Source code for ladybug_comfort.utci

# coding=utf-8
"""Utility functions for calculating Universal Thermal Climate Index (UTCI).

UTCI is a thermal comfort model strictly for the outdoors. It is an international
standard for outdoor temperature sensation (aka. "feels-like" temperature) and
is one of the most common of such "feels-like" temperatures used by
meteorologists. UTCI that attempts to satisfy the following requirements:

1) Thermo-physiological significance in the whole range of heat
    exchange conditions of existing thermal environments
2) Valid in all climates, seasons, and scales
3) Useful for key applications in human biometeorology.

While UTCI is designed to be valid in all climates and seasons, it assumes
that human subjects are walking (with a metabolic rate around 2.4 met) and
that they naturally adapt their clothing with the outdoor temperature.
For outdoor situations that do not fit these criteria, the Physiological
Equivalent Temperature (PET) model is recommended.
"""
from __future__ import division

from ladybug.rootfinding import secant
from ladybug.rootfinding import bisect

import math




def universal_thermal_climate_index(ta, tr, vel, rh):
    """Calculate Universal Thermal Climate Index (UTCI) using a polynomial approximation.

    This function here is a Python version of the original UTCI_approx
    application written in Fortran. Version a 0.002, October 2009
    The original Fortran code can be found at www.utci.org.

    Note:
        [1] Peter Bröde, Dusan Fiala, Krzysztof Blazejczyk, Yoram Epstein,
        Ingvar Holmér, Gerd Jendritzky, Bernhard Kampmann, Mark Richards,
        Hannu Rintamäki, Avraham Shitzer, George Havenith. 2009.
        Calculating UTCI Equivalent Temperature. In: JW Castellani & TL
        Endrusick, eds. Proceedings of the 13th International Conference
        on Environmental Ergonomics, USARIEM, Natick, MA.

    Args:
        ta: Air temperature [C]
        tr: Mean radiant temperature [C]
        vel: Wind speed 10 m above ground level [m/s].
            Note that this meteorological speed at 10 m is simply 1.5 times the
            speed felt at ground in the original Fiala model used to build UTCI.
        rh: Relative humidity [%]

    Returns:
        UTCI_approx -- The Universal Thermal Climate Index (UTCI) for the input
        conditions as approximated by a 4-D polynomial.
    """
    # set upper and lower limits of air velocity according to Fiala model scenarios
    vel = 0.5 if vel < 0.5 else vel
    vel = 17 if vel > 17 else vel

    # metrics derived from the inputs used in the polynomial equation
    eh_pa = saturated_vapor_pressure_hpa(ta) * (rh / 100.0)  # partial vapor pressure
    pa_pr = eh_pa / 10.0  # convert vapour pressure to kPa
    d_tr = tr - ta  # difference between radiant and air temperature

    # pre-calculate powers so we can re-use them
    utci_approx = _utci_polynomial(ta, d_tr, vel, pa_pr)

    return utci_approx





def saturated_vapor_pressure_hpa(db_temp):
    """Calculate saturated vapor pressure (hPa) at temperature (C).

    This equation of saturation vapor pressure is specific to the UTCI model.
    """
    g = (-2836.5744, -6028.076559, 19.54263612, -0.02737830188, 0.000016261698,
         7.0229056e-10, -1.8680009e-13)
    tk = db_temp + 273.15  # air temp in K
    es = 2.7150305 * math.log(tk)
    for i, x in enumerate(g):
        es = es + (x * (tk**(i - 2)))
    es = math.exp(es) * 0.01
    return es





def calc_missing_utci_input(target_utci, utci_inputs, low_bound=0., up_bound=100.,
                            tolerance=0.001):
    """Return the value of a missing_utci_input given a target_utci and the 3 other inputs.

    This is particularly useful when trying to draw comfort polygons on charts
    using the UTCI model.

    Args:
        target_utci: The target UTCI temperature that you are trying to produce
            from the inputs to the UTCI model.
        utci_inputs: A dictionary of 4 UTCI inputs with the following keys:
            'ta', 'tr', 'vel', 'rh'.  Each key should correspond to a value
            that represents that UTCI input but one of these inputs should
            have a value of None.
            The input corresponding to None will be solved for by this function.
            One can also input None for both 'ta' and 'tr' to solve for the operative
            temperature that meets the target_utci. In this case, both 'ta' and 'tr'
            in the output dictionary will be the same.
            Example (solving for relative humidity):

         .. code-block:: python

            {'ta': 20, 'tr': 20, 'vel': 0.05, 'rh': None}

        low_bound: The lowest possible value of the missing input you are tying to
            find. Putting in a good value here will help the model converge to a
            solution faster. (Default: 0).
        up_bound: The highest possible value of the missing input you are tying to
            find. Putting in a good value here will help the model converge to a
            solution faster. (Default: 100).
        tolerance: The acceptable error in the target_utci. (Default: 0.001).

