from typing import Dict, Union
from scipy.stats._distn_infrastructure import rv_frozen
[docs]class Surveyor:
"""Represents an individual who will participate in the survey.
Parameters
----------
name : str
Unique name for the surveyor
team_name : str
Name of the parent team
surveyor_type : str
A helpful way of grouping surveyors with like traits (e.g., 'student' or 'expert')
skill : Union[float, rv_frozen], optional
Assuming perfect visibility and ideal observation rate, what is the expected probability that this person would identify any feature that crossed their survey unit. The default is 1.0, which would mean this surveyor recorded everything they encountered (after controlling for other factors).
speed_penalty : Union[float, rv_frozen], optional
Time factor added to each of this surveyor's survey units. The default is 0.0, which applies no penalty. Penalties should range between 0.0 and 1.0.
Attributes
----------
name : str
Unique name for the surveyor
team_name : str
Name of the parent team
surveyor_type : str
A helpful way of grouping surveyors with like traits (e.g., 'student' or 'expert')
skill : Union[float, rv_frozen]
Assuming perfect visibility and ideal observation rate, what is the expected probability that this person would identify any feature that crossed their survey unit.
speed_penalty : Union[float, rv_frozen]
Time factor added to each of this surveyor's survey units.
"""
def __init__(
self,
name: str,
team_name: str,
surveyor_type: str,
skill: Union[float, rv_frozen] = 1.0,
speed_penalty: Union[float, rv_frozen] = 0.0,
):
"""[summary]"""
self.name = name
self.team_name = team_name
self.surveyor_type = surveyor_type
self.skill = skill
self.speed_penalty = speed_penalty
[docs] def to_dict(self) -> Dict:
"""Create dictionary from attributes to allow easy DataFrame creation by `Team`.
Returns
-------
dict
Dictionary containing pairs of class attributes and their values
"""
return {
"surveyor_name": self.name,
"team_name": self.team_name,
"surveyor_type": self.surveyor_type,
"skill": self.skill,
"speed_penalty": self.speed_penalty,
}
