Remove unnecessary semicolons at end of Python statements

OceanLab/dyn.py

@@ -396,7 +396,7 @@ def eqmodes(N2,z,nm,lat,pmodes=False):
     # F_j(z)=-g.he_j d/dzS_j
     #
     ####
-    if pmodes==True:
+    if pmodes:
         Fi=np.zeros(Si.shape)
         for i in np.arange(nm):
             Fi[1:-1,i] =\

OceanLab/eof.py

@@ -44,7 +44,7 @@ def eoft(trmat,nm=None):
     amp = np.dot(evecs_norm.T,trmat)
 
     #if was chosen a number of eigenvectors
-    if nm!=None:
+    if nm is not None:
         evecs_norm = evecs_norm[:,:nm]
         evals_perc = evals_perc[:nm]
         amp        = amp[:nm,:]
@@ -117,7 +117,7 @@ def my_eof_interp(M,nmodes,errmin=1e-15,repmax=None):
           print(err)
           if err < errmin:
              break
-       if repmax!=None:
+       if repmax is not None:
             if rep>=repmax:
                 break
        rep += 1
@@ -188,7 +188,8 @@ def ceof(lon, lat, data, nkp = 10, parallel = True):
     print('3: Solving the eigenvalue problem')
     lamda, loadings = delayed(la.eig)(c).compute() # lamda: eigenvalue, loadings: eigenvectors
 
-    l = lamda.conjugate().T; k = np.argsort(l)
+    l = lamda.conjugate().T
+    k = np.argsort(l)
     lamda, loadings = np.flip(l[k]), np.fliplr(loadings[:,k])
     loadings = loadings[:,:nkp]
     # In case there were nan values in the orginal data, we need to perform the approach below:

OceanLab/oa.py

@@ -94,12 +94,12 @@ def vectoa(Xg,Yg,X,Y,U,V,corrlenx,corrleny,err,b=0):
     tc = np.array(tc)
     tc.shape = ppc[0].shape
     d2=((np.tile(xc,(n,1)).T-np.tile(x,(nv,1)))**2+(np.tile(yc,(n,1)).T-np.tile(y,(nv,1)))**2)
-    R=np.exp(-lambd*d2)+bmo;
+    R=np.exp(-lambd*d2)+bmo
 
     P=np.zeros((nv,2*n))
     # streamfunction-velocity covariance
-    P[:,0:n]=np.sin(tc)*np.sqrt(d2)*R;
-    P[:,n:2*n]=-np.cos(tc)*np.sqrt(d2)*R;
+    P[:,0:n]=np.sin(tc)*np.sqrt(d2)*R
+    P[:,n:2*n]=-np.cos(tc)*np.sqrt(d2)*R
 
     PSI=np.dot(P,np.linalg.solve(A,uv))   # solvi the linear system
     PSI=PSI.reshape(nv2,nv1).T
@@ -163,7 +163,7 @@ def scaloa(xc, yc, x, y, t=[], corrlenx=None,corrleny=None, err=None, zc=None):
     # Gauss-Markov to get the weights that minimize the variance (OI).
     tp = None
     ep = 1 - np.sum(C.T * np.linalg.solve(A, C.T), axis=0) / (1 - err)
-    if any(t)==True: ##### was t!=None:
+    if any(t): ##### was t!=None:
         t = np.reshape(t, (n, 1))
         tp = np.dot(C, np.linalg.solve(A, t))
         #if 0: # NOTE: `scaloa2.m`
@@ -174,7 +174,7 @@ def scaloa(xc, yc, x, y, t=[], corrlenx=None,corrleny=None, err=None, zc=None):
         #  tp = tp + mD * np.ones(tp.shape)
         return tp, ep
 
-    if any(t)==False: ##### was t==None:
+    if not any(t): ##### was t==None:
         print("Computing just the interpolation errors.")
         #Normalized mean error. Taking the squared root you can get the
         #interpolation error in percentage.

OceanLab/utils.py

@@ -2,8 +2,7 @@
 import scipy.signal as sg
 import xarray as xr
 
-import dask
-from dask.distributed import Client, progress
+from dask.distributed import Client
 
 ##### User functions
 #=============================================================================