Hey @avd38, Curate Exposures only adjusts the contrast of the image from ctf/diag_image_path. The minimum and maximum values are set with this function:
def contrast_normalization(arr_bin, tile_size = 128):
'''
Computes the minimum and maximum contrast values to use
by calculating the median of the 2nd/98th percentiles
of the mic split up into tile_size * tile_size patches.
:param arr_bin: the micrograph represented as a numpy array
:type arr_bin: list
:param tile_size: the size of the patch to split the mic by
(larger is faster)
:type tile_size: int
'''
ny,nx = arr_bin.shape
# set up start and end indexes to make looping code readable
tile_start_x = n.arange(0, nx, tile_size)
tile_end_x = tile_start_x + tile_size
tile_start_y = n.arange(0, ny, tile_size)
tile_end_y = tile_start_y + tile_size
num_tile_x = len(tile_start_x)
num_tile_y = len(tile_start_y)
# initialize array that will hold percentiles of all patches
tile_all_data = n.empty((num_tile_y*num_tile_x, 2), dtype=n.float32)
index = 0
for y in range(num_tile_y):
for x in range(num_tile_x):
# cut out a patch of the mic
arr_tile = arr_bin[tile_start_y[y]:tile_end_y[y], tile_start_x[x]:tile_end_x[x]]
# store 2nd and 98th percentile values
tile_all_data[index:,0] = n.percentile(arr_tile, 98)
tile_all_data[index:,1] = n.percentile(arr_tile, 2)
index += 1
# calc median of non-NaN percentile values
all_tiles_98_median = n.nanmedian(tile_all_data[:,0])
all_tiles_2_median = n.nanmedian(tile_all_data[:,1])
vmid = 0.5*(all_tiles_2_median+all_tiles_98_median)
vrange = abs(all_tiles_2_median-all_tiles_98_median)
extend = 1.5
# extend vmin and vmax enough to not include outliers
vmin = vmid - extend*0.5*vrange
vmax = vmid + extend*0.5*vrange
return vmin, vmax
Then pyplot.imshow() with these params should give you the same plot as the job.