def ls(root=".", abspaths=False, recursive=False):
"""
Return a list of files in directory.
Directory listings are sorted alphabetically. If the named
directory is a file, return it's path.
Examples:
>>> fs.ls("foo")
["a", "b", "c"]
>>> fs.ls("foo/a")
["foo/a"]
>>> fs.ls("foo", abspaths=True)
["/home/test/foo/a", "/home/test/foo/b", "/home/test/foo/c"]
>>> fs.ls("foo", recursive=True)
["a", "b", "b/d", "b/d/e", "c"]
Arguments:
root (str): Path to directory. Can be relative or absolute.
abspaths (bool, optional): Return absolute paths if true.
recursive (bool, optional): Recursively list subdirectories if
true.
Returns:
list of str: A list of paths.
Raises:
OSError: If root directory does not exist.
"""
def _expand_subdirs(file):
if isdir(path(root, file)):
return [file] + [path(file, x) for x in ls(path(root, file), recursive=True)]
else:
return [file]
if isfile(root):
# If argument is a file, return path.
return [abspath(root)] if abspaths else [basename(root)]
elif abspaths:
# Get relative names.
relpaths = ls(root, recursive=recursive, abspaths=False)
# Prepend the absolute path to each relative name.
base = abspath(root)
return [path(base, relpath) for relpath in relpaths]
elif recursive:
# Recursively expand subdirectories.
paths = ls(root, abspaths=abspaths, recursive=False)
return lab.flatten([_expand_subdirs(file) for file in paths])
else:
# List directory contents.
return list(sorted(os.listdir(root)))
def xval_benchmarks(clf, data, **benchmark_name_opts):
"""cross-validate across benchmarks"""
benchmark_names = get_benchmark_names(data, **benchmark_name_opts)
return pd.DataFrame(
flatten([
leave_one_benchmark_out(clf, cgo13_features, data, b)
for b in benchmark_names
]))
def kernel_performance(db, output=None, **kwargs):
labels = ["synthetic"] + db.real_kernel_names
values = [lab.flatten([db.performance_of_kernels_with_name(name) for name in
db.synthetic_kernel_names])]
values += [db.performance_of_kernels_with_name(name) for name in
db.real_kernel_names]
title = kwargs.pop("title", "Workgroup size performance across kernels")
_performance_plot(output, labels, values, title,
color=sns.color_palette("Greens"), **kwargs)
def run_synthetic_benchmarks(iterations=250):
"""
Sample the space of synthetic benchmarks.
"""
allargs = list(experiment.SIMPLEBIG_ARGS)
random.shuffle(allargs)
for devargs in experiment.DEVARGS:
for simplebigargs in allargs:
args = labm8.flatten(simplebigargs + (devargs,))
io.debug(" ".join(args))
cmd_str = " ".join(args)
sample_simplebig(args, iterations=iterations)
def kernel_performance(db, output=None, **kwargs):
labels = ["synthetic"] + db.real_kernel_names
values = [
lab.flatten([
db.performance_of_kernels_with_name(name)
for name in db.synthetic_kernel_names
])
]
values += [
db.performance_of_kernels_with_name(name)
for name in db.real_kernel_names
]
title = kwargs.pop("title", "Workgroup size performance across kernels")
_performance_plot(output,
labels,
values,
title,
color=sns.color_palette("Greens"),
**kwargs)
def test_flatten(self):
self._test([1, 2, 3], lab.flatten([[1], [2, 3]]))
