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Made changes to plotting style, fixed a bug that was excluding some levels.

master
Karthik 2 years ago
parent
ea757ad62d
commit
124ef73439
2 changed files with 44 additions and 21 deletions
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  1. BIN
      DyLevelStructure/result.pdf
  2. 65
      DyLevelStructure/visualizeDyLevelStructureWithTransitions.py

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DyLevelStructure/result.pdf
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DyLevelStructure/visualizeDyLevelStructureWithTransitions.py
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@ -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")

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