Optimize caching

BBHX_Phenom.py

@@ -288,21 +288,23 @@ def _bbhx_fd(
     # To solve this we *round* the *logarithm* of this mass-dependent start
     # frequency. The factor of 25 ensures reasonable spacing while doing this.
     # So we round down to the nearest 1/25 of the logarithm of the frequency
-    log_mf_min = math.log(f_min*MTSUN_SI*(m1+m2)) * 25
+    # We only do this if `mf_min` is not specified. If it is then we set this
+    # None and can easily cache the generator.
+    if mf_min is None:
+        log_mf_min = math.log(f_min*MTSUN_SI*(m1+m2)) * 25
+        if cache_generator:
+            log_mf_min = int(log_mf_min)
+    else:
+        log_mf_min = None
     if cache_generator:
-        if mf_min is not None:
-            raise RuntimeError(
-                "Cannot use `cache_generator` when `mf_min` is specified"
-            )
-        # Use int to round down
         wave_gen = cached_get_waveform_genner(
-            int(log_mf_min),
-            mf_min=None,
+            log_mf_min=log_mf_min,
+            mf_min=mf_min,
             run_phenomd=run_phenomd,
         )
     else:
         wave_gen = get_waveform_genner(
-            log_mf_min,
+            log_mf_min=log_mf_min,
             mf_min=mf_min,
             run_phenomd=run_phenomd,
         )

tests.py

@@ -87,6 +87,7 @@ def test_cache_generator(params, cache_generator):
 
     params["approximant"] = "BBHX_PhenomD"
     params["cache_generator"] = cache_generator
+    params["mf_min"] = None
 
     # Build cache if using it
     get_fd_det_waveform(**params)
@@ -101,6 +102,27 @@ def test_cache_generator(params, cache_generator):
     else:
         assert cache_info.hits == 0
 
+def test_cache_generator_mf_min(params):
+    from BBHX_Phenom import cached_get_waveform_genner
+
+    # Clear cache for these tests
+    cached_get_waveform_genner.cache_clear()
+
+    params["approximant"] = "BBHX_PhenomD"
+    params["cache_generator"] = True
+    params["mf_min"] = 1e-4
+
+    masses = [2e6, 3e6]
+
+    # Build cache
+    get_fd_det_waveform(**params)
+
+    for m in masses:
+        params["mass1"] = m
+        get_fd_det_waveform(**params)
+
+    cache_info = cached_get_waveform_genner.cache_info()
+    assert cache_info.hits == len(masses)
 
 
 def test_length_in_time(params, approximant):