From 0673f6dea60aed8bdd7ae741b09e96a546c26442 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 02:27:35 -0400
Subject: [PATCH 01/14] rm dists

---
 dist/nt2py-0.2.1.tar.gz           | Bin 30984 -> 0 bytes
 dist/nt2py-0.2.tar.gz             | Bin 14772 -> 0 bytes
 dist/nt2py-0.3.0.tar.gz           | Bin 13702 -> 0 bytes
 dist/nt2py-0.3.1.tar.gz           | Bin 13723 -> 0 bytes
 dist/nt2py-0.4.0.tar.gz           | Bin 14105 -> 0 bytes
 dist/nt2py-0.4.1.tar.gz           | Bin 14184 -> 0 bytes
 dist/nt2py-0.5.0-py3-none-any.whl | Bin 26185 -> 0 bytes
 dist/nt2py-0.5.0.tar.gz           | Bin 21508 -> 0 bytes
 dist/nt2py-0.5.1-py3-none-any.whl | Bin 26217 -> 0 bytes
 dist/nt2py-0.5.1.tar.gz           | Bin 21527 -> 0 bytes
 dist/nt2py-0.5.2-py3-none-any.whl | Bin 26219 -> 0 bytes
 dist/nt2py-0.5.2.tar.gz           | Bin 21529 -> 0 bytes
 dist/nt2py-0.5.3-py3-none-any.whl | Bin 26220 -> 0 bytes
 dist/nt2py-0.5.3.tar.gz           | Bin 22573 -> 0 bytes
 dist/nt2py-0.6.0-py3-none-any.whl | Bin 28547 -> 0 bytes
 dist/nt2py-0.6.0.tar.gz           | Bin 24487 -> 0 bytes
 dist/nt2py-1.0.1-py3-none-any.whl | Bin 35061 -> 0 bytes
 dist/nt2py-1.0.1.tar.gz           | Bin 30073 -> 0 bytes
 dist/nt2py-1.1.0-py3-none-any.whl | Bin 36348 -> 0 bytes
 dist/nt2py-1.1.0.tar.gz           | Bin 30928 -> 0 bytes
 dist/nt2py-1.2.0-py3-none-any.whl | Bin 39719 -> 0 bytes
 dist/nt2py-1.2.0.tar.gz           | Bin 34374 -> 0 bytes
 dist/nt2py-1.2.1-py3-none-any.whl | Bin 39699 -> 0 bytes
 dist/nt2py-1.2.1.tar.gz           | Bin 34370 -> 0 bytes
 dist/nt2py-1.3.0-py3-none-any.whl | Bin 41964 -> 0 bytes
 dist/nt2py-1.3.0.tar.gz           | Bin 35855 -> 0 bytes
 dist/nt2py-1.4.0-py3-none-any.whl | Bin 43194 -> 0 bytes
 dist/nt2py-1.4.0.tar.gz           | Bin 35963 -> 0 bytes
 dist/nt2py-1.5.0-py3-none-any.whl | Bin 43701 -> 0 bytes
 dist/nt2py-1.5.0.tar.gz           | Bin 36789 -> 0 bytes
 dist/nt2py-1.5.1-py3-none-any.whl | Bin 43739 -> 0 bytes
 dist/nt2py-1.5.1.tar.gz           | Bin 36821 -> 0 bytes
 dist/nt2py-1.5.2-py3-none-any.whl | Bin 43848 -> 0 bytes
 dist/nt2py-1.5.2.tar.gz           | Bin 36905 -> 0 bytes
 dist/nt2py-1.5.3-py3-none-any.whl | Bin 43860 -> 0 bytes
 dist/nt2py-1.5.3.tar.gz           | Bin 36927 -> 0 bytes
 36 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 dist/nt2py-0.2.1.tar.gz
 delete mode 100644 dist/nt2py-0.2.tar.gz
 delete mode 100644 dist/nt2py-0.3.0.tar.gz
 delete mode 100644 dist/nt2py-0.3.1.tar.gz
 delete mode 100644 dist/nt2py-0.4.0.tar.gz
 delete mode 100644 dist/nt2py-0.4.1.tar.gz
 delete mode 100644 dist/nt2py-0.5.0-py3-none-any.whl
 delete mode 100644 dist/nt2py-0.5.0.tar.gz
 delete mode 100644 dist/nt2py-0.5.1-py3-none-any.whl
 delete mode 100644 dist/nt2py-0.5.1.tar.gz
 delete mode 100644 dist/nt2py-0.5.2-py3-none-any.whl
 delete mode 100644 dist/nt2py-0.5.2.tar.gz
 delete mode 100644 dist/nt2py-0.5.3-py3-none-any.whl
 delete mode 100644 dist/nt2py-0.5.3.tar.gz
 delete mode 100644 dist/nt2py-0.6.0-py3-none-any.whl
 delete mode 100644 dist/nt2py-0.6.0.tar.gz
 delete mode 100644 dist/nt2py-1.0.1-py3-none-any.whl
 delete mode 100644 dist/nt2py-1.0.1.tar.gz
 delete mode 100644 dist/nt2py-1.1.0-py3-none-any.whl
 delete mode 100644 dist/nt2py-1.1.0.tar.gz
 delete mode 100644 dist/nt2py-1.2.0-py3-none-any.whl
 delete mode 100644 dist/nt2py-1.2.0.tar.gz
 delete mode 100644 dist/nt2py-1.2.1-py3-none-any.whl
 delete mode 100644 dist/nt2py-1.2.1.tar.gz
 delete mode 100644 dist/nt2py-1.3.0-py3-none-any.whl
 delete mode 100644 dist/nt2py-1.3.0.tar.gz
 delete mode 100644 dist/nt2py-1.4.0-py3-none-any.whl
 delete mode 100644 dist/nt2py-1.4.0.tar.gz
 delete mode 100644 dist/nt2py-1.5.0-py3-none-any.whl
 delete mode 100644 dist/nt2py-1.5.0.tar.gz
 delete mode 100644 dist/nt2py-1.5.1-py3-none-any.whl
 delete mode 100644 dist/nt2py-1.5.1.tar.gz
 delete mode 100644 dist/nt2py-1.5.2-py3-none-any.whl
 delete mode 100644 dist/nt2py-1.5.2.tar.gz
 delete mode 100644 dist/nt2py-1.5.3-py3-none-any.whl
 delete mode 100644 dist/nt2py-1.5.3.tar.gz

diff --git a/dist/nt2py-0.2.1.tar.gz b/dist/nt2py-0.2.1.tar.gz
deleted file mode 100644
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From 1cda9449492af9c1da2736370378f8c9bb399368 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 02:28:32 -0400
Subject: [PATCH 02/14] dump pyright+black for pyrefly+ruff

---
 .vscode/extensions.json |  4 +++-
 pyrightconfig.json      | 12 ------------
 2 files changed, 3 insertions(+), 13 deletions(-)
 delete mode 100644 pyrightconfig.json

diff --git a/.vscode/extensions.json b/.vscode/extensions.json
index 00563da..0b56f9d 100644
--- a/.vscode/extensions.json
+++ b/.vscode/extensions.json
@@ -1,6 +1,8 @@
 {
   "recommendations": [
     "ms-python.python",
-    "ms-python.black-formatter"
+    "meta.pyrefly",
+    "charliermarsh.ruff",
+    "github.vscode-github-actions"
   ]
 }
\ No newline at end of file
diff --git a/pyrightconfig.json b/pyrightconfig.json
deleted file mode 100644
index cb36191..0000000
--- a/pyrightconfig.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "extraPath": [
-    "./"
-  ],
-  "reportAny": false,
-  "reportExplicitAny": false,
-  "reportUnknownVariableType": false,
-  "reportUnknownMemberType": false,
-  "reportUnknownArgumentType": false,
-  "reportArgumentType": false,
-  "reportPrivateImportUsage": false
-}

From d92284d6323ceb17a95c669d29cbe739067a736a Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 02:28:55 -0400
Subject: [PATCH 03/14] separate publish/test workflows

---
 .github/workflows/publish.yml                 | 52 +++++++++++++++++++
 .github/workflows/{unittests.yml => test.yml} | 16 +++---
 2 files changed, 58 insertions(+), 10 deletions(-)
 create mode 100644 .github/workflows/publish.yml
 rename .github/workflows/{unittests.yml => test.yml} (56%)

diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 0000000..ee744e1
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,52 @@
+name: publish to PyPI
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  test:
+    uses: ./.github/workflows/test.yml
+
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+
+      - name: Check version matches the release tag
+        run: |
+          PKG_VERSION=$(grep -oP '__version__ = "\K[^"]+' nt2/__init__.py)
+          TAG="${{ github.event.release.tag_name }}"
+          TAG="${TAG#v}"   # allow tags like v0.1.0
+          if [ "$PKG_VERSION" != "$TAG" ]; then
+            echo "::error::package version ($PKG_VERSION) != release tag ($TAG)"
+            exit 1
+          fi
+
+      - name: Build sdist and wheel
+        run: uv build
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: dist
+          path: dist/
+
+  publish:
+    needs: build
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/nt2py
+    permissions:
+      id-token: write # required for PyPI Trusted Publishing (OIDC)
+    steps:
+      - uses: actions/download-artifact@v4
+        with:
+          name: dist
+          path: dist/
+
+      - name: Publish to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
diff --git a/.github/workflows/unittests.yml b/.github/workflows/test.yml
similarity index 56%
rename from .github/workflows/unittests.yml
rename to .github/workflows/test.yml
index 005af95..cb5a96a 100644
--- a/.github/workflows/unittests.yml
+++ b/.github/workflows/test.yml
@@ -1,17 +1,19 @@
 name: Unit tests
 
-on: [push]
+on:
+  push:
+  workflow_call:
 
 jobs:
-  build:
+  test:
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13", "3.14"]
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13", "3.14"]
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           lfs: true
 
@@ -28,9 +30,3 @@ jobs:
       - name: Test with `pytest`
         run: |
           pytest
-
-      - name: Publish package
-        if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') && matrix.python-version == '3.14'
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          password: ${{ secrets.PYPI_API_TOKEN }}

From 34ece575f02dc4eb8ac8afbd21fa37f459299aa8 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 02:29:18 -0400
Subject: [PATCH 04/14] old containers moved to legacy

---
 legacy/containers/__init__.py    |   0
 legacy/containers/container.py   |  94 ++++
 legacy/containers/data.py        | 420 ++++++++++++++++
 legacy/containers/diagnostics.py | 108 ++++
 legacy/containers/fields.py      | 161 ++++++
 legacy/containers/particles.py   | 814 +++++++++++++++++++++++++++++++
 legacy/containers/spectra.py     | 164 +++++++
 7 files changed, 1761 insertions(+)
 create mode 100644 legacy/containers/__init__.py
 create mode 100644 legacy/containers/container.py
 create mode 100644 legacy/containers/data.py
 create mode 100644 legacy/containers/diagnostics.py
 create mode 100644 legacy/containers/fields.py
 create mode 100644 legacy/containers/particles.py
 create mode 100644 legacy/containers/spectra.py

diff --git a/legacy/containers/__init__.py b/legacy/containers/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/legacy/containers/container.py b/legacy/containers/container.py
new file mode 100644
index 0000000..dcb4092
--- /dev/null
+++ b/legacy/containers/container.py
@@ -0,0 +1,94 @@
+from typing import Callable, Optional, Dict, Tuple, List
+
+from nt2.readers.base import BaseReader
+
+
+class BaseContainer:
+    """Parent container class for holding any category data."""
+
+    __path: str
+    __reader: BaseReader
+    __remap: Optional[Dict[str, Callable[[str], str]]]
+
+    __valid_steps: Dict[str, List[int]]
+    __valid_files: Dict[str, List[str]]
+
+    def __init__(
+        self,
+        path: str,
+        reader: BaseReader,
+        remap: Optional[Dict[str, Callable[[str], str]]] = None,
+    ):
+        """Initializer for the BaseContainer class.
+
+        Parameters
+        ----------
+        path : str
+            The path to the data.
+        reader : BaseReader
+            The reader to be used for reading the data.
+        remap : Optional[dict[str, Callable[[str], str]]]
+            Remap dictionary to use to remap the data names (coords, fields, etc.).
+
+        """
+        super(BaseContainer, self).__init__()
+        self.__path = path
+        self.__reader = reader
+        self.__remap = remap
+
+    @property
+    def path(self) -> str:
+        """str: The main path of the data."""
+        return self.__path
+
+    @property
+    def reader(self) -> BaseReader:
+        """BaseReader: The reader used to read the data."""
+        return self.__reader
+
+    @property
+    def remap(self) -> Optional[Dict[str, Callable[[str], str]]]:
+        """{ str: (str) -> str } : The coordinate/field remap dictionary."""
+        return self.__remap
+
+    @property
+    def valid_steps(self) -> Dict[str, List[int]]:
+        """dict[str, list[int]]: The valid steps for each category."""
+        return self.__valid_steps
+
+    @property
+    def valid_files(self) -> Dict[str, List[str]]:
+        """dict[str, list[str]]: The valid files for each category."""
+        return self.__valid_files
+
+    @valid_steps.setter
+    def valid_steps(self, value: Dict[str, List[int]]) -> None:
+        """Set the valid steps for each category.
+
+        Parameters
+        ----------
+        value : dict[str, list[int]]
+            The valid steps for each category.
+
+        """
+        self.__valid_steps = value
+
+    @valid_files.setter
+    def valid_files(self, value: Dict[str, List[str]]) -> None:
+        """Set the valid files for each category.
+
+        Parameters
+        ----------
+        value : dict[str, list[str]]
+            The valid files for each category.
+
+        """
+        self.__valid_files = value
+
+    def __dask_tokenize__(self) -> Tuple[str, str, str]:
+        """Provide a deterministic Dask token for container instances."""
+        return (
+            self.__class__.__name__,
+            self.__path,
+            self.__reader.format.value,
+        )
diff --git a/legacy/containers/data.py b/legacy/containers/data.py
new file mode 100644
index 0000000..f5e28cc
--- /dev/null
+++ b/legacy/containers/data.py
@@ -0,0 +1,420 @@
+from typing import Callable, Any, Union, Optional, List, Dict
+
+import sys
+import logging
+
+if sys.version_info >= (3, 12):
+    from typing import override
+else:
+
+    def override(method):
+        return method
+
+
+from nt2.utils import ToHumanReadable
+
+import xarray as xr
+import pandas as pd
+
+from nt2.utils import (
+    DetermineDataFormat,
+    InheritClassDocstring,
+    Format,
+    CoordinateSystem,
+)
+from nt2.readers.base import BaseReader
+from nt2.readers.hdf5 import Reader as HDF5Reader
+from nt2.readers.adios2 import Reader as BP5Reader
+from nt2.containers.fields import Fields
+from nt2.containers.particles import Particles
+from nt2.containers.spectra import Spectra
+from nt2.containers.diagnostics import Diagnostics
+
+import nt2.plotters.polar as acc_polar
+import nt2.plotters.particles as acc_particles
+import nt2.plotters.inspect as acc_inspect
+import nt2.plotters.movie as acc_movie
+from nt2.plotters.export import makeFramesAndMovie
+
+
+@xr.register_dataset_accessor("polar")
+@InheritClassDocstring
+class DatasetPolarPlotAccessor(acc_polar.ds_accessor):
+    pass
+
+
+@xr.register_dataset_accessor("particles")
+@InheritClassDocstring
+class DatasetParticlesPlotAccessor(acc_particles.ds_accessor):
+    pass
+
+
+@xr.register_dataarray_accessor("polar")
+@InheritClassDocstring
+class PolarPlotAccessor(acc_polar.accessor):
+    pass
+
+
+@xr.register_dataset_accessor("inspect")
+@InheritClassDocstring
+class DatasetInspectPlotAccessor(acc_inspect.ds_accessor):
+    pass
+
+
+@xr.register_dataarray_accessor("movie")
+@InheritClassDocstring
+class MoviePlotAccessor(acc_movie.accessor):
+    pass
+
+
+# Cartesian remapping functions
+def remap_fields_cart(name: str) -> str:
+    name = name[1:]
+    fieldname = name.split("_")[0]
+    fieldname = fieldname.replace("0", "t")
+    fieldname = fieldname.replace("1", "x")
+    fieldname = fieldname.replace("2", "y")
+    fieldname = fieldname.replace("3", "z")
+    suffix = "_".join(name.split("_")[1:])
+    return f"{fieldname}{'_' + suffix if suffix != '' else ''}"
+
+
+def remap_coords_cart(name: str) -> str:
+    return {
+        "X1": "x",
+        "X2": "y",
+        "X3": "z",
+    }.get(name, name)
+
+
+def remap_prtl_quantities_cart(name: str) -> str:
+    shortname = name[1:]
+    return {
+        "X1": "x",
+        "X2": "y",
+        "X3": "z",
+        "U1": "ux",
+        "U2": "uy",
+        "U3": "uz",
+        "W": "w",
+    }.get(shortname, shortname)
+
+
+# Spherical remapping functions
+def remap_fields_sph(name: str) -> str:
+    name = name[1:]
+    fieldname = name.split("_")[0]
+    fieldname = fieldname.replace("0", "t")
+    fieldname = fieldname.replace("1", "r")
+    fieldname = fieldname.replace("2", "th")
+    fieldname = fieldname.replace("3", "ph")
+    suffix = "_".join(name.split("_")[1:])
+    return f"{fieldname}{'_' + suffix if suffix != '' else ''}"
+
+
+def remap_coords_sph(name: str) -> str:
+    return {
+        "X1": "r",
+        "X2": "th",
+        "X3": "ph",
+    }.get(name, name)
+
+
+def remap_prtl_quantities_sph(name: str) -> str:
+    shortname = name[1:]
+    return {
+        "X1": "r",
+        "X2": "th",
+        "X3": "ph",
+        "U1": "ur",
+        "U2": "uth",
+        "U3": "uph",
+        "W": "w",
+    }.get(shortname, shortname)
+
+
+def compactify(lst: Union[List[Any], Any]) -> str:
+    c = ""
+    cntr = 0
+    for l_ in lst:
+        if cntr > 5:
+            c += "\n|   "
+            cntr = 0
+        c += f"{l_}, "
+        cntr += 1
+    return c[:-2]
+
+
+class Data(Fields, Particles, Spectra):
+    """Main class to manage all the data containers.
+
+    Inherits from all category-specific containers.
+
+    """
+
+    __reader: BaseReader
+    __diagnostics: Optional[Diagnostics]
+
+    def __init__(
+        self,
+        path: str,
+        reader: Optional[BaseReader] = None,
+        remap: Optional[Dict[str, Callable[[str], str]]] = None,
+        coord_system: Optional[CoordinateSystem] = None,
+    ):
+        """Initializer for the Data class.
+
+        Parameters
+        ----------
+        path : str
+            Main path to the data
+        reader : BaseReader, optional
+            Reader to use to read the data. If None, it will be determined
+            based on the file format.
+        remap : dict[str, Callable[[str], str]], optional
+            Remap dictionary to use to remap the data names (coords, fields, etc.).
+        coord_system : CoordinateSystem, optional
+            Coordinate system of the data. If None, it will be determined
+            based on the data attrs (if remap is also None).
+
+        Raises
+        ------
+        NotImplementedError
+            If the data format or coordinate system support is not implemented.
+        ValueError
+            If the reader format does not match the data format or if coordinate system cannot be inferred.
+        """
+        # determine the reader from the format
+        fmt = DetermineDataFormat(path)
+        if reader is None:
+            if fmt == Format.HDF5:
+                self.__reader = HDF5Reader()
+            elif fmt == Format.BP5:
+                self.__reader = BP5Reader()
+            else:
+                raise NotImplementedError(
+                    "Only HDF5 & BP5 formats are supported at the moment."
+                )
+        else:
+            if fmt != reader.format:
+                raise ValueError(
+                    f"Reader format {reader.format} does not match data format {fmt}."
+                )
+            self.__reader = reader
+
+        # determine valid files & steps for each category
+        for category in ["fields", "particles", "spectra"]:
+            self.valid_files[category] = self.__reader.GetValidFiles(
+                path=path, category=category
+            )
+            if self.__reader.DefinesCategory(
+                path, category, self.valid_files[category]
+            ):
+                self.valid_steps[category] = self.__reader.GetValidSteps(
+                    path=path, category=category
+                )
+                if len(self.valid_steps[category]) == 0:
+                    raise ValueError(f"No valid steps found for category {category}.")
+
+        # determine the coordinate system and remapping
+        self.__attrs: Dict[str, Any] = {}
+        for category in ["fields", "particles", "spectra"]:
+            if (
+                category in self.valid_files
+                and len(self.valid_files[category]) > 0
+                and category in self.valid_steps
+                and len(self.valid_steps[category]) > 0
+            ):
+                first_step = self.valid_steps[category][0]
+                attrs = self.__reader.ReadAttrsAtTimestep(path, category, first_step)
+                self.__attrs.update(**attrs)
+                if "Coordinates" not in attrs:
+                    raise ValueError(
+                        f"Coordinates not found in attributes for category {category}."
+                    )
+                else:
+                    if attrs["Coordinates"] in [b"cart", "cart"]:
+                        coord_system = CoordinateSystem.XYZ
+                    elif attrs["Coordinates"] in [b"sph", "sph", b"qsph", "qsph"]:
+                        coord_system = CoordinateSystem.SPH
+
+                    else:
+                        raise NotImplementedError(
+                            f"Coordinate system {attrs['Coordinates']} not supported."
+                        )
+                    if remap is None:
+                        remap = {
+                            "coords": (
+                                remap_coords_cart
+                                if coord_system == CoordinateSystem.XYZ
+                                else remap_coords_sph
+                            ),
+                            "fields": (
+                                remap_fields_cart
+                                if coord_system == CoordinateSystem.XYZ
+                                else remap_fields_sph
+                            ),
+                            "particles": (
+                                remap_prtl_quantities_cart
+                                if coord_system == CoordinateSystem.XYZ
+                                else remap_prtl_quantities_sph
+                            ),
+                        }
+                    break
+
+        if coord_system is None:
+            raise ValueError("No coordinate system found in the data.")
+
+        self.__coordinate_system = coord_system
+
+        super(Data, self).__init__(path=path, reader=self.__reader, remap=remap)
+        try:
+            self.__diagnostics = Diagnostics(path)
+        except Exception as e:
+            logging.warning(f"Failed to read diagnostics: {e}")
+            self.__diagnostics = None
+
+    def makeMovie(
+        self,
+        plot: Callable,
+        time: Optional[List[float]] = None,
+        num_cpus: Optional[int] = None,
+        **movie_kwargs: Any,
+    ) -> bool:
+        """Create animation with provided plot function.
+
+        Parameters
+        ----------
+        plot : callable
+            A function that takes a single argument (time in physical units) and produces a plot.
+        time : array_like, optional
+            An array of time values to use for the animation. If not provided, the entire time range will be used.
+        num_cpus : int, optional
+            The number of CPUs to use for parallel processing. If None, it will use all available CPUs.
+        **movie_kwargs : dict
+            Additional keyword arguments to pass to the movie creation function.
+
+        Returns
+        -------
+        bool
+            True if the movie was created successfully, False otherwise.
+        """
+        if time is None:
+            if self.fields_defined:
+                time = self.fields.t.values
+            elif self.particles_defined and self.particles is not None:
+                time = list(self.particles.times)
+            else:
+                raise ValueError("No time values found.")
+        assert time is not None, "Time values must be provided."
+        name: str = ""
+        if self.attrs.get("simulation.name", None) is None:
+            name = movie_kwargs.pop("name", "movie")
+        else:
+            name_b = self.attrs.get("simulation.name")
+            if isinstance(name_b, bytes):
+                name = name_b.decode("utf-8")
+            else:
+                name = str(name_b)
+        return makeFramesAndMovie(
+            name=name,
+            data=self,
+            plot=plot,
+            times=time,
+            num_cpus=num_cpus,
+            **movie_kwargs,
+        )
+
+    @property
+    def coordinate_system(self) -> CoordinateSystem:
+        """CoordinateSystem: The coordinate system of the data."""
+        return self.__coordinate_system
+
+    @property
+    def attrs(self) -> Dict[str, Any]:
+        """dict[str, Any]: The attributes of the data."""
+        return self.__attrs
+
+    @property
+    def diagnostics(self) -> Union[pd.DataFrame, None]:
+        """pd.DataFrame or None: The diagnostics output if .out file is found, None otherwise."""
+        if self.__diagnostics is None:
+            return None
+        return self.__diagnostics.df
+
+    def to_str(self) -> str:
+        """str: String representation of the all the enclosed dataframes."""
+
+        string = ""
+        if self.fields_defined:
+            string += "FieldsDataset:\n"
+            string += "==============\n"
+            string += f"| Coordinates:\n|   {self.coordinate_system.value}\n|\n"
+            string += f"| Data axes:\n|   {compactify(self.fields.indexes.keys())}\n|\n"
+            delta_t = (
+                self.fields.coords["t"].values[1] - self.fields.coords["t"].values[0]
+            ) / (self.fields.coords["s"].values[1] - self.fields.coords["s"].values[0])
+            string += f"|   - dt: {delta_t:.2e}\n"
+            for key in self.fields.coords.keys():
+                crd = self.fields.coords[key].values
+                fmt = ""
+                if key != "s":
+                    fmt = ".2f"
+                string += f"|   - {key}: {crd.min():{fmt}} -> {crd.max():{fmt}} [{len(crd)}]\n"
+            string += "|\n"
+            string += f"| Quantities:\n|   {compactify(sorted(map(str, self.fields.data_vars.keys())))}\n|\n"
+            string += f"| Total size: {ToHumanReadable(self.fields.nbytes)}\n\n"
+        else:
+            string += "FieldsDataset:\n"
+            string += "==============\n"
+            string += "  empty\n\n"
+        if self.particles_defined and self.particles is not None:
+            species = sorted(self.particles.species)
+            string += "ParticleDataset:\n"
+            string += "================\n"
+            string += f"| Species:\n|   {compactify(species)}\n|\n"
+            string += f"| Timesteps:\n|   {len(self.particles.times)}\n|\n"
+            string += f"| Quantities:\n|   {compactify(self.particles.columns)}\n|\n"
+            string += f"| Total size: {ToHumanReadable(self.particles.nbytes)}\n|\n"
+            string += self.help_particles("| ")
+            string += "\n"
+        else:
+            string += "ParticleDataset:\n"
+            string += "================\n"
+            string += "  empty\n\n"
+        if self.spectra_defined and self.spectra is not None:
+            string += "SpectraDataset:\n"
+            string += "===============\n"
+            string += (
+                f"| Data axes:\n|   {compactify(self.spectra.indexes.keys())}\n|\n"
+            )
+            delta_t = (
+                self.spectra.coords["t"].values[1] - self.spectra.coords["t"].values[0]
+            ) / (
+                self.spectra.coords["s"].values[1] - self.spectra.coords["s"].values[0]
+            )
+            string += f"|   - dt: {delta_t:.2e}\n"
+            for key in self.spectra.coords.keys():
+                crd = self.spectra.coords[key].values
+                fmt = ""
+                if key != "s":
+                    fmt = ".2f"
+                string += f"|   - {key}: {crd.min():{fmt}} -> {crd.max():{fmt}} [{len(crd)}]\n"
+            string += "|\n"
+            string += f"| Quantities:\n|   {compactify(sorted(map(str, self.spectra.data_vars.keys())))}\n|\n"
+            string += f"| Total size: {ToHumanReadable(self.spectra.nbytes)}\n|\n"
+            string += self.help_spectra("| ")
+        else:
+            string += "SpectraDataset:\n"
+            string += "===============\n"
+            string += "  empty\n\n"
+
+        return string
+
+    @override
+    def __str__(self) -> str:
+        return self.to_str()
+
+    @override
+    def __repr__(self) -> str:
+        return self.to_str()
diff --git a/legacy/containers/diagnostics.py b/legacy/containers/diagnostics.py
new file mode 100644
index 0000000..98fe33b
--- /dev/null
+++ b/legacy/containers/diagnostics.py
@@ -0,0 +1,108 @@
+from typing import Union
+import pandas as pd
+
+
+class Diagnostics:
+    df: Union[pd.DataFrame, None]
+
+    def __init__(self, path: str):
+        import os
+        import logging
+        import re
+
+        outfiles = [o for o in os.listdir(path) if o.endswith(".out")]
+        if len(outfiles) == 0:
+            logging.warning(f"No .out files found in {path}")
+            self.df = None
+        else:
+            self.outfile = os.path.join(path, outfiles[0])
+
+            data = {}
+
+            with open(self.outfile, "r") as f:
+                content = f.read()
+                steps = re.findall(r"Step:\s+(\d+)\.+\[", content)
+                times = re.findall(r"Time:\s+([\d.]+\d)\.+\[", content)
+                substeps = re.findall(r"\s+([A-Za-z]+)\.+([\d.]+)\s+([mµn]?s)", content)
+                species = re.findall(
+                    r"\s+species\s+(\d+)\s+\(.+\)\.+([\deE+-.]+)(\s+\d+\%\s:\s\d+\%\s+)?([\deE+-.]+)?( : )?([\deE+-.]+)?",
+                    content,
+                )
+
+                data["steps"] = []
+                for step in steps:
+                    data["steps"].append(int(step))
+
+                data["times"] = []
+                for time in times:
+                    data["times"].append(float(time))
+
+                assert len(data["steps"]) == len(
+                    data["times"]
+                ), "Number of steps and times do not match"
+
+                data["substeps"] = {}
+                for substep in substeps:
+                    if substep[0] not in data["substeps"].keys():
+                        data["substeps"][substep[0]] = []
+
+                    def to_ns(value: float, unit: str) -> float:
+                        if unit == "s":
+                            return value * 1e9
+                        elif unit == "ms":
+                            return value * 1e6
+                        elif unit == "µs":
+                            return value * 1e3
+                        elif unit == "ns":
+                            return value
+                        else:
+                            raise ValueError(f"Unknown time unit: {unit}")
+
+                    data["substeps"][substep[0]].append(
+                        to_ns(float(substep[1]), substep[2])
+                    )
+
+                for key in data["substeps"].keys():
+                    assert len(data["substeps"][key]) == len(
+                        data["steps"]
+                    ), f"Number of substep entries for {key} does not match number of steps"
+
+                data["species"] = {}
+                data["species_min"] = {}
+                data["species_max"] = {}
+                for specie in species:
+                    if specie[0] not in data["species"].keys():
+                        data["species"][specie[0]] = []
+                        data["species_min"][specie[0]] = []
+                        data["species_max"][specie[0]] = []
+                    data["species"][specie[0]].append(int(float(specie[1])))
+                    if len(specie) == 6 and specie[3] != "" and specie[5] != "":
+                        data["species_min"][specie[0]].append(int(float(specie[3])))
+                        data["species_max"][specie[0]].append(int(float(specie[5])))
+
+                for key in data["species"].keys():
+                    assert len(data["species"][key]) == len(
+                        data["steps"]
+                    ), f"Number of species entries for {key} does not match number of steps"
+                    assert (len(data["species_min"][key]) == len(data["steps"])) or (
+                        len(data["species_min"][key]) == 0
+                    ), f"Number of species min entries for {key} does not match number of steps"
+                    assert (len(data["species_max"][key]) == len(data["steps"])) or (
+                        len(data["species_max"][key]) == 0
+                    ), f"Number of species max entries for {key} does not match number of steps"
+
+            self.df = pd.DataFrame(index=data["steps"])
+            self.df["Step"] = data["steps"]
+            self.df["Time"] = data["times"]
+            for key in data["substeps"].keys():
+                self.df[key] = data["substeps"][key]
+            for key in data["species"].keys():
+                self.df[f"species_{key}"] = data["species"][key]
+                if (
+                    len(data["species_min"][key]) > 0
+                    and len(data["species_max"][key]) > 0
+                ):
+                    self.df[f"species_{key}_min"] = data["species_min"][key]
+                    self.df[f"species_{key}_max"] = data["species_max"][key]
+
+            del data
diff --git a/legacy/containers/fields.py b/legacy/containers/fields.py
new file mode 100644
index 0000000..6cbb5b6
--- /dev/null
+++ b/legacy/containers/fields.py
@@ -0,0 +1,161 @@
+from typing import Any
+
+import dask
+import dask.array as da
+import xarray as xr
+from tqdm import tqdm
+
+from nt2.containers.container import BaseContainer
+from nt2.utils import Layout
+
+
+class Fields(BaseContainer):
+    """Parent class to manage the fields dataframe."""
+
+    def _read_field(self, layout: Layout, field: str, step: int) -> Any:
+        """Reads a field from the data.
+
+        This is a dask-delayed function used further to build the dataset.
+
+        Parameters
+        ----------
+        layout : Layout
+            Layout of the field.
+        field : str
+            Field to read.
+        step : int
+            Step to read.
+
+        Returns
+        -------
+        Any
+            Field data.
+
+        """
+        if layout == Layout.L:
+            return self.reader.ReadArrayAtTimestep(self.path, "fields", field, step)
+        else:
+            return self.reader.ReadArrayAtTimestep(self.path, "fields", field, step).T
+
+    def __init__(
+        self,
+        **kwargs: Any,
+    ) -> None:
+        """Initializer for the Fields class.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Keyword arguments to be passed to the parent BaseContainer class.
+
+        """
+        super(Fields, self).__init__(**kwargs)
+        if self.reader.DefinesCategory(self.path, "fields", self.valid_files["fields"]):
+            self.__fields_defined = True
+            self.__fields = self._read_fields()
+        else:
+            self.__fields_defined = False
+            self.__fields = xr.Dataset()
+
+    @property
+    def fields_defined(self) -> bool:
+        """bool: Whether the fields category is defined."""
+        return self.__fields_defined
+
+    @property
+    def fields(self) -> xr.Dataset:
+        """xr.Dataset: The fields dataframe."""
+        return self.__fields
+
+    def _read_fields(self) -> xr.Dataset:
+        """Helper function to read the fields dataframe."""
+        self.reader.VerifySameCategoryNames(
+            self.path, "fields", "f", self.valid_steps["fields"]
+        )
+        self.reader.VerifySameFieldShapes(self.path, self.valid_steps["fields"])
+        self.reader.VerifySameFieldLayouts(self.path, self.valid_steps["fields"])
+
+        field_names = self.reader.ReadCategoryNamesAtTimestep(
+            self.path, "fields", "f", self.valid_steps["fields"][0]
+        )
+
+        first_step = self.valid_steps["fields"][0]
+        first_name = next(iter(field_names))
+        layout = self.reader.ReadFieldLayoutAtTimestep(self.path, first_step)
+        shape = self.reader.ReadArrayShapeAtTimestep(
+            self.path, "fields", first_name, first_step
+        )
+        coords = self.reader.ReadFieldCoordsAtTimestep(self.path, first_step)
+        coords = {k: coords[k] for k in sorted(coords.keys())[::-1]}
+        # rename coordinates if remap is provided
+        if self.remap is not None and "coords" in self.remap:
+            new_coords = {}
+            for coord in coords.keys():
+                new_coords[self.remap["coords"](coord)] = coords[coord]
+            coords = new_coords
+
+        times = self.reader.ReadPerTimestepVariable(
+            self.path,
+            "fields",
+            "Time",
+            "t",
+            self.valid_files["fields"],
+        )
+        steps = self.reader.ReadPerTimestepVariable(
+            self.path,
+            "fields",
+            "Step",
+            "s",
+            self.valid_files["fields"],
+        )
+
+        edge_coords = self.reader.ReadEdgeCoordsAtTimestep(self.path, first_step)
+        new_edge_coords = {}
+        for coord in edge_coords.keys():
+            assoc_x = (
+                coord[:-1]
+                if (self.remap is None or "coords" not in self.remap)
+                else self.remap["coords"](coord[:-1])
+            )
+            new_edge_coords[assoc_x + "_min"] = (assoc_x, edge_coords[coord][:-1])
+            new_edge_coords[assoc_x + "_max"] = (assoc_x, edge_coords[coord][1:])
+        edge_coords = new_edge_coords
+
+        all_dims = {**times, **coords}.keys()
+        all_coords = {**times, **coords, "s": ("t", steps["s"]), **edge_coords}
+
+        return xr.Dataset(
+            {
+                (
+                    remapped_name := (
+                        self.remap["fields"](name)
+                        if (self.remap is not None and "fields" in self.remap)
+                        else name
+                    )
+                ): xr.DataArray(
+                    da.stack(
+                        [
+                            da.from_delayed(
+                                dask.delayed(self._read_field)(layout, name, step),
+                                shape=shape[:: -1 if layout == Layout.R else 1],
+                                dtype="float",
+                            )
+                            for step in tqdm(
+                                self.valid_steps["fields"],
+                                desc="steps",
+                                position=1,
+                                leave=False,
+                            )
+                        ],
+                        axis=0,
+                    ),
+                    name=remapped_name,
+                    dims=all_dims,
+                    coords=all_coords,
+                )
+                for name in tqdm(field_names, desc="fields", position=0, leave=False)
+            },
+            attrs=self.reader.ReadAttrsAtTimestep(
+                path=self.path, category="fields", step=first_step
+            ),
+        )
diff --git a/legacy/containers/particles.py b/legacy/containers/particles.py
new file mode 100644
index 0000000..910cfbd
--- /dev/null
+++ b/legacy/containers/particles.py
@@ -0,0 +1,814 @@
+from typing import (
+    Any,
+    Callable,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Literal,
+    Union,
+    Dict,
+    Type,
+)
+import numpy.typing as npt
+from copy import copy
+
+import dask
+import dask.dataframe as dd
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+import matplotlib.pyplot as plt
+import matplotlib.axes as maxes
+
+from nt2.containers.container import BaseContainer
+
+
+IntSelector = Union[int, Sequence[int], slice, Tuple[int, int]]
+FloatSelector = Union[float, slice, Sequence[float], Tuple[float, float]]
+
+
+class Selection:
+    def __init__(
+        self,
+        type: Literal["value", "range", "list"],
+        value: Optional[Union[int, float, list, tuple]] = None,
+    ):
+        self.type = type
+        self.value = value
+
+    def intersect(self, other: "Selection") -> "Selection":
+        if self.value is None:
+            return copy(other)
+        elif other.value is None:
+            return copy(self)
+        if self.type == "value" and other.type == "value":
+            if self.value == other.value:
+                return Selection("value", self.value)
+            else:
+                return Selection("value")
+        elif self.type == "value" and other.type == "list":
+            assert isinstance(other.value, list), "other.value must be a list"
+            if self.value in other.value:
+                return Selection("value", self.value)
+            else:
+                return Selection("value")
+        elif self.type == "value" and other.type == "range":
+            assert isinstance(other.value, tuple) and len(other.value) == 2, (
+                "other.value must be a tuple of length 2"
+            )
+            lo, hi = other.value
+            if lo <= self.value < hi:
+                return Selection("value", self.value)
+            else:
+                return Selection("value")
+        elif self.type == "list" and other.type == "value":
+            return other.intersect(self)
+        elif self.type == "list" and other.type == "list":
+            assert isinstance(self.value, list), "self.value must be a list"
+            assert isinstance(other.value, list), "other.value must be a list"
+            new_values = [v for v in self.value if v in other.value]
+            return Selection("list", new_values)
+        elif self.type == "list" and other.type == "range":
+            assert isinstance(other.value, tuple) and len(other.value) == 2, (
+                "other.value must be a tuple of length 2"
+            )
+            assert isinstance(self.value, list), "self.value must be a list"
+            lo, hi = other.value
+            new_values = [v for v in self.value if lo <= v <= hi]
+            return Selection("list", new_values)
+        elif self.type == "range" and other.type == "value":
+            return other.intersect(self)
+        elif self.type == "range" and other.type == "list":
+            return other.intersect(self)
+        elif self.type == "range" and other.type == "range":
+            assert isinstance(self.value, tuple) and len(self.value) == 2, (
+                "self.value must be a tuple of length 2"
+            )
+            assert isinstance(other.value, tuple) and len(other.value) == 2, (
+                "other.value must be a tuple of length 2"
+            )
+            lo1, hi1 = self.value
+            lo2, hi2 = other.value
+            new_lo = max(lo1, lo2)
+            new_hi = min(hi1, hi2)
+            if new_lo <= new_hi:
+                return Selection("range", (new_lo, new_hi))
+            else:
+                return Selection("value")
+        else:
+            raise ValueError(f"Unknown selection types: {self.type}, {other.type}")
+
+    def __repr__(self) -> str:
+        if self.type == "value":
+            return "all" if self.value is None else f"{self.value:.3g}"
+        elif self.type == "range":
+            if self.value is None:
+                return "all"
+            else:
+                assert isinstance(self.value, tuple) and len(self.value) == 2, (
+                    "value must be a tuple of length 2"
+                )
+                lo, hi = self.value
+                lo_str = "..." if lo is None or lo == -np.inf else f"{lo:.3g}"
+                hi_str = "..." if hi is None or hi == np.inf else f"{hi:.3g}"
+                return f"[ {lo_str} -> {hi_str} ]"
+        elif self.type == "list":
+            assert isinstance(self.value, list), "value must be a list"
+            return "{ " + ", ".join(f"{v:.3g}" for v in self.value) + " }"
+        else:
+            return "InvalidSelection"
+
+    def __str__(self) -> str:
+        return self.__repr__()
+
+
+def _coerce_selector_to_mask(
+    s: Union[IntSelector, FloatSelector],
+    series: Any,
+    inclusive_tuple: bool = True,
+    method="exact",
+):
+    from operator import ior
+    from functools import reduce
+
+    if isinstance(s, slice):
+        lo = s.start if s.start is not None else -np.inf
+        hi = s.stop if s.stop is not None else np.inf
+        step = s.step
+        mask = (series >= lo) & (series <= hi)
+        if step not in (None, 1):
+            mask = mask & (((series - lo) % step) == 0)
+        return mask, ("range", (lo, hi))
+    elif isinstance(s, tuple) and len(s) == 2 and inclusive_tuple:
+        lo, hi = s
+        if lo is None:
+            lo = -np.inf
+        if hi is None:
+            hi = np.inf
+        return (series >= lo) & (series <= hi), ("range", (lo, hi))
+    elif isinstance(s, (list, tuple, np.ndarray, pd.Index, pd.Series)):
+        if method == "exact":
+            return series.isin(list(s)), ("list", list(s))
+        else:
+            return reduce(
+                ior, [np.abs(series - v) == np.abs(series - v).min() for v in s]
+            ), ("list", list(s))
+    else:
+        if method == "exact":
+            return series == s, ("value", s)
+        else:
+            return np.abs(series - s) == np.abs(series - s).min(), ("value", s)
+
+
+def _attach_columns(
+    part: pd.DataFrame,
+    cols_tuple,
+    read_column,
+    metadtypes,
+) -> pd.DataFrame:
+    if len(part) == 0:
+        for c in cols_tuple:
+            part[c] = np.array([], dtype=metadtypes[c])
+        return part
+    st_val = int(part["st"].iloc[0])
+
+    arrays = {c: read_column(st_val, c) for c in cols_tuple}
+
+    sel = part["row"].to_numpy()
+    for c in cols_tuple:
+        part[c] = np.asarray(arrays[c])[sel]
+    return part
+
+
+class ParticleDataset:
+    steps: npt.NDArray[np.int64]
+    times: npt.NDArray[np.float64]
+    colnames: List[str]
+
+    def __init__(
+        self,
+        species: List[int],
+        steps: npt.NDArray[np.int64],
+        times: npt.NDArray[np.float64],
+        colnames: List[str],
+        read_column: Callable[
+            [int, str], npt.NDArray[Union[np.float64, np.int64, np.float32, np.int32]]
+        ],
+        fprec: Optional[Type] = np.float32,
+        selection: Optional[Dict[str, Selection]] = None,
+        ddf_index: Optional[dd.DataFrame] = None,
+    ):
+        self.species = species
+        self.steps = steps
+        self.times = times
+        self.colnames = colnames
+
+        self.read_column = read_column
+        self.fprec = fprec
+        self.index_cols = ("id", "sp")
+        self._all_columns_cache: Optional[List[str]] = None
+
+        if selection is not None:
+            self.selection = selection
+        else:
+            self.selection = {
+                "t": Selection("range"),
+                "st": Selection("range"),
+                "sp": Selection("range"),
+                "id": Selection("range"),
+            }
+
+        self._dtypes = {
+            "id": np.int64,
+            "sp": np.int32,
+            "row": np.int64,
+            "st": np.int64,
+            "t": fprec,
+            "x": fprec,
+            "y": fprec,
+            "z": fprec,
+            "ux": fprec,
+            "uy": fprec,
+            "uz": fprec,
+            "r": fprec,
+            "th": fprec,
+            "ph": fprec,
+            "ur": fprec,
+            "uth": fprec,
+            "uph": fprec,
+        }
+
+        if ddf_index is not None:
+            self._ddf_index = ddf_index
+        else:
+            self._ddf_index = self._build_index_ddf()
+
+    @property
+    def ddf(self) -> dd.DataFrame:
+        return self._ddf_index
+
+    @property
+    def nbytes(self) -> int:
+        return self.ddf.memory_usage(index=True, deep=True).sum().compute()
+
+    @property
+    def columns(self) -> List[str]:
+        if self._all_columns_cache is None:
+            self._all_columns_cache = self.colnames
+        return self._all_columns_cache
+
+    def sel(
+        self,
+        t: Optional[Union[IntSelector, FloatSelector]] = None,
+        st: Optional[IntSelector] = None,
+        sp: Optional[IntSelector] = None,
+        id: Optional[IntSelector] = None,
+        method: str = "exact",
+    ) -> "ParticleDataset":
+        ddf = self._ddf_index
+        new_selection = {k: copy(v) for k, v in self.selection.items()}
+        if st is not None:
+            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                st, ddf["st"], method="exact"
+            )
+            ddf = ddf[ddf_sel]
+            new_selection["st"] = new_selection["st"].intersect(
+                Selection(sel_type, sel_value)
+            )
+        if t is not None:
+            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                t, ddf["t"], method=method
+            )
+            ddf = ddf[ddf_sel]
+            new_selection["t"] = new_selection["t"].intersect(
+                Selection(sel_type, sel_value)
+            )
+        if sp is not None:
+            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                sp, ddf["sp"], method="exact"
+            )
+            ddf = ddf[ddf_sel]
+            new_selection["sp"] = new_selection["sp"].intersect(
+                Selection(sel_type, sel_value)
+            )
+        if id is not None:
+            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                id, ddf["id"], method="exact"
+            )
+            ddf = ddf[ddf_sel]
+            new_selection["id"] = new_selection["id"].intersect(
+                Selection(sel_type, sel_value)
+            )
+
+        return ParticleDataset(
+            species=self.species,
+            steps=self.steps,
+            times=self.times,
+            colnames=self.colnames,
+            read_column=self.read_column,
+            fprec=self.fprec,
+            selection=new_selection,
+            ddf_index=ddf,
+        )
+
+    def isel(
+        self, t: Optional[IntSelector] = None, st: Optional[IntSelector] = None
+    ) -> "ParticleDataset":
+        ddf = self._ddf_index
+        new_selection = {k: v for k, v in self.selection.items()}
+        for t_or_s, t_or_s_str, t_or_s_arr in zip(
+            [t, st], ["t", "st"], [self.times, self.steps]
+        ):
+            if t_or_s is not None:
+                if isinstance(t_or_s, slice):
+                    lo = t_or_s.start if t_or_s.start is not None else 0
+                    hi = t_or_s.stop if t_or_s.stop is not None else -1
+                    ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                        slice(t_or_s_arr[lo], t_or_s_arr[hi]),
+                        ddf[t_or_s_str],
+                        method="exact",
+                    )
+                    ddf = ddf[ddf_sel]
+                    new_selection[t_or_s_str] = new_selection[t_or_s_str].intersect(
+                        Selection(sel_type, sel_value)
+                    )
+                elif isinstance(t_or_s, (list, tuple, np.ndarray, pd.Index, pd.Series)):
+                    ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                        [t_or_s_arr[ti] for ti in t_or_s],
+                        ddf[t_or_s_str],
+                        method="exact",
+                    )
+                    ddf = ddf[ddf_sel]
+                    new_selection[t_or_s_str] = new_selection[t_or_s_str].intersect(
+                        Selection(sel_type, sel_value)
+                    )
+                else:
+                    ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                        t_or_s_arr[t_or_s], ddf[t_or_s_str], method="exact"
+                    )
+                    ddf = ddf[ddf_sel]
+                    new_selection[t_or_s_str] = new_selection[t_or_s_str].intersect(
+                        Selection(sel_type, sel_value)
+                    )
+        return ParticleDataset(
+            species=self.species,
+            steps=self.steps,
+            times=self.times,
+            colnames=self.colnames,
+            read_column=self.read_column,
+            fprec=self.fprec,
+            selection=new_selection,
+            ddf_index=ddf,
+        )
+
+    def _load_index_partition(self, st: int, t: float, index_cols: Tuple[str, ...]):
+        cols = {c: self.read_column(st, c) for c in index_cols}
+        n = len(next(iter(cols.values())))
+        df = pd.DataFrame(cols)
+        df["st"] = np.asarray(st, dtype=np.int64)
+        df["t"] = np.asarray(t, dtype=float)
+        df["row"] = np.arange(n, dtype=np.int64)
+        return df
+
+    def _build_index_ddf(self) -> dd.DataFrame:
+        delayed_parts = [
+            dask.delayed(self._load_index_partition)(st, t, self.index_cols)
+            for st, t in zip(self.steps, self.times)
+        ]
+
+        meta = pd.DataFrame(
+            {
+                **{
+                    c: np.array([], dtype=self._dtypes.get(c, "O"))
+                    for c in self.index_cols
+                },
+                "st": np.array([], dtype=self._dtypes.get("st", np.int64)),
+                "t": np.array([], dtype=self._dtypes.get("t", np.int64)),
+                "row": np.array([], dtype=self._dtypes.get("row", np.int64)),
+            }
+        )
+
+        ddf = dd.from_delayed(delayed_parts, meta=meta)
+        return ddf
+
+    def load(self, cols: Optional[Sequence[str]] = None) -> pd.DataFrame:
+        if cols is None:
+            cols = self.columns
+
+        cols = [c for c in cols if c not in ("t", "st", "row")]
+
+        meta_dict = {
+            c: np.array([], dtype=self._dtypes.get(c, np.float64)) for c in cols
+        }
+        meta = self._ddf_index._meta.assign(**meta_dict)
+
+        cols_tuple = tuple(cols)
+
+        return (
+            self._ddf_index.map_partitions(
+                _attach_columns,
+                cols_tuple=cols_tuple,
+                read_column=self.read_column,
+                metadtypes=meta.dtypes,
+                meta=meta,
+            )
+            .compute()
+            .drop(columns=["row"])
+        )
+
+    def help(self, prepend="") -> str:
+        ret = f"{prepend}- use .sel(...) to select particles based on criteria:\n"
+        ret += f"{prepend}  t  : time (float)\n"
+        ret += f"{prepend}  st : step (int)\n"
+        ret += f"{prepend}  sp : species (int)\n"
+        ret += f"{prepend}  id : particle id (int)\n{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #   .sel(t=slice(10.0, 20.0), sp=[1, 2, 3], id=[42, 22])\n{prepend}\n"
+        ret += f"{prepend}- use .isel(...) to select particles based on output step:\n"
+        ret += f"{prepend}  t  : timestamp index (int)\n"
+        ret += f"{prepend}  st : step index (int)\n{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #   .isel(t=-1)\n"
+        ret += f"{prepend}\n"
+        ret += f"{prepend}- .sel and .isel can be chained together:\n{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #   .isel(t=-1).sel(sp=1).sel(id=[55, 66])\n{prepend}\n"
+        ret += f"{prepend}- use .load(cols=[...]) to load data into a pandas DataFrame (`cols` defaults to all columns)\n{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #  .sel(...).load()\n"
+        return ret
+
+    def __repr__(self) -> str:
+        ret = "ParticleDataset:\n"
+        ret += "================\n"
+        ret += f"Variables:\n  {self.columns}\n\n"
+        ret += "Current selection:\n"
+        for k, v in self.selection.items():
+            ret += f"  {k:<5} : {v}\n"
+        ret += "\nHelp:\n"
+        ret += "-----\n"
+        ret += f"{self.help()}"
+        return ret
+
+    def __str__(self) -> str:
+        return self.__repr__()
+
+    def spectrum_plot(
+        self,
+        ax: Optional[maxes.Axes] = None,
+        bins: Optional[npt.NDArray] = None,
+        quantity: Optional[Callable[[pd.DataFrame], npt.NDArray]] = None,
+    ):
+        if ax is None:
+            ax = plt.gca()
+
+        def _uSqr_cart(df: pd.DataFrame):
+            return np.sum(
+                [
+                    np.asarray(df[c].to_numpy(), dtype=np.float64) ** 2
+                    for c in ["ux", "uy", "uz"]
+                ],
+                axis=0,
+            )
+
+        def _quantity_cart(df: pd.DataFrame):
+            uSqr = _uSqr_cart(df)
+            return uSqr * np.sqrt(1.0 + uSqr)
+
+        def _uSqr_sph(df: pd.DataFrame):
+            return np.sum(
+                [
+                    np.asarray(df[c].to_numpy(), dtype=np.float64) ** 2
+                    for c in ["ur", "uth", "uph"]
+                ],
+                axis=0,
+            )
+
+        def _quantity_sph(df: pd.DataFrame):
+            uSqr = _uSqr_sph(df)
+            return uSqr * np.sqrt(1.0 + uSqr)
+
+        if "ux" in self.columns:
+            cols = ["ux", "uy", "uz"]
+            if quantity is None:
+                quantity = _quantity_cart
+        else:
+            cols = ["ur", "uth", "uph"]
+            if quantity is None:
+                quantity = _quantity_sph
+        df = self.load(cols=["sp", *cols])
+        species = sorted(df["sp"].unique())
+        arrays = df.groupby("sp").apply(quantity, include_groups=False)
+        if bins is None:
+            bins = np.logspace(0, 4, 100)
+        hists = {sp: np.histogram(arrays[sp], bins=bins)[0] for sp in species}
+        bins = 0.5 * (bins[1:] + bins[:-1])
+        for sp in species:
+            ax.loglog(bins, hists[sp], label=f"{sp}")
+        if bins.min() > 0 and bins.max() / bins.min() > 100:
+            ax.set(xscale="log", yscale="log")
+
+    def phase_plot(
+        self,
+        ax: Optional[maxes.Axes] = None,
+        x_quantity: Optional[Callable[[pd.DataFrame], npt.NDArray]] = None,
+        y_quantity: Optional[Callable[[pd.DataFrame], npt.NDArray]] = None,
+        xy_bins: Optional[Tuple[npt.NDArray, npt.NDArray]] = None,
+        **kwargs: Any,
+    ):
+        if ax is None:
+            ax = plt.gca()
+
+        def _xquantity_cart(df: pd.DataFrame):
+            return np.asarray(df["x"].to_numpy(), dtype=np.float64)
+
+        def _yquantity_cart(df: pd.DataFrame):
+            return np.asarray(df["ux"].to_numpy(), dtype=np.float64)
+
+        def _xquantity_sph(df: pd.DataFrame):
+            return np.asarray(df["r"].to_numpy(), dtype=np.float64)
+
+        def _yquantity_sph(df: pd.DataFrame):
+            return np.asarray(df["ur"].to_numpy(), dtype=np.float64)
+
+        if "ux" in self.columns:
+            cols = ["ux", "uy", "uz"]
+            for c in "xyz":
+                if c in self.columns:
+                    cols.append(c)
+            if x_quantity is None:
+                x_quantity = _xquantity_cart
+            if y_quantity is None:
+                y_quantity = _yquantity_cart
+        else:
+            cols = ["ur", "uth", "uph"]
+            for c in ["r", "th", "ph"]:
+                if c in self.columns:
+                    cols.append(c)
+            if x_quantity is None:
+                x_quantity = _xquantity_sph
+            if y_quantity is None:
+                y_quantity = _yquantity_sph
+
+        df = self.load(cols=[*cols])
+        x_array = x_quantity(df)
+        y_array = y_quantity(df)
+
+        if xy_bins is None:
+            x_bins = np.linspace(x_array.min(), x_array.max(), 100)
+            y_bins = np.linspace(y_array.min(), y_array.max(), 100)
+            xy_bins = (x_bins, y_bins)
+        else:
+            x_bins, y_bins = xy_bins
+
+        h2d, xedges, yedges = np.histogram2d(x_array, y_array, bins=[x_bins, y_bins])
+        X, Y = np.meshgrid(
+            0.5 * (xedges[1:] + xedges[:-1]), 0.5 * (yedges[1:] + yedges[:-1])
+        )
+        pcm = ax.pcolormesh(
+            X,
+            Y,
+            h2d.T,
+            shading="auto",
+            rasterized=True,
+            **kwargs,
+        )
+        return pcm
+
+
+class Particles(BaseContainer):
+    """Parent class to manage the particles dataframe."""
+
+    __particles_defined: bool
+    __particles: Optional[ParticleDataset]
+    quantities: List[str]
+    sp_with_idx: List[int]
+    sp_without_idx: List[int]
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initializer for the Particles class.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Keyword arguments to be passed to the parent BaseContainer class.
+
+        """
+        super(Particles, self).__init__(**kwargs)
+        if (
+            self.reader.DefinesCategory(
+                self.path, "particles", self.valid_files["particles"]
+            )
+            and self.particles_present
+        ):
+            self.__particles_defined = True
+
+            valid_steps = self.nonempty_steps
+            quantities_ = [
+                self.reader.ReadCategoryNamesAtTimestep(
+                    self.path, "particles", "p", step
+                )
+                for step in valid_steps
+            ]
+            self.quantities = sorted(
+                np.unique([q for qtys in quantities_ for q in qtys])
+            )
+
+            unique_quantities = sorted(
+                list(
+                    set(
+                        f"{q}".split("_")[0]
+                        for q in self.quantities
+                        if not q.startswith("pIDX") and not q.startswith("pRNK")
+                    )
+                )
+            )
+            all_species = sorted(
+                list(set([int(f"{q}".split("_")[1]) for q in self.quantities]))
+            )
+
+            self.sp_with_idx = sorted(
+                [
+                    int(f"{q}".split("_")[1])
+                    for q in self.quantities
+                    if f"{q}".startswith("pIDX")
+                ]
+            )
+            self.sp_without_idx = sorted(
+                [sp for sp in all_species if sp not in self.sp_with_idx]
+            )
+
+            self.__particles = ParticleDataset(
+                species=all_species,
+                steps=self.valid_steps["particles"],
+                times=self.reader.ReadPerTimestepVariable(
+                    self.path,
+                    "particles",
+                    "Time",
+                    "t",
+                    self.valid_files["particles"],
+                )["t"],
+                colnames=[
+                    (
+                        self.remap["particles"](q)
+                        if (self.remap is not None and "particles" in self.remap)
+                        else q
+                    )
+                    for q in unique_quantities
+                ]
+                + ["id", "sp"],
+                read_column=self._read_column,
+            )
+        else:
+            self.__particles_defined = False
+            self.__particles = None
+
+    @property
+    def particles_present(self) -> bool:
+        """bool: Whether the particles are present in any of the timesteps."""
+        return len(self.nonempty_steps) > 0
+
+    @property
+    def nonempty_steps(self) -> List[int]:
+        """list[int]: List of timesteps that contain particles data."""
+        return [
+            step
+            for step in self.valid_steps["particles"]
+            if len(
+                set(
+                    q.split("_")[0]
+                    for q in self.reader.ReadCategoryNamesAtTimestep(
+                        self.path, "particles", "p", step
+                    )
+                    if q.startswith("p")
+                )
+            )
+            > 0
+        ]
+
+    @property
+    def particles_defined(self) -> bool:
+        """bool: Whether the particles category is defined."""
+        return self.__particles_defined
+
+    @property
+    def particles(self) -> Optional[ParticleDataset]:
+        """Returns the particles data.
+
+        Returns
+        -------
+        ParticleDataset
+            Dictionary of datasets for each step.
+
+        """
+        return self.__particles
+
+    def help_particles(self, prepend: str = "") -> str:
+        return self.particles.help(prepend) if self.particles is not None else ""
+
+    def _get_count(self, step: int, sp: int) -> np.int64:
+        try:
+            return np.int64(
+                self.reader.ReadArrayShapeAtTimestep(
+                    self.path, "particles", f"pX1_{sp}", step
+                )[0]
+            )
+        except Exception:
+            return np.int64(0)
+
+    def _species_has_quantity(self, read_colname: str, step: int, sp: int) -> bool:
+        return f"{read_colname}_{sp}" in self.reader.ReadCategoryNamesAtTimestep(
+            self.path, "particles", "p", step
+        )
+
+    def _get_quantity_for_species(
+        self,
+        read_colname: str,
+        step: int,
+        sp: int,
+    ) -> npt.NDArray[Union[np.float64, np.int64]]:
+        if f"{read_colname}_{sp}" in self.quantities:
+            return self.reader.ReadArrayAtTimestep(
+                self.path, "particles", f"{read_colname}_{sp}", step
+            )
+        else:
+            return np.zeros(self._get_count(step, sp)) * np.nan
+
+    def _read_column(
+        self, step: int, colname: str
+    ) -> npt.NDArray[Union[np.float64, np.int64, np.float32, np.int32]]:
+        read_colname = None
+        if colname == "id":
+            idx = np.concatenate(
+                [
+                    self.reader.ReadArrayAtTimestep(
+                        self.path, "particles", f"pIDX_{sp}", step
+                    ).astype(np.int64)
+                    for sp in self.sp_with_idx
+                ]
+                + [
+                    np.zeros(self._get_count(step, sp), dtype=np.int64) - 100
+                    for sp in self.sp_without_idx
+                ]
+            )
+            if (
+                len(self.sp_with_idx) > 0
+                and f"pRNK_{self.sp_with_idx[0]}" in self.quantities
+            ):
+                rnk = np.concatenate(
+                    [
+                        self.reader.ReadArrayAtTimestep(
+                            self.path, "particles", f"pRNK_{sp}", step
+                        ).astype(np.int64)
+                        for sp in self.sp_with_idx
+                    ]
+                    + [
+                        np.zeros(self._get_count(step, sp), dtype=np.int64) - 100
+                        for sp in self.sp_without_idx
+                    ]
+                )
+                return (idx + rnk) * (idx + rnk + 1) // 2 + rnk
+            else:
+                return idx
+        elif colname == "x" or colname == "r":
+            read_colname = "pX1"
+        elif colname == "y" or colname == "th":
+            read_colname = "pX2"
+        elif colname == "z" or colname == "ph":
+            read_colname = "pX3"
+        elif colname == "ux" or colname == "ur":
+            read_colname = "pU1"
+        elif colname == "uy" or colname == "uth":
+            read_colname = "pU2"
+        elif colname == "uz" or colname == "uph":
+            read_colname = "pU3"
+        elif colname == "w":
+            read_colname = "pW"
+        elif colname == "sp":
+            return np.concatenate(
+                [
+                    np.zeros(self._get_count(step, sp), dtype=np.int32) + sp
+                    for sp in self.sp_with_idx
+                ]
+                + [
+                    np.zeros(self._get_count(step, sp), dtype=np.int32) + sp
+                    for sp in self.sp_without_idx
+                ]
+            )
+        else:
+            read_colname = f"p{colname}"
+
+        return np.concatenate(
+            [
+                self._get_quantity_for_species(read_colname, step, sp)
+                for sp in self.sp_with_idx
+                if self._species_has_quantity(read_colname, step, sp)
+            ]
+            + [
+                self._get_quantity_for_species(read_colname, step, sp)
+                for sp in self.sp_without_idx
+                if self._species_has_quantity(read_colname, step, sp)
+            ]
+        )
diff --git a/legacy/containers/spectra.py b/legacy/containers/spectra.py
new file mode 100644
index 0000000..ff47dd1
--- /dev/null
+++ b/legacy/containers/spectra.py
@@ -0,0 +1,164 @@
+from typing import Any
+
+import dask
+import dask.array as da
+import xarray as xr
+import numpy as np
+from tqdm import tqdm
+
+from nt2.containers.container import BaseContainer
+from nt2.readers.base import BaseReader
+
+
+class Spectra(BaseContainer):
+    """Parent class to manager the spectra dataframe."""
+
+    @staticmethod
+    def read_spectrum(path: str, reader: BaseReader, spectrum: str, step: int) -> Any:
+        """Reads a spectrum from the data.
+
+        This is a dask-delayed function used further to build the dataset.
+
+        Parameters
+        ----------
+        path : str
+            Main path to the data.
+        reader : BaseReader
+            Reader to use to read the data.
+        spectrum : str
+            Spectrum array to read.
+        step : int
+            Step to read.
+
+        Returns
+        -------
+        Any
+            Spectrum data.
+
+        """
+        return reader.ReadArrayAtTimestep(path, "spectra", spectrum, step)
+
+    def __init__(self, **kwargs: Any) -> None:
+        super(Spectra, self).__init__(**kwargs)
+        if self.reader.DefinesCategory(
+            self.path,
+            "spectra",
+            self.valid_steps["spectra"],
+        ):
+            self.__spectra_defined = True
+            self.__spectra = self.__read_spectra()
+        else:
+            self.__spectra_defined = False
+            self.__spectra = xr.Dataset()
+
+    @property
+    def spectra_defined(self) -> bool:
+        """bool: Whether the spectra category is defined."""
+        return self.__spectra_defined
+
+    @property
+    def spectra(self) -> xr.Dataset:
+        """xr.Dataset: The spectra dataframe."""
+        return self.__spectra
+
+    def __read_spectra(self) -> xr.Dataset:
+        self.reader.VerifySameCategoryNames(
+            self.path,
+            "spectra",
+            "s",
+            self.valid_steps["spectra"],
+        )
+        first_step = self.valid_steps["spectra"]
+        spectra_names = self.reader.ReadCategoryNamesAtTimestep(
+            self.path, "spectra", "s", first_step
+        )
+        spectra_names = set(s for s in sorted(spectra_names) if s.startswith("sN"))
+        ebin_name = "sEbn"
+        first_spectrum_name = next(iter(spectra_names))
+        shape = self.reader.ReadArrayShapeExplicitlyAtTimestep(
+            self.path, "spectra", first_spectrum_name, first_step
+        )
+        times = self.reader.ReadPerTimestepVariable(
+            self.path,
+            "spectra",
+            "Time",
+            "t",
+            self.valid_files["spectra"],
+        )
+        steps = self.reader.ReadPerTimestepVariable(
+            self.path,
+            "spectra",
+            "Step",
+            "s",
+            self.valid_files["spectra"],
+        )
+
+        ebins = self.reader.ReadArrayAtTimestep(
+            self.path, "spectra", ebin_name, first_step
+        )
+
+        diffs = np.diff(ebins)
+        if np.isclose(diffs[1] - diffs[0], diffs[-1] - diffs[-2], atol=1e-2):
+            ebins = 0.5 * (ebins[1:] + ebins[:-1])
+        else:
+            ebins = (ebins[1:] * ebins[:-1]) ** 0.5
+
+        all_dims = {**times, "E": ebins}
+        all_coords = {**all_dims, "s": ("t", steps["s"])}
+
+        def remap_name(name: str) -> str:
+            return name[1:]
+
+        return xr.Dataset(
+            {
+                remap_name(spectrum): xr.DataArray(
+                    da.stack(
+                        [
+                            da.from_delayed(
+                                dask.delayed(self.read_spectrum)(
+                                    path=self.path,
+                                    reader=self.reader,
+                                    spectrum=spectrum,
+                                    step=step,
+                                ),
+                                shape=shape,
+                                dtype="float",
+                            )
+                            for step in tqdm(
+                                self.valid_steps["spectra"],
+                                desc="steps",
+                                position=1,
+                                leave=False,
+                            )
+                        ],
+                    ),
+                    name=remap_name(spectrum),
+                    dims=all_dims,
+                    coords=all_coords,
+                )
+                for spectrum in tqdm(
+                    spectra_names, desc="spectra", position=0, leave=False
+                )
+            },
+            attrs=self.reader.ReadAttrsAtTimestep(
+                path=self.path, category="spectra", step=first_step
+            ),
+        )
+
+    def help_spectra(self, prepend="") -> str:
+        ret = f"{prepend}- use .sel(...) to select specific energy or time intervals\n"
+        ret += f"{prepend}  t  : time (float)\n"
+        ret += f"{prepend}  st : step (int)\n"
+        ret += f"{prepend}  E  : energy bin (float)\n{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #   .sel(E=slice(10.0, 20.0)).sel(t=0, method='nearest')\n{prepend}\n"
+        ret += f"{prepend}- use .isel(...) to select spectra based on energy bin or time index:\n"
+        ret += f"{prepend}  t  : timestamp index (int)\n"
+        ret += f"{prepend}  st : step index (int)\n"
+        ret += f"{prepend}  E  : energy bin index (int)\n{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #   .isel(t=-1, E=11)\n"
+        ret += f"{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #  .spectra.N_1.sel(E=slice(None, 50)).isel(t=5).plot()\n"
+        return ret

From 59935153879c0d4283bc18a0364bf9d3e825db88 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 03:30:34 -0400
Subject: [PATCH 05/14] drop py3.8 support, new opt depends

---
 pyproject.toml | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index fef7be7..49ada55 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,8 +1,8 @@
 [project]
 name = "nt2py"
-description = "Post-processing & visualization toolkit for the Entity PIC code"
+description = "Post-processing & visualization package for the Entity PIC code"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 license-files = ["LICENSE"]
 authors = [{ name = "Hayk", email = "haykh.astro@gmail.com" }]
 maintainers = [{ name = "Hayk", email = "haykh.astro@gmail.com" }]
@@ -12,7 +12,6 @@ classifiers = [
   "Intended Audience :: Science/Research",
   "License :: OSI Approved :: BSD License",
   "Programming Language :: Python :: 3 :: Only",
-  "Programming Language :: Python :: 3.8",
   "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
@@ -47,7 +46,7 @@ Repository = "https://github.com/entity-toolkit/nt2py"
 nt2 = "nt2.cli.main:app"
 
 [project.optional-dependencies]
-dev = ["black", "pytest"]
+dev = ["ruff", "pyrefly", "pytest", "types-tqdm"]
 hdf5 = ["h5py"]
 
 [build-system]
@@ -59,7 +58,7 @@ path = "nt2/__init__.py"
 
 [tool.hatch.build.targets.wheel]
 packages = ["nt2"]
-exclude = ["/.git", "/.venv", "/dist", "/temp", "/nt2/tests/"]
+exclude = ["/nt2/tests/"]
 
 [tool.hatch.build.targets.sdist]
-exclude = ["/.git", "/.venv", "/dist", "/temp", "/nt2/tests/"]
+exclude = ["/legacy/"]
\ No newline at end of file

From d2361909192d4d0a8d33818bc2a0ba62a95a06ac Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 03:31:03 -0400
Subject: [PATCH 06/14] parallel init reading + valid_steps/files saved to
 containers

---
 nt2/readers/adios2.py |  21 ++-
 nt2/readers/base.py   | 360 ++++++++++++++++++++++++++++++------------
 nt2/readers/hdf5.py   |  26 ++-
 3 files changed, 292 insertions(+), 115 deletions(-)

diff --git a/nt2/readers/adios2.py b/nt2/readers/adios2.py
index 3b22ccf..4cb7ffe 100644
--- a/nt2/readers/adios2.py
+++ b/nt2/readers/adios2.py
@@ -41,12 +41,10 @@ def ReadPerTimestepVariable(
         category: str,
         varname: str,
         newname: str,
+        valid_files: List[str],
     ) -> Dict[str, npt.NDArray[Any]]:
         variables: List[float] = []
-        for filename in self.GetValidFiles(
-            path=path,
-            category=category,
-        ):
+        for filename in valid_files:
             with bp.FileReader(os.path.join(path, category, filename)) as f:
                 avail: Dict[str, Any] = f.available_variables()
                 vars: List[str] = list(avail.keys())
@@ -62,6 +60,21 @@ def ReadPerTimestepVariable(
                     raise ValueError(f"{varname} not found in the BP file {filename}")
         return {newname: np.array(variables)}
 
+    @override
+    def ReadParticleCountsAtTimestep(
+        self, path: str, step: int, species: List[int]
+    ) -> Dict[int, int]:
+        """Read all per-species counts from one BP file's metadata."""
+        with bp.FileReader(self.FullPath(path, "particles", step)) as f:
+            available = f.available_variables()
+            counts: Dict[int, int] = {}
+            for sp in species:
+                name = f"pX1_{sp}"
+                var = f.inquire_variable(name) if name in available else None
+                shape = var.shape() if var is not None else []
+                counts[sp] = int(shape[0]) if shape else 0
+            return counts
+
     @override
     def ReadEdgeCoordsAtTimestep(
         self,
diff --git a/nt2/readers/base.py b/nt2/readers/base.py
index d53280d..ae06489 100644
--- a/nt2/readers/base.py
+++ b/nt2/readers/base.py
@@ -1,10 +1,67 @@
-from typing import Any, List, Tuple, Dict, Set
+from concurrent.futures import as_completed
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple
+import os
+import re
+import logging
+
 import numpy.typing as npt
-import os, re, logging
+from loky import get_reusable_executor
+from tqdm import tqdm
 
 from nt2.utils import Format, Layout
 
 
+def _check_file(enter_file: Callable[[str], Any], filename: str) -> None:
+    with enter_file(filename):
+        pass
+
+
+def _get_field_shapes(
+    reader: "BaseReader", path: str, step: int
+) -> Dict[str, Tuple[int, ...]]:
+    names = reader.ReadCategoryNamesAtTimestep(
+        path=path,
+        category="fields",
+        prefix="f",
+        step=step,
+    )
+    return {
+        name: reader.ReadArrayShapeAtTimestep(
+            path=path,
+            category="fields",
+            quantity=name,
+            step=step,
+        )
+        for name in names
+    }
+
+
+def _verify_particle_shapes(reader: "BaseReader", path: str, step: int) -> None:
+    prtl_species = reader.ReadParticleSpeciesAtTimestep(path=path, step=step)
+    quantities = reader.ReadCategoryNamesAtTimestep(
+        path=path,
+        category="particles",
+        prefix="p",
+        step=step,
+    )
+    quantities = set(q.split("_")[0] for q in quantities if q.startswith("p"))
+    for species in prtl_species:
+        shape = None
+        for quantity in quantities:
+            current_shape = reader.ReadArrayShapeAtTimestep(
+                path=path,
+                category="particles",
+                quantity=f"{quantity}_{species}",
+                step=step,
+            )
+            if shape is None:
+                shape = current_shape
+            elif shape != current_shape:
+                raise ValueError(
+                    f"Different particle shapes found in the {reader.format.value} files for species {species} and quantity {quantity} in step {step}"
+                )
+
+
 class BaseReader:
     """Base virtual class for arbitrary format readers.
 
@@ -52,6 +109,7 @@ def ReadPerTimestepVariable(
         category: str,
         varname: str,
         newname: str,
+        valid_files: List[str],
     ) -> Dict[str, npt.NDArray[Any]]:
         """Read a variable at each timestep and return a dictionary with the new name.
 
@@ -65,6 +123,8 @@ def ReadPerTimestepVariable(
             The name of the variable to be read.
         newname : str
             The new name of the variable to be returned.
+        valid_files : list[str]
+            The valid files to be read.
 
         Returns
         -------
@@ -74,6 +134,33 @@ def ReadPerTimestepVariable(
         """
         raise NotImplementedError("ReadPerTimestepVariable is not implemented")
 
+    def ReadParticleCountsAtTimestep(
+        self,
+        path: str,
+        step: int,
+        species: List[int],
+    ) -> Dict[int, int]:
+        """Return particle counts by species without reading particle arrays.
+
+        Readers may override this method to collect all counts while opening the
+        timestep only once.  The default implementation uses array-shape
+        metadata and is kept for third-party readers.
+        """
+        counts: Dict[int, int] = {}
+        for sp in species:
+            try:
+                counts[sp] = int(
+                    self.ReadArrayShapeAtTimestep(
+                        path=path,
+                        category="particles",
+                        quantity=f"pX1_{sp}",
+                        step=step,
+                    )[0]
+                )
+            except (IndexError, KeyError, OSError, ValueError):
+                counts[sp] = 0
+        return counts
+
     def ReadAttrsAtTimestep(
         self,
         path: str,
@@ -360,6 +447,7 @@ def GetValidSteps(
         self,
         path: str,
         category: str,
+        num_cpus: Optional[int] = None,
     ) -> List[int]:
         """Get valid timesteps (sorted) in a given path and category.
 
@@ -369,6 +457,8 @@ def GetValidSteps(
             The path to the files.
         category : str
             The category of the files.
+        num_cpus : Optional[int]
+            The number of CPU cores to use for parallel processing.
 
         Returns
         -------
@@ -376,16 +466,33 @@ def GetValidSteps(
             A list of valid timesteps in the given path and category.
 
         """
-        steps: List[int] = []
-        for filename in BaseReader.CategoryFiles(
+        category_files = BaseReader.CategoryFiles(
             path=path,
             category=category,
             format=self.format.value,
+        )
+        num_cpus = num_cpus if num_cpus is not None else (os.cpu_count() or 1)
+        executor = get_reusable_executor(max_workers=num_cpus)
+        futures = {
+            executor.submit(
+                _check_file,
+                self.EnterFile,
+                os.path.join(path, category, filename),
+            ): filename
+            for filename in category_files
+        }
+
+        steps: List[int] = []
+        for future in tqdm(
+            as_completed(futures),
+            total=len(futures),
+            desc=f"getting valid steps for {category}",
+            leave=False,
         ):
+            filename = futures[future]
             try:
-                with self.EnterFile(os.path.join(path, category, filename)):
-                    step = int(filename.split(".")[1])
-                    steps.append(step)
+                future.result()
+                steps.append(int(filename.split(".")[1]))
             except OSError:
                 if filename not in self.skipped_files:
                     self.skipped_files.append(filename)
@@ -399,6 +506,7 @@ def GetValidFiles(
         self,
         path: str,
         category: str,
+        num_cpus: Optional[int] = None,
     ) -> List[str]:
         """Get valid files (sorted by timestep) in a given path and category.
 
@@ -408,6 +516,8 @@ def GetValidFiles(
             The path to the files.
         category : str
             The category of the files.
+        num_cpus : Optional[int]
+            The number of CPU cores to use for parallel processing.
 
         Returns
         -------
@@ -415,15 +525,33 @@ def GetValidFiles(
             A list of valid files in the given path and category.
 
         """
-        files: List[str] = []
-        for filename in BaseReader.CategoryFiles(
+        category_files = BaseReader.CategoryFiles(
             path=path,
             category=category,
             format=self.format.value,
+        )
+        num_cpus = num_cpus if num_cpus is not None else (os.cpu_count() or 1)
+        executor = get_reusable_executor(max_workers=num_cpus)
+        futures = {
+            executor.submit(
+                _check_file,
+                self.EnterFile,
+                os.path.join(path, category, filename),
+            ): filename
+            for filename in category_files
+        }
+
+        files: List[str] = []
+        for future in tqdm(
+            as_completed(futures),
+            total=len(futures),
+            desc=f"getting valid files for {category}",
+            leave=False,
         ):
+            filename = futures[future]
             try:
-                with self.EnterFile(os.path.join(path, category, filename)):
-                    files.append(filename)
+                future.result()
+                files.append(filename)
             except OSError:
                 if filename not in self.skipped_files:
                     self.skipped_files.append(filename)
@@ -438,6 +566,8 @@ def VerifySameCategoryNames(
         path: str,
         category: str,
         prefix: str,
+        valid_steps: List[int],
+        num_cpus: Optional[int] = None,
     ):
         """Verify that all files in a given category have the same names.
 
@@ -449,6 +579,10 @@ def VerifySameCategoryNames(
             The category of the files.
         prefix : str
             The prefix of the variables to be read.
+        valid_steps : list[int]
+            The valid timesteps to be checked.
+        num_cpus : Optional[int]
+            The number of CPU cores to use for parallel processing.
 
         Raises
         ------
@@ -456,32 +590,41 @@ def VerifySameCategoryNames(
             If different names are found.
 
         """
-        names = None
-        for step in self.GetValidSteps(
-            path=path,
-            category=category,
+        num_cpus = num_cpus if num_cpus is not None else (os.cpu_count() or 1)
+        executor = get_reusable_executor(max_workers=num_cpus)
+        futures = {
+            executor.submit(
+                self.ReadCategoryNamesAtTimestep,
+                path=path,
+                category=category,
+                prefix=prefix,
+                step=step,
+            ): step
+            for step in valid_steps
+        }
+        names_by_step = {}
+        for future in tqdm(
+            as_completed(futures),
+            total=len(futures),
+            desc=f"verifying same names for {category}",
+            leave=False,
         ):
+            names_by_step[futures[future]] = future.result()
+
+        names = None
+        for step in valid_steps:
             if names is None:
-                names = self.ReadCategoryNamesAtTimestep(
-                    path=path,
-                    category=category,
-                    prefix=prefix,
-                    step=step,
+                names = names_by_step[step]
+            elif names != names_by_step[step]:
+                raise ValueError(
+                    f"Different field names found in the {self.format.value} files for step {step}"
                 )
-            else:
-                if names != self.ReadCategoryNamesAtTimestep(
-                    path=path,
-                    category=category,
-                    prefix=prefix,
-                    step=step,
-                ):
-                    raise ValueError(
-                        f"Different field names found in the {self.format.value} files for step {step}"
-                    )
 
     def VerifySameFieldShapes(
         self,
         path: str,
+        valid_steps: List[int],
+        num_cpus: Optional[int] = None,
     ):
         """Verify that all fields in a given path have the same shape.
 
@@ -489,6 +632,10 @@ def VerifySameFieldShapes(
         ----------
         path : str
             The path to the files.
+        valid_steps : list[int]
+            The valid timesteps to be checked.
+        num_cpus : Optional[int]
+            The number of CPU cores to use for parallel processing.
 
         Raises
         ------
@@ -496,43 +643,49 @@ def VerifySameFieldShapes(
             If different shapes are found.
 
         """
-        shape = None
-        for step in self.GetValidSteps(
-            path=path,
-            category="fields",
+        num_cpus = num_cpus if num_cpus is not None else (os.cpu_count() or 1)
+        executor = get_reusable_executor(max_workers=num_cpus)
+        futures = {
+            executor.submit(_get_field_shapes, self, path, step): step
+            for step in valid_steps
+        }
+        shapes_by_step = {}
+        for future in tqdm(
+            as_completed(futures),
+            total=len(futures),
+            desc="verifying same shapes for fields",
+            leave=False,
         ):
-            names = self.ReadCategoryNamesAtTimestep(
-                path=path,
-                category="fields",
-                prefix="f",
-                step=step,
-            )
+            shapes_by_step[futures[future]] = future.result()
+
+        shape = None
+        for step in valid_steps:
+            names = set(shapes_by_step[step])
             if shape is None:
                 name = names.pop()
-                shape = self.ReadArrayShapeAtTimestep(
-                    path=path,
-                    category="fields",
-                    quantity=name,
-                    step=step,
-                )
+                shape = shapes_by_step[step][name]
             for name in names:
-                if shape != self.ReadArrayShapeAtTimestep(
-                    path=path,
-                    category="fields",
-                    quantity=name,
-                    step=step,
-                ):
+                if shape != shapes_by_step[step][name]:
                     raise ValueError(
                         f"Different field shapes found in the {self.format.value} files for field {name} in step {step}"
                     )
 
-    def VerifySameFieldLayouts(self, path: str):
+    def VerifySameFieldLayouts(
+        self,
+        path: str,
+        valid_steps: List[int],
+        num_cpus: Optional[int] = None,
+    ):
         """Verify that all timesteps in a given path have the same layout.
 
         Parameters
         ----------
         path : str
             The path to the files.
+        valid_steps : list[int]
+            The valid timesteps to be checked.
+        num_cpus : Optional[int]
+            The number of CPU cores to use for parallel processing.
 
         Raises
         ------
@@ -540,32 +693,50 @@ def VerifySameFieldLayouts(self, path: str):
             If different layouts are found.
 
         """
-        layout = None
-        for step in self.GetValidSteps(
-            path=path,
-            category="fields",
+        num_cpus = num_cpus if num_cpus is not None else (os.cpu_count() or 1)
+        executor = get_reusable_executor(max_workers=num_cpus)
+        futures = {
+            executor.submit(
+                self.ReadFieldLayoutAtTimestep,
+                path=path,
+                step=step,
+            ): step
+            for step in valid_steps
+        }
+        layouts_by_step = {}
+        for future in tqdm(
+            as_completed(futures),
+            total=len(futures),
+            desc="verifying same layouts for fields",
+            leave=False,
         ):
+            layouts_by_step[futures[future]] = future.result()
+
+        layout = None
+        for step in valid_steps:
             if layout is None:
-                layout = self.ReadFieldLayoutAtTimestep(
-                    path=path,
-                    step=step,
+                layout = layouts_by_step[step]
+            elif layout != layouts_by_step[step]:
+                raise ValueError(
+                    f"Different field layouts found in the {self.format.value} files for step {step}"
                 )
-            else:
-                if layout != self.ReadFieldLayoutAtTimestep(
-                    path=path,
-                    step=step,
-                ):
-                    raise ValueError(
-                        f"Different field layouts found in the {self.format.value} files for step {step}"
-                    )
 
-    def VerifySameParticleShapes(self, path: str):
+    def VerifySameParticleShapes(
+        self,
+        path: str,
+        valid_steps: List[int],
+        num_cpus: Optional[int] = None,
+    ):
         """Verify that all particle quantities in a given path have the same shape at specific timesteps.
 
         Parameters
         ----------
         path : str
             The path to the files.
+        valid_steps : list[int]
+            The valid timesteps to be checked.
+        num_cpus : Optional[int]
+            The number of CPU cores to use for parallel processing.
 
         Raises
         ------
@@ -573,40 +744,21 @@ def VerifySameParticleShapes(self, path: str):
             If different shapes are found.
 
         """
-        for step in self.GetValidSteps(
-            path=path,
-            category="particles",
+        num_cpus = num_cpus if num_cpus is not None else (os.cpu_count() or 1)
+        executor = get_reusable_executor(max_workers=num_cpus)
+        futures = [
+            executor.submit(_verify_particle_shapes, self, path, step)
+            for step in valid_steps
+        ]
+        for future in tqdm(
+            as_completed(futures),
+            total=len(futures),
+            desc="verifying same shapes for particles",
+            leave=False,
         ):
-            prtl_species = self.ReadParticleSpeciesAtTimestep(path=path, step=step)
-            quantities = self.ReadCategoryNamesAtTimestep(
-                path=path,
-                category="particles",
-                prefix="p",
-                step=step,
-            )
-            quantities = set(q.split("_")[0] for q in quantities if q.startswith("p"))
-            for sp in prtl_species:
-                shape = None
-                for q in quantities:
-                    if shape is None:
-                        shape = self.ReadArrayShapeAtTimestep(
-                            path=path,
-                            category="particles",
-                            quantity=f"{q}_{sp}",
-                            step=step,
-                        )
-                    else:
-                        if shape != self.ReadArrayShapeAtTimestep(
-                            path=path,
-                            category="particles",
-                            quantity=f"{q}_{sp}",
-                            step=step,
-                        ):
-                            raise ValueError(
-                                f"Different particle shapes found in the {self.format.value} files for species {sp} and quantity {q} in step {step}"
-                            )
-
-    def DefinesCategory(self, path: str, category: str) -> bool:
+            future.result()
+
+    def DefinesCategory(self, path: str, category: str, valid_files: List[str]) -> bool:
         """Check whether a given category is defined in the path.
 
         Parameters
@@ -622,6 +774,4 @@ def DefinesCategory(self, path: str, category: str) -> bool:
             True if the category is defined, False otherwise.
 
