Graph#

class psynet.trial.graph.GraphChainNetwork(*args, **kwargs)[source]#

Bases: ChainNetwork

A Network class for graph chains. A graph chain corresponds to the evolution of a vertex within a graph.

Parameters (for now stating the new ones)#

vertex_id: The id of the vertex that the network is representing within the graph.
dependent_vertex_ids: A list of the vertex ids on which the current node depends (incoming edges).
source_seed: Source seed to use when initializing the graph in the trialmaker.

creation_time#: the time at which the Network was created.

details#: a generic column for storing structured JSON data

failed#: boolean indicating whether the Network has failed which prompts Dallinger to ignore it unless specified otherwise. Objects are usually failed to indicate something has gone wrong.

failed_reason#: an optional reason the object was failed. If the object is failed as part of a cascading failure triggered from another object, the chain of objects will be captured in this field.

full#: Whether the network is currently full

id#: a unique number for every entry. 1, 2, 3 and so on…

max_size#: How big the network can get, this number is used by the full() method to decide whether the network is full

property1#: a generic column that can be used to store experiment-specific details in String form.

property2#: a generic column that can be used to store experiment-specific details in String form.

property3#: a generic column that can be used to store experiment-specific details in String form.

property4#: a generic column that can be used to store experiment-specific details in String form.

property5#: a generic column that can be used to store experiment-specific details in String form.

role#: The role of the network. By default dallinger initializes all networks as either “practice” or “experiment”

time_of_death#: the time at which failing occurred

type#: A String giving the name of the class. Defaults to “network”. This allows subclassing.

vars#

class psynet.trial.graph.GraphChainNode(*args, **kwargs)[source]#

Bases: ChainNode

A Node class for graph chains.

Parameters (for now stating the new ones)#

vertex_id: The id of the vertex that the network is representing within the graph.
dependent_vertex_ids: A list of the vertex ids on which the current node depends (incoming edges).

create_definition_from_seed(seed, experiment, participant)[source]#

(Built-in) In a graph chain, the next node in the chain is a faithful reproduction of the previous iteration.

Parameters:

seed – The seed being passed to the node.
experiment – An instantiation of psynet.experiment.Experiment, corresponding to the current experiment.
participant – Current participant, if relevant.

Returns:

object – The node’s new definition, which is a faithful reproduction of the seed that it was passed.

creation_time#: the time at which the Network was created.

details#: a generic column for storing structured JSON data

failed#: boolean indicating whether the Network has failed which prompts Dallinger to ignore it unless specified otherwise. Objects are usually failed to indicate something has gone wrong.

failed_reason#: an optional reason the object was failed. If the object is failed as part of a cascading failure triggered from another object, the chain of objects will be captured in this field.

id#: a unique number for every entry. 1, 2, 3 and so on…

network#: the network the node is in

network_id#: the id of the network that this node is a part of

participant#: the participant the node is associated with

participant_id#: the id of the participant whose node this is

property1#: a generic column that can be used to store experiment-specific details in String form.

property2#: a generic column that can be used to store experiment-specific details in String form.

property3#: a generic column that can be used to store experiment-specific details in String form.

property4#: a generic column that can be used to store experiment-specific details in String form.

property5#: a generic column that can be used to store experiment-specific details in String form.

summarize_trials(trials, experiment, participant)[source]#

(Abstract method, to be overridden) This method should summarize the answers to the provided trials. A default method is implemented for cases when there is just one trial per node; in this case, the method extracts and returns the trial’s answer, available in trial.answer. The method must be extended if it is to cope with multiple trials per node, however.

Parameters:

trials (list) – Trials to be summarized. By default only trials that are completed (i.e. have received a response) and processed (i.e. aren’t waiting for an asynchronous process) are provided here.
experiment – An instantiation of psynet.experiment.Experiment, corresponding to the current experiment.
participant – The participant who initiated the creation of the node.

Returns:

object – The derived seed. Should be suitable for serialisation to JSON.

time_of_death#: the time at which failing occurred

type#: A String giving the name of the class. Defaults to node. This allows subclassing.

vars#

class psynet.trial.graph.GraphChainTrial(*args, **kwargs)[source]#

Bases: ChainTrial

A Trial class for graph chains.

complete#: whether the info is ‘complete’, i.e. has received its contents

creation_time#: the time at which the Network was created.

details#: a generic column for storing structured JSON data

failed#: boolean indicating whether the Network has failed which prompts Dallinger to ignore it unless specified otherwise. Objects are usually failed to indicate something has gone wrong.

failed_reason#: an optional reason the object was failed. If the object is failed as part of a cascading failure triggered from another object, the chain of objects will be captured in this field.

id#: a unique number for every entry. 1, 2, 3 and so on…

make_definition(experiment, participant)[source]#

(Built-in) In an graph chain, the trial’s definition equals the definition of the node that created it.

Parameters:

experiment – An instantiation of psynet.experiment.Experiment, corresponding to the current experiment.
participant – Optional participant with which to associate the trial.

Returns:

object – The trial’s definition, equal to the node’s definition.

network#: the network the info is in

network_id#: the id of the network the info is in

origin#: the Node that created the info.

origin_id#: the id of the Node that created the info

property1#: a generic column that can be used to store experiment-specific details in String form.

property2#: a generic column that can be used to store experiment-specific details in String form.

property3#: a generic column that can be used to store experiment-specific details in String form.

property4#: a generic column that can be used to store experiment-specific details in String form.

property5#: a generic column that can be used to store experiment-specific details in String form.

time_of_death#: the time at which failing occurred

type#: a String giving the name of the class. Defaults to “info”. This allows subclassing.

vars#

class psynet.trial.graph.GraphChainTrialMaker(*, id_, node_class, trial_class, network_structure, chain_type, expected_trials_per_participant, max_trials_per_participant, chains_per_participant, trials_per_node, balance_across_chains, check_performance_at_end, check_performance_every_trial, recruit_mode, target_n_participants=typing.Optional[int], max_nodes_per_chain=None, fail_trials_on_premature_exit=False, fail_trials_on_participant_performance_check=False, propagate_failure=True, n_repeat_trials=0, wait_for_networks=False, allow_revisiting_networks_in_across_chains=False, sync_group_type=None)[source]#

Bases: ChainTrialMaker

A TrialMaker class for graph chains; see the documentation for ChainTrialMaker for usage instructions.

Parameters:: network_structure – A representation of the graph structure to instantiate. The representation consistes of a dictionary of vertices and edges. E.g. {“vertices”: [1,2], “edges”: [{“origin”: 1, “target”: 2, “properties”: {“type”: “default”}}]}

grow_network(network, experiment)[source]#

Extends the network if necessary by adding one or more nodes. Returns True if any nodes were added.

Parameters:

network – The network to be potentially extended.
experiment – An instantiation of psynet.experiment.Experiment, corresponding to the current experiment.

pre_deploy_routine(experiment)[source]#

Defines a routine for setting up the experiment prior to deployment. This is a good place to prepare networks etc.

Parameters:: experiment – An instantiation of psynet.experiment.Experiment, corresponding to the current experiment.