From 1b08c003883a43d1f05e17e8e35db3169051fd36 Mon Sep 17 00:00:00 2001
From: Prayush Kumar <prayush.kumar@gmail.com>
Date: Tue, 25 Jun 2024 21:54:36 +0530
Subject: [PATCH] Add debugging prints; use SEOBNRv4PHM as backup MR
 approximant for ESIGMA

---
 pycbc/waveform/waveform.py | 50 ++++++++++++++++++++++++++++++--------
 1 file changed, 40 insertions(+), 10 deletions(-)

diff --git a/pycbc/waveform/waveform.py b/pycbc/waveform/waveform.py
index d41d9aa07d5..69849e4bdd1 100644
--- a/pycbc/waveform/waveform.py
+++ b/pycbc/waveform/waveform.py
@@ -593,16 +593,31 @@ def get_td_waveform(template=None, **kwargs):
         os.environ["ModePNOrder"] = "8"
         os.environ["InspiralEndRadius"] = "4.0"
 
-        if "eccentricity" in input_params:
-            eccentricity = float(input_params.get("eccentricity", 0))
-        else:
+        verbose = input_params.get("verbose", False)
+        if verbose: print(f"Assigning eccentricity a value now")
+        if 'alpha' in input_params:
             eccentricity = float(input_params.get("alpha", 0))
-        if "mean_anomaly" in input_params:
+            if verbose:
+                print(f"Using eccentricity from `alpha` column, value = {eccentricity}")
+        elif "eccentricity" in input_params:
+            eccentricity = float(input_params.get("eccentricity", 0))
+            if verbose:
+                print(f"Using eccentricity from `eccentricity` column, value = {eccentricity}")
+
+        if verbose: print(f"Assigning mean_anomaly a value now")
+        if 'alpha1' in input_params:
+            mean_anomaly = float(input_params.get("alpha1", 0))
+            if verbose:
+                print(f"Using mean_anomaly from `alpha1` column, value = {mean_anomaly}")
+        elif "mean_anomaly" in input_params:
             mean_anomaly = float(input_params.get("mean_anomaly", 0))
+            if verbose:
+                print(f"Using mean_anomaly from `mean_anomaly` column, value = {mean_anomaly}")
         elif "mean_per_ano" in input_params:
             mean_anomaly = float(input_params.get("mean_per_ano", 0))
-        else:
-            mean_anomaly = float(input_params.get("alpha1", 0))
+            if verbose:
+                print(f"Using mean_anomaly from `mean_per_ano` column, value = {mean_anomaly}")
+
         esigma_params = dict(
             mass1=float(input_params["mass1"]),
             mass2=float(input_params["mass2"]),
@@ -666,10 +681,25 @@ def get_td_waveform(template=None, **kwargs):
                 )
             return hp, hc
         except Exception as exc:
-            print(f"""Failed to generate waveform with input_params={input_params},
-                  with error: {exc}
-                  """)
-            return None, None
+            try:
+                esigma_params["merger_ringdown_approximant"] = "SEOBNRv4PHM"
+                if input_params["approximant"] == "IMRESIGMAHM":
+                    hp, hc = esigma_utils.get_imr_esigma_waveform(
+                        **esigma_params, modes_to_use=[(2, 2), (2, 1), (3, 3), (3, 2), (4, 4), (4, 3)]
+                    )
+                elif input_params["approximant"] == "IMRESIGMA":
+                    hp, hc = esigma_utils.get_imr_esigma_waveform(
+                        **esigma_params, modes_to_use=[(2, 2)]
+                    )
+                else:
+                    print(f"""Failed to generate waveform with input_params={input_params},
+with errors: {exc}""")
+                    return None, None
+            except Exception as exc2:
+                print(f"""Failed to generate waveform with input_params={input_params},
+with errors: {exc}
+and: {exc2}""")
+                return None, None
     else:
         wav_gen = td_wav[type(_scheme.mgr.state)]
         if input_params['approximant'] not in wav_gen: