def H_angle_30_and_wind():
dirs = "../result_h/angle/angle_30_and_wind/"
main_v.mkdir(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
#angleデータの取得
_, _, _, data = main_v.main_data(
start, start,
span=30,
get_columns=["hermert"],
region=None,
accumulate=False
)
data.theta[data.R2<threshold_R2] = np.nan
#地衡風平均の出力
_, _, _, data_w = main_v.main_data(
start, end,
span=30,
get_columns=["w"],
region=None,
accumulate=True
)
data_array = np.array(data_w)
data_ave = np.nanmean(data_array, axis=0)
data_ave = pd.DataFrame(data_ave)
data_ave.columns = ["w_speed", "w_u", "w_v"]
save_name = dirs + "angle_30_and_wind_" + str(start)[:6] + ".png"
visualize.plot_map_multi(
data_ave.loc[:, ["w_u", "w_v"]],
data["theta"],
data_type="type_non_wind",
save_name=save_name,
show=False,
vmax=180,
vmin=-180,
cmap=cm_angle_2
)
print("\n")
def H_A_30_test():
dirs = "../result_h/A/A_30_test/"
main_v.mkdir(dirs)
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
_, _, _, data = main_v.main_data(
start, start,
span=30,
get_columns=["hermert"],
region=None,
accumulate=False
)
save_name = dirs + str(start)[:6] + ".png"
visualize.plot_map_once(
data["A"],
data_type="type_non_wind",
save_name=save_name,
show=False,
vmax=None,
vmin=None,
cmap=plt.cm.jet
)
print("\n")
def H_vec_mean_ocean_currents():
dirs = "../result_h/mean_vector/mean_ocean_currents/"
main_v.mkdir(dirs)
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
_, _, _, data = main_v.main_data(
start, start,
span=30,
get_columns=["hermert"],
region=None,
accumulate=False
)
save_name = dirs + "mean_ocean_currents_" + str(start)[:6] + ".png"
visualize.plot_map_once(
data.loc[:, ["ocean_u", "ocean_v"]],
data_type="type_wind",
save_name=save_name,
show=False,
vmax=None,
vmin=None,
cmap=None
)
print("\n")
def H_A_30_with_coef():
dirs = "../result_h/A/A_30_with_coef/"
main_v.mkdir(dirs)
df_latlon = pd.read_csv("../data/latlon_ex.csv")
lon = df_latlon.Lon
lat = df_latlon.Lat
lon = np.array(lon)
lat = np.array(lat)
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
_, _, _, data = main_v.main_data(
start, start,
span=30,
get_columns=["hermert"],
region=None,
accumulate=False
)
idx_low_coef = data[data.R2<threshold_R2].index
data_low_coef = np.array([np.nan]*(145**2))
data_low_coef[idx_low_coef] = 1
data_low_coef1 = np.reshape(data_low_coef, (145,145), order="F")
data_low_coef1 = np.ma.masked_invalid(data_low_coef1)
data.A.loc[data.R2<threshold_R2] = np.nan
data = np.array(data.A)
data1 = np.reshape(data, (145,145), order="F")
data1 = np.ma.masked_invalid(data1)
save_name = dirs + str(start)[:6] + ".png"
m = Basemap(lon_0=180, boundinglat=50, resolution='i', projection='npstere')
fig = plt.figure(figsize=(6.5, 6.5))
x, y = m(lon, lat)
x1 = np.reshape(x, (145,145), order="F")
y1 = np.reshape(y, (145,145), order="F")
dx1 = (x1[1,0]-x1[0,0])/2
dy1 = (y1[0,1]-y1[0,0])/2
x2 = np.linspace(x1[0,0], x1[144,0], 145)
y2 = np.linspace(y1[0,0], y1[0,144], 145)
xx, yy = np.meshgrid(x2, y2)
xx, yy = xx.T, yy.T
