Noe Brucy
1373 lines
45 KiB
Python
1373 lines
45 KiB
Python
# coding: utf-8
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import sys
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import os
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import glob as glob
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import tables
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import pymses
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import numpy as np
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from numpy.polynomial.polynomial import polyfit
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from scipy.stats import linregress
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from pymses.sources.ramses import output
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from pymses.sources.hop.file_formats import *
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from pymses.analysis import Camera, raytracing, slicing, splatting
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from pymses.filters import CellsToPoints
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from pymses.analysis import ScalarOperator, FractionOperator, MaxLevelOperator
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from mypool import MyPool as Pool
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from functools import partial
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from abc import ABCMeta, abstractmethod
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import contextlib
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import bunch
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from run_selector import *
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from units import *
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class Rule:
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def __init__(
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self,
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postproc,
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process,
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description="",
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group="",
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dependencies=[],
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is_valid=lambda arg: True,
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kind="classic",
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unit=cst.none,
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):
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self.postproc = postproc
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self.process_fn = process
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self.dependencies = dependencies
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self.is_valid_add = is_valid
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self.group = group
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self.description = description
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self.unit = unit
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self.kind = kind
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def process(self, arg, **kwargs):
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if not arg is None:
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return self.process_fn(arg, **kwargs)
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else:
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return self.process_fn(**kwargs)
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def is_valid(self, arg):
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save = self.postproc.save
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valid = True
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for dep in self.dependencies:
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if dep in self.postproc.rules:
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rule_dep = self.postproc.rules[dep]
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if not arg is None:
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valid = (
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valid and rule_dep.group + "/" + dep + "_" + str(arg) in save
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)
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else:
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valid = valid and rule_dep.group + "/" + dep in save
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return valid and self.is_valid_add(arg)
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class BaseProcessor:
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"""
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Base class for processors, should not be instanciated
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"""
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__metaclass__ = ABCMeta
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log_id = ""
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rules = {}
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solve_self_dep = True
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def __init__(self, path, path_out=None, pp_params=None, tag=None):
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if pp_params is None:
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self.pp_params = default_params()
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elif type(pp_params) == str:
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self.pp_params = load_params(pp_params)
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else:
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self.pp_params = pp_params
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if tag is not None:
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self.pp_params.out.tag = tag
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# Determining output directory
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if path_out is None:
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self.path_out = path
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else:
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self.path_out = path_out
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def _log(self, string, status=""):
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if self.pp_params.process.verbose:
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if len(status) > 0:
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print(status + ": " + self.log_id + string)
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else:
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print(self.log_id + string)
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def process(
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self,
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to_process_list,
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args=[None],
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overwrite=False,
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overwrite_dep=False,
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**kwargs
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):
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"""
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Render the data in to_process_list and save them
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"""
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if type(to_process_list) == str:
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to_process_list = [to_process_list]
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if type(args) == str or args is None:
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args = [args]
