def vec_0():
dirs = "../result/mean_vector/mean_ocean_currents/"
try:
os.makedirs(dirs)
except:
print('directory {} already exists'.format(dirs))
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
_, _, _, data = main_data(
start, start,
span=30,
get_columns=["coeff"],
region=None,
accumulate=False
)
save_name = dirs + "mean_ocean_currents_" + str(start)[:6] + ".png"
#visualize.pyで関数を選ぶ
visualize.plot_map_once(
data.loc[:, ["mean_ocean_u", "mean_ocean_v"]],
data_type="type_wind",
save_name=save_name,
show=False,
vmax=None,
vmin=None,
cmap=None
)
print("\n")
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):
print("****************** {}/{} *******************".format(
i + 1, M))
_, _, _, data = main_data(start,
start,
span=30,
get_columns=["hermert"],
region=None,
accumulate=False)
save_name_R2 = dirs_R2 + str(start)[:6] + ".png"
visualize.plot_map_once(data["R2"],
data_type="type_non_wind",
save_name=save_name_R2,
show=False,
vmax=1,
vmin=0,
cmap=plt.cm.jet)
save_name_e2 = dirs_e2 + str(start)[:6] + ".png"
visualize.plot_map_once(data["epsilon2"],
data_type="type_non_wind",
save_name=save_name_e2,
show=False,
vmax=None,
vmin=None,
cmap=plt.cm.jet)
print("\n")
def H_angle_30_high_coef():
dirs = "../result_h/angle/angle_30_high_coef/"
mkdir(dirs)
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
date_ax, date_ax_str, skipping_date_str, data = main_data(
start, start,
span=30,
get_columns=["hermert"],
region=None,
accumulate=False
)
data.theta[data.R2<0.4**2] = np.nan
save_name = dirs + "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
)
print("\n")
def H_A_30():
dirs = "../result_h/A/A_30/"
mkdir(dirs)
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
_, _, _, data = 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=0.025,
vmin=0,
cmap=plt.cm.jet
)
print("\n")
def angle_1():
dirs = "../result/angle/angle_30_modified_test/"
try:
os.makedirs(dirs)
except:
print('directory {} already exists'.format(dirs))
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
_, _, _, data = main_data(
start, start,
span=30,
get_columns=["coeff"],
region=None,
accumulate=False
)
data[(data.A<0) & (data.angle<0)] += 180
data[(data.A<0) & (data.angle>0)] -= 180
#data.angle.loc[data.coef**2<0.4**2] = np.nan
#data[data.data_idx==0.] = np.nan
save_name = dirs + "angle_30_modified_" + str(start)[:6] + ".png"
#visualize.pyで関数を選ぶ
visualize.plot_map_once(
data["angle"],
data_type="type_non_wind",
save_name=save_name,
show=False,
vmax=180,
vmin=-180,
cmap=cm_angle
)
print("\n")
def A_2():
dirs = "../result/A/A_30_modified_ocean/"
try:
os.makedirs(dirs)
except:
print('directory {} already exists'.format(dirs))
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
_, _, _, data = main_data(
start, start,
span=30,
get_columns=["coeff"],
region=None,
accumulate=False
)
data.A.loc[data.A<0] *= -1
data.A.loc[data.data_idx==0.] = np.nan
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=0.025,
vmin=0,
cmap="jet"
)
print("\n")
def A_5():
A_by_year_list = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
dirs = "../result/A/A_by_year_modified/"
try:
os.makedirs(dirs)
except:
print('directory {} already exists'.format(dirs))
for item in A_by_year_list:
fname = "../data/csv_A_by_year/ssc_amsr_ads" + item + "_fin.csv"
df_coeffs = pd.read_csv(fname, sep=',', dtype='float32')
df_coeffs.columns = ["index", "angle", "mean_ocean_u", "mean_ocean_v", "A", "coef", "data_num", "mean_ice_u", "mean_ice_v", "mean_w_u", "mean_w_v"]
df_coeffs = df_coeffs.drop("index", axis=1)
df_coeffs[df_coeffs==999.] = np.nan
df_coeffs.A[df_coeffs.A<0] *= -1
save_name = dirs + "A_" + item + ".png"
visualize.plot_map_once(
