data¶
-
class
zfit.core.data.
Data
(dataset: Union[tensorflow.python.data.ops.dataset_ops.DatasetV2, LightDataset], obs: Union[str, Iterable[str], zfit.Space] = None, name: str = None, weights=None, iterator_feed_dict: Dict[KT, VT] = None, dtype: tensorflow.python.framework.dtypes.DType = None)[source]¶ Bases:
zfit.util.cache.Cachable
,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: - () (dtype) – A dataset storing the actual values
- () – Observables where the data is defined in
- () – Name of the Data
- () –
- () –
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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.
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axes
¶ Return the axes.
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convert_sort_space
(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None, limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = None) → Optional[zfit.core.limits.Space][source]¶ Convert the inputs (using eventually obs, axes) to
Space
and sort them according to own obs.Parameters: - () (limits) –
- () –
- () –
Returns:
-
copy
(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject¶
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data_range
¶
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dtype
¶
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classmethod
from_numpy
(obs: Union[str, Iterable[str], zfit.Space], array: numpy.ndarray, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None)[source]¶ Create Data from a np.array.
Parameters: - () (obs) –
- array (numpy.ndarray) –
- name (str) –
Returns: Return type: zfit.Data
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classmethod
from_pandas
(df: pandas.core.frame.DataFrame, obs: Union[str, Iterable[str], zfit.Space] = None, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None)[source]¶ Create a Data from a pandas DataFrame. If obs is None, columns are used as obs.
Parameters:
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classmethod
from_root
(path: str, treepath: str, branches: List[str] = None, branches_alias: Dict[KT, VT] = None, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray, str] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None, root_dir_options=None) → zfit.core.data.Data[source]¶ Create a Data from a ROOT file. Arguments are passed to uproot.
Parameters: - path (str) –
- treepath (str) –
- branches (List[str]]) –
- branches_alias (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 (tf.Tensor, None, np.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 (str) –
- () (root_dir_options) –
Returns: Return type: zfit.Data
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classmethod
from_root_iter
(path, treepath, branches=None, entrysteps=None, name=None, **kwargs)[source]¶
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classmethod
from_tensor
(obs: Union[str, Iterable[str], zfit.Space], tensor: tensorflow.python.framework.ops.Tensor, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None) → zfit.core.data.Data[source]¶ Create a Data from a tf.Tensor. Value simply returns the tensor (in the right order).
Parameters: Returns: Return type: zfit.core.Data
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graph_caching_methods
= []¶
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iterator
¶
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n_obs
¶ Return the number of observables.
-
name
¶ The name of the object.
-
nevents
¶
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obs
¶ Return the observables.
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old_graph_caching_methods
= []¶
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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)¶
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reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
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set_weights
(weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray])[source]¶ Set (temporarily) the weights of the dataset.
Parameters: weights (tf.Tensor, np.ndarray, None) –
-
to_pandas
(obs: Union[str, Iterable[str], zfit.Space] = None)[source]¶ Create a pd.DataFrame from obs as columns and return it.
Parameters: () (obs) – The observables to use as columns. If None, all observables are used. Returns:
-
unstack_x
(obs: Union[str, Iterable[str], zfit.Space] = None, always_list: bool = False)[source]¶ Return the unstacked data: a list of tensors or a single Tensor.
Parameters: - () (obs) – which observables to return
- always_list (bool) – If True, always return a list (also if length 1)
Returns: List(tf.Tensor)
-
weights
¶
-
class
zfit.core.data.
SampleData
(dataset: Union[tensorflow.python.data.ops.dataset_ops.DatasetV2, LightDataset], sample_holder: tensorflow.python.framework.ops.Tensor, obs: Union[str, Iterable[str], zfit.Space] = None, weights=None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = tf.float64)[source]¶ Bases:
zfit.core.data.Data
-
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.
-
axes
¶ Return the axes.
-
convert_sort_space
(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None, limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = None) → Optional[zfit.core.limits.Space]¶ Convert the inputs (using eventually obs, axes) to
Space
and sort them according to own obs.Parameters: - () (limits) –
- () –
- () –
Returns:
-
copy
(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject¶
-
data_range
¶
-
dtype
¶
-
classmethod
from_numpy
(obs: Union[str, Iterable[str], zfit.Space], array: numpy.ndarray, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None)¶ Create Data from a np.array.
