zfit package¶
Toplevel 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
,zfit.core.interfaces.ZfitIndependentParameter
Class for fit parameters, derived from TF Variable class.
 Parameters
name (
str
) – name of the parametervalue (
Union
[int
,float
,complex
,Tensor
,ForwardRef
]) – starting valuelower_limit (
Union
[int
,float
,complex
,Tensor
,ForwardRef
,None
]) – lower limitupper_limit (
Union
[int
,float
,complex
,Tensor
,ForwardRef
,None
]) – upper limitstep_size (
Union
[int
,float
,complex
,Tensor
,ForwardRef
,None
]) – step size

DEFAULT_STEP_SIZE
= 0.001¶

property
lower
¶

property
upper
¶

property
at_limit
¶ If the value is at the limit (or over it).
The precision is up to 1e5 relative.
 Return type
Tensor
 Returns
Boolean tf.Tensor that tells whether the value is at the limits.

property
floating
¶

property
independent
¶

property
step_size
¶ Step size of the parameter, the estimated order of magnitude of the uncertainty.
This can be crucial to tune for the minimization. A too large step_size can produce NaNs, a too small won’t converge.
If the step size is not set, the DEFAULT_STEP_SIZE is used.
 Return type
Tensor
 Returns
The step size

set_value
(value)[source]¶ Set the
Parameter
to value (temporarily if used in a context manager).This operation won’t, compared to the assign, return the read value but an object that can act as a context manager.
 Parameters
value (
Union
[int
,float
,complex
,Tensor
,ForwardRef
]) – The value the parameter will take on.

randomize
(minval=None, maxval=None, sampler=<builtin method uniform of numpy.random.mtrand.RandomState object>)[source]¶ Update the parameter with a randomised value between minval and maxval and return it.
 Parameters
minval (
Union
[int
,float
,complex
,Tensor
,ForwardRef
,None
]) – The lower bound of the sampler. If not given, lower_limit is used.maxval (
Union
[int
,float
,complex
,Tensor
,ForwardRef
,None
]) – The upper bound of the sampler. If not given, upper_limit is used.sampler (
Callable
) – A sampler with the same interface as np.random.uniform
 Return type
Tensor
 Returns
The sampled value

get_params
(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)[source]¶  Return type

property
lower_limit
¶

property
upper_limit
¶

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_def – SaveSliceInfoDef 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 elementwise for equality.

add_cache_deps
(cache_deps, allow_non_cachable=True)¶ Add dependencies that render the cache invalid if they change.
 Parameters
cache_deps (
Union
[ForwardRef
,Iterable
[ForwardRef
]]) –allow_non_cachable (
bool
) – If True, allow cache_dependents to be noncachables. 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 batchwise.
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_delta – tf.IndexedSlices to be assigned to this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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=False, name=None, **overwrite_params)¶  Return type

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
 Return type
DType

eval
(session=None)¶ Evaluates and returns the value of this variable.

experimental_ref
()¶ DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

static
from_proto
(variable_def, import_scope=None)¶

gather_nd
(indices, name=None)¶ Reads the value of this variable sparsely, using gather_nd.

get_cache_deps
(only_floating=True)¶ Return a set of all independent
Parameter
that this object depends on.

get_dependencies
(only_floating=True)¶ DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).
 Return type
OrderedSet

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
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.

instances
= <_weakrefset.WeakSet object>¶

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

numpy
()¶

property
op
¶ The op for this variable.

property
params
¶  Return type
~ParametersType

ref
()¶ Returns a hashable reference object to this Variable.
The primary use case 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.
The following will raise an exception starting 2.0
>>> x = tf.Variable(5) >>> y = tf.Variable(10) >>> z = tf.Variable(10) >>> variable_set = {x, y, z} Traceback (most recent call last): ... TypeError: Variable is unhashable. Instead, use tensor.ref() as the key. >>> variable_dict = {x: 'five', y: 'ten'} Traceback (most recent call last): ... TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
Instead, we can use variable.ref().
>>> variable_set = {x.ref(), y.ref(), z.ref()} >>> x.ref() in variable_set True >>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'} >>> variable_dict[y.ref()] 'ten'
Also, the reference object provides .deref() function that returns the original Variable.
>>> x = tf.Variable(5) >>> x.ref().deref() <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>

register_cacher
(cacher)¶ Register a cacher that caches values produces by this instance; a dependent.
 Parameters
cacher (
Union
[ForwardRef
,Iterable
[ForwardRef
]]) –

reset_cache
(reseter)¶

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_delta – tf.IndexedSlices to be added to this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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_delta – tf.IndexedSlices to divide this variable by.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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_delta – tf.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
The updated variable.
 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_delta – tf.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
The updated variable.
 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_delta – tf.IndexedSlices to multiply this variable by.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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_{Q2}, 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 Q1+PK with shape:
` [d_0, ..., d_{Q2}, ref.shape[K], ..., ref.shape[P1]]. `
For example, say we want to add 4 scattered elements to a rank1 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
The updated variable.

scatter_nd_max
(indices, updates, name=None)¶ Updates this variable with the max of tf.IndexedSlices and itself.
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_{Q2}, 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 Q1+PK with shape:
` [d_0, ..., d_{Q2}, ref.shape[K], ..., ref.shape[P1]]. `
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
The updated variable.

scatter_nd_min
(indices, updates, name=None)¶ Updates this variable with the min of tf.IndexedSlices and itself.
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_{Q2}, 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 Q1+PK with shape:
` [d_0, ..., d_{Q2}, ref.shape[K], ..., ref.shape[P1]]. `
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
The updated variable.