df_coeffs["A"],
data_type="type_non_wind",
save_name=save_name,
show=False,
vmax=0.025,
vmin=0,
cmap="jet"
)
print("\n")
def H_A_and_theta_by_year():
A_by_year_list = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
dirs_A = "../result_h/A/A_by_year/"
mkdir(dirs_A)
dirs_theta = "../result_h/theta/theta_by_year/"
mkdir(dirs_theta)
for item in A_by_year_list:
fname = "../data/csv_Hermert_by_year/Hermert_by_year_" + item + ".csv"
df_coeffs = pd.read_csv(fname, sep=',', dtype='float32')
save_name_A = dirs_A + "Hermert_" + item + ".png"
visualize.plot_map_once(
df_coeffs["A"],
data_type="type_non_wind",
save_name=save_name_A,
show=False,
vmax=0.025,
vmin=0,
cmap=plt.cm.jet
)
save_name_theta = dirs_theta + "Hermert_" + item + ".png"
visualize.plot_map_once(
df_coeffs["theta"],
data_type="type_non_wind",
save_name=save_name_theta,
show=False,
vmax=180,
vmin=-180,
cmap=cm_angle_2
)
print("\n")
def w_1():
wind_files = sorted(glob.glob("../data/csv_iw/*.csv"))
lon = latlon_ex.Lon
lat = latlon_ex.Lat
lon = np.array(lon)
lat = np.array(lat)
dirs = "../result/test_ice_wind/"
try:
os.makedirs(dirs)
except:
print('directory {} already exists'.format(dirs))
for item in wind_files:
data = calc_data.get_1day_iw_data(item)
save_name = dirs + item[15:21] + ".png"
print(save_name)
visualize.plot_map_once(
data.loc[:, ["iw_u", "iw_v"]],
data_type="type_wind",
show=False,
save_name=save_name,
vmax=None,
vmin=None,
cmap=None)
def H_vec_mean_ocean_currents():
dirs = "../result_h/mean_vector/mean_ocean_currents/"
mkdir(dirs)
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
_, _, _, data = 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 compare_2_both_30_csv():
dirs = "../result_h/test/compare_2_both_30_csv/"
if not os.path.exists(dirs):
os.makedirs(dirs)
y_list = [
"03", "04", "05", "06", "07", "08", "09", "10", "13", "14", "15", "16"
]
month_list = [
"01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"
]
plot_kw_list = ["ocean_u", "ocean_v", "A", "theta", "R2", "epsilon2"]
v_lim_list = [(-0.01, 0.01), (-0.01, 0.01), (-0.0001, 0.0001),
(-0.01, 0.01), (-0.0001, 0.0001), (-0.01, 0.01)]
for month in month_list:
for year in y_list:
print(year + month)
file_mat = "../data/csv_Helmert_both_30/Helmert_both_30_20" + year + month + ".csv"
df_mat = pd.read_csv(file_mat)
file_non_mat = "../data/csv_Helmert_30_non_mat/Helmert_30_non_mat_20" + year + month + ".csv"
df_non_mat = pd.read_csv(file_non_mat)
for i, kw in enumerate(plot_kw_list):
N_c_h = df_mat[kw].values
N_c_c = df_non_mat[kw].values
N_c_diff = N_c_h - N_c_c
save_name = dirs + kw + "_" + year + month + ".png"
visualize.plot_map_once(N_c_diff,
data_type="type_non_wind",
show=False,
save_name=save_name,
vmax=v_lim_list[i][1],
vmin=v_lim_list[i][0],
cmap=plt.cm.jet)
def map_corr_median():
dirs = "../result_h/corr_map/corr_median/"
if not os.path.exists(dirs):
os.makedirs(dirs)
y_list = [
"03", "04", "05", "06", "07", "08", "09", "10", "13", "14", "15", "16"
]
month_list = [
"01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"
]
corr_all = []
for month in month_list:
file_list = sorted(
glob.glob("../data/csv_Helmert_both_30/Helmert_both_30_*" + month +
".csv"))
accumulate_data = []
for file in file_list:
data = pd.read_csv(file)
data = data.loc[:, [
"A", "theta", "R2", "epsilon2", "ic0_30_median",
"sit_30_median"
]]
print(data.columns)
print(data.dropna().head(2))
print(np.array(data.dropna())[0:2, :])
accumulate_data.append(np.array(data))
accumulate_data = np.array(accumulate_data)
#data_A_ic0 = accumulate_data[:, :, [0,4]]
corr_list = []
for i in range(145**2):
data_A = accumulate_data[:, i, 0]
#data_A = data_A[~np.isnan(data_A)]
