I0_gerade = unp.uarray(50636, 254) / 300 print('I0_gerade', I0_gerade) I0_schraeg1 = unp.uarray(16660, 146) / 100 I0_schraeg2 = unp.uarray(16417, 145) / 100 #I0_schraeg = np.mean([I0_schraeg1,I0_schraeg2]) I0_schraeg = avg_and_sem([noms(I0_schraeg1), noms(I0_schraeg2)]) I0_schraeg = unp.uarray(I0_schraeg[0], I0_schraeg[1]) print('I0_schraeg', I0_schraeg) makeTable([ noms([I0_gerade * 300, I0_schraeg1 * 100, I0_schraeg2 * 100]), stds([I0_gerade * 300, I0_schraeg1 * 100, I0_schraeg2 * 100]), [300, 100, 100], noms([I0_gerade, I0_schraeg1, I0_schraeg2]), stds([I0_gerade, I0_schraeg1, I0_schraeg2]) ], r'\multicolumn{2}{c}{' + r'$N_0$' + r'} & {' + r'$\Delta t_0/\si{\second}$' + r'} & \multicolumn{2}{c}{' + r'$I_0/\si{\becquerel}$' + r'}', 'tabReferenzmessung', [ 'S[table-format=5.0]', '@{${}\pm{}$}S[table-format=3.0]', 'S[table-format=3.0]', 'S[table-format=3.1]', '@{${}\pm{}$}S[table-format=1.1]' ], ["%5.0f", "%3.0f", "%3.0f", "%3.1f", "%1.1f"]) #Würfel 2 I2_gerade = unp.uarray(8054, 103) / 300 I2_schraeg1 = unp.uarray(4864, 81) / 300 I2_schraeg2 = unp.uarray(8707, 106) / 300 mu_21 = unp.log(I0_gerade / I2_gerade) / 3 mu_22 = unp.log(I0_schraeg1 / I2_schraeg1) / (3 * np.sqrt(2)) mu_23 = unp.log(I0_schraeg2 / I2_schraeg2) / (2 * np.sqrt(2)) print('mu_21', mu_21)
N_S = 11 R_H = 15.79 * 10**(-2) # m N_H = 154 B_S1 = B_field(I_S1, N_S, R_S) B_S2 = B_field(I_S2, N_S, R_S) B_H1 = B_field(I_H1, N_H, R_H) B_H2 = B_field(I_H2, N_H, R_H) B_1 = B_H1 + B_S1 B_2 = B_H2 + B_S2 makeTable([ v / 1000, I_S1 * 1000, I_H1 * 1000, B_S1 * 10**6, B_H1 * 10**6, B_1 * 10**6 ], r'{$\nu/\si{\kilo\hertz}$} & {$I_\text{S,A}/\si{\milli\ampere}$} & {$I_\text{H,A}/\si{\milli\ampere}$} & {$B_\text{S,A}/\si{\micro\tesla}$} & {$B_\text{H,A}/\si{\micro\tesla}$} & {$B_\text{Ges,A}/\si{\micro\tesla}$}', 'messung1A', [ 'S[table-format=4.0]', 'S[table-format=3.0]', 'S[table-format=3.0]', 'S[table-format=3.2]', 'S[table-format=3.2]', 'S[table-format=3.2]' ], ["%4.0f", "%3.0f", "%3.0f", "%3.2f", "%3.2f", "%3.2f"]) makeTable([ v / 1000, I_S2 * 1000, I_H2 * 1000, B_S2 * 10**6, B_H2 * 10**6, B_2 * 10**6 ], r'{$\nu/\si{\kilo\hertz}$} & {$I_\text{S,B}/\si{\milli\ampere}$} & {$I_\text{H,B}/\si{\milli\ampere}$} & {$B_\text{S,B}/\si{\micro\tesla}$} & {$B_\text{H,B}/\si{\micro\tesla}$} & {$B_\text{Ges,B}/\si{\micro\tesla}$}', 'messung1B', [ 'S[table-format=4.0]', 'S[table-format=3.0]', 'S[table-format=3.0]', 'S[table-format=3.2]', 'S[table-format=3.2]', 'S[table-format=3.2]' ], ["%4.0f", "%3.0f", "%3.0f", "%3.2f", "%3.2f", "%3.2f"]) def Linear(x, a, b): return a * x + b
P_Wind_schwach = 8 / 14 P_Feucht_hoch = 7 / 14 P_Temp_heiß = 1 / 14 P_Ausb_sonnig = 3 / 14 P_W = P_Wind_schwach * P_Feucht_hoch * P_Temp_heiß * P_Ausb_sonnig P_F_W = P_Wind_schwach_F * P_Feucht_hoch_F * P_Temp_heiß_F * P_Ausb_sonnig_F * P_F / P_W print(P_F_W) #Nr.17) temperature, weather, humidity, wind, soccer = np.genfromtxt( 'scripts/data.txt', unpack=True) makeTable( [temperature, weather, humidity, wind, soccer], r'{' + r'$Temperature/\si{\celsius}$' + r'} & {' + r'$Weather$' + r'} & {' + r'$Humidity/\si{\percent}$' + r'} & {' + r'$Wind$' + r'} & {' + r'$Soccer$' + r'}', 'tabData', [ 'S[table-format=2.1]', 'S[table-format=1.0]', 'S[table-format=2.0]', 'S[table-format=1.0]', 'S[table-format=1.0]' ], ["%2.1f", "%1.0f", "%2.0f", "%1.0f", "%1.0f"]) def entropy(p1, p2): if p1 == 0: return -p2 * np.log2(p2) elif p2 == 0: return -p1 * np.log2(p1) else: return -p1 * np.log2(p1) - p2 * np.log2(p2) p_soc_true = len(soccer[soccer == 1]) / len(soccer)