    Returns:
        complete_utci_inputs -- A copy of the utci_inputs dictionary but with values for
        all inputs. The missing input to the UTCI model will be filled by the value
        that returns the target_utci.
    """
    assert len(utci_inputs.keys()) == 4, \
        'utci_inputs must have 4 keys. Got {}.'.format(len(utci_inputs.keys()))

    # Determine the function that should be used given the missing input.
    if utci_inputs['ta'] is None and utci_inputs['tr'] is None:
        def fn(x):
            return universal_thermal_climate_index(
                x, x, utci_inputs['vel'], utci_inputs['rh']) - target_utci
        missing_key = ('ta', 'tr')
    elif utci_inputs['ta'] is None:
        def fn(x):
            return universal_thermal_climate_index(
                x, utci_inputs['tr'], utci_inputs['vel'], utci_inputs['rh']) \
                - target_utci
        missing_key = 'ta'
    elif utci_inputs['tr'] is None:
        def fn(x):
            return universal_thermal_climate_index(
                utci_inputs['ta'], x, utci_inputs['vel'], utci_inputs['rh']) \
                - target_utci
        missing_key = 'tr'
    elif utci_inputs['vel'] is None:
        def fn(x):
            return target_utci - universal_thermal_climate_index(
                utci_inputs['ta'], utci_inputs['tr'], x, utci_inputs['rh'])
        missing_key = 'vel'
    else:
        def fn(x):
            return universal_thermal_climate_index(
                utci_inputs['ta'], utci_inputs['tr'], utci_inputs['vel'], x) \
                - target_utci
        missing_key = 'rh'

    # Solve for the missing input using the function.
    missing_val = secant(low_bound, up_bound, fn, tolerance)
    if missing_val is None:
        missing_val = bisect(low_bound, up_bound, fn, tolerance, 0)

    # copy and complete the input dictionary
    utci_inputs = utci_inputs.copy()
    if isinstance(missing_key, str):
        utci_inputs[missing_key] = missing_val
    else:
        for key in missing_key:
            utci_inputs[key] = missing_val
    return utci_inputs



def _utci_polynomial(ta, d_tr, vel, pa_pr):
    """Polynomial approximation for UTCI.
    
    Args:
        ta: Air temperature [C].
        d_tr: Difference between radiant and air temperature [C].
        vel: Wind speed 10 m above ground level [m/s].
        pa_pr: Vapour pressure [kPa].
    """
    # pre-calculate powers so we can re-use them
    ta2 = ta ** 2
    ta3 = ta ** 3
    ta4 = ta ** 4
    ta5 = ta ** 5
    ta6 = ta ** 6
    vel2 = vel ** 2
    vel3 = vel ** 3
    vel4 = vel ** 4
    vel5 = vel ** 5
    vel6 = vel ** 6
    d_tr2 = d_tr ** 2
    d_tr3 = d_tr ** 3
    d_tr4 = d_tr ** 4
    d_tr5 = d_tr ** 5
    d_tr6 = d_tr ** 6
    pa_pr2 = pa_pr ** 2
    pa_pr3 = pa_pr ** 3
    pa_pr4 = pa_pr ** 4
    pa_pr5 = pa_pr ** 5
    pa_pr6 = pa_pr ** 6

