Adding pre-commit hook

.pre-commit-config.yaml

@@ -0,0 +1,7 @@
+repos:
+  - repo: https://github.com/charliermarsh/ruff-pre-commit
+    rev: v0.1.0
+    hooks:
+      - id: ruff
+        args: [--fix]  # This will auto-fix issues if possible
+      - id: ruff-format

README.md

@@ -23,7 +23,16 @@ https://www.cesm.ucar.edu/models/cam.
     ```
     pip install .
     ```
-It is recommended this is done from inside a virtual environment.
+3. (Optional) Install an additional package `pre-commit` to ensure consistent code format throughout development. If installed, it automatically runs on codebase before committing changes. Run below commands to install pre-commit and it's hooks:
+    ```
+    pip install pre-commit
+    pre-commit install
+    ```
+    The commands will first install the pre-commit package and then the formatting tools that pre-commit package is using on the code.
+
+Note: It is recommended this is done from inside a virtual environment.
+
+
 
 
 # Model Description

newCAM_emulation/Model.py

@@ -134,7 +134,7 @@ def train_loop(dataloader, model, loss_fn, optimizer):
 # validating loop
 def val_loop(dataloader, model, loss_fn):
     """
-    Validation loop.
+    Run the validation loop.
 
     Args:
         dataloader (DataLoader): DataLoader for validation data.

newCAM_emulation/NN_pred.py

@@ -16,57 +16,53 @@
 """
 Determine if any GPUs are available
 """
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(device)
 
 
 """
 Initialize Hyperparameters
 """
 ilev = 93
-dim_NN = 8*ilev + 4
-dim_NNout = 2*ilev
+dim_NN = 8 * ilev + 4
+dim_NNout = 2 * ilev
 
 batch_size = 8
 learning_rate = 1e-4
 num_epochs = 1
 
 
-
-
-
 ## load mean and std for normalization
-fm = np.load('Demodata/mean_demo.npz')
-fs = np.load('Demodata/std_demo.npz')
-
-Um        = fm['U']
-Vm        = fm['V']
-Tm        = fm['T']
-DSEm      = fm['DSE']
-NMm       = fm['NM']
-NETDTm    = fm['NETDT']
-Z3m    = fm['Z3']
-RHOIm    = fm['RHOI']
-PSm    = fm['PS']
-latm    = fm['lat']
-lonm    = fm['lon']
-UTGWSPECm    = fm['UTGWSPEC']
-VTGWSPECm    = fm['VTGWSPEC']
-
-Us        = fs['U']
-Vs        = fs['V']
-Ts        = fs['T']
-DSEs      = fs['DSE']
-NMs       = fs['NM']
-NETDTs    = fs['NETDT']
-Z3s    = fs['Z3']
-RHOIs    = fs['RHOI']
-PSs    = fs['PS']
-lats    = fs['lat']
-lons    = fs['lon']
-UTGWSPECs    = fs['UTGWSPEC']
-VTGWSPECs    = fs['VTGWSPEC']
-
+fm = np.load("Demodata/mean_demo.npz")
+fs = np.load("Demodata/std_demo.npz")
+
+Um = fm["U"]
+Vm = fm["V"]
+Tm = fm["T"]
+DSEm = fm["DSE"]
+NMm = fm["NM"]
+NETDTm = fm["NETDT"]
+Z3m = fm["Z3"]
+RHOIm = fm["RHOI"]
+PSm = fm["PS"]
+latm = fm["lat"]
+lonm = fm["lon"]
+UTGWSPECm = fm["UTGWSPEC"]
+VTGWSPECm = fm["VTGWSPEC"]
+
+Us = fs["U"]
+Vs = fs["V"]
+Ts = fs["T"]
+DSEs = fs["DSE"]
+NMs = fs["NM"]
+NETDTs = fs["NETDT"]
+Z3s = fs["Z3"]
+RHOIs = fs["RHOI"]
+PSs = fs["PS"]
+lats = fs["lat"]
+lons = fs["lon"]
+UTGWSPECs = fs["UTGWSPEC"]
+VTGWSPECs = fs["VTGWSPEC"]
 
