Hennebelle Patrick
1245 lines
42 KiB
Python
1245 lines
42 KiB
Python
# coding: utf-8
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"""
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This software is a helper to use pysmes tools to read and analyse RAMSES Outputs.
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It's a rule based interface.
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This is the plotter module.
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@author Noé Brucy 2019-2020
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"""
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import sys
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import os
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from functools import partial
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import tables
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import numpy as np
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from scipy.stats import linregress
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from numpy.polynomial.polynomial import polyfit
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from scipy.ndimage.filters import gaussian_filter1d
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from scipy import optimize
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import matplotlib as mpl
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if os.environ.get("DISPLAY", "") == "":
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print("No display found. Using non-interactive Agg backend")
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mpl.use("Agg")
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import pylab as P
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from comparator import *
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import pspec_read
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P.rcParams["image.cmap"] = "plasma"
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P.rcParams["savefig.dpi"] = 400
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tex_params = {"text.latex.preamble": [r"\usepackage{amsmath}"]}
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P.rcParams.update(tex_params)
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class PlotRule(Rule):
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"""
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The rule class, speficic to plot.
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Add an extra method, plot, that take the reference to an open hdf5 file (from pytables)
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"""
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def plot(self, save, arg, **kwargs):
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"""
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Set the plotter's storage to 'save' and exetute the rule
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Parameters
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----------
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save : opended pytables hdf5 file, where to find the data
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arg : main argument of the plotting function
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kargs : optional keyword arguments to the plotting function
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"""
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self.postproc.save = save
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return self.process_fn(arg, **kwargs)
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class Plotter(Aggregator, BaseProcessor):
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"""
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This class loads derived quantities and plot them
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"""
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solve_self_dep = False
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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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_ax_title = {"x": r"$x$", "y": r"$y$", "z": r"$z$"}
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G = 1.0 # Gravitational constant
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# Conversion table from namelist keys (from amses config file) into LaTex strings
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label_convert = {
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"turb_rms": "$f_{rms}$",
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"beta": "$\\beta$",
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"beta_cool": "$\\beta_{c}$",
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"dens0": "$n_0$",
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"coldens0": "$\Sigma_0$",
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"sfr_avg_window": "window",
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"comp_frac": "$\\zeta$",
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}
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# Conversion table from namelist values (from amses config file) into LaTex strings
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value_convert = {
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"sfr_avg_window": lambda x: "${:g}$ Myr".format(80 * x),
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"comp_frac": lambda x: "${:g}$".format(1 - x),
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}
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def __init__(
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self,
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path,
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in_runs=None,
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in_nums=None,
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path_out=None,
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pp_params=None,
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selector=None,
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tag=None,
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**kwargs
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):
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"""
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Create a new Plotter instance. Will select run and outputs via a RunSelector object.
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Parameters
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----------
