Noe Brucy
685 lines
28 KiB
Python
685 lines
28 KiB
Python
# coding: utf-8
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import sys
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import os
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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 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 scipy.stats import linregress
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from functools import partial
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from abc import ABCMeta, abstractmethod
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from pp_params import *
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class Rule:
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def __init__(self, postproc, process, description='', group='', dependencies=[], args_ok=['x', 'y', 'z'],
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is_valid=lambda arg:True):
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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.args_ok = args_ok
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self.description = description
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def process(self, arg):
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if not arg is None:
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return self.process_fn(arg)
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else:
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return self.process_fn()
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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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rule_dep = self.postproc.rules[dep]
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if not arg is None:
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valid = valid and rule_dep.group + '/' + dep + '_' + str(arg) in save
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else:
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valid = valid and rule_dep.group + '/' + dep in save
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return arg in self.args_ok and 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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@abstractmethod
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def __init__(self):
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self.def_rules()
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log_id = ""
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def _log(self, string, status=""):
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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(self, to_process_list, args=[None], overwrite=False, overwrite_dep=None):
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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 overwrite_dep is None:
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overwrite_dep = overwrite
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self.overwrite_dep = overwrite_dep
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just_done = [] # Computations done within this call
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self.save = tables.open_file(self.filename, mode="a")
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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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just_done = self._process_single(name, rule, arg, overwrite, just_done)
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else:
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self._log("{} is unknown, allowed rules are {}".format(name, self.rules.keys()), "ERROR")
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self.save.close()
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def _process_single(self, name, rule, arg, overwrite=False, just_done=[]):
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# Solve dependencies
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for dep in rule.dependencies:
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if dep in self.rules:
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rule_dep = self.rules[dep]
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just_done = self._process_single(dep, rule_dep, arg, self.overwrite_dep, just_done)
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else:
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self._log("Dependency {} for {} is unknown".format(dep, name), "ERROR")
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# Process rule
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done = self._process_rule(name, rule, arg, overwrite, just_done)
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return just_done + [done]
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def _process_rule(self, name, rule, arg, overwrite=False, just_done=[]):
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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 just_done:
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if overwrite or not name_full in self.save:
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self._log("Processing {}".format(name_full))
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data = rule.process(arg)
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self._save_data(name_full, data, rule.description)
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self._log("Data for {} computed".format(name_full), "SUCCESS")
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return name_full
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else:
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self._log("Data for {} is already computed, skipping...".format(name_full))
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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):
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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 not type(data) == dict:
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self.save.create_array(os.path.dirname(name_full), os.path.basename(name_full),
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data, description, createparents=True)
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else:
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for key in data:
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if type(description) == dict:
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self.save.create_array(name_full, key,
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data[key], description[key], createparents=True)
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else:
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self.save.create_array(name_full, key,
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data[key], description, createparents=True)
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@abstractmethod
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def def_rules(self):
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pass
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class PostProcessor(BaseProcessor):
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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. # Gravitational constant
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def __init__(self, path=None, num=None, path_out=None, filename=None, pp_params=Params()):
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"""
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Creates the basic structures needed for the outputs
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"""
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if not path is None and not num is None:
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# TODO : Make possible to load the HDF5 file even without the original file
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self.pp_params = pp_params
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# Determining output directory
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if (path_out is None):
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path_out = path
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# Open outfile
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if not pp_params.out.tag == '':
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tag_name = pp_params.out.tag + '_'
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else :
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tag_name = ''
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self.filename = (path_out + '/postproc_' +
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tag_name + format(num,'05') + '.h5')
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self.save = tables.open_file(self.filename, mode="a",
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title=os.path.basename(path)+ '_' + format(num,'05'))
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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._amr = self._ro.amr_source(["rho","vel","P"])
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# Density operator
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self._rho_op = ScalarOperator(lambda dset: dset["rho"], self._ro.info["unit_density"])
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# Density ray tracer
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if(pp_params.pymses.fft):
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self._rt = splatting.SplatterProcessor(self._amr, self._ro.info, self._rho_op)
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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._ro.info['boxlen']
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center = pp_params.out.center
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im_extent = [(- 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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# 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 = dict()
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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(center=pp_params.out.center,
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line_of_sight_axis=ax_los,
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region_size=[2.*self._radius, 2.*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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self._add_metadata()
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self.save.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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# Beta for the beta cooling
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if not (self.pp_params.disk.beta is None or self.pp_params.disk.beta == False):
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if type(self.pp_params.disk.beta) == int:
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self.save.root._v_attrs.beta = self.pp_params.disk.beta
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else:
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self.save.root._v_attrs.beta = int(self.save.root._v_attrs.run.split('_')[1][4:])
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def _coldens(self, ax_los):
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datamap = self._rt.process(self._cam[ax_los], surf_qty=True)
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return datamap.map.T * self._lbox
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def _rho(self, ax_los):
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datamap_rho = slicing.SliceMap(self._amr, self._cam[ax_los], self._rho_op, z=0.)
