5. API Reference

graphtik	Lightweight computation graphs for Python.
graphtik.op	About operation nodes (but not net-ops to break cycle).
graphtik.netop	About network operations (those based on graphs)
graphtik.network	Compile & execute network graphs of operations.
graphtik.plot
graphtik.config	Configurations for network execution, and utilities on them.
graphtik.base	Generic or specific utilities

Module: op

About operation nodes (but not net-ops to break cycle).

class graphtik.op.Operation

An abstract class representing an action with compute().

compute(named_inputs, outputs=None)

Compute (optional) asked outputs for the given named_inputs.

It is called by Network. End-users should simply call the operation with named_inputs as kwargs.

Parameters:named_inputs – the input values with which to feed the computation. Returns list:Should return a list values representing the results of running the feed-forward computation on inputs.

graphtik.op.reparse_operation_data(name, needs, provides)

Validate & reparse operation data as lists.

As a separate function to be reused by client code when building operations and detect errors aearly.

Module: netop

About network operations (those based on graphs)

class graphtik.netop.NetworkOperation(operations, name, *, outputs=None, predicate: Callable[[Any, Mapping[KT, VT_co]], bool] = None, rescheduled=None, endured=None, parallel=None, marshalled=None, merge=None, node_props=None)

An operation that can compute a network-graph of operations.

Tip

Use compose() factory to prepare the net and build instances of this class.

compile(inputs=None, outputs=<UNSET>, predicate: Callable[[Any, Mapping[KT, VT_co]], bool] = <UNSET>) → graphtik.network.ExecutionPlan

Produce a plan for the given args or outputs/predicate narrrowed earlier.

Parameters:

• named_inputs – a string or a list of strings that should be fed to the needs of all operations.
• outputs – A string or a list of strings with all data asked to compute. If None, all possible intermediate outputs will be kept. If not given, those set by a previous call to withset() or cstor are used.
• predicate – Will be stored and applied on the next compute() or compile(). If not given, those set by a previous call to withset() or cstor are used.

Returns:

the execution plan satisfying the given inputs, outputs & predicate

Raises:

ValueError –

• If outputs asked do not exist in network, with msg:

  Unknown output nodes: …

• If solution does not contain any operations, with msg:

  Unsolvable graph: …

• If given inputs mismatched plan’s needs, with msg:

  Plan needs more inputs…

• If outputs asked cannot be produced by the dag, with msg:

  Impossible outputs…

compute(named_inputs: Mapping[KT, VT_co], outputs: Union[Collection[T_co], str, None] = <UNSET>, predicate: Callable[[Any, Mapping[KT, VT_co]], bool] = <UNSET>) → graphtik.network.Solution

Compile a plan & execute the graph, sequentially or parallel.

Attention

If intermediate compilation is sucessfull, the “global abort run flag is reset before the execution starts.

Parameters:

• named_inputs – A maping of names –> values that will be fed to the needs of all operations. Cloned, not modified.
• outputs – A string or a list of strings with all data asked to compute. If None, all intermediate data will be kept.

Returns:

The solution which contains the results of each operation executed +1 for inputs in separate dictionaries.

Raises:

ValueError –

• If outputs asked do not exist in network, with msg:

  Unknown output nodes: …

• If plan does not contain any operations, with msg:

  Unsolvable graph: …

• If given inputs mismatched plan’s needs, with msg:

  Plan needs more inputs…

• If outputs asked cannot be produced by the dag, with msg:

  Impossible outputs…

See also Operation.compute().

last_plan = None

The execution_plan of the last call to compute(), stored as debugging aid.

name = None

The name for the new netop, used when nesting them.

outputs = None

The outputs names (possibly None) used to compile the plan.

predicate = None

The node predicate is a 2-argument callable(op, node-data) that should return true for nodes to include; if None, all nodes included.

withset(outputs: Union[Collection[T_co], str, None] = <UNSET>, predicate: Callable[[Any, Mapping[KT, VT_co]], bool] = <UNSET>, *, name=None, rescheduled=None, endured=None, parallel=None, marshalled=None) → graphtik.netop.NetworkOperation

Return a copy with a network pruned for the given needs & provides.