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path : path to the main folder of the simulations (ex '~/simus/myproject')
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in_runs : list of the runs to consider (ex ['run1', 'run2'])
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in_nums : list or dict of the outputs numbers to consider (ex [3, 5] or {'run1' : [3, 5], 'run2' : [4, 6])
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path_out : Path where the plot will be saved. By default set to `path`
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pp_params : Parameters for postprocessing. See pp_params module.
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selector : Existing instance of RunSelector, that selects runs and outputs. If set, in_runs and in_nums will be ignored
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tag : string to add in the output and data files.
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kwargs : Keyword arguments for RunSelector.
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"""
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super(Plotter, self).__init__(path, path_out, pp_params, tag)
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# Select runs
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if selector is None:
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selector = RunSelector(path, in_runs, in_nums, self.pp_params, **kwargs)
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# Save infos
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self.path = path
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self.runs = selector.runs
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self.nums = selector.nums
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# Get comparator object
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self.comp = Comparator(
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path, self.runs, self.nums, path_out, self.pp_params, selector=selector
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)
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# Get postprocesor objets for each run
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self.pp = self.comp.pp
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# Define log prefix
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self.log_id = "[plot {}] ".format(self.pp_params.out.tag)
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# Define rules
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self.def_rules()
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self.save = None
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def _not_self_dep(self, name, dep, dep_arg, overwrite, **kwargs):
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""""""
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if dep in self.comp.rules:
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done = self.comp.process(dep, dep_arg, overwrite, overwrite)
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self.just_done.extend(done)
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else:
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super(Plotter, self)._not_self_dep(name, dep, dep_arg, overwrite, **kwargs)
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def _needs_computation(self, overwrite, plot_filename):
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return (
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self.pp_params.out.interactive
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or overwrite
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or not os.path.exists(plot_filename)
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)
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def _process_rule(
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self,
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name,
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rule,
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arg,
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overwrite=False,
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ax=None,
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movie=False,
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from_cells=False,
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**kwargs
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):
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if not arg is None:
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name_full = name + "_" + str(arg)
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else:
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name_full = name
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if rule.is_valid(arg):
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if rule.kind == "classic" or rule.kind == "runs":
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try:
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runs = kwargs.pop("runs")
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if isinstance(runs, RunSelector):
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runs = runs.runs
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except KeyError:
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runs = self.runs
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i = 0
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for run in runs:
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files = []
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if rule.kind == "classic":
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nums = self.nums[run]
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else:
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nums = [None]
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for num in nums:
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plot_filename = self._find_filename(name_full, run, num)
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if from_cells or rule.kind == "cells":
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if not os.exists(self.pp[run][num].cells_filename):
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self.pp[run][num].load_cells()
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self.pp[run][num].unload_cells()
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save = tables.open_file(self.pp[run][num].cells_filename)
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elif rule.kind == "classic":
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save = tables.open_file(self.pp[run][num].filename)
