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hfpytrace.collision

Package

Collision-frequency models and NRLMSISE-backed neutral background support.

Key Classes

Class NRLMSISE2D
Class NRLMSISE3D
Class ComputeCollision

Key Methods

Method NRLMSISE2D.fetch_dataset()
Method NRLMSISE3D.fetch_dataset()
Method ComputeCollision.from_nrlmsise()
Method ComputeCollision.from_nrlmsise_3d()
Method ComputeCollision.calculate_FT_collision_frequency()

API

hfpytrace.collision

Electron collision-frequency models.

Provides NRLMSISE-00-based neutral background models (2D and 3D) and multiple formulations for computing electron collision frequencies:

  • Friedrich-Tonker (FT) — pressure-based approximation.
  • Schunk-Nagy (SN) electron-neutral — species-resolved (N₂, O₂, O, H, He).
  • Schunk-Nagy (SN) electron-ion — Coulomb logarithm formulation (O₂⁺, O⁺).

The results are stored as a nested :class:Collision dataclass, which can be passed directly to HF ray-tracing engine grid construction.

Requires

nrlmsise00 : for neutral atmosphere profiles (pip install 'nrlmsise00[dataset]').

Classes

Collision_en Per-species electron-neutral collision frequency container. Collision_ei Per-species electron-ion collision frequency container. Collision_SN Combined Schunk-Nagy collision container (SN-en + SN-ei + total). Collision Top-level collision dataclass (FT variants + SN). NRLMSISE2D NRLMSISE-00 neutral background on a 2D (height × route) grid. NRLMSISE3D NRLMSISE-00 neutral background on a 3D (lat × lon × height) grid. ComputeCollision Derives all collision profiles from plasma/neutral state arrays.

Collision_en dataclass

Per-species electron-neutral collision frequency arrays.

Each field has the same shape as the input plasma/neutral grids. Units are Hz (s⁻¹) throughout.

Attributes

N2, O2, O, H, He : np.ndarray or None Species-resolved collision frequency contributions.

np.ndarray or None

Sum of all species contributions.

Source code in hfpytrace/collision.py 
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@dataclass
class Collision_en:
    """Per-species electron-neutral collision frequency arrays.

    Each field has the same shape as the input plasma/neutral grids.
    Units are Hz (s⁻¹) throughout.

    Attributes
    ----------
    N2, O2, O, H, He : np.ndarray or None
        Species-resolved collision frequency contributions.
    total : np.ndarray or None
        Sum of all species contributions.
    """

    N2: np.ndarray | None = None
    O2: np.ndarray | None = None
    O: np.ndarray | None = None
    H: np.ndarray | None = None
    He: np.ndarray | None = None
    total: np.ndarray | None = None

Collision_ei dataclass

Per-species electron-ion collision frequency arrays.

Attributes

O2p, Op : np.ndarray or None Species-resolved collision frequency contributions.

np.ndarray or None

Sum of O₂⁺ and O⁺ contributions.

Source code in hfpytrace/collision.py 
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@dataclass
class Collision_ei:
    """Per-species electron-ion collision frequency arrays.

    Attributes
    ----------
    O2p, Op : np.ndarray or None
        Species-resolved collision frequency contributions.
    total : np.ndarray or None
        Sum of O₂⁺ and O⁺ contributions.
    """

    O2p: np.ndarray | None = None
    Op: np.ndarray | None = None
    total: np.ndarray | None = None

Collision_SN dataclass

Schunk-Nagy collision container combining electron-neutral and electron-ion terms.

Attributes
Collision_en or None

Electron-neutral contributions per species.

Collision_ei or None

Electron-ion contributions per species.

np.ndarray or None

Sum of all Schunk-Nagy contributions (en.total + ei.total).

Source code in hfpytrace/collision.py 
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@dataclass
class Collision_SN:
    """Schunk-Nagy collision container combining electron-neutral and electron-ion terms.

    Attributes
    ----------
    en : Collision_en or None
        Electron-neutral contributions per species.
    ei : Collision_ei or None
        Electron-ion contributions per species.
    total : np.ndarray or None
        Sum of all Schunk-Nagy contributions (en.total + ei.total).
    """

    en: Collision_en | None = None
    ei: Collision_ei | None = None
    total: np.ndarray | None = None

Collision dataclass

Top-level collision-frequency container.

Attributes
np.ndarray or None

Friedrich-Tonker electron-neutral collision frequency (frac=1.0).

np.ndarray or None

Friedrich-Tonker with classical Coulomb correction (frac=2.5).

np.ndarray or None

Friedrich-Tonker with Maxwell-Boltzmann correction (frac=1.5).

Collision_SN or None

Full Schunk-Nagy (electron-neutral + electron-ion) collision container.

Source code in hfpytrace/collision.py 
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@dataclass
class Collision:
    """Top-level collision-frequency container.

    Attributes
    ----------
    nu_ft : np.ndarray or None
        Friedrich-Tonker electron-neutral collision frequency (frac=1.0).
    nu_av_cc : np.ndarray or None
        Friedrich-Tonker with classical Coulomb correction (frac=2.5).
    nu_av_mb : np.ndarray or None
        Friedrich-Tonker with Maxwell-Boltzmann correction (frac=1.5).
    nu_sn : Collision_SN or None
        Full Schunk-Nagy (electron-neutral + electron-ion) collision container.
    """

    nu_ft: np.ndarray | None = None
    nu_av_cc: np.ndarray | None = None
    nu_av_mb: np.ndarray | None = None
    nu_sn: Collision_SN | None = None

NRLMSISE2D

Bases: object

Build NRLMSISE-00 neutral background on a 2D (height x path-range) grid.

Inputs: - date: datetime for model evaluation - lats, lons: 1D path coordinates, length Nr - heights_km: 1D heights, length Nh

Outputs are stored in self.msise as arrays of shape (Nh, Nr), with densities in m^-3 (SI, as returned by nrlmsise00) and temperatures in K.

Source code in hfpytrace/collision.py 
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class NRLMSISE2D(object):
    """
    Build NRLMSISE-00 neutral background on a 2D (height x path-range) grid.

    Inputs:
    - date: datetime for model evaluation
    - lats, lons: 1D path coordinates, length Nr
    - heights_km: 1D heights, length Nh

    Outputs are stored in `self.msise` as arrays of shape (Nh, Nr), with
    densities in m^-3 (SI, as returned by nrlmsise00) and temperatures in K.
    """

    def __init__(
        self,
        date: dt.datetime,
        lats,
        lons,
        heights_km,
        workers: int = 1,
        update_spaceweather: bool = False,
        suppress_spaceweather_warning: bool = True,
    ):
        self.date = date
        self.lats = np.asarray(lats, dtype=float)
        self.lons = np.asarray(lons, dtype=float)
        self.heights_km = np.asarray(heights_km, dtype=float)
        self.workers = max(1, int(workers))
        self.update_spaceweather = update_spaceweather
        self.suppress_spaceweather_warning = suppress_spaceweather_warning
        if self.lats.shape != self.lons.shape:
            raise ValueError("lats and lons must have the same shape")
        if self.lats.ndim != 1:
            raise ValueError("lats and lons must be 1D arrays")
        if self.heights_km.ndim != 1:
            raise ValueError("heights_km must be a 1D array")
        self.msise = self.fetch_dataset()

    def fetch_dataset(self) -> dict[str, np.ndarray]:
        try:
            from nrlmsise00.dataset import msise_4d
        except ImportError as exc:
            raise ImportError(
                "nrlmsise00 dataset interface is unavailable. "
                "Install extras: pip install 'nrlmsise00[dataset]'"
            ) from exc

