def plot_u1(f, amps, show=False, save=False): fig = plt.figure(figsize=(3, 1.85)) ax = fig.add_subplot(111) df = sb.dataframe_from_csv(f"../data/raw/{f}.txt", sep=" ", header=None) ax.plot(df[0], df[1], "k") ax.set_xlabel(r"$U$ [V]") ax.set_ylabel(r"$I$ [A]") # ax.legend() ax.set_title(f"$I_{{mag}} = \SI{{{amps}}}{{A}}$") ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0)) fig.tight_layout() if show: plt.show() if save: f_ = f.replace(".", "") fig.savefig(f"../fig/u1_{f_}.pdf") plt.close(fig)
ax.plot(x, calibration_curve(x)) ax.set_xlabel(r"Kanál") ax.set_ylabel(r"E [keV]") ax.legend() fig.tight_layout() if show: plt.show() if save: fig.savefig("../fig/calibration.pdf") plt.close(fig) df_kalib = sb.dataframe_from_csv("../data/kalibrace_ra226.Spe", header=None) df_cesium = sb.dataframe_from_csv("../data/cs137.Spe", header=None) df_sul = sb.dataframe_from_csv("../data/sul.Spe", header=None) df = pd.DataFrame() df["E"] = calibration_curve(df_kalib.index.values) df["ra"] = df_kalib[0] df["cs"] = df_cesium[0] df["na"] = df_sul[0] def plot_spectra(show=False, save=False): with sb.latex_style(): fig, axs = plt.subplots(nrows=3, figsize=(11, 8.5))
"1.6A_5Vs": 1.602, "1.7A_5Vs": 1.701, "1.8A_5Vs": 1.791, "1.9A_5Vs": 1.889, "2A_5Vs": 2.01 } def make_u1_plots(): for f, a in files_amps.items(): plot_u1(f, a, False, True) u2 = sb.dataframe_from_csv("../data/u2.csv") u2 = u2.sort_values("I_mag") def plot_u2(show=False, save=False): fig = plt.figure() ax = fig.add_subplot(111) fit = sb.FitCurve(sb.f_line, u2.I_mag**2, u2.U_kr) ax.plot(*fit.curve(), ":", c="gray", label="lineární fit") ax.plot(u2.I_mag**2, u2.U_kr, "kx", label="kritická napětí") ax.set_xlabel(r"$ I_{mag}^2 [\si{\ampere\squared}] $") ax.set_ylabel(r"$ U_{kr}$ [V]")
from custom_utils.science.imports import * from custom_utils.science import basics as sb sb.use_mpl_latex_style() df = sb.dataframe_from_csv("../data/u4.csv") print(df) cp = [["count", "Počet událostí", "", "4.0"], ["sigma", r"Chyba střední hodnoty $\sigma$", "", "1.3"], ["err", "Chyba chyby", "", "1.3"]] # print(sb.df_to_booktabs_table(df, cp)) def plot_u5(show=False, save=False): fig = plt.figure(figsize=[6, 3]) ax = fig.add_subplot(111) ax.errorbar(df["count"], df["sigma"], yerr=df["err"], fmt="kx", capsize=3, elinewidth=0.5) ax.set_xlabel(r"Počet událostí") ax.set_ylabel(r"Chyba střední hodnoty $\sigma$") # ax.legend() fig.tight_layout()
from custom_utils.science.imports import * from custom_utils.science import basics as sb from custom_utils.math.uncertainty import up_function u3 = sb.dataframe_from_csv("../data/u3.csv") def plot_u3(show=False, save=False): fig = plt.figure() ax = fig.add_subplot(111) fit40 = sb.FitCurve(sb.f_line, u3.I_40**2, u3.U_c) ax.plot(*fit40.curve(), ":", c="grey") ax.plot(u3.I_40**2, u3.U_c, "x", c="grey", label="$r = \SI{20}{mm}$") fit60 = sb.FitCurve(sb.f_line, u3.I_60**2, u3.U_c) ax.plot(*fit60.curve(), ":", c="C3") ax.plot(u3.I_60**2, u3.U_c, "x", c="C3", label="$r = \SI{30}{mm}$") fit80 = sb.FitCurve(sb.f_line, u3.I_80**2, u3.U_c) ax.plot(*fit80.curve(), ":", c="C1") ax.plot(u3.I_80**2, u3.U_c, "x", c="C1", label="$r = \SI{40}{mm}$") fit100 = sb.FitCurve(sb.f_line, u3.I_100**2, u3.U_c) ax.plot(*fit100.curve(), ":", c="C0") ax.plot(u3.I_100**2, u3.U_c, "x", c="C0", label="$r = \SI{50}{mm}$") ax.set_xlabel(r"$I_{mag}^2$ [A]") ax.set_ylabel(r"$U_c$ [V]")
from custom_utils.science.imports import * from custom_utils.science import basics as sb from scipy.constants import c u1 = sb.dataframe_from_csv("../data/u1.csv") u3 = sb.dataframe_from_csv("../data/data_planck.csv") sb.use_mpl_latex_style() def plot_u1(show=False, save=False): fig = plt.figure() ax = fig.add_subplot(111) ax.plot(u1.U, u1.GKV, "k", label="závislost vakuové fotonky GKV") ax.plot(u1.U, u1.GKE, c="grey", label="závislost plynové fotonky GKE") ax.set_xlabel(r"U[V]") ax.set_ylabel(r"I[nA]") ax.legend() fig.tight_layout() if show: plt.show() if save: fig.savefig("../plot/u1.pdf") def plot_u3_578(show=False, save=False): fig = plt.figure() ax = fig.add_subplot(111)
from custom_utils.science.imports import * from custom_utils.science import basics as sb from scipy.constants import h co = sb.dataframe_from_csv("../data/DERNER_CO.PRN", sep=" ", header=None) def plot_co(show=False, save=False): fig = plt.figure() ax = fig.add_subplot(111) ax.plot(co[0], co[1], "k", lw=0.5) ax.set_xlabel(r"$\nu[\si{\per\centi\metre}]$") ax.set_ylabel(r"Transmitance") # ax.legend() ax.autoscale(True, "x", True) fig.tight_layout() if show: plt.show() if save: fig.savefig("../fig/co.pdf") plt.close(fig) u1 = sb.dataframe_from_csv("../data/Tomas_Derner_CO.csv") u1 = pd.DataFrame({"Wavenumber": u1.Wavenumber.values})
from custom_utils.science.imports import * from custom_utils.science import basics as sb sb.use_mpl_latex_style() u2 = sb.dataframe_from_csv("../data/u2.csv") u3 = sb.dataframe_from_csv("../data/u3.csv") u6 = sb.dataframe_from_csv("../data/u6.csv") def plot_u2(show=False, save=False): fig = plt.figure(figsize=(3.7, 2.5)) ax = fig.add_subplot(111) ax.plot(u2.U, u2.I_6cm * 1e3, "+", c="k", label="$d = \SI{6}{cm}$") ax.plot(u2.U, u2.I_2cm * 1e3, "+", c="grey", label="$d = \SI{2}{cm}$") ax.set_xlabel(r"$U[\si{V}]$") ax.set_ylabel(r"$I[\si{pA}]$") ax.legend() fig.tight_layout() if show: plt.show() if save: fig.savefig("../fig/u2.pdf") plt.close(fig)