def make_hist_empir_model(datasets, analysis_ext, data_dir, fig_ext):
plt.figure()
for i, dataset in enumerate (datasets):
datafile = datafile = data_dir + dataset + analysis_ext
raw_data = import_abundance(datafile)
usites = np.sort(list(set(raw_data["site"])))
subplot = i + 1
ax = plt.subplot(4,3, subplot)
for site in usites:
subsites = raw_data["site"][raw_data["site"] == site]
abunds = raw_data["ab"][raw_data["site"] == site]
N = sum(abunds) # N = total abundance for a site
S = len(subsites) # S = species richness at a site
if S > 15:
#Graphing code
"""Make a histogram comparing the two models to the empirical data"""
xs = range(1, max(abunds) * 2)
pln_paras = get_par_multi_dists(abunds, 'pln') + (1,) #add truncation at 1
negbin_paras = get_par_multi_dists(abunds, 'negbin')
pln_pmf = pln.pmf(xs, *pln_paras)
negbin_pmf = nbinom_lower_trunc.pmf(xs, *negbin_paras)
hist_empir, hist_bins = preston_sad(abunds)
hist_empir = hist_empir / sum(hist_empir)
hist_pln, _ = hist_pmf(xs, pln_pmf, hist_bins)
hist_negbin, _ = hist_pmf(xs, negbin_pmf, hist_bins)
hist_bins_log = np.log2(hist_bins)
xticks = hist_bins_log[:-1] + 0.5
xvalues = [int(np.exp2(val)) for val in hist_bins_log[:-1]]
plt.bar(hist_bins_log[:-1], hist_empir, color='gray', width=1)
plt.plot(xticks, hist_pln, linewidth=2, color = 'm')
plt.plot(xticks, hist_negbin, linewidth=2, color = 'c')
plt.xticks(xticks, xvalues, rotation='vertical', fontsize = 'x-small')
plt.yticks(fontsize = 'x-small')
plt.title(dataset, fontsize = 'small')
plt.tight_layout()
break
ax = plt.subplot(4,3, 12)
plt.axis('off')
pln_line = plt.scatter([],[], s=100, marker = 's', facecolors='m', edgecolors='black')
negbin_line = plt.scatter([],[], s=100, marker = 's', facecolors='c', edgecolors='black')
labels = ["Poisson lognormal", "Negative binomial"]
plt.legend([pln_line, negbin_line], labels, frameon=False, fontsize=12, scatterpoints = 1)
output_file = data_dir + fig_ext
plt.savefig(output_file, dpi=250)
plt.show()
plt.close()
def get_llik(abundances, dist):
"""Get the loglikelihood for a given distribution and set of data"""
paras = get_par_multi_dists(abundances, dist)
if paras:
llik = get_loglik_multi_dists(abundances, dist, *paras)
else:
llik = None
return llik
def get_llik(abundances, dist):
"""Get the loglikelihood for a given distribution and set of data"""
paras = get_par_multi_dists(abundances, dist)
if paras:
llik = get_loglik_multi_dists(abundances, dist, *paras)
else:
llik = None
return llik
def make_hist_empir_model(datasets, analysis_ext, data_dir, fig_ext):
plt.figure()
for i, dataset in enumerate(datasets):
datafile = datafile = data_dir + dataset + analysis_ext
raw_data = import_abundance(datafile)
usites = np.sort(list(set(raw_data["site"])))
subplot = i + 1
ax = plt.subplot(4, 3, subplot)
for site in usites:
subsites = raw_data["site"][raw_data["site"] == site]
abunds = raw_data["ab"][raw_data["site"] == site]
N = sum(abunds) # N = total abundance for a site
S = len(subsites) # S = species richness at a site
if S > 15:
#Graphing code
"""Make a histogram comparing the two models to the empirical data"""
xs = range(1, max(abunds) * 2)
pln_paras = get_par_multi_dists(abunds, 'pln') + (
1, ) #add truncation at 1
negbin_paras = get_par_multi_dists(abunds, 'negbin')
pln_pmf = pln.pmf(xs, *pln_paras)
negbin_pmf = nbinom_lower_trunc.pmf(xs, *negbin_paras)
hist_empir, hist_bins = preston_sad(abunds)
hist_empir = hist_empir / sum(hist_empir)
hist_pln, _ = hist_pmf(xs, pln_pmf, hist_bins)
hist_negbin, _ = hist_pmf(xs, negbin_pmf, hist_bins)
hist_bins_log = np.log2(hist_bins)
xticks = hist_bins_log[:-1] + 0.5
xvalues = [int(np.exp2(val)) for val in hist_bins_log[:-1]]
plt.bar(hist_bins_log[:-1], hist_empir, color='gray', width=1)
plt.plot(xticks, hist_pln, linewidth=2, color='m')
plt.plot(xticks, hist_negbin, linewidth=2, color='c')
plt.xticks(xticks,
xvalues,
rotation='vertical',
fontsize='x-small')
plt.yticks(fontsize='x-small')
plt.title(dataset, fontsize='small')
plt.tight_layout()
break
ax = plt.subplot(4, 3, 12)
plt.axis('off')
pln_line = plt.scatter([], [],
s=100,
marker='s',
facecolors='m',
edgecolors='black')
negbin_line = plt.scatter([], [],
s=100,
marker='s',
facecolors='c',
edgecolors='black')
labels = ["Poisson lognormal", "Negative binomial"]
plt.legend([pln_line, negbin_line],
labels,
frameon=False,
fontsize=12,
scatterpoints=1)
output_file = data_dir + fig_ext
plt.savefig(output_file, dpi=250)
plt.show()
plt.close()