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PyIRoGlass v0.2.0 released!
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@sarahshi
sarahshi committed Sep 24, 2023
1 parent d5f7124 commit 1db290a
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Showing 2 changed files with 49 additions and 47 deletions.
49 changes: 24 additions & 25 deletions UnitTests/test_plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,15 +10,15 @@

class test_plotting_trace(unittest.TestCase):

@patch("matplotlib.pyplot.Figure.savefig")
@patch('matplotlib.pyplot.Figure.savefig')
def test_trace_savefile(self, mock_savefig):

pnames = ['B_mean',"B_PC1","B_PC2","B_PC3","B_PC4",'G1430_peak','G1430_std','G1430_amp',
pnames = ['B_mean','B_PC1','B_PC2','B_PC3','B_PC4','G1430_peak','G1430_std','G1430_amp',
'G1515_peak','G1515_std','G1515_amp','H1635_mean','H1635_PC1','H1635_PC2','m','b']

texnames = ['$\overline{B}$',"$\overline{B}_{PC1}$","$\overline{B}_{PC2}$","$\overline{B}_{PC3}$",'$\overline{B}_{PC4}$',
'$\mu_{1430}$','$\sigma_{1430}$','$a_{1430}$','$\mu_{1515}$','$\sigma_{1515}$','$a_{1515}$',
'$\overline{H_{1635}}$','$\overline{H_{1635}}_{PC1}$','$\overline{H_{1635}}_{PC2}$','$m$','$b$']
texnames = [r'$\overline{B}$',r'$\overline{B}_{PC1}$',r'$\overline{B}_{PC2}$',r'$\overline{B}_{PC3}$',r'$\overline{B}_{PC4}$',
r'$\mu_{1430}$',r'$\sigma_{1430}$',r'$a_{1430}$',r'$\mu_{1515}$',r'$\sigma_{1515}$',r'$a_{1515}$',
r'$\overline{H_{1635}}$',r'$\overline{H_{1635}}_{PC1}$',r'$\overline{H_{1635}}_{PC2}$',r'$m$',r'$b$']

file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../docs/examples/transmission_ftir/NPZTXTFILES/RESULTS/AC4_OL53_101220_256s_30x30_a.npz')
mcmc_npz = np.load(file_path)
Expand All @@ -28,19 +28,19 @@ def test_trace_savefile(self, mock_savefig):
# Check if savefig was called with the correct filename
mock_savefig.assert_called_once()
args, kwargs = mock_savefig.call_args
assert 'dummy_filename.png' in args[0]
self.assertIn('dummy_filename.png', args[0])
plt.close('all')


@patch("matplotlib.pyplot.Figure.savefig")
@patch('matplotlib.pyplot.Figure.savefig')
def test_trace_nosavefile(self, mock_savefig):

pnames = ['B_mean',"B_PC1","B_PC2","B_PC3","B_PC4",'G1430_peak','G1430_std','G1430_amp',
pnames = ['B_mean','B_PC1','B_PC2','B_PC3','B_PC4','G1430_peak','G1430_std','G1430_amp',
'G1515_peak','G1515_std','G1515_amp','H1635_mean','H1635_PC1','H1635_PC2','m','b']

texnames = ['$\overline{B}$',"$\overline{B}_{PC1}$","$\overline{B}_{PC2}$","$\overline{B}_{PC3}$",'$\overline{B}_{PC4}$',
'$\mu_{1430}$','$\sigma_{1430}$','$a_{1430}$','$\mu_{1515}$','$\sigma_{1515}$','$a_{1515}$',
'$\overline{H_{1635}}$','$\overline{H_{1635}}_{PC1}$','$\overline{H_{1635}}_{PC2}$','$m$','$b$']
texnames = [r'$\overline{B}$',r'$\overline{B}_{PC1}$',r'$\overline{B}_{PC2}$',r'$\overline{B}_{PC3}$',r'$\overline{B}_{PC4}$',
r'$\mu_{1430}$',r'$\sigma_{1430}$',r'$a_{1430}$',r'$\mu_{1515}$',r'$\sigma_{1515}$',r'$a_{1515}$',
r'$\overline{H_{1635}}$',r'$\overline{H_{1635}}_{PC1}$',r'$\overline{H_{1635}}_{PC2}$',r'$m$',r'$b$']

file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../docs/examples/transmission_ftir/NPZTXTFILES/RESULTS/AC4_OL53_101220_256s_30x30_a.npz')
mcmc_npz = np.load(file_path)
Expand All @@ -66,50 +66,49 @@ def setUp(self):
self.decimalPlace = 5


