mirror of
https://github.com/Findus23/halo_comparison.git
synced 2024-09-13 09:03:49 +02:00
Changed layout of comparison figures: introduced sup(x/y)label but kept old structure, changed size of rowcolumn_labels to fit other labels, changed position of comp information
Formatted everything with black
This commit is contained in:
glatterf42
parent
60bd933469
commit
39bb626a42
25 changed files with 947 additions and 427 deletions
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@ -33,10 +33,12 @@ from utils import figsize_from_page_fraction
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# rc('ytick',direction='in')
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# rc('legend',fontsize='x-large')
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base_shape = {'u': [np.array([0, 1]), np.array([1, 0]), np.array([0, -1])],
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'd': [np.array([0, -1]), np.array([-1, 0]), np.array([0, 1])],
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'r': [np.array([1, 0]), np.array([0, 1]), np.array([-1, 0])],
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'l': [np.array([-1, 0]), np.array([0, -1]), np.array([1, 0])]}
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base_shape = {
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"u": [np.array([0, 1]), np.array([1, 0]), np.array([0, -1])],
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"d": [np.array([0, -1]), np.array([-1, 0]), np.array([0, 1])],
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"r": [np.array([1, 0]), np.array([0, 1]), np.array([-1, 0])],
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"l": [np.array([-1, 0]), np.array([0, -1]), np.array([1, 0])],
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}
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def hilbert_curve(order, orientation):
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@ -44,26 +46,46 @@ def hilbert_curve(order, orientation):
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Recursively creates the structure for a hilbert curve of given order
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"""
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if order > 1:
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if orientation == 'u':
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return hilbert_curve(order - 1, 'r') + [np.array([0, 1])] + \
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hilbert_curve(order - 1, 'u') + [np.array([1, 0])] + \
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hilbert_curve(order - 1, 'u') + [np.array([0, -1])] + \
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hilbert_curve(order - 1, 'l')
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elif orientation == 'd':
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return hilbert_curve(order - 1, 'l') + [np.array([0, -1])] + \
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hilbert_curve(order - 1, 'd') + [np.array([-1, 0])] + \
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hilbert_curve(order - 1, 'd') + [np.array([0, 1])] + \
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hilbert_curve(order - 1, 'r')
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elif orientation == 'r':
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return hilbert_curve(order - 1, 'u') + [np.array([1, 0])] + \
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hilbert_curve(order - 1, 'r') + [np.array([0, 1])] + \
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hilbert_curve(order - 1, 'r') + [np.array([-1, 0])] + \
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hilbert_curve(order - 1, 'd')
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if orientation == "u":
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return (
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hilbert_curve(order - 1, "r")
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+ [np.array([0, 1])]
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+ hilbert_curve(order - 1, "u")
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+ [np.array([1, 0])]
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+ hilbert_curve(order - 1, "u")
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+ [np.array([0, -1])]
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+ hilbert_curve(order - 1, "l")
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)
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elif orientation == "d":
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return (
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hilbert_curve(order - 1, "l")
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+ [np.array([0, -1])]
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+ hilbert_curve(order - 1, "d")
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+ [np.array([-1, 0])]
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+ hilbert_curve(order - 1, "d")
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+ [np.array([0, 1])]
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+ hilbert_curve(order - 1, "r")
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)
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elif orientation == "r":
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return (
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hilbert_curve(order - 1, "u")
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+ [np.array([1, 0])]
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+ hilbert_curve(order - 1, "r")
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+ [np.array([0, 1])]
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+ hilbert_curve(order - 1, "r")
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+ [np.array([-1, 0])]
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+ hilbert_curve(order - 1, "d")
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)
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else:
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return hilbert_curve(order - 1, 'd') + [np.array([-1, 0])] + \
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hilbert_curve(order - 1, 'l') + [np.array([0, -1])] + \
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hilbert_curve(order - 1, 'l') + [np.array([1, 0])] + \
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hilbert_curve(order - 1, 'u')
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return (
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hilbert_curve(order - 1, "d")
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+ [np.array([-1, 0])]
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+ hilbert_curve(order - 1, "l")
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+ [np.array([0, -1])]
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+ hilbert_curve(order - 1, "l")
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+ [np.array([1, 0])]
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+ hilbert_curve(order - 1, "u")
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)
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else:
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return base_shape[orientation]
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@ -88,15 +110,17 @@ def hilbert_curve(order, orientation):
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order = 6
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curve = hilbert_curve(order, 'u')
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curve = hilbert_curve(order, "u")
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curve = np.array(curve) * 4