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self.overwrite_dep = overwrite_dep
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self.just_done = [] # Computations done within this call
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for name in to_process_list:
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if name in self.rules:
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rule = self.rules[name]
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for arg in args:
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self._solve_and_process_rule(name, rule, arg, overwrite, **kwargs)
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else:
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self._log(
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"{} is unknown, allowed rules are {}".format(
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name, self.rules.keys()
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),
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"ERROR",
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)
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return self.just_done
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def _solve_and_process_rule(self, name, rule, arg, overwrite=False, **kwargs):
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updated = self._solve_dependencies(name, rule, arg, overwrite, **kwargs)
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overwrite_rule = overwrite or updated
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self._process_rule(name, rule, arg, overwrite, **kwargs)
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def _solve_dependencies(self, name, rule, arg, overwrite=False, **kwargs):
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self.done_before_dep = len(self.just_done)
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if type(rule.dependencies) == dict:
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dep_arg = rule.dependencies[dep]
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else:
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dep_arg = arg
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# Solve dependencies
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for dep in rule.dependencies:
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if self.solve_self_dep and dep in self.rules:
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rule_dep = self.rules[dep]
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self._solve_and_process_rule(dep, rule_dep, dep_arg, self.overwrite_dep)
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else:
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self._not_self_dep(name, dep, dep_arg, self.overwrite_dep, **kwargs)
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# Whether dependencies where updated
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return len(self.just_done) > self.done_before_dep
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def _not_self_dep(self, name, dep, dep_arg, overwrite, **kwargs):
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self._log("Dependency {} for {} is unknown".format(dep, name), "ERROR")
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def _needs_computation(self, overwrite, name_full):
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return overwrite or not (name_full in self.save)
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def _process_rule(self, name, rule, arg, overwrite=False, **kwargs):
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if not arg is None:
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name_full = rule.group + "/" + name + "_" + str(arg)
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else:
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name_full = rule.group + "/" + name
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if rule.is_valid(arg):
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if not name_full in self.just_done:
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if self._needs_computation(overwrite, name_full):
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self._log("Processing {}".format(name_full))
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data = rule.process(arg, **kwargs)
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self._save_data(name_full, data, rule.description, rule.unit)
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self._log("Data for {} computed".format(name_full), "SUCCESS")
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self.just_done.append(name_full)
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else:
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self._log(
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"Data for {} is already computed, skipping...".format(name_full)
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)
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else:
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self._log("{} is not valid in this context".format(name_full), "ERROR")
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def _save_data(self, name_full, data, description, unit):
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"""
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Save data in the HDF5 structure, overwrite if necessary
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"""
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if name_full in self.save:
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self.save.remove_node(name_full, recursive=True)
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if type(data) == dict:
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if type(description) == str:
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self.save.create_group(
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os.path.dirname(name_full),
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os.path.basename(name_full),
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description,
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createparents=True,
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)
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else:
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self.save.create_group(
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os.path.dirname(name_full),
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os.path.basename(name_full),
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"",
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createparents=True,
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)
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if not type(unit) == dict:
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self.save.get_node(name_full)._v_attrs.unit = unit
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for key in data:
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if type(description) == dict:
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if type(unit) == dict:
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self._save_data(
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name_full + "/" + key,
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data[key],
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description[key],
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unit[key],
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)
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else:
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self._save_data(
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name_full + "/" + key, data[key], description[key], unit
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)
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else:
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if type(unit) == dict:
