analyze.py

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# %%
from datetime import date, timedelta as td
from download import fname
import pandas as pd
import numpy as np


# %%
def load_data(cdate):
    try:
        return pd.read_csv(fname(cdate), sep="\t")
    except FileNotFoundError:
        return pd.DataFrame(
            {
                key: []
                for key in [
                    "Date",
                    "Seconds",
                    "NumberPeople",
                    "Activity",
                    "Document",
                    "Category",
                    "Productivity",
                ]
            }
        )


# %%
def load_data_week(cdate):
    return [load_data(date.today() - td(days=i)) for i in range(7, -1, -1)]


# %%
def score_day(cdate):
    df = load_data(cdate)
    if np.sum(df.Seconds) == 0.0:
        return 0.0
    score = np.sum((df.Productivity + 2) * df.Seconds) / (4 * np.sum(df.Seconds))
    return score * 100


# %%
def weekday(cdate):
    return d.weekday()


# %%
def score_over_time(cdate, weeks=1):
    cweekday = weekday(cdate)
    days_to_monday = cweekday
    days_to_score = days_to_monday + 7 * weeks
    day_start = cdate - td(days=days_to_score)
    scores = -np.ones((weeks + 1, 7)).ravel()
    for i in range(days_to_score + 1):
        cur_day = day_start + td(days=i)
        try:
            cur_score = score_day(cur_day)
            scores[i] = cur_score
        except FileNotFoundError:
            continue

    scores = scores.reshape(weeks + 1, 7)
    return scores


# %%
def writing_time(cdate):
    df = load_data(cdate)
    sites = [
        "overleaf.com",
        "hyper",
        "scholar.google.com",
        "arxiv.org",
        "microsoft powerpoint",
        "Google Documents",
        "Google Presentations",
    ]
    minutes = df.query(f"Activity in {sites}").Seconds.sum() / 60.0
    return minutes