        if self.update_spaceweather:
            try:
                from spaceweather import sw

                sw.update_data()
                logger.info("Updated spaceweather local data files.")
            except Exception as exc:
                logger.warning(f"spaceweather update failed: {exc}")

        nh = self.heights_km.size
        nr = self.lats.size
        out = dict(
            N2=np.zeros((nh, nr), dtype=float),
            O2=np.zeros((nh, nr), dtype=float),
            O=np.zeros((nh, nr), dtype=float),
            H=np.zeros((nh, nr), dtype=float),
            He=np.zeros((nh, nr), dtype=float),
            Tn=np.zeros((nh, nr), dtype=float),
            t_nn=np.zeros((nh, nr), dtype=float),
        )

        logger.info(
            f"Running NRLMSISE-00 for {self.date} on grid Nh={nh}, Nr={nr} "
            f"with workers={self.workers}"
        )
        if self.workers == 1:
            for j, (lat, lon) in enumerate(zip(self.lats, self.lons)):
                with warnings.catch_warnings():
                    if self.suppress_spaceweather_warning:
                        warnings.filterwarnings(
                            "ignore",
                            message="Local data files are older than 30days.*",
                            category=UserWarning,
                        )
                    ds = msise_4d(
                        self.date, self.heights_km, np.array([lat]), np.array([lon])
                    )
                out["N2"][:, j] = ds.variables["N2"].values[0, :, 0, 0]
                out["O2"][:, j] = ds.variables["O2"].values[0, :, 0, 0]
                out["O"][:, j] = ds.variables["O"].values[0, :, 0, 0]
                out["H"][:, j] = ds.variables["H"].values[0, :, 0, 0]
                out["He"][:, j] = ds.variables["He"].values[0, :, 0, 0]
                out["Tn"][:, j] = ds.variables["Talt"].values[0, :, 0, 0]
        else:
            idx_chunks = np.array_split(np.arange(nr, dtype=int), self.workers)
            idx_chunks = [c for c in idx_chunks if c.size > 0]
            logger.info(
                f"Running NRLMSISE2D with process workers={self.workers} "
                f"on {len(idx_chunks)} route chunks"
            )
            with ProcessPoolExecutor(max_workers=self.workers) as ex:
                results = list(
                    ex.map(
                        _msise2d_chunk,
                        [
                            (
                                self.date,
                                self.heights_km,
                                self.lats[idx],
                                self.lons[idx],
                                idx,
                                self.suppress_spaceweather_warning,
                            )
                            for idx in idx_chunks
                        ],
                    )
                )
            for idx, chunk in results:
                out["N2"][:, idx] = chunk["N2"]
                out["O2"][:, idx] = chunk["O2"]
                out["O"][:, idx] = chunk["O"]
                out["H"][:, idx] = chunk["H"]
                out["He"][:, idx] = chunk["He"]
                out["Tn"][:, idx] = chunk["Tn"]

        out["t_nn"] = out["N2"] + out["O2"] + out["O"] + out["H"] + out["He"]
        return out

    def as_collision_kwargs(self) -> dict[str, np.ndarray]:
        """
        Convenience mapping for ComputeCollision neutral inputs.
        """
        return dict(
            Tn=self.msise["Tn"],
            N2=self.msise["N2"],
            O2=self.msise["O2"],
            O=self.msise["O"],
            H=self.msise["H"],
            He=self.msise["He"],
        )

as_collision_kwargs()

Convenience mapping for ComputeCollision neutral inputs.

Source code in hfpytrace/collision.py 
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def as_collision_kwargs(self) -> dict[str, np.ndarray]:
    """
    Convenience mapping for ComputeCollision neutral inputs.
    """
    return dict(
        Tn=self.msise["Tn"],
        N2=self.msise["N2"],
        O2=self.msise["O2"],
        O=self.msise["O"],
        H=self.msise["H"],
        He=self.msise["He"],
    )

NRLMSISE3D

Bases: object

Build NRLMSISE-00 neutral background on a 3D (lat x lon x height) grid.

This path is vectorized through one msise_4d call and is substantially faster than point-by-point evaluation for large 3D grids.

Source code in hfpytrace/collision.py 
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class NRLMSISE3D(object):
    """
    Build NRLMSISE-00 neutral background on a 3D (lat x lon x height) grid.

    This path is vectorized through one `msise_4d` call and is substantially
    faster than point-by-point evaluation for large 3D grids.
    """

    def __init__(
        self,
        date: dt.datetime,
        lats,
        lons,
        heights_km,
        workers: int = 1,
        update_spaceweather: bool = False,
        suppress_spaceweather_warning: bool = True,
    ):
        self.date = date
        self.lats = np.asarray(lats, dtype=float)
        self.lons = np.asarray(lons, dtype=float)
        self.heights_km = np.asarray(heights_km, dtype=float)
        self.workers = max(1, int(workers))
        self.update_spaceweather = update_spaceweather
        self.suppress_spaceweather_warning = suppress_spaceweather_warning
        if self.lats.ndim != 1 or self.lons.ndim != 1:
            raise ValueError("lats and lons must be 1D arrays for NRLMSISE3D")
        if self.heights_km.ndim != 1:
            raise ValueError("heights_km must be a 1D array for NRLMSISE3D")
        self.msise = self.fetch_dataset()

    def fetch_dataset(self) -> dict[str, np.ndarray]:
        try:
            from nrlmsise00.dataset import msise_4d
        except ImportError as exc:
            raise ImportError(
                "nrlmsise00 dataset interface is unavailable. "
                "Install extras: pip install 'nrlmsise00[dataset]'"
            ) from exc

        if self.update_spaceweather:
            try:
                from spaceweather import sw

                sw.update_data()
                logger.info("Updated spaceweather local data files.")
            except Exception as exc:
                logger.warning(f"spaceweather update failed: {exc}")

        if self.workers == 1:
            with warnings.catch_warnings():
                if self.suppress_spaceweather_warning:
                    warnings.filterwarnings(
                        "ignore",
                        message="Local data files are older than 30days.*",
                        category=UserWarning,
                    )
                ds = msise_4d(self.date, self.heights_km, self.lats, self.lons)

            def _llh(var_name: str) -> np.ndarray:
                arr = ds.variables[var_name].values[0, :, :, :]  # alt, lat, lon
                return np.transpose(arr, (1, 2, 0))  # lat, lon, alt

            out = dict(
                N2=_llh("N2"),
                O2=_llh("O2"),
                O=_llh("O"),
                H=_llh("H"),
                He=_llh("He"),
                Tn=_llh("Talt"),
            )
            out["t_nn"] = out["N2"] + out["O2"] + out["O"] + out["H"] + out["He"]
            return out

        lat_chunks = np.array_split(self.lats, self.workers)
        lat_chunks = [c for c in lat_chunks if c.size > 0]
        logger.info(
            f"Running NRLMSISE3D with process workers={self.workers} "
            f"on {len(lat_chunks)} latitude chunks"
        )
        with ProcessPoolExecutor(max_workers=self.workers) as ex:
            results = list(
                ex.map(
                    _msise3d_chunk,
                    [
                        (
                            self.date,
                            self.heights_km,
                            c,
                            self.lons,
                            self.suppress_spaceweather_warning,
                        )
                        for c in lat_chunks
                    ],
                )
            )

        results.sort(key=lambda x: x[0][0])
        keys = ["N2", "O2", "O", "H", "He", "Tn", "t_nn"]
        out = {k: np.concatenate([r[1][k] for r in results], axis=0) for k in keys}
        return out

    def as_collision_kwargs(self) -> dict[str, np.ndarray]:
        return dict(
            Tn=self.msise["Tn"],
            N2=self.msise["N2"],
            O2=self.msise["O2"],
            O=self.msise["O"],
            H=self.msise["H"],
            He=self.msise["He"],
        )

ComputeCollision

Bases: object

Estimate collision profiles from provided plasma/neutral state arrays.

