"""
Module containing the Population grid class object.
Here all the functionality of a Population object is defined.
Useful for the user to understand the functionality,
but copying functionality isn't recommended except if you know what you are doing
Tasks:
- TODO: add functionality to 'on-init' set arguments
- TODO: add functionality to return the initial_abundance_hash
- TODO: add functionality to return the isotope_hash
- TODO: add functionality to return the isotope_list
- TODO: add functionality to return the nuclear_mass_hash
- TODO: add functionality to return the nuclear_mass_list
- TODO: add functionality to return the source_list
- TODO: add functionality to return the ensemble_list
- TODO: consider spreading the functions over more files.
- TODO: type the private functions
- TODO: fix the correct object types for the default values of the bse_options
- TODO: uncomment and implement the HPC functionality
- TODO: think of a clean and nice way to unload and remove the custom_logging_info library from memory (and from disk)
- TODO: think of a nice way to remove the loaded grid_code/ generator from memory.
- TODO: Create a designated dict for results
"""
import os
import sys
import copy
import json
import time
import uuid
import logging
import datetime
import argparse
import importlib.util
import multiprocessing
from typing import Union, Any
from pathos.helpers import mp as pathos_multiprocess
from pathos.pools import _ProcessPool as Pool
from binarycpython.utils.grid_options_defaults import (
grid_options_defaults_dict,
moe_distefano_default_options,
)
from binarycpython.utils.custom_logging_functions import (
autogen_C_logging_code,
binary_c_log_code,
create_and_load_logging_function,
)
from binarycpython.utils.functions import (
get_defaults,
remove_file,
filter_arg_dict,
get_help_all,
return_binary_c_version_info,
binaryc_json_serializer,
verbose_print,
merge_dicts,
update_dicts,
extract_ensemble_json_from_string,
get_moe_distefano_dataset,
)
# from binarycpython.utils.hpc_functions import (
# get_condor_version,
# get_slurm_version,
# create_directories_hpc,
# path_of_calling_script,
# get_python_details,
# )
from binarycpython.utils.distribution_functions import (
Moecache,
LOG_LN_CONVERTER,
)
from binarycpython import _binary_c_bindings
class Population:
"""
Population Object. Contains all the necessary functions to set up, run and process a
population of systems
"""
def __init__(self):
"""
Initialisation function of the population class
"""
# Different sections of options
# get binary_c defaults and create a cleaned up dict
# Setting stuff will check against the defaults to see if the input is correct.
self.defaults = get_defaults()
self.cleaned_up_defaults = self._cleanup_defaults()
self.available_keys = list(self.defaults.keys())
self.special_params = [
el for el in list(self.defaults.keys()) if el.endswith("%d")
]
# make the input dictionary
self.bse_options = {} # bse_options is just empty.
# Grid options
self.grid_options = copy.deepcopy(grid_options_defaults_dict)
# Custom options
self.custom_options = {}
# Argline dict
self.argline_dict = {}
# Set main process id
self.grid_options["_main_pid"] = os.getpid()
# Set some memory dicts
self.persistent_data_memory_dict = {}
#
self.process_ID = 0
# Create location to store results. Users should write to this dictionary.
self.grid_results = {}
# Create location where ensemble results are written to
self.grid_ensemble_results = {}
###################################################
# Argument functions
###################################################
# General flow of generating the arguments for the binary_c call:
# - user provides parameter and value via set (or manually but that is risky)
# - The parameter names of these input get compared to the parameter names in the self.defaults;
# with this, we know that its a valid parameter to give to binary_c.
# - For a single system, the bse_options will be written as a arg line
# - For a population the bse_options will get copied to a temp_bse_options dict and updated with
# all the parameters generated by the grid
# I will NOT create the argument line by fully writing ALL the defaults and overriding user
# input, that seems not necessary because by using the get_defaults() function we already
# know for sure which parameter names are valid for the binary_c version
# And because binary_c uses internal defaults, its not necessary to explicitly pass them.
# I do however suggest everyone to export the binary_c defaults to a file, so that you know
# exactly which values were the defaults.
def set(self, **kwargs) -> None:
"""
Function to set the values of the population. This is the preferred method to set values
of functions, as it provides checks on the input.
the bse_options will get populated with all the those that have a key that is present
in the self.defaults
the grid_options will get updated with all the those that have a key that is present
in the self.grid_options
If neither of above is met; the key and the value get stored in a custom_options dict.
Args:
via kwargs all the arguments are either set to binary_c parameters, grid_options or custom_options (see above)
"""
# Select the params that end with %d
# Go over all the input
for key in kwargs:
# Filter out keys for the bse_options
if key in self.defaults.keys():
verbose_print(
"adding: {}={} to BSE_options".format(key, kwargs[key]),
self.grid_options["verbosity"],
1,
)
self.bse_options[key] = kwargs[key]
# Extra check to check if the key fits one of parameter names that end with %d
elif any(
[
True
if (key.startswith(param[:-2]) and len(param[:-2]) < len(key))
else False
for param in self.special_params
]
):
verbose_print(
"adding: {}={} to BSE_options by catching the %d".format(
key, kwargs[key]
),
self.grid_options["verbosity"],
1,
)
self.bse_options[key] = kwargs[key]
# Filter out keys for the grid_options
elif key in self.grid_options.keys():
verbose_print(
"adding: {}={} to grid_options".format(key, kwargs[key]),
self.grid_options["verbosity"],
1,
)
self.grid_options[key] = kwargs[key]
# The of the keys go into a custom_options dict
else:
print(
"!! Key doesnt match previously known parameter: \
adding: {}={} to custom_options".format(
key, kwargs[key]
)
)
self.custom_options[key] = kwargs[key]
def parse_cmdline(self) -> None:
"""
Function to handle settings values via the command line.
Best to be called after all the .set(..) lines, and just before the .evolve() is called
If you input any known parameter (i.e. contained in grid_options, defaults/bse_options
or custom_options), this function will attempt to convert the input from string
(because everything is string) to the type of the value that option had before.
The values of the bse_options are initially all strings, but after user input they
can change to ints.
The value of any new parameter (which will go to custom_options) will be a string.
Tasks:
- TODO: remove the need for --cmdline
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"--cmdline",
help='Setting values via the commandline. Input like --cmdline "metallicity=0.02"',
)
args = parser.parse_args()
# How its set up now is that as input you need to give --cmdline "metallicity=0.002"
# Its checked if this exists and handled accordingly.
if args.cmdline:
verbose_print(
"Found cmdline args. Parsing them now",
self.grid_options["verbosity"],
1,
)
# Grab the input and split them up, while accepting only non-empty entries
cmdline_args = args.cmdline
self.grid_options["_commandline_input"] = cmdline_args
split_args = [
cmdline_arg
for cmdline_arg in cmdline_args.split(" ")
if not cmdline_arg == ""
]
# Make dict and fill it
cmdline_dict = {}
for cmdline_arg in split_args:
split = cmdline_arg.split("=")
parameter = split[0]
value = split[1]
old_value_found = False
# Find an old value
if parameter in self.grid_options:
old_value = self.grid_options[parameter]
old_value_found = True
elif parameter in self.defaults:
old_value = self.defaults[parameter]
old_value_found = True
elif parameter in self.custom_options:
old_value = self.custom_options[parameter]
old_value_found = True
# (attempt to) convert
if old_value_found:
try:
verbose_print(
"Converting type of {} from {} to {}".format(
parameter, type(value), type(old_value)
),
self.grid_options["verbosity"],
1,
)
value = type(old_value)(value)
verbose_print("Success!", self.grid_options["verbosity"], 1)
except ValueError:
verbose_print(
"Tried to convert the given parameter {}/value {} to its correct type {} (from old value {}). But that wasn't possible.".format(
parameter, value, type(old_value), old_value
),
self.grid_options["verbosity"],
0,
)
# Add to dict
cmdline_dict[parameter] = value
# unpack the dictionary into the setting function that handles where the values are set
self.set(**cmdline_dict)
def _return_argline(self, parameter_dict=None):
"""
Function to create the string for the arg line from a parameter dict
"""
if not parameter_dict:
parameter_dict = self.bse_options
argline = "binary_c "
for param_name in sorted(parameter_dict):
argline += "{} {} ".format(param_name, parameter_dict[param_name])
argline = argline.strip()
return argline
def last_grid_variable(self):
"""
Functon that returns the last grid variable
(i.e. the one with the highest grid_variable_number)
"""
number = len(self.grid_options["_grid_variables"])
for grid_variable in self.grid_options["_grid_variables"]:
if (
self.grid_options["_grid_variables"][grid_variable][
"grid_variable_number"
]
== number - 1
):
return grid_variable
def add_grid_variable(
self,
name: str,
longname: str,
valuerange: Union[list, str],
resolution: str,
spacingfunc: str,
probdist: str,
dphasevol: Union[str, int],
parameter_name: str,
gridtype: str = "edge",
branchpoint: int = 0,
precode: Union[str, None] = None,
condition: Union[str, None] = None,
) -> None:
"""
Function to add grid variables to the grid_options.
TODO: Fix this complex function.
The execution of the grid generation will be through a nested forloop.
Each of the grid variables will get create a deeper for loop.
The real function that generates the numbers will get written to a new file in the TMP_DIR,
and then loaded imported and evaluated.
beware that if you insert some destructive piece of code, it will be executed anyway.
Use at own risk.
Args:
name:
name of parameter. This is evaluated as a parameter and you can use it throughout
the rest of the function
example: name = 'lnm1'
longname:
Long name of parameter
example: longname = 'Primary mass'
range:
Range of values to take. Does not get used really, the spacingfunction is used to
get the values from
example: range = [math.log(m_min), math.log(m_max)]
resolution:
Resolution of the sampled range (amount of samples).
TODO: check if this is used anywhere
example: resolution = resolution["M_1"]
spacingfunction:
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.
example:
spacingfunction = "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
sampling function (i.e. a value for lnm1 is chosen already)
example: precode = 'M_1=math.exp(lnm1);'
probdist:
FUnction determining the probability that gets asigned to the sampled parameter
example: probdist = 'Kroupa2001(M_1)*M_1'
dphasevol:
part of the parameter space that the total probability is calculated with. Put to -1
if you want to ignore any dphasevol calculations and set the value to 1
example: dphasevol = 'dlnm1'
condition:
condition that has to be met in order for the grid generation to continue
example: condition = 'self.grid_options['binary']==1'
gridtype:
Method on how the value range is sampled. Can be either 'edge' (steps starting at
the lower edge of the value range) or 'center'
(steps starting at lower edge + 0.5 * stepsize).
"""
# TODO: Add check for the gridtype input value
# TODO: add functionality for branchpoint
# Add grid_variable
grid_variable = {
"name": name,
"longname": longname,
"valuerange": valuerange,
"resolution": resolution,
"spacingfunc": spacingfunc,
"precode": precode,
"probdist": probdist,
"dphasevol": dphasevol,
"parameter_name": parameter_name,
"condition": condition,
"gridtype": gridtype,
"branchpoint": branchpoint,
"grid_variable_number": len(self.grid_options["_grid_variables"]),
}
# Load it into the grid_options
self.grid_options["_grid_variables"][grid_variable["name"]] = grid_variable
verbose_print(
"Added grid variable: {}".format(json.dumps(grid_variable, indent=4)),
self.grid_options["verbosity"],
1,
)
###################################################
# Return functions
###################################################
def return_population_settings(self) -> dict:
"""
Function that returns all the options that have been set.
Can be combined with json to make a nice file.
Returns:
dictionary containing "bse_options", "grid_options", "custom_options"
"""
options = {
"bse_options": self.bse_options,
"grid_options": self.grid_options,
"custom_options": self.custom_options,
}
return options
def _return_binary_c_version_info(self, parsed=False):
"""
Function that returns the version information of binary_c
"""
version_info = return_binary_c_version_info(parsed=parsed)
return version_info
def _return_binary_c_defaults(self):
"""
Function that returns the defaults of the binary_c version that is used.
"""
return self.defaults
def return_all_info(
self,
include_population_settings: bool = True,
include_binary_c_defaults: bool = True,
include_binary_c_version_info: bool = True,
include_binary_c_help_all: bool = True,
) -> dict:
"""
Function that returns all the information about the population and binary_c
Args:
include_population_settings:
whether to include the population_settings (see function return_population_settings)
include_binary_c_defaults:
whether to include a dict containing the binary_c parameters and their default
values
include_binary_c_version_info:
whether to include a dict containing all the binary_c version info
(see return_binary_c_version_info)
include_binary_c_help_all:
whether to include a dict containing all the information about
the binary_c parameters (see get_help_all)
Return:
dictionary containing all, or part of, the above dictionaries
"""
#
all_info = {}
#
if include_population_settings:
population_settings = self.return_population_settings()
all_info["population_settings"] = population_settings
#
if include_binary_c_defaults:
binary_c_defaults = self._return_binary_c_defaults()
all_info["binary_c_defaults"] = binary_c_defaults
if include_binary_c_version_info:
binary_c_version_info = return_binary_c_version_info(parsed=True)
all_info["binary_c_version_info"] = binary_c_version_info
if include_binary_c_help_all:
binary_c_help_all_info = get_help_all(print_help=False)
all_info["binary_c_help_all"] = binary_c_help_all_info
return all_info
def export_all_info(
self,
use_datadir: bool = True,
outfile: Union[str, None] = None,
include_population_settings: bool = True,
include_binary_c_defaults: bool = True,
include_binary_c_version_info: bool = True,
include_binary_c_help_all: bool = True,
) -> Union[str, None]:
"""
Function that exports the all_info to a json file
Tasks:
- TODO: if any of the values in the dicts here is of a not-serializable form, then we need
to change that to a string or something so, use a recursive function that goes over the
all_info dict and finds those that fit
- TODO: Fix to write things to the directory. which options do which etc
- TODO: theres flawed logic here. rewrite this part pls
- TODO: consider actually just removing the whole 'output to file' part and let the user do this.