data_ic0 = accumulate_data[:, i, 4]
tmp_df = pd.DataFrame({"data_A": data_A, "data_ic0": data_ic0})
if len(tmp_df.dropna()) <= 5:
corr = np.nan
else:
corr = tmp_df.dropna().corr()
corr = np.array(corr)[0, 1]
#print(i, corr)
corr_list.append(corr)
#corr_all.append(corr_list)
save_name = dirs + "ic0_A_median_" + month + ".png"
print(save_name)
visualize.plot_map_once(corr_list,
data_type="type_non_wind",
show=False,
save_name=save_name,
vmax=1,
vmin=-1,
cmap=plt.cm.jet)
def test_SIT():
filename = "../data/csv_sit/SIT_20020813.csv"
data = calc_data.get_1day_sit_data(filename)
data[data >= 10001] = np.nan
visualize.plot_map_once(data,
data_type="type_non_wind",
save_name=None,
show=True,
vmax=None,
vmin=None,
cmap=plt.cm.jet)
"""
def plot_data_corr_sit_1day_delay():
dirs_corr_map = "../result_h_1day_delay/corr_map/"
dirs_corr_map_search_grid = "../result_h_1day_delay/corr_map_search_grid/"
if not os.path.exists(dirs_corr_map):
os.makedirs(dirs_corr_map)
if not os.path.exists(dirs_corr_map_search_grid):
os.makedirs(dirs_corr_map_search_grid)
data_ex_dir = "../data/csv_Helmert_30_1day_delay/Helmert_30_1day_delay_200301.csv"
data_ex = pd.read_csv(data_ex_dir)
month_list = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
for month in month_list:
file_list = sorted(glob.glob("../data/csv_Helmert_30_1day_delay/Helmert_30_1day_delay_*" + month + ".csv"))
accumulate_data = []
for file in file_list:
data = pd.read_csv(file)
data = data.loc[:, ["A", "theta", "R2", "epsilon2", "ic0_30", "sit_30"]]
print(data.columns)
print(data.dropna().head(2))
print(np.array(data.dropna())[0:2,:])
accumulate_data.append(np.array(data))
accumulate_data = np.array(accumulate_data)
#data_A_ic0 = accumulate_data[:, :, [0,4]]
corr_list = []
for i in range(145**2):
data_A = accumulate_data[:, i, 0]
#data_A = data_A[~np.isnan(data_A)]
data_ic0 = accumulate_data[:, i, 5]
tmp_df = pd.DataFrame({"data_A": data_A, "data_sit": data_ic0})
if len(tmp_df.dropna()) <= 5:
corr = np.nan
else:
corr = tmp_df.dropna().corr()
corr = np.array(corr)[0,1]
#print(i, corr)
corr_list.append(corr)
save_name_corr = dirs_corr_map + "sit_A_" + month + ".png"
visualize.plot_map_once(corr_list, data_type="type_non_wind", show=False,
save_name=save_name_corr, vmax=1, vmin=-1, cmap=cm_angle_2)
"""
def vec_1():
dirs = "../result/mean_vector/mean_wind/"
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
date_ax, date_ax_str, skipping_date_str, data = main_data(
start, end,
span=30,
get_columns=["w"],
region=None,
accumulate=True
)
data_array = np.array(data)
#data_array_1 = np.ma.masked_invalid(data_array)
#data_count_nan = np.sum(data_array_1.recordmask, axis=0)
#date_ax_len = len(date_ax)
#data_ave_sum = np.sum(data_array, axis=0)
data_ave = np.nanmean(data_array, axis=0)
#data_ave[date_ax_len-data_count_nan<=20] = np.nan
data_ave = pd.DataFrame(data_ave)
data_ave.columns = ["w_speed", "w_u", "w_v"]
print(data_ave.head(3))
save_name = dirs + "mean_wind_" + str(start)[:6] + ".png"
visualize.plot_map_once(
data_ave.loc[:, ["w_u", "w_v"]],
data_type="type_wind",
save_name=save_name,
show=False,
vmax=None,
vmin=None,
cmap=None
)
print("\n")
def H_angle_30():
dirs_original = "../result_h/angle/angle_30/"
mkdir(dirs_original)
dirs_h_coef = "../result_h/angle/angle_30_high_coef/"
mkdir(dirs_h_coef)
for i, start in enumerate(start_list):
print("****************** {}/{} *******************".format(i+1, M))
_, _, _, data = 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
)
#angle_30_high_coef
data.loc[data.R2<threshold_R2, data.theta] = 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
)
print("\n")
def H_A_and_theta_by_year():
