def get_response_content(fs):
f_info = ctmcmi.get_mutual_info_known_distn
# define the R table headers
headers = ['log.probability.ratio', 'mutual.information']
# make the array
arr = []
for x in np.linspace(fs.x_min, fs.x_max, 101):
row = [x]
proc = evozoo.AlternatingHypercube_d_1(3)
X = np.array([x])
distn = proc.get_distn(X)
Q = proc.get_rate_matrix(X)
info = f_info(Q, distn, fs.t)
row.append(info)
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):
# validate and store user input
if fs.x_max <= fs.x_min:
raise ValueError('check the min and max logs')
f_info = divtime.get_fisher_info_known_distn_fast
# define the R table headers
headers = ['log.probability.ratio', 'fisher.information']
# make the array
arr = []
for x in np.linspace(fs.x_min, fs.x_max, 101):
row = [x]
proc = evozoo.DistinguishedCornerPairHypercube_d_1(3)
X = np.array([x])
distn = proc.get_distn(X)
Q = proc.get_rate_matrix(X)
info = f_info(Q, distn, fs.t)
row.append(info)
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 main(args):
# get the end positions,
# forcing the first end position to be 5
# and the last end position to be 898.
incr = (g_nchar - 5) / float(args.nlengths - 1)
stop_positions = [5 + int(i * incr) for i in range(args.nlengths)]
stop_positions[-1] = g_nchar
# run BEAST and create the R stuff
table_string, scripts = get_table_string_and_scripts(
stop_positions, args.nsamples)
# create the comboscript
out = StringIO()
print >> out, 'library(ggplot2)'
print >> out, 'par(mfrow=c(3,1))'
for script in scripts:
print >> out, script
comboscript = out.getvalue()
# create the R output image
device_name = Form.g_imageformat_to_r_function['pdf']
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, comboscript, device_name)
if retcode:
raise RUtil.RError(r_err)
# write the image data
with open(args.outfile, 'wb') as fout:
fout.write(image_data)
def get_response_content(fs):
M, R = get_input_matrices(fs)
# create the R table string and scripts
headers = [
't', 'mi.true.mut', 'mi.true.mutsel', 'mi.analog.mut',
'mi.analog.mutsel'
]
npoints = 100
t_low = 0.0
t_high = 5.0
t_incr = (t_high - t_low) / (npoints - 1)
t_values = [t_low + t_incr * i for i in range(npoints)]
# get the data for the R table
arr = []
for t in t_values:
mi_mut = ctmcmi.get_mutual_information(M, t)
mi_mutsel = ctmcmi.get_mutual_information(R, t)
mi_analog_mut = ctmcmi.get_ll_ratio_wrong(M, t)
mi_analog_mutsel = ctmcmi.get_ll_ratio_wrong(R, t)
row = [t, mi_mut, mi_mutsel, mi_analog_mut, mi_analog_mutsel]
arr.append(row)
# 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_latex_documentbody(fs):
"""
This is obsolete because I am now using pure R output.
The latex documentbody should have a bunch of tikz pieces in it.
Each tikz piece should have been generated from R.
"""
Q_mut, Q_sels = get_qmut_qsels(fs)
# compute the statistics
ER_ratios, NSR_ratios, ER_NSR_ratios = get_statistic_ratios(Q_mut, Q_sels)
M = zip(*(ER_ratios, NSR_ratios, ER_NSR_ratios))
column_headers = ('ER.ratio', 'NSR.ratio', 'ER.times.NSR.ratio')
table_string = RUtil.get_table_string(M, column_headers)
nsels = len(Q_sels)
# define the R scripts
scripts = []
for name in column_headers:
scripts.append(get_r_tikz_script(nsels, name))
# get the tikz codes from R, for each histogram
retcode, r_out, r_err, tikz_code_list = RUtil.run_plotter_multiple_scripts(
table_string, scripts, 'tikz', width=3, height=2)
if retcode:
raise RUtil.RError(r_err)
#
# show some timings
print 'R did not fail, but here is its stderr:'
print r_err
#
# write the latex code
out = StringIO()
#print >> out, '\\pagestyle{empty}'
for tikz_code in tikz_code_list:
print >> out, tikz_code
# return the latex code, consisting mainly of a bunch of tikz plots
return out.getvalue()
def main(args):
# set up the logger
f = logging.getLogger('toplevel.logger')
h = logging.StreamHandler()
h.setFormatter(logging.Formatter('%(message)s %(asctime)s'))
f.addHandler(h)
if args.verbose:
f.setLevel(logging.DEBUG)
else:
f.setLevel(logging.WARNING)
f.info('(local) permute columns of the alignment')
header_seq_pairs = beasttut.get_456_col_permuted_header_seq_pairs()
f.info('(local) run BEAST serially locally and build the R stuff')
table_string, scripts = get_table_string_and_scripts(
g_start_stop_pairs, args.nsamples, header_seq_pairs)
f.info('(local) create the composite R script')
out = StringIO()
print >> out, 'library(ggplot2)'
print >> out, 'par(mfrow=c(3,1))'
for script in scripts:
print >> out, script
comboscript = out.getvalue()
f.info('(local) run R to create the pdf')
device_name = Form.g_imageformat_to_r_function['pdf']
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, comboscript, device_name, keep_intermediate=True)
if retcode:
raise RUtil.RError(r_err)
f.info('(local) write the .pdf file')
with open(args.outfile, 'wb') as fout:
fout.write(image_data)
f.info('(local) return from toplevel')
def get_r_tikz_stub():
user_script = RUtil.g_stub
device_name = 'tikz'
retcode, r_out, r_err, tikz_code = RUtil.run_plotter_no_table(
user_script, device_name)
if retcode:
raise RUtil.RError(r_err)
return tikz_code
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):
# legend labels
label_a = 'N=%d mu=%f' % (fs.nstates_a, fs.mu_a)
label_b = 'N=%d mu=%f' % (fs.nstates_b, fs.mu_b)
arr, headers = make_table(fs)
# compute the max value
ymax = math.log(max(fs.nstates_a, fs.nstates_b))
nfifths = int(math.floor(ymax * 5.0)) + 1
ylim = RUtil.mk_call_str('c', 0, 0.2 * nfifths)
# write the R script body
out = StringIO()
print >> out, RUtil.mk_call_str(
'plot',
'my.table$t',
'my.table$alpha',
type='"n"',
ylim=ylim,
xlab='"time"',
ylab='"information"',
main='"comparison of an information criterion for two processes"',
)
# draw some horizontal lines
for i in range(nfifths + 1):
print >> out, RUtil.mk_call_str('abline',
h=0.2 * i,
col='"lightgray"',
lty='"dotted"')
colors = ('darkblue', 'darkred')
for c, header in zip(colors, headers[1:]):
print >> out, RUtil.mk_call_str(
'lines',
'my.table$t',
'my.table$%s' % header,
col='"%s"' % c,
)
legend_names = (label_a, label_b)
legend_name_str = 'c(' + ', '.join('"%s"' % s for s in legend_names) + ')'
legend_col_str = 'c(' + ', '.join('"%s"' % s for s in colors) + ')'
legend_lty_str = 'c(' + ', '.join('1' for s in colors) + ')'
print >> out, RUtil.mk_call_str(
'legend',
'"%s"' % fs.legend_placement,
legend_name_str,
col=legend_col_str,
lty=legend_lty_str,
)
script_body = out.getvalue()
# create the R plot image
table_string = RUtil.get_table_string(arr, headers)
device_name = Form.g_imageformat_to_r_function[fs.imageformat]
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, script_body, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
distn_modes = [x for x in g_ordered_modes if x in fs.distribution]
if not distn_modes:
raise ValueError('no distribution mode was specified')
colors = [g_mode_to_color[m] for m in distn_modes]
arr, headers = make_table(fs, distn_modes)
distn_headers = headers[1:]
# Get the largest value in the array,
# skipping the first column.
arrmax = np.max(arr[:, 1:])
# write the R script body
out = StringIO()
ylim = RUtil.mk_call_str('c', 0, arrmax + 0.1)
sel_str = {
BALANCED: 'balanced',
HALPERN_BRUNO: 'Halpern-Bruno',
}[fs.selection]
print >> out, RUtil.mk_call_str(
'plot',
'my.table$t',
'my.table$%s' % distn_headers[0],
type='"n"',
ylim=ylim,
xlab='""',
ylab='"relaxation time"',
main='"Effect of selection (%s) on relaxation time for %d states"' %
(sel_str, fs.nstates),
)
for c, header in zip(colors, distn_headers):
print >> out, RUtil.mk_call_str(
'lines',
'my.table$t',
'my.table$%s' % header,
col='"%s"' % c,
)
mode_names = [s.replace('_', ' ') for s in distn_modes]
legend_name_str = 'c(' + ', '.join('"%s"' % s for s in mode_names) + ')'
legend_col_str = 'c(' + ', '.join('"%s"' % s for s in colors) + ')'
legend_lty_str = 'c(' + ', '.join(['1'] * len(distn_modes)) + ')'
print >> out, RUtil.mk_call_str(
'legend',
'"%s"' % fs.legend_placement,
legend_name_str,
col=legend_col_str,
lty=legend_lty_str,
)
script_body = out.getvalue()
# create the R plot image
table_string = RUtil.get_table_string(arr, headers)
device_name = Form.g_imageformat_to_r_function[fs.imageformat]
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, script_body, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
# define some fixed values
N_diploid = 10
N_hap = 2 * N_diploid
#Nr = fs.Nr
plot_density = 2
# define some mutation rates
theta_values = [0.001, 0.01, 0.1, 1.0]
# define some selection coefficients to plot
Ns_low = 0.0
Ns_high = 3.0
Ns_values = np.linspace(Ns_low, Ns_high, 3 * plot_density + 1)
# get the values for each h
Nr_values = (0, 5)
arr_0 = get_plot_array(N_diploid, Nr_values[0], theta_values, Ns_values)
arr_1 = get_plot_array(N_diploid, Nr_values[1], theta_values, Ns_values)
if fs.scale_to_2N_200:
arr_0 = (200 / float(N_hap)) * np.array(arr_0)
arr_1 = (200 / float(N_hap)) * np.array(arr_1)
ylab = '"generations * theta * (200 / 2N)"'
else:
ylab = '"generations * theta"'
# define x and y plot limits
xlim = (Ns_low, Ns_high)
ylim = (np.min((arr_0, arr_1)), np.max((arr_0, arr_1)))
if fs.ylogscale:
ylogstr = '"y"'
else:
ylogstr = '""'
# http://sphaerula.com/legacy/R/multiplePlotFigure.html
out = StringIO()
print >> out, mk_call_str(
'par',
mfrow='c(1,2)',
oma='c(0,0,2,0)',
)
print >> out, get_plot('left', Nr_values[0], arr_0, theta_values,
Ns_values, xlim, ylim, ylogstr, ylab)
print >> out, get_plot('right', Nr_values[1], arr_1, theta_values,
Ns_values, xlim, ylim, ylogstr, '""')
print >> out, mk_call_str(
'title',
'"mean hitting time, 2N=%s"' % N_hap,
outer='TRUE',
)
script = out.getvalue().rstrip()
# 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_no_table(
script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def main(args):
# check args
if gmpy.popcount(args.ntiles) != 1:
raise ValueError('the number of tiles should be a power of two')
# set up the logger
f = logging.getLogger('toplevel.logger')
h = logging.StreamHandler()
h.setFormatter(logging.Formatter('%(message)s %(asctime)s'))
f.addHandler(h)
if args.verbose:
f.setLevel(logging.DEBUG)
else:
f.setLevel(logging.WARNING)
f.info('(local) read the xml contents')
if args.infile is None:
xmldata = sys.stdin.read()
else:
with open(args.infile) as fin:
xmldata = fin.read()
f.info('(local) modify the log filename and chain length xml contents')
xmldata = beast.set_nsamples(xmldata, args.mcmc_id, args.nsamples)
xmldata = beast.set_log_filename(xmldata, args.log_id, args.log_filename)
xmldata = beast.set_log_logevery(xmldata, args.log_id, args.log_logevery)
f.info('(local) define the hierarchically nested intervals')
start_stop_pairs = tuple(
(a + 1, b) for a, b in beasttiling.gen_hierarchical_slices(
args.tile_width, args.offset, args.tile_width * args.ntiles))
f.info('(local) run BEAST serially locally and build the R stuff')
table_string, full_table_string, scripts = get_table_strings_and_scripts(
xmldata, args.alignment_id, start_stop_pairs, args.nsamples)
if args.full_table_out:
f.info('(local) create the verbose R table')
with open(args.full_table_out, 'w') as fout:
fout.write(full_table_string)
f.info('(local) create the composite R script')
out = StringIO()
print >> out, 'library(ggplot2)'
print >> out, 'par(mfrow=c(3,1))'
for script in scripts:
print >> out, script
comboscript = out.getvalue()
f.info('(local) run R to create the pdf')
device_name = Form.g_imageformat_to_r_function['pdf']
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, comboscript, device_name, keep_intermediate=True)
if retcode:
raise RUtil.RError(r_err)
f.info('(local) write the .pdf file')
with open(args.outfile, 'wb') as fout:
fout.write(image_data)
f.info('(local) return from toplevel')
def get_latex_documentbody(fs):
"""
This is obsolete.
"""
out = StringIO()
table_string, scripts = get_table_string_and_scripts(fs)
for script in scripts:
retcode, r_out, r_err, tikz_code = RUtil.run_plotter(
table_string, script, 'tikz',
width=5, height=5)
if retcode:
raise RUtil.RError(r_err)
print >> out, tikz_code
return out.getvalue()
def get_response_content(fs):
# get the table string and scripts
table_string, scripts = get_table_string_and_scripts(fs)
# create a comboscript
out = StringIO()
print >> out, 'par(mfrow=c(3,1))'
for script in scripts:
print >> out, script
comboscript = out.getvalue()
# 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, comboscript, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
Q_mut, Q_sels = get_qmut_qsels(fs)
# compute the statistics
ER_ratios, NSR_ratios, ER_NSR_ratios = get_statistic_ratios(Q_mut, Q_sels)
M = zip(*(ER_ratios, NSR_ratios, ER_NSR_ratios))
column_headers = ('ER.ratio', 'NSR.ratio', 'ER.times.NSR.ratio')
table_string = RUtil.get_table_string(M, column_headers)
nsels = len(Q_sels)
# get the R script
comboscript = get_r_comboscript(nsels, column_headers)
# 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, comboscript, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
f_info = ctmcmi.get_mutual_info_known_distn
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
headers = ['t']
if fs.log4:
headers.append('log.4')
if fs.log3:
headers.append('log.3')
r_names = [a.replace('_', '.') for a, b, c in requested_triples]
headers.extend(r_names)
