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.
James Davenport jradavenport
danhey
/ mnras_sizer.py
Created
October 9, 2019 11:15
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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] |
RuthAngus
/ Teff_bv_conversion.py
Last active
September 28, 2016 21:49
Converting Teff into B-V using the Sekiguchi & Fukugita (2000) relation.
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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 + \ |
shoyer
/ George - 2D Example.ipynb
Created
November 2, 2015 19:38
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