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else:
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save = tables.open_file(self.comp.filename, "r")
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try:
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self._plot_rule(
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rule,
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save,
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arg,
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plot_filename,
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overwrite,
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ax=ax[i],
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run=run,
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**kwargs
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)
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except TypeError as e:
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if str(e) in [
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"'LocatableAxes' object does not support indexing",
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"'AxesSubplot' object does not support indexing",
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]:
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self._plot_rule(
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rule,
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save,
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arg,
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plot_filename,
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overwrite,
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ax=ax,
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run=run,
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**kwargs
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)
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elif ax is None:
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fig = P.figure()
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self._plot_rule(
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rule,
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save,
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arg,
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plot_filename,
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overwrite,
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ax=P.gca(),
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run=run,
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**kwargs
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)
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else:
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raise
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finally:
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save.close()
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i = i + 1
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files.append(plot_filename)
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else:
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if ax is None:
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ax = P.gca()
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if rule.kind == "series" and len(self.runs) == 1:
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run = self.runs[0]
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plot_filename = self._find_filename(name_full, run)
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else:
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plot_filename = self._find_filename(name_full)
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save = tables.open_file(self.comp.filename, "r")
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try:
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self._plot_rule(
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rule, save, arg, plot_filename, overwrite, ax, **kwargs
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)
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finally:
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save.close()
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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 _plot_rule(self, rule, save, arg, plot_filename, overwrite, ax, **kwargs):
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P.sca(ax)
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if self._needs_computation(overwrite, plot_filename):
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rule.plot(save, arg, **kwargs)
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P.tight_layout(pad=1)
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if not self.pp_params.out.interactive:
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P.savefig(plot_filename)
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P.close()
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self._log("{} plotted".format(plot_filename), "SUCCESS")
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else:
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self._log(
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"{} plotted".format(os.path.basename(plot_filename)), "SUCCESS"
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)
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else:
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self._log("Plot {} is already done, skipping...".format(plot_filename))
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def _find_filename(self, name_full, run=None, num=None, fmt=None):
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tag_name = self.pp_params.out.tag
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if fmt is None and self.pp_params.out.fmt == "":
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if not self.pp_params.out.tag == "":
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tag_name = "_" + tag_name
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if not run is None and not num is None:
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fmt = "{out}/{run}/{name}{tag}_{run}_{num:05}{ext}"
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elif not run is None:
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fmt = "{out}/{run}/{name}{tag}_{run}{ext}"
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else:
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fmt = "{out}/{name}{tag}{ext}"
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elif fmt is None:
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fmt = self.pp_params.out.fmt
