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174 lines
6.6 KiB
174 lines
6.6 KiB
import os, csv
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from pathlib import Path
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import numpy as np
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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sns.set_theme(style="ticks")
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def parse_NIST_data(path, min_J, max_J, max_wavenumber):
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data_list = []
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with open(path) as f:
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reader = csv.reader(f, delimiter="\t")
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data_list = list(reader)
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#collect data
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Parity = np.zeros(len(data_list))
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J = np.zeros(len(data_list))
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Wavenumber = np.zeros(len(data_list))
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for i in range(1, len(data_list)):
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try:
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tmp = data_list[:][i]
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if not tmp[0] == '' and tmp[0].find('*') != -1:
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Parity[i] = 1
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elif not tmp[0] == '':
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Parity[i] = 0
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J[i] = int(tmp[1])
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Wavenumber[i] = float(tmp[3])
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except ValueError:
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J[i] = np.nan
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Wavenumber[i] = np.nan
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remove_idxs = []
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for i in range(1, len(data_list)):
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p = Parity[i]
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j = J[i]
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wn = Wavenumber[i]
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if np.isnan(p) or np.isnan(j) or np.isnan(wn):
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remove_idxs.append(i)
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Parity = np.delete(Parity, remove_idxs)
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J = np.delete(J, remove_idxs)
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Wavenumber = np.delete(Wavenumber, remove_idxs)
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#sort data
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sorting_indices = np.argsort(J)
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Parity = Parity[sorting_indices]
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J = J[sorting_indices]
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Wavenumber = Wavenumber[sorting_indices]
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# splice data to within user-defined range of Js
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splice_idx_start = np.where(J==min_J)[0][0]
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splice_idx_stop = len(J) - 1 - np.where(J[::-1]==max_J)[0][0]
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Parity = Parity[splice_idx_start:splice_idx_stop]
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J = J[splice_idx_start:splice_idx_stop]
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Wavenumber = Wavenumber[splice_idx_start:splice_idx_stop]
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# splice data to within user-defined range of Wavenumbers
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splice_idxs = [i for i in range(len(Wavenumber)) if Wavenumber[i] > max_wavenumber]
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Parity = [ele for idx, ele in enumerate(Parity) if idx not in splice_idxs]
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J = [ele for idx, ele in enumerate(J) if idx not in splice_idxs]
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Wavenumber = [ele for idx, ele in enumerate(Wavenumber) if idx not in splice_idxs]
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# Create a Pandas data frame with the data
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dataset = pd.DataFrame(np.array(list(zip(Parity, J, Wavenumber))), columns=['Parity', 'J', 'Wavenumber'])
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return dataset
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def plot_level_structure_with_red_and_blue_transitions(*args, **kwargs):
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#start plotting
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f, ax = plt.subplots(figsize=(4, 8))
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named_colors = ['r', 'm']
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Red_Blue_colors = ['#ab162a', '#cf5246', '#eb9172', '#fac8af', '#faeae1', '#e6eff4', '#bbdaea', '#7bb6d6', '#3c8abe', '#1e61a5']
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#draw levels
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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)
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#write electronic configuration for GS
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ax.text(gs_J + 0.15, gs_wavenumber + 400, '$6s^2$')
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#draw guide line for GS
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#plt.axhline(y=gs_wavenumber, color='m', linestyle='--', linewidth=1, alpha=0.5)
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#write wavelength of red transition
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ax.text(red_J - 0.4, red_wavenumber * 0.5, '$626.082 ~ \mathrm{nm}$', color = '#db2929')
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ax.text(red_J - 0.4, red_wavenumber * 0.46, '$(\\Gamma = 2\\pi\\times 136 ~ \mathrm{kHz})$', fontsize = 8, color = '#db2929')
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#draw red transition arrow