Args:
include_population_settings: whether to include the population_settings (see function return_population_settings)
include_binary_c_defaults: whether to include a dict containing the binary_c parameters and their default values
include_binary_c_version_info: whether to include a dict containing all the binary_c version info (see return_binary_c_version_info)
include_binary_c_help_all: whether to include a dict containing all the information about the binary_c parameters (see get_help_all)
use_datadir: boolean whether to use the custom_options['data_dir'] to write the file to. If the custom_options["base_filename"] is set, the output file will be called <custom_options["base_filename"]>_settings.json. Otherwise a file called simulation_<date+time>_settings.json will be created
outfile: if use_datadir is false, a custom filename will be used
"""
all_info = self.return_all_info(
include_population_settings=include_population_settings,
include_binary_c_defaults=include_binary_c_defaults,
include_binary_c_version_info=include_binary_c_version_info,
include_binary_c_help_all=include_binary_c_help_all,
)
# Copy dict
all_info_cleaned = copy.deepcopy(all_info)
if use_datadir:
if not self.custom_options.get("base_filename", None):
base_name = "simulation_{}".format(
datetime.datetime.strftime(datetime.datetime.now(), "%Y%m%d_%H%M%S")
)
else:
base_name = os.path.splitext(self.custom_options["base_filename"])[0]
settings_name = base_name + "_settings.json"
# Check directory, make if necessary
os.makedirs(self.custom_options["data_dir"], exist_ok=True)
settings_fullname = os.path.join(
self.custom_options["data_dir"], settings_name
)
verbose_print(
"Writing settings to {}".format(settings_fullname),
self.grid_options["verbosity"],
1,
)
# if not outfile.endswith('json'):
with open(settings_fullname, "w") as file:
file.write(
json.dumps(
all_info_cleaned,
indent=4,
default=binaryc_json_serializer,
)
)
return settings_fullname
else:
verbose_print(
"Writing settings to {}".format(outfile),
self.grid_options["verbosity"],
1,
)
if not outfile.endswith("json"):
verbose_print(
"Error: outfile ({}) must end with .json".format(outfile),
self.grid_options["verbosity"],
0,
)
raise ValueError
with open(outfile, "w") as file:
file.write(
json.dumps(
all_info_cleaned, indent=4, default=binaryc_json_serializer
)
)
return outfile
def _set_custom_logging(self):
"""
Function/routine to set all the custom logging so that the function memory pointer
is known to the grid.
"""
# C_logging_code gets priority of C_autogen_code
verbose_print(
"Creating and loading custom logging functionality",
self.grid_options["verbosity"],
1,
)
if self.grid_options["C_logging_code"]:
# Generate entire shared lib code around logging lines
custom_logging_code = binary_c_log_code(
self.grid_options["C_logging_code"],
verbose=self.grid_options["verbosity"],
)
# Load memory adress
(
self.grid_options["custom_logging_func_memaddr"],
self.grid_options["_custom_logging_shared_library_file"],
) = create_and_load_logging_function(
custom_logging_code,
verbose=self.grid_options["verbosity"],
custom_tmp_dir=self.grid_options["tmp_dir"],
)
elif self.grid_options["C_auto_logging"]:
# Generate real logging code
logging_line = autogen_C_logging_code(
self.grid_options["C_auto_logging"],
verbose=self.grid_options["verbosity"],
)
# Generate entire shared lib code around logging lines
custom_logging_code = binary_c_log_code(
logging_line, verbose=self.grid_options["verbosity"]
)
# Load memory adress
(
self.grid_options["custom_logging_func_memaddr"],
self.grid_options["_custom_logging_shared_library_file"],
) = create_and_load_logging_function(
custom_logging_code,
verbose=self.grid_options["verbosity"],
custom_tmp_dir=self.grid_options["tmp_dir"],
)
###################################################
# Ensemble functions
###################################################
# Now they are stored in the _process_run_population thing.
# Needed less code since they all
###################################################
# Evolution functions
###################################################
def _pre_run_cleanup(self):
"""
Function to clean up some stuff in the grid before a run (like results, ensemble results etc)
"""
# empty results
self.grid_options["results"] = {}
def evolve(self) -> None:
"""
Entrypoint function of the whole object. From here, based on the settings,
we set up a SLURM or CONDOR grid, or if no setting is given we go straight
to evolving the population
There are no direct arguments to this function, rather it is based on the grid_options settings:
grid_options['slurm']: integer boolean whether to use a slurm_grid evolution
grid_options['condor']: integer boolean whether to use a condor_grid evolution
If neither of the above is set, we continue without using HPC routines
(that doesn't mean this cannot be run on a server with many cores)
Returns an dictionary containing the analytics of the run
TODO: change the way this is done. Slurm & CONDOR should probably do this different
"""
# Just to make sure we don't have stuff from a previous run hanging around
self._pre_run_cleanup()
# Check which type:
if self.grid_options["slurm"] == 1:
# Execute slurm subroutines
self._slurm_grid()
elif self.grid_options["condor"] == 1:
# Execute condor subroutines
self._condor_grid()
else:
# Execute population evolution subroutines
self.evolve_population()
# Put all interesting stuff in a variable and output that afterwards, as analytics of the run.
analytics_dict = {
"population_name": self.grid_options["_population_id"],
"evolution_type": self.grid_options["evolution_type"],
"failed_count": self.grid_options["_failed_count"],
"failed_prob": self.grid_options["_failed_prob"],
"failed_systems_error_codes": self.grid_options[
"_failed_systems_error_codes"
].copy(),
"errors_exceeded": self.grid_options["_errors_exceeded"],
"errors_found": self.grid_options["_errors_found"],
"total_probability": self.grid_options["_probtot"],
"total_count": self.grid_options["_count"],
"start_timestamp": self.grid_options["_start_time_evolution"],
"end_timestamp": self.grid_options["_end_time_evolution"],
"total_mass_run": self.grid_options["_total_mass_run"],
"total_probability_weighted_mass_run": self.grid_options[
"_total_probability_weighted_mass_run"
],
}
# print(Moecache)
print(Moecache.keys())
##
# Clean up code: remove files, unset values. This is placed in the general evolve function,
# because that makes for easier control
self._cleanup()
return analytics_dict
def evolve_population(self):
"""
Function to evolve populations. This handles the setting up, evolving
and cleaning up of a population of stars.
Choices here are:
to evolve a population via multiprocessing or linearly on 1 core.
NOT IMPLEMENTED YET to evolve a population via a variable grid, a source file or MC
Tasks:
- TODO: include options for different ways of generating a population here. (i.e. MC or source file)
"""
# Reset some settings: population_id, results, ensemble_results etc
self.grid_options["_population_id"] = uuid.uuid4().hex
##
# Prepare code/initialise grid.
# set custom logging, set up store_memaddr, build grid code. dry run grid code.
self._setup()
##
# Evolve systems: via grid_options one can choose to do this linearly, or
# multiprocessing method.
if (
self.grid_options["evolution_type"]
in self.grid_options["_evolution_type_options"]
):
if self.grid_options["evolution_type"] == "grid":
self._evolve_population_grid()
# elif self.grid_options["evolution_type"] == "mc":
# # TODO: add MC option
else:
print(
"Warning. you chose a wrong option for the grid evolution types.\
Please choose from the following: {}.".format(
self.grid_options["_evolution_type_options"]
)
)
#
self.grid_options["_end_time_evolution"] = time.time()
# Log and print some information
verbose_print(
"Population-{} finished! The total probability was: {}. It took a total of {}s to run {} systems on {} cores".format(
self.grid_options["_population_id"],
self.grid_options["_probtot"],
self.grid_options["_end_time_evolution"]
- self.grid_options["_start_time_evolution"],
self.grid_options["_total_starcount"],
self.grid_options["amt_cores"],
),
self.grid_options["verbosity"],
0,
)
if self.grid_options["_errors_found"]:
# Some information afterwards
verbose_print(
"During the run {} failed systems were found, with a total probability of {} and with the following unique error codes: {} ".format(
self.grid_options["_failed_count"],
self.grid_options["_failed_prob"],
self.grid_options["_failed_systems_error_codes"],
),
self.grid_options["verbosity"],
0,
)
# Some information afterwards
verbose_print(
"The full argline commands for {} these systems have been written to {}".format(
"ALL"
if not self.grid_options["_errors_exceeded"]
else "SOME (only the first ones, as there were too many to log all of them)",
os.path.join(
self.grid_options["tmp_dir"],
"failed_systems_{}_X.txt".format(
self.grid_options["_population_id"]
),
),
),
self.grid_options["verbosity"],
0,
)
else:
verbose_print(
"There were no errors found in this run.",
self.grid_options["verbosity"],
0,
)
def get_stream_logger(self, level=logging.DEBUG):
"""Return logger with configured StreamHandler."""
stream_logger = logging.getLogger("stream_logger")
stream_logger.handlers = []
stream_logger.setLevel(level)
sh = logging.StreamHandler()
sh.setLevel(level)
fmt = "[%(asctime)s %(levelname)-8s %(processName)s] --- %(message)s"
formatter = logging.Formatter(fmt)
sh.setFormatter(formatter)
stream_logger.addHandler(sh)
return stream_logger
def system_queue_filler(self, job_queue, amt_cores):
"""
Function that is responsible for keeping the queue filled.
This will generate the systems until it is full, and then keeps trying to fill it.
Will have to play with the size of this.
"""
stream_logger = self.get_stream_logger()
stream_logger.debug(f"setting up the system_queue_filler now")
# Setup of the generator
self._generate_grid_code(dry_run=False)
self._load_grid_function()
generator = self.grid_options["_system_generator"](self, print_results=False)
# TODO: build in method to handle with the HPC.
# Continously fill the queue
for system_number, system_dict in enumerate(generator):
# stream_logger.debug(f"producing: {system_number}") # DEBUG
job_queue.put((system_number, system_dict))
# Print current size
# print("Current size: {}".format(save_que.qsize()))
# Send closing signal to workers. When they receive this they will terminate
stream_logger.debug(f"Signaling stop to processes") # DEBUG
for _ in range(amt_cores):
job_queue.put("STOP")
def _evolve_population_grid(self):
"""
Function to evolve the population with multiprocessing approach.
Using pathos to be able to include class-owned functions.
This function will create a pool with <self.grid_options["amt_cores"]> processes, and
perform an imap_unordered to run the different `threads`.
Before this was done by giving a generator as the iterable, and have the processes get a
certain chunksize each round.
Later on this seemed to be a bad decision, because it is difficult to pass information
back to the main controller, and because with each new batch of systems a new object instance was created.
What I do now is I spawn these X amount of processes, and pass a range(self.grid_options["amt_cores"]) as iterable.
In that way, only once do they fetch a `job`, but that job is just a ID number.
With this ID number each thread/process loops over the whole generator,
but only runs the one <ID>'th system (if (localcounter+ID) % self.grid_options["amt_cores"]==0)'
When they are finished, these jobs are instructed to return a set of information
(the result dict, TODO: describe what more)
These resultation dictionaries are then merged and stored as object properties again.
