zfit package

Top-level package for zfit.

class zfit.Parameter(name, value, lower_limit=None, upper_limit=None, step_size=None, floating=True, dtype=tf.float64, **kwargs)[source]

Bases: zfit.core.parameter.ZfitParameterMixin, zfit.core.parameter.TFBaseVariable, zfit.core.parameter.BaseParameter

Class for fit parameters, derived from TF Variable class.

name : name of the parameter, value : starting value lower_limit : lower limit upper_limit : upper limit step_size : step size (set to 0 for fixed parameters)

class SaveSliceInfo(full_name=None, full_shape=None, var_offset=None, var_shape=None, save_slice_info_def=None, import_scope=None)

Bases: object

Information on how to save this Variable as a slice.

Provides internal support for saving variables as slices of a larger variable. This API is not public and is subject to change.

Available properties:

  • full_name

  • full_shape

  • var_offset

  • var_shape

Create a SaveSliceInfo.

Parameters
  • full_name – Name of the full variable of which this Variable is a slice.

  • full_shape – Shape of the full variable, as a list of int.

  • var_offset – Offset of this Variable into the full variable, as a list of int.

  • var_shape – Shape of this Variable, as a list of int.

  • save_slice_info_defSaveSliceInfoDef protocol buffer. If not None, recreates the SaveSliceInfo object its contents. save_slice_info_def and other arguments are mutually exclusive.

  • import_scope – Optional string. Name scope to add. Only used when initializing from protocol buffer.

property spec

Computes the spec string used for saving.

to_proto(export_scope=None)

Returns a SaveSliceInfoDef() proto.

Parameters

export_scope – Optional string. Name scope to remove.

Returns

A SaveSliceInfoDef protocol buffer, or None if the Variable is not in the specified name scope.

__iter__()

Dummy method to prevent iteration.

Do not call.

NOTE(mrry): If we register __getitem__ as an overloaded operator, Python will valiantly attempt to iterate over the variable’s Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.

Raises

TypeError – when invoked.

__ne__(other)

Compares two variables element-wise for equality.

add_cache_dependents(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependents that render the cache invalid if they change.

Parameters
  • cache_dependents (ZfitCachable) –

  • allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.

Raises

TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.

property aggregation
assign(value, use_locking=None, name=None, read_value=True)

Assigns a new value to this variable.

Parameters
  • value – A Tensor. The new value for this variable.

  • use_locking – If True, use locking during the assignment.

  • name – The name to use for the assignment.

  • read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

assign_add(delta, use_locking=None, name=None, read_value=True)

Adds a value to this variable.

Parameters
  • delta – A Tensor. The value to add to this variable.

  • use_locking – If True, use locking during the operation.

  • name – The name to use for the operation.

  • read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

assign_sub(delta, use_locking=None, name=None, read_value=True)

Subtracts a value from this variable.

Parameters
  • delta – A Tensor. The value to subtract from this variable.

  • use_locking – If True, use locking during the operation.

  • name – The name to use for the operation.

  • read_value – A bool. Whether to read and return the new value of the variable or not.

Returns

If read_value is True, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the Operation that does the assignment, and when in eager mode it will return None.

batch_scatter_update(sparse_delta, use_locking=False, name=None)

Assigns tf.IndexedSlices to this variable batch-wise.

Analogous to batch_gather. This assumes that this variable and the sparse_delta IndexedSlices have a series of leading dimensions that are the same for all of them, and the updates are performed on the last dimension of indices. In other words, the dimensions should be the following:

num_prefix_dims = sparse_delta.indices.ndims - 1 batch_dim = num_prefix_dims + 1 `sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[

batch_dim:]`

where

sparse_delta.updates.shape[:num_prefix_dims] == sparse_delta.indices.shape[:num_prefix_dims] == var.shape[:num_prefix_dims]

And the operation performed can be expressed as:

`var[i_1, …, i_n,
sparse_delta.indices[i_1, …, i_n, j]] = sparse_delta.updates[

i_1, …, i_n, j]`

When sparse_delta.indices is a 1D tensor, this operation is equivalent to scatter_update.

To avoid this operation one can looping over the first ndims of the variable and using scatter_update on the subtensors that result of slicing the first dimension. This is a valid option for ndims = 1, but less efficient than this implementation.

Parameters
  • sparse_deltatf.IndexedSlices to be assigned to this variable.

  • use_locking – If True, use locking during the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

property constraint

Returns the constraint function associated with this variable.

Returns

The constraint function that was passed to the variable constructor. Can be None if no constraint was passed.

copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject
count_up_to(limit)

Increments this variable until it reaches limit. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Dataset.range instead.

When that Op is run it tries to increment the variable by 1. If incrementing the variable would bring it above limit then the Op raises the exception OutOfRangeError.

If no error is raised, the Op outputs the value of the variable before the increment.

This is essentially a shortcut for count_up_to(self, limit).

Parameters

limit – value at which incrementing the variable raises an error.

Returns

A Tensor that will hold the variable value before the increment. If no other Op modifies this variable, the values produced will all be distinct.

property create

The op responsible for initializing this variable.

property device

The device this variable is on.

property dtype

The dtype of the object

eval(session=None)

Evaluates and returns the value of this variable.

experimental_ref()

Returns a hashable reference object to this Variable.

Warning: Experimental API that could be changed or removed.

The primary usecase for this API is to put variables in a set/dictionary. We can’t put variables in a set/dictionary as variable.__hash__() is no longer available starting Tensorflow 2.0.

```python import tensorflow as tf

x = tf.Variable(5) y = tf.Variable(10) z = tf.Variable(10)

# The followings will raise an exception starting 2.0 # TypeError: Variable is unhashable if Variable equality is enabled. variable_set = {x, y, z} variable_dict = {x: ‘five’, y: ‘ten’} ```

Instead, we can use variable.experimental_ref().

```python variable_set = {x.experimental_ref(),

y.experimental_ref(), z.experimental_ref()}

print(x.experimental_ref() in variable_set) ==> True

variable_dict = {x.experimental_ref(): ‘five’,

y.experimental_ref(): ‘ten’, z.experimental_ref(): ‘ten’}

print(variable_dict[y.experimental_ref()]) ==> ten ```

Also, the reference object provides .deref() function that returns the original Variable.

`python x = tf.Variable(5) print(x.experimental_ref().deref()) ==> <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5> `

property floating
static from_proto(variable_def, import_scope=None)

Returns a Variable object created from variable_def.

gather_nd(indices, name=None)

Reads the value of this variable sparsely, using gather_nd.

get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

Return a set of all independent Parameter that this object depends on.

Parameters

only_floating (bool) – If True, only return floating Parameter

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[ZfitParameter]

Return the parameters. If it is empty, automatically return all floating variables.

Parameters
  • () (names) – If True, return only the floating parameters.

  • () – The names of the parameters to return.

Returns

Return type

list(ZfitParameters)

get_shape()

Alias of Variable.shape.

property graph

The Graph of this variable.

graph_caching_methods = []
property handle

The handle by which this variable can be accessed.

property has_limits
property independent
property initial_value

Returns the Tensor used as the initial value for the variable.

initialized_value()

Returns the value of the initialized variable. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.

You should use this instead of the variable itself to initialize another variable with a value that depends on the value of this variable.

`python # Initialize 'v' with a random tensor. v = tf.Variable(tf.random.truncated_normal([10, 40])) # Use `initialized_value` to guarantee that `v` has been # initialized before its value is used to initialize `w`. # The random values are picked only once. w = tf.Variable(v.initialized_value() * 2.0) `

Returns

A Tensor holding the value of this variable after its initializer has run.

property initializer

The op responsible for initializing this variable.

is_initialized(name=None)

Checks whether a resource variable has been initialized.

Outputs boolean scalar indicating whether the tensor has been initialized.

Parameters

name – A name for the operation (optional).

Returns

A Tensor of type bool.

load(value, session=None)

Load new value into this variable. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Prefer Variable.assign which has equivalent behavior in 2.X.

