Most critically, however, the other libraries do not offer a graphical interface to create studies, which limits their suitability for undergraduate teaching. In comparison to these, PsychoPy offers a broader list of stimulus options, experimental designs, response options (such as rating scales), and hardware support, as well as a larger community of active developers. Since 2008, numerous additional libraries have been created in Python, such as Expyriment (Krause & Lindemann, 2014), PyGaze (Dalmaijer, Mathôt, & Van der Stigchel, 2014), mPsy ( ), and SMILE ( ). We discuss the current state of the project, as well as plans for the future.Īt the time that the core PsychoPy library was written, the other comparable packages were Vision Egg (Straw, 2008) and PyEPL (Geller, Schlefer, Sederberg, Jacobs, & Kahana, 2007), both of which subsequently ceased development. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility.
#Psychopy scale description code
The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. Here we describe the features that have been added over the last 10 years of its development. It now provides a choice of interface users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. Python backend system that decouples API from implementation unumpy provides a NumPy API. Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis.ĭevelop libraries for array computing, recreating NumPy's foundational concepts.
NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.ĭeep learning framework that accelerates the path from research prototyping to production deployment.Īn end-to-end platform for machine learning to easily build and deploy ML powered applications.ĭeep learning framework suited for flexible research prototyping and production.Ī cross-language development platform for columnar in-memory data and analytics.
Labeled, indexed multi-dimensional arrays for advanced analytics and visualization NumPy-compatible array library for GPU-accelerated computing with Python.Ĭomposable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU. NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.ĭistributed arrays and advanced parallelism for analytics, enabling performance at scale. With this power comes simplicity: a solution in NumPy is often clear and elegant. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. Nearly every scientist working in Python draws on the power of NumPy.
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