Commit dfa6553c by Tobias Jörg Theodor Kempe

### resolved merge conflict

parents 5a9d341d 306f34e3
 ... ... @@ -6,12 +6,12 @@ report.pdf: script: - pip3 install --requirement python/requirements.txt - python3 python/main.py - mkdir plots - find . -name '*.pdf' -exec mv {} plots/ \; - cd latex/ - latexmk -pdf report.tex - cd .. - mv latex/report.pdf Report\_$REPORT_NUMBER\_Kempe_Sensebat.pdf - mkdir plots - find . -name '*.pdf' -exec mv {} plots/ \; - mv plots/report.pdf Report\_$REPORT_NUMBER\_Kempe_Sensebat.pdf artifacts: paths: ... ...
 import numpy as np <<<<<<< HEAD import time import matplotlib.pyplot as plt ... ... @@ -108,3 +109,113 @@ print(f'total simulation duration: {round(duration, 2)} s') ======= import matplotlib.pyplot as plt import time # settings N = int(1e6) T_0 = 1000 W = 1 showPlots = True def simulate(phi1, phi2): # generate random phis thetas = np.empty((N, 2), dtype=np.float32) thetas[:, 0] = np.random.rand(N) * 2 * np.pi thetas[:, 1] = thetas[:, 0] + np.pi / 2 # generate random number for timestamp rs = np.random.rand(N) # calculate outcome of observation station x1 = (np.cos(2 * (phi1 - thetas[:, 0])) >= (2*np.random.rand(N) -1)).reshape(N, 1) t1 = T_0 * rs * np.sin(2 * (phi1 - thetas[:, 0])) ** 2 x2 = (np.cos(2 * (phi2 - thetas[:, 1])) >= (2*np.random.rand(N) -1)).reshape(N, 1) t2 = T_0 * rs * np.sin(2 * (phi2 - thetas[:, 1])) ** 2 # determine which events are conincidences (1) and which not (0) coincidences = np.abs(t1 - t2) < W # zip measurement outcome together x1x2 = np.concatenate((x1, x2), axis=1) counts = np.empty((2, 4), dtype=np.int32) counts[0, 0] = np.sum(np.all(x1x2 == np.array([0, 0]), axis=1), axis=0) counts[0, 1] = np.sum(np.all(x1x2 == np.array([0, 1]), axis=1), axis=0) counts[0, 2] = np.sum(np.all(x1x2 == np.array([1, 0]), axis=1), axis=0) counts[0, 3] = np.sum(np.all(x1x2 == np.array([1, 1]), axis=1), axis=0) counts[1, 0] = np.sum(np.all(x1x2 == np.array([0, 0]), axis=1) == coincidences, axis=0) counts[1, 1] = np.sum(np.all(x1x2 == np.array([0, 1]), axis=1) == coincidences, axis=0) counts[1, 2] = np.sum(np.all(x1x2 == np.array([1, 0]), axis=1) == coincidences, axis=0) counts[1, 3] = np.sum(np.all(x1x2 == np.array([1, 1]), axis=1) == coincidences, axis=0) E_all = np.sum(counts, axis=1).astype(float) E_12 = (counts[:, 0] - counts[:, 1] - counts[:, 2] + counts[:, 3]).astype(float) E_1 = (counts[:, 0] + counts[:, 1] - counts[:, 2] - counts[:, 3]).astype(float) E_2 = (counts[:, 0] - counts[:, 1] + counts[:, 2] - counts[:, 3]).astype(float) if (np.all(E_all > 0)): E_12 /= E_all E_1 /= E_all E_2 /= E_all return E_12, E_1, E_2 def try_all_configs(n_configs): E_12 = np.empty((n_configs, 2), dtype=np.float32) E_1 = np.empty((n_configs, 2), dtype=np.float32) E_2 = np.empty((n_configs, 2), dtype=np.float32) for i in range(n_configs): phi1 = 0 phi2 = i * 2 * np.pi / n_configs E_12_, E_1_, E_2_ = simulate(phi1, phi2) E_12[i, :] = E_12_ E_1[i, :] = E_1_ E_2[i, :] = E_2_ x = np.linspace(0, 360, n_configs) plt.figure() plt.plot(x, E_12[:, 0]) plt.show() # correlation phi = np.linspace(0, 360, n_configs) phi_theory = np.linspace(0, 360, n_configs*100) theory = -np.cos(2 * phi_theory * (2 * np.pi / 360)) plt.figure() plt.xlabel(r'$\Delta \varphi$') plt.ylabel(r'$$') plt.plot(phi_theory, theory, '.', markersize=1, color='orange') plt.plot(phi, E_12[:, 0], 'o', color='blue') #plt.savefig('correlation.pdf') if showPlots: plt.show() plt.close() # product of expectation values line = np.zeros(phi_theory.shape) plt.figure() plt.xlabel(r'\Delta \varphi') plt.ylabel(r'$$') plt.ylim([-1, 1]) plt.xticks([0, 45, 90, 135, 180, 225, 270, 305, 360]) plt.plot(phi_theory, line, '.', markersize=1, color='orange') plt.plot(phi, E_1[:, 0]*E_2[:, 0], 'o', color='blue') #plt.savefig('expectation_value.pdf') if showPlots: plt.show() plt.close() start = time.time() try_all_configs(32) duration = time.time() - start print(f'simulation took {round(duration, 2)} seconds') >>>>>>> 306f34e3cfd8a671917195d947d9ef22046bb21a
python/example.py 0 → 100644
 import numpy as np import matplotlib.pyplot as plt # some initialization wxyz = 1234 np.random.seed(wxyz) # input data nsteps = 32 nsamples = 100000 T0 = 1000 # (ns), maximum time delay W = 1 # (ns), time coincidence window HWP2 = 0 # degrees, orientation of wave plate 2 (EOM2) cHWP2 = np.cos(HWP2*np.pi/180) sHWP2 = np.sin(HWP2*np.pi/180) def analyzer(c, s, cHWP, sHWP, T0): # EOM : plane rotation c2 = cHWP*c + sHWP*s s2 = -sHWP*c + cHWP*s x = c2*c2 - s2*s2 # cos(2(x-a)) y = 2*c2*s2 # sin(2(x-a)) # Malus law r0 = np.random.rand() if(x>2*r0 - 1): j = 0 # +1 event else: j = 1 #-1 event # time delay r1 = np.random.rand() l = y**4*T0*r1 # delay time: T_0 sin(2*(theta1 - x))**4 return j, l count = np.zeros(shape=(2, 2, 2, nsteps), dtype=np.int32) # set all counts to zero for ipsi0 in range(nsteps): # loop over different settings of EOM1 cHWP1 = np.cos(ipsi0*2*np.pi/nsteps) sHWP1 = np.sin(ipsi0*2*np.pi/nsteps) for i in range(nsamples): # source r0 = np.random.rand() c1 = np.cos(r0*2*np.pi) # polarization angle x of particle going to station 1 s1 = np.sin(r0*2*np.pi) # polarization angle x + pi/2 of particle going to station 2 c2 = -s1 s2 = c1 # station 1 j1, l1 = analyzer(c1, s1, cHWP1, sHWP1, T0) # station 2 j2, l2 = analyzer(c2, s2, cHWP2, sHWP2, T0) # count count[j1, j2, 0, ipsi0] = count[j1, j2, 0, ipsi0] + 1 # Malus law model if(abs(l1 - l2) no time window, i = 1 <=> use time coincidences #tot[i, j] = np.sum(count[:, :, i, j]) tot[i, j] = count[0, 0, i, j] + count[1, 1, i, j] + count[1, 0, i, j] + count[0, 1, i, j] E12[i, j] = count[0, 0, i, j] + count[1, 1, i, j] - count[1, 0, i, j] - count[0, 1, i, j] E1[i, j] = count[0, 0, i, j] + count[0, 1, i, j] - count[1, 1, i, j] - count[1, 0, i, j] E2[i, j] = count[0, 0, i, j] + count[1, 0, i, j] - count[1, 1, i, j] - count[0, 1, i, j] if(tot[i, j]>0): E12[i, j] = E12[i, j]/tot[i, j] E1[i, j] = E1[i, j]/tot[i, j] E2[i, j] = E2[i, j]/tot[i, j] theta = np.linspace(0, 360, nsteps) theta_theory = np.linspace(0, 360, nsteps*100) theory = [] for j in range(nsteps*100): theory.append(-np.cos(2 * j * 2 * np.pi / nsteps / 100)) plt.plot(theta, E12[0, :], 'o') plt.plot(theta_theory, theory, '.', markersize=1) plt.savefig('e12.pdf') plt.close() \ No newline at end of file
 import example import eprb
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