def rows(session, row_data, sample_id=None, group_id=None):
"""Handle rows of sambamba output.
N.B. only handles single sample annotations.
Args:
session (Session): database session object
row_data (List[dict]): parsed sambamba output rows
sample_id (Optional[str]): id to reference sample
group_id (Optional[str]): id to group samples together
Yields:
ExonStatistic: stats model linked to exon and sample
"""
if sample_id is None:
# use first row to get information on sample
first_row = next(iter(row_data))
sample_id = first_row['sampleName']
# place the first row back in the stream
all_data = cons(first_row, row_data)
else:
all_data = row_data
exons = {exon.exon_id: exon.id for exon in session.query(Exon)}
sample_obj = Sample(sample_id=sample_id, group_id=group_id)
nested_stats = (row(session, data, sample_obj, exons) for data in all_data)
# flatten 2D nested list
return (stat for stats in nested_stats for stat in stats)
def rows(session, row_data, sample_id=None, group_id=None):
"""Handle rows of sambamba output.
N.B. only handles single sample annotations.
Args:
session (Session): database session object
row_data (dict): parsed sambamba output rows
sample_id (Optional[str]): id to reference sample
group_id (Optional[str]): id to group samples together
Yields:
ExonStatistic: stats model linked to exon and sample
"""
if sample_id is None:
# use first row to get information on sample
first_row = next(iter(row_data))
sample_id = first_row['sampleName']
# place the first row back in the stream
all_data = cons(first_row, row_data)
else:
all_data = row_data
sample_obj = Sample(sample_id=sample_id, group_id=group_id)
nested_stats = (row(session, data, sample_obj) for data in all_data)
# flatten 2D nested list
return (stat for stats in nested_stats for stat in stats)
def move(n: int, target_stack_index: int, context: Context,
logs: List[str]) -> Tuple[Context, List[str]]:
current_stack_index = where(n, context)
current_stack = nth(current_stack_index, context)
target_stack = nth(target_stack_index, context)
untouched_stack_index = nth(
0,
set(range(3)) - {target_stack_index, current_stack_index})
untouched_stack = nth(untouched_stack_index, context)
if n == min(current_stack):
if len(target_stack) == 0 or n < min(target_stack):
_, *new_current_stack = current_stack
new_target_stack = list(cons(n, target_stack))
new_context = tuple(
map(
lambda tup: nth(1, tup),
sorted(((target_stack_index, new_target_stack),
(current_stack_index, new_current_stack),
(untouched_stack_index, untouched_stack)))))
log = (f"{index_to_name[current_stack_index]}" +
f" to {index_to_name[target_stack_index]}")
new_logs = logs + [log]
return new_context, new_logs
else:
new_context, new_logs = move(min(target_stack),
untouched_stack_index, context, logs)
return move(n, target_stack_index, new_context, new_logs)
else:
new_context, new_logs = move(min(current_stack), untouched_stack_index,
context, logs)
return move(n, target_stack_index, new_context, new_logs)
def render_tabular(api, options=None):
"""Entry point for the tabular reporter interface."""
# determine separator
separator = options.get('report.separator', '\t')
human = options.get('report.human')
panel = options.get('report.panel')
samples = options.get('report.samples')
group = options.get('report.group')
# read gene panel file if it has been set
if panel:
superblock_ids = [line.rstrip() for line in panel]
else:
superblock_ids = None
# get sample ID, group and cutoff from metadata
sample_query = limit_query(api.samples(), group=group, samples=samples)
metadata = ((sample.id, sample.group_id, sample.cutoff)
for sample in sample_query)
# get the data
base_query = limit_query(
api.average_metrics(superblock_ids=superblock_ids),
group=group,
samples=samples)
queries = [
metadata, base_query,
api.diagnostic_yield(superblock_ids=superblock_ids,
group_id=group,
sample_ids=samples),
api.sex_checker(group_id=group, sample_ids=samples)
]
# group multiple queries by sample ID (first column)
key_metrics = groupby(get(0), concat(queries))
# get the column names dynamically from the query
headers = concatv(['sample_id', 'group_id', 'cutoff'],
(column['name']
for column in base_query.column_descriptions),
['diagnostic yield', 'gender'])
unique_headers = unique(headers)
# iterate over all values, concat different query results, and keep
# only the unique values (excluding second sample_id)
data = (unique(concat(values)) for values in itervalues(key_metrics))
if human:
# export key_metrics in a more human friendly format
return tabulate(data, unique_headers)
# yield headers
return '\n'.join(
cons('#' + separator.join(unique_headers),
stringify_list(data, separator=separator)))
def run_features(args):
"""Run image feature computation.