xx = np.hstack([xx, xx[:,0].reshape(145,1)])
xx_ex = np.vstack([xx, (xx[144,:] + (xx[1,0]-xx[0,0]))])
yy = np.vstack([yy, yy[0,:]])
yy_ex = np.hstack([(yy[:,0].reshape(146,1) + (yy[0,0]-yy[0,1])), yy])
m.drawcoastlines(color = '0.15')
# m.plot(xx[144,0], yy[144,0], "bo")
m.pcolormesh(xx_ex-dx1, yy_ex+dy1, data1, cmap=plt.cm.jet, vmax=0.025, vmin=0)
m.colorbar(location='bottom')
cm_brown = visualize.generate_cmap(["burlywood", "burlywood"])
m.pcolormesh(xx_ex-dx1, yy_ex+dy1, data_low_coef1, cmap=cm_brown)
#plt.show()
plt.savefig(save_name, dpi=900)
plt.close()
print("\n")
def H_angle_30():
dirs_original = "../result_h/angle/angle_30/"
main_v.mkdir(dirs_original)
dirs_h_coef = "../result_h/angle/angle_30_high_coef/"
main_v.mkdir(dirs_h_coef)
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
_, _, _, data = main_v.main_data(
start, start,
span=30,
get_columns=["hermert"],
region=None,
accumulate=False
)
#angle_30
save_name_original = dirs_original + "angle_30_" + str(start)[:6] + ".png"
visualize.plot_map_once(
data["theta"],
data_type="type_non_wind",
save_name=save_name_original,
show=False,
vmax=180,
vmin=-180,
cmap=cm_angle_2
)
#angle_30_high_coef
data.theta[data.R2<threshold_R2] = np.nan
save_name = dirs_h_coef + "angle_30_high_coef_" + str(start)[:6] + ".png"
visualize.plot_map_once(
data["theta"],
data_type="type_non_wind",
save_name=save_name,
show=False,
vmax=180,
vmin=-180,
cmap=cm_angle_2
)
print("\n")
def H_A_by_day_30():
"""
基本的に古い方のA_by_day_30と変わらないが，こっちの方がちょっとだけ正確かも
"""
dirs = "../result_h/A/A_by_day_30/"
main_v.mkdir(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
date_ax, date_ax_str, skipping_date_str, data = main_v.main_data(
start, end,
span=30,
get_columns=["ex_2"],
region=None,
accumulate=True
)
data_array = np.array(data)
data_ave = np.nanmean(data_array, axis=0)
#A_by_dayなので0列目
data_ave = pd.DataFrame(data_ave[:, 0])
data_ave.columns = ["A_by_day"]
save_name = dirs + "A_by_day_30_" + str(start)[:6] + ".png"
visualize.plot_map_once(
data_ave["A_by_day"],
data_type="type_non_wind",
save_name=save_name,
show=False,
vmax=0.025,
vmin=None,
cmap=plt.cm.jet
)
print("\n")
def get_helmert_ocean_90():
dirs = "../data/csv_Helmert_ocean_90/"
mkdir(dirs)
start_list_plus_1month = start_list + [20170901]
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
#csv_Helmert_90の当該月のcsvを読み込む
hermert_file_name = "../data/csv_Helmert_both_90/Helmert_both_90_" + str(start)[:6] + ".csv"
helmert_90_data = pd.read_csv(hermert_file_name)
#wデータの取得・整形
_, _, _, data_w = main_data(
start, end,
span=30,
get_columns=["w"],
region=None,
accumulate=True
)
data_array_w = np.array(data_w)
data_ave_w = np.nanmean(data_array_w, axis=0)
#iwデータの取得・整形
_, _, _, data_iw = main_data(
start, end,
span=30,
get_columns=["iw"],
region=None,
accumulate=True
)
data_array_iw = np.array(data_iw)
print("\n")
w_array = np.vstack((data_array_iw[:,:,1], data_array_iw[:,:,2]))
ocean_array_u = np.array(helmert_90_data["ocean_u_90"])
ocean_array_v = np.array(helmert_90_data["ocean_v_90"])
