Python API¶

class madspace.AlphaSGrid(self: madspace._madspace_py.AlphaSGrid, file: str)¶

Bases: pybind11_object

coefficients_shape(self: madspace._madspace_py.AlphaSGrid, batch_dim: bool = False) → list[int]¶

initialize_globals(self: madspace._madspace_py.AlphaSGrid, context: madspace._madspace_py.Context, prefix: str = '') → None¶

property logq2¶

logq2_shape(self: madspace._madspace_py.AlphaSGrid, batch_dim: bool = False) → list[int]¶

property q¶

property q_count¶

property region_sizes¶

property values¶

class madspace.BatchSize(*args, **kwargs)¶

Bases: pybind11_object

Overloaded function.

__init__(self: madspace._madspace_py.BatchSize) -> None
__init__(self: madspace._madspace_py.BatchSize, name: str) -> None

one = 1¶

class madspace.ChannelWeightNetwork(self: madspace._madspace_py.ChannelWeightNetwork, channel_count: SupportsInt, particle_count: SupportsInt, hidden_dim: SupportsInt = 32, layers: SupportsInt = 3, activation: madspace._madspace_py.MLP.Activation = <Activation.leaky_relu: 1>, prefix: str = '')¶

Bases: FunctionGenerator

initialize_globals(self: madspace._madspace_py.ChannelWeightNetwork, context: madspace._madspace_py.Context) → None¶

mask_name(self: madspace._madspace_py.ChannelWeightNetwork) → str¶

mlp(self: madspace._madspace_py.ChannelWeightNetwork) → madspace._madspace_py.MLP¶

preprocessing(self: madspace._madspace_py.ChannelWeightNetwork) → madspace._madspace_py.MomentumPreprocessing¶

class madspace.Context(*args, **kwargs)¶

Bases: pybind11_object

Overloaded function.

__init__(self: madspace._madspace_py.Context) -> None
__init__(self: madspace._madspace_py.Context, device: madspace._madspace_py.Device) -> None

define_global(self: madspace._madspace_py.Context, name: str, dtype: madspace._madspace_py.DataType, shape: collections.abc.Sequence[SupportsInt], requires_grad: bool = False) → madspace._madspace_py.Tensor¶

device(self: madspace._madspace_py.Context) → madspace._madspace_py.Device¶

get_global(self: madspace._madspace_py.Context, name: str) → madspace._madspace_py.Tensor¶

global_exists(self: madspace._madspace_py.Context, name: str) → bool¶

global_requires_grad(self: madspace._madspace_py.Context, name: str) → bool¶

load(self: madspace._madspace_py.Context, file: str) → None¶

load_matrix_element(self: madspace._madspace_py.Context, file: str, param_card: str) → madspace._madspace_py.MatrixElementApi¶

matrix_element(self: madspace._madspace_py.Context, index: SupportsInt) → madspace._madspace_py.MatrixElementApi¶

save(self: madspace._madspace_py.Context, file: str) → None¶

class madspace.CutItem(self: madspace._madspace_py.CutItem, observable: madspace._madspace_py.Observable, min: SupportsFloat = -inf, max: SupportsFloat = inf, mode: madspace._madspace_py.Cuts.CutMode = <CutMode.all: 1>)¶

Bases: pybind11_object

property max¶

property min¶

property mode¶

property observable¶

class madspace.Cuts(*args, **kwargs)¶

Bases: FunctionGenerator

Overloaded function.

__init__(self: madspace._madspace_py.Cuts, cut_data: collections.abc.Sequence[madspace._madspace_py.CutItem]) -> None
__init__(self: madspace._madspace_py.Cuts, particle_count: typing.SupportsInt) -> None

class CutMode(*args, **kwargs)¶

Bases: pybind11_object

Members:

any

all

Overloaded function.

__init__(self: madspace._madspace_py.Cuts.CutMode, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.Cuts.CutMode, name: str) -> None

all = <CutMode.all: 1>¶

any = <CutMode.any: 0>¶

Cuts.CutMode.name -> str

property value¶

all = <CutMode.all: 1>¶

any = <CutMode.any: 0>¶

eta_max(self: madspace._madspace_py.Cuts) → list[float]¶

pt_min(self: madspace._madspace_py.Cuts) → list[float]¶

sqrt_s_min(self: madspace._madspace_py.Cuts) → float¶

class madspace.DataType(*args, **kwargs)¶

Bases: pybind11_object

Members:

int

float

batch_sizes

Overloaded function.

__init__(self: madspace._madspace_py.DataType, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.DataType, name: str) -> None

batch_sizes = <DataType.batch_sizes: 2>¶

float = <DataType.float: 1>¶

int = <DataType.int: 0>¶

DataType.name -> str

property value¶

class madspace.Decay¶

Bases: pybind11_object

property child_indices¶

property e_max¶

property e_min¶

property index¶

property mass¶

property on_shell¶

property parent_index¶

property pdg_id¶

property width¶

class madspace.Device¶: Bases: pybind11_object

class madspace.Diagram(self: madspace._madspace_py.Diagram, incoming_masses: collections.abc.Sequence[SupportsFloat], outgoing_masses: collections.abc.Sequence[SupportsFloat], propagators: collections.abc.Sequence[madspace._madspace_py.Propagator], vertices: collections.abc.Sequence[collections.abc.Sequence[madspace._madspace_py.LineRef]])¶

Bases: pybind11_object

property incoming_masses¶

property incoming_vertices¶

property outgoing_masses¶

property outgoing_vertices¶

property propagator_vertices¶

property propagators¶

property vertices¶

class madspace.DifferentialCrossSection(self: madspace._madspace_py.DifferentialCrossSection, matrix_element: madspace._madspace_py.MatrixElement, cm_energy: SupportsFloat, running_coupling: madspace._madspace_py.RunningCoupling, energy_scale: madspace._madspace_py.EnergyScale, pid_options: collections.abc.Sequence[collections.abc.Sequence[SupportsInt]] = [], has_pdf1: bool = False, has_pdf2: bool = False, pdf_grid1: madspace._madspace_py.PdfGrid | None = None, pdf_grid2: madspace._madspace_py.PdfGrid | None = None, has_mirror: bool = False, input_momentum_fraction: bool = True)¶

Bases: FunctionGenerator

has_mirror(self: madspace._madspace_py.DifferentialCrossSection) → bool¶

matrix_element(self: madspace._madspace_py.DifferentialCrossSection) → madspace._madspace_py.MatrixElement¶

pid_options(self: madspace._madspace_py.DifferentialCrossSection) → list[list[int]]¶

class madspace.DiscreteFlow(self: madspace._madspace_py.DiscreteFlow, option_counts: collections.abc.Sequence[typing.SupportsInt], prefix: str = '', dims_with_prior: collections.abc.Sequence[typing.SupportsInt] = [], condition_dim: typing.SupportsInt = 0, subnet_hidden_dim: typing.SupportsInt = 32, subnet_layers: typing.SupportsInt = 3, subnet_activation: madspace._madspace_py.MLP.Activation = <Activation.leaky_relu: 1>)¶

Bases: Mapping

condition_dim(self: madspace._madspace_py.DiscreteFlow) → int¶

initialize_globals(self: madspace._madspace_py.DiscreteFlow, context: madspace._madspace_py.Context) → None¶

option_counts(self: madspace._madspace_py.DiscreteFlow) → list[int]¶

class madspace.DiscreteHistogram(self: madspace._madspace_py.DiscreteHistogram, option_counts: collections.abc.Sequence[SupportsInt])¶: Bases: FunctionGenerator

class madspace.DiscreteOptimizer(self: madspace._madspace_py.DiscreteOptimizer, context: madspace._madspace_py.Context, prob_names: collections.abc.Sequence[str])¶

Bases: pybind11_object

add_data(self: madspace._madspace_py.DiscreteOptimizer, values_and_counts: collections.abc.Sequence[object]) → None¶

optimize(self: madspace._madspace_py.DiscreteOptimizer) → None¶

class madspace.DiscreteSampler(self: madspace._madspace_py.DiscreteSampler, option_counts: collections.abc.Sequence[SupportsInt], prefix: str = '', dims_with_prior: collections.abc.Sequence[SupportsInt] = [])¶

Bases: Mapping

initialize_globals(self: madspace._madspace_py.DiscreteSampler, context: madspace._madspace_py.Context) → None¶

option_counts(self: madspace._madspace_py.DiscreteSampler) → list[int]¶

prob_names(self: madspace._madspace_py.DiscreteSampler) → list[str]¶

class madspace.EnergyScale(*args, **kwargs)¶

Bases: FunctionGenerator

Overloaded function.

