"""
This module contains layers acting on the x-grid input of the NN
The two operations included are:
- ``xDivide``
- ``xIntegrator``
The names are self-describing. The only subtlety is that they do not act equally
for all flavours. The choice of flavours on which to act in a different way is given
as an input argument.
"""
from typing import List, Optional
from n3fit.backends import MetaLayer
from n3fit.backends import operations as op
BASIS_SIZE = 14
[docs]class xDivide(MetaLayer):
"""
Create tensor of either 1/x or ones depending on the flavour,
to be used to divide some PDFs by x by multiplying with the result.
By default it utilizes the 14-flavour FK basis. In the unpolarized
case, one divides [v, v3, v8, v15] which corresponds to indices
(3, 4, 5, 6) from the FK basis:
(photon, sigma, g, v, v3, v8, v15, v24, v35, t3, t8, t15, t24, t35)
In the polarized case, only [T3, T8] are divided by `x` which
corresponds to the indices (9, 10).
Parameters:
-----------
output_dim: int
dimension of the pdf
div_list: list
list of indices to be divided by `x` (by default [3, 4, 5, 6]; [v, v3, v8, v15]
"""
def __init__(
self,
output_dim: int = BASIS_SIZE,
fitbasis: str = "NN31IC",
div_list: Optional[List[int]] = None,
**kwargs
):
if div_list is None: # Default value if unspecified for Unpolarized Case
div_list = [3, 4, 5, 6]
div_list = [9, 10] if fitbasis.startswith("POLARIZED_") else div_list
self.output_dim = output_dim
self.div_list = div_list
super().__init__(**kwargs)
self.powers = [-1 if i in div_list else 0 for i in range(output_dim)]
[docs] def call(self, x):
return op.pow(x, self.powers)
[docs] def get_config(self):
config = super().get_config()
config.update({"output_dim": self.output_dim, "div_list": self.div_list})
return config
[docs]class xIntegrator(MetaLayer):
"""
This layer performs a sum of the input layer/tensor on the axis corresponding to the x-grid
weighted by the weights of the grid.
The output shape is the input shape with the x-axis removed.
Parameters
----------
grid_weights: np.array
weights of the grid
x_axis: int (default=2)
axis of the input tensor that corresponds to the x-grid
"""
def __init__(self, grid_weights, x_axis=2, **kwargs):
self.x_axis = x_axis
self.grid_weights = op.flatten(op.numpy_to_tensor(grid_weights))
super().__init__(**kwargs)
[docs] def call(self, pdf):
return op.tensor_product(pdf, self.grid_weights, axes=[self.x_axis, 0])