diff --git a/binarycpython/utils/grid.py b/binarycpython/utils/grid.py
index 9cbb6872a91c2a7235b75a98032991192bd188ab..73e73a909373dab6cc42ce2ff8379abee07a9bf6 100644
--- a/binarycpython/utils/grid.py
+++ b/binarycpython/utils/grid.py
@@ -244,7 +244,7 @@ class Population:
key, kwargs[key]
),
self.grid_options["verbosity"],
- 0, # NOTE: setting this to be 0 prevents mistakes being overlooked.
+ 0, # NOTE: setting this to be 0 prevents mistakes being overlooked.
)
self.custom_options[key] = kwargs[key]
@@ -401,6 +401,22 @@ class Population:
1,
)
+ def delete_grid_variable(
+ self,
+ name: str,
+ ) -> None:
+ try:
+ del self.grid_options["_grid_variables"][name]
+ verbose_print(
+ "Deleted grid variable: {}".format(name),
+ self.grid_options["verbosity"],
+ 1,
+ )
+ except:
+ msg = "Failed to remove grid variable {} : please check it exists.".format(name)
+ raise ValueError(msg)
+
+
def rename_grid_variable(
self,
oldname: str,
@@ -425,6 +441,11 @@ class Population:
try:
self.grid_options["_grid_variables"][newname] = self.grid_options["_grid_variables"].pop(oldname)
self.grid_options["_grid_variables"][newname]["name"] = newname
+ verbose_print(
+ "Rename grid variable: {} to {}".format(oldname,newname),
+ self.grid_options["verbosity"],
+ 1,
+ )
except:
msg = "Failed to rename grid variable {} to {}.".format(oldname,newname)
raise ValueError(msg)
@@ -471,7 +492,7 @@ class Population:
Examples:
longname = 'Primary mass'
range:
- Range of values to take. Does not get used really, the spacingfunction is used to
+ Range of values to take. Does not get used really, the spacingfunc is used to
get the values from
Examples:
@@ -482,13 +503,13 @@ class Population:
Examples:
resolution = resolution["M_1"]
- spacingfunction:
+ spacingfunc:
Function determining how the range is sampled. You can either use a real function,
or a string representation of a function call. Will get written to a file and
then evaluated.
Examples:
- spacingfunction = "const(math.log(m_min), math.log(m_max), {})".format(resolution['M_1'])
+ spacingfunc = "const(math.log(m_min), math.log(m_max), {})".format(resolution['M_1'])
precode:
Extra room for some code. This code will be evaluated within the loop of the
@@ -831,7 +852,7 @@ class Population:
# set previous logging system number to 0
self.shared_memory["prev_log_system_number"] = multiprocessing.Value('i', 0)
-
+
# array to store memory use per-thread
mem = mem_use()
self.shared_memory["memory_use_per_thread"] = multiprocessing.Array('d', [1.0 * mem] * self.grid_options["amt_cores"])
@@ -864,7 +885,7 @@ class Population:
Returns an dictionary containing the analytics of the run
- TODO: change the way this is done. Slurm & CONDOR should probably do this different
+ TODO: change the way this is done. Slurm & CONDOR should probably do this differently
NOTE: SLURM and CONDOR options are not working properly yet
"""
@@ -1807,6 +1828,9 @@ class Population:
)
)
+ if self.grid_options['exit_after_dry_run']:
+ exit()
+
#######################
# Reset values and prepare the grid function
self.grid_options[
@@ -2083,13 +2107,26 @@ class Population:
# + "\n"
# )
- code_string += (
- indent * depth
- + "for {}_sample_number in range({}):".format(
- grid_variable["name"], grid_variable["resolution"]
+
+ if grid_variable["resolution"] == 0:
+ # generate resolution automatically
+ code_string += (
+ indent * depth
+ + "for {}_sample_number in range(len(sampled_values_{})):".format(
+ grid_variable["name"], grid_variable["name"]
+ )
+ + "\n"
)
- + "\n"
- )
+ else:
+ # use resolution passed in
+ code_string += (
+ indent * depth
+ + "for {}_sample_number in range({}):".format(
+ grid_variable["name"], grid_variable["resolution"]
+ )
+ + "\n"
+ )
+
if grid_variable['gridtype'] == 'edge':
code_string += (
indent * (depth+1)
@@ -2548,7 +2585,7 @@ class Population:
Requires the grid to be built as a dry run grid
"""
-
+ verbose_print("Dry run of the grid", self.grid_options["verbosity"], 1)
system_generator = self.grid_options["_system_generator"]
total_starcount = system_generator(self)
self.grid_options["_total_starcount"] = total_starcount
diff --git a/binarycpython/utils/grid_options_defaults.py b/binarycpython/utils/grid_options_defaults.py
index 00b3e0d07b42c91437b2b9b0184b1825c8063ed4..67c845abcfa93144b4b6f377da3bd10e1d3a80e9 100644
--- a/binarycpython/utils/grid_options_defaults.py
+++ b/binarycpython/utils/grid_options_defaults.py
@@ -43,6 +43,8 @@ grid_options_defaults_dict = {
"_zero_prob_stars_skipped": 0,
"ensemble_factor_in_probability_weighted_mass": False, # Whether to multiply the ensemble results by 1/probability_weighted_mass
"do_dry_run": True, # Whether to do a dry run to calculate the total probability for this run
+ "exit_after_dry_run": False, # Exit after dry run?