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nml = None
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if not run is None:
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nml = self.comp.namelist[run]
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return fmt.format(
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run=run,
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name=name_full,
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tag=tag_name,
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num=num,
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nml=nml,
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out=self.path_out,
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ext=self.pp_params.out.ext,
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)
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def _label_run(self, run, node, label, nml_key):
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def get_label_nml(nml_key):
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prop_name = os.path.basename(nml_key)
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if prop_name in self.label_convert:
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prop_label = self.label_convert[prop_name]
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else:
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prop_label = prop_name
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prop_value = self.comp.get_nml(nml_key, run)
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if prop_name in self.value_convert:
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prop_value_str = self.value_convert[prop_name](prop_value)
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elif type(prop_value) in [int, float]:
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prop_value_str = convert_exp(prop_value, digits=5)
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else:
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prop_value_str = str(prop_value)
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return r"{} = {}".format(prop_label, prop_value_str)
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if nml_key is None and label is None:
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if (
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"attrs" in self.save.root._v_attrs
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and run in self.save.root._v_attrs.attrs
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):
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label_run = r"{}".format(self.save.root._v_attrs.attrs[run].label)
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else:
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label_run = run
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elif not nml_key is None:
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if not type(nml_key) == list:
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nml_key = [nml_key]
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label_run = ", ".join(map(get_label_nml, nml_key))
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if not label is None:
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label_run = label + " (" + label_run + ")"
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else:
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label_run = label
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return label_run
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def _ax_label_unit(self, node, label, unit, unit_coeff):
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if label is None:
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if "label" in node._v_attrs:
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label = node._v_attrs.label
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elif node._v_name in self.label_convert:
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label = self.label_convert[node._v_name]
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elif not node._v_title == "":
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label = node._v_title
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else:
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label = node._v_name
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if "unit" in node._v_attrs:
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unit_old = node._v_attrs.unit
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else:
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unit_old = cst.none
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if unit is None:
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unit = unit_old
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if not unit_coeff == 1:
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base = unit
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unit = unit_coeff * unit
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label = label + unit_str(unit, base=base)
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else:
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label = label + unit_str(unit)
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return label, unit_old, unit
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def _plot_map(
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self,
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name,
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ax_los,
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run,
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label=None,
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unit=None,
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unit_coeff=1.0,
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overlays=[],
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overlays_kwargs=[],
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title=None,
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put_title=True,
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nml_key=None,
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put_time=True,
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time_unit=cst.Myr,
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unit_space=cst.pc,
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cmap="plasma",
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norm="log",
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put_cbar=True,
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autoscale=True,
