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7540540e177a568c94169af73b1747f4da9025d7 | akersten/cute-kitchen | src/router/annotations.py | [
"MIT"
] | Python | route_login_required | <not_specific> | def route_login_required():
"""
Decorates a function to indicate the requirement for a logged-in session.
:return: The decorated function.
"""
def decorator(fn):
fn.route_login_required = True
return fn
return decorator |
Decorates a function to indicate the requirement for a logged-in session.
:return: The decorated function.
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def decorator(fn):
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return fn
return decorator | 0 | 56 |
c5b891ac420cbeffb65019d562bc382e7bf6367b | MacFarlaneBro/compose-mode | compose_mode/generate.py | [
"MIT"
] | Python | fix_restart | <not_specific> | def fix_restart(restart_config):
""" Fix output of docker-compose.
docker-compose's "show config" mechanism--the internals of which we use to
merge configs--doesn't actually return valid configurations for the
"restart" property as they convert it to an internal representation which
they then forget to convert back to the yaml format. We do that by hand
here.
"""
try:
mrc = restart_config['MaximumRetryCount']
except TypeError:
name = restart_config
else:
name = restart_config['Name']
if name in ['always', 'unless-stopped', 'no']:
return name
else:
return '{}:{}'.format(name, mrc) | Fix output of docker-compose.
docker-compose's "show config" mechanism--the internals of which we use to
merge configs--doesn't actually return valid configurations for the
"restart" property as they convert it to an internal representation which
they then forget to convert back to the yaml format. We do that by hand
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| Fix output of docker-compose. | [
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] | def fix_restart(restart_config):
try:
mrc = restart_config['MaximumRetryCount']
except TypeError:
name = restart_config
else:
name = restart_config['Name']
if name in ['always', 'unless-stopped', 'no']:
return name
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name = restart_config
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name = restart_config['Name']
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65ef5aa7f6bb80020bfccdca5945fcdda9118ad3 | netceteragroup/da4e | assembler.py | [
"MIT"
] | Python | _read_file_and_join_lines | <not_specific> | def _read_file_and_join_lines(file_name):
'''
Reads all contents of a file and appends each line into one ',' separated string.
Leading and trailing whitespace is removed. Lines having a '#' character as first non-
whitespace character are considered as comment and are therefore ignored in the output.
@param fileName: the file to read
'''
def _normalizeString(line):
return line.replace('\n', '').strip()
def _isComment(line):
return not line.startswith("#")
def _isEmptyLine(line):
return line != ""
with open(file_name, 'r') as file:
return ','.join(filter(_isEmptyLine, filter(_isComment, map(_normalizeString, file.readlines())))) |
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Leading and trailing whitespace is removed. Lines having a '#' character as first non-
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@param fileName: the file to read
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whitespace character are considered as comment and are therefore ignored in the output. | [
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4ba9d06164e542a8b6d024e64d49a8773a1ff650 | WolfgangFahl/pyOnlineSpreadSheetEditing | onlinespreadsheet/tablequery.py | [
"Apache-2.0"
] | Python | match | <not_specific> | def match(pattern:str,string:str):
'''
re match search for the given pattern with ignore case
'''
return re.search(pattern=pattern, string=string, flags=re.IGNORECASE) |
re match search for the given pattern with ignore case
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def match(pattern:str,string:str):
return re.search(pattern=pattern, string=string, flags=re.IGNORECASE) | 3 | 994 |
ed05c8d76b4e4beff6fca1cee61e2aadf3732abe | tomerten/PyElegantSDDS | pyelegantsdds/elegantrun.py | [
"MIT"
] | Python | write_parallel_run_script | null | def write_parallel_run_script(sif):
"""
Method to generate parallel elegant run
script.
Parameters:
-----------
sif: str
path to singularity container
"""
bashstrlist = [
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"",
"$pele $cmd $1",
]
bashstr = "\n".join(bashstrlist)
# write to file
with open("run_pelegant.sh", "w") as f:
f.write(bashstr) |
Method to generate parallel elegant run
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Parameters:
-----------
sif: str
path to singularity container
| Method to generate parallel elegant run
script.
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path to singularity container | [
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bf09424b7fd16830410dec98a364876163fae903 | erikagardini/InferringMusicAndVisualArtStyleEvolution | python/utilities.py | [
"MIT"
] | Python | find_min_max_indexes | <not_specific> | def find_min_max_indexes(dates):
"""
Get the index of the min value and the max value (centroids are not considered)
Args:
[ndarray int] dates: dates matrix
Returns:
[float] index_min: index of the min value
[int] index_max: index of the max value
"""
min = dates[0]
index_min = 0
max = dates[0]
index_max = 0
for i in range(0, dates.shape[0]):
if dates[i] < min and dates[i] != -1:
min = dates[i]
index_min = i
if dates[i] > max and dates[i] != -1:
max = dates[i]
index_max = i
return index_min, index_max |
Get the index of the min value and the max value (centroids are not considered)
Args:
[ndarray int] dates: dates matrix
Returns:
[float] index_min: index of the min value
[int] index_max: index of the max value
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8eae126dbf8a8baeab1e2e976d363b1bcf02863a | muvr/muvr-ml | sensorcnn/dataset/utils.py | [
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] | Python | csv_file_iterator | null | def csv_file_iterator(root_directory):
"""Returns a generator (iterator) of absolute file paths for CSV files in a given directory"""
for root_path, _, files in os.walk(root_directory, followlinks=True):
for f in files:
if f.endswith("csv"):
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def csv_file_iterator(root_directory):
for root_path, _, files in os.walk(root_directory, followlinks=True):
for f in files:
if f.endswith("csv"):
yield os.path.join(root_path, f) | 7 | 671 |
4c8f7cefbccbd3770a96057fbf07a756c0ce334d | intrlocutr/nightcoreify | nightcorei.py | [
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"""Prepares tags for a new upload. Keeps as many old tags as possible while adding a "nightcore" tag."""
to_add = 'nightcore'
# The total number of characters in YouTube video tags can't exceed 400.
# We're adding the "nightcore" tag, so we'll only keep this many characters of the original tags.
target_len = 400 - len(to_add)
new_tags = []
length = 0
# Keep tags up until they can no longer fit within our target.
for tag in tags:
length += len(tag)
if length < target_len:
new_tags.append(tag)
else:
break
new_tags.append(to_add)
return new_tags | Prepares tags for a new upload. Keeps as many old tags as possible while adding a "nightcore" tag. | Prepares tags for a new upload. Keeps as many old tags as possible while adding a "nightcore" tag. | [
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903fc63fede8ca3f8a8dc0b5f4ca496131e1a105 | JustKowalski/sound_separation | datasets/fuss/reverberate_and_mix.py | [
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"""Write tab separated source/rir lists in files for train/validate/eval."""
if not separate:
with open(item_file, 'w') as f:
for subfolder in item_dict:
for example in item_dict[subfolder]:
line = '\t'.join([example] + item_dict[subfolder][example])
f.write(line + '\n')
else:
for subfolder in item_dict:
item_base, item_ext = item_file.split('.')
item_file_sub = item_base + '_' + subfolder + '.' + item_ext
with open(item_file_sub, 'w') as f:
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line = '\t'.join([example] + item_dict[subfolder][example])
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8f34f83cd4a60a8d8e72e31e4fde3b9e819b893f | BAMWelDX/weldx | weldx/tests/test_time.py | [
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"""Transform an array into a scalar, single value array or return in unmodified."""
if not is_array:
return data[0]
if is_scalar:
return [data[0]]
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cc94e3893f2982011fc08d2790d839d5a009c4f5 | EmersonAires/Introducao_a_ciencia_da_computacao_com_Python | Exercicios_Resolvidos/Parte 2/Semana 1/exercicio_12_1.py | [
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] | Python | extrair_coluna | <not_specific> | def extrair_coluna(b_mat, col):
'''retorna uma lista com os elementos da coluna indicada'''
coluna_b_mat = []
for linha in b_mat:
elemento = linha[col]
coluna_b_mat.append(elemento)
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coluna_b_mat = []
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coluna_b_mat.append(elemento)
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5bb08e1a378e746bf276fdb1704d41f5409d7d65 | trisongz/file_io | fileio/core/py_utils.py | [
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"""Temporarily assign obj.attr to value."""
original = getattr(obj, attr)
setattr(obj, attr, value)
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63484325eaa962f8f517d4993495bc314d2325ed | alchem0x2A/vasp-interactive-test | examples/ex11_k8s_minimal.py | [
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"""A threaded version of atoms.get_potential_energy. Energy is a one-member list
ideas taken from https://wiki.fysik.dtu.dk/ase/_modules/ase/neb.html#NEB
"""
energy[0] = atoms.get_potential_energy()
return | A threaded version of atoms.get_potential_energy. Energy is a one-member list
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e71a7da7f51510edc97fee6d78a7582b46d166d7 | seung-lab/mapbuffer | mapbuffer/mapbuffer.py | [
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Takes an ascendingly sorted input and
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Modified from:
https://algorithmica.org/en/eytzinger
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output[k - 1] = inpt[i]
i += 1
i = eytzinger_sort(inpt, output,i, 2 * k + 1)
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],
"outlier_params": [],
"others": []
} | def eytzinger_sort(inpt, output, i = 0, k = 1):
if k <= len(inpt):
i = eytzinger_sort(inpt, output, i, 2 * k)
output[k - 1] = inpt[i]
i += 1
i = eytzinger_sort(inpt, output,i, 2 * k + 1)
return i | 14 | 993 |
b2984037c6a8ed370b786cafaa04ec91d894c19a | GHzytp/PyLorentz | GUI/PyLorentz_GUI.py | [
"BSD-3-Clause"
] | Python | change_visibility | None | def change_visibility(window: sg.Window, elem_val_list: List[Tuple[sg.Element, Any]]) -> None:
""" Take a list of element keys and change
visibility of the element.