"""
# TODO: make further use of a queue to handle jobs or at least
# get information on the process ids etc
# https://stackoverflow.com/questions/10190981/get-a-unique-id-for-worker-in-python-multiprocessing-pool
# https://stackoverflow.com/questions/8640367/python-manager-dict-in-multiprocessing/9536888
# for muting values through dicts
# https://python-forum.io/Thread-Dynamic-updating-of-a-nested-dictionary-in-multiprocessing-pool
# https://stackoverflow.com/questions/28740955/working-with-pathos-multiprocessing-tool-in-python-and
# TODO: make good example of how to deal with a result_dict
# https://www.programcreek.com/python/example/58176/multiprocessing.Value
# https://stackoverflow.com/questions/17377426/shared-variable-in-pythons-multiprocessing
# Set up the manager object that can share info between processes
# pathos_multiprocess
manager = multiprocessing.Manager()
job_queue = manager.Queue(maxsize=self.grid_options['max_queue_size'])
result_queue = manager.Queue(maxsize=self.grid_options["amt_cores"])
# Create process instances
processes = []
for ID in range(self.grid_options["amt_cores"]):
processes.append(
multiprocessing.Process(
target=self._process_run_population_grid,
args=(job_queue, result_queue, ID),
)
)
# Activate the processes
for p in processes:
p.start()
# Set up the system_queue
self.system_queue_filler(job_queue, amt_cores=self.grid_options["amt_cores"])
# Join the processes
for p in processes:
p.join()
# Handle the results by merging all the dictionaries. How that merging happens exactly is
# described in the merge_dicts description.
combined_output_dict = {}
sentinel = object()
for output_dict in iter(result_queue.get, sentinel):
print(output_dict)
combined_output_dict = merge_dicts(combined_output_dict, output_dict)
if result_queue.empty():
break
# Put the values back as object properties
self.grid_results = combined_output_dict["results"]
self.grid_ensemble_results = combined_output_dict[
"ensemble_results"
] # Ensemble results are also passed as output from that dictionary
# Add some metadata
self.grid_ensemble_results["population_id"] = self.grid_options[
"_population_id"
]
self.grid_options["_failed_count"] = combined_output_dict["_failed_count"]
self.grid_options["_failed_prob"] = combined_output_dict["_failed_prob"]
self.grid_options["_failed_systems_error_codes"] = list(
set(combined_output_dict["_failed_systems_error_codes"])
)
self.grid_options["_errors_exceeded"] = combined_output_dict["_errors_exceeded"]
self.grid_options["_errors_found"] = combined_output_dict["_errors_found"]
self.grid_options["_probtot"] = combined_output_dict["_probtot"]
self.grid_options["_count"] = combined_output_dict["_count"]
self.grid_options["_total_mass_run"] = combined_output_dict["_total_mass_run"]
self.grid_options[
"_total_probability_weighted_mass_run"
] = combined_output_dict["_total_probability_weighted_mass_run"]
def _evolve_system_mp(self, full_system_dict):
"""
Function that the multiprocessing evolution method calls to evolve a system
this function is called by _process_run_population_grid
"""
binary_cmdline_string = self._return_argline(full_system_dict)
persistent_data_memaddr = -1
if self.bse_options.get("ensemble", 0) == 1:
persistent_data_memaddr = self.persistent_data_memory_dict[self.process_ID]
# print("thread {}: persistent_data_memaddr: ".format(self.process_ID), persistent_data_memaddr)
# Get results binary_c
out = _binary_c_bindings.run_system(
argstring=binary_cmdline_string,
custom_logging_func_memaddr=self.grid_options[
"custom_logging_func_memaddr"
],
store_memaddr=self.grid_options["_store_memaddr"],
population=1, # since this system is part of a population, we set this flag to prevent the store from being freed
persistent_data_memaddr=persistent_data_memaddr,
)
# Check for errors
_ = self._check_binary_c_error(out, full_system_dict)
# Have some user-defined function do stuff with the data.
if self.grid_options["parse_function"]:
self.grid_options["parse_function"](self, out)
def _process_run_population_grid(self, job_queue, result_queue, ID):
"""
Function that loops over the whole generator, but only runs
systems that fit to: if (localcounter+ID) % self.grid_options["amt_cores"] == 0
That way with e.g. 4 processes, process 1 runs sytem 0, 4, 8... process 2 runs system 1, 5, 9..., etc
This function is called by _evolve_population_grid
"""
# set start timer
start_process_time = datetime.datetime.now()
#
self.process_ID = (
ID # Store the ID as a object property again, lets see if that works.
)
stream_logger = self.get_stream_logger()
stream_logger.debug(f"Setting up processor: process-{self.process_ID}")
# Set to starting up
with open(
os.path.join(
self.grid_options["tmp_dir"],
"process_status",
"process_{}.txt".format(self.process_ID),
),
"w",
) as f:
f.write("STARTING")
# lets try out making stores for all the grids:
self.grid_options["_store_memaddr"] = _binary_c_bindings.return_store_memaddr()
verbose_print(
"Process {} started at {}.\tUsing store memaddr {}".format(
ID,
datetime.datetime.now().isoformat(),
self.grid_options["_store_memaddr"],
),
self.grid_options["verbosity"],
0,
)
# Set the ensemble memaddr
if self.bse_options.get("ensemble", 0) == 1:
# set persistent data memaddr if necessary.
persistent_data_memaddr = (
_binary_c_bindings.return_persistent_data_memaddr()
)
self.persistent_data_memory_dict = {
self.process_ID: persistent_data_memaddr
}
verbose_print(
"\tUsing persistent_data memaddr: {}".format(persistent_data_memaddr),
self.grid_options["verbosity"],
0,
)
# Set up local variables
localcounter = (
0 # global counter for the whole loop. (need to be ticked every loop)
)
probability_of_systems_run = (
0 # counter for the probability of the actual systems this tread ran
)
number_of_systems_run = (
0 # counter for the actual amt of systems this thread ran
)
total_time_calling_binary_c = 0
total_mass_run = 0
total_probability_weighted_mass_run = 0
# Go over the queue
for system_number, system_dict in iter(job_queue.get, "STOP"):
if localcounter == 0:
# Set status to running
with open(
os.path.join(
self.grid_options["tmp_dir"],
"process_status",
"process_{}.txt".format(self.process_ID),
),
"w",
) as f:
f.write("RUNNING")
# Combine that with the other settings
full_system_dict = self.bse_options.copy()
full_system_dict.update(system_dict)
# In the first system, explicitly check all the keys that are passed to see if
# they match the keys known to binary_c.
# Won't do that every system cause that is a bit of a waste of computing time.
if number_of_systems_run == 0:
# TODO: Put this someplace else and wrap in a functioncall
for key in full_system_dict.keys():
if not key in self.available_keys:
# Deal with special keys
if not any(
[
True
if (
key.startswith(param[:-2])
and len(param[:-2]) < len(key)
)
else False
for param in self.special_params
]
):
msg = "Error: Found a parameter unknown to binary_c: {}. Abort".format(
key
)
raise ValueError(msg)
# self._print_info(
# i + 1, self.grid_options["_total_starcount"], full_system_dict
# )
#
verbose_print(
"Process {} is handling system {}".format(ID, system_number),
self.grid_options["verbosity"],
2,
)
# In some cases, the whole run crashes. To be able to figure out which system
# that was on, we log each current system to a file (each thread has one).
# Each new system overrides the previous
with open(
os.path.join(
self.grid_options["tmp_dir"],
"current_system",
"process_{}.txt".format(self.process_ID),
),
"w",
) as f:
binary_cmdline_string = self._return_argline(full_system_dict)
f.write(binary_cmdline_string)
start_runtime_binary_c = time.time()
# Evolve the system
if self.grid_options["_actually_evolve_system"]:
self._evolve_system_mp(full_system_dict)
end_runtime_binary_c = time.time()
total_time_calling_binary_c += (
end_runtime_binary_c - start_runtime_binary_c
) # keep track of total binary_c call time
# Debug line: logging all the lines
if self.grid_options["log_runtime_systems"] == 1:
with open(
os.path.join(
self.grid_options["tmp_dir"],
"runtime_systems",
"process_{}.txt".format(self.process_ID),
),
"a+",
) as f:
binary_cmdline_string = self._return_argline(full_system_dict)
f.write(
"{} {} '{}'\n".format(
start_runtime_binary_c,
end_runtime_binary_c - start_runtime_binary_c,
binary_cmdline_string,
)
)
# Keep track of systems:
probability_of_systems_run += full_system_dict["probability"]
number_of_systems_run += 1
localcounter += 1
# Tally up some numbers
total_mass_system = (
full_system_dict.get("M_1", 0)
+ full_system_dict.get("M_2", 0)
+ full_system_dict.get("M_3", 0)
+ full_system_dict.get("M_4", 0)
)
total_mass_run += total_mass_system
total_probability_weighted_mass_run += (
total_mass_system * full_system_dict["probability"]
)
# Set status to finishing
with open(
os.path.join(
self.grid_options["tmp_dir"],
"process_status",
"process_{}.txt".format(self.process_ID),
),
"w",
) as f:
f.write("FINISHING")
stream_logger.debug(f"Process-{self.process_ID} is finishing.")
# Handle ensemble output: is ensemble==1, then either directly write that data to a file, or combine everything into 1 file.
ensemble_json = {} # Make sure it exists already
if self.bse_options.get("ensemble", 0) == 1:
verbose_print(
"Process {}: is freeing ensemble output (using persisten_data memaddr {})".format(
ID, self.persistent_data_memory_dict[self.process_ID]
),
self.grid_options["verbosity"],
2,
)
ensemble_raw_output = (
_binary_c_bindings.free_persistent_data_memaddr_and_return_json_output(
self.persistent_data_memory_dict[self.process_ID]
)
)
if ensemble_raw_output is None:
verbose_print(
"Process {}: Warning! Ensemble output is empty. ".format(ID),
self.grid_options["verbosity"],
1,
)
#
if self.grid_options["combine_ensemble_with_thread_joining"] is True:
verbose_print(
"Process {}: Extracting ensemble info from raw string".format(ID),
self.grid_options["verbosity"],
2,
)
ensemble_json["ensemble"] = extract_ensemble_json_from_string(
ensemble_raw_output
) # Load this into a dict so that we can combine it later
else:
# If we do not allow this, automatically we will export this to the data_dir, in
# some formatted way
output_file = os.path.join(
self.custom_options["data_dir"],
"ensemble_output_{}_{}.json".format(
self.grid_options["_population_id"], self.process_ID
),
)
# Write to file
with open(output_file, "w") as f:
f.write(ensemble_raw_output)
print(
"Thread {}: Wrote ensemble results directly to file: {}".format(
self.process_ID, output_file
)
)
# free store mem:
_binary_c_bindings.free_store_memaddr(self.grid_options["_store_memaddr"])
# Return a set of results and errors
output_dict = {
"results": self.grid_results,
"ensemble_results": ensemble_json,
"_failed_count": self.grid_options["_failed_count"],
"_failed_prob": self.grid_options["_failed_prob"],
"_failed_systems_error_codes": self.grid_options[
"_failed_systems_error_codes"
],
"_errors_exceeded": self.grid_options["_errors_exceeded"],
"_errors_found": self.grid_options["_errors_found"],
"_probtot": probability_of_systems_run,
"_count": number_of_systems_run,
"_total_mass_run": total_mass_run,
"_total_probability_weighted_mass_run": total_probability_weighted_mass_run,
}
end_process_time = datetime.datetime.now()
verbose_print(
"Process {} finished:\n\tgenerator started at {}, done at {} (total: {}s of which {}s interfacing with binary_c).\n\tRan {} systems with a total probability of {}.\n\tThis thread had {} failing systems with a total probability of {}".format(
ID,
start_process_time.isoformat(),
end_process_time.isoformat(),
(end_process_time - start_process_time).total_seconds(),
total_time_calling_binary_c,
number_of_systems_run,
probability_of_systems_run,
self.grid_options["_failed_count"],
self.grid_options["_failed_prob"],
),
self.grid_options["verbosity"],
0,
)
# Write summary
summary_dict = {
"population_id": self.grid_options["_population_id"],
"process_id": self.process_ID,
"start_process_time": start_process_time.timestamp(),
"end_process_time": end_process_time.timestamp(),
"total_time_calling_binary_c": total_time_calling_binary_c,
"number_of_systems_run": number_of_systems_run,
"probability_of_systems_run": probability_of_systems_run,
"failed_systems": self.grid_options["_failed_count"],
"failed_probability": self.grid_options["_failed_prob"],
"failed_system_error_codes": self.grid_options[
"_failed_systems_error_codes"
],
}
with open(
os.path.join(
self.grid_options["tmp_dir"],
"process_summary",
"process_{}.json".format(self.process_ID),
),
"w",
) as f:
f.write(json.dumps(summary_dict, indent=4))
# Set status to running
with open(
os.path.join(
self.grid_options["tmp_dir"],
"process_status",
"process_{}.txt".format(self.process_ID),
),
"w",
) as f:
f.write("FINISHED")
result_queue.put(output_dict)
stream_logger.debug(f"Process-{self.process_ID} is finished.")
return
# Single system
def evolve_single(self, clean_up_custom_logging_files: bool = True) -> Any:
"""
Function to run a single system, based on the settings in the grid_options
The output of the run gets returned, unless a parse function is given to this function.
Args:
clean_up_custom_logging_files: whether the clean up all the custom_logging files.
returns:
either returns the raw binary_c output, or whatever the parse_function does
"""
### Custom logging code:
self._set_custom_logging()
# Get argument line
argline = self._return_argline(self.bse_options)
verbose_print("Running {}".format(argline), self.grid_options["verbosity"], 1)
# Run system
out = _binary_c_bindings.run_system(
argstring=argline,
custom_logging_func_memaddr=self.grid_options[
"custom_logging_func_memaddr"
],
store_memaddr=self.grid_options["_store_memaddr"],
population=0,
)
# TODO: add call to function that cleans up the temp customlogging dir,
# and unloads the loaded libraries.
# TODO: make a switch to turn this off
if clean_up_custom_logging_files:
self._clean_up_custom_logging(evol_type="single")
# Parse
if self.grid_options["parse_function"]:
return self.grid_options["parse_function"](self, out)
return out
def _setup(self):
"""
Function to set up the necessary stuff for the population evolution.
The idea is to do all the stuff that is necessary for a population to run.
