FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data

Related tags

Deep LearningFAST
Overview

FAST (Fusion Abundant multi-Source data download Terminal)

介绍

FAST 针对目前GNSS数据下载步骤繁琐、下载速度慢等问题,开发了一套较为完备的融合多源数据下载终端软件——FAST。
软件目前包含GNSS科研学习过程中绝大部分所需的数据源,采用并行下载的方式极大的提升了下载的效率。

Git地址

软件特点

  • 多平台:同时支持windows与linux系统;
  • 资源丰富:基本囊括了GNSS科研学习中所需的数据源,目前支持15个大类、62个小类,具体支持数据见数据支持
  • 快速:软件采用并行下载方式,在命令行参数运行模式可自行指定下载线程数,经测试下载100天的brdc+igs+clk文件只需要48.93s!
  • 易拓展:如需支持更多数据源,可在FTP_Source.py、GNSS_TYPE.py中指定所需的数据与数据源;
  • 简单易行:程序有引导下载模式与命令行带参数运行模式两种方式下载,直接运行程序便可进入引导下载模式,命令行带参数运行FAST -h可查看带参数运行模式介绍;
  • 灵活:在带参数运行模式下,用户可灵活指定下载类型、下载位置、下载时间、是否解压、线程数等,可根据自我需求编写bat、shell、python等脚本运行;
  • 轻便:windows程序包仅有18.9 MB,Liunx程序包仅有6.63 MB.

安装教程

  • Windows系统下仅需解压程序包即可直接运行,CMD运行FAST.exe -h可查看带参数运行模式介绍;
  • Linux系统下需安装先导软件wget\lftp\ncompress\python3,以Ubuntu系统为例,于终端中输入以下代码:
apt-get install wget
apt-get install lftp
apt-get install ncompress
apt-get install python3

安装后如windows系统下相同可直接运行程序,或将程序配置至环境变量中。

使用说明

引导下载模式Windows系统双击运行FAST.exe便可进入引导下载,若为Linux系统终端输入FAST运行即可:

  1. 以下载武汉大学多系统精密星历为例,在一级选择目录中选择SP3,即为输入2后回车;
    一级目录

  2. 选择MGEX_WUH_sp3即为输入6并回车,其中MGEX代表多系统,WUH代表武汉大学IGS数据处理中心,SP3代表精密星历; 二级目录

  3. 根据引导输入时间,回车完成输入; 输入时间

  4. 下载完成,根据提示直接回车完成解压或者输入任意字符回车不解压; 下载完成 解压完成

  5. 根据提示输入y再次进入引导或退出;
    在此引导

命令行带参数运行模式Windows系统CMD或power shell运行FAST.exe -h可查看命令行运行帮助,若为Linux系统终端输入FAST -h查看帮助:

  FAST : Fusion Abundant multi-Source data download Terminal
  ©Copyright 2022.01 @ Chang Chuntao
  PLEASE DO NOT SPREAD WITHOUT PERMISSION OF THE AUTHOR !

  Usage: FAST 

  Where the following are some of the options avaiable:

  -v,  --version                   display the version of GDD and exit
  -h,  --help                      print this help
  -t,  --type                      GNSS type, if you need to download multiple data,
                                   Please separate characters with " , "
                                   Example : GPS_brdc,GPS_IGS_sp3,GPS_IGR_clk
  -l,  --loc                       which folder is the download in
  -y,  --year                      where year are the data to be download
  -d,  --day                       where day are the data to be download
  -o,  --day1                      where first day are the data to be download
  -e,  --day2                      where last day are the data to be download
  -m,  --month                     where month are the data to be download
  -u,  --uncomprss Y/N             Y - unzip file (default)
                                   N - do not unzip files
  -f,  --file                      site file directory,The site names in the file are separated by spaces.
                                   Example : bjfs irkj urum
  -p   --process                   number of threads (default 12)

  Example: FAST -t MGEX_IGS_atx
           FAST -t GPS_brdc,GPS_IGS_sp3,GPS_IGR_clk -y 2022 -d 22 -p 30
           FAST -t MGEX_WUH_sp3 -y 2022 -d 22 -u N -l D:\code\CDD\Example
           FAST -t MGEX_IGS_rnx -y 2022 -d 22 -f D:\code\cdd\mgex.txt
           FAST -t IVS_week_snx -y 2022 -m 1

数据支持

  1. BRDC : GPS_brdc / MGEX_brdm

  2. SP3 : GPS_IGS_sp3 / GPS_IGR_sp3 / GPS_IGU_sp3 / GPS_GFZ_sp3 / GPS_GRG_sp3
    MGEX_WUH_sp3 / MGEX_WUHU_sp3 / MGEX_GFZ_sp3 / MGEX_COD_sp3
    MGEX_SHA_sp3 / MGEX_GRG_sp3 / GLO_IGL_sp3

  3. RINEX :GPS_IGS_rnx / MGEX_IGS_rnx / GPS_USA_cors / GPS_HK_cors / GPS_EU_cors
    GPS_AU_cors

  4. CLK : GPS_IGS_clk / GPS_IGR_clk / GPS_IGU_clk / GPS_GFZ_clk / GPS_GRG_clk GPS_IGS_clk_30s MGEX_WUH_clk / MGEX_COD_clk / MGEX_GFZ_clk / MGEX_GRG_clk / WUH_PRIDE_clk