__init__(self: madspace._madspace_py.EnergyScale, particle_count: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.EnergyScale, particle_count: typing.SupportsInt, type: madspace._madspace_py.EnergyScale.DynamicalScaleType) -> None
__init__(self: madspace._madspace_py.EnergyScale, particle_count: typing.SupportsInt, fixed_scale: typing.SupportsFloat) -> None
__init__(self: madspace._madspace_py.EnergyScale, particle_count: typing.SupportsInt, dynamical_scale_type: madspace._madspace_py.EnergyScale.DynamicalScaleType, ren_scale_fixed: bool, fact_scale_fixed: bool, ren_scale: typing.SupportsFloat, fact_scale1: typing.SupportsFloat, fact_scale2: typing.SupportsFloat) -> None

class DynamicalScaleType(*args, **kwargs)¶

Bases: pybind11_object

Members:

transverse_energy

transverse_mass

half_transverse_mass

partonic_energy

Overloaded function.

__init__(self: madspace._madspace_py.EnergyScale.DynamicalScaleType, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.EnergyScale.DynamicalScaleType, name: str) -> None

half_transverse_mass = <DynamicalScaleType.half_transverse_mass: 2>¶

EnergyScale.DynamicalScaleType.name -> str

partonic_energy = <DynamicalScaleType.partonic_energy: 3>¶

transverse_energy = <DynamicalScaleType.transverse_energy: 0>¶

transverse_mass = <DynamicalScaleType.transverse_mass: 1>¶

property value¶

half_transverse_mass = <DynamicalScaleType.half_transverse_mass: 2>¶

partonic_energy = <DynamicalScaleType.partonic_energy: 3>¶

transverse_energy = <DynamicalScaleType.transverse_energy: 0>¶

transverse_mass = <DynamicalScaleType.transverse_mass: 1>¶

class madspace.EventGenerator(self: madspace._madspace_py.EventGenerator, context: madspace._madspace_py.Context, channels: collections.abc.Sequence[madspace._madspace_py.Integrand], temp_file_prefix: str, status_file: str = '', config: madspace._madspace_py.EventGeneratorConfig = EventGenerator.default_config, channel_subprocesses: collections.abc.Sequence[SupportsInt] = [], channel_names: collections.abc.Sequence[str] = [], channel_histograms: collections.abc.Sequence[madspace._madspace_py.ObservableHistograms] = [])¶

Bases: pybind11_object

channel_status(self: madspace._madspace_py.EventGenerator) → list[madspace._madspace_py.EventGeneratorStatus]¶

combine_to_compact_npy(self: madspace._madspace_py.EventGenerator, file_name: str) → None¶

combine_to_lhe(self: madspace._madspace_py.EventGenerator, file_name: str, lhe_completer: madspace::LHECompleter) → None¶

combine_to_lhe_npy(self: madspace._madspace_py.EventGenerator, file_name: str, lhe_completer: madspace::LHECompleter) → None¶

default_config = <madspace._madspace_py.EventGeneratorConfig object>¶

generate(self: madspace._madspace_py.EventGenerator) → None¶

histograms(self: madspace._madspace_py.EventGenerator) → list[madspace._madspace_py.EventGeneratorHistogram]¶

integrand_flags = 1077¶

status(self: madspace._madspace_py.EventGenerator) → madspace._madspace_py.EventGeneratorStatus¶

survey(self: madspace._madspace_py.EventGenerator) → None¶

class madspace.EventGeneratorConfig(self: madspace._madspace_py.EventGeneratorConfig)¶

Bases: pybind11_object

property batch_size¶

property freeze_max_weight_after¶

property max_batch_size¶

property max_overweight_truncation¶

property optimization_patience¶

property optimization_threshold¶

property start_batch_size¶

property survey_max_iters¶

property survey_min_iters¶

property survey_target_precision¶

property target_count¶

property vegas_damping¶

property verbosity¶

class madspace.EventGeneratorHistogram¶

Bases: pybind11_object

property bin_errors¶

property bin_values¶

property max¶

property min¶

property name¶

class madspace.EventGeneratorStatus(self: madspace._madspace_py.EventGeneratorStatus)¶

Bases: pybind11_object

property count¶

property count_after_cuts¶

property count_after_cuts_opt¶

property count_opt¶

property count_target¶

property count_unweighted¶

property done¶

property error¶

property index¶

property iterations¶

property mean¶

property rel_std_dev¶

class madspace.EventGeneratorVerbosity(*args, **kwargs)¶

Bases: pybind11_object

Members:

silent

log

pretty

Overloaded function.

__init__(self: madspace._madspace_py.EventGeneratorVerbosity, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.EventGeneratorVerbosity, name: str) -> None

log = <EventGeneratorVerbosity.log: 1>¶

EventGeneratorVerbosity.name -> str

pretty = <EventGeneratorVerbosity.pretty: 2>¶

silent = <EventGeneratorVerbosity.silent: 0>¶

property value¶

class madspace.FastRamboMapping(self: madspace._madspace_py.FastRamboMapping, n_particles: SupportsInt, massless: bool)¶: Bases: Mapping

class madspace.Flow(self: madspace._madspace_py.Flow, input_dim: SupportsInt, condition_dim: SupportsInt = 0, prefix: str = '', bin_count: SupportsInt = 10, subnet_hidden_dim: SupportsInt = 32, subnet_layers: SupportsInt = 3, subnet_activation: madspace._madspace_py.MLP.Activation = <Activation.leaky_relu: 1>, invert_spline: bool = True)¶

Bases: Mapping

condition_dim(self: madspace._madspace_py.Flow) → int¶

initialize_from_vegas(self: madspace._madspace_py.Flow, context: madspace._madspace_py.Context, grid_name: str) → None¶

initialize_globals(self: madspace._madspace_py.Flow, context: madspace._madspace_py.Context) → None¶

input_dim(self: madspace._madspace_py.Flow) → int¶

class madspace.Function¶

Bases: pybind11_object

property globals¶

property inputs¶

property instructions¶

static load(file: str) → madspace._madspace_py.Function¶

property locals¶

property outputs¶

save(self: madspace._madspace_py.Function, file: str) → None¶

class madspace.FunctionBuilder(self: madspace._madspace_py.FunctionBuilder, input_types: collections.abc.Sequence[madspace._madspace_py.Type], output_types: collections.abc.Sequence[madspace._madspace_py.Type])¶

Bases: pybind11_object

add(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value, in2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

add_int(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value, in2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

apply_subchannel_weights(self: madspace._madspace_py.FunctionBuilder, channel_weights_in: madspace._madspace_py.Value, subchannel_weights: madspace._madspace_py.Value, channel_indices: madspace._madspace_py.Value, subchannel_indices: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

argsort(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

batch_cat(self: madspace._madspace_py.FunctionBuilder, args: collections.abc.Sequence[madspace._madspace_py.Value]) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

batch_gather(self: madspace._madspace_py.FunctionBuilder, indices: madspace._madspace_py.Value, values: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

batch_scatter(self: madspace._madspace_py.FunctionBuilder, indices: madspace._madspace_py.Value, target: madspace._madspace_py.Value, source: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

batch_size(self: madspace._madspace_py.FunctionBuilder, args: collections.abc.Sequence[madspace._madspace_py.Value]) → madspace._madspace_py.Value¶

batch_split(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value, counts: madspace._madspace_py.Value) → list[madspace._madspace_py.Value]¶

boost_beam(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, x1: madspace._madspace_py.Value, x2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

boost_beam_inverse(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, x1: madspace._madspace_py.Value, x2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

breit_wigner_invariant(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, mass: madspace._madspace_py.Value, width: madspace._madspace_py.Value, s_min: madspace._madspace_py.Value, s_max: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

breit_wigner_invariant_inverse(self: madspace._madspace_py.FunctionBuilder, s: madspace._madspace_py.Value, mass: madspace._madspace_py.Value, width: madspace._madspace_py.Value, s_min: madspace._madspace_py.Value, s_max: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

cat(self: madspace._madspace_py.FunctionBuilder, args: collections.abc.Sequence[madspace._madspace_py.Value]) → madspace._madspace_py.Value¶

chili_forward(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, e_cm: madspace._madspace_py.Value, m_out: madspace._madspace_py.Value, pt_min: madspace._madspace_py.Value, y_max: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