plt.xlim(60,200) plt.legend(loc='best') plt.tight_layout(pad=0, h_pad=1.04, w_pad=1.08) plt.savefig('build/'+'longitudinal') #transversale Moden T00x, T00I = np.genfromtxt('scripts/T00mode.txt',unpack=True) #mm,nA T00x = 15-T00x T00x = T00x[::-1] T00I = T00I[::-1] T01x, T01I = np.genfromtxt('scripts/T01mode.txt',unpack=True) #mm.nA #T00x = T00x /1000 #m #T01x = T01x /1000 #m makeTable([T00x,T00I], r'{$\Delta x/ \si{\milli\meter}$} & {$ I / \si{\nano\ampere}$}','tabT00' , ['S[table-format=2.0]' , 'S[table-format=4.0]'] , ["%2.0f", "%4.0f"]) makeTable([T01x[:15],T01I[:15]], r'{$ \Delta x / \si{\milli\meter}$} & {$ I/ \si{\nano\ampere}$}','tabT011' , ['S[table-format=1.1]' , 'S[table-format=3.0]'] , ["%1.1f", "%3.0f"]) makeTable([T01x[15:],T01I[15:]], r'{$ \Delta x/ \si{\milli\meter}$} & {$ I/ \si{\nano\ampere}$}','tabT012' , ['S[table-format=2.1]' , 'S[table-format=3.0]'] , ["%2.1f", "%3.0f"]) #T00 mode fit def T00(x,a,b,c): return a*np.exp(-2*((x-c)**2)/(b**2)) params, covariance_matrix = curve_fit(T00,T00x,T00I) errors = np.sqrt(np.diag(covariance_matrix)) print('Die Parameter der T00 Mode:') print('a =', params[0], '±', errors[0]) print('b =', params[1], '±', errors[1]) print('c =', params[2], '±', errors[2]) #plot
plt.plot(xplot+273.15,expFunktion(xplot+273.15,*params),'b-',label='Ausgleichskurve') plt.xlabel(r'$T/\si{\kelvin}$') plt.ylabel(r'$i/\si{\pico\ampere}$') plt.xlim(220,335) plt.ylim(-10,50) plt.legend(loc='best') plt.tight_layout(pad=0, h_pad=1.08, w_pad=1.08) plt.savefig('content/images/plot1exp.pdf') paramsU=uncertainties.correlated_values(params, covar) print(paramsU) t_roh,T_roh,I_roh=np.genfromtxt('scripts/data1.txt',unpack=True) for i in range(len(I)): I[i]=I[i]-expFunktion(T[i], *params) makeTable([t[:26],T[:26],I_roh[:26],I[:26]], r'{'+r'$t_\text{1}/(\si{\minute})$'+r'} & {'+r'$T_\text{1}/(\si{\kelvin})$'+r'} & {'+r'$i_\text{roh,1}/(\si{\pico\ampere})$'+r'} & {'+r'$i_\text{ber,1}/(\si{\pico\ampere})$'+r'}','tabData1_1',['S[table-format=2.0]','S[table-format=3.1]','S[table-format=2.1]','S[table-format=2.1]'],["%2.0f","%3.1f","%2.1f","%2.1f"]) makeTable([t[26:],T[26:],I_roh[26:],I[26:]], r'{'+r'$t_\text{1}/(\si{\minute})$'+r'} & {'+r'$T_\text{1}/(\si{\kelvin})$'+r'} & {'+r'$i_\text{roh,1}/(\si{\pico\ampere})$'+r'} & {'+r'$i_\text{ber,1}/(\si{\pico\ampere})$'+r'}','tabData1_2',['S[table-format=2.0]','S[table-format=3.1]','S[table-format=2.1]','S[table-format=2.1]'],["%2.0f","%3.1f","%2.1f","%2.1f"]) makeTable([1/T[:30],np.log(I[:30])], r'{'+r'$T^{-1}_\text{1}/(\si{\kelvin^{-1}})$'+r'} & {'+r'$\ln{\frac{i_\text{1}}{i_\text{0}}}$'+r'}','tabLog11',['S[table-format=1.4]','S[table-format=1.4]'],["%1.4f","%1.4f"]) #W: 1.Möglichkeit print('erste Möglichkeit') x=1/T params4,covar4=cf(linear,x[4:15],np.log(I[4:15])) paramsU=uncertainties.correlated_values(params4, covar4) print(paramsU) plt.cla() plt.clf() plt.plot(x[:30],np.log(I[:30]),'y.',label='Messwerte')
