diff --git a/.gitignore b/.gitignore
index 7ec290c..e9fa6b4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
 .idea/
 *.png
 *.csv
+*.zip
diff --git a/2d.py b/2d.py
new file mode 100644
index 0000000..693d1ea
--- /dev/null
+++ b/2d.py
@@ -0,0 +1,31 @@
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from matplotlib.axes import Axes
+from matplotlib.colors import LogNorm
+from matplotlib.figure import Figure
+
+from readfiles import read_file
+
+
+def filter_for_2d(df: pd.DataFrame, group: int):
+    xs = df_ref.X
+    ys = df_ref.Y
+    return np.array([xs, ys]).T
+
+
+reference_dir = Path(f"/home/lukas/monofonic_tests/shannon_512_100")
+df_ref, _ = read_file(reference_dir)
+
+df = df_ref.loc[df_ref["FOFGroupIDs"] == 1]
+df2 = df_ref.loc[df_ref["FOFGroupIDs"] == 4]
+
+fig: Figure = plt.figure()
+ax: Axes = fig.gca()
+
+# ax.hist2d(df.X, df.Y, bins=500, norm=LogNorm())
+ax.hist2d(df2.X, df2.Y, bins=1000, norm=LogNorm())
+
+plt.show()
diff --git a/3d.py b/3d.py
index 001dbb1..32e1ced 100644
--- a/3d.py
+++ b/3d.py
@@ -8,7 +8,7 @@ from readfiles import read_file
 
 
 def file_to_arry(file: Path):
-    df_ref = read_file(file)
+    df_ref,_ = read_file(file)
     df_ref = df_ref.loc[df_ref["FOFGroupIDs"] == 34]
 
     xs = df_ref.X
diff --git a/3d2.py b/3d2.py
index c7ffd13..5519a1a 100644
--- a/3d2.py
+++ b/3d2.py
@@ -1,29 +1,31 @@
 from pathlib import Path
 
 import numpy as np
+import pandas as pd
 import pyvista
 
 from readfiles import read_file
 
 
-def file_to_arry(file: Path):
-    df_ref = read_file(file)
-    df_ref = df_ref.loc[df_ref["FOFGroupIDs"] == 1]
+def filter_for_3d(df: pd.DataFrame, group: int):
+    df_ref = df.loc[df["FOFGroupIDs"] == group]
 
     xs = df_ref.X
     ys = df_ref.Y
     zs = df_ref.Z
-    print()
     return np.array([xs, ys, zs]).T
 
 
 reference_dir = Path(f"/home/lukas/monofonic_tests/shannon_512_100")
+df_ref, _ = read_file(reference_dir)
 
-data = file_to_arry(reference_dir)
+data = filter_for_3d(df_ref, group=1)
+data2 = filter_for_3d(df_ref, group=2)
+data3 = filter_for_3d(df_ref, group=3)
 print(len(data))
-# point_cloud = np.random.random((100, 3))
-pdata = pyvista.PointSet(data)
-pdata.plot(point_size=3, render_points_as_spheres=True, eye_dome_lighting=True, parallel_projection=True,
-           anti_aliasing=True)
-# # create many spheres from the point cloud
-# pc.plot(cmap='Reds')
+
+for data in [filter_for_3d(df_ref, group=1),filter_for_3d(df_ref, group=2),filter_for_3d(df_ref, group=3)]:
+
+    pdata = pyvista.PointSet(data)
+    pdata.plot(point_size=3, render_points_as_spheres=True, eye_dome_lighting=True, parallel_projection=True,
+               anti_aliasing=True)
diff --git a/compare_halos.py b/compare_halos.py
index 99ff649..edeaca3 100644
--- a/compare_halos.py
+++ b/compare_halos.py
@@ -1,4 +1,5 @@
 from pathlib import Path
+from typing import Dict
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -6,7 +7,6 @@ from matplotlib.axes import Axes
 from matplotlib.figure import Figure
 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
@@ -30,26 +30,25 @@ 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)
 
+    print("precalculating halo memberships")
+    ref_halo_lookup = precalculate_halo_membership(df_ref, df_ref_halo)
+    comp_halo_lookup = precalculate_halo_membership(df_comp, df_comp_halo)
+
     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.iterrows():
         print(f"{index} of {len(df_ref_halo)} original halos")
-        particles_in_ref_halo = df_ref.loc[df_ref["FOFGroupIDs"] == index]
+        halo_particle_ids = ref_halo_lookup[int(index)]
         ref_halo = df_ref_halo.loc[index]
         # 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()
             scaler = IDScaler(reference_resolution, comparison_resolution)
-            # i = 0
             for id in halo_particle_ids:
-                # i += 1
-                # if i % 1000 == 0:
-                #     print(i)
                 upscaled_ids.update(set(scaler.upscale(id)))
             halo_particle_ids = upscaled_ids
             after_len = len(upscaled_ids)
@@ -61,20 +60,19 @@ def compare_halo_resolutions(reference_resolution: int, comparison_resolution: i
             for id in halo_particle_ids:
                 downscaled_ids.add(scaler.downscale(id))
             halo_particle_ids = downscaled_ids
-            print("done")
             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)]
+        halo_particles = df_comp.loc[list(halo_particle_ids)]
 