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cumulative_curve_int = np.array([np.sum(curve[:i], 0) for i in range(len(curve) + 1)])
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cumulative_curve = (np.array([np.sum(curve[:i], 0) for i in range(len(curve) + 1)]) + 2) / 2 ** (order + 2)
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cumulative_curve = (
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np.array([np.sum(curve[:i], 0) for i in range(len(curve) + 1)]) + 2
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) / 2 ** (order + 2)
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# plot curve using plt
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N = 2 ** (2 * order)
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sublevel = order - 4
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cmap = cm.get_cmap('jet')
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cmap = cm.get_cmap("jet")
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fig = plt.figure(figsize=figsize_from_page_fraction(height_to_width=1))
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t = {}
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@ -104,31 +128,38 @@ sublevel = 7
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for i in range(2 ** (2 * sublevel)):
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il = i * N // (2 ** (2 * sublevel))
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ir = (i + 1) * N // 2 ** (2 * sublevel)
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plt.plot(cumulative_curve[il:ir + 1, 0], cumulative_curve[il:ir + 1, 1], lw=0.5, c=cmap(i / 2 ** (2 * sublevel)))
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plt.plot(
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cumulative_curve[il : ir + 1, 0],
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cumulative_curve[il : ir + 1, 1],
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lw=0.5,
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c=cmap(i / 2 ** (2 * sublevel)),
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)
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plt.xlabel('$x$')
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plt.ylabel('$y$')
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plt.xlabel("$x$")
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plt.ylabel("$y$")
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plt.tight_layout()
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plt.savefig(Path(f"~/tmp/hilbert_indexcolor.eps").expanduser())
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key = b'0123456789ABCDEF'
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key = b"0123456789ABCDEF"
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num = 123
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print(siphash.SipHash_2_4(key, bytes(num)).hash())
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order = 6
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curve = hilbert_curve(order, 'u')
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curve = hilbert_curve(order, "u")
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curve = np.array(curve) * 4
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cumulative_curve_int = np.array([np.sum(curve[:i], 0) for i in range(len(curve) + 1)])
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cumulative_curve = (np.array([np.sum(curve[:i], 0) for i in range(len(curve) + 1)]) + 2) / 2 ** (order + 2)
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cumulative_curve = (
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np.array([np.sum(curve[:i], 0) for i in range(len(curve) + 1)]) + 2
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) / 2 ** (order + 2)
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# plot curve using plt
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N = 2 ** (2 * order)
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sublevel = order - 4
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cmap = cm.get_cmap('jet')
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cmap = cm.get_cmap("jet")
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plt.figure()
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key = b'0123456789ABCDEF'
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key = b"0123456789ABCDEF"
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fig = plt.figure(figsize=figsize_from_page_fraction(height_to_width=1))
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t = {}
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@ -137,10 +168,15 @@ for i in range(2 ** (2 * sublevel)):
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il = i * N // (2 ** (2 * sublevel))
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ir = (i + 1) * N // 2 ** (2 * sublevel)
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sipkey = siphash.SipHash_2_4(key, bytes(il)).hash()
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plt.plot(cumulative_curve[il:ir + 1, 0], cumulative_curve[il:ir + 1, 1], lw=0.5, c=cmap(sipkey / 2 ** 64))
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plt.plot(
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cumulative_curve[il : ir + 1, 0],
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cumulative_curve[il : ir + 1, 1],
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lw=0.5,
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c=cmap(sipkey / 2 ** 64),
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)
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plt.xlabel('$x$')
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plt.ylabel('$y$')
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plt.xlabel("$x$")
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plt.ylabel("$y$")
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plt.tight_layout()
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plt.savefig(Path(f"~/tmp/hilbert_indexcolor_scrambled.eps").expanduser())
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plt.show()
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@ -8,6 +8,7 @@ from typing import List
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import matplotlib.pyplot as plt
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import numpy as np
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# two-fold upsampling -- https://cnx.org/contents/xsppCgXj@8.18:H_wA16rf@16/Upsampling
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from matplotlib.axes import Axes
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from matplotlib.figure import Figure
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@ -69,12 +70,12 @@ def cascade_algorithm(h, g, maxit):
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for it in range(maxit):
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# perform repeated convolutions
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phi_it = np.sqrt(2) * np.convolve(h_it, phi_it, mode='full')
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phi_it = np.sqrt(2) * np.convolve(h_it, phi_it, mode="full")
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if it != maxit - 1:
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psi_it = np.sqrt(2) * np.convolve(h_it, psi_it, mode='full')
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psi_it = np.sqrt(2) * np.convolve(h_it, psi_it, mode="full")
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else:
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psi_it = np.sqrt(2) * np.convolve(g_it, psi_it, mode='full')
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psi_it = np.sqrt(2) * np.convolve(g_it, psi_it, mode="full")