Parameters
----------
args : argparse.Namespace
The arguments parsed by the argparse library.
"""
if args.global_threshold:
images = map(io.imread, args.images)
thresholds = pre.global_threshold(images, args.random_seed)
else:
thresholds = None
images = map(io.imread, args.images)
screen_info = screens.d[args.screen]
index_function, fmap = screen_info['index'], screen_info['fmap']
fmap = tz.partial(fmap,
threshold=thresholds,
sample_size=args.sample_size,
random_seed=args.random_seed)
indices = list(map(index_function, args.images))
f0, feature_names = fmap(next(images))
feature_vectors = tz.cons(f0, (fmap(im)[0] for im in images))
online_scaler = StandardScaler()
online_pca = cluster.OnlineIncrementalPCA(n_components=args.n_components,
batch_size=args.pca_batch_size)
nimages, nfeatures = len(args.images), len(f0)
emit = io.emitter_function(args.emitter)
with temporary_hdf5_dataset((nimages, nfeatures), 'float') as dset:
# First pass: compute the features, compute the mean and SD,
# compute the PCA
for i, (idx, v) in enumerate(zip(indices, feature_vectors)):
emit({'_id': idx, 'feature_vector': list(v)})
dset[i] = v
online_scaler.partial_fit(v.reshape(1, -1))
online_pca.add_sample(v)
# Second pass: standardise the feature vectors, compute PCA-transform
for i, (idx, v) in enumerate(zip(indices, dset)):
v_std = online_scaler.transform(v)
v_pca = online_pca.transform(v)
dset[i] = v_std
emit({
'_id': idx,
'feature_vector_std': list(v_std),
'pca_vector': list(v_pca)
})
online_pca.transform(v)
# Third pass: Compute the nearest neighbors graph.
# THIS ANNOYINGLY INSTANTIATES FULL ARRAY -- no out-of-core
# solution that I'm aware of...
ng = neighbors.kneighbors_graph(dset,
args.num_neighbours,
include_self=False,
mode='distance')
for idx, row in zip(indices, ng):
emit({'_id': idx, 'neighbours': [indices[i] for i in row.indices]})
def render_tabular(api, options=None):
"""Entry point for the tabular reporter interface."""
# determine separator
separator = options.get('report.separator', '\t')
human = options.get('report.human')
panel = options.get('report.panel')
samples = options.get('report.samples')
group = options.get('report.group')
# read gene panel file if it has been set
if panel:
superblock_ids = [line.rstrip() for line in panel]
else:
superblock_ids = None
# get sample ID, group and cutoff from metadata
sample_query = limit_query(api.samples(), group=group, samples=samples)
metadata = ((sample.id, sample.group_id, sample.cutoff)
for sample in sample_query)
# get the data
base_query = limit_query(api.average_metrics(superblock_ids=superblock_ids),
group=group,
samples=samples)
queries = [metadata,
base_query,
api.diagnostic_yield(superblock_ids=superblock_ids,
group_id=group, sample_ids=samples),
api.sex_checker(group_id=group, sample_ids=samples)]
# group multiple queries by sample ID (first column)
key_metrics = groupby(get(0), concat(queries))
# get the column names dynamically from the query
headers = concatv(['sample_id', 'group_id', 'cutoff'],
(column['name'] for column
in base_query.column_descriptions),
['diagnostic yield', 'gender'])
unique_headers = unique(headers)
# iterate over all values, concat different query results, and keep
# only the unique values (excluding second sample_id)
data = (unique(concat(values)) for values in itervalues(key_metrics))
if human:
# export key_metrics in a more human friendly format
return tabulate(data, unique_headers)
# yield headers
return '\n'.join(cons('#' + separator.join(unique_headers),
stringify_list(data, separator=separator)))
def test_actions_call_done_with_state_and_values():
def f(a, b):
return '%s%d' % (a, b)
fl = lift(f)
instructions = [fl(i) for i in range(randint(1, 1000))]
run = actions(instructions, result)
r = run('seed')
expect = ''.join(cons('seed', [str(i) for i in range(len(instructions))]))
assert r == expect
def run_features(args):
"""Run image feature computation.
Parameters
----------
args : argparse.Namespace
The arguments parsed by the argparse library.
"""
if args.global_threshold:
images = map(io.imread, args.images)
thresholds = pre.global_threshold(images, args.random_seed)
else:
thresholds = None
images = map(io.imread, args.images)
screen_info = screens.d[args.screen]
index_function, fmap = screen_info['index'], screen_info['fmap']
fmap = tz.partial(fmap, threshold=thresholds,
sample_size=args.sample_size,
random_seed=args.random_seed)
indices = list(map(index_function, args.images))
f0, feature_names = fmap(next(images))
feature_vectors = tz.cons(f0, (fmap(im)[0] for im in images))
online_scaler = StandardScaler()
online_pca = cluster.OnlineIncrementalPCA(n_components=args.n_components,
batch_size=args.pca_batch_size)
nimages, nfeatures = len(args.images), len(f0)
emit = io.emitter_function(args.emitter)
with temporary_hdf5_dataset((nimages, nfeatures), 'float') as dset:
# First pass: compute the features, compute the mean and SD,
# compute the PCA
for i, (idx, v) in enumerate(zip(indices, feature_vectors)):
emit({'_id': idx, 'feature_vector': list(v)})
dset[i] = v
online_scaler.partial_fit(v.reshape(1, -1))
online_pca.add_sample(v)
# Second pass: standardise the feature vectors, compute PCA-transform
for i, (idx, v) in enumerate(zip(indices, dset)):
v_std = online_scaler.transform(v.reshape(1, -1))[0]
v_pca = online_pca.transform(v)
dset[i] = v_std
emit({'_id': idx, 'feature_vector_std': list(v_std),
'pca_vector': list(v_pca)})
online_pca.transform(v)
# Third pass: Compute the nearest neighbors graph.
# THIS ANNOYINGLY INSTANTIATES FULL ARRAY -- no out-of-core
# solution that I'm aware of...
ng = neighbors.kneighbors_graph(dset, args.num_neighbours,
include_self=False, mode='distance')
for idx, row in zip(indices, ng):
emit({'_id': idx, 'neighbours': [indices[i] for i in row.indices]})
def compress(sexp):
