def test_ocean_mean_iw_plot():
dirs = "../result_h/test/test_ocean/"
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_90 = "../data/csv_Helmert_both_90/Helmert_both_90_" + str(
start)[:6] + ".csv"
helmert_90_data = pd.read_csv(hermert_file_name_90).dropna()
#csv_Helmert_30の当該月のcsvを読み込む
hermert_file_name_30 = "../data/csv_Helmert_30/Helmert_30_" + str(
start)[:6] + ".csv"
helmert_30_data = pd.read_csv(hermert_file_name_30).dropna()
#木村のcsvの読み込み(90)
coeff_file_name_90 = "../data/csv_A_90/ssc_amsr_ads" + str(
start)[2:6] + "_90_fin.csv"
coeff_90_data = calc_data.get_1month_coeff_data(
coeff_file_name_90).dropna()
#木村のcsvの読み込み(30)
coeff_file_name_30 = "../data/csv_A_30/ssc_amsr_ads" + str(
start)[2:6] + "_30_fin.csv"
coeff_30_data = calc_data.get_1month_coeff_data(
coeff_file_name_30).dropna()
ocean_90_h_u = helmert_90_data["ocean_u"].values
ocean_90_h_v = helmert_90_data["ocean_v"].values
ocean_90_h_speed = np.sqrt(ocean_90_h_u**2 + ocean_90_h_v**2)
ocean_30_h_u = helmert_30_data["ocean_u"].values
ocean_30_h_v = helmert_30_data["ocean_v"].values
ocean_30_h_speed = np.sqrt(ocean_30_h_u**2 + ocean_30_h_v**2)
ocean_90_c_u = coeff_90_data["mean_ocean_u"].values
ocean_90_c_v = coeff_90_data["mean_ocean_v"].values
ocean_90_c_speed = np.sqrt(ocean_90_c_u**2 + ocean_90_c_v**2)
ocean_30_c_u = coeff_30_data["mean_ocean_u"].values
ocean_30_c_v = coeff_30_data["mean_ocean_v"].values
ocean_30_c_speed = np.sqrt(ocean_30_c_u**2 + ocean_30_c_v**2)
fig, axes = plt.subplots(1, 2)
axes[0].hist(ocean_90_c_speed / ocean_90_h_speed)
axes[1].hist(ocean_30_c_speed / ocean_30_h_speed)
plt.savefig(dirs + "hist_ocean_" + str(start)[:6] + ".png", dpi=450)
plt.close()
def main_data(start, end, **kwargs):
span = kwargs["span"]
region = kwargs["region"]
get_columns = kwargs["get_columns"]
accumulate = kwargs["accumulate"]
date_ax, date_ax_str = get_date_ax(start, end)
N = len(date_ax_str)
skipping_date_str = []
accumulate_data = []
data = []
for i, day in enumerate(date_ax_str):
print("{}/{}: {}".format(i + 1, N, day))
print("start: {}, end: {}".format(start, end))
year = day[2:4]
month = day[4:6]
#ファイル名の生成
wind_file_name = "../data/csv_w/ecm" + day[2:] + ".csv"
ice_file_name = "../data/csv_iw/" + day[2:] + ".csv"
ic0_145_file_name = "../data/csv_ic0/IC0_" + day + ".csv"
sit_145_file_name = "../data/csv_sit/SIT_" + day + ".csv"
coeff_file_name = "../data/csv_A_30/ssc_amsr_ads" + str(year) + str(
month) + "_" + str(span) + "_fin.csv"
hermert_file_name = "../data/csv_Helmert_30/Helmert_30_" + str(
day)[:6] + ".csv"
# wind10m_file_name = "../data/netcdf4/" + day[2:] + ".csv"
# t2m_file_name = "../data/netcdf4/" + day[2:] + ".csv"
skipping_boolean = ("coeff" not in get_columns) and (not all([
os.path.isfile(wind_file_name),
os.path.isfile(ice_file_name),
os.path.isfile(coeff_file_name)
]))
if ("ic0_145" in get_columns):
skipping_boolean = ("coeff" not in get_columns) and (not all([
os.path.isfile(wind_file_name),
os.path.isfile(ice_file_name),
os.path.isfile(coeff_file_name),
os.path.isfile(ic0_145_file_name)
]))
if ("sit_145" in get_columns):
skipping_boolean = ("coeff" not in get_columns) and (not all([
os.path.isfile(wind_file_name),
os.path.isfile(ice_file_name),
os.path.isfile(coeff_file_name),
os.path.isfile(sit_145_file_name)
]))
if skipping_boolean == True:
print("\tSkipping " + day + " file...")
