feat(validation): added more of the scripts to make paper figures
This commit is contained in:
351
validation/ManuscriptFigures/pynucastro/plot.py
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351
validation/ManuscriptFigures/pynucastro/plot.py
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import argparse
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import json
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import os
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import sys
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import math
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.interpolate import interp1d
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from scipy.integrate import cumulative_trapezoid
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from astropy import constants as const
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from astropy import units as u
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from enum import Enum
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from typing import List, Dict, Any, Tuple
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from fourdst.atomic import species as spdict
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EXTERNAL_STYLE_PATH = "../ManuscriptFigures/utils/pub.mplstyle"
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class PlotVariable(Enum):
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COMPOSITION = "composition"
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EPS = "eps"
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DT = "dt"
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class OutputFormat(Enum):
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INTERACTIVE = "interactive"
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PDF = "pdf"
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PNG = "png"
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JPEG = "jpeg"
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def discover_runs(base_dir: str) -> List[str]:
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runs = set()
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gf_ok_dir = os.path.join(base_dir, "GridFire", "Ok")
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if os.path.exists(gf_ok_dir):
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for fname in os.listdir(gf_ok_dir):
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if fname.endswith("_OKAY.json"):
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runs.add(fname.replace("_OKAY.json", ""))
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return sorted(list(runs))
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def load_run_data(base_dir: str, run_name: str) -> Tuple[Dict[str, Any], Dict[str, Any]]:
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gf_data = {}
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pynuc_data = {}
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gf_ok_path = os.path.join(base_dir, "GridFire", "Ok", f"{run_name}_OKAY.json")
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gf_err_path = os.path.join(base_dir, "GridFire", "Err", f"{run_name}_FAIL.json")
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pynuc_path = os.path.join(base_dir, "pynucastro", f"{run_name}_pynucastro.json")
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if os.path.exists(gf_ok_path):
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with open(gf_ok_path, 'r') as f:
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gf_data = json.load(f)
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elif os.path.exists(gf_err_path):
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with open(gf_err_path, 'r') as f:
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gf_data = json.load(f)
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if os.path.exists(pynuc_path):
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with open(pynuc_path, 'r') as f:
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pynuc_data = json.load(f)
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return gf_data, pynuc_data
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def get_pynuc_eps(steps: List[Dict[str, Any]]) -> Tuple[np.ndarray, np.ndarray]:
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c_sq = (const.c.cgs.value)**2
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Na = const.N_A.cgs.value
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amu_to_g = const.u.cgs.value
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eps_history = []
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time_history = []
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last_Y = {}
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last_t = None
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for step in steps:
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t = step["t"]
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current_Y = step["Composition"]
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if last_t is None:
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last_t = t
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last_Y = current_Y.copy()
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eps_history.append(0.0)
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time_history.append(t)
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continue
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dt = t - last_t
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if dt > 0:
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dm_dt = 0.0
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all_species = set(current_Y.keys()) | set(last_Y.keys())
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for sp in all_species:
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y_curr = current_Y.get(sp, 0.0)
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y_prev = last_Y.get(sp, 0.0)
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dy = y_curr - y_prev
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if sp in spdict:
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mass_g = spdict[sp].mass() * amu_to_g
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dm_dt += mass_g * (dy / dt)
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rate = -dm_dt * Na * c_sq
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eps_history.append(rate)
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time_history.append(t)
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last_t = t
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last_Y = current_Y.copy()
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return np.array(time_history), np.array(eps_history)
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def _setup_axes(ax_main: plt.Axes, ax_diff: plt.Axes, var: PlotVariable, fig_opts: dict):
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ax_diff.set_xlabel("Time (s)")
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ax_main.set_xscale(fig_opts['x_scale'])
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ax_diff.set_xscale(fig_opts['x_scale'])
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ax_main.set_yscale(fig_opts['y_scale'])
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if var == PlotVariable.EPS:
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ax_main.set_ylabel("Cumulative Energy (eps)")
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elif var == PlotVariable.DT:
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ax_main.set_ylabel("Timestep Size (dt)")
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elif var == PlotVariable.COMPOSITION:
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ax_main.set_ylabel("Mass Fraction (X_i)")
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ax_diff.set_ylabel(r"$\Delta \log_{10}$")
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def _plot_single_run(ax_main: plt.Axes, ax_diff: plt.Axes, run_name: str, var: PlotVariable,
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base_dir: str, compare_pynuc: bool):
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gf_data, pynuc_data = load_run_data(base_dir, run_name)
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if not gf_data or gf_data.get("Metadata", {}).get("Status") == "Error":
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return
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gf_steps = gf_data.get("Steps", [])
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if not gf_steps:
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return
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if compare_pynuc and not pynuc_data:
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print(f"Warning: PyNucastro comparison requested but data not found for '{run_name}'.")