Since we have different methods of running a population, this setup function
will do different things depending on different settings
Tasks:
TODO: Make other kinds of populations possible. i.e, read out type of grid,
and set up accordingly
TODO: make this function more general. Have it explicitly set the system_generator
function
"""
# Make sure the subdirs of the tmp dir exist:
os.makedirs(
os.path.join(self.grid_options["tmp_dir"], "failed_systems"), exist_ok=True
)
os.makedirs(
os.path.join(self.grid_options["tmp_dir"], "current_system"), exist_ok=True
)
os.makedirs(
os.path.join(self.grid_options["tmp_dir"], "process_status"), exist_ok=True
)
os.makedirs(
os.path.join(self.grid_options["tmp_dir"], "process_summary"), exist_ok=True
)
os.makedirs(
os.path.join(self.grid_options["tmp_dir"], "runtime_systems"), exist_ok=True
)
# Check for parse function
if not self.grid_options["parse_function"]:
print("Warning: No parse function set. Make sure you intended to do this.")
# #######################
# ### Custom logging code:
self._set_custom_logging()
# ### Load store: Make sure this actually works.
### ensemble: make some checks for this
## check the settings and set all the warnings.
if self.bse_options.get("ensemble", None):
if not self.bse_options.get("ensemble_defer", 0) == 1:
verbose_print(
"Error, if you want to run an ensemble in a population, the output needs to be deferred. Please set 'ensemble_defer' to 1",
self.grid_options["verbosity"],
0,
)
raise ValueError
if not any(
[key.startswith("ensemble_filter_") for key in self.bse_options]
):
verbose_print(
"Warning: Running the ensemble without any filter requires alot of available RAM",
self.grid_options["verbosity"],
0,
)
if self.bse_options.get("ensemble_filters_off", 0) != 1:
verbose_print(
"Warning: Running the ensemble without any filter requires alot of available RAM",
self.grid_options["verbosity"],
0,
)
if self.grid_options["combine_ensemble_with_thread_joining"] == False:
if not self.custom_options.get("data_dir", None):
verbose_print(
"Error: chosen to write the ensemble output directly to files but data_dir isnt set",
self.grid_options["verbosity"],
0,
)
raise ValueError
# Check which type of population generation
if self.grid_options["evolution_type"] == "grid":
#######################
# Dry run and getting starcount
self.grid_options["_probtot"] = 0
# Put in check
if len(self.grid_options["_grid_variables"]) == 0:
print("Error: you havent defined any grid variables! Aborting")
raise ValueError
# Set up the grid code with a dry run option to see total probability
self._generate_grid_code(dry_run=True)
# Load the grid code
self._load_grid_function()
# Do a dry run
self._dry_run()
print(
"Total starcount for this run will be: {}".format(
self.grid_options["_total_starcount"]
)
)
#######################
# Reset values and prepare the grid function
self.grid_options[
"_probtot"
] = 0 # To make sure that the values are reset. TODO: fix this in a cleaner way
self.grid_options[
"_start_time_evolution"
] = time.time() # Setting start time of grid
# # Making sure the loaded grid code isn't lingering in the main PID
# self._generate_grid_code(dry_run=False)
# #
# self._load_grid_function()
#
self.grid_options["_system_generator"] = None
# Source file
elif self.grid_options["evolution_type"] == "source_file":
#######################
# Dry run and getting starcount
self.grid_options["_probtot"] = 0
# Load the grid code
# TODO: fix this function
# self._load_source_file_function()
# Do a dry run
self._dry_run_source_file()
print(
"Total starcount for this run will be: {}".format(
self.grid_options["_total_starcount"]
)
)
#######################
# Reset values and prepare the grid function
self.grid_options[
"_probtot"
] = 0 # To make sure that the values are reset. TODO: fix this in a cleaner way
self.grid_options[
"_start_time_evolution"
] = time.time() # Setting start time of grid
#
# TODO: fix this function
# self._load_source_file_function()
# self._load_source_file_function(dry_run=False)
#
self._load_grid_function()
# elif self.grid_options["evolution_type"] == "mc":
#######
def _cleanup(self):
"""
Function that handles all the cleaning up after the grid has been generated and/or run
- reset values to 0
- remove grid file
- unload grid function/module
- remove dry grid file
- unload dry grid function/module
"""
# Reset values
self.grid_options["_count"] = 0
self.grid_options["_probtot"] = 0
self.grid_options["_system_generator"] = None
self.grid_options["_failed_count"] = 0
self.grid_options["_failed_prob"] = 0
self.grid_options["_errors_found"] = False
self.grid_options["_errors_exceeded"] = False
self.grid_options["_failed_systems_error_codes"] = []
self.grid_options["_total_mass_run"] = 0
self.grid_options["_total_probability_weighted_mass_run"] = 0
# Remove files
# TODO: remove files
# Unload functions
# TODO: unload functions
# Unload/free custom_logging_code
# TODO: cleanup custom logging code.
###################################################
# Gridcode functions
#
# Function below are used to run populations with
# a variable grid
###################################################
def _generate_grid_code(self, dry_run=False):
"""
Function that generates the code from which the population will be made.
dry_run: when True, it will return the starcount at the end so that we know
what the total amount of systems is.
The phasevol values are handled by generating a second array
# TODO: Add correct logging everywhere
# TODO: add part to handle separation if orbital_period is added. Idea. use default values
# for orbital parameters and possibly overwrite those or something.
# TODO: add centering center left right for the spacing.
# TODO: add sensible description to this function.
# TODO: Check whether all the probability and phasevol values are correct.
# TODO: import only the necessary packages/functions
# TODO: Put all the masses, eccentricities and periods in there already
# TODO: Put the certain blocks that are repeated in some subfunctions
Results in a generated file that contains a system_generator function.
"""
verbose_print("Generating grid code", self.grid_options["verbosity"], 1)
# Some local values
code_string = ""
depth = 0
indent = " "
total_grid_variables = len(self.grid_options["_grid_variables"])
# Import packages
code_string += "import math\n"
code_string += "import numpy as np\n"
code_string += "from binarycpython.utils.distribution_functions import *\n"
code_string += "from binarycpython.utils.spacing_functions import *\n"
code_string += "from binarycpython.utils.useful_funcs import *\n"
code_string += "\n\n"
# Make the function
code_string += "def grid_code(self, print_results=True):\n"
# Increase depth
depth += 1
# Write some info in the function
code_string += (
indent * depth
+ "# Grid code generated on {}\n".format(
datetime.datetime.now().isoformat()
)
+ indent * depth
+ "# This function generates the systems that will be evolved with binary_c\n\n"
)
# Set some values in the generated code:
code_string += indent * depth + "# Setting initial values\n"
code_string += indent * depth + "_total_starcount = 0\n"
code_string += indent * depth + "starcounts = [0 for i in range({})]\n".format(
total_grid_variables
)
code_string += indent * depth + "probabilities = {}\n"
code_string += (
indent * depth
+ "probabilities_list = [0 for i in range({})]\n".format(
total_grid_variables
)
)
code_string += (
indent * depth
+ "probabilities_sum = [0 for i in range({})]\n".format(
total_grid_variables
)
)
code_string += indent * depth + "parameter_dict = {}\n"
code_string += indent * depth + "phasevol = 1\n"
code_string += indent * depth + "\n"
# Setting up the system parameters
code_string += indent * depth + "M_1 = None\n"
code_string += indent * depth + "M_2 = None\n"
code_string += indent * depth + "M_3 = None\n"
code_string += indent * depth + "M_4 = None\n"
code_string += indent * depth + "orbital_period = None\n"
code_string += indent * depth + "orbital_period_triple = None\n"
code_string += indent * depth + "orbital_period_quadruple = None\n"
code_string += indent * depth + "eccentricity = None\n"
code_string += indent * depth + "eccentricity2 = None\n"
code_string += indent * depth + "eccentricity3 = None\n"
code_string += indent * depth + "\n"
# Prepare the probability
code_string += indent * depth + "# setting probability lists\n"
for grid_variable_el in sorted(
self.grid_options["_grid_variables"].items(),
key=lambda x: x[1]["grid_variable_number"],
):
# Make probabilities dict
grid_variable = grid_variable_el[1]
code_string += indent * depth + 'probabilities["{}"] = 0\n'.format(
grid_variable["parameter_name"]
)
#################################################################################
# Start of code generation
#################################################################################
code_string += indent * depth + "\n"
# Generate code
print("Generating grid code")
for loopnr, grid_variable_el in enumerate(
sorted(
self.grid_options["_grid_variables"].items(),
key=lambda x: x[1]["grid_variable_number"],
)
):
print("Constructing/adding: {}".format(grid_variable_el[0]))
grid_variable = grid_variable_el[1]
#########################
# Setting up the forloop
# Add comment for forloop
code_string += (
indent * depth
+ "# for loop for {}".format(grid_variable["parameter_name"])
+ "\n"
)
code_string += (
indent * depth
+ "sampled_values_{} = {}".format(
grid_variable["name"], grid_variable["spacingfunc"]
)
+ "\n"
)
# TODO: Make clear that the phasevol only works good
# TODO: add option to ignore this phasevol calculation and set it to 1
# if you sample linearly in that thing.
# if phasevol is <= 0 then we SKIP that whole loop. Its not working then.
if (
not grid_variable["dphasevol"] == -1
): # A method to turn off this calculation and allow a phasevol = 1
code_string += (
indent * depth
+ "phasevol_{} = sampled_values_{}[1]-sampled_values_{}[0]".format(
grid_variable["name"],
grid_variable["name"],
grid_variable["name"],
)
+ "\n"
)
else:
code_string += indent * depth + "phasevol_{} = 1\n".format(
grid_variable["name"]
)
# # Some print statement
# code_string += (
# indent * depth
# + "print('phasevol_{}:', phasevol_{})".format(grid_variable["name"],
# grid_variable["name"])
# + "\n"
# )
# Adding for loop structure
code_string += (
indent * depth
+ "for {} in sampled_values_{}:".format(
grid_variable["name"], grid_variable["name"]
)
+ "\n"
)
#################################################################################
# Check condition and generate forloop
# If the grid variable has a condition, write the check and the action
if grid_variable["condition"]:
# Add comment
code_string += (
indent * (depth + 1)
+ "# Condition for {}".format(grid_variable["parameter_name"])
+ "\n"
)
# Add condition check
code_string += (
indent * (depth + 1)
+ "if not {}:".format(grid_variable["condition"])
+ "\n"
)
# Add condition failed action:
code_string += (
indent * (depth + 2)
+ 'print("Condition for {} not met!")'.format(
grid_variable["parameter_name"]
)
+ "\n"
)
code_string += indent * (depth + 2) + "continue" + "\n"
# Add some whiteline
code_string += indent * (depth + 1) + "\n"
##############3
# Add phasevol check:
code_string += (
indent * (depth + 1)
+ "if phasevol_{} <= 0:".format(grid_variable["name"])
+ "\n"
)
# Add phasevol check action:
code_string += (
indent * (depth + 2)
+ 'print("phasevol_{} <= 0!")'.format(grid_variable["name"])
+ "\n"
)
code_string += indent * (depth + 2) + "continue" + "\n"
# Add some whiteline
code_string += indent * (depth + 1) + "\n"
#########################
# Setting up pre-code and value in some cases
# Add pre-code
if grid_variable["precode"]:
code_string += (
indent * (depth + 1)
+ "{}".format(
grid_variable["precode"].replace(
"\n", "\n" + indent * (depth + 1)
)
)
+ "\n"
)
# Set phasevol
code_string += indent * (depth + 1) + "phasevol *= phasevol_{}\n".format(
grid_variable["name"],
)
#######################
# Probabilities
# Calculate probability
code_string += indent * (depth + 1) + "\n"
code_string += indent * (depth + 1) + "# Setting probabilities\n"
code_string += (
indent * (depth + 1)
+ "d{} = phasevol_{} * {}".format(
grid_variable["name"],
grid_variable["name"],
grid_variable["probdist"],
)
+ "\n"
)
# Saving probability sum
code_string += (
indent * (depth + 1)
+ "probabilities_sum[{}] += d{}".format(
grid_variable["grid_variable_number"], grid_variable["name"]
)
+ "\n"
)
if grid_variable["grid_variable_number"] == 0:
code_string += (
indent * (depth + 1)
+ "probabilities_list[0] = d{}".format(grid_variable["name"])
+ "\n"
)
else:
code_string += (
indent * (depth + 1)
+ "probabilities_list[{}] = probabilities_list[{}] * d{}".format(
grid_variable["grid_variable_number"],
grid_variable["grid_variable_number"] - 1,
grid_variable["name"],
)
+ "\n"
)
#######################
# Increment starcount for this parameter
code_string += "\n"
code_string += indent * (
depth + 1
) + "# Increment starcount for {}\n".format(grid_variable["parameter_name"])
code_string += (
indent * (depth + 1)
+ "starcounts[{}] += 1".format(
grid_variable["grid_variable_number"],
)
+ "\n"
)
# Add value to dict
code_string += (
indent * (depth + 1)
+ 'parameter_dict["{}"] = {}'.format(
grid_variable["parameter_name"], grid_variable["parameter_name"]
)
+ "\n"
)
# Add some space
code_string += "\n"
# The final parts of the code, where things are returned, are within the deepest loop,
# but in some cases code from a higher loop needs to go under it again
# SO I think its better to put an ifstatement here that checks
# whether this is the last loop.
if loopnr == len(self.grid_options["_grid_variables"]) - 1:
code_string = self._write_gridcode_system_call(
code_string,
indent,
depth,
grid_variable,
dry_run,
grid_variable["branchpoint"],
)
# increment depth
depth += 1
depth -= 1
code_string += "\n"