ocean_array = np.hstack((ocean_array_u.reshape((-1,1)), ocean_array_v.reshape((-1,1))))
ocean_array = np.tile(ocean_array, (data_array_iw.shape[0],1,1))
ocean_array = np.vstack((ocean_array[:,:,0], ocean_array[:,:,1]))
#print(ocean_array.shape)
#print(w_array[:, 0].shape)
Helmert = []
#for j in range(100):
for j in range(145**2):
print("j: {}".format(j))
#欠損データの処理
not_nan_idx = np.sum(np.isnan(data_array_iw[:, j, :]), axis=1)==False
#print("\tnot_nan_idx: {}".format(not_nan_idx))
x = data_array_w[:, j, 1][not_nan_idx].reshape((-1,1))
y = data_array_w[:, j, 2][not_nan_idx].reshape((-1,1))
w = (w_array[:, j]-ocean_array[:, j])[np.tile(not_nan_idx, 2)].reshape((-1,1))
iw_u_ave = np.nanmean(data_array_iw[:, j, 1])
iw_v_ave = np.nanmean(data_array_iw[:, j, 2])
iw_u_ave_ocean = np.nanmean(data_array_iw[:, j, 1] - ocean_array_u[j])
iw_v_ave_ocean = np.nanmean(data_array_iw[:, j, 2] - ocean_array_v[j])
N_c = np.sum(not_nan_idx==True)
if N_c <= 1:
print("\tskipping for loop...")
Helmert.append([np.nan, np.nan, np.nan, np.nan, N_c, np.nan, np.nan])
continue
one_N = np.ones(N_c).reshape((-1,1))
zero_N = np.zeros(N_c).reshape((-1,1))
#D_1 = np.hstack((one_N, zero_N, x, -y))
#D_2 = np.hstack((zero_N, one_N, y, x))
D_1 = np.hstack((x, -y))
D_2 = np.hstack((y, x))
Dt = np.vstack((D_1, D_2))
#print(N_c, Dt, np.dot(Dt.T, Dt))
D_inv = np.linalg.inv(np.dot(Dt.T, Dt))
gamma = np.dot(D_inv, np.dot(Dt.T, w))
print("\t{}".format(gamma.shape))
A = np.sqrt(gamma[0]**2 + gamma[1]**2)
theta = np.arctan2(gamma[1], gamma[0]) * 180/np.pi
R_denominator = np.dot(w.T, w) - N_c*(iw_u_ave_ocean**2 + iw_v_ave_ocean**2)
epsilon = w - np.dot(Dt, gamma)
R_numerator = np.dot(epsilon.T, epsilon)
R2 = 1 - (R_numerator/R_denominator)[0,0]
#print("\t{}".format([A[0], theta[0], gamma[0,0], gamma[1,0], R2, N_c]))
if N_c <= 20:
Helmert.append([np.nan, np.nan, np.nan, np.nan, N_c, iw_u_ave_ocean, iw_v_ave_ocean])
else:
Helmert.append([A, theta, R2, R_numerator, N_c, iw_u_ave_ocean, iw_v_ave_ocean])
result = np.hstack((Helmert, data_ave_w[:,[1,2]]))
#print(result.shape)
data = pd.DataFrame(result)
data.columns = ["A_30_90", "theta_30_90", "R2_30_90", "epsilon2_30_90", "N_c_30_90", "mean_iw_u_with_ocean_30_90", "mean_iw_v_with_ocean_30_90", "mean_w_u_30", "mean_w_v_30"]
data_ex_dir = "../data/csv_Helmert_ex/Helmert_ex_" + str(start)[:-2] + ".csv"
data_ = pd.read_csv(data_ex_dir).loc[:, ["coastal_region_1", "coastal_region_2", "area_label", "ic0_30", "ic0_30_median", "sit_30", "sit_30_median"]]
data = pd.concat([latlon_ex, data_, data], axis=1)
#print(data.head(3))
save_name = dirs + "Helmert_30_90_" + str(start)[:6] + ".csv"
print(save_name)
data.to_csv(save_name, index=False)
def get_helmert_both_90(data_ex_basic):
dirs = "../data/csv_Helmert_both_90/"
mkdir(dirs)
#start_list = [20030101]
start_list_plus_3month = start_list + [20170901, 20171001, 20171101]
for k, start_1 in enumerate(start_list):
if start_1 == 20170801:
print("Breaking the loop...")
break
print("******************* {}/{} *******************".format(k+1, M))
if k == 0:
month_end_1 = start_list_plus_3month[k+1]
month_end_1 = date(month_end_1//10000, (month_end_1%10000)//100, (month_end_1%10000)%100) - timedelta(days=1)
end_1 = start_1 + month_end_1.day - 1
start_2 = month_end_1 + timedelta(days=1)
month_end_2 = start_list_plus_3month[k+2]
month_end_2 = date(month_end_2//10000, (month_end_2%10000)//100, (month_end_2%10000)%100) - timedelta(days=1)
end_2 = start_2 + timedelta(days=month_end_2.day-1)
start_2 = int(start_2.strftime('%Y/%m/%d').replace("/", ""))
end_2 = int(end_2.strftime('%Y/%m/%d').replace("/", ""))
start_list_3month = [start_1, start_2, 0]
end_list_3month = [end_1, end_2, 0]
else:
kk = k - 1
start_1_1 = start_list[kk]
month_end_1 = start_list_plus_3month[kk+1]
month_end_1 = date(month_end_1//10000, (month_end_1%10000)//100, (month_end_1%10000)%100) - timedelta(days=1)
end_1 = start_1_1 + month_end_1.day - 1
start_2 = month_end_1 + timedelta(days=1)
month_end_2 = start_list_plus_3month[kk+2]
month_end_2 = date(month_end_2//10000, (month_end_2%10000)//100, (month_end_2%10000)%100) - timedelta(days=1)
end_2 = start_2 + timedelta(days=month_end_2.day-1)
start_3 = month_end_2 + timedelta(days=1)
month_end_3 = start_list_plus_3month[kk+3]
month_end_3 = date(month_end_3//10000, (month_end_3%10000)//100, (month_end_3%10000)%100) - timedelta(days=1)
end_3 = start_3 + timedelta(days=month_end_3.day-1)
start_2 = int(start_2.strftime('%Y/%m/%d').replace("/", ""))
start_3 = int(start_3.strftime('%Y/%m/%d').replace("/", ""))
end_2 = int(end_2.strftime('%Y/%m/%d').replace("/", ""))
end_3 = int(end_3.strftime('%Y/%m/%d').replace("/", ""))
start_list_3month = [start_1_1, start_2, start_3]
end_list_3month = [end_1, end_2, end_3]
data_w_90 = np.zeros((1, 145**2, 3))
data_iw_90 = np.zeros((1, 145**2, 3))
for i in range(3):
start = start_list_3month[i]
end = end_list_3month[i]
if start == 0 and end == 0:
continue
#wデータの取得・整形
_, _, _, data_w = main_data(
start, end,
span=30,
get_columns=["w"],
region=None,
accumulate=True
)
data_array_w = np.array(data_w)
#data_ave_w = np.nanmean(data_array_w, axis=0)
data_w_90 = np.concatenate([data_w_90, data_array_w], axis=0)
#iwデータの取得・整形
_, _, _, data_iw = main_data(
start, end,
span=30,
get_columns=["iw"],
region=None,
accumulate=True
)
data_array_iw = np.array(data_iw)
data_iw_90 = np.concatenate([data_iw_90, data_array_iw], axis=0)
print("\n")
data_w_90 = data_w_90[1:, :, :]
data_iw_90 = data_iw_90[1:, :, :]
data_ave_w = np.nanmean(data_w_90, axis=0)
data_ave_iw = np.nanmean(data_iw_90, axis=0)
w_array = np.vstack((data_iw_90[:,:,1], data_iw_90[:,:,2]))
Helmert = []
for j in range(145**2):
print("j: {}".format(j))
#欠損データの処理
not_nan_idx = np.sum(np.isnan(data_iw_90[:, j, :]), axis=1)==False
#print("\tnot_nan_idx: {}".format(not_nan_idx))
x = data_w_90[:, j, 1][not_nan_idx].reshape((-1,1))
y = data_w_90[:, j, 2][not_nan_idx].reshape((-1,1))
w = w_array[:, j][np.tile(not_nan_idx, 2)].reshape((-1,1))
iw_u_ave = np.nanmean(data_iw_90[:, j, 1])
iw_v_ave = np.nanmean(data_iw_90[:, j, 2])
N_c = np.sum(not_nan_idx==True)
if N_c <= 1:
print("\tskipping for loop...")
