This is a simple NLTK Python script which uses N-grams to construct phrases from a generative language model trained on the King James Bible.
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def mnras_size(fig_width_pt, square=False): | |
inches_per_pt = 1.0/72.00 # Convert pt to inches | |
golden_mean = (np.sqrt(5)-1.0)/2.0 # Most aesthetic ratio | |
fig_width = fig_width_pt*inches_per_pt # Figure width in inches | |
if square: | |
fig_height = fig_width | |
else: | |
fig_height = fig_width*golden_mean | |
return [fig_width,fig_height] |
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import numpy as np | |
def teff2bv(teff, logg, feh): | |
""" | |
Relation from Sekiguchi & Fukugita (2000). | |
""" | |
t = [-813.3175, 684.4585, -189.923, 17.40875] | |
f = [1.2136, 0.0209] | |
d1, g1, e1 = -0.294, -1.166, 0.3125 | |
return t[0] + t[1]*np.log10(teff) + t[2]*(np.log10(teff))**2 + \ |