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**kwargs
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):
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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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im_extent = self.save.root.maps._v_attrs.im_extent
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unit_length = self.save.root._v_attrs["unit_length"]
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im_extent = np.array(im_extent) * unit_length.express(unit_space)
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node = self.save.get_node("/maps/{}_{}".format(name, ax_los))
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dmap = node.read()
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label, unit_old, unit = self._ax_label_unit(node, label, unit, unit_coeff)
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dmap = dmap * unit_old.express(unit)
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if norm == "log":
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norm = mpl.colors.LogNorm()
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elif norm == "linear":
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norm = mpl.colors.NoNorm()
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if autoscale and not norm is None:
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norm.autoscale(dmap)
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im = P.imshow(
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dmap, extent=im_extent, origin="lower", norm=norm, cmap=cmap, **kwargs
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)
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P.locator_params(axis=ax_h, nbins=self.pp_params.plot.ntick)
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P.locator_params(axis=ax_v, nbins=self.pp_params.plot.ntick)
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P.xlabel(self._ax_title[ax_h] + unit_str(unit_space))
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P.ylabel(self._ax_title[ax_v] + unit_str(unit_space))
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try:
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cbar = P.colorbar(im, cax=P.gca().cax)
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except AttributeError:
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cbar = P.colorbar()
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if not label is None:
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cbar.set_label(label)
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if put_title:
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title = self._label_run(run, node, title, nml_key)
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if put_time:
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time = self.save.root._v_attrs.time * self.comp.info["unit_time"]
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time_str = self.pp_params.plot.time_fmt.format(
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time.express(time_unit), time_unit.latex
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)
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if len(title) > 0:
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title = title + " | " + time_str
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else:
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title = time_str
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P.title(title)
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for i, plot_overlay in enumerate(overlays):
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try:
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plot_overlay(ax_los, **overlays_kwargs[i])
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except:
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plot_overlay(ax_los)
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def _overlay_levels(self, ax_los):
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map_level = self.save.get_node("/maps/{}_{}".format("levels", ax_los)).read()
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# Computing linewidths
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levels_ar = np.arange(np.min(map_level), np.max(map_level) + 1)
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lw = np.ones(levels_ar.size) * 2
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lvl_th = 8 # Level threeshold for reducing linewidths
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lw[levels_ar >= lvl_th] = lw[levels_ar >= lvl_th] ** (
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lvl_th - levels_ar[levels_ar >= lvl_th]
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)
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lw[levels_ar < lvl_th] = 1.0
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cont = P.contour(
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map_level,
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extent=self.save.root.maps._v_attrs.im_extent,
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origin="lower",
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colors="grey",
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linewidths=lw,
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levels=levels_ar,
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)
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cont.levels = cont.levels + 1
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P.clabel(cont, cont.levels[cont.levels < 11], inline=1, fontsize=8.0, fmt="%1d")
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def _overlay_speed(
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self, ax_los, unit=cst.km_s, unit_coeff=1.0, key_v=None, **kwargs
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):
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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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dmap_vh_node = self.save.get_node("/maps/speed_h_{}".format(ax_los))
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dmap_vh = dmap_vh_node.read()
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dmap_vv = self.save.get_node("/maps/speed_v_{}".format(ax_los)).read()
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vel_red = self.pp_params.plot.vel_red
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radius = self.save.root.maps._v_attrs.radius
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center = self.save.root.maps._v_attrs.center