if sexp is None: return None
if not isinstance(sexp, list): return sexp
if all(not isinstance(x, list) for x in sexp): return sexp
# if both heads are the same then gut the children.
children = list(map(compress, sexp[1:]))
heads = set(extract_op(x) for x in t.pluck(0, filter(lambda x: isinstance(x,list),children)))
heads.add(extract_op(sexp[0]))
# number heads are fine, we just want to make sure that the
# string type heads are all the same
if len(heads) <= 1:
return list(t.cons(sexp[0], t.concat(x[1:] if isinstance(x,list) else [x]
for x in children)))
return [sexp[0]]+children
def clean_data(file, df, column_list, sample_size=None):
""" Reformat dates and fix null values """
df = make_sample(df, sample_size)
# Add event time and adjust day format
# TODO: Move to concat script
df['day'] = df['day'].astype(str).map(fix_day)
df['event_time'] = df['day'].map(lambda dy: arrow.get(dy).timestamp * 1000)
df = df[list(cons('event_time', column_list))]
# TOOD: Handle infinity values
df = df.mask(df.isin([np.inf, -np.inf]))
# df.replace([np.inf, -np.inf], np.nan)
# TODO: Handle null values. Setting to 0 until get way of handling nulls from Omar
clean_columns = ['ads_per_video_cnt_avg', 'ad_vs_video_time_avg']
df[clean_columns] = df[clean_columns].fillna(0.)
return file, df
def main():
"""Entry point of application."""
program_description = 'Lorenz system'
parser = argparse.ArgumentParser(program_description)
parser.add_argument(
'--params',
type=utils.argparse.ntuple(3, float),
default=(10, 28, 8 / 3),
help='parameters σ,r,b for the lorenz system (default: 10,28,8/3)')
parser.add_argument('--plot',
help='plot best results',
action='store_true')
app, args = application.default_console_app(Individual, AssessmentRunner,
parser)
app.assessment_runner.params['s'] = args.params[0]
app.assessment_runner.params['r'] = args.params[1]
app.assessment_runner.params['b'] = args.params[2]
app.run()
logger = logging.getLogger(__name__)
logger.info('\n')
logger.info('Hall of Fame:')
for individual in app.gp_runner.halloffame:
logger.info('{} {}'.format(individual.fitness.values,
str(individual)))
if not args.plot:
return
# Plot n best results.
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn
n = 2
seaborn.set_palette('husl', n + 2)
alpha = 0.7
label_size = 16
title_size = 20
params, yinit = app.assessment_runner.params, app.assessment_runner.yinit
x, target = app.assessment_runner.x, app.assessment_runner.target
title = program_description + '\nparams={}, yinit={}'.format(params, yinit)
ax0 = plt.subplot2grid((3, 2), (0, 0))
ax1 = plt.subplot2grid((3, 2), (1, 0))
ax2 = plt.subplot2grid((3, 2), (2, 0))
ax3 = plt.subplot2grid((3, 2), (1, 1), projection='3d', rowspan=2)
lines, labels = [], []
l, = ax0.plot(x, target, linestyle='dotted')
ax1.plot(x, target, linestyle='dotted')
ax2.plot(x, target, linestyle='dotted')
labels.append('target')
lines.append(l)
uncontrolled = Individual.from_string('Add(y_0, Neg(y_0))')
for ind in cons(uncontrolled, app.gp_runner.halloffame[:n]):
popt = getattr(ind, 'popt', np.zeros(len(ind.pset.constants)))
label = 'with $a({}) = {}$, $c={}$'.format(','.join(ind.pset.args),
str(ind), popt)
label = label.replace('**', '^').replace('*', '\cdot ')
y = app.assessment_runner.trajectory(ind, *popt)
l, = ax0.plot(x, y[0, :], alpha=alpha)
ax1.plot(x, y[1, :], alpha=alpha, color=l.get_color())
ax2.plot(x, y[2, :], alpha=alpha, color=l.get_color())
ax3.plot(y[0, :], y[1, :], y[2, :], alpha=alpha, color=l.get_color())
labels.append(label)
lines.append(l)
ax0.set_ylabel('$y_0$', fontsize=label_size)
ax0.set_xlabel('time', fontsize=label_size)
ax1.set_ylabel('$y_1$', fontsize=label_size)
ax1.set_xlabel('time', fontsize=label_size)
ax2.set_ylabel('$y_2$', fontsize=label_size)
ax2.set_xlabel('time', fontsize=label_size)
ax3.set_xlabel('$y_0$', fontsize=label_size)
ax3.set_ylabel('$y_1$', fontsize=label_size)
ax3.set_title('Phase Portrait', fontsize=label_size)
plt.figlegend(lines,
labels,
loc='upper right',
bbox_to_anchor=(0.9, 0.9),
fontsize=label_size)
plt.suptitle(title, fontsize=title_size)
plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05)
plt.show()
def _run(state):
ans = fn(*cons(state, args), **kwargs)
s = state_fn(state) if state_fn is not None else ans
return {'answer': ans, 'state': s}
def main():
"""Entry point of application."""
