app.engine.greencrowdStrategy module¶

app.engine.greencrowdStrategy.get_random_values_from_tasks(all_records)[source]¶

Extracts all dimensions from the tasks and generates random values within the range of the minimum and maximum values found in the tasks.

Parameters:: all_records (list) – A list of all tasks.
Returns:: dict – A dictionary containing random values for each dimension.

app.engine.greencrowdStrategy.get_average_values_from_tasks(task, all_records)[source]¶

Compute the average integer value of each dimension across tasks.

Parameters:

task – The current task (unused placeholder kept for signature compatibility).
all_records (list) – All task records to average over.

Returns:

dict – Average integer value for each dimension.

app.engine.greencrowdStrategy.get_dynamic_values_from_tasks(task, list_ids_tasks, all_records, user, variable_basic_points, variable_lbe_multiplier)[source]¶

Calculates dynamic values based on user participation in tasks.

Parameters:

task (object) – The task object.
list_ids_tasks (list) – A list of task IDs with externalTaskId references.
all_records (list) – A list of all task participation records.
user (object) – The user object participating in tasks.
variable_basic_points (int) – The base points per participation.
variable_lbe_multiplier (float) – The location-based equity multiplier.

Returns:

dict –

A dictionary containing calculated values for each dimension:

”DIM_BP”: Base points adjusted for task uniqueness.
”DIM_LBE”: Points adjusted for location-based equity.
”DIM_TD”: Points based on temporal distribution of participation.
”DIM_PP”: Points adjusted for participation periodicity.
”DIM_S”: Streak-based points for continuous participation.

app.engine.greencrowdStrategy.assign_random_scores(min_value, max_value)[source]¶

Generate random scores for each GREENCROWD scoring dimension.

Used to seed simulations/tests with plausible per-dimension values.

Parameters:

min_value (int) – Inclusive lower bound for each random score.
max_value (int) – Inclusive upper bound for each random score.

Returns:

dict – Random integer scores keyed by dimension (DIM_BP, DIM_TD, DIM_LBE, DIM_PP, DIM_S).

class app.engine.greencrowdStrategy.GREENCROWDGamificationStrategy[source]¶

Bases: BaseStrategy

generate_logic_graph(format='png')[source]¶

Render the GREENCROWD scoring logic as a Graphviz diagram.

Produces a labelled flowchart of the five scoring dimensions (base points, location-based equity, time diversity, personal performance and streak bonus) for display in the dashboard.

Parameters:: format (str) – Graphviz output format (e.g. "png", "svg").
Returns:: graphviz.Digraph – The constructed diagram.

generate_hash(response_data)[source]¶: Generate a hash for the response to ensure integrity.

simulate_strategy(data_to_simulate=None, userGroup='dynamic', user_last_task=None)[source]¶

Estimate the points a user would receive, without assigning them.

Five dimensions contribute to the score:

Task Diversity Base Points (DIM_BP)
Location-Based Equity (DIM_LBE)
Time Diversity (DIM_TD)
Personal Performance (DIM_PP)
Streak Bonus (DIM_S)

Parameters:

data_to_simulate (dict, optional) – Data for the simulation, with the structure shown below.
userGroup (str) – The user group to simulate for; one of random_range, average_score, dynamic_calculation.
user_last_task (dict, optional) – The user’s last task.

The data_to_simulate payload is structured as:

{
    "task": dict,            # single task object
    "allTasks": list,        # all tasks
    "externalUserId": str,   # the user the simulation is run for
}

Returns:

list –

One result dict per task, each structured as:

{
    "externalUserId": str,
    "dimensions": list,           # points per dimension
    "totalSimulatedPoints": int,  # total estimated points
}

Parameters:

data_to_simulate (dict)
userGroup (str)
user_last_task (dict)

checkISExpired(expiration_date)[source]¶: Check if the expiration date has passed.

async calculate_points(externalGameId, externalTaskId, externalUserId, data)[source]¶

Calculate the points for the GREENCROWD gamification strategy.

Parameters:

externalGameId (str) – The external game id.
externalTaskId (str) – The external task id.
externalUserId (str) – The external user id.
data (dict) – The dimensions used for the calculation, with the shape shown below.

The data payload is structured as:

{
    "experimentGroup": str,
    "simulationHash": str,
    "dimensions": [
        {"DIM_BP": int, "DIM_LBE": int, "DIM_TD": int,
         "DIM_PP": int, "DIM_S": int},
        ...
    ]
}