Top-level package for zfit.
zfit.
Parameter
Bases: zfit.core.parameter.ZfitParameterMixin, zfit.core.parameter.TFBaseVariable, zfit.core.parameter.BaseParameter, zfit.core.interfaces.ZfitIndependentParameter
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 (str) – name of the parameter
str
value (Union[int, float, complex, Tensor, ForwardRef]) – starting value
Union
int
float
complex
Tensor
ForwardRef
lower_limit (Union[int, float, complex, Tensor, ForwardRef, None]) – lower limit
None
upper_limit (Union[int, float, complex, Tensor, ForwardRef, None]) – upper limit
step_size (Union[int, float, complex, Tensor, ForwardRef, None]) – step size
DEFAULT_STEP_SIZE
lower
upper
has_limits
If the parameter has limits set or not.
bool
at_limit
If the value is at the limit (or over it).
The precision is up to 1e-5 relative.
Boolean tf.Tensor that tells whether the value is at the limits.
value
read_value
floating
independent
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.
The step size
set_value
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.
value (Union[int, float, complex, Tensor, ForwardRef]) – The value the parameter will take on.
randomize
Update the parameter with a randomised value between minval and maxval and return it.
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
Callable
The sampled value
get_params
Set[ZfitParameter]
Set
ZfitParameter
lower_limit
upper_limit
SaveSliceInfo
Bases: object
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.
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
Returns a SaveSliceInfoDef() proto.
export_scope – Optional string. Name scope to remove.
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.
TypeError – when invoked.
__ne__
Compares two variables element-wise for equality.
add_cache_deps
Add dependencies that render the cache invalid if they change.
cache_deps (Union[ForwardRef, Iterable[ForwardRef]]) –
Iterable
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.
TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
aggregation
assign
Assigns a new value to this variable.
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.
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
Adds a value to this variable.
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.
assign_sub
Subtracts a value from this variable.
delta – A Tensor. The value to subtract from this variable.
batch_scatter_update
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:
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.
sparse_delta – tf.IndexedSlices to be assigned to this variable.
name – the name of the operation.
The updated variable.
TypeError – if sparse_delta is not an IndexedSlices.
constraint
Returns the constraint function associated with this variable.
The constraint function that was passed to the variable constructor. Can be None if no constraint was passed.
copy
ZfitObject
count_up_to
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).
limit – value at which incrementing the variable raises an error.
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
DType
eval
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.
from_proto
gather_nd
Reads the value of this variable sparsely, using gather_nd.
get_cache_deps
Return a set of all independent Parameter that this object depends on.
only_floating (bool) – If True, only return floating Parameter
OrderedSet
get_dependencies
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_shape
Alias of Variable.shape.
graph
The Graph of this variable.
graph_caching_methods
handle
The handle by which this variable can be accessed.
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) `
A Tensor holding the value of this variable after its initializer has run.
initializer
instances
is_initialized
Checks whether a resource variable has been initialized.
Outputs boolean scalar indicating whether the tensor has been initialized.
name – A name for the operation (optional).
A Tensor of type bool.
load
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()
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]
```
value – New variable value
session – The session to use to evaluate this variable. If none, the default session is used.
ValueError – Session is not passed and no default session
name
numpy
op
The op for this variable.
params
~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
Register a cacher that caches values produces by this instance; a dependent.
cacher (Union[ForwardRef, Iterable[ForwardRef]]) –
reset_cache
reset_cache_self
Clear the cache of self and all dependent cachers.
scatter_add
Adds tf.IndexedSlices to this variable.
sparse_delta – tf.IndexedSlices to be added to this variable.
scatter_div
Divide this variable by tf.IndexedSlices.
sparse_delta – tf.IndexedSlices to divide this variable by.
scatter_max
Updates this variable with the max of tf.IndexedSlices and itself.
sparse_delta – tf.IndexedSlices to use as an argument of max with this variable.
scatter_min
Updates this variable with the min of tf.IndexedSlices and itself.
sparse_delta – tf.IndexedSlices to use as an argument of min with this variable.
scatter_mul
Multiply this variable by tf.IndexedSlices.
sparse_delta – tf.IndexedSlices to multiply this variable by.
scatter_nd_add
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:
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.
indices – The indices to be used in the operation.
updates – The values to be used in the operation.
scatter_nd_max
scatter_nd_min
scatter_nd_sub
Applies sparse subtraction to individual values or slices in a Variable.