A_by_year_list = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"]
dirs_A = "../result_h/A/A_by_year/"
mkdir(dirs_A)
dirs_theta = "../result_h/theta/theta_by_year/"
mkdir(dirs_theta)
for item in A_by_year_list:
fname = "../data/csv_Hermert_by_year/Hermert_by_year_" + item + ".csv"
df_coeffs = pd.read_csv(fname, sep=',', dtype='float32')
#df_coeffs.columns = ["index", "angle", "mean_ocean_u", "mean_ocean_v", "A", "coef", "data_num", "mean_ice_u", "mean_ice_v", "mean_w_u", "mean_w_v"]
#df_coeffs = df_coeffs.drop("index", axis=1)
#df_coeffs[df_coeffs==999.] = np.nan
save_name_A = dirs_A + "Hermert_" + item + ".png"
visualize.plot_map_once(
df_coeffs["A"],
data_type="type_non_wind",
save_name=save_name_A,
show=False,
vmax=0.025,
vmin=0,
cmap=plt.cm.jet
)
save_name_theta = dirs_theta + "Hermert_" + item + ".png"
visualize.plot_map_once(
df_coeffs["theta"],
data_type="type_non_wind",
save_name=save_name_theta,
show=False,
vmax=180,
vmin=-180,
cmap=cm_angle_1
)
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/"
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_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 plot_data_map_1day_delay(num):
dirs_A_30 = "../result_h_1day_delay/A/A_30/"
dirs_R2_30 = "../result_h_1day_delay/R2/R2_30/"
dirs_theta_30 = "../result_h_1day_delay/theta/theta_30/"
dirs_epsilon2_30 = "../result_h_1day_delay/epsilon2/epsilon2_30/"
dirs_A_90 = "../result_h_1day_delay/A/A_90/"
dirs_R2_90 = "../result_h_1day_delay/R2/R2_90/"
dirs_theta_90 = "../result_h_1day_delay/theta/theta_90/"
dirs_epsilon2_90 = "../result_h_1day_delay/epsilon2/epsilon2_90/"
dirs_A_by_year = "../result_h_1day_delay/A/A_by_year/"
dirs_R2_by_year = "../result_h_1day_delay/R2/R2_by_year/"
dirs_theta_by_year = "../result_h_1day_delay/theta/theta_by_year/"
dirs_epsilon2_by_year = "../result_h_1day_delay/epsilon2/epsilon2_by_year/"
dirs_list = [
dirs_A_30,
dirs_R2_30,
dirs_theta_30,
dirs_epsilon2_30,
dirs_A_90,
dirs_R2_90,
dirs_theta_90,
dirs_epsilon2_90,
dirs_A_by_year,
dirs_R2_by_year,
dirs_theta_by_year,
dirs_epsilon2_by_year
]
for dirs in dirs_list:
if not os.path.exists(dirs):
os.makedirs(dirs)
file_list_30 = sorted(glob.glob("../data/csv_Helmert_30_1day_delay/Helmert_30_1day_delay_*.csv"))
file_list_90 = sorted(glob.glob("../data/csv_Helmert_90_1day_delay/Helmert_90_1day_delay_*.csv"))
file_list_year = sorted(glob.glob("../data/csv_Helmert_by_year_1day_delay/Helmert_by_year_1day_delay_*.csv"))
if num == 1:
for file in file_list_30:
data = pd.read_csv(file)
save_name_A = dirs_A_30 + file[56:62] + ".png"
print(save_name_A)
visualize.plot_map_once(np.array(data["A"]), data_type="type_non_wind", show=False,
save_name=save_name_A, vmax=0.02, vmin=0, cmap=plt.cm.jet)
save_name_theta = dirs_theta_30 + file[56:62] + ".png"
print(save_name_theta)
visualize.plot_map_once(np.array(data["theta"]), data_type="type_non_wind", show=False,
save_name=save_name_theta, vmax=180, vmin=-180, cmap=cm_angle_2)
save_name_R2 = dirs_R2_30 + file[56:62] + ".png"
print(save_name_R2)
visualize.plot_map_once(np.array(data["R2"]), data_type="type_non_wind", show=False,
save_name=save_name_R2, vmax=1, vmin=0, cmap=plt.cm.jet)
save_name_e2 = dirs_epsilon2_30 + file[56:62] + ".png"
print(save_name_e2)
visualize.plot_map_once(np.array(data["epsilon2"]), data_type="type_non_wind", show=False,
save_name=save_name_e2, vmax=1.5, vmin=0, cmap=plt.cm.jet)
elif num == 2:
for file in file_list_90:
data = pd.read_csv(file)
save_name_A = dirs_A_90 + file[56:62] + ".png"
print(save_name_A)
visualize.plot_map_once(np.array(data["A"]), data_type="type_non_wind", show=False,
save_name=save_name_A, vmax=0.02, vmin=0, cmap=plt.cm.jet)
