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):
# 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',
'probability',
'log.prob',
]
# compute the transition matrix
P = np.dot(P_drift_selection, P_mutation)
# Compute the endpoint conditional probabilities for various states
# along the unobserved path.
nstates = fs.npop + 1
M = np.zeros((nstates, fs.ngenerations))
M[fs.nmutants_initial, 0] = 1.0
M[fs.nmutants_final, fs.ngenerations-1] = 1.0
for i in range(fs.ngenerations-2):
A_exponent = i + 1
B_exponent = fs.ngenerations - 1 - A_exponent
A = np.linalg.matrix_power(P, A_exponent)
B = np.linalg.matrix_power(P, B_exponent)
weights = np.zeros(nstates)
for k in range(nstates):
weights[k] = A[fs.nmutants_initial, k] * B[k, fs.nmutants_final]
weights /= np.sum(weights)
for k, p in enumerate(weights):
M[k, i+1] = p
arr = []
for g in range(fs.ngenerations):
for k in range(nstates):
p = M[k, g]
if p:
logp = math.log(p)
else:
logp = float('-inf')
row = [g, k, p, logp]
arr.append(row)
# 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):
# transform the arguments according to the diffusion approximation
mutation_ab = (fs.pop * fs.mutation_ab) / fs.pop_gran
mutation_ba = (fs.pop * fs.mutation_ba) / fs.pop_gran
if mutation_ab > 1 or mutation_ba > 1:
raise Exception('the mutation probability is not small enough '
'for the diffusion approximation to be meaningful')
selection_ratio = 1 + (fs.pop * fs.additive_selection) / fs.pop_gran
npop = fs.pop_gran
ngenerations = fs.ngenerations
nmutants_initial = int(fs.initial_freq * fs.pop_gran)
nmutants_final = int(fs.final_freq * fs.pop_gran)
# precompute some transition matrices
P_drift_selection = pgmsinglesite.create_drift_selection_transition_matrix(
npop, selection_ratio)
MatrixUtil.assert_transition_matrix(P_drift_selection)
P_mutation = pgmsinglesite.create_mutation_transition_matrix(
npop, mutation_ab, mutation_ba)
MatrixUtil.assert_transition_matrix(P_mutation)
# define the R table headers
headers = [
'generation',
'allele.frequency',
'probability',
'log.density',
]
# compute the transition matrix
P = np.dot(P_drift_selection, P_mutation)
# Compute the endpoint conditional probabilities for various states
# along the unobserved path.
nstates = npop + 1
M = np.zeros((nstates, ngenerations))
M[nmutants_initial, 0] = 1.0
M[nmutants_final, ngenerations - 1] = 1.0
for i in range(ngenerations - 2):
A_exponent = i + 1
B_exponent = ngenerations - 1 - A_exponent
A = np.linalg.matrix_power(P, A_exponent)
B = np.linalg.matrix_power(P, B_exponent)
weights = np.zeros(nstates)
for k in range(nstates):
weights[k] = A[nmutants_initial, k] * B[k, nmutants_final]
weights /= np.sum(weights)
for k, p in enumerate(weights):
M[k, i + 1] = p
arr = []
for g in range(ngenerations):
for k in range(nstates):
p = M[k, g]
allele_frequency = k / float(npop)
# Finer gridding needs larger scaling for the density
# because each interval has a smaller support.
density = p * nstates
if density:
log_density = math.log(density)
else:
log_density = float('-inf')
row = [g, allele_frequency, p, log_density]
arr.append(row)
# 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(nstates)
# 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):
# transform the arguments according to the diffusion approximation
mutation_ab = (fs.pop * fs.mutation_ab) / fs.pop_gran
mutation_ba = (fs.pop * fs.mutation_ba) / fs.pop_gran
if mutation_ab > 1 or mutation_ba > 1:
raise Exception(
'the mutation probability is not small enough '
'for the diffusion approximation to be meaningful')
selection_ratio = 1 + (fs.pop * fs.additive_selection) / fs.pop_gran
npop = fs.pop_gran
ngenerations = fs.ngenerations
nmutants_initial = int(fs.initial_freq * fs.pop_gran)
nmutants_final = int(fs.final_freq * fs.pop_gran)
# precompute some transition matrices
P_drift_selection = pgmsinglesite.create_drift_selection_transition_matrix(
npop, selection_ratio)
MatrixUtil.assert_transition_matrix(P_drift_selection)
P_mutation = pgmsinglesite.create_mutation_transition_matrix(
npop, mutation_ab, mutation_ba)
MatrixUtil.assert_transition_matrix(P_mutation)
# define the R table headers
headers = [
'generation',
'allele.frequency',
'probability',
'log.density',
]
# compute the transition matrix
P = np.dot(P_drift_selection, P_mutation)
# Compute the endpoint conditional probabilities for various states
# along the unobserved path.
nstates = npop + 1
M = np.zeros((nstates, ngenerations))
M[nmutants_initial, 0] = 1.0
M[nmutants_final, ngenerations-1] = 1.0
for i in range(ngenerations-2):
A_exponent = i + 1
B_exponent = ngenerations - 1 - A_exponent
A = np.linalg.matrix_power(P, A_exponent)
B = np.linalg.matrix_power(P, B_exponent)
weights = np.zeros(nstates)
for k in range(nstates):
weights[k] = A[nmutants_initial, k] * B[k, nmutants_final]
weights /= np.sum(weights)
for k, p in enumerate(weights):
M[k, i+1] = p
arr = []
for g in range(ngenerations):
for k in range(nstates):
p = M[k, g]
allele_frequency = k / float(npop)
# Finer gridding needs larger scaling for the density
# because each interval has a smaller support.
density = p * nstates
if density:
log_density = math.log(density)
else:
log_density = float('-inf')
row = [g, allele_frequency, p, log_density]
arr.append(row)
# 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(nstates)
# 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
