NAME: acormapexamples - Examples using correlator mapping routines: Starting idl. - To use the correlator mapping routines you need to tell idl where to look to get these procedures. You can do it manually each time you run idl, or you can put it in an id startup file. Manually: idl @phil @corinit Using a idl setup file: Suppose your home directory is ~jones. create the file ~jones/.idlstartup add the line !path = '/home/phil/idl/gen:' + !path to this file. In your .cshrc file (if you run csh) add the line setenv IDL_STARTUP ~/.idlstartup You can then run idl with : idl @corinit You can also put any other commands in the startup file that you want to be executed each time you start idl. My startup file contains: !EDIT_INPUT=500 @phil!path = '/home/phil/idl/gen:' + !path 1.Which data can use these routines. You should be able to process data taken with cormap1, cordrift, or cormapdec with these routines. 2. Cookbook processing of a map: A. inputing the spectra: - If the entire map is in a single file used: openr,lun,'/share/olcor/corfile.17dec00.a1397.1',/get_lun ; open the file scan=35253227L ; first scan of the map istat=cormapinp(lun,scan,1,2,m,cals) ; input the map from brds 1,2 free_lun,lun - If the map is spread over many files use: stat=cormapinplist( ) - You can also input a map using the data archive. see arch_getmap in the correlator section. m[2,nsmp,nstrips].d is in correlator power units B. Scale to Kelvins istat=cormapsclk(m,cals,tsys) ; scale to kelvins using the cals .. m[2,nsmp,nstrips].d is in kelvins C1. baseline the data. mb=cormapbl(m,mask,polyDeg) ; baseline the map C2. bandpass correct the data: numbpedge=2 mb=cormapbc(m,numbpedge) .. This will: 1. Create an average bandpass for each strip. It can come from: - bandpasses on the edge of each strip, - averageing the entire strip. 2. For each spectra of the strip compute spc[i]=spc[i]/normalized(spcBpc) 3. Remove Tsys from each spectra by: - interpolating by the spectra used for the band pass correction - interpolate using all of the spectra in a strip - remove Tsys from each spectra by computing the mean of each spectra D. gain correct the data cmgaincor(mb,gaincor) ; gain correct the map .. mb.d is now gain corrected. gaincor[] holds the correction applied. E. Things to watch out for.. - cmgaincor assumes the data is in nongaincorrected Kelvins. Once you've called it it is in true kelvins (or Jy). Don't recall it with the new data.. 3. The data structures. .. map data structure input by cormapinp help,m M STRUCT = ->Array[2, 15, 15] [npol,nsamplesperstrip,nstrips] help,m,/st H STRUCT -> HDR Array[1]. header (see cor documentation) P FLOAT 1.08431 ..total power AZ FLOAT 284.581 ..azimuth pos deg at end of sample ZA FLOAT 18.9583 ..za pos deg at end of sample AZERRASEC FLOAT 0.256720 ..az tracking error asecs ZAERRASEC FLOAT 0.289800 .. za tracking error asecs RAHR FLOAT 3.26740 ..RA in hours,middle of sample DECDEG FLOAT 12.5167 ..DEC in deg ,middle of sample D FLOAT Array[2048] ..spectral data. correlator units. .. cals data structure input by cormapinp: help,cals1,/st H STRUCT -> HDR Array[1] .. header for cal on record CALVAL FLOAT Array[2] .. pola,b cal value in Kelvins CALSCL FLOAT Array[2] .. pola,b. scale correlator counts to kelvins. 4. Displaying things: .. average the bandpasses for the entire map avgBpPolA=cmavgstrips(mbl,1) avgBpPolB=cmavgstrips(mbl,2) plot,avgBpPolA oplot,avgBpPolB .. average all spectra strips 3-10 counting from 1. avgBpPolA=cmavgstrips(mbl,1,first=3,last=10) avgBpPolB=cmavgstrips(mbl,2,first=2,last=10) .. display whole map overplotting all spectra in each strip ver,35,65 cmstrips,m,1,0,2 ;offset each strip by 2 kelvin cmstrips,m,1,0,2,/freq ;plot versus frequency cmstrips,m,1,0,2,/vel ;plot versus velocity .. plot individual spectra. plot,m[0,1,3].d ; pola,2nd sample,4th strip .. average over a range of frequency channels then display as an image. pol=1 ; do pol a chn1=500 ; first channel, count from 1 chn2=550 ; last channel avgchn=cmavgchn(mbl,pol,chn1,chn2); avgchn[nsmp,nstrips] xloadct ; start color widget, clip on grey scale imgdisp,avgchn,zx=10,zy=10,/histeq ; image display, zoom 10 by 10 ..Do an animation of the map. ..assume a 15,15 map with 2048 frequency channels. ..display channels 951 thru 1100 (150) expanding the ra,dec dimensions to also be 150,150 d=reform(mbl[0,*,*].d[951:1100],150,15,15); grab the data dh=imghisteq(rebin(d,150,150,150)) ;interpolate,histogram equalize xinteranimate,set=[150,150,150],/showload for i=0,149 do xinteranimate,frame=i,image=reform(dh[i,*,*],150,150) xinteranimate,/keep_pixmaps .. click on the colors button to setup the colormap you want .. click on the active slider to see which frame is displayed. .. change the display rate with the top slider (frames/sec). 5. Notes on scaling the map to kelvins. istat=cormapsclk(m,cals,tsys) .. m[2,n,m].d will now be in kelvins. .. tsys[2,n,m] will be the system temperature measured at each spectra. .. .. You should examine the cals data to make sure that there are not .. any bad cal samples. cals.calscl[0] should be a relatively constant .. value since it is converting from correlator counts to kelvins .. using (calInKelvins)/(calon-caloff). Variation in these values .. can come from the cal temperature changing with time, the gain .. of the if/lo changing, or rfi in a cal on and not in the off. .. The cals are temperature controlled. This value does change by .. about 1% per degree F change in the rotary floor room. .. This is probably some amplifier gains changing. .. look at the values with: plot,cals.calscl[0] .. polA plot,cals.calscl[1] .. polB .. If 2 cals/strip were taken then everyother strip has also .. had it's cals flipped (along with the data samples). .. Look at the system temperatures with: plot,tsys[0,*,*] polA all strips
(See /pkg/rsi/local/libao/phil/Cor2/cormap/acormapexamples.pro)
NAME: cmavgchn - average freq channels of a map SYNTAX: avgchn=cmavgchn(m,pol,chn1,ch2) ARGS: m[2,nsmp,nstrips] : map structure array pol : 1 or 2.. pol to return. chn1 : first freq chn for average. count from 1 chn2 : last freq chn for average. count from 1 RETURNS: avgchn[nsmp,nstrips: float the averaged channels. DESCRIPTION: Average frequency channels chn1 through chn2 of the requested polarization. Return a float array of the averaged channels. EXAMPLE: assume m[2,31,21] is a map with 2 polarizations, 31 samples per strip, 21 strips, and 1024 frequency channels. Then the call : avgchn=cmavgchn(m,1,500,520) returns the array avgchn[31,21] that is averaged over the frequency channels 500=520.
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cmavgchn.pro)
NAME: cmavgstrips- average bandpasses for a map SYNTAX: avgbp=cmavgstrips(m,pol,first=first,last=last) ARGS: m[2,nsmp,nstrips] : map structure array pol : 1 or 2.. pol to return KEYWORDS: first : int first strip to use. default =1 last : int last strip to use. default =last normal : if set then normalize averaged bandpass to unity RETURNS: avgbp[nfrqchn] : float .. averaged bandpass. DESCRIPTION: Average all spectra in 1 or more strips together. Return the averaged bandpass. EXAMPLES: Assume that m[2,31,21] is a map with 31 samples/strip, 21 strips,and 1024 frequency channels. avgbp=cmavgstrips(m,1) will average all of the bandpasses for pol A in the map avgbp=cmavgstrips(m,1,first=2,last=5) will average all samples in strips 2 through 5 (counting from 1). avgbp=cmavgstrips(m,1,first=3,last=3) will average all the samples in strip 3.
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cmavgstrips.pro)
NAME: cmgaincor - gain correct map(s) SYNTAX: cmgaincor,m,gaincor,jy=jy,undo=undo ARGS: m[2,nsmp,nstrips,(nmaps)]: array of map structure. routine will work with a single map or an array of maps RETURNS: gaincor[nsmp,nstrips,nmaps]: float. The gain correction done at each point. m.d: the data that is gain corrected. KEYWORDS: jy : if set then gain correct and return in Janskies. The default is to do a relative gain correction leaving the data in the input units (normally kelvins) undo : if set then routine will undo a previous gain correction. In this case the user passes in m and gaincor. note: undo is not currently implemented.. DESCRIPTION: The gain of the telescope in kelvins/Jy is modeled in the routine corhgainget. This routine will remove the telescope gain variation with azimuth and za. By default it will do a relative correction normalizing to the average gain begin between 5 and 10 degrees za. If the /jy keyword is set then it will convert the input data (which should already be in Kelvins) to janskies after correcting for the gain response of the telescope. The routine performs the following operation on each spectra: ; get the gain value for each az,za position of the map stat=corhgainget(m.h,gainval) ;here gainval is in K/Jy if jy keyword is set then gainCor=gainval ; this is Kelvain/Jy else gainCor=(gainval/avggainval) endelse m.d=m.d/gainCor NOTE: If you input multiple maps, they should be for the same data set parameters (frequency and receiver). WARNING: cmgaincor modifies the input map. You should not call cmgaincor using already corrected data. eg.. numbpedge=2 ; use 2 spectra each edge for bandpass mbpc=cormapbc(m,numbpedge) corgaincor,m,gaincor corgaincor,m,gaincor ; this is illegal to call a second time ; but.. mbpc=cormapbc(m,numbpedge) corgaincor,m,gaincor mbpc=cormapbc(m,numbpedge) corgaincor,m,gaincor ; this is Ok
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cmgaincor.pro)
NAME: cmstostr - combine map datasets into an array. SYNTAX: cmstostr,m,ind,marr,nmaps=nmaps,chn1=chn1,chn2=chn2 ARGS: m[2,nsmp,nstrips]: {cormap} map data for 1 map ind: long index for this map marr[2,nsmp,nstrips,nmaps]:{corget} store maps here KEYWORDS: chn1: long . first freq chn to keep. count from 0.def:0 chn2: long . last freq chn to keep. count from 0.def:last nmaps: long . if specified then allocate marr to hold this many maps. DESCRIPTION: The data returned by cormapinp is an anonymous structure. This allows it to be dynamically generated. A drawback is that you can't combine different anonymous structures into an array. This routine allows you to place cormapinp array structures from calls into an array. Each map must be the same dimensions. The chn1,chn2 keywords allow you to keeep only a subset of the frequency channels (to keep the memory usage down). EXAMPLE: alloc=nmaps for i=0,nmaps-1 do begin istat=cormapinp(lun,scan,1,2,m,cals) .. get the first record cmstostr,m,i,marr,nmaps=alloc,chn1=951,chn2=1100 alloc=0 endfor
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cmstostr.pro)
NAME: cmstrips - overplot spectra by strip. SYNTAX: cmstrips,m,pol,offset,step,smo=n,title=title,xtitle=xtitle, ytitle=ytitle,first=first,last=last,freq=freq,vel=vel, tp=tp,color=color ARGS: m[2,xdim,ydim] : {} map array pol : int 1 or 2 for pol A or pol B offset : float. the y offset to add to the first strip. step : float. the y offset to add between strips. KEYWORDS smooth : int .. smooth adjacent frequency channels by n. title : string.. title for plot xtitle : string.. x axes label ytitle : string.. y axes label first : int .. first strip to plot. default is 1 last : int .. last strip to plot. default is end of map freq : if set then plot versus frequency vel : if set then plot versus velocity tp : if set then plot total power instead of spectra. color : if set then change color for each strip (you should probably enter ldcolph before running this so that the color table are loaded..) DESCRIPTION: Overplot the spectra for all the strips in a map. Each strip is offset from the previous by offset. If the keyword tp is set, then plot the total power by strip for all strips in the map. Each horizontal line will be the total power for each sample of a strip. The freq,vel keywords are not used. You will probably need to use the ver,minver,maxver function to for the vertical scale to be within the range of the data plotted. EXAMPLE: Assume the map is dimensioned m[2,31,21] with 1024 frequency channels. cmstrips,m,1,.01,.02 will plot m[0,*,i].d+offset+(i-1)*step for strips 1 thru 21. cmstrips,m,1,.01,.02,/freq .. will plot versus frequency cmstrips,m,1,.01,.02,/vel .. will plot versus velocity To plot total power by strip with zero offset between strips: ver,.95,1.2 cmstrips,m,1.,0,0,/tp,/color SEE ALSO: ver,hor (generic idl routines).
