From 0ef41d72f67c5b8711c0f7e34f5406e23a993b3b Mon Sep 17 00:00:00 2001
From: ftong <95+ftong@noreply.example.org>
Date: Mon, 22 Jun 2026 19:06:19 +0200
Subject: [PATCH] Update src/seismic_hazard_forecasting.py

fix orientation issue attempt 1
---
 src/seismic_hazard_forecasting.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/src/seismic_hazard_forecasting.py b/src/seismic_hazard_forecasting.py
index 7cc3d0a..c3b0426 100644
--- a/src/seismic_hazard_forecasting.py
+++ b/src/seismic_hazard_forecasting.py
@@ -699,18 +699,22 @@ verbose: {verbose}")
         fxy = xy_kde[0]
         logger.debug(f"Normalization check; sum of all f(x,y) values = {np.sum(fxy)}")
 
-        xx, yy = np.meshgrid(x_range, y_range, indexing='ij')  # grid points
+        xx, yy = np.meshgrid(x_range, y_range)  # grid points
 
         # set every grid point to be a receiver
+        grid_shape = xx.shape
         x_rx = xx.flatten()
         y_rx = yy.flatten()
 
+        num_points = x_rx.size
+        distances = np.zeros(shape=(num_points, grid_shape[0], grid_shape[1]))
+        
         # compute distance matrix for each receiver
-        distances = np.zeros(shape=(nx * ny, nx, ny))
+        #distances = np.zeros(shape=(nx * ny, nx, ny))
         rx_lat = np.zeros(nx * ny)
         rx_lon = np.zeros(nx * ny)
 
-        for i in range(nx * ny):
+        for i in range(num_points):
             # Compute the squared distances directly using NumPy's vectorized operations
             squared_distances = (xx - x_rx[i]) ** 2 + (yy - y_rx[i]) ** 2
             distances[i] = np.sqrt(squared_distances)
@@ -726,7 +730,7 @@ verbose: {verbose}")
         if exclude_low_fxy:
             indices = list(np.where(fxy.flatten() > thresh_fxy)[0])
         else:
-            indices = range(0, len(distances))
+            indices = np.arange(num_points)
 
         if use_AOI:
             # Filter out receivers outside the AOI; Find indices where values are OUTSIDE the AOI
@@ -836,9 +840,6 @@ verbose: {verbose}")
 
             iml_grid_hd[iml_grid_hd == 0.0] = np.nan  # change zeroes back to nan
 
-            # correct orientation is AOI is used
-            if use_AOI:
-                iml_grid_hd = np.rot90(iml_grid_hd, k=-1)
                 
             #vmin_hd = min(x for x in iml_grid_hd.flatten() if not isnan(x))
             vmax_hd = max(x for x in iml_grid_hd.flatten() if not isnan(x))