    # UTCI approximation calculation
    utci_approx = ta + \
        0.607562052 + \
        -0.0227712343 * ta + \
        8.06470249e-4 * ta2 + \
        -1.54271372e-4 * ta3 + \
        -3.24651735e-6 * ta4 + \
        7.32602852e-8 * ta5 + \
        1.35959073e-9 * ta6 + \
        -2.25836520 * vel + \
        0.0880326035 * ta * vel + \
        0.00216844454 * ta2 * vel + \
        -1.53347087e-5 * ta3 * vel + \
        -5.72983704e-7 * ta4 * vel + \
        -2.55090145e-9 * ta5 * vel + \
        -0.751269505 * vel2 + \
        -0.00408350271 * ta * vel2 + \
        -5.21670675e-5 * ta2 * vel2 + \
        1.94544667e-6 * ta3 * vel2 + \
        1.14099531e-8 * ta4 * vel2 + \
        0.158137256 * vel3 + \
        -6.57263143e-5 * ta * vel3 + \
        2.22697524e-7 * ta2 * vel3 + \
        -4.16117031e-8 * ta3 * vel3 + \
        -0.0127762753 * vel4 + \
        9.66891875e-6 * ta * vel4 + \
        2.52785852e-9 * ta2 * vel4 + \
        4.56306672e-4 * vel5 + \
        -1.74202546e-7 * ta * vel5 + \
        -5.91491269e-6 * vel6 + \
        0.398374029 * d_tr + \
        1.83945314e-4 * ta * d_tr + \
        -1.73754510e-4 * ta2 * d_tr + \
        -7.60781159e-7 * ta3 * d_tr + \
        3.77830287e-8 * ta4 * d_tr + \
        5.43079673e-10 * ta5 * d_tr + \
        -0.0200518269 * vel * d_tr + \
        8.92859837e-4 * ta * vel * d_tr + \
        3.45433048e-6 * ta2 * vel * d_tr + \
        -3.77925774e-7 * ta3 * vel * d_tr + \
        -1.69699377e-9 * ta4 * vel * d_tr + \
        1.69992415e-4 * vel2 * d_tr + \
        -4.99204314e-5 * ta * vel2 * d_tr + \
        2.47417178e-7 * ta2 * vel2 * d_tr + \
        1.07596466e-8 * ta3 * vel2 * d_tr + \
        8.49242932e-5 * vel3 * d_tr + \
        1.35191328e-6 * ta * vel3 * d_tr + \
        -6.21531254e-9 * ta2 * vel3 * d_tr + \
        -4.99410301e-6 * vel4 * d_tr + \
        -1.89489258e-8 * ta * vel4 * d_tr + \
        8.15300114e-8 * vel5 * d_tr + \
        7.55043090e-4 * d_tr2 + \
        -5.65095215e-5 * ta * d_tr2 + \
        -4.52166564e-7 * ta2 * d_tr2 + \
        2.46688878e-8 * ta3 * d_tr2 + \
        2.42674348e-10 * ta4 * d_tr2 + \
        1.54547250e-4 * vel * d_tr2 + \
        5.24110970e-6 * ta * vel * d_tr2 + \
        -8.75874982e-8 * ta2 * vel * d_tr2 + \
        -1.50743064e-9 * ta3 * vel * d_tr2 + \
        -1.56236307e-5 * vel2 * d_tr2 + \
        -1.33895614e-7 * ta * vel2 * d_tr2 + \
        2.49709824e-9 * ta2 * vel2 * d_tr2 + \
        6.51711721e-7 * vel3 * d_tr2 + \
        1.94960053e-9 * ta * vel3 * d_tr2 + \
        -1.00361113e-8 * vel4 * d_tr2 + \
        -1.21206673e-5 * d_tr3 + \
        -2.18203660e-7 * ta * d_tr3 + \
        7.51269482e-9 * ta2 * d_tr3 + \
        9.79063848e-11 * ta3 * d_tr3 + \
        1.25006734e-6 * vel * d_tr3 + \
        -1.81584736e-9 * ta * vel * d_tr3 + \
        -3.52197671e-10 * ta2 * vel * d_tr3 + \
        -3.36514630e-8 * vel2 * d_tr3 + \
        1.35908359e-10 * ta * vel2 * d_tr3 + \
        4.17032620e-10 * vel3 * d_tr3 + \
        -1.30369025e-9 * d_tr4 + \
        4.13908461e-10 * ta * d_tr4 + \
        9.22652254e-12 * ta2 * d_tr4 + \
        -5.08220384e-9 * vel * d_tr4 + \
        -2.24730961e-11 * ta * vel * d_tr4 + \
        1.17139133e-10 * vel2 * d_tr4 + \
        6.62154879e-10 * d_tr5 + \
        4.03863260e-13 * ta * d_tr5 + \
        1.95087203e-12 * vel * d_tr5 + \
        -4.73602469e-12 * d_tr6 + \
        5.12733497 * pa_pr + \
        -0.312788561 * ta * pa_pr + \
        -0.0196701861 * ta2 * pa_pr + \
        9.99690870e-4 * ta3 * pa_pr + \
        9.51738512e-6 * ta4 * pa_pr + \
        -4.66426341e-7 * ta5 * pa_pr + \
        0.548050612 * vel * pa_pr + \
        -0.00330552823 * ta * vel * pa_pr + \
        -0.00164119440 * ta2 * vel * pa_pr + \
        -5.16670694e-6 * ta3 * vel * pa_pr + \
        9.52692432e-7 * ta4 * vel * pa_pr + \
        -0.0429223622 * vel2 * pa_pr + \
        0.00500845667 * ta * vel2 * pa_pr + \
        1.00601257e-6 * ta2 * vel2 * pa_pr + \
        -1.81748644e-6 * ta3 * vel2 * pa_pr + \
        -1.25813502e-3 * vel3 * pa_pr + \
        -1.79330391e-4 * ta * vel3 * pa_pr + \
        2.34994441e-6 * ta2 * vel3 * pa_pr + \
        1.29735808e-4 * vel4 * pa_pr + \
        1.29064870e-6 * ta * vel4 * pa_pr + \
        -2.28558686e-6 * vel5 * pa_pr + \
        -0.0369476348 * d_tr * pa_pr + \
        0.00162325322 * ta * d_tr * pa_pr + \
        -3.14279680e-5 * ta2 * d_tr * pa_pr + \
        2.59835559e-6 * ta3 * d_tr * pa_pr + \
        -4.77136523e-8 * ta4 * d_tr * pa_pr + \
        8.64203390e-3 * vel * d_tr * pa_pr + \
        -6.87405181e-4 * ta * vel * d_tr * pa_pr + \
        -9.13863872e-6 * ta2 * vel * d_tr * pa_pr + \
        5.15916806e-7 * ta3 * vel * d_tr * pa_pr + \
        -3.59217476e-5 * vel2 * d_tr * pa_pr + \
        3.28696511e-5 * ta * vel2 * d_tr * pa_pr + \
        -7.10542454e-7 * ta2 * vel2 * d_tr * pa_pr + \
        -1.24382300e-5 * vel3 * d_tr * pa_pr + \
        -7.38584400e-9 * ta * vel3 * d_tr * pa_pr + \
        2.20609296e-7 * vel4 * d_tr * pa_pr + \
        -7.32469180e-4 * d_tr2 * pa_pr + \
        -1.87381964e-5 * ta * d_tr2 * pa_pr + \
        4.80925239e-6 * ta2 * d_tr2 * pa_pr + \
        -8.75492040e-8 * ta3 * d_tr2 * pa_pr + \
        2.77862930e-5 * vel * d_tr2 * pa_pr + \
        -5.06004592e-6 * ta * vel * d_tr2 * pa_pr + \
        1.14325367e-7 * ta2 * vel * d_tr2 * pa_pr + \
        2.53016723e-6 * vel2 * d_tr2 * pa_pr + \
        -1.72857035e-8 * ta * vel2 * d_tr2 * pa_pr + \
        -3.95079398e-8 * vel3 * d_tr2 * pa_pr + \
        -3.59413173e-7 * d_tr3 * pa_pr + \
        7.04388046e-7 * ta * d_tr3 * pa_pr + \
        -1.89309167e-8 * ta2 * d_tr3 * pa_pr + \
        -4.79768731e-7 * vel * d_tr3 * pa_pr + \
        7.96079978e-9 * ta * vel * d_tr3 * pa_pr + \
        1.62897058e-9 * vel2 * d_tr3 * pa_pr + \
        3.94367674e-8 * d_tr4 * pa_pr + \
        -1.18566247e-9 * ta * d_tr4 * pa_pr + \
        3.34678041e-10 * vel * d_tr4 * pa_pr + \
        -1.15606447e-10 * d_tr5 * pa_pr + \
        -2.80626406 * pa_pr2 + \
        0.548712484 * ta * pa_pr2 + \
        -0.00399428410 * ta2 * pa_pr2 + \
