This is an early document describing my acm reduction methods. The software and the descriptions changed a lot as we learned more about the properties of this camera. I archive this doc now in Feb2019.
The motivation for this document centered around measuring night sky surface brightness values with the acm. The bias, and to a lesser extent the dark levels, must be subtracted before we can measure a mean flux level due to the sky signal. Some early numbers from an analysis of sky frames indicate that if we assume a bias uncertainty of &pm 6 adu (if no bias fraem are available for a given night) then our sky flux uncertainties will be 11% and 3% in g,r respecticvely. The derivation of these estimates is described in a quick first look at g,r sky levels..
In a "cart before the horse" approach, I have decided to first present a sequential description of how I go about reducing a single night of acm data. The variable nature of the acm instrument has dictated numeroud changes to this process, and this variation will doubtless continue. Hence, my solution is to link to a verbose document that outlines the best set of steps that are currently in use. I'll updat and/or replace this document as the need arises. The current process is outlined in an End-to-End Reduction of 20180403.
A big motivator fro this work was to measure night sky surface brightnees values with the acm using differential photometry from stars in ean acm field. Here are some wuicky notes.
# Use https://www.google.com/ and search on "dark sky g band sky brightness" http://www.ctio.noao.edu/noao/node/1218 *** Lots of good references and these mean values: U 22.12 mag arcsec-2 B 22.82 mag arcsec-2 V 21.79 mag arcsec-2 R 21.19 mag arcsec-2 I 19.85 mag arcsec-2 u 23.3 mag arcsec-2 g 22.3 mag arcsec-2 r 21.4 mag arcsec-2 i 20.5 mag arcsec-2There are other papers (de Vaucoleurs and Angione at McD, Violet Taylor at VATT, etcc) to collect good UBVR stuff, but the numbers above are a good ballpark start.
This document originally dealt with the reduction of a single night. In practice, many recution problems were encountered and in the end a number of nights were analyzed. I record information about these nights below.
A valuable night: 20180114 Moon rise at 11:50 UT Nearly all of the night was dark (no moon) and clear. I took acm cals and a number of acm images on sky associated with LRS2 observations. This night should have all we need for dark sky surface brightness measurements. Clear at start of night, no further weather notes A valuable night: 20180115 Moon rise at 12:40 UT Took sky brightness data in grB on acm with open cluster NGC2301. Took bias and dark frames with scripts, bias=1382 (normal). Cirrus at start of night, no further weather notes A valuable night: 20180116 Bad weather, no observing. Took extensive bias+dark with stacking by scripts. A valuable night: 20180206 Moon rise at 6:10UT Took acm bias and 5sec darks that appear to be normal. Observed many targets with dark time and bright moon time. Should be excellent for sky brightness work. Took mainly B,r acm images. Notes indicate night clear A valuable night: 20180402 Moon rise at 2:50UT Pointing test night where I took 30-60 stars each night almost all in g. I also took bias/dark data sets (which appear to be at normal levels). This night had a very bright moon a day or two after full moon. Mostly clear in the time range 6:30UT to 10:20UT. Some clouds, some clear A valuable night: 20180403 Moon rise at 3:40UT Pointing test nights where I took 30-60 stars each night almost all in g. I also took bias/dark data sets (which appear to be at normal levels). Both nights had a very bright moon a day or two after full moon. Clear in early part of the night with some clouds around 6:00UT. Heavy clouds and high wind after 8:23UT. Some clouds, some clearNote that I include the night of 20180116, a night of heavy clouds when we made no on-sky observations. A major obstacle in analyzing acm data for sky surface brightness analysis involves how best to subtract the bias and dark signals in these images. ON 20180116 we took numerous bias and dark frames to address these issues.
In a given night we'll usually see a wide variety of acm images. Some can be final (just prior to LRS2 or VIRUS observation) setup images, some are intermediate sky images taken during focus and telescope position, some may be various types of calibration images (skyflat, bias, dark, etc...). In some cases the RA logs may help you navigate the different image types, but a lot of times we just have a directory full of images. The routine fits_review is designed to operate on a variety of image types, and I initailly used it here to evaluate images taken with the HET acquisition camera (acm). This approach is described here.