@patch("matplotlib.pyplot.Figure.savefig")
@patch.object(matplotlib.pyplot.Figure, 'savefig')
def test_modelfit_savefile(self, mock_savefig):

file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../src/PyIRoGlass/BaselineAvgPC.npz')
wavenumber = pig.Load_Wavenumber(file_path)

pnames = ['B_mean',"B_PC1","B_PC2","B_PC3","B_PC4",'G1430_peak','G1430_std','G1430_amp',
pnames = ['B_mean','B_PC1','B_PC2','B_PC3','B_PC4','G1430_peak','G1430_std','G1430_amp',
'G1515_peak','G1515_std','G1515_amp','H1635_mean','H1635_PC1','H1635_PC2','m','b']

texnames = ['$\overline{B}$',"$\overline{B}_{PC1}$","$\overline{B}_{PC2}$","$\overline{B}_{PC3}$",'$\overline{B}_{PC4}$',
'$\mu_{1430}$','$\sigma_{1430}$','$a_{1430}$','$\mu_{1515}$','$\sigma_{1515}$','$a_{1515}$',
'$\overline{H_{1635}}$','$\overline{H_{1635}}_{PC1}$','$\overline{H_{1635}}_{PC2}$','$m$','$b$']
texnames = [r'$\overline{B}$',r'$\overline{B}_{PC1}$',r'$\overline{B}_{PC2}$',r'$\overline{B}_{PC3}$',r'$\overline{B}_{PC4}$',
r'$\mu_{1430}$',r'$\sigma_{1430}$',r'$a_{1430}$',r'$\mu_{1515}$',r'$\sigma_{1515}$',r'$a_{1515}$',
r'$\overline{H_{1635}}$',r'$\overline{H_{1635}}_{PC1}$',r'$\overline{H_{1635}}_{PC2}$',r'$m$',r'$b$']

file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../docs/examples/transmission_ftir/NPZTXTFILES/RESULTS/AC4_OL49_021920_30x30_H2O_a.npz')
mcmc_npz = np.load(file_path)

fig = pig.modelfit(self.spec_mc3, np.ones_like(self.spec_mc3) * 0.01, wavenumber, mcmc_npz['best_model'], title = 'AC4_OL49_021920_30x30_H2O_a', savefile='dummy_filename.png')

# Check if savefig was called with the correct filename
mock_savefig.assert_called_once()
args, kwargs = mock_savefig.call_args
assert "dummy_filename.png" in args[0]
self.assertIn('dummy_filename.png', args[0])
plt.close('all')


@patch("matplotlib.pyplot.Figure.savefig")
@patch.object(matplotlib.pyplot.Figure, 'savefig')
def test_modelfit_nosavefile(self, mock_savefig):

file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../src/PyIRoGlass/BaselineAvgPC.npz')
wavenumber = pig.Load_Wavenumber(file_path)

pnames = ['B_mean',"B_PC1","B_PC2","B_PC3","B_PC4",'G1430_peak','G1430_std','G1430_amp',
pnames = ['B_mean','B_PC1','B_PC2','B_PC3','B_PC4','G1430_peak','G1430_std','G1430_amp',
'G1515_peak','G1515_std','G1515_amp','H1635_mean','H1635_PC1','H1635_PC2','m','b']

texnames = ['$\overline{B}$',"$\overline{B}_{PC1}$","$\overline{B}_{PC2}$","$\overline{B}_{PC3}$",'$\overline{B}_{PC4}$',
'$\mu_{1430}$','$\sigma_{1430}$','$a_{1430}$','$\mu_{1515}$','$\sigma_{1515}$','$a_{1515}$',
'$\overline{H_{1635}}$','$\overline{H_{1635}}_{PC1}$','$\overline{H_{1635}}_{PC2}$','$m$','$b$']
texnames = [r'$\overline{B}$',r'$\overline{B}_{PC1}$',r'$\overline{B}_{PC2}$',r'$\overline{B}_{PC3}$',r'$\overline{B}_{PC4}$',
r'$\mu_{1430}$',r'$\sigma_{1430}$',r'$a_{1430}$',r'$\mu_{1515}$',r'$\sigma_{1515}$',r'$a_{1515}$',
r'$\overline{H_{1635}}$',r'$\overline{H_{1635}}_{PC1}$',r'$\overline{H_{1635}}_{PC2}$',r'$m$',r'$b$']

