def main_plot(data, **kwargs):
what_type = kwargs["what_type"]
mode = kwargs["mode"]
show = kwargs["show"]
save = kwargs["save"]
save_name = kwargs["save_name"]
if what_type == "non_line":
visualize.visual_non_line(data,
mode=mode,
save_name=save_name,
show=show)
elif what_type == "map":
mode0 = mode[0]
submode = mode[1]
if mode0 == 0:
visualize.visual_map(data,
mode0,
submode,
save_name=save_name,
show=show)
elif mode0 == 1:
visualize.visual_map(data,
mode0,
submode,
save_name=save_name,
show=show)
elif mode0 == 2:
data = [ic0_900_file_name, latlon900_file_name]
visualize.visual_map(data,
mode0,
submode,
save_name=save_name,
show=show)
def scatter_5():
dirs = "../result/scatter/scatter_A_30_and_sit_h_np/"
try:
os.makedirs(dirs)
except:
print('directory {} already exists'.format(dirs))
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
_, _, _, data_A = main_data(
start, start,
span=30,
get_columns=["coeff"],
region=None,
accumulate=False
)
data_A = data_A.loc[:, ["data_idx", "A", "coef"]]
data_A.A.loc[data_A.coef**2<0.4**2] = np.nan
data_A.A.loc[data_A.A<0] *= -1
#data_A.A.loc[data_A.data_idx==0.] = np.nan
date_ax, _, skipping_date_str, data_sit_30 = main_data(
start, end,
span=30,
get_columns=["sit_145"],
region=None,
accumulate=True
)
data_array = np.array(data_sit_30)
"""
data_array_1 = np.ma.masked_invalid(data_array)
data_count_nan = np.sum(data_array_1.recordmask, axis=0)
data_ave = np.sum(data_array, axis=0) / (len(date_ax)-data_count_nan)
"""
data_ave = np.nanmean(data_array, axis=0)
data_ave = pd.DataFrame(data_ave)
data_ave.columns = ["sit_30"]
data = pd.concat([latlon_ex, data_A, data_ave], axis=1)
data = data[data.Name=="north_polar"]
print(data.dropna().head())
save_name = dirs + str(start)[:6] + ".png"
#visualize.pyで関数を選ぶ
visualize.visual_non_line(
data,
mode=["scatter", ["sit_30", "A"]],
save_name=save_name,
show=False
)
print("\n")
def test_w_iw():
start_list = []
n = 20000000
y_list = [4, 6, 8, 10, 14, 16]
for i in y_list:
m = n + i * 10000
for j in [2, 4, 6, 8, 10]:
start_list.append(m + j * 100 + 1)
dirs = "../result_h/test/w_iw/"
mkdir(dirs)
for i, start in enumerate(start_list):
print("******************* {}/{} *******************".format(
i + 1, M))
month_end = start + 100
month_end = date(month_end // 10000, (month_end % 10000) // 100,
(month_end % 10000) % 100) - timedelta(days=1)
end = start + month_end.day - 1
_, _, _, data = main_data(start,
end,
span=30,
get_columns=["w", "iw"],
region=None,
accumulate=True)
np_region_idx = np.array(
latlon_ex.loc[latlon_ex.Name == "north_polar", :].index)
print(np_region_idx)
data = np.array(data)
#data_w = data[:, :, 0].flatten().reshape(-1,1)
#data_iw = data[:, :, 3].flatten().reshape(-1,1)
#data = np.hstack((data_w, data_iw))
#print(data.shape)
#print(data[10, :, [0,3]].shape)
data = data[10, :, [0, 3]].T
data = data[np_region_idx, :]
data = pd.DataFrame(data)
#print(data.shape)
data.columns = ["w", "iw"]
save_name = dirs + str(start)[:6] + ".png"
visualize.visual_non_line(data.dropna(),
mode=["scatter", ["w", "iw"]],
save_name=save_name,
show=False)
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")
def H_scatter_sit_np():
dirs_A_30_and_sit_all = "../result/scatter/scatter_A_30_and_sit_all/"
mkdir(dirs_A_30_and_sit_all)
dirs_A_30_and_sit_np = "../result/scatter/scatter_A_30_and_sit_np/"
mkdir(dirs_A_30_and_sit_np)
dirs_A_30_and_sit_h_np = "../result/scatter/scatter_A_30_and_sit_h_np/"
mkdir(dirs_A_30_and_sit_h_np)
dirs_angle_30_and_sit_all = "../result/scatter/scatter_angle_30_and_sit_all/"
mkdir(dirs_angle_30_and_sit_all)
dirs_angle_30_and_sit_np = "../result/scatter/scatter_angle_30_and_sit_np/"
mkdir(dirs_angle_30_and_sit_np)
dirs_angle_30_and_sit_h_np = "../result/scatter/scatter_angle_30_and_sit_h_np/"
mkdir(dirs_angle_30_and_sit_h_np)
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
_, _, _, data_A_original = main_data(
start, start,
span=30,
get_columns=["hermert"],
region=None,
accumulate=False
)
_, _, _, data_sit_30 = main_data(
start, end,
span=30,
get_columns=["sit_145"],
region=None,
accumulate=True
)
data_array = np.array(data_sit_30)
data_ave = np.nanmean(data_array, axis=0)
data_ave = pd.DataFrame(data_ave)
data_ave.columns = ["sit_30"]
#dirs_A_30_and_sit_all