chili_inverse(self: madspace._madspace_py.FunctionBuilder, p_ext: madspace._madspace_py.Value, e_cm: madspace._madspace_py.Value, m_out: madspace._madspace_py.Value, pt_min: madspace._madspace_py.Value, y_max: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

collect_channel_weights(self: madspace._madspace_py.FunctionBuilder, amp2: madspace._madspace_py.Value, channel_indices: madspace._madspace_py.Value, channel_count: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

com_p_in(self: madspace._madspace_py.FunctionBuilder, e_cm: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

cut_all(self: madspace._madspace_py.FunctionBuilder, obs: madspace._madspace_py.Value, min: madspace._madspace_py.Value, max: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

cut_any(self: madspace._madspace_py.FunctionBuilder, obs: madspace._madspace_py.Value, min: madspace._madspace_py.Value, max: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

cut_one(self: madspace._madspace_py.FunctionBuilder, obs: madspace._madspace_py.Value, min: madspace._madspace_py.Value, max: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

cut_unphysical(self: madspace._madspace_py.FunctionBuilder, w_in: madspace._madspace_py.Value, p: madspace._madspace_py.Value, x1: madspace._madspace_py.Value, x2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

diff_cross_section(self: madspace._madspace_py.FunctionBuilder, x1: madspace._madspace_py.Value, x2: madspace._madspace_py.Value, pdf1: madspace._madspace_py.Value, pdf2: madspace._madspace_py.Value, matrix_element: madspace._madspace_py.Value, e_cm2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

discrete_histogram(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, weights: madspace._madspace_py.Value, option_count: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

elu(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

fast_rambo_massive(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, e_cm: madspace._madspace_py.Value, masses: madspace._madspace_py.Value, p0: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

fast_rambo_massive_com(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, e_cm: madspace._madspace_py.Value, masses: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

fast_rambo_massive_inverse(self: madspace._madspace_py.FunctionBuilder, p_out: madspace._madspace_py.Value, e_cm: madspace._madspace_py.Value, masses: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

fast_rambo_massless(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, e_cm: madspace._madspace_py.Value, p0: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

fast_rambo_massless_com(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, e_cm: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

fast_rambo_massless_inverse(self: madspace._madspace_py.FunctionBuilder, p_out: madspace._madspace_py.Value, e_cm: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

full(self: madspace._madspace_py.FunctionBuilder, args: collections.abc.Sequence[madspace._madspace_py.Value]) → madspace._madspace_py.Value¶

function(self: madspace._madspace_py.FunctionBuilder) → madspace._madspace_py.Function¶

gather(self: madspace._madspace_py.FunctionBuilder, index: madspace._madspace_py.Value, choices: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

gather_int(self: madspace._madspace_py.FunctionBuilder, index: madspace._madspace_py.Value, choices: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

gelu(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

get_global(self: madspace._madspace_py.FunctionBuilder, name: str, dtype: madspace._madspace_py.DataType, shape: collections.abc.Sequence[SupportsInt]) → madspace._madspace_py.Value¶

histogram(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, weights: madspace._madspace_py.Value, min: madspace._madspace_py.Value, max: madspace._madspace_py.Value, bin_count: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

input(self: madspace._madspace_py.FunctionBuilder, index: SupportsInt) → madspace._madspace_py.Value¶

input_range(self: madspace._madspace_py.FunctionBuilder, start_index: SupportsInt, end_index: SupportsInt) → list[madspace._madspace_py.Value]¶

interpolate_alpha_s(self: madspace._madspace_py.FunctionBuilder, q2: madspace._madspace_py.Value, grid_logq2: madspace._madspace_py.Value, grid_coeffs: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

interpolate_pdf(self: madspace._madspace_py.FunctionBuilder, x: madspace._madspace_py.Value, q2: madspace._madspace_py.Value, pid_indices: madspace._madspace_py.Value, grid_logx: madspace._madspace_py.Value, grid_logq2: madspace._madspace_py.Value, grid_coeffs: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

invariants_from_momenta(self: madspace._madspace_py.FunctionBuilder, p_ext: madspace._madspace_py.Value, factors: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

leaky_relu(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

matmul(self: madspace._madspace_py.FunctionBuilder, x: madspace._madspace_py.Value, weight: madspace._madspace_py.Value, bias: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

matrix_element(self: madspace._madspace_py.FunctionBuilder, args: collections.abc.Sequence[madspace._madspace_py.Value]) → list[madspace._madspace_py.Value]¶

max(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value, in2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

min(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value, in2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

mirror_momenta(self: madspace._madspace_py.FunctionBuilder, p_ext: madspace._madspace_py.Value, mirror: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

momenta_to_x1x2(self: madspace._madspace_py.FunctionBuilder, p_ext: madspace._madspace_py.Value, e_cm: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

mul(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value, in2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

nonzero(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_delta_eta(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_delta_phi(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_delta_r(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_e(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_eta(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_eta_abs(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_mass(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_p_mag(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_phi(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_pt(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_px(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_py(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_pz(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_sqrt_s(self: madspace._madspace_py.FunctionBuilder, p_ext: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_theta(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_y(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

obs_y_abs(self: madspace._madspace_py.FunctionBuilder, p: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

offset_indices(self: madspace._madspace_py.FunctionBuilder, batch_sizes_offset: madspace._madspace_py.Value, batch_sizes_out: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

one_hot(self: madspace._madspace_py.FunctionBuilder, index: madspace._madspace_py.Value, option_count: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

output(self: madspace._madspace_py.FunctionBuilder, index: SupportsInt, value: madspace._madspace_py.Value) → None¶

output_range(self: madspace._madspace_py.FunctionBuilder, start_index: SupportsInt, values: collections.abc.Sequence[madspace._madspace_py.Value]) → None¶

permute_momenta(self: madspace._madspace_py.FunctionBuilder, momenta: madspace._madspace_py.Value, permutations: madspace._madspace_py.Value, index: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

pop(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

product(self: madspace._madspace_py.FunctionBuilder, values: collections.abc.Sequence[madspace._madspace_py.Value]) → madspace._madspace_py.Value¶

pt_eta_phi_x(self: madspace._madspace_py.FunctionBuilder, p_ext: madspace._madspace_py.Value, x1: madspace._madspace_py.Value, x2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

r_to_x1x2(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, s_hat: madspace._madspace_py.Value, s_lab: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

random(self: madspace._madspace_py.FunctionBuilder, batch_size: madspace._madspace_py.Value, count: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

reduce_product(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

reduce_sum(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

reduce_sum_vector(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

relu(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

rqs_find_bin(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, in_sizes: madspace._madspace_py.Value, out_sizes: madspace._madspace_py.Value, derivatives: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

rqs_forward(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, condition: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

rqs_inverse(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, condition: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

rqs_reshape(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, bin_count: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

s23_min_max(self: madspace._madspace_py.FunctionBuilder, pa: madspace._madspace_py.Value, pb: madspace._madspace_py.Value, p3: madspace._madspace_py.Value, t1_abs: madspace._madspace_py.Value, m1: madspace._madspace_py.Value, m2: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

s23_value_and_min_max(self: madspace._madspace_py.FunctionBuilder, pa: madspace._madspace_py.Value, pb: madspace._madspace_py.Value, p3: madspace._madspace_py.Value, t1_abs: madspace._madspace_py.Value, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

sample_discrete(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, option_count: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

sample_discrete_inverse(self: madspace._madspace_py.FunctionBuilder, index: madspace._madspace_py.Value, option_count: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

sample_discrete_probs(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, probs: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

sample_discrete_probs_inverse(self: madspace._madspace_py.FunctionBuilder, index: madspace._madspace_py.Value, probs: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

scale_half_transverse_mass(self: madspace._madspace_py.FunctionBuilder, momenta: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

scale_partonic_energy(self: madspace._madspace_py.FunctionBuilder, momenta: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

scale_transverse_energy(self: madspace._madspace_py.FunctionBuilder, momenta: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

scale_transverse_mass(self: madspace._madspace_py.FunctionBuilder, momenta: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

sde2_channel_weights(self: madspace._madspace_py.FunctionBuilder, invariants: madspace._madspace_py.Value, masses: madspace._madspace_py.Value, widths: madspace._madspace_py.Value, indices: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

select(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, indices: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

select_int(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, indices: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

select_vector(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, indices: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

sigmoid(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

softmax(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

softmax_prior(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, prior: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

softplus(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

sqrt(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

square(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

squeeze(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

stable_invariant(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, mass: madspace._madspace_py.Value, s_min: madspace._madspace_py.Value, s_max: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

