Major folder renaming.

Dy-Level-Structure/DyLevels.txt

@@ -0,0 +1,740 @@
+Term	J	Prefix	Level (cm-1)	Suffix
+"5I"	"8"	""	"0.000"	""	
+"5I"	"7"	""	"4134.222"	""	
+"5I"	"6"	""	"7050.603"	""	
+"5I"	"5"	""	"9211.591"	""	
+"5I"	"4"	""	"10925.277"	""	
+"7H*"	"2"	""	""	""	
+"7H*"	"8"	""	"7565.610"	""	
+"7H*"	"7"	""	"8519.210"	""	
+"7H*"	"6"	""	"10088.802"	""	
+"7H*"	"5"	""	"12298.551"	""	
+"7H*"	"4"	""	"13952.001"	""	
+"7H*"	"3"	""	"15254.936"	""	
+"7I*"	"3"	""	""	""	
+"7I*"	"4"	""	""	""	
+"7I*"	"9"	""	"9990.974"	""	
+"7I*"	"8"	""	"12007.121"	""	
+"7I*"	"7"	""	"14367.806"	""	
+"7I*"	"6"	""	"14970.701"	""	
+"7I*"	"5"	""	"16684.731"	""	
+"7F*"	"6"	""	"11673.49"	""	
+"7G*"	"7"	""	"12655.13"	""	
+"7K*"	"4"	""	""	""	
+"7K*"	"5"	""	""	""	
+"7K*"	"10"	""	"12892.76"	""	
+"7K*"	"8"	""	"16288.73"	""	
+"7K*"	"9"	""	"16717.79"	""	
+"7K*"	"7"	""	"17687.90"	""	
+"7K*"	"6"	""	"19182.66"	""	
+"5K*"	"9"	""	"13495.932"	""	
+"5K*"	"8"	""	"19688.595"	""	
+"5K*"	"7"	""	"21783.407"	""	
+"5K*"	"6"	""	"23464.019"	""	
+"5K*"	"5"	""	"24881.867"	""	
+"7F*"	"5"	""	"14153.49"	""	
+"5I*"	"8"	""	"14625.64"	""	
+"5I*"	"7"	""	"18339.80"	"o"	
+"5I*"	"6"	""	"20554.73"	""	
+"5I*"	"5"	""	"22294.88"	""	
+"5I*"	"4"	""	"23686.81"	""	
+"5H*"	"7"	""	"15194.83"	""	
+"(8,0)*"	"8"	""	"15567.38"	""	
+"*"	"6"	""	"15862.64"	""	
+"(8,1)*"	"9"	""	"15972.35"	""	
+"(8,1)*"	"7"	""	"16693.87"	""	
+"(8,1)*"	"8"	""	"16733.20"	""	
+"7P*"	"4"	""	"16069.98"	""	
+"*"	"4"	""	"16412.80"	""	
+"*"	"6"	""	"16591.38"	""	
+"*"	"5"	""	"17502.89"	""	
+"(8,2)*"	"10"	""	"17513.33"	""	
+"(8,2)*"	"9"	""	"17727.15"	""	
+"(8,2)*"	"8"	""	"18021.89"	""	
+"(8,2)*"	"7"	""	"18433.76"	"o"	
+"(8,2)*"	"6"	""	"18711.93"	""	
+"3[8]"	"9"	""	"17514.50"	""	
+"3[8]"	"8"	""	"18903.21"	""	
+"3[8]"	"7"	""	"21074.20"	"o"	
+"3[7]"	"8"	""	"17613.36"	""	
+"3[7]"	"7"	""	"18937.78"	""	
+"3[7]"	"6"	""	"21159.79"	""	
+"5F*"	"5"	""	"17804.24"	""	
+"3[6]"	"5"	""	""	""	
+"3[6]"	"6"	""	""	""	
