unit

Author: JayKuo1

README.md

@@ -52,6 +52,8 @@ rm tmp1.nc tmp2.nc
 
 After the data interpolation is completed, you can refer to the [demo.ipynb](https://github.com/OpenEarthLab/ORCA-DL/blob/main/demo.ipynb) to run ORCA-DL.
 
+**Note: you need to unify the units before using our statistics to normalize the data. See [demo.ipynb](https://github.com/OpenEarthLab/ORCA-DL/blob/main/demo.ipynb).**
+
 ## 📋 Updates
 
 - Training data and code are coming soon.

demo.ipynb

@@ -39,13 +39,15 @@
     "    'mean': {v: np.load(f\"./stat/mean/{v}.npy\") for v in variables},\n",
     "    'std': {v: np.load(f\"./stat/std/{v}.npy\") for v in variables}\n",
     "}\n",
+    "## Note: the unit for temperatue is °C, salinity is g/kg, current is m/s, ssh is m, wind stress is N/m²\n",
     "\n",
     "# load data\n",
     "month = 0  # the corresponding statistical values ​​for each month are different\n",
     "ocean_vars = []\n",
     "atmo_vars = []\n",
     "for v in variables[:-2]:\n",
     "    ds = xr.open_dataset(f\"./example_data/{v}.nc\")\n",
+    "    # Important: the units of input data should be consistent with the units of the statistical values\n",
     "    normed_data = (ds[v].values - stat['mean'][v][month]) / stat['std'][v][month]\n",
     "    ocean_vars.append(normed_data if len(normed_data.shape) == 3 else normed_data[None])\n",
     "for v in variables[-2:]:\n",

model/utils.py

@@ -153,10 +153,4 @@ def __call__(self):
         mask = mask.reshape((self.rand_size[0], self.rand_size[1]))
         mask = mask.repeat_interleave(self.scale[0], dim=0).repeat_interleave(self.scale[1], dim=1)
 
-        return mask
-
-
-
-
-
-
+        return mask