 
 """
@@ -77,94 +73,86 @@
 optimizer = torch.optim.Adam(GWnet.parameters(), lr=learning_rate)
 
 
-s_list = list(range(5,6))
+s_list = list(range(5, 6))
 
 for iter in s_list:
- if (iter > 0):
-   GWnet.load_state_dict(torch.load('./conv_torch.pth'))
-   GWnet.eval()
- print ('data loader iteration',iter)
- filename  = './Demodata/Demo_timestep_' + str(iter).zfill(3) + '.nc'
-
- F = nc.Dataset(filename)
- PS = np.asarray(F['PS'][0,:])
- PS = newnorm(PS, PSm, PSs)
-
- Z3 = np.asarray(F['Z3'][0,:,:])
- Z3 = newnorm(Z3, Z3m, Z3s)
-
- U = np.asarray(F['U'][0,:,:])
- U = newnorm(U, Um, Us)
-
- V = np.asarray(F['V'][0,:,:])
- V = newnorm(V, Vm, Vs)
-
- T = np.asarray(F['T'][0,:,:])
- T = newnorm(T, Tm, Ts)
-
- lat = F['lat']
- lat = newnorm(lat, np.mean(lat), np.std(lat))
-
- lon = F['lon']
- lon = newnorm(lon, np.mean(lon), np.std(lon))
-
- DSE = np.asarray(F['DSE'][0,:,:])
- DSE = newnorm(DSE, DSEm, DSEs)
-
- RHOI = np.asarray(F['RHOI'][0,:,:])
- RHOI = newnorm(RHOI, RHOIm, RHOIs)
+    if iter > 0:
+        GWnet.load_state_dict(torch.load("./conv_torch.pth"))
+        GWnet.eval()
+    print("data loader iteration", iter)
+    filename = "./Demodata/Demo_timestep_" + str(iter).zfill(3) + ".nc"
 
- NETDT = np.asarray(F['NETDT'][0,:,:])
- NETDT = newnorm(NETDT, NETDTm, NETDTs)
+    F = nc.Dataset(filename)
+    PS = np.asarray(F["PS"][0, :])
+    PS = newnorm(PS, PSm, PSs)
 
- NM = np.asarray(F['NMBV'][0,:,:])
- NM = newnorm(NM, NMm, NMs)
+    Z3 = np.asarray(F["Z3"][0, :, :])
+    Z3 = newnorm(Z3, Z3m, Z3s)
 
- UTGWSPEC = np.asarray(F['BUTGWSPEC'][0,:,:])
- UTGWSPEC = newnorm(UTGWSPEC, UTGWSPECm, UTGWSPECs)
+    U = np.asarray(F["U"][0, :, :])
+    U = newnorm(U, Um, Us)
 
- VTGWSPEC = np.asarray(F['BVTGWSPEC'][0,:,:])
- VTGWSPEC = newnorm(VTGWSPEC, VTGWSPECm, VTGWSPECs)
+    V = np.asarray(F["V"][0, :, :])
+    V = newnorm(V, Vm, Vs)
 
+    T = np.asarray(F["T"][0, :, :])
+    T = newnorm(T, Tm, Ts)
 
+    lat = F["lat"]
+    lat = newnorm(lat, np.mean(lat), np.std(lat))
 
- print('shape of PS',np.shape(PS))
- print('shape of Z3',np.shape(Z3))
- print('shape of U',np.shape(U))
- print('shape of V',np.shape(V))
- print('shape of T',np.shape(T))
- print('shape of DSE',np.shape(DSE))
- print('shape of RHOI',np.shape(RHOI))
- print('shape of NETDT',np.shape(NETDT))
- print('shape of NM',np.shape(NM))
- print('shape of UTGWSPEC',np.shape(UTGWSPEC))
- print('shape of VTGWSPEC',np.shape(VTGWSPEC))
+    lon = F["lon"]
+    lon = newnorm(lon, np.mean(lon), np.std(lon))
 