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ax.annotate('',
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xy=(red_J, red_wavenumber),
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xytext=(gs_J, gs_wavenumber),
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arrowprops=dict(color='#db2929', alpha=0.8, width=1.5),
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horizontalalignment='right',
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verticalalignment='top')
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#write electronic configuration for triplet excited state
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ax.text(red_J + 0.35, red_wavenumber + 200, '$6s6p(^3P_1)$', fontsize = 10)
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#draw guide line for triplet excited state
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plt.axhline(y=red_wavenumber, color='m', linestyle='--', linewidth=1, alpha=0.5)
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#write wavelength of red transition
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ax.text(blue_J - 1.5, blue_wavenumber * 0.55, '$421.291~ \mathrm{nm}$', color = '#2630ea')
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ax.text(blue_J - 1.55, blue_wavenumber * 0.52, '$(\\Gamma = 2\\pi\\times 32.2 ~ \mathrm{MHz})$', fontsize = 8, color = '#2630ea')
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#draw blue transition arrow
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ax.annotate('',
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xy=(blue_J, blue_wavenumber),
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xytext=(gs_J, gs_wavenumber),
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arrowprops=dict(color='#2630ea', alpha=0.8, width=3.5),
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horizontalalignment='right',
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verticalalignment='top')
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#write electronic configuration for singlet excited state
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ax.text(blue_J + 0.35, blue_wavenumber + 200, '$6s6p(^1P_1)$', fontsize = 10)
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#draw guide line for singlet excited state
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plt.axhline(y=blue_wavenumber, color='m', linestyle='--', linewidth=1, alpha=0.5)
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#figure options
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f.canvas.draw()
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plt.xlabel('$J$', fontsize=16)
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plt.ylabel('$\\tilde{v}~(cm^{-1})$', fontsize=16)
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plt.ylabel('$\\lambda~(nm)$', fontsize=16)
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plot_handle.set_xticks(range(min_J-1, max_J+2))
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ax.get_xticklabels()[0].set_visible(False)
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ax.get_xticklabels()[-1].set_visible(False)
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ax.get_xticklines()[0].set_visible(False)
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ax.get_xticklines()[-2].set_visible(False)
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yticklabels = [item.get_text() for item in ax.get_yticklabels()]
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yticklabels = ['' if item.startswith('−') or item.startswith('0') else item for item in yticklabels]
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yticks = [float(item) if item != '' else 0.0 for item in yticklabels]
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new_yticks = np.arange(min(yticks), max(yticks), 4000)
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plot_handle.set_yticks(new_yticks)
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new_yticklabels = [round(1e7/item) if item != 0 else item for item in new_yticks]
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ax.set_yticklabels(new_yticklabels)
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ax.get_yticklabels()[0].set_visible(False)
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ax.get_yticklabels()[-1].set_visible(False)
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ax.get_yticklines()[0].set_visible(False)
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ax.get_yticklines()[-2].set_visible(False)
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plt.tick_params(axis='both', which='major', labelsize=14)
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#plt.show()
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f.savefig(Path(home_path + os.sep + 'result.pdf'), format='pdf', bbox_inches = "tight")
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if __name__ == '__main__':
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min_J = 8
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max_J = 9
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max_wavenumber = 25500.0
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#Ground State: [Xe]4f^{10} 6s^2(^5I_8)
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gs_J = 8
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gs_wavenumber = 0.0
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#626 nm transition GS ---> [Xe]4f^{10}(^5I_8) 6s6p(^3P_1)(8,1)_9
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red_J = 9
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red_wavenumber = 15972.35
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#421 nm transition GS ---> [Xe]4f^{10}(^5I_8) 6s6p(^1P_1)(8,1)_9
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blue_J = 9
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blue_wavenumber = 23736.610
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NIST_Dy_level_data_filename= 'Dylevels.txt'
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home_path = str(Path(__file__).parent.resolve())
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dataframe = parse_NIST_data(home_path + os.sep + NIST_Dy_level_data_filename, min_J, max_J, max_wavenumber)
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plot_level_structure_with_red_and_blue_transitions(dataframe, gs_J, gs_wavenumber, red_J, red_wavenumber, blue_J, blue_wavenumber, home_path)
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