mirror of
https://github.com/Findus23/halo_comparison.git
synced 2024-09-13 09:03:49 +02:00
65 lines
1.7 KiB
Python
65 lines
1.7 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from matplotlib.axes import Axes
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from matplotlib.figure import Figure
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from halo_vis import get_comp_id
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from paths import base_dir
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num_bins = 5
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bins = np.geomspace(450, 80000, num_bins + 1)
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waveform = "shannon"
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comparisons_dir = base_dir / "comparisons"
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def read(mode, ref_res, comp_res):
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df = pd.read_csv(
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comparisons_dir / get_comp_id(waveform, ref_res, waveform, comp_res)
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)
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# df = pd.read_csv(f"{mode}_{ref_res}_100_{mode}_{comp_res}_100.csv")
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print(min(df.ref_Mvir), max(df.ref_Mvir))
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digits = np.digitize(df.ref_Mvir, bins)
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bin_means = []
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for i in range(num_bins):
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values = np.where(digits == i + 1)
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in_bin = df.iloc[values]
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matches = np.array(
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in_bin.match
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) # TODO: or instead fraction of halos that are matching?
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bin_means.append(matches.mean())
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return bin_means
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rows = [[1] * num_bins]
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resolutions = [128, 256, 512, 1024]
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ref_res = 128
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for res in resolutions:
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if res == ref_res:
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continue
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means = read(waveform, 128, res)
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rows.append(means)
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data = np.array(rows).T
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fig: Figure = plt.figure()
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ax: Axes = fig.gca()
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ax.set_xticks(range(len(resolutions)))
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ax.set_xticklabels(resolutions)
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ax.set_yticks(np.arange(len(bins)) - 0.5)
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ax.set_yticklabels(["{:.2f}".format(a) for a in bins])
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for x in range(data.shape[0]):
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for y in range(data.shape[1]):
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text = ax.text(
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y, x, "{:.2f}".format(data[x, y]), ha="center", va="center", color="w"
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)
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# print(data)
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p = ax.imshow(data, origin="lower", vmin=0.5, vmax=1)
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fig.colorbar(p)
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ax.set_title(waveform)
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# fig.savefig(method + ".png")
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plt.show()
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