import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from uncertainties import unumpy c_palette = sns.color_palette() red_palette = sns.color_palette("Reds") def eff(pairs, singles): p_u = unumpy.uarray(pairs, np.sqrt(pairs)) s_u = unumpy.uarray(singles, np.sqrt(singles)) return p_u / s_u def aom2freq(x): return ((x - 68) * 2) def duty_cycle(x): return x / 1.875 def brig(pairs, tau): return pairs * 2 * np.pi * tau * 1e-3 pscan = pd.read_csv('powerscans/power_scan2D.dat', delim_whitespace=True) pscan['Brig'] = brig(pscan.Pairs, pscan.tau) dscan = pd.read_csv('detunings/plot_data_tau.dat', delim_whitespace=True) dscan['delta'] = aom2freq(dscan['AOM_freq']) dscan['Delta'] = 60 dscan['Pump1'] = .45 dscan['Pump2'] = 15 dscan['Pairs_err'] = np.sqrt(dscan['Pairs']) dscan['Signal_err'] = np.sqrt(dscan['Signal']) dscan['Idler_err'] = np.sqrt(dscan['Idler']) eff_i_u = eff(dscan['Pairs'], dscan['Idler']) eff_s_u = eff(dscan['Pairs'], dscan['Signal']) dscan['eff_i'] = unumpy.nominal_values(eff_i_u) dscan['eff_s'] = unumpy.nominal_values(eff_s_u) dscan['eff_s_err'] = unumpy.std_devs(eff_s_u) dscan['eff_i_err'] = unumpy.std_devs(eff_i_u) dscan['OD'] = 29 dty = pd.read_csv('density/fwm_OD.dat', delim_whitespace=True) dty['eff_s'] = unumpy.nominal_values(eff(dty['Pairs'], dty['Signal'])) dty['eff_i'] = unumpy.nominal_values(eff(dty['Pairs'], dty['Idler'])) dty['eff_s_err'] = unumpy.std_devs(eff(dty['Pairs'], dty['Signal'])) dty['eff_i_err'] = unumpy.std_devs(eff(dty['Pairs'], dty['Idler'])) dty['delta'] = 12 dty['Delta'] = 60 dty['Pump2'] = 15 dty['Pump1'] = .3 new_t = pd.concat([pscan, dscan, dty]) # print(pscan.columns.values, dscan.columns.values, dty.columns.values) # print(new_t.columns.values) # print(new_t) # plt.plot(new_t['Pump2'], new_t['eff_i'], 'o-') # plt.plot(new_t['Pump2'], new_t['eff_s'], 'o-') # pivots a, b = pd.cut(pscan.tau, 5, retbins=True) pscan['tau_r'] = a ppiv = pscan.pivot(columns='tau_r', values='Pairs', index='eff_i') # ppiv.fillna(method='ffill').plot(style='o-') # ppiv.sort_valuesort_index().dropna(axis=1, how='all').plot(style='o-') # # plt.figure('Pair rate vs Tau') # plt.errorbar(pscan.tau, pscan.Pairs, # xerr=pscan.tau_err, yerr=pscan.Pairs_err, # fmt='o-', label='Power scan') # plt.errorbar(dscan.tau, dscan.Pairs, # xerr=dscan.tau_err, yerr=dscan.Pairs_err, # fmt='o', label='$\delta$ scan') # plt.errorbar(dty.tau, dty.Pairs, # xerr=dty.tau_err, yerr=dty.Pairs_err, # fmt='o', label='OD scan') # plt.legend() # plt.xlabel(r'$\tau$ (ns)') # plt.ylabel(r'pair rate (1/s)') # plt.figure('Eff_s vs Tau') # plt.errorbar(pscan.tau, pscan.eff_s, # xerr=pscan.tau_err, yerr=pscan.eff_s_err, # fmt='o', label='Power scan') # plt.errorbar(dscan.tau, dscan.eff_s, # xerr=dscan.tau_err, yerr=dscan.eff_s_err, # fmt='o', label='$\delta$ scan') # plt.errorbar(dty.tau, dty.eff_s, # xerr=dty.tau_err, yerr=dty.eff_s_err, # fmt='o', label='OD scan') # plt.legend() # plt.xlabel(r'$\tau$ (ns)') # plt.ylabel(r'$\eta_s$') # plt.figure('Eff_s vs spectral brightness') # plt.errorbar(pscan.Pairs * 2 * np.pi * pscan.tau * 1e-3, pscan.eff_s, # # xerr=pscan.tau_err, # yerr=pscan.eff_s_err, # fmt='o', label='Power scan') # plt.errorbar(dscan.Pairs * 2 * np.pi * dscan.tau * 1e-3, dscan.eff_s, # # xerr=dscan.tau_err, # yerr=dscan.eff_s_err, # fmt='o', label='$\delta$ scan') # plt.errorbar(dty.Pairs * 2 * np.pi * dty.tau * 1e-3, dty.eff_s, # # xerr=dty.tau_err, # yerr=dty.eff_s_err, # fmt='o', label='OD scan') # plt.legend() # plt.xlabel(r'brightness (MHz * s)$^{-1}$') # plt.ylabel(r'$\eta_s$') # plt.figure('Eff_s vs pair rate') # plt.errorbar(pscan.Pairs, pscan.eff_s, # # xerr=pscan.tau_err, # yerr=pscan.eff_s_err, # fmt='o', label='Power