Assays - Snailz Docs

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# Assays

Generate snail assays.

## `AssayParams`

Bases: `BaseModel`

Parameters for assay generation.

- baseline: Baseline reading value (must be positive)
- end_date: End date for assay generation
- mutant: Mutant reading value (must be positive)
- noise: Noise level for readings (must be positive)
- plate_size: Size of assay plate (must be positive)
- seed: Random seed for reproducibility
- start_date: Start date for assay generation (must not be after end_date)

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

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

        - baseline: Baseline reading value (must be positive)
        - end_date: End date for assay generation
        - mutant: Mutant reading value (must be positive)
        - noise: Noise level for readings (must be positive)
        - plate_size: Size of assay plate (must be positive)
        - seed: Random seed for reproducibility
        - start_date: Start date for assay generation (must not be after end_date)
        """

        baseline: float = Field(gt=0)
        end_date: date = Field()
        mutant: float = Field(gt=0)
        noise: float = Field(gt=0)
        plate_size: int = Field(gt=0)
        seed: int = Field()
        start_date: date = Field()

        @model_validator(mode="after")
        def validate_date_range(self):
            """Validate that start_date is not after end_date."""
            if self.start_date > self.end_date:
                raise ValueError("start date must be less than or equal to end date")
            return self

        model_config = {"extra": "forbid"}

### `validate_date_range()`

Validate that start_date is not after end_date.

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

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    @model_validator(mode="after")
    def validate_date_range(self):
        """Validate that start_date is not after end_date."""
        if self.start_date > self.end_date:
            raise ValueError("start date must be less than or equal to end date")
        return self

## `Assay`

Bases: `BaseModel`

A single assay.

- performed: date assay was performed
- ident: unique identifier
- specimen_id: which specimen
- person_id: who did the assay
- readings: grid of assay readings
- treatments: grid of samples or controls

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

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

        - performed: date assay was performed
        - ident: unique identifier
        - specimen_id: which specimen
        - person_id: who did the assay
        - readings: grid of assay readings
        - treatments: grid of samples or controls
        """

        performed: date
        ident: str
        specimen_id: str
        person_id: str
        readings: list[list[float]]
        treatments: list[list[str]]

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

            Parameters:
                data_type: Type of data to output, either "readings" or "treatments"

            Returns:
                A CSV-formatted string with the assay data in the format:
                id,<assay_id>
                specimen,<specimen_id>
                performed,<performed_date>
                performed_by,<person_id>
                ,A,B,C,...
                1,<data>,<data>,...
                2,<data>,<data>,...
                ...

                The CSV output uses Unix line endings (LF).

            Raises:
                ValueError: If data_type is not "readings" or "treatments"
            """
            if data_type 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 data_type == "readings" else self.treatments

            # Generate column headers (A, B, C, etc.) and calculate metadata padding
            plate_size = len(data)
            column_headers = [""] + [chr(65 + i) for i in range(plate_size)]
            max_columns = len(column_headers)
            padding = [""] * (max_columns - 2)

            # Write metadata rows with Unix line endings
            output = io.StringIO()
            writer = csv.writer(output, **utils.CSV_SETTINGS)
            writer.writerow(["id", self.ident] + padding)
            writer.writerow(["specimen", self.specimen_id] + padding)
            writer.writerow(["performed", self.performed.isoformat()] + padding)
            writer.writerow(["performed_by", self.person_id] + padding)

            # Write data rows with row numbers
            writer.writerow(column_headers)
            for i, row in enumerate(data, 1):
                writer.writerow([i] + row)

            return output.getvalue()

### `to_csv(data_type)`

Return a CSV string representation of the assay data.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`data_type` | `str` | Type of data to output, either "readings" or "treatments" | required

Returns:

Type | Description
--- | ---
`str` | A CSV-formatted string with the assay data in the format:
`str` | id,
`str` | specimen,
`str` | performed,
`str` | performed_by,
`str` | ,A,B,C,...
`str` | 1,,,...
`str` | 2,,,...
`str` | ...
`str` | The CSV output uses Unix line endings (LF).

Raises:

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

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

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

        Parameters:
            data_type: Type of data to output, either "readings" or "treatments"

        Returns:
            A CSV-formatted string with the assay data in the format:
            id,<assay_id>
            specimen,<specimen_id>
            performed,<performed_date>
            performed_by,<person_id>
            ,A,B,C,...
            1,<data>,<data>,...
            2,<data>,<data>,...
            ...

            The CSV output uses Unix line endings (LF).

        Raises:
            ValueError: If data_type is not "readings" or "treatments"
        """
        if data_type 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 data_type == "readings" else self.treatments

        # Generate column headers (A, B, C, etc.) and calculate metadata padding
        plate_size = len(data)
        column_headers = [""] + [chr(65 + i) for i in range(plate_size)]
        max_columns = len(column_headers)
        padding = [""] * (max_columns - 2)

        # Write metadata rows with Unix line endings
        output = io.StringIO()
        writer = csv.writer(output, **utils.CSV_SETTINGS)
        writer.writerow(["id", self.ident] + padding)
        writer.writerow(["specimen", self.specimen_id] + padding)
        writer.writerow(["performed", self.performed.isoformat()] + padding)
        writer.writerow(["performed_by", self.person_id] + padding)

        # Write data rows with row numbers
        writer.writerow(column_headers)
        for i, row in enumerate(data, 1):
            writer.writerow([i] + row)

        return output.getvalue()

## `AllAssays`

Bases: `BaseModel`

Keep track of generated assays.

