minor coreg fixes

trchudley · trchudley · commit 03fd1a8f7552 · 2025-01-27T15:44:23.000Z
diff --git a/batch/batch_download_and_coregister_is2.py b/batch/batch_download_and_coregister_is2.py
@@ -204,7 +204,7 @@
         # check whether coreg worked, and construct filename appropriately
         if metadata["coreg_status"] == "failed":
             out_fpath = out_fname + ".tif"
-        if metadata["coreg_status"] == "coregistered":
+        elif metadata["coreg_status"] == "coregistered":
             out_fpath = out_fname + "_coreg.tif"
         elif metadata["coreg_status"] == "dz_only":
             out_fpath = out_fname + "_coreg_dz.tif"
diff --git a/src/pdemtools/_accessor.py b/src/pdemtools/_accessor.py
@@ -233,14 +233,19 @@ def coregister_is2(
         # ADD ICESAT-2 SPECIFIC TO METADATA
         if "request_date_dt" in points_df.columns:
 
-            dt_days_max = int(
-                points_df["request_date_dt"].dt.round("d").dt.days.abs().max()
-            )
-            metadata_dict["points_dt_days_max"] = dt_days_max
+            if metadata_dict["coreg_status"] == "failed":
+                metadata_dict["points_dt_days_max"] = None
+                metadata_dict["points_dt_days_count"] = None
 
-            dt_days = points_df["request_date_dt"].dt.round("d").dt.days
-            dt_days_counts_dict = dt_days.value_counts().to_dict()
-            metadata_dict["points_dt_days_count"] = dt_days_counts_dict
+            else:
+                dt_days_max = int(
+                    points_df["request_date_dt"].dt.round("d").dt.days.abs().max()
+                )
+                metadata_dict["points_dt_days_max"] = dt_days_max
+
+                dt_days = points_df["request_date_dt"].dt.round("d").dt.days
+                dt_days_counts_dict = dt_days.value_counts().to_dict()
+                metadata_dict["points_dt_days_count"] = dt_days_counts_dict
 
         metadata_dict["coregistration_type"] = "reference_icesat2"
 
@@ -384,19 +389,6 @@ def coregister_dems(
 
         resolution = get_resolution(self._obj)
 
-        # new_dem_array, metadata_dict = coregisterdems(
-        #     reference.values,
-        #     self._obj.values,
-        #     reference.x.values,
-        #     reference.y.values,
-        #     stable_mask.values,
-        #     resolution,
-        #     max_horiz_offset=max_horiz_offset,
-        #     rmse_step_thresh=rmse_step_thresh,
-        #     max_iterations=max_iterations,
-        #     verbose=verbose,
-        # )
-
         new_dem_array, metadata_dict = coregister(
             self._obj.values,
             reference.values,
diff --git a/src/pdemtools/_coreg.py b/src/pdemtools/_coreg.py
@@ -265,6 +265,7 @@ def coregister(
         perr = np.full((3, 1), np.nan)
         d0 = np.nan
         status = "failed"
+        points_n = None
 
     else:
         status = "coregistered"
@@ -296,220 +297,6 @@ def coregister(
     return dem2out, metadata_dict
 