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cmstrips.pro)
NAME: cmtotpwr - compute total power for a map SYNTAX: mtp=cmtotpwr(m,chn1,ch2,mask=mask) ARGS: m[2,nsmp,nstrips] : map freq structure array chn1 : first freq chn for average. count from 1 chn2 : last freq chn for average. count from 1 ARGS: mask[m]: {cormask} mask to use when computing total power. If supplied, then ignore chn1,chn2. The dimension m can be: 1: use same mask for entire map nstrips: use same different mask for each strip nsmp*nstrips: use different mask for each sample RETURNS: mtpavg[2,nsmp,nstrips]: {cormaptp} return array of total power DESCRIPTION: Average frequency channels chn1 through chn2 of the map and return in the mtp structure. It is identical to the m[] structure except that the m.d[] frequency data is replaced by a single number which is the averaged total power. EXAMPLE: assume m[2,31,21] is a map with 2 polarizations, 31 samples per strip, 21 strips, and 1024 frequency channels. Then the call : mtp=cmtotpwr(m,100,900) returns the struct mtp with mtp.d the total power averaged over channels 100:900.
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cmtotpwr.pro)
NAME: cormapbc - band pass correct a map 1 strip at a time SYNTAX: mbpc=cormapbc(m,numbpedg,pol=pol,edge=edge,$ tsys=tsys,degfit=degfit,cumfilter=cumfilter,$ mask=mask,fractmask=fractmask,coef0=coef0,coef1=coef1 ARGS: m[2,pntstrip,numstrips] : {} input map array. numbpedg : int. How to do the band pass correction: ge 0. This is the number of bandpasses on each edge of a strip to use for the bandpass correction (0 is the same as 1). The edge keyword can choose just the left or right edge. -1 The bandpass correction is computed for each strip by doing a linear fit for each channel over the samples of a strip. Outliers are thrown out. The keyword edge will be ignored. -2 Use the entire strip to compute the bandpass by averaging over the strip. -3 Use the entire strip to compute the bandpass by computing the median by freq chn along the strip. KEYWORDS: pol : int, 1 or 2. if provided, then only process this pol edge[]: int Determine which edge of each strip to use for the bandpass,tsys correction. If edge is a single variable then the specified edge will be used for all strips. If edge is an array, then it must be dimensioned edge[numstrips]. In this case, the edge used for the bandpass correction for strip i (count from zero) will be edge[i]. This lets you change the edge used in case a continuum source appears at the edge of a strip. The values for edge are: -1 use numbpedg on the starting edge of each strip for the bandpass correction. 1 use numbpedg on the ending edge of each strip for the bandpass correction. 0 use numbpedge from both edges. If the edge keyword is not supplied then edge=0 is used. tsys: int This keyword determines how to remove the system temperature from each spectra: 0 -(default) compute tsys from bpc spectra. linearly interpolate between each edge if two edges. if a single edge, use that for the entire strip 1 - linearly interpolate the total power using all of the spectra of the strip. If numbpedg = -1 then the average of the linear fits by channel is used. 2 - compute the mean Tsys for each spectra of the strip and remove it. 3 - do not remove tsys 4 - compute the median Tsys for each spectra of the strip and remove it. 5 - compute Tsys with a robust average and then remove it. degfit: int if tsys eq 1 then this is the deg of the polynomial fit to use. The default is 1 cumfilter: If tsys!=2 and this keyword is set, a cumfilter is run on the Tsys before the fit. This will normally exlude the continuum sources from the fit. see cumfilter (under general software) mask[nchn] int mask to use for removing Tsys. If provided then it should have the same number of channels a m[0,0].d It will use the non-zero channels to compute and remove Tsys (depending on keyword tsys). The routine will always use all of the channels when doing the bandpass correction (since the cal was averaged over all channels). Note.. to use this polA and polB must have the same number of channels. fractmask: float fraction of channels on each side of spectra to ignore when computing tsys for tsys removal. Use this instead of mask= keyword. If you have 1024 channels then fractmask=.05 would ignore 51 channels from each side. RETURNS: mbpc[2,pntstrip,numstrips] : map after bandpass correction coef0[nlags,nstrips]: float bandpass fit by channel for each strip. This is the constant. coef1[nlags,nstrips]: float bandpass fit by channel for each strip. This is the linear term (-nsmp/2,nsmp/2) DESCRIPTION: Bandpass correct a map 1 strip at a time and then subtract off the system temperature. For the bandpass correction the options are: 1. use a set of bandpasses from 1 or both edges of each strip for the bandpass correction. This is controlled by numbpedge ge 0 and the edge keyword. By default numbpedge spectra from each edge are used. If edge is -1 or +1 then only the left or right edge is used for the entire map. If edge[nstrips] is an array then you can choose a different edge for each strip. This is overriden by /allstrip keyword and numbpedge =-1. 2. The /allstrip keyword will use the average bandpass of the entire strip ; for the bandpass correction (numbpedge is ignored). The Tsys removal is determined by the tsys= keyword. It will compute Tsys by averaging over all of the spectral channels. The mask= keyword lets you supply a mask that specifies which spectral points to use when computing Tsys (see blmask() in general routines). The fractedge= keyword specifies the fraction of channels on each edge of the bandpass to not use. The options for removing the Tsys by strip are: 3. If numbpedge is lt 0 then the bandpass correction will be done by doing a linear fit to each channel (over the samples in a strip). The fitting iterates throwing out any outliers over 2 sigma. The fit uses -nsmp/2 to +nsmp/2 for the x values. The constant term from the fit of each channel is used for the bandpass value for that channel. Tsys removal is determined by the tsys keyword. Tsys=0 For each strip compute Tsys from the spectra used for the bandpass correction. If bandpasses from both edges are used then linearly interpolate the tsys from the 2 sides across the entire strip. Tsys=1 Compute Tsys for each spectra of a strip and then linearly interpolate this data across the strip. Remove the fitted tsys. The degfit= keyword let's you use a higher order fit. The cumfilter keyword will not use outlier points (continuum sources) in the fit. Tsys=2 Compute Tsys for each spectra and remove it from each spectra. This is handy if you want to get rid of the continuum sources. Tsys=4 Same as Tsys=2 but use the median rather than the mean. Tsys=5 Same as Tsys=2 but use a robust average. All points 3 sigma away from mean are excluded, and then the mean is recomputed. Continue looping till no points are thrown out. Tsys=3 Do not remove Tsys. Take a look at: Comparing mean,median,robustAvg for a comparison of tsys=2,4,5. For each strip the routine will: 1. Average the bandpasses for the bandpass correction. 2. For the i'th spectra in each strip compute: spectra[i]=(spectra[i]/normalized(avgBP) -Tsys[i] where normalized(avbBp) is the normalized average bandpass computed in step 1. Tsys[i] is the system temperature computed from the keywords tsys= etc.. EXAMPLES: 1. Bandpass correct using 3 channels on each side of the map. Remove tsys using all spectral channels and interpolating between the 3 bandpassed on each edge. m=cormapbc(m,3) 2. Bandpass correct using only the starting edge. When computing Tsys ignore 5% of the channels on each edge. m=cormapbc(m,3,edge=-1,fractmask=.05) 3. Suppose you want to make a continuum map.. Bandpass correct averaging all of the spectra of a strip. Remove tsys by linearly interpolating the tsys from each spectra. Do not use 5% of the channels on each edge of the spectra. Use the cumfilter to not include the continuum sources in the tsys fit. ignore 5% of the channels on each edge. m=cormapbc(m,0,/allstrip,fractmask=.05,tsys=1,/cum) 4. You want to remove the continuum sources (ie. you are looking for galaxies) Use the average of each strip for the bandpass correction. Remove Tsys by subtracting the Tsys of each spectra. Throw out 5% of the channels from each edge. m=cormapbc(m,0,/allstrip,fractmask=.05,tsys=2) 5. Band pass correct averaging over all strips but do not remove tsys. m=cormapbc(m,0,/allstrip,tsys=3) RECENT HISTORY: 07jul04 - added tsys=5. use avgrobbychan for tsys removal. 06jul04 - added tsys=4. uses median rather than mean for Tsys removal. 07feb04 - bandpass by channel, after fit, recheck all the residuals for being with nsig, not just the current good ones. Bug in the residual compare, was not using abs(). 14jan04 - allstrip -> numbpedg=-2 07jan04 - added bcbychan option. does a linear fit by chan along each strip to determine the bandpass. 03sep03 - if mask supplied (or edge fract) then the normalizatino of the bandpass for bandpass correction should be over the mask, and not the entire bandpass. Before it was normalizing over the entire bandpass, dividing, and then using the mask to compute the tsys subtraction. 27jan03 - added cumfilter keyword 27jan03 - for tsys removal if tsys=1 then allow option to iterate throwing out pnts above 3sigma
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cormapbc.pro)
NAME: cormapbl - baseline all the spectra in a map. SYNTAX: mbl=cormapbl(m,mask,polyDeg,gotmask=gotmask) ARGS: m[2,nsmps,nstrips] - map array of structures. mask[nfrqchn]: float - mask to use for baselining polyDeg : int - degree of polynomial for baselining KEYWORDS: gotmask : if set then user supplies mask and polyDeg as input. RETURNS: mask[nfrqchn] : the mask array used. polyDeg : the degree of the polynomial used. mbl[2,nsmps,nstrips] : baselined map info DESCRIPTION: cormapbl will baseline all the spectra in a map. The user inputs the map array. The routine call bluser which allows the user to interactively define a mask and order for baselining. After the mask and order is chosen, the routine will fit the requested order polynomial to every spectra using the mask. A copy of the map array is returned with the baseline removed from the data. bluser is called from within the routine with the average of all spectra in pola. The users uses this as the reference to decide which polynomial and mask to use. EXAMPLE: let m[2,31,21] be the map structure with 1024 frequency channels. the call: mbl=cormapbl(m,mask,polyDeg) will: 1. call bluser with average of polA over the map. the output from bluser consists of: KEY ARGS FUNCTION m .. define mask f n .. fit polynomial of order n h h1 h2 .. change horizontal scale for plot to h1,h2 v v1 v2 .. change vertical scale for plot to v1,v2 c .. print coefficients p .. plot data - fit q .. quit You must first specify the mask with m.After than you can fit various polynomials with the f deg . Use p to plot the data - fit. Use v 1.1 1.5 or h 200 400 to change the vertical and horizontal display. When you are satisfied with the fit, enter q and the routine will then fit the polynomial to each spectra and return it in mbl. NOTE: If you called: cormapinp, cormapsclk, and then this routine, the spectra still need to be bandpass corrected before the temperature scale is correct.