Helmert.append([np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, N_c, np.nan, np.nan])
continue
one_N = np.ones(N_c).reshape((-1,1))
zero_N = np.zeros(N_c).reshape((-1,1))
D_1 = np.hstack((one_N, zero_N, x, -y))
D_2 = np.hstack((zero_N, one_N, y, x))
Dt = np.vstack((D_1, D_2))
#print(N_c, Dt, np.dot(Dt.T, Dt))
D_inv = np.linalg.inv(np.dot(Dt.T, Dt))
gamma = np.dot(D_inv, np.dot(Dt.T, w))
A = np.sqrt(gamma[2]**2 + gamma[3]**2)
theta = np.arctan2(gamma[3], gamma[2]) * 180/np.pi
R_denominator = np.dot(w.T, w) - N_c*(iw_u_ave**2 + iw_v_ave**2)
epsilon = w - np.dot(Dt, gamma)
R_numerator = np.dot(epsilon.T, epsilon)
R2 = 1 - (R_numerator/R_denominator)[0,0]
print("\t{}".format([A[0], theta[0], gamma[0,0], gamma[1,0], R2, N_c]))
if N_c < 45:
Helmert.append([np.nan, np.nan, gamma[0], gamma[1], np.nan, np.nan, N_c, iw_u_ave, iw_v_ave])
else:
Helmert.append([A, theta, gamma[0], gamma[1], R2, R_numerator, N_c, iw_u_ave, iw_v_ave])
result = np.hstack((Helmert, data_ave_w[:,[1,2]]))
#print(result.shape)
data = pd.DataFrame(result)
data.columns = ["A_90", "theta_90", "ocean_u_90", "ocean_v_90", "R2_90", "epsilon2_90", "N_c_90", "mean_iw_u_90", "mean_iw_v_90", "mean_w_u_90", "mean_w_v_90"]
data["ocean_speed_90"] = np.sqrt(data["ocean_u_90"]**2 + data["ocean_v_90"]**2)
#print(data.head(3))
data = pd.concat([latlon_ex, data_ex_basic, data], axis=1)
save_name = dirs + "Helmert_both_90_" + str(start_1)[:6] + ".csv"
print(save_name)
data.to_csv(save_name, index=False)
def get_csv_ex(array_label, array_coastal_region_1, array_coastal_region_2):
data_ex_dir = "../data/csv_Helmert_ex/"
mkdir(data_ex_dir)
dirs_pair = "../result_h/pairplot/"
mkdir(dirs_pair)
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_ic0_30 = main_data(start,
end,
span=30,
get_columns=["ic0_145"],
region=None,
accumulate=True)
data_array_ic0 = np.array(data_ic0_30)
data_ave_ic0 = np.nanmean(data_array_ic0, axis=0).ravel()
data_med_ic0 = np.nanmedian(data_array_ic0, axis=0).ravel()
data_ic0_ave_med_30 = pd.DataFrame({
"ic0_30": data_ave_ic0,
"ic0_30_median": data_med_ic0
})
_, _, _, data_sit_30 = main_data(start,
end,
span=30,
get_columns=["sit_145"],
region=None,
accumulate=True)
data_array_sit = np.array(data_sit_30)
data_ave_sit = np.nanmean(data_array_sit, axis=0).ravel()
data_med_sit = np.nanmedian(data_array_sit, axis=0).ravel()
data_sit_ave_med_30 = pd.DataFrame({
"sit_30": data_ave_sit,
"sit_30_median": data_med_sit
})
_, _, _, data_A_original = main_data(start,
start,
span=30,
get_columns=["hermert"],
region=None,
accumulate=False)
data_A_original["mean_ocean_speed"] = np.sqrt(
data_A_original["ocean_u"]**2 + data_A_original["ocean_v"]**2)
data_A_original["mean_iw_speed"] = np.sqrt(
data_A_original["mean_iw_u"]**2 + data_A_original["mean_iw_v"]**2)