         """
-        return os.path.exists(os.path.join(path, category)) and (
-            len(self.GetValidFiles(path=path, category=category)) > 0
-        )
+        return os.path.exists(os.path.join(path, category)) and (len(valid_files) > 0)
diff --git a/nt2/readers/hdf5.py b/nt2/readers/hdf5.py
index 0ba198b..14d9df1 100644
--- a/nt2/readers/hdf5.py
+++ b/nt2/readers/hdf5.py
@@ -47,9 +47,9 @@ def __extract_step0(f: "_h5py.File") -> "_h5py.Group":
             if isinstance(f0, h5.Group):
                 return f0
             else:
-                raise ValueError(f"Step0 is not a group in the HDF5 file")
+                raise ValueError("Step0 is not a group in the HDF5 file")
         else:
-            raise ValueError(f"Wrong structure of the hdf5 file")
+            raise ValueError("Wrong structure of the hdf5 file")
 
     @property
     @override
@@ -71,13 +71,11 @@ def ReadPerTimestepVariable(
         category: str,
         varname: str,
         newname: str,
+        valid_files: List[str],
     ) -> Dict[str, npt.NDArray[Any]]:
         variables: List[Any] = []
         h5 = _require_h5py()
-        for filename in self.GetValidFiles(
-            path=path,
-            category=category,
-        ):
+        for filename in valid_files:
             with h5.File(os.path.join(path, category, filename), "r") as f:
                 f0 = Reader.__extract_step0(f)
                 if varname in f0.keys():
@@ -93,6 +91,22 @@ def ReadPerTimestepVariable(
 
         return {newname: np.array(variables)}
 
+    @override
+    def ReadParticleCountsAtTimestep(
+        self, path: str, step: int, species: List[int]
+    ) -> Dict[int, int]:
+        """Read all per-species counts from one HDF5 file's metadata."""
+        h5 = _require_h5py()
+        with h5.File(self.FullPath(path, "particles", step), "r") as f:
+            f0 = Reader.__extract_step0(f)
+            return {
+                sp: int(f0[name].shape[0])
+                if (name := f"pX1_{sp}") in f0
+                and isinstance(f0[name], h5.Dataset)
+                else 0
+                for sp in species
+            }
+
     @override
     def ReadAttrsAtTimestep(
         self,

From 6579860867ca3f309fc9df185fadaf85b36acf91 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 03:31:38 -0400
Subject: [PATCH 07/14] plotter accessors moved to __init__.py

---
 nt2/__init__.py | 62 ++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 59 insertions(+), 3 deletions(-)

diff --git a/nt2/__init__.py b/nt2/__init__.py
index 1f5aee9..6355901 100644
--- a/nt2/__init__.py
+++ b/nt2/__init__.py
@@ -1,7 +1,63 @@
-__version__ = "1.5.3"
+__version__ = "1.6.0"
 
-import nt2.containers.data as nt2_data
+import xarray as xr
+from .containers.data import Data as nt2Data
 
+from .plotters import polar as acc_polar
+from .plotters import particles as acc_particles
+from .plotters import inspect as acc_inspect
+from .plotters import movie as acc_movie
+from .utils import InheritClassDocstring
 
-class Data(nt2_data.Data):
+
+class Data(nt2Data):
+    pass
+
+
+# patch until xarray decides to actually fix this stupid bug
+def __patch_xarray_dark(cls):
+    STYLE = """
+    <style>
+    .xr-wrap {
+    --xr-background-color-row-odd: rgba(100, 100, 100, 0.10) !important;
+    --xr-background-color-row-even: rgba(30, 30, 30, 0.10) !important;
+    }
+    </style>
+    """
+    cls._repr_html_orig_ = cls._repr_html_
+    cls._repr_html_ = lambda self: STYLE + cls._repr_html_orig_(self)
+
+
+__patch_xarray_dark(xr.DataArray)
+__patch_xarray_dark(xr.Dataset)
+
+
+# register custom plotters
+@xr.register_dataset_accessor("polar")
+@InheritClassDocstring
+class DatasetPolarPlotAccessor(acc_polar.ds_accessor):
+    pass
+
+
+@xr.register_dataset_accessor("particles")
+@InheritClassDocstring
+class DatasetParticlesPlotAccessor(acc_particles.ds_accessor):
+    pass
+
+
+@xr.register_dataarray_accessor("polar")
+@InheritClassDocstring
+class PolarPlotAccessor(acc_polar.accessor):
+    pass
+
+
+@xr.register_dataset_accessor("inspect")
+@InheritClassDocstring
+class DatasetInspectPlotAccessor(acc_inspect.ds_accessor):
+    pass
+
+
+@xr.register_dataarray_accessor("movie")
+@InheritClassDocstring
+class MoviePlotAccessor(acc_movie.accessor):
     pass

From be7954408924aa6e01d227c8b065946cbaed65f1 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 03:31:50 -0400
Subject: [PATCH 08/14] optimized data containers

---
 nt2/containers/base.py             | 112 +++++
 nt2/containers/container.py        |  57 ---
 nt2/containers/data.py             | 377 ++++++--------
 nt2/containers/diagnostics.py      | 387 ++++++++++----
 nt2/containers/fields.py           | 210 ++++++--
 nt2/containers/particle_dataset.py | 667 +++++++++++++++++++++++++
 nt2/containers/particles.py        | 776 ++++++-----------------------
 nt2/containers/spectra.py          | 136 +++--
 8 files changed, 1651 insertions(+), 1071 deletions(-)
 create mode 100644 nt2/containers/base.py
 delete mode 100644 nt2/containers/container.py
 create mode 100644 nt2/containers/particle_dataset.py

diff --git a/nt2/containers/base.py b/nt2/containers/base.py
new file mode 100644
index 0000000..7e2a943
--- /dev/null
+++ b/nt2/containers/base.py
@@ -0,0 +1,112 @@
+from typing import Callable, Optional, Dict, Tuple, List, Union
+
+from ..readers.base import BaseReader
+from ..utils import CoordinateSystem
+
+
+class BaseContainer:
+    """Parent container class for holding any category data."""
+
+    __path: str
+    __reader: BaseReader
+    __verify: bool
+    __remap: Optional[Dict[str, Callable[[str], str]]]
+    __coordinate_system: Optional[CoordinateSystem]
+    __num_cpus: Optional[int]
+
+    valid_steps: List[int] = []
+    valid_files: List[str] = []
+
+    def __init__(
+        self,
+        path: str,
+        reader: BaseReader,
+        verify: bool = False,
+        remap: Union[Dict[str, Callable[[str], str]], None] = None,
+        coord_system: Union[CoordinateSystem, None] = None,
+        num_cpus: Union[int, None] = None,
+    ):
+        """Initializer for the BaseContainer class.
+
+        Parameters
+        ----------
+        path : str
+            The path to the data.
+        reader : BaseReader
+            The reader to be used for reading the data.
+        verify : Optional[bool]
+            Whether to verify the data. If None, it will use the reader's default.
+        remap : Optional[dict[str, Callable[[str], str]]]
+            Remap dictionary to use to remap the data names (coords, fields, etc.).
+        coord_system : Optional[CoordinateSystem]
+            The coordinate system of the data.
+        num_cpus : Optional[int]
+            The number of CPUs to use for parallel processing. If None, it will use all available CPUs.
+
+        """
+        self.__path = path
+        self.__reader = reader
+        self.__verify = verify
+        self.__remap = remap
+        self.__coordinate_system = coord_system
+        self.__num_cpus = num_cpus
+
+    @property
+    def path(self) -> str:
+        """str: The main path of the data."""
+        return self.__path
+
+    @property
+    def reader(self) -> BaseReader:
+        """BaseReader: The reader used to read the data."""
+        return self.__reader
+    
+    @property
+    def verify(self) -> bool:
+        """bool: Whether to verify the data."""
+        return self.__verify
+
+    @property
+    def remap(self) -> Optional[Dict[str, Callable[[str], str]]]:
+        """{ str: (str) -> str } : The coordinate/field remap dictionary."""
+        return self.__remap
+
+    @property
+    def coordinate_system(self) -> Optional[CoordinateSystem]:
+        """CoordinateSystem: The coordinate system of the data."""
+        return self.__coordinate_system
+
+    @property
+    def num_cpus(self) -> Optional[int]:
+        """int: The number of CPUs to use for parallel processing."""
+        return self.__num_cpus
+
+    def set_remap(self, remap: Dict[str, Callable[[str], str]]) -> None:
+        """Set the remap dictionary for the container.
+
+        Parameters
+        ----------
+        remap : dict[str, Callable[[str], str]]
+            The remap dictionary to set.
+
+        """
+        self.__remap = remap
+
+    def set_coordinate_system(self, coord_system: CoordinateSystem) -> None:
+        """Set the coordinate system of the data.
+
+        Parameters
+        ----------
+        coord_system : CoordinateSystem
+            The coordinate system to set.
+
+        """
+        self.__coordinate_system = coord_system
+
+    def __dask_tokenize__(self) -> Tuple[str, str, str]:
+        """Provide a deterministic Dask token for container instances."""
+        return (
+            self.__class__.__name__,
+            self.__path,
+            self.__reader.format.value,
+        )
diff --git a/nt2/containers/container.py b/nt2/containers/container.py
deleted file mode 100644
index 430051b..0000000
--- a/nt2/containers/container.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from typing import Callable, Optional, Dict, Tuple
-
-from nt2.readers.base import BaseReader
-
-
-class BaseContainer:
-    """Parent container class for holding any category data."""
-
-    __path: str
-    __reader: BaseReader
-    __remap: Optional[Dict[str, Callable[[str], str]]]
-
-    def __init__(
-        self,
-        path: str,
-        reader: BaseReader,
-        remap: Optional[Dict[str, Callable[[str], str]]] = None,
-    ):
-        """Initializer for the BaseContainer class.
-
-        Parameters
-        ----------
-        path : str
-            The path to the data.
-        reader : BaseReader
-            The reader to be used for reading the data.
-        remap : Optional[dict[str, Callable[[str], str]]]
-            Remap dictionary to use to remap the data names (coords, fields, etc.).
-
-        """
-        super(BaseContainer, self).__init__()
-        self.__path = path
-        self.__reader = reader
-        self.__remap = remap
-
-    @property
-    def path(self) -> str:
-        """str: The main path of the data."""
-        return self.__path
-
-    @property
-    def reader(self) -> BaseReader:
-        """BaseReader: The reader used to read the data."""
-        return self.__reader
-
-    @property
-    def remap(self) -> Optional[Dict[str, Callable[[str], str]]]:
-        """{ str: (str) -> str } : The coordinate/field remap dictionary."""
-        return self.__remap
-
-    def __dask_tokenize__(self) -> Tuple[str, str, str]:
-        """Provide a deterministic Dask token for container instances."""
-        return (
-            self.__class__.__name__,
-            self.__path,
-            self.__reader.format.value,
-        )
diff --git a/nt2/containers/data.py b/nt2/containers/data.py
index dec97cd..26b2a74 100644
--- a/nt2/containers/data.py
+++ b/nt2/containers/data.py
@@ -1,136 +1,28 @@
 from typing import Callable, Any, Union, Optional, List, Dict
 
-import sys
-import logging
-
-if sys.version_info >= (3, 12):
-    from typing import override
-else:
-
-    def override(method):
-        return method
-
-
-from nt2.utils import ToHumanReadable
+import os
 
 import xarray as xr
 import pandas as pd
 
-from nt2.utils import (
+from ..utils import (
+    ToHumanReadable,
     DetermineDataFormat,
-    InheritClassDocstring,
     Format,
     CoordinateSystem,
+    CoordinateSystemType,
 )
-from nt2.readers.base import BaseReader
-from nt2.readers.hdf5 import Reader as HDF5Reader
-from nt2.readers.adios2 import Reader as BP5Reader
-from nt2.containers.fields import Fields
-from nt2.containers.particles import Particles
-from nt2.containers.spectra import Spectra
-from nt2.containers.diagnostics import Diagnostics
-
-import nt2.plotters.polar as acc_polar
-import nt2.plotters.particles as acc_particles
-import nt2.plotters.inspect as acc_inspect
-import nt2.plotters.movie as acc_movie
-from nt2.plotters.export import makeFramesAndMovie
-
-
-@xr.register_dataset_accessor("polar")
-@InheritClassDocstring
-class DatasetPolarPlotAccessor(acc_polar.ds_accessor):
-    pass
-
-
-@xr.register_dataset_accessor("particles")
-@InheritClassDocstring
-class DatasetParticlesPlotAccessor(acc_particles.ds_accessor):
-    pass
-
-
-@xr.register_dataarray_accessor("polar")
-@InheritClassDocstring
-class PolarPlotAccessor(acc_polar.accessor):
-    pass
-
-
-@xr.register_dataset_accessor("inspect")
-@InheritClassDocstring
-class DatasetInspectPlotAccessor(acc_inspect.ds_accessor):
-    pass
-
-
-@xr.register_dataarray_accessor("movie")
-@InheritClassDocstring
-class MoviePlotAccessor(acc_movie.accessor):
-    pass
-
+from ..readers.base import BaseReader
+from ..readers.hdf5 import Reader as HDF5Reader
+from ..readers.adios2 import Reader as BP5Reader
 
-# Cartesian remapping functions
-def remap_fields_cart(name: str) -> str:
-    name = name[1:]
-    fieldname = name.split("_")[0]
-    fieldname = fieldname.replace("0", "t")
-    fieldname = fieldname.replace("1", "x")
-    fieldname = fieldname.replace("2", "y")
-    fieldname = fieldname.replace("3", "z")
-    suffix = "_".join(name.split("_")[1:])
-    return f"{fieldname}{'_' + suffix if suffix != '' else ''}"
+from .fields import FieldContainer
+from .particles import ParticleContainer
+from .particle_dataset import ParticleDataset
+from .spectra import SpectraContainer
+from .diagnostics import Diagnostics
 
-
-def remap_coords_cart(name: str) -> str:
-    return {
-        "X1": "x",
-        "X2": "y",
-        "X3": "z",
-    }.get(name, name)
-
-
-def remap_prtl_quantities_cart(name: str) -> str:
-    shortname = name[1:]
-    return {
-        "X1": "x",
-        "X2": "y",
-        "X3": "z",
-        "U1": "ux",
-        "U2": "uy",
-        "U3": "uz",
-        "W": "w",
-    }.get(shortname, shortname)
-
-
-# Spherical remapping functions
-def remap_fields_sph(name: str) -> str:
-    name = name[1:]
-    fieldname = name.split("_")[0]
-    fieldname = fieldname.replace("0", "t")
-    fieldname = fieldname.replace("1", "r")
-    fieldname = fieldname.replace("2", "th")
-    fieldname = fieldname.replace("3", "ph")
-    suffix = "_".join(name.split("_")[1:])
-    return f"{fieldname}{'_' + suffix if suffix != '' else ''}"
-
-
-def remap_coords_sph(name: str) -> str:
-    return {
-        "X1": "r",
-        "X2": "th",
-        "X3": "ph",
-    }.get(name, name)
-
-
-def remap_prtl_quantities_sph(name: str) -> str:
-    shortname = name[1:]
-    return {
-        "X1": "r",
-        "X2": "th",
-        "X3": "ph",
-        "U1": "ur",
-        "U2": "uth",
-        "U3": "uph",
-        "W": "w",
-    }.get(shortname, shortname)
+from ..plotters.export import makeFramesAndMovie
 
 
 def compactify(lst: Union[List[Any], Any]) -> str:
@@ -145,19 +37,26 @@ def compactify(lst: Union[List[Any], Any]) -> str:
     return c[:-2]
 
 
-class Data(Fields, Particles, Spectra):
-    """Main class to manage all the data containers.
-
-    Inherits from all category-specific containers.
+class Data:
+    """Main class to manage all the data containers."""
 
-    """
+    _fields: Optional[FieldContainer] = None
+    _particles: Optional[ParticleContainer] = None
+    _spectra: Optional[SpectraContainer] = None
+    _diagnostics: Optional[Diagnostics] = None
 
     def __init__(
         self,
         path: str,
+        fields: bool = True,
+        particles: bool = True,
+        spectra: bool = True,
+        diagnostics: bool = False,
+        verify: bool = False,
         reader: Optional[BaseReader] = None,
         remap: Optional[Dict[str, Callable[[str], str]]] = None,
-        coord_system: Optional[CoordinateSystem] = None,
+        coord_system: Optional[CoordinateSystemType] = None,
+        num_cpus: Optional[int] = min(os.cpu_count() or 1, 16),
     ):
         """Initializer for the Data class.
 
@@ -165,14 +64,28 @@ def __init__(
         ----------
         path : str
             Main path to the data
+        components : list[OutputComponentType], optional
+            List of components to load. If None, all components will be loaded.
+        fields : bool, optional
+            Whether to load the fields component. Default is True.
+        particles : bool, optional
+            Whether to load the particles component. Default is True.
+        spectra : bool, optional
+            Whether to load the spectra component. Default is True.
+        diagnostics : bool, optional
+            Whether to load the diagnostics component. Default is False.
+        verify : bool, optional
+            Whether to verify the data. Default is False.
         reader : BaseReader, optional
             Reader to use to read the data. If None, it will be determined
             based on the file format.
         remap : dict[str, Callable[[str], str]], optional
             Remap dictionary to use to remap the data names (coords, fields, etc.).
-        coord_system : CoordinateSystem, optional
+        coord_system : Literal["XYZ", "SPH"], optional
             Coordinate system of the data. If None, it will be determined
             based on the data attrs (if remap is also None).
+        num_cpus : int, optional
+            Number of CPUs to use for parallel processing. If None, it will use all available CPUs.
 
         Raises
         ------
@@ -185,9 +98,9 @@ def __init__(
         fmt = DetermineDataFormat(path)
         if reader is None:
             if fmt == Format.HDF5:
-                self.__reader = HDF5Reader()
+                __reader = HDF5Reader()
             elif fmt == Format.BP5:
-                self.__reader = BP5Reader()
+                __reader = BP5Reader()
             else:
                 raise NotImplementedError(
                     "Only HDF5 & BP5 formats are supported at the moment."
@@ -197,63 +110,115 @@ def __init__(
                 raise ValueError(
                     f"Reader format {reader.format} does not match data format {fmt}."
                 )
-            self.__reader = reader
-
-        # determine the coordinate system and remapping
+            __reader = reader
+
+        if fields:
+            self._fields = FieldContainer(
+                path=path,
+                reader=__reader,
+                verify=verify,
+                remap=remap,
+                coord_system=CoordinateSystem.from_str(coord_system)
+                if coord_system
+                else None,
+                num_cpus=num_cpus,
+            )
+        if particles:
+            self._particles = ParticleContainer(
+                path=path,
+                reader=__reader,
+                verify=verify,
+                remap=remap,
+                coord_system=CoordinateSystem.from_str(coord_system)
+                if coord_system
+                else None,
+                num_cpus=num_cpus,
+            )
+        if spectra:
+            self._spectra = SpectraContainer(
+                path=path,
+                reader=__reader,
+                verify=verify,
+                remap=remap,
+                coord_system=CoordinateSystem.from_str(coord_system)
+                if coord_system
+                else None,
+                num_cpus=num_cpus,
+            )
+        if diagnostics:
+            self._diagnostics = Diagnostics(path=path)
         self.__attrs: Dict[str, Any] = {}
-        for category in ["fields", "particles", "spectra"]:
-            if self.__reader.DefinesCategory(path, category):
-                valid_steps = self.__reader.GetValidSteps(path, category)
-                if len(valid_steps) == 0:
-                    raise ValueError(f"No valid steps found for category {category}.")
-                first_step = valid_steps[0]
-                attrs = self.__reader.ReadAttrsAtTimestep(path, category, first_step)
-                self.__attrs.update(**attrs)
-                if "Coordinates" not in attrs:
-                    raise ValueError(
-                        f"Coordinates not found in attributes for category {category}."
-                    )
-                else:
-                    if attrs["Coordinates"] in [b"cart", "cart"]:
-                        coord_system = CoordinateSystem.XYZ
-                    elif attrs["Coordinates"] in [b"sph", "sph", b"qsph", "qsph"]:
-                        coord_system = CoordinateSystem.SPH
+        if self.fields_defined:
+            self.__attrs.update(**self._fields.attrs)
+        if self.particles_defined:
+            self.__attrs.update(**self._particles.attrs)
+        if self.spectra_defined:
+            self.__attrs.update(**self._spectra.attrs)
 
-                    else:
-                        raise NotImplementedError(
-                            f"Coordinate system {attrs['Coordinates']} not supported."
-                        )
-                    if remap is None:
-                        remap = {
-                            "coords": (
-                                remap_coords_cart
-                                if coord_system == CoordinateSystem.XYZ
-                                else remap_coords_sph
-                            ),
-                            "fields": (
-                                remap_fields_cart
-                                if coord_system == CoordinateSystem.XYZ
-                                else remap_fields_sph
-                            ),
-                            "particles": (
-                                remap_prtl_quantities_cart
-                                if coord_system == CoordinateSystem.XYZ
-                                else remap_prtl_quantities_sph
-                            ),
-                        }
-                    break
+    @property
+    def fields_defined(self) -> bool:
+        """bool: Whether fields are defined in the data."""
+        return (
+            self._fields is not None
+            and self._fields.fields_defined
+            and self._fields.fields is not None
+        )
+
+    @property
+    def particles_defined(self) -> bool:
+        """bool: Whether particles are defined in the data."""
+        return (
+            self._particles is not None
+            and self._particles.particles_defined
+            and self._particles.particles is not None
+        )
+
+    @property
+    def spectra_defined(self) -> bool:
+        """bool: Whether spectra are defined in the data."""
+        return (
+            self._spectra is not None
+            and self._spectra.spectra_defined
+            and self._spectra.spectra is not None
+        )
 
-        if coord_system is None:
-            raise ValueError("No coordinate system found in the data.")
+    @property
+    def diagnostics_defined(self) -> bool:
+        """bool: Whether diagnostics are defined in the data."""
+        return self._diagnostics is not None and self._diagnostics.df is not None
+
+    @property
+    def fields(self) -> xr.Dataset:
+        """xr.Dataset: The fields dataset."""
+        if not self.fields_defined:
+            raise ValueError("Fields are not defined in the data.")
+        return self._fields.fields
+
+    @property
+    def particles(self) -> ParticleDataset:
+        """ParticleDataset: The particles dataset."""
+        if not self.particles_defined:
+            raise ValueError("Particles are not defined in the data.")
+        return self._particles.particles
+
+    @property
+    def spectra(self) -> xr.Dataset:
+        """xr.Dataset: The spectra dataset."""
+        if not self.spectra_defined:
+            raise ValueError("Spectra are not defined in the data.")
+        return self._spectra.spectra
 
-        self.__coordinate_system = coord_system
+    @property
+    def diagnostics(self) -> pd.DataFrame:
+        """Diagnostics: The diagnostics dataset."""
+        if not self.diagnostics_defined:
+            raise ValueError("Diagnostics are not defined in the data.")
+        return self._diagnostics.df
 
-        super(Data, self).__init__(path=path, reader=self.__reader, remap=remap)
-        try:
-            self.__diagnostics = Diagnostics(path)
-        except Exception as e:
-            logging.warning(f"Failed to read diagnostics: {e}")
-            self.__diagnostics = None
+    @property
+    def attrs(self) -> Dict[str, Any]:
+        """dict: The attributes of the data."""
+        return self.__attrs
 
     def makeMovie(
         self,
@@ -262,8 +227,8 @@ def makeMovie(
         num_cpus: Optional[int] = None,
         **movie_kwargs: Any,
     ) -> bool:
-        f"""Create animation with provided plot function.
-        
+        """Create animation with provided plot function.
+
         Parameters
         ----------
         plot : callable
@@ -274,7 +239,7 @@ def makeMovie(
             The number of CPUs to use for parallel processing. If None, it will use all available CPUs.
         **movie_kwargs : dict
             Additional keyword arguments to pass to the movie creation function.
-        
+
         Returns
         -------
         bool
@@ -283,13 +248,14 @@ def makeMovie(
         if time is None:
             if self.fields_defined:
                 time = self.fields.t.values
-            elif self.particles_defined and self.particles is not None:
+            elif self.particles_defined:
                 time = list(self.particles.times)
             else:
                 raise ValueError("No time values found.")
-        assert time is not None, "Time values must be provided."
+        if time is None:
+            raise ValueError("No time values found.")
         name: str = ""
-        if self.attrs.get("simulation.name", None) == None:
+        if self.attrs.get("simulation.name", None) is None:
             name = movie_kwargs.pop("name", "movie")
         else:
             name_b = self.attrs.get("simulation.name")
@@ -306,23 +272,6 @@ def makeMovie(
             **movie_kwargs,
         )
 
-    @property
-    def coordinate_system(self) -> CoordinateSystem:
-        """CoordinateSystem: The coordinate system of the data."""
-        return self.__coordinate_system
-
-    @property
-    def attrs(self) -> Dict[str, Any]:
-        """dict[str, Any]: The attributes of the data."""
-        return self.__attrs
-
-    @property
-    def diagnostics(self) -> Union[pd.DataFrame, None]:
-        """pd.DataFrame or None: The diagnostics output if .out file is found, None otherwise."""
-        if self.__diagnostics is None:
-            return None
-        return self.__diagnostics.df
-
     def to_str(self) -> str:
         """str: String representation of the all the enclosed dataframes."""
 
@@ -330,7 +279,7 @@ def to_str(self) -> str:
         if self.fields_defined:
             string += "FieldsDataset:\n"
             string += "==============\n"
-            string += f"| Coordinates:\n|   {self.coordinate_system.value}\n|\n"
+            string += f"| Coordinates:\n|   {self._fields.coordinate_system.value}\n|\n"
             string += f"| Data axes:\n|   {compactify(self.fields.indexes.keys())}\n|\n"
             delta_t = (
                 self.fields.coords["t"].values[1] - self.fields.coords["t"].values[0]
@@ -343,21 +292,23 @@ def to_str(self) -> str:
                     fmt = ".2f"
                 string += f"|   - {key}: {crd.min():{fmt}} -> {crd.max():{fmt}} [{len(crd)}]\n"
             string += "|\n"
-            string += f"| Quantities:\n|   {compactify(sorted(self.fields.data_vars.keys()))}\n|\n"
+            string += f"| Quantities:\n|   {compactify(sorted(map(str, self.fields.data_vars.keys())))}\n|\n"
             string += f"| Total size: {ToHumanReadable(self.fields.nbytes)}\n\n"
         else:
             string += "FieldsDataset:\n"
             string += "==============\n"
             string += "  empty\n\n"
-        if self.particles_defined and self.particles is not None:
+        if self.particles_defined:
             species = sorted(self.particles.species)
             string += "ParticleDataset:\n"
             string += "================\n"
             string += f"| Species:\n|   {compactify(species)}\n|\n"
             string += f"| Timesteps:\n|   {len(self.particles.times)}\n|\n"
             string += f"| Quantities:\n|   {compactify(self.particles.columns)}\n|\n"
-            string += f"| Total size: {ToHumanReadable(self.particles.nbytes)}\n|\n"
-            string += self.help_particles("| ")
+            string += (
+                f"| Estimated index size: {ToHumanReadable(self.particles.nbytes)}\n|\n"
+            )
+            string += self._particles.help_particles("| ")
             string += "\n"
         else:
             string += "ParticleDataset:\n"
@@ -382,9 +333,9 @@ def to_str(self) -> str:
                     fmt = ".2f"
                 string += f"|   - {key}: {crd.min():{fmt}} -> {crd.max():{fmt}} [{len(crd)}]\n"
             string += "|\n"
-            string += f"| Quantities:\n|   {compactify(sorted(self.spectra.data_vars.keys()))}\n|\n"
+            string += f"| Quantities:\n|   {compactify(sorted(map(str, self.spectra.data_vars.keys())))}\n|\n"
             string += f"| Total size: {ToHumanReadable(self.spectra.nbytes)}\n|\n"
-            string += self.help_spectra("| ")
+            string += self._spectra.help_spectra("| ")
         else:
             string += "SpectraDataset:\n"
             string += "===============\n"
@@ -392,10 +343,8 @@ def to_str(self) -> str:
 
         return string
 
-    @override
     def __str__(self) -> str:
         return self.to_str()
 
-    @override
     def __repr__(self) -> str:
         return self.to_str()
diff --git a/nt2/containers/diagnostics.py b/nt2/containers/diagnostics.py
index 98fe33b..324fa82 100644
--- a/nt2/containers/diagnostics.py
+++ b/nt2/containers/diagnostics.py
@@ -1,108 +1,301 @@
-from typing import Union
+"""Parse Entity diagnostic log files into pandas dataframes."""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+from pathlib import Path
+from typing import Dict, Iterator, List, Optional, Union
+
 import pandas as pd
 
 
+_NUMBER = r"[+-]?(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][+-]?\d+)?"
+_STEP_RE = re.compile(r"^Step:\s+(\d+)\.+\[")
+_TIME_RE = re.compile(rf"^Time:\s+({_NUMBER})\.+\[")
+_SUBSTEP_RE = re.compile(
+    rf"^\s+(?P<name>[A-Za-z]+)\.+(?P<value>{_NUMBER})\s+"
+    rf"(?P<unit>ms|[µμu]s|ns|s)\b"
+)
+_SPECIES_RE = re.compile(
+    rf"^\s+species\s+(?P<species>\d+)\s+\([^)]*\)\.+"
+    rf"(?P<total>{_NUMBER})"
+    rf"(?:\s+\d+%\s*:\s*\d+%\s+(?P<minimum>{_NUMBER})"
+    rf"\s*:\s*(?P<maximum>{_NUMBER}))?"
+)
+
+_UNIT_TO_NS = {
+    "s": 1e9,
+    "ms": 1e6,
+    "µs": 1e3,
+    "μs": 1e3,
+    "us": 1e3,
+    "ns": 1.0,
+}
+
+
 class Diagnostics:
-    df: Union[pd.DataFrame, None]
+    """Diagnostic data parsed from an Entity ``.out`` file.
+
+    The source is read one line at a time and converted in bounded-size chunks.
+    By default, the parsed dataframe is cached next to the source as Parquet.
+    A valid cache is used on later construction instead of scanning the log.
+
+    Parameters
+    ----------
+    path
+        Directory containing a ``.out`` file, or the path to one ``.out`` file.
+    cache
+        Whether to read and write the Parquet cache.
+    chunk_size
+        Number of timestep records per Parquet row group.
+    cache_path
+        Optional cache filename. The default is
+        ``<outfile>.diagnostics.parquet``.
+    """
+
+    _CACHE_VERSION = 1
+
+    df: Optional[pd.DataFrame]
+    outfile: str
 
-    def __init__(self, path: str):
-        import os
-        import logging
-        import re
+    def __init__(
+        self,
+        path: Union[str, os.PathLike[str]],
+        *,
+        cache: bool = True,
+        chunk_size: int = 100_000,
+        cache_path: Optional[Union[str, os.PathLike[str]]] = None,
+    ) -> None:
+        if chunk_size <= 0:
+            raise ValueError("chunk_size must be greater than zero")
 
-        outfiles = [o for o in os.listdir(path) if o.endswith(".out")]
-        if len(outfiles) == 0:
-            logging.warning(f"No .out files found in {path}")
+        outfile = self._find_outfile(Path(path))
+        if outfile is None:
+            logging.warning("No .out files found in %s", path)
             self.df = None
-        else:
-            self.outfile = os.path.join(path, outfiles[0])
-
-            data = {}
-
-            with open(self.outfile, "r") as f:
-                content = f.read()
-                steps = re.findall(r"Step:\s+(\d+)\.+\[", content)
-                times = re.findall(r"Time:\s+([\d.]+\d)\.+\[", content)
-                substeps = re.findall(r"\s+([A-Za-z]+)\.+([\d.]+)\s+([mµn]?s)", content)
-                species = re.findall(
-                    r"\s+species\s+(\d+)\s+\(.+\)\.+([\deE+-.]+)(\s+\d+\%\s:\s\d+\%\s+)?([\deE+-.]+)?( : )?([\deE+-.]+)?",
-                    content,
+            return
+
+        self.outfile = str(outfile)
+        parquet_path = (
+            Path(cache_path)
+            if cache_path is not None
+            else Path(f"{outfile}.diagnostics.parquet")
+        )
+        metadata_path = Path(f"{parquet_path}.json")
+        if parquet_path.resolve() == outfile.resolve():
+            raise ValueError("cache_path must not overwrite the source .out file")
+
+        if cache and self._cache_is_valid(outfile, parquet_path, metadata_path):
+            try:
+                self.df = pd.read_parquet(parquet_path)
+                return
+            except (OSError, ValueError):
+                logging.warning(
+                    "Failed to read diagnostics cache %s; rebuilding it",
+                    parquet_path,
+                    exc_info=True,
                 )
 
-                data["steps"] = []
-                for step in steps:
-                    data["steps"].append(int(step))
-
-                data["times"] = []
-                for time in times:
-                    data["times"].append(float(time))
-
-                assert len(data["steps"]) == len(
-                    data["times"]
-                ), "Number of steps and times do not match"
-
-                data["substeps"] = {}
-                for substep in substeps:
-                    if substep[0] not in data["substeps"].keys():
-                        data["substeps"][substep[0]] = []
-
-                    def to_ns(value: float, unit: str) -> float:
-                        if unit == "s":
-                            return value * 1e9
-                        elif unit == "ms":
-                            return value * 1e6
-                        elif unit == "µs":
-                            return value * 1e3
-                        elif unit == "ns":
-                            return value
-                        else:
-                            raise ValueError(f"Unknown time unit: {unit}")
-
-                    data["substeps"][substep[0]].append(
-                        to_ns(float(substep[1]), substep[2])
+        if cache:
+            self.df = self._parse_to_cache(
+                outfile, parquet_path, metadata_path, chunk_size
+            )
+        else:
+            chunks = list(self._dataframe_chunks(outfile, chunk_size))
+            self.df = self._combine_chunks(chunks)
+
+    @staticmethod
+    def _find_outfile(path: Path) -> Optional[Path]:
+        if path.is_file():
+            if path.suffix != ".out":
+                raise ValueError(f"Expected a .out file, got {path}")
+            return path
+        if not path.exists():
+            raise FileNotFoundError(path)
+        if not path.is_dir():
+            raise ValueError(f"Expected a directory or .out file, got {path}")
+
+        outfiles = sorted(path.glob("*.out"))
+        return outfiles[0] if outfiles else None
+
+    @classmethod
+    def _cache_is_valid(
+        cls, source: Path, parquet_path: Path, metadata_path: Path
+    ) -> bool:
+        if not parquet_path.is_file() or not metadata_path.is_file():
+            return False
+        try:
+            with metadata_path.open("r", encoding="utf-8") as stream:
+                metadata = json.load(stream)
+            stat = source.stat()
+            return metadata == {
+                "parser_version": cls._CACHE_VERSION,
+                "source": str(source.resolve()),
+                "source_size": stat.st_size,
+                "source_mtime_ns": stat.st_mtime_ns,
+            }
+        except (OSError, ValueError, TypeError):
+            return False
+
+    @classmethod
+    def _cache_metadata(cls, source: Path) -> Dict[str, Union[int, str]]:
+        stat = source.stat()
+        return {
+            "parser_version": cls._CACHE_VERSION,
+            "source": str(source.resolve()),
+            "source_size": stat.st_size,
+            "source_mtime_ns": stat.st_mtime_ns,
+        }
+
+    @classmethod
+    def _parse_to_cache(
+        cls,
+        source: Path,
+        parquet_path: Path,
+        metadata_path: Path,
+        chunk_size: int,
+    ) -> pd.DataFrame:
+        # ParquetWriter creates multiple row groups in one file without retaining
+        # the full parsed table in memory. Import lazily so cache=False only needs
+        # pandas.
+        import pyarrow as pa
+        import pyarrow.parquet as pq
+
+        parquet_path.parent.mkdir(parents=True, exist_ok=True)
+        temporary_parquet = parquet_path.with_name(
+            f".{parquet_path.name}.{os.getpid()}.tmp"
+        )
+        temporary_metadata = metadata_path.with_name(
+            f".{metadata_path.name}.{os.getpid()}.tmp"
+        )
+
+        writer = None
+        source_metadata = cls._cache_metadata(source)
+        try:
+            for chunk in cls._dataframe_chunks(source, chunk_size):
+                table = pa.Table.from_pandas(chunk, preserve_index=True)
+                if writer is None:
+                    writer = pq.ParquetWriter(temporary_parquet, table.schema)
+                elif table.schema != writer.schema:
+                    raise ValueError(
+                        "Diagnostics columns or dtypes changed within the log"
+                    )
+                writer.write_table(table)
+
+            if writer is not None:
+                writer.close()
+                writer = None
+            else:
+                # Preserve the historical result for a log with no records.
+                empty = pd.DataFrame(columns=["Step", "Time"])
+                empty.index = pd.Index([], name="Step", dtype="int64")
+                empty.to_parquet(temporary_parquet)
+
+            with temporary_metadata.open("w", encoding="utf-8") as stream:
+                # Use the source state from before parsing. If a running
+                # simulation appended to the file meanwhile, this cache will
+                # intentionally be stale and rebuilt on the next construction.
+                json.dump(source_metadata, stream)
+
+            os.replace(temporary_parquet, parquet_path)
+            os.replace(temporary_metadata, metadata_path)
+        finally:
+            if writer is not None:
+                writer.close()
+            temporary_parquet.unlink(missing_ok=True)
+            temporary_metadata.unlink(missing_ok=True)
+
+        return pd.read_parquet(parquet_path)
+
+    @classmethod
+    def _dataframe_chunks(cls, source: Path, chunk_size: int) -> Iterator[pd.DataFrame]:
+        records: List[Dict[str, Union[int, float]]] = []
+        columns: Optional[List[str]] = None
+
+        for record in cls._records(source):
+            if columns is None:
+                columns = list(record)
+            else:
+                missing = set(columns) - set(record)
+                extra = set(record) - set(columns)
+                if missing or extra:
+                    raise ValueError(
+                        f"Inconsistent diagnostics record at step {record['Step']}: "
+                        f"missing={sorted(missing)}, extra={sorted(extra)}"
                     )
+            records.append(record)
+            if len(records) >= chunk_size:
+                yield cls._make_dataframe(records, columns)
+                records = []
+
+        if records:
+            # columns is necessarily set when records is non-empty.
+            yield cls._make_dataframe(records, columns or [])
+
+    @staticmethod
+    def _make_dataframe(
+        records: List[Dict[str, Union[int, float]]], columns: List[str]
+    ) -> pd.DataFrame:
+        dataframe = pd.DataFrame.from_records(records, columns=columns)
+        return dataframe.set_index("Step", drop=False)
+
+    @staticmethod
+    def _combine_chunks(chunks: List[pd.DataFrame]) -> pd.DataFrame:
+        if chunks:
+            return pd.concat(chunks)
+        dataframe = pd.DataFrame(columns=["Step", "Time"])
+        dataframe.index = pd.Index([], name="Step", dtype="int64")
+        return dataframe
+
+    @staticmethod
+    def _records(source: Path) -> Iterator[Dict[str, Union[int, float]]]:
+        record: Optional[Dict[str, Union[int, float]]] = None
+
+        with source.open("r", encoding="utf-8-sig", errors="replace") as stream:
+            for line in stream:
+                if line.startswith("Step:"):
+                    if record is not None:
+                        yield record
+                    match = _STEP_RE.match(line)
+                    record = {"Step": int(match.group(1))} if match else None
+                    continue
+
+                if record is None:
+                    continue
+
+                # Entity terminates each complete timestep with a dotted line.
+                # Yielding here means a final record interrupted while the
+                # simulation is still writing is not exposed as valid data.
+                if line.startswith("........................................"):
+                    yield record
+                    record = None
+                    continue
+
+                if line.startswith("Time:"):
+                    match = _TIME_RE.match(line)
+                    if match:
+                        record["Time"] = float(match.group(1))
+                    continue
+
+                if line.startswith("  species"):
+                    match = _SPECIES_RE.match(line)
+                    if match:
+                        species = match.group("species")
+                        record[f"species_{species}"] = int(float(match.group("total")))
+                        minimum = match.group("minimum")
+                        maximum = match.group("maximum")
+                        if minimum is not None and maximum is not None:
+                            record[f"species_{species}_min"] = int(float(minimum))
+                            record[f"species_{species}_max"] = int(float(maximum))
+                    continue
 
-                for key in data["substeps"].keys():
-                    assert len(data["substeps"][key]) == len(
-                        data["steps"]
-                    ), f"Number of substep entries for {key} does not match number of steps"
-
-                data["species"] = {}
-                data["species_min"] = {}
-                data["species_max"] = {}
-                for specie in species:
-                    if specie[0] not in data["species"].keys():
-                        data["species"][specie[0]] = []
-                        data["species_min"][specie[0]] = []
-                        data["species_max"][specie[0]] = []
-                    data["species"][specie[0]].append(int(float(specie[1])))
-                    if len(specie) == 6 and specie[3] != "" and specie[5] != "":
-                        data["species_min"][specie[0]].append(int(float(specie[3])))
-                        data["species_max"][specie[0]].append(int(float(specie[5])))
-
-                for key in data["species"].keys():
-                    assert len(data["species"][key]) == len(
-                        data["steps"]
-                    ), f"Number of species entries for {key} does not match number of steps"
-                    assert (len(data["species_min"][key]) == len(data["steps"])) or (
-                        len(data["species_min"][key]) == 0
-                    ), f"Number of species min entries for {key} does not match number of steps"
-                    assert (len(data["species_max"][key]) == len(data["steps"])) or (
-                        len(data["species_max"][key]) == 0
-                    ), f"Number of species max entries for {key} does not match number of steps"
-
-            self.df = pd.DataFrame(index=data["steps"])
-            self.df["Step"] = data["steps"]
-            self.df["Time"] = data["times"]
-            for key in data["substeps"].keys():
-                self.df[key] = data["substeps"][key]
-            for key in data["species"].keys():
-                self.df[f"species_{key}"] = data["species"][key]
-                if (
-                    len(data["species_min"][key]) > 0
-                    and len(data["species_max"][key]) > 0
-                ):
-                    self.df[f"species_{key}_min"] = data["species_min"][key]
-                    self.df[f"species_{key}_max"] = data["species_max"][key]
-
-            del data
+                if line.startswith("  "):
+                    match = _SUBSTEP_RE.match(line)
+                    if match:
+                        record[match.group("name")] = (
+                            float(match.group("value"))
+                            * _UNIT_TO_NS[match.group("unit")]
+                        )
diff --git a/nt2/containers/fields.py b/nt2/containers/fields.py
index 5f323a3..0cf2c75 100644
--- a/nt2/containers/fields.py
+++ b/nt2/containers/fields.py
@@ -1,40 +1,57 @@
-from typing import Any
+from typing import Any, Union, Dict
 
 import dask
 import dask.array as da
 import xarray as xr
+from tqdm import tqdm
 
-from nt2.containers.container import BaseContainer
-from nt2.utils import Layout
+from .base import BaseContainer
+from ..utils import Layout, CoordinateSystem
 
 
-class Fields(BaseContainer):
-    """Parent class to manage the fields dataframe."""
+def remap_fields_cart(name: str) -> str:
+    name = name[1:]
+    fieldname = name.split("_")[0]
+    fieldname = fieldname.replace("0", "t")
+    fieldname = fieldname.replace("1", "x")
+    fieldname = fieldname.replace("2", "y")
+    fieldname = fieldname.replace("3", "z")
+    suffix = "_".join(name.split("_")[1:])
+    return f"{fieldname}{'_' + suffix if suffix != '' else ''}"
 
-    def _read_field(self, layout: Layout, field: str, step: int) -> Any:
-        """Reads a field from the data.
 
-        This is a dask-delayed function used further to build the dataset.
+def remap_coords_cart(name: str) -> str:
+    return {
+        "X1": "x",
+        "X2": "y",
+        "X3": "z",
+    }.get(name, name)
 
-        Parameters
-        ----------
-        layout : Layout
-            Layout of the field.
-        field : str
-            Field to read.
-        step : int
-            Step to read.
 
-        Returns
-        -------
-        Any
-            Field data.
+def remap_fields_sph(name: str) -> str:
+    name = name[1:]
+    fieldname = name.split("_")[0]
+    fieldname = fieldname.replace("0", "t")
+    fieldname = fieldname.replace("1", "r")
+    fieldname = fieldname.replace("2", "th")
+    fieldname = fieldname.replace("3", "ph")
+    suffix = "_".join(name.split("_")[1:])
+    return f"{fieldname}{'_' + suffix if suffix != '' else ''}"
 
-        """
-        if layout == Layout.L:
-            return self.reader.ReadArrayAtTimestep(self.path, "fields", field, step)
-        else:
-            return self.reader.ReadArrayAtTimestep(self.path, "fields", field, step).T
+
+def remap_coords_sph(name: str) -> str:
+    return {
+        "X1": "r",
+        "X2": "th",
+        "X3": "ph",
+    }.get(name, name)
+
+
+class FieldContainer(BaseContainer):
+    """Parent class to manage the fields dataframe."""
+
+    __fields_defined: bool = False
+    __fields: Union[xr.Dataset, None] = None
 
     def __init__(
         self,
@@ -48,13 +65,21 @@ def __init__(
             Keyword arguments to be passed to the parent BaseContainer class.
 
         """
-        super(Fields, self).__init__(**kwargs)
-        if self.reader.DefinesCategory(self.path, "fields"):
+        super().__init__(**kwargs)
+        self.valid_files = self.reader.GetValidFiles(
+            path=self.path,
+            category="fields",
+            num_cpus=self.num_cpus,
+        )
+        self.valid_steps = self.reader.GetValidSteps(
+            path=self.path,
+            category="fields",
+            num_cpus=self.num_cpus,
+        )
+
+        if self.reader.DefinesCategory(self.path, "fields", self.valid_files):
             self.__fields_defined = True
             self.__fields = self._read_fields()
-        else:
-            self.__fields_defined = False
-            self.__fields = xr.Dataset()
 
     @property
     def fields_defined(self) -> bool:
@@ -62,22 +87,63 @@ def fields_defined(self) -> bool:
         return self.__fields_defined
 
     @property
-    def fields(self) -> xr.Dataset:
+    def fields(self) -> Union[xr.Dataset, None]:
         """xr.Dataset: The fields dataframe."""
         return self.__fields
 
+    def _read_field(self, layout: Layout, field: str, step: int) -> Any:
+        """Reads a field from the data.
+
+        This is a dask-delayed function used further to build the dataset.
+
+        Parameters
+        ----------
+        layout : Layout
+            Layout of the field.
+        field : str
+            Field to read.
+        step : int
+            Step to read.
+
+        Returns
+        -------
+        Any
+            Field data.
+
+        """
+        if layout == Layout.L:
+            return self.reader.ReadArrayAtTimestep(self.path, "fields", field, step)
+        else:
+            return self.reader.ReadArrayAtTimestep(self.path, "fields", field, step).T
+
     def _read_fields(self) -> xr.Dataset:
         """Helper function to read the fields dataframe."""
-        self.reader.VerifySameCategoryNames(self.path, "fields", "f")
-        self.reader.VerifySameFieldShapes(self.path)
-        self.reader.VerifySameFieldLayouts(self.path)
 
-        valid_steps = self.reader.GetValidSteps(self.path, "fields")
+        # ensure that the category names, shapes, and layouts are consistent across all steps
+        if self.verify:
+            self.reader.VerifySameCategoryNames(
+                self.path,
+                "fields",
+                "f",
+                self.valid_steps,
+                self.num_cpus,
+            )
+            self.reader.VerifySameFieldShapes(
+                self.path,
+                self.valid_steps,
+                self.num_cpus,
+            )
+            self.reader.VerifySameFieldLayouts(
+                self.path,
+                self.valid_steps,
+                self.num_cpus,
+            )
+
+        # read the field names, layout, shape, coordinates, and attributes from the first step
+        first_step = self.valid_steps[0]
         field_names = self.reader.ReadCategoryNamesAtTimestep(
-            self.path, "fields", "f", valid_steps[0]
+            self.path, "fields", "f", first_step
         )
-
-        first_step = valid_steps[0]
         first_name = next(iter(field_names))
         layout = self.reader.ReadFieldLayoutAtTimestep(self.path, first_step)
         shape = self.reader.ReadArrayShapeAtTimestep(
@@ -85,6 +151,37 @@ def _read_fields(self) -> xr.Dataset:
         )
         coords = self.reader.ReadFieldCoordsAtTimestep(self.path, first_step)
         coords = {k: coords[k] for k in sorted(coords.keys())[::-1]}
+        attributes = self.reader.ReadAttrsAtTimestep(
+            path=self.path, category="fields", step=first_step
+        )
+        if self.coordinate_system is None:
+            if "Coordinates" not in attributes:
+                raise ValueError("Coordinates not found in attributes for fields.")
+            if attributes["Coordinates"] in [b"cart", "cart"]:
+                self.set_coordinate_system(CoordinateSystem.XYZ)
+            elif attributes["Coordinates"] in [b"sph", "sph", b"qsph", "qsph"]:
+                self.set_coordinate_system(CoordinateSystem.SPH)
+            else:
+                raise NotImplementedError(
+                    f"Coordinate system {attributes['Coordinates']} not supported."
+                )
+
+        if self.remap is None:
+            self.set_remap(
+                {
+                    "coords": (
+                        remap_coords_cart
+                        if self.coordinate_system == CoordinateSystem.XYZ
+                        else remap_coords_sph
+                    ),
+                    "fields": (
+                        remap_fields_cart
+                        if self.coordinate_system == CoordinateSystem.XYZ
+                        else remap_fields_sph
+                    ),
+                }
+            )
+
         # rename coordinates if remap is provided
         if self.remap is not None and "coords" in self.remap:
             new_coords = {}
@@ -92,8 +189,20 @@ def _read_fields(self) -> xr.Dataset:
                 new_coords[self.remap["coords"](coord)] = coords[coord]
             coords = new_coords
 
-        times = self.reader.ReadPerTimestepVariable(self.path, "fields", "Time", "t")
-        steps = self.reader.ReadPerTimestepVariable(self.path, "fields", "Step", "s")
+        times = self.reader.ReadPerTimestepVariable(
+            self.path,
+            "fields",
+            "Time",
+            "t",
+            self.valid_files,
+        )
+        steps = self.reader.ReadPerTimestepVariable(
+            self.path,
+            "fields",
+            "Step",
+            "s",
+            self.valid_files,
+        )
 
         edge_coords = self.reader.ReadEdgeCoordsAtTimestep(self.path, first_step)
         new_edge_coords = {}
@@ -126,7 +235,12 @@ def _read_fields(self) -> xr.Dataset:
                                 shape=shape[:: -1 if layout == Layout.R else 1],
                                 dtype="float",
                             )
-                            for step in valid_steps
+                            for step in tqdm(
+                                self.valid_steps,
+                                desc="steps",
+                                position=1,
+                                leave=False,
+                            )
                         ],
                         axis=0,
                     ),
@@ -134,9 +248,15 @@ def _read_fields(self) -> xr.Dataset:
                     dims=all_dims,
                     coords=all_coords,
                 )
-                for name in field_names
+                for name in tqdm(field_names, desc="fields", position=0, leave=False)
             },
-            attrs=self.reader.ReadAttrsAtTimestep(
-                path=self.path, category="fields", step=first_step
-            ),
+            attrs=attributes,
         )
+
+    @property
+    def attrs(self) -> Dict[str, Any]:
+        """dict: The attributes of the fields dataframe."""
+        if self.fields_defined:
+            return self.fields.attrs
+        else:
+            return {}
diff --git a/nt2/containers/particle_dataset.py b/nt2/containers/particle_dataset.py
new file mode 100644
index 0000000..294d388
--- /dev/null
+++ b/nt2/containers/particle_dataset.py
@@ -0,0 +1,667 @@
+from typing import (
+    Any,
+    Callable,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Literal,
+    Union,
+    Dict,
+    Type,
+    cast,
+)
+import numpy.typing as npt
+from copy import copy
+
+import dask
+import dask.dataframe as dd
+from dask.delayed import Delayed
+from dask.optimization import cull
+import pandas as pd
+import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.axes as maxes
+
+
+IntSelector = Union[int, Sequence[int], slice, Tuple[int, int]]
+FloatSelector = Union[float, slice, Sequence[float], Tuple[float, float]]
+
+
+def _cull_dataframe_graph(ddf: dd.DataFrame) -> dd.DataFrame:
+    keys = ddf.__dask_keys__()
+    graph, _ = cull(ddf.__dask_graph__(), keys)
+    partitions = [Delayed(key, graph) for key in keys]
+    return cast(
+        dd.DataFrame,
+        dd.from_delayed(
+            partitions,
+            meta=ddf._meta,
+            divisions=ddf.divisions,
+        ),
+    )
+
+
+class Selection:
+    def __init__(
+        self,
+        type: Literal["value", "range", "list"],
+        value: Optional[Union[int, float, list, tuple]] = None,
+    ):
+        self.type = type
+        self.value = value
+
+    def intersect(self, other: "Selection") -> "Selection":
+        if self.value is None:
+            return copy(other)
+        elif other.value is None:
+            return copy(self)
+        if self.type == "value" and other.type == "value":
+            if self.value == other.value:
+                return Selection("value", self.value)
+            else:
+                return Selection("value")
+        elif self.type == "value" and other.type == "list":
+            assert isinstance(other.value, list), "other.value must be a list"
+            if self.value in other.value:
+                return Selection("value", self.value)
+            else:
+                return Selection("value")
+        elif self.type == "value" and other.type == "range":
+            assert isinstance(other.value, tuple) and len(other.value) == 2, (
+                "other.value must be a tuple of length 2"
+            )
+            lo, hi = other.value
+            if lo <= self.value < hi:
+                return Selection("value", self.value)
+            else:
+                return Selection("value")
+        elif self.type == "list" and other.type == "value":
+            return other.intersect(self)
+        elif self.type == "list" and other.type == "list":
+            assert isinstance(self.value, list), "self.value must be a list"
+            assert isinstance(other.value, list), "other.value must be a list"
+            new_values = [v for v in self.value if v in other.value]
+            return Selection("list", new_values)
+        elif self.type == "list" and other.type == "range":
+            assert isinstance(other.value, tuple) and len(other.value) == 2, (
+                "other.value must be a tuple of length 2"
+            )
+            assert isinstance(self.value, list), "self.value must be a list"
+            lo, hi = other.value
+            new_values = [v for v in self.value if lo <= v <= hi]
+            return Selection("list", new_values)
+        elif self.type == "range" and other.type == "value":
+            return other.intersect(self)
+        elif self.type == "range" and other.type == "list":
+            return other.intersect(self)
+        elif self.type == "range" and other.type == "range":
+            assert isinstance(self.value, tuple) and len(self.value) == 2, (
+                "self.value must be a tuple of length 2"
+            )
+            assert isinstance(other.value, tuple) and len(other.value) == 2, (
+                "other.value must be a tuple of length 2"
+            )
+            lo1, hi1 = self.value
+            lo2, hi2 = other.value
+            new_lo = max(lo1, lo2)
+            new_hi = min(hi1, hi2)
+            if new_lo <= new_hi:
+                return Selection("range", (new_lo, new_hi))
+            else:
+                return Selection("value")
+        else:
+            raise ValueError(f"Unknown selection types: {self.type}, {other.type}")
+
+    def __repr__(self) -> str:
+        if self.type == "value":
+            return "all" if self.value is None else f"{self.value:.3g}"
+        elif self.type == "range":
+            if self.value is None:
+                return "all"
+            else:
+                assert isinstance(self.value, tuple) and len(self.value) == 2, (
+                    "value must be a tuple of length 2"
+                )
+                lo, hi = self.value
+                lo_str = "..." if lo is None or lo == -np.inf else f"{lo:.3g}"
+                hi_str = "..." if hi is None or hi == np.inf else f"{hi:.3g}"
+                return f"[ {lo_str} -> {hi_str} ]"
+        elif self.type == "list":
+            assert isinstance(self.value, list), "value must be a list"
+            return "{ " + ", ".join(f"{v:.3g}" for v in self.value) + " }"
+        else:
+            return "InvalidSelection"
+
+    def __str__(self) -> str:
+        return self.__repr__()
+
+
+def _coerce_selector_to_mask(
+    s: Union[IntSelector, FloatSelector],
+    series: Any,
+    inclusive_tuple: bool = True,
+    method="exact",
+):
+    from operator import ior
+    from functools import reduce
+
+    if isinstance(s, slice):
+        lo = s.start if s.start is not None else -np.inf
+        hi = s.stop if s.stop is not None else np.inf
+        step = s.step
+        mask = (series >= lo) & (series <= hi)
+        if step not in (None, 1):
+            mask = mask & (((series - lo) % step) == 0)
+        return mask, ("range", (lo, hi))
+    elif isinstance(s, tuple) and len(s) == 2 and inclusive_tuple:
+        lo, hi = s
+        if lo is None:
+            lo = -np.inf
+        if hi is None:
+            hi = np.inf
+        return (series >= lo) & (series <= hi), ("range", (lo, hi))
+    elif isinstance(s, (list, tuple, np.ndarray, pd.Index, pd.Series)):
+        if method == "exact":
+            return series.isin(list(s)), ("list", list(s))
+        else:
+            return reduce(
+                ior, [np.abs(series - v) == np.abs(series - v).min() for v in s]
+            ), ("list", list(s))
+    else:
+        if method == "exact":
+            return series == s, ("value", s)
+        else:
+            return np.abs(series - s) == np.abs(series - s).min(), ("value", s)
+
+
+def _attach_columns(
+    part: pd.DataFrame,
+    cols_tuple: Tuple[str, ...],
+    read_column: Callable[[int, str], npt.NDArray[Any]],
+    metadtypes: Any,
+) -> pd.DataFrame:
+    if len(part) == 0:
+        return part.assign(**{c: pd.Series(dtype=metadtypes[c]) for c in cols_tuple})
+    st_val = int(part["st"].iloc[0])
+
+    columns_to_read = tuple(c for c in cols_tuple if c not in part.columns)
+    arrays = {c: read_column(st_val, c) for c in columns_to_read}
+
+    sel = part["row"].to_numpy()
+    for c in columns_to_read:
+        part[c] = np.asarray(arrays[c])[sel]
+    return part
+
+
+def _load_index_partition(
+    st: int,
+    t: float,
+    index_cols: Tuple[str, ...],
+    read_column: Callable[[int, str], npt.NDArray[Any]],
+) -> pd.DataFrame:
+    cols = {c: read_column(st, c) for c in index_cols}
+    n = len(next(iter(cols.values())))
+    return pd.DataFrame(
+        {
+            **cols,
+            "st": np.full(n, st, dtype=np.int64),
+            "t": np.full(n, t, dtype=float),
+            "row": np.arange(n, dtype=np.int64),
+        }
+    )
+
+
+class ParticleDataset:
+    steps: npt.NDArray[np.int64]
+    times: npt.NDArray[np.float64]
+    colnames: List[str]
+    _ddf_index: dd.DataFrame
+
+    def __init__(
+        self,
+        species: List[int],
+        steps: npt.NDArray[np.int64],
+        times: npt.NDArray[np.float64],
+        colnames: List[str],
+        read_column: Callable[
+            [int, str], npt.NDArray[Union[np.float64, np.int64, np.float32, np.int32]]
+        ],
+        fprec: Optional[Type] = np.float32,
+        selection: Optional[Dict[str, Selection]] = None,
+        ddf_index: Optional[dd.DataFrame] = None,
+        partition_lengths: Optional[Sequence[int]] = None,
+    ):
+        self.species = species
+        self.steps = steps
+        self.times = times
+        self.colnames = colnames
+
+        self.read_column = read_column
+        self.fprec = fprec
+        self.index_cols = ("id", "sp")
+        self._all_columns_cache: Optional[List[str]] = None
+        self._partition_lengths = (
+            tuple(int(length) for length in partition_lengths)
+            if partition_lengths is not None
+            else None
+        )
+
+        if selection is not None:
+            self.selection = selection
+        else:
+            self.selection = {
+                "t": Selection("range"),
+                "st": Selection("range"),
+                "sp": Selection("range"),
+                "id": Selection("range"),
+            }
+
+        self._dtypes = {
+            "id": np.int64,
+            "sp": np.int32,
+            "row": np.int64,
+            "st": np.int64,
+            "t": np.float64,
+            "x": fprec,
+            "y": fprec,
+            "z": fprec,
+            "ux": fprec,
+            "uy": fprec,
+            "uz": fprec,
+            "r": fprec,
+            "th": fprec,
+            "ph": fprec,
+            "ur": fprec,
+            "uth": fprec,
+            "uph": fprec,
+        }
+
+        if ddf_index is not None:
+            self._ddf_index = ddf_index
+            self._partition_indices: Optional[Tuple[int, ...]] = None
+        else:
+            self._ddf_index = self._build_index_ddf()
+            self._partition_indices = tuple(range(self._ddf_index.npartitions))
+
+        if (
+            self._partition_lengths is not None
+            and len(self._partition_lengths) != self._ddf_index.npartitions
+        ):
+            raise ValueError(
+                "partition_lengths must contain one value per Dask partition"
+            )
+
+    @property
+    def ddf(self) -> dd.DataFrame:
+        return self._ddf_index
+
+    @property
+    def nbytes(self) -> int:
+        """Estimated bytes occupied by the particle index's NumPy buffers.
+
+        The estimate excludes small pandas/Dask object overhead.  For datasets
+        filtered by particle values (for example ``sel(sp=1)``), it is an upper
+        bound because finding the exact surviving row count would require
+        computing the lazy index.
+        """
+        if self._partition_lengths is None:
+            # Compatibility fallback for ParticleDataset instances constructed
+            # directly by downstream code without reader-provided metadata.
+            return int(self.ddf.memory_usage(index=True, deep=True).sum().compute())
+
+        bytes_per_row = sum(
+            np.dtype(self._dtypes[column]).itemsize
+            for column in (*self.index_cols, "st", "t", "row")
+        )
+        return int(sum(self._partition_lengths) * bytes_per_row)
+
+    @property
+    def columns(self) -> List[str]:
+        if self._all_columns_cache is None:
+            self._all_columns_cache = self.colnames
+        return self._all_columns_cache
+
+    def sel(
+        self,
+        t: Optional[Union[IntSelector, FloatSelector]] = None,
+        st: Optional[IntSelector] = None,
+        sp: Optional[IntSelector] = None,
+        id: Optional[IntSelector] = None,
+        method: str = "exact",
+    ) -> "ParticleDataset":
+        ddf: dd.DataFrame = self._ddf_index
+        new_selection = {k: copy(v) for k, v in self.selection.items()}
+        if st is not None:
+            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                st, ddf["st"], method="exact"
+            )
+            ddf = cast(dd.DataFrame, ddf[ddf_sel])
+            new_selection["st"] = new_selection["st"].intersect(
+                Selection(sel_type, sel_value)
+            )
+        if t is not None:
+            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                t, ddf["t"], method=method
+            )
+            ddf = cast(dd.DataFrame, ddf[ddf_sel])
+            new_selection["t"] = new_selection["t"].intersect(
+                Selection(sel_type, sel_value)
+            )
+        if sp is not None:
+            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                sp, ddf["sp"], method="exact"
+            )
+            ddf = cast(dd.DataFrame, ddf[ddf_sel])
+            new_selection["sp"] = new_selection["sp"].intersect(
+                Selection(sel_type, sel_value)
+            )
+        if id is not None:
+            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                id, ddf["id"], method="exact"
+            )
+            ddf = cast(dd.DataFrame, ddf[ddf_sel])
+            new_selection["id"] = new_selection["id"].intersect(
+                Selection(sel_type, sel_value)
+            )
+
+        result = ParticleDataset(
+            species=self.species,
+            steps=self.steps,
+            times=self.times,
+            colnames=self.colnames,
+            read_column=self.read_column,
+            fprec=self.fprec,
+            selection=new_selection,
+            ddf_index=ddf,
+            partition_lengths=self._partition_lengths,
+        )
+        result._partition_indices = self._partition_indices
+        return result
+
+    def isel(
+        self, t: Optional[IntSelector] = None, st: Optional[IntSelector] = None
+    ) -> "ParticleDataset":
+        ddf: dd.DataFrame = self._ddf_index
+        partition_indices = self._partition_indices
+        partition_lengths = self._partition_lengths
+        new_selection = {k: v for k, v in self.selection.items()}
+        for t_or_s, t_or_s_str, t_or_s_arr in zip(
+            [t, st], ["t", "st"], [self.times, self.steps]
+        ):
+            if t_or_s is not None:
+                selector: Any
+                if isinstance(t_or_s, slice):
+                    lo = t_or_s.start if t_or_s.start is not None else 0
+                    hi = t_or_s.stop if t_or_s.stop is not None else -1
+                    selector = slice(t_or_s_arr[lo], t_or_s_arr[hi])
+                elif isinstance(t_or_s, (list, tuple, np.ndarray, pd.Index, pd.Series)):
+                    selector = [t_or_s_arr[ti] for ti in t_or_s]
+                else:
+                    selector = t_or_s_arr[t_or_s]
+
+                selector_for_mask = cast(Any, selector)
+                if partition_indices is not None:
+                    partition_mask, _ = _coerce_selector_to_mask(
+                        selector_for_mask,
+                        pd.Series(t_or_s_arr.tolist()),
+                        method="exact",
+                    )
+                    matching_indices = set(np.flatnonzero(np.asarray(partition_mask)))
+                    selected_partitions = [
+                        i
+                        for i, original_index in enumerate(partition_indices)
+                        if original_index in matching_indices
+                    ]
+                    if not selected_partitions:
+                        # Keep one partition for the filter to turn into an empty frame.
+                        selected_partitions = [0]
+                    ddf = cast(dd.DataFrame, ddf.partitions[selected_partitions])
+                    partition_indices = tuple(
+                        partition_indices[i] for i in selected_partitions
+                    )
+                    if partition_lengths is not None:
+                        partition_lengths = tuple(
+                            partition_lengths[i] for i in selected_partitions
+                        )
+
+                ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
+                    selector_for_mask,
+                    ddf[t_or_s_str],
+                    method="exact",
+                )
+                ddf = cast(dd.DataFrame, ddf[ddf_sel])
+                new_selection[t_or_s_str] = new_selection[t_or_s_str].intersect(
+                    Selection(sel_type, sel_value)
+                )
+
+        if partition_indices is not None:
+            ddf = _cull_dataframe_graph(ddf)
+
+        result = ParticleDataset(
+            species=self.species,
+            steps=self.steps,
+            times=self.times,
+            colnames=self.colnames,
+            read_column=self.read_column,
+            fprec=self.fprec,
+            selection=new_selection,
+            ddf_index=ddf,
+            partition_lengths=partition_lengths,
+        )
+        result._partition_indices = partition_indices
+        return result
+
+    def _build_index_ddf(self) -> dd.DataFrame:
+        delayed_parts = [
+            dask.delayed(_load_index_partition)(
+                st,
+                t,
+                self.index_cols,
+                self.read_column,
+            )
+            for st, t in zip(self.steps, self.times)
+        ]
+
+        meta = pd.DataFrame(
+            {
+                **{
+                    c: np.array([], dtype=self._dtypes.get(c, "O"))
+                    for c in self.index_cols
+                },
+                "st": np.array([], dtype=self._dtypes.get("st", np.int64)),
+                "t": np.array([], dtype=self._dtypes.get("t", np.int64)),
+                "row": np.array([], dtype=self._dtypes.get("row", np.int64)),
+            }
+        )
+
+        ddf = cast(dd.DataFrame, dd.from_delayed(delayed_parts, meta=meta))
+        return ddf
+
+    def load(self, cols: Optional[Sequence[str]] = None) -> pd.DataFrame:
+        if cols is None:
+            cols = self.columns
+
+        cols = [c for c in cols if c not in ("t", "st", "row")]
+
+        meta_dict = {
+            c: np.array([], dtype=self._dtypes.get(c, np.float64)) for c in cols
+        }
+        meta = self._ddf_index._meta.assign(**meta_dict)
+
+        cols_tuple = tuple(cols)
+
+        return (
+            self._ddf_index.map_partitions(
+                _attach_columns,
+                cols_tuple=cols_tuple,
+                read_column=self.read_column,
+                metadtypes=meta.dtypes,
+                meta=meta,
+            )
+            .compute()
+            .drop(columns=["row"])
+        )
+
+    def help(self, prepend="") -> str:
+        ret = f"{prepend}- use .sel(...) to select particles based on criteria:\n"
+        ret += f"{prepend}  t  : time (float)\n"
+        ret += f"{prepend}  st : step (int)\n"
+        ret += f"{prepend}  sp : species (int)\n"
+        ret += f"{prepend}  id : particle id (int)\n{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #   .sel(t=slice(10.0, 20.0), sp=[1, 2, 3], id=[42, 22])\n{prepend}\n"
+        ret += f"{prepend}- use .isel(...) to select particles based on output step:\n"
+        ret += f"{prepend}  t  : timestamp index (int)\n"
+        ret += f"{prepend}  st : step index (int)\n{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #   .isel(t=-1)\n"
+        ret += f"{prepend}\n"
+        ret += f"{prepend}- .sel and .isel can be chained together:\n{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #   .isel(t=-1).sel(sp=1).sel(id=[55, 66])\n{prepend}\n"
+        ret += f"{prepend}- use .load(cols=[...]) to load data into a pandas DataFrame (`cols` defaults to all columns)\n{prepend}\n"
+        ret += f"{prepend}  # example:\n"
+        ret += f"{prepend}  #  .sel(...).load()\n"
+        return ret
+
+    def __repr__(self) -> str:
+        ret = "ParticleDataset:\n"
+        ret += "================\n"
+        ret += f"Variables:\n  {self.columns}\n\n"
+        ret += "Current selection:\n"
+        for k, v in self.selection.items():
+            ret += f"  {k:<5} : {v}\n"
+        ret += "\nHelp:\n"
+        ret += "-----\n"
+        ret += f"{self.help()}"
+        return ret
+
+    def __str__(self) -> str:
+        return self.__repr__()
+
+    def spectrum_plot(
+        self,
+        ax: Optional[maxes.Axes] = None,
+        bins: Optional[npt.NDArray] = None,
+        quantity: Optional[Callable[[pd.DataFrame], npt.NDArray]] = None,
+    ):
+        if ax is None:
+            ax = plt.gca()
+
+        def _uSqr_cart(df: pd.DataFrame):
+            return np.sum(
+                [
+                    np.asarray(df[c].to_numpy(), dtype=np.float64) ** 2
+                    for c in ["ux", "uy", "uz"]
+                ],
+                axis=0,
+            )
+
+        def _quantity_cart(df: pd.DataFrame):
+            uSqr = _uSqr_cart(df)
+            return uSqr * np.sqrt(1.0 + uSqr)
+
+        def _uSqr_sph(df: pd.DataFrame):
+            return np.sum(
+                [
+                    np.asarray(df[c].to_numpy(), dtype=np.float64) ** 2
+                    for c in ["ur", "uth", "uph"]
+                ],
+                axis=0,
+            )
+
+        def _quantity_sph(df: pd.DataFrame):
+            uSqr = _uSqr_sph(df)
+            return uSqr * np.sqrt(1.0 + uSqr)
+
+        if "ux" in self.columns:
+            cols = ["ux", "uy", "uz"]
+            if quantity is None:
+                quantity = _quantity_cart
+        else:
+            cols = ["ur", "uth", "uph"]
+            if quantity is None:
+                quantity = _quantity_sph
+        assert quantity is not None
+        df = self.load(cols=["sp", *cols])
+        species = sorted(df["sp"].unique())
+        arrays = {
+            sp: quantity(group.drop(columns=["sp"])) for sp, group in df.groupby("sp")
+        }
+        if bins is None:
+            bins = np.logspace(0, 4, 100)
+        hists = {sp: np.histogram(arrays[sp], bins=bins)[0] for sp in species}
+        bins = 0.5 * (bins[1:] + bins[:-1])
+        for sp in species:
+            ax.loglog(bins, hists[sp], label=f"{sp}")
+        if bins.min() > 0 and bins.max() / bins.min() > 100:
+            ax.set(xscale="log", yscale="log")
+
+    def phase_plot(
+        self,
+        ax: Optional[maxes.Axes] = None,
+        x_quantity: Optional[Callable[[pd.DataFrame], npt.NDArray]] = None,
+        y_quantity: Optional[Callable[[pd.DataFrame], npt.NDArray]] = None,
+        xy_bins: Optional[Tuple[npt.NDArray, npt.NDArray]] = None,
+        **kwargs: Any,
+    ):
+        if ax is None:
+            ax = plt.gca()
+
+        def _xquantity_cart(df: pd.DataFrame):
+            return np.asarray(df["x"].to_numpy(), dtype=np.float64)
+
+        def _yquantity_cart(df: pd.DataFrame):
+            return np.asarray(df["ux"].to_numpy(), dtype=np.float64)
+
+        def _xquantity_sph(df: pd.DataFrame):
+            return np.asarray(df["r"].to_numpy(), dtype=np.float64)
+
+        def _yquantity_sph(df: pd.DataFrame):
+            return np.asarray(df["ur"].to_numpy(), dtype=np.float64)
+
+        if "ux" in self.columns:
+            cols = ["ux", "uy", "uz"]
+            for c in "xyz":
+                if c in self.columns:
+                    cols.append(c)
+            if x_quantity is None:
+                x_quantity = _xquantity_cart
+            if y_quantity is None:
+                y_quantity = _yquantity_cart
+        else:
+            cols = ["ur", "uth", "uph"]
+            for c in ["r", "th", "ph"]:
+                if c in self.columns:
+                    cols.append(c)
+            if x_quantity is None:
+                x_quantity = _xquantity_sph
+            if y_quantity is None:
+                y_quantity = _yquantity_sph
+
+        df = self.load(cols=[*cols])
+        x_array = x_quantity(df)
+        y_array = y_quantity(df)
+
+        if xy_bins is None:
+            x_bins = np.linspace(x_array.min(), x_array.max(), 100)
+            y_bins = np.linspace(y_array.min(), y_array.max(), 100)
+            xy_bins = (x_bins, y_bins)
+        else:
+            x_bins, y_bins = xy_bins
+
+        h2d, xedges, yedges = np.histogram2d(x_array, y_array, bins=[x_bins, y_bins])
+        X, Y = np.meshgrid(
+            0.5 * (xedges[1:] + xedges[:-1]), 0.5 * (yedges[1:] + yedges[:-1])
+        )
+        pcm = ax.pcolormesh(
+            X,
+            Y,
+            h2d.T,
+            shading="auto",
+            rasterized=True,
+            **kwargs,
+        )
+        return pcm
diff --git a/nt2/containers/particles.py b/nt2/containers/particles.py
index a352e4e..4c740b6 100644
--- a/nt2/containers/particles.py
+++ b/nt2/containers/particles.py
@@ -1,561 +1,59 @@
-from typing import (
-    Any,
-    Callable,
-    List,
-    Optional,
-    Sequence,
-    Tuple,
-    Literal,
-    Union,
-    Dict,
-    Type,
-)
+from typing import Any, List, Union, Dict, Set
 import numpy.typing as npt
-from copy import copy
 
-import dask
-import dask.dataframe as dd
-import pandas as pd
 import numpy as np
 
-import matplotlib.pyplot as plt
-import matplotlib.axes as maxes
-
-from nt2.containers.container import BaseContainer
-
-
-IntSelector = Union[int, Sequence[int], slice, Tuple[int, int]]
-FloatSelector = Union[float, slice, Sequence[float], Tuple[float, float]]
-
-
-class Selection:
-    def __init__(
-        self,
-        type: Literal["value", "range", "list"],
-        value: Optional[Union[int, float, list, tuple]] = None,
-    ):
-        self.type = type
-        self.value = value
-
-    def intersect(self, other: "Selection") -> "Selection":
-        if self.value is None:
-            return copy(other)
-        elif other.value is None:
-            return copy(self)
-        if self.type == "value" and other.type == "value":
-            if self.value == other.value:
-                return Selection("value", self.value)
-            else:
-                return Selection("value")
-        elif self.type == "value" and other.type == "list":
-            assert isinstance(other.value, list), "other.value must be a list"
-            if self.value in other.value:
-                return Selection("value", self.value)
-            else:
-                return Selection("value")
-        elif self.type == "value" and other.type == "range":
-            assert (
-                isinstance(other.value, tuple) and len(other.value) == 2
-            ), "other.value must be a tuple of length 2"
-            lo, hi = other.value
-            if lo <= self.value < hi:
-                return Selection("value", self.value)
-            else:
-                return Selection("value")
-        elif self.type == "list" and other.type == "value":
-            return other.intersect(self)
-        elif self.type == "list" and other.type == "list":
-            assert isinstance(self.value, list), "self.value must be a list"
-            assert isinstance(other.value, list), "other.value must be a list"
-            new_values = [v for v in self.value if v in other.value]
-            return Selection("list", new_values)
-        elif self.type == "list" and other.type == "range":
-            assert (
-                isinstance(other.value, tuple) and len(other.value) == 2
-            ), "other.value must be a tuple of length 2"
-            assert isinstance(self.value, list), "self.value must be a list"
-            lo, hi = other.value
-            new_values = [v for v in self.value if lo <= v <= hi]
-            return Selection("list", new_values)
-        elif self.type == "range" and other.type == "value":
-            return other.intersect(self)
-        elif self.type == "range" and other.type == "list":
-            return other.intersect(self)
-        elif self.type == "range" and other.type == "range":
-            assert (
-                isinstance(self.value, tuple) and len(self.value) == 2
-            ), "self.value must be a tuple of length 2"
-            assert (
-                isinstance(other.value, tuple) and len(other.value) == 2
-            ), "other.value must be a tuple of length 2"
-            lo1, hi1 = self.value
-            lo2, hi2 = other.value
-            new_lo = max(lo1, lo2)
-            new_hi = min(hi1, hi2)
-            if new_lo <= new_hi:
-                return Selection("range", (new_lo, new_hi))
-            else:
-                return Selection("value")
-        else:
-            raise ValueError(f"Unknown selection types: {self.type}, {other.type}")
-
-    def __repr__(self) -> str:
-        if self.type == "value":
-            return "all" if self.value is None else f"{self.value:.3g}"
-        elif self.type == "range":
-            if self.value is None:
-                return "all"
-            else:
-                assert (
-                    isinstance(self.value, tuple) and len(self.value) == 2
-                ), "value must be a tuple of length 2"
-                lo, hi = self.value
-                lo_str = "..." if lo is None or lo == -np.inf else f"{lo:.3g}"
-                hi_str = "..." if hi is None or hi == np.inf else f"{hi:.3g}"
-                return f"[ {lo_str} -> {hi_str} ]"
-        elif self.type == "list":
-            assert isinstance(self.value, list), "value must be a list"
-            return "{ " + ", ".join(f"{v:.3g}" for v in self.value) + " }"
-        else:
-            return "InvalidSelection"
-
-    def __str__(self) -> str:
-        return self.__repr__()
-
-
-def _coerce_selector_to_mask(
-    s: Union[IntSelector, FloatSelector],
-    series: Any,
-    inclusive_tuple: bool = True,
-    method="exact",
-):
-    from operator import ior
-    from functools import reduce
-
-    if isinstance(s, slice):
-        lo = s.start if s.start is not None else -np.inf
-        hi = s.stop if s.stop is not None else np.inf
-        step = s.step
-        mask = (series >= lo) & (series <= hi)
-        if step not in (None, 1):
-            mask = mask & (((series - lo) % step) == 0)
-        return mask, ("range", (lo, hi))
-    elif isinstance(s, tuple) and len(s) == 2 and inclusive_tuple:
-        lo, hi = s
-        if lo is None:
-            lo = -np.inf
-        if hi is None:
-            hi = np.inf
-        return (series >= lo) & (series <= hi), ("range", (lo, hi))
-    elif isinstance(s, (list, tuple, np.ndarray, pd.Index, pd.Series)):
-        if method == "exact":
-            return series.isin(list(s)), ("list", list(s))
-        else:
-            return reduce(
-                ior, [np.abs(series - v) == np.abs(series - v).min() for v in s]
-            ), ("list", list(s))
-    else:
-        if method == "exact":
-            return series == s, ("value", s)
-        else:
-            return np.abs(series - s) == np.abs(series - s).min(), ("value", s)
-
-
-def _attach_columns(
-    part: pd.DataFrame,
-    cols_tuple,
-    read_column,
-    metadtypes,
-) -> pd.DataFrame:
-    if len(part) == 0:
-        for c in cols_tuple:
-            part[c] = np.array([], dtype=metadtypes[c])
-        return part
-    st_val = int(part["st"].iloc[0])
-
-    arrays = {c: read_column(st_val, c) for c in cols_tuple}
-
-    sel = part["row"].to_numpy()
-    for c in cols_tuple:
-        part[c] = np.asarray(arrays[c])[sel]
-    return part
-
-
-class ParticleDataset:
-    steps: npt.NDArray[np.int64]
-    times: npt.NDArray[np.float64]
-    colnames: List[str]
-
-    def __init__(
-        self,
-        species: List[int],
-        steps: npt.NDArray[np.int64],
-        times: npt.NDArray[np.float64],
-        colnames: List[str],
-        read_column: Callable[
-            [int, str], npt.NDArray[Union[np.float64, np.int64, np.float32, np.int32]]
-        ],
-        fprec: Optional[Type] = np.float32,
-        selection: Optional[Dict[str, Selection]] = None,
-        ddf_index: Optional[dd.DataFrame] = None,
-    ):
-        self.species = species
-        self.steps = steps
-        self.times = times
-        self.colnames = colnames
-
-        self.read_column = read_column
-        self.fprec = fprec
-        self.index_cols = ("id", "sp")
-        self._all_columns_cache: Optional[List[str]] = None
-
-        if selection is not None:
-            self.selection = selection
-        else:
-            self.selection = {
-                "t": Selection("range"),
-                "st": Selection("range"),
-                "sp": Selection("range"),
-                "id": Selection("range"),
-            }
-
-        self._dtypes = {
-            "id": np.int64,
-            "sp": np.int32,
-            "row": np.int64,
-            "st": np.int64,
-            "t": fprec,
-            "x": fprec,
-            "y": fprec,
-            "z": fprec,
-            "ux": fprec,
-            "uy": fprec,
-            "uz": fprec,
-            "r": fprec,
-            "th": fprec,
-            "ph": fprec,
-            "ur": fprec,
-            "uth": fprec,
-            "uph": fprec,
-        }
-
-        if ddf_index is not None:
-            self._ddf_index = ddf_index
-        else:
-            self._ddf_index = self._build_index_ddf()
-
-    @property
-    def ddf(self) -> dd.DataFrame:
-        return self._ddf_index
-
-    @property
-    def nbytes(self) -> int:
-        return self.ddf.memory_usage(index=True, deep=True).sum().compute()
-
-    @property
-    def columns(self) -> List[str]:
-        if self._all_columns_cache is None:
-            self._all_columns_cache = self.colnames
-        return self._all_columns_cache
-
-    def sel(
-        self,
-        t: Optional[Union[IntSelector, FloatSelector]] = None,
-        st: Optional[IntSelector] = None,
-        sp: Optional[IntSelector] = None,
-        id: Optional[IntSelector] = None,
-        method: str = "exact",
-    ) -> "ParticleDataset":
-        ddf = self._ddf_index
-        new_selection = {k: copy(v) for k, v in self.selection.items()}
-        if st is not None:
-            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
-                st, ddf["st"], method="exact"
-            )
-            ddf = ddf[ddf_sel]
-            new_selection["st"] = new_selection["st"].intersect(
-                Selection(sel_type, sel_value)
-            )
-        if t is not None:
-            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
-                t, ddf["t"], method=method
-            )
-            ddf = ddf[ddf_sel]
-            new_selection["t"] = new_selection["t"].intersect(
-                Selection(sel_type, sel_value)
-            )
-        if sp is not None:
-            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
-                sp, ddf["sp"], method="exact"
-            )
-            ddf = ddf[ddf_sel]
-            new_selection["sp"] = new_selection["sp"].intersect(
-                Selection(sel_type, sel_value)
-            )
-        if id is not None:
-            ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
-                id, ddf["id"], method="exact"
-            )
-            ddf = ddf[ddf_sel]
-            new_selection["id"] = new_selection["id"].intersect(
-                Selection(sel_type, sel_value)
-            )
-
-        return ParticleDataset(
-            species=self.species,
-            steps=self.steps,
-            times=self.times,
-            colnames=self.colnames,
-            read_column=self.read_column,
-            fprec=self.fprec,
-            selection=new_selection,
-            ddf_index=ddf,
-        )
-
-    def isel(
-        self, t: Optional[IntSelector] = None, st: Optional[IntSelector] = None
-    ) -> "ParticleDataset":
-        ddf = self._ddf_index
-        new_selection = {k: v for k, v in self.selection.items()}
-        for t_or_s, t_or_s_str, t_or_s_arr in zip(
-            [t, st], ["t", "st"], [self.times, self.steps]
-        ):
-            if t_or_s is not None:
-                if isinstance(t_or_s, slice):
-                    lo = t_or_s.start if t_or_s.start is not None else 0
-                    hi = t_or_s.stop if t_or_s.stop is not None else -1
-                    ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
-                        slice(t_or_s_arr[lo], t_or_s_arr[hi]),
-                        ddf[t_or_s_str],
-                        method="exact",
-                    )
-                    ddf = ddf[ddf_sel]
-                    new_selection[t_or_s_str] = new_selection[t_or_s_str].intersect(
-                        Selection(sel_type, sel_value)
-                    )
-                elif isinstance(t_or_s, (list, tuple, np.ndarray, pd.Index, pd.Series)):
-                    ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
-                        [t_or_s_arr[ti] for ti in t_or_s],
-                        ddf[t_or_s_str],
-                        method="exact",
-                    )
-                    ddf = ddf[ddf_sel]
-                    new_selection[t_or_s_str] = new_selection[t_or_s_str].intersect(
-                        Selection(sel_type, sel_value)
-                    )
-                else:
-                    ddf_sel, (sel_type, sel_value) = _coerce_selector_to_mask(
-                        t_or_s_arr[t_or_s], ddf[t_or_s_str], method="exact"
-                    )
-                    ddf = ddf[ddf_sel]
-                    new_selection[t_or_s_str] = new_selection[t_or_s_str].intersect(
-                        Selection(sel_type, sel_value)
-                    )
-        return ParticleDataset(
-            species=self.species,
-            steps=self.steps,
-            times=self.times,
-            colnames=self.colnames,
-            read_column=self.read_column,
-            fprec=self.fprec,
-            selection=new_selection,
-            ddf_index=ddf,
-        )
-
-    def _load_index_partition(self, st: int, t: float, index_cols: Tuple[str, ...]):
-        cols = {c: self.read_column(st, c) for c in index_cols}
-        n = len(next(iter(cols.values())))
-        df = pd.DataFrame(cols)
-        df["st"] = np.asarray(st, dtype=np.int64)
-        df["t"] = np.asarray(t, dtype=float)
-        df["row"] = np.arange(n, dtype=np.int64)
-        return df
-
-    def _build_index_ddf(self) -> dd.DataFrame:
-        delayed_parts = [
-            dask.delayed(self._load_index_partition)(st, t, self.index_cols)
-            for st, t in zip(self.steps, self.times)
-        ]
-
-        meta = pd.DataFrame(
-            {
-                **{
-                    c: np.array([], dtype=self._dtypes.get(c, "O"))
-                    for c in self.index_cols
-                },
-                "st": np.array([], dtype=self._dtypes.get("st", np.int64)),
-                "t": np.array([], dtype=self._dtypes.get("t", np.int64)),
-                "row": np.array([], dtype=self._dtypes.get("row", np.int64)),
-            }
-        )
-
-        ddf = dd.from_delayed(delayed_parts, meta=meta)
-        return ddf
-
-    def load(self, cols: Optional[Sequence[str]] = None) -> pd.DataFrame:
-        if cols is None:
-            cols = self.columns
-
-        cols = [c for c in cols if c not in ("t", "st", "row")]
-
-        meta_dict = {
-            c: np.array([], dtype=self._dtypes.get(c, np.float64)) for c in cols
-        }
-        meta = self._ddf_index._meta.assign(**meta_dict)
-
-        cols_tuple = tuple(cols)
-
-        return (
-            self._ddf_index.map_partitions(
-                _attach_columns,
-                cols_tuple=cols_tuple,
-                read_column=self.read_column,
-                metadtypes=meta.dtypes,
-                meta=meta,
-            )
-            .compute()
-            .drop(columns=["row"])
-        )
-
-    def help(self, prepend="") -> str:
-        ret = f"{prepend}- use .sel(...) to select particles based on criteria:\n"
-        ret += f"{prepend}  t  : time (float)\n"
-        ret += f"{prepend}  st : step (int)\n"
-        ret += f"{prepend}  sp : species (int)\n"
-        ret += f"{prepend}  id : particle id (int)\n{prepend}\n"
-        ret += f"{prepend}  # example:\n"
-        ret += f"{prepend}  #   .sel(t=slice(10.0, 20.0), sp=[1, 2, 3], id=[42, 22])\n{prepend}\n"
-        ret += f"{prepend}- use .isel(...) to select particles based on output step:\n"
-        ret += f"{prepend}  t  : timestamp index (int)\n"
-        ret += f"{prepend}  st : step index (int)\n{prepend}\n"
-        ret += f"{prepend}  # example:\n"
-        ret += f"{prepend}  #   .isel(t=-1)\n"
-        ret += f"{prepend}\n"
-        ret += f"{prepend}- .sel and .isel can be chained together:\n{prepend}\n"
-        ret += f"{prepend}  # example:\n"
-        ret += f"{prepend}  #   .isel(t=-1).sel(sp=1).sel(id=[55, 66])\n{prepend}\n"
-        ret += f"{prepend}- use .load(cols=[...]) to load data into a pandas DataFrame (`cols` defaults to all columns)\n{prepend}\n"
-        ret += f"{prepend}  # example:\n"
-        ret += f"{prepend}  #  .sel(...).load()\n"
-        return ret
-
-    def __repr__(self) -> str:
-        ret = "ParticleDataset:\n"
-        ret += "================\n"
-        ret += f"Variables:\n  {self.columns}\n\n"
-        ret += "Current selection:\n"
-        for k, v in self.selection.items():
-            ret += f"  {k:<5} : {v}\n"
-        ret += "\nHelp:\n"
-        ret += "-----\n"
-        ret += f"{self.help()}"
-        return ret
-
-    def __str__(self) -> str:
-        return self.__repr__()
-
-    def spectrum_plot(
-        self,
-        ax: Optional[maxes.Axes] = None,
-        bins: Optional[npt.NDArray] = None,
-        quantity: Optional[Callable[[pd.DataFrame], npt.NDArray]] = None,
-    ):
-        if ax is None:
-            ax = plt.gca()
-
-        if "ux" in self.columns:
-            cols = ["ux", "uy", "uz"]
-            if quantity is None:
-                uSqr = lambda df: np.sum(
-                    [df[c].to_numpy(dtype=np.float64) ** 2 for c in cols], axis=0
-                )
-                quantity = lambda df: uSqr(df) * np.sqrt(1.0 + uSqr(df))
-        else:
-            cols = ["ur", "uth", "uph"]
-            if quantity is None:
-                uSqr = lambda df: np.sum(
-                    [df[c].to_numpy(dtype=np.float64) ** 2 for c in cols], axis=0
-                )
-                quantity = lambda df: uSqr(df) * np.sqrt(1.0 + uSqr(df))
-        df = self.load(cols=["sp", *cols])
-        species = sorted(df["sp"].unique())
-        arrays = df.groupby("sp").apply(quantity, include_groups=False)
-        if bins is None:
-            bins = np.logspace(0, 4, 100)
-        hists = {sp: np.histogram(arrays[sp], bins=bins)[0] for sp in species}
-        bins = 0.5 * (bins[1:] + bins[:-1])
-        for sp in species:
-            ax.loglog(bins, hists[sp], label=f"{sp}")
-        if bins.min() > 0 and bins.max() / bins.min() > 100:
-            ax.set(xscale="log", yscale="log")
-
-    def phase_plot(
-        self,
-        ax: Optional[maxes.Axes] = None,
-        x_quantity: Optional[Callable[[pd.DataFrame], np.ndarray]] = None,
-        y_quantity: Optional[Callable[[pd.DataFrame], np.ndarray]] = None,
-        xy_bins: Optional[Tuple[npt.NDArray, npt.NDArray]] = None,
-        **kwargs: Any,
-    ):
-        if ax is None:
-            ax = plt.gca()
-
-        if "ux" in self.columns:
-            cols = ["ux", "uy", "uz"]
-            for c in "xyz":
-                if c in self.columns:
-                    cols.append(c)
-            if x_quantity is None:
-                x_quantity = lambda df: df["x"].to_numpy(dtype=np.float64)
-            if y_quantity is None:
-                y_quantity = lambda df: df["ux"].to_numpy(dtype=np.float64)
-        else:
-            cols = ["ur", "uth", "uph"]
-            for c in ["r", "th", "ph"]:
-                if c in self.columns:
-                    cols.append(c)
-            if x_quantity is None:
-                x_quantity = lambda df: df["r"].to_numpy(dtype=np.float64)
-            if y_quantity is None:
-                y_quantity = lambda df: df["ur"].to_numpy(dtype=np.float64)
-
-        df = self.load(cols=[*cols])
-        x_array = x_quantity(df)
-        y_array = y_quantity(df)
-
-        if xy_bins is None:
-            x_bins = np.linspace(x_array.min(), x_array.max(), 100)
-            y_bins = np.linspace(y_array.min(), y_array.max(), 100)
-            xy_bins = (x_bins, y_bins)
-        else:
-            x_bins, y_bins = xy_bins
-
-        h2d, xedges, yedges = np.histogram2d(x_array, y_array, bins=[x_bins, y_bins])
-        X, Y = np.meshgrid(
-            0.5 * (xedges[1:] + xedges[:-1]), 0.5 * (yedges[1:] + yedges[:-1])
-        )
-        pcm = ax.pcolormesh(
-            X,
-            Y,
-            h2d.T,
-            shading="auto",
-            rasterized=True,
-            **kwargs,
-        )
-        return pcm
-
-
-class Particles(BaseContainer):
+from .base import BaseContainer
+from .particle_dataset import ParticleDataset
+from ..utils import CoordinateSystem
+
+
+def remap_prtl_quantities_cart(name: str) -> str:
+    shortname = name[1:]
+    return {
+        "X1": "x",
+        "X2": "y",
+        "X3": "z",
+        "U1": "ux",
+        "U2": "uy",
+        "U3": "uz",
+        "W": "w",
+    }.get(shortname, shortname)
+
+
+def remap_prtl_quantities_sph(name: str) -> str:
+    shortname = name[1:]
+    return {
+        "X1": "r",
+        "X2": "th",
+        "X3": "ph",
+        "U1": "ur",
+        "U2": "uth",
+        "U3": "uph",
+        "W": "w",
+    }.get(shortname, shortname)
+
+
+class ParticleContainer(BaseContainer):
     """Parent class to manage the particles dataframe."""
 