Writes new value to variable’s memory. Doesn’t add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used. See tf.compat.v1.Session for more information on launching a graph and on sessions.

```python v = tf.Variable([1, 2]) init = tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:

sess.run(init) # Usage passing the session explicitly. v.load([2, 3], sess) print(v.eval(sess)) # prints [2 3] # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. v.load([3, 4], sess) print(v.eval()) # prints [3 4]

```

Parameters
  • value – New variable value

  • session – The session to use to evaluate this variable. If none, the default session is used.

Raises

ValueError – Session is not passed and no default session

property lower_limit
property name

The name of the object.

numpy()
old_graph_caching_methods = []
property op

The op for this variable.

property params
randomize(minval=None, maxval=None, sampler=<built-in method uniform of numpy.random.mtrand.RandomState object>)[source]

Update the value with a randomised value between minval and maxval.

Parameters
  • minval (Numerical) –

  • maxval (Numerical) –

  • () (sampler) –

read_value()[source]

Constructs an op which reads the value of this variable.

Should be used when there are multiple reads, or when it is desirable to read the value only after some condition is true.

Returns

the read operation.

register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])

Register a cacher that caches values produces by this instance; a dependent.

Parameters

() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

scatter_add(sparse_delta, use_locking=False, name=None)

Adds tf.IndexedSlices to this variable.

Parameters
  • sparse_deltatf.IndexedSlices to be added to this variable.

  • use_locking – If True, use locking during the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered addition has completed.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_div(sparse_delta, use_locking=False, name=None)

Divide this variable by tf.IndexedSlices.

Parameters
  • sparse_deltatf.IndexedSlices to divide this variable by.

  • use_locking – If True, use locking during the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered division has completed.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_max(sparse_delta, use_locking=False, name=None)

Updates this variable with the max of tf.IndexedSlices and itself.

Parameters
  • sparse_deltatf.IndexedSlices to use as an argument of max with this variable.

  • use_locking – If True, use locking during the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered maximization has completed.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_min(sparse_delta, use_locking=False, name=None)

Updates this variable with the min of tf.IndexedSlices and itself.

Parameters
  • sparse_deltatf.IndexedSlices to use as an argument of min with this variable.

  • use_locking – If True, use locking during the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered minimization has completed.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_mul(sparse_delta, use_locking=False, name=None)

Multiply this variable by tf.IndexedSlices.

Parameters
  • sparse_deltatf.IndexedSlices to multiply this variable by.

  • use_locking – If True, use locking during the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered multiplication has completed.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_nd_add(indices, updates, name=None)

Applies sparse addition to individual values or slices in a Variable.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the K`th dimension of `ref.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) add = ref.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(add)

```

The resulting update to ref would look like this:

[1, 13, 3, 14, 14, 6, 7, 20]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters
  • indices – The indices to be used in the operation.

  • updates – The values to be used in the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

scatter_nd_sub(indices, updates, name=None)

Applies sparse subtraction to individual values or slices in a Variable.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the K`th dimension of `ref.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = ref.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

```

The resulting update to ref would look like this:

[1, -9, 3, -6, -6, 6, 7, -4]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters
  • indices – The indices to be used in the operation.

  • updates – The values to be used in the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

scatter_nd_update(indices, updates, name=None)

Applies sparse assignment to individual values or slices in a Variable.

ref is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into ref. It must be shape [d_0, …, d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or slices (if K < P) along the K`th dimension of `ref.

updates is Tensor of rank Q-1+P-K with shape:

` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. `

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

```python

ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) op = ref.scatter_nd_update(indices, updates) with tf.compat.v1.Session() as sess:

print sess.run(op)

```

The resulting update to ref would look like this:

[1, 11, 3, 10, 9, 6, 7, 12]

See tf.scatter_nd for more details about how to make updates to slices.

Parameters
  • indices – The indices to be used in the operation.

  • updates – The values to be used in the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

scatter_sub(sparse_delta, use_locking=False, name=None)

Subtracts tf.IndexedSlices from this variable.

Parameters
  • sparse_deltatf.IndexedSlices to be subtracted from this variable.