        -9.54009191e-4 * ta3 * pa_pr2 + \
        1.93090978e-5 * ta4 * pa_pr2 + \
        -0.308806365 * vel * pa_pr2 + \
        0.0116952364 * ta * vel * pa_pr2 + \
        4.95271903e-4 * ta2 * vel * pa_pr2 + \
        -1.90710882e-5 * ta3 * vel * pa_pr2 + \
        0.00210787756 * vel2 * pa_pr2 + \
        -6.98445738e-4 * ta * vel2 * pa_pr2 + \
        2.30109073e-5 * ta2 * vel2 * pa_pr2 + \
        4.17856590e-4 * vel3 * pa_pr2 + \
        -1.27043871e-5 * ta * vel3 * pa_pr2 + \
        -3.04620472e-6 * vel4 * pa_pr2 + \
        0.0514507424 * d_tr * pa_pr2 + \
        -0.00432510997 * ta * d_tr * pa_pr2 + \
        8.99281156e-5 * ta2 * d_tr * pa_pr2 + \
        -7.14663943e-7 * ta3 * d_tr * pa_pr2 + \
        -2.66016305e-4 * vel * d_tr * pa_pr2 + \
        2.63789586e-4 * ta * vel * d_tr * pa_pr2 + \
        -7.01199003e-6 * ta2 * vel * d_tr * pa_pr2 + \
        -1.06823306e-4 * vel2 * d_tr * pa_pr2 + \
        3.61341136e-6 * ta * vel2 * d_tr * pa_pr2 + \
        2.29748967e-7 * vel3 * d_tr * pa_pr2 + \
        3.04788893e-4 * d_tr2 * pa_pr2 + \
        -6.42070836e-5 * ta * d_tr2 * pa_pr2 + \
        1.16257971e-6 * ta2 * d_tr2 * pa_pr2 + \
        7.68023384e-6 * vel * d_tr2 * pa_pr2 + \
        -5.47446896e-7 * ta * vel * d_tr2 * pa_pr2 + \
        -3.59937910e-8 * vel2 * d_tr2 * pa_pr2 + \
        -4.36497725e-6 * d_tr3 * pa_pr2 + \
        1.68737969e-7 * ta * d_tr3 * pa_pr2 + \
        2.67489271e-8 * vel * d_tr3 * pa_pr2 + \
        3.23926897e-9 * d_tr4 * pa_pr2 + \
        -0.0353874123 * pa_pr3 + \
        -0.221201190 * ta * pa_pr3 + \
        0.0155126038 * ta2 * pa_pr3 + \
        -2.63917279e-4 * ta3 * pa_pr3 + \
        0.0453433455 * vel * pa_pr3 + \
        -0.00432943862 * ta * vel * pa_pr3 + \
        1.45389826e-4 * ta2 * vel * pa_pr3 + \
        2.17508610e-4 * vel2 * pa_pr3 + \
        -6.66724702e-5 * ta * vel2 * pa_pr3 + \
        3.33217140e-5 * vel3 * pa_pr3 + \
        -0.00226921615 * d_tr * pa_pr3 + \
        3.80261982e-4 * ta * d_tr * pa_pr3 + \
        -5.45314314e-9 * ta2 * d_tr * pa_pr3 + \
        -7.96355448e-4 * vel * d_tr * pa_pr3 + \
        2.53458034e-5 * ta * vel * d_tr * pa_pr3 + \
        -6.31223658e-6 * vel2 * d_tr * pa_pr3 + \
        3.02122035e-4 * d_tr2 * pa_pr3 + \
        -4.77403547e-6 * ta * d_tr2 * pa_pr3 + \
        1.73825715e-6 * vel * d_tr2 * pa_pr3 + \
        -4.09087898e-7 * d_tr3 * pa_pr3 + \
        0.614155345 * pa_pr4 + \
        -0.0616755931 * ta * pa_pr4 + \
        0.00133374846 * ta2 * pa_pr4 + \
        0.00355375387 * vel * pa_pr4 + \
        -5.13027851e-4 * ta * vel * pa_pr4 + \
        1.02449757e-4 * vel2 * pa_pr4 + \
        -0.00148526421 * d_tr * pa_pr4 + \
        -4.11469183e-5 * ta * d_tr * pa_pr4 + \
        -6.80434415e-6 * vel * d_tr * pa_pr4 + \
        -9.77675906e-6 * d_tr2 * pa_pr4 + \
        0.0882773108 * pa_pr5 + \
        -0.00301859306 * ta * pa_pr5 + \
        0.00104452989 * vel * pa_pr5 + \
        2.47090539e-4 * d_tr * pa_pr5 + \
        0.00148348065 * pa_pr6

    return utci_approx