data_A_original["mean_w_speed"] = np.sqrt(
data_A_original["mean_w_u"]**2 + data_A_original["mean_w_v"]**2)
data_A_original["w_iw_ratio"] = data_A_original[
"mean_iw_speed"] / data_A_original["mean_w_speed"]
data_A_original["mean_iw_speed_no_ocean"] = np.sqrt(
(data_A_original["mean_iw_u"] - data_A_original["ocean_u"])**2 +
(data_A_original["mean_iw_v"] - data_A_original["ocean_v"])**2)
data = pd.concat([
latlon_ex, data_A_original, data_ic0_ave_med_30,
data_sit_ave_med_30
],
axis=1)
#data["t_ocean"] = t_ocean
#data["t_iw"] = t_iw
#print(len(data))
#print(array_coastal_region_1.shape)
data["coastal_region_1"] = array_coastal_region_1
data["coastal_region_2"] = array_coastal_region_2
data["area_label"] = array_label
#print("\n\n{}\n".format(data.head(3)))
data_name = data_ex_dir + "Helmert_ex_" + str(start)[:-2] + ".csv"
data.to_csv(data_name, index=False)
data_no_nan = data_A_original.dropna()
sns.pairplot(
data_no_nan.loc[:, ["A", "R2", "epsilon2", "mean_ocean_speed"]])
plt.savefig(dirs_pair + "ocean_" + str(start)[:-2] + ".png")
plt.close()
sns.pairplot(data_no_nan.loc[:,
["A", "R2", "epsilon2", "mean_iw_speed"]])
plt.savefig(dirs_pair + "iw_" + str(start)[:-2] + ".png")
plt.close()
sns.pairplot(data_no_nan.loc[:, ["A", "R2", "epsilon2", "w_iw_ratio"]])
plt.savefig(dirs_pair + "ratio_" + str(start)[:-2] + ".png")
plt.close()
sns.pairplot(
data_no_nan.loc[:,
["A", "R2", "epsilon2", "mean_iw_speed_no_ocean"]])
plt.savefig(dirs_pair + "iw_no_ocean_" + str(start)[:-2] + ".png")
plt.close()
sns.pairplot(data_no_nan.loc[:, [
"mean_iw_speed", "mean_w_speed", "mean_ocean_speed",
"mean_iw_speed_no_ocean"
]])
plt.savefig(dirs_pair + "speed_" + str(start)[:-2] + ".png")
plt.close()
print("\n")
def H_scatter_ic0_np(mode):
if mode == "mean":
dirs_A_30_and_ic0_np = "../result_h/scatter/scatter_A_30_and_ic0_np/"
mkdir(dirs_A_30_and_ic0_np)
dirs_A_30_and_ic0_h_np = "../result_h/scatter/scatter_A_30_and_ic0_h_np/"
mkdir(dirs_A_30_and_ic0_h_np)
dirs_angle_30_and_ic0_np = "../result_h/scatter/scatter_angle_30_and_ic0_np/"
mkdir(dirs_angle_30_and_ic0_np)
dirs_angle_30_and_ic0_h_np = "../result_h/scatter/scatter_angle_30_and_ic0_h_np/"
mkdir(dirs_angle_30_and_ic0_h_np)
elif mode == "median":
dirs_A_30_and_ic0_median_np = "../result_h/scatter/scatter_A_30_and_ic0_median_np/"
mkdir(dirs_A_30_and_ic0_median_np)
dirs_A_30_and_ic0_median_h_np = "../result_h/scatter/scatter_A_30_and_ic0_median_h_np/"
mkdir(dirs_A_30_and_ic0_median_h_np)
dirs_angle_30_and_ic0_median_np = "../result_h/scatter/scatter_angle_30_and_ic0_median_np/"
mkdir(dirs_angle_30_and_ic0_median_np)
dirs_angle_30_and_ic0_median_h_np = "../result_h/scatter/scatter_angle_30_and_ic0_median_h_np/"
