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# %%
# %matplotlib inline
import datetime
import os
import sys

import seaborn as sns

nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
    sys.path.append(nb_dir)

# %%
import participants.query_db
from features import esm, helper, proximity

# %% [markdown]
# # 1. Get the relevant data

# %%
participants_inactive_usernames = participants.query_db.get_usernames(
    collection_start=datetime.date.fromisoformat("2020-08-01")
)
# Consider only two participants to simplify.
ptcp_2 = participants_inactive_usernames[0:2]

# %% [markdown]
# ## 1.1 Labels

# %%
df_esm = esm.get_esm_data(ptcp_2)
df_esm_preprocessed = esm.preprocess_esm(df_esm)

# %%
df_esm_PANAS = df_esm_preprocessed[
    (df_esm_preprocessed["questionnaire_id"] == 8)
    | (df_esm_preprocessed["questionnaire_id"] == 9)
]
df_esm_PANAS_clean = esm.clean_up_esm(df_esm_PANAS)

# %% [markdown]
# ## 1.2 Sensor data

# %%
df_proximity = proximity.get_proximity_data(ptcp_2)
df_proximity = helper.get_date_from_timestamp(df_proximity)
df_proximity = proximity.recode_proximity(df_proximity)

# %% [markdown]
# # 2. Grouping/segmentation

# %%
df_esm_PANAS_daily_means = (
    df_esm_PANAS_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
    .esm_user_answer_numeric.agg("mean")
    .reset_index()
    .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
)