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_{Q2}, 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 Q1+PK with shape:
` [d_0, ..., d_{Q2}, ref.shape[K], ..., ref.shape[P1]]. `
For example, say we want to add 4 scattered elements to a rank1 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
The updated variable.

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_{Q2}, 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 Q1+PK with shape:
` [d_0, ..., d_{Q2}, ref.shape[K], ..., ref.shape[P1]]. `
For example, say we want to add 4 scattered elements to a rank1 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
The updated variable.

scatter_sub
(sparse_delta, use_locking=False, name=None)¶ Subtracts tf.IndexedSlices from this variable.
 Parameters
sparse_delta – tf.IndexedSlices to be subtracted from this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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_delta – tf.IndexedSlices to be assigned to this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 Raises
TypeError – if sparse_delta is not an IndexedSlices.

set_shape
(shape)¶

property
shape
¶ The shape of this variable.

sparse_read
(indices, name=None)¶ Reads the value of this variable sparsely, using gather.

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
¶

class
zfit.
ComposedParameter
(name, value_fn, params=<class 'zfit.util.checks.NotSpecified'>, dtype=tf.float64, dependents=<class 'zfit.util.checks.NotSpecified'>)[source]¶ Bases:
zfit.core.parameter.BaseComposedParameter
Arbitrary composition of parameters.
A ComposedParameter allows for arbitrary combinations of parameters and correlations
 Parameters
name (
str
) – Unique name of the Parametervalue_fn (
Callable
) – Function that returns the value of the composed parameter and takes as arguments params as arguments.params (
Union
[Dict
[str
,ZfitParameter
],Iterable
[ZfitParameter
],ZfitParameter
]) – If it is a dict, this will direclty be used as the params attribute, otherwise the parameters will be automatically named with f”param_{i}”. The values act as arguments to value_fn.dtype (
DType
) – Output of value_fn dtypedependents (
Union
[Dict
[str
,ZfitParameter
],Iterable
[ZfitParameter
],ZfitParameter
]) –Deprecated since version unknown: use params instead.

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_def – SaveSliceInfoDef 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 elementwise for equality.

add_cache_deps
(cache_deps, allow_non_cachable=True)¶ Add dependencies that render the cache invalid if they change.
 Parameters
cache_deps (
Union
[ForwardRef
,Iterable
[ForwardRef
]]) –allow_non_cachable (
bool
) – If True, allow cache_dependents to be noncachables. 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=False, name=None, read_value=True)¶ Assigns a new value to the variable.
This is essentially a shortcut for assign(self, value).
 Parameters
value – A Tensor. The new value for this variable.
use_locking – If True, use locking during the assignment.
name – The name of the operation to be created
read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.
 Returns
The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_add
(delta, use_locking=False, name=None, read_value=True)¶ Adds a value to this variable.
This is essentially a shortcut for assign_add(self, delta).
 Parameters
delta – A Tensor. The value to add to this variable.
use_locking – If True, use locking during the operation.
name – The name of the operation to be created
read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.
 Returns
The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_sub
(delta, use_locking=False, name=None, read_value=True)¶ Subtracts a value from this variable.
This is essentially a shortcut for assign_sub(self, delta).
 Parameters
delta – A Tensor. The value to subtract from this variable.
use_locking – If True, use locking during the operation.
name – The name of the operation to be created
read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.
 Returns
The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

batch_scatter_update
(sparse_delta, use_locking=False, name=None)¶ Assigns tf.IndexedSlices to this variable batchwise.
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_delta – tf.IndexedSlices to be assigned to this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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=False, name=None, **overwrite_params)¶  Return type

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
device
¶ The device of this variable.

property
dtype
¶ The dtype of the object
 Return type
DType

eval
(session=None)¶ In a session, computes and returns the value of this variable.
This is not a graph construction method, it does not 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. print(v.eval(sess)) # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. print(v.eval())
 Parameters
session – The session to use to evaluate this variable. If none, the default session is used.
 Returns
A numpy ndarray with a copy of the value of this variable.

experimental_ref
()¶ DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

property
floating
¶

static
from_proto
(variable_def, import_scope=None)¶ Returns a Variable object created from variable_def.

gather_nd
(indices, name=None)¶ Gather slices from params into a Tensor with shape specified by indices.
See tf.gather_nd for details.
 Parameters
indices – A Tensor. Must be one of the following types: int32, int64. Index tensor.
name – A name for the operation (optional).
 Returns
A Tensor. Has the same type as params.

get_cache_deps
(only_floating=True)¶ Return a set of all independent
Parameter
that this object depends on.