program_description = "Damped oscillator d²y/dt² = -k*y - c*dy/dt"
parser = argparse.ArgumentParser(description=program_description)
parser.add_argument(
"--params",
type=utils.argparse.ntuple(2, float),
default=(3.0 / 8.0, 1.0),
help="parameters c,k for the damped oscillator (default: 3/8,1)",
)
parser.add_argument("--plot",
help="plot best results",
action="store_true")
app, args = application.default_console_app(Individual,
AssessmentRunner,
parser=parser)
app.assessment_runner.params["c"] = app.args.params[0]
app.assessment_runner.params["k"] = app.args.params[1]
app.run()
logger = logging.getLogger(__name__)
logger.info("\n")
logger.info("Hall of Fame:")
for individual in app.gp_runner.pareto_front:
popt = getattr(individual, "popt", ())
logger.info("{} {}, {} = {}".format(
individual.fitness.values,
str(individual),
individual.pset.constants,
popt,
))
if not args.plot:
return
# Plot n best results.
import matplotlib.pyplot as plt
import seaborn
n = 2
seaborn.set_palette("husl", n + 2)
alpha = 0.75
label_size = 16
title_size = 20
assessment_runner = AssessmentRunner()
params, yinit = assessment_runner.params, assessment_runner.yinit
x, ampl, omega = (
assessment_runner.x,
assessment_runner.ampl,
assessment_runner.omega,
)
title = program_description + "\nparams={}, yinit={}".format(
params, yinit, fontsize=title_size)
ax0 = plt.subplot2grid((2, 2), (0, 0))
ax1 = plt.subplot2grid((2, 2), (1, 0))
ax2 = plt.subplot2grid((2, 2), (1, 1))
lines, labels = [], []
target = (ampl * np.sin(omega * x), ampl * omega * np.cos(omega * x))
(l, ) = ax2.plot(target[0], target[1], linestyle="dotted", alpha=alpha)
ax0.plot(x,
target[0],
linestyle="dotted",
alpha=alpha,
color=l.get_color())
ax1.plot(x,
target[1],
linestyle="dotted",
alpha=alpha,
color=l.get_color())
labels.append("target")
lines.append(l)
uncontrolled = Individual.from_string("Add(y_0, Neg(y_0))")
for ind in cons(uncontrolled, reversed(app.gp_runner.pareto_front[:n])):
popt = getattr(ind, "popt", np.zeros(len(ind.pset.constants)))
label = "with $a(y_0, y_1) = {}$, $c={}$".format(str(ind), popt)
label = label.replace("**", "^").replace("*", "\cdot ")
y = assessment_runner.trajectory(ind, *popt)
(l, ) = ax0.plot(x, y[0, :], alpha=alpha)
ax1.plot(x, y[1, :], alpha=alpha, color=l.get_color())
ax2.plot(y[0, :], y[1, :], alpha=alpha, color=l.get_color())
labels.append(label)
lines.append(l)
ax0.set_ylabel("$y_0$", fontsize=label_size)
ax0.set_xlabel("time", fontsize=label_size)
ax1.set_ylabel("$y_1$", fontsize=label_size)
ax1.set_xlabel("time", fontsize=label_size)
ax2.set_xlabel("$y_0$", fontsize=label_size)
ax2.set_ylabel("$y_1$", fontsize=label_size)
ax2.set_title("Phase Portrait", fontsize=label_size)
plt.figlegend(lines,
labels,
loc="upper right",
bbox_to_anchor=(0.9, 0.9),
fontsize=label_size)
plt.suptitle(title, fontsize=title_size)
plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05)
plt.show()
def main():
"""Entry point of application."""