+"3[6]"	"7"	""	"18094.52"	""	
+"*"	"6"	""	"18172.87"	""	
+"3[9]"	"10"	""	"18462.65"	""	
+"3[9]"	"9"	""	"19240.82"	""	
+"3[9]"	"8"	""	"20193.60"	""	
+"9G*"	"8"	""	"18472.71"	""	
+"*"	"7"	""	"18528.55"	""	
+"*"	"6"	""	"18561.20"	""	
+"*"	"7"	""	"18857.04"	""	
+"3K2"	"8"	""	"19019.15"	""	
+"7H*"	"8"	""	"19092.30"	""	
+"*"	"6"	""	"19304.26"	""	
+"3[10]"	"11"	""	"19348.72"	""	
+"3[10]"	"10"	""	"19797.96"	""	
+"3[10]"	"9"	""	"20209.00"	""	
+"*"	"5"	""	"19480.87"	""	
+"*"	"9"	""	"19557.83"	""	
+"*"	"10"	""	"19797.96"	""	
+"*"	"5"	""	"19813.98"	""	
+"*"	"6"	""	"19856.88"	""	
+"(7,0)*"	"7"	""	"19907.51"	""	
+"(7,1)*"	"8"	""	"20341.32"	""	
+"(7,1)*"	"7"	""	"20766.29"	""	
+"(7,1)*"	"6"	""	"20817.61"	""	
+"*"	"4"	""	"20430.11"	""	
+"9K*"	"11"	""	"20448.44"	""	
+"*"	"4"	""	"20474.99"	""	
+"7G*"	"7"	""	"20485.40"	""	
+"(15/2,1/2)"	"7"	""	"20614.32"	"o"	
+"(15/2,1/2)"	"8"	""	"20789.85"	""	
+"*"	"8"	""	"20884.87"	""	
+"*"	"5"	""	"20891.64"	""	
+"*"	"5"	""	"20921.55"	""	
+"*"	"7"	""	"20954.18"	""	
+"*"	"6"	""	"21392.40"	""	
+"*"	"9"	""	"21540.68"	""	
+"3[7]"	"8"	""	"21603.04"	""	
+"5H*"	"7"	""	"21675.28"	""	
+"5H*"	"6"	""	"24040.59"	""	
+"5H*"	"4"	""	"26440.41"	""	
+"5H*"	"3"	""	"27601.33"	""	
+"3[6]"	"7"	""	"21778.43"	""	
+"*"	"10"	""	"21788.93"	""	
+"(7,2)*"	"9"	""	"21838.55"	""	
+"(7,2)*"	"8"	""	"21899.22"	""	
+"(7,2)*"	"7"	""	"22061.29"	""	
+"(7,2)*"	"6"	""	"22286.87"	""	
+"(7,2)*"	"5"	""	"22524.21"	""	
+"1[9]"	"9"	""	"22045.79"	""	
+"*"	"4"	""	"22099.06"	""	
+"1[10]"	"10"	""	"22487.14"	""	
+"9L*"	"8"	""	""	""	
+"9L*"	"6"	""	""	""	
+"9L*"	"12"	""	"22541.18"	""	
+"9L*"	"11"	""	"23677.38"	""	
+"9L*"	"10"	""	"24858.74"	""	
+"9L*"	"9"	""	"25955.10"	""	
+"9L*"	"7"	""	"27556.34"	""	
+"5G*"	"6"	""	"22633.23"	""	
+"*"	"7"	""	"22647.94"	""	
+"*"	"4"	""	"22696.82"	""	
+"*"	"8"	""	"22767.83"	""	
+"*"	"4"	""	"22938.03"	""	
+"(6,0)*"	"6"	""	"22956.84"	""	
+"(8,0)?"	"8"	""	"23031.46"	""	
+""	"9"	""	"23218.59"	""	