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# upsample the coefficients
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h_it = upsample(h_it)
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@ -108,55 +109,173 @@ xdb2, phidb2, psidb2 = cascade_algorithm(h_DB2, g_DB2, maxit)
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# DB3 -- http://wavelets.pybytes.com/wavelet/db3/
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h_DB3 = np.array(
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[0.3326705529509569, 0.8068915093133388, 0.4598775021193313, -0.13501102001039084, -0.08544127388224149,
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0.035226291882100656])
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[
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0.3326705529509569,
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0.8068915093133388,
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0.4598775021193313,
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-0.13501102001039084,
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-0.08544127388224149,
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0.035226291882100656,
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]
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)
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g_DB3 = np.array(
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[0.035226291882100656, 0.08544127388224149, -0.13501102001039084, -0.4598775021193313, 0.8068915093133388,
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-0.3326705529509569])
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[
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0.035226291882100656,
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0.08544127388224149,
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-0.13501102001039084,
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-0.4598775021193313,
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0.8068915093133388,
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-0.3326705529509569,
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]
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)
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xdb3, phidb3, psidb3 = cascade_algorithm(h_DB3, g_DB3, maxit)
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# DB4 -- http://wavelets.pybytes.com/wavelet/db4/
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h_DB4 = np.array(
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[0.23037781330885523, 0.7148465705525415, 0.6308807679295904, -0.02798376941698385, -0.18703481171888114,
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0.030841381835986965, 0.032883011666982945, -0.010597401784997278])
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[
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0.23037781330885523,
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0.7148465705525415,
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0.6308807679295904,
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-0.02798376941698385,
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-0.18703481171888114,
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0.030841381835986965,
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0.032883011666982945,
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-0.010597401784997278,
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]
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)
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g_DB4 = np.array(
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[-0.010597401784997278, -0.032883011666982945, 0.030841381835986965, 0.18703481171888114, -0.02798376941698385,
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-0.6308807679295904, 0.7148465705525415, -0.23037781330885523])
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[
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-0.010597401784997278,
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-0.032883011666982945,
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0.030841381835986965,
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0.18703481171888114,
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-0.02798376941698385,
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-0.6308807679295904,
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0.7148465705525415,
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-0.23037781330885523,
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]
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)
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xdb4, phidb4, psidb4 = cascade_algorithm(h_DB4, g_DB4, maxit)
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# DB8 -- http://wavelets.pybytes.com/wavelet/db8/
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h_DB8 = np.array(
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[0.05441584224308161, 0.3128715909144659, 0.6756307362980128, 0.5853546836548691, -0.015829105256023893,
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-0.2840155429624281, 0.00047248457399797254, 0.128747426620186, -0.01736930100202211, -0.04408825393106472,
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0.013981027917015516, 0.008746094047015655, -0.00487035299301066, -0.0003917403729959771, 0.0006754494059985568,
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-0.00011747678400228192])
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[
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0.05441584224308161,
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0.3128715909144659,
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0.6756307362980128,
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0.5853546836548691,
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-0.015829105256023893,
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-0.2840155429624281,
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0.00047248457399797254,
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0.128747426620186,
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-0.01736930100202211,
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-0.04408825393106472,
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0.013981027917015516,
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0.008746094047015655,
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-0.00487035299301066,
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-0.0003917403729959771,
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0.0006754494059985568,
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-0.00011747678400228192,
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]
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)
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g_DB8 = np.array(
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[-0.00011747678400228192, -0.0006754494059985568, -0.0003917403729959771, 0.00487035299301066, 0.008746094047015655,
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-0.013981027917015516, -0.04408825393106472, 0.01736930100202211, 0.128747426620186, -0.00047248457399797254,
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-0.2840155429624281, 0.015829105256023893, 0.5853546836548691, -0.6756307362980128, 0.3128715909144659,
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-0.05441584224308161])
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[
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-0.00011747678400228192,
