def day_map_plot(config_dict):
hdf_fn = config_dict.get("hdf_fn")
fig_fp = config_dict.get("fig_fp")
area_extent = config_dict.get("area_extent")
dataset_name = config_dict.get("dataset_name")
colorbar_title = config_dict.get("colorbar_title")
title = config_dict.get("title")
llcrnrlon = area_extent[1]
llcrnrlat = area_extent[0]
urcrnrlon = area_extent[3]
urcrnrlat = area_extent[2]
projection = "cyl"
lon_0 = ((area_extent[3] - area_extent[1]) / 2) + area_extent[1]
lat_0 = ((area_extent[2] - area_extent[0]) / 2) + area_extent[0]
hdf_reader = HDFReader(hdf_fn)
pred = hdf_reader.get_dataset(dataset_name)
fill_value = hdf_reader.get_dataset_attr(dataset_name, "FillValue")
pred[pred == fill_value] = np.nan
pred = np.flipud(pred)
map_drawer = PMMap(llcrnrlon, llcrnrlat, urcrnrlon, urcrnrlat, projection,
lon_0, lat_0)
map_drawer.add_data2D(pred, colorbar_title, title)
map_drawer.save_fig(fig_fp)
def day_scatter_plot(config_dict):
fig_fp = config_dict.get("fig_fp")
os.makedirs(os.path.basename(fig_fp), exist_ok=True)
hdf_fp = config_dict.get("hdf_fp")
actual = config_dict.get("actual")
pred = config_dict.get("pred")
area_extent = config_dict.get("area_extent")
res = config_dict.get("res")
title = config_dict.get("title")
x_label = config_dict.get("x_label")
y_label = config_dict.get("y_label")
dst_lat, dst_lon = get_roi_latlon(area_extent, res)
gbl_line, gbl_pixel = get_roi_rcs(dst_lat, dst_lon)
hdf_reader = HDFReader(hdf_fp)
pred_data = hdf_reader.get_dataset(pred)
actual_data = hdf_reader.get_dataset(actual)
fill_val = hdf_reader.get_dataset_attr(pred, "FillValue")
actual_data = actual_data[gbl_line, gbl_pixel]
pred_data = pred_data[gbl_line, gbl_pixel]
mask = actual_data != fill_val
mask &= pred_data != fill_val
pred_data = pred_data[mask]
actual_data = actual_data[mask]
pm_scatter = PMScatter()
pm_scatter.draw(actual_data, pred_data, title, x_label, y_label, fig_fp)
def mapping(self, mersi_fn, dataset_name, dst_lon, dst_lat):
h5_reader = HDFReader(mersi_fn)
data = self.mask_scale(dataset_name, h5_reader)
warped_data = self.down_sample(data, dst_lat, dst_lon)
self.__dataset_dict[dataset_name] = warped_data
self.__dataset_attr_dict[dataset_name] = {
"long_name": h5_reader.get_dataset_attr(dataset_name, "long_name"),
"units": h5_reader.get_dataset_attr(dataset_name, "units"),
"Slope": 1,
"Intercept": 0,
"FillValue": -32767,
"valid_range": self.__valid_range_dict.get(dataset_name)
}
return self.__dataset_dict, self.__dataset_attr_dict
def test_HDFReader():
hdf_fn = r"D:\01-work_directory\03-PM2.5\PMs\data\MERSI\FY3D_MERSI_GBAL_L2_AOD_MLT_GLL_20180817_POAD_5000M_MS.HDF"
h_reader = HDFReader(hdf_fn)
data = h_reader.get_dataset("AOT_550_Mean")
slope = h_reader.get_dataset_attr("AOT_550_Mean", "Slope")
global_attrs = h_reader.get_global_attrs()
print(global_attrs)
aod_attrs = h_reader.get_dataset_attrs("AOT_550_Mean")
print(aod_attrs)
print(data.shape)
print(slope)
def minmax(self, f):
hdf_reader = HDFReader(f)
for name in self.vr_dict.keys():
data = hdf_reader.get_dataset(name)
fill_val = hdf_reader.get_dataset_attr(name, "FillValue")
if len(data[data != fill_val]) == 0:
continue
max_val = data[data != fill_val].max()
min_val = data[data != fill_val].min()
if max_val > self.vr_dict.get(name)[1]:
self.vr_dict[name][1] = max_val
if min_val < self.vr_dict.get(name)[0]:
self.vr_dict[name][0] = min_val
def get_roi_mean_df(actual, gbl_line, gbl_pixel, hdf_list, pred):
df = pd.DataFrame(columns=["date", actual, pred])
for idx, f in enumerate(sorted(hdf_list)):
hdf_reader = HDFReader(f)
df_c = pd.DataFrame(columns=["date", actual, pred])
date_str = os.path.basename(f)[:8]
fill_value = hdf_reader.get_dataset_attr(actual, "FillValue")
pm25 = hdf_reader.get_dataset(actual)[gbl_line, gbl_pixel]
pm25[pm25 == fill_value] = np.nan
pred_pm25 = hdf_reader.get_dataset(pred)[gbl_line, gbl_pixel]
pred_pm25[pred_pm25 == fill_value] = np.nan
df_c.loc[idx, "date"] = date_str
df_c.loc[idx, actual] = np.nanmean(pm25)
df_c.loc[idx, pred] = np.nanmean(pred_pm25)
df = df.append(df_c, ignore_index=True)
df = df.dropna()
return df
def get_season_year_mean_data_dict(hdf_lst, dataset_name):
result_dict = dict()
season_fp_dict = dict()
for season in ["spring", "summer", "autumn", "winter"]:
season_fp_dict[season] = list()
for f in hdf_lst:
mon = str(int(os.path.basename(f)[4:6]))
if str(mon) in ["3", "4", "5"]:
season_fp_dict["spring"].append(f)
elif str(mon) in ["6", "7", "8"]:
season_fp_dict["summer"].append(f)
elif str(mon) in ["9", "10", "11"]:
season_fp_dict["autumn"].append(f)
else:
season_fp_dict["winter"].append(f)
year_data_lst = list()
for key in season_fp_dict.keys():
season_data_lst = list()
for f in season_fp_dict.get(key):
hdf_reader = HDFReader(f)
pred = hdf_reader.get_dataset(dataset_name)
fill_value = hdf_reader.get_dataset_attr(dataset_name, "FillValue")
pred[pred == fill_value] = np.nan
season_data_lst.append(pred)
season_data = np.asarray(season_data_lst)
season_mean = np.nanmean(season_data, axis=0)
season_mean = np.flipud(season_mean)
result_dict[key] = season_mean
year_data_lst.append(season_mean)
year_data = np.asarray(year_data_lst)
year_mean = np.nanmean(year_data, axis=0)
result_dict["year"] = year_mean
return result_dict