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self._save_data(name_full + "/" + key, data[key], "", unit[key])
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else:
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self._save_data(name_full + "/" + key, data[key], "", unit)
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else:
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self.save.create_array(
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os.path.dirname(name_full),
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os.path.basename(name_full),
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data,
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description,
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createparents=True,
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)
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self.save.get_node(name_full).attrs.unit = unit
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@abstractmethod
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def def_rules(self):
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pass
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class HDF5Container(BaseProcessor):
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filename = ""
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save = None
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opened = False
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def open(self):
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if not self.opened:
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self.save = tables.open_file(self.filename, mode="a")
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self.opened = True
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def close(self):
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if self.opened:
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self.save.close()
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self.opened = False
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def _process_rule(self, name, rule, arg, overwrite, **kwargs):
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self.open()
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try:
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super(HDF5Container, self)._process_rule(
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name, rule, arg, overwrite, **kwargs
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)
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finally:
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self.close()
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def get_value(self, node_name):
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self.open()
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try:
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node = self.save.get_node(node_name)
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if node._v_attrs.CLASS == "GROUP":
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value = {}
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for child_name in node._v_children:
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value[child_name] = self.get_value(node_name + "/" + child_name)
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else:
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value = node.read()
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finally:
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self.close()
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return value
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def get_attribute(self, node_name, attr_name):
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self.open()
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try:
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node = self.save.get_node(node_name)
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attr = node._v_attrs[attr_name]
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finally:
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self.close()
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return attr
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def _map_rule(rule, arg, overwrite, path, path_out, pp_params, run_num):
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pp = PostProcessor(
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path + "/" + run_num[0], run_num[1], path_out + "/" + run_num[0], pp_params
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)
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return pp.process(rule, arg, overwrite)
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class Aggregator:
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def _not_self_dep(self, name, dep, dep_arg, overwrite, **kwargs):
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if "runs" in kwargs:
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dep_runs = [run for run in self.runs if run in kwargs["runs"]]
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else:
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dep_runs = self.runs
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pps = [[self.pp_runs[run][num] for num in self.nums[run]] for run in dep_runs]
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run_num = [(run, num) for run in dep_runs for num in self.nums[run]]
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map_fn = partial(
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_map_rule, dep, dep_arg, overwrite, self.path, self.path_out, self.pp_params
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)
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if self.pp_params.process.num_process > 1:
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pool = Pool(processes=self.pp_params.process.num_process)
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done = pool.map(map_fn, run_num)
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pool.close()
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pool.join()
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else:
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done = map(map_fn, run_num)
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self.just_done.extend([item for li in done for item in li])
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def simple_getter(name, dset):
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return dset[name]
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mass_func = lambda dset: dset["rho"] * dset.get_sizes() ** 3 # Mass function
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class PostProcessor(HDF5Container):
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"""
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This class enable to compute and save derived quantities from the raw output
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"""
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# Axes information
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_ax_nb = {"x": 0, "y": 1, "z": 2} # Number of each axes
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_axes_h = {"x": "y", "y": "x", "z": "x"} # Associated horizontal axe
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_axes_v = {"x": "z", "y": "z", "z": "y"} # Associated vertical axe
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G = 1.0 # Gravitational constant
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cells_loaded = False
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def __init__(
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self,
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path=None,
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num=None,
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path_out=None,