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lbox = self.save.root._v_attrs.lbox
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map_vh_red = dmap_vh[::vel_red, ::vel_red] # take only a subset of velocities
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map_vv_red = dmap_vv[::vel_red, ::vel_red]
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nh = map_vh_red.shape[0]
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nv = map_vv_red.shape[1]
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vec_h = (
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np.arange(nh) * 2.0 / nh * radius - radius + center[0] + radius / nh
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) * lbox
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vec_v = (
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np.arange(nv) * 2.0 / nv * radius - radius + center[1] + radius / nv
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) * lbox
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hh, vv = np.meshgrid(vec_h, vec_v)
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norm_v = np.sqrt(map_vh_red ** 2 + map_vv_red ** 2)
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max_v = np.max(norm_v)
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min_v = np.min(norm_v)
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Q = P.quiver(hh, vv, map_vh_red, map_vv_red, units="width", **kwargs)
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|
label, unit_old, unit = self._ax_label_unit(dmap_vh_node, "", unit, unit_coeff)
|
|
|
|
if key_v is None:
|
|
key_v = (max_v + min_v) / 2.0
|
|
P.quiverkey(
|
|
Q,
|
|
0.6,
|
|
0.98,
|
|
key_v,
|
|
r"${:g}$".format(key_v) + label,
|
|
labelpos="E",
|
|
coordinates="figure",
|
|
)
|
|
|
|
def _overlay_B(self, ax_los, **kwargs):
|
|
ax_h = self._axes_h[ax_los]
|
|
ax_v = self._axes_v[ax_los]
|
|
dmap_Bh_node = self.save.get_node("/maps/B_h_{}".format(ax_los))
|
|
dmap_Bh = dmap_Bh_node.read()
|
|
dmap_Bv = self.save.get_node("/maps/B_v_{}".format(ax_los)).read()
|
|
|
|
vel_red = self.pp_params.plot.vel_red
|
|
radius = self.save.root.maps._v_attrs.radius
|
|
center = self.save.root.maps._v_attrs.center
|
|
lbox = self.save.root._v_attrs.lbox
|
|
|
|
map_Bh_red = dmap_Bh[::vel_red, ::vel_red] # take only a subset of velocities
|
|
map_Bv_red = dmap_Bv[::vel_red, ::vel_red]
|
|
nh = map_Bh_red.shape[0]
|
|
nv = map_Bv_red.shape[1]
|
|
vec_h = (
|
|
np.arange(nh) * 2.0 / nh * radius - radius + center[0] + radius / nh
|
|
) * lbox
|
|
vec_v = (
|
|
np.arange(nv) * 2.0 / nv * radius - radius + center[1] + radius / nv
|
|
) * lbox
|
|
hh, vv = np.meshgrid(vec_h, vec_v)
|
|
|
|
P.streamplot(hh, vv, map_Bh_red, map_Bv_red, **kwargs)
|
|
|
|
P.streamplot(hh, vv, map_Bh_red, map_Bv_red)
|
|
|
|
def _plot_radial(self, name, ax_los, label=None, xlog=False, ylog=False):
|
|
|
|
radial_bins = self.save.get_node("/radial/radial_bins_" + ax_los).read()
|
|
bin_centers = 0.5 * (radial_bins[1:] + radial_bins[:-1])
|
|
mean_bin = self.save.get_node("/radial/{}_{}".format(name, ax_los)).read()
|
|
|
|
P.grid()
|
|
P.xlabel(r"$r$")
|
|
|
|
if xlog:
|
|
P.xscale("log")
|
|
if ylog:
|
|
P.yscale("log")
|
|
P.plot(bin_centers, mean_bin)
|
|
|
|
if not label is None:
|
|
P.ylabel(label)
|
|
|
|
def _plot_hist(
|
|
self,
|
|
name,
|
|
ax_los,
|
|
run,
|
|
group="/hist/",
|
|
label=None,
|
|
unit=None,
|
|
unit_coeff=1.0,
|
|
title=None,
|
|
nml_key=None,
|
|
put_time=True,
|
|
time_unit=cst.Myr,
|
|
xlog=None,
|
|
ylog=False,
|
|
kind="bar",
|
|
ylabel="$\mathcal{P}$",
|
|
color=None,
|
|
colors=None,
|
|
nml_color=None,
|
|
fit=None,
|
|
fitlabel=None,
|
|
**kwargs
|
|
):
|
|
|
|
if not ax_los is None:
|
|
name = name + "_" + ax_los
|
|
|
|
node = self.save.get_node(group + name)
|
|
|
|
if xlog is None:
|
|
try:
|
|
xlog = node._v_attrs_.logbins
|
|
except:
|
|
xlog = False
|
|
|
|
label, unit_old, unit = self._ax_label_unit(node, label, unit, unit_coeff)
|
|
|
|
if "mean" in node:
|
|
index = node["runs"].read().index(run)
|
|
values, centers = node["mean"].read()[index]
|
|
else:
|
|
values, centers = node.read()
|
|
|
|
if xlog:
|
|
centers = centers + np.log10(unit_old.express(unit))
|
|
else:
|
|
centers = centers * unit_old.express(unit)
|
|
|
|
title = self._label_run(run, node, title, nml_key)
|
|
|
|
if put_time:
|
|
time = self.save.root._v_attrs.time * self.comp.info["unit_time"]
|
|
time_str = self.pp_params.plot.time_fmt.format(
|
|
time.express(time_unit), time_unit.latex
|
|
)
|
|
if len(title) > 0:
|
|
title = title + " | " + time_str
|
|
else:
|
|
title = time_str
|
|
|
|
P.title(title)
|
|
|
|
if color is None and not colors is None:
|
|
if nml_color is None:
|
|
color = colors[run]
|
|
else:
|
|
nml = self.comp.get_nml(nml_color, run)
|
|
try:
|
|
color = colors[nml]
|
|
except:
|
|
color = colors(nml)
|
|
|
|
if kind == "bar":
|
|
width = centers[1] - centers[0]
|
|
P.bar(centers, values, width, log=ylog, color=color, label=title, **kwargs)
|
|
elif kind == "step":
|
|
if ylog:
|
|
P.yscale("log")
|
|
P.step(centers, values, where="mid", color=color, label=title, **kwargs)
|
|
else:
|
|
raise ValueError("kind must be 'bar' or 'step'")
|
|
P.grid()
|
|
|
|
if not label is None:
|
|
P.xlabel(label)
|
|
if not ylabel is None:
|
|
P.ylabel(ylabel)
|
|
|
|
if not ax_los is None and "/hist/fit_" + name + "_" + ax_los in self.save:
|
|
slope = node.attrs.slope
|
|
origin = node.attrs.origin
|
|
P.plot(
|
|
centers,
|
|
10 ** (slope * centers + origin),
|
|
"--",
|
|
linewidth=2,
|
|
color="orange",
|
|
)
|
|
|
|
P.ylim([None, 1.0])
|
|
|
|
if not fit is None:
|
|
self._overlay_fit(
|
|
centers, values, kind=fit, ls="--", lw=1.5, label=fitlabel
|
|
)
|
|
|
|
def _plot(
|
|
self,
|
|
name_x,
|
|
name_y,
|
|
node_arg=None,
|
|
xlabel=None,
|
|
ylabel=None,
|
|
label=None,
|
|
xunit=None,
|
|
yunit=None,
|
|
xunit_coeff=1.0,
|
|
yunit_coeff=1.0,
|
|
ylog=False,
|
|
fit=None,
|
|
fitlabel=None,
|
|
smooth=0,
|
|
nml_key=None,
|
|
run=None,
|
|
runs=None,
|
|
yerr_kind="std",
|
|
sigma_err=2.0,
|
|
grid=True,
|
|
put_time=False,
|
|
time_unit=cst.Myr,
|
|
colors=None,
|
|
nml_color=None,
|
|
legend=None,
|
|
subname_x=None,
|
|
subname_y=None,
|
|
**kwargs
|
|
):
|
|
|
|
if not node_arg is None:
|
|
name_x, name_y = name_x + "_" + node_arg, name_y + "_" + node_arg
|
|
|
|
node_x = self.save.get_node(name_x)
|
|
node_y = self.save.get_node(name_y)
|
|
|
|
if subname_x:
|
|
hdf5_x = tables.open_file(node_x.read())
|
|
node_x = hdf5_x.get_node(subname_x)
|
|
if subname_y:
|
|
hdf5_y = tables.open_file(node_y.read())
|
|
node_y = hdf5_y.get_node(subname_y)
|
|
|
|
xlabel, xunit_old, xunit = self._ax_label_unit(
|
|