Parameters:

• outputs – Will be stored and applied on the next compute() or compile(). If not given, the value of this instance is conveyed to the clone.
• predicate – Will be stored and applied on the next compute() or compile(). If not given, the value of this instance is conveyed to the clone.
• name –

  the name for the new netop:

  • if None, the same name is kept;
  • if True, a distinct name is devised:

    <old-name>-<uid>
    

  • otherwise, the given name is applied.
• rescheduled – applies rescheduled to all contained operations
• endured – applies endurance to all contained operations
• parallel – mark all contained operations to be executed in parallel
• marshalled – mark all contained operations to be marshalled (usefull when run in parallel with a process pool).

Returns:

A narrowed netop clone, which MIGHT be empty!*

Raises:

ValueError –

• If outputs asked do not exist in network, with msg:

  Unknown output nodes: …

graphtik.netop.compose(name, op1, *operations, outputs: Union[Collection[T_co], str, None] = None, rescheduled=None, endured=None, parallel=None, marshalled=None, merge=False, node_props=None) → graphtik.netop.NetworkOperation

Composes a collection of operations into a single computation graph, obeying the merge property, if set in the constructor.

Parameters:

• name (str) – A optional name for the graph being composed by this object.
• op1 – syntactically force at least 1 operation
• operations – Each argument should be an operation instance created using operation.
• merge (bool) – If True, this compose object will attempt to merge together operation instances that represent entire computation graphs. Specifically, if one of the operation instances passed to this compose object is itself a graph operation created by an earlier use of compose the sub-operations in that graph are compared against other operations passed to this compose instance (as well as the sub-operations of other graphs passed to this compose instance). If any two operations are the same (based on name), then that operation is computed only once, instead of multiple times (one for each time the operation appears).
• rescheduled – applies rescheduled to all contained operations
• endured – applies endurance to all contained operations
• parallel – mark all contained operations to be executed in parallel
• marshalled – mark all contained operations to be marshalled (usefull when run in parallel with a process pool).
• node_props – added as-is into NetworkX graph, to provide for filtering by NetworkOperation.withset().

Returns:

Returns a special type of operation class, which represents an entire computation graph as a single operation.

Raises:

ValueError – If the net` cannot produce the asked outputs from the given inputs.

Module: network

Compile & execute network graphs of operations.

exception graphtik.network.AbortedException

Raised from Network when abort_run() is called, and contains the solution …

with any values populated so far.

__module__ = 'graphtik.network'

__weakref__

list of weak references to the object (if defined)

class graphtik.network.ExecutionPlan

A pre-compiled list of operation steps that can execute for the given inputs/outputs.

It is the result of the network’s compilation phase.

Note the execution plan’s attributes are on purpose immutable tuples.

net

The parent Network

needs

An iset with the input names needed to exist in order to produce all provides.

provides

An iset with the outputs names produces when all inputs are given.

dag

The regular (not broken) pruned subgraph of net-graph.

steps

The tuple of operation-nodes & instructions needed to evaluate the given inputs & asked outputs, free memory and avoid overwritting any given intermediate inputs.

evict

when false, keep all inputs & outputs, and skip prefect-evictions check.