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pp_params=None,
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tag=None,
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variables=["rho", "vel", "Br", "Bl", "P", "g", "phi"],
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):
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"""
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Creates the basic structures needed for the outputs
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"""
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super(PostProcessor, self).__init__(path, path_out, pp_params, tag)
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# Open outfile
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if not self.pp_params.out.tag == "":
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tag_name = self.pp_params.out.tag + "_"
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else:
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tag_name = ""
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self.filename = path_out + "/postproc_" + tag_name + format(num, "05") + ".h5"
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if not os.path.exists(path_out):
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os.makedirs(path_out)
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self.open()
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# Ramses Output
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self.path = path
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self.run = os.path.basename(path)
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self.num = num
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self._ro = pymses.RamsesOutput(path, num, order=pp_params.pymses.order)
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self.variables = variables
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self._amr = self._ro.amr_source(self.variables)
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self.info = self._ro.info.copy()
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# Density operator
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self._rho_op = ScalarOperator(
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lambda dset: dset["rho"], self._ro.info["unit_density"]
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)
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# Density ray tracer
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if pp_params.pymses.fft:
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self._rt = splatting.SplatterProcessor(
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self._amr, self._ro.info, self._rho_op
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)
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else:
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self._rt = raytracing.RayTracer(self._amr, self._ro.info, self._rho_op)
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# Set the extend of the image
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self._radius = 0.5 / pp_params.out.zoom
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self._lbox = self.info["boxlen"]
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center = pp_params.out.center
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im_extent = [
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(-self._radius + center[0]) * self._lbox,
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(self._radius + center[0]) * self._lbox,
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(-self._radius + center[1]) * self._lbox,
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(self._radius + center[1]) * self._lbox,
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]
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# Get time
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time = self._ro.info["time"] # time in codeunits
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# Set post processing attributes
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self.save.root._v_attrs.dir = os.path.dirname(path)
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self.save.root._v_attrs.run = os.path.basename(path)
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self.save.root._v_attrs.num = num
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self.save.root._v_attrs.lbox = self._lbox
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self.save.root._v_attrs.time = time
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if not "/maps" in self.save:
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self.save.create_group("/", "maps", "2D maps")
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self.save.root.maps._v_attrs.center = center
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self.save.root.maps._v_attrs.radius = self._radius
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self.save.root.maps._v_attrs.im_extent = im_extent
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# Initialize cameras
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self._cam = {}
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for ax_los in self._ax_nb: # los = line of sight
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ax_h = self._axes_h[ax_los]
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ax_v = self._axes_v[ax_los]
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self._cam[ax_los] = Camera(
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center=pp_params.out.center,
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line_of_sight_axis=ax_los,
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region_size=[2.0 * self._radius, 2.0 * self._radius],
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distance=self._radius,
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far_cut_depth=self._radius,
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up_vector=ax_v,
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map_max_size=pp_params.out.map_size,
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)
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self._add_metadata()
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self.close()
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self.log_id = "[{}, {}] ".format(self.run, self.num)
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self.def_rules()
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def _add_metadata(self):
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"""
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Add additional metadata to the file
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"""
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# Label of the run in the label.txt file
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label_filename = self.path + "/" + self.pp_params.input.label_filename
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if os.path.exists(label_filename):
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label_file = open(label_filename, "r")
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self.label = label_file.readline()
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label_file.close()
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else:
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self.label = self.run
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self.save.root._v_attrs.label = self.label
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# def open_pymlog(self):
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# if self.pp_params.pymses.verbose:
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# return sys.stdout
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# else:
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# return open(os.devnull, "w")
