Load fits problem

sacc/utils.py

@@ -194,7 +194,7 @@ def numpy_to_vanilla(x):
     Parameters
     ----------
     x : numpy scalar
-        A NumPy scalar value (e.g., np.str_, np.int64, np.float64, np.bool).
+        A NumPy scalar value (e.g., np.str_, np.int64, np.float64, np.bool_).
 
     Returns
     -------
@@ -207,6 +207,6 @@ def numpy_to_vanilla(x):
         x = int(x)
     elif type(x) == np.float64:
         x = float(x)
-    elif type(x) == np.bool:
+    elif type(x) == np.bool_:
         x = bool(x)
     return x

test/test_load_fits.py

@@ -0,0 +1,47 @@
+#!/usr/bin/env python
+
+import numpy as np
+import sacc
+
+def create_simple_sacc():
+    # Create a new Sacc object
+    s = sacc.Sacc()
+
+    # --- 1️⃣ Define tracers ---
+    # Add a survey tracer (usually represents the data source)
+    s.add_tracer("survey", "my_mock_survey", 100.0)  # 100 deg^2 area, for example
+
+    # Add two bin tracers (e.g., redshift and richness bins)
+    s.add_tracer("bin_z", "zbin_0", 0.2, 0.4)
+    s.add_tracer("bin_z", "zbin_1", 0.4, 0.6)
+    s.add_tracer("bin_richness", "rich_0", 10, 20)
+    s.add_tracer("bin_richness", "rich_1", 20, 40)
+    print(type(s.tracers["zbin_0"]))
+
+    # --- 2️⃣ Add mock data ---
+    # We’ll use a standard type — cluster counts
+    cluster_count = sacc.standard_types.cluster_counts
+
+    # reproducible random numbers
+    rng = np.random.default_rng(42)
+
+    for zbin in ["zbin_0", "zbin_1"]:
+        for rbin in ["rich_0", "rich_1"]:
+            count_value = rng.integers(50, 150)  # random counts
+            s.add_data_point(cluster_count, ("my_mock_survey", zbin, rbin), count_value)
+
+    # --- 3️⃣ Add covariance ---
+    # Create a simple diagonal covariance matrix (variance = 10 for each data point)
+    ndata = len(s.data)
+    covariance = np.diag(np.ones(ndata) * 10)
+    s.add_covariance(covariance)
+
+    # --- 4️⃣ Save the SACC file ---
+    s.to_canonical_order()
+    s.save_fits("test/data/simple_mock_clusters.sacc", overwrite=True)
+
+    print(f"SACC file saved with {ndata} data points: test/data/simple_mock_clusters.sacc")
+
+def test_bool_numpy_error():
+    create_simple_sacc()
+    t2 = sacc.Sacc.load_fits("test/data/simple_mock_clusters.sacc")