Eventually I realized that the acm image data suffers many problems. Poor or incorrect
header information, variable bias and dark signal, and a number of other annoying
trends make a direct approach to reduction impossible. A set of routines that are
specifically built for efficient acm processing were eventually developed.
One of the most fundamental problems encountered in acm image analysis is the variable
nature of the bias level. Using the early fits_review analyses I constructed the figures
below that demonstrate this problem.
 |
| The bias and dark frame signals found in different nights of acm image data. Initially I thought these values may have a strong temperature dpendence. However, looking at the temperature values given in the legend we see that this is not strictly true. In this plot I have the BIAS images hving the lowest images numbers and then I include dark images with progressively longer integrations times of 5sec, 10sec, and 20sec. |
 |
| The bias and dark frame signals found in different nights of acm image data. I have zoomed in on the bias images to show that for five different nights we encounter bias levels that range from 1382 adu to 1388 adu. Initially I had hoped this scatter over 6 adu would be explained by temperature vaiation, but this is not true. The two lowest values come from the coldest (20180101,green) and one of the warmest (20180107,cyan) nights. The lesson here is that we'll need at least a few acm bias frames per night if we are to properly correct for the mean bias level in acm images from a given night. |
In trying to outline how I would reduce a single nights-worth of acm images I uncovered a host of problems. To treat many of these required me to inspect and compare image properties from many nights.
Using fits_review to characterizethe acm images from a given night is useful. However, problems with variable bias and dark properties necessitated using many nights of daqta to address these issues. To easily summarize many nights of data I developed the routine acm_nights. Basically I want use a file that lists the dates I am interested in, and the location on disk where my date subdirectories are located (for example, on mcs we usually used /hetdata/data). Here is a brief example:
# In the file "list.1" I listed 13 nights of interest % acm_nights list.1 /home/sco/HET_work/acm_nights Results are written to: acm.nights_TABLE 20170906 220 23 148 49 20171108 878 573 305 0 20171129 94 60 19 15 20171231 544 42 120 382 20180101 164 40 123 1 20180102 682 41 123 518 20180105 213 40 120 53 20180106 272 40 120 112 20180107 305 62 13 230 20180109 387 41 45 301 20180114 352 12 27 313 20180115 529 11 21 497 20180116 309 224 84 1 The columns are: Col01: UT date Col02: total number of images Col03: number of bias images Col04: number of dark images Col05: number of "shutter-open" imagesUsing this approach I could survey in less than a minute the contents of the 13 nights (as of Jan22,2018) when bias frames were takenfor the acm. This would have taken considerably longer with separate runs using fits_review.
Once a night has been selected for reduction, we want to start with a simple summary of waht images are available.
# Procedure as of Mar20,2018 wheredata Y acm_list none none OR setenv basedir /home/sco/HET_work/acm_nights (in BaseDir) setenv date 20180206 (in Date) acm_list $date $basedirThis procedure will create lists of images like list.OPEN, list.DARK, and list.BIAS. Some of this files can be large (hundreds of lines) and not easily used for visual review, etc.. The list.OPEN file is usually very large, and this is the list of images that we presumably care the moset about (images taken on the sky mostly!). There are some useful scripts that can be used to divide them furtehr:
% list_split_exptime list.OPEN ListEXP Y # Divide into lists split by (integer) exposure tie in seconds % list_split_filter list.OPEN List N # Divide into lists split by filter nameYou can see exmaples of using list_split_exptime and list_split_filter.
Sometimes we want to view large numbers of images. Here are some ways to do this. The images can be marked and the resulting lists of marked and unmarked images can be used for various tasks.
