From 9641c0a39b5a7d75b7bc00d081255be44d79e089 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gi=C3=B9=20Marcer?= Date: Tue, 28 Apr 2020 23:44:24 +0200 Subject: [PATCH] ex-4: fit plot added --- ex-4/main.c | 49 ++++++++++++++++++++++++++++++------------- ex-4/note.py | 1 - notes/images/fit.pdf | Bin 0 -> 17733 bytes notes/sections/4.md | 9 +++++--- 4 files changed, 40 insertions(+), 19 deletions(-) create mode 100644 notes/images/fit.pdf diff --git a/ex-4/main.c b/ex-4/main.c index e2b0e4c..e0e1019 100644 --- a/ex-4/main.c +++ b/ex-4/main.c @@ -9,13 +9,14 @@ // Process CLI arguments. // -int parser(size_t *N, size_t *n, double *p_max, char argc, char **argv) +int parser(size_t *N, size_t *n, double *p_max, char argc, char **argv, size_t *go) { for (size_t i = 1; i < argc; i++) { if (!strcmp(argv[i], "-n")) *N = atol(argv[++i]); else if (!strcmp(argv[i], "-b")) *n = atol(argv[++i]); else if (!strcmp(argv[i], "-p")) *p_max = atof(argv[++i]); + else if (!strcmp(argv[i], "-o")) *go = 1; else { fprintf(stderr, "Usage: %s -[hnbp]\n", argv[0]); @@ -23,6 +24,7 @@ int parser(size_t *N, size_t *n, double *p_max, char argc, char **argv) fprintf(stderr, "\t-n N\tThe number of events to generate. (default: 50000)\n"); fprintf(stderr, "\t-b N\tThe number of bins of the histogram. (default: 50)\n"); fprintf(stderr, "\t-p PMAX\tThe maximum value of momentum. (default: 10)\n"); + fprintf(stderr, "\t-o \tPrint histogram to stdout.\n"); return 0; } } @@ -35,18 +37,19 @@ int main(int argc, char **argv) // Set default options. // - size_t N = 50000; // number of events. - size_t n = 50; // number of bins. - double p_max = 10; // maximum value of momentum module. - int res = parser(&N, &n, &p_max, argc, argv); - if (res == 1) + size_t N = 50000; + size_t n = 50; + double p_max = 10; + size_t go = 0; + int res = parser(&N, &n, &p_max, argc, argv, &go); + if (go == 0) { printf("\nGenerating histogram with:\n" "%ld points\n" "%ld bins\n" "p_max = %.3f\n\n", N, n, p_max); } - else return EXIT_FAILURE; + else if (res == 0) return EXIT_FAILURE; // printf("step: \t%.5f\n", step); @@ -116,12 +119,15 @@ int main(int argc, char **argv) // Compute the mean value of each bin and print it to stodut // together with other useful things to make the histogram. // - // printf("bins: \t%ld\n", n); - // printf("step: \t%.5f\n", step); + if (go == 1) + { + printf("bins: \t%ld\n", n); + printf("step: \t%.5f\n", step); + } for (size_t i = 0; i < n; i++) { histo[i].sum = histo[i].sum / histo[i].amo; // Average P_v - //printf("\n%.5f", histo[i].sum); + if (go == 1) printf("\n%.5f", histo[i].sum); }; // Compare the histigram with the expected function: @@ -165,9 +171,12 @@ int main(int argc, char **argv) double result = x; double