get_dependencies
(only_floating=True)¶ DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).
 Return type
OrderedSet

get_params
(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)¶ Recursively collect parameters that this object depends on according to the filter criteria.
 Which parameters should be included can be steered using the arguments as a filter.
 None: do not filter on this. E.g. floating=None will return parameters that are floating as well as
parameters that are fixed.
True: only return parameters that fulfil this criterion
 False: only return parameters that do not fulfil this criterion. E.g. floating=False will return
only parameters that are not floating.
 Parameters
floating (
Optional
[bool
]) – if a parameter is floating, e.g. iffloating()
returns Trueis_yield (
Optional
[bool
]) – if a parameter is a yield of the _current_ model. This won’t be applied recursively, but may include yields if they do also represent a parameter parametrizing the shape. So if the yield of the current model depends on other yields (or also nonyields), this will be included. If, however, just submodels depend on a yield (as their yield) and it is not correlated to the output of our model, they won’t be included.extract_independent (
Optional
[bool
]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.
 Return type

get_shape
()¶ Alias of Variable.shape.

property
graph
¶ The Graph of this variable.

graph_caching_methods
= []¶

property
independent
¶

property
initial_value
¶ Returns the Tensor used as the initial value for the variable.
Note that this is different from initialized_value() which runs the op that initializes the variable before returning its value. This method returns the tensor that is used by the op that initializes the variable.
 Returns
A Tensor.

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 initializer operation for this variable.

instances
= <_weakrefset.WeakSet object>¶

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

numpy
()¶

property
op
¶ The Operation of this variable.

property
params
¶

read_value
()¶

ref
()¶ Returns a hashable reference object to this Variable.
The primary use case 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.
The following will raise an exception starting 2.0
>>> x = tf.Variable(5) >>> y = tf.Variable(10) >>> z = tf.Variable(10) >>> variable_set = {x, y, z} Traceback (most recent call last): ... TypeError: Variable is unhashable. Instead, use tensor.ref() as the key. >>> variable_dict = {x: 'five', y: 'ten'} Traceback (most recent call last): ... TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
Instead, we can use variable.ref().
>>> variable_set = {x.ref(), y.ref(), z.ref()} >>> x.ref() in variable_set True >>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'} >>> variable_dict[y.ref()] 'ten'
Also, the reference object provides .deref() function that returns the original Variable.
>>> x = tf.Variable(5) >>> x.ref().deref() <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>

register_cacher
(cacher)¶ Register a cacher that caches values produces by this instance; a dependent.
 Parameters
cacher (
Union
[ForwardRef
,Iterable
[ForwardRef
]]) –

reset_cache
(reseter)¶

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_delta – tf.IndexedSlices to be added to this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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_delta – tf.IndexedSlices to divide this variable by.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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_delta – tf.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
The updated variable.
 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_delta – tf.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
The updated variable.
 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_delta – tf.IndexedSlices to multiply this variable by.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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.
The Variable has rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q2}, 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 self.
updates is Tensor of rank Q1+PK with shape:
` [d_0, ..., d_{Q2}, self.shape[K], ..., self.shape[P1]]. `
For example, say we want to add 4 scattered elements to a rank1 tensor to 8 elements. In Python, that update would look like this:
 ```python
v = 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 = v.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:
print sess.run(add)
The resulting update to v 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
The updated variable.

scatter_nd_sub
(indices, updates, name=None)¶ Applies sparse subtraction to individual values or slices in a Variable.
Assuming the variable has rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q2}, 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 self.
updates is Tensor of rank Q1+PK with shape:
` [d_0, ..., d_{Q2}, self.shape[K], ..., self.shape[P1]]. `
For example, say we want to add 4 scattered elements to a rank1 tensor to 8 elements. In Python, that update would look like this:
 ```python
v = 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 = v.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to v 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
The updated variable.