Args:
window : The element representing the main GUI window.
elem_val_list : The list of elements with values whose
state is to be changed.
Returns:
None
"""
for elem_key, val in elem_val_list:
window[elem_key].Update(visible=val) | Take a list of element keys and change
visibility of the element.
Args:
window : The element representing the main GUI window.
elem_val_list : The list of elements with values whose
state is to be changed.
Returns:
None
| Take a list of element keys and change
visibility of the element. | [
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] | def change_visibility(window: sg.Window, elem_val_list: List[Tuple[sg.Element, Any]]) -> None:
for elem_key, val in elem_val_list:
window[elem_key].Update(visible=val) | [
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for elem_key, val in elem_val_list:
window[elem_key].Update(visible=val) | 15 | 239 |
61d3a295b3c1cedde8c1eab628b02734cf1c0cd5 | BensonGathu/neighborhood | virtual/lib/python3.8/site-packages/PIL/ImageChops.py | [
"Unlicense"
] | Python | multiply | <not_specific> | def multiply(image1, image2):
"""
Superimposes two images on top of each other.
If you multiply an image with a solid black image, the result is black. If
you multiply with a solid white image, the image is unaffected.
.. code-block:: python
out = image1 * image2 / MAX
:rtype: :py:class:`~PIL.Image.Image`
"""
image1.load()
image2.load()
return image1._new(image1.im.chop_multiply(image2.im)) |
Superimposes two images on top of each other.
If you multiply an image with a solid black image, the result is black. If
you multiply with a solid white image, the image is unaffected.
.. code-block:: python
out = image1 * image2 / MAX
:rtype: :py:class:`~PIL.Image.Image`
| Superimposes two images on top of each other.
If you multiply an image with a solid black image, the result is black. If
you multiply with a solid white image, the image is unaffected.
code-block:: python
out = image1 * image2 / MAX | [
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"\"\"\"\n Superimposes two images on top of each other.\n\n If you multiply an image with a solid black image, the result is black. If\n you multiply with a solid white image, the image is unaffected.\n\n .. code-block:: python\n\n out = image1 * image2 / MAX\n\n :rtype: :py:class:`~PIL.Image.Image`\n \"\"\""
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],
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} | def multiply(image1, image2):
image1.load()
image2.load()
return image1._new(image1.im.chop_multiply(image2.im)) | 16 | 804 |
2d8da20bf2fb315a41e314223d05cc62ea9dcae5 | DeepanshS/mrsimulator-ui | app/sims/method/__init__.py | [
"BSD-3-Clause"
] | Python | generate_sidepanel | <not_specific> | def generate_sidepanel(method, index):
"""Generate scrollable side panel listing for methods"""
title = html.B(f"Method {index}", className="")
# method name
name = html.Div(method["name"], className="")
# method channel(s)
channels = ", ".join(method["channels"])
channels = html.Div(f"Channel: {channels}")
# n dimensions
n_dim = len(method["spectral_dimensions"])
n_dim = html.Div(f"Dimensions: {n_dim}")
a_tag = html.A([title, name, channels, n_dim])
# The H6(index) only shows for smaller screen sizes.
return html.Li(
[html.H6(index), html.Div(a_tag)],
# draggable="true",
className="list-group-item",
# id={"type": "select-method-index", "index": index},
) | Generate scrollable side panel listing for methods | Generate scrollable side panel listing for methods | [
"Generate",
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"listing",
"for",
"methods"
] | def generate_sidepanel(method, index):
title = html.B(f"Method {index}", className="")
name = html.Div(method["name"], className="")
channels = ", ".join(method["channels"])
channels = html.Div(f"Channel: {channels}")
n_dim = len(method["spectral_dimensions"])
n_dim = html.Div(f"Dimensions: {n_dim}")
a_tag = html.A([title, name, channels, n_dim])
return html.Li(
[html.H6(index), html.Div(a_tag)],
className="list-group-item",
) | [
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"# draggable=\"true\",",
"# id={\"type\": \"select-method-index\", \"index\": index},"
] | [
{
"param": "method",
"type": null
},
{
"param": "index",
"type": null
}
] | {
"returns": [],
"raises": [],
"params": [
{
"identifier": "method",
"type": null,
"docstring": null,
"docstring_tokens": [],
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"is_optional": null
},
{
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"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import html
def generate_sidepanel(method, index):
title = html.B(f"Method {index}", className="")
name = html.Div(method["name"], className="")
channels = ", ".join(method["channels"])
channels = html.Div(f"Channel: {channels}")
n_dim = len(method["spectral_dimensions"])
n_dim = html.Div(f"Dimensions: {n_dim}")
a_tag = html.A([title, name, channels, n_dim])
return html.Li(
[html.H6(index), html.Div(a_tag)],
className="list-group-item",
) | 18 | 196 |
6b1bbc653af085e7c5303ff686cc116c3b15f540 | QuantumJack/GNN | lib/net_gen.py | [
"Apache-2.0"
] | Python | __generate_nets | <not_specific> | def __generate_nets(gnw_path, main_netfile_path, net_size, num_nets_per_size, outdir_path):
""" Generate random network modules extracted from main_net_filename
Args:
gnw_path: path to GeneNetWeaver ".jar" file.
main_netfile_path: filepath for the main network used for module extraction.
net_size: size of the extracted modules.
num_nets_per_size: number of modules to be extracted for each size.
outdir_path: directory to store extracted network modules.
"""
if not os.path.exists(outdir_path):
os.mkdir(outdir_path)
command = ("java -jar {!s} --extract -c data/settings.txt --input-net {!s}"
" --input-net-format=0 --random-seed --greedy-selection --subnet-size={!s}"
" --num-subnets={!s} --output-net-format=0 --output-path={!s}"
).format(gnw_path, main_netfile_path, net_size, num_nets_per_size, outdir_path)
subprocess.run([command], shell=True)
file_prefix = os.path.basename(main_netfile_path)[:-4]
generated_files = glob.glob("{!s}/{!s}*.tsv".format(outdir_path, file_prefix))
return generated_files | Generate random network modules extracted from main_net_filename
Args:
gnw_path: path to GeneNetWeaver ".jar" file.
main_netfile_path: filepath for the main network used for module extraction.
net_size: size of the extracted modules.
num_nets_per_size: number of modules to be extracted for each size.
outdir_path: directory to store extracted network modules.
| Generate random network modules extracted from main_net_filename | [
"Generate",
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"modules",
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"from",
"main_net_filename"
] | def __generate_nets(gnw_path, main_netfile_path, net_size, num_nets_per_size, outdir_path):
if not os.path.exists(outdir_path):
os.mkdir(outdir_path)
command = ("java -jar {!s} --extract -c data/settings.txt --input-net {!s}"
" --input-net-format=0 --random-seed --greedy-selection --subnet-size={!s}"
" --num-subnets={!s} --output-net-format=0 --output-path={!s}"
).format(gnw_path, main_netfile_path, net_size, num_nets_per_size, outdir_path)
subprocess.run([command], shell=True)
file_prefix = os.path.basename(main_netfile_path)[:-4]
generated_files = glob.glob("{!s}/{!s}*.tsv".format(outdir_path, file_prefix))
return generated_files | [
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] | [
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{
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}
] | {
"returns": [],
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"params": [
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],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import subprocess
import os
import glob
def __generate_nets(gnw_path, main_netfile_path, net_size, num_nets_per_size, outdir_path):
if not os.path.exists(outdir_path):
os.mkdir(outdir_path)
command = ("java -jar {!s} --extract -c data/settings.txt --input-net {!s}"
" --input-net-format=0 --random-seed --greedy-selection --subnet-size={!s}"
" --num-subnets={!s} --output-net-format=0 --output-path={!s}"
).format(gnw_path, main_netfile_path, net_size, num_nets_per_size, outdir_path)
subprocess.run([command], shell=True)
file_prefix = os.path.basename(main_netfile_path)[:-4]
generated_files = glob.glob("{!s}/{!s}*.tsv".format(outdir_path, file_prefix))
return generated_files | 20 | 539 |
5258c905652f8e689e7f69238996a5b2f5d95f0d | clbarnes/pyboolnet | boolnet/misc.py | [
"MIT"
] | Python | prod | <not_specific> | def prod(numbers):
"""
Find the product of a sequence
:param numbers: Sequence of numbers
:return: Their product
"""
ret = 1
for number in numbers:
ret *= number
return ret |
Find the product of a sequence
:param numbers: Sequence of numbers
:return: Their product
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ret = 1
for number in numbers:
ret *= number
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ret = 1
for number in numbers:
ret *= number
return ret | 23 | 413 |
664e3c76f043a3063bee9e66192f6daf1dad61e8 | sullivanmj/pywemo | pywemo/ouimeaux_device/api/rules_db.py | [
"MIT"
] | Python | _unpack_db | <not_specific> | def _unpack_db(content, db_file):
"""Unpack the sqlite database from a .zip file content."""