+"*"	"9"	""	"23271.74"	""	
+""	"8"	""	"23280.46"	""	
+""	"6"	""	"23333.92"	""	
+"*"	"7"	""	"23340.12"	""	
+"*"	"6"	""	"23359.82"	""	
+""	"7"	""	"23360.66"	""	
+""	"8"	""	"23388.95"	""	
+"*"	"5"	""	"23440.46"	""	
+"(6,1)*"	"7"	""	"23479.77"	""	
+"(6,1)*"	"5"	""	"23552.65"	""	
+"(6,1)*"	"6"	""	"23687.87"	""	
+"*"	"6"	""	"23529.01"	""	
+"(15/2,3/2)"	"6"	""	""	""	
+"(15/2,3/2)"	"8"	""	"23534.50"	""	
+"(15/2,3/2)"	"7"	""	"23591.27"	""	
+"(15/2,3/2)"	"9"	""	"23780.26"	""	
+"3[8]"	"7"	""	"23655.36"	""	
+""	"6"	""	"23683.87"	""	
+"(8,1)*"	"9"	""	"23736.610"	""	
+"(8,1)*"	"8"	""	"23877.739"	""	
+"(8,1)*"	"7"	""	"24708.971"	""	
+""	"7"	""	"23799.41"	""	
+"5H*"	"3"	""	"23824.68"	""	
+"*"	"8"	""	"23832.060"	""	
+""	"9"	""	"23841.90"	""	
+"*"	"10"	""	"23953.30"	""	
+""	"7"	""	"23972.34"	""	
+""	"6"	""	"24062.88"	""	
+"*"	"8"	""	"24204.19"	""	
+"*"	"9"	""	"24229.22"	""	
+""	"6"	""	"24302.02"	""	
+"*"	"7"	""	"24353.58"	""	
+""	"7"	""	"24430.27"	""	
+"*"	"5"	""	"24634.07"	""	
+"*"	"3"	""	"24668.59"	""	
+"(6,2)*"	"4"	""	"24841.04"	""	
+"(6,2)*"	"7"	""	"24906.86"	""	
+"(6,2)*"	"6"	""	"24931.63"	""	
+"(6,2)*"	"8"	""	"24999.58"	""	
+"(6,2)*"	"5"	""	"25082.02"	"o"	
+""	"6"	""	"24867.17"	""	
+""	"6"	""	"24899.06"	""	
+""	"10"	""	"24978.98"	""	
+"*"	"5"	""	"24993.47"	""	
+"*"	"8"	""	"25012.21"	""	
+"*"	"9"	""	"25084.80"	""	
+""	"8"	""	"25095.66"	""	
+"(5,0)*"	"5"	""	"25127.52"	""	
+"*"	"4"	""	"25203.92"	""	
+"*"	"7"	""	"25268.87"	""	
+""	"7"	""	"25502.82"	""	
+""	"6"	""	"25506.38"	""	
+"*"	"7"	""	"25567.53"	""	
+"*"	"6"	""	"25670.45"	""	
+"(5,1)*"	"4"	""	"25687.20"	""	
+"(5,1)*"	"6"	""	"25825.83"	""	
+"(5,1)*"	"5"	""	"25912.63"	""	
+""	"5"	""	"25744.35"	""	
+"*"	"8"	""	"25760.39"	""	
+"*"	"4"	""	"25761.77"	""	
+"*"	"10"	""	"25774.39"	""	
+"*"	"6"	""	"25868.00"	""	
+""	"7"	""	"25879.15"	""	
+"*"	"6"	""	"25920.88"	""	
+""	"5"	""	"25993.57"	""	
+"*"	"5"	""	"26135.21"	""	
+""	"7"	""	"26200.05"	"?"	
+""	"9"	""	"26244.60"	""	
+""	"5"	""	"26284.69"	""	
+""	"8"	""	"26349.49"	""	
+""	"6"	""	"26387.81"	""	
+""	"7"	""	"26425.15"	""	
+"3[7]"	"8"	""	""	""	
+"3[7]"	"6"	""	""	""	
+"3[7]"	"7"	""	"26435.71"	""	
+""	"5"	""	"26506.51"	""	
+""	"6"	""	"26533.88"	""	
+"(5,2)*"	"3"	""	"26607.16"	""	
+"(5,2)*"	"4"	""	"26998.27"	""	
+"(5,2)*"	"5"	""	"27109.93"	""	
+"(5,2)*"	"6"	""	"27199.20"	""	
+"(5,2)*"	"7"	""	"27427.08"	""	
+"(4,0)*"	"4"	""	"26662.41"	""	
+""	"10"	""	"26752.29"	""	
+"3[8]"	"7"	""	""	""	
+"3[8]"	"9"	""	""	""	
+"3[8]"	"8"	""	"26759.81"	""	
+"(13/2,3/2)"	"5"	""	""	""	
+"(13/2,3/2)"	"6"	""	"26785.45"	""	
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+"*"	"8"	""	"26868.07"	""	
+"*"	"3"	""	"26886.01"	""	
+""	"9"	""	"26955.00"	""	
+"*"	"9"	""	"27014.02"	""	
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+"*"	"6"	""	"27190.74"	""	
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+""	"6"	""	"27474.64"	""	
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+"*"	"3"	""	"27643.57"	""	
+"*"	"4"	""	"27659.02"	""	
+""	"5"	""	"27680.12"	""	
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+"(7,1)*"	"8"	""	"27818.000"	""	
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+"*"	"7"	""	"27837.54"	""	
+"*"	"8"	""	"27851.435"	""	
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+""	"6"	""	"28326.48"	""	
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+""	"8"	""	"28971.42"	""	
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+"*"	"9"	""	"29119.11"	""	
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+"*"	"7"	""	"29161.38"	""	
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+"*"	"6"	""	"32431.61"	""	
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+"*"	"7"	""	"32564.97"	""	
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+""	"7"	""	"46391.45"	""	
+""	"8"	""	"47354.04"	""

Dy-Level-Structure/visualizeDyLevelStructureWithTransitions.py

@@ -0,0 +1,182 @@
+import os, csv
+from pathlib import Path
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+sns.set_theme(style="ticks")
+
+def parse_NIST_data(path, min_J, max_J, max_wavenumber):
+
+    data_list = []
+    with open(path) as f:
+        reader = csv.reader(f, delimiter="\t")
+        data_list = list(reader)
+    
+    #collect data
+    Parity = np.zeros(len(data_list))
+    J = np.zeros(len(data_list))
+    Wavenumber = np.zeros(len(data_list))
+    for i in range(1, len(data_list)):
+        try:
+            tmp = data_list[:][i]
+            if not tmp[0] == '' and tmp[0].find('*') != -1:
+                Parity[i] = 1
+            elif not tmp[0] == '':
+                Parity[i] = 0
+            J[i] = int(tmp[1])
+            Wavenumber[i] = float(tmp[3])
+        except ValueError:
+            J[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]
+        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)
+    
+    #sort data
+    sorting_indices = np.argsort(J)
+    Parity = Parity[sorting_indices]
+    J = J[sorting_indices]
+    Wavenumber = Wavenumber[sorting_indices]
+    
+    # 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]
+
+    # 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'])
+
+    return dataset
+
+def plot_level_structure_with_red_and_blue_transitions(*args, **kwargs):
+    
+    #start plotting
+    f, ax = plt.subplots(figsize=(4.5, 7))
+    #plt.subplots_adjust(top=0.973, bottom=0.121, left=0.244, right=0.959, hspace=0.2, wspace=0.2)
+
+    named_colors = ['r', 'm']