# Viewing multiple acm images. acm_basic_list list.BIAS # Optional step to setup labeleing of ds9_list_load images ds9_list_load list.BIAS # To see all images in list.BIAS bigds9 list.BIAS 4 4 # To see all images in list.BIAS in 4x4 sets bigds9 list.OPEN 311 329 # To see images for lines 311 to 329 in list.OPENThe bigds9 routine is the most convenient tool to use. It just makes multiple runs of the ds9_list_load routine, but uses setsof images that make tiled image sets that can be easily viewed. The acm_basic_list routine collects useful summary information for the acm images and prepares a ds9 (text) regions file that will is used to label the images dispalyed with ds9_list_load.
A better way to do this was developed in Jan2019.
# Viewing acm images with interactive cursor pas_imlistgen l acm N # Find list of acm images for a night acm_quick_table # Select plot points and inspect their imagesThis method requires further documentation, but will be used a lot in the future. After viewing the image selected for each point the user is asked if this target is to be selected. If the answer is "Y", then the point is flagged (in the xyf.in file) and the full image path name is appended to the local file named "Selected.Images".
As well see in the following sections of this document, the bias and dark levels of the acm appear to be variable. At this time (Jan2018) it remains unclear if this variability is a function of temperature or some other property. To help eliminate some possibilities I have prepare several scripts that should be used observers to obtain the bias and dark frames for acm. This scripts handle things like insuring that light-emitting sources, like the PV camera, are powwered off and that the FCU head is deployed abd the acm mirror is retracted. All of thses measures are designed to insure that unwanted sources of scattered light will not reach the acm when bias and dark images are being taken. Here are the commands the RA should use:
# Take 43 acm bias frames % acm_bias_run 43 arg1 - number of bias images to take # Take 21 5-second acm dark frames % acm_dark_run 21 5 arg1 - number of images to take arg2 - integration time (5, 10, 20)These scripts may change as we learn more about the acm system, but the basic calling syntax is straight-forward and should bot change very much.
By far the most important correction for the acm images is a simple subtraction of
the substantial fixed bias pattern present in this camera.
 |
| A typical example of the improvement we derive from a a simple bias correction with the acm. A mean fixed bias pattern frame was computed using nine bias images from the night of 20180115. The bias corrected image in the left panel is from a 5 second acm image in the (Gunn) r filter. The raw frame is shown in the right panel for comparison. It should be noted that the mean sky signal in this corrected frame is 123.98 ± 0.03 adu. For comparison, correcting a single bias frame (not used in the computing the mean fixed bias pattern) yields a mean signal of 0.24 ± 0.02 adu. |
The links in this paragraph are severly out of date. I retain them now only to preserve some figures that remain of interest. I have some early notes on bias abd dark frames with acm and some early notes on processing multiple nights to investigate the bias and dark properties of the acm data. I have also compiled other notes that describe early studies of acm bias and dark properties.
The acm has a substantial fixed bias pattern (FBP) and the mean bias level of the camera seems to float around the level of 1385 adu (± 6 adu).
# Building a master bias frame % setenv date 20180206 # optional % acm_basic_list list.BIAS # optional % ds9_list_load list.BIAS # optional % process_acm_bias list.BIAS 9 $date NAs you see, the only required step here is to run process_acm_bias, the script that will stack (most or all) of the bias frame listed in the file list.BIAS. In the first optional step I define a system variable named "date". I use this as the identification string I feed to process_acm_bias (as arg3). The next two optional lines allow me to view all of the images and summary information about this. This sort of a last quality control check before assmbling the bias fame we'll use to correct our acm images.
The images and files built isthis procedure are shown and explainsed below.