Parameters: - () (obs) –
- array (numpy.ndarray) –
- name (str) –
Returns: Return type: zfit.Data
-
classmethod
from_pandas
(df: pandas.core.frame.DataFrame, obs: Union[str, Iterable[str], zfit.Space] = None, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None)¶ Create a Data from a pandas DataFrame. If obs is None, columns are used as obs.
Parameters:
-
classmethod
from_root
(path: str, treepath: str, branches: List[str] = None, branches_alias: Dict[KT, VT] = None, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray, str] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None, root_dir_options=None) → zfit.core.data.Data¶ Create a Data from a ROOT file. Arguments are passed to uproot.
Parameters: - path (str) –
- treepath (str) –
- branches (List[str]]) –
- branches_alias (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 (tf.Tensor, None, np.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 (str) –
- () (root_dir_options) –
Returns: Return type: zfit.Data
-
classmethod
from_root_iter
(path, treepath, branches=None, entrysteps=None, name=None, **kwargs)¶
-
classmethod
from_sample
(sample: tensorflow.python.framework.ops.Tensor, obs: Union[str, Iterable[str], zfit.Space], name: str = None, weights=None)[source]¶
-
classmethod
from_tensor
(obs: Union[str, Iterable[str], zfit.Space], tensor: tensorflow.python.framework.ops.Tensor, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None) → zfit.core.data.Data¶ Create a Data from a tf.Tensor. Value simply returns the tensor (in the right order).
Parameters: Returns: Return type: zfit.core.Data
-
get_iteration
()¶
-
graph_caching_methods
= []¶
-
initialize
()¶
-
iterator
¶
-
n_obs
¶ Return the number of observables.
-
name
¶ The name of the object.
-
nevents
¶
-
numpy
()¶
-
obs
¶ Return the observables.
-
old_graph_caching_methods
= []¶
-
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.
-
set_data_range
(data_range)¶
-
set_weights
(weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray])¶ Set (temporarily) the weights of the dataset.
Parameters: weights (tf.Tensor, np.ndarray, None) –
-
sort_by_axes
(axes: Union[int, Iterable[int]], allow_superset: bool = False)¶
-
sort_by_obs
(obs: Union[str, Iterable[str], zfit.Space], allow_superset: bool = False)¶
-
to_pandas
(obs: Union[str, Iterable[str], zfit.Space] = None)¶ Create a pd.DataFrame from obs as columns and return it.
Parameters: () (obs) – The observables to use as columns. If None, all observables are used. Returns:
-
unstack_x
(obs: Union[str, Iterable[str], zfit.Space] = None, always_list: bool = False)¶ Return the unstacked data: a list of tensors or a single Tensor.
Parameters: - () (obs) – which observables to return
- always_list (bool) – If True, always return a list (also if length 1)
Returns: List(tf.Tensor)
-
value
(obs: Union[str, Iterable[str], zfit.Space] = None)¶
-
weights
¶
-
-
class
zfit.core.data.
Sampler
(dataset: zfit.core.data.LightDataset, sample_func: Callable, sample_holder: tensorflow.python.ops.variables.Variable, n: Union[int, float, complex, tensorflow.python.framework.ops.Tensor, Callable], weights=None, fixed_params: Dict[zfit.Parameter, Union[int, float, complex, tensorflow.python.framework.ops.Tensor]] = None, obs: Union[str, Iterable[str], zfit.Space] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = tf.float64)[source]¶ Bases:
zfit.core.data.Data
-
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.
-
axes
¶ Return the axes.
-
convert_sort_space
(obs: Union[str, Iterable[str], zfit.Space] = None, axes: Union[int, Iterable[int]] = None, limits: Union[Tuple[Tuple[Tuple[float, ...]]], Tuple[float, float], bool] = None) → Optional[zfit.core.limits.Space]¶ Convert the inputs (using eventually obs, axes) to
Space
and sort them according to own obs.Parameters: - () (limits) –
- () –
- () –
Returns:
-
copy
(deep: bool = False, name: str = None, **overwrite_params) → zfit.core.interfaces.ZfitObject¶
-
data_range
¶
-
dtype
¶
-
classmethod
from_numpy
(obs: Union[str, Iterable[str], zfit.Space], array: numpy.ndarray, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None)¶ Create Data from a np.array.
