Assays - Snailz Docs

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# Assays

Generate snail assays.

## `AssayParams`

Bases: `BaseModel`

Parameters for assay generation.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`baseline` | `float` | Baseline reading value | `2.0`
`degrade` | `float` | Rate at which sample responses decrease per day after first day (0..1) | `0.05`
`delay` | `int` | Maximum number of days between specimen collection and assay | `5`
`mutant` | `float` | Mutant reading value (must be positive) | `5.0`
`noise` | `float` | Noise level for readings (must be positive) | `0.2`
`plate_size` | `int` | Size of assay plate (must be positive) | `4`
`image_noise` | `int` | Plate image noise (grayscale 0-255) | `32`

Source code in `src/snailz/assays.py`

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    class AssayParams(BaseModel):
        """Parameters for assay generation."""

        baseline: float = Field(default=2.0, ge=0.0, description="Baseline reading value")
        degrade: float = Field(
            default=0.05,
            ge=0.0,
            le=1.0,
            description="Rate at which sample responses decrease per day after first day (0..1)",
        )
        delay: int = Field(
            default=5,
            gt=0,
            description="Maximum number of days between specimen collection and assay",
        )
        mutant: float = Field(
            default=5.0, gt=0.0, description="Mutant reading value (must be positive)"
        )
        noise: float = Field(
            default=0.2, ge=0.0, description="Noise level for readings (must be positive)"
        )
        plate_size: int = Field(
            default=DEFAULT_PLATE_SIZE,
            gt=0,
            description="Size of assay plate (must be positive)",
        )
        image_noise: int = Field(
            default=32,
            ge=0,
            le=255,
            description="Plate image noise (grayscale 0-255)",
        )

        model_config = {"extra": "forbid"}

        @model_validator(mode="after")
        def validate_fields(self):
            """Validate requirements on fields."""
            if self.mutant < self.baseline:
                raise ValueError("mutant value must be greater than baseline")
            return self

### `validate_fields()`

Validate requirements on fields.

Source code in `src/snailz/assays.py`

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    @model_validator(mode="after")
    def validate_fields(self):
        """Validate requirements on fields."""
        if self.mutant < self.baseline:
            raise ValueError("mutant value must be greater than baseline")
        return self

## `Assay`

Bases: `BaseModel`

A single assay.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`ident` | `str` | unique identifier | required
`specimen` | `str` | which specimen | required
`person` | `str` | who did the assay | required
`performed` | `date` | date assay was performed | required
`readings` | `Grid[float]` | assay readings | required
`treatments` | `Grid[str]` | samples or controls | required

Source code in `src/snailz/assays.py`

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    class Assay(BaseModel):
        """A single assay."""

        ident: str = Field(description="unique identifier")
        specimen: str = Field(description="which specimen")
        person: str = Field(description="who did the assay")
        performed: date = Field(description="date assay was performed")
        readings: Grid[float] = Field(description="assay readings")
        treatments: Grid[str] = Field(description="samples or controls")

        model_config = {"extra": "forbid"}

        @model_validator(mode="after")
        def show_fields(self):
            return self

        def to_csv(self, kind: str) -> str:
            """Return a CSV string representation of the assay data.

            Parameters:
                kind: Either "readings" or "treatments"

            Returns:
                A CSV-formatted string with the assay data.

            Raises:
                ValueError: If 'kind' is not "readings" or "treatments"
            """
            if kind not in ["readings", "treatments"]:
                raise ValueError("data_type must be 'readings' or 'treatments'")

            # Get the appropriate data based on data_type
            data = self.readings if kind == "readings" else self.treatments
            assert isinstance(data, Grid)

            # Generate column headers (A, B, C, etc.) and calculate metadata padding
            column_headers = [""] + [chr(ord("A") + i) for i in range(data.width)]
            max_columns = len(column_headers)
            padding = [""] * (max_columns - 2)

            # Write data
            output = io.StringIO()
            writer = csv.writer(output, lineterminator="\n")
            pre = [
                ["id", self.ident] + padding,
                ["specimen", self.specimen] + padding,
                ["date", self.performed.isoformat()] + padding,
                ["by", self.person] + padding,
                column_headers,
            ]
            for row in pre:
                writer.writerow(row)

            for i, y in enumerate(range(data.height - 1, -1, -1)):
                row = [i + 1] + [data[x, y] for x in range(data.width)]
                writer.writerow(row)

            return output.getvalue()

### `to_csv(kind)`

Return a CSV string representation of the assay data.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`kind` | `str` | Either "readings" or "treatments" | required

Returns:

Type | Description
--- | ---
`str` | A CSV-formatted string with the assay data.

Raises:

Type | Description
--- | ---
`ValueError` | If 'kind' is not "readings" or "treatments"

Source code in `src/snailz/assays.py`

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    def to_csv(self, kind: str) -> str:
        """Return a CSV string representation of the assay data.

        Parameters:
            kind: Either "readings" or "treatments"

        Returns:
            A CSV-formatted string with the assay data.

        Raises:
            ValueError: If 'kind' is not "readings" or "treatments"
        """
        if kind not in ["readings", "treatments"]:
            raise ValueError("data_type must be 'readings' or 'treatments'")

        # Get the appropriate data based on data_type
        data = self.readings if kind == "readings" else self.treatments
        assert isinstance(data, Grid)

        # Generate column headers (A, B, C, etc.) and calculate metadata padding
        column_headers = [""] + [chr(ord("A") + i) for i in range(data.width)]
        max_columns = len(column_headers)
        padding = [""] * (max_columns - 2)

        # Write data
        output = io.StringIO()
        writer = csv.writer(output, lineterminator="\n")
        pre = [
            ["id", self.ident] + padding,
            ["specimen", self.specimen] + padding,
            ["date", self.performed.isoformat()] + padding,
            ["by", self.person] + padding,
            column_headers,
        ]
        for row in pre:
            writer.writerow(row)

        for i, y in enumerate(range(data.height - 1, -1, -1)):
            row = [i + 1] + [data[x, y] for x in range(data.width)]
            writer.writerow(row)

        return output.getvalue()

## `AllAssays`

Bases: `BaseModel`

All generated assays.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`items` | `list[Assay]` | actual assays | required

Source code in `src/snailz/assays.py`

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    class AllAssays(BaseModel):
        """All generated assays."""

        items: list[Assay] = Field(description="actual assays")

        def to_csv(self) -> str:
            """Return a CSV string representation of the assay summary data.

            Returns:
                A CSV-formatted string containing a summary of all assays
            """
            return utils.to_csv(
                self.items,
                ["ident", "specimen", "person", "performed"],
                lambda r: [r.ident, r.specimen, r.person, r.performed.isoformat()],
            )

### `to_csv()`

Return a CSV string representation of the assay summary data.

Returns:

Type | Description
--- | ---
`str` | A CSV-formatted string containing a summary of all assays

Source code in `src/snailz/assays.py`

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    def to_csv(self) -> str:
        """Return a CSV string representation of the assay summary data.

        Returns:
            A CSV-formatted string containing a summary of all assays
        """
        return utils.to_csv(
            self.items,
            ["ident", "specimen", "person", "performed"],
            lambda r: [r.ident, r.specimen, r.person, r.performed.isoformat()],
        )

## `assays_generate(params, persons, specimens)`

Generate an assay for each specimen.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`params` | `AssayParams` | assay generation parameters | required
`persons` | `AllPersons` | all staff members | required
`specimens` | `AllSpecimens` | specimens to generate assays for | required

Returns:

Type | Description
--- | ---
`AllAssays` | Assay list object

Source code in `src/snailz/assays.py`

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    def assays_generate(
        params: AssayParams, persons: AllPersons, specimens: AllSpecimens
    ) -> AllAssays:
        """Generate an assay for each specimen.

        Parameters:
            params: assay generation parameters
            persons: all staff members
            specimens: specimens to generate assays for

        Returns:
            Assay list object
        """
        gen = utils.unique_id("assays", lambda: f"{random.randint(0, 999999):06d}")

        items = []
        for spec in specimens.items:
            performed = spec.collected + timedelta(days=random.randint(0, params.delay))
            person = random.choice(persons.items)
            treatments = _make_treatments(params)
            readings = _make_readings(params, spec, performed, treatments)
            ident = next(gen)
            assert isinstance(ident, str)  # to satisfy type checking
            items.append(
                Assay(
                    ident=ident,
                    performed=performed,
                    specimen=spec.ident,
                    person=person.ident,
                    treatments=treatments,
                    readings=readings,
                )
            )

        return AllAssays(items=items)

## `_calc_degradation(params, collected, assayed)`

Calculate degradation based on days since collection.

Source code in `src/snailz/assays.py`

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    def _calc_degradation(params: AssayParams, collected: date, assayed: date) -> float:
        """Calculate degradation based on days since collection."""
        return max(0.0, 1.0 - (params.degrade * (assayed - collected).days))

## `_make_readings(params, specimen, performed, treatments)`

Make a single assay.

Source code in `src/snailz/assays.py`

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    def _make_readings(
        params: AssayParams,
        specimen: Specimen,
        performed: date,
        treatments: Grid[str],
    ) -> Grid[float]:
        """Make a single assay."""
        degradation = _calc_degradation(params, specimen.collected, performed)
        readings = Grid(width=params.plate_size, height=params.plate_size, default=0.0)
        for x in range(params.plate_size):
            for y in range(params.plate_size):
                if treatments[x, y] == "C":
                    base_value = 0.0
                elif specimen.is_mutant:
                    base_value = params.mutant * degradation
                else:
                    base_value = params.baseline * degradation
                readings[x, y] = round(
                    base_value + random.uniform(0.0, params.noise), utils.PRECISION
                )

        return readings

## `_make_treatments(params)`

Generate random treatments.

Source code in `src/snailz/assays.py`

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    def _make_treatments(params: AssayParams) -> Grid[str]:
        """Generate random treatments."""
        size = params.plate_size
        size_sq = size**2
        half = size_sq // 2
        available = list(("S" * half) + ("C" * (size_sq - half)))
        random.shuffle(available)
        treatments = Grid(width=size, height=size, default="")
        for x in range(size):
            for y in range(size):
                treatments[x, y] = available.pop()
        return treatments