dantro.data_ops package
Contents
dantro.data_ops package#
Implements the data operations database of dantro, which is used in
the data transformation framework to apply
transformations on data using TransformationDAG
.
isort:skip_file
Submodules#
dantro.data_ops._base_ops module#
Implements operations that need to be importable from other modules
- BOOLEAN_OPERATORS = {'!=': <built-in function ne>, '<': <built-in function lt>, '<=': <built-in function le>, '==': <built-in function eq>, '>': <built-in function gt>, '>=': <built-in function ge>, '^': <built-in function xor>, 'contains': <built-in function contains>, 'eq': <built-in function eq>, 'ge': <built-in function ge>, 'gt': <built-in function gt>, 'in': <function <lambda>>, 'in interval': <function <lambda>>, 'le': <built-in function le>, 'lt': <built-in function lt>, 'ne': <built-in function ne>, 'not in': <function <lambda>>, 'not in interval': <function <lambda>>, 'xor': <built-in function xor>}#
Boolean binary operators
dantro.data_ops.apply module#
Implements the application of operations on the given arguments and data
- apply_operation(op_name: str, *op_args, _ops: Optional[dict] = None, _log_level: int = 5, **op_kwargs) Any [source]#
Apply an operation with the given arguments and then return its return value. This is used by the Data Transformation Framework and allows to invoke operations from the data operations database, see Data Processing.
- Parameters
op_name (str) – The name of the operation to carry out; need to be part of the operations database
_ops
.*op_args – The positional arguments to the operation
_ops (dict, optional) – The operations database object to use; if None, uses the dantro operations database.
_log_level (int, optional) – Log level of the log messages created by this function.
**op_kwargs – The keyword arguments to the operation
- Returns
The result of the operation
- Return type
Any
- Raises
BadOperationName – On invalid operation name
DataOperationError – On failure to apply the operation
dantro.data_ops.arr_ops module#
Implements data operations that work on array-like data, e.g. from numpy or xarray.
- apply_along_axis(func: Callable, axis: int, arr: ndarray, *args, **kwargs) ndarray [source]#
This is like numpy’s function of the same name, but does not try to cast the results of func to an
numpy.ndarray
but tries to keep them as dtype object. Thus, the return value of this function will always have one fewer dimension then the input array.This goes along the equivalent formulation of
numpy.apply_along_axis()
, outlined in their documentation of the function.- Parameters
- Returns
- with
func
applied alongaxis
, reducing the array dimensions by one.
- with
- Return type
- create_mask(data: DataArray, operator_name: str, rhs_value: float) DataArray [source]#
Given the data, returns a binary mask by applying the following comparison:
data <operator> rhs value
.- Parameters
- Raises
KeyError – On invalid operator name
- Returns
Boolean mask
- Return type
- where(data: DataArray, operator_name: str, rhs_value: float, **kwargs) DataArray [source]#
Filter elements from the given data according to a condition. Only those elemens where the condition is fulfilled are not masked.
Note
This typically leads to a dtype change to
numpy.float64
.- Parameters
data (DataArray) – The data to mask
operator_name (str) – The
operator
argument used inarr_ops.create_mask()
rhs_value (float) – The
rhs_value
argument used inarr_ops.create_mask()
**kwargs – Passed on to
.where()
method call
- count_unique(data, dims: List[str] = None) DataArray [source]#
Applies
numpy.unique()
to the given data and constructs axarray.DataArray
for the results.NaN values are filtered out.
- Parameters
data – The data
dims (List[str], optional) – The dimensions along which to apply np.unique. The other dimensions will be available after the operation. If not provided it is applied along all dims.
- populate_ndarray(objs: typing.Iterable, shape: typing.Optional[typing.Tuple[int]] = None, dtype: typing.Union[str, type, numpy.dtype] = <class 'float'>, order: str = 'C', out: typing.Optional[numpy.ndarray] = None, ufunc: typing.Optional[typing.Callable] = None) ndarray [source]#
Populates an empty
numpy.ndarray
of the givendtype
with the given objects by zipping over a new array of the givenshape
and the sequence of objects.- Parameters
objs (Iterable) – The objects to add to the
numpy.ndarray
. These objects are added in the order they are given here. Note that their final position inside the resulting array is furthermore determined by theorder
argument.shape (Tuple[int], optional) – The shape of the new array. Required if no
out
array is given.dtype (Union[str, type, dtype], optional) – dtype of the new array. Ignored if
out
is given.order (str, optional) – Order of the new array, determines iteration order. Ignored if
out
is given.out (ndarray, optional) – If given, populates this array rather than an empty array.
ufunc (Callable, optional) – If given, applies this unary function to each element before storing it in the to-be-returned ndarray.
