timelined_array 0.0.5

Manage easily 1 or multidimensionnal samples numpy arrays that are time related. Extends numpy without removing any of it's abilities on such arrays.

timelined_array

Overview

The TimelinedArray package provides a set of classes and utilities for working with time-indexed arrays. It extends the functionality of NumPy arrays to include time-based indexing and operations, making it easier to work with time-series data.

Classes

Timeline

A subclass of np.ndarray that represents a timeline associated with the array. It includes methods for creating uniformly spaced timelines and calculating time steps.

Boundary

An enumeration that defines inclusive and exclusive boundaries for time indexing.

TimeIndexer

A class that provides methods for converting time values to array indices and for indexing arrays based on time.

TimeMixin

A mixin class that adds time-related methods and properties to arrays, including methods for aligning, transposing, and moving axes.

TimePacker

A class for packing arrays with their associated timelines. Usefull to plot data fast to matplotlib.

TimelinedArray

A subclass of np.ndarray that includes a timeline and a time dimension. It provides methods for time-based indexing and operations.

MaskedTimelinedArray

A subclass of np.ma.MaskedArray that includes a timeline and a time dimension. It provides methods for time-based indexing and operations on masked arrays.

Seconds

A simple class for converting seconds to array indices based on a given sampling frequency.

Installation

To install the TimelinedArray package, simply type in your environment activated console :

pip install timelined_array

The package can be found on PyPI at : https://pypi.org/project/timelined_array/

Usage

Imports

from timelined_array import TimelinedArray, MaskedTimelinedArray, Boundary, Timeline

Creating a TimelinedArray

import numpy as np
from timelined_array import TimelinedArray

data = np.random.rand(100, 10)
timeline = np.linspace(0, 10, 100)
timelined_array = TimelinedArray(data, timeline=timeline, time_dimension=0)

Time-based Indexing

# Access data at a specific time
data_at_time = timelined_array.itime[5.0]

# Aligning arrays
aligned_array = TimelinedArray.align_from_iterable([timelined_array, another_timelined_array])

Masked TimelinedArray

from timelined_array import MaskedTimelinedArray

masked_data = np.ma.masked_array(data, mask=data > 0.5)
masked_timelined_array = MaskedTimelinedArray(masked_data, timeline=timeline, time_dimension=0)

Converting Seconds to Index

masked_data.itime.time_to_index(5.0)

Misc

License

This package is licensed under the MIT License. See the LICENSE file for more details.

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss any changes.

Contact

For any questions or issues, please contact the package maintainer.