util.py

import numpy as np
import xarray as xr
import datetime
from datetime import timezone
from metpy.units import units
from metpy.calc import thickness_hydrostatic
from collections import namedtuple
import os
import h5py

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

homedir = os.path.expanduser('~') + '/'


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


class EarlyStop:
    def __init__(self, window_length=3, patience=5):
        self.patience = patience
        self.min = np.finfo(np.single).max
        self.cnt = 0
        self.cnt_wait = 0
        self.window = np.zeros(window_length, dtype=np.single)
        self.window.fill(np.nan)

    def check_stop(self, value):
        self.window[:-1] = self.window[1:]
        self.window[-1] = value

        if np.any(np.isnan(self.window)):
            return False

        ave = np.mean(self.window)
        if ave < self.min:
            self.min = ave
            self.cnt_wait = 0
            return False
        else:
            self.cnt_wait += 1

        if self.cnt_wait > self.patience:
            return True
        else:
            return False


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


def get_timestamp(dt_str, format_code='%Y-%m-%d_%H:%M'):
    dto = datetime.datetime.strptime(dt_str, format_code).replace(tzinfo=timezone.utc)
    ts = dto.timestamp()
    return ts


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


def find_bin_index(nda, value_s):
    idxs = np.arange(nda.shape[0])
    iL_s = np.zeros(value_s.shape[0])
    iL_s[:,] = -1

    for k, v in enumerate(value_s):
        above = v >= nda
        if not above.any():
            continue

        below = v < nda
        if not below.any():
            continue

        iL = idxs[above].max()
        iL_s[k] = iL

    return iL_s.astype(np.int32)


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


# array solzen must be degrees, missing values must NaN. For small roughly 50x50km regions only
def is_night(solzen, test_angle=100.0):
    solzen = solzen.flatten()
    solzen = solzen[np.invert(np.isnan(solzen))]
    if len(solzen) == 0 or np.sum(solzen >= test_angle) < len(solzen):
        return False
    else:
        return True


def check_oblique(satzen, test_angle=70.0):
    satzen = satzen.flatten()
    satzen = satzen[np.invert(np.isnan(satzen))]
    if len(satzen) == 0 or np.sum(satzen <= test_angle) < len(satzen):
        return False
    else:
        return True


def get_grid_values_all(h5f, grid_name, scale_factor_name='scale_factor', add_offset_name='add_offset',
                        fill_value_name='_FillValue', range_name='actual_range', fill_value=None):
    hfds = h5f[grid_name]
    attrs = hfds.attrs

    if attrs is None:
        raise GenericException('No attributes object for: '+grid_name)

    grd_vals = hfds[:,]
    if fill_value is not None:
        grd_vals = np.where(grd_vals == fill_value, np.nan, grd_vals)

    if scale_factor_name is not None:
        attr = attrs.get(scale_factor_name)
        if attr is None:
            raise GenericException('Attribute: '+scale_factor_name+' not found for variable: '+grid_name)
        scale_factor = attr[0]
        grd_vals = grd_vals * scale_factor

    if add_offset_name is not None:
        attr = attrs.get(add_offset_name)
        if attr is None:
            raise GenericException('Attribute: '+add_offset_name+' not found for variable: '+grid_name)
        add_offset = attr[0]
        grd_vals = grd_vals + add_offset

    if range_name is not None:
        attr = attrs.get(range_name)
        if attr is None:
            raise GenericException('Attribute: '+range_name+' not found for variable: '+grid_name)
        low = attr[0]
        high = attr[1]
        grd_vals = np.where(grd_vals < low, np.nan, grd_vals)
        grd_vals = np.where(grd_vals > high, np.nan, grd_vals)
    elif fill_value_name is not None:
        attr = attrs.get(fill_value_name)
        if attr is None:
            raise GenericException('Attribute: '+fill_value_name+' not found for variable: '+grid_name)
        fill_value = attr[0]
        grd_vals = np.where(grd_vals == fill_value, np.nan, grd_vals)

    return grd_vals


# dt_str_0: start datetime string in format YYYY-MM-DD_HH:MM
# dt_str_1: stop datetime string, if not None num_steps is computed
# format_code: default '%Y-%m-%d_%H:%M'
# num_steps with increment of days, hours, minutes or seconds
# dt_str_1 and num_steps cannot both be None
# return num_steps+1 lists of datetime strings and timestamps (edges of a numpy histogram)
def make_times(dt_str_0, dt_str_1=None, format_code='%Y-%m-%d_%H:%M', num_steps=None, days=None, hours=None, minutes=None, seconds=None):
    if days is not None:
        inc = 86400*days
    elif hours is not None:
        inc = 3600*hours
    elif minutes is not None:
        inc = 60*minutes
    else:
        inc = seconds

