<jupyter_start><jupyter_text>Compute model correlations<jupyter_code>model = vega.compute_model()

print(model['lyaxlya'])<jupyter_output>[ 1.16949242e-02  6.50524223e-03  3.78344207e-03 ... -1.90820611e-06
 -1.80467177e-06 -1.70475048e-06]<jupyter_text>Define a new coordinate grid:Make sure you add this to the [data] section of lyaxlya.ini:distortion = False<jupyter_code># For example:
rp_vals = np.linspace(0, 200, 200)
rt_vals = np.linspace(0, 200, 100)

rt_grid, rp_grid = np.meshgrid(rp_vals, rt_vals)

# Overwrite rp_rt grid:
vega.corr_items['lyaxlya'].rp_rt_grid = [rp_grid.flatten(), rt_grid.flatten()]

# Overwrite z grid. This can be a vector or a float
vega.corr_items['lyaxlya'].z_grid = 2.4
new_model = vega.compute_model()

print(new_model['lyaxlya'])
print(np.shape(new_model['lyaxlya']))<jupyter_output>[ 0.00000000e+00  2.37123379e-02  2.05552972e-02 ... -1.71958354e-06
 -1.69531364e-06 -1.67129585e-06]
(20000,)<jupyter_text>To compute a single multipole add this line to the [model] section of lyaxlya.ini:single_multipole = 2<jupyter_code>vega = VegaInterface("D:\\work\\run\\DR16_CONFIG\\main.ini")

model = vega.compute_model()
print('ell = 2: ', model['lyaxlya'])<jupyter_output>INFO: reading input Pk d:\work\repo\dev\vega\vega\models\PlanckDR16\PlanckDR16.fits
Reading data file D:\\work\\data\\DR16\\cf_z_0_10-exp.fits

Warning: Prior specified for a parameter that is not sampled!
ell = 2:  [-3.95238790e-03 -3.33327586e-03 -2.23244413e-03 ... -3.09637239e-06
 -2.98264057e-06 -2.87183226e-06]
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