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-0.0006754494059985568,
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-0.0003917403729959771,
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0.00487035299301066,
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0.008746094047015655,
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-0.013981027917015516,
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-0.04408825393106472,
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0.01736930100202211,
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0.128747426620186,
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-0.00047248457399797254,
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-0.2840155429624281,
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0.015829105256023893,
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0.5853546836548691,
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-0.6756307362980128,
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0.3128715909144659,
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-0.05441584224308161,
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]
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)
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xdb8, phidb8, psidb8 = cascade_algorithm(h_DB8, g_DB8, maxit)
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# DB16 --
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# DB16 --
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h_DB16 = np.array(
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[0.0031892209253436892, 0.03490771432362905, 0.1650642834886438, 0.43031272284545874, 0.6373563320829833,
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0.44029025688580486, -0.08975108940236352, -0.3270633105274758, -0.02791820813292813, 0.21119069394696974,
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0.027340263752899923, -0.13238830556335474, -0.006239722752156254, 0.07592423604445779, -0.007588974368642594,
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-0.036888397691556774, 0.010297659641009963, 0.013993768859843242, -0.006990014563390751, -0.0036442796214883506,
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0.00312802338120381, 0.00040789698084934395, -0.0009410217493585433, 0.00011424152003843815,
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0.00017478724522506327, -6.103596621404321e-05, -1.394566898819319e-05, 1.133660866126152e-05,
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-1.0435713423102517e-06, -7.363656785441815e-07, 2.3087840868545578e-07, -2.1093396300980412e-08])
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g_DB16 = np.array([-2.1093396300980412e-08, -2.3087840868545578e-07, -7.363656785441815e-07, 1.0435713423102517e-06,
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1.133660866126152e-05, 1.394566898819319e-05, -6.103596621404321e-05, -0.00017478724522506327,
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0.00011424152003843815, 0.0009410217493585433, 0.00040789698084934395, -0.00312802338120381,
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-0.0036442796214883506, 0.006990014563390751, 0.013993768859843242, -0.010297659641009963,
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-0.036888397691556774, 0.007588974368642594, 0.07592423604445779, 0.006239722752156254,
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-0.13238830556335474, -0.027340263752899923, 0.21119069394696974, 0.02791820813292813,
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-0.3270633105274758, 0.08975108940236352, 0.44029025688580486, -0.6373563320829833,
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0.43031272284545874, -0.1650642834886438, 0.03490771432362905, -0.0031892209253436892])
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[
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0.0031892209253436892,
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0.03490771432362905,
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0.1650642834886438,
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0.43031272284545874,
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0.6373563320829833,
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0.44029025688580486,
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-0.08975108940236352,
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-0.3270633105274758,
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-0.02791820813292813,
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0.21119069394696974,
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0.027340263752899923,
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-0.13238830556335474,
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-0.006239722752156254,
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0.07592423604445779,
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-0.007588974368642594,
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-0.036888397691556774,
|
||||
0.010297659641009963,
|
||||
0.013993768859843242,
|
||||
-0.006990014563390751,
|
||||
-0.0036442796214883506,
|
||||
0.00312802338120381,
|
||||
0.00040789698084934395,
|
||||
-0.0009410217493585433,
|
||||
0.00011424152003843815,
|
||||
0.00017478724522506327,
|
||||
-6.103596621404321e-05,
|
||||
-1.394566898819319e-05,
|
||||
1.133660866126152e-05,
|
||||
-1.0435713423102517e-06,
|
||||
-7.363656785441815e-07,
|
||||
2.3087840868545578e-07,
|
||||
-2.1093396300980412e-08,
|
||||
]
|
||||
)
|
||||
g_DB16 = np.array(
|
||||
[
|
||||
-2.1093396300980412e-08,
|
||||
-2.3087840868545578e-07,
|
||||
-7.363656785441815e-07,
|
||||
1.0435713423102517e-06,
|
||||
1.133660866126152e-05,
|
||||
1.394566898819319e-05,
|
||||
-6.103596621404321e-05,
|
||||
-0.00017478724522506327,
|
||||
0.00011424152003843815,
|
||||
0.0009410217493585433,
|
||||
0.00040789698084934395,
|
||||
-0.00312802338120381,
|
||||
-0.0036442796214883506,
|
||||
0.006990014563390751,
|
||||
0.013993768859843242,
|
||||
-0.010297659641009963,
|
||||
-0.036888397691556774,
|
||||
0.007588974368642594,
|
||||
0.07592423604445779,
|
||||
0.006239722752156254,
|
||||
-0.13238830556335474,
|
||||
-0.027340263752899923,
|
||||
0.21119069394696974,
|
||||
0.02791820813292813,
|
||||
-0.3270633105274758,
|
||||
0.08975108940236352,
|
||||
0.44029025688580486,
|
||||
-0.6373563320829833,
|
||||
0.43031272284545874,
|
||||
-0.1650642834886438,
|
||||
0.03490771432362905,
|
||||
-0.0031892209253436892,
|
||||
]
|
||||
)
|
||||
|
||||
xdb16, phidb16, psidb16 = cascade_algorithm(h_DB16, g_DB16, maxit)
|
||||
|
||||
|
|
@ -164,14 +283,15 @@ xdb16, phidb16, psidb16 = cascade_algorithm(h_DB16, g_DB16, maxit)
|
|||
|
||||
fig: Figure
|
||||
fig, ax = plt.subplots(
|
||||
4, 2,
|
||||
4,
|
||||
2,
|
||||
figsize=figsize_from_page_fraction(height_to_width=12 / 8),
|
||||
# sharex="all", sharey="all"
|
||||
)
|
||||
labels = ['Haar', 'DB2', 'DB4', 'DB8', 'DB16']
|
||||
labels = ["Haar", "DB2", "DB4", "DB8", "DB16"]
|
||||
|
||||
ax[0, 0].set_title('scaling functions $\\varphi$')
|
||||
ax[0, 1].set_title('wavelets $\\psi$')
|
||||
ax[0, 0].set_title("scaling functions $\\varphi$")
|
||||
ax[0, 1].set_title("wavelets $\\psi$")
|
||||
|
||||
ax[0, 0].plot(xhaar, phihaar, lw=1)
|
||||
ax[0, 1].plot(xhaar, psihaar, lw=1)
|
||||
|
|
@ -188,7 +308,7 @@ ax[3, 1].plot(xdb8, psidb8, lw=1)
|
|||
# ax[4, 0].plot(xdb16, phidb16, lw=1)
|
||||
# ax[4, 1].plot(xdb16, psidb16, lw=1)
|
||||
for a in ax.flatten():
|
||||
a.set_xlabel('t')
|
||||
a.set_xlabel("t")
|
||||
|
||||
|
||||
def inset_label(ax: Axes, text: str):
|
||||
|
|
@ -198,7 +318,7 @@ def inset_label(ax: Axes, text: str):
|
