materials.diffusers.predicted_scattering

Scattering coefficients a book computed, kept apart from the ones measured.

.measured_scattering holds what turntables measured under ISO 17497-1. This holds the three tables of Cox & D’Antonio’s Appendix C, which are boundary element predictions of the correlation scattering coefficient, and they are a separate catalogue for one reason: a row that says 0.45 because a solver said so and a row that says 0.45 because a reverberation room said so are not interchangeable, and a caller who mixes them without meaning to has no way of finding out afterwards.

Table C.1 and Table C.2 are three-dimensional predictions of 3 m by 3 m single-plane diffusers, at normal and at random incidence, over the one-third octave bands from 250 Hz to 4 kHz; the angle is in the table’s title, so every row of one carries the same one. Table C.3 is two-dimensional, runs from 100 Hz to 5 kHz, and prints three lines per surface at 0, 56.9 and random incidence, so there the angle is a field that varies inside the table. All three are PredictedScatteringSpectrum, and PredictedScatteringSpectrum.model says which solver produced the row.

More than usual, because the book spends a page on it. The correlation scattering coefficient “interprets any absorption as being scattering”, so the formulation “needs to be revised for surfaces that partially absorb”; the random incidence values of the two-dimensional table “tend to have raised values at low frequencies”; and the coefficient “does not discriminate between different diffusers in a consistent manner”, because it reads a redirection as a dispersion. Each of those is quoted in the about of the table it belongs to, with the page. A row here is a modelling result to compare against, not a specification.

Each group of Tables C.1 and C.2 closes with a row labelled "h/L = 20" or "h/L = 40", which cannot be read at face value: every surface of every group has L = 20 cm and h between 2 and 10 cm, so the ratio is between 0.1 and 0.5. Read as a percentage they resolve, and three of the six carry exactly the values of a row of their own group, which is how the reading was settled. The book explains them nowhere. They are kept as rows, because they are rows.

Auto-generated from the source docstrings by scripts/generate_api_docs.py (make api-docs). Do not edit by hand.

predicted_scattering_named(
    name: str,
) -> tuple[PredictedScatteringSpectrum, ...]

Every predicted row whose description or heading contains name.

Parameters

Returns: The rows that match, in the order the tables are read, which is empty when no table has one.

PredictedScatteringSpectrum(
    *,
    name: str,
    source: str,
    table: str = '',
    variant: str = '',
    basis: Mapping[str, str] = ...,
    approximate: frozenset[str] = frozenset(),
    derived: Mapping[str, str] = ...,
    converted: Mapping[str, tuple[str, str]] = ...,
    carried: Mapping[str, str] = ...,
    ranges: Mapping[str, tuple[float | None, float | None]] = ...,
    bounded_above: frozenset[str] = frozenset(),
    bounded_below: frozenset[str] = frozenset(),
    reported: Mapping[str, tuple[float | tuple[float, float], ...]] = ...,
    unquantified: Mapping[str, str] = ...,
    uncertainty: Mapping[str, float] = ...,
    not_derivable: Mapping[str, str] = ...,
    misprinted: Mapping[str, str] = ...,
    attributed_to: Mapping[str, str] = ...,
    group: str = '',
    note: str = '',
    scattering_coefficient_100: float | None = None,
    scattering_coefficient_125: float | None = None,
    scattering_coefficient_160: float | None = None,
    scattering_coefficient_200: float | None = None,
    scattering_coefficient_250: float | None = None,
    scattering_coefficient_315: float | None = None,
    scattering_coefficient_400: float | None = None,
    scattering_coefficient_500: float | None = None,
    scattering_coefficient_630: float | None = None,
    scattering_coefficient_800: float | None = None,
    scattering_coefficient_1000: float | None = None,
    scattering_coefficient_1250: float | None = None,
    scattering_coefficient_1600: float | None = None,
    scattering_coefficient_2000: float | None = None,
    scattering_coefficient_2500: float | None = None,
    scattering_coefficient_3150: float | None = None,
    scattering_coefficient_4000: float | None = None,
    scattering_coefficient_5000: float | None = None,
    angle_of_incidence_deg: float | None = None,
    model: str = '',
)

One computed surface at one angle, band by band.

The band fields and the reading method come from ScatteringBands, which a measured row carries too, and the two classes are siblings rather than one inheriting from the other, so a caller narrowing on the type learns which kind of number they hold. The two fields below are what a prediction has and a measurement does not.

The two three-dimensional tables start at 250 Hz, so their four lowest band fields are None on every row: the book says the coefficient below that “should be taken to be 0” and prints nothing, and filling those cells with zeros would put the book’s advice into the data where nobody could tell it from a computed value.

Attributes

PredictedScatteringSpectrum.bands() -> tuple[int, ...]

The bands this row prints a value for, in hertz.

PredictedScatteringSpectrum.basis_of(field_name: str) -> str

What the source says this field is, one of CATALOGUE_BASES.

The five are measured, declared, calculated, estimated and extended.

Parameters

Returns: The field’s own entry in basis, else the row’s, else the empty string, which means the source does not say.

classmethod

PredictedScatteringSpectrum.from_printed(**cells: Any) -> Self

A row built from the cells its page prints, completed and marked.