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t_gf = np.array([s["t"] for s in gf_steps])
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if var == PlotVariable.COMPOSITION:
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final_comp = gf_steps[-1]["Composition"]
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top_species = sorted(final_comp, key=final_comp.get, reverse=True)[:3]
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for spec in top_species:
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y_gf = np.array([s["Composition"].get(spec, 1e-30) for s in gf_steps])
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line, = ax_main.plot(t_gf, y_gf, label=f"{run_name} {spec} (GF)")
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if compare_pynuc and pynuc_data:
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pynuc_steps = pynuc_data.get("Steps", [])
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if not pynuc_steps:
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continue
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t_pynuc = np.array([s["t"] for s in pynuc_steps])
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y_pynuc = np.array([s["Composition"].get(spec, 1e-30) for s in pynuc_steps])
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ax_main.plot(t_pynuc, y_pynuc, '--', color=line.get_color(), label=f"{run_name} {spec} (PyNuc)")
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if len(t_pynuc) > 1:
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f_interp = interp1d(t_pynuc, y_pynuc, kind='linear', bounds_error=False, fill_value=(y_pynuc[0], y_pynuc[-1]))
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y_pynuc_interp = f_interp(t_gf)
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log_diff = np.abs(np.log10(np.maximum(y_gf, 1e-30)) - np.log10(np.maximum(y_pynuc_interp, 1e-30)))
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ax_diff.plot(t_gf, log_diff, color=line.get_color(), linestyle=':', label=f"Δ {spec}")
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elif var == PlotVariable.EPS:
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y_gf = np.array([s["eps"] for s in gf_steps])
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line, = ax_main.plot(t_gf, y_gf, label=f"{run_name} (GF)")
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if compare_pynuc and pynuc_data:
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pynuc_steps = pynuc_data.get("Steps", [])
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if pynuc_steps:
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s_t, s_e = get_pynuc_eps(pynuc_steps)
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if len(s_t) > 1:
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s_cumE = cumulative_trapezoid(s_e, s_t, initial=0)
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ax_main.plot(s_t, s_cumE, '--', color=line.get_color(), label=f"{run_name} (PyNuc)")
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f_pynuc_interp = interp1d(s_t[np.isfinite(s_cumE)], s_cumE[np.isfinite(s_cumE)])
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f_gf_interp = interp1d(t_gf, y_gf)
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t_safe = np.logspace(
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8,
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np.log10(min(s_t.max(), t_gf.max())),
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1000
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)
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y_pynuc_interp = f_pynuc_interp(t_safe)
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y_gf_interp = f_gf_interp(t_safe)
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pynuc_safe = np.maximum(np.abs(y_pynuc_interp), 1e-30)
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gf_safe = np.maximum(np.abs(y_gf_interp), 1e-30)
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log_diff = np.log10(gf_safe) - np.log10(pynuc_safe)
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ax_diff.plot(t_safe, log_diff, color=line.get_color(), linestyle=':', label=f"Δ eps")
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ax_main.set_xlim(1e8)
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elif var == PlotVariable.DT:
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y_gf = np.array([s["dt"] for s in gf_steps])
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ax_main.plot(t_gf, y_gf, label=f"{run_name} (GF)")
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def _finalize_plot(fig: plt.Figure, ax_main: plt.Axes, ax_diff: plt.Axes, format_opt: OutputFormat, filename_base: str, is_subfigure: bool = False):
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ax_main.legend(loc='best', fontsize='small')
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if len(ax_diff.lines) > 0:
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ax_diff.legend(loc='best', fontsize='x-small')
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if is_subfigure:
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return
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fig.tight_layout()
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if format_opt != OutputFormat.INTERACTIVE:
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out_name = f"{filename_base}.{format_opt.value}"
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fig.savefig(out_name, format=format_opt.value, bbox_inches='tight')
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print(f"Saved figure: {out_name}")
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plt.close(fig)
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def plot_data(runs: List[str], plot_vars: List[PlotVariable], base_dir: str,
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compare_pynuc: bool, format_opt: OutputFormat, fig_opts: dict):
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if not runs:
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print("No valid runs to plot.")
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return
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if fig_opts['use_ext_style']:
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try:
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plt.style.use(EXTERNAL_STYLE_PATH)
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print(f"Using external style sheet: {EXTERNAL_STYLE_PATH}")
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except Exception as e:
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print(f"Warning: Failed to load external style sheet. Error: {e}")
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elif fig_opts['style']:
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try:
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plt.style.use(fig_opts['style'])
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except OSError:
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print(f"Warning: Style '{fig_opts['style']}' not found. Using default.")