 
-# def coregisterdems(
-#     dem1,  # Reference DEM
-#     dem2,  # DEM to be coregistered
-#     x,
-#     y,
-#     mask,
-#     res,
-#     max_horiz_offset=50,
-#     rmse_step_thresh=-0.001,
-#     max_iterations=5,
-#     verbose=True,
-# ):
-#     """
-#     Simplified version of Erik Husby's coregisterdems() Python function.
-
-#     INPUTS:
-#     dem_1, dem_2: 2D arrays (of same shape) of dems. dem2 is the dem to be coregistered
-#     mask: mask of regions to be used in coregistration process (1=VALID FOR COREGISTRATION)
-
-#     OUTPUTS:
-#     trans: the [dz,dx,dy] transformation parameter
-#     trans_err: 1-sigma errors of trans
-#     rms: root mean square of the transformation in the vertical from the residuals
-
-#     If the registration fails due to lack of overlap, NaNs are returned in p and perr.
-#     If the registration fails to converge or exceeds the maximum shift, the median
-#     vertical offset is applied.
-
-#     """
-
-#     # Verbose print lambda function
-#     print_verbose = lambda msg: print(msg) if verbose else None
-
-#     print_verbose("THIS IS THE OLD COREGISTERDEMS FUNCTION - WE SHOULD MOVE TO A SINGLE COREGISTER FUNCTION")
-
-#     # initial trans and RMSE settings
-#     p = np.zeros((3, 1))  # p  is prior iteration trans var
-#     pn = p.copy()  # pn is current iteration trans var
-#     perr = np.zeros((3, 1))  # perr is prior iteration regression errors
-#     pnerr = perr.copy()  # pnerr is current iteration regression errors
-#     d0 = np.inf  # initial RMSE
-
-#     # Edge case markers
-#     meddz = None
-#     return_meddz = False
-#     critical_failure = False
-
-#     it = 0
-#     while True:
-#         it += 1
-#         print_verbose(f"Planimetric Correction Iteration {it}")
-
-#         print_verbose(f"Offset (z,x,y): {pn[0, 0]:.3f}, {pn[1, 0]:.3f}, {pn[2, 0]:.3f}")
-#         # print(f"pn: {pn}")
-
-#         # Break loop if conditions reached
-#         if np.any(np.abs(pn[1:]) > max_horiz_offset):
-#             print(
-#                 f"Maximum horizontal offset ({max_horiz_offset}) exceeded."
-#                 "Consider raising the threshold if offsets are large."
-#             )
-#             return_meddz = True
-#             break
-
-#         # Apply offsets
-#         if pn[1] != 0 and pn[2] != 0:
-#             dem2n = shift_dem(dem2, pn.T[0], x, y, verbose=verbose).astype("float32")
-#         else:
-#             dem2n = dem2 - pn[0].astype("float32")
-
-#         # # Calculate slopes - original method from PGC
-#         # sy, sx = np.gradient(dem2n, res)
-#         # sx = -sx
-
-#         print(type(dem2n))
-
-#         # Calculate slope - using Florinsky slope method (p = sx, q = sy)
-#         sy = q_f(dem2n, res)
-#         sx = p_f(dem2n, res)
-#         sy = -sy
-#         sx = -sx
-
-#         # Difference grids.
-#         dz = dem2n - dem1
-
-#         # Mask (in full script, both m1 and m2 are applied)
-#         dz[mask == 0] = np.nan
-
-#         # If no overlap between scenes, break the loop
-#         if np.all(np.isnan(dz)):
-#             print("No overlapping data between DEMs")
-#             critical_failure = True
-#             break
-
-#         # Filter NaNs and outliers.
-#         n = (
-#             ~np.isnan(sx)
-#             & ~np.isnan(sy)
-#             & (np.abs(dz - np.nanmedian(dz)) <= 3 * np.nanstd(dz))
-#         )
-#         n_count = np.count_nonzero(n)
-
-#         if n_count < 10:
-#             print(f"Too few ({n_count}) registration points: 10 required")
-#             critical_failure = True
-#             break
-
-#         # Get RMSE
-#         d1 = np.sqrt(np.mean(np.power(dz[n], 2)))
-#         print_verbose(f"RMSE = {d1}")
-
-#         # Keep median dz if first iteration.
-#         if it == 1:
