# file to evaluate a measurement import os.path, glob from os.path import join import json import pandas as pd # from bec_analysis.settings import GROUP_STORAGE_PATH, LOCAL_GROUP_STORAGE import numpy as np import matplotlib.pyplot as pl # from bec_analysis.evaluation.shapiro_steps.shapiro_steps_batch import * # from bec_experiment.bec_style import * # from bec_analysis.calibration.barrier_height_calibration import * # from bec_analysis.calibration.chemical_potential import * # from helper import * from style import * # path to data path_data_repo = "../Fig_1" # def get_meas_data(meas): # # path_exp = join(path_data_repo, meas["path"]) # # Read Experiment Data # data = pd.read_csv(path_exp) # # data = data[data["discard"] != True] # # data = data[data["sum_fit_offsetcomp"] >= 50000] # # # remove reference # # data = data[data[SEGMENT_MULTIPLIER] == 1] # # data = data[data[Z_REF] > -1] # # # # data["chem_pot"] = mu_N(data["N"] * (1 + data['delta_z'])) - mu_N( # data["N"] * (1 - data['delta_z'])) # # print(len(data["chem_pot"])) # # f = data["f"].to_numpy()[0] # # I_m = data["I_m"].to_numpy()[0] # I_m / I_c # # I_c = current(1, v_c=1) # # # if store: # # # save to data repo # # df = pd.DataFrame(data={'vel': data[V]*scaling, # # 'delta_z': data[DELTA_Z], # # 'N': data["sum_fit_offsetcomp"], # # 'I_m': I_m/I_c, # # 'f': f}) # # df = df.set_index('vel') # # # # df.to_csv(os.path.join(path_data_repo, name_csv)) # # data = data[["delta_z", "vel", "chem_pot"]] # grouped = data.groupby(by="vel").agg(['mean', 'sem']) # # vel = grouped.index.to_numpy() # z = grouped['delta_z', 'mean'] # z_err = grouped['delta_z', 'sem'] # # mu = grouped['chem_pot', 'mean'] # mu_err = grouped['chem_pot', 'sem'] # # I = current(vel, v_c=0.42) # # return vel, I, mu, mu_err, I_m, f fig, ax = pl.subplot_mosaic( [["a", "a"], ["a", "a"], ["b", "c"]], layout="constrained", figsize=(7. / 2 * 1.5, 3.8 * 1.5)) meas_list = [ { "path": "Exp_Shapiro_steps_f_90_Hz_I_m_0.0.csv", "mod_amp": 0., "color": "#2e2e41", }, { "path": "Exp_Shapiro_steps_f_90_Hz_I_m_1.0.csv", "mod_amp": 0.7, "color": "navy", }, { "path": "Exp_Shapiro_steps_f_90_Hz_I_m_1.6.csv", "mod_amp": 1.2, "color": "#fb6d10" }, ] n = len(meas_list) colors = [RPTU_COLORS[c] for c in ["nacht", "schiefer", "mango", ]] ##### A ###### for i, meas in enumerate(meas_list): I_c = current(1, v_c=1) # get the critical current from the func keyword parameter v_max = 0.7 vel, I, mu, mu_err, I_m, f = get_meas_data(meas["path"], path_data_repo = path_data_repo) style = get_style(colors[i]) if I_m == 0: ax["a"].errorbar(I[vel <= v_max] / I_c, mu[vel <= v_max], mu_err[vel <= v_max], **style, label=fr'$I_\mathrm{{m}} = {0}$') else: ax["a"].errorbar(I[vel <= v_max] / I_c, mu[vel <= v_max], mu_err[vel <= v_max], **style, label=fr'$I_\mathrm{{m}} = {I_m / I_c:.1f}$ $I_\mathrm{{c}}$') ax["a"].axhline(y=90, color='dimgrey', ls='--') ax["a"].axhline(y=180, color='dimgrey', ls='--') ax["a"].annotate(text='$1 \cdot f_\mathrm{m}$', xy=(0, 90), verticalalignment='center', fontsize=12, bbox={'boxstyle': 