% ls
20180115T040325.2_acm_sci_BIAS_20180115.Explain 20180115T040325.2_acm_sci_BIAS_20180115_margdist_col.table
20180115T040325.2_acm_sci_BIAS_20180115.fits 20180115T040325.2_acm_sci_FBP_20180115.fits
20180115T040325.2_acm_sci_BIAS_20180115_margdist_col.parlab list.BIAS
20180115T040325.2_acm_sci_BIAS_20180115.fits
- The final master bias frame
20180115T040325.2_acm_sci_FBP_20180115.fits
- The fixed bias pattern image (with a mean level of 0)
20180115T040325.2_acm_sci_BIAS_20180115_margdist_col.parlab,table
- A table file of the column-averaged bias data from the stacked image
20180115T040325.2_acm_sci_BIAS_20180115.Explain
- A summary file that contains information about the stacking
process. One of the most useful parts of this file comes at the
end where I list the images used inthe stack and those image NOT
used in the stack. As we see below, it is usful to apply the final
master bias frame to a single bias that was not used in building
the master bias. This simple test will verfy that we properly remove
fixed bias pattern and that our resulting produc has a mean level
very close to zero.
Below I show two figures that demonstrate what we have done. The first (directly below)
show aspects of the master bias and it's application.
 |
| A section of the mean bias frame (a total of 21 acm bias frames) is shown in the upper left. We see numerous vertical lines. In the upper-right is a single bias frame of the same area (not included in the mean image stack). In the lower-left is the same area of the single bias frame after subtracting the master mean bias. We see a substantial improvement with regards to removing the fixed bias structure. Fianlly, it the lower-right I show the full field image of the master bias frame to show the large amount of bias structure in an acm image. |
% cat 20180115T040325.2_acm_sci_BIAS_20180115_margdist_col.parlab col Column Number mean Mean signal value (adu) sig Standard devistion (adu) me Mean error about mean (adu) Npix Number of pixels % xyplotter_auto 20180115T040325.2_acm_sci_BIAS_20180115_margdist_col col mean 1 # I use the matplotlib show() module I can adjust the plot as I see fit. I can also chhose to create a hard copy version of the plot, which I show below.
 |
| The fixed bias pattern computed with the process_acm_bias routine. Using the matplotlib show() routine I have set the scale so that we can clearly see the large scale properties of the acm fixed bias pattern. Despite the low resoltion of this plot many of the bad columns in an acm bias are evident. The placement and depth of these bad columns has remained fairly constant over the course of weeks. Another major feature is the large-scale rise and drop in the bias signal across the chip. The ammplitude of this feature is approximately 40 adu and hence is extremely significant given that our average sky signals typically fall in the range of a 50 to 300 adu. Furthermore, over a range of just 100 pixels (in the X direction) the bias signal changes systematically by 10 adu or so (over much of the image). Hence, without removing this source of systematic error, measurements of profiles, sky surface brightness, or integrated magnitudes can be adversely affected. |
This section need more detail.
# See example: /home/sco/ACM_Red1/20180115/old_dark5 I made a single good darkrate image: ./20180115/old_dark5/20180115T040719.0_acm_sci_DARKRATE_DARK5.fits # Main tool to make the darkrate image % process_acm_darkrate Usage: process_acm_darkrate list.DARK5 5 20180116DARK5 MeanBias.fits Y arg1 - list of input (bias) images (can be fullpath) arg2 - number of images in each sub-stack (NSIZE) arg3 - identification string (usually UT date of acm night) arg4 - name of bias image to be subtracted arg5 - run in verbose/debug mode (Y/N) # To split exp times % list_split_exptime ../list.OPEN ListEXP NIt seems best now to process darkrate images using darks that match as closely as possible to exposure times of the sky images being reduced. Note that I uusally find darkrates in range 0.3 adu/sec (good nights) to 2.0 adu/sec (bad nights).
Once we havethe bias and dark images, we can process the acm images. Make a list of the images to be processed by starting with my big list.OPEN from the run of acm_list.
% pwd /home/sco/ACM_Red1/20180206/sky_5sec % list_split_exptime ../list.OPEN ListEXP NWith the above command I created ListEXP.5sec (a list of my 444 5sec images). In practice, I use environmental variables to define the paths to my bias and dark images computed in the previous sections. Then I process the images listed in ListEXP.5sec using acm_ccd_correct.