stable_invariant_inverse(self: madspace._madspace_py.FunctionBuilder, s: madspace._madspace_py.Value, mass: madspace._madspace_py.Value, s_min: madspace._madspace_py.Value, s_max: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

stable_invariant_nu(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, mass: madspace._madspace_py.Value, nu: madspace._madspace_py.Value, s_min: madspace._madspace_py.Value, s_max: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

stable_invariant_nu_inverse(self: madspace._madspace_py.FunctionBuilder, s: madspace._madspace_py.Value, mass: madspace._madspace_py.Value, nu: madspace._madspace_py.Value, s_min: madspace._madspace_py.Value, s_max: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

stack(self: madspace._madspace_py.FunctionBuilder, args: collections.abc.Sequence[madspace._madspace_py.Value]) → madspace._madspace_py.Value¶

sub(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value, in2: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

subchannel_weights(self: madspace._madspace_py.FunctionBuilder, invariants: madspace._madspace_py.Value, masses: madspace._madspace_py.Value, widths: madspace._madspace_py.Value, indices: madspace._madspace_py.Value, on_shell: madspace._madspace_py.Value, group_sizes: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

t_inv_min_max(self: madspace._madspace_py.FunctionBuilder, pa: madspace._madspace_py.Value, pb: madspace._madspace_py.Value, m1: madspace._madspace_py.Value, m2: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

t_inv_value_and_min_max(self: madspace._madspace_py.FunctionBuilder, pa: madspace._madspace_py.Value, pb: madspace._madspace_py.Value, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

three_body_decay(self: madspace._madspace_py.FunctionBuilder, r_e1: madspace._madspace_py.Value, r_e2: madspace._madspace_py.Value, r_phi: madspace._madspace_py.Value, r_cos_theta: madspace._madspace_py.Value, r_beta: madspace._madspace_py.Value, m0: madspace._madspace_py.Value, m1: madspace._madspace_py.Value, m2: madspace._madspace_py.Value, m3: madspace._madspace_py.Value, p0: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(4)']¶

three_body_decay_com(self: madspace._madspace_py.FunctionBuilder, r_e1: madspace._madspace_py.Value, r_e2: madspace._madspace_py.Value, r_phi: madspace._madspace_py.Value, r_cos_theta: madspace._madspace_py.Value, r_beta: madspace._madspace_py.Value, m0: madspace._madspace_py.Value, m1: madspace._madspace_py.Value, m2: madspace._madspace_py.Value, m3: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(4)']¶

three_body_decay_com_inverse(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value, p3: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(10)']¶

three_body_decay_inverse(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value, p3: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(11)']¶

two_body_decay(self: madspace._madspace_py.FunctionBuilder, r_phi: madspace._madspace_py.Value, r_cos_theta: madspace._madspace_py.Value, m0: madspace._madspace_py.Value, m1: madspace._madspace_py.Value, m2: madspace._madspace_py.Value, p0: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

two_body_decay_com(self: madspace._madspace_py.FunctionBuilder, r_phi: madspace._madspace_py.Value, r_cos_theta: madspace._madspace_py.Value, m0: madspace._madspace_py.Value, m1: madspace._madspace_py.Value, m2: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

two_body_decay_com_inverse(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(6)']¶

two_body_decay_inverse(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(7)']¶

two_to_three_particle_scattering(self: madspace._madspace_py.FunctionBuilder, phi_choice: madspace._madspace_py.Value, pa: madspace._madspace_py.Value, pb: madspace._madspace_py.Value, p3: madspace._madspace_py.Value, s23: madspace._madspace_py.Value, t1_abs: madspace._madspace_py.Value, m1: madspace._madspace_py.Value, m2: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

two_to_three_particle_scattering_inverse(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value, p3: madspace._madspace_py.Value, pa: madspace._madspace_py.Value, pb: madspace._madspace_py.Value, t1_abs: madspace._madspace_py.Value, s23: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(4)']¶

two_to_two_particle_scattering(self: madspace._madspace_py.FunctionBuilder, r_phi: madspace._madspace_py.Value, pa: madspace._madspace_py.Value, pb: madspace._madspace_py.Value, t_abs: madspace._madspace_py.Value, m1: madspace._madspace_py.Value, m2: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

two_to_two_particle_scattering_com(self: madspace._madspace_py.FunctionBuilder, r_phi: madspace._madspace_py.Value, pa: madspace._madspace_py.Value, pb: madspace._madspace_py.Value, t_abs: madspace._madspace_py.Value, m1: madspace._madspace_py.Value, m2: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(3)']¶

two_to_two_particle_scattering_com_inverse(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value, pa: madspace._madspace_py.Value, pb: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(4)']¶

two_to_two_particle_scattering_inverse(self: madspace._madspace_py.FunctionBuilder, p1: madspace._madspace_py.Value, p2: madspace._madspace_py.Value, pa: madspace._madspace_py.Value, pb: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(4)']¶

uniform_invariant(self: madspace._madspace_py.FunctionBuilder, r: madspace._madspace_py.Value, s_min: madspace._madspace_py.Value, s_max: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

uniform_invariant_inverse(self: madspace._madspace_py.FunctionBuilder, s: madspace._madspace_py.Value, s_min: madspace._madspace_py.Value, s_max: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

unsqueeze(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value) → madspace._madspace_py.Value¶

unstack(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → list[madspace._madspace_py.Value]¶

unstack_sizes(self: madspace._madspace_py.FunctionBuilder, in1: madspace._madspace_py.Value) → list[madspace._madspace_py.Value]¶

unweight(self: madspace._madspace_py.FunctionBuilder, weights: madspace._madspace_py.Value, max_weight: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

vegas_forward(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, grid: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

vegas_histogram(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, weights: madspace._madspace_py.Value, bin_count: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

vegas_inverse(self: madspace._madspace_py.FunctionBuilder, input: madspace._madspace_py.Value, grid: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

x1x2_to_r(self: madspace._madspace_py.FunctionBuilder, x1: madspace._madspace_py.Value, x2: madspace._madspace_py.Value, s_lab: madspace._madspace_py.Value) → Annotated[list[madspace._madspace_py.Value], 'FixedSize(2)']¶

class madspace.FunctionGenerator(self: madspace._madspace_py.FunctionGenerator, name: str, arg_types: collections.abc.Sequence[madspace._madspace_py.Type], return_types: collections.abc.Sequence[madspace._madspace_py.Type])¶

Bases: pybind11_object

build_function(self: madspace._madspace_py.FunctionGenerator, builder: madspace._madspace_py.FunctionBuilder, args: collections.abc.Sequence[madspace._madspace_py.Value]) → list[madspace._madspace_py.Value]¶

function(self: madspace._madspace_py.FunctionGenerator) → madspace._madspace_py.Function¶

class madspace.FunctionRuntime(*args, **kwargs)¶

Bases: pybind11_object

Overloaded function.

__init__(self: madspace._madspace_py.FunctionRuntime, function: madspace._madspace_py.Function) -> None
__init__(self: madspace._madspace_py.FunctionRuntime, function: madspace._madspace_py.Function, context: madspace._madspace_py.Context) -> None

call(self: madspace._madspace_py.FunctionRuntime, arg0: collections.abc.Sequence[object]) → list[madspace._madspace_py.Tensor]¶

call_backward(self: madspace._madspace_py.FunctionRuntime, arg0: collections.abc.Sequence[object], arg1: collections.abc.Sequence[object], arg2: collections.abc.Sequence[bool]) → tuple[list[madspace._madspace_py.Tensor | None], list[tuple[str, madspace._madspace_py.Tensor | None]]]¶

call_with_grad(self: madspace._madspace_py.FunctionRuntime, arg0: collections.abc.Sequence[object], arg1: collections.abc.Sequence[bool]) → tuple[list[madspace._madspace_py.Tensor], list[madspace._madspace_py.Tensor | None], list[bool]]¶

class madspace.HistItem(self: madspace._madspace_py.HistItem, observable: madspace._madspace_py.Observable, min: SupportsFloat, max: SupportsFloat, bin_count: SupportsInt)¶