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)
[1, -9, 3, -6, -6, 6, 7, -4]
scatter_nd_update
Applies sparse assignment to individual values or slices in a Variable.
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:
[1, 11, 3, 10, 9, 6, 7, 12]
scatter_sub
Subtracts tf.IndexedSlices from this variable.
sparse_delta – tf.IndexedSlices to be subtracted from this variable.
scatter_update
Assigns tf.IndexedSlices to this variable.
set_shape
shape
The shape of this variable.
sparse_read
Reads the value of this variable sparsely, using gather.
synchronization
Converts a ResourceVariable to a VariableDef protocol buffer.
RuntimeError – If run in EAGER mode.
A VariableDef protocol buffer, or None if the Variable is not in the specified name scope.
trainable
ComposedParameter
Bases: zfit.core.parameter.BaseComposedParameter
zfit.core.parameter.BaseComposedParameter
Arbitrary composition of parameters.
A ComposedParameter allows for arbitrary combinations of parameters and correlations
name (str) – Unique name of the Parameter
value_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.
Dict
dtype (DType) – Output of value_fn dtype
dependents (Union[Dict[str, ZfitParameter], Iterable[ZfitParameter], ZfitParameter]) –
Deprecated since version unknown: use params instead.
Assigns a new value to the variable.
This is essentially a shortcut for assign(self, value).
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.
The updated variable. If read_value is false, instead returns None in Eager mode and the assign op in graph mode.
This is essentially a shortcut for assign_add(self, delta).
This is essentially a shortcut for assign_sub(self, delta).
The device of this variable.
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.
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())
A numpy ndarray with a copy of the value of this variable.
Returns a Variable object created from variable_def.
Gather slices from params into a Tensor with shape specified by indices.
See tf.gather_nd for details.
indices – A Tensor. Must be one of the following types: int32, int64. Index tensor.
A Tensor. Has the same type as params.
Recursively collect parameters that this object depends on according to the filter criteria.
parameters that are fixed.
True: only return parameters that fulfil this criterion
only parameters that are not floating.
floating (Optional[bool]) – if a parameter is floating, e.g. if floating() returns True
Optional
floating()
is_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 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 (Optional[bool]) – If the parameter is an independent parameter, i.e. if it is a ZfitIndependentParameter.
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.
A Tensor.
The initializer operation for this variable.
The Operation of this 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_{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 self.
` [d_0, ..., d_{Q-2}, self.shape[K], ..., self.shape[P-1]]. `
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:
The resulting update to v would look like this:
Assuming the variable has rank P and indices is a Tensor of rank Q.
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:
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:
Overrides the shape for this variable.
shape – the TensorShape representing the overridden shape.
Gather slices from params axis axis according to indices.
This function supports a subset of tf.gather, see tf.gather for details on usage.
indices – The index Tensor. Must be one of the following types: int32, int64. Must be in range [0, params.shape[axis]).
Converts a Variable to a VariableDef protocol buffer.
ComplexParameter
Bases: zfit.core.parameter.ComposedParameter
zfit.core.parameter.ComposedParameter
Create a complex parameter.
Note
Use the constructor class methods instead of the __init__() constructor:
ComplexParameter.from_cartesian()
ComplexParameter.from_polar()
from_cartesian
Create a complex parameter from cartesian coordinates.
name – Name of the parameter.
real – Real part of the complex number.
imag – Imaginary part of the complex number.
from_polar
Create a complex parameter from polar coordinates.
real – Modulus (r) the complex number.
imag – Argument (phi) of the complex number.
conj
Returns a complex conjugated copy of the complex parameter.
real
Real part of the complex parameter.
imag
Imaginary part of the complex parameter.
mod
Modulus (r) of the complex parameter.
arg
Argument (phi) of the complex parameter.
convert_to_parameter
Convert a numerical to a constant/floating parameter or return if already a parameter.
value –
name –
prefer_constant – If True, create a ConstantParameter instead of a Parameter, if possible.
Space
Bases: zfit.core.space.BaseSpace
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 use-cases, 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.
obs (Union[str, Iterable[str], Space, None]) –
limits (Union[ZfitLimit, Tensor, ndarray, Iterable[float], float, Tuple[float], List[float], bool, None]) –
ZfitLimit
ndarray
Tuple
List
name (Optional[str]) –
AUTO_FILL
ANY
ANY_LOWER
ANY_UPPER
get_limits
Union[Dict[str, Union[Dict[Tuple[int], ZfitLimit], Dict[Tuple[str], ZfitLimit]]], Dict[Tuple[int], ZfitLimit], Dict[Tuple[str], ZfitLimit]]
limits
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.
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.
inside()
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.
Tuple[Union[ndarray, Tensor, float], Union[ndarray, Tensor, float]]
The lower and upper limits.
LimitsNotSpecifiedError – If there are not limits set or they are False.
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.
Tuple[ndarray, ndarray]
dimension is always n_obs, the first can be vectorized. This allows unstacking with z.unstack_x() as can be done with data.
CannotConvertToNumpyError – In case the conversion fails.
LimitsNotSpecifiedError – If the limits are not set
rect_lower
The lower, rectangular limits, equivalent to rect_limits[0] with shape (…, n_obs)
Union[ndarray, Tensor, float]
The lower, rectangular limits as np.ndarray or tf.Tensor
LimitsNotSpecifiedError – If the limits are not set or are false
rect_upper
The upper, rectangular limits, equivalent to rect_limits[1] with shape (…, n_obs)
Union[ndarray, Tensor, None, bool]
The upper, rectangular limits as np.ndarray or tf.Tensor
rect_area
Calculate the total rectangular area of all the limits and axes. Useful, for example, for MC integration.
Union[float, ndarray, Tensor]
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.
If the rectangular limits are tensors.
has_rect_limits
If there are limits and whether they are rectangular.
limits_are_false
If the limits have been set to False, so the object on purpose does not contain limits.
True if limits is False
Whether there are limits set and they are not false.
Returns:
limits_are_set
n_events
Return the number of events, the dimension of the first shape.
Optional[int]
it’s vectorized.
limit2d
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.
limits1d
return the tuple(low_1, …, low_n, up_1, …, up_n).
Tuple[float]
low_1 to up_1 is the first interval, low_2 to up_2 is the second interval etc.
RuntimeError – if the conditions (n_obs or n_limits) are not satisfied.
Simplified .limits for exactly 1 obs, n limits
n_limits
The number of different limits.
int >= 1
iter_limits
REMOVED.Return the limits, either as Space objects or as pure limits-tuple. (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)
List[Space] or List[limit,…]
with_limits
Return a copy of the space with the new limits (and the new name).
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
name (Optional[str]) – Human readable name
ZfitSpace
Copy of the current object with the new limits.
reorder_x
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.
x (Union[Tensor, ndarray]) – Tensor to be reordered, last dimension should be n_obs resp. n_axes
x_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.
Union[ndarray, Tensor]
The reordered array-like object
with_obs
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 as-is and now correspond to the already existing axes in the object.