-    __particles_defined: bool
-    __particles: Optional[ParticleDataset]
+    __particles_defined: bool = False
+    __particles: Union[ParticleDataset, None] = None
+
+    nonempty_steps: List[int]
+    attributes: Dict[str, Any]
     quantities: List[str]
     sp_with_idx: List[int]
     sp_without_idx: List[int]
+    quantity_names_by_step: Dict[int, Set[str]]
+
+    def __getstate__(self) -> dict[str, Any]:
+        state = self.__dict__.copy()
+        state.pop("_ParticleContainer__particles", None)
+        return state
 
     def __init__(self, **kwargs: Any) -> None:
-        """Initializer for the Particles class.
+        """Initializer for the ParticleContainer class.
 
         Parameters
         ----------
@@ -563,50 +61,123 @@ def __init__(self, **kwargs: Any) -> None:
             Keyword arguments to be passed to the parent BaseContainer class.
 
         """
-        super(Particles, self).__init__(**kwargs)
+        super().__init__(**kwargs)
+        self.valid_files = self.reader.GetValidFiles(
+            path=self.path,
+            category="particles",
+            num_cpus=self.num_cpus,
+        )
+        self.valid_steps = self.reader.GetValidSteps(
+            path=self.path,
+            category="particles",
+            num_cpus=self.num_cpus,
+        )
+
+        # @TODO: parallelize
+        self.quantity_names_by_step = {
+            step: self.reader.ReadCategoryNamesAtTimestep(
+                self.path, "particles", "p", step
+            )
+            for step in self.valid_steps
+        }
+        self.nonempty_steps = [
+            step
+            for step, names in self.quantity_names_by_step.items()
+            if any(q.startswith("p") for q in names)
+        ]
+
         if (
-            self.reader.DefinesCategory(self.path, "particles")
-            and self.particles_present
+            self.reader.DefinesCategory(self.path, "particles", self.valid_files)
+            and len(self.nonempty_steps) > 0
         ):
             self.__particles_defined = True
+            (
+                self.quantities,
+                self.sp_with_idx,
+                self.sp_without_idx,
+                self.attributes,
+                self.__particles,
+            ) = self._read_particles()
+
+    def _read_particles(self):
+        # read unique quantities and species
+        quantities_ = [
+            self.quantity_names_by_step[step] for step in self.nonempty_steps
+        ]
+        quantities = sorted(np.unique([q for qtys in quantities_ for q in qtys]))
 
-            valid_steps = self.nonempty_steps
-            quantities_ = [
-                self.reader.ReadCategoryNamesAtTimestep(
-                    self.path, "particles", "p", step
+        unique_quantities = sorted(
+            list(
+                set(
+                    f"{q}".split("_")[0]
+                    for q in quantities
+                    if not q.startswith("pIDX") and not q.startswith("pRNK")
                 )
-                for step in valid_steps
-            ]
-            self.quantities = sorted(
-                np.unique([q for qtys in quantities_ for q in qtys])
             )
+        )
+        all_species = sorted(list(set([int(f"{q}".split("_")[1]) for q in quantities])))
 
-            unique_quantities = sorted(
-                list(
-                    set(
-                        str(q).split("_")[0]
-                        for q in self.quantities
-                        if not q.startswith("pIDX") and not q.startswith("pRNK")
-                    )
-                )
-            )
-            all_species = sorted(
-                list(set([int(str(q).split("_")[1]) for q in self.quantities]))
-            )
+        sp_with_idx = sorted(
+            [int(f"{q}".split("_")[1]) for q in quantities if f"{q}".startswith("pIDX")]
+        )
+        sp_without_idx = sorted([sp for sp in all_species if sp not in sp_with_idx])
 
-            self.sp_with_idx = sorted(
-                [int(q.split("_")[1]) for q in self.quantities if q.startswith("pIDX")]
+        partition_lengths = tuple(
+            sum(
+                self.reader.ReadParticleCountsAtTimestep(
+                    self.path, step, all_species
+                ).values()
             )
-            self.sp_without_idx = sorted(
-                [sp for sp in all_species if sp not in self.sp_with_idx]
+            for step in self.valid_steps
+        )
+
+        # save times and steps
+        steps = np.array(self.valid_steps)
+        times = self.reader.ReadPerTimestepVariable(
+            self.path,
+            "particles",
+            "Time",
+            "t",
+            self.valid_files,
+        )["t"]
+
+        # determine coordinate system and remap functions
+        first_step = self.valid_steps[0]
+        attributes = self.reader.ReadAttrsAtTimestep(
+            path=self.path, category="particles", step=first_step
+        )
+        if self.coordinate_system is None:
+            if "Coordinates" not in attributes:
+                raise ValueError("Coordinates not found in attributes for particles.")
+            if attributes["Coordinates"] in [b"cart", "cart"]:
+                self.set_coordinate_system(CoordinateSystem.XYZ)
+            elif attributes["Coordinates"] in [b"sph", "sph", b"qsph", "qsph"]:
+                self.set_coordinate_system(CoordinateSystem.SPH)
+            else:
+                raise NotImplementedError(
+                    f"Coordinate system {attributes['Coordinates']} not supported."
+                )
+
+        if self.remap is None:
+            self.set_remap(
+                {
+                    "particles": (
+                        remap_prtl_quantities_cart
+                        if self.coordinate_system == CoordinateSystem.XYZ
+                        else remap_prtl_quantities_sph
+                    ),
+                }
             )
 
-            self.__particles = ParticleDataset(
+        return (
+            quantities,
+            sp_with_idx,
+            sp_without_idx,
+            attributes,
+            ParticleDataset(
                 species=all_species,
-                steps=np.array(self.reader.GetValidSteps(self.path, "particles")),
-                times=self.reader.ReadPerTimestepVariable(
-                    self.path, "particles", "Time", "t"
-                )["t"],
+                steps=steps,
+                times=times,
                 colnames=[
                     (
                         self.remap["particles"](q)
@@ -617,34 +188,9 @@ def __init__(self, **kwargs: Any) -> None:
                 ]
                 + ["id", "sp"],
                 read_column=self._read_column,
-            )
-        else:
-            self.__particles_defined = False
-            self.__particles = None
-
-    @property
-    def particles_present(self) -> bool:
-        """bool: Whether the particles are present in any of the timesteps."""
-        return len(self.nonempty_steps) > 0
-
-    @property
-    def nonempty_steps(self) -> List[int]:
-        """list[int]: List of timesteps that contain particles data."""
-        valid_steps = self.reader.GetValidSteps(self.path, "particles")
-        return [
-            step
-            for step in valid_steps
-            if len(
-                set(
-                    q.split("_")[0]
-                    for q in self.reader.ReadCategoryNamesAtTimestep(
-                        self.path, "particles", "p", step
-                    )
-                    if q.startswith("p")
-                )
-            )
-            > 0
-        ]
+                partition_lengths=partition_lengths,
+            ),
+        )
 
     @property
     def particles_defined(self) -> bool:
@@ -652,17 +198,25 @@ def particles_defined(self) -> bool:
         return self.__particles_defined
 
     @property
-    def particles(self) -> Optional[ParticleDataset]:
+    def particles(self) -> Union[ParticleDataset, None]:
         """Returns the particles data.
 
         Returns
         -------
-        ParticleDataset
-            Dictionary of datasets for each step.
+        ParticleDataset | None
+            The particles data if defined, otherwise None.
 
         """
         return self.__particles
 
+    @property
+    def attrs(self) -> Dict[str, Any]:
+        """dict: The attributes of the particles dataframe."""
+        if self.particles_defined:
+            return self.attributes
+        else:
+            return {}
+
     def help_particles(self, prepend: str = "") -> str:
         return self.particles.help(prepend) if self.particles is not None else ""
 
@@ -673,7 +227,7 @@ def _get_count(self, step: int, sp: int) -> np.int64:
                     self.path, "particles", f"pX1_{sp}", step
                 )[0]
             )
-        except:
+        except Exception:
             return np.int64(0)
 
     def _species_has_quantity(self, read_colname: str, step: int, sp: int) -> bool:
diff --git a/nt2/containers/spectra.py b/nt2/containers/spectra.py
index 044656d..93b5c41 100644
--- a/nt2/containers/spectra.py
+++ b/nt2/containers/spectra.py
@@ -1,29 +1,58 @@
-from typing import Any
+from typing import Any, Union, Dict
 
 import dask
 import dask.array as da
 import xarray as xr
 import numpy as np
+from tqdm import tqdm
 
-from nt2.containers.container import BaseContainer
-from nt2.readers.base import BaseReader
+from .base import BaseContainer
 
 
-class Spectra(BaseContainer):
-    """Parent class to manager the spectra dataframe."""
+class SpectraContainer(BaseContainer):
+    """Parent class to manage the spectra dataframe."""
 
-    @staticmethod
-    def read_spectrum(path: str, reader: BaseReader, spectrum: str, step: int) -> Any:
+    __spectra_defined: bool = False
+    __spectra: Union[xr.Dataset, None] = None
+
+    def __init__(self, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        self.valid_files = self.reader.GetValidFiles(
+            path=self.path,
+            category="spectra",
+            num_cpus=self.num_cpus,
+        )
+        self.valid_steps = self.reader.GetValidSteps(
+            path=self.path,
+            category="spectra",
+            num_cpus=self.num_cpus,
+        )
+
+        if self.reader.DefinesCategory(
+            self.path,
+            "spectra",
+            self.valid_files,
+        ):
+            self.__spectra_defined = True
+            self.__spectra = self._read_spectra()
+
+    @property
+    def spectra_defined(self) -> bool:
+        """bool: Whether the spectra category is defined."""
+        return self.__spectra_defined
+
+    @property
+    def spectra(self) -> Union[xr.Dataset, None]:
+        """xr.Dataset: The spectra dataframe."""
+        return self.__spectra
+
+    def _read_spectrum(self, spectrum: str, step: int) -> Any:
         """Reads a spectrum from the data.
 
         This is a dask-delayed function used further to build the dataset.
 
         Parameters
         ----------
-        path : str
-            Main path to the data.
-        reader : BaseReader
-            Reader to use to read the data.
         spectrum : str
             Spectrum array to read.
         step : int
@@ -35,42 +64,40 @@ def read_spectrum(path: str, reader: BaseReader, spectrum: str, step: int) -> An
             Spectrum data.
 
         """
-        return reader.ReadArrayAtTimestep(path, "spectra", spectrum, step)
-
-    def __init__(self, **kwargs: Any) -> None:
-        super(Spectra, self).__init__(**kwargs)
-        if self.reader.DefinesCategory(self.path, "spectra"):
-            self.__spectra_defined = True
-            self.__spectra = self.__read_spectra()
-        else:
-            self.__spectra_defined = False
-            self.__spectra = xr.Dataset()
-
-    @property
-    def spectra_defined(self) -> bool:
-        """bool: Whether the spectra category is defined."""
-        return self.__spectra_defined
-
-    @property
-    def spectra(self) -> xr.Dataset:
-        """xr.Dataset: The spectra dataframe."""
-        return self.__spectra
-
-    def __read_spectra(self) -> xr.Dataset:
-        self.reader.VerifySameCategoryNames(self.path, "spectra", "s")
-        valid_steps = sorted(self.reader.GetValidSteps(self.path, "spectra"))
+        return self.reader.ReadArrayAtTimestep(self.path, "spectra", spectrum, step)
+
+    def _read_spectra(self) -> xr.Dataset:
+        if self.verify:
+            self.reader.VerifySameCategoryNames(
+                self.path,
+                "spectra",
+                "s",
+                self.valid_steps,
+            )
+        first_step = self.valid_steps[0]
         spectra_names = self.reader.ReadCategoryNamesAtTimestep(
-            self.path, "spectra", "s", valid_steps[0]
+            self.path, "spectra", "s", first_step
         )
         spectra_names = set(s for s in sorted(spectra_names) if s.startswith("sN"))
         ebin_name = "sEbn"
-        first_step = valid_steps[0]
         first_spectrum_name = next(iter(spectra_names))
         shape = self.reader.ReadArrayShapeExplicitlyAtTimestep(
             self.path, "spectra", first_spectrum_name, first_step
         )
-        times = self.reader.ReadPerTimestepVariable(self.path, "spectra", "Time", "t")
-        steps = self.reader.ReadPerTimestepVariable(self.path, "spectra", "Step", "s")
+        times = self.reader.ReadPerTimestepVariable(
+            self.path,
+            "spectra",
+            "Time",
+            "t",
+            self.valid_files,
+        )
+        steps = self.reader.ReadPerTimestepVariable(
+            self.path,
+            "spectra",
+            "Step",
+            "s",
+            self.valid_files,
+        )
 
         ebins = self.reader.ReadArrayAtTimestep(
             self.path, "spectra", ebin_name, first_step
@@ -85,6 +112,10 @@ def __read_spectra(self) -> xr.Dataset:
         all_dims = {**times, "E": ebins}
         all_coords = {**all_dims, "s": ("t", steps["s"])}
 
+        attributes = self.reader.ReadAttrsAtTimestep(
+            path=self.path, category="spectra", step=first_step
+        )
+
         def remap_name(name: str) -> str:
             return name[1:]
 
@@ -94,29 +125,40 @@ def remap_name(name: str) -> str:
                     da.stack(
                         [
                             da.from_delayed(
-                                dask.delayed(self.read_spectrum)(
-                                    path=self.path,
-                                    reader=self.reader,
+                                dask.delayed(self._read_spectrum)(
                                     spectrum=spectrum,
                                     step=step,
                                 ),
                                 shape=shape,
                                 dtype="float",
                             )
-                            for step in valid_steps
+                            for step in tqdm(
+                                self.valid_steps,
+                                desc="steps",
+                                position=1,
+                                leave=False,
+                            )
                         ],
                     ),
                     name=remap_name(spectrum),
                     dims=all_dims,
                     coords=all_coords,
                 )
-                for spectrum in spectra_names
+                for spectrum in tqdm(
+                    spectra_names, desc="spectra", position=0, leave=False
+                )
             },
-            attrs=self.reader.ReadAttrsAtTimestep(
-                path=self.path, category="spectra", step=first_step
-            ),
+            attrs=attributes,
         )
 
+    @property
+    def attrs(self) -> Dict[str, Any]:
+        """dict: The attributes of the spectra dataframe."""
+        if self.spectra_defined:
+            return self.spectra.attrs
+        else:
+            return {}
+
     def help_spectra(self, prepend="") -> str:
         ret = f"{prepend}- use .sel(...) to select specific energy or time intervals\n"
         ret += f"{prepend}  t  : time (float)\n"

From 2503c7c718aa63f8d525f0dca32d4004e635d6d2 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 03:32:00 -0400
Subject: [PATCH 09/14] tests adapted to new containers

---
 nt2/tests/test_cli.py                     | 166 +++++++++++++--------
 nt2/tests/test_containers.py              | 169 +++++++++++++++++-----
 nt2/tests/test_diagnostics.py             | 117 +++++++++++++++
 nt2/tests/test_particle_dataset_nbytes.py |  41 ++++++
 nt2/tests/test_reader.py                  |  71 ++++++---
 nt2/tests/test_tokenize.py                |  10 +-
 6 files changed, 454 insertions(+), 120 deletions(-)
 create mode 100644 nt2/tests/test_diagnostics.py
 create mode 100644 nt2/tests/test_particle_dataset_nbytes.py

diff --git a/nt2/tests/test_cli.py b/nt2/tests/test_cli.py
index 731c2be..49a00fe 100644
--- a/nt2/tests/test_cli.py
+++ b/nt2/tests/test_cli.py
@@ -1,6 +1,9 @@
+from unittest.mock import Mock
+
+import matplotlib.pyplot as plt
+import numpy as np
 import pytest
 from typer.testing import CliRunner
-import matplotlib.pyplot as plt
 
 import os
 import nt2
@@ -13,77 +16,116 @@
 def test_version():
     result = runner.invoke(app, ["version"])
     assert result.exit_code == 0, f"Expected exit code 0, got {result.exit_code}"
-    assert (
-        nt2.__version__ in result.output
-    ), f"Expected version {nt2.__version__} in output, got {result.output}"
+    assert nt2.__version__ in result.output, (
+        f"Expected version {nt2.__version__} in output, got {result.output}"
+    )
 
 
 @pytest.mark.parametrize(
     "test",
     [test for test in TESTS],
 )
-def test_show(test):
-    PATH = test["path"]
-    data = nt2.Data(PATH)
-    result = runner.invoke(app, ["show", PATH])
+def test_show(test, monkeypatch):
+    path = test["path"]
+    expected = f"Data summary for {path}"
+    data = Mock()
+    data.to_str.return_value = expected
+    data_factory = Mock(return_value=data)
+    monkeypatch.setattr(nt2, "Data", data_factory)
+
+    result = runner.invoke(app, ["show", path])
+
     assert result.exit_code == 0, f"Expected exit code 0, got {result.exit_code}"
-    assert (
-        data.to_str() in result.output
-    ), f"Expected data info in output, got {result.output}"
+    data_factory.assert_called_once_with(path)
+    data.to_str.assert_called_once_with()
+    assert expected in result.output
 
 
 @pytest.mark.parametrize(
     "test",
-    [test for test in TESTS],
+    [test for test in TESTS if test["fields"]],
 )
-def test_plot_png(test):
-    PATH = test["path"]
-    if test["fields"] == {}:
-        return
-    if test.get("coords", "cart") == "cart":
-        result = runner.invoke(
-            app,
-            [
-                "plot",
-                PATH,
-                "--what",
-                "fields",
-                "--sel",
-                "x=slice(None, 5);y=slice(-5.0, 5.0)",
-                "--isel",
-                f"t=0{';z=0' if test['dim'] == '3D' else ''}",
-            ],
+def test_plot_png(test, monkeypatch, tmp_path):
+    path = test["path"]
+    data_class = nt2.Data
+
+    def field_data(data_path):
+        return data_class(
+            data_path,
+            fields=True,
+            particles=False,
+            spectra=False,
+            num_cpus=1,
         )
-        assert result.exit_code == 0, f"Expected exit code 0, got {result.exit_code}"
-
-        data = nt2.Data(PATH)
-        fname = os.path.basename(PATH.strip("/"))
-
-        d = data.fields.sel(x=slice(None, 5), y=slice(-5, 5)).isel(t=0)
-        if test["dim"] == "3D":
-            d = d.isel(z=0)
-        d.inspect.plot(fig_kwargs={"dpi": 200})
-        plt.savefig(fname=f"{fname}-2.png")
-
-        def files_are_identical(path1, path2):
-            with open(path1, "rb") as f1, open(path2, "rb") as f2:
-                return f1.read() == f2.read()
-
-        assert files_are_identical(
-            f"{fname}-2.png", f"{fname}.png"
-        ), f"Files {fname}-2.png and {fname}.png are not identical."
-
-        os.remove(f"{fname}-2.png")
-        os.remove(f"{fname}.png")
-    # else:
-    #     result = runner.invoke(
-    #         app,
-    #         [
-    #             "plot",
-    #             PATH,
-    #             "--sel",
-    #             "r=slice(None, 5);th=slice(1.5, 2.5)",
-    #             "--isel",
-    #             "t=0",
-    #         ],
-    #     )
+
+    monkeypatch.setattr(nt2, "Data", field_data)
+    monkeypatch.chdir(tmp_path)
+
+    is_cartesian = test.get("coords", "cart") == "cart"
+    selection = (
+        "x=slice(None, 5);y=slice(-5.0, 5.0)"
+        if is_cartesian
+        else "r=slice(None, 5);th=slice(1.5, 2.5)"
+    )
+    index_selection = f"t=0{';z=0' if test['dim'] == '3D' else ''}"
+    result = runner.invoke(
+        app,
+        [
+            "plot",
+            path,
+            "--what",
+            "fields",
+            "--sel",
+            selection,
+            "--isel",
+            index_selection,
+        ],
+    )
+    assert result.exit_code == 0, f"Expected exit code 0, got {result.exit_code}"
+
+    fname = os.path.basename(path.strip("/"))
+    actual_path = tmp_path / f"{fname}.png"
+    expected_path = tmp_path / f"{fname}-expected.png"
+    assert actual_path.is_file()
+
+    data = field_data(path)
+    if is_cartesian:
+        selected = data.fields.sel(x=slice(None, 5), y=slice(-5, 5)).isel(t=0)
+    else:
+        selected = data.fields.sel(r=slice(None, 5), th=slice(1.5, 2.5)).isel(t=0)
+    if test["dim"] == "3D":
+        selected = selected.isel(z=0)
+
+    plt.close("all")
+    selected.inspect.plot(name=fname, fig_kwargs={"dpi": 200})
+    plt.savefig(expected_path)
+    plt.close("all")
+
+    np.testing.assert_array_equal(
+        plt.imread(actual_path),
+        plt.imread(expected_path),
+    )
+
+
+@pytest.mark.parametrize("test", [test for test in TESTS if not test["fields"]])
+def test_plot_without_fields_fails(test, monkeypatch, tmp_path):
+    path = test["path"]
+    data_class = nt2.Data
+
+    def field_data(data_path):
+        return data_class(
+            data_path,
+            fields=True,
+            particles=False,
+            spectra=False,
+            num_cpus=1,
+        )
+
+    monkeypatch.setattr(nt2, "Data", field_data)
+    monkeypatch.chdir(tmp_path)
+
+    result = runner.invoke(app, ["plot", path, "--what", "fields", "--isel", "t=0"])
+
+    assert result.exit_code == 1
+    assert isinstance(result.exception, FileNotFoundError)
+    assert str(result.exception).endswith("/fields'")
diff --git a/nt2/tests/test_containers.py b/nt2/tests/test_containers.py
index 9e8f058..c773d7e 100644
--- a/nt2/tests/test_containers.py
+++ b/nt2/tests/test_containers.py
@@ -1,13 +1,19 @@
+from typing import List, Type, Union
+
 import pytest
-from typing import Union, List, Type
 
 from nt2.readers.base import BaseReader
-from nt2.containers.fields import Fields
-from nt2.containers.particles import Particles
+from nt2.containers.fields import FieldContainer
+from nt2.containers.particles import ParticleContainer
 from nt2.containers.data import Data
 from nt2.tests.cases import TESTS
 
 
+FIELD_TESTS = [test for test in TESTS if test["fields"]]
+EMPTY_FIELD_TESTS = [test for test in TESTS if not test["fields"]]
+PARTICLE_TESTS = [test for test in TESTS if test["particles"]]
+
+
 def check_shape(shape1, shape2):
     """
     Check if two shapes are equal
@@ -16,45 +22,67 @@ def check_shape(shape1, shape2):
 
 
 @pytest.mark.parametrize(
-    "test,field_container", [[test, fc] for test in TESTS for fc in [Data, Fields]]
+    "test,field_container",
+    [[test, container] for test in FIELD_TESTS for container in [Data, FieldContainer]],
 )
-def test_fields(test, field_container: Union[Type[Data], Type[Fields]]):
+def test_fields(test, field_container: Union[Type[Data], Type[FieldContainer]]):
     reader: BaseReader = test["reader"]()
-    PATH = test["path"]
-    if test["fields"] == {}:
-        return
+    path = test["path"]
 
     coords: List[str] = ["x", "y", "z"]
     flds: List[str] = ["Ex", "Ey", "Ez", "Bx", "By", "Bz"]
 
-    def coord_remap(Xold: str) -> str:
+    def coord_remap_cart(Xold: str) -> str:
         return {
             "X1": "x",
             "X2": "y",
             "X3": "z",
         }.get(Xold, Xold)
 
-    if test.get("coords", "cart") != "cart":
-        coords = ["r", "th", "ph"]
-        flds = ["Er", "Eth", "Eph", "Br", "Bth", "Bph"]
-        coord_remap = lambda Xold: {
+    def coord_remap_sph(Xold: str) -> str:
+        return {
             "X1": "r",
             "X2": "th",
             "X3": "ph",
         }.get(Xold, Xold)
 
+    if test.get("coords", "cart") != "cart":
+        coords = ["r", "th", "ph"]
+        flds = ["Er", "Eth", "Eph", "Br", "Bth", "Bph"]
+
     def field_remap(Fold: str):
         return {
-            f"f{F}{i+1}": f"{F}{x}" for i, x in enumerate(coords) for F in "EB"
+            f"f{F}{i + 1}": f"{F}{x}" for i, x in enumerate(coords) for F in "EB"
         }.get(Fold, Fold)
 
-    fields = field_container(
-        path=PATH,
-        reader=reader,
-        remap={"coords": coord_remap, "fields": field_remap},
-    )
+    remap = {
+        "coords": (
+            coord_remap_cart
+            if test.get("coords", "cart") == "cart"
+            else coord_remap_sph
+        ),
+        "fields": field_remap,
+    }
+    if field_container is Data:
+        fields = field_container(
+            path=path,
+            fields=True,
+            particles=False,
+            spectra=False,
+            reader=reader,
+            remap=remap,
+            num_cpus=1,
+        )
+    else:
+        fields = field_container(
+            path=path,
+            reader=reader,
+            remap=remap,
+            coord_system=None,
+            num_cpus=1,
+        )
 
-    steps = reader.GetValidSteps(path=PATH, category="fields")
+    steps = reader.GetValidSteps(path=path, category="fields", num_cpus=1)
     nx1 = test["fields"]["nx1"]
     nx2 = test["fields"]["nx2"]
     assert fields.fields is not None, "Fields are None"
@@ -106,17 +134,52 @@ def field_remap(Fold: str):
             )
 
 
+@pytest.mark.parametrize(
+    "test,field_container",
+    [
+        [test, container]
+        for test in EMPTY_FIELD_TESTS
+        for container in [Data, FieldContainer]
+    ],
+)
+def test_missing_field_output_raises_file_not_found(
+    test, field_container: Union[Type[Data], Type[FieldContainer]]
+):
+    reader: BaseReader = test["reader"]()
+    kwargs = {
+        "path": test["path"],
+        "reader": reader,
+        "remap": None,
+        "num_cpus": 1,
+    }
+
+    with pytest.raises(FileNotFoundError, match="/fields"):
+        if field_container is Data:
+            field_container(
+                **kwargs,
+                fields=True,
+                particles=False,
+                spectra=False,
+            )
+        else:
+            field_container(**kwargs, coord_system=None)
+
+
 @pytest.mark.parametrize(
     "test,particle_container",
-    [[test, fc] for test in TESTS for fc in [Data, Particles]],
+    [
+        [test, container]
+        for test in PARTICLE_TESTS
+        for container in [Data, ParticleContainer]
+    ],
 )
-def test_particles(test, particle_container: Union[Type[Data], Type[Particles]]):
+def test_particles(
+    test, particle_container: Union[Type[Data], Type[ParticleContainer]]
+):
     reader: BaseReader = test["reader"]()
-    PATH = test["path"]
-    if test["particles"] == {}:
-        return
+    path = test["path"]
 
-    def prtl_remap(Xold: str) -> str:
+    def prtl_remap_cart(Xold: str) -> str:
         return {
             "pX1": "x",
             "pX2": "y",
@@ -127,8 +190,8 @@ def prtl_remap(Xold: str) -> str:
             "pW": "w",
         }.get(Xold, Xold)
 
-    if test.get("coords", "cart") != "cart":
-        prtl_remap = lambda Xold: {
+    def prtl_remap_sph(Xold: str) -> str:
+        return {
             "pX1": "r",
             "pX2": "th",
             "pX3": "ph",
@@ -137,9 +200,47 @@ def prtl_remap(Xold: str) -> str:
             "pU3": "uph",
             "pW": "w",
         }.get(Xold, Xold)
-    particles = particle_container(
-        path=PATH,
-        reader=reader,
-        remap={"particles": prtl_remap},
-    )
-    assert particles.particles is not None, "Particles are None"
+
+    remap = {
+        "particles": (
+            prtl_remap_cart if test.get("coords", "cart") == "cart" else prtl_remap_sph
+        )
+    }
+    if particle_container is Data:
+        particles = particle_container(
+            path=path,
+            fields=False,
+            particles=True,
+            spectra=False,
+            reader=reader,
+            remap=remap,
+            num_cpus=1,
+        )
+    else:
+        particles = particle_container(
+            path=path,
+            reader=reader,
+            remap=remap,
+            coord_system=None,
+            num_cpus=1,
+        )
+
+    dataset = particles.particles
+    assert dataset is not None, "Particles are None"
+    assert len(dataset.species) == test["particles"].get("nspec", 4)
+
+    selected = dataset.isel(t=-1).load(cols=["id", "sp", "w"])
+    last_step = int(dataset.steps[-1])
+    expected_counts = [
+        reader.ReadArrayShapeAtTimestep(
+            path=path,
+            category="particles",
+            quantity=f"pW_{species}",
+            step=last_step,
+        )[0]
+        for species in dataset.species
+    ]
+
+    assert selected["st"].unique().tolist() == [last_step]
+    assert selected.groupby("sp", sort=True).size().tolist() == expected_counts
+    assert selected["w"].notna().all()
diff --git a/nt2/tests/test_diagnostics.py b/nt2/tests/test_diagnostics.py
new file mode 100644
index 0000000..abc3cdd
--- /dev/null
+++ b/nt2/tests/test_diagnostics.py
@@ -0,0 +1,117 @@
+import os
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from nt2.containers.diagnostics import Diagnostics
+
+
+def _write_log(path: Path, steps: int = 3) -> None:
+    preamble = "Entity diagnostic output\n\n"
+    records = []
+    for step in range(1, steps + 1):
+        records.append(
+            f"""Step: {step}....................[of {steps}]
+Time: {step * 0.25:.2f}................[Δt = 0.25]
+
+[SUBSTEP]..................[DURATION]
+  Communications..............{step}.00 ms      3%
+  FieldSolver................{step + 1}.00 µs      1%
+  Injector.....................0.00 ns      0%
+
+[PARTICLE SPECIES]            [TOTAL] [% TOT]
+  species  1 (e-)............{step}.20e+03      1%
+  species  2 (i+)............2.00e+03      1%
+
+................................................................................
+
+"""
+        )
+    path.write_text(preamble + "".join(records), encoding="utf-8")
+
+
+def test_streaming_parser_without_cache(tmp_path: Path) -> None:
+    outfile = tmp_path / "simulation.out"
+    _write_log(outfile)
+
+    diagnostics = Diagnostics(tmp_path, cache=False, chunk_size=2)
+
+    expected = pd.DataFrame(
+        {
+            "Step": [1, 2, 3],
+            "Time": [0.25, 0.50, 0.75],
+            "Communications": [1e6, 2e6, 3e6],
+            "FieldSolver": [2e3, 3e3, 4e3],
+            "Injector": [0.0, 0.0, 0.0],
+            "species_1": [1200, 2200, 3200],
+            "species_2": [2000, 2000, 2000],
+        }
+    ).set_index("Step", drop=False)
+    pd.testing.assert_frame_equal(diagnostics.df, expected)
+
+
+def test_parquet_cache_is_reused_and_invalidated(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    outfile = tmp_path / "simulation.out"
+    cache = tmp_path / "diagnostics.parquet"
+    _write_log(outfile, steps=2)
+    first = Diagnostics(outfile, cache_path=cache, chunk_size=1)
+    assert first.df is not None
+    assert cache.is_file()
+    assert Path(f"{cache}.json").is_file()
+
+    def fail_if_parsed(source: Path):
+        raise AssertionError(f"Unexpected parse of {source}")
+
+    monkeypatch.setattr(Diagnostics, "_records", fail_if_parsed)
+    cached = Diagnostics(outfile, cache_path=cache)
+    pd.testing.assert_frame_equal(cached.df, first.df)
+
+    monkeypatch.undo()
+    with outfile.open("a", encoding="utf-8") as stream:
+        stream.write("\n")
+    os.utime(outfile, None)
+    reparsed = Diagnostics(outfile, cache_path=cache)
+    pd.testing.assert_frame_equal(reparsed.df, first.df)
+
+
+def test_inconsistent_record_raises(tmp_path: Path) -> None:
+    outfile = tmp_path / "simulation.out"
+    _write_log(outfile, steps=2)
+    text = outfile.read_text(encoding="utf-8")
+    outfile.write_text(
+        text.replace("  Injector.....................0.00 ns      0%\n", "", 1),
+        encoding="utf-8",
+    )
+
+    with pytest.raises(ValueError, match="Inconsistent diagnostics record at step 2"):
+        Diagnostics(outfile, cache=False)
+
+
+def test_incomplete_final_record_is_ignored(tmp_path: Path) -> None:
+    outfile = tmp_path / "simulation.out"
+    _write_log(outfile, steps=2)
+    with outfile.open("a", encoding="utf-8") as stream:
+        stream.write("Step: 3....................[of 3]\nTime: 0.75")
+
+    diagnostics = Diagnostics(outfile, cache=False)
+
+    assert diagnostics.df is not None
+    assert diagnostics.df["Step"].tolist() == [1, 2]
+
+
+def test_no_outfile(tmp_path: Path, caplog: pytest.LogCaptureFixture) -> None:
+    diagnostics = Diagnostics(tmp_path)
+
+    assert diagnostics.df is None
+    assert "No .out files found" in caplog.text
+
+
+def test_cache_cannot_overwrite_source(tmp_path: Path) -> None:
+    outfile = tmp_path / "simulation.out"
+    _write_log(outfile)
+
+    with pytest.raises(ValueError, match="must not overwrite"):
+        Diagnostics(outfile, cache_path=outfile)
diff --git a/nt2/tests/test_particle_dataset_nbytes.py b/nt2/tests/test_particle_dataset_nbytes.py
new file mode 100644
index 0000000..74cd07e
--- /dev/null
+++ b/nt2/tests/test_particle_dataset_nbytes.py
@@ -0,0 +1,41 @@
+import numpy as np
+
+from nt2.containers.particle_dataset import ParticleDataset
+
+
+def _unexpected_read(step: int, column: str):
+    raise AssertionError(f"nbytes unexpectedly read {column} at step {step}")
+
+
+def test_nbytes_uses_cached_partition_lengths_without_computing():
+    particles = ParticleDataset(
+        species=[1],
+        steps=np.array([10, 20], dtype=np.int64),
+        times=np.array([1.0, 2.0], dtype=np.float64),
+        colnames=["x", "id", "sp"],
+        read_column=_unexpected_read,
+        partition_lengths=(1, 2),
+    )
+
+    bytes_per_row = sum(
+        np.dtype(dtype).itemsize
+        for dtype in (np.int64, np.int32, np.int64, np.float64, np.int64)
+    )
+    assert particles.nbytes == 3 * bytes_per_row
+
+
+def test_nbytes_tracks_timestep_partition_selection_without_computing():
+    particles = ParticleDataset(
+        species=[1],
+        steps=np.array([10, 20], dtype=np.int64),
+        times=np.array([1.0, 2.0], dtype=np.float64),
+        colnames=["x", "id", "sp"],
+        read_column=_unexpected_read,
+        partition_lengths=(1, 2),
+    )
+
+    bytes_per_row = sum(
+        np.dtype(dtype).itemsize
+        for dtype in (np.int64, np.int32, np.int64, np.float64, np.int64)
+    )
+    assert particles.isel(t=-1).nbytes == 2 * bytes_per_row
diff --git a/nt2/tests/test_reader.py b/nt2/tests/test_reader.py
index 17a889e..d556e39 100644
--- a/nt2/tests/test_reader.py
+++ b/nt2/tests/test_reader.py
@@ -12,16 +12,16 @@ def pytest_generate_tests(metafunc):
 
 def check_equal_arrays(arr1, arr2):
     if isinstance(arr1, set):
-        assert len(arr1) == len(
-            arr2
-        ), f"Set lengths do not match: {len(arr1)} != {len(arr2)}"
+        assert len(arr1) == len(arr2), (
+            f"Set lengths do not match: {len(arr1)} != {len(arr2)}"
+        )
         assert arr1 == arr2, f"Sets do not match: {arr1} != {arr2}"
     else:
         arr1 = np.array(arr1)
         arr2 = np.array(arr2)
-        assert (
-            arr1.shape == arr2.shape
-        ), f"Shapes do not match: {arr1.shape} != {arr2.shape}"
+        assert arr1.shape == arr2.shape, (
+            f"Shapes do not match: {arr1.shape} != {arr2.shape}"
+        )
         assert np.all(np.isclose(arr1, arr2)), f"Arrays do not match: {arr1} != {arr2}"
 
 
@@ -78,9 +78,9 @@ def test_reader(test):
 
         field_names = test["fields"].get(
             "quantities",
-            [f"{f}{i+1}" for i in range(3) for f in "BE"]
+            [f"{f}{i + 1}" for i in range(3) for f in "BE"]
             + [f"N_{i}" for i in ["1_2", "3_4"]]
-            + [f"T0{c+1}_{i+1}" for i in range(4) for c in range(3)],
+            + [f"T0{c + 1}_{i + 1}" for i in range(4) for c in range(3)],
         )
         field_names = set(f"f{f}" for f in field_names)
         # Check that invalid_tstep raises OSError in fields
@@ -92,12 +92,22 @@ def test_reader(test):
                 OSError,
             )
 
+        valid_files = reader.GetValidFiles(path=PATH, category="fields")
+
         # Check that timesteps are read correctly from fields
         times = reader.ReadPerTimestepVariable(
-            path=PATH, category="fields", varname="Time", newname="t"
+            path=PATH,
+            category="fields",
+            varname="Time",
+            newname="t",
+            valid_files=valid_files,
         )["t"]
         steps = reader.ReadPerTimestepVariable(
-            path=PATH, category="fields", varname="Step", newname="s"
+            path=PATH,
+            category="fields",
+            varname="Step",
+            newname="s",
+            valid_files=valid_files,
         )["s"]
         check_equal_arrays(
             times,
@@ -119,7 +129,6 @@ def test_reader(test):
             check_equal_arrays(coords["X2"], x2)
 
             if test["dim"] == "3D":
-                sx3 = test["fields"]["sx3"]
                 x3min = dx / 2
                 nx3 = test["fields"]["nx3"]
                 x3 = np.array([x3min + i * dx for i in range(int(nx3))])
@@ -140,18 +149,27 @@ def test_reader(test):
                 shape, (nx1, nx2, nx3) if layout == Layout.R else (nx3, nx2, nx1)
             )
 
-        for step in reader.GetValidSteps(path=PATH, category="fields"):
+        valid_steps = reader.GetValidSteps(path=PATH, category="fields")
+        for step in valid_steps:
             for f in field_names:
                 field = reader.ReadArrayAtTimestep(
-                    path=PATH, category="fields", quantity=f, step=step
+                    path=PATH,
+                    category="fields",
+                    quantity=f,
+                    step=step,
                 )
                 check_equal_arrays(field.shape, shape)
 
-        reader.VerifySameCategoryNames(path=PATH, category="fields", prefix="f")
-        reader.VerifySameFieldLayouts(path=PATH)
+        reader.VerifySameCategoryNames(
+            path=PATH,
+            category="fields",
+            prefix="f",
+            valid_steps=valid_steps,
+        )
+        reader.VerifySameFieldLayouts(path=PATH, valid_steps=valid_steps)
 
         # Check that the shapes of the fields are read correctly
-        reader.VerifySameFieldShapes(path=PATH)
+        reader.VerifySameFieldShapes(path=PATH, valid_steps=valid_steps)
 
     if test["particles"] != {}:
         dt = 0
@@ -171,9 +189,9 @@ def test_reader(test):
         nspec: int = test["particles"].get("nspec", 4)
 
         prtl_names = (
-            [f"U{i+1}_{j+1}" for i in range(3) for j in range(nspec)]
+            [f"U{i + 1}_{j + 1}" for i in range(3) for j in range(nspec)]
             + [
-                f"X{i+1}_{j+1}"
+                f"X{i + 1}_{j + 1}"
                 for i in range(
                     2
                     if test["dim"] == "2D" and test.get("coords", "cart") == "cart"
@@ -181,16 +199,25 @@ def test_reader(test):
                 )
                 for j in range(nspec)
             ]
-            + [f"W_{i+1}" for i in range(nspec)]
+            + [f"W_{i + 1}" for i in range(nspec)]
         )
         prtl_names = set(f"p{p}" for p in prtl_names)
 
+        valid_files = reader.GetValidFiles(path=PATH, category="particles")
         # Check that timesteps are read correctly from particles
         times = reader.ReadPerTimestepVariable(
-            path=PATH, category="particles", varname="Time", newname="t"
+            path=PATH,
+            category="particles",
+            varname="Time",
+            newname="t",
+            valid_files=valid_files,
         )["t"]
         steps = reader.ReadPerTimestepVariable(
-            path=PATH, category="particles", varname="Step", newname="s"
+            path=PATH,
+            category="particles",
+            varname="Step",
+            newname="s",
+            valid_files=valid_files,
         )["s"]
 
         if dt is not None:
@@ -212,6 +239,6 @@ def test_reader(test):
                 reader.ReadArrayShapeAtTimestep(
                     path=PATH,
                     category="particles",
-                    quantity=f"pW_{sp+1}",
+                    quantity=f"pW_{sp + 1}",
                     step=step,
                 )
diff --git a/nt2/tests/test_tokenize.py b/nt2/tests/test_tokenize.py
index 7e49f74..10e46b3 100644
--- a/nt2/tests/test_tokenize.py
+++ b/nt2/tests/test_tokenize.py
@@ -1,6 +1,6 @@
 from dask.base import tokenize
 
-from nt2.containers.container import BaseContainer
+from nt2.containers.base import BaseContainer
 from nt2.readers.base import BaseReader
 from nt2.utils import Format
 
@@ -12,7 +12,13 @@ def format(self) -> Format:
 
 
 def test_base_container_has_deterministic_dask_token():
-    container = BaseContainer(path="/tmp/sim", reader=_Reader(), remap=None)
+    container = BaseContainer(
+        path="/tmp/sim",
+        reader=_Reader(),
+        remap=None,
+        coord_system=None,
+        num_cpus=1,
+    )
 
     token1 = tokenize(container)
     token2 = tokenize(container)

From 56f2d53172b3ab583bec431781dfac6f566c97ef Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 03:32:04 -0400
Subject: [PATCH 10/14] minor

---
 .gitignore   |  8 +++++++-
 LICENSE      |  2 +-
 nt2/utils.py | 15 ++++++++++++++-
 3 files changed, 22 insertions(+), 3 deletions(-)

diff --git a/.gitignore b/.gitignore
index 17eb961..430467a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -47,7 +47,7 @@ coverage.xml
 *.cover
 *.py,cover
 .hypothesis/
-.pytest_cache/
+.*cache/
 cover/
 
 # Translations
@@ -155,3 +155,9 @@ nt2/tests/testdata
 test/
 temp/
 *.bak
+.*venv
+
+# LLM
+.codex
+.agents
+.claude
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
index 3ac8c81..9f07b06 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 BSD 3-Clause License
 
-Copyright (c) 2025, Entity development team
+Copyright (c) 2026, Entity development team
 
 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met:
diff --git a/nt2/utils.py b/nt2/utils.py
index 12ea889..f6d7900 100644
--- a/nt2/utils.py
+++ b/nt2/utils.py
@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Union, Literal
 from enum import Enum
 import os
 import re
@@ -26,6 +26,19 @@ class CoordinateSystem(Enum):
     XYZ = "Cartesian"
     SPH = "Spherical"
 
+    @staticmethod
+    def from_str(s):
+        s = s.lower()
+        if s in ("cartesian", "xyz"):
+            return CoordinateSystem.XYZ
+        elif s in ("spherical", "sph"):
+            return CoordinateSystem.SPH
+        else:
+            raise ValueError(f"Unknown coordinate system: {s}")
+
+
+CoordinateSystemType = Literal["XYZ", "SPH"]
+
 
 def DetermineDataFormat(path: str) -> Format:
     """Determine the data format for the files in the given path.

