#X11INCPATH /usr/openwin/share/include/X11 # Specific to Linux...#X11INCPATH /usr/openwin/share/include/X11 # Specific to Linux...need the alias or link gg --> /opt/gamit10.34 Your Operating System type is: Linux...man setenv LIBPATH /usr/local/gmt/lib:/usr/lib:/usr/local/lib:/usr/i386-glibc21-linux...在终端输入 doy 返回 DOY: Converts various
文件（需要dbadmin用户登录vsql操作）: select export_objects('/tmp/t_jingyu.sql','test.t_jingyu'); vertica建分区表： 按doy...create table t_jingyu( col1 int, col2 varchar, col3 timestamp not null) PARTITION BY (date_part('doy...', t_jingyu.col3)); 这样的分区表卸载时： SELECT DROP_PARTITION('test.t_jingyu', EXTRACT('doy' FROM '2015-04-01'
of input layers DOY 161-193 0: Clear1: BadBit 3: State of input layers DOY 209-241 0:...DOY 305-337 0: Clear1: BadBit 6: State of input layers DOY 353-017 0: Clear1: BadBit 7...of input layers DOY 305-337 0: Clear1: CloudyBit 6: State of input layers DOY 353-017...of input layers DOY 065-097 0: Clear 1: Bad Bit 1: State of input layers DOY 113-145...layers DOY 209-241 0: Clear 1: Bad Bit 4: State of input layers DOY 257-289 0: Clear
', EXTRACT('doy' FROM '2014-08-02'::date)); SELECT DROP_PARTITION('test.t_jingyu', EXTRACT('doy' FROM...('doy' FROM '2014-08-07'::date)); SELECT DROP_PARTITION('test.t_jingyu', EXTRACT('doy' FROM '2014-08...', EXTRACT('doy' FROM '2014-08-11'::date)); SELECT DROP_PARTITION('test.t_jingyu', EXTRACT('doy' FROM...('doy' FROM '2014-08-16'::date)); SELECT DROP_PARTITION('test.t_jingyu', EXTRACT('doy' FROM '2014-08...('doy' FROM '2014-08-25'::date)); SELECT DROP_PARTITION('test.t_jingyu', EXTRACT('doy' FROM '2014-08
我这里测试均是以业务用户test登录建表： vsql -Utest 1.1 使用预定义函数创建分区表 按天分区(doy) --按天分区(doy) create table t_jingyu_doy( col1...int, col2 varchar, col3 timestamp not null) PARTITION BY (date_part('doy', col3)); 按月分区(month) --按月分区...入库具体方法可以参见：Vertica 业务用户指定资源池加载数据 4.删除历史分区数据 4.1 删除历史分区数据（使用预定义函数创建的分区表） --按天分区(doy)，删除”2015-08-01”这一时间的分区数据...SELECT DROP_PARTITION('test.t_jingyu_doy', EXTRACT('doy' FROM '2015-08-01'::date)); test=> SELECT DROP_PARTITION...('test.t_jingyu_doy', EXTRACT('doy' FROM '2015-08-01'::date)); DROP_PARTITION ------------------- Partition
return im.addBands(CIre).copyProperties(img,['system:time_start']); } function addDOY(im){ var doy...= im.date().getRelative('day', 'year'); var doyBand = ee.Image.constant(doy).uint16().rename('doy'...ee.Filter.calendarRange(7, 9, 'month')) .map(maskSCL) .map(getIndices) .map(addDOY) .select('CIre', 'doy...max:20,palette:['black','indigo','cyan','limegreen','yellow']},'CIre') Map.addLayer(CIre_max.select('doy...'), // {min:183,max:274,palette:['black','indigo','cyan','limegreen','yellow']},'doy_p98') ///print
')) var may = col1.getAt(1).addBands(pie.Image().constant(135).rename('doy')) var june = col1.getAt(2...(166).rename('doy')) var july2 = col2.getAt(4).addBands(pie.Image().constant(196).rename('doy')) var...oct]) //B1,B2,doy 阈值筛选/ //插秧期合成negVI最大值，并记录doy1 var flood = transplant.qualityMosaic('negVI').select...(['B2', 'negVI', 'doy']).clip(roi) //生长期合成EVI最大值，并记录doy2 var peak = growth.qualityMosaic('B2').select...(['B2', 'doy']).clip(roi) //B2 doy //计算水稻插秧期快速生长后EVI值的变化 var datediffer = (peak.select('doy').subtract
时序属性 col <- col$map(function(img) { doy <- ee$Date(img$get('system:time_start'))$getRelative('day',...'year') img$set('doy', doy) }) distinctDOY <- col$filterDate('2013-01-01', '2014-01-01') 定义一个过滤器，用于标识完整集合中的哪些图像与不同...DOY 集合中的 DOY 匹配。...= 'doy') 定义一个连接；将生成的 FeatureCollection 转换为 ImageCollection。..., col, filter)) 在匹配的 DOY 集合中应用中位数减少。