|||
text,
|
||||
horizontalalignment="left",
|
||||
verticalalignment="bottom",
|
||||
transform=ax.transAxes
|
||||
transform=ax.transAxes,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -238,32 +358,63 @@ def fourier_wavelet(h, g, n):
|
|||
# ax.plot([0, np.pi], [1., 1.], 'k:')
|
||||
|
||||
kh, fphih, fpsih = fourier_wavelet(h_Haar, g_Haar, 256)
|
||||
ax.plot(kh, np.abs(fphih) ** 2, label=r'$\hat\varphi_\textrm{Haar}$', c="C0")
|
||||
ax.plot(kh, np.abs(fpsih) ** 2, label=r'$\hat\psi_\textrm{Haar}$', c="C0", linestyle="dashed")
|
||||
ax.plot(kh, np.abs(fphih) ** 2, label=r"$\hat\varphi_\textrm{Haar}$", c="C0")
|
||||
ax.plot(
|
||||
kh,
|
||||
np.abs(fpsih) ** 2,
|
||||
label=r"$\hat\psi_\textrm{Haar}$",
|
||||
c="C0",
|
||||
linestyle="dashed",
|
||||
)
|
||||
|
||||
kdb2, fphidb2, fpsidb2 = fourier_wavelet(h_DB2, g_DB2, 256)
|
||||
ax.plot(kdb2, np.abs(fphidb2) ** 2, label=r'$\hat\varphi_\textrm{DB2}$', c="C1")
|
||||
ax.plot(kdb2, np.abs(fpsidb2) ** 2, label=r'$\hat\psi_\textrm{DB2}$', c="C1", linestyle="dashed")
|
||||
ax.plot(kdb2, np.abs(fphidb2) ** 2, label=r"$\hat\varphi_\textrm{DB2}$", c="C1")
|
||||
ax.plot(
|
||||
kdb2,
|
||||
np.abs(fpsidb2) ** 2,
|
||||
label=r"$\hat\psi_\textrm{DB2}$",
|
||||
c="C1",
|
||||
linestyle="dashed",
|
||||
)
|
||||
|
||||
kdb4, fphidb4, fpsidb4 = fourier_wavelet(h_DB4, g_DB4, 256)
|
||||
ax.plot(kdb4, np.abs(fphidb4) ** 2, label=r'$\hat\varphi_\textrm{DB4}$', c="C2")
|
||||
ax.plot(kdb4, np.abs(fpsidb4) ** 2, label=r'$\hat\psi_\textrm{DB4}$', c="C2", linestyle="dashed")
|
||||
ax.plot(kdb4, np.abs(fphidb4) ** 2, label=r"$\hat\varphi_\textrm{DB4}$", c="C2")
|
||||
ax.plot(
|
||||
kdb4,
|
||||
np.abs(fpsidb4) ** 2,
|
||||
label=r"$\hat\psi_\textrm{DB4}$",
|
||||
c="C2",
|
||||
linestyle="dashed",
|
||||
)
|
||||
|
||||
kdb8, fphidb8, fpsidb8 = fourier_wavelet(h_DB8, g_DB8, 256)
|
||||
ax.plot(kdb8, np.abs(fphidb8) ** 2, label=r'$\hat\varphi_\textrm{DB8}$', c="C3")
|
||||
ax.plot(kdb8, np.abs(fpsidb8) ** 2, label=r'$\hat\psi_\textrm{DB8}$', c="C3", linestyle="dashed")
|
||||
ax.plot(kdb8, np.abs(fphidb8) ** 2, label=r"$\hat\varphi_\textrm{DB8}$", c="C3")
|
||||
ax.plot(
|
||||
kdb8,
|
||||
np.abs(fpsidb8) ** 2,
|
||||
label=r"$\hat\psi_\textrm{DB8}$",
|
||||
c="C3",
|
||||
linestyle="dashed",
|
||||
)
|
||||
|
||||
|
||||
# all k* are np.linspace(0, np.pi, 256), so we can also use them for shannon
|
||||
|
||||
|
||||
def shannon(k):
|
||||
y = np.zeros_like(k)
|
||||
y[k > pi / 2] = 1
|
||||
return y
|
||||
|
||||
|
||||
ax.plot(kdb8, 1 - shannon(kdb8), label=r'$\hat\varphi_\textrm{shannon}$', c="C4")
|
||||
ax.plot(kdb8, shannon(kdb8), label=r'$\hat\psi_\textrm{shannon}$', c="C4", linestyle="dashed")
|
||||
ax.plot(kdb8, 1 - shannon(kdb8), label=r"$\hat\varphi_\textrm{shannon}$", c="C4")
|
||||
ax.plot(
|
||||
kdb8,
|
||||
shannon(kdb8),
|
||||
label=r"$\hat\psi_\textrm{shannon}$",
|
||||
c="C4",
|
||||
linestyle="dashed",
|
||||
)
|
||||
# ax.plot(kdb8, np.abs(fpsidb8) ** 2, label='$\\hat\\psi_{DB8}$', c="C3", linestyle="dashed")
|
||||
|
||||
# kdb16, fphidb16, fpsidb16 = fourier_wavelet(h_DB16, g_DB16, 256)
|
||||
|
|
@ -282,10 +433,12 @@ leg1 = ax.legend(frameon=False, handles=philines, loc="center left")
|
|||
leg2 = ax.legend(frameon=False, handles=psilines, loc="center right")
|
||||
ax.add_artist(leg1)
|
||||
ax.add_artist(leg2)
|
||||
ax.set_xlabel('k')
|
||||
ax.set_ylabel('P(k)')
|
||||
ax.set_xlabel("k")
|
||||
ax.set_ylabel("P(k)")
|
||||
ax.set_xticks([0, pi / 2, pi])
|
||||
ax.set_xticklabels(["0", r"$k_\textrm{coarse}^\textrm{ny}$", r"$k_\textrm{fine}^\textrm{ny}$"])
|
||||
ax.set_xticklabels(
|
||||
["0", r"$k_\textrm{coarse}^\textrm{ny}$", r"$k_\textrm{fine}^\textrm{ny}$"]
|
||||
)
|
||||
|
||||
# plt.semilogy()
|
||||
# plt.ylim([1e-4,2.0])
|
||||
|
|
|
|||
|
|
@ -32,7 +32,14 @@ mode = Mode.richings
|
|||
|
||||
|
||||
def dir_name_to_parameter(dir_name: str):
|
||||
return map(int, dir_name.lstrip("auriga6_halo").lstrip("richings21_").lstrip("bary_").lstrip("ramses_").split("_"))
|
||||
return map(
|
||||
int,
|
||||
dir_name.lstrip("auriga6_halo")
|
||||
.lstrip("richings21_")
|
||||
.lstrip("bary_")
|
||||
.lstrip("ramses_")
|
||||
.split("_"),
|
||||
)
|
||||
|
||||
|
||||
def levelmax_to_softening_length(levelmax: int) -> float:
|
||||
|
|
@ -46,8 +53,8 @@ fig2: Figure = plt.figure(figsize=figsize_from_page_fraction())
|
|||
ax2: Axes = fig2.gca()
|
||||
|
||||
for ax in [ax1, ax2]:
|
||||
ax.set_xlabel(r'R [Mpc]')
|
||||
ax1.set_ylabel(r'M [$10^{10} \mathrm{M}_\odot$]')
|
||||
ax.set_xlabel(r"R [Mpc]")
|
||||
ax1.set_ylabel(r"M [$10^{10} \mathrm{M}_\odot$]")
|
||||
ax2.set_ylabel("density [$\\frac{10^{10} \\mathrm{M}_\\odot}{Mpc^3}$]")
|
||||
|
||||
part_numbers = []
|
||||
|
|
@ -107,8 +114,10 @@ for dir in sorted(root_dir.glob("*")):
|
|||
|
||||
ideal_softening_length = levelmax_to_softening_length(levelmax)
|
||||
if not np.isclose(softening_length, levelmax_to_softening_length(levelmax)):
|
||||
raise ValueError(f"softening length for levelmax {levelmax} should be {ideal_softening_length} "
|
||||
f"but is {softening_length}")
|
||||
raise ValueError(
|
||||
f"softening length for levelmax {levelmax} should be {ideal_softening_length} "
|
||||
f"but is {softening_length}"
|
||||
)
|
||||
print(input_file)
|
||||
if mode == Mode.richings and is_by_adrian:
|
||||
h = 0.6777
|
||||
|
|
@ -141,12 +150,16 @@ for dir in sorted(root_dir.glob("*")):
|
|||
# halo = halos.loc[1]
|
||||
center = np.array([halo.X, halo.Y, halo.Z])
|
||||
log_radial_bins, bin_masses, bin_densities, center = halo_mass_profile(
|
||||
df, center, particles_meta, plot=False, num_bins=100,
|
||||
vmin=0.002, vmax=6.5
|
||||
df, center, particles_meta, plot=False, num_bins=100, vmin=0.002, vmax=6.5
|
||||
)
|
||||
i_min_border = np.argmax(0.01 < log_radial_bins) # first bin outside of specific radius
|
||||
i_min_border = np.argmax(
|
||||
0.01 < log_radial_bins
|
||||
) # first bin outside of specific radius
|
||||
i_max_border = np.argmax(1.5 < log_radial_bins)
|
||||
popt = fit_nfw(log_radial_bins[i_min_border:i_max_border], bin_densities[i_min_border:i_max_border]) # = rho_0, r_s
|
||||
popt = fit_nfw(
|
||||
log_radial_bins[i_min_border:i_max_border],
|
||||
bin_densities[i_min_border:i_max_border],
|
||||
) # = rho_0, r_s
|
||||
print(popt)
|
||||
# # Plot NFW profile
|
||||
# ax.loglog(
|
||||
|
|
@ -176,11 +189,11 @@ for dir in sorted(root_dir.glob("*")):
|
|||
ref_log_radial_bins, ref_bin_masses, ref_bin_densities = data
|
||||
mass_deviation: np.ndarray = np.abs(bin_masses - ref_bin_masses)
|
||||
density_deviation: np.ndarray = np.abs(bin_densities - ref_bin_densities)
|
||||
ax1.loglog(log_radial_bins[:-1], mass_deviation, c=f"C{i}",
|
||||
linestyle="dotted")
|
||||
ax1.loglog(log_radial_bins[:-1], mass_deviation, c=f"C{i}", linestyle="dotted")
|
||||
|
||||
ax2.loglog(log_radial_bins[:-1], density_deviation, c=f"C{i}",
|
||||
linestyle="dotted")
|
||||
ax2.loglog(
|
||||
log_radial_bins[:-1], density_deviation, c=f"C{i}", linestyle="dotted"
|
||||
)
|
||||
accuracy = mass_deviation / ref_bin_masses
|
||||
print(accuracy)
|
||||
print("mean accuracy", accuracy.mean())
|
||||
|
|
@ -209,11 +222,13 @@ for dir in sorted(root_dir.glob("*")):
|
|||
vmin = min(vmin, rho.min())
|
||||
vmax = max(vmax, rho.max())
|
||||
|
||||
images.append(Result(
|
||||
rho=rho,
|
||||
title=str(dir.name),
|
||||
levels=(levelmin, levelmin_TF, levelmax) if levelmin else None
|
||||
))
|
||||
images.append(
|
||||
Result(
|
||||
rho=rho,
|
||||
title=str(dir.name),
|
||||
levels=(levelmin, levelmin_TF, levelmax) if levelmin else None,
|
||||
)
|
||||
)
|
||||
i += 1
|
||||
# plot_cic(
|
||||
# rho, extent,
|
||||
|
|