Bases: pybind11_object

property bin_count¶

property max¶

property min¶

property observable¶

class madspace.Instruction¶

Bases: pybind11_object

property name¶

property opcode¶

class madspace.InstructionCall¶

Bases: pybind11_object

property inputs¶

property instruction¶

property outputs¶

class madspace.Integrand(self: madspace._madspace_py.Integrand, mapping: madspace._madspace_py.PhaseSpaceMapping, diff_xs: madspace._madspace_py.DifferentialCrossSection, adaptive_map: None | madspace._madspace_py.VegasMapping | madspace._madspace_py.Flow = None, discrete_before: None | madspace._madspace_py.DiscreteSampler | madspace._madspace_py.DiscreteFlow = None, discrete_after: None | madspace._madspace_py.DiscreteSampler | madspace._madspace_py.DiscreteFlow = None, pdf_grid: madspace._madspace_py.PdfGrid | None = None, energy_scale: madspace._madspace_py.EnergyScale | None = None, prop_chan_weights: madspace._madspace_py.PropagatorChannelWeights | None = None, subchan_weights: madspace._madspace_py.SubchannelWeights | None = None, chan_weight_net: madspace._madspace_py.ChannelWeightNetwork | None = None, chan_weight_remap: collections.abc.Sequence[SupportsInt] = [], remapped_chan_count: SupportsInt = 0, flags: SupportsInt = 0, channel_indices: collections.abc.Sequence[SupportsInt] = [], active_flavors: collections.abc.Sequence[SupportsInt] = [], flavor_remap: collections.abc.Sequence[SupportsInt] = [], flavor_factors: collections.abc.Sequence[SupportsFloat] = [])¶

Bases: FunctionGenerator

adaptive_map(self: madspace._madspace_py.Integrand) → None | madspace._madspace_py.VegasMapping | madspace._madspace_py.Flow¶

chan_weight_net(self: madspace._madspace_py.Integrand) → madspace._madspace_py.ChannelWeightNetwork | None¶

diff_xs(self: madspace._madspace_py.Integrand) → madspace._madspace_py.DifferentialCrossSection¶

discrete_after(self: madspace._madspace_py.Integrand) → None | madspace._madspace_py.DiscreteSampler | madspace._madspace_py.DiscreteFlow¶

discrete_before(self: madspace._madspace_py.Integrand) → None | madspace._madspace_py.DiscreteSampler | madspace._madspace_py.DiscreteFlow¶

energy_scale(self: madspace._madspace_py.Integrand) → madspace._madspace_py.EnergyScale | None¶

flags(self: madspace._madspace_py.Integrand) → int¶

latent_dims(self: madspace._madspace_py.Integrand) → tuple[list[int], list[bool]]¶

mapping(self: madspace._madspace_py.Integrand) → madspace._madspace_py.PhaseSpaceMapping¶

matrix_element_inputs = [<MatrixElementInput.momenta_in: 0>, <MatrixElementInput.alpha_s_in: 1>, <MatrixElementInput.flavor_in: 2>, <MatrixElementInput.random_color_in: 3>, <MatrixElementInput.random_helicity_in: 4>, <MatrixElementInput.random_diagram_in: 5>]¶

matrix_element_outputs = [<MatrixElementOutput.matrix_element_out: 0>, <MatrixElementOutput.diagram_amp2_out: 1>, <MatrixElementOutput.color_index_out: 2>, <MatrixElementOutput.helicity_index_out: 3>, <MatrixElementOutput.diagram_index_out: 4>]¶

particle_count(self: madspace._madspace_py.Integrand) → int¶

prop_chan_weights(self: madspace._madspace_py.Integrand) → madspace._madspace_py.PropagatorChannelWeights | None¶

random_dim(self: madspace._madspace_py.Integrand) → int¶

return_chan_weights = 256¶

return_channel = 128¶

return_cwnet_input = 512¶

return_discrete = 1024¶

return_discrete_latent = 2048¶

return_indices = 16¶

return_latent = 64¶

return_momenta = 4¶

return_random = 32¶

return_x1_x2 = 8¶

sample = 1¶

unweight = 2¶

vegas_grid_name(self: madspace._madspace_py.Integrand) → str | None¶

class madspace.IntegrandProbability(self: madspace._madspace_py.IntegrandProbability, integrand: madspace._madspace_py.Integrand)¶: Bases: FunctionGenerator

class madspace.Invariant(self: madspace._madspace_py.Invariant, power: SupportsFloat = 0.0, mass: SupportsFloat = 0.0, width: SupportsFloat = 0.0)¶: Bases: Mapping

class madspace.LHECompleter(self: madspace._madspace_py.LHECompleter, subproc_args: collections.abc.Sequence[madspace._madspace_py.SubprocArgs], bw_cutoff: SupportsFloat)¶

Bases: pybind11_object

complete_event_data(self: madspace._madspace_py.LHECompleter, event: madspace._madspace_py.LHEEvent, subprocess_index: SupportsInt, diagram_index: SupportsInt, color_index: SupportsInt, flavor_index: SupportsInt, helicity_index: SupportsInt) → None¶

property max_particle_count¶

class madspace.LHEEvent(self: madspace._madspace_py.LHEEvent, process_id: SupportsInt = 0, weight: SupportsFloat = 0.0, scale: SupportsFloat = 0.0, alpha_qed: SupportsFloat = 0.0, alpha_qcd: SupportsFloat = 0.0, particles: collections.abc.Sequence[madspace._madspace_py.LHEParticle] = [])¶

Bases: pybind11_object

property alpha_qcd¶

property alpha_qed¶

property particles¶

property process_id¶

property scale¶

property weight¶

class madspace.LHEFileWriter(self: madspace._madspace_py.LHEFileWriter, file_name: str, meta: madspace._madspace_py.LHEMeta)¶

Bases: pybind11_object

write(self: madspace._madspace_py.LHEFileWriter, event: madspace._madspace_py.LHEEvent) → None¶

write_string(self: madspace._madspace_py.LHEFileWriter, str: str) → None¶

class madspace.LHEHeader(self: madspace._madspace_py.LHEHeader, name: str = '', content: str = '', escape_content: bool = False)¶

Bases: pybind11_object

property content¶

property escape_content¶

property name¶

class madspace.LHEMeta(self: madspace._madspace_py.LHEMeta, beam1_pdg_id: SupportsInt = 0, beam2_pdg_id: SupportsInt = 0, beam1_energy: SupportsFloat = 0.0, beam2_energy: SupportsFloat = 0.0, beam1_pdf_authors: SupportsInt = 0, beam2_pdf_authors: SupportsInt = 0, beam1_pdf_id: SupportsInt = 0, beam2_pdf_id: SupportsInt = 0, weight_mode: SupportsInt = 0, processes: collections.abc.Sequence[madspace._madspace_py.LHEProcess] = [], headers: collections.abc.Sequence[madspace._madspace_py.LHEHeader] = [])¶

Bases: pybind11_object

property beam1_energy¶

property beam1_pdf_authors¶

property beam1_pdf_id¶

property beam1_pdg_id¶

property beam2_energy¶

property beam2_pdf_authors¶

property beam2_pdf_id¶

property beam2_pdg_id¶

property headers¶

property processes¶

property weight_mode¶

class madspace.LHEParticle(self: madspace._madspace_py.LHEParticle, pdg_id: SupportsInt = 0, status_code: SupportsInt = 0, mother1: SupportsInt = 0, mother2: SupportsInt = 0, color: SupportsInt = 0, anti_color: SupportsInt = 0, p_x: SupportsFloat = 0.0, p_y: SupportsFloat = 0.0, p_z: SupportsFloat = 0.0, energy: SupportsFloat = 0.0, mass: SupportsFloat = 0.0, lifetime: SupportsFloat = 0.0, spin: SupportsFloat = 0.0)¶

Bases: pybind11_object

property anti_color¶

property color¶

property energy¶

property lifetime¶

property mass¶

property mother1¶

property mother2¶

property pdg_id¶

property px¶

property py¶

property pz¶

property spin¶

property status_code¶

status_incoming = -1¶

status_intermediate_resonance = 2¶

status_outgoing = 1¶

class madspace.LHEProcess(self: madspace._madspace_py.LHEProcess, cross_section: SupportsFloat = 0.0, cross_section_error: SupportsFloat = 0.0, max-weight: SupportsFloat = 0.0, process_id: SupportsInt = 0)¶

Bases: pybind11_object

property cross_section¶

property cross_section_error¶

property max_weight¶

property process_id¶

class madspace.LineRef(self: madspace._madspace_py.LineRef, str: str)¶: Bases: pybind11_object

class madspace.Logger¶

Bases: pybind11_object

class LogLevel(*args, **kwargs)¶

Bases: pybind11_object

Members:

level_debug

level_info

level_warning

level_error

Overloaded function.

