This repository contains tutorials for the py4DSTEM Python package

Overview
Comments
  • Binder dev

    Binder dev

    • Binder link created, currently lands in Index.ipynb
    • data loaded as part of the notebooks, running all cells on notebooks inside binder will work.
    • Added file_getter.py which takes command-line arguments, which makes extending the download to more notebooks fairly straightforward.
    • Both notebooks work, make_probe_templates.ipynb required adding some clean-up steps to avoid going over 2GB ram limit, the alternative is to split them into more separate notebooks.
    • There's a slight issue that if people don't shutdown notebooks properly or if they have multiple notebooks over, they may cause kernel panics, both notebooks peak memory usage push the 2GB limit .
    • I haven't given much attention to style or formatting currently just wanted to get something functional and working to see if works as required.
    opened by alex-rakowski 1
  • SSB tutorial notebooks with new dataset

    SSB tutorial notebooks with new dataset

    These are two new tutorial notebooks I updated. One is for single-run reconstruction, the other is for interactive mode with ipywidgets and matplotlib visualization.

    opened by PhilippPelz 0
  • Binder dev

    Binder dev

    • Binder link created, currently lands in Index.ipynb
    • data loaded as part of the notebooks, running all cells on notebooks inside binder will work.
    • Added file_getter.py which takes command-line arguments, which makes extending the download to more notebooks fairly straightforward.
    • Both notebooks work, make_probe_templates.ipynb required adding some clean-up steps to avoid going over 2GB ram limit, the alternative is to split them into more separate notebooks.
    • There's a slight issue that if people don't shutdown notebooks properly or if they have multiple notebooks over, they may cause kernel panics, both notebooks peak memory usage push the 2GB limit .
    • I haven't given much attention to style or formatting currently just wanted to get something functional and working to see if works as required.
    opened by alex-rakowski 0
  • Add simulations for dynamical scattering

    Add simulations for dynamical scattering

    I found that there is almost no proper documentation for the dynamical scattering simulation in py4DSTEM unless you read the source code (actually I couldn't find the documentation for the whole diffraction module). So I created a tutorial using NaCl as an example. Hope I have done it right.

    opened by Taimin 0
  • py4DSTEM.process.virtualimage.get_virtualimage_circ (strain mapping tutorial)

    py4DSTEM.process.virtualimage.get_virtualimage_circ (strain mapping tutorial)

    in the strain mapping tutorial, this step doesn't work !

    [12]

    Next, create a BF virtual detector using the the center beam position (qxy0, qy0)

    We will expand the BF radius slightly (+ 2 px).

    The DF virtual detector can be set to all remaining pixels.

    expand_BF = 2.0 image_BF = py4DSTEM.process.virtualimage.get_virtualimage_circ( dataset, qx0, qy0, probe_semiangle + expand_BF) image_DF = py4DSTEM.process.virtualimage.get_virtualimage_ann( dataset, qx0, qy0, probe_semiangle + expand_BF, 1e3)

    [return]

    AttributeError Traceback (most recent call last) Input In [168], in <cell line: 5>() 1 # Next, create a BF virtual detector using the the center beam position (qxy0, qy0) 2 # We will expand the BF radius slightly (+ 2 px). 3 # The DF virtual detector can be set to all remaining pixels. 4 expand_BF = 2.0 ----> 5 image_BF = py4DSTEM.process.get_virtualimage_circ( 6 dataset, 7 qx0, qy0, 8 probe_semiangle + expand_BF) 9 image_DF = py4DSTEM.process.virtualimage.get_virtualimage_ann( 10 dataset, 11 qx0, qy0, 12 probe_semiangle + expand_BF, 13 1e3)

    AttributeError: module 'py4DSTEM.process' has no attribute 'get_virtualimage_circ'

    Any tips to fix that ?

    py4DSTEM.process.virtualimage.virtualimage.get_virtualimage_circ or py4DSTEM.process.virtualimage.get_virtualimage_circ ?

    opened by lylofu 0
  • ACOM_03_Au_NP_sim.ipynb bugs

    ACOM_03_Au_NP_sim.ipynb bugs

    Running the ACOM_03 notebook as downloaded, cell 25 gives the following error:

    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    /var/folders/ts/tq6v7mks7hvg37ys5zvs1c2w0000gn/T/ipykernel_3012/3733081456.py in <module>
         14 
         15 # Fit an ellipse to the elliptically corrected bvm
    ---> 16 qx0_corr,qy0_corr,a_corr,e_corr,theta_corr = py4DSTEM.process.calibration.fit_ellipse_1D(bvm_ellipsecorr,(qx0,qy0),(qmin,qmax))
         17 
         18 py4DSTEM.visualize.show_elliptical_fit(
    
    NameError: name 'qmin' is not defined
    

    I think someone changed qmin, qmax to be a list called qrange and never actually tested the notebook in a fresh state.

    opened by sezelt 0
  • AttributeError: module 'py4DSTEM.process' has no attribute 'diffraction'

    AttributeError: module 'py4DSTEM.process' has no attribute 'diffraction'

    When I run the "ACOM Tutorial Notebook 01", it gives a following error message.

    AttributeError: module 'py4DSTEM.process' has no attribute 'diffraction'

    version python 3.8.0 py4DSTEM 0.12.6 pywin32 302

    error

    opened by nomurayuki0503 0
Releases(v0.13.8-alpha)
Image augmentation library in Python for machine learning.

Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independe

Marcus D. Bloice 4.8k Jan 07, 2023
General Vision Benchmark, a project from OpenGVLab

Introduction We build GV-B(General Vision Benchmark) on Classification, Detection, Segmentation and Depth Estimation including 26 datasets for model e

174 Dec 27, 2022
N-Omniglot is a large neuromorphic few-shot learning dataset

N-Omniglot [Paper] || [Dataset] N-Omniglot is a large neuromorphic few-shot learning dataset. It reconstructs strokes of Omniglot as videos and uses D

11 Dec 05, 2022
Contenido del curso Bases de datos del DCC PUC versión 2021-2

IIC2413 - Bases de Datos Tabla de contenidos Equipo Profesores Ayudantes Contenidos Calendario Evaluaciones Resumen de notas Foro Política de integrid

54 Nov 23, 2022
Jax/Flax implementation of Variational-DiffWave.

jax-variational-diffwave Jax/Flax implementation of Variational-DiffWave. (Zhifeng Kong et al., 2020, Diederik P. Kingma et al., 2021.) DiffWave with

YoungJoong Kim 37 Dec 16, 2022
Powerful unsupervised domain adaptation method for dense retrieval.

Powerful unsupervised domain adaptation method for dense retrieval

Ubiquitous Knowledge Processing Lab 191 Dec 28, 2022
Cereal box identification in store shelves using computer vision and a single train image per model.

Product Recognition on Store Shelves Description You can read the task description here. Report You can read and download our report here. Step A - Mu

Nicholas Baraghini 1 Jan 21, 2022
Classification Modeling: Probability of Default

Credit Risk Modeling in Python Introduction: If you've ever applied for a credit card or loan, you know that financial firms process your information

Aktham Momani 2 Nov 07, 2022
Production First and Production Ready End-to-End Speech Recognition Toolkit

WeNet 中文版 Discussions | Docs | Papers | Runtime (x86) | Runtime (android) | Pretrained Models We share neural Net together. The main motivation of WeN

2.7k Jan 04, 2023
The Python code for the paper A Hybrid Quantum-Classical Algorithm for Robust Fitting

About The Python code for the paper A Hybrid Quantum-Classical Algorithm for Robust Fitting The demo program was only tested under Conda in a standard

Anh-Dzung Doan 5 Nov 28, 2022
TC-GNN with Pytorch integration

TC-GNN (Running Sparse GNN on Dense Tensor Core on Ampere GPU) Cite this project and paper. @inproceedings{TC-GNN, title={TC-GNN: Accelerating Spars

YUKE WANG 19 Dec 01, 2022
Official implementation of GraphMask as presented in our paper Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking.

GraphMask This repository contains an implementation of GraphMask, the interpretability technique for graph neural networks presented in our ICLR 2021

Michael Schlichtkrull 29 Sep 02, 2022
MolRep: A Deep Representation Learning Library for Molecular Property Prediction

MolRep: A Deep Representation Learning Library for Molecular Property Prediction Summary MolRep is a Python package for fairly measuring algorithmic p

AI-Health @NSCC-gz 83 Dec 24, 2022
Hough Transform and Hough Line Transform Using OpenCV

Hough transform is a feature extraction method for detecting simple shapes such as circles, lines, etc in an image. Hough Transform and Hough Line Transform is implemented in OpenCV with two methods;

Happy N. Monday 3 Feb 15, 2022
学习 python3 以来写的一些垃圾玩具……

和东哥做兄弟 Author: chiupam 版权 未经本人同意,仓库内所有资源文件,禁止任何公众号、自媒体、开发者进行任何形式的转载、发布、搬运。 声明 这不是一个开源项目,只是把 GitHub 当作一个代码的存储空间,本项目不接受任何开源要求。 仅用于学习研究,禁止用于商业用途,不能保证其合法性

Chiupam 67 Mar 26, 2022
Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

English | 简体中文 | 繁體中文 | 한국어 State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained models

Clara Meister 50 Nov 12, 2022
Hyper-parameter optimization for sklearn

hyperopt-sklearn Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn. See how to use hyperopt-sklearn

1.4k Jan 01, 2023
Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in ONNX

ONNX msg_chn_wacv20 depth completion Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20 model in

Ibai Gorordo 19 Oct 22, 2022
TransPrompt - Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification

TransPrompt This code is implement for our EMNLP 2021's paper 《TransPrompt:Towards an Automatic Transferable Prompting Framework for Few-shot Text Cla

WangJianing 23 Dec 21, 2022
Continuous Diffusion Graph Neural Network

We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.

Twitter Research 227 Jan 05, 2023