var ndvi = image.normalizedDifference(['B4', 'B8']).rename('ndvi'); var date = image.date(); var doy...date.getRelative('day', 'year'); var time = image.metadata('system:time_start'); var doyImage = ee.Image(doy...) .rename('doy') .int(); return ndvi.addBands(doyImage).addBands(time) .clip(image.geometry...function(array) { var time = array.arraySlice(bandAxis, -1); var sorted = array.arraySort(time); var doy...= sorted.arraySlice(bandAxis, -2, -1); var left = doy.arraySlice(timeAxis, 1); var right = doy.arraySlice
m-%d %H:%M:%S')#格式转为时间戳 day=[i.day for i in b5['datetime']] month=[i.month for i in b5['datetime']] doy...=[] for ij in range(len(day)): a=month[ij]*32+day[ij] doy.append(a) b2['doy']=doy group=b2.groupby...([b2['经度'],b2['纬度'],b2['doy']],as_index=False) b5=group.mean()###这里就是groupby的统计功能了，除了平均值还有一堆函数。。。...b6=b5.sort_values('doy',ascending=True)##排序也是可以的 b6.reset_index(drop=True, inplace=True) b3=b6[['经度',
例如，对于上图中DOY为1的blue这个单元格，那么求出来的平均值就是在全部名称为Ref_GRA_Y.csv格式的.csv文件之中，DOY为1且列名为blue的单元格的平均值。...combined_data = pd.concat([combined_data, df_filtered]) average_values = combined_data.groupby('DOY...完成所有文件的处理后，使用combined_data.groupby('DOY').mean()计算所有文件的平均值，按照DOY列进行分组并求平均值。
MODIS/006/MOD13A2')$select('NDVI') Group images by composite date col <- col$map(function(img) { doy...<- ee$Date(img$get('system:time_start'))$getRelative('day', 'year') img$set('doy', doy) }) distinctDOY...'2014-01-01') Define a filter that identifies which images from the complete collection match the DOY...from the distinct DOY collection. filter doy', rightField = 'doy');...<- ee$ImageCollection(join$apply(distinctDOY, col, filter)) Apply median reduction among matching DOY
ElementOperator { override var nameList: ArrayList = arrayListOf("Ana", "Bob", "Cris", "Doy...val element = ElementOperator.Element() // 对于字符串的操作符 element - "Ana" // 输出[Bob, Cris, Doy...("Cris Index:${element["Cris"]}") // 添加Ana，但是经过子类重载后修改 element + "Ana" // 输出[Bob, Cris, Doy...Override:Ana] println(element.nameList) // 不同参数类型的操作符，对应不同的操作 element - 1 // [Bob, Doy
dow = cal.get(Calendar.DAY_OF_WEEK); int dom = cal.get(Calendar.DAY_OF_MONTH); int doy...星期一输出为 2，以此类推 System.out.println("一月中的第几天: " + dom); System.out.println("一年的第几天: " + doy
function addNDVI(image){ // var ndvi = image.normalizedDifference(['B8','B4']).rename('ndvi'); // var doy...yyyyMMdd") // return image.addBands(ndvi).select('ndvi').clip(geometry) // .set({'Date':doy...divide(image.select('B11').add(image.select('B8'))) .float().rename('NDBI'); var doy...("yyyyMMdd") return image.addBands(ndbi).select('NDBI').clip(geometry) .set({'Date':doy
. , 6; 0 = Sunday doy(date) 一年内第几天; 1, 2, . . . , 366 clear input str20 var1 "2021-01-01" "2021-04-02...month = month(date1) //月份 gen quarter = quarter(date1) //季度 gen halfyear = halfyear(date1) //半年 gen doy...= doy(date1) // 一年内第几天 ?
dow = cale.get(Calendar.DAY_OF_WEEK); int dom = cale.get(Calendar.DAY_OF_MONTH); int doy...); System.out.println("Day of Month: " + dom); System.out.println("Day of Year: " + doy
matplotlib.pyplot as plt data = np.load('cbk12.npy') # Get minimum visibility visibility = data[:,4] # doy...doy = data[:,0] % 10000 doy_range = np.unique(doy) # Initialize arrays ndoy = len(doy_range) mist...= np.zeros(ndoy) haze = np.zeros(ndoy) # Compute frequencies for i, d in enumerate(doy_range): indices...= np.where(d == doy) selection = visibility[indices] mist_truth = (10 < selection) & (selection...): indices = np.where(d == doy) selection = visibility[indices] mist_truth = (10 < selection
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