+    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=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)
+    
+    #write wavelength of red transition
+    ax.text(red_J - 0.4, red_wavenumber * 0.54 - 500, '626.082 nm', color = '#db2929', fontsize = 17, fontweight = 'bold')
+    #ax.text(red_J - 0.43, red_wavenumber * 0.38 - 500 , '$(\\Gamma = 2\\pi\\times 136$'+ '\n' + '$~ \mathrm{kHz})$', fontsize = 14, color = '#db2929')
+    ax.text(red_J - 0.325, red_wavenumber * 0.45 - 500 , '$(136~ \mathrm{kHz})$', fontsize = 16, color = '#db2929')
+    #draw red transition arrow
+    ax.annotate('', 
+            xy=(red_J, red_wavenumber),
+            xytext=(gs_J, gs_wavenumber), 
+            arrowprops=dict(color='#db2929', width=1.5),
+            horizontalalignment='right',
+            verticalalignment='top')
+
+    #write electronic configuration for triplet excited state
+    #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 - 1.8, blue_wavenumber * 0.44, '421.291 nm', color = '#2630ea', fontsize = 17, fontweight = 'bold')
+    # ax.text(blue_J - 1.85, blue_wavenumber * 0.33, '$(\\Gamma = 2\\pi\\times 32.2$'+ '\n' + '$~ \mathrm{MHz})$', fontsize = 14, color = '#2630ea') #$(\\Gamma = 2\\pi\\times 32.2 ~ \mathrm{MHz})$
+    ax.text(blue_J - 1.775, blue_wavenumber * 0.38, '$(32.2~ \mathrm{MHz})$', fontsize = 16, color = '#2630ea') #$(\\Gamma = 2\\pi\\times 32.2 ~ \mathrm{MHz})$
+    #ax.text(blue_J - 1.5, blue_wavenumber * 0.3, '$32.2 ~ \mathrm{MHz})$', fontsize = 10, color = '#2630ea')
+    #draw blue transition arrow
+    ax.annotate('', 
+            xy=(blue_J, blue_wavenumber),
+            xytext=(gs_J, gs_wavenumber), 
+            arrowprops=dict(color='#2630ea', width=3.5),
+            horizontalalignment='right',
+            verticalalignment='top')
+
+    #write electronic configuration for singlet excited state
+    #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=20, fontweight = 'bold')
+    #plt.ylabel('$\\tilde{v}~(cm^{-1})$', fontsize=16)
+    plt.ylabel('Wavelength (nm)', fontsize=20, fontweight = 'bold')
+    
+    plot_handle.set_xticks(range(min_J-1, max_J+2))
+    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)
+    ax.set_xticklabels(ax.get_xticks(), weight='bold')
+
+    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_wavenumber, 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, weight='bold')
+    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=16)
+
+    #f.tight_layout()
+    #plt.show()
+    
+    f.savefig(Path(home_path + os.sep + 'result.pdf'), format='pdf', bbox_inches = "tight")
+    
+if __name__ == '__main__':
+    
+    min_J = 8
+    max_J = 9
+    #max_wavenumber = 24500.0
+    max_wavenumber = 28500.0
+
+    #Ground State: [Xe]4f^{10} 6s^2(^5I_8)
+    gs_J = 8
+    gs_wavenumber = 0.0
+
+    #626 nm transition GS ---> [Xe]4f^{10}(^5I_8) 6s6p(^3P_1)(8,1)_9
+    red_J = 9
+    red_wavenumber = 15972.35
+
+    #421 nm transition GS ---> [Xe]4f^{10}(^5I_8) 6s6p(^1P_1)(8,1)_9
+    blue_J = 9
+    blue_wavenumber = 23736.610
+
+    NIST_Dy_level_data_filename= 'Dylevels.txt'
+    home_path = str(Path(__file__).parent.resolve())
+    
+    dataframe = parse_NIST_data(home_path + os.sep + NIST_Dy_level_data_filename, min_J, max_J, max_wavenumber)
+    plot_level_structure_with_red_and_blue_transitions(dataframe, gs_J, gs_wavenumber, red_J, red_wavenumber, blue_J, blue_wavenumber, max_wavenumber, home_path)
+    
+    
+    
+