zfit package¶
Top-level package for zfit.
-
class
zfit.
Parameter
(name, value, lower_limit=None, upper_limit=None, step_size=None, floating=True, dtype=tf.float64, **kwargs)[source]¶ Bases:
zfit.util.execution.SessionHolderMixin
,zfit.core.parameter.ZfitParameterMixin
,zfit.core.parameter.TFBaseVariable
,zfit.core.parameter.BaseParameter
Class for fit parameters, derived from TF Variable class.
- Constructor.
- name : name of the parameter, value : starting value lower_limit : lower limit upper_limit : upper limit step_size : step size (set to 0 for fixed parameters)
-
class
SaveSliceInfo
(full_name=None, full_shape=None, var_offset=None, var_shape=None, save_slice_info_def=None, import_scope=None)¶ Bases:
object
Information on how to save this Variable as a slice.
Provides internal support for saving variables as slices of a larger variable. This API is not public and is subject to change.
Available properties:
- full_name
- full_shape
- var_offset
- var_shape
Create a SaveSliceInfo.
Parameters: - full_name – Name of the full variable of which this Variable is a slice.
- full_shape – Shape of the full variable, as a list of int.
- var_offset – Offset of this Variable into the full variable, as a list of int.
- var_shape – Shape of this Variable, as a list of int.
- save_slice_info_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.
-
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.
-
add_cache_dependents
(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)¶ Add dependents that render the cache invalid if they change.
Parameters: - cache_dependents (ZfitCachable) –
- allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.
Raises: TypeError
– if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
-
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 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_delta – 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: ValueError
– 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.
-
floating
¶
-
static
from_proto
(variable_def, import_scope=None)¶ Returns a Variable object created from variable_def.
-
gather_nd
(indices, name=None)¶ Reads the value of this variable sparsely, using gather_nd.
-
get_dependents
(only_floating: bool = True) → Set[zfit.core.parameter.Parameter]¶ Return a set of all independent
Parameter
that this object depends on.Parameters: only_floating (bool) – If True, only return floating Parameter
-
get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[ZfitParameter]¶ Return the parameters. If it is empty, automatically return all floating variables.
Parameters: - () (names) – If True, return only the floating parameters.
- () – The names of the parameters to return.
Returns: Return type: list(ZfitParameters)
-
get_shape
()¶ Alias of Variable.shape.
-
graph
¶ The Graph of this variable.
-
handle
¶ The handle by which this variable can be accessed.
-
has_limits
¶
-
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.
-
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: Union[int, float, complex, tensorflow.python.framework.ops.Tensor])[source]¶ Parameter
takes on the value. Is not part of the graph, does a session run.Parameters: value (numerical) –
-
lower_limit
¶
-
name
¶ The name of the object.
-
numpy
()¶
-
op
¶ The op for this variable.
-
params
¶
-
randomize
(minval=None, maxval=None, sampler=<built-in method uniform of mtrand.RandomState object>)[source]¶ Update the value with a randomised value between minval and maxval.
Parameters: - minval (Numerical) –
- maxval (Numerical) –
- () (sampler) –
-
read_value
()[source]¶ Constructs an op which reads the value of this variable.
Should be used when there are multiple reads, or when it is desirable to read the value only after some condition is true.
Returns: the read operation.
-
register_cacher
(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])¶ Register a cacher that caches values produces by this instance; a dependent.
Parameters: () (cacher) –
-
reset_cache
(reseter: zfit.util.cache.ZfitCachable)¶
-
reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
-
scatter_add
(sparse_delta, use_locking=False, name=None)¶ Adds IndexedSlices from this variable.
Parameters: - sparse_delta – 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 subtraction has completed.
Raises: ValueError
– 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.
Raises: ValueError
– if sparse_delta is not an IndexedSlices.
-
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.
Raises: ValueError
– if sparse_delta is not an IndexedSlices.
-
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.
Raises: ValueError
– if sparse_delta is not an IndexedSlices.
