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