Expected units: - Temperatures (Te, Ti, Tn): K - Neutral densities (N2, O2, O, H, He): m^-3 (SI; as returned by NRLMSISE-00) - Plasma densities (edens, O2p, Op): cm^-3 (as returned by IRI and RT model profiles)

The SN electron-neutral formulas apply a 1e-6 factor internally to handle the m^-3 neutral inputs. The FT formula uses neutral pressure (m^-3 × k_B × T) in SI throughout. The SN electron-ion formula converts plasma densities to m^-3 internally for both the Debye length and the collision rate.

Source code in hfpytrace/collision.py 
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class ComputeCollision(object):
    """
    Estimate collision profiles from provided plasma/neutral state arrays.

    Expected units:
    - Temperatures (Te, Ti, Tn): K
    - Neutral densities (N2, O2, O, H, He): m^-3  (SI; as returned by NRLMSISE-00)
    - Plasma densities (edens, O2p, Op): cm^-3  (as returned by IRI and RT model profiles)

    The SN electron-neutral formulas apply a 1e-6 factor internally to handle the
    m^-3 neutral inputs. The FT formula uses neutral pressure (m^-3 × k_B × T) in
    SI throughout. The SN electron-ion formula converts plasma densities to m^-3
    internally for both the Debye length and the collision rate.
    """

    def __init__(
        self,
        Te,
        Ti,
        Tn,
        edens,
        O2p,
        Op,
        N2,
        O2,
        O,
        H,
        He,
        date: dt.datetime | None = None,
    ):
        self.Te = np.asarray(Te, dtype=float)
        self.Ti = np.asarray(Ti, dtype=float)
        self.Tn = np.asarray(Tn, dtype=float)

        self.edens = np.asarray(edens, dtype=float)
        self.O2p = np.asarray(O2p, dtype=float)
        self.Op = np.asarray(Op, dtype=float)

        self.N2 = np.asarray(N2, dtype=float)
        self.O2 = np.asarray(O2, dtype=float)
        self.O = np.asarray(O, dtype=float)
        self.H = np.asarray(H, dtype=float)
        self.He = np.asarray(He, dtype=float)

        self.t_nn = self.N2 + self.O2 + self.O + self.H + self.He
        self.date = date
        if date:
            logger.info(f"Compute collision profiles for {date}")

        self.collision = Collision()
        self.collision.nu_sn = Collision_SN(
            en=Collision_en(),
            ei=Collision_ei(),
            total=np.zeros_like(self.Te),
        )

        self.collision.nu_ft = self.calculate_FT_collision_frequency()
        self.collision.nu_av_cc = self.calculate_FT_collision_frequency(2.5)
        self.collision.nu_av_mb = self.calculate_FT_collision_frequency(1.5)
        self.calculate_SN_en_collision_frequency()
        self.calculate_SN_ei_collision_frequency()
        return

    @classmethod
    def from_nrlmsise(
        cls,
        *,
        date: dt.datetime,
        lats,
        lons,
        heights_km,
        Te,
        Ti,
        edens,
        O2p,
        Op,
        workers: int = 1,
        update_spaceweather: bool = False,
        suppress_spaceweather_warning: bool = True,
    ):
        """
        Build collision model using neutral fields from NRLMSISE2D and plasma
        fields from the caller (e.g., IRI).
        """
        bg = NRLMSISE2D(
            date=date,
            lats=lats,
            lons=lons,
            heights_km=heights_km,
            workers=workers,
            update_spaceweather=update_spaceweather,
            suppress_spaceweather_warning=suppress_spaceweather_warning,
        )
        return cls(
            Te=Te,
            Ti=Ti,
            Tn=bg.msise["Tn"],
            edens=edens,
            O2p=O2p,
            Op=Op,
            N2=bg.msise["N2"],
            O2=bg.msise["O2"],
            O=bg.msise["O"],
            H=bg.msise["H"],
            He=bg.msise["He"],
            date=date,
        )

    @classmethod
    def from_nrlmsise_3d(
        cls,
        *,
        date: dt.datetime,
        lats,
        lons,
        heights_km,
        Te,
        Ti,
        edens,
        O2p,
        Op,
        workers: int = 1,
        update_spaceweather: bool = False,
        suppress_spaceweather_warning: bool = True,
    ):
        """
        Build collision model using neutral fields from NRLMSISE3D and plasma
        fields on a 3D (lat x lon x height) grid.
        """
        bg = NRLMSISE3D(
            date=date,
            lats=lats,
            lons=lons,
            heights_km=heights_km,
            workers=workers,
            update_spaceweather=update_spaceweather,
            suppress_spaceweather_warning=suppress_spaceweather_warning,
        )
        return cls(
            Te=Te,
            Ti=Ti,
            Tn=bg.msise["Tn"],
            edens=edens,
            O2p=O2p,
            Op=Op,
            N2=bg.msise["N2"],
            O2=bg.msise["O2"],
            O=bg.msise["O"],
            H=bg.msise["H"],
            He=bg.msise["He"],
            date=date,
        )

    def calculate_FT_collision_frequency(self, frac: float = 1.0):
        """
        Friedrich-Tonker electron-neutral collision frequency.

        t_nn is in cm^-3 (SI) need to convert; pressure p = n [m^-3] * k_B [J/K] * T [K] in Pa.
        """
        logger.info(
            f"Compute Friedrich-Tonker electron-neutral collision frequency with a={frac}"
        )
        t_nn = 1e6 * self.t_nn
        Te = np.clip(self.Te, 1.0, None)
        p = t_nn * self.Tn * pconst["boltz"]
        nu = (2.637e6 / np.sqrt(Te) + 4.945e5) * p
        return frac * nu

    def atmospheric_ion_neutral_collision_frequency(self):
        """
        Atmospheric ion-neutral collision frequency from total neutral density.

        Formula constant 3.8e-11 expects density in cm^-3; t_nn is in cm^-3,
        so multiply by 1e-6 to convert (if you think you set it in m^-3):
        ν = 3.8e-11 * n[cm^-3] = 3.8e-17 * n[m^-3].
        """
        return 3.8e-11 * self.t_nn

    def calculate_SN_ei_collision_frequency(self, gamma: float = 0.5572, zi: int = 2):
        """
        Schunk-Nagy electron-ion collision frequency profile.
        """
        logger.warning("Compute Schunk-Nagy electron-ion collision frequency")

        e = pconst["q_e"]
        k = pconst["boltz"]
        me = pconst["m_e"]
        eps0 = pconst["eps0"]
        k_e = 1 / (4 * np.pi * eps0)

        Te = np.clip(self.Te, 1.0, None)
        Ti = np.clip(self.Ti, 1.0, None)
        Ne = np.clip(self.edens, 1e-12, None)