zip_contents = io.BytesIO(content)
with zipfile.ZipFile(zip_contents) as zip_file:
inner_file_name = zip_file.namelist()[0]
with zip_file.open(inner_file_name) as zipped_db_file:
db_file.write(zipped_db_file.read())
db_file.flush()
return inner_file_name
raise RuntimeError("Could not find database within zip file") | Unpack the sqlite database from a .zip file content. | Unpack the sqlite database from a .zip file content. | [
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zip_contents = io.BytesIO(content)
with zipfile.ZipFile(zip_contents) as zip_file:
inner_file_name = zip_file.namelist()[0]
with zip_file.open(inner_file_name) as zipped_db_file:
db_file.write(zipped_db_file.read())
db_file.flush()
return inner_file_name
raise RuntimeError("Could not find database within zip file") | [
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] | [
"\"\"\"Unpack the sqlite database from a .zip file content.\"\"\""
] | [
{
"param": "content",
"type": null
},
{
"param": "db_file",
"type": null
}
] | {
"returns": [],
"raises": [],
"params": [
{
"identifier": "content",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
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{
"identifier": "db_file",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import zipfile
import io
def _unpack_db(content, db_file):
zip_contents = io.BytesIO(content)
with zipfile.ZipFile(zip_contents) as zip_file:
inner_file_name = zip_file.namelist()[0]
with zip_file.open(inner_file_name) as zipped_db_file:
db_file.write(zipped_db_file.read())
db_file.flush()
return inner_file_name
raise RuntimeError("Could not find database within zip file") | 24 | 619 |
ae5462bd222a5c5bd4c72139814f3a36c73c6486 | 4con/hello-gn | build/android/apksize.py | [
"BSD-3-Clause-No-Nuclear-License-2014",
"BSD-3-Clause"
] | Python | print_human_readable_size_info | null | def print_human_readable_size_info(apk):
"""Prints size information in human readable format.
Args:
apk: ApkSizeInfo object
"""
files = apk.processed_files
logging.critical('Stats for files as they exist within the apk:')
for ext in files:
logging.critical(' %-8s %s bytes in %s files', ext,
files[ext]['compressed_bytes'], files[ext]['number'])
logging.critical('--------------------------------------')
logging.critical(
'All Files: %s bytes in %s files', apk.compressed_size, apk.total_files)
logging.critical('APK Size: %s', apk.apk_size)
logging.critical('APK overhead: %s', apk.apk_size - apk.compressed_size)
logging.critical('--------------------------------------')
logging.critical('Stats for files when extracted from the apk:')
for ext in files:
logging.critical(' %-8s %s bytes in %s files', ext,
files[ext]['uncompressed_bytes'], files[ext]['number'])
logging.critical('--------------------------------------')
logging.critical(
'All Files: %s bytes in %s files', apk.uncompressed_size, apk.total_files) | Prints size information in human readable format.
Args:
apk: ApkSizeInfo object
| Prints size information in human readable format. | [
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files = apk.processed_files
logging.critical('Stats for files as they exist within the apk:')
for ext in files:
logging.critical(' %-8s %s bytes in %s files', ext,
files[ext]['compressed_bytes'], files[ext]['number'])
logging.critical('--------------------------------------')
logging.critical(
'All Files: %s bytes in %s files', apk.compressed_size, apk.total_files)
logging.critical('APK Size: %s', apk.apk_size)
logging.critical('APK overhead: %s', apk.apk_size - apk.compressed_size)
logging.critical('--------------------------------------')
logging.critical('Stats for files when extracted from the apk:')
for ext in files:
logging.critical(' %-8s %s bytes in %s files', ext,
files[ext]['uncompressed_bytes'], files[ext]['number'])
logging.critical('--------------------------------------')
logging.critical(
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}
] | {
"returns": [],
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"params": [
{
"identifier": "apk",
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"docstring": null,
"docstring_tokens": [
"None"
],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import logging
def print_human_readable_size_info(apk):
files = apk.processed_files
logging.critical('Stats for files as they exist within the apk:')
for ext in files:
logging.critical(' %-8s %s bytes in %s files', ext,
files[ext]['compressed_bytes'], files[ext]['number'])
logging.critical('--------------------------------------')
logging.critical(
'All Files: %s bytes in %s files', apk.compressed_size, apk.total_files)
logging.critical('APK Size: %s', apk.apk_size)
logging.critical('APK overhead: %s', apk.apk_size - apk.compressed_size)
logging.critical('--------------------------------------')
logging.critical('Stats for files when extracted from the apk:')
for ext in files:
logging.critical(' %-8s %s bytes in %s files', ext,
files[ext]['uncompressed_bytes'], files[ext]['number'])
logging.critical('--------------------------------------')
logging.critical(
'All Files: %s bytes in %s files', apk.uncompressed_size, apk.total_files) | 25 | 803 |
cfb9c7e09aba8ed937f808b341f34b7eee06405b | finex-dev/finex | finex/ratios.py | [
"BSD-3-Clause"
] | Python | roce | <not_specific> | def roce(net_income, preferred_dividends, average_common_equity):
"""Computes return on common equity.
Parameters
----------
net_income : int or float
Net income
preferred_dividends : int or float
Preferred dividends
average_common_equity : int or float
Average common equity
Returns
-------
out : int or float
Return on common equity
"""
return (net_income - preferred_dividends) / average_common_equity | Computes return on common equity.
Parameters
----------
net_income : int or float
Net income
preferred_dividends : int or float
Preferred dividends
average_common_equity : int or float
Average common equity
Returns
-------
out : int or float
Return on common equity
| Computes return on common equity.
Parameters
net_income : int or float
Net income
preferred_dividends : int or float
Preferred dividends
average_common_equity : int or float
Average common equity
Returns
out : int or float
Return on common equity | [
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],
"outlier_params": [],
"others": []
} | def roce(net_income, preferred_dividends, average_common_equity):
return (net_income - preferred_dividends) / average_common_equity | 26 | 306 |
177f8f7c5c114b75e46eb141489e8ed3694972a9 | guesswhohaha/learntools | learntools/core/asserts.py | [
"Apache-2.0"
] | Python | assert_has_columns | null | def assert_has_columns(df, cols, name="dataframe", strict=False):
"""Assert that the given dataframe contains columns with the given names.
If strict is True, then assert it has *only* those columns.
"""
for col in cols:
assert col in df.columns, "Expected {} to have column `{}`".format(
name, col
)
if strict:
for col in df.columns:
msg = "Unexpected column in {}: `{}`".format(name, col)
assert col in cols, msg | Assert that the given dataframe contains columns with the given names.
If strict is True, then assert it has *only* those columns.
| Assert that the given dataframe contains columns with the given names.
If strict is True, then assert it has *only* those columns. | [
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],
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} | def assert_has_columns(df, cols, name="dataframe", strict=False):
for col in cols:
assert col in df.columns, "Expected {} to have column `{}`".format(
name, col
)
if strict:
for col in df.columns:
msg = "Unexpected column in {}: `{}`".format(name, col)
assert col in cols, msg | 27 | 419 |
bb9bc874cfb0cf7a29aef5ba2b188b6dea437065 | maurogaravello/Pirati | plot.py | [
"MIT"
] | Python | check_negative | <not_specific> | def check_negative(value):
""" check if a value is negative"""
ivalue = int(value)
if ivalue <= 0:
raise argparse.ArgumentTypeError("%s is an invalid entry" % value)
return ivalue | check if a value is negative | check if a value is negative | [
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"value",
"is",
"negative"
] | def check_negative(value):
ivalue = int(value)
if ivalue <= 0:
raise argparse.ArgumentTypeError("%s is an invalid entry" % value)
return ivalue | [
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} | import argparse
def check_negative(value):
ivalue = int(value)
if ivalue <= 0:
raise argparse.ArgumentTypeError("%s is an invalid entry" % value)
return ivalue | 28 | 856 |
642f0f99428865d2e704e0a3535fd3e4cd3f9e98 | larsOhne/pvdn | pvdn/detection/train.py | [
"CC0-1.0"
] | Python | print_end_summary | null | def print_end_summary(epochs: int, output_dir: str, best_epochs: dict,
best_metrics: dict):
"""
Outputs the final summary to the terminal.
