_mover, wind_mean


@exporter.export
@preprocess_and_wrap()
@check_units('[pressure]', '[speed]', '[speed]', u_llj='[speed]', v_llj='[speed]')
def corfidi_storm_motion(pressure, u, v, *, u_llj=None, v_llj=None):
    r"""Calculate upwind- and downwind-developing MCS storm motions using the Corfidi method.

    Method described by ([Corfidi2003]_):

    * Cloud-layer winds, estimated as the mean of the wind from 850 hPa to 300 hPa
    * Convergence along the cold pool, defined as the negative of the low-level jet
        (estimated as maximum in winds below 850 hPa unless specified in u_llj and v_llj)
    * The vector sum of the above is used to describe upwind propagating MCSes
    * Downwind propagating MCSes are taken as propagating along the sum of the cloud-layer wind
        and negative of the low level jet, therefore the cloud-level winds are doubled
        to re-add storm motion

    Parameters
    ----------
    pressure : `pint.Quantity`
        Pressure from full profile

    u : `pint.Quantity`
        Full profile of the U-component of the wind

    v : `pint.Quantity`
        Full profile of the V-component of the wind

    u_llj : `pint.Quantity`, optional
        U-component of low-level jet

    v_llj : `pint.Quantity`, optional
        V-component of low-level jet

    Returns
    -------
    upwind_prop: (`pint.Quantity`, `pint.Quantity`)
        Scalar U- and V- components of Corfidi upwind propagating MCS motion

    downwind_prop: (`pint.Quantity`, `pint.Quantity`)
        Scalar U- and V- components of Corfidi downwind propagating MCS motion

    Examples
    --------
    >>> from metpy.calc import corfidi_storm_motion, wind_components
    >>> from metpy.units import units
    >>> p = [1000, 925, 850, 700, 500, 400] * units.hPa
    >>> wdir = [165, 180, 190, 210, 220, 250] * units.degree
    >>> speed = [5, 15, 20, 30, 50, 60] * units.knots
    >>> u, v = wind_components(speed, wdir)
    >>> corfidi_storm_motion(p, u, v)
    (<Quantity([16.4274315   7.75758388], 'knot')>,
    <Quantity([36.32782655 35.21132283], 'knot')>)
    >>> # Example with manually specified low-level jet as max wind below 1500m
    >>> import numpy as np
    >>> h = [100, 250, 700, 1500, 3100, 5720] * units.meters
    >>> lowest1500_index = np.argmin(h <= units.Quantity(1500, 'meter'))
    >>> llj_index = np.argmax(speed[:lowest1500_index])
    >>> llj_u, llj_v = u[llj_index], v[llj_index]
    >>> corfidi_storm_motion(p, u, v, u_llj=llj_u, v_llj=llj_v)
    (<Quantity([4.90039505   1.47297683], 'knot')>,
    <Quantity([24.8007901 28.92671577], 'knot')>)


    Notes
    -----
    Only functions on 1D profiles (not higher-dimension vertical cross sections or grids).
    Since this function returns scalar values when given a profile, this will return Pint
    Quantities even when given xarray DataArray profiles.

    """
    # If user specifies only one component of low-level jet, raise
    if (u_llj is None) ^ (v_llj is None):
        raise ValueError('Must specify both u_llj and v_llj or neither')

    # remove nans from input
    pressure, u, v = _remove_nans(pressure, u, v)

    # If LLJ specified, use that
    if u_llj is not None and v_llj is not None:
        # find inverse of low-level jet
        llj_inverse = units.Quantity.from_list((-1 * u_llj, -1 * v_llj))
    # If pressure values contain values below 850 hPa, find low-level jet
    elif np.max(pressure) >= units.Quantity(850, 'hectopascal'):
        # convert u/v wind to wind speed and direction
        wind_magnitude = wind_speed(u, v)
        # find inverse of low-level jet
        lowlevel_index = np.argmin(pressure >= units.Quantity(850, 'hectopascal'))
        llj_index = np.argmax(wind_magnitude[:lowlevel_index])
        llj_inverse = units.Quantity.from_list((-u[llj_index], -v[llj_index]))
    # If LLJ not specified and maximum pressure value is above 850 hPa, raise
    else:
        raise ValueError('Must specify low-level jet or '
                         'specify pressure values below 850 hPa')

    # cloud layer mean wind
    # don't select outside bounds of given data
    if pressure[0] < units.Quantity(850, 'hectopascal'):
        bottom = pressure[0]
    else:
        bottom = units.Quantity(850, 'hectopascal')
    if pressure[-1] > units.Quantity(300, 'hectopascal'):
        depth = bottom - pressure[-1]
    else:
        depth = units.Quantity(550, 'hectopascal')
    cloud_layer_winds = mean_pressure_weighted(pressure, u, v,
                                               bottom=bottom,
                                               depth=depth)

    cloud_layer_winds = units.Quantity.from_list(cloud_layer_winds)

    # calculate corfidi vectors
    upwind = cloud_layer_winds + llj_inverse

    downwind = 2 * cloud_layer_winds + llj_inverse

    return upwind, downwind


@exporter.export
@preprocess_and_wrap()
@check_units('[pressure]', '[speed]', '[speed]')
def bulk_shear(pressure, u, v, height=None, bottom=None, depth=None):
    r"""Calculate bulk shear through a layer.