file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../docs/examples/transmission_ftir/NPZTXTFILES/RESULTS/AC4_OL49_021920_30x30_H2O_a.npz')
mcmc_npz = np.load(file_path)

fig = pig.modelfit(self.spec_mc3, np.ones_like(self.spec_mc3) * 0.01, wavenumber, mcmc_npz['best_model'], title = 'AC4_OL49_021920_30x30_H2O_a')

# Check if savefig was called with the correct filename
# Check if savefig was never called
mock_savefig.assert_not_called()
plt.close('all')

Expand Down
47 changes: 25 additions & 22 deletions src/PyIRoGlass/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,7 +85,7 @@ def Load_PC(file_name):
PC_DF = PC_DF.set_index('Wavenumber')

PC_DF = PC_DF[wn_low:wn_high]
PC_matrix = np.matrix(PC_DF.to_numpy())
PC_matrix = np.ndarray(PC_DF.to_numpy())

return PC_matrix

Expand Down Expand Up @@ -202,13 +202,13 @@ def Carbonate(P, x, PCmatrix, Peak_1635_PCmatrix, Nvectors = 5):
peak_G1430, std_G1430, G1430_amplitude, peak_G1515, std_G1515, G1515_amplitude = P[Nvectors:-5]
m, b = P[-2:None]

Peak_1635 = Peak_Weights * Peak_1635_PCmatrix.T
Peak_1635 = Peak_Weights @ Peak_1635_PCmatrix.T
G1515 = Gauss(x, peak_G1515, std_G1515, A=G1515_amplitude)
G1430 = Gauss(x, peak_G1430, std_G1430, A=G1430_amplitude)

linear_offset = Linear(x, m, b)

baseline = PC_Weights * PCmatrix.T
baseline = PC_Weights @ PCmatrix.T
model_data = baseline + linear_offset + Peak_1635 + G1515 + G1430
model_data = np.array(model_data)[0,:]

Expand Down Expand Up @@ -545,7 +545,8 @@ def modelfit(data, uncert, indparams, model, title, nbins=75,
if savefile is not None:
plt.suptitle(title, fontsize=fs+1)
plt.ioff()
plt.savefig(savefile)
plt.savefig(savefile, bbox_inches='tight')

return ax, rax


Expand Down Expand Up @@ -582,12 +583,12 @@ def MCMC(data, uncert, indparams, log, savefile):
priorlow = np.array([0.00, 0.00, 0.00, 0.00, 0.00, 0.0, 5.00, 0.0, 0.0, 5.00, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
priorup = np.array([0.00, 0.00, 0.00, 0.00, 0.00, 0.0, 5.00, 0.0, 0.0, 5.00, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

pnames = ['B_mean',"B_PC1","B_PC2","B_PC3","B_PC4",'G1430_peak','G1430_std','G1430_amp',
pnames = ['B_mean','B_PC1','B_PC2','B_PC3','B_PC4','G1430_peak','G1430_std','G1430_amp',
'G1515_peak','G1515_std','G1515_amp','H1635_mean','H1635_PC1','H1635_PC2','m','b']

texnames = ['$\overline{B}$',"$\overline{B}_{PC1}$","$\overline{B}_{PC2}$","$\overline{B}_{PC3}$",'$\overline{B}_{PC4}$',
'$\mu_{1430}$','$\sigma_{1430}$','$a_{1430}$','$\mu_{1515}$','$\sigma_{1515}$','$a_{1515}$',
'$\overline{H_{1635}}$','$\overline{H_{1635}}_{PC1}$','$\overline{H_{1635}}_{PC2}$','$m$','$b$']
texnames = [r'$\overline{B}$',r'$\overline{B}_{PC1}$',r'$\overline{B}_{PC2}$',r'$\overline{B}_{PC3}$',r'$\overline{B}_{PC4}$',
r'$\mu_{1430}$',r'$\sigma_{1430}$','$a_{1430}$',r'$\mu_{1515}$',r'$\sigma_{1515}$','$a_{1515}$',
r'$\overline{H_{1635}}$',r'$\overline{H_{1635}}_{PC1}$',r'$\overline{H_{1635}}_{PC2}$','$m$','$b$']


mc3_output = mc3.sample(data=data, uncert=uncert, func=func, params=params, indparams=indparams,
Expand Down Expand Up @@ -781,15 +782,15 @@ def Run_All_Spectra(dfs_dict, exportpath):

