Python批量下载Landsat-8数据（II）

接上一篇Python批量下载Landsat-8数据（I），这次从Google Storage下载数据。
可参考Google Storage公开数据集介绍

1. 获取影像条带号

参考Python批量下载Landsat-8数据（I），不再赘述。

2. 根据条带号获取文件信息及下载地址

Google Storage的目录索引有500M，解压后有3G，直接读取进行检索，需要花费80s，为便于检索，建了一个简单的数据库：

import mysql.connector
#连接数据库
mydb = mysql.connector.connect(
  host="localhost",       # 数据库主机地址
  user="root",    # 数据库用户名
  passwd="",   # 数据库密码
)
mycursor = mydb.cursor() 
mycursor.execute("CREATE DATABASE landsat_index") #创建数据库
生成表
mycursor.execute("CREATE TABLE gc_index( sid INT UNSIGNED AUTO_INCREMENT, \
	SCENE_ID VARCHAR(30),\
	PRODUCT_ID VARCHAR(50),\
	SPACECRAFT_ID VARCHAR(10),\
	SENSOR_ID VARCHAR(10),\
	DATE_ACQUIRED VARCHAR(10),\
	COLLECTION_NUMBER VARCHAR(10),\
	COLLECTION_CATEGORY VARCHAR(10),\
	SENSING_TIME VARCHAR(30),\
	DATA_TYPE VARCHAR(10),\
	WRS_PATH INT,\
	WRS_ROW INT,\
	CLOUD_COVER FLOAT,\
	NORTH_LAT FLOAT,\
	SOUTH_LAT FLOAT,\
	WEST_LON FLOAT,\
	EAST_LON FLOAT,\
	TOTAL_SIZE INT,\
	BASE_URL VARCHAR(150),\
	PRIMARY KEY ( sid ))DEFAULT CHARSET=utf8")
# 查看表
mycursor.execute("SHOW TABLES")
for x in mycursor:
    print(x)
# 查看描述
mycursor.execute("desc gc_index")
for x in mycursor:
   print(x)

插入数据
sql = "INSERT INTO gc_index (SCENE_ID,PRODUCT_ID,SPACECRAFT_ID,SENSOR_ID,DATE_ACQUIRED,COLLECTION_NUMBER,COLLECTION_CATEGORY,SENSING_TIME,DATA_TYPE,WRS_PATH,WRS_ROW,CLOUD_COVER,NORTH_LAT,SOUTH_LAT,WEST_LON,EAST_LON,TOTAL_SIZE,BASE_URL)\
                           VALUES (%s,         %s,           %s,       %s,           %s,                %s,                %s,          %s,       %s,      %s,    %s,          %s,       %s,       %s,      %s,      %s,        %s,       %s)"

with open ('index.csv','r') as f:
   for line in f:
        line = line.replace('\n','')
        val = tuple(line.split(','))
        print(val)
        mycursor.execute(sql, val)     
mydb.commit()

检索数据，并保存下载链接：
Google Storage的下载有两种，样例url分别为

https://storage.cloud.google.com/gcp-public-data-landsat/LC08/01/001/004/LC08_L1GT_001004_20130910_20170502_01_T2/LC08_L1GT_001004_20130910_20170502_01_T2_B1.TIF
http://storage.googleapis.com/gcp-public-data-landsat/LC08/01/001/004/LC08_L1GT_001004_20130910_20170502_01_T2/LC08_L1GT_001004_20130910_20170502_01_T2_B1.TIF

其中googleapi无需*，主要保存成该样式下载链接。

import mysql.connector
import time
import os

CLOUD_MAX=20    #云量
PATH = 118      #行号
ROW = 39        #列号
SPACECRAFT = 'LANDSAT_8' # 
INFO = 'SCENE_ID, PRODUCT_ID,CLOUD_COVER,TOTAL_SIZE,BASE_URL'  # 保存的信息：产品ID，云量，大小，链接

BASE_URL = 'http://storage.googleapis.com/'


mydb = mysql.connector.connect(
  host="localhost",       # 数据库主机地址
  user="root",    # 数据库用户名
  passwd="",   # 数据库密码
  database = 'landsat_index',#连接数据库
)
mycursor = mydb.cursor() #创建指针

