lint

extract_model/extract_model.py

@@ -281,9 +281,9 @@ def select(
         * False: 2D array of points with 1 dimension the lons and the other dimension the lats.
         * True: lons/lats as unstructured coordinate pairs (in xESMF language, LocStream).
     locstreamT: boolean, optional
-        If False, interpolate in time dimension independently of horizontal points. If True, use advanced indexing/interpolation in xarray to interpolate times to each horizontal locstream point. If this is True, locstream must be True.
+        If False, interpolate in time dimension independently of horizontal points. If True, use advanced indexing/interpolation in xarray to interpolate times to each horizontal locstream point.
     locstreamZ: boolean, optional
-        If False, interpolate in depth dimension independently of horizontal points. If True, use advanced indexing after depth interpolation select depths to match each horizontal locstream point. If this is True, locstream must be True and locstreamT must be True.
+        If False, interpolate in depth dimension independently of horizontal points. If True, use advanced indexing after depth interpolation select depths to match each horizontal locstream point.
     new_dim : str
         This is the name of the new dimension created if we are interpolating to a new set of points that are not a grid.
     weights: xESMF netCDF file path, DataArray, optional
@@ -360,14 +360,15 @@ def select(
                 "Use extrap=True to extrapolate."
             )
 
-    if locstreamT:
-        if not locstream:
-            raise ValueError("if `locstreamT` is True, `locstream` must also be True.")
-    if locstreamZ:
-        if not locstream or not locstreamT:
-            raise ValueError(
-                "if `locstreamZ` is True, `locstream` and `locstreamT` must also be True."
-            )
+    # these are only true if interpolating in those directions too — need to fix them
+    # if locstreamT:
+    #     if not locstream:
+    #         raise ValueError("if `locstreamT` is True, `locstream` must also be True.")
+    # if locstreamZ:
+    #     if not locstream or not locstreamT:
+    #         raise ValueError(
+    #             "if `locstreamZ` is True, `locstream` and `locstreamT` must also be True."
+    #         )
 
     # Perform interpolation
     if horizontal_interp:
@@ -443,13 +444,12 @@ def select(
                 xs, ys = proj(xs, ys)
                 x, y = proj(longitude, latitude)
 
-            # import pdb; pdb.set_trace()
             # lam = calc_barycentric(x, y, xs.reshape((10,9,3)), ys.reshape((10,9,3)))
             lam = calc_barycentric(x.flatten(), y.flatten(), xs, ys)
             # lam = calc_barycentric(x, y, xs, ys)
             # interp_coords are the coords and indices that went into the interpolation
             da, interp_coords = interp_with_barycentric(da, ixs, iys, lam)
-            # import pdb; pdb.set_trace()
+
             # if not locstream:
             #     FIGURE OUT HOW TO RECONSTITUTE INTO GRID HERE
             kwargs_out["interp_coords"] = interp_coords
@@ -665,6 +665,7 @@ def pt_in_itriangle_proj(ix, iy):
 
     # advanced indexing to select all assuming coherent time series
     # make sure len of each dimension matches
+
     if locstreamZ:
 
         dims_to_index = [da.cf["T"].name]
@@ -809,7 +810,6 @@ def sel2d(
                 mask = mask.load()
 
             # Assume mask is 2D — but not true for wetting/drying
-            # import pdb; pdb.set_trace()
             # find indices representing mask
             eta, xi = np.where(mask.values)
 
@@ -898,6 +898,10 @@ def sel2d(
 
     else:
 
+        # make sure the mask matches
+        msg = f"Mask {mask.name} dimensions do not match horizontal var {var.name} dimensions. mask dims: {mask.dims}, var dims: {var.dims}"
+        assert len(set(mask.dims) - set(var.dims)) == 0, msg
+
         # currently lons, lats 1D only
 
         # if no mask, assume user just wants 1 nearest point to each input lons/lats pair
@@ -907,7 +911,7 @@ def sel2d(
         # if user inputs mask, use it to only return the nearest point that is active
         # so, find nearest 30 points to have options
         else:
-            k = 30
+            k = 50
 
         distances, (iys, ixs) = tree_query(var[lonname], var[latname], lons, lats, k=k)
 
@@ -916,7 +920,7 @@ def sel2d(
             raise ValueError("all found values are masked!")
 
         if mask is not None:
-            isorted_mask = np.argsort(-mask.values[iys, ixs], axis=-1)
+            isorted_mask = np.argsort(-mask.values[iys, ixs], axis=-1, kind="mergesort")
             # sort the ixs and iys according to this sorting so that if there are unmasked indices,
             # they are leftmost also, and we will use the leftmost values.
             ixs_brought_along = np.take_along_axis(ixs, isorted_mask, axis=1)