PC_BP = mc3_output['bestp'][0:Nvectors]
PC_STD = mc3_output['stdp'][0:Nvectors]
Baseline_Solve_BP = PC_BP * PCmatrix.T
Baseline_Solve_BP = PC_BP @ PCmatrix.T
Baseline_Solve_BP = np.asarray(Baseline_Solve_BP).ravel()

m_BP, b_BP = mc3_output['bestp'][-2:None]
m_STD, b_STD = mc3_output['stdp'][-2:None]
Line_BP = Linear(Wavenumber, m_BP, b_BP)
Baseline_Solve_BP = Baseline_Solve_BP + Line_BP

H1635_BP = H2OmP1635_BP * Peak_1635_PCmatrix.T
H1635_BP = H2OmP1635_BP @ Peak_1635_PCmatrix.T
H1635_BP = np.asarray(H1635_BP).ravel()
CO2P1430_BP = Gauss(Wavenumber, CO2P_BP[0], CO2P_BP[1], A=CO2P_BP[2])
CO2P1515_BP = Gauss(Wavenumber, CO2P_BP[3], CO2P_BP[4], A=CO2P_BP[5])
Expand Down Expand Up @@ -829,7 +830,7 @@ def Run_All_Spectra(dfs_dict, exportpath):
ax1.annotate("$\mathregular{OH^{-}_{4500}}$ Peak Height: " + f"{PH_4500_krige_M:.4f} ± {PH_4500_krige_STD:.4f}, S2N={STN_4500_M:.2f}", (0.025, 0.80), xycoords = 'axes fraction')
ax1.set_ylabel('Absorbance')
warnings.filterwarnings("ignore", category = UserWarning)
ax1.legend(['NIR Spectrum','_','_','$\mathregular{H_2O_{m, 5200}}$ Median Filtered','_','_','$\mathregular{OH^{-}_{4500}}$ Median Filtered','_','_','$\mathregular{H_2O_{m, 5200}}$ Baseline','_','_','$\mathregular{OH^{-}_{4500}}$ Baseline'], prop={'size': 10})
ax1.legend(['NIR Spectrum','_','_',r'$\mathregular{H_2O_{m, 5200}}$ Median Filtered','_','_',r'$\mathregular{OH^{-}_{4500}}$ Median Filtered','_','_',r'$\mathregular{H_2O_{m, 5200}}$ Baseline','_','_',r'$\mathregular{OH^{-}_{4500}}$ Baseline'], prop={'size': 10})
ax1.set_xlim([4200, 5400])
ax1.set_ylim([plotmin-0.075, plotmax+0.075])
ax1.invert_xaxis()
Expand All @@ -838,20 +839,20 @@ def Run_All_Spectra(dfs_dict, exportpath):
ax2 = plt.subplot2grid((2, 3), (1, 0))
ax2.plot(data_H2O5200_1.index, data_H2O5200_1['Subtracted_Peak'] - np.min(krige_output_5200_1['Absorbance']), 'k',
data_H2O5200_2.index, data_H2O5200_2['Subtracted_Peak'] - np.min(krige_output_5200_2['Absorbance']), 'k',
data_H2O5200_3.index, data_H2O5200_3['Subtracted_Peak'] - np.min(krige_output_5200_3['Absorbance']), 'k', label = '$\mathregular{H_2O_{m, 5200}}$ Baseline Subtracted')
data_H2O5200_3.index, data_H2O5200_3['Subtracted_Peak'] - np.min(krige_output_5200_3['Absorbance']), 'k', label = r'$\mathregular{H_2O_{m, 5200}}$ Baseline Subtracted')
ax2.plot(data_H2O4500_1.index, data_H2O4500_1['Subtracted_Peak'] - np.min(krige_output_4500_1['Absorbance']), 'k',
data_H2O4500_2.index, data_H2O4500_2['Subtracted_Peak'] - np.min(krige_output_4500_2['Absorbance']), 'k',
data_H2O4500_3.index, data_H2O4500_3['Subtracted_Peak'] - np.min(krige_output_4500_3['Absorbance']), 'k', label = '$\mathregular{OH^{-}_{4500}}$ Baseline Subtracted')
data_H2O4500_3.index, data_H2O4500_3['Subtracted_Peak'] - np.min(krige_output_4500_3['Absorbance']), 'k', label = r'$\mathregular{OH^{-}_{4500}}$ Baseline Subtracted')