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return (datamap_rho.map).T
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def _speed_h(self, ax_los):
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vh_op = ScalarOperator(lambda dset: dset["vel"][:, self._ax_nb[self._axes_h[ax_los]]],
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self._ro.info["unit_velocity"])
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dmap_vh = slicing.SliceMap(self._amr, self._cam[ax_los], vh_op, z=0.).map.T
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return dmap_vh
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def _speed_v(self, ax_los):
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vv_op = ScalarOperator(lambda dset: dset["vel"][:, self._ax_nb[self._axes_v[ax_los]]],
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self._ro.info["unit_velocity"])
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dmap_vv = slicing.SliceMap(self._amr, self._cam[ax_los], vv_op, z=0.).map.T
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return dmap_vv
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def _temperature(self, ax_los):
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P_op = ScalarOperator(lambda dset: dset["P"], self._ro.info["unit_pressure"])
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dmap_P = (slicing.SliceMap(self._amr, self._cam[ax_los], P_op, z=0.)).map.T
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dmap_rho = self.save.get_node("/maps/rho_{}".format(ax_los)).read()
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return dmap_P/dmap_rho
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def _levels(self, ax_los):
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self._amr.set_read_levelmax(20)
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level_op = MaxLevelOperator()
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rt_level = raytracing.RayTracer(self._amr, self._ro.info, level_op)
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datamap = rt_level.process(self._cam[ax_los], surf_qty=True)
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return datamap.map.T
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def _jeans(self, ax_los):
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dmap_T = self.save.get_node('/maps/T_' + ax_los).read()
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dmap_rho = self.save.get_node('/maps/rho_' + ax_los).read()
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dmap_jeans = np.sqrt(np.pi * dmap_T / dmap_rho)
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return dmap_jeans
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def _jeans_ratio(self, ax_los):
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dmap_jeans = self.save.get_node('/maps/jeans_' + ax_los).read()
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dmap_levels = self.save.get_node('/maps/levels_' + ax_los).read()
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dmap_jeans_ratio = dmap_jeans * 2**(dmap_levels)
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return dmap_jeans_ratio
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def _jeans_ratio(self, ax_los):
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dmap_jeans = self.save.get_node('/maps/jeans_' + ax_los).read()
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dmap_levels = self.save.get_node('/maps/levels_' + ax_los).read()
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dmap_jeans_ratio = dmap_jeans * 2**(dmap_levels)
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return dmap_jeans_ratio
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def _toomreQ_disk(self, ax_los):
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"""
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Compute the Toomre Q parameter in a Keplerian disk
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"""
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# Operator to compute the angular speed times rho
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def omega_rho_func(dset):
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pos = dset.get_cell_centers()
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pos = pos - (self.pp_params.disk.pos_star / self._lbox)
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xx = pos[:, :, 0]
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yy = pos[:, :, 1]
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rc = np.sqrt(xx**2 + yy**2) # cylindrical radius
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vx = dset["vel"][:, :, 0]
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vy = dset["vel"][:, :, 1]
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omega_rho = (1. / rc**2)
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omega_rho = omega_rho * dset["rho"]
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vyx = vy * xx
