mirror of
https://github.com/Findus23/halo_comparison.git
synced 2024-09-18 14:53:49 +02:00
pbh and more comparisons
This commit is contained in:
parent
1baaae41b4
commit
1af06dcd2c
GPG key ID:
54DE4D798D244853
6 changed files with 406 additions and 69 deletions
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@ -15,6 +15,7 @@ from matplotlib.figure import Figure
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from cache import HDFCache
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from cic import cic_from_radius, cic_range
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from find_center import find_center
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from halo_mass_profile import halo_mass_profile
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from nfw import fit_nfw
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from paths import auriga_dir, richings_dir
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@ -55,7 +56,8 @@ def main():
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ax1: Axes = fig1.gca()
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fig2: Figure = plt.figure(figsize=figsize_from_page_fraction())
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ax2: Axes = fig2.gca()
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fig4, axs_baryon = plt.subplots(nrows=3, ncols=5, sharex="all", sharey="all", figsize=(10, 4))
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baryon_plot_counter = 0
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for ax in [ax1, ax2]:
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ax.set_xlabel(r"R [Mpc]")
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ax1.set_ylabel(r"M [$10^{10} \mathrm{M}_\odot$]")
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@ -92,7 +94,7 @@ def main():
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print(levelmin, levelmin_TF, levelmax)
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if not has_baryons:
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continue
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if levelmax != 11:
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if is_ramses:
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continue
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input_file = dir / "output_0009.hdf5"
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@ -150,6 +152,7 @@ def main():
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part_numbers.append(len(df) * particles_meta.particle_mass)
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# halo = halos.loc[1]
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center = np.array([halo.X, halo.Y, halo.Z])
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center = find_center(df, center)
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log_radial_bins, bin_masses, bin_densities, center = halo_mass_profile(
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df, center, particles_meta, plot=False, num_bins=100, vmin=0.002, vmax=6.5
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)
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@ -231,13 +234,12 @@ def main():
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if has_baryons:
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interpolation_method = "nearest" # "linear"
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fig3, axs_baryon = plt.subplots(nrows=1, ncols=5, sharex="all", sharey="all", figsize=(10, 4))
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extent = [46, 52, 54, 60] # xrange[0], xrange[-1], yrange[0], yrange[-1]
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extent = [42, 62, 50, 70]
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for ii, property in enumerate(["cic", "Densities", "Entropies", "InternalEnergies", "Temperatures"]):
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key = f"grid_{property}_{interpolation_method}"
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cached_grid = cache.get(key, str(input_file))
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if cached_grid is not None and False:
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if cached_grid is not None:
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grid = cached_grid
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else:
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if property == "cic":
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@ -247,7 +249,7 @@ def main():
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grid = create_2d_slice(input_file, center, property=property,
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extent=extent, method=interpolation_method)
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cache.set(key, grid, str(input_file), compressed=True)
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ax_baryon: Axes = axs_baryon[ii]
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ax_baryon: Axes = axs_baryon[baryon_plot_counter, ii]
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img = ax_baryon.imshow(
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grid,
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norm=LogNorm(),
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@ -259,11 +261,8 @@ def main():
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# ax_baryon.set_xlabel("X")
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# ax_baryon.set_ylabel("Y")
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ax_baryon.set_aspect("equal")
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fig3.suptitle(input_file.parent.stem)
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fig3.tight_layout()
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fig3.savefig(Path("~/tmp/slice.png").expanduser(), dpi=300)
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# exit()
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plt.show()
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baryon_plot_counter += 1
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# plot_cic(
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# rho, extent,
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@ -298,9 +297,14 @@ def main():
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cbar_ax = fig3.add_axes([0.85, 0.05, 0.05, 0.9])
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fig3.colorbar(img, cax=cbar_ax)
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fig1.savefig(Path(f"~/tmp/auriga1.pdf").expanduser())
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fig2.savefig(Path(f"~/tmp/auriga2.pdf").expanduser())
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fig3.savefig(Path("~/tmp/auriga3.pdf").expanduser())
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fig4.tight_layout()
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fig4.savefig(Path("~/tmp/slice.png").expanduser(), dpi=300)
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pprint(centers)
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plt.show()
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print(part_numbers)
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@ -11,9 +11,13 @@ cache = HDFCache(Path("center_cache.hdf5"))
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def find_center(df: pd.DataFrame, center: np.ndarray, initial_radius=1):
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plt.figure()
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# plt.figure()
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all_particles = df[["X", "Y", "Z"]].to_numpy()
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hash = hashlib.sha256(np.ascontiguousarray(all_particles).data).hexdigest()