:param epochs: number of epochs the model has been trained
:type epochs: int
:param output_dir: directory where the results are written to
:type output_dir: str
:param best_epochs: dictionary with metrics as keys, and epoch number of
the model that has the best performance in it as value
:type best_epochs: dict
:param best_metrics: dictionary storing the best result for each metric
:type best_metrics: dict
"""
print(f"\nTraining completed successfully for {epochs} epochs.\n"
f"Logs written to {output_dir}.")
best_str = [f"{k}: {v:4f} (epoch {best_epochs[k]})" for k, v in
best_metrics.items()]
print(f"-------------- Best --------------")
for s in best_str:
print(s) |
Outputs the final summary to the terminal.
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:type epochs: int
:param output_dir: directory where the results are written to
:type output_dir: str
:param best_epochs: dictionary with metrics as keys, and epoch number of
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:type best_epochs: dict
:param best_metrics: dictionary storing the best result for each metric
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f"Logs written to {output_dir}.")
best_str = [f"{k}: {v:4f} (epoch {best_epochs[k]})" for k, v in
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print(f"-------------- Best --------------")
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],
"outlier_params": [],
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best_metrics: dict):
print(f"\nTraining completed successfully for {epochs} epochs.\n"
f"Logs written to {output_dir}.")
best_str = [f"{k}: {v:4f} (epoch {best_epochs[k]})" for k, v in
best_metrics.items()]
print(f"-------------- Best --------------")
for s in best_str:
print(s) | 29 | 22 |
8a51d4283261aa0674abb481617734ad2aaf10f1 | j19sch/pytest-logfest | pytest_logfest/plugin.py | [
"MIT"
] | Python | fxt_function_logger | null | def fxt_function_logger(request, module_logger, session_filememoryhandler):
"""
Yields a logger, child of the module logger and named the name of the function.
Adds records for test started, setup error, test fail, and test ended.
"""
logger = module_logger.getChild(request.node.name)
logger.info("TEST STARTED")
yield logger
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if request.node.rep_setup.failed:
logger.warning("SETUP ERROR")
except AttributeError:
pass
try:
if request.node.rep_call.failed:
logger.warning("TEST FAIL")
except AttributeError:
pass
logger.info("TEST ENDED\n")
session_filememoryhandler.flush_with_filter_on_info() |
Yields a logger, child of the module logger and named the name of the function.
Adds records for test started, setup error, test fail, and test ended.
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logger.info("TEST STARTED")
yield logger
try:
if request.node.rep_setup.failed:
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try:
if request.node.rep_call.failed:
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logger.info("TEST ENDED\n")
session_filememoryhandler.flush_with_filter_on_info() | 30 | 301 |
4d978bbc81c4e00039d1224dd4790f6483e5af9b | abugler/SMLFinalProject | runners/utils.py | [
"MIT"
] | Python | disp_script | null | def disp_script(spec):
"""
Displays the arguments for a script in a readable fashion in
logging.
Args:
spec (dict): Dictionary containing script parameters.
"""
logging.info(
f"\n"
f" Running {spec['script']} with args:\n"
f" config: {spec['config']}\n"
f" run_in: {spec['run_in']}\n"
f" num_gpus: {spec['num_gpus']}\n"
f" blocking: {spec['blocking']}\n"
) |
Displays the arguments for a script in a readable fashion in
logging.
Args:
spec (dict): Dictionary containing script parameters.
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logging.info(
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f" config: {spec['config']}\n"
f" run_in: {spec['run_in']}\n"
f" num_gpus: {spec['num_gpus']}\n"
f" blocking: {spec['blocking']}\n"
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"default": null,
"is_optional": false
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"outlier_params": [],
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} | import logging
def disp_script(spec):
logging.info(
f"\n"
f" Running {spec['script']} with args:\n"
f" config: {spec['config']}\n"
f" run_in: {spec['run_in']}\n"
f" num_gpus: {spec['num_gpus']}\n"
f" blocking: {spec['blocking']}\n"
) | 31 | 1,017 |
bcc04efb1ef7d3258b74f184ec0c0258e4e8c5fa | elyase/altair | altair/expr/core.py | [
"BSD-3-Clause"
] | Python | _js_repr | <not_specific> | def _js_repr(val):
"""Return a javascript-safe string representation of val"""
if val is True:
return 'true'
elif val is False:
return 'false'
elif val is None:
return 'null'
else:
return repr(val) | Return a javascript-safe string representation of val | Return a javascript-safe string representation of val | [
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if val is True:
return 'true'
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return 'false'
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return 'null'
else:
return repr(val) | [
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return 'false'
elif val is None:
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else:
return repr(val) | 32 | 126 |
892fd63e452f2cae6fe22559ccb6584141ee4b92 | Bielorusse/Erdorbit_random | script.py | [
"MIT"
] | Python | multiplyArrayByScalar | <not_specific> | def multiplyArrayByScalar(inputArray, scalar):
'''
Multiplying each component of a 2D array by a scalar
- Inputs:
inputArray input array
scal input scalar
- Outputs:
outputArray output array
'''
outputArray = []
for j in range(len(inputArray)):
outputArray.append(inputArray[j])
for k in range(len(inputArray[j])):
outputArray[j][k] = inputArray[j][k] * scalar
return outputArray |
Multiplying each component of a 2D array by a scalar
- Inputs:
inputArray input array
scal input scalar
- Outputs:
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| Multiplying each component of a 2D array by a scalar
Inputs:
inputArray input array
scal input scalar
Outputs:
outputArray output array | [
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outputArray = []
for j in range(len(inputArray)):
outputArray.append(inputArray[j])
for k in range(len(inputArray[j])):
outputArray[j][k] = inputArray[j][k] * scalar
return outputArray | 33 | 1,004 |
950f789d70c9ff60ddf38726d7b8992d103f289c | google-research/pyreach | pyreach/common/base/debug.py | [
"Apache-2.0"
] | Python | debug | None | def debug(msg: str) -> None:
"""Write debug message to stderr.
Debug message consists file name, line number and function name
of the calling routine.
Args:
msg: a custom message.
"""
parent_frame = inspect.stack()[1]
file = parent_frame[1]
line = parent_frame[2]
func = parent_frame[3]
sys.stderr.write("{}:{}-{}: {}\n".format(file, line, func, msg))
sys.stderr.flush() | Write debug message to stderr.
Debug message consists file name, line number and function name
of the calling routine.
Args:
msg: a custom message.
| Write debug message to stderr.
Debug message consists file name, line number and function name
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import sys
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file = parent_frame[1]
line = parent_frame[2]
func = parent_frame[3]
sys.stderr.write("{}:{}-{}: {}\n".format(file, line, func, msg))
sys.stderr.flush() | 34 | 504 |
9a6a097e06c1ab31ad5a3c4d60545ff85a3c54d4 | cookbrite/ebs-deploy | ebs_deploy/__init__.py | [
"MIT"
] | Python | override_scaling | <not_specific> | def override_scaling(option_settings, min_size, max_size):
""" takes the merged option_settings and injects custom min/max autoscaling sizes """
match_namespace = "aws:autoscaling:asg"
match_keys = {"MinSize": min_size, "MaxSize": max_size}
copied_option_settings = []
for (namespace, key, value) in option_settings:
new_option = (namespace, key, value)
if match_namespace == namespace and key in match_keys:
new_option = (namespace, key, match_keys[key])
copied_option_settings.append(new_option)
return copied_option_settings | takes the merged option_settings and injects custom min/max autoscaling sizes | takes the merged option_settings and injects custom min/max autoscaling sizes | [
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match_namespace = "aws:autoscaling:asg"
match_keys = {"MinSize": min_size, "MaxSize": max_size}
copied_option_settings = []
for (namespace, key, value) in option_settings:
new_option = (namespace, key, value)
if match_namespace == namespace and key in match_keys:
new_option = (namespace, key, match_keys[key])
copied_option_settings.append(new_option)
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match_keys = {"MinSize": min_size, "MaxSize": max_size}
copied_option_settings = []
for (namespace, key, value) in option_settings:
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if match_namespace == namespace and key in match_keys:
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copied_option_settings.append(new_option)
return copied_option_settings | 35 | 744 |
440b71cde91480a4acc2ad27be6b5c2c2c2604ee | iamholger/professor | pyext/professor2/ipol.py | [
"MIT"
] | Python | mk_ipolinputs | <not_specific> | def mk_ipolinputs(params):
"""
Make sorted run name and parameter name & value lists, suitable for passing to prof.Ipol
params is a dict (actually, prefer OrderedDict) of run_names -> param_vals,
as returned from read_rundata
"""
runs = sorted(params.keys())
if not runs:
return runs, [], [[]]
paramnames = params[runs[0]].keys()
paramslist = [[params[run][pn] for pn in paramnames] for run in runs]
return runs, paramnames, paramslist |
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params is a dict (actually, prefer OrderedDict) of run_names -> param_vals,
as returned from read_rundata
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params is a dict (actually, prefer OrderedDict) of run_names -> param_vals,
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paramslist = [[params[run][pn] for pn in paramnames] for run in runs]
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paramslist = [[params[run][pn] for pn in paramnames] for run in runs]
return runs, paramnames, paramslist | 36 | 703 |
6ba8475b25a768b98f6094c5b04ba522fbead45a | Hyperparticle/lct-master | charles-university/statistical-nlp/assignment-3/tag.py | [
"MIT"
] | Python | open_text | <not_specific> | def open_text(filename):
"""Reads a text line by line, applies light preprocessing, and returns an array of words and tags"""
with open(filename, encoding='iso-8859-2') as f:
content = f.readlines()
preprocess = lambda word: tuple(word.strip().rsplit('/', 1))
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9b15eca2a02be787e6c3a05fff838aba8bbda7f9 | jschmidtnj/CS115 | hw5.py | [
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'''Returns the nth Lucas number using the memoization technique
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[2, 1, 3, 4, 7, 11, ...]'''