% echo $bias /home/sco/ACM_Red1/20180115/Bias/20180115T040325.2_acm_sci_BIAS_20180115.fits % echo $drate /home/sco/ACM_Red1/20180115/dark5/20180115T040719.0_acm_sci_DARKRATE_DARK5.fits % acm_ccd_correct ListEXP.5sec $bias $drate *** To process 34 images tkaes about 10sec on scohome **** *** To process 444 images tkaes about 4min on scohome ****For convenience I move the processed (*_proc.fits) images to a subdirectory and then review them with bigds9.
% ls -1 ./images/2018*proc.fits > list.proc % bigds9 list.proc 16 16 # To see images in 4x4 tiled ds9 views
I have compiled some early rough, example-oriented, notes, but the sequence of processing steps to derive WCS solutions continues to evolve with time. The basic steps are fairly secure now, and I give a simple overview here. To see the specific details using in this document, the reader shoudl refer to the End-to-End description given above.
The basic steps for an acm image are as follows. We use the wcsf_rough routine to graphically identify a soirce of known Ra,Dec. This is done by simultaneously diplaying the acm image along with a DSS image of (approximately) the same field. The scale and position angle values in a raw acm image are usually approximately correct. IT is the sky zeropoint (i.e. the values of CRVAL1,CRVAL2,CRPIX1,CRPIX2) that are usually off in the header by 5-20 arcseconds. Once these values are reset, we have a WCS header that is already good enough to perform a lot of tasks. Next, I visually confirm these rough fits and generate USNO-B1.0 astrometric catalogs for each image using the wcsf_viscat routine. To create catlogs of all sources detected above a user-specified sky threshold we next run the wcsf_imgcat routine. In the last step we use the wcsf_final routine to cross-match each image catalog with the USNO sources and derive a new set of astrometric standards. A final, improved WCS solution id computed and installed in the header of each image. The r.m.s scatter in both X,Y for typical acm images vary between 0.2" to 0.8" and these values allow cross-matching with PS1 data to allow photometric zeropoint derivation (see below).
Here I use the 14 wcs-calibrated images described in previous sections to calibrate photometric zeropoints using PanSTARRS gri photometry. The basic tool for this is photcal_fitslist, which can use phtometry from both USNO-B1.0 and PanSTARRS to install photometric zeropoint data into the headers of an inpu list of FITS images.
My top ZP reduction director is: /home/sco/A_wcsf/ZP_may16 # In ./ZP_may16/S I make the image list ls -1 /home/sco/A_wcsf/Run_apr17/local_red/WCS/20*fits > list.0 I create the ./local_red archive directory, then: % photcal_fitslist ./S/list.0 ps1 NIf we reduce images using PanSTARRS photometry (via PS1) then we'll have to do a second pass after the gri photometry (actually gri and transformed BVR photometry) has been gathered.
After the images are calibrated when can make a summary table.
% cd ./local_red/ZP % gethead *.fits FILTER PSYSNAME ZPSEC ZPERR NUMZP F = FILTER name in the image header S = standard photometric syystem transformed to ZPSEC = ZP for a 1-sec exposure err = mean error of ZPSEC N = Number of points used to derive the calibration Image Name F S ZPSEC err N ----------------------------------- - - -------- -------- -- 20180206T022042.6_acm_sci_proc.fits B B -3.06280 0.020384 10 20180206T032156.5_acm_sci_proc.fits B B -3.30300 0.029670 4 20180206T035223.8_acm_sci_proc.fits B B -3.01700 0.023692 3 20180206T061218.4_acm_sci_proc.fits r` r -2.56400 0.015604 4 20180206T070902.2_acm_sci_proc.fits r` r -2.27517 0.023742 6 20180206T072507.6_acm_sci_proc.fits r` r -2.63950 0.006500 2 20180206T083937.3_acm_sci_proc.fits r` r -2.57000 0.025956 8 20180206T092518.0_acm_sci_proc.fits r` r -2.50075 0.042880 4 20180206T100533.3_acm_sci_proc.fits r` r -2.36000 0.028202 6 20180206T101258.1_acm_sci_proc.fits r` r -2.22300 0.022627 8 20180206T102445.6_acm_sci_proc.fits r` r -2.42300 0.027937 4 20180206T104913.4_acm_sci_proc.fits r` r -2.95500 0.023624 8 20180206T105144.7_acm_sci_proc.fits r` r -2.83483 0.020092 6
At the end of our reduction we want a working catalog that can be used for practical purposes. By this I mean we desire some file(s) that contain astrometric positons (from our WCS solutions) and calibarted photometry (from our zeropoint solution) in some standard system. A couple simple goals for the work that motivated this document are:
Below I list some of the basic tools I use for this step.