node_x, xlabel, xunit, xunit_coeff
|
|
)
|
|
ylabel, yunit_old, yunit = self._ax_label_unit(
|
|
node_y, ylabel, yunit, yunit_coeff
|
|
)
|
|
|
|
P.xlabel(xlabel)
|
|
P.ylabel(ylabel)
|
|
|
|
if grid:
|
|
P.grid()
|
|
|
|
if ylog:
|
|
P.yscale("log")
|
|
|
|
if put_time:
|
|
time = self.save.root._v_attrs.time * self.comp.info["unit_time"]
|
|
time_str = self.pp_params.plot.time_fmt.format(
|
|
time.express(time_unit), time_unit.latex
|
|
)
|
|
if len(label) > 0:
|
|
label = label + " | " + time_str
|
|
else:
|
|
label = time_str
|
|
|
|
yerr = None
|
|
if node_y._v_attrs.CLASS == "ARRAY":
|
|
x = node_x.read() * xunit_old.express(xunit)
|
|
y = node_y.read() * yunit_old.express(yunit)
|
|
mask = np.isfinite(x) & np.isfinite(y)
|
|
x, y = x[mask], y[mask]
|
|
if smooth > 0:
|
|
y = gaussian_filter1d(y, sigma=smooth)
|
|
if not run is None:
|
|
label = self._label_run(run, node_y, label, nml_key)
|
|
if colors is None:
|
|
(base_line,) = P.plot(x, y, label=label, **kwargs)
|
|
else:
|
|
if nml_color is None:
|
|
color = colors[run]
|
|
elif nml_color == "time":
|
|
time = (
|
|
self.save.root._v_attrs.time * self.comp.info["unit_time"]
|
|
).express(time_unit)
|
|
color = colors(time)
|
|
else:
|
|
nml = self.comp.get_nml(nml_color, run)
|
|
try:
|
|
color = colors[nml]
|
|
except:
|
|
color = colors(nml)
|
|
(base_line,) = P.plot(x, y, label=label, color=color, **kwargs)
|
|
elif "mean" in node_y:
|
|
x = node_x.read() * xunit_old.express(xunit)
|
|
y = node_y.mean.read() * yunit_old.express(yunit)
|
|
|
|
if yerr_kind == "std":
|
|
std = node_y.std.read() * yunit_old.express(yunit)
|
|
yerr_min = y - sigma_err * std
|
|
yerr_max = y + sigma_err * std
|
|
elif yerr_kind == "min_max":
|
|
yerr_min = node_y.min.read() * yunit_old.express(yunit)
|
|
yerr_max = node_y.max.read() * yunit_old.express(yunit)
|
|
elif yerr_kind == "95per":
|
|
yerr_min = node_y.q025.read() * yunit_old.express(yunit)
|
|
yerr_max = node_y.q975.read() * yunit_old.express(yunit)
|
|
elif yerr_kind == "68per":
|
|
yerr_min = node_y.q16.read() * yunit_old.express(yunit)
|
|
yerr_max = node_y.q84.read() * yunit_old.express(yunit)
|
|
else:
|
|
yerr_min = y
|
|
yerr_max = y
|
|
|
|
yerr = yerr_max - yerr_min
|
|
mask = np.isfinite(x) & np.isfinite(y) & np.isfinite(yerr)
|
|
x, y, yerr, yerr_min, yerr_max = (
|
|
x[mask],
|
|
y[mask],
|
|
yerr[mask],
|
|
yerr_min[mask],
|
|
yerr_max[mask],
|
|
)
|
|
if not run is None:
|
|
label = self._label_run(run, node_y, label, nml_key)
|
|
base_line, _, _ = P.errorbar(
|
|
x, y, yerr=[y - yerr_min, yerr_max - y], label=label, **kwargs
|
|
)
|
|
else:
|
|
if runs is None:
|
|
runs = self.runs
|
|
for i, run in enumerate(runs):
|
|
x_run, y_run = node_x[run], node_y[run]
|
|
x = x_run.read() * xunit_old.express(xunit)
|
|
y = y_run.read() * yunit_old.express(yunit)
|
|
mask = np.isfinite(x) & np.isfinite(y)
|
|
x, y = x[mask], y[mask]
|
|
if smooth > 0:
|
|
y = gaussian_filter1d(y, sigma=smooth)
|
|
label_run = self._label_run(run, y_run, label, nml_key)
|
|
if colors is None:
|
|
(base_line,) = P.plot(x, y, label=label_run, **kwargs)
|
|
else:
|
|
if nml_color is None:
|
|
color = colors[i % len(colors)]
|
|
else:
|
|
nml = self.comp.get_nml(nml_color, run)
|
|
try:
|
|
color = colors[nml]
|
|
except:
|
|
color = colors(nml)
|
|
(base_line,) = P.plot(x, y, label=label_run, color=color, **kwargs)
|
|
if legend is None:
|
|
legend = True
|
|
|
|
if legend:
|
|
P.legend()
|
|
|
|
if not fit is None:
|
|
self._overlay_fit(
|
|
x,
|
|
y,
|
|
yerr,
|
|
kind=fit,
|
|
ls="--",
|
|
lw=1.5,
|
|
color=base_line.get_color(),
|
|
label=fitlabel,
|
|
)
|
|
if subname_x:
|
|
hdf5_x.close()
|
|
if subname_y:
|
|
hdf5_y.close()
|
|
|
|
def _pspec(self, name, **kwargs):
|
|
del kwargs["run"]
|
|
file_pspec = self.save.get_node("/hdf5/pspec").read()
|
|
num = self.save.root._v_attrs.num
|
|
getattr(pspec_read, "pspec_" + name)(file_pspec, ".", num, **kwargs)
|
|
|
|
def _overlay_fit(self, x, y, yerr=None, kind="linear", label=None, **kwargs):
|
|
if kind == "linear":
|
|
if yerr is None:
|
|
(a, b, rho, _map_rule, stderr) = linregress(x, y)
|
|
self._log(
|
|
"Linear fit y = {} x + {} with R^2 = {} and error is {}".format(
|
|
a, b, rho, stderr
|
|
)
|
|
)
|
|
if label is None:
|
|
label = r"Linear fit with slope ${:.3g}$ and $R^2 = {:.3f}$".format(
|
|
a, rho
|
|
)
|
|
else:
|
|
fit = polyfit(x, y, 1, w=[1.0 / ty for ty in yerr], full=True)
|
|
c = fit[0]
|
|
residual = fit[1][0][0]
|
|
b, a = c[0], c[1]
|
|
self._log(
|
|
"Linear fit y = {} x + {} with residual {}".format(a, b, residual)
|
|
)
|
|
if label is None:
|
|
label = r"Linear fit with slope ${:.3g}$".format(a)
|
|
P.plot(x, a * x + b, label=label, **kwargs)
|
|
elif kind == "power_law":
|
|
if yerr is None:
|
|
(a, b, rho, _map_rule, stderr) = linregress(np.log10(x), np.log10(y))
|
|
self._log(
|
|
"Power law fit y = x^({}) * {} with R^2 = {} and error is {}".format(
|
|
a, 10 ** b, rho, stderr
|
|
)
|
|
)
|
|
else:
|
|
fitfunc = lambda p, x: p[0] + p[1] * x
|
|
errfunc = lambda p, x, y, err: (y - fitfunc(p, x)) / err
|
|
pinit = [1.0, -1.0]
|
|
out = optimize.leastsq(
|
|
errfunc,
|
|
pinit,
|
|
args=(np.log10(x), np.log10(y), yerr / y),
|
|
full_output=1,
|
|
)
|
|
|
|
c = out[0]
|
|
b, a = c[0], c[1]
|
|
residual = errfunc(c, np.log10(x), np.log10(y), yerr / y)
|
|
self._log(
|
|
"Power law fit y = x^({}) * {} with residual {}".format(
|
|
a, 10 ** b, residual
|
|
)
|
|
)
|
|
if label is None:
|
|
label = r"Power-law fit with index {:.1f}".format(a)
|
|
P.plot(x, (10 ** b) * x ** a, label=label, **kwargs)
|
|
|
|
def overlay_kennicutt(self, n0, step):
|
|
P.grid(False)
|
|
ylim = P.ylim()
|
|
(tmin, tmax) = P.xlim()
|
|
tmax = tmax + 20
|
|
ymax = P.ylim()[1]
|
|
ssfr_sun = 2.5e-9
|
|
ssfr_ken = ssfr_sun * n0 ** 1.4
|
|
|
|
coeff = ssfr_ken * 1e6 * (self.comp.info["unit_length"].express(cst.pc)) ** 2
|
|
for i in np.arange(tmin, max(tmax, tmin + ymax / coeff), step):
|
|
t = np.linspace(0, tmax, 1000)
|
|
P.plot(t + i, t * coeff, ls="--", lw=0.9, color="grey")
|
|
P.plot(t + tmin, (t + i - tmin) * coeff, ls="--", lw=0.9, color="grey")
|
|
P.xlim(tmin, tmax)
|
|
P.ylim(ylim)
|
|
|
|
def def_rules(self):
|
|
self.rules = {
|
|
# Generic rules
|
|
"plot": PlotRule(
|
|
self, lambda arg, **kwargs: self._plot(*arg, **kwargs), kind="comp"
|
|
),
|
|
"coldens": PlotRule(
|
|
self,
|
|
partial(self._plot_map, "coldens", label=r"$\Sigma$", unit=cst.coldens),
|
|
"Column density map",
|
|
dependencies=["coldens"],
|
|
),
|
|
"rho": PlotRule(
|
|
self,
|
|
partial(self._plot_map, "rho", label=r"$\rho$", unit=cst.Msun_pc3),
|
|
"Density slice at s = 0, with s = x, y or z.",