# 生成新文件夹   
file_path = '{}{:0>3d}'.format(PATH,ROW)
base_path = os.getcwd()
entity_dir = os.path.join(base_path, file_path)
os.makedirs(entity_dir, exist_ok=True)
os.chdir(entity_dir)

print('Retriving {}{:0>3d}'.format(PATH,ROW))
time_start = time.time()
sql = "SELECT {} FROM gc_index WHERE PRODUCT_ID !='' AND SPACECRAFT_ID = '{}' AND WRS_PATH={} AND WRS_ROW = {} AND CLOUD_COVER<={}".format(INFO,SPACECRAFT,PATH,ROW,CLOUD_MAX)
mycursor.execute(sql)
myresult = mycursor.fetchall()
time_end = time.time()
print('Time cost: ',time_end-time_start)
myresult.sort()
print("Data Num: ",len(myresult))
# for result in myresult:
    # print(result)
    

# 保存检索到的影像信息
file_result = 'gs{}{:0>3d}_result.csv'.format(PATH,ROW)
with open(file_result,'w') as f:
    f.write(INFO+'\n')
    for result in myresult:
        f.write(','.join(str(i) for i in result) + '\n')
# 保存下载链接
file_url = 'gs{}{:0>3d}_url.txt'.format(PATH,ROW)
down_url={}
for result in myresult:
    url_list=[]
    EntityID = result[1]
    url_middle = result[4].split('//')[-1]
    url0 = '{}{}/{}_ANG.txt'.format(BASE_URL,url_middle,EntityID)
    url1 = '{}{}/{}_B1.TIF'.format(BASE_URL,url_middle,EntityID)
    url2 = '{}{}/{}_B2.TIF'.format(BASE_URL,url_middle,EntityID)
    url3 = '{}{}/{}_B3.TIF'.format(BASE_URL,url_middle,EntityID)
    url4 = '{}{}/{}_B4.TIF'.format(BASE_URL,url_middle,EntityID)
    url5 = '{}{}/{}_B5.TIF'.format(BASE_URL,url_middle,EntityID)
    url6 = '{}{}/{}_B6.TIF'.format(BASE_URL,url_middle,EntityID)
    url7 = '{}{}/{}_B7.TIF'.format(BASE_URL,url_middle,EntityID)
    url8 = '{}{}/{}_B8.TIF'.format(BASE_URL,url_middle,EntityID)
    url9 = '{}{}/{}_B9.TIF'.format(BASE_URL,url_middle,EntityID)
    url10= '{}{}/{}_B10.TIF'.format(BASE_URL,url_middle,EntityID)
    url11= '{}{}/{}_B11.TIF'.format(BASE_URL,url_middle,EntityID)
    url12= '{}{}/{}_BQA.TIF'.format(BASE_URL,url_middle,EntityID)
    url13= '{}{}/{}_MTL.txt'.format(BASE_URL,url_middle,EntityID)
    url_list = [url0,url1,url2,url3,url4,url5,url6,url7,url8,url9,url10,url11,url12,url13]
    down_url[EntityID] = url_list
with open(file_url,'w') as f:
    f.write(str(down_url))

3下载

术业有专攻，不再在python中下载，在python中调用IDM，批量导入任务进行下载。

import wget,os
from subprocess import call

IDM = r'D:\Program Files\IDM\IDMan.exe'

file_path = './118039/gs118039_url.txt'
base_path = os.path.dirname(os.path.abspath(file_path))
with open(file_path,'r') as f:
    file = f.read()
file_list = eval(file)
for key in file_list.keys():
    entity_dir = os.path.join(base_path, key)
    os.makedirs(entity_dir, exist_ok=True)
    os.chdir(entity_dir)
    #print(os.getcwd())
    value = file_list[key]
    for url in value:
        name = url.split('/')[-1]        
        if os.path.exists(name):
            print('\nDownloaded: ',name)
            continue
        print('\nDownloading: ',name)
        try:
            #wget.download(url)
            call([IDM,'/d',url,'/p',entity_dir,'/f',name,'/n','/a'])
        except:
            continue

IDM中Amazon S3的下载速度大概在500K/s左右，Google Storage的下载速度在1M/s(垃圾网速，跑满1M/s(百度云开会员也是这个速度))。
感谢Google Storage，Google Storage公开数据集出了LANDSAT之外，还有哨兵二号，之后再研究。不知道百度云什么时候能出百度云公开数据集。