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Model

Key randomization functions in model.

assay_performed(params)

Number of days between collection and assay being performed.

Parameters:

Name Type Description Default
params AssayParams

assay parameters

required

Returns:

Type Description
timedelta

Number of days.

Source code in src/snailz/model.py
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def assay_performed(params: AssayParams) -> timedelta:
    """Number of days between collection and assay being performed.

    Parameters:
        params: assay parameters

    Returns:
        Number of days.
    """
    return timedelta(days=random.randint(0, params.delay))

assay_reading(params, specimen, treatment, performed)

Calculate individual assay reading.

Parameters:

Name Type Description Default
params AssayParams

assay parameters

required
specimen object

specimen being assayed

required
treatment str

"C" for control or "S" for sample

required
performed date

date assay performed

required

Returns:

Type Description
float

Reading value.

Source code in src/snailz/model.py
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def assay_reading(
    params: AssayParams, specimen: object, treatment: str, performed: date
) -> float:
    """
    Calculate individual assay reading.

    Parameters:
        params: assay parameters
        specimen: specimen being assayed
        treatment: "C" for control or "S" for sample
        performed: date assay performed

    Returns:
        Reading value.
    """
    degradation = max(
        0.0, 1.0 - (params.degrade * (performed - specimen.collected).days)
    )
    if treatment == "C":
        base_value = 0.0
        stdev = params.rel_stdev
    elif specimen.is_mutant:
        base_value = params.mutant * degradation
        stdev = base_value * params.rel_stdev
    else:
        base_value = params.baseline * degradation
        stdev = base_value * params.rel_stdev

    return abs(random.gauss(base_value, stdev))

assay_specimens(params, specimens)

Generate list of specimens to be assayed.

Parameters:

Name Type Description Default
params AssayParams

assay parameters

required
specimens BaseModel

all available specimens

required

Returns:

Type Description
list

List of specimens (possibly containing duplicates).

Source code in src/snailz/model.py
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def assay_specimens(params: AssayParams, specimens: BaseModel) -> list:
    """Generate list of specimens to be assayed.

    Parameters:
        params: assay parameters
        specimens: all available specimens

    Returns:
        List of specimens (possibly containing duplicates).
    """
    extra = random.choices(
        specimens.items,
        k=math.floor(params.p_duplicate_assay * len(specimens.items)),
    )
    subjects = specimens.items + extra
    random.shuffle(subjects)
    return subjects

days_to_next_survey(params)

Choose the number of days between surveys.

Parameters:

Name Type Description Default
params SurveyParams

specimen generation parameters

required

Returns:

Type Description
timedelta

Days to the next survey.

Source code in src/snailz/model.py
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def days_to_next_survey(params: SurveyParams) -> timedelta:
    """Choose the number of days between surveys.

    Parameters:
        params: specimen generation parameters

    Returns:
        Days to the next survey.
    """
    return timedelta(days=random.randint(1, params.max_interval))

image_noise(params, array, img_size)

Add noise effects to numpy array before conversion to image.

Parameters:

Name Type Description Default
params AssayParams

assay parameters

required
array ndarray

pristine numpy array

required
img_size int

size of the image

required

Returns:

Type Description
ndarray

Distorted numpy array.

Source code in src/snailz/model.py
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def image_noise(params: AssayParams, array: np.ndarray, img_size: int) -> np.ndarray:
    """Add noise effects to numpy array before conversion to image.

    Parameters:
        params: assay parameters
        array: pristine numpy array
        img_size: size of the image

    Returns:
        Distorted numpy array.
    """
    # Generate random noise array of the same shape
    noise = np.random.randint(
        -params.image_noise,
        params.image_noise + 1,
        size=(img_size, img_size),
        dtype=np.int16,
    )

    # Add noise to the original array
    noisy_array = np.clip(
        array.astype(np.int16) + noise, utils.BLACK, utils.WHITE
    ).astype(np.uint8)

    return noisy_array

image_blur(img)

Apply Gaussian blur to an image.

Parameters:

Name Type Description Default
img Image

image to blur

required

Returns:

Type Description
Image

Blurred image.

Source code in src/snailz/model.py
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def image_blur(img: PilImage) -> PilImage:
    """Apply Gaussian blur to an image.

    Parameters:
        img: image to blur

    Returns:
        Blurred image.
    """
    return img.filter(ImageFilter.GaussianBlur(BLUR_RADIUS))

machine_brightness(params)

Choose relative brightness of this machine's camera.

Parameters:

Name Type Description Default
params MachineParams

machine parameters

required

Returns:

Type Description
float

Brightness level in that range.

Source code in src/snailz/model.py
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def machine_brightness(params: MachineParams) -> float:
    """Choose relative brightness of this machine's camera.

    Parameters:
        params: machine parameters

    Returns:
        Brightness level in that range.
    """

    return random.uniform(1.0 - params.variation, 1.0 + params.variation)

mutation_loci(params)

Make a list of mutable loci positions.

Parameters:

Name Type Description Default
params SpecimenParams

specimen generation parameters

required

Returns:

Type Description
list[int]

Randomly selected positions that can be mutated.

Source code in src/snailz/model.py
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def mutation_loci(params: SpecimenParams) -> list[int]:
    """Make a list of mutable loci positions.

    Parameters:
        params: specimen generation parameters

    Returns:
        Randomly selected positions that can be mutated.
    """
    return list(
        sorted(random.sample(list(range(params.genome_length)), params.max_mutations))
    )

specimen_adjust_mass(survey, max_pollution, specimen)

Adjust mass of specimen depending on pollution levels.