__abstractmethods__ = frozenset()

__dict__ = mappingproxy({'__module__': 'graphtik.network', '__doc__': "\n A pre-compiled list of operation steps that can :term:`execute` for the given inputs/outputs.\n\n It is the result of the network's :term:`compilation` phase.\n\n Note the execution plan's attributes are on purpose immutable tuples.\n\n .. attribute:: net\n\n The parent :class:`Network`\n .. attribute:: needs\n\n An :class:`iset` with the input names needed to exist in order to produce all `provides`.\n .. attribute:: provides\n\n An :class:`iset` with the outputs names produces when all `inputs` are given.\n .. attribute:: dag\n\n The regular (not broken) *pruned* subgraph of net-graph.\n .. attribute:: steps\n\n The tuple of operation-nodes & *instructions* needed to evaluate\n the given inputs & asked outputs, free memory and avoid overwritting\n any given intermediate inputs.\n .. attribute:: evict\n\n when false, keep all inputs & outputs, and skip prefect-evictions check.\n ", '_build_pydot': <function ExecutionPlan._build_pydot>, '__repr__': <function ExecutionPlan.__repr__>, 'validate': <function ExecutionPlan.validate>, '_check_if_aborted': <function ExecutionPlan._check_if_aborted>, '_prepare_tasks': <function ExecutionPlan._prepare_tasks>, '_handle_task': <function ExecutionPlan._handle_task>, '_execute_thread_pool_barrier_method': <function ExecutionPlan._execute_thread_pool_barrier_method>, '_execute_sequential_method': <function ExecutionPlan._execute_sequential_method>, 'execute': <function ExecutionPlan.execute>, '__dict__': <attribute '__dict__' of 'ExecutionPlan' objects>, '__abstractmethods__': frozenset(), '_abc_impl': <_abc_data object>})

__module__ = 'graphtik.network'

__repr__()

Return a nicely formatted representation string

_abc_impl = <_abc_data object>

_build_pydot(**kws)

_check_if_aborted(solution)

_execute_sequential_method(solution: graphtik.network.Solution)

This method runs the graph one operation at a time in a single thread

Parameters:solution – must contain the input values only, gets modified

_execute_thread_pool_barrier_method(solution: graphtik.network.Solution)

This method runs the graph using a parallel pool of thread executors. You may achieve lower total latency if your graph is sufficiently sub divided into operations using this method.

Parameters:solution – must contain the input values only, gets modified

_handle_task(future, op, solution) → None

Un-dill parallel task results (if marshalled), and update solution / handle failure.

_prepare_tasks(operations, solution, pool, global_parallel, global_marshal) → Union[Future, graphtik.network._OpTask, bytes]

Combine ops+inputs, apply marshalling, and submit to execution pool (or not) …

based on global/pre-op configs.

execute(named_inputs, outputs=None, *, name='') → graphtik.network.Solution

Parameters:

• named_inputs – A maping of names –> values that must contain at least the compulsory inputs that were specified when the plan was built (but cannot enforce that!). Cloned, not modified.
• outputs – If not None, they are just checked if possible, based on provides, and scream if not.

Returns:

The solution which contains the results of each operation executed +1 for inputs in separate dictionaries.

Raises:

ValueError –

• If plan does not contain any operations, with msg:

  Unsolvable graph: …

• If given inputs mismatched plan’s needs, with msg:

  Plan needs more inputs…

• If outputs asked cannot be produced by the dag, with msg:

  Impossible outputs…

validate(inputs: Union[Collection[T_co], str, None], outputs: Union[Collection[T_co], str, None])

Scream on invalid inputs, outputs or no operations in graph.

Raises:ValueError –

• If cannot produce any outputs from the given inputs, with msg:

  Unsolvable graph: …

• If given inputs mismatched plan’s needs, with msg:

  Plan needs more inputs…

• If outputs asked cannot be produced by the dag, with msg:

  Impossible outputs…

exception graphtik.network.IncompleteExecutionError

Raised by scream_if_incomplete() when netop operations were canceled/failed.

The exception contains 3 arguments:

1. the causal errors and conditions (1st arg),
2. the list of collected exceptiond (2nd arg), and
3. the solution instance (3rd argument), to interrogate for more.

__module__ = 'graphtik.network'

__str__()

Return str(self).

__weakref__

list of weak references to the object (if defined)

class graphtik.network.Network(*operations, graph=None)

A graph of operations that can compile an execution plan.

needs

the “base”, all data-nodes that are not produced by some operation

provides

the “base”, all data-nodes produced by some operation

__abstractmethods__ = frozenset()

__init__(*operations, graph=None)

Parameters:

• operations – to be added in the graph
• graph – if None, create a new.

Raises:

ValueError –

if dupe operation, with msg:

Operations may only be added once, …

__module__ = 'graphtik.network'

__repr__()

Return repr(self).

_abc_impl = <_abc_data object>

_append_operation(graph, operation: graphtik.op.Operation)

Adds the given operation and its data requirements to the network graph.