def lucasMemo(n, memo):
if n in memo:
return memo[n]
if n == 0:
result = 2
elif n == 1:
result = 1
else:
result = lucasMemo(n-1, memo) + lucasMemo(n-2, memo)
memo[n] = result
return result
return lucasMemo(n, {}) | Returns the nth Lucas number using the memoization technique
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4b509323441ad1cfa76f24e0b712b509f5f23a01 | fangohr/oommf-python | vision/oommf/mifgen.py | [
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] | Python | _save_mif | <not_specific> | def _save_mif(sim_object, target):
"""
save_mif(sim_object)
Function takes a simulation instance and then saves the mif to file in
order to run a simulation, and also += 1 to the N_mifs in order to keep
track of multiple simulation steps, ie if a parameter will change.
Parameters
----------
sim_object : instance
A simulation instance.
Returns
-------
string
The path to the mif file
"""
path = sim_object.name + "_" + str(sim_object.t) + "_" + str(target) + ".mif"
f = open(path, 'w')
f.write(sim_object.mif)
return path |
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Function takes a simulation instance and then saves the mif to file in
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track of multiple simulation steps, ie if a parameter will change.
Parameters
----------
sim_object : instance
A simulation instance.
Returns
-------
string
The path to the mif file
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f = open(path, 'w')
f.write(sim_object.mif)
return path | 39 | 157 |
fdbaead8c186a6baca4ad86b5dbf22f4c5e3e1b2 | masenf/metaforward | src/metaforward.py | [
"BSD-2-Clause"
] | Python | shadowed_attributes_from_bases | <not_specific> | def shadowed_attributes_from_bases(mcs, bases, dct=None):
"""
Collect attributes from a base class and the dct of a class under construction.
:param bases: sequence of types
:param dct: optional dict of attributes for a class under construction
:return: set of attributes which should not be proxied
"""
shadowed_attributes = set()
if dct is not None:
shadowed_attributes.update(dct.keys())
for base in bases:
shadowed_attributes.update(dir(base))
return shadowed_attributes |
Collect attributes from a base class and the dct of a class under construction.
:param bases: sequence of types
:param dct: optional dict of attributes for a class under construction
:return: set of attributes which should not be proxied
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return shadowed_attributes | 40 | 867 |
efb7d56381ffb678d115f43d13a2057ef73b85d9 | nuxy/bittrex_autotrader | bittrex_autotrader/__main__.py | [
"MIT"
] | Python | _list_of_dict_filter_by | <not_specific> | def _list_of_dict_filter_by(data, key, value):
"""
Returns list of dictionary items filtered by key/value.
Args:
data (dict):
Data to filter.
key (str):
Dictionary key search.
value (str):
Dictionary key value match.
Returns:
list
"""
return [
item for i, item in enumerate(data) if data[i].get(key) == value
] |
Returns list of dictionary items filtered by key/value.
Args:
data (dict):
Data to filter.
key (str):
Dictionary key search.
value (str):
Dictionary key value match.
Returns:
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95e101001497dcd38c9653f10ac6de81199769ab | d-chambers/OpenSarToolkit | ost/helpers/scihub.py | [
"MIT"
] | Python | connect | <not_specific> | def connect(uname=None, pword=None, base_url="https://apihub.copernicus.eu/apihub"):
"""Generates an opener for the Copernicus apihub/dhus
:param uname: username of Copernicus' scihub
:type uname: str
:param pword: password of Copernicus' scihub
:type pword: str
:param base_url:
:return: an urllib opener instance for Copernicus' scihub
:rtype: opener object
"""
if not uname:
print(
" If you do not have a Copernicus Scihub user"
" account go to: https://scihub.copernicus.eu"
)
uname = input(" Your Copernicus Scihub Username:")
if not pword:
pword = getpass.getpass(" Your Copernicus Scihub Password:")
# create opener
manager = urllib.request.HTTPPasswordMgrWithDefaultRealm()
manager.add_password(None, base_url, uname, pword)
handler = urllib.request.HTTPBasicAuthHandler(manager)
opener = urllib.request.build_opener(handler)
return opener | Generates an opener for the Copernicus apihub/dhus
:param uname: username of Copernicus' scihub
:type uname: str
:param pword: password of Copernicus' scihub
:type pword: str
:param base_url:
:return: an urllib opener instance for Copernicus' scihub
:rtype: opener object
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if not pword:
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"opener",
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"the",
"Copernicus",
"apihub",
"/",
"dhus"
] | [
"\"\"\"Generates an opener for the Copernicus apihub/dhus\n\n\n :param uname: username of Copernicus' scihub\n :type uname: str\n :param pword: password of Copernicus' scihub\n :type pword: str\n :param base_url:\n :return: an urllib opener instance for Copernicus' scihub\n :rtype: opener object\n \"\"\"",
"# create opener"
] | [
{
"param": "uname",
"type": null
},
{
"param": "pword",
"type": null
},
{
"param": "base_url",
"type": null
}
] | {
"returns": [
{
"docstring": "an urllib opener instance for Copernicus' scihub",
"docstring_tokens": [
"an",
"urllib",
"opener",
"instance",
"for",
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"scihub"
],
"type": "opener object"
}
],
"raises": [],
"params": [
{
"identifier": "uname",
"type": null,
"docstring": "username of Copernicus' scihub",
"docstring_tokens": [
"username",
"of",
"Copernicus",
"'",
"scihub"
],
"default": null,
"is_optional": null
},
{
"identifier": "pword",
"type": null,
"docstring": "password of Copernicus' scihub",
"docstring_tokens": [
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"'",
"scihub"
],
"default": null,
"is_optional": null
},
{
"identifier": "base_url",
"type": null,
"docstring": null,
"docstring_tokens": [
"None"
],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import getpass
import urllib
def connect(uname=None, pword=None, base_url="https://apihub.copernicus.eu/apihub"):
if not uname:
print(
" If you do not have a Copernicus Scihub user"
" account go to: https://scihub.copernicus.eu"
)
uname = input(" Your Copernicus Scihub Username:")
if not pword:
pword = getpass.getpass(" Your Copernicus Scihub Password:")
manager = urllib.request.HTTPPasswordMgrWithDefaultRealm()
manager.add_password(None, base_url, uname, pword)
handler = urllib.request.HTTPBasicAuthHandler(manager)
opener = urllib.request.build_opener(handler)
return opener | 43 | 125 |
32e3f226d9e450744b37a06b1b5e8904828a3571 | beli302/Pitches | virtual/lib/python3.6/site-packages/flask_uploads.py | [
"MIT"
] | Python | patch_request_class | <not_specific> | def patch_request_class(app, size=64 * 1024 * 1024):
"""
By default, Flask will accept uploads to an arbitrary size. While Werkzeug
switches uploads from memory to a temporary file when they hit 500 KiB,
it's still possible for someone to overload your disk space with a
gigantic file.
This patches the app's request class's
`~werkzeug.BaseRequest.max_content_length` attribute so that any upload
larger than the given size is rejected with an HTTP error.
.. note::
In Flask 0.6, you can do this by setting the `MAX_CONTENT_LENGTH`
setting, without patching the request class. To emulate this behavior,
you can pass `None` as the size (you must pass it explicitly). That is
the best way to call this function, as it won't break the Flask 0.6
functionality if it exists.
.. versionchanged:: 0.1.1
:param app: The app to patch the request class of.
:param size: The maximum size to accept, in bytes. The default is 64 MiB.
If it is `None`, the app's `MAX_CONTENT_LENGTH` configuration
setting will be used to patch.
"""
if size is None:
if isinstance(app.request_class.__dict__['max_content_length'],
property):
return
size = app.config.get('MAX_CONTENT_LENGTH')
reqclass = app.request_class
patched = type(reqclass.__name__, (reqclass,),
{'max_content_length': size})
app.request_class = patched |
By default, Flask will accept uploads to an arbitrary size. While Werkzeug
switches uploads from memory to a temporary file when they hit 500 KiB,
it's still possible for someone to overload your disk space with a
gigantic file.