scatter_nd_update
(indices, updates, name=None)¶ Applies sparse assignment to individual values or slices in a Variable.
The Variable has rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q2}, 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 self.
updates is Tensor of rank Q1+PK with shape:
` [d_0, ..., d_{Q2}, self.shape[K], ..., self.shape[P1]]. `
For example, say we want to add 4 scattered elements to a rank1 tensor to 8 elements. In Python, that update would look like this:
 ```python
v = 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 = v.scatter_nd_assign(indices, updates) with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to v 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
The updated variable.

scatter_sub
(sparse_delta, use_locking=False, name=None)¶ Subtracts tf.IndexedSlices from this variable.
 Parameters
sparse_delta – tf.IndexedSlices to be subtracted from this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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_delta – tf.IndexedSlices to be assigned to this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 Raises
TypeError – if sparse_delta is not an IndexedSlices.

set_shape
(shape)¶ Overrides the shape for this variable.
 Parameters
shape – the TensorShape representing the overridden shape.

property
shape
¶

sparse_read
(indices, name=None)¶ Gather slices from params axis axis according to indices.
This function supports a subset of tf.gather, see tf.gather for details on usage.
 Parameters
indices – The index Tensor. Must be one of the following types: int32, int64. Must be in range [0, params.shape[axis]).
name – A name for the operation (optional).
 Returns
A Tensor. Has the same type as params.

property
synchronization
¶

to_proto
(export_scope=None)¶ Converts a Variable to a VariableDef protocol buffer.
 Parameters
export_scope – Optional string. Name scope to remove.
 Returns
A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

property
trainable
¶

value
()¶

class
zfit.
ComplexParameter
(*args, **kwargs)[source]¶ Bases:
zfit.core.parameter.ComposedParameter
Create a complex parameter.
Note
Use the constructor class methods instead of the __init__() constructor:

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_def – SaveSliceInfoDef 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 elementwise for equality.

add_cache_deps
(cache_deps, allow_non_cachable=True)¶ Add dependencies that render the cache invalid if they change.
 Parameters
cache_deps (
Union
[ForwardRef
,Iterable
[ForwardRef
]]) –allow_non_cachable (
bool
) – If True, allow cache_dependents to be noncachables. 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=False, name=None, read_value=True)¶ Assigns a new value to the variable.
This is essentially a shortcut for assign(self, value).
 Parameters
value – A Tensor. The new value for this variable.
use_locking – If True, use locking during the assignment.
name – The name of the operation to be created
read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.
 Returns
The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_add
(delta, use_locking=False, name=None, read_value=True)¶ Adds a value to this variable.
This is essentially a shortcut for assign_add(self, delta).
 Parameters
delta – A Tensor. The value to add to this variable.
use_locking – If True, use locking during the operation.
name – The name of the operation to be created
read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.
 Returns
The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

assign_sub
(delta, use_locking=False, name=None, read_value=True)¶ Subtracts a value from this variable.
This is essentially a shortcut for assign_sub(self, delta).
 Parameters
delta – A Tensor. The value to subtract from this variable.
use_locking – If True, use locking during the operation.
name – The name of the operation to be created
read_value – if True, will return something which evaluates to the new value of the variable; if False will return the assign op.
 Returns
The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.

batch_scatter_update
(sparse_delta, use_locking=False, name=None)¶ Assigns tf.IndexedSlices to this variable batchwise.
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_delta – tf.IndexedSlices to be assigned to this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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=False, name=None, **overwrite_params)¶  Return type

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
device
¶ The device of this variable.

property
dtype
¶ The dtype of the object
 Return type
DType

eval
(session=None)¶ In a session, computes and returns the value of this variable.
This is not a graph construction method, it does not 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. print(v.eval(sess)) # Usage with the default session. The ‘with’ block # above makes ‘sess’ the default session. print(v.eval())
 Parameters
session – The session to use to evaluate this variable. If none, the default session is used.
 Returns
A numpy ndarray with a copy of the value of this variable.

experimental_ref
()¶ DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use ref() instead.

property
floating
¶

static
from_proto
(variable_def, import_scope=None)¶ Returns a Variable object created from variable_def.

gather_nd
(indices, name=None)¶ Gather slices from params into a Tensor with shape specified by indices.
See tf.gather_nd for details.
 Parameters
indices – A Tensor. Must be one of the following types: int32, int64. Index tensor.
name – A name for the operation (optional).
 Returns
A Tensor. Has the same type as params.

get_cache_deps
(only_floating=True)¶ Return a set of all independent
Parameter
that this object depends on.

get_dependencies
(only_floating=True)¶ DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use get_params instead if you want to retrieve the independent parameters or get_cache_deps in case you need the numerical cache dependents (advanced).
 Return type
OrderedSet

get_params
(floating=True, is_yield=None, extract_independent=True, only_floating=<class 'zfit.util.checks.NotSpecified'>)¶ Recursively collect parameters that this object depends on according to the filter criteria.
 Which parameters should be included can be steered using the arguments as a filter.
 None: do not filter on this. E.g. floating=None will return parameters that are floating as well as
parameters that are fixed.
True: only return parameters that fulfil this criterion
 False: only return parameters that do not fulfil this criterion. E.g. floating=False will return
only parameters that are not floating.
 Parameters
floating (
Optional
[bool
]) – if a parameter is floating, e.g. iffloating()
returns Trueis_yield (
Optional
[bool
]) – if a parameter is a yield of the _current_ model. This won’t be applied recursively, but may include yields if they do also represent a parameter parametrizing the shape. So if the yield of the current model depends on other yields (or also nonyields), this will be included. If, however, just submodels depend on a yield (as their yield) and it is not correlated to the output of our model, they won’t be included.extract_independent (
Optional
[bool
]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.
 Return type

get_shape
()¶ Alias of Variable.shape.

property
graph
¶ The Graph of this variable.

graph_caching_methods
= []¶

property
independent
¶

property
initial_value
¶ Returns the Tensor used as the initial value for the variable.
Note that this is different from initialized_value() which runs the op that initializes the variable before returning its value. This method returns the tensor that is used by the op that initializes the variable.
 Returns
A Tensor.

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 initializer operation for this variable.

instances
= <_weakrefset.WeakSet object>¶

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

numpy
()¶

property
op
¶ The Operation of this variable.

property
params
¶

read_value
()¶

ref
()¶ Returns a hashable reference object to this Variable.
The primary use case 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.
The following will raise an exception starting 2.0
>>> x = tf.Variable(5) >>> y = tf.Variable(10) >>> z = tf.Variable(10) >>> variable_set = {x, y, z} Traceback (most recent call last): ... TypeError: Variable is unhashable. Instead, use tensor.ref() as the key. >>> variable_dict = {x: 'five', y: 'ten'} Traceback (most recent call last): ... TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
Instead, we can use variable.ref().
>>> variable_set = {x.ref(), y.ref(), z.ref()} >>> x.ref() in variable_set True >>> variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'} >>> variable_dict[y.ref()] 'ten'
Also, the reference object provides .deref() function that returns the original Variable.
>>> x = tf.Variable(5) >>> x.ref().deref() <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>

register_cacher
(cacher)¶ Register a cacher that caches values produces by this instance; a dependent.
 Parameters
cacher (
Union
[ForwardRef
,Iterable
[ForwardRef
]]) –

reset_cache
(reseter)¶

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_delta – tf.IndexedSlices to be added to this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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_delta – tf.IndexedSlices to divide this variable by.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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_delta – tf.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
The updated variable.
 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_delta – tf.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
The updated variable.
 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_delta – tf.IndexedSlices to multiply this variable by.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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.
The Variable has rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q2}, 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 self.
updates is Tensor of rank Q1+PK with shape:
` [d_0, ..., d_{Q2}, self.shape[K], ..., self.shape[P1]]. `
For example, say we want to add 4 scattered elements to a rank1 tensor to 8 elements. In Python, that update would look like this:
 ```python
v = 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 = v.scatter_nd_add(indices, updates) with tf.compat.v1.Session() as sess:
print sess.run(add)
The resulting update to v 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
The updated variable.