-
scatter_sub
(sparse_delta, use_locking=False, name=None)¶ Subtracts IndexedSlices from this variable.
Parameters: - sparse_delta – 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: ValueError
– if sparse_delta is not an IndexedSlices.
-
scatter_update
(sparse_delta, use_locking=False, name=None)¶ Assigns IndexedSlices to this variable.
Parameters: - sparse_delta – 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: ValueError
– if sparse_delta is not an IndexedSlices.
-
sess
¶
-
set_sess
(sess: tensorflow.python.client.session.Session)¶ Set the session (temporarily) for this instance. If None, the auto-created default is taken.
Parameters: sess (tf.Session) –
-
set_shape
(shape)¶ Unsupported.
-
set_value
(value: Union[int, float, complex, tensorflow.python.framework.ops.Tensor])[source]¶ Set the
Parameter
to value (temporarily if used in a context manager).Parameters: value (float) – The value the parameter will take on.
-
shape
¶ The shape of this variable.
-
sparse_read
(indices, name=None)¶ Reads the value of this variable sparsely, using gather.
-
step_size
¶
-
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_limit
¶
-
class
zfit.
ComposedParameter
(name, tensor, dtype=tf.float64, **kwargs)[source]¶ Bases:
zfit.core.parameter.BaseComposedParameter
-
add_cache_dependents
(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)¶ Add dependents that render the cache invalid if they change.
Parameters: - cache_dependents (ZfitCachable) –
- allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.
Raises: TypeError
– if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
-
assign
(value, use_locking=False, name=None, read_value=True)¶
-
copy
(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject¶
-
dtype
¶ The dtype of the object
-
floating
¶
-
get_dependents
(only_floating: bool = True) → Set[zfit.core.parameter.Parameter]¶ Return a set of all independent
Parameter
that this object depends on.Parameters: only_floating (bool) – If True, only return floating Parameter
-
get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[ZfitParameter]¶ Return the parameters. If it is empty, automatically return all floating variables.
Parameters: - () (names) – If True, return only the floating parameters.
- () – The names of the parameters to return.
Returns: Return type: list(ZfitParameters)
-
independent
¶
-
load
(value, session=None)¶
-
name
¶ The name of the object.
-
params
¶
-
read_value
()¶
-
register_cacher
(cacher: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]])¶ Register a cacher that caches values produces by this instance; a dependent.
Parameters: () (cacher) –
-
reset_cache
(reseter: zfit.util.cache.ZfitCachable)¶
-
reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
-
value
()¶
-
-
class
zfit.
ComplexParameter
(name, value, dtype=tf.complex128, **kwargs)[source]¶ Bases:
zfit.core.parameter.ComposedParameter
-
add_cache_dependents
(cache_dependents: Union[zfit.core.interfaces.ZfitCachable, Iterable[zfit.core.interfaces.ZfitCachable]], allow_non_cachable: bool = True)¶ Add dependents that render the cache invalid if they change.
Parameters: - cache_dependents (ZfitCachable) –
- allow_non_cachable (bool) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.
Raises: TypeError
– if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
-
arg
¶
-
assign
(value, use_locking=False, name=None, read_value=True)¶
-
conj
¶
-
copy
(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject¶
-
dtype
¶ The dtype of the object
-
floating
¶
-
get_dependents
(only_floating: bool = True) → Set[zfit.core.parameter.Parameter]¶ Return a set of all independent
Parameter
that this object depends on.Parameters: only_floating (bool) – If True, only return floating Parameter
-
get_params
(only_floating: bool = False, names: Union[str, List[str], None] = None) → List[ZfitParameter]¶ Return the parameters. If it is empty, automatically return all floating variables.
Parameters: - () (names) – If True, return only the floating parameters.
- () – The names of the parameters to return.
Returns: Return type: list(ZfitParameters)
-
imag
¶
-
independent
¶
-
load
(value, session=None)¶
-
mod
¶
-
name
¶ The name of the object.
