def get_response_content(fs):
# precompute some transition matrices
P_drift_selection = pgmsinglesite.create_drift_selection_transition_matrix(
fs.npop, fs.selection_ratio)
MatrixUtil.assert_transition_matrix(P_drift_selection)
P_mutation = pgmsinglesite.create_mutation_transition_matrix(
fs.npop, fs.mutation_ab, fs.mutation_ba)
MatrixUtil.assert_transition_matrix(P_mutation)
# define the R table headers
headers = ['generation', 'number.of.mutants']
# compute the path samples
P = np.dot(P_drift_selection, P_mutation)
mypath = PathSampler.sample_endpoint_conditioned_path(
fs.nmutants_initial, fs.nmutants_final, fs.ngenerations, P)
arr = [[i, nmutants] for i, nmutants in enumerate(mypath)]
# 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):
# precompute some transition matrices
P_drift_selection = pgmsinglesite.create_drift_selection_transition_matrix(
fs.npop, fs.selection_ratio)
MatrixUtil.assert_transition_matrix(P_drift_selection)
P_mutation = pgmsinglesite.create_mutation_transition_matrix(
fs.npop, fs.mutation_ab, fs.mutation_ba)
MatrixUtil.assert_transition_matrix(P_mutation)
# define the R table headers
headers = ['generation', 'number.of.mutants']
# compute the path samples
P = np.dot(P_drift_selection, P_mutation)
mypath = PathSampler.sample_endpoint_conditioned_path(
fs.nmutants_initial, fs.nmutants_final, fs.ngenerations, P)
arr = [[i, nmutants] for i, nmutants in enumerate(mypath)]
# 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):
initial_state = multiline_state_to_ndarray(fs.initial_state)
final_state = multiline_state_to_ndarray(fs.final_state)
if initial_state.shape != final_state.shape:
raise ValueError(
'initial and final states do not have the same dimensions')
nchromosomes, npositions = initial_state.shape
ndimcap = 12
if nchromosomes * npositions > ndimcap:
raise ValueError('at most 2^%d states are allowed per generation' %
ndimcap)
#
mutation = 0.5 * fs.mutation_param
recombination = 0.5 * fs.recombination_param
selection = fs.selection_param
#
out = StringIO()
print >> out, 'number of chromosomes:'
print >> out, nchromosomes
print >> out
print >> out, 'number of positions per chromosome:'
print >> out, npositions
print >> out
# define the transition matrix
results = pgmfancy.get_state_space_info(nchromosomes, npositions)
ci_to_short, short_to_count, sorted_chrom_lists = results
initial_ci = pgmfancy.chroms_to_index(
sorted(popgenmarkov.bin_to_int(row) for row in initial_state),
npositions)
initial_short = ci_to_short[initial_ci]
final_ci = pgmfancy.chroms_to_index(
sorted(popgenmarkov.bin_to_int(row) for row in final_state),
npositions)
final_short = ci_to_short[final_ci]
P_sr_s = pgmfancy.get_selection_recombination_transition_matrix_s(
ci_to_short, short_to_count, sorted_chrom_lists, selection,
recombination, nchromosomes, npositions)
P_m_s = pgmfancy.get_mutation_transition_matrix_s(ci_to_short,
short_to_count,
sorted_chrom_lists,
mutation, nchromosomes,
npositions)
P = np.dot(P_sr_s, P_m_s)
# sample the endpoint conditioned path
path = PathSampler.sample_endpoint_conditioned_path(
initial_short, final_short, fs.ngenerations, P)
print >> out, 'sampled endpoint conditioned path, including endpoints:'
print >> out
# print the initial state without shuffling of individuals
print >> out, ndarray_to_multiline_state(initial_state)
print >> out
# print the intermediate states with randomly permuted individuals
for short_index in path[1:-1]:
chroms = sorted_chrom_lists[short_index]
random.shuffle(chroms)
K = np.array([popgenmarkov.int_to_bin(x, npositions) for x in chroms])
print >> out, ndarray_to_multiline_state(K)
print >> out
# print the final state without shuffling of individuals
print >> out, ndarray_to_multiline_state(final_state)
print >> out
return out.getvalue()
def get_response_content(fs):
initial_state = multiline_state_to_ndarray(fs.initial_state)
final_state = multiline_state_to_ndarray(fs.final_state)
if initial_state.shape != final_state.shape:
raise ValueError(
'initial and final states do not have the same dimensions')
nchromosomes, npositions = initial_state.shape
ndimcap = 12
if nchromosomes * npositions > ndimcap:
raise ValueError(
'at most 2^%d states are allowed per generation' % ndimcap)
#
mutation = 0.5 * fs.mutation_param
recombination = 0.5 * fs.recombination_param
selection = fs.selection_param
#
out = StringIO()
print >> out, 'number of chromosomes:'
print >> out, nchromosomes
print >> out
print >> out, 'number of positions per chromosome:'
print >> out, npositions
print >> out
# define the transition matrix
results = pgmfancy.get_state_space_info(nchromosomes, npositions)
ci_to_short, short_to_count, sorted_chrom_lists = results
initial_ci = pgmfancy.chroms_to_index(
sorted(popgenmarkov.bin_to_int(row) for row in initial_state),
npositions)
initial_short = ci_to_short[initial_ci]
final_ci = pgmfancy.chroms_to_index(
sorted(popgenmarkov.bin_to_int(row) for row in final_state),
npositions)
final_short = ci_to_short[final_ci]
P_sr_s = pgmfancy.get_selection_recombination_transition_matrix_s(
ci_to_short, short_to_count, sorted_chrom_lists,
selection, recombination, nchromosomes, npositions)
P_m_s = pgmfancy.get_mutation_transition_matrix_s(
ci_to_short, short_to_count, sorted_chrom_lists,
mutation, nchromosomes, npositions)
P = np.dot(P_sr_s, P_m_s)
# sample the endpoint conditioned path
path = PathSampler.sample_endpoint_conditioned_path(
initial_short, final_short,
fs.ngenerations, P)
print >> out, 'sampled endpoint conditioned path, including endpoints:'
print >> out
# print the initial state without shuffling of individuals
print >> out, ndarray_to_multiline_state(initial_state)
print >> out
# print the intermediate states with randomly permuted individuals
for short_index in path[1:-1]:
chroms = sorted_chrom_lists[short_index]
random.shuffle(chroms)
K = np.array([popgenmarkov.int_to_bin(x, npositions) for x in chroms])
print >> out, ndarray_to_multiline_state(K)
print >> out
# print the final state without shuffling of individuals
print >> out, ndarray_to_multiline_state(final_state)
print >> out
return out.getvalue()