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cormapbl.pro)
NAME: cormapinp - input a correlator map. SYNTAX:istat=cormapinp(lun,scan,polABrdNum,polBBrdNum, m,cals,norev=norev, han=han,sl=sl,maxrecs=maxrecs,avgsmp=avgsmp, calrecs=calrecs) ARGS: lun: int logical unit number for file. It should already be opened. scan: long scan number for start of map. polABrdNum: int correlator board index (1 thru 4) to take polA data from. polBBrdNum: int correlator board index (1 thru 4) to take polB data from. KEYWORDS: norev: When you drive both directions, the routine will normally reverse the odd strips so that the data,header, and cals of adjacent strips will line up in RA. Setting norev to true will override this default. The data will be returned in the order it is taken. han: if set then hanning smooth the data on input. sl[]: {sl} scanlist array returned from call a previous call to getsl(). If this keyword is provided then direct access is used rather than sequentially reading the file. maxrecs: long If you have more than 300 records in a scan you will have to set maxrecs to that value. avgsmp: long Number of samples to average together. Handy if you've oversampled each strip. If this number does not divide evenly into the samples/strip, then samples at the end will be dropped. avsmp=0 or 1 is the same as no averaging RETURNS: istat: int 1: got all the strips 0: got part of requested map -1: got no data. m[2,pnts/strip,nstrips]:{} array of structures holding the returned data and header. (see below for a description). cals[nstrips*n] :{} array of structures containing the processed cal on,off data (cal value and scale factor) (see below for a description). calrecs[nstrips*n]:{calrecs} array holding the raw cal on, cal off input data. For the i'th calOn/off measured the the structure contains cal spectra as float arrays: calrecs[i].scan ..scan number forcalOnScan calrecs[i].calPerStrip ..0,1,or 2 calrecs[i].con[nchans,2] calrecs[i].coff[nchans,2] where i is the i'th calon/off, nchannels are the number of frequency channels, and [,2] has polA, polB You can use this data to do rfi excision and then recompute the cals[] array. DESCRIPTION: Input a map taken with the correlator routines cormap1,cormapdec, cordrift, or cormapbm. You open the datafile (eg openr,lun,filename,/get_lun) prior to calling this routine and then pass in the lun. The scan argument tells the routine where to start reading. This should be the first scan of the map. If the map starts with a calon/off and you gave the scan of the first strip, the routine will search backwards looking for the cal that goes with the first strip (so either is ok to enter). You need to also specify which boards of the correlator contain the polA, polB data you want to extract (it will only process 1 set of polA, polB data at a time). The board numbers are indexed 1 through 4. If polA, polB were input on on the same board, then use the same board number for both. The routine will figure out from the header the size of the map, whether it is a partial map, which directions you drove, and how the cals were taken. The map information is returned as an array m of structures. The dimensions of the array are: m[pol,pntsperstrip,numstrips]. pol=2 Each element of the array contains the information for a particular sample and polarization. The contents of m (for this example assume 2048 freq channels,and we are looking at sample 5 of strips 7 (counting from 1): m[0,5,7].h the header for pola, m[0,5,7].d[2048] the data for pola m[0,5,7].p the total power value for this sample (linear scale). m[0,5,7].az the azimuth position in degrees at the end of each sample. m[0,5,7].za the zenith angle position in degrees at the end of each sample. m[0,5,7].azErrAsec the azimuth tracking error in arcseconds at the end of each sample (great circle). m[0,5,7].zaErrAsec the zenith angle tracking error in arcseconds at the end of each sample. m[0,5,7].raHr the RA in hours at the middle of each sample. m[0,5,7].decDeg the declination in degrees at the middle of each sample. m[0,5,7].calscl cal scale factor for this position. calst,calend, or (calst+calEnd)/2. m.h holds the header information for this sample. It contains several sub structures: m.h.std - the standard header m.h.pnt - the pointing header m.h.iflo - the iflo header m.h.dop - the doppler,freq,velocity header m.h.cor - the correlator header m.h.proc - the datataking procedure part of the header. Some locations of interest are: m.h.std.scannumber: the scan number for the strip m.h.pnt.r.reqPosRd[2]: requested ra,dec for center of map in radians. If you drove both directions in ra then lining up the samples numbers from each strip would not give a map in increasing ra. By default the routine will flip the order of the even strips (counting from 1) in the entire map (it flips the order of the headers and cals as well as the data). eg.. suppose the map has 15 strips, counting from 1 it will flip the samples in strips 2,4,6,8,10,12,14,12,14 so the ra will line up. If you started in the middle of a map, then it will flip the strips relative to the number in the entire map. eg. suppose the map has 15 strips and your first strip was strip 4, then it would flip 1,3,5... of the returned strips (since these would normally be flipped in the entire map). Setting the /norev keyword will override the default and return the data in the order it was taken in each strip. The cal data is returned in an array of cal structures. It is dimensioned cals[nstrips*n]. n is 1 if you took 1 cal per strip and 2 if you took 2. The contents of the cal structure are: cals[i].h - the header for the on cal record. cals[i].calval[2] - the cal value in kelvins for polA,polB interpolated to your frequency. cals[i].calscl[2] - the cal scaling factor to go from correlator counts to kelvins for pol A,polB. it is: computed as : calValKelvins/(calOn-caloff) The correlator part of the header contains the cal on, cal off counts: cals[i].h.cor.calon[2] polA,polB cal on total power cals[i].h.cor.caloff[2] polA,polB cal off total power If you provide the calrecs keyword, then the actual cal spectra will also be returned in an array of calrecs structure. Each element contains the info for one cal on/off measurement. The contents are: calrecs[i].scan ..scan number forcalOnScan calrecs[i].calPerStrip ..0,1,or 2 calrecs[i].con[nchans,2].. cal on spectra correlator units calrecs[i].coff[nchans,2] cal off spectra correlator units nchans are the number of frequency channels per spectra. If there are two sbc per board then polA will be [nchans,0], polB will be [nchans,1] If there can be 0,1 or 2 cals per strip. This data can be used to recompute the cal values after rfi excision. EXAMPLE: Suppose we are mapping with 15 samples/strip, 11 strips, and two correlator boards with 1024 pola/polB on each board. We also took 1 cal per strip. Since arrays are indexed from 0,lets count our samples and strips from 0. Then: m[0,4,3].d[*] polA sample 4,strip 3 m[1,4,3].d[*] polB sample 4,strip 3 the headers would be: m[0,4,3].h polA header sample 4,strip 3 m[1,4,3].h polB header sample 4,strip 3 Assuming there is 1 cal rec/strip, you could scale the spectra in the first strip to kelvins with: m[0,*,0].d=m[0,*,0]* cals[0].calscl[0] m[1,*,0].d=m[1,*,0]* cals[0].calscl[1] If we took two cals per strip then the cals for the i'th strip (counting from zero would be: cals[2*i:2*i+1] SEE ALSO: cor2/corget, cor2/cormapsclK, cor2/arch_getmap,cormap/cormapinplist NOTES: 1. For lbw the routine assumes the cal values are for the measured polarizations. If the circular hybrid is in, then the cal values from the file (which are linear, need to be averaged). This probably needs a parameter to flag this (or maybe rely on the header to have the iflo info..) 2. cormapbm does a beam map in great circle azimuth,za relative to the center of the source. The m.raHr and m.decDeg values are the az,za offsets (great circle) in degrees rather than the ra and dec (sorry about this but it was too late to change the variable names to m.c1,m.c2). m.raHr -- azimuth offset relative to src center in degrees. m.decDeg -- zenth angle offset relative to src center in degrees. 3.The header contains a proc section that holds procedure dependant information m.h.proc.iar[10] integer data m.h.proc.dar[10] double data These contain some of the parameters that were used in the datataking procedure: --------------------- HDR.PROC.IAR definition for cormap1,cormapdec code : bit 0: coord : 0 J2000, 1 1950 : bit 1: rawidth : 0 great cirle, 1 little circle ra width : bit 2: direction: 0 both ways , 1 increasing ra only : bit 3: update doppler odd strips : bit 4: update doppler even strips : bit 5: cal on/off start of strip : bit 6: cal on/off end of strip : bit 8: cal on/off start of map (once only) : bit 9: 1--> adjust correlator power levels at start position. iar[0] - code value used iar[1] - number of strips requested in map (total.. not this call) iar[2] - 1st strip to start on (count from 1) iar[3] - integrations per strip iar[4] - stripNumber this strip (count from 1) (from start of map, not start of call) iar[5] - start time seconds from midnite ast this strip HDR.PROC.dAR definition for cormap1,cormapdec dar[0] - seconds per integration dar[1] - raOffAmin for map .. what user entered dar[2] - decOffAmin for map .. what user entered dar[3] - decStepAmin for map .. what was used strip info dar[4] - raRateRd for map .. dar[5] - raRateRd this strip little circle dar[6] - raOffRd this strip little circle dar[7] - decOffRd this strip --------------------- HDR.PROC.IAR for cordrift code code where each bit signifies an option: code code where each bit signifies an option: 1 bit0: coord : 0 J2000, 1 1950 2 bit1: cal on/off end of strip: 1 yes, 0 no 4 bit2: 0:raOffset=stripTime/2., 1:raOffset=stripTime*1.0027../2 .. convert to sidereal secs 8 bit 3: update doppler firstStrip (only) 0x10 bit 4: update doppler each strip (including first) 0x100 bit 8: coradjpwr at start of 1st strip 0x200 bit 9: coradjpwr at start of every strip 0x400 bit10: cal onoff