This patches the app's request class's
`~werkzeug.BaseRequest.max_content_length` attribute so that any upload
larger than the given size is rejected with an HTTP error.
.. note::
In Flask 0.6, you can do this by setting the `MAX_CONTENT_LENGTH`
setting, without patching the request class. To emulate this behavior,
you can pass `None` as the size (you must pass it explicitly). That is
the best way to call this function, as it won't break the Flask 0.6
functionality if it exists.
.. versionchanged:: 0.1.1
:param app: The app to patch the request class of.
:param size: The maximum size to accept, in bytes. The default is 64 MiB.
If it is `None`, the app's `MAX_CONTENT_LENGTH` configuration
setting will be used to patch.
| By default, Flask will accept uploads to an arbitrary size. While Werkzeug
switches uploads from memory to a temporary file when they hit 500 KiB,
it's still possible for someone to overload your disk space with a
gigantic file.
This patches the app's request class's
`~werkzeug.BaseRequest.max_content_length` attribute so that any upload
larger than the given size is rejected with an HTTP error.
In Flask 0.6, you can do this by setting the `MAX_CONTENT_LENGTH`
setting, without patching the request class. To emulate this behavior,
you can pass `None` as the size (you must pass it explicitly). That is
the best way to call this function, as it won't break the Flask 0.6
functionality if it exists.
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if size is None:
if isinstance(app.request_class.__dict__['max_content_length'],
property):
return
size = app.config.get('MAX_CONTENT_LENGTH')
reqclass = app.request_class
patched = type(reqclass.__name__, (reqclass,),
{'max_content_length': size})
app.request_class = patched | [
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] | [
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if size is None:
if isinstance(app.request_class.__dict__['max_content_length'],
property):
return
size = app.config.get('MAX_CONTENT_LENGTH')
reqclass = app.request_class
patched = type(reqclass.__name__, (reqclass,),
{'max_content_length': size})
app.request_class = patched | 44 | 142 |
303426d5ee7004b9dd1a7767a2408579d6fb2033 | neptune-ml/sacred | sacred/commandline_options.py | [
"MIT"
] | Python | apply | null | def apply(cls, args, run):
"""Add priority info for this run."""
try:
priority = float(args)
except ValueError:
raise ValueError(
"The PRIORITY argument must be a number! " "(but was '{}')".format(args)
)
run.meta_info["priority"] = priority | Add priority info for this run. | Add priority info for this run. | [
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] | def apply(cls, args, run):
try:
priority = float(args)
except ValueError:
raise ValueError(
"The PRIORITY argument must be a number! " "(but was '{}')".format(args)
)
run.meta_info["priority"] = priority | [
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} | def apply(cls, args, run):
try:
priority = float(args)
except ValueError:
raise ValueError(
"The PRIORITY argument must be a number! " "(but was '{}')".format(args)
)
run.meta_info["priority"] = priority | 45 | 194 |
866a30195e6c69b24064d40dcd0432c7464bdea9 | tvhahn/Tool-Wear-Files | load_data.py | [
"MIT"
] | Python | tool_no_apply | <not_specific> | def tool_no_apply(cols):
"""Gets the tool number from the PMC signal
Explanation
===========
Same explanation as in the cut_signal_apply function
"""
pmc = cols[0]
if (pmc - 64) > 0:
return int(pmc - 64)
else:
return int(pmc) | Gets the tool number from the PMC signal
Explanation
===========
Same explanation as in the cut_signal_apply function
| Gets the tool number from the PMC signal
Explanation
Same explanation as in the cut_signal_apply function | [
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pmc = cols[0]
if (pmc - 64) > 0:
return int(pmc - 64)
else:
return int(pmc) | [
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} | def tool_no_apply(cols):
pmc = cols[0]
if (pmc - 64) > 0:
return int(pmc - 64)
else:
return int(pmc) | 46 | 531 |
323c8a621d742c6892970c147bed4e6c27b1d7f0 | pypyjs/pypy | pypy/module/cpyext/import_.py | [
"Apache-2.0",
"OpenSSL"
] | Python | PyImport_Import | <not_specific> | def PyImport_Import(space, w_name):
"""
This is a higher-level interface that calls the current "import hook function".
It invokes the __import__() function from the __builtins__ of the
current globals. This means that the import is done using whatever import hooks
are installed in the current environment, e.g. by rexec or ihooks.
Always uses absolute imports."""
caller = space.getexecutioncontext().gettopframe_nohidden()
# Get the builtins from current globals
if caller is not None:
w_globals = caller.w_globals
w_builtin = space.getitem(w_globals, space.wrap('__builtins__'))
else:
# No globals -- use standard builtins, and fake globals
w_builtin = space.getbuiltinmodule('__builtin__')
w_globals = space.newdict()
space.setitem(w_globals, space.wrap("__builtins__"), w_builtin)
# Get the __import__ function from the builtins
if space.isinstance_w(w_builtin, space.w_dict):
w_import = space.getitem(w_builtin, space.wrap("__import__"))
else:
w_import = space.getattr(w_builtin, space.wrap("__import__"))
# Call the __import__ function with the proper argument list
# Always use absolute import here.
return space.call_function(w_import,
w_name, w_globals, w_globals,
space.newlist([space.wrap("__doc__")])) |
This is a higher-level interface that calls the current "import hook function".
It invokes the __import__() function from the __builtins__ of the
current globals. This means that the import is done using whatever import hooks
are installed in the current environment, e.g. by rexec or ihooks.
Always uses absolute imports. | This is a higher-level interface that calls the current "import hook function".
It invokes the __import__() function from the __builtins__ of the
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return space.call_function(w_import,
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space.newlist([space.wrap("__doc__")])) | 47 | 54 |
f134b7410f030c009eefe67e740f832d9cbaa2f8 | MirkoLedda/polyoligo | src/polyoligo/lib_utils.py | [
"BSD-2-Clause"
] | Python | seconds_to_hms | <not_specific> | def seconds_to_hms(t):
"""Formats a datetime.timedelta object to a HH:MM:SS string."""
hours, remainder = divmod(t.total_seconds(), 3600)
minutes, seconds = divmod(remainder, 60)
hours, minutes, seconds = int(hours), int(minutes), int(seconds)
if hours < 10:
hours = "0%s" % int(hours)
if minutes < 10:
minutes = "0%s" % minutes
if seconds < 10:
seconds = "0%s" % seconds
return "%s:%s:%s" % (hours, minutes, seconds) | Formats a datetime.timedelta object to a HH:MM:SS string. | Formats a datetime.timedelta object to a HH:MM:SS string. | [
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hours, remainder = divmod(t.total_seconds(), 3600)
minutes, seconds = divmod(remainder, 60)
hours, minutes, seconds = int(hours), int(minutes), int(seconds)
if hours < 10:
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hours, remainder = divmod(t.total_seconds(), 3600)
minutes, seconds = divmod(remainder, 60)
hours, minutes, seconds = int(hours), int(minutes), int(seconds)
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hours = "0%s" % int(hours)
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return "%s:%s:%s" % (hours, minutes, seconds) | 48 | 814 |
d37711957c262fbe690050937109571e502cf3e6 | Pandaaaa906/scrapy-playwright | scrapy_playwright/headers.py | [
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] | Python | use_playwright_headers | dict | async def use_playwright_headers(
browser_type: str,
playwright_request: PlaywrightRequest,
scrapy_headers: Headers,
) -> dict:
"""Return headers from the Playwright request, unaltered"""
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35a98f36b10113ace2ee5dac283ea5b6e9f91f5e | threefoldtech/jumpscaleX_core | install/threesdk/shell.py | [
"Apache-2.0"
] | Python | eval_code | <not_specific> | def eval_code(stmts, locals_=None, globals_=None):
"""
a helper function to ignore incomplete syntax erros when evaluating code
while typing incomplete lines, e.g.: j.clien...
"""
if not stmts:
return
try:
code = compile(stmts, filename=__name__, mode="eval")
except SyntaxError:
return
try:
return eval(code, globals_, locals_)
except:
return |
a helper function to ignore incomplete syntax erros when evaluating code
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code = compile(stmts, filename=__name__, mode="eval")
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return | 51 | 545 |
3d22d48525dfc4451cbff275e76eaf0f861d2502 | Allthemighty/SataniaBot | src/modules/misc/misc_util.py | [
"MIT"
] | Python | simple_check | <not_specific> | def simple_check(author, channel):
"""
A predicate used in the wait_for() function, to ensure the user input can only come
from the user who activated the command, and in the same channel.
:param author: Author object
:param channel: Channel object
:return: Check function
"""
def check(message):
return message.author == author and message.channel == channel
return check |
A predicate used in the wait_for() function, to ensure the user input can only come
from the user who activated the command, and in the same channel.
:param author: Author object
:param channel: Channel object
:return: Check function
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ecd832b296a9381f4398c16dcef906a078177dd4 | cmu-sei/usersim | tasks/frequency.py | [
"BSL-1.0"
] | Python | parameters | <not_specific> | def parameters(cls):
""" Returns a dictionary with human-readable descriptions of required arguments for the Frequency task.