  • use_locking – If True, use locking during the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

scatter_update(sparse_delta, use_locking=False, name=None)

Assigns tf.IndexedSlices to this variable.

Parameters
  • sparse_deltatf.IndexedSlices to be assigned to this variable.

  • use_locking – If True, use locking during the operation.

  • name – the name of the operation.

Returns

A Tensor that will hold the new value of this variable after the scattered subtraction has completed.

Raises

TypeError – if sparse_delta is not an IndexedSlices.

set_shape(shape)

Unsupported.

set_value(value: Union[int, float, complex, tensorflow.python.framework.ops.Tensor])[source]

Set the Parameter to value (temporarily if used in a context manager).

Parameters

value (float) – The value the parameter will take on.

property shape

The shape of this variable.

sparse_read(indices, name=None)

Reads the value of this variable sparsely, using gather.

property step_size
property synchronization
to_proto(export_scope=None)

Converts a ResourceVariable to a VariableDef protocol buffer.

Parameters

export_scope – Optional string. Name scope to remove.

Raises

RuntimeError – If run in EAGER mode.

Returns

A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

property trainable
property upper_limit
value()[source]

A cached operation which reads the value of this variable.

class zfit.ComposedParameter(name, value_fn, dependents, dtype=tf.float64, **kwargs)[source]

Bases: zfit.core.parameter.BaseComposedParameter

add_cache_dependents(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependents that render the cache invalid if they change.

Parameters
  • cache_dependents (ZfitCachable) –

  • allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.

Raises

TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.

assign(value, use_locking=False, name=None, read_value=True)
copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject
property dtype

The dtype of the object

property floating
get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

Return a set of all independent Parameter that this object depends on.

Parameters

only_floating (bool) – If True, only return floating Parameter

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[ZfitParameter]

Return the parameters. If it is empty, automatically return all floating variables.

Parameters
  • () (names) – If True, return only the floating parameters.

  • () – The names of the parameters to return.

Returns

Return type

list(ZfitParameters)

graph_caching_methods = []
property independent
property name
numpy()
old_graph_caching_methods = []
property params
read_value()
register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])

Register a cacher that caches values produces by this instance; a dependent.

Parameters

() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

property shape
value()
class zfit.ComplexParameter(name, value_fn, dependents, dtype=tf.complex128, **kwargs)[source]

Bases: zfit.core.parameter.ComposedParameter

add_cache_dependents(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependents that render the cache invalid if they change.

Parameters
  • cache_dependents (ZfitCachable) –

  • allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.

Raises

TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.

property arg
assign(value, use_locking=False, name=None, read_value=True)
property conj
copy(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject
property dtype

The dtype of the object

property floating
static from_cartesian(name, real, imag, dtype=tf.complex128, floating=True, **kwargs)[source]
static from_polar(name, mod, arg, dtype=tf.complex128, floating=True, **kwargs)[source]
get_dependents(only_floating: bool = True) -> OrderedSet(['z', 'f', 'i', 't', '.', 'P', 'a', 'r', 'm', 'e'])

Return a set of all independent Parameter that this object depends on.

Parameters

only_floating (bool) – If True, only return floating Parameter

get_params(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[ZfitParameter]

Return the parameters. If it is empty, automatically return all floating variables.

Parameters
  • () (names) – If True, return only the floating parameters.

  • () – The names of the parameters to return.

Returns

Return type

list(ZfitParameters)

graph_caching_methods = []
property imag
property independent
property mod
property name
numpy()
old_graph_caching_methods = []
property params
read_value()
property real
register_cacher(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])

Register a cacher that caches values produces by this instance; a dependent.

Parameters

() (cacher) –

reset_cache(reseter: zfit.util.cache.ZfitCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

property shape
value()
zfit.convert_to_parameter(value, name=None, prefer_constant=True, dependents=None, graph_mode=False) → zfit.core.interfaces.ZfitParameter[source]

Convert a numerical to a constant/floating parameter or return if already a parameter.

