oap_model.psd module

class oap_model.psd.CompositePSD(psds: list[oap_model.psd.PSD], bins: list[float] = None)[source]

Bases: PSD

A composite particle size distribution object.

Contains the distribution parameters, and binning information.

Parameters

psds (list[PSD]) – The list of PSDs to combine.
bins (np.ndarray, optional) – The bin edges in metres. Defaults to 100 bins between \(1 imes10^{-7}\) and \(1 imes10^{-7}\).

dn_dd(d: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray[source]: Calculate the particle size distribution value given diameters.

generate_diameters(n_particles) → tuple[list[float], list[oap_model.ast_model.ASTModel]][source]: Generate a particle diameter from the PSD.

class oap_model.psd.CrystalModel(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]

Bases: Enum

Enum for crystal types.

COL_AR5_ROT = 4

RECT_AR5 = 2

RECT_AR5_ROT = 5

ROS_6 = 3

SPHERE = 1

get_generator()[source]

min_diameter(pixel_size)[source]

class oap_model.psd.GammaPSD(intercept: float, slope: float, shape: float, bins: list[float] = None, model: CrystalModel = CrystalModel.SPHERE)[source]

Bases: PSD

Gamma particle size distribution object.

Contains the distribution parameters, and binning information.

\[n_N(r) = N_0 \left(\frac{r}{\text{1 m}}\right)^\mu \mathrm{e}^{-\Lambda r}.\]

Parameters

intercept (float) – \(N_0\) in \(\mathrm{m^{-3}}\).
slope (float) – \(\Lambda\) in \(\mathrm{m^{-1}}\).
shape (float) – \(\mu\).
bins (np.ndarray, optional) – The bin edges in metres. Defaults to 100 bins between \(1\times10^{-7}\) and \(1\times10^{-7}\).

dn_dd(d: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray[source]: Calculate the particle size distribution value given diameters.

classmethod fit(diameters, dn_dd, min_considered_diameter=5e-05)[source]

classmethod from_concentration(number_concentration, slope, shape, **kwargs)[source]

Calculate the number density per field.

Parameters

number_concentration (float) – The number concentration in \(\mathrm{m^{-3}}\).
slope (float) – \(\Lambda\) in \(\mathrm{m^{-1}}\).
shape (float) – \(\mu\).

Returns

The number density per field.

Return type

float

classmethod from_litres_cm(intercept_per_litre_per_cm, slope_per_cm, shape, **kwargs)[source]

classmethod from_litres_microns(intercept_per_litre_per_micron, slope_per_micron, shape, **kwargs)[source]

classmethod from_mean_variance(number_concentration, mean, variance, **kwargs)[source]: Create a GammaPSD object from the mean and variance of the particle diameter.

property mean: Calculate the mean particle diameter.

parameter_description() → str[source]

property variance: Calculate the variance of the particle diameter.

classmethod w19_parameterisation(temp, intercept=None, total_number_density=None, insitu_origin=False, liquid_origin=False)[source]

Using Wolf et al. 2019 parameterisation.

Returns: The number density per field.
Return type: float

class oap_model.psd.OSheaGammaPSD(intercept: float, slope: float, shape: float, bins: list[float] = None, model: CrystalModel = CrystalModel.SPHERE)[source]

Bases: GammaPSD

dn_dd(d: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray[source]: Calculate the particle size distribution value given diameters.

class oap_model.psd.PSD(bins: list[float] = None, model: CrystalModel = CrystalModel.SPHERE)[source]

Bases: ABC

Base class for particle size distribution objects.

adjusted_bins(retrieval)[source]

property binned_distribution

Calculate the binned particle size distribution.

Returns: The number of particles in each bin.
Return type: np.ndarray

abstract dn_dd(d: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray[source]: Calculate the particle size distribution value given diameters.

generate_diameters(n_particles) → tuple[list[float], list[oap_model.ast_model.ASTModel]][source]: Generate a particle diameter from the PSD.

property max_dn_dd: Calculate the maximum value of the particle size distribution.

property midpoints

Calculate the binned particle size distribution.

