fixes and improvements for tidal filter

oceans/filters.py

@@ -415,10 +415,7 @@ def medfilt1(x, L=3):
     >>> L = 103
     >>> xout = medfilt1(x=x, L=L)
     >>> ax = plt.subplot(212)
-    >>> (
-    ...     l1,
-    ...     l2,
-    ... ) = ax.plot(
+    >>> (l1, l2,) = ax.plot(
     ...     x
     ... ), ax.plot(xout)
     >>> ax.grid(True)
@@ -570,7 +567,7 @@ def md_trenberth(x):
     return y
 
 
-def pl33tn(x, dt=1.0, T=33.0, mode="valid"):
+def pl33tn(x, dt=1.0, T=33.0, mode="valid", t=None):
     """
     Computes low-passed series from `x` using pl33 filter, with optional
     sample interval `dt` (hours) and filter half-amplitude period T (hours)
@@ -608,14 +605,25 @@ def pl33tn(x, dt=1.0, T=33.0, mode="valid"):
     """
 
     import cf_xarray  # noqa: F401
+    import pandas as pd
     import xarray as xr
 
-    if isinstance(x, xr.Dataset):
-        raise TypeError("Input a DataArray not a Dataset.")
+    if isinstance(x, (xr.Dataset, pd.DataFrame)):
+        raise TypeError("Input a DataArray not a Dataset, or a Series not a DataFrame.")
 
+    if isinstance(x, pd.Series) and not isinstance(
+        x.index,
+        pd.core.indexes.datetimes.DatetimeIndex,
+    ):
+        raise TypeError("Input Series needs to have parsed datetime indices.")
+
+    # find dt in units of hours
     if isinstance(x, xr.DataArray):
-        # find dt in units of hours
-        dt = (x.cf["T"][1] - x.cf["T"][0]) * 1e-9 / 3600
+        dt = (x.cf["T"][1] - x.cf["T"][0]) / np.timedelta64(
+            360_000_000_000,
+        )
+    elif isinstance(x, pd.Series):
+        dt = (x.index[1] - x.index[0]) / pd.Timedelta("1H")
 
     pl33 = np.array(
         [
@@ -694,18 +702,20 @@ def pl33tn(x, dt=1.0, T=33.0, mode="valid"):
     dt = float(dt) * (33.0 / T)
 
     filter_time = np.arange(0.0, 33.0, dt, dtype="d")
-    # N = len(filter_time)
+    Nt = len(filter_time)
     filter_time = np.hstack((-filter_time[-1:0:-1], filter_time))
 
     pl33 = np.interp(filter_time, _dt, pl33)
     pl33 /= pl33.sum()
 
     if isinstance(x, xr.DataArray):
+        x = x.interpolate_na(dim=x.cf["T"].name)
+
         weight = xr.DataArray(pl33, dims=["window"])
         xf = (
             x.rolling({x.cf["T"].name: len(pl33)}, center=True)
             .construct({x.cf["T"].name: "window"})
-            .dot(weight)
+            .dot(weight, dims="window")
         )
         # update attrs
         attrs = {
@@ -715,7 +725,26 @@ def pl33tn(x, dt=1.0, T=33.0, mode="valid"):
         }
         xf.attrs = attrs
 
+    elif isinstance(x, pd.Series):
+        xf = x.to_frame().apply(np.convolve, v=pl33, mode=mode)
+
+        # nan out edges which are not good values anyway
+        if mode == "same":
+            xf[: Nt - 1] = np.nan
+            xf[-Nt + 2 :] = np.nan
+
     else:  # use numpy
         xf = np.convolve(x, pl33, mode=mode)
 
+        # times to match xf
+        if t is not None:
+            # Nt = len(filter_time)
+            tf = t[Nt - 1 : -Nt + 1]
+            return xf, tf
+
+        # nan out edges which are not good values anyway
+        if mode == "same":
+            xf[: Nt - 1] = np.nan
+            xf[-Nt + 2 :] = np.nan
+
     return xf