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def load_cells(self):
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if not self.cells_loaded:
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# with self.open_pymlog() as f, contextlib.redirect_stdout(f):
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cell_source = CellsToPoints(self._amr)
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self.cells = cell_source.flatten()
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self.cells_loaded = True
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def unload_cells(self):
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if self.cells_loaded:
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del self.cells
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self.cells_loaded = False
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def _slice(self, getter, ax_los="z", z=0, unit=cst.none):
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op = ScalarOperator(getter, unit)
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datamap = slicing.SliceMap(self._amr, self._cam[ax_los], op, z=z)
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return datamap.map.T
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def _ax_avg(
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self, getter, ax_los, unit=cst.none, mass_weighted=True, surf_qty=False
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):
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if mass_weighted:
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def num(cells):
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value = getter(cells)
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mass = mass_func(cells)
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# Transpose (.T) is for vectorial values
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return (mass * value.T).T
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op = FractionOperator(num, mass_function, unit)
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else:
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op = ScalarOperator(getter, unit)
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if pp_params.pymses.fft:
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rt = splatting.SplatterProcessor(self._amr, self._ro.info, op)
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else:
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rt = raytracing.RayTracer(self._amr, self._ro.info, op)
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datamap = rt.process(self._cam[ax_los], surf_qty=surf_qty)
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return datamap.map.T
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def _vol_avg(self, getter, mass_weighted=True):
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self.load_cells()
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value = getter(self.cells)
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if mass_weighted:
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mass = mass_func(self.cells)
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# Transpose (.T) is for vectorial values
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return np.sum((mass * value.T).T, axis=0) / np.sum(mass)
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else:
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return np.sum(value, axis=0)
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def _mwa_sigma(self):
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mw_speed = self.save.get_node("/globals/mwa_speed").read()
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def getter(dset):
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return np.sum((dset["vel"] - mw_speed) ** 2, axis=1)
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return np.sqrt(self._vol_avg(getter, mass_weighted=True))
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|
def _coldens(self, ax_los):
|
|
datamap = self._rt.process(self._cam[ax_los], surf_qty=True)
|
|
return datamap.map.T
|
|
|
|
def _rho(self, ax_los, z=0.0):
|
|
datamap_rho = slicing.SliceMap(self._amr, self._cam[ax_los], self._rho_op, z=z)
|
|
return (datamap_rho.map).T
|
|
|
|
def _speed_h(self, ax_los, z=0.0):
|
|
vh_op = ScalarOperator(
|
|
lambda dset: dset["vel"][:, self._ax_nb[self._axes_h[ax_los]]],
|
|
self._ro.info["unit_velocity"],
|
|
)
|
|
dmap_vh = slicing.SliceMap(self._amr, self._cam[ax_los], vh_op, z=z).map.T
|
|
return dmap_vh
|
|
|
|
def _speed_v(self, ax_los, z=0.0):
|
|
vv_op = ScalarOperator(
|
|
lambda dset: dset["vel"][:, self._ax_nb[self._axes_v[ax_los]]],
|
|
self._ro.info["unit_velocity"],
|
|
)
|
|
dmap_vv = slicing.SliceMap(self._amr, self._cam[ax_los], vv_op, z=z).map.T
|
|
return dmap_vv
|
|
|
|
def _temperature(self, ax_los, z=0.0):
|
|
P_op = ScalarOperator(lambda dset: dset["P"], self._ro.info["unit_pressure"])
|
|
dmap_P = (slicing.SliceMap(self._amr, self._cam[ax_los], P_op, z=z)).map.T
|
|
dmap_rho = self.save.get_node("/maps/rho_{}".format(ax_los)).read()
|
|
return dmap_P / dmap_rho
|
|
|
|
def _levels(self, ax_los):
|
|
self._amr.set_read_levelmax(20)
|
|
level_op = MaxLevelOperator()
|
|
rt_level = raytracing.RayTracer(self._amr, self._ro.info, level_op)
|
|
datamap = rt_level.process(self._cam[ax_los], surf_qty=True)
|
|
return datamap.map.T
|
|
|
|
def _jeans(self, ax_los):
|
|
dmap_T = self.save.get_node("/maps/T_" + ax_los).read()
|
|
dmap_rho = self.save.get_node("/maps/rho_" + ax_los).read()
|
|
dmap_jeans = np.sqrt(np.pi * dmap_T / dmap_rho)
|
|
return dmap_jeans
|
|
|
|
def _jeans_ratio(self, ax_los):
|
|
dmap_jeans = self.save.get_node("/maps/jeans_" + ax_los).read()
|
|
dmap_levels = self.save.get_node("/maps/levels_" + ax_los).read()
|
|
dmap_jeans_ratio = dmap_jeans * 2 ** (dmap_levels)
|
|
return dmap_jeans_ratio
|
|
|
|
def _toomreQ_disk(self, ax_los):
|
|
"""
|
|
Compute the Toomre Q parameter in a Keplerian disk
|
|
"""
|
|
|
|
# Operator to compute the angular speed times rho
|
|
def omega_rho_func(dset):
|
|
pos = dset.get_cell_centers()
|
|
pos = pos - (self.pp_params.disk.pos_star / self._lbox)
|
|
xx = pos[:, :, 0]
|
|
yy = pos[:, :, 1]
|
|
rc = np.sqrt(xx ** 2 + yy ** 2) # cylindrical radius
|
|
vx = dset["vel"][:, :, 0]
|
|
vy = dset["vel"][:, :, 1]
|
|
omega_rho = 1.0 / rc ** 2
|
|
omega_rho = omega_rho * dset["rho"]
|
|
vyx = vy * xx
|
|
vxy = vx * yy
|
|
omega_rho = omega_rho * (vyx - vxy)
|
|
return omega_rho
|
|
|
|
# Operator to compute the angular speed
|
|
omega_op = FractionOperator(
|
|
omega_rho_func, lambda dset: dset["rho"], 1.0 / self._ro.info["unit_time"]
|
|
)
|
|
|
|
# Operator to compute the sound speed
|
|
cs_op = FractionOperator(
|
|
lambda dset: dset["P"],
|
|
lambda dset: dset["rho"],
|
|
self._ro.info["unit_velocity"],
|
|
)
|
|
|
|
# Ray tracer for the angular speed
|
|
rt_omega = raytracing.RayTracer(self._amr, self._ro.info, omega_op)
|
|
|
|
# Ray tracer for the sound speed
|
|
if self.pp_params.pymses.fft:
|
|
rt_cs = splatting.SplatterProcessor(
|
|
self._amr, self._ro.info, cs_op, surf_qty=False
|
|
)
|
|
else:
|
|
rt_cs = raytracing.RayTracer(self._amr, self._ro.info, cs_op)
|
|
|
|
dmap_omega = rt_omega.process(self._cam[ax_los])
|
|
dmap_cs = rt_cs.process(self._cam[ax_los])
|
|
dmap_col = self.save.root.maps.coldens_z.read()
|
|
map_Q = (
|
|
(self._lbox * dmap_cs.map.T)
|
|
* dmap_omega.map.T
|
|
/ (np.pi * self.G * dmap_col)
|
|
)
|
|
return map_Q
|
|
|
|
def _radial_bins(self, _):
|
|
pos_star = self.pp_params.disk.pos_star
|
|
im_extent = self.save.root.maps._v_attrs.im_extent
|
|
|
|
# radius of the corner of the box plus a margin
|
|
rad_of_box = (
|
|
np.sqrt(
|
|
(im_extent[1] - pos_star[0]) ** 2 + (im_extent[3] - pos_star[1]) ** 2
|
|
)
|
|
+ 0.1
|
|
)
|
|
|
|
bin_in = self.pp_params.disk.bin_in
|
|
bin_out = self.pp_params.disk.bin_out
|
|
nb_bin = self.pp_params.disk.nb_bin
|
|
|
|
# radial bins
|
|
if self.pp_params.disk.binning == "log":
|
|
lrad_in = np.log10(bin_in)
|
|
lrad_ext = np.log10(bin_out)
|
|
rad_bins = np.logspace(lrad_in, lrad_ext, num=nb_bin)
|
|
elif self.pp_params.disk.binning == "lin":
|
|
rad_bins = np.linspace(bin_in, bin_out, num=nb_bin)
|
|
|
|
# Add boundaries
|
|
rad_bins = np.concatenate(([0.0], rad_bins, [rad_of_box]))
|
|
return rad_bins
|
|
|
|
def _rr(self, _):
|
|
im_extent = self.save.root.maps._v_attrs.im_extent
|
|
map_size = self.pp_params.out.map_size
|
|
pos_star = self.pp_params.disk.pos_star
|
|
|
|
x = np.linspace(im_extent[0], im_extent[1], map_size)
|
|
y = np.linspace(im_extent[2], im_extent[3], map_size)
|
|
xx, yy = np.meshgrid(x, y)
|
|
rr = np.sqrt((xx - pos_star[0]) ** 2 + (yy - pos_star[1]) ** 2)
|
|
return rr
|
|
|
|
def _bins_on_map(self, ax_los):
|
|
rad_bins = self.save.get_node("/radial/radial_bins_" + ax_los).read()
|
|
rr = self.save.get_node("/maps/rr_" + ax_los).read()
|
|
|
|
# Find appropriate bin for each coordinate set
|
|
bins = np.zeros(rr.shape, dtype=int)
|
|
for r in rad_bins[1:]:
|
|
bins = bins + (rr >= r).astype(int)
|
|
return bins
|
|
|
|
def _rad_avg(self, name, ax_los):
|
|
radial_bins = self.save.get_node("/radial/radial_bins_" + ax_los).read()
|
|
bins_on_map = self.save.get_node("/maps/bins_on_map_" + ax_los).read()
|
|
dmap = self.save.get_node("/maps/" + name + "_" + ax_los).read()
|
|
|
|
# mean of all the cells in the bin
|
|
mean_bin = np.zeros(len(radial_bins) - 1)
|
|
for j in range(len(radial_bins) - 1):
|
|
mean_bin[j] = np.mean(dmap[bins_on_map == j])
|
|
return mean_bin
|
|
|
|
def _rad_avg_map(self, name, ax_los):
|
|
|
|
radial_bins = self.save.get_node("/radial/radial_bins_" + ax_los).read()
|
|
bins_on_map = self.save.get_node("/maps/bins_on_map_" + ax_los).read()
|
|
rr = self.save.get_node("/maps/rr_" + ax_los).read()
|
|
mean_bin = self.save.get_node("/radial/rad_avg_" + name + "_" + ax_los).read()
|
|
|
|
# Add value for border
|
|
mean_bin = np.concatenate(([mean_bin[0]], mean_bin))
|
|
|
|
rr_flat = rr.flatten()
|
|
bins_on_map_flat = bins_on_map.flatten()
|
|
|
|
# Compute the map azimuthally averaged
|
|
# use linear interpolation to improve accuracy
|
|
avg_flat = (radial_bins[bins_on_map_flat + 1] - rr_flat) * mean_bin[
|
|
bins_on_map_flat
|
|
]
|
|
avg_flat = (
|
|
avg_flat
|
|
+ (rr_flat - radial_bins[bins_on_map_flat]) * mean_bin[bins_on_map_flat + 1]
|
|
)
|
|
avg_flat = avg_flat / (
|
|
radial_bins[bins_on_map_flat + 1] - radial_bins[bins_on_map_flat]
|
|
)
|
|
avg_map = np.reshape(avg_flat, rr.shape)
|
|
|
|
return avg_map
|
|
|
|
def _fluct_map(self, name, ax_los):
|
|
|
|
dmap = self.save.get_node("/maps/" + name + "_" + ax_los).read()
|
|
avg_map = self.save.get_node("/maps/avg_map_" + name + "_" + ax_los).read()
|
|
|
|
return dmap / avg_map
|
|
|
|
def _pdf(self, name, ax_los):
|
|
fluct_map = self.save.get_node("/maps/fluct_" + name + "_" + ax_los).read()
|
|
rr = self.save.get_node("/maps/rr_" + ax_los).read()
|
|
|
|
mask_pdf = (rr > self.pp_params.disk.rmin_pdf) & (
|
|
rr < self.pp_params.disk.rmax_pdf
|
|
)
|
|
|
|
nb_cells = np.sum(mask_pdf.flatten())
|
|
values, edges = np.histogram(
|
|
np.log10(fluct_map[mask_pdf].flatten()),
|
|
self.pp_params.pdf.nb_bin,
|
|
weights=np.ones(nb_cells) / nb_cells,
|
|
)
|
|
centers = 0.5 * (edges[1:] + edges[:-1])
|
|
return np.stack([values, centers])
|
|
|
|
def _fit_pdf(self, name, ax_los):
|
|
pdf = self.save.get_node("/hist/pdf_" + name + "_" + ax_los)
|
|
values, centers = pdf.read()
|
|
mask_fit = (
|
|
(centers > self.pp_params.pdf.xmin_fit)
|
|
& (centers < self.pp_params.pdf.xmax_fit)
|
|
& (values > 0)
|
|
)
|
|
(slope, origin, correlation, _, stderr) = linregress(
|
|
centers[mask_fit], np.log10(values[mask_fit])
|
|
)
|
|
|
|
pdf.attrs.slope = slope
|
|
pdf.attrs.origin = origin
|
|
pdf.attrs.correlation = correlation
|
|
pdf.attrs.stderr = stderr
|
|
pdf.attrs.var = np.var
|
|
return True
|
|
|
|
def _sinks(self):
|
|
csv_name = (
|
|
self.path
|
|
+ "/output_"
|
|
+ str(self.num).zfill(5)
|
|
+ "/sink_"
|
|
+ str(self.num).zfill(5)
|
|
+ ".csv"
|
|
)
|
|
sinks = np.loadtxt(csv_name, delimiter=",")
|
|
header = [
|
|
"Id",
|
|
"M",
|
|
"dmf",
|
|
"x",
|
|
"y",
|
|
"z",
|
|
"vx",
|
|
"vy",
|
|
"vz",
|
|
"rot_period",
|
|
"lx",
|
|
"ly",
|
|
"lz",
|
|
"acc_rate",
|
|
"acc_lum",
|
|
"age",
|
|
"int_lum",
|
|
"Teff",
|
|
]
|
|
if len(sinks) == 0:
|
|
sinks = np.zeros(len(header))
|
|
|
|
sinks_dict = {}
|
|
for key, a in zip(header, sinks):
|
|
sinks_dict[key] = a
|
|
|
|
return sinks_dict
|
|
|
|
def def_rules(self):
|
|
|
|
self.rules = {
|
|
# Base rules
|
|
"coldens": Rule(
|
|
self,
|
|
self._coldens,
|
|
"Column density",
|
|
"/maps",
|
|
unit=self.info["unit_density"] * self.info["unit_length"],
|
|
),
|
|
"rho": Rule(
|
|
self,
|
|
self._rho,
|
|
"Density slice",
|
|
"/maps",
|
|
unit=self.info["unit_density"],
|
|
),
|
|
"speed_h": Rule(
|
|
self,
|
|
self._speed_h,
|
|
"Horizontal speed slice wrt the line of sight",
|
|
"/maps",
|
|
unit=self.info["unit_velocity"],
|
|
),
|
|
"speed_v": Rule(
|
|
self,
|
|
self._speed_v,
|
|
"Vertical speed slice wrt the line of sight",
|
|
"/maps",
|
|
unit=self.info["unit_velocity"],
|
|
),
|
|
"T": Rule(
|
|
self,
|
|
self._temperature,
|
|
"Temperature slice",
|
|
"/maps",
|
|
dependencies=["rho"],
|
|
unit=self.info["unit_temperature"],
|
|
),
|
|
"levels": Rule(
|
|
self, self._levels, "Max level within line of sight", "/maps"
|
|
),
|
|
"jeans": Rule(
|
|
self,
|
|
self._jeans,
|
|
"Jeans lenght slice",
|
|
"/maps",
|
|
dependencies=["rho", "T"],
|
|
),
|
|
"jeans_ratio": Rule(
|
|
self,
|
|
self._jeans_ratio,
|
|
"Jeans' lenght divided by the max resolution",
|
|
"/maps",
|
|
dependencies=["jeans", "levels"],
|
|
),
|
|
"Q": Rule(
|
|
self,
|
|
self._toomreQ_disk,
|
|
"Toomre Q parameter for a Keplerian disk",
|
|
"/maps",
|
|
dependencies=["coldens"],
|
|
is_valid=lambda _: self.pp_params.disk.on,
|
|
),
|
|
"sinks": Rule(
|
|
self,
|
|
self._sinks,
|
|
group="/datasets",
|
|
unit={
|
|
"Id": cst.none,
|
|
"M": cst.Msun,
|
|
"dmf": cst.Msun,
|
|
"x": "",
|
|
"y": "",
|
|
"z": "",
|
|
"vx": "",
|
|
"vy": "",
|
|
"vz": "",
|
|
"rot_period": "[y]",
|
|
"lx": "|l|",
|
|
"ly": "|l|",
|
|
"lz": "|l|",
|
|
"acc_rate": "[Msol/y]",
|
|
"acc_lum": "[Lsol]",
|
|
"age": cst.year,
|
|
"int_lum": "[Lsol]",
|
|
"Teff": cst.K,
|
|
},
|
|
),
|
|
# Helpers
|
|
"radial_bins": Rule(self, self._radial_bins, "Radial bins", "/radial"),
|
|
"rr": Rule(self, self._rr, "Coordinate map", "/maps"),
|
|
"bins_on_map": Rule(
|
|
self,
|
|
self._bins_on_map,
|
|
"Convert map coordinates to bins",
|
|
"/maps",
|
|
dependencies=["radial_bins", "rr"],
|
|
),
|
|
# globals
|
|
"time_num": Rule(
|
|
self,
|
|
lambda: self.info["time"],
|
|
"Time",
|
|
"/globals",
|
|
unit=self.info["unit_time"],
|
|
),
|
|
"mwa_speed": Rule(
|
|
self,
|
|
partial(self._vol_avg, partial(simple_getter, "vel")),
|
|
"Mass weighted speed average",
|
|
"/globals",
|
|
unit=self.info["unit_velocity"],
|
|
),
|
|
"mwa_sigma": Rule(
|
|
self,
|
|
self._mwa_sigma,
|
|
"Mass weighted speed average",
|
|
"/globals",
|
|
dependencies=["mwa_speed"],
|
|
unit=self.info["unit_velocity"],
|
|
),
|
|
}
|
|
|
|
# Average and other
|
|
averageables = ["coldens", "rho", "T", "Q"]
|
|
for name in averageables:
|
|
self.rules["rad_avg_" + name] = Rule(
|
|
self,
|
|
partial(self._rad_avg, name),
|
|
"Azimuthal average of {}".format(name),
|
|
"/radial",
|
|
dependencies=["radial_bins", "bins_on_map", name],
|
|
)
|
|
|
|
self.rules["avg_map_" + name] = Rule(
|
|
self,
|
|
partial(self._rad_avg_map, name),
|
|
"Interpolated map of azimuthal average of {}".format(name),
|
|
"/maps",
|
|
dependencies=["radial_bins", "bins_on_map", "rr", "rad_avg_" + name],
|
|
)
|
|
self.rules["fluct_" + name] = Rule(
|
|
