cellpy.readers.dbreader

Module Contents

Classes

DbSheetCols
Reader	Simple excel reader.

class DbSheetCols

class Reader(db_file=None, db_datadir=None, db_datadir_processed=None, db_frame=None, batch=None, batch_col_name=None)

Bases: cellpy.readers.core.BaseDbReader

Simple excel reader.

Parameters:

• db_file (str, pathlib.Path) – xlsx-file to read.

• db_datadir (str, pathlib.Path) – path where raw date is located.

• db_datadir_processed (str, pathlib.Path) – path where cellpy files are located.

• db_frame (pandas.DataFrame) – use this instead of reading from xlsx-file.

• batch (str) – batch name to use.

• batch_col_name (str) – name of the column in the db-file that contains the batch name.

extract_date_from_cell_name(force=False)

filter_by_col(column_names)

filters sheet/table by columns (input is column header)

The routine returns the serial numbers with values>1 in the selected columns.

Parameters:

column_names (list) – the column headers.

Returns:

pandas.DataFrame

filter_by_col_value(column_name, min_val=None, max_val=None)

filters sheet/table by column.

The routine returns the serial-numbers with min_val <= values >= max_val in the selected column.

Parameters:

• column_name (str) – column name.

• min_val (int) – minimum value of serial number.

• max_val (int) – maximum value of serial number.

Returns:

pandas.DataFrame

filter_by_slurry(slurry, appender='_')

Filters sheet/table by slurry name.

Input is slurry name or list of slurry names, for example ‘es030’ or [“es012”,”es033”,”es031”].

Parameters:

• slurry (str or list of strings) – slurry names.

• appender (chr) – char that surrounds slurry names.

Returns:

List of serial_number (ints).

filter_selected(serial_numbers)

abstract from_batch(batch_name: str, include_key: bool = False, include_individual_arguments: bool = False) → dict

get_all()

get_area(serial_number)

abstract get_areal_loading(serial_number)

get_args(serial_number: int) → dict

get_by_column_label(column_name, serial_number)

get_cell_name(serial_number)

get_cell_type(serial_number)

get_comment(serial_number)

get_experiment_type(serial_number)

get_fileid(serial_number, full_path=True)

get_group(serial_number)

get_instrument(serial_number)

get_label(serial_number)

get_loading(serial_number)

get_mass(serial_number)

get_nom_cap(serial_number)

get_total_mass(serial_number)

inspect_exists(serial_number)

inspect_hd5f_fixed(serial_number)

static intersect(lists)

pick_table()

Pick the table and return a pandas.DataFrame.

print_serial_number_info(serial_number, print_to_screen=True)

Print information about the run.

Parameters:

• serial_number – serial number.

• print_to_screen – runs the print statement if True, returns txt if not.

Returns:

txt if print_to_screen is False, else None.

select_all(serial_numbers)

Select rows for identification for a list of serial_number.

Parameters:

serial_numbers – list (or ndarray) of serial numbers

Returns:

pandas.DataFrame

select_batch(batch, batch_col_name=None, case_sensitive=True, drop=True, clean=False, **kwargs) → List[int]

Selects the rows in column batch_col_number.

Parameters:

• batch – batch to select

• batch_col_name – column name to use for batch selection (default: DbSheetCols.batch).

• case_sensitive – if True, the batch name must match exactly (default: True).

• drop – if True, all un-selected rows are dropped from the table (default: True).

• clean – if True and drop is True, the table is cleaned from duplicates and NaNs (default: False).

Returns:

List of row indices

select_serial_number_row(serial_number)

Select row for identification number serial_number

Parameters:

serial_number – serial number

Returns:

pandas.DataFrame

static subtract(list1, list2)

static subtract_many(list1, lists)

static union(lists)