save_name_theta = dirs_theta_90 + file[56:62] + ".png"
print(save_name_theta)
visualize.plot_map_once(np.array(data["theta"]), data_type="type_non_wind", show=False,
save_name=save_name_theta, vmax=180, vmin=-180, cmap=cm_angle_2)
save_name_R2 = dirs_R2_90 + file[56:62] + ".png"
print(save_name_R2)
visualize.plot_map_once(np.array(data["R2"]), data_type="type_non_wind", show=False,
save_name=save_name_R2, vmax=1, vmin=0, cmap=plt.cm.jet)
save_name_e2 = dirs_epsilon2_90 + file[56:62] + ".png"
print(save_name_e2)
visualize.plot_map_once(np.array(data["epsilon2"]), data_type="type_non_wind", show=False,
save_name=save_name_e2, vmax=1.5, vmin=0, cmap=plt.cm.jet)
elif num == 3:
for file in file_list_year:
data = pd.read_csv(file)
save_name_A = dirs_A_by_year + file[66:68] + ".png"
print(save_name_A)
visualize.plot_map_once(np.array(data["A"]), data_type="type_non_wind", show=False,
save_name=save_name_A, vmax=0.02, vmin=0, cmap=plt.cm.jet)
save_name_theta = dirs_theta_by_year + file[66:68] + ".png"
print(save_name_theta)
visualize.plot_map_once(np.array(data["theta"]), data_type="type_non_wind", show=False,
save_name=save_name_theta, vmax=180, vmin=-180, cmap=cm_angle_2)
save_name_R2 = dirs_R2_by_year + file[66:68] + ".png"
print(save_name_R2)
visualize.plot_map_once(np.array(data["R2"]), data_type="type_non_wind", show=False,
save_name=save_name_R2, vmax=1, vmin=0, cmap=plt.cm.jet)
save_name_e2 = dirs_epsilon2_by_year + file[66:68] + ".png"
print(save_name_e2)
visualize.plot_map_once(np.array(data["epsilon2"]), data_type="type_non_wind", show=False,
save_name=save_name_e2, vmax=1.5, vmin=0, cmap=plt.cm.jet)
np.savetxt(savename, sit, delimiter=',') """ #landmask_low_NPのテスト filename = "../../data/landmask_low_NP" fp = open(filename, 'rb') ary = np.fromfile(fp, '<b', -1) savename = filename + ".csv" #np.savetxt(savename, ary, delimiter=',') #ocean_grid145との比較 data_0 = ary ocean_0 = np.where(data_0 == 0.)[0] data_1 = ocean_grid_145 ocean_1 = np.where(data_1 == 1.)[0] print(len(ocean_0)) print(len(ocean_1)) """ data, ic0_idx_t = calc_data.get_1day_ic0_data(savename, grid900to145_file_name) visualize.plot_map_once(data, data_type="type_non_wind", show=True, save_name=None, vmax=None, vmin=None) """ #visualize.plot_ic0_900(savename, None, True)
def plot_data_corr_sit_1day_delay():
dirs_corr_map = "../result_h_1day_delay/corr_map/"
dirs_corr_map_search_grid = "../result_h_1day_delay/corr_map_search_grid/"
if not os.path.exists(dirs_corr_map):
os.makedirs(dirs_corr_map)
if not os.path.exists(dirs_corr_map_search_grid):
os.makedirs(dirs_corr_map_search_grid)
data_ex_dir = "../data/csv_Helmert_30_1day_delay/Helmert_30_1day_delay_200301.csv"
data_ex = pd.read_csv(data_ex_dir)
month_list = [
"01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"
]
for month in month_list:
file_list = sorted(
glob.glob(
"../data/csv_Helmert_30_1day_delay/Helmert_30_1day_delay_*" +
month + ".csv"))
accumulate_data = []
for file in file_list:
data = pd.read_csv(file)
data = data.loc[:, [
"A", "theta", "R2", "epsilon2", "ic0_30", "sit_30"
]]
print(data.columns)
print(data.dropna().head(2))
print(np.array(data.dropna())[0:2, :])
accumulate_data.append(np.array(data))
accumulate_data = np.array(accumulate_data)
#data_A_ic0 = accumulate_data[:, :, [0,4]]
corr_list = []
for i in range(145**2):
data_A = accumulate_data[:, i, 0]
#data_A = data_A[~np.isnan(data_A)]
data_ic0 = accumulate_data[:, i, 5]
tmp_df = pd.DataFrame({"data_A": data_A, "data_sit": data_ic0})
if len(tmp_df.dropna()) <= 5:
corr = np.nan
else:
corr = tmp_df.dropna().corr()
corr = np.array(corr)[0, 1]
#print(i, corr)
corr_list.append(corr)