@ -226,15 +241,21 @@ fig2.tight_layout()
|
|||
|
||||
# fig3: Figure = plt.figure(figsize=(9, 9))
|
||||
# axes: List[Axes] = fig3.subplots(3, 3, sharex=True, sharey=True).flatten()
|
||||
fig3: Figure = plt.figure(figsize=figsize_from_page_fraction(columns=2, height_to_width=1))
|
||||
fig3: Figure = plt.figure(
|
||||
figsize=figsize_from_page_fraction(columns=2, height_to_width=1)
|
||||
)
|
||||
axes: List[Axes] = fig3.subplots(3, 3, sharex=True, sharey=True).flatten()
|
||||
|
||||
for result, ax in zip(images, axes):
|
||||
data = 1.1 + result.rho
|
||||
vmin_scaled = 1.1 + vmin
|
||||
vmax_scaled = 1.1 + vmax
|
||||
img = ax.imshow(data.T, norm=LogNorm(vmin=vmin_scaled, vmax=vmax_scaled), extent=extent,
|
||||
origin="lower")
|
||||
img = ax.imshow(
|
||||
data.T,
|
||||
norm=LogNorm(vmin=vmin_scaled, vmax=vmax_scaled),
|
||||
extent=extent,
|
||||
origin="lower",
|
||||
)
|
||||
ax.set_title(result.title)
|
||||
|
||||
fig3.tight_layout()
|
||||
|
|
|
|||
51
cic.py
51
cic.py
|
|
@ -39,10 +39,15 @@ def cic_deposit(X, Y, ngrid, periodic=True) -> np.ndarray:
|
|||
|
||||
|
||||
def cic_range(
|
||||
X: np.ndarray, Y: np.ndarray,
|
||||
ngrid: int,
|
||||
xmin: float, xmax: float,
|
||||
ymin: float, ymax: float, *args, **kwargs
|
||||
X: np.ndarray,
|
||||
Y: np.ndarray,
|
||||
ngrid: int,
|
||||
xmin: float,
|
||||
xmax: float,
|
||||
ymin: float,
|
||||
ymax: float,
|
||||
*args,
|
||||
**kwargs
|
||||
) -> Tuple[np.ndarray, Extent]:
|
||||
xrange = xmax - xmin
|
||||
yrange = ymax - ymin
|
||||
|
|
@ -57,16 +62,25 @@ def cic_range(
|
|||
|
||||
|
||||
def cic_from_radius(
|
||||
X: np.ndarray, Y: np.ndarray,
|
||||
ngrid: int,
|
||||
x_center: float, y_center: float,
|
||||
radius: float, *args, **kwargs
|
||||
X: np.ndarray,
|
||||
Y: np.ndarray,
|
||||
ngrid: int,
|
||||
x_center: float,
|
||||
y_center: float,
|
||||
radius: float,
|
||||
*args,
|
||||
**kwargs
|
||||
) -> Tuple[np.ndarray, Extent]:
|
||||
return cic_range(
|
||||
X, Y, ngrid,
|
||||
x_center - radius, x_center + radius,
|
||||
y_center - radius, y_center + radius,
|
||||
*args, **kwargs
|
||||
X,
|
||||
Y,
|
||||
ngrid,
|
||||
x_center - radius,
|
||||
x_center + radius,
|
||||
y_center - radius,
|
||||
y_center + radius,
|
||||
*args,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -87,18 +101,21 @@ def plot_cic(rho: np.ndarray, extent: Extent, title: str):
|
|||
data = np.log(data)
|
||||
norm = plt.Normalize(vmin=data.min(), vmax=data.max())
|
||||
image = cmap(norm(data.T))
|
||||
plt.imsave((Path("~/tmp").expanduser() / title).with_suffix(".png"), image, origin="lower")
|
||||
plt.imsave(
|
||||
(Path("~/tmp").expanduser() / title).with_suffix(".png"), image, origin="lower"
|
||||
)
|
||||
# ax.hist2d(df.X, df.Y, bins=500, norm=LogNorm())
|
||||
# ax.hist2d(df2.X, df2.Y, bins=1000, norm=LogNorm())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
input_file = Path(sys.argv[1])
|
||||
df_ref, _ = read_file(input_file)
|
||||
rho, extent = cic_from_radius(df_ref.X.to_numpy(), df_ref.Y.to_numpy(), 1500, 48.8, 57, 1, periodic=False)
|
||||
rho, extent = cic_from_radius(
|
||||
df_ref.X.to_numpy(), df_ref.Y.to_numpy(), 1500, 48.8, 57, 1, periodic=False
|
||||
)
|
||||
# rho, extent = cic_range(df_ref.X.to_numpy(), df_ref.Y.to_numpy(), 800, 0, 85.47, 0, 85.47, periodic=False)
|
||||
|
||||
plot_cic(
|
||||
rho, extent,
|
||||
title=str(input_file.relative_to(input_file.parent.parent).name)
|
||||
rho, extent, title=str(input_file.relative_to(input_file.parent.parent).name)
|
||||
)
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@ for wf in ["DB2", "DB4", "DB8", "shannon"]:
|
|||
plot=False,
|
||||
plot3d=False,
|
||||
velo_halos=True,
|
||||
single=False
|
||||
single=False,
|
||||
)
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
|
|
|
|||
111
compare_halos.py
111
compare_halos.py
|
|
@ -45,10 +45,16 @@ def apply_offset(value, offset):
|
|||
|
||||
|
||||
def compare_halo_resolutions(
|
||||
ref_waveform: str, comp_waveform: str,
|
||||
reference_resolution: int, comparison_resolution: int,
|
||||
plot=False, plot3d=False, plot_cic=False,
|
||||
single=False, velo_halos=False, force=False
|
||||
ref_waveform: str,
|
||||
comp_waveform: str,
|
||||
reference_resolution: int,
|
||||
comparison_resolution: int,
|
||||
plot=False,
|
||||
plot3d=False,
|
||||
plot_cic=False,
|
||||
single=False,
|
||||
velo_halos=False,
|
||||
force=False,
|
||||
):
|
||||
reference_dir = base_dir / f"{ref_waveform}_{reference_resolution}_100"
|
||||
comparison_dir = base_dir / f"{comp_waveform}_{comparison_resolution}_100/"
|
||||
|
|
@ -68,14 +74,18 @@ def compare_halo_resolutions(
|
|||
print("reading reference file")
|
||||
df_ref, ref_meta = read_file(reference_dir / "output_0004.hdf5")
|
||||
if velo_halos:
|
||||
df_ref_halo, ref_halo_lookup, ref_unbound = read_velo_halo_particles(reference_dir)
|
||||
df_ref_halo, ref_halo_lookup, ref_unbound = read_velo_halo_particles(
|
||||
reference_dir
|
||||
)
|
||||
else:
|
||||
df_ref_halo = read_halo_file(reference_dir / "fof_output_0004.hdf5")
|
||||
|
||||
print("reading comparison file")
|
||||
df_comp, comp_meta = read_file(comparison_dir / "output_0004.hdf5")
|
||||
if velo_halos:
|
||||
df_comp_halo, comp_halo_lookup, comp_unbound = read_velo_halo_particles(comparison_dir)
|
||||
df_comp_halo, comp_halo_lookup, comp_unbound = read_velo_halo_particles(
|
||||
comparison_dir
|
||||
)
|
||||
|
||||
else:
|
||||
df_comp_halo = read_halo_file(comparison_dir / "fof_output_0004.hdf5")
|
||||
|
|
@ -137,18 +147,22 @@ def compare_halo_resolutions(
|
|||
print(f"{prev_len} => {after_len} (factor {prev_len / after_len:.2f})")
|
||||
|
||||
halo_distances = np.linalg.norm(
|
||||
ref_halo[['X', 'Y', 'Z']].values
|
||||
- df_comp_halo[['X', 'Y', 'Z']].values,
|
||||
axis=1)
|
||||
ref_halo[["X", "Y", "Z"]].values - df_comp_halo[["X", "Y", "Z"]].values,
|
||||
axis=1,
|
||||
)
|
||||
# print(list(halo_distances))
|
||||
|
||||
print(f"find nearby halos (50x{ref_halo.Rvir:.1f})")
|
||||
print(ref_halo[['X', 'Y', 'Z']].values)
|
||||
print(ref_halo[["X", "Y", "Z"]].values)
|
||||
# Find IDs of halos that are less than 50 Rvir away
|
||||
nearby_halos = set(df_comp_halo.loc[halo_distances < ref_halo.Rvir * 50].index.to_list())
|
||||
nearby_halos = set(
|
||||
df_comp_halo.loc[halo_distances < ref_halo.Rvir * 50].index.to_list()
|
||||
)
|
||||
if len(nearby_halos) < 10:
|
||||
print(f"only {len(nearby_halos)} halos, expanding to 150xRvir")
|
||||
nearby_halos = set(df_comp_halo.loc[halo_distances < ref_halo.Rvir * 150].index.to_list())
|
||||
nearby_halos = set(
|
||||
df_comp_halo.loc[halo_distances < ref_halo.Rvir * 150].index.to_list()
|
||||
)
|
||||
counters.checking_150 += 1
|
||||
|
||||
if not nearby_halos:
|
||||
|
|
@ -179,9 +193,13 @@ def compare_halo_resolutions(
|
|||
if plot:
|
||||
fig: Figure = plt.figure()
|
||||
ax: Axes = fig.gca()
|
||||
ax.scatter(apply_offset_to_list(halo_particles["X"], offset_x),
|
||||
apply_offset_to_list(halo_particles["Y"], offset_y), s=1,
|
||||
alpha=.3, label="Halo")
|
||||
ax.scatter(
|
||||
apply_offset_to_list(halo_particles["X"], offset_x),
|
||||
apply_offset_to_list(halo_particles["Y"], offset_y),
|
||||
s=1,
|
||||
alpha=0.3,
|
||||
label="Halo",
|
||||
)
|
||||
if plot_cic:
|
||||
diameter = ref_halo["R_size"]
|
||||
X = ref_halo["Xc"]
|
||||
|
|
@ -207,6 +225,7 @@ def compare_halo_resolutions(
|
|||
|
||||
if plot3d:
|
||||
from pyvista import Plotter
|
||||
|
||||
pl = Plotter()
|
||||
plotdf3d(pl, halo_particles, color="#b3cde3") # light blue
|
||||
pl.set_focus((ref_halo.X, ref_halo.Y, ref_halo.Z))
|
||||
|
|
@ -223,7 +242,11 @@ def compare_halo_resolutions(
|
|||
particle_ids_in_comp_halo = comp_halo_lookup[halo_id]
