Loading PEC CSV Data¶

This notebook shows a minimal PEC workflow with the built-in pec_csv loader. It uses the example export that ships with cellpy, inspects the raw file header, loads the cycling data, and generates step and summary tables.

from pathlib import Path
import sys
import re

# Changes to the cellpy repo can directly be used without installing the package. This is useful for development and testing.
repo_root = next(
    (path for path in [Path.cwd(), Path.cwd().parent] if (path / "cellpy" / "__init__.py").exists()),
    None,
)
if repo_root is not None and str(repo_root) not in sys.path:
    sys.path.insert(0, str(repo_root))
# IF not using the cellpy repo, make sure to install cellpy in the current environment (e.g., pip install cellpy) and comment the above code.
import cellpy

local_pec_file = Path("data/pec.csv").resolve()
if local_pec_file.is_file():
    pec_file = local_pec_file
    print(f"Using local PEC file: {pec_file}")
else:
    from cellpy.utils import example_data

    pec_file = example_data.pec_file_path()
    print(f"Using example PEC file: {pec_file}")

pec_file

Look at the first few lines of the PEC file to understand its structure.

with open(pec_file, encoding="utf-8-sig") as handle:
    for line_number, line in zip(range(1, 13), handle):
        print(f"{line_number:02d}: {line.rstrip()}")

Load the PEC data with the built-in pec_csv loader. The mass value can be adjusted for the dataset.

c = cellpy.get(
    filename=pec_file,
    instrument="pec_csv",
    mass=1.0,
    auto_summary=False,
)
c

The raw table is a pandas DataFrame. Prefer c.schema so column names track the native 2.1 headers.

raw = c.data.raw
r = c.schema.raw
raw[[r.datapoint_num, r.cycle_num, r.step_num, r.test_time, r.current, r.potential]].head()

Build the step table and summary so cycle and step information is available.

c.make_step_table()
c.make_summary()

print(f"Loaded rows: {len(c.data.raw)}")
print(f"Cycles: {c.get_cycle_numbers()[:10]}")
print(f"Start time: {c.data.start_datetime}")

c.data.steps.head()

c.data.summary.head()

Save in HDF5 / cellpy format.

c.save("pec_data.h5")

Loading Multiple tests for the same CellID¶

In PEC testers setup at IFE, have LotID to differentiate between cells and tests performed on that cell in terms of TestID.

By default, cellpy allows merging up to 20 files in a single cellpy.get() call. This limit exists to catch accidental over-selection (e.g. a glob matching hundreds of files). If you genuinely need to merge more files, raise the limit before loading — either in your script or in your config file.

# Raise the merge limit if you need to combine more than 20 files.
# Option A - change it for the current session only:
from cellpy import config

config.reader.max_raw_files_to_merge = 50  # or whatever you need

# Option B - set it permanently in your cellpy.toml:
#   [reader]
#   max_raw_files_to_merge = 50

Multiple files can be loaded at once by passing a list of file paths to cellpy.get(). The files are sorted by the test number extracted from the file name.

def test_number(path):
    """Extract the test number from the file name. Expects file names in the format "TestXXXXX.csv"."""
    return int(re.search(r"Test(\d+)\.csv$", path.name).group(1))

files = [
    Path("data/pec_multiple_tests/Test25195.csv"),
    Path("data/pec_multiple_tests/Test25205.csv"),
    Path("data/pec_multiple_tests/Test25209.csv"),
]

files = sorted(files, key=test_number)

c = cellpy.get(files, instrument="pec_csv")
c.make_step_table()
c.make_summary()