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# %%
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.esm import *

# %%
participants_inactive_usernames = participants.query_db.get_usernames(
    collection_start=datetime.date.fromisoformat("2020-08-01")
)
df_esm_inactive = get_esm_data(participants_inactive_usernames)

# %%
df_esm_preprocessed = preprocess_esm(df_esm_inactive)

# %% [markdown]
# # PANAS

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

# %% [markdown]
# Group by participants, date, and subscale and calculate daily means.

# %%
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"})
)

# %% [markdown]
# Next, calculate mean, median, and standard deviation across all days for each participant.

# %%
df_esm_PANAS_summary_participant = (
    df_esm_PANAS_daily_means.groupby(["participant_id", "questionnaire_id"])
    .agg(["mean", "median", "std"])
    .reset_index(col_level=1)
)
df_esm_PANAS_summary_participant.columns = df_esm_PANAS_summary_participant.columns.get_level_values(
    1
)
df_esm_PANAS_summary_participant[
    "PANAS_subscale"
] = df_esm_PANAS_daily_means.questionnaire_id.astype("category").cat.rename_categories(
    {8.0: "PA", 9.0: "NA"}
)

# %%
sns.displot(
    data=df_esm_PANAS_summary_participant, x="mean", hue="PANAS_subscale", binwidth=0.2
)

# %%
sns.displot(
    data=df_esm_PANAS_summary_participant,
    x="median",
    hue="PANAS_subscale",
    binwidth=0.2,
)

# %%
sns.displot(
    data=df_esm_PANAS_summary_participant, x="std", hue="PANAS_subscale", binwidth=0.05
)

# %%
df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]

# %% [markdown]
# # Stress appraisal measure

# %%
df_esm_SAM = df_esm_preprocessed[
    (df_esm_preprocessed["questionnaire_id"] >= 87)
    & (df_esm_preprocessed["questionnaire_id"] <= 93)
]

# %%
clean_up_esm(df_esm_SAM)[["esm_user_answer", "esm_user_answer_numeric"]].head(9)

# %%
df_esm_PANAS_clean[["esm_user_answer", "esm_user_answer_numeric"]].head(n=10)

# %%
df_esm_SAM[
    [
        "esm_instructions",
        "question_id",
        "questionnaire_id",
        "esm_user_answer",
        "esm_type",
    ]
].head(n=10)

# %%