file = filepath('endocell.jpg', subdir=['examples', 'data'])
read_jpeg, file, cell
.compile adapt_hist_equal
resolve_all
profiler, /system
profiler
ahe_cell = adapt_hist_equal(cell)
profiler, /report, data=data
profiler, /clear
print, 'Name', 'Count', 'Only time', 'Time', 'System', format='(A-20, A7, A12, A10, A9)'
print, data[sort(-data.time)], format='(A-20, I7, F12.5, F10.5, I9)'
ones = fltarr(100) + 1.0
endoFilename = file_which('endocell.jpg')
endo = read_image(endoFilename)
tvscl, endo gt 170
tv, endo * (endo gt 120 and endo lt 180)
all = bytarr(n)
if (ind[0] ne -1L) then all[ind] = 1B
complement = where(all eq 0, nComplement)
d = dist(20)
ind = where(d gt 13, count)
print, ind
print, array_indices(d, ind)
arr = fix(randomu(0L, 6, 3) * 10)
print, arr
print, total(arr, 2, /preserve_type)
print, total(arr, 1, /preserve_type)
print, max(arr, dimension=2, min=min) - min
y = findgen(3, 5)
print, y
x = fltarr(3, 5) + 1.
a = 2.
blas_axpy, y, a, x
print, y
blas_axpy, y, a, fltarr(3) + 1., 1, [0, 2]
print, y
blas_axpy, y, a, fltarr(3) + 1., 1, [0, 1], 2, [0, 2, 3]
print, y
a = 10
print, a[0]
multi = indgen(2, 3)
print, multi[4]
print, multi
print, indgen(6)
print, multi[0, 2]
print, multi[4]
filename = filepath('people.jpg', subdir=['examples', 'data'])
ali = read_image(filename)
help, ali
arr = randomu(seed, 1000)
ind = mg_sample(1000, 10)
print, mg_convert_type(1, 2)
print, mg_convert_type(5, 6)
a = indgen(4, 5)
print, a
a[*, 4] = indgen(4)
a[0, 4] = reform(indgen(4), 4, 1)
print, a
seed = 0L
arr = randomu(seed, 3, 5)
print, arr
v = randomu(seed, 5)
print, v
repeatV = rebin(reform(v, 1, 5), 3, 5)
print, repeatV
print, arr * repeatV
print, arr * ((fltarr(3) + 1.0) # v)
restore, filename=filepath('cow10.sav', subdir=['examples', 'data'])
verts = transpose([[x], [y], [z]])
nverts = (size(points, /dimensions))[1]
pt = [1, 1, 0]
distances = sqrt(total((verts - rebin(reform(pt, 3, 1), 3, nverts))^2, 1))
tv, rebin(reform(bindgen(256), 256, 1), 256, 8)
tv, rebin(reform(bindgen(256), 1, 256), 8, 256)
print, reform(rebin([-1, 0, 1], 3, 9), 27)
print, reform(rebin([-1, 0, 1], 9, 3), 27)
print, rebin([-1, 0, 1], 27)
arr = findgen(5)
print, arr[[-1, 0, 3, 4, 5]]
compile_opt strictarrsubs
print, arr[[-1, 0, 3, 4, 5]]
im = findgen(3, 4, 3)
x_loc = (lindgen(4, 3) + 2L) mod 4
y_loc = (lindgen(4, 3) + 1L) mod 3
p = transpose([[[x_loc]], [[y_loc]]], [2, 0, 1])
x = rebin(reform(lindgen(3), [3, 1, 1]), [3, 4, 3])
y = rebin(p[0, *, *], [3, 4, 3])
z = rebin(p[1, *, *], [3, 4, 3])
im2 = im[x, y, z]
print, im2[2, 3, 1], im[2, p[0, 3, 1], p[1, 3, 1]]
print, (intarr(3) + 1) # indgen(4)
print, indgen(4) # indgen(5)
x = findgen(10) / 9. - 0.5
y = findgen(20) / 19. - 0.5
surface, x^2 # (fltarr(20) + 1.) + (fltarr(10) + 1.) # y^2, x, y
im = read_image(file_which('endocell.jpg'))