__init__(self: madspace._madspace_py.Logger.LogLevel, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.Logger.LogLevel, name: str) -> None

level_debug = <LogLevel.level_debug: 0>¶

level_error = <LogLevel.level_error: 3>¶

level_info = <LogLevel.level_info: 1>¶

level_warning = <LogLevel.level_warning: 2>¶

Logger.LogLevel.name -> str

property value¶

static debug(message: str) → None¶

static error(message: str) → None¶

static info(message: str) → None¶

level_debug = <LogLevel.level_debug: 0>¶

level_error = <LogLevel.level_error: 3>¶

level_info = <LogLevel.level_info: 1>¶

level_warning = <LogLevel.level_warning: 2>¶

static log(level: madspace._madspace_py.Logger.LogLevel, message: str) → None¶

static set_log_handler(func: collections.abc.Callable[[madspace._madspace_py.Logger.LogLevel, str], None]) → None¶

static warning(message: str) → None¶

class madspace.Luminosity(self: madspace._madspace_py.Luminosity, s_lab: SupportsFloat, s_hat_min: SupportsFloat, s_hat_max: SupportsFloat = 0.0, invariant_power: SupportsFloat = 0.0, mass: SupportsFloat = 0.0, width: SupportsFloat = 0.0)¶: Bases: Mapping

class madspace.MLP(self: madspace._madspace_py.MLP, input_dim: SupportsInt, output_dim: SupportsInt, hidden_dim: SupportsInt = 32, layers: SupportsInt = 3, activation: madspace._madspace_py.MLP.Activation = <Activation.leaky_relu: 1>, prefix: str = '')¶

Bases: FunctionGenerator

class Activation(*args, **kwargs)¶

Bases: pybind11_object

Members:

relu

leaky_relu

elu

gelu

sigmoid

softplus

linear

Overloaded function.

__init__(self: madspace._madspace_py.MLP.Activation, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.MLP.Activation, name: str) -> None

elu = <Activation.elu: 2>¶

gelu = <Activation.gelu: 3>¶

leaky_relu = <Activation.leaky_relu: 1>¶

linear = <Activation.linear: 6>¶

MLP.Activation.name -> str

relu = <Activation.relu: 0>¶

sigmoid = <Activation.sigmoid: 4>¶

softplus = <Activation.softplus: 5>¶

property value¶

elu = <Activation.elu: 2>¶

gelu = <Activation.gelu: 3>¶

initialize_globals(self: madspace._madspace_py.MLP, context: madspace._madspace_py.Context) → None¶

input_dim(self: madspace._madspace_py.MLP) → int¶

leaky_relu = <Activation.leaky_relu: 1>¶

linear = <Activation.linear: 6>¶

output_dim(self: madspace._madspace_py.MLP) → int¶

relu = <Activation.relu: 0>¶

sigmoid = <Activation.sigmoid: 4>¶

softplus = <Activation.softplus: 5>¶

class madspace.Mapping(self: madspace._madspace_py.Mapping, name: str, input_types: collections.abc.Sequence[madspace._madspace_py.Type], output_types: collections.abc.Sequence[madspace._madspace_py.Type], condition_types: collections.abc.Sequence[madspace._madspace_py.Type])¶

Bases: pybind11_object

build_forward(self: madspace._madspace_py.Mapping, builder: madspace._madspace_py.FunctionBuilder, inputs: collections.abc.Sequence[madspace._madspace_py.Value], conditions: collections.abc.Sequence[madspace._madspace_py.Value]) → tuple[list[madspace._madspace_py.Value], madspace._madspace_py.Value]¶

build_inverse(self: madspace._madspace_py.Mapping, builder: madspace._madspace_py.FunctionBuilder, inputs: collections.abc.Sequence[madspace._madspace_py.Value], conditions: collections.abc.Sequence[madspace._madspace_py.Value]) → tuple[list[madspace._madspace_py.Value], madspace._madspace_py.Value]¶

forward_function(self: madspace._madspace_py.Mapping) → madspace._madspace_py.Function¶

inverse_function(self: madspace._madspace_py.Mapping) → madspace._madspace_py.Function¶

map_forward(inputs, conditions=[])¶

map_inverse(inputs, conditions=[])¶

class madspace.MatrixElement(*args, **kwargs)¶

Bases: FunctionGenerator

Overloaded function.

__init__(self: madspace._madspace_py.MatrixElement, matrix_element_index: typing.SupportsInt, particle_count: typing.SupportsInt, inputs: collections.abc.Sequence[madspace._madspace_py.MatrixElement.MatrixElementInput], outputs: collections.abc.Sequence[madspace._madspace_py.MatrixElement.MatrixElementOutput], diagram_count: typing.SupportsInt = 1, sample_random_inputs: bool = False) -> None
__init__(self: madspace._madspace_py.MatrixElement, matrix_element_api: madspace._madspace_py.MatrixElementApi, inputs: collections.abc.Sequence[madspace._madspace_py.MatrixElement.MatrixElementInput], outputs: collections.abc.Sequence[madspace._madspace_py.MatrixElement.MatrixElementOutput], sample_random_inputs: bool = False) -> None

class MatrixElementInput(*args, **kwargs)¶

Bases: pybind11_object

Members:

momenta_in

alpha_s_in

flavor_in

random_color_in

random_helicity_in

random_diagram_in

helicity_in

diagram_in

Overloaded function.

__init__(self: madspace._madspace_py.MatrixElement.MatrixElementInput, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.MatrixElement.MatrixElementInput, name: str) -> None

alpha_s_in = <MatrixElementInput.alpha_s_in: 1>¶

diagram_in = <MatrixElementInput.diagram_in: 7>¶

flavor_in = <MatrixElementInput.flavor_in: 2>¶

helicity_in = <MatrixElementInput.helicity_in: 6>¶

momenta_in = <MatrixElementInput.momenta_in: 0>¶

MatrixElement.MatrixElementInput.name -> str

random_color_in = <MatrixElementInput.random_color_in: 3>¶

random_diagram_in = <MatrixElementInput.random_diagram_in: 5>¶

random_helicity_in = <MatrixElementInput.random_helicity_in: 4>¶

property value¶

class MatrixElementOutput(*args, **kwargs)¶

Bases: pybind11_object

Members:

matrix_element_out

diagram_amp2_out

color_index_out

helicity_index_out

diagram_index_out

Overloaded function.

__init__(self: madspace._madspace_py.MatrixElement.MatrixElementOutput, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.MatrixElement.MatrixElementOutput, name: str) -> None

color_index_out = <MatrixElementOutput.color_index_out: 2>¶

diagram_amp2_out = <MatrixElementOutput.diagram_amp2_out: 1>¶

diagram_index_out = <MatrixElementOutput.diagram_index_out: 4>¶

helicity_index_out = <MatrixElementOutput.helicity_index_out: 3>¶

matrix_element_out = <MatrixElementOutput.matrix_element_out: 0>¶

MatrixElement.MatrixElementOutput.name -> str

property value¶

alpha_s_in = <MatrixElementInput.alpha_s_in: 1>¶

color_index_out = <MatrixElementOutput.color_index_out: 2>¶

diagram_amp2_out = <MatrixElementOutput.diagram_amp2_out: 1>¶

diagram_count(self: madspace._madspace_py.MatrixElement) → int¶

diagram_in = <MatrixElementInput.diagram_in: 7>¶

diagram_index_out = <MatrixElementOutput.diagram_index_out: 4>¶

flavor_in = <MatrixElementInput.flavor_in: 2>¶

helicity_in = <MatrixElementInput.helicity_in: 6>¶

helicity_index_out = <MatrixElementOutput.helicity_index_out: 3>¶

matrix_element_index(self: madspace._madspace_py.MatrixElement) → int¶

matrix_element_out = <MatrixElementOutput.matrix_element_out: 0>¶

momenta_in = <MatrixElementInput.momenta_in: 0>¶

particle_count(self: madspace._madspace_py.MatrixElement) → int¶

random_color_in = <MatrixElementInput.random_color_in: 3>¶

random_diagram_in = <MatrixElementInput.random_diagram_in: 5>¶

random_helicity_in = <MatrixElementInput.random_helicity_in: 4>¶

class madspace.MatrixElementApi(self: madspace._madspace_py.MatrixElementApi, file: str, param_card: str, index: SupportsInt = 0)¶