save_name_corr = dirs_corr_map + "sit_A_" + month + ".png"
visualize.plot_map_once(corr_list,
data_type="type_non_wind",
show=False,
save_name=save_name_corr,
vmax=1,
vmin=-1,
cmap=plt.cm.jet)
df_corr = pd.DataFrame({"corr": corr_list})
df_corr = pd.concat([
latlon_ex, df_corr,
data_ex.loc[:,
["coastal_region_1", "coastal_region_2", "area_label"]]
],
axis=1)
corr_grid_pos = df_corr.loc[(df_corr["corr"] >= 0.7) & (
df_corr["area_label"].isin([0, 1, 4, 5, 7, 8, 10, 12, 16])
), :].dropna().index
corr_grid_neg = df_corr.loc[(df_corr["corr"] <= -0.7) & (
df_corr["area_label"].isin([0, 1, 4, 5, 7, 8, 10, 12, 16])
), :].dropna().index
try:
plot_grids_pos = random.sample(
np.array(corr_grid_pos).tolist(), 15)
plot_grids_neg = random.sample(
np.array(corr_grid_neg).tolist(), 15)
except:
plot_grids_pos = random.sample(np.array(corr_grid_pos).tolist(), 5)
plot_grids_neg = random.sample(np.array(corr_grid_neg).tolist(), 5)
for grid in plot_grids_pos:
plot_A = accumulate_data[:, grid, 0]
plot_ic0 = accumulate_data[:, grid, 5]
sns.set_style("darkgrid")
sns.jointplot(x=plot_ic0, y=plot_A, kind="reg")
save_name = dirs_corr_map_search_grid + "sit_A_pos_grid_" + month + "_" + str(
grid) + ".png"
plt.savefig(save_name)
plt.close()
for grid in plot_grids_neg:
plot_A = accumulate_data[:, grid, 0]
plot_ic0 = accumulate_data[:, grid, 5]
sns.set_style("darkgrid")
sns.jointplot(x=plot_ic0, y=plot_A, kind="reg")
save_name = dirs_corr_map_search_grid + "sit_A_neg_grid_" + month + "_" + str(
grid) + ".png"
plt.savefig(save_name)
plt.close()
m = Basemap(lon_0=180,
boundinglat=65,
resolution='l',
projection='npstere')
m.drawcoastlines(color='0.15')
m.fillcontinents(color='#555555')
lon = np.array(latlon_ex.Lon)
lat = np.array(latlon_ex.Lat)
x, y = m(lon, lat)
m.scatter(x[plot_grids_pos],
y[plot_grids_pos],
marker='o',
color="r",
s=2,
alpha=0.9)
m.scatter(x[plot_grids_neg],
y[plot_grids_neg],
marker='o',
color="b",
s=2,
alpha=0.9)
for grid in plot_grids_pos:
plt.annotate(str(grid),
xy=(x[grid], y[grid]),
xycoords='data',
xytext=(x[grid], y[grid]),
textcoords='data',
color='r')
for grid in plot_grids_neg:
plt.annotate(str(grid),
xy=(x[grid], y[grid]),
xycoords='data',
xytext=(x[grid], y[grid]),
textcoords='data',
color='b')
plt.savefig(dirs_corr_map_search_grid + "sit_A_grid_info_" + month +
".png",
dpi=200)
plt.close()
region=None,
accumulate=False)
#print(data.head())
#data = data["A"]
#print(len(data))
# latlon_exで絞り込む場合，ここに処理を書く
#data = pd.concat([latlon_ex, data], axis=1)
data.loc[data.A < 0] *= -1
data[data.data_idx == 0.] = np.nan
save_name = "../result/A_30/A_30_" + str(start)[:6] + ".png"
#visualize.pyで関数を選ぶ
visualize.plot_map_once(data["A"],
data_type="type_non_wind",
save_name=save_name,
show=False,
vmax=0.025)
print("\n")
"""
#A_by_dayのマップを月ごとに出力して保存するコード
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)
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)
data_array_1 = np.ma.masked_invalid(data_array)
data_count_nan = np.sum(data_array_1.recordmask, axis=0)
date_ax_len = len(date_ax)
data_ave_sum = np.sum(data_array, axis=0)
data_ave_sum[date_ax_len - data_count_nan <= 20] = np.nan
data_ave = data_ave_sum / (date_ax_len - data_count_nan)
#A_by_dayなので0列目
data_ave = pd.DataFrame(data_ave)
data_ave.columns = ["w_speed", "w_u", "w_v"]
print(data_ave.head(3))
save_name = "../result/mean_vector/mean_wind/mean_wind_" + str(
start)[:6] + ".png"
visualize.plot_map_once(data_ave.loc[:, ["w_u", "w_v"]],
data_type="type_wind",
save_name=save_name,
show=False,
vmax=None,
vmin=None)
print("\n")
"""
#海流の平均のマップ出力
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)
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)