Returns:
dict of dicts: Configuration dictionary with the keys 'required' and 'optional', where values are dicts
containing the required and optional parameters and their descriptions for the Frequency task,
respectively.
"""
params = {'required': {'task': 'task| the configuration of another task',
'frequency': 'number| positive decimal number - avg number of triggers per hour',
'repetitions': 'int| non-negative integer - 0 for unlimited'},
'optional': {}}
return params | Returns a dictionary with human-readable descriptions of required arguments for the Frequency task.
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dict of dicts: Configuration dictionary with the keys 'required' and 'optional', where values are dicts
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3a0d16ef8d51a02e0c9568006f90794aa64c9e5c | russhughes/TurtlePlotBot | lib/oledui.py | [
"MIT"
] | Python | _screen_shot | null | def _screen_shot(uio, *_):
"""
_screen_shot
Append a text screen shot of the OLED display in the screenshot.txt
file. This can be triggered by a call to the routine or by long
pressing a button on the Pin 0 any time the UI is waiting for a
button press.
Each screen shot starts with a line consisting of the word "BEGIN".
Each row of the display is represented as a line of '.' and 'X'
characters where a dark pixel is represented by a '.' and each light
pixel is represented by a 'X'. The screenshot ends with a line
consisting of the word "END". The screenshot.txt file can contain
multiple screenshots.
"""
print("Writing screenshot... ")
with open('screenshots.txt', 'a') as output:
print('BEGIN', file=output)
for row in range(uio.display.height):
for col in range(uio.display.width):
if uio.display.pixel(col, row):
print('X', sep="", end="", file=output)
else:
print(".", sep="", end="", file=output)
print("", file=output)
print("END", file=output)
print("done.") |
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multiple screenshots.
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Append a text screen shot of the OLED display in the screenshot.txt
file. This can be triggered by a call to the routine or by long
pressing a button on the Pin 0 any time the UI is waiting for a
button press.
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Append a text screen shot of the OLED display in the screenshot.txt
file. | [
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] | [
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} | def _screen_shot(uio, *_):
print("Writing screenshot... ")
with open('screenshots.txt', 'a') as output:
print('BEGIN', file=output)
for row in range(uio.display.height):
for col in range(uio.display.width):
if uio.display.pixel(col, row):
print('X', sep="", end="", file=output)
else:
print(".", sep="", end="", file=output)
print("", file=output)
print("END", file=output)
print("done.") | 54 | 918 |
2e79e2b725ac9e7e5c08e4e2dcbc552df0a4a442 | ooici/coi-services | ion/agents/platform/test/helper.py | [
"BSD-2-Clause"
] | Python | using_actual_rsn_oms_endpoint | <not_specific> | def using_actual_rsn_oms_endpoint(cls):
"""
Determines whether we are testing against the actual RSN OMS endpoint.
This is based on looking up the "USING_ACTUAL_RSN_OMS_ENDPOINT"
environment variable, which normally will only be defined as
convenient while doing local tests. See OOIION-1352.
"""
return "yes" == os.getenv('USING_ACTUAL_RSN_OMS_ENDPOINT') |
Determines whether we are testing against the actual RSN OMS endpoint.
This is based on looking up the "USING_ACTUAL_RSN_OMS_ENDPOINT"
environment variable, which normally will only be defined as
convenient while doing local tests. See OOIION-1352.
| Determines whether we are testing against the actual RSN OMS endpoint.
This is based on looking up the "USING_ACTUAL_RSN_OMS_ENDPOINT"
environment variable, which normally will only be defined as
convenient while doing local tests. | [
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} | import os
def using_actual_rsn_oms_endpoint(cls):
return "yes" == os.getenv('USING_ACTUAL_RSN_OMS_ENDPOINT') | 55 | 657 |
cc179d17e4e35730d499aba1ba8c9c604fca0dad | crahal/lit_reviewer | src/pubmed_functions.py | [
"MIT"
] | Python | return_pag | <not_specific> | def return_pag(paper):
""" Return the pagination from the paper nest"""
try:
return paper['MedlineCitation']['Article']['Pagination']['MedlinePgn']
except KeyError:
return None | Return the pagination from the paper nest | Return the pagination from the paper nest | [
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try:
return paper['MedlineCitation']['Article']['Pagination']['MedlinePgn']
except KeyError:
return None | [
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try:
return paper['MedlineCitation']['Article']['Pagination']['MedlinePgn']
except KeyError:
return None | 56 | 359 |
2e17d39b74ad622e3ac0ae25d5f59143463bfa85 | rafaelcn/cryptography | aes/src/aes/common.py | [
"MIT"
] | Python | add_round_key | null | def add_round_key(state, key):
"""
Add the given key to the state using XOR.
"""
for i in range(4):
for j in range(4):
state[i][j] ^= key[i][j] |
Add the given key to the state using XOR.
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for j in range(4):
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for i in range(4):
for j in range(4):
state[i][j] ^= key[i][j] | 57 | 727 |
e18bd85473e5921ae439f6af3476d616402d6ba0 | waikato-datamining/video-frame-processor | src/vfp/_processor.py | [
"MIT"
] | Python | decode_fourcc | <not_specific> | def decode_fourcc(cc):
"""
Turns the float into a four letter codec string.
Taken from here:
https://stackoverflow.com/a/49138893/4698227
:param cc: the codec as float
:type cc: float
:return: the codec string
:rtype: str
"""
return "".join([chr((int(cc) >> 8 * i) & 0xFF) for i in range(4)]) |
Turns the float into a four letter codec string.
Taken from here:
https://stackoverflow.com/a/49138893/4698227
:param cc: the codec as float
:type cc: float
:return: the codec string
:rtype: str
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3378601f401cc65b610da88dac290768b028faa7 | mwhit74/sau | sau/sau.py | [
"MIT"
] | Python | ac1_mx | <not_specific> | def ac1_mx(w, l, x):
"""Moment at x - Beam simply supported - Uniformly dist. loads
Calculates the moment in the beam at any location, x, along the
beam due to a uniformly distributed load.
m = w*x/2*(l-x)
Args:
w (float): uniformly distributed load
l (float): length of beam between supports
E (float): modulus of elasticity
I (float): section modulus
x (float): distance along beam from left support
Returns:
m (tuple(float, str)): maximum positive moment at midspan
Notes:
1. Consistent units are the responsibility of the user.
3. For maximum positive moment use x = L/2.
"""
m = w*x/2.*(l-x)
text = (f'm = w*x/2*(l-x) \n' +
f'm = {w:.2f}*{x:.2f}/2*({l:.2f}-{x:.2f}) \n' +
f'm = {m:.2f}')
return m, text | Moment at x - Beam simply supported - Uniformly dist. loads
Calculates the moment in the beam at any location, x, along the
beam due to a uniformly distributed load.
m = w*x/2*(l-x)
Args:
w (float): uniformly distributed load
l (float): length of beam between supports
E (float): modulus of elasticity
I (float): section modulus
x (float): distance along beam from left support
Returns:
m (tuple(float, str)): maximum positive moment at midspan
Notes:
1. Consistent units are the responsibility of the user.
3. For maximum positive moment use x = L/2.
| Moment at x - Beam simply supported - Uniformly dist. loads
Calculates the moment in the beam at any location, x, along the
beam due to a uniformly distributed load.
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m = w*x/2.*(l-x)
text = (f'm = w*x/2*(l-x) \n' +
f'm = {w:.2f}*{x:.2f}/2*({l:.2f}-{x:.2f}) \n' +
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m = w*x/2.*(l-x)
text = (f'm = w*x/2*(l-x) \n' +
f'm = {w:.2f}*{x:.2f}/2*({l:.2f}-{x:.2f}) \n' +
f'm = {m:.2f}')
return m, text | 59 | 185 |
85c81e489f3398ee5c56508f8cd0e49341a902fc | amole-arup/eng_utilities | eng_utilities/section_utilities.py | [
"MIT"
] | Python | deck_props | <not_specific> | def deck_props(DD, DR, B, TT, TB):
"""Properties of a trapezoidal deck when given deck depth (DD), rib depth (DR)
rib spacing (B), rib width at top (TT), rib width at bottom (TB)
- P is the length of the deck on the underside (topside is ignored)
- A is the cross-sectional area of concrete per rib
- D_AVE is the average depth of concrete
NB At the moment no flexural properties are calculated"""
A = DD * B + 0.5 * (TT + TB) * DR
P = B - TT + TB + ((TT - TB)**2 + 4 * DR**2)**0.5
return {'P': P, 'A': A, 'D_AVE': A / B} | Properties of a trapezoidal deck when given deck depth (DD), rib depth (DR)
rib spacing (B), rib width at top (TT), rib width at bottom (TB)
- P is the length of the deck on the underside (topside is ignored)
- A is the cross-sectional area of concrete per rib
- D_AVE is the average depth of concrete
NB At the moment no flexural properties are calculated | Properties of a trapezoidal deck when given deck depth (DD), rib depth (DR)
rib spacing (B), rib width at top (TT), rib width at bottom (TB)
P is the length of the deck on the underside (topside is ignored)
A is the cross-sectional area of concrete per rib
D_AVE is the average depth of concrete
NB At the moment no flexural properties are calculated | [
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"outlier_params": [],
"others": []
} | def deck_props(DD, DR, B, TT, TB):
A = DD * B + 0.5 * (TT + TB) * DR
P = B - TT + TB + ((TT - TB)**2 + 4 * DR**2)**0.5
return {'P': P, 'A': A, 'D_AVE': A / B} | 60 | 30 |
4158684ebdf6a0f7e38126105f4c44de5ba48d04 | turnoutnow/game-analytics-pipeline | source/demo/publish_data.py | [
"MIT-0"
] | Python | send_record_batch | null | def send_record_batch(kinesis_client, stream_name, raw_records):
"""Send a batch of records to Amazon Kinesis."""
