From 71ca7c33fcfafb1d4c49103bbf737081a091a327 Mon Sep 17 00:00:00 2001
From: David Hendriks <davidhendriks93@gmail.com>
Date: Tue, 8 Jun 2021 18:27:28 +0100
Subject: [PATCH] Modifying the behaviour of the multiplicty fraction
calculating function
---
binarycpython/utils/distribution_functions.py | 84 ++++++++++---------
binarycpython/utils/grid_options_defaults.py | 13 +--
2 files changed, 53 insertions(+), 44 deletions(-)
diff --git a/binarycpython/utils/distribution_functions.py b/binarycpython/utils/distribution_functions.py
index f37efc8ca..5e32f4544 100644
--- a/binarycpython/utils/distribution_functions.py
+++ b/binarycpython/utils/distribution_functions.py
@@ -1069,7 +1069,9 @@ def normalize_dict(result_dict, verbosity=0):
def Moe_de_Stefano_2017_multiplicity_fractions(options, verbosity=0):
"""
- Function that returns a list of multiplicity fractions for a given list
+ Function that creates a list of probability fractions and
+ normalizes and merges them according to the users choice.
+
TODO: make an extrapolation functionality in this. log10(1.6e1)
is low, we can probably go a bit further
@@ -1083,8 +1085,20 @@ def Moe_de_Stefano_2017_multiplicity_fractions(options, verbosity=0):
# Use the global Moecache
global Moecache
- result = {}
- new_result = {}
+ # Check for length
+ if not len(options["multiplicity_modulator"])==4:
+ msg = "Multiplicity modulator has to have 4 elements. Now it is {}, len: {}".format(options["multiplicity_modulator"], len(options["multiplicity_modulator"]))
+ verbose_print(
+ msg,
+ verbosity,
+ 0,
+ )
+ raise ValueError(msg)
+
+ # Set up some arrays
+ full_fractions_array = np.zeros(4) # Meant to contain the real fractions
+ weighted_fractions_array = np.zeros(4) # Meant to contain the fractions multiplied by the multiplicity modulator
+ multiplicity_modulator_array = np.array(options["multiplicity_modulator"]) # Modulator array
# ... it's better to interpolate the multiplicity and then
# use a Poisson distribution to calculate the fractions
@@ -1104,25 +1118,16 @@ def Moe_de_Stefano_2017_multiplicity_fractions(options, verbosity=0):
[np.log10(options["M1"])]
)[0]
+ # Fill the multiplicity array
for n in range(4):
new_result[n] = poisson(multiplicity, n, 3, verbosity)
- result[n] = (
- options["multiplicity_modulator"][n]
- * poisson(multiplicity, n, 3, verbosity)
- if options["multiplicity_modulator"][n] > 0
- else 0
- )
- print("For mass M1={}".format(options['M1']))
- print("Multiplicty modulator: {}".format(options["multiplicity_modulator"]))
- print("Old results: {}".format(result))
+ full_fractions_array[n] = poisson(multiplicity, n, 3, verbosity)
- # Normalize dict
- new_result = normalize_dict(new_result, verbosity=verbosity)
- sum_result = sum([new_result[key] for key in new_result])
- print("New result: {} (sum = {})".format(new_result, sum_result))
+ # Normalize it so it fills to one when taking all the multiplicities:
+ full_fractions_array = full_fractions_array/np.sum(full_fractions_array)
verbose_print(
- "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: using model {}".format("Poisson"),
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: using model {}: full_fractions_array: {}".format("Poisson", full_fractions_array),
verbosity,
_MS_VERBOSITY_LEVEL,
)
@@ -1133,27 +1138,26 @@ def Moe_de_Stefano_2017_multiplicity_fractions(options, verbosity=0):
# note that in this case, there are no quadruples: these
# are combined with triples
+ # Fill with the raw values
for n in range(3):
- result[n] = (
+ full_fractions_array[n] = (
Moecache["rinterpolator_multiplicity"].interpolate(
[np.log10(options["M1"])]
- )[n + 1]
- * options["multiplicity_modulator"][n]
- )
- result[3] = 0.0 # no quadruples
+ )[n + 1])
+
+ # Set last value
+ full_fractions_array[3] = 0.0 # no quadruples
verbose_print(
- "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: using model {}".format("data"),
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: using model {}: full_fractions_array: {}".format("data", full_fractions_array),
verbosity,
_MS_VERBOSITY_LEVEL,
)
# Normalisation:
if options["normalize_multiplicities"] == "raw":
- # TODO: Not sure what actually happens here. It eems the same as above
- # do nothing : use raw Poisson predictions
-
+ # Don't multiply by the multiplicity_array, but do give a fractions array
verbose_print(
- "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: Not normalizing (using raw results)",
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: Not normalizing (using raw results): results: {}".format(full_fractions_array),
verbosity,
_MS_VERBOSITY_LEVEL,
)
@@ -1161,34 +1165,37 @@ def Moe_de_Stefano_2017_multiplicity_fractions(options, verbosity=0):
pass
elif options["normalize_multiplicities"] == "norm":
- # simply normalize so all multiplicities add to 1
- result = normalize_dict(result, verbosity=verbosity)
+ # Multiply the full_multiplicity_fraction array by the multiplicity_multiplier_array, creating a weighted fractions array
+ weighted_fractions_array = full_fractions_array * multiplicity_modulator_array
+
+ # Normalize this so it is intotal 1:
+ result = weighted_fractions_array/np.sum(weighted_fractions_array)
verbose_print(
- "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: Normalizing with {}".format("norm"),
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: Normalizing with {}. result: {}".format("norm", result),
verbosity,
_MS_VERBOSITY_LEVEL,
)
elif options["normalize_multiplicities"] == "merge":
+ # Get max multiplicity
max_multiplicity = get_max_multiplicity(options["multiplicity_modulator"])
- # Merge the multiplicities
- result = merge_multiplicities(result, max_multiplicity, verbosity=verbosity)
-
- new_result = merge_multiplicities(new_result, max_multiplicity, verbosity=verbosity)
+ # We first take the full multiplicity array
+ # (i.e. not multiplied by multiplier) and do the merging
+ result = merge_multiplicities(full_fractions_array, max_multiplicity, verbosity=verbosity)
- # Normalising results again
- result = normalize_dict(result, verbosity=verbosity)
+ # Then normalize to be sure
+ result = result/np.sum(result)
verbose_print(
- "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: Normalizing with {}, max_multiplicity={}".format("merge", max_multiplicity),
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: Normalizing with {}, max_multiplicity={} result={}".format("merge", max_multiplicity, result),
verbosity,
_MS_VERBOSITY_LEVEL,
)
verbose_print(
- "\tM&S: Multiplicity array: {}".format(str(result)),
+ "\tM&S: Moe_de_Stefano_2017_multiplicity_fractions: {}".format(str(result)),
verbosity,
_MS_VERBOSITY_LEVEL,
)
@@ -1196,7 +1203,6 @@ def Moe_de_Stefano_2017_multiplicity_fractions(options, verbosity=0):
# return array reference
return result
-
def build_q_table(options, m, p, verbosity=0):
############################################################
#
diff --git a/binarycpython/utils/grid_options_defaults.py b/binarycpython/utils/grid_options_defaults.py
index 3e53a23cb..8b7f14c1c 100644
--- a/binarycpython/utils/grid_options_defaults.py
+++ b/binarycpython/utils/grid_options_defaults.py
@@ -603,7 +603,7 @@ combined (and there are NO quadruples).
e.g. [1,0,0,0] for single stars only
[0,1,0,0] for binary stars only
-defaults to [1,1,1,1] i.e. all types
+defaults to [1,1,0,0] i.e. singles and binaries
""",
"normalize_multiplicities": """
given a mix of multiplities, you can either (noting that
@@ -628,13 +628,16 @@ here (S,B,T,Q) = appropriate modulator * model(S,B,T,Q) )
Note: if multiplicity_modulator == [1,1,1,1] this
option does nothing (equivalent to 'raw').
-note: if you only set one multiplicity_modulator
-to 1, and all the others to 0, then normalizing
+Note: If you only set one multiplicity modulator to 1, and all others to zero,
+we will not multiply the fractions by the modulator. The grid will run fine (i.e. it will only run e.g. singles), but it will still take into account the real fraction
+This is unlike how this setting was previously implemented: normalizing
will mean that you effectively have the same number
of stars as single, binary, triple or quad (whichever
is non-zero) i.e. the multiplicity fraction is ignored.
-This is probably not useful except for
-testing purposes or comparing to old grids.
+
+
+
+
""",
"q_low_extrapolation_method": """
q extrapolation (below 0.15) method
--
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