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hashdata = hashlib.sha256()
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hashdata.update(np.ascontiguousarray(all_particles).data)
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hashdata.update(np.ascontiguousarray(center).data)
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hashdata.update(np.array(initial_radius))
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hash = hashdata.hexdigest()
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cached_center = cache.get(hash)
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if cached_center is not None:
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return np.array(cached_center)
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@ -31,7 +35,7 @@ def find_center(df: pd.DataFrame, center: np.ndarray, initial_radius=1):
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center_of_mass = in_radius_particles.mean(axis=0)
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new_center = (center_of_mass + center) / 2
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shift = np.linalg.norm(center - new_center)
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radius = max(2 * shift, radius * 0.9)
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radius = max(0.8 * shift, radius * 0.9)
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center = new_center
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i += 1
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center_history = np.array(center_history)
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@ -24,7 +24,6 @@ def halo_mass_profile(
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plot=False,
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num_bins=30,
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):
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center = find_center(particles, center)
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positions = particles[["X", "Y", "Z"]].to_numpy()
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distances = np.linalg.norm(positions - center, axis=1)
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group_radius = distances.max()
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66
pbh_comparison.py
Normal file
66
pbh_comparison.py
Normal file
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@ -0,0 +1,66 @@
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import numpy as np
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import pandas as pd
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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 cic import cic_from_radius, plot_cic
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from find_center import find_center
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from halo_mass_profile import halo_mass_profile
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from paths import pbh_dir
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from readfiles import read_g4_file, ParticlesMeta
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from utils import figsize_from_page_fraction
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def cic_comparison(pbh_high_coord, ref_high_coord,center):
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rhos = []
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i = 0
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for coord in [ref_high_coord, pbh_high_coord]:
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rho, extent = cic_from_radius(
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coord[::, 0], coord[::, 2], 3000, center[0], center[2], .2, periodic=False
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)
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rhos.append(rho)
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plot_cic(
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rho, extent, title=f"test{i}"
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)
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i += 1
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plot_cic(
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np.absolute(rhos[0] - rhos[1]), extent, title="abs(diff)"
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)
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def main():
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ref_data = read_g4_file(
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pbh_dir / "CDM-L50-N128" / "snapshot_039.hdf5",
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zoom_type="cdm")
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pbh_data = read_g4_file(
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pbh_dir / "DM-L50-N128" / "snapshot_039.hdf5",
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zoom_type="pbh")
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fig1: Figure = plt.figure(figsize=figsize_from_page_fraction())
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ax1: Axes = fig1.gca()
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fig2: Figure = plt.figure(figsize=figsize_from_page_fraction())
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ax2: Axes = fig2.gca()
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center = [32.423177, 37.255220, 36.026005]
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centered = False
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for data in [ref_data, pbh_data]:
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highres_coords, lowres_coords, highres_mass, lowres_mass = data
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df = pd.DataFrame(highres_coords, columns=["X", "Y", "Z"])
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particles_meta = ParticlesMeta(particle_mass=highres_mass)
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# center = np.median(highres_coords, axis=0)
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print(center)
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# if not centered:
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# center = find_center(df, center, initial_radius=0.01)
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centered = True
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log_radial_bins, bin_masses, bin_densities, center = halo_mass_profile(
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df, center, particles_meta, plot=False, num_bins=100, vmin=0.002, vmax=5
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)
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ax1.loglog(log_radial_bins[:-1], bin_masses)
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ax2.loglog(log_radial_bins[:-1], bin_densities)
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plt.show()
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cic_comparison(ref_data[0], pbh_data[0],center)
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if __name__ == '__main__':
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main()
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327
poetry.lock
generated
327
poetry.lock
generated
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@ -27,7 +27,7 @@ python-versions = ">=3.6"
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[[package]]
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name = "cython"
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version = "0.29.30"
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version = "0.29.32"
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description = "The Cython compiler for writing C extensions for the Python language."