# Write parts to write below the part that yield the results.
# this has to go in a reverse order:
# Here comes the stuff that is put after the deepest nested part that calls returns stuff.
# Here we will have a
reverse_sorted_grid_variables = sorted(
self.grid_options["_grid_variables"].items(),
key=lambda x: x[1]["grid_variable_number"],
reverse=True,
)
for loopnr, grid_variable_el in enumerate(reverse_sorted_grid_variables):
grid_variable = grid_variable_el[1]
code_string += indent * (depth + 1) + "#" * 40 + "\n"
code_string += (
indent * (depth + 1)
+ "# Code below is for finalising the handling of this iteration of the parameter\n"
)
# Set phasevol
# TODO: fix. this isnt supposed to be the value that we give it here. discuss
code_string += indent * (depth + 1) + "phasevol /= phasevol_{}\n".format(
grid_variable["name"]
)
code_string += indent * (depth + 1) + "\n"
depth -= 1
# Check the branchpoint part here. The branchpoint makes sure that we can construct
# a grid with several multiplicities and still can make the system calls for each
# multiplicity without reconstructing the grid each time
if grid_variable["branchpoint"] == 1:
# Add comment
code_string += (
indent * (depth + 1)
+ "# Condition for branchpoint at {}".format(
reverse_sorted_grid_variables[loopnr + 1][1]["parameter_name"]
)
+ "\n"
)
# Add condition check
code_string += (
indent * (depth + 1)
+ "if not {}:".format(grid_variable["condition"])
+ "\n"
)
code_string = self._write_gridcode_system_call(
code_string,
indent,
depth + 1,
reverse_sorted_grid_variables[loopnr + 1][1],
dry_run,
grid_variable["branchpoint"],
)
code_string += "\n"
################
# Finalising print statements
#
# code_string += indent * (depth + 1) + "\n"
code_string += indent * (depth + 1) + "#" * 40 + "\n"
code_string += indent * (depth + 1) + "if print_results:\n"
code_string += (
indent * (depth + 2)
+ "print('Grid has handled {} stars'.format(_total_starcount))\n"
)
code_string += (
indent * (depth + 2)
+ "print('with a total probability of {}'.format(self.grid_options['_probtot']))\n"
)
################
# Finalising return statement for dry run.
#
if dry_run:
code_string += indent * (depth + 1) + "return _total_starcount\n"
#################################################################################
# Stop of code generation. Here the code is saved and written
# Save the gridcode to the grid_options
verbose_print(
"Saving grid code to grid_options", self.grid_options["verbosity"], 1
)
self.grid_options["code_string"] = code_string
# Write to file
gridcode_filename = os.path.join(
self.grid_options["tmp_dir"],
"binary_c_grid_{}.py".format(self.grid_options["_population_id"]),
)
self.grid_options["gridcode_filename"] = gridcode_filename
verbose_print(
"Writing grid code to {}".format(gridcode_filename),
self.grid_options["verbosity"],
1,
)
with open(gridcode_filename, "w") as file:
file.write(code_string)
def _write_gridcode_system_call(
self, code_string, indent, depth, grid_variable, dry_run, branchpoint
):
#################################################################################
# Here are the calls to the queuing or other solution. this part is for every system
# Add comment
code_string += indent * (depth + 1) + "#" * 40 + "\n"
if branchpoint:
code_string += (
indent * (depth + 1)
+ "# Code below will get evaluated for every system at this level of multiplicity (last one of that being {})\n"
).format(grid_variable["name"])
else:
code_string += (
indent * (depth + 1)
+ "# Code below will get evaluated for every generated system\n"
)
# Calculate value
code_string += (
indent * (depth + 1)
+ 'probability = self.grid_options["weight"] * probabilities_list[{}]'.format(
grid_variable["grid_variable_number"]
)
+ "\n"
)
# TODO: ask rob if just replacing this with probability is enough
code_string += (
indent * (depth + 1)
# + 'repeat_probability = probability / self.grid_options["repeat"]'
+ 'probability = probability / self.grid_options["repeat"]'
+ "\n"
)
# For each repeat of the system this has to be done yes.
code_string += (
indent * (depth + 1) + 'for _ in range(self.grid_options["repeat"]):' + "\n"
)
code_string += indent * (depth + 2) + "_total_starcount += 1\n"
# set probability and phasevol values
code_string += (
indent * (depth + 2)
+ 'parameter_dict["{}"] = {}'.format("probability", "probability")
+ "\n"
)
code_string += (
indent * (depth + 2)
+ 'parameter_dict["{}"] = {}'.format("phasevol", "phasevol")
+ "\n"
)
# Some prints. will be removed
# code_string += indent * (depth + 1) + "print(probabilities)\n"
# code_string += (
# indent * (depth + 1) + 'print("_total_starcount: ", _total_starcount)\n'
# )
# code_string += indent * (depth + 1) + "print(probability)\n"
# Increment total probability
code_string += indent * (depth + 2) + "self._increment_probtot(probability)\n"
if not dry_run:
# Handling of what is returned, or what is not.
# TODO: think of whether this is a good method
# code_string += indent * (depth + 2) + "if (self.grid_options['multiplicity'] >= 2): print('phasevol_q: ',phasevol_q); print('phasevol_log10per: ',phasevol_log10per);\n"
# code_string += indent * (depth + 2) + "print('phasevol_lnm1: ',phasevol_lnm1); print('phasevol_multiplicity: ',phasevol_multiplicity);\n"
# code_string += indent * (depth + 2) + "print(probabilities_list)\n"
# code_string += indent * (depth + 2) + "print(parameter_dict)\n"
# code_string += indent * (depth + 2) + "print('YOO IK GA LEKKER NOG EEN RONDJE')\n"
code_string += indent * (depth + 2) + "yield(parameter_dict)\n"
# If its a dry run, dont do anything with it
else:
# code_string += indent * (depth + 2) + "if (self.grid_options['multiplicity'] >= 2): print(phasevol_q)\n"
# code_string += indent * (depth + 2) + "print(parameter_dict)\n"
code_string += indent * (depth + 2) + "pass\n"
code_string += indent * (depth + 1) + "#" * 40 + "\n"
return code_string
def _load_grid_function(self):
"""
Functon that loads the script containing the grid code.
TODO: Update this description
Test function to run grid stuff. mostly to test the import
"""
# Code to load the
verbose_print(
message="Loading grid code function from {}".format(
self.grid_options["gridcode_filename"]
),
verbosity=self.grid_options["verbosity"],
minimal_verbosity=1,
)
spec = importlib.util.spec_from_file_location(
"binary_c_python_grid",
os.path.join(self.grid_options["gridcode_filename"]),
)
grid_file = importlib.util.module_from_spec(spec)
spec.loader.exec_module(grid_file)
generator = grid_file.grid_code
self.grid_options["_system_generator"] = generator
verbose_print("Grid code loaded", self.grid_options["verbosity"], 1)
def _dry_run(self):
"""
Function to dry run the grid and know how many stars it will run
Requires the grid to be built as a dry run grid
"""
system_generator = self.grid_options["_system_generator"]
total_starcount = system_generator(self)
self.grid_options["_total_starcount"] = total_starcount
def _print_info(self, run_number, total_systems, full_system_dict):
"""
Function to print info about the current system and the progress of the grid.
# color info tricks from https://ozzmaker.com/add-colour-to-text-in-python/
https://stackoverflow.com/questions/287871/how-to-print-colored-text-in-terminal-in-python
"""
# Define frequency
if self.grid_options["verbosity"] == 1:
print_freq = 1
else:
print_freq = 10
# Calculate amount of time left
# calculate amount of time passed
# time_passed = time.time() - self.grid_options["_start_time_evolution"]
if run_number % print_freq == 0:
binary_cmdline_string = self._return_argline(full_system_dict)
info_string = "{color_part_1} \
{text_part_1}{end_part_1}{color_part_2} \
{text_part_2}{end_part_2}".format(
color_part_1="\033[1;32;41m",
text_part_1="{}/{}".format(run_number, total_systems),
end_part_1="\033[0m",
color_part_2="\033[1;32;42m",
text_part_2="{}".format(binary_cmdline_string),
end_part_2="\033[0m",
)
print(info_string)
###################################################
# Montecarlo functions
#
# Functions below are used to run populations with
# Monte carlo
###################################################
###################################################
# Population from file functions
#
# Functions below are used to run populations from
# a file containing binary_c calls
###################################################
def _dry_run_source_file(self):
"""
Function to go through the source_file and count the amount of lines and the total probability
"""
system_generator = self.grid_options["_system_generator"]
total_starcount = 0
total_probability = 0
contains_probability = False
for line in system_generator:
total_starcount += 1
total_starcount = system_generator(self)
self.grid_options["_total_starcount"] = total_starcount
def _load_source_file(self, check=False):
"""
Function that loads the source_file that contains a binary_c calls
"""
if not os.path.isfile(self.grid_options["source_file_filename"]):
verbose_print("Source file doesnt exist", self.grid_options["verbosity"], 0)
verbose_print(
message="Loading source file from {}".format(
self.grid_options["gridcode_filename"]
),
verbosity=self.grid_options["verbosity"],
minimal_verbosity=1,
)
# We can choose to perform a check on the sourcefile, which checks if the lines start with 'binary_c'
if check:
source_file_check_filehandle = open(
self.grid_options["source_file_filename"], "r"
)
for line in source_file_check_filehandle:
if not line.startswith("binary_c"):
failed = True
break
if failed:
verbose_print(
"Error, sourcefile contains lines that do not start with binary_c",
self.grid_options["verbosity"],
0,
)
raise ValueError
source_file_filehandle = open(self.grid_options["source_file_filename"], "r")
self.grid_options["_system_generator"] = source_file_filehandle
verbose_print("Source file loaded", self.grid_options["verbosity"], 1)
def _dict_from_line_source_file(self, line):
"""
Function that creates a dict from a binary_c argline
"""
if line.startswith("binary_c "):
line = line.replace("binary_c ", "")
split_line = line.split()
arg_dict = {}
for i in range(0, len(split_line), 2):
if "." in split_line[i + 1]:
arg_dict[split_line[i]] = float(split_line[i + 1])
else:
arg_dict[split_line[i]] = int(split_line[i + 1])