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vxy = vx * yy
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omega_rho = omega_rho * (vyx - vxy)
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return omega_rho
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# Operator to compute the angular speed
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omega_op = FractionOperator(omega_rho_func, lambda dset: dset["rho"],
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1. / self._ro.info["unit_time"])
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# Operator to compute the sound speed
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cs_op = FractionOperator(lambda dset: dset["P"],
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lambda dset: dset["rho"], self._ro.info["unit_velocity"])
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# Ray tracer for the angular speed
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rt_omega = raytracing.RayTracer(self._amr, self._ro.info, omega_op)
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# Ray tracer for the sound speed
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if self.pp_params.pymses.fft:
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rt_cs = splatting.SplatterProcessor(self._amr, ro.info, cs_op, surf_qty=False)
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else :
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rt_cs = raytracing.RayTracer(self._amr, self._ro.info, cs_op)
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dmap_omega = rt_omega.process(self._cam[ax_los])
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dmap_cs = rt_cs.process(self._cam[ax_los])
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dmap_col = self.save.root.maps.coldens_z.read()
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map_Q = (self._lbox * dmap_cs.map.T) * dmap_omega.map.T / (np.pi * self.G * dmap_col)
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return map_Q
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def _radial_bins(self, ax_los):
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pos_star = self.pp_params.disk.pos_star
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im_extent = self.save.root.maps._v_attrs.im_extent
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# radius of the corner of the box plus a margin
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rad_of_box = np.sqrt((im_extent[1] - pos_star[0])**2 + (im_extent[3] - pos_star[1])**2) + 0.1
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bin_in = self.pp_params.disk.bin_in
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bin_out = self.pp_params.disk.bin_out
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nb_bin = self.pp_params.disk.nb_bin
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# radial bins
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if self.pp_params.disk.binning == 'log':
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lrad_in = np.log10(bin_in)
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lrad_ext = np.log10(bin_out)
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rad_bins = np.logspace(lrad_in, lrad_ext, num=nb_bin)
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elif binning == 'lin':
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rad_bins = np.linspace(bin_in, bin_out, num=nb_bin)
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# Add boundaries
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rad_bins = np.concatenate(([0.], rad_bins, [rad_of_box]))
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return rad_bins
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def _rr(self, ax_los):
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im_extent = self.save.root.maps._v_attrs.im_extent
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map_size = self.pp_params.out.map_size
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pos_star = self.pp_params.disk.pos_star
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x = np.linspace(im_extent[0], im_extent[1], map_size)
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y = np.linspace(im_extent[2], im_extent[3], map_size)
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xx, yy = np.meshgrid(x, y)
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rr = np.sqrt((xx - pos_star[0])**2 + (yy - pos_star[1])**2)
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return rr
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def _bins_on_map(self, ax_los):
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rad_bins = self.save.get_node('/radial/radial_bins_' + ax_los).read()
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rr = self.save.get_node('/maps/rr_' + ax_los).read()
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# Find appropriate bin for each coordinate set
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bins = np.zeros(rr.shape, dtype=int)