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category = "main"
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optional = false
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@ -35,7 +35,7 @@ python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
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[[package]]
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name = "fonttools"
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version = "4.33.3"
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version = "4.36.0"
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description = "Tools to manipulate font files"
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category = "main"
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optional = false
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@ -76,7 +76,7 @@ numpy = ">=1.14.5"
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[[package]]
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name = "imageio"
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version = "2.19.3"
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version = "2.21.1"
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description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
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category = "main"
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optional = false
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@ -105,7 +105,7 @@ tifffile = ["tifffile"]
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[[package]]
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name = "kiwisolver"
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version = "1.4.3"
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version = "1.4.4"
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description = "A fast implementation of the Cassowary constraint solver"
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category = "main"
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optional = false
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@ -121,7 +121,7 @@ python-versions = ">=3.7,<3.11"
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[[package]]
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name = "matplotlib"
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version = "3.5.2"
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version = "3.5.3"
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description = "Python plotting package"
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category = "main"
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optional = false
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@ -136,7 +136,7 @@ packaging = ">=20.0"
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pillow = ">=6.2.0"
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pyparsing = ">=2.2.1"
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python-dateutil = ">=2.7"
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setuptools_scm = ">=4"
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setuptools_scm = ">=4,<7"
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[[package]]
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name = "numba"
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@ -153,11 +153,11 @@ setuptools = "*"
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[[package]]
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name = "numexpr"
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version = "2.8.1"
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version = "2.8.3"
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description = "Fast numerical expression evaluator for NumPy"
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category = "main"
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optional = false
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python-versions = "*"
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python-versions = ">=3.7"
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[package.dependencies]
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numpy = ">=1.13.3"
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@ -184,7 +184,7 @@ pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
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[[package]]
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name = "pandas"
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version = "1.4.2"