'round', 'fc': "white", 'ec': 'dimgrey', 'ls': '-', 'lw': 1.5}, zorder=5) ax["a"].annotate(text='$2 \cdot f_\mathrm{m}$', xy=(0, 2 * 90), verticalalignment='center', fontsize=12, bbox={'boxstyle': 'round', 'fc': "white", 'ec': 'dimgrey', 'ls': '-', 'lw': 1.5}, zorder=5) # label = r'$n \cdot f_\mathrm{m}$' ax["a"].set_ylim(top=264) ax["a"].get_yaxis().set_ticks([0, 100, 200]) ax["a"].get_yaxis().set_ticklabels([0, 100, 200], fontsize=12) ax["a"].get_xaxis().set_ticks([0, 0.5, 1, 1.5]) ax["a"].get_xaxis().set_ticklabels([0, 0.5, 1, 1.5], fontsize=12) ##### B ############ data = { "path": "Exp_Shapiro_steps_f_90_Hz_I_m_1.0.csv", "mod_amp": 0.7, } vel, I, mu, mu_err, I_m, f = get_meas_data(data["path"], path_data_repo = path_data_repo) ax["b"].errorbar(I[vel <= v_max] / I_c, mu[vel <= v_max], mu_err[vel <= v_max], **get_style(RPTU_COLORS["schiefer"]), label=fr'$I_\mathrm{{m}} = {I_m / I_c:.1f}$ $I_\mathrm{{c}}$') ax["b"].axhline(y=90, color='dimgrey', ls='--') ax["b"].axhline(y=180, color='dimgrey', ls='--') ax["b"].annotate(text='$1 \cdot f_\mathrm{m}$', xy=(0, 90), verticalalignment='center', fontsize=8, bbox={'boxstyle': 'round', 'fc': "white", 'ec': 'dimgrey', 'ls': '-', 'lw': 1.5}, zorder=5) # plot b experiment theory comparison tab_b = pd.read_csv(join(path_data_repo, "Sim_Shapiro_steps_f_90_Hz_I_m_1.0.csv")) vel = tab_b["I/I_c"].to_numpy() chem_pot_data = tab_b["chem_pot"].to_numpy() I_m = tab_b["I_m"].to_numpy()[0] ax['b'].errorbar(vel, chem_pot_data, **get_style(RPTU_COLORS["himbeere"], ls='-', marker='.'), label=fr"Sim. {I_m:.1f} $I_c$") ##### C ###### # Simulation data sim_list_names = ["Sim_Shapiro_steps_f_90_Hz_I_m_0.0.csv", "Sim_Shapiro_steps_f_90_Hz_I_m_0.5.csv", "Sim_Shapiro_steps_f_90_Hz_I_m_0.7.csv", "Sim_Shapiro_steps_f_90_Hz_I_m_1.0.csv", "Sim_Shapiro_steps_f_90_Hz_I_m_1.4.csv",] sim_list_mod_amps = np.asarray([0, 0.4, 0.5, 0.75, 1]) * 1e-3 # modulation amplitudes in µm n = len(sim_list_names) colors = rptu_centered(np.linspace(0.2, 0.8, n)) # plot c theory for i, name in enumerate(sim_list_names): table = pd.read_csv(join(path_data_repo, name)) vel = table["I/I_c"].to_numpy() chem_pot_data = table["chem_pot"].to_numpy() I_m = table["I_m"].to_numpy()[0] ax['c'].errorbar(vel, chem_pot_data, **get_style(colors[i], ls='-', marker='.'), label=fr"Sim. {I_m:.1f} $I_c$") ax["c"].axhline(y=90, color='dimgrey', ls='--') ax["c"].axhline(y=2 * 90, color='dimgrey', ls='--') ax["c"].axhline(y=3 * 90, color='dimgrey', ls='--') ax["a"].set_xlim(right=1.7) ax["a"].set_xlabel(r"Current $I \, / \, I_\mathrm{c}$") ax["a"].set_ylabel(r" $\Delta \mu \, / \, h$ [Hz] ") ax["a"].legend(loc='upper left') ax["b"].set_xlim(right=1.65) ax["b"].legend(loc='upper left', fontsize=8) ax["b"].set_xlabel(r" $I \, / \, I_\mathrm{c}$") ax["b"].set_ylabel("$\Delta \mu \, / \, h$ [Hz]") ax["c"].set_xlim(right=1.85) ax["c"].legend(loc='upper left', fontsize=6) ax["c"].set_xlabel(r" $I \, / \, I_\mathrm{c}$") ax["c"].set_ylabel(r"$\Delta \mu \, / \, h$ [Hz]") fig.tight_layout() pl.show() pl.close()