Modifier

Modifier#

class BooleanMask(*args: Any, **kwargs: Any)[source]#

Bases: ModifierBase

Applies a modifier only to bins selected by a boolean mask.

Parameters:

  • mask – Boolean array indicating which bins receive the modifier.

  • modifier – Modifier that provides the offsets and scales.

Examples

>>> import jax.numpy as jnp
>>> import evermore as evm
>>> hist = jnp.array([5, 20, 30])
>>> syst = evm.NormalParameter(value=0.1)
>>> norm = syst.scale_log_asymmetric(up=1.1, down=0.9)
>>> mask = jnp.array([True, False, True])
>>> modifier = evm.modifier.BooleanMask(mask, norm)
>>> modifier(hist)
Array([ 5.049494, 20.      , 30.296963], dtype=float32)
class Compose(*args: Any, **kwargs: Any)[source]#

Bases: ModifierBase

Combines multiple modifiers and applies them sequentially.

Parameters:

*modifiers – Modifiers to compose. They are flattened if nested Compose instances are provided.

Examples

>>> import jax.numpy as jnp
>>> import evermore as evm
>>> mu = evm.Parameter(value=1.1)
>>> syst = evm.NormalParameter(value=0.1)
>>> hist = jnp.array([10, 20, 30])
>>> composition = evm.modifier.Compose(
...     mu.scale(offset=0, slope=1),
...     syst.scale_log_asymmetric(up=1.1, down=0.9),
... )
>>> composition(hist)
Array([11.155, 22.237, 33.318], dtype=float32)
class Modifier(*args: Any, **kwargs: Any)[source]#

Bases: ModifierBase

Pairs a parameter with an effect to build a modifier.

Parameters:

  • parameter – Parameter instance that provides the nuisance strength.

  • effect – Effect describing how the histogram is altered.

Examples

>>> import jax.numpy as jnp
>>> import evermore as evm
>>> modifier = evm.Modifier(
...     value=1.1,
...     effect=evm.effect.Linear(offset=0.0, slope=1.0),
... )
>>> modifier(jnp.array([10, 20, 30]))
Array([11., 22., 33.], dtype=float32)
class ModifierBase(*args: Any, **kwargs: Any)[source]#

Bases: Module

Base class for modules that modify histogram templates.

Subclasses implement offset_and_scale() and automatically gain a callable interface as well as support for composition via the matrix multiplication operator.

Examples

>>> import jax.numpy as jnp
>>> import evermore as evm
>>> modifier = evm.Parameter(1.0).scale()
>>> modifier(jnp.array([10.0, 20.0]))
Array([10., 20.], dtype=float32)
class Transform(*args: Any, **kwargs: Any)[source]#

Bases: ModifierBase

Applies a transformation to both offset and scale of a modifier.

Parameters:

  • transform_fn – Callable applied to each leaf of the offset and scale.

  • modifier – Modifier supplying the original offset and scale values.

Examples

>>> import jax.numpy as jnp
>>> import evermore as evm
>>> hist = jnp.array([5, 20, 30])
>>> syst = evm.NormalParameter(value=0.1)
>>> norm = syst.scale_log_asymmetric(up=1.1, down=0.9)
>>> transformed_norm = evm.modifier.Transform(jnp.sqrt, norm)
>>> transformed_norm(hist)
Array([ 5.024686, 20.098743, 30.148115], dtype=float32)
class TransformOffset(*args: Any, **kwargs: Any)[source]#

Bases: ModifierBase

Transforms only the offset component of another modifier.

Parameters:

  • transform_fn – Callable applied to the offset leaves.

  • modifier – Modifier providing the original offset values.

class TransformScale(*args: Any, **kwargs: Any)[source]#

Bases: ModifierBase

Transforms only the multiplicative scale component of another modifier.

Parameters:

  • transform_fn – Callable applied to the scale leaves.

  • modifier – Modifier providing the original scale values.

class Where(*args: Any, **kwargs: Any)[source]#

Bases: ModifierBase

Chooses between two modifiers based on a boolean condition.

Parameters:

  • condition – Boolean array indicating where to apply modifier_true.

  • modifier_true – Modifier evaluated where condition is True.

  • modifier_false – Modifier evaluated where condition is False.

Examples

>>> import jax.numpy as jnp
>>> import evermore as evm
>>> hist = jnp.array([5, 20, 30])
>>> syst = evm.NormalParameter(value=0.1)
>>> norm = syst.scale_log_asymmetric(up=jnp.array([1.1]), down=jnp.array([0.9]))
>>> shape = syst.morphing(
...     up_template=jnp.array([7, 22, 31]),
...     down_template=jnp.array([4, 16, 27]),
... )
>>> modifier = evm.modifier.Where(hist < 10, norm, shape)
>>> modifier(hist)
Array([ 5.049494, 20.281374, 30.181376], dtype=float32)
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