Adjusts numerical values in tranfer.py

Author: KevinShuman

clouddrift/adapters/gdp/gdp1h.py

@@ -623,10 +623,10 @@ def to_raggedarray(
     # set dynamic global attributes
     if ra.attrs_global:
         ra.attrs_global["time_coverage_start"] = (
-            f"{datetime(1970,1,1) + timedelta(seconds=int(np.min(ra.coords['time']))):%Y-%m-%d:%H:%M:%SZ}"
+            f"{datetime(1970, 1, 1) + timedelta(seconds=int(np.min(ra.coords['time']))):%Y-%m-%d:%H:%M:%SZ}"
         )
         ra.attrs_global["time_coverage_end"] = (
-            f"{datetime(1970,1,1) + timedelta(seconds=int(np.max(ra.coords['time']))):%Y-%m-%d:%H:%M:%SZ}"
+            f"{datetime(1970, 1, 1) + timedelta(seconds=int(np.max(ra.coords['time']))):%Y-%m-%d:%H:%M:%SZ}"
         )
 
     return ra

clouddrift/adapters/gdp/gdp6h.py

@@ -523,10 +523,10 @@ def to_raggedarray(
 
     # update dynamic global attributes
     ra.attrs_global["time_coverage_start"] = (
-        f"{datetime.datetime(1970,1,1) + datetime.timedelta(seconds=int(np.min(ra.coords['time']))):%Y-%m-%d:%H:%M:%SZ}"
+        f"{datetime.datetime(1970, 1, 1) + datetime.timedelta(seconds=int(np.min(ra.coords['time']))):%Y-%m-%d:%H:%M:%SZ}"
     )
     ra.attrs_global["time_coverage_end"] = (
-        f"{datetime.datetime(1970,1,1) + datetime.timedelta(seconds=int(np.max(ra.coords['time']))):%Y-%m-%d:%H:%M:%SZ}"
+        f"{datetime.datetime(1970, 1, 1) + datetime.timedelta(seconds=int(np.max(ra.coords['time']))):%Y-%m-%d:%H:%M:%SZ}"
     )
 
     return ra

clouddrift/transfer.py

@@ -774,21 +774,21 @@ def kvtilde(
 
     # Prepare zk for vectorized computation
     zk = np.tile(z, (nterms, 1)).T
-    zk[:, 0] = 1
+    zk[:, 0] = 1.0
     zk = np.cumprod(zk, axis=1)
 
     k = np.arange(nterms)
-    ak = 4 * nu**2 - (2 * k - 1) ** 2
-    ak[0] = 1
-    ak = np.cumprod(ak) / (factorial(k) * (8**k))
+    ak = 4.0 * nu**2 - (2.0 * k - 1.0) ** 2
+    ak[0] = 1.0
+    ak = np.cumprod(ak) / (factorial(k) * (8.0**k))
 
     # Handling potential non-finite values in ak
     if not np.all(np.isfinite(ak)):
         first_nonfinite = np.where(~np.isfinite(ak))[0][0]
         ak = ak[:first_nonfinite]
         zk = zk[:, :first_nonfinite]
 
-    K = sqrt(np.pi / (2 * z)) * (np.dot(1.0 / zk, ak))
+    K = sqrt(np.pi / (2.0 * z)) * (np.dot(1.0 / zk, ak))
     K = K.reshape(sizez)
 
     return K
@@ -811,21 +811,21 @@ def ivtilde(
 
     # Prepare zk for vectorized computation with alternating signs for each term
     zk = np.tile(-z, (nterms, 1)).T
-    zk[:, 0] = 1
+    zk[:, 0] = 1.0
     zk = np.cumprod(zk, axis=1)
 
     k = np.arange(nterms)
-    ak = 4 * nu**2 - (2 * k - 1) ** 2
-    ak[0] = 1
-    ak = np.cumprod(ak) / (factorial(k) * (8**k))
+    ak = 4.0 * nu**2 - (2.0 * k - 1.0) ** 2
+    ak[0] = 1.0
+    ak = np.cumprod(ak) / (factorial(k) * (8.0**k))
 
     # Handling potential non-finite values in ak
     if not np.all(np.isfinite(ak)):
         first_nonfinite = np.where(~np.isfinite(ak))[0][0]
         ak = ak[:first_nonfinite]
         zk = zk[:, :first_nonfinite]
 
-    I = 1 / sqrt(2 * z * np.pi) * (np.dot(1.0 / zk, ak))
+    I = 1.0 / sqrt(2.0 * z * np.pi) * (np.dot(1.0 / zk, ak))
     I = I.reshape(sizez)
 
     return I