def plot_1d_comp_Poisson(model,
data,
fig_num=None,
residual='Anscombe',
plot_masked=False,
show=True):
"""
Poisson comparison between 1d model and data.
model: 1-dimensional model SFS
data: 1-dimensional data SFS
fig_num: Clear and use figure fig_num for display. If None, an new figure
window is created.
residual: 'Anscombe' for Anscombe residuals, which are more normally
distributed for Poisson sampling. 'linear' for the linear
residuals, which can be less biased.
plot_masked: Additionally plots (in open circles) results for points in the
model or data that were masked.
show: If True, execute pylab.show command to make sure plot displays.
"""
if fig_num is None:
f = pylab.gcf()
else:
f = pylab.figure(fig_num, figsize=(7, 7))
pylab.clf()
if data.folded and not model.folded:
model = model.fold()
masked_model, masked_data = Numerics.intersect_masks(model, data)
ax = pylab.subplot(2, 1, 1)
pylab.semilogy(masked_data, '-ob', label='data')
pylab.semilogy(masked_model, '-or', label='model')
if plot_masked:
pylab.semilogy(masked_data.data, '--ob', mfc='w', zorder=-100)
pylab.semilogy(masked_model.data, '--or', mfc='w', zorder=-100)
ax.legend(loc='upper right')
pylab.subplot(2, 1, 2, sharex=ax)
if residual == 'Anscombe':
resid = Inference.Anscombe_Poisson_residual(masked_model, masked_data)
elif residual == 'linear':
resid = Inference.linear_Poisson_residual(masked_model, masked_data)
else:
raise ValueError("Unknown class of residual '%s'." % residual)
pylab.plot(resid, '-og')
if plot_masked:
pylab.plot(resid.data, '--og', mfc='w', zorder=-100)
ax.set_xlim(0, data.shape[0] - 1)
if show:
pylab.show()
def plot_2d_comp_Poisson(model, data, vmin=None, vmax=None,
resid_range=None, fig_num=None,
pop_ids=None, residual='Anscombe',
adjust=True, saveplot=False,
nomplot="plot_2d_comp_Poisson",
showplot=True):
"""
Poisson comparison between 2d model and data.
model: 2-dimensional model SFS
data: 2-dimensional data SFS
vmin, vmax: Minimum and maximum values plotted for sfs are vmin and
vmax respectively.
resid_range: Residual plot saturates at +- resid_range.
fig_num: Clear and use figure fig_num for display. If None, an new figure
window is created.
pop_ids: If not None, override pop_ids stored in Spectrum.
residual: 'Anscombe' for Anscombe residuals, which are more normally
distributed for Poisson sampling. 'linear' for the linear
residuals, which can be less biased.
adjust: Should method use automatic 'subplots_adjust'? For advanced
manipulation of plots, it may be useful to make this False.
"""
if data.folded and not model.folded:
model = model.fold()
masked_model, masked_data = Numerics.intersect_masks(model, data)
if fig_num is None:
f = pylab.gcf()
else:
f = pylab.figure(fig_num, figsize=(7,7))
pylab.clf()
if adjust:
pylab.subplots_adjust(bottom=0.07, left=0.07, top=0.94, right=0.95,
hspace=0.26, wspace=0.26)
max_toplot = max(masked_model.max(), masked_data.max())
min_toplot = min(masked_model.min(), masked_data.min())
if vmax is None:
vmax = max_toplot
if vmin is None:
vmin = min_toplot
extend = _extend_mapping[vmin <= min_toplot, vmax >= max_toplot]
if pop_ids is not None:
data_pop_ids = model_pop_ids = resid_pop_ids = pop_ids
if len(pop_ids) != 2:
raise ValueError('pop_ids must be of length 2.')
else:
data_pop_ids = masked_data.pop_ids
model_pop_ids = masked_model.pop_ids
if masked_model.pop_ids is None:
model_pop_ids = data_pop_ids
if model_pop_ids == data_pop_ids:
resid_pop_ids = model_pop_ids
else:
resid_pop_ids = None
ax = pylab.subplot(2,2,1)
plot_single_2d_sfs(masked_data, vmin=vmin, vmax=vmax,
pop_ids=data_pop_ids, colorbar=False)
ax.set_title('data')
ax2 = pylab.subplot(2,2,2, sharex=ax, sharey=ax)
plot_single_2d_sfs(masked_model, vmin=vmin, vmax=vmax,
pop_ids=model_pop_ids, extend=extend )
ax2.set_title('model')
if residual == 'Anscombe':
resid = Inference.Anscombe_Poisson_residual(masked_model, masked_data,
mask=vmin)
elif residual == 'linear':
resid = Inference.linear_Poisson_residual(masked_model, masked_data,
mask=vmin)
else:
raise ValueError("Unknown class of residual '%s'." % residual)
if resid_range is None:
resid_range = max((abs(resid.max()), abs(resid.min())))
resid_extend = _extend_mapping[-resid_range <= resid.min(),
resid_range >= resid.max()]
ax3 = pylab.subplot(2,2,3, sharex=ax, sharey=ax)
plot_2d_resid(resid, resid_range, pop_ids=resid_pop_ids,
extend=resid_extend)
ax3.set_title('residuals')
ax = pylab.subplot(2,2,4)
flatresid = numpy.compress(numpy.logical_not(resid.mask.ravel()),
resid.ravel())
ax.hist(flatresid, bins=20, normed=True)
ax.set_title('residuals')
ax.set_yticks([])
if saveplot:
nomplot=nomplot + ".png"
pylab.savefig(nomplot)
if showplot:
pylab.show()
print('Optimized parameters', str(repr(popt)))
model = func_ex(popt[0], ns, pts_l)
ll_opt = dadi.Inference.ll_multinom(model, pop)
print('Optimized log-likelihood:', str(ll_opt))
print('Theta0_2:', str(theta0))
##Print out the scaled SFS and the Anscombe Poisson residuals
print("==============================================")
print(Inference.optimally_scaled_sfs(model, pop))
rescaled = Inference.optimally_scaled_sfs(model, pop)
print(Inference.Anscombe_Poisson_residual(rescaled,pop))
dadi.Plotting.plot_1d_comp_multinom(model,pop)
print("==============================================")
####Neutral Fit#########
###########################
print("NEUTRAL MODEL")
params = [1]
lowerbounds = [100]
upperbounds = [0.001]