def A_6():
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)
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/A/A_by_day_30/"
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
date_ax, date_ax_str, skipping_date_str, data = main_data(
start, end,
span=30,
get_columns=["ex_1"],
region=None,
accumulate=True
)
data_array = np.array(data)
"""
data_array = np.ma.masked_invalid(data_array)
data_count_nan = np.sum(data_array.recordmask, axis=0)
#print(data_count_nan)
#print(len(date_ax))
#print(len(date_ax_str))
data_ave = np.sum(data_array, axis=0) / (len(date_ax)-data_count_nan)
"""
data_ave = np.nanmean(data_array, axis=0)
#A_by_dayなので0列目
data_ave = pd.DataFrame(data_ave[:, 0])
#print(data_ave)
data_ave.columns = ["A_by_day"]
# 閾値を設ける場合
#data_ave.loc[data_ave.A_by_day>=0.05, :] = np.nan
# data_aveにLabelとかNameをくっつける場合、以下のdataをmain_plotに渡す
# Labelなどで絞り込む場合は、ここに操作を付け足す
#data = pd.concat([latlon_ex, data], axis=1)
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="jet"
)
print("\n")
def map_corr_slide_month(slide):
dirs = "../result_h/corr_map/corr_slide_month/"
if not os.path.exists(dirs):
os.makedirs(dirs)
y_list = [
"03", "04", "05", "06", "07", "08", "09", "10", "13", "14", "15", "16"
]
month_list = [
"01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"
]
product_ym = list(itertools.product(y_list, month_list))
data_dict = {}
for month in month_list:
data_dict[month] = {}
for year in y_list:
data_dict[month][year] = {}
data_dict[month][year]["ym_A"] = np.array([np.nan] * (145**2))
data_dict[month][year]["ym_ic0"] = np.array([np.nan] * (145**2))
data_dict[month][year]["data_A"] = np.array([np.nan] * (145**2))
data_dict[month][year]["data_ic0"] = np.array([np.nan] * (145**2))
for k, item in enumerate(product_ym):
if k + slide > len(product_ym) - 1:
break
x_ym = product_ym[k][0] + product_ym[k][1]
y_ym = product_ym[k + slide][0] + product_ym[k + slide][1]
print(x_ym, y_ym)
if (k <= 95) & (k + slide > 95):
print("\tcontinue: {}".format((x_ym, y_ym)))
data_dict[product_ym[k][1]][product_ym[k][0]]["ym_A"] = x_ym
data_dict[product_ym[k][1]][product_ym[k][0]]["ym_ic0"] = y_ym
data_dict[product_ym[k][1]][product_ym[k][0]]["data_A"] = np.array(
[np.nan] * (145**2))
data_dict[product_ym[k][1]][
product_ym[k][0]]["data_ic0"] = np.array([np.nan] * (145**2))
continue
else:
helmert_axis_x_file = "../data/csv_Helmert_both_30/Helmert_both_30_20" + x_ym + ".csv"
helmert_axis_y_file = "../data/csv_Helmert_both_30/Helmert_both_30_20" + y_ym + ".csv"
data_A = pd.read_csv(helmert_axis_x_file)["A"]
data_ic0 = pd.read_csv(helmert_axis_y_file)["ic0_30"]
data_dict[product_ym[k][1]][product_ym[k][0]]["ym_A"] = x_ym
data_dict[product_ym[k][1]][product_ym[k][0]]["ym_ic0"] = y_ym
data_dict[product_ym[k][1]][product_ym[k][0]]["data_A"] = np.array(
data_A)
data_dict[product_ym[k][1]][product_ym[k]
[0]]["data_ic0"] = np.array(data_ic0)
for month in month_list:
accumulate_data_A = []
accumulate_data_ic0 = []
for year in y_list:
#print(month, year)
accumulate_data_A.append(data_dict[month][year]["data_A"])
accumulate_data_ic0.append(data_dict[month][year]["data_ic0"])
accumulate_data_A = np.array(accumulate_data_A)
accumulate_data_ic0 = np.array(accumulate_data_ic0)
corr_list = []
for i in range(145**2):
plot_data_A = accumulate_data_A[:, i]
#data_A = data_A[~np.isnan(data_A)]
plot_data_ic0 = accumulate_data_ic0[:, i]
tmp_df = pd.DataFrame({
"data_A": plot_data_A,
"data_ic0": plot_data_ic0
})
if len(tmp_df.dropna()) <= 5:
corr = np.nan
else:
corr = tmp_df.dropna().corr()
corr = np.array(corr)[0, 1]
corr_list.append(corr)
corr_array = np.array(corr_list)
save_name = dirs + "ic0_A_start_month_" + month + "_slide_" + str(
