param

class zfit.param.ConstantParameter(name, value, dtype=tf.float64)[source]

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

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

Add dependencies that render the cache invalid if they change.

Parameters:
  • cache_deps (ZfitGraphCachable) –
  • 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.

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

The dtype of the object

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

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).

get_params(floating: Optional[bool] = True, is_yield: Optional[bool] = None, extract_independent: Optional[bool] = True, only_floating=<class 'zfit.util.checks.NotSpecified'>) → Set[zfit.core.interfaces.ZfitParameter]

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 – if a parameter is floating, e.g. if floating() returns True
  • is_yield – 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 non-yields), 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 – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.
graph_caching_methods = []
independent
instances = <_weakrefset.WeakSet object>
name

The name of the object.

params
read_value() → tensorflow.python.framework.ops.Tensor[source]
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.ZfitGraphCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

shape
value() → tensorflow.python.framework.ops.Tensor[source]
class zfit.param.Parameter(name: str, value: Union[int, float, complex, tensorflow.python.framework.ops.Tensor], lower_limit: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, upper_limit: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, step_size: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, floating: bool = True, dtype: tensorflow.python.framework.dtypes.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.

name : name of the parameter, value : starting value lower_limit : lower limit upper_limit : upper limit step_size : step size

DEFAULT_STEP_SIZE = 0.001
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.
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_deps(cache_deps: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependencies that render the cache invalid if they change.

Parameters:
  • cache_deps (ZfitGraphCachable) –
  • 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.

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.

at_limit

If the value is at the limit (or over it).

The precision is up to 1e-5 relative.

Returns:Boolean tf.Tensor that tells whether the value is at the limits.
Return type:tf.Tensor
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.

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.
create

The op responsible for initializing this variable.

device

The device this variable is on.

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> `

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

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).

get_params(floating: Optional[bool] = True, is_yield: Optional[bool] = None, extract_independent: Optional[bool] = True, only_floating=<class 'zfit.util.checks.NotSpecified'>) → Set[zfit.core.interfaces.ZfitParameter][source]

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 – if a parameter is floating, e.g. if floating() returns True
  • is_yield – 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 non-yields), 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 – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.
get_shape()

Alias of Variable.shape.

graph

The Graph of this variable.

graph_caching_methods = []
handle

The handle by which this variable can be accessed.

has_limits

If the parameter has limits set or not

Returns:bool
independent
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.
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

lower
lower_limit
name

The name of the object.

numpy()
op

The op for this variable.

params
randomize(minval: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, maxval: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, None] = None, sampler: Callable = <function random_uniform>) → tensorflow.python.framework.ops.Tensor[source]

Update the parameter with a randomised value between minval and maxval and return it.

Parameters:
  • minval (Numerical) – The lower bound of the sampler. If not given, lower_limit is used.
  • maxval (Numerical) – The upper bound of the sampler. If not given, upper_limit is used.
  • () (sampler) – A sampler with the same interface as tf.random.uniform
Returns:

The sampled value

Return type:

tf.Tensor

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.ZfitGraphCachable)
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).

This operation won’t, compared to the assign, return the read value but an object that can act as a context manager.

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

The shape of this variable.

sparse_read(indices, name=None)

Reads the value of this variable sparsely, using gather.

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.

Returns:the step size
Return type:tf.Tensor
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.
trainable
upper
upper_limit
value()[source]

A cached operation which reads the value of this variable.

class zfit.param.ComposedParameter(name: str, value_fn: Callable, params: Union[Dict[str, zfit.core.interfaces.ZfitParameter], Iterable[zfit.core.interfaces.ZfitParameter], zfit.core.interfaces.ZfitParameter] = <class 'zfit.util.checks.NotSpecified'>, dtype: tensorflow.python.framework.dtypes.DType = tf.float64, dependents: Union[Dict[str, zfit.core.interfaces.ZfitParameter], Iterable[zfit.core.interfaces.ZfitParameter], zfit.core.interfaces.ZfitParameter] = <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 – Unique name of the Parameter
  • value_fn – Function that returns the value of the composed parameter and takes as arguments params as arguments.
  • params – 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 – Output of value_fn dtype
  • dependents – DEPRECEATED, use params instead.
add_cache_deps(cache_deps: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)

Add dependencies that render the cache invalid if they change.

Parameters:
  • cache_deps (ZfitGraphCachable) –
  • 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.

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

The dtype of the object

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

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).

get_params(floating: Optional[bool] = True, is_yield: Optional[bool] = None, extract_independent: Optional[bool] = True, only_floating=<class 'zfit.util.checks.NotSpecified'>) → Set[zfit.core.interfaces.ZfitParameter]

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 – if a parameter is floating, e.g. if floating() returns True
  • is_yield – 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 non-yields), 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 – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.
graph_caching_methods = []
independent
instances = <_weakrefset.WeakSet object>
name

The name of the object.

numpy()
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.ZfitGraphCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

shape
value()
class zfit.param.ComplexParameter(name, value_fn, params, dtype=tf.complex128)[source]

Bases: zfit.core.parameter.ComposedParameter

Create a complex parameter.

Warning

Use the constructor class methods instead of the __init__() constructor:

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

Add dependencies that render the cache invalid if they change.

Parameters:
  • cache_deps (ZfitGraphCachable) –
  • 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.

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

The dtype of the object

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

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).

get_params(floating: Optional[bool] = True, is_yield: Optional[bool] = None, extract_independent: Optional[bool] = True, only_floating=<class 'zfit.util.checks.NotSpecified'>) → Set[zfit.core.interfaces.ZfitParameter]

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 – if a parameter is floating, e.g. if floating() returns True
  • is_yield – 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 non-yields), 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 – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.
graph_caching_methods = []
imag
independent
instances = <_weakrefset.WeakSet object>
mod
name

The name of the object.

numpy()
params
read_value()
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.ZfitGraphCachable)
reset_cache_self()

Clear the cache of self and all dependent cachers.

shape
value()
zfit.param.convert_to_parameter(value, name=None, prefer_constant=True, dependents=None) → 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_.
zfit.param.set_values(params: Union[zfit.core.parameter.Parameter, Iterable[zfit.core.parameter.Parameter]], values: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, Iterable[Union[int, float, complex, tensorflow.python.framework.ops.Tensor]], zfit.minimizers.interface.ZfitResult])[source]

Set the values (using a context manager or not) of multiple parameters.

Parameters:
  • params – Parameters to set the values
  • values – list-like object that supports indexing

Returns: