Data¶
-
class
zfit.data.
Data
(dataset, obs=None, name=None, weights=None, iterator_feed_dict=None, dtype=None)[source]¶ Bases:
zfit.util.cache.GraphCachable
,zfit.core.interfaces.ZfitData
,zfit.core.dimension.BaseDimensional
,zfit.core.baseobject.BaseObject
Create a data holder from a dataset used to feed into models.
- Parameters
dataset (
Union
[DatasetV2
,LightDataset
]) – A dataset storing the actual valuesobs (
Union
[str
,Iterable
[str
],Space
,None
]) – Observables where the data is defined inname (
Optional
[str
]) – Name of the Dataiterator_feed_dict (
Optional
[Dict
]) –dtype (
Optional
[DType
]) – The DType of the return value. Defaults to the zfit default (usually float64).
-
set_weights
(weights)[source]¶ Set (temporarily) the weights of the dataset.
- Parameters
weights (
Union
[Tensor
,None
,ndarray
]) –
-
classmethod
from_root
(path, treepath, branches=None, branches_alias=None, weights=None, name=None, dtype=None, root_dir_options=None)[source]¶ Create a Data from a ROOT file. Arguments are passed to uproot.
The arguments are passed to uproot directly.
- Parameters
path (
str
) –treepath (
str
) –branches (
Optional
[List
[str
]]) –branches_alias (
Optional
[Dict
]) – A mapping from the branches (as keys) to the actual observables (as values). This allows to have different observable names, independent of the branch name in the file.weights (
Union
[Tensor
,None
,ndarray
,str
]) – Weights of the data. Has to be 1-D and match the shape of the data (nevents). Can be a column of the ROOT file by using a string corresponding to a column.name (
Optional
[str
]) –root_dir_options –
- Returns
- Return type
zfit.Data
-
classmethod
from_pandas
(df, obs=None, weights=None, name=None, dtype=None)[source]¶ Create a Data from a pandas DataFrame. If obs is None, columns are used as obs.
- Parameters
df (
DataFrame
) –weights (
Union
[None
,ndarray
,Tensor
]) – Weights of the data. Has to be 1-D and match the shape of the data (nevents).obs (
Union
[str
,Iterable
[str
],Space
,None
]) –name (
Optional
[str
]) –
-
classmethod
from_numpy
(obs, array, weights=None, name=None, dtype=None)[source]¶ Create Data from a np.array.
- Parameters
obs (
Union
[str
,Iterable
[str
],Space
]) –array (
ndarray
) –name (
Optional
[str
]) –
Returns:
-
classmethod
from_tensor
(obs, tensor, weights=None, name=None, dtype=None)[source]¶ Create a Data from a tf.Tensor. Value simply returns the tensor (in the right order).
- Parameters
obs (
Union
[str
,Iterable
[str
],Space
]) –tensor (
Tensor
) –name (
Optional
[str
]) –
Returns:
- Return type
Data
-
to_pandas
(obs=None)[source]¶ Create a pd.DataFrame from obs as columns and return it.
- Parameters
obs (
Union
[str
,Iterable
[str
],Space
,None
]) – The observables to use as columns. If None, all observables are used.
Returns:
-
unstack_x
(obs=None, always_list=False)[source]¶ Return the unstacked data: a list of tensors or a single Tensor.
- Parameters
obs (
Union
[str
,Iterable
[str
],Space
,None
]) – which observables to returnalways_list (
bool
) – If True, always return a list (also if length 1)
- Returns
List(tf.Tensor)
-
convert_sort_space
(obs=None, axes=None, limits=None)[source]¶ Convert the inputs (using eventually obs, axes) to
Space
and sort them according to own obs.- Parameters
obs (
Union
[str
,Iterable
[str
],Space
,None
]) –axes (
Union
[int
,Iterable
[int
],None
]) –limits (
Union
[ZfitLimit
,Tensor
,ndarray
,Iterable
[float
],float
,Tuple
[float
],List
[float
],bool
,None
]) –
Returns:
- Return type
Optional
[Space
]
-
add_cache_deps
(cache_deps, allow_non_cachable=True)¶ Add dependencies that render the cache invalid if they change.
- Parameters
cache_deps (
Union
[ForwardRef
,Iterable
[ForwardRef
]]) –allow_non_cachable (
bool
) – If True, allow cache_dependents to be non-cachables. If False, any cache_dependents that is not a ZfitCachable will raise an error.
- Raises
TypeError – if one of the cache_dependents is not a ZfitCachable _and_ allow_non_cachable if False.
-
property
name
¶ The name of the object.
- Return type
str
-
register_cacher
(cacher)¶ Register a cacher that caches values produces by this instance; a dependent.
- Parameters
cacher (
Union
[ForwardRef
,Iterable
[ForwardRef
]]) –
-
reset_cache_self
()¶ Clear the cache of self and all dependent cachers.