def get_response_content(fs):
f_info = divtime.get_fisher_info_known_distn_fast
requested_triples = []
for triple in g_process_triples:
name, desc, zoo_obj = triple
if getattr(fs, name):
requested_triples.append(triple)
if not requested_triples:
raise ValueError('nothing to plot')
# define the R table headers
r_names = [a.replace('_', '.') for a, b, c in requested_triples]
headers = ['t'] + r_names
# Spend a lot of time doing the optimizations
# to construct the points for the R table.
arr = []
for t in cbreaker.throttled(progrid.gen_binary(fs.start_time,
fs.stop_time),
nseconds=5,
ncount=200):
row = [t]
for python_name, desc, zoo_class in requested_triples:
zoo_obj = zoo_class(fs.d)
df = zoo_obj.get_df()
opt_dep = OptDep(zoo_obj, t, f_info)
if df:
X0 = np.random.randn(df)
xopt = scipy.optimize.fmin(opt_dep,
X0,
maxiter=10000,
maxfun=10000)
# I would like to use scipy.optimize.minimize
# except that this requires a newer version of
# scipy than is packaged for ubuntu right now.
# fmin_bfgs seems to have problems sometimes
# either hanging or maxiter=10K is too big.
"""
xopt = scipy.optimize.fmin_bfgs(opt_dep, X0,
gtol=1e-8, maxiter=10000)
"""
else:
xopt = np.array([])
info_value = -opt_dep(xopt)
row.append(info_value)
arr.append(row)
arr.sort()
npoints = len(arr)
# create the R table string and scripts
# get the R table
table_string = RUtil.get_table_string(arr, headers)
# get the R script
script = get_ggplot()
# create the R plot image
device_name = Form.g_imageformat_to_r_function[fs.imageformat]
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
f_info = divtime.get_fisher_info_known_distn_fast
requested_triples = []
for triple in g_process_triples:
name, desc, zoo_obj = triple
if getattr(fs, name):
requested_triples.append(triple)
if not requested_triples:
raise ValueError('nothing to plot')
# define the R table headers
r_names = [a.replace('_', '.') for a, b, c in requested_triples]
headers = ['t'] + r_names
# Spend a lot of time doing the optimizations
# to construct the points for the R table.
arr = []
for t in cbreaker.throttled(
progrid.gen_binary(fs.start_time, fs.stop_time),
nseconds=5, ncount=200):
row = [t]
for python_name, desc, zoo_class in requested_triples:
zoo_obj = zoo_class(fs.d)
df = zoo_obj.get_df()
opt_dep = OptDep(zoo_obj, t, f_info)
if df:
X0 = np.random.randn(df)
xopt = scipy.optimize.fmin(
opt_dep, X0, maxiter=10000, maxfun=10000)
# I would like to use scipy.optimize.minimize
# except that this requires a newer version of
# scipy than is packaged for ubuntu right now.
# fmin_bfgs seems to have problems sometimes
# either hanging or maxiter=10K is too big.
"""
xopt = scipy.optimize.fmin_bfgs(opt_dep, X0,
gtol=1e-8, maxiter=10000)
"""
else:
xopt = np.array([])
info_value = -opt_dep(xopt)
row.append(info_value)
arr.append(row)
arr.sort()
npoints = len(arr)
# create the R table string and scripts
# get the R table
table_string = RUtil.get_table_string(arr, headers)
# get the R script
script = get_ggplot()
# create the R plot image
device_name = Form.g_imageformat_to_r_function[fs.imageformat]
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
M = get_input_matrix(fs)
nstates = len(M)
nsites = fs.nsites
if nstates**nsites > 16:
raise ValueError('the site dependent rate matrix is too big')
# precompute some stuff
M_site_indep = get_site_independent_process(M, nsites)
v = mrate.R_to_distn(M)
v_site_indep = get_site_independent_distn(v, nsites)
if fs.info_fis:
f_info = divtime.get_fisher_info_known_distn_fast
elif fs.info_mut:
f_info = ctmcmi.get_mutual_info_known_distn
else:
raise ValueError('no info type specified')