program_description = 'Damped oscillator d²y/dt² = -k*y - c*dy/dt'
parser = argparse.ArgumentParser(description=program_description)
parser.add_argument(
'--params',
type=utils.argparse.ntuple(2, float),
default=(3.0 / 8.0, 1.0),
help='parameters c,k for the damped oscillator (default: 3/8,1)')
parser.add_argument('--plot',
help='plot best results',
action='store_true')
app, args = application.default_console_app(Individual,
AssessmentRunner,
parser=parser)
app.assessment_runner.params['c'] = app.args.params[0]
app.assessment_runner.params['k'] = app.args.params[1]
app.run()
logger = logging.getLogger(__name__)
logger.info('\n')
logger.info('Hall of Fame:')
for individual in app.gp_runner.halloffame:
popt = getattr(individual, 'popt', ())
logger.info('{} {}, {} = {}'.format(individual.fitness.values,
str(individual),
individual.pset.constants, popt))
if not args.plot:
return
# Plot n best results.
import matplotlib.pyplot as plt
import seaborn
n = 2
seaborn.set_palette('husl', n + 2)
alpha = 0.75
label_size = 16
title_size = 20
assessment_runner = AssessmentRunner()
params, yinit = assessment_runner.params, assessment_runner.yinit
x, ampl, omega = assessment_runner.x, assessment_runner.ampl, assessment_runner.omega
title = program_description + '\nparams={}, yinit={}'.format(
params, yinit, fontsize=title_size)
ax0 = plt.subplot2grid((2, 2), (0, 0))
ax1 = plt.subplot2grid((2, 2), (1, 0))
ax2 = plt.subplot2grid((2, 2), (1, 1))
lines, labels = [], []
target = (ampl * np.sin(omega * x), ampl * omega * np.cos(omega * x))
l, = ax2.plot(target[0], target[1], linestyle='dotted', alpha=alpha)
ax0.plot(x,
target[0],
linestyle='dotted',
alpha=alpha,
color=l.get_color())
ax1.plot(x,
target[1],
linestyle='dotted',
alpha=alpha,
color=l.get_color())
labels.append('target')
lines.append(l)
uncontrolled = Individual.from_string('Add(y_0, Neg(y_0))')
for ind in cons(uncontrolled, reversed(app.gp_runner.halloffame[:n])):
popt = getattr(ind, 'popt', np.zeros(len(ind.pset.constants)))
label = 'with $a(y_0, y_1) = {}$, $c={}$'.format(str(ind), popt)
label = label.replace('**', '^').replace('*', '\cdot ')
y = assessment_runner.trajectory(ind, *popt)
l, = ax0.plot(x, y[0, :], alpha=alpha)
ax1.plot(x, y[1, :], alpha=alpha, color=l.get_color())
ax2.plot(y[0, :], y[1, :], alpha=alpha, color=l.get_color())
labels.append(label)
lines.append(l)
ax0.set_ylabel('$y_0$', fontsize=label_size)
ax0.set_xlabel('time', fontsize=label_size)
ax1.set_ylabel('$y_1$', fontsize=label_size)
ax1.set_xlabel('time', fontsize=label_size)
ax2.set_xlabel('$y_0$', fontsize=label_size)
ax2.set_ylabel('$y_1$', fontsize=label_size)
ax2.set_title('Phase Portrait', fontsize=label_size)
plt.figlegend(lines,
labels,
loc='upper right',
bbox_to_anchor=(0.9, 0.9),
fontsize=label_size)
plt.suptitle(title, fontsize=title_size)
plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05)
plt.show()
def _run(state):
ans = fn(*cons(state, args), **kwargs)
s = state_fn(state) if state_fn is not None else ans
return {'answer': ans, 'state': s}
def main():
"""Entry point of application."""