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for r in rad_bins[1:]:
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bins = bins + (rr >= r).astype(int)
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return bins
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def _rad_avg(self, name, ax_los):
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radial_bins = self.save.get_node('/radial/radial_bins_' + ax_los).read()
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bins_on_map = self.save.get_node('/maps/bins_on_map_' + ax_los).read()
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dmap = self.save.get_node('/maps/' + name + '_' + ax_los).read()
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# mean of all the cells in the bin
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mean_bin = np.zeros(len(radial_bins) - 1)
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for j in range(len(radial_bins) - 1):
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mean_bin[j] = np.mean(dmap[bins_on_map == j])
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return mean_bin
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def _rad_avg_map(self, name, ax_los):
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radial_bins = self.save.get_node('/radial/radial_bins_' + ax_los).read()
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bins_on_map = self.save.get_node('/maps/bins_on_map_' + ax_los).read()
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rr = self.save.get_node('/maps/rr_' + ax_los).read()
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mean_bin = self.save.get_node('/radial/rad_avg_' + name + '_' + ax_los).read()
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# Add value for border
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mean_bin = np.concatenate(([mean_bin[0]], mean_bin))
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rr_flat = rr.flatten()
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bins_on_map_flat = bins_on_map.flatten()
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# Compute the map azimuthally averaged
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# use linear interpolation to improve accuracy
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avg_flat = (radial_bins[bins_on_map_flat + 1] - rr_flat) * mean_bin[bins_on_map_flat]
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avg_flat = avg_flat + (rr_flat - radial_bins[bins_on_map_flat]) * mean_bin[bins_on_map_flat + 1]
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avg_flat = avg_flat / (radial_bins[bins_on_map_flat + 1] - radial_bins[bins_on_map_flat])
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avg_map = np.reshape(avg_flat, rr.shape)
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return avg_map
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def _fluct_map(self, name, ax_los):
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dmap = self.save.get_node('/maps/' + name + '_' + ax_los).read()
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avg_map = self.save.get_node('/maps/avg_map_' + name + '_' + ax_los).read()
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return dmap / avg_map
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def _pdf(self, name, ax_los):
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fluct_map = self.save.get_node('/maps/fluct_' + name + '_' + ax_los).read()
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rr = self.save.get_node('/maps/rr_' + ax_los).read()
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mask_pdf = (rr > self.pp_params.disk.rmin_pdf) & (rr < self.pp_params.disk.rmax_pdf)
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|
|
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 = 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'),
|
|
'rho' : Rule(self, self._rho, "Density slice", '/maps'),
|
|
'speed_h' : Rule(self, self._speed_h, "Horizontal speed slice wrt the line of sight", '/maps'),
|
|
'speed_v' : Rule(self, self._speed_v, "Vertical speed slice wrt the line of sight", '/maps'),
|
|
'T' : Rule(self, self._temperature, "Temperature slice", '/maps', dependencies=['rho']),
|
|
'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'], args_ok=['z'],
|
|
is_valid=lambda _: self.pp_params.disk.on),
|
|
'sinks' : Rule(self, self._sinks, group="/datasets", args_ok=[None],
|
|
description={'Id': '', 'M':'[Msol]', 'dmf':'[Msol]',
|
|
'x': '', 'y': '', 'z': '', 'vx': '', 'vy': '', 'vz': '',
|
|
'rot_period':'[y]', 'lx':'|l|', 'ly':'|l|', 'lz':'|l|',
|
|
'acc_rate':'[Msol/y]', 'acc_lum':'[Lsol]', 'age':'[y]',
|
|
'int_lum':'[Lsol]', 'Teff':'[K]'}),
|
|
|
|
# Helpers
|
|
'radial_bins' : Rule(self, self._radial_bins, "Radial bins", '/radial', args_ok=['z']),
|
|
'rr' : Rule(self, self._rr, "Coordinate map", '/maps', args_ok=['z']),
|
|
'bins_on_map' : Rule(self, self._bins_on_map, "Convert map coordinates to bins", '/maps',
