Assays - Snailz Docs

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# Assays

Generate snail assays.

## `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
`machine` | `str` | machine ID | 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")
        machine: str = Field(description="machine ID")
        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 as_chunk(self) -> list[list[str]]:
            """Generate chunk for inclusion in overall assay CSV.

            Returns:
                Rows of data.
            """
            result = []
            col_names = [chr(ord("A") + i) for i in range(self.treatments.width)]
            for i_col, col_name in zip(range(self.treatments.width), col_names):
                for i_row in range(self.treatments.height):
                    result.append(
                        [
                            self.ident,
                            self.specimen,
                            col_name,
                            str(i_row + 1),
                            self.treatments[i_col, i_row],
                            self.readings[i_col, i_row],
                        ]
                    )
            return result

        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,
                ["machine", self.machine] + 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()

### `as_chunk()`

Generate chunk for inclusion in overall assay CSV.

Returns:

Type | Description
--- | ---
`list[list[str]]` | Rows of data.

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

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    def as_chunk(self) -> list[list[str]]:
        """Generate chunk for inclusion in overall assay CSV.

        Returns:
            Rows of data.
        """
        result = []
        col_names = [chr(ord("A") + i) for i in range(self.treatments.width)]
        for i_col, col_name in zip(range(self.treatments.width), col_names):
            for i_row in range(self.treatments.height):
                result.append(
                    [
                        self.ident,
                        self.specimen,
                        col_name,
                        str(i_row + 1),
                        self.treatments[i_col, i_row],
                        self.readings[i_col, i_row],
                    ]
                )
        return result

### `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,
            ["machine", self.machine] + 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 max_reading(self) -> int:
            """Find maximum assay reading value.

            Returns:
                Largest reading value across all assays.
            """
            result = 0.0
            for a in self.items:
                for x in range(a.readings.width):
                    for y in range(a.readings.height):
                        result = max(result, a.readings[x, y])
            return result

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

            Parameters:
                summary: produce summary (default) or include all results

            Returns:
                A CSV-formatted string containing assay data
            """
            if summary:
                return utils.to_csv(
                    self.items,
                    ["ident", "specimen", "person", "performed", "machine"],
                    lambda r: [
                        r.ident,
                        r.specimen,
                        r.person,
                        r.performed.isoformat(),
                        r.machine,
                    ],
                )
            else:
                rows = []
                for assay in self.items:
                    rows.extend(assay.as_chunk())
                return utils.to_csv(
                    rows,
                    ["ident", "specimen", "col", "row", "treatment", "reading"],
                    lambda r: r,
                )

        @staticmethod
        def generate(
            params: AssayParams,
            persons: AllPersons,
            machines: AllMachines,
            specimens: AllSpecimens,
        ) -> "AllAssays":
            """Generate an assay for each specimen.

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

            Returns:
                Assay list object
            """
            # Duplicate a few specimens and randomize order.
            subjects = model.assay_specimens(params, specimens)

            gen = utils.unique_id("assays", lambda: f"{random.randint(0, 999999):06d}")
            items = []
            for spec in subjects:
                performed = spec.collected + model.assay_performed(params)
                person = random.choice(persons.items)
                machine = random.choice(machines.items)
                treatments = _make_treatments(params)
                readings = _make_readings(params, spec, performed, machine, 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,
                        machine=machine.ident,
                        treatments=treatments,
                        readings=readings,
                    )
                )

            return AllAssays(items=items)

### `max_reading()`

Find maximum assay reading value.

Returns:

Type | Description
--- | ---
`int` | Largest reading value across all assays.

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

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    def max_reading(self) -> int:
        """Find maximum assay reading value.

        Returns:
            Largest reading value across all assays.
        """
        result = 0.0
        for a in self.items:
            for x in range(a.readings.width):
                for y in range(a.readings.height):
                    result = max(result, a.readings[x, y])
        return result

### `to_csv(summary=True)`

Return a CSV string representation of the assay summary data.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`summary` | `bool` | produce summary (default) or include all results | `True`

Returns:

Type | Description
--- | ---
`str` | A CSV-formatted string containing assay data

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

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

        Parameters:
            summary: produce summary (default) or include all results

        Returns:
            A CSV-formatted string containing assay data
        """
        if summary:
            return utils.to_csv(
                self.items,
                ["ident", "specimen", "person", "performed", "machine"],
                lambda r: [
                    r.ident,
                    r.specimen,
                    r.person,
                    r.performed.isoformat(),
                    r.machine,
                ],
            )
        else:
            rows = []
            for assay in self.items:
                rows.extend(assay.as_chunk())
            return utils.to_csv(
                rows,
                ["ident", "specimen", "col", "row", "treatment", "reading"],
                lambda r: r,
            )

### `generate(params, persons, machines, specimens)` `staticmethod`

Generate an assay for each specimen.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`params` | `AssayParams` | assay generation parameters | required
`persons` | `AllPersons` | all staff members | required
`machines` | `AllMachines` | all laboratory equipment | 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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    @staticmethod
    def generate(
        params: AssayParams,
        persons: AllPersons,
        machines: AllMachines,
        specimens: AllSpecimens,
    ) -> "AllAssays":
        """Generate an assay for each specimen.

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

        Returns:
            Assay list object
        """
        # Duplicate a few specimens and randomize order.
        subjects = model.assay_specimens(params, specimens)

        gen = utils.unique_id("assays", lambda: f"{random.randint(0, 999999):06d}")
        items = []
        for spec in subjects:
            performed = spec.collected + model.assay_performed(params)
            person = random.choice(persons.items)
            machine = random.choice(machines.items)
            treatments = _make_treatments(params)
            readings = _make_readings(params, spec, performed, machine, 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,
                    machine=machine.ident,
                    treatments=treatments,
                    readings=readings,
                )
            )

        return AllAssays(items=items)

## `_make_readings(params, specimen, performed, machine, 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,
        machine: Machine,
        treatments: Grid[str],
    ) -> Grid[float]:
        """Make a single assay."""
        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):
                readings[x, y] = round(
                    model.assay_reading(params, specimen, treatments[x, y], performed),
                    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