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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Intensity Weighting
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
Step1:  As always, let's do imports and initialize a logger and a new Bundle.  See Building a System for more details.
Step2:  Relevant Parameters
Step3:  Influence on Light Curves (fluxes)
Let's (roughtly) reproduce Figure 5 from Prsa et al. 2016 which shows the difference between photon and energy intensity weighting.
<img src="prsa+2016_fig5.png" alt="Figure 8" width="600px"/>
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Python Code:
!pip install -I "phoebe>=2.2,<2.3"
Explanation: Intensity Weighting
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
%matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
b.add_dataset('lc', times=np.linspace(0,1,101))
Explanation: As always, let's do imports and initialize a logger and a new Bundle.  See Building a System for more details.
End of explanation
b['intens_weighting']
print(b['intens_weighting'])
Explanation: Relevant Parameters
End of explanation
for teff_primary in [5000,7500,10000,12500,15000]:
    b['teff@primary'] = teff_primary
    b['teff@secondary'] = 0.9 * teff_primary
    
    for weighting in ['energy', 'photon']:
        b['intens_weighting'] = weighting
        b.run_compute(irrad_method='none', model='{}_{}'.format(teff_primary, weighting))
teff_colormap = {5000: 'm', 7500: 'r', 10000: 'g', 12500: 'c', 15000: 'b'}
fig = plt.figure()
ax1, ax2 = fig.add_subplot(211), fig.add_subplot(212)
for teff, color in teff_colormap.items():
    fluxes_energy = b.get_value('fluxes@{}_energy'.format(teff))
    fluxes_photon = b.get_value('fluxes@{}_photon'.format(teff))
    phases = b.to_phase('times@lc@dataset')
    
    # alias data from -0.6 to 0.6
    fluxes_energy = np.append(fluxes_energy, fluxes_energy[abs(phases) > 0.4])
    fluxes_photon = np.append(fluxes_photon, fluxes_photon[abs(phases) > 0.4])
    phases = np.append(phases, phases[abs(phases)>0.4]+1.0)
    phases[phases > 1.0] = phases[phases > 1.0] - 2.0
    
    sort = phases.argsort()
    phases = phases[sort]
    fluxes_energy = fluxes_energy[sort]
    fluxes_photon = fluxes_photon[sort]
    
    ax1.plot(phases, fluxes_energy, color=color)
    ax2.plot(phases, fluxes_photon-fluxes_energy, color=color)
lbl = ax1.set_xlabel('')
lbl = ax1.set_ylabel('flux')
lbl = ax2.set_xlabel('phase')
lbl = ax2.set_ylabel('flux diff')
Explanation: Influence on Light Curves (fluxes)
Let's (roughtly) reproduce Figure 5 from Prsa et al. 2016 which shows the difference between photon and energy intensity weighting.
<img src="prsa+2016_fig5.png" alt="Figure 8" width="600px"/>
End of explanation
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