Returns: The number of particles in each bin.
Return type: np.ndarray

moment(n)[source]: Calculate the nth moment of the PSD.

plot(ax, retrieval: Retrieval = None, **kwargs)[source]: Plot the PSD value against diameter.

property total_number_density: float: Calculate the total number density of particles.

class oap_model.psd.Particle(diameter, angle, model)

Bases: tuple

angle: Alias for field number 1

diameter: Alias for field number 0

model: Alias for field number 2

class oap_model.psd.PositionedParticle(diameter, angle, model, position)

Bases: tuple

angle: Alias for field number 1

diameter: Alias for field number 0

model: Alias for field number 2

position: Alias for field number 3

class oap_model.psd.SamplingModel(psd: GammaPSD, z_dist: callable = None, inlet_length: float = 0.00735)[source]

Bases: object

Particle size distribution model.

A modelling class that contains a PSD object, and simulates particle measurement from it. Particles are assumed to be distributed uniformly across the inlet. Particle size is assumed to be independent of position along the inlet.

Parameters

psd (PSD) – The particle size distribution function, dN/dr in \(\mathrm{m^{-3}}\).
z_dist (callable, optional, unimplimented) – The probability distribution of particles along the z-axis, normalised to 1 when integrated wrt z, in m^-1. Defaults to uniform across array.
inlet_length (float, optional) – The length of the inlet in metres.

generate(n_particles: int) → ndarray[source]

Generate a sample of particles from the PSD, at random z positions.

Parameters: n_particles (int) – The number of particles to generate.
Returns: The particles represented by tuples of (diameter, z).
Return type: np.ndarray

simulate_distribution(n_particles: int, single_particle: bool = False, keep_models=False) → ndarray[source]

Simulate the observation of spherical particles.

Generates a sample of spherical particles and then simulates the observation of the particles by producing new AST models.

Parameters

n_particles (int) – The number of particles to generate.
single_particle (bool, optional) – Whether to consider only the largest particle in each diffraction pattern. Defaults to False.
keep_models (bool, optional) – Whether to keep the AST models after simulation. Warning: this will use a lot of memory. Defaults to False.

Returns

The measured particle diameters.

Return type

np.ndarray

simulate_distribution_from_scaling(n_particles: int, single_particle: bool = False, base_model: ASTModel = None, keep_models=False) → ndarray[source]

Simulate the observation of similar particles.

Generates a sample of particles, produces new AST models by scaling a base model, and then simulates the observation of the particles.

Parameters

n_particles (int) – The number of particles to generate.
single_particle (bool, optional) – Whether to consider only the largest particle in each diffraction pattern. Defaults to False.
base_model (ASTModel, optional) – The base model to scale. Should have a high resolution relative to typical particle sizes. Defaults to a 1000 nm diameter particle.
keep_models (bool, optional) – Whether to keep the AST models after simulation. Warning: this will use a lot of memory. Defaults to False.

Returns

The measured particle diameters.

Return type

np.ndarray

class oap_model.psd.TwoMomentGammaPSD(m2, m3, bins: list[float] = None, model: CrystalModel = CrystalModel.SPHERE)[source]

Bases: PSD

property characteristic_diameter

dn_dd(d: Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → ndarray[source]: Calculate the particle size distribution value given diameters.

classmethod from_m2_tc(m2, tc, **kwargs)[source]: Create a TwoMomentGammaPSD object from the second moment and in-cloud temperature.

oap_model.psd.rejection_sampler(p, xbounds, pmax)[source]

Returns a value sampled from a bounded probability distribution.

Parameters

p (callable) – The probability distribution function.
xbounds (list) – The bounds of the distribution.
pmax (float) – The maximum value of the probability distribution.