start of each strip iar[0] - code value used iar[1] - requested number of times to loop (strips if decStp != 0) iar[2] - requested seconds to drift iar[3] - integrations per strip iar[4] - current strip count from 1 iar[5] - start time seconds from midnite ast this strip iar[6] - extra settle time secs.. iar[7] - stripsPerStep in dec HDR.PROC.dAR definition for cordrift dar[0] - seconds per integration dar[1] - haOffRd for map .. what user entered current coord.. dar[2] - decOffAmin for map .. what user entered dar[3] - decStepAmin for map .. what user entered strip info dar[4] - decOffsetThis strip amin --------------------- HDR.PROC.IAR for cormapbm (beam maps) code code where each bit signifies an option: : bit 0: coord : 0 J2000, 1 1950 : bit 1: azwidth : always great circle : bit 2: direction: 0 both ways , 1 increasing az only : bit 3: : bit 4: : bit 5: cal on/off start of strip : bit 6: cal on/off end of strip : bit 8: cal on/off start of map (once only) : bit 9: 1--> adjust correlator power levels at start position. iar[0] - code value used iar[1] - number of strips requested in map (total.. not this call) iar[2] - 1st strip to start on (count from 1) iar[3] - integrations per strip iar[4] - stripNumber this strip (count from 1) (from start of map, not start of call) iar[5] - start time seconds from midnite ast this strip HDR.PROC.dAR definition for cormapbm dar[0] - seconds per integration dar[1] - azOffAmin for map .. great circle dar[2] - zaOffAmin for map .. dar[3] - zaStepAmin for map .. w strip info dar[4] - azRateRd for map ..(great circle dar[5] - azRateRd this strip great circle dar[6] - azOffRd this strip great circle dar[7] - zaOffRd this strip modhistory 31jun00 update for new corget format 20dec00 updated to use h.proc information 09jun01 added support for sl keyword 04nov01 when computing positions, the rate seconds are sidereal. need to compute duration solar->sidereal 07dec01 fixed up m.rahr for cordrift. Had constant position for each strip. 16dec02 added maxrecs 16dec02 added avgsmp. updated: all h.cor.dumpsPerInteg h.proc.iar[3] - integrations per strip h.proc.dar[0] secs per integration cordrift iar[2] secs req to drift 07apr03 check procname and source name.. 01jun03idlversion >= 5.5 now treat embedded structures as arrays of 1. This breaks the reform code. Need to test version of idl to do reform correctly. 05dec03 start adding support for cormapbm 10jan04 it now searches backwards for the first cal if the user enters the scan number of the first strip.. 03feb04 was not computing number of cals to return if cals on both edge of strip 20feb04 newer versions of idl were creating different dimensions of the arrays .. eg d[128,1,30] and d[128,30]. This was causing problems on the reversing.. I tried to fix it here.. 07feb04 case statement line 580.. first test was cormap1 or cordrift 2nd test was cordrift, should have been cormapdec. This caused cormapdec patterns to fail. 26jul04 need to check rcvr number. Some clever people were interleaving maps of same name, but different receivers..
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cormapinp.pro)
NAME: cormapinpcal - process a cal on/off for cormapinp SYNTAX: stat=cormapinpcal(lun,b,calAr,brdIndA,brdIndB,calrec,totcals,stripNum, rdrec=rdrec,han=han) ARGS: lun : int unit number assigned to file to read. b : {corget} optional. calOn rec if already read in. brdIndA : int board index to process for polA count(0..3) brdIndb : int board index to process for polB count(0..3) calrec : int index into calAr[] to place this cal data If sucessful, this variable gets incremented on return. totcals : int total number of cals that will be placed in calAr. On the first cal with calrec=0, totcals is used to initially allocate calAr[totcals]. stripNum: int the strip number for this cal (count from 1) KEYWORDS: rdrec if set, then the cal on rec should be read from the current location of lun and placed in b. If rdrec is not set, then the user has already read the the calon rec and it is passed in via b. han: if set and returning calrecs, hanning smooth the cal spectra. RETURNS: stat : int 1 successful, 0 error. calAr[n]: {calstr} array holding calinfo for each strip of map. The data for the current cal will be stored in calar[calrec] (before calrec is incremented). calrec : int will be incremented on successful return. This keeps track of where the next cal will go in calAr. b :{corget} If rdrec is not set, then the calOn rec is returned DESCRIPTION: This routine is normally called by cormapinp. It will process the next calonoff pair of scans. If rdrec is set, then lun should point to the calOnRec. If rdrec is not set, then the cal on rec should already have been read into the variable b and lun should point at the cal off rec. The routine calls corcalonoffrec(). It loads the cal results in the strcuture calAr[calrec]. Calrec is then incremented.
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cormapinpcal.pro)
NAME: cormapinplist - input a map from a list of files. SYNTAX:istat=cormapinplist(flist,scanlist,polABrdNum,polBBrdNum, m,cals, maxstrips=maxstrips,norev=norev,han=han, maxrecs=maxrecs,avgsmp=avgsmp) ARGS: cormapinplist arguments: flist[n]: string array of files to read data from scanlist[n]: long starting scannumber of the map in each file cormapinp arguments: polABrdNum: int correlator board index (1 thru 4) to take polA data from. polBBrdNum: int correlator board index (1 thru 4) to take polB data from. KEYWORDS: maxstrips: long The maximum number of strips for the completed map The program will allocate an array of this size when inputting the data. The default max size is 41 strips The following keywords are from cormapinp. norev: When you drive both directions, the routine will normally reverse the odd strips so that the data,header, and cals of adjacent strips will line up in RA. Setting norev to true will override this default. The data will be returned in the order it is taken. han: if set then hanning smooth the data on input. sl[]: {sl} scanlist array returned from call a previous call to getsl(). If this keyword is provided then direct access is used rather than sequentially reading the file. maxrecs: long If you have more than 300 records in a scan you will have to set maxrecs to that value. avgsmp: long Number of samples to average together. Handy if you've oversampled each strip. If this number does not divide evenly into the samples/strip, then samples at the end will be dropped. avsmp=0 or 1 is the same as no averaging RETURNS: istat: int 1: got map -1: trouble getting the data m[2,pnts/strip,nstrips]:{} array of structures holding the returned data and header. (see below for a description). cals[nstrips*n] :{} array of structures containing the cal on,off data (see below for a description). DESCRIPTION: cormapinp is used to input an on the fly map from a data file. Large maps may be taken a few strips per day and be spread over many datafiles. cormapinplist will input a map that is spread over many files. See cormapinp for a description of the m,cals structures that are returned. EXAMPLE: Suppose the map is spread over 3 days and that the files are located in /share/olcor/ with the specified starting scan numbers. The data is in brdpolA=1 and brdPolB=2 with a total of 49 strips. You could input the map using: flist=['corfile.14dec02.a1632.1','corfile.15dec02.A1632.1',$ 'corfile.16dec02.A1632.1',$ dir='/share/olcor/' scanlist=[234800055L,234900056L,235000053L] polABrd=1 polBBrd=2 istat=cormapinplist(dir+flist,scanlist,polABrd,polBBrd,m,cals,maxstrips=49) SEE ALSO: cormapinp(), cor2/arch_getmap modhistory 19dec02 started 07mar04 added calrecs keyword
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cormapinplist.pro)
NAME: cormapsclK - scale a map to Kelvins. SYNTAX: istat=cormapsclK(m,cals,tsys,use1cal=use1cal) ARGS: m[pol,smpPerStrip,nstrips] : map array input from cormapinp. cals[ncals] : cals data input with cormapinp. KEYWORDS: use1cal: int 1 or 2. if two cals per strips you can should to use only the first or 2nd cal by setting use1cal to 1 or 2. RETURNS: m.d : will be scaled to kelvins tsys[2,smpPerStrip,nstrips] : average system temp each sample istat : 1 ok, 0.. trouble DESCRIPTION: Scale the map data to kelvins. To use this routine the data must have been taken with 1 or 2 cals per strip. For each strip in the map the cals.calscl scale factor is used to convert from correlator counts to kelvins. If two cals per strip were taken then the cal values are interpolated across the strip. The original data in m.d is overwritten with the new values. The location m.calscl is also updated if we have 2 cals/strip. The interpolated value is used instead of the average that was loaded in cormapinp. EXAMPLE: istat=cormapinp(lun,scan,brda,brdB,m,cals) istat=cormapsclk(m,cals,tsys) When calling this routine, make sure that the data array m is still in correlator units (as returned by cormapinp). If you call cormapbc() to bandpass correct the data, then the returned data array is no longer in correlator units and this routine should not be used with that data array. NOTE: 1. Since the original data is overwritten, you do not want to run this routine more than once on the same array. The conversion only works if the input data is in correlator counts. 2. This is normally the first routine called. After calling the routine, you will notice that the system temperatures in tsys are smaller than the central portion of the spectra. This is because the bandpass correction has not yet been done. Tsys is the average of the entire spectra. Since the spectra falls off at the edges (because of the bandpass) the center or the spectra will be higher than the average. You will have the correct temperatures only after the bandpass correction is done ( by dividing through by a normalized bandpass). This implies that scaling to kelvin and then fitting a baseline will not give the correct temperatures. If this is really what is wanted, then the scaling factor from correlator units to kelvins should be computed over the central part of the spectra.
