cellpy.readers.sql_dbreader

This module is an example of how to implement a custom database reader for the batch utility in cellpy.

Module Contents

Classes

Base	Base class for the database models.
Batch	Model for batch objects in the database.
Cell	Model for cell objects in the database.
RawData	Model for raw data objects in the database.
SQLReader	A custom database reader for the batch utility in cellpy.

Attributes

DB_FILE_EXCEL
DB_FILE_SQLITE
DB_URI
HEADER_ROW
TABLE_NAME_EXCEL
UNIT_ROW
batch_cell_association_table
hdr_journal

class Base

Bases: sqlalchemy.orm.DeclarativeBase

Base class for the database models.

class Batch

Bases: Base

Model for batch objects in the database.

cells: sqlalchemy.orm.Mapped[List[Cell]]

comment: sqlalchemy.orm.Mapped[str | None]

name: sqlalchemy.orm.Mapped[str]

pk: sqlalchemy.orm.Mapped[int]

class Cell

Bases: Base

Model for cell objects in the database.

active_material_mass_fraction: sqlalchemy.orm.Mapped[float | None]

area: sqlalchemy.orm.Mapped[float | None]

argument: sqlalchemy.orm.Mapped[str | None]

batches: sqlalchemy.orm.Mapped[List[Batch] | None]

cell_design: sqlalchemy.orm.Mapped[str | None]

cell_exists: sqlalchemy.orm.Mapped[bool | None]

cell_group: sqlalchemy.orm.Mapped[str | None]

cell_type: sqlalchemy.orm.Mapped[str | None]

cellpy_file_name: sqlalchemy.orm.Mapped[str | None]

channel: sqlalchemy.orm.Mapped[str | None]

comment_cell: sqlalchemy.orm.Mapped[str | None]

comment_general: sqlalchemy.orm.Mapped[str | None]

comment_history: sqlalchemy.orm.Mapped[str | None]

comment_slurry: sqlalchemy.orm.Mapped[str | None]

electrolyte: sqlalchemy.orm.Mapped[str | None]

experiment_type: sqlalchemy.orm.Mapped[str | None]

formation: sqlalchemy.orm.Mapped[str | None]

frozen: sqlalchemy.orm.Mapped[bool | None]

inactive_additive_mass: sqlalchemy.orm.Mapped[float | None]

instrument: sqlalchemy.orm.Mapped[str | None]

label: sqlalchemy.orm.Mapped[str | None]

loading_active: sqlalchemy.orm.Mapped[float | None]

mass_active: sqlalchemy.orm.Mapped[float | None]

mass_total: sqlalchemy.orm.Mapped[float | None]

material_class: sqlalchemy.orm.Mapped[str | None]

material_group_label: sqlalchemy.orm.Mapped[str | None]

material_label: sqlalchemy.orm.Mapped[str | None]

material_pre_processing: sqlalchemy.orm.Mapped[str | None]

material_solvent: sqlalchemy.orm.Mapped[str | None]

material_sub_label: sqlalchemy.orm.Mapped[str | None]

material_surface_processing: sqlalchemy.orm.Mapped[str | None]

name: sqlalchemy.orm.Mapped[str]

nominal_capacity: sqlalchemy.orm.Mapped[float | None]

pasting_thickness: sqlalchemy.orm.Mapped[str | None]

pk: sqlalchemy.orm.Mapped[int]

project: sqlalchemy.orm.Mapped[str | None]

raw_data: sqlalchemy.orm.Mapped[List[RawData]]

schedule: sqlalchemy.orm.Mapped[str | None]

selected: sqlalchemy.orm.Mapped[bool | None]

separator: sqlalchemy.orm.Mapped[str | None]

solvent_solid_ratio: sqlalchemy.orm.Mapped[str | None]

temperature: sqlalchemy.orm.Mapped[float | None]

test_date: sqlalchemy.orm.Mapped[str | None]

class RawData

Bases: Base

Model for raw data objects in the database.

cell: sqlalchemy.orm.Mapped[Cell]

cell_pk: sqlalchemy.orm.Mapped[int]

is_file: sqlalchemy.orm.Mapped[bool]

name: sqlalchemy.orm.Mapped[str]

pk: sqlalchemy.orm.Mapped[int]

class SQLReader(db_connection: str = None, batch: str = None, **kwargs)

Bases: cellpy.readers.core.BaseDbReader

A custom database reader for the batch utility in cellpy.

