In [15]:

    

using MethylClust
using PyPlot


    

    




INFO: Loading help data...

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ff_file = "/Users/alex/Dropbox/meth_data/test1.tsv"#"/Users/alex/Dropbox/meth_data/allc_ff_15.tsv"
h1_file = "/Users/alex/Dropbox/meth_data/test2.tsv"#"/Users/alex/Dropbox/meth_data/allc_h1_15.tsv"
positions = find_common_positions(ff_file,h1_file;strand="-")
ff_p = est_prob_allc(ff_file, positions)
h1_p = est_prob_allc(h1_file, positions)

js_divergence(ff_p,h1_p)


    

    
Out[17]:





0.0165661045548326

In [18]:

    

function draw_mixture(num_reads, read_len; proportion_ff=0.5)
    I1,J1,V1 = draw_reads(ff_p, round(Int,num_reads*proportion_ff), read_len)
    I2,J2,V2 = draw_reads(h1_p, round(Int,num_reads*(1-proportion_ff)), read_len)
    return sparse([I1,I2+I1[end]],[J1,J2],[V1,V2])
end


    

    
Out[18]:





draw_mixture (generic function with 1 method)

In [23]:

    

figure()
imshow(draw_mixture(100,30),cmap=ColorMap("bwr"),interpolation="none")
ylabel("reads"),xlabel("cytosine positions")


    

    













    
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(PyObject <matplotlib.text.Text object at 0x1144ade90>,PyObject <matplotlib.text.Text object at 0x111175050>)

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