res_chi = chi2(result, ¶ms); - printf("Results:\n"); - printf("χ² = %.3f\n", res_chi); - printf("p_max = %.3f\n", result); + if (go == 0) + { + printf("Results:\n"); + printf("χ² = %.3f\n", res_chi); + printf("p_max = %.3f\n", result); + } // Compute the second derivative of χ² in its minimum for the result error. // @@ -195,9 +204,19 @@ int main(int argc, char **argv) B = exp2d(x, result) * (1 - pow((histo[i].sum / expecto), 2)); error = error + A + B; }; - error = 1/error; - printf("ΔP_max = %.3f\n\n", error); + error = 1/error; + if (go == 0) printf("ΔP_max = %.3f\n", error); + // Check compatibility + // + double t = fabs(result - p_max)/error; + double alpha = 1 - erf(t/sqrt(2)); + if (go == 0) + { + printf("\nCompatibility:\n"); + printf("t = %.3f\n", t); + printf("α = %.3f\n\n", alpha); + } // Free memory. // diff --git a/ex-4/note.py b/ex-4/note.py index fa955c5..cf23f4e 100644 --- a/ex-4/note.py +++ b/ex-4/note.py @@ -12,7 +12,6 @@ def main(): y = f(x) plt.rcParams['font.size'] = 20 - plt.figure() plt.title('Expected distribution', loc='right') plt.ylabel('$\\langle |P_v| \\rangle$') plt.xlabel('$P_h$') diff --git a/notes/images/fit.pdf b/notes/images/fit.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7299f99f0bec43e862a86f6d8ca4e7d2706d73e8 GIT binary patch literal 17733 zcmeHvcRZF~_`gDUkd;k6+56d#oxS%~M)ux&gp|=hWs^umRx*o3C3{6i5=FKU5?X%u z{YW2`@8{QdynerbzPyfe?sK1Uo$I>JIoG+)`^Kp)uPBHRLPI!Tcx( zk&uAGv@Pvzy`e}TM;8jy5A?8w!sIM{EM45~p(jp2Y~5_Yf+Bk=CG@dt*h0}e zl@#6Ge4wbEw6>*>kFA#*6azj&VA@{p)_S%+P!pgZc}1Xo+W;RZOvM%GQ}#zJ_ajz; znn7WD4wg2KZuU^@PIY4~D<@lPAE+>{@qfrcz=4e20m!(yx%+qnT>$mv08(t-fG5$N z29&(qeLbMyFy-{1FnL=)M{8SMC7^5IN#D!T&D+D$%ht_0aChW8<$;c18n!l$ma^^v zK)YaPq9Q0E3=)b#poK)hj~UbdF5kbsPV4ABIZ7OKc^TePmahe&;G z%(xgisE%LZIEOrfzBV1-m$Sze7z`sz?*}ssW@Xizf7hxjDGCeUm>aynzs#x>fXd)h zz4T(G-l{-3wI&hmzUX=W*x9W(&d!-<*D8)JlirfQJjoMiO@8Ir+4ZRLQC{xYUJjJm zNpW@e3$~xwga<#&w)6z`P>t0@BCME(fp!>$s#35sC6aC*@ZoRDB5IyJ?_hW&GHoP4 zFClite59G}s1)8Bt6HiNiSJWsmNow5xe#W~^_sE^EfkN>v`|mG5)=py&8MC&lQ=c& zSZ$1@TiHC2x@`RP%%nE4*-B?AL&G`g>>-oOuip|`)LWBy*Cr_&+!mcSA`8pg=)YH2 zb)gY#XLU(mH&X$)KoFRU>-_o$Dqzu`>eEBd1vI=8dx7uDZ(1ejOO`<^f}OhNm?HP;x%a zDzW0qBV26{6HS~&KH?Sovy6{&vlaW5o?=y8g}5H>+h{)eW4t}o#Cj`*1D3Yx84nBP z-xxm|2k8&=kAEqb7+>e)oBE7u`u(FWaA3!z)+E(Z?U&fpf(Pc|7kSAPPue(BMeoSv za>SU`I?zr$7xkm=zMt$dRdjH)1G>$#3BFhW(7uA>zUXMeSiVuo;l}$UuJS>n#?Rj2 zn6{>Q|2c;bjh=m_kXgOtag&;jNGQ>hb1#s#Gg! 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In this case: - - t = 0.278 - - p = 0.781 + - t = 0.295 + - p = 0.768 which allows to assert that the sampled points actually follow the predicted -distribution. +distribution. In @fig:fit, the fit function superimposed on the histogram is +shown. + +![Fitted data.](images/fit.pdf){#fig:fit}