        # Plasma densities (edens, O2p, Op) are in cm^-3; convert to m^-3
        # for the SI Coulomb-logarithm and collision rate formula.
        Ne_m3 = Ne * 1e6

        for key, Ni in {"O2p": self.O2p, "Op": self.Op}.items():
            Ni = np.clip(Ni, 1e-12, None)
            Ni_m3 = Ni * 1e6

            ki2 = 4 * np.pi * Ni_m3 * e**2 * zi**2 * k_e / (k * Ti)
            ke2 = 4 * np.pi * Ne_m3 * e**2 * k_e / (k * Te)

            ki2 = np.clip(ki2, 1e-30, None)
            ke2 = np.clip(ke2, 1e-30, None)

            ke = np.sqrt(ke2)
            lam = np.log(4 * k * Te / (gamma**2 * zi * e**2 * k_e * ke)) - (
                ((ke2 + ki2) / ki2) * np.log(np.sqrt((ke2 + ki2) / ke2))
            )
            lam = np.clip(lam, 1e-6, None)

            nu_ei = (
                4
                * np.sqrt(2 * np.pi)
                * Ni_m3  # SI formula requires m^-3 here
                * (zi * e**2 * k_e) ** 2
                * lam
                / (3 * np.sqrt(me) * (k * Te) ** 1.5)
            )
            setattr(self.collision.nu_sn.ei, key, nu_ei)

        self.collision.nu_sn.ei.total = (
            self.collision.nu_sn.ei.O2p + self.collision.nu_sn.ei.Op
        )
        self.collision.nu_sn.total += self.collision.nu_sn.ei.total

    def calculate_SN_en_collision_frequency(self):
        """
        Schunk-Nagy electron-neutral collision frequency profile.
        """
        logger.warning("Compute Schunk-Nagy electron-neutral collision frequency")

        Te = np.clip(self.Te, 1.0, None)
        sqrt_Te = np.sqrt(Te)

        self.collision.nu_sn.en.N2 = 1e-6 * 2.33e-11 * self.N2 * (1 - 1.12e-4 * Te) * Te
        self.collision.nu_sn.en.O2 = (
            1e-6 * 1.82e-10 * self.O2 * (1 + 3.6e-2 * sqrt_Te) * sqrt_Te
        )
        self.collision.nu_sn.en.O = (
            1e-6 * 8.9e-11 * self.O * (1 + 5.7e-4 * Te) * sqrt_Te
        )
        self.collision.nu_sn.en.He = 1e-6 * 4.6e-10 * self.He * sqrt_Te
        self.collision.nu_sn.en.H = (
            1e-6 * 4.5e-9 * self.H * (1 - 1.35e-4 * Te) * sqrt_Te
        )

        self.collision.nu_sn.en.total = (
            self.collision.nu_sn.en.N2
            + self.collision.nu_sn.en.O2
            + self.collision.nu_sn.en.O
            + self.collision.nu_sn.en.He
            + self.collision.nu_sn.en.H
        )
        self.collision.nu_sn.total += self.collision.nu_sn.en.total

    @staticmethod
    def atmospheric_collision_frequency(ni, nn, T):
        """
        Atmospheric collision profile from ion(electron)/neutral density and temperature.
        """
        na_profile = lambda T, nn: (1.8 * 1e-8 * nn * np.sqrt(T / 300))
        ni_profile = lambda T, ni: (6.1 * 1e-3 * ni * (300 / T) * np.sqrt(300 / T))
        return ni_profile(T, ni) + na_profile(T, nn)

from_nrlmsise(*, date, lats, lons, heights_km, Te, Ti, edens, O2p, Op, workers=1, update_spaceweather=False, suppress_spaceweather_warning=True) classmethod

Build collision model using neutral fields from NRLMSISE2D and plasma fields from the caller (e.g., IRI).

Source code in hfpytrace/collision.py 
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@classmethod
def from_nrlmsise(
    cls,
    *,
    date: dt.datetime,
    lats,
    lons,
    heights_km,
    Te,
    Ti,
    edens,
    O2p,
    Op,
    workers: int = 1,
    update_spaceweather: bool = False,
    suppress_spaceweather_warning: bool = True,
):
    """
    Build collision model using neutral fields from NRLMSISE2D and plasma
    fields from the caller (e.g., IRI).
    """
    bg = NRLMSISE2D(
        date=date,
        lats=lats,
        lons=lons,
        heights_km=heights_km,
        workers=workers,
        update_spaceweather=update_spaceweather,
        suppress_spaceweather_warning=suppress_spaceweather_warning,
    )
    return cls(
        Te=Te,
        Ti=Ti,
        Tn=bg.msise["Tn"],
        edens=edens,
        O2p=O2p,
        Op=Op,
        N2=bg.msise["N2"],
        O2=bg.msise["O2"],
        O=bg.msise["O"],
        H=bg.msise["H"],
        He=bg.msise["He"],
        date=date,
    )

from_nrlmsise_3d(*, date, lats, lons, heights_km, Te, Ti, edens, O2p, Op, workers=1, update_spaceweather=False, suppress_spaceweather_warning=True) classmethod

Build collision model using neutral fields from NRLMSISE3D and plasma fields on a 3D (lat x lon x height) grid.

Source code in hfpytrace/collision.py 
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@classmethod
def from_nrlmsise_3d(
    cls,
    *,
    date: dt.datetime,
    lats,
    lons,
    heights_km,
    Te,
    Ti,
    edens,
    O2p,
    Op,
    workers: int = 1,
    update_spaceweather: bool = False,
    suppress_spaceweather_warning: bool = True,
):
    """
    Build collision model using neutral fields from NRLMSISE3D and plasma
    fields on a 3D (lat x lon x height) grid.
    """
    bg = NRLMSISE3D(
        date=date,
        lats=lats,
        lons=lons,
        heights_km=heights_km,
        workers=workers,
        update_spaceweather=update_spaceweather,
        suppress_spaceweather_warning=suppress_spaceweather_warning,
    )
    return cls(
        Te=Te,
        Ti=Ti,
        Tn=bg.msise["Tn"],
        edens=edens,
        O2p=O2p,
        Op=Op,
        N2=bg.msise["N2"],
        O2=bg.msise["O2"],
        O=bg.msise["O"],
        H=bg.msise["H"],
        He=bg.msise["He"],
        date=date,
    )

calculate_FT_collision_frequency(frac=1.0)

Friedrich-Tonker electron-neutral collision frequency.

t_nn is in cm^-3 (SI) need to convert; pressure p = n [m^-3] * k_B [J/K] * T [K] in Pa.

Source code in hfpytrace/collision.py 
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def calculate_FT_collision_frequency(self, frac: float = 1.0):
    """
    Friedrich-Tonker electron-neutral collision frequency.

    t_nn is in cm^-3 (SI) need to convert; pressure p = n [m^-3] * k_B [J/K] * T [K] in Pa.
    """
    logger.info(
        f"Compute Friedrich-Tonker electron-neutral collision frequency with a={frac}"
    )
    t_nn = 1e6 * self.t_nn
    Te = np.clip(self.Te, 1.0, None)
    p = t_nn * self.Tn * pconst["boltz"]
    nu = (2.637e6 / np.sqrt(Te) + 4.945e5) * p
    return frac * nu

atmospheric_ion_neutral_collision_frequency()

Atmospheric ion-neutral collision frequency from total neutral density.

Formula constant 3.8e-11 expects density in cm^-3; t_nn is in cm^-3, so multiply by 1e-6 to convert (if you think you set it in m^-3): ν = 3.8e-11 * n[cm^-3] = 3.8e-17 * n[m^-3].

Source code in hfpytrace/collision.py 
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def atmospheric_ion_neutral_collision_frequency(self):
    """
    Atmospheric ion-neutral collision frequency from total neutral density.

    Formula constant 3.8e-11 expects density in cm^-3; t_nn is in cm^-3,
    so multiply by 1e-6 to convert (if you think you set it in m^-3):
    ν = 3.8e-11 * n[cm^-3] = 3.8e-17 * n[m^-3].
    """
    return 3.8e-11 * self.t_nn

calculate_SN_ei_collision_frequency(gamma=0.5572, zi=2)

Schunk-Nagy electron-ion collision frequency profile.

Source code in hfpytrace/collision.py 
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def calculate_SN_ei_collision_frequency(self, gamma: float = 0.5572, zi: int = 2):
    """
    Schunk-Nagy electron-ion collision frequency profile.
    """
    logger.warning("Compute Schunk-Nagy electron-ion collision frequency")

    e = pconst["q_e"]
    k = pconst["boltz"]
    me = pconst["m_e"]
    eps0 = pconst["eps0"]
    k_e = 1 / (4 * np.pi * eps0)

    Te = np.clip(self.Te, 1.0, None)
    Ti = np.clip(self.Ti, 1.0, None)
    Ne = np.clip(self.edens, 1e-12, None)

    # Plasma densities (edens, O2p, Op) are in cm^-3; convert to m^-3
    # for the SI Coulomb-logarithm and collision rate formula.
    Ne_m3 = Ne * 1e6

    for key, Ni in {"O2p": self.O2p, "Op": self.Op}.items():
        Ni = np.clip(Ni, 1e-12, None)
        Ni_m3 = Ni * 1e6

        ki2 = 4 * np.pi * Ni_m3 * e**2 * zi**2 * k_e / (k * Ti)
        ke2 = 4 * np.pi * Ne_m3 * e**2 * k_e / (k * Te)

        ki2 = np.clip(ki2, 1e-30, None)
        ke2 = np.clip(ke2, 1e-30, None)

        ke = np.sqrt(ke2)
        lam = np.log(4 * k * Te / (gamma**2 * zi * e**2 * k_e * ke)) - (
            ((ke2 + ki2) / ki2) * np.log(np.sqrt((ke2 + ki2) / ke2))
        )
        lam = np.clip(lam, 1e-6, None)

        nu_ei = (
            4
            * np.sqrt(2 * np.pi)
            * Ni_m3  # SI formula requires m^-3 here
            * (zi * e**2 * k_e) ** 2
            * lam
            / (3 * np.sqrt(me) * (k * Te) ** 1.5)
        )
        setattr(self.collision.nu_sn.ei, key, nu_ei)

    self.collision.nu_sn.ei.total = (
        self.collision.nu_sn.ei.O2p + self.collision.nu_sn.ei.Op
    )
    self.collision.nu_sn.total += self.collision.nu_sn.ei.total

calculate_SN_en_collision_frequency()

Schunk-Nagy electron-neutral collision frequency profile.

Source code in hfpytrace/collision.py 
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def calculate_SN_en_collision_frequency(self):
    """
    Schunk-Nagy electron-neutral collision frequency profile.
    """
    logger.warning("Compute Schunk-Nagy electron-neutral collision frequency")

    Te = np.clip(self.Te, 1.0, None)
    sqrt_Te = np.sqrt(Te)

    self.collision.nu_sn.en.N2 = 1e-6 * 2.33e-11 * self.N2 * (1 - 1.12e-4 * Te) * Te
    self.collision.nu_sn.en.O2 = (
        1e-6 * 1.82e-10 * self.O2 * (1 + 3.6e-2 * sqrt_Te) * sqrt_Te
    )
    self.collision.nu_sn.en.O = (
        1e-6 * 8.9e-11 * self.O * (1 + 5.7e-4 * Te) * sqrt_Te
    )
    self.collision.nu_sn.en.He = 1e-6 * 4.6e-10 * self.He * sqrt_Te
    self.collision.nu_sn.en.H = (
        1e-6 * 4.5e-9 * self.H * (1 - 1.35e-4 * Te) * sqrt_Te
    )

    self.collision.nu_sn.en.total = (
        self.collision.nu_sn.en.N2
        + self.collision.nu_sn.en.O2
        + self.collision.nu_sn.en.O
        + self.collision.nu_sn.en.He
        + self.collision.nu_sn.en.H
    )
    self.collision.nu_sn.total += self.collision.nu_sn.en.total

atmospheric_collision_frequency(ni, nn, T) staticmethod

Atmospheric collision profile from ion(electron)/neutral density and temperature.

Source code in hfpytrace/collision.py 
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@staticmethod
def atmospheric_collision_frequency(ni, nn, T):
    """
    Atmospheric collision profile from ion(electron)/neutral density and temperature.
    """
    na_profile = lambda T, nn: (1.8 * 1e-8 * nn * np.sqrt(T / 300))
    ni_profile = lambda T, ni: (6.1 * 1e-3 * ni * (300 / T) * np.sqrt(300 / T))
    return ni_profile(T, ni) + na_profile(T, nn)

Source Code

hfpytrace/collision.py
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"""Electron collision-frequency models.

Provides NRLMSISE-00-based neutral background models (2D and 3D) and
multiple formulations for computing electron collision frequencies:

* **Friedrich-Tonker (FT)** — pressure-based approximation.
* **Schunk-Nagy (SN) electron-neutral** — species-resolved (N₂, O₂, O, H, He).
* **Schunk-Nagy (SN) electron-ion** — Coulomb logarithm formulation (O₂⁺, O⁺).

The results are stored as a nested :class:`Collision` dataclass, which can
be passed directly to HF ray-tracing engine grid construction.

Requires
--------
nrlmsise00 : for neutral atmosphere profiles (``pip install 'nrlmsise00[dataset]'``).

Classes
-------
Collision_en
    Per-species electron-neutral collision frequency container.
Collision_ei
    Per-species electron-ion collision frequency container.
Collision_SN
    Combined Schunk-Nagy collision container (SN-en + SN-ei + total).
Collision
    Top-level collision dataclass (FT variants + SN).
NRLMSISE2D
    NRLMSISE-00 neutral background on a 2D (height × route) grid.
NRLMSISE3D
    NRLMSISE-00 neutral background on a 3D (lat × lon × height) grid.
ComputeCollision
    Derives all collision profiles from plasma/neutral state arrays.
"""

import datetime as dt
import warnings
from concurrent.futures import ProcessPoolExecutor
from dataclasses import dataclass

import numpy as np
from loguru import logger

from hfpytrace.utils import pconst


@dataclass
class Collision_en:
    """Per-species electron-neutral collision frequency arrays.

    Each field has the same shape as the input plasma/neutral grids.
    Units are Hz (s⁻¹) throughout.

    Attributes
    ----------
    N2, O2, O, H, He : np.ndarray or None
        Species-resolved collision frequency contributions.
    total : np.ndarray or None
        Sum of all species contributions.
    """

    N2: np.ndarray | None = None
    O2: np.ndarray | None = None
    O: np.ndarray | None = None
    H: np.ndarray | None = None
    He: np.ndarray | None = None
    total: np.ndarray | None = None


@dataclass
class Collision_ei:
    """Per-species electron-ion collision frequency arrays.

    Attributes
    ----------
    O2p, Op : np.ndarray or None
        Species-resolved collision frequency contributions.
    total : np.ndarray or None
        Sum of O₂⁺ and O⁺ contributions.
    """

    O2p: np.ndarray | None = None
    Op: np.ndarray | None = None
    total: np.ndarray | None = None


@dataclass
class Collision_SN:
    """Schunk-Nagy collision container combining electron-neutral and electron-ion terms.