date_ax_str.remove(day)
bb = date(int(day[:4]), int(day[4:6]), int(day[6:]))
date_ax.remove(bb)
skipping_date_str.append(day)
continue
data = pd.DataFrame({"data_idx": np.array(ocean_grid_145).ravel()})
if "ex_1" in get_columns:
print("\t{}\n\t{}\n\t{}\n\t{}".format(wind_file_name,
ice_file_name,
coeff_file_name))
tmp = calc_data.get_w_regression_data(wind_file_name,
ice_file_name,
coeff_file_name)
data = pd.concat([data, tmp], axis=1)
if "ex_2" in get_columns:
print("\t{}\n\t{}\n\t{}\n\t{}".format(wind_file_name,
ice_file_name,
hermert_file_name))
tmp = calc_data.get_w_hermert_data(wind_file_name, ice_file_name,
hermert_file_name)
data = pd.concat([data, tmp], axis=1)
if "w" in get_columns:
print("\t{}".format(wind_file_name))
tmp = calc_data.get_1day_w_data(wind_file_name)
data = pd.concat([data, tmp], axis=1)
if "iw" in get_columns:
print("\t{}".format(ice_file_name))
tmp = calc_data.get_1day_iw_data(ice_file_name)
data = pd.concat([data, tmp], axis=1)
if "ic0_145" in get_columns:
print("\t{}".format(ic0_145_file_name))
tmp = calc_data.get_1day_ic0_data(ic0_145_file_name)
data = pd.concat([data, tmp], axis=1)
if "sit_145" in get_columns:
print("\t{}".format(sit_145_file_name))
tmp = calc_data.get_1day_sit_data(sit_145_file_name)
data = pd.concat([data, tmp], axis=1)
if "coeff" in get_columns:
print("\t{}".format(coeff_file_name))
tmp = calc_data.get_1month_coeff_data(coeff_file_name)
data = pd.concat([data, tmp], axis=1)
if "hermert" in get_columns:
print("\t{}".format(hermert_file_name))
tmp = calc_data.get_1month_hermert_data(hermert_file_name)
data = pd.concat([data, tmp], axis=1)
"""
if "w10m" in get_columns:
tmp = calc_data.get_1day_w10m_data(wind10m_file_name)
data = pd.concat([data, tmp], axis=1)
if "t2m" in get_columns:
tmp = calc_data.get_1day_t2m_data(t2m_file_name)
data = pd.concat([data, tmp], axis=1)
"""
data = calc_data.get_masked_region_data(data, region)
if ("coeff" in get_columns):
print("\tSelected only coeff data. Getting out of the loop...")