• Invoked during constructor only (immutability).
• Identities are based on the name of the operation, the names of the operation’s needs, and the names of the data it provides.

Parameters:

• graph – the networkx graph to append to
• operation – operation instance to append

_apply_graph_predicate(graph, predicate)

_build_execution_steps(pruned_dag, inputs: Collection[T_co], outputs: Optional[Collection[T_co]]) → List[T]

Create the list of operation-nodes & instructions evaluating all

operations & instructions needed a) to free memory and b) avoid overwritting given intermediate inputs.

Parameters:

• pruned_dag – The original dag, pruned; not broken.
• outputs – outp-names to decide whether to add (and which) evict-instructions

Instances of _EvictInstructions are inserted in steps between operation nodes to reduce the memory footprint of solutions while the computation is running. An evict-instruction is inserted whenever a need is not used by any other operation further down the DAG.

_build_pydot(**kws)

_cached_plans = None

Speed up compile() call and avoid a multithreading issue(?) that is occuring when accessing the dag in networkx.

_prune_graph(inputs: Union[Collection[T_co], str, None], outputs: Union[Collection[T_co], str, None], predicate: Callable[[Any, Mapping[KT, VT_co]], bool] = None) → Tuple[<sphinx.ext.autodoc.importer._MockObject object at 0x7f8607077a90>, Collection[T_co], Collection[T_co], Collection[T_co]]

Determines what graph steps need to run to get to the requested outputs from the provided inputs: - Eliminate steps that are not on a path arriving to requested outputs; - Eliminate unsatisfied operations: partial inputs or no outputs needed; - consolidate the list of needs & provides.

Parameters:

• inputs – The names of all given inputs.
• outputs – The desired output names. This can also be None, in which case the necessary steps are all graph nodes that are reachable from the provided inputs.
• predicate – the node predicate is a 2-argument callable(op, node-data) that should return true for nodes to include; if None, all nodes included.

Returns:

a 3-tuple with the pruned_dag & the needs/provides resolved based on the given inputs/outputs (which might be a subset of all needs/outputs of the returned graph).

Use the returned needs/provides to build a new plan.

Raises:

ValueError –

• if outputs asked do not exist in network, with msg:

  Unknown output nodes: …

_topo_sort_nodes(dag) → List[T]

Topo-sort dag respecting operation-insertion order to break ties.

compile(inputs: Union[Collection[T_co], str, None] = None, outputs: Union[Collection[T_co], str, None] = None, predicate=None) → graphtik.network.ExecutionPlan

Create or get from cache an execution-plan for the given inputs/outputs.

See _prune_graph() and _build_execution_steps() for detailed description.

Parameters:

• inputs – A collection with the names of all the given inputs. If None`, all inputs that lead to given outputs are assumed. If string, it is converted to a single-element collection.
• outputs – A collection or the name of the output name(s). If None`, all reachable nodes from the given inputs are assumed. If string, it is converted to a single-element collection.
• predicate – the node predicate is a 2-argument callable(op, node-data) that should return true for nodes to include; if None, all nodes included.

Returns:

the cached or fresh new execution plan

Raises:

ValueError –

• If outputs asked do not exist in network, with msg:

  Unknown output nodes: …

• If solution does not contain any operations, with msg:

  Unsolvable graph: …

• If given inputs mismatched plan’s needs, with msg:

  Plan needs more inputs…

• If outputs asked cannot be produced by the dag, with msg:

  Impossible outputs…

class graphtik.network.Solution(plan, input_values)

Collects outputs from operations, preserving overwrites.

plan

the plan that produced this solution

executed

A dictionary with keys the operations executed, and values their status:

• no key: not executed yet
• value None: execution ok
• value Exception: execution failed

canceled

A sorted set of canceled operations due to upstream failures.

finalized

a flag denoting that this instance cannot acccept more results (after the finalized() has been invoked)

__abstractmethods__ = frozenset()

__delitem__(key)

__init__(plan, input_values)

Initialize a ChainMap by setting maps to the given mappings. If no mappings are provided, a single empty dictionary is used.