mkdir(dirs_angle_30_and_ic0_median_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_ic0_30 = main_data(start,
end,
span=30,
get_columns=["ic0_145"],
region=None,
accumulate=True)
data_array = np.array(data_ic0_30)
if mode == "mean":
data_ave = np.nanmean(data_array, axis=0)
data_ave = pd.DataFrame(data_ave)
data_ave.columns = ["ic0_30"]
#dirs_A_30_and_ic0_np
data_A = data_A_original["A"]
data = pd.concat([latlon_ex, data_A, data_ave], axis=1)
data = data[data.Name == "north_polar"]
save_name = dirs_A_30_and_ic0_np + str(start)[:6] + ".png"
visualize.visual_non_line(data,
mode=["scatter", ["ic0_30", "A"]],
save_name=save_name,
show=False)
#dirs_A_30_and_ic0_h_np
"""
data_A_1 = data_A_original.loc[:, ["A", "R2"]]
data_A_1.A[data_A_1.R2<threshold_R2] = np.nan
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_ic0_h_np + str(start)[:6] + ".png"
visualize.visual_non_line(
data_1,
mode=["scatter", ["ic0_30", "A"]],
save_name=save_name,
show=False
)
"""
#dirs_angle_30_and_ic0_np
data_angle = data_A_original["theta"]
data_2 = pd.concat([latlon_ex, data_angle, data_ave], axis=1)
data_2 = data_2[data_2.Name == "north_polar"]
save_name = dirs_angle_30_and_ic0_np + str(start)[:6] + ".png"
visualize.visual_non_line(data_2,
mode=["scatter", ["ic0_30", "theta"]],
save_name=save_name,
show=False)
#dirs_angle_30_and_ic0_h_np
"""
data_angle_1 = data_A_original.loc[:, ["theta", "R2"]]
data_angle_1.theta[data_angle_1.R2<threshold_R2] = 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_ic0_h_np + str(start)[:6] + ".png"
visualize.visual_non_line(
data_3,
mode=["scatter", ["ic0_30", "theta"]],
save_name=save_name,
show=False
)
"""
elif mode == "median":
data_ave = np.nanmedian(data_array, axis=0)
data_ave = pd.DataFrame(data_ave)
data_ave.columns = ["ic0_30"]
#dirs_A_30_and_ic0_np
data_A = data_A_original["A"]
data = pd.concat([latlon_ex, data_A, data_ave], axis=1)
data = data[data.Name == "north_polar"]
save_name = dirs_A_30_and_ic0_median_np + str(start)[:6] + ".png"
visualize.visual_non_line(data,
mode=["scatter", ["ic0_30", "A"]],
save_name=save_name,
show=False)
#dirs_A_30_and_ic0_h_np
"""
data_A_1 = data_A_original.loc[:, ["A", "R2"]]
data_A_1.A[data_A_1.R2<threshold_R2] = np.nan
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_ic0_median_h_np + str(start)[:6] + ".png"
visualize.visual_non_line(
data_1,
mode=["scatter", ["ic0_30", "A"]],
save_name=save_name,
show=False
)
"""
#dirs_angle_30_and_ic0_np
data_angle = data_A_original["theta"]
data_2 = pd.concat([latlon_ex, data_angle, data_ave], axis=1)
data_2 = data_2[data_2.Name == "north_polar"]
save_name = dirs_angle_30_and_ic0_median_np + str(
start)[:6] + ".png"
visualize.visual_non_line(data_2,
mode=["scatter", ["ic0_30", "theta"]],
save_name=save_name,
