allow approximant to be read from config file (gwastro#4805)

* allow approximant to be read from config file

* fix

* cc

bin/bank/pycbc_brute_bank

@@ -48,8 +48,8 @@ parser.add_argument('--min',
     help='list of the minimum parameter values', nargs='+', type=float)
 parser.add_argument('--max',
     help='list of the maximum parameter values', nargs='+', type=float)
-parser.add_argument('--approximant',  required=True,
-    help='The waveform approximant to place')
+parser.add_argument('--approximant',  required=False,
+    help='The waveform approximant to place.')
 parser.add_argument('--minimal-match', default=0.97, type=float)
 parser.add_argument('--buffer-length', default=2, type=float,
     help='size of waveform buffer in seconds')
@@ -71,12 +71,13 @@ parser.add_argument('--min-mchirp', type=float)
 parser.add_argument('--max-mchirp', type=float)
 parser.add_argument('--fixed-params', type=str, nargs='*')
 parser.add_argument('--fixed-values', type=float, nargs='*')
+parser.add_argument('--use-cross', action='store_true')
 parser.add_argument('--max-q', type=float)
 parser.add_argument('--tau0-crawl', type=float)
 parser.add_argument('--tau0-start', type=float)
 parser.add_argument('--tau0-end', type=float)
 parser.add_argument('--tau0-cutoff-frequency', type=float, default=15.0)
-parser.add_argument('--nprocesses', type=int, default=1, 
+parser.add_argument('--nprocesses', type=int, default=1,
     help='Number of processes to use for waveform generation parallelization. If not given then only a single core will be used.')
 pycbc.psd.insert_psd_option_group(parser)
 args = parser.parse_args()
@@ -254,7 +255,7 @@ class TriangleBank(object):
 
         pool = pycbc.pool.choose_pool(args.nprocesses)
         for return_wf in pool.imap_unordered(
-                wf_wrapper, 
+                wf_wrapper,
                 ({k: params[k][idx] for k in params} for idx in range(total_num))
             ):
             waveform_cache += [return_wf]
@@ -263,7 +264,7 @@ class TriangleBank(object):
         for hp in waveform_cache:
             if hp is not None:
                 hp.gen = gen
-                hp.threshold = threshold 
+                hp.threshold = threshold
                 if hp not in self:
                     num_added += 1
                     self.insert(hp)
@@ -302,11 +303,14 @@ class GenUniformWaveform(object):
                 f = self.f_lower
 
         kwds['f_lower'] = f
+        if hasattr(kwds['approximant'], 'decode'):
+            kwds['approximant'] = kwds['approximant'].decode()
 
         if kwds['approximant'] in pycbc.waveform.fd_approximants():
             hp, hc = pycbc.waveform.get_fd_waveform(delta_f=self.delta_f,
                                                     f_ref=10.0, **kwds)
-
+            if  args.use_cross:
+                hp = hc
 
             if 'fratio' in kwds:
                 hp = hc * kwds['fratio'] + hp * (1 - kwds['fratio'])
@@ -316,12 +320,13 @@ class GenUniformWaveform(object):
             hp = pycbc.waveform.get_waveform_filter(
                         pycbc.types.zeros(self.flen, dtype=numpy.complex64),
                         delta_f=self.delta_f, delta_t=dt,
-                        f_lower=self.f_lower, **kwds)
+                        **kwds)
 
         hp.resize(self.flen)
         hp = hp.astype(numpy.complex64)
         hp[self.kmin:-1] *= self.w
-        s = float(1.0 / pycbc.filter.sigmasq(hp, low_frequency_cutoff=self.f_lower) ** 0.5)
+        s = float(1.0 / pycbc.filter.sigmasq(hp,
+                                             low_frequency_cutoff=f) ** 0.5)
         hp *= s
         hp.params = kwds
         hp.view = hp[self.kmin:-1]
@@ -350,7 +355,8 @@ def wf_wrapper(p):
     try:
         hp = gen.generate(**p)
         return hp
-    except Exception:
+    except Exception as e:
+        print(e)
         return None
 
 if args.input_file:
@@ -399,7 +405,8 @@ def draw(rtype):
         if args.input_config is not None and waveform_transforms is not None:
             params = transforms.apply_transforms(params, waveform_transforms)
 
-    params['approximant'] = numpy.array([args.approximant]*size)
+    if args.approximant is not None:
+        params['approximant'] = numpy.array([args.approximant]*size)
 
     # Filter out stuff (kde method may also generate samples outside boundaries).
     l = None
@@ -429,7 +436,6 @@ def draw(rtype):
         l = dists_joint.contains(params)
 
     params = {k: params[k][l] for k in params}
-
     return params
 
 def cdraw(rtype, ts, te):