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1d067638ebb7eb7862938e528b755c5db2ec52a7
LaudateCorpus1/code-align-evals-data
human_eval/6a183ce5-8d03-4fc0-a3c2-41e5b5be11b8.py
[ "MIT" ]
Python
find_max
<not_specific>
def find_max(words): """Write a function that accepts a list of strings. The list contains different words. Return the word with maximum number of unique characters. If multiple strings have maximum number of unique characters, return the one which comes first in lexicographical order. find_max(["name", "of", "string"]) == "string" find_max(["name", "enam", "game"]) == "enam" find_max(["aaaaaaa", "bb" ,"cc"]) == ""aaaaaaa" """ #[SOLUTION] return sorted(words, key = lambda x: (-len(set(x)), x))[0]
Write a function that accepts a list of strings. The list contains different words. Return the word with maximum number of unique characters. If multiple strings have maximum number of unique characters, return the one which comes first in lexicographical order. find_max(["name", "of", "string"]) == "string" find_max(["name", "enam", "game"]) == "enam" find_max(["aaaaaaa", "bb" ,"cc"]) == ""aaaaaaa"
Write a function that accepts a list of strings. The list contains different words. Return the word with maximum number of unique characters. If multiple strings have maximum number of unique characters, return the one which comes first in lexicographical order.
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def find_max(words): return sorted(words, key = lambda x: (-len(set(x)), x))[0]
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Write a function that accepts a list of strings.
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[ "\"\"\"Write a function that accepts a list of strings.\n The list contains different words. Return the word with maximum number\n of unique characters. If multiple strings have maximum number of unique\n characters, return the one which comes first in lexicographical order.\n\n find_max([\"name\", \"of\", \"string\"]) == \"string\"\n find_max([\"name\", \"enam\", \"game\"]) == \"enam\"\n find_max([\"aaaaaaa\", \"bb\" ,\"cc\"]) == \"\"aaaaaaa\"\n \"\"\"", "#[SOLUTION]" ]
[ { "param": "words", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "words", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def find_max(words): return sorted(words, key = lambda x: (-len(set(x)), x))[0]
610,297
694
1563b7a0cc90c26e1c4d9a394ed3939197bcf886
kaspermunch/argweaver
test/test_install.py
[ "MIT" ]
Python
run_cmd
null
def run_cmd(cmd, ret_code=0): """ Run shell command and assert success exit code. """ assert os.system(cmd) == ret_code
Run shell command and assert success exit code.
Run shell command and assert success exit code.
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def run_cmd(cmd, ret_code=0): assert os.system(cmd) == ret_code
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Run shell command and assert success exit code.
[ "Run", "shell", "command", "and", "assert", "success", "exit", "code", "." ]
[ "\"\"\"\n Run shell command and assert success exit code.\n \"\"\"" ]
[ { "param": "cmd", "type": null }, { "param": "ret_code", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cmd", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "ret_code", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import os def run_cmd(cmd, ret_code=0): assert os.system(cmd) == ret_code
610,298
1,009
4d3cd58a515acaa7e0ff402f7c16eedea3c458cd
BTBTravis/army_cook
armycook/cli.py
[ "MIT" ]
Python
load_master_txt
<not_specific>
def load_master_txt(): """Load file containing text parts to be used in tweet""" if(os.environ.get('DEV_ENV', '') is "1"): txt_file = open("../master.txt", "r") else: txt_file = open("/txts/master/master.txt", "r") txt = txt_file.read() txt_parts = txt.split('--') split_on_newline = lambda l: l.split("\n") non_empty = lambda s: len(s) > 0 filter_nonempty = lambda ls: list(filter(non_empty, ls)) break_apart_row = lambda row: { 'title': filter_nonempty(split_on_newline(row))[0], 'body': filter_nonempty(split_on_newline(row))[1] } txt_parts = list(map(break_apart_row, txt_parts)) return txt_parts
Load file containing text parts to be used in tweet
Load file containing text parts to be used in tweet
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def load_master_txt(): if(os.environ.get('DEV_ENV', '') is "1"): txt_file = open("../master.txt", "r") else: txt_file = open("/txts/master/master.txt", "r") txt = txt_file.read() txt_parts = txt.split('--') split_on_newline = lambda l: l.split("\n") non_empty = lambda s: len(s) > 0 filter_nonempty = lambda ls: list(filter(non_empty, ls)) break_apart_row = lambda row: { 'title': filter_nonempty(split_on_newline(row))[0], 'body': filter_nonempty(split_on_newline(row))[1] } txt_parts = list(map(break_apart_row, txt_parts)) return txt_parts
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Load file containing text parts to be used in tweet
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[ "\"\"\"Load file containing text parts to be used in tweet\"\"\"" ]
[]
{ "returns": [], "raises": [], "params": [], "outlier_params": [], "others": [] }
import os def load_master_txt(): if(os.environ.get('DEV_ENV', '') is "1"): txt_file = open("../master.txt", "r") else: txt_file = open("/txts/master/master.txt", "r") txt = txt_file.read() txt_parts = txt.split('--') split_on_newline = lambda l: l.split("\n") non_empty = lambda s: len(s) > 0 filter_nonempty = lambda ls: list(filter(non_empty, ls)) break_apart_row = lambda row: { 'title': filter_nonempty(split_on_newline(row))[0], 'body': filter_nonempty(split_on_newline(row))[1] } txt_parts = list(map(break_apart_row, txt_parts)) return txt_parts
610,299
688
1aee590c82de256b8807c3e33aee6e4dd2e5f143
Simske/exostriker
lib/RV_mod/functions.py
[ "MIT" ]
Python
randomString
<not_specific>
def randomString(stringLength=5): """ Generate a random string of fixed length """ letters = string.ascii_lowercase return ''.join(random.choice(letters) for i in range(stringLength))
Generate a random string of fixed length
Generate a random string of fixed length
[ "Generate", "a", "random", "string", "of", "fixed", "length" ]
def randomString(stringLength=5): letters = string.ascii_lowercase return ''.join(random.choice(letters) for i in range(stringLength))
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Generate a random string of fixed length
[ "Generate", "a", "random", "string", "of", "fixed", "length" ]
[ "\"\"\"\n Generate a random string of fixed length\n \"\"\"" ]
[ { "param": "stringLength", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "stringLength", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import string import random def randomString(stringLength=5): letters = string.ascii_lowercase return ''.join(random.choice(letters) for i in range(stringLength))
610,300
316
8bb45507ba5223af53d7feb9bd4a27216cc4d760
Cosive/chromewhip
chromewhip/protocol/storage.py
[ "MIT" ]
Python
trackCacheStorageForOrigin
<not_specific>
def trackCacheStorageForOrigin(cls, origin: Union['str'], ): """Registers origin to be notified when an update occurs to its cache storage list. :param origin: Security origin. :type origin: str """ return ( cls.build_send_payload("trackCacheStorageForOrigin", { "origin": origin, }), None )
Registers origin to be notified when an update occurs to its cache storage list. :param origin: Security origin. :type origin: str
Registers origin to be notified when an update occurs to its cache storage list.
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def trackCacheStorageForOrigin(cls, origin: Union['str'], ): return ( cls.build_send_payload("trackCacheStorageForOrigin", { "origin": origin, }), None )
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Registers origin to be notified when an update occurs to its cache storage list.
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[ "\"\"\"Registers origin to be notified when an update occurs to its cache storage list.\n :param origin: Security origin.\n :type origin: str\n \"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "origin", "type": "Union['str']" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "origin", "type": "Union['str']", "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def trackCacheStorageForOrigin(cls, origin: Union['str'], ): return ( cls.build_send_payload("trackCacheStorageForOrigin", { "origin": origin, }), None )
610,301
442
c189304925ad7368229a7e61ccf28a9c84260580
Kitware/trame
trame/app/dev.py
[ "Apache-2.0" ]
Python
clear_triggers
null
def clear_triggers(server): """ Helper function to remove all triggers. :param server: server on which we want to clear the triggers :type server: trame_server.core.Server """ names = list(server._triggers.keys()) for name in names: fn = server._triggers.pop(name) server._triggers_fn2name.pop(fn) print(f"unregister trigger {name}")
Helper function to remove all triggers. :param server: server on which we want to clear the triggers :type server: trame_server.core.Server
Helper function to remove all triggers.
[ "Helper", "function", "to", "remove", "all", "triggers", "." ]
def clear_triggers(server): names = list(server._triggers.keys()) for name in names: fn = server._triggers.pop(name) server._triggers_fn2name.pop(fn) print(f"unregister trigger {name}")
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Helper function to remove all triggers.
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[ "\"\"\"\n Helper function to remove all triggers.\n\n :param server: server on which we want to clear the triggers\n :type server: trame_server.core.Server\n \"\"\"" ]
[ { "param": "server", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "server", "type": null, "docstring": "server on which we want to clear the triggers", "docstring_tokens": [ "server", "on", "which", "we", "want", "to", "clear", "the", "triggers" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def clear_triggers(server): names = list(server._triggers.keys()) for name in names: fn = server._triggers.pop(name) server._triggers_fn2name.pop(fn) print(f"unregister trigger {name}")
610,302
497
e59084b51c000616f08ea2828647d08173037f65
adambyram/bite-project-mod
build/tools/base.py
[ "Apache-2.0" ]
Python
_GetExecutable
<not_specific>
def _GetExecutable(executable, location=None): """Returns the path to the given executable or None if it is not found. This algorithm provides a reasonably accurate determination of whether or not the given executable is on the machine and can be used without knowing its full path. An optional path can be added to the search. The algorithm returns the path or None. Args: executable: The name of the executable (no path info).(string) location: Optional location to search for the tool. (string) Returns: The path to the executable or None. (string) """ extensions = os.environ.get('PATHEXT', '').split(os.pathsep) paths = os.environ.get('PATH', '').split(os.pathsep) if location: paths.append(location) # Loop over every combination of path and file extension. for extension in extensions: for path in paths: full_path = os.path.join(path, '%s%s' % (executable, extension)) if os.path.isfile(full_path) and os.access(full_path, os.X_OK): return full_path return None
Returns the path to the given executable or None if it is not found. This algorithm provides a reasonably accurate determination of whether or not the given executable is on the machine and can be used without knowing its full path. An optional path can be added to the search. The algorithm returns the path or None. Args: executable: The name of the executable (no path info).(string) location: Optional location to search for the tool. (string) Returns: The path to the executable or None. (string)
Returns the path to the given executable or None if it is not found. This algorithm provides a reasonably accurate determination of whether or not the given executable is on the machine and can be used without knowing its full path. An optional path can be added to the search. The algorithm returns the path or None.
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def _GetExecutable(executable, location=None): extensions = os.environ.get('PATHEXT', '').split(os.pathsep) paths = os.environ.get('PATH', '').split(os.pathsep) if location: paths.append(location) for extension in extensions: for path in paths: full_path = os.path.join(path, '%s%s' % (executable, extension)) if os.path.isfile(full_path) and os.access(full_path, os.X_OK): return full_path return None
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Returns the path to the given executable or None if it is not found.
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[ "\"\"\"Returns the path to the given executable or None if it is not found.\n\n This algorithm provides a reasonably accurate determination of whether or\n not the given executable is on the machine and can be used without knowing\n its full path. An optional path can be added to the search. The algorithm\n returns the path or None.\n\n Args:\n executable: The name of the executable (no path info).(string)\n location: Optional location to search for the tool. (string)\n\n Returns:\n The path to the executable or None. (string)\n \"\"\"", "# Loop over every combination of path and file extension." ]
[ { "param": "executable", "type": null }, { "param": "location", "type": null } ]
{ "returns": [ { "docstring": "The path to the executable or None. (string)", "docstring_tokens": [ "The", "path", "to", "the", "executable", "or", "None", ".", "(", "string", ")" ], "type": null } ], "raises": [], "params": [ { "identifier": "executable", "type": null, "docstring": "The name of the executable (no path info).(string)", "docstring_tokens": [ "The", "name", "of", "the", "executable", "(", "no", "path", "info", ")", ".", "(", "string", ")" ], "default": null, "is_optional": null }, { "identifier": "location", "type": null, "docstring": "Optional location to search for the tool. (string)", "docstring_tokens": [ "Optional", "location", "to", "search", "for", "the", "tool", ".", "(", "string", ")" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import os def _GetExecutable(executable, location=None): extensions = os.environ.get('PATHEXT', '').split(os.pathsep) paths = os.environ.get('PATH', '').split(os.pathsep) if location: paths.append(location) for extension in extensions: for path in paths: full_path = os.path.join(path, '%s%s' % (executable, extension)) if os.path.isfile(full_path) and os.access(full_path, os.X_OK): return full_path return None
610,303
361
72ddceb682be19986f92cf069b181361ae4b6c32
zc-alexfan/faster_rcnn_0.4
extract_vg_feature.py
[ "MIT" ]
Python
formalize_bbox
<not_specific>
def formalize_bbox(_im_summary): """ Extract bboxes from all classes and return a list of bbox. Each element of the list is in the form: [x1, y1, x2, y2, class_id, score]. The returned list is sorted descendingly according to score. """ boxes = [] # each element: x, y, w, h, class_id, score probs = [] # prob distribution for each bounding box feats = [] # pooled features for class_id, items in enumerate(_im_summary.pred.boxes): for bbox in items: x1, y1, x2, y2, score = bbox boxes.append([x1, y1, x2, y2, class_id, score]) for class_id, items in enumerate(_im_summary.pred.cls_prob): for cls_prob in items: probs.append(cls_prob) for class_id, items in enumerate(_im_summary.pred.pooled_feat): for f in items: feats.append(f) assert len(boxes) == len(probs) assert len(boxes) == len(feats) bundles = list(zip(boxes, probs, feats)) bundles = sorted(bundles, key=lambda x: x[0][-1], reverse = True) # sort by confidence descendingly boxes, probs, feats = zip(*bundles) return (list(boxes), list(probs), list(feats))
Extract bboxes from all classes and return a list of bbox. Each element of the list is in the form: [x1, y1, x2, y2, class_id, score]. The returned list is sorted descendingly according to score.
Extract bboxes from all classes and return a list of bbox. Each element of the list is in the form: [x1, y1, x2, y2, class_id, score]. The returned list is sorted descendingly according to score.
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def formalize_bbox(_im_summary): boxes = [] probs = [] feats = [] for class_id, items in enumerate(_im_summary.pred.boxes): for bbox in items: x1, y1, x2, y2, score = bbox boxes.append([x1, y1, x2, y2, class_id, score]) for class_id, items in enumerate(_im_summary.pred.cls_prob): for cls_prob in items: probs.append(cls_prob) for class_id, items in enumerate(_im_summary.pred.pooled_feat): for f in items: feats.append(f) assert len(boxes) == len(probs) assert len(boxes) == len(feats) bundles = list(zip(boxes, probs, feats)) bundles = sorted(bundles, key=lambda x: x[0][-1], reverse = True) boxes, probs, feats = zip(*bundles) return (list(boxes), list(probs), list(feats))
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Extract bboxes from all classes and return a list of bbox.
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[ "\"\"\"\n Extract bboxes from all classes and return a list of bbox. \n Each element of the list is in the form: [x1, y1, x2, y2, class_id, score]. \n The returned list is sorted descendingly according to score. \n \"\"\"", "# each element: x, y, w, h, class_id, score ", "# prob distribution for each bounding box", "# pooled features", "# sort by confidence descendingly " ]
[ { "param": "_im_summary", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "_im_summary", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def formalize_bbox(_im_summary): boxes = [] probs = [] feats = [] for class_id, items in enumerate(_im_summary.pred.boxes): for bbox in items: x1, y1, x2, y2, score = bbox boxes.append([x1, y1, x2, y2, class_id, score]) for class_id, items in enumerate(_im_summary.pred.cls_prob): for cls_prob in items: probs.append(cls_prob) for class_id, items in enumerate(_im_summary.pred.pooled_feat): for f in items: feats.append(f) assert len(boxes) == len(probs) assert len(boxes) == len(feats) bundles = list(zip(boxes, probs, feats)) bundles = sorted(bundles, key=lambda x: x[0][-1], reverse = True) boxes, probs, feats = zip(*bundles) return (list(boxes), list(probs), list(feats))
610,306
838
ef8c7f541b81229223b8d706278c1e5b5219598c
Zagorlu/PythonWorkSpace
Util/ConnectionUtil/FtpUtil.py
[ "BSD-3-Clause" ]
Python
transferedByteDisplay
null
def transferedByteDisplay(fileName, bytesToFar, totalByte): """ INFO: This function will display number of the how many bytes transfered. This function usefull for ftp, sftp Internal modules inner callable functions. :param fileName: Filename :param bytesToFar: The proceeded byte count :param totalByte: Total byte count of the filename :return: NONE """ print('Transfer of %r is at %d/%d bytes (%.1f%%)' % (fileName, bytesToFar, totalByte, 100. * bytesToFar / totalByte))
INFO: This function will display number of the how many bytes transfered. This function usefull for ftp, sftp Internal modules inner callable functions. :param fileName: Filename :param bytesToFar: The proceeded byte count :param totalByte: Total byte count of the filename :return: NONE
This function will display number of the how many bytes transfered. This function usefull for ftp, sftp Internal modules inner callable functions.
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def transferedByteDisplay(fileName, bytesToFar, totalByte): print('Transfer of %r is at %d/%d bytes (%.1f%%)' % (fileName, bytesToFar, totalByte, 100. * bytesToFar / totalByte))
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INFO: This function will display number of the how many bytes transfered.
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[ "\"\"\"\n INFO: This function will display number of the how many bytes transfered. This function usefull for ftp, sftp\n Internal modules inner callable functions.\n :param fileName: Filename\n :param bytesToFar: The proceeded byte count\n :param totalByte: Total byte count of the filename\n :return: NONE\n \"\"\"" ]
[ { "param": "fileName", "type": null }, { "param": "bytesToFar", "type": null }, { "param": "totalByte", "type": null } ]
{ "returns": [ { "docstring": null, "docstring_tokens": [ "None" ], "type": null } ], "raises": [], "params": [ { "identifier": "fileName", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null }, { "identifier": "bytesToFar", "type": null, "docstring": "The proceeded byte count", "docstring_tokens": [ "The", "proceeded", "byte", "count" ], "default": null, "is_optional": null }, { "identifier": "totalByte", "type": null, "docstring": "Total byte count of the filename", "docstring_tokens": [ "Total", "byte", "count", "of", "the", "filename" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def transferedByteDisplay(fileName, bytesToFar, totalByte): print('Transfer of %r is at %d/%d bytes (%.1f%%)' % (fileName, bytesToFar, totalByte, 100. * bytesToFar / totalByte))
610,308
611
5f0ee81684056a551bdcaf4bf4f679468a1d5dd3
ggirelli/fasta-tools-gg
src/fasta_lines.py
[ "MIT" ]
Python
format_element
<not_specific>
def format_element(eid, eseq, k): """Format a sequence element using FASTA format and k sequence length. Args: eid (string): element ID. eseq (string): element sequence. k (int): sequence line length. Return: string: "> %s\n%s" % (eid, eseq_split_by_k) """ if k > 0: eseq = [eseq[i:min(i + k, len(eseq))]for i in range(0, len(eseq), k)] eseq = "\n".join(eseq) return("> %s\n%s" % (eid, eseq))
Format a sequence element using FASTA format and k sequence length. Args: eid (string): element ID. eseq (string): element sequence. k (int): sequence line length. Return: string: "> %s\n%s" % (eid, eseq_split_by_k)
Format a sequence element using FASTA format and k sequence length.
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def format_element(eid, eseq, k): if k > 0: eseq = [eseq[i:min(i + k, len(eseq))]for i in range(0, len(eseq), k)] eseq = "\n".join(eseq) return("> %s\n%s" % (eid, eseq))
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Format a sequence element using FASTA format and k sequence length.
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[ "\"\"\"Format a sequence element using FASTA format and k sequence length.\n\n\tArgs:\n\t\teid (string): element ID.\n\t\teseq (string): element sequence.\n\t\tk (int): sequence line length.\n\n\tReturn:\n\t\tstring: \"> %s\\n%s\" % (eid, eseq_split_by_k)\n\t\"\"\"" ]
[ { "param": "eid", "type": null }, { "param": "eseq", "type": null }, { "param": "k", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "eid", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": false }, { "identifier": "eseq", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": false }, { "identifier": "k", "type": null, "docstring": "sequence line length.", "docstring_tokens": [ "sequence", "line", "length", "." ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
def format_element(eid, eseq, k): if k > 0: eseq = [eseq[i:min(i + k, len(eseq))]for i in range(0, len(eseq), k)] eseq = "\n".join(eseq) return("> %s\n%s" % (eid, eseq))
610,310
119
70d1df3039ac9d4a2ce893a6ba651d1c12e219ed
pedro-mgb/pedestrian-arc-lstm-smf
models/interaction_modules/train_aux.py
[ "MIT" ]
Python
create_gt_generator
<not_specific>
def create_gt_generator(model): """ Create a "gt generator". This will be a copy of the model, minus the shape configuration. It will be fixed throughout one epoch, and will generate the ground truth to train the shape configuration model. This ground truth will be the pooling shape dimensions that yielded the lowest loss - simulating what could be the real shape dimension of the pedestrian. May also disable gradient computation for parts of the original model (that are not relevant from a point of view of training). :param model: the original model :return: the gt generator, and the interaction model """ # gt_model a model that will be used to generate "predictions" that will be simulated as ground truth data shape_config = model.shape_config model.shape_config = None # deepcopy might not work without this line, due to there being some extra tensors gt_generator = copy.deepcopy(model) gt_generator.eval() gt_generator.shape_config = None # disable all gradients - except for the ones associated to the shape configuration network model.shape_config = shape_config for param in gt_generator.parameters(): param.requires_grad = False for param in model.parameters(): param.requires_grad = True # because the actual pooling data will come from a dataset, and not computed by the module, the goal of this # interaction module is just to embed the data, and send it as direct input to the LSTM cell interaction_module = copy.deepcopy(gt_generator.interaction_module) return gt_generator, interaction_module
Create a "gt generator". This will be a copy of the model, minus the shape configuration. It will be fixed throughout one epoch, and will generate the ground truth to train the shape configuration model. This ground truth will be the pooling shape dimensions that yielded the lowest loss - simulating what could be the real shape dimension of the pedestrian. May also disable gradient computation for parts of the original model (that are not relevant from a point of view of training). :param model: the original model :return: the gt generator, and the interaction model
Create a "gt generator". This will be a copy of the model, minus the shape configuration. It will be fixed throughout one epoch, and will generate the ground truth to train the shape configuration model. This ground truth will be the pooling shape dimensions that yielded the lowest loss - simulating what could be the real shape dimension of the pedestrian. May also disable gradient computation for parts of the original model (that are not relevant from a point of view of training).
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def create_gt_generator(model): shape_config = model.shape_config model.shape_config = None gt_generator = copy.deepcopy(model) gt_generator.eval() gt_generator.shape_config = None model.shape_config = shape_config for param in gt_generator.parameters(): param.requires_grad = False for param in model.parameters(): param.requires_grad = True interaction_module = copy.deepcopy(gt_generator.interaction_module) return gt_generator, interaction_module
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Create a "gt generator".
[ "Create", "a", "\"", "gt", "generator", "\"", "." ]
[ "\"\"\"\n Create a \"gt generator\". This will be a copy of the model, minus the shape configuration. It will be fixed\n throughout one epoch, and will generate the ground truth to train the shape configuration model. This ground truth\n will be the pooling shape dimensions that yielded the lowest loss - simulating what could be the real shape\n dimension of the pedestrian.\n May also disable gradient computation for parts of the original model (that are not relevant from a point of view\n of training).\n :param model: the original model\n :return: the gt generator, and the interaction model\n \"\"\"", "# gt_model a model that will be used to generate \"predictions\" that will be simulated as ground truth data", "# deepcopy might not work without this line, due to there being some extra tensors", "# disable all gradients - except for the ones associated to the shape configuration network", "# because the actual pooling data will come from a dataset, and not computed by the module, the goal of this", "# interaction module is just to embed the data, and send it as direct input to the LSTM cell" ]
[ { "param": "model", "type": null } ]
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import copy def create_gt_generator(model): shape_config = model.shape_config model.shape_config = None gt_generator = copy.deepcopy(model) gt_generator.eval() gt_generator.shape_config = None model.shape_config = shape_config for param in gt_generator.parameters(): param.requires_grad = False for param in model.parameters(): param.requires_grad = True interaction_module = copy.deepcopy(gt_generator.interaction_module) return gt_generator, interaction_module
610,311
631
2f6939d46124b482bbe624e60b8f654d810e3041
lightvault/mlfinlab
mlfinlab/bet_sizing/ch10_snippets.py
[ "BSD-3-Clause" ]
Python
inv_price_sigmoid
<not_specific>
def inv_price_sigmoid(forecast_price, w_param, m_bet_size): """ Part of SNIPPET 10.4 Calculates the inverse of the bet size with respect to the market price. Based on a sigmoid function for a bet size algorithm. :param forecast_price: (float) Forecast price. :param w_param: (float) Coefficient regulating the width of the bet size function. :param m_bet_size: (float) Bet size. :return: (float) Inverse of bet size with respect to market price. """ return forecast_price - m_bet_size * (w_param / (1 - m_bet_size**2))**0.5
Part of SNIPPET 10.4 Calculates the inverse of the bet size with respect to the market price. Based on a sigmoid function for a bet size algorithm. :param forecast_price: (float) Forecast price. :param w_param: (float) Coefficient regulating the width of the bet size function. :param m_bet_size: (float) Bet size. :return: (float) Inverse of bet size with respect to market price.
Part of SNIPPET 10.4 Calculates the inverse of the bet size with respect to the market price. Based on a sigmoid function for a bet size algorithm.
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def inv_price_sigmoid(forecast_price, w_param, m_bet_size): return forecast_price - m_bet_size * (w_param / (1 - m_bet_size**2))**0.5
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Part of SNIPPET 10.4 Calculates the inverse of the bet size with respect to the market price.
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[ "\"\"\"\n Part of SNIPPET 10.4\n Calculates the inverse of the bet size with respect to the market price.\n Based on a sigmoid function for a bet size algorithm.\n\n :param forecast_price: (float) Forecast price.\n :param w_param: (float) Coefficient regulating the width of the bet size function.\n :param m_bet_size: (float) Bet size.\n :return: (float) Inverse of bet size with respect to market price.\n \"\"\"" ]
[ { "param": "forecast_price", "type": null }, { "param": "w_param", "type": null }, { "param": "m_bet_size", "type": null } ]
{ "returns": [ { "docstring": "(float) Inverse of bet size with respect to market price.", "docstring_tokens": [ "(", "float", ")", "Inverse", "of", "bet", "size", "with", "respect", "to", "market", "price", "." ], "type": null } ], "raises": [], "params": [ { "identifier": "forecast_price", "type": null, "docstring": "(float) Forecast price.", "docstring_tokens": [ "(", "float", ")", "Forecast", "price", "." ], "default": null, "is_optional": null }, { "identifier": "w_param", "type": null, "docstring": "(float) Coefficient regulating the width of the bet size function.", "docstring_tokens": [ "(", "float", ")", "Coefficient", "regulating", "the", "width", "of", "the", "bet", "size", "function", "." ], "default": null, "is_optional": null }, { "identifier": "m_bet_size", "type": null, "docstring": "(float) Bet size.", "docstring_tokens": [ "(", "float", ")", "Bet", "size", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def inv_price_sigmoid(forecast_price, w_param, m_bet_size): return forecast_price - m_bet_size * (w_param / (1 - m_bet_size**2))**0.5
610,312
885
b39a896ba2d8b2db4d218fdecd89644a8862db07
phy-q/benchmark
sciencebirdsagents/MultiAgentTestOnly.py
[ "MIT" ]
Python
sample_levels
<not_specific>
def sample_levels(training_level_set, num_agents, agent_idx): ''' given idx, return the averaged distributed levels ''' n = math.ceil(len(training_level_set) / num_agents) total_list = [] for i in range(0, len(training_level_set), n): total_list.append(training_level_set[i:i + n]) if agent_idx >= len(total_list): return None return total_list[agent_idx]
given idx, return the averaged distributed levels
given idx, return the averaged distributed levels
[ "given", "idx", "return", "the", "averaged", "distributed", "levels" ]
def sample_levels(training_level_set, num_agents, agent_idx): n = math.ceil(len(training_level_set) / num_agents) total_list = [] for i in range(0, len(training_level_set), n): total_list.append(training_level_set[i:i + n]) if agent_idx >= len(total_list): return None return total_list[agent_idx]
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given idx, return the averaged distributed levels
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[ "'''\n given idx, return the averaged distributed levels\n '''" ]
[ { "param": "training_level_set", "type": null }, { "param": "num_agents", "type": null }, { "param": "agent_idx", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "training_level_set", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "num_agents", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "agent_idx", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import math def sample_levels(training_level_set, num_agents, agent_idx): n = math.ceil(len(training_level_set) / num_agents) total_list = [] for i in range(0, len(training_level_set), n): total_list.append(training_level_set[i:i + n]) if agent_idx >= len(total_list): return None return total_list[agent_idx]
610,313
659
e55b3bf2bd3c1bb3470fe6d7bdac45ebd5bd555e
kennethlove/django
django/contrib/admin/templatetags/admin_modify.py
[ "BSD-3-Clause" ]
Python
cell_count
<not_specific>
def cell_count(inline_admin_form): """Returns the number of cells used in a tabular inline""" count = 1 # Hidden cell with hidden 'id' field for fieldset in inline_admin_form: # Loop through all the fields (one per cell) for line in fieldset: for field in line: count += 1 if inline_admin_form.formset.can_delete: # Delete checkbox count += 1 return count
Returns the number of cells used in a tabular inline
Returns the number of cells used in a tabular inline
[ "Returns", "the", "number", "of", "cells", "used", "in", "a", "tabular", "inline" ]
def cell_count(inline_admin_form): count = 1 for fieldset in inline_admin_form: for line in fieldset: for field in line: count += 1 if inline_admin_form.formset.can_delete: count += 1 return count
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Returns the number of cells used in a tabular inline
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[ "\"\"\"Returns the number of cells used in a tabular inline\"\"\"", "# Hidden cell with hidden 'id' field", "# Loop through all the fields (one per cell)", "# Delete checkbox" ]
[ { "param": "inline_admin_form", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "inline_admin_form", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def cell_count(inline_admin_form): count = 1 for fieldset in inline_admin_form: for line in fieldset: for field in line: count += 1 if inline_admin_form.formset.can_delete: count += 1 return count
610,314
105
9d2e5ac0e46e8b9a4d1dcf3049633f8bfe0d0456
Mu-L/PaddleNLP
examples/language_model/xlm/xnli_train.py
[ "Apache-2.0" ]
Python
convert_example
<not_specific>
def convert_example(example, tokenizer, max_seq_length=256, language="en"): """convert a example into necessary features""" # Get the label label = example["label"] premise = example["premise"] hypothesis = example["hypothesis"] # Convert raw text to feature example = tokenizer(premise, text_pair=hypothesis, max_length=max_seq_length, return_attention_mask=True, return_token_type_ids=False, lang=language) return example["input_ids"], example["attention_mask"], label
convert a example into necessary features
convert a example into necessary features
[ "convert", "a", "example", "into", "necessary", "features" ]
def convert_example(example, tokenizer, max_seq_length=256, language="en"): label = example["label"] premise = example["premise"] hypothesis = example["hypothesis"] example = tokenizer(premise, text_pair=hypothesis, max_length=max_seq_length, return_attention_mask=True, return_token_type_ids=False, lang=language) return example["input_ids"], example["attention_mask"], label
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convert a example into necessary features
[ "convert", "a", "example", "into", "necessary", "features" ]
[ "\"\"\"convert a example into necessary features\"\"\"", "# Get the label", "# Convert raw text to feature" ]
[ { "param": "example", "type": null }, { "param": "tokenizer", "type": null }, { "param": "max_seq_length", "type": null }, { "param": "language", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "example", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "tokenizer", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "max_seq_length", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "language", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def convert_example(example, tokenizer, max_seq_length=256, language="en"): label = example["label"] premise = example["premise"] hypothesis = example["hypothesis"] example = tokenizer(premise, text_pair=hypothesis, max_length=max_seq_length, return_attention_mask=True, return_token_type_ids=False, lang=language) return example["input_ids"], example["attention_mask"], label
610,315
171
9e70e8dde69f94d78567176d5ac581edba3c3a5d
giserh/gpkit
gpkit/small_scripts.py
[ "MIT" ]
Python
maybe_flatten
<not_specific>
def maybe_flatten(value): "Extract values from 0-d numpy arrays, if necessary" if hasattr(value, "shape") and not value.shape: return value.flatten()[0] # 0-d numpy arrays return value
Extract values from 0-d numpy arrays, if necessary
Extract values from 0-d numpy arrays, if necessary
[ "Extract", "values", "from", "0", "-", "d", "numpy", "arrays", "if", "necessary" ]
def maybe_flatten(value): if hasattr(value, "shape") and not value.shape: return value.flatten()[0] return value
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Extract values from 0-d numpy arrays, if necessary
[ "Extract", "values", "from", "0", "-", "d", "numpy", "arrays", "if", "necessary" ]
[ "\"Extract values from 0-d numpy arrays, if necessary\"", "# 0-d numpy arrays" ]
[ { "param": "value", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "value", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def maybe_flatten(value): if hasattr(value, "shape") and not value.shape: return value.flatten()[0] return value
610,316
798
52c9a2d7b3c4d45eebfb12cb45f958d5bc4823c8
Mindhome/field_service
mindhome_alpha/erpnext/patches/v12_0/set_purchase_receipt_delivery_note_detail.py
[ "MIT" ]
Python
row_is_mappable
<not_specific>
def row_is_mappable(doc_row, return_doc_row, detail_field): """Checks if two rows are similar enough to be mapped.""" if doc_row.item_code == return_doc_row.item_code and not return_doc_row.get(detail_field): if doc_row.get('batch_no') and return_doc_row.get('batch_no') and doc_row.batch_no == return_doc_row.batch_no: return True elif doc_row.get('serial_no') and return_doc_row.get('serial_no'): doc_sn = doc_row.serial_no.split('\n') return_doc_sn = return_doc_row.serial_no.split('\n') if set(doc_sn) & set(return_doc_sn): # if two rows have serial nos in common, map them return True elif doc_row.rate == return_doc_row.rate: return True else: return False
Checks if two rows are similar enough to be mapped.
Checks if two rows are similar enough to be mapped.
[ "Checks", "if", "two", "rows", "are", "similar", "enough", "to", "be", "mapped", "." ]
def row_is_mappable(doc_row, return_doc_row, detail_field): if doc_row.item_code == return_doc_row.item_code and not return_doc_row.get(detail_field): if doc_row.get('batch_no') and return_doc_row.get('batch_no') and doc_row.batch_no == return_doc_row.batch_no: return True elif doc_row.get('serial_no') and return_doc_row.get('serial_no'): doc_sn = doc_row.serial_no.split('\n') return_doc_sn = return_doc_row.serial_no.split('\n') if set(doc_sn) & set(return_doc_sn): return True elif doc_row.rate == return_doc_row.rate: return True else: return False
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Checks if two rows are similar enough to be mapped.
[ "Checks", "if", "two", "rows", "are", "similar", "enough", "to", "be", "mapped", "." ]
[ "\"\"\"Checks if two rows are similar enough to be mapped.\"\"\"", "# if two rows have serial nos in common, map them" ]
[ { "param": "doc_row", "type": null }, { "param": "return_doc_row", "type": null }, { "param": "detail_field", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "doc_row", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "return_doc_row", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "detail_field", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def row_is_mappable(doc_row, return_doc_row, detail_field): if doc_row.item_code == return_doc_row.item_code and not return_doc_row.get(detail_field): if doc_row.get('batch_no') and return_doc_row.get('batch_no') and doc_row.batch_no == return_doc_row.batch_no: return True elif doc_row.get('serial_no') and return_doc_row.get('serial_no'): doc_sn = doc_row.serial_no.split('\n') return_doc_sn = return_doc_row.serial_no.split('\n') if set(doc_sn) & set(return_doc_sn): return True elif doc_row.rate == return_doc_row.rate: return True else: return False
610,317
505
4290c09bd48b420798cd63629f95f40d10e92ead
Jiaolong/gcn-parking-slot
psdet/utils/precision_recall.py
[ "MIT" ]
Python
match_gt_with_preds
<not_specific>
def match_gt_with_preds(ground_truth, predictions, match_labels): """Match a ground truth with every predictions and return matched index.""" max_confidence = 0. matched_idx = -1 for i, pred in enumerate(predictions): if match_labels(ground_truth, pred[1]) and max_confidence < pred[0]: max_confidence = pred[0] matched_idx = i return matched_idx
Match a ground truth with every predictions and return matched index.
Match a ground truth with every predictions and return matched index.
[ "Match", "a", "ground", "truth", "with", "every", "predictions", "and", "return", "matched", "index", "." ]
def match_gt_with_preds(ground_truth, predictions, match_labels): max_confidence = 0. matched_idx = -1 for i, pred in enumerate(predictions): if match_labels(ground_truth, pred[1]) and max_confidence < pred[0]: max_confidence = pred[0] matched_idx = i return matched_idx
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Match a ground truth with every predictions and return matched index.
[ "Match", "a", "ground", "truth", "with", "every", "predictions", "and", "return", "matched", "index", "." ]
[ "\"\"\"Match a ground truth with every predictions and return matched index.\"\"\"" ]
[ { "param": "ground_truth", "type": null }, { "param": "predictions", "type": null }, { "param": "match_labels", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "ground_truth", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "predictions", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "match_labels", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def match_gt_with_preds(ground_truth, predictions, match_labels): max_confidence = 0. matched_idx = -1 for i, pred in enumerate(predictions): if match_labels(ground_truth, pred[1]) and max_confidence < pred[0]: max_confidence = pred[0] matched_idx = i return matched_idx
610,318
415
fc4c8627d96961eb19b1e97f5dcf360877e78777
brytemorio/WavesGatewayFramework
waves_gateway/common/utils.py
[ "MIT" ]
Python
map_array
list
def map_array(func, arr: list) -> list: """ Calls the given function on every element on the given array and returns the resulting elements. """ res = list() for el in arr: res.append(func(el)) return res
Calls the given function on every element on the given array and returns the resulting elements.
Calls the given function on every element on the given array and returns the resulting elements.
[ "Calls", "the", "given", "function", "on", "every", "element", "on", "the", "given", "array", "and", "returns", "the", "resulting", "elements", "." ]
def map_array(func, arr: list) -> list: res = list() for el in arr: res.append(func(el)) return res
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Calls the given function on every element on the given array and returns the resulting elements.
[ "Calls", "the", "given", "function", "on", "every", "element", "on", "the", "given", "array", "and", "returns", "the", "resulting", "elements", "." ]
[ "\"\"\"\n Calls the given function on every element on the given array and returns the resulting elements.\n \"\"\"" ]
[ { "param": "func", "type": null }, { "param": "arr", "type": "list" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "func", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "arr", "type": "list", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def map_array(func, arr: list) -> list: res = list() for el in arr: res.append(func(el)) return res
610,319
844
6a80ea554f72cf8c13686e53c42ff5e2244d083f
wibbico/bcpy
bcpy/format_file_builder.py
[ "MIT" ]
Python
_scaper
<not_specific>
def _scaper(input_string): """Adds the required scape characters to the format file. :param input_string: Value before scaping :type input_string: str :return: Scaped string """ scaped_string = input_string.replace('"', '\\"').replace("'", "\\'") \ .replace('\r', '\\r').replace('\n', '\\n') return scaped_string
Adds the required scape characters to the format file. :param input_string: Value before scaping :type input_string: str :return: Scaped string
Adds the required scape characters to the format file.
[ "Adds", "the", "required", "scape", "characters", "to", "the", "format", "file", "." ]
def _scaper(input_string): scaped_string = input_string.replace('"', '\\"').replace("'", "\\'") \ .replace('\r', '\\r').replace('\n', '\\n') return scaped_string
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Adds the required scape characters to the format file.
[ "Adds", "the", "required", "scape", "characters", "to", "the", "format", "file", "." ]
[ "\"\"\"Adds the required scape characters to the format file.\n :param input_string: Value before scaping\n :type input_string: str\n :return: Scaped string\n \"\"\"" ]
[ { "param": "input_string", "type": null } ]
{ "returns": [ { "docstring": null, "docstring_tokens": [ "None" ], "type": null } ], "raises": [], "params": [ { "identifier": "input_string", "type": null, "docstring": "Value before scaping", "docstring_tokens": [ "Value", "before", "scaping" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _scaper(input_string): scaped_string = input_string.replace('"', '\\"').replace("'", "\\'") \ .replace('\r', '\\r').replace('\n', '\\n') return scaped_string
610,320
202
14679d745d6ce0f2fc1dc8ef1430ba861dcc3559
seefrye/waaaaagh-list
_notes/.obsidian/scripts/sympy-1.9/sympy/strategies/rl.py
[ "MIT" ]
Python
distribute
<not_specific>
def distribute(A, B): """ Turns an A containing Bs into a B of As where A, B are container types >>> from sympy.strategies import distribute >>> from sympy import Add, Mul, symbols >>> x, y = symbols('x,y') >>> dist = distribute(Mul, Add) >>> expr = Mul(2, x+y, evaluate=False) >>> expr 2*(x + y) >>> dist(expr) 2*x + 2*y """ def distribute_rl(expr): for i, arg in enumerate(expr.args): if isinstance(arg, B): first, b, tail = expr.args[:i], expr.args[i], expr.args[i+1:] return B(*[A(*(first + (arg,) + tail)) for arg in b.args]) return expr return distribute_rl
Turns an A containing Bs into a B of As where A, B are container types >>> from sympy.strategies import distribute >>> from sympy import Add, Mul, symbols >>> x, y = symbols('x,y') >>> dist = distribute(Mul, Add) >>> expr = Mul(2, x+y, evaluate=False) >>> expr 2*(x + y) >>> dist(expr) 2*x + 2*y
Turns an A containing Bs into a B of As where A, B are container types
[ "Turns", "an", "A", "containing", "Bs", "into", "a", "B", "of", "As", "where", "A", "B", "are", "container", "types" ]
def distribute(A, B): def distribute_rl(expr): for i, arg in enumerate(expr.args): if isinstance(arg, B): first, b, tail = expr.args[:i], expr.args[i], expr.args[i+1:] return B(*[A(*(first + (arg,) + tail)) for arg in b.args]) return expr return distribute_rl
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Turns an A containing Bs into a B of As where A, B are container types
[ "Turns", "an", "A", "containing", "Bs", "into", "a", "B", "of", "As", "where", "A", "B", "are", "container", "types" ]
[ "\"\"\" Turns an A containing Bs into a B of As\n\n where A, B are container types\n\n >>> from sympy.strategies import distribute\n >>> from sympy import Add, Mul, symbols\n >>> x, y = symbols('x,y')\n >>> dist = distribute(Mul, Add)\n >>> expr = Mul(2, x+y, evaluate=False)\n >>> expr\n 2*(x + y)\n >>> dist(expr)\n 2*x + 2*y\n \"\"\"" ]
[ { "param": "A", "type": null }, { "param": "B", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "A", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "B", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def distribute(A, B): def distribute_rl(expr): for i, arg in enumerate(expr.args): if isinstance(arg, B): first, b, tail = expr.args[:i], expr.args[i], expr.args[i+1:] return B(*[A(*(first + (arg,) + tail)) for arg in b.args]) return expr return distribute_rl
610,322
365
913b3b136d852dee9672717386445abaad6a0056
De117/scanner
SSLTester/SSLTester.py
[ "MIT" ]
Python
make_ssocket
<not_specific>
def make_ssocket(protocol, ciphers=ssl._DEFAULT_CIPHERS): """Creates a TCP socket wrapped in SSL.""" return ssl.wrap_socket( socket.socket(), ssl_version=protocol, ciphers=ciphers)
Creates a TCP socket wrapped in SSL.
Creates a TCP socket wrapped in SSL.
[ "Creates", "a", "TCP", "socket", "wrapped", "in", "SSL", "." ]
def make_ssocket(protocol, ciphers=ssl._DEFAULT_CIPHERS): return ssl.wrap_socket( socket.socket(), ssl_version=protocol, ciphers=ciphers)
[ "def", "make_ssocket", "(", "protocol", ",", "ciphers", "=", "ssl", ".", "_DEFAULT_CIPHERS", ")", ":", "return", "ssl", ".", "wrap_socket", "(", "socket", ".", "socket", "(", ")", ",", "ssl_version", "=", "protocol", ",", "ciphers", "=", "ciphers", ")" ]
Creates a TCP socket wrapped in SSL.
[ "Creates", "a", "TCP", "socket", "wrapped", "in", "SSL", "." ]
[ "\"\"\"Creates a TCP socket wrapped in SSL.\"\"\"" ]
[ { "param": "protocol", "type": null }, { "param": "ciphers", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "protocol", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "ciphers", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import ssl import socket def make_ssocket(protocol, ciphers=ssl._DEFAULT_CIPHERS): return ssl.wrap_socket( socket.socket(), ssl_version=protocol, ciphers=ciphers)
610,324
357
c177d018efecea5a18366149644a7ba9caab240a
vc1492a/graphtransliterator
graphtransliterator/initialize.py
[ "MIT" ]
Python
_num_tokens_of
<not_specific>
def _num_tokens_of(rule): """Calculate the total number of tokens in a rule.""" total = len(rule.get("tokens")) for _ in ("prev_classes", "prev_tokens", "next_tokens", "next_classes"): val = rule.get(_) if val: total += len(val) return total
Calculate the total number of tokens in a rule.
Calculate the total number of tokens in a rule.
[ "Calculate", "the", "total", "number", "of", "tokens", "in", "a", "rule", "." ]
def _num_tokens_of(rule): total = len(rule.get("tokens")) for _ in ("prev_classes", "prev_tokens", "next_tokens", "next_classes"): val = rule.get(_) if val: total += len(val) return total
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Calculate the total number of tokens in a rule.
[ "Calculate", "the", "total", "number", "of", "tokens", "in", "a", "rule", "." ]
[ "\"\"\"Calculate the total number of tokens in a rule.\"\"\"" ]
[ { "param": "rule", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "rule", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _num_tokens_of(rule): total = len(rule.get("tokens")) for _ in ("prev_classes", "prev_tokens", "next_tokens", "next_classes"): val = rule.get(_) if val: total += len(val) return total
610,325
98
6ad10b900bfb8f871b8237f634b696692e25c70b
tiltowait/inconnu
inconnu/character/update/paramupdate.py
[ "MIT" ]
Python
__update_xp
str
def __update_xp(character: VChar, xp_type: str, delta: str) -> str: """ Update a character's XP. Args: character (VChar): The character to update xp_type (str): "total" or "current" delta (str): The amount to add or remove Does not catch exceptions. """ setting = False # If the user doesn't supply a sign, they are setting the XP total rather # than modifying it if isinstance(delta, str): if delta[0] not in ["+", "-"]: setting = True if not xp_type in ["total", "current"]: raise SyntaxError(f"Unknown XP type: {xp_type}.") # Should never be seen delta = int(delta) new_xp = None if setting: new_xp = delta else: current = getattr(character, f"{xp_type.lower()}_xp") new_xp = current + delta # When displaying the update, we want to say whether they are doing a delta vs # set and, if doing a delta, the *final* amound added/subtracted, after doing # bounds-checking. current = character.current_xp if xp_type == "current": character.current_xp = new_xp cur_delta = character.current_xp - current if setting: return f"Set current/unspent XP to `{new_xp}`." return f"`{cur_delta:+}` current/unspent XP." total = character.total_xp character.total_xp = new_xp tot_delta = character.total_xp - total cur_delta = character.current_xp - current if setting: return f"Set unspent XP to `{character.current_xp}`.\nSet lifetime XP to `{new_xp}`." return f"`{cur_delta:+}` unspent XP.\n`{tot_delta:+}` lifetime XP."
Update a character's XP. Args: character (VChar): The character to update xp_type (str): "total" or "current" delta (str): The amount to add or remove Does not catch exceptions.
Update a character's XP.
[ "Update", "a", "character", "'", "s", "XP", "." ]
def __update_xp(character: VChar, xp_type: str, delta: str) -> str: setting = False if isinstance(delta, str): if delta[0] not in ["+", "-"]: setting = True if not xp_type in ["total", "current"]: raise SyntaxError(f"Unknown XP type: {xp_type}.") delta = int(delta) new_xp = None if setting: new_xp = delta else: current = getattr(character, f"{xp_type.lower()}_xp") new_xp = current + delta current = character.current_xp if xp_type == "current": character.current_xp = new_xp cur_delta = character.current_xp - current if setting: return f"Set current/unspent XP to `{new_xp}`." return f"`{cur_delta:+}` current/unspent XP." total = character.total_xp character.total_xp = new_xp tot_delta = character.total_xp - total cur_delta = character.current_xp - current if setting: return f"Set unspent XP to `{character.current_xp}`.\nSet lifetime XP to `{new_xp}`." return f"`{cur_delta:+}` unspent XP.\n`{tot_delta:+}` lifetime XP."
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Update a character's XP.
[ "Update", "a", "character", "'", "s", "XP", "." ]
[ "\"\"\"\n Update a character's XP.\n Args:\n character (VChar): The character to update\n xp_type (str): \"total\" or \"current\"\n delta (str): The amount to add or remove\n\n Does not catch exceptions.\n \"\"\"", "# If the user doesn't supply a sign, they are setting the XP total rather", "# than modifying it", "# Should never be seen", "# When displaying the update, we want to say whether they are doing a delta vs", "# set and, if doing a delta, the *final* amound added/subtracted, after doing", "# bounds-checking." ]
[ { "param": "character", "type": "VChar" }, { "param": "xp_type", "type": "str" }, { "param": "delta", "type": "str" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "character", "type": "VChar", "docstring": "The character to update", "docstring_tokens": [ "The", "character", "to", "update" ], "default": null, "is_optional": false }, { "identifier": "xp_type", "type": "str", "docstring": "\"total\" or \"current\"", "docstring_tokens": [ "\"", "total", "\"", "or", "\"", "current", "\"" ], "default": null, "is_optional": false }, { "identifier": "delta", "type": "str", "docstring": "The amount to add or remove", "docstring_tokens": [ "The", "amount", "to", "add", "or", "remove" ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
def __update_xp(character: VChar, xp_type: str, delta: str) -> str: setting = False if isinstance(delta, str): if delta[0] not in ["+", "-"]: setting = True if not xp_type in ["total", "current"]: raise SyntaxError(f"Unknown XP type: {xp_type}.") delta = int(delta) new_xp = None if setting: new_xp = delta else: current = getattr(character, f"{xp_type.lower()}_xp") new_xp = current + delta current = character.current_xp if xp_type == "current": character.current_xp = new_xp cur_delta = character.current_xp - current if setting: return f"Set current/unspent XP to `{new_xp}`." return f"`{cur_delta:+}` current/unspent XP." total = character.total_xp character.total_xp = new_xp tot_delta = character.total_xp - total cur_delta = character.current_xp - current if setting: return f"Set unspent XP to `{character.current_xp}`.\nSet lifetime XP to `{new_xp}`." return f"`{cur_delta:+}` unspent XP.\n`{tot_delta:+}` lifetime XP."
610,326
31
e873743178f538a5c1a82b0f66bcb08ba27a1f3a
mohamadmansourX/minihack
minihack/level_generator.py
[ "Apache-2.0" ]
Python
_validate_coord
<not_specific>
def _validate_coord(coord): """Validate a given typle of coordinates.""" assert ( isinstance(coord, tuple) and len(coord) == 2 and isinstance(coord[0], int) and isinstance(coord[1], int) ) return coord
Validate a given typle of coordinates.
Validate a given typle of coordinates.
[ "Validate", "a", "given", "typle", "of", "coordinates", "." ]
def _validate_coord(coord): assert ( isinstance(coord, tuple) and len(coord) == 2 and isinstance(coord[0], int) and isinstance(coord[1], int) ) return coord
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Validate a given typle of coordinates.
[ "Validate", "a", "given", "typle", "of", "coordinates", "." ]
[ "\"\"\"Validate a given typle of coordinates.\"\"\"" ]
[ { "param": "coord", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "coord", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _validate_coord(coord): assert ( isinstance(coord, tuple) and len(coord) == 2 and isinstance(coord[0], int) and isinstance(coord[1], int) ) return coord
610,327
546
9b61e3e930687d581573d474a4b394e15115cc33
KyleLavorato/Backpropagation-Machine-Learning
WineQualityAnalysis/ResultQuality7.py
[ "MIT" ]
Python
appendLine
null
def appendLine(line): """Append the supplied string to file""" snOut = open("ResultsSeven.csv", "a") snOut.write(line) snOut.close()
Append the supplied string to file
Append the supplied string to file
[ "Append", "the", "supplied", "string", "to", "file" ]
def appendLine(line): snOut = open("ResultsSeven.csv", "a") snOut.write(line) snOut.close()
[ "def", "appendLine", "(", "line", ")", ":", "snOut", "=", "open", "(", "\"ResultsSeven.csv\"", ",", "\"a\"", ")", "snOut", ".", "write", "(", "line", ")", "snOut", ".", "close", "(", ")" ]
Append the supplied string to file
[ "Append", "the", "supplied", "string", "to", "file" ]
[ "\"\"\"Append the supplied string to file\"\"\"" ]
[ { "param": "line", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "line", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def appendLine(line): snOut = open("ResultsSeven.csv", "a") snOut.write(line) snOut.close()
610,328
200
406593bd1e04e7cee53412c50948f49161d9faa8
lawclaw/sudoku-cw
GameEngine/game.py
[ "Apache-2.0" ]
Python
save_game
None
def save_game(board) -> None: """ Save board into json save file :param board: Board object :return: None """ board.to_json()
Save board into json save file :param board: Board object :return: None
Save board into json save file
[ "Save", "board", "into", "json", "save", "file" ]
def save_game(board) -> None: board.to_json()
[ "def", "save_game", "(", "board", ")", "->", "None", ":", "board", ".", "to_json", "(", ")" ]
Save board into json save file
[ "Save", "board", "into", "json", "save", "file" ]
[ "\"\"\"\n Save board into json save file\n :param board: Board object\n :return: None\n \"\"\"" ]
[ { "param": "board", "type": null } ]
{ "returns": [ { "docstring": null, "docstring_tokens": [ "None" ], "type": null } ], "raises": [], "params": [ { "identifier": "board", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def save_game(board) -> None: board.to_json()
610,329
485
9a6c13a480b7082dfc5388a2abf5a3ea8375275c
Muhammada3178/checkov
checkov/common/util/http_utils.py
[ "Apache-2.0" ]
Python
normalize_prisma_url
<not_specific>
def normalize_prisma_url(url: str): """ Correct common Prisma Cloud API URL misconfigurations """ if not url: return None return url.lower().replace('//app', '//api').replace('http:', 'https:').rstrip('/')
Correct common Prisma Cloud API URL misconfigurations
Correct common Prisma Cloud API URL misconfigurations
[ "Correct", "common", "Prisma", "Cloud", "API", "URL", "misconfigurations" ]
def normalize_prisma_url(url: str): if not url: return None return url.lower().replace('//app', '//api').replace('http:', 'https:').rstrip('/')
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Correct common Prisma Cloud API URL misconfigurations
[ "Correct", "common", "Prisma", "Cloud", "API", "URL", "misconfigurations" ]
[ "\"\"\" Correct common Prisma Cloud API URL misconfigurations \"\"\"" ]
[ { "param": "url", "type": "str" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "url", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def normalize_prisma_url(url: str): if not url: return None return url.lower().replace('//app', '//api').replace('http:', 'https:').rstrip('/')
610,330
976
c8a09034cfe1c9b23c365dbfba45931865d6386e
olaaustine/igsr_archive
tests/conftest.py
[ "Apache-2.0" ]
Python
chObject_withdrawn
<not_specific>
def chObject_withdrawn(ct_obj, db_dict): """ Fixture returning a ChangeEvents object with a withdrawn path """ ct_obj.prod_tree = os.getenv('DATADIR') + "/ctree/current.plus1.tree" file_dict = ct_obj.get_file_dict() chObject = ct_obj.cmp_dicts(db_dict=db_dict, file_dict=file_dict) return chObject
Fixture returning a ChangeEvents object with a withdrawn path
Fixture returning a ChangeEvents object with a withdrawn path
[ "Fixture", "returning", "a", "ChangeEvents", "object", "with", "a", "withdrawn", "path" ]
def chObject_withdrawn(ct_obj, db_dict): ct_obj.prod_tree = os.getenv('DATADIR') + "/ctree/current.plus1.tree" file_dict = ct_obj.get_file_dict() chObject = ct_obj.cmp_dicts(db_dict=db_dict, file_dict=file_dict) return chObject
[ "def", "chObject_withdrawn", "(", "ct_obj", ",", "db_dict", ")", ":", "ct_obj", ".", "prod_tree", "=", "os", ".", "getenv", "(", "'DATADIR'", ")", "+", "\"/ctree/current.plus1.tree\"", "file_dict", "=", "ct_obj", ".", "get_file_dict", "(", ")", "chObject", "=", "ct_obj", ".", "cmp_dicts", "(", "db_dict", "=", "db_dict", ",", "file_dict", "=", "file_dict", ")", "return", "chObject" ]
Fixture returning a ChangeEvents object with a withdrawn path
[ "Fixture", "returning", "a", "ChangeEvents", "object", "with", "a", "withdrawn", "path" ]
[ "\"\"\"\n Fixture returning a ChangeEvents object\n with a withdrawn path\n \"\"\"" ]
[ { "param": "ct_obj", "type": null }, { "param": "db_dict", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "ct_obj", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "db_dict", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import os def chObject_withdrawn(ct_obj, db_dict): ct_obj.prod_tree = os.getenv('DATADIR') + "/ctree/current.plus1.tree" file_dict = ct_obj.get_file_dict() chObject = ct_obj.cmp_dicts(db_dict=db_dict, file_dict=file_dict) return chObject
610,332
438
b1223cb588ee3c17d3ecddd827dc25f86cc0505a
iashraful/fast-drf
fast_drf/tests/test_app/models.py
[ "MIT" ]
Python
api_version_fields
<not_specific>
def api_version_fields(cls, **kwargs): """ *** DEFAULT VERSION `v1` *** This method will return a dictionary object with version number and fields name. Fields are similar like serializer fields. Or you can say exactly as same as serializer fields. :param kwargs: Currently nothing to receive on kwargs :return: a dictionary object with version number """ versions = { 'v1': ['id', 'author', 'title', 'description'], 'v2': { 'fields': ['pk', 'author', 'title', 'description', 'meta', 'photo'], 'read_only_fields': ['pk', 'meta', 'photo'], 'optional_fields': ['description', 'author'] } } return versions
*** DEFAULT VERSION `v1` *** This method will return a dictionary object with version number and fields name. Fields are similar like serializer fields. Or you can say exactly as same as serializer fields. :param kwargs: Currently nothing to receive on kwargs :return: a dictionary object with version number
DEFAULT VERSION `v1` This method will return a dictionary object with version number and fields name. Fields are similar like serializer fields. Or you can say exactly as same as serializer fields.
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def api_version_fields(cls, **kwargs): versions = { 'v1': ['id', 'author', 'title', 'description'], 'v2': { 'fields': ['pk', 'author', 'title', 'description', 'meta', 'photo'], 'read_only_fields': ['pk', 'meta', 'photo'], 'optional_fields': ['description', 'author'] } } return versions
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DEFAULT VERSION `v1` This method will return a dictionary object with version number and fields name.
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[ "\"\"\"\n *** DEFAULT VERSION `v1` ***\n\n This method will return a dictionary object with version number and fields name. Fields are similar like\n serializer fields. Or you can say exactly as same as serializer fields.\n :param kwargs: Currently nothing to receive on kwargs\n :return: a dictionary object with version number\n \"\"\"" ]
[ { "param": "cls", "type": null } ]
{ "returns": [ { "docstring": "a dictionary object with version number", "docstring_tokens": [ "a", "dictionary", "object", "with", "version", "number" ], "type": null } ], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [ { "identifier": "kwargs", "type": null, "docstring": "Currently nothing to receive on kwargs", "docstring_tokens": [ "Currently", "nothing", "to", "receive", "on", "kwargs" ], "default": null, "is_optional": null } ], "others": [] }
def api_version_fields(cls, **kwargs): versions = { 'v1': ['id', 'author', 'title', 'description'], 'v2': { 'fields': ['pk', 'author', 'title', 'description', 'meta', 'photo'], 'read_only_fields': ['pk', 'meta', 'photo'], 'optional_fields': ['description', 'author'] } } return versions
610,333
294
6d7ac876fdf65fe0e85cceb94c311a93d9ea39c2
lijiancheng0614/Paddle
python/paddle/reader/decorator.py
[ "Apache-2.0" ]
Python
firstn
<not_specific>
def firstn(reader, n): """ Limit the max number of samples that reader could return. :param reader: the data reader to read from. :type reader: callable :param n: the max number of samples that return. :type n: int :return: the decorated reader. :rtype: callable """ # TODO(yuyang18): Check if just drop the reader, could clean the opened # resource or not? def firstn_reader(): for i, item in enumerate(reader()): if i == n: break yield item return firstn_reader
Limit the max number of samples that reader could return. :param reader: the data reader to read from. :type reader: callable :param n: the max number of samples that return. :type n: int :return: the decorated reader. :rtype: callable
Limit the max number of samples that reader could return.
[ "Limit", "the", "max", "number", "of", "samples", "that", "reader", "could", "return", "." ]
def firstn(reader, n): def firstn_reader(): for i, item in enumerate(reader()): if i == n: break yield item return firstn_reader
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Limit the max number of samples that reader could return.
[ "Limit", "the", "max", "number", "of", "samples", "that", "reader", "could", "return", "." ]
[ "\"\"\"\n Limit the max number of samples that reader could return.\n\n :param reader: the data reader to read from.\n :type reader: callable\n :param n: the max number of samples that return.\n :type n: int\n :return: the decorated reader.\n :rtype: callable\n \"\"\"", "# TODO(yuyang18): Check if just drop the reader, could clean the opened", "# resource or not?" ]
[ { "param": "reader", "type": null }, { "param": "n", "type": null } ]
{ "returns": [ { "docstring": "the decorated reader.", "docstring_tokens": [ "the", "decorated", "reader", "." ], "type": "callable" } ], "raises": [], "params": [ { "identifier": "reader", "type": null, "docstring": "the data reader to read from.", "docstring_tokens": [ "the", "data", "reader", "to", "read", "from", "." ], "default": null, "is_optional": null }, { "identifier": "n", "type": null, "docstring": "the max number of samples that return.", "docstring_tokens": [ "the", "max", "number", "of", "samples", "that", "return", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def firstn(reader, n): def firstn_reader(): for i, item in enumerate(reader()): if i == n: break yield item return firstn_reader
610,334
23
215e1f50415caf80892021491f379d9a63a2bd27
dendisuhubdy/two1-python
two1/bitcoin/crypto.py
[ "BSD-2-Clause-FreeBSD" ]
Python
from_private_key
<not_specific>
def from_private_key(private_key): """ Generates a public key object from a PrivateKey object. Args: private_key (PrivateKey): The private key object from which to derive this object. Returns: PublicKey: A PublicKey object. """ return private_key.public_key
Generates a public key object from a PrivateKey object. Args: private_key (PrivateKey): The private key object from which to derive this object. Returns: PublicKey: A PublicKey object.
Generates a public key object from a PrivateKey object.
[ "Generates", "a", "public", "key", "object", "from", "a", "PrivateKey", "object", "." ]
def from_private_key(private_key): return private_key.public_key
[ "def", "from_private_key", "(", "private_key", ")", ":", "return", "private_key", ".", "public_key" ]
Generates a public key object from a PrivateKey object.
[ "Generates", "a", "public", "key", "object", "from", "a", "PrivateKey", "object", "." ]
[ "\"\"\" Generates a public key object from a PrivateKey object.\n\n Args:\n private_key (PrivateKey): The private key object from\n which to derive this object.\n\n Returns:\n PublicKey: A PublicKey object.\n \"\"\"" ]
[ { "param": "private_key", "type": null } ]
{ "returns": [ { "docstring": "A PublicKey object.", "docstring_tokens": [ "A", "PublicKey", "object", "." ], "type": "PublicKey" } ], "raises": [], "params": [ { "identifier": "private_key", "type": null, "docstring": "The private key object from\nwhich to derive this object.", "docstring_tokens": [ "The", "private", "key", "object", "from", "which", "to", "derive", "this", "object", "." ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
def from_private_key(private_key): return private_key.public_key
610,335
794
b4d192aa6e6a43a512a7e870476010fb0055e5df
mbargull/snakemake
snakemake/io.py
[ "MIT" ]
Python
limit
<not_specific>
def limit(pattern, **wildcards): """ Limit wildcards to the given values. Arguments: **wildcards -- the wildcards as keyword arguments with their values as lists """ return pattern.format( **{ wildcard: "{{{},{}}}".format(wildcard, "|".join(values)) for wildcard, values in wildcards.items() } )
Limit wildcards to the given values. Arguments: **wildcards -- the wildcards as keyword arguments with their values as lists
Limit wildcards to the given values. Arguments: wildcards -- the wildcards as keyword arguments with their values as lists
[ "Limit", "wildcards", "to", "the", "given", "values", ".", "Arguments", ":", "wildcards", "--", "the", "wildcards", "as", "keyword", "arguments", "with", "their", "values", "as", "lists" ]
def limit(pattern, **wildcards): return pattern.format( **{ wildcard: "{{{},{}}}".format(wildcard, "|".join(values)) for wildcard, values in wildcards.items() } )
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Limit wildcards to the given values.
[ "Limit", "wildcards", "to", "the", "given", "values", "." ]
[ "\"\"\"\n Limit wildcards to the given values.\n\n Arguments:\n **wildcards -- the wildcards as keyword arguments\n with their values as lists\n \"\"\"" ]
[ { "param": "pattern", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "pattern", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def limit(pattern, **wildcards): return pattern.format( **{ wildcard: "{{{},{}}}".format(wildcard, "|".join(values)) for wildcard, values in wildcards.items() } )
610,336
988
4ca72457efaa5e88f390b2b95946a5473af7a4de
RudolfCardinal/pythonlib
cardinal_pythonlib/rnc_web.py
[ "Apache-2.0" ]
Python
print_result_for_plain_cgi_script
None
def print_result_for_plain_cgi_script(contenttype: str, headers: TYPE_WSGI_RESPONSE_HEADERS, content: bytes, status: str = '200 OK') -> None: """ Writes HTTP request result to stdout. """ headers = [ ("Status", status), ("Content-Type", contenttype), ("Content-Length", str(len(content))), ] + headers sys.stdout.write("\n".join([h[0] + ": " + h[1] for h in headers]) + "\n\n") sys.stdout.buffer.write(content)
Writes HTTP request result to stdout.
Writes HTTP request result to stdout.
[ "Writes", "HTTP", "request", "result", "to", "stdout", "." ]
def print_result_for_plain_cgi_script(contenttype: str, headers: TYPE_WSGI_RESPONSE_HEADERS, content: bytes, status: str = '200 OK') -> None: headers = [ ("Status", status), ("Content-Type", contenttype), ("Content-Length", str(len(content))), ] + headers sys.stdout.write("\n".join([h[0] + ": " + h[1] for h in headers]) + "\n\n") sys.stdout.buffer.write(content)
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Writes HTTP request result to stdout.
[ "Writes", "HTTP", "request", "result", "to", "stdout", "." ]
[ "\"\"\"\n Writes HTTP request result to stdout.\n \"\"\"" ]
[ { "param": "contenttype", "type": "str" }, { "param": "headers", "type": "TYPE_WSGI_RESPONSE_HEADERS" }, { "param": "content", "type": "bytes" }, { "param": "status", "type": "str" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "contenttype", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "headers", "type": "TYPE_WSGI_RESPONSE_HEADERS", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "content", "type": "bytes", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "status", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import sys def print_result_for_plain_cgi_script(contenttype: str, headers: TYPE_WSGI_RESPONSE_HEADERS, content: bytes, status: str = '200 OK') -> None: headers = [ ("Status", status), ("Content-Type", contenttype), ("Content-Length", str(len(content))), ] + headers sys.stdout.write("\n".join([h[0] + ": " + h[1] for h in headers]) + "\n\n") sys.stdout.buffer.write(content)
610,337
241
7250f31f0856f540bacb7df2ef6ad8d151135858
HMProenca/RuleList
rulelist/rulelistmodel/gain_add_rule.py
[ "MIT" ]
Python
compute_statistics_newrules
<not_specific>
def compute_statistics_newrules(rulelist, data, bitarray_subgroup): """ Computes the statistics of 3 rules: 1. the new subgroup rule 2. the old "default" rule that covered the subgroup (only the cover of the subgroup not the rest) 3. the new default rule that covers the region not covered by any subgroup. """ rulelist.tmp_subgroup_statistic = rulelist.tmp_subgroup_statistic.replace_stats(data, bitarray_subgroup) bitarray_new_defaultrule =rulelist.bitset_uncovered &~ bitarray_subgroup rulelist.tmp_default_statistic = rulelist.tmp_default_statistic.replace_stats(data, bitarray_new_defaultrule) return rulelist.tmp_subgroup_statistic, rulelist.tmp_default_statistic
Computes the statistics of 3 rules: 1. the new subgroup rule 2. the old "default" rule that covered the subgroup (only the cover of the subgroup not the rest) 3. the new default rule that covers the region not covered by any subgroup.
Computes the statistics of 3 rules: 1. the new subgroup rule 2. the old "default" rule that covered the subgroup (only the cover of the subgroup not the rest) 3. the new default rule that covers the region not covered by any subgroup.
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def compute_statistics_newrules(rulelist, data, bitarray_subgroup): rulelist.tmp_subgroup_statistic = rulelist.tmp_subgroup_statistic.replace_stats(data, bitarray_subgroup) bitarray_new_defaultrule =rulelist.bitset_uncovered &~ bitarray_subgroup rulelist.tmp_default_statistic = rulelist.tmp_default_statistic.replace_stats(data, bitarray_new_defaultrule) return rulelist.tmp_subgroup_statistic, rulelist.tmp_default_statistic
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Computes the statistics of 3 rules: 1. the new subgroup rule 2. the old "default" rule that covered the subgroup (only the cover of the subgroup not the rest) 3. the new default rule that covers the region not covered by any subgroup.
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[ "\"\"\" Computes the statistics of 3 rules:\n 1. the new subgroup rule\n 2. the old \"default\" rule that covered the subgroup (only the cover of the subgroup not the rest)\n 3. the new default rule that covers the region not covered by any subgroup.\n\n \"\"\"" ]
[ { "param": "rulelist", "type": null }, { "param": "data", "type": null }, { "param": "bitarray_subgroup", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "rulelist", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "data", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "bitarray_subgroup", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def compute_statistics_newrules(rulelist, data, bitarray_subgroup): rulelist.tmp_subgroup_statistic = rulelist.tmp_subgroup_statistic.replace_stats(data, bitarray_subgroup) bitarray_new_defaultrule =rulelist.bitset_uncovered &~ bitarray_subgroup rulelist.tmp_default_statistic = rulelist.tmp_default_statistic.replace_stats(data, bitarray_new_defaultrule) return rulelist.tmp_subgroup_statistic, rulelist.tmp_default_statistic
610,338
130
3b5a9203126f1cdf3d6b2e2421d2fa8c53288537
nicolay-r/are-networks-experiments
callback_log_exp.py
[ "MIT" ]
Python
parse_last_epoch_results
<not_specific>
def parse_last_epoch_results(filepath): """ Perform results reading from the related filepath """ # Example to parse: # F1-last per iterations: [0.32, 0.229, 0.311] iters = None with open(filepath) as f: for line in f.readlines(): if u'last per iterations' in line: arr = line.split(':')[1].strip() vals = arr[1:-1] iters = [float(v) for v in vals.split(',')] return iters
Perform results reading from the related filepath
Perform results reading from the related filepath
[ "Perform", "results", "reading", "from", "the", "related", "filepath" ]
def parse_last_epoch_results(filepath): iters = None with open(filepath) as f: for line in f.readlines(): if u'last per iterations' in line: arr = line.split(':')[1].strip() vals = arr[1:-1] iters = [float(v) for v in vals.split(',')] return iters
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Perform results reading from the related filepath
[ "Perform", "results", "reading", "from", "the", "related", "filepath" ]
[ "\"\"\" Perform results reading from the related filepath\n \"\"\"", "# Example to parse:", "# F1-last per iterations: [0.32, 0.229, 0.311]" ]
[ { "param": "filepath", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "filepath", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def parse_last_epoch_results(filepath): iters = None with open(filepath) as f: for line in f.readlines(): if u'last per iterations' in line: arr = line.split(':')[1].strip() vals = arr[1:-1] iters = [float(v) for v in vals.split(',')] return iters
610,339
952
43bc49202b4a98d782f9b383db73722119dd67e4
Vyryn/davidbot
functions.py
[ "MIT" ]
Python
order
<not_specific>
def order(x, count=0): """Returns the base 10 order of magnitude of a number""" if x / 10 >= 1: count += order(x / 10, count) + 1 return count
Returns the base 10 order of magnitude of a number
Returns the base 10 order of magnitude of a number
[ "Returns", "the", "base", "10", "order", "of", "magnitude", "of", "a", "number" ]
def order(x, count=0): if x / 10 >= 1: count += order(x / 10, count) + 1 return count
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Returns the base 10 order of magnitude of a number
[ "Returns", "the", "base", "10", "order", "of", "magnitude", "of", "a", "number" ]
[ "\"\"\"Returns the base 10 order of magnitude of a number\"\"\"" ]
[ { "param": "x", "type": null }, { "param": "count", "type": null } ]
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def order(x, count=0): if x / 10 >= 1: count += order(x / 10, count) + 1 return count
610,340
1,006
6326ebf3524a095a74e36281dbd8fbbb65af942e
dacb/elvizCluster
abundance_plot_utils.py
[ "BSD-3-Clause" ]
Python
axd_landscape
<not_specific>
def axd_landscape(axs): """ axis dictionary for landscape pages. """ return {('Low', 1): axs[0, 0], ('Low', 2): axs[0, 1], ('Low', 3): axs[0, 2], ('Low', 4): axs[0, 3], ('High', 1): axs[1, 0], ('High', 2): axs[1, 1], ('High', 3): axs[1, 2], ('High', 4): axs[1, 3]}
axis dictionary for landscape pages.
axis dictionary for landscape pages.
[ "axis", "dictionary", "for", "landscape", "pages", "." ]
def axd_landscape(axs): return {('Low', 1): axs[0, 0], ('Low', 2): axs[0, 1], ('Low', 3): axs[0, 2], ('Low', 4): axs[0, 3], ('High', 1): axs[1, 0], ('High', 2): axs[1, 1], ('High', 3): axs[1, 2], ('High', 4): axs[1, 3]}
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axis dictionary for landscape pages.
[ "axis", "dictionary", "for", "landscape", "pages", "." ]
[ "\"\"\"\n axis dictionary for landscape pages.\n \"\"\"" ]
[ { "param": "axs", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "axs", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def axd_landscape(axs): return {('Low', 1): axs[0, 0], ('Low', 2): axs[0, 1], ('Low', 3): axs[0, 2], ('Low', 4): axs[0, 3], ('High', 1): axs[1, 0], ('High', 2): axs[1, 1], ('High', 3): axs[1, 2], ('High', 4): axs[1, 3]}
610,341
400
52788d70613495656c8e909b2542817317e55865
zamberjo/tron-api-python
tronapi/tron.py
[ "Apache-2.0" ]
Python
string_utf8_to_hex
<not_specific>
def string_utf8_to_hex(name): """Convert a string to Hex format Args: name (str): string """ return bytes(name, encoding='utf-8').hex()
Convert a string to Hex format Args: name (str): string
Convert a string to Hex format
[ "Convert", "a", "string", "to", "Hex", "format" ]
def string_utf8_to_hex(name): return bytes(name, encoding='utf-8').hex()
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Convert a string to Hex format
[ "Convert", "a", "string", "to", "Hex", "format" ]
[ "\"\"\"Convert a string to Hex format\n\n Args:\n name (str): string\n\n \"\"\"" ]
[ { "param": "name", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "name", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
def string_utf8_to_hex(name): return bytes(name, encoding='utf-8').hex()
610,342
755
ffb7943a5e6f9276de04202036593148ae6db2ab
CGATOxford/Optic
Optic/MaliIO.py
[ "MIT" ]
Python
removeGappedColumns
<not_specific>
def removeGappedColumns(mali, gap_char="-"): """remove all gapped columns in mali.""" if len(mali) == 0: return mali keys = mali.keys() lmali = len(mali[keys[0]]) wmali = len(keys) gaps_per_column = [0] * lmali for m in mali.values(): for x in range(lmali): if m[x] == gap_char: gaps_per_column[x] += 1 new_mali = {} for k in keys: s = [] m = mali[k] for x in range(lmali): if gaps_per_column[x] < wmali: s += m[x] new_mali[k] = string.join(s, "") return new_mali
remove all gapped columns in mali.
remove all gapped columns in mali.
[ "remove", "all", "gapped", "columns", "in", "mali", "." ]
def removeGappedColumns(mali, gap_char="-"): if len(mali) == 0: return mali keys = mali.keys() lmali = len(mali[keys[0]]) wmali = len(keys) gaps_per_column = [0] * lmali for m in mali.values(): for x in range(lmali): if m[x] == gap_char: gaps_per_column[x] += 1 new_mali = {} for k in keys: s = [] m = mali[k] for x in range(lmali): if gaps_per_column[x] < wmali: s += m[x] new_mali[k] = string.join(s, "") return new_mali
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remove all gapped columns in mali.
[ "remove", "all", "gapped", "columns", "in", "mali", "." ]
[ "\"\"\"remove all gapped columns in mali.\"\"\"" ]
[ { "param": "mali", "type": null }, { "param": "gap_char", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "mali", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "gap_char", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import string def removeGappedColumns(mali, gap_char="-"): if len(mali) == 0: return mali keys = mali.keys() lmali = len(mali[keys[0]]) wmali = len(keys) gaps_per_column = [0] * lmali for m in mali.values(): for x in range(lmali): if m[x] == gap_char: gaps_per_column[x] += 1 new_mali = {} for k in keys: s = [] m = mali[k] for x in range(lmali): if gaps_per_column[x] < wmali: s += m[x] new_mali[k] = string.join(s, "") return new_mali
610,344
945
18466c32671c98fab43d5fb4c276bb79d0c7d04d
cameron-freshworks/ppr
ppr-api/src/ppr_api/models/search_utils.py
[ "Apache-2.0" ]
Python
format_mhr_number
null
def format_mhr_number(request_json): """Trim and pad with zeroes search query mhr number query.""" mhr_num: str = request_json['criteria']['value'] mhr_num = mhr_num.strip().rjust(6, '0') request_json['criteria']['value'] = mhr_num
Trim and pad with zeroes search query mhr number query.
Trim and pad with zeroes search query mhr number query.
[ "Trim", "and", "pad", "with", "zeroes", "search", "query", "mhr", "number", "query", "." ]
def format_mhr_number(request_json): mhr_num: str = request_json['criteria']['value'] mhr_num = mhr_num.strip().rjust(6, '0') request_json['criteria']['value'] = mhr_num
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Trim and pad with zeroes search query mhr number query.
[ "Trim", "and", "pad", "with", "zeroes", "search", "query", "mhr", "number", "query", "." ]
[ "\"\"\"Trim and pad with zeroes search query mhr number query.\"\"\"" ]
[ { "param": "request_json", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "request_json", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def format_mhr_number(request_json): mhr_num: str = request_json['criteria']['value'] mhr_num = mhr_num.strip().rjust(6, '0') request_json['criteria']['value'] = mhr_num
610,345
97
87f25981d11d7b420f22f0f7f1a04dbcd458c247
jeremie-gauthier/al-go-rithms
sort/selection_sort/python/selection_sort2.py
[ "CC0-1.0" ]
Python
selection_sort
<not_specific>
def selection_sort(items): """Implementation of the selection sort algorithm using Python inside parameter we can pass some items which are heterogeneous comparable """ length = len(items) for i in range(length): least = i for j in range(i + 1, length): if items[j] < items[least]: least = j items[least], items[i] = ( items[i], items[least] ) return items
Implementation of the selection sort algorithm using Python inside parameter we can pass some items which are heterogeneous comparable
Implementation of the selection sort algorithm using Python inside parameter we can pass some items which are heterogeneous comparable
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def selection_sort(items): length = len(items) for i in range(length): least = i for j in range(i + 1, length): if items[j] < items[least]: least = j items[least], items[i] = ( items[i], items[least] ) return items
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Implementation of the selection sort algorithm using Python inside parameter we can pass some items which are heterogeneous comparable
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[ "\"\"\"Implementation of the selection sort algorithm using Python\n inside parameter we can pass some items which are heterogeneous\n comparable\n \"\"\"" ]
[ { "param": "items", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "items", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def selection_sort(items): length = len(items) for i in range(length): least = i for j in range(i + 1, length): if items[j] < items[least]: least = j items[least], items[i] = ( items[i], items[least] ) return items
610,346
792
0c6f756e4a668819a1361332e48a1468d48c9f4a
JonathanBodine/stale-news
code/replication/utils.py
[ "MIT" ]
Python
bookValue
<not_specific>
def bookValue(firm, date, pdatabase, printWarnings=True, bookUnit=1000000): """ Firm's book value as of latest quarterly earnings report preceding date Relies on naming of pdatabase (compustat) and chronological data for each firm Returns -1 if no data available Returns FLOAT """ bookQuery = pdatabase.data.query('(tic == "' + firm + '") & (datadate < ' + date + ')') # print(bookQuery) if bookQuery.empty: if printWarnings: print("NO BOOK DATA: " + firm + ", " + date) return -1 bookSeries = bookQuery["ceqq"] return float(bookSeries.iat[bookSeries.size - 1] * bookUnit)
Firm's book value as of latest quarterly earnings report preceding date Relies on naming of pdatabase (compustat) and chronological data for each firm Returns -1 if no data available Returns FLOAT
Firm's book value as of latest quarterly earnings report preceding date Relies on naming of pdatabase (compustat) and chronological data for each firm Returns -1 if no data available Returns FLOAT
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def bookValue(firm, date, pdatabase, printWarnings=True, bookUnit=1000000): bookQuery = pdatabase.data.query('(tic == "' + firm + '") & (datadate < ' + date + ')') if bookQuery.empty: if printWarnings: print("NO BOOK DATA: " + firm + ", " + date) return -1 bookSeries = bookQuery["ceqq"] return float(bookSeries.iat[bookSeries.size - 1] * bookUnit)
[ "def", "bookValue", "(", "firm", ",", "date", ",", "pdatabase", ",", "printWarnings", "=", "True", ",", "bookUnit", "=", "1000000", ")", ":", "bookQuery", "=", "pdatabase", ".", "data", ".", "query", "(", "'(tic == \"'", "+", "firm", "+", "'\") & (datadate < '", "+", "date", "+", "')'", ")", "if", "bookQuery", ".", "empty", ":", "if", "printWarnings", ":", "print", "(", "\"NO BOOK DATA: \"", "+", "firm", "+", "\", \"", "+", "date", ")", "return", "-", "1", "bookSeries", "=", "bookQuery", "[", "\"ceqq\"", "]", "return", "float", "(", "bookSeries", ".", "iat", "[", "bookSeries", ".", "size", "-", "1", "]", "*", "bookUnit", ")" ]
Firm's book value as of latest quarterly earnings report preceding date Relies on naming of pdatabase (compustat) and chronological data for each firm Returns -1 if no data available Returns FLOAT
[ "Firm", "'", "s", "book", "value", "as", "of", "latest", "quarterly", "earnings", "report", "preceding", "date", "Relies", "on", "naming", "of", "pdatabase", "(", "compustat", ")", "and", "chronological", "data", "for", "each", "firm", "Returns", "-", "1", "if", "no", "data", "available", "Returns", "FLOAT" ]
[ "\"\"\"\n Firm's book value as of latest quarterly earnings report preceding date\n Relies on naming of pdatabase (compustat) and chronological data for each firm\n Returns -1 if no data available\n Returns FLOAT\n \"\"\"", "# print(bookQuery)" ]
[ { "param": "firm", "type": null }, { "param": "date", "type": null }, { "param": "pdatabase", "type": null }, { "param": "printWarnings", "type": null }, { "param": "bookUnit", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "firm", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "date", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "pdatabase", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "printWarnings", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "bookUnit", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def bookValue(firm, date, pdatabase, printWarnings=True, bookUnit=1000000): bookQuery = pdatabase.data.query('(tic == "' + firm + '") & (datadate < ' + date + ')') if bookQuery.empty: if printWarnings: print("NO BOOK DATA: " + firm + ", " + date) return -1 bookSeries = bookQuery["ceqq"] return float(bookSeries.iat[bookSeries.size - 1] * bookUnit)
610,347
423
2f414201eb94bd364da5c0e1db5d60819b258684
eltoredo/ChatBot
venv/lib/python3.6/site-packages/falcon/testing/resource.py
[ "Apache-2.0" ]
Python
capture_responder_args
null
def capture_responder_args(req, resp, resource, params): """Before hook for capturing responder arguments. Adds the following attributes to the hooked responder's resource class: * captured_req * captured_resp * captured_kwargs """ resource.captured_req = req resource.captured_resp = resp resource.captured_kwargs = params
Before hook for capturing responder arguments. Adds the following attributes to the hooked responder's resource class: * captured_req * captured_resp * captured_kwargs
Before hook for capturing responder arguments. Adds the following attributes to the hooked responder's resource class.
[ "Before", "hook", "for", "capturing", "responder", "arguments", ".", "Adds", "the", "following", "attributes", "to", "the", "hooked", "responder", "'", "s", "resource", "class", "." ]
def capture_responder_args(req, resp, resource, params): resource.captured_req = req resource.captured_resp = resp resource.captured_kwargs = params
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Before hook for capturing responder arguments.
[ "Before", "hook", "for", "capturing", "responder", "arguments", "." ]
[ "\"\"\"Before hook for capturing responder arguments.\n\n Adds the following attributes to the hooked responder's resource\n class:\n\n * captured_req\n * captured_resp\n * captured_kwargs\n \"\"\"" ]
[ { "param": "req", "type": null }, { "param": "resp", "type": null }, { "param": "resource", "type": null }, { "param": "params", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "req", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "resp", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "resource", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "params", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def capture_responder_args(req, resp, resource, params): resource.captured_req = req resource.captured_resp = resp resource.captured_kwargs = params
610,348
657
4fc87b44d73d035a06d1285767817d16bf257e50
xflows/clowdflows
workflows/templatetags/paginator.py
[ "MIT" ]
Python
paginator
<not_specific>
def paginator(context, adjacent_pages=2): """ To be used in conjunction with the object_list generic view. Adds pagination context variables for use in displaying first, adjacent and last page links in addition to those created by the object_list generic view. Required context variables: paged: The Paginator.page() instance. """ paged = context['paged'] # the paginator.page(page) instance paginator = paged.paginator startPage = max(paged.number - adjacent_pages, 1) if startPage <= 3: startPage = 1 endPage = paged.number + adjacent_pages + 1 if endPage >= paginator.num_pages - 1: endPage = paginator.num_pages + 1 page_numbers = [n for n in range(startPage, endPage) \ if n > 0 and n <= paginator.num_pages] return { 'paged': paged, 'paginator': paginator, 'page': paged.number, 'pages': paginator.num_pages, 'page_numbers': page_numbers, 'next': paged.next_page_number(), 'previous': paged.previous_page_number(), 'has_next': paged.has_next(), 'has_previous': paged.has_previous(), 'show_first': 1 not in page_numbers, 'show_last': paginator.num_pages not in page_numbers, 'last':paginator.num_pages, }
To be used in conjunction with the object_list generic view. Adds pagination context variables for use in displaying first, adjacent and last page links in addition to those created by the object_list generic view. Required context variables: paged: The Paginator.page() instance.
To be used in conjunction with the object_list generic view. Adds pagination context variables for use in displaying first, adjacent and last page links in addition to those created by the object_list generic view. Required context variables: paged: The Paginator.page() instance.
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def paginator(context, adjacent_pages=2): paged = context['paged'] paginator = paged.paginator startPage = max(paged.number - adjacent_pages, 1) if startPage <= 3: startPage = 1 endPage = paged.number + adjacent_pages + 1 if endPage >= paginator.num_pages - 1: endPage = paginator.num_pages + 1 page_numbers = [n for n in range(startPage, endPage) \ if n > 0 and n <= paginator.num_pages] return { 'paged': paged, 'paginator': paginator, 'page': paged.number, 'pages': paginator.num_pages, 'page_numbers': page_numbers, 'next': paged.next_page_number(), 'previous': paged.previous_page_number(), 'has_next': paged.has_next(), 'has_previous': paged.has_previous(), 'show_first': 1 not in page_numbers, 'show_last': paginator.num_pages not in page_numbers, 'last':paginator.num_pages, }
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To be used in conjunction with the object_list generic view.
[ "To", "be", "used", "in", "conjunction", "with", "the", "object_list", "generic", "view", "." ]
[ "\"\"\"\r\n To be used in conjunction with the object_list generic view.\r\n\r\n Adds pagination context variables for use in displaying first, adjacent and\r\n last page links in addition to those created by the object_list generic\r\n view.\r\n\r\n Required context variables: paged: The Paginator.page() instance.\r\n\r\n \"\"\"", "# the paginator.page(page) instance\r" ]
[ { "param": "context", "type": null }, { "param": "adjacent_pages", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "context", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "adjacent_pages", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def paginator(context, adjacent_pages=2): paged = context['paged'] paginator = paged.paginator startPage = max(paged.number - adjacent_pages, 1) if startPage <= 3: startPage = 1 endPage = paged.number + adjacent_pages + 1 if endPage >= paginator.num_pages - 1: endPage = paginator.num_pages + 1 page_numbers = [n for n in range(startPage, endPage) \ if n > 0 and n <= paginator.num_pages] return { 'paged': paged, 'paginator': paginator, 'page': paged.number, 'pages': paginator.num_pages, 'page_numbers': page_numbers, 'next': paged.next_page_number(), 'previous': paged.previous_page_number(), 'has_next': paged.has_next(), 'has_previous': paged.has_previous(), 'show_first': 1 not in page_numbers, 'show_last': paginator.num_pages not in page_numbers, 'last':paginator.num_pages, }
610,349
909
ff5a5d6b21dcafb0ce8bcf79349adb89926842a0
cloudpassage/ec2-halo-delta
app/ehdlib/report.py
[ "BSD-3-Clause" ]
Python
format_aws_instance
<not_specific>
def format_aws_instance(cls, aws_instance): """Format an AWS instance's metadata for reporting. Args: aws_instance(tuple): Formatted like this: ("i-231423452", {"aws_account": "12345", "aws_region": "us-east-1", "key_name": "my_ssh_key", "launch_time": "1999-12-31T23:59.9"}} """ instance_id = "Instance ID: {instance}".format(instance=aws_instance[0]) # NOQA aws_account = "AWS Account: {account}".format(account=aws_instance[1]["aws_account"]) # NOQA aws_region = "AWS Region: {region}".format(region=aws_instance[1]["aws_region"]) # NOQA key_name = "Key Name: {key_}".format(key_=aws_instance[1]["key_name"]) launch = "Launched at: {launch}".format(launch=aws_instance[1]["launch_time"]) # NOQA vpc_id = "VPC ID: {vpc}".format(vpc=aws_instance[1]["vpc_id"]) ordered_fields = [aws_account, aws_region, key_name, vpc_id, instance_id, launch] return "\n".join(ordered_fields)
Format an AWS instance's metadata for reporting. Args: aws_instance(tuple): Formatted like this: ("i-231423452", {"aws_account": "12345", "aws_region": "us-east-1", "key_name": "my_ssh_key", "launch_time": "1999-12-31T23:59.9"}}
Format an AWS instance's metadata for reporting.
[ "Format", "an", "AWS", "instance", "'", "s", "metadata", "for", "reporting", "." ]
def format_aws_instance(cls, aws_instance): instance_id = "Instance ID: {instance}".format(instance=aws_instance[0]) aws_account = "AWS Account: {account}".format(account=aws_instance[1]["aws_account"]) aws_region = "AWS Region: {region}".format(region=aws_instance[1]["aws_region"]) key_name = "Key Name: {key_}".format(key_=aws_instance[1]["key_name"]) launch = "Launched at: {launch}".format(launch=aws_instance[1]["launch_time"]) vpc_id = "VPC ID: {vpc}".format(vpc=aws_instance[1]["vpc_id"]) ordered_fields = [aws_account, aws_region, key_name, vpc_id, instance_id, launch] return "\n".join(ordered_fields)
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Format an AWS instance's metadata for reporting.
[ "Format", "an", "AWS", "instance", "'", "s", "metadata", "for", "reporting", "." ]
[ "\"\"\"Format an AWS instance's metadata for reporting.\n\n Args:\n aws_instance(tuple): Formatted like this:\n (\"i-231423452\", {\"aws_account\": \"12345\",\n \"aws_region\": \"us-east-1\",\n \"key_name\": \"my_ssh_key\",\n \"launch_time\": \"1999-12-31T23:59.9\"}}\n\n \"\"\"", "# NOQA", "# NOQA", "# NOQA", "# NOQA" ]
[ { "param": "cls", "type": null }, { "param": "aws_instance", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "aws_instance", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": false } ], "outlier_params": [], "others": [] }
def format_aws_instance(cls, aws_instance): instance_id = "Instance ID: {instance}".format(instance=aws_instance[0]) aws_account = "AWS Account: {account}".format(account=aws_instance[1]["aws_account"]) aws_region = "AWS Region: {region}".format(region=aws_instance[1]["aws_region"]) key_name = "Key Name: {key_}".format(key_=aws_instance[1]["key_name"]) launch = "Launched at: {launch}".format(launch=aws_instance[1]["launch_time"]) vpc_id = "VPC ID: {vpc}".format(vpc=aws_instance[1]["vpc_id"]) ordered_fields = [aws_account, aws_region, key_name, vpc_id, instance_id, launch] return "\n".join(ordered_fields)
610,350
396
7fc9df65c287b5d93f2e704f922065e8b34ff911
nir-lavee/server_utils
auto/SafeUpdater.py
[ "MIT" ]
Python
run
<not_specific>
def run(commands, input_string="", fail_abort=True): """ Run the given commands as a subprocess, wait for it to finish. If fail_abort is set, then a non-zero return code will trigger an exception. Return (return_code, stdout, stderr). """ process = subprocess.Popen(commands, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = process.communicate(input=input_string) return_code = process.returncode if return_code != 0 and fail_abort: raise Exception("Command returned non-zero: %s\n" "Return code: %s\n" "Stdout: %s\n" "Stderr: %s\n" % (commands, return_code, stdout, stderr)) return (return_code, stdout, stderr)
Run the given commands as a subprocess, wait for it to finish. If fail_abort is set, then a non-zero return code will trigger an exception. Return (return_code, stdout, stderr).
Run the given commands as a subprocess, wait for it to finish. If fail_abort is set, then a non-zero return code will trigger an exception. Return (return_code, stdout, stderr).
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def run(commands, input_string="", fail_abort=True): process = subprocess.Popen(commands, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = process.communicate(input=input_string) return_code = process.returncode if return_code != 0 and fail_abort: raise Exception("Command returned non-zero: %s\n" "Return code: %s\n" "Stdout: %s\n" "Stderr: %s\n" % (commands, return_code, stdout, stderr)) return (return_code, stdout, stderr)
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Run the given commands as a subprocess, wait for it to finish.
[ "Run", "the", "given", "commands", "as", "a", "subprocess", "wait", "for", "it", "to", "finish", "." ]
[ "\"\"\"\n Run the given commands as a subprocess, wait for it to finish.\n If fail_abort is set, then a non-zero return code will trigger\n an exception.\n Return (return_code, stdout, stderr).\n \"\"\"" ]
[ { "param": "commands", "type": null }, { "param": "input_string", "type": null }, { "param": "fail_abort", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "commands", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "input_string", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "fail_abort", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import subprocess def run(commands, input_string="", fail_abort=True): process = subprocess.Popen(commands, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = process.communicate(input=input_string) return_code = process.returncode if return_code != 0 and fail_abort: raise Exception("Command returned non-zero: %s\n" "Return code: %s\n" "Stdout: %s\n" "Stderr: %s\n" % (commands, return_code, stdout, stderr)) return (return_code, stdout, stderr)
610,351
1,002
4f4201ab1b3d5f291e37c13f6fb55bb12fa43766
shamirtowsif/soft_patterns
util.py
[ "MIT" ]
Python
nub_by
<not_specific>
def nub_by(xs, key): """ Removes elements with duplicate keys, maintaining original order. """ seen = set() def check_and_add(x): k = key(x) if k not in seen: seen.add(k) return True return False return (x for x in xs if check_and_add(x))
Removes elements with duplicate keys, maintaining original order.
Removes elements with duplicate keys, maintaining original order.
[ "Removes", "elements", "with", "duplicate", "keys", "maintaining", "original", "order", "." ]
def nub_by(xs, key): seen = set() def check_and_add(x): k = key(x) if k not in seen: seen.add(k) return True return False return (x for x in xs if check_and_add(x))
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Removes elements with duplicate keys, maintaining original order.
[ "Removes", "elements", "with", "duplicate", "keys", "maintaining", "original", "order", "." ]
[ "\"\"\" Removes elements with duplicate keys, maintaining original order. \"\"\"" ]
[ { "param": "xs", "type": null }, { "param": "key", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "xs", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "key", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def nub_by(xs, key): seen = set() def check_and_add(x): k = key(x) if k not in seen: seen.add(k) return True return False return (x for x in xs if check_and_add(x))
610,352
946
6d6cb7975937ff4d353701b9f384fc97664e6109
SelfAdjointOperator/adb-sync
src/ADBSync/__init__.py
[ "Apache-2.0" ]
Python
pruneTree
<not_specific>
def pruneTree(cls, tree): """Remove all Nones from a tree. May return None if tree is None however.""" if not isinstance(tree, dict): return tree else: returnDict = {} for key, value in tree.items(): value_pruned = cls.pruneTree(value) if value_pruned is not None: returnDict[key] = value_pruned return returnDict or None
Remove all Nones from a tree. May return None if tree is None however.
Remove all Nones from a tree. May return None if tree is None however.
[ "Remove", "all", "Nones", "from", "a", "tree", ".", "May", "return", "None", "if", "tree", "is", "None", "however", "." ]
def pruneTree(cls, tree): if not isinstance(tree, dict): return tree else: returnDict = {} for key, value in tree.items(): value_pruned = cls.pruneTree(value) if value_pruned is not None: returnDict[key] = value_pruned return returnDict or None
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Remove all Nones from a tree.
[ "Remove", "all", "Nones", "from", "a", "tree", "." ]
[ "\"\"\"Remove all Nones from a tree. May return None if tree is None however.\"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "tree", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "tree", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def pruneTree(cls, tree): if not isinstance(tree, dict): return tree else: returnDict = {} for key, value in tree.items(): value_pruned = cls.pruneTree(value) if value_pruned is not None: returnDict[key] = value_pruned return returnDict or None
610,353
114
080d3bca8afc2f008ffd64f5a8a292632dc78f2e
JFlynnXYZ/LichtensteinGenerator
bin/colour.py
[ "Apache-2.0" ]
Python
rgb_unflatten
<not_specific>
def rgb_unflatten(rgbCols): '''Converts a list of separated RGB values into a 3 tuple groups of (R,G,B) Parameters: rgbCols [list][tuple] : A list of separate R,G,B values all being ints. On Exit: Combines and groups every 3 values into it's own tuple and returns a complete list of grouped RGB values. ''' return [tuple(rgbCols[i:i+3]) for i in range(0,len(rgbCols),3)]
Converts a list of separated RGB values into a 3 tuple groups of (R,G,B) Parameters: rgbCols [list][tuple] : A list of separate R,G,B values all being ints. On Exit: Combines and groups every 3 values into it's own tuple and returns a complete list of grouped RGB values.
Converts a list of separated RGB values into a 3 tuple groups of (R,G,B)
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def rgb_unflatten(rgbCols): return [tuple(rgbCols[i:i+3]) for i in range(0,len(rgbCols),3)]
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Converts a list of separated RGB values into a 3 tuple groups of (R,G,B)
[ "Converts", "a", "list", "of", "separated", "RGB", "values", "into", "a", "3", "tuple", "groups", "of", "(", "R", "G", "B", ")" ]
[ "'''Converts a list of separated RGB values into a 3 tuple groups of (R,G,B)\n \n Parameters:\n rgbCols [list][tuple] : A list of separate R,G,B values all being ints.\n \n On Exit:\n Combines and groups every 3 values into it's own tuple and returns a \n complete list of grouped RGB values.\n \n '''" ]
[ { "param": "rgbCols", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "rgbCols", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [ { "identifier": "rgbCols [list][tuple] ", "type": null, "docstring": "A list of separate R,G,B values all being ints.", "docstring_tokens": [ "A", "list", "of", "separate", "R", "G", "B", "values", "all", "being", "ints", "." ], "default": null, "is_optional": null } ], "others": [] }
def rgb_unflatten(rgbCols): return [tuple(rgbCols[i:i+3]) for i in range(0,len(rgbCols),3)]
610,355
913
4bf9f4222c417dce11140fda6bf0634f2e72ac2b
minhtriet8752/Medusa
Degeneration/FeederPlot.py
[ "MIT" ]
Python
autolabel
null
def autolabel(rects, ax, xpos='center'): """ Attach a text label above each bar in *rects*, displaying its height. *xpos* indicates which side to place the text w.r.t. the center of the bar. It can be one of the following {'center', 'right', 'left'}. """ xpos = xpos.lower() # normalize the case of the parameter ha = {'center': 'center', 'right': 'left', 'left': 'right'} offset = {'center': 0.5, 'right': 0.57, 'left': 0.43} # x_txt = x + w*off for rect in rects: height = rect.get_height() ax.text(rect.get_x() + rect.get_width()*offset[xpos], 1.01*height, '{}'.format(height), ha=ha[xpos], va='bottom')
Attach a text label above each bar in *rects*, displaying its height. *xpos* indicates which side to place the text w.r.t. the center of the bar. It can be one of the following {'center', 'right', 'left'}.
Attach a text label above each bar in *rects*, displaying its height. xpos* indicates which side to place the text w.r.t. the center of the bar.
[ "Attach", "a", "text", "label", "above", "each", "bar", "in", "*", "rects", "*", "displaying", "its", "height", ".", "xpos", "*", "indicates", "which", "side", "to", "place", "the", "text", "w", ".", "r", ".", "t", ".", "the", "center", "of", "the", "bar", "." ]
def autolabel(rects, ax, xpos='center'): xpos = xpos.lower() ha = {'center': 'center', 'right': 'left', 'left': 'right'} offset = {'center': 0.5, 'right': 0.57, 'left': 0.43} for rect in rects: height = rect.get_height() ax.text(rect.get_x() + rect.get_width()*offset[xpos], 1.01*height, '{}'.format(height), ha=ha[xpos], va='bottom')
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Attach a text label above each bar in *rects*, displaying its height.
[ "Attach", "a", "text", "label", "above", "each", "bar", "in", "*", "rects", "*", "displaying", "its", "height", "." ]
[ "\"\"\"\n Attach a text label above each bar in *rects*, displaying its height.\n\n *xpos* indicates which side to place the text w.r.t. the center of\n the bar. It can be one of the following {'center', 'right', 'left'}.\n \"\"\"", "# normalize the case of the parameter", "# x_txt = x + w*off" ]
[ { "param": "rects", "type": null }, { "param": "ax", "type": null }, { "param": "xpos", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "rects", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "ax", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "xpos", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def autolabel(rects, ax, xpos='center'): xpos = xpos.lower() ha = {'center': 'center', 'right': 'left', 'left': 'right'} offset = {'center': 0.5, 'right': 0.57, 'left': 0.43} for rect in rects: height = rect.get_height() ax.text(rect.get_x() + rect.get_width()*offset[xpos], 1.01*height, '{}'.format(height), ha=ha[xpos], va='bottom')
610,356
732
2f7109b51f8f8ddcd6b0e0a0c8a087863b3b4c87
msyaifullah/Stargate
stargate/decorator_auth.py
[ "MIT" ]
Python
check_device
<not_specific>
def check_device(device): """ This function is called to check if a device id is valid. """ uuid = device.get('Device-Id') if not uuid: return False return True
This function is called to check if a device id is valid.
This function is called to check if a device id is valid.
[ "This", "function", "is", "called", "to", "check", "if", "a", "device", "id", "is", "valid", "." ]
def check_device(device): uuid = device.get('Device-Id') if not uuid: return False return True
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This function is called to check if a device id is valid.
[ "This", "function", "is", "called", "to", "check", "if", "a", "device", "id", "is", "valid", "." ]
[ "\"\"\"\n This function is called to check if a device id is valid.\n\n \"\"\"" ]
[ { "param": "device", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "device", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def check_device(device): uuid = device.get('Device-Id') if not uuid: return False return True
610,357
798
c36e32ccbecfa21558b38b6225165cb007f09915
auroracramer/slurmjobs
slurmjobs/util.py
[ "MIT" ]
Python
make_executable
null
def make_executable(file_path): '''Grant permission to execute a file.''' # https://stackoverflow.com/a/30463972 mode = os.stat(file_path).st_mode mode |= (mode & 0o444) >> 2 os.chmod(file_path, mode)
Grant permission to execute a file.
Grant permission to execute a file.
[ "Grant", "permission", "to", "execute", "a", "file", "." ]
def make_executable(file_path): mode = os.stat(file_path).st_mode mode |= (mode & 0o444) >> 2 os.chmod(file_path, mode)
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Grant permission to execute a file.
[ "Grant", "permission", "to", "execute", "a", "file", "." ]
[ "'''Grant permission to execute a file.'''", "# https://stackoverflow.com/a/30463972" ]
[ { "param": "file_path", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "file_path", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import os def make_executable(file_path): mode = os.stat(file_path).st_mode mode |= (mode & 0o444) >> 2 os.chmod(file_path, mode)
610,358
300
f862e40f7957e74e24227dc2add2f1885ed898d1
KURDO72/DiscordGBcreator
bot/utils/time.py
[ "MIT", "BSD-3-Clause" ]
Python
wait_until
None
async def wait_until(time: datetime.datetime, start: Optional[datetime.datetime] = None) -> None: """ Wait until a given time. :param time: A datetime.datetime object to wait until. :param start: The start from which to calculate the waiting duration. Defaults to UTC time. """ delay = time - (start or datetime.datetime.utcnow()) delay_seconds = delay.total_seconds() # Incorporate a small delay so we don't rapid-fire the event due to time precision errors if delay_seconds > 1.0: await asyncio.sleep(delay_seconds)
Wait until a given time. :param time: A datetime.datetime object to wait until. :param start: The start from which to calculate the waiting duration. Defaults to UTC time.
Wait until a given time.
[ "Wait", "until", "a", "given", "time", "." ]
async def wait_until(time: datetime.datetime, start: Optional[datetime.datetime] = None) -> None: delay = time - (start or datetime.datetime.utcnow()) delay_seconds = delay.total_seconds() if delay_seconds > 1.0: await asyncio.sleep(delay_seconds)
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Wait until a given time.
[ "Wait", "until", "a", "given", "time", "." ]
[ "\"\"\"\n Wait until a given time.\n\n :param time: A datetime.datetime object to wait until.\n :param start: The start from which to calculate the waiting duration. Defaults to UTC time.\n \"\"\"", "# Incorporate a small delay so we don't rapid-fire the event due to time precision errors" ]
[ { "param": "time", "type": "datetime.datetime" }, { "param": "start", "type": "Optional[datetime.datetime]" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "time", "type": "datetime.datetime", "docstring": "A datetime.datetime object to wait until.", "docstring_tokens": [ "A", "datetime", ".", "datetime", "object", "to", "wait", "until", "." ], "default": null, "is_optional": null }, { "identifier": "start", "type": "Optional[datetime.datetime]", "docstring": "The start from which to calculate the waiting duration. Defaults to UTC time.", "docstring_tokens": [ "The", "start", "from", "which", "to", "calculate", "the", "waiting", "duration", ".", "Defaults", "to", "UTC", "time", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import asyncio import datetime async def wait_until(time: datetime.datetime, start: Optional[datetime.datetime] = None) -> None: delay = time - (start or datetime.datetime.utcnow()) delay_seconds = delay.total_seconds() if delay_seconds > 1.0: await asyncio.sleep(delay_seconds)
610,359
985
789a047fa6b69f3fff5c5ab5988fc529ad97bd93
marciogameiro/hysteretic-paths
Hysteretic_Paths/HystereticPaths.py
[ "MIT" ]
Python
reverse_topological_sort
<not_specific>
def reverse_topological_sort(graph): """Return list of vertices in reverse topologically sorted order. """ result = [] explored = set() dfs_stack = [(v,0) for v in graph.vertices] while dfs_stack: (v,i) = dfs_stack.pop() if (v,i) in explored: continue explored.add((v,i)) if i == 0: # preordering visit dfs_stack.extend([(v,1)] + [(u,0) for u in graph.adjacencies(v)]) elif i == 1: # postordering visit result.append(v) return result
Return list of vertices in reverse topologically sorted order.
Return list of vertices in reverse topologically sorted order.
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def reverse_topological_sort(graph): result = [] explored = set() dfs_stack = [(v,0) for v in graph.vertices] while dfs_stack: (v,i) = dfs_stack.pop() if (v,i) in explored: continue explored.add((v,i)) if i == 0: dfs_stack.extend([(v,1)] + [(u,0) for u in graph.adjacencies(v)]) elif i == 1: result.append(v) return result
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Return list of vertices in reverse topologically sorted order.
[ "Return", "list", "of", "vertices", "in", "reverse", "topologically", "sorted", "order", "." ]
[ "\"\"\"Return list of vertices in reverse topologically sorted order.\n \"\"\"", "# preordering visit", "# postordering visit" ]
[ { "param": "graph", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "graph", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def reverse_topological_sort(graph): result = [] explored = set() dfs_stack = [(v,0) for v in graph.vertices] while dfs_stack: (v,i) = dfs_stack.pop() if (v,i) in explored: continue explored.add((v,i)) if i == 0: dfs_stack.extend([(v,1)] + [(u,0) for u in graph.adjacencies(v)]) elif i == 1: result.append(v) return result
610,360
750
2f6750c1f45ddc2d70877f2692d25d494776990f
LPD-EPFL/DifferentialByzantine
train.py
[ "MIT" ]
Python
compute_avg_dev
<not_specific>
def compute_avg_dev(values): """ Compute the arithmetic mean and standard deviation of a list of values. Args: values Iterable of values Returns: Arithmetic mean, standard deviation """ avg = sum(values) / len(values) var = 0 for value in values: var += (value - avg) ** 2 var /= len(values) - 1 return avg, math.sqrt(var)
Compute the arithmetic mean and standard deviation of a list of values. Args: values Iterable of values Returns: Arithmetic mean, standard deviation
Compute the arithmetic mean and standard deviation of a list of values. Args: values Iterable of values Returns: Arithmetic mean, standard deviation
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def compute_avg_dev(values): avg = sum(values) / len(values) var = 0 for value in values: var += (value - avg) ** 2 var /= len(values) - 1 return avg, math.sqrt(var)
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Compute the arithmetic mean and standard deviation of a list of values.
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[ "\"\"\" Compute the arithmetic mean and standard deviation of a list of values.\n Args:\n values Iterable of values\n Returns:\n Arithmetic mean, standard deviation\n \"\"\"" ]
[ { "param": "values", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "values", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import math def compute_avg_dev(values): avg = sum(values) / len(values) var = 0 for value in values: var += (value - avg) ** 2 var /= len(values) - 1 return avg, math.sqrt(var)
610,361
845
f3eaa21c9b46c00ae011261c8e51c07273a16c43
Alig1493/arrow
arrow/util.py
[ "Apache-2.0" ]
Python
is_timestamp
bool
def is_timestamp(value: Any) -> bool: """Check if value is a valid timestamp.""" if isinstance(value, bool): return False if not isinstance(value, (int, float, str)): return False try: float(value) return True except ValueError: return False
Check if value is a valid timestamp.
Check if value is a valid timestamp.
[ "Check", "if", "value", "is", "a", "valid", "timestamp", "." ]
def is_timestamp(value: Any) -> bool: if isinstance(value, bool): return False if not isinstance(value, (int, float, str)): return False try: float(value) return True except ValueError: return False
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Check if value is a valid timestamp.
[ "Check", "if", "value", "is", "a", "valid", "timestamp", "." ]
[ "\"\"\"Check if value is a valid timestamp.\"\"\"" ]
[ { "param": "value", "type": "Any" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "value", "type": "Any", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def is_timestamp(value: Any) -> bool: if isinstance(value, bool): return False if not isinstance(value, (int, float, str)): return False try: float(value) return True except ValueError: return False
610,363
395
dc79e803218ebb2622ca23609eb6109ad2cb5283
thailee/VoiceIP
freePBX/html/admin/modules/core/sip_to_pjsip/astconfigparser.py
[ "MIT" ]
Python
try_option
<not_specific>
def try_option(line): """Parses the line as an option, returning the key/value pair.""" data = re.split('=>?', line, 1) # should split in two (key/val), but either way use first two elements return data[0].rstrip(), data[1].lstrip()
Parses the line as an option, returning the key/value pair.
Parses the line as an option, returning the key/value pair.
[ "Parses", "the", "line", "as", "an", "option", "returning", "the", "key", "/", "value", "pair", "." ]
def try_option(line): data = re.split('=>?', line, 1) return data[0].rstrip(), data[1].lstrip()
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Parses the line as an option, returning the key/value pair.
[ "Parses", "the", "line", "as", "an", "option", "returning", "the", "key", "/", "value", "pair", "." ]
[ "\"\"\"Parses the line as an option, returning the key/value pair.\"\"\"", "# should split in two (key/val), but either way use first two elements" ]
[ { "param": "line", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "line", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import re def try_option(line): data = re.split('=>?', line, 1) return data[0].rstrip(), data[1].lstrip()
610,364
225
01644573328900914e845d0bf65c90c7390628fd
ekr/ietfdb
ietf/ipr/utils.py
[ "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]
Python
iprs_from_docs
<not_specific>
def iprs_from_docs(aliases,**kwargs): """Returns a list of IPRs related to doc aliases""" iprdocrels = [] for alias in aliases: if alias.document.ipr(**kwargs): iprdocrels += alias.document.ipr(**kwargs) return list(set([i.disclosure for i in iprdocrels]))
Returns a list of IPRs related to doc aliases
Returns a list of IPRs related to doc aliases
[ "Returns", "a", "list", "of", "IPRs", "related", "to", "doc", "aliases" ]
def iprs_from_docs(aliases,**kwargs): iprdocrels = [] for alias in aliases: if alias.document.ipr(**kwargs): iprdocrels += alias.document.ipr(**kwargs) return list(set([i.disclosure for i in iprdocrels]))
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Returns a list of IPRs related to doc aliases
[ "Returns", "a", "list", "of", "IPRs", "related", "to", "doc", "aliases" ]
[ "\"\"\"Returns a list of IPRs related to doc aliases\"\"\"" ]
[ { "param": "aliases", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "aliases", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def iprs_from_docs(aliases,**kwargs): iprdocrels = [] for alias in aliases: if alias.document.ipr(**kwargs): iprdocrels += alias.document.ipr(**kwargs) return list(set([i.disclosure for i in iprdocrels]))
610,365
703
e5da316ef81f45e6229bbcb98db7a365c360be07
rosqual/rosdiscover
src/rosdiscover/models/rosdiscover_error_reproducer.py
[ "Apache-2.0" ]
Python
error_reproducer
null
def error_reproducer(c: NodeContext): """ This model is intended for evaluation of rosdiscover on detecting misconfigurations. IT SHOULD NOT BE USED IN GENERAL. This model subscribes and publishes to topics that are passed in as parameters. In this way, we can easily construct a node that can be used to reproduce a configuration error that exists in our database. """ topics = c.read("~topic", []) for t in topics: if t["direction"] == 'pub': c.pub(t["name"], t["type"]) elif t["direction"] == 'sub': c.sub(t["name"], t["type"])
This model is intended for evaluation of rosdiscover on detecting misconfigurations. IT SHOULD NOT BE USED IN GENERAL. This model subscribes and publishes to topics that are passed in as parameters. In this way, we can easily construct a node that can be used to reproduce a configuration error that exists in our database.
This model is intended for evaluation of rosdiscover on detecting misconfigurations. IT SHOULD NOT BE USED IN GENERAL. This model subscribes and publishes to topics that are passed in as parameters. In this way, we can easily construct a node that can be used to reproduce a configuration error that exists in our database.
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def error_reproducer(c: NodeContext): topics = c.read("~topic", []) for t in topics: if t["direction"] == 'pub': c.pub(t["name"], t["type"]) elif t["direction"] == 'sub': c.sub(t["name"], t["type"])
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This model is intended for evaluation of rosdiscover on detecting misconfigurations.
[ "This", "model", "is", "intended", "for", "evaluation", "of", "rosdiscover", "on", "detecting", "misconfigurations", "." ]
[ "\"\"\"\n This model is intended for evaluation of rosdiscover on\n detecting misconfigurations. IT SHOULD NOT BE USED IN GENERAL.\n\n This model subscribes and publishes to topics that are passed\n in as parameters. In this way, we can easily construct a node\n that can be used to reproduce a configuration error that exists\n in our database.\n \"\"\"" ]
[ { "param": "c", "type": "NodeContext" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "c", "type": "NodeContext", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def error_reproducer(c: NodeContext): topics = c.read("~topic", []) for t in topics: if t["direction"] == 'pub': c.pub(t["name"], t["type"]) elif t["direction"] == 'sub': c.sub(t["name"], t["type"])
610,366
240
7f679aacf799bef75b10def7cd3858d1edcd83d2
mauriceDevsM/networking-python-sdk
ibm_cloud_networking_services/user_agent_blocking_rules_v1.py
[ "Apache-2.0" ]
Python
from_dict
'UseragentRuleObjectConfiguration'
def from_dict(cls, _dict: Dict) -> 'UseragentRuleObjectConfiguration': """Initialize a UseragentRuleObjectConfiguration object from a json dictionary.""" args = {} if 'target' in _dict: args['target'] = _dict.get('target') else: raise ValueError('Required property \'target\' not present in UseragentRuleObjectConfiguration JSON') if 'value' in _dict: args['value'] = _dict.get('value') else: raise ValueError('Required property \'value\' not present in UseragentRuleObjectConfiguration JSON') return cls(**args)
Initialize a UseragentRuleObjectConfiguration object from a json dictionary.
Initialize a UseragentRuleObjectConfiguration object from a json dictionary.
[ "Initialize", "a", "UseragentRuleObjectConfiguration", "object", "from", "a", "json", "dictionary", "." ]
def from_dict(cls, _dict: Dict) -> 'UseragentRuleObjectConfiguration': args = {} if 'target' in _dict: args['target'] = _dict.get('target') else: raise ValueError('Required property \'target\' not present in UseragentRuleObjectConfiguration JSON') if 'value' in _dict: args['value'] = _dict.get('value') else: raise ValueError('Required property \'value\' not present in UseragentRuleObjectConfiguration JSON') return cls(**args)
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Initialize a UseragentRuleObjectConfiguration object from a json dictionary.
[ "Initialize", "a", "UseragentRuleObjectConfiguration", "object", "from", "a", "json", "dictionary", "." ]
[ "\"\"\"Initialize a UseragentRuleObjectConfiguration object from a json dictionary.\"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "_dict", "type": "Dict" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "_dict", "type": "Dict", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def from_dict(cls, _dict: Dict) -> 'UseragentRuleObjectConfiguration': args = {} if 'target' in _dict: args['target'] = _dict.get('target') else: raise ValueError('Required property \'target\' not present in UseragentRuleObjectConfiguration JSON') if 'value' in _dict: args['value'] = _dict.get('value') else: raise ValueError('Required property \'value\' not present in UseragentRuleObjectConfiguration JSON') return cls(**args)
610,367
550
639fc70e0a95f5c147500888547ab6da4f169141
denesbartha/tree-graph-labeling
tree_labeling.py
[ "MIT" ]
Python
sort_tree
null
def sort_tree(t, node): """Sorts the given directed tree's branch. Sorting here means that at every level (starting from the root node) the nodes should follow an increasing order at the count of their children. Args: t: a dictionary that contains the nodes of a labeled tree node: the actual node of the tree (Node object) """ node.ordervect = [len(node.children_list)] if node.children_list: # iterate through the children for n in node.children_list: sort_tree(t, t[n]) node.ordervect.append(t[n].ordervect) # if there is more then one children, sort the children list and the ordervect at the same time (the keys are # the elements of the ordervect) if len(node.children_list) > 1: node.children_list, node.ordervect[1:] = (list(x) for x in zip( *sorted(zip(node.children_list, node.ordervect[1:]), key=lambda pair: t[pair[0]].ordervect)))
Sorts the given directed tree's branch. Sorting here means that at every level (starting from the root node) the nodes should follow an increasing order at the count of their children. Args: t: a dictionary that contains the nodes of a labeled tree node: the actual node of the tree (Node object)
Sorts the given directed tree's branch. Sorting here means that at every level (starting from the root node) the nodes should follow an increasing order at the count of their children.
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def sort_tree(t, node): node.ordervect = [len(node.children_list)] if node.children_list: for n in node.children_list: sort_tree(t, t[n]) node.ordervect.append(t[n].ordervect) if len(node.children_list) > 1: node.children_list, node.ordervect[1:] = (list(x) for x in zip( *sorted(zip(node.children_list, node.ordervect[1:]), key=lambda pair: t[pair[0]].ordervect)))
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Sorts the given directed tree's branch.
[ "Sorts", "the", "given", "directed", "tree", "'", "s", "branch", "." ]
[ "\"\"\"Sorts the given directed tree's branch.\n\n Sorting here means that at every level (starting from the root node) the nodes should follow an increasing order\n at the count of their children.\n\n Args:\n t: a dictionary that contains the nodes of a labeled tree\n node: the actual node of the tree (Node object)\n \"\"\"", "# iterate through the children", "# if there is more then one children, sort the children list and the ordervect at the same time (the keys are", "# the elements of the ordervect)" ]
[ { "param": "t", "type": null }, { "param": "node", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "t", "type": null, "docstring": "a dictionary that contains the nodes of a labeled tree", "docstring_tokens": [ "a", "dictionary", "that", "contains", "the", "nodes", "of", "a", "labeled", "tree" ], "default": null, "is_optional": null }, { "identifier": "node", "type": null, "docstring": "the actual node of the tree (Node object)", "docstring_tokens": [ "the", "actual", "node", "of", "the", "tree", "(", "Node", "object", ")" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def sort_tree(t, node): node.ordervect = [len(node.children_list)] if node.children_list: for n in node.children_list: sort_tree(t, t[n]) node.ordervect.append(t[n].ordervect) if len(node.children_list) > 1: node.children_list, node.ordervect[1:] = (list(x) for x in zip( *sorted(zip(node.children_list, node.ordervect[1:]), key=lambda pair: t[pair[0]].ordervect)))
610,368
314
4b44d61b8c0782d6ea3b771c511fdc97628dfde2
tlovett/vvp-validation-scripts
ice_validator/tests/utils/network_roles.py
[ "Apache-2.0", "CC-BY-4.0" ]
Python
is_valid_ipv4_address
<not_specific>
def is_valid_ipv4_address(ip_address): """ check if an ip address of the type ipv4 is valid """ try: socket.inet_pton(socket.AF_INET, ip_address) except AttributeError: try: socket.inet_aton(ip_address) except (OSError, socket.error): return False return ip_address.count(".") == 3 except (OSError, socket.error): return False return True
check if an ip address of the type ipv4 is valid
check if an ip address of the type ipv4 is valid
[ "check", "if", "an", "ip", "address", "of", "the", "type", "ipv4", "is", "valid" ]
def is_valid_ipv4_address(ip_address): try: socket.inet_pton(socket.AF_INET, ip_address) except AttributeError: try: socket.inet_aton(ip_address) except (OSError, socket.error): return False return ip_address.count(".") == 3 except (OSError, socket.error): return False return True
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check if an ip address of the type ipv4 is valid
[ "check", "if", "an", "ip", "address", "of", "the", "type", "ipv4", "is", "valid" ]
[ "\"\"\"\n check if an ip address of the type ipv4\n is valid\n \"\"\"" ]
[ { "param": "ip_address", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "ip_address", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import socket def is_valid_ipv4_address(ip_address): try: socket.inet_pton(socket.AF_INET, ip_address) except AttributeError: try: socket.inet_aton(ip_address) except (OSError, socket.error): return False return ip_address.count(".") == 3 except (OSError, socket.error): return False return True
610,369
1,017
f77136f0f45cadc21d9054e8e5497112756da897
sjvs/immunant-c2rust-copy
scripts/test_translator.py
[ "BSD-3-Clause" ]
Python
readable_directory
str
def readable_directory(directory: str) -> str: """ Check that a directory is exists and is readable """ if not os.path.isdir(directory): msg = "directory:{0} is not a valid path".format(directory) raise argparse.ArgumentTypeError(msg) elif not os.access(directory, os.R_OK): msg = "directory:{0} cannot be read".format(directory) raise argparse.ArgumentTypeError(msg) else: return directory
Check that a directory is exists and is readable
Check that a directory is exists and is readable
[ "Check", "that", "a", "directory", "is", "exists", "and", "is", "readable" ]
def readable_directory(directory: str) -> str: if not os.path.isdir(directory): msg = "directory:{0} is not a valid path".format(directory) raise argparse.ArgumentTypeError(msg) elif not os.access(directory, os.R_OK): msg = "directory:{0} cannot be read".format(directory) raise argparse.ArgumentTypeError(msg) else: return directory
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Check that a directory is exists and is readable
[ "Check", "that", "a", "directory", "is", "exists", "and", "is", "readable" ]
[ "\"\"\"\n Check that a directory is exists and is readable\n \"\"\"" ]
[ { "param": "directory", "type": "str" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "directory", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import argparse import os def readable_directory(directory: str) -> str: if not os.path.isdir(directory): msg = "directory:{0} is not a valid path".format(directory) raise argparse.ArgumentTypeError(msg) elif not os.access(directory, os.R_OK): msg = "directory:{0} cannot be read".format(directory) raise argparse.ArgumentTypeError(msg) else: return directory
610,370
270
f8ee921b09888c34ba9f6ba78d8351db425c7660
GoldJohn/dds
buildscripts/idl/idl/compiler.py
[ "Apache-2.0" ]
Python
_write_dependencies
<not_specific>
def _write_dependencies(spec): # type: (syntax.IDLSpec) -> None """Write a list of dependencies to standard out.""" if not spec.imports: return dependencies = sorted(spec.imports.dependencies) for resolved_file_name in dependencies: print(resolved_file_name)
Write a list of dependencies to standard out.
Write a list of dependencies to standard out.
[ "Write", "a", "list", "of", "dependencies", "to", "standard", "out", "." ]
def _write_dependencies(spec): if not spec.imports: return dependencies = sorted(spec.imports.dependencies) for resolved_file_name in dependencies: print(resolved_file_name)
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Write a list of dependencies to standard out.
[ "Write", "a", "list", "of", "dependencies", "to", "standard", "out", "." ]
[ "# type: (syntax.IDLSpec) -> None", "\"\"\"Write a list of dependencies to standard out.\"\"\"" ]
[ { "param": "spec", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "spec", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _write_dependencies(spec): if not spec.imports: return dependencies = sorted(spec.imports.dependencies) for resolved_file_name in dependencies: print(resolved_file_name)
610,371
232
0d082263c69eba4564d217d6755dbf43d60368f1
kbren/uwnet
ext/sam/SRC/python/module.py
[ "MIT" ]
Python
to_normal_units_nn_columns
<not_specific>
def to_normal_units_nn_columns(ds): """Convert output from uwnet.columns to have proper SI units""" scales = { 'FQT': 1 / 86400 / 1000, 'FSL': 1 / 86400, 'Q1NN': 1 / 86400 / 1000, 'qt': 1 / 1000, 'qtOBS': 1 / 1000, 'Q2NN': 1 / 86400 / 1000, } for key, scale in scales.items(): if key in ds: ds = ds.assign(**{key: ds[key] * scale}) return ds
Convert output from uwnet.columns to have proper SI units
Convert output from uwnet.columns to have proper SI units
[ "Convert", "output", "from", "uwnet", ".", "columns", "to", "have", "proper", "SI", "units" ]
def to_normal_units_nn_columns(ds): scales = { 'FQT': 1 / 86400 / 1000, 'FSL': 1 / 86400, 'Q1NN': 1 / 86400 / 1000, 'qt': 1 / 1000, 'qtOBS': 1 / 1000, 'Q2NN': 1 / 86400 / 1000, } for key, scale in scales.items(): if key in ds: ds = ds.assign(**{key: ds[key] * scale}) return ds
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Convert output from uwnet.columns to have proper SI units
[ "Convert", "output", "from", "uwnet", ".", "columns", "to", "have", "proper", "SI", "units" ]
[ "\"\"\"Convert output from uwnet.columns to have proper SI units\"\"\"" ]
[ { "param": "ds", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "ds", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def to_normal_units_nn_columns(ds): scales = { 'FQT': 1 / 86400 / 1000, 'FSL': 1 / 86400, 'Q1NN': 1 / 86400 / 1000, 'qt': 1 / 1000, 'qtOBS': 1 / 1000, 'Q2NN': 1 / 86400 / 1000, } for key, scale in scales.items(): if key in ds: ds = ds.assign(**{key: ds[key] * scale}) return ds
610,372
142
e1829df6d8e7582b37d68bd1aae07127d0fc8386
albertozurli/mammoth
backbone/__init__.py
[ "MIT" ]
Python
xavier
None
def xavier(m: nn.Module) -> None: """ Applies Xavier initialization to linear modules. :param m: the module to be initialized Example:: >>> net = nn.Sequential(nn.Linear(10, 10), nn.ReLU()) >>> net.apply(xavier) """ if m.__class__.__name__ == 'Linear': fan_in = m.weight.data.size(1) fan_out = m.weight.data.size(0) std = 1.0 * math.sqrt(2.0 / (fan_in + fan_out)) a = math.sqrt(3.0) * std m.weight.data.uniform_(-a, a) if m.bias is not None: m.bias.data.fill_(0.0)
Applies Xavier initialization to linear modules. :param m: the module to be initialized Example:: >>> net = nn.Sequential(nn.Linear(10, 10), nn.ReLU()) >>> net.apply(xavier)
Applies Xavier initialization to linear modules.
[ "Applies", "Xavier", "initialization", "to", "linear", "modules", "." ]
def xavier(m: nn.Module) -> None: if m.__class__.__name__ == 'Linear': fan_in = m.weight.data.size(1) fan_out = m.weight.data.size(0) std = 1.0 * math.sqrt(2.0 / (fan_in + fan_out)) a = math.sqrt(3.0) * std m.weight.data.uniform_(-a, a) if m.bias is not None: m.bias.data.fill_(0.0)
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Applies Xavier initialization to linear modules.
[ "Applies", "Xavier", "initialization", "to", "linear", "modules", "." ]
[ "\"\"\"\n Applies Xavier initialization to linear modules.\n\n :param m: the module to be initialized\n\n Example::\n >>> net = nn.Sequential(nn.Linear(10, 10), nn.ReLU())\n >>> net.apply(xavier)\n \"\"\"" ]
[ { "param": "m", "type": "nn.Module" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "m", "type": "nn.Module", "docstring": "the module to be initialized\nExample::\n>>> net = nn.Sequential(nn.Linear(10, 10), nn.ReLU())\n>>> net.apply(xavier)", "docstring_tokens": [ "the", "module", "to", "be", "initialized", "Example", "::", ">>>", "net", "=", "nn", ".", "Sequential", "(", "nn", ".", "Linear", "(", "10", "10", ")", "nn", ".", "ReLU", "()", ")", ">>>", "net", ".", "apply", "(", "xavier", ")" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import math def xavier(m: nn.Module) -> None: if m.__class__.__name__ == 'Linear': fan_in = m.weight.data.size(1) fan_out = m.weight.data.size(0) std = 1.0 * math.sqrt(2.0 / (fan_in + fan_out)) a = math.sqrt(3.0) * std m.weight.data.uniform_(-a, a) if m.bias is not None: m.bias.data.fill_(0.0)
610,373
644
7f76108af694d99daf50e3fbfb90cb68d18e1ffa
florian-lapp/tmcm-lib-py
tmcm_lib/module_3110/motor.py
[ "MIT" ]
Python
__velocity_external
float
def __velocity_external( cls, pulse_divisor_exponent : int, portion : int, microstep_resolution : int ) -> float : """ Converts a velocity of a motor from a pulse divisor exponent and a portion of `__VELOCITY_PORTIONS` into units of fullsteps per second. """ return ( portion * cls.__VELOCITY_DIVIDEND / ( cls.__VELOCITY_PORTIONS * cls.__DIVISOR_BASE ** pulse_divisor_exponent ) / microstep_resolution )
Converts a velocity of a motor from a pulse divisor exponent and a portion of `__VELOCITY_PORTIONS` into units of fullsteps per second.
Converts a velocity of a motor from a pulse divisor exponent and a portion of `__VELOCITY_PORTIONS` into units of fullsteps per second.
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def __velocity_external( cls, pulse_divisor_exponent : int, portion : int, microstep_resolution : int ) -> float : return ( portion * cls.__VELOCITY_DIVIDEND / ( cls.__VELOCITY_PORTIONS * cls.__DIVISOR_BASE ** pulse_divisor_exponent ) / microstep_resolution )
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Converts a velocity of a motor from a pulse divisor exponent and a portion of `__VELOCITY_PORTIONS` into units of fullsteps per second.
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[ "\"\"\"\r\n Converts a velocity of a motor from a pulse divisor exponent and a portion of\r\n `__VELOCITY_PORTIONS` into units of fullsteps per second.\r\n \"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "pulse_divisor_exponent", "type": "int" }, { "param": "portion", "type": "int" }, { "param": "microstep_resolution", "type": "int" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "pulse_divisor_exponent", "type": "int", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "portion", "type": "int", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "microstep_resolution", "type": "int", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def __velocity_external( cls, pulse_divisor_exponent : int, portion : int, microstep_resolution : int ) -> float : return ( portion * cls.__VELOCITY_DIVIDEND / ( cls.__VELOCITY_PORTIONS * cls.__DIVISOR_BASE ** pulse_divisor_exponent ) / microstep_resolution )
610,374
308
f70d8714e61a4d5271dd1487b84f2e80f3c91e72
alexbarcelo/daos
src/tests/ftest/util/check_for_pool.py
[ "Apache-2.0" ]
Python
check_for_pool
<not_specific>
def check_for_pool(host, uuid): """ Function to check if pool folder exist on server Args: host: Server host name uuid: Pool uuid to check if exists return: resp: subprocess return code """ cmd = "test -e /mnt/daos/" + uuid resp = subprocess.call(["ssh", host, cmd]) if resp == 0: print ('%s exists' %uuid) else: print ('%s does not exist' %uuid) return resp
Function to check if pool folder exist on server Args: host: Server host name uuid: Pool uuid to check if exists return: resp: subprocess return code
Function to check if pool folder exist on server
[ "Function", "to", "check", "if", "pool", "folder", "exist", "on", "server" ]
def check_for_pool(host, uuid): cmd = "test -e /mnt/daos/" + uuid resp = subprocess.call(["ssh", host, cmd]) if resp == 0: print ('%s exists' %uuid) else: print ('%s does not exist' %uuid) return resp
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Function to check if pool folder exist on server
[ "Function", "to", "check", "if", "pool", "folder", "exist", "on", "server" ]
[ "\"\"\"\n Function to check if pool folder exist on server\n Args:\n host: Server host name\n uuid: Pool uuid to check if exists\n return:\n resp: subprocess return code\n \"\"\"" ]
[ { "param": "host", "type": null }, { "param": "uuid", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "host", "type": null, "docstring": "Server host name", "docstring_tokens": [ "Server", "host", "name" ], "default": null, "is_optional": null }, { "identifier": "uuid", "type": null, "docstring": "Pool uuid to check if exists", "docstring_tokens": [ "Pool", "uuid", "to", "check", "if", "exists" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import subprocess def check_for_pool(host, uuid): cmd = "test -e /mnt/daos/" + uuid resp = subprocess.call(["ssh", host, cmd]) if resp == 0: print ('%s exists' %uuid) else: print ('%s does not exist' %uuid) return resp
610,376
599
d77bf1bf9518c9df1151dd647ac04a35bf6a19c2
drew-loukusa/ID-CAS
tree.py
[ "MIT" ]
Python
must_be
<not_specific>
def must_be(_, c): """ NOT IN USE ANYMORE: I changed how my parsing works to not use this. I should probably have some sort of check, but I'll worry about that later. It works for now. Mostly used to ensure if there is an opening brace, then there is a matching closing brace somewhere in the expression. This function also saves the location of the last found closing brace into MatchParenIndex. If we start looking for another closing brace, then we start looking from MatchParenIndex. """ #print("MatchParenIndex",MatchParenIndex) index = _.NextTokenIndex if _.MatchParenIndex > _.NextTokenIndex: index = _.MatchParenIndex #print("* _.NextToken",_.NextToken) #print("* NextTokenIndex", NextTokenIndex) #print("* Index", index) for i in range(index, len(_.token_stream)): if _.token_stream[i][1] == c: _.MatchParenIndex = i #GetNextToken() #std.write("Found closing paren") #print("* _.NextToken",_.NextToken) #print("* NextTokenIndex", NextTokenIndex) #print("* Index", index) return True raise Exception("Missing closing '{}'".format(c))
NOT IN USE ANYMORE: I changed how my parsing works to not use this. I should probably have some sort of check, but I'll worry about that later. It works for now. Mostly used to ensure if there is an opening brace, then there is a matching closing brace somewhere in the expression. This function also saves the location of the last found closing brace into MatchParenIndex. If we start looking for another closing brace, then we start looking from MatchParenIndex.
Mostly used to ensure if there is an opening brace, then there is a matching closing brace somewhere in the expression. This function also saves the location of the last found closing brace into MatchParenIndex. If we start looking for another closing brace, then we start looking from MatchParenIndex.
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def must_be(_, c): index = _.NextTokenIndex if _.MatchParenIndex > _.NextTokenIndex: index = _.MatchParenIndex for i in range(index, len(_.token_stream)): if _.token_stream[i][1] == c: _.MatchParenIndex = i return True raise Exception("Missing closing '{}'".format(c))
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NOT IN USE ANYMORE: I changed how my parsing works to not use this.
[ "NOT", "IN", "USE", "ANYMORE", ":", "I", "changed", "how", "my", "parsing", "works", "to", "not", "use", "this", "." ]
[ "\"\"\" \n NOT IN USE ANYMORE: I changed how my parsing works to not use this.\n\n I should probably have some sort of check, but I'll worry about that\n later. It works for now.\n\n Mostly used to ensure if there is an opening brace, then there is \n a matching closing brace somewhere in the expression. \n\n This function also saves the location of the last found closing brace\n into MatchParenIndex. If we start looking for another closing brace, \n then we start looking from MatchParenIndex.\n \"\"\"", "#print(\"MatchParenIndex\",MatchParenIndex)", "#print(\"* _.NextToken\",_.NextToken)", "#print(\"* NextTokenIndex\", NextTokenIndex)", "#print(\"* Index\", index)", "#GetNextToken()", "#std.write(\"Found closing paren\")", "#print(\"* _.NextToken\",_.NextToken)", "#print(\"* NextTokenIndex\", NextTokenIndex)", "#print(\"* Index\", index)" ]
[ { "param": "_", "type": null }, { "param": "c", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "_", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "c", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def must_be(_, c): index = _.NextTokenIndex if _.MatchParenIndex > _.NextTokenIndex: index = _.MatchParenIndex for i in range(index, len(_.token_stream)): if _.token_stream[i][1] == c: _.MatchParenIndex = i return True raise Exception("Missing closing '{}'".format(c))
610,377
29
7870bff228bca4a16194530620b94ddefabda054
discopop-project/discopop-profiler
discopop_explorer/utils.py
[ "BSD-3-Clause" ]
Python
is_depend_in_out
bool
def is_depend_in_out(var: Variable, in_deps: List[Tuple[str, str, Dependency]], out_deps: List[Tuple[str, str, Dependency]]) -> bool: """there is an in and out dependency :param var: Variable :param in_deps: in dependencies :param out_deps: out dependencies :return: true if dependency is both in and out """ for in_dep in in_deps: for out_dep in out_deps: if var.name == in_dep[2].var_name and in_dep[2].var_name == out_dep[2].var_name: return True return False
there is an in and out dependency :param var: Variable :param in_deps: in dependencies :param out_deps: out dependencies :return: true if dependency is both in and out
there is an in and out dependency
[ "there", "is", "an", "in", "and", "out", "dependency" ]
def is_depend_in_out(var: Variable, in_deps: List[Tuple[str, str, Dependency]], out_deps: List[Tuple[str, str, Dependency]]) -> bool: for in_dep in in_deps: for out_dep in out_deps: if var.name == in_dep[2].var_name and in_dep[2].var_name == out_dep[2].var_name: return True return False
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there is an in and out dependency
[ "there", "is", "an", "in", "and", "out", "dependency" ]
[ "\"\"\"there is an in and out dependency\n\n :param var: Variable\n :param in_deps: in dependencies\n :param out_deps: out dependencies\n :return: true if dependency is both in and out\n \"\"\"" ]
[ { "param": "var", "type": "Variable" }, { "param": "in_deps", "type": "List[Tuple[str, str, Dependency]]" }, { "param": "out_deps", "type": "List[Tuple[str, str, Dependency]]" } ]
{ "returns": [ { "docstring": "true if dependency is both in and out", "docstring_tokens": [ "true", "if", "dependency", "is", "both", "in", "and", "out" ], "type": null } ], "raises": [], "params": [ { "identifier": "var", "type": "Variable", "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null }, { "identifier": "in_deps", "type": "List[Tuple[str, str, Dependency]]", "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null }, { "identifier": "out_deps", "type": "List[Tuple[str, str, Dependency]]", "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def is_depend_in_out(var: Variable, in_deps: List[Tuple[str, str, Dependency]], out_deps: List[Tuple[str, str, Dependency]]) -> bool: for in_dep in in_deps: for out_dep in out_deps: if var.name == in_dep[2].var_name and in_dep[2].var_name == out_dep[2].var_name: return True return False
610,378
832
0bd98deac4297ab61d37a82c9ca0b56409f10fc0
changusmc/stone
stone/ir/data_types.py
[ "MIT" ]
Python
record_custom_annotation_imports
null
def record_custom_annotation_imports(annotation, namespace): """ Records imports for custom annotations in the given namespace. """ # first, check the annotation *type* if annotation.annotation_type.namespace.name != namespace.name: namespace.add_imported_namespace( annotation.annotation_type.namespace, imported_annotation_type=True) # second, check if we need to import the annotation itself # the annotation namespace is currently not actually used in the # backends, which reconstruct the annotation from the annotation # type directly. This could be changed in the future, and at # the IR level it makes sense to include the dependency if annotation.namespace.name != namespace.name: namespace.add_imported_namespace( annotation.namespace, imported_annotation=True)
Records imports for custom annotations in the given namespace.
Records imports for custom annotations in the given namespace.
[ "Records", "imports", "for", "custom", "annotations", "in", "the", "given", "namespace", "." ]
def record_custom_annotation_imports(annotation, namespace): if annotation.annotation_type.namespace.name != namespace.name: namespace.add_imported_namespace( annotation.annotation_type.namespace, imported_annotation_type=True) if annotation.namespace.name != namespace.name: namespace.add_imported_namespace( annotation.namespace, imported_annotation=True)
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Records imports for custom annotations in the given namespace.
[ "Records", "imports", "for", "custom", "annotations", "in", "the", "given", "namespace", "." ]
[ "\"\"\"\n Records imports for custom annotations in the given namespace.\n\n \"\"\"", "# first, check the annotation *type*", "# second, check if we need to import the annotation itself", "# the annotation namespace is currently not actually used in the", "# backends, which reconstruct the annotation from the annotation", "# type directly. This could be changed in the future, and at", "# the IR level it makes sense to include the dependency" ]
[ { "param": "annotation", "type": null }, { "param": "namespace", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "annotation", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "namespace", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def record_custom_annotation_imports(annotation, namespace): if annotation.annotation_type.namespace.name != namespace.name: namespace.add_imported_namespace( annotation.annotation_type.namespace, imported_annotation_type=True) if annotation.namespace.name != namespace.name: namespace.add_imported_namespace( annotation.namespace, imported_annotation=True)
610,379
832
a2445768bf596acff7bf73796f395883036cb6c8
Jamaliela/The-Game-of-Nim
jamalie_alfaro_a05.py
[ "MIT" ]
Python
player_2_turn
<not_specific>
def player_2_turn(num_balls): """ This function divides the number of balls remaining by 5 with modulus so that the computer ends up winning by always taking the remainder of the division. :param num_balls: integer. number of balls that changes as user and computer pick :return: num_balls """ player2 = num_balls % 5 # modulus to calcuate how many balls for the computer to pick if player2 == 0: # if the result from modulus is 0 then something else happens player2 = random.randrange(1, 4) # a random number is assigned to player2 num_balls = num_balls - player2 # recalculating the number of balls after the computer picks print("The computer takes", player2, "balls") # telling user how many balls they pick print("there are", num_balls, "left") # telling user how many balls are remaining return num_balls
This function divides the number of balls remaining by 5 with modulus so that the computer ends up winning by always taking the remainder of the division. :param num_balls: integer. number of balls that changes as user and computer pick :return: num_balls
This function divides the number of balls remaining by 5 with modulus so that the computer ends up winning by always taking the remainder of the division.
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def player_2_turn(num_balls): player2 = num_balls % 5 if player2 == 0: player2 = random.randrange(1, 4) num_balls = num_balls - player2 print("The computer takes", player2, "balls") print("there are", num_balls, "left") return num_balls
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This function divides the number of balls remaining by 5 with modulus so that the computer ends up winning by always taking the remainder of the division.
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[ "\"\"\"\n This function divides the number of balls remaining by 5 with modulus so that the computer ends up winning by always taking the remainder of the division.\n :param num_balls: integer. number of balls that changes as user and computer pick\n :return: num_balls\n \"\"\"", "# modulus to calcuate how many balls for the computer to pick", "# if the result from modulus is 0 then something else happens", "# a random number is assigned to player2", "# recalculating the number of balls after the computer picks", "# telling user how many balls they pick", "# telling user how many balls are remaining" ]
[ { "param": "num_balls", "type": null } ]
{ "returns": [ { "docstring": null, "docstring_tokens": [ "None" ], "type": null } ], "raises": [], "params": [ { "identifier": "num_balls", "type": null, "docstring": "integer. number of balls that changes as user and computer pick", "docstring_tokens": [ "integer", ".", "number", "of", "balls", "that", "changes", "as", "user", "and", "computer", "pick" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import random def player_2_turn(num_balls): player2 = num_balls % 5 if player2 == 0: player2 = random.randrange(1, 4) num_balls = num_balls - player2 print("The computer takes", player2, "balls") print("there are", num_balls, "left") return num_balls
610,380
259
02d1eae971e56bfa1d46e28cef9a7dd256607891
tiero/snet-cli
snet_cli/utils.py
[ "MIT" ]
Python
abi_get_element_by_name
<not_specific>
def abi_get_element_by_name(abi, name): """ Return element of abi (return None if fails to find) """ if (abi and "abi" in abi): for a in abi["abi"]: if ("name" in a and a["name"] == name): return a return None
Return element of abi (return None if fails to find)
Return element of abi (return None if fails to find)
[ "Return", "element", "of", "abi", "(", "return", "None", "if", "fails", "to", "find", ")" ]
def abi_get_element_by_name(abi, name): if (abi and "abi" in abi): for a in abi["abi"]: if ("name" in a and a["name"] == name): return a return None
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Return element of abi (return None if fails to find)
[ "Return", "element", "of", "abi", "(", "return", "None", "if", "fails", "to", "find", ")" ]
[ "\"\"\" Return element of abi (return None if fails to find) \"\"\"" ]
[ { "param": "abi", "type": null }, { "param": "name", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "abi", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "name", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def abi_get_element_by_name(abi, name): if (abi and "abi" in abi): for a in abi["abi"]: if ("name" in a and a["name"] == name): return a return None
610,381
13
d6912afe42f431756c06863bf360535402fd25c5
eichin/thok-ztwitgw
ztwitgw.py
[ "MIT" ]
Python
display_rate_limit
<not_specific>
def display_rate_limit(tag, limit): """Display rate limit if it isn't at maximum; return available count for convenience""" if limit["remaining"] != limit["limit"]: print(limit["remaining"], "out of", limit["limit"], tag, "queries available") reset_time = limit["reset"] print(" Will reset in", reset_time - time.time(), "seconds, at", time.ctime(reset_time)) return limit["remaining"]
Display rate limit if it isn't at maximum; return available count for convenience
Display rate limit if it isn't at maximum; return available count for convenience
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def display_rate_limit(tag, limit): if limit["remaining"] != limit["limit"]: print(limit["remaining"], "out of", limit["limit"], tag, "queries available") reset_time = limit["reset"] print(" Will reset in", reset_time - time.time(), "seconds, at", time.ctime(reset_time)) return limit["remaining"]
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Display rate limit if it isn't at maximum; return available count for convenience
[ "Display", "rate", "limit", "if", "it", "isn", "'", "t", "at", "maximum", ";", "return", "available", "count", "for", "convenience" ]
[ "\"\"\"Display rate limit if it isn't at maximum; return available count for convenience\"\"\"" ]
[ { "param": "tag", "type": null }, { "param": "limit", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "tag", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "limit", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import time def display_rate_limit(tag, limit): if limit["remaining"] != limit["limit"]: print(limit["remaining"], "out of", limit["limit"], tag, "queries available") reset_time = limit["reset"] print(" Will reset in", reset_time - time.time(), "seconds, at", time.ctime(reset_time)) return limit["remaining"]
610,382
98
d26d7a6f9ce5839d9642d2081579251531f2c276
fkooman/python-eduvpn
eduvpn/storage.py
[ "Apache-2.0" ]
Python
write_config
null
def write_config(config: str, private_key: str, certificate: str, target: Path): """ Write the configuration to target. """ with open(target, mode='w+t') as f: f.writelines(config) f.writelines(f"\n<key>\n{private_key}\n</key>\n") f.writelines(f"\n<cert>\n{certificate}\n</cert>\n")
Write the configuration to target.
Write the configuration to target.
[ "Write", "the", "configuration", "to", "target", "." ]
def write_config(config: str, private_key: str, certificate: str, target: Path): with open(target, mode='w+t') as f: f.writelines(config) f.writelines(f"\n<key>\n{private_key}\n</key>\n") f.writelines(f"\n<cert>\n{certificate}\n</cert>\n")
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Write the configuration to target.
[ "Write", "the", "configuration", "to", "target", "." ]
[ "\"\"\"\n Write the configuration to target.\n \"\"\"" ]
[ { "param": "config", "type": "str" }, { "param": "private_key", "type": "str" }, { "param": "certificate", "type": "str" }, { "param": "target", "type": "Path" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "config", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "private_key", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "certificate", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "target", "type": "Path", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def write_config(config: str, private_key: str, certificate: str, target: Path): with open(target, mode='w+t') as f: f.writelines(config) f.writelines(f"\n<key>\n{private_key}\n</key>\n") f.writelines(f"\n<cert>\n{certificate}\n</cert>\n")
610,383
507
2e1ba8922cf58bb90287d078e5a54ad5ae1af3bf
matteoNunz/ImmunoPoli
Database/DataBaseGenerator.py
[ "MIT" ]
Python
readSurnames
<not_specific>
def readSurnames(): """ Method that reads the possible surnames from a file :return: a list containing the surnames """ surnamesRead = [] with open("Files/Surnames.txt", 'r', encoding='utf8') as f: for line in f: if line == "\n": continue surnamesRead.append(line.rstrip('\n').rstrip().lstrip()) f.close() return surnamesRead
Method that reads the possible surnames from a file :return: a list containing the surnames
Method that reads the possible surnames from a file
[ "Method", "that", "reads", "the", "possible", "surnames", "from", "a", "file" ]
def readSurnames(): surnamesRead = [] with open("Files/Surnames.txt", 'r', encoding='utf8') as f: for line in f: if line == "\n": continue surnamesRead.append(line.rstrip('\n').rstrip().lstrip()) f.close() return surnamesRead
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Method that reads the possible surnames from a file
[ "Method", "that", "reads", "the", "possible", "surnames", "from", "a", "file" ]
[ "\"\"\"\n Method that reads the possible surnames from a file\n :return: a list containing the surnames\n \"\"\"" ]
[]
{ "returns": [ { "docstring": "a list containing the surnames", "docstring_tokens": [ "a", "list", "containing", "the", "surnames" ], "type": null } ], "raises": [], "params": [], "outlier_params": [], "others": [] }
def readSurnames(): surnamesRead = [] with open("Files/Surnames.txt", 'r', encoding='utf8') as f: for line in f: if line == "\n": continue surnamesRead.append(line.rstrip('\n').rstrip().lstrip()) f.close() return surnamesRead
610,384
35
d7c17c92fbe22c08e17a62d517cc379af6e6d031
hermajan/algorithms
src/algorithms/math/Number.py
[ "MIT" ]
Python
is_odd
<not_specific>
def is_odd(number): """ Checks if the number is odd. :param number: Number. :return: True if is odd, false if it is even. """ return (number % 2 != 0)
Checks if the number is odd. :param number: Number. :return: True if is odd, false if it is even.
Checks if the number is odd.
[ "Checks", "if", "the", "number", "is", "odd", "." ]
def is_odd(number): return (number % 2 != 0)
[ "def", "is_odd", "(", "number", ")", ":", "return", "(", "number", "%", "2", "!=", "0", ")" ]
Checks if the number is odd.
[ "Checks", "if", "the", "number", "is", "odd", "." ]
[ "\"\"\"\n\t\tChecks if the number is odd.\n\t\t:param number: Number.\n\t\t:return: True if is odd, false if it is even.\n\t\t\"\"\"" ]
[ { "param": "number", "type": null } ]
{ "returns": [ { "docstring": "True if is odd, false if it is even.", "docstring_tokens": [ "True", "if", "is", "odd", "false", "if", "it", "is", "even", "." ], "type": null } ], "raises": [], "params": [ { "identifier": "number", "type": null, "docstring": null, "docstring_tokens": [ "None" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def is_odd(number): return (number % 2 != 0)
610,385
529
9fc18168e0b22faeca6c11a4eb9c2a7ca24a9200
hutoTUM/MakeAdditions
makeadditions/parse.py
[ "Apache-2.0" ]
Python
check_debugshell_and_makefile
null
def check_debugshell_and_makefile(makeoutput): """ Check if the Makefile output can be parsed and transformed automatically. Raises exceptions, if something looks weird """ # makeoutput must start with directory information. # Reason: The --print-directory flag for make flag was given if (not makeoutput or not (makeoutput.startswith("make: Entering directory ") or makeoutput.startswith("make[1]: Entering directory "))): raise Exception( "Directory changes cannot be recognized: " + makeoutput[0:35])
Check if the Makefile output can be parsed and transformed automatically. Raises exceptions, if something looks weird
Check if the Makefile output can be parsed and transformed automatically. Raises exceptions, if something looks weird
[ "Check", "if", "the", "Makefile", "output", "can", "be", "parsed", "and", "transformed", "automatically", ".", "Raises", "exceptions", "if", "something", "looks", "weird" ]
def check_debugshell_and_makefile(makeoutput): if (not makeoutput or not (makeoutput.startswith("make: Entering directory ") or makeoutput.startswith("make[1]: Entering directory "))): raise Exception( "Directory changes cannot be recognized: " + makeoutput[0:35])
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Check if the Makefile output can be parsed and transformed automatically.
[ "Check", "if", "the", "Makefile", "output", "can", "be", "parsed", "and", "transformed", "automatically", "." ]
[ "\"\"\"\n Check if the Makefile output can be parsed and transformed automatically.\n Raises exceptions, if something looks weird\n \"\"\"", "# makeoutput must start with directory information.", "# Reason: The --print-directory flag for make flag was given" ]
[ { "param": "makeoutput", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "makeoutput", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def check_debugshell_and_makefile(makeoutput): if (not makeoutput or not (makeoutput.startswith("make: Entering directory ") or makeoutput.startswith("make[1]: Entering directory "))): raise Exception( "Directory changes cannot be recognized: " + makeoutput[0:35])
610,386
147
fc5586d3025f0b07fd7a7c919dd2b3fb4f742d53
Gee-3/pygna
pygna/converters.py
[ "MIT" ]
Python
convert_e2s
list
def convert_e2s(cls, geneset: pd.DataFrame, tsv_data: pd.DataFrame, entrez_col: str = "NCBI Gene ID", symbol_col: str = "Approved symbol") -> list: """ Method to convert the entrez genes to symbols :param tsv_data: the dataframe to work on :param symbol_col: the column containing the symbols :param entrez_col: the column containing the entrez ID :param geneset: column containing the entrez to convert :return: list containing the string names Example _______ >>> gmt_data = rc.ReadGmt(".gmt", True).get_data() >>> converted = [] >>> for k, d in gmt_data.items(): >>> converted[k] = Converters.convert_e2s(d["genes"], tsv_data,entrez_col, symbol_col) """ logging.info("Converting Entrez ID -> Symbols") unknown_counter = 0 geneset_symbol = [] for i in geneset: name = tsv_data[tsv_data[entrez_col] == int(i)][symbol_col].values.tolist() if len(name) > 0: geneset_symbol.append(str(name[0])) else: unknown_counter += 1 geneset_symbol.append("<" + i + ">") if unknown_counter > 0: logging.warning("%d/%d terms that couldn't be mapped" % (unknown_counter, len(geneset))) return geneset_symbol
Method to convert the entrez genes to symbols :param tsv_data: the dataframe to work on :param symbol_col: the column containing the symbols :param entrez_col: the column containing the entrez ID :param geneset: column containing the entrez to convert :return: list containing the string names Example _______ >>> gmt_data = rc.ReadGmt(".gmt", True).get_data() >>> converted = [] >>> for k, d in gmt_data.items(): >>> converted[k] = Converters.convert_e2s(d["genes"], tsv_data,entrez_col, symbol_col)
Method to convert the entrez genes to symbols
[ "Method", "to", "convert", "the", "entrez", "genes", "to", "symbols" ]
def convert_e2s(cls, geneset: pd.DataFrame, tsv_data: pd.DataFrame, entrez_col: str = "NCBI Gene ID", symbol_col: str = "Approved symbol") -> list: logging.info("Converting Entrez ID -> Symbols") unknown_counter = 0 geneset_symbol = [] for i in geneset: name = tsv_data[tsv_data[entrez_col] == int(i)][symbol_col].values.tolist() if len(name) > 0: geneset_symbol.append(str(name[0])) else: unknown_counter += 1 geneset_symbol.append("<" + i + ">") if unknown_counter > 0: logging.warning("%d/%d terms that couldn't be mapped" % (unknown_counter, len(geneset))) return geneset_symbol
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Method to convert the entrez genes to symbols
[ "Method", "to", "convert", "the", "entrez", "genes", "to", "symbols" ]
[ "\"\"\"\n Method to convert the entrez genes to symbols\n\n :param tsv_data: the dataframe to work on\n :param symbol_col: the column containing the symbols\n :param entrez_col: the column containing the entrez ID\n :param geneset: column containing the entrez to convert\n :return: list containing the string names\n\n Example\n _______\n >>> gmt_data = rc.ReadGmt(\".gmt\", True).get_data()\n >>> converted = []\n >>> for k, d in gmt_data.items():\n >>> converted[k] = Converters.convert_e2s(d[\"genes\"], tsv_data,entrez_col, symbol_col)\n\n \"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "geneset", "type": "pd.DataFrame" }, { "param": "tsv_data", "type": "pd.DataFrame" }, { "param": "entrez_col", "type": "str" }, { "param": "symbol_col", "type": "str" } ]
{ "returns": [ { "docstring": "list containing the string names\nExample\n>>> gmt_data = rc.ReadGmt(\".gmt\", True).get_data()\n>>> converted = []\n>>> for k, d in gmt_data.items():\n>>> converted[k] = Converters.convert_e2s(d[\"genes\"], tsv_data,entrez_col, symbol_col)", "docstring_tokens": [ "list", "containing", "the", "string", "names", "Example", ">>>", "gmt_data", "=", "rc", ".", "ReadGmt", "(", "\"", ".", "gmt", "\"", "True", ")", ".", "get_data", "()", ">>>", "converted", "=", "[]", ">>>", "for", "k", "d", "in", "gmt_data", ".", "items", "()", ":", ">>>", "converted", "[", "k", "]", "=", "Converters", ".", "convert_e2s", "(", "d", "[", "\"", "genes", "\"", "]", "tsv_data", "entrez_col", "symbol_col", ")" ], "type": null } ], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "geneset", "type": "pd.DataFrame", "docstring": "column containing the entrez to convert", "docstring_tokens": [ "column", "containing", "the", "entrez", "to", "convert" ], "default": null, "is_optional": null }, { "identifier": "tsv_data", "type": "pd.DataFrame", "docstring": "the dataframe to work on", "docstring_tokens": [ "the", "dataframe", "to", "work", "on" ], "default": null, "is_optional": null }, { "identifier": "entrez_col", "type": "str", "docstring": "the column containing the entrez ID", "docstring_tokens": [ "the", "column", "containing", "the", "entrez", "ID" ], "default": null, "is_optional": null }, { "identifier": "symbol_col", "type": "str", "docstring": "the column containing the symbols", "docstring_tokens": [ "the", "column", "containing", "the", "symbols" ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import logging def convert_e2s(cls, geneset: pd.DataFrame, tsv_data: pd.DataFrame, entrez_col: str = "NCBI Gene ID", symbol_col: str = "Approved symbol") -> list: logging.info("Converting Entrez ID -> Symbols") unknown_counter = 0 geneset_symbol = [] for i in geneset: name = tsv_data[tsv_data[entrez_col] == int(i)][symbol_col].values.tolist() if len(name) > 0: geneset_symbol.append(str(name[0])) else: unknown_counter += 1 geneset_symbol.append("<" + i + ">") if unknown_counter > 0: logging.warning("%d/%d terms that couldn't be mapped" % (unknown_counter, len(geneset))) return geneset_symbol
610,387
54
0224f727a16636369024c0b8fb2b16edd5d26e4f
AtharvaPusalkar/MITx-6.00.1x
PSET 4/problem-4.py
[ "MIT" ]
Python
calculateHandlen
<not_specific>
def calculateHandlen(hand): """ Returns the length (number of letters) in the current hand. hand: dictionary (string int) returns: integer """ count = 0 for i in hand: count += hand[i] return count
Returns the length (number of letters) in the current hand. hand: dictionary (string int) returns: integer
Returns the length (number of letters) in the current hand. hand: dictionary (string int) returns: integer
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def calculateHandlen(hand): count = 0 for i in hand: count += hand[i] return count
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Returns the length (number of letters) in the current hand.
[ "Returns", "the", "length", "(", "number", "of", "letters", ")", "in", "the", "current", "hand", "." ]
[ "\"\"\" \n Returns the length (number of letters) in the current hand.\n \n hand: dictionary (string int)\n returns: integer\n \"\"\"" ]
[ { "param": "hand", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "hand", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def calculateHandlen(hand): count = 0 for i in hand: count += hand[i] return count
610,388
413
59d9d976f42cf0a6cbb0dd903908ba0d941f9d9b
choderalab/fah-xchem
fah_xchem/analysis/extract_work.py
[ "MIT" ]
Python
_get_num_steps
int
def _get_num_steps(df: pd.DataFrame) -> int: """ Get the number of steps described in the dataframe Parameters ---------- df : pandas.DataFrame The dataframe containing `Step` column Returns ------- n_steps : int The number of steps described in the dataframe """ if df.empty: raise ValueError("Empty dataframe") step = df["Step"].astype(int) return int(step.iloc[-1] - step.iloc[0])
Get the number of steps described in the dataframe Parameters ---------- df : pandas.DataFrame The dataframe containing `Step` column Returns ------- n_steps : int The number of steps described in the dataframe
Get the number of steps described in the dataframe Parameters df : pandas.DataFrame The dataframe containing `Step` column Returns n_steps : int The number of steps described in the dataframe
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def _get_num_steps(df: pd.DataFrame) -> int: if df.empty: raise ValueError("Empty dataframe") step = df["Step"].astype(int) return int(step.iloc[-1] - step.iloc[0])
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Get the number of steps described in the dataframe Parameters
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[ "\"\"\"\n Get the number of steps described in the dataframe\n\n Parameters\n ----------\n df : pandas.DataFrame\n The dataframe containing `Step` column\n\n Returns\n -------\n n_steps : int\n The number of steps described in the dataframe\n\n \"\"\"" ]
[ { "param": "df", "type": "pd.DataFrame" } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "df", "type": "pd.DataFrame", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _get_num_steps(df: pd.DataFrame) -> int: if df.empty: raise ValueError("Empty dataframe") step = df["Step"].astype(int) return int(step.iloc[-1] - step.iloc[0])
610,389
504
acb87cb757722d2362b46268c1505ec8458db96d
getslash/confetti
confetti/config.py
[ "BSD-3-Clause" ]
Python
from_string
<not_specific>
def from_string(cls, s, namespace=None): """ Initializes the config from a string. The string is expected to contain the config as a variable named ``CONFIG``. """ if namespace is None: namespace = {} else: namespace = dict(namespace) exec(s, namespace) return cls(namespace['CONFIG'])
Initializes the config from a string. The string is expected to contain the config as a variable named ``CONFIG``.
Initializes the config from a string. The string is expected to contain the config as a variable named ``CONFIG``.
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def from_string(cls, s, namespace=None): if namespace is None: namespace = {} else: namespace = dict(namespace) exec(s, namespace) return cls(namespace['CONFIG'])
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Initializes the config from a string.
[ "Initializes", "the", "config", "from", "a", "string", "." ]
[ "\"\"\"\n Initializes the config from a string. The string is expected to contain the config as a variable named ``CONFIG``.\n \"\"\"" ]
[ { "param": "cls", "type": null }, { "param": "s", "type": null }, { "param": "namespace", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "cls", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "s", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "namespace", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def from_string(cls, s, namespace=None): if namespace is None: namespace = {} else: namespace = dict(namespace) exec(s, namespace) return cls(namespace['CONFIG'])
610,390
127
2f88875e58598aa36b9eb07a6b3e26a667cc3692
Saharae/Final-Project-Group2
joshua-ting-individual-project/Code/my_other_work.py
[ "MIT" ]
Python
ratio_capital_letters
<not_specific>
def ratio_capital_letters(df, col_name): ''' Function to get ratio of interesting characters in string Parameter --------- df: dataset col_name: column name to get number of words Return df: dataset transformed ''' df['{}_ratio_capital_letters'.format(col_name)] = df[col_name].map(lambda x: len([i for i in x if i.isupper()])/len([i for i in x]) if isinstance(x, str) else 0) # print(df[[col_name, '{}_ratio_capital_letters'.format(col_name)]].head()) return df
Function to get ratio of interesting characters in string Parameter --------- df: dataset col_name: column name to get number of words Return df: dataset transformed
Function to get ratio of interesting characters in string Parameter dataset col_name: column name to get number of words Return df: dataset transformed
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def ratio_capital_letters(df, col_name): df['{}_ratio_capital_letters'.format(col_name)] = df[col_name].map(lambda x: len([i for i in x if i.isupper()])/len([i for i in x]) if isinstance(x, str) else 0) return df
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Function to get ratio of interesting characters in string Parameter
[ "Function", "to", "get", "ratio", "of", "interesting", "characters", "in", "string", "Parameter" ]
[ "'''\n Function to get ratio of interesting characters in string\n\n Parameter\n ---------\n df: dataset\n col_name: column name to get number of words\n\n Return\n df: dataset transformed\n '''", "# print(df[[col_name, '{}_ratio_capital_letters'.format(col_name)]].head())" ]
[ { "param": "df", "type": null }, { "param": "col_name", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "df", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "col_name", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def ratio_capital_letters(df, col_name): df['{}_ratio_capital_letters'.format(col_name)] = df[col_name].map(lambda x: len([i for i in x if i.isupper()])/len([i for i in x]) if isinstance(x, str) else 0) return df
610,391
966
1be9aef821fa946560784d3d6616a9b0dc25382e
VMarty/CityEnergyAnalyst
cea/analysis/clustering/sax/sax_optimization.py
[ "MIT" ]
Python
calc_complexity
<not_specific>
def calc_complexity(names_of_clusters): """ Calculated according to 'Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions' by H. Acosta-Mesa et al., 2014 :param names_of_clusters: list containing a word which clusters the time series. e.g., ['abcffs', dddddd'...'svfdab'] :return: level of complexity which penalizes the objective function """ single_words_length = len(list(set(names_of_clusters))) len_timeseries = len(names_of_clusters) # number of observations result = 1- (single_words_length/ len_timeseries) return result
Calculated according to 'Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions' by H. Acosta-Mesa et al., 2014 :param names_of_clusters: list containing a word which clusters the time series. e.g., ['abcffs', dddddd'...'svfdab'] :return: level of complexity which penalizes the objective function
Calculated according to 'Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions' by H. Acosta-Mesa et al., 2014
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def calc_complexity(names_of_clusters): single_words_length = len(list(set(names_of_clusters))) len_timeseries = len(names_of_clusters) result = 1- (single_words_length/ len_timeseries) return result
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Calculated according to 'Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions' by H. Acosta-Mesa et al., 2014
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[ "\"\"\"\n Calculated according to 'Application of time series discretization using evolutionary programming for classification\n of precancerous cervical lesions' by H. Acosta-Mesa et al., 2014\n :param names_of_clusters: list containing a word which clusters the time series. e.g., ['abcffs', dddddd'...'svfdab']\n :return: level of complexity which penalizes the objective function\n \"\"\"", "# number of observations" ]
[ { "param": "names_of_clusters", "type": null } ]
{ "returns": [ { "docstring": "level of complexity which penalizes the objective function", "docstring_tokens": [ "level", "of", "complexity", "which", "penalizes", "the", "objective", "function" ], "type": null } ], "raises": [], "params": [ { "identifier": "names_of_clusters", "type": null, "docstring": "list containing a word which clusters the time series.", "docstring_tokens": [ "list", "containing", "a", "word", "which", "clusters", "the", "time", "series", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def calc_complexity(names_of_clusters): single_words_length = len(list(set(names_of_clusters))) len_timeseries = len(names_of_clusters) result = 1- (single_words_length/ len_timeseries) return result
610,392
747
28953e59dd04e1ee5ea79c7fa66b4afe72794756
notapresent/rutracker_rss
feeds.py
[ "Apache-2.0" ]
Python
feed_size
<not_specific>
def feed_size(category): """Returns number of feed entries for category""" if category.key.id() == 'r0': # Root category return 100 elif category.key.id().startswith('c'): # Level 2 category return 50 return 25 # category with subcategories
Returns number of feed entries for category
Returns number of feed entries for category
[ "Returns", "number", "of", "feed", "entries", "for", "category" ]
def feed_size(category): if category.key.id() == 'r0': return 100 elif category.key.id().startswith('c'): return 50 return 25
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Returns number of feed entries for category
[ "Returns", "number", "of", "feed", "entries", "for", "category" ]
[ "\"\"\"Returns number of feed entries for category\"\"\"", "# Root category", "# Level 2 category", "# category with subcategories" ]
[ { "param": "category", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "category", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def feed_size(category): if category.key.id() == 'r0': return 100 elif category.key.id().startswith('c'): return 50 return 25
610,393
841
d49b427031eeb31006bbdc73c306a216d045bf92
hsinyy02/Paddy-Field-Segmentation
code/stitching.py
[ "MIT" ]
Python
point_homography
<not_specific>
def point_homography(point, H): """Apply projective transformation (homography) on a point. Parameters: point: (x, y) tuple. A point for projective transformation. H: A homography matrix. Return: homo: (x, y) tuple. The point after projective transformation. """ h0, h1, h2 = H[0] h3, h4, h5 = H[1] h6, h7, h8 = H[2] x, y = point tx = h0*x + h1*y + h2 ty = h3*x + h4*y + h5 tz = h6*x + h7*y + h8 homo = (tx/tz, ty/tz) return homo
Apply projective transformation (homography) on a point. Parameters: point: (x, y) tuple. A point for projective transformation. H: A homography matrix. Return: homo: (x, y) tuple. The point after projective transformation.