- Returns
- The populated
out
array or the newly created one (if out
was not given)
- The populated
- Return type
- Raises
TypeError – On missing
shape
argument ifout
is not givenValueError – If the number of given objects did not match the array size
- build_object_array(objs: Union[Dict, Sequence], *, dims: Tuple[str] = ('label',), fillna: Any = None) DataArray [source]#
Creates a simple labelled multidimensional object array.
It accepts simple iterable types like dictionaries or lists and unpacks them into the array, using the key or index (respectively) as coordinate for the entry. For dict-like entries, multi-dimensional coordinates can be specified by using tuples for keys. Subsequently, list-like iterable types (list, tuple etc.) will result in one-dimensional output array.
Warning
This data operation is built for flexibility, not for speed. It will call the
merge()
operation for every element in theobjs
iterable, thus being slow and potentially creating an array with many empty elements. To efficiently populate an n-dimensional object array, use thepopulate_ndarray()
operation instead and build a labelled array from that output.- Parameters
objs (Union[Dict, Sequence]) – The objects to populate the object array with. If dict-like, keys are assumed to encode coordinates, which can be of the form
coord0
or(coord0, coord1, …)
, where the tuple-form requires as many coordinates as there are entries in thedims
argument. If list- or tuple-like (more exactly: if missing theitems
attribute) trivial indexing is used anddims
needs to be 1D.dims (Tuple[str], optional) – The names of the dimensions of the labelled array.
fillna (Any, optional) – The fill value for entries that are not covered by the dimensions specified by
objs
. Note that this will replace all null values, which includes NaN but alsoNone
. This operation is only called iffillna is not None
.
- Raises
ValueError – If coordinates and/or
dims
argument for individual entries did not match.
- multi_concat(arrs: ndarray, *, dims: Sequence[str]) DataArray [source]#
Concatenates
xarray.Dataset
orxarray.DataArray
objects usingxarray.concat()
. This function expects the xarray objects to be pre-aligned inside the numpy object arrayarrs
, with the number of dimensions matching the number of concatenation operations desired. The position inside the array carries information on where the objects that are to be concatenated are placed inside the higher dimensional coordinate system.Through multiple concatenation, the dimensionality of the contained objects is increased by
dims
, while their dtype can be maintained.For the sequential application of
xarray.concat()
along the outer dimensions, the customapply_along_axis()
function is used.- Parameters
arrs (ndarray) – The array containing xarray objects which are to be concatenated. Each array dimension should correspond to one of the given
dims
. For each of the dimensions, thexarray.concat()
operation is applied along the axis, effectively reducing the dimensionality ofarrs
to a scalar and increasing the dimensionality of the contained xarray objects until they additionally contain the dimensions specified in thedims
argument.dims (Sequence[str]) – A sequence of dimension names that is assumed to match the dimension names of the array. During each concatenation operation, the name is passed along to
xarray.concat()
where it is used to select the dimension of the content ofarrs
along which concatenation should occur.
- Raises
ValueError – If number of dimension names does not match the number of data dimensions.
- merge(arrs: Union[Sequence[Union[DataArray, Dataset]], ndarray], *, reduce_to_array: bool = False, **merge_kwargs) Union[Dataset, DataArray] [source]#
Merges the given sequence of xarray objects into an
xarray.Dataset
.As a convenience, this also allows passing a
numpy.ndarray
of dtypeobject
containing the xarray objects. Furthermore, if the resultingxarray.Dataset
contains only a single data variable, that variable can be extracted as axarray.DataArray
by setting thereduce_to_array
flag, making that array the return value of this operation.
- expand_dims(d: Union[ndarray, DataArray], *, dim: dict = None, **kwargs) DataArray [source]#
Expands the dimensions of the given object.
If the object does not support a
expand_dims
method call, it will be attempted to convert it to anxarray.DataArray
first.- Parameters
d (Union[ndarray, DataArray]) – The object to expand the dimensions of
dim (dict, optional) – Keys specify the dimensions to expand, values can either be an integer specifying the length of the dimension, or a sequence of coordinates.