-#             meddz = np.median(dz[n])
-#             meddz_err = np.std(dz[n] / np.sqrt(n_count))
-#             d00 = np.sqrt(np.mean(np.power(dz[n] - meddz, 2)))
-
-#         # Get improvement in RMSE
-#         rmse_step = d1 - d0  # initial d0 == inf
-
-#         # break if rmse above threshold
-#         if rmse_step > rmse_step_thresh or np.isnan(d0):
-#             print_verbose(
-#                 f"RMSE step in this iteration ({rmse_step:.5f}) is above threshold "
-#                 f"({rmse_step_thresh}), stopping and returning values of prior iteration."
-#             )
-#             # If fails after first registration attempt,
-#             # set dx and dy to zero and subtract the median offset.
-#             if it == 2:
-#                 print("Second iteration regression failure")
-#                 return_meddz = True
-#             break
-#         elif it == max_iterations:
-#             print_verbose(f"Maximum number of iterations ({max_iterations}) reached")
-#             break
-
-#         # Keep this adjustment.
-#         dem2out = dem2n.copy()
-#         p = pn.copy()
-#         perr = pnerr.copy()
-#         d0 = d1
-
-#         # Build design matrix.
-#         X = np.column_stack((np.ones(n_count, dtype=np.float32), sx[n], sy[n]))
-#         sx, sy = None, None  # release for data amangement
-
-#         # Solve for new adjustment.
-#         p1 = np.reshape(np.linalg.lstsq(X, dz[n], rcond=None)[0], (-1, 1))
-
-#         # Calculate p errors.
-#         _, R = np.linalg.qr(X)
-#         RI = np.linalg.lstsq(R, np.identity(3, dtype=np.float32), rcond=None)[0]
-#         nu = X.shape[0] - X.shape[1]  # residual degrees of freedom
-#         yhat = np.matmul(X, p1)  # predicted responses at each data point
-#         r = dz[n] - yhat.T[0]  # residuals
-#         normr = np.linalg.norm(r)
-
-#         dz = None  # release for memory managment
-
-#         rmse = normr / np.sqrt(nu)
-#         tval = stats.t.ppf((1 - 0.32 / 2), nu)
-
-#         se = rmse * np.sqrt(np.sum(np.square(np.abs(RI)), axis=1, keepdims=True))
-#         p1err = tval * se
-
-#         # Update shifts.
-#         pn = p + p1
-#         pnerr = np.sqrt(np.square(perr) + np.square(p1err))
-
-#         # END OF LOOP
-
-#     if return_meddz:
-#         print(f"Returning median vertical offset: {meddz:.3f}")
-#         dem2out = dem2 - meddz
-#         p = np.array([[meddz, 0, 0]]).T
-#         perr = np.array([[meddz_err, 0, 0]]).T
-#         d0 = d00
-#         status = "dz_only"
-
-#     elif critical_failure:
-#         print("Regression critical failure, returning original DEM, NaN trans, and RMSE")
-#         dem2out = dem2
-#         p = np.full((3, 1), np.nan)
-#         perr = np.full((3, 1), np.nan)
-#         d0 = np.nan
-#         status = "failed"
-
-#     else:
-#         status = "coregistered"
-
-#     print(f"Final offset (z,x,y): {p[0, 0]:.3f}, {p[1, 0]:.3f}, {p[2, 0]:.3f}")
-#     print(f"Final RMSE = {d0:.3f}")
-
-#     # Construct metadata:
-#     metadata_dict = {
-#         "coreg_status": status,
-#         "x_offset": p[1, 0],
-#         "y_offset": p[2, 0],
-#         "z_offset": p[0, 0],
-#         "x_offset_err": perr[1, 0],
-#         "y_offset_err": perr[2, 0],
-#         "z_offset_err": perr[0, 0],
-#         "rmse": d0,
-#     }
-#     # Convert all numerical values to regular Python floats
-#     metadata_dict = {
-#         key: float(value) if isinstance(value, (np.float64, np.float32)) else value
-#         for key, value in metadata_dict.items()
-#     }
-
-#     # Return
-#     return dem2out, metadata_dict  # p.T[0], perr.T[0], d0
-
-
 def shift_dem(dem, trans, x, y, verbose=True):
     """
     Shifts DEM according to translation factors ascertained in coregisterdems function