Parameters:

Name Type Description Default
survey Survey

survey that specimen is taken from

required
max_pollution float

maximum pollution level seen across all surveys

required
specimen Specimen

specimen to adjust

required
Source code in src/snailz/model.py
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def specimen_adjust_mass(
    survey: Survey, max_pollution: float, specimen: Specimen
) -> float:
    """Adjust mass of specimen depending on pollution levels.

    Parameters:
        survey: survey that specimen is taken from
        max_pollution: maximum pollution level seen across all surveys
        specimen: specimen to adjust
    """
    pollution = survey.cells[specimen.location.x, specimen.location.y]
    if (pollution is None) or (pollution == 0.0):
        return specimen.mass
    scaling = 1.0 + 2.0 * utils.sigmoid(pollution / max_pollution)
    return specimen.mass * scaling

specimen_collection_date(survey)

Choose a collection date for a specimen.

Parameters:

Name Type Description Default
survey BaseModel

survey that specimen belongs to

required

Returns:

Type Description
date

Date specimen was collected.

Source code in src/snailz/model.py
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def specimen_collection_date(survey: BaseModel) -> date:
    """Choose a collection date for a specimen.

    Parameters:
        survey: survey that specimen belongs to

    Returns:
        Date specimen was collected.
    """
    return date.fromordinal(
        random.randint(survey.start_date.toordinal(), survey.end_date.toordinal())
    )

specimen_genome(params, specimens)

Generate genome for a particular specimen.

Parameters:

Name Type Description Default
specimens BaseModel

all specimens

required

Returns:

Type Description
tuple[int, str]

Random genome produced by mutating reference genome.

Source code in src/snailz/model.py
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def specimen_genome(params: SpecimenParams, specimens: BaseModel) -> tuple[int, str]:
    """Generate genome for a particular specimen.

    Parameters:
        specimens: all specimens

    Returns:
        Random genome produced by mutating reference genome.
    """
    num_species = len(specimens.references)
    species = utils.choose_one(list(range(num_species)), weights=params.prob_species)
    genome = list(specimens.references[species])
    max_mutations = random.randint(1, len(specimens.loci[species]))
    locations = random.sample(specimens.loci[species], max_mutations)
    for loc in locations:
        genome[loc] = utils.choose_one(utils.BASES)
    result = "".join(genome)
    return species, result

specimen_initial_mass(params, species, collected, is_mutant)

Generate mass of a specimen.

Parameters:

Name Type Description Default
params SpecimenParams

specimen generation parameters

required
collected date

specimen collection date

required
is_mutant bool

whether this specimen is a mutant

required

Returns:

Type Description
float

Random mass.

Source code in src/snailz/model.py
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def specimen_initial_mass(
    params: SpecimenParams,
    species: int,
    collected: date,
    is_mutant: bool,
) -> float:
    """Generate mass of a specimen.

    Parameters:
        params: specimen generation parameters
        collected: specimen collection date
        is_mutant: whether this specimen is a mutant

    Returns:
        Random mass.
    """

    # Initial mass
    mass_scale = params.mut_mass_scale if is_mutant else 1.0
    mean_mass = mass_scale * params.mean_masses[species]
    mass = abs(random.gauss(mean_mass, mean_mass * params.mass_rel_stdev))

    # Growth effects
    days_passed = (collected - params.start_date).days
    mass += params.daily_growth * days_passed * mass

    return mass

specimens_num_per_survey(params, survey)

Number of specimens per survey.

Parameters:

Name Type Description Default
params SpecimenParams

specimen generation parameters

required
survey Survey

particular survey

required

Returns:

Type Description
int

Number of specimens.

Source code in src/snailz/model.py
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def specimens_num_per_survey(params: SpecimenParams, survey: Survey) -> int:
    """Number of specimens per survey.

    Parameters:
        params: specimen generation parameters
        survey: particular survey

    Returns:
        Number of specimens.
    """
    return random.randint(survey.size // 2, (3 * survey.size) // 2)

specimens_place(survey, specimens)

Place specimens in grid.

Parameters:

Name Type Description Default
survey Survey

survey from which specimens taken

required
specimens Sequence[Specimen]

to place

required
Source code in src/snailz/model.py
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def specimens_place(survey: Survey, specimens: Sequence[Specimen]) -> None:
    """Place specimens in grid.

    Parameters:
        survey: survey from which specimens taken
        specimens: to place
    """
    anneal(survey.size, specimens)

specimen_reference_genome(params)

Make a random reference genome.

Parameters:

Name Type Description Default
params SpecimenParams

SpecimenParams with length attribute

required

Returns:

Type Description
str

A randomly generated genome string of the specified length

Source code in src/snailz/model.py
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def specimen_reference_genome(params: SpecimenParams) -> str:
    """Make a random reference genome.

    Parameters:
        params: SpecimenParams with length attribute

    Returns:
        A randomly generated genome string of the specified length
    """
    return "".join(random.choices(utils.BASES, k=params.genome_length))

survey_initialize_grid(size)

Initialize values in survey grid.

Parameters:

Name Type Description Default
size int

size of survey grid

required

Returns:

Type Description
Grid[int]

Initialized grid.

Source code in src/snailz/model.py
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def survey_initialize_grid(size: int) -> Grid[int]:
    """Initialize values in survey grid.

    Parameters:
        size: size of survey grid

    Returns:
        Initialized grid.
    """
    cells = Grid(width=size, height=size, default=0)
    size_1 = size - 1
    center = size // 2
    moves = [[-1, 0], [1, 0], [0, -1], [0, 1]]
    x, y = center, center
    while (x != 0) and (x != size_1) and (y != 0) and (y != size_1):
        cells[x, y] += 1
        m = random.choice(moves)
        x += m[0]
        y += m[1]
    return cells