scatter_nd_sub
(indices, updates, name=None)¶ Applies sparse subtraction to individual values or slices in a Variable.
Assuming the variable has rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q2}, 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 self.
updates is Tensor of rank Q1+PK with shape:
` [d_0, ..., d_{Q2}, self.shape[K], ..., self.shape[P1]]. `
For example, say we want to add 4 scattered elements to a rank1 tensor to 8 elements. In Python, that update would look like this:
 ```python
v = 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 = v.scatter_nd_sub(indices, updates) with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to v 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
The updated variable.

scatter_nd_update
(indices, updates, name=None)¶ Applies sparse assignment to individual values or slices in a Variable.
The Variable has rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into self. It must be shape [d_0, …, d_{Q2}, 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 self.
updates is Tensor of rank Q1+PK with shape:
` [d_0, ..., d_{Q2}, self.shape[K], ..., self.shape[P1]]. `
For example, say we want to add 4 scattered elements to a rank1 tensor to 8 elements. In Python, that update would look like this:
 ```python
v = 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 = v.scatter_nd_assign(indices, updates) with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to v 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
The updated variable.

scatter_sub
(sparse_delta, use_locking=False, name=None)¶ Subtracts tf.IndexedSlices from this variable.
 Parameters
sparse_delta – tf.IndexedSlices to be subtracted from this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 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_delta – tf.IndexedSlices to be assigned to this variable.
use_locking – If True, use locking during the operation.
name – the name of the operation.
 Returns
The updated variable.
 Raises
TypeError – if sparse_delta is not an IndexedSlices.

set_shape
(shape)¶ Overrides the shape for this variable.
 Parameters
shape – the TensorShape representing the overridden shape.

property
shape
¶

sparse_read
(indices, name=None)¶ Gather slices from params axis axis according to indices.
This function supports a subset of tf.gather, see tf.gather for details on usage.
 Parameters
indices – The index Tensor. Must be one of the following types: int32, int64. Must be in range [0, params.shape[axis]).
name – A name for the operation (optional).
 Returns
A Tensor. Has the same type as params.

property
synchronization
¶

to_proto
(export_scope=None)¶ Converts a Variable to a VariableDef protocol buffer.
 Parameters
export_scope – Optional string. Name scope to remove.
 Returns
A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.

property
trainable
¶

value
()¶

classmethod
from_cartesian
(name, real, imag, dtype=tf.complex128, floating=True)[source]¶ Create a complex parameter from cartesian coordinates.
 Parameters
name – Name of the parameter.
real – Real part of the complex number.
imag – Imaginary part of the complex number.

classmethod
from_polar
(name, mod, arg, dtype=tf.complex128, floating=True, **kwargs)[source]¶ Create a complex parameter from polar coordinates.
 Parameters
name – Name of the parameter.
real – Modulus (r) the complex number.
imag – Argument (phi) of the complex number.

property
conj
¶ Returns a complex conjugated copy of the complex parameter.

property
real
¶ Real part of the complex parameter.

property
imag
¶ Imaginary part of the complex parameter.

property
mod
¶ Modulus (r) of the complex parameter.

property
arg
¶ Argument (phi) of the complex parameter.

class

zfit.
convert_to_parameter
(value, name=None, prefer_constant=True, dependents=None)[source]¶ Convert a numerical to a constant/floating parameter or return if already a parameter.
 Parameters
value –
name –
prefer_constant – If True, create a ConstantParameter instead of a Parameter, if possible.
 Return type

class
zfit.
Space
(obs=None, limits=None, axes=None, rect_limits=None, name='Space')[source]¶ Bases:
zfit.core.space.BaseSpace
Define a space with the name (obs) of the axes (and it’s number) and possibly it’s limits.
A space can be thought of as coordinates, possibly with the definition of a range (limits). For most usecases, it is sufficient to specify a Space via observables; simple string identifiers. They can be multidimensional.
Observables are like the columns of a spreadsheet/dataframe, and are therefore needed for any object that does numerical operations or holds data in order to match the right axes. On object creation, the observables are assigned using a Space. This is often used as the default space of an object and can be used as the default norm_range, sampling limits etc.
Axes are the same concept as observables, but numbers, indexes, and are used inside an object. There, axes 0 corresponds to the 0th data column we get (which corresponds to a certain observable).
Every space can have limits; they are either rectangular or an arbitrary function (together with rectangular limits). Spaces can be combined (multiplied) to create higher dimensional spaces. Spaces can be added, which combines them into one Space consisting of two disconnected limits.
So integrating over the space consisting of the two added disconnected ranges, e.g. 0 to 1 and 2 to 3 will return the sum of the two separate integrals.
lower_band = zfit.Space('obs1', (0, 1)) upper_band = zfit.Space('obs1', (2, 3)) combined_obs = lower_band + upper_band integral_comb = model.integrate(limits=combined_obs) # which is equivalent to the lower integral_sep = model.integrate(limits=lower_band) + model.integrate(limits=upper_band) assert integral_comb == integral_sep
In principle, the same behavior could also be achieved by specifying an arbitrary function. Using the addition allows for certain optimizations inside.
 Parameters