The one path that works anything out. The cells are what the page prints, under the field names of the class and with the hedges each cell carries, as a data file writes them. A figure written under a unit the class takes as an alias of its own (an AbsorptionAreaSpectrum takes absorption_area_125_ft2 for absorption_area_125_m2) is converted on its digits with an exact factor and rounded once, and converted records the figure and its unit. The row is then built and held to the contract the class docstring lists, so every cell is checked before any arithmetic reads it. Last, the class fills what follows from those cells (a modulus from a plate speed, a density and a Poisson ratio), never over a cell that holds a value or one the row says something else about, and derived says how each filled value was reached and, when the cells it rests on do not share one basis, the basis of each. Cells the arithmetic cannot take are refused rather than turned into a value that would be wrong: a modulus of 1 GPa and a shear modulus of 0.1 GPa give a Poisson ratio of 4, which no isotropic solid has, and a row whose cells are not meant to give a value says so in not_derivable, which keeps the arithmetic from running.

Cls(...) stays literal: it holds what it is given and works nothing out. To change a cell of a row and have what follows from it follow again, change it in printed_fields and build again here; dataclasses.replace would copy the derived values as they were:

cells = row.printed_fields()
cells["density_kg_m3"] = 2400.0
row = type(row).from_printed(**cells)

Parameters

Returns: The row, with what follows from its cells filled in.

Raises

PredictedScatteringSpectrum.is_approximate(field_name: str) -> bool

Whether the page prints this field with a ~.

Parameters

Returns: True when the page rounded the cell on purpose.

PredictedScatteringSpectrum.is_derived(field_name: str) -> bool

Whether this library computed this field instead of reading it.

Parameters

Returns: True when the page did not print it and the value follows from cells that it did. derived says how. A value the page prints in another unit, or gives by reference to another of its rows, answers False: the number is the page’s, and converted or carried says so.

PredictedScatteringSpectrum.printed(
    field_name: str,
    *,
    wanted_by: str = 'the caller',
) -> float

One quantity this page prints, or a refusal that says what it had.

Every quantity of a row is optional, because the pages print different columns, so a caller passing one into a function that requires a float has to narrow it. Doing it here beats an assertion at each call site: the refusal names the field, who wanted it and what the page had in that cell, which is the difference between a cell the book left empty and a cell holding the word “model”.

Parameters

Returns: The value, as a float.

Raises

PredictedScatteringSpectrum.printed_fields() -> dict[str, Any]

The cells the page prints, as from_printed takes them.

Every field, the name, the citation, the table and every hedge included, except the values this library derived, derived itself, and a field left at a default that holds nothing (a quantity the page leaves out, an empty text or hedge). A text field whose default says something is kept even when it holds no text: the per of an area per unit is a person unless the row says otherwise, and a row that leaves it empty has said otherwise. A value converted from the page’s unit and one the page gives by reference to another of its rows are the page’s, so they stay, with converted and carried beside them.

For every row from_printed builds, and so for every packaged one, type(row).from_printed(**row.printed_fields()) is the row again, and changing a cell before building it again is how a row is edited without carrying a derived value that no longer follows from it. A derived passed to the literal constructor is the caller’s own; it is left out here with its value, like every derived one, so building again gives back only what the class works out.

Returns: A new dictionary of constructor keywords. The values are the ones the row holds, frozen as the row holds them.

PredictedScatteringSpectrum.scattering_coefficient(band_hz: int) -> float

The coefficient in one band, or a refusal that says what the page had.

Parameters

Returns: The scattering coefficient, dimensionless.

Raises

PredictedScatteringSpectrum.spectrum() -> dict[int, float]

The row as {band_hz: value} over the bands it prints.

A band the page left empty, or printed as something other than a number, is left out rather than filled with a zero; why_missing on that band’s field says which it was.

PredictedScatteringSpectrum.values_at(
    frequencies_hz: ArrayLike,
) -> NDArray[np.float64]

The row’s value at each of frequencies_hz, or a refusal.

The adapter for a function that takes one value per band by position (the absorption of a surface in a reverberation formula, the transmission loss of a panel at the frequencies an enclosure model asks for), because an array handed over by position cannot say which band is missing and a row can. Each frequency is read as the band of these tables whose centre is nearest on a logarithmic scale, and matches it when it lies within a sixth of the spacing between bands: a sixth of an octave for octave bands, an eighteenth for one-third octave bands. That takes the nominal centre, the exact base-ten one and the exact base-two one alike (160 Hz, 158.49 Hz and 157.49 Hz are one band) and never reaches the next band. A band the row does not print is refused, as printed refuses it, with what the page had there; it is never read as zero and never filled from its neighbours.

Parameters

Returns: A new array of the same shape, one value per frequency.

Raises

PredictedScatteringSpectrum.why_missing(field_name: str) -> str

Why this field is None, in the page’s own terms.

A catalogue that answers None and stops is asking the caller to guess whether the material has no such property, whether the book measured it and printed a dash, or whether the cell holds something that is not a number. Each of those is a different answer.

Parameters

Returns: What the page had in that cell, or the empty string when the field is not missing at all. A field the page has no column for and this library cannot derive, because the cells it would need are themselves a range, answers that it does not follow.

Raises

Constant (mapping).