    Layer top and bottom specified in meters or pressure.

    Parameters
    ----------
    pressure : `pint.Quantity`
        Atmospheric pressure profile

    u : `pint.Quantity`
        U-component of wind

    v : `pint.Quantity`
        V-component of wind

    height : `pint.Quantity`, optional
        Heights from sounding

    depth: `pint.Quantity`, optional
        The depth of the layer in meters or hPa. Defaults to 100 hPa.

    bottom: `pint.Quantity`, optional
        The bottom of the layer in height or pressure coordinates.
        If using a height, it must be in the same coordinates as the given
        heights (i.e., don't use meters AGL unless given heights
        are in meters AGL.) Defaults to the highest pressure or lowest height given.

    Returns
    -------
    u_shr: `pint.Quantity`
        U-component of layer bulk shear
    v_shr: `pint.Quantity`
        V-component of layer bulk shear

    Examples
    --------
    >>> from metpy.calc import bulk_shear, wind_components
    >>> from metpy.units import units
    >>> p = [1000, 925, 850, 700, 500] * units.hPa
    >>> wdir = [165, 180, 190, 210, 220] * units.degree
    >>> sped = [5, 15, 20, 30, 50] * units.knots
    >>> u, v = wind_components(sped, wdir)
    >>> bulk_shear(p, u, v)
    (<Quantity(2.41943319, 'knot')>, <Quantity(11.6920573, 'knot')>)

    Notes
    -----
    Only functions on 1D profiles (not higher-dimension vertical cross sections or grids).
    Since this function returns scalar values when given a profile, this will return Pint
    Quantities even when given xarray DataArray profiles.

    .. versionchanged:: 1.0
       Renamed ``heights`` parameter to ``height``

    """
    _, u_layer, v_layer = get_layer(pressure, u, v, height=height,
                                    bottom=bottom, depth=depth)

    u_shr = u_layer[-1] - u_layer[0]
    v_shr = v_layer[-1] - v_layer[0]

    return u_shr, v_shr


@exporter.export
@preprocess_and_wrap(wrap_like='mucape')
@check_units('[energy] / [mass]', '[speed] * [speed]', '[speed]')
def supercell_composite(mucape, effective_storm_helicity, effective_shear):
    r"""Calculate the supercell composite parameter.

    The supercell composite parameter is designed to identify
    environments favorable for the development of supercells,
    and is calculated using the formula developed by
    [Thompson2004]_:

    .. math::  \text{SCP} = \frac{\text{MUCAPE}}{1000 \text{J/kg}} *
               \frac{\text{Effective SRH}}{50 \text{m}^2/\text{s}^2} *
               \frac{\text{Effective Shear}}{20 \text{m/s}}

    The effective_shear term is set to zero below 10 m/s and
    capped at 1 when effective_shear exceeds 20 m/s.

    Parameters
    ----------
    mucape : `pint.Quantity`
        Most-unstable CAPE

    effective_storm_helicity : `pint.Quantity`
        Effective-layer storm-relative helicity

    effective_shear : `pint.Quantity`
        Effective bulk shear

    Returns
    -------
    `pint.Quantity`
        Supercell composite

    Examples
    --------
    >>> from metpy.calc import supercell_composite
    >>> from metpy.units import units
    >>> supercell_composite(2500 * units('J/kg'), 125 * units('m^2/s^2'),
    ...                     50 * units.knot).to_base_units()
    <Quantity([6.25], 'dimensionless')>

    """
    effective_shear = np.clip(np.atleast_1d(effective_shear), None, units.Quantity(20, 'm/s'))
    effective_shear[effective_shear < units.Quantity(10, 'm/s')] = units.Quantity(0, 'm/s')
    effective_shear = effective_shear / units.Quantity(20, 'm/s')

    return ((mucape / units.Quantity(1000, 'J/kg'))
            * (effective_storm_helicity / units.Quantity(50, 'm^2/s^2'))
            * effective_shear).to('dimensionless')


@exporter.export
@preprocess_and_wrap(wrap_like='sbcape')
@check_units('[energy] / [mass]', '[length]', '[speed] * [speed]', '[speed]')
def significant_tornado(sbcape, surface_based_lcl_height, storm_helicity_1km, shear_6km):
    r"""Calculate the significant tornado parameter (fixed layer).

    The significant tornado parameter is designed to identify
    environments favorable for the production of significant
    tornadoes contingent upon the development of supercells.
    It's calculated according to the formula used on the SPC
    mesoanalysis page, updated in [Thompson2004]_:

    .. math::  \text{SIGTOR} = \frac{\text{SBCAPE}}{1500 \text{J/kg}} * \frac{(2000 \text{m} -
               \text{LCL}_\text{SB})}{1000 \text{m}} *
               \frac{SRH_{\text{1km}}}{150 \text{m}^\text{s}/\text{s}^2} *
               \frac{\text{Shear}_\text{6km}}{20 \text{m/s}}

    The lcl height is set to zero when the lcl is above 2000m and
    capped at 1 when below 1000m, and the shr6 term is set to 0
    when shr6 is below 12.5 m/s and maxed out at 1.5 when shr6
    exceeds 30 m/s.

