Examples of using f2py to get high-speed Fortran integrated with Python easily

Overview

f2py Examples

Actions Status

Simple examples of using f2py to get high-speed Fortran integrated with Python easily. These examples are also useful to troubleshoot problems with f2py.

Build

Fortran compiler is needed:

  • Mac: brew install gcc
  • Linux: apt install gfortran or yum install gfortran
  • Windows

Install

pip install -e .

This will compile the Fortran code (in .f and .f90 files). It creates a file pyprod.* where * depends on operating system and Python version:

  • Linux/Mac: .so
  • Windows: .pyd

Examples

.f2py_f2cmap required

A file .f2py_f2cmap as in this repository must be in the top-level (same as setup.py) of the project directory tree. If this file is missing, all "real" kinds map to float32, which is not in general what is wanted. A missing .f2py_f2cmap will lead float64 values to be completely incorrect.

The names in the .f2py_f2cmap must exactly match the Fortran variable names used for the real kind. If you use dp=>real64 in the Fortran code, then .f2py_f2cmap must map dp as well.

Fortran Intents

python f2py_demo.py

output:

x = 3
y = 2
x * y = 6.0
Your system did this in Python using Fortran-compiled library

Fortran comment syntax

Fortran 77 is officially full-line comments only. Inline comments are not allowed with f2py as a result in Fortran 77 files. Demonstrate this with:

f2py -m badcomment -c badcomment.f

Troubleshooting f2py

f2py normally Just Works on Linux, MacOS and Windows Subsystem for Linux. However, Windows itself can be more challenging due to inconsistencies in Microsoft Visual Studio.

See the Windows f2py installation guide and troubleshooting guide.

Owner
Michael
Scientific computing enabling new frontiers in aerospace and geospace discovery. Language expertise includes Fortran, Python, Matlab, CMake.
Michael
PyTorch trainer and model for Sequence Classification

PyTorch-trainer-and-model-for-Sequence-Classification After cloning the repository, modify your training data so that the training data is a .csv file

NhanTieu 2 Dec 09, 2022
Detection of drones using their thermal signatures from thermal camera through YOLO-V3 based CNN with modifications to encapsulate drone motion

Drone Detection using Thermal Signature This repository highlights the work for night-time drone detection using a using an Optris PI Lightweight ther

Chong Yu Quan 6 Dec 31, 2022
Official Repository for "Robust On-Policy Data Collection for Data Efficient Policy Evaluation" (NeurIPS 2021 Workshop on OfflineRL).

Robust On-Policy Data Collection for Data-Efficient Policy Evaluation Source code of Robust On-Policy Data Collection for Data-Efficient Policy Evalua

Autonomous Agents Research Group (University of Edinburgh) 2 Oct 09, 2022
A method to perform unsupervised cross-region adaptation of crop classifiers trained with satellite image time series.

TimeMatch Official source code of TimeMatch: Unsupervised Cross-region Adaptation by Temporal Shift Estimation by Joachim Nyborg, Charlotte Pelletier,

Joachim Nyborg 17 Nov 01, 2022
RodoSol-ALPR Dataset

RodoSol-ALPR Dataset This dataset, called RodoSol-ALPR dataset, contains 20,000 images captured by static cameras located at pay tolls owned by the Ro

Rayson Laroca 45 Dec 15, 2022
2.86% and 15.85% on CIFAR-10 and CIFAR-100

Shake-Shake regularization This repository contains the code for the paper Shake-Shake regularization. This arxiv paper is an extension of Shake-Shake

Xavier Gastaldi 294 Nov 22, 2022
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)

Intro This repository contains code to generate data and reproduce experiments from our NeurIPS 2019 paper: Boris Knyazev, Graham W. Taylor, Mohamed R

Boris Knyazev 242 Jan 06, 2023
[CVPR 2022 Oral] Balanced MSE for Imbalanced Visual Regression https://arxiv.org/abs/2203.16427

Balanced MSE Code for the paper: Balanced MSE for Imbalanced Visual Regression Jiawei Ren, Mingyuan Zhang, Cunjun Yu, Ziwei Liu CVPR 2022 (Oral) News

Jiawei Ren 267 Jan 01, 2023
Yolov5-lite - Minimal PyTorch implementation of YOLOv5

Yolov5-Lite: Minimal YOLOv5 + Deep Sort Overview This repo is a shortened versio

Kadir Nar 57 Nov 28, 2022
HODEmu, is both an executable and a python library that is based on Ragagnin 2021 in prep.

HODEmu HODEmu, is both an executable and a python library that is based on Ragagnin 2021 in prep. and emulates satellite abundance as a function of co

Antonio Ragagnin 1 Oct 13, 2021
Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Hiroshechka Y 33 Dec 26, 2022
TriMap: Large-scale Dimensionality Reduction Using Triplets

TriMap TriMap is a dimensionality reduction method that uses triplet constraints to form a low-dimensional embedding of a set of points. The triplet c

Ehsan Amid 235 Dec 24, 2022
Code for the SIGGRAPH 2021 paper "Consistent Depth of Moving Objects in Video".

Consistent Depth of Moving Objects in Video This repository contains training code for the SIGGRAPH 2021 paper "Consistent Depth of Moving Objects in

Google 203 Jan 05, 2023
PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks This is the official repository for our paper: Sharpness-aware Quantization for Deep Neural Netw

Zhuang AI Group 30 Dec 19, 2022
This package contains deep learning models and related scripts for RoseTTAFold

RoseTTAFold This package contains deep learning models and related scripts to run RoseTTAFold This repository is the official implementation of RoseTT

1.6k Jan 03, 2023
BoxInst: High-Performance Instance Segmentation with Box Annotations

Introduction This repository is the code that needs to be submitted for OpenMMLab Algorithm Ecological Challenge, the paper is BoxInst: High-Performan

88 Dec 21, 2022
Apply AnimeGAN-v2 across frames of a video clip

title emoji colorFrom colorTo sdk app_file pinned AnimeGAN-v2 For Videos 🔥 blue red gradio app.py false AnimeGAN-v2 For Videos Apply AnimeGAN-v2 acro

Nathan Raw 36 Oct 18, 2022
Simple Python project using Opencv and datetime package to recognise faces and log attendance data in a csv file.

Attendance-System-based-on-Facial-recognition-Attendance-data-stored-in-csv-file- Simple Python project using Opencv and datetime package to recognise

3 Aug 09, 2022
Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks This repository contains the code that accompanies our CVPR 20

Despoina Paschalidou 161 Dec 20, 2022