    Attributes
    ----------
    en : Collision_en or None
        Electron-neutral contributions per species.
    ei : Collision_ei or None
        Electron-ion contributions per species.
    total : np.ndarray or None
        Sum of all Schunk-Nagy contributions (en.total + ei.total).
    """

    en: Collision_en | None = None
    ei: Collision_ei | None = None
    total: np.ndarray | None = None


@dataclass
class Collision:
    """Top-level collision-frequency container.

    Attributes
    ----------
    nu_ft : np.ndarray or None
        Friedrich-Tonker electron-neutral collision frequency (frac=1.0).
    nu_av_cc : np.ndarray or None
        Friedrich-Tonker with classical Coulomb correction (frac=2.5).
    nu_av_mb : np.ndarray or None
        Friedrich-Tonker with Maxwell-Boltzmann correction (frac=1.5).
    nu_sn : Collision_SN or None
        Full Schunk-Nagy (electron-neutral + electron-ion) collision container.
    """

    nu_ft: np.ndarray | None = None
    nu_av_cc: np.ndarray | None = None
    nu_av_mb: np.ndarray | None = None
    nu_sn: Collision_SN | None = None


def _msise3d_chunk(args):
    """
    Process-safe NRLMSISE chunk evaluator on a latitude subset.
    Returns (lat_chunk, out_dict) with arrays in (lat, lon, alt).
    """
    date, heights_km, lat_chunk, lons, suppress_spaceweather_warning = args
    from nrlmsise00.dataset import msise_4d

    with warnings.catch_warnings():
        if suppress_spaceweather_warning:
            warnings.filterwarnings(
                "ignore",
                message="Local data files are older than 30days.*",
                category=UserWarning,
            )
        ds = msise_4d(date, heights_km, lat_chunk, lons)

    def _llh(var_name: str) -> np.ndarray:
        arr = ds.variables[var_name].values[0, :, :, :]  # alt, lat, lon
        return np.transpose(arr, (1, 2, 0))  # lat, lon, alt

    out = dict(
        N2=_llh("N2"),
        O2=_llh("O2"),
        O=_llh("O"),
        H=_llh("H"),
        He=_llh("He"),
        Tn=_llh("Talt"),
    )
    out["t_nn"] = out["N2"] + out["O2"] + out["O"] + out["H"] + out["He"]
    return np.asarray(lat_chunk, dtype=float), out


def _msise2d_chunk(args):
    """
    Process-safe NRLMSISE evaluator on a route-point subset.
    Returns (idx_chunk, out_dict) where each array is (height, n_chunk).
    """
    (
        date,
        heights_km,
        lats_chunk,
        lons_chunk,
        idx_chunk,
        suppress_spaceweather_warning,
    ) = args
    from nrlmsise00.dataset import msise_4d

    nh = int(np.asarray(heights_km).size)
    nc = int(np.asarray(idx_chunk).size)
    out = dict(
        N2=np.zeros((nh, nc), dtype=float),
        O2=np.zeros((nh, nc), dtype=float),
        O=np.zeros((nh, nc), dtype=float),
        H=np.zeros((nh, nc), dtype=float),
        He=np.zeros((nh, nc), dtype=float),
        Tn=np.zeros((nh, nc), dtype=float),
        t_nn=np.zeros((nh, nc), dtype=float),
    )

    for j, (lat, lon) in enumerate(zip(lats_chunk, lons_chunk)):
        with warnings.catch_warnings():
            if suppress_spaceweather_warning:
                warnings.filterwarnings(
                    "ignore",
                    message="Local data files are older than 30days.*",
                    category=UserWarning,
                )
            ds = msise_4d(date, heights_km, np.array([lat]), np.array([lon]))
        out["N2"][:, j] = ds.variables["N2"].values[0, :, 0, 0]
        out["O2"][:, j] = ds.variables["O2"].values[0, :, 0, 0]
        out["O"][:, j] = ds.variables["O"].values[0, :, 0, 0]
        out["H"][:, j] = ds.variables["H"].values[0, :, 0, 0]
        out["He"][:, j] = ds.variables["He"].values[0, :, 0, 0]
        out["Tn"][:, j] = ds.variables["Talt"].values[0, :, 0, 0]

    out["t_nn"] = out["N2"] + out["O2"] + out["O"] + out["H"] + out["He"]
    return np.asarray(idx_chunk, dtype=int), out


class NRLMSISE2D(object):
    """
    Build NRLMSISE-00 neutral background on a 2D (height x path-range) grid.

    Inputs:
    - date: datetime for model evaluation
    - lats, lons: 1D path coordinates, length Nr
    - heights_km: 1D heights, length Nh

    Outputs are stored in `self.msise` as arrays of shape (Nh, Nr), with
    densities in m^-3 (SI, as returned by nrlmsise00) and temperatures in K.
    """

    def __init__(
        self,
        date: dt.datetime,
        lats,
        lons,
        heights_km,
        workers: int = 1,
        update_spaceweather: bool = False,
        suppress_spaceweather_warning: bool = True,
    ):
        self.date = date
        self.lats = np.asarray(lats, dtype=float)
        self.lons = np.asarray(lons, dtype=float)
        self.heights_km = np.asarray(heights_km, dtype=float)
        self.workers = max(1, int(workers))
        self.update_spaceweather = update_spaceweather
        self.suppress_spaceweather_warning = suppress_spaceweather_warning
        if self.lats.shape != self.lons.shape:
            raise ValueError("lats and lons must have the same shape")
        if self.lats.ndim != 1:
            raise ValueError("lats and lons must be 1D arrays")
        if self.heights_km.ndim != 1:
            raise ValueError("heights_km must be a 1D array")
        self.msise = self.fetch_dataset()

    def fetch_dataset(self) -> dict[str, np.ndarray]:
        try:
            from nrlmsise00.dataset import msise_4d
        except ImportError as exc:
            raise ImportError(
                "nrlmsise00 dataset interface is unavailable. "
                "Install extras: pip install 'nrlmsise00[dataset]'"
            ) from exc

        if self.update_spaceweather:
            try:
                from spaceweather import sw

                sw.update_data()
                logger.info("Updated spaceweather local data files.")
            except Exception as exc:
                logger.warning(f"spaceweather update failed: {exc}")

        nh = self.heights_km.size
        nr = self.lats.size
        out = dict(
            N2=np.zeros((nh, nr), dtype=float),
            O2=np.zeros((nh, nr), dtype=float),
            O=np.zeros((nh, nr), dtype=float),
            H=np.zeros((nh, nr), dtype=float),
            He=np.zeros((nh, nr), dtype=float),
            Tn=np.zeros((nh, nr), dtype=float),
            t_nn=np.zeros((nh, nr), dtype=float),
        )