-
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.ZfitCachable)¶
-
reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
-
value
()¶
-
-
zfit.
convert_to_parameter
(value, name=None, prefer_floating=False) → zfit.core.interfaces.ZfitParameter[source]¶ Convert a numerical to a fixed parameter or return if already a parameter.
Parameters: () (value) –
-
class
zfit.
Space
(obs: Union[str, Iterable[str], zfit.Space], limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool, None] = None, name: Optional[str] = 'Space')[source]¶ Bases:
zfit.core.interfaces.ZfitSpace
,zfit.core.baseobject.BaseObject
Define a space with the name (obs) of the axes (and it’s number) and possibly it’s limits.
Parameters: -
ANY
= <Any>¶
-
ANY_LOWER
= <Any Lower Limit>¶
-
ANY_UPPER
= <Any Upper Limit>¶
-
AUTO_FILL
= <object object>¶
-
add
(other: Union[zfit.Space, Iterable[zfit.Space]])[source]¶ Add the limits of the spaces. Only works for the same obs.
In case the observables are different, the order of the first space is taken.
Parameters: other ( Space
) –Returns: Return type: Space
-
area
() → float[source]¶ Return the total area of all the limits and axes. Useful, for example, for MC integration.
-
axes
¶ The axes (“obs with int”) the space is defined in.
Returns:
-
combine
(other: Union[zfit.Space, Iterable[zfit.Space]]) → zfit.core.interfaces.ZfitSpace[source]¶ Combine spaces with different obs (but consistent limits).
Parameters: other ( Space
) –Returns: Return type: Space
-
copy
(name: Optional[str] = None, **overwrite_kwargs) → zfit.Space[source]¶ Create a new
Space
using the current attributes and overwriting with overwrite_overwrite_kwargs.Parameters: - name (str) – The new name. If not given, the new instance will be named the same as the current one.
- () (**overwrite_kwargs) –
Returns:
-
classmethod
from_axes
(axes: Union[int, Iterable[int]], limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool, None] = None, name: str = None) → zfit.Space[source]¶ Create a space from axes instead of from obs.
Parameters: - () (limits) –
- () –
- name (str) –
Returns:
-
get_axes
(obs: Union[str, Iterable[str], zfit.Space] = None, as_dict: bool = False, autofill: bool = False) → Union[Tuple[int], None, Dict[str, int]][source]¶ Return the axes corresponding to the obs (or all if None).
Parameters: Returns: Tuple, OrderedDict
Raises: ValueError
– if the requested obs do not match with the one defined in the rangeAxesNotSpecifiedError
– If the axes in thisSpace
have not been specified.
-
get_obs_axes
(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None)[source]¶
-
get_reorder_indices
(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None) → Tuple[int][source]¶ Indices that would order self.obs as obs respectively self.axes as axes.
Parameters: - () (axes) –
- () –
Returns:
-
get_subspace
(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None, name: Optional[str] = None) → zfit.Space[source]¶ Create a
Space
consisting of only a subset of the obs/axes (only one allowed).Parameters: Returns:
-
iter_areas
(rel: bool = False) → Tuple[float, ...][source]¶ Return the areas of each interval
Parameters: rel (bool) – If True, return the relative fraction of each interval Returns: Return type: Tuple[float]
-
iter_limits
(as_tuple: bool = True) → Union[Tuple[zfit.Space], Tuple[Tuple[Tuple[float]]], Tuple[Tuple[float]]][source]¶ Return the limits, either as
Space
objects or as pure limits-tuple.This makes iterating over limits easier: for limit in space.iter_limits() allows to, for example, pass limit to a function that can deal with simple limits only or if as_tuple is True the limit can be directly used to calculate something.
Example
for lower, upper in space.iter_limits(as_tuple=True): integrals = integrate(lower, upper) # calculate integral integral = sum(integrals)
Returns: Return type: List[ Space
] or List[limit,…]
-
limit1d
¶ return the tuple(lower, upper).
