n3fit.tests package

Subpackages

Submodules

n3fit.tests.conftest module

n3fit.tests.test_backend module

This module tests the mathematical functions in the n3fit backend and ensures they do the same thing as their numpy counterparts

n3fit.tests.test_backend.are_equal(result, reference, threshold=1e-06)[source]

checks the difference between array reference and tensor result is below threshold for all elements

n3fit.tests.test_backend.numpy_check(backend_op, python_op, mode='same')[source]

Receives a backend operation (backend_op) and a python operation python_op and asserts that, applied to two random arrays, the result is the same. The option mode selects the two arrays to be tested and accepts the following options:

  • same (default): two arrays of the same dimensionality

  • diff: first array has one extra dimension that second array

  • single: only one array enters the operation

  • (tensor, array): if passed a tuple (backend tensor, numpy array), uses these

    values as tensor and array inputs for the operations

n3fit.tests.test_backend.test_boolean_mask()[source]
n3fit.tests.test_backend.test_c_to_py_fun()[source]
n3fit.tests.test_backend.test_flatten()[source]
n3fit.tests.test_backend.test_op_log()[source]
n3fit.tests.test_backend.test_op_multiply()[source]
n3fit.tests.test_backend.test_op_multiply_dim()[source]
n3fit.tests.test_backend.test_sum()[source]
n3fit.tests.test_backend.test_tensor_product()[source]

n3fit.tests.test_checks module

n3fit.tests.test_evolven3fit module

n3fit.tests.test_fit module

n3fit.tests.test_hyperopt module

n3fit.tests.test_layers module

Tests for the layers of n3fit This module checks that the layers do what they would do with numpy

class n3fit.tests.test_layers.FakePhoton[source]

Bases: object

n3fit.tests.test_layers.generate_DIS(nfk=1)[source]
n3fit.tests.test_layers.generate_had(nfk=1)[source]
n3fit.tests.test_layers.generate_input_DIS(flavs=3, xsize=2, ndata=5, n_combinations=-1)[source]

Generates fake input (fktable and array of combinations) for the DIS convolution

Parameters:

  • flavs (int) – number of flavours to consider

  • xsize (int) – size of the grid on x

  • ndata (int) – number of experimental datapoints

  • n_combinations (int) – number of combinations of flavours to take into account default: flavs (all)

n3fit.tests.test_layers.generate_input_had(flavs=3, xsize=2, ndata=4, n_combinations=None)[source]

Generates fake input (fktable and array of combinations) for the hadronic convolution

Parameters:

  • flavs (int) – number of flavours to consider

  • xsize (int) – size of the grid on x

  • ndata (int) – number of experimental datapoints

  • n_combinations (int) – number of combinations of flavours to take into account default: flavs*flavs (all)

n3fit.tests.test_layers.test_DIS()[source]
n3fit.tests.test_layers.test_DIS_basis()[source]
n3fit.tests.test_layers.test_DY()[source]
n3fit.tests.test_layers.test_DY_basis()[source]
n3fit.tests.test_layers.test_addphoton_init()[source]

Test AddPhoton class.

n3fit.tests.test_layers.test_computation_bc()[source]

Test the computation of the boundary conditions.

n3fit.tests.test_layers.test_compute_photon()[source]
n3fit.tests.test_layers.test_mask()[source]

Test the mask layer

n3fit.tests.test_layers.test_rotation_evol()[source]
n3fit.tests.test_layers.test_rotation_flavour()[source]

n3fit.tests.test_losses module

Test the losses layers

n3fit.tests.test_losses.test_l_invcovmat()[source]
n3fit.tests.test_losses.test_l_positivity()[source]

n3fit.tests.test_modelgen module

Test for the model generation

These tests check that the generated NN are as expected It checks that both the number of layers and the shape of the weights of the layers are what is expected

n3fit.tests.test_modelgen.test_generate_dense_network()[source]
n3fit.tests.test_modelgen.test_generate_dense_per_flavour_network()[source]
n3fit.tests.test_modelgen.test_generate_multi_dense_network()[source]

n3fit.tests.test_msr module

n3fit.tests.test_multidense module

n3fit.tests.test_multidense.test_initializers()[source]
n3fit.tests.test_multidense.test_multidense()[source]

n3fit.tests.test_multireplica module

n3fit.tests.test_multireplica.test_replica_split()[source]

Check that multi replica pdf and concatenated single output pdfs agree

n3fit.tests.test_penalties module

Test the penalties for n3fit hyperopt

n3fit.tests.test_penalties.test_integrability_numbers()[source]

Check that the integrability penalty runs and returns a float

n3fit.tests.test_penalties.test_patience()[source]

Check that the patience penalty runs and returns a float

n3fit.tests.test_penalties.test_saturation()[source]

Check that the saturation penalty runs and returns a float

n3fit.tests.test_preprocessing module

n3fit.tests.test_preprocessing.setup_layer(replica_seeds)[source]

Setup a layer for testing

n3fit.tests.test_preprocessing.test_constraint()[source]

Test the constraint

n3fit.tests.test_preprocessing.test_preprocessing()[source]

Regression test

n3fit.tests.test_rotations module

n3fit.tests.test_rotations.test_fk()[source]

n3fit.tests.test_stopwatch module

Tests the stopwatch does what is supposed to do

n3fit.tests.test_stopwatch.test_register_ref()[source]
n3fit.tests.test_stopwatch.test_register_times()[source]
n3fit.tests.test_stopwatch.time_comparer(internal_dict, computed_dict, base_time)[source]

n3fit.tests.test_vpinterface module

n3fit.tests.test_xops module

Test the x operations

n3fit.tests.test_xops.test_xdivide_default()[source]

Check that the default xDivide works as expected

n3fit.tests.test_xops.test_xdivide_indices()[source]

Check that xDivide with custom indices works as expected

n3fit.tests.test_xops.test_xintegrator()[source]

Module contents