Saving / Loading Data#

All PEPT objects can be saved in an efficient binary format using and pept.load:

import pept
import numpy as np

# Create some dummy data
lines_raw = np.arange(70).reshape((10, 7)
lines = pept.LineData(lines_raw)

# Save data"data.pickle", lines)

# Load data
lines_loaded = pept.load("data.pickle")

The binary approach has the advantage of preserving all your metadata saved in the object instances - e.g. columns, sample_size - allowing the full state to be reloaded.

Matrix-like data like pept.LineData and pept.PointData can also be saved in a slower, but human-readable CSV format using their class methods .to_csv; such tabular data can then be reinitialised using pept.read_csv:

# Save data in CSV format

# Load data back - *this will be a simple NumPy array!*
lines_raw = pept.read_csv("data.csv")

# Need to put the array back into a `pept.LineData`
lines = pept.LineData(lines_raw)