From 48a4eec9160fdeb87c5b26f233ecf62c96f0d206 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Wed, 15 Jul 2026 17:43:22 -0400
Subject: [PATCH 11/14] speed up valid time/step reading + explicit
 timerange/steprange options

---
 nt2/containers/base.py                        | 112 ++++++++++-
 nt2/containers/data.py                        |  23 ++-
 nt2/containers/fields.py                      |  39 +---
 nt2/containers/particles.py                   |  57 +++---
 nt2/containers/spectra.py                     |  35 +---
 nt2/readers/adios2.py                         |  40 +++-
 nt2/readers/base.py                           | 180 +++++++++++-------
 nt2/readers/hdf5.py                           |  46 ++++-
 nt2/tests/test_containers.py                  |   4 +-
 .../test_particle_container_serialization.py  |  34 ++++
 nt2/tests/test_reader.py                      |  45 ++---
 nt2/tests/test_tokenize.py                    |  10 +
 12 files changed, 442 insertions(+), 183 deletions(-)
 create mode 100644 nt2/tests/test_particle_container_serialization.py

diff --git a/nt2/containers/base.py b/nt2/containers/base.py
index 7e2a943..8c5b758 100644
--- a/nt2/containers/base.py
+++ b/nt2/containers/base.py
@@ -1,5 +1,8 @@
 from typing import Callable, Optional, Dict, Tuple, List, Union
 
+import numpy.typing as npt
+import numpy as np
+
 from ..readers.base import BaseReader
 from ..utils import CoordinateSystem
 
@@ -8,20 +11,29 @@ class BaseContainer:
     """Parent container class for holding any category data."""
 
     __path: str
+    __category: str
     __reader: BaseReader
     __verify: bool
+    __timerange: Optional[Tuple[Union[float, None], Union[float, None]]]
+    __steprange: Optional[Tuple[Union[int, None], Union[int, None]]]
     __remap: Optional[Dict[str, Callable[[str], str]]]
     __coordinate_system: Optional[CoordinateSystem]
     __num_cpus: Optional[int]
 
-    valid_steps: List[int] = []
-    valid_files: List[str] = []
+    __valid_steps: List[int] = []
+    __valid_files: List[str] = []
+
+    __times: npt.NDArray = np.array([])
+    __steps: npt.NDArray = np.array([])
 
     def __init__(
         self,
         path: str,
+        category: str,
         reader: BaseReader,
         verify: bool = False,
+        timerange: Union[Tuple[Union[float, None], Union[float, None]], None] = None,
+        steprange: Union[Tuple[Union[int, None], Union[int, None]], None] = None,
         remap: Union[Dict[str, Callable[[str], str]], None] = None,
         coord_system: Union[CoordinateSystem, None] = None,
         num_cpus: Union[int, None] = None,
@@ -32,10 +44,16 @@ def __init__(
         ----------
         path : str
             The path to the data.
+        category : str
+            The category of the data.
         reader : BaseReader
             The reader to be used for reading the data.
         verify : Optional[bool]
             Whether to verify the data. If None, it will use the reader's default.
+        timerange : Optional[Tuple[Union[float, None], Union[float, None]]]
+            Time range to load. If None, all times will be loaded.
+        steprange : Optional[Tuple[Union[int, None], Union[int, None]]]
+            Step range to load. If None, all steps will be loaded.
         remap : Optional[dict[str, Callable[[str], str]]]
             Remap dictionary to use to remap the data names (coords, fields, etc.).
         coord_system : Optional[CoordinateSystem]
@@ -45,22 +63,40 @@ def __init__(
 
         """
         self.__path = path
+        self.__category = category
         self.__reader = reader
+        self.__timerange = timerange
+        self.__steprange = steprange
         self.__verify = verify
         self.__remap = remap
         self.__coordinate_system = coord_system
         self.__num_cpus = num_cpus
 
+        self.__valid_files, self.__valid_steps = self.__reader.GetValidFilesAndSteps(
+            self.path,
+            self.category,
+            self.steprange,
+            self.num_cpus,
+        )
+        self.__times, self.__steps = self.read_times_and_steps()
+        if self.steprange is None:
+            self.narrow_timerange()
+
     @property
     def path(self) -> str:
         """str: The main path of the data."""
         return self.__path
 
+    @property
+    def category(self) -> str:
+        """str: The category of the data."""
+        return self.__category
+
     @property
     def reader(self) -> BaseReader:
         """BaseReader: The reader used to read the data."""
         return self.__reader
-    
+
     @property
     def verify(self) -> bool:
         """bool: Whether to verify the data."""
@@ -81,6 +117,76 @@ def num_cpus(self) -> Optional[int]:
         """int: The number of CPUs to use for parallel processing."""
         return self.__num_cpus
 
+    @property
+    def timerange(self) -> Optional[Tuple[Union[float, None], Union[float, None]]]:
+        """tuple[float | None, float | None]: The time range of the data."""
+        return self.__timerange
+
+    @property
+    def steprange(self) -> Optional[Tuple[Union[int, None], Union[int, None]]]:
+        """tuple[int | None, int | None]: The step range of the data."""
+        return self.__steprange
+
+    @property
+    def valid_files(self) -> List[str]:
+        """list[str]: The valid files of the data."""
+        return self.__valid_files
+
+    @property
+    def valid_steps(self) -> List[int]:
+        """list[int]: The valid steps of the data."""
+        return self.__valid_steps
+
+    @property
+    def times(self) -> npt.NDArray:
+        """npt.NDArray: The times of the data."""
+        return self.__times
+
+    @property
+    def steps(self) -> npt.NDArray:
+        """npt.NDArray: The steps of the data."""
+        return self.__steps
+
+    def read_times_and_steps(self) -> Tuple[npt.NDArray, npt.NDArray]:
+        """Reads the times and steps for the given category.
+
+        Parameters
+        ----------
+        category : str
+            The category to read the times and steps for.
+
+        Returns
+        -------
+        tuple[npt.NDArray, npt.NDArray]
+            A tuple containing the times and steps for the given category.
+
+        """
+        vars = self.reader.ReadPerTimestepVariables(
+            path=self.path,
+            category=self.category,
+            varnames=["Time", "Step"],
+            newnames=["t", "s"],
+            valid_files=self.valid_files,
+        )
+        return (vars["t"], vars["s"])
+
+    def narrow_timerange(self):
+        if self.timerange is not None:
+            start_time, end_time = self.timerange
+            start_idx, end_idx = 0, -1
+            if start_time is None:
+                start_idx = 0
+            else:
+                start_idx = int(np.searchsorted(self.__times, start_time))
+            if end_time is None:
+                end_idx = len(self.__times) - 1
+            else:
+                end_idx = int(np.searchsorted(self.__times, end_time))
+            self.__times = self.__times[start_idx : end_idx + 1]
+            self.__steps = self.__steps[start_idx : end_idx + 1]
+            self.__valid_files = self.__valid_files[start_idx : end_idx + 1]
+            self.__valid_steps = self.__valid_steps[start_idx : end_idx + 1]
+
     def set_remap(self, remap: Dict[str, Callable[[str], str]]) -> None:
         """Set the remap dictionary for the container.
 
diff --git a/nt2/containers/data.py b/nt2/containers/data.py
index 26b2a74..abefcd2 100644
--- a/nt2/containers/data.py
+++ b/nt2/containers/data.py
@@ -1,4 +1,4 @@
-from typing import Callable, Any, Union, Optional, List, Dict
+from typing import Callable, Any, Union, Optional, List, Dict, Tuple
 
 import os
 
@@ -53,6 +53,8 @@ def __init__(
         spectra: bool = True,
         diagnostics: bool = False,
         verify: bool = False,
+        timerange: Optional[Tuple[Union[float, None], Union[float, None]]] = None,
+        steprange: Optional[Tuple[Union[int, None], Union[int, None]]] = None,
         reader: Optional[BaseReader] = None,
         remap: Optional[Dict[str, Callable[[str], str]]] = None,
         coord_system: Optional[CoordinateSystemType] = None,
@@ -76,6 +78,10 @@ def __init__(
             Whether to load the diagnostics component. Default is False.
         verify : bool, optional
             Whether to verify the data. Default is False.
+        timerange : tuple[float | None, float | None], optional
+            Time range to load. If None, all times will be loaded.
+        steprange : tuple[int | None, int | None], optional
+            Step range to load. If None, all steps will be loaded.
         reader : BaseReader, optional
             Reader to use to read the data. If None, it will be determined
             based on the file format.
@@ -117,6 +123,8 @@ def __init__(
                 path=path,
                 reader=__reader,
                 verify=verify,
+                timerange=timerange,
+                steprange=steprange,
                 remap=remap,
                 coord_system=CoordinateSystem.from_str(coord_system)
                 if coord_system
@@ -128,6 +136,8 @@ def __init__(
                 path=path,
                 reader=__reader,
                 verify=verify,
+                timerange=timerange,
+                steprange=steprange,
                 remap=remap,
                 coord_system=CoordinateSystem.from_str(coord_system)
                 if coord_system
@@ -139,6 +149,8 @@ def __init__(
                 path=path,
                 reader=__reader,
                 verify=verify,
+                timerange=timerange,
+                steprange=steprange,
                 remap=remap,
                 coord_system=CoordinateSystem.from_str(coord_system)
                 if coord_system
@@ -255,14 +267,19 @@ def makeMovie(
         if time is None:
             raise ValueError("No time values found.")
         name: str = ""
-        if self.attrs.get("simulation.name", None) is None:
-            name = movie_kwargs.pop("name", "movie")
+        provided_name = movie_kwargs.pop("name", None)
+        if provided_name is not None:
+            name = provided_name
+        elif self.attrs.get("simulation.name", None) is None:
+            name = provided_name
         else:
             name_b = self.attrs.get("simulation.name")
             if isinstance(name_b, bytes):
                 name = name_b.decode("utf-8")
             else:
                 name = str(name_b)
+        if name is None:
+            raise ValueError("No name provided for the movie.")
         return makeFramesAndMovie(
             name=name,
             data=self,
diff --git a/nt2/containers/fields.py b/nt2/containers/fields.py
index 0cf2c75..a630e3f 100644
--- a/nt2/containers/fields.py
+++ b/nt2/containers/fields.py
@@ -65,18 +65,7 @@ def __init__(
             Keyword arguments to be passed to the parent BaseContainer class.
 
         """
-        super().__init__(**kwargs)
-        self.valid_files = self.reader.GetValidFiles(
-            path=self.path,
-            category="fields",
-            num_cpus=self.num_cpus,
-        )
-        self.valid_steps = self.reader.GetValidSteps(
-            path=self.path,
-            category="fields",
-            num_cpus=self.num_cpus,
-        )
-
+        super().__init__(category="fields", **kwargs)
         if self.reader.DefinesCategory(self.path, "fields", self.valid_files):
             self.__fields_defined = True
             self.__fields = self._read_fields()
@@ -189,21 +178,6 @@ def _read_fields(self) -> xr.Dataset:
                 new_coords[self.remap["coords"](coord)] = coords[coord]
             coords = new_coords
 
-        times = self.reader.ReadPerTimestepVariable(
-            self.path,
-            "fields",
-            "Time",
-            "t",
-            self.valid_files,
-        )
-        steps = self.reader.ReadPerTimestepVariable(
-            self.path,
-            "fields",
-            "Step",
-            "s",
-            self.valid_files,
-        )
-
         edge_coords = self.reader.ReadEdgeCoordsAtTimestep(self.path, first_step)
         new_edge_coords = {}
         for coord in edge_coords.keys():
@@ -216,8 +190,8 @@ def _read_fields(self) -> xr.Dataset:
             new_edge_coords[assoc_x + "_max"] = (assoc_x, edge_coords[coord][1:])
         edge_coords = new_edge_coords
 
-        all_dims = {**times, **coords}.keys()
-        all_coords = {**times, **coords, "s": ("t", steps["s"]), **edge_coords}
+        all_dims = {"t": self.times, **coords}.keys()
+        all_coords = {"t": self.times, **coords, "s": ("t", self.steps), **edge_coords}
 
         return xr.Dataset(
             {
@@ -248,7 +222,12 @@ def _read_fields(self) -> xr.Dataset:
                     dims=all_dims,
                     coords=all_coords,
                 )
-                for name in tqdm(field_names, desc="fields", position=0, leave=False)
+                for name in tqdm(
+                    field_names,
+                    desc="fields",
+                    position=0,
+                    leave=False,
+                )
             },
             attrs=attributes,
         )
diff --git a/nt2/containers/particles.py b/nt2/containers/particles.py
index 4c740b6..31a3623 100644
--- a/nt2/containers/particles.py
+++ b/nt2/containers/particles.py
@@ -49,9 +49,40 @@ class ParticleContainer(BaseContainer):
 
     def __getstate__(self) -> dict[str, Any]:
         state = self.__dict__.copy()
+        particles = state.get("_ParticleContainer__particles")
+        if particles is not None:
+            state["_ParticleContainer__particle_dataset_state"] = {
+                "species": particles.species,
+                "steps": particles.steps,
+                "times": particles.times,
+                "colnames": particles.colnames,
+                "fprec": particles.fprec,
+                "selection": particles.selection,
+                "partition_lengths": particles._partition_lengths,
+            }
         state.pop("_ParticleContainer__particles", None)
         return state
 
+    def __setstate__(self, state: dict[str, Any]) -> None:
+        particle_dataset_state = state.pop(
+            "_ParticleContainer__particle_dataset_state", None
+        )
+        self.__dict__.update(state)
+        if self.__particles_defined:
+            if particle_dataset_state is None:
+                (
+                    self.quantities,
+                    self.sp_with_idx,
+                    self.sp_without_idx,
+                    self.attributes,
+                    self.__particles,
+                ) = self._read_particles()
+            else:
+                self.__particles = ParticleDataset(
+                    **particle_dataset_state,
+                    read_column=self._read_column,
+                )
+
     def __init__(self, **kwargs: Any) -> None:
         """Initializer for the ParticleContainer class.
 
@@ -61,17 +92,7 @@ def __init__(self, **kwargs: Any) -> None:
             Keyword arguments to be passed to the parent BaseContainer class.
 
         """
-        super().__init__(**kwargs)
-        self.valid_files = self.reader.GetValidFiles(
-            path=self.path,
-            category="particles",
-            num_cpus=self.num_cpus,
-        )
-        self.valid_steps = self.reader.GetValidSteps(
-            path=self.path,
-            category="particles",
-            num_cpus=self.num_cpus,
-        )
+        super().__init__(category="particles", **kwargs)
 
         # @TODO: parallelize
         self.quantity_names_by_step = {
@@ -131,16 +152,6 @@ def _read_particles(self):
             for step in self.valid_steps
         )
 
-        # save times and steps
-        steps = np.array(self.valid_steps)
-        times = self.reader.ReadPerTimestepVariable(
-            self.path,
-            "particles",
-            "Time",
-            "t",
-            self.valid_files,
-        )["t"]
-
         # determine coordinate system and remap functions
         first_step = self.valid_steps[0]
         attributes = self.reader.ReadAttrsAtTimestep(
@@ -176,8 +187,8 @@ def _read_particles(self):
             attributes,
             ParticleDataset(
                 species=all_species,
-                steps=steps,
-                times=times,
+                steps=self.steps,
+                times=self.times,
                 colnames=[
                     (
                         self.remap["particles"](q)
diff --git a/nt2/containers/spectra.py b/nt2/containers/spectra.py
index 93b5c41..68f2148 100644
--- a/nt2/containers/spectra.py
+++ b/nt2/containers/spectra.py
@@ -16,17 +16,7 @@ class SpectraContainer(BaseContainer):
     __spectra: Union[xr.Dataset, None] = None
 
     def __init__(self, **kwargs: Any) -> None:
-        super().__init__(**kwargs)
-        self.valid_files = self.reader.GetValidFiles(
-            path=self.path,
-            category="spectra",
-            num_cpus=self.num_cpus,
-        )
-        self.valid_steps = self.reader.GetValidSteps(
-            path=self.path,
-            category="spectra",
-            num_cpus=self.num_cpus,
-        )
+        super().__init__(category="spectra", **kwargs)
 
         if self.reader.DefinesCategory(
             self.path,
@@ -84,20 +74,6 @@ def _read_spectra(self) -> xr.Dataset:
         shape = self.reader.ReadArrayShapeExplicitlyAtTimestep(
             self.path, "spectra", first_spectrum_name, first_step
         )
-        times = self.reader.ReadPerTimestepVariable(
-            self.path,
-            "spectra",
-            "Time",
-            "t",
-            self.valid_files,
-        )
-        steps = self.reader.ReadPerTimestepVariable(
-            self.path,
-            "spectra",
-            "Step",
-            "s",
-            self.valid_files,
-        )
 
         ebins = self.reader.ReadArrayAtTimestep(
             self.path, "spectra", ebin_name, first_step
@@ -109,8 +85,8 @@ def _read_spectra(self) -> xr.Dataset:
         else:
             ebins = (ebins[1:] * ebins[:-1]) ** 0.5
 
-        all_dims = {**times, "E": ebins}
-        all_coords = {**all_dims, "s": ("t", steps["s"])}
+        all_dims = {"t": self.times, "E": ebins}
+        all_coords = {**all_dims, "s": ("t", self.steps)}
 
         attributes = self.reader.ReadAttrsAtTimestep(
             path=self.path, category="spectra", step=first_step
@@ -145,7 +121,10 @@ def remap_name(name: str) -> str:
                     coords=all_coords,
                 )
                 for spectrum in tqdm(
-                    spectra_names, desc="spectra", position=0, leave=False
+                    spectra_names,
+                    desc="spectra",
+                    position=0,
+                    leave=False,
                 )
             },
             attrs=attributes,
diff --git a/nt2/readers/adios2.py b/nt2/readers/adios2.py
index 4cb7ffe..f63d202 100644
--- a/nt2/readers/adios2.py
+++ b/nt2/readers/adios2.py
@@ -1,6 +1,7 @@
 from typing import Any, List, Dict, Tuple, Set
 
 import sys
+from tqdm import tqdm
 
 if sys.version_info >= (3, 12):
     from typing import override
@@ -44,7 +45,12 @@ def ReadPerTimestepVariable(
         valid_files: List[str],
     ) -> Dict[str, npt.NDArray[Any]]:
         variables: List[float] = []
-        for filename in valid_files:
+        for filename in tqdm(
+            valid_files,
+            desc=f"Reading {category} {varname}",
+            position=0,
+            leave=False,
+        ):
             with bp.FileReader(os.path.join(path, category, filename)) as f:
                 avail: Dict[str, Any] = f.available_variables()
                 vars: List[str] = list(avail.keys())
@@ -60,6 +66,38 @@ def ReadPerTimestepVariable(
                     raise ValueError(f"{varname} not found in the BP file {filename}")
         return {newname: np.array(variables)}
 
+    @override
+    def ReadPerTimestepVariables(
+        self,
+        path: str,
+        category: str,
+        varnames: List[str],
+        newnames: List[str],
+        valid_files: List[str],
+    ) -> Dict[str, npt.NDArray[Any]]:
+        variables = {newname: [] for newname in newnames}
+        for filename in tqdm(
+            valid_files,
+            desc=f"Reading {category} {varnames}",
+            position=0,
+            leave=False,
+        ):
+            with bp.FileReader(os.path.join(path, category, filename)) as f:
+                avail: Dict[str, Any] = f.available_variables()
+                vars: List[str] = list(avail.keys())
+                for varname, newname in zip(varnames, newnames):
+                    if varname in vars:
+                        var = f.inquire_variable(varname)
+                        if var is not None:
+                            variables[newname].append(f.read(var))
+                        else:
+                            raise ValueError(
+                                f"{varname} is not a variable in the BP file {filename}"
+                            )
+                    else:
+                        raise ValueError(f"{varname} not found in the BP file {filename}")
+        return {newname: np.array(variables[newname]) for newname in newnames}
+
     @override
     def ReadParticleCountsAtTimestep(
         self, path: str, step: int, species: List[int]
diff --git a/nt2/readers/base.py b/nt2/readers/base.py
index ae06489..bee5349 100644
--- a/nt2/readers/base.py
+++ b/nt2/readers/base.py
@@ -1,5 +1,5 @@
 from concurrent.futures import as_completed
-from typing import Any, Callable, Dict, List, Optional, Set, Tuple
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
 import os
 import re
 import logging
@@ -134,6 +134,37 @@ def ReadPerTimestepVariable(
         """
         raise NotImplementedError("ReadPerTimestepVariable is not implemented")
 
+    def ReadPerTimestepVariables(
+        self,
+        path: str,
+        category: str,
+        varnames: List[str],
+        newnames: List[str],
+        valid_files: List[str],
+    ) -> Dict[str, npt.NDArray[Any]]:
+        """Read multiple variables at each timestep and return a dictionary with the new names.
+
+        Parameters
+        ----------
+        path : str
+            The path to the files.
+        category : str
+            The category of the files.
+        varnames : list[str]
+            The names of the variables to be read.
+        newnames : list[str]
+            The new names of the variables to be returned.
+        valid_files : list[str]
+            The valid files to be read.
+
+        Returns
+        -------
+        dict[str, NDArray[Any]]
+            A dictionary with the new names and the variables at each timestep.
+
+        """
+        raise NotImplementedError("ReadPerTimestepVariables is not implemented")
+
     def ReadParticleCountsAtTimestep(
         self,
         path: str,
@@ -443,13 +474,73 @@ def FullPath(self, path: str, category: str, step: int) -> str:
             path, category, f"{category}.{step:08d}.{self.format.value}"
         )
 
-    def GetValidSteps(
+    # def GetValidSteps(
+    #     self,
+    #     path: str,
+    #     category: str,
+    #     num_cpus: Optional[int] = None,
+    # ) -> List[int]:
+    #     """Get valid timesteps (sorted) in a given path and category.
+
+    #     Parameters
+    #     ----------
+    #     path : str
+    #         The path to the files.
+    #     category : str
+    #         The category of the files.
+    #     num_cpus : Optional[int]
+    #         The number of CPU cores to use for parallel processing.
+
+    #     Returns
+    #     -------
+    #     list[int]
+    #         A list of valid timesteps in the given path and category.
+
+    #     """
+    #     category_files = BaseReader.CategoryFiles(
+    #         path=path,
+    #         category=category,
+    #         format=self.format.value,
+    #     )
+    #     num_cpus = num_cpus if num_cpus is not None else (os.cpu_count() or 1)
+    #     executor = get_reusable_executor(max_workers=num_cpus)
+    #     futures = {
+    #         executor.submit(
+    #             _check_file,
+    #             self.EnterFile,
+    #             os.path.join(path, category, filename),
+    #         ): filename
+    #         for filename in category_files
+    #     }
+
+    #     steps: List[int] = []
+    #     for future in tqdm(
+    #         as_completed(futures),
+    #         total=len(futures),
+    #         desc=f"getting valid steps for {category}",
+    #         leave=False,
+    #     ):
+    #         filename = futures[future]
+    #         try:
+    #             future.result()
+    #             steps.append(int(filename.split(".")[1]))
+    #         except OSError:
+    #             if filename not in self.skipped_files:
+    #                 self.skipped_files.append(filename)
+    #                 logging.warning(f"Could not read {filename}, skipping it")
+    #         except Exception as e:
+    #             raise e
+    #     steps.sort()
+    #     return steps
+
+    def GetValidFilesAndSteps(
         self,
         path: str,
         category: str,
+        steprange: Optional[Tuple[Union[int, None], Union[int, None]]] = None,
         num_cpus: Optional[int] = None,
-    ) -> List[int]:
-        """Get valid timesteps (sorted) in a given path and category.
+    ) -> Tuple[List[str], List[int]]:
+        """Get valid files (sorted by timestep) and steps in a given path and category.
 
         Parameters
         ----------
@@ -457,13 +548,15 @@ def GetValidSteps(
             The path to the files.
         category : str
             The category of the files.
+        steprange : Optional[tuple[int | None, int | None]]
+            The range of timesteps to be considered. If None, all timesteps are considered.
         num_cpus : Optional[int]
             The number of CPU cores to use for parallel processing.
 
         Returns
         -------
-        list[int]
-            A list of valid timesteps in the given path and category.
+        tuple[list[str], list[int]]
+            A tuple containing a list of valid files and a list of valid timesteps in the given path and category.
 
         """
         category_files = BaseReader.CategoryFiles(
@@ -473,65 +566,18 @@ def GetValidSteps(
         )
         num_cpus = num_cpus if num_cpus is not None else (os.cpu_count() or 1)
         executor = get_reusable_executor(max_workers=num_cpus)
-        futures = {
-            executor.submit(
-                _check_file,
-                self.EnterFile,
-                os.path.join(path, category, filename),
-            ): filename
-            for filename in category_files
-        }
-
-        steps: List[int] = []
-        for future in tqdm(
-            as_completed(futures),
-            total=len(futures),
-            desc=f"getting valid steps for {category}",
-            leave=False,
-        ):
-            filename = futures[future]
-            try:
-                future.result()
-                steps.append(int(filename.split(".")[1]))
-            except OSError:
-                if filename not in self.skipped_files:
-                    self.skipped_files.append(filename)
-                    logging.warning(f"Could not read {filename}, skipping it")
-            except Exception as e:
-                raise e
-        steps.sort()
-        return steps
-
-    def GetValidFiles(
-        self,
-        path: str,
-        category: str,
-        num_cpus: Optional[int] = None,
-    ) -> List[str]:
-        """Get valid files (sorted by timestep) in a given path and category.
-
-        Parameters
-        ----------
-        path : str
-            The path to the files.
-        category : str
-            The category of the files.
-        num_cpus : Optional[int]
-            The number of CPU cores to use for parallel processing.
 
-        Returns
-        -------
-        list[str]
-            A list of valid files in the given path and category.
+        def is_inrange(filename: str) -> bool:
+            step = int(filename.split(".")[1])
+            if steprange is None:
+                return True
+            start, end = steprange
+            if start is not None and step < start:
+                return False
+            if end is not None and step >= end:
+                return False
+            return True
 
-        """
-        category_files = BaseReader.CategoryFiles(
-            path=path,
-            category=category,
-            format=self.format.value,
-        )
-        num_cpus = num_cpus if num_cpus is not None else (os.cpu_count() or 1)
-        executor = get_reusable_executor(max_workers=num_cpus)
         futures = {
             executor.submit(
                 _check_file,
@@ -539,19 +585,22 @@ def GetValidFiles(
                 os.path.join(path, category, filename),
             ): filename
             for filename in category_files
+            if is_inrange(filename)
         }
 
         files: List[str] = []
+        steps: List[int] = []
         for future in tqdm(
             as_completed(futures),
             total=len(futures),
-            desc=f"getting valid files for {category}",
+            desc=f"getting valid files & steps for {category}",
             leave=False,
         ):
             filename = futures[future]
             try:
                 future.result()
                 files.append(filename)
+                steps.append(int(filename.split(".")[1]))
             except OSError:
                 if filename not in self.skipped_files:
                     self.skipped_files.append(filename)
@@ -559,7 +608,8 @@ def GetValidFiles(
             except Exception as e:
                 raise e
         files.sort(key=lambda x: int(x.split(".")[1]))
-        return files
+        steps.sort()
+        return (files, steps)
 
     def VerifySameCategoryNames(
         self,
diff --git a/nt2/readers/hdf5.py b/nt2/readers/hdf5.py
index 14d9df1..4c24226 100644
--- a/nt2/readers/hdf5.py
+++ b/nt2/readers/hdf5.py
@@ -3,6 +3,7 @@
 from typing import Any, TYPE_CHECKING, List, Dict, Tuple, Set
 
 import sys
+from tqdm import tqdm
 
 if sys.version_info >= (3, 12):
     from typing import override
@@ -75,7 +76,12 @@ def ReadPerTimestepVariable(
     ) -> Dict[str, npt.NDArray[Any]]:
         variables: List[Any] = []
         h5 = _require_h5py()
-        for filename in valid_files:
+        for filename in tqdm(
+            valid_files,
+            desc=f"Reading {category}/{varname}",
+            position=0,
+            leave=False,
+        ):
             with h5.File(os.path.join(path, category, filename), "r") as f:
                 f0 = Reader.__extract_step0(f)
                 if varname in f0.keys():
@@ -91,6 +97,41 @@ def ReadPerTimestepVariable(
 
         return {newname: np.array(variables)}
 
+    @override
+    def ReadPerTimestepVariables(
+        self,
+        path: str,
+        category: str,
+        varnames: List[str],
+        newnames: List[str],
+        valid_files: List[str],
+    ) -> Dict[str, npt.NDArray[Any]]:
+        variables = {newname: [] for newname in newnames}
+        h5 = _require_h5py()
+        for filename in tqdm(
+            valid_files,
+            desc=f"Reading {category} {varnames}",
+            position=0,
+            leave=False,
+        ):
+            with h5.File(os.path.join(path, category, filename), "r") as f:
+                f0 = Reader.__extract_step0(f)
+                for varname, newname in zip(varnames, newnames):
+                    if varname in f0.keys():
+                        var = f0[varname]
+                        if isinstance(var, h5.Dataset):
+                            variables[newname].append(var[()])
+                        else:
+                            raise ValueError(
+                                f"{varname} is not a group in the HDF5 file {filename}"
+                            )
+                    else:
+                        raise ValueError(
+                            f"{varname} not found in the HDF5 file {filename}"
+                        )
+
+        return {newname: np.array(variables[newname]) for newname in newnames}
+
     @override
     def ReadParticleCountsAtTimestep(
         self, path: str, step: int, species: List[int]
@@ -101,8 +142,7 @@ def ReadParticleCountsAtTimestep(
             f0 = Reader.__extract_step0(f)
             return {
                 sp: int(f0[name].shape[0])
-                if (name := f"pX1_{sp}") in f0
-                and isinstance(f0[name], h5.Dataset)
+                if (name := f"pX1_{sp}") in f0 and isinstance(f0[name], h5.Dataset)
                 else 0
                 for sp in species
             }
diff --git a/nt2/tests/test_containers.py b/nt2/tests/test_containers.py
index c773d7e..5994100 100644
--- a/nt2/tests/test_containers.py
+++ b/nt2/tests/test_containers.py
@@ -82,7 +82,9 @@ def field_remap(Fold: str):
             num_cpus=1,
         )
 
-    steps = reader.GetValidSteps(path=path, category="fields", num_cpus=1)
+    _, steps = reader.GetValidFilesAndSteps(
+        path=path, category="fields", num_cpus=1
+    )
     nx1 = test["fields"]["nx1"]
     nx2 = test["fields"]["nx2"]
     assert fields.fields is not None, "Fields are None"
diff --git a/nt2/tests/test_particle_container_serialization.py b/nt2/tests/test_particle_container_serialization.py
new file mode 100644
index 0000000..768a723
--- /dev/null
+++ b/nt2/tests/test_particle_container_serialization.py
@@ -0,0 +1,34 @@
+import pickle
+
+import numpy as np
+
+from nt2.containers.particle_dataset import ParticleDataset
+from nt2.containers.particles import ParticleContainer
+
+
+class _RestoringParticleContainer(ParticleContainer):
+    def _read_particles(self):
+        raise AssertionError("deserialization should not rescan particle files")
+
+    def _read_column(self, step, colname):
+        return np.array([], dtype=np.int64)
+
+
+def test_particle_dataset_is_restored_after_serialization():
+    container = _RestoringParticleContainer.__new__(_RestoringParticleContainer)
+    container.__dict__["_ParticleContainer__particles_defined"] = True
+    container.__dict__["_ParticleContainer__particles"] = ParticleDataset(
+        species=[1],
+        steps=np.array([10]),
+        times=np.array([2.5]),
+        colnames=["x", "id", "sp"],
+        read_column=container._read_column,
+        partition_lengths=(0,),
+    )
+
+    restored = pickle.loads(pickle.dumps(container))
+
+    assert restored.particles_defined
+    assert restored.particles is not None
+    assert restored.particles.species == [1]
+    assert restored.particles.times.tolist() == [2.5]
diff --git a/nt2/tests/test_reader.py b/nt2/tests/test_reader.py
index d556e39..a0d6582 100644
--- a/nt2/tests/test_reader.py
+++ b/nt2/tests/test_reader.py
@@ -92,23 +92,20 @@ def test_reader(test):
                 OSError,
             )
 
-        valid_files = reader.GetValidFiles(path=PATH, category="fields")
+        valid_files, valid_steps = reader.GetValidFilesAndSteps(
+            path=PATH, category="fields"
+        )
 
         # Check that timesteps are read correctly from fields
-        times = reader.ReadPerTimestepVariable(
-            path=PATH,
-            category="fields",
-            varname="Time",
-            newname="t",
-            valid_files=valid_files,
-        )["t"]
-        steps = reader.ReadPerTimestepVariable(
+        timestep_variables = reader.ReadPerTimestepVariables(
             path=PATH,
             category="fields",
-            varname="Step",
-            newname="s",
+            varnames=["Time", "Step"],
+            newnames=["t", "s"],
             valid_files=valid_files,
-        )["s"]
+        )
+        times = timestep_variables["t"]
+        steps = timestep_variables["s"]
         check_equal_arrays(
             times,
             np.array([s * dt for s in steps]),
@@ -149,7 +146,6 @@ def test_reader(test):
                 shape, (nx1, nx2, nx3) if layout == Layout.R else (nx3, nx2, nx1)
             )
 
-        valid_steps = reader.GetValidSteps(path=PATH, category="fields")
         for step in valid_steps:
             for f in field_names:
                 field = reader.ReadArrayAtTimestep(
@@ -203,22 +199,19 @@ def test_reader(test):
         )
         prtl_names = set(f"p{p}" for p in prtl_names)
 
-        valid_files = reader.GetValidFiles(path=PATH, category="particles")
+        valid_files, valid_steps = reader.GetValidFilesAndSteps(
+            path=PATH, category="particles"
+        )
         # Check that timesteps are read correctly from particles
-        times = reader.ReadPerTimestepVariable(
+        timestep_variables = reader.ReadPerTimestepVariables(
             path=PATH,
             category="particles",
-            varname="Time",
-            newname="t",
+            varnames=["Time", "Step"],
+            newnames=["t", "s"],
             valid_files=valid_files,
-        )["t"]
-        steps = reader.ReadPerTimestepVariable(
-            path=PATH,
-            category="particles",
-            varname="Step",
-            newname="s",
-            valid_files=valid_files,
-        )["s"]
+        )
+        times = timestep_variables["t"]
+        steps = timestep_variables["s"]
 
         if dt is not None:
             check_equal_arrays(
@@ -234,7 +227,7 @@ def test_reader(test):
         check_equal_arrays(names, prtl_names)
 
         # Check prtl shapes
-        for step in reader.GetValidSteps(path=PATH, category="particles"):
+        for step in valid_steps:
             for sp in range(nspec):
                 reader.ReadArrayShapeAtTimestep(
                     path=PATH,
diff --git a/nt2/tests/test_tokenize.py b/nt2/tests/test_tokenize.py
index 10e46b3..be73014 100644
--- a/nt2/tests/test_tokenize.py
+++ b/nt2/tests/test_tokenize.py
@@ -1,3 +1,4 @@
+import numpy as np
 from dask.base import tokenize
 
 from nt2.containers.base import BaseContainer
@@ -10,10 +11,19 @@ class _Reader(BaseReader):
     def format(self) -> Format:
         return Format.HDF5
 
+    def GetValidFilesAndSteps(self, path, category, steprange=None, num_cpus=None):
+        return (["fields.00000001.h5"], [1])
+
+    def ReadPerTimestepVariables(
+        self, path, category, varnames, newnames, valid_files
+    ):
+        return {"t": np.array([0.5]), "s": np.array([1])}
+
 
 def test_base_container_has_deterministic_dask_token():
     container = BaseContainer(
         path="/tmp/sim",
+        category="fields",
         reader=_Reader(),
         remap=None,
         coord_system=None,

From 6b1597d30c7591f44a9dbab9e11381e7f99e8188 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Tue, 15 Sep 2026 17:12:13 -0400
Subject: [PATCH 12/14] support for spatially binned spectra & deprecated
 annotations rm

---
 .gitignore                         |   8 +-
 devenv.lock                        |  65 +++++++++++
 devenv.nix                         |  43 ++++++++
 devenv.yaml                        |   4 +
 legacy/containers/data.py          |  39 ++++---
 nt2/__init__.py                    |   6 +-
 nt2/cli/main.py                    |  31 +++---
 nt2/containers/base.py             |  58 +++++-----
 nt2/containers/data.py             |  82 +++++++-------
 nt2/containers/diagnostics.py      |  30 +++---
 nt2/containers/fields.py           |  22 ++--
 nt2/containers/particle_dataset.py |  98 ++++++++---------
 nt2/containers/particles.py        |  44 ++++----
 nt2/containers/spectra.py          |  95 +++++++++++++---
 nt2/plotters/annotations.py        |  23 +++-
 nt2/plotters/export.py             |  29 +++--
 nt2/plotters/inspect.py            | 101 +++++++++--------
 nt2/plotters/movie.py              |  22 ++--
 nt2/plotters/particles.py          |  25 ++---
 nt2/plotters/polar.py              |  16 +--
 nt2/readers/adios2.py              |  68 ++++++------
 nt2/readers/base.py                | 168 ++++++++++-------------------
 nt2/readers/hdf5.py                |  63 +++++------
 nt2/tests/test_cli.py              |   4 +-
 nt2/tests/test_containers.py       |  26 +++--
 nt2/utils.py                       |  12 ++-
 26 files changed, 689 insertions(+), 493 deletions(-)
 create mode 100644 devenv.lock
 create mode 100644 devenv.nix
 create mode 100644 devenv.yaml

diff --git a/.gitignore b/.gitignore
index 430467a..5567ed1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -160,4 +160,10 @@ temp/
 # LLM
 .codex
 .agents
-.claude
\ No newline at end of file
+.claude
+
+# devenv
+.devenv*
+devenv.local.nix
+devenv.local.yaml
+.direnv
diff --git a/devenv.lock b/devenv.lock
new file mode 100644
index 0000000..85e76bb
--- /dev/null
+++ b/devenv.lock
@@ -0,0 +1,65 @@
+{
+  "nodes": {
+    "devenv": {
+      "locked": {
+        "dir": "src/modules",
+        "lastModified": 1789480904,
+        "narHash": "sha256-anHWHTkxI7423/eUroN9Rc4+H8VIGyIUWq/hV1egBDQ=",
+        "owner": "cachix",
+        "repo": "devenv",
+        "rev": "0fd5e6d3a9b2ae6f10205a3e95e0ca58d526c5a6",
+        "type": "github"
+      },
+      "original": {
+        "dir": "src/modules",
+        "owner": "cachix",
+        "repo": "devenv",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "inputs": {
+        "nixpkgs-src": "nixpkgs-src"
+      },
+      "locked": {
+        "lastModified": 1787753358,
+        "narHash": "sha256-Tl77VbWyAKrOfRNQhL6JbQtb/MLzbYa/1RG1gWWfICk=",
+        "owner": "cachix",
+        "repo": "devenv-nixpkgs",
+        "rev": "256551e45f6303e142ab4a98be1bf243feb77dc0",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "ref": "rolling",
+        "repo": "devenv-nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs-src": {
+      "flake": false,
+      "locked": {
+        "lastModified": 1787394516,
+        "narHash": "sha256-pRGOQSClnXNI2iLUG6DYpsGvYcuw0drOutVZFTJNw90=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "c8f90650c15282fa8656a041bfbbd2403997a9a7",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixpkgs-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "devenv": "devenv",
+        "nixpkgs": "nixpkgs"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}
\ No newline at end of file
diff --git a/devenv.nix b/devenv.nix
new file mode 100644
index 0000000..e6f6ca2
--- /dev/null
+++ b/devenv.nix
@@ -0,0 +1,43 @@
+{ pkgs, lib, ... }:
+
+let
+  # override with
+  #   devenv shell -O languages.python.package:pkg python312
+  py = "313";
+in
+{
+  name = "nt2dev";
+
+  languages.python = {
+    enable = true;
+    package = pkgs."python${py}";
+
+    venv = {
+      enable = true;
+      requirements = ''
+        ipykernel
+        jupyterlab
+        pytest
+        -e .
+      '';
+    };
+  };
+
+  # https://devenv.sh/packages/
+  packages = with pkgs; [
+    black
+    pyright
+    taplo
+    vscode-langservers-extracted
+    zlib
+  ];
+
+  env.LD_LIBRARY_PATH = lib.makeLibraryPath [
+    pkgs.stdenv.cc.cc
+    pkgs.zlib
+  ];
+
+  enterShell = ''
+    echo "nt2dev devenv activated: $DEVENV_STATE/venv/bin/python"
+  '';
+}
diff --git a/devenv.yaml b/devenv.yaml
new file mode 100644
index 0000000..68616a4
--- /dev/null
+++ b/devenv.yaml
@@ -0,0 +1,4 @@
+# yaml-language-server: $schema=https://devenv.sh/devenv.schema.json
+inputs:
+  nixpkgs:
+    url: github:cachix/devenv-nixpkgs/rolling
diff --git a/legacy/containers/data.py b/legacy/containers/data.py
index f5e28cc..570f872 100644
--- a/legacy/containers/data.py
+++ b/legacy/containers/data.py
@@ -1,7 +1,6 @@
-from typing import Callable, Any, Union, Optional, List, Dict
-
-import sys
 import logging
+import sys
+from typing import Any, Callable, Dict, List, Optional, Union
 
 if sys.version_info >= (3, 12):
     from typing import override
@@ -11,30 +10,28 @@ def override(method):
         return method
 
 
-from nt2.utils import ToHumanReadable
-
-import xarray as xr
 import pandas as pd
+import xarray as xr
 
-from nt2.utils import (
-    DetermineDataFormat,
-    InheritClassDocstring,
-    Format,
-    CoordinateSystem,
-)
-from nt2.readers.base import BaseReader
-from nt2.readers.hdf5 import Reader as HDF5Reader
-from nt2.readers.adios2 import Reader as BP5Reader
+import nt2.plotters.inspect as acc_inspect
+import nt2.plotters.movie as acc_movie
+import nt2.plotters.particles as acc_particles
+import nt2.plotters.polar as acc_polar
+from nt2.containers.diagnostics import Diagnostics
 from nt2.containers.fields import Fields
 from nt2.containers.particles import Particles
 from nt2.containers.spectra import Spectra
-from nt2.containers.diagnostics import Diagnostics
-
-import nt2.plotters.polar as acc_polar
-import nt2.plotters.particles as acc_particles
-import nt2.plotters.inspect as acc_inspect
-import nt2.plotters.movie as acc_movie
 from nt2.plotters.export import makeFramesAndMovie
+from nt2.readers.adios2 import Reader as BP5Reader
+from nt2.readers.base import BaseReader
+from nt2.readers.hdf5 import Reader as HDF5Reader
+from nt2.utils import (
+    CoordinateSystem,
+    DetermineDataFormat,
+    Format,
+    InheritClassDocstring,
+    ToHumanReadable,
+)
 
 
 @xr.register_dataset_accessor("polar")
diff --git a/nt2/__init__.py b/nt2/__init__.py
index 6355901..4a1f655 100644
--- a/nt2/__init__.py
+++ b/nt2/__init__.py
@@ -1,12 +1,12 @@
 __version__ = "1.6.0"
 
 import xarray as xr
-from .containers.data import Data as nt2Data
 
-from .plotters import polar as acc_polar
-from .plotters import particles as acc_particles
+from .containers.data import Data as nt2Data
 from .plotters import inspect as acc_inspect
 from .plotters import movie as acc_movie
+from .plotters import particles as acc_particles
+from .plotters import polar as acc_polar
 from .utils import InheritClassDocstring
 
 
diff --git a/nt2/cli/main.py b/nt2/cli/main.py
index a7ca8cf..ff4f6c7 100644
--- a/nt2/cli/main.py
+++ b/nt2/cli/main.py
@@ -1,7 +1,12 @@
-from typing import Union, Dict
-import typer, nt2, os
-from typing_extensions import Annotated
+from __future__ import annotations
+
+import os
+from typing import Annotated
+
 import matplotlib.pyplot as plt
+import typer
+
+import nt2
 
 app = typer.Typer()
 
@@ -23,18 +28,18 @@ def check_path(path: str) -> str:
     return path
 
 
-def check_sel(sel: str) -> Dict[str, Union[int, float, slice]]:
+def check_sel(sel: str) -> dict[str, float | slice]:
     if sel == "":
         return {}
     sel_list = sel.strip().split(";")
-    sel_dict: Dict[str, Union[int, float, slice]] = {}
+    sel_dict: dict[str, float | slice] = {}
     for _, s in enumerate(sel_list):
         coord, arg = s.strip().split("=", 1)
         coord = coord.strip()
         arg_exec = eval(arg.strip())
-        assert isinstance(
-            arg_exec, (int, float, slice)
-        ), f"Invalid selection argument for '{coord}': {arg_exec}. Must be int, float, or slice."
+        assert isinstance(arg_exec, (int, float, slice)), (
+            f"Invalid selection argument for '{coord}': {arg_exec}. Must be int, float, or slice."
+        )
         sel_dict[coord] = arg_exec
     return sel_dict
 
@@ -122,19 +127,19 @@ def plot(
 ):
     fname = os.path.basename(path.strip("/"))
     data = nt2.Data(path)
-    assert isinstance(
-        sel, dict
-    ), f"Invalid selection format: {sel}. Must be a dictionary."
+    assert isinstance(sel, dict), (
+        f"Invalid selection format: {sel}. Must be a dictionary."
+    )
     assert isinstance(isel, dict), f"Invalid isel format: {isel}. Must be a dictionary."
     if what == "fields":
         d = data.fields
         if sel != {}:
             slices = {}
             sels = {}
-            slices: Dict[str, Union[slice, float, int]] = {
+            slices: dict[str, slice | float] = {
                 k: v for k, v in sel.items() if isinstance(v, slice)
             }
-            sels: Dict[str, Union[slice, float, int]] = {
+            sels: dict[str, slice | float] = {
                 k: v for k, v in sel.items() if not isinstance(v, slice)
             }
             d = d.sel(**sels, method="nearest")
diff --git a/nt2/containers/base.py b/nt2/containers/base.py
index 8c5b758..69b7a3c 100644
--- a/nt2/containers/base.py
+++ b/nt2/containers/base.py
@@ -1,7 +1,9 @@
-from typing import Callable, Optional, Dict, Tuple, List, Union
+from __future__ import annotations
+
+from typing import Callable
 
-import numpy.typing as npt
 import numpy as np
+import numpy.typing as npt
 
 from ..readers.base import BaseReader
 from ..utils import CoordinateSystem
@@ -14,17 +16,17 @@ class BaseContainer:
     __category: str
     __reader: BaseReader
     __verify: bool
-    __timerange: Optional[Tuple[Union[float, None], Union[float, None]]]
-    __steprange: Optional[Tuple[Union[int, None], Union[int, None]]]
-    __remap: Optional[Dict[str, Callable[[str], str]]]
-    __coordinate_system: Optional[CoordinateSystem]
-    __num_cpus: Optional[int]
+    __timerange: tuple[float | None, float | None] | None
+    __steprange: tuple[int | None, int | None] | None
+    __remap: dict[str, Callable[[str], str]] | None
+    __coordinate_system: CoordinateSystem | None
+    __num_cpus: int | None
 
-    __valid_steps: List[int] = []
-    __valid_files: List[str] = []
+    __valid_steps: list[int]
+    __valid_files: list[str]
 
-    __times: npt.NDArray = np.array([])
-    __steps: npt.NDArray = np.array([])
+    __times: npt.NDArray
+    __steps: npt.NDArray
 
     def __init__(
         self,
@@ -32,11 +34,11 @@ def __init__(
         category: str,
         reader: BaseReader,
         verify: bool = False,
-        timerange: Union[Tuple[Union[float, None], Union[float, None]], None] = None,
-        steprange: Union[Tuple[Union[int, None], Union[int, None]], None] = None,
-        remap: Union[Dict[str, Callable[[str], str]], None] = None,
-        coord_system: Union[CoordinateSystem, None] = None,
-        num_cpus: Union[int, None] = None,
+        timerange: tuple[float | None, float | None] | None = None,
+        steprange: tuple[int | None, int | None] | None = None,
+        remap: dict[str, Callable[[str], str]] | None = None,
+        coord_system: CoordinateSystem | None = None,
+        num_cpus: int | None = None,
     ):
         """Initializer for the BaseContainer class.
 