Parameters
  • () (name) –

  • ()

  • prefer_constant – If True, create a ConstantParameter instead of a Parameter _if possible_.

class zfit.Space(obs: Union[str, Iterable[str], zfit.Space], limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool, None] = None, name: Optional[str] = 'Space')[source]

Bases: zfit.core.interfaces.ZfitSpace, zfit.core.baseobject.BaseObject

Define a space with the name (obs) of the axes (and it’s number) and possibly it’s limits.

Parameters
  • obs (str, List[str,..]) –

  • () (limits) –

  • name (str) –

ANY = <Any>
ANY_LOWER = <Any Lower Limit>
ANY_UPPER = <Any Upper Limit>
AUTO_FILL = <object object>
add(other: Union[zfit.Space, Iterable[zfit.Space]])[source]

Add the limits of the spaces. Only works for the same obs.

In case the observables are different, the order of the first space is taken.

Parameters

other (Space) –

Returns

Return type

Space

area() → float[source]

Return the total area of all the limits and axes. Useful, for example, for MC integration.

property axes

The axes (“obs with int”) the space is defined in.

Returns:

combine(other: Union[zfit.Space, Iterable[zfit.Space]]) → zfit.core.interfaces.ZfitSpace[source]

Combine spaces with different obs (but consistent limits).

Parameters

other (Space) –

Returns

Return type

Space

copy(name: Optional[str] = None, **overwrite_kwargs) → zfit.Space[source]

Create a new Space using the current attributes and overwriting with overwrite_overwrite_kwargs.

Parameters
  • name (str) – The new name. If not given, the new instance will be named the same as the current one.

  • () (**overwrite_kwargs) –

Returns

Space

classmethod from_axes(axes: Union[int, Iterable[int]], limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool, None] = None, name: str = None) → zfit.Space[source]

Create a space from axes instead of from obs.

Parameters
  • () (limits) –

  • ()

  • name (str) –

Returns

Space

get_axes(obs: Union[str, Iterable[str], zfit.Space] = None, as_dict: bool = False, autofill: bool = False) → Union[Tuple[int], None, Dict[str, int]][source]

Return the axes corresponding to the obs (or all if None).

Parameters
  • () (obs) –

  • as_dict (bool) – If True, returns a ordered dictionary with {obs: axis}

  • autofill (bool) – If True and the axes are not specified, automatically fill them with the default numbering and return (not setting them).

Returns

Tuple, OrderedDict

Raises
get_obs_axes(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None)[source]
get_reorder_indices(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None) → Tuple[int][source]

Indices that would order self.obs as obs respectively self.axes as axes.

Parameters
  • () (axes) –

  • ()

Returns:

get_subspace(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None, name: Optional[str] = None) → zfit.Space[source]

Create a Space consisting of only a subset of the obs/axes (only one allowed).

Parameters
  • obs (str, Tuple[str]) –

  • axes (int, Tuple[int]) –

  • () (name) –

Returns:

iter_areas(rel: bool = False) → Tuple[float, ...][source]

Return the areas of each interval

Parameters

rel (bool) – If True, return the relative fraction of each interval

Returns

Return type

Tuple[float]

iter_limits(as_tuple: bool = True) → Union[Tuple[zfit.Space], Tuple[Tuple[Tuple[float]]], Tuple[Tuple[float]]][source]

Return the limits, either as Space objects or as pure limits-tuple.

This makes iterating over limits easier: for limit in space.iter_limits() allows to, for example, pass limit to a function that can deal with simple limits only or if as_tuple is True the limit can be directly used to calculate something.

Example

for lower, upper in space.iter_limits(as_tuple=True):
    integrals = integrate(lower, upper)  # calculate integral
integral = sum(integrals)
Returns

Return type

List[Space] or List[limit,…]

property limit1d

return the tuple(lower, upper).

Returns

so lower, upper = space.limit1d for a simple, 1 obs limit.

Return type

tuple(float, float)

Raises

RuntimeError – if the conditions (n_obs or n_limits) are not satisfied.

Type

Simplified limits getter for 1 obs, 1 limit only

property limit2d

return the tuple(low_obs1, low_obs2, up_obs1, up_obs2).

