serazing

def mld_thres(phi, phi_step, ref_depth=10, dim='depth'):
    
    # Get the depth vector
    z = phi[dim]
    # Find the index closest to the reference depth
    k_ref = int((np.abs(z - ref_depth)).argmin())
    
    from numba import guvectorize
    @guvectorize(['void(float64[:], float64[:], intp, float64, float64[:])'],
              '(n),(n),(),()->()', nopython=True)

serazing

import xarray as xr

def expfit(phi, dim):
    
    def exp_func(z, phi_bottom, delta_phi, z0):
        return phi_bottom - delta_phi * np.exp(- z / z0)
    
    mean_phi_max = phi.max(dim).mean()
    mean_phi_min = phi.min(dim).mean()
    delta_phi = mean_phi_max - mean_phi_min

serazing

import numpy as np
import numba
import xarray as xr

@numba.jit(nopython=True)
def lowess_1d(y, x, alpha=2. / 3., it=10):
    """lowess(x, y, f=2./3., iter=3) -> yest
    Lowess smoother: Robust locally weighted regression.
    The lowess function fits a nonparametric regression curve to a scatterplot.
    The arrays x and y contain an equal number of elements; each pair

serazing

import xarray as xr
import numpy as np
import numba
import pandas as pd
from obspy.geodetics import kilometers2degrees


@numba.jit(nopython=True, parallel=True)
def _interp_over_lon_lat(data_in, lon_in, lat_in, data_out, lon_out, lat_out, dlon, dlat):
    """

serazing

def bin_lat_lon(data, lon_min=0., lon_max=360., lon_res=1., 
                lat_min=-80., lat_max=80, lat_res=1.):
    """
    Bin unidimensional data onto a regular latidude and longitude grid
    by computing the median value and the number of corresponding 
    observations
    
    Paraneters
    ----------
    data : xarray.DataArray

serazing

import xarray as xr
import numpy as np
import numba
import pandas as pd
import pyresample

def interpolate_rossby_radius(Rd, lon, lat, interp_type='gaussian', 
                              radius_of_influence=500e3, fill_value=None, 
                              reduce_data=True, nprocs=1, neighbours=8):
    lat_min, lat_max = np.min(lat), np.max(lat)

serazing

import dask.delayed as delayed
import xarray as xr
import numpy as np


@delayed(pure=True)
def lagged_difference(a, dim, lag, order=1):
	return (a.shift(**{dim: -lag}) - a) ** order

serazing

import xarray as xr
import numpy as np
import pandas as pd

EARTH_RADIUS = 6371 * 1e3


def latlon2yx(lat, lon):
    """
    Convert latitude and longitude arrays to y and x arrays in m