def test_01():
from psana.pyalgos.generic.NDArrGenerators import random_standard
print('%s\n%s\n' % (80 * '_', 'Test method subtract_bkgd(...):'))
shape1 = (32, 185, 388)
winds = [(s, 10, 155, 20, 358) for s in (0, 1)]
data = random_standard(shape=shape1, mu=300, sigma=50)
bkgd = random_standard(shape=shape1, mu=100, sigma=10)
cdata = subtract_bkgd(data, bkgd, mask=None, winds=winds, pbits=0o377)
def test_entropy():
print('In %s' % sys._getframe().f_code.co_name)
from psana.pyalgos.generic.NDArrGenerators import random_standard
from psana.pyalgos.generic.NDArrUtils import print_ndarr
from time import time
arr_float = random_standard(shape=(100000, ),
mu=200,
sigma=25,
dtype=np.float)
arr_int16 = arr_float.astype(np.int16)
print_ndarr(arr_int16, name='arr_int16', first=0, last=10)
t0_sec = time()
ent1 = entropy(arr_int16)
t1_sec = time()
ent2 = entropy_v1(arr_int16)
t2_sec = time()
ent3 = entropy_cpo(arr_int16)
t3_sec = time()
print('entropy(arr_int16) = %.6f, time=%.6f sec' %
(ent1, t1_sec - t0_sec))
print('entropy_v1(arr_int16) = %.6f, time=%.6f sec' %
(ent2, t2_sec - t1_sec))
print('entropy_cpo(arr_int16) = %.6f, time=%.6f sec' %
(ent3, t3_sec - t2_sec))
def test01():
"""imshow"""
img = random_standard(shape=(40, 60), mu=200, sigma=25)
fig, axim = fig_img_axes()
move_fig(fig, x0=50, y0=20)
imsh = imshow(axim, img, amp_range=None, extent=None,\
interpolation='nearest', aspect='auto', origin='upper',\
orientation='horizontal', cmap='jet')
def test_10():
from psana.pyalgos.generic.NDArrGenerators import random_standard
image = random_standard()
verbosity = True
save_image_tiff(image, fname='image.tiff', verb=verbosity)
save_image_file(image, fname='image.png', verb=verbosity)
save_image_file(image, fname='image.xyz', verb=verbosity)
def test_serialize_numpy_array():
nda = random_standard(shape=(4,6), mu=100, sigma=10, dtype=np.float32)
#nda = aranged_array(shape=(2,3), dtype=np.float) # uint32)
print_ndarr(nda, 'nda', first=0, last=12)
d = serialize_numpy_array(nda)
print('serialize_numpy_array: %s' % d)
nda2 = deserialize_numpy_array(d)
print_ndarr(nda, 'de-serialized nda', first=0, last=12)
def unitest_entropy():
import sys
from psana.pyalgos.generic.NDArrGenerators import random_standard
print('In %s' % sys._getframe().f_code.co_name)
np.random.seed(42)
arr_int16 = random_standard(shape=(100000,), mu=200, sigma=25, dtype=np.int16)
ent1 = entropy(arr_int16);
print('entropy(arr_int16) = %.6f' % ent1)
assert('%.6f'%ent1 == '6.690948')
def test02() :
""" hist
"""
mu, sigma = 200, 25
arr = random_standard((500,), mu, sigma)
#fig = figure(figsize=(6,5), title='Test hist', dpi=80, facecolor='w', edgecolor='w', frameon=True, move=(100,10))
#axhi = add_axes(fig, axwin=(0.10, 0.08, 0.85, 0.88))
fig, axhi = fig_img_axes()
his = hist(axhi, arr, bins=100, amp_range=(mu-6*sigma,mu+6*sigma), weights=None, color=None, log=False)
def test01():
""" imshow
"""
img = random_standard(shape=(40, 60), mu=200, sigma=25)
#fig = figure(figsize=(6,5), title='Test imshow', dpi=80, facecolor='w', edgecolor='w', frameon=True, move=(100,10))
#axim = add_axes(fig, axwin=(0.10, 0.08, 0.85, 0.88))
fig, axim = fig_img_axes()
move_fig(fig, x0=200, y0=100)
imsh = imshow(axim, img, amp_range=None, extent=None,\
interpolation='nearest', aspect='auto', origin='upper',\
orientation='horizontal', cmap='jet')
def test_serialize_dict():
d = {'val':123,
'nda1': aranged_array(shape=(3,4), dtype=np.int),
'nda2': random_standard(shape=(2,3), mu=100, sigma=10, dtype=np.float)
}
print('initial dict:')
print_dict(d)
serialize_dict(d)
print(80*'_')
print('serialized dict:')
print_dict(d)
print(80*'_')
deserialize_dict(d)
print('deserialized dict:')
print_dict(d)
print(80*'_')
def test04() :
""" Update histogram in the event loop
"""
mu, sigma = 200, 25
#fig = figure(figsize=(6,5), title='Test hist', dpi=80, facecolor='w', edgecolor='w', frameon=True, move=(100,10))
#axhi = add_axes(fig, axwin=(0.10, 0.08, 0.85, 0.88))
fig, axhi = fig_img_axes()
for i in range(10) :
print('Event %3d' % i)
arr = random_standard((500,), mu, sigma, dtype=np.float)
axhi.cla()
set_win_title(fig, 'Event %d' % i)
his = hist(axhi, arr, bins=100, amp_range=(mu-6*sigma,mu+6*sigma), weights=None, color=None, log=False)
show(mode=1) # !!!!!!!!!!
