Additional bug fixes from trying to run

heron/inference.py

@@ -6,7 +6,7 @@
 
 import click
 
-from gwpy.timeseries import TimeSeries
+from .types import TimeSeries
 import astropy.units as u
 
 from nessai.flowsampler import FlowSampler
@@ -84,6 +84,7 @@ def heron_inference(settings):
     if "data files" in settings.get("data", {}):
         # Load frame files from disk
         for ifo in settings["interferometers"]:
+            print(f"Loading {ifo} data")
             logger.info(
                 f"Loading {ifo} data from "
                 f"{settings['data']['data files'][ifo]}/{settings['data']['channels'][ifo]}"
@@ -93,14 +94,16 @@ def heron_inference(settings):
                 channel=settings["data"]["channels"][ifo],
                 format="gwf",
             )
-    elif "injection" in other_settings:
-        pass
+    #elif "injection" in other_settings:
+    #    pass
 
     # Make Likelihood
     if len(settings["interferometers"]) > 1:
         likelihoods = []
+        print("Creating likelihoods")
         waveform_model = KNOWN_WAVEFORMS[settings["waveform"]["model"]]()
         for ifo in settings["interferometers"]:
+            print(f"\t {ifo}")
             likelihoods.append(
                 KNOWN_LIKELIHOODS[settings.get("likelihood").get("function")](
                     data[ifo],
@@ -113,7 +116,7 @@ def heron_inference(settings):
                     ),
                 )
             )
-            likelihood = MultiDetector(*likelihoods)
+        likelihood = MultiDetector(*likelihoods)
 
     priors = heron.priors.PriorDict()
     priors.from_dictionary(settings["priors"])

heron/injection.py

@@ -115,7 +115,7 @@ def injection_parameters_add_units(parameters):
     UNITS = {"luminosity_distance": u.megaparsec, "m1": u.solMass, "m2": u.solMass}
 
     for parameter, value in parameters.items():
-        if not isinstance(value, u.Quantity):
+        if not isinstance(value, u.Quantity) and parameter in UNITS:
             parameters[parameter] = value * UNITS[parameter]
     return parameters
 

heron/likelihood.py

@@ -60,7 +60,6 @@ def __init__(
         self.timeseries = data
         self.data = np.array(data.data)
         self.times = data.times
-
         self.C = self.psd.covariance_matrix(times=self.times)
         self.inverse_C = np.linalg.inv(self.C)
 
@@ -96,7 +95,11 @@ def snr(self, waveform):
         return np.sqrt(np.abs(h_h))
 
     def log_likelihood(self, waveform, norm=True):
-        a, b = self.timeseries.determine_overlap(self, waveform)
+        w = self.timeseries.determine_overlap(self, waveform)
+        if w is not None:
+            (a,b) = w
+        else:
+            return -np.inf
         residual = np.array(self.data.data[a[0]:a[1]]) - np.array(waveform.data[b[0]:b[1]])
         weighted_residual = (
             (residual) @ self.solve(self.C[a[0]:a[1],b[0]:b[1]], residual) * (self.dt * self.dt / 4) / 4

heron/models/lalsimulation.py

@@ -91,7 +91,6 @@ def _convert_units(self, args):
                 args[name] = argument.to_value(units[mappings[name]])
             elif name in mappings.keys() and argument:
                 # This is commented out as it causes problems if e.g. lalnative values are passed
-                print(f"Performing a mapping on {name}, {argument}")
                 args[name] = (argument * default_units[mappings[name]]).to_value(
                     units[mappings[name]]
                 )

heron/priors.py

@@ -13,6 +13,7 @@
     "Uniform": bilby.prior.Uniform,
     "PowerLaw": bilby.prior.PowerLaw,
     "Sine": bilby.prior.Sine,
+    "Cosine": bilby.prior.Cosine,
     "UniformSourceFrame": bilby.gw.prior.UniformSourceFrame,
     "UniformInComponentsMassRatio": bilby.gw.prior.UniformInComponentsMassRatio,
 }

heron/types.py

@@ -50,6 +50,9 @@ def is_in(time, timeseries):
             overlap = timeseries_a.times[0], timeseries_a.times[-1]
         else:
             overlap = None
+            #print("No overlap found")
+            #print(timeseries_a.times[0], timeseries_a.times[-1])
+            #print(timeseries_b.times[0], timeseries_b.times[-1])
             return None
 
         start_a = np.argmin(np.abs(timeseries_a.times - overlap[0]))

requirements.txt

@@ -16,6 +16,6 @@ click
 asimov
 pesummary
 nessai
-gpytorch==1.0.1
-torch==2.4.0
-torchvision==0.5.0
+gpytorch
+torch
+torchvision