A pyparsing-based library for parsing SOQL statements

Overview

CONTRIBUTORS WANTED!!

Installation

pip install python-soql-parser

or, with poetry

poetry add python-soql-parser

Usage

from python_soql_parser import parse


parse_result = parse("SELECT Id FROM Account")

where parse_result is a ParseResults object from pyparsing.

Notable caveats

Unsupported features

  • Subqueries (e.g., SELECT Name, (SELECT LastName FROM Contacts) FROM Account)
  • Aggregate queries
  • SOQL specific WHERE-clause tokens (e.g., LAST_N_DAYS: )

Partially supported

These are either partially supported or weakly supported

  • Related attributes (e.g., SELECT Id, Account.Name FROM Contact). The object name is not yet parsed out on its own. This will simply parse out "Account.Name"

Contributing

A lot of work remains to be done. Practically no SOQL-specific features are supported as of yet. If you want to contribute, just open a PR! (and add a test for your new feature)

Setting up locally

First install poetry. Afterwards, to install the dependencies, run

poetry install

Running the tests

Simply execute

pytest

House cleaning

Please sort imports with isort and format the code with black (in that order).

Owner
Kicksaw
Salesforce implementation, data migration, process optimization, tool integration, & data sourcing / enrichment services to boost your revenue teams.
Kicksaw
A Python 3 package for state-of-the-art statistical dimension reduction methods

direpack: a Python 3 library for state-of-the-art statistical dimension reduction techniques This package delivers a scikit-learn compatible Python 3

Sven Serneels 32 Dec 14, 2022
This reposityory contains the PyTorch implementation of our paper "Generative Dynamic Patch Attack".

Generative Dynamic Patch Attack This reposityory contains the PyTorch implementation of our paper "Generative Dynamic Patch Attack". Requirements PyTo

Xiang Li 8 Nov 17, 2022
Custom implementation of Corrleation Module

Pytorch Correlation module this is a custom C++/Cuda implementation of Correlation module, used e.g. in FlowNetC This tutorial was used as a basis for

Clément Pinard 361 Dec 12, 2022
Neural Magic Eye: Learning to See and Understand the Scene Behind an Autostereogram, arXiv:2012.15692.

Neural Magic Eye Preprint | Project Page | Colab Runtime Official PyTorch implementation of the preprint paper "NeuralMagicEye: Learning to See and Un

Zhengxia Zou 56 Jul 15, 2022
Roger Labbe 13k Dec 29, 2022
PyTorch implementation of paper "StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement" (ICCV 2021 Oral)

StarEnhancer StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement (ICCV 2021 Oral) Abstract: Image enhancement is a subjective process w

IDKiro 133 Dec 28, 2022
Like ThreeJS but for Python and based on wgpu

pygfx A render engine, inspired by ThreeJS, but for Python and targeting Vulkan/Metal/DX12 (via wgpu). Introduction This is a Python render engine bui

139 Jan 07, 2023
Reproduces the results of the paper "Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations".

Finite basis physics-informed neural networks (FBPINNs) This repository reproduces the results of the paper Finite Basis Physics-Informed Neural Netwo

Ben Moseley 65 Dec 28, 2022
HandFoldingNet ✌️ : A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton

HandFoldingNet ✌️ : A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton Wencan Cheng, Jae Hyun Park, Jong

cwc1260 23 Oct 21, 2022
The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier')

The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography

James 135 Dec 23, 2022
Deep Learning for Time Series Forecasting.

nixtlats:Deep Learning for Time Series Forecasting [nikstla] (noun, nahuatl) Period of time. State-of-the-art time series forecasting for pytorch. Nix

Nixtla 5 Dec 06, 2022
[SIGGRAPH 2022 Journal Track] AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars

AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars Fangzhou Hong1*  Mingyuan Zhang1*  Liang Pan1  Zhongang Cai1,2,3  Lei Yang2 

Fangzhou Hong 749 Jan 04, 2023
PyTorch implementation of PNASNet-5 on ImageNet

PNASNet.pytorch PyTorch implementation of PNASNet-5. Specifically, PyTorch code from this repository is adapted to completely match both my implemetat

Chenxi Liu 314 Nov 25, 2022
Differentiable rasterization applied to 3D model simplification tasks

nvdiffmodeling Differentiable rasterization applied to 3D model simplification tasks, as described in the paper: Appearance-Driven Automatic 3D Model

NVIDIA Research Projects 336 Dec 30, 2022
Using this codebase as a tool for my own research. Making some modifications to the original repo for my own purposes.

For SwapNet Create a list.txt file containing all the images to process. This can be done with the GNU find command: find path/to/input/folder -name '

Andrew Jong 2 Nov 10, 2021
Cortex-compatible model server for Python and TensorFlow

Nucleus model server Nucleus is a model server for TensorFlow and generic Python models. It is compatible with Cortex clusters, Kubernetes clusters, a

Cortex Labs 14 Nov 27, 2022
A high performance implementation of HDBSCAN clustering.

HDBSCAN HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates

2.3k Jan 02, 2023
Predictive Maintenance LSTM

Predictive-Maintenance-LSTM - Predictive maintenance study for Complex case study, we've obtained failure causes by operational error and more deeply by design mistakes.

Amir M. Sadafi 1 Dec 31, 2021
ECCV2020 paper: Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards. Code and Data.

This repo contains some of the codes for the following paper Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards. Code

Xuewen Yang 56 Dec 08, 2022
Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network.

Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network

111 Dec 27, 2022