mirror/halo_comparison
1
0
Fork 0
mirror of https://github.com/Findus23/halo_comparison.git synced 2024-09-19 16:03:50 +02:00
Code Releases Activity

better output

Browse source
This commit is contained in:
Lukas Winkler 2022-05-05 11:53:43 +02:00
parent 27af0fdbb1
commit cb8ed5f349
2 changed files with 22 additions and 28 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

49
compare_halos.py
View file

@ -9,16 +9,18 @@ from pandas import DataFrame
from cumulative_mass_profiles import cumulative_mass_profile
from readfiles import read_file, read_halo_file
from remap_particle_IDs import IDScaler
from utils import print_progress, memory_usage
def compare_halo_resolutions(reference_resolution: int, comparison_resolution: int, plot=False, single=False):
reference_dir = Path(f"/home/lukas/monofonic_tests/shannon_{reference_resolution}_100")
comparison_dir = Path(f"/home/lukas/monofonic_tests/DB8_{comparison_resolution}_100/")
comparison_dir = Path(f"/home/lukas/monofonic_tests/shannon_{comparison_resolution}_100/")
ref_masses = []
comp_masses = []
ref_sizes = []
comp_sizes = []
matches = []
print("reading reference file")
df_ref, ref_meta = read_file(reference_dir)
@ -28,24 +30,20 @@ def compare_halo_resolutions(reference_resolution: int, comparison_resolution: i
df_comp, comp_meta = read_file(comparison_dir)
df_comp_halo = read_halo_file(comparison_dir)
bytes_used = df_ref.memory_usage(index=True).sum()
print(f"Memory: {bytes_used / 1024 / 1024:.2f} MB")
print(df_ref.dtypes)
print(f"Memory ref: {memory_usage(df_ref):.2f} MB")
print(f"Memory comp: {memory_usage(df_comp):.2f} MB")
for index, original_halo in df_ref_halo[:5].iterrows():
print(index)
print(len(df_ref))
for index, original_halo in df_ref_halo.iterrows():
print(f"{index} of {len(df_ref_halo)} original halos")
particles_in_ref_halo = df_ref.loc[df_ref["FOFGroupIDs"] == index]
ref_halo = df_ref_halo.loc[index]
print("halo", ref_halo, len(particles_in_ref_halo))
cumulative_mass_profile(particles_in_ref_halo, ref_halo, ref_meta, plot=PLOT)
# cumulative_mass_profile(particles_in_ref_halo, ref_halo, ref_meta, plot=plot)
halo_particle_ids = set(particles_in_ref_halo.index.to_list())
prev_len = len(halo_particle_ids)
if reference_resolution < comparison_resolution:
print("upscaling IDs")
upscaled_ids = set()
prev_len = len(halo_particle_ids)
print(prev_len)
scaler = IDScaler(reference_resolution, comparison_resolution)
# i = 0
for id in halo_particle_ids:
@ -55,29 +53,24 @@ def compare_halo_resolutions(reference_resolution: int, comparison_resolution: i
upscaled_ids.update(set(scaler.upscale(id)))
halo_particle_ids = upscaled_ids
after_len = len(upscaled_ids)
print(after_len)
print(after_len / prev_len)
print("done")
print(f"{prev_len} => {after_len} (factor {after_len / prev_len})")
if comparison_resolution < reference_resolution:
print("downscaling IDs")
prev_count = len(halo_particle_ids)
print(prev_count)
downscaled_ids = set()
scaler = IDScaler(comparison_resolution, reference_resolution)
for id in halo_particle_ids:
downscaled_ids.add(scaler.downscale(id))
halo_particle_ids = downscaled_ids
print("done")
after_count = len(halo_particle_ids)
print(after_count)
print(prev_count / after_count)
after_len = len(halo_particle_ids)
print(f"{prev_len} => {after_len} (factor {prev_len / after_len})")
print("look up halo particles in comparison dataset")
particles = df_comp.loc[list(halo_particle_ids)]
# print(particles)
halos_in_particles = set(particles["FOFGroupIDs"])
halos_in_particles.discard(2147483647)
# print(halos_in_particles)
print(f"{len(halos_in_particles)} halos found in new particles")
if plot:
fig: Figure = plt.figure()
ax: Axes = fig.gca()
@ -88,10 +81,10 @@ def compare_halo_resolutions(reference_resolution: int, comparison_resolution: i
best_halo = None
best_halo_match = 0
for halo in halos_in_particles:
for i, halo in enumerate(halos_in_particles):
# print("----------", halo, "----------")
print(halo)
halo_data = df_comp_halo.loc[halo]
print_progress(i, len(halos_in_particles), halo)
# halo_data = df_comp_halo.loc[halo]
particles_in_comp_halo: DataFrame = df_comp.loc[df_comp["FOFGroupIDs"] == halo]
halo_size = len(particles_in_comp_halo)
@ -117,6 +110,7 @@ def compare_halo_resolutions(reference_resolution: int, comparison_resolution: i
ref_masses.append(ref_halo["Masses"])
comp_sizes.append(comp_halo["Sizes"])
comp_masses.append(comp_halo["Masses"])
matches.append(best_halo_match / len(particles))
# exit()
if plot:
ax.legend()
@ -126,16 +120,17 @@ def compare_halo_resolutions(reference_resolution: int, comparison_resolution: i
if single:
break
df = DataFrame(np.array([ref_sizes, comp_sizes, ref_masses, comp_masses]).T,
columns=["ref_sizes", "comp_sizes", "ref_masses", "comp_masses"])
df = DataFrame(np.array([matches, ref_sizes, comp_sizes, ref_masses, comp_masses]).T,
columns=["matches", "ref_sizes", "comp_sizes", "ref_masses", "comp_masses"])
print(df)
df.to_csv("sizes.csv", index=False)
return df, reference_dir.name + "_" + comparison_dir.name
if __name__ == '__main__':
compare_halo_resolutions(
reference_resolution=128,
comparison_resolution=512,
plot=True,
plot=False,
single=False
)

1
readfiles.py
View file

@ -22,7 +22,6 @@ def read_file(path: Path) -> Tuple[pd.DataFrame, ParticlesMeta]:
reference_file = h5py.File(file)
masses = reference_file["PartType1"]["Masses"]
print(masses == masses[0])
if not np.all(masses == masses[0]):
raise ValueError("only equal mass particles are supported for now")
df = pd.DataFrame(reference_file["PartType1"]["Coordinates"], columns=["X", "Y", "Z"])