def task(filename, task_params):
''' Return a hot pixel in the defined region.
@author
@param task_params -
@return
'''
# Assume the arguments are passed in this format:
# [region_of_interest, N(the threshold value)]
# if ROI is 'all', then by default it will find all the hot pixels in the image.
# NOTE: for test purpose we ignore [region file] for now.
# CMD: hot_pixel ffview max rect 200 200 400 400
# Example JSON:
# {"filename":"default", "threshold":"max", "region":{"rect":[0,0,100,100]}}
hdulist = fits.open(filename)
threshold, roi = task_params['threshold'], task_params['region']
region_type, value = roi['type'], roi['value']
# Region
region = hdulist[0].data
try:
if (region_type=='rect'):
region_slice = parseRegion_rect(value)
elif (region_type=='all'):
region_slice = slice(None)
else:
return ['Input region type not supported.']
except Exception as e:
return ["Error reading the input region."]
# print(region_slice)
region = region[region_slice]
# os.system("echo %d %d %d %d > /www/algorithm/debug_file" % (x_start, x_end, y_start, y_end))
# Threshold
if (threshold == 'max'):
threshold = float(np.max(region))
else:
try:
threshold = float(threshold)
except Exception as e:
return ["Error reading the input threshold."]
cols, rows = np.where(region>=threshold)
num_points = 500 # Set to 500 so that at most it will return 1000 points
num_points = rows.size if num_points>rows.size else num_points
rows = rows[::rows.size//num_points]
cols = cols[::cols.size//num_points]
l = np.zeros((rows.size, 2))
l[:,0] = cols + region_slice[0].start
l[:,1] = rows + region_slice[1].start
hdulist.close()
return l.tolist(), None
def task(filename, task_params):
''' Simple task calculating average value in a region. Boundary assumes the expected format being sent in.
@author Wei Ren
@param filename - file name of the FITS image
@param task_params - Region to calculate mean value on. Currently support `rect` and `circle` regions.
@return Mean value of the results or Error info.
'''
hdulist = fits.open(filename)
region = hdulist[0].data
region_type, value = task_params['type'], task_params['value']
if (region_type=='rect'):
region_slice = parseRegion_rect(value)
ROI = region[region_slice]
avg = str(np.mean(ROI))
elif (region_type=='circle'):
mask = circle_mask(region, value)
avg = str(gf(region, np.mean, footprint=mask))
else:
avg = "Region type is not recognized."
hdulist.close()
return {"result":avg},None