-        halos_in_particles = set(particles["FOFGroupIDs"])
+        halos_in_particles = set(halo_particles["FOFGroupIDs"])
         halos_in_particles.discard(2147483647)
         print(f"{len(halos_in_particles)} halos found in new particles")
         if plot:
             fig: Figure = plt.figure()
             ax: Axes = fig.gca()
-            ax.scatter(particles["X"], particles["Y"], s=1, alpha=.3, label="Halo")
+            ax.scatter(halo_particles["X"], halo_particles["Y"], s=1, alpha=.3, label="Halo")
         #     ax.scatter(particles_in_ref_halo["X"], particles_in_ref_halo["Y"], s=1, alpha=.3, label="RefHalo")
         # plt.legend()
         # plt.show()
@@ -83,18 +81,18 @@ def compare_halo_resolutions(reference_resolution: int, comparison_resolution: i
 
         for i, halo in enumerate(halos_in_particles):
             # print("----------", 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)
-
-            df = particles_in_comp_halo.join(particles, how="inner", rsuffix="ref")
-            shared_size = len(df)
+            # particles_in_comp_halo: DataFrame = df_comp.loc[df_comp["FOFGroupIDs"] == halo]
+            particle_ids_in_comp_halo = comp_halo_lookup[halo]
+            halo_size = len(particle_ids_in_comp_halo)
+            # df = particles_in_comp_halo.join(halo_particles, how="inner", rsuffix="ref")
+            shared_particles = particle_ids_in_comp_halo.intersection(halo_particle_ids)
+            shared_size = len(shared_particles)
             match = shared_size / halo_size
-            # print(match, halo_size, shared_size)
-            # print(df)
             if plot:
+                df = df_comp.loc[list(shared_particles)]
                 ax.scatter(df["X"], df["Y"], s=1, alpha=.3, label=f"shared {halo}")
+            # print_progress(i, len(halos_in_particles), halo)
             # ax.scatter(particles_in_comp_halo["X"], particles_in_comp_halo["Y"], s=2, alpha=.3, label=f"shared {halo}")
             if shared_size > best_halo_match:
                 best_halo_match = shared_size
@@ -110,9 +108,10 @@ 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))
+        matches.append(best_halo_match / len(halo_particles))
         # exit()
         if plot:
+            print("plotting")
             ax.legend()
             ax.set_title(f"{reference_dir.name} vs. {comparison_dir.name} (Halo {index})")
             fig.savefig("out.png", dpi=300)
@@ -127,10 +126,28 @@ def compare_halo_resolutions(reference_resolution: int, comparison_resolution: i
     return df, reference_dir.name + "_" + comparison_dir.name
 
 
+def precalculate_halo_membership(df_comp, df_comp_halo):
+    pointer = 0
+    comp_halo_lookup: Dict[int, set[int]] = {}
+    for i, halo in df_comp_halo.iterrows():
+        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]
+
+        # check_id = halo_particles["FOFGroupIDs"].to_numpy()
+        # assert (check_id == i).all()
+        # assert (check_id==check_id[0]
+        pointer += size
+        ids = set(halo_particles.index.to_list())
+        comp_halo_lookup[halo_id] = ids
+    return comp_halo_lookup
+
+
 if __name__ == '__main__':
     compare_halo_resolutions(
         reference_resolution=128,
         comparison_resolution=512,
-        plot=False,
+        plot=True,
         single=False
     )
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..40eac59
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,650 @@
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+python-versions = "*"
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+
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+
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+description = "Read and write HDF5 files from Python"
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+
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+]
+six = [
+    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
+    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+]
+tomli = [
+    {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
+    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
+]
+vtk = []
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..c5d53f0
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,22 @@
+[tool.poetry]
+name = "halo_comparison"
+version = "0.1.0"
+description = ""
+authors = ["Lukas Winkler <git@lw1.at>"]
+
+[tool.poetry.dependencies]
+python = "^3.9,<3.11"
+matplotlib = "^3.5.2"
+numpy = "^1.22.3"
+h5py = "^3.6.0"
+vtk = {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"}
+pyvista = "^0.34.1"
+pandas = "^1.4.2"
+scipy = "^1.8.0"
+pynbody = "^1.1.0"
+
+[tool.poetry.dev-dependencies]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
diff --git a/readfiles.py b/readfiles.py
index d73a6a1..03f01a0 100644
--- a/readfiles.py
+++ b/readfiles.py
@@ -1,4 +1,5 @@
 import pickle
+import sqlite3
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Tuple
@@ -33,6 +34,8 @@ def read_file(path: Path) -> Tuple[pd.DataFrame, ParticlesMeta]:
         df = df.merge(df3, "outer", left_index=True, right_index=True)
         del df3
         df.set_index("ParticleIDs", inplace=True)
+        print("sorting")
+        df.sort_values("FOFGroupIDs",inplace=True)
         meta = ParticlesMeta(
             particle_mass=masses[0]
         )
diff --git a/sizes.py b/sizes.py
index 3ae5b9a..ff3f8ba 100644
--- a/sizes.py
+++ b/sizes.py
@@ -7,12 +7,12 @@ df = pd.read_csv("sizes.csv")
 
 print(df)
 
-df = df.iloc[:50]
+# df = df.iloc
 
 fig: Figure = plt.figure()
 ax: Axes = fig.gca()
-# ax.scatter(df["ref_sizes"], df["comp_sizes"], s=1, alpha=.3)
-ax.scatter(df["ref_masses"], df["comp_masses"], s=3)
+ax.scatter(df["ref_sizes"], df["comp_sizes"], s=1, alpha=.3)
+# ax.scatter(df["ref_masses"], df["comp_masses"], s=3)
 
 ax.set_xscale("log")
 ax.set_yscale("log")
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..a1dcb95
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,10 @@
+import pandas as pd
+
+
+def print_progress(i, total, extra_data=""):
+    print(f"{i} of {total} ({extra_data})" + " " * 20, end="\r" if i != total else "\n", flush=True)
+
+
+def memory_usage(df: pd.DataFrame):
+    bytes_used = df.memory_usage(index=True).sum()
+    return bytes_used / 1024 / 1024