|
||||
mass_factor_limit = 5
|
||||
|
||||
if not (1 / mass_factor_limit < (comp_halo_masses[halo_id] / ref_halo_mass) < mass_factor_limit):
|
||||
if not (
|
||||
1 / mass_factor_limit
|
||||
< (comp_halo_masses[halo_id] / ref_halo_mass)
|
||||
< mass_factor_limit
|
||||
):
|
||||
# print("mass not similar, skipping")
|
||||
num_skipped_for_mass += 1
|
||||
continue
|
||||
|
|
@ -235,7 +258,10 @@ def compare_halo_resolutions(
|
|||
|
||||
# similarity = len(shared_particles) / len(union_particles)
|
||||
similarity = len(shared_particles) / (
|
||||
len(halo_particle_ids) + len(particle_ids_in_comp_halo) - len(shared_particles))
|
||||
len(halo_particle_ids)
|
||||
+ len(particle_ids_in_comp_halo)
|
||||
- len(shared_particles)
|
||||
)
|
||||
# assert similarity_orig == similarity
|
||||
# print(shared_size)
|
||||
# if not similarity:
|
||||
|
|
@ -247,12 +273,24 @@ def compare_halo_resolutions(
|
|||
color = f"C{i + 1}"
|
||||
comp_halo: pd.Series = df_comp_halo.loc[halo_id]
|
||||
|
||||
ax.scatter(apply_offset_to_list(df["X"], offset_x), apply_offset_to_list(df["Y"], offset_y), s=1,
|
||||
alpha=.3, c=color)
|
||||
circle = Circle((apply_offset(comp_halo.X, offset_x), apply_offset(comp_halo.Y, offset_y)),
|
||||
comp_halo["Rvir"], zorder=10,
|
||||
linewidth=1, edgecolor=color, fill=None
|
||||
)
|
||||
ax.scatter(
|
||||
apply_offset_to_list(df["X"], offset_x),
|
||||
apply_offset_to_list(df["Y"], offset_y),
|
||||
s=1,
|
||||
alpha=0.3,
|
||||
c=color,
|
||||
)
|
||||
circle = Circle(
|
||||
(
|
||||
apply_offset(comp_halo.X, offset_x),
|
||||
apply_offset(comp_halo.Y, offset_y),
|
||||
),
|
||||
comp_halo["Rvir"],
|
||||
zorder=10,
|
||||
linewidth=1,
|
||||
edgecolor=color,
|
||||
fill=None,
|
||||
)
|
||||
ax.add_artist(circle)
|
||||
if plot3d:
|
||||
plotdf3d(pl, df, color="#fed9a6") # light orange
|
||||
|
|
@ -270,13 +308,16 @@ def compare_halo_resolutions(
|
|||
comp_halo: pd.Series = df_comp_halo.loc[best_halo]
|
||||
|
||||
# merge the data of the two halos with fitting prefixes
|
||||
halo_data = pd.concat([
|
||||
ref_halo.add_prefix("ref_"),
|
||||
comp_halo.add_prefix("comp_")
|
||||
])
|
||||
distance = linalg.norm(
|
||||
np.array([ref_halo.X, ref_halo.Y, ref_halo.Z]) - np.array([comp_halo.X, comp_halo.Y, comp_halo.Z])
|
||||
) / ref_halo.Rvir
|
||||
halo_data = pd.concat(
|
||||
[ref_halo.add_prefix("ref_"), comp_halo.add_prefix("comp_")]
|
||||
)
|
||||
distance = (
|
||||
linalg.norm(
|
||||
np.array([ref_halo.X, ref_halo.Y, ref_halo.Z])
|
||||
- np.array([comp_halo.X, comp_halo.Y, comp_halo.Z])
|
||||
)
|
||||
/ ref_halo.Rvir
|
||||
)
|
||||
halo_data["distance"] = distance
|
||||
halo_data["match"] = best_halo_match
|
||||
halo_data["num_skipped_for_mass"] = num_skipped_for_mass
|
||||
|
|
@ -285,7 +326,9 @@ def compare_halo_resolutions(
|
|||
if plot:
|
||||
print(f"plotting with offsets ({offset_x},{offset_y})")
|
||||
# ax.legend()
|
||||
ax.set_title(f"{reference_dir.name} vs. {comparison_dir.name} (Halo {index})")
|
||||
ax.set_title(
|
||||
f"{reference_dir.name} vs. {comparison_dir.name} (Halo {index})"
|
||||
)
|
||||
fig.savefig("out.png", dpi=300)
|
||||
plt.show()
|
||||
if plot3d:
|
||||
|
|
@ -310,7 +353,7 @@ def precalculate_halo_membership(df_comp, df_comp_halo):
|
|||
print_progress(i, len(df_comp_halo), halo["Sizes"])
|
||||
size = int(halo["Sizes"])
|
||||
halo_id = int(i)
|
||||
halo_particles = df_comp.iloc[pointer:pointer + size]
|
||||
halo_particles = df_comp.iloc[pointer : pointer + size]
|
||||
|
||||
# check_id = halo_particles["FOFGroupIDs"].to_numpy()
|
||||
# assert (check_id == i).all()
|
||||
|
|
@ -321,7 +364,7 @@ def precalculate_halo_membership(df_comp, df_comp_halo):
|
|||
return comp_halo_lookup
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
compare_halo_resolutions(
|
||||
ref_waveform="shannon",
|
||||
comp_waveform="shannon",
|
||||
|
|
@ -332,5 +375,5 @@ if __name__ == '__main__':
|
|||
plot_cic=False,
|
||||
velo_halos=True,
|
||||
single=False,
|
||||
force=True
|
||||
force=True,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -21,32 +21,32 @@ from utils import figsize_from_page_fraction, rowcolumn_labels, waveforms, tex_f
|
|||
|
||||
G = 43.022682 # in Mpc (km/s)^2 / (10^10 Msun)
|
||||
|
||||
vmaxs = {
|
||||
"Mvir": 52,
|
||||
"Vmax": 93,
|
||||
"cNFW": 31
|
||||
}
|
||||
vmaxs = {"Mvir": 52, "Vmax": 93, "cNFW": 31}
|
||||
|
||||
units = {
|
||||
"distance": "Mpc",
|
||||
"Mvir": r"10^{10} \textrm{M}_\odot",
|
||||
"Vmax": r"\textrm{km} \textrm{s}^{-1}" # TODO
|
||||
"Mvir": r"10^{10} \textrm{ M}_\odot",
|
||||
"Vmax": r"\textrm{km } \textrm{s}^{-1}", # TODO
|
||||
}
|
||||
|
||||
|
||||
def concentration(row, halo_type: str) -> bool:
|
||||
r_200crit = row[f'{halo_type}_R_200crit']
|
||||
r_200crit = row[f"{halo_type}_R_200crit"]
|
||||
if r_200crit <= 0:
|
||||
cnfw = -1
|
||||
colour = 'orange'
|
||||
colour = "orange"
|
||||
return False
|
||||
# return cnfw, colour
|
||||
|
||||
r_size = row[f'{halo_type}_R_size'] # largest difference from center of mass to any halo particle
|
||||
m_200crit = row[f'{halo_type}_Mass_200crit']
|
||||
vmax = row[f'{halo_type}_Vmax'] # largest velocity coming from enclosed mass profile calculation
|
||||
rmax = row[f'{halo_type}_Rmax']
|
||||
npart = row[f'{halo_type}_npart']
|
||||
r_size = row[
|
||||
f"{halo_type}_R_size"
|
||||
] # largest difference from center of mass to any halo particle
|
||||
m_200crit = row[f"{halo_type}_Mass_200crit"]
|
||||
vmax = row[
|
||||
f"{halo_type}_Vmax"
|
||||
] # largest velocity coming from enclosed mass profile calculation
|
||||
rmax = row[f"{halo_type}_Rmax"]
|
||||
npart = row[f"{halo_type}_npart"]
|
||||
VmaxVvir2 = vmax ** 2 * r_200crit / (G * m_200crit)
|
||||
if VmaxVvir2 <= 1.05:
|
||||
if m_200crit == 0:
|
||||
|
|
@ -59,7 +59,7 @@ def concentration(row, halo_type: str) -> bool:
|
|||
# colour = 'white'
|
||||
else:
|
||||
if npart >= 100: # only calculate cnfw for groups with more than 100 particles
|
||||
cnfw = row[f'{halo_type}_cNFW']
|
||||
cnfw = row[f"{halo_type}_cNFW"]
|
||||
return True
|
||||
# colour = 'black'
|
||||
else:
|
||||
|
|
@ -91,12 +91,12 @@ def plot_comparison_hist2d(ax: Axes, file: Path, property: str):
|
|||
max_x = max([max(df[x_col]), max(df[y_col])])
|
||||
num_bins = 100
|
||||
bins = np.geomspace(min_x, max_x, num_bins)
|
||||
if property == 'cNFW':
|
||||
if property == "cNFW":
|
||||
rows = []
|
||||
for i, row in df.iterrows():
|
||||
comp_cnfw_normal = concentration(row, halo_type="comp")
|
||||
|
||||
ref_cnfw_normal = concentration(row, halo_type='ref')
|
||||
ref_cnfw_normal = concentration(row, halo_type="ref")
|
||||
cnfw_normal = comp_cnfw_normal and ref_cnfw_normal
|
||||
if cnfw_normal:
|
||||
rows.append(row)
|
||||
|
|
@ -118,13 +118,10 @@ def plot_comparison_hist2d(ax: Axes, file: Path, property: str):
|
|||
stds.append(std)
|
||||
means = np.array(means)
|
||||
stds = np.array(stds)
|
||||
args = {
|
||||
"color": "C2",
|
||||
"zorder": 10
|
||||
}
|
||||
ax.fill_between(bins, means - stds, means + stds, alpha=.2, **args)
|
||||
ax.plot(bins, means + stds, alpha=.5, **args)
|
||||
ax.plot(bins, means - stds, alpha=.5, **args)
|
||||
args = {"color": "C2", "zorder": 10}
|
||||
ax.fill_between(bins, means - stds, means + stds, alpha=0.2, **args)
|
||||
ax.plot(bins, means + stds, alpha=0.5, **args)
|
||||
ax.plot(bins, means - stds, alpha=0.5, **args)
|
||||
# ax_scatter.plot(bins, stds, label=f"{file.stem}")
|
||||
|
||||
if property in vmaxs:
|
||||
|
|
@ -133,8 +130,13 @@ def plot_comparison_hist2d(ax: Axes, file: Path, property: str):
|
|||
vmax = None
|
||||
print("WARNING: vmax not set")
|
||||
image: QuadMesh
|
||||