scan') # plt.errorbar(dscan.Pairs, dscan.eff_s, # # xerr=dscan.tau_err, # yerr=dscan.eff_s_err, # fmt='o', label='$\delta$ scan') # plt.errorbar(dty.Pairs, dty.eff_s, # # xerr=dty.tau_err, # yerr=dty.eff_s_err, # fmt='o', label='OD scan') # plt.legend() # plt.xlabel(r'pairs rate s$^{-1}$') # plt.ylabel(r'$\eta_s$') # plt.figure('spectral brightness vs power') # plt.errorbar(pscan.Pump2, pscan.Pairs * 2 * np.pi * pscan.tau * 1e-3, # # xerr=pscan.tau_err, yerr=pscan.eff_s_err, # fmt='o', label='Power scan') # plt.legend() # plt.xlabel(r'$P_2$ (mW)') # plt.ylabel(r'brightness (MHz * s)$^{-1}$') # plt.figure('spectral brightness vs OD') # plt.errorbar(dty.OD, dty.Pairs * 2 * np.pi * dty.tau * 1e-3, # # xerr=pscan.tau_err, yerr=pscan.eff_s_err, # fmt='o', label='Power scan') # plt.legend() # plt.xlabel(r'OD') # plt.ylabel(r'brightness (MHz * s)$^{-1}$') # plt.figure('spectral brightness vs detuning') # plt.errorbar(dscan.delta, dscan.Pairs * 2 * np.pi * dscan.tau * 1e-3, # # xerr=pscan.tau_err, yerr=pscan.eff_s_err, # fmt='o', label='Power scan') # plt.legend() # plt.xlabel(r'$\delta$ (MHz)') # plt.ylabel(r'brightness (MHz * s)$^{-1}$') replacements = {'Pump2': r'$P_2$ (mW)', 'Brig': r'brightness (MHz * s)$^{-1}$', 'tau': r'$\tau$ (ns)', 'eff_s': r'$\eta_s$', 'delta': r'$\delta$ (MHz)'} pscan[['Pump2', 'Brig', 'tau', 'eff_s', 'eff_i', 'tau_err', 'eff_s_err', 'eff_i_err', 'Pump1']].to_csv('Power_data_table.dat', sep='\t') g = sns.PairGrid(pscan, vars=['Pump2', 'Brig', 'tau', 'eff_s'], hue='Pump1', palette=red_palette) g.map_lower(plt.scatter) for i, j in np.ndindex(np.shape(g.axes)): xlabel = g.axes[i][j].get_xlabel() ylabel = g.axes[i][j].get_ylabel() if xlabel in replacements.keys(): g.axes[i][j].set_xlabel(replacements[xlabel]) if ylabel in replacements.keys(): g.axes[i][j].set_ylabel(replacements[ylabel]) for i, j in zip(*np.triu_indices_from(g.axes, 0)): # plt.delaxes(g.axes[i, j]) g.axes[i, j].axis('off') g.add_legend() for i in range(len(g.fig.get_children()[-1].texts)): label = g.fig.get_children()[-1].texts[i].get_text() g.fig.get_children()[-1].texts[i].set_text(label + ' mW') # if label in replacements.keys(): # g.fig.get_children()[-1].texts[i].set_text(replacements[label]) g.fig.get_children()[-1].set_bbox_to_anchor((.7, 0.6, 0, 0)) plt.tight_layout() dscan['Brig'] = brig(dscan.Pairs, dscan.tau) dscan['color'] = np.int_(dscan.delta > dscan.delta[np.argmax(dscan.Pairs)]) dscan[['delta', 'Brig', 'tau', 'eff_s', 'color', 'eff_i', 'tau_err', 'eff_s_err', 'eff_i_err']].to_csv('det_data_table.dat', sep='\t', encoding='utf-8') d = sns.PairGrid(dscan, vars=['delta', 'Brig', 'tau', 'eff_s'], hue='color', palette="Set1", hue_order=[0, 1]) d.map_lower(plt.scatter) for i, j in np.ndindex(np.shape(d.axes)): xlabel = d.axes[i][j].get_xlabel() ylabel = d.axes[i][j].get_ylabel() if xlabel in replacements.keys(): d.axes[i][j].set_xlabel(replacements[xlabel]) if ylabel in replacements.keys(): d.axes[i][j].set_ylabel(replacements[ylabel]) for i, j in zip(*np.triu_indices_from(d.axes, 0)): plt.delaxes(d.axes[i, j]) plt.tight_layout() dty['Brig'] = brig(dty.Pairs, dty.tau) dty[['OD', 'Brig', 'tau', 'eff_s', 'eff_i', 'tau_err', 'eff_s_err', 'eff_i_err']].to_csv('OD_data_table.dat', sep='\t', encoding='utf-8') r = sns.PairGrid(dty, vars=['OD', 'Brig', 'tau', 'eff_s']) r.map_lower(plt.scatter) for i, j in np.ndindex(np.shape(r.axes)): xlabel = r.axes[i][j].get_xlabel() ylabel = r.axes[i][j].get_ylabel() if xlabel in replacements.keys(): r.axes[i][j].set_xlabel(replacements[xlabel]) if ylabel in replacements.keys(): r.axes[i][j].set_ylabel(replacements[ylabel]) for i, j in zip(*np.triu_indices_from(r.axes, 0)): plt.delaxes(r.axes[i, j]) plt.subplots_adjust() plt.tight_layout() plt.savefig('od_table.pdf') # plt.show()