+
##########################
# Execution log:
##########################
diff --git a/binarycpython/utils/spacing_functions.py b/binarycpython/utils/spacing_functions.py
index 2284267736e45386d9db541e94081bb8dbce748d..0f96a1c9e3ff61671fad68f206c63ddbe41ac388 100644
--- a/binarycpython/utils/spacing_functions.py
+++ b/binarycpython/utils/spacing_functions.py
@@ -7,7 +7,7 @@ Tasks:
from typing import Union
import numpy as np
-
+import math
def const(
min_bound: Union[int, float], max_bound: Union[int, float], steps: int
@@ -18,10 +18,78 @@ def const(
Args:
min_bound: lower bound of range
max_bound: upper bound of range
- steps: amount of segments between min_bound and max_bound
+ steps: number of segments between min_bound and max_bound
Returns:
np.linspace(min_bound, max_bound, steps+1)
"""
return np.linspace(min_bound, max_bound, steps+1)
+
+############################################################
+def peak_normalized_gaussian_func(
+ x: Union[int, float], mean: Union[int, float], sigma: Union[int, float]
+) -> Union[int, float]:
+ """
+ Function to evaluate a Gaussian at a given point, note
+ that the normalization is such that the peak is always 1.0,
+ not that the integral is 1.0
+
+ Args:
+ x: location at which to evaluate the distribution
+ mean: mean of the Gaussian
+ sigma: standard deviation of the Gaussian
+
+ Returns:
+ value of the Gaussian at x
+ """
+ gaussian_prefactor = 1.0 #/ math.sqrt(2.0 * math.pi)
+
+ r = 1.0 / sigma
+ y = (x - mean) * r
+ return math.exp(-0.5 * y ** 2)
+
+############################################################
+def gaussian_zoom(
+ min_bound: Union[int, float],
+ max_bound: Union[int, float],
+ zoom_mean: Union[int, float],
+ zoom_dispersion: Union[int,float],
+ zoom_magnitude: Union[int,float],
+ steps: int
+) -> list:
+ """
+ Samples such that a region is zoomed in according to a 1-Gaussian function
+
+ Args:
+ min_bound: lower bound of range
+ max_bound: upper bound of range
+ zoom_mean: mean of the Gaussian zoom location
+ zoom_dispersion: dispersion of the Gaussian
+ zoom_magnitude: depth of the Gaussian (should be 0<= zoom_magntiude <1)
+ steps: number of segments between min_bound and max_bound assuming a linear step
+ this is what you'd normally call "resolution"
+
+ Returns:
+ np.linspace(min_bound, max_bound, steps+1)
+ """
+
+ # linear spacing: this is what we'd have
+ # in the absence of a Gaussian zoom
+ linear_spacing = (max_bound - min_bound)/(steps-1)
+
+ # make the list of values
+ x = min_bound
+ array = np.array([])
+ while x <= max_bound:
+ array = np.append(array,x)
+ g = peak_normalized_gaussian_func(x,zoom_mean,zoom_dispersion)
+ f = 1.0 - zoom_magnitude * g
+ dx = linear_spacing * f
+ x = x + dx
+
+ # force the last array member to be max_bound if it's not
+ if array[-1] != max_bound:
+ array[-1] = max_bound
+
+ return array