for shot in range(5):
conductor_json_path = data_folder + '\\{}'.format(shot) + '.conductor.json'
tcam_hdf5_path = data_folder + '\\{}'.format(shot) + '.ixon.hdf5'
f = open(conductor_json_path, 'r')
f1 = f.read()
f2 = json.loads(f1)
t = f2['sequencer.DO_parameters.red_mot_tof'] + t_delay
images = {}
with h5py.File(tcam_hdf5_path, 'r') as images_h5:
for key in images_h5:
images[key] = np.array(images_h5[key], dtype = 'float64')
images_h5.close()
n0 = process_images_g(images)
n = n0
# yc, xc = np.unravel_index(n0.argmax(), n0.shape)
# n = n0[yc - roi_width: yc + roi_width, xc - roi_width: xc + roi_width]
n = np.flipud(n)
# print(yc, xc)
X = range(n.shape[1])
Y = range(n.shape[0])
x_trace = np.sum(n, axis = 0)
y_trace = np.sum(n, axis = 1)
p0x = (x_trace.argmax(), fit_width, np.max(x_trace), np.min(x_trace))
p0y = (y_trace.argmax(), fit_width, np.max(y_trace), np.min(y_trace))
def process_red_mot_tof_ccd(settings):
shot = settings['shot']
roi_width = settings['kwargs']['roi_width']
fit_width = settings['kwargs']['fit_width']
x_key = settings['kwargs']['x_key']
y_key = settings['kwargs']['y_key']
method = settings['kwargs']['method']
if shot >= 0:
data = []
data_folder = os.path.join(PROJECT_DATA_PATH, settings['data_path'])
save_folder = os.path.join(PROJECT_DATA_PATH, settings['data_path'],
'processed_data')
save_path = os.path.join(save_folder, str(shot))
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
conductor_json_path = data_folder + '\\{}'.format(
shot) + '.conductor.json'
ccd_hdf5_path = data_folder + '\\{}'.format(shot) + '.{}.hdf5'.format(
method)
try:
f = open(conductor_json_path, 'r')
f1 = f.read()
f2 = json.loads(f1)
tof = f2['sequencer.DO_parameters.red_mot_tof'] + t_delay
try:
em_gain = f2['andor.record_path']['em_gain']
except:
em_gain = 1
images = {}
with h5py.File(ccd_hdf5_path, 'r') as images_h5:
for key in images_h5:
images[key] = np.array(images_h5[key], dtype='float64')
images_h5.close()
n0 = process_images_g(images, em_gain)
x_trace0 = np.sum(n0, axis=0)
y_trace0 = np.sum(n0, axis=1)
xc = x_trace0.argmax()
yc = y_trace0.argmax()
n = n0
n = np.flipud(n)
X = range(n.shape[1])
Y = range(n.shape[0])
x_trace = np.sum(n, axis=0)
y_trace = np.sum(n, axis=1)
p0x = (x_trace.argmax(), fit_width, np.max(x_trace),
np.min(x_trace))
p0y = (y_trace.argmax(), fit_width, np.max(y_trace),
np.min(y_trace))
try:
poptx, pcovx = optimize.curve_fit(fit_Gaussian,
X,
x_trace,
p0=p0x)
popty, pcovy = optimize.curve_fit(fit_Gaussian,
Y,
y_trace,
p0=p0y)
except Exception as e1:
print(e1)
poptx = p0x
popty = p0y
xcen = poptx[0]
ycen = popty[0]
xwidth = poptx[1]
ywidth = popty[1]
count1 = np.sum(x_trace) - poptx[-1] * len(x_trace) # test
count2 = np.sum(y_trace) - popty[-1] * len(y_trace) # test
count = (count1 + count2) / 2
count = round(count, 1)
data.append({
'shot': shot,
x_key: tof,
'fit': {
'xcen': xcen,
'ycen': ycen,
'xwidth': xwidth,
'ywidth': ywidth,
'count': count
}
})
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(20, 8)
n1 = n0[yc - roi_width:yc + roi_width,
xc - roi_width:xc + roi_width]
X = range(n1.shape[1])
Y = range(n1.shape[0])
