def obsprd_plot(plots, flavour):
pops = {
'obs': { 'sw': [], 'aw': [] },
'prd': { 'sw': [], 'aw': [] },
}
for plotname in plots:
if plotname in ignore:
continue
plot = plots[plotname]
L, U, N, T, x, plotfile = plot['attrs']
nsw, naw = plot['nsw'], plot['naw']
sw, aw = abb_init_kernels(L, U, N, flavour, plotname)
for obs_year in sw.years[1:]:
pops['obs']['sw'].append(len(sw.meas['SW'][obs_year]) / sw.plot_size * 1e4)
pops['obs']['aw'].append(len(aw.meas['AW'][obs_year]) / sw.plot_size * 1e4)
j = T.index(obs_year)
pops['prd']['sw'].append(sw.population(nsw[j]))
pops['prd']['aw'].append(aw.population(naw[j]))
tmp = np.asarray(pops['obs']['sw'])
if any(tmp > 15000):
print plotname
stats = { 'sw': {}, 'aw': {} }
for species in [ 'sw', 'aw' ]:
from utils.stats import dent_blackie, theil, MSEP
from scipy.stats import ttest_ind, mannwhitneyu, pearsonr
obs = pops['obs'][species]
prd = pops['prd'][species]
F, pval, (a, b) = dent_blackie(obs, prd)
stats[species]['F'] = (F, pval)
stats[species]['theil'] = theil(obs, prd)
stats[species]['t'] = ttest_ind(prd, obs)
stats[species]['mannwhitneyu'] = mannwhitneyu(prd, obs)
stats[species]['pearsonr'] = pearsonr(prd, obs)
mc, sc, rc, msep = MSEP(obs, prd)
stats[species]['msep'] = msep
stats[species]['mc'] = mc
stats[species]['sc'] = sc
stats[species]['rc'] = rc
x = np.asarray(sorted(prd))
yhat = a + b*x
yex = x
plt.figure()
plt.plot(prd, obs, 'ok', label='data', alpha=0.5)
plt.plot(x, yex, '-r', label='y=x')
plt.plot(x, yhat, '-b', label='best fit')
plt.ylabel('observed')
plt.xlabel('predicted')
plt.legend(loc='best')
plt.savefig('plots/obsprd_%s_%s.png' % (flavour, species))
plt.close()
# plt.title({'sw': 'Spruce', 'aw': 'Aspen'}[species])
return stats
# XXX: should really remove these plots
if p1 > p0:
print "NEW TREES:", plotname, obs_year
continue
j = T.index(obs_year)
n1 = sw.population(nsw1[j])
n2 = sw.population(nsw2[j])
obs.append(p1 / p0)
prd["const"].append(n1 / p0)
prd["model"].append(n2 / p0)
for flavour in prd:
F, pval, (a, b) = dent_blackie(obs, prd[flavour])
mrstats[flavour]["F"] = (F, pval)
x = np.asarray(sorted(prd[flavour]))
yhat = a + b * x
yex = x
plt.figure()
plt.plot(prd[flavour], obs, "ok", label="data", alpha=0.5)
plt.plot(x, yex, "-r", label="exact")
plt.plot(x, yhat, "-b", label="fit")
plt.ylabel("observed")
plt.xlabel("predicted")
plt.legend(loc="best")
plt.savefig("plots/mortobsprd_sw_mort_%s.pdf" % flavour)