return arg_dict
###################################################
# SLURM functions
#
# subroutines to run SLURM grids
###################################################
# def _slurm_grid(self):
# """
# Main function that manages the SLURM setup.
# Has three stages:
# - setup
# - evolve
# - join
# Which stage is used is determined by the value of grid_options['slurm_command']:
# <empty>: the function will know its the user that executed the script and
# it will set up the necessary condor stuff
# 'evolve': evolve_population is called to evolve the population of stars
# 'join': We will attempt to join the output
# """
# # Check version
# # TODO: Put in function
# slurm_version = get_slurm_version()
# if not slurm_version:
# verbose_print(
# "SLURM: Error: No installation of slurm found",
# self.grid_options["verbosity"],
# 0,
# )
# else:
# major_version = int(slurm_version.split(".")[0])
# minor_version = int(slurm_version.split(".")[1])
# if major_version > 17:
# verbose_print(
# "SLURM: Found version {} which is new enough".format(slurm_version),
# self.grid_options["verbosity"],
# 1,
# )
# else:
# verbose_print(
# "SLURM: Found version {} which is too old (we require 17+)".format(
# slurm_version
# ),
# self.grid_options["verbosity"],
# 0,
# )
# verbose_print(
# "SLURM: Running slurm grid. command={}".format(
# self.grid_options["slurm_command"]
# ),
# self.grid_options["verbosity"],
# 1,
# )
# if not self.grid_options["slurm_command"]:
# # Setting up
# verbose_print(
# "SLURM: Main controller script. Setting up",
# self.grid_options["verbosity"],
# 1,
# )
# # Set up working directories:
# verbose_print(
# "SLURM: creating working directories", self.grid_options["verbosity"], 1
# )
# create_directories_hpc(self.grid_options["slurm_dir"])
# # Create command
# python_details = get_python_details()
# scriptname = path_of_calling_script()
# command = "{} {}".format(python_details["executable"], scriptname)
# command += ' --cmdline "{}"'.format(
# " ".join(
# [
# "{}".format(self.grid_options["_commandline_input"]),
# "offset=$jobarrayindex",
# "modulo={}".format(self.grid_options["slurm_njobs"]),
# "vb={}".format(self.grid_options["verbosity"]),
# "slurm_jobid=$jobid",
# "slurm_jobarrayindex=$jobarrayindex",
# "slurm_jobname='binary_grid_'$jobid'.'$jobarrayindex",
# "slurm_njobs={}".format(self.grid_options["slurm_njobs"]),
# "slurm_dir={}".format(self.grid_options["slurm_dir"]),
# "rungrid=1",
# "slurm_command=evolve",
# ]
# ).strip()
# )
# # Construct dict with settings for the script while checking the settings at the same time
# # Check settings:
# # TODO: check settings
# # Create SLURM_DIR script:
# slurm_script_options = {}
# slurm_script_options["n"] = self.grid_options["slurm_njobs"]
# slurm_script_options["njobs"] = self.grid_options["slurm_njobs"]
# slurm_script_options["dir"] = self.grid_options["slurm_dir"]
# slurm_script_options["memory"] = self.grid_options["slurm_memory"]
# slurm_script_options["working_dir"] = self.grid_options[
# "slurm_dir"
# ] # TODO: check this
# slurm_script_options["command"] = command
# # slurm_script_options['streams'] = self.grid_options['streams']
# # Construct the script
# slurm_script_contents = ""
# slurm_script_contents += "#!/bin/bash\n"
# slurm_script_contents += "# Slurm file for binary_grid and slurm\n"
# slurm_script_contents += "#SBATCH --error={}/stderr/%A.%a\n".format(
# self.grid_options["slurm_dir"]
# )
# slurm_script_contents += "#SBATCH --output={}/stdout/%A.%a\n".format(
# self.grid_options["slurm_dir"]
# )
# slurm_script_contents += "#SBATCH --job-name={}\n".format(
# self.grid_options["slurm_jobname"]
# )
# slurm_script_contents += "#SBATCH --partition={}\n".format(
# self.grid_options["slurm_partition"]
# )
# slurm_script_contents += "#SBATCH --time={}\n".format(
# self.grid_options["slurm_time"]
# )
# slurm_script_contents += "#SBATCH --mem={}\n".format(
# self.grid_options["slurm_memory"]
# )
# slurm_script_contents += "#SBATCH --ntasks={}\n".format(
# self.grid_options["slurm_ntasks"]
# )
# slurm_script_contents += "#SBATCH --array={}\n".format(
# self.grid_options["slurm_array"]
# )
# slurm_script_contents += "\n"
# if self.grid_options["slurm_extra_settings"]:
# slurm_script_contents += "# Extra settings by user:"
# slurm_script_contents += "\n".join(
# [
# "--{}={}".format(
# key, self.grid_options["slurm_extra_settings"][key]
# )
# for key in self.grid_options["slurm_extra_settings"]
# ]
# )
# slurm_script_contents += '# set status to "running"\n'
# slurm_script_contents += (
# 'echo "running" > {}/status/$jobid.$jobarrayindex\n\n'.format(
# self.grid_options["slurm_dir"]
# )
# )
# slurm_script_contents += "# run grid of stars\n"
# slurm_script_contents += "{}\n\n".format(command)
# slurm_script_contents += '# set status to "finished"\n'
# slurm_script_contents += (
# 'echo "finished" > {}/status/$jobid.$jobarrayindex\n'.format(
# self.grid_options["slurm_dir"]
# )
# )
# slurm_script_contents += "\n"
# if self.grid_options["slurm_postpone_join"]:
# slurm_script_contents += "{} rungrid=0 results_hash_dumpfile={}/results/$jobid.all slurm_command=join\n".format(
# command, self.grid_options["slurm_dir"]
# )
# # Write script to file
# slurm_script_filename = os.path.join(
# self.grid_options["slurm_dir"], "slurm_script"
# )
# with open(slurm_script_filename, "w") as slurm_script_file:
# slurm_script_file.write(slurm_script_contents)
# # Execute or postpone
# if self.grid_options["slurm_postpone_sbatch"]:
# # Execute or postpone the real call to sbatch
# sbatch_command = "sbatch {}".format(slurm_script_filename)
# verbose_print(
# "running slurm script {}".format(slurm_script_filename),
# self.grid_options["verbosity"],
# 0,
# )
# # subprocess.Popen(sbatch_command, close_fds=True)
# # subprocess.Popen(sbatch_command, creationflags=subprocess.DETACHED_PROCESS)
# verbose_print("Submitted scripts.", self.grid_options["verbosity"], 0)
# else:
# verbose_print(
# "Slurm script is in {} but hasnt been executed".format(
# slurm_script_filename
# ),
# self.grid_options["verbosity"],
# 0,
# )
# verbose_print("all done!", self.grid_options["verbosity"], 0)
# exit()
# elif self.grid_options["slurm_command"] == "evolve":
# # Part to evolve the population.
# # TODO: decide how many CPUs
# verbose_print(
# "SLURM: Evolving population", self.grid_options["verbosity"], 1
# )
# #
# self.evolve_population()
# elif self.grid_options["slurm_command"] == "join":
# # Joining the output.
# verbose_print("SLURM: Joining results", self.grid_options["verbosity"], 1)
###################################################
# CONDOR functions
#
# subroutines to run CONDOR grids
###################################################
# def _condor_grid(self):
# """
# Main function that manages the CONDOR setup.
# Has three stages:
# - setup
# - evolve
# - join
# Which stage is used is determined by the value of grid_options['condor_command']:
# <empty>: the function will know its the user that executed the script and
# it will set up the necessary condor stuff
# 'evolve': evolve_population is called to evolve the population of stars
# 'join': We will attempt to join the output
# """
# # TODO: Put in function
# condor_version = get_condor_version()
# if not condor_version:
# verbose_print(
# "CONDOR: Error: No installation of condor found",
# self.grid_options["verbosity"],
# 0,
# )
# else:
# major_version = int(condor_version.split(".")[0])
# minor_version = int(condor_version.split(".")[1])
# if (major_version == 8) and (minor_version > 4):
# verbose_print(
# "CONDOR: Found version {} which is new enough".format(
# condor_version
# ),
# self.grid_options["verbosity"],
# 0,
# )
# elif major_version > 9:
# verbose_print(
# "CONDOR: Found version {} which is new enough".format(
# condor_version
# ),
# self.grid_options["verbosity"],
# 0,
# )
# else:
# verbose_print(
# "CONDOR: Found version {} which is too old (we require 8.3/8.4+)".format(
# condor_version
# ),
# self.grid_options["verbosity"],
# 0,
# )
# verbose_print(
# "Running Condor grid. command={}".format(
# self.grid_options["condor_command"]
# ),
# self.grid_options["verbosity"],
# 1,
# )
# if not self.grid_options["condor_command"]:
# # Setting up
# verbose_print(
# "CONDOR: Main controller script. Setting up",
# self.grid_options["verbosity"],
# 1,
# )
# # Set up working directories:
# verbose_print(
# "CONDOR: creating working directories",
# self.grid_options["verbosity"],
# 1,
# )
# create_directories_hpc(self.grid_options["condor_dir"])
# # Create command
# current_workingdir = os.getcwd()
# python_details = get_python_details()
# scriptname = path_of_calling_script()
# # command = "".join([
# # "{}".python_details['executable'],
# # "{}".scriptname,
# # "offset=$jobarrayindex",
# # "modulo={}".format(self.grid_options['condor_njobs']),
# # "vb={}".format(self.grid_options['verbosity'])
# # "results_hash_dumpfile=$self->{_grid_options}{slurm_dir}/results/$jobid.$jobarrayindex",
# # 'slurm_jobid='.$jobid,
# # 'slurm_jobarrayindex='.$jobarrayindex,
# # 'slurm_jobname=binary_grid_'.$jobid.'.'.$jobarrayindex,
# # "slurm_njobs=$njobs",
# # "slurm_dir=$self->{_grid_options}{slurm_dir}",
# # );
# # Create directory with info for the condor script. By creating this directory we also check whether all the values are set correctly
# # TODO: create the condor script.
# condor_script_options = {}
# # condor_script_options['n'] =
# condor_script_options["njobs"] = self.grid_options["condor_njobs"]
# condor_script_options["dir"] = self.grid_options["condor_dir"]
# condor_script_options["memory"] = self.grid_options["condor_memory"]
# condor_script_options["working_dir"] = self.grid_options[
# "condor_working_dir"
# ]
# condor_script_options["command"] = self.grid_options["command"]
# condor_script_options["streams"] = self.grid_options["streams"]
# # TODO: condor works with running an executable.
# # Create script contents
# condor_script_contents = ""
# condor_script_contents += """
# #################################################
# #
# # Condor script to run a binary_grid via python
# #
# #################################################
# """
# condor_script_contents += "Executable\t= {}".format(executable)
# condor_script_contents += "arguments\t= {}".format(arguments)
# condor_script_contents += "environment\t= {}".format(environment)
# condor_script_contents += "universe\t= {}".format(
# self.grid_options["condor_universe"]
# )
# condor_script_contents += "\n"
# condor_script_contents += "output\t= {}/stdout/$id\n".format(
# self.grid_options["condor_dir"]
# )
# condor_script_contents += "error\t={}/sterr/$id".format(
# self.grid_options["condor_dir"]
# )
# condor_script_contents += "log\t={}\n".format(
# self.grid_options["condor_dir"]
# )
# condor_script_contents += "initialdir\t={}\n".format(current_workingdir)
# condor_script_contents += "remote_initialdir\t={}\n".format(
# current_workingdir
# )
# condor_script_contents += "\n"
# condor_script_contents += "steam_output\t={}".format(stream)
# condor_script_contents += "steam_error\t={}".format(stream)
# condor_script_contents += "+WantCheckpoint = False"
# condor_script_contents += "\n"
# condor_script_contents += "request_memory\t={}".format(
# self.grid_options["condor_memory"]
# )
# condor_script_contents += "ImageSize\t={}".format(
# self.grid_options["condor_memory"]
# )
# condor_script_contents += "\n"
# if self.grid_options["condor_extra_settings"]:
# slurm_script_contents += "# Extra settings by user:"
# slurm_script_contents += "\n".join(
# [
# "{}\t={}".format(
# key, self.grid_options["condor_extra_settings"][key]
# )
# for key in self.grid_options["condor_extra_settings"]
# ]
# )
# condor_script_contents += "\n"
# # request_memory = $_[0]{memory}
# # ImageSize = $_[0]{memory}
# # Requirements = (1) \&\& (".
# # $self->{_grid_options}{condor_requirements}.")\n";
# #
# # file name: my_program.condor
# # Condor submit description file for my_program
# # Executable = my_program
# # Universe = vanilla
# # Error = logs/err.$(cluster)
# # Output = logs/out.$(cluster)
# # Log = logs/log.$(cluster)
# # should_transfer_files = YES
# # when_to_transfer_output = ON_EXIT
# # transfer_input_files = files/in1,files/in2
# # Arguments = files/in1 files/in2 files/out1
# # Queue
# # Write script contents to file
# if self.grid_options["condor_postpone_join"]:
# condor_script_contents += "{} rungrid=0 results_hash_dumpfile={}/results/$jobid.all condor_command=join\n".format(
# command, self.grid_options["condor_dir"]
# )
# condor_script_filename = os.path.join(
# self.grid_options["condor_dir"], "condor_script"
# )
# with open(condor_script_filename, "w") as condor_script_file:
# condor_script_file.write(condor_script_contents)
# if self.grid_options["condor_postpone_sbatch"]:
# # Execute or postpone the real call to sbatch
# submit_command = "condor_submit {}".format(condor_script_filename)
# verbose_print(
# "running condor script {}".format(condor_script_filename),
# self.grid_options["verbosity"],
# 0,
# )
# # subprocess.Popen(sbatch_command, close_fds=True)
# # subprocess.Popen(sbatch_command, creationflags=subprocess.DETACHED_PROCESS)
# verbose_print("Submitted scripts.", self.grid_options["verbosity"], 0)
# else:
# verbose_print(
# "Condor script is in {} but hasnt been executed".format(
# condor_script_filename
# ),
# self.grid_options["verbosity"],
# 0,
# )
# verbose_print("all done!", self.grid_options["verbosity"], 0)
# exit()
# elif self.grid_options["condor_command"] == "evolve":
# # TODO: write this function
# # Part to evolve the population.
# # TODO: decide how many CPUs
# verbose_print(
# "CONDOR: Evolving population", self.grid_options["verbosity"], 1
# )
# #
# self.evolve_population()
# elif self.grid_options["condor_command"] == "join":
# # TODO: write this function
# # Joining the output.
# verbose_print("CONDOR: Joining results", self.grid_options["verbosity"], 1)
# pass
###################################################
# Unordered functions
#
# Functions that arent ordered yet
###################################################
def write_binary_c_calls_to_file(
self,
output_dir: Union[str, None] = None,
output_filename: Union[str, None] = None,
include_defaults: bool = False,
) -> None:
"""
Function that loops over the gridcode and writes the generated parameters to a file.
In the form of a commandline call
Only useful when you have a variable grid as system_generator. MC wouldnt be that useful
Also, make sure that in this export there are the basic parameters
like m1,m2,sep, orb-per, ecc, probability etc.
On default this will write to the datadir, if it exists
WARNING; dont use yet. not fully tested.
