import matplotlib.pyplot as plt from rebound import SimulationArchive, Particle, Simulation from extradata import ExtraData from utils import filename_from_argv, plot_settings, is_ci plot_settings() fn = filename_from_argv() sa = SimulationArchive(str(fn.with_suffix(".bin"))) ed = ExtraData.load(fn) print(ed.meta) data = {} sim: Simulation print(f"{len(sa)} Snapshots found") for sim in sa: t = sim.t for pn in range(1, sim.N): part: Particle = sim.particles[pn] hash = part.hash.value if hash not in data: data[hash] = ([], []) data[hash][0].append(t) data[hash][1].append(part.a) for name, d in data.items(): times, values = d print(list(map(len, [times, values]))) if False: plt.scatter(times, values, label=name, s=.9)
from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from imageio import imread import sys sys.path.insert(0, "../") import linear_regression import utils import stat_tools import crossvalidation import bootstrap from FrankeFunction import FrankeFunction utils.plot_settings() # LaTeX fonts in Plots! def terrain_analysis_plots( spacing=100, max_degree=20, n_lambdas=30, k_folds=5, n_bootstraps=50, do_boot=False, do_subset=False, ): # Setting up the terrain data: # Note structure! X-coordinates are on the rows of terrain_data # Point_selection.flatten() moves most rapidly over the x-coordinates