show=False)
#dirs_angle_30_and_ic0_h_np
"""
data_angle_1 = data_A_original.loc[:, ["theta", "R2"]]
data_angle_1.theta[data_angle_1.R2<threshold_R2] = 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_ic0_median_h_np + str(start)[:6] + ".png"
visualize.visual_non_line(
data_3,
mode=["scatter", ["ic0_30", "theta"]],
save_name=save_name,
show=False
)
"""
print("\n")
def H_scatter_6win_2area(worker):
dirs_basic = "../result_h/scatter/"
if worker == 0:
dirs_A_ic0 = dirs_basic + "A_ic0/"
mkdir(dirs_A_ic0)
dirs_theta_ic0 = dirs_basic + "theta_ic0/"
mkdir(dirs_theta_ic0)
dirs_e2_ic0 = dirs_basic + "e2_ic0/"
mkdir(dirs_e2_ic0)
elif worker == 1:
dirs_A_sit = dirs_basic + "A_sit/"
mkdir(dirs_A_sit)
dirs_theta_sit = dirs_basic + "theta_sit/"
mkdir(dirs_theta_sit)
dirs_e2_sit = dirs_basic + "e2_sit/"
mkdir(dirs_e2_sit)
sns.set_style("darkgrid")
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_hermert = main_data(start,
start,
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_cross = data_array[:, :, 0] * data_array[:, :, 1]
data_cross_ave = np.nanmean(data_cross, axis=0) / 100
data_ave["cross_ic0_sit"] = data_cross_ave
data_tmp = data_hermert.loc[:, ["A", "theta", "R2", "epsilon2"]]
data_basic = pd.concat([latlon_ex, data_tmp, data_ave], axis=1)
rank_np = np.zeros(145**2)
rank_np[data_basic[data_basic.Name == "north_polar"].index] = 1
rank_R2 = np.ones(145**2)
rank_R2[data_basic[data_basic.R2 <= (0.5)**2].index] = 0
rank_R2[data_basic[data_basic.R2 > (0.6)**2].index] = 2
data_rank = pd.DataFrame({"rank_np": rank_np, "rank_R2": rank_R2})
data = pd.concat([data_basic, data_rank], axis=1)
if worker == 0:
save_name = dirs_A_ic0 + str(start)[:6] + ".png"
sns.lmplot(x="ic0_30",
y="A",
row="rank_R2",
col="rank_np",
data=data,
size=3,
fit_reg=True)
plt.savefig(save_name, dpi=900)
plt.close()
save_name = dirs_theta_ic0 + str(start)[:6] + ".png"
sns.lmplot(x="ic0_30",
y="theta",
row="rank_R2",
col="rank_np",
data=data,
size=3)
plt.savefig(save_name, dpi=900)
plt.close()
save_name = dirs_e2_ic0 + str(start)[:6] + ".png"
sns.lmplot(x="ic0_30",
y="epsilon2",
row="rank_R2",
col="rank_np",
data=data,
size=3)
plt.savefig(save_name, dpi=900)
plt.close()
elif worker == 1:
save_name = dirs_A_sit + str(start)[:6] + ".png"
sns.lmplot(x="sit_30",
y="A",
row="rank_R2",
col="rank_np",
data=data,
size=3)
plt.savefig(save_name, dpi=900)
plt.close()
save_name = dirs_theta_sit + str(start)[:6] + ".png"
sns.lmplot(x="sit_30",
y="theta",
row="rank_R2",
col="rank_np",
data=data,
size=3)
plt.savefig(save_name, dpi=900)
plt.close()
save_name = dirs_e2_sit + str(start)[:6] + ".png"
sns.lmplot(x="sit_30",
y="epsilon2",
row="rank_R2",
col="rank_np",
data=data,
size=3)
plt.savefig(save_name, dpi=900)
plt.close()
print("\n")