# Spend a lot of time doing the optimizations
# to construct the points for the R table.
times = np.linspace(fs.start_time, fs.stop_time, 101)
arr = []
for t in times:
row = [t]
if fs.log4:
row.append(math.log(4))
if fs.log3:
row.append(math.log(3))
for python_name, desc, zoo_obj in requested_triples:
X = np.array([])
info_value = f_info(zoo_obj.get_rate_matrix(X),
zoo_obj.get_distn(X), t)
row.append(info_value)
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 main(args):
# set up the logger
f = logging.getLogger('toplevel.logger')
h = logging.StreamHandler()
h.setFormatter(logging.Formatter('%(message)s %(asctime)s'))
f.addHandler(h)
if args.verbose:
f.setLevel(logging.DEBUG)
else:
f.setLevel(logging.WARNING)
if args.run == 'hpc':
r = RemoteBeast(g_start_stop_pairs, args.nsamples)
f.info('run BEAST remotely')
r.run(verbose=args.verbose)
f.info('(local) build the R table string and scripts')
table_string, scripts = beasttut.get_table_string_and_scripts_from_logs(
g_start_stop_pairs, r.local_log_paths, args.nsamples)
elif args.run == 'serial':
f.info('(local) run BEAST serially locally and build the R stuff')
table_string, scripts = beasttut.get_table_string_and_scripts(
g_start_stop_pairs, args.nsamples)
elif args.run == 'parallel':
f.info('(local) run BEAST locally in parallel and build the R stuff')
table_string, scripts = beasttut.get_table_string_and_scripts_par(
g_start_stop_pairs, args.nsamples)
else:
raise ValueError('invalid execution model')
f.info('(local) create the composite R script')
out = StringIO()
print >> out, 'library(ggplot2)'
print >> out, 'par(mfrow=c(3,1))'
for script in scripts:
print >> out, script
comboscript = out.getvalue()
f.info('(local) run R to create the pdf')
device_name = Form.g_imageformat_to_r_function['pdf']
retcode, r_out, r_err, image_data = RUtil.run_plotter(
table_string, comboscript, device_name, keep_intermediate=True)
if retcode:
raise RUtil.RError(r_err)
f.info('(local) write the .pdf file')
with open(args.outfile, 'wb') as fout:
fout.write(image_data)
f.info('(local) return from toplevel')
def get_response_content(fs):
# define the initial frequency
p = 0.8
# define some selection coefficients to plot
low = -5
high = 5
s_values = np.linspace(low, high, (high - low) * 20 + 1)
# define some dominance coefficients
h_values = [2, 3, 5, 7]
# get the values for each h
arr = []
for h in h_values:
v = [kimura.get_fixation_probability_chen(p, s, h) for s in s_values]
arr.append(v)
#
# define the r script
out = StringIO()
print >> out, 'title.string <- "my title"'
print >> out, 's.values <- c', str(tuple(s_values))
print >> out, 'ha <- c', str(tuple(arr[0]))
print >> out, 'hb <- c', str(tuple(arr[1]))
print >> out, 'hc <- c', str(tuple(arr[2]))
print >> out, 'hd <- c', str(tuple(arr[3]))
print >> out, mk_call_str(
'plot',
's.values',
'ha',
type='"l"',
xlab='"selection coefficient (s)"',
ylab='"probability of eventual fixation"',
main='"fixation probabilities for various h ; p0 = 0.8"',
ylim='c(0.5,1)',
)
print >> out, mk_call_str('lines', 's.values', 'hb', col='"red"')
print >> out, mk_call_str('lines', 's.values', 'hc', col='"green"')
print >> out, mk_call_str('lines', 's.values', 'hd', col='"blue"')
script = out.getvalue().rstrip()
# 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_no_table(
script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
# create the R table string and scripts
headers = ['entropy', 'analog']
distributions = []
nstates = 4
npoints = 5000
arr = []
best_pair = None
for i in range(npoints):
weights = [random.expovariate(1) for j in range(nstates)]
total = sum(weights)
distn = [x / total for x in weights]
entropy = -sum(p * math.log(p) for p in distn)
sum_squares = sum(p * p for p in distn)
sum_cubes = sum(p * p * p for p in distn)
analog = math.log(sum_squares / sum_cubes)
row = [entropy, analog]
arr.append(row)
dist = (entropy - 1.0)**2 + (analog - 0.4)**2
if (best_pair is None) or (dist < best_pair[0]):
best_pair = (dist, distn)
# get the R table
table_string = RUtil.get_table_string(arr, headers)
# get the R script
out = StringIO()
title = ', '.join(str(x) for x in best_pair[1])
print >> out, RUtil.mk_call_str('plot',
'my.table$entropy',
'my.table$analog',
pch='20',
main='"%s"' % title)
script = out.getvalue()