Initialize the SQLReader.

add_batch_object(batch: Batch) → None

Add a batch object to the database.

For this to work, you will have to create a batch object first, then populate it with data (including the cell objects that the batch refers to, see .add_cell_object), and finally add it to the database using this method.

Examples

>>> from cellpy.readers import sql_dbreader
>>> db = sql_dbreader.SQLReader()
>>> db.open_db("my_db.sqlite")

>>> # create a batch object:
>>> batch = sql_dbreader.Batch()
>>> batch.name = "my_batch"
>>> batch.comment = "my_comment"

>>> # add the cells to the batch:
>>> batch.cells = [cell1, cell2, cell3]

>>> db.add_batch_object(batch)

add_cell_object(cell: Cell) → None

Add a cell object to the database.

For this to work, you will have to create a cell object first, then populate it with data, and finally add it to the database using this method.

Examples

>>> from cellpy.readers import sql_dbreader
>>> cell = sql_dbreader.Cell()
>>> cell.name = "my_cell"
>>> cell.label = "my_label"
>>> cell.project = "my_project"
>>> cell.cell_group = "my_cell_group"
>>> # ...and so on...

>>> db = sql_dbreader.SQLReader()
>>> db.open_db("my_db.sqlite")
>>> db.add_cell_object(cell)

Parameters:

cell – cellpy.readers.sql_dbreader.Cell object

Returns:

None

add_raw_data_object(raw_data: RawData) → None

create_db(db_uri: str = DB_URI, echo: bool = False, **kwargs) → None

extract_date_from_cell_name(force=False)

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

Get a dictionary with the data from a batch for the journal.

Parameters:

• batch_name – name of the batch.

• include_key – include the key (the cell ids).

• include_individual_arguments – include the individual arguments.

Returns:

dictionary with the data.

Return type:

dict

get_area(pk: int) → float

get_args(pk: int) → dict

get_by_column_label(pk: int, name: str) → Any

get_cell_name(pk: int) → str

get_cell_type(pk: int) → str

get_comment(pk: int) → str

get_experiment_type(pk: int) → str

get_group(pk: int) → str

get_instrument(pk: int) → str

get_label(pk: int) → str

get_loading(pk: int) → float

get_mass(pk: int) → float

get_nom_cap(pk: int) → float

get_total_mass(pk: int) → float

import_cells_from_excel_sqlite(db_path: str = None, echo: bool = False, allow_duplicates: bool = False, allow_updates: bool = True, process_batches=True, clear=False) → None

Import cells from old db to new db.

Parameters:

• db_path – path to old db (if not provided, it will use the already loaded db if it exists).

• echo – will echo sql statements (if loading, i.e. if db_path is provided).

• allow_duplicates – will not import if cell already exists in new db.

• allow_updates – will update existing cells in new db.

• process_batches – will process batches (if any) in old db.

• clear – will clear all rows in new db before importing (asks for confirmation).

Returns:

None

inspect_hd5f_fixed(pk: int) → int

load_excel_sqlite(db_path: str, echo: bool = False) → None

Load an old sqlite cellpy database created from an Excel file.

You can use the cellpy.utils.batch_tools.sqlite_from_excel.run() function to convert an Excel file to a sqlite database.

open_db(db_uri: str = DB_URI, echo: bool = False, **kwargs) → None

select_batch(batch_name: str) → List[int]

view_old_excel_sqlite_table_columns() → None

Prints the columns of the old sqlite database.

DB_FILE_EXCEL

DB_FILE_SQLITE

DB_URI

HEADER_ROW

TABLE_NAME_EXCEL

UNIT_ROW

batch_cell_association_table

hdr_journal