program_description = "Lorenz system"
parser = argparse.ArgumentParser(program_description)
parser.add_argument(
"--params",
type=utils.argparse.ntuple(3, float),
default=(10, 28, 8 / 3),
help="parameters σ,r,b for the lorenz system (default: 10,28,8/3)",
)
parser.add_argument("--plot",
help="plot best results",
action="store_true")
app, args = application.default_console_app(Individual, AssessmentRunner,
parser)
app.assessment_runner.params["s"] = args.params[0]
app.assessment_runner.params["r"] = args.params[1]
app.assessment_runner.params["b"] = args.params[2]
app.run()
logger = logging.getLogger(__name__)
logger.info("\n")
logger.info("Hall of Fame:")
for individual in app.gp_runner.pareto_front:
logger.info("{} {}".format(individual.fitness.values,
str(individual)))
if not args.plot:
return
# Plot n best results.
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn
n = 2
seaborn.set_palette("husl", n + 2)
alpha = 0.7
label_size = 16
title_size = 20
params, yinit = app.assessment_runner.params, app.assessment_runner.yinit
x, target = app.assessment_runner.x, app.assessment_runner.target
title = program_description + "\nparams={}, yinit={}".format(params, yinit)
ax0 = plt.subplot2grid((3, 2), (0, 0))
ax1 = plt.subplot2grid((3, 2), (1, 0))
ax2 = plt.subplot2grid((3, 2), (2, 0))
ax3 = plt.subplot2grid((3, 2), (1, 1), projection="3d", rowspan=2)
lines, labels = [], []
(l, ) = ax0.plot(x, target, linestyle="dotted")
ax1.plot(x, target, linestyle="dotted")
ax2.plot(x, target, linestyle="dotted")
labels.append("target")
lines.append(l)
uncontrolled = Individual.from_string("Add(y_0, Neg(y_0))")
for ind in cons(uncontrolled, app.gp_runner.pareto_front[:n]):
popt = getattr(ind, "popt", np.zeros(len(ind.pset.constants)))
label = "with $a({}) = {}$, $c={}$".format(",".join(ind.pset.args),
str(ind), popt)
label = label.replace("**", "^").replace("*", "\cdot ")
y = app.assessment_runner.trajectory(ind, *popt)
(l, ) = ax0.plot(x, y[0, :], alpha=alpha)
ax1.plot(x, y[1, :], alpha=alpha, color=l.get_color())
ax2.plot(x, y[2, :], alpha=alpha, color=l.get_color())
ax3.plot(y[0, :], y[1, :], y[2, :], alpha=alpha, color=l.get_color())
labels.append(label)
lines.append(l)
ax0.set_ylabel("$y_0$", fontsize=label_size)
ax0.set_xlabel("time", fontsize=label_size)
ax1.set_ylabel("$y_1$", fontsize=label_size)
ax1.set_xlabel("time", fontsize=label_size)
ax2.set_ylabel("$y_2$", fontsize=label_size)
ax2.set_xlabel("time", fontsize=label_size)
ax3.set_xlabel("$y_0$", fontsize=label_size)
ax3.set_ylabel("$y_1$", fontsize=label_size)
ax3.set_title("Phase Portrait", fontsize=label_size)
plt.figlegend(lines,
labels,
loc="upper right",
bbox_to_anchor=(0.9, 0.9),
fontsize=label_size)
plt.suptitle(title, fontsize=title_size)
plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.05)
plt.show()
def port(x):
p = x[2].split(',')
p1 = ['/'.join((x[1], y)) for y in p]
return list(cons(x[0], p1))
def sieve(numbers):
p = next(numbers)
yield from cons(p, sieve(n for n in numbers if n % p > 0))