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 as-is and now correspond to the already existing axes in the object.
obs (Union[str, Iterable[str], Space, None]) – Observables to sort/associate this instance with
allow_superset (bool) – if False and a strict superset of the own observables is given, an error
raised. (is) –
allow_subset (bool) – if False and a strict subset of the own observables is given, an error
raised. –
A copy of the object with the new ordering/observables
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
Create a new instance that has axes; sorted by or set or dropped.
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 as-is and now correspond to the already existing obs in the object.
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 as-is and now correspond to the already existing obs in the object.
axes (Union[int, Iterable[int], None]) – Axes to sort/associate this instance with
allow_superset (bool) – if False and a strict superset of the own axeservables is given, an error
allow_subset (bool) – if False and a strict subset of the own axeservables is given, an error
A copy of the object with the new ordering/axes
AxesIncompatibleError – if axes is a superset and allow_superset is False or a subset and allow_allow_subset is False
with_coords
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 non-matchin axes results in axes being dropped.
coords (ZfitOrderableDimensional) – An instance of Coordinates
ZfitOrderableDimensional
Coordinates
allow_superset (bool) – If False and a strict superset is given, an error is raised
allow_subset (bool) – If False and a strict subset is given, an error is raised
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 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)
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.
The object with the new axes
AxesIncompatibleError – if the axes are already set and overwrite is False.
get_subspace
Create a Space consisting of only a subset of the obs/axes (only one allowed).
obs (Union[str, Iterable[str], Space, None]) – Observables of the subspace to return.
axes (Union[int, Iterable[int], None]) – Axes of the subspace to return.
name (Optional[str]) – Human readable names
A space containing only a subspace (and sublimits etc.)
with_obs_axes
get_obs_axes
obs_axes
area
Create a new Space using the current attributes and overwriting with overwrite_overwrite_kwargs.
name – The new name. If not given, the new instance will be named the same as the current one.
**overwrite_kwargs –
limit1d
from_axes
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), …)
rect_limits –
axes (Union[int, Iterable[int]]) –
__eq__
Compares two Limits for equality without graph mode allowed.
IllegalInGraphModeError – it the comparison happens with tensors in a graph context.
__le__
Set-like 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.
Result of the comparison
add
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.
other (Union[Space, Iterable[Space]]) –
axes
The axes (“obs with int”) the space is defined in.
Optional[Tuple[int]]
combine
Combine spaces with different obs (but consistent limits).
equal
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.
Union[bool, Tensor]
filter
Filter x by removing the elements along axis that are not inside the limits.
This is similar to tf.boolean_mask.
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.
Data
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.
removed.
get_reorder_indices
Indices that would order the instances obs as obs respectively the instances axes as axes.
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.
Tuple[int]
New indices that would reorder the instances obs to be obs respectively axes.
CoordinatesUnderdefinedError – If neither obs nor axes is given
get_sublimits
inside
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
x (Union[ndarray, Tensor, Data]) – Values to be checked whether they are inside of the limits. The shape is expected to have the last dimension equal to n_obs.
Union[ndarray, Tensor, Data]
last dimension removed.
less_equal
other – Any other object to compare with
allow_graph – If False and the function returns a symbolic tensor, raise IllegalInGraphModeError instead.
n_obs
Return the number of observables/axes.
The name of the object.
obs
The observables (“axes with str”)the space is defined in.
Optional[Tuple[str, …]]
convert_to_space
Convert limits to a Space object if not already None or False.
axes (Union[int, Iterable[int], None]) –
overwrite_limits (bool) – If obs or axes is a Space _and_ limits are given, return an instance of Space with the new limits. If the flag is False, the limits argument will be ignored if
one_dim_limits_only (bool) –
simple_limits_only (bool) –
Union[Space, False, None]
OverdefinedError – if obs or axes is a Space and axes respectively obs is not None.
supports
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.
norm_range (bool) – If False, no norm_range argument will be passed through resp. will be None
multiple_limits (bool) – If False, only simple limits are to be expected and no iteration is therefore required.