101 lines
5.5 KiB
Python
101 lines
5.5 KiB
Python
import pandas as pd
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import numpy as np
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import re
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import settings
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timelog = pd.read_csv(settings.pomodoro_logfile())
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# Dump bad data. The real solution here is to get rid of the damned 'Cancel'
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# button on the Pomodoro Prompt dialog, but i don't know how to do that, so we
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# need to drop the rows where the work task description is blank, which is
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# coming in as not a number for reasons i'm not entirely clear on. Maybe
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# because it's the last row of the spreadsheet? Anyway we cannot do anything
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# with no data in the description, so drop them at the outset.
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timelog = timelog.dropna()
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timelog = timelog.reset_index(drop=True)
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# For debugging, keep original description around.
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timelog["orig_desc"] = timelog["description"]
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# Clean up description before we go to work on it.
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timelog['description'] = timelog['description'].str.strip()
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# Allow multiple entries to be put into one prompt by splitting with semicolon.
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# TODO make this a flag since it's possible to use semicolons without meaning
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# to make multiple task entries at once.
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timelog["description"] = list(timelog["description"].str.split(";"))
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timelog = timelog.explode("description").reset_index()
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timelog["started"] = pd.to_datetime(timelog["started"]).dt.tz_convert("US/Eastern")
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timelog["recorded"] = pd.to_datetime(timelog["recorded"]).dt.tz_convert("US/Eastern")
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latest_recorded = settings.pomodoro_latest_recorded()
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if latest_recorded:
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timelog = timelog.query("recorded>" + latest_recorded)
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timelog["time"] = 30
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# A pomodoro started before 3am Eastern time is considered to be a continuation
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# of the day before, so we are, effectively, on West Coast time for determining
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# the day we want to associate a time entry with. PomodoroPrompt saves as UTC.
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timelog["date"] = timelog["started"].dt.tz_convert("US/Pacific").dt.date
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timelog["day_of_week"] = pd.to_datetime(timelog["date"]).dt.day_name()
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# If a project has been specified (task prefixed with a colon), then put the
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# project in its own column.
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timelog['project'] = (np.where(timelog['description'].str.contains(': '), timelog['description'].str.split(': ', 1).str[0], None))
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timelog['description'] = (np.where(timelog['description'].str.contains(': '), timelog['description'].str.split(': ', 1).str[1], timelog['description']))
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# Mid-work clean up of description and new project.
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timelog['description'] = timelog['description'].str.strip()
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timelog['project'] = timelog['project'].str.strip()
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# If a multiplier has been provided (an asterisk and an integer at the end of a
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# task), then multiply the time by it and remove it from the description.
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# Ensure we're splitting on the same asterisk we found: Use the end of string
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# signifier in the regular expression ($), and split from the right.
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p = re.compile(r'\*\s*\d$')
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# On some systems, using np.where worked but others failed. Why it worked is
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# unknown but why it failed is because numpy where evaluates all parts, even
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# the parts that will never get used because the where clause does not apply!
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# This caused the chained strings to fail because— no string.
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# timelog['tmp_multiplier'] = (np.where(timelog['description'].str.contains('\*\s*\d$'), timelog['description'].str.rsplit('*', 1).str[1].str.strip(), 1))
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# timelog['description'] = (np.where(timelog['description'].str.contains('\*\s*\d$'), timelog['description'].str.rsplit('*', 1).str[0], timelog['description']))
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timelog['tmp_multiplier'] = timelog['description'].apply(lambda x: x.rsplit('*', 1)[1].strip() if p.search(x) else 1)
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timelog['description'] = timelog['description'].apply(lambda x: x.rsplit('*', 1)[0] if p.search(x) else x)
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timelog["time"] = timelog["time"] * timelog['tmp_multiplier'].astype(int)
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timelog.drop(columns=['tmp_multiplier'], inplace=True)
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# Clean up description again, after it has been sliced and diced.
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timelog['description'] = timelog['description'].str.strip()
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# Replace irregular-but-known project names with ones timetracking tools use.
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replacement_project_names = {
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"Find It Cambridge": ["Find It", "FIC", "Cambridge"],
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"The Propaganda Site": ["TPS", "Propaganda Site"],
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"MASS Design Group": ["MASS"],
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"Teachers with GUTS": ["TWIG", "GUTS"],
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"NICHQ Support": ["NICHQ", "NICHQ support", "nichq"],
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"Network engagement": ["Network Engagement", "network engagement", "Network engagment", "Social media", "Network building", "Agaric network engagement"],
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"Agaric internal": ["Agaric", "Internal"],
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"Agaric contrib": ["Contributing", "Contrib"],
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"Leads": ["Lead", "Agaric leads", "Lead followups"],
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"Learning": ["Personal learning"],
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"Drutopia": ["Drutopia improvements"],
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"Personal / external": ["Personal/external", "Personal", "External"],
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"Near North camp": ["Near North Camp", "Near North defense", "Encampment support", "Camp support"],
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}
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# TODO Probably put all alternatives in lower case and do str.lower() on
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# project just before the "is in" check.
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for preferred, alternatives in replacement_project_names.items():
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timelog.loc[timelog.project.isin(alternatives), "project"] = preferred
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# Condense duplicate entries by date, summing the minutes spent, and listing
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# the first started and last recorded times for each task.
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# The fillna is essential or we drop entries with blank ('None') projects.
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tl = timelog.groupby(["date", timelog.project.fillna(""), "description"]).agg({"time": 'sum', "started": 'min', "recorded": 'max'}).reset_index()
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if hasattr(sys, 'ps1'):
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tl.to_csv('harvest-ready.csv', index=False)
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settings.pomodoro_latest_recorded(tl.recorded.max())
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else:
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print("We do not write to the harvest-ready.csv nor update the latest recorded setting when run interactively in the python shell.")
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