(See /pkg/rsi/local/libao/phil/Cor2/cormap/cormapsclk.pro)


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Support for IPP Fax Out
Wrapping proprietary printer drivers into a Printer Application
IPP scan (or virtual MF device) server (Scanner Application)
IPP scan (or virtual MF device) server (Scanner Application)


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- packet - Variable in exception com.whirvis.jraknet.client.PacketBufferException
-
- Packet - Class in com.whirvis.jraknet
-
Used to read and write data with ease.
- Packet(ByteBuf) - Constructor for class com.whirvis.jraknet.Packet
-
Constructs a Packet
that reads from and writes to the
specified ByteBuf
.
- Packet(DatagramPacket) - Constructor for class com.whirvis.jraknet.Packet
-
Constructs a Packet
that reads from and writes to the
specified DatagramPacket
- Packet(byte[]) - Constructor for class com.whirvis.jraknet.Packet
-
Constructs a Packet
that reads from and writes to the
specified byte array.
- Packet(Packet) - Constructor for class com.whirvis.jraknet.Packet
-
Constructs a Packet
that reads from and writes to the
specified Packet
.
- Packet() - Constructor for class com.whirvis.jraknet.Packet
-
Constructs a blank Packet
using an empty
ByteBuf
.
- packet - Variable in class com.whirvis.jraknet.RakNet.BootstrapHandler
-
- packet - Variable in class com.whirvis.jraknet.stream.PacketDataInput
-
- packet - Variable in class com.whirvis.jraknet.stream.PacketDataOutput
-
- PACKET_SEND_COUNT - Static variable in class com.whirvis.jraknet.SequencedPacketTest
-
- PacketBufferException - Exception in com.whirvis.jraknet.client
-
Signals that a packet critical to the RakNetClient
failed to
encode or decode correctly.
- PacketBufferException(RakNetClient, RakNetPacket) - Constructor for exception com.whirvis.jraknet.client.PacketBufferException
-
Constructs a PacketBufferException
with the specified
RakNetClient
and RakNetPacket
.
- PacketDataInput - Class in com.whirvis.jraknet.stream
-
Used to read data from a Packet
with ease, to retrieve a
Packet
's DataInput
simply use
getDataInput()
.
- PacketDataInput(Packet) - Constructor for class com.whirvis.jraknet.stream.PacketDataInput
-
Constructs a PacketDataInput
with the specified
Packet
.
- PacketDataOutput - Class in com.whirvis.jraknet.stream
-
Used to write data to a Packet
with ease, to retrieve a
Packet
's DataOutput
simply use
getDataOutput()
.
- PacketDataOutput(Packet) - Constructor for class com.whirvis.jraknet.stream.PacketDataOutput
-
Constructs a PacketDataOutput
with the specified
Packet
.
- packetIds - Static variable in class com.whirvis.jraknet.protocol.MessageIdentifier
-
- packetNames - Static variable in class com.whirvis.jraknet.protocol.MessageIdentifier
-
- PacketOverloadException - Exception in com.whirvis.jraknet.session
-
Signals that a RakNetSession
has attempted to send too many
packets at once.
- PacketOverloadException() - Constructor for exception com.whirvis.jraknet.session.PacketOverloadException
-
Constructs a PacketOverloadException
.
- packetReceiveCount - Static variable in class com.whirvis.jraknet.SequencedPacketTest
-
- packetsReceived - Static variable in class com.whirvis.jraknet.SequencedPacketTest
-
- packetsReceivedThisSecond - Variable in class com.whirvis.jraknet.session.RakNetSession
-
- packetsSentThisSecond - Variable in class com.whirvis.jraknet.session.RakNetSession
-
- pad(int) - Method in class com.whirvis.jraknet.Packet
-
Writes the specified amount of null (0x00) bytes to the packet.
- parseAddress(String, int) - Static method in class com.whirvis.jraknet.RakNet
-
Parses a single String as an address and port and converts it to an
InetSocketAddress
.
- parseAddress(String) - Static method in class com.whirvis.jraknet.RakNet
-
Parses a single String as an address and port and converts it to an
InetSocketAddress
.
- parseAddressPassive(String, int) - Static method in class com.whirvis.jraknet.RakNet
-
Parses a single String as an address and port and converts it to an
InetSocketAddress
.
- parseAddressPassive(String) - Static method in class com.whirvis.jraknet.RakNet
-
Parses a single String as an address and port and converts it to an
InetSocketAddress
.
- parseBytePassive(String) - Static method in class com.whirvis.jraknet.RakNet
-
Parses a String as a byte and returns -1 in the case of a
NumberFormatException
.
- parseIntPassive(String) - Static method in class com.whirvis.jraknet.RakNet
-
Parses a String as an int and returns -1 in the case of a
NumberFormatException
.
- parseLongPassive(String) - Static method in class com.whirvis.jraknet.RakNet
-
Parses a String as a long and returns -1 in the case of a
NumberFormatException
.
- parseShortPassive(String) - Static method in class com.whirvis.jraknet.RakNet
-
Parses a String as a short and returns -1 in the case of a
NumberFormatException
.
- payload - Variable in class com.whirvis.jraknet.protocol.message.EncapsulatedPacket
-
- PAYLOAD_LENGTH_LENGTH - Static variable in class com.whirvis.jraknet.protocol.message.EncapsulatedPacket
-
- payloads - Variable in class com.whirvis.jraknet.session.SplitPacket
-
- PERMANENT_BLOCK - Static variable in class com.whirvis.jraknet.server.BlockedAddress
-
- PING_ID - Static variable in class com.whirvis.jraknet.RakNet
-
- PING_RETRIES - Static variable in class com.whirvis.jraknet.RakNet
-
- PING_SEND_INTERVAL - Static variable in class com.whirvis.jraknet.RakNet
-
- PING_TIMESTAMP - Static variable in class com.whirvis.jraknet.RakNet
-
- pingId - Variable in class com.whirvis.jraknet.client.RakNetClient
-
- pingId - Variable in class com.whirvis.jraknet.protocol.status.UnconnectedPing
-
- pongId - Variable in class com.whirvis.jraknet.protocol.status.UnconnectedPong
-
- pongId - Variable in class com.whirvis.jraknet.server.RakNetServer
-
- pongsReceived - Variable in class com.whirvis.jraknet.session.RakNetSession
-
- port - Variable in class com.whirvis.jraknet.server.RakNetServer
-
- POWERSHELL_ADMINISTRATIVE_TIMEOUT - Static variable in class com.whirvis.jraknet.windows.PowerShellAdministrativeClient
-
- POWERSHELL_ADMINISTRATIVE_TIMEOUT - Static variable in class com.whirvis.jraknet.windows.PowerShellCommand
-
- POWERSHELL_BASE64_CHARSET - Static variable in class com.whirvis.jraknet.windows.PowerShellCommand
-
- POWERSHELL_EXECUTABLE - Static variable in class com.whirvis.jraknet.windows.PowerShellCommand
-
- PowerShellAdministrativeClient - Class in com.whirvis.jraknet.windows
-
Used to execute commands with administrative privileges on Windows machines.
- PowerShellAdministrativeClient() - Constructor for class com.whirvis.jraknet.windows.PowerShellAdministrativeClient
-
- PowerShellCommand - Class in com.whirvis.jraknet.windows
-
Represents a command that can be executed in the Windows PowerShell
environment under the Windows 10 operating system.
- PowerShellCommand(String) - Constructor for class com.whirvis.jraknet.windows.PowerShellCommand
-
- PowerShellException - Exception in com.whirvis.jraknet.windows
-
Signals that an error occurred when executing a Windows PowerShell command
(other than that Windows PowerShell is not installed.)
- PowerShellException(String) - Constructor for exception com.whirvis.jraknet.windows.PowerShellException
-
- preparation - Variable in class com.whirvis.jraknet.client.RakNetClient
-
- printResults() - Static method in class com.whirvis.jraknet.SequencedPacketTest
-
Prints the results of the test
- protocolVersion - Variable in class com.whirvis.jraknet.protocol.login.OpenConnectionRequestOne
-
- put(byte, T) - Method in class com.whirvis.jraknet.map.ByteMap
-
Associates the specified value with the specified key in this map.
- put(byte, T) - Method in class com.whirvis.jraknet.map.concurrent.ConcurrentByteMap
-
Associates the specified value with the specified key in this map.
- put(int, T) - Method in class com.whirvis.jraknet.map.concurrent.ConcurrentIntMap
-
Associates the specified value with the specified key in this map.
- put(long, T) - Method in class com.whirvis.jraknet.map.concurrent.ConcurrentLongMap
-
Associates the specified value with the specified key in this map.
- put(short, T) - Method in class com.whirvis.jraknet.map.concurrent.ConcurrentShortMap
-
Associates the specified value with the specified key in this map.
- put(int, T) - Method in class com.whirvis.jraknet.map.IntMap
-
Associates the specified value with the specified key in this map.
- put(long, T) - Method in class com.whirvis.jraknet.map.LongMap
-
Associates the specified value with the specified key in this map.
- put(short, T) - Method in class com.whirvis.jraknet.map.ShortMap
-
Associates the specified value with the specified key in this map.


bidi_fd_watcher, bidi_fd_watcher_impl
// Members of dasynq::event_loop<T> instantiation: class bidi_fd_watcher; template <class Derived> class bidi_fd_watcher_impl; // : public bidi_fd_watcher;
bidi_fd_watcher
Brief: bidi_fd_watcher is a member type of the event_loop template class. It represents an event watcher for file-descriptor readiness events, similar to the fd_watcher type, but with certain event-loop backend details abstracted away. In particular, bidi_fd_watcher always allows watching for both input and output readiness using a single watcher. The bidi_fd_watcher class should not be subclassed directly; the bidi_fd_watcher_impl template provides a means for subclassing.
Members
Types
- event_loop_t — the event loop type that this watcher registers with.
Constructors
- bidi_fd_watcher — default constructor.