Apply projective transformation (homography) on a point.
[ "Apply", "projective", "transformation", "(", "homography", ")", "on", "a", "point", "." ]
def point_homography(point, H): h0, h1, h2 = H[0] h3, h4, h5 = H[1] h6, h7, h8 = H[2] x, y = point tx = h0*x + h1*y + h2 ty = h3*x + h4*y + h5 tz = h6*x + h7*y + h8 homo = (tx/tz, ty/tz) return homo
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Apply projective transformation (homography) on a point.
[ "Apply", "projective", "transformation", "(", "homography", ")", "on", "a", "point", "." ]
[ "\"\"\"Apply projective transformation (homography) on a point.\n\n Parameters:\n point: (x, y) tuple. \n A point for projective transformation.\n\n H: A homography matrix.\n \n Return:\n homo: (x, y) tuple.\n The point after projective transformation.\n \"\"\"" ]
[ { "param": "point", "type": null }, { "param": "H", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "point", "type": null, "docstring": "(x, y) tuple.\nA point for projective transformation.", "docstring_tokens": [ "(", "x", "y", ")", "tuple", ".", "A", "point", "for", "projective", "transformation", "." ], "default": null, "is_optional": null }, { "identifier": "H", "type": null, "docstring": "A homography matrix.", "docstring_tokens": [ "A", "homography", "matrix", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def point_homography(point, H): h0, h1, h2 = H[0] h3, h4, h5 = H[1] h6, h7, h8 = H[2] x, y = point tx = h0*x + h1*y + h2 ty = h3*x + h4*y + h5 tz = h6*x + h7*y + h8 homo = (tx/tz, ty/tz) return homo
610,394
281
72eec153e741c8731540655b0bff97137fe62117
hkgopal/tf-test
tcutils/util.py
[ "Apache-2.0" ]
Python
read_config_option
<not_specific>
def read_config_option(config, section, option, default_option): ''' Read the config file. If the option/section is not present, return the default_option ''' if not config: return default_option try: val = config.get(section, option) except (configparser.NoOptionError, configparser.NoSectionError): return default_option if val.lower() == 'true': val = True #elif val.lower() == 'false' or val.lower() == 'none': elif val.lower() == 'false': val = False elif not val: val = default_option if type(val) is not bool and val is not None and ( '$__' in val or val.lower() == 'none'): # ie. having a template value unpopulated(for $__xyz__) # or None val = '' if val == '' : val = default_option return val
Read the config file. If the option/section is not present, return the default_option
Read the config file. If the option/section is not present, return the default_option
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def read_config_option(config, section, option, default_option): if not config: return default_option try: val = config.get(section, option) except (configparser.NoOptionError, configparser.NoSectionError): return default_option if val.lower() == 'true': val = True elif val.lower() == 'false': val = False elif not val: val = default_option if type(val) is not bool and val is not None and ( '$__' in val or val.lower() == 'none'): val = '' if val == '' : val = default_option return val
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Read the config file.
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[ { "param": "config", "type": null }, { "param": "section", "type": null }, { "param": "option", "type": null }, { "param": "default_option", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "config", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "section", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "option", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "default_option", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import configparser def read_config_option(config, section, option, default_option): if not config: return default_option try: val = config.get(section, option) except (configparser.NoOptionError, configparser.NoSectionError): return default_option if val.lower() == 'true': val = True elif val.lower() == 'false': val = False elif not val: val = default_option if type(val) is not bool and val is not None and ( '$__' in val or val.lower() == 'none'): val = '' if val == '' : val = default_option return val
610,395
53
9b3cfb5734765325ae3a96f1ed3a0f939f9e14ce
lucasb-eyer/datasets
tensorflow_datasets/core/download/kaggle.py
[ "Apache-2.0" ]
Python
_kaggle_dir_name
str
def _kaggle_dir_name(competition_or_dataset: str) -> str: """Returns path where the dataset is to be downloaded. Args: competition_or_dataset: Name of the kaggle competition/dataset. Returns: Path to the dir where the dataset is to be downloaded. """ return competition_or_dataset.replace('/', '_')
Returns path where the dataset is to be downloaded. Args: competition_or_dataset: Name of the kaggle competition/dataset. Returns: Path to the dir where the dataset is to be downloaded.
Returns path where the dataset is to be downloaded.
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def _kaggle_dir_name(competition_or_dataset: str) -> str: return competition_or_dataset.replace('/', '_')
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Returns path where the dataset is to be downloaded.
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[ "\"\"\"Returns path where the dataset is to be downloaded.\n\n Args:\n competition_or_dataset: Name of the kaggle competition/dataset.\n\n Returns:\n Path to the dir where the dataset is to be downloaded.\n \"\"\"" ]
[ { "param": "competition_or_dataset", "type": "str" } ]
{ "returns": [ { "docstring": "Path to the dir where the dataset is to be downloaded.", "docstring_tokens": [ "Path", "to", "the", "dir", "where", "the", "dataset", "is", "to", "be", "downloaded", "." ], "type": null } ], "raises": [], "params": [ { "identifier": "competition_or_dataset", "type": "str", "docstring": "Name of the kaggle competition/dataset.", "docstring_tokens": [ "Name", "of", "the", "kaggle", "competition", "/", "dataset", "." ], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def _kaggle_dir_name(competition_or_dataset: str) -> str: return competition_or_dataset.replace('/', '_')
610,396
866
201f5488d1884708f4227d61239a4105b46eb1a6
arsinclair/Chromagnon
chromagnon/classicalOutput.py
[ "BSD-3-Clause" ]
Python
classicalOutput
null
def classicalOutput(queryResult, separator="\t"): """ Display the data separated by the specified separator """ for line in queryResult: for element in line: sys.stdout.write(element) sys.stdout.write(separator) sys.stdout.write('\n')
Display the data separated by the specified separator
Display the data separated by the specified separator
[ "Display", "the", "data", "separated", "by", "the", "specified", "separator" ]
def classicalOutput(queryResult, separator="\t"): for line in queryResult: for element in line: sys.stdout.write(element) sys.stdout.write(separator) sys.stdout.write('\n')
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Display the data separated by the specified separator
[ "Display", "the", "data", "separated", "by", "the", "specified", "separator" ]
[ "\"\"\"\n Display the data separated by the specified separator\n \"\"\"" ]
[ { "param": "queryResult", "type": null }, { "param": "separator", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "queryResult", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "separator", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import sys def classicalOutput(queryResult, separator="\t"): for line in queryResult: for element in line: sys.stdout.write(element) sys.stdout.write(separator) sys.stdout.write('\n')
610,397
905
2170655fd1b3b04b7b7bf4abc8519c9dace4bdda
bshephar/directord
directord/iodict.py
[ "Apache-2.0" ]
Python
_get_item_key
<not_specific>
def _get_item_key(path: str): """Return the key name from a file object. :returns: String """ try: key = os.getxattr(path, "user.key") except OSError: if not os.path.exists(path): raise FileNotFoundError(path) from None return os.path.basename(path) else: return key.decode()
Return the key name from a file object. :returns: String
Return the key name from a file object.
[ "Return", "the", "key", "name", "from", "a", "file", "object", "." ]
def _get_item_key(path: str): try: key = os.getxattr(path, "user.key") except OSError: if not os.path.exists(path): raise FileNotFoundError(path) from None return os.path.basename(path) else: return key.decode()
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Return the key name from a file object.
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[ "\"\"\"Return the key name from a file object.\n\n :returns: String\n \"\"\"" ]
[ { "param": "path", "type": "str" } ]
{ "returns": [ { "docstring": null, "docstring_tokens": [ "None" ], "type": null } ], "raises": [], "params": [ { "identifier": "path", "type": "str", "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import os def _get_item_key(path: str): try: key = os.getxattr(path, "user.key") except OSError: if not os.path.exists(path): raise FileNotFoundError(path) from None return os.path.basename(path) else: return key.decode()
610,398
921
8e72fcda175f510d6a24529d813a5c58c67df05f
wezil/principles-computing
7-word-wrangler.py
[ "MIT" ]
Python
gen_all_strings
<not_specific>
def gen_all_strings(word): """ Generate all strings that can be composed from the letters in word in any order. Returns a list of all strings that can be formed from the letters in word. This function should be recursive. """ # base case with no length word if len(word) == 0: return [""] # recursive case head = word[0] tail = word[1: ] # keep track of a master list while generating sub list master_list = [] sub_list = gen_all_strings(tail) # add sub list to master list master_list.extend(sub_list) # for each sub list word add to master list a combination of all # head character positions in sub word for sub_word in sub_list: for index in range(len(sub_word) + 1): master_list.append(sub_word[:index] + head + sub_word[index: ]) return master_list
Generate all strings that can be composed from the letters in word in any order. Returns a list of all strings that can be formed from the letters in word. This function should be recursive.
Generate all strings that can be composed from the letters in word in any order. Returns a list of all strings that can be formed from the letters in word. This function should be recursive.
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def gen_all_strings(word): if len(word) == 0: return [""] head = word[0] tail = word[1: ] master_list = [] sub_list = gen_all_strings(tail) master_list.extend(sub_list) for sub_word in sub_list: for index in range(len(sub_word) + 1): master_list.append(sub_word[:index] + head + sub_word[index: ]) return master_list
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Generate all strings that can be composed from the letters in word in any order.
[ "Generate", "all", "strings", "that", "can", "be", "composed", "from", "the", "letters", "in", "word", "in", "any", "order", "." ]
[ "\"\"\"\n Generate all strings that can be composed from the letters in word\n in any order.\n\n Returns a list of all strings that can be formed from the letters\n in word.\n\n This function should be recursive.\n \"\"\"", "# base case with no length word", "# recursive case", "# keep track of a master list while generating sub list", "# add sub list to master list", "# for each sub list word add to master list a combination of all", "# head character positions in sub word" ]
[ { "param": "word", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "word", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def gen_all_strings(word): if len(word) == 0: return [""] head = word[0] tail = word[1: ] master_list = [] sub_list = gen_all_strings(tail) master_list.extend(sub_list) for sub_word in sub_list: for index in range(len(sub_word) + 1): master_list.append(sub_word[:index] + head + sub_word[index: ]) return master_list
610,399
619
e7690ec0872dd31fcd7b0ca82e4142a3b41fbeee
seatim/dwave-system
dwave/embedding/chain_strength.py
[ "Apache-2.0" ]
Python
scaled
<not_specific>
def scaled(bqm, embedding=None, prefactor=1.0): """Chain strength that is scaled to the problem bias range. Args: bqm (:obj:`.BinaryQuadraticModel`): A binary quadratic model. embedding (dict/:class:`.EmbeddedStructure`, default=None): Included to satisfy the `chain_strength` callable specifications for `embed_bqm`. prefactor (float, optional, default=1.0): Prefactor used for scaling. Returns: float: The chain strength, or 1 if chain strength is not applicable. """ if bqm.num_interactions > 0: max_bias = max(max(bqm.linear.max(), -bqm.linear.min()), max(bqm.quadratic.max(), -bqm.quadratic.min())) return prefactor * max_bias else: return 1 # won't matter (chain strength isn't needed to embed this problem)
Chain strength that is scaled to the problem bias range. Args: bqm (:obj:`.BinaryQuadraticModel`): A binary quadratic model. embedding (dict/:class:`.EmbeddedStructure`, default=None): Included to satisfy the `chain_strength` callable specifications for `embed_bqm`. prefactor (float, optional, default=1.0): Prefactor used for scaling. Returns: float: The chain strength, or 1 if chain strength is not applicable.
Chain strength that is scaled to the problem bias range.
[ "Chain", "strength", "that", "is", "scaled", "to", "the", "problem", "bias", "range", "." ]
def scaled(bqm, embedding=None, prefactor=1.0): if bqm.num_interactions > 0: max_bias = max(max(bqm.linear.max(), -bqm.linear.min()), max(bqm.quadratic.max(), -bqm.quadratic.min())) return prefactor * max_bias else: return 1
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Chain strength that is scaled to the problem bias range.
[ "Chain", "strength", "that", "is", "scaled", "to", "the", "problem", "bias", "range", "." ]
[ "\"\"\"Chain strength that is scaled to the problem bias range.\n\n Args:\n bqm (:obj:`.BinaryQuadraticModel`):\n A binary quadratic model.\n\n embedding (dict/:class:`.EmbeddedStructure`, default=None):\n Included to satisfy the `chain_strength` callable specifications \n for `embed_bqm`. \n\n prefactor (float, optional, default=1.0):\n Prefactor used for scaling. \n\n Returns:\n float: The chain strength, or 1 if chain strength is not applicable.\n\n \"\"\"", "# won't matter (chain strength isn't needed to embed this problem)" ]
[ { "param": "bqm", "type": null }, { "param": "embedding", "type": null }, { "param": "prefactor", "type": null } ]
{ "returns": [ { "docstring": "The chain strength, or 1 if chain strength is not applicable.", "docstring_tokens": [ "The", "chain", "strength", "or", "1", "if", "chain", "strength", "is", "not", "applicable", "." ], "type": "float" } ], "raises": [], "params": [ { "identifier": "bqm", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "embedding", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "prefactor", "type": null, "docstring": "Prefactor used for scaling.", "docstring_tokens": [ "Prefactor", "used", "for", "scaling", "." ], "default": null, "is_optional": false } ], "outlier_params": [ { "identifier": "bqm (", "type": null, "docstring": "`.BinaryQuadraticModel`):\nA binary quadratic model.", "docstring_tokens": [ "`", ".", "BinaryQuadraticModel", "`", ")", ":", "A", "binary", "quadratic", "model", "." ], "default": null, "is_optional": null }, { "identifier": "embedding (dict/", "type": null, "docstring": "`.EmbeddedStructure`, default=None):\nIncluded to satisfy the `chain_strength` callable specifications\nfor `embed_bqm`.", "docstring_tokens": [ "`", ".", "EmbeddedStructure", "`", "default", "=", "None", ")", ":", "Included", "to", "satisfy", "the", "`", "chain_strength", "`", "callable", "specifications", "for", "`", "embed_bqm", "`", "." ], "default": null, "is_optional": null } ], "others": [] }
def scaled(bqm, embedding=None, prefactor=1.0): if bqm.num_interactions > 0: max_bias = max(max(bqm.linear.max(), -bqm.linear.min()), max(bqm.quadratic.max(), -bqm.quadratic.min())) return prefactor * max_bias else: return 1
610,400
674
a9a438bf555b78ef0bc4007be4642596f449b0ec
HarshCasper/trankit
trankit/utils/mwt_lemma_utils/seq2seq_utils.py
[ "Apache-2.0" ]
Python
sort
<not_specific>
def sort(packed, ref, reverse=True): """ Sort a series of packed list, according to a ref list. Also return the original index before the sort. """ assert (isinstance(packed, tuple) or isinstance(packed, list)) and isinstance(ref, list) packed = [ref] + [range(len(ref))] + list(packed) sorted_packed = [list(t) for t in zip(*sorted(zip(*packed), reverse=reverse))] return tuple(sorted_packed[1:])
Sort a series of packed list, according to a ref list. Also return the original index before the sort.
Sort a series of packed list, according to a ref list. Also return the original index before the sort.
[ "Sort", "a", "series", "of", "packed", "list", "according", "to", "a", "ref", "list", ".", "Also", "return", "the", "original", "index", "before", "the", "sort", "." ]
def sort(packed, ref, reverse=True): assert (isinstance(packed, tuple) or isinstance(packed, list)) and isinstance(ref, list) packed = [ref] + [range(len(ref))] + list(packed) sorted_packed = [list(t) for t in zip(*sorted(zip(*packed), reverse=reverse))] return tuple(sorted_packed[1:])
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Sort a series of packed list, according to a ref list.
[ "Sort", "a", "series", "of", "packed", "list", "according", "to", "a", "ref", "list", "." ]
[ "\"\"\"\r\n Sort a series of packed list, according to a ref list.\r\n Also return the original index before the sort.\r\n \"\"\"" ]
[ { "param": "packed", "type": null }, { "param": "ref", "type": null }, { "param": "reverse", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "packed", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "ref", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "reverse", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def sort(packed, ref, reverse=True): assert (isinstance(packed, tuple) or isinstance(packed, list)) and isinstance(ref, list) packed = [ref] + [range(len(ref))] + list(packed) sorted_packed = [list(t) for t in zip(*sorted(zip(*packed), reverse=reverse))] return tuple(sorted_packed[1:])
610,401
542
03fce7299a054f6894e3611b9fd87dae92cf6514
buffet/sequestrum
sequestrum/logging.py
[ "MIT" ]
Python
print_verbose
null
def print_verbose(error_message): """ Prints message with green color to show success """ print("\033[1;32mVERBOSE\033[0m {}".format(error_message))
Prints message with green color to show success
Prints message with green color to show success
[ "Prints", "message", "with", "green", "color", "to", "show", "success" ]
def print_verbose(error_message): print("\033[1;32mVERBOSE\033[0m {}".format(error_message))
[ "def", "print_verbose", "(", "error_message", ")", ":", "print", "(", "\"\\033[1;32mVERBOSE\\033[0m {}\"", ".", "format", "(", "error_message", ")", ")" ]
Prints message with green color to show success
[ "Prints", "message", "with", "green", "color", "to", "show", "success" ]
[ "\"\"\"\n Prints message with green color to show success\n \"\"\"" ]
[ { "param": "error_message", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "error_message", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def print_verbose(error_message): print("\033[1;32mVERBOSE\033[0m {}".format(error_message))
610,402
136
a8922ba6075898495ae0f13f19d81db683a3c485
BennettDixon/holbertonschool-higher_level_programming
0x0B-python-input_output/1-number_of_lines.py
[ "MIT" ]
Python
number_of_lines
<not_specific>
def number_of_lines(filename=""): """gets the number of lines from filename """ with open(filename, encoding='utf-8') as myFile: return sum([1 for line in myFile])
gets the number of lines from filename
gets the number of lines from filename
[ "gets", "the", "number", "of", "lines", "from", "filename" ]
def number_of_lines(filename=""): with open(filename, encoding='utf-8') as myFile: return sum([1 for line in myFile])
[ "def", "number_of_lines", "(", "filename", "=", "\"\"", ")", ":", "with", "open", "(", "filename", ",", "encoding", "=", "'utf-8'", ")", "as", "myFile", ":", "return", "sum", "(", "[", "1", "for", "line", "in", "myFile", "]", ")" ]
gets the number of lines from filename
[ "gets", "the", "number", "of", "lines", "from", "filename" ]
[ "\"\"\"gets the number of lines from filename\n \"\"\"" ]
[ { "param": "filename", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "filename", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def number_of_lines(filename=""): with open(filename, encoding='utf-8') as myFile: return sum([1 for line in myFile])
610,403
389
d4ed4bc4191a35a42387fbf8a7c93bbc6b9905b2
screw-pack/Plumbit
plumbit.py
[ "MIT" ]
Python
add
<not_specific>
def add(circuit, current): """Ajoute le tuyau courant au circuit""" for pipe in circuit: if pipe.rect.topleft == current.rect.topleft: circuit.remove(pipe) circuit.append(current) return circuit
Ajoute le tuyau courant au circuit
Ajoute le tuyau courant au circuit
[ "Ajoute", "le", "tuyau", "courant", "au", "circuit" ]
def add(circuit, current): for pipe in circuit: if pipe.rect.topleft == current.rect.topleft: circuit.remove(pipe) circuit.append(current) return circuit
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Ajoute le tuyau courant au circuit
[ "Ajoute", "le", "tuyau", "courant", "au", "circuit" ]
[ "\"\"\"Ajoute le tuyau courant au circuit\"\"\"" ]
[ { "param": "circuit", "type": null }, { "param": "current", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "circuit", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "current", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def add(circuit, current): for pipe in circuit: if pipe.rect.topleft == current.rect.topleft: circuit.remove(pipe) circuit.append(current) return circuit
610,404
702
339e15552a0da69107e346603283d35cfe72ee68
fandulu/MPLT
utils.py
[ "MIT" ]
Python
rotate
<not_specific>
def rotate(point, angle): """ Rotate a point counterclockwise by a given angle around a given origin. The angle should be given in radians. """ ox, oy = 0, 0 px, py = point qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy) qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy) return qx, qy
Rotate a point counterclockwise by a given angle around a given origin. The angle should be given in radians.
Rotate a point counterclockwise by a given angle around a given origin. The angle should be given in radians.
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def rotate(point, angle): ox, oy = 0, 0 px, py = point qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy) qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy) return qx, qy
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Rotate a point counterclockwise by a given angle around a given origin.
[ "Rotate", "a", "point", "counterclockwise", "by", "a", "given", "angle", "around", "a", "given", "origin", "." ]
[ "\"\"\"\n Rotate a point counterclockwise by a given angle around a given origin.\n\n The angle should be given in radians.\n \"\"\"" ]
[ { "param": "point", "type": null }, { "param": "angle", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "point", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "angle", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import math def rotate(point, angle): ox, oy = 0, 0 px, py = point qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy) qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy) return qx, qy
610,405
805
1f3993bf85cd6d42600fc6b8e82db0bd3ecf0425
GoogleCloudPlatform/deactivate_tunnel
deactivate_tunnel.py
[ "Apache-2.0" ]
Python
delete_route
<not_specific>
def delete_route(compute, project, route): """ Deletes an existing route asynchronously.""" route_deleted = compute.routes().delete(project=project, route=route).execute() return route_deleted
Deletes an existing route asynchronously.
Deletes an existing route asynchronously.
[ "Deletes", "an", "existing", "route", "asynchronously", "." ]
def delete_route(compute, project, route): route_deleted = compute.routes().delete(project=project, route=route).execute() return route_deleted
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Deletes an existing route asynchronously.
[ "Deletes", "an", "existing", "route", "asynchronously", "." ]
[ "\"\"\" Deletes an existing route asynchronously.\"\"\"" ]
[ { "param": "compute", "type": null }, { "param": "project", "type": null }, { "param": "route", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "compute", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "project", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "route", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
def delete_route(compute, project, route): route_deleted = compute.routes().delete(project=project, route=route).execute() return route_deleted
610,406
607
59e111f6833d68afa0c40e04d48fc2a6407b091c
frictionlessdata/data-quality-cli
data_quality/utilities.py
[ "MIT" ]
Python
resolve_dir_name
<not_specific>
def resolve_dir_name(config_filepath, dir_path): """Create an absolute path from the file path and the path given in the config""" if not os.path.isabs(dir_path): config_path = os.path.abspath(os.path.dirname(config_filepath)) return os.path.join(config_path, dir_path) else: return dir_path
Create an absolute path from the file path and the path given in the config
Create an absolute path from the file path and the path given in the config
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def resolve_dir_name(config_filepath, dir_path): if not os.path.isabs(dir_path): config_path = os.path.abspath(os.path.dirname(config_filepath)) return os.path.join(config_path, dir_path) else: return dir_path
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Create an absolute path from the file path and the path given in the config
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[ "\"\"\"Create an absolute path from the file path and the path given in the config\"\"\"" ]
[ { "param": "config_filepath", "type": null }, { "param": "dir_path", "type": null } ]
{ "returns": [], "raises": [], "params": [ { "identifier": "config_filepath", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "dir_path", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] }
import os def resolve_dir_name(config_filepath, dir_path): if not os.path.isabs(dir_path): config_path = os.path.abspath(os.path.dirname(config_filepath)) return os.path.join(config_path, dir_path) else: return dir_path
610,407
830