@@ -50,9 +52,9 @@ def __init__(
             The reader to be used for reading the data.
         verify : Optional[bool]
             Whether to verify the data. If None, it will use the reader's default.
-        timerange : Optional[Tuple[Union[float, None], Union[float, None]]]
+        timerange : Optional[tuple[Union[float, None], Union[float, None]]]
             Time range to load. If None, all times will be loaded.
-        steprange : Optional[Tuple[Union[int, None], Union[int, None]]]
+        steprange : Optional[tuple[Union[int, None], Union[int, None]]]
             Step range to load. If None, all steps will be loaded.
         remap : Optional[dict[str, Callable[[str], str]]]
             Remap dictionary to use to remap the data names (coords, fields, etc.).
@@ -103,37 +105,37 @@ def verify(self) -> bool:
         return self.__verify
 
     @property
-    def remap(self) -> Optional[Dict[str, Callable[[str], str]]]:
+    def remap(self) -> dict[str, Callable[[str], str]] | None:
         """{ str: (str) -> str } : The coordinate/field remap dictionary."""
         return self.__remap
 
     @property
-    def coordinate_system(self) -> Optional[CoordinateSystem]:
+    def coordinate_system(self) -> CoordinateSystem | None:
         """CoordinateSystem: The coordinate system of the data."""
         return self.__coordinate_system
 
     @property
-    def num_cpus(self) -> Optional[int]:
+    def num_cpus(self) -> int | None:
         """int: The number of CPUs to use for parallel processing."""
         return self.__num_cpus
 
     @property
-    def timerange(self) -> Optional[Tuple[Union[float, None], Union[float, None]]]:
+    def timerange(self) -> tuple[float | None, float | None] | None:
         """tuple[float | None, float | None]: The time range of the data."""
         return self.__timerange
 
     @property
-    def steprange(self) -> Optional[Tuple[Union[int, None], Union[int, None]]]:
+    def steprange(self) -> tuple[int | None, int | None] | None:
         """tuple[int | None, int | None]: The step range of the data."""
         return self.__steprange
 
     @property
-    def valid_files(self) -> List[str]:
+    def valid_files(self) -> list[str]:
         """list[str]: The valid files of the data."""
         return self.__valid_files
 
     @property
-    def valid_steps(self) -> List[int]:
+    def valid_steps(self) -> list[int]:
         """list[int]: The valid steps of the data."""
         return self.__valid_steps
 
@@ -147,7 +149,7 @@ def steps(self) -> npt.NDArray:
         """npt.NDArray: The steps of the data."""
         return self.__steps
 
-    def read_times_and_steps(self) -> Tuple[npt.NDArray, npt.NDArray]:
+    def read_times_and_steps(self) -> tuple[npt.NDArray, npt.NDArray]:
         """Reads the times and steps for the given category.
 
         Parameters
@@ -187,7 +189,7 @@ def narrow_timerange(self):
             self.__valid_files = self.__valid_files[start_idx : end_idx + 1]
             self.__valid_steps = self.__valid_steps[start_idx : end_idx + 1]
 
-    def set_remap(self, remap: Dict[str, Callable[[str], str]]) -> None:
+    def set_remap(self, remap: dict[str, Callable[[str], str]]) -> None:
         """Set the remap dictionary for the container.
 
         Parameters
@@ -209,7 +211,7 @@ def set_coordinate_system(self, coord_system: CoordinateSystem) -> None:
         """
         self.__coordinate_system = coord_system
 
-    def __dask_tokenize__(self) -> Tuple[str, str, str]:
+    def __dask_tokenize__(self) -> tuple[str, str, str]:
         """Provide a deterministic Dask token for container instances."""
         return (
             self.__class__.__name__,
diff --git a/nt2/containers/data.py b/nt2/containers/data.py
index abefcd2..0fd67a7 100644
--- a/nt2/containers/data.py
+++ b/nt2/containers/data.py
@@ -1,31 +1,30 @@
-from typing import Callable, Any, Union, Optional, List, Dict, Tuple
+from __future__ import annotations
 
 import os
+from typing import Any, Callable
 
-import xarray as xr
 import pandas as pd
+import xarray as xr
 
+from ..plotters.export import makeFramesAndMovie
+from ..readers.adios2 import Reader as BP5Reader
+from ..readers.base import BaseReader
+from ..readers.hdf5 import Reader as HDF5Reader
 from ..utils import (
-    ToHumanReadable,
-    DetermineDataFormat,
-    Format,
     CoordinateSystem,
     CoordinateSystemType,
+    DetermineDataFormat,
+    Format,
+    ToHumanReadable,
 )
-from ..readers.base import BaseReader
-from ..readers.hdf5 import Reader as HDF5Reader
-from ..readers.adios2 import Reader as BP5Reader
-
+from .diagnostics import Diagnostics
 from .fields import FieldContainer
-from .particles import ParticleContainer
 from .particle_dataset import ParticleDataset
+from .particles import ParticleContainer
 from .spectra import SpectraContainer
-from .diagnostics import Diagnostics
-
-from ..plotters.export import makeFramesAndMovie
 
 
-def compactify(lst: Union[List[Any], Any]) -> str:
+def compactify(lst: list[Any] | Any) -> str:
     c = ""
     cntr = 0
     for l_ in lst:
@@ -40,10 +39,10 @@ def compactify(lst: Union[List[Any], Any]) -> str:
 class Data:
     """Main class to manage all the data containers."""
 
-    _fields: Optional[FieldContainer] = None
-    _particles: Optional[ParticleContainer] = None
-    _spectra: Optional[SpectraContainer] = None
-    _diagnostics: Optional[Diagnostics] = None
+    _fields: FieldContainer | None = None
+    _particles: ParticleContainer | None = None
+    _spectra: SpectraContainer | None = None
+    _diagnostics: Diagnostics | None = None
 
     def __init__(
         self,
@@ -53,12 +52,12 @@ def __init__(
         spectra: bool = True,
         diagnostics: bool = False,
         verify: bool = False,
-        timerange: Optional[Tuple[Union[float, None], Union[float, None]]] = None,
-        steprange: Optional[Tuple[Union[int, None], Union[int, None]]] = None,
-        reader: Optional[BaseReader] = None,
-        remap: Optional[Dict[str, Callable[[str], str]]] = None,
-        coord_system: Optional[CoordinateSystemType] = None,
-        num_cpus: Optional[int] = min(os.cpu_count() or 1, 16),
+        timerange: tuple[float | None, float | None] | None = None,
+        steprange: tuple[int | None, int | None] | None = None,
+        reader: BaseReader | None = None,
+        remap: dict[str, Callable[[str], str]] | None = None,
+        coord_system: CoordinateSystemType | None = None,
+        num_cpus: int | None = min(os.cpu_count() or 1, 16),
     ):
         """Initializer for the Data class.
 
@@ -159,7 +158,7 @@ def __init__(
             )
         if diagnostics:
             self._diagnostics = Diagnostics(path=path)
-        self.__attrs: Dict[str, Any] = {}
+        self.__attrs: dict[str, Any] = {}
         if self.fields_defined:
             self.__attrs.update(**self._fields.attrs)
         if self.particles_defined:
@@ -228,15 +227,15 @@ def diagnostics(self) -> pd.DataFrame:
         return self._diagnostics.df
 
     @property
-    def attrs(self) -> Dict[str, Any]:
+    def attrs(self) -> dict[str, Any]:
         """dict: The attributes of the data."""
         return self.__attrs
 
     def makeMovie(
         self,
         plot: Callable,
-        time: Optional[List[float]] = None,
-        num_cpus: Optional[int] = None,
+        time: list[float] | None = None,
+        num_cpus: int | None = None,
         **movie_kwargs: Any,
     ) -> bool:
         """Create animation with provided plot function.
@@ -268,9 +267,10 @@ def makeMovie(
             raise ValueError("No time values found.")
         name: str = ""
         provided_name = movie_kwargs.pop("name", None)
-        if provided_name is not None:
-            name = provided_name
-        elif self.attrs.get("simulation.name", None) is None:
+        if (
+            provided_name is not None
+            or self.attrs.get("simulation.name", None) is not None
+        ):
             name = provided_name
         else:
             name_b = self.attrs.get("simulation.name")
@@ -298,11 +298,19 @@ def to_str(self) -> str:
             string += "==============\n"
             string += f"| Coordinates:\n|   {self._fields.coordinate_system.value}\n|\n"
             string += f"| Data axes:\n|   {compactify(self.fields.indexes.keys())}\n|\n"
-            delta_t = (
-                self.fields.coords["t"].values[1] - self.fields.coords["t"].values[0]
-            ) / (self.fields.coords["s"].values[1] - self.fields.coords["s"].values[0])
-            string += f"|   - dt: {delta_t:.2e}\n"
-            for key in self.fields.coords.keys():
+            if (
+                len(self.fields.coords["t"].values) > 1
+                and len(self.fields.coords["s"].values) > 1
+            ):
+                delta_t = (
+                    self.fields.coords["t"].values[1]
+                    - self.fields.coords["t"].values[0]
+                ) / (
+                    self.fields.coords["s"].values[1]
+                    - self.fields.coords["s"].values[0]
+                )
+                string += f"|   - dt: {delta_t:.2e}\n"
+            for key in self.fields.coords:
                 crd = self.fields.coords[key].values
                 fmt = ""
                 if key != "s":
@@ -343,7 +351,7 @@ def to_str(self) -> str:
                 self.spectra.coords["s"].values[1] - self.spectra.coords["s"].values[0]
             )
             string += f"|   - dt: {delta_t:.2e}\n"
-            for key in self.spectra.coords.keys():
+            for key in self.spectra.coords:
                 crd = self.spectra.coords[key].values
                 fmt = ""
                 if key != "s":
diff --git a/nt2/containers/diagnostics.py b/nt2/containers/diagnostics.py
index 324fa82..0a8d001 100644
--- a/nt2/containers/diagnostics.py
+++ b/nt2/containers/diagnostics.py
@@ -6,12 +6,11 @@
 import logging
 import os
 import re
+from collections.abc import Iterator
 from pathlib import Path
-from typing import Dict, Iterator, List, Optional, Union
 
 import pandas as pd
 
-
 _NUMBER = r"[+-]?(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][+-]?\d+)?"
 _STEP_RE = re.compile(r"^Step:\s+(\d+)\.+\[")
 _TIME_RE = re.compile(rf"^Time:\s+({_NUMBER})\.+\[")
@@ -58,23 +57,24 @@ class Diagnostics:
 
     _CACHE_VERSION = 1
 
-    df: Optional[pd.DataFrame]
+    df: pd.DataFrame | None
     outfile: str
 
     def __init__(
         self,
-        path: Union[str, os.PathLike[str]],
+        path: str | os.PathLike[str],
         *,
         cache: bool = True,
         chunk_size: int = 100_000,
-        cache_path: Optional[Union[str, os.PathLike[str]]] = None,
+        cache_path: str | os.PathLike[str] | None = None,
     ) -> None:
         if chunk_size <= 0:
             raise ValueError("chunk_size must be greater than zero")
 
         outfile = self._find_outfile(Path(path))
+        logger = logging.getLogger(__name__)
         if outfile is None:
-            logging.warning("No .out files found in %s", path)
+            logger.warning("No .out files found in %s", path)
             self.df = None
             return
 
@@ -93,7 +93,7 @@ def __init__(
                 self.df = pd.read_parquet(parquet_path)
                 return
             except (OSError, ValueError):
-                logging.warning(
+                logger.warning(
                     "Failed to read diagnostics cache %s; rebuilding it",
                     parquet_path,
                     exc_info=True,
@@ -108,7 +108,7 @@ def __init__(
             self.df = self._combine_chunks(chunks)
 
     @staticmethod
-    def _find_outfile(path: Path) -> Optional[Path]:
+    def _find_outfile(path: Path) -> Path | None:
         if path.is_file():
             if path.suffix != ".out":
                 raise ValueError(f"Expected a .out file, got {path}")
@@ -141,7 +141,7 @@ def _cache_is_valid(
             return False
 
     @classmethod
-    def _cache_metadata(cls, source: Path) -> Dict[str, Union[int, str]]:
+    def _cache_metadata(cls, source: Path) -> dict[str, int | str]:
         stat = source.stat()
         return {
             "parser_version": cls._CACHE_VERSION,
@@ -212,8 +212,8 @@ def _parse_to_cache(
 
     @classmethod
     def _dataframe_chunks(cls, source: Path, chunk_size: int) -> Iterator[pd.DataFrame]:
-        records: List[Dict[str, Union[int, float]]] = []
-        columns: Optional[List[str]] = None
+        records: list[dict[str, float]] = []
+        columns: list[str] | None = None
 
         for record in cls._records(source):
             if columns is None:
@@ -237,13 +237,13 @@ def _dataframe_chunks(cls, source: Path, chunk_size: int) -> Iterator[pd.DataFra
 
     @staticmethod
     def _make_dataframe(
-        records: List[Dict[str, Union[int, float]]], columns: List[str]
+        records: list[dict[str, float]], columns: list[str]
     ) -> pd.DataFrame:
         dataframe = pd.DataFrame.from_records(records, columns=columns)
         return dataframe.set_index("Step", drop=False)
 
     @staticmethod
-    def _combine_chunks(chunks: List[pd.DataFrame]) -> pd.DataFrame:
+    def _combine_chunks(chunks: list[pd.DataFrame]) -> pd.DataFrame:
         if chunks:
             return pd.concat(chunks)
         dataframe = pd.DataFrame(columns=["Step", "Time"])
@@ -251,8 +251,8 @@ def _combine_chunks(chunks: List[pd.DataFrame]) -> pd.DataFrame:
         return dataframe
 
     @staticmethod
-    def _records(source: Path) -> Iterator[Dict[str, Union[int, float]]]:
-        record: Optional[Dict[str, Union[int, float]]] = None
+    def _records(source: Path) -> Iterator[dict[str, float]]:
+        record: dict[str, float] | None = None
 
         with source.open("r", encoding="utf-8-sig", errors="replace") as stream:
             for line in stream:
diff --git a/nt2/containers/fields.py b/nt2/containers/fields.py
index a630e3f..7636244 100644
--- a/nt2/containers/fields.py
+++ b/nt2/containers/fields.py
@@ -1,12 +1,14 @@
-from typing import Any, Union, Dict
+from __future__ import annotations
+
+from typing import Any
 
 import dask
 import dask.array as da
 import xarray as xr
 from tqdm import tqdm
 
+from ..utils import CoordinateSystem, Layout
 from .base import BaseContainer
-from ..utils import Layout, CoordinateSystem
 
 
 def remap_fields_cart(name: str) -> str:
@@ -51,7 +53,7 @@ class FieldContainer(BaseContainer):
     """Parent class to manage the fields dataframe."""
 
     __fields_defined: bool = False
-    __fields: Union[xr.Dataset, None] = None
+    __fields: xr.Dataset | None = None
 
     def __init__(
         self,
@@ -76,7 +78,7 @@ def fields_defined(self) -> bool:
         return self.__fields_defined
 
     @property
-    def fields(self) -> Union[xr.Dataset, None]:
+    def fields(self) -> xr.Dataset | None:
         """xr.Dataset: The fields dataframe."""
         return self.__fields
 
@@ -155,7 +157,11 @@ def _read_fields(self) -> xr.Dataset:
                     f"Coordinate system {attributes['Coordinates']} not supported."
                 )
 
-        if self.remap is None:
+        if (
+            self.remap is None
+            or self.remap.get("coords", None) is None
+            or self.remap.get("fields", None) is None
+        ):
             self.set_remap(
                 {
                     "coords": (
@@ -174,13 +180,13 @@ def _read_fields(self) -> xr.Dataset:
         # rename coordinates if remap is provided
         if self.remap is not None and "coords" in self.remap:
             new_coords = {}
-            for coord in coords.keys():
+            for coord in coords:
                 new_coords[self.remap["coords"](coord)] = coords[coord]
             coords = new_coords
 
         edge_coords = self.reader.ReadEdgeCoordsAtTimestep(self.path, first_step)
         new_edge_coords = {}
-        for coord in edge_coords.keys():
+        for coord in edge_coords:
             assoc_x = (
                 coord[:-1]
                 if (self.remap is None or "coords" not in self.remap)
@@ -233,7 +239,7 @@ def _read_fields(self) -> xr.Dataset:
         )
 
     @property
-    def attrs(self) -> Dict[str, Any]:
+    def attrs(self) -> dict[str, Any]:
         """dict: The attributes of the fields dataframe."""
         if self.fields_defined:
             return self.fields.attrs
diff --git a/nt2/containers/particle_dataset.py b/nt2/containers/particle_dataset.py
index 294d388..91bd675 100644
--- a/nt2/containers/particle_dataset.py
+++ b/nt2/containers/particle_dataset.py
@@ -1,32 +1,26 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+from copy import copy
 from typing import (
     Any,
     Callable,
-    List,
-    Optional,
-    Sequence,
-    Tuple,
     Literal,
-    Union,
-    Dict,
-    Type,
     cast,
 )
-import numpy.typing as npt
-from copy import copy
 
 import dask
 import dask.dataframe as dd
+import matplotlib.axes as maxes
+import matplotlib.pyplot as plt
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
 from dask.delayed import Delayed
 from dask.optimization import cull
-import pandas as pd
-import numpy as np
-
-import matplotlib.pyplot as plt
-import matplotlib.axes as maxes
-
 
-IntSelector = Union[int, Sequence[int], slice, Tuple[int, int]]
-FloatSelector = Union[float, slice, Sequence[float], Tuple[float, float]]
+IntSelector = int | Sequence[int] | slice | tuple[int, int]
+FloatSelector = float | slice | Sequence[float] | tuple[float, float]
 
 
 def _cull_dataframe_graph(ddf: dd.DataFrame) -> dd.DataFrame:
@@ -47,12 +41,12 @@ class Selection:
     def __init__(
         self,
         type: Literal["value", "range", "list"],
-        value: Optional[Union[int, float, list, tuple]] = None,
+        value: float | list | tuple | None = None,
     ):
         self.type = type
         self.value = value
 
-    def intersect(self, other: "Selection") -> "Selection":
+    def intersect(self, other: Selection) -> Selection:
         if self.value is None:
             return copy(other)
         elif other.value is None:
@@ -92,9 +86,9 @@ def intersect(self, other: "Selection") -> "Selection":
             lo, hi = other.value
             new_values = [v for v in self.value if lo <= v <= hi]
             return Selection("list", new_values)
-        elif self.type == "range" and other.type == "value":
-            return other.intersect(self)
-        elif self.type == "range" and other.type == "list":
+        elif (self.type == "range" and other.type == "value") or (
+            self.type == "range" and other.type == "list"
+        ):
             return other.intersect(self)
         elif self.type == "range" and other.type == "range":
             assert isinstance(self.value, tuple) and len(self.value) == 2, (
@@ -139,13 +133,13 @@ def __str__(self) -> str:
 
 
 def _coerce_selector_to_mask(
-    s: Union[IntSelector, FloatSelector],
+    s: IntSelector | FloatSelector,
     series: Any,
     inclusive_tuple: bool = True,
     method="exact",
 ):
-    from operator import ior
     from functools import reduce
+    from operator import ior
 
     if isinstance(s, slice):
         lo = s.start if s.start is not None else -np.inf
@@ -178,7 +172,7 @@ def _coerce_selector_to_mask(
 
 def _attach_columns(
     part: pd.DataFrame,
-    cols_tuple: Tuple[str, ...],
+    cols_tuple: tuple[str, ...],
     read_column: Callable[[int, str], npt.NDArray[Any]],
     metadtypes: Any,
 ) -> pd.DataFrame:
@@ -198,7 +192,7 @@ def _attach_columns(
 def _load_index_partition(
     st: int,
     t: float,
-    index_cols: Tuple[str, ...],
+    index_cols: tuple[str, ...],
     read_column: Callable[[int, str], npt.NDArray[Any]],
 ) -> pd.DataFrame:
     cols = {c: read_column(st, c) for c in index_cols}
@@ -216,22 +210,22 @@ def _load_index_partition(
 class ParticleDataset:
     steps: npt.NDArray[np.int64]
     times: npt.NDArray[np.float64]
-    colnames: List[str]
+    colnames: list[str]
     _ddf_index: dd.DataFrame
 
     def __init__(
         self,
-        species: List[int],
+        species: list[int],
         steps: npt.NDArray[np.int64],
         times: npt.NDArray[np.float64],
-        colnames: List[str],
+        colnames: list[str],
         read_column: Callable[
-            [int, str], npt.NDArray[Union[np.float64, np.int64, np.float32, np.int32]]
+            [int, str], npt.NDArray[np.float64 | np.int64 | np.float32 | np.int32]
         ],
-        fprec: Optional[Type] = np.float32,
-        selection: Optional[Dict[str, Selection]] = None,
-        ddf_index: Optional[dd.DataFrame] = None,
-        partition_lengths: Optional[Sequence[int]] = None,
+        fprec: type | None = np.float32,
+        selection: dict[str, Selection] | None = None,
+        ddf_index: dd.DataFrame | None = None,
+        partition_lengths: Sequence[int] | None = None,
     ):
         self.species = species
         self.steps = steps
@@ -241,7 +235,7 @@ def __init__(
         self.read_column = read_column
         self.fprec = fprec
         self.index_cols = ("id", "sp")
-        self._all_columns_cache: Optional[List[str]] = None
+        self._all_columns_cache: list[str] | None = None
         self._partition_lengths = (
             tuple(int(length) for length in partition_lengths)
             if partition_lengths is not None
@@ -280,7 +274,7 @@ def __init__(
 
         if ddf_index is not None:
             self._ddf_index = ddf_index
-            self._partition_indices: Optional[Tuple[int, ...]] = None
+            self._partition_indices: tuple[int, ...] | None = None
         else:
             self._ddf_index = self._build_index_ddf()
             self._partition_indices = tuple(range(self._ddf_index.npartitions))
@@ -318,19 +312,19 @@ def nbytes(self) -> int:
         return int(sum(self._partition_lengths) * bytes_per_row)
 
     @property
-    def columns(self) -> List[str]:
+    def columns(self) -> list[str]:
         if self._all_columns_cache is None:
             self._all_columns_cache = self.colnames
         return self._all_columns_cache
 
     def sel(
         self,
-        t: Optional[Union[IntSelector, FloatSelector]] = None,
-        st: Optional[IntSelector] = None,
-        sp: Optional[IntSelector] = None,
-        id: Optional[IntSelector] = None,
+        t: IntSelector | FloatSelector | None = None,
+        st: IntSelector | None = None,
+        sp: IntSelector | None = None,
+        id: IntSelector | None = None,
         method: str = "exact",
-    ) -> "ParticleDataset":
+    ) -> ParticleDataset:
         ddf: dd.DataFrame = self._ddf_index
         new_selection = {k: copy(v) for k, v in self.selection.items()}
         if st is not None:
@@ -381,8 +375,8 @@ def sel(
         return result
 
     def isel(
-        self, t: Optional[IntSelector] = None, st: Optional[IntSelector] = None
-    ) -> "ParticleDataset":
+        self, t: IntSelector | None = None, st: IntSelector | None = None
+    ) -> ParticleDataset:
         ddf: dd.DataFrame = self._ddf_index
         partition_indices = self._partition_indices
         partition_lengths = self._partition_lengths
@@ -479,7 +473,7 @@ def _build_index_ddf(self) -> dd.DataFrame:
         ddf = cast(dd.DataFrame, dd.from_delayed(delayed_parts, meta=meta))
         return ddf
 
-    def load(self, cols: Optional[Sequence[str]] = None) -> pd.DataFrame:
+    def load(self, cols: Sequence[str] | None = None) -> pd.DataFrame:
         if cols is None:
             cols = self.columns
 
@@ -543,9 +537,9 @@ def __str__(self) -> str:
 
     def spectrum_plot(
         self,
-        ax: Optional[maxes.Axes] = None,
-        bins: Optional[npt.NDArray] = None,
-        quantity: Optional[Callable[[pd.DataFrame], npt.NDArray]] = None,
+        ax: maxes.Axes | None = None,
+        bins: npt.NDArray | None = None,
+        quantity: Callable[[pd.DataFrame], npt.NDArray] | None = None,
     ):
         if ax is None:
             ax = plt.gca()
@@ -601,10 +595,10 @@ def _quantity_sph(df: pd.DataFrame):
 
     def phase_plot(
         self,
-        ax: Optional[maxes.Axes] = None,
-        x_quantity: Optional[Callable[[pd.DataFrame], npt.NDArray]] = None,
-        y_quantity: Optional[Callable[[pd.DataFrame], npt.NDArray]] = None,
-        xy_bins: Optional[Tuple[npt.NDArray, npt.NDArray]] = None,
+        ax: maxes.Axes | None = None,
+        x_quantity: Callable[[pd.DataFrame], npt.NDArray] | None = None,
+        y_quantity: Callable[[pd.DataFrame], npt.NDArray] | None = None,
+        xy_bins: tuple[npt.NDArray, npt.NDArray] | None = None,
         **kwargs: Any,
     ):
         if ax is None:
diff --git a/nt2/containers/particles.py b/nt2/containers/particles.py
index 31a3623..e19c76d 100644
--- a/nt2/containers/particles.py
+++ b/nt2/containers/particles.py
@@ -1,11 +1,13 @@
-from typing import Any, List, Union, Dict, Set
-import numpy.typing as npt
+from __future__ import annotations
+
+from typing import Any
 
 import numpy as np
+import numpy.typing as npt
 
+from ..utils import CoordinateSystem
 from .base import BaseContainer
 from .particle_dataset import ParticleDataset
-from ..utils import CoordinateSystem
 
 
 def remap_prtl_quantities_cart(name: str) -> str:
@@ -38,14 +40,14 @@ class ParticleContainer(BaseContainer):
     """Parent class to manage the particles dataframe."""
 
     __particles_defined: bool = False
-    __particles: Union[ParticleDataset, None] = None
+    __particles: ParticleDataset | None = None
 
-    nonempty_steps: List[int]
-    attributes: Dict[str, Any]
-    quantities: List[str]
-    sp_with_idx: List[int]
-    sp_without_idx: List[int]
-    quantity_names_by_step: Dict[int, Set[str]]
+    nonempty_steps: list[int]
+    attributes: dict[str, Any]
+    quantities: list[str]
+    sp_with_idx: list[int]
+    sp_without_idx: list[int]
+    quantity_names_by_step: dict[int, set[str]]
 
     def __getstate__(self) -> dict[str, Any]:
         state = self.__dict__.copy()
@@ -128,15 +130,13 @@ def _read_particles(self):
         quantities = sorted(np.unique([q for qtys in quantities_ for q in qtys]))
 
         unique_quantities = sorted(
-            list(
-                set(
-                    f"{q}".split("_")[0]
-                    for q in quantities
-                    if not q.startswith("pIDX") and not q.startswith("pRNK")
-                )
-            )
+            {
+                f"{q}".split("_")[0]
+                for q in quantities
+                if not q.startswith("pIDX") and not q.startswith("pRNK")
+            }
         )
-        all_species = sorted(list(set([int(f"{q}".split("_")[1]) for q in quantities])))
+        all_species = sorted({int(f"{q}".split("_")[1]) for q in quantities})
 
         sp_with_idx = sorted(
             [int(f"{q}".split("_")[1]) for q in quantities if f"{q}".startswith("pIDX")]
@@ -209,7 +209,7 @@ def particles_defined(self) -> bool:
         return self.__particles_defined
 
     @property
-    def particles(self) -> Union[ParticleDataset, None]:
+    def particles(self) -> ParticleDataset | None:
         """Returns the particles data.
 
         Returns
@@ -221,7 +221,7 @@ def particles(self) -> Union[ParticleDataset, None]:
         return self.__particles
 
     @property
-    def attrs(self) -> Dict[str, Any]:
+    def attrs(self) -> dict[str, Any]:
         """dict: The attributes of the particles dataframe."""
         if self.particles_defined:
             return self.attributes
@@ -251,7 +251,7 @@ def _get_quantity_for_species(
         read_colname: str,
         step: int,
         sp: int,
-    ) -> npt.NDArray[Union[np.float64, np.int64]]:
+    ) -> npt.NDArray[np.float64 | np.int64]:
         if f"{read_colname}_{sp}" in self.quantities:
             return self.reader.ReadArrayAtTimestep(
                 self.path, "particles", f"{read_colname}_{sp}", step
@@ -261,7 +261,7 @@ def _get_quantity_for_species(
 
     def _read_column(
         self, step: int, colname: str
-    ) -> npt.NDArray[Union[np.float64, np.int64, np.float32, np.int32]]:
+    ) -> npt.NDArray[np.float64 | np.int64 | np.float32 | np.int32]:
         read_colname = None
         if colname == "id":
             idx = np.concatenate(
diff --git a/nt2/containers/spectra.py b/nt2/containers/spectra.py
index 68f2148..6add10c 100644
--- a/nt2/containers/spectra.py
+++ b/nt2/containers/spectra.py
@@ -1,19 +1,38 @@
-from typing import Any, Union, Dict
+from __future__ import annotations
+
+from typing import Any
 
 import dask
 import dask.array as da
-import xarray as xr
 import numpy as np
+import xarray as xr
 from tqdm import tqdm
 
+from ..utils import CoordinateSystem
 from .base import BaseContainer
 
 
+def remap_coords_cart(name: str) -> str:
+    return {
+        "X1": "x",
+        "X2": "y",
+        "X3": "z",
+    }.get(name, name)
+
+
+def remap_coords_sph(name: str) -> str:
+    return {
+        "X1": "r",
+        "X2": "th",
+        "X3": "ph",
+    }.get(name, name)
+
+
 class SpectraContainer(BaseContainer):
     """Parent class to manage the spectra dataframe."""
 
     __spectra_defined: bool = False
-    __spectra: Union[xr.Dataset, None] = None
+    __spectra: xr.Dataset | None = None
 
     def __init__(self, **kwargs: Any) -> None:
         super().__init__(category="spectra", **kwargs)
@@ -32,7 +51,7 @@ def spectra_defined(self) -> bool:
         return self.__spectra_defined
 
     @property
-    def spectra(self) -> Union[xr.Dataset, None]:
+    def spectra(self) -> xr.Dataset | None:
         """xr.Dataset: The spectra dataframe."""
         return self.__spectra
 
@@ -68,24 +87,76 @@ def _read_spectra(self) -> xr.Dataset:
         spectra_names = self.reader.ReadCategoryNamesAtTimestep(
             self.path, "spectra", "s", first_step
         )
-        spectra_names = set(s for s in sorted(spectra_names) if s.startswith("sN"))
+        spectra_names = {s for s in sorted(spectra_names) if s.startswith("sN")}
         ebin_name = "sEbn"
         first_spectrum_name = next(iter(spectra_names))
         shape = self.reader.ReadArrayShapeExplicitlyAtTimestep(
             self.path, "spectra", first_spectrum_name, first_step
         )
 
-        ebins = self.reader.ReadArrayAtTimestep(
+        energy_binedges = self.reader.ReadArrayAtTimestep(
             self.path, "spectra", ebin_name, first_step
         )
+        num_spatial_dims = len(shape) - 1
+
+        x_binedges = [
+            self.reader.ReadArrayAtTimestep(
+                self.path, "spectra", f"sX{i + 1}bn", first_step
+            )
+            for i in range(num_spatial_dims)
+        ]
+
+        def edges_to_bins(edges: np.ndarray) -> np.ndarray:
+            diffs = np.diff(edges)
+            if len(diffs) == 1 or np.isclose(
+                diffs[1] - diffs[0], diffs[-1] - diffs[-2], atol=1e-2
+            ):
+                return 0.5 * (edges[1:] + edges[:-1])
+            else:
+                return (edges[1:] * edges[:-1]) ** 0.5
+
+        attributes = self.reader.ReadAttrsAtTimestep(
+            path=self.path, category="spectra", step=first_step
+        )
+        if self.coordinate_system is None:
+            if "Coordinates" not in attributes:
+                raise ValueError("Coordinates not found in attributes for particles.")
+            if attributes["Coordinates"] in [b"cart", "cart"]:
+                self.set_coordinate_system(CoordinateSystem.XYZ)
+            elif attributes["Coordinates"] in [b"sph", "sph", b"qsph", "qsph"]:
+                self.set_coordinate_system(CoordinateSystem.SPH)
+            else:
+                raise NotImplementedError(
+                    f"Coordinate system {attributes['Coordinates']} not supported."
+                )
+
+        if self.remap is None or self.remap.get("coords", None) is None:
+            self.set_remap(
+                {
+                    "coords": (
+                        remap_coords_cart
+                        if self.coordinate_system == CoordinateSystem.XYZ
+                        else remap_coords_sph
+                    ),
+                }
+            )
+
+        ebins = edges_to_bins(energy_binedges)
+        xbins = [edges_to_bins(xb) for xb in x_binedges]
 
-        diffs = np.diff(ebins)
-        if np.isclose(diffs[1] - diffs[0], diffs[-1] - diffs[-2], atol=1e-2):
-            ebins = 0.5 * (ebins[1:] + ebins[:-1])
+        if self.remap is not None and "coords" in self.remap:
+            new_xbins = {}
+            for i in range(num_spatial_dims):
+                new_xbins[self.remap["coords"](f"X{i + 1}")] = xbins[i]
+            xbins = new_xbins
         else:
-            ebins = (ebins[1:] * ebins[:-1]) ** 0.5
+            xbins = {f"X{i + 1}": xbins[i] for i in range(num_spatial_dims)}
 
-        all_dims = {"t": self.times, "E": ebins}
+        all_dims = {
+            "t": self.times,
+            **xbins,
+            "E": ebins,
+        }
         all_coords = {**all_dims, "s": ("t", self.steps)}
 
         attributes = self.reader.ReadAttrsAtTimestep(
@@ -131,7 +202,7 @@ def remap_name(name: str) -> str:
         )
 
     @property
-    def attrs(self) -> Dict[str, Any]:
+    def attrs(self) -> dict[str, Any]:
         """dict: The attributes of the spectra dataframe."""
         if self.spectra_defined:
             return self.spectra.attrs
diff --git a/nt2/plotters/annotations.py b/nt2/plotters/annotations.py
index 4a768b4..1373ab1 100644
--- a/nt2/plotters/annotations.py
+++ b/nt2/plotters/annotations.py
@@ -2,11 +2,24 @@
 
 
 def annotatePulsar(
-    ax, data, rmax, rstar=1.1, ti=None, time=None, attrs={}, ax_props={}, star_props={}
+    ax,
+    data,
+    rmax,
+    rstar=1.1,
+    ti=None,
+    time=None,
+    attrs=None,
+    ax_props=None,
+    star_props=None,
 ):
     import numpy as np
-    from matplotlib import lines
-    from matplotlib import patches
+    from matplotlib import lines, patches
+
+    logger = logging.getLogger(__name__)
+
+    attrs = attrs or {}
+    ax_props = ax_props or {}
+    star_props = star_props or {}
 
     if ti is None and time is None:
         raise ValueError("Must provide either ti or time")
@@ -21,7 +34,7 @@ def annotatePulsar(
             )
         )
     ) is None:
-        logging.warning(
+        logger.warning(
             "No spinup time or spin period found, please specify explicitly as `attrs = {'psr_omega': ..., 'psr_spinup_time': ...}`"
         )
         demo_rotation = False
@@ -45,7 +58,7 @@ def annotatePulsar(
         xy=(0.0, rmax * 0.95),
         xytext=(0.0, -rmax * 0.95),
         zorder=4,
-        arrowprops=dict(arrowstyle="->", color=ax_props.get("color", "k"), lw=0.5),
+        arrowprops={"arrowstyle": "->", "color": ax_props.get("color", "k"), "lw": 0.5},
     )
     for i in range(-int(rmax * 0.8) // 2 - 1, int(rmax * 0.8) // 2):
         if i != -1:
diff --git a/nt2/plotters/export.py b/nt2/plotters/export.py
index eb7dafd..2c89712 100644
--- a/nt2/plotters/export.py
+++ b/nt2/plotters/export.py
@@ -1,11 +1,14 @@
-from typing import Any, Callable, Union, Optional, List
+from __future__ import annotations
+
+from typing import Any, Callable
+
 import matplotlib.pyplot as plt
 
 
 def makeFramesAndMovie(
     name: str,
     plot: Callable,
-    times: List[float],
+    times: list[float],
     data: Any = None,
     **kwargs: Any,
 ) -> bool:
@@ -36,7 +39,7 @@ def makeFramesAndMovie(
         raise ValueError("Failed to make frames")
 
 
-def makeMovie(**ffmpeg_kwargs: Union[str, int, float]) -> bool:
+def makeMovie(**ffmpeg_kwargs: str | float) -> bool:
     """
     Create a movie from frames using the `ffmpeg` command-line tool.
 
@@ -62,9 +65,7 @@ def makeMovie(**ffmpeg_kwargs: Union[str, int, float]) -> bool:
     """
     import subprocess
 
-    input_pattern: str = (
-        f"{ffmpeg_kwargs.get('input', 'step_')}%0{ffmpeg_kwargs.get('number', 3)}d.{ffmpeg_kwargs.get('extension', 'png')}"
-    )
+    input_pattern: str = f"{ffmpeg_kwargs.get('input', 'step_')}%0{ffmpeg_kwargs.get('number', 3)}d.{ffmpeg_kwargs.get('extension', 'png')}"
 
     command = [
         ffmpeg_kwargs.get("ffmpeg", "ffmpeg"),
@@ -86,7 +87,12 @@ def makeMovie(**ffmpeg_kwargs: Union[str, int, float]) -> bool:
     ]
     command = [str(c) for c in command if c is not None]
     print("Command:\n", " ".join(command))
-    result = subprocess.run(command, capture_output=True, text=True)
+    result = subprocess.run(
+        command,
+        capture_output=True,
+        text=True,
+        check=False,
+    )
 
     if result.returncode == 0:
         print("ffmpeg -- [OK]")
@@ -112,11 +118,11 @@ def _plot_and_save(ti: int, t: float, fpath: str, plot: Callable, data: Any) ->
 
 def makeFrames(
     plot: Callable,
-    times: List[float],
+    times: list[float],
     fpath: str,
     data: Any = None,
-    num_cpus: Optional[int] = None,
-) -> List[bool]:
+    num_cpus: int | None = None,
+) -> list[bool]:
     """
     Create plot frames from a set of timesteps of the same dataset.
 
@@ -160,9 +166,10 @@ def makeFrames(
     >>> makeFrames(plot_func, range(100), 'output/', num_cpus=16)
 
     """
+    import os
+
     from loky import get_reusable_executor
     from tqdm import tqdm
-    import os
 
     os.makedirs(fpath, exist_ok=True)
 
diff --git a/nt2/plotters/inspect.py b/nt2/plotters/inspect.py
index 2e64ec5..c4b55d8 100644
--- a/nt2/plotters/inspect.py
+++ b/nt2/plotters/inspect.py
@@ -1,9 +1,13 @@
-from typing import Any, Callable, Optional, Union, List, Dict, Tuple
-import matplotlib.pyplot as plt
+from __future__ import annotations
+
+from typing import Any, Callable
+
 import matplotlib.figure as mfigure
+import matplotlib.pyplot as plt
 import xarray as xr
-from nt2.utils import DataIs2DPolar
+
 from nt2.plotters.export import makeFramesAndMovie
+from nt2.utils import DataIs2DPolar
 
 
 class ds_accessor:
@@ -12,7 +16,7 @@ def __init__(self, xarray_obj: xr.Dataset):
 
     def __axes_grid(
         self,
-        grouped_fields: Dict[str, List[str]],
+        grouped_fields: dict[str, list[str]],
         makeplot: Callable,
         nrows: int,
         ncols: int,
@@ -20,7 +24,7 @@ def __axes_grid(
         size: float,
         aspect: float,
         **fig_kwargs: Any,
-    ) -> Tuple[mfigure.Figure, List[plt.Axes]]:
+    ) -> tuple[mfigure.Figure, list[plt.Axes]]:
         vpad = fig_kwargs.pop("vpad", 0.5)
         hpad = fig_kwargs.pop("hpad", 0.5)
         if aspect > 1:
@@ -51,7 +55,7 @@ def __axes_grid(
 
     @staticmethod
     def _fixed_axes_grid_with_cbars(
-        fields: List[str],
+        fields: list[str],
         makeplot: Callable,
         makecbar: Callable,
         nrows: int,
@@ -61,7 +65,7 @@ def _fixed_axes_grid_with_cbars(
         aspect: float,
         cbar_w: float,
         **fig_kwargs: Any,
-    ) -> Tuple[mfigure.Figure, List[plt.Axes]]:
+    ) -> tuple[mfigure.Figure, list[plt.Axes]]:
         from mpl_toolkits.axes_grid1 import Divider, Size
 
         vpad = fig_kwargs.pop("vpad", 0.5)
@@ -89,7 +93,7 @@ def _fixed_axes_grid_with_cbars(
         v += [Size.Fixed(vpad)]
 
         divider = Divider(fig, (0, 0, 1, 1), h, v, aspect=False)
-        axes: List[plt.Axes] = []
+        axes: list[plt.Axes] = []
 
         cntr = 0
         for i in range(nrows):
@@ -117,15 +121,15 @@ def _fixed_axes_grid_with_cbars(
 
     def plot(
         self,
-        fig: Optional[mfigure.Figure] = None,
-        name: Optional[str] = None,
-        skip_fields: Optional[List[str]] = None,
-        only_fields: Optional[List[str]] = None,
-        fig_kwargs: Optional[Dict[str, Any]] = None,
-        plot_kwargs: Optional[Dict[str, Any]] = None,
-        movie_kwargs: Optional[Dict[str, Any]] = None,
-        set_aspect: Optional[str] = "equal",
-    ) -> Union[mfigure.Figure, bool]:
+        fig: mfigure.Figure | None = None,
+        name: str | None = None,
+        skip_fields: list[str] | None = None,
+        only_fields: list[str] | None = None,
+        fig_kwargs: dict[str, Any] | None = None,
+        plot_kwargs: dict[str, Any] | None = None,
+        movie_kwargs: dict[str, Any] | None = None,
+        set_aspect: str | None = "equal",
+    ) -> mfigure.Figure | bool:
         """
         Plots the overview plot for fields at a given time or step (or as a movie).
 