Returns: so lower, upper = space.limit1d
for a simple, 1 obs limit.Return type: tuple(float, float) Raises: RuntimeError
– if the conditions (n_obs or n_limits) are not satisfied.Type: Simplified limits getter for 1 obs, 1 limit only
-
limit2d
¶ return the tuple(low_obs1, low_obs2, up_obs1, up_obs2).
Returns: - so low_x, low_y, up_x, up_y = space.limit2d for a single, 2 obs limit.
- low_x is the lower limit in x, up_x is the upper limit in x etc.
Return type: tuple(float, float, float, float) Raises: RuntimeError
– if the conditions (n_obs or n_limits) are not satisfied.Type: Simplified limits for exactly 2 obs, 1 limit
-
limits
¶ Return the limits.
Returns:
-
limits1d
¶ return the tuple(low_1, …, low_n, up_1, …, up_n).
Returns: - so low_1, low_2, up_1, up_2 = space.limits1d for several, 1 obs limits.
- low_1 to up_1 is the first interval, low_2 to up_2 is the second interval etc.
Return type: tuple(float, float, ..) Raises: RuntimeError
– if the conditions (n_obs or n_limits) are not satisfied.Type: Simplified .limits for exactly 1 obs, n limits
-
lower
¶ Return the lower limits.
Returns:
-
n_limits
¶ The number of different limits.
Returns: int >= 1
-
n_obs
¶ Return the number of observables/axes.
Returns: int >= 1
-
name
¶ The name of the object.
-
obs
¶ The observables (“axes with str”)the space is defined in.
Returns:
-
obs_axes
¶
-
reorder_by_indices
(indices: Tuple[int])[source]¶ Return a
Space
reordered by the indices.Parameters: () (indices) –
-
upper
¶ Return the upper limits.
Returns:
-
with_autofill_axes
(overwrite: bool = False) → zfit.Space[source]¶ Return a
Space
with filled axes corresponding to range(len(n_obs)).Parameters: overwrite (bool) – If self.axes is not None, replace the axes with the autofilled ones. If axes is already set, don’t do anything if overwrite is False. Returns: Space
-
with_axes
(axes: Union[int, Iterable[int]]) → zfit.Space[source]¶ Sort by obs and return the new instance.
Parameters: () (axes) – Returns: Space
-
with_limits
(limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool], name: Optional[str] = None) → zfit.Space[source]¶ Return a copy of the space with the new limits (and the new name).
Parameters: - () (limits) –
- name (str) –
Returns:
-
with_obs
(obs: Union[str, Iterable[str], zfit.Space]) → zfit.Space[source]¶ Sort by obs and return the new instance.
Parameters: () (obs) – Returns: Space
-
-
zfit.
convert_to_space
(obs: Union[str, Iterable[str], zfit.Space, None] = None, axes: Union[int, Iterable[int], None] = None, limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool, None] = None, *, overwrite_limits: bool = False, one_dim_limits_only: bool = True, simple_limits_only: bool = True) → Union[None, zfit.core.limits.Space, bool][source]¶ Convert limits to a
Space
object if not already None or False.Parameters: - obs (Union[Tuple[float, float],
Space
]) – - () (axes) –
- () –
- overwrite_limits (bool) – If obs or axes is a
Space
_and_ limits are given, return an instance ofSpace
with the new limits. If the flag is False, the limits argument will be ignored if - one_dim_limits_only (bool) –
- simple_limits_only (bool) –
Returns: Return type: Union[
Space
, False, None]Raises: OverdefinedError
– if obs or axes is aSpace
and axes respectively obs is not None.- obs (Union[Tuple[float, float],
-
zfit.
supports
(*, norm_range: bool = False, multiple_limits: bool = False) → Callable[source]¶ Decorator: Add (mandatory for some methods) on a method to control what it can handle.
If any of the flags is set to False, it will check the arguments and, in case they match a flag (say if a norm_range is passed while the norm_range flag is set to False), it will raise a corresponding exception (in this example a NormRangeNotImplementedError) that will be catched by an earlier function that knows how to handle things.
Parameters: