def fill_missed(data, sea):
if sea != 'chukchi': #this sea has ice full year
fill_zeros(data, sea)
interp_funcs = interpolate_missed(data)
prev = init_prev(data)
for year in sorted(data):
for month in sorted(data[year]):
for dec in sorted(data[year][month]):
curr_global_dec = est.get_global_dec(year, month, dec)
prev_global_dec = curr_global_dec if prev['date']['year'] == 0 else \
est.get_global_dec(prev['date']['year'], prev['date']['month'], prev['date']['dec'])
dist = curr_global_dec - prev_global_dec
if dist > 1:
for d in range(1, dist):
local_data = est.get_local_date(prev_global_dec + d)
#print(local_data)
append_to_data(data, local_data['year'], local_data['month'], local_data['dec'], {
'avg_area': float(interp_funcs[0](prev_global_dec + d)),
'avg_conc': float(interp_funcs[1](prev_global_dec + d)),
'avg_vol': float(interp_funcs[2](prev_global_dec + d))
})
prev['date'] = {'year': year, 'month': month, 'dec': dec}
return data
def calc_rates(self, data, field_name, k, t, jb, je, l, u, last_year, prec,
pair_coeffs, num):
max_val_l = est.find_max_val(data[l], field_name)
states_l = [(max_val_l / prec * i) for i in range(prec + 1)]
rates_sum = 0
for i in range(len(states_l) - 1):
rates = self.chosen_rates(data, field_name, k, t + 1, jb, je, l,
u + 1, states_l[i], states_l[i + 1],
last_year)
if (states_l[i],
states_l[i + 1]) not in self.global_probs[l][u].keys():
cur_date = est.get_local_date(u + 1)
try:
known_data = data[l][cur_date['year']][cur_date['month']][
cur_date['dec']][field_name]
cond = False
if i < len(states_l) - 2:
cond = known_data >= states_l[
i] and known_data < states_l[i + 1]
else:
cond = known_data >= states_l[
i] and known_data <= states_l[i + 1]
if cond:
self.global_probs[l][u][(states_l[i],
states_l[i + 1])] = 1.0
else:
self.global_probs[l][u][(states_l[i],
states_l[i + 1])] = 0.0
except:
self.global_probs[l][u][(states_l[i], states_l[i + 1])] = \
self.get_prob(data, field_name, l, u, states_l[i], states_l[i + 1], last_year, prec, pair_coeffs, num + 1)
rates_sum += rates * self.global_probs[l][u][(states_l[i],
states_l[i + 1])]
return rates_sum