def test_significant_digits():
s = significant_digits(0.637282, 1e-3)
assert s[0] == "0.6373"
s = significant_digits(0.9837473385, 0.000065323)
assert s[0] == "0.98375"
assert s[1] == "0.00007"
s = significant_digits(0.0338577, 0.0015473)
assert s[0] == "0.0339"
assert s[1] == "0.0015"
def calc_beta_inst(name, position, results_df, magnet1_df, magnet2_df,
kmod_input_params):
betas = np.zeros((2, 2))
sign = np.array([[0, 0], [1, 0], [-1, 0], [0, 1], [0, -1]])
for i, plane in enumerate(PLANES):
waist = results_df.loc[:, f"{WAIST}{plane}"].values
if magnet1_df.headers['POLARITY'] == 1 and magnet2_df.headers[
'POLARITY'] == -1:
waist = -waist
if plane == 'Y':
waist = -waist
beta = propagate_beta_in_drift(
(results_df.loc[:, f"{BETA}{WAIST}{plane}"].values + sign[:, 0] *
results_df.loc[:, f"{ERR}{BETA}{WAIST}{plane}"].values),
((waist - position) +
sign[:, 1] * results_df.loc[:, f"{ERR}{WAIST}{plane}"].values))
beta_err = get_err(beta[1::2] - beta[0])
if kmod_input_params.no_sig_digits:
betas[i, 0], betas[i, 1] = beta[0], beta_err
else:
betas[i, 0], betas[i, 1] = tfstools.significant_digits(
beta[0], beta_err, return_floats=True)
return name, betas[0, 0], betas[0, 1], betas[1, 0], betas[1, 1]
def calc_betastar(kmod_input_params, results_df, l_star):
sign = return_sign_for_err(2)
for plane in PLANES:
betastar = propagate_beta_in_drift(
(results_df.loc[:, f"{BETA}{WAIST}{plane}"].values + sign[:, 0] *
results_df.loc[:, f"{ERR}{BETA}{WAIST}{plane}"].values),
(results_df.loc[:, f"{WAIST}{plane}"].values +
sign[:, 1] * results_df.loc[:, f"{ERR}{WAIST}{plane}"].values))
betastar_err = get_err(betastar[1::2] - betastar[0])
if kmod_input_params.no_sig_digits:
results_df[f"{BETA}{STAR}{plane}"], results_df[
f"{ERR}{BETA}{STAR}{plane}"] = (betastar[0], betastar_err)
else:
results_df[f"{BETA}{STAR}{plane}"], results_df[
f"{ERR}{BETA}{STAR}{plane}"] = tfstools.significant_digits(
betastar[0], betastar_err, return_floats=True)
# reindex df to put betastar first
cols = results_df.columns.tolist()
cols = [cols[0]] + cols[-4:] + cols[1:-4]
results_df = results_df.reindex(columns=cols)
for plane in PLANES:
results_df[f"{PHASEADV}{plane}"], results_df[
f"{ERR}{PHASEADV}{plane}"] = phase_adv_from_kmod(
l_star, betastar[0], betastar_err,
results_df.loc[:, f"{WAIST}{plane}"].values,
results_df.loc[:, f"{ERR}{WAIST}{plane}"].values)
return results_df
def test_significant_digits():
s = significant_digits(0.637282, 1e-3)
assert s[0] == "0.6373"
s = significant_digits(0.9837473385, 0.000065323)
assert s[0] == "0.98375"
assert s[1] == "0.00007"
s = significant_digits(0.0338577, 0.0015473)
assert s[0] == "0.0339"
assert s[1] == "0.0015"
s = significant_digits(0.0338577, 0.0015473, return_floats=True)
assert s[0] == 0.0339
assert s[1] == 0.0015
with pytest.raises(ValueError):
s = significant_digits(0.0338577, 0.0)
def _get_scaled_labels(val, std, scale):
scaled_vas, scaled_std = val*scale, std*scale
if abs(scaled_std) > 1 or scaled_std == 0:
return f'{int(round(scaled_vas)):d}', f'{int(round(scaled_std)):d}'
return significant_digits(scaled_vas, scaled_std)