cmap = plt.get_cmap('jet')
ax.set_aspect('equal')
plt.pcolormesh(X, Y, n1, cmap=cmap, vmin=0)
plt.colorbar()
plt.title('Atom number: {:.2e}'.format(count))
plt.savefig(save_path + '.png', bbox_inches='tight')
X = range(n.shape[1])
Y = range(n.shape[0])
plt.figure()
t_plot = np.linspace(np.min(X), np.max(X), 100)
plt.scatter(X, x_trace, c='blue')
plt.plot(
t_plot,
fit_Gaussian(t_plot, poptx[0], poptx[1], poptx[2], poptx[3]))
plt.savefig(save_path + '_traceX.png', bbox_inches='tight')
plt.figure()
t_plot = np.linspace(np.min(Y), np.max(Y), 100)
plt.scatter(Y, y_trace, c='red')
plt.plot(
t_plot,
fit_Gaussian(t_plot, popty[0], popty[1], popty[2], popty[3]))
plt.savefig(save_path + '_traceY.png', bbox_inches='tight')
plt.clf()
plt.close('all')
except Exception as e:
print(e)
data = []
xcen = 0
ycen = 0
xwidth = 0
ywidth = 0
count = 0
data.append({
'shot': shot,
x_key: tof,
'fit': {
'xcen': xcen,
'ycen': ycen,
'xwidth': xwidth,
'ywidth': ywidth,
'count': count
}
})
return data
else:
return data
def process_lattice_accelerate_image_ccd(settings):
shot = settings['shot']
roi_center = settings['kwargs']['roi_center']
roi_width = settings['kwargs']['roi_width']
fit_width = settings['kwargs']['fit_width']
x_key = settings['kwargs']['x_key']
y_key = settings['kwargs']['y_key']
method = settings['kwargs']['method']
image = settings['kwargs']['image']
if shot >= 0:
data = []
data_folder = os.path.join(PROJECT_DATA_PATH, settings['data_path'])
save_folder = os.path.join(PROJECT_DATA_PATH, settings['data_path'],
'processed_data')
save_path = os.path.join(save_folder, str(shot))
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
conductor_json_path = data_folder + '\\{}'.format(
shot) + '.conductor.json'
ccd_hdf5_path = data_folder + '\\{}'.format(shot) + '.{}.hdf5'.format(
method)
with open(conductor_json_path, 'r') as file:
f1 = file.read()
f2 = json.loads(f1)
try:
em_gain = f2['andor.record_path']['em_gain']
except:
em_gain = 1
try:
images = {}
with h5py.File(ccd_hdf5_path, 'r') as images_h5:
for key in images_h5:
images[key] = np.array(images_h5[key], dtype='float64')
images_h5.close()
if image == 'absorption':
n0 = process_images_g(images, em_gain)
xc = n0.shape[1] - roi_center[1]
yc = roi_center[0]
n = n0[:, xc - roi_width:xc + roi_width]
x_trace0 = np.sum(n, axis=0)
y_trace0 = np.sum(n, axis=1)
X = range(n.shape[1])
Y = range(n.shape[0])
count = np.sum(n)
count = round(float(count), 1)
elif image == 'fluorescence':
n0 = process_images_g_fluorescence(images)
xc = n0.shape[1] - roi_center[1]
yc = roi_center[0]
n = n0[:, xc - roi_width:xc + roi_width]
x_trace0 = np.sum(n, axis=0)
y_trace0 = np.sum(n, axis=1)
X = range(n.shape[1])
Y = range(n.shape[0])
count = np.sum(n)
count = round(float(count), 1)
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(20, 8)
cmap = plt.get_cmap('jet')
ax.set_aspect('equal')
# X = range(n0.shape[1])
# Y = range(n0.shape[0])
plt.pcolormesh(X, Y, n, cmap=cmap, vmin=0)
ax.set_xlim(0, n.shape[1])
ax.set_ylim(0, n.shape[0])
# plt.plot(X, x_trace0/max(x_trace0)*roi_width, c = 'yellow', label = 'x_trace')
# plt.plot(y_trace0/max(y_trace0)*roi_width, Y, c = 'yellow', label = 'y_trace')
# plt.legend()
plt.colorbar()