ax2.plot(krige_output_5200_1.index, krige_output_5200_1['Absorbance'] - np.min(krige_output_5200_1['Absorbance']),
krige_output_5200_2.index, krige_output_5200_2['Absorbance'] - np.min(krige_output_5200_2['Absorbance']),
krige_output_5200_3.index, krige_output_5200_3['Absorbance'] - np.min(krige_output_5200_3['Absorbance']), label = '$\mathregular{H_2O_{m, 5200}}$ Kriged Peak')
krige_output_5200_3.index, krige_output_5200_3['Absorbance'] - np.min(krige_output_5200_3['Absorbance']), label = r'$\mathregular{H_2O_{m, 5200}}$ Kriged Peak')
ax2.plot(krige_output_4500_1.index, krige_output_4500_1['Absorbance'] - np.min(krige_output_4500_1['Absorbance']),
krige_output_4500_2.index, krige_output_4500_2['Absorbance'] - np.min(krige_output_4500_2['Absorbance']),
krige_output_4500_3.index, krige_output_4500_3['Absorbance'] - np.min(krige_output_4500_3['Absorbance']), label = '$\mathregular{OH^{-}_{4500}}$ Kriged Peak')
krige_output_4500_3.index, krige_output_4500_3['Absorbance'] - np.min(krige_output_4500_3['Absorbance']), label = r'$\mathregular{OH^{-}_{4500}}$ Kriged Peak')
ax2.set_xlabel('Wavenumber $(\mathregular{cm^{-1}})$')
ax2.set_ylabel('Absorbance')
warnings.filterwarnings("ignore", category = UserWarning)
ax2.legend(['$\mathregular{H_2O_{m, 5200}}$ Baseline Subtracted','_','_','$\mathregular{OH^{-}_{4500}}$ Baseline Subtracted','_','_','_','_','$\mathregular{H_2O_{m, 5200}}$ Kriged','_','_','$\mathregular{OH^{-}_{4500}}$ Kriged'], prop={'size': 10})
ax2.legend([r'$\mathregular{H_2O_{m, 5200}}$ Baseline Subtracted','_','_',r'$\mathregular{OH^{-}_{4500}}$ Baseline Subtracted','_','_','_','_',r'$\mathregular{H_2O_{m, 5200}}$ Kriged','_','_',r'$\mathregular{OH^{-}_{4500}}$ Kriged'], prop={'size': 10})
ax2.set_xlim([4200, 5400])
ax2.set_ylim([0, plotmax+0.05])
ax2.invert_xaxis()
Expand All @@ -861,7 +862,7 @@ def Run_All_Spectra(dfs_dict, exportpath):
plotmin = np.round(np.min(data[CO2_wn_low:CO2_wn_low+2750]['Absorbance'].to_numpy()), decimals = 0)
ax3 = plt.subplot2grid((2, 3), (0, 1), rowspan = 2)
ax3.plot(data.index, data['Absorbance'], 'k')
ax3.plot(data_H2O3550_1['Absorbance'].index, data_H2O3550_1['BL_MIR_3550'], 'silver', label = '$\mathregular{H_2O_{t, 3550}}$ Baseline')
ax3.plot(data_H2O3550_1['Absorbance'].index, data_H2O3550_1['BL_MIR_3550'], 'silver', label = r'$\mathregular{H_2O_{t, 3550}}$ Baseline')
ax3.plot(data_H2O3550_2['Absorbance'].index, data_H2O3550_2['BL_MIR_3550'], 'silver')
ax3.plot(data_H2O3550_3['Absorbance'].index, data_H2O3550_3['BL_MIR_3550'], 'silver')
ax3.plot(plot_output_3550_1.index, (plot_output_3550_1['Subtracted_Peak_Hat']+plot_output_3550_1['BL_MIR_3550']), 'r', linewidth = 2)
Expand All @@ -887,7 +888,7 @@ def Run_All_Spectra(dfs_dict, exportpath):
lineerror = masked_posterior[:, -2:None]
lineerror = lineerror[0:np.shape(masked_posterior[:, :])[0]:int(np.shape(masked_posterior[:, :])[0] / 100), :]

