helikite.instruments.base

Attributes

logger

Classes

Instrument

Helper class that provides a standard way to create an ABC using

Module Contents

helikite.instruments.base.logger

class helikite.instruments.base.Instrument(dtype: Dict[Any, Any] = {}, na_values: List[Any] | None = None, header: int | None = 0, delimiter: str = ',', lineterminator: str | None = None, comment: str | None = None, names: List[str] | None = None, index_col: bool | int | None = None, cols_export: List[str] = [], cols_housekeeping: List[str] = [], export_order: int | None = None, pressure_variable: str | None = None)

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

dtype

na_values = None

header = 0

delimiter = ','

lineterminator = None

comment = None

names = None

index_col = None

cols_export = []

cols_housekeeping = []

export_order = None

pressure_variable = None

filename: str | None = None

date: datetime.datetime | None = None

pressure_offset_housekeeping: float | None = None

time_offset: Dict[str, int]

name: str | None = None

time_range: Tuple[Any, Any] | None = None

add_config(yaml_props: Dict[str, Any])

Adds the application’s config to the Instrument class

This is called from the main() function in helikite.py

abstractmethod data_corrections(df, *args, **kwargs)

Default callback function for data corrections.

Return with no changes

create_plots(df: pandas.DataFrame) → List[plotly.graph_objects.Figure | None]

Default callback for generated figures from dataframes

Return nothing, as anything else will populate the list that is written out to HTML.

abstractmethod file_identifier(first_lines_of_csv: List[str])

Default file identifier callback

Must return false. True would provide false positives.

date_extractor(first_lines_of_csv: List[str])

Returns the date of the data sample from a CSV header

Can be used for an instrument that reports the date in header instead of in the data field.

Return None if there is nothing to do here

add_device_name_to_columns(df: pandas.DataFrame) → pandas.DataFrame: Updates dataframe with column names prefixed by instrument name

property housekeeping_columns: List[str]

Returns housekeeping columns, prefixed with the instrument name

If there are no housekeeping variables, return an empty list

property export_columns: List[str]

Returns export datafile columns, prefixed with the instrument name

If there are no variables, return an empty list

The export data file is a combined output, similar to housekeeping

abstractmethod set_time_as_index(df: pandas.DataFrame) → pandas.DataFrame

This function is used to set the date in the file to the index

By default no action is taken, but this can be overridden in the instrument class to set the date as the index.

correct_time_from_config(df: pandas.DataFrame, trim_start: pandas.Timestamp | None = None, trim_end: pandas.Timestamp | None = None) → pandas.DataFrame: Correct the time offset and trim from the configuration

set_housekeeping_pressure_offset_variable(df: pandas.DataFrame, column_name=constants.HOUSEKEEPING_VAR_PRESSURE) → pandas.DataFrame

Generate variable to offset pressure value for housekeeping

Using an offset in the configuration, a new variable is created that offset’s the instruments pressure variable. This is used to align the pressure value on the plot to help align pressure.

abstractmethod read_data() → pandas.DataFrame

Read data into dataframe

This allows a custom read function to parse the CSV/TXT into a dataframe, for example cleaning dirty data at the end of the file in memory without altering the input file (see flight computer conf).

detect_from_folder(input_folder: str, lines_to_read=constants.QTY_LINES_TO_IDENTIFY_INSTRUMENT, quiet=False)

Scans an input folder for the instrument file

If there are two files that match the instrument, the function will raise an error. This is to prevent the program from trying to process the same file twice.

read_from_folder(input_folder: str, quiet=False) → pandas.DataFrame: Reads the data from the detected file in the input folder

remove_duplicates(df: pandas.DataFrame) → pandas.DataFrame: Remove duplicate rows from the dataframe