# 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):
# create the R table string and scripts
headers = [
'z',
'c.neg2.0',
'c.neg0.5',
'c.0.5',
'c.2.0',
#'c.a',
#'c.b',
#'c.c',
#'c.d',
]
#C = numpy.array([-0.5, -0.2, 0.2, 0.5], dtype=float)
#C = numpy.array([-1.0, -0.4, 0.4, 1.0], dtype=float)
C = numpy.array([-2.0, -0.5, 0.5, 2.0], dtype=float)
Z = numpy.linspace(-5, 5, 101)
# get the data for the R table
arr = []
for z in Z:
row = [z]
for c in C:
rate = 1.0 / kimrecessive.denom_piecewise(c, z * numpy.sign(c))
row.append(rate)
arr.append(row)
# 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):
#
N_diploid = 10
N_haploid = 2 * N_diploid
t1_20_exact = get_t1_exact(N_diploid)
t1_20_approx = get_t1_approx(N_diploid)
proportions_20 = np.linspace(0.0, 1.0, 2 * N_diploid + 1)[1:]
#
N_diploid = 5
N_haploid = 2 * N_diploid
t1_10_exact = get_t1_exact(N_diploid)
t1_10_approx = get_t1_approx(N_diploid)
proportions_10 = np.linspace(0.0, 1.0, 2 * N_diploid + 1)[1:]
#
# define the r script
out = StringIO()
print >> out, 'title.string <- "my title"'
print >> out, 't1.20.approx <- c', str(tuple(t1_20_approx))
print >> out, 't1.20.exact <- c', str(tuple(t1_20_exact))
print >> out, 'p.20 <- c', str(tuple(proportions_20.tolist()))
print >> out, mk_call_str('plot',
'p.20',
't1.20.approx',
col='"red"',
type='"l"',
ylim='c(0,45)',
xaxp='c(0,1,10)')
print >> out, mk_call_str('points', 'p.20', 't1.20.exact', col='"blue"')
#
print >> out, 't1.10.approx <- c', str(tuple(t1_10_approx))
print >> out, 't1.10.exact <- c', str(tuple(t1_10_exact))
print >> out, 'p.10 <- c', str(tuple(proportions_10.tolist()))
print >> out, mk_call_str('lines', 'p.10', 't1.10.approx', col='"red"')
print >> out, mk_call_str('points', 'p.10', 't1.10.exact', col='"blue"')
#
"""
'barplot',
'mdat',
'legend.text=' + mk_call_str(
'c',
'"exact discrete distribution"',
'"continuous approximation"',
#'"two-allele large N limit"',
#'"two-allele"',
#'"four-allele without mutational bias"',
#'"four-allele with mutational bias (kappa_{1,2}=2)"',
#'"four-allele with mutational bias, large N limit"',
),
'args.legend = list(x="topright", bty="n")',
'names.arg = 1:%s' % (N-1),
main='title.string',
xlab='"frequency of allele 1"',
ylab='"frequency"',
col=mk_call_str(
'c',
#'"red"',
#'"white"',
'"black"',
#'"gray"',
'"red"',
),
beside='TRUE',
border='NA',
)
#print >> out, 'box()'
"""
script = out.getvalue().rstrip()
# 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_no_table(
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):
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 get_response_content(fs):
M = get_input_matrix(fs)
# create the R table string and scripts
headers = ['t']
if fs.show_entropy:
headers.append('ub.entropy')
headers.extend([
'ub.jc.spectral',
'ub.f81.spectral',
'mutual.information',
'lb.2.state.spectral',
'lb.2.state',
'lb.f81',
])
npoints = 100
t_low = fs.start_time
t_high = fs.stop_time
t_incr = (t_high - t_low) / (npoints - 1)
t_values = [t_low + t_incr*i for i in range(npoints)]
# define some extra stuff
v = mrate.R_to_distn(M)
entropy = -np.dot(v, np.log(v))
n = len(M)
gap = sorted(abs(x) for x in np.linalg.eigvals(M))[1]
print 'stationary distn:', v
print 'entropy:', entropy
print 'spectral gap:', gap
M_slow_jc = gap * (1.0 / n) * (np.ones((n,n)) - n*np.eye(n))
M_slow_f81 = gap * np.outer(np.ones(n), v)
M_slow_f81 -= np.diag(np.sum(M_slow_f81, axis=1))
M_f81 = msimpl.get_fast_f81(M)
M_2state = msimpl.get_fast_two_state_autobarrier(M)
M_2state_spectral = -gap * M_2state / np.trace(M_2state)
# get the data for the R table
arr = []
for u in t_values:
# experiment with log time
#t = math.exp(u)
t = u
mi_slow_jc = ctmcmi.get_mutual_information(M_slow_jc, t)
mi_slow_f81 = ctmcmi.get_mutual_information(M_slow_f81, t)
mi_mut = ctmcmi.get_mutual_information(M, t)
mi_2state_spectral = ctmcmi.get_mutual_information(M_2state_spectral, t)
mi_f81 = ctmcmi.get_mutual_information(M_f81, t)
mi_2state = ctmcmi.get_mutual_information(M_2state, t)
row = [u]
if fs.show_entropy:
row.append(entropy)
row.extend([mi_slow_jc, mi_slow_f81,
mi_mut, mi_2state_spectral, mi_2state, mi_f81])
arr.append(row)
# 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):
# define the initial frequency
p = 0.1
# define some selection coefficients to plot
h_low = 0.2
h_high = 0.8