- x_test,y_test = data_loader (U,V,T, DSE, NM, NETDT, Z3,
-                              RHOI, PS,lat,lon,UTGWSPEC, VTGWSPEC)
+    DSE = np.asarray(F["DSE"][0, :, :])
+    DSE = newnorm(DSE, DSEm, DSEs)
 
- print('shape of x_test', np.shape(x_test))
- print('shape of y_test', np.shape(y_test))
+    RHOI = np.asarray(F["RHOI"][0, :, :])
+    RHOI = newnorm(RHOI, RHOIm, RHOIs)
 
+    NETDT = np.asarray(F["NETDT"][0, :, :])
+    NETDT = newnorm(NETDT, NETDTm, NETDTs)
 
- data = Model.myDataset(X=x_test, Y=y_test)
- test_loader = DataLoader(data, batch_size=len(data), shuffle=False)
- print(test_loader)
+    NM = np.asarray(F["NMBV"][0, :, :])
+    NM = newnorm(NM, NMm, NMs)
 
+    UTGWSPEC = np.asarray(F["BUTGWSPEC"][0, :, :])
+    UTGWSPEC = newnorm(UTGWSPEC, UTGWSPECm, UTGWSPECs)
 
- for batch, (X, Y) in enumerate(test_loader):
-  print(np.shape(Y))
-  pred = GWnet(X)
-  truth = Y.cpu().detach().numpy()
-  predict = pred.cpu().detach().numpy()
+    VTGWSPEC = np.asarray(F["BVTGWSPEC"][0, :, :])
+    VTGWSPEC = newnorm(VTGWSPEC, VTGWSPECm, VTGWSPECs)
 
- print(np.corrcoef(truth.flatten(), predict.flatten())[0, 1])
- print('shape of truth ',np.shape(truth))
- print('shape of prediction',np.shape(predict))
+    print("shape of PS", np.shape(PS))
+    print("shape of Z3", np.shape(Z3))
+    print("shape of U", np.shape(U))
+    print("shape of V", np.shape(V))
+    print("shape of T", np.shape(T))
+    print("shape of DSE", np.shape(DSE))
+    print("shape of RHOI", np.shape(RHOI))
+    print("shape of NETDT", np.shape(NETDT))
+    print("shape of NM", np.shape(NM))
+    print("shape of UTGWSPEC", np.shape(UTGWSPEC))
+    print("shape of VTGWSPEC", np.shape(VTGWSPEC))
 
- np.save('./pred_data_' + str(iter) + '.npy', predict)
+    x_test, y_test = data_loader(
+        U, V, T, DSE, NM, NETDT, Z3, RHOI, PS, lat, lon, UTGWSPEC, VTGWSPEC
+    )
 
+    print("shape of x_test", np.shape(x_test))
+    print("shape of y_test", np.shape(y_test))
 
+    data = Model.myDataset(X=x_test, Y=y_test)
+    test_loader = DataLoader(data, batch_size=len(data), shuffle=False)
+    print(test_loader)
 
+    for batch, (X, Y) in enumerate(test_loader):
+        print(np.shape(Y))
+        pred = GWnet(X)
+        truth = Y.cpu().detach().numpy()
+        predict = pred.cpu().detach().numpy()
 
+    print(np.corrcoef(truth.flatten(), predict.flatten())[0, 1])
+    print("shape of truth ", np.shape(truth))
+    print("shape of prediction", np.shape(predict))
 
+    np.save("./pred_data_" + str(iter) + ".npy", predict)

newCAM_emulation/__init__.py

@@ -0,0 +1 @@
+"""newCAM_emulation package."""