WorktimeCalculator

class WorktimeCalculator(user_config_path='.user_config.ini')[source]

Class to calculate the worktime.

Parameters: user_config_path (str, optional) – Path to the user specific config, which will overwrite default settings. by default “.user_config.ini”

Attributes Summary

default_config_path

Methods Summary

`add_time_columns`	Add Year, Month, Week and Day columns to an existing Dataframe.
`calc_file_hashes`	Calculate hashvalues for files.
`clean_db`	Remove rows where the session work was less than 1min.
`generate_contract_worktime_df`	Generate a Dataframe with 'worktime' column.
`get_daily_worktime`	Calculate the mean daily work time based on the contract details.
`get_datetime_now`	Helpermethod for mocking of datetime.datetime.now() in unittests.
`get_holiday_df`	Generate a Dataframe containing all needed holidays.
`get_manual_df_with_workime`	Read the manual_db file.
`get_pandas_now`	Return datetime.now as pd.Timestamp.
`get_plot_df`	Return a Dataframe prepared for plotting.
`get_remote_db`	Download the db_file to db_path_online from the SFTP server.
`get_total_df`	Calculate total Dataframe.
`init_holidays`	Initialize the holidays with the custom holidays from the config.
`load_config`	Load the config files and sets all necessary properties.
`load_db`	Load Database remote or locally.
`merge_dbs`	Merge local db with remote db.
`push_remote_db`	Push the db_file from db_path_offline to the SFTP server.
`split_date_overlap_session`	Split sessions which contain midnight to to sessions.

Methods Documentation

classmethod add_time_columns(df: DataFrame, date_time_column_name: str = 'start') → DataFrame[source]

Add Year, Month, Week and Day columns to an existing Dataframe.

Parameters

df (pd.DataFrame) – Dataframe the columns should be added to.
date_time_column (str) – Name of the column containing the used date

Returns

Dataframe with added Year, Month, Week and Day columns.

Return type

pd.DataFrame

calc_file_hashes() → DataFrame

Calculate hashvalues for files.

Returns: Dataframe with file hashes.
Return type: pd.DataFrame

clean_db() → None: Remove rows where the session work was less than 1min.

generate_contract_worktime_df() → DataFrame[source]

Generate a Dataframe with ‘worktime’ column.

Generate a template like DataFrame containing the mean daily work time for each day counting as workday based on the contract details. This Template will be used later on to generate the manual_df and holiday_df.

Returns: contract_worktime_df – Dataframe containing all workdays and their mean daily working time, since the 1st contract started until now
Return type: pandas.DataFrame

get_daily_worktime(frequenzy: str, worktime: Union[int, float], weekmask: str) → float[source]

Calculate the mean daily work time based on the contract details.

Parameters

frequenzy (str) – Timeperiod in which the the user is supposed to to work worktime*1h Supported values are: monthly, weekly
worktime (int, float) – Time in hours that the user is supposed to work in frequenzy
weekmask (str) – Listing of the abbreviated weekdays the user is supposed to work i.e.: “Mon Tue Wed Thu Fri Sat”

Returns

daily_worktime

Return type

float

get_datetime_now() → datetime

Helpermethod for mocking of datetime.datetime.now() in unittests.

Returns: datetime.now()
Return type: datetime.datetime

get_holiday_df() → DataFrame[source]

Generate a Dataframe containing all needed holidays.

They begin at the start of the first contract until now, as well as work time based on the contract parameters for those days.

Returns: holiday_df – Dataframe containing all holidays from the start of the first contract until now, as well as work time based on the contract parameters for those days.
Return type: pandas.DataFrame

get_manual_df_with_workime() → DataFrame[source]

Read the manual_db file.

That file contains information like sick time | vacation, specified by manual_db in the config. Then expands the given time periods to date_ranges on a daily base, drops the none workdays and adds the daily work time based on the contract parameters for that day.

Returns: Dataframe containing the to a daily base expanded entry’s of manual_df.
Return type: pandas.DataFrame

get_pandas_now() → Timestamp

Return datetime.now as pd.Timestamp.

Returns: current time as timestamp: pd.Timestamp
Return type: pd.Timestamp

get_plot_df(rule='D', date_time_column='start') → DataFrame[source]

Return a Dataframe prepared for plotting.

Dataframe with a DateTimeIndex, columns named by occupation and containing the worked time of that occupation for the given samplingrate

Parameters: rule (str) – Resampling rule see pandas.DataFrame.resample
Returns: Dataframe with a DateDimeIndex, columns named by occupation and containing the worktime of that occupation for the samplingrate
Return type: pandas.DataFrame

get_remote_db() → bool

Download the db_file to db_path_online from the SFTP server.

This uses the values specified at [“login”][“db_path”] in the config file.

Returns: Whether database retrieval succeeded or not.
Return type: bool

get_total_df() → DataFrame[source]

Calculate total Dataframe.

Returns: Dataframe with ‘worktime’ and time columns
Return type: pd.DataFrame

See also

add_time_columns

init_holidays() → Union[HolidayBase, Dict][source]

Initialize the holidays with the custom holidays from the config.

Returns the class instance from holiday, which matches the country and province given in the config and updates it with the special holidays, also given in the config

Returns: HolidayBase object for the given country and province or an empty dict.
Return type: Union[HolidayBase, Dict]

load_config()[source]: Load the config files and sets all necessary properties.

load_db() → DataFrame[source]

Load Database remote or locally.

Tries to load the database directly from the server if possible, else it loads the local database or throws an exception that isn’t possible either.

Returns: db – Database with the actually worked time
Return type: pandas.DataFrame

merge_dbs() → DataFrame

Merge local db with remote db.

The overlap (same start) is replaced with the max value of end.

Returns: Local db merged with remote db, with striped overlap.
Return type: pd.Dataframe

push_remote_db() → bool

Push the db_file from db_path_offline to the SFTP server.

This uses the values specified at [“login”][“db_path”] in the config file.

Returns: Whether database upload succeeded or not.
Return type: bool

split_date_overlap_session() → None[source]

Split sessions which contain midnight to to sessions.

The first lasting until midnight and the second starting at midnight.

df before:
start   end
1.1.1970 21:00:00   2.1.1970 02:00:00

df after:
start   end
1.1.1970 21:00:00   2.1.1970 00:00:00
2.1.1970 00:00:00   2.1.1970 02:00:00