diff --git a/DyLevelStructure/result.pdf b/DyLevelStructure/result.pdf index b149646..2a7aeca 100644 Binary files a/DyLevelStructure/result.pdf and b/DyLevelStructure/result.pdf differ diff --git a/DyLevelStructure/visualizeDyLevelStructureWithTransitions.py b/DyLevelStructure/visualizeDyLevelStructureWithTransitions.py index fb0b240..852a010 100644 --- a/DyLevelStructure/visualizeDyLevelStructureWithTransitions.py +++ b/DyLevelStructure/visualizeDyLevelStructureWithTransitions.py @@ -5,7 +5,7 @@ import pandas as pd import seaborn as sns import matplotlib.pyplot as plt -sns.set_theme(style="white") +sns.set_theme(style="ticks") def parse_NIST_data(path, min_J, max_J, max_wavenumber): @@ -17,7 +17,7 @@ def parse_NIST_data(path, min_J, max_J, max_wavenumber): #collect data Parity = np.zeros(len(data_list)) J = np.zeros(len(data_list)) - wavenumber = np.zeros(len(data_list)) + Wavenumber = np.zeros(len(data_list)) for i in range(1, len(data_list)): try: tmp = data_list[:][i] @@ -26,42 +26,45 @@ def parse_NIST_data(path, min_J, max_J, max_wavenumber): elif not tmp[0] == '': Parity[i] = 0 J[i] = int(tmp[1]) - wavenumber[i] = float(tmp[3]) - if wavenumber[i] > max_wavenumber: - J[i] = np.nan - wavenumber[i] = np.nan + Wavenumber[i] = float(tmp[3]) except ValueError: J[i] = np.nan - wavenumber[i] = np.nan + Wavenumber[i] = np.nan remove_idxs = [] for i in range(1, len(data_list)): p = Parity[i] j = J[i] - wn = wavenumber[i] + wn = Wavenumber[i] if np.isnan(p) or np.isnan(j) or np.isnan(wn): remove_idxs.append(i) Parity = np.delete(Parity, remove_idxs) J = np.delete(J, remove_idxs) - wavenumber = np.delete(wavenumber, remove_idxs) + Wavenumber = np.delete(Wavenumber, remove_idxs) #sort data sorting_indices = np.argsort(J) Parity = Parity[sorting_indices] J = J[sorting_indices] - wavenumber = wavenumber[sorting_indices] + Wavenumber = Wavenumber[sorting_indices] - # splice data to within user-defined range + # splice data to within user-defined range of Js splice_idx_start = np.where(J==min_J)[0][0] splice_idx_stop = len(J) - 1 - np.where(J[::-1]==max_J)[0][0] Parity = Parity[splice_idx_start:splice_idx_stop] J = J[splice_idx_start:splice_idx_stop] - wavenumber = wavenumber[splice_idx_start:splice_idx_stop] + Wavenumber = Wavenumber[splice_idx_start:splice_idx_stop] + + # splice data to within user-defined range of Wavenumbers + splice_idxs = [i for i in range(len(Wavenumber)) if Wavenumber[i] > max_wavenumber] + Parity = [ele for idx, ele in enumerate(Parity) if idx not in splice_idxs] + J = [ele for idx, ele in enumerate(J) if idx not in splice_idxs] + Wavenumber = [ele for idx, ele in enumerate(Wavenumber) if idx not in splice_idxs] # Create a Pandas data frame with the data - dataset = pd.DataFrame(np.array(list(zip(Parity, J, wavenumber))), columns=['Parity', 'J', 'Wavenumber']) + dataset = pd.DataFrame(np.array(list(zip(Parity, J, Wavenumber))), columns=['Parity', 'J', 'Wavenumber']) return dataset @@ -73,15 +76,16 @@ def plot_level_structure_with_red_and_blue_transitions(*args, **kwargs): Red_Blue_colors = ['#ab162a', '#cf5246', '#eb9172', '#fac8af', '#faeae1', '#e6eff4', '#bbdaea', '#7bb6d6', '#3c8abe', '#1e61a5'] #draw levels - plot_handle = sns.scatterplot(x='J', y='Wavenumber', data = dataframe, s=500, hue = 'Parity', palette = sns.color_palette(named_colors), marker = '_', linewidth=1, legend=False) + plot_handle = sns.scatterplot(x='J', y='Wavenumber', data = dataframe, s=2000, hue = 'Parity', palette = sns.color_palette(named_colors), marker = '_', linewidth=1.5, legend=False) #write electronic configuration for GS ax.text(gs_J + 0.15, gs_wavenumber + 400, '$6s^2$') #draw guide line for GS - plt.axhline(y=gs_wavenumber, color='m', linestyle='--', linewidth=1, alpha=0.5) + #plt.axhline(y=gs_wavenumber, color='m', linestyle='--', linewidth=1, alpha=0.5) #write wavelength of red transition - ax.text(red_J - 0.4, red_wavenumber * 0.5, '$626.082 ~ \mathrm{nm}$') + ax.text(red_J - 0.4, red_wavenumber * 0.5, '$626.082 ~ \mathrm{nm}$', color = '#db2929') + ax.text(red_J - 0.4, red_wavenumber * 0.46, '$(\\Gamma = 2\\pi\\times 136 ~ \mathrm{kHz})$', fontsize = 8, color = '#db2929') #draw red transition arrow ax.annotate('', xy=(red_J, red_wavenumber), @@ -91,12 +95,13 @@ def plot_level_structure_with_red_and_blue_transitions(*args, **kwargs): verticalalignment='top') #write electronic configuration for triplet excited state - ax.text(red_J + 0.18, red_wavenumber + 400, '$6s6p(^3P_1)$') + ax.text(red_J + 0.35, red_wavenumber + 200, '$6s6p(^3P_1)$', fontsize = 10) #draw guide line for triplet excited state plt.axhline(y=red_wavenumber, color='m', linestyle='--', linewidth=1, alpha=0.5) #write wavelength of red transition - ax.text(blue_J - 1.5, blue_wavenumber * 0.55, '$421.291~ \mathrm{nm}$') + ax.text(blue_J - 1.5, blue_wavenumber * 0.55, '$421.291~ \mathrm{nm}$', color = '#2630ea') + ax.text(blue_J - 1.55, blue_wavenumber * 0.52, '$(\\Gamma = 2\\pi\\times 32.2 ~ \mathrm{MHz})$', fontsize = 8, color = '#2630ea') #draw blue transition arrow ax.annotate('', xy=(blue_J, blue_wavenumber), @@ -106,18 +111,36 @@ def plot_level_structure_with_red_and_blue_transitions(*args, **kwargs): verticalalignment='top') #write electronic configuration for singlet excited state - ax.text(blue_J + 0.18, blue_wavenumber + 400, '$6s6p(^1P_1)$') + ax.text(blue_J + 0.35, blue_wavenumber + 200, '$6s6p(^1P_1)$', fontsize = 10) #draw guide line for singlet excited state plt.axhline(y=blue_wavenumber, color='m', linestyle='--', linewidth=1, alpha=0.5) #figure options + f.canvas.draw() plt.xlabel('$J$', fontsize=16) plt.ylabel('$\\tilde{v}~(cm^{-1})$', fontsize=16) - #plt.title('Dysprosium I Energy Level Structure', fontsize=20) + plt.ylabel('$\\lambda~(nm)$', fontsize=16) + plot_handle.set_xticks(range(min_J-1, max_J+2)) - plt.tick_params(axis='both', which='major', labelsize=12) ax.get_xticklabels()[0].set_visible(False) ax.get_xticklabels()[-1].set_visible(False) + ax.get_xticklines()[0].set_visible(False) + ax.get_xticklines()[-2].set_visible(False) + + yticklabels = [item.get_text() for item in ax.get_yticklabels()] + yticklabels = ['' if item.startswith('−') or item.startswith('0') else item for item in yticklabels] + yticks = [float(item) if item != '' else 0.0 for item in yticklabels] + new_yticks = np.arange(min(yticks), max(yticks), 4000) + plot_handle.set_yticks(new_yticks) + new_yticklabels = [round(1e7/item) if item != 0 else item for item in new_yticks] + ax.set_yticklabels(new_yticklabels) + ax.get_yticklabels()[0].set_visible(False) + ax.get_yticklabels()[-1].set_visible(False) + ax.get_yticklines()[0].set_visible(False) + ax.get_yticklines()[-2].set_visible(False) + + plt.tick_params(axis='both', which='major', labelsize=14) + #plt.show() f.savefig(Path(home_path + os.sep + 'result.pdf'), format='pdf', bbox_inches = "tight")