__module__ = 'graphtik.network'

__repr__()

Return repr(self).

_abc_impl = <_abc_data object>

_build_pydot(**kws)

delegate to network

finalize()

invoked only once, after all ops have been executed

is_failed(op)

operation_executed(op, outputs)

Invoked once per operation, with its results.

It will update executed with the operation status and if outputs were partials, it will update canceled with the unsatisfiead ops downstream of op.

Parameters:

• op – the operation that completed ok
• outputs – The names of the outputs values the op` actually produced, which may be a subset of its provides. Sideffects are not considered.

operation_failed(op, ex)

Invoked once per operation, with its results.

It will update executed with the operation status and the canceled with the unsatisfiead ops downstream of op.

overwrites

The data in the solution that exist more than once.

A “virtual” property to a dictionary with keys the names of values that exist more than once, and values, all those values in a list, ordered:

• before finsihed(), as computed;
• after finsihed(), in reverse.

scream_if_incomplete()

Raise a IncompleteExecutionError when netop operations failed/canceled.

class graphtik.network._DataNode

Dag node naming a data-value produced or required by an operation.

__module__ = 'graphtik.network'

__repr__()

Return repr(self).

__slots__ = ()

class graphtik.network._EvictInstruction

A step in the ExecutionPlan to evict a computed value from the solution.

It’s a step in ExecutionPlan.steps for the data-node str that frees its data-value from solution after it is no longer needed, to reduce memory footprint while computing the graph.

__module__ = 'graphtik.network'

__repr__()

Return repr(self).

__slots__ = ()

class graphtik.network._OpTask(op, sol, solid)

Mimic concurrent.futures.Future for sequential execution.

This intermediate class is needed to solve pickiling issue with process executor.

__call__()

Call self as a function.

__init__(op, sol, solid)

Initialize self. See help(type(self)) for accurate signature.

__module__ = 'graphtik.network'

__repr__()

Return repr(self).

__slots__ = ('op', 'sol', 'result', 'solid')

get()

Call self as a function.

logname = 'graphtik.network'

marshalled()

op

result

sol

solid

graphtik.network._do_task(task)

Un-dill the simpler _OpTask & Dill the results, to pass through pool-processes.

See https://stackoverflow.com/a/24673524/548792

graphtik.network._optionalized(graph, data)

Retain optionality of a data node based on all needs edges.

graphtik.network._unsatisfied_operations(dag, inputs: Collection[T_co]) → List[T]

Traverse topologically sorted dag to collect un-satisfied operations.

Unsatisfied operations are those suffering from ANY of the following:

• They are missing at least one compulsory need-input.

  Since the dag is ordered, as soon as we’re on an operation, all its needs have been accounted, so we can get its satisfaction.

• Their provided outputs are not linked to any data in the dag.

  An operation might not have any output link when _prune_graph() has broken them, due to given intermediate inputs.

Parameters:

• dag – a graph with broken edges those arriving to existing inputs
• inputs – an iterable of the names of the input values

Returns:

a list of unsatisfied operations to prune

graphtik.network._yield_datanodes(nodes)

May scan dag nodes.

graphtik.network.collect_requirements(graph) → Tuple[<sphinx.ext.autodoc.importer._MockObject object at 0x7f8607390668>, <sphinx.ext.autodoc.importer._MockObject object at 0x7f8607390668>]

Collect & split datanodes in (possibly overlapping) needs/provides.

graphtik.network.is_endure_operations() → Optional[bool]

graphtik.network.is_marshal_tasks() → Optional[bool]

graphtik.network.is_parallel_tasks() → Optional[bool]

graphtik.network.is_reschedule_operations() → Optional[bool]

graphtik.network.is_skip_evictions() → Optional[bool]

graphtik.network.yield_ops(nodes)

May scan (preferably) plan.steps or dag nodes.