    Parameters
    ----------
    sbcape : `pint.Quantity`
        Surface-based CAPE

    surface_based_lcl_height : `pint.Quantity`
        Surface-based lifted condensation level

    storm_helicity_1km : `pint.Quantity`
        Surface-1km storm-relative helicity

    shear_6km : `pint.Quantity`
        Surface-6km bulk shear

    Returns
    -------
    `pint.Quantity`
        Significant tornado parameter

    Examples
    --------
    >>> from metpy.calc import significant_tornado
    >>> from metpy.units import units
    >>> significant_tornado(3000 * units('J/kg'), 750 * units.meters,
    ...                     150 * units('m^2/s^2'), 25 * units.knot).to_base_units()
    <Quantity([1.28611111], 'dimensionless')>

    """
    surface_based_lcl_height = np.clip(np.atleast_1d(surface_based_lcl_height),
                                       units.Quantity(1000., 'm'), units.Quantity(2000., 'm'))
    surface_based_lcl_height = ((units.Quantity(2000., 'm') - surface_based_lcl_height)
                                / units.Quantity(1000., 'm'))
    shear_6km = np.clip(np.atleast_1d(shear_6km), None, units.Quantity(30, 'm/s'))
    shear_6km[shear_6km < units.Quantity(12.5, 'm/s')] = units.Quantity(0, 'm/s')
    shear_6km /= units.Quantity(20, 'm/s')

    return ((sbcape / units.Quantity(1500., 'J/kg'))
            * surface_based_lcl_height
            * (storm_helicity_1km / units.Quantity(150., 'm^2/s^2'))
            * shear_6km)


@exporter.export
@preprocess_and_wrap()
@check_units('[pressure]', '[speed]', '[speed]', '[length]', '[speed]', '[speed]')
def critical_angle(pressure, u, v, height, u_storm, v_storm):
    r"""Calculate the critical angle.

    The critical angle is the angle between the 10m storm-relative inflow vector
    and the 10m-500m shear vector. A critical angle near 90 degrees indicates
    that a storm in this environment on the indicated storm motion vector
    is likely ingesting purely streamwise vorticity into its updraft, and [Esterheld2008]_
    showed that significantly tornadic supercells tend to occur in environments
    with critical angles near 90 degrees.

    Parameters
    ----------
    pressure : `pint.Quantity`
        Pressures from sounding

    u : `pint.Quantity`
        U-component of sounding winds

    v : `pint.Quantity`
        V-component of sounding winds

    height : `pint.Quantity`
        Heights from sounding

    u_storm : `pint.Quantity`
        U-component of storm motion

    v_storm : `pint.Quantity`
        V-component of storm motion

    Returns
    -------
    `pint.Quantity`
        Critical angle in degrees

    Examples
    --------
    >>> from metpy.calc import critical_angle, wind_components
    >>> from metpy.units import units
    >>> p = [1000, 925, 850, 700, 500, 400] * units.hPa
    >>> h = [250, 700, 1500, 3100, 5720, 7120] * units.meters
    >>> wdir = [165, 180, 190, 210, 220, 250] * units.degree
    >>> sped = [5, 15, 20, 30, 50, 60] * units.knots
    >>> u, v = wind_components(sped, wdir)
    >>> critical_angle(p, u, v, h, 7 * units.knots, 7 * units.knots)
    <Quantity(67.0942521, 'degree')>

    Notes
    -----
    Only functions on 1D profiles (not higher-dimension vertical cross sections or grids).
    Since this function returns scalar values when given a profile, this will return Pint
    Quantities even when given xarray DataArray profiles.

    .. versionchanged:: 1.0
       Renamed ``heights`` parameter to ``height``

    """
    # Convert everything to m/s
    u = u.to('m/s')
    v = v.to('m/s')
    u_storm = u_storm.to('m/s')
    v_storm = v_storm.to('m/s')

    sort_inds = np.argsort(pressure[::-1])
    pressure = pressure[sort_inds]
    height = height[sort_inds]
    u = u[sort_inds]
    v = v[sort_inds]

    # Calculate sfc-500m shear vector
    shr5 = bulk_shear(pressure, u, v, height=height, depth=units.Quantity(500, 'meter'))

    # Make everything relative to the sfc wind orientation
    umn = u_storm - u[0]
    vmn = v_storm - v[0]

    vshr = np.asarray([shr5[0].magnitude, shr5[1].magnitude])
    vsm = np.asarray([umn.magnitude, vmn.magnitude])
    angle_c = np.dot(vshr, vsm) / (np.linalg.norm(vshr) * np.linalg.norm(vsm))
    critical_angle = units.Quantity(np.arccos(angle_c), 'radian')

    return critical_angle.to('degrees')