Tasks:
- TODO: test this function
- TODO: make sure the binary_c_python .. output file has a unique name
Args:
output_dir: (optional, default = None) directory where to write the file to. If custom_options['data_dir'] is present, then that one will be used first, and then the output_dir
output_filename: (optional, default = None) filename of the output. If not set it will be called "binary_c_calls.txt"
include_defaults: (optional, default = None) whether to include the defaults of binary_c in the lines that are written. Beware that this will result in very long lines, and it might be better to just export the binary_c defaults and keep them in a seperate file.
"""
# Check if there is no compiled grid yet. If not, lets try to build it first.
if not self.grid_options["_system_generator"]:
## check the settings:
if self.bse_options.get("ensemble", None):
if self.bse_options["ensemble"] == 1:
if not self.bse_options.get("ensemble_defer", 0) == 1:
verbose_print(
"Error, if you want to run an ensemble in a population, the output needs to be deferred",
self.grid_options["verbosity"],
0,
)
raise ValueError
# Put in check
if len(self.grid_options["_grid_variables"]) == 0:
print("Error: you havent defined any grid variables! Aborting")
raise ValueError
#
self._generate_grid_code(dry_run=False)
#
self._load_grid_function()
# then if the _system_generator is present, we go through it
if self.grid_options["_system_generator"]:
# Check if there is an output dir configured
if self.custom_options.get("data_dir", None):
binary_c_calls_output_dir = self.custom_options["data_dir"]
# otherwise check if theres one passed to the function
else:
if not output_dir:
print(
"Error. No data_dir configured and you gave no output_dir. Aborting"
)
raise ValueError
binary_c_calls_output_dir = output_dir
# check if theres a filename passed to the function
if output_filename:
binary_c_calls_filename = output_filename
# otherwise use default value
else:
binary_c_calls_filename = "binary_c_calls.txt"
binary_c_calls_full_filename = os.path.join(
binary_c_calls_output_dir, binary_c_calls_filename
)
print("Writing binary_c calls to {}".format(binary_c_calls_full_filename))
# Write to file
with open(binary_c_calls_full_filename, "w") as file:
# Get defaults and clean them, then overwrite them with the set values.
if include_defaults:
# TODO: make sure that the defaults here are cleaned up properly
cleaned_up_defaults = self.cleaned_up_defaults
full_system_dict = cleaned_up_defaults.copy()
full_system_dict.update(self.bse_options.copy())
else:
full_system_dict = self.bse_options.copy()
for system in self.grid_options["_system_generator"](self):
# update values with current system values
full_system_dict.update(system)
binary_cmdline_string = self._return_argline(full_system_dict)
file.write(binary_cmdline_string + "\n")
else:
print("Error. No grid function found!")
raise ValueError
def _cleanup_defaults(self):
"""
Function to clean up the default values:
from a dictionary, removes the entries that have the following values:
- "NULL"
- ""
- "Function"
Uses the function from utils.functions
TODO: Rethink this functionality. seems a bit double, could also be just outside of the class
"""
binary_c_defaults = self._return_binary_c_defaults().copy()
cleaned_dict = filter_arg_dict(binary_c_defaults)
return cleaned_dict
def _clean_up_custom_logging(self, evol_type):
"""
Function to clean up the custom logging.
Has two types:
'single':
- removes the compiled shared library
(which name is stored in grid_options['_custom_logging_shared_library_file'])
- TODO: unloads/frees the memory allocated to that shared library
(which is stored in grid_options['custom_logging_func_memaddr'])
- sets both to None
'multiple':
- TODO: make this and design this
"""
if evol_type == "single":
verbose_print(
"Cleaning up the custom logging stuff. type: single",
self.grid_options["verbosity"],
1,
)
# TODO: Unset custom logging code
# TODO: Unset function memory adress
# print(self.grid_options["custom_logging_func_memaddr"])
# remove shared library files
if self.grid_options["_custom_logging_shared_library_file"]:
remove_file(
self.grid_options["_custom_logging_shared_library_file"],
self.grid_options["verbosity"],
)
if evol_type == "population":
verbose_print(
"Cleaning up the custom logging stuffs. type: population",
self.grid_options["verbosity"],
1,
)
# TODO: make sure that these also work. not fully sure if necessary tho.
# whether its a single file, or a dict of files/memaddresses
if evol_type == "MC":
pass
def _increment_probtot(self, prob):
"""
Function to add to the total probability. For now not used
"""
self.grid_options["_probtot"] += prob
def _increment_count(self):
"""
Function to add to the total amount of stars. For now not used
"""
self.grid_options["_count"] += 1
def _set_loggers(self):
"""
Function to set the loggers for the execution of the grid
"""
# Set logfile
binary_c_logfile = self.grid_options["log_file"]
# Create directory
os.makedirs(os.path.dirname(binary_c_logfile), exist_ok=True)
# Set up logger
self.logger = logging.getLogger("binary_c_python_logger")
self.logger.setLevel(self.grid_options["verbosity"])
# Reset handlers
self.logger.handlers = []
# Set formatting of output
log_formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
# Make and add filehandlers
# make handler for output to file
handler_file = logging.FileHandler(filename=os.path.join(binary_c_logfile))
handler_file.setFormatter(log_formatter)
handler_file.setLevel(logging.INFO)
# Make handler for output to stdout
handler_stdout = logging.StreamHandler(sys.stdout)
handler_stdout.setFormatter(log_formatter)
handler_stdout.setLevel(logging.INFO)
# Add the loggers
self.logger.addHandler(handler_file)
self.logger.addHandler(handler_stdout)
# def join_result_dicts(self):
# """
# Function to join the result dictionaries
# """
def _check_binary_c_error(self, binary_c_output, system_dict):
"""
Function to check whether binary_c throws an error and handle accordingly.
"""
if binary_c_output:
if (binary_c_output.splitlines()[0].startswith("SYSTEM_ERROR")) or (
binary_c_output.splitlines()[-1].startswith("SYSTEM_ERROR")
):
verbose_print(
"FAILING SYSTEM FOUND",
self.grid_options["verbosity"],
0,
)
# Keep track of the amount of failed systems and their error codes
self.grid_options["_failed_prob"] += system_dict["probability"]
self.grid_options["_failed_count"] += 1
self.grid_options["_errors_found"] = True
# Try catching the error code and keep track of the unique ones.
try:
error_code = int(
binary_c_output.splitlines()[0]
.split("with error code")[-1]
.split(":")[0]
.strip()
)
if (
not error_code
in self.grid_options["_failed_systems_error_codes"]
):
self.grid_options["_failed_systems_error_codes"].append(
error_code
)
except ValueError:
verbose_print(
"Failed to extract the error-code",
self.grid_options["verbosity"],
1,
)
# Check if we have exceeded the amount of errors
if (
self.grid_options["_failed_count"]
> self.grid_options["failed_systems_threshold"]
):
if not self.grid_options["_errors_exceeded"]:
verbose_print(
"Process {} exceeded the maximum ({}) amount of failing systems. Stopped logging them to files now".format(
self.process_ID,
self.grid_options["failed_systems_threshold"],
),
self.grid_options["verbosity"],
1,
)
self.grid_options["_errors_exceeded"] = True
# If not, write the failing systems to files unique to each process
else:
# Write arglines to file
argstring = self._return_argline(system_dict)
with open(
os.path.join(
self.grid_options["tmp_dir"],
"failed_systems",
"process_{}.txt".format(self.process_ID),
),
"a+",
) as f:
f.write(argstring + "\n")
else:
verbose_print(
"binary_c had 0 output. Which is strange",
self.grid_options["verbosity"],
2,
)
################################################################################################
def Moe_de_Stefano_2017(self, options=None):
"""
Class method of the population class
Takes a dictionary as its only argument
The dictionary should contain a file to
"""
# Require input options
if not options:
print("Please provide a options dictionary.")
raise ValueError
# TODO: put this in defaults
default_options = copy.deepcopy(moe_distefano_default_options)
# Take the option dictionary that was given and override.
options = update_dicts(default_options, options)
# Write options to a file
with open("/tmp/moeopts.dat", "w") as f:
f.write(json.dumps(options, indent=4))
json_data = get_moe_distefano_dataset(options)
# entry of log10M1 is a list containing 1 dict. We can take the dict out of the list
json_data["log10M1"] = json_data["log10M1"][0]
# Get all the masses
logmasses = sorted(json_data["log10M1"].keys())
if not logmasses:
print("The table does not contain masses")
raise ValueError
with open("/tmp/moe.log", "w") as logfile:
logfile.write("log₁₀Masses(M☉) {}\n".format(logmasses))
# Get all the periods and see if they are all consistently present
logperiods = []
for logmass in logmasses:
if not logperiods:
logperiods = sorted(json_data["log10M1"][logmass]["logP"])
dlog10P = float(logperiods[1]) - float(logperiods[0])
current_logperiods = sorted(json_data["log10M1"][logmass]["logP"])
if not (logperiods == current_logperiods):
print("Period values are not consistent throughout the dataset")
raise ValueError
############################################################
# log10period binwidth : of course this assumes a fixed
# binwidth, so we check for this too.
for i in range(len(current_logperiods) - 1):
if not dlog10P == (
float(current_logperiods[i + 1]) - float(current_logperiods[i])
):
print("Period spacing is not consistent throughout the dataset")
raise ValueError
with open("/tmp/moe.log", "a") as logfile:
logfile.write("log₁₀Periods(days) {}\n".format(logperiods))
# Fill the global dict
for logmass in logmasses:
# print("Logmass: {}".format(logmass))
# Create the multiplicity table
if not Moecache.get("multiplicity_table", None):
Moecache["multiplicity_table"] = []
# multiplicity as a function of primary mass
Moecache["multiplicity_table"].append(
[
float(logmass),
json_data["log10M1"][logmass]["f_multi"],
json_data["log10M1"][logmass]["single star fraction"],
json_data["log10M1"][logmass]["binary star fraction"],
json_data["log10M1"][logmass]["triple/quad star fraction"],
]
)
print("Size multiplicity table: {}", len(Moecache["multiplicity_table"]))
############################################################
# a small log10period which we can shift just outside the
# table to force integration out there to zero
epslog10P = 1e-8 * dlog10P
############################################################
# loop over either binary or triple-outer periods
first = 1
# Go over the periods
for logperiod in logperiods:
# print("logperiod: {}".format(logperiod))
############################################################
# distributions of binary and triple star fractions
# as a function of mass, period.
#
# Note: these should be per unit log10P, hence we
# divide by $dlog10P
if first:
first = 0
# Create the multiplicity table
if not Moecache.get("period_distributions", None):
Moecache["period_distributions"] = []
############################################################
# lower bound the period distributions to zero probability
Moecache["period_distributions"].append(
[
float(logmass),
float(logperiod) - 0.5 * dlog10P - epslog10P,
0.0,
0.0,
]
)
Moecache["period_distributions"].append(
[
float(logmass),
float(logperiod) - 0.5 * dlog10P,
json_data["log10M1"][logmass]["logP"][logperiod][
"normed_bin_frac_p_dist"
]
/ dlog10P,
json_data["log10M1"][logmass]["logP"][logperiod][
"normed_tripquad_frac_p_dist"
]
/ dlog10P,
]
)
Moecache["period_distributions"].append(
[
float(logmass),
float(logperiod),
json_data["log10M1"][logmass]["logP"][logperiod][
"normed_bin_frac_p_dist"
]
/ dlog10P,
json_data["log10M1"][logmass]["logP"][logperiod][
"normed_tripquad_frac_p_dist"
]
/ dlog10P,
]
)
print(
"Size period_distributions table: {}",
len(Moecache["period_distributions"]),
)
############################################################
# distributions as a function of mass, period, q
#
# First, get a list of the qs given by Moe
#
qs = sorted(json_data["log10M1"][logmass]["logP"][logperiod]["q"])
qdata = {}
I_q = 0.0
# get the q distribution from M&S, which ranges 0.15 to 0.95
for q in qs:
val = json_data["log10M1"][logmass]["logP"][logperiod]["q"][q]
qdata[q] = val
I_q += val
# hence normalize to 1.0
for q in qs:
qdata[q] = qdata[q] / I_q
# Create the multiplicity table
if not Moecache.get("q_distributions", None):
Moecache["q_distributions"] = []
for q in qs:
Moecache["q_distributions"].append(
[float(logmass), float(logperiod), float(q), qdata[q]]
)
############################################################
# eccentricity distributions as a function of mass, period, ecc
eccs = sorted(json_data["log10M1"][logmass]["logP"][logperiod]["e"])
ecc_data = {}
I_e = 0
# Read out the data
for ecc in eccs:
val = json_data["log10M1"][logmass]["logP"][logperiod]["e"][ecc]
ecc_data[ecc] = val
I_e += val
# Normalize the data
for ecc in eccs:
ecc_data[ecc] = ecc_data[ecc] / I_e
# Create the multiplicity table
if not Moecache.get("ecc_distributions", None):
Moecache["ecc_distributions"] = []
for ecc in eccs:
Moecache["ecc_distributions"].append(
[float(logmass), float(logperiod), float(ecc), ecc_data[ecc]]
)
############################################################
# upper bound the period distributions to zero probability
Moecache["period_distributions"].append(
[
float(logmass),
float(logperiods[-1]) + 0.5 * dlog10P,
json_data["log10M1"][logmass]["logP"][logperiods[-1]][
"normed_bin_frac_p_dist"
]
/ dlog10P,
json_data["log10M1"][logmass]["logP"][logperiods[-1]][
"normed_tripquad_frac_p_dist"
]
/ dlog10P,
]
)
Moecache["period_distributions"].append(
[
float(logmass),
float(logperiods[-1]) + 0.5 * dlog10P + epslog10P,
0.0,
0.0,
]
)
print(
"Size period_distributions table: {}".format(
len(Moecache["period_distributions"])
)
)
# Write to logfile
with open("/tmp/moecache.json", "w") as cache_filehandle:
cache_filehandle.write(json.dumps(Moecache, indent=4))