Parameters: - () (obs) –
- array (numpy.ndarray) –
- name (str) –
Returns: Return type: zfit.Data
-
classmethod
from_pandas
(df: pandas.core.frame.DataFrame, obs: Union[str, Iterable[str], zfit.Space] = None, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None)¶ Create a Data from a pandas DataFrame. If obs is None, columns are used as obs.
Parameters:
-
classmethod
from_root
(path: str, treepath: str, branches: List[str] = None, branches_alias: Dict[KT, VT] = None, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray, str] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None, root_dir_options=None) → zfit.core.data.Data¶ Create a Data from a ROOT file. Arguments are passed to uproot.
Parameters: - path (str) –
- treepath (str) –
- branches (List[str]]) –
- branches_alias (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 (tf.Tensor, None, np.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 (str) –
- () (root_dir_options) –
Returns: Return type: zfit.Data
-
classmethod
from_root_iter
(path, treepath, branches=None, entrysteps=None, name=None, **kwargs)¶
-
classmethod
from_sample
(sample_func: Callable, n: Union[int, float, complex, tensorflow.python.framework.ops.Tensor], obs: Union[str, Iterable[str], zfit.Space], fixed_params=None, name: str = None, weights=None, dtype=None)[source]¶
-
classmethod
from_tensor
(obs: Union[str, Iterable[str], zfit.Space], tensor: tensorflow.python.framework.ops.Tensor, weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray] = None, name: str = None, dtype: tensorflow.python.framework.dtypes.DType = None) → zfit.core.data.Data¶ Create a Data from a tf.Tensor. Value simply returns the tensor (in the right order).
Parameters: Returns: Return type: zfit.core.Data
-
get_iteration
()¶
-
graph_caching_methods
= []¶
-
initialize
()¶
-
iterator
¶
-
n_obs
¶ Return the number of observables.
-
n_samples
¶
-
name
¶ The name of the object.
-
nevents
¶
-
numpy
()¶
-
obs
¶ Return the observables.
-
old_graph_caching_methods
= []¶
-
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) –
-
resample
(param_values: Mapping[KT, VT_co] = None, n: Union[int, tensorflow.python.framework.ops.Tensor] = None)[source]¶ Update the sample by newly sampling. This affects any object that used this data already.
All params that are not in the attribute fixed_params will use their current value for the creation of the new sample. The value can also be overwritten for one sampling by providing a mapping with param_values from Parameter to the temporary value.
Parameters:
-
reset_cache
(reseter: zfit.util.cache.ZfitCachable)¶
-
reset_cache_self
()¶ Clear the cache of self and all dependent cachers.
-
set_data_range
(data_range)¶
-
set_weights
(weights: Union[tensorflow.python.framework.ops.Tensor, None, numpy.ndarray])¶ Set (temporarily) the weights of the dataset.
Parameters: weights (tf.Tensor, np.ndarray, None) –
-
sort_by_axes
(axes: Union[int, Iterable[int]], allow_superset: bool = False)¶
-
sort_by_obs
(obs: Union[str, Iterable[str], zfit.Space], allow_superset: bool = False)¶
-
to_pandas
(obs: Union[str, Iterable[str], zfit.Space] = None)¶ Create a pd.DataFrame from obs as columns and return it.
Parameters: () (obs) – The observables to use as columns. If None, all observables are used. Returns:
-
unstack_x
(obs: Union[str, Iterable[str], zfit.Space] = None, always_list: bool = False)¶ Return the unstacked data: a list of tensors or a single Tensor.
Parameters: - () (obs) – which observables to return
- always_list (bool) – If True, always return a list (also if length 1)
Returns: List(tf.Tensor)
-
value
(obs: Union[str, Iterable[str], zfit.Space] = None)¶
-
weights
¶
-
-
zfit.core.data.
feed_function
(data, feed_val)¶
-
zfit.core.data.
feed_function_for_partial_run
(data)¶
-
zfit.core.data.
fetch_function
(data)¶