95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
from collections.abc import Collection
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import pandas as pd
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from config.models import Participant, Proximity
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from setup import db_engine, session
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FEATURES_PROXIMITY = ["freq_prox_near", "prop_prox_near"]
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def get_proximity_data(usernames: Collection) -> pd.DataFrame:
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"""
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Read the data from the proximity sensor table and return it in a dataframe.
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Parameters
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----------
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usernames: Collection
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A list of usernames to put into the WHERE condition.
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Returns
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-------
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df_proximity: pd.DataFrame
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A dataframe of proximity data.
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"""
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query_proximity = (
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session.query(Proximity, Participant.username)
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.filter(Participant.id == Proximity.participant_id)
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.filter(Participant.username.in_(usernames))
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)
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with db_engine.connect() as connection:
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df_proximity = pd.read_sql(query_proximity.statement, connection)
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return df_proximity
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def recode_proximity(df_proximity: pd.DataFrame) -> pd.DataFrame:
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"""
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This function recodes proximity from a double to a boolean value.
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Different proximity sensors report different values,
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but in our data only several distinct values have ever been found.
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These are therefore converted into "near" and "far" binary values.
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See expl_proximity.ipynb for additional info.
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Parameters
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----------
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df_proximity: pd.DataFrame
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A dataframe of proximity data.
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Returns
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-------
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df_proximity: pd.DataFrame
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The same dataframe with an additional column bool_prox_near,
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indicating whether "near" proximity was reported.
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False values correspond to "far" reported by this sensor.
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"""
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df_proximity = df_proximity.assign(bool_prox_near=lambda x: x.double_proximity == 0)
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return df_proximity
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def count_proximity(
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df_proximity: pd.DataFrame, group_by: Collection = None
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) -> pd.DataFrame:
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"""
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The function counts how many times a "near" value occurs in proximity
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and calculates the proportion of this counts to all proximity values (i.e. relative count).
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Parameters
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----------
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df_proximity: pd.DataFrame
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A dataframe of proximity data.
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group_by: Collection
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A list of strings, specifying by which parameters to group.
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By default, the features are calculated per participant, but could be "date_lj" etc.
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Returns
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-------
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df_proximity_features: pd.DataFrame
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A dataframe with the count of "near" proximity values and their relative count.
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"""
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if group_by is None:
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group_by = []
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if "bool_prox_near" not in df_proximity:
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df_proximity = recode_proximity(df_proximity)
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df_proximity["bool_prox_far"] = ~df_proximity["bool_prox_near"]
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df_proximity_features = df_proximity.groupby(["participant_id"] + group_by).sum()[
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["bool_prox_near", "bool_prox_far"]
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]
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df_proximity_features = df_proximity_features.assign(
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prop_prox_near=lambda x: x.bool_prox_near / (x.bool_prox_near + x.bool_prox_far)
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)
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df_proximity_features = df_proximity_features.rename(
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columns={"bool_prox_near": "freq_prox_near"}
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).drop(columns="bool_prox_far", inplace=False)
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return df_proximity_features
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