slide) + ".png"
print("\t{}".format(save_name))
visualize.plot_map_once(corr_array,
data_type="type_non_wind",
show=False,
save_name=save_name,
vmax=1,
vmin=-1,
cmap=plt.cm.jet)
def presentation_03(slide):
"""
SICとの相関係数のslide month
"""
y_list = [
"03", "04", "05", "06", "07", "08", "09", "10", "13", "14", "15", "16"
]
month_list = [
"01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"
]
product_ym = list(itertools.product(y_list, month_list))
data_dict = {}
for month in month_list:
data_dict[month] = {}
for year in y_list:
data_dict[month][year] = {}
data_dict[month][year]["ym_A"] = np.array([np.nan] * (145**2))
data_dict[month][year]["ym_ic0"] = np.array([np.nan] * (145**2))
data_dict[month][year]["data_A"] = np.array([np.nan] * (145**2))
data_dict[month][year]["data_ic0"] = np.array([np.nan] * (145**2))
for k, item in enumerate(product_ym):
if k + slide > len(product_ym) - 1:
break
x_ym = product_ym[k][0] + product_ym[k][1]
y_ym = product_ym[k + slide][0] + product_ym[k + slide][1]
print(x_ym, y_ym)
if (k <= 95) & (k + slide > 95):
print("\tcontinue: {}".format((x_ym, y_ym)))
data_dict[product_ym[k][1]][product_ym[k][0]]["ym_A"] = x_ym
data_dict[product_ym[k][1]][product_ym[k][0]]["ym_ic0"] = y_ym
data_dict[product_ym[k][1]][product_ym[k][0]]["data_A"] = np.array(
[np.nan] * (145**2))
data_dict[product_ym[k][1]][
product_ym[k][0]]["data_ic0"] = np.array([np.nan] * (145**2))
continue
else:
helmert_axis_x_file = "../data/csv_Helmert_30_1day_delay/Helmert_30_1day_delay_20" + x_ym + ".csv"
helmert_axis_y_file = "../data/csv_Helmert_30_1day_delay/Helmert_30_1day_delay_20" + y_ym + ".csv"
data_A = pd.read_csv(helmert_axis_x_file)["A"]
data_ic0 = pd.read_csv(helmert_axis_y_file)["ic0_30"]
data_dict[product_ym[k][1]][product_ym[k][0]]["ym_A"] = x_ym
data_dict[product_ym[k][1]][product_ym[k][0]]["ym_ic0"] = y_ym
data_dict[product_ym[k][1]][product_ym[k][0]]["data_A"] = np.array(
data_A)
data_dict[product_ym[k][1]][product_ym[k]
[0]]["data_ic0"] = np.array(data_ic0)
for month in month_list:
accumulate_data_A = []
accumulate_data_ic0 = []
for year in y_list:
#print(month, year)
accumulate_data_A.append(data_dict[month][year]["data_A"])
accumulate_data_ic0.append(data_dict[month][year]["data_ic0"])
accumulate_data_A = np.array(accumulate_data_A)
accumulate_data_ic0 = np.array(accumulate_data_ic0)
corr_list_ic0_p = []
corr_list_ic0_r = []
for i in range(145**2):
plot_data_A = accumulate_data_A[:, i]
#data_A = data_A[~np.isnan(data_A)]
plot_data_ic0 = accumulate_data_ic0[:, i]
tmp_df = pd.DataFrame({
"data_A": plot_data_A,
"data_ic0": plot_data_ic0
})
if len(tmp_df.dropna()) <= 5:
r = np.nan
p = np.nan
else:
corr_ic0 = np.array(tmp_df.dropna())
r, p = stats.pearsonr(corr_ic0[:, 0], corr_ic0[:, 1])
corr_list_ic0_p.append(p)
corr_list_ic0_r.append(r)
corr_array_p = np.array(corr_list_ic0_p)
corr_array_r = np.array(corr_list_ic0_r)
th = 0.05
corr_array_p = np.where(corr_array_p < th, 0, 1)
month_slided = str((int(month) + slide) % 12)
if len(month_slided) == 1:
month_slided = "0" + month_slided
save_name_p = "../result_h_1day_delay/corr_map_slide_month/ic0_A_start_month_" + month + "_" + month_slided + "_p.png"
save_name_r = "../result_h_1day_delay/corr_map_slide_month/ic0_A_start_month_" + month + "_" + month_slided + "_r.png"
print("\t{}".format(save_name_p))
print("\t{}".format(save_name_r))
visualize.plot_map_once(corr_array_p,
data_type="type_non_wind",
show=False,
save_name=save_name_p,
vmax=1,
vmin=0,
cmap=cm_p_value)
visualize.plot_map_once(corr_array_r,
data_type="type_non_wind",
show=False,
save_name=save_name_r,
vmax=1,
vmin=-1,
cmap=plt.cm.jet)