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version = "1.4.3"
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description = "Powerful data structures for data analysis, time series, and statistics"
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category = "main"
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optional = false
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@ -205,14 +205,14 @@ test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
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[[package]]
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name = "pillow"
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version = "9.1.1"
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version = "9.2.0"
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description = "Python Imaging Library (Fork)"
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category = "main"
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optional = false
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python-versions = ">=3.7"
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[package.extras]
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docs = ["olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinx-rtd-theme (>=1.0)", "sphinxext-opengraph"]
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docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"]
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tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
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[[package]]
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@ -233,7 +233,7 @@ python-versions = "*"
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[[package]]
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name = "pynbody"
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version = "1.2.1"
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version = "1.2.3"
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description = "Light-weight astronomical N-body/SPH analysis for python"
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category = "main"
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optional = false
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@ -293,7 +293,7 @@ doc = ["guzzle-sphinx-theme", "scipy", "nbsphinx", "numpy"]
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[[package]]
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name = "pytz"
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version = "2022.1"
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version = "2022.2.1"
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description = "World timezone definitions, modern and historical"
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category = "main"
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optional = false
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@ -301,7 +301,7 @@ python-versions = "*"
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[[package]]
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name = "pyvista"
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version = "0.34.1"
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version = "0.34.2"
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description = "Easier Pythonic interface to VTK"
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category = "main"
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optional = false
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@ -330,37 +330,37 @@ python-versions = ">=3.6"
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[[package]]
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name = "scipy"
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version = "1.8.1"
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version = "1.9.0"
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description = "SciPy: Scientific Library for Python"
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category = "main"
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optional = false
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python-versions = ">=3.8,<3.11"
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python-versions = ">=3.8,<3.12"
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[package.dependencies]
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numpy = ">=1.17.3,<1.25.0"
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numpy = ">=1.18.5,<1.25.0"
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[[package]]