  5. ERP : IGS_erp / WUH_erp / COD_erp / GFZ_erp

  6. BIA : MGEX_WHU_bia / GPS_COD_bia / MGEX_COD_bia / MGEX_GFZ_bia

  7. ION : IGS_ion / WUH_ion / COD_ion

  8. SINEX : IGS_day_snx / IGS_week_snx / IVS_week_snx / ILS_week_snx / IDS_week_snx

  9. CNES_AR : CNES_post / CNES_realtime

  10. ATX : MGEX_IGS_atx

  11. DCB : GPS_COD_dcb / MGEX_CAS_dcb / MGEX_WHU_OSB / P1C1 / P1P2 / P2C2

  12. Time_Series : IGS14_TS_ENU / IGS14_TS_XYZ / Series_TS_Plot

  13. Velocity_Fields : IGS14_Venu / IGS08_Venu / PLATE_Venu

  14. SLR : HY_SLR / GRACE_SLR / BEIDOU_SLR

  15. OBX : GPS_COD_obx / GPS_GRG_obx / MGEX_WUH_obx / MGEX_COD_obx / MGEX_GFZ_obx

  16. TRO : IGS_zpd / COD_tro / JPL_tro / GRID_1x1_VMF3 / GRID_2.5x2_VMF1 / GRID_5x5_VMF3

参与贡献

  1. 常春涛@中国测绘科学研究院
    程序思路、主程序编写、文档编写、程序测试

  2. 蒋科材博士后@武汉大学
    程序思路、并行计算处理思路

  3. 慕任海博士@武汉大学
    程序思路、程序编写、程序测试

  4. 李博博士@辽宁工程技术大学&中国测绘科学研究院
    程序测试、文档编写、节点汇总

  5. 李勇熹@兰州交通大学&中国测绘科学研究院
    程序测试、节点汇总

  6. 曹多明@山东科技大学&中国测绘科学研究院
    程序测试、节点汇总

Owner
ChangChuntao
QQ 1252443496 WECHAT amst-jazz
ChangChuntao
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
Official code repository for the EMNLP 2021 paper

Integrating Visuospatial, Linguistic and Commonsense Structure into Story Visualization PyTorch code for the EMNLP 2021 paper "Integrating Visuospatia

Adyasha Maharana 23 Dec 19, 2022
U-Net implementation in PyTorch for FLAIR abnormality segmentation in brain MRI

U-Net for brain segmentation U-Net implementation in PyTorch for FLAIR abnormality segmentation in brain MRI based on a deep learning segmentation alg

562 Jan 02, 2023
A PyTorch implementation of PointRend: Image Segmentation as Rendering

PointRend A PyTorch implementation of PointRend: Image Segmentation as Rendering [arxiv] [Official Implementation: Detectron2] This repo for Only Sema

AhnDW 336 Dec 26, 2022
A tool for making map images from OpenTTD save games

OpenTTD Surveyor A tool for making map images from OpenTTD save games. This is not part of the main OpenTTD codebase, nor is it ever intended to be pa

Aidan Randle-Conde 9 Feb 15, 2022
ICNet for Real-Time Semantic Segmentation on High-Resolution Images, ECCV2018

ICNet for Real-Time Semantic Segmentation on High-Resolution Images by Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi, Jiaya Jia, details a

Hengshuang Zhao 594 Dec 31, 2022
PyTorch code for SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised DA

PyTorch Code for SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation Viraj Prabhu, Shivam Khare, Deeks

Viraj Prabhu 46 Dec 24, 2022
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

Aladdin Persson 4.7k Jan 08, 2023
Finite-temperature variational Monte Carlo calculation of uniform electron gas using neural canonical transformation.

CoulombGas This code implements the neural canonical transformation approach to the thermodynamic properties of uniform electron gas. Building on JAX,

FermiFlow 9 Mar 03, 2022
PyToch implementation of A Novel Self-supervised Learning Task Designed for Anomaly Segmentation

Self-Supervised Anomaly Segmentation Intorduction This is a PyToch implementation of A Novel Self-supervised Learning Task Designed for Anomaly Segmen

WuFan 2 Jan 27, 2022
E2VID_ROS - E2VID_ROS: E2VID to a real-time system

E2VID_ROS Introduce We extend E2VID to a real-time system. Because Python ROS ca

Robin Shaun 7 Apr 17, 2022
Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

PRP Introduction This is the implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

yuanyao366 39 Dec 29, 2022
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).

This is the original implementation of our paper, A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (arXiv:1706.1

Zhengyao Jiang 1.5k Dec 29, 2022
Customer Segmentation using RFM

Customer-Segmentation-using-RFM İş Problemi Bir e-ticaret şirketi müşterilerini segmentlere ayırıp bu segmentlere göre pazarlama stratejileri belirlem

Nazli Sener 7 Dec 26, 2021
Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval PyTorch This is the PyTorch implementation of Retrieve in Style: Unsupervised Fa

60 Oct 12, 2022
Deep Reinforcement Learning based Trading Agent for Bitcoin

Deep Trading Agent Deep Reinforcement Learning based Trading Agent for Bitcoin using DeepSense Network for Q function approximation. For complete deta

Kartikay Garg 669 Dec 29, 2022
Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh

generate_cloud_points Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh. Run python disp_mesh.py Or you

Peng Yu 2 Dec 24, 2021
Using contrastive learning and OpenAI's CLIP to find good embeddings for images with lossy transformations

Creating Robust Representations from Pre-Trained Image Encoders using Contrastive Learning Sriram Ravula, Georgios Smyrnis This is the code for our pr

Sriram Ravula 26 Dec 10, 2022
A novel framework to automatically learn high-quality scanning of non-planar, complex anisotropic appearance.

appearance-scanner About This repository is an implementation of the neural network proposed in Free-form Scanning of Non-planar Appearance with Neura

Xiaohe Ma 14 Oct 18, 2022
PyTorch implementations of the beta divergence loss.

Beta Divergence Loss - PyTorch Implementation This repository contains code for a PyTorch implementation of the beta divergence loss. Dependencies Thi

Billy Carson 7 Nov 09, 2022