continue
if accumulate == True:
data_1 = data.drop("data_idx", axis=1)
print("\t{}".format(data_1.columns))
accumulate_data.append(np.array(data_1))
if accumulate == True:
print("accumulate: True\tdata type: array")
return date_ax, date_ax_str, skipping_date_str, accumulate_data
else:
print("accumulate: False\tdata type: DataFrame")
return date_ax, date_ax_str, skipping_date_str, data
def test_ocean_plot():
dirs = "../result_h/test/test_iw/"
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
try:
#csv_Helmert_90の当該月のcsvを読み込む
#hermert_file_name_90 = "../data/csv_Helmert_both_90/Helmert_both_90_" + str(start)[:6] + ".csv"
#helmert_90_data = pd.read_csv(hermert_file_name_90)
#csv_Helmert_30の当該月のcsvを読み込む
hermert_file_name_30 = "../data/csv_Helmert_30/Helmert_30_" + str(
start)[:6] + ".csv"
helmert_30_data = pd.read_csv(hermert_file_name_30)
#木村のcsvの読み込み(90)
#coeff_file_name_90 = "../data/csv_A_90/ssc_amsr_ads" + str(start)[2:6] + "_90_fin.csv"
#coeff_90_data = calc_data.get_1month_coeff_data(coeff_file_name_90)
#木村のcsvの読み込み(30)
coeff_file_name_30 = "../data/csv_A_30/ssc_amsr_ads" + str(
start)[2:6] + "_30_fin.csv"
coeff_30_data = calc_data.get_1month_coeff_data(coeff_file_name_30)
except:
continue
#ocean_90_h_u = helmert_90_data["ocean_u_90"].values
#ocean_90_h_v = helmert_90_data["ocean_v_90"].values
#ocean_90_h_speed = np.sqrt(ocean_90_h_u**2 + ocean_90_h_v**2)
ocean_30_h_u = helmert_30_data["ocean_u"].values
ocean_30_h_v = helmert_30_data["ocean_v"].values
ocean_30_h_speed = np.sqrt(ocean_30_h_u**2 + ocean_30_h_v**2)
#ocean_90_c_u = coeff_90_data["mean_ocean_u"].values
#ocean_90_c_v = coeff_90_data["mean_ocean_v"].values
#ocean_90_c_speed = np.sqrt(ocean_90_c_u**2 + ocean_90_c_v**2)
ocean_30_c_u = coeff_30_data["mean_ocean_u"].values
ocean_30_c_v = coeff_30_data["mean_ocean_v"].values
ocean_30_c_speed = np.sqrt(ocean_30_c_u**2 + ocean_30_c_v**2)
"""
fig, axes = plt.subplots(1, 2)
ratio_90 = ocean_90_c_speed/ocean_90_h_speed
ratio_30 = ocean_30_c_speed/ocean_30_h_speed
axes[0].hist(ratio_30[ocean_idx][~np.isnan(ratio_30[ocean_idx])], range=(0,3), bins=150)
axes[1].hist(ratio_90[ocean_idx][~np.isnan(ratio_90[ocean_idx])], range=(0,3), bins=150)
"""
ratio_w = ocean_30_c_u / ocean_30_h_u
N_c_h = helmert_30_data["mean_iw_v"].values
N_c_c = coeff_30_data["mean_ice_v"].values
N_c_diff = N_c_h - N_c_c
#plt.hist(N_c_diff[~np.isnan(N_c_diff)], range=(-5,5), bins=11, alpha=0.5)
#tmp = N_c_diff[~np.isnan(N_c_diff)]
#print(len(np.where(tmp>0)[0]))
m = Basemap(lon_0=180,
boundinglat=50,
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)
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])
data = np.ma.masked_invalid(N_c_diff)
data1 = np.reshape(data, (145, 145), order='F')
m.pcolormesh(xx_ex - dx1,
yy_ex + dy1,
data1,
cmap=plt.cm.jet,
vmax=0.5,
vmin=-0.5)
#fig.colorbar(im, ax=ax)
m.colorbar()
#plt.hist(N_c_c[~np.isnan(N_c_c)], range=(20,35), bins=16)
plt.savefig(dirs + "iw_v_" + str(start)[:6] + ".png", dpi=450)
plt.close()
def main_data(start, end, **kwargs):
