# ---
# jupyter:
#   jupytext:
#     formats: ipynb,py:percent
#     text_representation:
#       extension: .py
#       format_name: percent
#       format_version: '1.3'
#       jupytext_version: 1.11.2
#   kernelspec:
#     display_name: straw2analysis
#     language: python
#     name: straw2analysis
# ---

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.esm import *

# %% [markdown]
# # ESM data

# %% [markdown]
# Only take data from the main part of the study. The pilot data have different structure, there were especially many additions to ESM_JSON.

# %%
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)
df_esm_preprocessed.head()

# %%
df_esm_preprocessed.columns

# %% [markdown]
# # Concordance

# %% [markdown]
# The purpose of concordance is to count the number of EMA sessions that a participant answered in a day and possibly compare it to some maximum number of EMAs that could theoretically be presented for that day.

# %% [markdown]
# ## Session IDs

# %% [markdown]
# One approach would be to count distinct session IDs which are incremented for each group of EMAs. However, since not every question answered counts as a fulfilled EMA, some unique session IDs should be eliminated first.

# %%
session_counts = df_esm_preprocessed.groupby(["participant_id", "esm_session"])[
    "esm_session"
].count()

# %%
sns.displot(session_counts.to_numpy(), binwidth=1, height=8)

# %% [markdown]
# ### Unique session IDs

# %%
df_session_counts = pd.DataFrame(session_counts)
df_session_1 = df_session_counts[(df_session_counts["esm_session"] == 1)]
df_esm_unique_session = df_session_1.join(
    df_esm_preprocessed.set_index(["participant_id", "esm_session"])
)

# %%
df_esm_unique_session["esm_user_answer"].value_counts()

# %% [markdown]
# The "DayFinished3421" tag marks the last EMA, where the participant only marked "I finished with work for today" and did not answer any questions.
# What do the answers "Ne" represent?

# %%
df_esm_unique_session.query("esm_user_answer == 'Ne'")[
    ["esm_trigger", "esm_instructions", "esm_user_answer"]
].head()

# %%
df_esm_unique_session.loc[
    df_esm_unique_session["esm_user_answer"].str.contains("Ne"), "esm_trigger"
].value_counts()

# %% [markdown]
# These are all "first" questions of EMAs which serve as a way to postpone the daytime or evening EMAs.

# %% [markdown]
# The other answers signify expired or interrupted EMAs.

# %% [markdown]
# ### "Almost" unique session IDs

# %% [markdown]
# There are some session IDs that only appear twice or three times.

# %%
df_session_counts[
    (df_session_counts["esm_session"] < 4) & (df_session_counts["esm_session"] > 1)
]

# %% [markdown]
# Some represent the morning EMAs that only contained three questions.

# %%
df_esm_preprocessed.query("participant_id == 89 & esm_session == 158")[
    ["esm_trigger", "esm_instructions", "esm_user_answer"]
]

# %%
df_esm_preprocessed.query("participant_id == 89 & esm_session == 157")[
    ["esm_trigger", "esm_instructions", "esm_user_answer"]
]

# %% [markdown]
# Others represent interrupted EMA sessions.

# %%
df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[
    ["esm_trigger", "esm_instructions", "esm_user_answer"]
]

# %% [markdown]
# ## Other possibilities

# %% [markdown]
# There are also answers that describe what happened to a pending question: "Removed%"