# Set and measure regions in the images Usage: ds9_imstats a.fits Y arg1 - FITS image name arg2 - run in interactive mode (Y/N) # Establish a table of Ra,Dec positions for eavery region (must have WCS) Usage: midowcs ./S/20180206T022042.6_acm_sci_proc.fits N arg1 - Name of FITS image (can be full path) arg2 - run in debug mode (Y/N) # Establish table and cdfp files with calibrated photometry Usage: midophotcat.sh a.fits Y arg1 - name of the LOCAL input FITS image file arg2 - Run in debug modeThe basic catalog(s) made in this step can be used to address our two goals above.
The best wrapper tool for all of the aboveroutines is called "ds9_fitslist". It allows you build ds9 region and mido information files for a list of images. If the proper header information is installed, then a phtometric catalog is also prepared in the form of a table file and a cdfp file. If desired, these data products for each images are archived (in the CAT subdirectory). On subsequent runs of ds9_fitslist you will be asked if you wish to start with these archived products. Here is a sample run:
% cd /home/sco/A_wcsf/ZP_may20 % cat ./S/list.test /home/sco/A_wcsf/ZP_may20/local_red/ZP/20180206T020222.8_acm_sci_proc.fits /home/sco/A_wcsf/ZP_may20/local_red/ZP/20180206T022042.6_acm_sci_proc.fits /home/sco/A_wcsf/ZP_may20/local_red/ZP/20180206T032156.5_acm_sci_proc.fits % ds9_fitslist ./S/list.test N arg1 - Name of file with list of FITS images (can be full path) arg2 - run in debug mode (Y/N)The last step of the process here is to now take the results from our ds9 runs, primarily the sky box flux measurements, and install them and other useful values into the FITS header of each image. In particular, we insert values aboit the moon (mmon illumination, monn separation angle) and predicted model sky surface brightness into the header. I also run a routine that computes the totaltracker offset from center and puts this into the header. These setpas are all run by the routine acm_skysb_stats. Here are some typical calls:
% acm_skysb_stats 20180403T042947.5_acm_sci_proc.fits Y N % acm_skysb_stats 20180403T052406.0_acm_sci_proc.fits Y N % acm_skysb_stats 20180403T053306.8_acm_sci_proc.fits Y NAs discussed in the End-toEnd doc presented above, I usually build a script that runs acm_skysb_stats for all of the images I have processed in a given night. A more efficient wrapper script that takes a list of input FITS files may eventually be built.
In the Appendix section below I have retained a set of notes that demonstrate an early analysis of the acm phtometry results derived with this pipeline. The most useful portion of this early work shows how I can compare the final acm photometry with other sources (e.g. PS1 gri photometry). My use of the title "Analysis" for this section is intentionally vague. The scope and golas of any analysis of an acm reduction will surely change over time. Hence, I refer to a help document that illustrates an anaysis of the header information installed in the previous section. Basically, I use a variety of Table tools to extract and order the header values for 90 images (my Bgr images from five different nights) into a Table file. Using munging and table tools I have developed I created the plots below. Note that the files and plots generated in this work are arcived with this html document in $scohtm/Night_of_acm/work_analysis.