h_values = np.linspace(h_low, h_high, 6 * 10 + 1)
# define some dominance coefficients
hs_values = [-0.2, -0.05, 0, 0.05, 0.2]
colors = ['blue', 'green', 'black', 'orange', 'red']
# get the values for each h
arr = []
for hs in hs_values:
v = [
kimura.get_fixation_probability_chen(p, hs / h, h)
for h in h_values
]
arr.append(v)
#
# define the r script
out = StringIO()
print >> out, 'h.values <- c', str(tuple(h_values))
print >> out, 'ha <- c', str(tuple(arr[0]))
print >> out, 'hb <- c', str(tuple(arr[1]))
print >> out, 'hc <- c', str(tuple(arr[2]))
print >> out, 'hd <- c', str(tuple(arr[3]))
print >> out, 'he <- c', str(tuple(arr[4]))
print >> out, mk_call_str(
'plot',
'h.values',
'ha',
type='"l"',
xlab='"h"',
ylab='"probability of eventual fixation"',
main=('"fixation probabilities for various h*s ; '
's = 2*N*sigma ; p0 = %s"' % p),
ylim='c(0, 0.2)',
col='"%s"' % colors[0],
)
print >> out, mk_call_str('lines',
'h.values',
'hb',
col='"%s"' % colors[1])
print >> out, mk_call_str('lines',
'h.values',
'hc',
col='"%s"' % colors[2])
print >> out, mk_call_str('lines',
'h.values',
'hd',
col='"%s"' % colors[3])
print >> out, mk_call_str('lines',
'h.values',
'he',
col='"%s"' % colors[4])
script = out.getvalue().rstrip()
# 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_no_table(
script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
N_diploid = fs.N_diploid
N = N_diploid * 2
k = 2
gamma = fs.gamma
# define the fitnesses and the selection value
f0 = 1.0
f1 = 1.0 - gamma / N
s = 1 - f1 / f0
if f1 <= 0:
raise ValueError('the extreme selection caused a non-positive fitness')
# get a wright fisher transition matrix
P = np.exp(wfengine.create_genic_diallelic(N_diploid, s))
"""
# condition on no fixation
for i in range(N):
P[i] /= 1 - P[i, N]
# remove the fixed state from the transition matrix
P = P[:N, :N]
"""
# add mutations
P[0, 0] = 0
P[0, 1] = 1
P[N, N] = 0
P[N, 1] = 1
# compute the stationary distribution
v = MatrixUtil.get_stationary_distribution(P)
# get the distribution over dimorphic states
h = v[1:-1]
h /= np.sum(h)
# look at continuous approximations
w = np.zeros(N + 1)
for i in range(1, N):
x = i / float(N)
#x0 = i / float(N)
#x1 = (i + 1) / float(N)
#value = sojourn_definite(x0, x1, gamma)
value = sojourn_kernel(x, gamma)
w[i] = value
w = w[1:-1]
w /= np.sum(w)
# get the array for the R plot
arr = [h.tolist(), w.tolist()]
# define the r script
out = StringIO()
print >> out, 'title.string <- "allele 1 vs allele 2"'
print >> out, 'mdat <-', RUtil.matrix_to_R_string(arr)
print >> out, mk_call_str(
'barplot',
'mdat',
'legend.text=' + mk_call_str(
'c',
'"exact discrete distribution"',
'"continuous approximation"',
#'"two-allele large N limit"',
#'"two-allele"',
#'"four-allele without mutational bias"',
#'"four-allele with mutational bias (kappa_{1,2}=2)"',
#'"four-allele with mutational bias, large N limit"',
),
'args.legend = list(x="topright", bty="n")',
'names.arg = 1:%s' % (N - 1),
main='title.string',
xlab='"frequency of allele 1"',
ylab='"frequency"',
col=mk_call_str(
'c',
#'"red"',
#'"white"',
'"black"',
#'"gray"',
'"red"',
),
beside='TRUE',
border='NA',
)
#print >> out, 'box()'
script = out.getvalue().rstrip()
# 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_no_table(
script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
N_small = 10
N_big_diploid = fs.N_big_diploid
N_big_haploid = N_big_diploid * 2
if N_big_haploid < N_small:
raise ValueError('use a larger diploid population size')
if fs.with_replacement:
f_subsample = StatsUtil.subsample_pmf_with_replacement
elif fs.without_replacement:
f_subsample = StatsUtil.subsample_pmf_without_replacement
else:
raise ValueError('subsampling option error')
k = 4
gamma = fs.gamma_1
params_list = [(0.008, 1, 1, fs.gamma_0, fs.gamma_1, fs.gamma_2),
(0.008, 2, 1, fs.gamma_0, fs.gamma_1, fs.gamma_2)]
allele_histograms = np.zeros((2, N_big_haploid + 1))
for i, params in enumerate(params_list):
mutation, selection = kaizeng.params_to_mutation_fitness(
N_big_haploid, params)
P = kaizeng.get_transition_matrix(N_big_diploid, k, mutation,
selection)
v = MatrixUtil.get_stationary_distribution(P)
for state_index, counts in enumerate(
kaizeng.gen_states(N_big_haploid, k)):
if counts[0] and counts[1]:
allele_histograms[i, counts[0]] += v[state_index]
# Define the r table.
# There are nine columns each corresponding to an allele frequency.
# There are three rows each corresponding to a configuration.
arr = []
# Use the two allele approximation
# from mcvean and charlesworth 1999 referred to by zeng 2011.
# I'm not sure if I am using the right equation.
g0 = fs.gamma_0
g1 = fs.gamma_1
"""
s_0 = -gamma_0 / float(N_big)
s_1 = -gamma_1 / float(N_big)
hist = np.zeros(N_small+1)
for i in range(1, N_small):
x = i / float(N_small)
hist[i] = math.exp(1*N_big*(s_0 - s_1)*x) / (x*(1-x))
h = hist[1:-1]
h /= np.sum(h)
arr.append(h.tolist())
"""
arr.append(diallelic_approximation(N_small, g0, g1).tolist())
# Use the exact two allele distribution.
# Well, it is exact if I understand the right scaling
# of the population size and fitnesses.
f0 = 1.0
f1 = 1.0 - gamma / N_big_haploid
#f0 = 1.0 + gamma / N
#f1 = 1.0
#f0 = 1.0 + 1.5 / (4*N)
#f1 = 1.0 - 1.5 / (4*N)
h = get_two_allele_distribution(N_big_haploid, N_small, f0, f1,
f_subsample)
arr.append(h.tolist())
# Get frequencies for the other two configurations
for hist in allele_histograms:
# Get probabilities conditional on dimorphism.
hist[0] = 0
hist[-1] = 0
hist /= np.sum(hist)
# Get the subsampled pmf.
distn = f_subsample(hist, N_small)
MatrixUtil.assert_distribution(distn)
# Get probabiities conditional on dimorphism of the sample.
distn[0] = 0
distn[-1] = 0
distn /= np.sum(distn)
# Add to the table of densities.
arr.append(distn[1:-1].tolist())
# Get a large population approximation
# when there is mutational bias.
params = (0.008, 2, 1, fs.gamma_0, fs.gamma_1, fs.gamma_2)
mutation, fitness = kaizeng.params_to_mutation_fitness(
N_big_haploid, params)
gammas = np.array([fs.gamma_0, fs.gamma_1, fs.gamma_2, 0])
h = kaizeng.get_large_population_approximation(N_small, k, gammas,
mutation)
arr.append(h.tolist())
# define the r script
out = StringIO()
print >> out, 'title.string <- "allele 1 vs allele 2"'
print >> out, 'mdat <-', RUtil.matrix_to_R_string(arr)
print >> out, mk_call_str(
'barplot',
'mdat',
'legend.text=' + mk_call_str(
'c',
'"two-allele large N limit"',
'"two-allele"',
'"four-allele without mutational bias"',
'"four-allele with mutational bias (kappa_{1,2}=2)"',
'"four-allele with mutational bias, large N limit"',
),
'args.legend = list(x="topleft", bty="n")',
'names.arg = c(1,2,3,4,5,6,7,8,9)',
main='title.string',
xlab='"frequency of allele 1"',
ylab='"frequency"',
col=mk_call_str(
'c',
'"red"',
'"white"',
'"black"',
'"gray"',
'"blue"',
),
beside='TRUE',
)
#print >> out, 'box()'
script = out.getvalue().rstrip()
# 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_no_table(
script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
# define the initial frequency
p = 0.01
density = 2
# dominance
if fs.h_dense:
h_low = 0.2
h_high = 0.8
h_values = np.linspace(h_low, h_high, 6 * density + 1)
elif fs.h_sparse:
h_low = 0
h_high = 1
h_values = np.linspace(h_low, h_high, 2 + 1)
# selection
s_low = -1.0
s_high = 1.0
s_values = fs.s_scale * np.linspace(s_low, s_high, 20 * density + 1)
# hs product
hs_values = np.outer(h_values, s_values)
# compute all values in the grid
f_values = np.zeros((len(h_values), len(s_values)))
for i, h in enumerate(h_values):
for j, s in enumerate(s_values):
f_values[i, j] = kimura.get_fixation_probability_chen(p, s, h)
if fs.x_is_log_pfix:
f_values = np.log(f_values / p)
# define the r script
out = StringIO()
if fs.infer_s:
ylab_string = '"selection coefficient (s)"'
ylim_low = np.min(s_values)
ylim_high = np.max(s_values)
elif fs.infer_hs:
ylab_string = '"product of dominance and selection (hs)"'
ylim_low = np.min(hs_values)
ylim_high = np.max(hs_values)
if fs.x_is_pfix:
xlab_string = '"probability of eventual fixation"'
elif fs.x_is_log_pfix:
xlab_string = '"log of ratios of probability of eventual fixation"'
xlim_low = np.min(f_values)
xlim_high = np.max(f_values)
colors = ['orange', 'green', 'blue']
