def get_date_ax(start, end): start_date = [start//10000, (start%10000)//100, (start%10000)%100] end_date = [end//10000, (end%10000)//100, (end%10000)%100] d1 = date(start_date[0], start_date[1], start_date[2]) d2 = date(end_date[0], end_date[1], end_date[2]) L = (d2-d1).days+1 dt = d1 date_ax = [] date_ax_str = [] for i in range(L): date_ax.append(dt) date_ax_str.append(calc_data.cvt_date(dt)) dt = dt + timedelta(days=1) return date_ax, date_ax_str
def H_corr_1month():
dirs_ic0 = "../result_h/corr/H_corr_1month_ic0/"
mkdir(dirs_ic0)
dirs_sit = "../result_h/corr/H_corr_1month_sit/"
mkdir(dirs_sit)
start_list = []
n = 20000000
y_list = [3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16]
for i in y_list:
m = n + i * 10000
for j in range(12):
start_list.append(m + (j + 1) * 100 + 1)
M = len(start_list)
start_list_plus_1month = start_list + [20170101]
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(
i + 1, M))
month_end = start_list_plus_1month[i + 1]
month_end = date(month_end // 10000, (month_end % 10000) // 100,
(month_end % 10000) % 100) - timedelta(days=1)
end = start + month_end.day - 1
start_1month_before = date(start // 10000, (start % 10000) // 100,
(start % 10000) % 100) - timedelta(days=1)
start_1month_before = int(calc_data.cvt_date(start_1month_before))
print("\tA month: {}\n\tIC0 & SIT month: {}, {}".format(
start_1month_before, start, end))
_, _, _, data_A_original = main_data(start_1month_before,
start_1month_before,
span=30,
get_columns=["hermert"],
region=None,
accumulate=False)
_, _, _, data_ic0_sit = main_data(start,
end,
span=30,
get_columns=["ic0_145", "sit_145"],
region=None,
accumulate=True)
data_array = np.array(data_ic0_sit)
data_ave = np.nanmean(data_array, axis=0)
data_ave = pd.DataFrame(data_ave)
data_ave.columns = ["ic0_30", "sit_30"]
data_A = data_A_original["A"]
data = pd.concat([latlon_ex, data_A, data_ave], axis=1)
save_name = dirs_ic0 + str(start)[:6] + ".png"
visualize.visual_non_line(data,
mode=["scatter", ["ic0_30", "A"]],
save_name=save_name,
show=False)
save_name = dirs_sit + str(start)[:6] + ".png"
visualize.visual_non_line(data,
mode=["scatter", ["sit_30", "A"]],
save_name=save_name,
show=False)
print("\n")