- items: actual assays
- params: parameters used in generation

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

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    class AllAssays(BaseModel):
        """Keep track of generated assays.

        - items: actual assays
        - params: parameters used in generation
        """

        items: list[Assay]
        params: AssayParams

        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 with fields:
                - ident: assay identifier
                - specimen_id: specimen identifier
                - performed: date the assay was performed
                - performed_by: person identifier

                The CSV output uses Unix line endings (LF).
            """
            output = io.StringIO()
            writer = csv.writer(output, utils.CSV_SETTINGS)
            writer.writerow(["ident", "specimen_id", "performed", "performed_by"])
            for assay in self.items:
                writer.writerow(
                    [
                        assay.ident,
                        assay.specimen_id,
                        assay.performed.isoformat(),
                        assay.person_id,
                    ]
                )

            return output.getvalue()

### `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 with fields:
`str` | ident: assay identifier
`str` | specimen_id: specimen identifier
`str` | performed: date the assay was performed
`str` | performed_by: person identifier
`str` | The CSV output uses Unix line endings (LF).

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 with fields:
            - ident: assay identifier
            - specimen_id: specimen identifier
            - performed: date the assay was performed
            - performed_by: person identifier

            The CSV output uses Unix line endings (LF).
        """
        output = io.StringIO()
        writer = csv.writer(output, utils.CSV_SETTINGS)
        writer.writerow(["ident", "specimen_id", "performed", "performed_by"])
        for assay in self.items:
            writer.writerow(
                [
                    assay.ident,
                    assay.specimen_id,
                    assay.performed.isoformat(),
                    assay.person_id,
                ]
            )

        return output.getvalue()

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

Generate an assay for each specimen.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`params` | `AssayParams` | AssayParams object containing assay generation parameters | required
`specimens` | `AllSpecimens` | Specimens object with individual specimens to generate assays for | required
`people` | `AllPersons` | People object with staff members | required

Returns:

Type | Description
--- | ---
`AllAssays` | Assays object containing generated assays and parameters

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

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

        Parameters:
            params: AssayParams object containing assay generation parameters
            specimens: Specimens object with individual specimens to generate assays for
            people: People object with staff members

        Returns:
            Assays object containing generated assays and parameters
        """
        days_delta = (params.end_date - params.start_date).days + 1
        individuals = specimens.individuals
        susc_locus = specimens.susceptible_locus
        susc_base = specimens.susceptible_base
        items = []

        gen = utils.UniqueIdGenerator("assays", lambda: f"{random.randint(0, 999999):06d}")

        for individual in individuals:
            assay_date = params.start_date + timedelta(
                days=random.randint(0, days_delta - 1)
            )
            assay_id = gen.next()

            # Generate treatments randomly with equal probability
            treatments = []
            for row in range(params.plate_size):
                treatment_row = []
                for col in range(params.plate_size):
                    treatment_row.append(random.choice(["S", "C"]))
                treatments.append(treatment_row)

            # Generate readings based on treatments and susceptibility
            readings = []
            is_susceptible = individual.genome[susc_locus] == susc_base
            for row in range(params.plate_size):
                reading_row = []
                for col in range(params.plate_size):
                    if treatments[row][col] == "C":
                        # Control cells have values uniformly distributed between 0 and noise
                        reading_row.append(random.uniform(0, params.noise))
                    elif is_susceptible:
                        # Susceptible specimens (with susceptible base at susceptible locus)
                        # Base mutant value plus noise scaled by mutant/baseline ratio
                        scaled_noise = round(
                            params.noise * params.mutant / params.baseline, utils.PRECISION
                        )
                        reading_row.append(params.mutant + random.uniform(0, scaled_noise))
                    else:
                        # Non-susceptible specimens
                        # Base baseline value plus uniform noise
                        reading_row.append(
                            params.baseline + random.uniform(0, params.noise)
                        )
                # Handle limited precision.
                reading_row = [round(r, utils.PRECISION) for r in reading_row]
                readings.append(reading_row)

            # Randomly select a person to perform the assay
            person = random.choice(people.individuals)

            # Create the assay record
            items.append(
                Assay(
                    performed=assay_date,
                    ident=assay_id,
                    specimen_id=individual.ident,
                    person_id=person.ident,
                    readings=readings,
                    treatments=treatments,
                )
            )

        return AllAssays(items=items, params=params)

## `assays_to_csv(input, output)`

Write assays to standard output or files.

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

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    def assays_to_csv(input: str, output: str | None) -> None:
        """Write assays to standard output or files."""
        data = utils.load_data("assays", input, AllAssays)

        # For stdout, only output the summary
        if output is None:
            content = data.to_csv()
            print(content, end="")
            return

        output_path = Path(output)
        with open(output_path / "assays.csv", "w") as writer:
            writer.write(data.to_csv())

        # Create assays subdirectory
        assays_dir = output_path / ASSAYS_SUBDIR
        assays_dir.mkdir(exist_ok=True)

        # Write individual assay files
        for assay in data.items:
            # Design file
            design_file = assays_dir / f"{assay.ident}_design.csv"
            with open(design_file, "w") as writer:
                writer.write(assay.to_csv(data_type="treatments"))

            # Readings file
            assay_file = assays_dir / f"{assay.ident}_assay.csv"
            with open(assay_file, "w") as writer:
                writer.write(assay.to_csv(data_type="readings"))