Functions
- (#1) void add_watch(event_loop_t &eloop, int fd, int flags, int inprio = DEFAULT_PRIORITY, int outprio = DEFAULT_PRIORITY) —
add a watch for the specified file descriptor. The flags argument specifies a combination of dasynq::IN_EVENTS and dasynq::OUT_EVENTS specifying which readiness events to initially enable. - (#2) template <typename T>
static bidi_fd_watcher *add_watch(event_loop_t &eloop, int fd, int flags, T watchHndlr) —
add a dynamically-allocated watch with the specified callback. See add_watch(#1). The watch is automatically deleted when removed. The watchHandlr should be a lambda of the form [](event_loop_t &eloop, int fd, int flags) -> dasynq::rearm or a compatible function object. See details below. - void add_watch_noemu(event_loop_t &eloop, int fd, int flags,
int inprio = DEFAULT_PRIORITY, int outprio = DEFAULT_PRIORITY) —
add a watch as per add_watcher, but without watch emulation for regular files. If the backend does not support the specified file descriptor, throws std::system_error rather than emulating readiness events. - void set_in_watch_enabled(event_loop_t &eloop, bool b) noexcept — enable or disable watching input readiness events.
- void set_out_watch_enabled(event_loop_t &eloop, bool b) noexcept — enable or disable watching output readiness events.
- void set_watches(event_loop_t &eloop, int newFlags) noexcept — enable/disable both input and output readiness watches at the same time. newFlags is a combination of dasynq::IN_EVENTS and dasynq::OUT_EVENTS which specifies which watches will be enabled (if not set, the corresponding watch is disabled).
- void deregister(event_loop_t &eloop) noexcept
— request removal from the event loop. - int get_watched_fd() — returns the file descriptor associated with this watcher
- virtual void watch_removed() noexcept — called when the watcher has been removed from the event loop.
Details and Usage
Please read the usage details for the fd_watcher class as they generally also apply to bidi_fd_watcher.
A bidi_fd_watcher largely acts as two separate fd_watcher instances — one for input readiness and one for output readiness — controlled via a single object. The two halves can be enabled/disabled independently and, importantly, may be queued independently (meaning that their callbacks may be issued concurrently if multiple threads are used to poll the event loop). The rules for the manipulating each half is the same as for watchers in general, including that they must not be enabled while already queued or processing callbacks if the event loop could be polled from a second thread. See the event_loop documentation for details.
Subclassing bidi_fd_watcher
To specify callback behaviour, bidi_fd_watcher can be subclassed — however, it should not be directly subclassed; instead, use the bidi_fd_watcher_impl implementation wrapper template.
add_watch (#2)
// member of dasynq::event_loop<T>::bidi_fd_watcher template <typename T> static fd_watcher<event_loop_t> *add_watch(event_loop_t &eloop, int fd, int flags, T watch_hndlr)
This variant of the add_watch function can be used to create and register a dynamically-allocated fd_watcher. The first three parameters are the same as for add_watch(#1). The watch_hndlr parameter is a function or lambda of the form:
[](event_loop_t &eloop, int fd, int flags) -> rearm { ... }
It acts as the callback function for the generated watcher (for both input and output readiness events). The flags parameter will be set to either dasynq::IN_EVENTS or dasynq:OUT_EVENTS. The watcher will delete itself when it is removed from the event loop.
This function can throw std::system_error or std::bad_alloc on failure.
bidi_fd_watcher_impl
Brief: The bidi_fd_watcher_impl class provides a basis for implementing bidi_fd_watcher, using the "curiously recurring template pattern". Instead of subclassing bidi_fd_watcher directly, subclass an instantiation of bidi_fd_watcher_impl with the template parameter specified as the subclass itself. For example:
class my_watcher : public event_loop_t::bidi_fd_watcher_impl<my_watcher> { // ... }
Details and Usage
The callback functions must be provided in the subclass and named read_ready and write_ready, with signatures compatible with the following:
rearm read_ready(event_loop_t & loop, int fd); rearm write_ready(event_loop_t & loop, int fd);
The callback functions must be public, but need not be virtual. They will be called with the following parameters:
- loop — a reference to the event loop.
- fd — the file descriptor being watched, as specified at registration.
The return value specifies the rearm action. The action is performed independently for the input and output halves of the watcher. If the rearm::REMOVE action is returned by either callback function, the relevant callback is disabled and the watch (as a whole) will be de-registered once the other callback function also returns rearm::REMOVE (or the watcher is explicitly deregistered).
To deregister the watcher (disarming both callbacks) from inside either callback, the deregister function can be used; the callback function should then return rearm::REMOVED.
Note that, if the event queue is polled from multiple threads, both callbacks may be active simultaneously.
A bidi_fd_watcher_impl instantiation has no public or protected members, other than those inherited from bidi_fd_watcher, which it publicly derives from.


The Remote Method Invocation (RMI) module helps you write programs that use RMI technology. It contains RMI templates, which can generate most of the code needed for RMI programs. It also includes an RMI Registry browser that generates the code for RMI registry binding and lookup.
For more information see:
RMI Concepts
Generating, Programming, and Compiling RMI Files
Browsing RMI Registries
Specifying RMI Settings
Using the RMI Activation Browser


{% extends "base.html" %} {% block title %}Rate Limiting{% endblock %} {% block content %}
TO BE UPDATED: Rate limiting is applied to the Address Index API to ensure a high-quality service is delivered for all users, and to protect client applications from unexpected loops.
{% endblock %}


Followings are the details of your platform
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I am experienced in web development, both in the backend using java with spring frameworks, and in the frontend using Angular. Also am experience with development for high platform (mainframe), where i was develop systems to financial institution.
Respond by developing the accreditation system for payment method!
Analysis and coding programs Cobol (Batch and Online), often using in these programs access to DB2 database, and building JCL's. Testing in unit test environment, integration environment, and also performing validations with managers in homologation environment!
Coding and testing of Cobol programs (Batch and Online) using DB2 and CICS, to divers systems of Banco Bradesco S / A!
Apart from being a web developer, I enjoy most of my free time to watch series and movies that i like. I like going to the movies theater, and dining out. And I also spend a lot of time with my dog ... Rocky. I take the time to always study some new technology.


#BayelsaKogiDecide: Armed men overwhelmed security agents –PSC
by Adelani AdepegbaAdelani Adepegba, Abuja
The Police Service Commission has said armed men overwhelmed security agents during the Saturday governorship elections in Bayelsa and Kogi states.
The commission called on the leadership of the security agencies to immediately take steps to mop up illegal firearms to safeguard the nation’s democracy.
The PSC spokesman, Ikechukwu Ani, in a statement on Sunday expressed satisfaction with the roles played by the Nigeria Police Force and other security agencies in the elections.
It said the interim report of the commission indicated that the police were very professional despite numerous challenges in the two states.
The PSC said, “The strong monitoring team of the commission to the two states observed the proliferation of illegal weapons, inadequate logistics and challenging terrains as militating factors.
“The commission monitored the conduct of police officers on election duties in Bayelsa and Kogi states on Saturday, November 16th, 2019, and observed that the police were professional in their conduct during the elections, while the activities of illegal armed men overstretched the capacity of these officers to protect the voters and the electoral materials.”
Copyright PUNCH.
All rights reserved. This material, and other digital content on this website, may not be reproduced, published, broadcast, rewritten or redistributed in whole or in part without prior express written permission from PUNCH.
Contact: theeditor@punchng.com


Hi, I'm Linda
and I'm a full-stack developer
Hi, I'm Linda, an italian girl born in 1997 who's studying software engineering in Brescia. My passion for coding arrived when I was 15: it was new, affascinating and so stimulating. I started with HTML and CSS, then discovered PHP and MySQL and last but not least, Java and C. At first I didn't even know full-stack developing was a thing, along with the concepts of front-end and back-end developing. As I grew up, I came to learn about these jobs and noticed that what I was studying at university wasn't really preparing me at all. That's when I started studying on my own: I started from focusing and deeping what I alredy knew and then learnt about Javascript, React.js, Git, Phyton and Unix systems.
Here is my portfolio, it contains a small gallery of my favourite and/or most recent projects. I've done most of them all by my self, because I don't have a team yet, and I think it's a great way to display my progess and, most importantly,both my mistakes and improvement. How else could I learn? If you're interested in seeing all of them, just explore
|
As my very first project,
I built an e-commerce website. |
My first collaborative project:
a b&b booking management system. |
My first creative project:
rooms and their white noises. |
Do you want to know more about me, my projects, my experience or just talk? Don't esitate to contact me!


`Toy Story 2' – A Great Holiday Gift by Homer Yen (c) 1999
Yee-haw! The long-awaited sequel featuring Cowboy Woody (voiced by Tom Hanks) and Buzz Lightyear (voiced by Tim Allen) has finally arrived. And, if there is any movie to see this holiday season, this is it!
When the first Toy Story came to the big screen. It was a fabulous achievement in both Disney storytelling and computer-animation. We fell in love with not only Woody and Buzz, but the supporting cast of toys that included a piggy bank, Mr. Potato Head, green army men, and other playthings from the pre-Atari era. These toys stimulated the child's imagination in every way that TS2 will stimulate yours.
The underlying story is about friendship and sacrifice. Woody learns the frailty of his existence when during playtime with his owner, his arm is accidentally damaged. He is exiled away on the top shelf where he learns the fate of a toy that had not been seen in quite some time. It's Weezy, a squeaking penguin whose squeaker no longer works. Weezy will be heading for the dreaded garage sale. Woody attempts to save him. This beginning sequence is important to the concept of Toy Story. Toys do have a life of their own, and the happiness that they experience is dependent on the love that the owner gives, much like a pet. Toys feel, they long for affection, and they watch out for one another.
In trying to save Weezy, Woody winds up as an item in the garage sale. A shopper recognizes Woody as a rare collector's item. His name is Big Al (voiced by Wayne Knight) and he quickly snatches this rare find and drives off. Buzz and his crew of toys must get Woody back. Using the unique mapping abilities of Etch-a-Sketch, they embark on a dangerous search and rescue mission across town. To cross a street for example, they hide underneath orange traffic cones, which force oncoming cars to swerve left and right through the ad hoc construction zone.
Meanwhile, Woody learns why he was of such interest to Big Al. Woody is one of an ensemble of toys called the Roundup Gang. The other members of the gang include Cowgirl Jessie (voiced by Joan Cusack), Stinky Pete (voiced by Kelsey Grammer), and a horse named Bullseye. He is further amazed to learn that his popularity stemmed from an old black and white television puppet show. A Japanese museum wants to collect the entire ensemble. Without Woody, the others are worthless and would be locked away forever. It's a fate that no toy ever wants to experience. Jessie pleads with Woody to stay recalling her experience as a toy once loved but then discarded once her owner grew up. Stinky Pete, still in its original box, laments the fact that nobody would ever love a doll the way he looks. Their concerns touch Woody, for he knows that it may just be a matter of time before Andy forgets about him, especially with his damaged arm. But Buzz tells Woody that it's better to be loved for a little while than to be locked away behind a glass partition in some museum.
