In [1]:

    

import sys
sys.path.append('..')


    

In [7]:

    

import os

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline

from trout import load_file, get_bname, Nbs


    

In [8]:

    

os.chdir('..')
pref = 'trt'
case = load_file(pref)
os.chdir('notebooks')


    

In [16]:

    

ncase = case['Newb']
nbs = []
ests = []
ratios = []
for (model, N0), vals in ncase.items():
    nb = Nbs[(model, N0)]
    nbs.append(nb)
    bname = get_bname(model)
    est = stats.hmean(vals[(None, 50, 100, 'SNP')][0])
    ests.append(est)
    ratios.append(est / nb)
    print(model, N0, est/nb)
fig, ax = plt.subplots()
ax.plot(nbs, ratios, '.')
ax.set_ylim(1.0, 1.5)


    

    




bullpred 387 1.0966434895
bulltrout 180 1.04646624262
bullt2 3050 1.1016332039
bulltrout 90 1.11360403374
fraley 641 1.10807998905
bullt2 305 1.29869413387
btrout 6476 0.855320370707
bullt2 1830 1.14247231208
restricted 90 1.09703212621
wfrog 600 2.10825687338
bullt2 4575 1.06534089539
bullt2 610 1.28124543862
bulltrout 722 1.06881522045
bullt2 2440 1.10255666462
shepard 1036 1.09873175499
restricted 722 1.08112611542
bullpred 775 1.09912253355
bullt2 915 1.25720536429
bullt2 1220 1.20489016564
btrout 1619 0.847723134999
bullpred 193 1.09960734177
bulltrout 361 1.04399185631
shepard 518 1.04428351955
fraley 1282 1.07791991677
bullt2 1525 1.14830306974
restricted 180 1.07483740944
bullt2 6100 1.05440078447
mosquito 436 2.26152323954
restricted 361 1.06899037225






    
Out[16]:





(1.0, 1.5)






    

In [4]: