def plot_casewise(var):
f, ax = plt.subplots(1, 4, sharey=True)
ax[0].set_ylabel(var + ' ({})'.format(TILTCASE))
CASE = {}
for cnv, v in enumerate(['NOT_B', 'NOT_Y', 'BOTH', 'NEITHER']):
SMPAnalysis.fetch_from(DATA_DIR + '/DUMMY_' + v)
CASE[v] = SMPAnalysis('')
# for j in range(CASE[v].pray.shape[0]):
z = CASE[v][:, 1][VARS]
# if v=='NOT_B':
# z['B'] *= 0
# if v=='NOT_Y':
# z['Y'] *= 0
# if v=='NEITHER':
# z['Y'] *= 0
# z['B'] *= 0
ax[cnv].plot(z['Y'] * 1e3, ST[var](z), '--.', label='PYTHON')
ax[cnv].plot(CASE[v][:, 1:]['Y'][1:] * 1e3,
CASE[v].stunee[var][1:, 1:],
'--.',
label='POLVAL')
ax[cnv].legend()
ax[cnv].set_title(v)
ax[cnv].grid()
ax[cnv].set_xlabel('Y [mm]')
def load_case(dir_name):
dir_path = '../data/ARTEM_TEST/' + dir_name
SMPAnalysis.fetch_from(dir_path)
cw_mrk = sorted(SMPAnalysis.list_markers('CW'), key=lambda x: float(x[2:]))
cw_arr = RunContainer(*cw_mrk)
if len(SMPAnalysis.list_markers('DECOH')) > 0:
decoh_data = np.loadtxt(dir_path + '/STUNEE:DECOH.dat',
dtype=[('pid', int)] + MU_TYPE)
decoh_pray = NLF['PRAY'][1](dir_path + '/PRAY:DECOH.dat')
cw_arr.decoh = Bundle(data=decoh_data, pray=decoh_pray)
return cw_arr
def __init__(self, *marker_list):
self.count = len(marker_list)
self.shape = (self.count, )
self._mrk_list = marker_list
self.run_list = np.empty(self.count, dtype=object)
self.fit_parameters = Bundle()
for i, mrk in enumerate(marker_list):
self.run_list[i] = SMPAnalysis(mrk)
def plot_3D_opt_cases(var, tilt_case):
data_dir = '../data/TSS/{}/{}'.format(tilt_case, 'UNOPT')
SMPAnalysis.fetch_from(data_dir + '/DUMMY_BOTH')
case = SMPAnalysis('')
z = case[VARS]
fig = plt.figure(figsize=plt.figaspect(0.5))
for i, opt_case in enumerate(['UNOPT', 'OPTIM']):
data_dir = '../data/TSS/{}/{}'.format(tilt_case, opt_case)
st = load_davecs(data_dir)
ax = fig.add_subplot(2, 2, i + 1, projection='3d')
ax.set_xlabel('Y [mm]')
ax.set_ylabel('B')
ax.set_title(var + ' ({}, {})'.format(tilt_case, opt_case))
var_vals = np.array([st[var](v) for v in z.T]).T
for j, v in enumerate(var_vals.T):
ax.plot(z['Y'][:, j], z['B'][:, j], var_vals[:, j])
art.form(ax)
for i, opt_case in enumerate(['UNOPT', 'OPTIM']):
data_dir = '../data/TSS/{}/{}'.format(tilt_case, opt_case)
ax = fig.add_subplot(2, 2, i + 3, projection='3d')
ax.set_xlabel('Y [mm]')
ax.set_ylabel('B')
SMPAnalysis.fetch_from(data_dir + '/DUMMY_BOTH')
case = SMPAnalysis('')
z = case[VARS]
var_vals = case.stunee[var]
for j, v in enumerate(var_vals.T):
ax.plot(z['Y'][:, j], z['B'][:, j], var_vals[:, j])
art.form(ax)
def plot_3D(var, tilt_case, opt_case):
data_dir = '../data/TSS/{}/{}'.format(tilt_case, opt_case)
SMPAnalysis.fetch_from(data_dir + '/DUMMY_BOTH')
case = SMPAnalysis('')
z = case[VARS]
st = load_davecs(data_dir)
po_vals = case.stunee[var]
py_vals = np.array([st[var](v) for v in z.T]).T
fig = plt.figure(figsize=plt.figaspect(0.5))
ax = fig.add_subplot(121, projection='3d')
ax.set_xlabel('Y [mm]')
ax.set_ylabel('B')
ax.set_title(var + ' ({}, {}, {})'.format(tilt_case, opt_case, 'POLVAL'))
for i, v in enumerate(po_vals.T):
ax.plot(z['Y'][:, i], z['B'][:, i], po_vals[:, i])
art.form(ax)
ax = fig.add_subplot(122, projection='3d')
ax.set_xlabel('Y [mm]')
ax.set_ylabel('B')
ax.set_title(var + ' ({}, {}, {})'.format(tilt_case, opt_case, 'PYTHON'))
for i, v in enumerate(py_vals.T):
ax.plot(z['Y'][:, i], z['B'][:, i], py_vals[:, i])
art.form(ax)
import lmfit
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
from SMP.spin_motion_perturbation import guess_freq, guess_phase, SMPAnalysis
import artem_test as art
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
sine = art.sine
jitsine = art.jitsine
SMPAnalysis.fetch_from('../data/ARTEM_TEST/SEXTVAR/SEXT1')
def relLik(model0, model1):
return np.exp((model0.aic - model1.aic) / 2)
dat = SMPAnalysis('CW1')
n = dat.trpspi.shape[0]
sx = dat.trpspi['S_X'][:, 28] #+ np.random.normal(0,1e-2,n)
sy = dat.trpspi['S_Y'][:, 28] #+ np.random.normal(0,1e-2,n)
Py = dat.P['Y'] #+ np.random.normal(0,1e-2,n)
t = dat.trpspi['TURN'][:, 28] * 1e-6
syf0g = guess_freq(t, sy)
syp0g = guess_phase(t, sy)
smodel = lmfit.Model(sine)
jsmodel = lmfit.Model(jitsine)
## analysis script for the making of my seminar presentation
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
from SMP.spin_motion_perturbation import guess_freq, guess_phase, SMPAnalysis
import artem_test as art
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
font = {'size': 14}
plt.rc('font', **font)
SMPAnalysis.fetch_from('../data/ARTEM_TEST/SEXTVAR/SEXT1')
dat = SMPAnalysis('CW1')
f, ax = plt.subplots(1, 1)
pidmin = 28
ymin = dat.pray[pidmin]['Y'] * 1e3
pidmax = 24
ymax = dat.pray[pidmax]['Y'] * 1e3
ax.plot(dat['T'][:, pidmin],
dat['D'][:, pidmin],
'--.',
label='y-offset: {:4.2f} [mm]'.format(ymin))
ax.plot(dat['T'][:, pidmax],
dat['D'][:, pidmax],
'--.',
label='y-offset: {:4.2f} [mm]'.format(ymax))
ax.set_xlabel('Phase difference')
ax.set_ylabel('(K-K0)/K0')
ax.legend()
ax.plot(traj[x] * 1e3,
traj[y] * 1e3,
ST[VAR](traj),
label='{:4.2} [mm]'.format(traj[x][0] * 1e3))
ax.legend()
art.form(ax)
if __name__ == '__main__':
TILTCASE = sys.argv[1].upper() if len(sys.argv) > 1 else 'IMPERFECT'
OPTCASE = sys.argv[2].upper() if len(sys.argv) > 2 else 'UNOPT'
DATA_DIR = '../data/TSS/{}/{}'.format(TILTCASE, OPTCASE)
VAR = sys.argv[3] if len(sys.argv) > 3 else 'mu0'
ST = load_davecs(DATA_DIR)
SMPAnalysis.fetch_from(DATA_DIR + '/DUMMY_BOTH')
case = SMPAnalysis('')
z = case[VARS]
var_X, var_Y = 'Y', 'B'
z0 = np.zeros_like(z)
## Trajectory: line
z0[var_X] = z[var_X]
plot_main(z0, var_X, var_Y, VAR, 'line')
## Trajectory: (cos, sin)
t = np.linspace(0, 1, 51)
xmax = z[var_X].max()
ymax = z[var_Y].max()
nray = z.shape[1]