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_Figure_S2.py
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_Figure_S2.py
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#!/usr/bin/env python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# load the raw data
df = pd.read_pickle('Properties/full_props.pkl')
# load the raw spectra
spec = pd.read_pickle('Properties/raw_spectra.pkl')
# helper functions
def overlap(absorb, emission):
return np.sum(absorb * emission) / np.sqrt(np.sum(absorb * absorb) * np.sum(emission * emission))
# function to calculate peak scores
def peak_scorer(roi, x_nm, spectra):
# area in the roi
roi_spectra = spectra[ x_nm<roi[1] ][ x_nm >= roi[0] ]
roi_x_nm = x_nm[ x_nm < roi[1] ][ x_nm >= roi[0] ]
roi_area = trapz(roi_spectra,roi_x_nm)
tot_area = trapz(spectra, x_nm)
return roi_area/tot_area
def get_spectra(spectra, smiles):
d = list(filter(lambda mol: mol['smiles'] == smiles+'\n', spectra))[0]['spec']
return d
min_peak_score = df[df['fluo_peak_1']==df['fluo_peak_1'].min()]
max_peak_score = df[df['fluo_peak_1']==df['fluo_peak_1'].max()]
min_overlap = df[df['overlap']==df['overlap'].min()]
max_overlap = df[df['overlap']==df['overlap'].max()]
min_peak_score['smiles'].tolist()[0]
min_peak_score_spec = get_spectra(spec, min_peak_score['smiles'].tolist()[0])
max_peak_score_spec = get_spectra(spec, max_peak_score['smiles'].tolist()[0])
min_overlap_spec = get_spectra(spec, min_overlap['smiles'].tolist()[0])
max_overlap_spec = get_spectra(spec, max_overlap['smiles'].tolist()[0])
# make plot
fluo = '#011627'
ext = '#ff3366'
lw = 3
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=False, sharey=True, figsize=(10, 4))
# minimum peak score --------------------------------------------------------------
axes[0, 0].plot(min_peak_score_spec['x_nm'],
min_peak_score_spec['ext_norm'],
label='extinction',
lw=lw,
c=ext)
axes[0, 0].plot(min_peak_score_spec['x_nm'],
min_peak_score_spec['fluo_psd_norm'],
label='fluorescence',
lw=lw,
c=fluo)
axes[0, 0].set_title(f'minimum peak score={round(min_peak_score["fluo_peak_1"].tolist()[0], 4)}')
axes[0, 0].axvspan(400, 460, alpha=0.2, color='#95B9DB')
axes[0, 0].axvline(400, color='#95B9DB')
axes[0, 0].axvline(460, color='#95B9DB')
axes[0, 0].set_xlim(200, 550.)
# maximum peak score --------------------------------------------------------------
axes[0, 1].plot(max_peak_score_spec['x_nm'],
max_peak_score_spec['ext_norm'],
lw=lw,
c=ext)
axes[0, 1].plot(max_peak_score_spec['x_nm'],
max_peak_score_spec['fluo_psd_norm'],
lw=lw,
c=fluo)
axes[0, 1].set_title(f'maximum peak score={round(max_peak_score["fluo_peak_1"].tolist()[0],3)}')
axes[0, 1].axvspan(400, 460, alpha=0.2, color='#95B9DB')
axes[0, 1].axvline(400, color='#95B9DB')
axes[0, 1].axvline(460, color='#95B9DB')
axes[0, 1].set_xlim(200, 550.)
# minimum spectral overlap --------------------------------------------------------
axes[1, 0].plot(min_overlap_spec['x_nm'],
min_overlap_spec['ext_norm'],
lw=lw,
c=ext)
axes[1, 0].plot(min_overlap_spec['x_nm'],
min_overlap_spec['fluo_psd_norm'],
lw=lw,
c=fluo)
axes[1, 0].set_title(f'minimum spectral overlap={round(min_overlap["overlap"].tolist()[0],3)}')
axes[1, 0].set_xlim(200., 800.)
# maximum spectral overlap --------------------------------------------------------
axes[1, 1].plot(max_overlap_spec['x_nm'],
max_overlap_spec['ext_norm'],
label='extinction',
lw=lw,
c=ext)
axes[1, 1].plot(max_overlap_spec['x_nm'],
max_overlap_spec['fluo_psd_norm'],
label='fluorescence',
lw=lw,
c=fluo)
axes[0, 0].legend(fontsize=12)
axes[1, 1].set_title(f'maximum spectral overlap={round(max_overlap["overlap"].tolist()[0],2)}')
axes[1, 1].set_xlim(300., 550.)
axes[0, 0].set_ylabel('Normalized spectra', fontsize=12)
axes[1, 0].set_ylabel('Normalized spectra', fontsize=12)
axes[1, 0].set_xlabel('Wavelength [nm]', fontsize=12)
axes[1, 1].set_xlabel('Wavelength [nm]', fontsize=12)
plt.tight_layout()
plt.show()