#!/usr/bin/env python
# -*- coding: utf-8 -*-
# common
from datetime import datetime
# pip
import numpy as np
import xarray as xr
[docs]
def MJO_Categories(rmm1, rmm2, phase):
'''
Divides MJO data in 25 categories.
rmm1, rmm2, phase - MJO parameters
returns array with categories time series
and corresponding rmm
'''
rmm = np.sqrt(rmm1**2 + rmm2**2)
categ = np.empty(rmm.shape) * np.nan
for i in range(1,9):
s = np.squeeze(np.where(phase == i))
rmm_p = rmm[s]
# categories
categ_p = np.empty(rmm_p.shape) * np.nan
categ_p[rmm_p <=1] = 25
categ_p[rmm_p > 1] = i + 8*2
categ_p[rmm_p > 1.5] = i + 8
categ_p[rmm_p > 2.5] = i
categ[s] = categ_p
# get rmm_categ
rmm_categ = {}
for i in range(1,26):
s = np.squeeze(np.where(categ == i))
rmm_categ['cat_{0}'.format(i)] = np.column_stack((rmm1[s],rmm2[s]))
return categ.astype(int), rmm_categ
[docs]
def MJO_Phases(rmm1, rmm2):
'calculates and returns MJO phases (1-8) and degrees'
# mjo degrees phase
degr = (np.arctan2(rmm2, rmm1))*360/(2*np.pi)
degr[degr<0] = degr[degr<0] + 360
# degree to mjo phase (1-8)
phases_sector = [
(5, [0, 45]),
(6, [45, 90]),
(7, [90, 135]),
(8, [135, 180]),
(1, [180, 225]),
(2, [225, 270]),
(3, [270, 315]),
(4, [315, 360]),
]
# calculate phase
phase = np.zeros(len(degr))*np.nan
for p, s in phases_sector:
phase[np.where((degr[:]>s[0]) & (degr[:]<=s[1]))] = p
phase = phase.astype(int)
return phase, degr