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name = "scooby"
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version = "0.5.12"
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version = "0.6.0"
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description = "A Great Dane turned Python environment detective"
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category = "main"
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optional = false
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python-versions = ">=3.7.*"
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[package.extras]
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cpu = ["psutil", "mkl"]
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cpu = ["mkl", "psutil"]
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[[package]]
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name = "setuptools"
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version = "62.6.0"
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version = "65.2.0"
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description = "Easily download, build, install, upgrade, and uninstall Python packages"
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category = "main"
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optional = false
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python-versions = ">=3.7"
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[package.extras]
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docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "furo"]
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testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-enabler (>=1.0.1)", "pytest-perf", "mock", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)"]
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docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)", "sphinx-notfound-page (==0.8.3)", "sphinx-hoverxref (<2)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "furo"]
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testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-enabler (>=1.3)", "pytest-perf", "mock", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)"]
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testing-integration = ["pytest", "pytest-xdist", "pytest-enabler", "virtualenv (>=13.0.0)", "tomli", "wheel", "jaraco.path (>=3.2.0)", "jaraco.envs (>=2.2)", "build", "filelock (>=3.4.0)"]
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[[package]]
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@ -434,7 +434,6 @@ numpy = ["numpy (>=1.9)"]
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[package.source]
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type = "url"
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url = "https://github.com/pyvista/pyvista-wheels/raw/main/vtk-9.1.0.dev0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl"
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[metadata]
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lock-version = "1.1"
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python-versions = "^3.9,<3.11"
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@ -444,32 +443,288 @@ content-hash = "46efb5a91582357165918cbcebeab75afc0049abaf7e88c21880385c88b41ef4
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appdirs = []
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beniget = []
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cycler = []
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cython = []
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fonttools = []
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cython = [
|
||||
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{file = "numexpr-2.8.3.tar.gz", hash = "sha256:cb647c9d9c785dae0759bf6c875cde2bec472b5c3f7a6015734b161ae766d141"},
|
||||
]
|
||||
numpy = []
|
||||
packaging = []
|
||||
pandas = []
|
||||
pandas = [
|
||||
{file = "pandas-1.4.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d51674ed8e2551ef7773820ef5dab9322be0828629f2cbf8d1fc31a0c4fed640"},
|
||||
{file = "pandas-1.4.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:16ad23db55efcc93fa878f7837267973b61ea85d244fc5ff0ccbcfa5638706c5"},
|
||||
{file = "pandas-1.4.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:958a0588149190c22cdebbc0797e01972950c927a11a900fe6c2296f207b1d6f"},
|
||||
{file = "pandas-1.4.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e48fbb64165cda451c06a0f9e4c7a16b534fcabd32546d531b3c240ce2844112"},