_, _, _, image = ax.hist2d(df[x_col], df[y_col] / df[x_col], bins=(bins, np.linspace(0, 2, num_bins)),
|
||||
norm=LogNorm(vmax=vmax), rasterized=True)
|
||||
_, _, _, image = ax.hist2d(
|
||||
df[x_col],
|
||||
df[y_col] / df[x_col],
|
||||
bins=(bins, np.linspace(0, 2, num_bins)),
|
||||
norm=LogNorm(vmax=vmax),
|
||||
rasterized=True,
|
||||
)
|
||||
# ax.plot([rep_x_left, rep_x_left], [mean - std, mean + std], c="C1")
|
||||
# ax.annotate(
|
||||
# text=f"std={std:.2f}", xy=(rep_x_left, mean + std),
|
||||
|
|
@ -148,7 +150,9 @@ def plot_comparison_hist2d(ax: Axes, file: Path, property: str):
|
|||
# ax.set_yscale("log")
|
||||
ax.set_xlim(min(df[x_col]), max(df[y_col]))
|
||||
|
||||
ax.plot([min(df[x_col]), max(df[y_col])], [1, 1], linewidth=1, color="C1", zorder=10)
|
||||
ax.plot(
|
||||
[min(df[x_col]), max(df[y_col])], [1, 1], linewidth=1, color="C1", zorder=10
|
||||
)
|
||||
|
||||
return x_col, y_col
|
||||
# ax.set_title(file.name)
|
||||
|
|
@ -193,7 +197,9 @@ def plot_comparison_hist(ax: Axes, file: Path, property: str, m_min=None, m_max=
|
|||
ax.plot(bin_centers, hist_val, label=label)
|
||||
else:
|
||||
patches: List[Polygon]
|
||||
hist_val, bin_edges, patches = ax.hist(df[property], bins=bins, histtype=histtype, label=label, density=density)
|
||||
hist_val, bin_edges, patches = ax.hist(
|
||||
df[property], bins=bins, histtype=histtype, label=label, density=density
|
||||
)
|
||||
|
||||
|
||||
comparisons_dir = base_dir / "comparisons"
|
||||
|
|
@ -206,8 +212,10 @@ def compare_property(property, show: bool):
|
|||
is_hist_property = property in hist_properties
|
||||
fig: Figure
|
||||
fig, axes = plt.subplots(
|
||||
len(waveforms), len(comparisons),
|
||||
sharey="all", sharex="all",
|
||||
len(waveforms),
|
||||
len(comparisons),
|
||||
sharey="all",
|
||||
sharex="all",
|
||||
figsize=figsize_from_page_fraction(columns=2),
|
||||
)
|
||||
for i, waveform in enumerate(waveforms):
|
||||
|
|
@ -227,24 +235,82 @@ def compare_property(property, show: bool):
|
|||
}
|
||||
x_col, y_col = plot_comparison_hist2d(ax, file, property)
|
||||
lab_a, lab_b = x_labels[property]
|
||||
unit = f"[{units[property]}]" if property in units and units[property] else ""
|
||||
unit = (
|
||||
f"[{units[property]}]"
|
||||
if property in units and units[property]
|
||||
else ""
|
||||
)
|
||||
if is_bottom_row:
|
||||
if lab_b:
|
||||
ax.set_xlabel(tex_fmt(r"$AA_{\textrm{BB},CC} DD$", lab_a, lab_b, ref_res, unit))
|
||||
ax.set_xlabel(
|
||||
tex_fmt(
|
||||
r"$AA_{\textrm{BB},\textrm{ CC}} \textrm{ } DD$",
|
||||
lab_a,
|
||||
lab_b,
|
||||
ref_res,
|
||||
unit,
|
||||
)
|
||||
)
|
||||
# fig.supxlabel(tex_fmt(r"$AA_{\textrm{BB},\textrm{ } CC} \textrm{ } DD$", lab_a, lab_b, ref_res, unit), fontsize='medium')
|
||||
else:
|
||||
ax.set_xlabel(tex_fmt(r"$AA_{BB} CC$", lab_a, ref_res, unit))
|
||||
ax.set_xlabel(
|
||||
tex_fmt(
|
||||
r"$AA_{\textrm{BB}} \textrm{ } CC$",
|
||||
lab_a,
|
||||
ref_res,
|
||||
unit,
|
||||
)
|
||||
)
|
||||
# fig.supxlabel(tex_fmt(r"$AA_{BB} \textrm{ } CC$", lab_a, ref_res, unit), fontsize='medium')
|
||||
if is_left_col:
|
||||
if lab_b:
|
||||
ax.set_ylabel(
|
||||
tex_fmt(r"$AA_{\textrm{BB},\textrm{comp}} / AA_{\textrm{BB},\textrm{CC}}$",
|
||||
lab_a, lab_b, ref_res))
|
||||
# ax.set_ylabel(
|
||||
# tex_fmt(r"$AA_{\textrm{BB},\textrm{comp}} \textrm{ } / \textrm{ } AA_{\textrm{BB},\textrm{CC}}$",
|
||||
# lab_a, lab_b, ref_res))
|
||||
# fig.text(0.015, 0.5, tex_fmt(r"$AA_{\textrm{BB},\textrm{ comp}} \textrm{ } / \textrm{ } AA_{\textrm{BB},\textrm{ CC}}$", lab_a, lab_b, ref_res), va='center', rotation='vertical', size='medium')
|
||||
fig.supylabel(
|
||||
tex_fmt(
|
||||
r"$AA_{\textrm{BB},\textrm{ comp}} \textrm{ } / \textrm{ } AA_{\textrm{BB},\textrm{ CC}}$",
|
||||
lab_a,
|
||||
lab_b,
|
||||
ref_res,
|
||||
),
|
||||
fontsize="medium",
|
||||
fontvariant="small-caps",
|
||||
)
|
||||
else:
|
||||
ax.set_ylabel(
|
||||
tex_fmt(r"$AA_{\textrm{comp}} / AA_{\textrm{BB}}$",
|
||||
lab_a, ref_res))
|
||||
# ax.set_ylabel(
|
||||
# tex_fmt(r"$AA_{\textrm{comp}} \textrm{ } / \textrm{ } AA_{\textrm{BB}}$",
|
||||
# lab_a, ref_res))
|
||||
# fig.text(0.015, 0.5, tex_fmt(r"$AA_{\textrm{comp}} \textrm{ } / \textrm{ } AA_{\textrm{BB}}$", lab_a, ref_res), va='center', rotation='vertical', size='medium')
|
||||
fig.supylabel(
|
||||
tex_fmt(
|
||||
r"$AA_{\textrm{comp}} \textrm{ } / \textrm{ } AA_{\textrm{BB}}$",
|
||||
lab_a,
|
||||
ref_res,
|
||||
),
|
||||
fontsize="medium",
|
||||
)
|
||||
# ax.set_ylabel(f"{property}_{{comp}}/{property}_{ref_res}")
|
||||
ax.text(
|
||||
0.975,
|
||||
0.9,
|
||||
f"comp = {comp_res}",
|
||||
horizontalalignment="right",
|
||||
verticalalignment="top",
|
||||
transform=ax.transAxes,
|
||||
)
|
||||
else:
|
||||
if property == "match":
|
||||
if not (is_bottom_row and is_left_col):
|
||||
ax.text(
|
||||
0.05,
|
||||
0.9,
|
||||
f"comp = {comp_res}",
|
||||
horizontalalignment="left",
|
||||
verticalalignment="top",
|
||||
transform=ax.transAxes,
|
||||
)
|
||||
# mass_bins = np.geomspace(10, 30000, num_mass_bins)
|
||||
plot_comparison_hist(ax, file, property)
|
||||
|
||||
|
|
@ -257,18 +323,25 @@ def compare_property(property, show: bool):
|
|||
ax.legend()
|
||||
|
||||
else:
|
||||
ax.text(
|
||||
0.05,
|
||||
0.9,
|
||||
f"comp = {comp_res}",
|
||||
horizontalalignment="left",
|
||||
verticalalignment="top",
|
||||
transform=ax.transAxes,
|
||||
)
|
||||
plot_comparison_hist(ax, file, property)
|
||||
x_labels = {
|
||||
"match": "$J$",
|
||||
"distance": "$D$ [$R_{vir}$]"
|
||||
}
|
||||
x_labels = {"match": "$J$", "distance": "$D$ [$R_\mathrm{{vir}}$]"}
|
||||
if is_bottom_row:
|
||||
ax.set_xlabel(x_labels[property])
|
||||
if is_left_col:
|
||||
if property == "match":
|
||||
ax.set_ylabel(r"$p(J)$")
|
||||
# ax.set_ylabel(r"$p(J)$")
|
||||
fig.supylabel(r"$p(J)$", fontsize="medium")
|
||||
else:
|
||||
ax.set_ylabel(r"\# Halos")
|
||||
# ax.set_ylabel(r"\# Halos")
|
||||
fig.supylabel(r"\# Halos", fontsize="medium")
|
||||
if property == "distance":
|
||||
ax.set_xscale("log")
|
||||
ax.set_yscale("log")
|
||||
|
|
@ -278,11 +351,7 @@ def compare_property(property, show: bool):
|
|||
last_ytick: YTick = ax.yaxis.get_major_ticks()[-1]
|
||||
last_ytick.set_visible(False)
|
||||
if property == "Mvir" and is_top_row:
|
||||
particle_masses = {
|
||||
256: 0.23524624,
|
||||
512: 0.02940578,
|
||||
1024: 0.0036757225
|
||||
}
|
||||
particle_masses = {256: 0.23524624, 512: 0.02940578, 1024: 0.0036757225}
|
||||
partmass = particle_masses[ref_res]
|
||||
|
||||
def mass2partnum(mass: float) -> float:
|
||||
|
|
@ -291,10 +360,12 @@ def compare_property(property, show: bool):
|
|||
def partnum2mass(partnum: float) -> float:
|
||||
return partnum * partmass
|
||||
|
||||
sec_ax = ax.secondary_xaxis("top", functions=(mass2partnum, partnum2mass))
|
||||
sec_ax.set_xlabel(r"[\# \textrm{particles}]")
|
||||
sec_ax = ax.secondary_xaxis(
|
||||
"top", functions=(mass2partnum, partnum2mass)
|
||||
)
|
||||
sec_ax.set_xlabel(r"\textrm{Halo Size }[\# \textrm{particles}]")
|
||||
|
||||
rowcolumn_labels(axes, comparisons, isrow=False)
|
||||
# rowcolumn_labels(axes, comparisons, isrow=False)
|
||||
rowcolumn_labels(axes, waveforms, isrow=True)
|
||||
fig.tight_layout()
|
||||
fig.subplots_adjust(hspace=0)
|
||||
|
|
@ -315,5 +386,5 @@ def main():
|
|||
compare_property(property, show=len(argv) == 2)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
|
|||
|
|
@ -17,20 +17,22 @@ const_boltzmann_k_cgs = 1.380649e-16
|
|||
def calculate_gas_internal_energy(omegab, hubble_param_, zstart_):
|
||||
astart_ = 1.0 / (1.0 + zstart_)
|
||||
if fabs(1.0 - gamma) > 1e-7:
|
||||
npol = 1.0 / (gamma - 1.)
|
||||
npol = 1.0 / (gamma - 1. | ||||