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plt.rcParams["figure.figsize"] = fig_opts['figsize']
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plt.rcParams["figure.dpi"] = fig_opts['dpi']
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for var in plot_vars:
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if fig_opts['merge_runs']:
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fig, (ax_main, ax_diff) = plt.subplots(2, 1, sharex=True, gridspec_kw={'height_ratios': [3, 1]})
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ax_main.set_title(f"Comparison of {var.value.upper()} (Merged Runs)")
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_setup_axes(ax_main, ax_diff, var, fig_opts)
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for run_name in runs:
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_plot_single_run(ax_main, ax_diff, run_name, var, base_dir, compare_pynuc)
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_finalize_plot(fig, ax_main, ax_diff, format_opt, f"ValidationPlot_Merged_{var.value}")
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else:
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num_runs = len(runs)
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cols = math.ceil(math.sqrt(num_runs))
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rows = math.ceil(num_runs / cols)
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base_w, base_h = fig_opts['figsize']
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fig = plt.figure(figsize=(base_w * cols, base_h * rows), layout='constrained')
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fig.suptitle(f"{var.value.upper()} Comparison", fontsize=16, fontweight='bold')
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subfigs_raw = fig.subfigures(rows, cols)
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if hasattr(subfigs_raw, 'flatten'):
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subfigs = subfigs_raw.flatten()
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else:
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subfigs = [subfigs_raw]
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for i, run_name in enumerate(runs):
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subfig = subfigs[i]
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subfig.suptitle(f"{run_name}", fontsize=12)
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axes = subfig.subplots(2, 1, sharex=True, gridspec_kw={'height_ratios': [3, 1]})
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ax_main, ax_diff = axes[0], axes[1]
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_setup_axes(ax_main, ax_diff, var, fig_opts)
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_plot_single_run(ax_main, ax_diff, run_name, var, base_dir, compare_pynuc)
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_finalize_plot(fig, ax_main, ax_diff, format_opt, "", is_subfigure=True)
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for j in range(num_runs, len(subfigs)):
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subfigs[j].set_visible(False)
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if format_opt != OutputFormat.INTERACTIVE:
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out_name = f"ValidationPlot_Grid_{var.value}.{format_opt.value}"
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fig.savefig(out_name, format=format_opt.value, bbox_inches='tight')
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print(f"Saved grid figure: {out_name}")
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plt.close(fig)
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if format_opt == OutputFormat.INTERACTIVE:
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plt.show()
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def main():
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parser = argparse.ArgumentParser(description="GridFire Validation Suite Output Parser and Plotter")
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parser.add_argument("-d", "--data-dir", type=str, default="GF_Validation_Output",
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help="Path to the directory containing the JSON output folders.")
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parser.add_argument("--runs", nargs="+", type=str, required=False,
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help="Which validation runs to analyze. Use 'all' to process all available runs.")
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parser.add_argument("--plot", nargs="*", type=lambda x: PlotVariable[x.upper()], choices=list(PlotVariable), default=[],
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help="Variables to plot. Leave empty to skip plotting.")
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parser.add_argument("--compare-pynucastro", action="store_true",
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help="Include pynucastro data and calculate log residuals.")
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parser.add_argument("--merge-runs", action="store_true",
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help="Merge all specified runs onto a single figure per variable. (Default: Grid layout of subfigures)")
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parser.add_argument("--x-scale", type=str, choices=["log", "linear"], default="log",
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help="Scale for the x-axis (time). Default is 'log'.")
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parser.add_argument("--y-scale", type=str, choices=["log", "linear"], default="log",
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help="Scale for the y-axis (main plots). Default is 'log'.")
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parser.add_argument("--format", type=lambda x: OutputFormat[x.upper()], choices=list(OutputFormat), default=OutputFormat.INTERACTIVE,
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help="Output format for the plots. Default is interactive window.")
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parser.add_argument("--use-external-style", action="store_true",
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help="Load the custom style sheet defined in EXTERNAL_STYLE_PATH.")
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parser.add_argument("--style", type=str, default=None,
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help="Built-in Matplotlib stylesheet name (e.g., 'seaborn-v0_8-whitegrid'). Ignored if --use-external-style is set.")
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parser.add_argument("--figsize", nargs=2, type=float, default=[8.0, 6.0],
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metavar=("WIDTH", "HEIGHT"), help="Base figure size in inches per subfigure (e.g., --figsize 10 8).")
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parser.add_argument("--dpi", type=int, default=150, help="DPI resolution for saved figures.")
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parser.add_argument("--list", action="store_true", default=False, help="list available runs")
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args = parser.parse_args()
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available_runs = discover_runs(args.data_dir)
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if not available_runs:
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print(f"Error: No successful run data found in {args.data_dir} (Checked GridFire/Ok/).")
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sys.exit(1)
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if args.list:
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for run in available_runs:
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print(f"==> {run}")
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exit()
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if "all" in [r.lower() for r in args.runs]:
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target_runs = available_runs
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else:
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target_runs = [r for r in args.runs if r in available_runs]
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missing = [r for r in args.runs if r.lower() != "all" and r not in target_runs]
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if missing:
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print(f"Warning: The following runs were skipped because they failed or weren't found: {', '.join(missing)}")
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if args.plot and target_runs:
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fig_opts = {
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"figsize": tuple(args.figsize),
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"dpi": args.dpi,
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"style": args.style,
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"use_ext_style": args.use_external_style,
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"merge_runs": args.merge_runs,
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"x_scale": args.x_scale,
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"y_scale": args.y_scale
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}
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plot_data(target_runs, args.plot, args.data_dir, args.compare_pynucastro, args.format, fig_opts)
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elif args.plot:
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print("Error: No valid runs matched your selection.")
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if __name__ == "__main__":
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main()
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