aboutsummaryrefslogtreecommitdiffstats
diff options
context:
space:
mode:
-rw-r--r--fg21sim/configs/20-extragalactic.conf.spec7
-rw-r--r--fg21sim/extragalactic/clusters/main.py6
2 files changed, 10 insertions, 3 deletions
diff --git a/fg21sim/configs/20-extragalactic.conf.spec b/fg21sim/configs/20-extragalactic.conf.spec
index dbebe4e..6e023e5 100644
--- a/fg21sim/configs/20-extragalactic.conf.spec
+++ b/fg21sim/configs/20-extragalactic.conf.spec
@@ -113,6 +113,13 @@
# WARNING: for testing usage.
boost = float(default=1.0, min=0.1, max=1e4)
+ # Minimal elongated fraction for creating the images of radio halos
+ # The ``felong`` is defined as ``felong = b/a``, similar to the Hubble
+ # classification for the elliptical galaxies. ``felong_min = 1.0``
+ # means no elongation, and ``felong_min = 0.6`` is a good choice as
+ # the observed radio halos are generally regular.
+ felong_min = float(default=1.0, min=0.1, max=1.0)
+
# Number of most powerful halos to be dropped out.
halo_dropout = integer(default=0, min=0)
diff --git a/fg21sim/extragalactic/clusters/main.py b/fg21sim/extragalactic/clusters/main.py
index 1c3f8e7..0af41ab 100644
--- a/fg21sim/extragalactic/clusters/main.py
+++ b/fg21sim/extragalactic/clusters/main.py
@@ -82,6 +82,7 @@ class GalaxyClusters:
self.dump_halos_data = configs.getn(comp+"/dump_halos_data")
self.use_dump_halos_data = configs.getn(
comp+"/use_dump_halos_data")
+ self.felong_min = configs.getn(comp+"/felong_min")
self.halo_dropout = configs.getn(comp+"/halo_dropout")
self.prefix = configs.getn(comp+"/prefix")
self.output_dir = configs.get_path(comp+"/output_dir")
@@ -154,10 +155,9 @@ class GalaxyClusters:
logger.info("Preliminary processes to the catalog ...")
num = len(self.catalog)
lon, lat = self.sky.random_points(n=num)
- felong_min = 0.6
- sigma = (1.0 - felong_min) / 3.0
+ sigma = (1.0 - self.felong_min) / 3.0
felong = 1.0 - np.abs(np.random.normal(scale=sigma, size=num))
- felong[felong < felong_min] = felong_min
+ felong[felong < self.felong_min] = self.felong_min
rotation = np.random.uniform(low=0.0, high=360.0, size=num)
for i, cdict in enumerate(self.catalog):