Like the first Toy Story, TS2 uses the same state-of-the-art computer animation. There is enormous attention to details, and the film goes much further than being just a Sunday school lesson about friends. Terrific shots of humor abound throughout the film, and for the older set, there is also a great deal of action, as the fate of Woody becomes unclear. Will Buzz be able to convince him to return? Will Big Al get his way and ship him off to Japan. The 20-minute finale is nothing less than spectacular. I was thoroughly impressed with the skillfulness of the animation, the pace of the story, and the ability of the writers to remind us that toys are as magical as our own childhood. Far better than the original, TS2 should be atop your movie wishlist.
Grade: A
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


Documentation
Table of Contents
1 World Bank Data in Julia
As part of the World Bank's Open Data initiative, the World Bank provides free access to their extensive database through their Data Catalog API. This Julia package makes use of the API to interactively access and download World Bank data in Julia.
Similar to the WDI R package, data can be downloaded using function
wdi
. This function accepts indicators and countries as String
or
Array{String, 1}
. Thereby, countries must be given as World Bank's
iso2c code. Two additional Int
inputs allow specifying beginning
and end of the data period, while input true
for keyword argument
extra
attaches further country information like the capital,
longitude, latitude, income range, etc. to each observation. Output
will be of type TimeData
by default, but can be set to DataFrame
through keyword argument format
.
Pkg.clone("https://github.com/JuliaFinMetriX/WorldBankDataTd.jl.git")
using WorldBankDataTd ## single indicator, single country gnp = WorldBankDataTd.wdi("NY.GNP.PCAP.CD", "BR") ## single indicator, multiple countries gnp = WorldBankDataTd.wdi("NY.GNP.PCAP.CD", ["BR", "US", "DE"]) ## multiple indicators, single country data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"], "BR") ## multiple indicators, multiple countries data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"], ["BR", "US", "DE"]) ## same, but as DataFrame data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"], ["BR", "US", "DE"], format = DataFrame) ## multiple indicators, multiple countries, additional information data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"], ["BR", "US", "DE"], extra = true) data[1:5, :]
| idx | iso2c | country | NY.GNP.PCAP.CD | SP.DYN.LE00.IN | iso3c | name | region | regionId | capital | longitude | latitude | income | incomeId | lending | lendingId |
| 1960-12-31 | BR | Brazil | NA | 54.6921463414634 | BRA | Brazil | Latin America & Caribbean (all income levels) | LCN | Brasilia | -47.9292 | -15.7801 | Upper middle income | UMC | IBRD | IBD |
| 1961-12-31 | BR | Brazil | NA | 55.1696341463415 | BRA | Brazil | Latin America & Caribbean (all income levels) | LCN | Brasilia | -47.9292 | -15.7801 | Upper middle income | UMC | IBRD | IBD |
| 1962-12-31 | BR | Brazil | 230 | 55.6330975609756 | BRA | Brazil | Latin America & Caribbean (all income levels) | LCN | Brasilia | -47.9292 | -15.7801 | Upper middle income | UMC | IBRD | IBD |
| 1963-12-31 | BR | Brazil | 250 | 56.08 | BRA | Brazil | Latin America & Caribbean (all income levels) | LCN | Brasilia | -47.9292 | -15.7801 | Upper middle income | UMC | IBRD | IBD |
| 1964-12-31 | BR | Brazil | 270 | 56.5102926829268 | BRA | Brazil | Latin America & Caribbean (all income levels) | LCN | Brasilia | -47.9292 | -15.7801 | Upper middle income | UMC | IBRD | IBD |
2 Indicator and country metadata
In addition to the indicator itself, the World Bank also provides a
lot of metadata for countries and indicators which can be downloaded
with function getWBMeta
.
countryData = getWBMeta("countries") countryData[1:5, :]
| iso3c | iso2c | name | region | regionId | capital | longitude | latitude | income | incomeId | lending | lendingId |
| ABW | AW | Aruba | Latin America & Caribbean (all income levels) | LCN | Oranjestad | -70.0167 | 12.5167 | High income: nonOECD | NOC | Not classified | LNX |
| AFG | AF | Afghanistan | South Asia | SAS | Kabul | 69.1761 | 34.5228 | Low income | LIC | IDA | IDX |
| AFR | A9 | Africa | Aggregates | NA | NA | NA | NA | Aggregates | NA | Aggregates | NA |
| AGO | AO | Angola | Sub-Saharan Africa (all income levels) | SSF | Luanda | 13.242 | -8.81155 | Upper middle income | UMC | IBRD | IBD |
| ALB | AL | Albania | Europe & Central Asia (all income levels) | ECS | Tirane | 19.8172 | 41.3317 | Upper middle income | UMC | IBRD | IBD |
Country metadata provides information for each country or aggregated entity with respect to the following characteristics:
names(countryData)
| iso3c |
| iso2c |
| name |
| region |
| regionId |
| capital |
| longitude |
| latitude |
| income |
| incomeId |
| lending |
| lendingId |
Overall, the country metadata is a DataFrame
of the following size:
size(countryData)
| 262 |
| 12 |
Quite similarly, you can access the complete indicator metadata
through input indicators
.
indicatorData = getWBMeta("indicators") indicatorData[1:5, :]
| indicator | name | description | sourcedatabase | sourcedatabaseId | sourceorganization |
| 1.0.HCount.1.25usd | Poverty Headcount ($1.25 a day) | The poverty headcount index measures the proportion of the population with daily per capita income below the poverty line. | LAC Equity Lab | 37 | LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank). |
| 1.0.HCount.10usd | Under Middle Class ($10 a day) Headcount | The poverty headcount index measures the proportion of the population with daily per capita income below the poverty line. | LAC Equity Lab | 37 | LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank). |
| 1.0.HCount.2.5usd | Poverty Headcount ($2.50 a day) | The poverty headcount index measures the proportion of the population with daily per capita income below the poverty line. | LAC Equity Lab | 37 | LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank). |
| 1.0.HCount.Mid10to50 | Middle Class ($10-50 a day) Headcount | The poverty headcount index measures the proportion of the population with daily per capita income below the poverty line. | LAC Equity Lab | 37 | LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank). |
| 1.0.HCount.Ofcl | Official Moderate Poverty Rate-National | The poverty headcount index measures the proportion of the population with daily per capita income below the poverty line. | LAC Equity Lab | 37 | LAC Equity Lab tabulations of data from National Statistical Offices. |
The metadata contains information about each indicator regarding the following characteristics:
names(indicatorData)
| indicator |
| name |
| description |
| sourcedatabase |
| sourcedatabaseId |
| sourceorganization |
Just take a look at the size of this table in order to get an impression about the huge amount of indicators that are provided by the World Bank.
size(indicatorData)
| 13074 |
| 6 |
You can use these two DataFrames
of country and indicator metadata
to easily search for information through function search_wdi
. Before
looking at that, however, let's first spend a few words on downloading
and caching of country and indicator metadata.
Function getWBMeta
is required to force a fresh download of
metadata. In general, however, you do not need to download metadata
each time that you want to conduct a search. Hence, you should rather
use function loadWBMeta
instead, which only downloads metadata in
case that it can neither be accessed from disk nor from the workspace
of the current Julia session. getWBMeta
should be called only if you
think that your local metadata was corrupted in some way and needs to
be refreshed.
Calling loadWBMeta
will first try to access metadata stored in
global variables WorldBankDataTd.country_cache
and
WorldBankDataTd.indicator_cache
of the current session. If these
variables have not been assigned yet, it looks for a local version of
the data in directory WorldBankDataTd/data. If there is no local
version yet, it will automatically call getWBMeta
, which will
download the data and store it both on local disk and as caching
variables in the current session.
Hence, as we already did load the data, we can access the cached versions in our current workspace.
WorldBankDataTd.country_cache[1:5, :]
| iso3c | iso2c | name | region | regionId | capital | longitude | latitude | income | incomeId | lending | lendingId |
| ABW | AW | Aruba | Latin America & Caribbean (all income levels) | LCN | Oranjestad | -70.0167 | 12.5167 | High income: nonOECD | NOC | Not classified | LNX |
| AFG | AF | Afghanistan | South Asia | SAS | Kabul | 69.1761 | 34.5228 | Low income | LIC | IDA | IDX |
| AFR | A9 | Africa | Aggregates | NA | NA | NA | NA | Aggregates | NA | Aggregates | NA |
| AGO | AO | Angola | Sub-Saharan Africa (all income levels) | SSF | Luanda | 13.242 | -8.81155 | Upper middle income | UMC | IBRD | IBD |
| ALB | AL | Albania | Europe & Central Asia (all income levels) | ECS | Tirane | 19.8172 | 41.3317 | Upper middle income | UMC | IBRD | IBD |
WorldBankDataTd.indicator_cache[1:5, :]
| indicator | name | description | sourcedatabase | sourcedatabaseId | sourceorganization |
| 1.0.HCount.1.25usd | Poverty Headcount ($1.25 a day) | The poverty headcount index measures the proportion of the population with daily per capita income below the poverty line. | LAC Equity Lab | 37 | LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank). |
| 1.0.HCount.10usd | Under Middle Class ($10 a day) Headcount | The poverty headcount index measures the proportion of the population with daily per capita income below the poverty line. | LAC Equity Lab | 37 | LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank). |
| 1.0.HCount.2.5usd | Poverty Headcount ($2.50 a day) | The poverty headcount index measures the proportion of the population with daily per capita income below the poverty line. | LAC Equity Lab | 37 | LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank). |
| 1.0.HCount.Mid10to50 | Middle Class ($10-50 a day) Headcount | The poverty headcount index measures the proportion of the population with daily per capita income below the poverty line. | LAC Equity Lab | 37 | LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank). |
| 1.0.HCount.Ofcl | Official Moderate Poverty Rate-National | The poverty headcount index measures the proportion of the population with daily per capita income below the poverty line. | LAC Equity Lab | 37 | LAC Equity Lab tabulations of data from National Statistical Offices. |
3 Searching
The most convenient way to explore indicators probably still is the World Bank webpage, where you can easily use the search functionality to find what your are looking for. Once you found the indicator of interest, you can read off the indicator shortcut name (e.g. SP.DYN.LE00.IN) from the URL of the indicator webpage.