|
||||
{file = "pandas-1.4.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6f803320c9da732cc79210d7e8cc5c8019aad512589c910c66529eb1b1818230"},
|
||||
{file = "pandas-1.4.3-cp310-cp310-win_amd64.whl", hash = "sha256:2893e923472a5e090c2d5e8db83e8f907364ec048572084c7d10ef93546be6d1"},
|
||||
{file = "pandas-1.4.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:24ea75f47bbd5574675dae21d51779a4948715416413b30614c1e8b480909f81"},
|
||||
{file = "pandas-1.4.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:d5ebc990bd34f4ac3c73a2724c2dcc9ee7bf1ce6cf08e87bb25c6ad33507e318"},
|
||||
{file = "pandas-1.4.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:d6c0106415ff1a10c326c49bc5dd9ea8b9897a6ca0c8688eb9c30ddec49535ef"},
|
||||
{file = "pandas-1.4.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:78b00429161ccb0da252229bcda8010b445c4bf924e721265bec5a6e96a92e92"},
|
||||
{file = "pandas-1.4.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6dfbf16b1ea4f4d0ee11084d9c026340514d1d30270eaa82a9f1297b6c8ecbf0"},
|
||||
{file = "pandas-1.4.3-cp38-cp38-win32.whl", hash = "sha256:48350592665ea3cbcd07efc8c12ff12d89be09cd47231c7925e3b8afada9d50d"},
|
||||
{file = "pandas-1.4.3-cp38-cp38-win_amd64.whl", hash = "sha256:605d572126eb4ab2eadf5c59d5d69f0608df2bf7bcad5c5880a47a20a0699e3e"},
|
||||
{file = "pandas-1.4.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:a3924692160e3d847e18702bb048dc38e0e13411d2b503fecb1adf0fcf950ba4"},
|
||||
{file = "pandas-1.4.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:07238a58d7cbc8a004855ade7b75bbd22c0db4b0ffccc721556bab8a095515f6"},
|
||||
{file = "pandas-1.4.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:755679c49460bd0d2f837ab99f0a26948e68fa0718b7e42afbabd074d945bf84"},
|
||||
{file = "pandas-1.4.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:41fc406e374590a3d492325b889a2686b31e7a7780bec83db2512988550dadbf"},
|
||||
{file = "pandas-1.4.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d9382f72a4f0e93909feece6fef5500e838ce1c355a581b3d8f259839f2ea76"},
|
||||
{file = "pandas-1.4.3-cp39-cp39-win32.whl", hash = "sha256:0daf876dba6c622154b2e6741f29e87161f844e64f84801554f879d27ba63c0d"},
|
||||
{file = "pandas-1.4.3-cp39-cp39-win_amd64.whl", hash = "sha256:721a3dd2f06ef942f83a819c0f3f6a648b2830b191a72bbe9451bcd49c3bd42e"},
|
||||
{file = "pandas-1.4.3.tar.gz", hash = "sha256:2ff7788468e75917574f080cd4681b27e1a7bf36461fe968b49a87b5a54d007c"},
|
||||
]
|
||||
pillow = []
|
||||
ply = []
|
||||
posix-ipc = []
|
||||
pynbody = []
|
||||
pynbody = [
|
||||
{file = "pynbody-1.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a08861697578fa3f680b7edfb937ba4460ecb04e0659655ad0cdcd741a00450d"},
|
||||
{file = "pynbody-1.2.3-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:ff42901c208a136cc4e6d863673f256b7c6aeb3abce9acda9a96549526edf965"},
|
||||
{file = "pynbody-1.2.3-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:f04f049b2f9604908b7759449746b1fd97bba78aba5ea82f5fe4a4d117efde58"},
|
||||
{file = "pynbody-1.2.3-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:b0e22f53d94365a7f7ab34c8e872a44872ba63b37c530966b705739591e17c09"},
|
||||
{file = "pynbody-1.2.3-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:47c57475611fc69df96bbf03d86c298c4ad64494b2f63f63c78f815f804da99e"},
|
||||
{file = "pynbody-1.2.3-cp36-cp36m-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:e0b0e307e07a1976c38cf697ee4f2175d6cf60a46487cdcfe5c811ad52f43cb6"},
|
||||
{file = "pynbody-1.2.3-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:7f99a333cb614bebfa5e5597cbc6bed84b5dbddfd368fe291c48157d869d52a4"},
|
||||
{file = "pynbody-1.2.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:87566a58937addc85ba16eb280a4ae0b5bdb1c7eb56e7434b84b997f333ac1a6"},
|
||||
{file = "pynbody-1.2.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:db67f0a211c786f7aa59ab90e3a5e606f3f2de94e41922c70db2fba633599440"},
|
||||
{file = "pynbody-1.2.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:1d033b958bee2a38064a521a8742b261795e41ce144c565c66d3f4b628b78b17"},
|
||||
{file = "pynbody-1.2.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:57bb98c8e1ae05defe31a137f76b1bd1e161589db3140acc577332789d1b120e"},
|
||||
{file = "pynbody-1.2.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:69b96b914c5839634a895f4463685a5fc296ec2e0444fe5476392d4bb8951365"},
|
||||
{file = "pynbody-1.2.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:52dadf088ac0fe59d338192b87ec65d3f86ac1e0d7aa387b6bb33b433e6794dd"},
|
||||