 |
| The sky surface brightness values from acm images taken in the B, g, and r filters. These data were gathered from the FITS headers of 90 images and manipulated with the table tools discussed in a document with several examples. On the X axis above I plot the sky surface brightness in the V-band predicted with a model developed from data obtained from a LaPalm web site. The zeropoint of this relation (i.e. the V sky brightness in a moonless sky) was taken from a study of the sky brightness at Mt. Graham by Taylor etal (2004, PASP,116,762). This model uses the moon phase (illumination) and angular separtion from the moon to predic the night sky brightness for each acm image. On the Y axis I plot the g, r, abd B sky surface brightness estimates (in mag per sq.arcsec) derived from acm images taken on 5 different nights. It should be noted that some of the g,r data from the last night (20180403) may have been taken under partly cloudy conditions. One thing to note is that the large clump of points around mu_V=21.9 are measurements made under moonless conditions. It is peculiar that the r points taken under near full-moon conditions lie above the unity line (fainter than the unity line) but below the unity line in the case of moonless points. More g,r data taken on clear nights, under varying moon conditions, is needed to resolve the source of this feature. |
 |
| The photometric zeropoints for a 1 second exposure in g, r, and B as a function of tracker offset. Here we see the clear trend in ZP with the distance from tracker center for all 3 bands. The trackker offset, RSTRT, is derived by combining the X_STRT and Y_STRT values in quadrature. The sign of the X_STRT value is arbitrarily assigned to the value of RSTRT. Basically, we see the role of pupil illumination variation as the HET tracker departs from the track center. With more data from photometric nights we should be able to derive or confirm a model of pupil illumination for the HET. |
 |
| The sky surdace brightness in g, r, and B as a function of HET structure azimuth. We have mixed the moon/moonless data sets, but it seems that the g data indicates the sky is brightest in the south. However, most of these southern points were taken when the moon was up (and in the south), and hence this is not surprising. What is needed is a way to mask out the moonless nights (images with MILLUM=-999.0) and reconstruct this plot. We need a tool to compute a table mask based on numerial values and this is surrently being pursued. |
It is useful to summarize the location and purose of the image directories used in this exercise. All of this work was done on scohome, so I include the fullptah names for that machine. In the section below I list the size of each major subdirectory. I start with an example of how these sizes are found.
% du -sh /home/sco/HET_work/acm_nights/
21G /home/sco/HET_work/acm_nights/
acm_nights
==========
21G /home/sco/HET_work/acm_nights/
- The raw acm images and the nightly RA logs are stored here. The sets are
broken into the usual PAS data structures by UT night.
ACM_Red1
========
3.5G /home/sco/ACM_Red1
- Directories where I reduced and calibrated the acm images by UT night.
To get a list of all images that are fully ZP-calibrated:
% ls -1 /home/sco/ACM_Red1/2018????/SkyBoxes/local_red/CAT/*.fits >all.acm
SBsky_May2018_acm_reduced
=========================
207M ACM_reduced_SBsky_May2018/
- Collection of all calibrated acm image that I used to derive sky
surface brightness values in g,r,B.
-----------------------------------------------------------------------------------
To collect these images in the SBsky_May2018_acm_reduced/acm I used:
% cp /home/sco/ACM_Red1/20180114/SkyBoxes/local_red/CAT/*.fits . # 12 images
% cp /home/sco/ACM_Red1/20180115/SkyBoxes/local_red/CAT/*.fits . # 20 images
% cp /home/sco/ACM_Red1/20180206/SkyBoxes/local_red/CAT/*.fits . # 14 images
% cp /home/sco/ACM_Red1/20180402/SkyBoxes/local_red/CAT/*.fits . # 23 images
% cp /home/sco/ACM_Red1/20180403/SkyBoxes/local_red/CAT/*.fits . # 21 imags
*** This is a collection of 90 images.
-----------------------------------------------------------------------------------
We see the redection in total size with the sequence above.
A host of early notes and links to useful documents have been compiled in an Appendix Section.