if fs.h_dense:
main_string = ('"s = 2*N*sigma ; '
'%s < h < %s ; '
'p0 = %s"' % (h_low, h_high, p))
elif fs.h_sparse:
main_string = ('"s = 2*N*sigma ; ' 'p0 = %s"' % p)
print >> out, mk_call_str(
'plot',
'c(0,0)',
'c(0,0)',
type='"n"',
xlab=xlab_string,
ylab=ylab_string,
main=main_string,
xlim='c(%s, %s)' % (xlim_low, xlim_high),
ylim='c(%s, %s)' % (ylim_low, ylim_high),
)
if fs.h_dense:
for h, s_row in zip(h_values, f_values):
if fs.infer_s:
y_vect = s_values
elif fs.infer_hs:
y_vect = h * s_values
print >> out, mk_call_str(
'lines',
'c' + str(tuple(s_row)),
'c' + str(tuple(y_vect)),
)
for s, h_col in zip(s_values, f_values.T):
if fs.infer_s:
y_vect = s * np.ones_like(h_col)
elif fs.infer_hs:
y_vect = s * h_values
print >> out, mk_call_str(
'lines',
'c' + str(tuple(h_col)),
'c' + str(tuple(y_vect)),
)
elif fs.h_sparse:
for i, (h, s_row) in enumerate(zip(h_values, f_values)):
if fs.infer_s:
y_vect = s_values
elif fs.infer_hs:
y_vect = h * s_values
print >> out, mk_call_str(
'lines',
'c' + str(tuple(s_row)),
'c' + str(tuple(y_vect)),
col='"%s"' % colors[i],
)
if fs.h_sparse:
print >> out, mk_call_str(
'legend',
'"topleft"',
'c' + str(tuple('h = %s' % x for x in h_values)),
lty='c' + str(tuple([1] * len(h_values))),
lwd='c' + str(tuple([2.5] * len(h_values))),
col='c' + str(tuple(colors)),
)
script = out.getvalue().rstrip()
# 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_no_table(
script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data
def get_response_content(fs):
# define the initial frequency
p = 0.1
# define some selection coefficients to plot
h_low = 0.2
h_high = 0.8
h_values = np.linspace(h_low, h_high, 6*10 + 1)
# define some dominance coefficients
hs_values = [-0.2, -0.1, -0.04, 0, 0.04, 0.1, 0.2]
#colors = ['blue', 'green', 'black', 'orange', 'red']
colors = ['darkviolet', 'blue', 'green', 'black', 'yellow', 'orange', 'red']
# get the values for each h
arr = []
xaxis = []
for hs in hs_values:
s_values = hs / h_values
v = [kimura.get_fixation_probability_chen(p, hs/h, h) for h in h_values]
xaxis.append(s_values)
arr.append(v)
#
# define the r script
out = StringIO()
print >> out, 'h.values <- c', str(tuple(h_values))
print >> out, 'ha <- c', str(tuple(arr[0]))
print >> out, 'hb <- c', str(tuple(arr[1]))
print >> out, 'hc <- c', str(tuple(arr[2]))
print >> out, 'hd <- c', str(tuple(arr[3]))
print >> out, 'he <- c', str(tuple(arr[4]))
print >> out, 'hf <- c', str(tuple(arr[5]))
print >> out, 'hg <- c', str(tuple(arr[6]))
print >> out, 'ha.x <- c', str(tuple(xaxis[0]))
print >> out, 'hb.x <- c', str(tuple(xaxis[1]))
print >> out, 'hc.x <- c', str(tuple(xaxis[2]))
print >> out, 'hd.x <- c', str(tuple(xaxis[3]))
print >> out, 'he.x <- c', str(tuple(xaxis[4]))
print >> out, 'hf.x <- c', str(tuple(xaxis[5]))
print >> out, 'hg.x <- c', str(tuple(xaxis[6]))
print >> out, mk_call_str('plot', 'ha.x', 'ha',
type='"l"',
xlab='"selection coefficient (s)"',
ylab='"probability of eventual fixation"',
main=(
'"fixation probabilities for various h*s ; '
's = 2*N*sigma ; p0 = %s"' % p),
xlim='c(-1, 1)',
ylim='c(0.05, 0.15)',
col='"%s"' % colors[0],
)
print >> out, mk_call_str('lines', 'hb.x', 'hb', col='"%s"' % colors[1])
print >> out, mk_call_str('lines', 'hc.x', 'hc', col='"%s"' % colors[2])
print >> out, mk_call_str('lines', 'hd.x', 'hd', col='"%s"' % colors[3])
print >> out, mk_call_str('lines', 'he.x', 'he', col='"%s"' % colors[4])
print >> out, mk_call_str('lines', 'hf.x', 'hf', col='"%s"' % colors[5])
print >> out, mk_call_str('lines', 'hg.x', 'hg', col='"%s"' % colors[6])
print >> out, mk_call_str(
'legend',
'"topleft"',
'c' + str(rev('hs = %s' % x for x in hs_values)),
lty='c' + str(rev([1]*7)),
lwd='c' + str(rev([2.5]*7)),
col='c' + str(rev(colors)),
)
script = out.getvalue().rstrip()
# 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_no_table(
script, device_name)
if retcode:
raise RUtil.RError(r_err)
return image_data