|
|
dependencies=["rho"],
|
|
),
|
|
"coldens_l": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_map,
|
|
"coldens",
|
|
label=r"$\Sigma$",
|
|
unit=cst.coldens,
|
|
overlays=[self._overlay_levels],
|
|
),
|
|
"Column density with level overlay",
|
|
dependencies=["coldens", "levels"],
|
|
),
|
|
"rho_v": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_map,
|
|
"rho",
|
|
label=r"$\rho$",
|
|
unit=cst.Msun_pc3,
|
|
overlays=[self._overlay_speed],
|
|
),
|
|
"Density slice with speed overlay",
|
|
dependencies=["rho", "speed_h", "speed_v"],
|
|
),
|
|
"rho_B": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_map,
|
|
"rho",
|
|
label=r"$\rho$",
|
|
unit=cst.Msun_pc3,
|
|
overlays=[self._overlay_B],
|
|
),
|
|
"Density slice with magnetic field overlay",
|
|
dependencies=["rho", "B_h", "B_v"],
|
|
),
|
|
"rho_B_vel": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_map,
|
|
"rho",
|
|
label=r"$\rho$",
|
|
unit=cst.Msun_pc3,
|
|
overlays=[self._overlay_B, self._overlay_speed],
|
|
),
|
|
"Density slice with magnetic field and velocity overlay",
|
|
dependencies=["rho", "B_h", "B_v", "speed_h", "speed_v"],
|
|
),
|
|
"jeans_ratio": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_map,
|
|
"jeans_ratio",
|
|
vmin=0.1,
|
|
vmax=100,
|
|
cmap="RdBu_r",
|
|
overlays=[self._overlay_levels],
|
|
),
|
|
"Jeans' lenght divided by the max resolution",
|
|
dependencies=["jeans_ratio", "levels"],
|
|
),
|
|
"Q": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_map,
|
|
"rho",
|
|
label=r"$Q$",
|
|
vmin=0.01,
|
|
vmax=100,
|
|
cmap="RdBu_r",
|
|
),
|
|
"Toomre Q parameter for a Keplerian disk",
|
|
dependencies=["Q"],
|
|
),
|
|
"rho_pdf": PlotRule(
|
|
self,
|
|
partial(self._plot_hist, "rho_pdf"),
|
|
"$\rho$-PDF",
|
|
dependencies=["rho_pdf"],
|
|
),
|
|
"cos_pdf": PlotRule(
|
|
self,
|
|
partial(self._plot_hist, "cos_pdf"),
|
|
"cos-PDF",
|
|
dependencies=["cos_pdf"],
|
|
),
|
|
"avg_coldens_pdf": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_hist,
|
|
"avg_time_coldens_pdf_z",
|
|
group="/comp/",
|
|
xlog=True,
|
|
put_time=False,
|
|
),
|
|
"Column density PDF, averaged in time",
|
|
kind="runs",
|
|
dependencies={"avg_time_coldens_pdf": "z"},
|
|
),
|
|
"T_pdf": PlotRule(
|
|
self,
|
|
partial(self._plot_hist, "T_pdf"),
|
|
"T-PDF on a 2D slice",
|
|
dependencies=["T_pdf"],
|
|
),
|
|
"P_pdf": PlotRule(
|
|
self,
|
|
partial(self._plot_hist, "P_pdf"),
|
|
"P-PDF on a 2D slice ",
|
|
dependencies=["P_pdf"],
|
|
),
|
|
"B_int": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_map, "B_int", label=r"$\mid \mathrm{B} \mid$", unit=cst.T
|
|
),
|
|
"Magnetic intensity map",
|
|
dependencies=["B_int"],
|
|
),
|
|
"Brho": PlotRule(
|
|
self,
|
|
partial(self._plot, "/datasets/Brho/rho", "/datasets/Brho/B"),
|
|
"Brho on a 2D slice ",
|
|
dependencies=["Brho"],
|
|
),
|
|
"rho_prof": PlotRule(
|
|
self,
|
|
partial(self._plot, "/profile/axis", "/profile/rho_prof"),
|
|
"Density profile",
|
|
dependencies=["axis", "rho_prof"],
|
|
),
|
|
"pspec": PlotRule(self, self._pspec, dependencies={"pspec": None}),
|
|
"kappa_beta": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot,
|
|
"/comp/nml_cloud_params/beta_cool",
|
|
"/comp/avg_time_pdf_slope_coldens",
|
|
),
|
|
"Slope of the Sigma-PDF against cooling beta factor",
|
|
kind="comp",
|
|
dependencies={
|
|
"nml": "cloud_params/beta_cool",
|
|
"avg_time_pdf_slope_coldens": None,
|
|
},
|
|
),
|
|
"sink_mass": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot,
|
|
"/series/sinks_from_log/time",
|
|
"/series/sinks_from_log/mass_sink",
|
|
xunit=cst.Myr,
|
|
yunit=cst.Msun,
|
|
),
|
|
"Mass of the sinks against time",
|
|
kind="series",
|
|
dependencies=["sinks_from_log"],
|
|
),
|
|
"assfr": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot,
|
|
"/series/sfr_from_log/time",
|
|
"/series/sfr_from_log/sfr",
|
|
ylabel="Averaged surfacic SFR",
|
|
xunit=cst.Myr,
|
|
yunit=cst.ssfr,
|
|
),
|
|
kind="series",
|
|
dependencies=["sfr_from_log"],
|
|
),
|
|
"issfr": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot,
|
|
"/series/sinks_from_log/time",
|
|
"/series/sinks_from_log/issfr",
|
|
ylabel="Surfacic SFR",
|
|
xunit=cst.Myr,
|
|
yunit=cst.ssfr,
|
|
),
|
|
kind="series",
|
|
dependencies=["issfr"],
|
|
),
|
|
"turb_rms": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot,
|
|
"/series/rms_from_log/time",
|
|
"/series/rms_from_log/turb_rms",
|
|
xunit=cst.Myr,
|
|
),
|
|
"Turbulent RMS",
|
|
kind="series",
|
|
dependencies=["rms_from_log"],
|
|
),
|
|
"turb_energy": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot,
|
|
"/series/rms_from_log/time",
|
|
"/series/rms_from_log/turb_energy",
|
|
xunit=cst.Myr,
|
|
),
|
|
"Turbulent energy",
|
|
kind="series",
|
|
dependencies=["rms_from_log"],
|
|
),
|
|
"turb_power": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot,
|
|
"/series/rms_from_log/time",
|
|
"/series/rms_from_log/turb_power",
|
|
xunit=cst.Myr,
|
|
),
|
|
"Turbulent power",
|
|
kind="series",
|
|
dependencies=["turb_power"],
|
|
),
|
|
"sigma": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot,
|
|
"/series/time",
|
|
"/series/time_sigma",
|
|
ylabel="$\\sigma$",
|
|
xunit=cst.Myr,
|
|
yunit=cst.km_s,
|
|
),
|
|
"Velocity dispersion",
|
|
kind="series",
|
|
dependencies=["time_sigma"],
|
|
),
|
|
"max_fluct_coldens": PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot,
|
|
"/series/time",
|
|
"/series/time_max_fluct_coldens_z",
|
|
ylabel="$\\max(\Sigma/\overline{\Sigma})$",
|
|
xunit=cst.Myr,
|
|
),
|
|
"Maximal fluctuation of the column density against time",
|
|
kind="series",
|
|
dependencies={"time_max_fluct_coldens": "z"},
|
|
),
|
|
}
|
|
|
|
averageables = ["coldens", "rho", "T", "Q"]
|
|
for name in averageables:
|
|
self.rules["rad_" + name] = PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_radial,
|
|
"rad_avg_" + name,
|
|
label=name,
|
|
xlog=True,
|
|
ylog=True,
|
|
),
|
|
"Azimuthal average of {}".format(name),
|
|
dependencies=["radial_bins", "rad_avg_" + name],
|
|
)
|
|
|
|
self.rules["fluct_" + name] = PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_map,
|
|
"fluct_" + name,
|
|
vmin=0.01,
|
|
vmax=100,
|
|
cmap="RdBu_r",
|
|
label="{}/avg({})".format(name, name),
|
|
),
|
|
"Fluctuation of {}".format(name),
|
|
dependencies=["fluct_" + name],
|
|
)
|
|
self.rules["pdf_" + name] = PlotRule(
|
|
self,
|
|
partial(
|
|
self._plot_hist,
|
|
"pdf_" + name,
|
|
ylog=True,
|
|
label="{}/avg({})".format(name, name),
|
|
),
|
|
"Probability density function of {} fluctuations".format(name),
|
|
dependencies=["fit_pdf_" + name],
|
|
)
|
|
|
|
super(Plotter, self).def_rules()
|