AUTO_FILL
= <object object>¶

ANY
= <Any>¶

ANY_LOWER
= <Any Lower Limit>¶

ANY_UPPER
= <Any Upper Limit>¶

property
limits
¶

property
rect_limits
¶ Return the rectangular limits as np.ndarray``tf.Tensor if they are set and not false.
The rectangular limits can be used for sampling. They do not in general represent the limits of the object as a functional limit can be set and to check if something is inside the limits, the method
inside()
should be used.In order to test if the limits are False or None, it is recommended to use the appropriate methods limits_are_false and limits_are_set.

property
rect_limits_np
¶ Return the rectangular limits as np.ndarray. Raise error if not possible.
Rectangular limits are returned as numpy arrays which can be useful when doing checks that do not need to be involved in the computation later on as they allow direct interaction with Python as compared to tf.Tensor inside a graph function.
In order to test if the limits are False or None, it is recommended to use the appropriate methods limits_are_false and limits_are_set.
 Return type
Tuple
[ndarray
,ndarray
] Returns
 A tuple of two np.ndarray with shape (1, n_obs) typically. The last
dimension is always n_obs, the first can be vectorized. This allows unstacking with z.unstack_x() as can be done with data.
 Raises
CannotConvertToNumpyError – In case the conversion fails.
LimitsNotSpecifiedError – If the limits are not set

property
rect_lower
¶ The lower, rectangular limits, equivalent to rect_limits[0] with shape (…, n_obs)
 Return type
 Returns
The lower, rectangular limits as np.ndarray or tf.Tensor
 Raises
LimitsNotSpecifiedError – If the limits are not set or are false

property
rect_upper
¶ The upper, rectangular limits, equivalent to rect_limits[1] with shape (…, n_obs)
 Return type
 Returns
The upper, rectangular limits as np.ndarray or tf.Tensor
 Raises
LimitsNotSpecifiedError – If the limits are not set or are false

rect_area
()[source]¶ Calculate the total rectangular area of all the limits and axes. Useful, for example, for MC integration.

property
rect_limits_are_tensors
¶ Return True if the rectangular limits are tensors.
If a limit with tensors is evaluated inside a graph context, comparison operations will fail.
 Return type
 Returns
If the rectangular limits are tensors.

property
limits_are_false
¶ If the limits have been set to False, so the object on purpose does not contain limits.
 Return type
 Returns
True if limits is False

property
limits_are_set
¶

property
n_events
¶ Return the number of events, the dimension of the first shape.

property
limit2d
¶ DEPRECATED FUNCTION
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Depreceated, use .rect_limits or .inside to check if a value is inside or userect_limits to receive the rectangular limits.

property
limits1d
¶ return the tuple(low_1, …, low_n, up_1, …, up_n).
 Return type
 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.
 Raises
RuntimeError – if the conditions (n_obs or n_limits) are not satisfied.
 Type
Simplified .limits for exactly 1 obs, n limits

property
lower
¶

property
upper
¶

property
iter_limits
¶ REMOVED.Return the limits, either as
Space
objects or as pure limitstuple. (deprecated)Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Iterate over the space directly and use the limits from the spaces.
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,…]

with_limits
(limits=None, rect_limits=None, name=None)[source]¶ Return a copy of the space with the new limits (and the new name).
 Parameters
limits (
Union
[ZfitLimit
,Tensor
,ndarray
,Iterable
[float
],float
,Tuple
[float
],List
[float
],bool
,None
]) – Limits to use. Can be rectangular, a function (requires to also specify rect_limits or an instance of ZfitLimit.rect_limits (
Union
[Tensor
,ndarray
,Iterable
[float
],float
,Tuple
[float
],List
[float
],None
]) – Rectangular limits that will be assigned with the instance
 Return type
 Returns
Copy of the current object with the new limits.

