Loading, saving and exporting data¶

import pathlib

import numpy as np
import pandas as pd
from rich import print

import cellpy

Set the paths and filename(s). You can either load a single file filename, or add a list of filenames filenamelist (if several files belong to the same experiment):

filedir = pathlib.Path("data")  # foldername within the same directory

# single filename
filename = "20210210_FC_01_cc_01.res"
# list of files (continuations within same experiment)
filenamelist = [
    "20210210_FC_01_cc_01.res",
    "20210210_FC_01_cc_02.res",
    "20210210_FC_01_cc_03.res",
    "20210210_FC_01_cc_04.res",
]

filepaths = [filedir / file for file in filenamelist]

Loading data¶

Use cellpy.get() to load the rawdatafile(s):

c = cellpy.get(filepaths, mass=1.2, cycle_mode="full-cell")

Note: Without any further specifications, cellpy.get() will use the standard instrument loader as defined in your config file (here the one for loading arbin .res files). For loading different data formats, have a look at Loading different formats or Custom loaders.

Now you have created your CellpyCell object and can start to explore it further. The cellpy.get() function conveniently created a so-called step-table and a summary for you (both are pandas dataframes):

Data inspection¶

c.data.summary.head(5)

c.data.steps.head(5)

It also contains the raw data:

c.data.raw.head(5)

Metadata¶

Cellpy fills in some standard values for meta-data for you (based on your config-file), these can be updated and adjusted.

E.g., we can set a new cell name and add a value active electrode area:

c.active_electrode_area = 1.767
c.cell_name = "20210210_FC"

c.make_summary()

To check the units that are used within cellpy:

print(c.cellpy_units)

CellpyUnits(
    current='A',
    charge='mAh',
    voltage='V',
    time='sec',
    resistance='ohm',
    power='W',
    energy='Wh',
    frequency='hz',
    mass='mg',
    nominal_capacity='mAh/g',
    specific_gravimetric='g',
    specific_areal='cm**2',
    specific_volumetric='cm**3',
    length='cm',
    area='cm**2',
    volume='cm**3',
    temperature='C',
    pressure='bar'
)

Metadata can also be included by the use of a database file containing the required values. The information on database filename and content has to be set in the config file.

Saving & exporting data¶

You can easily save all of this in the cellpy .HDF5 format:

c.save(filedir / "out" / "20210210_FC")

or export to csv or excel

c.to_csv(filedir / "out", sep=";", raw=True)

c.to_excel(filedir / "out" / "20210210_FC.xlsx")

Loading saved files¶

To load saved files, you can use the cellpy.get() function again:

candidates = [
    filedir / "20210210_FC.h5",
    filedir / "out" / "20210210_FC.h5",
]
cellpy_path = next((p for p in candidates if p.exists()), None)
if cellpy_path is None:
    raise FileNotFoundError(
        "Could not find 20210210_FC.h5 in data/ or data/out/. "
        "Run the save cell above, or place the file in examples/data/."
    )
c = cellpy.get(cellpy_path)

c.data.summary.head()