# TODO: remove this and make the plotting of the multiplicity fractions more modular
# options['M1'] = 10
# # multiplicity_fractions = Moe_de_Stefano_2017_multiplicity_fractions(options)
# import matplotlib.pyplot as plt
# mass_range = range(1, 80)
# multiplicity_dict = {}
# for mass in mass_range:
# options["M1"] = mass
# multiplicity_fractions = Moe_de_Stefano_2017_multiplicity_fractions(options)
# multiplicity_dict[mass] = multiplicity_fractions
# single_values = [multiplicity_dict[key][0] for key in multiplicity_dict.keys()]
# binary_values = [multiplicity_dict[key][1] for key in multiplicity_dict.keys()]
# triple_values = [multiplicity_dict[key][2] for key in multiplicity_dict.keys()]
# quad_values = [multiplicity_dict[key][3] for key in multiplicity_dict.keys()]
# print("mass = {}".format(list(mass_range)))
# print("single_values={}".format(single_values))
# print("binary_values={}".format(binary_values))
# print("triple_values={}".format(triple_values))
# print("quad_values={}".format(quad_values))
# plt.plot(mass_range, single_values, label="single")
# plt.plot(mass_range, binary_values, label="binary")
# plt.plot(mass_range, triple_values, label="triple")
# plt.plot(mass_range, quad_values, label="Quadruple")
# plt.legend()
# plt.xscale("log")
# plt.show()
############################################################
# construct the grid here
#
############################################################
# make a version of the options which can be passed to
# the probability density function in the gridcode
# TODO: check if that works, if we just pass the options as a dict
############################################################
# first, the multiplicity, this is 1,2,3,4, ...
# for singles, binaries, triples, quadruples, ...
max_multiplicity = 0
for i in range(1, 5):
mod = options["multiplicity_modulator"][i - 1]
print("Check mod {} = {}".format(i, mod))
if mod > 0:
max_multiplicity = i
print("Max multiplicity = {}".format(max_multiplicity))
######
# Setting up the grid variables
# Multiplicity
self.add_grid_variable(
name="multiplicity",
parameter_name="multiplicity",
longname="multiplicity",
valuerange=[1, max_multiplicity],
resolution=1,
spacingfunc="range(1, 5)",
precode='self.grid_options["multiplicity"] = multiplicity; options={}'.format(
options
),
condition="({}[multiplicity-1] > 0)".format(
str(options["multiplicity_modulator"])
),
gridtype="edge",
dphasevol=-1,
probdist=1,
)
############################################################
# always require M1, for all systems
#
# TODO: put in option for the time-adaptive grid.
# log-spaced m1 with given resolution
self.add_grid_variable(
name="lnm1",
parameter_name="M_1",
longname="Primary mass",
resolution="options['resolutions']['M'][0]",
spacingfunc="const(np.log({}), np.log({}), {})".format(
options["ranges"]["M"][0],
options["ranges"]["M"][1],
options["resolutions"]["M"][0],
),
valuerange=[
"np.log({})".format(options["ranges"]["M"][0]),
"np.log({})".format(options["ranges"]["M"][1]),
],
gridtype="centred",
dphasevol="dlnm1",
precode='M_1 = np.exp(lnm1); options["M1"]=M_1',
probdist="Moe_de_Stefano_2017_pdf({{{}, {}, {}}}) if multiplicity == 1 else 1".format(
str(options)[1:-1], "'multiplicity': multiplicity", "'M1': M_1"
),
)
# Go to higher multiplicities
if max_multiplicity >= 2:
# binaries: period
self.add_grid_variable(
name="log10per",
parameter_name="orbital_period",
longname="log10(Orbital_Period)",
resolution=options["resolutions"]["logP"][0],
probdist=1.0,
condition='(self.grid_options["multiplicity"] >= 2)',
# branchpoint=1,
gridtype="centred",
dphasevol="({} * dlog10per)".format(LOG_LN_CONVERTER),
valuerange=[options["ranges"]["logP"][0], options["ranges"]["logP"][1]],
spacingfunc="const({}, {}, {})".format(
options["ranges"]["logP"][0],
options["ranges"]["logP"][1],
options["resolutions"]["logP"][0],
),
precode="""orbital_period = 10.0**log10per
qmin={}/M_1
qmax=maximum_mass_ratio_for_RLOF(M_1, orbital_period)
""".format(
options.get("Mmin", 0.07)
),
)
# binaries: mass ratio
self.add_grid_variable(
name="q",
parameter_name="M_2",
longname="Mass ratio",
valuerange=[
options["ranges"]["q"][0]
if options.get("ranges", {}).get("q", None)
else "options.get('Mmin', 0.07)/M_1",
options["ranges"]["q"][1]
if options.get("ranges", {}).get("q", None)
else "qmax",
],
resolution=options["resolutions"]["M"][1],
probdist=1,
gridtype="centred",
dphasevol="dq",
precode="""
M_2 = q * M_1
sep = calc_sep_from_period(M_1, M_2, orbital_period)
""",
spacingfunc="const({}, {}, {})".format(
options["ranges"]["q"][0]
if options.get("ranges", {}).get("q", [None, None])[0]
else "{}/M_1".format(options.get("Mmin", 0.07)),
options["ranges"]["q"][1]
if options.get("ranges", {}).get("q", [None, None])[1]
else "qmax",
options["resolutions"]["M"][1],
),
)
# (optional) binaries: eccentricity
if options["resolutions"]["ecc"][0] > 0:
self.add_grid_variable(
name="ecc",
parameter_name="eccentricity",
longname="Eccentricity",
resolution=options["resolutions"]["ecc"][0],
probdist=1,
gridtype="centred",
dphasevol="decc",
precode="eccentricity=ecc",
valuerange=[
options["ranges"]["ecc"][0], # Just fail if not defined.
options["ranges"]["ecc"][1],
],
spacingfunc="const({}, {}, {})".format(
options["ranges"]["ecc"][0], # Just fail if not defined.
options["ranges"]["ecc"][1],
options["resolutions"]["ecc"][0],
),
)
# Now for triples and quadruples
if max_multiplicity >= 3:
# Triple: period
self.add_grid_variable(
name="log10per2",
parameter_name="orbital_period_triple",
longname="log10(Orbital_Period2)",
resolution=options["resolutions"]["logP"][1],
probdist=1.0,
condition='(self.grid_options["multiplicity"] >= 3)',
branchpoint=1,
gridtype="centred",
dphasevol="({} * dlog10per2)".format(LOG_LN_CONVERTER),
valuerange=[
options["ranges"]["logP"][0],
options["ranges"]["logP"][1],
],
spacingfunc="const({}, {}, {})".format(
options["ranges"]["logP"][0],
options["ranges"]["logP"][1],
options["resolutions"]["logP"][1],
),
precode="""orbital_period_triple = 10.0**log10per2
q2min={}/(M_1+M_2)
q2max=maximum_mass_ratio_for_RLOF(M_1+M_2, orbital_period_triple)
""".format(
options.get("Mmin", 0.07)
),
)
# Triples: mass ratio
# Note, the mass ratio is M_outer/M_inner
print("M2 resolution {}".format(options["resolutions"]["M"][2]))
self.add_grid_variable(
name="q2",
parameter_name="M_3",
longname="Mass ratio outer/inner",
valuerange=[
options["ranges"]["q"][0]
if options.get("ranges", {}).get("q", None)
else "options.get('Mmin', 0.07)/(M_1+M_2)",
options["ranges"]["q"][1]
if options.get("ranges", {}).get("q", None)
else "q2max",
],
resolution=options["resolutions"]["M"][2],
probdist=1,
gridtype="centred",
dphasevol="dq2",
precode="""
M_3 = q2 * (M_1 + M_2)
sep2 = calc_sep_from_period((M_1+M_2), M_3, orbital_period_triple)
eccentricity2=0
""",
spacingfunc="const({}, {}, {})".format(
options["ranges"]["q"][0]
if options.get("ranges", {}).get("q", None)
else "options.get('Mmin', 0.07)/(M_1+M_2)",
options["ranges"]["q"][1]
if options.get("ranges", {}).get("q", None)
else "q2max",
options["resolutions"]["M"][2],
),
)
# (optional) triples: eccentricity
if options["resolutions"]["ecc"][1] > 0:
self.add_grid_variable(
name="ecc2",
parameter_name="eccentricity2",
longname="Eccentricity of the triple",
resolution=options["resolutions"]["ecc"][1],
probdist=1,
gridtype="centred",
dphasevol="decc2",
precode="eccentricity2=ecc2",
valuerange=[
options["ranges"]["ecc"][0], # Just fail if not defined.
options["ranges"]["ecc"][1],
],
spacingfunc="const({}, {}, {})".format(
options["ranges"]["ecc"][0], # Just fail if not defined.
options["ranges"]["ecc"][1],
options["resolutions"]["ecc"][1],
),
)
if max_multiplicity == 4:
# Quadruple: period
self.add_grid_variable(
name="log10per3",
parameter_name="orbital_period_quadruple",
longname="log10(Orbital_Period3)",
resolution=options["resolutions"]["logP"][2],
probdist=1.0,
condition='(self.grid_options["multiplicity"] >= 4)',
branchpoint=1,
gridtype="centred",
dphasevol="({} * dlog10per3)".format(LOG_LN_CONVERTER),
valuerange=[
options["ranges"]["logP"][0],
options["ranges"]["logP"][1],
],
spacingfunc="const({}, {}, {})".format(
options["ranges"]["logP"][0],
options["ranges"]["logP"][1],
options["resolutions"]["logP"][2],
),
precode="""orbital_period_quadruple = 10.0**log10per3
q3min={}/(M_3)
q3max=maximum_mass_ratio_for_RLOF(M_3, orbital_period_quadruple)
""".format(
options.get("Mmin", 0.07)
),
)
# Quadruple: mass ratio : M_outer / M_inner
print("M3 resolution {}".format(options["resolutions"]["M"][3]))
self.add_grid_variable(
name="q3",
parameter_name="M_4",
longname="Mass ratio outer low/outer high",
valuerange=[
options["ranges"]["q"][0]
if options.get("ranges", {}).get("q", None)
else "options.get('Mmin', 0.07)/(m3)",
options["ranges"]["q"][1]
if options.get("ranges", {}).get("q", None)
else "q3max",
],
resolution=options["resolutions"]["M"][3],
probdist=1,
gridtype="centred",
dphasevol="dq3",
precode="""
M_4 = q3 * M_3
sep3 = calc_sep_from_period((M_3), M_4, orbital_period_quadruple)
eccentricity3=0
""",
spacingfunc="const({}, {}, {})".format(
options["ranges"]["q"][0]
if options.get("ranges", {}).get("q", None)
else "options.get('Mmin', 0.07)/(m3)",
options["ranges"]["q"][1]
if options.get("ranges", {}).get("q", None)
else "q3max",
options["resolutions"]["M"][2],
),
)
# TODO: Ask about which periods should be used for the calc_sep_from_period
# (optional) triples: eccentricity
if options["resolutions"]["ecc"][2] > 0:
self.add_grid_variable(
name="ecc3",
parameter_name="eccentricity3",
longname="Eccentricity of the triple+quadruple/outer binary",
resolution=options["resolutions"]["ecc"][2],
probdist=1,
gridtype="centred",
dphasevol="decc3",
precode="eccentricity3=ecc3",
valuerange=[
options["ranges"]["ecc"][
0
], # Just fail if not defined.
options["ranges"]["ecc"][1],
],
spacingfunc="const({}, {}, {})".format(
options["ranges"]["ecc"][
0
], # Just fail if not defined.
options["ranges"]["ecc"][1],
options["resolutions"]["ecc"][2],
),
)
# Now we are at the last part.
# Here we should combine all the information that we calculate and update the options
# dictionary. This will then be passed to the Moe_de_Stefano_2017_pdf to calculate
# the real probability. The trick we use is to strip the options_dict as a string
# and add some keys to it:
probdist_addition = "Moe_de_Stefano_2017_pdf({{{}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}}})".format(
str(options)[1:-1],
'"multiplicity": multiplicity',
'"M1": M_1',
'"M2": M_2',
'"M3": M_3',
'"M4": M_4',
'"P": orbital_period',
'"P2": orbital_period_triple',
'"P3": orbital_period_quadruple',
'"ecc": eccentricity',
'"ecc2": eccentricity2',
'"ecc3": eccentricity3',
)
# and finally the probability calculator
self.grid_options["_grid_variables"][self.last_grid_variable()][
"probdist"
] = probdist_addition