plot, histogram(im), psym=10, xstyle=9, ystyle=8, charsize=0.75
d = randomu(0L, 100)
print, histogram(d, min=0., max=1., nbins=10)
print, histogram(d, min=0., max=0.1 * (10 - 1) + 0., nbins=10)
print, histogram(d, min=0., max=1., binsize=0.1)
seed = 0L
a = fix(15 * randomu(seed, 6))
b = fix(15 * randomu(seed, 6))
print, a, b
print, where(histogram([a, b], omin=omin)) + omin
a = [0]
b = [999999]
minAB = min(a, max=maxA) > min(b, max=maxB)
maxAB = maxA <
ahist = histogram(a, min=minAB, max=maxAB)
bhist = histogram(b, min=minAB, max=maxAB)
print, where(ahist and bhist, count) + minAB
vec = randomu(0L, 10)
removeInd = [0, 3, 4, 6, 9, 9]
h = histogram(removeInd, min=0, max=n_elements(vec) - 1L)
keepInd = where(h eq 0, count)
print, keepInd
seed = 0L
values = randomu(seed, 4)
print, values
repeats = [2, 0, 1, 2]
resultInd = [0, total(repeats, /cumulative, /preserve_type)]
print, resultInd
startBlocks = histogram(resultInd)
print, startBlocks
blocks = total(startBlocks, /cumulative, /preserve_type) - 1L
print, blocks
print, values[blocks[0:n_elements(startBlocks) - 2L]]
seed = 0L
arr = fix(randomu(seed, 4) * 10)
print, arr
h = histogram(arr, reverse_indices=r)
print, h
print, r
print, indgen(n_elements(r)), r, format='(13I6)'
a = fix(100 * randomu(seed, 10000))
i = 50
ind = where(a eq i, count)
if (count ne 0L) then a[ind] = 0
h = histogram(a, reverse_indices=r)
if (r[i] lt r[i + 1]) then a[r[r[i] : r[i+1]-1]] = 0
.run mg_where_vs_histogram
print, mg_n_smallest(randomu(seed, 1000), 3)
x = findgen(11) / 10.
print, x
print, value_locate([0.2, 0.4, 0.8], x)
d = randomu(12345678L, 20)
print, d
cutoffs = [0.3, 0.4, 0.8]
bins = value_locate(cutoffs, d) + 1L
print, bins
h = histogram(bins, reverse_indices=r)
print, h
print, d[r[r[0]:r[1] - 1]]
print, d[r[r[1]:r[2] - 1]]
print, d[r[r[2]:r[3] - 1]]
print, d[r[r[3]:r[4] - 1]]
startMemory = memory(/current)
image = 255B - image
print, memory(/highwater) - startMemory
startMemory = memory(/current)
image = 255B - temporary(image)
print, memory(/highwater) - startMemory
image = image - smooth(image, 5)
image = image - smooth(temporary(image), 5)
image = temporary(image) - smooth(image, 5)
s = { image: bytscl(dist(256)) }
s.image++
s.image = s.image + 1B
a = [0, 5, 6]
a = [a, 8]
print, a
restore, filename=filepath('cow10.sav', subdir=['examples', 'data'])
verts = transpose([[x], [y], [z]])
nverts = (size(points, /dimensions))[1]
pt = [1, 1, 0]
distances = sqrt(total((verts - rebin(reform(pt, 3, 1), 3, nverts))^2, 1))
im = findgen(3, 4, 3)
x_loc = (lindgen(4, 3) + 2L) mod 4
y_loc = (lindgen(4, 3) + 1L) mod 3
p = transpose([[[x_loc]], [[y_loc]]], [2, 0, 1])
sz = size(im, /dimensions)
nx = sz[1]
ny = sz[2]
x = rebin(reform(lindgen(3), [3, 1, 1]), [3, nx, ny])
y = rebin(p[0, *, *], [3, nx, ny])
z = rebin(p[1, *, *], [3, nx, ny])
im2 = im[x, y, z]