**kwargs – Passed on to the
expand_dims
method call. For an xarray objects that would bexarray.DataArray.expand_dims()
.
- Returns
The input data with expanded dimensions.
- Return type
- expand_object_array(d: DataArray, *, shape: Sequence[int] = None, astype: Union[str, type, dtype] = None, dims: Sequence[str] = None, coords: Union[dict, str] = 'trivial', combination_method: str = 'concat', allow_reshaping_failure: bool = False, **combination_kwargs) DataArray [source]#
Expands a labelled object-array that contains array-like objects into a higher-dimensional labelled array.
d
is expected to be an array of arrays, i.e. each element of the outer array is an object that itself is annumpy.ndarray
-like object. Theshape
is the expected shape of each of these inner arrays. Importantly, all these arrays need to have the exact same shape!Typically, e.g. when loading data from HDF5 files, the inner array will not be labelled but will consist of simple
numpy.ndarray
objects. The argumentsdims
andcoords
are used to label the inner arrays.This uses
multi_concat()
for concatenating ormerge()
for merging the object arrays into a higher-dimensional array, where the latter option allows for missing values.Todo
Make reshaping and labelling optional if the inner array already is a labelled array. In such cases, the coordinate assignment is already done and all information for combination is already available.
- Parameters
d (DataArray) – The labelled object-array containing further arrays as elements (which are assumed to be unlabelled).
shape (Sequence[int], optional) – Shape of the inner arrays. If not given, the first element is used to determine the shape.
astype (Union[str, type, dtype], optional) – All inner arrays need to have the same dtype. If this argument is given, the arrays will be coerced to this dtype. For numeric data,
float
is typically a good fallback. Note that withcombination_method == "merge"
, the choice here might not be respected.dims (Sequence[str], optional) – Dimension names for labelling the inner arrays. This is necessary for proper alignment. The number of dimensions need to match the
shape
. If not given, will useinner_dim_0
and so on.coords (Union[dict, str], optional) – Coordinates of the inner arrays. These are necessary to align the inner arrays with each other. With
coords = "trivial"
, trivial coordinates will be assigned to all dimensions. If specifying a dict and giving"trivial"
as value, that dimension will be assigned trivial coordinates.combination_method (str, optional) – The combination method to use to combine the object array. For
concat
, will use dantro’smulti_concat()
, which preserves dtype but does not allow missing values. Formerge
, will usemerge()
, which allows missing values (masked usingnp.nan
) but leads to the dtype decaying to float.allow_reshaping_failure (bool, optional) – If true, the expansion is not stopped if reshaping to
shape
fails for an element. This will lead to missing values at the respective coordinates and thecombination_method
will automatically be changed tomerge
.**combination_kwargs – Passed on to the selected combination function,
multi_concat()
ormerge()
.
- Returns
A new, higher-dimensional labelled array.
- Return type
- Raises
TypeError – If no
shape
can be extracted from the first element in the input datad
ValueError – On bad argument values for
dims
,shape
,coords
orcombination_method
.
- transform_coords(d: DataArray, dim: Union[str, Sequence[str]], func: Callable, *, func_kwargs: dict = None) DataArray [source]#
Assigns new, transformed coordinates to a data array by applying a function on the existing coordinates.
Uses
xarray.DataArray.assign_coords()
to set the new coordinates, which returns a shallow copy of the given object.- Parameters
d (DataArray) – The array to transform the
dim
coordinates ofdim (Union[str, Sequence[str]]) – The name or names of the coordinate dimension(s) to apply
func
to.func (Callable) – The callable to apply to
d.coords[dim]
func_kwargs (dict, optional) – Passed to the function invocation like
func(d.coords[dim], **func_kwargs)
dantro.data_ops.ctrl_ops module#
Implements operations that control the behaviour of the transformation or pipeline in general, including functions that can be used for debugging.
- raise_SkipPlot(cond: bool = True, *, reason: str = '', passthrough: Optional[Any] = None)[source]#
Raises
SkipPlot
to trigger that a plot is skipped without error, see Skipping Plots.If
cond
is False, this will do nothing but return the passthrough.
- print_data(data: Any, *, end: str = '\n', fstr: Optional[str] = None, **fstr_kwargs) Any [source]#
Prints and passes on the data using
print()
.The print operation distinguishes between dantro types (in which case some more information is shown) and non-dantro types. If a custom format string is given, will always use that one.
Note
This is a passthrough-function:
data
is always returned without any changes. However, the print operation may lead to resolution ofproxy
objects.- Parameters
data (Any) – The data to print
end (str, optional) – The
end
argument to theprint
callfstr (str, optional) – If given, will use this to format the data for printing. The data will be the passed as first positional argument to the format string, thus addressable by
{0:}
ordata
(e.g. to access attributes via format-string syntax). If the format string is notNone
, will always use the format string and not use the custom formatting for dantro objects.**fstr_kwargs – Keyword arguments passed to the
format()
call.
- Returns
the given
data
- Return type
Any
dantro.data_ops.db module#
This module holds the data operations database
dantro.data_ops.db_tools module#
Tools that help to monitor and manipulate the operations database
- register_operation(func: Callable, name: Optional[str] = None, *, skip_existing: bool = False, overwrite_existing: bool = False, _ops: Optional[dict] = None) None [source]#
Adds an entry to the shared operations registry.
- Parameters
func (Callable) – The callable that is to be registered as operation.
name (str, optional) – The name of the operation. If not given (and the callable not being a lambda), will use the function name instead.
skip_existing (bool, optional) – Whether to skip registration if the operation name is already registered. This suppresses the ValueError raised on existing operation name.
overwrite_existing (bool, optional) – Whether to overwrite a potentially already existing operation of the same name. If given, this takes precedence over
skip_existing
._ops (dict, optional) – The operations database object to use; if None, uses the dantro operations database.
- Raises
TypeError – On invalid name or non-callable for the func argument
ValueError – On already existing operation name and no skipping or overwriting enabled. Also if no
name
was given but the given callable is a lambda (which only has<lambda>
as name).
- is_operation(arg: Optional[Union[str, Callable]] = None, /, *, _ops: Optional[dict] = None, _reg_func: Optional[Callable] = None, **kws)[source]#
Decorator for registering functions with the operations database.
As an alternative to
register_operation()
, this decorator can be used to register a function with the operations database right where its defined:from dantro.data_ops import is_operation # Operation name deduced from function name @is_operation def my_operation(data, *args): # ... do stuff here ... return data # Custom operation name @is_operation("do_something") def my_operation_with_a_custom_name(foo, bar): pass # Overwriting an operation of the same name @is_operation("do_something", overwrite_existing=True) def actually_do_something(spam, fish): pass
See Registering operations for general information. For instructions on how to overwrite this decorator with a custom one, see Customizing database tools.
- Parameters
arg (Union[str, Callable], optional) – The name that should be used in the operation registry. If not given, will use the name of the decorated function instead. If a callable, this refers to the
@is_operation
call syntax and will use that as a function._ops (dict, optional) – The operations database to use. If not given, uses the dantro operations database.
_reg_func (Callable, optional) – If given, uses that callable for registration, which should have the same signature as
register_operation()
. If None, uses dantro’s registration function,register_operation()
.**kws – Passed to
register_operation()
or a potentially given custom_reg_func
.
- available_operations(*, match: Optional[str] = None, n: int = 5, _ops: Optional[dict] = None) Sequence[str] [source]#
Returns all available operation names or a fuzzy-matched subset of them.
Also see Available operations for an overview.
- Parameters
match (str, optional) – If given, fuzzy-matches the names and only returns close matches to this name.
n (int, optional) – Number of close matches to return. Passed on to
difflib.get_close_matches()
_ops (dict, optional) – The operations database object to use; if None, uses the dantro operations database.
- Returns
- All available operation names or the matched subset.
The sequence is sorted alphabetically.
- Return type
Sequence[str]
- get_operation(op_name: str, *, _ops: Optional[Dict[str, Callable]] = None) Callable [source]#
Retrieve the operation’s callable
- Parameters
op_name (str) – Name of the operation
_ops (Dict[str, Callable], optional) – The operations database object to use; if None, uses the dantro operations database.
- Raises
BadOperationName – Upon invalid operation name
dantro.data_ops.expr_ops module#
Implements data operations that work with expressions, e.g. lambda function definitions or symbolic math
- expression(expr: str, *, symbols: typing.Optional[dict] = None, evaluate: bool = True, transformations: typing.Optional[typing.Tuple[typing.Callable]] = None, astype: typing.Union[type, str] = <class 'float'>)[source]#
Parses and evaluates a symbolic math expression using SymPy.