|
|
dependencies=['radial_bins', 'rr'], args_ok=['z'])
|
|
}
|
|
|
|
# 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],
|
|
args_ok=['z'])
|
|
|
|
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],
|
|
args_ok=['z'])
|
|
self.rules['fluct_' + name] = Rule(self, partial(self._fluct_map, name),
|
|
"Fluctuation wrt to average of {}".format(name),
|
|
'/maps',
|
|
dependencies=[name, 'avg_map_' + name],
|
|
args_ok=['z'])
|
|
self.rules['pdf_' + name] = Rule(self, partial(self._pdf, name),
|
|
"Probability density function of {} fluctuations".format(name),
|
|
'/hist',
|
|
dependencies=['rr', 'fluct_' + name],
|
|
args_ok=['z'])
|
|
|
|
self.rules['fit_pdf_' + name] = Rule(self, partial(self._fit_pdf, name),
|
|
"Fit the PDF of {} fluctuations".format(name),
|
|
'/hist',
|
|
dependencies=['pdf_' + name],
|
|
args_ok=['z'])
|
|
|
|
class Comparator(BaseProcessor):
|
|
"""
|
|
Do comparaison between outputs and runs
|
|
"""
|
|
|
|
def __init__(self, path, runs, nums, path_out=None, pp_params=Params()):
|
|
"""
|
|
Creates the basic structures needed for the outputs
|
|
"""
|
|
|
|
self.pp_params = pp_params
|
|
|
|
# Determining output directory
|
|
if (path_out is None):
|
|
path_out = path
|
|
|
|
# Open outfile
|
|
if not pp_params.out.tag == '':
|
|
tag_name = '_' + pp_params.out.tag
|
|
else :
|
|
tag_name = ''
|
|
|
|
self.filename = (path_out + '/comp' + tag_name + '.h5')
|
|
self.save = tables.open_file(self.filename, mode="a", title="Comparaison file")
|
|
|
|
# Get postprocesor objets for each run
|
|
self.pp_runs = dict()
|
|
if not type(nums) == dict:
|
|
nums_tmp = nums
|
|
nums = dict()
|
|
for run in runs:
|
|
nums[run] = nums_tmp
|
|
|
|
for run in runs:
|
|
path_run = path + '/' + run
|
|
path_out_run = path_out + '/' + run
|
|
self.pp_runs[run] = dict()
|
|
for num in nums[run]:
|
|
self.pp_runs[run][num] = PostProcessor(path_run, num, path_out=path_out_run, pp_params=pp_params)
|
|
|
|
# save metadata
|
|
self.save.root._v_attrs.runs = runs
|
|
self.save.root._v_attrs.nums = nums
|
|
|
|
# log info
|
|
self.log_id = "[comp {}] ".format(self.pp_params.out.tag)
|
|
|
|
self.save.close()
|
|
self.def_rules()
|
|
|
|
def _time_series(self, name, getter):
|
|
nums = self.save.root._v_attrs.nums
|
|
series = dict()
|
|
for run in self.save.root._v_attrs.runs:
|
|
series[run] = np.zeros(len(nums[run]))
|
|
for i, num in enumerate(nums[run]):
|
|
series[run][i] = getter(self.pp_runs[run][num])
|
|
return series
|
|
|
|
def _comp(self, name, getter):
|
|
runs = self.save.root._v_attrs.runs
|
|
nums = self.save.root._v_attrs.nums
|
|
prop = np.zeros(len(runs))
|
|
for i, run in enumerate(runs):
|
|
num = nums[run][0]
|
|
prop[i] = getter(self.pp_runs[run][num])
|
|
return prop
|
|
|
|
def _time_avg(self, name):
|
|
runs = self.save.root._v_attrs.runs
|
|
mean = np.zeros(len(runs))
|
|
std = np.zeros(len(runs))
|
|
for i, run in enumerate(runs):
|
|
serie = self.save.get_node('/series/' + name + '/' + run).read()
|
|
mean[i] = np.mean(serie)
|
|
std[i] = np.std(serie)
|
|
return {"mean": mean, "std": std}
|
|
|
|
def _get_attr(self, attr_name, pp):
|
|
h5file = tables.open_file(pp.filename, "r")
|
|
attr = h5file.root._v_attrs[attr_name]
|
|
h5file.close()
|
|
return attr
|
|
|
|
def _get_pdf_slope(self, name, pp):
|
|
pp.process(['fit_pdf_' + name], ['z'], overwrite=self.overwrite_dep)
|
|
h5file = tables.open_file(pp.filename, "r")
|
|
pdf = h5file.get_node('/hist/pdf_' + name +'_z')
|
|
slope = pdf.attrs.slope
|
|
h5file.close()
|
|
return slope
|
|
|
|
def _get_sinks_mass(self, pp):
|
|
pp.process(['sinks'], overwrite=self.overwrite_dep)
|
|
h5file = tables.open_file(pp.filename, "r")
|
|
sinks_mass = h5file.get_node('/datasets/sinks/M').read()
|
|
h5file.close()
|
|
return np.sum(sinks_mass)
|
|
|
|
def def_rules(self):
|
|
averageables = ['coldens', 'rho', 'T', 'Q']
|
|
self.rules = {
|
|
'beta' : Rule(self, lambda arg: self._comp("beta", partial(self._get_attr, 'beta')), group='/comp',
|
|
args_ok = [None]),
|
|
'time_pdf_slope' : Rule(self,
|
|
lambda name: self._time_series("pdf_slope_" + name,
|
|
partial(self._get_pdf_slope, name)),
|
|
group='/series', args_ok = averageables),
|
|
'time_sinks_mass' : Rule(self, partial(self._time_series, "sinks", self._get_sinks_mass),
|
|
group='/series', args_ok=[None]),
|
|
'time' : Rule(self, partial(self._time_series, "time", partial(self._get_attr, 'time')),
|
|
group='/series', args_ok=[None]),
|
|
'avg_pdf_slope' : Rule(self,
|
|
lambda name: self._time_avg("time_pdf_slope_" + name),
|
|
group='/comp', dependencies=['time_pdf_slope'],
|
|
args_ok=averageables,
|
|
description={"mean": "Temporal average", "std": "Standard deviation"})
|
|
}
|
|
|
|
|
|
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
|
|
# ro = pymses.RamsesOutput(path, num, order='>')
|
|
# time = ro.info['time'] # time in codeunits
|
|
f.close()
|
|
return time
|
|
except IOError as e:
|
|
print(e)
|
|
return np.nan
|