        logger.info(
            f"Running NRLMSISE-00 for {self.date} on grid Nh={nh}, Nr={nr} "
            f"with workers={self.workers}"
        )
        if self.workers == 1:
            for j, (lat, lon) in enumerate(zip(self.lats, self.lons)):
                with warnings.catch_warnings():
                    if self.suppress_spaceweather_warning:
                        warnings.filterwarnings(
                            "ignore",
                            message="Local data files are older than 30days.*",
                            category=UserWarning,
                        )
                    ds = msise_4d(
                        self.date, self.heights_km, np.array([lat]), np.array([lon])
                    )
                out["N2"][:, j] = ds.variables["N2"].values[0, :, 0, 0]
                out["O2"][:, j] = ds.variables["O2"].values[0, :, 0, 0]
                out["O"][:, j] = ds.variables["O"].values[0, :, 0, 0]
                out["H"][:, j] = ds.variables["H"].values[0, :, 0, 0]
                out["He"][:, j] = ds.variables["He"].values[0, :, 0, 0]
                out["Tn"][:, j] = ds.variables["Talt"].values[0, :, 0, 0]
        else:
            idx_chunks = np.array_split(np.arange(nr, dtype=int), self.workers)
            idx_chunks = [c for c in idx_chunks if c.size > 0]
            logger.info(
                f"Running NRLMSISE2D with process workers={self.workers} "
                f"on {len(idx_chunks)} route chunks"
            )
            with ProcessPoolExecutor(max_workers=self.workers) as ex:
                results = list(
                    ex.map(
                        _msise2d_chunk,
                        [
                            (
                                self.date,
                                self.heights_km,
                                self.lats[idx],
                                self.lons[idx],
                                idx,
                                self.suppress_spaceweather_warning,
                            )
                            for idx in idx_chunks
                        ],
                    )
                )
            for idx, chunk in results:
                out["N2"][:, idx] = chunk["N2"]
                out["O2"][:, idx] = chunk["O2"]
                out["O"][:, idx] = chunk["O"]
                out["H"][:, idx] = chunk["H"]
                out["He"][:, idx] = chunk["He"]
                out["Tn"][:, idx] = chunk["Tn"]

        out["t_nn"] = out["N2"] + out["O2"] + out["O"] + out["H"] + out["He"]
        return out

    def as_collision_kwargs(self) -> dict[str, np.ndarray]:
        """
        Convenience mapping for ComputeCollision neutral inputs.
        """
        return dict(
            Tn=self.msise["Tn"],
            N2=self.msise["N2"],
            O2=self.msise["O2"],
            O=self.msise["O"],
            H=self.msise["H"],
            He=self.msise["He"],
        )


class NRLMSISE3D(object):
    """
    Build NRLMSISE-00 neutral background on a 3D (lat x lon x height) grid.

    This path is vectorized through one `msise_4d` call and is substantially
    faster than point-by-point evaluation for large 3D grids.
    """

    def __init__(
        self,
        date: dt.datetime,
        lats,
        lons,
        heights_km,
        workers: int = 1,
        update_spaceweather: bool = False,
        suppress_spaceweather_warning: bool = True,
    ):
        self.date = date
        self.lats = np.asarray(lats, dtype=float)
        self.lons = np.asarray(lons, dtype=float)
        self.heights_km = np.asarray(heights_km, dtype=float)
        self.workers = max(1, int(workers))
        self.update_spaceweather = update_spaceweather
        self.suppress_spaceweather_warning = suppress_spaceweather_warning
        if self.lats.ndim != 1 or self.lons.ndim != 1:
            raise ValueError("lats and lons must be 1D arrays for NRLMSISE3D")
        if self.heights_km.ndim != 1:
            raise ValueError("heights_km must be a 1D array for NRLMSISE3D")
        self.msise = self.fetch_dataset()

    def fetch_dataset(self) -> dict[str, np.ndarray]:
        try:
            from nrlmsise00.dataset import msise_4d
        except ImportError as exc:
            raise ImportError(
                "nrlmsise00 dataset interface is unavailable. "
                "Install extras: pip install 'nrlmsise00[dataset]'"
            ) from exc

        if self.update_spaceweather:
            try:
                from spaceweather import sw

                sw.update_data()
                logger.info("Updated spaceweather local data files.")
            except Exception as exc:
                logger.warning(f"spaceweather update failed: {exc}")

        if self.workers == 1:
            with warnings.catch_warnings():
                if self.suppress_spaceweather_warning:
                    warnings.filterwarnings(
                        "ignore",
                        message="Local data files are older than 30days.*",
                        category=UserWarning,
                    )
                ds = msise_4d(self.date, self.heights_km, self.lats, self.lons)

            def _llh(var_name: str) -> np.ndarray:
                arr = ds.variables[var_name].values[0, :, :, :]  # alt, lat, lon
                return np.transpose(arr, (1, 2, 0))  # lat, lon, alt

            out = dict(
                N2=_llh("N2"),
                O2=_llh("O2"),
                O=_llh("O"),
                H=_llh("H"),
                He=_llh("He"),
                Tn=_llh("Talt"),
            )
            out["t_nn"] = out["N2"] + out["O2"] + out["O"] + out["H"] + out["He"]
            return out

        lat_chunks = np.array_split(self.lats, self.workers)
        lat_chunks = [c for c in lat_chunks if c.size > 0]
        logger.info(
            f"Running NRLMSISE3D with process workers={self.workers} "
            f"on {len(lat_chunks)} latitude chunks"
        )
        with ProcessPoolExecutor(max_workers=self.workers) as ex:
            results = list(
                ex.map(
                    _msise3d_chunk,
                    [
                        (
                            self.date,
                            self.heights_km,
                            c,
                            self.lons,
                            self.suppress_spaceweather_warning,
                        )
                        for c in lat_chunks
                    ],
                )
            )

        results.sort(key=lambda x: x[0][0])
        keys = ["N2", "O2", "O", "H", "He", "Tn", "t_nn"]
        out = {k: np.concatenate([r[1][k] for r in results], axis=0) for k in keys}
        return out

    def as_collision_kwargs(self) -> dict[str, np.ndarray]:
        return dict(
            Tn=self.msise["Tn"],
            N2=self.msise["N2"],
            O2=self.msise["O2"],
            O=self.msise["O"],
            H=self.msise["H"],
            He=self.msise["He"],
        )


class ComputeCollision(object):
    """
    Estimate collision profiles from provided plasma/neutral state arrays.

    Expected units:
    - Temperatures (Te, Ti, Tn): K
    - Neutral densities (N2, O2, O, H, He): m^-3  (SI; as returned by NRLMSISE-00)
    - Plasma densities (edens, O2p, Op): cm^-3  (as returned by IRI and RT model profiles)

    The SN electron-neutral formulas apply a 1e-6 factor internally to handle the
    m^-3 neutral inputs. The FT formula uses neutral pressure (m^-3 × k_B × T) in
    SI throughout. The SN electron-ion formula converts plasma densities to m^-3
    internally for both the Debye length and the collision rate.
    """

    def __init__(
        self,
        Te,
        Ti,
        Tn,
        edens,
        O2p,
        Op,
        N2,
        O2,
        O,
        H,
        He,
        date: dt.datetime | None = None,
    ):
        self.Te = np.asarray(Te, dtype=float)
        self.Ti = np.asarray(Ti, dtype=float)
        self.Tn = np.asarray(Tn, dtype=float)

        self.edens = np.asarray(edens, dtype=float)
        self.O2p = np.asarray(O2p, dtype=float)
        self.Op = np.asarray(Op, dtype=float)

        self.N2 = np.asarray(N2, dtype=float)
        self.O2 = np.asarray(O2, dtype=float)
        self.O = np.asarray(O, dtype=float)
        self.H = np.asarray(H, dtype=float)
        self.He = np.asarray(He, dtype=float)

        self.t_nn = self.N2 + self.O2 + self.O + self.H + self.He
        self.date = date
        if date:
            logger.info(f"Compute collision profiles for {date}")

        self.collision = Collision()
        self.collision.nu_sn = Collision_SN(
            en=Collision_en(),
            ei=Collision_ei(),
            total=np.zeros_like(self.Te),
        )

        self.collision.nu_ft = self.calculate_FT_collision_frequency()
        self.collision.nu_av_cc = self.calculate_FT_collision_frequency(2.5)
        self.collision.nu_av_mb = self.calculate_FT_collision_frequency(1.5)
        self.calculate_SN_en_collision_frequency()
        self.calculate_SN_ei_collision_frequency()
        return