Returns

so low_x, low_y, up_x, up_y = space.limit2d for a single, 2 obs limit.

low_x is the lower limit in x, up_x is the upper limit in x etc.

Return type

tuple(float, float, float, float)

Raises

RuntimeError – if the conditions (n_obs or n_limits) are not satisfied.

Type

Simplified limits for exactly 2 obs, 1 limit

property limits

Return the limits.

Returns:

property limits1d

return the tuple(low_1, …, low_n, up_1, …, up_n).

Returns

so low_1, low_2, up_1, up_2 = space.limits1d for several, 1 obs limits.

low_1 to up_1 is the first interval, low_2 to up_2 is the second interval etc.

Return type

tuple(float, float, ..)

Raises

RuntimeError – if the conditions (n_obs or n_limits) are not satisfied.

Type

Simplified .limits for exactly 1 obs, n limits

property lower

Return the lower limits.

Returns:

property n_limits

The number of different limits.

Returns

int >= 1

property n_obs

Return the number of observables/axes.

Returns

int >= 1

property name

The name of the object.

property obs

The observables (“axes with str”)the space is defined in.

Returns:

property obs_axes
reorder_by_indices(indices: Tuple[int])[source]

Return a Space reordered by the indices.

Parameters

() (indices) –

property upper

Return the upper limits.

Returns:

with_autofill_axes(overwrite: bool = False) → zfit.Space[source]

Return a Space with filled axes corresponding to range(len(n_obs)).

Parameters

overwrite (bool) – If self.axes is not None, replace the axes with the autofilled ones. If axes is already set, don’t do anything if overwrite is False.

Returns

Space

with_axes(axes: Union[int, Iterable[int]]) → zfit.Space[source]

Sort by obs and return the new instance.

Parameters

() (axes) –

Returns

Space

with_limits(limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool], name: Optional[str] = None) → zfit.Space[source]

Return a copy of the space with the new limits (and the new name).

Parameters
  • () (limits) –

  • name (str) –

Returns

Space

with_obs(obs: Union[str, Iterable[str], zfit.Space]) → zfit.Space[source]

Sort by obs and return the new instance.

Parameters

() (obs) –

Returns

Space

with_obs_axes(obs_axes: Dict[str, int], ordered: bool = False, allow_subset=False) → zfit.Space[source]

Return a new Space with reordered observables and set the axes.

Parameters
  • obs_axes (OrderedDict[str, int]) – An ordered dict with {obs: axes}.

  • ordered (bool) – If True (and the obs_axes is an OrderedDict), the

  • () (allow_subset) –

Returns

Return type

Space

zfit.convert_to_space(obs: Union[str, Iterable[str], zfit.Space, None] = None, axes: Union[int, Iterable[int], None] = None, limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool, None] = None, *, overwrite_limits: bool = False, one_dim_limits_only: bool = True, simple_limits_only: bool = True) → Union[None, zfit.core.limits.Space, bool][source]

Convert limits to a Space object if not already None or False.

Parameters
  • obs (Union[Tuple[float, float], Space]) –

  • () (axes) –

  • ()

  • overwrite_limits (bool) – If obs or axes is a Space _and_ limits are given, return an instance of Space with the new limits. If the flag is False, the limits argument will be ignored if

  • one_dim_limits_only (bool) –

  • simple_limits_only (bool) –

Returns

Return type

Union[Space, False, None]

Raises

OverdefinedError – if obs or axes is a Space and axes respectively obs is not None.

zfit.supports(*, norm_range: bool = False, multiple_limits: bool = False) → Callable[source]

Decorator: Add (mandatory for some methods) on a method to control what it can handle.

If any of the flags is set to False, it will check the arguments and, in case they match a flag (say if a norm_range is passed while the norm_range flag is set to False), it will raise a corresponding exception (in this example a NormRangeNotImplementedError) that will be catched by an earlier function that knows how to handle things.

Parameters
  • norm_range (bool) – If False, no norm_range argument will be passed through resp. will be None

  • multiple_limits (bool) – If False, only simple limits are to be expected and no iteration is therefore required.