span = kwargs["span"]
region = kwargs["region"]
get_columns = kwargs["get_columns"]
accumulate = kwargs["accumulate"]
date_ax, date_ax_str = get_date_ax(start, end)
N = len(date_ax_str)
skipping_date_str = []
accumulate_data = []
for i, day in enumerate(date_ax_str):
print("{}/{}: {}".format(i + 1, N, day))
year = day[2:4]
month = day[4:6]
#ファイル名の生成
wind_file_name = "../data/wind_data/ecm" + day[2:] + ".csv"
ice_file_name = "../data/ice_wind_data/" + day[2:] + ".csv"
ic0_145_file_name = "../data/IC0_csv/2" + day + "A.csv"
ic0_900_file_name = "../data/IC0_csv/2" + day + "A.csv"
coeff_file_name = "../data/A_csv/ssc_amsr_ads" + str(year) + str(
month) + "_" + str(span) + "_fin.csv"
wind10m_file_name = "../data/netcdf4/" + day[2:] + ".csv"
t2m_file_name = "../data/netcdf4/" + day[2:] + ".csv"
if not all([
os.path.isfile(wind_file_name),
os.path.isfile(ice_file_name),
os.path.isfile(ic0_145_file_name),
os.path.isfile(coeff_file_name)
]):
print("\tSkipping " + day + " file...")
date_ax_str.remove(day)
date_ax.remove(datetime(day[:4] + "-" + day[4:6] + "-" + day[6:]))
skipping_date_str.append(day)
continue
data = pd.DataFrame({"data_idx": np.zeros(145 * 145)})
iw_idx_t, ic0_idx_t = np.array([-1]), np.array([-1])
if "ex_1" in get_columns:
tmp = calc_data.get_w_regression_data(wind_file_name,
ice_file_name,
coeff_file_name)
iw_idx_t = np.array(tmp["data_idx"])
data = pd.concat([data, tmp.loc[:, ["A_by_day", "theta_by_day"]]],
axis=1)
if "w" in get_columns:
tmp = calc_data.get_1day_w_data(wind_file_name)
data = pd.concat([data, tmp], axis=1)
if "iw" in get_columns:
tmp = calc_data.get_1day_ice_data(ice_file_name)
iw_idx_t = np.array(tmp["iw_idx_t"])
data = pd.concat([data, tmp.loc[:, ["iw_u", "iw_v", "iw_speed"]]],
axis=1)
if "ic0_145" in get_columns:
tmp = calc_data.get_1day_ic0_data(ic0_file_name,
grid900to145_file_name)
ic0_idx_t = np.array(tmp["ic0_idx_t"])
data = pd.concat([data, tmp.loc[:, ["ic0_145"]]], axis=1)
if "coeff" in get_columns:
tmp = calc_data.get_1month_coeff_data(coeff_file_name)
data = pd.concat([data, tmp], axis=1)
if "w10m" in get_columns:
tmp = calc_data.get_1day_w10m_data(wind10m_file_name)
data = pd.concat([data, tmp], axis=1)
if "t2m" in get_columns:
tmp = calc_data.get_1day_t2m_data(t2m_file_name)
data = pd.concat([data, tmp], axis=1)
mat = iw_idx_t.ravel().tolist() + ic0_idx_t.ravel().tolist()
data_t_idx = calc_data.get_non_nan_idx(mat, ocean_idx, strict=True)
data.loc[data.data_idx, data_t_idx] = 1
data = calc_data.get_masked_region_data(data, region)
if ("coeff" in get_columns) and (len(get_columns) == 1):
print("\tSelected only coeff data. Getting out of the loop...")
continue
if accumulate == True:
data = data.loc[:, get_columns]
print("\t{}".format(data.columns))
accumulate_data.append(np.array(data))
if accumulate == True:
print("accumulate: True\tdata type: array")
return date_ax, date_ax_str, skipping_date_str, accumulate_data
else:
print("accumulate: False\tdata type: DataFrame")
return date_ax, date_ax_str, skipping_date_str, data