# Translate input records into the format needed by the boto3 SDK
formatted_records = []
for rec in raw_records:
formatted_records.append({'PartitionKey': rec['event']['event_id'], 'Data': json.dumps(rec)})
kinesis_client.put_records(StreamName=stream_name, Records=formatted_records)
print('Sent %d records to stream %s.' % (len(formatted_records), stream_name)) | Send a batch of records to Amazon Kinesis. | Send a batch of records to Amazon Kinesis. | [
"Send",
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"batch",
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"records",
"to",
"Amazon",
"Kinesis",
"."
] | def send_record_batch(kinesis_client, stream_name, raw_records):
formatted_records = []
for rec in raw_records:
formatted_records.append({'PartitionKey': rec['event']['event_id'], 'Data': json.dumps(rec)})
kinesis_client.put_records(StreamName=stream_name, Records=formatted_records)
print('Sent %d records to stream %s.' % (len(formatted_records), stream_name)) | [
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] | [
"\"\"\"Send a batch of records to Amazon Kinesis.\"\"\"",
"# Translate input records into the format needed by the boto3 SDK"
] | [
{
"param": "kinesis_client",
"type": null
},
{
"param": "stream_name",
"type": null
},
{
"param": "raw_records",
"type": null
}
] | {
"returns": [],
"raises": [],
"params": [
{
"identifier": "kinesis_client",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "stream_name",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "raw_records",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | import json
def send_record_batch(kinesis_client, stream_name, raw_records):
formatted_records = []
for rec in raw_records:
formatted_records.append({'PartitionKey': rec['event']['event_id'], 'Data': json.dumps(rec)})
kinesis_client.put_records(StreamName=stream_name, Records=formatted_records)
print('Sent %d records to stream %s.' % (len(formatted_records), stream_name)) | 61 | 111 |
21cda7ead5143cd6dba8fbe256ccb44e0a991113 | baajur/Anakin | tools/external_converter_v2/parser/tensorflow/run_pb.py | [
"Apache-2.0"
] | Python | convert_name_tf2ak | <not_specific> | def convert_name_tf2ak(tf_name, perfix='record_'):
'''
conver tf name to ak name
:param tf_name:
:param perfix:
:return:
'''
ak_name = tf_name[:]
for index, x in enumerate(tf_name):
if x == '/':
ak_name = ak_name[:index] + '_' + ak_name[index + 1:]
return perfix + ak_name |
conver tf name to ak name
:param tf_name:
:param perfix:
:return:
| conver tf name to ak name | [
"conver",
"tf",
"name",
"to",
"ak",
"name"
] | def convert_name_tf2ak(tf_name, perfix='record_'):
ak_name = tf_name[:]
for index, x in enumerate(tf_name):
if x == '/':
ak_name = ak_name[:index] + '_' + ak_name[index + 1:]
return perfix + ak_name | [
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] | [
"'''\n conver tf name to ak name\n :param tf_name:\n :param perfix:\n :return:\n '''"
] | [
{
"param": "tf_name",
"type": null
},
{
"param": "perfix",
"type": null
}
] | {
"returns": [
{
"docstring": null,
"docstring_tokens": [
"None"
],
"type": null
}
],
"raises": [],
"params": [
{
"identifier": "tf_name",
"type": null,
"docstring": null,
"docstring_tokens": [
"None"
],
"default": null,
"is_optional": null
},
{
"identifier": "perfix",
"type": null,
"docstring": null,
"docstring_tokens": [
"None"
],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
} | def convert_name_tf2ak(tf_name, perfix='record_'):
ak_name = tf_name[:]
for index, x in enumerate(tf_name):
if x == '/':
ak_name = ak_name[:index] + '_' + ak_name[index + 1:]
return perfix + ak_name | 62 | 80 |
End of preview. Expand
in Dataset Viewer.
Dataset Card for "thevault-filtered"
Filtered version of The Vault (function). Restricted only to Python, then:
- Light AST filtering for self-contained functions
- Run through CodeBERT embeddings, clustered with k-means to 1024 clusters, and then the clusters were manually skimmed for seemingly uninformative functions.
The clusters excluded and their reasons are as follows:
excluded = [
4, # biochem stuff? DEcompiled code
9, # Empty functions
33, # Empty functions
34, # UI stuff, just returns arguments
37, # Empty functions
40, # Empty functions
42, # Empty functions
44, # _namespace_SIO stuff
55, # Trivial, e.g. add(a, b) = a + b
66, # find_by class methods
67, # Mostly methods, seems not very informative
77, # openapi_types, returns a fixed dictionary
78, # Minimal, method stuff
83, # Locale configuration
87, # Just returns argument
101, # Incomplete
102, # Class methods
108, # openapi_types
156, # Empty functions
164, # Trivial, function aliases
168, # Class methods
172, # Empty functions
173, # Class methods
175, # Class methods
181, # Empty functions
182, # Fixed API stuff
190, # Fixed specific stuff
197, # from_dictionary class methods
198, # Empty functions
234, # Unimplemented
246, # Fixed specific stuff
277, # Empty functions
280, # Empty functions
282, # Empty functions
287, # Trivial, e.g. helloWorld()
299, # Mostly unfinished
304, # Empty functions
310, # Fixed API stuff
313, # Just modifies globals
320, # Empty functions
329, # Takes a credentials object, and runs methods on it
332, # MangoPi bot
334, # Empty
338, # namespace_SIO nonsense
339, # fn(x) = x
363, # Empty functions
370, # Empty
379, # Empty
388, # Empty
392, # Empty functions
393, # Fixed lists
409, # Fixed dictionaries
416, # Aliases to print
428, # Empty functions
437, # Empty functions
444, # Empty
454, # Mostly just calls methods on arguments
463, # Mostly just calls methods on arguments
470, # Fixed dictionaries
474, # Mostly fixed printing
465, # OpenAPI fixed dictionaries
476, # Empty
477, # Fixed dictionaries
491, # Trivial
494, # Lots of fixed string stuff
496, # Empty
511, # Empty
518, # OpenAPI
521, # Fixed API stuff
536, # Empty
540, # Fixed API stuff
553, # Empty
555, # Empty
564, # Empty
566, # Empty
568, # cls methods
573, # Mostly fixed dict stuff
574, # namespace_SO stuff, more biochem?
582, # namespace_SO stuff, more biochem?
602, # Fixed lists
608, # Mostly cls methods
617, # Mostly cls methods
629, # cls methods, fixed lists
641, # Fixed API stuff
642, # Empty
647, # Windows API stuff
648, # jupyter stuff
649, # mostly fixed dicts
652, # Empty
660, # Empty
665, # cls methods
666, # Empty
672, # Empty
680, # fixed dicts
682, # Empty
686, # Empty
687, # Fixed lists elements_sequence
692, # cls methods
693, # ASCII art
704, # Empty
709, # mqtt send message
712, # Empty
715, # Fixed data recoding
717, # Empty
722, # cls methods
725, # cls methods
734, # cls methods
737, # Empty
741, # Trivial cls methods
742, # Empty
745, # Fixed strings
752, # Empty
758, # Mostly fixed printing
768, # Empty
783, # Empty
784, # Mostly fixed dicts
802, # Fixed printing
806, # Empty
821, # Empty
824, # stuff like load_performance_win_x64_win_x64_vs2017_settings
825, # Trivial
835, # Empty
851, # Empty
862, # Empty
876, # Trivial
878, # Empty
887, # Empty
888, # Mostly fixed dicts
890, # Mostly fixed dicts
893, # Empty
898, # cls methods
899, # Fixed ['str'] stuff
906, # Auto-generated or something
912, # Empty
924, # Empty
933, # namespace_SO biochem stuff
938, # Trivial
959, # Mostly fixed printing
963, # API-specific
965, # cls methods
967, # cls methods
970, # Mostly fixed printing
971, # cls methods
972, # cls methods
973, # Empty
979, # cls methods
982, # Empty
983, # Empty
989, # cls methods
990, # API specific
1007, # API specific
1014, # Empty
]
MIT licensed, like the original dataset
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