# plt.title('Atom number: {:.2e}'.format(count))
plt.savefig(save_path + '.png', bbox_inches='tight')
plt.clf()
plt.close('all')
except Exception as e:
print(e)
count = 0
data.append({'shot': shot, 'fit': {y_key: count}})
return data
else:
return data
def process_red_mot_image_ccd(settings):
shot = settings['shot']
roi_width = settings['kwargs']['roi_width']
fit_width = settings['kwargs']['fit_width']
x_key = settings['kwargs']['x_key']
y_key = settings['kwargs']['y_key']
method = settings['kwargs']['method']
image = settings['kwargs']['image']
if shot >= 0:
data = []
data_folder = os.path.join(PROJECT_DATA_PATH, settings['data_path'])
save_folder = os.path.join(PROJECT_DATA_PATH, settings['data_path'],
'processed_data')
save_path = os.path.join(save_folder, str(shot))
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
conductor_json_path = data_folder + '\\{}.conductor.json'.format(shot)
ccd_hdf5_path = data_folder + '\\{}.{}.hdf5'.format(shot, method)
with open(conductor_json_path, 'r') as file:
f1 = file.read()
f2 = json.loads(f1)
try:
em_gain = f2['andor.record_path']['em_gain']
except:
em_gain = 1
try:
images = {}
with h5py.File(ccd_hdf5_path, "r") as images_h5:
for key in images_h5:
images[key] = np.array(images_h5[key], dtype='float64')
images_h5.close()
if image == 'absorption':
n0 = process_images_g(images, em_gain)
x_trace0 = np.sum(n0, axis=0)
y_trace0 = np.sum(n0, axis=1)
xc = x_trace0.argmax()
yc = y_trace0.argmax()
n = n0[yc - roi_width:yc + roi_width,
xc - roi_width:xc + roi_width]
n = np.flipud(n)
X = range(n.shape[1])
Y = range(n.shape[0])
# x_trace = np.sum(n, axis = 0)
# y_trace = np.sum(n, axis = 1)
# p0x = (x_trace.argmax(), fit_width, np.max(x_trace), np.min(x_trace))
# p0y = (y_trace.argmax(), fit_width, np.max(y_trace), np.min(y_trace))
# try:
# poptx, pcovx = optimize.curve_fit(fit_Gaussian, X, x_trace, p0 = p0x)
# popty, pcovy = optimize.curve_fit(fit_Gaussian, Y, y_trace, p0 = p0y)
# except Exception as e1:
# print(e1)
# poptx = p0x
# popty = p0y
# count1 = np.sum(x_trace) - poptx[-1]*len(x_trace) # test
# count2 = np.sum(y_trace) - popty[-1]*len(y_trace) # test
# count = (count1+count2)/2
# count = round(count, 1)
count = np.sum(n)
count = round(float(count), 1)
elif image == 'fluorescence':
n0 = process_images_g_fluorescence(images)
(yc, xc) = np.unravel_index(np.argmax(n0, axis=None), n0.shape)
n = n0[yc - roi_width:yc + roi_width,
xc - roi_width:xc + roi_width]
n = np.flipud(n)
X = range(n.shape[1])
Y = range(n.shape[0])
count = float(np.sum(n))
count = round(count, 1)
data.append({x_key: shot, 'fit': {y_key: count}})
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(20, 8)
cmap = plt.get_cmap('jet')
ax.set_aspect('equal')
plt.pcolormesh(X, Y, n, cmap=cmap)
plt.colorbar()
plt.title('Atom number: {:.2e}'.format(count))
plt.savefig(save_path + '.png', bbox_inches='tight')
plt.clf()
plt.close('all')
except Exception as e:
print(e)
count = 0
data.append({x_key: shot, 'fit': {y_key: count}})
return data
else:
return data