Bases: pybind11_object

device(self: madspace._madspace_py.MatrixElementApi) → madspace._madspace_py.Device¶

diagram_count(self: madspace._madspace_py.MatrixElementApi) → int¶

helicity_count(self: madspace._madspace_py.MatrixElementApi) → int¶

index(self: madspace._madspace_py.MatrixElementApi) → int¶

particle_count(self: madspace._madspace_py.MatrixElementApi) → int¶

class madspace.MomentumPreprocessing(self: madspace._madspace_py.MomentumPreprocessing, particle_count: SupportsInt)¶

Bases: FunctionGenerator

output_dim(self: madspace._madspace_py.MomentumPreprocessing) → int¶

class madspace.MultiChannelFunction(self: madspace._madspace_py.MultiChannelFunction, functions: collections.abc.Sequence[madspace._madspace_py.FunctionGenerator])¶: Bases: FunctionGenerator

class madspace.MultiChannelIntegrand(self: madspace._madspace_py.MultiChannelIntegrand, integrands: collections.abc.Sequence[madspace._madspace_py.Integrand])¶: Bases: FunctionGenerator

class madspace.MultiChannelMapping(self: madspace._madspace_py.MultiChannelMapping, mappings: collections.abc.Sequence[madspace._madspace_py.Mapping])¶: Bases: Mapping

class madspace.Observable(self: madspace._madspace_py.Observable, pids: collections.abc.Sequence[SupportsInt], observable: madspace._madspace_py.Observable.ObservableOption, select_pids: collections.abc.Sequence[collections.abc.Sequence[SupportsInt]], sum_momenta: bool = False, sum_observable: bool = False, order_observable: madspace._madspace_py.Observable.ObservableOption | None = None, order_indices: collections.abc.Sequence[SupportsInt] = [], ignore_incoming: bool = True, name: str = '')¶

Bases: FunctionGenerator

class ObservableOption(*args, **kwargs)¶

Bases: pybind11_object

Members:

obs_e

obs_px

obs_py

obs_pz

obs_mass

obs_pt

obs_p_mag

obs_phi

obs_theta

obs_y

obs_y_abs

obs_eta

obs_eta_abs

obs_delta_eta

obs_delta_phi

obs_delta_r

obs_sqrt_s

Overloaded function.

__init__(self: madspace._madspace_py.Observable.ObservableOption, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.Observable.ObservableOption, name: str) -> None

Observable.ObservableOption.name -> str

obs_delta_eta = <ObservableOption.obs_delta_eta: 13>¶

obs_delta_phi = <ObservableOption.obs_delta_phi: 14>¶

obs_delta_r = <ObservableOption.obs_delta_r: 15>¶

obs_e = <ObservableOption.obs_e: 0>¶

obs_eta = <ObservableOption.obs_eta: 11>¶

obs_eta_abs = <ObservableOption.obs_eta_abs: 12>¶

obs_mass = <ObservableOption.obs_mass: 4>¶

obs_p_mag = <ObservableOption.obs_p_mag: 6>¶

obs_phi = <ObservableOption.obs_phi: 7>¶

obs_pt = <ObservableOption.obs_pt: 5>¶

obs_px = <ObservableOption.obs_px: 1>¶

obs_py = <ObservableOption.obs_py: 2>¶

obs_pz = <ObservableOption.obs_pz: 3>¶

obs_sqrt_s = <ObservableOption.obs_sqrt_s: 16>¶

obs_theta = <ObservableOption.obs_theta: 8>¶

obs_y = <ObservableOption.obs_y: 9>¶

obs_y_abs = <ObservableOption.obs_y_abs: 10>¶

property value¶

bottom_pids = [-5, 5]¶

jet_pids = [1, 2, 3, 4, -1, -2, -3, -4, 21]¶

lepton_pids = [11, 13, 15, -11, -13, -15]¶

missing_pids = [12, 14, 16, -12, -14, -16]¶

obs_delta_eta = <ObservableOption.obs_delta_eta: 13>¶

obs_delta_phi = <ObservableOption.obs_delta_phi: 14>¶

obs_delta_r = <ObservableOption.obs_delta_r: 15>¶

obs_e = <ObservableOption.obs_e: 0>¶

obs_eta = <ObservableOption.obs_eta: 11>¶

obs_eta_abs = <ObservableOption.obs_eta_abs: 12>¶

obs_mass = <ObservableOption.obs_mass: 4>¶

obs_p_mag = <ObservableOption.obs_p_mag: 6>¶

obs_phi = <ObservableOption.obs_phi: 7>¶

obs_pt = <ObservableOption.obs_pt: 5>¶

obs_px = <ObservableOption.obs_px: 1>¶

obs_py = <ObservableOption.obs_py: 2>¶

obs_pz = <ObservableOption.obs_pz: 3>¶

obs_sqrt_s = <ObservableOption.obs_sqrt_s: 16>¶

obs_theta = <ObservableOption.obs_theta: 8>¶

obs_y = <ObservableOption.obs_y: 9>¶

obs_y_abs = <ObservableOption.obs_y_abs: 10>¶

photon_pids = [22]¶

class madspace.ObservableHistograms(self: madspace._madspace_py.ObservableHistograms, observables: collections.abc.Sequence[madspace._madspace_py.HistItem])¶: Bases: FunctionGenerator

class madspace.PartonDensity(self: madspace._madspace_py.PartonDensity, grid: madspace._madspace_py.PdfGrid, pids: collections.abc.Sequence[SupportsInt], dynamic_pid: bool = False, prefix: str = '')¶: Bases: FunctionGenerator

class madspace.PdfGrid(self: madspace._madspace_py.PdfGrid, file: str)¶

Bases: pybind11_object

coefficients_shape(self: madspace._madspace_py.PdfGrid, batch_dim: bool = False) → list[int]¶

property grid_point_count¶

initialize_globals(self: madspace._madspace_py.PdfGrid, context: madspace._madspace_py.Context, prefix: str = '') → None¶

property logq2¶

logq2_shape(self: madspace._madspace_py.PdfGrid, batch_dim: bool = False) → list[int]¶

property logx¶

logx_shape(self: madspace._madspace_py.PdfGrid, batch_dim: bool = False) → list[int]¶

property pids¶

property q¶

property q_count¶

property region_sizes¶

property values¶

property x¶

class madspace.PhaseSpaceMapping(*args, **kwargs)¶

Bases: Mapping

Overloaded function.

__init__(self: madspace._madspace_py.PhaseSpaceMapping, topology: madspace._madspace_py.Topology, cm_energy: typing.SupportsFloat, leptonic: bool = False, invariant_power: typing.SupportsFloat = 0.8, t_channel_mode: madspace._madspace_py.PhaseSpaceMapping.TChannelMode = <TChannelMode.propagator: 0>, cuts: madspace._madspace_py.Cuts | None = None, permutations: collections.abc.Sequence[collections.abc.Sequence[typing.SupportsInt]] = []) -> None
__init__(self: madspace._madspace_py.PhaseSpaceMapping, masses: collections.abc.Sequence[typing.SupportsFloat], cm_energy: typing.SupportsFloat, leptonic: bool = False, invariant_power: typing.SupportsFloat = 0.8, mode: madspace._madspace_py.PhaseSpaceMapping.TChannelMode = <TChannelMode.rambo: 1>, cuts: madspace._madspace_py.Cuts | None = None) -> None

class TChannelMode(*args, **kwargs)¶

Bases: pybind11_object

Members:

propagator

rambo

chili

Overloaded function.

