plot_max = 12.0
# Use autograph for performance
@tf.function
def nll(coeffs_, signal_events_):
return bmf.signal.normalized_nll(coeffs_, signal_events_)
def try_nll(pos_, val):
# Override the coeff we want to vary
try_coeffs[pos_] = tf.constant(val)
return nll(try_coeffs, signal_events)
with bmf.Script() as script:
signal_coeffs = bmf.coeffs.signal(bmf.coeffs.SM)
signal_events = bmf.signal.generate(signal_coeffs)
fit_coeffs = bmf.coeffs.fit()
# For each amplitude
for a_idx in range(0, bmf.coeffs.amplitude_count):
# If no coeffs for this amplitude are trainable, then skip this plot
coeff_id_alpha = a_idx * bmf.coeffs.param_count
if not bmf.coeffs.is_trainable(fit_coeffs[coeff_id_alpha]) \
and not bmf.coeffs.is_trainable(fit_coeffs[coeff_id_alpha + 1]) \
and not bmf.coeffs.is_trainable(fit_coeffs[coeff_id_alpha + 2]):
continue
fig, axes = plt.subplots(bmf.coeffs.param_count)
fig.suptitle(bmf.coeffs.amplitude_latex_names[a_idx])
choices=bmf.coeffs.signal_models,
default=bmf.coeffs.SM,
help='signal model (default: {})'.format(bmf.coeffs.SM)
)
parser.add_argument(
'-w',
'--write-svg',
dest='write_svg',
metavar='SVG_PATH',
help='write plots as SVGs using this filepath. this string must contain \'%%name%%\''
)
args = parser.parse_args()
if args.write_svg and '%name%' not in args.write_svg:
parser.error('-w/--write-svg must contain \'%name%\'')
with bmf.Script(device=args.device) as script:
if args.write_svg is not None:
matplotlib.use('SVG')
# Import these after we optionally set SVG backend - otherwise matplotlib may bail on a missing TK backend when
# running from the CLI
import matplotlib.pylab as plt
from matplotlib.ticker import FuncFormatter, MultipleLocator
import seaborn as sns
signal_coeffs = bmf.coeffs.signal(args.signal_model)
if args.left_transform:
bmf.stdout('Performing transformations: a_para_l -> -a_perp_l, a_perp_l -> -a_para_l/2')
a_para_ls = signal_coeffs[0:6]
a_perp_ls = signal_coeffs[12:18]
# Use autograph for performance
@tf.function
def nll(coeffs_, signal_events_):
return bmf.signal.normalized_nll(coeffs_, signal_events_)
def try_nll(c):
# Override the two coeffs we want to vary
fit_coeffs[cx_idx] = tf.constant(c[0])
fit_coeffs[cy_idx] = tf.constant(c[1])
return nll(fit_coeffs, signal_events)
with bmf.Script():
signal_coeffs = bmf.coeffs.signal(bmf.coeffs.SM)
signal_events = bmf.signal.generate(signal_coeffs)
# Set fit coeffs to our constant signal ones
fit_coeffs = bmf.coeffs.signal(bmf.coeffs.SM)
cx = np.linspace(grid_min, grid_max, grid_points, dtype=np.float32)
cy = np.linspace(grid_min, grid_max, grid_points, dtype=np.float32)
X, Y = tf.meshgrid(cx, cy)
points_grid = tf.stack([X, Y], axis=2)
# Turn our grid into (x, y) pairs
points = tf.reshape(points_grid, [grid_points**2, 2])
# Calculate likelihoods
likelihoods = tf.map_fn(try_nll, points)
# Convert likelihoods back into meshgrid shape