Update algo parameters to point at new overrides (#205)

configs/algorithm_parameters_historical.yaml

@@ -1,7 +1,7 @@
 # JSON file containing frame-specific algorithm parameters to override the defaults passed
 #   in the `algorithm_parameters.yaml`.
 #   Type: string | null.
-algorithm_parameters_overrides_json: opera-disp-s1-algorithm-parameters-overrides-2024-10-14.json
+algorithm_parameters_overrides_json: opera-disp-s1-algorithm-parameters-overrides-2024-11-26.json
 ps_options:
   # Amplitude dispersion threshold to consider a pixel a PS.
   #   Type: number.

configs/opera-disp-s1-algorithm-parameters-overrides-2024-11-26.json

@@ -0,0 +1,28 @@
+{
+  "18903": {
+    "unwrap_options": {
+      "unwrap_method": "phass",
+      "run_interpolation": false
+    }
+  },
+  "23210": {
+    "unwrap_options": {
+      "unwrap_method": "spurt"
+    }
+  },
+  "23211": {
+    "unwrap_options": {
+      "unwrap_method": "spurt"
+    }
+  },
+  "33038": {
+    "unwrap_options": {
+      "unwrap_method": "spurt"
+    }
+  },
+  "33039": {
+    "unwrap_options": {
+      "unwrap_method": "spurt"
+    }
+  }
+}

src/disp_s1/_ps.py

@@ -239,7 +239,6 @@ def run_combine(
         mean = reader_mean[rows, cols][np.newaxis].filled(0)
         dispersion = reader_dispersion[rows, cols][np.newaxis].filled(0)
 
-        # Fit a line to each pixel with weighted least squares
         dispersions = np.vstack([compslc_dispersion, dispersion])
 
         means = np.vstack([compslc_mean, mean])

src/disp_s1/main.py

@@ -402,7 +402,7 @@ def create_displacement_products(
     los_north_file: Path | None = None,
     near_far_incidence_angles: tuple[float, float] = (30.0, 45.0),
     water_mask: Path | None = None,
-    max_workers: int = 3,
+    max_workers: int = 4,
 ) -> None:
     """Run parallel processing for all interferograms.
 

src/disp_s1/plotting.py

@@ -15,7 +15,7 @@ def plot_product(
     filename: Filename,
     downsample=3,
     use_recommended_mask: bool = True,
-    figsize: tuple[float, float] = (9, 6),
+    figsize: tuple[float, float] = (10, 10),
     disp_cmap: str = DEFAULT_CMAP,
     disp_limits: tuple[float, float] = (-0.20, 0.20),
     filtered_disp_limits: tuple[float, float] = (-0.05, 0.05),
@@ -97,10 +97,12 @@ def plot_product(
         bad_mask = ds["recommended_mask"][::downsample, ::downsample] == 0
 
     fig, axes = plt.subplots(
-        ncols=5, nrows=2, sharex=True, sharey=True, figsize=figsize
+        ncols=4, nrows=3, sharex=True, sharey=True, figsize=figsize
     )
 
-    for ax, dset_name, cmap, vm in zip(axes.ravel(), dsets, cmaps, vms, strict=True):
+    for ax, dset_name, cmap, vm in zip(
+        axes.ravel()[: len(dsets)], dsets, cmaps, vms, strict=True
+    ):
         dset = ds[dset_name][::downsample, ::downsample]
         if not use_recommended_mask:
             bad_mask = dset == 0