f_selection = mrate.to_gtr_hb_known_energies
# Spend a lot of time doing the optimizations
# to construct the points for the R table.
arr = []
for t in cbreaker.throttled(progrid.gen_binary(fs.start_time,
fs.stop_time),
nseconds=4,
ncount=100):
row = [t]
# get the site-dependent mutation selection balance information
if fs.dep_balance:
dep_balance = OptDep(M_site_indep, v_site_indep, t, f_info,
f_selection)
X0 = np.random.randn(nstates**nsites - 1)
xopt = scipy.optimize.fmin(dep_balance, X0)
max_dep_balance_info = -dep_balance(xopt)
row.append(max_dep_balance_info)
# for debug
Q_bal, v_bal = dep_balance.get_process(xopt)
print 'dependent balance:'
print max_dep_balance_info
print v_bal
print Q_bal
print
# get the site-independent mutation selection balance information
if fs.indep_balance:
indep_balance = OptIndep(M, v, nsites, t, f_info, f_selection)
X0 = np.random.randn(nstates - 1)
xopt = scipy.optimize.fmin(indep_balance, X0)
max_indep_balance_info = -indep_balance(xopt)
row.append(max_indep_balance_info)
# for debug
Q_bal, v_bal = indep_balance.get_process(xopt)
print 'independent balance:'
print max_indep_balance_info
print v_bal
print Q_bal
print
# get the site-independent mutation process information
if fs.indep_mutation:
indep_mut_info = f_info(M_site_indep, v_site_indep, t)
row.append(indep_mut_info)
# add the data row to the table
arr.append(row)
arr.sort()
npoints = len(arr)
# create the R table string and scripts
headers = ['t']
if fs.dep_balance:
headers.append('max.site.dep.balance')
if fs.indep_balance:
headers.append('max.site.indep.balance')
if fs.indep_mutation:
headers.append('site.indep.mutation')
# get the R table
table_string = RUtil.get_table_string(arr, headers)
# get the R script
script = get_ggplot()
# create the R plot image
device_name = Form.g_imageformat_to_r_function[fs.imageformat]
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def test_binary(self):
observed = list(cbreaker.throttled(gen_binary(0, 1), ncount=9))
expected = [0.0, 1.0, 0.5, 0.25, 0.75, 0.125, 0.375, 0.625, 0.875]
self.assertEqual(observed, expected)
def get_response_content(fs):
M = get_input_matrix(fs)
nstates = len(M)
nsites = fs.nsites
if nstates ** nsites > 16:
raise ValueError('the site dependent rate matrix is too big')
# precompute some stuff
M_site_indep = get_site_independent_process(M, nsites)
v = mrate.R_to_distn(M)
v_site_indep = get_site_independent_distn(v, nsites)
if fs.info_fis:
f_info = divtime.get_fisher_info_known_distn_fast
elif fs.info_mut:
f_info = ctmcmi.get_mutual_info_known_distn
else:
raise ValueError('no info type specified')
f_selection = mrate.to_gtr_hb_known_energies
# Spend a lot of time doing the optimizations
# to construct the points for the R table.
arr = []
for t in cbreaker.throttled(
progrid.gen_binary(fs.start_time, fs.stop_time),
nseconds=4, ncount=100):
row = [t]
# get the site-dependent mutation selection balance information
if fs.dep_balance:
dep_balance = OptDep(
M_site_indep, v_site_indep, t, f_info, f_selection)
X0 = np.random.randn(nstates ** nsites - 1)
xopt = scipy.optimize.fmin(dep_balance, X0)
max_dep_balance_info = -dep_balance(xopt)
row.append(max_dep_balance_info)
# for debug
Q_bal, v_bal = dep_balance.get_process(xopt)
print 'dependent balance:'
print max_dep_balance_info
print v_bal
print Q_bal
print
# get the site-independent mutation selection balance information
if fs.indep_balance:
indep_balance = OptIndep(
M, v, nsites, t, f_info, f_selection)
X0 = np.random.randn(nstates-1)
xopt = scipy.optimize.fmin(indep_balance, X0)
max_indep_balance_info = -indep_balance(xopt)
row.append(max_indep_balance_info)
# for debug
Q_bal, v_bal = indep_balance.get_process(xopt)
print 'independent balance:'
print max_indep_balance_info
print v_bal
print Q_bal
print
# get the site-independent mutation process information
if fs.indep_mutation:
indep_mut_info = f_info(M_site_indep, v_site_indep, t)
row.append(indep_mut_info)
# add the data row to the table
arr.append(row)
arr.sort()
npoints = len(arr)
# create the R table string and scripts
headers = ['t']
if fs.dep_balance:
headers.append('max.site.dep.balance')
if fs.indep_balance:
headers.append('max.site.indep.balance')
if fs.indep_mutation:
headers.append('site.indep.mutation')
# get the R table
table_string = RUtil.get_table_string(arr, headers)
# get the R script
script = get_ggplot()
# create the R plot image
device_name = Form.g_imageformat_to_r_function[fs.imageformat]
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