Alternatively, however, this package also contains functionality to
interactively search the database from Julia itself. Thereby, function
search_wdi
makes use of cached country and indicator metadata to
speed up search operations. As first argument, you need to choose the
metadata to be searched. This can be either "countries" or
"indicators". The second argument needs to specify the column to be
searched, while the actual search term needs to be given as regex as
third argument.
For example, searching the :name
column of countries for a case
insensitive occurrence of "united":
res = search_wdi("countries", :name, r"united"i) res
| iso3c | iso2c | name | region | regionId | capital | longitude | latitude | income | incomeId | lending | lendingId |
| ARE | AE | United Arab Emirates | Middle East & North Africa (all income levels) | MEA | Abu Dhabi | 54.3705 | 24.4764 | High income: nonOECD | NOC | Not classified | LNX |
| GBR | GB | United Kingdom | Europe & Central Asia (all income levels) | ECS | London | -0.126236 | 51.5002 | High income: OECD | OEC | Not classified | LNX |
| USA | US | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
Or, searching indicators by some given description:
res = search_wdi("indicators", :description, r"gross national expenditure"i) res[:name]
| Gross national expenditure deflator (base year varies by country) |
| Gross national expenditure (current US$) |
| Gross national expenditure (current LCU) |
| Gross national expenditure (constant 2005 US$) |
| Gross national expenditure (constant LCU) |
| Gross national expenditure (% of GDP) |
Some further search examples:
search_wdi("countries", :iso2c, r"TZ"i) search_wdi("countries", :income, r"upper middle"i) search_wdi("countries", :region, r"Latin America"i) search_wdi("countries", :capital, r"^Ka"i) search_wdi("countries", :lending, r"IBRD"i) search_wdi("indicators", :name, r"gross national expenditure"i) search_wdi("indicators", :description, r"gross national expenditure"i) search_wdi("indicators", :source_database, r"Sustainable"i) search_wdi("indicators", :source_organization, r"Global Partnership"i)[1:5, :]
| indicator | name | description | sourcedatabase | sourcedatabaseId | sourceorganization |
| 2.1PRE.PRIMARY.GER | School enrolment, preprimary, national source (% gross) | Pre-Primary Gross Enrolment Rate (GER): The number of pupils enrolled in pre-primary school, regardless of age, expressed as a percentage of the population in the theoretical age group in pre-primary school. The purpose of this indicator is to measure the general level of participation of children in Early Childhood Education (ECE) programs. Country-specific definition, method and targets are determined by countries themselves. | Global Partnership for Education | 34 | Data were collected from national and other publicly available sources, and validated by the Local Education Group (LEG) in each country. LEGs are typically led by the Ministry of Education and include development partners and other education stakeholders. Data were not processed or analyzed by the Global Partnership for Education. It is reported as it was presented in the original sources, or as it was communicated to us through the Coordinating Agency or Lead Donor of the LEG. |
| 2.2GIR | Gross intake ratio in grade 1, total, national source (% of relevant age group) | Gross intake ratio (GIR): This indicator measures the total number of new entrants in the first grade of primary education, regardless of age, expressed as a percentage of the population at the official primary school-entrance age. Country-specific definition, method and targets are determined by countries themselves. | Global Partnership for Education | 34 | Data were collected from national and other publicly available sources, and validated by the Local Education Group (LEG) in each country. LEGs are typically led by the Ministry of Education and include development partners and other education stakeholders. Data were not processed or analyzed by the Global Partnership for Education. It is reported as it was presented in the original sources, or as it was communicated to us through the Coordinating Agency or Lead Donor of the LEG. |
| 2.3GIR.GPI | Gender parity index for gross intake ratio in grade 1 | Ratio of female to male values of gross intake ratio for primary first grade. Country-specific definition, method and targets are determined by countries themselves. | Global Partnership for Education | 34 | Data were collected from national and other publicly available sources, and validated by the Local Education Group (LEG) in each country. LEGs are typically led by the Ministry of Education and include development partners and other education stakeholders. Data were not processed or analyzed by the Global Partnership for Education. It is reported as it was presented in the original sources, or as it was communicated to us through the Coordinating Agency or Lead Donor of the LEG. |
| 2.4OOSC.RATE | Rate of out of school children, national source (% of relevant age group) | Number of children of official primary school age who are not enrolled in primary or secondary school, expressed as a percentage of the population of official primary school age. This indicator is intended to measure the size of the population in the official primary school age range that should be targeted by policies and efforts to achieve universal primary education. Country-specific definition, method and targets are determined by countries themselves. | Global Partnership for Education | 34 | Data were collected from national and other publicly available sources, and validated by the Local Education Group (LEG) in each country. LEGs are typically led by the Ministry of Education and include development partners and other education stakeholders. Data were not processed or analyzed by the Global Partnership for Education. It is reported as it was presented in the original sources, or as it was communicated to us through the Coordinating Agency or Lead Donor of the LEG. |
| 2.5PCR | Primary completion rate, total, national source (% of relevant age group) | The Primary Completion Rate (PCR) is the percentage of pupils who completed the last year of primary schooling. It is computed by dividing the total number of students in the last grade of primary school minus repeaters in that grade, divided by the total number of children of official completing age. Country-specific definition, method and targets are determined by countries themselves. | Global Partnership for Education | 34 | Data were collected from national and other publicly available sources, and validated by the Local Education Group (LEG) in each country. LEGs are typically led by the Ministry of Education and include development partners and other education stakeholders. Data were not processed or analyzed by the Global Partnership for Education. It is reported as it was presented in the original sources, or as it was communicated to us through the Coordinating Agency or Lead Donor of the LEG. |
4 Tips and Tricks
You can subset your data with respect to rows, columns or individual
entries through the ordinary TimeData
functions. For example,
selecting entries of US only:
data = wdi("NY.GNP.PCAP.CD", ["US","BR"], 1980, 2012, extra = true) usData = chkDates(x-> x[:iso2c] .== "US", eachdate(data)) |> x -> asArr(x, Bool, false) |> x -> data[x[:], :] usData
| idx | iso2c | country | NY.GNP.PCAP.CD | iso3c | name | region | regionId | capital | longitude | latitude | income | incomeId | lending | lendingId |
| 1980-12-31 | US | United States | 13410 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1981-12-31 | US | United States | 14400 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1982-12-31 | US | United States | 14230 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1983-12-31 | US | United States | 14590 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1984-12-31 | US | United States | 16230 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1985-12-31 | US | United States | 17510 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1986-12-31 | US | United States | 19160 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1987-12-31 | US | United States | 21460 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1988-12-31 | US | United States | 23580 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1989-12-31 | US | United States | 23860 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1990-12-31 | US | United States | 24150 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1991-12-31 | US | United States | 24370 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1992-12-31 | US | United States | 25780 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1993-12-31 | US | United States | 26480 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1994-12-31 | US | United States | 27750 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1995-12-31 | US | United States | 29150 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1996-12-31 | US | United States | 30380 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1997-12-31 | US | United States | 31390 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1998-12-31 | US | United States | 32150 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 1999-12-31 | US | United States | 33800 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2000-12-31 | US | United States | 36090 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2001-12-31 | US | United States | 36840 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2002-12-31 | US | United States | 37460 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2003-12-31 | US | United States | 39950 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2004-12-31 | US | United States | 43690 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2005-12-31 | US | United States | 46350 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2006-12-31 | US | United States | 48080 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2007-12-31 | US | United States | 48640 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2008-12-31 | US | United States | 49350 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2009-12-31 | US | United States | 48040 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2010-12-31 | US | United States | 48960 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2011-12-31 | US | United States | 50660 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
| 2012-12-31 | US | United States | 52350 | USA | United States | North America | NAC | Washington D.C. | -77.032 | 38.8895 | High income: OECD | OEC | Not classified | LNX |
Furthermore, data can be visualized through the TimeData
plotting
functions. Simply call function loadPlotting
to load Winston
and
Gadfly
packages, and directly plot the data as Timenum
object
through wstPlot
or gdfPlot
:
data = wdi("AG.LND.ARBL.HA.PC", "US", 1900, 2011) arableLand = convert(Timematr, data[symbol("AG.LND.ARBL.HA.PC")]) loadPlotting() ## using Winston wstPlot(arableLand) ## using Gadfly gdfPlot(arableLand)
In case of missing values, wdi
will return an empty TimeData object
without warning.
dfAS = wdi("EN.ATM.CO2E.KT", "AS")


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Functions for (1) ranking, selecting, and prioritising genes, proteins, and metabolites from high dimensional biology experiments, (2) multivariate hit calling in high content screens, and (3) combining data from diverse sources.
| Version: | 1.2.1 |
| Depends: | R (≥ 2.10) |
| Suggests: | knitr |
| Published: | 2016-12-10 |
| Author: | Stanley E. Lazic |
| Maintainer: | Stanley E. Lazic <stan.lazic at cantab.net> |
| BugReports: | https://github.com/stanlazic/desiR/issues |
| License: | GPL-3 |
| URL: | https://github.com/stanlazic/desiR |
| NeedsCompilation: | no |
| Citation: | desiR citation info |
| CRAN checks: | desiR results |
| Reference manual: | desiR.pdf |
| Vignettes: |
Gene ranking with desirability functions |
| Package source: | desiR_1.2.1.tar.gz |
| Windows binaries: | r-devel: desiR_1.2.1.zip, r-release: desiR_1.2.1.zip, r-oldrel: desiR_1.2.1.zip |
| OS X binaries: | r-release: desiR_1.2.1.tgz, r-oldrel: desiR_1.2.1.tgz |
| Old sources: | desiR archive |
| Reverse imports: | survivALL |
Please use the canonical form https://CRAN.R-project.org/package=desiR to link to this page.


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payu.client-secret
payu.authorization-uri
payu.merchant-pos-id
payu.order-url
Required order data
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We want a set of questions that reflect examples of both hateful and non-hateful discussions
about a mix of identities, that we can use to screen potential raters.
Good test questions should:
Not be ambiguous. (If you need to spend more than a few seconds figuring out if the comment is
hateful or not, or think it needs more context, it's probably a bad test question).
Have a clear reference to an identity. If the comment is too vague (e.g. mentions religious
people in general but not a specific religious group), then it can't be used to gauge a rater's
understanding of what is and isn't identity hate.
Be examples of both hateful and not hateful things. We want to determine if a rater can tell the
difference.
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question_id: {{question.id}}
Would this comment make a good test question for the identity hate labeling job?


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