    dt_obj_s = []
    ts_s = []
    dto_0 = datetime.datetime.strptime(dt_str_0, format_code).replace(tzinfo=timezone.utc)
    ts_0 = dto_0.timestamp()

    if dt_str_1 is not None:
        dto_1 = datetime.datetime.strptime(dt_str_1, format_code).replace(tzinfo=timezone.utc)
        ts_1 = dto_1.timestamp()
        num_steps = int((ts_1 - ts_0)/inc)

    dt_obj_s.append(dto_0)
    ts_s.append(ts_0)
    dto_last = dto_0
    for k in range(num_steps):
        dt_obj = dto_last + datetime.timedelta(seconds=inc)
        dt_obj_s.append(dt_obj)
        ts_s.append(dt_obj.timestamp())
        dto_last = dt_obj

    return dt_obj_s, ts_s


def make_histogram(values, edges):
    h = np.histogram(values, bins=edges)
    return h


def normalize(data, param, mean_std_dict, add_noise=False, noise_scale=1.0, seed=None):

    if mean_std_dict.get(param) is None:
        return data

    shape = data.shape
    data = data.flatten()

    mean, std, lo, hi = mean_std_dict.get(param)
    data -= mean
    data /= std

    if add_noise:
        if seed is not None:
            np.random.seed(seed)
        rnd = np.random.normal(loc=0, scale=noise_scale, size=data.size)
        data += rnd

    not_valid = np.isnan(data)
    data[not_valid] = 0

    data = np.reshape(data, shape)

    return data

# ------------ This code will not be needed when we implement a Fully Connected CNN -----------------------------------
# Example GOES file to retrieve GEOS parameters in MetPy form (CONUS)
exmp_file_conus = '/Users/tomrink/data/OR_ABI-L1b-RadC-M6C14_G16_s20193140811215_e20193140813588_c20193140814070.nc'
# Full Disk
exmp_file_fd = '/Users/tomrink/data/OR_ABI-L1b-RadF-M6C16_G16_s20212521800223_e20212521809542_c20212521809596.nc'


def make_for_full_domain_predict(clvrx_file, name_list=None, domain='FD'):
    w_x = 16
    w_y = 16

    if domain == 'CONUS':
        exmpl_ds = xr.open_dataset(exmp_file_conus)
    elif domain == 'FD':
        exmpl_ds = xr.open_dataset(exmp_file_fd)
    mdat = exmpl_ds.metpy.parse_cf('Rad')
    geos = mdat.metpy.cartopy_crs
    xlen = mdat.x.values.size
    ylen = mdat.y.values.size
    exmpl_ds.close()

    h5f = h5py.File(clvrx_file, 'r')

    grd_dct = {name: None for name in name_list}

    cnt_a = 0
    for didx, ds_name in enumerate(name_list):
        gvals = get_grid_values_all(h5f, ds_name)
        if gvals is not None:
            grd_dct[ds_name] = gvals
            cnt_a += 1

    if cnt_a > 0 and cnt_a != len(name_list):
        raise GenericException('weirdness')

    grd_dct_n = {name: [] for name in name_list}

    n_x = int(xlen/w_x)
    n_y = int(ylen/w_y)

    i_0 = 0
    j_0 = 0

    cc = []
    ll = []

    for didx, ds_name in enumerate(name_list):
        for j in range(4, n_y-4, 1):
            j_ul = j_0 + j * w_y
            for i in range(4, n_x-4, 1):
                i_ul = i_0 + i * w_x
                if didx == 0:
                    ll.append(j_ul)
                    cc.append(i_ul)
                grd_dct_n[ds_name].append(grd_dct[ds_name][j_ul:j_ul+w_y, i_ul:i_ul+w_x])

    grd_dct = {name: None for name in name_list}
    for didx, ds_name in enumerate(name_list):
        grd_dct[ds_name] = np.stack(grd_dct_n[ds_name])

    h5f.close()

    return grd_dct, ll, cc
# -------------------------------------------------------------------------------------------