Baseline_Array = np.array(samplingerror * PCmatrix[:, :].T)
Baseline_Array = np.array(samplingerror @ PCmatrix[:, :].T)
Baseline_Array_Plot = Baseline_Array

ax4 = plt.subplot2grid((2, 3), (0, 2), rowspan = 2)
Expand All @@ -896,9 +897,9 @@ def Run_All_Spectra(dfs_dict, exportpath):
Baseline_Array_Plot[i, :] = Baseline_Array[i, :] + Linearray
plt.plot(Wavenumber, Baseline_Array_Plot[i, :], 'dimgray', linewidth = 0.25)
ax4.plot(Wavenumber, spec_mc3, 'tab:blue', linewidth = 2.5, label = 'FTIR Spectrum')
ax4.plot(Wavenumber, H1635_SOLVE, 'tab:orange', linewidth = 1.5, label = '$\mathregular{H_2O_{m, 1635}}$')
ax4.plot(Wavenumber, CO2P1515_SOLVE, 'tab:green', linewidth = 2.5, label = '$\mathregular{CO_{3, 1515}^{2-}}$')
ax4.plot(Wavenumber, CO2P1430_SOLVE, 'tab:red', linewidth = 2.5, label = '$\mathregular{CO_{3, 1430}^{2-}}$')
ax4.plot(Wavenumber, H1635_SOLVE, 'tab:orange', linewidth = 1.5, label = r'$\mathregular{H_2O_{m, 1635}}$')
ax4.plot(Wavenumber, CO2P1515_SOLVE, 'tab:green', linewidth = 2.5, label = r'$\mathregular{CO_{3, 1515}^{2-}}$')
ax4.plot(Wavenumber, CO2P1430_SOLVE, 'tab:red', linewidth = 2.5, label = r'$\mathregular{CO_{3, 1430}^{2-}}$')
ax4.plot(Wavenumber, Carbonate(mc3_output['meanp'], Wavenumber, PCmatrix, Peak_1635_PCmatrix, Nvectors), 'tab:purple', linewidth = 1.5, label = 'MC$^\mathregular{3}$ Fit')
ax4.plot(Wavenumber, Baseline_Solve_BP, 'k', linewidth = 1.5, label = 'Baseline')
ax4.annotate("$\mathregular{H_2O_{m, 1635}}$ Peak Height: " + f"{H2OmP1635_BP[0]:.3f} ± {H2OmP1635_STD[0]:.3f}", (0.025, 0.95), xycoords = 'axes fraction')
Expand All @@ -913,7 +914,9 @@ def Run_All_Spectra(dfs_dict, exportpath):
plt.savefig(path_beg + figurepath + files + '.pdf')
plt.close('all')

texnames = ['$\overline{B}$',"$\overline{B}_{PC1}$","$\overline{B}_{PC2}$","$\overline{B}_{PC3}$",'$\overline{B}_{PC4}$','$\mu_{1430}$','$\sigma_{1430}$','$a_{1430}$','$\mu_{1515}$','$\sigma_{1515}$','$a_{1515}$','$\overline{H_{1635}}$','$\overline{H_{1635}}_{PC1}$','$\overline{H_{1635}}_{PC2}$','$m$','$b$']
texnames = [r'$\overline{B}$',r'$\overline{B}_{PC1}$',r'$\overline{B}_{PC2}$',r'$\overline{B}_{PC3}$',r'$\overline{B}_{PC4}$',
r'$\mu_{1430}$',r'$\sigma_{1430}$','$a_{1430}$',r'$\mu_{1515}$',r'$\sigma_{1515}$','$a_{1515}$',
r'$\overline{H_{1635}}$',r'$\overline{H_{1635}}_{PC1}$',r'$\overline{H_{1635}}_{PC2}$','$m$','$b$']

posterior, zchain, zmask = mu.burn(mc3_output)
post = mp.Posterior(posterior, texnames)
Expand Down

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