Module: plot

Plotting of graphtik graphs.

graphtik.plot.build_pydot(graph, steps=None, inputs=None, outputs=None, solution=None, title=None, node_props=None, edge_props=None, clusters=None, legend_url='https://graphtik.readthedocs.io/en/latest/_images/GraphtikLegend.svg') → <sphinx.ext.autodoc.importer._MockObject object at 0x7f8606fadda0>

Build a Graphviz out of a Network graph/steps/inputs/outputs and return it.

See Plotter.plot() for the arguments, sample code, and the legend of the plots.

graphtik.plot.default_jupyter_render = {'svg_container_styles': '', 'svg_element_styles': 'width: 100%; height: 300px;', 'svg_pan_zoom_json': '{controlIconsEnabled: true, zoomScaleSensitivity: 0.4, fit: true}'}

A nested dictionary controlling the rendering of graph-plots in Jupyter cells,

as those returned from Plotter.plot() (currently as SVGs). Either modify it in place, or pass another one in the respective methods.

The following keys are supported.

Parameters:

• svg_pan_zoom_json –

  arguments controlling the rendering of a zoomable SVG in Jupyter notebooks, as defined in https://github.com/ariutta/svg-pan-zoom#how-to-use if None, defaults to string (also maps supported):

  "{controlIconsEnabled: true, zoomScaleSensitivity: 0.4, fit: true}"
  

• svg_element_styles –

  mostly for sizing the zoomable SVG in Jupyter notebooks. Inspect & experiment on the html page of the notebook with browser tools. if None, defaults to string (also maps supported):

  "width: 100%; height: 300px;"
  

• svg_container_styles – like svg_element_styles, if None, defaults to empty string (also maps supported).

graphtik.plot.legend(filename=None, show=None, jupyter_render: Mapping[KT, VT_co] = None, arch_url='https://graphtik.readthedocs.io/en/latest/arch.html')

Generate a legend for all plots (see Plotter.plot() for args)

Parameters:arch_url – the url to the architecture section explaining graphtik glassary.

See render_pydot() for the rest argyments.

graphtik.plot.render_pydot(dot: <sphinx.ext.autodoc.importer._MockObject object at 0x7f8606ff70b8>, filename=None, show=False, jupyter_render: str = None)

Plot a Graphviz dot in a matplotlib, in file or return it for Jupyter.

Parameters:

• dot – the pre-built Graphviz pydot.Dot instance
• filename (str) – Write diagram into a file. Common extensions are .png .dot .jpg .jpeg .pdf .svg call plot.supported_plot_formats() for more.
• show – If it evaluates to true, opens the diagram in a matplotlib window. If it equals -1, it returns the image but does not open the Window.
• jupyter_render –

  a nested dictionary controlling the rendering of graph-plots in Jupyter cells. If None, defaults to default_jupyter_render (you may modify those in place and they will apply for all future calls).

  You may increase the height of the SVG cell output with something like this:

  netop.plot(jupyter_render={"svg_element_styles": "height: 600px; width: 100%"})
  

Returns:

the matplotlib image if show=-1, or the dot.

See Plotter.plot() for sample code.

graphtik.plot.supported_plot_formats() → List[str]

return automatically all pydot extensions

Module: config

Configurations for network execution, and utilities on them.

graphtik.config.abort_run()

Sets the abort run global flag, to halt all currently or future executing plans.

This global flag is reset when any NetworkOperation.compute() is executed, or manually, by calling reset_abort().

graphtik.config.evictions_skipped(enabled)

Like set_skip_evictions() as a context-manager to reset old value.

graphtik.config.get_execution_pool() → Optional[Pool]

Get the process-pool for parallel plan executions.

graphtik.config.is_abort()

Return True if networks have been signaled to stop execution.

graphtik.config.is_endure_operations() → Optional[bool]

see set_endure_operations()

graphtik.config.is_marshal_tasks() → Optional[bool]

see set_marshal_tasks()

graphtik.config.is_parallel_tasks() → Optional[bool]

see set_parallel_tasks()

graphtik.config.is_reschedule_operations() → Optional[bool]

see set_reschedule_operations()

graphtik.config.is_skip_evictions() → Optional[bool]

see set_skip_evictions()

graphtik.config.operations_endured(enabled)

Like set_endure_operations() as a context-manager to reset old value.

graphtik.config.operations_reschedullled(enabled)

Like set_reschedule_operations() as a context-manager to reset old value.

graphtik.config.reset_abort()

Reset the abort run global flag, to permit plan executions to proceed.

graphtik.config.set_endure_operations(enabled)

Enable/disable globally endurance to keep executing even if some operations fail.

Parameters:enable –

• If None (default), respect the flag on each operation;
• If true/false, force it for all operations.

Returns:a “reset” token (see ContextVar.set())

.

graphtik.config.set_execution_pool(pool: Optional[Pool])

Set the process-pool for parallel plan executions.

You may have to :also func:set_marshal_tasks() to resolve pickling issues.

graphtik.config.set_marshal_tasks(enabled)

Enable/disable globally marshalling of parallel operations, …

inputs & outputs with dill, which might help for pickling problems.

Parameters:enable –

• If None (default), respect the respective flag on each operation;
• If true/false, force it for all operations.

Returns:a “reset” token (see ContextVar.set())

graphtik.config.set_parallel_tasks(enabled)

Enable/disable globally parallel execution of operations.

Parameters:enable –

• If None (default), respect the respective flag on each operation;
• If true/false, force it for all operations.

Returns:a “reset” token (see ContextVar.set())

graphtik.config.set_reschedule_operations(enabled)

Enable/disable globally rescheduling for operations returning only partial outputs.

Parameters:enable –

• If None (default), respect the flag on each operation;
• If true/false, force it for all operations.

Returns:a “reset” token (see ContextVar.set())

.

graphtik.config.set_skip_evictions(enabled)

When true, disable globally evictions, to keep all intermediate solution values, …

regardless of asked outputs.

Returns:a “reset” token (see ContextVar.set())

graphtik.config.tasks_in_parallel(enabled)

Like set_parallel_tasks() as a context-manager to reset old value.

graphtik.config.tasks_marshalled(enabled)

Like set_marshal_tasks() as a context-manager to reset old value.

Module: base

Generic or specific utilities

graphtik.base.NO_RESULT = <NO_RESULT>

When an operation function returns this special value, it implies operation has no result at all, (otherwise, it would have been a single result, None).`

class graphtik.base.Plotter

Classes wishing to plot their graphs should inherit this and …

implement property plot to return a “partial” callable that somehow ends up calling plot.render_pydot() with the graph or any other args binded appropriately. The purpose is to avoid copying this function & documentation here around.

plot(filename=None, show=False, jupyter_render: Union[None, Mapping[KT, VT_co], str] = None, **kws)

Entry-point for plotting ready made operation graphs.

Parameters:

• filename (str) – Write diagram into a file. Common extensions are .png .dot .jpg .jpeg .pdf .svg call plot.supported_plot_formats() for more.
• show – If it evaluates to true, opens the diagram in a matplotlib window. If it equals -1, it plots but does not open the Window.
• inputs – an optional name list, any nodes in there are plotted as a “house”
• outputs – an optional name list, any nodes in there are plotted as an “inverted-house”
• solution – an optional dict with values to annotate nodes, drawn “filled” (currently content not shown, but node drawn as “filled”). It extracts more infos from a Solution instance, such as, if solution has an executed attribute, operations contained in it are drawn as “filled”.
• title – an optional string to display at the bottom of the graph
• node_props – an optional nested dict of Grapvhiz attributes for certain nodes
• edge_props – an optional nested dict of Grapvhiz attributes for certain edges
• clusters – an optional mapping of nodes –> cluster-names, to group them
• jupyter_render – a nested dictionary controlling the rendering of graph-plots in Jupyter cells, if None, defaults to jupyter_render (you may modify it in place and apply for all future calls).
• legend_url – a URL to the graphtik legend; if it evaluates to false, none is added.

Returns:

a pydot.Dot instance (for for API reference visit: https://pydotplus.readthedocs.io/reference.html#pydotplus.graphviz.Dot)

Tip

The pydot.Dot instance returned is rendered directly in Jupyter/IPython notebooks as SVG images.