def image_with_random_peaks(shape=(500, 500), add_water_ring=True):
img = random_standard(shape, mu=0, sigma=10)
if add_water_ring:
rad = 0.3 * shape[0]
sigm = rad / 4
add_ring(img,
amp=20,
row=shape[0] / 2,
col=shape[1] / 2,
rad=rad,
sigma=sigm)
peaks = add_random_peaks(img,
npeaks=10,
amean=100,
arms=50,
wmean=1.5,
wrms=0.3)
return img, peaks
def test05():
""" Update image with color bar in the event loop
"""
fig, axim, axcb = fig_img_cbar_axes()
move_fig(fig, x0=200, y0=0)
imsh = None
for i in range(20):
print('Event %3d' % i)
img = random_standard((1000, 1000), mu=i, sigma=10)
#axim.cla()
set_win_title(fig, 'Event %d' % i)
if imsh is None:
imsh, cbar = imshow_cbar(fig, axim, axcb, img, amin=None, amax=None, extent=None,\
interpolation='nearest', aspect='auto', origin='upper',\
orientation='vertical', cmap='inferno')
else:
imsh.set_data(img)
ave, rms = img.mean(), img.std()
imsh.set_clim(ave - 1 * rms, ave + 3 * rms)
show(mode=1) # !!!!!!!!!!
def test03():
""" Update image in the event loop
"""
#fig = figure(figsize=(6,5), title='Test hist', dpi=80, facecolor='w', edgecolor='w', frameon=True, move=(100,10))
#axim = add_axes(fig, axwin=(0.10, 0.08, 0.85, 0.88))
fig, axim = fig_img_axes()
imsh = None
for i in range(10):
print('Event %3d' % i)
img = random_standard((1000, 1000), mu=200, sigma=25)
#axim.cla()
set_win_title(fig, 'Event %d' % i)
if imsh is None:
imsh = imshow(axim, img, amp_range=None, extent=None,\
interpolation='nearest', aspect='auto', origin='upper',\
orientation='horizontal', cmap='jet')
else:
imsh.set_data(img)
show(mode=1) # !!!!!!!!!!
def test_08():
import psana.pyalgos.generic.Graphics as gg
from psana.pyalgos.generic.NDArrGenerators import random_standard
from psana.pyalgos.generic.NDArrUtils import reshape_to_2d
print('%s\n%s\n' %
(80 * '_', 'Test method locxymax(nda, order, mode):'))
#data = random_standard(shape=(32,185,388), mu=0, sigma=10)
data = random_standard(shape=(2, 185, 388), mu=0, sigma=10)
t0_sec = time()
mask = locxymax(data, order=1, mode='clip')
print('Consumed t = %10.6f sec' % (time() - t0_sec))
if True:
img = data if len(data.shape) == 2 else reshape_to_2d(data)
msk = mask if len(mask.shape) == 2 else reshape_to_2d(mask)
ave, rms = img.mean(), img.std()
amin, amax = ave - 2 * rms, ave + 2 * rms
gg.plotImageLarge(img, amp_range=(amin, amax), title='random')
gg.plotImageLarge(msk, amp_range=(0, 1), title='mask loc max')
gg.show()
def test06():
""" fig_img_cbar
"""
img = random_standard((1000, 1000), mu=100, sigma=10)
fig, axim, axcb, imsh, cbar = fig_img_cbar(img) #, **kwa)
move_fig(fig, x0=200, y0=0)
def test_02():
from psana.pyalgos.generic.NDArrGenerators import random_standard
shape1 = (32, 185, 388)
data = random_standard(shape=shape1, mu=300, sigma=50)
print(info_ndarr(data, 'test_02: info_ndarr', first=0, last=3))
print(info_ndarr(shape1, 'test_02: info_ndarr'))
def test07():
""" r-phi fig_img_proj_cbar
"""
img = random_standard((200, 200), mu=100, sigma=10)
fig, axim, axcb, imsh, cbar = fig_img_proj_cbar(img)
move_fig(fig, x0=200, y0=0)