@@ -239,20 +243,20 @@ def plot_func(ti: int, _):
 
     @staticmethod
     def _get_fields_to_plot(
-        data: xr.Dataset, skip_fields: List[str], only_fields: List[str]
-    ) -> List[str]:
+        data: xr.Dataset, skip_fields: list[str], only_fields: list[str]
+    ) -> list[str]:
         import re
 
         nfields = len(data.data_vars)
         if nfields > 0:
-            keys: List[str] = [str(k) for k in data.keys()]
+            keys: list[str] = [str(k) for k in data]
             if len(only_fields) == 0:
                 fields_to_plot = [
-                    f for f in keys if not any([re.match(sf, f) for sf in skip_fields])
+                    f for f in keys if not any(re.match(sf, f) for sf in skip_fields)
                 ]
             else:
                 fields_to_plot = [
-                    f for f in keys if any([re.match(sf, f) for sf in only_fields])
+                    f for f in keys if any(re.match(sf, f) for sf in only_fields)
                 ]
         else:
             fields_to_plot = []
@@ -265,9 +269,9 @@ def _get_fields_to_plot(
 
     @staticmethod
     def _get_fields_minmax(
-        data: xr.Dataset, fields: List[str]
-    ) -> Dict[str, Optional[Tuple[float, float]]]:
-        minmax: Dict[str, Optional[Tuple[float, float]]] = {
+        data: xr.Dataset, fields: list[str]
+    ) -> dict[str, tuple[float, float] | None]:
+        minmax: dict[str, tuple[float, float] | None] = {
             "E": None,
             "B": None,
             "J": None,
@@ -304,22 +308,23 @@ def _get_fields_minmax(
     def plot_frame_1d(
         self,
         data: xr.Dataset,
-        fig: Optional[mfigure.Figure],
-        skip_fields: List[str],
-        only_fields: List[str],
-        fig_kwargs: Dict[str, Any],
-        plot_kwargs: Dict[str, Any],
+        fig: mfigure.Figure | None,
+        skip_fields: list[str],
+        only_fields: list[str],
+        fig_kwargs: dict[str, Any],
+        plot_kwargs: dict[str, Any],
     ) -> mfigure.Figure:
         if len(data.dims) != 1:
             raise ValueError("Pass 1D data; use .sel or .isel to reduce dimension.")
 
-        import math, re
+        import math
+        import re
 
         # count the number of subplots
         fields_to_plot = self._get_fields_to_plot(data, skip_fields, only_fields)
 
         # group fields by their first letter
-        grouped_fields: Dict[str, List[str]] = {}
+        grouped_fields: dict[str, list[str]] = {}
         for f in fields_to_plot:
             key = f[0]
             if key not in grouped_fields:
@@ -329,8 +334,8 @@ def plot_frame_1d(
         nplots = len(grouped_fields)
 
         aspect = 0.5
-        ncols = max(1, int(math.floor(nplots * 1.5 * aspect / (1 + 1.5 * aspect))))
-        nrows = max(1, int(math.ceil(nplots / ncols)))
+        ncols = max(1, math.floor(nplots * 1.5 * aspect / (1 + 1.5 * aspect)))
+        nrows = max(1, math.ceil(nplots / ncols))
 
         figsize0 = 3.0
 
@@ -338,9 +343,9 @@ def plot_frame_1d(
         kwargs = {}
         for fld in fields_to_plot:
             kwargs[fld] = {}
-            for fld_kwargs in plot_kwargs:
+            for fld_kwargs, val in plot_kwargs.items():
                 if re.match(fld_kwargs, fld):
-                    kwargs[fld] = {**plot_kwargs[fld_kwargs]}
+                    kwargs[fld] = {**val}
                     break
 
         def make_plot(ax: plt.Axes, fld: str):
@@ -373,21 +378,23 @@ def make_plot(ax: plt.Axes, fld: str):
     def plot_frame_2d(
         self,
         data: xr.Dataset,
-        fig: Optional[mfigure.Figure],
-        skip_fields: List[str],
-        only_fields: List[str],
-        fig_kwargs: Dict[str, Any],
-        plot_kwargs: Dict[str, Any],
-        set_aspect: Optional[str],
+        fig: mfigure.Figure | None,
+        skip_fields: list[str],
+        only_fields: list[str],
+        fig_kwargs: dict[str, Any],
+        plot_kwargs: dict[str, Any],
+        set_aspect: str | None,
     ) -> mfigure.Figure:
         if len(data.dims) != 2:
             raise ValueError("Pass 2D data; use .sel or .isel to reduce dimension.")
 
         x1, x2 = data.dims
 
+        import math
+        import re
+
         import matplotlib.colors as mcolors
         import numpy as np
-        import math, re
 
         # count the number of subplots
         fields_to_plot = self._get_fields_to_plot(data, skip_fields, only_fields)
@@ -402,8 +409,8 @@ def plot_frame_2d(
         else:
             aspect = 1.5
 
-        ncols = max(1, int(math.floor(nfields * 1.5 * aspect / (1 + 1.5 * aspect))))
-        nrows = max(1, int(math.ceil(nfields / ncols)))
+        ncols = max(1, math.floor(nfields * 1.5 * aspect / (1 + 1.5 * aspect)))
+        nrows = max(1, math.ceil(nfields / ncols))
 
         figsize0 = 3.0
 
@@ -443,9 +450,9 @@ def plot_frame_2d(
                 "vmax": vmax,
             }
             kwargs[fld] = default_kwargs
-            for fld_kwargs in plot_kwargs:
+            for fld_kwargs, val in plot_kwargs.items():
                 if re.match(fld_kwargs, fld):
-                    kwargs[fld] = {**default_kwargs, **plot_kwargs[fld_kwargs]}
+                    kwargs[fld] = {**default_kwargs, **val}
                     break
             if "norm" in kwargs[fld]:
                 vmin = kwargs[fld].pop("vmin")
diff --git a/nt2/plotters/movie.py b/nt2/plotters/movie.py
index 9481c4e..6fb0b2c 100644
--- a/nt2/plotters/movie.py
+++ b/nt2/plotters/movie.py
@@ -1,8 +1,12 @@
-from typing import Any, Optional, Dict
+from __future__ import annotations
+
+from typing import Any
+
+import xarray as xr
+
 from nt2.plotters.export import (
     makeFramesAndMovie,
 )
-import xarray as xr
 
 
 class accessor:
@@ -21,8 +25,8 @@ def __init__(self, xarray_obj: xr.DataArray) -> None:
     def plot(
         self,
         name: str,
-        movie_kwargs: Dict[str, Any] = {},
-        fig_kwargs: Dict[str, Any] = {},
+        movie_kwargs: dict[str, Any] | None = None,
+        fig_kwargs: dict[str, Any] | None = None,
         aspect_equal: bool = False,
         **kwargs: Any,
     ) -> bool:
@@ -58,12 +62,16 @@ def plot(
 
         import matplotlib.pyplot as plt
 
+        movie_kwargs = movie_kwargs or {}
+        fig_kwargs = fig_kwargs or {}
+
         def plot_func(ti: int, _: Any) -> None:
             if len(self._obj.isel(t=ti).dims) == 2:
                 if aspect_equal:
                     x1, x2 = self._obj.isel(t=ti).dims
-                    nx1, nx2 = len(self._obj.isel(t=ti)[x1]), len(
-                        self._obj.isel(t=ti)[x2]
+                    nx1, nx2 = (
+                        len(self._obj.isel(t=ti)[x1]),
+                        len(self._obj.isel(t=ti)[x2]),
                     )
                     aspect = nx1 / nx2
                     figsize = fig_kwargs.get("figsize", (6, 4 * aspect))
@@ -75,7 +83,7 @@ def plot_func(ti: int, _: Any) -> None:
                 plt.gca().set_aspect("equal")
             plt.tight_layout()
 
-        num_cpus: Optional[int] = movie_kwargs.pop("num_cpus", None)
+        num_cpus: int | None = movie_kwargs.pop("num_cpus", None)
         return makeFramesAndMovie(
             name=name,
             data=self._obj,
diff --git a/nt2/plotters/particles.py b/nt2/plotters/particles.py
index a001e9a..f9557dd 100644
--- a/nt2/plotters/particles.py
+++ b/nt2/plotters/particles.py
@@ -1,7 +1,8 @@
-import xarray as xr
+from __future__ import annotations
+
 import numpy as np
 import numpy.typing as npt
-from typing import Optional, Tuple
+import xarray as xr
 
 
 class ds_accessor:
@@ -12,10 +13,10 @@ def phaseplot(
         self,
         x: str = "x",
         y: str = "ux",
-        xbins: Optional[npt.NDArray] = None,
-        ybins: Optional[npt.NDArray] = None,
-        xlims: Optional[Tuple[float, float]] = None,
-        ylims: Optional[Tuple[float, float]] = None,
+        xbins: npt.NDArray | None = None,
+        ybins: npt.NDArray | None = None,
+        xlims: tuple[float, float] | None = None,
+        ylims: tuple[float, float] | None = None,
         xnbins: int = 100,
         ynbins: int = 100,
         **kwargs,
@@ -57,12 +58,12 @@ def phaseplot(
         --------
         >>> ds.phaseplot(x='x', y='ux', xbins=np.linspace(0, 1000, 100), ybins=np.linspace(-5, 5, 50))
         """
-        assert x in list(self._obj.keys()) and y in list(
-            self._obj.keys()
-        ), "x and y must be valid variable names in the dataset"
-        assert (
-            len(self._obj[x].dims) == 1 and len(self._obj[y].dims) == 1
-        ), "x and y must be 1D variables"
+        assert x in list(self._obj.keys()) and y in list(self._obj.keys()), (
+            "x and y must be valid variable names in the dataset"
+        )
+        assert len(self._obj[x].dims) == 1 and len(self._obj[y].dims) == 1, (
+            "x and y must be 1D variables"
+        )
         assert "t" not in self._obj.dims, "Dataset must not have time dimension"
 
         import matplotlib.pyplot as plt
diff --git a/nt2/plotters/polar.py b/nt2/plotters/polar.py
index a9dfbeb..8488cd0 100644
--- a/nt2/plotters/polar.py
+++ b/nt2/plotters/polar.py
@@ -1,5 +1,8 @@
+from __future__ import annotations
+
+from typing import Any
+
 import numpy as np
-from typing import Any, Dict
 
 from nt2.utils import DataIs2DPolar
 
@@ -176,7 +179,7 @@ def fieldlines(self, fr, fth, start_points, **kwargs):
         fxs = self._obj[fr] * np.sin(ths) + self._obj[fth] * np.cos(ths)
         fys = self._obj[fr] * np.cos(ths) - self._obj[fth] * np.sin(ths)
 
-        props: Dict[str, Any] = {
+        props: dict[str, Any] = {
             "method": "nearest",
             "bounds_error": False,
             "fill_value": 0,
@@ -188,9 +191,10 @@ def fieldlines(self, fr, fth, start_points, **kwargs):
         ]
 
     def _fieldline(self, interp_fx, interp_fy, r_th_start, **kwargs):
-        import numpy as np
         from copy import copy
 
+        import numpy as np
+
         direction = kwargs.pop("direction", "both")
         stopWhen = kwargs.pop("stopWhen", lambda _, __: False)
         ds = kwargs.pop("ds", 0.1)
@@ -292,10 +296,9 @@ def pcolor(self, **kwargs) -> Any:
         Additional keyword arguments are passed to `pcolormesh`.
         """
 
-        import matplotlib.pyplot as plt
-        from matplotlib import colors
-        from matplotlib import tri
         import matplotlib as mpl
+        import matplotlib.pyplot as plt
+        from matplotlib import colors, tri
         from mpl_toolkits.axes_grid1 import make_axes_locatable
 
         ax = kwargs.pop("ax", plt.gca())
@@ -434,6 +437,7 @@ def contour(self, **kwargs):
         """
 
         import warnings
+
         import matplotlib.pyplot as plt
 
         ax = kwargs.pop("ax", plt.gca())
diff --git a/nt2/readers/adios2.py b/nt2/readers/adios2.py
index f63d202..5086793 100644
--- a/nt2/readers/adios2.py
+++ b/nt2/readers/adios2.py
@@ -1,6 +1,8 @@
-from typing import Any, List, Dict, Tuple, Set
+from __future__ import annotations
 
 import sys
+from typing import Any
+
 from tqdm import tqdm
 
 if sys.version_info >= (3, 12):
@@ -11,15 +13,15 @@ def override(method):
         return method
 
 
-import re
 import os
-import numpy as np
-import numpy.typing as npt
+import re
 
 import adios2 as bp
+import numpy as np
+import numpy.typing as npt
 
-from nt2.utils import Format, Layout
 from nt2.readers.base import BaseReader
+from nt2.utils import Format, Layout
 
 
 class Reader(BaseReader):
@@ -42,9 +44,9 @@ def ReadPerTimestepVariable(
         category: str,
         varname: str,
         newname: str,
-        valid_files: List[str],
-    ) -> Dict[str, npt.NDArray[Any]]:
-        variables: List[float] = []
+        valid_files: list[str],
+    ) -> dict[str, npt.NDArray[Any]]:
+        variables: list[float] = []
         for filename in tqdm(
             valid_files,
             desc=f"Reading {category} {varname}",
@@ -52,8 +54,8 @@ def ReadPerTimestepVariable(
             leave=False,
         ):
             with bp.FileReader(os.path.join(path, category, filename)) as f:
-                avail: Dict[str, Any] = f.available_variables()
-                vars: List[str] = list(avail.keys())
+                avail: dict[str, Any] = f.available_variables()
+                vars: list[str] = list(avail.keys())
                 if varname in vars:
                     var = f.inquire_variable(varname)
                     if var is not None:
@@ -71,10 +73,10 @@ def ReadPerTimestepVariables(
         self,
         path: str,
         category: str,
-        varnames: List[str],
-        newnames: List[str],
-        valid_files: List[str],
-    ) -> Dict[str, npt.NDArray[Any]]:
+        varnames: list[str],
+        newnames: list[str],
+        valid_files: list[str],
+    ) -> dict[str, npt.NDArray[Any]]:
         variables = {newname: [] for newname in newnames}
         for filename in tqdm(
             valid_files,
@@ -83,8 +85,8 @@ def ReadPerTimestepVariables(
             leave=False,
         ):
             with bp.FileReader(os.path.join(path, category, filename)) as f:
-                avail: Dict[str, Any] = f.available_variables()
-                vars: List[str] = list(avail.keys())
+                avail: dict[str, Any] = f.available_variables()
+                vars: list[str] = list(avail.keys())
                 for varname, newname in zip(varnames, newnames):
                     if varname in vars:
                         var = f.inquire_variable(varname)
@@ -95,17 +97,19 @@ def ReadPerTimestepVariables(
                                 f"{varname} is not a variable in the BP file {filename}"
                             )
                     else:
-                        raise ValueError(f"{varname} not found in the BP file {filename}")
+                        raise ValueError(
+                            f"{varname} not found in the BP file {filename}"
+                        )
         return {newname: np.array(variables[newname]) for newname in newnames}
 
     @override
     def ReadParticleCountsAtTimestep(
-        self, path: str, step: int, species: List[int]
-    ) -> Dict[int, int]:
+        self, path: str, step: int, species: list[int]
+    ) -> dict[int, int]:
         """Read all per-species counts from one BP file's metadata."""
         with bp.FileReader(self.FullPath(path, "particles", step)) as f:
             available = f.available_variables()
-            counts: Dict[int, int] = {}
+            counts: dict[int, int] = {}
             for sp in species:
                 name = f"pX1_{sp}"
                 var = f.inquire_variable(name) if name in available else None
@@ -118,11 +122,11 @@ def ReadEdgeCoordsAtTimestep(
         self,
         path: str,
         step: int,
-    ) -> Dict[str, Any]:
-        dct: Dict[str, npt.NDArray[Any]] = {}
+    ) -> dict[str, Any]:
+        dct: dict[str, npt.NDArray[Any]] = {}
         with bp.FileReader(self.FullPath(path, "fields", step)) as f:
-            avail: Dict[str, Any] = f.available_variables()
-            vars: List[str] = list(avail.keys())
+            avail: dict[str, Any] = f.available_variables()
+            vars: list[str] = list(avail.keys())
             for var in vars:
                 if var.startswith("X") and var.endswith("e"):
                     var_obj = f.inquire_variable(var)
@@ -136,7 +140,7 @@ def ReadAttrsAtTimestep(
         path: str,
         category: str,
         step: int,
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         with bp.FileReader(self.FullPath(path, category, step)) as f:
             return {k: f.read_attribute(k) for k in f.available_attributes()}
 
@@ -168,9 +172,9 @@ def ReadCategoryNamesAtTimestep(
         category: str,
         prefix: str,
         step: int,
-    ) -> Set[str]:
+    ) -> set[str]:
         with bp.FileReader(self.FullPath(path, category, step)) as f:
-            keys: List[str] = f.available_variables()
+            keys: list[str] = f.available_variables()
             return set(
                 filter(
                     lambda c: c.startswith(prefix),
@@ -181,7 +185,7 @@ def ReadCategoryNamesAtTimestep(
     @override
     def ReadArrayShapeAtTimestep(
         self, path: str, category: str, quantity: str, step: int
-    ) -> Tuple[int, ...]:
+    ) -> tuple[int, ...]:
         with bp.FileReader(filename := self.FullPath(path, category, step)) as f:
             if quantity in f.available_variables():
                 var = f.inquire_variable(quantity)
@@ -199,7 +203,7 @@ def ReadArrayShapeAtTimestep(
     @override
     def ReadArrayShapeExplicitlyAtTimestep(
         self, path: str, category: str, quantity: str, step: int
-    ) -> Tuple[int, ...]:
+    ) -> tuple[int, ...]:
         with bp.FileReader(filename := self.FullPath(path, category, step)) as f:
             if quantity in f.available_variables():
                 var = f.inquire_variable(quantity)
@@ -217,7 +221,7 @@ def ReadArrayShapeExplicitlyAtTimestep(
     @override
     def ReadFieldCoordsAtTimestep(
         self, path: str, step: int
-    ) -> Dict[str, npt.NDArray[Any]]:
+    ) -> dict[str, npt.NDArray[Any]]:
         with bp.FileReader(filename := self.FullPath(path, "fields", step)) as f:
 
             def get_coord(c: str) -> npt.NDArray[Any]:
@@ -227,13 +231,13 @@ def get_coord(c: str) -> npt.NDArray[Any]:
                 else:
                     raise ValueError(f"Field {c} is not a group in the {filename}")
 
-            keys: List[str] = list(f.available_variables())
+            keys: list[str] = list(f.available_variables())
             return {c: get_coord(c) for c in keys if re.match(r"^X[1|2|3]$", c)}
 
     @override
     def ReadFieldLayoutAtTimestep(self, path: str, step: int) -> Layout:
         with bp.FileReader(filename := self.FullPath(path, "fields", step)) as f:
-            attrs: Dict[str, Any] = f.available_attributes()
+            attrs: dict[str, Any] = f.available_attributes()
             keys = list(attrs.keys())
             if "LayoutRight" not in keys:
                 raise ValueError(f"LayoutRight attribute not found in the {filename}")
diff --git a/nt2/readers/base.py b/nt2/readers/base.py
index bee5349..501e0a7 100644
--- a/nt2/readers/base.py
+++ b/nt2/readers/base.py
@@ -1,8 +1,10 @@
-from concurrent.futures import as_completed
-from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
+from __future__ import annotations
+
+import logging
 import os
 import re
-import logging
+from concurrent.futures import as_completed
+from typing import Any, Callable
 
 import numpy.typing as npt
 from loky import get_reusable_executor
@@ -17,8 +19,8 @@ def _check_file(enter_file: Callable[[str], Any], filename: str) -> None:
 
 
 def _get_field_shapes(
-    reader: "BaseReader", path: str, step: int
-) -> Dict[str, Tuple[int, ...]]:
+    reader: BaseReader, path: str, step: int
+) -> dict[str, tuple[int, ...]]:
     names = reader.ReadCategoryNamesAtTimestep(
         path=path,
         category="fields",
@@ -36,7 +38,7 @@ def _get_field_shapes(
     }
 
 
-def _verify_particle_shapes(reader: "BaseReader", path: str, step: int) -> None:
+def _verify_particle_shapes(reader: BaseReader, path: str, step: int) -> None:
     prtl_species = reader.ReadParticleSpeciesAtTimestep(path=path, step=step)
     quantities = reader.ReadCategoryNamesAtTimestep(
         path=path,
@@ -44,7 +46,7 @@ def _verify_particle_shapes(reader: "BaseReader", path: str, step: int) -> None:
         prefix="p",
         step=step,
     )
-    quantities = set(q.split("_")[0] for q in quantities if q.startswith("p"))
+    quantities = {q.split("_")[0] for q in quantities if q.startswith("p")}
     for species in prtl_species:
         shape = None
         for quantity in quantities:
@@ -69,7 +71,7 @@ class BaseReader:
 
     """
 
-    skipped_files: List[str]
+    skipped_files: list[str]
 
     def __init__(self) -> None:
         """Initializer for the BaseReader class."""
@@ -109,8 +111,8 @@ def ReadPerTimestepVariable(
         category: str,
         varname: str,
         newname: str,
-        valid_files: List[str],
-    ) -> Dict[str, npt.NDArray[Any]]:
+        valid_files: list[str],
+    ) -> dict[str, npt.NDArray[Any]]:
         """Read a variable at each timestep and return a dictionary with the new name.
 
         Parameters
@@ -138,10 +140,10 @@ def ReadPerTimestepVariables(
         self,
         path: str,
         category: str,
-        varnames: List[str],
-        newnames: List[str],
-        valid_files: List[str],
-    ) -> Dict[str, npt.NDArray[Any]]:
+        varnames: list[str],
+        newnames: list[str],
+        valid_files: list[str],
+    ) -> dict[str, npt.NDArray[Any]]:
         """Read multiple variables at each timestep and return a dictionary with the new names.
 
         Parameters
@@ -169,15 +171,15 @@ def ReadParticleCountsAtTimestep(
         self,
         path: str,
         step: int,
-        species: List[int],
-    ) -> Dict[int, int]:
+        species: list[int],
+    ) -> dict[int, int]:
         """Return particle counts by species without reading particle arrays.
 
         Readers may override this method to collect all counts while opening the
         timestep only once.  The default implementation uses array-shape
         metadata and is kept for third-party readers.
         """
-        counts: Dict[int, int] = {}
+        counts: dict[int, int] = {}
         for sp in species:
             try:
                 counts[sp] = int(
@@ -197,7 +199,7 @@ def ReadAttrsAtTimestep(
         path: str,
         category: str,
         step: int,
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         """Read the attributes of a given timestep.
 
         Parameters
@@ -221,7 +223,7 @@ def ReadEdgeCoordsAtTimestep(
         self,
         path: str,
         step: int,
-    ) -> Dict[str, npt.NDArray[Any]]:
+    ) -> dict[str, npt.NDArray[Any]]:
         """Read the coordinates of cell edges at a given timestep.
 
         Parameters
@@ -273,7 +275,7 @@ def ReadCategoryNamesAtTimestep(
         category: str,
         prefix: str,
         step: int,
-    ) -> Set[str]:
+    ) -> set[str]:
         """Read the names of the variables in a given category and timestep.
 
         Parameters
@@ -295,7 +297,7 @@ def ReadCategoryNamesAtTimestep(
         """
         raise NotImplementedError("ReadCategoryNamesAtTimestep is not implemented")
 
-    def ReadParticleSpeciesAtTimestep(self, path: str, step: int) -> Set[int]:
+    def ReadParticleSpeciesAtTimestep(self, path: str, step: int) -> set[int]:
         """Read the particle species indices at a given timestep.
 
         Parameters
@@ -311,10 +313,10 @@ def ReadParticleSpeciesAtTimestep(self, path: str, step: int) -> Set[int]:
             A set of particle species indices at a given timestep.
 
         """
-        return set(
+        return {
             int(f.split("_")[1])
             for f in self.ReadCategoryNamesAtTimestep(path, "particles", "p", step)
-        )
+        }
 
     def ReadArrayShapeAtTimestep(
         self,
@@ -322,7 +324,7 @@ def ReadArrayShapeAtTimestep(
         category: str,
         quantity: str,
         step: int,
-    ) -> Tuple[int, ...]:
+    ) -> tuple[int, ...]:
         """Read the shape of an array at a given timestep.
 
         Parameters
@@ -350,7 +352,7 @@ def ReadArrayShapeExplicitlyAtTimestep(
         category: str,
         quantity: str,
         step: int,
-    ) -> Tuple[int, ...]:
+    ) -> tuple[int, ...]:
         """Read the shape of an array at a given timestep, without relying on metadata.
 
         Parameters
@@ -378,7 +380,7 @@ def ReadFieldCoordsAtTimestep(
         self,
         path: str,
         step: int,
-    ) -> Dict[str, npt.NDArray[Any]]:
+    ) -> dict[str, npt.NDArray[Any]]:
         """Read the coordinates of the fields at a given timestep.
 
         Parameters
@@ -419,7 +421,7 @@ def ReadFieldLayoutAtTimestep(self, path: str, step: int) -> Layout:
     # # # # # # # # # # # # # # # # # # # # # # # #
 
     @staticmethod
-    def CategoryFiles(path: str, category: str, format: str) -> List[str]:
+    def CategoryFiles(path: str, category: str, format: str) -> list[str]:
         """Get the list of files in a given category and format.
 
         Parameters
@@ -442,6 +444,8 @@ def CategoryFiles(path: str, category: str, format: str) -> List[str]:
             If no files are found.
 
         """
+        if not os.path.exists(os.path.join(path, category)):
+            return []
         files = [
             f
             for f in os.listdir(os.path.join(path, category))
@@ -474,72 +478,13 @@ def FullPath(self, path: str, category: str, step: int) -> str:
             path, category, f"{category}.{step:08d}.{self.format.value}"
         )
 
-    # def GetValidSteps(
-    #     self,
-    #     path: str,
-    #     category: str,
-    #     num_cpus: Optional[int] = None,
-    # ) -> List[int]:
-    #     """Get valid timesteps (sorted) in a given path and category.
-
-    #     Parameters
-    #     ----------
-    #     path : str
-    #         The path to the files.
-    #     category : str
-    #         The category of the files.
-    #     num_cpus : Optional[int]
-    #         The number of CPU cores to use for parallel processing.
-
-    #     Returns
-    #     -------
-    #     list[int]
-    #         A list of valid timesteps in the given path and category.
-
-    #     """
-    #     category_files = BaseReader.CategoryFiles(
-    #         path=path,
-    #         category=category,
-    #         format=self.format.value,
-    #     )
-    #     num_cpus = num_cpus if num_cpus is not None else (os.cpu_count() or 1)
-    #     executor = get_reusable_executor(max_workers=num_cpus)
-    #     futures = {
-    #         executor.submit(
-    #             _check_file,
-    #             self.EnterFile,
-    #             os.path.join(path, category, filename),
-    #         ): filename
-    #         for filename in category_files
-    #     }
-
-    #     steps: List[int] = []
-    #     for future in tqdm(
-    #         as_completed(futures),
-    #         total=len(futures),
-    #         desc=f"getting valid steps for {category}",
-    #         leave=False,
-    #     ):
-    #         filename = futures[future]
-    #         try:
-    #             future.result()
-    #             steps.append(int(filename.split(".")[1]))
-    #         except OSError:
-    #             if filename not in self.skipped_files:
-    #                 self.skipped_files.append(filename)
-    #                 logging.warning(f"Could not read {filename}, skipping it")
-    #         except Exception as e:
-    #             raise e
-    #     steps.sort()
-    #     return steps
-
     def GetValidFilesAndSteps(
         self,
         path: str,
         category: str,
-        steprange: Optional[Tuple[Union[int, None], Union[int, None]]] = None,
-        num_cpus: Optional[int] = None,
-    ) -> Tuple[List[str], List[int]]:
+        steprange: tuple[int | None, int | None] | None = None,
+        num_cpus: int | None = None,
+    ) -> tuple[list[str], list[int]]:
         """Get valid files (sorted by timestep) and steps in a given path and category.
 
         Parameters
@@ -548,9 +493,9 @@ def GetValidFilesAndSteps(
             The path to the files.
         category : str
             The category of the files.
-        steprange : Optional[tuple[int | None, int | None]]
+        steprange : tuple[int | None, int | None] | None
             The range of timesteps to be considered. If None, all timesteps are considered.
-        num_cpus : Optional[int]
+        num_cpus : int | None
             The number of CPU cores to use for parallel processing.
 
         Returns
@@ -574,9 +519,7 @@ def is_inrange(filename: str) -> bool:
             start, end = steprange
             if start is not None and step < start:
                 return False
-            if end is not None and step >= end:
-                return False
-            return True
+            return not (end is not None and step >= end)
 
         futures = {
             executor.submit(
@@ -587,9 +530,10 @@ def is_inrange(filename: str) -> bool:
             for filename in category_files
             if is_inrange(filename)
         }
+        logger = logging.getLogger(__name__)
 
-        files: List[str] = []
-        steps: List[int] = []
+        files: list[str] = []
+        steps: list[int] = []
         for future in tqdm(
             as_completed(futures),
             total=len(futures),
@@ -604,9 +548,9 @@ def is_inrange(filename: str) -> bool:
             except OSError:
                 if filename not in self.skipped_files:
                     self.skipped_files.append(filename)
-                    logging.warning(f"Could not read {filename}, skipping it")
-            except Exception as e:
-                raise e
+                    logger.warning(f"Could not read {filename}, skipping it")
+            except Exception:
+                raise
         files.sort(key=lambda x: int(x.split(".")[1]))
         steps.sort()
         return (files, steps)
@@ -616,8 +560,8 @@ def VerifySameCategoryNames(
         path: str,
         category: str,
         prefix: str,
-        valid_steps: List[int],
-        num_cpus: Optional[int] = None,
+        valid_steps: list[int],
+        num_cpus: int | None = None,
     ):
         """Verify that all files in a given category have the same names.
 
@@ -631,7 +575,7 @@ def VerifySameCategoryNames(
             The prefix of the variables to be read.
         valid_steps : list[int]
             The valid timesteps to be checked.
-        num_cpus : Optional[int]
+        num_cpus : int | None
             The number of CPU cores to use for parallel processing.
 
         Raises
@@ -673,8 +617,8 @@ def VerifySameCategoryNames(
     def VerifySameFieldShapes(
         self,
         path: str,
-        valid_steps: List[int],
-        num_cpus: Optional[int] = None,
+        valid_steps: list[int],
+        num_cpus: int | None = None,
     ):
         """Verify that all fields in a given path have the same shape.
 
@@ -684,7 +628,7 @@ def VerifySameFieldShapes(
             The path to the files.
         valid_steps : list[int]
             The valid timesteps to be checked.
-        num_cpus : Optional[int]
+        num_cpus : int | None
             The number of CPU cores to use for parallel processing.
 
         Raises
@@ -723,8 +667,8 @@ def VerifySameFieldShapes(
     def VerifySameFieldLayouts(
         self,
         path: str,
-        valid_steps: List[int],
-        num_cpus: Optional[int] = None,
+        valid_steps: list[int],
+        num_cpus: int | None = None,
     ):
         """Verify that all timesteps in a given path have the same layout.
 
@@ -734,7 +678,7 @@ def VerifySameFieldLayouts(
             The path to the files.
         valid_steps : list[int]
             The valid timesteps to be checked.
-        num_cpus : Optional[int]
+        num_cpus : int | None
             The number of CPU cores to use for parallel processing.
 
         Raises
@@ -774,8 +718,8 @@ def VerifySameFieldLayouts(
     def VerifySameParticleShapes(
         self,
         path: str,
-        valid_steps: List[int],
-        num_cpus: Optional[int] = None,
+        valid_steps: list[int],
+        num_cpus: int | None = None,
     ):
         """Verify that all particle quantities in a given path have the same shape at specific timesteps.
 
@@ -785,7 +729,7 @@ def VerifySameParticleShapes(
             The path to the files.
         valid_steps : list[int]
             The valid timesteps to be checked.
-        num_cpus : Optional[int]
+        num_cpus : int | None
             The number of CPU cores to use for parallel processing.
 
         Raises
@@ -808,7 +752,7 @@ def VerifySameParticleShapes(
         ):
             future.result()
 
-    def DefinesCategory(self, path: str, category: str, valid_files: List[str]) -> bool:
+    def DefinesCategory(self, path: str, category: str, valid_files: list[str]) -> bool:
         """Check whether a given category is defined in the path.
 
         Parameters
diff --git a/nt2/readers/hdf5.py b/nt2/readers/hdf5.py
index 4c24226..ae82e36 100644
--- a/nt2/readers/hdf5.py
+++ b/nt2/readers/hdf5.py
@@ -1,8 +1,8 @@
 from __future__ import annotations
 
-from typing import Any, TYPE_CHECKING, List, Dict, Tuple, Set
-
 import sys
+from typing import TYPE_CHECKING, Any
+
 from tqdm import tqdm
 
 if sys.version_info >= (3, 12):
@@ -13,8 +13,9 @@ def override(method):
         return method
 
 
-import re
 import os
+import re
+
 import numpy as np
 import numpy.typing as npt
 
@@ -26,8 +27,8 @@ def override(method):
 if TYPE_CHECKING:
     import h5py as _h5py
 
-from nt2.utils import Format, Layout
 from nt2.readers.base import BaseReader
+from nt2.utils import Format, Layout
 
 
 def _require_h5py():
@@ -41,9 +42,9 @@ def _require_h5py():
 
 class Reader(BaseReader):
     @staticmethod
-    def __extract_step0(f: "_h5py.File") -> "_h5py.Group":
+    def __extract_step0(f: _h5py.File) -> _h5py.Group:
         h5 = _require_h5py()
-        if "Step0" in f.keys():
+        if "Step0" in f:
             f0 = f["Step0"]
             if isinstance(f0, h5.Group):
                 return f0
@@ -61,7 +62,7 @@ def format(self) -> Format:
     @override
     def EnterFile(
         filename: str,
-    ) -> "_h5py.File":
+    ) -> _h5py.File:
         h5 = _require_h5py()
         return h5.File(filename, "r")
 
@@ -72,9 +73,9 @@ def ReadPerTimestepVariable(
         category: str,
         varname: str,
         newname: str,
-        valid_files: List[str],
-    ) -> Dict[str, npt.NDArray[Any]]:
-        variables: List[Any] = []
+        valid_files: list[str],
+    ) -> dict[str, npt.NDArray[Any]]:
+        variables: list[Any] = []
         h5 = _require_h5py()
         for filename in tqdm(
             valid_files,
@@ -84,7 +85,7 @@ def ReadPerTimestepVariable(
         ):
             with h5.File(os.path.join(path, category, filename), "r") as f:
                 f0 = Reader.__extract_step0(f)
-                if varname in f0.keys():
+                if varname in f0:
                     var = f0[varname]
                     if isinstance(var, h5.Dataset):
                         variables.append(var[()])
@@ -102,10 +103,10 @@ def ReadPerTimestepVariables(
         self,
         path: str,
         category: str,
-        varnames: List[str],
-        newnames: List[str],
-        valid_files: List[str],
-    ) -> Dict[str, npt.NDArray[Any]]:
+        varnames: list[str],
+        newnames: list[str],
+        valid_files: list[str],
+    ) -> dict[str, npt.NDArray[Any]]:
         variables = {newname: [] for newname in newnames}
         h5 = _require_h5py()
         for filename in tqdm(
@@ -117,7 +118,7 @@ def ReadPerTimestepVariables(
             with h5.File(os.path.join(path, category, filename), "r") as f:
                 f0 = Reader.__extract_step0(f)
                 for varname, newname in zip(varnames, newnames):
-                    if varname in f0.keys():
+                    if varname in f0:
                         var = f0[varname]
                         if isinstance(var, h5.Dataset):
                             variables[newname].append(var[()])
@@ -134,8 +135,8 @@ def ReadPerTimestepVariables(
 
     @override
     def ReadParticleCountsAtTimestep(
-        self, path: str, step: int, species: List[int]
-    ) -> Dict[int, int]:
+        self, path: str, step: int, species: list[int]
+    ) -> dict[int, int]:
         """Read all per-species counts from one HDF5 file's metadata."""
         h5 = _require_h5py()
         with h5.File(self.FullPath(path, "particles", step), "r") as f:
@@ -153,7 +154,7 @@ def ReadAttrsAtTimestep(
         path: str,
         category: str,
         step: int,
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         h5 = _require_h5py()
         with h5.File(self.FullPath(path, category, step), "r") as f:
             return {k: v for k, v in f.attrs.items()}
@@ -163,7 +164,7 @@ def ReadEdgeCoordsAtTimestep(
         self,
         path: str,
         step: int,
-    ) -> Dict[str, npt.NDArray[Any]]:
+    ) -> dict[str, npt.NDArray[Any]]:
         h5 = _require_h5py()
         with h5.File(self.FullPath(path, "fields", step), "r") as f:
             f0 = Reader.__extract_step0(f)
@@ -180,7 +181,7 @@ def ReadArrayAtTimestep(
         h5 = _require_h5py()
         with h5.File(filename := self.FullPath(path, category, step), "r") as f:
             f0 = Reader.__extract_step0(f)
-            if quantity in f0.keys():
+            if quantity in f0:
                 var = f0[quantity]
                 if isinstance(var, h5.Dataset):
                     return np.array(var[:])
@@ -196,21 +197,21 @@ def ReadCategoryNamesAtTimestep(
         category: str,
         prefix: str,
         step: int,
-    ) -> Set[str]:
+    ) -> set[str]:
         h5 = _require_h5py()
         with h5.File(self.FullPath(path, category, step), "r") as f:
             f0 = Reader.__extract_step0(f)
-            keys: List[str] = list(f0.keys())
-            return set(c for c in keys if c.startswith(prefix))
+            keys: list[str] = list(f0.keys())
+            return {c for c in keys if c.startswith(prefix)}
 
     @override
     def ReadArrayShapeAtTimestep(
         self, path: str, category: str, quantity: str, step: int
-    ) -> Tuple[int, ...]:
+    ) -> tuple[int, ...]:
         h5 = _require_h5py()
         with h5.File(filename := self.FullPath(path, category, step), "r") as f:
             f0 = Reader.__extract_step0(f)
-            if quantity in f0.keys():
+            if quantity in f0:
                 var = f0[quantity]
                 if isinstance(var, h5.Dataset):
                     return var.shape
@@ -226,11 +227,11 @@ def ReadArrayShapeAtTimestep(
     @override
     def ReadArrayShapeExplicitlyAtTimestep(
         self, path: str, category: str, quantity: str, step: int
-    ) -> Tuple[int, ...]:
+    ) -> tuple[int, ...]:
         h5 = _require_h5py()
         with h5.File(self.FullPath(path, category, step), "r") as f:
             f0 = Reader.__extract_step0(f)
-            if quantity in f0.keys():
+            if quantity in f0:
                 var = f0[quantity]
                 if isinstance(var, h5.Dataset) and (read := var[:]) is not None:
                     return read.shape
@@ -246,7 +247,7 @@ def ReadArrayShapeExplicitlyAtTimestep(
     @override
     def ReadFieldCoordsAtTimestep(
         self, path: str, step: int
-    ) -> Dict[str, npt.NDArray[Any]]:
+    ) -> dict[str, npt.NDArray[Any]]:
         h5 = _require_h5py()
         with h5.File(filename := self.FullPath(path, "fields", step), "r") as f:
             f0 = Reader.__extract_step0(f)
@@ -256,9 +257,9 @@ def get_coord(c: str) -> Any:
                 if isinstance(f0_c, h5.Dataset):
                     return f0_c[:]
                 else:
-                    raise ValueError(f"Field {c} is not a group in the {filename}")
+                    raise TypeError(f"Field {c} is not a group in the {filename}")
 
-            keys: List[str] = list(f0.keys())
+            keys: list[str] = list(f0.keys())
             return {c: get_coord(c) for c in keys if re.match(r"^X[1|2|3]$", c)}
 
     @override
diff --git a/nt2/tests/test_cli.py b/nt2/tests/test_cli.py
index 49a00fe..f43a9da 100644
--- a/nt2/tests/test_cli.py
+++ b/nt2/tests/test_cli.py
@@ -127,5 +127,5 @@ def field_data(data_path):
     result = runner.invoke(app, ["plot", path, "--what", "fields", "--isel", "t=0"])
 
     assert result.exit_code == 1
-    assert isinstance(result.exception, FileNotFoundError)
-    assert str(result.exception).endswith("/fields'")
+    assert isinstance(result.exception, ValueError)
+    assert "Fields are not defined" in str(result.exception)
diff --git a/nt2/tests/test_containers.py b/nt2/tests/test_containers.py
index 5994100..a770a40 100644
--- a/nt2/tests/test_containers.py
+++ b/nt2/tests/test_containers.py
@@ -144,7 +144,7 @@ def field_remap(Fold: str):
         for container in [Data, FieldContainer]
     ],
 )
-def test_missing_field_output_raises_file_not_found(
+def test_missing_field_output_is_undefined(
     test, field_container: Union[Type[Data], Type[FieldContainer]]
 ):
     reader: BaseReader = test["reader"]()
@@ -155,16 +155,20 @@ def test_missing_field_output_raises_file_not_found(
         "num_cpus": 1,
     }
 
-    with pytest.raises(FileNotFoundError, match="/fields"):
-        if field_container is Data:
-            field_container(
-                **kwargs,
-                fields=True,
-                particles=False,
-                spectra=False,
-            )
-        else:
-            field_container(**kwargs, coord_system=None)
+    if field_container is Data:
+        container = field_container(
+            **kwargs,
+            fields=True,
+            particles=False,
+            spectra=False,
+        )
+        assert not container.fields_defined, "Fields are unexpectedly defined"
+        with pytest.raises(ValueError, match="Fields are not defined"):
+            _ = container.fields
+    else:
+        container = field_container(**kwargs, coord_system=None)
+        assert not container.fields_defined, "Fields are unexpectedly defined"
+        assert container.fields is None, "Fields are not None"
 
 
 @pytest.mark.parametrize(
diff --git a/nt2/utils.py b/nt2/utils.py
index f6d7900..8509d71 100644
--- a/nt2/utils.py
+++ b/nt2/utils.py
@@ -1,10 +1,12 @@
-from typing import Union, Literal
-from enum import Enum
+from __future__ import annotations
+
+import inspect
 import os
 import re
-import inspect
-import numpy as np
+from enum import Enum
+from typing import Literal
 
+import numpy as np
 import xarray as xr
 
 
@@ -77,7 +79,7 @@ def DetermineDataFormat(path: str) -> Format:
     raise ValueError("Could not determine file format.")
 
 
-def ToHumanReadable(num: Union[float, int], suffix: str = "B") -> str:
+def ToHumanReadable(num: float, suffix: str = "B") -> str:
     """Convert a number to a human-readable format with SI prefixes.
 
     Parameters

From 7dddd56012e4d128bcfec30505743dd395eac066 Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Tue, 15 Sep 2026 17:21:02 -0400
Subject: [PATCH 13/14] py 3.9 incompat rm

---
 nt2/containers/particle_dataset.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/nt2/containers/particle_dataset.py b/nt2/containers/particle_dataset.py
index 91bd675..70cdaf6 100644
--- a/nt2/containers/particle_dataset.py
+++ b/nt2/containers/particle_dataset.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import sys
 from collections.abc import Sequence
 from copy import copy
 from typing import (
@@ -19,8 +20,12 @@
 from dask.delayed import Delayed
 from dask.optimization import cull
 
-IntSelector = int | Sequence[int] | slice | tuple[int, int]
-FloatSelector = float | slice | Sequence[float] | tuple[float, float]
+if sys.version_info >= (3, 10):
+    IntSelector = int | Sequence[int] | slice | tuple[int, int]
+    FloatSelector = float | slice | Sequence[float] | tuple[float, float]
+else:
+    IntSelector = Any
+    FloatSelector = Any
 
 
 def _cull_dataframe_graph(ddf: dd.DataFrame) -> dd.DataFrame:

From ba1aeb3ac1fbaff260fa4b893138cefa3809bc1a Mon Sep 17 00:00:00 2001
From: haykh <haykh.astro@gmail.com>
Date: Tue, 15 Sep 2026 18:01:36 -0400
Subject: [PATCH 14/14] py3.9 support for cli app

---
 nt2/cli/main.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/nt2/cli/main.py b/nt2/cli/main.py
index ff4f6c7..718fe99 100644
--- a/nt2/cli/main.py
+++ b/nt2/cli/main.py
@@ -1,7 +1,5 @@
-from __future__ import annotations
-
 import os
-from typing import Annotated
+from typing import Annotated, Dict, Union
 
 import matplotlib.pyplot as plt
 import typer
@@ -28,11 +26,11 @@ def check_path(path: str) -> str:
     return path
 
 
-def check_sel(sel: str) -> dict[str, float | slice]:
+def check_sel(sel: str) -> Dict[str, Union[float, slice]]:
     if sel == "":
         return {}
     sel_list = sel.strip().split(";")
-    sel_dict: dict[str, float | slice] = {}
+    sel_dict: Dict[str, Union[float, slice]] = {}
     for _, s in enumerate(sel_list):
         coord, arg = s.strip().split("=", 1)
         coord = coord.strip()
@@ -136,10 +134,10 @@ def plot(
         if sel != {}:
             slices = {}
             sels = {}
-            slices: dict[str, slice | float] = {
+            slices: Dict[str, Union[slice, float]] = {
                 k: v for k, v in sel.items() if isinstance(v, slice)
             }
-            sels: dict[str, slice | float] = {
+            sels: Dict[str, Union[slice, float]] = {
                 k: v for k, v in sel.items() if not isinstance(v, slice)
             }
             d = d.sel(**sels, method="nearest")