import pandas as pd
import numpy as np
import itertools
from scipy.stats import entropy


def compute_metrics(filtered_data, apps_type, metrics, apps_features):
    if "count" in metrics:
        apps_features["apps_" + day_segment + "_count" + apps_type] = filtered_data.groupby(["local_date"]).count()["timestamp"]
    if "timeoffirstuse" in metrics:
        time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_date", keep="first").set_index("local_date")
        apps_features["apps_" + day_segment + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
    if "timeoflastuse" in metrics:
        time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_date", keep="first").set_index("local_date")
        apps_features["apps_" + day_segment + "_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"]
    if "frequencyentropy" in metrics:
        apps_with_count = filtered_data.groupby(["local_date","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
        apps_features["apps_" + day_segment + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_date")["timestamp"].agg(entropy)
    return apps_features


apps_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
day_segment = snakemake.params["day_segment"]
single_categories = snakemake.params["single_categories"]
multiple_categories_with_genres = snakemake.params["multiple_categories"]
single_apps = snakemake.params["single_apps"]
excluded_categories = snakemake.params["excluded_categories"]
excluded_apps = snakemake.params["excluded_apps"]
metrics = snakemake.params["metrics"]

single_categories = list(set(single_categories) - set(excluded_categories))
multiple_categories = list(multiple_categories_with_genres.keys() - set(excluded_categories))
apps = list(set(single_apps) - set(excluded_apps))

# exclude categories in the excluded_categories list
if "system_apps" in excluded_categories:
    apps_data = apps_data[apps_data["is_system_app"] == 0]
apps_data = apps_data[~apps_data["genre"].isin(excluded_categories)]
# exclude apps in the excluded_apps list
apps_data = apps_data[~apps_data["application_name"].isin(excluded_apps)]

# deep copy the apps_data for the top1global computation
apps_data_global = apps_data.copy()

apps_features = pd.DataFrame(columns=["local_date"] + ["apps_" + day_segment + "_" + x for x in ["".join(metric) for metric in itertools.product(metrics, single_categories + multiple_categories + apps)]])
if not apps_data.empty:
    apps_features = pd.DataFrame()
    if day_segment != "daily":
        apps_data =apps_data[apps_data["local_day_segment"] == day_segment]
    
    # single category
    for sc in single_categories:
        if sc == "all":
            apps_features = compute_metrics(apps_data, "all", metrics, apps_features)
        else:
            filtered_data = apps_data[apps_data["genre"].isin([sc])]
            apps_features = compute_metrics(filtered_data, sc, metrics, apps_features)
    # multiple category
    for mc in multiple_categories:
        filtered_data = apps_data[apps_data["genre"].isin(multiple_categories_with_genres[mc])]
        apps_features = compute_metrics(filtered_data, mc, metrics, apps_features)
    # single apps
    for app in apps:
        col_name = app
        if app == "top1global":
            # get the most used app
            apps_with_count = apps_data_global.groupby(["local_date","package_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
            app = apps_with_count.iloc[0]["package_name"]
            col_name = "top1global"
        
        filtered_data = apps_data[apps_data["package_name"].isin([app])]
        apps_features = compute_metrics(filtered_data, col_name, metrics, apps_features)

    apps_features = apps_features.reset_index()

apps_features.to_csv(snakemake.output[0], index=False)