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
Creating Multi Task Models With Keras

Creating Multi Task Models With Keras About The Project! I used the keras and Tensorflow Library, To build a Deep Learning Neural Network to Creating

Srajan Chourasia 4 Nov 28, 2022
LocUNet is a deep learning method to localize a UE based solely on the reported signal strengths from a set of BSs.

LocUNet LocUNet is a deep learning method to localize a UE based solely on the reported signal strengths from a set of BSs. The method utilizes accura

4 Oct 05, 2022
Training BERT with Compute/Time (Academic) Budget

Training BERT with Compute/Time (Academic) Budget This repository contains scripts for pre-training and finetuning BERT-like models with limited time

Intel Labs 263 Jan 07, 2023
DenseNet Implementation in Keras with ImageNet Pretrained Models

DenseNet-Keras with ImageNet Pretrained Models This is an Keras implementation of DenseNet with ImageNet pretrained weights. The weights are converted

Felix Yu 568 Oct 31, 2022
PyTorch implementation of PP-LCNet

PP-LCNet-Pytorch Pre-Trained Models Google Drive p018 Accuracy Models Top1 Top5 PPLCNet_x0_25 0.5186 0.7565 PPLCNet_x0_35 0.5809 0.8083 PPLCNet_x0_5 0

24 Dec 12, 2022
An Inverse Kinematics library aiming performance and modularity

IKPy Demo Live demos of what IKPy can do (click on the image below to see the video): Also, a presentation of IKPy: Presentation. Features With IKPy,

Pierre Manceron 481 Jan 02, 2023
Repository for MuSiQue: Multi-hop Questions via Single-hop Question Composition

🎵 MuSiQue: Multi-hop Questions via Single-hop Question Composition This is the repository for our paper "MuSiQue: Multi-hop Questions via Single-hop

21 Jan 02, 2023
Train DeepLab for Semantic Image Segmentation

Train DeepLab for Semantic Image Segmentation Martin Kersner, [email protected]

Martin Kersner 172 Dec 14, 2022
Keyword2Text This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation"

Keyword2Text This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation", if you find this useful and use

57 Dec 27, 2022
Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds."

DeltaConv [Paper] [Project page] Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds" by Ru

98 Nov 26, 2022
CLIP+FFT text-to-image

Aphantasia This is a text-to-image tool, part of the artwork of the same name. Based on CLIP model, with FFT parameterizer from Lucent library as a ge

vadim epstein 690 Jan 02, 2023
Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks

CyGNet This repository reproduces the AAAI'21 paper “Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Network

CunchaoZ 89 Jan 03, 2023
PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC

DeepLab with PyTorch This is an unofficial PyTorch implementation of DeepLab v2 [1] with a ResNet-101 backbone. COCO-Stuff dataset [2] and PASCAL VOC

Kazuto Nakashima 995 Jan 08, 2023
Progressive Coordinate Transforms for Monocular 3D Object Detection

Progressive Coordinate Transforms for Monocular 3D Object Detection This repository is the official implementation of PCT. Introduction In this paper,

58 Nov 06, 2022
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling For Official repo of NU-Wave: A Diffusion Probabilistic Model for Neural Audio Up

Rishikesh (ऋषिकेश) 38 Oct 11, 2022
PyTorch implementation of saliency map-aided GAN for Auto-demosaic+denosing

Saiency Map-aided GAN for RAW2RGB Mapping The PyTorch implementations and guideline for Saiency Map-aided GAN for RAW2RGB Mapping. 1 Implementations B

Yuzhi ZHAO 20 Oct 24, 2022
Implement slightly different caffe-segnet in tensorflow

Tensorflow-SegNet Implement slightly different (see below for detail) SegNet in tensorflow, successfully trained segnet-basic in CamVid dataset. Due t

Tseng Kuan Lun 364 Oct 27, 2022
Pytorch Implementation of "Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation"

CRL_EGPG Pytorch Implementation of Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation We use contrastive loss implemented b

YHR 25 Nov 14, 2022
PyTorch implementation of an end-to-end Handwritten Text Recognition (HTR) system based on attention encoder-decoder networks

AttentionHTR PyTorch implementation of an end-to-end Handwritten Text Recognition (HTR) system based on attention encoder-decoder networks. Scene Text

Dmitrijs Kass 31 Dec 22, 2022
TensorFlow Tutorials with YouTube Videos

TensorFlow Tutorials Original repository on GitHub Original author is Magnus Erik Hvass Pedersen Introduction These tutorials are intended for beginne

9.1k Jan 02, 2023