#!/usr/bin/env python

# data area
#latN = 35
#latS = -35
#lonW = -100
#lonE = 45
data_lat = (-35, 35)
data_lon = (-100, 45)

aew_clim_dir = "/home/muffin/phd/data/aew/clim/"
cv_data_dir = "/home/muffin/phd/data/aew/cv/" # reference where ALL cv data is (for clim calc.)

# construct clim file, regenerate for each window?
def get_clim_file():
    fn = (aew_clim_dir + "cv_clim_" + str(abs(data_lat[1])) + ('N' if data_lat[1] > 0 else 'S') + "_"
          + str(abs(data_lat[0])) + ('N' if data_lat[0] > 0 else 'S') + "_"
          + str(abs(data_lon[0])) + ('E' if data_lon[0] > 0 else 'W') + "_"
          + str(abs(data_lon[1])) + ('E' if data_lon[1] > 0 else 'W')) + ".nc"
    return fn

# wave area to be extracted
wave_filter_lat = (-45, 45)
wave_filter_lon = (-110, 55)

levels = [70000, 85000]  # 700 hPa

# time of interest
start_date = '2008-08-15T00:00'
end_date = '2008-08-15T12:00'

# Algorithm parameters
# max u wind (m/s) (0 = only keep west-propagating). Belanger: 2.5; Berry: 0.0
max_u_thresh = 0.0  # m/s

# CV anomaly percentile
# NOTE: this is precomputed from the climatology, so delete climatology file and change this after to be re-executed.
cv_percentile = 66  # 66% percentile of PV anomalies as reference on what areas to consider

# need positive 2nd time derivative
second_advection_min_thr = 0.0

### FILTERING

# spatial filtering: if wave to small, discard
# threshold in degrees of wave length (sum of wave segments)
spatial_thr = 5

### TRACKING

# speed range of AEWs
# at 10°N we have in longitude direction 0.00914 degrees/km (360/(40,000*cos(10deg)))
speed_range_m_per_s = [5.0, 10.0]
speed_deg_per_h = [-m_per_s * 3.6 * 0.00914 for m_per_s in speed_range_m_per_s]  # negative -> westward [-5, -10]