util.py

import numpy as np
import datetime
from datetime import timezone
from metpy.units import units
from metpy.calc import thickness_hydrostatic
from collections import namedtuple
import os

LatLonTuple = namedtuple('LatLonTuple', ['lat', 'lon'])


class GenericException(Exception):
    def __init__(self, message):
        self.message = message


def get_time_tuple_utc(timestamp):
    dt_obj = datetime.datetime.fromtimestamp(timestamp, timezone.utc)
    return dt_obj, dt_obj.timetuple()


def add_time_range_to_filename(pathname, tstart, tend):
    dt_obj, _ = get_time_tuple_utc(tstart)
    str_start = dt_obj.strftime('%Y%m%d%H')

    dt_obj, _ = get_time_tuple_utc(tend)
    str_end = dt_obj.strftime('%Y%m%d%H')

    filename = os.path.split(pathname)[1]
    w_o_ext, ext = os.path.splitext(filename)
    filename = w_o_ext+'_'+str_start+'_'+str_end+ext

    path = os.path.split(pathname)[0]
    path = path+'/'+filename
    return path


def haversine_np(lon1, lat1, lon2, lat2):
    """
    Calculate the great circle distance between two points
    on the earth (specified in decimal degrees)

    (lon1, lat1) must be broadcastable with (lon2, lat2).

    """
    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])

    dlon = lon2 - lon1
    dlat = lat2 - lat1

    a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2

    c = 2 * np.arcsin(np.sqrt(a))
    km = 6367 * c
    return km


def bin_data_by(a, b, bin_ranges):
    nbins = len(bin_ranges)
    binned_data = []

    for i in range(nbins):
        rng = bin_ranges[i]
        idxs = (b >= rng[0]) & (b < rng[1])
        binned_data.append(a[idxs])

    return binned_data


def get_bin_ranges(lop, hip, bin_size=100):
    bin_ranges = []
    delp = hip - lop
    nbins = int(delp/bin_size)

    for i in range(nbins):
        rng = [lop + i*bin_size, lop + i*bin_size + bin_size]
        bin_ranges.append(rng)

    return bin_ranges


# t must be monotonic increasing
def get_breaks(t, threshold):
    t_0 = t[0:t.shape[0]-1]
    t_1 = t[1:t.shape[0]]
    d = t_1 - t_0
    idxs = np.nonzero(d > threshold)
    return idxs


def pressure_to_altitude(pres, temp, prof_pres, prof_temp, sfc_pres=None, sfc_temp=None, sfc_elev=0):
    if not np.all(np.diff(prof_pres) > 0):
        raise GenericException("target pressure profile must be monotonic increasing")

    if pres < prof_pres[0]:
        raise GenericException("target pressure less than top of pressure profile")

    if temp is None:
        temp = np.interp(pres, prof_pres, prof_temp)

    i_top = np.argmax(np.extract(prof_pres <= pres, prof_pres)) + 1

    pres_s = prof_pres.tolist()
    temp_s = prof_temp.tolist()

    pres_s = [pres] + pres_s[i_top:]
    temp_s = [temp] + temp_s[i_top:]

    if sfc_pres is not None:
        if pres > sfc_pres:  # incoming pressure below surface
            return -1

        prof_pres = np.array(pres_s)
        prof_temp = np.array(temp_s)
        i_bot = prof_pres.shape[0] - 1

        if sfc_pres > prof_pres[i_bot]:  # surface below profile bottom
            pres_s = pres_s + [sfc_pres]
            temp_s = temp_s + [sfc_temp]
        else:
            idx = np.argmax(np.extract(prof_pres < sfc_pres, prof_pres))
            if sfc_temp is None:
                sfc_temp = np.interp(sfc_pres, prof_pres, prof_temp)
            pres_s = prof_pres.tolist()
            temp_s = prof_temp.tolist()
            pres_s = pres_s[0:idx+1] + [sfc_pres]
            temp_s = temp_s[0:idx+1] + [sfc_temp]

    prof_pres = np.array(pres_s)
    prof_temp = np.array(temp_s)

    prof_pres = prof_pres[::-1]
    prof_temp = prof_temp[::-1]

    prof_pres = prof_pres * units.hectopascal
    prof_temp = prof_temp * units.kelvin
    sfc_elev = sfc_elev * units.meter

    z = thickness_hydrostatic(prof_pres, prof_temp) + sfc_elev

    return z


# http://fourier.eng.hmc.edu/e176/lectures/NM/node25.html
def minimize_quadratic(xa, xb, xc, ya, yb, yc):
    x_m = xb + 0.5*(((ya-yb)*(xc-xb)*(xc-xb) - (yc-yb)*(xb-xa)*(xb-xa)) / ((ya-yb)*(xc-xb) + (yc-yb)*(xb-xa)))
    return x_m


def value_to_index(nda, value):
    diff = np.abs(nda - value)
    idx = np.argmin(diff)
    return idx


# array solzen must be degrees, missing values must NaN. For small 50x50km regions only
def is_night(solzen, test_angle=80.0, threshold=0.10):
    solzen = solzen.flatten()
    solzen = solzen[np.invert(np.isnan(solzen))]
    if len(solzen) == 0 or (np.sum(solzen > test_angle) / len(solzen)) > threshold:
        return True
    else:
        return False