__init__(self: madspace._madspace_py.PhaseSpaceMapping.TChannelMode, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.PhaseSpaceMapping.TChannelMode, name: str) -> None

chili = <TChannelMode.chili: 2>¶

PhaseSpaceMapping.TChannelMode.name -> str

propagator = <TChannelMode.propagator: 0>¶

rambo = <TChannelMode.rambo: 1>¶

property value¶

channel_count(self: madspace._madspace_py.PhaseSpaceMapping) → int¶

chili = <TChannelMode.chili: 2>¶

particle_count(self: madspace._madspace_py.PhaseSpaceMapping) → int¶

propagator = <TChannelMode.propagator: 0>¶

rambo = <TChannelMode.rambo: 1>¶

random_dim(self: madspace._madspace_py.PhaseSpaceMapping) → int¶

class madspace.PrettyBox(self: madspace._madspace_py.PrettyBox, title: str, rows: SupportsInt, columns: collections.abc.Sequence[SupportsInt], offset: SupportsInt = 0, box_width: SupportsInt = 91)¶

Bases: pybind11_object

property line_count¶

print_first(self: madspace._madspace_py.PrettyBox) → None¶

print_update(self: madspace._madspace_py.PrettyBox) → None¶

set_cell(self: madspace._madspace_py.PrettyBox, row: SupportsInt, column: SupportsInt, value: str) → None¶

set_column(self: madspace._madspace_py.PrettyBox, column: SupportsInt, values: collections.abc.Sequence[str]) → None¶

set_row(self: madspace._madspace_py.PrettyBox, row: SupportsInt, values: collections.abc.Sequence[str]) → None¶

class madspace.Propagator(self: madspace._madspace_py.Propagator, mass: SupportsFloat = 0.0, width: SupportsFloat = 0.0, integration_order: SupportsInt = 0, e_min: SupportsFloat = 0.0, e_max: SupportsFloat = 0.0, pdg_id: SupportsInt = 0)¶

Bases: pybind11_object

property e_max¶

property e_min¶

property integration_order¶

property mass¶

property pdg_id¶

property width¶

class madspace.PropagatorChannelWeights(self: madspace._madspace_py.PropagatorChannelWeights, topologies: collections.abc.Sequence[madspace._madspace_py.Topology], permutations: collections.abc.Sequence[collections.abc.Sequence[collections.abc.Sequence[SupportsInt]]], channel_indices: collections.abc.Sequence[collections.abc.Sequence[SupportsInt]])¶: Bases: FunctionGenerator

class madspace.RunningCoupling(self: madspace._madspace_py.RunningCoupling, grid: madspace._madspace_py.AlphaSGrid, prefix: str = '')¶: Bases: FunctionGenerator

class madspace.SubchannelWeights(self: madspace._madspace_py.SubchannelWeights, topologies: collections.abc.Sequence[collections.abc.Sequence[madspace._madspace_py.Topology]], permutations: collections.abc.Sequence[collections.abc.Sequence[collections.abc.Sequence[SupportsInt]]], channel_indices: collections.abc.Sequence[collections.abc.Sequence[SupportsInt]])¶

Bases: FunctionGenerator

channel_count(self: madspace._madspace_py.SubchannelWeights) → int¶

class madspace.SubprocArgs(self: madspace._madspace_py.SubprocArgs, process_id: SupportsInt = 0, topologies: collections.abc.Sequence[madspace._madspace_py.Topology] = [], permutations: collections.abc.Sequence[collections.abc.Sequence[collections.abc.Sequence[SupportsInt]]] = [], diagram_indices: collections.abc.Sequence[collections.abc.Sequence[SupportsInt]] = [], diagram_color_indices: collections.abc.Sequence[collections.abc.Sequence[collections.abc.Sequence[SupportsInt]]] = [], color_flows: collections.abc.Sequence[collections.abc.Sequence[collections.abc.Sequence[tuple[SupportsInt, SupportsInt]]]] = [], pdg_color_types: collections.abc.Mapping[SupportsInt, SupportsInt] = {}, helicities: collections.abc.Sequence[collections.abc.Sequence[SupportsFloat]] = [], pdg_ids: collections.abc.Sequence[collections.abc.Sequence[collections.abc.Sequence[SupportsInt]]] = [], matrix_flavor_indices: collections.abc.Sequence[SupportsInt] = [])¶

Bases: pybind11_object

property color_flows¶

property diagram_color_indices¶

property diagram_indices¶

property helicities¶

property matrix_flavor_indices¶

property pdg_color_types¶

property pdg_ids¶

property permutations¶

property process_id¶

property topologies¶

class madspace.TPropagatorMapping(self: madspace._madspace_py.TPropagatorMapping, integration_order: collections.abc.Sequence[SupportsInt], invariant_power: SupportsFloat = 0.0)¶: Bases: Mapping

class madspace.Tensor¶

Bases: pybind11_object

numpy()¶

torch()¶

class madspace.ThreeBodyDecay(self: madspace._madspace_py.ThreeBodyDecay, com: bool)¶: Bases: Mapping

class madspace.Topology(self: madspace._madspace_py.Topology, diagram: madspace._madspace_py.Diagram)¶

Bases: pybind11_object

property decay_integration_order¶

property decays¶

property incoming_masses¶

property outgoing_indices¶

property outgoing_masses¶

propagator_momentum_terms(self: madspace._madspace_py.Topology, arg0: bool) → list[tuple[list[int], float, float]]¶

property t_integration_order¶

property t_propagator_count¶

property t_propagator_masses¶

property t_propagator_widths¶

static topologies(diagram: madspace._madspace_py.Diagram) → list[madspace._madspace_py.Topology]¶

class madspace.TwoBodyDecay(self: madspace._madspace_py.TwoBodyDecay, com: bool)¶: Bases: Mapping

class madspace.TwoToThreeParticleScattering(self: madspace._madspace_py.TwoToThreeParticleScattering, t_invariant_power: SupportsFloat = 0.0, t_mass: SupportsFloat = 0.0, t_width: SupportsFloat = 0.0, s_invariant_power: SupportsFloat = 0.0, s_mass: SupportsFloat = 0.0, s_width: SupportsFloat = 0.0)¶: Bases: Mapping

class madspace.TwoToTwoParticleScattering(self: madspace._madspace_py.TwoToTwoParticleScattering, com: bool, invariant_power: SupportsFloat = 0.0, mass: SupportsFloat = 0.0, width: SupportsFloat = 0.0)¶: Bases: Mapping

class madspace.Type(*args, **kwargs)¶

Bases: pybind11_object

Overloaded function.

__init__(self: madspace._madspace_py.Type, dtype: madspace._madspace_py.DataType, batch_size: madspace._madspace_py.BatchSize, shape: collections.abc.Sequence[typing.SupportsInt]) -> None
__init__(self: madspace._madspace_py.Type, batch_size_list: collections.abc.Sequence[madspace._madspace_py.BatchSize]) -> None

property batch_size¶

property dtype¶

property shape¶

class madspace.Unweighter(self: madspace._madspace_py.Unweighter, types: collections.abc.Sequence[madspace._madspace_py.Type])¶: Bases: FunctionGenerator

class madspace.Value(*args, **kwargs)¶

Bases: pybind11_object

Overloaded function.

__init__(self: madspace._madspace_py.Value, value: typing.SupportsInt) -> None
__init__(self: madspace._madspace_py.Value, value: typing.SupportsFloat) -> None

property literal_value¶

property local_index¶

property type¶

class madspace.VegasGridOptimizer(self: madspace._madspace_py.VegasGridOptimizer, context: madspace._madspace_py.Context, grid_name: str, damping: SupportsFloat)¶

Bases: pybind11_object

add_data(self: madspace._madspace_py.VegasGridOptimizer, values: object, counts: object) → None¶

optimize(self: madspace._madspace_py.VegasGridOptimizer) → None¶

class madspace.VegasHistogram(self: madspace._madspace_py.VegasHistogram, dimension: SupportsInt, bin_count: SupportsInt)¶: Bases: FunctionGenerator

class madspace.VegasMapping(self: madspace._madspace_py.VegasMapping, dimension: SupportsInt, bin_count: SupportsInt, prefix: str = '')¶

Bases: Mapping

grid_name(self: madspace._madspace_py.VegasMapping) → str¶

initialize_globals(self: madspace._madspace_py.VegasMapping, context: madspace._madspace_py.Context) → None¶

madspace.batch_float_array(count: SupportsInt) → madspace._madspace_py.Type¶

madspace.batch_four_vec_array(count: SupportsInt) → madspace._madspace_py.Type¶

madspace.cpu_device() → madspace._madspace_py.Device¶

madspace.cuda_device() → madspace._madspace_py.Device¶

madspace.default_context() → madspace._madspace_py.Context¶

madspace.default_cuda_context() → madspace._madspace_py.Context¶

madspace.default_hip_context() → madspace._madspace_py.Context¶

madspace.format_progress(progress: SupportsFloat, width: SupportsInt) → str¶

madspace.format_si_prefix(value: SupportsFloat) → str¶

madspace.format_with_error(value: SupportsFloat, error: SupportsFloat) → str¶

madspace.hip_device() → madspace._madspace_py.Device¶

madspace.initialize_vegas_grid(context: madspace._madspace_py.Context, grid_name: str) → None¶

madspace.multichannel_batch_size(count: SupportsInt) → madspace._madspace_py.Type¶

madspace.set_lib_path(lib_path: str) → None¶

madspace.set_simd_vector_size(vector_size: SupportsInt) → None¶

madspace.set_thread_count(new_count: SupportsInt) → None¶