reorder_x
(x, *, x_obs=None, x_axes=None, func_obs=None, func_axes=None)[source]¶ Reorder x in the last dimension either according to its own obs or assuming a function ordered with func_obs.
There are two obs or axes around: the one associated with this Coordinate object and the one associated with x. If x_obs or x_axes is given, then this is assumed to be the obs resp. the axes of x and x will be reordered according to self.obs resp. self.axes.
If func_obs resp. func_axes is given, then x is assumed to have self.obs resp. self.axes and will be reordered to align with a function ordered with func_obs resp. func_axes.
Switching func_obs for x_obs resp. func_axes for x_axes inverts the reordering of x.
 Parameters
x (
Union
[Tensor
,ndarray
]) – Tensor to be reordered, last dimension should be n_obs resp. n_axesx_obs (
Union
[str
,Iterable
[str
],Space
,None
]) – Observables associated with x. If both, x_obs and x_axes are given, this has precedency over the latter.x_axes (
Union
[int
,Iterable
[int
],None
]) – Axes associated with x.func_obs (
Union
[str
,Iterable
[str
],Space
,None
]) – Observables associated with a function that x will be given to. Reorders x accordingly and assumes self.obs to be the obs of x. If both, func_obs and func_axes are given, this has precedency over the latter.func_axes (
Union
[int
,Iterable
[int
],None
]) – Axe associated with a function that x will be given to. Reorders x accordingly and assumes self.axes to be the axes of x.
 Return type
Union
[ndarray
,Tensor
] Returns
The reordered arraylike object

with_obs
(obs, allow_superset=True, allow_subset=True)[source]¶ Create a new Space that has obs; sorted by or set or dropped.
The behavior is as follows:
obs are already set:
input obs are None: the observables will be dropped. If no axes are set, an error will be raised, as no coordinates will be assigned to this instance anymore.
input obs are not None: the instance will be sorted by the incoming obs. If axes or other objects have an associated order (e.g. data, limits,…), they will be reordered as well. If a strict subset is given (and allow_subset is True), only a subset will be returned. This can be used to take a subspace of limits, data etc. If a strict superset is given (and allow_superset is True), the obs will be sorted accordingly as if the obs not contained in the instances obs were not in the input obs.
obs are not set:
if the input obs are None, the same object is returned.
if the input obs are not None, they will be set asis and now correspond to the already existing axes in the object.
 Parameters
obs (
Union
[str
,Iterable
[str
],Space
,None
]) – Observables to sort/associate this instance withallow_superset (
bool
) – if False and a strict superset of the own observables is given, an errorraised. (is) –
allow_subset (
bool
) – if False and a strict subset of the own observables is given, an errorraised. –
 Return type
 Returns
A copy of the object with the new ordering/observables
 Raises
CoordinatesUnderdefinedError – if obs is None and the instance does not have axes
ObsIncompatibleError – if obs is a superset and allow_superset is False or a subset and allow_allow_subset is False

with_axes
(axes, allow_superset=True, allow_subset=True)[source]¶ Create a new instance that has axes; sorted by or set or dropped.
The behavior is as follows:
axes are already set:
input axes are None: the axes will be dropped. If no observables are set, an error will be raised, as no coordinates will be assigned to this instance anymore.
input axes are not None: the instance will be sorted by the incoming axes. If obs or other objects have an associated order (e.g. data, limits,…), they will be reordered as well. If a strict subset is given (and allow_subset is True), only a subset will be returned. This can be used to retrieve a subspace of limits, data etc. If a strict superset is given (and allow_superset is True), the axes will be sorted accordingly as if the axes not contained in the instances axes were not present in the input axes.
axes are not set:
if the input axes are None, the same object is returned.
if the input axes are not None, they will be set asis and now correspond to the already existing obs in the object.
 Parameters
 Return type
 Returns
A copy of the object with the new ordering/axes
 Raises
CoordinatesUnderdefinedError – if obs is None and the instance does not have axes
AxesIncompatibleError – if axes is a superset and allow_superset is False or a subset and allow_allow_subset is False

with_coords
(coords, allow_superset=True, allow_subset=True)[source]¶ Create a new
Space
with reordered observables and/or axes.The behavior is that _at least one coordinate (obs or axes) has to be set in both instances (the space itself or in coords). If both match, observables is taken as the defining coordinate. The space is sorted according to the defining coordinate and the other coordinate is sorted as well. If either the space did not have the “weaker coordinate” (e.g. both have observables, but only coords has axes), then the resulting Space will have both. If both have both coordinates, obs and axes, and sorting for obs results in nonmatchin axes results in axes being dropped.
 Parameters
coords (
ZfitOrderableDimensional
) – An instance ofCoordinates
allow_superset (
bool
) – If False and a strict superset is given, an error is raisedallow_subset (
bool
) – If False and a strict subset is given, an error is raised
 Returns
 Return type
 Raises
CoordinatesUnderdefinedError – if neither both obs or axes are specified.
CoordinatesIncompatibleError – if coords is a superset and allow_superset is False or a subset and allow_allow_subset is False

with_autofill_axes
(overwrite=False)[source]¶ Overwrite the axes of the current object with axes corresponding to range(len(n_obs)).
This effectively fills with (0, 1, 2,…) and can be used mostly when an object enters a PDF or similar. overwrite allows to remove the axis first in case there are already some set.
object.obs > ('x', 'z', 'y') object.axes > None object.with_autofill_axes() object.obs > ('x', 'z', 'y') object.axes > (0, 1, 2)
 Parameters
overwrite (
bool
) – If axes are already set, replace the axes with the autofilled ones. If axes is already set and overwrite is False, raise an error. Return type
 Returns
The object with the new axes
 Raises
AxesIncompatibleError – if the axes are already set and overwrite is False.

get_subspace
(obs=None, axes=None, name=None)[source]¶ Create a
Space
consisting of only a subset of the obs/axes (only one allowed).

property
obs_axes
¶

copy
(**overwrite_kwargs)[source]¶ Create a new
Space
using the current attributes and overwriting with overwrite_overwrite_kwargs.