{file = "pynbody-1.2.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:66125e69363f0d34186e93a40976e9367fede2f98170febee593b1468e3b1522"},
|
||||
{file = "pynbody-1.2.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:b9606330274c9c02ba4fa2f91ccb009d7ca53c816788333104eb3062c99ec920"},
|
||||
{file = "pynbody-1.2.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:dfa7310d721e481c6c7ccb7145f80cbc24016f9d7ea6a12ccd26582a5a825364"},
|
||||
{file = "pynbody-1.2.3-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:31f665dc026394c8c6de630fcdc3eaf48b9fde08d7cc9db02d64970f6871086e"},
|
||||
{file = "pynbody-1.2.3-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:7b860f962d28b8524c8e52b30c37f1979c7578ba9ca8b0f90ed9745119bdae52"},
|
||||
{file = "pynbody-1.2.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:bb0335cbbb3f3fd7ab4984247795aef0bac2cff107a62cdc9913593be9b59320"},
|
||||
{file = "pynbody-1.2.3.tar.gz", hash = "sha256:7dba913c7be137a44e9c01dd7e109481ce001a3d928aa9fcb63f7c4cb498bccb"},
|
||||
]
|
||||
pyparsing = []
|
||||
python-dateutil = []
|
||||
pythran = []
|
||||
pytz = []
|
||||
pyvista = []
|
||||
pytz = [
|
||||
{file = "pytz-2022.2.1-py2.py3-none-any.whl", hash = "sha256:220f481bdafa09c3955dfbdddb7b57780e9a94f5127e35456a48589b9e0c0197"},
|
||||
{file = "pytz-2022.2.1.tar.gz", hash = "sha256:cea221417204f2d1a2aa03ddae3e867921971d0d76f14d87abb4414415bbdcf5"},
|
||||
]
|
||||
pyvista = [
|
||||
{file = "pyvista-0.34.2-py3-none-any.whl", hash = "sha256:8634016ca20a466d4cb242a9df66a07a7e24c232ad67c29f48eb46fd859fc194"},
|
||||
{file = "pyvista-0.34.2.tar.gz", hash = "sha256:410d52a9c4d57c86823e8db0f1b51bd947702c82685da8bfdb78e98f82b3ae24"},
|
||||
]
|
||||
pyyaml = []
|
||||
scipy = []
|
||||
scooby = []
|
||||
setuptools = []
|
||||
scipy = [
|
||||
{file = "scipy-1.9.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0424d1bbbfa51d5ddaa16d067fd593863c9f2fb7c6840c32f8a08a8832f8e7a4"},
|
||||
{file = "scipy-1.9.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:8f2232c9d9119ec356240255a715a289b3a33be828c3e4abac11fd052ce15b1e"},
|
||||
{file = "scipy-1.9.0-cp310-cp310-macosx_12_0_universal2.macosx_10_9_x86_64.whl", hash = "sha256:e2004d2a3c397b26ca78e67c9d320153a1a9b71ae713ad33f4a3a3ab3d79cc65"},
|
||||
{file = "scipy-1.9.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:45f0d6c0d6e55582d3b8f5c58ad4ca4259a02affb190f89f06c8cc02e21bba81"},
|
||||
{file = "scipy-1.9.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:79dd7876614fc2869bf5d311ef33962d2066ea888bc66c80fd4fa80f8772e5a9"},
|
||||
{file = "scipy-1.9.0-cp310-cp310-win_amd64.whl", hash = "sha256:10417935486b320d98536d732a58362e3d37e84add98c251e070c59a6bfe0863"},
|
||||
{file = "scipy-1.9.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:adb6c438c6ef550e2bb83968e772b9690cb421f2c6073f9c2cb6af15ee538bc9"},
|
||||
{file = "scipy-1.9.0-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:8d541db2d441ef87afb60c4a2addb00c3af281633602a4967e733ef4b7050504"},
|
||||
{file = "scipy-1.9.0-cp38-cp38-macosx_12_0_universal2.macosx_10_9_x86_64.whl", hash = "sha256:97a1f1e51ea30782d7baa8d0c52f72c3f9f05cb609cf1b990664231c5102bccd"},
|
||||
{file = "scipy-1.9.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:16207622570af10f9e6a2cdc7da7a9660678852477adbcd056b6d1057a036fef"},
|
||||
{file = "scipy-1.9.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb687d245b6963673c639f318eea7e875d1ba147a67925586abed3d6f39bb7d8"},
|
||||
{file = "scipy-1.9.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:73b704c5eea9be811919cae4caacf3180dd9212d9aed08477c1d2ba14900a9de"},
|
||||
{file = "scipy-1.9.0-cp38-cp38-win32.whl", hash = "sha256:12005d30894e4fe7b247f7233ba0801a341f887b62e2eb99034dd6f2a8a33ad6"},
|
||||
{file = "scipy-1.9.0-cp38-cp38-win_amd64.whl", hash = "sha256:fc58c3fcb8a724b703ffbc126afdca5a8353d4d5945d5c92db85617e165299e7"},
|
||||
{file = "scipy-1.9.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:01c2015e132774feefe059d5354055fec6b751d7a7d70ad2cf5ce314e7426e2a"},
|
||||
{file = "scipy-1.9.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:f7c3c578ff556333f3890c2df6c056955d53537bb176698359088108af73a58f"},
|
||||
{file = "scipy-1.9.0-cp39-cp39-macosx_12_0_universal2.macosx_10_9_x86_64.whl", hash = "sha256:e2ac088ea4aa61115b96b47f5f3d94b3fa29554340b6629cd2bfe6b0521ee33b"},
|
||||
{file = "scipy-1.9.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:5d1b9cf3771fd921f7213b4b886ab2606010343bb36259b544a816044576d69e"},
|
||||
{file = "scipy-1.9.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d3a326673ac5afa9ef5613a61626b9ec15c8f7222b4ecd1ce0fd8fcba7b83c59"},
|
||||
{file = "scipy-1.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:693b3fe2e7736ce0dbc72b4d933798eb6ca8ce51b8b934e3f547cc06f48b2afb"},
|
||||
{file = "scipy-1.9.0-cp39-cp39-win32.whl", hash = "sha256:7bad16b91918bf3288089a78a4157e04892ea6475fb7a1d9bcdf32c30c8a3dba"},
|
||||
{file = "scipy-1.9.0-cp39-cp39-win_amd64.whl", hash = "sha256:bd490f77f35800d5620f4d9af669e372d9a88db1f76ef219e1609cc4ecdd1a24"},
|
||||
{file = "scipy-1.9.0.tar.gz", hash = "sha256:c0dfd7d2429452e7e94904c6a3af63cbaa3cf51b348bd9d35b42db7e9ad42791"},
|
||||
]
|
||||
scooby = [
|
||||
{file = "scooby-0.6.0-py3-none-any.whl", hash = "sha256:a6f128975ec7ffd0ca1cdd7db1def4368045d81a671ad2659e1ededeee3eef7c"},
|
||||
{file = "scooby-0.6.0.tar.gz", hash = "sha256:9c6536cd11b1832f22d7a0d5ed49b529b5d3c8783f9f2481b76be57e75b7781b"},
|
||||
]
|
||||
setuptools = [
|
||||
{file = "setuptools-65.2.0-py3-none-any.whl", hash = "sha256:a3ca5857c89f82f5c9410e8508cb32f4872a3bafd4aa7ae122a24ca33bccc750"},
|
||||
{file = "setuptools-65.2.0.tar.gz", hash = "sha256:7f4bc85450898a09f76ebf28b72fa25bc7111f6c7d665d514a60bba9c75ef2a9"},
|
||||
]
|
||||
setuptools-scm = []
|
||||
siphash = []
|
||||
six = []
|
||||
|
|
|
|||
49
readfiles.py
49
readfiles.py
|
|
@ -21,9 +21,14 @@ def read_file(file: Path) -> Tuple[pd.DataFrame, ParticlesMeta]:
|
|||
reference_file = h5py.File(file)
|
||||
has_fof = "FOFGroupIDs" in reference_file["PartType1"]
|
||||
|
||||
masses = reference_file["PartType1"]["Masses"]
|
||||
if not np.all(masses == masses[0]):
|
||||
raise ValueError("only equal mass particles are supported for now")
|
||||
try:
|
||||
masses = reference_file["PartType1"]["Masses"]
|
||||
if not np.all(masses == masses[0]):
|
||||
raise ValueError("only equal mass particles are supported for now")
|
||||
meta = ParticlesMeta(particle_mass=masses[0])
|
||||
|
||||
except KeyError:
|
||||
meta = ParticlesMeta(particle_mass=0)
|
||||
df = pd.DataFrame(
|
||||
reference_file["PartType1"]["Coordinates"], columns=["X", "Y", "Z"]
|
||||
)
|
||||
|
|
@ -43,7 +48,6 @@ def read_file(file: Path) -> Tuple[pd.DataFrame, ParticlesMeta]:
|
|||
if has_fof:
|
||||
print("sorting")
|
||||
df.sort_values("FOFGroupIDs", inplace=True)
|
||||
meta = ParticlesMeta(particle_mass=masses[0])
|
||||
print("saving cache")
|
||||
with meta_cache_file.open("wb") as f:
|
||||
pickle.dump(meta, f)
|
||||
|
|
@ -74,23 +78,28 @@ def read_halo_file(file: Path) -> DataFrame:
|
|||
def read_fof_file(path: Path):
|
||||
file = path / ""
|
||||
|
||||
def read_g4_file(file: Path, zoom_type: str) -> Tuple[np.ndarray, np.ndarray]:
|
||||
reference_file = h5py.File(file)
|
||||
|
||||
hubble_param = reference_file["Parameters"].attrs["HubbleParam"]
|
||||
if zoom_type == 'pbh':
|
||||
highres_parttype = 'PartType0'
|
||||
lowres_parttype = 'PartType1'
|
||||
elif zoom_type == 'cdm':
|
||||
highres_parttype = 'PartType1'
|
||||
lowres_parttype = 'PartType2'
|
||||
highres_mass = reference_file['Header'].attrs['MassTable'][1] / hubble_param
|
||||
else:
|
||||
raise ValueError('Please select pbh or cdm as zoom_type!')
|
||||
def read_g4_file(file: Path, zoom_type: str) -> Tuple[np.ndarray, np.ndarray, float, float]:
|
||||
with h5py.File(file) as reference_file:
|
||||
|
||||
highres_coordinates = reference_file[highres_parttype]["Coordinates"][:] #all coordinates in Mpc/h without adaption!
|
||||
lowres_coordinates = reference_file[lowres_parttype]["Coordinates"][:]
|
||||
hubble_param = reference_file["Parameters"].attrs["HubbleParam"]
|
||||
masstable = reference_file['Header'].attrs['MassTable']
|
||||
if zoom_type == 'pbh':
|
||||
highres_parttype = 'PartType0'
|
||||
lowres_parttype = 'PartType1'
|
||||
highres_mass = masstable[0] / hubble_param
|
||||
lowres_mass = masstable[1] / hubble_param
|
||||
|
||||
reference_file.close()
|
||||
elif zoom_type == 'cdm':
|
||||
highres_parttype = 'PartType1'
|
||||
lowres_parttype = 'PartType2'
|
||||
highres_mass = masstable[1] / hubble_param
|
||||
lowres_mass = masstable[2] / hubble_param
|
||||
else:
|
||||
raise ValueError('Please select pbh or cdm as zoom_type!')
|
||||
|
||||
return highres_coordinates, lowres_coordinates
|
||||
# all coordinates in Mpc/h without adaption!
|
||||
highres_coordinates = reference_file[highres_parttype]["Coordinates"][:]
|
||||
lowres_coordinates = reference_file[lowres_parttype]["Coordinates"][:]
|
||||
|
||||
return highres_coordinates, lowres_coordinates, highres_mass, lowres_mass
|
||||
|
|
|
|||
Loading…
Reference in a new issue