data_A = data_A_original["A"]
data = pd.concat([latlon_ex, data_A, data_ave], axis=1)
save_name = dirs_A_30_and_sit_all + str(start)[:6] + ".png"
visualize.visual_non_line(
data,
mode=["scatter", ["sit_30", "A"]],
save_name=save_name,
show=False
)
#dirs_A_30_and_sit_np
data_A_1 = data_A_original.loc[:, ["A", "R2"]]
data_1 = pd.concat([latlon_ex, data_A_1, data_ave], axis=1)
data_1 = data_1[data_1.Name=="north_polar"]
save_name = dirs_A_30_and_sit_np + str(start)[:6] + ".png"
visualize.visual_non_line(
data_1,
mode=["scatter", ["sit_30", "A"]],
save_name=save_name,
show=False
)
#dirs_A_30_and_sit_h_np
data_A_2 = data_A_original.loc[:, ["A", "R2"]]
data_A_2.loc[data_A_2.R2<threshold_R2, data_A_2.A] = np.nan
data_1_2 = pd.concat([latlon_ex, data_A_2, data_ave], axis=1)
data_1_2 = data_1_2[data_1_2.Name=="north_polar"]
save_name = dirs_A_30_and_sit_h_np + str(start)[:6] + ".png"
visualize.visual_non_line(
data_1_2,
mode=["scatter", ["sit_30", "A"]],
save_name=save_name,
show=False
)
#dirs_angle_30_and_sit_all
data_angle = data_A_original["theta"]
data_2 = pd.concat([latlon_ex, data_angle, data_ave], axis=1)
save_name = dirs_angle_30_and_sit_all + str(start)[:6] + ".png"
visualize.visual_non_line(
data_2,
mode=["scatter", ["sit_30", "theta"]],
save_name=save_name,
show=False
)
#dirs_angle_30_and_sit_np
data_angle_1 = data_A_original.loc[:, ["theta", "R2"]]
data_3 = pd.concat([latlon_ex, data_angle_1, data_ave], axis=1)
data_3 = data_3[data_3.Name=="north_polar"]
save_name = dirs_angle_30_and_sit_np + str(start)[:6] + ".png"
visualize.visual_non_line(
data_3,
mode=["scatter", ["sit_30", "theta"]],
save_name=save_name,
show=False
)
#dirs_angle_30_and_sit_h_np
data_angle_1 = data_A_original.loc[:, ["theta", "R2"]]
data_angle_1.loc[data_angle_1.R2<threshold_R2, data_angle_1.theta] = np.nan
data_3 = pd.concat([latlon_ex, data_angle_1, data_ave], axis=1)
data_3 = data_3[data_3.Name=="north_polar"]
save_name = dirs_angle_30_and_sit_h_np + str(start)[:6] + ".png"
visualize.visual_non_line(
data_3,
mode=["scatter", ["sit_30", "theta"]],
save_name=save_name,
show=False
)
print("\n")
_, _, _, data = main_data( start, end, span=30, get_columns=["w", "iw"], region=None, accumulate=True ) ! data = data[:, :, [0, 3]] data = pd.DataFrame(data) data.columns = ["iw", "w"] save_name = dirs + str(start)[:6] + ".png" visualize.visual_non_line( data, mode=["scatter", ["w", "iw"]], save_name=save_name, show=False ) ############################################################################################################### #R2_30, epsilon2_30 def H_R2_e2_30(): dirs_R2 = "../result_h/R2/R2_30/" mkdir(dirs_R2) dirs_e2 = "../result_h/epsilon2/epsilon2_30/" mkdir(dirs_e2) for i, start in enumerate(start_list):
def scatter_0():
"""
start_list = []
n = 20000000
y_list = [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)
start_ex_list = [20170101, 20170201, 20170301, 20170401, 20170501, 20170601,20170701,20170801]
start_list = np.sort(np.array(list(set(start_list)|set(start_ex_list)))).tolist()
M = len(start_list)
start_list_plus_1month = start_list + [20170901]
"""
dirs = "../result/scatter/scatter_A_30_and_ic0_np/"
try:
os.makedirs(dirs)
except:
print('directory {} already exists'.format(dirs))
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
_, _, _, data_A = main_data(
start, start,
span=30,
get_columns=["coeff"],
region=None,
accumulate=False
)
data_A = data_A.loc[:, ["data_idx", "A"]]
data_A.A.loc[data_A.A<0] *= -1
#data_A.A.loc[data_A.data_idx==0.] = np.nan
date_ax, _, _, data_ic0_30 = main_data(
start, end,
span=30,
get_columns=["ic0_145"],
region=None,
accumulate=True
)
data_array = np.array(data_ic0_30)
"""
data_array_1 = np.ma.masked_invalid(data_array)
data_count_nan = np.sum(data_array_1.recordmask, axis=0)
data_ave = np.sum(data_array, axis=0) / (len(date_ax)-data_count_nan)
"""
data_ave = np.nanmean(data_array, axis=0)
data_ave = pd.DataFrame(data_ave)
data_ave.columns = ["ic0_30"]
data = pd.concat([latlon_ex, data_A, data_ave], axis=1)
data = data[data.Name=="north_polar"]
#print(data.dropna().head())
save_name = dirs + str(start)[:6] + ".png"
#visualize.pyで関数を選ぶ
visualize.visual_non_line(
data,
mode=["scatter", ["ic0_30", "A"]],
save_name=save_name,
show=False
)
print("\n")
