Bins

quantflow.utils.bins.pdf

pdf(data, *, num_bins=None, delta=None, symmetric=None, precision=6)

Extract a probability density function from the data as a DataFrame with index given by the bin centers and a single column pdf with the estimated probability density function values

PARAMETER	DESCRIPTION
data	The data to extract the PDF from TYPE: FloatArray
num_bins	The number of bins to use in the histogram. if not provided it is calculated from the delta parameter (if provided) or set to 50 TYPE: int | None DEFAULT: None
delta	The spacing between bins, if not provided it is calculated from the num_bins TYPE: float | None DEFAULT: None
symmetric	If provided, the bins are centered around this value TYPE: float | None DEFAULT: None
precision	The precision to use in the calculation TYPE: int DEFAULT: 6

Source code in quantflow/utils/bins.py

def pdf(
    data: Annotated[
        FloatArray,
        Doc("The data to extract the PDF from"),
    ],
    *,
    num_bins: Annotated[
        int | None,
        Doc(
            "The number of bins to use in the histogram. if not provided "
            "it is calculated from the `delta` parameter (if provided) "
            "or set to 50"
        ),
    ] = None,
    delta: Annotated[
        float | None,
        Doc(
            "The spacing between bins, if not provided "
            " it is calculated from the `num_bins`"
        ),
    ] = None,
    symmetric: Annotated[
        float | None, Doc("If provided, the bins are centered around this value")
    ] = None,
    precision: Annotated[int, Doc("The precision to use in the calculation")] = 6,
) -> DataFrame:
    """Extract a probability density function from the data as a DataFrame
    with index given by the bin centers and a single column `pdf` with the
    estimated probability density function values
    """
    max_value = cast(float, np.max(data))
    min_value = cast(float, np.min(data))
    domain: float = max(abs(data)) if symmetric is not None else max_value - min_value  # type: ignore
    if num_bins is None:
        if not delta:
            num_bins = 50
            delta_ = round(domain / (num_bins - 1), precision)
        else:
            delta_ = delta
            num_bins = round(domain / delta_)
    else:
        if delta:
            raise ValueError("Cannot specify both num_bins and delta")
        if num_bins < 2:
            raise ValueError("num_bins must be greater than 1")
        delta_ = round(domain / (num_bins - 1), precision)
    if symmetric is not None:
        b = (num_bins + 0.5) * delta_
        min_value = symmetric - b
        max_value = symmetric + b
    x = np.arange(min_value - delta_, max_value + 2 * delta_, delta_)
    bins = (x[:-1] + x[1:]) * 0.5
    pdf, _ = np.histogram(data, bins=bins, density=True)
    return DataFrame(dict(pdf=pdf), index=x[1:-1])

quantflow.utils.bins.event_density

event_density(df, columns, num=10)

Calculate the probability density of the number of events in the dataframe columns

PARAMETER	DESCRIPTION
df	The dataframe to extract the event density from TYPE: DataFrame
columns	The columns to calculate the event density for TYPE: Sequence[str]
num	The number of events to consider TYPE: int DEFAULT: 10

Source code in quantflow/utils/bins.py

def event_density(
    df: Annotated[DataFrame, Doc("The dataframe to extract the event density from")],
    columns: Annotated[
        Sequence[str], Doc("The columns to calculate the event density for")
    ],
    num: Annotated[int, Doc("The number of events to consider")] = 10,
) -> dict[str, Any]:
    """Calculate the probability density of the number of events
    in the dataframe columns
    """
    bins = np.linspace(-0.5, num - 0.5, num + 1)
    data = dict(n=np.arange(num))
    for col in columns:
        counts, _ = np.histogram(df[col], bins=bins)
        counts = counts / np.sum(counts)
        data[col] = counts[:num]  # type: ignore
    return data