You may increase the height of the SVG cell output with something like this:

netop.plot(jupyter_render={"svg_element_styles": "height: 600px; width: 100%"})

Check default_jupyter_render for defaults.

Note that the graph argument is absent - Each Plotter provides its own graph internally; use directly render_pydot() to provide a different graph.

NODES:

oval

function

egg

subgraph operation

house

given input

inversed-house

asked output

polygon

given both as input & asked as output (what?)

square

intermediate data, neither given nor asked.

red frame

evict-instruction, to free up memory.

filled

data node has a value in solution OR function has been executed.

thick frame

function/data node in execution steps.

ARROWS

solid black arrows

dependencies (source-data need-ed by target-operations, sources-operations provides target-data)

dashed black arrows

optional needs

blue arrows

sideffect needs/provides

wheat arrows

broken dependency (provide) during pruning

green-dotted arrows

execution steps labeled in succession

To generate the legend, see legend().

Sample code:

>>> from graphtik import compose, operation
>>> from graphtik.modifiers import optional
>>> from operator import add

>>> netop = compose("netop",
...     operation(name="add", needs=["a", "b1"], provides=["ab1"])(add),
...     operation(name="sub", needs=["a", optional("b2")], provides=["ab2"])(lambda a, b=1: a-b),
...     operation(name="abb", needs=["ab1", "ab2"], provides=["asked"])(add),
... )

>>> netop.plot(show=True);                 # plot just the graph in a matplotlib window # doctest: +SKIP
>>> inputs = {'a': 1, 'b1': 2}
>>> solution = netop(**inputs)             # now plots will include the execution-plan

>>> netop.plot('plot1.svg', inputs=inputs, outputs=['asked', 'b1'], solution=solution);           # doctest: +SKIP
>>> dot = netop.plot(solution=solution);   # just get the `pydoit.Dot` object, renderable in Jupyter
>>> print(dot)
digraph G {
    URL="https://graphtik.readthedocs.io/en/latest/_images/GraphtikLegend.svg";
    fontname=italic;
    label=netop;
    a [fillcolor=wheat, shape=invhouse, style=filled, tooltip=1];
...

class graphtik.base.Token(*args)

hashid

graphtik.base.aslist(i, argname, allowed_types=<class 'list'>)

Utility to accept singular strings as lists, and None –> [].

graphtik.base.astuple(i, argname, allowed_types=<class 'tuple'>)

graphtik.base.jetsam(ex, locs, *salvage_vars, annotation='jetsam', **salvage_mappings)

Annotate exception with salvaged values from locals() and raise!

Parameters:

• ex – the exception to annotate
• locs –

  locals() from the context-manager’s block containing vars to be salvaged in case of exception

  ATTENTION: wrapped function must finally call locals(), because locals dictionary only reflects local-var changes after call.

• annotation – the name of the attribute to attach on the exception
• salvage_vars – local variable names to save as is in the salvaged annotations dictionary.
• salvage_mappings – a mapping of destination-annotation-keys –> source-locals-keys; if a source is callable, the value to salvage is retrieved by calling value(locs). They take precendance over`salvae_vars`.

Raises:

any exception raised by the wrapped function, annotated with values assigned as atrributes on this context-manager

• Any attrributes attached on this manager are attached as a new dict on the raised exception as new jetsam attrribute with a dict as value.
• If the exception is already annotated, any new items are inserted, but existing ones are preserved.

Example:

Call it with managed-block’s locals() and tell which of them to salvage in case of errors:

try:
    a = 1
    b = 2
    raise Exception()
exception Exception as ex:
    jetsam(ex, locals(), "a", b="salvaged_b", c_var="c")
    raise

And then from a REPL:

import sys
sys.last_value.jetsam
{'a': 1, 'salvaged_b': 2, "c_var": None}

** Reason:**

Graphs may become arbitrary deep. Debugging such graphs is notoriously hard.

The purpose is not to require a debugger-session to inspect the root-causes (without precluding one).

Naively salvaging values with a simple try/except block around each function, blocks the debugger from landing on the real cause of the error - it would land on that block; and that could be many nested levels above it.