The psychopy package contains different modules for different features. However, as mentioned on the Getting started page, getting the psychopy package to work is not easy, which is why we recommend the “batteries included” standalone version of PsychoPy. If you plan on programming your PsychoPy experiment (so not use the Builder interface), you technically do not need the entire “standalone” PsychoPy package installing the psychopy Python package would suffice and you could just write your experiments in your favorite editor (like Visual Studio Code). Whereas the Builder interface generates such code from your graphical experiment, in the Coder interface you’ll write your experiment using functionality from the psychopy package directly! If you look at this generated Python script closely, you’ll see that most of the code is based on functions and classes from the psychopy Python package. When using the Builder interace, you’ve seen that, “under the hood”, PsychoPy converts your Builder experiment to a Python script, which is then executed to run your experiment. This time, we will create a variant of the classical color-word Stroop task, the emotion-word Stroop task, in which participants are presented with images of emotional facial expressions in combination with words describing emotions that are congruent with the images (e.g., an angry expression with the word “angry”) or incongruent with the images (e.g., a happy exression with the word “angry”). ![]() Like in the previous Builder tutorial, we will explain the concepts by walking you through the process of programming a real experiment. It will be a somewhat more “dry” tutorial because we won’t actually create any stimuli or trials in this tutorial, because we’ll save that for the next tutorial. Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.Introduction to the PsychoPy Coder (tutorial) #Īt last, we’ll discuss the PsychoPy Coder! In this tutorial, we explain the basics of the Coder interface. 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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