mirror of
https://github.com/Findus23/halo_comparison.git
synced 2024-09-19 16:03:50 +02:00
97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
import numpy as np
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from matplotlib import pyplot as plt
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from matplotlib.axes import Axes
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from matplotlib.figure import Figure
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from paths import base_dir
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from read_vr_files import read_velo_halos
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def counts_without_inf(number_halos):
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with np.errstate(divide='ignore', invalid='ignore'):
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number_halos_inverse = 1 / np.sqrt(number_halos)
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number_halos_inverse[np.abs(number_halos_inverse) == np.inf] = 0
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return number_halos_inverse
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def main():
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fig: Figure = plt.figure()
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ax: Axes = fig.gca()
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num_bins = 30
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sim_volume = 100 ** 3
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linestyles = ["solid", "dashed", "dotted"]
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colors = ["C1", "C2"]
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for i, waveform in enumerate(["DB2", "shannon"]):
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for j, resolution in enumerate([128, 256, 512]):
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print(waveform, resolution)
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dir = base_dir / f"{waveform}_{resolution}_100"
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halos = read_velo_halos(dir)
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halos = halos[halos["Mvir"] > 2] # there seem to be multiple halos with a mass of 1.88196993
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# halos.to_csv("weird_halos.csv")
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halo_masses: np.ndarray = halos["Mvir"].to_numpy()
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bins = np.geomspace(halo_masses.min(), halo_masses.max(), num_bins + 1)
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digits = np.digitize(halo_masses, bins)
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number_densities = []
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widths = []
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centers = []
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left_edges = []
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Ns = []
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deltas = []
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for bin_id in range(num_bins):
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mass_low = bins[bin_id]
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mass_high = bins[bin_id + 1]
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counter = 0
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for val in halo_masses:
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if mass_low <= val < mass_high:
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counter += 1
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delta_mass = mass_high - mass_low
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widths.append(delta_mass)
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centers.append(mass_low + delta_mass / 2)
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left_edges.append(mass_low)
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values = np.where(digits == bin_id + 1)[0]
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# print(halo_masses[values])
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# print(values)
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num_halos = values.shape[0]
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assert num_halos == counter
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nd = num_halos / sim_volume / delta_mass
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number_densities.append(nd)
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Ns.append(num_halos)
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deltas.append(delta_mass)
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deltas = np.array(deltas)
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ax.set_xscale("log")
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ax.set_yscale("log")
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# ax.bar(centers, number_densities, width=widths, log=True, fill=False)
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name = f"{waveform} {resolution}"
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number_densities = np.array(number_densities)
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Ns = np.array(Ns)
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ax.step(left_edges, number_densities, where="post", color=colors[i], linestyle=linestyles[j], label=name)
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lower_error_limit = number_densities - counts_without_inf(Ns) / sim_volume / deltas
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upper_error_limit = number_densities + counts_without_inf(Ns) / sim_volume / deltas
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ax.fill_between(
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left_edges,
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lower_error_limit,
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upper_error_limit, alpha=.5, linewidth=0, step='post')
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# break
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# break
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plt.legend()
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plt.show()
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if __name__ == '__main__':
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main()
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