self,
|
|
partial(self._fluct_map, name),
|
|
"Fluctuation wrt to average of {}".format(name),
|
|
"/maps",
|
|
dependencies=[name, "avg_map_" + name],
|
|
)
|
|
self.rules["pdf_" + name] = Rule(
|
|
self,
|
|
partial(self._pdf, name),
|
|
"Probability density function of {} fluctuations".format(name),
|
|
"/hist",
|
|
dependencies=["rr", "fluct_" + name],
|
|
)
|
|
|
|
self.rules["fit_pdf_" + name] = Rule(
|
|
self,
|
|
partial(self._fit_pdf, name),
|
|
"Fit the PDF of {} fluctuations".format(name),
|
|
"/hist",
|
|
dependencies=["pdf_" + name],
|
|
)
|
|
|
|
|
|
class Comparator(Aggregator, HDF5Container):
|
|
"""
|
|
Do comparaison between outputs and runs
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
path,
|
|
in_runs,
|
|
in_nums,
|
|
path_out=None,
|
|
pp_params=default_params(),
|
|
selector=None,
|
|
tag=None,
|
|
**kwargs
|
|
):
|
|
"""
|
|
Creates the basic structures needed for the outputs
|
|
"""
|
|
|
|
super(Comparator, self).__init__(path, path_out, pp_params, tag)
|
|
|
|
# Open outfile
|
|
if not self.pp_params.out.tag == "":
|
|
tag_name = "_" + self.pp_params.out.tag
|
|
else:
|
|
tag_name = ""
|
|
|
|
self.filename = path_out + "/comp" + tag_name + ".h5"
|
|
|
|
# Select runs
|
|
if selector is None:
|
|
selector = RunSelector(path, in_runs, in_nums, self.pp_params, **kwargs)
|
|
|
|
# Save infos
|
|
self.path = path
|
|
self.runs = selector.runs
|
|
self.nums = selector.nums
|
|
|
|
# Get postprocesor objets for each run
|
|
self.pp_runs = {}
|
|
attrs = {}
|
|
|
|
for run in self.runs:
|
|
path_run = path + "/" + run
|
|
path_out_run = path_out + "/" + run
|
|
self.pp_runs[run] = {}
|
|
for num in self.nums[run]:
|
|
self.pp_runs[run][num] = PostProcessor(
|
|
path_run, num, path_out=path_out_run, pp_params=self.pp_params
|
|
)
|
|
|
|
self.namelist = selector.namelist
|
|
self.info = self.pp_runs[self.runs[0]][self.nums[self.runs[0]][0]].info
|
|
|
|
# log info
|
|
self.log_id = "[comp {}] ".format(self.pp_params.out.tag)
|
|
|
|
# Define rules
|
|
self.def_rules()
|
|
|
|
def _needs_computation(self, overwrite, name_full):
|
|
"""
|
|
Returns True if a new computation of the rule is needed
|
|
"""
|
|
if overwrite or not (name_full in self.save):
|
|
return True
|
|
elif not "nums" in self.save.get_node(name_full)._v_attrs:
|
|
return True
|
|
else:
|
|
saved_nums = self.save.get_node(name_full)._v_attrs.nums
|
|
missing_runs = len([run for run in self.nums if not run in saved_nums]) > 0
|
|
missing_nums = missing_runs and all(
|
|
[
|
|
len([num for num in self.nums[run] if not num in saved_nums[run]])
|
|
> 0
|
|
for run in self.nums
|
|
if run in saved_nums
|
|
]
|
|
)
|
|
return missing_nums
|
|
|
|
def _save_data(self, name_full, data, description, unit):
|
|
super(Comparator, self)._save_data(name_full, data, description, unit)
|
|
self.save.get_node(name_full)._v_attrs.nums = self.nums
|
|
|
|
def _time_series(self, getter):
|
|
series = {}
|
|
for run in self.runs:
|
|
series[run] = np.zeros(len(self.nums[run]))
|
|
for i, num in enumerate(self.nums[run]):
|
|
series[run][i] = getter(run, num)
|
|
return series
|
|
|
|
def _comp(self, getter):
|
|
prop = np.zeros(len(self.runs))
|
|
for i, run in enumerate(self.runs):
|
|
num = self.nums[run][0]
|
|
prop[i] = getter(run, num)
|
|
return prop
|
|
|
|
def _time_avg(self, name, start=None, end=None, span=None, group="/series"):
|
|
mean = np.zeros(len(self.runs))
|
|
median = np.zeros(len(self.runs))
|
|
std = np.zeros(len(self.runs))
|
|
|
|
for i, run in enumerate(self.runs):
|
|
serie = self.save.get_node(group + "/" + name + "/" + run).read()
|
|
time = self.save.get_node(group + "/time/" + run).read()
|
|
mask = abs(serie) != np.inf
|
|
|
|
if not ((start, end, span) == (None, None, None)):
|
|
start_r, end_r = start, end
|
|
# Be sure that start_r and end_r are defined
|
|
if start_r is None:
|
|
if end_r is None:
|
|
end_r = time[-1]
|
|
start_r = end_r - span
|
|
elif end_r is None:
|
|
end_r = start_r + span
|
|
|
|
mask = mask & (time >= start_r) & (time <= end_r)
|
|
|
|
mean[i] = np.nanmean(serie[mask])
|
|
median[i] = np.nanmedian(serie[mask])
|
|
std[i] = np.nanstd(serie[mask])
|
|
return {"runs": self.runs, "mean": mean, "std": std, "median": median}
|
|
|
|
def get_attr(self, attr_name, run, num, node_name="/"):
|
|
pp = self.pp_runs[run][num]
|
|
return pp.get_attribute(node_name, attr_name)
|
|
|
|
def get_global(self, node_name, run, num, args=[None], unload_cells=False):
|
|
pp = self.pp_runs[run][num]
|
|
if unload_cells:
|
|
pp.unload_cells()
|
|
value = pp.get_value(node_name)
|
|
return value
|
|
|
|
def get_nml(self, nml_key, run, num=0):
|
|
"""
|
|
num parameter is ignored
|
|
"""
|
|
res = self.namelist[run]
|
|
for key in nml_key.split("/"):
|
|
res = res[key]
|
|
return res
|
|
|
|
def get_pdf_slope(self, name, run, num):
|
|
pp = self.pp_runs[run][num]
|
|
pp.process(["fit_pdf_" + name], ["z"], overwrite=self.overwrite_dep)
|
|
slope = pp.get_attribute("/hist/pdf_" + name + "_z", "slope")
|
|
return slope
|
|
|
|
def _extract_sinks_from_log(self, series, log_filename, run):
|
|
cmd_grep = "grep 'Number of sink' {} -A 2".format(log_filename)
|
|
content = os.popen(cmd_grep).readlines()
|
|
for i in range(0, len(content), 4):
|
|
series["nb_sink"][run].append(np.int(content[i].split("=")[1]))
|
|
series["mass_sink"][run].append(np.float(content[i + 1].split("=")[1]))
|
|
series["time"][run].append(np.float(content[i + 2].split("=")[1]))
|
|
return series
|
|
|
|
def _extract_sfr_from_log(self, series, log_filename, run):
|
|
cmd_grep = "grep '\[SFR' {} ".format(log_filename)
|
|
content = os.popen(cmd_grep).readlines()
|
|
for i in range(0, len(content)):
|
|
time = np.float(content[i].split("]")[0].split("=")[1].split()[0])
|
|
sfr = np.float(content[i].split("]")[1].split("=")[1].split()[0])
|
|
series["time"][run].append(time)
|
|
series["sfr"][run].append(sfr)
|
|
return series
|
|
|
|
def _extract_rms_from_log(self, series, log_filename, run):
|
|
cmd_grep = "grep 'turbulent rms' {} -B 1".format(log_filename)
|
|
content = os.popen(cmd_grep).readlines()
|
|
for i in range(0, len(content), 3):
|
|
series["time"][run].append(np.float(content[i].split("=")[2].split()[0]))
|
|
series["turb_rms"][run].append(np.float(content[i + 1].split(":")[1]))
|
|
return series
|
|
|
|
def _from_log(self, keys, extractor):
|
|
nums = self.nums
|
|
|
|
# Initialize series
|
|
series = {}
|
|
for key in keys:
|
|
series[key] = {}
|
|
|
|
for run in self.runs:
|
|
# Initialize list
|
|
for key in keys:
|
|
series[key][run] = []
|
|
|
|
# get one preprocessor
|
|
path_run = self.path + "/" + run
|
|
|
|
# Get list of run files
|
|
log_files = path_run + "/" + self.pp_params.input.log_prefix + "*"
|
|
|
|
# Parse files
|
|
for log_filename in glob.glob(log_files):
|
|
series = extractor(series, log_filename, run)
|
|
|
|
# Numpify the lists
|
|
for key in series:
|
|
series[key][run] = np.array(series[key][run])
|
|
|
|
# Sort the arrays
|
|
ind_sort = series["time"][run].argsort()
|
|
for key in series:
|
|
series[key][run] = series[key][run][ind_sort]
|
|
return series
|
|
|
|
def _ssfr_from_mass_sink(self, avg_window=None):
|
|
"""
|
|
avg_window in year
|
|
"""
|
|
time_unit = self.save.get_node("/series/sinks_from_log/time")._v_attrs.unit
|
|
mass_unit = self.save.get_node("/series/sinks_from_log/mass_sink")._v_attrs.unit
|
|
# Surface of the box in pc^2
|
|
surface = (self.info["unit_length"].express(cst.pc)) ** 2
|
|
# WARNING : We do not multiply by boxlen since already done in 'unit_length' (pymses)
|
|
ssfr = {}
|
|
for run in self.runs:
|
|
time = self.save.get_node("/series/sinks_from_log/time/" + run).read()
|
|
time = time * time_unit.express(cst.year)
|
|
mass_sink = self.save.get_node(
|
|
"/series/sinks_from_log/mass_sink/" + run
|
|
).read()
|
|
mass_sink = mass_sink * mass_unit.express(cst.Msun)
|
|
if avg_window is None:
|
|
shift = 1
|
|
else:
