Äîêóìåíò âçÿò èç êýøà ïîèñêîâîé ìàøèíû. Àäðåñ îðèãèíàëüíîãî äîêóìåíòà : http://www.adass.org/adass/proceedings/adass98/reprints/pirzkaln.pdf Äàòà èçìåíåíèÿ: Fri Jul 16 22:13:44 1999 Äàòà èíäåêñèðîâàíèÿ: Tue Oct 2 15:08:52 2012 Êîäèðîâêà: koi8-r Ïîèñêîâûå ñëîâà: ï ï ï ï ï ï ï ï ï ï ï ï ï ï ï ï ï ï

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import futils

Import the author's file utility module the ASCII file and store the result in an array d

d = futils.parseasciifile("assoc.clear")Parse

d[0] ['assoc_id', 'sub_assoc_id', 'dataset', 'xoff', 'yoff', 'ra_targ', 'dec_targ', 'ra_sd', 'dec_sd', 'actual_duration', 'ass_release_date_dmf']
The association ID is in the first column, the dataset member's name in the third column d[2] ['O46P3X010', 1.0, 'O46P3X010', 0.0, 0.0, 271.378077, -19.90682, 0.0, 1.0, 300.0, 558543382.0]

The first row of the file contained the column descriptions

len(d) 14 dataset={} dic={} for i in d[2:]: tmp={}

Get the length of the array d.

Initialize an empty Dictionary to contain all the datasets (each line in the original ASCII file) and another one to contain the dataset associations Loop over the rows of d Initialize an empty Dictionary to hold the associations Assign each element of a row to a Dictionary using the column description of the original ASCII file as a key for this element

for j in range(3,len(i)): tmp[d[0][j]]=i[j] dataset[i[2]] = tmp

In the dataset Dictionary, create a new element using the dataset's name as the key, and assign to it the Dictionary that was just created previously for this dataset

if dic.has_key(i[0]): dic[i[0]][i[2]]=dataset[i[2]] else: dic[i[0]]={} dic[i[0]][i[2]]=dataset[i[2]]

If an entry for the current association of which the current row/dataset is a member exists then add this dataset to what is already in the association dictionary. Otherwise, create a new one.

The association Dictionary should now contain a table of known associations, which we display here by querying the valid keys of this Dictionary dic.keys() ['O46P3X010', 'O46P46010', 'O46P49010', 'O46P40010', 'O46P47010', 'O46P4D010', 'O46P4A010'] We can now query the valid keys of a particular association, hence returning the list of

dic['O46P49010'].keys() datasets in this association ['O46P4C010', 'O46P4B010', 'O46P49010'] dic['O46P49010']['O46P4B010']['xoff'] 0.04 dataset['O46P4B010']['xoff']=-1.0 dic['O46P49010']['O46P4B010']['xoff'] -1.0 dataset['O46P4B010']['xoff'] -1.0
Find out the x-offset of the dataset O46P4B010 member of the association O46P49010

Changing a value in one of the entries of the dataset dictionary properly affects the content of the association Dictionary

for item in dic['O46P49010'].keys(): print item,dic['O46P49010'][item]['xoff'],\ dic['O46P49010'][item]['yoff'] O46P4C010 0.06 10.76 O46P4B010 -1.0 10.78 O46P49010 0.0 0.0

Once the association dictionary is populated, we can easily query information about association, loop over its member datasets etc...

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import import import import

Numeric string pfitsio Statistics

Load the Numerical module to handle array algebra, the string module for string manipulation, the pfitsio module to access FITS files, and the Statistics module to provide some basic statistical functions.

for item in dic['O46P49010'].keys():of

data = pfitsio.read_data(string.lower(item)+"_crj.fits",2)
Get the x and y direction offsets from our dataset Dictionary, and convert those to integer values

xoff = -int(dic['O46P49010'][item]['yoff']) yoff = -int(dic['O46P49010'][item]['xoff']) print "Cleaning the image",item
Compute the standard deviation and the median of the data, ignoring the borders of the image which are known to be noisy

stdv =Statistics.standardDeviation(Numeric.ravel(data[50:950,50:950])) median = Statistics.median(Numeric.ravel(data[50:950,50:950]))
Replace all elements of data which have values higher than one sigma above the median of the entire image

data = Numeric.where(Numeric.greater(data,median+1*stdv),median,data)
Get the shape of the data array and store the x and y sizes in xm and ym respectively

xm,ym = Numeric.shape(data) print "Adding ",item," xoff=",xoff," yoff=",yoff
Compute the x and y coordinates of the overlap regions

x1,x2 y1,y2 v1,v2 w1,w2

= = = =

max(0,xoff),min(xm-1,xm-1+xoff) max(0,yoff),min(ym-1,ym-1+yoff) max(0,-xoff),min(xm-1,xm-1-xoff) max(0,-yoff),min(xm-1,xm-1-yoff)
Try to add the computed overlap region of the image that was just read to the current image sum. If this fail (if the image sum does not already exist, then create a new combined image of the appropriate size and store the current image in it

try: comb[v1:v2,w1:w2] = comb[v1:v2,w1:w2] + data[x1:x2,y1:y2] except: comb = Numeric.zeros((xm,ym),Numeric.Float) comb[v1:v2,w1:w2] = data[x1:x2,y1:y2]

Once all the images have been summed up into the array comb, write this array to a file called test.fits

Cleaning the image Adding O46P49010 0 Cleaning the image Adding O46P4B010 0 Cleaning the image Adding O46P4C010 0

O46P49010 0 O46P4B010 10 O46P4C010 0

pfitsio.write_data(comb,"test.fits")

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Following the previous example, iterate over the member datasets the association O46P49010 Read the data from the second extension of the file "item"_crj.fits, and store the result in the array data

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