#B_max = B_max*1.75 print('max B field [mT]:', B_max*10**3) z,B = np.genfromtxt('scripts/magnetfeld.txt',unpack=True) # mm, mT #B = B*1.75 plt.cla() plt.clf() plt.plot(z,B,'rx') plt.xlabel(r'$z/\si{\milli\metre}$') plt.ylabel(r'$B/\si{\milli\tesla}$') #plt.xlim(-30,30) plt.tight_layout(pad=0, h_pad=1.08, w_pad=1.08) #plt.legend(loc='best') plt.savefig('build/magnetfeld.pdf') makeTable([z[:10], B[:10]], r'{$z/\si{\milli\metre}$} & {$B/\si{\milli\tesla}$}', 'magnetfeld', ['S[table-format=2.0]', 'S[table-format=3.0]'], ["%2.0f", "%3.0f"]) makeTable([z[9:], B[9:]], r'{$z/\si{\milli\metre}$} & {$B/\si{\milli\tesla}$}', 'magnetfeld2', ['S[table-format=2.0]', 'S[table-format=3.0]'], ["%2.0f", "%3.0f"]) #reinprobe print('reinprobe') l,t1,t2 = np.genfromtxt('scripts/reinprobe.txt',unpack=True) # micrometer, degree tr = (t1-t2)/2 tr = 2*np.pi*tr/360 # rad tr = tr/5.11*10**3 #rad/m def hyperbel(l,a): return a/l**2 params, covariance_matrix = curve_fit(hyperbel,l,tr) errors = np.sqrt(np.diag(covariance_matrix))
A = A_0*unp.exp(-np.log(2)*t/tau) print('A =', A) #Energieverlustmessung print('Energieverlustmessung') p_ohne,U1_ohne,U2_ohne,U3_ohne = np.genfromtxt('scripts/dataOhne.txt',unpack=True) #p in mbar, U in V U_ohne_m = [] U_ohne_s = [] for i in range(len(U1_ohne)): U_ohne_m = U_ohne_m + [avg_and_sem([U1_ohne[i],U2_ohne[i],U3_ohne[i]])[0]] U_ohne_s = U_ohne_s + [avg_and_sem([U1_ohne[i],U2_ohne[i],U3_ohne[i]])[1]] U_ohne = unp.uarray(U_ohne_m,U_ohne_s) makeTable([p_ohne,U1_ohne,U2_ohne,U3_ohne,noms(U_ohne),stds(U_ohne)], r'{'+r'$p_\text{ohne}/(\si{\milli\bar})$'+r'} & {'+r'$U_\text{high,ohne}/\si{\volt}$'+r'} & {'+r'$U_\text{low,ohne}/\si{\volt}$'+r'} & {'+r'$U_\text{mid,ohne}/\si{\volt}$'+r'} & \multicolumn{2}{c}{'+r'$\bar{U}_\text{ohne}/(\si{\volt})$'+r'}','tabDataOhne',['S[table-format=3.2]','S[table-format=1.2]','S[table-format=1.2]','S[table-format=1.2]','S[table-format=1.2]','@{${}\pm{}$}S[table-format=1.2]'],["%3.2f","%1.2f","%1.2f","%1.2f","%1.2f","%1.2f"]) #params_ohne, covar = curve_fit(Line, p_ohne, noms(U_ohne)) #uParams=uncertainties.correlated_values(params, covar) p_mit,U1_mit,U2_mit,U3_mit = np.genfromtxt('scripts/dataMit.txt',unpack=True) #p in mbar, U in V U_mit_m = [] U_mit_s = [] for i in range(len(U1_mit)): U_mit_m = U_mit_m + [avg_and_sem([U1_mit[i],U2_mit[i],U3_mit[i]])[0]] U_mit_s = U_mit_s + [avg_and_sem([U1_mit[i],U2_mit[i],U3_mit[i]])[1]] U_mit = unp.uarray(U_mit_m,U_mit_s) makeTable([p_mit,U1_mit,U2_mit,U3_mit,noms(U_mit),stds(U_mit)], r'{'+r'$p_\text{mit}/(\si{\milli\bar})$'+r'} & {'+r'$U_\text{high,mit}/\si{\volt}$'+r'} & {'+r'$U_\text{low,mit}/\si{\volt}$'+r'} & {'+r'$U_\text{mid,mit}/\si{\volt}$'+r'} & \multicolumn{2}{c}{'+r'$\bar{U}_\text{mit}/(\si{\volt})$'+r'}','tabDataMit',['S[table-format=3.2]','S[table-format=1.2]','S[table-format=1.2]','S[table-format=1.2]','S[table-format=1.2]','@{${}\pm{}$}S[table-format=1.2]'],["%3.2f","%1.2f","%1.2f","%1.2f","%1.2f","%1.2f"]) plot = True if(plot):