|
|
# We assume that the timestep do not vary a lot ...
|
|
shift = np.searchsorted(time, avg_window, side="left")
|
|
sfr = (mass_sink[shift:] - mass_sink[:-shift]) / (
|
|
time[shift:] - time[:-shift]
|
|
)
|
|
ssfr[run] = np.zeros(len(mass_sink))
|
|
ssfr[run][shift:] = sfr / surface
|
|
return ssfr
|
|
|
|
def _gen_rule_avg(self, rule_src_name, subarray_name=None):
|
|
|
|
rule_src = self.rules[rule_src_name]
|
|
|
|
if subarray_name is None:
|
|
src_name = rule_src_name
|
|
group_src = rule_src.group
|
|
unit = rule_src.unit
|
|
descr = rule_src.description
|
|
else:
|
|
src_name = subarray_name
|
|
group_src = rule_src.group + "/" + rule_src_name
|
|
unit = rule_src.unit[subarray_name]
|
|
descr = rule_src.description[subarray_name]
|
|
|
|
description = {
|
|
"runs": "List of runs",
|
|
"mean": "Temporal average of {}".format(descr),
|
|
"std": "Standard deviation of {}".format(descr),
|
|
"median": "Median of {}".format(descr),
|
|
}
|
|
name = "avg_" + src_name
|
|
|
|
def fn(arg=None, **kwargs):
|
|
if arg is None:
|
|
return self._time_avg(src_name, group=group_src, **kwargs)
|
|
else:
|
|
return self._time_avg(
|
|
src_name + "_" + str(arg), group=group_src, **kwargs
|
|
)
|
|
|
|
self.rules[name] = Rule(
|
|
self,
|
|
fn,
|
|
group="/comp",
|
|
unit=unit,
|
|
description=description,
|
|
dependencies=[rule_src_name],
|
|
)
|
|
|
|
def def_rules(self):
|
|
averageables = ["coldens", "rho", "T", "Q"]
|
|
self.rules = {
|
|
# Read from log
|
|
"sinks_from_log": Rule(
|
|
self,
|
|
partial(
|
|
self._from_log,
|
|
["time", "mass_sink", "nb_sink"],
|
|
self._extract_sinks_from_log,
|
|
),
|
|
group="/series",
|
|
unit={
|
|
"time": self.info["unit_time"],
|
|
"mass_sink": cst.Msun,
|
|
"nb_sink": cst.none,
|
|
},
|
|
description={
|
|
"time": "Time",
|
|
"mass_sink": "Total mass of stars",
|
|
"nb_sink": "Number of stars",
|
|
},
|
|
),
|
|
"issfr": Rule(
|
|
self,
|
|
self._ssfr_from_mass_sink,
|
|
group="/series/sinks_from_log",
|
|
unit=cst.ssfr,
|
|
description="Instantaneous surfacic star formation rate",
|
|
dependencies=["sinks_from_log"],
|
|
),
|
|
"sfr_from_log": Rule(
|
|
self,
|
|
partial(self._from_log, ["time", "sfr"], self._extract_sfr_from_log),
|
|
group="/series",
|
|
unit={"time": cst.year, "sfr": cst.ssfr},
|
|
description={
|
|
"time": "Time",
|
|
"sfr": "Averaged surfacic star formation rate",
|
|
},
|
|
),
|
|
"rms_from_log": Rule(
|
|
self,
|
|
partial(
|
|
self._from_log, ["time", "turb_rms"], self._extract_rms_from_log
|
|
),
|
|
group="/series",
|
|
unit={"time": self.info["unit_time"], "turb_rms": cst.none},
|
|
description={"time": "Time", "turb_rms": "Computed turbulent RMS"},
|
|
),
|
|
# Read from outputs
|
|
"time": Rule(
|
|
self,
|
|
partial(
|
|
self._time_series, partial(self.get_global, "/globals/time_num")
|
|
),
|
|
group="/series",
|
|
unit=self.info["unit_time"],
|
|
dependencies=["time_num"],
|
|
),
|
|
"time_sigma": Rule(
|
|
self,
|
|
partial(
|
|
self._time_series,
|
|
partial(self.get_global, "/globals/mwa_sigma", unload_cells=True),
|
|
),
|
|
group="/series",
|
|
unit=self.info["unit_velocity"],
|
|
dependencies=["time", "mwa_sigma"],
|
|
),
|
|
# namelist
|
|
"nml": Rule(
|
|
self,
|
|
lambda nml_key: self._comp(partial(self.get_nml, nml_key)),
|
|
group="/comp",
|
|
),
|
|
}
|
|
|
|
for name in ["issfr", "time_sigma"]:
|
|
self._gen_rule_avg(name)
|
|
|
|
self._gen_rule_avg("sinks_from_log", "mass_sink")
|
|
self._gen_rule_avg("sinks_from_log", "nb_sink")
|
|
self._gen_rule_avg("sfr_from_log", "sfr")
|
|
|
|
|
|
def get_time(path, num):
|
|
"""
|
|
Return the time of the output (code units)
|
|
|
|
Parameters
|
|
----------
|
|
num output number
|
|
path_out path of the pipeline output
|
|
|
|
Returns
|
|
-------
|
|
time the time of the output (code units)
|
|
"""
|
|
try:
|
|
f = open(
|
|
path
|
|
+ "/output_"
|
|
+ str(num).zfill(5)
|
|
+ "/info_"
|
|
+ str(num).zfill(5)
|
|
+ ".txt"
|
|
)
|
|
for line in f:
|
|
ls = line.split()
|
|
if len(ls) > 1 and ls[0] == "time":
|
|
time = float(ls[2])
|
|
break
|
|
f.close()
|
|
return time
|
|
except IOError as e:
|
|
print(e)
|
|
return np.nan
|