property
limit1d
¶

classmethod
from_axes
(cls, axes, limits=None, rect_limits=None, name=None)[source]¶ Create a space from axes instead of from obs. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use directly the class to create a Space. E.g. zfit.Space(axes=(0, 1), …)

__eq__
(other)¶ Compares two Limits for equality without graph mode allowed.
Returns:
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.
 Return type

__le__
(other)¶ Setlike comparison for compatibility. If an object is less_equal to another, the limits are combatible.
This can be used to determine whether a fitting range specification can handle another limit.
 Return type
 Returns
Result of the comparison
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

add
(*other)¶ 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.

property
axes
¶ The axes (“obs with int”) the space is defined in.
Returns:

combine
(*other)¶ Combine spaces with different obs (but consistent limits).

equal
(other, allow_graph)¶ Compare the limits on equality. For ANY objects, this also returns true.
If called inside a graph context and the limits are tensors, this will return a symbolic tf.Tensor.
 Return type
 Returns
Result of the comparison
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

filter
(x, guarantee_limits=False, axis=None)¶ Filter x by removing the elements along axis that are not inside the limits.
This is similar to tf.boolean_mask.
 Parameters
x (
Union
[ndarray
,Tensor
,Data
]) – Values to be checked whether they are inside of the limits. If not, the corresonding element (in the specified axis) is removed. The shape is expected to have the last dimension equal to n_obs.guarantee_limits (
bool
) – Guarantee that the values are already inside the rectangular limits.axis (
Optional
[int
]) – The axis to remove the elements from. Defaults to 0.
 Return type
Union
[ndarray
,Tensor
] Returns
 Return an object with the same shape as x except that along axis elements have been
removed.

get_reorder_indices
(obs=None, axes=None)¶ Indices that would order the instances obs as obs respectively the instances axes as axes.
 Parameters
obs (
Union
[str
,Iterable
[str
],Space
,None
]) – Observables that the instances obs should be ordered to. Does not reorder, but just return the indices that could be used to reorder.axes (
Union
[int
,Iterable
[int
],None
]) – Axes that the instances obs should be ordered to. Does not reorder, but just return the indices that could be used to reorder.
 Return type
 Returns
New indices that would reorder the instances obs to be obs respectively axes.
 Raises
CoordinatesUnderdefinedError – If neither obs nor axes is given

get_sublimits
()¶

inside
(x, guarantee_limits=False)¶ Test if x is inside the limits.
This function should be used to test if values are inside the limits. If the given x is already inside the rectangular limits, e.g. because it was sampled from within them
 Parameters
 Return type
 Returns
 Return a boolean tensorlike object with the same shape as the input x except of the
last dimension removed.

less_equal
(other, allow_graph)¶ Setlike comparison for compatibility. If an object is less_equal to another, the limits are combatible.
This can be used to determine whether a fitting range specification can handle another limit.
If called inside a graph context and the limits are tensors, this will return a symbolic tf.Tensor.
 Parameters
other – Any other object to compare with
allow_graph – If False and the function returns a symbolic tensor, raise IllegalInGraphModeError instead.
 Returns
Result of the comparison
 Raises
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.

zfit.
convert_to_space
(obs=None, axes=None, limits=None, *, overwrite_limits=False, one_dim_limits_only=True, simple_limits_only=True)[source]¶ Convert limits to a
Space
object if not already None or False. Parameters
limits (
Union
[ZfitLimit
,Tensor
,ndarray
,Iterable
[float
],float
,Tuple
[float
],List
[float
],bool
,None
]) –overwrite_limits (
bool
) – If obs or axes is aSpace
_and_ limits are given, return an instance ofSpace
with the new limits. If the flag is False, the limits argument will be ignored ifone_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=False, multiple_limits=False)[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.
Subpackages¶
 zfit.core package
 Submodules
 zfit.core.basefunc module
 zfit.core.basemodel module
 zfit.core.baseobject module
 zfit.core.basepdf module
 zfit.core.constraint module
 zfit.core.coordinates module
 zfit.core.data module
 zfit.core.dependents module
 zfit.core.dimension module
 zfit.core.integration module
 zfit.core.interfaces module
 zfit.core.loss module
 zfit.core.operations module
 zfit.core.parameter module
 zfit.core.sample module
 zfit.core.space module
 zfit.core.testing module
 Submodules
 zfit.minimizers package
 Submodules
 zfit.minimizers.base_tf module
 zfit.minimizers.baseminimizer module
 zfit.minimizers.errors module
 zfit.minimizers.fitresult module
 zfit.minimizers.interface module
 zfit.minimizers.minimizer_minuit module
 zfit.minimizers.minimizer_tfp module
 zfit.minimizers.minimizers_scipy module
 zfit.minimizers.optimizers_tf module
 zfit.minimizers.tf_external_optimizer module
 Submodules
 zfit.models package
 zfit.util package
 Submodules
 zfit.util.cache module
 zfit.util.checks module
 zfit.util.container module
 zfit.util.deprecation module
 zfit.util.diverse module
 zfit.util.exception module
 zfit.util.execution module
 zfit.util.graph module
 zfit.util.legacy module
 zfit.util.logging module
 zfit.util.temporary module
 zfit.util.warnings module
 zfit.util.ztyping module
 Submodules
 zfit.z package