    @classmethod
    def from_nrlmsise(
        cls,
        *,
        date: dt.datetime,
        lats,
        lons,
        heights_km,
        Te,
        Ti,
        edens,
        O2p,
        Op,
        workers: int = 1,
        update_spaceweather: bool = False,
        suppress_spaceweather_warning: bool = True,
    ):
        """
        Build collision model using neutral fields from NRLMSISE2D and plasma
        fields from the caller (e.g., IRI).
        """
        bg = NRLMSISE2D(
            date=date,
            lats=lats,
            lons=lons,
            heights_km=heights_km,
            workers=workers,
            update_spaceweather=update_spaceweather,
            suppress_spaceweather_warning=suppress_spaceweather_warning,
        )
        return cls(
            Te=Te,
            Ti=Ti,
            Tn=bg.msise["Tn"],
            edens=edens,
            O2p=O2p,
            Op=Op,
            N2=bg.msise["N2"],
            O2=bg.msise["O2"],
            O=bg.msise["O"],
            H=bg.msise["H"],
            He=bg.msise["He"],
            date=date,
        )

    @classmethod
    def from_nrlmsise_3d(
        cls,
        *,
        date: dt.datetime,
        lats,
        lons,
        heights_km,
        Te,
        Ti,
        edens,
        O2p,
        Op,
        workers: int = 1,
        update_spaceweather: bool = False,
        suppress_spaceweather_warning: bool = True,
    ):
        """
        Build collision model using neutral fields from NRLMSISE3D and plasma
        fields on a 3D (lat x lon x height) grid.
        """
        bg = NRLMSISE3D(
            date=date,
            lats=lats,
            lons=lons,
            heights_km=heights_km,
            workers=workers,
            update_spaceweather=update_spaceweather,
            suppress_spaceweather_warning=suppress_spaceweather_warning,
        )
        return cls(
            Te=Te,
            Ti=Ti,
            Tn=bg.msise["Tn"],
            edens=edens,
            O2p=O2p,
            Op=Op,
            N2=bg.msise["N2"],
            O2=bg.msise["O2"],
            O=bg.msise["O"],
            H=bg.msise["H"],
            He=bg.msise["He"],
            date=date,
        )

    def calculate_FT_collision_frequency(self, frac: float = 1.0):
        """
        Friedrich-Tonker electron-neutral collision frequency.

        t_nn is in cm^-3 (SI) need to convert; pressure p = n [m^-3] * k_B [J/K] * T [K] in Pa.
        """
        logger.info(
            f"Compute Friedrich-Tonker electron-neutral collision frequency with a={frac}"
        )
        t_nn = 1e6 * self.t_nn
        Te = np.clip(self.Te, 1.0, None)
        p = t_nn * self.Tn * pconst["boltz"]
        nu = (2.637e6 / np.sqrt(Te) + 4.945e5) * p
        return frac * nu

    def atmospheric_ion_neutral_collision_frequency(self):
        """
        Atmospheric ion-neutral collision frequency from total neutral density.

        Formula constant 3.8e-11 expects density in cm^-3; t_nn is in cm^-3,
        so multiply by 1e-6 to convert (if you think you set it in m^-3):
        ν = 3.8e-11 * n[cm^-3] = 3.8e-17 * n[m^-3].
        """
        return 3.8e-11 * self.t_nn

    def calculate_SN_ei_collision_frequency(self, gamma: float = 0.5572, zi: int = 2):
        """
        Schunk-Nagy electron-ion collision frequency profile.
        """
        logger.warning("Compute Schunk-Nagy electron-ion collision frequency")

        e = pconst["q_e"]
        k = pconst["boltz"]
        me = pconst["m_e"]
        eps0 = pconst["eps0"]
        k_e = 1 / (4 * np.pi * eps0)

        Te = np.clip(self.Te, 1.0, None)
        Ti = np.clip(self.Ti, 1.0, None)
        Ne = np.clip(self.edens, 1e-12, None)

        # Plasma densities (edens, O2p, Op) are in cm^-3; convert to m^-3
        # for the SI Coulomb-logarithm and collision rate formula.
        Ne_m3 = Ne * 1e6

        for key, Ni in {"O2p": self.O2p, "Op": self.Op}.items():
            Ni = np.clip(Ni, 1e-12, None)
            Ni_m3 = Ni * 1e6

            ki2 = 4 * np.pi * Ni_m3 * e**2 * zi**2 * k_e / (k * Ti)
            ke2 = 4 * np.pi * Ne_m3 * e**2 * k_e / (k * Te)

            ki2 = np.clip(ki2, 1e-30, None)
            ke2 = np.clip(ke2, 1e-30, None)

            ke = np.sqrt(ke2)
            lam = np.log(4 * k * Te / (gamma**2 * zi * e**2 * k_e * ke)) - (
                ((ke2 + ki2) / ki2) * np.log(np.sqrt((ke2 + ki2) / ke2))
            )
            lam = np.clip(lam, 1e-6, None)

            nu_ei = (
                4
                * np.sqrt(2 * np.pi)
                * Ni_m3  # SI formula requires m^-3 here
                * (zi * e**2 * k_e) ** 2
                * lam
                / (3 * np.sqrt(me) * (k * Te) ** 1.5)
            )
            setattr(self.collision.nu_sn.ei, key, nu_ei)

        self.collision.nu_sn.ei.total = (
            self.collision.nu_sn.ei.O2p + self.collision.nu_sn.ei.Op
        )
        self.collision.nu_sn.total += self.collision.nu_sn.ei.total

    def calculate_SN_en_collision_frequency(self):
        """
        Schunk-Nagy electron-neutral collision frequency profile.
        """
        logger.warning("Compute Schunk-Nagy electron-neutral collision frequency")

        Te = np.clip(self.Te, 1.0, None)
        sqrt_Te = np.sqrt(Te)

        self.collision.nu_sn.en.N2 = 1e-6 * 2.33e-11 * self.N2 * (1 - 1.12e-4 * Te) * Te
        self.collision.nu_sn.en.O2 = (
            1e-6 * 1.82e-10 * self.O2 * (1 + 3.6e-2 * sqrt_Te) * sqrt_Te
        )
        self.collision.nu_sn.en.O = (
            1e-6 * 8.9e-11 * self.O * (1 + 5.7e-4 * Te) * sqrt_Te
        )
        self.collision.nu_sn.en.He = 1e-6 * 4.6e-10 * self.He * sqrt_Te
        self.collision.nu_sn.en.H = (
            1e-6 * 4.5e-9 * self.H * (1 - 1.35e-4 * Te) * sqrt_Te
        )

        self.collision.nu_sn.en.total = (
            self.collision.nu_sn.en.N2
            + self.collision.nu_sn.en.O2
            + self.collision.nu_sn.en.O
            + self.collision.nu_sn.en.He
            + self.collision.nu_sn.en.H
        )
        self.collision.nu_sn.total += self.collision.nu_sn.en.total

    @staticmethod
    def atmospheric_collision_frequency(ni, nn, T):
        """
        Atmospheric collision profile from ion(electron)/neutral density and temperature.
        """
        na_profile = lambda T, nn: (1.8 * 1e-8 * nn * np.sqrt(T / 300))
        ni_profile = lambda T, ni: (6.1 * 1e-3 * ni * (300 / T) * np.sqrt(300 / T))
        return ni_profile(T, ni) + na_profile(T, nn)