For parsing, uses sympy’s
sympy.parsing.sympy_parser.parse_expr()
. Thesymbols
are provided aslocal_dict
; theglobal_dict
is not explicitly set and subsequently uses the sympy default value, containing all basic sympy symbols and notations.Note
The expression given here is not Python code, but symbolic math. You cannot call arbitrary functions, but only those that are imported by
from sympy import *
.Hint
When using this expression as part of the Data Transformation Framework, it is attached to a so-called syntax hook that makes it easier to specify the
symbols
parameter. See here for more information.Warning
While the expression is symbolic math, be aware that smypy by default interprets the
^
operator as XOR, not an exponentiation! For exponentiation, use the**
operator or adjust thetransformations
argument as specified in the sympy documentation.Warning
The return object of this operation will only contain symbolic sympy objects if
astype is None
. Otherwise, the type cast will evaluate all symbolic objects to the numerical equivalent specified by the givenastype
.- Parameters
expr (str) – The expression to evaluate
symbols (dict, optional) – The symbols to use
evaluate (bool, optional) – Controls whether sympy evaluates
expr
. This may lead to a fully evaluated result, but does not guarantee that no sympy objects are contained in the result. For ensuring a fully numerical result, see theastype
argument.transformations (Tuple[Callable], optional) – The
transformations
argument for sympy’ssympy.parsing.sympy_parser.parse_expr()
. By default, the sympy standard transformations are performed.astype (Union[type, str], optional) – If given, performs a cast to this data type, fully evaluating all symbolic expressions. Default: Python
float
.
- Raises
TypeError – Upon failing
astype
cast, e.g. due to free symbols remaining in the evaluated expression.ValueError – When parsing of
expr
failed.
- Returns
The result of the evaluated expression.
- generate_lambda(expr: str) Callable [source]#
Generates a lambda from a string. This is useful when working with callables in other operations.
The
expr
argument needs to be a valid Python lambda expression.Inside the lambda body, the following names are available for use:
A large part of the
builtins
moduleEvery name from the Python
math
module, e.g.sin
,cos
, …These modules (and their long form):
np
,xr
,scipy
Internally, this uses
eval
but imposes the following restrictions:The following strings may not appear in
expr
:;
,__
.There can be no nested
lambda
, i.e. the only allowed lambda string is that in the beginning ofexpr
.The dangerous parts from the
builtins
module are not available.
- Parameters
expr (str) – The expression string to evaluate into a lambda.
- Returns
The generated Callable.
- Return type
Callable
- Raises
SyntaxError – Upon failed evaluation of the given expression, invalid expression pattern, or disallowed strings in the lambda body.
dantro.data_ops.hooks module#
Implements operation hooks for the DAG parser implemented in
_dag_utils
.
- DAG_PARSER_OPERATION_HOOKS = {'expression': <function op_hook_expression>}#
Contains hooks that are invoked when a certain operation is parsed. The values should be callables that receive
operation, *args, **kwargs
and return a 3-tuple of the manipulatedoperation, args, kwargs
. The return values will be those that the Transformation object is created from.See the DAG Syntax Operation Hooks page for more information on integration and available hooks.
Example of defining a hook and registering it:
# Define hook function def _op_hook_my_operation( operation, *args, **kwargs ) -> Tuple[str, list, dict]: # ... do stuff here ... return operation, args, kwargs # Register with hooks registry from dantro.data_ops import DAG_PARSER_OPERATION_HOOKS DAG_PARSER_OPERATION_HOOKS["my_operation"] = _op_hook_my_operation
Todo
Implement a decorator to automatically register operation hooks
- op_hook_expression(operation, *args, **kwargs) Tuple[str, list, dict] [source]#
An operation hook for the
expression
operation, attempting to auto-detect which symbols are specified in the given expression. From those,DAGTag
objects are created, making it more convenient to specify an expression that is based on other DAG tags.The detected symbols are added to the
kwargs.symbols
, if no symbol of the same name is already explicitly defined there.This hook accepts as positional arguments both the
(expr,)
form and the(prev_node, expr)
form, making it more robust when thewith_previous_result
flag was set.If the expression contains the
prev
orprevious_result
symbols, the correspondingDAGNode
will be added to the symbols additionally.For more information on operation hooks, see DAG Syntax Operation Hooks.